Research Methodology in Strategy and Management, Volume 3 (Research Methodology in Strategy and Management)

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RESEARCH METHODOLOGY IN STRATEGY AND MANAGEMENT

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RESEARCH METHODOLOGY IN STRATEGY AND MANAGEMENT Series Editors: David J. Ketchen, Jr. and Donald D. Bergh Volume 1:

Research Methodology in Strategy and Management. Edited by David J. Ketchen, Jr. and Don D. Bergh

Volume 2:

Research Methodology in Strategy and Management. Edited by David J. Ketchen, Jr. and Don D. Bergh

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RESEARCH METHODOLOGY IN STRATEGY AND MANAGEMENT VOLUME 3

RESEARCH METHODOLOGY IN STRATEGY AND MANAGEMENT EDITED BY

DAVID J. KETCHEN, JR. College of Business, Auburn University, USA

DONALD D. BERGH Daniels College of Business, University of Denver, USA

Amsterdam – Boston – Heidelberg – London – New York – Oxford Paris – San Diego – San Francisco – Singapore – Sydney – Tokyo JAI Press is an imprint of Elsevier

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JAI Press is an imprint of Elsevier The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA First edition 2006 Copyright r 2006 Elsevier Ltd. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent veriﬁcation of diagnoses and drug dosages should be made British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN-13: 978-0-7623-1339-6 ISBN-10: 0-7623-1339-0 ISSN: 1479-8387 (Series) For information on all JAI Press publications visit our website at books.elsevier.com Printed and bound in The Netherlands 06 07 08 09 10 10 9 8 7 6 5 4 3 2 1

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CONTENTS ix

LIST OF CONTRIBUTORS

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INTRODUCTION SCHOLARSHIP THAT ENDURES Sumantra Ghoshal

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THEORY, PRACTICE, AND SCHOLARSHIP Raymond E. Miles and Charles C. Snow

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CONSTRUCTS AND CONSTRUCT MEASUREMENT IN UPPER ECHELONS RESEARCH Mason A. Carpenter and Gregory P. Reilly

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SURVEYING THE CORPORATE ELITE: THEORETICAL AND PRACTICAL GUIDANCE ON IMPROVING RESPONSE RATES AND RESPONSE QUALITY IN TOP MANAGEMENT SURVEY QUESTIONNAIRES Michael K. Bednar and James D. Westphal

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MANAGERIAL CONSTRAINT: THE INTERSECTION BETWEEN ORGANIZATIONAL TASK ENVIRONMENT AND DISCRETION Brian K. Boyd and Steve Gove

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CONTENTS

ASSESSING THE EXTERNAL ENVIRONMENT: AN ENRICHMENT OF THE ARCHIVAL TRADITION C. Chet Miller, dt ogilvie and William H. Glick

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ANALYSIS OF EXTREMES IN MANAGEMENT STUDIES Joel A. C. Baum and Bill McKelvey

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THE ROLE OF FORMATIVE MEASUREMENT MODELS IN STRATEGIC MANAGEMENT RESEARCH: REVIEW, CRITIQUE, AND IMPLICATIONS FOR FUTURE RESEARCH Nathan P. Podsakoff, Wei Shen and Philip M. Podsakoff

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INDIVIDUALS AND ORGANIZATIONS: THOUGHTS ON A MICRO-FOUNDATIONS PROJECT FOR STRATEGIC MANAGEMENT AND ORGANIZATIONAL ANALYSIS Teppo Felin and Nicolai Foss

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RIGOR AND RELEVANCE USING REPERTORY GRID TECHNIQUE IN STRATEGY RESEARCH Robert P. Wright

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STUDYING THE DYNAMICS OF REPUTATION: A FRAMEWORK FOR RESEARCH ON THE REPUTATIONAL CONSEQUENCES OF CORPORATE ACTIONS Matthew S. Kraatz and E. Geoffrey Love

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Contents

AN ASSESSMENT OF THE USE OF STRUCTURAL EQUATION MODELING IN INTERNATIONAL BUSINESS RESEARCH G. Tomas M. Hult, David J. Ketchen, Jr., Anna Shaojie Cui, Andrea M. Prud’homme, Steven H. Seggie, Michael A. Stanko, Alex Shichun Xu and S. Tamer Cavusgil

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LIST OF CONTRIBUTORS Joel A. C. Baum

Rotman School of Management, University of Toronto, Toronto, Canada

Michael K. Bednar

Department of Management, The University of Texas at Austin, Red McCombs School of Business, Austin, TX, USA

Brian K. Boyd

W.P. Carey School of Business, Arizona State University, Tempe, AZ, USA

Mason A. Carpenter

School of Business, University of WisconsinMadison, Madison, WI, USA

S. Tamer Cavusgil

Eli Broad Graduate School of Management, Michigan State University, East Lansing, MI, USA

Anna Shaojie Cui

Department of Marketing and Supply Chain Management, Michigan State University, MI, USA

Teppo Felin

Organizational Leadership & Strategy, Marriott School of Management, Brigham Young University, Provo, UT, USA

Nicolai Foss

Center for Strategic Management and Globalization, Copenhagen Business School, Denmark

Sumantra Ghoshal y

London Business School, London, UK

William H. Glick

Jones Graduate School of Management, Rice University, Houston, TX, USA

Steve Gove

Department of Management and Marketing, University of Dayton, Dayton, OH, USA ix

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LIST OF CONTRIBUTORS

G. Tomas M. Hult

Center for International Business Education and Research, Eli Broad Graduate School of Management, Michigan State University, East Lansing, MI, USA

David J. Ketchen, Jr.

College of Business, Auburn University, Auburn, AL, USA

Matthew S. Kraatz

College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, USA

E. Geoffrey Love

College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, USA

Bill McKelvey

The Anderson School of Management at UCLA, Los Angeles, CA, USA

Raymond E. Miles

Haas School of Business, University of California, Berkeley, CA, USA

C. Chet Miller

Babcock Graduate School of Management, Wake Forest University, Winston-Salem, NC, USA

dt ogilvie

Rutgers Business School – Newark and New Brunswick, Rutgers University, Newark, NJ, USA

Philip M. Podsakoff

Department of Management, Indiana University, Bloomington, IN, USA

Nathan P. Podsakoff

Department of Management, University of Florida, Gainesville, FL, USA

Andrea M. Prud’homme

Department of Marketing and Supply Chain Management, Michigan State University, MI, USA

Gregory P. Reilly

School of Business, University of WisconsinMadison, Madison, WI, USA

Steven H. Seggie

Department of Marketing and Supply Chain Management, Michigan State University, MI, USA

List of Contributors

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Wei Shen

Department of Management, University of Florida, Gainesville, FL, USA

Charles C. Snow

Department of Management and Organization, The Pennsylvania State University, University Park, PA, USA

Michael A. Stanko

Department of Marketing and Supply Chain Management, Michigan State University, MI, USA

James D. Westphal

Department of Management, Red McCombs School of Business, The University of Texas at Austin, Austin, TX, USA

Robert P. Wright

Department of Management and Marketing, Faculty of Business, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

Alex Shichun Xu

Department of Marketing and Supply Chain Management, Michigan State University, MI, USA

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INTRODUCTION Welcome to the third volume of Research Methodology in Strategy and Management. This book series’ mission is to provide a forum for critique, commentary, and discussion about key research methodology issues in the strategic management ﬁeld. Strategic management relies on an array of complex methods drawn from various allied disciplines to examine how managers attempt to lead their ﬁrms toward success. The ﬁeld is undergoing a rapid transformation in methodological rigor, and researchers face many new challenges about how to conduct their research and in understanding the implications that are associated with their research choices. For example, as the ﬁeld progresses, what new methodologies might be best suited for testing the developments in thinking and theorizing? Many long-standing issues remain unresolved as well. What methodological challenges persist as we consider those matters? This book series seeks to bridge the gap between what researchers know and what they need to know about methodology. We seek to provide wisdom, insight, and guidance from some of the best methodologists inside and outside the strategic management ﬁeld. Before we discuss the contents of this volume, let us brieﬂy reﬂect on its predecessor. Volume 2 debuted at the 2005 Academy of Management meeting in Hawaii. The volume was showcased in a symposium sponsored by the Business Policy and Strategy division. We were thrilled that our large meeting room was ﬁlled to capacity. We believe this turnout reﬂects a strong desire by strategy researchers to improve their methodology skills; a desire we hope this book series is serving capably. We want to thank Phil Bromiley, Scott Johnson, Kent Miller, Caron St. John, Devi Gnyawali, Beverly Tyler, James Combs, T. Russell Crook, and Christopher Shook for offering excellent presentations of their chapters. We hope that Volume 3 also will be well received. The volume you hold in your hands offers 12 chapters. Sumantra Ghoshal provided the lead chapter shortly before his untimely death. His chapter encourages researchers to aspire to conduct research that offers wisdom that endures, using the work of Raymond E. Miles and Charles C. Snow as an exemplar. Sumantra’s writing once again beckons us to think critically and comprehensively about our research, and he challenges us to ﬁnd the courage to pursue big and broad xiii

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INTRODUCTION

questions and to develop useful frameworks within the realities of our institutional and individual constraints. In what is a testimony to his important legacy, the chapter exhorts us to seek enduring scholarship and to embark on a long and meaningful intellectual journey that will help make the world a better place. We are deeply indebted to Professor Ghoshal’s perspective and drive to help us deﬁne, challenge, and improve our lives. Next, Miles and Snow react to Ghoshal’s insights by discussing their views on the links among theory, practice, and scholarship. They also take the reader ‘‘behind the scenes’’ of the formation of their ideas on strategy that have profoundly shaped the strategy ﬁeld since the 1970s. Taken together, we believe these companion pieces offering budding and experienced scholars alike many great ideas to guide their research. In remembrance of Sumantra Ghoshal and his many significant contributions to research and practice, the editors and the publisher are donating a portion of the proceeds from this volume to the scholarship fund set up in his name within the Business Policy and Strategy division. The volume includes two other pairs of chapters that each center on a general theme. The ﬁrst of these themes is the study of top managers. Mason A. Carpenter and Gregory P. Reilly focus on three key tasks: how to identify upper echelons constructs, how to embed such constructs to build theory, and how to operationalize such constructs. This chapter comprehensively reviews how upper echelons research has been conducted, and it provides a practical and user-friendly process for improving how we study this very important subject. Eliciting strong response rates in surveys of top managers has long been notoriously difﬁcult. In response, Michael K. Bednar and James D. Westphal outline some empirically based guidance for maximizing the effectiveness of top management surveys. They delve into important survey design factors and ﬁnd how researcher decisions inﬂuence the rate and quality of survey responses. This interesting chapter gives practical advice about what to do and what not to, when surveying subjects that tend to have low response histories. We believe that any researcher attempting to study top managers would beneﬁt considerably by following the action plans laid out in these two chapters. Understanding the environment is the second theme that has attracted two chapters. Brian K. Boyd and Steve Gove describe key conceptual and methodological issues surrounding the investigation of task environments and managerial discretion. Drawing on a review of published studies and original data analysis, they offer important suggestions to guide future research. In sum, the chapter offers a great primer for those interested in the organization/ environment interface. C. Chet Miller, d.t. Ogilvie, and William H. Glick seek

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to improve how researchers assess the environment using archival measures. Although there is a long tradition of using archival sources to measure the environment, problems involving constitutive definitions and mismatches between constitutive and operational definitions plague this work. In response, Miller et al. clarify existing definitions and propose new ones where needed. They extend our understanding of the archival tradition and provide us with new ideas about how to study a central and critical factor in the ﬁeld of strategic management. Overall, we are conﬁdent that absorbing the insights offered by the above array of scholars about three key issues (enduring scholarship, top managers, and the environment) will prove quite valuable to our colleagues. The remaining chapters address six diverse topics of importance. Joel A. C. Baum and Bill McKelvey discuss how extreme value theory can enhance management research. As the authors note, the ﬁeld tends to ﬁxate on central tendencies, but often studying unusual events is more valuable to efforts to understand organizations. They provide a detailed and interesting insight into perspectives such as power law and the concept of normal extreme behavior. These authors offer perhaps the most interesting set of examples we have ever encountered in one chapter, and we are certain that you will be intrigued by the ideas they offer. More generally, they offer very relevant insights into explaining unusual outcomes, a critically relevant and important factor in the ﬁeld of strategic management. Like Carpenter and Reilly, Nathan P. Podsakoff, Wei Shen, and Philip M. Podsakoff focus on construct validity. However, the latters’ focus is on the strategy ﬁeld as a whole. These authors provide a thorough and expansive discussion of measurement models within the ﬁeld, identifying common problems and highlighting methods for how to improve measurement and integration between concept and variable. They argue convincingly that many key constructs in strategic management would be better modeled as having formative indicators rather than as having reﬂective indicators. This insight has important implications not just for future research, but also for interpreting the ﬁndings of past research. In Volume 2 of this series, Caron St. John laid out some foundational insights on the role of levels of analysis within strategic management research. In what could be viewed as a companion piece to St. John’s chapter, Teppo Felin and Nicolai Foss develop what they refer to as the microfoundations of strategic management. They seek to enhance strategic management theory by explicitly acknowledging that ‘‘organizations are made up of individuals, and there is no organization without individuals.’’ The chapter provides new insights into the organizational capabilities concept,

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INTRODUCTION

and creatively applies Coleman’s General Model of Social Explanations to evaluate management research. The result is a very lively treatise offering novel ideas that promise to greatly enrich future theory development. While building theory among scholars is vital, understanding the conceptual frameworks used by managers to make sense of their reality is also important. Repertory grid technique is a valuable tool to tap into these frameworks, but few strategy scholars possess detailed knowledge of the technique. In response, Robert P. Wright provides ample examples and guidance on how to use repertory grid technique. He notes that concepts traditionally linked with a cognitive perspective provide important methodological challenges and he describes several accessible approaches for testing such dimensions. Next, Matt Kraatz and Geoffrey Love focus on corporate reputation, a topic of growing conceptual and practical importance. The norm for corporate reputation research has been to explain the antecedents of reputation through static or cross sectional models. In response, Kraatz and Love discuss how to study reputation in a dynamic, longitudinal fashion, as well as how to link changes in corporate reputation to other structures and practices that ﬁrms adopt over time (e.g., governance changes, downsizing). They provide insightful recommendations that not only apply to reputation but can also guide researchers interested in understanding intangible assets in general. Volume 2 of this series offered a chapter on entrepreneurship that signaled that our intended domain reaches beyond the strategy ﬁeld. Similarly, the ﬁnal chapter in this volume takes seriously the ‘‘and Management’’ portion of our book series’ title. G. Tomas M. Hult and his co-authors enlighten us about the use of structural equation modeling in the international business ﬁeld. There are interesting parallels between the current development of the international area and that of strategic management research a couple of decades ago. Just as with strategy in the 1980s, international business tackles issues of practical importance, but critics question its theoretical robustness and methodological rigors. The chapter explains the strengths and weaknesses of extant practices, chronicles the effects of certain decisions, and offers suggestions for improvement. As with one of the chapters in Volume 1 of our series, this chapter is the product of a doctoral seminar led by the ﬁrst author. Such projects are not only valuable to the ﬁeld but also provide excellent learning experiences for doctoral students. Overall, the work offered in this volume strives to gently prod the ﬁeld toward better using method and theory. If the advice offered is followed, the

Introduction

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consequence will be an enhanced capability to understand how organizations behave and perform. We hope your research beneﬁts from these chapters as much as we enjoyed working with their respective authors. We are very grateful to all of the contributors for their insights and efforts. David J. Ketchen, Jr. Donald D. Bergh Editors

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SCHOLARSHIP THAT ENDURES Sumantra Ghoshaly As academics, we collectively publish thousands of articles and hundreds of books each year. We spend a large part of our lives producing them – sacriﬁcing, in the process, sleep, time with our families, reading things we want to read, seeing places we wish to see. Most of these books and articles soon vanish without a trace, helping us get tenure perhaps, but taking with them into oblivion very large parts of the best years of our lives. Few – very few – of the outputs of our intellectual endeavors endure. What is it that distinguishes scholarship that endures from scholarship that does not? Painters hone their skills by copying grandmasters. Doctors and lawyers often learn by observing the greats of their profession at work. Scientists, too, analyze the work of those who have blazed new trails, in order to learn from their styles. In the same spirit, I describe some of the features of the research presented by Professors Raymond Miles and Charles Snow (M&S, in future references) in their book Organizational Strategy, Structure and Process (OSSP in future references) that, to my mind, still remain valid as hallmarks of ‘‘scholarship that endures.’’

THE ADAPTIVE CYCLE AND THE ORGANIZATIONAL TYPOLOGY Most readers of this chapter are very familiar with OSSP. The book’s basic proposition is that successful companies need to develop consistency among their strategy, the business model they adopt, including the choice of technology, and their organizational capability including human resource Research Methodology in Strategy and Management, Volume 3, 1–10 r 2006 Published by Elsevier Ltd. ISSN: 1479-8387/doi:10.1016/S1479-8387(06)03001-3

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practices. The basic framing of the problem in OSSP is not a static analysis of ﬁt but rather the dynamic problem of adaptation. How do organizations adapt to changing environments? Why do adaptive failures occur? These were the starting questions for M&S. They saw the adaptive process as consisting of three sets of problems that a company has to solve in a mutually consistent way (see Fig. 1). The ﬁrst is what they described as the entrepreneurial problem, focused on the choice of product-markets that the company would serve. In an established company, this choice is constrained by the existing activities of the ﬁrm: Should it remain within its historical domain, or should it venture out to exploit new opportunities that may lie outside that domain? The second problem in the adaptation process is what M&S described as the engineering problem, involving ‘‘the creation of a system which puts into actual operation management’s solution to the entrepreneurial problem.’’ In other words, the company has to select an appropriate business model including a choice of

The Entrepreneurial Problem

Choice of product-market domain

Selection of areas for future innovation

The Engineering Problem

The Administrative Problem Rationalization of structure and processes

Fig. 1.

Choice of technologies for production and distribution

The Adaptive Cycle (Reproduced from Fig. 2.1 in OSSP).

Scholarship that Endures

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technology. The administrative problem relates to the structure and processes of the organization: how to build the organizational arrangements necessary to efﬁciently implement existing activities without jeopardizing the company’s ability to create new activities in response to evolving market demands. Perhaps the most enduring and insightful contribution of M&S lies in their developing a categorization scheme for organizations based on how they respond to the adaptive challenge. In contrast to most work on strategic and organizational ‘‘archetypes’’ that tend to be grounded in static analysis of organizational snapshots, M&S’s scheme is based on a view of organizations as ‘‘integrated wholes in dynamic interaction with their environments.’’ Basically, they argued that organizations can create the coherence in their strategy, structure, and processes that is needed for effective adaptation in some distinct ways. While theoretically a very large number of different permutations and combinations of the different dimensions was possible, M&S’s ﬁeld research in companies revealed only four archetypes. Defenders are organizations that focus on a well-deﬁned and narrow market in which they attempt to remain competitive by constantly improving their efﬁciency and productivity. Prospectors focus on constant innovations. They search for new market opportunities and tend to be highly ﬂexible and entrepreneurial. Analyzers are more complex organizations that combine some aspects of both Defenders and Prospectors. They operate in some businesses that are relatively stable, in which they compete on the Defender’s strength of efﬁciency and low cost, and others that are dynamic, in which they act like Prospectors, searching for innovations. Finally, Reactors are organizations that lack consistency in their strategy, structure, and processes. They are, therefore, unable to create a coherent and sustainable position for themselves, and unable to respond effectively to environmental change, unless somehow forced to do so by external forces. What is remarkable about this categorization scheme is the comprehensiveness of organizational attributes – across strategic orientation, organizational features, and management processes – they capture (see Table 1). It is in the comprehensiveness of the scheme that the M&S framework distinguishes itself from other categorization schemes that relate primarily to the strategy of ﬁrms, with limited insights into the associated organizational attributes, such as Michael Porter’s generic strategies,1 or to organizational structural attributes, with limited attention to either strategy or process, as in Oliver Williamson’s distinctions among F, H, M, and corrupted M forms,2 or even in schemes that span strategy and organization, such as Henry Minzberg’s or Danny Miller’s typologies.3

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Table 1. The Four Organizational Archetypes (Summarized from Tables 3.1, 4.1, and 5.1 in OSSP). Defender

Prospector

Analyser

Entrepreneurial problem

Focused and narrow market segment Limited environment scanning Focus on efﬁciency and productivity Incremental growth through deeper penetration within segment Some product development close to existing products

Engineering problem

Single core technology Investment in technology to improve efﬁciency Tendency toward vertical integration

Multiple markets, Broad market with some stable others continuous changing development Broad environment Marketing and R&D oriented scanning Growth through environmental scanning product and Steady growth market through market development Acquisition to obtain penetration and new business new technologies development Tendency toward a fast follower strategy Multiple Dual technological technologies core with stable Low degree of and ﬂexible routinization components Technology Large and inﬂuential embedded in applied research people group Moderate degree of technical efﬁciency Product division Matrix structure Marketing and organizations Marketing and R&D applied research carry most carry most inﬂuence inﬂuence Large, diverse, and Moderately transitory top centralized control management with horizontal Proliferation of task sharing of forces and project information Complex, teams Decentralized multidimensional control co-ordination Extensive rather than Complex planning intensive planning and multi-attribute performance management

Administrative problem Functional organization Production and ﬁnancial control carry most inﬂuence Long tenure of top management Centralized control Intensive rather than extensive planning Co-ordination through standardization and scheduling Performance measurement against own history Difﬁcult for Effective in dynamic Robust but needs Risks and beneﬁts competitors to environments, but constant review of dislodge but major vulnerable to low portfolio Can be overwhelmed shift in market can proﬁtability and threaten survival loss of focus by internal Unable to exploit complexity new opportunities

Reactor Inarticulated or ambiguous strategy with no clear direction or prioritising Adherence to a strategic path rendered unviable by environmental changes

No clear business model

Organizational features not consistent with strategy Organizational features not coherent among themselves Persistence with an unsuitable strategy–structure ﬁt

Inability to respond effectively to market changes Poor performance and potential crisis

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A JOURNEY OF ADVENTURE What can we learn from the enduring relevance and insightfulness of OSSP, about the conduct of scholarship that endures? At one level, an academic career, like any other career, is a career. It has its usual career demands – publishing ‘‘A’’ journal articles to get promoted, getting enough numbers of such articles to obtain tenure, and so on. Even after tenure, the need to maintain a scholarly reputation within the ﬁeld poses its own ongoing demands. Amid all these demands of the career, it is all too easy to forget what the profession is all about. I believe the profession is all about enduring scholarship. Admittedly, very few of us are likely to make contributions that will maintain their relevance and salience for over 25 years, as OSSP has done. Therefore, at the individual level, it is perhaps inadvisable to make such contributions the test of one’s own worth, because that will only set most of us up for disappointment or failure. Besides, there is considerable value in contributing the incremental pieces that are always useful in the process of knowledge development, even if they are quickly lost from sight as the intellectual ediﬁce builds on top of them. But, at the level of the overall profession, it is enduring scholarship that counts. It is these bursts of insights and ideas that move academic ﬁelds forward and that create the energy and momentum that keep the rest of us engaged. And, even at the individual level, while it may be unwise to make it the goal, is it not true that the possibility of making such contributions was what seduced most of us into the profession in the ﬁrst place? What does it take? What can OSSP tell us about the path that those who make such contributions follow? In describing himself and his work, Sigmund Freud wrote (p. 297): You often estimate me too highly. I am not really a man of science, not an experimenter, and not a thinker. I am nothing but by temperament a conquistador – an adventurer, if you want to translate the word – with the curiosity, the boldness, and the tenacity that belong to that type of being. Such people are apt to be treasured if they succeed, if they really discover something; otherwise they are thrown out. And that is not altogether unjust.4

This, it seems to me, is a necessary, though by no means sufﬁcient, condition for enduring scholarship: the courage and curiosity to embark on a long intellectual journey. And it is precisely such a journey that M&S undertook. It started with a study of 16 college textbook publishers that led to some initial ideas about the different organizational types. The process was

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inductive and, being so, the data were insufﬁcient to provide a complete speciﬁcation for each type. A follow-up study of three of those publishers completed the models. But these were all ﬁrms in the same industry. To look for inter-industry comparisons and also some empirical validation, the authors turned to their Berkeley colleagues, Henry Coleman Jr. and Alan Meyer, who went out to the ﬁeld to collect data from 22 electronics ﬁrms and 27 food processing companies (Coleman), and then to 19 voluntary hospitals (Meyer). While OSSP was published in 1978, the same journey has continued over the next two decades, as the authors have kept on probing into the same question of organizational adaptation.5 Each step in the journey has produced ideas but has also left gaps that have led to the next step, with genuine and relentless curiosity as the driving engine. This is essentially a model of research as detective work that combines information with imagination to search for ideas and insights – much more in-line with the recommendations of Karl Weick than with the doctrine of Karl Popper.6 I emphasize this if only because so many of us today, when asked what we are working on, list a diverse range of ‘‘papers’’ that we are writing, typically with different co-authors, perhaps on a variety of relatively unrelated topics. There is little adventure in this process, and little sustained curiosity. Such an approach to research, while necessary perhaps for meeting career needs or personal preferences for variety, is unlikely to create enduring contributions. Freud highlighted the risks of adventurers: if they do not discover something important, they are thrown out. To untenured colleagues, the prospect of working for a long period of time on one topic with highly uncertain outcomes may give the words ‘‘thrown out’’ some very tangible and immediate meaning. What then can those willing to undertake such adventures do to enhance the likelihood that something worthwhile will really be found?

THE COURAGE TO PURSUE BIG, BROAD QUESTIONS Exciting adventures require big, broad, and relatively unchartered territories to explore. Again, this does not assure success, but its obverse fails to qualify as adventure. If scholarship that endures is a product of intellectual adventure, then it must start with similarly broad issues to address. M&S started with a set of fundamental and enduring questions. Indeed, if I try to recall any work in the strategy ﬁeld that has endured over many decades – Chandler (1962), Rumelt (1974), Mintzberg (1973) – this is the one feature all of them shared.7 Each of these scholars identiﬁed – and then had

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the courage or the foolhardiness to pursue – big, broad questions. It is also not true that they took up such challenges only in their post-tenure years: two of the three pieces I have identiﬁed as examples were doctoral theses. One consequence of focusing on such enduring questions and issues was that each of these authors ended up presenting a broad framework rather than a tightly argued causal theory. M&S highlight the importance of such frameworks in their introduction to the recently published Stanford Business Classics reissue of OSSP: Of utmost importance is the need for a broad, ﬂexible conceptual framework. Such a framework serves several purposes: it helps to classify and explain what is already known, it allows you to interpret the work of others, and it guides your future research. A truly ﬂexible framework is also never complete; it can always be modiﬁed and extended to make it more useful. Indeed, it is the conceptual framework itself that is probably of most value to other researchers, not any single idea derived from, or study spawned by, the framework.

I recognize that such pursuit of big, broad questions runs directly counter to some of the conventions of our career. First, it requires a degree of tolerance for ambiguities and incompleteness that can easily be viewed as lack of rigor and as signs of a poor intellect. Second, such work is hard to publish in journals – indeed as M&S wrote in the preface of OSSP, ‘‘During the course of this process, it became clear that we could not compress the entire framework and its related research evidence into a single article or monograph. Therefore, we decided that a book would be more appropriate y’’ How many of today’s promotion review committees will accept such an explanation while reviewing a tenure case? Beyond the issue of limited space in journal articles, there is also the associated problem of methodology. In exploring big, complex issues, it is hard to maintain methodological purity – particularly if one is unwilling to pay the price of extreme reductionism. As Robert Sutton and Barry Staw wrote (p. 383), ‘‘We ask the reader to consider whether the evidence provided by people such as Freud, Marx or Darwin would meet the empirical standards of the top journals in organizational research.’’8 In the same vein one can ask, would any top journal today publish the key arguments of OSSP, even if M&S found a way to dice up the overall story? In his book, Scholarship Revisited, Ernest Boyer described four different kinds of scholarship: The scholarship of discovery (research), the scholarship of integration (synthesis), the scholarship of practice (application), and the scholarship of teaching (pedagogy).9 Over the last two decades, we have collectively narrowed the deﬁnition of scholarship to only that of research, and have excluded from it the other three categories. Although the focus on

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discovery has yielded great beneﬁts, one of the costs has been the delegitimization of the scholarship of integration. OSSP is, in essence, an outstanding example of scholarship of integration, and it demonstrates the enormous value of creative and parsimonious synthesis in academic ﬁelds connected with professional practice. If we celebrate this book, then surely we will like to see more work of this kind. To do so, perhaps we need to once again legitimize the role of books – not only as complements to but also, in exceptional cases, as substitutes for, journal articles. Beyond that, perhaps we also need to create a new journal – to be called the Academy of Management Essays, perhaps – as a fully legitimate outlet for the scholarship of integration.

MAKING THE WORLD A BETTER PLACE All of us live in two worlds. One is our own world, within the academic community, in which we derive great joy from speaking to one another – whether physically or metaphorically, through our papers. The language of these conversations is theory, and its grammar is shaped by rules of logical and empirical rigor. The other world is the real world – of companies, managers, employees, consumers, regulators, students and y our children, most of whom will be residents of that world. The two worlds sometimes connect, as when theory addresses a real-world issue. A lot of the time, however, we keep the two worlds apart. Enduring scholarship always exists at the intersection of these two worlds. Without theory, there is no scholarship; without the real world, there is no endurance. The ultimate purpose of all scholarship is to help, directly or indirectly, make the world a better place, often by ﬁrst making it a better understood place. In this sense, scholarship that divorces the endeavor of building positive theory from normative purposes is unlikely to endure. I know that there are many in our profession who disagree with this proposition. In response to them, I offer a quote from James Coleman, perhaps the most pre-eminent sociologist of the last quarter century (p. 14): The rational reconstruction of society y is now upon us in full force y It is the task of sociologists to aid in that reconstruction, to bring to it the understanding of social processes, to ensure that this reconstruction of society is not naı¨ ve but sophisticated; to ensure, one might say, that it is indeed a rational reconstruction of society.10

What Coleman asserted about the tasks of sociology is doubly true for all of us involved in the Academy of Management. This is indeed the promise to

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society, on the strength of which all other diverse scholarly interests related to business and management have derived their legitimacy, and because of which the Academy has enjoyed its enormous expansion. The promise is not one of merely observing, describing, or even explaining. It is a promise to help in the reconstruction – in other words, a promise to build an intellectual grounding that can guide the actions of those responsible for effective performance of key social institutions, including companies. OSSP is normative theory. It is grounded in a rich understanding of the existing literatures, and is informed through the observation of several institutions. But its goal is to help managers understand their existing situations, and to aid them in improving the effectiveness of their organizations. It is not normative in the sense of providing a simple and universal prescription that cures all ills; it is normative in providing perspective, a basis for diagnosis, and a method for reﬂection on further action. There are some very practical career beneﬁts that can accrue from this form of scholarship: it allows the scholar to harmonize the different aspects of his or her professional work. While focusing on research and publications, most of us also do some teaching, and perhaps some consulting. Typically, these aspects of our professional work tend to be unconnected. For M&S, however, not only were they connected, but the connections were self-reinforcing. As they wrote, in describing the process of writing the book, ‘‘we have used portions of these materials in classes at several universities, with managers in university executive development programs, and in private consulting activities with top-management groups. The verbal and written responses of these individuals were more than adequate encouragement to keep us at our task.’’ In the world of business schools, this is an increasingly rare form of scholarship – one based on creating a tight link between research, teaching, and consulting. The conventional advice is exactly to the contrary. Yet, such a synthesis is of great advantage for scholarship that attempts to illuminate and improve professional practice. And once again, if I apply the test of long-term endurance, it is this form of scholarship that I believe has the best chance of being remembered and celebrated 25 years after publication.

NOTES 1. Porter, M.E., 1980. Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: The Free Press; Treacy, M. and F. Wiersema, 1995. The Discipline of Market Leaders. Reading, MA: Addison-Wesley.

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2. Williamson, O.E., 1975. Markets and Hierarchies: Analysis and Antitrust Implications. New York: Free Press. 3. Miller, D., 1990. The Icarus Paradox: How Exceptional Companies Bring about Their Own Downfall. New York: Harper Business; Mintzberg, H., 1979. The Structuring of Organizations: A Synthesis of the Research. Englewood Cliffs, NJ: PrenticeHall. 4. See Jones, E., 1964. The Life and Work of Sigmund Freud. London: Penguin Books. 5. Miles, R.E. and C.C. Snow, 1986. ‘‘Network Organizations: New Concepts for New Forms,’’ California Management Review, Vol. 28, 66–73; Miles, R. E. and C. C. Snow, 1994. Fit, Failure and the Hall of Fame: How Companies Succeed or Fail. New York: The Free Press. 6. Popper, K.R., 1968. The Logic of Scientific Discovery. New York: Harper & Row; Weick, K.E., 1989. ‘‘Theory Construction as Disciplined Imagination,’’ Academy of Management Review, Vol. 14, No. 4, 516–531. 7. Chandler, A.D., Jr., 1962. Strategy and Structure: Chapters in the History of the American Industrial Enterprise. Cambridge, MA: The MIT Press; Mintzberg, H., 1973. The Nature of Managerial Work. New York: Harper & Row; Rumelt, R., 1974. Strategy, Structure and Economic Performance. Boston, MA: Division of Research, Harvard Business School. 8. Sutton, R.I. and B.M. Staw, 1995. ‘‘What Theory is Not,’’ Administrative Science Quarterly, Vol. 40, 371–384. 9. Boyer, E.L., 1990. Scholarship Reconsidered: Priorities of The Professoriate. Princeton, NJ: The Carnegie Foundation for the Advancement of Teaching. 10. Coleman, J.S., 1992. ‘‘The Rational Reconstruction of Society,’’ American Sociological Review, Vol. 58, 1–15.

THEORY, PRACTICE, AND SCHOLARSHIP Raymond E. Miles and Charles C. Snow In theory, there is no difference between theory and practice. In practice, there is. – Yogi Berra

While you are pondering this observation on the relationship between theory and practice, let us note that Sumantra Ghoshal has been saying essentially the same thing about theories of management for over a decade now (e.g., Ghoshal & Moran, 1996). His most insightful observations are contained in Ghoshal (2005), where he points out that not only are theory and practice inextricably intertwined, but ﬂawed or ‘‘bad’’ theories can even do damage to good management practices. We are honored and pleased that Ghoshal, who was one of the world’s great organization theorists, has cited our work as exemplary scholarship – ‘‘good’’ theory if you will. Ghoshal identiﬁes four main reasons why he believes that the research reported in our 1978 book has endured. First, he says that we had ‘‘y the courage and curiosity to embark on a long intellectual journey.’’ We agree with his observation, though we hasten to add that at the time our curiosity far exceeded our courage. Miles wanted to widen the context of his research on individual managers’ philosophies of management to include organization design and development, and Snow (his doctoral student) was interested in the ﬁrm as a unit of analysis. Thus, our research collaboration was a natural partnership – Snow was the student learning how to do research, and Miles was expanding his research focus to include the entire organization. Research Methodology in Strategy and Management, Volume 3, 11–15 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1479-8387/doi:10.1016/S1479-8387(06)03002-5

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Ghoshal’s second observation concerns the scope of the issues addressed by our research: ‘‘If scholarship that endures is a product of intellectual adventure, then it must start with similarly broad issues to address.’’ In the year before the Miles–Snow research collaboration began, Snow served as the research assistant to Professor Charles Perrow, who was on sabbatical in Berkeley at the time and writing his book Organizational Analysis: A Sociological View (Perrow, 1970). While searching for examples for Perrow’s book, as well as taking a seminar from him, Snow became intrigued with the idea of how organizations interacted with their various environments and the role that top managers’ perceptions played in the process. Approaching Miles with the request to serve as his dissertation chair, and after many intellectual conversations that ranged across the topics of managerial decision making, organizational change and development, and management ideologies, Snow came to believe that his dissertation research was about organizational adaptation. Ghoshal notes that Miles and Snow ‘‘y ended up presenting a broad framework rather than a tightly argued causal theory,’’ and we are especially pleased that he attaches as much theoretical importance to our adaptive cycle – a dynamic construct embodying ideas drawn from the work of Chandler (1962), Lawrence and Lorsch (1967), Thompson (1967), and Weick (1977) – as he does to the organizational typology. We were fortunate that two other doctoral students, Alan Meyer and Henry Coleman, arrived at Berkeley soon after we had developed our overall theoretical framework. Their dissertation research provided valuable tests and extensions of the adaptive cycle and organizational typology, and their work helped to make this the rich framework that has served us (and others) well for many years. Thus, we are not suggesting that every organizational researcher should seek to develop a broad theoretical framework. We do believe, however, that any particular piece of research should examine issues of practical significance and should be clearly embedded in a broad theoretical perspective. Using Boyer’s (1990) four types of scholarship, Ghoshal discusses a third reason for the endurance of our framework – the scholarship of integration (or synthesis). Our framework did indeed provide a measure of integration, not because we set out to do so, but rather because we needed to bridge two somewhat separate ﬁelds in order to explain our ﬁndings. For example, the emerging ﬁeld of business policy portrayed a ﬁrm’s strategy as an essentially unique solution to the problems posed by environmental threats and opportunities. The more established ﬁeld of organization theory, on the other hand, hardly acknowledged strategy at all. Our research showed that (a) patterns in top managers’ perceptions and decisions could be identiﬁed

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across ﬁrms in an industry, (b) an evolving industry could support several different competitive strategies, and (c) a particular ﬁrm’s strategy had to be ﬁtted with an appropriate organization structure and set of management processes in order for the ﬁrm to succeed. These are, to use Ghoshal’s words, ‘‘big, broad’’ ideas that span ﬁelds and stimulate further, more detailed research. Lastly, Ghoshal says that our theoretical framework is normative: ‘‘It is not normative in the sense of providing a simple and universal prescription that cures all ills; it is normative in providing perspective, a basis for diagnosis, and a method for reﬂection on further action.’’ We strongly believe, as does Ghoshal, that we are members of an applied academic discipline and that our efforts as researchers should be focused heavily on the improvement of management practice. For our current thinking on this point, see Snow et al. (2006), where we argue that the sub-ﬁeld of organization design should devote more attention to issues such as formulating theories that help managers anticipate new organizational forms, and building diagnostic and investment tools that aid managers in preparing their organizations for the future. Ghoshal’s comments on our dynamic theoretical framework of strategy, structure, and process are so laudatory that we hesitate to draw attention to any omissions on his part. Nevertheless, because one piece of our framework that he did not mention is so closely linked to his own work, we feel that we should point it out. In Chapter 8 of Miles and Snow (1978), we discussed the impact of managers’ beliefs and assumptions on their strategic, structural, and process choices. Using Miles’ (1975) typology of management theories, we argued that both the prospector strategy and the decentralized decision-making process it required probably would not be successful unless managers use a ‘‘human resources’’ theory of management – that is, unless they have positive views of the motivation and capabilities of those managers and employees below them in the hierarchy and are genuinely committed to joint goal-setting and delegation. Essentially, we argued that the prevailing ‘‘traditional’’ or ‘‘human relations’’ theories constrained managers’ strategic choices. Our overall argument was conﬁrmed by Alan Meyer’s research which is reported in Chapter 13 of the book and which is the basis of his highly cited article on how management ideology affects an organization’s mode of adaptation to environmental jolts or crises (Meyer, 1982). Moreover, we have included the link between management theory and organizational form in our subsequent work. In Miles and Snow (1994), we pointed out that multi-ﬁrm network organizations required a willingness on

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the part of management to invest heavily in the development of new individual skills and collective capabilities. We argued, for example, that managers who attempted to pursue innovation-based strategies supported by new structural arrangements needed a new theory of management, which we called the ‘‘human investment’’ model, a view that expanded the human resources perspective. Thus, while Ghoshal has passionately described the negative impact of ‘‘bad’’ theory on management practice, we have argued that ‘‘good’’ theory allows managers more freedom to create innovative strategies, structures, and processes. Nevertheless, the theories that Ghoshal and Moran (1996) and Ghoshal (2005) cite as bad for practice – most notably, agency theory (Jensen & Meckling, 1976) and transaction cost economics (Williamson, 1975) – have endured, and progress toward the development of new organizational forms has been slow in coming. Why? Is it possible to clearly specify the factors that separate good theories from bad ones? We’d like to close our commentary by offering our thoughts on this matter. We agree with Ghoshal’s controversial point that agency theory and transaction cost economics are bad for practice – in the sense that strict adherence to the dictates and implications of those approaches can create a self-fulﬁlling prophecy that results in the widespread use of undesirable management practices. We also agree with his analysis of what is wrong with such theories, especially his notions of partial analysis and the exclusion of human intentionality and choice. For example, both agency theory and transaction cost economics have a narrow view of organizational behavior (e.g., shareholders are the only stakeholders of a ﬁrm or costs are the most important consideration in managerial decision making). Therefore, it seems to us, a theory can endure but still be bad if it is too narrowly deﬁned – if it only permits the partial analysis of some important organizational or managerial phenomenon. Such a theory can be valid, and can even garner substantial empirical support, but still not fully or properly reﬂect management practice. Also, both agency theory and transaction cost economics have a ﬂawed conception of human motivation and behavior (e.g., people act only in their self-interest and often with guile). Such theories can survive, partly because some managers do in fact behave as the theories presume, but also because the theories are at least somewhat self-fulﬁlling (Ferraro, Pfeffer, & Sutton, 2005). To the extent that a management theory is self-fulﬁlling, it crowds out the development of useful management practices (as well as ‘‘good’’ theories) based on positive human attributes such as generosity, trusting in and caring for others, intrinsic motivation, and a desire to cooperate and

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collaborate. In short, bad management theories do not adequately incorporate human volition, and they will not, as Ghoshal says, make the world a better place. Indeed, not only may they lead managers to make bad choices but also adherence to them inhibits managers from experimenting with new and more desirable approaches. In conclusion, perhaps it bears repeating that researchers need not begin their careers, or launch a particular study, with the objective of producing enduring scholarship. They simply need to focus on important management issues, both theoretical and practical, and study how people go about resolving them.

REFERENCES Boyer, E. L. (1990). Scholarship reconsidered: Priorities of the professoriate. Princeton, NJ: The Carnegie Foundation for the Advancement of Teaching. Chandler, A. D., Jr. (1962). Strategy and structure: Chapters in the history of the American industrial enterprise. Cambridge, MA: MIT Press. Ferraro, F., Pfeffer, J., & Sutton, R. I. (2005). Economics language and assumptions: How theories can become self-fulﬁlling. Academy of Management Review, 30, 8–24. Ghoshal, S. (2005). Bad management theories are destroying good management practices. Academy of Management Learning & Education, 4, 75–91. Ghoshal, S., & Moran, P. (1996). Bad for practice: A critique of the transaction cost theory. Academy of Management Review, 21, 13–47. Jensen, M., & Meckling, W. (1976). Theory of the ﬁrm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3, 305–360. Lawrence, P. R., & Lorsch, J. W. (1967). Organization and environment: Managing differentiation and integration. Boston: Harvard Graduate School of Business Administration. Meyer, A. D. (1982). Adapting to environmental jolts. Administrative Science Quarterly, 27, 515–536. Miles, R. E. (1975). Theories of management: Implications for organizational behavior and development. New York: McGraw-Hill. Miles, R. E., & Snow, C. C. (1978). Organizational strategy, structure, and process. New York: McGraw-Hill. Miles, R. E., & Snow, C. C. (1994). Fit, failure, and the Hall of Fame: How companies succeed or fail. New York: Free Press. Perrow, C. (1970). Organizational analysis: A sociological view. Belmont, CA: Wadsworth. Snow, C. C., Miles, R. E., & Miles, G. (2006). The conﬁgurational approach to organization design: Four recommended research initiatives. In: R. M. Burton, D. D. Ha˚konsson, B. Eriksen & C. C. Snow (Eds), Organization design: The evolving state-of-the-art. New York: Springer. Thompson, J. D. (1967). Organizations in action. New York: McGraw-Hill. Weick, K. E. (1977). Enactment processes in organizations. In: B. M. Staw & G. R. Salancik (Eds), New directions in organizational behavior (pp. 267–300). Chicago: St. Clair Press. Williamson, O. E. (1975). Markets and hierarchies: Analysis and antitrust implications. New York: Free Press.

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CONSTRUCTS AND CONSTRUCT MEASUREMENT IN UPPER ECHELONS RESEARCH Mason A. Carpenter and Gregory P. Reilly ABSTRACT Upper echelons research considers the relationship of top executives to organizational attributes or outcomes, vis-a`-vis, their individual or group demographic characteristics such as tenure or experience. The upper echelons perspective is typically associated with the theorizing of Hambrick and Mason in their 1984 Academy of Management Review article, but also has much broader and deeper organizational theory roots as demonstrated by Pfeffer’s (1983) earlier exhaustive review of organizational demography. Since the early 1980s, hundreds of upper echelons studies have been published – some explicitly invoking the upper echelons theoretical perspective, while others employing its underlying methodology of relying on executive demographic characteristics as proxies for executive and top management team (TMT) related constructs. This chapter examines three important features and their related challenges and opportunities in future upper echelons research. Specifically, we focus on (1) the identification of upper echelons constructs, (2) embedding those constructs in a meaningful way to develop new theory or better our understanding of extant theory, and (3) the related operationalization and measurement of those constructs that are eventually included in qualitative and quantitative analyses using TMT demographics. We Research Methodology in Strategy and Management, Volume 3, 17–35 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1479-8387/doi:10.1016/S1479-8387(06)03003-7

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conclude our chapter by drawing these three features together to provide a benchmark process to gauge the theoretical and methodological contributions of upper echelons-related work, and ultimately improve the chances of getting such research published.

INTRODUCTION The purpose of this chapter is to help researchers develop new studies and get their work published when they adopt, either explicitly or implicitly, the upper echelons perspective (UEP) introduced by Hambrick and Mason (1984) (henceforth H&M). In doing so, we outline the challenges and issues related to constructs and construct measurement that must be considered by researchers when embarking upon UEP research. We will approach this task by ﬁrst describing what novel research looks like, and then move on quickly to describe the UEP model and identify the important constructs that comprise it. Next, we will describe strategies for connecting these constructs to ensure the advancement of theory in the ﬁeld. After that, we will turn our focus to a discussion of how construct validity can be attained in an UEP study. Finally, we will bring these topics together in a checklist intended to help future UEP researcher to select interesting and appropriate constructs for study and to ensure that these constructs are operationalized with high internal and external validity. Interesting? Regardless of the domain, UEP or not, your research won’t go beyond working paper status if it is not interesting. Is ‘‘interesting’’ subjective? Sure. Is ‘‘interesting’’ purely deﬁned by academic fad and fashion? No. Fortunately, there is a concise and easily accessible starting point, the classic paper by Murray S. Davis (1971), for determining if your UEP research question stands up to this test. Davis sets up his paper by stating: ‘‘It has long been thought that a theorist is considered great because his theories are true, but this is false. A theorist [or theory] is considered great, not because his theories are true, but because they are interesting’’ (1971, p. 309). Davis asserts a theory to be interesting if it has been given wide circulation (e.g., is cited in other research, textbooks or taught in courses). As an introductory footnote, we’d say that the UEP would pass Davis’ test given the fact that H&M is cited in over 500 subsequent refereed journal articles, across

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domains as diverse as psychology and economics, and the citation rate and domain diffusion of the UEP appears to be continuing at a healthy clip (Carpenter, Sanders, & Geletkanycz, 2004). In general terms, Davis ﬁnds that, ‘‘A new theory will be noticed only when it denies an old truth, proverb, platitude, maxim, adage, saying, commonplace, etc.’’ (1971, p. 310). He adds, ‘‘all interesting theories, at least all interesting social theories, then, constitute an attack on the takenfor-granted world of their audience. y If it does not challenge but merely conﬁrms one of their taken-for-granted beliefs, [the audience] will respond to it by rejecting its value while afﬁrming its truth’’ (1971, p. 310) Finally, ‘‘an interesting proposition [is] always the negation of an accepted one’’ (1971, p. 311). In summary then, what Davis is saying is that your UEP theory or study will be considered interesting if it challenges widely shared, but weakly held assumptions about the world, organizations and human behavior. Such research is not only interesting, it is also important because it helps us to better understand extant knowledge and theories, and create opportunities and motivation for new research. Valuable, Inimitable, and Rare? Recall that the UEP is typically considered within the domain of strategic management, and there too you have a useful tool for determining if your proposed study meets the ‘‘that’s interesting!’’ test. Speciﬁcally, a recent editorial in the Academy of Management Journal (2003) by Don Bergh walks through the resource-based view (RBV) as a tool to evaluate whether you are making a valuable contribution with your UEP study. While we encourage you to read the full text yourself, the gist of his suggestion is that your research must be considered (a) valuable (would academics and practitioners see enough value in your work to cite or apply it?), (b) inimitable (have you controlled for all reasonable competing explanations of the proposed advance, either through the logical consistency of your theoretical model or through controlling for rival theories in the research method?) and, (c) rare (is your contribution novel, surprising, and unexpected?). Although we agree that there remains great promise and opportunity in UEP research, we also strongly endorse the view that ‘‘the bar has been raised such that any new UEP study that looks at the effects of demographic characteristic X on organizational outcome Y is likely to be viewed as simply reafﬁrming that which is already understood – namely, that executives and their demographics matter’’ (Carpenter et al., 2004, p. 770). In many ways, because UEP relationships are taken as given, particularly in certain quarters of

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strategy researchers, they are no longer novel from a Davis (1971) perspective. Admittedly, replication of existing work does have tremendous academic value, and there are venues that will publish it. However, editors and reviewers in the top management journals will apply the ‘‘that’s interesting’’ and resource based criteria we summarized above.

THE UPPER ECHELONS PERSPECTIVE AND ITS CONSTITUENT CONSTRUCTS It should be clear to you that the ﬁrst step in developing and publishing research that employs the UEP is to pass the ‘‘that’s interesting!’’ hurdle. This is a very high, non-trivial hurdle, particularly if you are aiming to publish your work in one of our top academic journals. But you are a savvy social scientist, and let us assume you have done a reasonably good job of identifying an interesting research question. The second step in developing and publishing your UEP research is an understanding of the theoretical model originally developed by H&M, its constituent constructs, and how the model and UEP research has evolved in the past 20 years. Fortunately, much of this review has been undertaken for you already. If your research is a dissertation, then you would be well served by reading H&M, Pfeffer (1983), Jackson (1992), Hambrick (1992), Hambrick (1994), Finkelstein and Hambrick (1996), Carpenter et al. (2004), and the dialogue by several authors in Dansereau and Yammarino (2005, pp. 197–273) on the various multi-level analytical issues embedded in the UEP. Admittedly this is a long laundry list of readings on the UEP perspective, but if you are aiming for something interesting and publishable, you had best arm yourself with the requisite knowledge of the UEP history and landscape. H&M’s original model identiﬁes characteristics of a top management team (TMT) as being important factors in the determination of overall ﬁrm performance. H&M reason that TMT members are most likely to make decisions that will have the greatest impact on ﬁrm actions and ultimately on ﬁrm performance outcomes. H&M proposed that the personal characteristics of these central decision makers will inﬂuence their individual problem-framing, habits, values and biases and, in turn, affect the decision-making processes of the ﬁrm and its success or failure. An updated H&M model provided by Carpenter et al. (2004), that accounts for UEP research to-date is presented in Fig. 1. Additionally, H&M proposed that demographic information about TMT members could be used as a proxy for their personal (and difﬁcult to measure and access) characteristics. In fact,

Antecedents E xternal E nvironment • E xternal stakeholders • E xternal managerial labor markets • E nvironmental characteristics Org anizational • Firm characteristics • Board characteristics • Internal labor markets

• • • • • • • • •

Fig. 1.

Skills and orientations Cognitions or social cognitions Behavioral propensities Access to information Access to resources H uman capital Social capital Relative status – within TMT or across firms H eir apparent

Moderators/ Mediators of TMT D emog raphic E ffects • • • • •

Power D iscretion Incentives Integration Team processes

Org anizational Outcomes

Strateg ic • Business • Corporate • International • Change • Strategic interactions • Policies

Performance • Financial • Market • Social • Innovation

TMT/ Board • Turnover • Composition

Constructs and Construct Measurement in Upper Echelons Research

Theoretical Constructs Proxied by TMT D emog raphics

Carpenter, Geletkanycz, and Sanders’ (2004) Stylized Model of the Upper Echelons Perspective. 21

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the ease of collection of both ﬁrm performance information and demographic information such as education, experience, age and functional background may have contributed to the boom in UEP research in the past decades. If you undertook the literature review we recommended above, you will know that a typical study speciﬁes one or more measures of TMT demographics and investigates their relationships with a measurable outcome variable (i.e., strategy, action, performance, etc.) using the logic of H&M to build a causal chain and explain why the relationship is found. While early UEP research often focused only on specifying measures for these two constructs in the model, to make a contribution to the ﬁeld today researchers are more likely to expand their investigations to explicitly measure variables which represent other components of the original model. In this spirit, we present in Fig. 2 an overview of the key constructs in the UEP and their relationships to each other. This model incorporates the conclusions elaborated in the UEP review by Carpenter et al. (2004), and shown in Fig. 1. Fig. 2 is intended to serve as construct selection guide to future UEP researchers. Speciﬁcally, it is a tool that you can use in developing your UEP research agenda. In the remainder of this section we will describe potential variables for each major construct area and review examples of actual variables that have been studied in past UEP research.

TMT Demographics Although their work may be among the most well-known, H&M are not the only authors, nor the ﬁrst, to argue for the inﬂuence of the demographics of an organization on its performance outcomes. Pfeffer’s (1983) review of organizational demography illustrates the long history of this approach. He identiﬁed organizational characteristics such as growth rate, technology and human resource practices as being important antecedents of organizational demographics. Additionally, he identiﬁed overall ﬁrm performance and other outcomes (e.g., cohort conﬂict, form of organizational control) as being affected by demographics. Even prior to that, Song (1982) showed a relationship between TMT characteristics and ﬁrm diversiﬁcation strategies. The UEP’s main contributions to the existing demographic literature are its focus on the TMT (rather than the ﬁrm or other employees as a whole) and its identiﬁcation of demographic information as a proxy for ﬁrm characteristics rather than as a mediating variable between ﬁrm characteristics and outcomes. While TMT demographic variables are likely to remain an important part of UEP research, it has already been pointed out in this chapter

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Early Upper Echelons Research

Demographic proxies for TMT Characteristics Mediating Operational Processes

Mediating Strategy Processes

- Design - Execution - Evolution

- Planning - Scanning, etc.

TMT Characteristics - Individual level - Group level

Decision Making Processes

Organizational Outcomes

- TMT-level - Lo wer-level - E xternal

- Process - Output - Overall

Context (Antecedents and/or Moderators) Organization-level Characteristics

Fig. 2.

Environment-level Characteristics

Upper Echelons Constructs.

that demographics, even novel ones, will be unlikely to give a study any unique advantage or novelty.

TMT Characteristics This category of UEP constructs are at the heart of the causal model proposed by H&M. The authors view organizational outcomes as ‘‘reﬂections of the values and cognitive bases of powerful actors in the organization’’ (Hambrick & Mason, 1984, p. 193). Carpenter et al. (2004) enrich this original idea by more speciﬁcally describing characteristics that might have the greatest impact on ﬁrm outcomes. Again, these are summarized in Fig. 1. They identify skills, behavioral tendencies, access to resources and social status as being among the most inﬂuential attributes of a top manager. However, these characteristics have only rarely been directly modeled and tested in the UEP literature. While researchers have for the most part used TMT demographics as a proxy for leader or leadership team characteristics, there are some important exceptions. In one notable recent example, Peterson et al. (2003) describe how Big 5 personality characteristics in CEOs affect TMT dynamics, and subsequent ﬁrm performance. By directly measuring these psychological characteristics of the CEO and the

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TMT rather than using demography-based proxies, these authors begin to provide a glimpse inside the causal black box of UEP. Attributes of Decision-Making Processes Although Hambrick and Mason (1984) do not explicitly discuss the causal path between top managers and ﬁrm outcomes being mediated by strategic decisions, Carpenter et al. (2004) point out that such mediation was in the spirit of a ﬁrst draft toward theory building. Twenty years of research has further deﬁned the story of UEP to include an understanding that managers’ characteristics affect ﬁrms through their strategic choices and decisionmaking processes. It follows then that operationalizing characteristics of the decision-making process holds promise and opportunity for interested researchers. Baum and Wally’s (2003) exploration of the impact of speed of decision making on performance may provide guidance for future UEP researchers who want to link this construct back to TMT characteristics. Similarly, a study could use a small case-study approach to compare UEPbased predictions derived from demographic characteristics, and the actual behaviors manifested over time by the managers being studied. Mediating Strategy Processes Another conceptual opportunity that has not been adequately explored is the inclusion of characteristics of other strategy process activities into UEP research. While it may seem logical to assume that characteristics of TMT individuals or the group as a whole will affect ﬁrm decision-making, one could argue that a large portion of this effect will be realized through their participation in the planning process. That is, characteristics of the TMT will affect characteristics of strategic plans, and the strategic plans will affect the nature of ﬁrm decision-making. Markoczy (2001), for instance, looked at the consensus process, but did not factor in any of the respective players demographic characteristics. In contrast, West and Schwenk (1996) tried to link demographics, consensus and performance, but were unable to detect any signiﬁcant relationships. Most recently, Arendt et al. (2005) introduced a CEO-advisor model of strategic decision making where they explored how the use of formal versus informal advisory systems and how advisors were selected was affected by context. While they did not include an exhaustive set of demographic characteristics in their theorizing, Arendt et al. (2005, p. 690) did propose that longer-tenured CEOs would be more likely to rely on formal advisory systems in making strategic decisions. Such a study

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provides a nice example of integrating the constituent constructs of the UEP to develop a novel theoretical perspective. Organizational Outcomes While H&M’s original model identiﬁed performance, growth, variability and survival as potential outcome variables, UEP researchers have experimented in more broadly in the past 20 years with their choices of dependent variable outcome measures. Perhaps mirroring the ﬁeld of strategy as a whole, researchers have become interested in innovation-related outcomes (e.g., team innovativeness, West & Anderson, 1996), socio-cognitive outcomes (e.g., group conﬂicts Amason & Sapienza, 1997), and speciﬁc functional practices (e.g., HR practices, Collins & Clark, 2003). We would encourage research in this vein, particularly work that shows how the UEP is really capturing a cascade of decisions and outcomes, with performance as but one important albeit rather ﬁnal outcome. Mediating Operational Processes While use of alternative outcome variables is beneﬁcial for development of the UEP, we suggest that an opportunity exists in further investigating, intermediate operational outcome variables as mediating variables between strategic decision making and overall ﬁrm performance. In a recent review of the strategic consensus literature, Kellermans, Walter, Lechner, and Floyd identiﬁed the need for ‘‘(a) measures of consensus that take account of locus as well as differences in how the context of strategy is perceived by top-, middle-, and lower-level managers, (b) research designs wherein assumptions about the locus and content of consensus govern the choice of antecedents, and (c) more consistent use of moderators’’ (2005, p. 719). Such an agenda provides the perfect context for integrating and extending the UEP because it can be shown how particular constellations of managers affect the strategic process, and the conditions under which they are most likely to affect particular pieces of the process. Contextual Forces Our last category of conceptual pieces of the UEP model is contextual variables that describe the organizational and environmental circumstances

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under which other relationships occur. These characteristics might be important moderators of the relationship between two previously mentioned types of variables or might provide insight if modeled as antecedents to one speciﬁc construct. Previous examples of such work include a ﬁrm’s global strategy or the discretion afforded the CEO or members of the TMT (see Carpenter et al., 2004, for a summary). However, just as Kellermans et al. (2005) identiﬁed the need for the greater use of moderators in strategy consensus research, so too does there remain tremendous opportunity to understand when, where and why UEP effects play out in certain contexts versus others, particularly if the studies are simultaneously able to shed light on the previously black-boxed dynamics explaining those contingent UEP effects.

EMBEDDING CONSTRUCTS TO CREATE NEW THEORY Having laid out a framework for helping researchers to broadly consider a variety of constructs in their UEP research, we turn now to a discussion of how to select and piece together these elements to create a study that will move the ﬁeld forward. In a sample of 111 manuscripts that he had personally reviewed ‘‘over a four-year period,’’ Daft (1995) identiﬁes the lack of coherent theory as being the most signiﬁcant problem he found. He concludes that more than half the studies he reviewed failed to tell a compelling story and explain why relationships between variables exist as they do. The idea that success in publishing requires scholars to contribute new theory is applicable to research in the UEP. As Carpenter et al. (2004) pointed out, we already understand that demographic characteristics affect ﬁrm outcomes. And as Davis (1971) and Bergh (2003) emphasize, the study has to be interesting (and it is inherently not interesting if it is incoherent!). To make a contribution in the ﬁeld, researchers must explore and relate some richer combination of the constructs identiﬁed in the previous section and then tell a compelling story that explains why the relationships exist. In the remainder of this section, we will outline a process for theory development that can serve to guide UEP researchers in this effort. Crafting the Causal Story Again, we are assuming that you have come up with what you believe to be an interesting theoretical question – you have addressed the questions of

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value, inimitability and rareness. Beyond this, Whetten proposes that ‘‘theoretical insights come from demonstrating how the addition of a new variable signiﬁcantly alters our understanding of the phenomena by reorganizing our causal maps’’ (Whetten, 1989, p. 493). It is not enough just to add new variables described in the ﬁrst section to existing UEP equations. Instead, future UEP researchers must help us to better understand why the relationships have been found. Daft suggests storytelling as one technique for ensuring that a clear rationale is laid out as to why variable X relates to variable Y. He suggests that ‘‘storytelling explains the ‘why’ of the data and gives meaning to observed relationships’’ (1995, p. 178). In the case of UEP research, explanations of what happens inside the TMT and within the strategy process of the organization to cause speciﬁc organizational outcomes have only been offered at a general level of theorizing. Sometimes these stories are simple – for instance, research shows that when individuals are faced with uncertainty they are more likely to fall back on what they know to process information and make decisions. Such an explanation is central to H&M’s original model, was ﬁrst tested in Carpenter and Fredrickson (2001), and later elaborated upon in Hambrick, Finkelstein, and Mooney (2005) in a broader theory on the job demands facing top executives. Demographic characteristics often provide us with clues about what individuals already know. Thus, a signiﬁcant opportunity exists to provide the details of a causal story about why particular characteristics of leaders translate into speciﬁc plans or decision-making models within an organization. Similarly, researchers who could explain the effect of a speciﬁc characteristic on ﬁrm actions and link them both to outcomes would advance the ﬁeld. Also, a contribution can be made by scholars who are better able to explain boundary conditions associated with UEP relationships. We outline one possible tool that you can use to experiment with different possible UEP relationships in Fig. 3. Based on your review of the literature, see if you can ﬁll in the boxes such that you have identiﬁed important gaps in our knowledge base. Then imagine, and try to write out the story that links up these pieces of the puzzle so that your newly developed theory satisﬁes Daft’s (1995) coherence criterion. In effect, this is a tool to structure different UEP causal scenarios or theories. For instance, researchers might proﬁtably test whether there are signiﬁcant organizational or environmental-level factors that strengthen or weaken cause-effect linkages predicted by the UEP. In developing a causal story, researchers must pay careful attention to how the level-of-analysis issues are managed and to explaining the signiﬁcance of temporal dimensions of relationships. This attention to detail

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Demographic

Mediating Strategy Processes TMT Characteristic

Attribute of Operating Process

Attribute of TMT Decision Making

Organizational Outcome

Contextual Factor Organization-level Characteristic

Fig. 3.

Environment-level Characteristic

Hypothesis Development Map.

in theory development will lend credence to any causal story advanced to explain new proposed UEP relationships.

Link to Existing Research Development of the UEP is ongoing. Carpenter et al. (2004) found that H&M’s original work has been cited in excess of 500 times in journals covering a wide variety of disciplines. Besides minimizing the risk of repeating what has already been done, a careful and appropriate review of relevant work provides evidence to potential reviewers that a researcher can be taken seriously, and this chapter has provided you with ample resources to conduct such a thorough review. Any new UEP work should focus on at least one community of researchers within the ﬁeld and explicitly aim to extend a conversation they have initiated in the form of articles in recent publications. A causal story that does not explicitly incorporate past knowledge and address issues seen as important by current UEP researchers has little chance of being published. At the same time, researchers must not fall into the trap that was described by Ketchen (2002) as ‘‘argumentation by citation.’’ That is, hypotheses cannot be developed based only on the ﬁndings in previous studies. Building on the work of others is necessary but not

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sufﬁcient for making a contribution. Successful research must be grounded in what has been done while offering something new within a concise, logical and compelling story. Ensure a Contribution While explicitly building on existing research is an absolute necessity for research in the UEP, development of a compelling causal story requires a researcher to depart somewhat from the accepted wisdom of the ﬁeld and ﬁnd a part of the bigger story that has not been adequately explored or explained. Yes, we return here to Davis’ ‘‘that’s interesting!’’ criterion. One potentially promising approach in UEP research could be classiﬁed as ‘‘testing black box assumptions.’’ Beginning with H&M’s landmark paper, UEP researchers for the most part describe a detailed causal chain similar to our Fig. 2. However, in most cases, they only operationalize demographics and outcome variables. Testing the assumptions found in the intermediate steps in the causal chain would contribute to the ﬁeld by empirically showing that previous assumptions about causal linkages were correct. A second approach to making a contribution would be to understand a relationship in some part of the model that does not conform to expectations. Davis’ (1971) categorization scheme for identifying approaches to developing ‘‘interesting’’ research helps us identify several possible paths for ﬁnding an unexpected result. One approach would theorize about constructs in the UEP framework that would be expected to be correlated but turn out to uncorrelated. In another, researchers might identify a negative correlation between constructs where the more established general UEP model would predict a positive correlation. Finally, UEP scholars could propose causation that contradicts the expected direction of causation in the established UEP thinking. Given that very little of the ‘‘black box’’ linkages in the UEP model have been tested empirically, there may be signiﬁcant opportunity for surprising ﬁndings.

ENSURING CONSTRUCT VALIDITY IN UEP RESEARCH As important as selecting applicable constructs and linking them together via new theory are, effective research must also ensure that the operationalization of the constructs is properly executed. Such construct validity is critical to all research endeavors to ensure that theory is tested in as accurate

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a manner as is possible. Schwab said that construct validity is ‘‘present when there is a high correspondence between cases’ scores on a measure and the mental deﬁnition of the construct it is designed to represent’’ (1999, p. 26). In UEP research, there are several components of the generally accepted framework for which high levels of construct validity are especially difﬁcult to attain. The ﬁrst area of difﬁculty is the identiﬁcation of the TMT itself. Sidestepping the fact that prominent UEP researchers themselves take issue with the ‘‘team’’ label (Hambrick, 1994), most research employing the UEP identiﬁes the TMT (a construct in the theory) using those individuals identiﬁed in public documents. This practice raises two questions. First, would these be the strategic decision makers identiﬁed by the CEO if she or he were asked who comprises their top management team? Roberto (2003) and Arendt et al. (2005), among others, suggest not. Roberto (2003), for instance, argues that there is a common core group for all strategic decisions, but that other members come in and out of the team depending on the strategic issue. Arendt et al. (2005) suggest that strategic choices are inﬂuenced not only by TMT members, but also other internal and external advisors. To the extent that the team demographic characteristics obtained based on public data diverge from the characteristics of the team members actually involved in strategic processes (and your theory), such divergence is highly problematic for construct validity. The second area of difﬁculty is related and has similar validity consequences. Speciﬁcally, should team membership (and therefore the TMT demographic characteristics) be deﬁned by the organization’s structure (i.e., who is shown to be a member of the team) or by the strategic decision (i.e., a merger or acquisition versus the launch of a new product or diversiﬁcation)? Determining Construct Valid Measures Researchers in the upper echelons perspective, as in most streams of research, have not organized a well orchestrated process through which to develop valid measures of the constructs in the ﬁeld. Instead, there have been a variety of approaches to measure development, which have contributed to learning and serve to steer the ﬁeld toward increasingly more valid measurements of its constructs. One implication of this idea for a new UEP researcher is that it is important to understand the process of developing construct valid measures even though he or she is joining a process already very much underway. Nunnally and Bernstein (1994) describe three primary steps to achieving construct valid measures. First, they suggest that

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researchers should theorize about the domain of observables that could represent a construct. Inherent in this idea is that the process will speciﬁcally exclude observables thought to not represent the construct. This intuitive theorizing process does not, on its own, yield construct valid measures. Rather, it helps researchers to narrow the group of observables that will be tested. In the UEP, researchers use demographic measures to represent the mental models of top managers. While these measures are useful because they are readily available, it is recognized by researchers that more construct valid measures of TMT mental models are possible to ﬁnd (Calori, Johnson, & Sarnin, 1994). A second step in determining construct valid measures is an analysis of the extent to which multiple measures of a single construct go together. This can be tested empirically by assessing the level of inter-correlation between two supposed measures of the same construct. Researchers doing UEP work might compare multiple demographic indicators to understand the extent to which they represent constructs related to TMT decision-making processes. For example, the researcher could employ Agresti and Agresti’s (1978) Index of Qualitative Variation (IQV). The IQV indicates demographic dispersion over multiple nominal categories, such as within education (i.e., engineering, science and arts/humanities). A ﬁnal step in Nunnally and Bernstein’s (1994) framework for developing construct valid measures is to look at existing relationships between focal measures and measures of other constructs. If a particular measure behaves as expected in relation to variables not related to the focal construct, the researcher gains information about the focal measure’s construct validity. For example, if an UEP researcher wanted to use a subjective measure of ﬁrm performance (such as the response to a survey question) instead of a more typical objective measure (e.g., proﬁtability in the form of return on sales or return on investment), he or she could test the correlation of the subjective measure to an unrelated measure that has been shown to be positively correlated with objective measures of ﬁrm performance, such as the degree of a ﬁrm’s multinationality (Delios & Beamish, 1999). Challenges to Construct Validity One way to avoid problems in construct deﬁnition in an UEP study is to be aware of the most common threats to construct validity found in published research. Schwab (1999) categorizes these threats as being due to either deﬁciency (under-representation of the construct), contamination (constructs including surplus irrelevancies), or unreliability (randomness in measurement).

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Cook and Campbell (1979) list measure development practices, which lead to one or more of the above problems. One practice is described as poor explication of the construct prior to identiﬁcation of the measure. If a construct is vague, it is less likely that different researchers will utilize similar measures to operationalize it. One example of this problem can be found in the literature related to the goal commitment construct. Goal commitment was originally deﬁned as determination to reach a goal (Locke & Latham, 1990). This vague deﬁnition led to disagreement about measures. Hollenbeck et al. (1989) introduced a scale that was attacked by others (Tubbs, 1993), who questioned the correspondence between the measure and the construct. A second practice is use of a single operation or method to generate measures for variables. As noted by Cook and Campbell, ‘‘Since single operations both under-represent constructs and contain irrelevancies, construct validity will be lower in single exemplar research than in research where each construct is multiply operationalized’’ (1979, p. 65). Tests of Construct Validity Schwab notes that ‘‘Since the criterion in construct validity is conceptual, direct tests are not possible. As a consequence, construct validity must be inferred on the basis of indirect assessments’’ (Schwab, 1980, p. 34). In this spirit, he offers ﬁve procedures for indirectly testing construct validity (Schwab, 1999). First, a researcher can assess the content validity of a measure by using expert judges to review proposed measures. Second, reliability statistics can be used to assess the extent to which a measure is free from random errors. Third, a researcher can test the convergent validity of multiple indicators that are supposed to measure the same construct. Fourth, tests of discriminant validity can show that measures of different constructs do not converge with measures of a focal construct. Finally, nomological networks of construct relationships can help to provide evidence of construct validity when research provides support for the relationships proposed in the network.

CONCLUDING WITH A BENCHMARK PROCESS FOR UPPER ECHELONS RESEARCH The aim of this chapter has been to provide speciﬁc guidance and tools to students and scholars interested in using the UEP in their research. These frameworks and tools are expected to help ensure the theoretical importance

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and internal and external construct validity of their work. In that spirit, we conclude our essay by providing a checklist tool to readers, summarized in Table 1, which categorizes the steps researchers should consider to ensure that the constructs chosen are relevant and valid. The tool provides a reminder for researchers to think broadly about what constructs from the UEP to include in a study, to identify interesting relationships and to ensure that constructs are validly measured. We remain steadfastly optimistic about the import and bountifulness of future research opportunities in the UEP tradition and expect such research to continue to proliferate, albeit Table 1.

Benchmark Process for Upper Echelons Perspective Research Development.

1. Define UEP Constructs of Interest from the Categories below &TMT demographics ___________________________________________________ &TMT characteristics ___________________________________________________ &Attributes of TMT decision-making ___________________________________________________ &Attributes of other strategy processes ___________________________________________________ &Organizational outcomes ___________________________________________________ &Attributes of operating processes ___________________________________________________ &Contextual factors ___________________________________________________ 2. Combine Constructs to Develop Hypotheses &Use Fig. 2 to diagram planned hypotheses &Describe in words your causal story 3. Test your Construct-Level Idea &Does the causal story make sense? &Is proposed relationship linked to existing theory and a current conversation? &Would the results of your study be interesting? 4. Select Valid Measures &Theorize about the domain of variables &Identify potential measures of each construct &Check intercorrelation of multiple measures 5. Test Construct Validity of Measures &Assess content validity &Assess reliability &Assess convergent validity &Assess discriminant validity &Assess ﬁt with nomological network

_______________________ _______________________ _______________________ _______________________ _______________________ _______________________ _______________________

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subject to increasingly stringent hurdles in terms of theoretical novelty and empirical rigor. Best wishes with your research.

REFERENCES Agresti, A., & Agresti, B. (1978). Statistical analysis of qualitative variation. Sociological Methodology, 9, 204–237. Amason, A. C., & Sapienza, H. G. (1997). The effects of top management team size and interaction norms on cognitive and affective conﬂict. Journal of Management, 23(4), 495– 601. Arendt, L., Priem, R., & Ndofor, H. (2005). A CEO-advisor model of strategic decision making. Journal of Management, 31(5), 680–699. Baum, J. R., & Wally, S. (2003). Strategic decision speed and ﬁrm performance. Strategic Management Journal, 24(11), 1107–1129. Bergh, D. (2003). Thinking strategically about contribution. Academy of Management Journal, 46(2), 135–136. Calori, R., Johnson, G., & Sarnin, P. (1994). CEO’s cognitive maps and the scope of the organization. Strategic Management Journal, 15, 437–458. Carpenter, M. A., & Fredrickson, J. W. (2001). Top management teams, global strategic posture, and the moderating role of uncertainty. Academy of Management Journal, 44, 533–546. Carpenter, M. A., Sanders, W. G., & Geletkanycz, M. A. (2004). The upper echelons revisited: The antecedents, elements, and consequences of TMT composition. Journal of Management, 30(6), 749–778. Collins, C. J., & Clark, K. D. (2003). Strategic human resource practices, top management team social networks, and ﬁrm performance: The role of human resource practices in creating organizational competitive advantage. Academy of Management Journal, 46(6), 740–751. Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis for field settings. Chicago: Rand McNally. Daft, R. L. (1995). Why I recommended that your manuscript be rejected and what you can do about it. In: L. L. Cummings & P. J. Frost (Eds), Publishing in the organizational sciences (2nd edn., pp. 164–182). Thousand Oaks, CA: Sage. Dansereau, F., & Yammarino, F. (Eds). (2005). Multi-level issues in strategy and methods. Research in Multi-Level Issues, 4, 197–276. Davis, M. (1971). That’s interesting!: Towards a phenomenology or sociology and a sociology of phenomenology. Philosophy of the Social Sciences, 1, 309–344. Delios, A., & Beamish, P. W. (1999). Geographic scope, product diversiﬁcation and the corporate performance of Japanese ﬁrms. Strategic Management Journal, 20, 711–728. Finkelstein, S., & Hambrick, D. (1996). Top executives and their effects on organizations. St. Paul, MN: West Publishing Company. Hambrick, D. C. (1992). Commentary: Consequences of group composition for the interpersonal dynamics of strategic issue processing. In: P. Shrivastava, A. Huff & J. Dutton (Eds), Advances in strategic management (pp. 383–389). Greenwich, CT: JAI Press. Hambrick, D. C. (1992). Top management groups: A conceptual integration and reconsideration of the ‘‘team’’ label. In: B. Staw & L. L. Cummings (Eds), Research in organizational behavior (Vol. 16 pp. 171–213). Beverly Hill: JAI Press.

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Hambrick, D. C., Finkelstein, S., & Mooney, A. C. (2005). Executive job demands: New insights for explaining strategic decisions and leader behaviors. Academy of Management Review, 30, 472–491. Hambrick, D. C., & Mason, P. A. (1984). Upper echelons – The organization as a reﬂection of its top managers. Academy of Management Review, 9(2), 193–206. Hollenbeck, J., Williams, C., & Klien, H. (1989). An empirical examination of the antecedents of commitment to difﬁcult goals. Journal of Applied Psychology, 74(1), 18–23. Jackson, S. (1992). Consequences of group composition for the interpersonal dynamics of strategic issue processing. In: P. Shrivastava, A. Huff & J. Dutton (Eds), Advances in strategic management (pp. 345–382). Greenwich, CT: JAI Press. Kellermans, F., Walter, J., Lechner, C., & Floyd, S. (2005). The lack of consensus about strategic consensus: Advancing theory and research. Journal of Management, 31(5), 719–737. Ketchen, D. (2002). Some candid thoughts on the publication process. Journal of Management, 28(5), 585–590. Locke, E., & Latham, G. (1990). A theory goal setting and task performance. Engelwood Cliffs, NJ: Prentice-Hall. Markoczy, L. (2001). Consensus formation during strategic change. Strategic Management Journal, 12, 1013–1031. Nunnally, J., & Bernstein, I. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill. Peterson, R. S., Smith, D. B., Martorana, P. V., & Owens, P. D. (2003). The impact of chief executive ofﬁcer personality on top management team dynamics: One mechanism by which leadership affects organizational performance. Journal of Applied Psychology, 88, 795–808. Pfeffer, J. (1983). Organizational demography In: L. L. Cummings & B. M. Staw (Eds), Research in organizational behavior (Vol. 5. pp. 299–357). Greenwich, CT: JAI Press. Roberto, M. (2003). The stable core and the dynamic periphery in top management teams. Management Decision, 41, 120–131. Schwab, D. P. (1980). Construct validity in organizational behavior. Research in Organizational Behavior, 2, 3–43. Schwab, D. P. (1999). Research methods for organizational studies. Mahwah, NJ: Lawrence Erlbaum. Song, J. (1982). Diversiﬁcation strategies and the experience of top executives of large ﬁrms. Strategic Management Journal, 3, 377–380. Tubbs, M. (1993). Commitment as a moderator of the goal–performance re-lation: A case for clearer construct deﬁnition. Journal of Applied Psychology, 78(1), 86–97. West, C. T., & Schwenk, C. R. (1996). Top management team strategic consensus, demographic homogeneity and ﬁrm performance: A report of resounding non-ﬁndings. Strategic Management Journal, 17, 571–576. West, M. A., & Anderson, N. R. (1996). Innovation in top management teams. Journal of Applied Psychology, 81(6), 680–693. Whetten, D. A. (1989). What constitutes a theoretical contribution. Academy of Management Review, 14(4), 490–495.

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SURVEYING THE CORPORATE ELITE: THEORETICAL AND PRACTICAL GUIDANCE ON IMPROVING RESPONSE RATES AND RESPONSE QUALITY IN TOP MANAGEMENT SURVEY QUESTIONNAIRES Michael K. Bednar and James D. Westphal ABSTRACT Survey research of top managers is critical to addressing many contemporary research questions in the field of strategic management. Yet, the threat of low response rates has discouraged many researchers from attempting this type of work, steering the field of strategic management away from issues related to strategic process. This article provides an empirical examination of factors that determine the likelihood and quality of response to top management surveys. More generally, we advance a theoretical perspective on survey response rooted in social influence theory that should help researchers make better choices about the design of their survey questionnaires.

Research Methodology in Strategy and Management, Volume 3, 37–55 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1479-8387/doi:10.1016/S1479-8387(06)03004-9

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Questionnaire surveys of top management have historically suffered from very low response rates, which increase the risk of sample selection bias (Heckman, 1979; Fowler, 1993). Sample selection bias, in turn, threatens the internal and external validity of statistical tests performed on the data (Berk, 1983). Thus, for those interested in studying top executives, survey research is perceived to be a risky endeavor and many researchers are deterred from attempting this type of work. Yet, questionnaire surveys, especially aimed at top executives, are critical to addressing many contemporary research questions in the ﬁeld of strategic management. Many topics of interest simply cannot be addressed with available archival data and top executives are often the only individuals with the necessary knowledge to answer questions concerning organizational-level phenomena, especially issues related to strategic process (Zajac, 1990; Pettigrew, 1992). The aversion to survey research directed at top executives has thus limited the types of questions that strategy scholars have asked, and steered the ﬁeld of strategic management toward macro and content oriented research that can be examined with more easily accessible archival data. Some have lamented that issues relating to strategic process and the human side of strategy have been greatly underexplored (Hambrick, 2004). For example, more ﬁne grained phenomena among top managers including various social and cognitive processes involved in the formulation and implementation of strategy remain largely understudied. There are many reasons why executives do not respond to questionnaire surveys. Executives are exceedingly busy individuals who often lack sufﬁcient time to perform critical job demands, let alone respond to an academic survey. In addition, some surveys call for potentially sensitive data about the ﬁrm that executives are reluctant to reveal, despite promises of conﬁdentiality. Some executives may not respond due to lack of interest in the study or a company policy against returning questionnaires (Baruch, 1999). Despite these obstacles, however, some research demonstrates that executives can be successfully surveyed with adequate response rates to yield valid measures of theoretically important constructs (Zajac, 1990; Westphal, 1998, 1999; Steensma & Corley, 2001; Christmann, 2004). Nevertheless, most survey research of executives suffers from very low response rates with the authors citing previous studies with similarly low response rates as a justiﬁcation. Perhaps more importantly, the prospect of low response rates has discouraged researchers from attempting surveys of corporate elites, and has ultimately helped steer the strategy ﬁeld away from empirical research on micro-behavioral processes. The purpose of this chapter is to take a ﬁrst step toward understanding how speciﬁc survey characteristics affect not only the rate of response

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among executives, but also the quality of the responses. We hope that this chapter will serve as a practical guide for researchers interested in conducting survey research on the corporate elite. In addition to practical guidance about speciﬁc techniques for increasing survey effectiveness among executives, more theoretical grounding is needed to guide our survey research practices. The literature on survey practices is generally not theory-driven (see Groves, Cialdini, & Couper, 1992 for an exception). In this chapter, we draw on theory from social psychology to provide a better understanding of why executives may or may not ﬁll out questionnaire surveys, and what factors may inﬂuence the quality of responses. We develop a theoretical framework that suggests how social inﬂuence theory, including the wellestablished principles of reciprocity, social proof, and legitimacy and authority, can be applied by strategy researchers to improve survey response rates and the quality of responses among the population of corporate elites. We test our theoretical framework with a large scale executive survey and conclude by discussing the implications of this study and the prospects for future survey research on top managers.

DETERMINANTS OF SURVEY RESPONSE AND RESPONSE QUALITY From a purely rational perspective, individuals who receive a survey must analyze the costs and beneﬁts of participation. To encourage participation in a survey, researchers should seek to minimize the costs of completing the questionnaire (Dillman, 1991). For example, lengthy surveys require more time and effort to complete and would generally be perceived as more costly to potential respondents than shorter questionnaires. Research on survey methods generally shows that longer questionnaires prompt lower response rates (Yammarino, Skinner, & Childers, 1991; Jobber & Saunders, 1993). Given the time constraints facing executives, we expect that survey length will be an especially important determinant of response rates for corporate elites. Longer surveys may also decrease the quality of response. We speculate that individuals tend to devote a ﬁxed amount of time and effort to ﬁlling out a questionnaire, regardless of its length (e.g. an executive may decide to spend 5 min on a survey and will spend that amount of time whether the questionnaire is 1 page or 5 pages). Hence, as the survey increases in length, executives may give hurried, less reliable answers in an attempt to ﬁnish in the predetermined amount of time, resulting in less reliable responses.

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While the length of the survey is extremely important to induce quality responses, the decision to complete a questionnaire does not simply involve a rational calculation of costs and beneﬁts. From a social inﬂuence perspective, the decision to ﬁll out a survey is an act of compliance, which can be affected by social norms. Researchers can use their knowledge of these social norms to increase the likelihood that executives will respond to their surveys and that they will make the necessary effort to produce high quality responses. Norm of Reciprocity The norm of reciprocity is a nearly universal code of moral conduct wherein individuals perceive an obligation to reciprocate a received beneﬁt in some manner (Gouldner, 1960). Individuals who receive favors, gifts, concessions, or other favorable treatment often experience a psychological sense of indebtedness. Because the sense of indebtedness is unpleasant for most people, they typically seek opportunities to reduce its psychological burden through reciprocation (Greenberg, 1980; Cialdini, 1993; Uehara, 1995; Settoon, Bennett, & Liden, 1996; Cialdini, 2001). In short, when we receive something, there is typically a powerful perceived obligation to give something in return. There is also theory and evidence to suggest that the norm of reciprocity can be initiated by unsolicited favors (Befu, 1980; Cialdini, 1993) and that the substance and value of what is exchanged between parties can vary somewhat (Deckop, Cirka, & Andersson, 2003; Molm, 2003). Thus, small favors can potentially induce greater favors in return. The norm of reciprocity can prompt exchange between parties in situations where exchange does not seem likely from a purely rational or economic perspective. There is evidence that the norm of reciprocity persists even in situations where the recipient has no expectation of receiving further beneﬁts from the giver, as in exchanges between strangers (Hoffman, McCabe, & Smith, 1998; Whatley, Webster, & Smith, 1999; Perugini, Gallucci, Presaghi, & Ercolani, 2003). The powerful nature of this norm is further highlighted by research suggesting that individuals often incur signiﬁcant economic costs to return a favor (Fehr & Gachter, 2000) and sometimes reciprocate favors from individuals whom they dislike (Regan, 1971; Cialdini, 1993). Individuals who are familiar with the norm of reciprocity can exploit their knowledge of the norm by rendering favors to powerful individuals who have the potential to beneﬁt or harm them. In fact, personal favors aimed at powerful actors in organizations can result in a variety of positive outcomes including improved performance evaluations, higher pay, and increased

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likelihood of promotions (e.g.,Yukl & Tracey, 1992; Westphal & Stern, 2005) while decreasing the likelihood of negative actions such as ﬁrings, demotions or pay cuts (Westphal, 1998). It is important to note that the norm of reciprocity can break down if the initial favor, gift, or concession is viewed by the other party as a bribe or as a measure to apply pressure to coerce compliance with the request (see Brehm & Cole, 1966; Weiner & Brehm, 1966; Groves et al., 1992). In fact, if the initial behavior is viewed as a bribe, then subsequent compliance to the request may actually be less likely. While there is a strong norm to respond to a genuine gift, no such action is required in response to a sales trick, gimmick or bribe (Cialdini, 2001). In survey research, the norm of reciprocity provides a theoretical basis for including monetary and non-monetary incentives with a questionnaire. Research has shown that the inclusion of even a small amount of money or other incentive with a survey can signiﬁcantly increase response rates, even if the reward is not made contingent upon completion of the survey (Church, 1993). Individuals who receive a small gift from the researcher should feel an obligation to reciprocate this treatment by ﬁlling out the survey. In addition, potential respondents who receive a gift or a promise of some favor may increase the amount of time and effort they devote to completing the survey, thus resulting in more considered responses and ultimately higher reliability. Some may ask whether the inclusion of a small monetary incentive can inﬂuence the behavior of executives who often earn very high salaries. As mentioned, the norm of reciprocity transcends rational consideration of costs and beneﬁts, such that even small gifts can induce large favors in return (Cialdini, 2001). Owing to the pervasiveness of this norm, we would not expect executives to be exempt from its inﬂuence. Social Proof Individuals often rely on others as a standard of comparison before making decisions about an appropriate course of action (Festinger, 1954). In their studies of bystander intervention, Latane and Darley (1970) found that bystanders to a possible emergency were inﬂuenced by the actions of those around them. When others failed to act in a concerned manner, individuals were less likely to respond to the emergency. Other studies have similarly found that the actions of groups of people can greatly inﬂuence the behavior of others. Milgram, Bickman, and Berkowitz (1969) conducted an experiment in which a researcher would stop in a busy street and look up at nothing in particular. When just one individual looked up, the action did

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not garner much attention, but when ﬁve individuals engaged in the behavior, the research team found that over 80% of those passing by would stop to look up as well. These examples illustrate the powerful principle of social proof. Cialdini (1993) asserts that we tend to view a behavior as correct in a given situation to the degree to which we see others performing it. Whether the question is what to do with an empty popcorn box in a movie theater, how fast to drive on a certain stretch of highway, or how to eat chicken in a restaurant, the actions of those around us will be important guides in deﬁning the answer (p. 95).

He further states that if a lot of people are doing the same thing, they must know something we don’t. Especially when we are uncertain, we are willing to place an enormous amount of trust in the collective knowledge of the crowd (Cialdini, 1993, p. 131).

Social proof is derived from the idea that most individuals are followers rather than initiators and seems to work best when the proof is provided by the actions of many other people (Cialdini, 2001). Advertisers often use this idea to their advantage by claiming that a product is the best-selling or fastest growing. If lots of people are already using a certain product, we often infer that it must be a quality product. Although the idea of social proof is often associated with inefﬁcient outcomes, as in the bystander intervention studies, the use of social proof as a decision-making tool is not necessarily maladaptive. Very often, many people engage in a behavior because it is the appropriate thing to do, such that looking to others before deciding how to act can lead to effective solutions. Thus, the tendency to rely on the actions of others is a natural decisionmaking tool in many situations (Cialdini, 2001). The principle of social proof is consistent with organizational scholars’ explanation for why ﬁrms often imitate others in response to uncertainty in decision-making (Cyert & March, 1963; Rao, Greve, & Davis, 2001). The idea of mimetic isomorphism from institutional theory for example, suggests that under conditions of uncertainty, organizations will look to others and mimic their behavior (DiMaggio & Powell, 1983). Through the imitation of others, ﬁrms can gain legitimacy that is often an important antecedent to survival. An understanding of social proof can be applied by survey researchers to increase the likelihood that executives will complete a questionnaire and to increase the quality of responses. According to social proof, to the extent that executives believe that similar others have already completed a survey, they should be more willing to do so. When surveying executives, researchers

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can include information in the instructions or on the cover letter indicating that many other executives have already participated in the same survey (or similar surveys conducted by the same researchers). On the basis of this information, some executives should make the assumption, consciously or otherwise, that if many other top managers have ﬁlled out the survey, it must be important and worth the time to complete. The fact that similar others have participated in the survey gives it credibility and suggests that taking the time to respond would not be counter normative. If the survey is viewed as credible and important, respondents should devote more time to each question resulting in more considered, accurate, and reliable responses. Legitimacy and Authority People are typically more willing to comply with a request if it is made by an individual or organization perceived as having legitimate authority (Cialdini, 2001). From birth, we are socialized to respond to requests from individuals in positions of authority. Research has demonstrated that this norm of responding to perceived legitimate authority is extremely powerful in affecting behavior. For example, Milgram’s famous study demonstrated just how far individuals were willing to go when asked to do something by a person in a position of authority. In his study, Milgram found that individuals were willing to inﬂict great amounts of pain on others if a ‘‘scientist’’ in a position of legitimate authority made the request to do so (Milgram, 1963). As with the principle of social proof, responding to requests from individuals in positions of authority is often a beneﬁcial decision-making heuristic. People with legitimate authority can often supply access to valuable resources and information. Generally, individuals in positions of authority have satisﬁed certain requirements pertaining to their education, work experience or professional certiﬁcation to obtain their position and in the process, have accumulated a wealth of knowledge or expertise. Research from the literature on persuasion demonstrates that individuals possessing expert power, or relevant knowledge and information not possessed by the inﬂuence target, are more successful in their attempts at persuasion (Porter, Allen, & Angle, 1981; Raven, 1999). In fact, research suggests that we often take for granted the appropriateness of requests made by legitimate experts (Eagly & Chaiken, 1993; Ziegler, Diehl, Zigon, & Fett, 2004). Individuals often look for cues concerning the authority or legitimacy of others when responding to requests. For example, titles such as educational degrees or professional certiﬁcation are indicators of legitimate expertise. Requests made by high status individuals or organizations should also be

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viewed as more legitimate. For this reason, survey researchers sometimes include reference to a sponsoring institution or include an endorsement of the survey from prominent executives. Research on survey methods suggests that the inclusion of a university sponsor can signiﬁcantly increase the expected response rate in surveys (Green, Boser, & Hutchinson, 1998; Jobber & O’Reilly, 1998; Greer, Chuchinprakam, & Seshadri, 2000). Sponsorship of the survey by a prominent university should be an important signal to potential survey respondents about the legitimacy and importance of the survey and should provide assurance to the respondent that the survey will be conducted in a professional manner. Endorsement by a prominent executive should also legitimize the survey in the eyes of the potential respondent, increasing the likelihood of response. In addition, respondents who perceive the survey to be legitimate may increase the amount of time and effort they devote to the survey, thus increasing the quality of response. Helping Norm In most societies, a norm of helping, often referred to as a norm of social responsibility, exists wherein people feel a moral obligation to help those in need and who are dependent upon them for aid (Berkowitz & Daniels, 1964; Groves et al., 1992). For example, an interviewer standing at a doorstep may be more likely to get into homes on a rainy day than on a sunny day because people feel a greater obligation to help under such circumstances. Researchers can appeal to this norm to potentially increase the rate and quality of response. For example, Mowen and Cialdini (1980) found that response rates in interview surveys increased dramatically simply by adding the phrase ‘‘it would really help us out’’ to the end of the request. Survey researchers are entirely dependent on the potential respondents for the success of the survey. Making this point salient with a plea for help could induce increased participation due to the helping norm. Similarly, respondents may be willing to devote more time and effort to the survey in order to help out the researcher, potentially resulting in higher quality responses.

METHOD The preceding discussion outlines principles from the social inﬂuence literature that can inform our understanding of executive survey response rates and the quality of their responses. We tested these ideas in a large-sample

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survey of top executives. Speciﬁcally, we sent survey questionnaires to top managers at 500 companies randomly selected from the Reference USA index of mid-sized companies. Companies in the sample frame were between $50 million and $100 million in total revenues. We selected up to seven senior ofﬁcers from each company with the title of Vice President or higher. If the ﬁrm had more than seven senior ofﬁcers, seven were randomly selected. This resulted in a sample frame of 2,632 top managers. To assess how features of the survey questionnaire affect the likelihood and quality of response, different versions of the questionnaire were randomly assigned to managers in the sample frame. The survey response rate was 36%, resulting in a sample of 958 top managers from 387 companies. We randomly assigned surveys to recipients based on the following characteristics. Survey Length. The length of the survey may be an especially important determinant of response rates and quality of responses from executives. Shorter questionnaires require less time to complete, thus reducing the perceived cost of response. To test this idea, we developed four versions of the survey, which were randomly assigned to executives in the sample frame. The ﬁrst version included questions about board monitoring of top management (Westphal, 1999), the provision and seeking of strategic advice from other managers (McDonald & Westphal, 2003), and friendship and advice ties to outside directors (Westphal, 1999). The second version of the questionnaire included survey scales to assess task conﬂict and relational conﬂict among members of the top management team; these scales were based on measures developed by Jehn (1995). The third version of the questionnaire included all the questions from version 1 followed by questions from version 2, and the fourth version of questionnaire included questions from the second version followed by questions from the ﬁrst version. Pre-testing indicated that the ﬁrst version required about 10 min to complete, the second version required about 7 min to complete, and the third and fourth versions required about 17 min to complete. Reciprocity. The norm of reciprocity suggests that individuals should be more likely to ﬁll out a survey, and produce higher quality responses after receiving a favor or gift. We tested this idea in several ways. Two different types of monetary incentives were included in the survey. Some individuals were given a dollar while others were given 50 cents. This manipulation was used to determine how the amount of the monetary incentive affects response. We also sent surveys in which respondents were promised to receive a summary report of the results of the study. To examine whether the conditionality of a gift affects the likelihood or quality of response, we made

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half of the promised reports unconditional on the response while telling the other recipients that they would only receive a report upon the successful completion of the survey. Social Proof. To test our theoretical arguments about the affect of social proof, in one manipulation of the survey, we included a line in the cover letter indicating that many of the executive’s peers had responded to prior surveys by the same researchers. Speciﬁcally, the cover letter states that ‘‘over 5000 top managers and directors have responded to our prior surveys [on corporate governance and strategy].’’ Legitimacy and Authority. We included appeals to legitimate authority in two ways. First, in some surveys we included an endorsement of the survey by a prominent executive. The cover letter notes that the survey is endorsed by the executive, and his/her signature is included at the bottom of the letter. Second, some surveys were printed on ofﬁcial letterhead from the McCombs School of Business at the University of Texas at Austin and enclosed in envelopes that contained the university name and printed symbol. Helping Norm. We tested for possible effects of the helping norm in one condition by including a plea for help from the researchers to the potential respondents. After describing the topic of the survey, the cover letter states: ‘‘our knowledge about corporate governance depends on information from top executives such as yourself. Please help us learn more about this important subject by responding to this survey.’’ Because we had data from multiple executives at the same company, we could test for inter-rater agreement among executives at the same ﬁrm. We used inter-rater agreement as a measure of the quality of the survey response. To the extent that executives make an effort to carefully respond to survey items, the level of agreement between executives should increase. Moreover, inter-rater agreement is an indicator of the validity of the survey measures. Thus, if a particular survey characteristic increases inter-rater agreement of a survey measure, it increases the validity of that measure. We measured inter-rater agreement as the average difference between the focal manager’s standardized responses to survey items about board monitoring and task and relational conﬂict and the responses of another manager from the same company to the same set of questions. When there was more than one other respondent from the same company, one respondent was randomly selected. We used logit regression to estimate the likelihood of responding to the survey, and we used a Heckman selection model to estimate inter-rater agreement. The Heckman model is a two-stage procedure in which the ﬁrststage model estimates the likelihood of responding to the survey with

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probit regression and the second-stage model incorporates parameter estimates from the selection equation to predict inter-rater agreement using multiple regression (Heckman, 1979). This procedure ensures that regression estimates are not biased by unmeasured differences between managers who responded to the survey and managers in the larger sample frame.

RESULTS Results of the logit and heckman selection models are presented in Table 1. The results show that the length of the questionnaire signiﬁcantly inﬂuenced responses to the survey. Managers who received the long version of the questionnaire were less likely to respond than managers who received the medium-length version. Moreover, inter-rater agreement was lower for the long version of the survey, suggesting that the length of the survey lowered the quality of responses. Individuals receiving the short version were also signiﬁcantly more likely to respond than individuals receiving the medium-length survey. However, the quality of the responses to the short version was not signiﬁcantly different from the quality of responses to the medium condition. The comparison of the medium-length survey vs. the short survey is a highly conservative test of the effect of survey length on the likelihood of response and inter-rater agreement, as the medium-length version is only 3 min longer than the short version. Evidence that an increment of 3 min in survey length has a signiﬁcant effect on the likelihood of response suggests that survey length is a very important determinant of the response decision for this population. In fact, based on the estimated coefﬁcients in the logit analysis, the odds of receiving a response to the short survey were 72% greater than with the medium version. Compared to the medium-length version of the survey, using the longer version decreased the odds of a positive response by 65% for version one and 55% for version two. The results also provide evidence that the norm of reciprocity inﬂuences response to executive surveys. The inclusion of monetary incentives of either 1 dollar or 50 cents was associated with increased likelihood of response. Individuals receiving either type of monetary incentive were approximately 50% more likely to respond than those not receiving the incentive. Inter-rater agreement was also higher for individuals receiving either type of monetary incentive. Interestingly, the size of the incentive did not seem to matter: The magnitude of the incentive effect on the likelihood of response and inter-rater

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Table 1. Models of Response Likelihood and Inter-Rater Argeement. Independent Variable

Length: short version vs. medium version Length: long version (1) vs. medium version Length: long version (2) vs. medium version UT letterhead Endorsement by executive Fifty cents included with survey Dollar included with survey Promise of summary report (if respond) Promise of summary report (no strings attached) Plea for help Note that peers have responded Constant Wald w2

Logit Regression Model of Survey Response

Heckman Selection Model of Inter-Rater Agreement

0.542 (0.216) 1.041 (0.156) 0.788 (0.145) 0.049 (0.111) 1.000 (0.150) 0.404 (0.123) 0.446 (0.127) 0.974 (0.217) 0.674 (0.165) 0.100 (0.093) 0.292 (0.098) 1.366 (0.230) 237.16

0.062 (0.036) 0.109 (0.024) 0.067 (0.017) 0.014 (0.015) 0.020 (0.016) 0.060 (0.017) 0.089 (0.016) 0.064 (0.028) 0.070 (0.023) 0.021 (0.013) 0.002 (0.013) 0.129 (0.031) 160.86

N ¼ 2,632. Note: z-statistics are one-tailed for hypothesized effects, two-tailed for control variables. Standard errors are in parentheses. po0.05. po0.01. po0.001.

agreement was not signiﬁcantly greater for 1 dollar vs. 50 cents. The results suggest that the inclusion of monetary incentives is an effective means of invoking the norm of reciprocity, and that even very small favors can prompt a response. This ﬁnding speaks to the strength of the norm or reciprocity and the obligation that individuals feel to reciprocate a gift or favor. Some may be surprised to ﬁnd that an unsolicited gift of 50 cents to an executive, to whom the money is materially insigniﬁcant, signiﬁcantly

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increased the likelihood of response. It also appears that the gift elicited more considered and accurate responses, as evidenced by the effect on interrater reliability. The promise of a summary report also increased the likelihood of response. Those promised a report conditional on their completion of the survey were over two and a half times more likely to respond than those not promised a report. Similarly, for those receiving an unconditional promise of a report, the odds of responding increased by 96%. As for the quality of response, when the report was promised regardless of whether or not the survey was completed, inter-rater agreement was higher than in cases where no promise was given. Interestingly, when the report was made conditional on the completion of the survey, the quality of response actually decreased. Cialdini (1993) suggests that incentives are helpful to the extent that they are seen as gifts and not as bribes. Perhaps in the conditional case, the incentive (i.e., the report) was viewed by the respondent as a bribe to induce compliance and the individual was less likely to devote the effort necessary to produce a quality response. Alternatively, whereas the unconditional case may induce respondents to devote extra effort to ﬁlling out the survey by triggering the norm of reciprocity, the conditional case may trigger a rational exchange mindset in which respondents do the minimum required to receive a report. Social proof also appears to have had an effect on the response rate. Indicating on the survey that similar others had responded to the questionnaire had a positive effect on the likelihood of response, increasing the odds of response by 40% compared to surveys with no such inclusion. It seems that the reassurance that many similar individuals had participated in surveys by the same researchers made managers more likely to respond. However, it did not affect inter-rater agreement. Endorsement by an executive was associated with an increased likelihood of response, although inter-rater agreement was unaffected. The coefﬁcient from the logit regression suggests that an endorsement by an executive increased the odds of getting a response by a factor of 2.71. In other words, surveys with an executive endorsement were almost three times more likely to be completed and returned. This result is consistent with Cialdini’s idea that individuals are more likely to respond to a request from an individual or group perceived to be a legitimate authority ﬁgure (Cialdini, 1993). The presence of University letterhead had no statistically signiﬁcant impact on the likelihood of response or inter-rater agreement. Moreover, the inclusion of a plea for help on the survey also did not affect the response rate or the quality of response. In this case, it does not appear that the norm of helping was an important determinant of survey response (Table 2).

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Table 2. Summary of Advice about Conducting Surveys of Top Executives. Limit the number of items on the survey Get an endorsement from a prominent executive Include some type of incentive (i.e. small amount of money, promise of summary report) If you promise a summary report, do so whether or not they respond Inform respondents that others have responded to similar surveys Pleas for help and using university letterhead have no effect

CONCLUSION In this chapter, we have sought to invoke principles of social inﬂuence to suggest how certain features of surveys are more likely to elicit a response from top executives. In addition, we have provided initial evidence of how these principles can affect the quality of response, a subject that has received scant research attention in the past. This chapter suggests that survey researchers can use principles of reciprocity, social proof and appeals to legitimate authority to obtain better response rates and higher quality responses from top executives. Consideration of these principles can aid in survey design by indicating what characteristics are important to response rates and quality of response, and what characteristics are not. Thus, in addition to providing a theoretical framework to enhance our understanding of responses to survey questionnaires, our study may serve as a practical guide to researchers who are interested in conducting surveys of corporate elites. The ﬁrst ﬁnding suggests the importance of keeping executive surveys short in length. Long versions of the survey were less likely to be completed and the quality of response suffered as well. It is important to note that even the long version of the survey in this study, which took only 17 min to complete, is rather short in comparison to most surveys conducted in psychology or organizational behavior. On the basis of the empirical ﬁndings of this chapter, it seems that surveys of top executives must include only essential questions. Results from this study further suggest that the use of incentives is a viable option for increasing response rates from executives. Even small amounts of money, as little as 50 cents, increased response rates signiﬁcantly. Some research budgets may limit the extent to which monetary incentives can be used. Our results suggest that non-monetary incentives such as the promise of a summary report of the ﬁndings can be used to increase

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response. To ensure the quality of response, such promised incentives may be more effective when they are given unconditionally (i.e., regardless of whether a survey is returned). These ﬁndings suggest that survey characteristics, which invoke the norm of reciprocity can be effective in enhancing the rate and quality of survey responses by corporate elites. Executives also responded to cues about the legitimacy of the survey. Following the logic of social proof, they were more likely to return a survey when it was noted that many peers had already ﬁlled out the questionnaire. This information sends a strong signal to the executives that the survey must be worthwhile if others are participating. Researchers can increase executive response rates by including information in the cover letter or instructions about peers who have also responded to the survey. This ﬁnding points to the importance of having a meaningful stream of survey research, and may suggest that success begets success when working with executives. Noting that peers have responded is essentially costless, and yet it had a strong impact on the likelihood of response. Executive endorsement also appears to be a very useful tool in increasing response rates. Endorsement by a prominent executive is a powerful signal about the legitimacy and importance of the research. Survey researchers will beneﬁt by establishing ties with executives who can endorse their work. Although some investment is required to develop and maintain such ties, the returns are considerable: Endorsement by a prominent executive increased the likelihood of response by a factor of 2.7. Of course, researchers must be able to explain the potential importance of their work to executives if they hope to receive such an endorsement. Two attributes of surveys studied here did not produce a signiﬁcant increase in response rates for this sample. Speciﬁcally, the use of university letterhead and a plea for help did not have a signiﬁcant effect on response rates or the quality of response. Perhaps afﬁliation with a university does not send a strong signal of legitimacy and importance to business executives on a par with endorsement by a prominent executive. Alternatively, the use of letterhead may have less impact than an ofﬁcial endorsement. Researchers may be better served by focusing their resources on the other survey attributes mentioned previously. It is useful to compare our theoretical framework and empirical results with previous research on the total design method, a popular survey design and administration system used by some researchers to increase survey response rates (see Dillman, 1978, 1991 for a review). The total design method provides detailed recommendations for the entire process of survey research. For example, the method gives guidance about the layout and ordering of

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questions, the size of the survey booklet, the type of return envelope to be used, the timing of follow-up responses, and many other practical suggestions for administering a survey. When the total design method is strictly followed, research has shown that response rates can be signiﬁcantly increased (Dillman, 1991). The method is based on the idea that respondents will be most likely to respond to a survey when the perceived costs of participation are less than the perceived beneﬁts. Thus, the recommendations advocated by the total design method represent an attempt to increase the perceived rewards of participation, decrease the perceived costs, and increase the participant’s trust that the rewards will be realized (Dillman, 1991). These principles are consistent with many of the theoretical ideas presented in this chapter although we have not attempted to empirically test the value of the total design method for an executive population. It seems that survey researchers interested in studying top managers would be well served by incorporating aspects of the total design method that reduce costs and increase the perceived beneﬁts of participation for managers. Our study complements the total design method in several ways. Consistent with the total design method logic, we consider the effect of reducing perceived costs to respondents (i.e. shortening the questionnaire length). However, we also provide evidence that the decision to respond to a survey goes beyond a rational calculation of costs and beneﬁts and that invoking social norms and applying other social inﬂuence processes can encourage participation and increase response quality. While the total design method focuses on increasing response rate, we examine the effect of several survey characteristics on both response rate and response quality. Moreover, we have included in our study survey characteristics that seem especially pertinent to executives such as endorsement of the survey by a prominent executive and the promise of reports detailing the survey results. Thus, our study is directly applicable to researchers interested in studying executive populations. In addition, while the total design method does not explicitly address the role of incentives in survey research, we have evidence suggesting that several types of incentives can effectively increase the likelihood that executives will respond to questionnaires. Finally, we test social inﬂuence mechanisms such as pleas for help and social proof that are not addressed by the total design method. Through an improved understanding and application of the principles outlined in this chapter, we hope that scholars of strategic management will be better equipped to engage in surveys of corporate elites. We realize that executive surveys will always be perceived by some as overly risky, especially in comparison to archival methods, and thus many will be deterred from

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attempting this type of work. However, we believe that the potential returns to survey research of top executives are extremely high – both to individual scholars and to the ﬁeld of strategic management as a whole. The effective use of survey methods could signiﬁcantly broaden the kinds of research questions that scholars investigate. Surveys of corporate elites are critical to the development of cognitive and micro-social perspectives on the formulation and implementation of strategy. More generally, such research is essential to the study of strategy process phenomena, or what Hambrick (2004, p. 94) called ‘‘the human element’’ of strategy formulation and implementation. Similarly, executive surveys could accelerate progress in addressing a variety of understudied topics in organization theory, including institutional entrepreneurship, symbolic management, and the interface of politics and institutional processes. We hope that the theoretical and practical guidance offered in this chapter will be a ﬁrst step in that direction.

ACKNOWLEDGMENTS We thank Dave Ketchen and seminar participants at Penn State University for their helpful comments.

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Deckop, J. R., Cirka, C. C., & Andersson, L. M. (2003). Doing unto others: The reciprocity of helping behavior in organizations. Journal of Business Ethics, 47, 101–113. Dillman, D. A. (1978). Mail and telephone surveys: The total design method. New York: WileyInterscience. Dillman, D. A. (1991). The design and administration of mail surveys. Annual Review of Sociology, 17, 225–249. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational ﬁelds. American Sociological Review, 48, 147–160. Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Ft Worth, TX: Harcourt Brace Jovanovich College Publishers. Fehr, E., & Gachter, S. (2000). Fairness and retaliation: The economics of reciprocity. Journal of Economic Perspectives, 14, 159–181. Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7, 117–140. Fowler, F. J. (1993). Survey research methods. Newbury Park, CA: Sage. Gouldner, A. W. (1960). The norm of reciprocity: A preliminary statement. American Sociological Review, 25, 161–178. Green, K. E., Boser, J. A., & Hutchinson, S. R. (1998). Response-rate differences and responseenhancement effects by population type. Psychological Reports, 83, 336–338. Greenberg, M. S. (1980). A theory of indebtedness. In: K. J. Gergen, M. S. Greenberg & R. H. Willis (Eds), Social exchange: Advances in theory and research (pp. 3–26). New York: Plenum Press. Greer, T. V., Chuchinprakam, N., & Seshadri, S. (2000). Likelihood of participating in mail survey research: Business respondents’ perspective. Industrial Marketing Management, 29, 97–109. Groves, R. M., Cialdini, R. B., & Couper, M. P. (1992). Understanding the decision to participate in a survey. Public Opinion Quarterly, 56, 475–495. Hambrick, D. (2004). The disintegration of strategic management: It’s time to consolidate our gains. Strategic Organization, 2, 91–98. Heckman, J. J. (1979). Sample selection bias as a speciﬁcation error. Econometrica, 47, 153–161. Hoffman, E., McCabe, K. A., & Smith, V. L. (1998). Behavioral foundations of reciprocity: Experimental economics and evolutionary psychology. Economic Inquiry, 36, 335–352. Jehn, K. A. (1995). A multimethod examination of the beneﬁts and detriments of intragroup conﬂict. Administrative Science Quarterly, 40, 256–282. Jobber, D., & O’Reilly, D. (1998). Industrial mail surveys: A methodological update. Industrial Marketing Management, 27, 95–107. Jobber, D., & Saunders, J. (1993). A note on the applicability of the Bruvold–Comer model of mail survey response rates to commercial populations. Journal of Business Research, 26(3), 223–236. Latane, B., & Darley, J. (1970). The unresponsive bystander: Why doesn’t he help? New York: Appleton-Century-Crofts. McDonald, M. L., & Westphal, J. D. (2003). Getting by with the advice of their friends: Ceos’ advice networks and ﬁrms’ strategic responses to poor performance. Administrative Science Quarterly, 48, 1–32. Milgram, S. (1963). Behavioral study of obedience. Journal of Abnormal and Social Psychology, 67, 371–378. Milgram, S., Bickman, L., & Berkowitz, L. (1969). Note on the drawing power of crowds of different size. Journal of Personality and Social Psychology, 13, 79–82.

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MANAGERIAL CONSTRAINT: THE INTERSECTION BETWEEN ORGANIZATIONAL TASK ENVIRONMENT AND DISCRETION Brian K. Boyd and Steve Gove ABSTRACT Managerial constraint is a central theme in strategic management research. Although discussed using a variety of labels (including choice and determinism) and theoretical perspectives (including resource dependence and population ecology), the common question is the degree to which executives have choices or options when making decisions. Two of the most commonly used approaches for discussing constraint are organizational task environments (Dess & Beard, 1984) and managerial discretion (Hambrick & Finkelstein, 1987). These two papers share substantial commonalities in both their theoretical background and operationalization, raising the question of whether discretion and task environment are indeed separate constructs. This chapter reviews both conceptual and methodological issues associated with the use of task environment and discretion. Drawing on a review of published studies and original data analysis, we offer methodological suggestions for future research.

Research Methodology in Strategy and Management, Volume 3, 57–95 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1479-8387/doi:10.1016/S1479-8387(06)03005-0

57

58

BRIAN K. BOYD AND STEVE GOVE

Do managers matter? And, if so, under what conditions? Managerial constraint is a key component in the debate between population ecology and strategic choice perspectives. Two frameworks: Dess and Beard’s (1984) model of organizational task environments, and Hambrick and Finkelstein’s (1987) managerial discretion are commonly cited sources for studies of managerial constraint. These two papers also share some interesting characteristics. First, while both papers are widely cited, there are only a small number of empirical applications of each framework. Within this set of empirical studies, there are widely varying practices regarding variable selection and the measurement of these variables; differences which can limit the generalizability of results. Additionally, the two perspectives draw on similar theory and employ similar – and sometimes identical – measures. Thus, although the two papers are rarely cited concurrently, it is not clear whether task environment are unique, overlapping, or identical constructs. The purpose of this chapter is to facilitate the use of task environment and discretion variables in future studies. We begin with a review of the two constructs, including theoretical foundations and intended focus. Second, we review empirical studies that have used task environment or discretion variables, including a content analysis of methodological practices. Next, we discuss the implications of different approaches to deﬁnition and measurement. We assess these measurement options based on an analysis of data from 130 industry groups. We offer suggestions based on these analyses for future studies. Finally, we evaluate the overlap between discretion and task environment.

CONSTRUCT DEVELOPMENT AND APPLICATION In this section, we will review two topics. In a theoretical overview, we compare the notions of strategic choice and determinism. Additionally, we examine how both task environment and discretion relates to these perspectives. In the second section, we present content analyses of how task environment and discretion have been used in prior studies. We identiﬁed nearly 500 journal articles which have cited either Dess and Beard (1984) or Hambrick and Finkelstein (1987). From these articles, we identiﬁed 87 studies that used task environment or discretion variables in their analyses. Common Roots, Different Applications What constrains managerial action? Inﬂuenced by general systems theory (Bertalanffy, 1968), management scholars began to explore the role of

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59

external forces in shaping a company’s direction. One example of this trend was Emery and Trist’s (1965) depiction of four environmental archetypes of increasing complexity: placid randomized, placid clustered, disturbed reactive, and turbulent ﬁeld. A prominent research stream during this period was the creation of measures to assess uncertainty due to environmental factors (e.g., Duncan, 1972; Lawrence & Lorsch, 1967). However, a number of methodological limitations were identiﬁed with these studies (Downey, Hellriegel, & Slocum, 1975; Tosi, Aldag, & Storey, 1973), resulting in little consensus on either the deﬁnition or measurement of ﬁrm constraints. Subsequently, competing theoretical models emerged to explain the effect of external factors on organizations. Population ecology (Aldrich, 1979; Hannan & Freeman, 1977) characterized the ﬁrm’s interaction with outside forces as a Darwinian model of natural selection: Companies are essentially unable to control their environment, and internal inertia prevents successful adaptation. Survival, therefore, is contingent on having the right set of attributes at the right time. The conclusion that senior managers are largely interchangeable with one another is representative of this perspective (Lieberson & O’Connor, 1972). In contrast, strategists argued that adaptation to external constraints was the key to organizational survival, and that top managers played a central role in this process (Andrews, 1971; Chandler, 1962). A number of strategy models are descendents of strategic choice: Resource dependence theory (Pfeffer & Salancik, 1978) focused on mechanisms that companies could use to help buffer outside forces. Similarly, research on top management teams and upper echelons (Gupta, 1984; Hambrick & Mason, 1984) addressed how attributes of senior managers drove decision-making and subsequent ﬁrm performance. Despite this growing interest in external constraint on organizations, there was little consensus – at either conceptual or empirical levels – on the articulation of these factors. As part of the population ecology framework, Aldrich (1979) integrated prior work to propose six dimensions of business environments: capacity, heterogeneity, stability, concentration, domain consensus, and turbulence. Drawing on these common roots, two prominent papers emerged in the 1980s that offered frameworks to characterize factors that constrain managerial action: Dess and Beard’s (1984) study of organizational task environments, and Hambrick and Finkelstein’s (1987) paper on managerial discretion. On the basis of citations, both articles have been very inﬂuential: The Social Science Citation Index reports 309 citations to Dess and Beard,1 and 172 citations to Hambrick and Finkelstein. However, inﬂuence does not translate directly to application: despite the large number of citations, there

60

BRIAN K. BOYD AND STEVE GOVE

have been only 19 empirical applications of Dess and Beard’s framework that have used all three dimensions and comparable variables; with a number of other papers that used either a partial set of dimensions or very different measures. Similarly, there have been only 16 empirical applications of the discretion construct. Of greater concern, there has been little consistency in the use of either construct: Empirical studies have used a wide range of variables, with an equally wide range of parameters to deﬁne and measure these variables. Additionally, empirical studies often tap only limited aspects of these two constructs. Looking across the two perspectives, the concepts of task environment and discretion have been used interchangeably: multiple articles, for example, have equated highly muniﬁcent industry environments with high levels of discretion (e.g., Datta, Rajagopalan, & Zhang, 2003; Goll & Rasheed, 1997). Similarly, high levels of industry volatility have been equated with high levels of discretion (Haleblian & Finkelstein, 1993). Additionally, similar or identical indicators are often used to measure both discretion and task environment: industry growth rates, for example, have been used to tap both task environments (e.g., Boyd, 1990; Keats & Hitt, 1988) and discretion (e.g., Hambrick & Abrahamson, 1995). Additionally, this same variable, measured at the ﬁrm level, has also been used to measure discretion (Finkelstein & Boyd, 1998). Thus, although the two papers are rarely cited in the same article, the question remains: How distinct are task environments and discretion from one another? Fig. 1 shows some possible answers to this question. First, the lack of co-citation may simply reﬂect that these are separate and distinct – i.e., they are orthogonal or independent constructs. Alternately, these may be loosely linked – sharing some aspects, but still largely independent of each other. Finally, the task environment and discretion labels may simply another case a common problem: The use of inconsistent labels used to describe a common external pressure (Boyd, Dess, & Rasheed, 1993). Both papers share a common theoretical foundation: The distinction between strategic choice and determinism. However, there are substantial differences in the two frameworks. Dess and Beard’s focus is at the industry level, and their intent was to develop a reliable set of indicators to measure how levels of uncertainty varied from industry to industry. In contrast, Hambrick and Finkelstein (1987, p. 370) were interested in constraints at the individual level: It is important to stress that our focus is on the discretion of top managers – speciﬁcally chief executives – and not the discretion of organizations per se. Obviously, managerial discretion is limited by organizational discretion, so part of our analysis will still pertain to those who are interested solely in restrictions on organizations. Our interest in chief

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61

(a) Unrelated

Task Environment

(b) Loosely Linked

Task Environment

Discretion

Discretion

(c) Virtually Identical

Task Environment Discretion

Fig. 1.

Possible Relationships between Organizational Task Environment and Discretion.

executive discretion stems from related research we are conducting on executive characteristics, compensation, and succession – phenomena we believe will be far better understood if discretion is considered.

Both papers treat their respective constructs as multidimensional. Aspects of each are shown in Fig. 2. Dess and Beard integrated work by Aldrich (1979), Child (1972), and others to propose that environmental uncertainty would be shaped by three elements of the task environment for a given industry: Munificence is the availability of resources, and is negatively associated with uncertainty. Muniﬁcent environments are resource rich, with ample ability to support organizational growth. Dynamism refers to volatility or unpredictability in the environment, and is positively associated with uncertainty. Finally, complexity refers to the variety of an industry – concentration of inputs, for example, or organizational density. Complexity is positively associated with uncertainty. A number of subsequent articles have focused speciﬁcally on the conceptualization and measurement of Dess and Beard’s framework (Castrogiovanni, 2002; Harris, 2004; Sharfman & Dean, 1991). Hambrick and Finkelstein also proposed three factors that would shape the level of discretion2 – generally deﬁned as the latitude of action available to an executive. Their determinants were found at the individual, ﬁrm, and industry levels. First, they noted that managerial characteristics would drive discretion. Speciﬁcally, executive personality characteristics (e.g., cognitive complexity, commitment), personal power base, and political skills would

62

BRIAN K. BOYD AND STEVE GOVE

Munificence Capacity to Support Growth Organizational Slack Resource Scarcity

Environmental Uncertainty

Complexity

Dynamism

Organizational Density and Concentration

Unpredictable Change

(a) Three Elements of Task Environment

Internal Forces Inertia Resource Availability Powerful Inside Forces

Managerial Discretion

Managerial Characteristics Aspiration Level Commitment Cognitive Complexity Internal Locus of Control Power Base Political Acumen

Task Environment Product Differentiation Market Growth Industry Structure Demand Instability Quasi-legal Constraints Powerful Outside Forces

(b) Three Elements of Discretion

Fig. 2. Conceptualization of Task Environment and Discretion (a) Dess and Beard’s (1984) Three Elements of Task Environment and (b) Hambrick and Finkelstein’s (1987) Three Elements of Discretion.

Managerial Constraint

63

serve to create, or limit, options available to a particular manager. Second, internal forces, such as organizational slack, ﬁrm inertia, or political power held by others, can also shape discretion. Finally, the task environment was expected to shape discretion as well. As shown in Fig. 2, many of the discretion task environment elements closely resemble muniﬁcence, dynamism, and complexity. Because Hambrick and Finkelstein’s article was a conceptual one, as opposed to Dess and Beard, subsequent researchers had less guidance – one might even say greater latitude of action – in how to deﬁne and measure discretion. As a result, in subsequent applications, discretion has been studied at the individual, ﬁrm, and industry levels, has been observed directly and inferred indirectly, and measured in a variety of ways, including expert assessment, survey, and archival measure (Boyd & Salamin, 2001). A few studies have focused primarily on the measurement of discretion (Carpenter & Golden, 1997; Finkelstein & Boyd, 1998; Hambrick & Abrahamson, 1995). In the next two sections, we describe empirical studies that have operationalized both the task environment and discretion frameworks.

Empirical Applications of Dess and Beard’s Task Environment Using the Social Science Citation Index, we identiﬁed 306 published papers that cited Dess and Beard’s (1984) article on task environments. We should note that this article pool is not an exhaustive list of possible uses, as books and book chapters are not included in the SSCI, nor are some journals. We then reviewed each article on the SSCI list, excluding papers that were not available as hard copy journals, electronic journals, or via interlibrary loan. We then classiﬁed papers into three categories: Articles which cited Dess and Beard, but did not use any of their measures; articles which used a subset of Dess and Beard dimensions (e.g., used muniﬁcence and dynamism, but not complexity), and articles which used all three of the Dess and Beard dimensions. Overall, we identiﬁed 71 papers that used Dess and Beard constructs in their analyses: 28 studies, which used all three dimensions, and 43 studies which used a subset of papers. In three quarters of the papers, task environment variables were used as predictor or contingency variables. The remaining one quarter of papers used these dimensions to control for industry effects. Interestingly, while the Dess and Beard model was framed to study objective aspects of industry constraint, a number of studies measured muniﬁcence, dynamism, or complexity with perceptual, or survey, items. In fact, of the 28 papers that used all three dimensions, nine relied solely on

64

BRIAN K. BOYD AND STEVE GOVE

perceptual measures. Therefore, of the 306 papers, we identiﬁed only 19 articles that used industry-level data for all three constructs. Attributes of the full-use studies are shown in Table 1, and partial-use studies are shown in Table 2.

Time Horizon The ﬁrst aspect of the papers we reviewed was the time horizon. Muniﬁcence and dynamism scores are calculated by regressing an industry variable against time. While 5-and 10-year time windows are the most commonly used horizons, studies also used 2-, 3-, 7-, 9-, and even 19-year windows. Five- and ten-year windows typically cited prior work as precedent; otherwise, the rationale for a speciﬁc horizon was not emphasized. Additionally, studies did not explore whether different time horizons might affect results.

Industry Variables The variables used to measure muniﬁcence and dynamism varied widely. Industry sales and total employment were the most commonly used measures. The price-cost margin and value added were also used by a number of studies. Less commonly used variables included the number of establishments, capital expenditures, and return on assets. Prior use of a variable was the most common basis for justifying its use. The overwhelming majority of studies represented each dimension with a single indicator – Harris (2004) and Jarley, Fiorito, and Delaney (1997) are the two prominent exceptions. Because of the reliance on single indicators, most studies were not able to report tests of dimensionality3 for the industry variables used. Among studies that did test dimensionality, three quarters relied on partial (e.g., reliability of individual dimensions) versus extensive (e.g., conﬁrmatory factor analysis) testing. Also, some studies with multiple indicators reported correlations among composite variables versus raw indicators, which limits comparison against other studies (Boyd, Gove, & Hitt, 2005). Complexity is most frequently measured by economic concentration. Boyd (1990) reported results of the MINL transformation (see Schmalensee, 1977) that yields an approximation of the Herﬁndahl index based on traditional concentration ratios (4-ﬁrm, 8-ﬁrm, etc). The use of H with the MINL transformation is the most widely used approach to operationalize complexity. Other studies have measured this dimension via concentration or dispersion of other variables, however, such as value-added or employees.

Article

Studies Using all Three Dess and Beard Dimensions.

Muniﬁcence Variables

Time

Dynamism Adjust

Variable

Time

Complexity Adjust

Variable

(a) Studies Using Archival Measures Bamford, Dean, and Douglas (2004)

Bank deposits

5

Log

Bank deposits

5

Log

Bamford, Dean, and McDougall (2000)

Bank deposits

5

Log

Bank deposits

5

Log

Bergh (1998) Berman, Wicks, Kotha, and Jones (1999) Boyd (1990) Boyd (1995) Castrogiovanni (2002)

Sales Sales

5 9

Mean Mean

Sales Sales

5 9

Mean Mean

Sales Sales Sales, employees, value added, price-cost margin Sales, price-cost margin, employees, value-added, # of establishments

5 5 5

Mean Mean Mean

Sales Sales Sales

5 5 5

Mean Mean Mean

H (MINL) H (MINL) Specialization ratio, coverage ratio

10

Mean

Sales, price-cost margin, employees, value-added, intermediate industry output Not fully described

10

Mean

Dispersion of sales, value-added, employees, # of establishments

Log

Industry size, regional production dispersion, concentration

Harris (2004)

Jarley et al. (1997)

Not fully described

5

Log

5

Number of bank branches divided by log of population Number of bank branches divided by log of population H (MINL) C (4-ﬁrm)

Managerial Constraint

Table 1.

65

66

Table 1. (Continued ) Article

Muniﬁcence Variables

Karimi, Somers, and Gupta (2004)

Pagell and Krause (2004) Palmer and Wiseman (1999) Sharfman and Dean (1991)

Snell, Lepak, Dean, and Youndt (2000)

Adjust

Variable

NA NA Sales, price-cost Sales, price-cost margin, margin, employment, employment, value-added value-added Sales, net income 5 Log Sales, net income Summary MDC scores taken directly from Dess and Beard (1984) Sales Sales

Time

Complexity Adjust

Variable

NA

NA

5

Log

Concentration of sales, value-added, employment, # of establishments Grossack

5 5

Mean Mean

H (MINL) H (MINL)

10

Mean

Concentration of sales, value-added, employment, inputs, # of establishments

5 5

Mean Mean

Sales Sales

Sales, price-cost, employment, value-added, # of establishments Sales

10

Mean

Sales, price-cost, employment, value-added

5

Log

Sales

5

Log

C (MINL)

Sales, employment Sales, employees, C-8 ratio

5

Log

Sales, net income

5

Log

Mean

Sales, employees

10

Mean

Log

Sales

NA

Log

C (4-ﬁrm), count of competitors Geographic concentration, product breadth, percentage of scientists and engineers in the workforce C (MINL)

Sales

10

NA

BRIAN K. BOYD AND STEVE GOVE

Keats and Hitt (1988) Lawless and Finch (1989) Lepak and Snell (2002) Lepak, Takeuchi, and Snell (2005) McArthur and Nystrom (1991)

Time

Dynamism

Muniﬁcence Indicators

Items Reported

Dynamism Reliability

Indicators

Items Reported

Complexity Reliability

Indicators

Items Reported

Reliability

(b) Studies Using Survey Measures Baum, Locke, and Smith (2001) Bensaou and Venkatraman (1995) Camison (2004) Chen and Lin (2004) Hart and Banbury (1994) Hart and Quinn (1993) Luo (1999) Luo and Peng (1999) Panayotopoulou, Bourantas, and Papalexandris (2003)

4

None

1

Full

0.73 Single item

4

None

0.84

2

None

3

Partial

0.79

1

Full

0.68 Single item

Managerial Constraint

Authors

25 Survey items for all three dimensions, aggregated into a single variable. Individual items are not reported, but a Cronbach’s alpha of 0.93 is reported 2 Partial 0.60 4 Partial 0.66 4 Partial 0.97 2

Full

1

Full

0.63

8

Full

Single item

1

Full

0.63

2

Full

Single item

1

Full

16 10

Full Partial

0.70+ 0.70+

5

Partial

0.81

16 10

Full Partial

0.70+ 0.70+

16 10

Full Partial

0.70+ 0.70+

3

Partial

0.60

11

Partial

0.79

0.67 Single item

67

Article

Studies Using a Subset of Dess and Beard Dimensions. Muniﬁcence

Variables

Time

Dynamism Adjust

3 Survey items

Variable

Complexity

Time

Adjust

Sales

10

Mean

Sales, income 5 Survey items Sales

10

Std. error

19

NA

Sales

5

Mean

Sales Sales

5 5

Mean Mean

Sales

5

Mean

Sales

5

Mean

Sales

NA

NA 5

NA Mean

7

Sales NA

5 10

Sales

5

Sales

NA

Input–output

# of ﬁrm SIC codes

NA Production Sales

Sales, capital expenditures, assets

Variable

NA

Sales, capital expenditures, assets Sales

7

NA

NA

Std. dev.

Sales, employees

10

Mean

5

1r2

H

Growth rate NA Log

NA

H (MINL)

Sales

BRIAN K. BOYD AND STEVE GOVE

Andersen (2001) concentration Andersen (2004) Bantel (1998) Baucus and Near (1991) Bergh (1993) Bergh and Lawless (1998) Bloom and Michel (2002) Boeker (1997) Buchko (1994) Carpenter and Fredrickson (2001) Chattopadhyay, Glick, Miller, and Huber (1999) David et al. (2002) Dawley et al. (2002) Dean and Sharfman (1993) Dean and Snell (1996) Dean and Sharfman (1996) Delacroix and Swaminathan (1991)

68

Table 2.

5

Mean

Sales

5

Mean

Sales

5

Mean

Sales

5

Mean

Sales

10

Mean

Sales

10

Mean

Sales

10

Mean

Sales

10

Mean

Sales

10

NA

Change in Japan GNP

C (4-ﬁrm) Sales Sales 3 Survey items

3 Survey items

5 5

Mean Mean

Sales

5

Mean

2 Survey items Sales

5

Mean

Sales

5

NA

Sales

5

5 10

NA Mean

Sales

5

Coefﬁcient of variation NA

Sales

5

Mean

Sales Sales Sales

NA 5 5

s Mean Mean

Sales

2

NA Sales, cost of operations, ROA, # of establishments Sales

10

Mean

5

(1r2)

Sales Sales, employees

Sales, cost of operations, ROA, number of establishments Sales

10

5

Mean

NA

69

Stoeberl et al. (1998)

Sales

Managerial Constraint

Floyd and Wooldridge (1992) Floyd and Wooldridge (1997) Goll and Rasheed (1997) Goll and Rasheed (2004) Goll and Rasheed (2005) Kotha and Nair (1995) Li and Simerly (1998) Li and Ye (1999) Lumpkin and Dess (2001) Luo (2005) Mishina, Pollock, and Porac (2004) Rajagopalan and Datta (1996) Rasheed (2005) Sharfman and Dean (1997) Sheppard (1995) Simerly (1997) Simerly and Li (2000) Spanos, Zaralis, and Lioukas (2004) Stetz and Beehr (2000)

70

Table 2. (Continued ) Article

Muniﬁcence Variables

Time

Tushman and Anderson (1986)

Annual sales growth

5

Wally and Baum (1994)

Ordinal ranking of industries based on environmental uncertainty Sales

NA

Adjust

Complexity

Variable

Time

Adjust

Mean growth

Ratio of forecasted to actual industry growth

5

Mean

Sales

NA

Mean

Sales

3

Mean

Sales, employment

3

Mean

4 Survey items

Variable

BRIAN K. BOYD AND STEVE GOVE

Weinzimmer et al. (1998) Wholey and Brittain (1989) Wiklund and Shepherd (2005) Zhang and Rajagopalan (2004)

Dynamism

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Standardization To facilitate comparison across industries, muniﬁcence and dynamism scores are standardized, using either the mean of the industry variable, or a log transform. Keats and Hitt (1988) used the log transform approach, while Boyd (1990) used mean standardization; the latter approach is more widely used. These papers are typically cited as rationale in the article. A number of papers, however, used unstandardized scores; the comparability of scores across industries using this approach is not known. Perceptual Measures As noted previously, roughly one-third of the papers that use the full set of task environment variables do so via perceptual measures. These articles are noted in section (b) of Table 1. We also identiﬁed two studies that used only a subset of task environment dimensions and perceptual measures: Lumpkin and Dess (2001) and Luo (2005). While all studies report reliabilities of each dimension of 0.60 or greater, there is virtually no consistency in the survey measures used: The number of survey items used for a dimension, for instance, range between 1 and 16. Some studies will use very different numbers of indicators for each dimension as well – both Hart and Banbury (1994) and Panayotopoulou, Bourantas, and Papalexandris (2003), for example, use twice as many survey items for dynamism than for muniﬁcence or complexity. Also, while each paper created a unique set of survey items, limited testing is done in regard to reliability and validity. Most studies reported either the survey item itself, or topic of survey questions. Partial Use of Task Environment Dimensions The majority of studies that include a measure based on Dess and Beard (1984) include only a subset of the muniﬁcence, dynamism, and complexity dimensions. These studies typically focus on muniﬁcence and dynamism; of the 43 partial studies, only 5 included the complexity dimension. Partial studies are more likely to include task environment as a control variable, and most often rely on single indicator measures.

Empirical Applications of Hambrick and Finkelstein’s Discretion We identiﬁed 172 articles through the Social Sciences Citation Index citing Hambrick and Finkelstein’s (1987) article. As with our review of articles citing Dess and Beard’s (1984) work, this approach excludes most books and chapters, and we excluded papers not available through our libraries,

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BRIAN K. BOYD AND STEVE GOVE

electronic means, or via interlibrary loan. This may represent a limiting factor in our review. We examined each article citing Hambrick and Finkelstein (1987) and classiﬁed how discretion was utilized in the methodology. In total, we identiﬁed 16 papers4 that used discretion in some form in the analysis, some with multiple operationalizations (e.g., Hambrick & Abrahamson, 1995), and some with measures at multiple levels of analysis (e.g., Boyd & Salamin, 2001; Magnan & St. Onge, 1997). Unlike Dess and Beard’s work, discretion has evolved in the literature from conceptual discussion (Hambrick & Finkelstein, 1987). Early applications (Finkelstein & Hambrick, 1990; Haleblian & Finkelstein, 1993; Hambrick, Geletkanycz, & Fredrickson, 1993) measured discretion as ordered categories – i.e., either high/low discretion or high/ medium/low discretion settings. These rankings of discretion were based on reviews of archival data by study authors, but with little explicit information as to analyses conducted, cutoff levels, or other considerations presented in their methodologies. Additionally, while Hambrick and Finkelstein initially characterized discretion at the level of the individual executive, these high/low groupings were done at the industry level: Neither internal forces nor managerial characteristics were included in these analyses. As developed by Hambrick and Finkelstein, executive discretion was expected to be shaped by three types of forces: Individual level (managerial characteristics), ﬁrm level (internal forces), and industry level (task environment). Subsequently, there have been efforts to reﬁne the measurement of discretion at all three levels, with varying degrees of conformance with Hambrick and Finkelstein. At the industry level, Hambrick and Abrahamson (1995) created discretion measures via an unafﬁliated panel of expert raters, (surveys of academic and securities analysts). They reported a high level of agreement within both groups of experts. Following a multiple-trait, multiple-measure approach, the authors examined the correlation between expert ratings and quantitative measures from archival sources. Their methodology did not include a factor analysis, but rather utilized a regression-based approach of predicting the expert panel discretion scores using quantitative indicators. At the ﬁrm level, Finkelstein and Boyd (1998) developed a multi-indicator factor model of discretion in a structural equation model predicting CEO pay. Included in their model were indicators of product differentiability, growth, demand instability, capital intensity, regulation, and industry concentration. These indicators can best be described as micro versions of the industry task environment, and differ substantially from Hambrick and Finkelstein’s discussion of ﬁrm-level forces such as inertia or the presence of powerful inside forces. Finally, at the individual level,

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Carpenter and Golden (1997) reported results based on a 15-item scale (a ¼ 0.82) assessing discretion as part of a simulation. Their approach is notable as it included sub-scales assessing both low discretion (5 items; a ¼ 0.74) and high discretion (10 items; a ¼ 78) environment, though actual items were not reported, for the use of perceived managerial discretion as a dependent variable, and usage of a lab study using a simulation. Again, the Carpenter and Golden measures differ substantially from the individuallevel forces as presented by Hambrick and Finkelstein. We use these levels (industry, ﬁrm, and individual) to summarize empirical use of discretion, as shown in Table 3. Next, we will highlight some of the key characteristics of this research. Level of Analysis While conceptualized as a multi-level construct, the vast majority of studies have measured discretion at a single level. As shown in Table 3, the industry level of analysis is dominant, with only a handful of studies tapping either ﬁrm- or individual-level aspects of discretion. Only two studies (Boyd & Salamin, 2001; Magnum & St. Onge, 1997) used measures from multiple levels. At the industry level, indicators typically include some of the usual suspects seen in the Dess and Beard applications – i.e., growth and volatility. Unique indicators included the intensity of advertising and R&D to tap differentiation, capital intensity, and regulation. While industry level studies mainly used archival data, Hambrick and Abrahamson (1995) also used expert ratings of industry discretion. Two different approaches have been used to measure discretion at the ﬁrm level. First, Finkelstein and Boyd (1998) used many of the same archival indicators as the industry studies, but measured at the ﬁrm level. So, for example, industry growth became ﬁrm growth, and industry concentration became a weighted composite of Herﬁndahl scores from each of the ﬁrm’s business segments. Second, Rajagopalan and Finkelstein (1992) extended the scope of discretion by proposing that discretion ﬂowed from company strategy. Using the Miles and Snow (1978) typology, they developed hypotheses based on the premise that executives in Prospector ﬁrms would enjoy greater latitude in decision options than Defender executives. Strategic orientation has been used in subsequent studies as well (Boyd & Salamin, 2001; Rajagopalan, 1997). Similarly, Magnum and St. Onge (1997) used aspects of ﬁrm strategy (product mix, scope, and internationalization) to measure discretion. Finally, the individual level has seen the least empirical use. Additionally, the measures for individual-level studies are very different from those proposed by Hambrick and Finkelstein: Two studies used hierarchical

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BRIAN K. BOYD AND STEVE GOVE

Table 3. Studies Using Hambrick and Finkelstein Discretion. Article

Level of Analysis Industry

Abrahamson and Hambrick (1997)

Firm

Expert assessment data from Hambrick and Abrahamson (1995)

Aragon-Correa, MatiasReche, and SeniseBarrio (2004)

Boyd and Salamin (2001) Carpenter and Golden (1997) Datta, Guthrie, and Wright (2005) Datta and Rajagopalan (1998) Datta et al. (2003)

Strategic orientation

Capital intensity, growth, R&D intensity, and industry volatility Advertising intensity, growth, and capital intensity Capital intensity, growth, advertising intensity

Finkelstein and Boyd (1998)

Finkelstein and Hambrick (1990)

Haleblian and Finkelstein (1993)

Hambrick and Abrahamson (1995)

Individual

Market growth, R&D intensity, advertising intensity, demand instability, capital intensity, concentration, and regulation Industries ranked high, medium, and low discretion based on a review of product differentiation, growth, demand instability, capital intensity, and degree of regulation Industries ranked high and low discretion based on R&D intensity, growth, advertising intensity, instability, and regulation Archival measures: R&D intensity, advertising intensity, capital intensity, growth,

Political power based on membership in a dominant coalition Hierarchical position Perceived discretion

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Table 3. (Continued ) Article

Level of Analysis Industry

Hambrick et al. (1993)

Magnan and St. Onge (1997)

Rajagopalan (1997) Rajagopalan and Finkelstein (1992)

demand instability, and regulation. Expert (academic and analyst) ratings Industries ranked high and low discretion, based on Finkelstein and Hambrick (1990) Banking laws that prohibit takeovers or branching

Firm

Individual

Company strategy, including product mix, internationalization, and geographic scope Strategic orientation Strategic orientation

position (Boyd & Salamin, 2001; Aragon-Correa, Matias-Reche, & SeniseBarrio, 2004) as proxies of individual power. A third paper (Carpenter & Golden, 1997) developed a perceptual measure of individual discretion. Measurement Practices As with the pool of Dess and Beard articles, there was considerable variation in the manner that discretion variables were operationalized. For example, some studies standardized growth and volatility scores to adjust for differences in industry size, while others did not. Time windows used to measure variables varied as well, including single and multi-year composites. As with the Dess and Beard articles, the rationale for a speciﬁc time window was often not justiﬁed when creating growth or volatility measures. The sophistication of measurement also varied widely across studies, including ordinal categories, single measures, and multiple measures. Among the studies with multiple measures, indicators have been aggregated into a single index measure, treated as separate predictors, and loaded onto a multiindicator latent factor model.

IMPLICATIONS FOR FUTURE RESEARCH We address three topics in this section. First, as demonstrated in our content analysis, prior studies have used a wide range of options when deﬁning

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BRIAN K. BOYD AND STEVE GOVE

variables, particularly for growth and volatility scores. Using data from 130 industry groups, we examine how choices in deﬁnition affect variables. Second, we examine the degree of overlap between discretion and task environment variables. For this analysis, we compare data from a sample of 400 ﬁrms with our industry-level measures. Finally, we use the Hrebiniak and Joyce (1985) model of strategic choice and determinism to integrate task environment and discretion.

Recommendations for Measurement In this section, we review the implications of different choices in the construction of growth and volatility measures, which are central to both discretion and task environment frameworks. While we illustrate these issues with industry level data, the analyses are applicable to ﬁrm level measures as well. We collected data for 130 SIC industry groups from U.S. Industrial Outlook. Based on our content analysis of prior studies, we created muniﬁcence and dynamism scores using a variety of approaches, and then compared the correlations among these different measures. Results are reported in Tables 4 and 5. Prior studies used a wide range of time horizons to calculate scores, ranging from as little as 2 years, to as long as 19 years for Dess and Beard articles. Time periods varied for discretion studies as well. We examined the effect of temporal stability by creating scores with four different time horizons: 3, 5, 7, and 10 years. To facilitate comparison, all time windows end in 1986 – i.e., the 10-year window includes 1977–1986 data, while the 5-year window includes 1982–1986 data. To address the effect on the choice of industry variable, we constructed separate measures based on industry sales (labeled value of shipments in the Industrial Outlook), and industry employment – these were the two most commonly used industry variables as reported in the content analysis of prior studies. Next, we used three different approaches to standardizing scores: Mean and log standardization, and unstandardized scores. While mean standardization is the most commonly used approach, Keats and Hitt (1988) standardized via log transforms, and this alternate method has been used in a minority of studies. For mean standardization, a regression model is run using the industry variable (i.e., sales or employment) as the dependent variable, with time as the predictor. Both the parameter estimate and standard error of the regression slope coefﬁcient are then divided by the mean of the industry variable to create muniﬁcence and dynamism scores, respectively. For log standardization,

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Table 4. Comparison of Muniﬁcence Scores. Variable 1 2 3 4 5 6 7 8 9 10 11 12 13

VoS TE VoS TE VoS TE VoS TE VoS TE VoS VoS TE Mean: Std. Devn:

Basis Window Mean Mean Log Log Mean Mean Mean Mean Log Log None Mean Mean

10 10 10 10 7 7 5 5 5 5 5 3 3

1 1.00 0.74 1.00 0.89 0.91 0.89 0.73 0.73 0.73 0.73 0.16 0.57 0.61

2

1.00 0.74 0.94 0.68 0.69 0.56 0.58 0.55 0.57 0.12 0.41 0.48

3

4

5

1.00 0.89 0.88 0.88 0.69 0.71 0.71 0.71 0.16 0.54 0.60

1.00 0.81 0.85 0.63 0.69 0.63 0.67 0.14 0.48 0.57

1.00 0.93 0.90 0.86 0.87 0.84 0.07 0.69 0.69

6

1.00 0.78 0.91 0.80 0.89 0.12 0.61 0.77

7

1.00 0.85 0.95 0.85 0.14 0.84 0.66

8

1.00 0.88 0.98 0.07 0.66 0.87

9

1.00 0.92 0.08 0.82 0.75

10

11

12

13

1.00 0.05 1.00 0.70 0.35 1.00 0.87 0.07 0.66 1.00

0.05 0.02 1.07 0.99 0.04 0.02 0.05 0.01 1.06 1.00 5475 0.03 0.00 0.05 0.06 0.06 0.05 0.07 0.06 0.08 0.07 0.12 0.09 29492 0.10 0.08

Note: VoS ¼ Value of Shipments; TE ¼ Total Employment. All data from US Industrial Outlook. All time windows end with 1986 data – e.g., 10-year window includes years 1977–1986, 3year window includes 1984–1986. Correlations in italics nonsigniﬁcant. All other correlations signiﬁcant at 0.001.

a log transform is used on the industry variable prior to regression. The antilog of the parameter estimate and standard error of the regression slope coefﬁcient are then used to calculate muniﬁcence and dynamism scores, respectively. Unstandardized scores have been used as indicators for both task environment and discretion studies, and are more commonly used in the pool of discretion studies. This approach uses the unadjusted parameter estimate and standard error of the regression slope coefﬁcient. Temporal Stability Muniﬁcence and dynamism scores are only minimally affected by the choice of time horizon. For example, muniﬁcence scores based on any time window will have a correlation between 0.84 and 0.91 with any adjacent window – e.g., comparing a 5-year window to either 3- or 7-year windows. Scores are also very similar even when comparing extreme ranges of time windows: Using mean-based standardization, for example, 3- and 10-year windows correlate on average at 0.53. Similarly, mean standardized dynamism scores correlate at 0.83 on average. The importance of the time window has been raised in a number of articles. Boyd et al. (1993) noted that scores based on a broad time horizon may have limited meaning, as older data may be less relevant to current organizational issues. Additionally, Castrogiovanni

78

Table 5. Variable VoS TE VoS TE VoS TE VoS TE VoS TE VoS VoS TE Mean: Std. Devn:

Basis

Window

1

2

3

4

5

6

7

8

9

10

11

12

13

Mean Mean Log Log Mean Mean Mean Mean Log Log None Mean Mean

10 10 10 10 7 7 5 5 5 5 5 3 3

1.00 0.32 0.94 0.39 0.96 0.90 0.91 0.82 0.93 0.93 0.13 0.78 0.88

1.00 0.29 0.99 0.29 0.31 0.26 0.29 0.27 0.32 0.04 0.23 0.29

1.00 0.38 0.91 0.83 0.89 0.87 0.88 0.85 0.20 0.77 0.74

1.00 0.36 0.40 0.34 0.41 0.35 0.41 0.06 0.25 0.34

1.00 0.88 0.95 0.78 0.96 0.91 0.15 0.81 0.84

1.00 0.81 0.89 0.86 0.95 0.11 0.59 0.87

1.00 0.76 0.98 0.86 0.25 0.83 0.77

1.00 0.90 0.99 0.09 0.58 0.86

1.00 0.92 0.18 0.84 0.85

1.00 0.08 0.68 0.89

1.00 0.38 0.03

1.00 0.72

1.00

0.01 0.01

0.01 0.03

1.01 0.02

0.01 0.02

0.01 0.01

1.01 0.02

2960 16982

0.02 0.04

0.01 0.03

1.01 0.01

0.02 0.02

0.02 0.02

1.02 0.03

Note: VoS ¼ Value of Shipments; TE ¼ Total Employment. All data from US Industrial Outlook. All time windows end with 1986 data – e.g., 10-year window includes years 1977–1986, 3-year window includes 1984–1986. Correlations in italics nonsigniﬁcant. Signiﬁcant at 0.05. All other correlations signiﬁcant at 0.001.

BRIAN K. BOYD AND STEVE GOVE

1 2 3 4 5 6 7 8 9 10 11 12 13

Comparison of Dynamism Scores.

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(2002) reported that muniﬁcence, dynamism, and complexity scores tended to decline as an industry matured. However, the close correspondence of scores across time horizons suggests that the choice of a 5- versus 7-, or even 10-year window may not be a meaningful concern. Industry Variable While industry sales is the most widely used variable, a variety of other indicators have been used, including capital expenditures, net income, proﬁtability ratios, and assets. Total employment is the second most widely used industry variable. As shown in Tables 4 and 5, muniﬁcence and dynamism scores based on sales and employment data correlated strongly with each other. However, the 5- and 7-year window mean-adjusted scores tracked much more closely than scores based on either the shortest or longest time windows. For muniﬁcence, scores based on industry sales and employment at 7- and 5-year windows correlated at 0.93 and 0.85, respectively. In comparison, 10-year scores correlated at 0.74, and 3-year scores correlated at 0.66. Dynamism scores based on industry sales and employment reported correlations of 0.88 and 0.76 at 7- and 5-year windows, respectively. The 10-year window reported a correlation of only 0.32, and the 3-year window correlated at 0.72. Part of this disparity may be the upward bias of industry sales – typically studies do not discuss transforming sales to constant dollars to control for inﬂationary pressures. Standardization Comparisons of mean and log standardization were made for 5- and 10-year windows, and using both industry sales and employment. Boyd (1990) had previously reported that both approaches correlated strongly; however, this analysis was based on a relatively small sample of industry groups. Our analysis is based on a much larger pool of industries, and report similar, but stronger results than Boyd (1990). On average, muniﬁcence measures correlated at 0.97, and dynamism measures correlated at 0.98. There was no noticeable pattern of variation based on time window or the choice of industry variable. Unstandarized scores, however, report a very different pattern of correlations. Muniﬁcence scores using industry sales and a 5-year window were not signiﬁcantly correlated with any other estimates at either 5-, 7-, or 10-year windows. The unstandardized muniﬁcence score did report a signiﬁcant correlation of 0.35 with the 3-year estimate also based on industry sales, however. Unstandardized dynamism scores reported slightly better results, with a correlation of 0.25 with the 5-year mean-adjusted

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BRIAN K. BOYD AND STEVE GOVE

industry sales score, and a correlation of 0.38 with the 3-year mean-adjusted industry sales score.

Potential for Omitted Variable Problems As noted in earlier Tables, the majority of studies that use task environment variables use only a subset of the muniﬁcence, dynamism, and complexity constructs. Similarly, the potential for omitted variable problems are substantial for discretion studies as well: Not only are there multiple levels of discretion that need to be addressed, but most studies use a small number of indicators to represent a particular level. Given the nature of both phenomena, researchers should be alert to potential omitted variable problems. We will illustrate this issue using data on task environments, but the concern is applicable to discretion as well. In their article, Dess and Beard (1984) proposed that muniﬁcence, dynamism, and complexity would be independent dimensions. Thus, their factor model reported orthogonal versus oblique factors. It is important to note that there was no a priori theoretical basis for this – rather, Dess and Beard viewed their model as an exploratory one, and reported independent factors solely in the interest of parsimony. In a subsequent replication, Harris (2004) reported signiﬁcant covariances across all three dimensions.5 Based on our review of prior studies, both the strength and the direction of covariance among task environment dimensions is highly inﬂuenced by the characteristics of a particular sample. Table 6 aggregates the correlations for these dimensions, based on section (a) articles from Table 1. Using on a weighted mean of these articles, the three dimensions would appear to be virtually independent: Muniﬁcence and dynamism correlate at 0.11, muniﬁcence and complexity at 0.02, and dynamism and complexity at 0.02. However, these scores vary dramatically across studies. The largest correlation between muniﬁcence and dynamism was 0.88; at the other extreme, one study reported a negative correlation of 0.46. Similarly, muniﬁcence and complexity reported correlations ranging from 0.34 to 0.62. Finally, dynamism and complexity has correlations ranging from 0.46 to 0.48. Omitted variable problems occur when two predictor variables have overlapping variance, and one of the predictors is excluded from the model. As a result, a statistical model may overestimate the effect of the included predictor on the dependent variable. Given the range of strong correlations that have been reported between muniﬁcence, dynamism, and complexity, and the general tendency to use only a subset of these dimensions, there is ample basis to question the accuracy of prior studies.

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Table 6. Correlations between Muniﬁcence, Dynamism, and Complexity. Article

N

Correlations Muniﬁcence– Dynamism

Bamford et al. (2004) Bamford et al. (2000) Bergh (1998) Berman et al. (1999) Boyd (1990) Boyd (1995) Castrogiovanni (2002) Harris (2004) Jarley et al. (1997) Karimi et al. (2004) Keats and Hitt (1988) Lepak and Snell (2002) Lepak et al. (2003) McArthur and Nystrom (1991) Pagell and Krause (2004) Palmer and Wiseman (1999) Snell et al. (2000) Weighted mean Minimum Maximum

Muniﬁcence– Complexity

Dynamism– Complexity

490

0.04

0.33

0.01

140

0.14

0.15

0.07

168 486

0.21 0.10

0.13 0.39

0.03 0.46

147 192 45

0.36 0.49 0.19

0.58 0.02 0.38

0.29 0.13 0.24

247 50

0.46 0.28

0.17 0.62

0.48 0.20

77

0.35

0.29

0.39

262

0.06

0.34

0.14

206

0.20

0.03

0.13

148

0.22

0.01

0.16

109

0.26

0.03

0.18

168

0.88

0.04

0.05

235

0.15

0.06

0.13

74

0.86

0.21

0.15

0.11 0.46 0.88

0.02 0.34 0.62

0.02 0.46 0.48

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BRIAN K. BOYD AND STEVE GOVE

Net Recommendations The most common approach to building industry-level muniﬁcence and dynamism scores is to use a 5-year time window, mean standardization, and industry sales. Continued use of this approach facilitates comparison of results across different samples and analyses. However, if authors have a theoretical basis for either a longer or shorter time window, this change should have only a minimal effect on scores. Similarly, the choice of standardization method should not have an effect on results. Many studies, however, either do not standardize their measures, or are unclear whether or not they standardize scores. As shown in our comparison, unstandardized growth or volatility scores show, at best, only tenuous correspondence with either the mean or log adjusted measures. Thus, some form of standardization should be included for any studies that utilize multi-industry samples. The choice of an industry variable is more important than either the time window or standardization approach, as there is some variation between scores based in industry sales and employment. These differences are likely affected by multiple factors, including failure to control for inﬂation with sales scores, and advances in man-hour productivity, such as lean manufacturing. Scores based on industry sales should ideally be restated to constant dollars. Additionally, the use of multiple measures would be helpful. Finally, studies should consider including all three task environment dimensions, even as control variables, to address potential omitted variable problems. In the case of discretion, studies should include a broader pool of indicators. A summary of measurement recommendations is shown in Table 7.

Different Labels, Common Phenomenon? Based on our review, there appear to be more similarities than differences in the applied use of task environment and discretion. Although intended to capture individual psychological traits, and organizational factors such as inertia or internal politics, as Table 3 shows, the de facto indicators for discretion are typically measured at the industry level. Consequently, industry-level growth rates, volatility, economic concentration, and capital intensity have been used to characterize both discretion and task environment. So, are these truly different constructs, or simply different labels? Boyd et al. (1993) noted that organizational environments can be measured at multiple levels, and with multiple approaches. A modiﬁed version of their framework for classifying measures is shown in Fig. 3. Consider a regression slope coefﬁcient for sales, standardized by its mean. When

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Table 7.

Recommended Measurement Practices for Future Studies.

Issue Time window for growth and volatility scores

Standardization for growth and volatility scores

Choice of variables for growth and volatility scores Omitted variables

Level of analysis

83

Description Five-year windows offer greatest generalizability to other studies. However, measures are generally comparable if window size is adjusted up or downwards by two years Mean and log standardization yield almost identical results, but mean is most widely used practice. Unstandardized scores correlate poorly with either mean or log standardization, and should be limited to single-industry samples Sales offer the greatest generalizability to other studies Correlations between dimensions have varied widely from study to study, creating a realistic potential for omitted variable problems. Task environment studies should include measures for muniﬁcence, dynamism, and complexity even if hypotheses relate to just one dimension Discretion studies should use multiple measures, ideally at different levels – e.g., ﬁrm versus individual – when appropriate Scores based on industry- versus ﬁrm-level data will covary but still measure different aspects of constraint. Authors should provide explicit rationale for use of a given level

‘‘sales’’ is measured as ‘‘industry sales,’’ this is a measure of muniﬁcence, an aspect of industry task environment. However, when ‘‘sales’’ is measured as ‘‘ﬁrm sales’’ the variable is now less macro, and now represents the level of discretion facing a ﬁrm. In practice, how similar or different are these two measures, separated only by the level of analysis? Boyd et al. (1993) reported comparative data for the semiconductor industry, based on sales data for 1984–1989. While semiconductor is often described as a ‘‘boom or bust’’ business, this time period was relatively stable, and reported moderate levels of muniﬁcence and dynamism. However, scores based on data for individual members of this industry reported considerably more variation. Intel, for instance, reported muniﬁcence scores

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BRIAN K. BOYD AND STEVE GOVE

Environment Dimensions

Objective

Perceptual

Industry

Industry Munificence Firm

Outsiders

Simulation

Discretion Insiders

Slope of Sales Mean of Sales TMT

Middle Managers

Fig. 3.

Levels of Environmental Measurement. Source: Boyd et al. (1993).

of 60 percent higher than that of the industry; in contrast, Siliconix reported a muniﬁcence score, only one quarter that of the industry. Additionally, deviation from the industry norm on a single task environment dimension did not guarantee similar abnormality on another: Chips and Technologies, for instance, reported an average level of dynamism, despite having a muniﬁcence score ﬁve times that of the industry. Overall, Boyd et al. (1993, p. 215) concluded that industry level measures of uncertainty are ‘‘less relevant for characterizing the level and nature of uncertainty felt by individual ﬁrms.’’ Since the comparison by Boyd et al. (1993) was anecdotal, and limited to a single industry, we conducted a more comprehensive analysis of ﬁrm and industry level muniﬁcence and dynamism scores. We obtained ﬁrm-level growth and volatility scores for 400 ﬁrms; these were a subset of the sample used by Finkelstein and Boyd (1998). We then matched these data via 4-digit SIC codes with the industry-level scores that we reported earlier. Both sets of measures were based on the same time period, and correlations are reported in Table 8. Muniﬁcence scores based on sales at the two levels reported correlations of approximately 0.46, and were essentially unaffected by the basis for

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Table 8. Comparison of Industry-Level and Firm-Level Scores. Variable (a) Munificence Variables 1 VoS 2 TE 3 VoS 4 TE 5 Firm Sales

Basis

1

2

3

4

5

Mean Mean Log Log Mean

1.00 0.84 0.95 0.84 0.45

1.00 0.86 0.98 0.21

1.00 0.91 0.46

1.00 0.18

1.00

0.06 0.07

0.00 0.05

1.06 0.08

1.00 0.06

0.08 0.12

1.00 0.36 0.97 0.43 0.16

1.00 0.54 0.99 0.19

1.00 0.57 0.17

1.00 0.29

1.00

0.02 0.02

0.01 0.01

1.02 0.02

1.01 0.01

0.03 0.03

Mean: Std. Devn: (b) Dynamism Variables 1 VoS 2 TE 3 VoS 4 TE 5 Firm Sales Mean: Std. Devn:

Mean Mean Log Log Mean

Note: All correlations signiﬁcant at a ¼ 0.001. All variables based on 1982–1986 data. Industry data from US Industrial Outlook. Firm data based on a subset of the sample from Finkelstein and Boyd (1998).

standardizing the industry-level scores. Firm-level growth scores based on sales correlated less strongly with industry-level measures based on total employment, approximately at 0.20. On average, dynamism scores were more loosely linked: Firm and industry-level scores based on sales correlated at approximately 0.17. Interestingly, the ﬁrm-level score based on sales correlated more strongly with industry measured based on employment. While recognizing the limitations of correlational analysis, these results should be considered as preliminary evidence that task environment and discretion are loosely linked versus independent or totally overlapping constructs. Future research could help clarify the degree of overlap by reporting results with scores based on different levels of analysis. Integrating Task Environment and Discretion Recognizing that task environment and secretion are distinct yet overlapping constructs, what are the implications for management research? One salient issue is a broader form of an omitted variable problem: If a

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researcher is interested in ﬁrm-level discretion, and includes it as a predictor in a model, an unknown degree of explained variance may actually be due to an unmeasured, industry-level counterpart. As a result, industry-level studies may have overestimated the role of muniﬁcence, dynamism, and complexity, while ﬁrm-level studies may have overstated the effect of discretion. One solution may simply be for studies to create both ﬁrm- and industrylevel variables. A superior approach, however, would be to go beyond integrating measures and instead integrate both task environment and discretion frameworks in future research. A model developed by Hrebiniak and Joyce (1985) offers an excellent basis for developing integrative hypotheses. Brieﬂy, the authors proposed that choice and determinism were not competing perspectives, but rather, complementary ones. A modiﬁed version of their model is shown in Fig. 4a. Environmental determinism is driven by structural characteristics of industries – a perfectly competitive market, for instance, is highly deterministic. Alternately, an environment rich in resources – which Hrebiniak and Joyce (1985) described as ‘‘benign’’ – is less deterministic. Independent of these structural characteristics, individual ﬁrms will have varying degrees of strategic choice available to them. For example, a ﬁrm in a commodity market – e.g., Nucor and steelmaking – may develop innovative ways to build a cost advantage. Alternately, other ﬁrms might develop strategies to consolidate industries that were previously heavily fragmented; Blockbuster and video rental, or Service Corp International and funeral homes are two examples. Hrebiniak and Joyce (1985, p. 342) noted ‘‘The essential point is that external constraints and high environmental determinism need not necessarily prevent individual choice and impact on strategic adaptation.’’ Hrebiniak and Joyce (1985) proposed that different strategies would be appropriate for each of the choice – determinism combinations, and that ﬁrm performance would be affected by the match between strategy and context. Lawless and Finch (1989) conducted the ﬁrst empirical test of the choice – determinism model, using the task environment variables muniﬁcence, dynamism, and complexity. Overall, they reported that high deterministic quadrants had higher levels of environmental uncertainty; i.e., high determinism translated into less muniﬁcence, more dynamism, and more complexity. However, Lawless and Finch were unable to ﬁnd support for the Hrebiniak and Joyce’s propositions regarding matching structure to context, or the accompanying performance effects. A probable explanation for their lack of results was the use of task environment variables to characterize both determinism and strategic choice. Hrebiniak and Joyce (1985)

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Fig. 4. Using Hrebiniak and Joyce (1985) to Integrate Task Environment and Discretion (a) Original Model, (b) Diagonal Elements, and (c) Off-Diagonal Elements.

deﬁned determinism as an external constraint, which is consistent with Dess and Beard’s (1984) model of organizational task environments. Strategic choice, however, was framed either at the level of the ﬁrm, or the CEO. In either case, Hambrick and Finkelstein’s (1987) discretion model tailors closely to this dimension of the Hrebiniak and Joyce 2 2 model. A subsequent paper (Bamford, Dean, & McDougall, 2000) also used the Hrebiniak and Joyce framework; however, their 21 strategic choice indicators were measures of decisions made at the time of company founding,

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versus the latitude of choice available to ﬁrms. More recently, Dawley, Hoffman, and Lamont (2002) applied the Hrebiniak and Joyce’s framework: They used muniﬁcence to measure the degree of determinism, and ﬁrm slack to measure strategic choice. They found that, for ﬁrms emerging from bankruptcy, strategic choice was more important than environmental conditions in predicting ﬁrm survival. For many companies, their ﬁrm-level discretion will closely mirror the broader industry conditions: Settings with low industry uncertainty (e.g., high muniﬁcence, low dynamism, and low complexity) will also have high levels of ﬁrm-level discretion, and highly uncertain industry environments will also tend to offer less ﬁrm-level discretion. These diagonal elements are shown in Fig. 4b, and are the sectors of Maximum Choice and Restricted Choice. The off-diagonal elements are shown in Fig. 4c. From a research perspective, these sectors are more interesting: they include Differentiated Choice, where ﬁrms have managed to cultivate discretion despite a highly uncertain environment, and Incremental Choice, where ﬁrms have little discretion despite a benign industry structure. In Fig. 4b settings, task environment and discretion variables are likely to have very similar effects. However, in Fig. 4c conditions, the effects of discretion and task environment might vary dramatically. Thus, one avenue for future research would be to include both industry- and ﬁrm-level indicators of constraint, and to test hypotheses that integrate task environment and discretion perspectives.

CONCLUSION Strategic management research has been characterized as placing less emphasis on construct measurement than is warranted (Boyd et al., 2005; Hitt, Boyd, & Li, 2004; Venkatraman & Grant, 1986). This criticism is applicable to research on both organizational task environments and discretion. Prior studies on both topics have used a wide array of variables, and inconsistent approached to measuring these variables. Greater consistency in measurement will facilitate comparisons of ﬁndings and generalizability of future studies. In addition to greater consistency in measurement, studies should consider using multiple indicators of respective phenomena. Finally, we would encourage authors to develop hypotheses that explicitly integrate both discretion and task environment frameworks.

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NOTES 1. As of August, 2005. 2. It is relevant to note that the concept of discretion did not originate with Hambrick and Finkelstein (1987). Pfeffer and Salancik (1978, pp. 244–247), for example, discussed determinism, constraint, and superior-subordinate relations as determinants of individual discretion. Similarly, Mintzberg (1983) discussed discretion in the context of organizational power. 3. Although studies did not typically report tests of dimensionality, virtually all papers followed the convention of treating muniﬁcence, dynamism, and complexity as separate predictors. Exceptions to this practice included Camison (2004), who aggregated 24 survey items into a single environmental uncertainty measure. Similarly, Wally and Baum (1994) reduced the three task environment dimensions to a composite score of environmental uncertainty. 4. We included Datta and Rajagopalan (1998) in this list. Discretion is not developed as a prominent theory in the paper, but the measures and approach are consistent with Hambrick and Finkelstein (1987), and another discretion paper (Hambrick & Abrahamson, 1995) is used to justify measures. 5. Harris concludes that the Dess and Beard’s framework does not meet the requirements of construct validity, based on (a) covariance among dimensions, (b) lack of predictive validity, and (c) methods bias. As we noted previously, Dess and Beard reported that their model used orthogonal versus oblique factors because they viewed their analysis to be exploratory in nature. Subsequent applications (e.g., Keats & Hitt, 1988; Boyd, 1990) have allowed muniﬁcence, dynamism, and complexity to covary. Regarding predictive validity, Harris cites Sharfman and Dean (1991), but none of the 70 other empirical applications of Dess and Beard that are listed in Tables 1 and 2. Finally, the assessment of methods bias is made based on a comparison of two models (Models 4 and 5, on p. 868). While Harris (2004, p. 869) noted that ‘‘the ﬁnal model (5) suggests good ﬁt with the data,’’ the w2 difference between models is not signiﬁcant, and the NFI, CFI, and GFI for the two models are identical. Thus, while acknowledging the importance of covariation across dimensions, the conclusions of Harris (2004) appear to overstep available data.

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ASSESSING THE EXTERNAL ENVIRONMENT: AN ENRICHMENT OF THE ARCHIVAL TRADITION C. Chet Miller, dt ogilvie and William H. Glick ABSTRACT Organization theorists and strategy researchers have effectively leveraged archival assessments of the environment to better understand organizational actions and performance. Despite the successes, several issues continue to plague research. Vague constitutive definitions and mismatches between constitutive and operational definitions are among the most pressing of these issues. To further develop the archival tradition, we clarified existing definitions and proposed new definitions where warranted. Our work has implications not only for the selection of concepts and measures in future work but also for interpretations of past research.

The external environment has been a concern of organization theorists for many years. Although not emphasized in any distinct way until after World War II, its status has risen steadily over the past several decades with the advent of environmental contingency theory, institutional theory, and ecological theory. In the relatively young ﬁeld of strategy, the importance of the environment has been recognized from the very beginning. By its very nature, the strategy ﬁeld emphasizes an open-system conceptualization of Research Methodology in Strategy and Management, Volume 3, 97–122 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1479-8387/doi:10.1016/S1479-8387(06)03006-2

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organizations, which assigns a central role to the environment. From the resource-based view of the ﬁrm to upper-echelons research, the external circumstances of an organization have been key considerations. The external environment is a set of elements (organizations, individuals, and so on) that lie beyond the organization’s boundary. It has been formally deﬁned as those elements that are important to the tasks of the organization – those elements ‘‘potentially relevant to goal setting and goal attainment’’ (Dill, 1958). Although frequently considered to be a single construct with several correlated dimensions, the environment is best conceptualized as a multi-construct phenomenon with several connected but clearly distinct constructs. Variation, including dimensions such as instability and unpredictability, is perhaps the most popular of these constructs (for example studies, see Hough & White, 2004; Huber, Miller, & Glick, 1990; Li & Atuahene-Gima, 2001; March, 1991). Complexity, including dimensions such as number and heterogeneity of external elements, is another (for examples, see George, 2005; Keats & Hitt; 1988; Luo & Peng, 1999). Munificence is a third commonly used environmental construct (see Baum & Wally, 2003; Castrogiovanni, 1991; Park & Mezias, 2005). Two distinct strategies have been used to assess the environment. The ﬁrst utilizes the perceptions of organizational decision makers or outside panels of experts. Informants are asked to respond to survey or interview questions in order to characterize an organization’s external circumstances. Although perceptions can be biased, the perceptual approach has been used successfully in hundreds of studies. From Child’s inﬂuential study of strategic choice (Child, 1972), to Miller’s studies of strategy and structure (e.g., Miller, 1988), to Priem’s studies of strategy process (e.g., Priem, Rasheed, & Kotulic, 1995), many important insights have been generated with this approach. The second strategy utilizes archival records. Here, archival data from sources such as Standard and Poor’s Compustat database and the U.S. Census of Manufactures are used to characterize the environments of organizations. One drawback of this approach is data availability. An organization theorist or strategy researcher with a nuanced view of an environmental property may be unable to ﬁnd archival data suitable for capturing the appropriate phenomena. Despite this and other concerns, many studies have successfully relied upon the archival assessment strategy. Glick, Miller, and Huber (1993), for example, assessed turbulence through Compustat data. They found that comprehensiveness of strategic decisionmaking positively affected ﬁrm performance in turbulent industries. This ﬁnding complemented existing work based on different methods (Bourgeois

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& Eisenhardt, 1988) and helped to motivate subsequent work that supported and elaborated the earlier ﬁndings (e.g., Priem et al., 1995; Zahra, Neubaum, & El-Hagrassey, 2002). As another example, Beckman, Haunschild, and Phillips (2004) assessed market volatility, ﬁnding that ﬁrms tend to exploit and reinforce their existing alliance networks under volatile market conditions. Their work complemented and extended existing research on alliances (e.g., Podolny, 1994). Although both the perceptual and archival traditions have contributed to the development of important organizational and strategic insights, each has shortcomings that must be addressed as research continues to evolve. For the perceptual approach, the propensity for inaccurate assessments must be considered. Bourgeois (1985) studied perceptual errors and found that they negatively impacted ﬁrm performance. Boyd, Dess, and Rasheed (1993), Sutcliffe (1994), and others have built on this research by examining sources of perceptual error. For the archival approach, limitations in constitutive definitions and mismatches between constitutive and operational definitions are pressing issues. Although some work has addressed these deﬁnitional concerns (e.g., Wholey & Brittain, 1989; Rasheed & Prescott, 1992), a great deal remains to be done. It is this deﬁnitional task within the archival tradition to which we now turn. While clariﬁcation of deﬁnitional issues has direct implications for the perceptional tradition, our focus is the archival tradition where these issues are most acute.

BACKGROUND AND PURPOSE The environment can be characterized in terms of its variation, complexity, and munificence (Aldrich, 1979; Dess & Beard, 1984). For each of these three constructs, multiple dimensions exist, and this is where the difﬁculties begin. First, organizational and strategy researchers are not always clear about the speciﬁc dimension being used to develop theoretical arguments. The fundamental problem is that constitutive definitions (i.e., conceptual definitions used in theory building) often are not speciﬁed in precise terms. In some cases, definitions are not explicitly provided (e.g., Anderson, 2001; Goll & Rasheed, 1997; Pelham, 1999). In other cases, constitutive deﬁnitions are provided but are overly generic. For example, variation is often labeled instability, dynamism, or volatility and deﬁned in terms of amount or rate of change (e.g., Harris, 2004; Li & Simerly, 1998). Change, however, has speciﬁc, fundamental aspects related to magnitude, frequency,

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and unpredictability, and this should be taken into consideration. In order for theory development to build from one study to the next, and in order to avoid confusion in the organization theory and strategy communities, more precision is called for. Second, and perhaps more problematic, dimensions of environmental constructs are often unintentionally mishandled within a given study. The primary problem here is that constitutive definitions do not always match operational definitions. Keats and Hitt (1988), for example, deﬁned instability in terms of ‘‘difﬁcult to predict discontinuities’’ (p. 579). Their operational definition, however, corresponded to average magnitude of change, a variable that may or may not be strongly correlated with unpredictability. Similarly, Deepak, Guthrie, and Wright (2005) deﬁned dynamism in terms of unpredictable change but adopted an operational definition focused on average magnitude of change. Although the two studies cited here seem to have produced meaningful results and interpretations, mismatches between constitutive and operational definitions can cause inaccurate or unwarranted conclusions to be drawn from a study. Cumulative science is impossible without greater agreement on core definitions. Previous critiques of archival environmental assessments have been focused on additional issues, such as the lack of consensus concerning the most important dimensions of the environment, inconsistencies in labels applied to various dimensions, the weak connection between perceptual and archival measures, and possible weaknesses in convergent and discriminant validity among common measures, (e.g., Harris, 2004; Rasheed & Prescott, 1992; Sharfman & Dean, 1991). Summing all of the relevant issues, the archival tradition to environmental assessment is in need of attention. One purpose of our work is to address the above problems by clarifying and organizing constitutive definitions of environmental dimensions. A second purpose is to clarify existing operational definitions and to propose new ones where warranted. Implications of our work relate to interpretations of past research as well as future theory building and empirical assessment.

VARIATION Organizations are open systems that engage in transactions with their task environments (Thompson, 1967; Yuchtman & Seashore, 1967). As noted earlier, important constructs associated with the environment include variation, complexity, and munificence. Variation and complexity, the two most popular constructs, are discussed in this chapter, starting with variation.

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Instability In simple terms, instability is the extent to which an environment exhibits change. Although this common definition has merit, clariﬁcation is necessary in order to highlight fundamental aspects of change. As is well known, instability can be decomposed into magnitude and frequency of change (see, for example, Child, 1972; Wholey & Brittain, 1989). Magnitude is the size of changes while frequency is the number of changes over some time period. Less established in the research literature is the fact that magnitude and frequency have different representations based on outliers versus the full texture of variation. Depending upon the focus and purpose of a particular research study, outlying versus full representations might be more useful. To date, however, the outlier approach has received almost no empirical attention in the archival tradition of environmental assessment. Similarly, frequency as an overall concept has received almost no empirical attention. Amplitude is the representation of magnitude based on outliers. It represents the difference between the most extreme states of the environment within discrete units of time. Differences for each unit of time are averaged to characterize the entire time period of interest (e.g., differences within years can be averaged to characterize a 5-year period). Environments characterized by high amplitude exhibit large changes. Environments that exhibit low amplitude exhibit small changes. Operationally, amplitude corresponds to the largest observed difference per unit of time in the plot of a variable such as industry sales, with differences being averaged across units of time to characterize the overall time period (see, for example, Lea´o, 2005; Wholey & Brittain, 1989). If, for example, changes in industry-level sales are used to indicate industry instability for a 5-year period, then amplitude might be assessed annually as the difference between maximum and minimum quarterly sales and the annual differences then would be averaged across the 5 years. Average magnitude of instability is the overall representation of magnitude of change. It represents the average size of changes in the environment for the time period of interest. This dimension has been by far the most popular in empirical examinations of instability based on the archival tradition. In operational terms, average magnitude corresponds roughly to the mean difference in environmental states. It is typically assessed using the coefﬁcient of variation (e.g., Tosi, Aldag, & Storey, 1973) or the standard error of regression divided by the mean level of the dependent variable for a time series (e.g., Dess & Beard, 1984).

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Frequency of changes in fortune is the representation of frequency based on outliers. It is the number of valence reversing changes that occur across the relevant time period. Environments characterized by high frequency exhibit many changes that constitute a move from a positive to a negative trajectory or a move from a negative to a positive trajectory. Environments characterized by low frequency exhibit few such changes. Operationally, frequency of changes in fortune corresponds to a count of shifts in direction (e.g., Wholey & Brittain, 1989). If changes in industry-level sales are being used to indicate industry instability, frequency would be the count of positive to negative and negative to positive shifts in the slope of the sales plot over time (controlling for the general trend in sales). Total frequency is the overall representation of frequency. It is the number of changes in an environment, similar to Monge’s (1990) theoretical discussion of rate of change and periodicity. Operationally, total frequency corresponds to a count of changes. Again using sales as an example, this dimension of instability is the count of all shifts in the slope of the sales plot over time. Such shifts would not necessarily entail changes in slope valence, but could instead entail simple points of inﬂection. These clariﬁcations of instability distinguish four dimensions: two based on outlying data points and two based on the full texture of variation. As a graphical method of clariﬁcation, the four dimensions are portrayed in Fig. 1. In this ﬁgure, the dotted line exhibits many changes. Because the changes involve many slope reversals that are quite small, both dimensions of frequency are high while both dimensions of magnitude are low. The solid bold line exhibits few but large changes. Thus, both dimensions of magnitude are high while both frequency dimensions are low. Finally, the solid non-emboldened line exhibits a more complex pattern. Here, there are both small and large changes, resulting in amplitude being high but average magnitude being moderate. Because there are a moderate number of changes in fortune (8) but many shifts in the slope (45), total frequency is high while frequency of fortune changes is moderate. Which of the four dimensions of instability is most important? This in fact is the wrong question to ask. Importance of a particular dimension depends upon speciﬁc research questions, theoretical issues, and the overall goals of a study. If, for example, managerial perceptions, behavior, and reactions to the environment are the focus, then amplitude and frequency of changes in fortune should be strongly considered. Both amplitude and frequency of fortune changes depend on extreme data points. Amplitude is focused on maximum and minimum values of resource ﬂows in an industry (sales, profits, number of employees, and so on) while frequency is focused on

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Resource Flows

Different Patterns of Environmental Instability

Overall Time Period

------------- Low amplitude, Low average magnitude, High frequency of changes in fortune, and High total frequency

_____ ________

High amplitude, High average magnitude, Low frequencyof changes in fortune, and Low total frequency High amplitude, Moderate average magnitude, Low Frequency of changes in fortune, and High total frequency

Fig. 1.

Different Patterns of Environmental Instability.

reversals of fortune in those resource ﬂows. Research on behavioral decision theory (see, for example, Bazerman, 2006), and particularly the availability heuristic (Tversky & Kahneman, 1973, 1974), suggest that these extreme data points are likely to have disproportionate effects on perceptions and behavior, and by implication disproportionate effects on decisions about strategy and organizational design. Upper-echelon managers are more likely to notice and allocate attention to extreme values because they are vivid (Barnes, 1984). Amplitude and frequency of changes in fortune have been utilized in only a very limited way in the archival tradition, and this perhaps has been to our collective detriment. For example, studies of congruence between archival assessments of instability and perceptions of instability (e.g., Sharfman & Dean, 1991) probably would result in stronger relationships if the more vivid dimensions of amplitude and frequency of fortune changes were examined. Studies of congruence, however, have typically focused on average magnitude for the archival dimension and a mixed array of dimensions for perceived instability. Average magnitude of instability and total frequency also have important roles to play. By assessing the full texture of variation, these variables

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probably capture important trends and cycles more accurately and consistently than do simple amplitude and frequency of changes in fortune. If so, they would be more useful in contingency studies involving performance. Unfortunately, to our knowledge, total frequency has never been used in an empirical study in the archival assessment tradition. Unpredictability Closely related to instability is unpredictability. Unpredictability refers to a lack of regularity in the pattern of change in an environment. In more unpredictable environments, changes are less foreseeable (Hannan & Freeman, 1977), and the probability of the environment being in a particular future state is less knowable. When a pattern is absent, change is difﬁcult to predict (Bourgeois, 1980; Cameron, Kim, & Whetten, 1987; Duncan, 1972; Emery & Trist, 1965; Singh, 1986; Snyder & Glueck, 1982). Similar to the initial definition of instability, this definition of unpredictability requires clariﬁcation. Below, two speciﬁc dimensions are discussed. Magnitude of unpredictability is the extent to which irregular, nonsystematic change is present in an environment. In an environment with little change, this aspect of unpredictability is low, by definition. In an environment with a great deal of change, this aspect can be low or high depending upon the presence or absence of pattern in the ﬂow of change over time. With regard to operational definitions, magnitude of unpredictability is the average size of ﬂuctuations after controlling for systematic change involving growth, decline, and cyclicality. Despite its importance, this dimension has been neglected in empirical work. Proportional unpredictability is the proportion of change that is irregular or nonsystematic. If an environment exhibits modest change but much of that change does not follow a pattern, then proportional unpredictability would be high. If an environment exhibits a great deal of change, this aspect of unpredictability could be high if most of the change does not follow a pattern. In operational terms, proportional unpredictability roughly corresponds to unsystematic change divided by total change. In the few cases where it has been used in empirical research, proportional unpredictability has been assessed using the inverse of the R2 from times-series regression (McCabe & Dutton, 1993; Miller, Burke, & Glick, 1998; Wholey & Brittain, 1989). If changes in sales are the foundation for the measure of unpredictability, then sales in one year are used to predict sales in the next (higher order terms can be used to detect non-linearity). A small R2 indicates unpredictability.

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Resource Flows

Magnitude of unpredictability Versus Proportional Unpredictability

Time period ----------- Low magnitude of unpredictability, High proportional unpredictability

____

High magnitude of unpredictability, High proportional unpredictability

___ ____

Low magnitude of unpredictability, Low proportional unpredictability

Fig. 2.

Magnitude of Unpredictability Versus Proportional Unpredictability.

As a ﬁnal effort to clarify unpredictability, its two dimensions are portrayed graphically in Fig. 2. The thin solid and dotted lines reﬂect low magnitude of unpredictability because the average size of the unsystematic change is low in both cases. The thin solid line reﬂects low proportional unpredictability because a large proportion of the total change revolves around a predictable upward trend line. The dotted line, however, reﬂects high-proportional unpredictability because a higher proportion of the total change revolves around a trend line that is unpredictable. The bold solid line reﬂects high magnitude of unpredictability because the average size of the unsystematic change is quite large. It also reﬂects high-proportional unpredictability because a large proportion of the total change revolves around an unpredictable trend line. Many of the constitutive definitions used by previous researchers (e.g., Child, 1972) appear to emphasize proportional unpredictability and this aspect of unpredictability has been emphasized in the few relevant empirical analyses, but magnitude of unpredictability is probably more salient to managers and probably more relevant to organization theorists and strategy researchers. Managers are more likely to be concerned with overall unforeseeable change rather than trivial nonsystematic change that occurs in the context of general stability (i.e., changes that cause proportional unpredictability to be high in the absence of large unforeseeable change). For trivial

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changes occurring in the context of general stability, managers can maintain a modest amount of ﬂexibility to handle the minor disturbances. Large unsystematic change, however, is difﬁcult, if not impossible, to plan for and can disrupt proposed managerial actions. It follows that important decisions regarding organizational design and strategy would be more tied to magnitude of unpredictability than to proportional unpredictability. As noted earlier, one of the chief issues in the archival tradition relates to a speciﬁc mismatch between constitutive and operational definitions of variation – variation is often deﬁned constitutively as amount and unpredictability of change but then operationalized only as average magnitude of instability (e.g., Deepak et al., 2005; Goll & Rasheed, 2004; Simerly & Li, 2000). The relationship between average magnitude of instability and unpredictability is not, however, certain. For example, average magnitude of instability and magnitude of unpredictability are not the same and may not be strongly correlated in all contexts. In some contexts, there may be large changes that are mostly predictable. This seems to have been the case for the forest products industry studied by Fredrickson and Mitchell (1984) in their work on comprehensiveness (see Bourgeois & Eisenhardt, 1988). Similarly, average magnitude of instability and proportional unpredictability are not the same and may not be strongly correlated. Wholey and Brittain (1989), for example, found a correlation of 0.05 for a sample of manufacturing industries and 0.07 for a sample of cities. In our own analysis of this issue (Glick, ogilvie, & Miller, 1990), 583 industries were assessed using Compustat data. Exploratory factor analysis suggested that average magnitude of instability and proportional unpredictability are two distinct factors (variation was assessed in terms of changes in sales, net income, capital expenditures, return on assets, and total assets). The correlation between the two dimensions was only 0.44.

Aggregate Versus Intra-Industry Variation An additional issue that has received very little attention in the research literature involves the speciﬁc method used to compute scores for the various dimensions of variation. In the dominant method (e.g., Dess & Beard, 1984; Tushman & Anderson, 1986; Wholey & Brittain, 1989), scores are based on ﬁrm-level data aggregated to the industry level without regard for variation among ﬁrms within an industry. Fluctuations within an industry are not considered. This method ignores intra-industry competition and dynamics, which is particularly problematic if the industry is in a state of

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zero-sum competition. In such an industry, aggregate data would suggest very little industry variation across time while each ﬁrm is having many ups and downs in the face of competitor tactics and strategies. When aggregate industry variables such as sales and asset levels are constant, organizations may still face unstable, unpredictable environments if the distribution of sales and assets is shifting rapidly and unpredictably from one competitor to another within an industry. Rueﬂi, Adaniya, Gallegos, and Limb (1993), in their longitudinal investigation of rank shift behavior at the inter and intra-industry levels, found evidence that patterns of behavior are different at the two levels. For instance, they found stability across industries, but instability from year to year within industries. Although their sample was only two industries, their ﬁnding clearly suggests the dangers of using only aggregate data. The patterns observed at the aggregate level are not necessarily duplicated at the intra-industry level (Rueﬂi et al., 1993). In our analysis of 583 industries (Glick et al., 1990), the dominant method based on aggregate data and an alternative method that included intraindustry variation were compared. For the dominant method, ﬁrms’ sales, capital expenditures, total assets, and so on were averaged within an industry for each year, and then average magnitude of instability and proportional unpredictability were assessed based on year to year ﬂuctuations in the averages. For the alternative method, each ﬁrm’s sales, capital expenditures, and so on within an industry were used to assess instability and unpredictability for that ﬁrm in that industry. Then, these ﬁrm-level scores were averaged at the industry level. The latter method captures both ﬁrmlevel and industry-level variation. For instability, the shared variance between the two approaches was moderately high (66%), but for unpredictability, the shared variance was lower (46%). Moving forward, significant attention must be paid to this issue. Research based on the common aggregate industry approach misses a great deal of variation.

Summary for Variation Two components of environmental variation, instability and unpredictability, have been clariﬁed (see Table 1). Instability can be decomposed into four separate dimensions – amplitude, average magnitude of instability, frequency of changes in fortune, and total frequency. For those interested in examining the full texture of instability, average magnitude of instability and total frequency seem most appropriate. For those interested in predicting managerial

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Table 1. Constitutive and Operational Definitions of Variation. Dimension

Constitutive Definition

Operational Definition

Amplitude

Difference between the most extreme states of the environment within a given unit of time, with differences being averaged across units of time to characterize the overall time period

Average magnitude of of instability

Average size of changes in the environment for the time period

Frequency of changes in fortune

The number of valence reversing changes that occur during the time period Number of changes in the environment during the relevant time period Average size of irregular, nonsystematic changes in the environment during the relevant time period

Largest observed difference for a given unit of time in the plot of a variable such as industry sales, with differences being averaged across units of time to characterize the overall time period Standard error of regression divided by the mean level of the dependent variable for a time series regression Count of the number of directional shifts in the slope of a plot over time Count of the number of shifts in the slope of a plot over time Average size of ﬂuctuations after controlling for systematic change involving growth, decline, and cyclicality Unsystematic change divided by total change

Total Frequency

Magnitude of unpredictability

Proportional unpredictability

Proportion of change that is irregular/nonsystematic

perceptions and reactions to instability, amplitude and frequency of changes in fortune are likely to be more appropriate. Unpredictability can be decomposed into two dimensions – magnitude of unpredictability and proportional unpredictability. Magnitude of unpredictability is likely to be more useful for both managers and researchers.

COMPLEXITY In very simple terms, complexity is the degree to which an environment is difﬁcult to understand and effectively manage at a given moment in time. Four primary dimensions of complexity have been emphasized by organization theorists and strategy researchers: numerosity or the number of relevant elements in the environment (see, for example, Gifford, Bobbitt,

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& Slocum, 1979; Huber, 1984; Shortell, 1977; Williamson, 1975); dispersion across the elements (see Dess & Beard, 1984; George, 2005; Pelham, 1999); heterogeneity or diversity of the elements (see Bourgeois, 1980; Child, 1972; Keats & Hitt, 1988; Tung, 1979); and interconnectedness among the elements in the ﬁrm’s environment (see Galbraith, 1977; Huber, 1984; Keats & Hitt, 1988; Lawless & Finch, 1989; Tung, 1979). Rather than conceiving of these dimensions as competing representations of complexity, we believe it is more helpful to view them as a developmental progression of complexity. The dimensions move from simple to more elaborate aspects of the complexity construct. Although simpler dimensions such as dispersion have been emphasized in operational definitions, the more elaborate dimensions, particularly when combined as we discuss below, better represent the complexity construct originally developed by Emery and Trist (1965) and Galbraith (1972). Unlike the dimensions of variation, which characterize the common environment experienced by a number of ﬁrms (e.g., within an industry), the complexity dimensions characterize the environment experienced by a particular ﬁrm. Complexity is experienced differently by different ﬁrms based on the number of elements in the environment with which they have relations and the nature and characteristics of the resource ﬂows with those elements. Numerosity A basic definition of complexity is focused on the number of elements in the environment. Shortell (1977) argued that environmental complexity should be based solely on numerosity because inclusion of other dimensions causes a lack of clarity and imprecision, resulting in confusion. This position carries an implicit assumption that each element in the environment is discrete and that transactions with each element are of equal size. Numerosity seems deﬁcient as an indicator of complexity because it ignores variations in the size of the resource ﬂows with each element, which are captured by measures of dispersion. Dispersion Dispersion, a very common dimension of complexity in empirical work, indicates whether a ﬁrm’s resource ﬂows (inputs and outputs) involve a large number of external elements and whether the exchanges with these elements vary in size. In comparison with numerosity, dispersion is sensitive to both

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the number of elements and the size of transactions with each. This is similar to Pfeffer and Salancik’s (1978) resource concentration – ‘‘the relative number of alternatives available, as well as the size or importance of these alternatives’’ (p. 50). For example, two ﬁrms with 100 external elements have equal numerosity but face very different dispersion if one ﬁrm has 80% of its transactions with four of the elements while the other has 1% of its transactions with each of the 100 elements. In the ﬁrst case, the environment is concentrated and is generally considered to be less complex because the ﬁrm must monitor and concern itself with only four elements. In the second case, the environment is dispersed and is generally considered to be more complex because issues (1) can come from anywhere and (2) can have an almost random feel as knowledge related to the activities and concerns of any one element may be somewhat superﬁcial. Consider a third case in which a ﬁrm has transactions that are of equal size with only 10 external elements. Although numerosity is lower in this environment, the ﬁrm faces more dispersion than the ﬁrst ﬁrm discussed above but not the second. In sum, dispersion is a reﬁnement of numerosity that reﬂects both number of elements and size of transactions with those elements. Increasing the number of elements and decreasing the variation in the size of transactions together contribute to increasing dispersion. Operationally, dispersion has been .P with the Gibbs–Martin forP assessed 2 mula (Gibbs & Martin, 1962): 1 x2 x (for an example study, see Dess & Beard, 1984). It can also be measured in terms of entropy by considering a ﬁrm, i, selling to and buying from different organizations, j. Let Pij be the proportion of ﬁrm i’s total sales to organization j and let Pji be the proportion Pn of ﬁrm i’s purchases Pn from organization j. The entropy for the ﬁrm is P lnð1=P Þ þ ij ij j¼1 j¼1 Pji lnð1=Pji Þ: Note that this equation captures both resource outﬂows and inﬂows to reﬂect the dispersion of the overall environment. It captures dispersion more accurately than the Gibbs– Martin formula because it is more sensitive to variation among smaller environmental elements (Jacquemin & Berry, 1979). As suggested above, dispersion’s conceptual connection to complexity has been based largely on information processing arguments with the common position being that increasing dispersion is associated with increasing complexity (e.g., Harris, 2004; Keats & Hitt, 1988). Several counterarguments can be made, however. One argument suggests a negative relationship – complexity decreases with increases in dispersion. Highly dispersed environments suggest low power and impact for any one external element (Schiller, 1989), and suggest that each element can be ignored to

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some degree. Low dispersion suggests a strong need to develop very close relationships and manage very closely the power dynamics associated with each important external element (Pfeffer, 1978; Pfeffer & Salancik, 1978). Thus, low dispersion may present the more complex case. A second and potentially more helpful counter argument suggests that dispersion’s relationship with complexity depends on heterogeneity among the external elements. This argument suggests there is no straightforward relationship between dispersion and complexity, as elaborated below. This is a very important issue, given dispersion’s popularity in empirical research.

Heterogeneity Heterogeneity has been identiﬁed as a key aspect of complexity. In terms of constitutive definitions, this aspect of complexity is most common (see, for example, Castrogiovanni, 2002; Child, 1972; Dess & Beard, 1984; Tung, 1979). Heterogeneity relates to differentiation among elements in the environment – the extent to which these elements are qualitatively different from each other. Qualitative differences among suppliers, customers, and competitors lead to heightened learning requirements and development of multiple interaction which lead to increased complexity. In formal P routines,P terms, H i ¼ nj¼1 H ij =n þ nj¼1 H ji =n; where Hi is the heterogeneity faced by ﬁrm i, Hij the dissimilarity of ﬁrm i to ﬁrm j (a customer), and Hji the dissimilarity of ﬁrm j (a supplier) to ﬁrm i. Entropy measures reﬂect differences in sizes of transactions with various elements, but not the qualitative differences among types. In contrast with measures of dispersion, heterogeneity measures should be sensitive to the average degree of qualitative difference. Thus, the operational definition of heterogeneity must be based on the dissimilarity of each element relative to other elements. Dissimilarity of the environmental elements might be operationally deﬁned in terms of differences along a variety of dimensions or more simply in terms of different taxonomic categories. Although useful in some ways, neither dispersion nor heterogeneity adequately captures the meaning of complexity, despite one being commonly used in operational definitions and the other being commonly used in constitutive definitions. Dispersion emphasizes the number of elements and variation in the size of transactions with those elements. An implicit assumption associated with the dispersion dimension is that all elements are similar in nature. If, in fact, all of the environmental elements are similar, it is not clear that having smaller or larger transactions with more or fewer

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elements contributes much to complexity. The assumption of similarity does not allow for distinguishing qualitative differences, but such differences are no doubt crucial in creating more difﬁcult managerial situations. The heterogeneity dimension is sensitive to important differences in environmental elements, but the implicit assumption underlying heterogeneity is that transactions with each element are of equal size. This may or may not be true in a given case. Dispersion and Heterogeneity Elaborated as Proximal Complexity Dispersion and heterogeneity are conceptually distinct and both are integral aspects of the complexity construct. Thus, the interaction of dispersion and heterogeneity is important, an interaction we label proximal complexity. Formally, this interaction can be calculated as follows: PC i ¼

n X j¼1

ðPij lnð1=Pij ÞðH ij Þ þ

n X

ðPji lnð1=Pji ÞðH ji Þ

j¼1

where PCi is the proximal complexity of ﬁrm i (the terms corresponding to entropy and dissimilarity in the above equation should be normalized to ensure equal contributions to the variance of the composite value of proximal complexity). This operational definition explicitly incorporates all three elements of complexity from the preceding discussion: numerosity (through the entropy measure of dispersion), dispersion, and heterogeneity. By specifying a multiplicative interaction, this definition implies that the relationship between dispersion and complexity depends on heterogeneity. Although suggesting, in essence, that dispersion be weighted by heterogeneity is in many ways a simple idea, it is important given the nature of dispersion and heterogeneity and the lack of attention to the issue in past practice. In sum, if high levels of heterogeneity are coupled with broad dispersion, then proximal complexity is high. If low levels of heterogeneity are coupled with broad dispersion, then proximal complexity is not high. The interaction of dispersion and heterogeneity is labeled proximal complexity because it captures local rather than total complexity. Proximal complexity focuses on resource ﬂows from the focal organization to the environment and from the environment to the organization. Thus, resource ﬂows among external elements in the environment are assumed to be unimportant. This assumption can be relaxed to consider resource ﬂows among all environmental elements because they are interconnected. Network

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research highlights the importance of overall interconnectedness (see, for example, Brass, Galaskiewicz, Greve, & Tsai, 2004). Total Complexity Long ago, Emery and Trist (1965) argued that: A comprehensive understanding of organizational behavior requires some knowledge of each member of the following set, where L indicates some potentially lawful connection, and the sufﬁx 1 refers to the organization and the sufﬁx 2 to the environment:

L11 ; L12 L21 ; L22 L11 here refers to processes within the organization – the area of internal interdependencies; L12 and L21 to exchanges between the organization and its environment – the area of transactional interdependencies, from either direction; and L22 to processes through which parts of the environment become related to each other – i.e., its causal texture–the area of interdependencies that belong within the environment itself (p. 22).

Galbraith (1972) deﬁned interconnectedness (which he also referred to as interrelatedness) as the ‘‘amount of connectedness or interdependence among the elements’’ (p. 55, emphasis added). Galbraith (1977) later noted that ‘‘the focal organization recognizes that those elements of the task environment on whom it is dependent also have dependence problems of their own’’ (p. 209). Thus, there are two types of environmental interdependence. Most researchers (e.g., Stearns, Hoffman, & Heide, 1987; Baker, 1990) have restricted their examination to transactional interdependence between the organization and its direct exchange partners (L12, L21). Extra-organizational interactions occurring among exchange partners and their partners are, however, very important aspects of interdependence (L22). Total complexity incorporates both types of environmental interdependence in order to augment more rudimentary aspects of complexity. Operationally, total complexity is the sum of (1) the focal ﬁrm’s proximal complexity, (2) a weighted average of the proximal complexities of the ﬁrm’s direct partners, and (3) a weighted average of the proximal complexities of indirect exchange partners (where the weights for direct partners correspond to relative transaction sizes with the focal ﬁrm and the weights for indirect partners depend upon transaction sizes with direct partners). Two ﬁnal points are relevant to the discussion. First, under our scheme a ﬁrm’s competitors do not have a direct effect on complexity because the

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complexity definitions are transactionally based. Competitors, however, are included because they are represented through the proximal complexity of a ﬁrm’s customers and suppliers. Second, the choice of proximal complexity or total complexity should be made on the basis of research questions and purposes. In cases where managerial perceptions and reactions to the environment are the focus, proximal complexity should be considered for study because direct relationships are likely to be very salient. In cases where, for example, ﬁrm performance is the focus, total complexity might be the better alternative. Summary for Complexity Complexity can be decomposed into progressively more encompassing dimensions (see Table 2). Numerosity is the least encompassing, but can be elaborated as dispersion. Dispersion does not have a direct relationship with complexity, but is made useful through weighting by heterogeneity. Numerosity, dispersion, and heterogeneity collectively form proximal complexity for a given ﬁrm. Taking into account a focal ﬁrm’s proximal Table 2. Dimension

Constitutive and Operational Definitions of Complexity. Constitutive Definition

Numerosity

Number of elements in the environment

Dispersion

The extent to which resource ﬂows are even across external elements Qualitative differences among external elements

Heterogeneity

Proximal complexity

Total complexity

The extent to which resource ﬂows are even across elements and the diversity of those elements The extent to which resource ﬂows are even across elements and the diversity of those elements, for the focal ﬁrm and for its direct and indirect exchange partners

Operational Definition Count of number of elements with which transactions occur Entropy for resource ﬂows across elements with which transactions occur Proﬁle dissimilarity averaged across external elements with which transactions occur Dispersion weighted by heterogeneity

Proximal complexity of the focal ﬁrm plus weighted average of the proximal complexities of direct and indirect exchange partners, where weight factor involves transaction sizes

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complexity and the proximal complexities of its direct and indirect exchange partners yields total complexity for that ﬁrm. The choice of proximal versus total complexity should be made on the basis of speciﬁc research questions and purposes.

DISCUSSION AND CONCLUSION Our efforts in this chapter represent an attempt to contribute to the archival tradition of environmental assessment. As part of our work, existing constitutive definitions have been discussed, clariﬁed, and organized. Existing operational definitions also have been discussed, and several new ones proposed. Importantly, our clariﬁcations and extensions have implications for the interpretation of past research and for the design of future research. Implications Related to Variation Our clariﬁcations of variation suggest that researchers should select environmental dimensions contingent on whether they are interested in (1) variation that strongly inﬂuences managerial perceptions and reactions or (2) the full texture of variation that no doubt plays a role in ﬁrm performance. Unlike the full texture of variation, extreme variation, such as that associated with amplitude, is likely to inﬂuence managers’ perceptions and reactions in a strong way. If researchers investigating, for example, the connection between perceived environmental uncertainty and archivally assessed variation (e.g., Tosi et al., 1973) had focused on either amplitude or frequency of fortune changes, then stronger relationships might have been observed. Amplitude and frequency of changes in fortune, and also magnitude of unpredictability, are likely to be the aspects of variation that cause what Milliken (1987, p. 137) refers to as state uncertainty among managers. See Table 3 for a summary of applications. Perhaps our most important contribution for variation is the clariﬁcation of operational definitions for average magnitude of instability and two types of unpredictability. As it stands, researchers frequently develop theory related to unpredictable change and then measure average magnitude of instability. The result is invalid tests of theory and erroneous interpretations of empirical ﬁndings. Going forward, researchers, reviewers, and editors must be alert to this problem. Our discussion of variation suggests several research questions. First, how do effective executives cope with industries that exhibit many changes in

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Table 3.

Environmental Dimensions Matched to Research Goals.

Focus of Research

Managerial perceptions, behavior, and reactions to the environment

Suggested Environmental Dimension(s) Instability: amplitude and frequency of changes in fortune Unpredictability: magnitude of unpredictability

Complexity: proximal complexity Firm performance

Instability: average magnitude of instability and total frequency

Unpredictability: magnitude of unpredictability Complexity: total complexity

Rationale

Emphasis is on outliers, which are likely to inﬂuence managerial perceptions, behavior, and reactions Emphasis is on large unpredictable changes, which are likely to inﬂuence managerial perceptions, behavior, and reactions Emphasis is on direct rather than remote transaction partners Emphasis is on the full texture of variation in the environment (which presents forces to which ﬁrms must adapt for success) Emphasis is on large-scale nonsystematic variation that a ﬁrm must handle effectively for success Emphasis is on resource ﬂows that are local and distant, where the two types of ﬂows can interact to exert important forces on the ﬁrm, and where distant ﬂows can cause unforeseen complications, which must be handled successfully in order to secure positive outcomes

fortune? Strategic planning and organizational buffering are no doubt important. Less obvious mechanisms such as establishing illicit inter-ﬁrm cooperation, hiring executives with lower needs for achievement, and keeping stock closely held may also be critical. Second, do ﬁrms react similarly to high average magnitude of instability and many changes in fortune? It may be that very different strategies are used to cope with these different aspects of variation. Third, does magnitude of unpredictability inﬂuence perceived

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environmental uncertainty to a greater extent than amplitude or frequency of fortune changes? Related to this, does magnitude of unpredictability inﬂuence such variables as inter-ﬁrm coordination to a greater extent than do changes in fortune?

Implications Related to Complexity Our clariﬁcations of complexity also have a number of implications. Perhaps the most important one relates to the common use of dispersion to characterize the complexity faced by a ﬁrm. Dispersion has a complicated relationship with complexity, one that must be viewed in light of prevailing heterogeneity. Use of dispersion alone is a problematic strategy. To effectively capture complexity, researchers should go beyond common but simple definitions and measures. Our work may help to promote richer measures by highlighting proximal and total complexity, the latter focused on broad networks. By focusing on these networks, researchers can include remote disturbances in their work. The effects of remote elements can be dramatic. First, changes occurring in remote elements may be large in magnitude and may strongly affect the focal ﬁrm through multiple indirect channels. Second, as system dynamics research (see Forrester, 1971) has shown, small disturbances occurring in interconnected systems can create deviation amplifying chains of events that strongly inﬂuence elements systemically distant from the original disturbance. That is, changes in remote elements may become magniﬁed as they inﬂuence successive elements. Managers may not be able to control these remote events, but they may be able to design organizations to accommodate them. Our concept of total complexity, reﬂecting dispersion, heterogeneity, and interconnectedness, suggests a number of research questions. First, how do ﬁrms cope with complexity generated by remote, indirectly relevant environmental elements? Are these coping mechanisms the same as those used to cope with complexity generated by more immediate exchange partners? Second, how do effective ﬁrms embedded in complex environments protect themselves from inter-ﬁrm conﬂict? Complex environments are characterized by differentiated, interconnected elements and thus are prone to such conﬂict (see Alter, 1990). Third, do ﬁrms embedded in complex environments require different sets of managerial characteristics or skills than ﬁrms that operate in less complex environments? What strategies do ﬁrms embedded in interconnected, differentiated environments use to manage the many uncontrollable factors they encounter?

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CONCLUSION Effectively conceptualizing and assessing the task environment is critical for organization theorists and strategy researchers. This, however, has proven to be a difﬁcult task. In some instances, constitutive definitions have been vague, which harms theory building. In many instances, constitutive definitions and theory building have not matched empirical measures, which harms the value of empirical research. As we move forward, attention to the pixels of environmental assessment is crucial. Despite past problems, a strong platform of research has been built over the years, and this platform can serve as the basis for future contributions.

ACKNOWLED