Call Centre Work: Smile by Wire: Special Issue of the European Journal of Work and Organisational Psychology, Vol. 12 Issue 4 (European Journal of Work and Organizational Psychology)

EUROPEAN JOURNAL OF WORK AND ORGANIZATIONAL PSYCHOLOGY

Editor Fred Zijlstra, Department of Psychology, School of Human Sciences, University of Surrey, Guildford, Surrey GU2 5XH, UK. Email: [email protected] Associate Editors Christian Dormann, Institut für Psychologie, Johann Wolfgang Goethe-Universität, Germany José Maria Peiró, Faculdad de Psicología, Universidad de Valencia, Spain Michael West, Aston Business School, Aston University, Birmingham, UK Book Reviews Editor Robert A.Roe, Universiteit Maastricht, Department of Organization Studies, P.O. Box 616, 6200 MD Maastricht, The Netherlands Editorial Board

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European Journal of Work and Organizational Psychology, 2003, 12 (4), 305–310

Call centres: High on technology—high on emotions Christian Dormann Johann Wolfgang Goethe-University Frankfurt, Germany Fred R.H.Zijlstra University of Surrey, Guildford, UK In our current “service economy” delivery of services is a major task for industry. Many organizations are now involved in delivering services of a kind, and like to think of themselves as being “client-oriented”, or “client-centred”. As part of their company policy these organizations regard contact with their clients as a key feature of their company philosophy. Technological developments, such as information technology, have boosted this option. The internet offers appealing options in this respect since all kinds of information concerning the companies’ products or services can be provided via a website on the internet. These products and services can often also be bought via the internet. Nevertheless, customers may want to contact the organization and talk to a customer representative, either to order the product or service, or to ask additional and specific questions. Therefore organizations are looking for ways to be accessible to their costumers, to be able to promptly answer questions customers may have, or to provide reliable and up-to-date information. In particular when the company sells products that are complex and entail all kind of technical features, customers may have all sorts of questions. Therefore, organizations see the necessity to set up a “helpdesk” where customers can get answers to their queries without having to come to the shop again. And as a result of enlargement of scale, many organizations have their customers spread around the country, in Europe often across multiple countries, and therefore have a large area to cover. This evidently implies a huge number of contacts, and organizations are looking for efficient ways to organize these contacts. The process of rationalization often results, according to Tayloristic tradition, in the decision to specialize the delivery of services. This has resulted in the creation of specialized departments for customer contacts: the call centre.

Correspondence should be addressed to C.Dormann, Johann Wolfgang GoetheUniversity Frankfurt, Dept. of Psychology, Mertonstr. 17, D-60054, Frankfurt a.M., Germany. Email: [email protected]

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Call centres are organizations or departments that are specifically dedicated to contacting clients and customers. This can either be the helpdesk, or client service department of an organization, but companies may also have outsourced this to a dedicated call centre, which handles all client contacts for a variety of organizations. Different types of call centres can be distinguished: (1) Inbound call centres mainly respond to incoming calls and primarily deal with questions and complaints that costumers may have. Clients’ questions are frequently straightforward and simple requiring standard answers, but sometimes clients have complex requests for help requiring non-standard answers. (2) Outbound call centres are mainly for contacts that are initiated by the organization, they are primarily for attempts to sell a product or service. This also implies that call centres can harbour jobs of different levels of qualification, ranging from unskilled people who are providing standard information (sometimes even reading from prescribed scripts) to frequently asked questions, to highly qualified personnel who deal with unique complex problems (i.e., technology helpdesks). This means that the popular claim that call centres are the nowadays Tayloristic “sweat shops” is an unjustified generalization and oversimplification of the issue. Call centres indeed are the result of a modern rationalization process, but that does not mean that all people working in call centres have little variety and no control over their work (although it certainly is true for particular groups). Moreover, call centre employees have to have a deep understanding of their “matter”. As will become clear from this special issue, a particular requirement for customer service representatives in call centres is that they need to have an emotional understanding of clients’ needs requiring a considerable level of empathy. Call centres are a relatively new type of organization; they are a typical product of the service economy, and closely related to technological developments. There are numerous estimates of the growth rate of the number of call centres. Although these estimates vary considerably, they are unanimously high. It is clear, though, that the introduction of call centres represents a global development and they have the interest of researchers from all around the world; this special issue demonstrates this with contributions from Europe, Australia, and Asia. Two major reasons are frequently given for the rapid increase in number of call centres. First of all, technological developments had a great impact. It has been noted before that technology has led to a disentanglement of “time” and “place” (cf. Roe, van den Berg, Zijlstra, Schalk, Taillieu, & van der Wielen, 1994). With the help of technology, activities are no longer confined to a particular place or a particular time. This applies both to individuals (tele- or home working) and organizations. Call centres are clear illustrations of this phenomenon. Companies can concentrate their customer information desk in a particular country, and automatically route calls from a number of countries to this centre, without the customer having to know that he or she is actually calling long distance or internationally. This offers companies an opportunity to move some of their labour intensive operations to low wage countries. The contribution by Shah and Bandi in this issue illustrates that high skilled service employees (as in the IT sector) can be hired anywhere in the world.

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Secondly, call centres are believed to be a cost effective way of achieving increased service orientation and customer satisfaction. Major facets of service quality focus on reliability (e.g., fixing a problem as soon as it occurs), responsiveness (e.g., to be accessible), assurance (e.g., creating the belief that the service involves high quality equipment), tangibles (e.g., appearance of communication materials), and empathy (e.g., the individualized attention that an organization provides to its customers; Parasuraman, Berry, & Zeithaml, 1991; cf. Dormann & Kaiser, 2002). In many ways, call centres are obviously likely to confirm such requirements. Thus, not only anticipated cost reduction but also increases in profits via customer satisfaction have triggered the development of call centres. Call centres, to some extent, are also a reflection of the current Zeitgeist phenomenon; they are thought of being: clean, fast, precise, nice-to-have, and “always there” for us. However, behind the scenes, researchers and practitioners have detected fault lines in the smooth surface. Undoubtedly there have been economic advantages associated with the introduction of call centres; however, downsizing and closing of call centres have become a new reality. Similarly, the promised benefits for customers have frequently not shown up as they were expected. Many of us are more likely to remember discomfort, frustration, and anger rather than joy and pleasure when thinking about past experiences with call centres. Actually, although cost reduction and customer care are both cited as common reasons for the introduction of call centres, they are partially incompatible with each other. At least in parts, some of the current problems associated with call centres are founded on the ongoing neglect of organizations as socio-technical systems. In case of call centres, the development of the technical systems has typically dominated the development of the social system, with sometimes detrimental effects on call centre employees. The present issue of the European Journal of Work and Organizational Psychology presents evidence that working in call centres does put specific demands upon employees, which makes working in a call centre different from other jobs. The technological and contextual circumstances have a great impact on call centre jobs. However, the traditional sociotechnical systems approach is not sufficient to describe all the problems. In fact, call centres, like most service organizations, should be better viewed as socio-sociotechnical systems. Focus on social systems is too frequently limited to selection and training of employees and their attitudes, values, and behaviours; the social customer system, including their wishes, desires, aims, and behaviours, and its impact on the employee system are frequently overlooked. Similarly, a focus on technical systems, such as monitoring the input-output flow or making technology flexible with the product mix, often ignores the customer system, too. For instance, although consideration of the potential negative impact of performance monitoring practices on employees represents a sound socio-technical perspective, it should also be taken into account that monitoring practices may impact on the customer system, too. Customers are usually informed that their calls are monitored, and they may feel observed and controlled, which may lead to a wide range of negative consequences such as dissatisfaction or verbal aggression. Thus an integrated strategy of organizational development should simultaneously consider employees (e.g., human

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resources departments), technology (e.g., IT departments), and customers (e.g., marketing departments). Obviously, this is not a trivial task because services are dynamic and cannot be readily planned, created, stored, and sold on demand. One way to fit the social employee system to the social customer systems is emotional labour or emotion work. Employees are paid by organizations to display (usually positive) emotions towards customers irrespective of their current feelings. Call centre employees should make customers feel as if they are really interested in the customers’ problems, and be friendly as if they are happy to talk to them. Empirical evidence for the beneficial effects of emotion work on customers does not yet exist, but its detrimental effects on call centre agents’ health is clearly demonstrated by Lewig and Dollard (this issue) and Grebner, Semmer, Faso, Gut, Kälin, and Elfering (this issue). Since employee ill-health is negatively related to customer satisfaction (Leiter, Harvie, & Frizell, 1998) one can doubt whether emotion work is worth its effort in terms of revenues due to customer satisfaction or the like. Various studies have shown that turnover rates are particularly high in call centres (e.g., Michel, 2001), illustrating that working in call centres is not always as nice as people would think. Moreover, it undermines the strategic goal of cost reduction because expenditures for training new employees increase. It is not the workload, pressure, and stress that make call centre jobs problematic. Rather, results (Bakker, Demerouti, & Schaufeli, this issue; Grebner et al., this issue) point in the direction that, in order to overcome problems with turnover, call centre jobs should be enriched with complexity, control, and variety. At a first glance, this seems to be incompatible with the establishment of low and semiskilled jobs, but most contributors to this Special Issue offer some useful practical guidance. Most articles compiled in this Special Issue were concerned with the working conditions at call centres. Zapf, Isic, Bechtoldt, and Blau report results of a comparison of a variety of variables between call centres and different kinds of jobs (service jobs, nonservice jobs). One important question is whether these differences lead to consequences such as impaired health or decreased performance. Bakker et al. argue that different kinds of working conditions have different kinds of effects; whilst job demands affect absenteeism via health problems, job resources affect turnover via involvement. They found good support for their model, which is useful because most authors acknowledge the particular relevance of absenteeism and turnover in call centres. A more differentiated picture with regard to health-related outcomes is provided by Grebner et al. They show how a great variety of resources and stressors including aspects of emotion work, which Zapf et al. identified as particularly high in call centre jobs, are related to health outcomes in call centres. Lewig and Dollard found similar results in Australian call centres, showing that the effects are similar across countries and cultures. Their findings stress that the imbalance between rewards on the one hand and emotional demands on the other causes health problems in call centre agents. Finally, Shah and Bandi present a case study, in which the demand for personnel development in high-knowledge customer-contactcentres is vividly described. Their study explicitly shows that there is no technological determinism since the work of the agents in their study is relatively enriched. Although

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this may be an extreme case, it reflects the conclusions of many authors that call centres are not a bad place to work per se—it depends. Since, from an employer’s perspective, call centres represent an efficient means to improve organizations’ economic performance, the relative number of job in call centres (or customer-contact centres) will continue to rise for years. However, almost all authors acknowledge that much has to be done until such jobs become attractive and motivating, with no or little effects on ill-health, with development opportunities, and with good performance from the viewpoint of the customer. REFERENCES Bakker, A.B., Demerouti, E., & Schaufeli, W.B. (2003). Dual processes at work in a call centre: An application of the job demands-resources model. European Journal of Work and Organizational Psychology, 12(4), 393–417. Dormann, C, & Kaiser, D. (2002). Job conditions and customer satisfaction. European Journal of Work and Organizational Psychology, 11, 257– 283. Grebner, S., Semmer, N.K., Faso, L.L., Gut, S., Kälin, W., & Elfering, A. (2003). Working conditions, well-being, and job-related attitudes among call centre agents. European Journal of Work and Organizational Psychology, 12(4), 341–365. Leiter, M.P., Harvie, P., & Frizell, C. (1998). The correspondence of patient satisfaction and nurse burnout. Social Science and Medicine, 47, 1611–1617. Lewig, K.A., & Dollard, M.F. (2003). Emotional dissonance, emotional exhaustion, and job satisfaction in call centre workers. European Journal of Work and Organizational Psychology, 12(4), 366–392. Michel, L.P. (2001). Call centres in Germany: Employment market and qualification requirements. Economic and Industrial Democracy, 22, 143–153. Parasuraman, A., Berry, L.L., & Zeithaml, V.A. (1991). Refinement and reassessment of the SERVQUAL scale. Journal of Retailing, 67, 420–450. Roe, R.A., van den Berg, P.T., Zijlstra, F.R.H., Schalk, M.J.D., Taillieu, T.C.B., & van der Wielen, J.M.M. (1994). New concepts for a new age: Information service organizations and mental information work. The European Work and Organizational Psychologist, 3, 177– 192. Shah, V., & Bandi, R.K. (2003). Capability development in knowledge intensive IT enabled services. European Journal of Work and Organizational Psychology, 12(4), 418–427. Zapf, D., Isic, A., Bechtoldt, M., & Blau, P. (2003). What is typical for call centre jobs? Job characteristics and service interactions in different call centres. European Journal of Work and Organizational Psychology, 12(4), 311–340.

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What is typical for call centre jobs? Job characteristics, and service interactions in different call centres Dieter Zapf, Amela Isic, Myriam Bechtoldt, and Patricia Blau J.W.Goethe University Frankfurt, Germany

Call centres have been one of the few booming branches in recent years. The main task of call centre operators is to interact with customers by telephone, usually supported by computer systems. It has been argued that call centre work is a modern form of “Taylorism”, because it is characterized by routine tasks, and low level of control for the employees. Moreover, it has been suggested that there is a high level of stress at work, both with regard to the work tasks and to the interactions with customers. In the present study a sample of 375 call centre employees from eight different call centres was compared with a sample of noncall centre workers (N = 405) in terms of job characteristics, job stressors, and emotional labour (emotion work). The results showed that call centre workers had worse job characteristics, but were better off with regard to most job stressors compared to representative comparison groups of no-service workers, service workers, and workers in human services respectively. Moreover, compared to the other groups,

Correspondence should be addressed to D.Zapf, Department of Psychology, Johann Wolfgang Goethe-University Frankfurt, Mertonstr. 17, D-60054 Frankfurt, Germany. Email: [email protected] earlier version of this article was presented as a poster at the 25th International Congress of Applied Psychology, 7–12 July 2002, Singapore. The present study was supported by the German Federal Ministry of Work and Social Affairs. Overall coordination of participating projects: Verwaltungsberufsgenossenschaft VBG. Project organization of the present study: Rationalisierungskuratorium der Deutschen Wirtschaft RKW, Eschborn. The support of the project organizers is gratefully acknowledged.

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customer service representatives (CSRs) had to express less negative emotions, but were most frequently exposed to states of emotional dissonance. A comparison of the working conditions of the eight call centres revealed that in most call centres the working conditions could be substantially improved. In addition, various call centre parameters such as inbound vs. outbound, or inhouse vs. external service centres were examined. The strongest effects were found for the percentage of time spent on the telephone. With some exceptions, the results support the view that the majority of call centres have been established to organize mass service for customers, that the work in the call centres is characterized by routine work and low task control, and that call centre employees are required to suggest a “friendly smile” when they are on the phone. No prospering without customers—a fact of prominent importance for service-oriented economies worldwide. Competing for customers has inspired companies to invent new ways of service. One of these ways is a call centre. As the name suggests, call centres are offices assigned to telephonic contact with customers. An official definition says: Call centres are “tools for organising communication with customers…with the help of telecommunication” (Henn, Kruse, & Strawe, 1996, p. 14). Call centres may either be part of the company (“inhouse” call centres) or be external services (“service bureaux”) usually working on behalf of several companies. The ways call centres get in contact with customers may differ. Whereas inbound call centres are restricted to a passive role (i.e., being called up exclusively by customers having any questions or complaints concerning a product), outbound call centres actively engage in phoning people up, e.g., telemarketing call centres. However, there are also call centres with both inbound and outbound activities. Basically, with the help of call centres companies aim to demonstrate their customer orientation, and try to ensure their clients’ satisfaction and commitment. From the companies’ point of view, the advantages are manifold: lower costs in the area of field work because even sophisticated services may be rendered by phone; more satisfied customers because, ideally, the call centre can be contacted 7 days a week, 24 hours a day (Henn et al., 1996; Holman, 2003). To do his job, the customer service representative (CSR) of a call centre usually sits at a table in front of a computer, wearing a headset for communicating with the customer, leaving his/her hands free in order to input data into the computer if necessary. Depending on the business, a CSR talks to between 60 and 250 clients per 8 hour shift (Dieckhoff, Freigang-Bauer, Schröter, & Viereck, 2002; Henn et al., 1996). The more customers are talked to, the less time is available for each of them and the more routine (and boring) these conversations may become for the CSR. The high rate of turnover and absenteeism in many call centres suggests that working in call centres is a stressful experience (e.g., Baumgart et al., 2002; Deery, Iverson, & Walsh, 2002; Holman, 2002, 2003). Although some studies seem to suggest that working in call centres can be interesting (Batt, 2002), there are still too few studies to give a definite answer here. Therefore, it is interesting to analyse the profile of CSRs’ jobs from a work psychology point of view.

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Recent studies suggest that most jobs in call centres can be characterized as unskilled work, which some authors (e.g., Knights & McCabe, 1998) called an advanced form of Taylorism (see also Dieckhoff et al., 2002). Relatively short-cycle routine interactions with customers mostly controlled by automatic call distribution systems and supported by networked information technologies allow little control of when and whom to speak to (e.g., Holman, 2003). Moreover, CSRs are expected to be always friendly (as if they are “smiling”) on the telephone (Holman, 2003; Schuler, 2000)—a fact described in literature as imposing emotional demands on the CSRs. Thus the main research question of this article is: Can call centres be characterized as low skilled routine jobs with little control, high job stressors, and high emotional demands to be customer-friendly due to organizational rules? Several studies (e.g., Richter & Schulze, 2001; Wieland, Metz, & Richter, 2001) showed that CSRs have low levels of job control. Isic, Dormann, and Zapf (1999) compared the working conditions of 250 call centre employees to those in banks and administrative offices. While call centres did not stand out in terms of job stressors such as uncertainty, organizational problems, and time pressure, they were distinguished by very low task control and timing control. According to that, CSRs suffered significantly more from psychosomatic complaints than employees in banks and administrative offices. Gerlmaier, Böcker, and Kastner (2001), and Richter and Schulze (2001) reported similar results. Metz, Rothe, and Degener (2001) analysed 37 CSR jobs with the help of experts. The experts criticized the poor decision latitude as well as the low complexity and high division of the work: CSRs continuously had to repeat the same activities thereby scarcely having the opportunity to make use of their professional know-how. Most call centre employees working in the front office do not complete a professional training for their telephone work (Baumgart et al., 2002; Isic et al., 1999). Therefore, to prevent inexperienced CSRs from making mistakes, complexity of work is often massively restricted. For example, with the help of standardized computer programs, employees in call centres of banks book orders for bonds even without any comprehensive knowledge of the matter (cf. Holman, 2003; Holman, Chissic, & Totterdell, 2002). More complicated enquiries are diverted to the few specialists who work in the back office (Henn et al., 1996). Following Bowen and Schneider (1988) and Batt (2002), Holman (2003) differentiated between two call centre models: the “mass service” and the “high commitment service” model. The mass service model aims at a high market volume and low added value. Cost minimization is the primary goal here. The jobs are characterized by routine work (low complexity) and low control. Employees are often required to follow a scripted dialogue when interacting with customers and follow highly detailed instructions (Deery et al., 2002). Frenkel, Tam, Korczynski, and Shire (1998) argued that despite the rhetoric of service quality, management appears to place a greater emphasis on the quantity of calls, thus showing preference for the mass service model (see also Dieckhoff et al., 2002). This contrasts the high commitment service. In this case, the market volume is low but the added value is high. Jobs are empowered, i.e., tasks are complex and there is high control for the service providers. The interactions with customers correspond to the relationship

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model (see below). Moreover, there are human resource incentives such as ongoing investment in training, employment security and relatively high pay (Batt, 2002). In this study we compare employees from several call centres with a representative sample of employees not working in call centres. This sample consisted of three groups: employees doing manufacturing or administrative work (the “no-service group”), people working in the service sector (e.g., sales or banks), and people working in the human services (e.g., nurses). In order to analyse typical profiles of call centre work, we first analysed whether call centre work is specific in the way that it differs significantly from the three groups of the representative sample (Hypothesis 1). Based on the existing studies cited above we expected most call centres to follow the mass service model, i.e., jobs will be characterized by lower complexity and control (task and timing control, participation) in comparison to the comparison samples (Hypothesis la). Moreover, in these types of call centres employees are supposed to handle as many call as possible. This is assumed to lead to high time pressure and requiring considerable attention and concentration. Therefore it was hypothesized that call centre jobs are characterized by higher task-related and organizational job stressors than the comparison samples (Hypothesis 1b). So far, we have discussed organizational and task aspects of call centre jobs focusing on cognitive aspects of internal information processing. Another aspect is the CSRs’ social interaction with the customer. As in any social interaction, the regulation of emotions plays a central role here. Hochschild (1983) coined the term “emotional labour” for this kind of job requirement occurring in service interactions. Emotional labour or emotion work (Zapf, 2002) refers to the quality of interactions between employees and clients. “Client” refers to any person who interacts with an employee, for example, patients, children, customers, passengers, or guests. Expressing appropriate emotions during faceto-face or voice-to-voice interactions is a job demand for many employees in the service industry, particularly in call centre jobs. Hochschild drew upon the work of Goffman (1959) to argue that while interacting, people nearly always tend to play roles and try to create certain impressions. Impressions include the display of normatively appropriate emotions following certain display rules. This general social phenomenon also applies to interactions between CSRs and their customers or clients. Certainly, CSRs cannot be assumed to be always in a good mood. Rather, they frequently encounter situations where anger is likely to be the dominant emotion (Deery et al., 2002; Grandey, Dickter, & Sin, 2002). Emotion work as part of the job, however, implies the display of organizationally desired emotions even in such unpleasant situations. Accordingly, emotion work can be defined as the psychological processes necessary to regulate organizationally desired emotions as part of one’s job (Grandey, 2000; Zapf, 2002). In the service sector, customer orientation is a label for such norms, rules, and standards of behaviour in service interactions that require to regulate emotions (Zeithaml & Bitner, 2000). In the case of a call centre where employees interact with customers by telephone, there may be rules such as: talking to customers should not exceed 5 minutes; customers should be addressed by their names; or customers should be talked to in a friendly tone throughout the interaction which means to display certain (usually positive) emotions towards clients.

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Recent studies have differentiated various dimensions of emotion work. Most of them comprise the frequency of emotion expression and emotional dissonance (e.g., Brotheridge & Grandey, 2002; Büssing & Glaser, 1999; Grandey, 2000; Morris & Feldman, 1996; Schaubroeck & Jones, 2000). This also applies to the concept used in the present study. Zapf, Vogt, Seifert, Mertini, and Isic (1999) differentiated the following aspects of emotion work: (1) the requirement to display positive emotions (abbreviated as “positive emotions”), (2) the requirement to display and handle negative emotions, which also implies a high variety of emotions (“negative emotions”), (3) the requirement to sense the interaction partner’s emotions (“sensitivity requirements”), and (4) the dissonance between felt and displayed emotions (“emotional dissonance”). In line with most empirical studies (e.g., Adelmann, 1995; Brotheridge & Lee, 2003; Morris & Feldman, 1997) the frequency of emotional display is considered to be an important aspect of emotion work. Factor analyses (Zapf et al., 1999) demonstrated the necessity of distinguishing between showing positive and showing negative emotions. Having to display positive and negative emotions usually implies demonstrating a high variety of emotions because positive emotions have to be shown in most of the jobs. Therefore, the requirement to display negative emotions comes close to the concept of variety of emotion display suggested by Morris and Feldman (1996). The expression of organizationally desired emotions is not an end in itself. Emotions are shown to have an influence on clients (Kruml & Geddes, 2000). Expressing emotions is one possible way to influence the clients’ emotions. To be able to do so, their accurate perception is an important prerequisite. Therefore, sensitivity requirements as the necessity to be sensitive and to consider the clients’ emotions is another aspect of the emotion work concept (Zapf et al., 1999). Finally, as most of the other studies of emotion work, we included the concept of emotional dissonance (e.g., Brotheridge & Lee, 2003; Büssing & Glaser, 1999; Kruml & Geddes, 2000; Morris & Feldman, 1996, 1997; Nerdinger & Röper, 1999; Zapf et al., 1999). Emotional dissonance occurs when an employee is required to express emotions that are not genuinely felt in the particular situation. A person may feel neutral while required to display a particular emotion, or alternatively the display rule may require the suppression of undesired emotions and the expression of neutrality or a positive emotion instead of a negative one. Studies on emotional dissonance consistently found correlations with emotional exhaustion (e.g., Morris & Feldman, 1997; Nerdinger & Röper, 1999; Schaubroeck & Jones, 2000). Based on Gutek (1997), Holman (2003) classified customer-employee interactions in call centres as “relationships” and “encounters”. Encounters involve single short interactions between strangers and are standardized with little room for authentic emotional expression. Contrary to that, persons in relationships know each other and share a common history. Trust and loyalty are important elements of these relationships. Applying the concept of emotion work, it can be assumed that the expression of positive emotions (the friendly “smile”) is the predominant requirement in the encounter. Since encounters involve short interactions between strangers having no shared history in common, requirements to express negative emotions are unlikely. Of course, both interaction partners may have negative feelings; for example, the CSR might feel impatient,

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the customer dissatisfied or angry at the quality of service. However, the CSR is always expected to react in a positive manner and to “appear happy, nice and glad to serve the customer” (Erickson & Wharton, 1997, p. 188). This may lead to a high level of emotional dissonance, which is the discrepancy between expressed and felt emotions. Gutek (1997) argued that relationships serve as a model for encounters because of their positive features and that organizations try to emulate some of the qualities of relationships by developing “pseudo-relationships”. By showing interest and concern, CSRs may create the impression of a trusting relationship to customize the service. This may be supported by customer-relationship systems that track customers’ interaction with the organization (Holman, 2003) and provide information about the customers’ service histories and their needs. Thus there may be relatively high requirements to be sensitive to the needs and emotions of customers compared to what one would normally expect from impersonal short-cycle encounters. With regard to our study we expected that most call centres follow the encounter model implying a high requirement to express positive emotions (Hypothesis 1c), but a low requirement to express negative emotions (Hypothesis 1d). Expectations for sensitivity requirements were unclear; however, emotional dissonance was expected to be high (Hypothesis 1e). The reason for this is that in the encounter model the interaction is only superficial and comprises just a few cues to elicit the expected positive emotion. The room for authenticity is assumed to be limited in these interactions, i.e., adequate negative emotion may hardly be expressed. Being strictly required to show positive emotions in situations where one would normally show negative ones leads to emotional dissonance. Moreover, to further analyse what is typical for call centres, we were interested in comparing the various call centres under study. Although it is likely that most of the call centres follow the mass service model, there may be some call centres following the customer relationship service model or some hybrid model. Therefore, we expected diversity among the call centres with regard to complexity and control. That is, although the majority of call centres may be characterized by unskilled work, there may be a few call centres with complex tasks and high control (Hypothesis 2a). Moreover, we expected no or only minor differences among the call centres with regard to the emotion work variables because the various call centres claimed to have similar degrees of customer orientation and numbers of CSR-customer interactions (Hypothesis 2b). We were also interested in explaining the differences among the call centres. Therefore we looked at various organizational variables that were hypothesized to cause such differences. We compared inhouse call centres vs. service bureaux, providing simple information vs. simple counselling vs. complex counselling, percentage of frontline work, inbound vs. outbound vs. both, leadership responsibilities, mean call time, and number of customers per hour as covariates. If these covariates reduced the variance between the call centres then the differences could be mainly attributed to these variables. Finally, we were interested to compare call centre jobs with regard to the organizational call centre variables. First, we expected differences between inhouse call centres and service bureaux. We assumed inhouse CSRs to be better off while they could have the opportunity to combine telephone work with other, more challenging, work. In service bureaux such work might often be not available. Apart from that, the CSRs may

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have worked in other departments of the organization and may, therefore, be familiar with a variety of these tasks. Service bureaux usually work for different clients, which would probably not be effective if the tasks they take over are very complex, because that would require high training efforts. Therefore, complexity and control should be higher in inhouse call centres compared to service bureaux (Hypothesis 3a). Moreover, the studies of Bongard and al’Absi (2003) suggest that emotional display rules are less rigid the more familiar the situation is. Therefore, we expected that service bureaux were more rigid with regard to their display rules, whereas CSRs dealing with customers of their own company may be allowed to be somewhat more authentic. This usually implies that they are allowed to express negative emotions in certain situations, e.g., if the customer shows negative behaviours. Agents in service bureaux, in contrast, are required to express positive emotions even in such situations. Therefore, we expected that the requirement to express negative emotions was higher in inhouse call centres compared to service bureaux (Hypothesis 3b). As a consequence emotional dissonance was expected to be higher in service bureaux (Hypothesis 3c). The nature of the tasks in the call centre is believed to affect the working conditions of the CSRs. CSRs whose main task is to pass on simple information and to book orders as well as to do simple counselling were expected to have jobs of low complexity and control (Hypothesis 4a). However, they were also expected to have less job stressors, because it is a typical finding that high task complexity goes along with more job stressors (e.g., Dormann, Zapf, & Isic, 2002; Zapf, 1993, Zapf et al., in press) (Hypothesis 4b). Also as for inhouse call centres we expected less strict display rules and more possibilities to be authentic for CSRs with complex counselling tasks. Displaying of negative emotions was, therefore, expected to be higher for CSRs with complex counselling tasks (Hypothesis 4c) and emotional dissonance was expected to be lower (Hypothesis 4d). We also compared participants with leadership responsibilities (team leaders) vs. CSRs who had no leadership role. Team leaders were expected to be confronted with more complex tasks and to have more control (Hypothesis 5a). In addition, we also expected that they would be more exposed to job stressors, since they were supposed to be in charge for all the unusual tasks that lack clear procedures how to be handled and that are unpredictable with respect to outcomes and time required (Hypothesis 5b). Because team leaders were hypothesized to have more other tasks than interacting with customers, they were supposed to be less involved in emotion work (Hypothesis 5c). Next, we asked for the percentage of telephone work (frontline work in comparison to backstage work). In the study by Metz et al. (2001), working conditions in the back office were rated more favourably because people working there are dealing with specialist, administrative, and managerial tasks that are much more challenging and less restricted. These employees do not interact with customers, so they are not directly controlled by them. Consequently they had more timing and task control and the tasks were in average more complex. Also people in the back office are believed to have more control because the affairs they are dealing with can hardly be managed within a few minutes—the time employees in the front office have available. Similar results were reported by Baumgart et al. (2002). Therefore, we hypothesized that less telephone work should imply higher

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complexity and control (Hypothesis 6a). With less interactions with customers, there should be less emotion work, too (Hypothesis 6b). Finally, we also compared inbound and outbound activities. In the inbound service customers are making the call, which means that the CSRs are in control neither of time nor action (timing control and task control, see Method section)—they have no choice but to accept the call. We, therefore, expected that inbound activities would be characterized by the lowest job control (Hypothesis 7). METHOD Sample A sample of employees working in different call centres was compared with a random sample of employees of two German cities. A variety of call centres were contacted, e.g., both inhouse call centres and external service bureaux, call centres with inbound and outbound activities, etc. The majority of the call centre sample was located in Hessen (a federal state of Germany). In total, nine call centres agreed to take part in this study. From the 506 questionnaires that were distributed, 375 questionnaires were returned. The response rate was 71%. From one call centre only very few questionnaires were returned; this call centre was left out of the comparison of call centres. The data was included in other analyses. Eight call centres, representing different types of call centres, could therefore be compared. Participation was voluntary and anonymity was guaranteed. Two thirds of the sample (66.8%) was women. Mean age was 31.9 years, ranging from 18 to 59 years. There were 44.1% who had some kind of high school degree, 37.3% who had attended modern secondary school (middle stream school-leaving certificate), and 16.6% having a lower stream school-leaving certificate. Only 8% were in possession of some kind of university degree. There were 59.6% persons reporting to have completed a professional training, while 25.4% had not. However, more than half (50.9%) of the persons with professional training had no specific training concerning working in call centres. Instead, they had changed their vocation and started working as CSRs without special experience. On average, persons had more than 5 years but no longer than 10 years work experience, but they had not been employed for longer than 6–12 months by their current call centre at the time of study. The jobs of the call centre employees consisted of providing information on the phone (33.2%) or simple counselling (46%); 19.5% were specialists responsible for complex counselling. Eighty-five per cent of the persons worked as CSRs without leadership responsibilities, 10.5% were head of a team. Sixty-six per cent of the sample worked in service bureaux, the rest in inhouse call centres. More than half (54.8%) of the persons did inbound calls only, 12.3% outbound calls only, and 32.6% were engaged in both. Call centre 1 was a telemarketing call centre with outbound activities only. In Call centres 4 and 6, only inbound activities were carried out, whereas all the other call centres combined both inbound and outbound activities. Call centres 2, 5, and 6 were inhouse call centres; the others were external service bureaux. On average the participants

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reported a single call to last between 3 and 5 minutes (range: 1 to more than 15 minutes). Per hour, they talked to 6–10 clients, ranging from 1 to more than 20. The comparison sample was a random sample of persons from two large German cities who worked for at least 30 hours a week, who were not self-employed, and whose German was reasonably good so that they were able to fill in the questionnaire. Participants were randomly chosen from a citizen database. They received a letter inviting them to participate. After some days the potential participants were contacted by telephone. Many people were excluded because they did not fulfil the criteria mentioned above. We contacted 767 persons who met the criteria for participation, and 405 persons returned the questionnaire (anonymously), which corresponds to a response rate of 52.8%. This estimation is the lower bound of the response rate, because among the 767 persons contacted some refused to take part in the study and finished the telephone call before the researchers received all information to decide whether the person would have fulfilled all criteria to participate. The mean age of the control sample was 40.9 years, ranging from 19 to 73 years; 37.8% were women. There were 55.9% who had some kind of high school degree, 23% who had attended modern secondary school, and 19.8% had a lower stream schoolleaving certificate. Few (1.8%) had no certificate at all, and 35.6% had some kind of university degree. Seventy-nine per cent of the persons reported to have completed a professional training relevant for their current job. On average (median category), persons had worked for 15–20 years, of which 2–5 years were in their current job. For some of the analyses the comparison sample was divided into subsamples: service employees not working in the service sector (N = 217, e.g., manufacturing, repair, administrative work), employees working in the service sector (N = 131, e.g., sales, banks, insurance, transportation, hotels and restaurants), and human service workers (N = 52, e.g., nurses, physicians, teachers, social workers). Instruments The items used in the present study were part of a more wide-ranging questionnaire. Time to fill in the whole questionnaire took from 45 to 90 minutes. The Instrument of Stress Oriented Job-Analysis (ISTA 6.0; Semmer, Zapf, & Dunckel, 1995, 1998, 1999) was used to examine differences concerning complexity, control, and job stressors. All ISTA scales, except the one measuring “participation”, consisted of five items and used various response formats ranging from 1 to 5, with 1 = “very seldom/ never” to 5=“very often”. Some items required a response on a 5-point scale that ranged from 1 = “very few” to 5 = “very much”. For some items we used the “A vs. B”-format (e.g., “‘A’ has documents and information that are always correct and up to date—‘B’ has documents and information that are often incomplete and out of date. What is your job like?”). Items using the “A vs. B” format required a response on a 5-point scale from 1 = “exactly like ‘A’” to 5 = “exactly like ‘B’”. The aspects considered were as follows. Complexity. This assessed the complexity of decisions and planning processes required to fulfil the task, how often difficult tasks have to be accomplished, and if the job offers the

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chance for enlarging one’s professional know-how. An example item is: “Colleague A has to plan in detail how the task can be solved. Colleague B’s tasks do not require any planning processes. Which job is more similar to yours?” Task control. This referred to the number and kind of decision possibilities concerning the tasks (e.g., “Is it possible in your job to make one’s own decisions how to carry out the tasks?”). Timing control. This referred to decision possibilities with regard to time aspects of the task, for example, if the person is allowed to choose his or her own pace and take a break when feeling for it (e.g., “To what extent can you decide how long to work on a certain task?”). Participation. This referred to more general decision possibilities with regard to planning vacations or shifts, employing new staff, or composition of the team. The scale consisted of seven items and asked to what extent the person may take part in these decisions. Organizational problems. This asked about problems in the work organization that typically cause additional effort to perform the tasks (e.g., “‘A’ has to use tricks to be able to fulfil his/her work. ‘B’ is equipped in such a way that he/she can manage without additional effort. Which job is similar to yours?”). This scale bears resemblance to the constraints scale of Spector and Jex (1998). Uncertainty. This aimed at unclear or contradictory goals, conditions, or outcomes of actions and included contradictory or unclear external tasks of the organization (e.g., “How often do you get contradictory orders?”). Time pressure. This described quantitative aspects of the job and referred to problems caused by speed and quantity of information processing so that tasks cannot be executed within a given time frame (e.g., “How often do you have to work during your break because there is so much work?”). Concentration demands. This referred to the problem of informational overload of the working memory during action execution. In this case, too much concurrent information is required to be available in the working memory to accomplish the task (e.g., “Do you have to make mental notes of things that are difficult to remember (number of units, names, addresses, codes, file names, folders, etc.)?”). Cooperation demands. The necessity of cooperating with other persons to accomplish one’s tasks may cause stress if these persons cannot be relied on. This is why proximity of cooperation was considered to be a potential job stressor. The scale asked if one’s process of work is hindered by colleagues or team members.

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Emotion work or emotional labour. This was measured using the Frankfurt Emotion Work Scales (FEWS 4.0; Zapf, Mertini, Seifert, Vogt, Isic, & Fischbach, 2000; Zapf et al., 1999). Response formats corresponded to those of the ISTA instrument. Positive emotions. This scale referred to the requirement to display pleasant emotions (e.g., “In your job how often does it occur that you have to display pleasant emotions towards your clients?”). Negative emotions. This asked for the necessity of displaying and dealing with unpleasant emotions (e.g., “How often does it occur in your job that you have to display unpleasant emotions towards your clients?”). Sensitivity requirements. This examined whether empathy or knowledge about clients’ current feelings are required by the job (e.g., “Does your job require paying attention to the feelings of your clients?”). Emotional dissonance. This referred to the display of unfelt emotions and to the suppression of felt but (from an organizational perspective) undesired emotions (e.g., “How often does it occur in your job that one has to display positive emotions that do not correspond to what you feel in this situation?”) Moreover, we used single items to measure characteristics of the call centres. We asked whether the participants worked in inhouse call centres or in service bureaux, whether they worked inbound, outbound, or both, whether their main task was to give information or to do simple order bookings, simple counselling, or complex counselling, whether they had managerial responsibility, and the percentage of time spent at the telephone. Means and standard deviations of the above-mentioned scales are presented in Table 1; the intercorrelations are presented in Table 2. RESULTS First, we analysed whether the overall call centre sample significantly differed from the three groups of the comparison sample. Corresponding analyses are presented in Table 3. The call centre sample was characterized by significantly less complexity, task control, timing control, and participation than all other groups. This result was in line with previous studies and supported Hypothesis 1a. As far as job stressors are concerned, no differences were found for “uncertainty”. For all other job stressors, the call centres scored better, although there was no significant difference with the human services group regarding the “cooperation demands”. Thus, Hypothesis 1b that job stressors would be higher in call centres compared to other organizations was rejected. Third, we investigated the emotion work variables. Again, substantial differences occurred across groups. With regard to the requirement to express positive emotions, all groups differed significantly from each other. They were lowest for the “no-service” group followed by the service, call centre, and human service groups, thus partly supporting Hypothesis 1c. The same result was found for sensitivity requirements. In line with Hypothesis 1d, the

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TABLE 1 Psychometric data of study variables

Gender: female = 1, male = 2.

call centre group was significantly less required to express negative emotions than all other groups. Finally, the “no-service” group experienced the least emotional dissonance, whereas the call centre sample reported the highest emotional dissonance. Although the call centre scores were not statistically different from the human service group, this finding is in line with Hypothesis 1e. Next, we compared the eight call centre samples with the comparison samples (Figures 1–3). Differences were tested with analyses of variance (with post hoc Bonferroni tests; see Table 4). As can be seen in Figure 1, complexity and control were generally lower for call centres than for the comparison groups. However, there were exceptions. Call centre 5, a computer hotline for technical assistance, was comparable to the comparison groups except for participation. In all, 11 of 28 possible mean differences among the call centres were significant for complexity, 9 for task control, 8 for timing control, and 2 for participation (cf. Table 4). Thus Hypothesis 2a was partly supported. With regard to job stressors, no significant differences were found for “uncertainty” (therefore not displayed in Figure 2). Two call centres differed from the human service group with regard to organizational problems, but not from the service group (Table 4 and Figure 2). Moreover, the eight call centres were not significantly different from each other. In contrast, time pressure was significantly lower in Call centre 1 (the only outbound call centre) than in the other call centres and significantly higher in Call centre

Cronbach’s α (total sample) = diagnonal (in parentheses); call centres (n=375) = lower triangle; control groups (n = 405) = upper triangle; *p < .05; **p < .01. Gender: female = 1, male = 2.

Intercorrelation of study variables

TABLE 2

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TABLE 3 Comparison of the total call centre sample with comparison groups

**p < .01; n.s.: not significant; variance analyses with Bonferroni post hoc analyses; groups with different letters are significantly different (p < .05).

Figure 1. Comparison of means of eight call centres and comparison groups of job characteristics.

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Figure 2. Comparison of means of eight call centres and comparison groups of job stressors.

Figure 3. Comparison of means of eight call centres and comparison groups of emotion work variables.

**p < .01; variance analyses with Bonferroni post hoc analyses; groups with different letters are significantly different (p < .05).

Comparison of eight call centres with comparison groups

TABLE 4

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5 than in the other call centres. In half of the call centres time pressure was lower than in the service and human service group. Similar but less pronounced results were found for concentration and cooperation demands. In all, there were no significant differences among call centres for uncertainty and organizational problems, but 12 significant differences for time pressure, 7 for concentration demands and 5 for cooperation demands (see Table 4: Group means with different letters are significantly different). With regard to the emotion work variables (Figure 3) differences occurred for the requirements to express positive emotions which might be attributed to varying degrees of customer contacts. Similar results were found for sensitivity requirements. In contrast, all call centres were characterized by lower requirements to express negative emotions, whereas they featured a higher level of emotional dissonance at the same time— although not statistically different from the service and human service group. Among the call centres, there were five significant differences for the expression of positive emotions, and two for sensitivity requirements. No significant differences were found for the expression of negative emotions and emotional dissonance. Thus there was support for Hypothesis 2b only with regard to negative emotions and emotional dissonance. Next we were interested in how far some of the organizational call centre variables were able to explain the differences described in Figures 1–3. In Table 5, results of covariance analyses with call centres as the independent variable, stressors, resources, and emotion work as the dependent variables and the organizational call centre variables as covariates are shown. η2 refers to the variance between call centres and Δη2 refers to the variance between call centres after controlling for the organizational call centre variables inhouse vs. external services, percentage of telephone work, leadership responsibilities, task type, inbound vs. outbound, call time, and number of customers per hour. As can be seen from Table 5, the variance between call centres was significantly reduced for complexity and control, most substantially for task control. Similar results were found for time pressure, concentration, and cooperation demands, whereas the stressors uncertainty and organizational problems were not or only little affected. The covariates reduced the variance between call centres by 50% for positive emotions, but had little effect on the other variables. In all cases, the percentage of telephone time was the most influential variable. Complexity and task control were higher in inhouse call centres, thus partly supporting Hypothesis 3a (Table 6). No differences were found for timing control and participation. Time pressure, concentration demands, and cooperation demands were also higher in inhouse call centres. Moreover, in line with Hypothesis 3b, CSRs in external service bureaux were more frequently required to show positive emotions and less frequently to show negative emotions. However, no differences were found for sensitivity requirements and emotional dissonance, thus partly rejecting Hypothesis 3b. Not surprisingly, CSRs reporting to do complex counselling also reported higher complexity and control (Hypothesis 4a) as well as job stressors except uncertainty (Hypothesis 4b). Positive emotions and sensitivity requirements were highest and negative emotions were lowest for simple counselling (Hypothesis 4c), whereas no differences occurred for emotional dissonance. Thus, Hypothesis 4d was rejected.

**p < .01; *p < .05; ap = .06. η2: variance between call centres; Δη2: variance between call centres after controlling for organizational call centre variables; columns 3–11, line 1: results of multiple regressions: betas with stressors and resources as dependent variables; line 2, numbers in parentheses: zero order correlations; column 12: explained variance; columns 4–8: dummy coded variables (cf. Table 6).

Job stressors and resources and characteristics of call centres

TABLE 5

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**p < .01; *p < .05; variance analyses with Bonferroni post hoc analyses: line 1: means; SD in parentheses;a and b: groups with different letters are significantly different (p < .05); cimhomogeneous variance, T-test for pooled variances used.

Job stressors and resources and characteristics of call centres: Comparison of means

TABLE 6

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Also in line with expectations, team leaders reported higher complexity and control (Hypothesis 5a) as well as time pressure, concentration demands, and cooperation demands (Hypothesis 5b). No differences were detected for the other stressors and emotion work (Hypothesis 5c). Thus Hypothesis 5a was fully supported, Hypothesis 5b was partly supported, whereas Hypothesis 5c was rejected. The effects for task and timing control, however, disappeared when controlling for the other organizational call centre variables, primarily due to the percentage of telephone time (Table 6). Thus, the data suggest that team leaders have more task control and timing control which might be explained by the fact that they spend less time interacting with customers on the phone. Table 5 also shows correlations between the percentage of time working on the telephone. As expected there were substantial negative correlations between complexity and control and telephone time, with the highest correlation for timing control (hypothesis 6a). Negative correlations were also found for time pressure, concentration, and cooperation demands. As expected, there was a positive correlation with positive emotions; however, contrary to expectations, a negative correlation with the frequency to express negative emotions occurred and no correlation was detectable for sensitivity requirements and emotional dissonance. Thus Hypothesis 6b could only be supported for one of the four emotion work variables. With regard to inbound vs. outbound activities, those jobs with both inbound and outbound activities were distinguished by the highest complexity and control, whereas inbound calls only and outbound calls only were not statistically different (Hypothesis 7). Outbound activities were characterized by less job stressors than inbound activities as well as the combination of both. Only for uncertainty no differences were found. Finally, positive emotions were higher for outbound activities but no differences were realized for the other emotion work variables. In addition, Table 5 also reports results of multiple regressions with resources, job stressors, and emotion work as the dependent variables and the organizational call centre variables as the independent variables. The table shows that the call centre variables explain considerable variance for complexity and job control and they explain least variance for the emotion work variables. The most important independent variable in these analyses is percentage of telephone time, which significantly contributes to complexity and all control variables, concentration and cooperation demands, and the requirement to display positive emotions. All other call centre variables are only occasionally significant in these analyses. DISCUSSION Call centres are among the few booming operations in recent years. The main task in call centres is to interact with customers by telephone, usually supported by computer systems used to organize and automate parts of the job. In this article we compared complexity, control, job stressors, and emotion work in a sample of call centre employees and in a comparison group consisting of service workers, human service workers, and workers who had no service jobs (mainly blue collar jobs and administrative jobs). We found that jobs in call centres were characterized by lower complexity and control, but call

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centre workers were better off with regard to most job stressors compared to the comparison groups. These effects become even stronger when CSRs without leadership responsibilities are considered only (cf. Table 6, column 9). Moreover, CSRs had to express less negative emotions but were most frequently exposed to emotional dissonance although the statistical differences were not all significant for the single call centres. Concerning requirements to express positive emotions and to demonstrate sensitivity they were between other service workers, the group they are most comparable with, and human service workers. Looking at the differentiated results of eight call centres revealed job-related deficits in most but not all call centres. Mixed results arose from the analysis of job stressors. The percentage of time working with customers on the telephone explained a substantial part of the differences among call centres. The percentage of telephone time was negatively correlated with complexity and control as well as with some of the job stressors and it was positively correlated with the requirement to display positive emotions. The present results support the view that the majority of call centres— that is both inbound and outbound call centres with relatively simple tasks—have been established to organize customer mass service (Holman, 2003). However, there are also call centres in our study that belong to the “customer relationship” type, at least with regard to complexity and control. This applies for Call centre 5, which is a hotline for technical questions where people perform relatively complex tasks. Similar but less pronounced results were found for Call centre 2, which is an inhouse call centre in the tourism sector. Contrary to many expectations, the job stressors in some call centres were lower than in the comparison groups. This was especially so in Call centre 1, the only outbound call centre. Looking at the various call centres substantial differences between the call centres were revealed. Workers in Call centre 5 scored maximally with regard to time pressure and concentration demands, as did people in human service work. Again, similar, but less pronounced results were found for Call centre 2. On the other hand, three call centres had significantly less organizational problems and two call centres had significantly lower cooperation demands than all comparison groups. Our results do not support the conclusion that call centre jobs are generally more stressful with regard to job stressors. The results for job complexity, control, and job stressors support the findings of an earlier study (Isic et al., 1999). Call centres seem to be well organized as indicated by the low levels of uncertainty and organizational problems, certainly in comparison with many other service organizations. This may be the result of relying heavily on information technology supporting the distribution of calls and directing the course of CSR-customer interactions. However, the data also show that this leads to lower control and participation by the CSRs. Thus it can be concluded that the problem in many call centres is not the high level of job stressors but the low level of resources that could help to buffer the negative effects of stressors (Hobfoll, 2001; Lazarus, 1999; Zapf & Semmer, in press). This conclusion has to be validated, however, because although we measured a variety of stressors, we were not able to measure all stressful aspects of the job. For example, we did not check whether performance was monitored electronically, which appeared to be a stressor in the study by Holman (2002). With regard to emotion work, there usually are strict display rules. Observing these rules is often enforced by electronic performance monitoring in many call centres

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(Baumgartner, Good, & Udris, 2002; Holman, 2003; Schuler, 2000). The frequency of customer contacts in call centres is generally higher than in other service jobs. This could explain the fact that CSRs are more often required to express positive emotions in comparison to other comparable service professions. However, the absence of having to display negative emotions seems to be specific for all call centres. Indeed, CSRs’ tasks do not seem to include the demonstration of negative emotions (the mean is between “almost never” and “once a month”). This is in contrast to the jobs of human service workers. A social worker, for example, sometimes has to express negative emotions to reach certain goals. Being allowed to display a variety of emotions may sometimes alleviate the task for human service workers (Zapf, Seifert, Schmutte, Mertini, & Holz, 2001). The display rules for human service workers are much less restrictive. In most call centres CSRs are expected to display friendliness and politeness (see, e.g., the study of Deery et al., 2002). The organizational display rules do not allow the display of any negative emotion. This could explain why emotion work in human services jobs seems to be more frequent and more intensive compared to work in call centres, although emotional dissonance seems to be higher in call centres. The organizational call centre variables have relatively little effect on emotion work. Only a few relations were significant, supporting the view that a high level of the requirement to express positive emotions, a very low level to express negative emotions, and a relatively high level of emotional dissonance resulting from both seem to be typical for call centres in general, almost independent of the specific call centre organization. When considering organizational and task characteristics of the call centres, it turns out that the percentage of time spent at the telephone interacting with customers is one of the key variables. The more time CSRs spend on the telephone the less complex the tasks are and the less control they have. However, there is also an increase of time pressure, concentration requirements, and cooperation demands. It is a frequent finding in our studies that more complex jobs are also more stressful (e.g., Dormann et al., 2002). So interacting by telephone in call centres is not necessarily related to high task-related job stressors. This contradicts findings that indicators of emotion work, such as frequency of interactions, are usually positively correlated with time pressure and concentration demands (Zapf et al., 2001). One speculative explanation could be that in many call centres only the time spent at the telephone is seen as productive (see the discussion on electronic monitoring in Holman, 2003). This might enhance the pressure on other tasks to be carried out as fast as possible. Although there is a pressure in many call centres to increase the number of calls, which could be hypothesized to go along with an heightened level of job stressors, there is, on the other hand, some evidence that call centres are better organized than other organizations or other organizational units. Most call centres have invested heavily in modern information technology to support the CSRs (Holman, 2003; Schuler, 2000). Therefore, organizational problems, time pressure, concentration requirements, and cooperation demands are relatively low compared to the comparison groups. In the present study, inhouse call centres differ in several respects from external service bureaux. When controlling for other organizational call centre variables, external service bureaux are still characterized by less complexity and a higher requirement to express

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positive emotions. Differences with regard to job stressors and control disappear. They are explained by the more complex tasks carried out in in-house call centres. Contrary to expectations, outbound activities compared to inbound activities are related to less complexity, control, and job stressors. Obviously, the low task complexity in outbound call centres offers little substantial control for the CSRs. An example is Call centre 1, the only outbound call centre in the study. Not surprisingly, team leaders report higher complexity and control, but also more job stressors except for uncertainty. Whereas the higher extent of complexity and participation can be attributed to their organizational role, task control and timing control can obviously be explained by the less time the team leaders spend at the telephone. Obviously, when interacting with customers on the telephone, they do not have more control over the situation than the CSRs without leadership responsibilities. In summary, the results of this study suggest that low complexity, low resources, and a relatively high level of emotional dissonance are the prevailing problems of working in call centres. CSRs are strongly controlled by customers. They often have to adhere to clear rules about how to interact with customers both on the task level (with scripts on how to proceed) as well as on the interaction level (display rules to be positive and friendly), thereby being limited in their possibilities to cope with stressors. Practical conclusions to be drawn from the present study would include job enrichment strategies, especially for inhouse call centres. A mixture of customer-related interaction work and administrative work, and other backstage work as well, such as taking over tasks from other departments of the company, could offer possibilities to improve jobs. However, these kinds of improvement would probably be hard to introduce in specialized call centres carrying out mass services. In this type of call centre it may be difficult to find a substantial amount of nonroutine work, making the implementation of job enrichment strategies almost impossible. Countries like Germany, which relocated a substantial part of its production to low salary countries, are characterized by a shortage of unskilled work and low salaried jobs. A look at the sociodemographic data of call centre staff shows that although the percentage of employees with a university degree is very low, only 17% are in possession of a lower stream school-leaving certificate and there is nobody without such a level of education. This reflects that most call centres recruit staff with some level of qualifications, like good or very good communication skills, a friendly voice, verbal fluency, and no accent (Baumgart et al., 2002; Baumgartner et al., 2002; Dieckhoff et al., 2002). These skills are difficult to find among unskilled workers. Therefore, candidates with higher education are usually preferred. Because of their high basic education, many of these employees find call centre work too undemanding after a while. This may contribute to the high turnover rate frequently found in call centres (Holman, 2003; Schuler, 2000). The present study has strengths and limitations. One of its strengths is the reasonable sample size and the comparison group design with a randomly drawn sample, which may, however, have a bias towards academic professions. A limitation is that the call centres were not randomly drawn. However, because the present results are very similar to other studies (Baumgart et al., 2002; Baumgartner et al., 2002; Dormann et al., 2002; Isic et al., 1999), especially with regard to sociodemographic variables, we assume that

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the call centres are not untypical. Despite the fact that the data are all self-reported, in most analyses, the independent variable can be assumed to be bias free (e.g., call centre sample vs. comparison samples, inbound vs. outbound, inhouse vs. service bureaux, team leader responsibilities). Therefore, an overestimation of relations or differences of results due to measurement artifacts is unlikely in this article. Whereas the study replicates previous findings suggesting that job design could be considerable improved in call centres, it clearly shows that call centre workers are not generally exposed to higher levels of job stressors. One of the strengths of this study is that it provides a more differentiated picture of the positive and negative implications of CSR—customer interactions. The relatively high level of emotional dissonance which has been shown to be an important stressor in service work (Zapf, 2002) questions the application of too strict display rules and the electronic performance monitoring techniques to enforce these rules. Although strict scripts and display rules may have a protective function because they are reassuring with regard to the behaviour preferred by the organization, they often inhibit the CSRs’ autonomy and their ability to provide customized service (Deery et al., 2002). The data of the present study suggest giving the agents more decision latitude to personalize the organizational display rules and to develop an individual style how to interact with customers. However, our practical experience is that fewer defined display and interaction rules often lead to uncertainty for the CSRs with regard to what is and what is not allowed. Currently, there seems to be progress in the research on job stressors and resources in call centres. However, little research has been carried out on the social interaction of service provider and customer. Future research should, therefore, supplement recent research by looking more on the service provider-customer interaction, which seems to be the major source of stress in the present study. REFERENCES Adelmann, P.K. (1995). Emotional labor as a potential source of job stress. In S.L.Sauter & L. R.Murphy (Eds.), Organizational risk factors for job stress (pp. 371–381). Washington, DC: American Psychological Association. Batt, R. (2002). Managing customer services: Human resource practices, quit rates, and sales growth. Academy of Management Journal, 45, 587–597. Baumgart, U., Debitz, U., Metz, A.-M., Richter, P., Schulze, F., Timm, E., & Wieland, R. (2002). CCall Report 11. Call Center auf dem arbeitspsychologischen Prüfstand. Teil 2: Arbeitsgestaltung, Belastung, Beanspruchung & Ressourcen [Call centre at the work psychology test bench. Part 2: Job design, stressor, strain, and resources]. Hamburg: VerwaltungsBerufsgenossenschaft. Baumgartner, M., Good, K., & Udris, I. (2002). Call Centers in der Schweiz. Psychologische Untersuchungen in 14 Organisationen [Call centres in Switzerland: Psychological investigations in 14 organizations]. Zurich, Switzerland: Institut für Arbeitspsychologie, ETH Zurich. Bongard, S., & al’Absi, M. (2003). Domain-specific anger expression assessment and blood pressure during rest and acute stress. Personality and Individual Differences, 34, 1381–1402.

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Hochschild, A.R. (1983). The managed heart. Berkeley, CA: University of California Press. Holman, D.J. (2002). Employee well being in call centres. Human Resource Management Journal, 12, 35–50. Holman, D.J. (2003). Call centres. In D.J.Holman, T.D.Wall, C.W.Clegg, P.Sparrow, & A. Howard (Eds.), The new workplace: A guide to the human impact of modern working practices. Chichester, UK: Wiley. Holman, D.J., Chissic, C., & Totterdell, P. (2002). The effects of performance monitoring on emotional labor and well being in call centers. Motivation and Emotion, 26, 57–81. Isic, A., Dormann, C., & Zapf, D. (1999). Belastungen und Ressourcen an Call CenterArbeitsplätzen [Stressors and resources at call centre jobs]. Zeitschrift für Arbeitswissenschaft, 53, 202–208. Knights, D., & McCabe, D. (1998). What happens when the phone goes wild? Staff, stress and spaces for escape in a BPR telephone banking call regime. Journal of Management Studies, 35, 163–194. Kruml, S.M., & Geddes, D. (2000). Catching fire without burning out: Is there an ideal way to perform emotional labor? In N.M.Ashkanasy, C.E.J.Härtel, & W.J.Zerbe (Eds.), Emotions in the workplace: Research, theory and practice (pp. 177–188). Westport, CT: Quorum Books. Lazarus, R.S. (1999). Stress and emotion: A new synthesis. New York: Springer. Metz, A.-M., Rothe, H.-J., & Degener, M. (2001). Belastungsprofile von Beschäftigen in Call Centern [Stressor profiles of employees working in call centres]. Zeitschrift für Arbeits- und Organisationspsychologie, 45, 124–135. Morris, J.A., & Feldman, D.C. (1996). The dimensions, antecedents, and consequences of emotional labor. Academy of Management Review, 21, 986–1010. Morris, J.A., & Feldman, D.C. (1997). Managing emotions in the workplace. Journal of Managerial Issues, 9, 257–274. Nerdinger, F.W., & Röper, M. (1999). Emotionale Dissonanz und Burnout. Eine empirische Untersuchung im Pflegebereich eines Universitätskrankenhauses [Emotional dissonance and burnout: An empirical examination in the nursing sector of a university hospital]. Zeitschrift für Arbeitswissenschaft, 53, 187–193. Richter, P., & Schulze, F. (2001). Arbeitsorganisation als Möglichkeit der Beanspruchungsoptimierung an Call-Center-Arbeitsplätzen [Work organization as a possibility of strain optimization at call centre jobs]. In I. Matuschek, A.Henninger, & F.Kleemann (Eds.), Neue Medien im Arbeitsalltag (pp. 131–146). Wiesbaden, Germany: Westdeutscher Velag. Schaubroeck, J., & Jones, J.R. (2000). Antecedents of workplace emotional labor dimensions and moderators of their effects on physical symptoms. Journal of Organizational Behavior, 21, 163–183. Schuler, H. (2000). Die Call Center der Zukunft: Vom Call Center zum multimedialen KundenInteraktions-Center [The call centre of the future: From the call centre to the multimedia customer interaction centre]. In H.Schuler & J.Pabst (Eds.), Personalentwicklung im Call Center der Zukunft (pp. 1–11). Neuwied, Germany: Luchterhand. Semmer, N.K., Zapf, D., & Dunckel, H. (1995). Assessing stress at work: A framework and an instrument. In O.Svane & C.Johansen (Eds.), Work and health—scientific basis of progress in the

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working environment (pp. 105–113). Luxembourg: Office for Official Publications of the European Communities. Semmer, N.K., Zapf, D., & Dunckel, H. (1998). Instrument zur Streßbezogenen Arbeitsanalyse ISTA Version 6.0. Bern, Germany: Psychologisches Institut Bern. Semmer, N.K., Zapf, D., & Dunckel, H. (1999). Instrument zur Stressbezogenen Tätigkeitsanalyse ISTA [Instrument for stress-related job analysis]. In H.Dunckel (Ed.), Handbuch psychologischer Arbeitsanalyseverfahren (pp. 179–204). Zürich, Switzerland: vdf Hochschulverlag. Spector, P.E., & Jex, S.M. (1998). Development of four self-report measures of job stressors and strain: Interpersonal Conflict at Work Scale, Organizational Constraints Scale, Quantitative Workload Inventory, and Physical Symptoms Inventory. Journal of Occupational Health Psychology, 3, 356–367. Wieland, R., Metz, A.-M., & Richter, P. (2001). CCall Report 3. Call Center auf dem arbeitspsychologischen Prüfstand. Teil 1: Verfahren, Tätigkeitsmerkmale und erste Ergebnisse zur psychischen Belastung [Call centres at the work psychology test bench. Part 1: Instruments, task characteristics and first results regarding psychological stress]. Hamburg, Germany: Verwaltungs-Berufsgenossenschaft. Zapf, D. (1993). Stress-oriented job analysis of computerized office work. The European Work and Organizational Psychologist, 3, 85–100. Zapf, D. (2002). Emotion work and psychological strain: A review of the literature and some conceptual considerations. Human Resource Management Review, 12, 237–268. Zapf, D., Bechtoldt, M., & Dormann, C. (in press). Instrument zur Streßbezogenen Arbeitsanalyse (ISTA), Fragebogen Version 6.0 [Instrument for stress-related job analysis (ISTA) questionnaire version 6.0]. Zeitschrift für Arbeits- und Organisationspsychologie. Zapf, D., Mertini, H., Seifert, C., Vogt, C., Isic, A., & Fischbach, A. (2000). Frankfurt Emotion Work Scales—Frankfurter Skalen zur Emotionsarbeit FEWS 4.0. Frankfurt, Germany: Department of Psychology, J.W. Goethe-University Frankfurt. Zapf, D., Seifert, C., Schmutte, B., Mertini, H., & Holz, M. (2001). Emotion work and job stressors and their effects on burnout. Psychology and Health, 16, 527–545. Zapf, D., & Semmer, N.K. (in press). Stress und Gesundheit in Organisationen [Stress and health in organizations]. In H.Schuler (Ed.), Enzyklopädie der Psychologie, Themenbereich D, Serie III, Band 3, Organisationspsychologie (2nd ed.). Göttingen, Germany: Hogrefe. Zapf, D., Vogt, C., Seifert, C., Mertini, H., & Isic, A. (1999). Emotion work as a source of stress: The concept and development of an instrument. European Journal of Work and Organizational Psychology, 8, 371–400. Zeithaml, V.A., & Bitner, M.J. (2000). Services marketing: Integrating customer focus across the firm (2nd ed.). Boston: McGraw-Hill.

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Working conditions, well-being, and job-related attitudes among call centre agents Simone Grebner, Norbert K.Semmer, Luca Lo Faso, Stephan Gut, Wolfgang Kälin, and Achim Elfering University of Berne, Switzerland

A comparison of 234 call centre agents with 572 workers in traditional jobs with long lasting training revealed lower job control and task complexity/ variety and higher uncertainty among call agents. However, time pressure, concentration demands, and work interruptions were lower in call agents. Within the call agent sample, controlling for negative affectivity and other working conditions, job control predicted intention to quit, and job complexity/variety predicted job satisfaction and affective commitment. Social stressors and task-related stressors predicted uniquely indicators of well-being and job-related attitudes. Furthermore, data confirm the role of emotional dissonance as a stressor in its own right, as it explained variance in irritated reactions and psychosomatic complaints beyond other working conditions. Results indicate that strong division of labour may be a rather general phenomenon in call centres. Therefore, working conditions of call agents require a redesign by means of job enrichment or—better—organization development. Moreover, measures of social stressors and emotional dissonance should be integrated routinely into stressrelated job analyses in service jobs. Call centres that execute customer care by phone, represent a new form of work organization, which often is designed “from scratch”. This might offer a unique opportunity to design jobs according to established principles of job design, creating work

Correspondence should be addressed to S.Grebner, Dept. of Psychology, University of Berne, Muesmattstr. 45, CH-3000 Berne 9, Switzerland. Email: [email protected]

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that is motivating, and enhances productivity (Parker & Wall, 1998). Typically, however, when new jobs are designed, such principles tend not to play a major role (Clegg et al., 1997). Rather, work is designed around technical solutions or existing organizational principles, and this may imply unfavourable working conditions for employees (Parker & Wall, 1998). This seems to apply to call centres as well. Of course, one cannot lump together all call centre jobs in an undifferentiated way. Nevertheless, there are indications that, at present, many call centre agents predominantly carry out tasks that are rather specialized and often simplified (cf. Isic, Dormann, & Zapf, 1999; Taylor, Mulvey, Hyman, & Bain, 2002). This can be attributed to a very high degree of structural division of labour. For instance call agents mainly answer incoming calls (inbound) or call customers (outbound), whereas back office employees often execute post-processing of requests (cf. Isic et al., 1999; Moltzen & van Dick, 2002). High-grade division of labour certainly promises some obvious microeconomic advantages. As it simplifies tasks, only a relatively short period of vocational training is required (e.g., 4–6 weeks of training, cf. Baumgartner, Good, & Udris, 2002; Toomingas et al., 2001). Also, simplified tasks do not require specialized personnel. Altogether this might serve to keep personnel costs low. However, possible disadvantages are easily overlooked. Several studies have shown that job simplification by division of labour comes along with routine work (low task variety, i.e., repetition of the same task over extended periods), low task complexity (i.e., only few necessities of own decisions; Frese & Zapf, 1994), and consequential low utilization of qualification (knowledge, skills, and abilities). Moreover, many call agents have low influence on one’s own work in terms of work-related resources such as job control, not only over work pace (i.e., decision possibilities over time frame of task conduct such as time point, succession, and duration of actions), but also with regard to planning and organizing one’s own work (cf. Deery, Iverson, & Walsh, 2002; Isic et al., 1999; Metz, Rothe, & Degener, 2001). This is in conflict with the fact that the majority of call agents in Switzerland are skilled workers (cf. Baumgartner et al., 2002). Thus, in comparison with more traditional jobs that require extensive job-specific vocational training (e.g., several years), the work of call agents that are employed in front line jobs is often characterized by elements of Taylorism, with its emphasis on strict division of labour, and consequential limited job demands in terms of low complexity, low variability, and low control, in particular with regard to inbound jobs (Isic et al., 1999). Although there are not many studies concerning call centres, supporting evidence is growing. A German study involving 250 call agents from 14 call centres (mostly inbound) found that call agents had poorer working conditions in terms of task variability and complexity and lower job control as well as higher psychosomatic complaints than people in comparable, but more traditional work places (administrative clerks, bank clerks; cf. Isic et al., 1999) controlling for age, sex, and education level. A Swiss study among 242 call agents from 14 call centres (primarily inbound) showed that task variety predicted psychosocial well-being, qualification requirements predicted job satisfaction, and lack of complexity was related to low organizational commitment (Baumgartner et al., 2002). Moreover, a recent study by Holman and Wall (2002) found that low job control predicted depression among inbound call agents of a national UK bank in crosssectional as well as in longitudinal data. Furthermore, in a study among US teleservice

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centre representatives, lack of job control was associated with musculoskeletal disorders (Hoekstra, Hurrell, Swanson, & Tepper, 1995). Finally, there is some evidence that many call centres suffer from high turnover rates of agents. Baumgartner et al. (2002) report turnover rates of 8–50%. They found that experienced monotony is one of the most frequent reasons that call agents cite for quitting their job. In line with this, low complexity and variety predicted intention to quit and were negatively related to actual job tenure. It seems, therefore, that there is a tendency in the design of many call centre jobs to show low control (i.e., limited resources) as well as low complexity and variety (i.e., limited job demands), that have not only been associated with poor outcomes in terms of well-being and turnover both in call centre studies but also in the general literature (e.g., Kahn & Byosiere, 1992; Sonnentag & Frese, 2003). From these considerations we derive two hypotheses, which ask about the working conditions of call agents as compared to employees in more traditional jobs that require a longer vocational training period (e.g., several years). We expect that due to the high division of labour in call centres and the short training period job control and task complexity and variety of call agents are lower than among employees in traditional jobs with a long training period (Hypothesis 1). With regard to call centre studies, the picture is less clear for stressors, such as taskrelated stressors (e.g., work overload, concentration demands, and uncertainty in terms of role ambiguity) and social stressors (e.g., conflicts with supervisors and colleagues). However, there are reports that high call volumes often lead to a fast pace of work (cf. Moltzen & van Dick, 2002). Isic et al. (1999) found, controlling for age, sex, and educational level, rather high levels of time pressure, concentration demands, and uncertainty among call agents, and two of those were significantly higher among call agents than among administrative clerks; the task-related stressors of bank clerks were, however, comparable to those of the call agents. From our point of view it seems plausible that the picture is less clear for stressors: In contrast to limited job resources and job demands (i.e., low control, low complexity, and limited variety), which seem to reflect a general tendency for job design in call centres, the level of some task-related stressors (work interruptions, problems of work organization, uncertainty, and concentration demands), and social stressors is in any organization likely to depend more on specific circumstances within that organization (e.g., organization structure, information flow, leadership behaviour, etc.) than on job demands and resources, and therefore is not built in the tasks of call agents as strongly as control, variety, and complexity of tasks. Therefore, we do not expect differences between call agents and employees in more traditional jobs concerning work interruptions and problems of work organization (i.e., regulation obstacles such as lack of updated information and deficient tools; cf. Frese & Zapf, 1994; Semmer, 2003), uncertainty (unclear or conflicting goals), concentration demands, and social stressors (Hypothesis 2a). However, we expect call agents to have higher workload respectively time pressure than employees in traditional jobs because of paced work, as reported in the literature (Hypothesis 2b). Moreover, because of limited job demands and resources we expect call agents to report worse well-being (e.g., context-free well-being such as irritation and psychosomatic complaints, and job-specific well-being like work-home spillover), and

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impaired job-related attitudes in terms of lower job satisfaction, less affective commitment, more resigned attitude towards the job, and higher intention to quit than among employees in traditional jobs (Hypothesis 3). It is important to note that job satisfaction might be classified as an indicator of well-being (cf. Warr, 1999) as well as a job attitude. We classified it as an attitude because of its clear attitudinal component. Hypotheses 1–3 intend primarily to replicate earlier findings (Isic et al., 1999). However, in two respects they go beyond a mere replication. First, they include problems of work organization and work interruptions in terms of regulation obstacles, a category of taskrelated stressors that is not often employed in occupational stress research, but that is interesting from many perspectives. Obstacles impede or even thwart to pursue and reach a goal. From this point of view, obstacles underscore the importance of goals (Frese & Zapf, 1994; Semmer, 2003), and they also underscore that people are motivated to do good work—that is—to reach their goals, and they are stressed if they do not find the conditions for doing so. Moreover, coping with regulation obstacles requires additional effort (e.g., to start again, to repeat parts of the action process or to enhance physical strength, etc.), or even the use of more risky actions in order to reach the goal despite the obstacles (e.g., Frese & Zapf, 1994). This type of task-related stressor has been proposed in the 1980s (Keenan & Newton, 1984; O’Connor, Peters, Pooynan, Weekley, Frank, & Erenkrantz, 1984; Peters & O’Connor, 1980; Semmer, 1984), and its relationships to a number of outcomes (e.g., psychosomatic complaints, cf. Semmer, 1984; Semmer, Zapf, & Greif, 1996b) have been demonstrated in these and some following studies (Dormann, Zapf, & Isic, 2002; Greiner, Ragland, Krause, Syme, & Fisher, 1997; Isic et al., 1999; Leitner, 1993; Semmer, Zapf, & Dunckel, 1995; Spector & Jex, 1998). Secondly, Hypotheses 1–3 go beyond a simple replication because negative affectivity is controlled, which is a stable affective disposition or personality trait, predisposing to negative perceptions of the world and leading to experiences of distress and negative emotions. Negative affectivity (NA) might not only influence self-reports of working conditions and strain, but may also lead to inflated correlations of stressors and strains (common method variance, e.g., Brief et al., 1988). The influence of NA should be controlled in studies that use exclusively self-reports and cross-sectional data (cf. Spector, Zapf, Chen, & Frese, 2000). For the assessment of job design in call centres it is not only important to compare working conditions and strain of call agents with working conditions and strain of other samples. It is also important to know what effects working conditions have on strain. Numerous studies have investigated effects of working conditions on strain (cf. Kahn & Byosiere, 1992; Sonnentag & Frese, 2003). For instance, resources at work such as job control are in general positively related to well-being, health and job-related attitudes (e.g., Semmer, 1998; Terry & Jimmieson, 1999). Moreover, job demands like job complexity and variety have the same effects on well-being and job-related attitudes as resources at work as long as they do not overtax a person’s capabilities, and as long as they allow to utilize one’s skills, knowledge, and abilities and, therefore, promote learning. Positive relationships of job complexity and variety with well-being and job-related attitudes have been reported both in the literature on stress at work in general (e.g., Kahn & Byosiere, 1992; Sonnentag & Frese, 2003; Warr, 1999) and specifically for call agents

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(Baumgartner et al., 2002; Isic et al., 1999). While control, complexity, and variety are associated with well-being, good health, and positive job-related attitudes, the opposite applies to stressors at work. In general, stressors are a possible source for chronic stress, such as impaired well-being and health (e.g., irritation, psychosomatic complaints; cf. Kahn & Byosiere, 1992; Sonnentag & Frese, 2003) and negatively affect job-related attitudes, too. For instance they might reduce job satisfaction and affective commitment over time, and, in turn, enhance intentions to quit a job (e.g., Sonnentag & Frese, 2003). However, although many studies have investigated stressor-strain relations, only a few studies tested unique effects of specific working conditions (e.g., job control) controlling at the same time for other types of working conditions (e.g., task-related and social stressors). From our point of view this is important because—although they are theoretically clearly distinguishable—different types of working conditions are usually moderately correlated and might contain redundant information. Therefore, it would be important to study the conceptual independence of specific types of working conditions in their effect on strain. Moreover, from a practical point of view, if the unique contribution of specific types of working conditions to strain is known job design could be tailored to improve that specific working conditions and therefore, to prevent systematically detrimental effects. For instance, Dormann et al. (2002) found an independent contribution of problems of work organization to psychosomatic complaints beyond other task-related stressors (time pressure, uncertainty), social stressors, job control, complexity, variety, emotion work scales (e.g., emotional dissonance), and NA. Hence, we expect negative relations between job control, job complexity, and variety on the one hand and measures of impaired well-being and impaired job-related attitudes on the other, which go beyond other influences including task-related stressors, social stressors, and emotional dissonance (Hypothesis 4). Moreover, we expect positive relations between task-related stressors (time pressure, concentration demands, uncertainty, problems of work organization, work interruptions) and measures of impaired well-being and lowered job attitudes beyond other influences (Hypothesis 5a). There exist many studies that investigated the effect of stressors at work on strain. However, most of them concentrated on task-related stressors. In general there exists not much evidence with regard to the effect of social stressors at work (e.g., conflicts with supervisors and colleagues, social animosities at work, negative group climate, and unfair behaviour) on strain, although available evidence suggests that social stressors may have a strong impact on well-being and health (e.g., Dormann & Zapf, 2002; Semmer, McGrath, & Beehr, in press). A possible explanation is that social stress situations involve attributions of blame (Reicherts & Pihet, 2000)— which increases stress—as well as negative social evaluations, which also are particularly stressful (Leary & Kowalski, 1995) because they offend self-worth. Hence, even if social stressors might share variance with taskrelated stressors (e.g., an impatient supervisor might not only cause time pressure but also conflicts) they might contribute uniquely to strain beyond task-related stressors because they involve negative social evaluations. Therefore, we expect social stressors to predict positively impaired well-being and impaired job-related attitudes beyond other influences (Hypothesis 5b).

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Social situations that require to control one’s owns emotions do not only occur in interactions with supervisors and colleagues (e.g., conflict with a co-worker), but are likely to occur in interactions with clients (e.g., customers). Call agents communicate most of the time voice-to-voice with customers (cf. Dormann et al., 2002; Holman & Wall, 2002; Moltzen & van Dick, 2002). Therefore, they have to deal with a variety of emotions of customers (e.g., anger, frustration). In such situations call agents have to display emotions as required by the organization (e.g., to show empathy and friendliness)— regardless of their real emotions (e.g., anger), in order to influence customers emotions in a goal-oriented manner. Therefore, their job involves emotion work according to Hochschild (1983) and Morris and Feldman (1996). Emotion work implies a stressor—emotional dissonance—that occurs when an employee has to display emotions that are appropriate for customer contact (e.g., friendliness), but differ from emotions he or she might feel actually (e.g., anger; cf. Zapf, 2002). It is important to note that in the literature emotional dissonance is seen either as a dependent variable (i.e., a state of tension that results when emotional expressions are actually different from internal feelings, e.g., Ashforth & Humphrey, 1993), or as a stressor that results when the organizationally desired emotion is not felt spontaneously (e.g., Grandey, 1998), or as a stressor located in the social environment in terms of a job demand (Zapf, 2002). We rely on the latter definition, according to the multidimensional concept of emotion work (Zapf, Vogt, Seifert, Mertini, & Isic, 1999) where emotional dissonance is defined as the demand to display emotions that are not truly felt, such as being friendly to disrespectful customers, even though the feeling that is experienced might be anger (Zapf et al., 1999). Research has shown that emotional dissonance is in general associated with impaired well-being (e.g., emotional exhaustion, depersonalization, irritation, psychosomatic complaints, reduced job satisfaction; cf. Dormann et al., 2002; Zapf, 2002; Zapf et al., 1999; Zapf, Seifert, Schmutte, Mertini, & Holz, 2001). Moreover, Dormann et al. (2002) have shown that emotional dissonance explains variance in emotional exhaustion and depersonalization beyond other working conditions (e.g., task-related and social stressors) and, therefore is considered as a stressor in its own right. However, these authors did not control for NA. From our point of view this seems to be important in particular with regard to emotional dissonance, because people high in NA are more likely to report high levels of emotional dissonance. Therefore, we expect that emotional dissonance is a task characteristic that is uniquely associated with impaired well-being and impaired job-related attitudes beyond other influences including NA (Hypothesis 5c).

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METHOD Samples Call centre sample The analyses are based on a field study of 339 male and female call centre employees from a company located in the French and German speaking area of Switzerland, corresponding to a response rate of 93%. Data collection took place in spring 2001. Overall, 163 employees in the French-speaking and 176 in the German-speaking area filled in questionnaires. The sample consists of 234 call centre agents, 40 team leaders, and 65 back office clerks. Mean age was 27.6 years (SD = 7.2, range 18–59), and 52.8% were female. The vast majority of the participants were employed full time (93.8%). Most of them (53.1%) had completed an apprenticeship or technical or secondary school. Another 30.6% had a college or university degree. Mean job tenure in the call centre of the present organization was 15 months (SD = 7.6, range 1–36), and 64.5% reported an overall job experience in customer care between 1 and 10 years, only 16% less than 6 months. Because team leaders and back office employees do not have personal contact with customers on a regular basis, data analyses are based on the subsample of n = 234 call agents who exclusively carried out inbound tasks. Work tasks and division of labour. Most of the time call agents are occupied by inbound calls. Primarily they provide information (i.e., concerning new products and services) and execute orders of customers (e.g., cancellation of contracts). The mean duration of calls is 3 min (SD = 42.3 s) and the mean duration of reworking per call 5 min (SD = 71.2 s). Remaining activities (13.5% of work time) concern team meetings and processing of information (i.e., updating own knowledge). The training period lasts few weeks, as usual for call agents in Switzerland (Baumgartner et al., 2002). However, follow-up tasks arising from inbound calls, such as processing of contracts, bills, and letters by mail, are handled by back-office employees. Comparison sample A sample of N = 572 young workers from five traditional occupations (cooks, sales assistants, nurses, bank clerks, and electronic technicians) was used as a comparison sample. These jobs require extensive vocational training between 2 and 4 years. All of them had 2 years’ job experience after finishing vocational trainings. Their mean age was 22.7 years (SD = 3.15). Slightly more than half of the sample was female (57.7%), and a similar percentage (55%) was working in the German-speaking and the others in the French-speaking area of Switzerland. The sample emanates from the third wave of the longitudinal research project “Work experiences and quality of life in Switzerland” (AEQUAS; cf. Kälin et al., 2000). The first wave took place in spring 1997, before participants completed their last year of vocational training. For the first wave, questionnaires were handed out in classrooms, in vocational schools. For waves two and

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three, they were sent out by mail. A stratified sample was drawn with the aim of having an equal representation of French- and German-speaking apprentices within each occupation, of both sexes in the overall sample, and in all selected occupations except nursing and electronics, where we simply targeted all participants of the minority gender that were available. Service and hightech occupations were chosen because they characterize the ongoing economic development in Switzerland. Therefore, the comparison sample represents a heterogeneous spectrum of job characteristics and traditional tasks (people work including service work, nursing, sales, and technical tasks, for instance programming and maintenance). Comparability of the samples Both samples are comparable in their proportion of females and French-speaking participants. Furthermore, both samples are on average in their twenties, even though the comparison sample is somewhat younger. Moreover, the overall education level is comparable. The crucial difference is that call agents are working in jobs requiring a few weeks’ training, whereas jobs in the comparison sample require longer training periods. Measures Working conditions. Working conditions (job control, job complexity/ variety, and taskrelated stressors) were measured through a short version of the Instrument for Stress Oriented Task Analysis (ISTA; Semmer et al., 1995). The instrument shows satisfying reliabilities with Cronbach’s alpha between .68 and .82 except uncertainty (α =.62; see Table 1; N = 572). All ISTA-scales consist of items that have a 5-point Likert format, reflecting either intensity or frequency. Job control captures aspects of method control (e.g., independently plan and organize one’s own work) and time control (e.g., influence on work pace and schedule). Moreover, job complexity/ variety measures complexity of tasks (e.g., necessity of complex decisions) and task variety (e.g., number of tasks). There were five task stressors: time pressure, concentration demands, problems of work organization (e.g., having to work with obsolete information), uncertainty (e.g., unclear instructions), and work interruptions. For some analyses, these five stressors were combined into a single index of task-related stressors by averaging the five scale means (cf. Frese, 1985, or Grebner, 2001, for a similar procedure). Moreover, social stressors were measured by an instrument of Frese and Zapf (1987), which captures personal animosities, poor group climate, and conflicts based on problems within the relationship to supervisor(s) and colleagues (5-point scale). Furthermore, emotional dissonance from the FEWS (Frankfurt Emotion Work Scales, Version 3.0; Zapf et al., 1999) was used to assess the frequency of the necessity to display emotions that are not genuinely felt (e,g., “How often do you have to suppress your feelings in your job in order to appear neutral?”; 5-point Likert scale). This scale was employed exclusively in the call centre sample.

Means shown are not corrected. Significances are related to estimated marginal means. Dashes indicate calculation of internal consistency is not appropriate because a mean is used. a scored from 1 to 4;b scored from 1 to 5;c scored from 1 to 6;d scored from 1 to 7;e In the call agents sample a 14-item version and in the comparison sample a 13-item version was used. Emotional dissonance was measured only among call agents. fMultivariate and univariate analyses of covariance (MANCOVAs) with age, sex, neuroticism, education level, and language region as covariates. Sample size: Call agents: N=234. Comparison sample: N=572. **p < .01; ***p< .001.

Descriptive statistics and internal consistencies (Cronbach’s alpha) for all study variables, multivariate (MANCOVA) and univariate analysis of variance (ANCOVA) for effects of group (call agents vs. comparison sample) on working conditions, well-being, and job-related attitudes

TABLE 1

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Well-being. Well-being was assessed in terms of context-free and job-related well-being (Warr, 1999). As an indicator for context-free well-being, psychosomatic complaints were measured with a scale developed by Mohr (1986, 1991; 5-point scale) on the basis of Fahrenberg (1975), asking for frequency of headaches, stomachaches, nervousness, etc. during the preceding year. The scale is comparable to many similar ones used in this type of research (e.g., the Physical Symptoms Inventory by Spector & Jex, 1998) and might be considered as a psychological long-term stress response. It has been used in a variety of studies on stress at work in German-speaking countries (e.g., Büssing, 1999; Frese, 1985; Garst, Frese, & Molenaar, 2000). Furthermore, well-being was measured by a scale on irritation/strain (Mohr, 1986, 1991; 8 items, 7-point Likert scale). This scale captures aspects of context-free well-being as well as aspects of job-related well-being. Therefore some of the analyses are based on two subscales of the Irritation/ Strain scale that differentiate both aspects. One subscale refers to the inability to “switch off” after work and to ruminate about work problems instead in terms of spillover from work to private life (e.g., “It is hard for me to switch off my mind after work”; 4 items) and, therefore measures job-related wellbeing. Garst et al. (2000) call this subscale “worrying”. The other subscale refers to context-free well-being and measures irritated reactions (e.g., “I am easily annoyed”; 4 items). Job-related attitudes. Job satisfaction was assessed by a scale that contains three items developed by Oegerli (1984) plus a Kunin Faces. It has been shown to be a good predictor of turnover (Baillod & Semmer, 1994; Semmer, Baillod, Stadler, & Gail, 1996a). Resigned attitude towards one’s job is based on Bruggemann’s (1974; for an English description see Büssing, 1992) concept of “resigned job satisfaction”. Items are again from Oegerli, and ask how often one has thoughts like “my job is not ideal, but it could be worse”, aiming at a defensive, or resentful, adaptation to working conditions that are not optimal (cf. Semmer, 2003; see also Kälin et al., 2000). Affective commitment in terms of strong positive attitudes towards the organization manifested by emotional attachment to, identification with, and involvement in the organization was measured by a scale of Dunham, Grube, and Castaneda (1994; 7-point scale, e.g., “I enjoy discussing my organization with people outside of it”). Intention to quit was measured with two items (5-point Likert scale), which ask for the subjective probability of staying in the same organization for 6 months or 2 years, respectively (cf. Bluedorn, 1982). Control variable. As an indicator for negative affectivity (NA) neuroticism was measured, based on the five-factor model of personality (McCrae & Costa, 1985). The short version of the bipolar adjective rating list was used (Ostendorf, 1990; Schallberger & Venetz, 1999). Data analysis In order to test Hypotheses 1, 2a, 2b, and 3, comparisons between call agents and the comparison sample with regard to working conditions, well-being, and job-related

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attitudes were conducted by analysis of covariance (ANCOVA), with gender (dummy coded), language area (dummy coded), level of education (dummy coded), and NA as covariates. Additionally, in order to consider existing correlations within categories of dependent variables three multivariate analyses of covariance (MANCOVA) were calculated separately for three categories of dependent variables: (1) job control and job complexity/ variety, (2) time pressure, work interruptions, problems of work organization, concentration demands, uncertainty, and social stressors, and (3) all measures of wellbeing and job-related attitudes. Within the call centre sample, hierarchical regression analyses were performed to predict indicators of well-being, as well as job-related attitudes by working conditions (Hypotheses 4, 5a, 5b, and 5c). In all regression analyses, control variables—NA, gender, language area, level of education, and age—were entered in a first step. Task-related stressors (time pressure, uncertainty, problems of work organization, concentration demands, and work interruptions) were entered in a second step. In order to determine the amount of additional variance explained by social stressors, emotional dissonance, job demands (complexity/variety) and resources (job control), these predictors were entered each in separate steps. Therefore, in a third step social stressors were entered. Emotional dissonance was added in the fourth step. In the fifth step job complexity/ variety was added. Finally in the last sixth step job control was entered. Due to entering all types of working conditions in each regression model, each effect of a specific working condition on a dependent variable is not only controlled for “control variables” but also for all other types of working conditions. RESULTS Table 1 presents descriptive statistics and reliability of all study variables for the call agents and the comparison sample. All measures in both samples indicate mostly satisfying levels of reliability in terms of internal consistency (coefficient α). Comparison of working conditions, well-being, and job-related attitudes between call agents and the comparison sample Working conditions. In Table 1, the means of working conditions, indicators of well-being, and job-related attitudes are shown for call agents and the comparison sample. In analyses of covariance, the means are controlled for age, sex, level of education, language area, and NA. As expected (Hypothesis 1), call agents showed significantly lower job control and job complexity/variety compared to employees in traditional jobs. The multivariate term was also significant indicating that the differences are not due to shared variance. Hypothesis 1 is therefore supported. Unexpectedly, the comparison sample showed higher values in task-related stressors. This applies to the index of task-related stressors as well as to three of the five underlying scales (time pressure, work interruptions, and concentration demands). Call agents had higher values only with regard to uncertainty. The multivariate term was significant,

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indicating again that the differences are not due to shared variance of dependent variables. However, with regard to organizational problems and social stressors no group differences were found as expected. Hypothesis 2a is not supported with regard to work interruptions, concentration demands, and uncertainty. However, Hypothesis 2a reveals some support concerning social stressors and problems of work organization. Hypothesis 2b is not supported. Well-being and job-related attitudes. In line with expectations, call agents reported higher psychosomatic complaints and resigned attitude towards the job (corrected means). However, they were also lower on the irritation/strain scale than the comparison sample. For job satisfaction, and affective commitment no difference was found between the samples. Moreover, call agents reported lower intention to quit than the comparison sample. Altogether Hypothesis 3 is hardly supported. The multivariate term was significant indicating again that differences are not due to shared variance of well-being and job-related attitudes. Moreover, it is important to note that the comparison sample showed a higher mean in NA. However, group differences in working conditions, well-being, and job-related attitudes remain the same if neuroticism is controlled, indicating that group differences are not based on NA. Correlations between working conditions, well-being, and job-related attitudes among call agents Intercorrelations of all study variables within the call centre sample are shown in Table 2. In line with expectations, task-related stressors, social stressors, and emotional dissonance showed a pattern of positive associations with psychosomatic complaints, irritated reactions, inability to switch off, resigned attitude towards the job and intention to quit, as well as negative associations with job satisfaction and affective commitment. Against expectations, positive relationships with concentration demands were found for job satisfaction, and affective commitment and a negative relationship was found with intention to quit. For job control, the pattern was similar as for the stressors but with a reversed sign. Job control was positively related to job satisfaction and affective commitment and negatively associated with irritated reactions, psychosomatic complaints, resigned attitude towards the job, and intention to quit. For job complexity/variety, the pattern is similar as for control, with regard to jobrelated attitudes. Job complexity/variety has clear positive associations with job satisfaction and affective commitment and a negative association with intention to quit. However, we found no associations for the well-being variables. Working conditions predicting well-being and job-related attitudes Table 3 shows the prediction of well-being and job-related attitudes by working conditions, controlling for age, sex, language area, education level, and NA.

Sample size N = 212–234. Coefficients above r = .12 are significant at p < .05; above r = .16 at p < .01; and above r = .22 at p < .001.

Intercorrelations of all study variables within the call agents sample

TABLE 2

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sex, language region, and level of education were included in Step 1 but are not shown. Sample sizes: N=209–222 call agents. Standardized regression coefficients (beta-weights) are from the final model. *p < .05; **p < .01; ***p < .001.

aAge,

Prediction of well-being and job attitudes by working conditions within the call agents sample

TABLE 3

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Control variables predicting well-being, and job-related attitudes. Control variables are important for all dependent variables except for job satisfaction, affective commitment and intention to quit. Typically, it is NA and language region that are responsible for this. Positive associations of both variables are found with inability to switch off, irritated reactions, psychosomatic complaints, and resigned attitude towards the job. Moreover, women show higher psychosomatic complaints. Job control and job complexity/variety predicting well-being, and job- related attitudes. Controlling for demographic variables (age, sex, educational level, and language area) and NA, job control predicts negatively intention to quit beyond other working conditions (i.e., task-related stressors, social stressors, emotional dissonance, and job complexity/ variety) (ΔR2 = .02, p < .05). Job complexity/variety predicts job satisfaction (ΔR2 = .04, p < .01) and affective commitment (ΔR2 = .04, p < .01), beyond control variables and other working conditions. Therefore, support for Hypothesis 4 is limited to job-related attitudes concerning job control and job complexity/variety. Altogether, Hypothesis 4 is not very well supported. Task-related and social stressors predicting well-being and job-related attitudes. Some task-related stressors explain additional variance beyond job control, and job complexity/variety and each of the dependent variables is affected by at least one type of task-related stressors (see Table 3). Problems of work organization explain variance in psychosomatic complaints, job satisfaction, resigned attitude towards the job, and affective commitment. Time pressure explains variance in inability to switch off, and uncertainty predicts negatively job satisfaction. Altogether, Hypothesis 5a is moderately supported. Altogether social stressors are the most consistent predictor among stressors showing unique effects on inability to switch off (ΔR2 = .02, p < .05), psychosomatic complaints (ΔR2 = .02, < .05), job satisfaction (ΔR2 = .06, p < .001), resigned attitude towards the job (ΔR2 = .06, p < .001), and intention to quit (ΔR2 = .02, p < .05) beyond task-related stressors, emotional dissonance, job control, and complexity/variety. Altogether we revealed satisfying support for Hypothesis 5b with regard to context-free well-being, jobrelated well-being, and job-related attitudes. In Hypothesis 5c, we expected emotional dissonance to predict well-being and jobrelated attitudes over and above the types of stressors that are more established and more often employed in occupational stress research (task-related stressors and social stressors), and controlling for job control and job complexity/variety. For irritated reactions (ΔR 2 = .04, p < .01), and psychosomatic complaints (ΔR2 = .07, p < .001), emotional dissonance yields a unique contribution. For job satisfaction, resigned attitude, personal accomplishment, and intention to quit, however, this is not the case, although bivariate relationships were statistically significant for all these variables. Altogether, Hypothesis 5c receives support with regard to context-free well-being, but not for job-related attitudes. In general, it should be noted that, in all analyses, regression coefficients hardly changed when NA was removed as predictor.

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DISCUSSION This article had two major objectives. First, we wanted to investigate if working conditions of call agents are characterized by low control and low complexity and variety, as has been reported in the literature. Moreover, we wanted to study whether task-related stressors (except time pressure) and social stressors do not differ from other occupations. Moreover we tested if time pressure was higher among call agents. Second, we wanted to investigate the prediction of various indicators of well-being by aspects of the work situation of call agents, with a special emphasis on (1) the unique role of social stressors, (2) the unique role of emotional dissonance as a stressor, and (3) the specific outcome variables that are linked to particular aspects of the work situation. Therefore, one general aim of this article is to replicate previous findings concerning (1) differences in working conditions and well-being between call agents and traditional jobs and (2) associations of working conditions with well-being and job-related attitudes among call agents. Beyond the mere replication it contributes to occupational stress research firstly because a broad variety of dependent variables is used (context-free wellbeing, job-specific well-being, and job-related attitudes). Moreover a unique contribution is, that sample comparisons as well as predictions are controlled for negative affectivity, and that predictions of well-being and job related attitudes by stressors are also controlled for other working conditions. Job design With regard to the first issue, we do, indeed, find significantly lower control and lower complexity and variety for call agents as compared to a sample of employees in more traditional jobs that require long lasting vocational training, thus confirming the general trend reported in the literature that reports overly simplified and repetitive tasks with low control among call centre agents. With regard to task-related stressors, however, the picture for our sample of call agents is more favourable than for the comparison sample. This suggests that, apart from initial design decisions, which seem to have been taken according to the usual pattern of strong labour division, the investigated organization had undertaken respectable efforts to install acceptable working conditions. The combination of less control and complexity/fewer variety but also lower task-related stressors are likely to be responsible for the finding that, overall, well-being among call-agents is at a similar level as it is in the comparison sample, which we had not expected. Specifically, call agents have higher values on psychosomatic complaints but lower ones on irritation, with no difference in job satisfaction, resigned attitude towards one’s work, and affective commitment. Interestingly, intention to quit is even lower among call agents. This may be due to the fact that the comparison sample is younger, and therefore might anticipate changes more than would be true for an older sample. Nevertheless, given the rather high turnover rates sometimes reported for call agents (Baumgartner et al., 2002), this result seems surprising. It could also be that call agents plan their changes less actively but rather react more spontaneously to opportunities that arise, which might imply that their threshold to quit their job is lower, and thus would explain why a low mean intention to

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quit might still be associated with a rather high actual turnover rate. Moreover, this seems plausible because call agents did not invest much in their current job in terms of training and if they quit their job they do not lose much in terms of job demands and resources. That job control and complexity and variety are rather low, and that both predict intention to quit, certainly has implications for job design. Increasing job control (job enrichment) seems to be the most urgent need. Reducing computer control, for instance by making decisions about breaks, or even planning their shifts, by themselves, would increase time control (and also help to guard against fatigue—Matthews, Davies, Westerman, & Stammers, 2000). Deciding on how to deal with questions that one cannot answer immediately (or may not answer immediately even if one knew the answer, because they are outside of one’s competences), rather than being required to refer them to a specialist, could be examples of how method control could be improved. Combining direct customer contact with post-processing tasks, rather than having these executed by back-office people, could be a good way of improving complexity and variety (see Isic et al., 1999, for similar suggestions). On the stressor side, social stressors and problems in work organization are the two aspects most consistently linked to well-being. With regard to the latter, qualitative results revealed that many of these problems are related to poor information flow. For instance, it can happen that a new product is introduced and heavily advertised, but call agents are not informed in advance, and thus are confronted with customer questions that take them by surprise. Furthermore, if call agents were not only informed in advance, but also consulted, they could be very helpful in avoiding problems, as their customer contacts often enable them to anticipate typical difficulties. This would also improve complexity and variety and increase overall control through participation in product design. However, to rebind call agents, for instance, into product development and development of marketing strategies, would even offend against the principle of division of labour respectively task sharing—the basic idea of call centres. In fact, sufficient improvement of job control, complexity, and variety might require organization development, for instance, in terms of systematically reintegrating customer care into preliminary departments. Reducing social stressors would probably require specific training, supervision, or coaching for supervisors and/or teams. However, since social stressors often may arise from difficulties at work (Euler, 1977), social aspects may well profit from being treated in conjunction with problems of work organization—for instance, in the context of a quality circle (Cordery, 1996). Working conditions, well-being, and the specific role of emotional dissonance Task-related and social stressors. Overall, our results with regard to stressors and wellbeing confirm our expectations: Task-related stressors predict well-being, and so do social stressors. This conforms to the picture that is generally reported in the literature, and thus does not require much additional comment. A few aspects do, however, deserve to be mentioned. First, these relationships are found even when controlling for NA, thus

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countering an often-heard criticism (see Spector et al., 2000). Second, social stressors are especially powerful in predicting well-being, and this is important given that, over many years, this type of stressors has received less attention than seems warranted. Only in recent years can one observe an increased focus on this variable, and this research also demonstrated its powerful effects (Dormann & Zapf, 2002; Spector & Jex, 1998). Note that these are social stressors arising within the organization, that is, with colleagues and supervisors, not with customers! Third, the role of “Problems in Work Organization” (barriers to task fulfilment respectively regulation obstacles, cf. Frese & Zapf, 1994) should also be stressed. Our results concerning this variable underscore its importance, as do the findings in call centre samples by Zapf and colleagues (Isic et al., 1999; Zapf et al., 2001). Emotional dissonance. Based on previous findings (e.g., Zapf et al., 2001) we hypothesized that emotional dissonance would explain variance over and above the other investigated stress factors (job control, job complexity/variety, time pressure, concentration demands, work interruptions, problems of work organization uncertainty, and social stressors; Hypothesis 5), and this was confirmed for irritated reactions and psychosomatic complaints (context-free well-being). This adds further evidence to the role of emotion work in service occupations and underscores the role of emotional dissonance as a stressor in its own right. Strengths and limitations The greatest weaknesses of this study are certainly its cross-sectional design and exclusive use of self-reports. Furthermore, the focus on one organization limits the generalizability of our results to other populations, both within and beyond call centres. On the strong side of our study is the fact that we could demonstrate unique relationships between work characteristics and well-being after controlling for NA and other working conditions, and that we employed a broad set of well-being measures, including job-specific well-being and context-free indicators. It should also be mentioned that our results in many respect resemble those obtained by Dormann et al. (2002) and by Zapf et al. (1999), thus lending additional credibility to both studies. However, our approach of testing the influence of working conditions on well-being and job-related attitudes by controlling each of the tested effects for numerous other work-related variables has advantages and disadvantages. The advantage of that kind of simultaneous testing is, that results show which working condition contributes independently (uniquely) of other working conditions to well-being and job-related attitudes. However, a disadvantage is, that several effects that would appear by testing less comprehensively using for instance only task-related and social stressors as predictors, are hidden because of overlapping variance of the numerous predictor variables. For instance, using exclusively job control and job complexity/variety as work-related predictors, job control predicts beyond the above-described effects also psychosomatic complaints, job satisfaction, and resigned attitude towards the job, whereas job complexity/variety

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predicts beyond the above-described effects also intention to quit (all effects in the expected direction). CONCLUSIONS In sum, our study shows, once again, the tendency for a strong division of labour in call agent jobs, and it documents again the relationship of these work characteristics with lower levels of well-being and impaired job-related attitudes. This calls for efforts to redesign such jobs, yielding more autonomy, variety, and complexity for instance by job enrichment. At the same time, our data show comparatively low levels in terms of taskrelated stressors, indicating that job design in the organization we investigated acts more strongly on stressors, and this is, in our sample, in a positive direction. Furthermore, our study shows relationships between complexity and variety, control, and task-related stressors on well-being and intention to quit. It replicates findings that emotional dissonance is a stress factor in its own right. Moreover, it suggests that social stressors should be measured on a regular basis in addition to task-related stressors. REFERENCES Ashforth, B.E., & Humphrey, R.H. (1993). Emotional labor in service roles: The influence of identity. Academy of Management Review, 18, 88–115. Baillod, J., & Semmer, N. (1994). Fluktuation und Berufsverläufe bei Computerfachleuten [Turnover and career paths among computer specialists]. Zeitschrift für Arbeits- und Organisationspsychologie, 38, 152–163. Baumgartner, M., Good, K., & Udris, I. (2002). Call centers in der Schweiz. Psychologische Untersuchungen in 14 Organisationen [Call centres in Switzerland. Psychological investigations in 14 organizations] [Reports from the Institute for Work Psychology]. Zurich, Switzerland: Swiss Federal Institute of Technology . Bluedorn, A.C. (1982). The theories of turnover: Causes, effects, and meaning. Research in the Sociology of Organizations, 1, 75–128. Brief, A.P., Burke, M.J., George, J.M., Robinson, B., & Webster, J. (1988). Should negative affectivity remain an unmeasured variable in the study of job stress? Journal of Applied Psychology, 73, 193–198. Bruggemann, A. (1974). Zur Unterscheidung verschiedener Formen von “Arbeitszufriedenheit” [Different forms of job satisfaction]. Arbeit und Leistung, 28, 281–284. Büssing, A. (1992). A dynamic view of job satisfaction in psychiatric nurses in Germany. Work and Stress, 6, 239–259. Büssing, A. (1999). Can control at work and social support moderate psychological consequences of job insecurity? Results from a quasi-experimental study in the steel industry. European Journal of Work and Organizational Psychology, 8, 219–242. Clegg, C., Axtell, C., Damodaran, L., Farbey, B., Hull, R., Lloyd-Jones, R., Nicholls, J., Sell, R., & Tomlinson, C. (1997). Information technology: A study of performance and the role of human and organizational factors. Ergonomics, 40, 851–871.

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Emotional dissonance, emotional exhaustion and job satisfaction in call centre workers K.A.Lewig and M.F.Dollard Work & Stress Research Group, School of Psychology, The University of South Australia, Adelaide, Australia

The rapid rise of the service sector, and in particular the call centre industry has made the study of emotional labour increasingly important within the area of occupational stress research. Given high levels of turnover and absenteeism in the industry this article examines the emotional demands (emotional labour) of call centre work and their relationship to the job satisfaction and emotional exhaustion in a sample of South Australian call centre workers (N = 98) within the theoretical frameworks of the job demand-control model, the effortreward imbalance model, and the job demands—resources model. Qualitatively the research confirmed the central role of emotional labour variables in the experience of emotional exhaustion and satisfaction at work. Specifically the research confirmed the preeminence of emotional dissonance compared to a range of emotional demand variables in its potency to account for variance in emotional exhaustion and job satisfaction. Specifically, emotional dissonance mediated the effect of emotional labour (positive emotions) on emotional exhaustion. Furthermore emotional dissonance was found to be equal in its capacity to explain variance in the outcomes compared to the most frequently researched demand measure in the work stress literature (psychosocial demands). Finally, emotional dissonance was found to exacerbate the level of emotional exhaustion at high levels of psychosocial demands, indicating jobs combining high levels of both kinds of demands are much more risky. Future theorizing Correspondence should be addressed to M.F.Dollard, School of Psychology, University of South Australia, City East Campus, Adelaide South Australia, 5600. Email: [email protected] Kind thanks for the helpful suggestions of the anonymous reviewers.

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about work stress needs to account for emotional demands, dissonance in particular. Potential ways to alleviate emotional exhaustion due to emotional dissonance is to reduce other psychosocial demands, increase rewards, support and control as conceptualized in the JDR model. Ways to boost job satisfaction are to increase control, support, and rewards. The last two decades of the twentieth century witnessed a major global shift in the distribution of employment away from agriculture and industry into the service sector (Godbout, 1993). Concomitant with this transition has been the creation of a relatively new labour market characterized by work roles that emphasize interactions between front-line service workers and customers. As a consequence a new type of work demand, that of emotional labour, has emerged as a key component of interactive service work. One such example of work requiring emotional labour is that of call centre work. This work requires constant interaction with customers, and the requirement to regulate emotions at work. It is not unusual to experience constant abuse from angry customers, and in these situations the call centre worker (CCW) must maintain organizational standards with respect to customer service—adherence to the organizational value that the customer is always right. Paradoxically, while the unique role of the call centre is the creation and maintenance of good customer relationships, call centres themselves have evolved in response to significant technological advances as well as global demands for cost-cutting initiatives. The CCW is therefore faced with the opposing goals of optimizing productivity while delivering superior customer service. Even in call centres driven by quality rather than quantity, call centre work is of itself demanding, repetitive, and often stressful (Taylor & Bain, 1999; Wallace, Eagleson, & Waldersee, 2000). This is reflected in high levels of turnover and absenteeism. Staff turnover in the Australian call centre industry is estimated to be 18% per year, representing a cost of Australian $330m annually (Information Industries Training Advisory Board, 2001). In call centres characterized by high stress, turnover is reported to be almost double the industry average. Stress-related absenteeism is estimated to cost the industry $A 7.5m per year (ACTU Call Centre Unions Group, 2001). Call centres are growing at an astonishing 40% per year globally. In Australia, call centre growth is forecast at around 20–25% annually (ACTU Call Centre Unions Group, 2001). Given the rapid growth of the call centre industry it is important from a practical perspective that organizations are aware of the impact of the emotional and psychological demands of call centre work on their employees in order to optimize the effectiveness and well-being of front-line workers and decrease the costs of turnover and absenteeism. From a theoretical perspective it is important that emotional labour is acknowledged in existing theories of occupational stress in order to assess its interaction with, and impact on, other workplace influences such as job and organizational characteristics (Abraham, 1998). The aims of this article are twofold. The first aim is to develop a more detailed understanding of the emotional demands associated with call centre work and to assess the relationship between these emotional demands and CCW well-being. To achieve this aim the different components of emotional labour and their relationship to emotional exhaustion and job satisfaction among CCWs will be explored. The second aim is to assess

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the salience of emotional dissonance in the prediction of CCW well-being and, further, to identify resources that may moderate the impact of emotional dissonance on call centre worker well-being. It is proposed to achieve these aims by (1) assessing the contribution of emotional dissonance to CCW emotional well-being and job satisfaction, (2) examining the relationship between emotional dissonance, organizational stressors, and resources and their combined effects on CCW emotional well-being and job satisfaction, and (3) placing the analysis of emotional dissonance within the context of a variety of theoretical viewpoints in order to arrive at a model that best predicts emotional well-being and job satisfaction among CCWs. In order to explicate these aims we will first discuss the concept of emotional labour and outline recent attempts to operationalize it. The relationship between emotional labour, other organizational work characteristics and employee well-being will then be reviewed. Finally three theoretical frameworks selected for the analysis of emotional labour will be presented and discussed. EMOTIONAL LABOUR The concept of emotional labour was first used by sociologist Arlie Hochschild (1983) to analyse the jobs of flight attendants and bill collectors and has been defined as “the effort, planning, and control needed to express organizationally desired emotions during interpersonal transactions” (Morris & Feldman, 1996, p. 98). According to Ashforth and Humphrey (1993, p. 96), “emotional labour is a double-edged sword”. In its functional capacity, emotional labour can serve to facilitate task effectiveness by providing the service worker with a means to regulate what are often dynamic and emergent interactions and thus provide the worker with a sense of increased self-efficacy. Emotional labour makes interactions with customers more predictable, and allows the service worker to maintain objectivity and emotional equilibrium by cognitively distancing him/herself from the implicated emotion. Emotional labour may also facilitate self-expression by enabling the service worker to “project at least some of the ‘authentic self into the enactment” (Ashforth & Humphrey, 1993, p. 94). On the other hand, emotional labour can become dysfunctional for the worker when dissonance between felt emotions and displayed emotions is experienced. This incongruence between feeling and action, termed emotional dissonance, may ultimately lead to lowered self-esteem, depression, cynicism, and alienation from work. Similarly, selfalienation may result when the worker ceases to recognize or even feel authentic emotions (Ashforth & Humphrey, 1993). There is a wide discrepancy in the literature exploring the relationship between emotional labour and employee well-being. Adelmann (1995) for example found no relationship between emotional labour and job outcomes in a study of table servers, whereas Wharton (1993) found that emotional labour actually enhanced job satisfaction. The relationship between emotional labour and job outcomes appears to be further complicated by the interaction of emotional labour with other work conditions such as job autonomy, job involvement, self-monitoring, and organizational identification (Adelmann, 1995; Schaubroeck & Jones, 2000; Wharton, 1993). In contrast Pugliesi

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(1999) found an independent effect of job conditions and emotional labour on job strain, job satisfaction, and psychological distress. Morris and Feldman (1996, 1997) posit that one reason for the discrepancies noted in the literature is the incomplete operationalization of the emotional labour construct. They have proposed a more rigorous conceptualization of emotional labour that considers both its qualitative (emotional dissonance) and quantitative (frequency and duration of emotional display) components and predict three outcomes of emotional labour based on these components. First, emotional exhaustion is predicted via emotional dissonance, based on the argument that emotional dissonance is a type of role conflict and role conflict has been shown to be a key antecedent of emotional exhaustion. Second, job dissatisfaction due to emotional dissonance is predicted through person-environment fit theory, which suggests that not all workers would find the requirement to express organizationally desired emotions dissatisfying. Thus frequency and duration of emotional labour (quantitative components) may not be relevant to job dissatisfaction. Rather it is the workers who experience dissonance (qualitative component) who will experience decreased levels of job satisfaction. A third outcome, role internalization, encompasses the argument put forward by Ashforth and Humphrey (1993) that work roles requiring emotional labour also carry pressure to internalize role demands because failure to internalize organizational display rules will ultimately lead to poor perceived job performance and job loss. However, overidentification with the work role so that too much emotional labour is expended in meeting high work demands can increase the risk of emotional exhaustion (Schaufeli & Enzmann, 1998). Expanding on the propositions of Morris and Feldman (1996, 1997), Zapf, Vogt, Seifert, Mertini, and Isic (1999) have recently developed a quantitative measure of emotional labour. The Frankfurt Emotion Work Scale (FEWS) differentiates five factors of emotional labour, namely the requirement to display positive emotions, the requirement to display negative emotions, the necessity to display sensitivity to the needs of the client (sensitivity requirements), the ability of an employee to decide when to engage in an interaction with a client and when that interaction will end (interaction control), and emotional dissonance. Following from Morris and Feldman’s (1996, 1997) proposition that frequency and duration of emotional labour need not directly impact on employee well-being, but may do so through emotional dissonance, Zapf et al. (1999) propose that the requirement to display positive emotions, negative emotions, and sensitivity requirements are not necessarily stressful but may become so through emotional dissonance. In a test of the FEWS scale on employees from social service institutions, the hospitality industry, and call centres, emotional dissonance was highly correlated with emotional exhaustion, depersonalization, irritation, and psychosomatic complaints. Emotional dissonance was negatively associated with job satisfaction in all but the hospitality industry sample (Zapf et al., 1999). In a further study using the FEWS to investigate the relationship between organizational stressors, social stressors, emotional labour, and burnout, emotional dissonance was identified as the most stressful aspect of emotional labour. Further, the contribution of emotional dissonance to emotional exhaustion and depersonalization was similar to that of task and organizational stressors (Zapf, Seifert,

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Schmutte, Mertini, & Holz, 2001). Based on the concepts discussed above we hypothesized that of all the emotional labour factors emotional dissonance would account for the most variance in emotional exhaustion and job dissatisfaction (Hypothesis 1). EMOTIONAL LABOUR AND ORGANIZATIONAL STRESSORS AND RESOURCES Recent attempts to clarify the relationship between emotional labour and other organizational variables in the prediction of employee well-being have consistently reported that job related stressors, especially work overload, time pressures, and role conflicts, are more strongly associated with emotional exhaustion than client-related stressors such as interactions with difficult clients (Lee & Ashforth, 1996; Schaufeli & Enzmann, 1998). However, Zapf et al. (2001) observe very few of these studies have directly measured emotional demands. Further Schaufeli and Enzmann argue that the high correlations reported between workload and emotional exhaustion may result from the conceptual overlap between task-related and client-related job characteristics. For example, call centre workers who are expected to provide a service to the customer (client related) and at the same time answer as many calls as possible (workload) may experience time pressure and/or role conflict. Based on the dual level exchange theory of burnout, Zapf et al. (2001) posit that one can expect to find interactions between organizational stressors and emotional demands in the development of emotional exhaustion due to the combined effect of lack of perceived client reciprocity when emotional demands are high, and lack of perceived organizational reciprocity when organizational stressors are high. However, a unique contribution of emotional demands to emotional exhaustion can also be expected, as emotional dissonance by its definition may act as a stressor independent of other organizational stressors. In Zapf et al.’s (2001) comparison of the relationship between emotional labour variables, organizational variables, and social variables in the prediction of burnout across a range of service jobs including call centre work, a unique contribution of emotional labour variables to burnout was noted over and above the contribution of other variables. Interaction effects between task-related stressors and emotional dissonance were also noted in the prediction of emotional exhaustion. Job resources are also relevant in the prediction of employee well-being. As found in the broader work stress literature researching psychosocial demands and employee wellbeing, job resources such as social support and autonomy also appear to moderate the relationship between emotional demands and employee well-being (see Zapf, 2002). Guided by the concepts discussed above we hypothesized that emotional dissonance would explain a unique proportion of the variance in emotional exhaustion and job satisfaction beyond that accounted for by psychosocial demands (Hypothesis 2). To further conceptualize how the various work demands and resources may combine together we searched the literature for a theoretical framework in which to place an analysis of emotional dissonance and its relationship to employee well-being. As de Jonge and Dormann (2003) observe, although a variety of theoretical frameworks are available for the analysis of workplace stressors, it is difficult to decide what framework is relevant

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to a particular work setting, a decision that is complicated further by the general lack of consensus on the value of the contribution made by existing theories to the understanding of work stress. In response to this predicament, three current models of work stress: the job demand-control model (JDC; Karasek, 1979); the effort—reward imbalance model (ERI; Siegrist, 1998); and the job demands-resources model (JDR; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001) were examined. The JDC and ERI models of work stress were selected for analysis of call centre work because they are predominant theories that have been critically examined in the literature and empirically supported. The JDC and the ERI model both predict that stress arises as a consequence of an imbalance between the worker and the work environment. However, while the JDC model identifies the environmental constraint, decision latitude, as the central modifier of the impact of workplace demands on strain (Baker, 1985), the ERI identifies occupational reward as the key modifier. Further, where the JDC model focuses attention on work content, the ERI makes a distinction between situational and personal characteristics. Specifically the ERI model identifies individuals who engage in a pattern of active coping with work demands, characterized by excessive effort and a higher than average need for approval and esteem (overcommitment), as more susceptible to the adverse effects of effort-reward imbalance in the long run (Joksimovic, Siegrist, Peter, Meyer-Hammar, Klimek, & Heintzen, 1999). The JDC model and the ERI model have been criticized on the grounds that the measurement of psychological demands employed by the models may not be applicable across occupational groups (de Jonge & Dormann, 2003; Kasl, 1996). It is claimed that the indices commonly used to measure global job demands are operationalized in terms of physical effort and time pressures, to the exclusion of other potential sources of strain (Melamed, Kushnir, & Meir, 1991; Van Der Doef & Maes, 1999). This study will partially address this criticism by assessing demands specific to service work (emotional demands). De Jonge, Mulder, and Nijhuis (1999) assessed the impact of emotional, physical, and psychosocial demands on the well-being of health care workers within the framework of the JDC model, and found that only psychological demands had a significant direct effect on emotional exhaustion. No direct or indirect effect of emotional demands on emotional exhaustion was evident. Using the ERI model as a theoretical framework, Van Vegchel, de Jonge, Meijer, and Hamers (2001) also failed to find an association between emotional demands and risk of emotional exhaustion among ancillary health care workers. The operationalization of the emotional labour construct may account for the failure to find an association between emotional demands and psychological well-being in these studies. A more recent theoretical model of work stress, the job demands— resources model (Demerouti et al., 2001) was further included for comparison in this study. The JDR model was chosen as it conceptually resembles a combined JDC/ERI model. Calnan, Wainwright, and Almond (2000) have reported that a combined JDC/ERI model improved the prediction of stress in general practitioners. The JDR model proposes that employee well-being is related to a wide range of workplace variables that can be conceptualized as either job demands (the physical, social, or organizational aspects of the job that require sustained physical or psychological effort) or job resources (those aspects

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of work that may reduce job demands, aid in achieving work goals, or stimulate personal growth, learning, and development) (Bakker, Demerouti, Taris, Schaufeli, & Schreurs, 2003; Demerouti et al., 2001). The JDR model predicts that burnout occurs through exposure to job demands (via emotional exhaustion) and lack of resources (via cynicism and a reduced sense of personal accomplishment), and that an interaction between job demands and job resources is the most important for the development of burnout. A recent test of the model across four different home care organizations found a significant interactive effect of job demands and resources in the prediction of exhaustion in two of the organizations over and above the main effects for these two variables (Bakker et al., 2003). Thus using the concepts of the JDC and ERI models, the JDR model predicts that employees experiencing high job demands, and low levels of resources (control, support, rewards) are the most likely to experience the highest levels of work stress. In summary, while there is evidence to support the distinctive contribution of both the JDC and the ERI in the prediction of work stress, there is also evidence to suggest that both models combined may enhance the overall explanatory power. It is therefore hypothesized that the JDR model will account for more variance than either the JDC or ERI alone (Hypothesis 3). METHOD Survey sample The study surveyed call centre workers in metropolitan Adelaide. Contact details for call centres in Adelaide were obtained from: the Australian Services Union; through contacts given by participating call centres; and through the researcher’s personal contacts. A total of 16 call centres were contacted and permission to recruit volunteers was obtained from 9 of these centres. The reasons given by the call centres for not wishing to participate were that employees had recently been surveyed by the company (f = 2), the call centre was in the process of moving to new premises (f = 1), the manager who had authority to approve participation was on holidays for a number of weeks (f = 1), and the type of work that the call centre handled was unusual and not suited to the study (f = 1). Of the 195 questionnaires given to managers to be handed out to volunteers, 99 were returned (1 of which was unusable), representing a response rate of 50.7%. The participating call centres were drawn from a variety of industry sectors as shown in Table 1. The survey respondents were predominantly female (M = 27, F = 71) and ranged in age from 18 to 63 years (M = 32 years, SD = 10.6 years). Fifty-three per cent were employed on a permanent basis and 47% were employed on a temporary or casual basis. Length of service was relatively short with 65% of respondents having worked less than 1 year in their current position. In terms of call type, 37% of respondents handled inbound calls only, 24% handled outbound calls only, and 37% handled both inbound and outbound calls.

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TABLE 1 Industry participation and response rates

Note: Numbers in brackets indicate number of participating organizations.

Demographics Demographic data was collected with regard to age, sex, education level, work status, and length of service. Participants were also asked to estimate the number of calls and the length of calls taken each day and whether their work predominantly involved making outbound calls, receiving inbound calls, or a mixture of both. Measures Emotional demands. The emotional demands of call centre work were measured using five subscales of the recently developed Frankfurt Emotion Work Scales-E (FEWS; Zapf et al., 2001). The FEWS are the only theoretically based, empirical measures of emotion work developed to date. The FEWS subscale, Display of Positive Emotions (EP), comprises five items measuring the requirement to display positive emotions (e.g., “How often in your job do you have to display pleasant emotions towards customers?”). The subscale, Display of Negative Emotions (EV) is made up of seven items designed to assess the requirement to display negative emotions when dealing with customers (e.g., “How often do you have to display unpleasant emotions towards customers?”). The Demand for Sensitivity subscale (ES) comprises four items measuring the extent to which empathy or knowledge of the customers’ current feelings are a requirement of the job (e.g., “How often in your job is it of importance to know how the customer is feeling at the moment?”). The Interaction Control subscale (EH) comprises four items designed to measure the degree of influence an employee has in his or her interactions with customers (e.g., “How often does your job allow you to end conversations with customers if you consider it to be appropriate?”). Finally, the five items of the Emotional Dissonance subscale (ED) assess the level of suppression of organizationally undesirable emotions and the display of unfelt emotions (e.g., “How often in your job do you have to suppress emotions in order to appear ‘neutral’ on the outside?”). Responses for each of the scales were rated from 1 = very rarely/never to 5 = very often (several times an hour). Internal reliabilities for the scales as measured by Cronbach’s alpha were .34 for positive emotions, .79 for negative emotions, .26 sensitivity demands, .24 for interaction

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control, and .72 for emotional dissonance. We will address the low reliabilities in the results section. Psychosocial demands. The Effort—Reward Imbalance Questionnaire (ERI) was used to measure psychosocial demands (extrinsic effort). The measure of psychosocial demands from the ERI Questionnaire is conceptually and operationally similar to the measure of psychosocial demands in the Job Content Questionnaire (used to test the JDC model). Six items were used to measure work place demands (effort) including statements such as “I have constant time pressure due to a heavy work load”. Respondents were asked whether they agreed or disagreed with the statements on a 2-point scale. The alpha coefficient was .67. Rewards. The ERI questionnaire was also used to measure rewards (monetary, esteem, status). Eleven items of the ERI scale are designed to measure the perceived rewards of the job and include statements such as “considering all my efforts and achievements, I receive the respect and prestige I deserve at work”. The reward scale items were rated agree or disagree on a 2-point scale. The reward scale was reverse scored so that a high score reflected high reward and low score reflected low reward. The alpha coefficient was .82. Autonomy. The Job Control Scale of the Job Content Questionnaire (Karasek, 1998) was used to measure job autonomy. The scale includes nine items designed to measure skill discretion and decision authority. The scale is rated from 1 = strongly disagree to 4 = strongly agree and includes items such as “my job allows me to make a lot of decisions on my own” and “my job requires me to be creative”. Cronbach’s alpha for the scale was .82. Social support. The Social Support Scale of the Job Content Questionnaire (Karasek, 1998) was used to measure social support. The scale includes four items designed to measure co-worker support and four items designed to measure supervisor support. The scale is rated from 1 = strongly disagree to 4=strongly agree and includes items such as “the people I work with take a personal interest in me” and “my supervisor is helpful in getting the job done”. Cronbach’s alpha for the scale was .88. Emotional exhaustion. The emotional exhaustion scale of the Maslach Burnout Inventory (MBI; Maslach & Jackson, 1986) was used to measure feelings of being emotionally extended and depleted of one’s resources. The scale comprises eight items and includes statements such as “I feel emotionally drained from my work” and “I feel frustrated by my job”. Items were rated from 0 = never to 6 = every day. Cronbach’s alpha was .92. Job satisfaction. A single item taken from the Job Satisfaction Scale (Warr, Cook, & Wall, 1979) was used to measure job satisfaction. The item, “taking everything into consideration how do you feel about your job as a whole?” was assessed on a 7-point scale from 1 = extremely dissatisfied to 7 = extremely satisfied.

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Qualitative data. Qualitative data was obtained from two open-ended questions asking for “aspects of work contributing most to job satisfaction” and “most stressful aspects of call centre work”. The data was sought to confirm (or not) the centrality of emotional labour components of call centre work. Statistical treatment Overall scores for emotional exhaustion, demands, reward, control, support, and the emotion work subscales (emotional dissonance, display of negative emotions, display of positive emotions, interaction control, and sensitivity) were obtained by summing the individual items for each scale. Descriptive and frequency information was derived to assess the representativeness of the sample. Bivariate correlation analyses were then undertaken to delineate the relationship between the type of emotional labour performed, and its relationship to emotional exhaustion and job satisfaction, and to determine the relationship between emotional labour and other work place characteristics. Given the possibility that different statistical procedures used to test core theoretical aspects of the models could themselves lead to different findings (Cotton, Dollard, & de Jonge, 2003) we uniformly used standard hierarchical multiple regression analyses to examine the main and interaction effects proposed in each hypotheses as recommended by Cohen and Cohen (1983). Prior to the analysis we standardized the independent measures to deal with problems of multicollinearity that arise from cross-product terms (Aiken & West, 1991). As moderated regression leads to a lack of power to detect interactions of significance the criterion for the significance of the increase in R2 was .1 (Frese, 1999). To test Hypothesis 1, a standard regression model was used to regress emotional exhaustion and job satisfaction on each of the emotional demands measures. To test Hypothesis 2, the main effects of demand and emotional dissonance were assessed at Step 1, followed by the interactive effects of demands and emotional dissonance at the second step. To test Hypothesis 3, that the JDR model would account for more variance than either the JDC or ERI model in both emotional exhaustion and job satisfaction, we adopted the following procedure. Given that, of the emotional demands, emotional dissonance was the only one associated with the outcome measures, an important question became: What resources could best reduce the negative impacts of emotional dissonance at work? Also, given that psychosocial demands have been shown in numerous studies to be reduced by control and rewards, to give every possibility of finding an effect if it existed (i.e., to increase the power), we focused only on emotional dissonance in the models. With respect to the JDC model, emotional dissonance and control were entered at the first step, to test for the main effects of each variable, then the interaction (Emotional dissonance×Control) was entered at the second step. Reward was added at the third step to see whether the addition of reward to the JDC model would improve its predictive power. The two interactions (Emotional dissonance×Reward and Emotional dissonance ×Reward×Control) were entered at the fourth step.

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TABLE 2 Means, standard deviations, ranges, and sample sizes for the study variables

To test the ERI model, emotional dissonance and reward were entered at the first step, followed by their interactions (Emotional dissonance×Reward) at the second step. Control was then entered at the third step to ascertain whether the addition of control to the ERI model would improve its predictive power. The two interaction terms (Emotional dissonance×Control and Emotional dissonance×Control×Reward) were entered at the fourth step. Finally, to test the combined JDR model, emotional dissonance, rewards, and control were entered at the first step of the analysis. Support was entered at the second step to ascertain whether this additional resource would add any variance. Then six interaction terms were entered (Emotional dissonance ×Control, Emotional dissonance×Reward, Emotional dissonance× Support, Emotional dissonance×Control×Reward, Emotional dissonance×Control×Support, and Emotional dissonance×Control× Support×Reward). RESULTS Descriptives Table 2 presents the descriptive statistics for all variables used in the regression analysis. As can be seen the alpha coefficients of positive emotions, interaction control, and sensitivity requirements are low. We examined the interitem correlations and removed items that were contributing to a low alpha level. By the removal of one item from the positive emotion scale the alpha improved to .54, the removal of one item from the interaction control scale improved the alpha to .47, and removal of one item from the sensitivity scale improved the alpha to .76. We decided not to try to make further improvements to the scales as, despite two being of low reliability, interaction control was now only three items. Further the reliabilities were similar to those reported by Zapf et al. (1999) for positive emotions (.52) and for interaction control (.51).

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TABLE 3 Frequency of emotional labour requirements

Emotional exhaustion. Descriptive statistics revealed that call centre workers in this sample experienced moderate levels of emotional exhaustion. A comparison with other high strain professions suggests call centre workers in this sample experienced levels of exhaustion similar to that of police officers (N = 430, M = 17.55, SD = 10.90) and probation/correction officers (N = 386, M = 19.49, SD = 11.33) (Schaufeli & Enzmann, 1998), but less than human service workers (e.g., social workers) from a large public sector in South Australia (N = 770, M = 20.06, SD = 11.11; Dollard, Winefield, & Winefield, 2001). Twenty-four per cent of respondents reported high levels of emotional exhaustion, twenty-nine per cent reported moderate levels of emotional exhaustion, and forty-seven per cent reported low levels of emotional exhaustion. Job satisfaction. Seventy-five per cent of call centre workers reported being satisfied with their jobs. The results were as follows: extremely satisfied 7%, very satisfied 29%, moderately satisfied 39%, not sure 8%, moderately dissatisfied 12%, very dissatisfied 3%, and extremely dissatisfied 1%. The levels of job satisfaction (M=5.02) were slightly higher than other South Australian public sector human service workers (N = 771, M = 4.84, SD=1.37) (Dollard et al, 2001). Emotional labour. As shown in Table 3, call centre workers reported that their jobs entailed high positive emotional display and emotional dissonance requirements and low negative emotion display and sensitivity requirements. Around 21% of workers report the experience of emotional dissonance several times an hour. Respondents also reported that they were often in control of the duration of their interactions with customers. The length of interaction on the phone was short with 78% of calls lasting less than 5 minutes, 16% of calls lasting 5–10 minutes, and 5% over 10 minutes. Examination of bivariate correlations (refer to Table 4) between the emotional labour variables and the outcome variables revealed a significant relationship between emotional dissonance and emotional exhaustion, r(98) = .43, p < .01, dissonance and job satisfaction, r(98) = −.27, p <.01, and positive emotions and emotional exhaustion, r(98) = .21, p <.05. Positive correlations between all other emotional labour variables and outcome variables were nonsignificant. Positive emotions were significantly positively

*p < .05; **p < .01. Sex, 1=male, 2=female. All other scores, high scores indicate high scores on the variable.

Pearson intercorrelations of variables

TABLE 4

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Figure 1. The interaction effect of emotional dissonance and demands on emotional exhaustion

correlated with sensitivity requirements and emotional dissonance. Negative emotions were significantly positively associated with interaction control. Further, sensitivity requirements were significantly positively correlated with emotional dissonance and support. There was no correlation between psychosocial demands and emotional dissonance indicating conceptual distinction between the variables. Regression analysis Emotional exhaustion and job satisfaction were regressed onto all of the emotional demand scales in two separate regression analyses. In each case only emotional dissonance was associated with the outcome measures: with emotional exhaustion (beta=.44, p < .001) and with job satisfaction (beta = .30, p < .05). This is unequivocal support for Hypothesis 1. To further test the possibility that emotional dissonance mediated the effect of positive emotion display on emotional exhaustion we entered positive emotions at the first step (beta = .21, p < .05). We then entered emotional dissonance at the second step (beta = .40, p < .001) at which point positive emotions became nonsignificant indicating a mediation effect (Baron & Kenny, 1986). Hierarchical regression analyses A hierarchical regression model regressing emotional exhaustion on the main effects of emotional dissonance and demands, and their interactions, showed significant and equal effects for both demands (emotional dissonance, beta = .39, p < .001; demands, beta = .40, p < .001) and a significant interaction effect (beta = .14, p < .10). (See Figure 1.)

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TABLE 5 Hierarchical regression analysis predicting emotional exhaustion from emotional dissonance, control, and rewards

*p < .01; ***p < .001. ED=emotional dissonance, C=control, R=rewards.

A similar regression model showed main effects for emotional dissonance (beta=.25, p < .01) and for psychosocial demands (beta = .23, p < .05) on job satisfaction. There were no interaction effects. Hypothesis 2—that emotional dissonance would account for unique variance in the outcome measures beyond that of psychosocial demands—was supported. Emotional exhaustion. Consistent with the JDC model, hierarchical regression analysis revealed significant main effects for dissonance and control with respect to emotional exhaustion (see Table 5 for beta values, and significance). However, the addition of rewards to the model appeared to mediate the impact of control on emotional exhaustion. In the presence of rewards, control was no longer significant in the prediction of emotional exhaustion. The model R2 for the JDC model was .29. There were no significant interaction effects.

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TABLE 6 Hierarchical regression analysis predicting job satisfaction from emotional dissonance, control, and rewards

*p < .05; **p < .01; ***p < .001. ED = emotional dissonance, C = control, R = rewards.

The main effects of dissonance and rewards using the ERI model were significant, and the interactive term did not add any further significant variance. The model R2 for the ERI model was .44. Finally the JDR model, adding key ingredients of the ERI/JDC model together, identified dissonance, rewards, and support as significant predictors and accounted for .47 for the variance and adding in social support a total of .50. No interaction terms were significant (see Table 7). These results show that the JDR accounted for the most variance, supporting Hypothesis 3. Overall the rewards aspect in the models seemed to be the most important resource for reducing the impact of emotional dissonance on emotional exhaustion, but the best model includes all three resources.

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Job satisfaction. Testing the main effects of emotional dissonance and control on job satisfaction, as per the JDC model, revealed a significant effect for control, as shown in Table 6. There were no significant interaction effects. The R2 for the model was .35. With respect to the ERI model, the main effects of emotional dissonance and rewards were significant, although there interactions were not. When control was added to the model it seemed to mediate the effect of emotional dissonance rather than add to it. Emotional dissonance was no longer significant in the presence of control in predicting job satisfaction. R2 for the model was .25. Finally the JDR model showed main effects for rewards and control but not for emotional dissonance (as before mediated through control). The R 2 for the combined ERI/JDC was .42 and when social support was added, also as a significant main effect, the R2 increased to .46. No interaction terms were significant (see Table 8). These results again support Hypothesis 3, that the key ingredients of the combined JDC/ERI model would account for the most variance in job satisfaction than either model alone. Further when social support was added, completing the full JDC model, the variance was highest.*p < .05; ***p < .001. ED = emotional dissonance, R = rewards, C = control, S = support. Qualitative data. The most frequently reported contributor to job satisfaction was providing good customer service, followed by good relationships with co-workers. The aspect of work considered to be most stressful was having to deal with angry and abusive customers, followed by pressure to meet targets and the repetitiveness of the job. These results confirm the centrality of emotion labour variables (dealing with angry/ aggressive customers) in the experience of stress at work, as well as in the experience of satisfaction at work (making customers feel happy). DISCUSSION The focus of this research was on the increasingly important aspect of service work, emotional labour, within the context of the call centre industry. The research aimed to assess the importance of emotional dissonance in relation to other work demands both emotional and psychosocial. Next the research specifically drew on various theoretical frameworks to assess the best combination of emotional dissonance and key resources in the work environment (control, rewards, supports) to account for variance in stress outcomes, with an eye to intervention. Role of emotional labour Qualitatively the research confirmed the central role of emotional labour variables in the experience of stress and satisfaction at work. Quantitatively the research confirmed the importance of emotional dissonance compared to a range of emotional demand variables in its potency to account for variance in emotional exhaustion and job satisfaction.

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TABLE 7 Hierarchical regression analysis predicting emotional exhaustion from emotional dissonance, control, rewards, and support

*p < .05; ***p < .001. ED=emotional dissonance, R=rewards, C=control, S=support.

This result confirms Morris and Feldman’s (1997) finding that of the three components of emotional labour examined, namely frequency of interactions, duration of interactions, and emotional dissonance, only emotional dissonance was associated with emotional exhaustion and job satisfaction. Specifically, we found that emotional dissonance fully mediated the relationship between positive emotional display and emotional exhaustion. The results of the present study are consistent with Brotheridge and Lee’s (1998, cited in Zapf et al., 1999) view that the emotional demands of work do not directly lead to emotional exhaustion but do so through their relationship with emotional dissonance. Support for this proposal is also evident in the comments made by call centre workers themselves. Overwhelmingly, the most stressful aspect of call centre work was dealing with angry, abusive, and dissatisfied customers. This suggests that dissonance between felt emotions and emotional display rather than the requirement to express positive and negative emotions per se contributes to strain and job dissatisfaction among this group of call centre workers. Surprisingly negative emotional display, sensitivity requirements, and interaction control were not associated with any of the outcome measures as found by Zapf et al. (2001) (correlations ranged from .20 to .10). A possible reason is our smaller sample size and lower power. Another possibility, in relation to negative emotional display, is that the characteristics of the call centre work in the study required infrequent negative emotional display (compared to positive emotional display). Workers may simply have ample opportunity to recover from negative emotional displays. Further, emotional dissonance was found to be equal to the most frequently researched demand measure in the work stress literature (psychosocial demands) in its capacity to

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TABLE 8 Hierarchical regression analysis predicting job satisfaction from emotional dissonance, control, rewards, and support

*p <. 05; **p < .01; ***p <. 001. ED = emotional dissonance, R = rewards, C = control, S = support.

explain variance in both emotional exhaustion and job satisfaction. Emotional dissonance was found to exacerbate the level of emotional exhaustion at high levels of psychosocial demands, indicating jobs combining high levels of both kinds of demands are much more risky (see Figure 1). This is entirely consistent with Zapf et al. (2001), who also found numerous emotional dissonance*job stressor interactions: “If all stressors are high at the same time exaggerated levels of emotional exhaustion [will] occur” (p. 543). A lack of correlation between psychosocial demands and emotional dissonance suggests that the two constructs act independently as workplace stressors and adds weight to the argument about the importance of including both kinds of demands especially in call centre or other human service style work. These findings add to the literature in a significant way and underscore the importance of exploring the emotional aspects of the work environment and looking at their possible interactions with other job characteristics (Abraham, 1998). Levels of emotional exhaustion and job satisfaction On average the level of emotional exhaustion among the call centre workers was moderate, levels of satisfaction were moderate to high compared with other occupations. As discussed below the levels of stress maybe underrepresented in this particular sample as we suspect a high level of turnover in the immediate population. Halik, Dollard, and de Jonge (2003), in a study of 102 South Australian CCWs, confirmed the link between emotional exhaustion and absenteeism (r = .24, p < .05) (but not between job satisfaction

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and absenteeism), confirming the importance of our findings in relation to broader industry issues. Modelling the effects of emotional dissonance on call centre workers The study found that the JDR accounted for the most variance in emotional exhaustion. Overall the rewards aspect in the models seemed to be the most important resource for reducing the impact of emotional dissonance, but the best model includes all three resources (rewards, control, support). In relation to job satisfaction again, support was found for the JDR model. The results further suggested the possible mediating role of control and the notion that emotional dissonance affects job satisfaction through control. None of the demand*resources interaction terms were significant. Indeed the observation was recently made in modelling the unique contributions of job demands and job resources to burnout that there is little evidence of an interactive effect (see Demerouti et al., 2001). Specifically the JDR was able to explain emotional exhaustion amongst this sample of call centre workers in terms of high levels of emotional dissonance, low rewards, and low levels of support. Further, support would appear to mediate the relationship between control and emotional exhaustion. These results however are in contrast to those of de Jonge et al. (1999), who found that while emotional demands had a direct effect on psychosomatic symptoms among their sample of health care workers, there was no direct significant effect on emotional exhaustion. Likewise van Vegchel et al. (2001) found that the relationship between high emotional demands and low rewards and emotional exhaustion was nonsignificant. In their study de Jonge et al. (1999) noted a significant positive correlation between emotional demands and psychosocial demands, which could account for the difference in results. Further, the Frankfurt Emotion Work Scale, used in the current study, may have provided an improved operationalization of emotion work and thus improved the construct validity of emotional demands. In addition, high rewards, high control, and high support as conceptualized by the JDR appears to contribute to job satisfaction amongst this group of call centre workers. Emotional dissonance on the other hand appears to have an insignificant effect on job satisfaction, a finding consistent with that of de Jonge et al. (1999). In summary, the potential ways to alleviate emotional exhaustion due to emotional dissonance is to reduce other psychosocial demands, and increase rewards, support, and control as conceptualized in the JDR model. The ways to boost job satisfaction is to boost control, support, and rewards. Methodological considerations The demographic characteristics in terms of gender, age, education level, and permanent/ temporary/casual ratios of the call centre workers in this study were generally reflective of the national profile of Australian call centre workers as reported by the ACTU Call Centre Unions Group (2001) and the Information Industries Training Advisory Board (2001). This gives us confidence that the sample obtained (with some difficulty given the

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apparent oversurveillance of this group), is representative and the results generalizable to other call centre workers. One difference however was that the average length of service was less than the national average of 2.5 years. Sixty-three per cent of call centre workers in the current study reported that they had worked in their current jobs for less than 12 months. Further, length of service was positively correlated with emotional exhaustion. This finding is consistent with the notion that stress increases with duration of exposure to stressors (Beehr, 1995; Dollard, 1996). The implication of this is that it is likely that we underestimate the level of distress, and the impact of emotional demands on stress in the call centre workers because their length of service is shorter (possibly due to the stressful nature of the work itself and a high turnover level). The present study has a number of limitations that need to be considered. First, data from this study was derived entirely from self-report questionnaires. This could lead to problems such as common method effects. There is no reason to expect that this problem would lead to some associations and not others, rather a general inflation of associations. Second, the present study did not attempt to control for the personality trait negative affectivity. Negative affectivity has been shown to potentially confound the relationship between stressors and strain in self-report research (Moyle, 1995). Indeed recent research (Chrisopolous, Dollard, & Dormann, 2003) shows a stronger correlation between negative affectivity and emotional dissonance (r = .33, p < .05) than with any other work environment measure (max r = .16), as well as an association between negative affectivity and emotional exhaustion (r = .48, p < .05). Although this may suggest that the solution is to control for negative affectivity in future research, the notion that prevailing negative affect states are determined by the work environment itself leading to an underestimation of the effect of stressors is a counter indication (Dollard & Winefield, 1998). Clearly longitudinal research is needed untangle this problem of interpretation. Third, the sample size in the current study was relatively small, and this no doubt has led to a failure to find associations where small effects could be expected (e.g., between some of the emotional labour variables and outcomes). Fourth, although the response rate was low we have confidence that it is representative of the larger population of call centre workers. Fifth, it is widely acknowledged that cross-sectional studies are unable to determine causality. While various models guided the hypotheses made in this study, causal connections cannot be assumed. Longitudinal research in the area would make an important contribution in confirming the findings of this study. Finally the reliability coefficients of two of the FEWS subscales were very low. This could be due to two different response formats being required within each of the scales. Interestingly, Halik et al. (2003), in a study of 102 South Australian CCWs, omitted questions using the A—B format (i.e., Person A can openly display his/her true feelings—Person B has to display feelings towards clients which do not match his/her true feelings. What is your job like?) and reported much higher reliabilities (positive .73; negative .71; sensitivity .72; dissonance . 82) but reported a similarly poor result for interaction control (.44). One of the scales with low reliability (positive emotions) correlated with emotional exhaustion at least where as the other scale (interaction control) did not. It is possible that the low reliability

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of scales affected results such that no or low associations were found with outcome measures. IMPLICATIONS Jobs that expose workers to high levels of emotional dissonance, such as having to deal with angry or aggressive customers and displaying positive emotions inconsistent with those genuinely experienced, may potentially be assisted through the provision of a range of key resources. Emotional dissonance affects all human service workers, even though they may vary in the extent to which their work involves lasting relationships with clients/customers, and in the amount of training they have received to deal with client/customer-related social stressors. For example health professionals typically develop long-lasting relationships with their clients, whereas call centre workers may have only a single brief interaction. In accordance with contemporary theories of work stress (conservation of resources, effort—reward imbalance, demand—control— support), Dollard, Dormann, Boyd, Winefield, and Winefield (2003) argue that social support and training designed to develop “role separation” are crucial resources needed to help service workers cope with the unique emotion stressors of their jobs. CONCLUSIONS Emotional labour is emerging as a key issue in modern work settings. This article underscores the importance of looking at emotional labour, in particular emotional dissonance in modelling and theorizing about workplace stress in call centre workers. Emotional dissonance is pre-eminent in comparison to other quantitative aspects of emotional demands (emotional labour), and is equally important as the often-explored psychosocial job demands, in accounting for affects on emotional exhaustion and job satisfaction. Further, emotional dissonance combines with psychosocial demands in an interactive way, such that workers exposed to high levels of both kinds of demands are at much greater risk for the development of emotional exhaustion. Theories of work stress can thus be improved by taking account of occupation specific demands and the broader social and economic environment within which contemporary workers operate. Further, a deeper understanding of emotional labour and its role in service work may be achieved by placing it within the framework of organizational psychological models of work stress. As the demand for call centre staff grows, it will be the organizations that provide healthy work environments that attract and retain the most valuable workers. The results of this article show that both employees and organizations alike can benefit from the creation of service jobs that enrich the working lives of call centre workers.

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REFERENCES Abraham, R. (1998). Emotional dissonance in organizations: A conceptualization of consequences, mediators and moderators. Leadership and Organizational Development Journal, 19(3), 137–146. ACTU Call Centre Unions Group. (2001). On the line: The future of Australia’s call centre industry. Sydney: Australian Council of Trade Unions. Adelmann, P.K. (1995). Emotional labor as a potential source of job stress. In S.L.Sauter & L. R.Murphy (Eds.), Organizational risk factors for job stress. Washington, DC: American Psychological Association. Aiken, L.S., & West, S.G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage Publications. Ashforth, B.E., & Humphrey, R.H. (1993). Emotional labour in service roles: The influence of identity. Academy of Management Review, 18(1), 88–115. Baker, D.E. (1985). The study of stress at work. Annual Review of Public Health, 6, 367–381. Bakker, A.B., Demerouti, A., Taris, T.W., Schaufeli, W.B., & Schreurs, P.J.G. (2003). A multigroup analysis of the Job Demands—Resources model in four home care organisations. International Journal of Stress Management, 10(1), 16–38. Baron, R.M., & Kenny, D.A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Beehr, T.A. (1995). Psychological stress in the workplace. London: Routledge. Calnan, M., Wainright, D., & Almond, S. (2000). Job strain, effort-reward imbalance and mental distress: A study of occupations in general medical practice. Work and Stress, 14(4), 297–311. Chrisopoulos, S., Dollard, M.F., & Dormann, C. (2003). Customer-related social stressors and reciprocity in radiation therapists: Main and interactive effects on burnout. Manuscript submitted for publication. Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Cotton, S.J., Dollard, M.F., de Jonge, J., & Whetham, P. (2003). Stress in the clergy. In M.F. Dollard, A.H.Winefield, & H.R.Winefield (Eds.), Occupational stress in the service professions (pp. 307–353). London: Taylor & Francis. de Jonge, J., & Dormann, C. (2003). The DISC model: Demand induced strain compensation mechanisms in job stress. In M.F.Dollard, A.H.Winefield, & H.R.Winefield (Eds.), Occupational stress in the service professions. London: Taylor & Francis. de Jonge, J., Mulder, M.J.G.P., & Nijhuis, F.J.N. (1999). The incorporation of different demand concepts into the job demand-control model: Effects on health care professionals. Social Science and Medicine, 48, 1149–1160. Demerouti, E., Bakker, A.B., Nachreiner, F., & Schaufeli, W.B. (2001). The job demands—resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. Dollard, M.F. (1996). Work stress: Conceptualisations and implications for research methodology and workplace intervention [PhD thesis]. Whyalla, Australia: Work and Stress Research Group, University of South Australia. Dollard, M.F., Dormann, C., Boyd, L., Winefield, A.H., & Winefield, H.R. (2003). Work demands and stress in human service workers. Australian Psychologist, 38, 84–91.

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Dollard, M.F., & Winefield, A.H. (1998). A test of the demand-control/support model of work stress in correctional officers. Journal of Occupational Health Psychology, 3(3), 243– 264. Dollard, M.F., Winefield, H.R., & Winefield, A.H. (2001). Occupational strain in human service workers: When the rescuer becomes the victim. Dordrecht, The Netherlands: Kluwer Academic Publishers. Frese, M. (1999). Social support as a moderator of the relationship between work stressors and psychological dysfunctioning: A longitudinal study with objective measures. Journal of Occupational Health Psychology, 4, 179–192. Godbout, J.M. (1993, October). Employment change and sectoral distribution in 10 countries, 1970–1990. Monthly Labour Review, 3–20. Halik, N., Dollard, M.F., & de Jonge, J. (2003). Emotional labour, emotional exhaustion and absenteeism in call centre workers. Manuscript submitted for publication. Hochschild, A.R. (1983). The managed heart: The commercialization of human feeling. Berkeley, CA: University of California Press. Information Industries Training Advisory Board. (2001). 2001–2003 industry training plan. Adelaide: SA Government. Joksimovic, L., Siegrist, J., Peter, R., Meyer-Hammar, M., Klimek, W., & Heintzen, M. (1999). Overcommitment predicts restenosis after coronary angioplasty in cardiac patients. International Journal of Behavioural Medicine, 6(4), 356–368. Karasek, R. (1979). Job demands, job decision latitude and mental strain: Implications for job redesign. Administrative Science Quarterly, 24, 285–308. Karasek, R. (1998). Demand/control model: A social, emotional, and physiological approach to stress risk and active behaviour development. Encyclopedia of Occupational Health and Safety (pp. 34.6–34.14). Geneva, Switzerland: International Labour Office. Kasl, S.V. (1996). The influence of the work environment on cardiovascular health: A historical, conceptual, and methodological perspective. Journal of Occupational Health Psychology, 1(1), 42–56. Lee, R.T., & Ashforth, B.E. (1996). A meta-analytic examination of the correlates of the three dimensions of job burnout. Journal of Applied Psychology, 81, 123–133. Maslach, C, & Jackson, S.E. (1986). Maslach Burnout Inventory—Manual (2nd ed.). Palo Alto, CA: Consulting Psychologists Press. Melamed, S., Kushnir, T., & Meir, E. (1991). Attenuating the impact of job demands: Additive and interactive effects of perceived control and social support. Journal of Vocational Behaviour, 39, 40–53. Morris, J.A., & Feldman, D.C. (1996). The dimensions, antecedents, and consequences of emotional labour. Academy of Management Review, 21(4), 986–1010. Morris, J.A., & Feldman, D.C. (1997). Managing emotions in the workplace. Journal of Managerial Issues, 9(3), 257–274. Moyle, P. (1995). The role of negative affectivity in the stress process: Tests of alternative models. Journal of Organizational Behaviour, 16, 647–668. Pugliesi, K. (1999). The consequences of emotional labour: Effects on work stress, job satisfaction, and well-being. Motivation and Emotion, 23(2), 125–154.

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Schaubroek, J., & Jones, J.R. (2000). Antecedents of workplace emotional labor dimensions and moderators of their effects on physical symptoms. Journal of Organizational Behavior, 21, 163–183. Schaufeli, W., & Enzmann, D. (1998). The burnout companion to study and practice: A critical analysis. London: Taylor & Francis. Siegrist, J. (1998). Adverse health effects of effort-reward imbalance at work: Theory, empirical support, and implications for prevention. In C.L.Cooper (Ed.), Theories of organizational stress. Oxford, UK: Oxford University Press. Taylor, P., & Bain, P. (1999). “An assembly line in the head”: Work and employee relations in the call centre. Industrial Relations Journal, 30(2), 101–117. Van Der Doef, M., & Maes, S. (1999). The job demand-control-support model and psychological wellbeing: A review of 20 years of empirical research. Work and Stress, 13(2), 87–114. van Vegchel, N., de Jonge, J., Meijer, T., & Hamers, J.P.H. (2001). Different effort constructs and effort-reward imbalance: Effects on employee well-being in ancillary health care workers. Journal of Advanced Nursing, 34(1), 128–136. Wallace, C.M., Eagleson, G., & Waldersee, R. (2000). The sacrificial HR strategy in call centres. International Journal of Service Industry Management, 11(2), 174–185. Warr, P.B., Cook, J.D., & Wall, T.D. (1979). Scales for the measurement of some work attitudes and aspects of psychological well-being. Journal of Occupational Psychology, 52, 129–148. Wharton, A.S. (1993). The affective consequences of service work: Managing emotions on the job. Work and Occupations, 20(2), 205–233. Zapf, D. (2002). Emotion work and psychological well-being: A review of the literature and some conceptual considerations. Human Resource Management Review, 12, 237–268. Zapf, D., Seifert, C., Schmutte, B., Mertini, H., & Holz, M. (2001). Emotion work and job stressors and their effects on burnout. Psychology and Health, 16, 527–545. Zapf, D., Vogt, C., Seifert, C., Mertini, H., & Isic, A. (1999). Emotion work as a source of stress: The concept and development of an instrument. European Journal of Work and Organizational Psychology, 8(3), 371–400.

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European Journal of Work and Organizational Psychology, 2003, 12 (4), 393–417

Dual processes at work in a call centre: An application of the job demands—resources model Arnold B.Bakker, Evangelia Demerouti, and Wilmar B.Schaufeli Department of Social and Organizational Psychology and Research Institute Psychology & Health, Utrecht University, The Netherlands

This study among 477 employees working in the call centre of a Dutch telecom company (response 88%) examined the predictive validity of the job demands—resources (JD—R) model for self-reported absenteeism and turnover intentions. The central hypothesis was that job demands would be the most important predictors of absenteeism, through their relationship with health problems (i.e., exhaustion and Repetitive Strain Injury—RSI), whereas job resources would be the most important predictors of turnover intentions, through their relationship with involvement (i.e., organizational commitment and dedication). Results of a series of SEM analyses largely supported these dual processes. In the first energy-driven process, job demands (i.e., work pressure, computer problems, emotional demands, and changes in tasks) were the most important predictors of health problems, which, in turn, were related to sickness absence (duration and long-term absence). In the second motivation-driven process, job resources (i.e., social support, supervisory coaching, performance feedback and time control) were the only predictors of involvement, which, in turn, was related to turnover intentions. Additionally, job resources had a weak negative relationship with health problems, and health problems positively influenced turnover intentions. The application of the JD—R model as a human resource management tool in call centres as well as in other organizations is discussed.

Correspondence should be addressed to A.Bakker, Utrecht University, Dept. of Social & Organizational Psychology, Heidelberglaan 1, PO Box 80. 140, 3508 TC Utrecht, The Netherlands. Email: [email protected]

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A call centre can be defined as a work environment in which the main business is mediated by computer and telephone-based technologies that enable the efficient distribution of incoming calls (or allocation of outgoing calls) to available staff, and permit customer—employee interaction to occur simultaneously with use of display screen equipment and the instant access to, and inputting of, information (Holman, 2003). Organizations have benefited from call centres because it has enabled them to reduce the costs of existing functions, and to extend and improve customer service facilities. However, Holman (2003) has outlined that the benefits for call centre employees are less clear. He has argued and shown that, whereas some employees enjoy call centre work, for many it is demanding and stressful. Call centre operators use interactive display terminals during telephone calls and thus perform multiple-tasks with frequent interruptions. Furthermore, their jobs are characterized by repetitive movements, while complex information is processed. Meanwhile, communication skills and efficiency are expected. In addition, call centre employees often work in noisy environments under high time pressure, and their performance is usually monitored on line (Ferreira & Saldiva, 2002). Some scholars have even argued that call centre jobs are an expression of an advanced form of Taylorism (Knights & McCabe, 1998; Taylor & Bain, 1999). It is therefore not surprising that absenteeism and personnel turnover are important problems for many call centres (e.g., Michel, 2001), and represent significant disadvantages for organizations that use call centres. Recent research in call centres has indeed shown that lack of job control, role stress, performance monitoring, inadequate coaching and training, emotional labour, and lack of team leader support can all lead to job stress—including depression, emotional exhaustion, and anxiety (e.g., De Ruyter, Wetzels, & Feinberg, 2001; Holman, Chissick, & Totterdell, 2002; Knights & McCabe, 1998; Taylor & Bain, 1999; Zapf, Vogt, Seifert, Mertini, & Isic, 1999). These studies are informative, since they all add to our knowledge regarding working conditions that may undermine well-being in call centres. The present study uses an overall theoretical framework of employee well-being—the job demands—resources (JD—R) model (Bakker, Demerouti, De Boer, & Schaufeli, 2003a; Bakker, Demerouti, Schaufeli, Taris, & Schreurs, 2003b; Demerouti, Bakker, Nachreiner, & Schaufeli, 2000, 2001)—to examine how different categories of working conditions in a Dutch call centre are related to self-reported sickness absenteeism and turnover intentions. The central tenet of the JD—R model is that job demands evoke an energy depletion process, whereas job resources induce a motivational process. As far as we know, previous call centres studies did not examine the concomitants of absenteeism and personnel turnover, although anecdotal evidence suggests that these employee behaviours pose important problems to call centres (see also Michel, 2001). JOB DEMANDS—RESOURCES MODEL The JD—R model is a heuristic model that specifies how health impairment and motivation or involvement in any organization may be produced by two specific sets of working conditions. The first set concerns job demands that represent characteristics of the job that potentially evoke strain, in cases where they exceed the employee’s adaptive

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capability. More specifically, job demands refer to those physical, social, or organizational aspects of the job that require sustained physical and/or psychological (i.e., cognitive or emotional) effort on the part of the employee and are therefore associated with certain physiological and/or psychological costs (e.g., exhaustion) (cf. Hockey, 1997). Although job demands are not necessarily negative, they may turn into job stressors when meeting those demands requires high effort from which the employee has not adequately recovered (Meijman & Mulder, 1998). Karasek’s (1979) influential demands-control model uses a rather restricted definition of job demands that are mainly quantitative in nature, such as workload and time pressure. The JD—R model expands this view by proposing that several demanding characteristics of the working environment, including emotional demands, problems with the work equipment (i.e., computers) or changes in the task (see also Semmer, 1984; Semmer, Zapf, & Dunckel, 1995; Zapf et al., 1999), may lead to the impairment of health and consequently to absenteeism. The second set of working conditions concerns the extent to which the job offers resources to individual employees. Job resources refer to those physical, psychological, social, or organizational aspects of the job that either/or: (1) reduce job demands and the associated physiological and psychological costs; (2) are functional in achieving work goals; (3) stimulate personal growth, learning, and development (Demerouti et al., 2001; Hacker, 1998). Hence resources are not only necessary to deal with job demands, but they also are important in their own right (Elsass & Veiga, 1997; Ganster & Fussilier, 1989; Hobfoll, 2001; Terry & Jimmieson, 1999). What we call job resources has been recognized by Kahn (1990) as characteristics of work situations that shape the degree to which people employ and express themselves physically, cognitively, and emotionally during role performance. In a similar vein, Hackman and Oldham (1980) refer to specific job characteristics with motivational potential. Such job characteristics foster so-called critical psychological states (e.g., meaningfulness), which—in their turn—drive people’s attitudes and behaviours. Examples of job resources are time control, performance feedback, a supportive leader, and trusting relationships with colleagues. In conclusion, according to the JD—R model, two sets of working conditions may each evoke a different process. First, badly designed jobs or high job demands (e.g., work overload, emotional demands) may exhaust employees’ mental and physical resources and may therefore lead to the depletion of energy (i.e., a state of exhaustion) and to health problems (e.g., Demerouti et al., 2000, 2001; Lee & Ashforth, 1996; Leiter, 1993) (health impairment hypothesis). Second, the presence of adequate job resources reduces job demands, fosters goal accomplishment and stimulates personal growth and development. In turn, this may lead to a stronger involvement in terms of organizational commitment and dedication to one’s work, and thus to a lower intention to leave the organization (motivational hypothesis). In fact, resources boost employees’ motivation (cf. Antonovski, 1987; Hackman & Oldham, 1980). HEALTH IMPAIRMENT PROCESS According to the health impairment hypothesis, perceived job demands lead to job strain such as feelings of exhaustion and repetitive strain injury (RSI), which are, in turn, related

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to increased sickness absenteeism. Exhaustion refers to the depletion of mental resources, and particularly to the experience of severe fatigue. Thus call centre employees who are repeatedly exposed to high job demands are expected to develop feelings of exhaustion. RSI is something of a misnomer, as repetition is only one of many possible causes of injury, and many practitioners question the use of the term “strain” as this in itself is a vague term to describe injury caused by overexertion (Baird, 1996). Despite differences in description, Occupational Overuse Syndrome (OOS), Cumulative Trauma Disorder (CTD), and Work Related Upper Limb Disorder (WRULD) all refer to damage to the hand, arm, shoulder, or neck caused, or exacerbated by some aspect of the physical working situation. In reality, common usage has effectively transformed RSI from an acronym to a term in its own right, which is associated most often with pain in the wrist or forearm/elbow area. Many experts now use RSI to describe a particular variation of a WRULD condition (Baird, 1996). The relationship between specific job demands (e.g., workload and emotional demands) and exhaustion has been reported by various studies on burnout, of which exhaustion is the core symptom (see Lee & Ashforth, 1996, for a meta-analysis). Moreover, research by Demerouti and her colleagues among different occupational groups shows that (self-reported and observed) job demands can have a strong impact on feelings of exhaustion (Demerouti et al., 2001; Demerouti, Bakker, & Bulters, in press). RSI is known to occur more often among employees who carry out repetitive tasks with their hands or arms and who work without rest breaks (Tyrer, 1994). Moreover, RSI occurs among computer workers and is associated with high quantitative demands and poor developmental possibilities (Jensen, Ryholt, Burr, Villadsen, & Christensen, 2002). This makes RSI particularly relevant for call centre jobs that are characterized by repetitive tasks using both telephone and computer. Furthermore, the incidence of RSI is increased in organiza tions with a poor working environment and with high levels of strain (Hopkins, 1990). Thus we expect that demanding aspects of the job will also be related to the experience of RSI, since experiencing high work pressure, emotional demands, and computer problems are constraints that force the employee to work nonstop and consequently to experience pain. Furthermore, particularly the exhaustion component of burnout has consistently been related to absence duration or time lost measures, for example in studies among airline reservations personnel (Saxton, Phillips, & Blakeney, 1991) and nurses (Firth & Britton, 1989). Research on RSI and absenteeism is still lacking, but studies on musculoskeletal pain found strong positive relationships (e.g., Maentyselkae, Kumpusalo, Ahonen, & Takala, 2002). However, it should be noted that several meta-analytic studies on absenteeism show that job strain is but one of many variables accounting for employee absence behaviour, so we should not expect job strain and absenteeism to be strongly correlated (Beehr, 1995; Nicholson, 1993). Nevertheless, we expect that call centre employees’ job demands will be related to sickness absenteeism through their impact on both exhaustion and RSI. In other words, we hypothesize that exhaustion and RSI will mediate the relationship between job demands and absenteeism (Hypothesis 1).

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MOTIVATIONAL PROCESS As previously noted, the motivational hypothesis assumes that job resources lead to involvement, which, in turn, is negatively related to turnover intentions. In the present study, we included two types of involvement: affective commitment and dedication. Affective commitment is most clearly an indicator of involvement at the level of the organization, and has been defined as “the strength of an individual’s identification with an organization” (Mowday, Steers, & Porter, 1979, p. 226). Dedication, on the other hand, is more directly related to the job itself, and is characterized by a sense of significance, enthusiasm, inspiration, pride, and challenge (Schaufeli, Salanova, González-Romá, & Bakker, 2002). Thus whereas commitment refers to positive attitudes towards the organization, dedication refers to positive attitudes towards one’s job. Several studies have shown that job resources are important predictors of involvement. For example, Bakker et al. (2003a) showed that job resources such as autonomy and participation in decision making had strong positive relationships with different types of commitment. In addition, a recent metaanalysis has shown that job resources such as organizational support and transformational leadership are all related to affective commitment (Meyer, Stanley, Herscovitch, & Topolnytsky, 2002). Less research has focused on dedication as an outcome of job resources. Demerouti et al. (2001) found that (self-reported and observed) job resources, such as performance feedback, supervisor support, and job control, were the only predictors of dedication—they use the term (dis) engagement. In addition, Schaufeli and Bakker (in press) found evidence for a positive relationship between three job resources (performance feedback, social support, and supervisory coaching) and engagement (of which dedication is a core aspect) in four occupational groups. They used structural equation modelling to show that engagement (including dedication) is exclusively predicted by job resources, and that engagement is a mediator of the relationship between job resources and turnover intentions. Among the consequences of organizational commitment, withdrawal cognitions and behaviours are the most salient. Particularly affective commitment demonstrates substantial correlations with intentions to leave one’s job (for meta-analyses, see Mathieu & Zajac, 1990; Meyer et al., 2002). In addition, affective commitment has been related to actual turnover (Mathieu & Zajac, 1990; Meyer et al., 2002). Evidence for a negative relationship between commitment and turnover intentions has also been found in the call centre study of De Ruyter et al. (2001). Taken together, we expect that committed and dedicated call centre employees will be less likely to look for another job. Specifically, we hypothesize that commitment and dedication will mediate the relationship between job

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Figure 1. Maximum likelihood estimates for the final JD—R model (M6, see Table 4), N = 477. All factor loadings and path coefficients are significant at the p < .05 level. Solid lines represent hypothesized paths.

resources and turnover intentions (Hypothesis 2). The two main hypotheses are graphically depicted in Figure 1 (see Results section, p. 408).1 Before testing these hypotheses, we want to explore differences between call centre employees in different job positions including operators, advisors, consultants, and supervisors. To this end, we compared their levels of job demands, job resources, health problems, involvement, self-reported absenteeism, and turnover intention. If such differences exist, they may be valuable in tracing specific job demands and resources responsible for health problems and involvement, respectively.

1

One may argue that also the interactions between job demands and resources are important (cf. Kahn & Byosiere, 1992). Such a view is consistent with the demand-control model (DCM; Karasek, 1979) and the effort—reward imbalance (ERI) model (Siegrist, 1996), and there is indeed some evidence for demands—resources interaction effects (Bakker et al., 2002, 2003b). In order to test whether the interaction between job demands and job resources may predict health problems and involvement in the present study, we followed the procedure for modelling latent interaction proposed by Dormann and Zapf (1999). The results showed that only one out of 16 possible job demands×resources interactions was significant. Employees with high emotional demands reported significantly higher levels of dedication when they had high time control than when they had low time control. Taken together, these findings suggest that high levels of job resources can only to a very limited degree mitigate the negative health effects of high job demands (see also Van der Doef & Maes, 1999).

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METHOD Participants and procedure The study was conducted among 477 customer services employees working in the only call centre of a large Dutch telecom company (response = 88%). After meetings with the floor managers and the human resources department, it was agreed upon that all employees would have the possibility to fill out an electronic questionnaire (published on a secured website) during work time, in a silent, separate room. A newsletter and an email of the management announced to all employees that the questionnaire could be filled out. Employees on sickness absence received a paper-and-pencil questionnaire through surface mail at their home address. In total, 467 employees filled out the questionnaire online, and 10 sick employees filled out the paper-and-pencil version at home (total N = 477). The study population includes 205 males (43%) and 272 females (57%). Their mean age is 30 years (SD = 8.80), and the mean organizational tenure is 1 year (SD = .83). The call centre under study is an in-built centre in a telecom company that takes care of incoming customer calls (inbound services). Four different types of job positions were represented in the sample: Tele-Operators (n = 24) are responsible for the provision of number information to the company’s customers. Their tasks almost exclusively include the answering of the phone and the provision of information from a computerized database to customers about the sought-after numbers. Tele-Advisors (n = 220) are the first contact for customers who have questions or problems regarding the company’s products and services. Their most important tasks include handling of incoming calls, writing down the question or complaint in a computer file, and referring this to the correct department. Tele-Consultants (n = 130) are responsible for analysing and solving the problems or complaints of customers, on the telephone as well as in writing. In addition to telephone contact with customers, they answer customers’ questions. Supervisors (n = 61) are responsible for supporting, monitoring, and coaching a group of approximately 12 employees each. In addition, each supervisor has additional tasks on a project basis. Fortytwo employees did not fill out their job position. Measures Job demands. Workload was assessed with a five-item scale developed by Bakker et al. (2003b). The items refer to quantitative, demanding aspects of the job. An example item is: “My job requires working very hard”. Items are scored on a 5-point Likert scale, ranging from (1) “never” to (5) “always”. Unless otherwise indicated, all following demands and resources used the same response categories. Changes in the task was measured with a scale developed by Van Veldhoven and Meijman (1994; see also Van Veldhoven, De Jonge, Broersen, Kompier, & Meijman, 2002). The scale includes eight items, such as “Do changes in your tasks pose difficulties to you?” Emotional demands were assessed with six of the seven items proposed by Van Veldhoven et al. (2002). An example item is: “Is your work emotionally demanding?” Computer problems was measured

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with two items, namely: “During your work, are you confronted with malfunctioning equipment (e.g., computers or printers)?” and “Are you confronted in your work with computer problems?” Both items were highly and positively related (r = .70, p < .001), and were summed to constitute one index for computer problems. Job resources. Social support was measured with the three-item scale developed by Bakker et al. (2003b). An example item is: “Can you ask your colleagues for help if necessary?” Coaching by the supervisor was measured using a validated Dutch adaptation (Le Blanc, 1994) of Graen and UhlBien’s (1991) 12-item Leader-Member Exchange scale; e.g., “My supervisor uses his/her influence to help me solve my problems at work”. Performance feedback was assessed with four items, based upon Karasek’s (1985) job content instrument. For example: “I get information/feedback from my supervisor about how well I do my job”. Finally, Time control was measured with a 5-item scale that we constructed ourselves, including “Within our call centre, there are sufficient possibilities for short breaks” (1 = totally disagree, 5 = totally agree). All responses were coded such that higher scores referred to higher job demands and more job resources, respectively. Health problems. Exhaustion was assessed using the Dutch version (Schaufeli & Van Dierendonck, 2000) of the Maslach Burnout Inventory— General Survey (Schaufeli, Leiter, Maslach, & Jackson, 1996). The scale includes five items, such as: “I feel emotionally drained from my work” (0 = never, 6 = every day). Repetitive strain injury (RSI) was assessed with a recently developed 5-item scale (Bakker, 2001). The scale includes symptoms that are usually associated with RSI such as pain and stiffness in the wrist and forearm/elbow (Baird, 1996; Tyrer, 1994). Example items are: “During the last year, did you experience pain, a stiff feeling, or other discomfort in your arms, wrists, or elbows?” and “During the last year, did you experience a loss of power in your arms, hands, or fingers?” (1 = never, 5 = always). Involvement. Organizational commitment refers to the relationship of employees to the organization in which they work. It is measured with six items of Mowday et al.’s (1979) affective commitment scale, including: “I tell my friends and family that my organization is a pleasant organization to work for” (1 = totally disagree, 5 = totally agree). Dedication is one of three subscales of the Utrecht Work Engagement Scale (Schaufeli et al., 2002). The subscale includes five items, and measures the extent to which employees are dedicated to their work, that is, how often they experience a sense of significance, enthusiasm, inspiration, pride, and challenge at their jobs. An example item is: “I am enthusiastic about my job” (0 = never, 6 = always). Absenteeism was assessed with two items, namely: “During the past 12 months, how many working days did you not work because of ill health?” (absence duration) and “During the past 12 months, have you been sick longer than 2 weeks in a row one or more times?” (long-term absence). The average number of days that employees reported themselves sick was 12 days (SD = 19.49) during the preceding year, and 17.6% indicated that they had been sick for 2 weeks in a row. The two items were highly and positively related (r = .56, p < .001).

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Turnover intention was assessed with the three-item scale of Schaufeli and Bakker (in press); e.g., “I intend to change jobs during the next year” (1 = completely disagree, 5 = completely agree). At the time of the study, the actual percentage of personnel turnover in the call centre was 22%. Analyses The model as displayed in Figure 1 on p. 408 (solid lines) was tested with structural equation modelling (SEM) analyses using the AMOS software package (Arbuckle, 1997). Maximum likelihood estimation methods were used and the covariance matrix of the scales/items was the input for the analysis. The goodness-of-fit of the model was evaluated using absolute and relative indices. The absolute goodness-of-fit indices calculated were the χ2 goodness-of-fit statistic and the Root Mean Square Error of Approximation (RMSEA). Nonsignificant χ 2 values indicate that the hypothesized model fits the data, and RMSEA values smaller than or equal to .08 are indicative of an acceptable fit (Cudeck & Browne, 1993). However, the χ2 goodness-of-fit statistic is sensitive to sample size, so that the probability of rejecting the hypothesized model increases with increasing sample size. Therefore, as recommended by Marsh, Balla, and Hau (1996), we used three relative goodness-of-fit indices, namely the Non-Normed Fit Index, the Incremental Fit Index (IFI), and the Comparative Fit Index (CFI). For these relative fit-indices, as a rule of thumb, values of .90 or higher are considered as indicating a good fit (Hoyle, 1995). The latent exogenous factors, job demands and job resources, were both operationalized by four exogenous observed variables each (see Figure 1). The manifest indicators of job demands were workload, emotional demands, changes in the task, and computer problems. Job resources were indicated by social support, coaching by the supervisor, performance feedback, and time control. In addition, the structural model includes two types of endogenous latent variables: (1) health problems and involvement as latent (mediator) variables, and (2) self-reported absenteeism and turnover intentions. The latent “health problems” factor was assessed by two observed variables, namely exhaustion and RSI, whereas the latent “involvement” factor was indicated by organizational commitment and dedication. Furthermore, the latent “self-reported absenteeism” factor included two indicators: absence duration and long-term absence. A single indicator operationalized turnover intention; we corrected for random measurement error by setting the random error variance of turnover intention equal to the product of its variance and the quantity one minus its internal consistency (Jöreskog & Sörbom, 1993). Additionally, the model included the following correlations: (1) among the latent factors job demands and job resources; (2) among the uniquenesses of the latent factors “health problems” and “involvement”; and (3) among the uniquenesses of the latent factors “self-reported absenteeism” and “turnover intentions”. Using the chi-square difference test, this model was compared with several nested models that specify various alternative relationships (see Results section).

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RESULTS Descriptive statistics Means, standard deviations, internal consistencies (Cronbach’s alphas), and correlations among all study variables are presented in Table 1. Differences between job positions In order to explore differences between call centre employees in different job positions, we performed two MANOVAs with job position as the independent variable, and the model variables as the dependent variables. The first MANOVA included all job demands and resources as dependent variables, whereas the second included the intervening variables (exhaustion, RSI, commitment, and dedication), and the most distal outcome variables (absenteeism and turnover intentions) as the dependent variables. The results are presented in Tables 2 and 3. The MANOVA on the working conditions (job demands and resources) resulted in a multivariate significant effect, F(24) = 11.95, p < .001. As can be seen in Table 2, several univariate effects are significant, namely for the job demands “workload’, “changes in tasks”, and “computer problems”, and for the job resources “performance feedback” and “time control”. Teleconsultants and supervisors score relatively high on each of the three job demands for which a significant univariate effect is found. These two groups differ regarding performance feedback: Supervisors receive more feedback than teleconsultants. The second MANOVA with the intervening and outcome variables as the dependent variables resulted in a multivariate significant effect as well, F(21) = 4.93, p < .001. As can be seen in Table 3, all univariate effects are significant, except for exhaustion. Consistent with the findings regarding the working conditions, teleconsultants report the highest scores on exhaustion and RSI complaints, and they report most sickness absence. Teleoperators report the lowest score on dedication and the highest score on turnover intention. Finally, although supervisors do not differ from the three other groups regarding their feelings of exhaustion, they report less RSI complaints, and are more strongly involved in their job and the organization (higher scores on dedication and commitment). Consistently, they also report the lowest sickness absenteeism, and they are least inclined to search for alternative jobs. Model testing Results of the SEM analysis showed that the proposed model (displayed in Figure 1) did not fit adequately to the data, χ 2(84) = 297.55, GFI = .92, AGFI = .89, IFI = .88, NNFI=.85, CFI = .88, RMSEA = .07. Inspection of the modification indices revealed that this lack of fit between the model and the data was mainly due to a covariation between the measurement errors of “emotional demands” and “time control”. The existence of an additional variable that is not included in the model may be responsible for such an error

All correlations ≥ .10 are significant; r ≥ .12, p < .01; .10 ≤ r ≤ .11, p < .05.

Means, standard deviations, internal consistencies (Cronbach’s alpha-on diagonal in italics), and correlations between the model variables (N = 477)

TABLE 1

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TABLE 2 Results of MANOVA: Comparison of job demands and resources for four groups of call centre employees

***p < .001. TABLE 3 Results of MANOVA: Comparison of well-being and outcomes for four groups of call centre employees

*p < .001; **p < .01; *p < .05.

correlation (De Jonge, Dormann, Janssen, Dollard, Landeweerd, & Nijhuis, 2001), and this correlation is necessary in order to explain the outcome variables more fully (MacCallum, Wegener, Uchino, & Fabrigar, 1993). The statistical explanation for this correlation is that items with comparable rating scales often have measurement errors that are correlated (Byrne, 1989). A theoretical explanation could be that having time control may alleviate the frequency of emotional demands. The revised model (called the “basic dual process model”—M1), including this covariation, shows a reasonable fit to the data (see first row in Table 4). All fit indices have values higher than .90 (except for the NNFI), and the RMSEA is .07. Importantly, all working conditions had significant loadings on the intended job demands and resources latent factors, and the direction of the relationships in the model were as predicted. The

χ

= chi-square; df = degrees of freedom; GFI=goodness-of-fit index; IF1=incremental fit index; NNFI=Non-normed fit index; CFI=comparative fit index; RMSEA=root mean square error of approximation; Δχ 2=chi-square difference; Δdf=difference in degrees of freedom. aAll models are significant at p < .001.

2

Goodness-of-fit indices of the alternative models (N=477)

TABLE 4

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coefficient of the path from job demands to health problems was positive and significant (β = .60, t = 6.94, p < .01), and the coefficient of the path from job resources to involvement was highly positive and significant as well (β = .52, t = 8.83, p < .01). Furthermore, health problems had a positive relationship with self-reported absenteeism (β = .25, t = 3.11, p < .01), while involvement had a strong negative relationship with turnover intentions (β = −.77, t = 13.79, p < .01). In order to test the alternative hypothesis that job demands are also related to involvement, and that job resources are also related to health problems, we included both diagonal paths in the model (partial cross-link model 1—M2). Compared to the previous model, adding both paths resulted in a significant improvement of the fit between model and data, M1 −M2; Δχ2(2) = 15.02, p < .01. However, only job resources showed a significant and negative relationship with health problems (β = −.25, t = 3.66, p < .01). Importantly, this relationship was significantly lower than the relationship between job resources and involvement (critical ratio for difference=−8.53, p < .01; see Arbuckle, 1997). Consistently, the model in which the paths from job resources to involvement and to health problems were constrained to be equal was significantly worse than M2, Δχ 2(1) = 79.84, p < .001. In an alternative model (partial cross-link model 2—M3), we included the additional paths from health problems to turnover intentions and from involvement to self-reported absenteeism. These two additional paths also increased model fit, M2−M3; Δχ 2(2) = 11. 85, p < .01. This was due to the fact that the coefficient of the path from health problems to turnover intentions reached significance (β = .18, t = 2.57, p < .01). However, in line with the JD—R model, the relationship between involvement and turnover intentions was significantly stronger than the relationship between health impairment and turnover intentions (critical ratio for difference=8.77, p < .01). Consistently, the model in which the paths from health problems to self-reported absenteeism as well as to turnover intention were constrained to be equal was significantly worse than M3, Δχ 2(1) = 14.50, p < .001. The third alternative model included additional direct relationships between job demands and self-reported absenteeism and between job resources and turnover intentions (partial mediation model—M4), while the fourth alternative model included all paths of the previous models together with the cross paths from job demands to turnover intentions and from job resources to self-reported absenteeism (full cross-link model—5). The inclusion of these additional paths did not lead to an improvement of the model (see also Table 4), M3−M4: Δχ 2(2) = 3.35, n.s.; M4−M5: Δχ2(2)=2.00, n.s. Moreover, the coefficients of all additional paths were nonsignificant. It can be concluded that involvement fully mediated the relationships between job resources and turnover intentions. Health problems acted as a conditional variable in the relationship between job demands and absenteeism (see Discussion section). In sum, this series of SEM analyses shows that the proposed JD—R model with dual processes fits well to the data, even though we found two additional paths that were not predicted. Accordingly, job demands are the most important predictors of health problems (i.e., exhaustion and RSI), which, in turn, predict self-reported absenteeism. In contrast, job resources are the most important predictors of involvement (i.e., commitment and

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dedication), which, in turn, predicts (reduced) turnover intentions. In addition, we found that job resources also had a small but significant, negative relationship with health problems, which, in turn, made a unique contribution to explaining variance in turnover intentions. These relationships are included in the final model, which is displayed graphically in Figure 1. In total, the JD—R model explained 9% of the variance in self-reported absenteeism and 60% of the variance in turnover intentions. Hence, SEM analyses generally supported the hypothesized dual processes of energy depletion and motivation among call centre employees. DISCUSSION The present study used the job demands—resources (JD—R) model (Bakker et al, 2003a, 2003b; Demerouti et al., 2000, 2001) to examine how different categories of working conditions—job demands and job resources—are related to absenteeism and turnover intentions among call centre employees. Our theoretical framework was successful in revealing two different processes responsible for absenteeism and turnover intentions in call centres. The first process can best be described as an energy depletion process starting with high job demands, which lead to health problems and, consequently, to longer periods of absence. The second process is motivational in nature, and starts with job resources. Call centre employees who can draw upon job resources such as social support from colleagues and performance feedback feel more dedicated to their work and more committed to their organization, and, consequently, are less inclined to leave the organization. These findings integrate and expand previous studies, in which moderate support was found for the idea that employees who experience job stress are absent longer (e.g., Firth & Britton, 1989; Saxton et al., 1991), and for the notion that employees low in organizational commitment are more inclined to look for an alternative employer (Mathieu & Zajac, 1990; Meyer et al., 2002). Note, however, that we used a crosssectional design, and that the present study does not provide evidence for causal relationships between the model variables. The proposed links between working conditions, well-being, and outcomes (sickness absence and turnover intentions) thus need to be tested using a more rigorous design before we can conclude what the exact order of the variables is. On the positive side, the model tested in our study is one of the few that incorporates individually assessed job characteristics, stress reactions, and workrelated attitudes for the explanation of different organizational outcomes. The specific findings will be discussed in more detail below. Dual processes at work in a call centre Results provided support for the hypothesized dual processes, although health problems did not act as a pure mediator. Job demands (i.e., work overload, changes in the task, emotional demands, and computer problems) were the most important predictors of call centre employees’ levels of exhaustion and RSI. The latter two indicators of health problems, in turn, were the only predictors of absence duration and long-term absence

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(positive relationships). Job resources (i.e., social support by colleagues, supervisory coaching, performance feedback, and time control) were unique predictors of commitment and dedication (positive relationship), and indirectly of turnover intentions (negative relationship). Because the correlational analysis revealed that job demands were not significantly related to the two absenteeism measures, exhaustion and RSI did not act as pure mediators. Instead, they seem to act as so-called conditional variables: If job demands lead to health problems, then absenteeism may follow. In contrast, all job resources were significantly related to turnover intentions, which means that involvement (commitment and dedication) acted as a pure mediator between job resources and turnover intentions. Alternative models, including direct paths from job demands and job resources to absenteeism and turnover intentions, did not fit better to the data than the proposed JD—R model. However, analyses of cross-links between both processes revealed two paths that were not predicted: the path from job resources to health problems, and from health problems to turnover intentions. Although the coefficients of these paths were significantly lower than the proposed paths, it is warranted to elaborate on these findings. First of all, an increase in job resources coincided with a small decrease in health problems. This suggests that some resources may directly prevent energy-depletion. Indeed, previous research has, for instance, shown that social support may play such a role (Lee & Ashforth, 1996). Second, the relationship between health problems and turnover intentions, over and above the impact of involvement, has been reported in the literature as well. For example, Schaufeli and Enzmann (1998, p. 90) calculated a metacorrelation between exhaustion and intention to quit across 13 studies and found a weighted population effect size of .45, indicating that both constructs share 20% of their variance. For RSI complaints, the relationship with turnover intentions is still unknown. Nevertheless, Schaufeli and Enzmann’s finding suggests that health problems or job strain may directly result in psychological (and eventually physical) withdrawal (see also Schaufeli & Bakker, in press). Taken together, these findings among call centre employees replicate and expand previous findings with the JD—R model among other occupational groups, showing that job demands are the most important predictors of absence duration among production personnel (Bakker et al., 2003a) and of in-role performance among human service professionals (Bakker, Demerouti, & Verbeke, 2003c), through their relationship with job strain variables. In contrast, in these previous studies, job resources were the most important predictors of short spells and extra-role performance, through their impact on motivational variables. Furthermore, in their study among air traffic controllers, human service professionals, and production workers, Demerouti et al. (2001) found unique relationships between job demands and fatigue and between job resources and disengagement, even when using independent observers’ ratings of job demands and resources. In addition, evidence for relationships between exhaustion and absenteeism, and between commitment and turnover (intentions) has been found in several other studies (for overviews, see Johns, 1997; Schaufeli & Enzmann, 1998). Thus the underlying processes of energy depletion and motivation do not seem to differ between call centre employees and employees in other professions. Yet, the specific job demands

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and job resources may differ to some extent, which also applies to the present study, since we found that computer problems and time control were particularly relevant for this occupational group. The current findings also emphasize the differences between the JD—R model on the one hand, and classic models such as the job characteristics model (JCM; Hackman & Oldham, 1980) and the demand—control model (DCM; Karasek, 1979). Whereas the JD—R model simultaneously investigates the roots of job stress and work motivation, the JCM focuses primarily on job resources and work motivation (even though absenteeism is also included as an outcome variable). In addition, whereas the DCM mainly concentrates on the combination of high job demands (mainly workload and time pressure) and low autonomy as a possible cause of job stress and reduced motivation, the JD—R model goes one step further and proposes that many different demands and resources may influence employee well-being. We also explored differences between call centre employees in different job positions. The results showed that employees in the different job positions differed regarding their demands and resources, their health problems and involvement, and regarding their absenteeism and turnover intentions. Particularly teleconsultants and supervisors scored relatively high on three job demands: “workload”, “changes in tasks”, and “computer problems”. Teleconsultants reported the highest scores on RSI complaints, and they had been most often absent for longer time periods. Teleoperators reported the lowest score on dedication and the highest score on turnover intention. Finally, although supervisors did not differ from the other groups (their subordinates) regarding their feelings of exhaustion, they reported less RSI complaints, and were stronger involved in their job and the organization (higher scores on dedication and commitment). Consistently, they also reported the lowest sickness absenteeism, and they were least inclined to search for alternative jobs. Such information can be used in practice to optimize call centre employees’ working conditions, by developing interventions that are tailor-made to the specific job positions. Limitations Like most studies, the present research has limitations as well. First, the measurement of the model variables was based solely on self-reports, which increases the possibility that the relationships between, for example, job demands and resources on the one hand, and health problems and involvement on the other hand might be due to common method variance. Therefore, some scholars (e.g., Brief, Burke, George, Robinson, & Webster, 1988; Payne, 1988) have argued that we should control for negative affectivity (NA; Watson & Clark, 1984) in job stress research (see Spector, Zapf, Chen, & Frese, 2000, for a different view). NA may bias self-reports of working conditions and job strains, and controlling for NA in a cross-sectional study may deal with a possible confounding of independent and dependent variables. The present study did not include a measure of NA, but we were able to use a measure of “general job satisfaction” as a proxy to control for NA. Thus in an additional SEM analysis, this measure was included in the final JD—R model, and covariations with each of the model variables were allowed. The results

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showed that the satisfaction measure did not have a substantial influence on the proposed relationships in the JD—R model, although the strength of some of the proposed relationships was slightly reduced. Interestingly, the cross paths from job resources to health problems, and from health problems to turnover intentions, became nonsignificant. Taken together, these findings suggest that the affective state of the participants did lead to a slight overestimation of the strength of the relationships in the JD—R model, but that controlling for affect did not produce different findings; instead, we found more evidence for the JD—R model, since the cross links proved to become nonsignificant. Nevertheless, future research should ideally use other sources of information as well, such as company registrations of absenteeism and personnel turnover. A second limitation of the present study is that we could only include self-reports of absenteeism instead of personnel records of absenteeism in order to maintain respondent anonymity and confidentiality. In addition, since the telecom organization started its business only a few years before, the personnel department was not functioning optimally, and consequently, absenteeism records were not kept adequately. Johns (1994) has shown that such practice is far from uncommon. On the basis of the available validity coefficients of previous studies, he calculated that the sample-size weighted estimate of the correlation between self-reported absenteeism and recordsbased measures was .64. Although this validity coefficient is not perfect, it does show that self-reports generally mirror reality. In addition, Spector (1987) has shown that in 20 out of 20 correlations with various measures of commitment and job characteristics, self-reportbased and records-based absence measures revealed no significant differences. Practical implications and suggestions for future research Despite these limitations, the present findings may have important implications for organizational practice within call centres. First and foremost, our study suggests that different organizational outcomes are the result of two different processes. This underlines the importance of a systematic distinction between reasons for absenteeism and personnel turnover by human resource managers. Results clearly suggest that, in order to decrease absenteeism, specific countermeasures have to be taken regarding the working environment. Specifically, in order to reduce or prevent exhaustion and the risk of RSI and consequently absenteeism, specific job demands (in the present study: work overload, emotional demands, changes in tasks, and computers problems) should be reduced or optimized. In addition, in order to increase involvement and lower turnover intentions, the availability of job resources (in this study: social support, supervisory coaching, time control, and performance feedback) should be considered. Schaufeli and Enzmann (1998) have described several interven tions at the organizational level that can be used to attain this, including job redesign, job coaching, and organizational development programmes. Our study was restricted to the examination of four specific job demands and four specific resources. At the heart of Demerouti et al.’s (2001) JD—R model lies the assumption that, whereas every organization may have its own specific characteristics, these factors can still be classified in two general categories (i.e., job demands and job

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resources). Future studies should examine a broader range of demands and resources, potentially related to absenteeism and withdrawal from work in a similar way. Ideally, research with the JD—R model starts with a qualitative analysis, including organizational document research and explorative interviews with job incumbents from different layers of the organization (representatives from management, staff, shop floor). Such an analysis can reveal a wide range of potentially relevant job demands and resources, which can then be examined quantitatively by including these constructs in a questionnaire. Thus a task of researchers and practitioners is to uncover the specific constellations of job demands and job resources that are prevalent in specific job types, since this may facilitate primary and secondary workplace interventions. Although the JD—R model was originally constructed for examining the causes of burnout at the organizational level, recently, we have successfully developed a computerized tool that may be used at the individual, employee level. Specifically, using the internet as a medium, employees can fill out the electronic questionnaire and they receive individual feedback about their own levels of job strain and its causes in terms of histograms and short written descriptions. The most extreme cases also receive advice about contacting occupational health professionals, their human resources departments, or the like. The information can be used by those willing to begin conversations with management, a company doctor, or a therapist. This can be the start of individual job (re) design and for changing suboptimal working conditions into a healthier workplace. REFERENCES Antonovski, A. (1987). Unravelling the mystery of health: How people manage stress and stay well. San Francisco: Jossey-Bass. Arbuckle, J.L. (1997). Amos users’ guide. Version 3.6. Chicago: Smallwaters. Baird, A. (1996). “Repetitive Strain Injury” (“RSI”): An ergonomist’s view. Loughborough, UK: Human Applications. Bakker, A.B. (2001). Research on well-being in a call centre [Internal Rep.]. Utrecht, The Netherlands: Utrecht University. Bakker, A.B., Demerouti, E., De Boer, E., & Schaufeli, W.B. (2003a). Job demands and job resources as predictors of absence duration and frequency. Journal of Vocational Behavior, 62, 341–356. Bakker, A.B., Demerouti, E., & Euwema, M.C. (2002). Which job resources buffer the impact of job demands on burnout? Manuscript submitted for publication. Bakker, A.B., Demerouti, E., Taris, T., Schaufeli, W.B., & Schreurs, P. (2003b). A multi-group analysis of the job demands—resources model in four home care organizations. International Journal of Stress Management, 10, 16–38. Bakker, A.B., Demerouti, E., & Verbeke, W. (2003c). Using the job demands—resources model to predict burnout and performance. Manuscript submitted for publication. Beehr, T.A. (1995). Psychological stress in the workplace. New York: Routledge.

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Capability development in knowledge intensive IT enabled services Vishal Shah and Rajendra K.Bandi Indian Institute of Management Bangalore, India

Most of the call centre literature discusses cases where the customer support task is routine and low in complexity. Call centres are considered to be modern equivalents of factory sweatshops in this literature. Technical support, however, is an example of a knowledge intensive support service. The article presents a case study of a call centre providing remote technical support and illustrates the nature of capabilities required for consistent service performance. The practices adopted at this technical support call centre do not confirm to the sweatshop stereotype mentioned in the literature. The rapid evolution of the internet and global telecommunication infrastructure has provided organizations with a choice of service providers located anywhere in the world. This coupled with an increasing desire of organizations to focus on their core competencies has given rise to the booming IT enabled services industry. The remote customer support industry or the call centre industry in India has been forecasted to grow at an annual rate of 50%, and employ as many as 700,000 people by 2008 (McKinsey, 1999). Much of the research on call centres is focused on the coercive employment systems adopted (Batt, 1999; Taylor & Bain, 1999). Critics of call centres have called them the modern equivalent of the factory sweatshops (Incomes Data Services, 1997). However, most studies have focused on call centres, which provide customer support for typically routine tasks like providing support for bank customers who have account related queries.

Correspondence should be addressed to V.Shah, Indian Institute of Management Bangalore (IIMB), Bannerghatta Road, Bangalore, 560076, India. Email: [email protected]

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The article presents a case of a technical support call centre that employs agents who help customers with technical problems and queries through email, text chat, and the telephone. Providing customer support for high technology products is knowledge intensive work. Technical support work and technicians have not traditionally been discussed in the knowledge work literature. However increasingly researchers are studying this class of work due to its growing importance (Barley, 1996; Pentland, 1992). We focus on the practices that build the call centre’s capability to performeffectively in a knowledge intensive environment. In particular we identify the role that organizational elements like people, process, and technology play in the call centre’s ability to manage knowledge. The practices adopted at this technical support call centre do not confirm to the sweatshop stereotype mentioned in the literature. CHARACTERISTICS OF REMOTE TECHNICAL SUPPORT WORK Remote technical support work exhibits many characteristics of knowledge work. Providing technical support involves work that is surrounded by uncertainty. Technical service personnel have to continuously deal with nonstandard problems, originating from the customer, try to make sense out of them, and then try to provide satisfactory solutions to them (Pentland, 1995). They are forced to come up with situation specific solutions and thus they engage in creating and applying knowledge. However, there are also some specific characteristics of technical support operations, which may not be common to all kinds of knowledge work. There is a significant service component to the technical support work. The success of products depends not only on the resolution of technical problems but also on the way the solution is delivered to the customer. Technical support specialists repair problems and repair relationships (Pentland, 1995). Support people perform the role of a broker between two communities. They link the community of users to the technologies they are using and to the technical community that produced these technologies (Barley, 1996). Hence they simplify the jargons of the technical community for the user community is needed. In most cases, except when service is delivered through email, the work has a real time quality to it like any other customer support work. Their performance is “live” and fast paced, accomplished while interacting with the customer. Especially in remote technical support, the lack of copresence with the customer introduces interpretative problems in the support situation. The support person is dependent on the customer to get information about the customer’s problem situation. However, the support person must consider the caller’s level of understanding and the correctness of the customer’s actions as part of his or her diagnosis. A support person thus needs to interpret the actual problem as well as the caller’s understanding of the problem, which may be faulty. Such interpretation difficulties are a constant feature of technical support work (Pentland, 1995). With the rise of the IT enabled services industry, remote outsourced customer support services have become independent businesses in their own right. Little research focuses on

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the organizational aspects of knowledge intensive support services especially for capability development and service delivery. Our purpose in this study was to examine the organizational capabilities that are required in knowledge intensive support services. In particular we examine the role of people, process, and technology elements in building organizational capability (Evenson, Patrick, & Frei, 1999) in knowledge intensive services. The next section describes our research approach. RESEARCH METHODOLOGY We adopted the case study method for our study. The rational for this approach arises from the exploratory nature of this study. The setting is that of a call centre that specialized in technical support and did not take on any other type of inbound or outbound call centre activity. Because of the call centre’s exclusive focus on the technical domain it was an ideal choice for the study. The primary source of data collection was the interview. A total of 24 semistructured and unstructured interviews were carried out in the organization and these spanned across hierarchies and departments. The respondents included the technical support agents, the team leaders, the support centre head, quality personnel, human resource personnel, the knowledge base in charge, and the top management. Two unstructured interviews were conducted with the CEO and the support centre head. These were initial interviews aimed at understanding the evolution of the organization, the services offered, and the nature of clients. The rest of the interviews were semistructured. An interview guide was prepared prior to the interviews, to serve as a checklist for information collection. The interview questions addressed five broad areas—the nature of task, nature of customer interaction, the people characteristics, process characteristics, and technology characteristics of the organization. In particular, questions addressed the nature of the task variety and complexity that the agents faced. Information was sought on the service delivery process, performance management process, and the knowledge management and organizational learning processes employed within the organization. Questions also related to employee qualifications, knowledge, and skill requirements for effective service delivery. The role of technology in the query resolution process was investigated through questions on the knowledge management technology employed in the organization. Many unplanned questions typically emerged during the course of the interview with reference to the particular context. The interviews lasted approximately hours on the average and were transcribed within 24 hours. The interviews were supplemented with notes on observations of support agents at work, and two weekly meetings. One hundred and twenty emails were related to the topic of support centre management were also used as primary data. A review of documentary evidence including Minutes of Meetings (MOMs), Process Manual, and samples of daily reports was carried out. Notes regarding these documents were made. The data was analysed using qualitative analysis techniques. Initially the same concepts that had been used for developing the interview guide were used for this process. The interview transcripts, the emails as well as the notes on meetings, documents reviewed, and observations were examined for recurring themes and issues. A few new concepts

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that emerged from the data were further added to the initial set of concepts to form a coding scheme. This scheme was then used for reexamining the data and arriving at the findings. The interviews provided the bulk of findings. For instance the existing literature did not suggest a need for “continuous on the job learning” in call centres. This emerged as an important theme from the interviews. The use of different data sources provided additional information as well as helped in checking the validity of the information. The observations of agents at work provided information about the nature of the task and service delivery processes. The need for multiple information sources for query resolution and the need for specific expertise to perform the task were first noted during observations of agents at work. Interviews confirmed such information and provided additional details such as the need for more complex, hands on problem solving for nonroutine queries. Though the concept of distributed knowledge among agents was discussed in the interviews, actual observations of the extent of the agents’ dependence on each other and the high levels of informal knowledge sharing substantiated this. Likewise the emails and the MOMs provided an opportunity to compare the organizational policies that were actually practised in the organization versus the policies that were stated in the interviews and the process manual. The concern for performance metrics like First Contact resolution, the existence of multiple organizational learning processes, and the emphasis on customer focus came out clearly from the emails as well as the MOMs. Hence the use of multiple data sources enabled data triangulation increased the confidence in the findings that we present next. CASE BACKGROUND TechHelp1 is a call centre that provides outsourced technical support services to client organizations. It is a mid-sized organization consisting of 80 employees. TechHelp is based in India and has an office in the US. At the time of the study the call centre offered chatand email-based technical support services from its Indian office for Indian and American corporate clients and their customers. They had also established a small five-seat voicebased call centre in the US for a client. They had plans to transition that to India and offer full-fledged voice based support services. At the time of the study TechHelp provided support over email, chat and phone for various technical domains ranging from MS Office packages to wireless modems for different clients. The next section presents our findings on the roles that the people, process and technology elements played in the organization.

1

TechHelp is a pseudonym.

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CASE FINDINGS People capabilities Employee profile The support agents were officially designated as experts in the call centre. This illustrates the difference between working in a traditional call centre and a knowledge intensive call centre. Only those agents who had a technical background or a technical diploma were recruited. The characteristic of the technical support task is such that knowledge is distributed among individuals. No one agent could be an expert in all the areas and agents specialize in different domains. Agents moved through different stages of expertise say from supporting MS office products to supporting Linux-based applications guided by personal interest as well as new client acquisition. The varied expertise that existed within the team was an important source of knowledge for agents. The operations area in which the agents performed the query resolution activities had no cubicles and the agents could easily access each other while responding to emails or chatting with customers. Knowledge and expertise was more concentrated in some positions than others. Team leaders and quality analysts are expected to have higher levels of expertise in technical as well as customer support skills compared to agents. However in an organization full of technical experts these pockets of higher expertise were used to increase the collective knowledge available. Knowledge sharing The concept of “Communities of Practice” in literature (Lave & Wenger, 1991; Orr, 1996) depicts the formal and informal ways in which groups of people with similar interests share and gain knowledge from each other. The knowledge sharing behaviour of technical support agents in TechHelp significantly demonstrated the characteristics of a community of practice. During working hours and off hours like lunch breaks, agents regularly talked of interesting and difficult incidents on query resolution that they had come experienced. In the data collection interviews agents referred to not only their own experiences but also the experiences of their colleagues while illustrating some point. This illustrates that knowledge was treated as belonging to a community and not an individual. The notions of shared knowledge and the interdependence of agents on each other are missing from the traditional call centre literature. Employee training and development Employee training was the most preferred route to formal knowledge acquisition within the support team. Training was frequent and spanned multiple areas. All new agents had to take a training programme that covered technical knowledge, account specific

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knowledge, problem-solving skills, and customer service skills. Once the agents were on the job, internal and external experts conducted regular training sessions in advanced technical areas, apart from refresher training sessions. This high emphasis on training is again different from the practice followed in traditional call centres where the routine nature of work usually requires only introductory training for fresh recruits. Training programmes had specific sessions on information searching skills. In a remote customer support environment information searching ability is an important skill for the agent. However, the important role played by information searching skills in achieving targeted service levels in call centres has not been addressed in the literature. Searching was particularly useful when the technical problem had specific error messages since most vendors have knowledge bases on their websites that have detailed information on solutions for different error messages. The importance accorded to information searching in training highlights the way in which modern technologies like the internet are changing the traditional call centres. On the job learning The need to keep oneself updated was important. One agent emphasized the need for this: You don’t have a choice. You have to find the time to update yourself. In traditional call centres the routine nature of work does not expose the agents to a fast changing environment that requires regular knowledge updating (Taylor & Bain, 1999). In this organization, due to the nature of the task, customer queries belonging to new technical areas spurred the agents to regularly update themselves through the internet, books, computer-based tutorials, and screen shots of new software products and applications. Apart from this new products were regularly installed in-house for agents to familiarize themselves with. The agents also managed to get additional experience through participating in various technology-related message boards and mailing lists on the internet. These avenues allowed them to experiment and sharpen their skills since they did not have to take as many precautions as they had to with customer queries. Team leaders also had their own methods of agent development. One team leader made sure that the new agents got queries that were challenging and stretched their skill sets. Another said that he communicated the solutions of the queries that were escalated to him to the team so that they could learn from his experience Employee mentoring Mentoring as a way for transmitting tacit knowledge and increasing the competence levels of new agents was frequently employed. Training programmes could not teach nuances of customer handling and problem solving. Mentors were expected to devote a considerable amount of time especially for new hires. At the same time new agents learnt the tricks of the trade through observing their mentors at work.

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The organization also encouraged employees to enrol in formal technical courses and sponsored such initiatives. As a result any employees who had joined the organization with a basic technical diploma later acquired certifications from Brainbench and Microsoft that had wide industry recognition. Such a broad range of employee development initiatives are rarely seen in traditional call centres. Process capability Service delivery process The support agent’s job essentially consisted of problem solving. This was different from a traditional call centre where the agent would simply provide information to customers. Agents referred to various sources of information in the process of solving a query, their own expertise and experience with similar problems in the past, as well as the expertise of their team leaders and team members to solve the query. Apart from this the agents considered the internet and the internal knowledge base as important information sources. TechHelp agents even consulted external specialists affiliated to their organization if necessary. TechHelp encouraged agents to resolve queries through hands on experimentation wherever possible. A set of 15 computers had been installed with different combinations of software and hardware in a laboratory kind of set-up. This helped the agents to get a feel of different components. One PC was designated as a guinea pig where one could carry out all kinds of tests and simulations even if the PC crashed. Providing support on text base chat and phone required real time problem solving as opposed to email and hence the pressure on the agent to perform was higher. As one agent said: It’s much tougher to handle chat. The customer is interacting with you continuously and you have to be able to think in such circumstances and figure out the solution. Hence new agents were first given responsibility for email-based queries and then moved to the more interactive mediums as they became more experienced. This ensured that the more capable agents handled the more difficult communication mediums. The customer’s technical expertise also played an important role in the query resolution. The agents were expected to customize their responses according to the customer’s perceived level of expertise. The language of the query and the way the customer phrased the query would give an experienced agent clues about the customer’s expertise. As a team leader said: If the customer says he has tried out the registry keys you can’t start with basic instructions.

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Based upon the perceived expertise level of the customer the TechHelp agent would send different combinations of information kinds in the response. With novice customers an agent would try and shift the interaction to a richer interaction medium if possible, say from email to chat or voice. Depending on the complexity of the queries, they were categorized into basic, average, and complex queries by the quality personnel. These categories were then used in rating the responses to queries in all the mediums—emails, text-based chat, and phone-based support. These ratings were used as inputs to performance appraisals of the agents. This categorization helped in tracking the performance of the support centre according to complexity of queries and hence highlighted areas that needed focus. The metrics and measures that were emphasized in the call centre further lead to improvement of employee capabilities. First contact resolution was an important measure that was regularly tracked. This indicated the percentage of customer queries that were solved in the first customer contact whether over email, chat, or phone. TechHelp had set a high target for first contact resolution. This required high level of capability in all phases of query resolution—information collection, diagnosis, and communication. For instance, one of the norms that the team leaders tried to implement was that the response to a complex query should contain at least three alternate recommendations for query resolution. To develop this ability the organization started focusing not only on agent knowledge and problem-solving skills but also the internal knowledge base. Technology capability Internal knowledge base One of the important sources of knowledge for new agents was the in-house knowledge base. It allowed them to become productive and start responding to customer queries in a short period of time. It also increased their exposure to different kinds of queries faster since new agents could use the information present in the knowledge base to tackle queries instead of escalating them to seniors or experts. CONCLUSION The case study illustrates the manner in which a call centre providing knowledge-based services has approached the issue of developing service delivery capability. Many aspects of the organization, whether it is the employee profile, the employee development and knowledge dissemination practices, or the service delivery process, differentiate it from the sweatshop model of a traditional call centre. The ability of the organization to recognize the expertise and knowledge involved in the task and hence mould its organizational characteristics accordingly resulted in an employee friendly work environment as well as successful performance.

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REFERENCES Barley, S.R. (1996). Technicians in the workplace: Ethnographic evidence for bringing work into organization studies. Administrative Science Quarterly, 41(3), 404–441. Batt, R. (1999). Work organization technology and performance in customer service and sales. Industrial and Labour Relations Review, 52(4), 539–564. Evenson, A., Patrick, H., & Frei, F.X. (1999). Effective call center management: Evidence from financial services [Working Paper No. 99–16]. The Wharton Financial Institutions Center, The Wharton School of Business: University of Pennsylvania, Philadelphia, USA. Incomes Data Services. (1997). Pay and conditions in call centers. London: Author. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press. McKinsey. (1999). Highlights of the NASSCOM, McKinsey study report on the it enabled service segment. New Delhi, India: National Association for Software and Service Companies. Orr, J.E. (1996/1997). Talking about machines: Ethnography of a modern job. Ithaca, NY: ILR Press. Pentland, B.T. (1992). Organizing moves in software support hot lines. Administrative Science Quarterly, 37(4), 527–548. Pentland, B.T. (1995). Read me what it says on your screen: The interpretative problem in technical service work. Technology Studies, 2(1), 50–79. Taylor, P., & Bain, P. (1999). An assembly line in the head: Work and employee relations in the call center. Industrial Relations Journal, 30(2), 101–117.

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European Journal of Work and Organizational Psychology Volume 12, 2003, Contents

Issue 1

Issue 2

424 VOLUME 12 CONTENTS

Issue 3

Issue 4 Special issue on Call centre work: Smile by wire (Guest editors: Christian Dormann and Fred R.H.Zijlstra)

European Journal of Work and Organizational Psychology Volume 12, 2003, List of reviewers

The editors would like to thank the following for reviewing papers for the journal between August 2002 and July 2003:

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Aims and Scope ● How do busy professionals find time to keep up-to-date in their area of expertise? ● How do academics discover what are the real problems facing employees and organizations? ● Answer: they all read the European Journal of Work and Organizational Psychology.

It is the express aim of the European Journal of Work and Organizational Psychology (EJWOP) to bring professionals and academics into closer collaboration; integrating European professional and academic responses to problems and issues which arise in practice. The Editor invites submission of original high quality articles of value to professionals and academics in the field of work and organizational psychology. Submissions may take several forms: empirical research articles, theoretical contributions, reviews, case studies, descriptions and evaluations of instruments and systems, dialogues between academics and practitioners, intervention methods, and so on. Methodological contributions will be considered, as long as they are relevant to the field of work and organizational psychology. However, studies dealing with “student subjects” will usually not be accepted. When submitting articles, authors should make clear the impact of their work for the professional field, and each article should include a paragraph that clearly discusses the implications for the field. In addition, EJWOP occasionally publishes theme issues on topics that are of immediate relevance to the world of work and organizational psychology, and proposals for theme issues are welcome. In particular, professionals working in the field of work and organizational psychology are invited to send in contributions, whether they are reactions or comments on articles that have been published, or news and new developments that are relevant for colleagues. Submissions of Manuscripts Manuscripts should be prepared following the guidelines in the APA Publication Manual (5th ed.). The publisher would actively encourage authors to submit papers electronically to expedite the peer review process. Please email your paper saved in a standard document format type such as Word, Rich Text Format, or PDF to: [email protected] If you are unable to supply a version of your paper by email, please send two hard copies of the manuscript and a disk version (Microsoft Word in PC format or PDF format) to: Sophie Forster, Journals Editorial Assistant, Psychology Press Ltd, 27 Church Road, Hove, East Sussex BN3 2FA, UK. Tel: +44 (0) 1273 225007; Fax: +44 (0) 1273 205612; Email: [email protected] Full instructions for authors are available on the journal website at: www.tandf.co.uk/ journals/pp/1359432X.html Professional News Section The Professional News Section comprises regular items together with one or two special features. Regular items include appointments, news about grants, international conferences, news of networks, legal issues, and research collaboration. Special features

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are aimed at giving an up-to-date picture of professional practice and current issues. Please send items for the Professional News Section to the Journal Editor, Professor Fred Zijlstra, Department of Psychology, School of Human Sciences, University of Surrey, Guildford, Surrey GU2 5XH, UK. Email: [email protected]

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European Journal of Work and Organizational Psychology Volume 12, 2003, Author index

Anderson, N., 1

Kälin, W., 337 Kivimäki, M., 19 Konradt, U., 61

Bakker, A.B., 81, 387 Bandi, R.K., 411 Bechtoldt, M., 309 Blau, P., 309 Brandstätter, V., 37

Latham, G.P., 245 Lewig, K.A., 361 Lo Faso, L., 337

Coyne, I., 209 Cropley, M., 195

Malzacher, J.T., 37 Millward Purvis, L.J., 195 Muhonen, T., 171

Daghighi Latham, S., 245 Demerouti, E., 387 Dollard, M.F., 361 Dormann, C., iii

Randall, P., 209 Roe, R.A., 257 Salgado, J.F., 1 Sandal, G.M., 147 Schaufeli, W.B., 387 Schmook, R., 61 Seigne, E., 209 Semmer, N.K., 337 Shah, V., 411 Smith-Lee Chong, P., 209

Elfering, A., 337 Elovainio, M., 19 Frese, M., 37 Grebner, S., 337 Gut, S., 337 Hacker, W., 105 Heimbeck, D., 37 Herriot, P., 131 Hertel, G., 61 Hetland, H., 147 Heuven, E., 81

Torkelson, E., 171

Isic, A., 309

Zapf, D., 309 Zijlstra, F.R.H., 193, iii

Van den Berg, P.T., 257 Van Yperen, N.W., 229 Virtanen, M., 19 Virtanen, P., 19

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