Interpersonal Networks in Organizations: Cognition, Personality, Dynamics, and Culture (Structural Analysis in the Social Sciences)

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Interpersonal Networks in Organizations Cognition, Personality, Dynamics, and Culture This book brings a social network perspective to bear on topics of leadership, decision making, turnover, organizational crises, organizational culture, and other major organizational behavior topics. It offers a new direction for organizational behavior theory and research by drawing from social network ideas. Across diverse research topics, the authors pursue an integrated focus on social ties both as they are represented in the cognitions of individuals and as they operate as constraints and opportunities in organizational settings. The authors bring their twenty years’ worth of research experience together to provide a programmatic social network approach to understanding the internal functioning of organizations. By focusing a distinctive research lens on interpersonal networks, they attempt to discover the keys to the whole realm of organizational behavior through the social network approach. Martin Kilduff is the Kleberg/King Ranch Centennial Professor of Management at the University of Texas at Austin. He is also editor of Academy of Management Review (2006–8) and coauthor of Social Networks and Organizations (with Wenpin Tsai; 2003). He has served on the faculties of Penn State and INSEAD, and he has been a visiting professor at Cambridge University, London Business School, Keele University, and Hong Kong University of Science and Technology. David Krackhardt is Professor of Organizations at the Heinz School of Public Policy and Management and at the Tepper School of Business at Carnegie Mellon University. Prior appointments include faculty positions at Cornell’s Graduate School of Management, the University of Chicago’s Graduate School of Business, INSEAD (France), and the Harvard Business School (Marvin Bower Fellow).

Structural Analysis in the Social Sciences Mark Granovetter, editor The series Structural Analysis in the Social Sciences presents approaches that explain social behavior and institutions by reference to relations among such concrete entities as persons and organizations. This contrasts with at least four other popular strategies: (a) reductionist attempts to explain by a focus on individuals alone; (b) explanations stressing the causal primacy of such abstract concepts as ideas, values, mental harmonies, and cognitive maps (thus, “structuralism” on the Continent should be distinguished from structural analysis in the present sense); (c) technological and material determination; and (d) explanations using “variables” as the main analytic concepts (as in the “structural equation” models that dominated much of the sociology of the 1970s), where structure is that connecting variables rather than actual social entities. The social network approach is an important example of the strategy of structural analysis; the series also draws on social science theory and research that is not framed explicitly in network terms but stresses the importance of relations rather than the atomization of reduction or the determination of ideas, technology, or material conditions. Though the structural perspective has become extremely popular and influential in all the social sciences, it does not have a coherent identity, and no series yet pulls together such work under a single rubric. By bringing the achievements of structurally oriented scholars to a wider public, the Structural Analysis series hopes to encourage the use of this very fruitful approach.

Recent Books in the Series Philippe Bourgois, In Search of Respect: Selling Crack in El Barrio (Second Edition) Nan Lin, Social Capital: A Theory of Social Structure and Action Roberto Franzosi, From Words to Numbers Sean O’Riain, The Politics of High-Tech Growth James Lincoln and Michael Gerlach, Japan’s Network Economy Patrick Doreian, Vladimir Batagelj, and Anujka Ferligoj, Generalized Blockmodeling Eiko Ikegami, Bonds of Civility: Aesthetic Networks and Political Origins of Japanese Culture Wouter de Nooy, Andrej Mrvar, and Vladimir Batagelj, Exploratory Social Network Analysis with Pajek Peter Carrington, John Scott, and Stanley Wasserman, Models and Methods in Social Network Analysis Robert C. Feenstra and Gary C. Hamilton, Emergent Economies, Divergent Paths Ari Adut, On Scandal: Moral Disturbances in Society, Politics, and Art

Interpersonal Networks in Organizations Cognition, Personality, Dynamics, and Culture

MARTIN KILDUFF University of Texas at Austin

DAVID KRACKHARDT Carnegie Mellon University

CAMBRIDGE UNIVERSITY PRESS

Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521866606 © Martin Kilduff and David Krackhardt 2008 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published in print format 2008

ISBN-13 978-0-511-42908-8

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ISBN-13

978-0-521-86660-6

hardback

ISBN-13

978-0-521-68558-0

paperback

Cambridge University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate.

Contents

Acknowledgments 1 Introduction I. Perceiving Networks 2 A Network Approach to Leadership 3 An Analysis of the Internal Market for Reputation in Organizations 4 Systematic Biases in Network Perception 5 Effects of Network Accuracy on Individuals’ Perceived Power II. 6 7 8 III. 9 10 11

The Psychology of Network Differences Social Structure and Decision Making in an MBA Cohort The Social Networks of Low and High Self-Monitors Centrality in the Emotion Helping Network: An Interactionist Approach Network Dynamics and Organizational Culture Network Perceptions and Turnover in Three Organizations Organizational Crises The Control of Organizational Diversity

12 Future Directions

page ix 1 13 39 59 84 101 131 157 181 208 236 259

References

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Index

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vii

Acknowledgments

We drew upon a number of published articles in preparing this book. We are happy to acknowledge the sources of these articles here. We thank our coauthors on these articles for their contributions and thank the journals for permission to reuse these materials. We also thank Ranjay Gulati, David A. Harrison, and Ajay Mehra for helpful comments during the preparation of the book. Chapter 2 draws from Balkundi, P., and Kilduff, M. 2005. The ties that lead: A social network approach to leadership. Leadership Quarterly, 16: 941–61. Chapter 3 includes material from Kilduff, M., and Krackhardt, D. 1994. Bringing the individual back in: A structural analysis of the internal market for reputation in organizations. Academy of Management Journal, 37: 87–108. Chapter 4 (and parts of Chapter 1) draws from Krackhardt, D., and Kilduff, M. 1999. Whether close or far: Social distance effects on perceived balance in friendship networks. Journal of Personality and Social Psychology, 76: 770–82. © 1999 by the American Psychological Association. Adapted with permission. Chapter 5 contains material reprinted from Krackhardt, D., Assessing the political landscape: Structure, cognition, and power in organizations, Administrative Science Quarterly, 35 (2) by permission of Administrative Science Quarterly, © 1990 Cornell University. Chapter 6 draws from the following three articles: Mehra, A., Kilduff, M., and Brass, D. J. 1998. At the margins: A distinctiveness approach to the social identity and social networks of under-represented groups. Academy of Management Journal, 41: 441–52; Kilduff, M. 1990. The interpersonal structure of decisionmaking: A social comparison approach to organizational choice. Organizational Behavior and Human Decision Processes, 47: 270–88; and Kilduff, M. 1992. The friendship network as a decision-making resource: Dispositional moderators of social influences on organizational choice. Journal of Personality and Social Psychology, 62: 168–80. © 1992 by the American Psychological Association, adapted with permission. Chapter 7 ix

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contains material reprinted from Mehra, A., Kilduff, M., and Brass, D. J., The social networks of high and low self-monitors: Implications for workplace performance, Administrative Science Quarterly, 46 (1) by permission of Administrative Science Quarterly © 2001 by Cornell University. Chapter 8 draws from Toegel, G., Anand, N., and Kilduff, M. 2007. Emotion helpers: The role of high positive affectivity and high self-monitoring managers. Personnel Psychology, 60: 337–65. Chapter 9 draws upon the following two articles: Krackhardt, D., and Porter, L. T. 1986. The snowball effect: Turnover embedded in communication networks. Journal of Applied Psychology, 71: 1–6 © 1986 by the American Psychological Association (adapted with permission); and Krackhardt, D., and Porter, L. T. 1985. When friends leave: A structural analysis of the relationship between turnover and stayers’ attitudes. Administrative Science Quarterly, 30 (2) © 1985, adapted and reprinted by permission of Administrative Science Quarterly, Cornell University. Chapter 10 includes material from Krackhardt, D., and Stern, R. 1988. Informal networks and organizational crises: An experimental simulation. Social Psychology Quarterly, 51: 123–40. Chapter 11 draws from two sources: Krackhardt, D., and Kilduff, M. 1990. Friendship patterns and culture: The control of organizational diversity. American Anthropologist, 92: 142–54; and Krackhardt, D., and Kilduff, M. 2002. Structure, culture and Simmelian ties in entrepreneurial firms. Social Networks, 24: 279– 90. Finally, Chapter 12 includes material adapted from the following sources: Ibarra, H., Kilduff, M., and Tsai, W. 2005. Zooming in and out: Connecting individuals and collectivities at the frontiers of organizational network research. Organization Science, 16 (4): 359–71. © 2005, the Institute for Operations Research and the Management Sciences, 7240 Parkway Drive, Suite 310, Hanover, MD 21076, USA, reprinted by permission; and Kilduff, M., Tsai, W., and Hanke, R. 2006. A paradigm too far? A dynamic stability reconsideration of the social network research program. Academy of Management Review, 31: 1031–48.

1 Introduction

Human beings are social creatures who depend on links to others to accomplish many of life’s tasks. The networks of relations within which each person is embedded include family, friends, and acquaintances. The embeddedness of human activity in such networks is true not just for primal activities such as child-rearing but also for economic activities such as finding a job (Granovetter, 1974). Indeed, business organizations themselves are held together not only by formal relations of authority but also by informal links that connect people across departmental and hierarchical boundaries. Starting with the Hawthorne studies (Roethlisberger and Dickson, 1939), researchers have investigated the importance of informal networks for job satisfaction (e.g., Roy, 1954), organizational conflict (e.g., Whyte, 1948), worker output (e.g., Jones, 1990), organizational power (e.g., Brass, 1984), and many other aspects of social and organizational life (see Kilduff and Tsai, 2003, for a review). Only recently, however, has research attention focused on actors’ perceptions of the structure of relations in social settings and on how actors’ individual differences may affect the network positions they occupy. These topics – actor perceptions and actor individual differences – provide the inspiration for our book. Actors’ perceptions of social networks within which they are embedded affect the decisions they make (see the discussion in Burt, 1982, chapter 5), and these perceptions are subject to considerable bias (Krackhardt, 1987a). How other people perceive the structure of relations surrounding the individual affects not only the individual’s power to act (Krackhardt, 1990) but also the individual’s reputation (Kilduff and Krackhardt, 1994). Actor individual differences can range from the visible attributes of ethnicity and gender (shown to affect patterns of centrality and exclusion in social networks – e.g., Mehra, Kilduff, and Brass, 1998) to specific personality characteristics such as self-monitoring (Snyder, 1974) that may be particularly 1

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Interpersonal Networks in Organizations

predictive of individuals’ network positions (Mehra, Kilduff, and Brass, 2001). This is a book about the cognitive and personality distinctiveness of individuals and the ways in which such distinctiveness affects relationships in organizations. The different chapters in the book unfold stories about how people perceive themselves and others in networks of friendship and advice, the biases people exhibit in their mental representations of who is connected to whom, the rewards and penalties that people experience as a result of such biases, and ways in which individual differences affect network positions and outcomes. We draw from cognitive network theory and an emerging personality approach to social network positions to examine (in organizational contexts) perceptions of networks, the psychology of network differences, and the dynamics of social network turnover, crisis, and culture. Unlike conventional network studies that tend to focus on interchangeable position holders, our focus throughout is on individual human beings and their distinctive patterns of network thinking and interaction. Across diverse research covering organizational behavior topics, we pursue an integrated focus on social ties both as they are represented in the perceptions of individuals and as they relate to individual differences.

Perceiving Networks Cognitions concerning social networks are important to the extent that people are uncertain concerning who is connected to whom. People may try to reduce such uncertainty by paying particular attention to the connections of those who are prominent. Savvy network entrepreneurs can take advantage of such uncertain knowledge to create social capital that may be merely fleeting but can, nonetheless, be valuable. The following story illustrates how a prominent banker used his visibility to bestow social capital that could be traded by his prot´eg´e for financial capital: At the height of his wealth and success, the financier Baron de Rothschild was petitioned for a loan by an acquaintance. Reputedly, the great man replied, “I won’t give you a loan myself; but I will walk arm-in-arm with you across the floor of the Stock Exchange, and you soon shall have willing lenders to spare” (Cialdini, 1989: 45). The baron in this story assumed that perceivers scan the social network connections of individuals for signals concerning difficult-to-discern underlying quality – such signals including connections to prominent

Introduction

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others. Research interest in such cognitive interpretations of network connections increased throughout the 1990s, concurrent with the cognitive turn in sociological approaches more generally (e.g., DiMaggio, 1997; Schwarz, 1998). Research concerning interorganizational relationships has increasingly focused on how network links affect perceived reputation and status (Zuckerman, 1999). Social networks are not just pipes through which resources flow; these networks are also potentially distorting prisms through which actors’ reputations can be discerned (cf. Podolny, 2001). Interest in a cognitive approach to social networks developed earlier in organizational behavior approaches than in more macro-oriented approaches. Pioneering work suggested that organizations and environments interacted as networked cognitions in the minds of participants: “what ties an organization together is what ties thought together” (Bougon, Weick, and Binkhorst, 1977: 626). Social equals in organizations tend to change their perceptions to establish consensus concerning environmental changes, whereas people connected to high-status individuals tend to be overly influenced by these high-status individuals’ perceptions of environmental change (Sampson, 1968; Walker, 1985). A recent review of the relationship between network connections and perceptions of the environment suggested that “knowledge emergence, as opposed to knowledge transfer, may occur . . . between social equals from different social circles, rather than between dyads divided by differences in mutual esteem and power” (Ibarra, Kilduff, and Tsai, 2005: 366). Building on this legacy of work in organizational behavior, our cognitive emphasis in this book is predicated on the finding that different individuals looking at the same networks tend to see different sets of connections (cf. Krackhardt, 1987a). To the extent that each individual occupies a specific position in a social network, the complexity of the network is likely to be viewed differently by each individual (Kilduff, Tsai, and Hanke, 2006). Some of these idiosyncratic views are likely to be more accurate in terms of mapping more closely on to a consensually validated representation of the network determined by the agreement of members of each interacting dyad. Such accuracy with respect to the organizational advice network can correlate with the power to influence others (Krackhardt, 1990). The perception of social networks begins as soon as an individual enters a new organizational context. People are motivated to generate an overall picture of a social group that they have joined, they seek to identify subgroups that might complicate or facilitate their putative plans, and they look for others to whom they can attach themselves (cf. von Hecker, 1993). Seeing the new interacting group into which they have just stepped as a distinct social system (cf. Campbell, 1958), people bring with them

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preconceptions from their interactions in previous social systems concerning the expectation that friendship overtures are likely to be reciprocated and the “transitivity” expectation that people who share a mutual friend will be friends themselves (Heider, 1958). These expectations bias the cognitive maps that people develop to represent social networks (Krackhardt and Kilduff, 1999). But people are not slaves to default expectations about friendship reciprocity and transitivity – people can also learn from vivid experiences (Ahn, Brewer, and Mooney, 1992) that social life can be riven with gaps where one might have expected ties (Janicik and Larrick, 2005). Each of us brings to our organizational sense making a different recipe for constructing representations of social networks. To the extent that people are cognitive misers who try to economize on memory demands (Fiske and Taylor, 1991), their mental representations of social networks are likely to exhibit simplifications such as excess clustering of people into densely connected groups and overattribution of popularity to people perceived to be central (Kilduff, Crossland, Tsai, and Krackhardt, forthcoming).

Individualizing Networks In bringing the individual back into social network research, we emphasize in this book not only the importance of individual cognition but also raise questions concerning how different types of people establish different network positions and experience different network outcomes. People differ with respect to whether or not they occupy brokerage positions in social networks (Burt, 1992) and outcomes from brokerage include both benefits such as higher job performance ratings (e.g., Mehra, Kilduff, and Brass, 2001) and potential costs such as reputation loss (e.g., Podolny and Baron, 1997). Despite this exploration in prior literature on the outcomes of brokerage, we still know relatively little about why some individuals rather than others are more central in social networks and occupy brokerage positions (Burt, 2005: 28). We explore in this book the likelihood that the patterning of social relations in organizations – including the elevation of some individuals to positions of centrality and brokerage – derives from stable individual differences. Visible individual differences such as ethnicity and gender function as bases for identification and network formation (Hughes, 1946). People tend to interact with similar others in organizations and this is particularly true for relations, such as friendship, that are more expressive than instrumental (Blau, 1977). Together with exclusionary pressures from the majority, this preference for similar, or “homophilous,” others

Introduction

5

may contribute to segregation within informal networks (Brass, 1985) and the marginalization of minority members. But going beyond this emphasis on demographic differences, we also explore in the book the likelihood that brokerage is related to selfmonitoring personality orientation. Those high in self-monitoring resemble successful actors in their ability to play different roles for different audiences (Snyder, 1987). Self-monitoring, in comparison to other personality variables, may be particularly relevant to the prediction of brokerage because of the theoretical (Day and Kilduff, 2003) and empirical (Flynn, Reagans, Amanatullah, and Ames, 2006) emphases on how personal identity affects the structuring of relationships. Other major personality variables tend to suffer from limited predictive validity when it comes to explaining why some individuals are more central than others (see, for example, the exemplary investigation of the network correlates of the Big Five personality constructs by Klein, Lim, Saltz, and Mayer, 2004). Because high self-monitors compared to low self-monitors tend to adapt their underlying personalities to allow themselves to become part of distinct social groups (Snyder and Gangestad, 1982), self-monitoring orientation is one key factor in understanding how individuals span across social divides in organizations.

Positioning the Book Interest in social networks has increased rapidly over the past decade, but few books focus specifically on interpersonal networks within organizations, and none pursue the topics we cover here. There are some excellent recent research monographs. The book by Noah Friedkin (1998) entitled A Structural Theory of Social Influence is unusual in bringing a social psychology approach to bear on questions of influence from a social network perspective. But there are few topics of overlap between that book and our own. The recent book by Peter Monge and Noshir Contractor (2003) entitled Theories of Communication Networks takes a programmatic approach in synthesizing the authors’ collaborative research in developing a multitheoretical and multilevel model. Again, there are few topics of overlap here. We see both of these books as companions to our own rather than as rivals. One of us has coauthored a recent social network book that critiques and extends social network theory in general in the context of offering a theoretical and methodological introduction (Kilduff and Tsai, 2003). There is one chapter in that book (pages 66– 86) that urges researchers to pursue structural research from a cognitive and individual difference perspective in pursuit of questions that have often been neglected. We see that chapter as whetting the appetite for

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Interpersonal Networks in Organizations

a more programmatic and comprehensive treatment of such topics as they apply to organizational behavior. Finally, the recent edited book by Cross, Parker, and Sasson (2003) focuses on networks in the knowledge economy with a particular appeal to managerial rather than research concerns. In studying interpersonal networks within organizations, we embrace a realist philosophy of science in terms of a research focus on three levels: the actual, the perceived, and underlying structures. The actual network of relationships in an organization can be perceived and experienced by individuals in many different ways, and, thus, actual and perceived networks can be discrepant with each other for any specific individual. In terms of the tendency for perceived networks to adhere to structural patterns, we know that perceptions of social relations tend to be shaped by cognitive heuristics such as the balance schema that individuals employ to make sense of complex realities (Krackhardt and Kilduff, 1999). Actual networks are also structured by underlying tendencies – for example, the tendency for people to cluster themselves together on the basis of similarity on dimensions that are considered important (such as ethnicity and gender – cf. Mehra et al., 1998). Our research engages all three levels of analysis, and investigates the discrepancies and tensions between these levels. We anticipate that the book will advance theory and research concerning organizational behavior and also push forward the social network research program itself. In tackling issues at the microbehavior level within organizations, we bring a traditionally sociological approach (structural social network theory) to dwell on topics (such as organizational turnover) typically studied from a more psychological approach. This book synthesizes research interests across the micro–macro divide to open new arenas for social network theory and methods. In bringing a distinctive research lens focused on interpersonal networks, we hope to unlock the whole realm of organizational behavior to the social network approach.

Overview of the Book This book emphasizes the importance of interpersonal networks, particularly friendship networks, for understanding people’s behaviors in organizations. There are three major sections, following this introduction. In the first part – “Perceiving Networks” – we focus on how individuals perceive networks in organizations, and explore the consequences of such perceptions. In the second part – “The Psychology of Network Differences” – we analyze how individuals differentially draw upon network resources, with particular attention on how network position and individual

Introduction

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personality contribute to performance outcomes. In the third part – “Network Dynamics and Organizational Culture” – we study how individuals in organizations respond to network influences, looking at turnover, crisis, and culture. A common theme runs throughout the book: We are bringing the importance of individual cognition, personality, and action back into a network research area that has tended to neglect if not completely ignore the importance of the microfoundations of structural constraint. Chapter 2, “A Network Approach to Leadership,” is a key resource for the whole book in providing a focused review of our major themes and their relevance for leadership in organizations. In this chapter, we articulate four interrelated principles that generate network theories and hypotheses and present a theoretical framework of leader effectiveness from the perspective of cognitive network theory. In Chapter 3, “An Analysis of the Internal Market for Reputation in Organizations,” we address whether perceptions of networks matter more than reality. We address how network perceptions are aggregated to create “real” networks, and how misperceptions of networks affect competitive outcomes in organizations, such as the reputations of individuals as good performers. We look specifically at the question of whether, if you are perceived by others in the organization to have a prominent friend, will this affect others’ perceptions of your job performance. In Chapter 4, “Systematic Biases in Network Perception,” we continue our focus on the systematic biasing of perceptions of organizational networks. We develop the theme of whether boundedly rational people in organizations tend to rely on heuristics to establish the friendship boundaries around themselves and others. In Chapter 5, “Effects of Network Accuracy on Individuals’ Perceived Power,” we examine the consequences of accurate perceptions of social networks in relation to individuals’ political power in organizational settings. The second part of the book, “The Psychology of Network Differences,” focuses on the use of networks with respect to decision making, individual performance in organizations, and helping behaviors. In terms of bringing the individual back in, we examine the possibility that individuals’ network positions are, to some extent, an expression of individual personality. In Chapter 6, “Social Structures and Decision Making in an MBA Cohort,” we address the issue of how people make decisions about complex issues, drawing upon network research that investigated such decision making in an environment overflowing with relatively complete information. We trace how individuals group themselves into clusters on the basis of ethnicity and gender, examine the extent to which the cohesion and structural equivalence perspectives predict these individuals’ decision making, and look at whether self-monitoring personality orientation offers a basis for understanding why some people

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Interpersonal Networks in Organizations

relative to others tend to draw more heavily upon network resources in making complex decisions. We follow up this self-monitoring theme in Chapter 7, “The Social Networks of Low and High Self-Monitors,” in our examination of whether low and high self-monitors build distinctly different network structures and whether self-monitoring and network position combine to affect individual performance in organizations. This chapter continues our attempt to open a productive new seam of structural research that brings together psychological richness at the individual level and sociological context at the network level. In Chapter 8, “Centrality in the Emotion Helping Network: An Interactionist Approach,” the last chapter in Part II, we again examine the twin effects of network position and personality, this time with respect to centrality in the emotion helping network in organizations. The third part of the book, “Network Dynamics and Organizational Culture,” takes a more dynamic perspective concerning how individuals in organizations are influenced in their behaviors and attitudes by those to whom they are connected either cognitively or actually. Chapter 9, “Network Perceptions and Turnover in Three Organizations,” investigates, from a social network perspective, the process and consequences of people leaving organizations. If someone occupying a role similar to my own leaves, how likely am I to also quit the organization? If I decide to stay despite the fact that a friend has left, what will be my attitude toward the organization – more or less committed? Chapter 10, “Organizational Crises,” continues the theme of network influence between and within organizational units in focusing on how internal and external friendship ties affect organizations’ responses to crises. Chapter 11, “The Control of Organizational Diversity,” advances a distinctive approach to organizational culture as a cognitive system developed and supported within local social networks. From this perspective, the organization resembles a magnetic field within which individual components attract and repel each other, with friends establishing mutually reinforcing interpretive systems. Our emphasis is on the local construction of cultural meaning within an overarching set of shared cultural understandings and the extent to which individuals’ cultural attitudes are controlled by their network ties. Finally, Chapter 12, “Future Directions,” looks forward to further research in terms of new approaches and phenomena to be addressed within the evolving research program that we have articulated.

Motivation for Writing This Book Because of the eclectic nature of the social network field, our research has appeared in leading journals in a variety of different areas including

Introduction

9

anthropology, psychology, sociology, and management. Indeed, we know of no other research program that has encompassed such different audiences. We have not had a chance to bring our different contributions together to emphasize the programmatic nature of our research interests. In this book, we bring our research themes under one overarching umbrella so that the significance of the work can be appreciated as a whole rather than in the particular fragments that happen to show up in each discipline’s journals. Rather than just reprinting articles, however, we have integrated material from different sources, updated our arguments, reduced redundancy, and emphasized core themes throughout. A major motivation for us in writing this book is the opportunity to comment on the different themes that we have been working on together for twenty years. We have synthesized and edited so that the book adds value beyond what has already been published. The book offers a theoretical and empirical alternative for organizational behavior research that often gets lost in the intricacies of microlevel attitudes at the expense of perceived and actual social context. Social network research has often critiqued other approaches in the social sciences. But it is time we went beyond critique to offer our fellow researchers a clear alternative that addresses topics they hold dear. In this book, we provide a blueprint for how theoretically motivated research can be accomplished on both traditional topics such as turnover and organizational culture as well as new topics such as the perception of social relations.

Target Readership This book is targeted at the research community of scholars interested in social network research. A primary audience consists of professors in schools of management, psychology departments, and sociology departments who want an up-to-date, theory-driven treatment of network research on organizational behavior topics. The book will also be of interest to doctoral students in the same areas. We are honored to have this book included in the distinguished Structural Analysis in the Social Sciences series edited by Mark Granovetter. In summary, the potential synergy between micro-organizational behavior research and social network approaches is huge. A focus on the social networks – both cognitive and actual – of organizational members is likely to enhance our understanding of organizational behavior, given the importance of social structures of interaction to the understanding of attitudes and behavior. The social network perspective has

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Interpersonal Networks in Organizations

traditionally avoided a focus on specific people, preferring to examine systematic patterns of interaction. Our aim in this book is to bring the individual back into the picture – to account for the cognitions and personalities of individuals in connection with the structural patterns that constrain and enable.

I Perceiving Networks

2 A Network Approach to Leadership

Good administrators sometimes fail to understand social structure and fail to anticipate its consequences for organizational survival. This can leave organizations vulnerable to manipulation by skilled political entrepreneurs. In one example, the entire top management team of a manufacturing company learned from a network analysis that the bomb threats, shootings, and vandalism threatening the future of the company were instigated by partisans of a lower-ranking manager, who had systematically recruited family, friends, and neighbors into the company over a thirty-year period. In a district desperate for jobs, these partisans felt loyalty to the informal leader, who had provided them information that allowed them to be first in line for vacancies on Monday morning. The CEO, confronted with an analysis of the deep cleavages existing in the social structure of the organization resulting from the informal patterns of recruiting over decades, had this to say about those who had been hired: “. . . they just seemed like waves of turtles coming over the hill; hired as they made it to our door” (Burt, 1992: 1). This story illustrates the gap at the heart of our understanding of organizational behavior. It illustrates how important it is for managers and would-be leaders to accurately perceive the network relations that connect people, and to actively manage these network relations. This story also illustrates how informal leaders who may lack formal authority can emerge to frustrate organizational functioning through the manipulation of network structures and the exercise of social influence. Our goal in this book is to investigate the implications of new directions in network theory that emphasize networks as both cognitive structures in the minds of organizational members and opportunity structures that facilitate and constrain action. In this chapter, we emphasize the importance of individual cognition for understanding social networks. We do this through an exploration of how the cognitions in the mind of the individual influence the network relationships negotiated by the individual 13

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Perceiving Networks

and how this individual network contributes to leadership effectiveness both directly and through informal networks. We understand “leadership” to be a general concept applicable at many different levels in the organization, and to include both formally designated leaders as well as informal leaders. We link together social cognitions and social structure to forge a distinctive network approach to leadership that builds upon, but extends, previous work in both the network and the leadership realms.

Organizational Network Research Core Ideas The organizational network perspective is a broad-based research program that continually draws inspiration from a set of distinctive ideas to investigate new empirical phenomena. The “hard-core” ideas at the heart of network research define its special character and distinguish it from rival research programs (cf. Lakatos, 1970). What are these ideas familiar to all organizational network researchers? At least four interrelated principles generate network theories and hypotheses: the importance of relations between organizational actors, actors’ embeddedness in social fields, the social utility of network connections, and the structural patterning of social life (Kilduff et al., 2006). An emphasis on relations between actors is the most important distinguishing feature of the network research program. As a recent historical treatment of social network research (Freeman, 2004: 16) pointed out, a core belief underlying modern social network analysis is the importance of understanding the interactions between actors (rather than a focus exclusively on the attributes of actors). An early treatment of network research on organizations stated that “the social network approach views organizations in society as a system of objects (e.g., people, groups, organizations) joined by a variety of relationships” (Tichy, Tushman, and Fombrum, 1979: 507), whereas the importance of understanding relationships as constitutive of human nature was stated as follows in a recent book: “Human beings are by their very nature gregarious creatures, for whom relationships are defining elements of their identities and creativeness. The study of such relationships is therefore the study of human nature itself” (Kilduff and Tsai, 2003: 131). Our network approach locates leadership in the relationships connecting individuals. The second principle that gives organizational network research its distinctiveness as a research program is the emphasis on embeddedness. For organizational network researchers, human behavior is seen as embedded in networks of interpersonal relationships (Granovetter, 1985; Uzzi, 1996). People in organizations and as representatives of organizations tend to enter exchange relationships not with complete strangers but

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with family, friends, or acquaintances. Embeddedness at the system level can refer to a preference for interacting with those within the community rather than those outside the community. We emphasize that people’s perceptions of others as leaders are reflected through the sets of embedded ties within which people are located. The third driving principle of social network research is the belief that network connections constitute social capital that provides value – including economic returns (Burt, 2000). As a previous review of network research on leadership pointed out, “Social capital is at the heart of social network analysis” (Brass and Krackhardt, 1999: 180). Depending upon the arrangement of social connections surrounding an actor, more or less value can be extracted (Burt, 1992; Gnyawali and Madhavan, 2001). At the system level, a generalized civic spirit emerges from and contributes to the many interactions of trust and interdependence between individual actors within the system (Coleman, 1990; Portes, 2000). Leadership, from the network perspective we develop, involves building and using social capital. The fourth leading idea distinctive to the social network research program – the emphasis on structural patterning – often leads social network research to be referred to as the “structural approach.” Network researchers look for the patterns of “connectivity and cleavage” in social systems (Wellman, 1988: 26). Not content with merely describing the surface pattern of ties, researchers look for the underlying structural factors through which actors generate and re-create network ties. At the local level surrounding a particular actor, the structure of ties can be described, for example, as relatively closed (actors tend to be connected to each other) or open (actors tend to be disconnected from each other) (Burt, 1992). At the system level, organizational networks can be assessed for the degree of clustering they exhibit and the extent to which any two actors can reach each other through a short number of network connections (e.g., Kogut and Walker, 2001). To understand who is a leader from a network perspective is to investigate the social-structural positions occupied by particular individuals in the social system. These four leading ideas – the importance of relationships, the principle of embeddedness, the social utility of network connections, and the emphasis on structural patterning – provide the common culture for organizational network research that allows the diversity of viewpoints from which fresh theoretical initiatives emerge (cf. Burns and Stalker, 1961: 119). Network research is also characterized by vigorous development of methods and analytical programs to facilitate the examination of phenomena highlighted by theory (see Wasserman and Faust, 1994, for a review of methods; and the UCINET suite of programs – Borgatti, Everett, and Freeman, 2002 – for statistical software).

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Perceiving Networks

The organizational network research program is progressive in the sense that new theory is constantly being developed from the metaphysical core of ideas that makes up the heart of the research program, highlighting new areas of application. It is the purpose of this chapter to highlight the area of leadership from a network perspective. The four leading ideas that comprise the intellectual source of theory development for organizational network research are best understood as mutually reinforcing core beliefs that, like the planks of a ship, keep the research program afloat – in terms of new theory development and exploration of new phenomena. At the level of network theory and research, all four ideas tend to be inextricably involved. We will invoke these ideas as appropriate throughout the chapter. In contrast to network research, traditional leadership research has focused on human capital attributes of leaders and situational attributes of leadership contexts. Human capital attributes of leaders include traits (e.g., House, 1977; Kenny and Zaccaro, 1983) and behavioral styles (e.g., Lewin, Lippitt, and White, 1939; Podsakoff, Todor, and Skov, 1982), whereas situational attributes of leadership contexts include task structure (Fiedler, 1971), the availability of leadership substitutes (Kerr and Jermier, 1978), the nature of the decision process (Vroom and Yetton, 1973), and the quality of leader–member exchange (Dansereau, Graen, and Haga, 1975; Graen, Novak, and Sommerkamp, 1982). A social network perspective does not eclipse the valuable results of conventional leadership research; rather, a network perspective can complement existing work without repeating it. In particular, in this review we amplify the voices that have called for a new understanding of leadership effectiveness to include leaders’ cognitions about networks and the actual structure of leaders’ ties (e.g., Hooijberg, Hunt, and Dodge, 1997; see also Bass, 1990: 19). As with all theoretical perspectives, the network approach has boundary conditions that limit its range of application. Social network processes are less likely to have the effects we discuss to the extent that organizations are characterized by perfect competition between equally informed actors all of whom have the same opportunities (see the discussion in Burt, 1992). (Even under conditions of perfect information, however, some actors are likely to be more influenced by social networks than others – see Kilduff, 1992.) A further limiting condition is the extent of work interdependence: Under conditions of low interdependence between actors and little or no social interaction, network processes and their effects will tend to be minimized. In network terms, leadership embodies the four principles that we articulated earlier. Leadership can be understood as social capital that collects around certain individuals – whether formally designated as leaders or

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not – based on the acuity of their social perceptions and the structure of their social ties (cf. Pastor, Meindl, and Mayo, 2002). Patterns of informal leadership can complement or complicate the patterns of formal leadership in organizations. Individuals can invest in social relations with others, can structure their social networks by adding and subtracting relationships, and can reap rewards both in terms of their own personal performance and organizational unit performance (Sparrowe, Liden, Wayne, and Kraimer, 2001). But embeddedness in social networks always involves the paradox that social relations, particularly those outside the immediate circle of the individual, may be difficult both to perceive accurately and to manage (cf. Uzzi, 1997). Thus, although the social structure of the organization determines opportunities and constraints for emergent leaders, the social structure is not within the control of any particular individual.

Leadership and the Structure of Ties We start our network approach to leadership theory with a discussion of actor cognitions concerning networks, move out to the inner circle around the actor, and then further zoom out to include progressively more of the social structure of the organization and the interorganizational realm. The theoretical framework is illustrated in Figure 2.1, and represents a tentative model of leadership effectiveness from a network perspective. We provide an overview of the causal connections of the model before zooming in to discuss in more detail the dynamics within each part of the model. As Figure 2.1 shows, the first step in the conceptual model indicates that leaders’ cognitions about social networks affect the “ego networks” that surround each leader. Cognitive network theory (see Kilduff and Tsai, 2003: 70–9, for a review) suggests that people in general shape their immediate social ties to others to be congruent with their schematic expectations concerning how relationships such as friendship and influence should be structured. The schematic expectations of leaders affect their ability to notice and change the structure of social ties (e.g., Janicik and Larrick, 2005). Thus, cognitions in the mind of the leader are the starting point for our theorizing concerning the formation of ties connecting the leader to others. The network cognitions of leaders concerning such crucial organizational phenomena as the flow of social capital within and across organizational boundaries and the presence and meaning of social divides are hypothesized to affect the extent to which leaders occupy strategically important positions in the organizational network. An accurate

18

•Range • Cohesion

•Accuracy

•Schemas

Leader Effectiveness

• Mentoring distributed leadership • Brokering

Organizational Network • Centrality

• Coalition-building

Intra-organizational Level

--------------------------------------

•Innovation

• Growth

•Survival

Organizational Level

Figure 2.1. Theoretical framework linking a leader’s network accuracy to leader-relevant outcomes.

•Density

Network Acuity

Ego Network

•Alliances

• Boundary spanning

Interorganizational Network

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perception of the informal influence network can itself be a base of power in the organization (see Chapter 5) and can facilitate the leader’s ability to forge successful coalitions (Janicik and Larrick, 2005). We extend these insights to hypothesize that the acuity of leader cognitions will affect the extent to which a leader plays a strategically important role in the relevant interorganizational network. We know of no research bearing on this thesis, although recent work concerning interorganizational relationships increasingly concerns itself with hypothesized perceptual processes such as organizational reputation and status (e.g., Podolny, 1998; Zuckerman, 1999). The extent to which a leader plays a role in these three actual networks – the ego network, the organizational network, and the interorganizational network – is hypothesized to affect leader effectiveness. This critical hypothesis derives from our basic understanding of how the four guiding principles of the network approach extend leadership theory. Modern concepts of leadership identify the relational content of the interaction between people as the key aspect involved in the structuring of situations and the altering of perceptions and expectations (e.g., Bass, 1990: 19). Modern network theory suggests that individuals who are central in the immediate networks around them and in the larger networks that connect them to others throughout the organization and beyond the organization are likely to acquire a particular type of expert power: knowledge of and access to those few powerful others whose words and deeds control resource flows and business opportunities (e.g., Burt, 2005). Leaders may not be able to move into the center of every important network, of course. Embeddedness in one social network may come at the price of marginality in another network. There are trade-offs involved in building social capital, particularly when brokerage across social divides may engender distrust rather than gains. One blow-by-blow account of an organizational power struggle contrasted the networking strategies of two combatants for sole control of the CEO position they currently shared. Whereas co-CEO Louis Glucksman was central within the Lehman Brothers organization as a whole and occupied a particularly strategic position among the traders, his rival and co-CEO Pete Petersen neglected internal networking in pursuit of connections with the leaders of other organizations (Auletta, 1986). Both men were effective leaders – Glucksman contributing to internal effectiveness and Petersen building and maintaining the external relationships that brought contracts to the partnership. But both had built quite different social network bases of power. The role of external affective ties with the representatives of other organizations in providing vital help to companies in financial trouble has been emphasized by research on the survival prospects of small firms

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Perceiving Networks

in the New York garment industry (Uzzi, 1996). More generally, the organizational theory and strategy literatures have examined the extent to which ties between organizations constitute a knowledge base important for outcomes such as firm growth (e.g., Powell, Koput, and Smith-Doerr, 1996), new ties (e.g., Gulati and Gargiulo, 1999; Larson, 1992), and innovation (Hargadon and Sutton, 1997). Thus, the extent to which leaders are effective in terms of accessing important resources is likely to depend on the social-structural positions they occupy in the key networks within and between organizations. What are the outcomes associated with leader effectiveness from a social network perspective? We have thus far mentioned such aspects of leader effectiveness as organizational growth, survival, and innovation. These are the responsibility of formal leaders and are outcomes at the organizational level of analysis. As Figure 2.1 summarizes, leader effectiveness from the network perspective that we articulate would also include such components of internal organizational functioning as coalition building, mentoring, and brokering. These are intrinsically networking outcomes of both formal and informal leadership that can enhance coordination across functions within the organization. We return to these internal measures of leader effectiveness later in the chapter. The model outlined in Figure 2.1 necessarily simplifies the relationships between cognition, social networks, and leadership effectiveness. We neglect, for example, the ways in which occupancy of social-structural positions in networks affects individuals’ cognitions and expectations about networks (see Ibarra et al., 2005, for a review). The organization and the environment within which it operates can be jointly considered a set of cyclical processes captured in networks of cognitions (cf. Bougon et al., 1977). We focus in this chapter on leadership, and therefore emphasize the proactive enactment of outcomes leading to leader effectiveness.

Network Cognition and Leadership A key discovery of modern social network research is that cognitions matter (see Chapter 3), and thus we start the in-depth discussion of the theoretical framework with an emphasis on network cognition, a topic relatively neglected within conventional leadership research (but see early leader-member exchange [LMX] work on whether peers within units accurately perceive the quality of dyadic leader–subordinate relations – Graen and Cashman, 1975). Depending upon how the boundary is drawn around a particular individual in an organization, that individual may or may not appear to be influential in the eyes of others. That implicit leadership theories may be triggered by the structural position

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of certain individuals in the eyes of others is a possibility hinted at in recent leadership theory (Lord and Emrich, 2001) but which has yet to be systematically examined. From the perspective of perceivers located in small groups, certain actors may appear influential, but perceivers surveying the larger context of the whole organization may dismiss these same actors as relatively inconsequential (see the discussion in Brass, 1992). Conversely, people who seem relatively powerless within one local group may be revealed to have close connections with powerful others outside the group. Thus, we organize our discussion by progressively zooming out from individuals’ network cognitions to include expanding social circles within and beyond the organization. From a network perspective that emphasizes the importance of relationships, embeddedness, social capital, and social structure, the ability of formal or would-be informal leaders to implement any leadership strategy depends on the accurate perception of how these principles operate in the social context of the organization. To be an effective leader of a social unit is to be aware of (a) the relations between actors in that unit, (b) the extent to which such relationships involve embedded ties including kinship and friendship, (c) the extent to which social entrepreneurs are extracting value from their personal networks to facilitate or frustrate organizational goals, and (d) the extent to which the social structure of the unit includes cleavages between different factions. The accurate perception of this complex social reality is fraught with difficulty, and, therefore, network cognition is an arena for innovative research. If a leader wants to use social network ties to lead others, the leader must be able to perceive the existence, nature, and structure of these ties – not just the ties surrounding the leader, but the ties connecting others in the organization both near and far. Actors who are perceived to have power in terms of the structure of their social ties to others may wield influence even though they seldom or never exercise their potential power (Wrong, 1968; see the discussion in Brass, 1992: 299). To a considerable extent, organizations and environments exist as cognitions in the minds of leaders and followers within organizations (Bougon et al., 1977; Kilduff, 1990) and in the interorganizational arena of reputation and status (Podolny, 1998; Zuckerman, 1999). Thus the question arises, how do people perceive network ties within and between organizations? How does anyone tell whether, for example, two individuals are personal friends? Even a small organization of fifty people represents a considerable cognitive challenge in terms of trying to perceive accurately the presence or absence of 2,450 friendship links between all pairs of individuals, links that may well be relatively invisible except to the individuals concerned. To create and manage the networks that promote leadership effectiveness, it may be necessary to possess an

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accurate representation of network links involving not just friendship and kinship, but also advice, communication, and other important network ties. What happens when formal leaders pay no attention to the four principles we have enunciated as representing the network approach to leadership? Is there any penalty consequent upon leader ignorance of social relations inside organizations, leader blindness to the embeddedness of working relationships in extra-organizational arrangements such as kinship, leader neglect of the extent to which social entrepreneurs manipulate embeddedness for their own ends, and leader unconsciousness of the social cleavages within the organization? The answer, provided in the case study alluded to in the opening paragraph of this chapter, is shocking in its illustration of diseased social capital. When the management fired, in a routine cost-cutting exercise, the informal leader to whom so many people were beholden not just for jobs but for the references necessary to actually get jobs inside the industrial plant, deep trouble ensued between employees loyal to the informal leader and those helping the management keep the industrial plant solvent. Shootings, bomb threats, and leakings of confidential management documents were the order of the day. The formal leadership team had no comprehension of what was happening, not having noticed that the workforce included so many people with strong social ties to a particular individual. (For the full case study, see Burt and Ronchi, 1990.) The CEO in this case was a good administrator and a skilled engineer who failed to understand the necessity of keeping track of the social structure of competition within and outside the organization. Social networks interpenetrate the boundary between employees and nonemployees, and the management of this boundary has important consequences for organizational functioning. Job applicants with social contacts (such as friends) inside the organization can exploit social capital advantages to extract critical information at both the interview and job offer stages. These referred individuals (compared to those who are not referred by current organizational members) tend to present more appropriate r´esum´es and to apply when market conditions are more favorable (Fernandez and Weinberg, 1997). Referred individuals have a significantly greater likelihood of being offered a job as a result of these advantages. Further, referrals (relative to nonreferrals) can use inside knowledge to boost their starting salaries in the negotiation process. Thus, what might appear to a corporate leader as a systematic process of institutionalized racism involving higher starting salary increases to ethnic majorities relative to ethnic minorities can be revealed through social network analysis as a function of who has friends inside the organization (Seidel, Polzer, and Stewart, 2000). The fairness of a hiring process

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may be fundamentally compromised because it is invisibly embedded in kinship and friendship networks. The perception of this otherwise invisible process of homophilous hiring is crucial to any effort by the leadership team to increase workforce diversity. The explicit management of external ties to recruit new members who are known to existing members of the organization can enhance the organization’s economic returns (Fernandez, Castilla, and Moore, 2000). If leaders comprehend the social network relationships not just among organizational employees but also between employees and those outside the organization, then leaders can build the social capital of the organization by putting individuals’ personal social networks to work for the organization’s benefit. Typically, managers are busy people whose work is fragmented and interrupted (Mintzberg, 1973). Much of our research in organization theory focuses on the formal arrangement of titles, offices, and reporting relationships, whether with respect to the integration and differentiation of the organization (e.g., Lawrence and Lorsch, 1967), the inertia of the organization (e.g., Hannan and Freeman, 1984), or the ceremonial fac¸ade created to be isomorphic with institutional demands (Meyer and Rowan, 1977). Leadership research, to the extent that it has considered social network relations, has also focused overwhelmingly (from an LMX perspective) on managers and the extent to which subordinates, for example, established networks that mirror those of their formally appointed managerial leaders (Sparrowe and Liden, 2005). The cognitive revolution in leadership research has focused not on the cognitions of leaders, but on leadership factors in the minds of followers (Eden and Leviatan, 1975; Lord and Emrich, 2001). There is an opportunity to extend both LMX research and cognitive approaches to leadership from the perspective of cognitive network theory (see Kilduff and Tsai, 2003: 70–9, for a review) with a focus on how leaders and followers comprehend (a) the structure of social relations (cf. Chapter 5), (b) the embeddedness of economic action in affect-laden networks (cf. Uzzi, 1996), and (c) the opportunities for social entrepreneurship across structural divides (Burt, 2005). A greater understanding of how leaders and followers comprehend the social structure from which action in organizations proceeds can enhance research on the management of relationships. Accuracy From a cognitive network theory perspective, leadership involves not just social intelligence (i.e., the accurate perception of social relationships in organizations) but also the management of others’ perceptions. First, let

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Perceiving Networks

us consider accuracy. People perceive the same network differently, with some individuals achieving a high degree of accurate perception, whereas other individuals lead their organizational lives in relative ignorance of the actual network of relationships within which work is accomplished (Chapter 3). In general, perceptions of networks involving sentiment relations such as friendship suffer from a series of predictable biases. People prefer to see their own relationships as reciprocated – they prefer not to perceive their friendship overtures as unrequited. Similarly, people prefer to believe that their friendship circles are transitively complete – they like to believe that their own friends are friends with each other (Heider, 1958). This cognitive balance schema operates also as a default mechanism for filling in the blanks concerning ties between relative strangers at the individual’s perceived organizational network’s periphery. In the absence of contrary information, people tend to assume that friendship ties of others are reciprocated, and that two friends of a distant stranger are themselves friends (Freeman, 1992; Chapter 4). These cognitive distortions can affect leadership emergence. People in organizations see themselves as more popular than they actually are (Krackhardt, 1987a), a tendency that can, perhaps, lead some individuals to neglect the vital process of maintaining their social capital (on the assumption that they are already popular), whereas other individuals, through a self-fulfilling prophecy process, may transform the illusion of popularity into actual friendship links that initially did not exist. Assuming that others like them, some people may reciprocate nonexistent liking and thereby create friends. Slight initial differences with respect to how people perceive their connections to others can potentially lead to cumulative advantages through this self-fulfilling prophecy process. Further, there may be a tendency to perceive popular actors as being even more popular than they really are (Kilduff et al., forthcoming). Human beings, in their perceptions of social networks, are “cognitive misers” (Chapter 4) who may tend to simplify networks by perceiving them as dominated by a few central actors even if the actual network has no dominant cluster. A misattribution of popularity to a few actors can result in these actors actually increasing their popularity. An emerging leader who is perceived to be popular may benefit from a bandwagon effect: People may want to associate with someone perceived to be a rising star. On the other hand, the perception that a social network is dominated by an elite group of leaders may discourage those who perceive themselves on the periphery from attempting to pursue leadership options.

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Schemas New research (Kilduff et al., forthcoming) suggests that individuals may tend to perceive friendship networks in organizations as small worlds. Small world network structures are unusual in that they exhibit both high local clustering and short average path lengths – two characteristics that are usually divergent (Watts and Strogatz, 1998). Clustering refers to the extent that actors are connected within local groups, whereas path lengths refer to the number of network connections between one actor and another in the network. A small world network resembles the huband-spoke structure of the U.S. commercial air traffic system: local hubs with lots of connections and short average path lengths because journeys from one city to another are routed through the hubs. (Compare this with the distinctly non-small world of the U.S. interstate highway system.) The small world effect, investigated originally in the 1960s by Milgram (1967), has become a burgeoning area of organizational social network research (e.g., Kogut and Walker, 2001; Uzzi and Spiro, 2005). As social networks become larger and more global, the discovery that some of the largest social networks such as the World Wide Web exhibit small world properties has excited considerable research interest (see Dorogovtsev and Mendes, 2003, for a review). Leadership within extremely large networks is a neglected topic but one that seems tractable from a small world perspective, given that small world networks are organized for efficient communication and coordination. We focus here on the possibility that some individuals more than others misperceive the extent to which organizational networks resemble small worlds (Kilduff et al., forthcoming). Such a bias has distinct implications for leadership research. Simplifying perceptions to perceive a friendship network as a small world offers a considerable advantage to the aspiring informal leader in terms of reducing the cognitive load required to keep track of so many different relationships. The rules for creating a cognitive map of the friendship network are relatively simple from this perspective: Put similar people (with similarity defined on some relevant dimension such as demography or interests) into clusters and connect the clusters. Further research is needed to examine the extent to which the match between the “small worldedness” of the individual’s cognitive network and the small worldedness of the actual network predicts leader effectiveness. Cognitive network schemas play a significant role in one important aspect of leadership, namely coalition building (cf. Stevenson, Pearce, and Porter, 1985). Leaders are constantly involved in appointing people to task forces and committees. Ensuring that these teams consist of the right

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Perceiving Networks

balance of people can make the difference between gridlock and effective action. In a pioneering set of studies, researchers found that individuals with experience of networks characterized by disconnections – structural holes – were better at perceiving the potential to bridge structural holes by identifying suitable collaborators – a key to successful coalition (Janicik and Larrick, 2005). By making sure that different constituencies are represented at the top of the organization, the leader may facilitate the engagement of widely different groups in the organizational mission. But in order to make these representative appointments, the leader must first be able to accurately perceive any existing social system cleavages. This recent research on the structural hole schema is interesting in suggesting that people are able to move beyond reliance on default modes of thinking (such as balance) when trying to make sense of the social network in organizations. People learn from experience to expect certain patterns in the social world, and tend to see new situations in the light of their anticipations. Thus, the leaders of an organization, familiar with the patterns of activity taking place from day to day, may impose on these patterns of interaction their own preconceptions of who shows up for meetings. Leaders anticipate that regular attendees will show up and remember these people as having showed up even if they did not, while forgetting that more peripheral members of the organization were actually present on a specific occasion (cf. Freeman, Romney, and Freeman, 1987). Further, people in general tend to perceive themselves to be more central in friendship networks in organizations than they actually are (Krackhardt, 1987a). Thus, network cognition can depart from actual patterns of network activity, with consequences for the leader’s ability to uncover political conflicts, spot communication problems between culturally divided groups, avoid reliance on problematic individuals for the transmission of important resources, achieve strategic objectives through the appointment of key people to influential positions, and manage relations within and across departments (Krackhardt and Hanson, 1993). Leaders who perceive important social networks accurately in their organizations are likely themselves to be perceived as powerful (Chapter 5). This perceived power can itself represent an important supplement to formal authority. But for those who want to span across structural holes and gain the reputed benefits of this activity, it may be crucially important to be perceived by others as not pursuing personal agendas (Fernandez and Gould, 1994). Social perceptions take place within reputational markets (Chapter 3) and, in the subtle battle to achieve prominence, individuals may strive to appear to others to be associated with leaders of high status. The perceived status of exchange partners can act like a distorting prism to filter attributions concerning the focal individual (cf. Podolny, 2001).

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Individuals move in and out of organizational contexts, and as they do so, their structural positions change. In one context, someone assumes a leadership position, but the same individual may be a follower in another context in the same organization. Partly this is due to shifting perceptions. Individuals self-perceive themselves as powerful in some contexts and as less powerful in other contexts, and their self-attributions may be concordant with or discrepant with others’ attributions. Actors in organizations may exert power without having to request compliance with their demands, simply on the basis of possibly false perceptions: Just as players can successfully “bluff” in poker, employees can also act as if they control scarce resources, as if they were potentially powerful. . . . Persons who are in a position to control information can withhold, disclose, and modify it in order to influence others’ attributions of power (Brass, 1992: 299). Thus, the importance of perceptions of leadership emergence and individual influence may reside in the extent to which they are never tested. In one recorded instance of a battle between dual CEOs for the exclusive control of the Lehman Brothers investment banking house, Louis Glucksman convinced his rival Pete Petersen that Petersen had lost friendships with board members, whereas Glucksman had retained their regard. But neither rival checked to see whether their perceptions of their social relations with the all-important board members were accurate (Auletta, 1986). To summarize our general ideas concerning the importance of acuity in leaders’ perceptions of social networks, we indicate in Figure 2.1 that accuracy is likely to improve the extent to which a leader occupies a strategic position in three social network structures relevant to organizational behavior: the ego network, comprising the individuals immediately connected to the leader; the complete organizational network, comprising not just direct connections but also the leaders’ indirect connections to everyone in the organization; and the interorganizational network of relationships important to the leader’s work outside the focal organization. In Figure 2.1, we also include the role of cognitive schemas in determining the match between leaders’ perceptions of networks and actual networks. We need more research concerning the extent to which cognitive schemas help or hurt leaders develop accurate maps of the social networks within which they operate. Whereas research on cognitive shortcuts implies that perceivers who rely on such shortcuts tend to make errors (Kahneman and Tversky, 1973), others see positive benefits deriving from the use of such schemas (e.g., Taylor and Brown, 1988), including greater satisfaction in close relationships (Murray, Holmes, and Griffin, 1996;

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see Kenny, Bond, Mohr, and Horn, 1996, for evidence concerning the effects of relational schemas on accuracy). We have spent considerable time on the social cognition of networks of relationships given the growing recognition within leadership research of how leader cognitions affect leader behaviors with implications for both leader effectiveness and organizational effectiveness (e.g., Hooijberg et al., 1997). Leadership research has long recognized the importance of implicit leadership schemas in the minds of followers (see Lord and Emrich, 2001, for review). Building on this emphasis on cognition and cognitive schemas, we seek to extend leadership research from a distinctively network emphasis on social relations, embeddedness, social capital, and social structure.

The Ego Network Moving on from the network cognitions in the head of the individual, we now consider the social circle of relations actually surrounding the individual. A strong argument could be made that it is this ego network that fundamentally affects all the other network relationships a leader forms and influences – hence the centrality of the ego network in Figure 2.1. It is this personal network that forms the basis of, for example, the influential structural hole perspective (Burt, 1992, 2005). A major task of future research is to assess whether the structure of direct connections leaders have with colleagues is as important as the structural hole approach implies, or whether more indirect connections involving intermediaries can dampen or enhance leadership effectiveness, as implied in embeddedness research (Burt, 2007; Uzzi, 1996). Density A key theoretical concept concerning how direct connections within the ego network relate to leadership is density, as indicated in Figure 2.1. Individuals whose social contacts are themselves connected to each other have dense social circles, whereas individuals whose social contacts have few connections among themselves have sparse social circles (Wasserman and Faust, 1994). Members of a dense network tend to share similar attitudes and values toward the leader of the organization (Krackhardt, 1999). From a network perspective, whether the members of a dense network tend to enhance or neutralize the leader’s effectiveness is likely to depend upon whether the shared attitudes toward the leader are positive or negative. A dense network of people favorably disposed toward the

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leader represents a pool of social capital available to the leader. Messages communicated to this group are likely to be favorably received and expeditiously transmitted. A dense network of people negatively inclined toward the leader represents a potentially distorting prism, likely to take any message or initiative from the leader and cast it in the most unflattering light. More research is needed on the ways in which dense networks distort or enhance leadership initiatives. Range Structural-hole theory (Burt, 1992), following on from the weak-tie hypothesis (Granovetter, 1973), suggests that individuals whose personal contacts include a diverse range of disconnected others gain benefits. These benefits (including faster promotions – Burt, 1992) derive from the information and control possibilities of being the “third in the middle” between other individuals who must pass resources and information through the focal individual. Thus, the focal individual has access to diverse communications within his or her immediate contacts. If the individual (conventionally referred to as “ego” in network research) is embedded in a clique, then the diversity of information and resources reaching ego from immediate contacts may be low. Further, the opportunity for ego to play an informal leadership role, distributing ideas and other valued resources throughout the immediate social circle, vanishes if ego is simply one more person in a highly connected group. As simple as the implied principle appears to be – connect oneself to diverse others who themselves are not connected to each other in order to enhance leadership potential in the informal network of relationships – it is much harder to realize than might at first be apparent. The principle of embeddedness operates strongly in this context. Simply stated, individuals prefer to associate with homophilous others – those who are similar to themselves (McPherson, Smith-Lovin, and Cook, 2001). This tendency is likely to be just as strong among putative leaders as it is among people in general – even economic transactions at the firm level tend to be embedded in kinship and friendship networks (Uzzi, 1996). Homophilous networks represent information restriction (Popielarz, 1999). Individuals embedded in such networks, established not just in terms of kinship but also on the basis of proximity (Festinger, Schachter, and Back, 1950), ethnicity, or gender (Mehra et al., 1998), are likely to experience strong cohesion (many ties among the similar others) but also information restriction. Groups as powerful as the dominant coalition (Cyert and March, 1963), the top management team (Hambrick and Mason, 1984), and the board of directors (Palmer, 1983) may exhibit in-group homogeneity under the pressures of ease of communication,

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shared backgrounds, and demographic similarity (see the review in Westphal and Milton, 2000). Social capital advantages are likely to be significantly diminished as leaders embed themselves in homogenous groups, leading to negative effects on market share and profits (Hambrick, Cho, and Chen, 1996). Business survival prospects tend to be better for those businesses whose owners establish a large range of personal contacts with important representatives of the task environment relative to those owners who establish a smaller range of such contacts (Oh, Kilduff, and Brass, 2005). Cohesion The cohesiveness of a dominant coalition may be sharply increased if the coalition perceives it is challenged by a set of actors (pursuing a hostile takeover, for example) or by negative outcomes of previous decisions (Kilduff, Angelmar, and Mehra, 2000). This increased homophily, while facilitating coordinated action by the top management team, may adversely restrict decision-making options. The extent to which leaders turn to their personal contacts for advice following poor firm performance predicts subsequent tendencies to minimize changes in corporate strategy (McDonald and Westphal, 2003). There are strong pressures in organizations for people to agree with their personal friends concerning important values and ideas. For an informal leader, embedded in a coalition of like-minded individuals, to challenge the hegemony of the official culture is always possible. But it is much more difficult for an informal leader to resist the social pressure from within his or her social circle to agree with close friends concerning how to interpret widely shared core values (Chapter 11). It is interesting to note that, from a network perspective, the social pressure on ego differs little irrespective of the size of the clique within which ego is embedded, given that the clique contains people who all have ties to each other within the clique but no common ties to those outside. Whether ego is embedded in a three-person clique or a larger clique, ego still experiences group pressure to conform (Simmel, 1950). This pressure becomes powerful as soon as a dyadic interaction (between two people) expands to include three people. To the extent that a leader belongs to two or more of these cliques (of three or more people), the leader is vulnerable to cross-pressures from the different cliques to which he or she belongs. Different cliques tend to reinforce different interpretations of reality, and these discrepant interpretations may place the leader, who links the two different cliques together and who may play a brokerage role between these different groups, in a complicated situation. Each clique

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may present the leader with demands that, considered jointly, may be difficult to meet. One case study described how an informal leader who strongly favored the ongoing unionization drive in an entrepreneurial company found himself unable to use his influential position in his personal social circle to influence others. This individual was a member of eight different threeperson friendship cliques and was thus “frozen by the set of constraints imposed by the numerous cliques” (Krackhardt, 1999: 206). Three of this person’s cliques contained vociferous opponents of unionization. So unpleasant was his position in his social circle that he resigned from the firm ten days before the unionization vote was taken and rejoined the firm two days after the vote had failed. This individual’s apparent power in the social circle of personal friends was stultified by his embeddedness in cohesive, but mutually discrepant, cliques. Informal Leadership Emergence Within the social circle surrounding the formal leader, there are likely to be some individuals who play informal leadership roles. These informal leaders tend to spring up in teams in which formally appointed leaders play little or no role in the coordination of team activity (perhaps because the formal leaders are focused on activities external to the team). Thus, informal leadership is likely to be a feature of teams in which formal leadership is, relatively speaking, absent. One study of leaderless teams found that informal leaders disproportionately influenced team efficacy – the extent to which team members evaluated their abilities to perform specific work-related tasks (Pescosolido, 2001). Such informal leaders also play a role in regulating team members’ emotions (Pescosolido, 2002). Key process variables, such as team efficacy and team emotions, affect team performance (Barsade, 2002; Gibson and Vermeulen, 2003). Given the potential power of these informal leaders to manage the cognitions and emotions of group members, even in the absence of any formal authority, formally appointed leaders’ relationships with these informal leaders become more important than perhaps approaches that have focused on leader–member exchange relations have recognized. We suggest that within the leader’s in-group there are some ties that are more crucial for leader effectiveness than others; and, outside the leader’s ingroup, neglect of individuals with considerable social influence is likely to imperil leader effectiveness. To summarize this section is to recognize that structural hole theory (Burt, 1992) suggests that would-be leaders should structure their interpersonal networks to reach diverse constituencies, using relatively few ties

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to expand the range of information and resources accessed. An effective network strategy, according to this interpretation of structural-hole theory, is likely to involve leaders building links to a variety of different constituencies and delegating to trusted “lieutenants” the task of managing relationships with the other members of each constituency. Information would flow to leaders through the trusted lieutenants from all around the organization. It is with each trusted lieutenant that the informal leader develops and maintains a strong tie (as suggested in the dyadic approach to leadership – see Dansereau, 1995, for a review). It is this emphasis on extending the leader’s ties throughout the organization that we turn to next.

The Organizational Network There are some caveats to the “divide and conquer” strategy advocated from the influential structural-hole perspective (Burt, 1992, 2005). From this perspective, would-be leaders are recommended to divide social networks in organizations into non-overlapping groups and to harvest social capital benefits from brokering information and other resources between these groups. However, as structural-hole theory recognizes, there are some groups (such as boards of directors) whose importance may require a much more intensive relational strategy. To the extent that all the members of a particular group have power over ego’s leadership effectiveness, it makes sense for ego to invest in a personal relationship with every member of the group. Second, the effectiveness of informal leadership is likely to depend not just on direct links to others but also on the pattern of links beyond the immediate ties. The important idea here, then, is that the structural position of ego in the social network affects the leadership potential of the individual in the organization, and this principle extends beyond the immediate social circle of the individual. From an embeddedness perspective (Uzzi, 1996, 1997), an effective leadership network is a multistep process, only one step of which is under the control of ego. First, ego needs to build ties to individuals who represent access to and from key constituencies within and outside the organization. But, second, ego needs to monitor whether representatives of these key constituencies themselves have access to networks. And third, ego must monitor the interrelationships between these representatives (cf. Sherony and Green, 2002; Sparrowe and Liden, 2005). Leadership success can crucially depend upon these secondary networks and the interrelationships between people beyond the leader’s ego network. At present, we know little about how a leader within an organization functions in the context of the social networks of informal leaders who

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may or may not be occupying positions of official authority. Informal leaders, typically of lower rank than the primary leaders (to whom they may or may not report directly), wield considerable influence derived from their positions in the social network (Mechanic, 1962). We can glean some insight into how a leader at one level can benefit or suffer from the activities of socially well-connected informal leaders by considering the literature on substitutes for leadership. Leaders whose subordinates possess expert power, for example, may find themselves to be relatively redundant. Subordinate expertise can act as a substitute for leadership in some cases and in other cases subordinates, representing the leader, can deputize for the leader (Gronn, 1999; Kerr and Jermier, 1978; Podsakoff and MacKenzie, 1997). This form of distributed leadership (Mayo, Meindl, and Pastor, 2003) is still poorly understood. Mentoring Distributed Leadership From the network perspective articulated in this chapter, leader effectiveness involves building social capital that benefits individuals in the organization and extending the social networks of subordinates to facilitate career advancement. One measure of leader effectiveness, therefore, is the success of the leader in promoting the social networks and leadership potential of subordinates. By systematically sponsoring subordinates’ development of social capital through introductions to key people in the organization and the environment, leaders can enhance the overall leadership potential in the organization and groom their subordinates for organizational success. Hence the emphasis on the mentoring of distributed leadership as an aspect of leader effectiveness in Figure 2.1. The perceived influence of prot´eg´es in the organization is likely to be related to the extent to which the prot´eg´es build links across demographic boundaries. Thus, helping a man build links to the network of women or a woman build links to the network of men within an organization can enhance the prot´eg´e’s leadership potential measured in terms of perceived power (Brass, 1985). Such sponsorship is likely to be especially important in the case of members of underrepresented groups whose own attempts at brokerage across social divides may rebound to hurt rather than help their careers, according to research in one firm (Burt, 1992). Members of underrepresented groups tend to form homophilous networks among themselves and may also experience discrimination from majority group members (Mehra et al., 1998). The mentoring of underrepresented group subordinates involves facilitating the development of the subordinates’ own networks that may expand in directions not covered by the leader’s own connections (cf. Higgins and Kram, 2001). Research suggests that such

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mentoring relationships can be successful even when the sponsor and the prot´eg´e are from different ethnic groups (Thomas, 1993). Network leadership, then, can be measured in terms of how much social capital it creates for others, especially those members of underrepresented groups whose social network ties may be restricted because of in-group pressures toward homophily and out-group bias (Chapter 6). A particularly important test of network leadership occurs in the case of isolates. G. K. Chesterton wrote, “There are no words to express the abyss between isolation and having one ally.” Members of work teams who consistently fail to communicate with their colleagues may represent wasted resources in today’s coordinated organizations whether or not they suffer the “abyss” of isolation. Research in three high-technology military organizations showed that isolates, relative to “participants,” tended to rely more on written and telephone communication, to withhold information, to express less commitment to the organization, to experience lower satisfaction with both communication and with their jobs, and to be rated as lower performers (Roberts and O’Reilly, 1979). Clearly, such isolated individuals represent a networking challenge. The extent to which such isolates are part of work groups may predict the extent of leader effectiveness in such groups. A related issue concerns the extent to which workgroups exhibit disconnects between subgroups. Although recent work suggests that too few or too many structural holes in a team may adversely affect communication (Oh, Chung, and Labianca, 2004) and team effectiveness, the question of how such structural holes affect team performance and functioning remains unanswered (Balkundi, Kilduff, Barsness, and Michael, 2007). Positive Emotion Isolates and structural holes in groups tend to signal the existence of emotional distress. Research attention has started to focus on the role of formal leaders in the emotion management network in organizations (Chapter 8). Vertical dyad linkage theory alerted researchers to the benefits – emotional and vocational – associated with membership in the leader’s in-group (see Dansereau, 1995, for a review). Building on this legacy, the positive psychology movement suggests that leaders have responsibility for maintaining the emotional health of all employees (Frost, 2003) rather than just those with privileged access to the leader. Yet, some people in formal leadership roles fail to attend to the toxic emotions created in organizational contexts and thereby fail to perform as effective leaders (Maitlis and Ozcelik, 2004). The question of the management of affective bonds and emotional health has been neglected in the leadership and in the network literatures and begs for more attention.

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The Interorganizational Network: Boundary Spanning and Alliances Leaders, both formal and informal, can potentially network within their organizational units and outside their units. As representatives of their organizational units, leaders forge interorganizational links that may or may not lead to or coincide with formally contracted relationships. Beneath most formal alliance ties between organizations “lies a sea of informal ties” (Powell et al., 1996: 120). Interpersonal friendships and other strong links such as kinship between CEOs can lead to business alliances, just as business alliances can lead to warmth and trust between representatives of different organizations (Larson, 1992; Uzzi, 1997). One dramatic case study, referred to earlier in this chapter, highlighted the danger of two individuals dividing the networking task between them into its internal and external components (Auletta, 1986). Lehman Brothers was a venerable Wall Street investment banking firm in which partner Louis Glucksman operated as the inside networker, maintaining cohesion and rapport with the company’s traders, whereas partner Pete Petersen operated as the outside networker, responsible for bringing in new business from the rich and famous. When both partners were appointed as joint CEOs, the ensuing battle for supremacy led to a financial crisis and a takeover by American Express, bringing to an inglorious end one chapter in the saga of a proud and independent institution. In the furious battle for control between the inside and outside networkers, Glucksman had the upper hand, having developed social capital within the organization among the partners who controlled the firm through their votes. As this example illustrates, managing the boundary between inside and outside networking is a crucial task for formal leaders. The formal leader can be considered a boundary spanner who manages not only an internal constituency within the organization but who also represents the organization in the community of organizations. Network links between organizations tend to build from within the existing network. Organizational leaders create stable relationships with trusted partners, and, over time, these stable ties accumulate into a network that provides to members of the network information about future alliance partners (Gulati and Gargiulo, 1999). Organizational leaders, for example, tend to recommend to one trusted partner the formation of a business relationship with another trusted partner, thus creating a three-member clique (Larson, 1992; Uzzi, 1996). With knowledge increasingly emerging from the interstices between hierarchical boundaries (Powell et al., 1996), leaders who pursue policies of splendid isolation are likely to see their organizations suffer “the liability of unconnectedness” (Baum and Oliver, 1992) in failing to capture intellectual developments as they arise and expand.

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An innovative organization such as Digital Equipment Company, once famed for its fortress-like culture and its devotion to in-house technical development (Kunda, 1993), is likely to fade away in a knowledge economy in which innovations are increasingly the product of industrial clusters rather than individual companies (Saxenian, 1990). Given the inertia of organizations relative to the speed of change in many environments (Hannan and Freeman, 1984), even large and apparently dominant organizations in knowledge-intensive industries need to build connections with a range of other organizations to access developing technology. However, leadership effectiveness in this knowledge economy may depend not just on the direct network links to other organizations under the leader’s control, but also on the links beyond the leader’s control. As we noted with respect to networking within the organization, it is often the links beyond the immediate social circle of the leader that affect many desired outcomes. Research suggests that the survival of the organization itself may be affected by the secondary links to organizations beyond the leader’s immediate control. For example, in the New York garment industry, CEOs who developed strong personal relationships with the heads of “jobbing” firms (that distribute work orders) increased the survival chances of their firms if they were able to access through these strong connections networks of balanced relationships. It was not just the primary ties to the jobbing firms that were important for the focal firms. Survival was enhanced for the firms of those CEOs strongly connected through a primary tie to a set of secondary ties that include a balanced mix of arm’s-length and close ties with a jobbing firm (Uzzi, 1997). Although the CEO may have some control over whether to develop close, personal ties or more market-based exchanges with heads of jobbing firms, the CEO may not even be aware of the types of business relationships that jobbers have with other firms. Thus, leadership effectiveness (and the survival of the organization) may depend on second-order network links beyond the control of the CEO. What of the leader’s centrality in the community of organizational leaders? Research shows that organizational leaders tend to interact with each other across a range of social events, with representatives of elite organizations tending to form their own elite social circles (Galaskiewicz, 1985; Kilduff and Tsai, 2003: 22). However, centrality in this community of leaders may distract leaders from the strategic management of their own organizations. One study of an ethnic community of Korean expatriate entrepreneurs showed that the extent to which organizational owners were central (in terms of spanning across divided social groups within the community) correlated negatively with performance and predicted organizational demise (Oh et al., 2005). Of compelling interest, however, is the extent to which the leader’s ties to organizational leaders

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outside the immediate community affect the flow of important resources and, thereby, organizational survival. It may be in the interorganizational arena that new network methods focused on social network dynamics emerge, given the strong interest in understanding the evolution of strategic alliances (e.g., Gulati and Gargiulo, 1999). Conventional wisdom suggests that networks tend to be relatively stable, but this apparent stability can mask many types of change that can be captured in network “movies” showing the dance of interactions over time (Moody, McFarland, and Bender-DeMoll, 2005).

Conclusion Leadership requires the management of social relationships. Starting with the cognitions in the mind of the leader concerning the patterns of relationships in the ego network, the organizational network, and the interorganizational network, social ties are formed and maintained, initiatives are launched or avoided, and through these actions and interactions, the work of the leader is accomplished. Building on the idea that networks are both cognitive structures in the minds of individuals and actual structures of relationships that link individuals, this chapter views organizational networks as constructed and maintained by boundedly rational actors, subject to biases in their perceptions. Leadership research from a network perspective has the opportunity to forge a new understanding of the interplay between the psychology of individuals and the complexity of the networks through which actors exchange information, affect, and other resources. Leadership research also has the opportunity to renew our understanding of how patterns of informal leadership complement or detract from the work of formally appointed leaders. If leaders rely solely on their formally assigned authority and bring into their leadership circles likeminded others, they may isolate themselves from new ideas (as represented by, for example, the slow learners investigated by March, 1991). Further, the influence of visible leaders, both informal and formal, is likely to be affected by network ties that may not show up at all in the organizational chart. The members of governing coalitions, for example, are likely to be tied to powerful individuals temporarily removed from positions of authority and deal makers who operate quietly to influence organizational outcomes. Only recently has research attention focused on these virtual actors whose “ghost” ties constrain network change and action (see, for example, Moody et al., 2005). The network approach articulated in this chapter emphasizes the extent to which individuals’ thoughts and actions are embedded in their

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perceptions of networks, in the immediate ego networks that surround them, in the organizational networks within which their ego networks are embedded, and in the interorganizational networks that connect them to leaders of other organizations. Leaders, we have emphasized, generate and use social capital through the acuity with which they perceive social structures and the actions that they take to build connections with important constituencies within and across social divides. To understand leadership effectiveness from a social network perspective is to study the individual’s position in the larger networks within which the individual is located. The network approach, therefore, allows a more macro focus on the full repertoire of network relationships than has been the case in previous leadership research. The network approach also incorporates actors within the network who may or may not be connected with the leader, but whose actions, in creating new ties, for example, can affect leader outcomes by changing the structures within which the leader operates. Clearly, the network perspective – in its emphasis on social relations, embeddedness, social capital, and social structure – both incorporates strands emphasized within previous leadership research and points in new directions. This chapter has emphasized the importance of individuals’ perceptions of network relations, and this theme is continued in the following three chapters. In the next chapter, we investigate the extent to which individuals who are perceived to have prominent friends gain advantages in terms of performance reputation in organizations.

3 An Analysis of the Internal Market for Reputation in Organizations

The basic idea we investigate in this chapter is whether the relative importance, reputation, and value of any particular individual in an organization (in the eyes of others) are affected by the company the individual is perceived to keep. The assessment of reputation, we suggest, is likely to be enhanced if the individual is perceived to have a high-status friend. Becoming the friend of a high-status person is not easy, and those who are fortunate to enjoy such access are likely to gain considerable social capital. High-status people are carefully scrutinized to see who their associates are. This is not a new insight. Shakespeare’s Falstaff, an intimate acquaintance of Crown Prince Harry in the Henry IV plays, is depicted as reveling in reflected glory. Certainly, the Baron de Rothschild (according to the anecdote in Chapter 1) was in no doubt concerning the value his apparent friendship would confer in terms of tangible financial capital becoming available from those impressed with his public social endorsement of the person with whom he walked “arm-in-arm.”

How Perceptions Affect Reputation The theoretical framework within which we investigated the determinants of reputation in organizational labor markets was balance theory (Heider, 1958). From this perspective, someone perceived to be the friend of a positively valued other is also likely to be perceived positively: There is a strain toward cognitive balance in the perceptions of observers. We argue that the performance reputations of people with prominent friends will tend to benefit from the public perception that they are linked to those friends. This basking-in-reflected-glory effect has been hypothesized to involve a deliberate strategy on the part of individuals to garner positive 39

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evaluations from those who perceive their ties to prominent others: “It is our contention that people make known their noninstrumental connections with positive sources because they understand that observers to these connections tend to evaluate connected objects similarly” (Cialdini et al., 1976: 374). Previous researchers have not investigated the basking-in-reflected-glory effect in a performance context, despite the phenonemon’s apparent relevance to such issues as performance rating. Building from this psychological base in balance theory, we propose to extend the study of the basking phenomenon to all the actors in a social system. The structural approach to social networks suggests looking at performance reputations in terms of the structure of relations in an entire organization. Structuralists are familiar with the use of the metaphor of the market to describe any competitive situation in which people jockey for valuable outcomes, such as a reputation as a good performer (cf. White, 1970). In looking at an organization as a market for reputation, one’s focus is implicitly on the process of exchange. The higher an individual’s reputation, the more valuable he or she becomes in the internal labor market. In looking for signals of quality (Spence, 1973), people ask: Does the person hold a high position in the organization? Is the person a friend of a prominent leader? In this cognitive assessment process, both individual attributes and social ties may contribute to the determination of performance reputation. To recapitulate, we are predicting that an observer’s perception of an individual’s performance will be significantly influenced by the degree to which the observer perceives that individual to have a prominent friend. The basis for this prediction at the psychological level is the strain toward cognitive balance in the mind of observers. Within the internal labor market of an organization, people are assumed to be jockeying to increase their reputations as high performers by publicizing links to prominent others. This assumption is supported by research on basking in reflected glory (see Cialdini [1989] for a review) showing that people actively seek to enhance their public images by proclaiming bonds to successful others: “It is as if strains toward cognitive balance are at some level of consciousness understood to exist by observers and action is taken to exploit the consequences of the balance process” (Cialdini and Richardson, 1980: 414). We assume that each individual is especially active in drawing attention to his or her most prominent friend because this friend offers the individual the most opportunity for basking in reflected glory. Organization members may also be alerting others to such relevant factors as their position in the organizational hierarchy and their organizational accomplishments. Further, members of the internal labor market are assumed to be searching for signals of the underlying performance quality of colleagues

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and competitors (cf. Spence, 1973) in an ongoing social comparison process (cf. Festinger, 1954). Formally stated, our first hypothesis is: Hypothesis 1: The prominence of an individual’s most prominent friend will influence the individual’s performance reputation in an organization, controlling for the individual’s formal status and job performance. However, this proposition can be further refined in accordance with balance theory to distinguish between perceived and actual friendship links. From a balance theory perspective (Heider, 1958), it is an individual’s perception of social relations, rather than the actual structure of social relations, that affects individual attitudes. What matters are the friends a person is perceived to have, not actual friendships. Balance theory suggests, therefore, that mental representations of patterns of relations will be more important determinants of attitudes than the actual patterns of relations within which individuals are located. From this perspective, perceptual measures of network relations should be more predictive of attitudes than objective measures. Social network links can be derived either from each observer’s cognitive map of how he or she perceives the connections between organizational actors or from an aggregate map built up from the agreement of each party to each link. We follow Weick and Bougon (1986: 105– 6) in using the term “cognitive map” to refer to an individual’s mental representation of relations within a system of connections. An individual’s cognitive map of a friendship network, for example, consists of the individual’s picture of who is friends with whom in a particular social system. Individuals are assumed to use these maps to negotiate their journeys through their social worlds. An alternative to deriving a separate set of network links from each observer’s cognitive map is to use a map of the actual network assembled in a conventional structural fashion from the agreement of each party to each link. The network map in this case is not idiosyncratic to any one individual. The aggregate network map represents reality because it is compiled from the observations of all relevant observers rather than from the observations of just one observer (Krackhardt, 1987a). Hypothesis 2: Measures of perceived network relations will lead to better predictions of performance reputation than will measures of actual network relations.

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Methods Silicon Systems (a pseudonym), the organization selected as the research site, was a small entrepreneurial firm located on the west coast of the United States in an area known for its many small startup companies. Silicon Systems specialized in the sale, installation, and maintenance of stateof-the-art information systems for clients such as local banks, schools, manufacturing firms, and research and development (R&D) labs. Not long before this research began, giant competitors, such as the International Business Machines Corporation (IBM) and the American Telephone and Telegraph Company (AT&T), had begun to focus attention on Silicon Systems’ market because of its perceived growth potential. According to the top managers of Silicon Systems, the small firm’s competitive edge remained its ability to respond more efficiently than its giant competitors to idiosyncratic customer demands. Silicon Systems was wholly owned by its three top managers, each of whom owned an equal share. All employees worked in the company’s single-floored building. The employees saw each other regularly and were familiar with each others’ work practices. The firm had grown from three to thirty-six people in fifteen years, with much of the growth occurring in the five years prior to this study. During those years, the firm had been generally profitable, and the owners anticipated no downward trend in their business. Of the thirty-six people in the company (twenty-eight men and eight women), thirty-three people, or 92 percent, accepted $3 each from us to complete a lengthy questionnaire. We described the research as a study of the effects of networks in organizations. All respondents were promised and given individual reports showing their cognitive maps of the networks and how those perceived networks compared to the actual networks. Measures Networks Network Indexes: Friendship and Advice Networks To capture respondents’ cognitive maps of the friendship and advice relations in this organization, we asked each person about his or her perceptions concerning every other person’s network links. For friendship, each person responded to the following question about every other person in the organization: “Who would this person consider to be a personal friend? Please place a check next to all the names of those people who that person would consider to be a friend of theirs.” For advice relations, the corresponding question was: “Who would this person go to for help or advice at work?” Thus, for the friendship network, John Meredith was asked a series of thirty-six questions of the form: “Who would Jane Asch

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Jim

Pat

Ev Irv

Fred

Zoe

Upton

Steve

Ivo

Chris

Figure 3.1. An employee’s cognitive map of the friendship relations at Silicon Systems.

consider to be a personal friend?” “Who would Jerry Bonavue consider to be a personal friend?” Each question was followed by the list of thirtyfive employees’ names. Meredith then checked the names that indicated, for example, his perceptions concerning who Asch considered to be her personal friends. Each respondent, then, gave us a complete cognitive map of his or her perceptions concerning who was friends with whom in the organization. To measure perceived friendship links, we used the following procedure: A friendship tie as perceived by person k existed between person i and person j only if k responded on the questionnaire that i considered j a friend. To measure actual friendship links, we determined the locally aggregated structure, or LAS (Krackhardt, 1987a) as follows: A friendship tie existed between persons i and j only if person i claimed person j as a friend and person j agreed that person i claimed person j as a friend. Thus, a friendship link from i to j was defined as existing when both parties agreed that it existed. Figure 3.1 presents an example of a respondent’s cognitive map of the friendship network. The striking aspect of this particular map is the relatively low number of connections that this individual perceived. Comparing this individual’s cognitive map with the actual structure of friendship

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relations that emerged from the reports of all respondents (see Figure 3.2) shows that perceptions concerning friendship links can be considerably discrepant from actuality (Krackhardt, 1990). In this case, the discrepancy is the result of the individual not perceiving many friendship links that actually existed. In other cases, the discrepancy occurs because an individual perceives friendship relations where none exist. To measure a perceived advice link, we followed the following procedure: A person was considered to go to another person for advice if the respondent’s cognitive map showed that person as going to the other for advice. That is, for respondent k, a perceived advice link existed between persons i and j only if respondent k perceived that person i went to person j for advice. To measure an actual advice link, we did this: A person was considered to go to another for advice only if that person claimed that he or she went to the other for advice. That is, person i was considered to actually go to j for advice only if i’s cognitive map showed i going to j for advice. This definition of an actual advice link is known as the row-dominated locally aggregated structure (Krackhardt, 1987a) and follows the standard procedure in network analysis in that it relies on the self-report of the individual concerned. This measurement preserves the asymmetry inherent in the relation, an asymmetry that is critical to our prominence argument, as discussed in the following subsection. Independent Variable: Friend’s Prominence Matrix We hypothesized that each person’s performance reputation would be influenced by the extent to which each person had a prominent friend in the organization. We chose to focus on each person’s most prominent friend rather than on an average of all friends’ prominence ratings because of the theoretical basis of the research. An average measure would not capture an individual’s ability to bask in reflected glory. With an average measure, two individuals might have the same friends’ prominence score even though one person had no prominent friends whereas the other had both highly prominent and highly obscure friends. We measured each friend’s prominence in four different ways and obtained four separate matrixes. Table 3.1 summarizes the differences among these four measurements. To contrast the predictive validity of the perceived and externalized structures, we measured prominence using both perceived and actual network links. To check for common method variance, we measured prominence both from questionnaire responses, as indegree centrality in the advice network – that is, the total number of others who went to the friend for advice (Scott, 1991: 72) – and from the organization chart, as formal status in the organizational hierarchy. The four measures of friend’s prominence were, therefore, (1) the indegree

45

Ovi

Bob

Nan

Wal

Vic

Gar

Hug

Chr

Rob

Ken

Ivo

Mel

Dal

Tom

Ric

Figure 3.2. The actual structure of friendship relations at Silicon Systems.

Ben

Jac

Ale

Ger

Jim

Dan

Ev

Upi

Pat

Zoe

Irv

Ste

Len

Fre

Car

Ear

Abe

Hal

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Table 3.1. Summary of Research Variables Variable Dependent Performance reputation matrix Independent Perceived friend’s indegree centrality matrix Actual friend’s indegree centrality matrix Perceived friend’s formal status matrix Actual friend’s formal status matrix Control Job performance matrix Formal status matrix

Definition of Each Cell in Matrix Respondent i’s perception of the job performance of person j, rated on a seven-point scale, for all js not supervised by i Advice indegree centrality of person j’s most central friend, based on friendship and advice networks perceived by i Advice indegree centrality of person j’s most central friend, based on aggregate (LAS) friendship and advice networks Level in organizational hierarchy occupied by person j’s highest-level friend, based on friendship network perceived by i Level in organizational hierarchy occupied by j’s highest-level friend, based on aggregate (LAS) friendship network Supervisor’s evaluation of the job performance of person j on a seven-point scale Level in the organizational hierarchy occupied by person j

centrality of the perceived friend, (2) the indegree centrality of the actual friend, (3) the formal status of the perceived friend, and (4) the formal status of the actual friend. The indegree centrality measure of prominence was derived from the advice network of relations. In social network research, “Prominent leaders are the objects of extensive relations from followers, while the latter are the objects of few relations” (Knoke and Burt, 1983: 199). To capture this kind of prominence, therefore, an asymmetric measure was needed, one that counted nonreciprocated ties. Further, our theoretical assumption was that individuals publicize the existence of friendship links to prominent others and that perceivers scan an organization for clues as to who the prominent actors are. We needed, then, a measure of visible prominence, one that emphasized direct ties rather than indirect ties. We wanted to capture the kind of prominence represented by someone whose desk is often surrounded by people seeking help and advice rather than the kind of prominence represented by someone with relatively invisible influence based on indirect links. Because of our concern with asymmetry and visibility, we chose to measure informal prominence in terms of indegree centrality in the advice network, which refers to the extent to which others seek help or advice about work-related matters from a focal person. Technically, indegree centrality can be defined as the number of

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other people who go to an actor for advice (Freeman, 1979). Indegree centrality has been widely used in organizational research when direct, asymmetric ties are being measured (e.g., Burkhardt and Brass, 1990), although measures that include indirect ties may be more appropriate in other situations (cf. Ibarra, 1992, 1993b). In the current research, we measured indegree centrality for both the perceived and actual networks. For the perceived measure, we looked at each respondent’s cognitive map of perceived relations. Within each cognitive map, we identified, for each person, the friend with the highest indegree centrality rating. This rating was then recorded as the first measurement of the independent variable. The second measurement of the independent variable – the actual friend’s indegree centrality – was based on the actual friendship and advice networks aggregated from the responses of all respondents. As described previously, the existence of a friendship link in the actual network meant that the two people involved both agreed that the friendship link existed. Similarly, the existence of an advice link from, for example, John to Bill meant that John had indicated on his questionnaire that he went to Bill for advice. For each person, therefore, we identified the actual friend with the highest indegree centrality rating and recorded that value. The third and fourth measurements of the independent variable were based on the friend’s formal status rather than on the friend’s indegree centrality. Because of potential common method variance, it was necessary to demonstrate that the critical variables in the study were not correlated simply because they were derived from the same source. The obvious remedy for common method variance is to use different sources for the independent and dependent variables, if doing so is consistent with the conceptual framework of a study (Sackett and Larson, 1990: 474). Following this strategy, we derived prominence ratings from the organizational chart recorded in company records. In many organizations, those higher up in the hierarchy are also more prominent because many others go to them for help and advice about work-related matters. Formal status has been shown to be predictive of organizational power (Brass and Burkhardt, 1993; Krackhardt, 1990) and to correlate highly with network centrality (Ibarra, 1992; Krackhardt, 1990). Formal status, then, provides an alternative to perceived measures of prominence in organizations. We were therefore able to test our hypotheses with the independent variable ratings derived from company records and our dependent variable ratings derived from questionnaire responses. In this way, we avoided the problem of common method variance. Also, we were able to assess the convergent validity of the independent variable by seeing whether

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different definitions of the same variable produced the same results (cf. Campbell and Fiske, 1959). At Silicon Systems, there were three levels of formal authority. The three owner-managers occupied the top level. Even though each ownermanager had different responsibilities and titles, all three were equal partners, and they made all major company decisions as a team. The next level consisted of five managers, each of whom had supervisory responsibility over certain operational features in the organization. The remaining twenty-eight employees had no formal supervisory titles or authority. Formal status, then, was rated as follows: We gave each of the three owners a status rating of 3, each of the five managers a rating of 2, and each of the remaining twenty-eight employees a rating of 1. We assigned formal status to both perceived friends and actual friends. For the perceived measure, we looked at each respondent’s cognitive map of perceived relations. Within each cognitive map, we identified, for each person, the friend with the highest formal status. This status rating was recorded as the third measurement of the independent variable. The measure of the actual friend’s formal status was based on the real friendship network aggregated from the responses of all respondents. For each person, we identified the friend with the highest status rating and recorded the rating as the fourth measurement of the independent variable. In summary, we measured each friend’s prominence in four ways, pitting perceived and actual network measures against each other and pitting a network measure of prominence against an organizational chart measure of prominence. For each of the four measures, we created a 36-by-36 matrix, with cell entries representing the prominence ratings of friends. For example, for the matrix of perceived friends’ indegree centrality ratings, a “9” in a cell formed by the intersection of row 12 and column 25 meant that, among all the friendships that person 12 perceived person 25 to be involved in, 9 was the highest indegree centrality rating that any of person 25’s friends achieved. Dependent Variable: Performance Reputation Matrix Each respondent provided his or her perception of the job performance of every person in the organization by circling numbers on a seven-point scale next to people’s names. We collected these performance reputation ratings in a 36-by-36 matrix. Each row in the dependent variable matrix represented the impressions in the mind of one respondent concerning the performance of those others not actually under the respondent’s supervision. Similarly, each column in the matrix represented the impressions of one individual held by all those respondents not actually supervising that

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individual. The performance reputation matrix contained the actual raw ratings that the respondents provided. Social network analysts typically retain raw ratings in matrix form rather than seeking to perform analyses on average scores (see Kilduff and Tsai, 2003, for an introduction to social network analysis). We elicited the raw ratings with the following instructions: “If you think the person is performing their job extremely well, you might circle the ‘7’ next to their name. If you think the person is performing their job reasonably well, you might circle the ‘4’ next to their name. If you think they are not performing their job at all well, you might circle the ‘1’ next to their name.” Each cell in the performance reputation matrix contained the rating provided by one respondent concerning one other person. For example, if person 12 rated person 25 as performing extremely well on the job, then a “7” would appear in the cell formed by the intersection of row 12 and column 25. Our reliance on a one-item measure of performance increases the possibility of random error and makes significant results harder to find. Our tests, therefore, are likely to be conservative assessments of the hypothesized basking-in-reflected-glory effect. First Control Variable: Job Performance Matrix Supervisors’ ratings of subordinates’ performance were excluded from the dependent variable matrix previously described because these supervisory ratings constituted the measurement of job performance. Supervisors therefore used the same seven-point scale that was used for the performance reputation measure. The job performance of people in organizations is typically difficult to ascertain, especially for work with many different aspects. However, one conventional measure of job performance in many companies is the supervisory rating: “The vast majority of performance ratings come directly from the immediate manager” (Bretz, Milkovich, and Read, 1992: 331). Previous research has shown that performance ratings obtained for research purposes are more reliable and valid than those obtained for administrative purposes (Wherry and Bartlett, 1982), perhaps because issues other than ratee performance bias official performance ratings (Longenecker, Sims, and Gioia, 1987; Tsui and O’Reilly, 1989: 410). The reporting relations between supervisors and subordinates were obtained from company records. The three owners of the company in the present research had nobody above them in the organizational chart to provide a supervisory rating. For each owner, therefore, we used the mean rating given by the other two owners as the supervisory rating. The owners’ ratings of each other did not differ in any case by more than two points on the seven-point scale.

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The supervisory ratings were collected in a column vector thirty-six cells long containing values from 1 through 7, indicating the job performance of each person in the company. Thus, each cell (i,j) in this matrix contained j’s supervisor’s rating of j’s performance. The vector was repeated thirty-six times to create a matrix of the same size (36 by 36) as the other matrixes in the analyses. Second Control Variable: Formal Status Matrix This variable controlled for the effects of formal status on the performance reputation of each focal person. In this small, organic organization, there was little apparent status differentiation based on educational differences or functional specialization. From our visits to the company, we concluded that the major status difference was between those who owned the company and those who only worked for it. Therefore, we defined formal status as the level in the organizational hierarchy that each person occupied (3 = owner-manager, 2 = manager, and 1 = nonmanager). The formal status scores were collected in a column vector thirty-six cells long containing the numbers 1 through 3. Thus, each cell (i,j) in this matrix contained j’s formal status. The vector was repeated thirty-six times to create a matrix of the same size (36 by 36) as the other matrixes in the analysis.

Analysis Social network data are often not amenable to standard statistical tests because the observations cannot be assumed to be independent. For example, in the current research, the matrix of friend’s indegree centrality ratings includes thirty-six ratings from each person in the study. Each of the thirty-six ratings within a row of this matrix derives from the same source – the cognitive map of the respondent – and therefore exhibits systematic interdependence. Indeed, in some of the matrices, the cell values are repeated across rows. Krackhardt (1988) showed that such row or column interdependence can bias ordinary-least-squares (OLS) tests of significance. The size of this bias is substantial: Results based on samples drawn from a population for which the null hypothesis is true (that is, there is no relationship between the independent and dependent variables) have a 70 percent chance of appearing significant under standard parametric methods. To deal with this problem of bias, we used the Multiple Regression Quadratic Assignment Procedure (MRQAP) suggested by Krackhardt (1993). The procedure builds on earlier bivariate work done by Hubert and others (Baker and Hubert, 1981; Hubert, 1987; Hubert and Schultz, 1976) and extended to the multiple regression case by Krackhardt (1987b,

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1988). (Note that the use of MRQAP in this book generally avoids the serious problems outlined by Dekker, Krackhardt, and Snijders, forthcoming, concerning the dependent variable permutations often used in social network research.) The method is straightforward. First, OLS estimates of regression coefficients are calculated in the usual manner. Then the rows and columns of the dependent variable matrix are permuted to give a new, mixed up matrix. The OLS regression calculation is then repeated with the new dependent variable. This new regression produces different beta coefficients and overall R2 values that are stored away. Another permutation of the dependent variable is then drawn, another regression is performed, and these new values are also stored. This permutation-regression process is repeated an arbitrarily large number of times (in our case, one thousand). The distribution of the stored betas and R2 s for each of the independent variables under the set of permuted regressions becomes the reference distribution against which the observed original values are compared. If fewer than 5 percent of the betas derived from the permuted regressions are larger than the observed beta, the beta is considered significant at the .05 level (one-tailed test). If fewer than 1 percent of the betas are larger than the observed beta, it is considered significant at the .01 level. The advantage of this simple procedure is that it is robust against varying and unknowable amounts of row and column autocorrelation in the dyadic data. That is, if a sample is drawn from an autocorrelated population in which the null hypothesis is true, the probability that the results will appear significant by this MRQAP test is .05 (where alpha equals .05). This remarkable feature of the MRQAP occurs because the test is a conditional nonparametric test. That is, each permutation of the dependent variable retains the structure of the original dyadic data and therefore preserves all the autocorrelation in each permuted regression; the test is conditioned on the degree of autocorrelation that exists in the data. The permutation version of MRQAP (Krackhardt, 1993) differs from the earlier analytic version (Krackhardt, 1988). The analytic solution to the multiple regression problem was based on Mantel’s formula (Mantel, 1967) for the first two moments of the distribution of all permutations. The current version has several demonstrated advantages. First, it permits an unbiased test of the overall R2 . Second, it is relatively powerful in the face of missing data. Finally, whereas the analytic test necessarily contains the assumption that the reference distribution of betas based on the permutations is normally distributed, the permutation-based sampling procedure used here does not have such a requirement. Permutation MRQAP is now available in user-friendly form in the UCINET social network analysis package (Borgatti et al., 2002).

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Table 3.2. Means, Standard Deviations, and Correlationsa Variables

Means

s.d.

1. Performance reputation 2. Friend’s prominence a. Perceived friend’s indegree centrality b. Actual friend’s indegree centrality c. Perceived friend’s status d. Actual friend’s status 3. Job performance 4. Formal status

4.93

1.39

6.70 7.87 1.46 1.55 4.91 1.31

8.86 7.00 0.73 0.83 1.15 0.62

a ∗

1

2a

2b

2c

2d

3

.23 .26 .28 .28 .33 .30

.22 .68 .15∗ .14∗ .17

.30 .83 .31∗ .54

.30 .28 .38

.49 .65

.47

All correlations are significant at p < .01, except for those with an asterisk. p < .05

One of the advantages of the permutation version of MRQAP is that it can handle missing values with much more statistical efficiency than the prior version. In the current research, several of the variables had values missing, either because there were three nonrespondents or because we assigned cells in a matrix missing value status when defining the variable. For example, in the case of the dependent variable, performance reputation was only considered in (i,j) pairs in which i was not j’s direct supervisor, and a missing value was inserted when i was the direct supervisor of j.

Results The descriptive statistics shown in Table 3.2 indicate a reasonably high level of performance at Silicon Systems, with both performance reputation and actual performance averaging around 4.9 on a seven-point scale. The zero-order correlations in Table 3.2 show that the two measures of perceived friend’s prominence – perceived friend’s indegree centrality and perceived friend’s formal status – were highly correlated (r = .68, p < .01), as were the two measures of actual friend’s prominence (r = .83, p < .01). Further, these correlations suggest that the actual friends of high-status individuals tended to also be of high status (r = .65, p < .01) and indegree centrality (r = .54, p < .01). Table 3.2 also shows that the dependent variable, performance reputation, was significantly correlated (at p < .01) with all four measurements of the independent variable (friend’s prominence), as well as with both control variables (job performance and formal status). To answer the question of whether these significant correlations would remain significant when other variables were controlled for, we conducted a multiple regression analysis.

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Table 3.3. Results of Multiple Regression Analysisa Status Models Variables Friend’s prominence Perceived friend’s indegree centrality Actual friend’s indegree centrality Perceived friend’s status Actual friend’s status Job performance Formal status Intercept R2

1

2

3

Centrality Models 4

5

6

7

.028∗∗

.026∗∗

.024 .319∗∗ .296∗∗ .407 2.93∗∗ .136

.109 .277∗ .326 2.96∗∗ .139

.273∗ .281 2.74∗∗ .161

.315∗∗ .090 .258∗ .217 2.76∗∗ .162

.286∗ .263 2.99∗∗ .146

.018

.284∗ .350 2.88∗∗ .166

.277∗ .245 2.92∗∗ .171

a

Beta coefficients are unstandardized. Their significance was determined by MRQAP (Krackhardt, 1993). p < .05, one-tailed test. ∗∗ p < .01, one-tailed test. ∗

Results of the first model, shown in Table 3.3, suggest that high performance on the job in this organization helped people achieve reputations as high performers (p < .01) but that formal status did not significantly affect performance reputations. The two control variables explained 14 percent of the variance in performance reputation. The question of interest, then, is whether the measures of the independent variable significantly increased explained variance above that already explained by the control variables. Did the existence of a friendship link to a prominent person boost individuals’ performance reputations in this organization, as hypothesis 1 predicts? Table 3.3 shows that friendship with prominent others did boost individuals’ performance reputations, but this effect depended on how the friendship links were assessed. Recall that hypothesis 2 predicts that perceived friendship links will lead to better predictions of performance reputation than actual links. The results shown in Table 3.3 support this prediction. Models 2, 3, and 4 in Table 3.3 employed two different definitions of the status of the highest-status friend to measure the independent variable. Model 2 shows that entering the status of the actual friend into the regression equation together with the control variables resulted in no significant increase in the variance explained. Model 3 shows that the introduction of the status of the perceived friend did increase explained variance significantly (p < .01), from 14 to 16 percent. Entering both measurements of friend’s status simultaneously (model 4) confirmed that only

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the perceived measure had a significant effect on performance reputation (p < .01). In support of hypothesis 2, then, these models show that, with individuals’ job performance and organizational status controlled, only the perceived measure of friend’s status had an effect on individuals’ performance reputations. Being perceived to have a friend at a high level in the organization helped boost an individual’s reputation as a high performer, whereas actually having such a friend had no significant effect on performance reputation. Models 5, 6, and 7 in Table 3.3 repeat the analyses already performed in models 2, 3, and 4, with a measure of the friend’s indegree centrality in the informal advice network substituting for a measure of the friend’s formal status. The results for models 5, 6, and 7 repeat the pattern seen in models 2, 3, and 4, indicating that the results favoring perceived friendship over actual friendship were not artifacts of the way that prominence was measured. Model 5 shows that entering the indegree centrality of the actual friend in the regression equation did not significantly increase explained variance. Model 6 indicates that the introduction of the indegree centrality of the perceived friend did increase explained variance significantly (p < .01), from 14 to 17 percent. Finally, model 7 confirms that when both the actual and the perceived measures of friend’s indegree centrality were entered together, only the perceived measure had a significant effect on performance reputation (p < .01). Paralleling the results from the status models, the results from the centrality models show that, with individuals’ job performance and organizational status controlled, only the perceived measure of friend’s indegree centrality had an effect on individuals’ performance reputations. In other words, being perceived to have a friend to whom many others go for help and advice helped boost an individual’s reputation as a high performer, whereas actually having such a friend had no significant effect on performance reputation. In summary, Table 3.3 shows that the status models (2, 3, and 4) and the centrality models (5, 6, and 7) are similar both in terms of the superiority of perceived measures of friend’s prominence over actual measures and in terms of the variance explained by each set of models. These consistent results support the convergent validity of our measures of perceived and actual prominence. The results show that it doesn’t matter whether the prominence ratings derive from the friend’s position in the organizational hierarchy or from questionnaire items concerning who goes to whom for advice at work. The robustness of the results across measures derived from two different sources supports the conclusion that the significant correlations are not artifacts of common method variance.

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One other concern, however, is that the effect of actual prominence might have been suppressed as a result of the correlational structure of the data. Table 3.2 shows that, relative to measures of perceived prominence, measures of actual prominence were more highly correlated with the control variable (job performance) that contributed significantly to explained variance in all the models of Table 3.3. To check whether the high correlations between measures of actual prominence and the control variable distorted the results, we conducted the analyses again without controlling for job performance and found the same pattern of results (albeit with less explained variance): Only the perceived measures of friend’s prominence significantly predicted performance reputation. The measures of actual friend’s prominence continued to be nonsignificant in all models.

Discussion In support of the hypothesized basking-in-reflected-glory effect, the results show that performance reputation is partly a function of an individual’s job performance and partly a function of the individual having a prominent friend. Perhaps the most intriguing aspect of the results is the finding that the actual existence of friendship links, recognized by both parties to the links, had no significant effect on performance reputation. Rather, it was the perceptions in the minds of organization members that mattered. To explain outcomes such as performance reputation in organizations, it may be necessary to explore the perceived networks that influence the attitudes of organization members. Structure, as it exists in the minds of individuals, may be more predictive of important outcomes than has been recognized. Bringing the individual back into structural analysis, therefore, may enhance rather than detract from the effectiveness of a structural approach. The results, then, support the utility of combining variables derived from individuals’ cognitive maps with more conventional structural variables. The thesis that psychological and structural approaches represent incommensurable paradigms militates against the kind of cross-level approaches that appear well adapted to the complex realities of organizations. We used MRQAP as one way to combine levels of analysis. This study demonstrates the use of the procedure as a flexible tool for combining multiple observations from each individual’s cognitive map with single measures on each individual within the same analysis. The theoretical basis for the current research is balance theory (Heider, 1958), which has a long history of use within social network analysis; Davis (1979) reviewed the relevant research. Much of this previous work

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modeled relations in social rather than cognitive space, following the influential mathematical extension of Heider’s ideas from the concept of cognitive balance to that of interpersonal balance (Cartwright and Harary, 1956). Social network analysts continue to develop sophisticated mathematical approaches to social structure (e.g., Boyd, 1991), but Blau’s warning remains pertinent: “There is a danger that the refined methods that network analysis . . . has developed will lead to sterile descriptive studies” (1982: 279). In examining Heider’s predictions concerning the strain toward cognitive balance, we have sought to return social network analysis to a theory-driven mode rather than a purely method-driven mode. The research presented here is both an example of how structural methods can incorporate individuals’ cognitive maps and a contribution to the literature on performance reputation. We have interpreted the results as supporting the idea that observers’ perceptions of individuals’ friendship links to prominent others positively influence the observers’ evaluations of the individuals concerned. This interpretation is compatible with balance theory in general and with research on the basking-in-reflected-glory effect in particular However, the data are cross-sectional and can support other causal arguments. For example, it is possible that individuals perceived by their colleagues to be high performers are assumed to have prominent friends. Without more detailed observations on the process by which perceptions concerning performance and friendship links are formed, the present results must remain suggestive rather than conclusive. Future research could investigate how reputations change over time in response to impression management techniques (cf. Tsui and Barry, 1986), and possible personality differences between individuals in their impression management strategies. For example, high self-monitors – individuals who are highly sensitive to social cues – may actively gather and use information concerning who is friends with whom; whereas low self-monitors – those who rely on their own attitudes and feelings for guidance – may be averse to trying to influence perceptions of their social relations (cf. Kilduff, 1992). A second limitation of the current research concerns the small size of the organization studied and the correspondingly high degree of interaction among its employees. Silicon Systems may be untypical because all the employees were at least weakly tied to each other, if Granovetter’s (1973) definition of interacting more than once a year is used. The question of whether the results generalize to large organizations will be difficult to answer given the methodological limitations of social network research. Typically, social networkers attempt to include the complete network of people in a social setting. For research concerning people’s cognitive maps

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of entire networks of relations, data collection and analysis constraints dictate an upper bound of about fifty people (Krackhardt, 1987a). However, in large organizations, where people may not know each other as well as did the people in our study and where, therefore, specific information about others may be scarce, performance reputations may be even more reflective of perceptions and impressions. Research has shown that when decision makers lack information about an employee, they rely on prevailing cognitions, such as stereotypes (Drazin and Auster, 1987), and that halo errors are more likely to occur when raters are evaluating people with whom they are unfamiliar (Kozlowski and Kirsch, 1987). Thus, we would expect individuals’ perceptions to be even more important in determining others’ reputations in large organizations than they were in this small organization. We assumed throughout this research that individuals act strategically to emphasize friendship links to prominent others. This assumption is compatible with the basking-in-reflected-glory effect and with evidence of wide variation with respect to how accurately people perceive network relations (Krackhardt, 1990). The relative opaqueness of friendship relations may provide opportunities for the strategic management of impressions. Research on impression management suggests that individuals perceived to be linked to prominent others may be credited with the ability to form powerful coalitions and the ability to influence higher-status persons (Tedeschi and Melburg, 1984). In other words, individuals perceived to have prominent friends may gain important advantages in the market for power and influence in an organization. Research on these phenomena in organizational settings is lacking, although anecdotes abound. For example, in the struggle for the control of the Lehman Brothers investment banking house, Louis Glucksman gained a crucial advantage by convincing his rival Pete Petersen that he, Petersen, had lost friendships with board members that Glucksman had retained. Neither Petersen nor Glucksman ever checked with the board members to see whether those impressions were accurate (Auletta, 1986). More empirical research on the impression management of friendship ties in organizations would be useful. The declared aim of structural analysis has been to reveal the structural form beneath the apparent content of social relations. According to structuralists, the unit of analysis is “the social network, never the individual” (Mayhew, 1980: 349). Structuralists have tended to “shun the ‘person’ construct as polluting” (White, 1992: 3). In this chapter, we have challenged the notion that structure can be understood apart from the cognitions of individuals. Our argument is compatible with the critique of structuralist claims by poststructuralists (see Agger, 1991, for a

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general introduction). In particular, by including each individual’s cognitive map in the analysis, we follow poststructuralist writers in rejecting the privileged status of any one particular interpretation of structure. We have also challenged the claimed incommensurability of individualism and structuralism by pointing to the influence on structural analysis of the psychology it has purported to reject and by providing an explicit demonstration of how a cognitive theory can guide the use of structural methods. In the next chapter, we continue our investigation of balance theory and the perception of network relations, looking systematically at how people distort perceptions close to and far from their own positions in friendship networks in organizations.

4 Systematic Biases in Network Perception

In Chapter 3, we predicted and found that the perceptions in people’s minds concerning whether a target individual was a friend of a prominent person significantly affected the target individual’s reputation concerning work performance in an organization (Kilduff and Krackhardt, 1994). The actual existence of friendship links, recognized by both parties in each link, had no significant effect on other people’s perceptions of an individual’s reputation as a high performer. This research showed that people’s perceptions of relations helped to determine reputations, whereas the actual structure of relations had no effect. In this chapter, we focus again on perceptions of the friendship network, this time investigating how perceptions are shaped by preexisting expectations. We chose the friendship network to study because this network affects important choices individuals make. We ask, under what circumstances are individuals’ perceptions of the friendship network shaped by schemas concerning how people typically behave in the friendship role? The role of friend is well understood in society, as indicated by the high level of agreement within societies concerning how friends should act in relation to each other (Argyle and Henderson, 1985: 92). People have access to a schema or strategy that specifies how individuals typically act in this role (see the discussions in DiMaggio, 1991; Swidler, 1986). Cognitive psychologists have described schemas as mental structures that enable people to anticipate the general features of recurring situations (Neisser, 1976: 51–78). Schemas allow people to search for and recognize relevant features of the person, situation, or process. The schema that has been most actively researched in the literature on friendship is the balance schema (for reviews, see Crockett, 1982; Markus and Zajonc, 1985; Wasserman and Faust, 1994, chap. 6). According to Heider’s explanation of the balance schema (1958: 205), perceivers tend to treat positive sentiment relations such as friendship as 59

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if they were symmetric and transitive. Symmetry refers to the perceiver’s assumption that friendship relations will be reciprocated. Thus, if the perceiver sees that A chooses B as a friend, the perceiver will anticipate that B will also choose A as a friend. Transitivity refers to the perceiver’s assumption that friendship relations will be complete. Thus, if the perceiver knows both that A is friends with B and that A is friends with C, the perceiver will anticipate that B and C will also be friends. The balance schema, then, consists of a set of cognitive expectations concerning the likely structure of the social world in terms of reciprocity and transitivity. Balance theory literature, however, offers several explanations for why people tend to perceive friendship relationships as balanced. We explore three perspectives: an emotional tension model, a cognitive miser model, and a composite model that combines the predictions of the other two. The Emotional Tension Model The first model emphasizes the emotional tension that results from discrepant cognitions, such as the perception of unbalanced friendship relations. People are motivated to resolve such cognitive discrepancies either by altering cognitions or by taking action in the world. Thus, if Jack perceives that his friendship overtures to his colleague Randolph are unreciprocated, the discrepant cognitions (e.g., “I’m Randolph’s friend, but Randolph doesn’t like me”) will prompt either a change in cognition (“Perhaps I’m not really his friend”) or a change in behavior (“I need to work harder to make this friendship work”). Individuals who perceive that their friendship relations are unbalanced may react with strong emotions rather than with cool analytical reasoning. The balance schema, from this perspective, functions as a deep-seated goal of human interaction (see the discussions in D’Andrade, 1992; Fiske, 1992). People strive to see friendship relations as balanced because the perception of unbalance induces feelings of uncertainty, instability (Festinger and Hutte, 1954), and nervousness (Sampson and Insko, 1964). As Heider suggested, ego’s perception of an unstructured region in the environment functions as a barrier that “makes action and therefore control difficult if not impossible” (1958: 71). The region closest to ego includes ego’s own personal friendships. Ego has power to directly influence whether these friendships are balanced or not. If, for example, Jane finds that her attempts at friendship with Ruby are unrequited, then Jane can sever the friendship link or try even harder to elicit tokens of friendship from Ruby. Ego has considerable potential power to balance friendship relations through direct action of this sort. Similarly, if Jane finds that her friendships with Alice and Shirley have not led Alice and Shirley themselves to become friends, then Jane can

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endeavor to bring her two friends together – over lunch in the cafeteria, for example. Within the region of the network where ego is connected to his or her friends, then, ego is potentially able to balance relationships through direct action. As ego surveys the friendship relations of his or her friends and of friends of friends, however, ego’s power to impose balance becomes considerably weakened. Further, if an alter (i.e., another individual) at some distance from ego is perceived to be involved in friendship relations that are unbalanced, this can disturb ego, because alter is still part of ego’s social world and as such is part of the mutually shared environment in which ego is involved every day. Perturbations affecting alter affect ego because ego encounters alter and alter’s friends in the daily round. The region occupied by an alter with unbalanced relations is likely to be perceived as one of uncertainty and tension. For relations close to ego, therefore, motivation is strong to balance relationships, and ego has the power to impose balance. As ego surveys relations at distances farther and farther from ego’s own position, however, the power to act is diminished, but the emotional uncertainty induced by perceived imbalance is also likely to be diminished. The unbalanced friendship relations of friends of friends represent areas of uncertainty and tension in the social world as perceived by ego. However, the prospect of uncertainty and tension derived from unbalanced distant relations is likely to be less troublesome than the immediacy of uncertainty and tension derived from unbalanced relations within ego’s own friendship circle. How quickly are the tension and uncertainty that are induced by perceived unbalanced friendship relations reduced as ego scans the social relations of friends, friends of friends, and so on out to the periphery of the social world? There has been some discussion of this issue. Heider (1958: 71) mentioned the problematic nature of unstructured regions anywhere in the mutually shared environment, whereas Insko (1981: 322–3) suggested that ego is likely to suffer little tension to the extent that ego has low involvement with individuals whose relations are unbalanced. Our working assumption is that the emotional pressure to perceive relations as balanced sharply diminishes but does not disappear as ego looks beyond his or her own friendship circle. Previous research has shown that people will alter relations or cognitions to preserve balance in close relations (Kumbasar, Romney, and Batchelder, 1994; Newcomb, 1961), and that individuals tend to seek out information that reduces dissonance and to avoid information that increases it (Ehrlich, Guttman, Schonbach, and Mills, 1957). Considerable evidence also indicates that people prefer balanced relations in general, even when they themselves are not directly connected to the

Perceiving Networks

Proportion

62

Distance Figure 4.1. Illustration of the emotional tension model’s prediction that the proportion of relations perceived by ego as balanced declines with social distance from ego.

individuals concerned (De Soto, 1960; Freeman, 1992). In the everyday world of social relationships, individuals are frequently brought into contact with acquaintances whose friendship relations may be unbalanced. In other words, individuals may be required to negotiate social pathways that are perceived to be unstructured and therefore problematic. Avoiding people with friendship problems may be either not possible or not compatible with, for example, a productive career. Thus, if Alice sees John as someone whose attempts at friendship are unreciprocated, she may want to avoid John; interacting with him may be bothersome because of the presumed tension he is under. However, Alice may have to work with John to accomplish her own tasks. Thus, the perceived imbalance in John’s friendships can affect Alice even if John himself is not a personal friend of Alice. We assume that the effect of alter’s unbalanced relations on ego diminishes sharply but does not disappear as ego considers alters farther and farther away from ego. To summarize, an emotional tension perspective on perceived balance leads to the prediction that ego’s close relations will tend to be perceived by ego as balanced, because ego has both the motivation and the power to arrange for them to be balanced. The curve in Figure 4.1 illustrates how ego’s perception of balance may be affected by social distance. As ego looks beyond the immediate circle of close friends, the emotional

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pressure to perceive relations as balanced sharply diminishes. Thus, as ego assesses the likelihood of balance among strangers on the remote horizon of ego’s social world, the degree of perceived balance should approach its minimum. The Cognitive Miser Model A contrasting perspective that represents a more recent paradigm in the field of social cognition views the person as a cognitive miser who, under circumstances of “unavailability or indeterminacy of important information” (Taylor, 1991: 195), relies on short cuts or heuristics to fill in the blanks in knowledge. The cognitive miser perspective emphasizes that even when people believe they are using complete information to form impressions or make decisions, they may be relying on only one or two salient cues (Dawes, 1976; Taylor, 1981; Taylor and Fiske, 1978). People are cognitive misers in the sense that they tend to avoid devoting the time and effort required to locate and use all relevant information prior to forming an opinion or perception. Applied to perceptions of friendship networks, this perspective suggests that people utilize schemas to help make sense of the mass of potential relations they observe. People may avoid expending cognitive energy keeping track of the potential relations characteristic of social groups. To the extent that an individual uses a well-developed schema, many details of the social world may be filled in by the schema rather than derived from actual perception (see the review by Mandler, 1979). The balance schema, then, provides ego with a way to infer the existence of relations when information is incomplete (Freeman, 1992). In particular, as people consider the friendship relations of those increasingly distant from themselves, they will have less and less knowledge of possible unbalanced relations (cf. McPherson, Popielarz, and Drobnic, 1992: 155). The farther away the relationship, the less information ego has regarding it and the more likely, therefore, ego is to assume that relations are balanced (Kuethe, 1962). There is a further reason why, from a cognitive miser perspective, people may rely increasingly on assumptions concerning balance as they scan distant relations: As people scan relations at greater and greater distances from themselves, they incorporate more and more people into their social world, and the number of possible relationships they must keep track of increases disproportionately. The effect of increasing group size on the number of possible relations in a group was dubbed “the law of family interaction” in an influential article by Bossard (1945: 292). As the group increases from four to eight members, for example, Bossard calculated that the number of possible relations increased from six to

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twenty-eight. Other researchers have offered refinements concerning how quickly the number of possible relations increases as each additional member is added to the group (e.g., Kephart, 1950), but all echo Bossard’s observation that the larger the group becomes, the more disproportionate the increase in possible personal relationships between members. From a cognitive miser perspective, people are likely to use the balance schema to fill in the blanks in their social knowledge rather than try to keep track of the large numbers of possible relations involving people at farther and farther distances from their own friendship circles. The law of family interaction suggests, therefore, that ego faces a daunting cognitive task in trying to keep track of relations of people at farther and farther distances. From this perspective, ego may tend to rely on the balance schema as a useful heuristic for making sense of distant relationships. Instead of keeping track of which relationships are reciprocated or transitive, ego may tend to assume that distant relationships are generally balanced. Within ego’s own social circle, however, unbalanced relations concerning ego’s own personal friends may be hard to ignore. Research suggests that people are more likely to notice evidence of unbalance than balance. For example, angry faces are found more efficiently in happy crowds than are happy faces in angry crowds (Hansen and Hansen, 1988). Heider suggested that unbalanced relations about which we have personal knowledge “stimulate us to further thinking” and “have the character of interesting puzzles” (1958: 180). In their review of the literature on the recall of schema-consistent and schema-inconsistent information, Markus and Zajonc (1985) suggested that schema-inconsistent information is likely to be recalled if it competes with the information in the schema and if the cognitive task requires the participant to make use of it. People are likely to remember the existence of unbalanced friendship relations in which they themselves are involved because this imbalance competes with the structures suggested by the balance schema, and the cognitive task of making sense of the immediate social world requires people to keep track of unbalance (cf. Janicik and Larrick, 2005). This is true, for example, in work organizations where individuals are likely to see their friends every workday and are therefore reminded daily of the absence of reciprocity and transitivity. In summary, the cognitive miser perspective suggests that people are likely to notice unbalanced relations among those close to themselves. However, as people scan distant relations, they are likely to rely on the balance heuristic to fill in the blanks of the relations of these distant others. Figure 4.2 offers one possible representation of the cognitive miser model, depicting an increasing probability of perceived balance for relations farther and farther away from ego.

65

Proportion

Systematic Biases in Network Perception

Distance Figure 4.2. Illustration of the cognitive miser model’s prediction that the proportion of relations perceived by ego as balanced increases with social distance from ego.

The Composite Model Despite the apparent contradiction between the emotional tension and cognitive miser models, there is a way to combine these two views: The balance schema may be imposed on close relationships (to avoid emotional tension) and attributed to the friendship relations of distant others (to fill in the blanks in social knowledge). If one accepts the extensive evidence that people are likely to suffer discomfort when they perceive their own friendship relations as imbalanced, then the major results of the emotional tension model are accommodated. According to this model, people are relatively unaffected by the perception of imbalance among those with whom they have no friendship ties. The motivation to change perceptions in favor of balance, then, is likely to affect mainly the perceptions of ego’s own friendship relations. If ego is not directly involved, little discomfort results from perceived imbalance. According to the cognitive miser model, however, while casting one’s gaze outward over the friendship relations of those with whom one has no direct links, one is likely to have less and less knowledge concerning such details as whether the relations are reciprocated or transitive. The less information that is available, the more one relies on the balance schema to fill in the blanks in one’s knowledge. The composite model, then, suggests that the hypothesized graphs in Figures 4.1 and 4.2 can be joined to display a curvilinear relationship between social distance and the degree of balance perceived. In summary,

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according to the composite model, individuals perceive both their immediate friendship circle and the periphery of their social networks as more balanced than social worlds of intermediate distance.

Method At each of the four sites described in this section, participants were promised and given an overview of the findings. At all four sites, the same questionnaire was used, as described in the “Perceived Friendship Network” section. Nonrespondents were excluded from all analyses. The relatively high response rates (which varied from 86 percent to 100 percent) reduced problems associated with nonresponse bias. Site 1: High-Tech Managers (HT) The participants at this site consisted of all twenty-one managers of a West Coast entrepreneurial firm of approximately 100 people employed in the manufacture of high-tech machinery. The managers were all men. David Krackhardt collected the data as part of an effort to explore the effects of a previous organizational development intervention conducted by external consultants. All twenty-one participants completed the questionnaire, and no compensation was offered to the participants. (See Krackhardt, 1987a, for further details.) Site 2: Government Office (Gov) This workgroup consisted of thirty-six professional staffers in the federal bureaucracy. Their job included advising the executive branch concerning courses of action that would facilitate the current public policy agenda. Each person in this workgroup had an advanced degree at the master’s level or higher. The group’s composition changed yearly as new staffers were added from different departments and others rotated out. The leadership of the group, however, had been in place for years. Thirty-one of the thirty-six people in this office completed a questionnaire, and no compensation was offered to the participants. Site 3: Silicon Systems (Sil) The participants included all thirty-six employees (twenty-eight men and eight women) of a small entrepreneurial firm located in the Bay Area of California. The employees were mostly semiskilled workers who installed computers and trained their clients in their use. Thirty-three of the thirtysix employees accepted $3 each to complete the questionnaire. (See Kilduff and Krackhardt, 1994, for more details.) Site 4: Pacific Distributors (Pac) The participants included all thirty-three supervisory and managerial personnel (fifteen men and eighteen women) located at the headquarters of

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67

a small, rapidly growing regional distributor of electronic components. The company employed 162 people in its headquarters and four branch offices. All thirty-three participants accepted $10 each to complete the questionnaire. (See Krackhardt and Kilduff, 1990, for more details.) Measures Perceived Friendship Network To capture participants’ perceptions of friendship relations, we used the same questionnaire across all four sites. At each site, every respondent answered the following question about every other person in the organization: “Who would this person consider to be a personal friend? Please place a check next to the names of those people who that person would consider to be a friend of theirs.” For example, John Meredith of the Sil sample was asked a series of thirty-six questions concerning the friendships of his thirty-six coworkers. The questions were in this form: “Who would Jane Asch consider to be a personal friend?” Each question was followed by the list of thirty-five employees’ names. John Meredith then checked the names that indicated his perceptions of who Jane Asch considered to be her personal friends. (For more details, see Kilduff and Krackhardt, 1994.) Each respondent, then, gave us a complete cognitive map of his or her perceptions concerning who was friends with whom in the organization. To measure perceived friendship links, we used the following procedure: A friendship tie as perceived by person k existed between person i and person j only if k responded on the questionnaire that i considered j a friend. Perceived Reciprocity We measured the extent to which each person in the network was perceived by every other person in the network to be involved in reciprocated friendships. We created a matrix of scores for each site, with each cell in the matrix indicating (according to person i’s perceptions) the proportion of person j’s dyadic relationships that were reciprocated. More formally, the perceived reciprocity matrix was defined as follows: Sij = NSij / (NSij + NNij ), where NSij is the number of reciprocated dyadic relations that i perceives j to be involved in, and NNij is the number of unreciprocated dyadic relations that i perceives j to be involved in. Perceived Transitivity We measured the extent to which each person in the network was perceived by every other person in the network to be involved in transitive friendship relations. We created a matrix of scores for each site, with each ij cell representing (according to i’s perceptions) the proportion of j’s triadic relationships that were transitive. Given that we separately analyzed

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perceptions of reciprocity from perceptions of transitivity, we chose the conservative path of considering only reciprocated ties in the transitivity analysis. Although there is a tradition within social network analysis of considering transitivity among unreciprocated relations as balanced (e.g., Holland and Leinhardt, 1977), it is clear from Heider’s (1958: 206–7) discussion of transitivity that he considered positive relations to be balanced only if the transitive relations were also reciprocated. Therefore, following Heider, when we refer to friendship relations in our definition of what constitutes a transitive triad, we are referring to reciprocated friendship relations. To compute transitivity, we temporarily symmetrized ego’s perceptions of friendship relations using the intersection rule that for a friendship relation between i and j to exist in ego’s perceptions, ego must perceive both a friendship link from i to j and a friendship link from j to i. Formally speaking, then, for any triple of actors i, j, and k, given that i and j are friends and j and k are friends, the triple ijk is transitive if and only if i and k are friends. Transitivity is violated (i.e., the triple is intransitive) if, given that i and j are friends and j and k are friends, i and k are not friends. Vacuously transitive triples, triples that do not meet the conditional requirement that i and j are friends and j and k are friends, are not considered in this analysis. The formula for computing each cell in the matrix T of perceived transitivity scores was as follows: Tij = NTij /(NTij + NTij ), where NTij is the number of transitive triples that i perceives j to be involved in, and NIij is the number of intransitive triples that i perceives j to be involved in. If NTij + NIij = 0 (i.e., if none of the triads perceived by i that include j meet the preconditions of transitivity), then Tij was set equal to a missing value. Actual Friendship Network To measure actual friendship links (distinct from perceived friendship links), we determined the locally aggregated structure (Krackhardt, 1987a) as follows: A friendship tie existed between persons i and j only if person i claimed person j as a friend and person j agreed that person i claimed person j as a friend. Thus, an actual friendship link from i to j was defined as existing when both parties agreed that it existed. Actual Reciprocity We created a matrix for each site in which each column in the matrix indicated the proportion of person j’s dyadic relationships that were actually reciprocated (as validated by both parties to the friendships). Thus, whereas person i might perceive that person j’s friendships were 40 percent reciprocated, if j’s friendships were in fact reported by j and j’s friendship partners to be 60 percent reciprocated, the column of scores for j in

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the actual reciprocity matrix would consist of .6 repeated in each cell of the column. Actual Transitivity We created a matrix for each site in which each column in the matrix indicated the proportion of person j’s triadic relationships that were actually transitive. We first symmetrized the actual friendship matrix, using the intersection rule that for a reciprocated tie to exist between persons i and j, person i had to report a friendship tie from i to j and person j had to report a friendship tie from j to i. Then, following the procedure outlined in the computation for perceived transitivity, we calculated the actual proportion of triads involving person j that were transitive. Each column in the matrix indicated the extent to which person j was involved in transitive triads. Social Distance We measured the extent to which each person in the network perceived himself or herself to be distant from every other person. Thus, our measure of social distance was a perceptual measure of how close or far ego perceived alter to be from ego. In a graph, the path distance between two points is the length of the shortest path (or geodesic) that connects them (Harary, 1969). As Feld and Grofman (1989) pointed out, when networks are represented as graphs, the path distance between any two points is a good proxy for social distance. We measured the shortest path distance between each pair of individuals i and j as perceived by i. Thus, if respondent Sam Berkowitz perceived that the shortest path connecting him to Alan Hobbs consisted of four lines, the distance from Berkowitz to Hobbs was measured as 4. We treated social distance within any individual’s cognitive map as a symmetric concept. That is, if ego perceived the distance from ego to alter as equal to some value x, then this implied that ego also perceived the distance from alter to ego as equal to x. Thus, we symmetrized ego’s perception of the friendship network before calculating social distance. For example, if ego perceived that ego was a friend of j and also perceived that j considered k a friend, then we deemed the social distance between ego and k equal to 2. Conversely, if ego perceived that k considered j a friend and that j considered ego a friend, then this was also deemed to be a distance of 2 between ego and k. In calculating the social distance measure used in predicting the degree of reciprocity in ego’s perceptions, we symmetrized ego’s cognitive map of the network using the union rule: A friendship relationship existed if ego perceived either of the people to have a friendship tie to the other. In contrast to the reciprocity analyses, the transitivity analyses were conducted

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on matrices that had already been symmetrized using the intersection rule: A friendship relation existed if ego perceived both of the people to have a friendship tie to each other. To be consistent, we calculated the social distance measure used in predicting the degree of transitivity in ego’s perceptions on the matrix symmetrized using the intersection rule. For both the reciprocity and transitivity tests, we considered distances of infinity (indicative of the absence of paths between the two nodes) to be missing values. We also performed analyses with the infinite distances recoded as distance N (the number of nodes in the network), and although the meta-analysis results were the same, the large distances tended to act as outliers, obscuring the true underlying relationships. In this chapter, we restricted our presentation of the data to those cases where distances were “real” (that is, the actors were mutually reachable) and not infinite. Distance squared was calculated in a straightforward manner but was mean-centered (i.e., the mean was subtracted from all values) before the term was squared. This reduced collinearity problems in the regression because the correlation between a variable and its mean-centered squared term is 0, whereas the correlation between a variable and its (non-meancentered) square can be high, resulting in unstable coefficient estimates. Density We assessed the density of each respondent’s cognitive map as the number of lines in the map divided by the maximum possible number of lines (Scott, 1991: 74). Some respondents perceived many friendship links, whereas other respondents perceived few links. We controlled for this variation in density across perceivers’ cognitive maps as follows: For each respondent, we calculated a number between 0 and 1 that indicated the proportion of all possible friendship links that were perceived to exist. As with the distance measure, the density measure was based on the specific matrix that predicted either transitivity or reciprocity. That is, densities in the models used to predict transitivity were based on the symmetrized friendship networks from which the transitivities were calculated. Densities in the models used to predict reciprocity, on the other hand, were based on the nonsymmetrized friendship networks from which the reciprocity proportions were calculated.

Data Analysis Social network data are often not amenable to standard statistical tests, such as ordinary-least-squares analysis, because the observations cannot be assumed to be independent. For example, in the current research, the transitivity matrix for the Pac site includes thirty-three scores from each person in the sample. Each of the thirty-three scores within a row of this

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matrix derives from the same source (the cognitive map of the respondent) and therefore exhibits systematic interdependence. To deal with this problem, we used the Multiple Regression Quadratic Assignment Procedure (MRQAP), which has been explained in detail in Chapter 3 and in previous work (e.g., Kilduff and Krackhardt, 1994; Krackhardt, 1987b, 1988). To assess the extent to which the combined results of our analyses across four sites offered support to the expected relationships between variables, we performed a meta-analysis (Hedges and Olkin, 1985). Typically, the null hypothesis for a meta-analysis is that all samples are drawn from populations in which there is no relationship between the variables of interest (these variables being, in our case, distance, distance squared, and proportions of balance). If the meta-analysis shows that the null hypothesis is rejected, the conclusion follows that for at least one of the samples there is a significant relationship between the variables of interest. However, finding an effect for only one of the samples scarcely ranks as “persuasive evidence of the efficacy of a treatment” (Hedges and Olkin, 1985: 45). However, the studies from four sites being combined in our analysis use exactly the same measurement instruments and replicate exactly the same regression model. In such a case, we can test the likelihood that the distance-squared coefficients that we are interested in are not significantly different across the four samples, and whether we can therefore interpret the combined p value to refer to a common population. Hedges and Olkin (1985) described many situations for which such a test can be performed using a Q statistic. However, the data that we collected, with their autocorrelated structure requiring a nonparametric MRQAP analysis, falls outside the situations described. This is unfortunate because the problems of autocorrelated data are common in the study of social networks. The arguments of Hedges and Olkin can nevertheless be extended to our case by using the information in the permuted values of the regression coefficients to replicate the weighted mean estimates of the population beta and the estimate of the standard error of the beta. Because this is the first time, to our knowledge, that such a Q test for meta-analysis has been applied to social network models such as ours, we describe here in detail the procedure that we used in calculating and testing Q. Notation is drawn from Hedges and Olkin (1985). Q is a test statistic that compares the observed betas β i from each sample i with a weighted estimate of the population beta β + :

Q=

  k  (βi − β+ )2 2

i=1

σˆ (βi )

(1)

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where k is the number of samples, and σˆ (βi) 2 is the estimated variance of each beta, which has been estimated by calculating the variance of the betas generated across all 999 permuted values of the dependent variable under the null hypothesis. The Q statistic is asymptotically distributed as a chi-square with (k − 1) degrees of freedom:   k  βi 2

β+ =

i=1 k 



σˆ (βi )

.

1 2

i=1

σˆ (βi )

The β + parameter is estimated by calculating a weighted average across the k sites, where each weight is inversely proportional to the variance of β i in each sample. For both the reciprocity and transitivity analyses, the following computations had to be made for each site i: β i , σˆ (βi) 2 , β i −β + , (β i −β + )2 , (β i −β + )2 /σˆ (βi) 2 . To test for the possibility (suggested by a colleague) that in calculating transitivity we might have created a positive correlation with distance (and distance squared), we randomly generated raw friendship data. For one hundred samples, the regression coefficients for distance squared in the prediction of reciprocity and transitivity were not significantly different from 0, and thus the distance-squared analyses appear to be unbiased. A slight but significant negative bias was uncovered for the coefficient for distance in the transitivity model. To ensure that the results were not affected by this bias, we recalculated the significance levels of this coefficient against a null hypothesis of the mean of the simulated samples, using the standard error generated by the simulations. The results of the meta-analysis become even stronger when we use this conservative test. Thus, the results that we report in the following section cannot be attributed to an artificial bias introduced by the analytic procedure itself.

Results Table 4.1 provides a summary of the variables used in this analysis. The proportions of reciprocity and transitivity perceived by individuals in friendship networks differed slightly across the four sites, as shown in Table 4.2. The mean proportion of perceived reciprocity ranged from .39 to .49, whereas the mean proportion of perceived transitivity ranged from .20 to .30. Tables 4.3 and 4.4 present information addressing the issue of whether the regression coefficients differed significantly across the four sites and whether it is therefore acceptable to interpret meta-analysis results as

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Table 4.1. Summary of Research Variables Variable Dependent Matrices Perceived reciprocity

Perceived transitivity

Independent Matrices Distance

Distance squared Control Matrices Density

Actual reciprocity and transivity

Definition of Each Cell in Matrix Respondent i’s perception of the proportion of pairs including j that were reciprocated Respondent i’s perception of the proportion of triads including j that were transitive Length of the shortest path between i and j as perceived by i Mean-centered distance squared Number of links in respondent i’s cognitive map divided by the maximum number of links possible Using the rule that both individuals must agree that each considers the other a friend before a friendship link is established, each cell contains the actual proportion of pairs (or trials) including j that were reciprocated (or transitive)

referring to a common population. The Q statistics at the bottom of Tables 4.3 and 4.4 are nonsignificant, indicating that the regression coefficients for distance and distance squared did not differ significantly across the four sites in either the reciprocity or the transitivity analyses. For example, in Table 4.3, the overall β + for distance squared was .016, which was calculated by summing the four entries for distance squared in the third column of the table. This yielded a nonsignificant Q of 0.476 ( p = .924, d f = 3). Thus we conclude that the four distance-squared coefficients in the reciprocity model were not significantly different from one another. On the basis of nonsignificant Q statistics for distance and

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Perceiving Networks Table 4.2. Proportions of Perceived Balance in Friendship Networks Site Variable Reciprocity M SD Transitivity M SD

HT n = 21

Gov n = 31

Sil n = 33

Pac n = 33

.39 .35

.44 .35

.44 .36

.49 .35

.20 .26

.30 .31

.25 .27

.24 .30

Notes: HT = high-tech managers; Gov = government office; Pac = Pacific Distributors; Sil = Silicon Systems.

Table 4.3. Summary of Q Analyses Determining Whether Reciprocity Regression Coefficients Differed across Four Samples Individual Site Statistics (βi − β+ )2 Variable Site 1 Distance Distance squared Site 2 Distance Distance squared Site 3 Distance Distance squared Site 4 Distance Distance squared

βi

Variance of βi

Variance of βi

−0.0894209 0.0111083

0.0017081 0.0003310

0.3814442 0.0790133

−0.0777464 0.0235878

0.0011510 0.0002434

0.1666685 0.2228960

−0.0717410 0.0145674

0.0005120 0.0000565

0.1202192 0.0484558

−0.0057950 0.0249318

0.0013735 0.0006016

2.4577374 0.1260871

All Four Sites Combined (df = 3) Distance Distance squared

All Four Sites Combined β+

−0.0638959 0.0162220

Q

p

3.127 .373 0.476 .924

distance squared in Tables 4.3 and 4.4, we can assume that the results of meta-analyses on these coefficients refer to a common population. Figure 4.3 presents the results of the reciprocity analyses for the combined data across all four sites and for each site individually. The overall graph shows a distinct curvilinear shape, indicating a tendency for ego to perceive close and distant relations as more reciprocated than relations in

Table 4.4. Summary of Q Analyses Determining Whether Transitivity Regression Coefficients Differed across Four Samples Individual Site Statistics (βi − β+ )2 βi

Variable Site 1 Distance Distance squared Site 2 Distance Distance squared Site 3 Distance Distance squared Site 4 Distance Distance squared

Variance of βi Variance of βi

−0.l242139 0.0640057

0.0039149 0.0016400

0.8911572 1.0929966

−0.1134550 0.0511434

0.0017719 0.0005701

1.3169683 1.5240775

−0.0484082 0.0164252

0.0011504 0.0004052

0.2494559 0.0678201

−0.0293083 −0.0029419

0.0012974 0.0003810

0.9900603 1.5893159

All Four Sites Combined (df = 3) Distance Distance squared

All Four Sites Combined β+

Q

p

−0.0651483 3.448 .328 0.0216671 4.274 .233

0.8 Pac

0.7

Gov Overall

Proportion

0.6 Sil

0.5 0.4 0.3 0.2

HT

0.1 0 1

2

3

4

5 Distance

6

7

8

9

Figure 4.3. Proportions of perceived reciprocity of people’s relations at four sites as a function of social distance from ego. HT = high-tech managers; Gov = government office; Pac = Pacific Distributors; Sil = Silicon Systems.

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Perceiving Networks 0.5 Overall HT

0.4

Gov

Proportion

0.3

Sil

0.2

Pac

0.1

0 1

2

3

4

5

6

7

Distance Figure 4.4. Proportions of perceived transitivity of people’s relations at four sites as a function of social distance from ego. HT = high-tech managers; Gov = government office; Pac = Pacific Distributors; Sil = Silicon Systems.

the middle distance of about two to five links from ego. This overall Ushaped curve provides support for a composite model that includes both the emotional tension model’s prediction (higher perceived reciprocity for close relations) and the cognitive miser model’s prediction (higher perceived reciprocity for more distant relations). As Figure 4.3 shows, the graphs reached their minima at values of social distance ranging from 2.12 (at the Pac site) to 5.8 (at the HT site). These minima were within the range of the data that we collected: The maximum values for social distance for each site were as follows: Pac = 5, Gov = 7, Sil = 9, and HT = 7. Figure 4.4 presents the results of the transitivity analyses for the combined data across all four sites and for each site individually. Again, the overall graph shows distinct U-shaped curvilinearity, supporting the predictions of the composite model. The graph for the Pac site, however, differs from the graphs for the other sites, showing a rather linear downward slope. At this site, therefore, the proportion of relations perceived as transitive tended to decrease with increasing distance from ego. As Figure 4.4 shows, for the three sites exhibiting positive curvilinear graphs, the graphs reached their minima at values of social distance ranging from 2.8 (at the HT site) to 3.5 (at the Sil site), all within range of the data that we collected. The maximum values for social distance for each site were as follows: HT = 5, Gov = 5, Sil = 7, and Pac = 7.

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Table 4.5. Summary of Multiple Regressions Predicting Proportions of Perceived Reciprocity in Friendship Networks B

β

p

Site 1: HT (n = 21) Distance Density Distance squared Actual proportion

−8.942 −2.443 1.111 9.768

−26.659 −0.370 8.600 9.895

.984 .502 .275 .170

Site 2: Gov (n = 31) Distance Density Distance squared Actual proportion

−7.775 46.564 2.359 −3.454

−21.294 12.767 15.605 −2.514

.990 .109 .069 .686

Site 3: Sil (n = 33) Distance Density Distance squared Actual proportion

−7.174 −27.498 1.457 17.616

−25.013 −3.883 16.381 16.718

.999 .693 .034 .033

Site 4: Pac (n = 33) Distance Density Distance squared Actual proportion

−0.580 21.742 2.493 14.174

−1.603 3.736 9.808 12.649

.542 .449 .169 .067

Variable

Note: Coefficients were multiplied by 100 for ease of presentation. Infinite distances are deemed missing values. All tests are one-tailed; thus, all negative coefficients have p values greater than .5 HT = high-tech managers; Gov = government office; Pac = Pacific Distributors; Sil = Silicon Systems.

Table 4.5 presents the results of regression analyses predicting proportions of perceived reciprocity at each of the four sites, whereas Table 4.6 presents the equivalent results for perceptions of transitivity. These results provide the basis for the meta-analysis results summarized in Table 4.7, where data from all four sites are combined. Consistent with the information evident in Figures 4.3 and 4.4, the meta-analysis confirms that the overall data exhibited curvilinearity (as assessed by the positive distance-squared term): The coefficients for overall distance squared were significant for both reciprocity (Z = 2.432, p = .008) and transitivity (Z = 2.301, p = .011). We can conclude from the evidence in the graphs and in the statistical analyses that the data are probably best fit by a U-shaped curve. In other words, the evidence lends support to a composite model that combines the downward curving prediction of the emotional tension model (less perceived reciprocity as ego looks beyond his or her immediate friendship

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Perceiving Networks Table 4.6. Summary of Multiple Regressions Predicting Proportions of Perceived Transitivity in Friendship Networks B

β

p

Site 1: HT (n = 21) Distance Density Distance squared Actual proportion

−12.421 281.260 6.401 6.666

−45.997 36.541 39.184 4.952

.973 .058 .065 .357

Site 2: Gov (n = 31) Distance Density Distance squared Actual proportion

−11.346 203.022 5.114 47.936

−38.802 47.681 31.453 28.180

.994 .004 .014 .012

Site 3: Sil (n = 33) Distance Density Distance squared Actual proportion

−4.821 152.721 1.643 21.258

−21.331 17.578 17.709 14.661

.929 .118 .167 .114

Site 4: Pac (n = 33) Distance Density Distance squared Actual proportion

−2.931 262.219 −0.294 12.161

−13.123 35.824 −2.999 7.965

.768 .122 .530 .278

Variable

Note: Coefficients were multiplied by 100 for ease of presentation. Infinite distances are deemed missing values. All tests are one-tailed; thus, all negative coefficients have p values greater than .5. HT = high-tech managers; Gov = government office; Pac = Pacific Distributors; Sil = Silicon Systems.

circle) and the upward curving prediction of the cognitive miser model (more perceived reciprocity as ego’s gaze includes the friendship relations between dyads relatively unfamiliar to ego). In the statistical analyses reported in the tables, we controlled not just for the linear effects of social distance and the density of ego’s network but also for the actual proportion of balance in specific friendship dyads. We were able to focus explicitly on the question of how social distance affected perceptions of balance in friendship relations while controlling for the possibility that perceptions might, in fact, align with reality. Controlling for people’s tendency to perceive balance where the members of the friendship pair confirmed that it existed, we found evidence for a curvilinear effect of social distance on perceived balance. In other words, the analyses allow us to reject the possibility that curvilinearity in the data derives not from perceptions but from the distribution of actually occurring friendship reciprocity and transitivity.

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Table 4.7. Summary of Meta-Analysis Results across Four Sites Predicting Proportions of Perceived Balance in Friendship Networks Reciprocity Variable

Transitivity

p, one-tailed

Z

p, one-tailed

Z

Site I: HT (n =21) Distance Distance squared

.984 .275

−2.144 0.598

.973 .065

−1.927 1.514

Site 2: Gov (n = 31) Distance Distance squared

.990 .069

−2.326 1.483

.994 .014

−2.512 2.197

Site 3: Sil (n = 33) Distance Distance squared

.999 .034

−3.090 1.825

.929 .167

−1.468 0.966

Site 4: Pac (n = 33) Distance Distance squared

.542 .169

−0.105 0.958

.768 .530

−0.732 −0.075

All Four Sites Combined Distance Distance squared

.999 .008

−3.833 2.432

.999 .011

−3.320 2.301

Note: All tests are one-tailed; thus, all negative coefficients have p values greater than .5. HT = high-tech managers; Gov = government office; Pac = Pacific Distributors; Sil = Silicon Systems.

Discussion The results of both the graphical and statistical analyses suggest that individuals tend to perceive both close and distant relations as balanced. We found support for a composite model that includes both an emotional tension effect (close relationships tend to be perceived as balanced) and a cognitive miser effect (distant relationships tend to be perceived as balanced). The results of the meta-analyses were consistent for both perceived reciprocity and perceived transitivity and suggest a unifying perspective on how individuals cognitively structure their social worlds. Previous research has shown that people tend to prefer balanced rather than imbalanced relations in both perceived networks (De Soto, 1960; Freeman, 1992) and behavioral networks (Davis, 1979). The current research across work organizations suggests that this general preference has specific significant effects on ego’s perceptions of friendship relations both close to and distant from ego. Close to ego, the motivation to perceive one’s own interpersonal world as balanced may be to avoid emotional upset. Far from ego, the motivation to perceive the relations of relative strangers as balanced may be to fill in the blanks in social

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structure. Thus, the explanation of balanced structures in social networks can combine both hot (emotional) and cold (knowledge) approaches to social cognition. One of the puzzles of the current research concerns the anomalous result for perceptions of transitivity in site 4. The sample for this organization differed from the samples from the other organizations in that we collected data only from the managerial core at headquarters, not from all the people at headquarters. The composite model that we present may well apply only to bounded groups such as those in sites 1, 2, and 3. Site 4 was also different in being a relatively large organization compared with the other three sites. In larger groups, the task of organizing the relations among the large set of alters may be difficult. Extensive previous research (reviewed by Moreland and Levine, 1992) has shown that most natural groups are quite small, averaging two to three members and rarely exceeding five or six members. People appear to have difficulty coordinating social interactions that involve more than five persons (Despartes and Lemaine, 1986). The minimum for reciprocity varied more than the minimum for transitivity across the different sites. Transitivity, as an indicator of balance, may be difficult to assess beyond a distance of four because it involves organizing six directional ties rather than the two ties involved in reciprocated relations. The task of mapping transitivity relations is relatively complex even in small organizations once ego’s gaze travels beyond his or her familiar acquaintances. Our study differs from previous research in the way that we measured social distance. Whereas in the present study we used a continuous measure of distance, previous work (Kumbasar et al., 1994) dichotomized alters into those who were at a distance of one from ego and those who were at a distance greater than one from ego. The dichotomization of distance prevents discovery of a curvilinear relationship even if one exists in the data. In the present research, we aimed to go beyond the laboratory to study the effects of schema use in actual social settings, in keeping with calls for more field-based studies of human cognition (e.g., Funder, 1987). We tested our models in four quite different social arenas rather than resting content with the standard single setting common in social network studies (e.g., Burkhardt and Brass, 1990; Kilduff, 1992; Krackhardt, 1990; Walker, 1985). A further strength of the current research is the inclusion in the statistical tests of variables derived from naturally formed networks of friendships as well as perceptions of those networks. This inclusion allowed us to focus on how schemas shape perceptions while taking account of the possibility that reality shapes perceptions. One of the strengths of the current research – data collected from actual social settings – is accompanied by a potential weakness. Because we

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have not experimentally manipulated the causal factor (social distance) to which we attribute the findings, the possibility remains that our causal logic could be reversed. Thus, it is possible that people tend to either draw close to or keep quite distant from those whose relationships they perceive as balanced. In the current cross-sectional analyses, we are unable to track dynamic processes of this sort, and this suggests that future work could explore the effects of social distance on perceived balance in more controlled settings. The attempt to understand transitivity in triads has a long but rather confused history. One of the leading researchers in the field was led to declare that “after a decade of matrix grinding, I have no more idea of why triads are transitive than I did when I began” (Davis, 1979: 60). Whereas reciprocity is well established as a defining feature of human society (Gouldner, 1960) and is especially evident among adults in the world of work (Gouldner, 1973: 268), transitivity appears most prominently in groups of junior high school students (Davis, 1979: 61). Transitivity, of course, involves an ordering of relations among a three-person group, whereas reciprocity involves relations among only two people at a time. Ego has greater control over whether a friendship link from alter to ego is reciprocated than whether two of ego’s friends decide to complete the third link of a transitive triplet. In other words, it is easier for ego to impose reciprocity relative to transitivity on perceptions of friendship relations (Doreian, Kapuscinski, Krackhardt, and Szczypula, 1996). The current research, in proposing that perceived transitivity is a function of social distance, offers a parsimonious explanation for when transitivity in social organizations is likely to be found, an explanation that works equally well for transitivity as for reciprocity. The importance of reciprocity and transitivity as structural principles of group organization has been widely recognized. Gouldner (1960) suggested that reciprocity functions to counter bureaucratic impersonality and to maintain the division of labor in work organizations. He quoted Simmel to the effect that all contacts among people “rest on the schema of giving and returning the equivalence” (Gouldner, 1960: 162 162). Recent work examining social relations across diverse studies has confirmed the pervasiveness of the reciprocity heuristic in perceptions of liking (Kenny et al., 1996; Kenny and DePaulo, 1993). The principle of transitivity has been described as the “key structural concept in the analysis of sociometric data” (Holland and Leinhardt, 1977: 49–50). The expectation that friendships will be balanced may serve to stabilize but not rigidify organizational systems in which patterns of interaction are reproduced daily. The assessment of perceived friendship in our research is two-valued, consistent with Heider’s (1958) discussion: People are either friends or not

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friends. Transitivity, therefore, can be understood as an instance of the logical principle known as the multiplicative rule. This rule implies that, for example, the multiplication of two positives (i.e., friendship relations between A and B and between A and C) results in a positive (i.e., a friendship relation between B and C). Because a two-valued approach to balance theory is perfectly compatible with traditional logic (as Insko, 1999, and Runkel and Peizer, 1968, point out), a preference for perceiving two-valued relations as transitive can be understood as a preference for perceiving the world as a logical, ordered place. One potentially valuable extension of the current research would be to examine preferences for balance using a many-valued rather than a two-valued approach (for two experimental investigations, see Tashakkori and Insko, 1979, 1981). In focusing on perceived balance in work organizations, we are helping to uncover the ways in which people structure the social worlds where careers are established and where much of the business of the modern world is conducted. The current research suggests that if people in organizations perceive unbalanced relations close to themselves, they will act to balance these relations either by changing relationships or by changing cognitions. Further, people tend to perceive friendship relations far from ego as balanced because of increased reliance on the balance schema to organize perception. It is in the middle ground – the area around the minimum – that ego is likely to be troubled by persistent imbalance. In this middle area, ego has no power to act decisively to change relationships, and ego may know too much about the relations of these people on the margins of ego’s world to be able to organize their relations using the principles of balance. Future research, then, could focus on this area around the minimum as the site of ego’s perceived dissatisfactions and opportunities. Ego is likely to be unhappy at work to the extent that he or she perceives relations in the middle distance as unbalanced. However, unbalanced relationships represent structural holes to be bridged (see the discussion in Burt, 1992). To the extent that ego perceives, for example, that groups of individuals who should be communicating with each other are not doing so or are doing so ineffectively, ego may be able to seize the initiative to bridge the gap and bring the people together. As Weick (1995) has emphasized, perceptions have a way of becoming reality. Thus, perceived gaps in communication and friendship patterns may lead to actual movements of people to bridge perceived gaps, irrespective of whether such gaps actually exist. In a striking example of the “grass is greener” effect, people in organizations may perceive more opportunities for entrepreneurial action just outside their own friendship circles. The reproduction and transformation of structure in social systems depend in part on the systemwide effects of the positions (Brass, 1984) and roles (DiMaggio, 1991: 94) occupied by individuals. A sense-making

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perspective emphasizing the perception of relations can help explain how individuals structure the social worlds to which they belong. Individual perceptions of social structure are important because such perceptions shape reputations across internal labor markets (Kilduff and Krackhardt, 1994). The process we have described connects the intimate world of friends and acquaintances with the distant world of relative strangers. The extent to which the perceiver finds that the periphery of the social world resembles the proximate may enable individuals to anticipate familiar patterns of interaction across social boundaries and structural holes. The individual, then, in extending a vision of a balanced world to the relations between comparative strangers, may sustain a logic of confidence that promotes action across social divides. In the next chapter, we tackle the question of accuracy: Given the biases that we have documented, does it help the individual to perceive accurately the friendship and advice relations in an organization? What benefits flow to those who see more clearly the connections among others?

5 Effects of Network Accuracy on Individuals’ Perceived Power

In the previous two chapters, we showed that perceptions of social networks matter and that such perceptions are systematically biased. But some people are more accurate than others in perceiving network patterns. If this is so, do these accurate people gain benefits in organizational arenas of competition and power? This is the theme we investigate in this chapter. We expand the discussion to include perceptions of both friendship and advice networks, and investigate whether an accurate perception of the political landscape – including who are the central players – predicts who has power in the organization. How does one assess the political landscape in an organization? One way of addressing this question is to identify the key political actors in the organization (Pfeffer, 1981). But simply identifying the most powerful actors may not give sufficient information to anticipate the overall dynamics of resistance and support for political acts. Additional questions about these actors come to mind: Are these powerful actors organized such that they tend to act in unison? Do they represent different political constituencies? Precisely whom does each have influence over? Beyond knowing who is powerful, it is useful to know how the powerful and powerless are organized or structured (Bailey, 1969: 108). One way to approach the answers to these deeper questions about the political landscape is to study access to and the control of information flow in the organization (Pettigrew, 1973). As far back as 1965, Hubbell derived both a measure of the power of individual actors and an identification of powerful coalitions, using the actors’ networks of ties. Laumann and Pappi (1976) documented how power accrued to those in central network positions in a community of elites. Brass (1984) discovered that centrality in work-related communication networks was a robust predictor of power in a printing company. As Pfeffer (1981: 130) stated: “Clearly, the power that comes from information control . . . derives largely from one’s position in both the formal and informal communication networks.” 84

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More to the point, the study reported in this chapter suggests that power accrues not only to those who occupy central network positions in organizations but also to those who have an accurate perception of the network in which they are embedded. An individual who has an astute knowledge of where the network links are can have a substantial advantage. First, this information provides a good assessment of who is powerful in the organization, because the central actors in the network can be easily ascertained. Knowing who the central – and powerful – actors are in the organization is essential political knowledge. Second, this information can be used to identify where the coalitions are in an organization. Knowing where the coalitions are, how large they are, and where their support comes from gives one an edge in anticipating resistance and in mobilizing support for action or change. Third, an accurate assessment of the network can also reveal the weaknesses in political groups by exposing holes, gaps, and locations of lack of support for any particular coalition. Thus, understanding the network provides a source of power independent of centrality in the network. The central point in this chapter is precisely that: Cognitive accuracy of the informal network is, in and of itself, a base of power. Both the concepts of power and cognitive accuracy are further developed in this chapter. In addition, we will argue that these two concepts are embedded in a structural context that must be taken into account in any empirical exploration.

Power There has been much disagreement as to the precise meaning of power. Some writers have referred to it as the ability to get things done despite the will and resistance of others, the ability to “win” political fights, or a capacity to outmaneuver the opposition (Bierstadt, 1950; Emerson, 1962). Others (e.g., Kanter, 1979; McClelland, 1975; Roberts, 1986) have stressed the positive sum nature of power, suggesting that it is the raw ability to mobilize resources to accomplish some end (without specific reference to organized opposition). Still others refer to power as the ability to control premises of actions, such that power becomes almost unobservable (Bachrach and Baratz, 1962; Lukes, 1974; Mizruchi, 1983). Salancik and Pfeffer (1977) preferred to ignore these distinctions, noting that, while academics may quibble over the definition of power, those actually experiencing the effects of power in the real world seem to exhibit a consensus as to who has it. Without fully resolving this debate, it is reasonable to assume that the answer to the question of who has power depends in part on an

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answer to the question, Power to do what? If the influence being sought is within the routine operation of the organization, then people who are “experts,” people in “authority,” and, generally, people who know how things work around the organization are likely to be seen as powerful. If, however, the influence entails a radical departure from prior operations, then the uncertainty that emerges is likely to arouse emotional responses to influence attempts. Affect-laden issues such as trust, respect, or liking may become important in evaluating who has the ability to mobilize support for the radical change (Chapter 10; Krackhardt and Stern, 1988). In such cases, the powerful person may be someone who has referent power (French and Raven, 1959) or charisma (Bradley, 1987; Fiedler and House, 1988; House, 1977) in the organization rather than someone who simply has authority or expertise. We included multiple kinds of power in this study (as recommended by Pfeffer, 1981). The assumption is that some actors are powerful because they are acknowledged as adept at getting things done in the organization, despite some resistance (e.g., Brass, 1984) and that some actors are influential because of an ascribed individual trait that reflects intangible qualities of trust and personal charm. These two different assessments of power are offered as ones that actors will readily recognize as influence bases in organizations: the ability to get things done in spite of resistance and the ability to influence people through personal appeal and magnetism (which is termed charisma).

Cognitive Social Structure and Accuracy The current study was motivated by the question, How closely does each person’s perception of the network approximate the “actual” network and how does this relate to power? To address this question, two types of aggregations were employed: The set of N individual perceived maps of the whole network, called “slices,” of Ri,j,k ; and the “actual” network, as defined by the two people actually involved in the relationship, referred to as the locally aggregated structure, or LAS (Krackhardt, 1987a). Just as power itself is a multidimensional concept, network relationships may be assessed on several dimensions. But the specific question is, What network relations are critical for the assessment of power? For example, a network composed of incidental communication links, such as perfunctory “hello’s,” may not be as rich in power information as a network composed of critical advice relationships. The study reported here was based on the cognitive social structures for two different types of networks that have been shown to be useful in understanding the

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dynamics of informal organizations (e.g., Brass, 1984; Burt, 1982: 25; Krackhardt and Porter, 1985, 1986; Lincoln and Miller, 1979). First, the advice network (who goes to whom for work-related advice) represents the instrumental, workflow-based network in the organization. The second network assessed was the friendship network, or what Lincoln and Miller (1979: 186) called the “primary network,” which we have focused on in the previous three chapters. The friendship network captures important affective and social bonds that can affect trust, especially in times of change (Chapter 10; Krackhardt and Stern, 1988).

Structural Influences In pursuing issues of power, one cannot ignore critical contextual and structural factors that also operate to give certain actors privilege and power in an organization. Brass (1984) found that centrality in the informal network itself predicts power. But centrality also has important theoretical links to cognition (see Krackhardt, 1987a, for a comparison of different types of centrality). A series of studies has found that central involvement in a social system increases one’s ability to “see” the social system accurately (Freeman and Romney, 1987; Freeman et al., 1987). Freeman, Freeman, and Michaelson (1988) noted that “social intelligence,” the ability to discern social groups and boundaries, evolves over time as participants gain experience in the social group. Freeman and Romney (1987) demonstrated that people’s ability to recall social structure accurately was a function of whether they were members of the core group or were peripheral, transitory members. These results, combined with Brass’s (1984) findings, suggest that centrality in the informal structure can lead to both cognitive accuracy and power. Another structural power base that cannot be ignored is the formal position that a person holds in the organization. Clearly, those with more authority will have more power, on the average, than those with less authority. In addition, those higher in the organizational chart are responsible for a larger part of the organization. A first-line supervisor is responsible for the activities of his or her immediate subordinates. A manager of several supervisors is responsible for these supervisors and ultimately for the activities of all their subordinates. People’s positions require them to pay attention to the way in which those under them work together and relate to each other. Thus, those higher up in the organization will have, by virtue of their position, a better opportunity to observe and take note of a larger part of the informal network. Consequently, they are likely to have a more accurate picture of the informal network.

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STRUCTURE

COGNITION

POWER

Formal Power

Cognitive Accuracy

Reputational Power

Informal Network Centrality Figure 5.1. Model relating structure, cognition. and power.

This should be particularly true in a small, entrepreneurial firm, where the owners-managers are known to be heavily involved in the details and day-to-day workings of the entire organization. Those higher in the formal organization are forced to relate to a wider base of people. A first-line supervisor must coordinate the activities of a limited number of people, all of whom are likely to interact informally with each other and be doing similar work. A top-level manager must coordinate the activities of supervisors and managers from different functions and sectors of the organization. This responsibility gives higher-level managers more central positions in the formal organization, in that they will find themselves dealing with more issues that surface between departments or groups. This formal role is likely, in turn, to lead to opportunities to be in the middle of the informal network, acting as a bridge between groups of employees. Therefore, it is expected that formal hierarchical level will also contribute to network centrality. There are thus both structural and cognitive power bases in an organization. Although it is proposed here that cognitive accuracy is a power base in and of itself, one must take into account the fact that this cognitive power base is influenced by formal and informal structural factors. Because these structural factors are sources of power in their own right, these sources are explicitly included as part of the cognitive model of power presented here. Figure 5.1 displays this model, which relates structure, cognition, and power. Formal structure is shown as an exogenous variable leading directly to informal structure, cognitive accuracy, and power. Informal structure, in turn, contributes to cognitive accuracy and power. Finally, in accordance with the central theme of this chapter, cognitive accuracy

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is predicted to contribute to power over and above the power already explained by the structural factors. This last link represents the main proposition of this chapter: Proposition: Controlling for formal and informal bases of power, cognitive accuracy of the informal network will be correlated with individual power in the organization. To test the model in Figure 5.1 and the proposition posed above, a network study was conducted of a small high-tech firm. Questionnaire and interview data were collected from which the cognitive social structures and “actual” networks were determined, and each employee provided assessments of how powerful and charismatic every employee was in the organization. From these data, the central proposition and model were tested directly.

Method The company studied and the sample were identical to that studied in Chapter 3: thirty-six members of the high-tech company Silicon Systems, of whom thirty-three filled out our questionnaire. Reputational Power Previous work (Brass, 1984) established internal consistency and predictive validity for a reputational measure derived from ratings of supervisors and peers. Building on this previous work, we asked each employee to rate all the employees (including himself or herself) on the two dimensions of power: the ability to get things done despite resistance and the ability to influence through personal magnetism (charisma). This procedure avoided the problem of availability bias, incomparable sources, and dichotomization. Moreover, with this multiple-source method, the internal reliability of the two power scores can be estimated. Each person rated each other person on a seven-point Likert scale on both charisma and the ability to get things done (potency). Two anchors were provided for each scale: “Not at all charismatic” to “Highly charismatic” for charisma, and “Not at all powerful” to “Highly powerful” for potency. To assess the reliability of the two measures, Cronbach’s alpha was calculated for charisma and potency (for the formula, see Carmines and Zeller, 1979: 44). Both charisma and potency had high reliability coefficients (Cronbach’s alpha = .96 and .99, respectively), demonstrating that there was very high consensus in the organization on who was

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influential on each of these dimensions. The correlation between the two power indicators was .63, indicating considerable overlap between the two measures. For this reason, the two measures were combined into a single dependent variable, overall power, using the factor scores from the first component of a principal components analysis of the two variables. Formal Position Although power derived from formal position may be ambiguous in some larger organizations, this organizational base of power was quite clear in Silicon Systems. There were three distinct levels of formal authority. At the top level were the three owner-managers. Even though they took on different responsibilities and had different titles, they were equal partners and made all major company decisions jointly. The next level consisted of five managers, each of whom had supervisory responsibility over certain operational features in the organization. The remaining twenty-eight employees had no formal supervisory title or authority. Formal position, then, was scored as follows: Each of the three owners was given a formal position score of 3; the five managers were given a formal position score of 2; and the remaining twenty-eight employees were given a formal position score of 1. Cognitive Social Structure The cognitive social structure is a three-dimensional array of linkages, Ri,j,k , among a set of N actors, where i is the sender of the relation, j is the receiver of the relation, and k is the perceiver of the relation. Using Krackhardt’s (1987a) methodology, a questionnaire was designed to assess the cognitive social structure of two relations in the organization: friendship and advice (see Chapter 3 for more details). Actual Network Although work in the area of recall of network relations has cast doubt on an informant’s ability to relate accurately to whom they actually talk on any given day (see Bernard, Killworth, Kronenfeld, and Sailer, 1984, for a review), Freeman et al. (1987) have shown that people are remarkably good at recounting enduring patterns of relations that they have with others. Thus, although people may not remember whom they talked to today or this week, they can accurately tell you whom they are in the habit of relating to over an extended period of time. Consistent with these results, Brass (1984) found that the workflow network in his study closely corresponded to the network reported by respondents. Because it

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is these enduring relational patterns that are of interest – as evidenced by the wording in the questions – the locally aggregated structure, or LAS (Krackhardt, 1987a), was used as a proxy for the “actual” network. The LAS is an aggregation defined by the local participants in the network. It mimics the typical form in which network data are collected. (See Chapter 3 for technical details.) Both i and j must agree that i goes to j for help and advice before the i → j link is recorded as existing in the “actual” advice network. Similarly, both i and j must agree that i considers j a friend before the i → j link is recorded as existing in the “actual” friendship network. Because the relationship is defined as existing when both parties agree that it exists, this measure of the “actual” network is direct and has obvious face validity. In the case of Silicon Systems, data were missing for three of the thirty-six employees, so we adopted the following procedure to deal with these cases. If information concerning a link between two people was missing from one but not both parties, then the existence or nonexistence of a link is determined by the information provided by the reporting party. If information was missing from both parties, then a link was deemed to exist if five or more people in the network reported that it existed. Cognitive Accuracy Each participant’s cognitive map of the network (representing each participant’s “perceived network”) was taken from the set of responses that he or she selected on the network questionnaire. We then correlated cognitive maps of the network with the actual network to derive a measure of accuracy between perceived and actual networks for each participant. The measure that we used was the point correlation coefficient and is equal to the value obtained by computing a Pearson correlation coefficient between the elements of the matrix representing each participant’s cognition of the network and the elements of the matrix representing the “actual” network. (See Gower and Legendre, 1986, for a review of correspondence measures.) Centrality Of the many different ways to measure of centrality, betweenness is the one most closely aligned with the idea of power (see Freeman, 1979, for the formula). The individual who is in between other actors has more control over information flow from one sector of the network to another. That person becomes a gatekeeper of information flow. Moreover, betweenness is an indication of the nonredundancy of the source of

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Mean

Standard Deviation

Power Advice accuracy Friend accuracy Advice centrality Friend centrality Formal position

0 .406 .326 14.443 17.014 1.333

1.00 .0624 .0704 27.3039 25.1941 .6454

Correlation Structure Variable

Power

1

2

3

4

1. Advice accuracy 2. Friend accuracy 3. Advice centrality 4. Friend centrality 5. Formal position

.340∗ .146 .453∗∗ .506∗∗ .656∗∗∗

.282∗ .210 .172 .240

.031 .236 .041

.222 .566∗∗∗

.161

∗

p < .05. p < .01. ∗∗∗ p < .001. ∗∗

information. To the extent that a person is connected to different parts of the network and therefore has access to different, nonredundant sources of information, that person will have a wider variety of information at his or her disposal. The higher the betweenness score of an actor, the greater the extent to which the actor serves as a conduit connecting others in the network. Formally, betweenness centrality measures the frequency with which an actor falls between other pairs of actors on the shortest or geodesic paths connecting them (Freeman, 1979: 221). Measures of betweenness centrality are difficult to interpret for nonsymmetric data. In preparing the matrices prior to the calculation of betweenness centrality, the networks were symmetrized according to the rule that if either member of a pair nominated the other, the pair was considered to have a tie – reflecting the assumption that the presence of even an asymmetric relationship represented an opportunity for exchange of information in both directions.

Results The means, standard deviations, and intercorrelations among all the variables used in this study are presented in Table 5.1. To test more completely the model in Figure 5.1, a set of hierarchical regressions was performed on the dependent variable, overall power. The

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Table 5.2. Hierarchical Regression Analysis of Reduced-Form Equations with Reputational Power as Dependent Variablea Equation Independent Variables

(1)

Formal position Advice centrality Friend centrality Advice accuracy Friend accuracy R2

1.107∗∗∗ (.210)

Hierarchical Test of Model R2 F df p

.431

(2) .879∗∗∗ (.222) .0015 (.005) .161∗∗∗ (.0048)

.597 .166 5.966 2,29 .007

(3) .782∗∗∗ −.0004 .0195∗∗∗ 5.091∗∗ −.559 .678

(.210) (.005) (.0049) (2.02) (1.74)

.082 3.425 2,27 .047

a

Standard errors are in parentheses. p < .05 ∗∗ p < .01 ∗∗∗ p < .001. ∗

results are presented in Table 5.2 as reduced-form equations (Cohen and Cohen, 1983: 361–6). Formal position explains 43 percent of the variance in overall power. The two informal structure sources of power, centrality in the advice and friendship networks, add another 17 percent of explained variance (significant at the .007 level). Note, however, that advice centrality is not significant in the equation; only centrality in the friendship network is significantly related to power when controlling for formal position (rho < .01). It appears, then, that any advantage that a person has by being central in the routine advice network is attributable to his or her formal position of power in the organization. In equation 3 of Table 5.2, cognitive accuracy in the advice and friendship networks explains an additional 8.2 percent variance (p <.047). Again, however, only one of the two added variables is significant: accuracy on the advice network. Understanding the friendship network is not significantly related to one’s power reputation over and above being in the center of the networks and having a position of formal authority. However, an understanding of the advice network is significantly related to one’s power reputation. These results reveal an interesting juxtaposition of effects. Clearly, formal authority is correlated with reputed power, as expected, but the two networks relate in different ways to one’s power base. Centrality in the friendship network – not the advice network – is a key factor in reputed power; but it is cognitive accuracy of the advice network – and

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not the friendship network – that adds a significant amount of explained variance to one’s power reputation. A closer inspection of the simple relationships among the variables in Table 5.2 provides a partial explanation for these findings. Advice centrality and friendship centrality are both strong simple predictors of power (.45 and .51, respectively). But, although advice centrality is correlated with formal authority (.57), friendship centrality is not significantly related (.16). Most of the variance in power explained by advice centrality is already explained by formal authority: Those central in the advice network are also those with higher authority. Because friendship centrality is not related to formal authority, however, it provides a unique contribution to power in the second step of the hierarchical regression. Knowledge of the advice network does not significantly covary with formal authority and therefore also provides a unique contribution to power in the third step of the regression.

Discussion The network analysis conducted on Silicon Systems confirms the major proposition of this study: that an accurate picture of the informal network significantly correlates with power. But, the overall model presented in Figure 5.1, relating structural factors to cognition and power, received only qualified support (see Table 5.1). As predicted, formal position is significantly related to power and advice centrality. Contrary to the model’s predictions, formal position does not significantly correlate with cognitive accuracy. Also, contrary to prior research (Freeman and Romney, 1987), centrality was not directly related to cognitive accuracy. Because these simple correlations were not confirmed in Table 5.2, more elaborate tests of the path coefficients for Figure 5.1 were not necessary, beyond the overall tests provided for by the hierarchical regressions reported in Table 5.2. The question remains why the other relationships in Figure 5.1, which form the theoretical building blocks for the basic proposition of this chapter, were not confirmed. One possible explanation for the lack of support for parts of the causal model may rest in the size of the firm. Because the firm is small, people all know each other and are relatively better informed on each other’s relationships than they might be in a large organization. Thus, being in the center of the network or at the top of the formal hierarchy does not provide as strong an informational edge over others’ vantage points. Perhaps in a larger organization, where many people are not even aware of each other’s existence, these structural advantages may

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prove more predictive of cognitive accuracy. Future research on this topic could shed light on whether these results are generalizable to – or perhaps even enhanced in – larger firms. Because the structural links to cognitive accuracy were not confirmed, the test of the major proposition of this study could have been reduced to a simple correlation between cognitive accuracy and reputational power without controlling for formal or informal positional power. It was necessary, nonetheless, to present the analysis in full, controlling for these theoretically important sources of power. The amount of variance in power explained by cognitive accuracy in the advice network (by itself in the absence of other variables) was 11.6 percent (= .342 ; see the correlation in Table 5.1); the direct contribution of the two accuracy indicators in column 3 (Table 5.2) in the hierarchical analysis was 8.2 percent. This difference indicates that, as predicted by the theory, there is some, albeit small, spurious correlation due to structural effects. In the conservative approach taken here, the hierarchical test of the main proposition removes this spuriousness. The study showed that reputational power of the members of the firm was significantly related to cognitive accuracy of the advice network, not the friendship network. Perhaps this is an indication of the extent to which power surrounded those who were capable of handling relatively routine operational problems. In answering questions about influence and power, employees were responding according to their experiences in their dayto-day lives in the organization. As mentioned previously, those people central in the advice network, the experts, are likely to derive power from such routine situations. Had the organization faced a nonroutine situation such as a crisis, however, it is possible that an understanding of the friendship network could have been more predictive of power in dealing with the crisis. Dealing with crises does not require routine information but, rather, it requires trust (Chapter 10; Krackhardt and Stern, 1988). It is reasonable to speculate that understanding the friendship network, which better represents the trust relations in the organization, could prove more critical than understanding the advice network in such a nonroutine situation. Of course, this is only speculation, because the data reported here do not involve anything but routine operations. Caveats and Limitations The theory presented at the beginning of the chapter argued that knowledge of the network is in its own right a base of power above and beyond the power accrued through other formal and informal bases. This causal claim leads directly to the prediction of association. However, as is always

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the case in field studies such as this, one cannot infer the causal link from the data. There are three possible reasons for an association between two variables, A and B: A leads to B, B leads to A, or there is a third variable (or set of variables) that leads to both A and B, in which case we say that the relationship is spurious. It is worth speculating about each of these possible reasons for the underlying association. In the current study, the theoretical claim underlying the observed association is that network knowledge leads to reputational power. But is it possible that one’s reputation as a powerful person leads to a better understanding of the social network? Perhaps, for instance, as one becomes reputed to have more power, one is fed differentially more social information. However, if this were the case, then it is likely that this differential focus of information would in turn lead to the actor becoming central in the network, and partialling out network centrality would remove the association between reputational power and network knowledge. A more serious concern is whether the observed relationship is spurious. Despite some statistical attempts to control for clear sources of spuriousness, there are potentially an infinite number of variables that are unaccounted for. For example, suppose that power reputation is an attribution based on the fact that certain people are closer to the action in the organization. Suppose that being closer to the action also gives people certain advantages in knowing the social network. Then one could argue that the observed relationship between reputational power and network knowledge is spurious. In part, one could also argue that being “closer to the action” is already controlled for by controlling for centrality in the network; but then again, it may not control for all of it. We have controlled for the most obvious sources of spuriousness. But, clearly, one cannot conclude that all sources of spuriousness have been eliminated. However, the raw data from the analyses are available (Krackhardt, 1990) and we invite scholars to explore alternative models that might explain the reported relationships. Another important issue surrounds the use of the term “power” in this study. The literature on power in organizations is extensive, and on a theoretical level, this study sheds light on only a small part of that literature. The emphasis here is on the power induced through information flows in the informal network. Dependencies and power in an organization can emanate from many sources, not simply how information is passed from one person to the next. For example, we have no information concerning workflow interdependencies (cf. Brass, 1984). Nor do we know who has the critical resources or who has control over them (cf. Pfeffer and Salancik, 1978). Nor do we have information on who is performing the important functions in the organization and the

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exclusivity with which they perform them (see Dubin, 1957: 62, for the first explicit treatment of this two-component definition of “power”). The use of the term “power” in this study is relatively specialized and may not generalize to other conceptualizations or other contexts. Whether understanding the social network has any bearing on whether an individual has control over critical resources is an interesting question that must be left to future research. Second, there is a methods limitation, one discussed in full by Pfeffer (1981: 54–7). Using reputation to measure the relative power of an individual has potential biases. The measure assumes that the raters know who is powerful and that they are willing to tell the researcher honestly what they know. Despite these possible problems, Pfeffer (1981: 57) noted that when raters seem to agree on their power attributions, “this consensus and consistency in power ratings provides some evidence for at least a shared social definition of the distribution of power.” Given the high reliability scores of the components of the reputational measure in this study, we share Pfeffer’s conclusion that that there is consensus in power attributions.

Conclusion This chapter demonstrates that knowledge of the relevant network is itself associated with reputational power, independent of other structural bases of power. In particular, further work exploring the importance of the structure of different kinds of relations in organizations may prove fruitful in understanding the dynamics of organizational behavior. As Mintzberg (1983b: 1) put it, “Power is a major factor, one that cannot be ignored by anyone interested in understanding how organizations work and end up doing what they do.” We have focused on a neglected aspect of organizational power, namely the power that derives from accurate knowledge of the informal network of advice relations. Given all the attention paid to structural and resource bases of power, it is surprising that so few have investigated the power drawn from such political knowledge. In the four chapters that comprise this part of the book, we have focused on “bringing the individual back in” through systematic investigation of how perceptions of networks affect such outcomes as leadership effectiveness, individuals’ reputation, and individuals’ power. In the next three chapters that comprise the second part of the book, we extend the current chapter’s introduction of an individual difference variable (charisma) in the context of a network study by looking systematically at

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how a personality variable particularly relevant to the understanding of network structuring – self-monitoring – combines with network position to help explain the differential uses of social networks. How do low and high self-monitors differ with respect to the social networks they create and are shaped by? This is one of the main questions we systematically investigate in the following chapters.

II The Psychology of Network Differences

6 Social Structure and Decision Making in an MBA Cohort

The sample for the three related studies covered in this chapter consists of individuals enrolled in an elite master of business administration (MBA) program that functioned as one of the portals to management in corporate America (Kilduff and Day, 1994). These managers-in-training made network and social identity choices in a campus setting that imposed relatively few of the hierarchical constraints on interaction characteristic of formal organizations. We examine how individuals’ networks are shaped by ethnicity and gender identifications and how individuals differentially respond to network influences in making complex decisions. In the first section of the chapter, we ask whether within-race and within-gender preferences can explain patterns of network marginality for members of underrepresented groups or whether such marginality results from exclusionary pressures from the majority. In the second perspective on this same MBA cohort, we examine the structure of social influence. Individuals, faced with the organizational choice decision, tend to be influenced by others, according to theory. Are these others people perceived to be especially similar? Or are these others friends, or perhaps social rivals (i.e., occupants of the same social position)? Following this examination of the social comparison other, we provide a further analysis of this MBA cohort in examining whether some people more than others are likely to be influenced by network contacts in decision making. We push social network research in the direction of incorporating personality, specifically the self-monitoring personality construct. The student’s second year in a top MBA program is dominated by one question: Which organization should I join? The organizational choice decision is the culmination of two years of social and academic training. These students are in transition between their previous careers as engineers, waitpersons, students, and so on, and their new careers as executives. They spend two years constructing new identities for themselves 101

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through continuous socialization by peers drawing on the culture of the business school (cf. Van Maanen, 1983), preparing for the fateful choice of an organization for which to work. During these two years, many students are relatively isolated from their families and previous social contacts. Further, the ambiguity of the organizational choice decision itself in the absence of any clearcut scale on which to compare organizations, together with the importance of the decision in terms of future career success, makes organizational choice an arena in which social comparisons and pressures can be expected to operate. Indeed, social comparison processes, concerning both academic and social prowess, are intense for MBAs at prestigious schools of business.

Study 1: Distinctiveness and Social Identity What determines individuals’ identifications with others? Distinctiveness theory (McGuire, 1984) suggests a parsimonious answer: People in a social context tend to identify with others with whom they share characteristics that are relatively rare in that context. Thus, two African Americans in a crowd of whites will tend to notice and identify with each other because of their common race; however, when in a group of other African Americans, the same two people are unlikely to notice or identify with each other on the basis of race. Drawing on distinctiveness theory, we predicted that, within the MBA program, members of numerically underrepresented groups, relative to those in the majority, would exhibit a stronger tendency to identify within the group. But this prediction still left unanswered the question of which of several possible underrepresented groups any particular individual will tend to identify with most strongly. For example, when is an African American woman more likely to feel strongly African American, and when is she more likely to feel strongly female? Distinctiveness theory suggests that the relative rarity of a social category in a particular social setting will promote members’ use of that social category as a basis for social identification. In our MBA sample, members of racial minorities were numerically rarer than women. For racial minorities, we predicted that race would be a stronger category for social identification than sex. However, for whites, the same reasoning suggested that sex, not race, would be a stronger category for social identification. Similarly, we predicted that the salience of race relative to sex would help determine whether people more often chose same-sex or same-race friends. Formally stated, the relative rarity of a social category in a particular social setting will tend to promote members’ use of that social category as a basis for friendship formation.

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Marginality Members of underrepresented groups are likely to be less central in friendship networks than members of well-represented groups because of the former’s tendency to select friends from the distinctive groups to which they belong rather than from the social network as a whole (see the discussion in Ibarra, 1993a). Previous theorizing has emphasized exclusionary pressures that tend to relegate underrepresented group members to the margins of social networks (Kanter, 1977). Thus, the structural marginality of members of underrepresented groups may well be overdetermined; we predict that is due both to the friendship choices of underrepresented group members and to exclusionary pressures and biases that focus on visible demographic characteristics such as race and sex. Method Sample The sample for this study consisted of a class of second-year MBA candidates enrolled in a nationally ranked MBA program. Nonresidents of the United States were excluded from the sample (and from all questionnaires) because the research design focused on the job choice process and included only those eligible to work in the United States. The average age of the respondents was twenty-seven years. Of the 209 students sampled, 181 (87 percent) completed mailed copies of the sociometric questionnaire. Nonrespondents did not differ significantly from respondents with respect to race or sex. Measures Friendship and Identity Friendship was measured by asking subjects to look carefully down a list of second-year MBAs and place checks next to the names of people they considered to be personal friends. To measure identity, subjects were asked to look carefully down a list of second-year MBAs and place checks next to the names of people they considered to be especially similar to themselves. A pair of individuals was considered to be a friendship pair (or an identity pair) if at least one member of the pair nominated the other member as a friend (or as especially similar). Compared with the various measures of the structural equivalence concept (e.g., Breiger, Boorman, and Arabie, 1975; Burt, 1976; White and Reitz, 1983) that attempt to estimate who is structurally identical or equivalent to whom, the direct measure of identification with others is closer to the subjective perceptions emphasized by social comparison theory (Festinger, 1954).

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Homophily In measuring race and sex homophily in the identity and friendship networks, we controlled for the relative availability of different groups (cf. Ibarra, 1992) because what may appear as a tendency on the part of, for example, women to form friendships with men may be attributable to the proportionally higher number of men in a group. The adjusted homophily index, known as the point correlation coefficient (see Gower and Legendre, 1986, for a review and Krackhardt, 1990, for the formula), ranged from −1 (indicative of extreme “heterophily”) to + 1 (indicative of extreme homophily). Sex This was coded as 1 for men and 0 for women. Race Using photographs from the school directory and information from publicly available student r´esum´es detailing membership in societies such as the Black Students Association, two people independently coded respondents as either white, African American, Asian American, or Hispanic (these were standard categories used by the administration at this school). Agreement between the two coders was high (98 percent interrater agreement). Disputed cases were resolved through discussion and a search for further information in the r´esum´e book published by the school. For the homophily and regression analyses, we dichotomized race as 0 for whites and 1 for all others. As a check on how reliably the coding reproduced individuals’ selfcoding of race, our coding was compared with the official school records on 113 individuals who had voluntarily reported their race. Only one person had been misclassified (our classification was white, but the selfclassification was Hispanic). There was complete agreement between the two codings for all of those we had classified as minority group members and for whom self-report records existed (seventeen people). We concluded that our coding of race reproduced self-ratings at an acceptable degree of accuracy. The absence of questionnaire items concerning race or sex ensured that the questionnaire itself did not trigger salient categories for reporting social identity or friendship. Structural Marginality Those on the margins have difficulty accessing the center of a network either through their own friends (direct ties) or through friends of friends (indirect ties). To capture both direct and indirect friendship ties, we used an eigenvector measure (Borgatti et al., 2002) that computed centrality as the summed connections to others weighted by the centrality of those others (see Bonacich, 1972, for the formula). Marginality was defined as

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the converse of centrality: Those scoring low on centrality scored high on marginality. Because the eigenvector analysis program handled only symmetric data, for this analysis we symmetrized the friendship matrix, using the rule that if either member of a pair nominated the other, then the pair was a friendship pair. This operational definition preserved information on weak ties (cf. Granovetter, 1973) and produced the most robust indicator of centrality as measured by the ratio of the largest eigenvalue to the next highest eigenvalue. To check whether the results were affected by this definition of the friendship measure, we also symmetrized the matrix using two alternate rules: (1) replace Xij and Xji by the minimum of (Xij or Xji) and (2) replace Xij and Xji by the average of (Xij or Xji). The pattern of results remained the same. Major Most of the students in the sample had chosen one of two majors: finance (56 percent of the sample) or marketing (26 percent), with the remaining students (18 percent) choosing a number of other possible concentrations. Because we were interested in the core/periphery structure of the social world of the MBA students, we dichotomized choice of major to differentiate those students choosing popular majors (finance or marketing, coded as 1) from those choosing unpopular majors (coded as 0). This dichotomization resulted in a significant effect for the control variable in our analyses, whereas a coding representing all possible majors had no significant effects. Analysis and Results Missing data reduced the sample to 159 people, 76 percent of the original population. The final sample included ninety-five white men, forty-four white women, ten racial minority men, and ten racial minority women. The mean homophily values given in Table 6.1 show that, with availability controlled for, individuals tended to identify with and form friendships with others of the same race. Similarly, individuals tended to identify with and form friendships with others of the same sex. Individuals tended to establish smaller identity networks than friendship networks, although the two networks were significantly correlated (r = .28, p < .001). Recall that we predicted that people would tend to identify with those with whom they shared a demographic characteristic that was relatively rare. The mean homophily values in Table 6.2 provide support for this idea. The results for the identity network presented in the top half of Table 6.2 show that the tendency for minorities to identify within-group was significantly stronger (t = −2.03, df = 19.5, p < .05) than that of

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Table 6.1. Descriptive Statistics and Correlationsa Variable 1. Majorb 2. Sexc 3. Raced 4. Centrality Race Homophily 5. Friendship network 6. Identity network Sex Homophily 7. Friendship network 8. Identity network

Mean S.D.

1

2

3

.13

−.22∗∗∗

9.09

.02 −.09 6.53 .21∗∗

0.04 0.04

0.12 0.11

.15 .07

−.00 −.08

0.04 0.05

0.13 0.08

.12 .03

−.13 −.19∗ −.27∗∗∗ −.06

4

5

6

7

.25∗∗∗ .16∗∗ .32∗∗∗ .25∗∗ .47∗∗∗ .15 .07

.07 .05

.18∗ .17∗ .34∗∗∗

Notes: a N = 159. b Finance and marketing = 1, other majors = 0. c Men = 1, women = 0. d Minorities = 1, whites = 0. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

Table 6.2. Mean Homophily Values Showing Tendency to Choose Partners Similar to Selfa Type of Homophily Group Identity Network Whites Minorities Men Women Friendship Network Whites Minorities Men Women

n

Sexb

Racec

t

df

139 20 105 54

0.06 (0.72) 0.04 (0.68) 0.04 (0.77) 0.09 (0.60)

0.02 (0.93) 0.13 (0.36) 0.03 (0.85) 0.06 (0.85)

−3.99∗∗∗ 1.72∗ −0.96 −1.45

137 18 103 52

139 20 105 54

0.05 (0.65) −0.06 (0.40) 0.02 (0.69) 0.06 (0.41)

0.02 (0.90) 0.16 (0.27) 0.03 (0.86) 0.05 (0.82)

−2.62∗∗ 4.63∗∗∗ 0.46 −0.68

137 18 103 52

Notes: a Unadjusted homophily values are in parentheses. b Men = 1, women = 0. c Minorities = 1, whites = 0. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

whites. Similarly, the tendency for women to identify within-group was significantly stronger (t = 3.82, df = 157, p < .001) than that of men. Further, the paired comparison t-tests in the first two rows of Table 6.2 show that, as predicted, whites were significantly more likely to identify

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Table 6.3. Summary of Regression Analyses Predicting Homophilya Type of Homophily Variable Identity Network Major Sex Race Model F R2 Friendship Network Major Sex Race Model F R2

Sexb

Racec

0.01 −0.05∗∗∗ −0.02 5.56∗∗∗ 0.10

(0.02) (0.01) (0.02)

−0.01 −0.02 0.10∗∗∗ 6.80∗∗∗ 0.11

(0.02) (0.02) (0.02)

0.03 −0.05∗∗∗ −0.11∗∗∗ 6.34∗∗∗ 0.11

(0.03) (0.02) (0.03)

0.04 −0.01 0.14∗∗∗ 10.98∗∗∗ 0.18

(0.02) (0.01) (0.02)

Notes: a N = 159. Values represent unstandardized coefficients; standard errors are in parentheses. b Men = 1, women = 0. c Minorities = 1, whites = 0. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

with others on the basis of sex rather than race (t = −3.99, df = 137, p < .001), whereas minorities were significantly more likely to identify with others on the basis of race rather than sex (t = 1.72, df = 18, p < .05). The patterns of friendship choices paralleled these results. Looking at the bottom half of Table 6.2, the tendency for members of minority groups to make friends within-group was significantly stronger (t = −3.28, df = 20.3, p < .01) than that of whites. Similarly, the tendency for women to make friends within-group was significantly stronger (t = 2.11, df = 137, p < .05) than that of men. The paired comparison t-tests in rows 5 and 6 in Table 6.2 show that, as predicted, whites were significantly more likely to make friends with others on the basis of sex rather than race (t = −2.62, df = 137, p < .01), whereas minorities were significantly more likely to make friends with others on the basis of race rather than sex (t = 4.63, df = 18, p < .001). The results presented in Table 6.3 confirm that these univariate effects of race and sex on the tendency to make in-group network choices remained significant when control variables were introduced into the analyses. The regression analysis results presented in the first column (labeled “Sex”) show that the tendency to identify with and make friends with members of one’s own sex was stronger for women than for men,

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with an individual’s race and choice of major controlled for. The regression results under the second column (labeled “Race”) show that minorities were more likely than whites to identify and make friends withingroup, with sex and choice of major controlled for. We further suggested that members of underrepresented groups were likely to be structurally marginal in the friendship network. This hypothesis was supported. Men were more central than women, and this difference was significant (t = −2.02, df = 157, p < .05). Similarly, whites were more central than minorities, and this difference was also significant (t = 2.58, df = 157, p < .01). The first regression model in Table 6.4 confirms that, with major and sex controlled for, members of racial minorities tended to be more marginal than whites (p < .05). This same model shows that women were only marginally less central than men (p < .10), with major and race controlled for. Going further, we suggested that the tendency to make in-group (i.e., homophilous) friendship ties would be negatively related to the centrality of underrepresented group members and positively related to the centrality of majority group members. The results of subsample analyses offered support for this hypothesis. The subsample results presented in column 4 of Table 6.4 show that sex homophily (the tendency to choose friends of the same sex) was positively associated with centrality for men. But the separate analysis for women presented in column 5 showed no significant effect for sex homophily. In an analysis not reported in the table, the positive correlation between sex homophily and centrality for the male subsample (r = .26, p < .01) was significantly higher (Z = 2.57, p < .05) than the negative correlation for the female subsample (r = −.17, ns). Similarly, the subsample regression results shown in the last two columns of Table 6.4 show that race homophily (the tendency to choose same-race friends) was positively associated with centrality for whites, but marginally negatively associated with centrality for minorities. The correlation between race homophily and centrality for the white subsample (r = .46, p < .05) was significantly higher (Z = 3.92, p < .001) than the same correlation for minorities (r = −.46, p < .05). Finally, we suggested that visible demographic characteristics, such as sex and race, would be negatively related to centrality for underrepresented group members and positively related to centrality for majority group members. Model 2 in Table 6.4 shows that, with a marginally significant (p < .10) effect of sex homophily controlled for, sex had a significant effect (p < .05) on centrality. This pattern of results suggests that women were less central in the friendship network not so much because of their tendency to prefer woman friends, but more as a result of their exclusion on the basis of gender. Model 3 in Table 6.4 shows

109

(1.54) 4.44∗∗ 0.08

(1.40) 7.13† (3.89) 2.46∗ (1.08)

3.07∗

Model 2 (1.37)

15.03∗∗ (4.71) −5.83∗∗ (1.64) 7.48∗∗ 0.13

2.49†

Model 3

4.07∗ 0.07

1.24 (1.84) 12.24∗∗ (4.68)

Men

4.61∗∗ 0.15

−7.44 (6.97)

5.39∗∗ (1.97)

Women

(1.47)

18.50∗∗ 0.21

32.77∗∗∗ (5.79)

1.11

Whites

Subsamples

2.43 0.22

−12.72† (6.39)

1.45 (2.68)

Minorities

Notes: a Values represent unstandardized coefficients; standard errors are in parentheses. For the full sample, N = 159. For the subsamples, n’s are as follows: men, 105; women, 54; whites, 139; minorities, 20. b Finance and marketing = 1, other majors = 0. c Men = 1, women = 0. d Minorities = 1, whites = 0. † p < .10. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

4.91∗∗ 0.09

−3.33∗

1.86† (1.07)

(1.39)

3.03∗

Majorb

Sex homophily Sexc Race homophily Raced Model F R2

Model 1

Independent Variable

Full Sample

Table 6.4. Summary of Regression Analyses Predicting Centrality in the Friendship Networka

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that, with a significant effect (p < .01) of race homophily controlled for, race had a significant effect (p < .01) on centrality. These results suggest that the marginality of members of racial minorities was due both to race homophily and to exclusion on the basis of race. One caveat is in order: The significance of the race variable in model 3 (and similarly, of the sex variable in model 2) indicates only that an individual’s race (or sex) tends to contribute to the individual’s centrality. These results do not allow us to say that race (or sex) was used as a basis for friendship exclusion by majority group members more than it was by underrepresented group members. To examine the structural network positions of whites and minorities in greater detail, we used multidimensional scaling (MDS) (Krackhardt, Blythe, and McGrath, 1994) on the unsymmetrized 159-by-159 friendship matrix. Figure 6.1 shows that the center of the network was occupied exclusively by whites, with a cluster of African Americans located in the upper right of the graph and other racial minority members located around the periphery. The MDS analysis depicted in Figure 6.2 shows just the friendship patterns among racial minorities. African Americans (represented by ovals surrounding “Bill”) formed a relatively tight friendship group, with many links between members. However, the members of other racial groups depended less on cohesive links among themselves than on the network-spanning activities of particular individuals. For example, the African American “Fay” represented a link to the Hispanic community, and the Hispanic “Jen” linked the African Americans, the Hispanics, and the Asian Americans. Study 1 Discussion The results show consistent support for a distinctiveness approach to the patterning of social networks. The lower the relative proportion of group members in a social context, the higher the likelihood of withingroup identification and friendship. Previous homophily research (e.g., Tuma and Hallinan, 1979) has shown that people tend to interact with similar others. Our research refined this general proposition by suggesting that perceptions of similarity are based on distinctiveness within specific contexts. Further, in extending distinctiveness theory from the realm of identity relations to the realm of friendship relations, we have shown how this approach can help explain patterns of structural marginalization in organizations. The marginalization of racial minority members in the friendship network appeared to result both from exclusionary pressures and from the preferences of the minorities for same-race friends. The marginalization

111

Figure 6.1. Friendship relations among individuals.a, b Letters indicate race of individual: W = white, A = Asian American, B = African American, and H = Hispanic. b To preserve visual clarity, some whites near the center of the sociogram are not shown here.

a

112

Figure 6.2. Friendship relations among minorities.a African Americans’ names are enclosed in ovals, Asian Americans’ in rectangles, and Hispanics’ in diamonds. Names are sex-specific.

a

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of women in the friendship network appeared to result more from exclusionary pressures than from women’s preferences for woman friends. Previous research has shown that the lower hierarchical rank of women and minorities in many organizations exacerbates the difficulties they encounter in integrating themselves into informal networks of influential others (e.g., Ibarra, 1992). The results that we report from a sample lacking a formal hierarchy suggest that segregation in informal networks may persist even in “delayered” organizational forms. Our results provide insights into the social networks likely to emerge in educational organizations – specifically, competitive MBA programs in which a relatively large cohort of would-be executives are socialized together. Similar patterns may emerge in training programs for cohorts of new recruits in work organizations. In addition, to the extent that people depend on friendships formed in MBA programs for job referrals and support throughout their careers, patterns established in these programs may have long-lasting effects. Our research raises practical questions concerning whether network patterns formed in graduate or company training programs have lasting effects on interaction patterns in work settings. To the extent that people belong to multiple groups, they have multiple bases of similarity on which to build bridges of social identification and friendship. Simmel (1955: 125–95) discussed this issue. Our study demonstrates that the relative rarity of a group in a social context is likely to promote members’ use of that group as a basis for shared identity and social interaction. All people, at some point in their organizational careers, are likely to be members of underrepresented groups, whether this involves race, gender, working in a foreign country as an expatriate, or simply joining a cross-functional team composed mainly of those with different expertise. From this perspective, organizations offer rich environments for identity development based on the shared characteristics individuals can discover. The discovery and promotion of shared bases of identification may be one of the most challenging tasks of management.

Study 2: Friends’, Rivals’, and Similars’ Influences on Decision Making We have shown that friendship patterns and identity patterns are formed around ethnicity and gender, and that these patterns are particularly cohesive for members of underrepresented groups. But do these patterns of friendship and identity affect outcomes, such as decision making? This is the question we explore in study 2, using the same cohort of MBA students.

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Decision-making research has been generally silent concerning social influences on choices. Both the normative models, such as expected utility theory (e.g., Becker, 1976), and the descriptive models, such as prospect theory (Kahneman and Tversky, 1979), consider individual decision makers in splendid isolation from the force field of influences that surround them. Although studies of social influences on organizational choice are rare (but see Higgins, 2001), we do know that people generally acquire information about job vacancies through their informal networks of friends, family, and acquaintances rather than through official sources such as advertisements or employment offices (Granovetter, 1974; Reynolds, 1951; Schwab, 1982; Schwab, Reines, and Aldag, 1987: 135–8). It would seem likely, therefore, that people rely on these same networks for help in evaluating potential employers. The present research uses social comparison theory as a framework to study the effects of social networks on the organizational choice process. According to Festinger’s (1954) formulation of social comparison theory, (1) human beings learn about themselves by comparing themselves to others, (2) people choose similar others with whom to compare, and (3) social comparisons will have strong effects when no objective nonsocial basis of comparison is available and when the opinion is very important to the individual (see Goethals and Darley, 1987, for a review of social comparison research). Sources of Social Information Friends. Much research has focused on social influence processes between friends and acquaintances (e.g., Coleman, Katz, and Menzel, 1966; Festinger et al., 1950; Krackhardt and Porter, 1985; Newcomb, Koenig, Flacks, and Warwick, 1967). From a social comparison perspective, friends are readily available as comparison others. People are hypothesized to shape their opinions and decisions through direct discussion with these important members of their social circle. Structurally equivalent others. A perspective that disputes the relevance of friendship to social comparison has focused on comparisons among people who occupy similar positions in the social network (e.g., Lorrain and White, 1971). These individuals are competing with each other to maintain and enhance their social positions. They are, therefore, keen to adopt attitudes and behaviors that they see their rivals using successfully. Those who are structurally equivalent in the network are hypothesized to “put themselves in one another’s roles as they form an opinion” (Burt, 1983: 272). Influence proceeds through symbolic communication between these equivalent actors whether or not they interact with each other (Burt, 1987; Knoke and Kuklinski, 1982: 60).

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Similar others. Some structural equivalence research explicitly assumes that individuals identified by the researcher as equivalent perceive each other as such. As Burt has asserted: “[An actor’s] evaluation is affected by other actors to the extent that he perceives them to be socially similar to himself” (1982: 178). This assumption has proved necessary in order to explain how structurally equivalent individuals in a social system influence each other. Interpersonal influence is hard to explain if individuals interact neither personally nor cognitively. To transform objective stimuli into subjective perceptions, Burt uses Stevens’ (1957, 1962) law of psychophysics. Perceived similarity is calculated as a power function of objective similarity. But as Krackhardt (1987a: 112) has pointed out, the use of a simple translation formula to generate subjective social perceptions from so-called objective stimuli is questionable. The implication is that “those studying cognitive networks should . . . measure perceptions of networks directly” (Krackhardt, 1987a: 113). Thus, there are three main questions that this research tries to answer. First, did pairs of friends tend to bid for interviews with the same organizations? Second, did pairs who perceived each other as similar tend to bid for interviews with the same organizations? Finally, did those pairs with similar patterns of friendships tend to bid for interviews with the same organizations? Method The sample consisted of the same class of 209 second-year MBA students as in the previous study. Of the 181 people who completed questionnaires, 11 people either did not participate in the bidding or were excluded because they were foreign exchange students. Both questionnaire and behavioral data were available for a total of 170 people (81 percent of the original sample). The MBA Bidding Process Organizational choice in the present study was measured in terms of those organizations students tried to interview with over the five-month recruiting period. The business school used a computerized bidding system under which each student could spend a total of 1,300 points bidding for interviews with the 119 organizations that recruited at the school. In general, those students who made the highest bids for particular interview slots were automatically selected. The bidding data were sensitive to student preferences over a five-month period, and the collection of the data was unobtrusive. Thus it was possible to monitor the behavioral preferences

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of subjects and to compare, for example, the degree of bidding overlap between pairs of friends compared to pairs of non-friends. Measures Independsent Variables Friendship and perceived similarity. As in the previous study, a pair of individuals was considered to be a friendship pair (or a similar pair) if at least one member of the pair nominated the other member as a friend (or as a similar individual). Structural equivalence. Structural equivalence was measured as the similarity in patterns of relations with other individuals in the friendship network. Thus, a pair of individuals who had exactly the same ties to other individuals (even though they had no ties to each other) would have a score of zero, indicating no difference in their structural positions. A pair of individuals who had very different ties to other actors in the system would have a large difference score. The difference scores were calculated as continuous measures in a Euclidean social space, where the distance between any two actors equals the square root of the sum of squared differences across all third actors (for the formula, see Knoke and Kuklinski, 1982: 61). Control Variables For each individual, it was possible to construct a vector of job preferences composed of zeros and ones, indicating, for each of sixteen job categories, whether or not the student had shown a preference for that type of job. The preference data were collected by the Career Services Center prior to the recruiting season. A job preference correlation matrix was created by calculating the Pearson correlation coefficients between the vectors of all pairs of individuals. This matrix contained information on how similar pairs of individuals were with respect to their job preferences. To create a matrix that would show which pairs of individuals had the same majors, we derived a list of seven categories of MBA majors from the academic concentrations claimed on student resumes. As reported in study 1, MBA students overwhelmingly chose two majors: finance (chosen by 56 percent) and marketing (chosen by 26 percent). In the few cases where it was impossible to identify an academic concentration from the available evidence, the students were categorized as miscellaneous. For each student, then, it was possible to allocate a number from 1 to 7 indicating the focus of his or her studies. We then created a majors similarity matrix; the 170–by-170 matrix consists of cell entries of 1 indicating that two individuals had the same major and cell entries of 0 indicating that the two had different majors. We used this matrix to control for similarity in academic concentration in the regression analyses.

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Table 6.5. Summary of Research Variables Variables

Source of Data

Operationalization

Dependent Overlapping choices of organizations

Computerized bidding system

For each pair, the correlation between bidding patterns across 119 organizations

Independent Friendship

Friendship questionnaire

For each pair, whether either person claimed the other as a friend The Euclidean distance between each pair For each pair, whether either person claimed the other as similar

Structural equivalence Perceived similarity Control Overlapping job preferences Overlapping majors

Friendship questionnaire Perceived similarity questionnaire MBA r´esum´e book MBA r´esum´e book

For each pair, the correlation across choices of 16 jobs For each pair, whether the individuals chose the same of seven majors

Dependent Variable The dependent variable was similarity in bidding behavior. Each individual could bid for interviews with 119 organizations. Thus, for each individual, it was possible to construct a bidding vector, 119 cells long, that showed for each organization whether or not a bid had been made. A bidding correlation matrix was constructed by correlating these bidding vectors for all pairs of individuals. The bidding correlation matrix, like the sociometric choice matrices, was 170 by 170 and consisted of Pearson correlation coefficients. These coefficients indicated how similar in their bidding behavior each pair of individuals had been. For example, a coefficient of .45 in cell (123, 81) indicated that the bids of persons 123 and 81 were correlated at the .45 level. Table 6.5 provides an overview of the variables and their measures. Analyses The basic analysis can be expressed as a multiple regression equation with five regressors and one dependent variable, where Y is the bidding correlation matrix, Pref the preference correlation matrix, Maj the majors similarity matrix, Euc the Euclidean distance matrix, Sim the perceived similarity matrix, Fr the friendship matrix, and ε the matrix of error terms: Y = β 0 + β1 (Pref) + β 2 (Maj) + β 3 (Euc) + β4 (Sim) + β 5 (Fr) + ε. Ordinary-least-squares (OLS) analysis is not appropriate for these data because the error terms are autocorrelated within rows and columns. The

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The Psychology of Network Differences Table 6.6. Summary Statistics for Number of Friends, Number of Similar Others, and Number of Bids

Similars Friends Bids

Maxa

Median

Mean

Range

SD

169 169 119

3 13 19

4.46 17.49 19.66

40 70 54

5.21 14.50 9.72

Note: a Maximum number of friends, similars, or bids that could be chosen.

OLS procedure requires that the observations be independent, whereas observations concerning all possible pairs in a social network exhibit systematic dependence. A solution to the problem of how to test the significance of the βs in a multiple regression equation when the data are structurally autocorrelated has been demonstrated by Krackhardt (1988) in terms of the Multiple Regression Quadratic Assignment Procedure (MRQAP) and is followed here. (See also Baker and Hubert, 1981; Hubert and Golledge, 1981; Hubert and Schultz, 1976; Krackhardt, 1987b.) Results The descriptive statistics in Table 6.6 show that the social networks among the MBAs were quite sparse. The median number of friends chosen was thirteen, compared to a median of three chosen as especially similar, with each student choosing from 169 possible names. Table 6.6 also shows that the mean number of organizations bid for was 19.66 (out of 119 available). The mean number of successful bids (those that resulted in interviews) was 16. The 84 percent success rate indicates that the bidding system was not characterized by cutthroat competition. There was little apparent incentive, in fact, for friends to collude in spreading their bids among different organizations. Table 6.7 shows that, comparing mean correlations, people’s job preferences were over twice as similar (r = .181) as their bidding behavior (r = .076). The base rate for bidding similarity was, then, quite low. In fact, the median bidding correlation between pairs of individuals was only .042. The bivariate correlations in Table 6.8 indicate preliminary support for the prediction that friends (relative to non-friends) and similars (relative to non-similars) would tend to bid for the same interviews. Contrary to the expectations, however, those individuals who were structurally equivalent – that is, friends with the same other people – were no more alike in their patterns of bids than those individuals who were friends with different people (Z = −0.08, ns). Table 6.8 also reveals that the

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Table 6.7. Means and Standard Deviations of Variables Variable

Min

Max

Mean

S.D.

Biddinga Equivalenceb Friendshipc Similarityc Majorsc Preferencesa

−0.259 2.449 0 0 0 −0.540

0.789 13.491 1 1 1 1

0.076 8.730 0.146 0.047 0.385 0.181

0.166 1.668 0.353 0.211 0.487 0.310

Notes: Results are based on 28,730 dyadic observations. a Pearson correlations. b Euclidean distance scores. c Each observation was either 0 or 1.

Table 6.8. Significance of Zero-Order Correlations among the Variables Variable 1. Bidding r Z 2. Equivalence r Z 3. Friendship r Z 4. Similarity r Z 5. Majors r Z 6. Preferences r Z

1

2

3

4

5

6

– –

−.01 −.08

.11 11.59∗∗

.10 13.05∗∗

.35 23.43∗∗

.40 32.08∗∗

.00 −0.18

.01 0.34

.00 0.31

.33 33.24∗∗

.08 4.67∗∗

.11 7.40∗∗

.04 2.93∗

.07 6.91∗∗

– –

.07 2.90∗ – – – –

– –

.48 22.05∗∗ – –

Notes: The Z scores were calculated by means of the Quadratic Assignment Procedure (QAP) and are based on 28,730 dyadic observations. ∗ p < .005 (two-tailed). ∗∗ p < .0001 (two-tailed).

control variables – similarity of job preferences and overlapping majors – were the most powerful predictors of bidding similarity. The first model in the Table 6.9 summary of regression results confirms the importance of the control variables – those who had either similar job preferences or similar majors tended to bid for the same organizations.

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Table 6.9. Five Multiple Regression Models Predicting Bidding Similarity Model Independent Variable Equivalence β Z Friendship β Z Similarity β Z Majors β Z Preferences β Z

1

2

3

4

5

−0.002 −0.856

−0.002 −1.026

0.030 5.787∗

0.021 4.407∗

0.059 8.503∗

0.047 6.675∗

0.072 11.371∗

0.072 11.420∗

0.071 11.286∗

0.072 11.348∗

0.072 11.360∗

0.160 19.119∗

0.157 18.895∗

0.157 18.888∗

0.160 19.094∗

0.156 18.722∗

Notes: The Z scores were calculated by means of the Quadratic Assignment Procedure (QAP). ∗ p < .0001 (two-tailed).

The Z scores indicate significant relationships between bidding similarity and both similarity of preferences and similarity of majors (p < .000l). The second model in Table 6.9 shows that, relative to non-similars, those who perceived each other as similar were significantly more likely to bid for interviews with the same organizations, even controlling for similarities in preferences and majors (Z = 8.503, p < .000l). Friends, too, had similar patterns of bids, relative to non-friends, even when the control variables were included in the analysis (Z = 5.787, p < .000l), as model 3 shows. Model 4, however, indicates that structural equivalence failed to predict bidding similarity when the control variables were included in the regression (Z = −0.856, ns), replicating the finding from the bivariate analysis. Apparently, pairs who had similar patterns of friendship ties were not significantly more similar in their choices of organizations than pairs who had different patterns of friendship ties. Model 5 shows that friendship and perceived similarity had independent main effects on bidding similarity. This was true despite the fact that friendship choices and choices of similar others were significantly intercorrelated. Model 5 in Table 6.9 also shows that structural equivalence remained insignificant in the full model (Z = −1.026, ns). The analyses indicated, as expected, that people with similar job preferences and similar academic concentrations tended to choose to interview with the same organizations. More interesting were the results showing

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that individuals who perceived each other as similar members of the MBA cohort, or who perceived each other as friends, tried to interview with the same organizations. These results remained significant even controlling for the powerful effects of overlapping job preferences and academic concentrations. Finally, structural equivalence, which is often used as a measure of perceived similarity, failed to predict bidding similarity, although the effect was in the expected direction. Study 2 Discussion One of the surprises of this research was the failure of structural equivalence to predict patterns of homogeneity in bidding behavior. Early claims concerning the superiority of structural equivalence over explanations based on friendship and acquaintanceship (Burt, 1987) have been heavily discounted given reanalyses of the same data (Kilduff and Oh, 2006). The general argument made in favor of structural equivalence has been that the perception of similarity is crucial to the diffusion of influence, and that perceived similarity may have little to do with direct social interaction. This general argument is certainly supported by the present research, but the most effective measure of perceived similarity was based on the direct perceptions of the individual rather than on structural equivalent estimates of relative positions in the friendship network. The relative superiority of perceived similarity and friendship over structural equivalence must be kept in perspective. The best predictors of bidding homogeneity were not the sociometric variables, but job preference similarity and similarity of majors. For the first time in the empirical literature on organizational choice (see Schwab et al., 1987, for a review), it was possible to focus explicitly on choices of organizations, controlling for the confounding effects of job preferences and academic specialization. The social influence independent variables operated on the margins of choices, perhaps determining which organizations would be selected by students for bids from the set of those offering the particular jobs that interested the students. This rather peripheral role of social influences should not, however, lead us to dismiss them as unimportant. This research has examined social influences only at the very end of a long decision process. The same kind of analysis could be applied to the actual choices of job preferences and MBA majors. These previous choices restrict the extent to which later choices can be influenced by social or other forces. But these previous choices may themselves have been influenced by friends, rivals, and family. The present research differs from previous work (e.g., Granovetter, 1974) that has found strong effects of social networks on the transmission of job vacancy information in imperfect labor markets. The MBAs

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in the present research made choices in a relatively perfect market characterized by full advance information concerning vacancies. Information concerning the characteristics of recruiting organizations was also widely disseminated by means of special company presentations on campus and through the Career Services Center. The market appeared to work quite effectively, with about 70 percent of the students actually obtaining jobs through the school-organized system, receiving an average of three job offers each. In a relatively perfect market, therefore, with an abundance of information, social networks may help to create and validate choice criteria. Previous research has shown that friends do indeed mutually influence evaluative criteria (Duck, 1973) and the way these criteria are used in organizations (Krackhardt and Kilduff, 1990). The opinions of strangers, however, concerning trivial choices are unlikely to influence behavior (Kilduff and Regan, 1988). As social comparison theory would predict, only important and ambiguous decisions, such as organizational choice, motivate individuals to seek comparative information from peers. Organizational choice was selected by Soelberg (1967) as an example of the kind of nonroutine decision making that is so little understood and yet which “forms the basis for allocating billions of dollars worth of resources in our economy every year” (Soelberg, 1967: 20). Study 2 suggests that in order to understand how individuals make such complex decisions, it is essential to study their interactions in the social systems to which they belong. What study 2 leaves unanswered, however, is whether some people, more than others, are susceptible to social network influence or whether network influence is such a strong force that it overwhelms individual personality differences.

Study 3: Does Self-Monitoring Moderate Network Influences on Decision Making? Missing from social network studies has been any discussion of dispositional differences. In moving from the analysis of attributes toward relation-centered analysis, network researchers appear to have lost sight of individual variability and its potential effects on the strength of relations. Social comparison research in general has long been criticized for its neglect of individual differences (e.g., Radloff, 1968: 945). At its most radical, the network view has suggested that the study of individuals is “a dead end” (Mayhew, 1980: 335) that leads to platitudes rather than scientific explanations of human behavior. Both the network approach and the dispositional approach can be used to understand decision making. The network perspective emphasizes that

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systems of relations influence decisions through such processes as cohesion among friends, social comparison among those who regard each other as similar, or rivalry among occupants of equivalent social positions (see study 2 for a discussion). The personality perspective emphasizes that relatively stable underlying dispositions influence decisions by predisposing individuals to prefer some outcomes rather than others. For example, the choice of a career may be partly determined by the degree of compatibility between the individual’s personality and the requirements of the vocation (Holland, 1985). Because of these different emphases, the two approaches are characterized by different research strategies. The network approach tends to take snapshots of decisions across the network at one moment in time (e.g., Walker, 1985), whereas the dispositional approach is generating more and more longitudinal research tracking individuals over the life course (e.g., Burns and Seligman, 1989; Gerhart, 1987; Staw, Bell, and Clausen, 1986; Staw and Ross, 1985). In summary, the two approaches offer opposing perspectives both in terms of variables (relations in networks versus individual dispositions) and research design (snapshot across the network versus history across time). In bringing the two approaches together, we may find it possible to pose new questions that have not previously been considered. For example, are there systematic differences between individuals in the degree to which they rely on friendship networks when making important decisions? To answer this question requires an examination of the effects of friendship networks on decision making for different personality types. Self-Monitoring The self-monitoring construct (Snyder, 1974, 1979; Snyder and Gangestad, 1986) distinguishes between those who are especially attuned to the role expectations of other people (high self-monitors) and those who insist on being themselves despite social expectations (low self-monitors). The basic idea is that compared with high self-monitors, “low self-monitors rely less on social cues to direct behavior and more on introspection” (Caldwell and O’Reilly, 1982b: 125). High self-monitoring individuals “are more likely than low self-monitoring individuals to seek out relevant social-comparison information” (Snyder and Cantor, 1980: 223). Low self-monitors (identified by their low scores on the Self-Monitoring Scale) “are controlled from within by their affective states and attitudes” (Snyder, 1979: 89). High self-monitors (identified by their high scores on the Self-Monitoring Scale) use cues from others as guidelines for monitoring (that is, regulating and controlling) their verbal and nonverbal selfpresentation. Whereas high self-monitors are “highly responsive to social

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and interpersonal cues of situationally appropriate performances,” the behavior of low self-monitors reflects “their own enduring and momentary inner states” (Snyder and Gangestad, 1986: 125). High self-monitors in a social situation ask the following: “Who does this situation want me to be and how can I be that person?”. By contrast, low self-monitors ask this: “Who am I and how can I be me in this situation?” (Snyder, 1979). In summary, high self-monitors, relative to lows, are more likely to shape their behavior in accordance with cues supplied by the social circles to which they belong. For the present research, which focuses on the choices of MBAs at a prestigious school of management located in a relatively isolated rural area, the relevant social circle was taken to be the network of personal friendships within the MBA cohort itself. Thus, we predict that the organizational choices of high self-monitors will be more highly correlated with the organizational choices of their friends. Research has also suggested that high and low self-monitors differ in the evaluative criteria they bring to the choice process. Particularly relevant to the present research is the evidence that high self-monitors choose on the basis of socially defined realities, whereas low self-monitors choose on the basis of intrinsic quality. For example, whereas high self-monitors choose products on the basis of the image they project, low self-monitors choose on the basis of the products’ quality (Snyder and DeBono, 1985). Adapting this research to the organizational choice process, one might suspect that compared with low self-monitors, high self-monitors would be more interested in the reputation, public image, and prestige of organizations. The image of the organization, like the image projected about consumption goods in advertising, should have particular salience for high self-monitors. The organization that one joins becomes an integral part of one’s self-image, and high self-monitors are very concerned with the “images of self that they project in social situations” (Snyder and DeBono, 1985: 588). Furthermore, high self-monitors prefer job situations that offer clearly defined roles (Snyder and Gangestad, 1982). It is as if high self-monitors wish to place themselves in situations that have strong social norms. By contrast, low self-monitors prefer situations that allow them the freedom to be themselves (Snyder and Gangestad, 1982). Relative to high self-monitors, they will perhaps evaluate organizations on how much autonomy in work procedures is encouraged. Furthermore, one can expect that low self-monitors will choose work compatible with the values and beliefs that are central to their self-identity. On the basis of this discussion, we predict that high and low selfmonitors will place different values on certain factors relevant to organizational choice. The six factors listed in Table 6.10 were selected to discriminate between the value systems of high and low self-monitors.

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Table 6.10. Factors Important to Organizational Choice Factor

Source

Individual Freedom 1. Freedom from pressures to conform both on and off the job. 3. The opportunity to determine my own work methods and procedures. 5. Work that is compatible with my personal values and beliefs. Social Conformity 2. Work that is of high status and prestige. 4. A clear idea of exactly what my role in the organization will be. 6. The organization’s reputation and public image.

Vroom, 1966. Lawler, Kuleck, Rhode, and Sorensen, 1975. Snyder and Gangestad, 1982.

Vroom, 1966; Lawler et al., 1975. Snyder and Gangestad, 1982. Pieters, Hundert, and Beer, 1968.

Factors 1, 3, and 5 can be categorized as individual freedom factors, likely to appeal to low self-monitors. These factors consist of freedom from pressures to conform both on and off the job, the opportunity to determine one’s own work methods and procedures, and work that is compatible with one’s personal values and beliefs. Factors 2, 4, and 6 in Table 6.10 can be categorized as social conformity factors, likely to appeal to high self-monitors. These factors consist of work that is of high status and prestige, a clear idea of exactly what one’s role in the organization will be, and the organization’s reputation and public image. Method Sample The sample consisted of the same 170 MBA students surveyed in study 2. Independent Variable Friendship, as measured in study 2, was the independent variable. Moderating Variable: Self-Monitoring This was measured on the questionnaire with the revised eighteen-item true-false version of the Self-Monitoring Scale (Snyder and Gangestad, 1986). In the present research, the scale’s reliability as measured by Cronbach’s (1951) alpha was .75. Dependent Variables Bidding Similarity This is the pairwise similarity in bidding behavior (as in study 2).

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Factors Important to Organizational Choice On the questionnaire, students were asked to rank the six factors in Table 6.10 in order of importance for their choices of organizations. Analyses Analyses were made using Quadratic Assignment Procedure (QAP) and Random Assignment Procedure (RAP). We also employed nonparametric tests of ranked factors. Quadratic Assignment Procedure As with study 2, we used QAP to provide a Z score of the significance of the relation between two matrices. To measure the strength of the correlations between the matrices, Goodman and Kruskal’s (1963) gamma was calculated, a nonparametric correlation coefficient for skewed and binary data such as is contained in the friendship matrix (Hubert and Schultz, 1976). Random Assignment Procedure Our prediction concerning the moderating effects of self-monitoring on friends’ influence was not amenable to analysis by the QAP procedure because the separate matrices for those high and low on the personality measures were not square. For example, the friendship matrix for high self-monitors was rectangular because the subsample of highs had chosen friends from the full sample of both lows and highs. That is, 70 high self-monitors selected friends from 170 high and low self-monitors. To determine whether the observed difference in correlations between low and high self-monitors could have resulted from chance alone, a distribution of ten thousand possible correlation differences was generated by randomly allocating people to the low and high self-monitoring categories. In the randomly reordered matrices, the first one hundred people were treated as if they were low self-monitors, whereas the last seventy people were treated as high self-monitors. The correlations between friendship patterns and bidding patterns were calculated separately for the lows and the highs, and the difference between the correlations was stored in a file. The random assignment of people to low and high categories was repeated ten thousand times to create a distribution of possible differences in correlations. This distribution was then examined to see how many times the observed difference in correlations had been generated by chance alone. For example, if the observed difference in correlations was equaled or exceeded in 250 of 10,000 trials, then the observed difference would be significant at the .025 level (one-tailed test).

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Nonparametric Tests of Ranked Factors To test whether there were significant differences in the way high and low self-monitors ranked the factors in Table 6.10, the ranks attributed to each set of factors by each individual were summed. The first set of factors (1, 3, and 5) were predicted to be preferred by low self-monitors. Each individual’s rank scores for factors 1, 3, and 5 were summed, giving a total that could range from 6 (i.e., 3 + 2 + 1) to 15 (i.e., 4 + 5 + 6). Similarly, each individual’s rank scores for factors 2, 4, and 6 (predicted to be preferred by high self-monitors) were summed. The mean rank score for each set of factors was calculated for low and for high selfmonitors. To test whether these means were significantly different from each other, a nonparametric analysis featuring the Kruskal-Wallis (1952) test (chi-square approximation) was performed. Results We know from the previous study, that friends, relative to non-friends, tended in general to bid for the same interviews. But was there a personality effect? We predicted that self-monitoring would moderate the significant correlation between friendship ties and bidding similarity, and this prediction was supported. The correlation between friendship and bidding similarity was higher for high (γ = .19) than for low self-monitors (γ = .13). To determine whether this difference was significant, RAP was used to output a distribution of ten thousand possible differences between the two groups. The observed difference was equaled or exceeded in 404 of 10,000 trials. The result, then, was significant at the .04 level (onetailed test). Table 6.11 provides averaged information on a range of indicators, including the number of job interviews obtained, the number of bids made, and the number of friends. There were no significant differences between low and high self-monitors on any of these indicators. Furthermore, individuals’ self-monitoring scores were not significantly correlated with the number of friends (r = .11, p = .l7, ns). High and low self-monitors may therefore adopt different interview strategies, but do they also tend to evaluate organizations differently? We predicted significant differences between low and high self-monitors with regard to the criteria by which they chose organizations. And, indeed, compared with low self-monitors, high self-monitors ranked more highly all three factors in Table 6.10 concerned with social conformity. Conversely, low self-monitors, compared with highs, ranked more highly all three factors concerned with freedom from social pressures. The difference between low and high self-monitors on the overall ranking of the social conformity and individual freedom factors was significant

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Table 6.11. Summary Statistics for Number of Interviews, Number of Bids, and Number of Friends by Self-Monitoring Category No. of Interviews

No. of Bids SD

No. of Friends

Median No. of Friends

M

SD

Group

M

SD

M

Low self-monitor High self-monitor

15.77

7.22

19.39

9.12

12

16.20

12.59

16.19

7.71

20.04

10.58

13

19.33

16.79

Note: For the low self-monitor group, n = 100; for the high self-monitor group, n = 70.

(p < .02). The summed ranked scores for the three individual freedom factors differed by approximately one scale point (8.95 for the lows, 9.94 for the highs), as did the summed ranked scores for the three social conformity factors (12.05 for the lows, 11.06 for the highs). It is, however, interesting to note that the individual freedom factors were more appealing than the social conformity factors to the sample in general. That is, although relative to the low self-monitors the high self-monitors ranked the individual freedom factors as less important, both highs and lows agreed that individual freedom was a more important criterion than social conformity in choosing an organization for which to work. The results of the tests on self-monitoring can be summarized as follows: Compared with those who tended to rely on their own counsel (low self-monitors), those who were more sensitive to social information (high self-monitors) were more like their friends in their choices of employment interviews with organizations. Looking now at attitudes toward organizations, the results confirmed that low and high self-monitors differed with regard to their rankings of criteria by which potential employers might be judged. Study 3 Discussion In partitioning the friendship network by self-monitoring, study 3 posed the following question: Do some people, more than others, tend to rely on their friends when making complex decisions? The results confirmed that personality types hypothesized to differ in their preferences for social comparison information did differ significantly, both with respect to how much their decision patterns resembled those of their friends and with respect to the criteria they used in the decision-making process. Previous research suggested that people rely on social information in making ambiguous decisions (Pfeffer, Salancik, and Leblebici, 1976) and when information is scarce (Granovetter, 1974; Kunreuther, 1978). The

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possibility that social influences on decision outcomes may depend not only on situational factors but also on dispositional factors has been neglected, despite a long history of research suggesting that individuals differ with regard to their susceptibility to social influence (McGuire, 1968). The reported differences between personality types in the degree to which people’s choices overlapped with those of their friends were small but significant. In this relatively perfect labor market, differences among people in the degree to which they relied on social networks for actual information concerning job opportunities were minimized. Future research could determine whether the relative differences between personality types reported in this chapter would be accentuated in an environment in which information was scarce rather than abundant. One of the goals of future research is to answer this question: Does reliance on the social network improve decision making? Previous research has suggested that people tend to rely on the advice of similar others rather than on the best information available (Brock, 1965) and that the benefits of using social networks are matched by the costs (Rook, 1984). Individuals appear to differ in their ability to derive support from social ties (Riley and Eckenrode, 1986). For the individuals studied in this chapter, we know that high self-monitors, relative to low self-monitors, tended to gain faster promotions in managerial careers (Kilduff and Day, 1994). But we don’t know whether the faster promotions were due to self-monitoring personality characteristics directly or whether the effect of self-monitoring on outcomes such as early promotions was mediated by social network position. The competing and possibly combinative effects of self-monitoring and social network position are examined in the next two chapters of this book. The research reported in this study supports the conclusion that even in conditions approaching those of perfect information and equal opportunity, individuals differ systematically in the extent to which they rely on the social network in making decisions. Self-monitoring was a significant moderator of social influence even in an environment overflowing with relevant facts. In a world in which such an excess of information is increasingly becoming a burden to be borne, the social network, as a decision-making resource, may be as much an expression of personality as it is a constraint on choice.

General Discussion In relying on unobtrusive measures of social influences (cf. Pfeffer et al., 1976) rather than on systematic observation of influence processes, the

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present research raises the question of whether the overlapping patterns of behavior actually resulted from social influences. Surely, students had an interest in keeping quiet about their interview choices to reduce the likelihood that their friends would compete for scarce interview slots. In fact, the interview market was not characterized by cutthroat competition: Eighty-four percent of bids resulted in interviews. Students averaged sixteen interviews and three job offers each. There was little apparent incentive, then, for friends to hide their preferences from each other. Indeed, the job preferences of all students were published by the Career Services Center before the start of the recruiting season. People started off, then, with a basic knowledge of who was likely to be interested in the various opportunities arising. Participant observations throughout the five-month interview period confirmed that students constantly exchanged information concerning their interview preferences. In the corridors, where upcoming recruitment schedules were posted, students discussed company pros and cons. These discussions continued in the student lounge, in the computer lab, and even in the bathrooms. To check our interpretation of the present findings, we conducted interviews with several members of the sample after the data collection. The consistent theme revealed by these interviews was of a highly interactive MBA cohort, the members of which studied and socialized together almost exclusively. Within this social world, friends not only discussed their interview preferences, but also, on occasion, jointly targeted selected companies. For example, one group of students not only decided to focus on specific investment banking firms but also decided to evaluate these firms on a consensually agree-upon criterion: the number of hours per week employees actually had to show their face in the office. As social comparison theory would predict, the evidence points to an intense and continuing exchange of opinions between friends concerning an important set of decisions. What was not being exchanged was any information concerning which companies were visiting or which kinds of vacancies were available. It was possible, then, to conduct a relatively pure test of differential social influence in a context in which everyone had complete information concerning vacancies. In the following chapter, we follow up the intriguing possibility that low and high self-monitors differ with respect to how they arrange and benefit from social networks.

7 The Social Networks of Low and High Self-Monitors

One of the enduring questions we face as human beings concerns why some people outcompete others in the race for life’s prizes. In work organizations, for example, why are some people better performers than others? One answer to this question is provided by research on the importance of structural position. Within each specific work context, some individuals occupy more advantageous positions in social networks than other individuals. These positions allow access to people who are otherwise disconnected from each other. The individuals who act as go-betweens, bridging the “structural holes” between disconnected others, facilitate resource flows and knowledge sharing across the organization. Their contributions to organizational functioning may lead to enhanced rewards, including faster promotions (Burt, 1992) and higher performance ratings. Research on structural position has emphasized the importance of being in the right place (Brass, 1984) but has neglected both the possibility that the network positions occupied by individuals might be influenced by their psychology and the possibility that personality and social network position might combine to influence important outcomes such as work performance. The structural approach to organizational dynamics tends to emphasize the structure of positions in social space (Blau, 1993; Pfeffer, 1991) and avoids dependence on difficult-to-measure psychological properties of actors (e.g., McPherson et al., 1992). Recent calls for more insight into the origins of network positions and the importance of individual characteristics (e.g., Emirbayer and Goodwin, 1994) prompt us to investigate why some individuals occupy structurally advantageous positions and how individual differences in psychology and structural position combine to determine performance in organizational contexts. The structuralist approach is not alone in disregarding the possible effects of individual characteristics on social structures. Despite a long history of psychological research suggesting that individuals differ with respect to social influence (e.g., McGuire, 1968; Riley and Eckenrode, 131

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1986), there has been relatively little work in psychology on how individual differences affect the structures of the social worlds in which people live and work. Rather than neglecting either the structure of the social world or the psychology of the individual, we investigate how individuals strive within social structures that both enable and constrain action. We follow in the tradition of those who recognize the importance of understanding the microfoundations of structural patterns (e.g., Granovetter, 1973; Ibarra, 1993a; Uzzi, 1996). Earlier work by social network pioneers included personality measures (e.g., Newcomb, 1961; Sampson, 1968) and interpersonal orientations (e.g., Breiger and Ennis, 1979; see also recent work by Janicik and Larrick, 2005). In bringing the individual back into social network analysis, we build on this previous work. Rather than treat individual attributes and social attributes as separate realms of inquiry, we seek to understand how the social networks that significantly affect the performance of organizational participants are shaped by the attributes of interacting individuals.

Theory The Structural Advantage Individuals may outperform their peers because of differences in the networks to which they belong. Links to friends and work partners can provide the assistance and social support necessary for high performance, but not all network configurations are likely to be equally helpful. Forming a large network, for example, may be less important than acquiring a structurally advantageous position within a network (Burt, 1992). Social actors who connect disconnected others tend to gain both information and control benefits. Information concerning projects, crises, resources, and other contingencies flow from a diversity of social actors to the central actor whose ties link disconnected others. Actors whose social ties are limited to one clique are less likely to receive diverse information than are actors whose ties span cliques because information that circulates within a clique of highly connected workers is likely to be redundant. Evidence for the benefits of structural holes comes from both small-group and organizational research (see the review in Burt, Jannotta, and Mahoney, 1998). Small-group experiments showed that people with exclusive relations to otherwise disconnected contacts tended to gain greater resources (Cook and Emerson, 1978; Cook, Emerson, Gillmore, and Yamagishi, 1983). One organizational study examined the importance for nonsupervisory personnel of occupying high-betweenness centrality positions – that is, positions that enable occupants to act as potential go-betweens for those not connected with each other. Results showed that the higher

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the betweenness centrality in the informal communication network, the greater the social influence and the higher the likelihood of promotion to supervisor within the following three-year period (Brass, 1984). Occupying a position between disconnected others is important not only for nonsupervisory personnel but also for those in the managerial ranks. A study of the individual networks and achievements of senior managers in a high-tech firm showed that nonredundant contacts to diverse clusters of others were related to early promotions (Burt, 1992). Similar findings emerged in another study of mobility among employees of a high-technology firm: People with sparse social networks that tied them to unconnected others tended to have high mobility (Podolny and Baron, 1997). The accumulating evidence suggests that individuals with ties across social divides gain nonredundant information concerning opportunities and resources. The ability to obtain resources such as information is directly related to individual and group performance (O’Reilly, 1977; O’Reilly and Roberts, 1977a, 1977b). Further, actors who connect disconnected others can facilitate the flow of information across the whole system of coordinated activity that makes up the organization, thereby contributing to the accomplishment of organizationwide goals. Given this, when we discuss individual performance in this chapter, we refer to the extent to which individuals contribute to organizational purposes, building on the work in organization theory that emphasizes that job performance consists of individuals contributing to the tasks specific to the organization (Burns and Stalker, 1994: 97). Previous work has focused on the effects of structural position on outcome variables such as power and promotions but has offered little conclusive evidence concerning performance in organizations. One of the few studies that did examine work performance found that employees occupying central positions in the workflow network were no more likely to be high performers than employees occupying less central positions (Brass, 1981). In contrast, research on officers and enlisted men in three high-technology military organizations showed that people with two or more network contacts performed better than people with one or no network contacts (Roberts and O’Reilly, 1979). This research did not examine the importance of network centrality or ties that link disconnected others. Given these suggestive but inconclusive findings, it is useful to examine directly whether structural position predicts workplace performance. Self-Monitoring Individuals in organizations may outperform their peers not only because of differences in the networks to which they belong but also because of individual differences in personality. Of the many personality variables

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that could potentially affect performance, self-monitoring, a variable centrally concerned with individuals’ “active construction of public selves to achieve social ends” (Gangestad and Snyder, 2000: 546), stands out for three reasons. First, self-monitoring theory provides compelling arguments linking individual differences in self-monitoring with a range of job outcomes, such as performance in the workplace, leadership emergence in workgroups, conflict management, information management, impression management, and boundary spanning (Kilduff and Day, 1994; Snyder, 1987: 88–90). Second, self-monitoring theory makes clear predictions concerning the effects of self-monitoring orientation on how individuals shape social worlds (Snyder, 1987: 59–84). And, third, as one leading structuralist has noted, the cutting edge of personality research of interest to social networkers may lie in approaches that recognize individual differences in predictable patterns of variability across situations, as selfmonitoring does (White, 1992: 206). According to self-monitoring theory, individuals differ in the extent to which they are willing and able to monitor and control their selfexpressions in social situations. Some people resemble successful actors or politicians in their ability to find the appropriate words and behaviors for a range of quite different social situations. With chameleonlike ease, they present the right image for the right audience. Other people, by contrast, appear to take to heart the advice Polonius gave to Laertes in Shakespeare’s Hamlet, “To thine own self be true”: They insist on being themselves, no matter how incongruent their self-expression may be with the requirements of the social situation. Research on self-monitoring has provided important insights into individual differences in how individuals present themselves in social contexts (see Gangestad and Snyder, 2000, for a review). In a social situation, high self-monitors ask, “Who does this situation want me to be and how can I be that person?” (Snyder, 1979). By contrast, low self-monitors ask, “Who am I and how can I be me in this situation?” (Kilduff and Day, 1994; Snyder, 1979). Self-monitoring theory, therefore, provides new insight into the age-old question of whether behavior is a function of consistent dispositions or strong situational pressures. From a self-monitoring perspective, some individuals (the low self-monitors) are consistent in demonstrating behavior that expresses inner feelings, attitudes, and beliefs. Other individuals (the high self-monitors) are consistent in adjusting behavior to the demands of different situations. Because high self-monitors rely on social cues from others to guide their behaviors rather than on their own inner attitudes and emotions, high self-monitors are more likely than low self-monitors to resolve conflicts through collaboration and compromise (Baron, 1989). Further, high selfmonitors tend to emerge as group leaders (Zaccaro, Foti, and Kenny,

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1991), particularly in situations calling for high levels of verbal interaction (Garland and Beard, 1979) and in normative climates that support the emergence of leadership (Whitmore and Klimoski, 1984). High self-monitors tend to emerge as leaders perhaps in part because they are more skilled at social interactions (Furnham and Capon, 1983). One study found that low self-monitors attended more to internal cues to produce effective work, whereas high self-monitors attended to situational cues, including the leadership behavior of supervisors (Anderson and Tolson, 1989). High self-monitors are more active in conversations (Ickes and Barnes, 1977) and tend to talk about the other person (and other people) instead of talking about themselves (Ickes, Reidhead, and Patterson, 1986). High self-monitors are better than low self-monitors at pacing conversations (Dabbs, Evans, Hooper, and Purvis, 1980), using humor (Turner, 1980), and reciprocating self-disclosures during acquaintance processes (Shaffer, Smith, and Tomarelli, 1982). In a review of studies of interpersonal strategies used by high and low self-monitors, Snyder wrote that the “lubricating” techniques employed by high self-monitors “would have warmed the heart of Dale Carnegie” (1987: 42). The social skills and leadership abilities of high self-monitors, therefore, may enable them to perform significantly better than low self-monitors in the modern workplace, where cooperation with others to achieve organizational purposes is the norm and where leadership emergence is encouraged (see the review by Baron and Markman, 2000). Although there is no reason to suppose that self-monitoring orientation affects the proficiency with which individuals perform technical duties, contextual activities, such as cooperating with others and following procedures even when they are personally inconvenient, are also a major part of workplace performance (Borman and Motowidlo, 1993). Much managerial work involves communicating with others (Gronn, 1983), performing a variety of different roles (Mintzberg, 1973), and relating to the needs of a large number of diverse people (Kotter, 1982). The social skills and leadership abilities characteristic of high self-monitors may enable them to perform better than low self-monitors in such contexts. Previous research has shown that individual differences in how people approach social situations affect individual attainment in managerial careers. Self-monitoring effects have been demonstrated on managerial promotions over a five-year period: High self-monitors are more likely to be promoted in managerial careers than low self-monitors (Kilduff and Day, 1994). Much of the pioneering work concerning the effects of selfmonitoring on performance-related variables has consisted of laboratory studies on students (e.g., Caldwell and O’Reilly, 1982a). The occasional field study has tended to focus either on the eventual outcomes of performance differences, such as early promotions (e.g., Kilduff and Day,

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1994), or has focused on specific types of workers, such as boundary spanners (e.g., Caldwell and O’Reilly, 1982b). It is important, therefore, to test whether self-monitoring predicts workplace performance across the full range of organizational positions in an organization.

Three Models Given the separate and unrelated literatures on social networks and personality, the question is how structural position and self-monitoring combine to affect individual performance in organizations. We explore three perspectives: a mediation model, an interaction model, and an additive model. Mediation Model Performance differences among individuals in organizations may be due to the tendency of a particular personality type (the high self-monitor) to occupy structurally central positions that link otherwise disconnected people and provide differential resources. Research across a range of social relationships shows that high and low self-monitors tend to inhabit different social worlds (Snyder, Gangestad, and Simpson, 1983; Snyder and Simpson, 1984; Snyder, Simpson, and Gangestad, 1986). Able to tailor behavior to a range of different social situations, the high selfmonitor tends to belong to a number of distinct social groups. The low self-monitor, by contrast, prefers to belong to a clique within which the individual can express a characteristic disposition (Snyder, 1987: 68–9). The high self-monitor likes to have one friend for tennis, another friend for basketball, and yet another friend for chess. High self-monitors maintain flexibility and make little emotional investment in relationships. Friends are chosen based on how closely their skills match activity domains. As one high self-monitoring tennis player observed, “When I want to play tennis, I select a partner who can challenge me” (quoted in Snyder, 1987: 65). Low self-monitors, by contrast, tend to choose friends on the basis of liking, irrespective of whether the friends are proficient in tennis, basketball, or chess. They like to be with the same friends across activity domains (Snyder et al., 1983). As one low self-monitor commented about her choice of an activity partner, “Jan’s my best friend. Besides, she’s the most fun to be around, whatever the activity” (quoted in Snyder, 1987: 65). Self-monitoring theory predicts, therefore, that high self-monitors, relative to low self-monitors, will tend to develop friendship relations at work

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with distinctly different people. Whereas low self-monitors will tend to occupy relatively homogenous social worlds, high self-monitors will tend to develop relationships across groups, using their flexible identities to play different roles in different groups. In a workplace, high self-monitors are therefore likely to bridge social worlds, acting as conduits through which otherwise unconnected people exchange information. According to the mediation perspective, high self-monitors will occupy central positions in social networks in organizations and reap the benefits of access to diverse resource flows and information detailed by structural sociologists (e.g., Burt, 1992). Because they tend to serve as go-betweens between disconnected others, high self-monitors will enhance their value to the organization by facilitating resource flows and knowledge sharing across the organization and thereby achieve superior performance. Thus, high self-monitors will tend to perform better than low self-monitors as a direct result of their differential success in occupying structurally advantageous positions in social networks. Complete mediation would suggest that any effect of self-monitoring on work performance is due to the individual’s structural position in social networks. Complete mediation, therefore, offers some support for the structuralist view (e.g., Burt et al., 1998) that individual dispositions can serve as proxies for the network positions that individuals are likely to occupy. Interaction Model The different, but not incompatible, interaction perspective suggests that different personality types may differentially take advantage of structural positions. High self-monitors may be more able and motivated than low self-monitors to seek out and use the resources available from the different social groups accessible from bridging positions in social networks. The success of high self-monitors in organizations may occur not because the high self-monitors tend to occupy structurally advantageous positions in social networks (the mediation argument) but because, irrespective of who happens to occupy the bridging positions in social networks, only the high self-monitors are willing and able to take advantage of the opportunities represented by such positions. The interaction model suggests that both a high self-monitoring disposition and a structurally advantageous position in the social network are necessary for the individual to achieve high work performance. Numerous studies have confirmed that high self-monitors, compared with low self-monitors, tend to be more responsive to the specific characteristics of situations (see the review in Snyder, 1987: 33–46). For example, in one study, high self-monitors showed themselves acutely sensitive to the differing contexts in which social interaction took place.

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The high self-monitors were conformist in social situations in which conformity was the most appropriate interpersonal orientation and were nonconformist when reference group norms favored autonomy. By contrast, low self-monitoring group members were virtually unaffected by their social settings (Snyder and Monson, 1975). This differential responsiveness is likely to affect work performance. In a field study of people whose jobs required that they interact with groups whose norms differed from one another, high self-monitors outperformed low self-monitors (Caldwell and O’Reilly, 1982b). This study, which focused on workers’ links outside the organization, provides support for the interaction model. Extending this research to the current study of workers within the organization, we might expect to find that only high self-monitors are able to take advantage of structurally advantageous network positions to enhance performance. A further reason to expect performance differences for high and low self-monitors occupying bridging positions relates to the detection of useful social information. High self-monitors are better at scanning the social world for information about people and their intentions. High selfmonitors are more likely than low self-monitors to notice and remember information concerning others (Berscheid, Graziano, Monson, and Dermer, 1976), to be more successful at detecting people’s intentions (Jones and Baumeister, 1976), and to be more accurate at eyewitness identification (e.g., Hosch, Leippe, Marchioni, and Cooper, 1984). If valuable information is available to those occupying bridging positions in social networks, then it is more likely to be detected by high self-monitors than by low self-monitors. Additive Model We have argued that high and low self-monitors may differentially succeed in organizations because they differentially occupy structurally advantageous positions in social networks (the mediation perspective) or because high self-monitors may be differentially able to capitalize on structurally advantageous positions (the interaction perspective). A third possibility is that structural position and self-monitoring may have relatively independent, additive effects on performance in organizations. The additive model involves twin predictions concerning work performance. The structural position prediction is that the greater the extent to which individuals act as potential go-betweens for those not connected to each other, the higher the work performance. The self-monitoring prediction is that the higher the individual’s self-monitoring score, the higher the performance. Support for the additive model would suggest two independent but not mutually exclusive ways for individuals to gain advantages

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MEDIATION MODEL

Structural Position

Self-Monitoring

Performance

INTERACTION MODEL Self-Monitoring

Performance

Structural Position

ADDITIVE MODEL

Self-Monitoring

Performance

Structural Position

Figure 7.1. Three models of how self-monitoring and structural position affect individual performance in organizations.

in work performance: (1) occupying a structurally advantageous network position, and (2) possessing a high self-monitoring orientation. Figure 7.1 summarizes the three models of the possible effects of structural position and self-monitoring on performance that we tested in our study.

Method Bayou Corporation (a pseudonym) was a small high-technology company involved in the chemical analysis of complex compounds. Employees researched, produced, and marketed high-precision chromatographic equipment for laboratories and other clients that analyzed the chemical composition of foods, fragrances, petrochemicals, pharmaceuticals, and other products. Bayou was founded in 1985 by an entrepreneur who left his job at a medium-sized chemical company to take advantage of a business-incubator program at a major university. By 1998, Bayou

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Corporation had grown to 116 employees, all located in one state-ofthe-art facility. The company had won numerous awards for the quality of its products and its environmentally conscious business practices. The self-styled “head-coach” and founder of the organization had created an entrepreneurial culture that emphasized informality rather than bureaucracy. Bayou competed in fast-moving markets against much larger companies such as Hewlett-Packard. The company founder emphasized the importance of innovation and creativity as the keys to survival in this competitive marketplace. Organizational structure was kept deliberately flat, with only three levels of hierarchy. Instead of establishing departments, Bayou organized its employees into fluid workgroups that ranged in size from two to sixteen people. The company prided itself on being in the forefront of equal opportunity employment and had won awards for its success in recruiting and promoting women. We collected network and personality data by means of a questionnaire sent to all 116 employees (68 men and 48 women). We collected performance-rating data by means of a separate questionnaire sent to all 22 supervisors (17 men and 5 women). Data about reporting relationships, demography, and tenure came from company records. The response rate was 88 percent for the questionnaire sent to all employees and 100 percent for the questionnaire sent only to supervisors. Nonrespondents did not differ significantly from respondents with regard to sex, tenure, or performance. Missing data on self-monitoring reduced the usable sample from 102 to 93 individuals for analyses involving this variable. Because there were no performance measures for the head of the company, analyses concerning both performance and self-monitoring used a sample of 92. Measures Social Networks We collected data on friendship relations and workflow relations using the roster method. For each network, we asked respondents to look down an alphabetical list of employees and place checks next to the names of people they considered friends or work partners. Data for each relation were arranged in 102-by-102 binary matrices. In each matrix, cell Xij corresponded to i’s relation to j as reported by i. For example, if i reported j as a friend, then cell Xij in the friendship matrix was coded as 1; otherwise, Xij was coded as 0. Each matrix contained 10,302 observations on all possible pairs of people. For each network question, respondents were free to nominate as many network contacts as they deemed appropriate. This format is preferable to

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a fixed-choice design in which respondents are asked, for example, “List your four best friends,” because it is unlikely that all people have exactly four best friends. Limiting respondents to a fixed number of choices tends to introduce measurement error into network data (Holland and Leinhardt, 1973). We depart from recent research on structural holes in ego networks (e.g., Burt, 1997) by including in our sampling all the actors in the organization rather than just the actors mentioned by the focal individual. In ego-network research, the individual (or “ego”) is the source of information concerning whether ego’s contacts are themselves connected or disconnected. Research has shown that individuals are reliable sources of information concerning the membership of stable networks to which they themselves belong (Freeman et al., 1987), but ego’s responses concerning possible interconnections between people to whom ego is tied are subject to systematic bias (Krackhardt and Kilduff, 1999; Kumbasar et al., 1994). Thus, ego-network data used to assess structural holes are potentially distorted by perceptual biases. Comparing Workflow and Friendship Networks As research on social networks has pointed out (e.g., Roethlisberger and Dickson, 1939: 493–510), in considering the importance of network position in an organization, researchers must consider two types of networks: the workflow and informal networks. The workflow network is the formally prescribed set of interdependencies between employees established by the division of labor in the organization. Work flows through the organization as workers exchange inputs and outputs. A successful interaction in the workflow network enables the flow of work from one person to another (Brass and Burkhardt, 1992: 197). By contrast, informal social networks, such as the friendship network, derive from mutual liking, similarity of attitudes, or personal choice. Compared to the workflow network, the friendship network represents more individual choice and initiative. People have more discretion in the choice of friends than they have in the choice of with whom to interact to accomplish work. Achieving a structurally advantageous position in either the more formal workflow network or the more informal friendship network can bring benefits to the individual in terms of diverse information and other resources. Friendship Network Respondents were asked to look down an alphabetical list of fellow employees and place checks next to the names of those individuals they considered “especially good friends.” Friends were defined as “people with whom you like to spend your free time, people you have been with

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most often for informal social activities, such as visiting each other’s homes, attending concerts or other public performances.” Workflow Network The workflow network was modeled after Brass (1981: 332), who argued that “task positions and the workers occupying these positions [can be] viewed as interrelated on the basis of the flow of work through the organization.” Respondents were asked to place a check next to the names of their workflow contacts. We combined workflow inputs and workflow outputs to make the questionnaire more manageable and because Brass (1984) found no differences between the predictive power of input and output contacts. Workflow contacts were defined as the “set of people that provide you with your workflow inputs taken together with the set of people to whom you provide your workflow output.” We defined workflow inputs as “any materials, information, clients, etc., that you must acquire in order to do your job.” Workflow output was defined as “the work that you send to someone else when your job is complete.” This network was, therefore, anchored in the actual work processes of the organization rather than in the more discretionary task advice networks studied by others (e.g., Podolny and Baron, 1997). Network Size and Structure A large network, one with many contacts, can enable the individual to access numerous others for information and other resources. But the benefits of a large network may be offset by the costs involved in maintaining a large number of relationships (Rook, 1984). People who interact with numerous others in organizations run the risk of running short of time and other resources necessary for work performance. Thus, people with large networks within the organization may not necessarily achieve the highest performance ratings. They may be so busy maintaining ties at work that their work performance suffers (see Burt and Ronchi, 1990, for a case study). In considering how network position relates to work performance, it is therefore important to examine simultaneously the relationships between network size and performance and between betweenness centrality and performance. One of the questions that our research attempts to answer is, controlling for the size of the individual’s network, does the extent to which the individual’s network spans social divides predict workplace performance? By looking at both network measures simultaneously, we can assess whether network size and network betweenness have independent relationships with work performance. Betweenness Centrality As a measure of the extent to which each individual occupied a structurally advantageous position, connecting otherwise unconnected others

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in the friendship and workflow networks, we used betweenness centrality (Freeman, 1979). We chose this measure rather than a more local measure of autonomy, such as constraint (Burt, 1992), because betweenness centrality takes both direct and indirect ties into account (Brass, 1984; Brass and Burkhardt, 1993; Krackhardt, 1990), whereas constraint focuses primarily on the direct ties in ego’s immediate circle of contacts. More local measures of the extent to which individuals span structural holes are useful when sampling from large populations for which whole network data are unavailable (e.g, Burt, 1992). The (102 × 102) friendship matrix and the (102 × 102) workflow matrix were each submitted to the betweenness procedure in the network program UCINET IV (Borgatti et al., 2002; see Freeman, 1979, for the formula). The higher the betweenness score of an actor, the greater the extent to which that actor serves as a structural conduit connecting others in the network. More formally, betweenness centrality measures the frequency with which an actor falls between other pairs of actors on the shortest or geodesic paths connecting them (Freeman, 1979: 221). Because it is difficult to interpret measures of betweenness centrality for nonsymmetric data, we symmetrized the friendship and workflow matrices using the rule that if either member of a pair nominated the other, the pair was considered to have a tie. To check whether the results were affected by this definition, we also symmetrized each matrix using the rule that there was a link between two people only if each member of the pair nominated the other. The pattern of results remained unchanged. Network Size Network size was measured as the total number of each individual’s direct links with other actors in the network, a measure also known as degree centrality (Scott, 1991: 86–7). To be compatible with measures of betweenness centrality, we calculated size on friendship and workflow matrices symmetrized according to the rule that if either member of a pair nominated the other, the pair was considered to have a tie. Performance Our theory of job performance emphasizes the extent to which individuals succeed (in the eyes of management) in contributing to organizational ends. In the absence of objective measures of performance across job types in this organization, we relied on supervisory ratings. Using a six-item scale arranged in a five-point Likert format, supervisors rated the performance of those subordinates who reported directly to them. As researchers have noted, in work organizations, “the vast majority of performance ratings come directly from the immediate supervisor” (Bretz et al., 1992: 331; see also Scullen, Mount, and Goff, 2000). A recent comprehensive review of performance evaluation in work settings concluded

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that supervisory ratings “are most likely valid reflections of true performance” (Arvey and Murphy, 1998: 163). We informed supervisors that performance ratings would be confidential and used only for research purposes. Performance ratings obtained for research purposes tend to be more reliable and valid than those obtained for administrative purposes (Wherry and Bartlett, 1982). The six performance items were selected after extensive discussions with the firm’s human resource director and a group of four employees representing a range of job types at the firm. Supervisors first evaluated subordinates’ performance on these three items: (1) “the overall job performance of the individual” (1 = poor, 5 = excellent); (2) the likelihood that the subordinate would “achieve future career related success (such as promotions, awards, bonuses, and involvement in high-profile projects)” at Bayou (1 = very unlikely, 5 = very likely); and (3) “the likelihood that you would pick [the subordinate] to succeed you in your job” (1 = very unlikely, 5 = very likely). Given the strong emphasis placed on innovation at Bayou and the growing recognition among researchers of the importance of contextual aspects of job performance (e.g., Arvey and Murphy, 1998; Borman and Motowidlo, 1993), we also included three items, taken from Scott and Bruce (1994), to capture employees’ workplace innovativeness. Supervisors rated subordinates’ innovativeness (using five-point scales) on these three items: (1) the degree to which the subordinate generated creative work-related ideas; (2) the degree to which the subordinate promoted and championed work-related ideas to others; and (3) the degree to which the subordinate searched out new technologies, processes, techniques, and/or product-related ideas. The reliability of the six-item scale, as measured by Cronbach’s (1951) alpha, was .90. The results of a component analysis showed all six items loaded on the same component (eigenvalue = 4.06; all loadings were above .76) that explained 68 percent of the overall variance. To check whether our results were an artifact of the composition of our performance measure, for all analyses that included performance, we ran separate tests using (1) the final six-item measure of performance, (2) a three-item measure that excluded the three innovativeness items, and (3) a three-item measure that included only the innovativeness items. The pattern of results was unchanged irrespective of the performance measure used. Self-Monitoring Self-monitoring was measured with the eighteen-item true-false version of the Self-Monitoring Scale (Snyder and Gangestad, 1986). Items include “I would probably make a good actor,” and “In different situations and

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with different people, I often act like very different persons.” The selfmonitoring score, used as a continuous variable, indicates the probability that an individual is a high or low self-monitor (Gangestad and Snyder, 1985). Control Variables Rank Differences in formal rank are likely to influence patterns of interaction in organizations. For example, high-ranking individuals, by virtue of their control over resources and their decision-making authority, may be better positioned to emerge as central actors in social networks (e.g., Ibarra, 1992; Lincoln and Miller, 1979). There were three levels of hierarchy in the company. From company records, we coded rank as 0 for nonsupervisors, 1 for supervisors, and 2 for top management team members. Tenure The length of time a person has been with the company is also likely to affect the pattern of participation in social networks. For example, individuals who have been with the company longer may be more likely to occupy central positions in social networks. Using company records, we coded tenure as the number of months that the company had employed an individual. Sex We controlled for sex in each of the regression models because of its possible impact on network configuration (Brass, 1985; Ibarra, 1993b) and performance evaluation (Burt, 1992). Sex was coded as 0 for women and 1 for men.

Analysis Our approach to testing the mediation, moderation, and additive models follows standard statistical procedures (detailed in Baron and Kenny, 1986). We controlled for rank, tenure, and sex in each test. To assess support for mediation, we conducted three statistical tests to see whether any significant relation between self-monitoring and performance was eliminated or significantly reduced once network position was controlled for. First, we used ordinary-least-squares (OLS) regression to examine the relationship between self-monitoring and performance. Second, we used MANOVA to examine whether self-monitoring significantly predicted the four network variables taken as a set. Finally, to evaluate support for the overall mediation model, we used hierarchical regression analysis to

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examine whether the inclusion of the four network variables significantly affected the relationship between self-monitoring and performance. If a significant relationship between self-monitoring and performance is eliminated or significantly reduced as a result of controlling for the four network variables, then this would indicate support for mediation. We used hierarchical regression analysis to test the interaction model. To correct for the multicollinearity that arises when testing moderated relationships among continuous variables, we centered self-monitoring and the centrality variables before generating interaction terms (Aiken and West, 1996; Cohen and Cohen, 1983). Centering consists of subtracting the sample mean from each independent variable. The adjusted variables each have a mean of zero, but their sample distribution remains unchanged. We computed four interaction terms by multiplying the centered self-monitoring score with each of the four centered centrality scores. Interaction terms were entered in a separate step after the main terms had already been entered. If the addition of the interaction terms results in a statistically significant improvement over the regression model containing the main terms, then this would indicate support for the interaction model. Testing the additive model was straightforward: Self-monitoring and the four network variables were included simultaneously as independent variables. If self-monitoring and the centrality variables were significantly related to performance, then the additive model would be supported. Size and Betweenness Centrality Collinearity Despite the clear conceptual distinction between the size of the individual’s network and the extent to which the individual’s network links otherwise disconnected employees, size and betweenness centrality are often highly correlated (Bonacich, Oliver, and Snijders, 1998: 135). Popular individuals tend to have high-betweenness centrality scores. Based on our theoretical arguments, we were interested in examining how betweenness centrality relates to dependent variables while controlling for network size. Collinearity between variables such as size and betweenness centrality tends to inflate the standard errors of their regression coefficients, making it more difficult to obtain significant values, but the inflation of standard errors does not affect the validity of any significant results that are found. As one regression expert explained, a significant value for the beta coefficient in a regression “is just as conclusive when collinearity is present as when it is absent” (Darlington, 1990: 130). To check on the severity of the multicollinearity between size and betweenness centrality, we examined the conditioning index and variance proportions associated with each independent and control variable (see Belsley, Kuh, and Welsch, 1980, for a discussion). According to

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Table 7.1. Means, Standard Deviations, and Correlationsa Variable

Mean

1. Rank 2. Tenure (months) 3. Sex 4. Self-monitoring Workflow Network 5. Betweenness centrality 6. Size

SD

1

2

0.31 53.95 0.62 7.12

0.61 39.03 0.49 3.93

3

4

5

.16 .14 .14

−.06 .07

63.90

77.19

.18∗

.04

.10 .12

49.27

19.50

.24∗∗

.04

.15 .24∗∗ .87∗∗∗∗

6

7

8

.12

Friendship Network 7. Betweenness 146.63 243.61 −.07 .33∗∗∗ .01 .18∗∗ .07 centrality 8. Size 7.24 5.50 .03 .36∗∗ .01 .04 .13 9. Performance 20.25 5.08 .36∗∗∗ −.26∗∗ .02 .23∗∗ .26∗∗

.14 .21∗∗ .80∗∗∗∗ .17 .04 −.10

Notes: a N = 93, except performance (N = 92). ∗ p < .10. ∗∗ p < .05. ∗∗∗ p < .01. ∗∗∗∗ p < .001.

Tabachnik and Fidell (1996: 86–7), a conditioning index greater than 30 and at least two variance proportions greater than .50 indicates serious multicollinearity. None of our independent variables violated this criterion; multicollinearity thus posed no serious threats to the validity of our analyses.

Results Table 7.1 presents means, standard deviations, and zero-order correlations among the variables. The typical employee had been with the firm for fifty-four months. Men made up 62 percent of the sample. Individuals who were higher in rank, self-monitoring, and betweenness centrality tended to have higher job performance ratings in these univariate tests. The density of the workflow network, as measured by the average cell value in the 102-by-102 binary workflow matrix, was .34. The friendship network was considerably sparser, with a mean density of .04. The Mediation Model According to the mediation model, the success of high self-monitors in outperforming low self-monitors is due to the greater success of the high self-monitors in occupying strategically advantageous positions in social

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Table 7.2. Standardized Regression Coefficients from Analyses Predicting Performance (N = 92) Model Independent Variable Rank Tenure Sex Self-monitoring (SM) Workflow Network Betweenness centrality Size Friendship Network Betweenness centrality Size SM × Workflow betweenness SM × Size of workflow network SM × Friendship betweenness SM × Size of friendship network Model F F R2 R2 Adjusted R2

1 .40∗∗∗∗ −.31∗∗∗ −.06

8.51∗∗∗∗ .23 .20

2

3∗

4

5

.38∗∗∗∗ −.32∗∗∗ −.08 .21∗∗

.42∗∗∗∗ −.36∗∗∗∗ −.07

.40∗∗∗∗ −.37∗∗∗∗ −.08 .19∗∗

.40∗∗∗∗ −.39∗∗∗∗ −.09 .20∗∗

.53∗∗∗ −.37∗∗

.59∗∗∗ −.47∗∗

.67∗∗∗ −.51∗∗∗

.41∗∗∗ −.29∗∗

.32∗∗ −.22∗

6.39∗∗∗∗ 3.95∗∗∗ .35 .12 .29

6.33∗∗∗∗ 4.19∗∗ .38 .03 .32

.28∗ −.19 −.11 .11 .11 −.09 4.10∗∗∗∗ 0.26 .38 .00 .29

7.89∗∗∗∗ 4.29∗∗ .27 .04 .23

Notes: a F and R2 report changes from previous model, except for model 3, which reports change statistics from model 1 to 3. ∗ p < .10. ∗∗ p < .05. ∗∗∗ p < .01. ∗∗∗∗ p < .001.

networks in organizations. To test this model, we first examined the relationship between self-monitoring and performance. The regression results presented in model 2 of Table 7.2 show that high self-monitors, as expected, tended to outperform low self-monitors. When we controlled for rank, tenure, and sex, self-monitoring significantly predicted performance (β = 0.21, p < .05), explaining an additional 4 percent of the variance over the baseline model. Although high self-monitors may achieve higher job performance than low self-monitors, we still need to know whether they also tend to occupy structurally advantageous positions in social networks. The MANOVA results presented in the last three columns of Table 7.3 show that when we control for rank, tenure, and sex, self-monitoring significantly predicted the four network variables taken as a set (F = 3.40, p < .05), explaining an additional 14 percent of the variance over the baseline model. Table 7.3 also shows that higher self-monitoring scores predicted both higher betweenness centrality in the friendship network and larger

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Table 7.3. Standardized Regression and MANOVA Coefficients from Analyses Predicting Structural Position and Size (N = 93) Friendship Network Variable Rank Tenure Sex Self-monitoring Model F R2 Adjusted R2

Betweenness Centrality −.15 .34∗∗∗∗ .03 .17∗∗ 3.92∗∗∗∗ .15 .11

Size −.03 .37∗∗∗∗ .04 .02 3.35∗∗ .13 .09

Workflow Network Betweenness Centrality .15 .01 .07 .09 1.06 .05 .003

Size .20∗ .00 .10 .20∗∗ 2.76∗∗ .11 .07

MANOVA Wilk’s Lambda

EtaSquared

F

.92 .86 .99 .86

.09 .15 .01 .14

1.96 3.59∗∗∗ 0.26 3.40∗∗

Notes: ∗ p < .10. ∗∗ p < .05. ∗∗∗ p < .01. ∗∗∗∗ p < .001.

size in the workflow network. Thus, high self-monitors, relative to low self-monitors, did tend to occupy strategically advantageous positions in the friendship network and to have larger workflow networks. To evaluate support for the overall mediation model, we examined whether the relationship between self-monitoring and performance was due to the significant relationship between self-monitoring and the network variables. Including the four network variables in the regression equation, however, did not significantly affect the relationship between self-monitoring and performance. The results presented in model 4 of Table 7.2 show that even though the high self-monitors tended to occupy high-betweenness positions in friendship networks, and even though the occupants of these positions tended to have higher performance, the higher performance of high self-monitors was not explained by their differential success in occupying high-betweenness positions. After we controlled for the significant relationships between the four network variables and performance, self-monitoring continued to explain significant variance in performance. The full set of results indicates that although self-monitoring explains significant variance in performance and in the set of structural variables, and the structural variables predict performance, the mediation model is not supported. There is no evidence of either full mediation or partial mediation. To understand these results more fully, we looked at the differing relationships between self-monitoring and the structural variables. Table 7.3 shows that higher self-monitoring scores predict higher betweenness centrality in the friendship network but also larger size in the workflow network. High self-monitors, relative to low self-monitors, not only occupy strategically advantageous positions in the friendship network,

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they also find themselves busier than the low self-monitors providing work outputs and receiving work inputs from more people. Table 7.2 suggests that the advantages the high self-monitors may gain from occupying central positions in the friendship network may be counterbalanced by the disadvantages of having to maintain large workflow networks. Whereas betweenness centrality in the friendship network has a positive relationship with individual performance, size in the workflow network has a negative relationship with performance. These counterbalanced results suggest that the performance of high self-monitors, relative to low self-monitors, is simultaneously increased and decreased by the structure of social networks. The high self-monitors’ success in spanning structural holes in the friendship network may help them increase their performance, but their acceptance of large workflow networks may make successfully accomplishing tasks more difficult. The Interaction Model The interaction model suggests that the relationship between network position and performance depends on the self-monitoring orientation of the person occupying the network position: High self-monitors (relative to low self-monitors) should be able to exploit high-betweenness positions more effectively. We found no support for this prediction. Model 5 in Table 7.2 shows that high self-monitors were no more likely than low self-monitors to benefit from occupying high-betweenness positions. Adding the four interaction terms as a set failed to significantly improve variance explained over the direct-effects model 4. There was, therefore, no support for the interaction model. The Additive Model According to the additive model, self-monitoring and structural position should independently predict performance in organizations. To test this model, we included self-monitoring and the four network variables in the same regression equation. In support of the additive model, the results show that high self-monitors tended to outperform low self-monitors, and those occupying high-betweenness centrality positions tended to outperform those occupying low-betweenness centrality positions: model 4 in Table 7.2 shows that (controlling for rank, sex, and tenure) selfmonitoring and each of the four network variables explained significant variance in performance. The full model explained significantly more variance in performance than model 2, which contained only the controls and the self-monitoring variable, and model 3, which contained only the controls and the four network variables.

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Self-Monitoring

Performance

Structural Position Figure 7.2. Emergent model of self-monitoring and structural position effects on individuals’ work performance.

An Emergent Model Of the three proposed models, the additive model best explains the data, but the overall results suggest a more complex relationship among selfmonitoring, structural position and performance than anticipated by any of the three proposed models. High self-monitors tended to achieve higher performance, as did individuals who occupied high-betweenness centrality positions in the friendship and workflow networks. Consistent with the additive model, self-monitoring and structural position were relatively independent predictors of performance. But we also found that self-monitoring explained significant variance in the set of structural variables: High self-monitors (compared with low self-monitors) tended to occupy high-betweenness positions in the friendship network and tended to interact with more people to get their work done. These results indicate that the variance shared between self-monitoring and the set of structural variables did not overlap with the variance that either of these variables shared with performance, which leads us to the emergent model summarized in Figure 7.2. Network Differences over Time To further explore the relationship between self-monitoring and social network position, we looked closely at the network that was most amenable to individual preferences: the friendship network. According to self-monitoring theory, high self-monitors should move over time into positions in the friendship network that link different social worlds, whereas low self-monitors should remain in homogeneous social worlds. In the absence of longitudinal data, we tested this argument by looking at whether the interaction of self-monitoring and organizational tenure predicted betweenness centrality in the friendship network. We first

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The Psychology of Network Differences Table 7.4. Standardized Regression Coefficients Predicting Betweenness Centrality in the Friendship Network (N = 93) Model Variable Rank Sex Tenure Self-monitoring Self-monitoring × tenure Model F F R2 R2 Adjusted R2

1

2

3

−.07 .02

−.15 .03 .34∗∗∗ .17∗∗

21

3.92∗∗∗ 7.59∗∗∗ .15 .14 .11

−.15 .06 .27∗∗∗ .11 .35∗∗∗∗ 6.24∗∗∗∗ 13.33∗∗∗∗ .26 .11 .22

.01 .00

Notes: ∗ p < .10. ∗∗ p < .05. ∗∗∗ p < .01. ∗∗∗∗ p < .001.

centered self-monitoring and tenure and then added the interaction between these centered variables. The results shown in model 3 in Table 7.4 suggest that the longer the tenure, the more likely were high self-monitors to occupy high-betweenness positions, but length of tenure made no apparent difference to the likelihood that low selfmonitors would occupy high-betweenness positions. The interaction term explained an additional 11 percent of the variance in betweenness centrality in the friendship network, a statistically significant improvement (p < .001) over model 2, which assessed the direct relationships between self-monitoring and betweenness centrality (controlling for tenure, rank, and sex). To chart this significant interaction, we partitioned the sample so that individuals with scores of 11 or greater were classified as high selfmonitors (e.g., Gangestad and Snyder, 1985; Kilduff, 1992). Figure 7.3 shows that longer-serving high self-monitors tended to have higher betweenness-centrality scores, whereas length of time in the organization made little difference to the betweenness centrality of low self-monitors. These results are compatible with the idea that high and low self-monitors tend to develop different social network structures over time.

Discussion This research represents a theory-driven examination of how personality relates to social structure and how social structure and personality

Betweenness Centrality in the Friendship Network

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700 600 500 400 300 200 100 0

10

20

40

60

80

100

120

140

160

Tenure (in months) ------- Low Self-Monitors –––––– High Self-Monitors

Figure 7.3. Relationship between tenure and betweenness centrality for high and low self-monitors.

combine to predict work performance. Consistent with self-monitoring theory, we found that high self-monitors tend to occupy positions of highbetweenness centrality. Further, we found that the relation between selfmonitoring orientation and performance in the organization remained significant despite controlling for several other significant variables, including four measures of network structure. Although strong claims of causality would require studying the effects of self-monitoring on social structure over time, we did find that for high self-monitors (but not for low self-monitors), longer service in the organization predicted the occupancy of strategically advantageous network positions. Our research therefore suggests three important conclusions. First, personality predicts social structure: The high self-monitors tended to occupy central positions in social networks. Second, personality affects the way individuals build friendship networks over time: The high self-monitors (but not the low self-monitors) became more central the longer they stayed in the organization. Third, self-monitoring and centrality in social networks independently predict individuals’ workplace performance. The results paint a picture of individuals shaping the networks that constrain and enable performance. It appears that high and low self-monitors pursue different network strategies, with high self-monitors tending to occupy positions that span social divides, whereas low self-monitors remain tied to more homogenous social worlds. High and low self-monitors, therefore, appear to be active agents in the structuring of distinctive social worlds at work. In formulating three models of how self-monitoring and network position together might affect work performance, we have emphasized the

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importance of considering alternative linkages between our constructs (cf. Elder, 1973). The particular site that we examined consisted of a relatively small, cohesive organization in which there were relatively few high self-monitors. We need further research in other organizational settings to understand more fully how self-monitoring orientation and network position might combine to affect workplace performance. Future research could also examine different types of performance outcomes to supplement our reliance on supervisory ratings. We are reassured by considerable research evidence that, even when supervisors and ratees are members of the same network (as in our sample), supervisors tend to like subordinates who prove themselves as high performers (Robbins and DeNisi, 1994). It is unlikely, therefore, that ratings were biased by liking, given that “affect is likely to be a function of how well or poorly a person performs his or her job” (Arvey and Murphy, 1998: 151). The picture we present in this chapter of people taking advantage of their personality orientations to forge different types of network structures offers a new direction for social network analysis. In the past, network research focused almost exclusively on “the overall structure of network ties” (Emirbayer and Goodwin, 1994: 1415), neglecting or omitting individual-level variables (see, for example, Mayhew’s 1980 manifesto). Individual dispositions, to the extent that they have been discussed at all in recent network research, have tended to be dismissed as “the spuriously significant attributes of people temporarily occupying particular positions in social structure” (Burt, 1986: 106). In this chapter, we demonstrate that self-monitoring theory can enrich our understanding of such vital network topics as who is likely to bridge structural holes and the connection between structural position and work performance. We encourage further examination of the ways in which different types of people forge distinctively different patterns of social ties in the workplace. One of the major unanswered questions concerning self-monitoring and social networks is, What motivates high and low self-monitors to build such different social worlds? A recent review of the self-monitoring literature suggested that high and low self-monitors might have different orientations toward status enhancement. High self-monitors might seek, above all, to “create public images . . . , that connote social status.” Low self-monitors, by contrast, may be more interested in investing in “close social relationships in which they and their partners can be trusted” (Gangestad and Snyder, 2000: 547). High and low self-monitors may be building different types of social capital, with high self-monitors focused on constructing social worlds that function as “effective instruments of status enhancement” and low self-monitors focusing on constructing social worlds that support their reputations as “genuine and sincere people” (Gangestad and Snyder, 2000: 547). Future research could

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investigate whether high and low self-monitors are differentially aware of the structural holes in social networks and whether they consider the career consequences of different social network strategies. The theory and results that we present in this chapter suggest that high self-monitors, the chameleons of the social world, resemble the prototypical person featured in sociological research on social networks. In sociological research, individuals tend to take on the attributes and ideas of their associates rather than relying on their own inner beliefs and values (e.g., Carley, 1991). According to sociologists, people strive to occupy central positions in social networks in order to advance their careers (Burt, 1992). Low self-monitors, despite making up approximately 60 percent of the population (Snyder, 1987), seem strangely absent from the sociological literature. In our research as well, low self-monitors have featured mainly as the background against which we have discussed the contributions and outcomes of the more visible high self-monitors. For future research, the question remains, How do the organizational networks of low self-monitors affect contributions and outcomes? Self-monitoring theory suggests that the social networks of low selfmonitors may help enhance several aspects of organizational effectiveness. Low self-monitors’ tendency to forge deep emotional attachments, for example, may facilitate the development of strong network ties useful in crisis situations (see Chapter 10) and in the transfer of tacit knowledge (Hansen, 1999). The networks of low self-monitoring individuals, therefore, may help organizations respond to unexpected jolts and to transmit expertise. Further, low self-monitors’ greater commitment to work relationships may lead to greater commitment to the organization (Jenkins, 1993). But if a low self-monitor does leave an organization, there may be a larger impact on coworkers (in terms of turnover, for example) than when a high self-monitor leaves (see the discussion of turnover effects on coworkers in Chapter 9). Self-monitoring orientation is a stable component of the individual’s personality, but a stable personality trait can be expressed through a range of possible behaviors. The practical implications of our findings, therefore, can involve individuals changing behaviors even if they are unable to change self-monitoring orientations. High self-monitors, for example, are more other-directed than low self-monitors, meaning that high self-monitors tend to be more susceptible to pressure from other people (Kilduff, 1992; Snyder and Gangestad, 1986). In our results, this otherdirectedness shows up as an increased workload for high self-monitors in terms of a larger number of connections in the workflow network. The challenge for the high self-monitor is how to avoid accepting too many different work responsibilities while maintaining friendship ties that span social divides. The challenge for low self-monitors is to build on their

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ability to say no to an overload of work responsibilities by spending more time overcoming their marked inclination to retreat into stable friendship cliques. Structural analysis offers tools for identifying role structures within groups (e.g., White, Boorman, and Breiger, 1976) and dynamics between groups (e.g., McPherson et al., 1992). Adding personality theory to structural analysis can help forge a powerful approach to understanding individual behavior in the context of social structure. Rather than accepting an inevitable duality between those interested in the psychological determinants of behavior and those interested in how network structure affects social processes, we need more interdisciplinary research that draws from different perspectives and contributes to an enhanced picture of how action affects outcomes in organizations. In the next chapter, we pursue this interdisciplinary agenda with respect to how personality variables (including self-monitoring) predict position in a social network that has not previously been studied: the emotion helping network in an organization.

8 Centrality in the Emotion Helping Network: An Interactionist Approach

An organization can be considered a socio-emotional system in which energy must be continually expended in order to keep the system on course (cf. Katz and Kahn, 1966). Among the many threats to system functioning are negative emotions. The workplace is a site where people often experience negative emotions associated with stress, anxiety, tension, and emotional pain (Basch and Fisher, 2000). Two classic experiments (Latan´e and Arrowood, 1963; Schachter, Willerman, Hyman, and Festinger, 1961) established that workers involved in all but the most routinized tasks who are subject to negative emotions tend to suffer decrements in the quality and quantity of their production. Further, these negative emotions correlate with individuals’ negative work-related attitudes (Weiss and Cropanzano, 1996) and health problems (Frost, 2003: 3), and can prove contagious (Hatfield, Cacioppo, and Rapson, 1994) with deleterious effects for other employees’ levels of cooperation and performance (Barsade, 2002). We know that some people become central actors in dealing with the negative emotions of colleagues (Frost, 2003; Frost and Robinson, 1999), but there is still little understanding of who these unusual people are. In this chapter, we spotlight the emotion helping network and its central players from a personality interaction perspective. Research concerning social support in general has examined the beneficial effect of such support on health and well-being (for reviews, see Uchino, Capioppo, and Kiecolt-Glaser, 1996; Viswesvaran, Sanchez, and Fisher, 1999), the features of the support recipient (e.g. Collins and Feeney, 2004), the antecedents of social support (e.g., Zellars and Perrew´e, 2001), and the role of cultural norms (Taylor et al., 2004). But “almost no attention has been paid to social integration, networks, or supports as dependent variables” (House, Umberson, and Landis, 1988: 308). We remedy this omission in focusing on centrality in the emotion 157

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helping network. We build on the finding in the previous chapter that personality predicts individuals’ social network positions. In general, pro-social behavior (such as emotion help) results from a combination of dispositional and status-based characteristics (Batson, 1991). We anticipate that people central in the emotion helping network are likely to have the dispositional skills necessary to notice and alleviate emotional suffering and the managerial responsibility necessary to take action. We draw from personality interaction theory to suggest that as managerial responsibility increases, the effects of relevant personality variables – self-monitoring and positive affectivity – are magnified. We also anticipate and control for the role of friendship and coworker social networks in ameliorating individuals’ negative emotions. We contribute to the ongoing effort to understand how individual personality interacts with organizational variables to affect important organizational outcomes (cf. Barrick, Parks, and Mount, 2005; Flynn and Ames, 2006).

Theory and Hypotheses Personality Dispositions and Emotion Helping Of the many personality variables that might conceivably affect individuals’ tendency to become central in the emotion helping network, we focused on two: positive affectivity and self-monitoring. These two variables emerge from strong theoretical traditions and have shown their importance in major studies of organizational behavior relevant to our research topic. Positive Affectivity Those who engage in the role of alleviating others’ negative emotions on a regular basis are exposed to considerable pain and suffering that has the potential to lead to burnout and stress (cf. Frost, 2003). There is evidence, however, that some individuals relative to others may enjoy the experience of helping those in distress deal with their problems and may be protected from the contagious effects of negative emotions. People high in positive affectivity, relative to those low in positive affectivity, are less likely to suffer the harmful consequences of helping others experiencing emotional difficulty (Zellars, Perrew´e, Hochwarter, and Anderson, 2006). Meta-analytic results show that high positive affectivity is significantly related to organizational commitment, job satisfaction, lower emotional exhaustion, and less depersonalization of others (Thoresen et al., 2003). An individual’s positive affectivity disposition tends to “permeate all of an individual’s experiences” (Barsade, Ward, Turner, and Sonnenfeld, 2000: 803). Positive affectivity is a classic personality trait in the sense

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of being a consistent disposition over time (Staw et al., 1986; Watson, Clark, and Tellegen, 1988). People high in positive affectivity, relative to those low in positive affectivity, tend to experience a “pleasurable engagement with the environment” (Watson, 1988: 128). One of the most robust findings in this research literature is that people high in positive affectivity tend to “enjoy, feel more confident in, and even attract social contact” (Lucas and Diener, 2003: 48). High positive affectivity people are more sensitive and attentive to those with whom they interact (Isen, 1970). Subjects in whom a positive mood has been induced show increased helping behavior related to the maintenance of the positive state of the beneficiary (Cunningham, Steinberg, and Grev, 1980) and exhibit more overt friendliness with others (Cunningham, 1988). Extensive evidence suggests that those high in positive affectivity (relative to those low in positive affectivity) tend to be more willing to perform organizational citizenship behaviors (Williams and Shiaw, 1999) and more willing to engage in altruistic behaviors (Diener, Lyubomirsky, and King, 2001). High positive affect may well be reinforced by the positive feelings that people experience when they perform altruistic and helpful behaviors (Carlson, Charlin, and Miller, 1988). People with low positive affectivity experience good mood and happy feelings to a lesser extent than those with high positive affectivity (Lyubomirsky, King, and Diener, 2005) and as a result may be less prone to helping others spontaneously (George and Brief, 1992). High positive affectivity individuals – who tend to be energetic, enthusiastic, and upbeat – are likely to be viewed by employees as open to helping others regulate their negative emotions; these people are also likely to enjoy the role of ameliorating others’ distress and solving organizational problems. Hypothesis 1: Positive affectivity will be positively related to the extent to which individuals occupy central positions in the emotion helping network. Self-Monitoring Self-monitoring theory concerns the monitoring and control of expressive behavior (Snyder, 1974), including emotional display (Graziano and Bryant, 1998). Individuals high in self-monitoring monitor and control how they present themselves to others in response to social cues concerning what is appropriate and expected in specific social situations, whereas individuals low in self-monitoring pay less attention to the social appropriateness of self-presentations and more attention to inner affective states and attitudes (Snyder, 1974). As the theory has developed, two

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prototypical types of people have been described: the high self-monitor who strives to generate affective states and behaviors appropriate to specific well-defined situations, and the low self-monitor who generates expressive behavior from inner affective states and attitudes (Snyder, 1979). Compared to high self-monitors, “low self-monitors express it as they feel it” (Graziano and Bryant, 1998: 251). High self-monitors, by contrast, are skilled at communicating socially appropriate impressions both vocally and nonverbally (Snyder, 1974). With respect to emotions, high self-monitors, because of their greater attentiveness to the social situation, tend to be better at adjusting their emotional displays to the needs of others, but are also more alert to the emotions being experienced by other people (Ickes, Stinson, Bissonette, and Garcia, 1990). According to recent thinking and research, the high self-monitoring advantage in expressing a range of socially appropriate emotions and in reading the emotions of others requires considerable effort and represents the strong motive of high self-monitors to produce successful social interactions (Ickes, Holloway, Stinson, and Hoodenpyle, 2006). This greater effort on the part of high self-monitors, relative to low self-monitors, at making social interactions go well is illustrated by research showing that in unstructured social interactions between strangers, high self-monitors tend to speak first and to use conversational overtures to break periods of silence (Ickes and Barnes, 1977). A review of this early research summarized it as showing that high self-monitors “were concerned that their interaction would go well, they acted to ensure that it would, and they reported being increasingly self-conscious to the extent that it didn’t” (Ickes et al., 2006: 662). Not only do the high self-monitors, relative to the low self-monitors, try to make social interactions work by taking the initiative in terms of conversational openings, they also try to inject positive affect into social interactions through the use of humor to lift the spirits of others (Turner, 1980). Other research shows that high self-monitors express more positive affect in their self-presentations than do low self-monitors (Levine and Feldman, 1997). A recent summary of the evidence concerning the motivations of high and low self-monitors concludes that high self-monitors, more than low self-monitors, derive positive self-affect from successful self-presentations to others: “if the self-presentation of a high self-monitor appears to have the desired effect on his or her interaction partner, the high self-monitor would experience a sense of acceptance and validation in the form of positive self-affect” (Ickes et al., 2006: 682). Thus, the evidence suggests that high self-monitors, relative to low selfmonitors, strive to pay attention to the emotions of others and to provide appropriate emotional displays that will produce successful social interactions. As part of this effort to make social interactions work, high

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self-monitors tend to talk about the other person instead of talking about themselves (Ickes, Reidhead, and Patterson, 1986), to pace conversations appropriately (Dabbs et al., 1980), and to reciprocate self-disclosures (Shaffer et al., 1982). Not surprisingly, given the substantial cognitive and emotional resources that high self-monitors bring to their social performances (Ickes et al., 2006: 681), high self-monitors tend to emerge as leaders in a variety of situations (Garland and Beard, 1979; Zaccaro et al., 1991). Further, a series of studies of self-monitoring and helping behavior suggests that high self-monitors are more accurate in perceiving the status dynamics of exchange relationships, and elevate their social status by establishing reputations as generous exchange partners willing to help others but reluctant to ask for help in return (Flynn et al., 2006). We build on this cumulating theory and evidence to suggest that it will be high self-monitors who will tend to monitor and ameliorate the negative emotions of others in the workplace. Hypothesis 2: Self-monitoring will be positively related to the extent to which individuals occupy central positions in the emotion helping network. Managerial Responsibility and Emotion Helping Managerial responsibility for the maintenance of morale in organizations has long been a part of organization theory. Henry Fayol (1916) emphasized the responsibility of managers for the promotion of harmony and union among organizational personnel, whereas Chester Barnard (1938) included among the functions of the executive responsibility for managing informal communication and morale in organizations. Frederick Taylor’s (1911: 74) principles of scientific management included the need for “harmony, not discord.” These classic recognitions of the importance of managerial responsibility for employees’ emotional welfare have been extended by examinations of emotion management in contemporary firms. The founder and chief executive officer of a cosmetics company was quoted as being “mystified by the fact that the business world is apparently proud to be seen as hard and uncaring and detached from human values . . . the word ‘love’ was as threatening in business as talking about a loss on the balance sheet” (Martin, Knopoff, and Beckman, 1998: 447). One research report of how middle managers coped with radical change in a large information technology firm summarized the findings as follows: “Managers’ emotion-attending behaviors reduced a potentially higher state of anger and fear among the employees driven by emotional contagion” (Huy, 2002: 60). Managers of many organizations tend to spend considerable

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time and attention dealing with the negative emotions prevalent in the workplace (Frost, 2003: 157–8). Thus, the processing of employee emotions is a crucial, if overlooked, organizational task (Frost and Robinson, 1999; Maitlis and Ozcelik, 2004). Managers, compared to other employees, are expected to help solve problems in the workplace, including employees’ emotional problems (Ostell, 1996). To the extent that negative emotions threaten the proper functioning of the organization, it is management’s duty to help process these emotions, and return the workplace to a state of emotional balance (Huy, 2002). The managerial role, relative to non-managerial roles, involves less programmed work, more discretion, and a greater license to intervene in others’ affairs to solve problems (Mintzberg, 1973). People with more managerial responsibility (compared to those with lower managerial responsibility) have more decision-making authority and greater control over resources (e.g., Ibarra, 1992). Therefore, those with managerial authority, compared to other employees, are expected to help solve problems in the workplace, to intervene in troublesome situations and to make use of the discretion and resources at their disposal to alleviate emotional problems of employees. We summarize this discussion in the following hypothesis. Hypothesis 3: Managerial responsibility will be positively related to the extent to which individuals occupy central positions in the emotion helping network. An Interactionist Approach From an interactionist perspective, the effects of personality traits on outcomes are likely to be magnified to the extent that constraints on the expression of personality are weakened (Barrick et al., 2005). Particular personality characteristics tend not to be evident indiscriminately, but rather to appear in the specific circumstances that make those characteristics salient (Reis, 2001: 69). For example, personality predicts job performance for occupants of jobs with high as opposed to low autonomy (Barrick and Mount, 1993). One general indication of autonomy in organizations is the extent of managerial responsibility: The higher the managerial responsibility, the more discretion people have in the solution of workplace problems (Mintzberg, 1973). Building on research suggesting that situations that provide individuals with considerable discretion are likely to facilitate the effects of personality (see Weiss and Adler, 1984, for a review), we suggest that the more managerial responsibility individuals possess, the more likely it is that personality traits will relate to the provision of emotional help to organizational members.

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Thus, as an individual accepts more managerial responsibility, selfmonitoring and positive affectivity are likely, we suggest, to become more predictive of discretionary, problem-solving behaviors such as helping others with their emotional problems. The acceptance of more managerial responsibility allows people more discretion, a greater license to intervene in others’ affairs to solve problems (Mintzberg, 1973) and greater power and status (Hambrick and Finkelstein, 1987). Managerial responsibility allows people to express their personality dispositions in the workplace in ways that are not available to those with low power or status (Anderson and Thompson, 2004). For example, the high self-monitoring tendency to take the initiative in speaking first in social interactions may well be suppressed among those with little or no managerial responsibility to intervene in cases where others appear to be suffering emotional distress. Hypothesis 4: The higher an individual’s managerial responsibility, the greater the effect of positive affectivity on the extent to which the individual occupies a central position in the emotion helping network. Hypothesis 5: The higher an individual’s managerial responsibility, the greater the effect of self-monitoring on the extent to which the individual occupies a central position in the emotion helping network. Alternative Explanations Social Network Covariates People suffering anxiety seek to affiliate with others (Schachter, 1959). More generally, as one review summarized, “social relationships are a major source of happiness, relief from distress, and health” (Argyle, 1987: 31). Therefore, any discussion of who is likely to be prominent in helping others with emotions in organizations must control for prominence in the informal social networks that provide a primary context for seeking and receiving help. The inclusion of network centrality variables as predictors seems to be relevant and necessary to show the added explanatory power of the personality interactions above and beyond the effects of friendship and work-related interaction. Two networks in organizational settings provide most of the basis for social interaction. One is the affective network of friendship relations, and the other is the instrumental network of work relationships. The friendship network is based on choices concerning whom to like and socialize with, whereas the workflow network reflects choices concerning work collaboration. These two networks – friendship and workflow – have

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long been emphasized as constituting bases of interaction in organizations (e.g., Brass, 1985; Lincoln and Miller, 1979; Roethlisberger and Dickson, 1939). Two people in an organizational setting joined by a friendship tie are likely to seek each other out frequently for social exchange, whereas two people joined by a workflow tie are likely to interact on a regular basis as part of their work. Note that the workflow network may differ from the formally designated set of task interdependencies in an organization given that, for example, people may choose not to cooperate with formally designated work partners (to the detriment of work performance – cf. Gargiulo and Benassi, 1999) and may provide information and other resources to people who are not formally recognized as work partners. Note also that the dependent variable – emotion helping – is different from these network variables: Helping lots of people with their negative emotions is not the same as enjoying friendship relations with lots of people, nor is it the same as having lots of work partners. But a person who is central in either the friendship or the workflow network has many opportunities to interact with others and is therefore likely to be called upon by friends or by work partners to solve problems (Sparrowe et al., 2001), including emotion problems. Therefore, we controlled for the degree of centrality in these two networks in our analyses. Gender and Tenure Evidence suggests that women relative to men are more likely to respond empathetically to the distress of others, show greater concern, and provide more comfort (see the review in Baron-Cohen, 2003). Providing sympathy may be more socially acceptable for women compared to men (Eagly and Crowley, 1986) and women tend to be more effective in roles that are defined in terms of interpersonal ability (Eagly, Karau, and Makhijani, 1995). We used tenure as a control variable because it seems likely that longer-serving employees might be relied upon more than shorter-serving employees for emotion support.

Method Site We studied a recruiting agency that provided managerial staff for retail outlets such as supermarkets and grocery stores. The organization had 104 employees and was structured as a professional bureaucracy in that trained professionals in the operating core of the business worked closely with clients (Mintzberg, 1983a). The company received about five hundred original r´esum´es each week and had developed a reputation for professionalism in the education and training of its employees.

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Procedure We collected personality, emotion helping, and network data by means of a questionnaire sent by e-mail attachment to all 104 employees (62 women and 42 men). We sent a reminder after two weeks and a second reminder after a further week. The questionnaire was sent directly by the research team to employees without any pressure from the company on employees to respond, although approval for this research project was provided to the research team in advance by the CEO. All respondents were assured of confidentiality in the handling of their responses, which, given the nature of the social network questions, were not anonymous. Respondents were promised and provided with debriefing sessions with the research team following data collection. These debriefing workshops proved popular, with approximately 60 percent of respondents showing up to learn more about how to interpret their individualized reports. In general, a high level of trust was developed and maintained throughout the data collection and debriefing process. Information concerning managerial responsibility scores, gender, and tenure were derived from company records. The response rate was 92 percent for the questionnaire. Nonrespondents did not differ significantly from respondents with regard to managerial responsibility, tenure, gender, or age. Missing data on self-monitoring reduced the usable sample from ninety-six to ninety-four respondents. Measures We were able to test our models with one independent variable (managerial responsibility) derived from company records whereas other independent variables (self-monitoring and positive affectivity) were derived from self-reports. The network control variables (centrality in friendship and workflow networks) and the dependent variable (extent of emotion helping) were derived not from the self-reports of the focal individual but from the reports of others concerning friendship relations, workflow interaction, and the tendency to go to the focal individual for emotion help. Thus, we avoided some potential common method variance problems by using different sources for our independent and dependent variables, consistent with the conceptual framework of our study (cf. Sackett and Larson, 1990: 474). Independent Variables Positive Affectivity Respondents rated on a five-point scale the extent to which they generally and on average experienced each of the ten relevant adjectives

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(active, alert, attentive, determined, enthusiastic, excited, inspired, interested, proud, and strong) that comprise the positive affectivity trait portion of the Positive Affectivity-Negative Affectivity Scale (Watson et al., 1988). For a review of the scale’s predictive and construct validity, see Watson et al. (1988). Cronbach α for the 10-item scale was .80. Self-Monitoring This variable was measured using the eighteen-item true-false SelfMonitoring Scale (Snyder and Gangestad, 1986). As a continuous variable, the self-monitoring score indicates the probability that an individual is a high or low self-monitor (Gangestad and Snyder, 1985): The higher the score, the higher the probability of a high self-monitoring orientation. Representative items include “In different situations and with different people, I often act like very different persons,” and “I may deceive people by being friendly when I really dislike them.” The Kuder-Richardson reliability for the eighteen-item scale in the present research was .67. For a recent review of the scale’s predictive and construct validity see Gangestad and Snyder (2000). Managerial Responsibility We measured this with a “job points” score used within the company to determine the salary and compensation for each position. The scores ranged from 40 for entry-level positions to 100 for that of the CEO. The scoring system shared the Hay Method’s principles of job evaluation (Milkovich and Newman, 1990): (1) More complex or more responsible work should receive greater compensation than less complex or responsible work, and (2) there should be like pay for like work within the organization. A follow-up interview with the CEO confirmed that higher scores indicated more complex business and supervisory responsibility, less programmed work, and use of greater discretion, consistent with how managerial jobs are defined (e.g., Mintzberg, 1973). Control Variables Network Centrality We followed the roster method for capturing network data (cf. Mehra et al., 2001). Respondents were asked to look down alphabetical lists of all employees and check the names of those they considered to be friends or work partners. The exact instructions concerning the friendship network were as follows: Whom would you consider to be your especially good friends? Friends are people with whom you like to spend your free time,

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people you have been with most often for informal social activities, such as visiting each others’ homes, having lunch together often, attending concerts or other public performances, going out to pubs and clubs, etc. The exact instructions concerning the workflow network (following Brass, 1981,1984) were as follows: Whom would you consider to be your most important work partners? Workflow contacts are a set of people who provide you with your workflow inputs, as well as the set of people to whom you provide your workflow output. Workflow inputs are any materials, information, clients, etc., that you might acquire in order to do your job. Workflow output is the work that you send to someone else when your job is complete. From these data, we constructed (following the example of previous research, i.e. Mehra et al., 2001), for both the friendship network and the workflow network, a (94 × 94) matrix with (ij) cell entries equal to 1 if i indicated a relationship to j and equal to 0 otherwise. We used the most basic measure of centrality – degree – in calculating the exact number of direct links between the individual and others in the network (Freeman, 1979). The pattern of results did not change if we defined a tie as existing only if each member of the pair nominated the other. Degree centrality captures the popularity of the individual in the network. The higher an individual’s degree centrality score, the greater the number of friends or workflow partners the individual has. The (94 × 94) friendship and workflow matrices were submitted to the degree centrality procedure in the network program UCINET VI (Borgatti et al., 2002). Gender Using company records, we coded gender as 0 for men and 1 for women. Tenure This was measured, using company records, as the number of years that an individual had been employed by the company. Dependent Variable: Indegree Centrality in the Emotion Helping Network To assess the extent to which each individual engaged in providing emotion help to colleagues, we collected information from respondents concerning who helped them deal with their negative emotions. Note that

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our measure recognizes that emotion helping may be unreciprocated in the sense that an individual can provide emotion help to others without seeking emotional help from any of those others. Thus, the dependent variable – emotion help – was measured as a count of how many others in the organization nominated the focal individual as someone to whom they turned to for emotional help. Whereas intra-organizational network research often considers a general form of “advice” network (e.g., Krackhardt, 1990), we measured the extent to which people helped others deal with a specific type of problem – stressful and negative emotions. We provided organizational members with an alphabetical list of colleagues and the following instructions: Whom do you go to when you experience anxiety, tension or emotional pain? Please look down the alphabetical list of your fellow employees and place a check mark to indicate all the names of those people who you think help you when you need support in times of trouble to cope with your personal problems and your negative emotions. Some people may go to several people for help and support. Some may only go to one person. Some may not go to anyone within the organization, in which case do not check anyone’s name under that question. For each individual in our sample, we calculated indegree centrality – the number of nominations received from others. Individuals who received many nominations were considered to be actively taking on the role of regulating others’ negative emotions, whereas individuals who received few or no nominations were considered to be relatively inactive in this role.

Results Our dependent variable, centrality in the emotion helping network, is measured by the number of nominations received from others. Therefore, it is a count variable that takes only non-negative integer values. We analyzed the data using both standard linear regression and Poisson regression recommended for count variables (cf. Long, 1997). Because there were no significant differences between the results of the two regression procedures, we report the data from the more familiar linear regression models. For all tests involving interaction, we corrected for potential multicollinearity among continuous variables by centering self-monitoring, positive affectivity, and managerial responsibility (Aiken and West, 1996; Cohen and Cohen, 1983).

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Table 8.1. Means, Standard Deviations, and Correlations Variable

M

SD

1. 2. 3. 4.

3.79 0.61 2.58 6.74

4.09 0.49 3.00 4.34

– .19 .47∗∗∗ .25∗

– −.05 −.14

– .10

–

32.84

16.26

.43∗∗∗

−.17

.21∗

11.31 36.73

3.21 5.01

.18 .26∗

−.24∗ −.08

53.51

15.40

.60∗∗

.03

5. 6. 7. 8.

Emotion Help Gender Tenure Friendship centrality Workflow centrality Self-monitoring Positive affectivity Managerial responsibility

1

2

3

4

5

6

7

8

.19

–

.07 .03

.25∗ .01

.14 .29∗∗

– .11

–

.59∗∗∗

.01

.31∗∗

.21∗

.20

–

Notes: N = 94. ∗ p < .05. ∗∗ p < 0.01. ∗∗∗ p < 0.001.

Table 8.1 presents means, standard deviations, and zero-order correlations among the variables. Individuals averaged seven friends and thirtythree workflow relationships, which compares to seven friends and fortynine workflow relationships reported in previous research on a similarly sized company (Mehra et al., 2001). Importantly for the independence of our hypotheses, self-monitoring and positive affectivity represented two different constructs – the scores were not significantly correlated (r = .11, ns). Women tended to have lower self-monitoring scores than men (r = −.24, p < .05). Both self-monitoring (r = .18) and positive affectivity (r = .26) were positively correlated with the extent of emotion help, but only the positive affectivity correlation with emotion help reached conventional levels of significance (p < .05). Individuals active in providing emotional help to many others also tended to have a longer tenure (r = .47, p < .001), and to be higher in workflow centrality (r = .43, p < .001), friendship centrality (r = .25, p < .05), and managerial responsibility (r = .60, p < .001). Table 8.2 shows the results of regression analyses in which the dependent variable is, for each individual in the organization, the number of people who nominated that individual as someone they turned to for emotional help. Model 1 shows that the control variables explained 34 percent of the adjusted variance. Recall that hypotheses 1 and 2 suggested that people with higher scores on positive affectivity and self-monitoring would tend to help more people with their negative emotions. Model 2 in Table 8.2 shows no main effects of positive affectivity or self-monitoring on the dependent variable. The introduction of these personality variables

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Table 8.2. Linear Regression Predicting Centrality in the Emotion Helping Network Variables Gender (0 for men; 1 for women) Tenure Friendship centrality Workflow centrality Positive affectivity (PA) Self-monitoring (SM) Managerial responsibility (MR) Managerial responsibility∗ PA Managerial responsibility∗ SM F R2 Adjusted R2 R2 Change

Model 1

Model 2

Model 3

Model 4a 0.16∗ 0.02 0.17∗ 0.06 0.18∗∗ 0.11 0.40∗∗∗ 0.44∗∗∗ 0.24∗∗ 18.78∗∗∗ 0.67 0.63 0.17∗∗∗

0.09 0.40∗∗∗ 0.16 0.33∗∗∗

0.11 0.40∗∗∗ 0.15 0.27∗∗ 0.16 0.09

0.10 0.18 0.20∗ 0.21∗ 0.11 0.01 0.41∗∗∗

12.90∗∗∗ 0.37 0.34 0.37∗∗∗

9.63∗∗∗ 0.40 0.36 0.03

12.03∗∗∗ 0.50 0.45 0.10∗∗∗

Notes: N = 94; standardized coefficients are presented. a Positive affectivity, self-monitoring, and managerial responsibility have been centered. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001.

did not significantly improve variance explained, so hypotheses 1 and 2 were not supported (although the positive affectivity coefficient of 0.16 was close to conventional levels of significance at p < .07). Model 3 in Table 8.2, however, presents evidence to support hypothesis 3’s suggestion that the extent of each person’s managerial responsibility would relate to how much emotional help each person provided. The addition of the managerial responsibility variable (β = .41, p < .001) significantly increased explained variance by 9 percent to an adjusted total of 45 percent. But is it the case that increasing managerial responsibility brings into play personality differences with respect to the provision of emotional help in the workplace? Specifically, in line with hypothesis 4, did increased managerial responsibility interact with positive affectivity to predict the extent of emotion helping in the organization? The answer is yes: As model 4 in Table 8.2 shows, the interaction between managerial responsibility and positive affectivity was significant (β = 0.44, p < .001). To illustrate the significant interaction between positive affectivity and managerial responsibility, we grouped the employees into three categories (low, medium, and high) on the managerial responsibility score that ranged from 40 to 100. As Figure 8.1 shows, at low levels of managerial responsibility, there is no discernible difference between those low and

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20

High

16

12 Emotion Help 8

Medium

4 Low 0 -12

-9

-6

-3

0

3

6

9

Figure 8.1. Relationship between high, medium, and low managerial responsibility, positive affectivity, and emotion help.

high on positive affectivity on the extent to which they provided emotional help, but positive affectivity differences are discernible for those with medium levels of managerial responsibility, especially for those with high levels of managerial responsibility. Thus, individuals high in positive affectivity, to the extent that they have the discretion that comes with managerial responsibility, are active in using their social skills to help ameliorate the effects of negative emotions in the workplace. Was there also support for hypothesis 5’s prediction that increased managerial responsibility would interact with self-monitoring to predict the extent of emotion helping in the organization? The answer is yes. As model 4 in Table 8.2 shows, the interaction between managerial responsibility and self-monitoring was significant (β = 0.24, p = .01). The interaction plots in Figure 8.2 resemble those in Figure 8.1 in showing an increasing effect of self-monitoring on the extent of emotion helping as managerial responsibility increases. In support of the interactionist approach, the addition of the interaction terms involving personality traits and managerial responsibility (model 4 in Table 8.2) significantly improved explained variance over the maineffects model – by 18 percent to an adjusted total of 63 percent. Because self-monitoring is a complex construct, it is sometimes useful to examine whether subscales (representing underlying factors) contribute to the statistical effects. Therefore, we conducted a post hoc analysis to explore the effects of two subscales (Public Performing and Other-Directedness)

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High

12

9 Medium

Emotion Help 6

3 Low 0 -7

-5

-3

-1

1

3

5

7

Self-Monitoring (centered)

Figure 8.2. Relationship between high, medium, and low managerial responsibility, self-monitoring, and emotion help.

identified in previous studies (Briggs and Cheek, 1988; Kilduff, 1992; Miller and Thayer, 1989) on the dependent variable. The analysis suggested that, as far as the interaction with managerial responsibility was concerned, it was the public performing aspect of self-monitoring that was associated with the significant tendency to provide emotional help (β = 0.28, p = .001, model adjusted R2 = .66), whereas the interaction of managerial responsibility with other-directedness was not significant (β = .54, ns, model adjusted R2 = .59). We were intrigued by the suggestion of two anonymous reviewers that the higher the individual’s centrality in the friendship and workflow networks, the more personality effects would be unlocked with respect to the extent of emotion helping. Indeed, post hoc results show that workflow centrality significantly (p < .05) interacted with both positive affectivity and with self-monitoring to predict the extent of centrality in the emotion helping network (even when the significant effects of managerial responsibility interactions with the personality variables were included). There was no significant effect of the interaction between friendship centrality and the personality variables. In summary, the full model (model 4) suggests that those active in providing emotional help to others in the workplace tended to be women, tended to have ties of friendship to many people, and tended to possess a combination of managerial responsibility and a high self-monitoring or high positive affectivity disposition.

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Discussion The results support an interaction approach to the understanding of centrality in emotion helping in organizations. As managerial responsibility increased, self-monitoring and positive affectivity effects on the extent of emotion helping also tended to increase. The significant interactions between managerial responsibility and the personality variables provide support for the idea that the increasing discretion that accompanies managerial responsibility unlocks discretionary behavior related to personality differences (cf. Anderson and Thompson, 2004; Barrick and Mount, 1993). There were no significant main effects for self-monitoring and positive affectivity on centrality in the emotion helping network in the absence of the interactions. This should not definitively rule out the main-effect hypotheses, however, given evidence that positive affectivity is generally related to helping behavior (see Brief and Weiss, 2002, for a review) and given emerging theory and evidence that high self-monitors, relative to low self-monitors, strive to provide more help than they receive (Flynn et al., 2006). The zero-order correlations in Table 8.1 between positive affectivity and emotion helping centrality (r = .26) and between self-monitoring and emotion helping centrality (r = .18) are similar to or exceed the meta-analytic estimates of correlations between Big Five personality dimensions and measures of work performance (Barrick and Mount, 1993; Hurtz and Donovan, 2000). The finding that the high self-monitoring managers are more central in the emotion helping network than low self-monitoring managers enhances our theoretical understanding of self-monitoring and contrasts with recent claims that high self-monitors resemble sociopaths in their general unfitness for managerial responsibility (Bedeian and Day, 2004: 689). We show that it is precisely managerial responsibility that reveals the distinctive advantages high self-monitors bring to the task of providing emotional help. To the extent that the managerial role involves a pragmatic willingness to intervene to solve organizational problems, it is the high self-monitors who are likely to develop a repertoire of well-honed problem-solving scripts (Dabbs et al., 1980; Douglas, 1983) involving directive attention to employees’ behavioral symptoms. High self-monitoring and high positive affectivity managers may actively reach out to those suffering negative emotions rather than wait for colleagues to bring these problems to their attention. In our post hoc analysis of self-monitoring subscales, we found that the significant interaction between self-monitoring and managerial responsibility tended to derive from the public performing aspect of self-monitoring rather than the other-directedness aspect. This suggests that high self-monitors may have

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been actively engaged in taking the initiative to help others rather than passively waiting to be asked to help. Future research can investigate the intriguing possibility that high selfmonitoring managers’ proclivity for extending help to others is part of an overall high self-monitoring pursuit of organizational status, a possibility suggested in a recent review article (Gangestad and Snyder, 2000) and in recent empirical research (Flynn et al., 2006). Whether managers high in self-monitoring try to enhance their own status by helping others deal with emotional problems is still unknown. It is possible that employees tend to seek out managers with the appropriate personalities for emotional support and help. Further, it is not entirely clear that an individual’s status in an organization is enhanced by being perceived to be a solver of others’ emotional problems. The significant effect of gender, a control variable in our study, is interesting in its own right. We found that women, relative to men, tended to be more likely to enact the role of emotion helper. This finding is compatible with research showing that in contrast to men, women tend to be more empathetic, more loving, and more able to perceive as well as express negative emotions (see the review in Baron-Cohen, 2003). There is some suggestive evidence that women may be slightly better than men at perceiving emotion (Mayer, Caruso, and Salovey, 2000) and may, therefore, be more predisposed to offering emotional support. Providing sympathy may be more socially acceptable for women compared to men (Eagly and Crowley, 1986). Furthermore, women are more likely than men to discuss personal issues and to self-disclose their emotions (Caldwell and O’Reilly, 1982a), which in turn might encourage reciprocity. Women, in our results, tended to have lower self-monitoring scores than men. This pattern is consistent with evidence from a recent metaanalysis that suggested the sex-related effects for self-monitoring to be partially responsible for the persistence of the glass ceiling effect in terms of noted disparities between men and women at higher organizational levels (Day, Schleicher, Unckless, and Hiller, 2002). Clearly, we need more research on the differing effects of self-monitoring for men and women, particularly in light of new research showing that a high selfmonitoring orientation provides women (but not men) with more influence in workgroup contexts (Flynn and Ames, 2006). If women are more likely than men to provide emotional help to fellow employees and if low self-monitoring is an obstacle to promotability (Kilduff and Day, 1994) then an interesting implication emerges: Emotion helpers at the lower levels of the organization may tend to be women, whereas those at higher levels may tend to be high self-monitoring men. The results for the friendship centrality control variable are also interesting and contribute to social network research a new focus on how

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individuals with central network positions are actively engaged in the maintenance of others’ emotional health. This focus adds to the robust finding from the research literature that individuals with central network positions in terms of the diversity of ties tend to be healthier in terms of mortality (Berkman and Syme, 1979) and resistance to infection (Cohen et al., 1997). Central network positions are typically associated in the research literature with personal advancement of individuals in terms of faster promotions (Burt, 1992; Podolny and Baron, 1997) and higher performance ratings (Mehra et al., 2001). Our research adds to these findings a new emphasis on how centrality in the friendship network involves responsibilities for others. Limitations A limitation of our study is that, because of the relatively flat organization studied, we were unable to investigate how far down the hierarchy managers’ involvement in emotional support tends to reach in organizations with tall hierarchies. Also, we did not examine the possible consequences for individuals of emotion helping behavior. Handling the negative emotions of others can cause negative outcomes such as frustration, burnout, illness, and failed personal and professional relationships (Frost, 2003; Kahn, 1993; Meyerson, 2000). Finally, we did not examine the types of negative emotions for which individuals sought help. Our results suggest that there might be two types of support systems at work: one that is informal and based on social network relations, and another activated by those with both higher formal responsibility and relevant dispositions. Future research efforts could examine the relative effectiveness of these two support systems and whether these two systems provide different forms of emotional help. Practical Implications We draw practical implications for the management of organizations from the personality and social network results. With respect to personality interaction effects, the results indicate one important way in which organizational systems may be able to cope with the inevitable production of negative emotions in the workplace. Going beyond previous work showing the general helpfulness of those high in positive affectivity (Carlson et al., 1988) and the superior work performance of those high in self-monitoring (Mehra et al., 2001), our results suggest the onerous task of dealing with upset employees may require employees who demonstrate both an appropriate disposition and the requisite managerial discretion. A positive affectivity disposition provides many benefits to an individual

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across domains as varied as work performance and health (Lyubomirsky et al., 2005). Similarly, high self-monitors tend to outcompete low selfmonitors on a range of organizational outcomes, including faster promotions (Kilduff and Day, 1994). Our research suggests ways in which these benefits can be extended to the improvement of the well-being of others in organizational settings. However, there are likely to be different perceptions across and within organizations concerning whether the psychological contract between employees and management anticipates the provision of emotional help (cf. Lester, Turnley, Bloodgood, and Bolino, 2002). To the extent that employees perceive the psychological contract to include the provision of emotional help, those with managerial responsibility who are low on positive affectivity or low on self-monitoring may be seen as less effective in their managerial roles, and this could affect promotion possibilities to the extent that employee expectations are endorsed by top management. In such organizations, ambitious people who are either low self-monitors or low in positive affectivity might be advised to engage in other types of extra-role behavior particularly valued by top management to the extent that such behavior is compatible with their personality orientations. For example, to the extent that low self-monitors are “independent nonconformists who think for themselves and stick to their beliefs” (Krosnick and Sedikides, 1990: 724), they might take on the valuable managerial role of devil’s advocate, putting forward ideas that run counter to organizational norms (cf. Premeaux and Bedeian, 2003) and providing accurate personnel assessments in the face of pressure to be lenient (cf. Jawahar, 2001). The question has also been raised as to whether individuals occupying central positions in social networks benefit only themselves or whether their activities also benefit the collectivity (Ibarra et al., 2005). Our research shows that for at least one measure of collective benefit – the number of others helped with emotional problems – individuals centrally located in the friendship network do contribute to the collective good. Thus, in organizations in which the emotional welfare of employees is important, the encouragement of social networking clubs among employees (cf. Friedman, 1996) may take on added strategic importance. Indeed, meta-analytic results show that the density of informal expressive ties in team social networks predicts team performance and team cohesion (Balkundi and Harrison, 2006). Negative emotions are likely to affect work performance across a range of organizations from the industrial (e.g., Schachter et al., 1961) to the artistic (e.g., Maitlis and Ozcelik, 2004), and there have been calls for those with managerial responsibility to take on the “obvious but important task of . . . handling interpersonal and personal problems of staff ”

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(Ostell, 1996: 525). However, there are probably boundary conditions affecting the extent of emotion helping and, by extension, the role of personality interactions affecting such emotion helping. We would expect to find emotion helping in contexts in which emotional labor is an intrinsic part of the work and in contexts in which there is a necessary coordination across tasks. Thus, it is no surprise to learn that, for example, flight attendants try to improve the morale of depressed coworkers prior to flights (Hochschild, 1983) given the importance of emotional labor in the service sector and given the importance of coordination between flight personnel. In contexts in which individuals pursue their own goals in the absence of client interaction (e.g., the famous Lincoln Electric plant; cf. Handlin, 1992), a managerial emphasis on emotion helping is likely to be less important.

Conclusion The question of who deals with the negative emotions that can threaten to overwhelm organizational initiatives (cf. Huy, 2002) and contribute to lower performance (Staw and Barsade, 1993) is an important one that relates to the health and well-being of organizational members. In this chapter, we have made a theoretical and empirical attempt at identifying those central in the emotion helping network in one organization. To the extent that organizations are arenas in which all human emotions are likely to emerge, it is vital to understand the actions and motivations of those who help others manage the inevitable frustrations and stresses that arise.

III Network Dynamics and Organizational Culture

9 Network Perceptions and Turnover in Three Organizations

We have emphasized in previous chapters the importance of individuals’ perceptions of networks within which they are embedded. In this chapter, we continue this theme, looking this time at the question of organizational turnover. Do people who perceive each other as playing similar roles in the organization tend to affect each other’s turnover decisions? And what about the attitudes of the people left behind when somebody leaves – how do people react to the departure of those perceived to be their friends? These are the issues we address in two studies of turnover across three fast food restaurants. Several reviews of turnover research (e.g., Griffeth, Horn, and Gaertner, 2000; Horn and Griffeth, 1995) have underscored the continuing interest in this area. Models of turnover have become complex (e.g., Steel, 2002), incorporating in excess of forty organizational, individual, and societal variables in at least one case (Mobley, Griffith, Hand, and Meglino, 1979). This complexity suggests the value of exploring new kinds of variables rather than clouding the picture with more variables of the same nature. Research on the dynamics of voluntary groups indicates the general importance of considering social ties inside and outside groups in order to understand rates of turnover (McPherson et al., 1992). A case study of an organization in crisis illustrated the potentially devastating effects of turnover on the attitudes of those left behind (Burt and Ronchi, 1990), but without specifying the social psychology of the attitude formation process. In general, psychological models of the turnover process tend to assume that turnover occurs atomistically within a workgroup. Each person’s behavior in a workgroup is considered a stochastic function determined by various personal and situational characteristics attributed to the person. Once those attributes are known, a regression model predicts an independent probability of each person leaving. In contrast to this work, we present two studies examining (1) whether there is a snowball effect 181

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such that turnover itself causes more turnover (cf. Mowday, Steers, and Porter, 1982) and (2) what the effect of friends’ turnover is on the attitudes of those who remain behind.

Study 1 The network approach to the question of whether turnover itself can cause more turnover is illustrated by a snowball metaphor. A snowball does not randomly accumulate snowflakes in the area. Rather, snow adheres to the snowball in a discernible path. Similarly, the patterns of turnover will not be independently distributed across any workgroup. People are not independent actors. They affect each other in their behavior. Moreover, the degree to which they affect each other depends on the relationship between them. Social network analysis provides a framework for assessing these relationships and for predicting their effects on individual members. Role Equivalence in Informal Networks A defining contribution to network analysis was the concept of structural equivalence (Lorrain and White, 1971). In their landmark article, Lorrain and White proposed that actors could be grouped into similar categories based on their patterns of interactions in a social system. Two people would be considered to have equivalent roles (or to be structurally equivalent) if they talked to exactly the same other people (although not necessarily to each other). To the extent that they talked to mostly the same people, they would occupy similar roles to each other. If they talked to no one in common, they would occupy very dissimilar roles. Breiger and his colleagues (Breiger et al., 1975) developed an algorithm for operationalizing Lorrain and White’s theory. Since then, many studies have used this algorithm (Arabie and Boorman, 1982) to identify and interpret informal groups in social systems. This idea has been generalized to a concept of role that is more directly relevant to organizations. Sailer (1978) has argued that two people are equivalent in their roles if they communicate with equivalent others. Thus, two supervisors would be equivalent to each other because they each communicate with a group of equivalent linespeople and to equivalent middle managers. To the extent that they communicate with people who are in different roles, then these two supervisors would be less similar to each other. This differs from Lorrain and White’s concept of structural equivalence because Sailer does not require that two people talk to the same others in order to be equivalent in their roles. Just as supervisors do not have to supervise the

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A

C

B

D

E

F

G

Figure 9.1. Hypothetical advice network of workgroups (B → A indicates that person B goes to person A for help and advice).

same exact people to be in equivalent role patterns, the informal role structure of any group in an organization is most appropriately assessed using this Sailer modification of the original Lorrain and White definition. (Sailer’s modification has been formalized and expanded by White and Reitz, 1983, 1985, who have coined the term regular equivalence to differentiate it from Lorrain and White’s structural equivalence.) To illustrate this Sailer concept, consider a group of seven organizational participants who work together. Assume that over the years they have developed a pattern of whom they go to when there is a problem or when they have a question about work-related matters. This pattern will be termed the advice network. For this illustration, assume persons B and C go to A for help and advice, D and E go to B, and F and G go to C. This hypothetical advice network is depicted in Figure 9.1. Note that this network parallels a typical formal organizational chart. This coincidence is intentional for demonstrative purposes, but one should not infer from this example that the informal organization usually mirrors the organizational chart. Seldom does it do so. Using the Sailer concept of equivalence (White and Reitz, 1985), one can measure the equivalence, or at least the similarity, of each pair of people in the hypothetical network. In this example, D, E, F, and G are equivalent in their roles. This is so because they go to the same others (B and C) and no one goes to them. On the other hand, B and C are equivalent because (1) they both go to A and (2) all those (D, E, F, G) who go to them are equivalent to each other. The matrix in Figure 9.2 shows the results of the algorithm as applied to this simple example in Figure 9.1. Scores of 100 (e.g., for the pair B, C), indicate that each member of the pair has a role equivalent to the other. A score of 0 indicates that the members of the pair have maximally

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Network Dynamics and Organizational Culture A A B C D E F G

40 40 0 0 0 0

B

C

40

40 100

100 40 40 40 40

40 40 40 40

D 0 40 40 100 100 100

E

F

G

0 40 40 100

0 40 40 100 100

0 40 40 100 100 100

100 100

100

Figure 9.2. Similarity scores between pairs in hypothetical network depicted in Figure 9.1.

dissimilar roles (such as the pair A, D, one member of which no one goes to, the other of which goes to no one). Intermediate scores indicate varying degrees of similarity in their patterns of communication. For example, B and A both have people coming to them. However, D and E (who go to B) are not equivalent to B and C (who go to A). Therefore, A and B will not be equivalent either; their similarity is attenuated by the degree to which those going to them are dissimilar to each other. Thus, A and B are only moderately similar to each other (score = 40). To emphasize a point, equivalence between two people is not based on whether they go to each other. For example, D and G do not go to each other; they do not even go to the same people. Yet they are perfectly equivalent in roles (similarity score = 100). Conversely, D goes to B and yet is not very similar to B (similarity score = 40). Importance of Perceived Similarity Thus, each pair of actors in a workgroup can be evaluated as to how similar they are in their patterns of communication with fellow workers. In keeping with the theme of this book, we also emphasize that in predicting the effect that a network of interactions has on any given individual’s attitudes, it is important to differentiate between the actual and perceived networks (cf. Burt, 1982). It is the network that is perceived by the individual that enables that individual to evaluate whether he or she is similar to a coworker. To the extent that they perceive each other to be similar, then they are more likely to affect each other’s behavior. For example, suppose that two supervisors viewed each other as equivalent. If one were to leave, the second is likely to view that leaving as relevant information for himself or herself. The reevaluation process that Mowday and his colleagues (1982) propose would be activated in such a

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situation. On the other hand, suppose that the two were quite dissimilar to each other in their informal communication patterns (e.g., one may be an isolated custodian while the second is an active middle manager). In this case, one’s leaving may be viewed as irrelevant. The two have little in common, and as such little dissonance is created when one quits. In this way, role equivalence creates a bond between participants in a workgroup. The role similarity is determined by the informal communication patterns. The resulting bond increases the effect that one’s behavior has on another. The purpose of study 1 is to test the proposition that such similarity in informal communication patterns can increase the effect of turnover behavior on a coworker. Specifically, the more similar coworkers are to each other, the more likely it is they will leave together (or stay together). If this is the case, then turnover should occur in clusters, which can be predicted by the informal communication channels. Hypothesis: Turnover will occur in clusters as defined by the perceived social network, such that those who are perceived similar in position to each other in the communication network will either stay together or leave together. Methods A questionnaire was administered to employees in three fast food restaurants (N = 16, 27, and 20, respectively, in restaurants A, B, and C). The average age of the employees was nineteen, with 73 percent being eighteen years old or less. Forty-eight percent of the employees were female. The only full-time (forty hours or more per week) employees were the store managers; however, all the people in the sample worked at least twenty hours per week. The average job longevity for the employees was less than eight months, although this number is skewed because turnover averages 200 percent per year. The questionnaire asked each person in the workgroup to list his or her perception of whom people go to for help and advice at their restaurant. The responses were used to assess how similar people are in terms of the role they had in giving and receiving advice from others. These data enabled us to identify an informal leadership hierarchy beyond that described by the organizational chart. The directions in part for this section were as follows: In this section, you will find several lists of people who work with you. Each list is started with the question, “Who would this person go to for help and advice at work?” That is if this person

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Network Dynamics and Organizational Culture had a question or ran into a problem at work, who would they likely go to ask for advice or help? Please answer the question by placing a check next to the names of all the people the person is likely to go to.

The network data collected in this study were perceptual (following the same methods used in Chapters 3 and 4). Each individual provided an entire picture of his or her perception of the social network in which he or she is embedded. This permits the testing of the perception-based hypothesis directly. The turnover data were collected during the one-month period following the questionnaire administration. All turnover was counted, including one transfer and one involuntary turnover. The reasons for including both of these events in the turnover count were twofold. First, both of these turnover events resolved a personnel problem in the organization. Had the turnover not occurred, a voluntary separation was likely anyway. Thus, the processes that led to the transfer and to the involuntary termination were similar to voluntary terminations, leading one reasonably to expect that the effects they would have on coworkers would be similar. The second reason that these events were not excluded was that the disruption in the social network was just as severe. A person leaving creates a hole in the network, no matter what the reason. It is this disruption that, it is argued here, snowballs. The hypothesis was tested using the Quadratic Assignment Procedure (QAP), developed by Hubert and his colleagues (Hubert and Schultz, 1976). (See Chapter 3 for more details.) Analysis and Results Turnover in the three restaurants combined to 25 percent in the onemonth time period under study (range: 20 percent to 38 percent in the three sites). The differences in turnover rates, χ 2 (2, N = 63) = 1.69, and questionnaire response rates (range 81 percent to 85 percent), χ 2 (2, N = 63) = .13, were not significant among the three restaurants. The hypothesis predicted that employees would leave in clusters that would map onto perceived role similarity clusters. This hypothesis was tested using three separate approaches with different assumptions underlying each approach. The first used all the information available: It tested the hypothesis for each individual employee’s perception of the social network. That is, each person had a perception of the entire network. Each perceived network is translated into a role similarity matrix using Sailer’s definition. Each similarity matrix can then be compared to the turnover matrix and tested to see whether the two are significantly related.

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Although this test took advantage of all the information, it was subject to methodological criticism, because the meta-analysis was based on correlated observations (i.e., each individual was observing the “same” network). The second test eliminated the statistical independence problem, but in the process destroyed the perceptual map information. This test used one representation of the network for each site by aggregating perceptions of whether person i goes to person j. This single representation then was tested against the turnover matrix. The third test represented a compromise, restoring much of the perceptual information and retaining statistical integrity for the significance test. First, individual perceptual maps are translated into similarity measures, as done in the first test. Then these similarity measures are aggregated into one similarity matrix, as in test 2. This summary matrix for each site is tested against the turnover matrix for significance. Before we proceed to the specifics of the testing procedures, we should note that the aggregations for tests 2 and 3 do not alter the basic dyadic nature of this study. The aggregations are of people’s perspectives. The dyadic relation between each (i, j) pair is still the unit of analysis that forms the basis for the significance tests that follow. As we will show, the robustness of the findings is emphasized by the similarity in the results of each test. Test 1: Individual Perceived Networks The first step was to determine the perceived similarity between pairs of coworkers. Let k represent the respondent who filled out the questionnaire, i represent the coworker who seeks advice at work, and j represent the coworker who potentially could be approached by i for advice. Then, let A(i, j, k) (referring to the raw advice matrix) be a matrix of dimension N × N × K (that is, K is the number of respondents, N is the number of coworkers, including K), such that A(i, j, k) = 1 if k perceives that i goes to j for help or advice at work, and A(i, j, k) = 0 otherwise. This matrix can be separated into k adjacency matrices, A(i, j), each representing who goes to whom for advice as perceived by k. Using the algorithm proposed by White and Reitz (1985), each A(i, j) is transformed into a Regular Similarity matrix, RS(i, j), representing k’s perception of how similar (in the Sailer sense) i is to j. If the hypothesis is correct, then each individual’s map of the role similarity should closely correspond to the turnover clusters. This was tested by creating a Turnover matrix (N × N) whose cells T(i, j) = 1 if i and j both either left or stayed (that is, both i and j behaved in similar ways), and whose cells T(i, j) = 0 if either i or j (but not both) left (that is, i and j behaved dissimilarly). This matrix was then compared to the RS(i, j) matrix for Person k to see whether, in general, their similar behavior in turnover (the 1s in the T[i, j] matrix) matched

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the perceptions that k had of how similar the two people (i and j) were in their roles in the advice network. QAP yielded a normalized statistic (expressed in Z scores), which would be large to the extent that this match up was greater than would occur by chance reassignment of turnovers (i.e., if a different group, but the same number, of people had left). In addition to the significance test, a gamma (Goodman and Kruskal, 1963) was calculated between the matched cells of the two matrices, RS(i, j) and T(i, j), where i does not = j. Gamma, a nonparametric correlation measure, is particularly appropriate here, because one of the matrices is composed of a dichotomous variable turnover. The results of these tests are summarized in Table 9.1. Each person (k) was tested against the T(i, j) matrix, producing a Z score and a gamma. For each site, the gammas and the Z scores were averaged. Because the standard error was estimated, a t test was used to test the null hypothesis that, on the average, no relation exists between turnover clusters and perceived similarity. Each site was considered an independent test. The overall relation was assessed using Rosenthal’s (1978) suggested metaanalysis approach. Each of the three tests was transformed into a Z score that corresponds to the same significance level. These three scores were then summed and divided by the square root of N (N = 3 sites), yielding a joint Z. The overall significance level is determined by this joint Z value in the normal distribution. The overall gamma is the simple average of the three sample gammas. It should be noted that correlation statistics, such as gamma or Pearson’s r, are not normally distributed, but rather are skewed toward 0. This leads to conservative estimates of the population parameter when simple averages are calculated, as is done in Table 9.1. Also, in the interest of conservatism, the correlations are not weighted by the sample N sizes. The largest sample had the strongest correlation (as is true in practically all the analyses reported in this work), which reduces the magnitude of what might be considered the appropriate overall strength of association. The size of both of these biases is not substantial, however, and the resulting overall average can be considered a reasonable, if somewhat conservative, estimate. Given these caveats, a discernable trend is observed in Table 9.1. Two of the three sites show significant relations between the pattern of perceived role similarities and turnover. Combining these results, the significance level is persuasive (less than .0005). The strength of these results, on the other hand, is modest. Ranging from 0 to .23 by group, the average gamma is only .10. Thus, using individual maps of the perceived role similarities in the test of the hypothesis, it is concluded that the null hypothesis of no relation is rejected in favor of a positive relation. The strength of that relation is questionable. One may wonder how an impressive significance

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Table 9.1. Quadratic Assignment Procedure Test of Association between Individual Perceptions of Role Similarity and Turnover Group and Measure Group A Z¯ N SD SE t p γ¯

Result .055 13 .9629 .2780 .1993 .NS .00

Group B Z¯ N SD SE t p γ¯

1.907 23 1.9725 .4205 4.536 <.0001 .23

Group C Z¯ N SD SE t p γ¯

.515 17 1.0228 .2557 2.013 <.03 .07

Meta-Analysis N Joint Z p γ¯

3 3.37 <.0005 .10

level can be associated with such a modest correlation and N size. The answer lies in the fact that the standard error of the average associations between individuals’ perceived maps and turnover is quite small. That is, the association may not be strong, but practically all the subjects “agree” it was there. This tight standard deviation leads to a small standard error and thus highly significant mean. Test 2: Local Aggregated Networks and Turnover Clusters It may be argued, and justifiably so, that averaging individuals’ relationships between perceived networks and turnover clusters is an inappropriate test of the hypothesis, because these observations are not

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independent of one another (i.e., each person is presumably perceiving the “same” social network). A more appropriate test, this logic continues, would be to summarize the perceived network in one matrix and test that one matrix against the turnover matrix for the particular site. As an alternative test of the hypothesis, then, the following procedure was undertaken. The transformation of information for the Role Similarity matrix will be described first. Recall that A(i, j, k) is the original data matrix containing all of k’s perceptions of whether i goes to j for advice, for all i and j (i not = j). Let the Local Aggregated Advice matrix, or LAA, entries LAA(i, j) = 1 if and only if both A(i, j, i) = 1 and A(i, j, j) = 1. Let LAA(i, j) = 0 otherwise. In other words, if i and j both agree about the fact that i goes to j for help and advice at work, then in the summary matrix LAA cell, (i, j) = 1. If they disagree, or if they agree that i does not go to j, the cell is set equal to 0. This strict intersection rule is consistent with the conservative stance repeatedly adopted in this research effort. If one person claims to be going to another for advice, but the second person denies it, such a claim is considered unreliable. When both agree that the first goes to the second, it is reasonable to assume that the connection actually does take place. With the same algorithm used to create the RS(i, j) matrix, this summary matrix LAA(i, j) is transformed into a Local Aggregated Regular Similarity matrix, LARS(i, j;). Again, the cells take on continuous values from 0 to 100, where 0 indicates that i and j are totally dissimilar in their roles in the advice network, and 100 indicates that the two have identical roles. We test the hypothesis by comparing this matrix with the T(i, j) matrix. Each of the three sites yields one summary test (instead of averaging the tests of each individual’s matrix). The QAP test results are given in Table 9.2. Groups B and C show significant, if again somewhat weak, associations between the role similarity matrix and the turnover similarity matrix. Overall, the relation is reconfirmed as before: significant (p < .05) but not strong (unweighted average gamma = .16). Test 3: Average Perceived Similarities Although the preceding computations of local-aggregated networks addressed the problem of independent observations, they did so at considerable cost. It was argued earlier that it is the individual’s perception of the network that affects him or her, and not links in the network as determined or perceived by others. The use of traditional local-aggregated networks abolishes the perceived network, beyond the individual’s local input. To take advantage of all the perceptual information focused on an individual, we performed a third test. Instead of aggregating the raw data

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Table 9.2. Quadratic Assignment Procedure Test Association between Local Aggregated Network of Role Similarity and Turnover Group and Measure

Result

Group A Z γ p

−1.23 −.11 ns

Group B Z γ p

2.98 .34 <.002

Group C Z γ p

1.70 .24 <.05

Meta-Analysis Z (Joint) γ p

1.99 .16 <.05

and then transforming to similarity (or distance) matrices, we performed the aggregation this time just prior to the QAP test. Recall that RS(i, j, k) is the matrix of role similarity scores perceived by k between i and j. The Average Perceived Role Similarity, or APRS, is the average of i and j’s overall perception of how similar i and j are. Then let APRS(i, j) = (RS(i, j, i) + RS(i, j, j))/2. This summary matrix, then, retains all the perceptual information calculated in A(i, j, k), distorted only to the extent that i and j disagree on their mutual similarity. Table 9.3 displays the results of the QAP test between the summary matrix APRS(i, j) and T(i, j) for each site. One of the three sites (group B) is significant. However, once again, the overall results were convincingly significant (p < .004) but not strong (average gamma = .15). Study 1 Discussion Three separate analyses pointed to the same conclusion: Turnover does not occur randomly throughout a workgroup. Rather, it is concentrated in patterns that can be delineated by role similarities in a communication network. Although these are not the only analyses possible, the fact

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Network Dynamics and Organizational Culture Table 9.3. QAP Test of Association between Average Perceived Role Similarity and Turnover Group and Measure

Result

Group A Z γ P

.83 .11 ns

Group B Z γ P

4.66 .42 <.000l

Group C Z γ p

−.79 −.09 ns

Meta-Analysis Z (Joint) γ p

2.71 .15 <.004

that this snowball pattern was so consistent across the three analytic techniques presented here emphasizes the robustness of this finding. Of interest to the manager in these results are the implications they hold for dealing with turnover phenomena in the organization. Frequently, interventions to reduce turnover are spread out over organizational levels (see, for example, Krackhardt, McKenna, Porter, and Steers, 1981). A more cost-efficient approach might be to concentrate resources on those who are observing similar coworkers leave. In doing so, expenses of turnover reduction programs could be minimized. Before projected savings of such a strategy are calculated, however, it is important to recall the relative strength of the findings reported here. The snowball effects were attenuated by two major factors. First, methodological issues must be considered. The single-item measure of connection – which is the building block of our role equivalence assessment – may be unreliable. Unsystematic error may be present in the data, reducing any observed correlations. Given the lack of power gained from using holistic perceptions, it may be preferable in future research to invest in multiple indicators of local connections rather than attempting to capture each individual’s perception of the entire network. In a similar vein, the correlation statistic used here, the gamma, does not have the usual upper bound of +1 in such matrices. Thus the apparent low values may be misleading. At the least, they are conservative.

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The second significant factor contributing to lower-than-expected correlations is inherent in the nature of the problem being studied. Turnover decisions are determined by a complex set of variables (Mobley, 1982; Steel, 2002), not simply whether a certain coworker leaves. The data reported here suggest that turnover of role-equivalent coworkers contributes only a part of the picture. Left unstudied is the effect that turnover has on people left behind in the organizations, and it is to this that we turn next.

Study 2 Does an individual who decides to continue working for an organization after a coworker leaves become more negative or more positive toward the organization? Prior research suggests, on the positive side, that turnover creates internal promotion opportunities for those who remain (Dalton and Todor, 1979; Staw, 1980a). Further, employees may increase their satisfaction with the job and the organization to justify their own decision to stay (as suggested by Mowday, 1981). Also, if those who left were poor performers, those who stay on are likely to benefit and be more satisfied with their jobs (cf. Mowday et al., 1982). Conversely, turnover could leave behind more discouraged, less satisfied coworkers. Each of the reasons for positive consequences previously mentioned could be turned around to predict negative consequences. For example, the termination of a coworker could require more work of those who remain to make up for the work not being accomplished by the person who left (cf. Mowday et al., 1982). This would be particularly true if the person who left was a valued employee. Particularly critical may be the social relationship the stayer has to the leaver: When the person leaving is a close friend, the effect on the stayer “may be particularly traumatic” ( Mowday et al., 1982: 148; cf. Burt and Ronchi, 1990). Perhaps the most useful model to organize the possible outcomes of this dynamic social interaction process is Heider’s (1958) balance theory. In this model, a triangle of relationships is described between an observer (self), another person, and an object of common interest. In this case, the observer (stayer) is faced with a coworker (who is a friend) and the job (see Figure 9.3). For the purpose of exposition, it is assumed that the link between each pair of vertices is positive prior to the departure of the coworker. That is, the triangle is balanced: The observer has positive affect for the job, the observer has positive affect for the coworker, and the coworker has positive affect toward the job. How this triangle might change (or not change) as a result of the termination of the coworker is depicted in Figure 9.3 (effects A, B, and C). In

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Network Dynamics and Organizational Culture AFTER TURNOVER Effect A: External Attribution Friend

No Change

Self BEFORE TURNOVER Friend

Job

Self

Effect B: Dissonance Reduction Friend Job Negative Attitude Change

Self

Job

Effect C: Insufficient Justification Friend

Positive Attitude Change

Self

Job

Figure 9.3. Possible effects of turnover of friend on stayer.

each of these predictions, it is assumed that positive attitudes held toward the friends remain, or at least do not become negative. This assumption is supported in friendship studies, where such links are generally stable over long periods of time (e.g., Newcomb et al., 1967). The first prediction is that no change in attitude toward the job would occur. This could happen if the employee attributed exogenous reasons to the friend’s departure (effect A). In this way, an attribution of job

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satisfaction to the friend can be maintained in the face of the friend’s leaving (e.g., “My friend liked the job, but she had to leave because of school”). Mowday (1981) proposed a similiar argument to explain his results, referring to such external attributions as the “pull” forces of turnover. Effect B in Figure 9.3 depicts the possibility of a negative change in attitude resulting from a friend’s leaving. In this scenario, the employee attributes dissatisfaction to the friend who left. This creates dissonance, which is resolved in the triangle by the stayer becoming more dissatisfied with his or her job. Effect C represents a possibility not predicted directly from balance theory but that has some support in dissonance studies. If the person observes a coworker leaving and attributes dissatisfaction to the leaver, then the person’s decision to stay may require more justification (Staw, 1976; 1980b). One way this justification could occur is for the stayer to develop more and stronger positive attitudes toward the workplace. Turnover Embedded in Network Structures These scenarios represent the possibilities at the micro level between two people and their job. However, the workplace is seldom restricted to two people in their organization. Instead, each of N employees must balance N-1 such triangles in his or her head. Few probably actually do so, but it is likely that such forces on a person’s psychology are to some extent additive, at least figuratively. That is, if many of a person’s friends leave, then the effects described in Figure 9.3 are likely to be stronger than if only one friend leaves. Moreover, the closer the friends are to the person, the stronger the effect is likely to be. Viewed from a more macro perspective, this phenomenon dictates that effects of turnover on stayers will not be uniformly nor randomly distributed among the stayers in the organization. Rather, these effects will be localized and focused on those stayers who are closest to those who left. The social network, then, describes the topology of forces that reverberate throughout an organization when someone leaves (Burt, 1977; Lewin, 1936). The friendship network in Figure 9.4 illustrates this proposed effect. Each letter represents an employee; a line connecting two employees indicates that the two employees are friends. Thus, A is a friend of B and C but not a friend of the remaining employees (D through H). If A were to leave, it is proposed that B and C would be most strongly affected. A person who is not a friend but is seen as a friend of a friend is more apt to have more influence than someone who is not seen as a friend of a friend. By extension, one is more affected by a friend of a friend of a friend than by someone further out in the friendship chain. Thus, it is

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Network Dynamics and Organizational Culture B G

A D

E

F H

C

Figure 9.4. Hypothetical friendship network.

proposed that A’s termination would affect D more than E and that H and G would be least affected. Another contextual effect must be considered when moving from simple dyads to the entire network. An individual is influenced by those who stay as well as by those who leave. That is, in Figure 9.3, if the person’s friend does not leave, then the triangle in “Before Turnover” is reinforced. If many of the coworkers who remain are friends and only one friend leaves, then the impact that this termination will have on the individual will be attenuated. This balancing effect of leavers versus stayers is depicted in Figure 9.5. Four extreme scenarios are represented. In each case, person A has eight coworkers, four of whom leave. Scenario 1 (in the upper-left corner of Figure 9.5) predicts the maximum impact on person A of the four turnovers. That is, because A is close to all four leavers and not close to any of the four stayers, whatever impact the turnover will have would be relatively large. At the other extreme (scenario 4), when A is close to the stayers and not close to the leavers, the impact of the turnovers would be less. Scenarios 2 and 3 represent two more moderate effects; however, they represent moderate positions for different reasons. In scenario 2, the impact is neutral because each of the actors is not connected (either directly or indirectly) to A; thus, there is little impact from either stayers or leavers. In scenario 3, the relatively strong impact of those who left is balanced by the impact of an equal number of coworkers who stayed. To be consistent with the psychological foundation of this book, however, we must make one final modification to the preceding structural arguments. Person A’s leaving will affect person B, assuming that person B perceives that person A is a friend. The effect is attenuated if person B perceives that person A is only a friend of a friend, and so on. For example, in Figure 9.4, if person D does not perceive person A to be a friend of B or C, and thus person C sees no connection at all between

197 2. Neutral Impact on A

A

4. Lowest Impact on A

1. Highest Impact on A

A

3. Neutral (Balanced) Impact on A

Circled dots represent Leavers. Uncircled dots represent Stayers. A line connecting a dot (coworker) to A indicates that a perceives the coworker to be a close friend.

A

A

Not Close to Leavers

Figure 9.5. Four extreme scenarios depicting various degrees of impact from leavers.

Close to Stayers

Not Close to Stayers

Close to Leavers

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herself and A, then the effect of A leaving will not be felt by D, even though in “reality” A is connected indirectly to D. Specifically, the effect of turnover on coworkers will depend, it is hypothesized, on how close in the friendship network the leaver was to the stayer as perceived by the stayer. We examined the relationship between turnover and subsequent organizational attitudes of those who remained and, in particular, how this relationship was moderated by the perceived position of leavers in the friendship network. Methods The three-restaurant sample was identical to that described in study 1. A pre-post natural quasi-experimental design was used. At time 1, a questionnaire was administered that included network questions and attitude items. One month later, at time 2, a second questionnaire with the attitude items was administered. The major treatment variable, turnover, was recorded during the interval between time 1 and time 2 at each of the sites. Using this design, we could determine the relationship each respondent had to each of the coworkers who left, and we could assess the degree of change in stayers’ attitudes subsequent to the turnover of their coworkers. Our analysis this time focused on perceived friendship rather than advice relations. Thus, we were interested in each person’s perceptions of who was a friend of whom, using the same question outlined in Chapter 3. Each person provided an entire picture of his or her perception of the friendship network in the restaurant in which he or she was embedded. These data allowed us to construct, for example, Henry’s perception of the entire network in the group, Rita’s perception of this network, and so on. Independent Variable From these data, we calculated how close in the perceived friendship network the respondent perceived himself or herself to be to each other coworker. These friendship links were combined with subsequent turnover data to create the independent variable in this study – the IMPACT index. This variable is a summary indication of how much potential influence there is on an individual stayer (k in the following formula) from friends who terminated, relative to those friends who stayed (see Figures 9.4 and 9.5). IMPACTk =

N  j=1

[FDk−1 ( j) × T ( j)].

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for all j not = k; where IMPACTk = the potential influence of the leavers relative to the stayers F Dk−1 ( j) = person k’s perceived friendship closeness to T( j)

coworker j (closeness = 1/distance) = turnover of coworker j ( = 1 if j left; = −1 if j stayed)

Dependent Variables Organizational Commitment This was measured by the fifteen-item Likert-scale Organizational Commitment Questionnaire (Mowday, Steers, and Porter, 1979) that assesses the degree to which the employee is committed to the firm for which he or she works. It has been shown to predict turnover reliably and consistently (Mowday, Steers, and Porter, 1979). Relative Job Satisfaction (Self) One section of the questionnaire assessed perceptions of how satisfied employees were. The respondent was instructed to place beside each individual’s name a number that indicated the relative amount of job satisfaction that individual had (e.g., a “1” beside the most satisfied coworker, a “2” beside the next most satisfied, etc.). Where the respondent placed him or herself in this ranking provided an indication of his or her satisfaction relative to the other coworkers. This self-ranking score was then transformed into a percentile by reverse scoring and normalizing. In assigning a rank to those people who had already left, the respondent was to recall “how satisfied they were just before they left this job.” Analysis and Results A rank-order correlation was used (Goodman and Kruskal’s gamma) to assess the degree to which individuals attributed relatively more dissatisfaction to those who left than they did to those who stayed. This comparison was done within individuals. That is, each respondent received a score equal to his or her gamma, indicating his or her association between coworkers leaving and the relative job satisfaction that he or she attributed to those coworkers. These gammas were then averaged for each group. Because, following Figure 9.5, three different predictions were made about the effect that turnover of friends might have on those who

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Network Dynamics and Organizational Culture Table 9.4. Associations within Individuals between Turnover of Others and Attributed Job Satisfaction Attributed Satisfaction T1

Attributed Satisfaction T2

 Attributed Satisfaction

Group A (N = 16)

γ = −.38 N = 13 SD = .201

γ = −.45 N=8 SD = .290

γ = −.21 N=8 SD = .317

Group B (N = 27)

γ = −.34 N = 23 SD = .392

γ = −.51 N = 15 SD = .365

γ = −.18 N = 15 SD = .364

Group C (N = 20)

γ = .01 N = 17 SD = .282

γ = −.10 N = 12 SD = .363

γ = −.15 N = 12 SD = .365

Weighted average association

γ = −.24 SD = .310

γ = −.36 SD = .347

γ = −.18 SD = .354

Sample

remained, two-tailed tests were used to evaluate the significance of the relationship observed in these links. Overall correlations were calculated based on the pooling of the information from all three sites, rather than the simple averaging of the three individual correlations (cf. Hunter, Schmidt, and Jackson, 1982), for two reasons. First, the dependent variables – organizational commitment and satisfaction – were all normalized for group size and may be considered continuous variables. Second, the independent variable, IMPACT, is theoretically meaningful in its absolute form. If a clique of close-knit friends exists and all members but one leave the clique, the effect on the remaining group member should be most pronounced, even in comparison with employees in other work groups. To average the three within-site correlations would destroy this information. The Organizational Commitment Questionnaire (OCQ) ranged in reliability (Cronbach’s alpha) from .84 to .90 across the three sites in both administrations. In addition, both the job satisfaction (self-ranked) measure and the OCQ at time 1 predicted subsequent turnover. Correlations with turnover ranged for the three sites from −.16 to −.52 for the two measures. Thus, these instruments displayed both reliability and predictive validity properties that were acceptable and consistent with prior assessments of similar measures (Mobley, 1982; Mowday et al., 1979). The results, shown in Table 9.4, indicate that people do attribute more dissatisfaction to those who leave. The overall gamma indicates that stayers rank those people who left lower in satisfaction after they leave (gamma = −.18). The lack of a particularly strong relationship is explainable by referring to the first two columns of Table 9.4. It is

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Table 9.5. Correlation of IMPACT with Organizational Commitment Sample

at T1

at T2

 (T2 – T1 )

Group A

r = .21 N=8 p = NS

r = .35 N=8 p = NS

r = .34 N=8 p = NS

Group B

r = .49 N = 21 p < .05

r = .16 N = 15 p = NS

r = −.17 N = 15 p = NS

Group C

r = .41 N = 13 p = NS

r = 39 N = 12 p = NS

r = .00 N = 12 p = NS

Combined

r = .51 N = 42 p < .001

r = .34 N = 32 p < .05

r = −.04 N = 35 p = NS

Note: Probability levels are all based on two-tailed tests.

clear that at time 1 employees were able to predict who was dissatisfied enough to leave (average gamma = −.24). At time 2, this predictability was somewhat stronger (average gamma = −.36). That is, employees were attributing dissatisfaction to those who left; however, the change in dissatisfaction ratings was attenuated by the fact that by time 1 they already had been able to anticipate who would leave. (Although the significance of these scores could be tested, this would be inappropriate, because the errors are not independent in this case [cf. Box, Hunter, and Hunter, 1978: 78–82]; therefore, the data are provided for descriptive purposes only.) IMPACT was a summary index of the relative closeness of the individual to those who left. A relationship between this index and organizational commitment would indicate that commitment was differentially affected by leavers depending on how close the leavers, relative to stayers, were perceived to be to the employee. As with attributed satisfaction, reported previously, the relationship between IMPACT and commitment is shown for time 1, time 2, and changes between time 1 and time 2 in Table 9.5. The changes in commitment were inconsistently related to IMPACT, as is evident from the last column in Table 9.4. In group A, the correlation was moderate (.34), although it was insignificant. Groups B and C showed weaker correlations. The combined correlation was practically zero. On the other hand, the correlations between IMPACT and commitment at time 1 and time 2 were significant and relatively strong (.51 and .34). Thus, commitment does seem to be related to the degree to which friends leave. The direction of this relationship is particularly interesting: The

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Network Dynamics and Organizational Culture Table 9.6. Correlation of IMPACT with Job Satisfaction (Self-Ranking) Sample

at T1

at T2

 (T2 – T1 )

Group A

t = .32 N=8 p = NS r = .08 N = 21 p = NS r = .04 N = 13 p = NS r = .11 N = 42 p = NS

r = .34 N=8 p = NS r = .36 N = 15 p = NS r = .42 N = 11 p = NS r = .35 N = 34 p < .05

r = .02 N=8 p = NS r = .40 N = 15 p = NS r = .50 N = 11 p = NS r = .38 N = 34 p < .05

Group B

Group C

Combined

Note: Probability levels are all based on two-tailed tests.

closer in friendship distance the leavers were to the respondent, the higher the degree of respondent commitment, both at time 1 and time 2. It is difficult to ascertain a causal direction in this relationship, however, because the change in commitment score was negligible. It was predicted that the turnover of close friends would result in differential satisfaction in stayers. In other words, IMPACT would be related to the change in satisfaction. As can be seen in Table 9.6, in each of the three groups a positive correlation existed between IMPACT and the change in self-ranked satisfaction (although none reaches significance because of small N sizes). The overall relationship is .38 and is significant. Thus, it would appear that when closer friends leave, the person who stays becomes even more satisfied relative to the other stayers. The relationship at time 2 between satisfaction and IMPACT is also positive (.35). At time 1, the relationship is even weaker (.11) and not significant. Because the change in satisfaction is more strongly correlated with IMPACT than satisfaction at either time 1 or time 2, it would appear plausible that the turnover of friends may contribute to the job satisfaction of stayers. Our primary concern here was to assess the net effect turnover had on those who remained. To this end, we presented the relationship between job attitudes and IMPACT, a summary index representing the effects illustrated in Figures 9.4 and 9.5. Thus far, we have ignored in this analysis the role of attributed satisfaction. The following results explore more fully the extent to which attributed satisfaction moderates or explains the observed relationship between IMPACT and the dependent variables.

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One question is whether the correlation between IMPACT and attitudes is spurious due to attributed satisfaction. As shown in Table 9.4, attributed satisfaction is a reasonably good predictor of turnover. Thus, it is possible that IMPACT is a surrogate for the influence from those who were dissatisfied. To test this, hierarchical regressions were performed on the set of six dependent variables (self-ranked satisfaction and organizational commitment at time 1, time 2, and the change from time 1 to time 2) on the data combined across the three sites (Cohen and Cohen, 1983). Forced to enter at step 1 of the regression was a new composite variable, FD∗AttrSat, which was similiar to IMPACT except that the binary turnover component was replaced with the extent to which the attributed satisfaction of the coworker was above or below the median (50 percent). In other words, this index was strongly positive when the close coworkers were relatively satisfied; it was strongly negative when the close coworkers were relatively dissatisfied. (See Krackhardt and Porter, 1985, for formulae for the additional attributed satisfaction measures discussed here.) At step 2 of the regression, IMPACT was added to the equation. If FD∗AttrSat is a source of spuriousness, then it would be significant at step 1 of the hierarchical regression, and the addition of IMPACT would not improve the R square significantly. Of the six hierarchical regressions, only one (where commitment at time 1 was the dependent variable) was significant at either step 1 or step 2. The fact that commitment at time 1 emerged as the most significant finding is not surprising given the results reported previously in Tables 9.4 and 9.5. The N size was larger at time 1, reducing the standard error of the estimates in the regression. Moreover, at time 1, self-ranked satisfaction was not related to IMPACT, whereas commitment showed a strong correlation (.51). Although the pattern of coefficients was similar across all six regressions, only the significant regression is detailed here. The results, shown in Table 9.7, indicate strong support for the IMPACT index over and above attributed satisfaction as a correlate of commitment. FD∗AttrSat is not significantly related to commitment at either step 1 or step 2. The amount of variance explained by adding IMPACT, however, is substantial (R2 = .251, p = .001). A second question of interest is whether attributed satisfaction interacts with turnover to influence attitudes and whether IMPACT explains any variance over and above the satisfaction-turnover interaction. One could reasonably expect that satisfaction would moderate the effect that turnover has on stayers. We could observe this interaction by separating the attributed satisfaction of stayers from the attributed satisfaction of leavers (see Table 9.8). Again, six hierarchical regressions were

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Table 9.7. Hierarchical Regression (Dependent Variable: Commitment at Time 1) Overall

Independent Variable

Standardized Beta

Step l FD∗AttrSat

.08

.52

Step 2 FD∗AttrSat IMPACT

.03 .50

Test of Increment R2 = .251

F = 13.17

p

R2

NS

.007

.18 3.63

NS .001

.258

df = 1,39

p < .001

t

F .274

6.76

(df)

p

(1,40)

NS

(2,39)

.005

Table 9.8. Hierarchical Regression (Dependent Variable: Commitment at Time 1) Independent Variable

Standardized Beta

Step 1 FD∗AttrSat (stayers) FD∗AttrSat(leavers)

.24 −.16

Step 2 FD∗AttrSat (stayers) FD∗AttrSat (leavers) IMPACT Test of increment R2 = .231

Overall p

R2

F

(df)

p

1.52 −1.02

NS NS

.103

2.34

(2,39)

NS

.14 −.21 .49

.90 −1.50 3.62

NS NS .001

.333

6.34

(3,38)

.005

F = 11.73

df = 1,38

p < .001

t

performed. Step 1 forced in both these interaction variables; step 2 added IMPACT. As in the preceding case, five of the six regressions were insignificant. Only commitment at time 1 resulted in a significant equation. The results for this regression are in Table 9.8. Again, the interaction terms for attributed satisfaction of stayers and leavers were not significant in step 1 or step 2. With the addition of IMPACT at step 2, however, the regression became significant, and the increment in explained variance was also significant (R2 = .231, p = .001). Attributed satisfaction thus added little to our ability to understand the process that enables IMPACT to predict attitudes. Study 2 Discussion It appears that the insufficient justification model in Figure 9.3 received the strongest support from the data. In general, dissatisfaction was

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attributed to those who left. This suggests that external attributions, if there were any, were not strong enough to justify the coworker’s departure. Thus the model of effect A depicted in Figure 9.3 is not supported. Effect B in Figure 9.3, although predicting correctly the attributed negative link between coworker and job, was incorrect in its prediction of the subsequent dissatisfaction of the stayer. Effect C correctly identified both the negative link between coworker and job and the positive subsequent change in stayers’ attitudes. It is worth noting that the two dependent variables, organizational commitment and job satisfaction, did not respond in identical patterns. Although commitment was correlated with IMPACT at time 1 and time 2, the change in commitment was not. It is reasonable to expect this, given the strength of the correlation at time 1. If the employee knows that a close coworker is about to leave, and at the same time knows that he himself or she herself is going to stay, then the insufficient justification process proposed earlier is likely to be operating at time 1. Given this, one would expect that little change would be observed. This anticipatory effect does not explain the satisfaction pattern. Correlations with the change in satisfaction subsequent to the turnover of coworkers indicate that employees were affected by the turnover itself. If the insufficient justification was enough to force stayers to be positively disposed toward the organization at time 1, why did the same forces not work to improve their satisfaction at time 1, also? There are two possible explanations of this inconsistency, one based on methods, the second based on theory. The inconsistency may be a result of the satisfaction measures, which are partially ipsative (Smith, 1967). That is, they measure satisfaction only in a relative sense. Consequently, an increase in a satisfaction score could result from a person becoming more satisfied or from a person perceiving that others are less satisfied. This makes the interpretation of these change scores somewhat tentative as compared, for example, to Likert scales. In contrast, the Organizational Commitment Questionnaire is a standardized instrument whose psychometric properties are well established (Mowday et al., 1979). The OCQ score provides an absolute indication of commitment. As such, increases or decreases in commitment are readily interpretable. The problem lies not in the advantages or disadvantages of either ipsative or nonipsative scales (cf. Kerlinger, 1973; Smith, 1967). Rather, of concern here is that the difference in results between the satisfaction and commitment measures could be partly a function of how they were measured. Although this is a possibility, it should be remembered that the results in Table 9.3 are based on correlation, not on absolute differences. The fact that IMPACT is positively related to satisfaction at time 2

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can be interpreted as meaning that those with high IMPACT scores reported themselves to be relatively more satisfied than their coworkers. This interpretation is not substantially different from that given to the similar positive correlation to commitment in Table 9.2: Those with high IMPACT scores report themselves to be relatively more committed than their coworkers. The similarity in interpretation can be extended to the change scores in both satisfaction and commitment. Thus, although the two measures do exhibit different psychometric properties, there is no reason to assume these differences would lead to the observed discrepancies between the satisfaction and commitment results. A more interesting and theoretically based explanation lies in another model of work attitude formation, social information processing theory (Salancik and Pfeffer, 1978). Suppose that employee A is about to leave, and B is A’s good friend. A’s behavior during these last weeks may include providing B with an earful of why it is that A is leaving (complaining about the work, the supervisor, etc.). B’s evaluation of the work during this time is influenced by A on two counts. First, because A is a friend, the frequency of interaction will be higher, allowing A more opportunity to provide negative social cues. Second, and equally important, because B perceives A to be a friend, B may take cues coming from A more seriously than cues coming from a stranger. Thus, not only are the social cues from A more frequent, but also B’s receptivity to such cues is enhanced by the friendship link. Once A has terminated, this source of negative information about the workplace also diminishes, resulting in a higher percentage of positive social cues about the work. Hence, B’s job attitude toward the job itself improves. Moreover, because the job is more immediate to the employees’ experience of work than is an evaluation of the organization, it would seem reasonable that shared communications would focus more frequently on the job than on the organization. Organizational commitment, then, was probably not as susceptible to social information cues; thus, changes in this attitude were less likely to be affected by the turnover itself and more likely to be governed by the anticipation of turnover as described previously.

Conclusion Calls for more studies of the effects of network change have increased in recent years, with the realization that “enormous research remains to be done in the dynamics of social networks” (Degenne and Fors´e, 1999: 159). However, one of the limitations with respect to formal organizations is that networks tend to be relatively inertial. It is difficult to study change processes in interpersonal social networks if ties between people

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remained stubbornly fixed (Mollica, Gray, and Trevino, 2003). Thus, the advantage of studying network change and its effects in fast food restaurants is that change is ever present in the fluctuating population of employees joining and leaving. It would be helpful to have further studies of network dynamics in other types of organizations given that most of the employees in the fast food restaurants studied here were adolescents less concerned with careers than with high school and social relationships. These adolescents were not trapped in this type of work or these organizations, but were freer to quit than would be the case for more career-oriented employees. Indeed, with turnover running at about 200 percent annually in these restaurants, employees may become inured to role equivalents or friends quitting. Thus, we would expect the effects in more typical career-oriented locations to be stronger than the ones observed in this study. Individual actors behave in organizations in ways that are influenced by the larger context in which they find themselves. Researchers, however, have a tendency to focus on either micro or macro factors, perhaps because of training as psychologists or sociologists. Organizational psychology and social psychology have explored individuals’ values, beliefs, perceptions, and motives, which can lead to their observed behavior. And organizational sociology has focused on the structural constraints to such behavior. The purpose of the current chapter, following up from the previous section, is to demonstrate that the combination of both orientations can lead to new insights into organizational dynamics. This demonstration employs a distinctly macro, structural lens to look at micro-organizational research questions concerning the effects of network turnover in organizations. The results confirm the importance of structural approaches, but at the same time reaffirm the richness of psychological explanations. We continue our examination of network dynamics in the next chapter, which focuses on organizational crisis.

10 Organizational Crises

The previous chapter discussed the ways in which social networks systematically affect patterns of turnover in organizations and attitudes of people left behind. The current chapter takes a closer look at how such patterns of cross-unit network links help organizations deal with crises. It is inevitable that people cross organizational boundaries in pursuit of careers. But as they move – from one department to another, for example – they also connect units that might not have been linked previously. Thus, a somewhat haphazard network of informal relations is likely to characterize any system of organizational units within an overall umbrella organization. Recently, there has been considerable attention given to the ways in which patterns of clustering and connectivity develop in networks (Dorogovtsev and Mendes, 2003), but little of this work has focused on the design of organizational networks. In general, the new science of “small worlds” has been content to assume optimal designs emerge through relatively serendipitous processes, although the possibility of more goaldirected network design has been discussed (e.g., Kilduff and Tsai, 2003). In this chapter, the emphasis is on comparing informal patterns of organization across two types of structure: an optimal structure, constructed on the basis of theory; and a “natural” structure that emerges on the basis of social interaction. It is argued here that emergent networks, left to themselves without the aid of conscious design, will form naturally in ways that are suboptimal, even dysfunctional, for the organization. Moreover, we posit that the degree to which the informal organization is designed optimally is measurable. The argument behind this theory will be built up from a set of seven assumptions presented and defended in the sections that follow; the assumptions will culminate in a set of propositions. The first of these propositions is tested in a set of organizational simulations. The results of these tests provide strong support for this primary proposition. 208

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The Structuring of Organizations Differentiation produces work units at many levels in an organization (Lawrence and Lorsch, 1967; Miller, 1958). We will refer to formal subdivisions within organizations as “subunits”; these units may vary widely in size. Subunits usually are considered in terms such as department, unit, center, or workgroup. The characteristic that is important to the following discussion is that these units are recognized formally as differentiated from other units.

Friendship Patterns Assumption 1: Organizations tend to evolve into friendship cliques (dense friendship networks) primarily within subunits. As we have shown in Chapter 6, similarity is a basis for identification and friendship formation. One important type of similarity is physical propinquity: similarity in geographic location. It is well known that such geographic closeness induces frequent interaction and leads to friendship. Festinger, Schachter, and Back (1950) studied a group of students to determine whether a causal link between propinquity and friendship could be inferred reasonably. Specifically, married students were assigned to housing units according to when they applied to school. This order of application was independent of other factors that may have led to friendship, such as similarity of interests or college majors. Yet those living next door to each other were far more likely to become friends with each other than were other pairs of couples. Those who lived at the physical ends of the buildings were more likely not to have any friends in the complex. Other researchers have found similar relationships. Several studies show that students who sat close to each other in classes in a boarding school were more likely to like each other (Byrne and Buehler, 1955; Maissoneuve, Palmade, and Fourment, 1952). Sykes, Larntz, and Fox (1976) found that those who bunked next to each other interacted with each other and liked each other more. In summarizing this research, Shaw (1981: 84–5) notes that propinquity creates at least an opportunity for friendship formation: “Clearly, persons who are physically close to each other are more likely to form affiliative relationships than those who are more distant from each other.” Subunits usually are organized physically so that members are proximate to each other. More interaction during the workday occurs within subunits than between subunits; as a result, friendships are more likely to occur within than across subunits. Miller (1958) describes how subunits,

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particularly those based on territory and time divisions, develop group solidarities and inward connectedness. Obvious examples include individuals working in the same department or the manner in which workers on the same shift are likely to form relationships. Such structurally constrained interaction also is likely to lead to some common perceptions of the units and the individuals in the organization. Subunits divided by technology often derive solidarity from what Selznick (1957) termed “distinctive competence.” The arrangements of the task not only make them specialists but also give them a particular vantage point and perspective on the organization. To the extent that individuals are grouped by occupation, common points of view may derive from the creation or maintenance of occupational communities. The creation of common points of view also is strengthened by another force at work in the friendship network. Allocation of resources usually is made to subunits in order to facilitate coordination and control of resources within the organization. Monitoring and budgeting funds to subunits is much easier than monitoring and budgeting individuals. As a result, those in a subunit are viewed as allies in the battle of the budget, and those within a unit may see other units as competitors for resources in organizational decision processes. Such a view is explicit in the work of both Allison (1971) on decision making in the government and Pfeffer (1981) on organizational power. In summary, organizations are structured in subunits that have sufficient boundaries to be structurally conducive to the formation of friendships with units. Although friendship links between individuals occur across subunits, these ties occur at a lower rate than within-unit ties. We now digress to introduce the idea of organizational crisis. It will be argued subsequently that crises create conditions in which the arrangement of friends becomes especially critical. Organizational Crises Organizational crisis has been the subject of a variety of analyses that debate the definition of crisis itself (e.g., Billings, Milburn, and Schaalman, 1980; Hermann, 1969; Milburn, Schuler, and Watman, 1983; Smart and Vertinsky, 1978). Most discussions use Hermann’s (1963) definition: A crisis is a stimulus (situation) that consists of a threat to desired organizational goals, in which decision time is short and surprise has found decision makers unprepared to act. Billings and his colleagues (1980) find this concept incomplete, and suggest the additional importance of the triggering event and the manner in which it is sensed. The stimulus needs to be considered in comparison with current standards of performance outcomes; otherwise, there will

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be no apparent threat. A discrepancy would produce an evaluation of the probability that the organization would face a loss, and of the magnitude of that loss. Crises are most likely when required responses are uncertain and must be original (Billings et al., 1980: 302–5). In building a general model of organizational crisis, Milburn and his colleagues (1983) add that a crisis may occur not only because of the opportunity to achieve desired goals but also because of a threat to those goals. They emphasize the organization’s need to resolve the crisis and the likelihood that the “resolution strategy is uncertain” (Milburn et al., 1983: 1144). From the crisis research, we can extract a working definition for this analysis. “Crisis” refers to a situation facing an organization that requires the organization to engage, under time constraint, in new, untested, unlearned behaviors to obtain or maintain the organization’s desired goal states. A crisis could result from events within an organization, such as a planned effort for change or the sudden loss of critical personnel, as well as from external sources. In the simplest terms, a crisis requires uncertain action under time pressure. When uncertain action is required without time pressure, the situation may be viewed as a problem rather than a crisis. When required actions and outcomes are known but when time pressure exists, organizations engage standard, albeit critical, procedures or routines. The importance of friendship networks for organizational crises is revealed in the conditions that exist within an organization during a crisis and in the behaviors that are required to manage the situation. Assumption 2: Crisis leads to a perception of uncertainty and threat of change. This assumption follows closely from the definition of crisis itself. A threat or opportunity requires uncertain response, the outcomes of which are also uncertain. Billings and his colleagues (1980) discuss the perceived probability of loss. Milburn and his colleagues (1983) examine the stress that results from this uncertainty. Meyer (1982) provides one of the clearest statements in discussing environmental jolts: “When they are labeled crises, jolts infuse organizations with energy, legitimize unorthodox acts and destabilize power structures” (Meyer, 1982: 553). The behaviors required are “unorthodox,” unlearned, and untried; especially to the point, they have unpredictable consequences. Thus the power structures, which enforce order and stability, are challenged directly. A wide range of threats to the current order must be acknowledged. Resources may be reallocated or changed in absolute availability. Perceived resource scarcity may increase. Power may be redistributed and

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day-to-day organizational procedures may shift. Expectations of behavior and overall performance may change. Threats of change in the normative, political, and physical environments can arrive in widely ranging forms. Assumption 3: Such perceived uncertainty and threat of change will result in conflict among subunits. The perceived threat will lead units to defend their resource base and patterns of action. Hermann (1963) suggests that preexisting conflicts will be aggravated during crises. The internal allocation of resources will seem more like a zero-sum game. Milburn and his colleagues (1983) argue that strategies of centralization tend to be applied in crises. They believe that such strategies reduce the cohesiveness of the organization as a system. There will be a greater tendency for conflict to develop among units (Milburn et al., 1983: 1171) and for cooperation among units to decline (Schein, 1967). Milburn and his colleagues (1983) posit a cycle of deepening crisis as a result. This argument leads to further elaboration of the nature of the conflict. Assumption 4: Conflict will lead to two separate but commensurate outcomes: (1) increased commitment to the home subunit and (2) reduced cooperation with other subunits in the organization. Theory suggests that intergroup conflict leads to increased group cohesiveness, a stereotyped image of other groups, an inability to cooperate, hostility, and clear formulation of group beliefs (Coser, 1956; Simmel, 1955). Most of these hypotheses have been verified in experiments since the work of Sherif, Harvey, White, Hood, and Sherif (1961) (see also Schein, 1967). Wheaton (1974) also shows that conflict can lead to increased group cohesion when members are forced to rally around a common set of principles, as one might observe within an organizational subunit. Cooperation, in this case, entails the notion that people are willing to work with others, even though some of the behavior is not likely to benefit their unit to the maximum degree. In addition to the preceding discussion, Hermann’s (1963) analysis of crises includes a similar observation. The centralizing tendency of crisis management reduces the use of normal channels to collect and disseminate information. As a result, cooperation and coordinating in general become more difficult. Centralization is an attempt to force integrated action by the subunits in order to manage the crisis. The ordinary level of differentiation of the subunits must be overcome temporarily.

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Assumption 5: Adaptation to crisis requires increased cooperation. Crises, by their nature, have uncertain outcomes; usually it is not clear who will benefit from the new required behaviors. Some of these behaviors require organization and coordination across subunits; thus some of these behaviors must be cooperative. Khandwalla (1978) discusses the response phase of crisis as one that involves increased collaborative relations and the establishment of integrative mechanisms. Solutions to major crises described by Starbuck, Greve, and Hedberg (1978) show the need for increased connectedness between units that previously were unconnected. Assumption 6: Trust enhances cooperation. In a trusting relationship, one imputes “honorable” motives to another. That is, if person A trusts person B, by implication A expects that B will not intentionally use information or engage in behavior at A’s expense. To violate this expectation is to violate trust. Once trust has been violated, cooperation is diminished greatly; cooperation without initial trust is very difficult to implement. Forced compliance to cooperative behaviors is not an efficient answer to the problem. As Kelman (1958) points out, forced compliance requires expensive and constant surveillance; it engenders distrust and negative affect. People are tremendously creative at undermining systems of control; the popular press often has highlighted examples of resistance, such as the now-classic case of the auto workers at Lordstown, Ohio, in the early 1970s (Lee, 1983). Assumption 7: Strong friendship includes trust. Although not isomorphic with trust, friendship implies trust. Without trust, friendship does not exist. As Bell (1981) observes in his research on friendship, “When we asked people to describe what was important to friendship, their most common answer was ‘trust’. This was because close friendships are possible only if certain barriers are eliminated and the two people can come to an understanding. This further means that what they do and get from each other is based on trust” (Bell, 1981: 16). These arguments suggest the reasonableness of the following proposition: Proposition 1: In times of crisis, more effective organizations will be those with friendship links between subunits (as opposed to strong friendship links within subunits).

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Insofar as strong friendship ties exist across subunit boundaries, more trust and cooperation will be possible. Uncertain solutions are more likely both to be suggested and to be implemented. More cooperation will result in greater ability to adapt to the new situation created by crisis. The importance of this proposition lies not only in its prediction that the tendencies toward differentiation in organizations work against the existence of optimal conditions, but also in its contradiction of the general principle of impersonality as found in the traditional bureaucratic model, which advocates eliminating friendship ties as a strategy for efficiency. Caution should be exercised in interpreting this proposition. It applies to crisis situations rather than to normal, routine operations. Although links among units still are important even in ordinary organizational functioning, ties within subunits improve normal performance by permitting coordination and cooperation in unit work tasks. Intraunit ties promote a more positive social environment for unit members who must spend most of their workday within the unit. This proposition is not intended to disregard the importance of internal group ties to ordinary group functioning. Rather, it emphasizes the extraordinary circumstances created by crisis conditions, which make an abundance of external ties more effective than internal ties. Dimensions of Network Characteristics In the model of crisis management proposed in this chapter, the focus is on friendship as the link among members of an organization. Friendship ties (links) have been identified as both external and internal to organizational subunits. External links are friendship links between members of different subunits; internal links are friendships between members of the same subunit. According to proposition 1, for any given density of friendship relations, external links are more important for the management of a crisis. According to assumption 1, however, internal links are more likely to form. An index of the relationship between external and internal links is required to evaluate the proposition. Therefore the E-I index is proposed as follows: E-I index =

EL − I L EL + I L

where: EL = the number of external friendship links IL = the number of internal friendship links

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The possible scores for this index range from –1.0 to +1.0. As the E-I index approaches +1, all the links would be external to the subunits. A score of –1 would indicate that all the links are internal. If the links are divided equally, the index will equal zero. Several facets of this index are worth exploring. One may note, for example, that the index is a measure of dominance of external over internal ties, not simply a measure of external links. Thus the index not only decreases with a decrease in external ties, as can be deduced directly from the theory developed here, but also can be decreased by increasing the internal ties. The rationale for calculating the index as a ratio instead of as a simple count of external ties is threefold. First, on the theoretical side, the higher the density of internal ties, the greater the identification that members will make with the subunit per se. Such an identification will exacerbate the problem of increased commitment to the home unit (see assumption 4). The lower the density of friends, the easier it will be to induce the organizationwide identification and commitment necessary for the cooperation required to face the crisis successfully. Second, one may assume that individuals have a limited amount of time, energy, and need for the social interaction and intimacy that are demanded in maintaining friendships. Given this assumption, one will find, on the average, a trade-off between the number of friends that one can maintain outside the subunit and the number one can maintain inside the subunit. In this sense, the more internal links one has, the fewer links one can foster outside the subunit. Thus internal links represent an “opportunity cost” to the subunit. Third, there is a practical, methodological reason for including internal links in the E-I measure. The concept of “friend” is somewhat elusive; some people may respond to a specific question about what constitutes friendship (and therefore who their friends are) differently from other people. That is, some may have a high threshold of friendship (and thus report few such friends), whereas others may have a low threshold (and thus report many such friends). By comparing the external to the internal links, we are controlling automatically for this source of variation in the measure. Those with low thresholds will contribute correspondingly to both internal and external links; those with high thresholds will deprive, in a sense, both external and internal links. On average, then, in a ratio measure such as the E-I index the “overestimates” of externals would be balanced by “overestimates” of internals. Thus this measure is not so sensitive to such kinds of measurement error. Other facets of this index are worth noting as well. As the organization grows larger, for example, the potential number of external links grows

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much faster than the potential number of internal links. This point can be demonstrated easily, as follows: The maximum possible number of external links (E∗ ) is a function of the size of each subgroup (Si ) and the number of subgroups (N). This relationship is computed readily as: E∗ =

N−1 

N 

Si S j

i=1 j=i+1

If we assume that the size of all the subgroups is equal (i.e., Si = Sj for all i, j), this formula reduces to:   N (N − 1) 2 N ∗ E = Si2 = Si 2 2 The maximum possible number of internal links (I∗ ) also is a function of N and of the size of each Si : 1 Si (Si − 1) 2 N

I∗ =

i=1

Again, if we assume that the size of all subgroups is equal, this formula reduces to: I∗ =

NSi (Si − 1) 2

The external possibilities are approximately proportional to the square of N times the square of Si . The internals are approximately proportional to N times the square of Si . In other words, the potential number of external links usually will be greater – by a factor of about N – than the potential number of internals. Moreover, when the groups are of the same size, the potential number of external links exceeds the number of internal links approximately by a factor of N. In an organization of any reasonable size, it would seem difficult to find an E-I index of less than zero because E∗ outnumbers I∗ so strongly. Of course, assumption 1 argues that most links in organizations will tend naturally to be internal; thus it is our conjecture that negative E-I indices would be common, despite the handicap that I∗ imposes relative to E∗ . Mariolis (1985) points to this problem and argues that such indices should be normalized against the maximum possible to give a true indication of the propensity to be dominated by external or internal relations. We have chosen not to do this for three reasons. First, calculating the number of possible external and internal ties is cumbersome, relative to the E-I index calculation. Second, if assumption 1 is correct, it would be difficult

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to find examples of positive E-I values. That is, external links are more difficult to form and are costly to maintain. To normalize this already small number by dividing by the large number of potential external links would place too much constraint on the index. The observed index values would be very small (i.e., all the values would approach –1.0) and difficult to interpret. Third, and most important, the theory rests in part on the degree to which members of subunits are oriented inwardly (inside the subunit) or outwardly (to the organization as whole). An individual who has many ties to other parts of the organization, even though those ties represent a tiny fraction of the maximum “possible” ties, will share a wider, more organizational view of the world. He or she need not be tied to everyone else (an impossible task anyway, in most cases) in order to share that view. As long as most of those ties are oriented outward, the individual will be influenced toward cooperation. If all members of the organization tend generally to be tied to more members outside their subunit than inside, the E-I index will summarize that trend with a positive value. Because we seek to measure this trend, we prefer the simple, unstandardized E-1 index to the more complex, more sophisticated alternatives proposed by Mariolis (1985). This index allows the formulation of a specific hypothesis deducible from proposition 1: Hypothesis 1: Organizations with a high (positive) E-I index will be more effective in the face of crises than organizations with a low (negative) E-I index. Further Propositions The dominant theme in this argument is that the relative density of external friendship links is the critical determinant of effectiveness in facing an organizational crisis. It would be misleading, however, to suggest that this is the only contributing factor. As suggested elsewhere (Tichy, 1981), one must pay attention to several contingencies in the design of organizations. Indeed, the theoretical discussion that led to proposition 1 can be used to develop additional propositions regarding other such contingencies and design factors. (These propositions will not be tested in this chapter.) Propositions 2 through 4, which follow, address some of these design issues. They are phrased specifically to retain the preeminence of the first proposition. Proposition 2: If the E-I index is held constant, the number of subunits in an organization is correlated inversely with organizational effectiveness in the face of a crisis.

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We stated earlier that subunits are any number of types of organizational units. They are distinct and usually there is consensus in the organization about their distinctness. From the earlier discussion, it is clear that the fewer subunits exist, the fewer problems will be created by differentiation. In effect, the greater the level of organizational complexity based on the number of subunits, the greater the potential problem of insufficient ratios of external to internal links. In addition to the dominance of external ties and the number of subunits, the distribution of external ties may be important, as suggested in the following proposition: Proposition 3: If the E-I index is held constant, the variance of the distribution of links between pairs of subunits is correlated inversely with organizational effectiveness in the face of a crisis. Variance is defined here as follows:   Ns −1  Ns −1 2  Ns −1  Ns −1 2 j=i Li j j=i Li j i=1 i=1 − Var = 1 1 N − 1) N − 1) 2 s (Ns 2 s (Ns where Ns = number of subunits in organization Li j = number of external links between subunit i and subunit j This measure is simply the variance in the number of links (Lij ) between all pairs of subunits. Given a fixed number of subunits and a fixed number of external links, we can arrange those links in several different ways, but not all such arrangements would be equally effective in promoting the necessary cooperation. For example, if all the links were concentrated between two subunits and if no links were present between any of the other subgroups (i.e., if variance were high), one would predict trouble in cooperative behaviors among those other subunits. On the other hand, if the links were distributed equally across all pairs of subunits (variance = 0), potential for cooperation and cooperative attitudes would be distributed equally across the organization. Thus a high-variance condition (in which the links are concentrated in a few pairs of subunits) will not be as robust or as suitable to crisis situations as a low-variance condition (in which the links are parceled out among pairs of subunits). Proposition 4: If the E-I index and the variance in external links are held constant, the match between density of external links and needs for coordination of those pairs of subunits will be correlated positively with organizational effectiveness in the face of crisis.

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One additional highly probable contingency follows from the preceding discussion: Subunits will vary in the amounts of coordinative activity they require (Thompson, 1967). Pairs of subunits that require such coordination will be sensitive to the extent to which external links exist to handle them. The creation of a high density of links is imperative to high needs for coordination (e.g., Lawrence and Lorsch, 1967). When two subunits have little need for coordination, the corresponding need for external links is attenuated. If the linkage pattern and the need for coordination overlap, greater human resources are available for crisis management. We are not arguing that external links between pairs are unnecessary where coordination needs are low. Indeed, such links can prevent problems that might arise independently from technological or other demands. We simply are arguing that they are needed more where coordination needs are higher. We turn now to a description of the empirical support found in an organizational experiment that we conducted to test this theory. Our intention was to test only hypothesis 1 (and, by inference, only proposition 1). Testing other parts of this model for the remaining propositions is left to future research.

Method To test hypothesis 1, we made six trials of an experiment. In each trial, two organizations were designed to be comparable in every way except for the arrangement of their friendship links. In one organization, the number of friendship links between subunits of the organization was maximized at the expense of the number of links within any one subunit. In the second organization, the number of links within the subunits was maximized so that there was a minimum number of links between subunits. We simulated the “organizations” using Miles and Randolph’s (1979) Organization Game. Other researchers also have found the game a useful device for simulating the complexities and demands found in real organizations (Cameron and Whetten, 1981). It provides a fertile ground for testing the hypothesis, for two reasons. First, it allows for experimental control of the design variable of interest – namely, the pattern of friendship links. Second, and equally important, the Organization Game is the epitome of crisis as we defined it previously. This definition has several components, including the requirement that organizations, “under time constraints, engage in new, untested, unlearned behaviors. . .”; the Organization Game meets these requirements. Consider the first condition, that of “time constraints.” The game is divided into several periods, each lasting between 30 and 75 minutes, with the longer periods at the beginning of

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the game. Within these strictly enforced time limits, each participant must figure out what to do and then do it. With the exception of a few people who find out that they have nothing to contribute, all the “employees” discover that they are under pressure to complete certain tasks (gather and disseminate information, fill out forms, allocate resources) before the end of the session. The penalty for not completing these tasks varies but frequently affects the entire organization. Invariably, some of these tasks are not completed on time. The second condition in our definition of crisis – that the organization is required to “engage in new, untested, unlearned behaviors” – also is endemic to the Organization Game. Wealth is accumulated or lost over time through a complex formula that incorporates productivity measures and the quality of decisions about resource allocation (see this chapter’s appendix for more details). The formula was not given directly to the participants; most players learn it through trial and error and through the hints given in the participant’s manual. Moreover, only a cursory skeleton of an organizational structure is provided. Even communication between units is restricted through a formal mechanism involving a limited number of passes. Some people are assigned to positions in the organization, but they must determine for themselves their formal and informal responsibilities in that position. If the participants do not organize themselves, allocate jobs, and begin to produce outputs, the indicators of performance will drop and the organization will fail. The ultimate creation of a hierarchic or centralized control structure also is a function of participants’ decisions. In reference to the definition given previously, the initial crisis is a threat to the organization’s goals of survival and performance; decision makers are surprised by the organization’s complexity and operating rules, which dictate behaviors that are verv different from the accustomed behaviors. The required responses are uncertain and original in their configuration. Some known and some unfamiliar behaviors must be organized in new ways to stabilize the organization. This initial crisis approximates the concept as developed by Billings and his colleagues (1980), Hermann (1963), and Milburn and his colleagues (1983). Little stability or reduction in confusion occurs for at least three rounds of play (Stern, 1974). After some stability has developed, periodic special events are introduced simultaneously into the paired games to initiate a further crisis condition. Such events have included simulations of disasters such as fires or earthquakes, the introduction of designated minority players who must be given positions of power within a certain period (thus current power holders must be replaced), competitors in the product market and takeover attempts by outside corporations, and government regulatory rulings. These crises, usually introduced during session 5, approximate the environmental jolts regarded commonly as organizational crisis situations.

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Thus the experience for each participant is one of uncertainty about what to do or how to do it. Roles develop through time in the game, through negotiations, interaction, and agreements. Yet these roles change frequently because of political infighting or renegotiations, or simply because organizational goals were not being met. The Organization Game, then, amounts to a continuous, interunit crisis environment, where new, untested, unlearned behaviors emerge and then frequently are discarded in favor of new behaviors. Each simulation was conducted as part of a class at one of two universities. Four of the trials used undergraduates (trials 2, 4, 5, and 6) and two used graduate (MBA) students (trials 1 and 3). The procedure for designing the organizations was as follows. Everyone in the class completed a questionnaire that asked them to rate every other person in the class as to how close a friend he or she was. The directions for this questionnaire included the following: Please place a check in the space that best describes your relationship with each person on the list. The names of everyone participating in the game were listed below, with five categories from which the respondent could choose: “trust as a friend,” “know well,” “acquaintance,” “associate name with face,” and “do not know.” Only the first category, “trust as a friend,” was used to assess the friendship links. The pattern of these links was used to assign individuals to positions in the organization. Two organizations were created from each class. In the first organization, called here the “optimal” organization, we broke up friendship clusters by assigning the members to different organizational divisions and taking care not to put friends within the same division (there are four divisions in the Organization Game). In the second organization, called the “natural” organization, we maintained the cluster of friends by assigning as many friends as possible within the same division and as few as possible in different divisions. This organization was termed “natural” because it reflected how organizations tend to form in the working world (see assumption 1). The actual procedure for assigning subjects to groups was constrained by the need to maintain some friendship cliques and to break up others. The friendship checkoff procedure was used to identify cliques; these cliques were assigned randomly to the “optimal” and the “natural” simulations. Individuals then were assigned to one of the four units, depending on whether the clique was to be preserved within a unit or spread out across units. We began to place the members of the largest cliques first, then members of smaller cliques, then dyads; finally we assigned isolates to even out the sizes of the four units and the two simulations. This procedure did not guarantee that no friendships existed within units in the

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“optimal” organization or that no friendships existed between units in the “natural” organization, but it did result in organizations that approached these ideal conditions. In all cases, the “optimal” organizations had positive E-I indices and the “natural” organizations had negative E-I indices (see Table 10.1). The two organizations were run simultaneously in separate areas of the building. We took care to ensure that each organization was exposed to the same resources, enforcement of rules, time deadlines, and other variables that can affect performance of the organization. Each trial lasted from five to seven rounds, depending on the time constraints placed by the host institution. Total playing time for the trials ranged from seven to nine hours. Although individuals were not provided with any external incentives to motivate them in the simulation, they were all asked to specify individual goals at the outset and to evaluate periodically how well they thought their organization was doing. Further, participants were aware that another organizational simulation involving their classmates was going on at the same time. The dependent variables in these trials were the four Organization Game performance indicators, determined by the rules given in the game manual (Miles and Randolph, 1979). Each indicator summarizes how well the organization is doing on one of the four dimensions: (1) resource base (RB), or how effectively the organization is replenishing the resources it consumes; 2) total output (TO), or the effectiveness of the organization at producing goods and services; (3) internal cohesion (IC), or the state of collaboration between individuals and groups in the organizations; and (4) member commitment (MC), or how many members are satisfied with the organization’s functioning, structure, and values. We determined the total performance of the organization by averaging the four indicators. The rules stipulate that if any organization drops to zero on any indicator, that organization is declared bankrupt (see the appendix at the end of this chapter for details on calculating the four indicators).

Results The means, standard deviations, and intercorrelations among the four performance measures are provided in Table 10.2. These measures were calculated in each session, but for reasons of clarity and brevity, we report the data only for the first and last sessions. Our primary interest here is in the scores for the last session because these represent how well the organization coped with the various problems encountered during the entire game.

223

Natural

30 55 −0.294

26

Links

E I Index

Number of participants

25

36 7 0.674

Optimal

Trial 1 Spring 1983

31

1 50 −0.961

Natural

32

25 1 0.923

Optimal

Trial 2 Fall 1983

29

4 32 −0.778

Natural

29

49 3 0.885

Optimal

Trial 3 Spring 1984

Table 10.1. E-I Index of Organizations in Each Trial

36

0 56 −1.000

Natural

35

25 7 0.562

Optimal

Trial 4 Fall 1984

31

12 20 −0.250

Natural

31

70 10 0.750

Optimal

Trial 5 Spring 1985

33

16 25 −0.220

Natural

33

38 4 0.810

Optimal

Trial 6 Spring 1986

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Table 10.2. Means, Standard Deviations, and Intercorrelations of Four Performance Measures after First Session and after Last Sessiona

Mean SD RBI TO1 IC1 MCI RB7 TO7 IC7 MC7

RB1

TO1

IC1

MC1

RB7

TO7

IC7

MC7

76.25 14.771

72.0 10.804

67.333 8.793

73.0 13.300

58.25 52.127

110.333 55.516

64.417 41.816

90.667 41.121

1.0000

0.3121 1.0000

0.4513 0.9300 1.0000

Correlations 0.4053 0.5112 0.9805 0.4174 0.9171 0.4817 1.0000 0.4320 1.0000

0.5389 0.1499 0.2781 0.2254 0.8971 1.0000

0.5665 0.2603 0.4117 0.3255 0.8970 0.9331 1.0000

0.6210 0.3607 0.4983 0.4238 0.8875 0.9049 0.9640 1.0000

Notes: A “1” following the performance abbreviation indicates that the score was measured after the first session. A “7” indicates that the score was measured after the last session.

a

The design of the study allows direct comparison between the two paired organizations when we control for many extraneous factors that frequently affect the performance of such simulations. It is worth emphasizing here that the trials are comparable only in such a pairwise manner. In any given pair of games, the researcher would insert a “crisis” simultaneously into both organizations. Resources were dispensed or (more frequently) withdrawn at arbitrary times. Players were reassigned arbitrarily (through fake “affirmative action” dicta). We made every effort to minimize any differences in such resources or rule enforcements between organizations within an experimental pair. It was impossible, however, to control such differences from one administration of the experiment to the next. Thus the appropriate analysis is a nested multivariate analysis of variance with four dependent variables, one blocking variable (experimental trials), and one treatment variable (the “optimal” versus the “natural” organizational design). We performed a MANOVA on these data, using a test of Pillai’s trace as a test of significance of the independent variable on the set of final performance indicators. Pillai’s trace was equal to 3.018, and the approximate F24,20 = 2.56 (p <.018). Thus the treatment variable (friendship link patterns) affected the set of performance indicators significantly. We conducted the MANOVA because we expected the four dependent variables to be intercorrelated (their determinants shared common elements – see the appendix). Indeed, as Table 10.2 shows, the four variables are related strongly in the last session of the simulation (generally they are related less strongly after session 1). The strength of these correlations

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Table 10.3. Results of Organization Game Experiment First Session

Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Trial 6 Significance test: paired ta df 1-tailed p-level

Last Session

Natural

Optimal

Session #

Natural

Optimal

78 49.25 63 74.75 82.75 72.5

83 67 62 72.75 78.75 81.5

7 6 7 7 6 5

127 11 50 79.5 34 75

150 77.5 62 161.25 45 98

1.16 5 NS

2.94 5 p <.02

Note: a A paired t-test was used here because the organizations were matched in each trial on several important dimensions. These dimensions included size, type of participant (undergraduate, MBA), degree of enforcement, and interpretation of ambiguous rules, number and length of game sessions, and, perhaps most important, the precise timing and character of the artificial “crises” that were introduced into the organizations.

provided an opportunity to perform a simpler, more straightforward analysis of the data. We combined the four dependent variables into one average performance score (Cronbach’s alpha = .89 for the average scores calculated after session 1, and Cronbach’s alpha = .98 for the average after the last session). With a single dependent variable, the data lend themselves to a simple paired t-test. The results are shown in Table 10.3. A graphic representation of these results appears in Figure 10.1. The graph shows the difference in average performance scores between the paired optimal and natural organizations. Points below zero represent more effective performance by the natural organization; those above zero represent more effective performance by the optimally designed organization. Scores were plotted at the completion of each simulation session. In reviewing Table 10.3 and Figure 10.1, one can see that neither the natural nor the optimal organizations have a significant edge over their counterparts after the first round. That situation would be expected at the start because everyone is still learning the rules and no one has figured out yet what is required to make the organization succeed. By the last round (indicated by a circle in Figure 10.1), however, each of the optimal groups outperformed its natural counterpart. The paired t-test indicates that those organizations designed with a high density of friendship links across subunits did significantly better than the organizations in which most friendships are within the subunits. These significance levels were attained despite the small number of observations (six trial

226

1

2

Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Trial 6

3

4

5

6

Indicates final session

Figure 10.1. Difference between optimal and natural performance indicators for each session in each trial.

–25

–15

–5

5

15

25

35

45

55

65

75

85

7

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227

pairs). In addition, an acute observer will note the frequent recurrence of inflection points at session 5, when the secondary, external crisis was introduced into the organization. At this point in trials 1, 2, 3, and 5, the optimal organizations begin to demonstrate substantially better performance than the natural groups.

Cooperation and Conflict Processes The results described in the preceding section are reasonably strong and convincing when we consider that a minimum number of experiments was conducted and that the focus was on only the first hypothesis in the theory. The size of the differences, on the whole, was substantial and relatively consistent. The conclusion one draws from these results is that the structure of friendship patterns in such situations was an important contributor to organizational success. It is important to note, however, that the theory as described in the introduction to this chapter has not been tested in full. This experiment is only a surrogate for organizational phenomena; the simulation lacks the history and the resultant culture that characterize most organizations. Such cultures could act to moderate the effects of friendship links (or the lack thereof). That history and culture also might include greater centralized authority than participants create in the organizational simulation; the experiment uses a relatively young organization. In addition, the performance indicators give little insight into the processes that produce cooperation, trust, and success or their opposites in the game. Even so, observing the participants’ activity provides a means for interpreting the levels of internal cohesion and resource accumulation that develop. Observation of participants also produces support for the process implied by the assumptions in the model. During the experimental trials, participants’ patterns of activity were observed and each participant kept a diary describing events and reactions to those events. Entries were made after each session, and the diaries were submitted to the instructor as part of the class assignment. Frequently, those in the optimal organizations were seen to cooperate with each other in the face of the dilemmas that they encountered. Divisions in the natural organizations, however, frequently directed participants’ attention to protecting or enhancing the resources of their own division rather than those of the entire group. Some examples will help to illustrate this difference in cooperation. At the close of the first session in trial 1, the Red Division in (coincidentally) both organizations failed to turn in some necessary forms. The penalty for failing to do so was reasonably severe for each organization,

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although those in the Red Division would bear the brunt of the penalty. In both cases, the members of the Red Division felt disillusioned, embarrassed, and angry, but the responses of those in the other divisions differed markedly between the two organizations. In the natural organization, the people in the Red Division were blamed for the oversight; they were labeled as incompetents who were going to ruin the organization and were isolated from the rest of the organization. Future attempts by those in the Red Division to help the organization were met with suspicion. In the optimal organization, a delegation of representatives from the other divisions (who were friends of those in the Red Division) approached those in Red to ask what had happened. Those in the Red Division replied that they were simply unaware of the rule that had required the forms to be turned in. (The same lack of awareness was responsible for Red’s mistake in the natural organization.) The group asked whether there was anything it could do to help Red at this point. It was decided jointly to spread out the penalty in such a way as to minimize the impact on the organization as a whole rather than to let Red suffer the penalty alone. By the middle of session 2, those in the Red Division were integrated into useful roles throughout the organization. The fact that the optimal organization outperformed the natural one in this trial is particularly interesting because, as was discovered during the postgame debriefing, one person in the natural organization had had a copy of the solutions to production problems in the game. This person was part of the Yellow Division, which was responsible for producing solutions to puzzles (for which the organization received profits). The ability to solve these puzzles was one of the primary ways in which the organization made money and increased its various performance indicators, especially resource base and total output. Having these solutions put the organization at a tremendous advantage. The advantage was not sufficient, however, to overcome the disadvantage that the natural organization suffered because of its suboptimal informal design. Trial 2, which ended after six rounds because of the bankruptcy of the natural organization, illustrates the process involved in cooperation and speaks to the strength of the assumptions of our model. The description focuses first on the action of the production units, which were solving word puzzles as their production activity, and second on the way in which these units interacted with the other units in their respective simulations. Production in the optimal organization was not confined solely to efforts of the assigned production units. An integrative form of subcontracting developed in which the Red Division, job- and resource-poor, was given puzzles to solve on commission. Red borrowed funds from other units to supplement what little money it owned and had the production

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units buy it puzzles. The return on solved puzzles was split among the production units and the Red Division. In the fourth round of this trial, several diaries show that staff units loaned funds to the production units to purchase raw materials. Units in the optimal game also began to pool resources for reinvestment in the firm as early as round 2. They cooperated in an effort to deal with the unexpected absence of a unit head, which could have cost the entire group available resources and opportunities. This cooperative effort contrasts sharply with events in the parallel natural organization. Two production units that must cooperate were merged through the actions of one of those units and then changed their name to Supercomtin. One member’s diary for session 3 says, “I really do think we are the most important (unit). We are really tight now. We have decided to stay together no matter what. It is also evident that the rest of the organization is against us.” The head of the super production unit was quite explicit about these views. In session 3, a member of another unit came to ask whether the production group would contribute to reinvestment in the organization. The head of Supercomtin wrote in his diary: They never invested any money in us to buy puzzles and they kept giving money to Routin [the other production unit] which was less successful than we were at production. We just felt that we were supposed to support ourselves. This theme continues in the next session (session 4): Although we did well, there was a lot of inter-group tension. I think people were jealous of us and were p . . . off at our attitude. We felt we didn’t need the rest of the organization and showed it in our relationship with the other group. Because of this, we had a meeting between the leaders of the groups. I, however, chose not to attend. During this trial, both groups were presented with a high-risk opportunity to expand their markets and to make a substantial improvement in performance indicators. Chance of success was only 50 percent, however, and failure meant a major decline in the indicators. In the optimal organization, the unit heads sent delegates to a meeting arranged by the communications unit and decided that they were doing well enough and should not take the risk. The natural organization proceeded in a dramatically different manner. The unit with information on the current indicator levels decided that for the “good of the organization,” the market expansion should be attempted. The members of the unit drafted a statement that they read to other units, telling them that the group could afford to take the risk and would be all right even if the attempt failed.

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To persuade the other units, the communication group presented false information to the others regarding current indicator levels. One diary describes this episode by saying, “Crunode [communication] was lying about some of the indicators in order to get the other units to contribute to the investment.” Then she points out that there was “complete lack of trust and sympathy between Emrel [personnel], Crunode and Comtin [production] which I understand since Crunode has been totally dishonest with us all until now.” The dynamic described here points out the individual-unit–centered activity in the natural organization and the integrative intergroup activity in the optimal structure. One member of the natural organization provides a succinct summary for sessions 1 and 2: “There was a lot of cooperation within the Blue Division. However, when members of other divisions came into the room, there was deception and non-cooperation.” Figure 10.1 shows dramatically the effects of those differences. Session 5 of trial 2 shows the adverse effects of the efforts devoted to the decision on the high-risk option. The difference in session 6 demonstrates the clear superiority of the optimal organization’s treatment of this issue. One additional example from trial 4 illustrates the differing dynamics of the games. Several members of both organizations became idle permanently because they had no job or income for two consecutive sessions. These members were sent back into the games as “government observers” in round 4. In round 5, they regained active status as “minorities” who had to be given positions of responsibility. In the optimal organization, the Board of Directors met and decided on a position for each “minority” member in the organization. The person currently occupying that position was given compensation including a vacation, a permanent salary, and a bonus. In the natural game, the “minority” member went from division to division asking for work (the group received a penalty if the person did not find a job). After a substantial effort, the “minority” member made an arrangement by written contract with one unit. When the next round of play began, however, that unit said that the deal was a lie, made up only to avoid the penalty. Substantial conflict erupted immediately. One of the “minority” members who moved from an ordinary player in one game to a “minority” in the other characterized the two situations in terms of his own enjoyment. “In Ingot [natural] it was fun screwing over the environment and other people. In Extol [optimal] it was fun making decisions in a corporate manner with results that reinforce your belief in yourself.” Differences between the organizations also are reflected in the personal assessment forms collected in trials 1 and 3 (these forms were not collected for the other four trials). One question on these forms asked how well participants thought the organization was doing. The response options

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consisted of a three-point scale: 1 = the organization is not doing well; 2 = the organization is doing fairly well; and 3 = the organization is doing very well. After round 1, the average responses in the natural organizations were 2.25 and 1.87 respectively for trials 1 and 3. These averages were slightly better than in the optimal organizations, whose corresponding scores were 2.11 and 1.61 respectively. By the end of the last round, however, the self-evaluations were decidedly different. The naturals had dropped their average evaluations to 2.06 and 1.47 respectively in trials 1 and 3. The optimals, in contrast, raised their evaluations to 2.71 and 2.44.

Discussion Although descriptions of game events and diary entries tend to support the contentions of the major proposition of the model, they also contribute to the viability of the set of assumptions underlying the model. In the model, assumptions such as conflict leading to commitment to the home unit (assumption 4) were taken as axiomatic, and we did not undertake a test of such statements. The participants’ diaries, however, give evidence that the players saw the work in ways that were consistent with assumptions 3 through 7. Resource scarcity and uncertainty about game rules led initially to conflict over proper strategy. Groups sought to ensure the security of their home units and attended to attainment of unit goals in the initial sessions. The learning of roles clarified the need for distribution of resources, and units began to approach one another. Efforts at interunit cooperation resulted where friendship links extended across groups, but concentrations of within-group friendships produced cooperation only within single units. The outcome was differential performance on the overall levels of performance that required organizationwide cooperation. Several particularly observant participants noted aspects of this effect in their diary entries: [Trial 2, session 5, optimal] We are all participating in investments and sinking money into buying puzzles. At the start of the game, this never happened. Every department was concerned with their [sic] own success and didn’t discuss matters of an organizational nature. Now it seems like everyone is out for the good of the organization. I think we have established a trust based on competency and effectiveness. [Trial 2, postgame, natural] Together with friends the members didn’t need to trust those they didn’t know because they already

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These two participants illustrate the importance of preexisting friendship ties, the formation of cohesive, within-group interests, and the difficulty of cooperating across units. The latter example focuses on the difficulties created by strong within-group ties; the former exemplifies the benefits of ties between groups. The importance of external links is demonstrated by the superior speed with which the optimal groups raised the performance indicators and solved unexpected problems presented during the simulations (e.g., Figure 10.1, trials 2, 3, and 5). The qualitative observations and diary records enhanced our faith in the validity of the model by illuminating the process that led to these results. Future Considerations We do not expect that this theory will be universally applicable. Not all organizations will benefit from friendships that cross division boundaries; such friendships may incur costs (cronyism, for example). Another issue is whether all organizations require the same amount of cooperation, even during a crisis. When the organization, such as a bank, is facing pooled interdependence, very little coordination is required across subunits to be efficient. That is, some crises may be restricted to single units or may require much less coordinated effort because of the technology employed by the organization or the limited scope of the crisis itself. In such cases, the cooperation that results from friendship links between subunits may be less useful. In any event, finding the boundary conditions and the associated moderating variables will be an important part of the development of this model. Let us ask a related question: Is there a linear relationship between the E-I index in the organization and the cooperation between subunit pairs? Perhaps the cooperation reaches a maximum asymptotically with relatively few links between the pairs of subunits. Answers, even speculation, await further research. Issues of External Validity As with any laboratory experiment, questions arise as to how far the results can be generalized to “real-life” situations. We have tried to

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emulate an organization in chaos and crisis, but there are significant differences between these simulated organizations and typical business organizations. In our organizations, for example, no hierarchy of divisions or centralized authorities was imposed on the players. Part of their task was to create their own hierarchy or centralization, if they chose to do so. It is possible that a centralized, hierarchic structure would respond to crises differently from organizations without such central authority. Another similar argument could be raised about the existence of formal communication links that cut across divisional boundaries. These links may not be based on friendships, but it could be argued that such formal channels might facilitate a cooperative effort in the face of a crisis. We have no formal basis for discounting such objections. Indeed, organizations often have more centralized authority and more formal communication channels than those in our experiment. Yet, as mentioned earlier, the literature on crises indicates that these formal, routinized links often are abandoned during crises and may be ineffective. Informal, nonhierarchic ties become more prominent. Nonetheless, it would be interesting to test whether the density of friendship ties across work-unit boundaries has as great an effect on performance in highly centralized firms as in decentralized firms. This simulation also has other limitations that prevent us from generalizing too quickly into the field. The performance indicators of necessity are objectively quantifiable – and consequently somewhat arbitrary. Although arbitrary indicators of performance often exist as well in the real world, they do not necessarily take the same form as in our simulation. Absences, for example, carry some cost to an organization, but to state that they deplete “total output” by precisely two points each may not reflect most organizations’ assessment of such costs. In fact, if the right people were absent, the organization might enjoy increased efficiency and output. As arbitrary as these indicators are, they are also known to the players of the Organization Game. Our intent here was not to mimic exactly the performance and reward system of the “real” world. Rather, we created a complex system of performance indicators that the players would have to work to fathom, unravel, and deal with – just as they would in the real world. In our current design, however, we cannot attest whether the results reported here are sensitive to these arbitrary reward structures. Again, this is an area for future research.

Conclusion Organizations are cooperative systems by nature. We have suggested here that the friendships that exist in all organizations can either hinder or

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facilitate that cooperation in times of crisis, depending on whether those friendships cut across subunit boundaries. In particular, we argue here that organizations, left to their natural progression, will develop dysfunctional structures that concentrate friendships within subunits. Effective friendship structures, then, must be designed consciously. The theory would suggest that more attention be given to organizing informal relationships and nurturing their development across organizational subunits. Organizations that must deal with crises should encourage personal relationships outside their immediate work unit. The existence of a dense network of cross-unit ties may result in the resolution of complex situations before they escalate to the point where organization members define them as crises. That is, external ties may affect the magnitude and frequency of crises in the organization. In contrast to the view that impersonality is preferred in organizations and that personal relationships are dysfunctional (Merton, 1957), we believe that personalized ties are a reserve resource that provides the potential for the coordination needed to meet rapidly changing circumstances. The challenge in organizational design, therefore, is to take the natural tendency for people to cluster together in friendship groups and use this to help bridge across administrative units. New research suggests that this challenge is not to be underestimated. Members of organizations prefer to see informal organizational structure in terms of clusters of friends, with clusters connected within organizational units (Kilduff, Crossland, Tsai, and Krackhardt, forthcoming). The task of changing this set of cognitive expectations to incorporate the different design principles articulated in this chapter would seem to require intensive training (cf. Janicik and Larrick, 2005). And, as we discuss in the next chapter, friendship clusters tend to enforce their own normative interpretations on their members and therefore may be difficult for management to influence.

Appendix to Chapter 10: Detail on the Calculation of the Performance Indicators The performance indicators are functions of actions taken by game participants. All four indicators are set initially at 100. The following Organization Game rules stipulate how each indicator is calculated after each session: r Resource base. RB is a function of natural decline (–10 percent of RB in the prior session); investments in organizational improvements (+20 percent of the amount invested); production raw materials purchased (–1 or –2 per unit, depending on type); new

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job assignments (+1 per assignee); absences (–2 per absence); job quits and firings (–3 per action); vacations (–1 per vacation); temporary unemployment (–1 per person); and producing an accounting report (+2 if produced, –3 if not). r Total output. TO is a function of natural decline (–10 percent of TO in prior session); investment in organizational improvement programs (+10 percent of amount invested) and human resource development programs (+10 percent of amount invested); production (+3 or +4, depending on type); new job assignments (–1 each); absences (–2 each); firings and quits (–3 each); vacations (–1 each); temporary unemployment (–1 per person); and production of management consulting report (+2 if produced, –3 if not). r Internal cohesion. IC is a function of natural decline (–10 percent of IC in the prior session); investment in human resource development programs (+20 percent of the amount invested); interunit cooperation in production (+2 per unit produced); new job assignments (–1 each); absences, firings, and quits (−3 each); vacations (–1 each); permanent unemployment (–3 per person); temporary unemployment (–1 per person); and producing a report on organizational communication (+ 2 if produced, –3 if not). r Member commitment. MC is a function of natural decline (−10 percent of MC in the prior session); investment in human resource development programs (+20 percent of the amount invested); overall production (+2 per unit); absences (–2 each); firings and quits (–3 each); permanent unemployment (–5 each); temporary unemployment (–1 each); and production of a management consulting report (+2 if produced, –3 if not). These adjustments to the indicators are applied at the end of each session of the simulation in which they occur, except for certain investments that are delayed one round pending actual implementation of the appropriate program. Finally, players in certain positions are given a “salary,” the size of which depends on how well the organization is doing (as defined by the preceding performance indicators). These funds can be used to invest in programs, buy raw materials, or pay for other items; such decisions affect the performance scores in succeeding rounds.

11 The Control of Organizational Diversity

In the previous two chapters, we showed the relevance of social networks for the understanding of turnover and crises in organizations. But what enables organizations to promote coordination and collectivity? How do people with diverse backgrounds, goals, and values successfully coordinate their activities in organizations? The usual answer to these questions is that organizational culture provides the glue that keeps the organization together. But the organizational culture literature has neglected the importance of social connections in producing shared systems of meaning. In this chapter, we begin the process of remedying this oversight through an emphasis on how people who are tied to each other create locally shared cognitive understandings. First, we provide an in-depth analysis of how the diversity of cultural interpretations within one organization is controlled through the friendship network. Second, we extend the discussion to include how network embeddedness affects agreement concerning the structuring of networks across three different organizations. Previous organizational culture research has tended to treat the culture of an organization as an independent variable that can be manipulated to control deviant behavior (e.g., Ouchi, 1980). From this culture-as-amanagerial-tool perspective, an effective organization is like a clan, in that it relies on mechanical solidarity – a religious adherence to common beliefs and practices – to ensure cooperation (Durkheim, 1933: 175–8). The clan cannot tolerate any divergence from the “totality of belief and sentiments common to all members of the group” ( Durkheim, 1933: 129). In contrast to this managerial emphasis, anthropological research has shown that organizational culture is an emergent property of informal relationships within workgroups (for reviews, see Baba, 1986; Holzberg and Giovannini, 1981; Trice 1985). Researchers within this tradition have investigated how norms, beliefs, attributions, behaviors, and other aspects of organizational culture are controlled through the informal networks of coworkers. Each culture develops its own system of knowledge 236

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(Romney and D’Andrade, 1964) and this knowledge is dispersed both among experts and novices (Romney, Weller, and Batchelder, 1986). Interaction between group members results in knowledge diffusion (Carley, 1991) concerning important aspects of the culture, such as the distribution of roles and relations (D’Andrade, 1984: 110). Effective action within a specific culture requires an understanding of how that particular world is organized (D’Andrade, 1995: 182). That is, an important part of cultural knowledge is the knowledge of how to operate in this complex web of relations and dependencies. This knowledge in turn depends in part on knowing who is related to whom in important ways (cf. Chapter 5). Thus we see two different views on organizational culture in the literature. From the culture-as-a-managerial-tool perspective, culture is a unifying force that binds people together (Siehl, 1985). Culture as an emergent property of personal relationships suggests a more fragmented view of culture, with the possibility of competing subcultures existing within the same organization (cf. Gregory, 1983). This chapter builds on both of these perspectives to suggest that institutionalized traditions, set in place by the organization’s founders, shape and are shaped by emergent beliefs and actions. Organizational culture, at any point in time, can be expressed as a set of social constructs negotiated between organizational members to anticipate and control the motivational and cognitive diversity in the organization (cf. Wallace, 1970: 36). These shared constructs allow organizational members to make sense of ongoing organizational activities. In this chapter, we treat culture as a cognitive system (as defined by Keesing, 1974) that is negotiated between interacting individuals who create what Geertz (1973) has referred to as locally shared systems of meaning. In study 1, we describe a method for eliciting the overarching cultural constructs utilized by people in organizations, and we look at how the network of informal relationships in the organization controls the way people use culturally defined constructs. In study 2, we investigate how clusters of individuals reinforce idiosyncratic understandings, with a specific focus on locally reinforced perceptions of network relations.

Study 1 The organization selected as the research site was a small regional distributor called Pacific Distributors Incorporated (PacDis) that employed a total of 162 people at its headquarters and four branch offices. The company had been founded by its current president, John Briggs, but was run on a day-to-day basis by Bob Jamison, who had been with the firm

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since its inception. Jamison had worked his way up from salesperson to chief operating officer. According to consultants’ reports, there was an ongoing ideological struggle between two main groups in the organization. On the one hand, there were those like Jamison, who believed in the primary importance of maintaining good social relations within the organization. On the other hand, there were those like Ralph Gibson, the chief financial officer, who believed that financial control and the bottom line were of paramount importance. Compared to Jamison and Gibson, President Briggs was removed from the everyday running of the organization, but as the founder of PacDis he had been instrumental in establishing the cultural and expressive components of the company (cf. Pettigrew 1979: 574). He was a strong believer in the importance of a friendly, open style of management that placed a great deal of trust in each employee. We conducted a series of structured interviews with a sample of PacDis employees to uncover the cultural dimensions that characterize the workplace. This phase was designed to elicit a set of constructs used by these employees to organize the diversity of styles and approaches that we had observed and to anticipate each other’s behavior. Based on the results of this first phase, we developed a questionnaire to examine how the network of relationships influenced the applications and interpretations of these cultural constructs. Phase 1: Eliciting the Cultural Constructs Phase 1 consisted of eliciting the cultural constructs from our structured interviews of PacDis personnel. Method Subjects We interviewed six men and four women, chosen from the full sample of key management and operational personnel whom we planned to include in the second phase (see the “Method” section of phase 2). Previous research has indicated that the majority of all constructs can be generated by a relatively small sample within a population (Dunn, Cahill, Dukes, and Ginsberg, 1986: 372). We wanted to capture the diversity of perspectives that existed in the organization. To this end, we interviewed the three top executives who epitomized the cultural tensions in the firm (the owner, the chief operating officer, and the chief financial officer) and seven other employees selected to represent four diverse functional areas and various levels in the firm (two department heads, three supervisors, and two nonsupervisory employees). Each individual in the sample was promised and provided with personalized feedback concerning the constructs elicited in the interviews.

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Procedure Given our cognitive approach to organizational culture, we turned to personal construct theory (Kelly, 1955) for a technique designed specifically to elicit the cognitive constructs that individuals use to anticipate the behavior of others. George Kelly’s repertory grid technique has been used in a wide variety of settings to enable individuals to verbalize the cognitive constructs they use to organize and anticipate events (e.g., Romney and D’Andrade, 1964: Wexler and Romney, 1972). Each of the ten employees was interviewed at the work site by a researcher for up to ninety minutes using the structured but informal grid technique outlined by Eden, Jones, and Sims (1983). The interviewers presented the participants with three names at a time, asking, “In what important way are two of these people alike but different from the third?” and, “How is this person different?” Nine names of PacDis employees were utilized, and twenty-four triads were presented to each participant so that each pair of names occurred twice (Burton and Nerlove, 1976). Research has shown that each individual has only a limited number of constructs relevant to any particular domain and that few new constructs are elicited after twenty or so triads have been presented (Hunt, 1951). For each triad, a similarity and a difference were elicited to form the verbal labels of two poles of a bipolar construct. The interviewers followed Kelly’s method of encouraging participants to articulate the distinctions and similarities suggested by the triads, to elaborate spontaneously on the bases for their discriminations. Facile similarities, such as, “They’re both in marketing,” were not ignored, but following Kelly (1955: 222), participants were encouraged to keep talking so that important psychological similarities and differences would emerge. As verbal labels for construct poles were elicited, they were written down by the researchers and confirmed by the participants. The informality of this technique was designed to encourage “thinking aloud,” the verbalization of unconscious and taken-for-granted constructs. This flexible form of the repertory grid technique provides much more information about the subjects’ constructs than paper and pencil tests (Kelly, 1955: 224). Results of Phase I On average, each subject used twenty-one constructs (standard deviation = 4.5), with the number ranging from thirteen to twenty-nine. We examined the ten lists of elicited constructs to see whether any common constructs were present. According to Kelly, verbal labels are not the constructs themselves but merely signify processes that may or may not have been previously verbalized. Therefore, we looked for similarities in ideas rather than in exact wording. For example, “Follows procedures vs. More freewheeling” and “Likely to go by the book vs. Likely to break rules” were counted as representing the same basic construct. Seven such

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Table 11.1. The Seven Elicited Constructs and the Number of People Who Used Each Construct Number of People Who Used Construct (Maximum = 10) 10 8 9 6 9 6 10

Constructs Inflexible, critical Does the job and nothing more Goes by the book Lets things slide Easy-going, relaxed Tactful, diplomatic People-oriented

←→ Flexible, tolerant ←→ Eats, sleeps, and breathes PacDis ←→ Prepared to cut corners ←→ Efficient, organized ←→ Aggressive, competitive ←→ Straightforward, blunt ←→ Task-oriented

constructs were identified, each of which had been spontaneously generated by at least six out of the ten participants. Verbal labels for the poles of the seven constructs were selected from individual protocols to accurately reflect their consensual ideas. The seven constructs are presented in Table 11.1. These constructs summarize the major contrasts in behavioral styles experienced by organizational members. According to Wallace’s (1970) view of culture, these constructs allow organizational members to anticipate the diversity of behaviors in the organization. The constructs capture various aspects of the organization’s main ideological struggle as delineated by the consultants’ reports and supported by our own observations. This struggle was between those who, like Bob Jamison, favored a flexible, easy-going company, and those who, like Ralph Gibson, preferred a critical, rule-based approach. In summary, a set of seven constructs was elicited from a subsample of ten people using Kelly’s repertory grid technique. By eliciting the cultural constructs from organizational members, we were able to approach culture from the participants’ rather than the survey researchers’ point of view. The seven constructs were assumed to express vital aspects of the organization’s culture and to possess psychological resonance for each individual in the organization. Phase 2: Network Relations and Cultural Attributions Our view of organizational culture as a cognitive system negotiated between interacting individuals suggests that people use the social network to find support for their own interpretations of experience. We expected that PacDis employees would tend to agree with their friends on how flexible or inflexible other employees were, how people-oriented versus task-oriented they were, and so on. Through processes of social

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comparison (Festinger, 1954), the attributions people make about others in the organization tend to be influenced by and aligned with the attributions of their friends. From the social comparison perspective, people evaluate beliefs (such as whether Smith is efficient) by comparing their uncertain opinions with others in their social network (Festinger et al., 1950; Kilduff, 1990). Based on the preceding discussion, we hypothesized that, relative to nonfriends, friends would construe fellow workers similarly on each of the seven cultural dimensions (hypothesis 1). The social network operates not only to support idiosyncratic patterns of attributions but also to control the diversity of possible attributions. People who can find little support for their opinions among their friends are likely to be in a state of discomfort or cognitive dissonance (Festinger, 1957) because they hold two beliefs that are incongruous with each other, namely, “I like my friends,” and, “My friends dislike my opinions.” This discomfort is likely to manifest itself in a number of ways, including a reduction in overall satisfaction with work. Thus, we predicted a positive correlation between how closely individuals agree with their friends and how satisfied they would be with their jobs (hypothesis 2). Method Subjects Forty-seven of the 162 PacDis employees (twenty-four men and twentythree women) were paid $10 each to complete and return a lengthy questionnaire. (Although the questionnaire contained forty-eight names, only the responses from the forty-seven people who completed the instrument were included in the subsequent analysis.) We used two criteria for selection in the sample. First, we included all the supervisors and management personnel at headquarters and at each of the four branches. Second, we asked the chief operating officer, Mr. Jamison, to assist us in identifying key operating personnel in accounting, purchasing, and manufacturing in the organization. We added these employees to our sample to ensure that we captured the entire operational core of the organization. Each subject was thoroughly briefed concerning the aims and outcomes of the research after the study was completed. Measures To measure friendship choices, each person was provided with a list of all forty-eight people in the sample and asked to check the names of his or her personal friends. On a separate form, each respondent was also asked to check the names of individuals whom the respondent thought would consider the respondent a personal friend. These data were aggregated into one N × N matrix using the following rule: If persons i and j both

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agree that person i considers person j to be a personal friend, then entry (i, j) in the matrix equals 1. Otherwise, entry (i, j) equals 0. The resulting adjacency matrix was labeled the Friendship Matrix. Attributions about fellow workers were measured using the seven elicited constructs from phase 1. For each of the seven constructs, each person rated every other person on a seven-point Likert scale (where 1 = one end of the bipolar dimension, and 7 = the opposing end of the same dimension). For example, Bob Jamison rated Ralph Gibson on how flexible and tolerant he was (as opposed to inflexible and critical), how task-oriented he was (as opposed to people-oriented), and so on. Jamison then rated each of the other forty-seven people (including himself) on the same scales. From these data, a coefficient of similarity for each pair of individuals for each construct was created. We accomplished this by calculating the Pearson correlation between their vectors of ratings. For example, the coefficient of similarity on the flexibility construct for Jamison and Gibson was the correlation between Jamison’s vector of “flexibility” ratings for each of the forty-eight employees and Gibson’s corresponding “flexibility” ratings for those same forty-eight employees. Repeating this procedure for each pair of individuals permitted the creation of an N × N similarity matrix of such scores for each of the seven constructs. These seven Attribution Similarity matrices were hypothesized to map onto the friendship social network. Overall job satisfaction was measured using the five items from the Michigan Organizational Assessment Questionnaire (Cammann, Fichman, Jenkins, and Klesh, 1983). These items consisted of seven-point Likert scales with end points labeled “strongly disagree” and “strongly agree.” Analyses To test hypothesis 1 (relative to nonfriends, friends would construe fellow workers similarly), the Friendship Matrix was correlated with each of the seven Attribution Similarity Matrices. To the extent that the hypothesis is true, a positive correlation should be observed between these matrices (i.e., the 1s in the Friendship Matrix should match up with high similarity scores in the Attribution Similarity Matrix). Because the unit of analysis for this correlation was the dyad, the test for this correlation was based on N × (N – 1) nonindependent observations. Thus, a nonparametric test, the Quadratic Assignment Procedure (QAP), was used to test the significance of the correlation (Baker and Hubert, 1981; Hubert, 1987; Hubert and Schultz, 1976). The QAP is a permutation-based test of significance for interdependent data of the sort encountered here (Krackhardt, 1988; see Chapter 3 for more details).

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Although the QAP procedure provides a significance test (expressed as a Z score), it does not reveal the strength of the relationship between two matrices. To measure the strength of the correlations, we calculated Goodman and Kruskal’s (1963) gamma, a nonparametric correlation coefficient that is a more appropriate descriptive measure than Pearson’s r for skewed and binary data such as are contained in the Friendship Matrix. We tested the second hypothesis – that relative to those who agreed with their friends, those who disagreed would be less satisfied – by creating an index of average agreement with friends for each individual. This index was the arithmetic mean of the attributional similarity scores that were calculated between each person and his or her friends. The agreement index was correlated with satisfaction scores for each person. Pearson correlations and t-tests were used instead of the gammas and QAP tests used for the first hypothesis, because hypothesis 2 involved predictions at the level of the individual. Results of Phase 2 A map of the friendship links in the organization (see Figure 11.1) shows a center-periphery structure in which there are no obvious cliques. At the center of the network, with many friends, is the chief operating officer, Bob Jamison (21). Close to Jamison, in terms of his role in the informal network, is the president, John Briggs (13), but far removed from the center of the network is the chief financial officer, Ralph Gibson (41). Consistent with our informal observations, the map shows that both Gibson and Jamison were friends with the president, but not with each other. There were five individuals who had no friendship links with anyone. These individuals were either from the computer group or from outlying branches. Their contact with other organizational members was minimal and mainly involved questions of technical support. The first question to ask is, How much diversity was there concerning attributions about fellow workers in this organization? Table 11.2 indicates that the diversity of evaluations on the shared constructs was extreme: Some pairs of individuals agreed completely on how they viewed others, whereas other pairs disagreed completely (correlations ranged from – 1 to + 1). The diversity of opinions that existed in this organization is also indicated by the magnitude of the standard deviations of the average correlations between attributional vectors. For example, on the construct “Prepared to cut corners vs. Goes by the book,” Table 11.2 shows that the average Pearson correlation between the 1,081 possible pairs of individuals’ vectors was .21, with a standard deviation of .24. On this dimension, 17 percent of the correlations were actually less than zero,

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Table 11.2. Distributions of Attribution Similarity Scores for Each Construct

Construct

Mean Attribution Similarity Standard % of Scores Score Deviation Minimum Maximum below .0

Flexible, tolerant Eats, sleeps, and breathes PacDis Prepared to cut corners Efficient, organized Aggressive, competitive Straightforward, blunt Task-oriented

.33 .44 .21 .30 .37 .32 .26

41

16

1.00 1.00 1.00 1.00 .93 .96 1.00

6.3 1.7 17.3 8.9 3.8 8.8 11.5

36

5

15

27

10

40

13 28

−.50 −1.00 −.98 −.78 −.58 −.98 −.81

.21 .21 .24 .22 .21 .24 .22

31

12

1 22

18

35 25

7

43 26

34

46 23

21 38 8

9

45

14

42

19

44

32

33

48

37

24

20 4

39

3

17

11

29

Figure 11.1. Friendship sociogram on multidimensional scaling representation of path distances.

indicating considerable differences in how individuals construed their fellow workers. Another descriptive issue is whether the seven dimensions can be reduced to fewer dimensions for analysis (i.e., did people think of “flexibility” and “prepared to cut corners” as the same thing?). To shed light on this issue, the correlations among the seven dimensions are reported

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Table 11.3. Correlations among the Seven Dimensions Dim1 Dim2 Dim3 Dim4 Dim5 Dim6 Diml Dim2 Dim3 Dim4 Dim5 Dim6 Dim7

Dim7

Flexible, tolerant 1.000 .091 .149 .126 .275 .220 .224 Eats, sleeps, and breathes PacDis .091 1.000 −.012 .330 .167 .111 .113 Prepared to cut corners .149 −.012 1.000 .061 .083 .117 .095 Efficient, organized .126 .330 .061 1.000 .069 .023 .088 Aggressive, competitive .275 .167 .083 .069 1.000 .181 .181 Straightforward, blunt .220 .111 .117 .023 .181 1.000 .249 Task-oriented .224 .113 .095 .088 .181 .249 1.000

Table 11.4. The Relationship between Friendship Links and Similarity of Cultural Attributions Construct

Gamma

Z (QAP)

p-Level

Flexible, tolerant Eats, sleeps, and breathes PacDis Prepared to cut corners Efficient, organized Aggressive, competitive Straightforward, blunt Task-oriented

.33 .28 .31 .33 .24 .27 .32

3.960 3.330 3.854 4.276 2.866 3.205 4.342

.0001 .0005 .0001 .0001 .005 .001 .0001

in Table 11.3. Most of the correlations were small. Of twenty-one pairs of dimensions, only one pair was correlated higher than .3: “Eats, sleeps, and breathes PacDis” and “Efficient, organized” were correlated at .33. Rather than collapsing these dimensions into subscales, we considered these dimensions to be sufficiently independent to warrant separate analyses. The first hypothesis asks, Was the diversity of attributions random or was the diversity patterned by the friendship network? The answer is given in Table 11.4, which shows that the attributions of friends were significantly more similar than those of nonfriends for each of the seven constructs (p ≤.005 for each construct). The gammas, ranging from .24 to .33, indicate a moderately strong relationship between friendship and attribution similarity. There are at least two alternative explanations for this relationship. From a demographic perspective, those who join a firm around the same time form a cohort within which attitudes are likely to be similar (because of similar experiences) and friendships are likely to develop (Pfeffer, 1983; Wagner, Pfeffer, and O’Reilly, 1984). From this perspective, we would expect the observed relationship between friendship and attributional similarity to disappear when we control for tenure in the organization.

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Table 11.5. Partial Correlations between the Friendship Network and Agreement on Each Construct Controlling for Alternative Explanations Construct Flexible, tolerant Eats, sleeps, and breathes PacDis Prepared to cut corners Efficient, organized Aggressive, competitive Straightforward, blunt Task-oriented

Controlling for: Tenure Formal organization Tenure Formal organization Tenure Formal organization Tenure Formal organization Tenure Formal organization Tenure Formal organization Tenure Formal organization

Gamma

Z (QAP)

p-Level

.29 .26 .27 .25 .27 .25 .31 .27 .22 .18 .26 .20 .27 .25

3.961 3.735 3.327 3.302 3.855 3.777 4.277 4.081 2.872 2.619 3.203 2.911 4.355 4.098

.0001 .0001 .0005 .0005 .0001 .0001 .0001 .0001 .005 .005 .001 .005 .0001 .0001

To test for this alternative explanation, we used a multiple regression extension of QAP (Krackhardt 1987b, 1988). To partial out the effects of tenure, we created an N × N matrix whose (i, j) cell was set equal to one if i and j arrived the same year at PacDis (i.e., were cohorts in the same entering “class”); otherwise the cell (i, j) was set equal to zero. Table 11.5 shows that the hypothesized relationship remained strong and significant, even controlling for tenure. The p-values ranged from .005 to .0001 and the gammas ranged from .22 to .31 (compared to .24 to .33 if tenure is not controlled for). The second alternative explanation that we considered derives from the idea that people in similar organizational positions make similar kinds of judgments (Walker, 1985). Perhaps people make friendship choices from among those in similar roles, and thus the observed correlation between friendship and attribution similarity is spurious. To test this alternative explanation, we controlled for formal organizational position. An N × N matrix describing the formal organization was created such that the (i, j) cell was set equal to one if i reported to j in the formal organizational chart; the (i, j) cell was set to zero otherwise. As the results in Table 11.5 show, the hypothesized relation between friendship and attributional similarity was still highly significant (p-values ranging from .005 to .0001), and the gammas, ranging from .18 to .27, continued to indicate a moderately strong correlation. Thus the hypothesis that pairs of friends, compared to pairs of nonfriends, would be more similar in how they construed their fellow workers received strong support. The relationship between friendship and

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Table 11.6. Correlations between Attitudes and Average Agreement with Friends Construct

r

t-Value

p-Level

Flexible, tolerant Eats, sleeps, and breathes PacDis Prepared to cut corners Efficient, organized Aggressive, competitive Straightforward, blunt Task-oriented

.48 .47 .69 .47 .22 .45 .23

3.627 3.615 6.355 3.540 1.484 3.384 1.620

.0005 .0005 .0001 .0005 NS .001 NS

attributional similarity remained significant even controlling for either cohort or organizational structure effects. Friends, then, tend to see the world similarly. But what if individuals disagree with their friends? The results in Table 11.6 suggest that such disagreement reduces job satisfaction, as predicted in hypothesis 2. For five of the seven dimensions, the Pearson correlations between agreement and satisfaction ranged from .45 to .69 (p-values from .001 to .0001). These high correlations indicate that whether individuals agreed or disagreed with how their friends viewed others in the organization had a powerful influence on their levels of job satisfaction. Study 1 Discussion We have described a method for uncovering the cultural constructs that people use to make sense of their interpersonal experiences in organizations. In the first phase of the research, we found that the repertory grid technique captured the ongoing tension in the PacDis organization between established and emergent norms. The original values of flexibility and people-orientation stressed by the organization’s founders were being challenged by a more rule-bounded and task-oriented approach. In the second phase of the research, we confirmed that interpersonal networks support individual interpretations of experience, and that these networks help control the diversity of possible interpretations. Interpersonal networks are one of the media through which organizational culture is maintained and challenged. Those who find support among their friends for idiosyncratic interpretations of the culture are more satisfied with their jobs than those whose interpretations run counter to friends’ views. The two-phase research design allowed us to capture some of the richness of a particular organizational setting in the actual questionnaire used to test theoretically derived hypotheses. But the present study lacks much

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of the thick description and longitudinal analysis that characterizes earlier ethnographic studies of informal relationships at work (e.g., Dalton, 1959; Whyte, 1948). In addition, the question arises as to whether perceptions of the structure of an organizational network are themselves affected by people’s structural positioning in the network. It is to this latter question that we turn next.

Study 2 Anthropological studies of culture have emphasized the degree to which consensus concerning kinship and other social relations serves to define different cultures (Romney and D’Andrade, 1964; Romney, Batchelder, and Weller, 1987; Romney, Weller, and Batchelder, 1986). Network structures in traditional societies determine “most of one’s positions . . . and most of what one will be expected to do” (D’Andrade, 1995: 19). An important question that any approach to culture must address is the relationship between culture and social structure (see discussion in D’Andrade, 1984). Cultural knowledge is clustered in the minds of interacting individuals. The organization resembles a magnetic field “of personal forces” (Barnard, 1938: 75) in which individuals and groups attract and repel each other, developing idiosyncratic interpretations of the culture that are reinforced through social interactions (as shown in study 1). Respondents “may give strikingly different descriptions” of the network relations within a particular group (Geertz and Geertz, 1975: 1). To understand the culture is to understand how the network ties between individuals shape their perceptions of the social world. The Structure of Cultural Agreement Individuals who interact with each other are likely to have a higher agreement concerning the culture than non-interacting individuals (as we demonstrated in study 1). Further, some relations (strong ties, for example) are likely to produce more cultural agreement than others. Simmel (1950) moved beyond the distinction between strong and weak ties by examining the special nature of dyadic ties embedded within triads. He suggested that relations embedded in a triad are stronger, more durable, and in particular more able to produce agreement between actors than relations that are not so embedded. Research confirms that dyadic relations embedded in triads (relative to dyadic relations in general) are more stable over time (Krackhardt, 1998) and exert more pressure on people to conform to clique norms and behavior (Krackhardt, 1999).

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Due to Simmel’s pioneering work in this area, we refer to dyadic ties embedded in three-person cliques as Simmelian ties. The culture of the organization is communicated through social networks (Krackhardt and Kilduff, 1990). But part of what is communicated is information about the social network itself. The social network is, therefore, both the vehicle through which cultural meaning is communicated and an important topic of cultural communication (see the related discussion in D’Andrade, 1984). Clusters of individuals reinforce potentially idiosyncratic understandings of many aspects of organizational culture, including the structure of roles and relationships. Advice and Friendship Relations in Organizations Thus, cognitions about social relations are an important aspect of culture. In modern organizations, informal advice and friendship relations are critical for decision making and resource allocation (see the discussion in Krackhardt and Hanson, 1993). The structure of these networks resides as tacit knowledge in the minds of organizational members in the form of cognitive maps (Krackhardt and Kilduff, 1999; Kumbasar et al., 1994). To the extent that people agree about the structure of advice and friendship relations in organizations, they share an understanding of important aspects of the culture of the organization. Our focus on knowledge concerning advice and friendship networks enables us to examine both instrumental and expressive domains (cf. Lincoln and Miller, 1979). Knowledge about advice relations is instrumental in the sense that such knowledge is the key to understanding how work gets done, how daily routine exceptions are handled, and who the experts are in the organization. Knowledge of who goes to whom for advice can be advantageous in short-circuiting long indirect chains of information gathering in the firm. Knowledge about friendship relations, on the other hand, is useful in determining who can trust whom, who is more likely to cooperate with whom, and who is likely to go to whose defense in a political scrap (Krackhardt, 1992). Dyadic and Simmelian Ties As advice and friendship networks develop in a firm, how does cultural agreement emerge? Certainly, agreement could follow the structure of the ties themselves. As two people interact in an advice relationship, for example, they are likely to share information about who else advises others. Dyadic ties are, therefore, likely to induce similarity in beliefs about the advice network. Similarly, friends may influence each other in their beliefs about who is a friend of whom in the firm. But, if Simmel is to be believed, these similarities in perceptions should be enhanced through the agreement-creating force of Simmelian triads

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(see the discussion in Krackhardt, 1999). The likelihood that two friends or advice partners will reach agreement concerning the structure of social networks should increase if these two people are members of the same strong clique. Disagreements within the clique are more likely to be mediated by a third party friendly to two antagonists in a three-person clique than in a dyad. Further, sense-making processes within such cliques are likely to be particularly effective in providing individuals with opportunities to compare beliefs with similar others (cf. Festinger, 1954), thus facilitating the process of clarification concerning many aspects of organizational culture, including information about who the informal leaders are and who is connected to whom. In summary, we predict that relative to dyads in general, dyads embedded in Simmelian triads are likely to have higher agreement concerning who is tied to whom in the organization (hypothesis 3). Dyads embedded in Simmelian triads (relative to dyads in general) are likely to exhibit agreement on many other aspects of the structure of the social worlds in which individuals’ careers are formed. The social structure of organizations is likely to be opaque and subject to discussion and interpretation. For example, an important aspect of social structure is the organization of the network into cliques. From the perspective of coalition formation, knowledge about cliques is likely to be useful in predicting where alliances might form (see discussion in Murnighan and Brass, 1991). The members of Simmelian triads are, we argue, likely to share understandings concerning who is Simmelian-tied to whom. Of course, dyads in general will tend to share beliefs about who is in which informal group, but dyads embedded in Simmelian triads are likely to have higher agreement concerning who are embedded together in triads in the organization (hypothesis 4). Method To test the general proposition that Simmelian ties produce more conformity in cultural beliefs than raw dyadic ties, we examined the structural relations and beliefs about these structural relations among employees in three entrepreneurial firms. These firms were all small (less than two hundred employees) and involved in state-of-the-art technologies in each of their areas. They all faced stiff competition from much larger players in their industries but were doing well within their particular niches. At each of the three organizations (Silicon Systems, Pacific Distributors, and High-Tech Managers – all described in Chapter 3), participants were promised and given an overview of the findings. At all three sites, the same questionnaire was used as described in Chapter 4. The high response rates

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(varying from 92 percent to 100 percent) reduced problems associated with nonresponse bias. Each respondent gave us a complete cognitive map of his or her perceptions concerning who were friends with whom in the organization, and who went to whom for advice about work-related matters (as described in Chapter 3). Dyadic Ties The raw dyadic ties (Ri j ) were created from the locally aggregated structure (Krackhardt, 1987a). The procedure was the same for both the friendship and advice networks. A tie existed from person i to j only if person i claimed that i was a friend of (or asked advice from) j and person j agreed that person i was a friend of (or asked advice from) j. Thus, a friendship or advice link from i to j was defined as existing when both parties agreed that it existed. If both respondents did not confirm the existence of this relationship, then the tie was considered not to exist. If either person did not fill out the questionnaire, then the other’s response was taken as a valid indication of the relationship. If neither of the two people filled out the questionnaire, then the relationship was deemed to exist if and only if the majority of others in the sample said that the particular relationship existed. Simmelian Ties and Hypergraphs Simmelian ties are dyadic in nature (they occur between pairs of people) but they require more than dyadic information to ascertain. To generate the S matrix of Simmelian ties from R, the matrix of raw dyadic ties, the hypergraphH matrix was first created (see Berge, 1989; Wasserman and Faust, 1994, for more information on hypergraphs). This hypergraph recorded every instance in which an actor belonged to a complete triad (defined as a triad in which each actor was tied to every other actor). LetH represent the hypergraph of all N actors mapped onto the set of complete triads,Hi j = 1 if and only if actor i is a member of the triad j; otherwise,Hi j = 0. We can use this representation to uncover Simmelian ties by multiplying the matrix form ofH by its transpose and then taking the boolean of that matrix: S = bool [HH] S will be an N×N matrix such thatSi j = 1, if actors i and j are Simmeliantied to each other, andSi j = 0 otherwise. One implication of this S matrix is that it not only reveals who are in the same connected triple but also, by implication, who are in the same strongly connected informal group

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Network Dynamics and Organizational Culture Person 2’s Perception of i’s Relation to j i tied to j i not tied to j i tied to j Person 1’s Perception of i’s Relation to j i not tied to j

A

B

C

D

Figure 11.2. Comparison of network perceptions of two people.

or clique in the organization (Krackhardt, 1999). That two actors are Simmelian-tied implies that they are comembers of the same clique and vice versa; or, in other words, S is a dichotomized clique comembership matrix. Cultural Agreement within Dyads We assessed the degree to which dyads reached an agreement concerning the structure of social networks as follows. Each person’s cognitive slice, B, of the structure of raw ties in the organization was taken directly from the individual’s responses to the questionnaire (see Krackhardt, 1987a). Thus, for the advice network for respondent k,B i j(k) = 1 if and only if person k checked person j’s name in response to the question “Who does i go to help or advice?”; otherwise,B i j(k) = 0. Thus, B is a matrix of what person k perceives the network to be. We created a cultural similarity matrix, C, by calculating an agreement measure (Pearson’s r) between each pair of individuals’ perceptions as given in B. As illustrated in Figure 11.2, the 2×2 table reflected two individuals’ perceptions of the set of dyads among all the actors in the firm. Methods of calculating Pearson’s r for such a table are numerous, using different nomenclatures (e.g., ϕ, point biserial correlation, Spearman’s rho, or S14), but all yield identical values (for a discussion, see Harris, 1975: 226). We used the S14 formula to calculate r (Gower and Legendre, 1986; see Krackhardt, 1990, for an example). The four cells in Figure 11.2 contain frequency counts. For example, A is the number of dyads where both persons 1 and 2 agree a tie goes between two other actors in the system (from i to j); B is the number of dyads where person 1 claimed a tie existed from i to j and person 2 claimed that no tie existed from i to j, and so on. C, then, is the matrix of these measures of agreement, whereCi j = r for the corresponding cell values in B(i) (B as perceived by respondent i) and B(j) (B as perceived by respondent j).

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To calculate the extent of dyadic agreement concerning Simmelian ties, we had to convert each person k’s belief matrix B(k) to a corresponding perceived Simmelian tie matrix, S(k) . Each S(k) was created in the same manner as before. A hypergraph matrixH(k) was created based on B(k) and then converted to a boolean S(k) . We created a matrix of agreement concerning these Simmelian ties, K, by calculating r for each pair of S(k) . Thus,K i j is the Pearson correlation of the corresponding cell values in S(i) and S(j) . Data Analysis To test the hypotheses, we assessed the overall relationship between the cultural agreement matrices (C and K) and the structural matrices (R and S) using the Goodman and Kruskal (1963) gamma. We chose gamma as a measure of association because of its direct interpretation in this context: It reveals the proportional reduction in error in “guessing” whether one pair of people will be in more agreement than a second pair given that the first pair of people is related (directly or Simmelian-tied) and the second pair is not. In other words, gamma tells us the extent to which our theory is making correct predictions (those people linked together are more similar in their perceptions than those who are not linked together). The other important methodological issue to be raised here is that C, S, and K are all symmetric matrices by construction. However, R (the matrix of raw dyadic ties) is not symmetric; indeed, many of the advice ties themselves are asymmetric. It is much more difficult for two matrices, one being symmetric and the other being nonsymmetric, to be strongly correlated with each other than two symmetric matrices. Thus, our predictions that Simmelian tie structures (which are symmetric) predict cultural agreement (also symmetric) better than raw dyadic ties (which are nonsymmetric) would become artificially supported. To eliminate this source of substantial bias, we temporarily symmetrized R (using a union rule for symmetry) before calculating the correlation (gamma) between R and the two cultural agreement matrices C and K. This union rule (as opposed to intersection rule) was chosen because it more closely reflects a solid theoretical interpretation of the social phenomenon under scrutiny. To see this, consider the two cases separately, one with a union rule and one with an intersection rule. For the advice network (the more asymmetric of the two relations under consideration), a tie is retained in the symmetrized version if either person i goes to person j or vice versa. In either of these cases, interaction occurs between i and j, and this interaction (no matter who initiates it) can lead to the exchange of information and influence in assessing what the rest of the network looks like. If we were to restrict the symmetrized network to an intersection case, then those cases where one person goes to another

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Table 11.7. Gamma Correlations Showing the Extent to Which (for Advice Networks in Three Organizations) Dyads Linked by Raw or Simmelian Ties Exhibited Agreement Concerning Organizationwide Raw and Simmelian Ties Dyadic Structure Dyadic Agreement about Ties

Raw Tie

Simmelian Tie

Raw tie

SilSys = 0. 06 PacDis = 0.19 HiTecMgrs = −0.10

SilSys = 0.20 PacDis = 0.24 HiTecMgrs = 0.17

Simmelian tie

SilSys = 0.07 PacDis = 0.34 HiTecMgrs = 0.06

SilSys = 0.37 PacDis = 0.49 HiTecMgrs = 0.57

Note: The highest correlations for each site are bolded.

for advice (but not vice versa) would be ignored (set to 0). Thus, they would be considered the same as two people who do not interact at all. It is the interaction of these people, and not just who initiates it, that creates the opportunity for influence and exchange of information. Thus, the union rule for symmetry makes more theoretical sense in this context than the intersection rule. This temporary symmetry adjustment permitted us to interpret the gamma correlation between the raw network R and C (or K) as the extent to which a tie from either i to j or from j to i, through interaction and the exchange of information, predicted cultural agreement between i and j. Results Our hypotheses, derived from cultural agreement theory, were that dyads embedded in Simmelian triads (relative to dyads in general) would exhibit greater agreement in the social structure of the organization. Table 11.7 presents the results of tests of the hypotheses for the network of advice relations. In general, the results supported the hypotheses. Specifically, when two people joined by an advice relation were embedded in a three-person advice clique, then those two people were more likely to have higher agreement concerning which other people in the organization were (a) joined by an advice relation and (b) joined by an advice relation that was embedded in a clique. The gamma correlations on the right side of Table 11.7 are larger than those on the left side, showing that dyads embedded in Simmelian triads had higher agreement than ordinary dyads. The strongest

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Table 11.8. Gamma Correlations Showing the Extent to Which (for Friendship Networks in Three Organizations) Dyads Linked by Raw or Simmelian Ties Exhibited Agreement Concerning Organizationwide Raw Ties and Simmelian Ties Dyadic Structure Dyadic Agreement about Ties

Raw Tie

Simmelian Tie

Raw tie

SilSys = 0.42 PacDis = 0.50 HiTecMgrs = 0.49

SilSys = 0.65 PacDis = 0.64 HiTecMgrs = 0.67

Simmelian tie

Sil Sys = 0.33 PacDis = 0.31 HiTecMgrs = 0.56

SilSys = 0.44 PacDis = 0.53 HiTecMgrs = 0.57

Note: The highest correlations for each site are bolded.

result was found in the bottom-right quadrant of the table, where the gammas for the three organizations were .37, .49, and .57. Being a member of a Simmelian advice clique appeared to predict particularly high agreement concerning which other people were embedded in similar cliques. Dyads embedded in Simmelian advice cliques may be prone to reaching agreement concerning the structure of social worlds, but is the same true for the friendship network? The answer is yes. Table 11.8 shows that, relative to ordinary friendship dyads, dyads embedded in Simmelian friendship cliques tended to have high agreement concerning both aspects of social structure that we investigated. All of the correlations on the right side of Table 11.8 are higher than the corresponding correlations on the left side of the table, indicating that Simmelian-tied dyads tended to have higher agreement concerning (a) who was friends with whom in the organization and (b) which friendship pairs were embedded in Simmelian triads. Comparing Table 11.8 to Table 11.7, we see that the correlations for friendship agreement were consistently higher than those for advice agreement. Further, whereas dyads embedded in Simmelian advice triads tended to reach the highest agreement on the question of which advice pairs were similarly embedded in Simmelian triads, the pattern was different for the friendship network. The highest friendship correlations are in the top-right quadrant of Table 11.8: Dyads embedded in Simmelian triads tended to be most in agreement concerning which others in the organization formed friendship pairs (irrespective of whether the pairs were Simmelian-tied). The correlations for this quadrant across the three organizations were .64, .65, and .67.

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The results support the idea that Simmelian-tied dyads (relative to dyads in general) reach a higher agreement concerning the informal social structure of organizations. The degree of agreement appears to vary depending on the type of structure, the type of network, and the particular organization. In testing the effects of Simmelian ties on cultural agreement, we built on study 1’s investigation of the ways in which social structures constrain the expression and interpretation of culture. We articulated the idea that the structure of an organization consists of the relationships among the actors of that organization. In network terms, the structure is a set of dyadic statements describing who is related to whom on particular dimensions, such as friendship and advice. The results are compatible with the Simmelian argument that we have presented but leave room for alternative explanations. For example, the data are binary and provide no information concerning tie strength. An alternative explanation might be that Simmelian ties are stronger ties, and that if one were to measure the strength of actors’ relations and not simply whether actors are members of the same clique, one might find that stronger ties predict more agreement. Although this “strength of ties” argument is plausible, such an explanation is certainly consistent with the Simmelian argument. Simmel would argue that co-cliqued relations will be stronger relations. But if stronger ties lead to more cliquing (rather than the other way around), then Simmelian ties are spuriously related to agreement. Our guess is that both explanations are true: Cliques lead to stronger ties and stronger ties lead to cliques in a reciprocating process that reinforces the relationship between Simmelian ties and agreement. It would be useful to have better access to “strength of tie” data to be able to explore this alternative explanation in more detail. Despite this possible tweaking of the underlying explanation of these results, we find support consistent with the theory that the social structure influences cultural understandings. The relation between social structure and culture appears much stronger for friendship structures than for advice structures. It is possible that friendship structures, with their implications of trust and cooperation, are more critical to the dynamic operation of work organizations. More energy may be spent on monitoring and sharing information about friendships than about advice relations. Simmelian ties predict higher levels of cultural agreement than raw ties. This appears to be true, independent of firm or of cultural domain (raw ties or Simmelian ties; advice or friendship). That such group-based ties are sources of powerful conformities speaks to the wisdom of Simmel’s original thesis. As Romney and his associates (1986) discovered in a

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similar argument, the agreement takes on a group form, and different groups can create their own cultural definitions. Their insight was an important first step. We have gone one step further in suggesting that dyadic processes of agreement formation become particularly powerful in the context of a specific type of group – the Simmelian triad.

General Discussion In these two studies, we argued that cultural beliefs emerged through a negotiated process. Cultural agreements are far from uniform across the organization but rather occur between sets of actors. Subcultures evolve as one group within the system forges agreement on one set of beliefs while other groups emphasize different cultural truths. Culture itself, then, becomes structured to the extent that different actors agree with other specific actors within the system. At the micro level, each dyad can be characterized by the extent to which the two individuals in the dyad agree on a particular cultural domain; this level of agreement between the actors constitutes a belief relationship between those two actors. At a more macro level, the aggregate set of dyadic belief relations among the actors of an organization can be considered one aspect of the structure of culture. One implication of these findings is that organizational culture can be only an imperfect management control device. To the extent that informal networks transmit and transmute the culture of the organization, culture is clearly outside the control of the formal organizational socialization and reward system. A subculture can flourish among a group of friends who use the same constructs as everyone else but interpret them differently. For example, everyone in the organization may believe in the virtues of both honesty and initiative, but people may differ as to how a specific behavior such as insider trading should be interpreted. Should one view those engaging in insider trading with admiration, because they display great initiative? Or should one condemn these traders because they are dishonest? The present research suggests that within any organizational culture the same set of cultural values can lead to discrepant attributions about the same people. In conclusion, we have found that friends significantly affect each other’s evaluations of fellow employees on culturally relevant criteria; and dyads embedded within three-person cliques reach higher agreement concerning who is tied to whom and who are embedded together in triads in organizations. People’s attributions are to some extent controlled by the need to be in harmony with others in their networks. These networks are likely to resist management attempts to initiate discrepant

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cultural values or interpretations (cf. Siehl, 1985). The organization can be depicted as a magnetic field in which individual components attract and repel each other (Nord, 1985). Within this fragmented universe, pairs of people establish mutually reinforcing interpretive systems. The control of organizational diversity, therefore, may be as much an interpersonal initiative as it is a prerogative of management manipulation.

12 Future Directions

In this book, we have emphasized the distinctiveness of the individual in the context of the structuring of organizational social networks. This relationship between the micro and macro has proved elusive for network research. Thus, we have renewed the call to “bring the individual back in” when conducting structural analysis (Kilduff and Krackhardt, 1994). Our objective includes helping the next generation of network researchers understand the benefits of simultaneously considering individuals and social structures. In this last chapter, we anticipate future directions for the research program described in this book. In looking to the future, we try to adopt some of the advantages and overcome some of the limitations of existing approaches to social network research. The influential structural hole perspective and similar work focused on actor centrality have brought a welcome focus on the agency of central individuals, but have tended to deliberately neglect the cognitions and personalities of actors in favor of an assumption of rational pursuit of personal advantage (Burt, 1992). By contrast, the new surge of work focused on small worlds is welcome in bringing an emphasis on dynamics to the network field, but too often this work tends to treat actors as pawns subject to all-powerful system forces (e.g., Dorogovtsev and Mendes, 2003). In looking to the future, we first review possible extensions of cognitive social network research and then explore topics related to the dynamic interplay of distinctive individuals in complex social networks in organizations.

Network Structure Affects Cognition Granovetter (1973) famously described how individuals could experience cohesion within cliquelike groups that were disconnected from each other in social space. This paradox of perceived local cohesion within 259

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overall fragmentation can be fruitfully revisited by cognitive network researchers. Individuals’ positions in friendship networks can bias perceptions of the environment to the relative exclusion of more objective outside views, potentially reinforcing similar views within friendship clusters (cf. Krackhardt and Kilduff, 1990; Chapter 7 this book). CEOs of poorly performing firms tend to seek advice from within their network of friends concerning perceived market opportunities (McDonald and Westphal, 2003), but the extent to which top management team perceptions of the environment coalesce over time tends to predict firm performance (Kilduff et al., 2000). Thus, there are considerable opportunities for research that specifically investigates how the network of ties within and across management teams affects the cognitive construction of strategic opportunities and how these cognitive constructions differ across competitive landscapes. Future research can examine the importance of network effects on individual perceptions at the organizational level. We know that managers whose previous organizations featured structural holes tend to be better able to see such holes in new organizational settings and are thereby more likely to forge viable top management team coalitions (Janicik and Larrick, 2005). Going further with such research may require the examination of the links between network structure, perceptions, and actions in a dynamic field of interaction. For example, it would be interesting to investigate the extent to which individuals occupying brokerage positions can profit from such brokerage if the two parties connected by the broker themselves perceive the network opportunity and view the broker to be self-interested. Actors who span across structural holes in networks may be able to exploit advantages only if they are seen by others to be not openly pursuing their own agendas (Fernandez and Gould, 1994). People occupying brokerage positions in organizations are reported to benefit in many ways (including higher salaries and faster promotions; Burt, 2004). However, we do not know the extent to which the benefits flowing to brokers depend on the misperceptions of other (more marginally located) actors concerning their own potential for activating potential links instead of depending on brokers. What might be the implications for members of two different cliques of their absence of knowledge concerning the extent to which the two cliques constitute an overlapping social circle (cf. Kadushin, 1966)? Although researchers have identified many discrete structures in organizational social networks, including dyads, triads, cliques, and social circles, the extent to which individuals automatically encode and therefore perceive these structures as entities in themselves remains unknown. Research suggests that there may be important differences flowing from the tendency to recognize certain group structures as entities (e.g.,

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Campbell, 1958; McConnell, Sherman, and Hamilton, 1997; see also Chapter 11). We also know little about people’s ability to record changes in an organizational social structure. We know, for example, that humans have difficulty keeping track of the movements of more than four units at a time (Dehaene, 1997), but we have little research on whether members of even relatively small organizational networks are able to accurately record changes in connections. To the extent that organizational members remain unaware of their structural constraints and opportunities, many of the purported benefits that can flow from network embeddedness and connectedness may fail to materialize. The organization can be understood as a marketplace of perceptions in which different schemas compete for adoption, alerting people to different signaling options. According to signaling theory (Spence, 1973), for a signal to be convincing, it must be difficult or expensive to produce (e.g., a Harvard diploma). How might this be relevant to network ties? High-status partners, with whom it is difficult to form ties, may serve as signals of an individual’s or an organization’s quality (see Chapter 3). The extent to which individual actors are perceived to have a high reputation may depend on which perceptual framings currently dominate social constructions, and these perceptual framings may vary between groups and subcultures. There may be a cognitive tipping point, such that perceptions, shared among a few key players, may create consensus in the whole network. Central actors tend to persist in seeing expected patterns, ignoring potentially important but fleeting information discrepant with their expectations (Freeman et al., 1987). The social construction of reputation can therefore be a fragile undertaking, subject to sudden disconfirmation. Such social constructions can extend not only to individual people, but also to the creation of “celebrity firms” (Rindova, Pollock, and Hayward, 2006).

Cognition Affects Network Structure We know that cognitive biases affect perceptions of social structure. Experimental evidence (De Soto, 1960; Freeman, 1992) suggests that people think of friendship relations in terms of reciprocated ties (if John likes Alan, Alan will like John) and in terms of transitive ties (if John has two friends, the two friends will be friends of each other) in support of Heider’s (1958) notion of a strain toward balance in relations involving sentiment. People tend to bias their own friendship relations in favor of balance (thus preserving their own emotional tranquility), and they tend to bias in favor of balance their perceptions of the relations of comparative strangers far removed from them in the organization (thus

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economizing on the necessity of keeping track of partially learned relationships). As part of this biased set of perceptions concerning friendship, people are also likely to see themselves as closer to the center of friendship networks in organizations than do the other members of the organization (Kumbasar et al., 1994). Thus, people may strive to preserve a perspective of a just world in which relations are ordered appropriately and in which they perceive themselves as more important (as measured by centrality in the network) than they are regarded by others. Actual data from organizations tend to show surprisingly low average levels of perceived reciprocity and transitivity in friendship networks (see Chapter 2). Perceptions of the balanced world may, therefore, be surprisingly fragile and subject to recurrent disconfirmation, perhaps motivating individuals to try to repair gaps in the network or to try to impose inaccurate perceptions on recalcitrant structures. As we consider how perception structures networks, a host of important questions emerge concerning accuracy, schemas, and cognitive ties between actors. Under which circumstances does it matter whether individuals have accurate cognitions regarding who is connected to whom? If individuals’ accurate perceptions of advice networks (but not friendship networks) lead to positions of power (as cross-sectional work has implied; see Chapter 5), do individual differences with respect to social intelligence predict who in the network is likely to be most accurate? High self-monitoring individuals (acutely aware of the demands of social situations; Snyder, 1974, 1979) tend to occupy more central positions in networks (Chapter 7; Oh and Kilduff, forthcoming), perhaps because of their greater accuracy in attending to such relevant signals as nonverbal behavior (Mill, 1984) and others’ emotions (Geizer, Rarick, and Soldow, 1977; Chapter 8 of this book). Given the importance of cognitive heuristics in the structuring of network relations, can more accurate individuals potentially take advantage of others’ biased perceptions to promote their own agendas? There are many different types of network relations, but research on cognitive schemas has tended to focus almost exclusively on friendship and influence networks (e.g., De Soto, 1960; Chapter 4 of this book). Network relations range from the primal (such as kinship, which remains an important determinant of outcomes in the many large and small familyrun firms) to the fleeting (such as homophily that can change depending on the specific mix of people in a social context; see Chapter 6). Different cognitive schemas may help structure different types of networks (see the discussion of communication rules in Monge and Contractor, 2003: 88). Evidence suggests that people in organizations differ in the extent to which they develop new schemas to codify their perceptions of recurring network patterns (such as structural holes; Janicik and Larrick, 2005).

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To what extent is behavior a function of competition between activated network schemas (cf. Macrae and Bodenhausen, 2000)? Some schemas – such as the balance schema – tend to be chronically accessible for most individuals as default options in the perception of social relationships. Most of us, for example, tend to perceive friendship as a reciprocal relationship. Evidence suggests that people can be taught new schemas through exposure to patterns of social relationships and that such schemas can provide advantages in the structuring of relationships in organizations (Janicik and Larrick, 2005). To the extent that schemas in general are slow to change and represent generic expectations about the world, we need to know more about how slow (i.e., schematic) learning of social network connections combines with the fast learning of novel connections to produce cognitive maps and social consensus (cf. March, 1991). As cognitions, many disparate organizational elements can be included in the same analysis, thus fulfilling one of the aims articulated by the actor-network theory research program (e.g., Law and Hassard, 1999) that has proved relatively intractable for the more quantitatively oriented social network research perspective. Building on the traditional assumption that humans are the nodes of the network, researchers can explore how novel kinds of ties between these nodes (including similarity of cognitions concerning technology) structure patterns of interaction. Block-modeling analysis can incorporate different kinds of cognitive ties between the same set of nodes in the search for underlying structure, but it may also be possible to discover alternative structural configurations deriving from cognitions relative to more “concrete” kinds of relations. For example, two people may be said to have a tie between them in that they have the same perception of the importance of the organizational database, or, alternatively, the same two people may have a tie to the extent that they both routinely input information (or extract information) from the database. The perceptual and the behavioral networks are unlikely to be identical and may differ in the extent to which they predict outcomes such as the extent to which people rely on technology to mediate workflow (rather than using human beings).

The Dynamic Interplay between Distinctive Individuals and Complex Social Networks Building on the ideas suggested so far in this chapter, we see possibilities for extending research in terms of the dynamic interplay between the psychology of individuals and the complexity of social networks within which they interact. Networks in which people (as organizational

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members or as representatives of organizations) constitute the nodes are unusual in that each node is itself a complex adaptive system. The nodes are constituted in part through their relationships with others in the network, but they also bring to any particular network idiosyncratic network expectations and perceptions. Thus, network stability and change involve both the patterns of interactions within the overall network system and the idiosyncrasies of the network actors in terms of their cognitions, personalities, and expectations regarding the social network. Therefore, social networks as complex systems are constituted by and help constitute the complexity of the nodes making up the system. This recursiveness between complex nodes linked together in complex systems might be the focus of future research. Actual networks are reflected in, constituted by, and sometimes discrepant with the perceptions of individuals. Both actual network patterns and perceived patterns can be approached in terms of underlying structures. We suggest that organizational network research could move forward by incorporating actors’ memories and desires, their bounded rationality and structural biases, and their creation and re-creation of structures that exhibit both stability and change. Organizational networks change constantly in some respects and yet remain stable in other respects, just as organizations can be considered both rapidly changing engines of creativity (Burns and Stalker, 1961) and stable bundles of routines (Nelson and Winter, 1982). At the perceptual level, perceptions of network structures evolve as individuals learn to perceive structural holes and other unusual features of the interpersonal landscape. Perceptions tend to be stable given that people rely on default schemas such as the balance schema (see Chapter 4). But people can learn to change their perceptions to include new types of schematic knowledge, such as the expectation that friendship networks will exhibit surprising gaps (Janicik and Larrick, 2005). Differences in perceptions can lead to differences in how people try to change the network. Small changes of network relations at the local level – one individual adding a single friendship tie, for example – can have global implications for change for the whole network – by bringing disconnected clusters of people much closer together, for instance. Conventional wisdom suggests that networks tend to be relatively stable, but this apparent stability can mask many types of change. For example, reciprocated relationships among strangers brought together in a college dormitory tend to stabilize over a period of about three weeks (Newcomb, 1961). But a closer examination of these data suggests that “reciprocity never converges in any meaningful sense, but instead fluctuates substantially over the entire observation period” of fifteen weeks (Moody et al., 2005: 1227). Some actors form stable relations, but others

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“dance between friends throughout the observation period” (Moody et al., 2005: 1229). This combination of stability and change offers considerable opportunities for organizational network research. The complexity of individuals is immense, with the human brain commonly understood to be the most complex object in the universe. Social networks of relations are also complex; even a small social network of fifty individuals on one dimension such as friendship involves the presence or absence of 2,450 ties. Further, each individual is distinctive in terms of social attributes, personality, and membership in associations, whereas each network, comprising the interactions of individuals, is distinctive in terms of size, dynamics, and structural attributes. We suggest that organizational network research can advance by seeking to capture the complexity and distinctiveness of both individuals and social networks in terms of mutual constitution and change. Structuration theory is one approach to interaction between agents and systems that has sensitized researchers to the duality of social structure – the ways in which knowledgeable agents draw on rules and resources in constituting and reconstituting the social structures that both enable and constrain (Giddens, 1984). The system properties of networks are nonreducible to the properties or the motivations of individuals. The structures that evolve from the interactions of individuals take on system-specific characteristics (Barley, 1986) in terms of centralization and density, for example. Organizational network research can enhance the structuration approach by investigating the dynamic interplay between the psychology of individuals and the complexity of social networks within which they interact; and by investigating how perceived and actual network systems mutually constitute each other. Networks are constituted in the minds of individuals as memories, thoughts, and desires. Network change can be traced in the changing perceptions of individuals concerning the creation and disappearance of ties between actors. Networks undergo constant change as actors repel and attract each other like components in a magnetic field (Barnard, 1938: 75). Thus, network change tends to be messy, with links appearing and disappearing in different places rather than the whole system shifting from one steady state to another. Locally shared systems of meaning are created when friendship groups form. The stability of such friendship groups depends on the continual efforts by members to engage in a mutual adjustment concerning how the world is perceived (see Chapter 11). Beyond these overall research directions, we can draw specific insights and research ideas from considering how the core constructs at the heart of network research can be reinterpreted to emphasize the dynamic interplay of distinctive individuals in complex social networks. In the spirit

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of Lakatos (1970), we offer emergent research directions regarding relational ties, embeddedness, the social utility of connections, and structural patterning. The goal is not to delineate small puzzles whose outcomes are already predetermined (as advocated from a paradigm perspective on scientific progress; Kuhn, 1962), but to signal new ideas about phenomena unanticipated by conventional wisdom.

Primacy of Relations Social networks can be understood as sets of interlinked actors continually forming and reforming, continually in the process of becoming. Social networks, as outcomes of human agency, carry with them in the cognitions of their members, memories of their past states, as well as hopes of their future states (Emirbayer and Mische, 1998: 963). At the level of the individual actors, the social network includes absent actors in the form of their memories in the minds of actors currently present in the network. Like the legendary Japanese soldier who retained his organizational loyalty during decades of hiding in the Philippine jungle, organizations survive in the memories and purposes of their actors. Some organizations process people through their cultures and then return them to the external world. Examples include universities and military organizations. We suggest that organizational network research can incorporate a focus on the neglected phenomenon of the influence of exiles on the organizations they have left behind. Thus, the tension between stability and change is affected by connections (both cognitive and actual) to absent nodes that continue as an active force within the network. People who remain in the organization selectively remember the history of who was in which office, who used to say what at meetings, and who could be relied upon to help when times were difficult. Similarly, the continuing activities of some of those no longer formally part of the current network continue to be important to the network. Prominent exiles continue to influence the network from afar through their examples of what can be achieved by network members. Their successes are envied, their failures commented on, their ups and downs serving as important social comparison indicators. The network may be considered a virtual set of nodes that stretches backward in time, forward to include those anticipated to join, and is dispersed spatially to include those whose continuing histories are vividly present (as exiles) even though formally they have no official links. When a person leaves an organization, this signals the appropriateness of exit for all those individuals who play similar roles in the social network (cf. Chapter 9).

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These signals affect not just interpersonal relations but also interorganizational networks. One recent example, in the realm of intercollegiate athletics, involved three members of the Big East athletic network successively yielding to the temptation to switch allegiance to the Atlantic Coast Conference (Spirer, 2003). The social network, therefore, exists as layer upon layer of relations built up over time and space in the cognitions of members. The past, for some members, may be more vividly present than the present. Oldtimers may wander the halls carrying on imaginary conversations with colleagues long departed (cf. Berendt, 1994). Relative newcomers may mourn the departure of high-flying friends to other organizations, and may benchmark their own ideas and progress against those of people with whom they rarely share a conversation. Current members of governing coalitions in organizations are likely to be influenced by those temporarily out of power. Deal makers may operate behind the scenes to influence appointments and policies. For each individual in the network, its reach may be idiosyncratically defined, shaped by memory and desire, reaching outside the set of obvious colleagues to include those forgotten by others. An organizational example of the importance of nostalgia for vanished times discussed the case of a university faculty who mourned the vanished past in which standards were higher, purposes were clearer, and cohesion in the network of faculty and students was greater (Brown and Humphreys, 2002). Such nostalgia can constitute a powerful integrating narrative of resistance to change and can propel actors to organize against apparently overwhelming forces for modernization (Welcomer, Gioia, and Kilduff, 2000). Nostalgia provides a framework for interpreting knowledge flows, a framework that may differ sharply from other sense-making recipes. To summarize this section, therefore, is to emphasize that apparent stability masks continual adjustment and negotiation. The core concept of the primacy of relations must be understood to include virtual actors whose “ghost” ties may constrain and enable network member actions. (See the related discussion of how past relationships continue to affect current relationships in Moody et al., 2005.) An important new research direction involves, therefore, a focus on how the history of the network, retained in the selective memories and interactions of its members, influences network change (Soda, Usai, and Zaheer, 2004). We propose that to the extent that members of the social network retain and remember ghost ties to former members of the network, this ongoing strengthening of relationships with exiles will restrict the extent to which the internal organizational network can shape cognition and behavior.

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Dynamic Embeddedness We conceive networks as evolving through a dynamic embeddedness process that takes into account both individual network positions as well as system-level network change. We build on recent work showing that individuals in the same network may be embedded in idiosyncratic positions that subject them to unique constraints and opportunities. An individual who is a member of many different cliques is potentially subject to the distinctive norms and values of all of those cliques. If the individual shares membership in many cliques with one or more other individuals, these co-clique members may function as watchdogs, alert to signs that the individual is violating in one clique the norms and values important to members in one of the other cliques to which they both belong. (See our discussion of Simmelian ties in Chapter 11; Krackhardt, 1998, 1999.) Building on this work, we emphasize how actors in these local structures can experience significant network change even though their direct ties remain the same. We propose that, as friends’ friends change, actors may find themselves serving as central conduits for the exchange of knowledge and other resources between distant actors to whom they are tied only indirectly. Because of changes in ties over which they have no control, their structural positions shift. (For a visualization of how actors’ positions can change even though their own ties do not change, see Moody et al., 2005.) Changes in network ties far from the individual can, therefore, affect individual outcomes in ways not currently incorporated in research that emphasizes the importance of the direct ties of actors. Individuals are likely to differ in their ability to notice and respond to these changes in embeddedness in the larger social environment. Some individuals (high self-monitors) are acutely aware of and responsive to the modulations of the interlinked system that creates the roles they are able to play (Snyder, 1987). These individuals scan the system for cues as to how to behave in ways familiar to sociological thinking concerning the responsiveness of individuals to the ideas and attributes of their associates (Kilduff, 1992). Other individuals (low self-monitors) look to a subset of the system for support for the roles they have decided to play, roles that may or may not find support or encouragement in the larger system where ideas and actions are traded and careers are traversed (Kilduff and Day, 1994; Chapter 7). New research investigating network structuring among a community of expatriate Korean small-business owners (Oh and Kilduff, forthcoming) shows that low self-monitors, relative to high self-monitors, are more embedded in social networks in terms of transitivity (their acquaintances tend to be mutual acquaintances of each other), betweenness centrality (the acquaintances of their acquaintances tend to

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be acquainted), and diversity of ties outside the community. The individual’s personality appears to have ripple effects across social structure. A fruitful research direction involves investigating how self-monitoring orientation contributes to the ongoing re-creation of the network system of constraint and facilitation in ways unanticipated by sociological approaches that treat structural positions as system constraints rather than as emergent properties of the interactions of distinctive individuals. Each actor, occupying a distinctly different position in the network, possesses a cognitive map of all the connections between all of the actors in the network (Krackhardt, 1987a). Each actor sees the network differently. Thus, if the perspectives of all the different actors in a forty-eightperson network are collected, it may appear that there are forty-eight different networks. Some of these cognitive maps are more accurate than other maps. Accuracy refers to the degree to which an actor’s cognitive map of ties overlaps with a consensus map (e.g., Chapter 5). Some actors will have only confused perceptions of the quality of relationships between network members whereas other actors will be able to describe such relationships with great clarity in terms of their strength, frequency, existence of mutual admiration, and so on. Accuracy of perceptions of networks is likely to predict the skill with which actors engage in social interaction. Occupants of the same social space may anticipate very different versions of the social network to which they both belong. Actors, embedded cognitively in their own perceptions of social networks, and drawing from their biased perceptions of social ties, may, we suggest, attempt to enact idiosyncratic structures of constraint and opportunity at the local level. Changes to local level structures can drastically affect global properties of networks (Robins, Pattison, and Woolcock, 2005). Meanings and other resource flows may tend to move through rather narrow conduits that can compress knowledge, distort it, or exclude important parts of it. We know that certain types of network connections can handle richer streams of knowledge than other types (Hansen, 1999; Tsai, 2002). But there is little research on how the embeddedness of individual actors can interrupt or supplement flows of knowledge across networks. We suggest that actors embedded in relatively open structures, with ties to several clusters, may become experienced facilitators of new knowledge flow, whereas actors in relatively closed structures may block incoming knowledge flow discrepant with taken-for-granted assumptions. Further, we think it likely that certain signals, because of the asymmetric nature of network ties, may fail to be amplified above the threshold necessary to move beyond a certain status level in the organizational network. There is a rich set of research opportunities relating to how embeddedness restricts or facilitates rumor transmission (see Burt,

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2001, for one review), and how such transmission across asymmetric boundaries in organizations alters social network structures. To summarize, changes in the larger context within which local networks are embedded can affect flows of knowledge and other resources through those local clusters (within which ties may remain stable). The extent to which people can track changes in such global and local connections is likely to correlate with self-monitoring orientation and is also likely to promote purposive social action. Actors who change ties at the local level may affect overall network functioning by, for example, bridging across clusters of hitherto isolated actors.

Social Utility of Network Connections In this section, we focus on how perceptions of centrality and centralization affect the utility of network connections. There are two questions here, one related to how actors perceive themselves and the other related to how actors are perceived by others. People tend to overestimate the number of friends they actually have in organizations (Kumbasar et al., 1994) and they may, therefore, anticipate that they have more social capital to support initiatives than is actually the case. The extent of this popularity illusion is likely to differ across individuals, perhaps as a function of self-monitoring orientation, given the greater social acuity of high self-monitors (Berscheid et al., 1976; Hosch et al., 1984; Jones and Baumeister, 1976). There may be penalties attached to miscalculating the extent of personal popularity in organizational contexts in which, for example, people jockey for support for leadership positions. A chairperson of a department who miscalculates how much support exists for the renewal of his or her tenure may suffer a damaging blow if the majority of the department members vote for nonrenewal. On the other hand, the illusion of popularity may facilitate a self-fulfilling prophecy: Those who think others like them may reciprocate this perceived liking and thereby create the very friendship links that initially did not exist. To the extent that actors in a network are connected to each other, each actor is exposed to perceptions from both proximate and distant actors. Perceptions concerning network structures (such as who is connected to whom) are an important aspect of the shared knowledge in the minds of organizational members that constitutes organizational culture (see Chapter 11). We suspect that slight, initial differences with respect to perceptions of popularity may be transmitted to different parts of the network and can lead to accumulating advantages in actual networks (cf. the “popularity is attractive” principle – Dorogovtsev and Mendes, 2003). Social networks may take on different social capital characteristics

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depending on the characteristics of the central actors who emerge from this process. Order – as represented by the emergence of consensus concerning who is central in the network – is possible because each actor’s perceptions are appraised in the eyes of proximate actors. As Adam Smith famously observed, “the countenance and behavior of those [we live] with . . . is the only looking glass by which we can, in some measure, with the eyes of other people, scrutinize the propriety of our conduct” (quoted in Bryson, 1945: 161). Thus, perceptions and behavior are subject to the scrutiny and appraisal of neighboring actors, whose perceptions also flow through the network establishing reputations through a collective process of all the actors in the network (see Chapter 3). As part of this system by which centralization emerges, there may be a tendency to perceive popular actors as being even more popular than they really are. Given that humans, as “cognitive misers,” tend to simplify complex social network information (see Chapter 4), people may tend to perceive networks as dominated by a few central actors rather than spending the cognitive energy to keep track of the fine gradations of popularity. If there is a tendency to cognitively enhance the popularity of central actors (cf. Kilduff et al., forthcoming), this attributional bias is likely to affect important outcomes, including the extent to which people are perceived to be performing well in their jobs (see Chapter 3). Further, it is possible that a misattribution of popularity can enhance the possibility of the actor actually becoming popular. For example, a researcher whose work is assumed to be highly cited is likely to receive more citations, thus propelling the researcher further into the center of the relevant citation network. On the negative side of the ledger, it is possible that being falsely perceived to be connected to many others may increase others’ expectations concerning the focal actor’s performance. Higher standards may be applied to those perceived to be part of a central elite. These issues may take on particular salience when the network is perceived to be centralized around a few central actors. Those who perceive themselves to be on the margins may self-select not to attempt to pursue options that appear to be controlled by a self-perpetuating elite. Of course, the question of who is central and who is marginal is affected by the structural configuration of the network itself, and by the type of centrality being discussed. An individual who is influential within one part of the social structure may be revealed as relatively marginal in the larger context of the whole social system. Conversely, people perceived by local group members to be insignificant may, like Swann in Marcel Proust’s (2003) masterpiece, maintain close connections with (absent) kings and princes. Further, an actor who is popular may be less influential than an actor with fewer ties who bridges between disconnected others (Brass,

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1992). The meaning and relevance of network ties are likely to vary from one social context to another even when the structural form is identical (Gould and Fernandez, 1989). Perceptions of centrality and of centralization are, therefore, fluid interpretations subject to change depending upon social context. Given the well-known asymmetry of relations within organizational networks, central players, relative to less central players, are likely to be able to communicate their own reputational messages with high fidelity. Thus, network flows are likely to be nonlinear. Most actors will be able to initiate relatively inconsequential network flows, but a few actors will be able to exploit the clustering and connectivity of the network to influence a large proportion of the network members.

Structural Patterning The small world effect (Watts, 1999) originally investigated in the 1960s (Milgram, 1967) has grown into a dominant force in structural configuration research (e.g., Kogut and Walker, 2001; Uzzi and Spiro, 2005). A small world network structure is unusual in that the network exhibits two network characteristics – high local clustering and short average paths – that are normally divergent (Watts and Strogatz, 1998). Local clustering means that actors in the network tend to link together in several clusters, whereas short average path length means that any actor in the network has a good chance of reaching any other actor through a small number of intermediaries. Thus, the hub-and-spoke U.S. airline system is an example of a small world network, whereas the interstate highway system is not. There are many questions yet unasked that could be opened up from a dynamic stability perspective. Structural configuration researchers neglect the question of how actors discover the shortest paths connecting them to others in organizational small world networks. For individual actors, the discovery of short paths is critical to their occupation of and exploitation of strategically central positions. But such discovery may prove difficult because network paths are properties of the whole global network system whereas actors are likely to have information heavily biased toward their own local network position (Watts, 2003). Actors who already occupy central positions may have advantages in terms of gaining diverse information about the structure of the network through short paths. Actors may gain these central positions in part through small initial advantages that translate into accumulating network ties as the network changes and grows over time. From this perspective, growing networks tend to produce a surprisingly robust topology, with distinct regions. (See Simon, 1996, on the

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evolutionary advantages of decomposable complex systems.) Such selforganized networks may prove highly resilient to disruption and highly efficient in the transmission of information across large distances. Research questions might include the following: What is the effect of differences in early structuring on the likelihood that small worlds will emerge in particular organizational arenas? What are the consequences, in terms of the social utility at the system level, of differing network structures? There has long been interest in the topology of human cognition (e.g., Lewin, 1936). We know that each individual in an organization has a cognitive map of the relations between all individuals (cf. Krackhardt, 1990). To what extent does the system of cognitions concerning network relations tend to organize according to small world principles? Organizing and keeping track of organizational relationships is likely to be especially challenging for a difficult-to-discern relationship such as friendship. We propose that boundedly rational people keep track of friendship relations in organizational settings by using a simple set of cognitive small world rules that can be summarized as follows: Arrange people in dense clusters and connect the clusters with short paths. Cognitive small-worldedness can, we argue, facilitate the systemwide organization of perceptions and reduce the cognitive burden of trying to keep track of hundreds of discrete relationships (cf. Kilduff et al., forthcoming). Each person within a network might exhibit a different level of reliance on small world perceptual organization with respect to network perceptions. Of course, researchers could compare individuals’ perceptions with actual maps of the “real” relations existing between actors. But it may be possible to discover benefits to individuals, irrespective of accuracy, consequent upon the organization of perceptions according to small world principles. Potential research questions could be the following: To what extent does the organization of individuals’ cognitive maps in terms of small worlds facilitate sense making and action by the individuals? Are there cognitive biases evident in the way people organize their perceptions in terms of small world structures, and if so, what are the advantages and disadvantages of such biases? Is it possible to “rewire” individuals’ cognitions concerning organizational networks without damaging the efficacy of their social cognition as long as small world structure is preserved?

Conclusion In this concluding chapter, we have drawn attention to the ongoing mutual constitution of complexity and distinctiveness by both networks and actors. The activities of the social actor cannot be understood except in terms of the network of relationships within which the actor

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is embedded; and the emergence of system-level properties cannot be understood except in terms of the relationships forged by individual actors. Actors, as complex systems themselves, bring distinctive qualities to the network that can provide initial advantages or drawbacks in the relationship-forging process. Small initial advantages can lead to longterm structural advantages for the actor, and small changes at the level of local networks surrounding particular actors can have large effects on system-level properties such as average path length. Apparent stability of networks can mask many types of change, and the network system, at any point in time, carries memories of its past states and anticipation of its future states distributed in the minds of actors. We offered several future research ideas relating to, among other topics, the importance of ghost ties, the likelihood that people’s cognitions of networks are organized as small worlds, and the likelihood that individual dispositions predict embeddedness in personal, organizational, and extra-organizational networks. It is through systems of relationships that people are able to enact their desires, pursue their affections, and get work done. In studying the evolution of social network relationships as the reciprocally emergent and re-created outcomes of purposive action, we need to discover why network connections bypass or avoid crossing certain territories. As one organizational theorist demanded to know some years ago, we need to discover not just the effects of structural holes but the reason why they exist in the first place (Salancik, 1995). What is the nature of the prohibition that prevents connections crossing between clusters? The idiosyncrasies of social actors and the flows of meanings between them help sustain the social structures that guide action.

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Index

Ahn,W. K., 4 Amanatullah, E. T., 5 Ames, D. R., 5, 158, 174 Arabie, P., 103, 182 Argyle, M., 59, 163 Auletta, K., 19, 27, 35, 57 Back, K. W., 29, 209 Baker, F. B., 50, 118, 242 balance schema, 6, 24, 26, 39–41, 55–6, 59, 61–5, 82, 193, 195, 261, 263–4 Balkundi, P., 34, 176 Barley, S. R., 265 Barnard, C., 161, 248, 265 Baron, J. N., 133, 142, 175 Baron, R. A., 134–5 Baron, R. M., 145 Baron-Cohen, S., 164, 174 Barrick, M. R., 158, 162, 173 Barsade, S. G., 31, 157–8, 177 basking-in-reflected-glory effect, 39–40, 49, 55–7 Bass, B. M., 16, 19 Batchelder, W. H., 61, 237, 248 Baum, J., 35 Beard, J. F., 135, 161 Becker, G., 114 Bedeian, A. G., 173, 176 Bell, R. R., 213 Benassi, M., 164 Berge, C., 251 305

Berkman, L. F., 175 Bernard, H. R., 90 Berscheid, E., 138, 270 Binkhorst, D., 3 Blau, P. M., 4, 56, 131 Bonacich, P., 104, 146 Boorman, S., 103, 156, 182 Borgatti, S. P., 15, 51, 104, 143, 167 Bossard, J. H., 63–4 Bougon, M. G., 3, 20–1, 41 Brass, D. J., 1–2, 4–5, 15, 21, 27, 30, 33, 47, 80, 82, 84, 86–7, 89–90, 96, 131, 133, 141–3, 145, 164, 167, 250, 271 Breiger, R. L., 103, 132, 156, 182 Brief, A. P., 159, 173 Briggs, S. R., 172 brokerage, 4–5, 19, 30, 33, 260 Bryant, W. H., 159–60 Burkhardt, M. E., 47, 80, 141, 143 Burns, T., 15, 133, 264 Burt, R. S., 1, 4, 13, 15–16, 19, 22–3, 28, 29, 31–3, 46, 82, 87, 103, 114–15, 121, 131–3, 137, 141–3, 145, 154–5, 175, 181, 184, 193, 195, 259–60, 269 Caldwell, D. F., 123, 135–6, 138, 174 Cameron, K. S., 219 Campbell, D. T., 3, 48, 261 Carley, K., 155, 237 Cartwright, D., 56

306

Index

centrality betweenness, 91, 132, 142–3, 146, 148–53, 268 degree, 143, 167 eigenvector, 105 indegree, 44, 46–8, 50, 52, 54, 167–8 Cheek, J. M., 172 Chen, M.-J., 30 Chesterton, G. K., 34 Cialdini, R. B., 2, 40 cliques, 30–1, 132, 156, 209, 221, 243, 249–50, 255, 257, 260, 268 cognitive approach to leadership, 23 to organizational culture, 239 to social networks, 3 cognitive dissonance theory, 241 cognitive maps, 4, 25, 41–2, 44, 47–8, 50, 67, 69, 73, 91, 249, 251, 269, 273 cognitive miser model, 60, 64–5, 76, 78 cognitive network theory, 2, 7, 17, 23 Cohen, S., 175 Coleman, J. S., 15, 114 Contractor, N. S., 5, 262 Cook, K. S., 132 Coser, L. A., 212 Crockett, W. H., 59 Cross, R., 6 Crossland, C., 4, 24, 234 Cunningham, M. R., 159 Cyert, R. M., 29 D’Andrade, R., 60, 237, 239, 248–9 Dalton, D. R., 193 Dalton, M., 248 Darley, J. M., 114 Davis, J. A., 55, 79, 81 Dawes, R., 63 Day, D. V., 5, 173–4, 176, 268 De Soto, C. B., 62, 79, 261–2 Degenne, A., 206 Dickson, W. J., 1, 141, 164 DiMaggio, P., 3, 59, 82 distinctiveness theory, 102, 110

Doreian, P., 81 Dorogovtsev, S., 25, 208, 259, 270 Duck, S. W., 122 Durkheim, E., 236 Eckenrode, J., 129, 131 E-I index, 214–17 embeddedness, 1, 14, 19, 22, 23, 28, 29, 31, 32, 38, 236, 261, 266, 268 Emerson, R. M., 85, 132 Emirbayer, M., 131, 154, 266 emotion helping network, 8, 156, 157, 159, 162, 163, 167, 172 emotional tension model, 60, 62, 65, 76, 77 Everett, M. G., 15 Faust, K., 15, 28, 59, 251 Fayol, H., 161 Feld, S. L., 69 Fernandez, R. M., 22–3, 26, 260, 272 Festinger, L., 29, 41, 60, 103, 114, 157, 209, 241, 250 Fiedler, F. E., 16, 86 Finkelstein, S., 163 Fiske, A. P., 60 Fiske, D. W., 48 Fiske, S. T., 4, 63 Flynn, F. J., 5, 158, 161, 173–4 Fombrum, C., 14 Fors`e, M., 206 Freeman, J., 23, 36 Freeman, L. C., 14–15, 24, 26, 47, 62–3, 79, 87, 90–2, 94, 141, 143, 167, 261 Freeman, S. C., 26, 87 French, J. R. P., 86 Friedkin, N. E., 5 Friedman, R. A., 176 Frost, P., 34, 157–8, 162, 175 Funder, D. C., 80 Gaertner, S., 181 Galaskiewicz, J., 36 Gangestad, S. W., 5, 123–5, 134, 136, 144–5, 152, 154–5, 166, 174

Index Gargiulo, M., 20, 35, 37, 164 Garland, J., 135, 161 Geertz, C., 237, 248 George, J. M., 159 Gerhart, B., 123 ghost ties, 267, 274 Gibson, C., 31 Giddens, A., 265 Gioia, D. A., 49, 267 Gnyawali, D. R., 15 Goethals, G. R., 114 Goodwin, J., 131, 154 Gould, R. V., 26, 260, 272 Gouldner, A. W., 81 Graen, G. B., 16, 20 Granovetter, M., 1, 9, 14, 29, 56, 105, 114, 121, 128, 132, 259 Gray, B., 207 Graziano, W. G., 138, 159–60 Gregory, K. L., 237 Griffeth, R. W., 181 Gronn, P. C., 33, 135 Gulati, R., 20, 35, 37 Hambrick, D. C., 29–30, 163 Hanke, R., 3 Hannan, M., 23, 36 Hansen, C. H., 64 Hansen, M. T., 155, 269 Hansen, R. D., 64 Harary, F., 56, 69 Hargadon, A. B., 20 Harrison, D. A., 176 Hayward, M. L. A., 261 Hedges, L. V., 71 Heider, F., 4, 24, 39, 41, 55–6, 59–61, 64, 68, 81, 193, 261 Higgins, M. C., 33, 114 Hochschild, A. R., 177 Holland, J. L., 123 Holland, P. W., 68, 81, 141 homophilous networks, 29 homophily, 104–5, 108, 262 Hooijberg, R., 16, 28 Horn, P. W., 181 House, R. J., 16, 86 Hubbell, C. H., 84

307 Hubert, L. J., 50, 118, 126, 186, 242 Hughes, E. C., 4 Huy, Q. N., 161–2, 177 Ibarra, H., 3, 20, 47, 103–4, 113, 132, 145, 162, 176 Ickes, W. J., 135, 160–1 identity network, 105–7 Insko, C. A., 60–1, 82 interorganizational networks, 38, 267 Isen, A. M., 159 Janicik, G. A., 4, 17, 19, 26, 64, 132, 234, 260, 262–4 Kadushin, C., 260 Kahn, R. L., 157 Kahn, W. A., 175 Kahneman, D., 27, 114 Kanter, R. M., 85, 103 Katz, D., 157 Katz, E., 114 Keesing, R. M., 237 Kelly, G. A., 239–40 Kenny, D. A., 16, 28, 81, 134, 145 Kephart, W. M., 64 Kilduff, M., 1–6, 14, 16–17, 21, 23–5, 30, 34, 36, 49, 56, 59, 66–7, 71, 80, 83, 101, 121–2, 129, 134–5, 141, 152, 155, 172, 174, 176, 208, 234, 241, 249, 259–60, 262, 267–8, 271, 273 Killworth, P., 90 Klein, K. J., 5 Knoke, D., 46, 114, 116 Kogut, B., 15, 25, 272 Kotter, J. B., 135 Krackhardt, D., 1, 3–4, 6, 15, 24, 26, 28, 31, 41, 43–4, 47, 50–1, 53, 57, 59, 66–8, 71, 80–1, 83, 86–7, 90–1, 95–6, 104, 110, 114–15, 118, 122, 141, 143, 168, 192, 203, 234, 242, 246, 248–52, 259–60, 268–9, 273 Kraimer, M. L., 17 Kram, K., 33 Kronenfeld, D., 90

308

Index

Kuethe, J. L., 63 Kuhn, T. S., 266 Kumbasar, E. A., 61, 80, 141, 249, 262, 270 Kunda, G., 36 Labianca, G., 34 Lakatos, I., 14, 266 Larrick, R. P., 4, 17, 19, 26, 64, 132, 234, 260, 262–4 Larson, A., 20, 35 Laumann, E. O., 84 law of family interaction, 63–4 Lawrence, P. R., 209, 219 leader effectiveness social network effects on, 19, 28, 31, 34 social network measures of, 33 social network outcomes of, 20, 33 leadership accuracy of network perceptions and, 21, 270 cognitive revolution in research and, 23 distributed, 33 ego network density and, 28 emergence of, 24, 27, 31, 134–5 formal, 22 implicit, 20, 28 informal, 29, 31–2, 185 in large networks, 25 secondary networks and, 32 self-monitoring and, 135 Leinhardt, S., 68, 81, 141 Levine, J. M., 80, 160 Lewin, K., 16, 195, 273 Liden, R. C., 17, 23, 32 Lincoln, J. R., 87, 145, 164, 249 Lord, R. G., 21, 23, 28 Lorrain, F. P., 114, 182–3 Lorsch, J. W., 23, 209, 219 Madhavan, R., 15 Maitlis, S., 34, 162, 176 March, J. G., 29, 37, 263 marginality in networks, 19, 101, 103–4, 110

Markus, H., 59, 64 Martin, J., 161 Mayhew, B., 57, 122, 154 Mayo, M. C., 17, 33 McDonald, M. L., 30, 260 McPherson, M., 29, 63, 131, 156, 181 Mehra, A., 1–2, 4, 6, 29–30, 33, 166–7, 169, 175 Meindl, J. R., 17, 33 Mendes, J., 25, 208, 259, 270 Menzel, H., 114 Merton, R. K., 234 Meyer, A. D., 211 Meyer, J., 23 Milgram, S., 25, 272 Milton, L. P., 30 Mintzberg, H., 23, 97, 135, 162–4, 166 Mische, A., 266 Mizruchi, M. S., 85 Mobley, W. H., 181, 193, 200 Mollica, K. A., 207 Monge, P. R., 5, 262 Moody, J., 37, 264–5, 267–8 Moreland, R. L., 80 Mowday, R. T., 182, 184, 193, 195, 199–200, 205 Murnighan, J. K., 250 Neisser, U., 59 network cognition and accuracy, 21, 26 and leadership, 20 network research core ideas, 14, 16, 21, 28, 38, 265 embeddedness, 268 primacy of relations, 266 social utility of network connections, 270 structural patterning of social life, 272 Newcomb, T. M., 61, 114, 132, 194, 264 Oh, H., 30, 34, 36, 121, 262, 268 Oliver, A., 146 Oliver, C., 35

Index Olkin, I., 71 O’Reilly, C. A., 34, 49, 123, 133, 135–6, 138, 174 Ozcelik, H., 34, 162, 176 Palmer, D., 29 Pappi, F. U., 84 Pastor, J. C., 17, 33 Pattison, P., 269 perceptions of social networks, 1–3, 6–7, 23–4, 27, 39, 44, 55, 59, 63, 65, 67, 79–80, 97, 115, 187, 198, 248, 260, 262 personal construct theory, 239 personality and social networks positive affectivity, 158, 162–3, 172 self-monitoring, 1, 5, 7, 98, 123, 133, 154–5, 158, 162–3, 172 Pfeffer, J., 84–6, 96–7, 128–9, 131, 206, 210, 245 Podolny, J. M., 3–4, 19, 21, 26, 133, 142, 175 Pollock, T. G., 261 Popielarz, P. A., 29, 63 Porter, L. T., 25, 87, 114, 182, 192, 199, 203 Portes, A., 15 Powell, W. W., 20, 35 Raven, B., 86 Reagans, R. E., 5 reciprocity actual, 68, 81, 264 perceived, 67, 69, 72, 76–7, 79, 262 Regan, D. T., 122 regular equivalence, 183 Reitz, K. P., 103, 183, 187 reputation and power, 96 and self-monitoring, 124, 154, 161 individual, 1, 7 interorganizational, 21 market for, 26, 40 organizational, 125 perceived, 3, 39, 83 performance, 38, 41, 48, 53 social construction of, 261, 271

309 Riley, D., 129, 131 Rindova, V. P., 261 Roberts, K. H., 34, 133 Robins, G., 269 Robinson, S., 157, 162 Roethlisberger, F., 1, 141, 164 Romney, A. K., 26, 61, 87, 94, 236, 237, 239, 248, 256 Ronchi, D., 22, 142, 181, 193 Rook, K. S., 129, 142 Rowan, B., 23 Roy, D., 1 Sailer, L., 90, 182–3, 186–7 Salancik, G., 85, 96, 128, 206, 274 Sampson, F. S., 3, 132 Saxenian, A., 36 Schachter, S., 29, 157, 163, 176, 209 Schein, E. H., 212 Schleicher, D. J., 174 Seidel, M. D. L., 22 Sherif, C., 212 Sherif, M., 212 Simmel, G., 30, 81, 113, 212, 248–9, 256 Simmelian ties, 249–51, 253, 255–6 Simon, H. A., 272 small worlds, 25, 208, 259, 272–4 Smith-Lovin, L., 29 Snijders, T. A., 51, 146 Snyder, M., 1, 5, 123–5, 134–8, 144, 145, 152, 154–5, 159–60, 166, 174, 262, 268 social capital, 2, 15–16, 21, 28–9, 33, 38–9, 154, 270 social comparison theory, 41, 103, 114, 122, 130, 241 social distance, 62, 65, 69, 76, 78, 81 Soda, G., 267 Sparrowe, R. T., 17, 23, 32, 164 Spence, A. M., 40–1, 261 Stalker, G. M., 15, 133, 264 Staw, B. M., 123, 159, 177, 193, 195 Steers, R. M., 182, 192, 199 Stern, R., 86–7, 95, 220 Stevenson, W. B., 25

310

Index

structural equivalence, 7, 103, 115–16, 120, 182 structural hole theory, 31, 259 structural holes, 26, 28, 34, 82–3, 131–2, 141, 143, 150, 154–5, 260, 262, 264, 274 structural patterning of social life, 14–15, 132, 266 structuration theory, 265 Sutton, R. I., 20 Swidler, A., 59 Syme, S. L., 175 Taylor, S. E., 4, 27, 63, 157 Thomas, D. A., 34 Thompson, J. D., 219 Tichy, N. M., 14, 217 transitivity actual, 69, 81, 268 perceived, 68, 72, 77, 79–81, 262 Trevino, L. K., 207 Tsai, W., 1, 3–5, 14, 17, 23–4, 36, 49, 208, 234, 269 Tsui, A. S., 49, 56 Tushman, M. L., 14 Tversky, A., 27, 114 underrepresented groups, 33, 101–3, 108, 113 Usai, A., 267

Uzzi, B., 14, 17, 20, 23, 25, 28–9, 32, 35–6, 132, 272 Van Maanen, J., 102 Vermeulen, F., 31 Vroom, V. H., 16, 125 Walker, G., 3, 15, 25, 80, 123, 246, 272 Wallace, A. F. C., 237, 240 Wasserman, S., 15, 28, 59, 251 Watts, D. J., 25, 272 Wayne, S. J., 17 Weick, K. E., 3, 41, 82 Wellman, B., 15 Westphal, J. D., 30, 260 Whetten, D. A., 219 White, D. R., 103, 183, 187 White, H. C., 40, 57, 114, 134, 156, 182–3 Whyte, W. F., 1, 248 Woolcock, J., 269 workflow network, 90, 133, 141–3, 147, 149, 151, 155, 163–5, 167, 172 Zaccaro, S. J., 16, 134, 161 Zaheer, A., 267 Zajonc, R. B., 59, 64 Zuckerman, E. W., 3, 19, 21