The Effects of Performance Monitoring on Emotional ... - Springer Link

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Style file version Nov. 19th, 1999

C 2002) Motivation and Emotion, Vol. 26, No. 1, March 2002 (°

The Effects of Performance Monitoring on Emotional Labor and Well-Being in Call Centers David Holman,1,2 Claire Chissick,1 and Peter Totterdell1

A study was conducted to investigate the relationship between performance monitoring and well-being. It also examined a mechanism, namely emotional labor, that might mediate the relationship between them, assessed the effect of the work context on the relationship between performance monitoring and well-being, and examined the relative effects of performance monitoring and work context on well-being. Three aspects of performance monitoring were covered, namely, its performance-related content (i.e., immediacy of feedback, clarity of performance criteria), its beneficial-purpose (i.e., developmental rather than punitive aims), and its perceived intensity. The participants were 347 customer service agents in two U.K. call centers who completed a battery of questionnaire scales. Regression analyses revealed that the performance-related content and the beneficial-purpose of monitoring were positively related to well-being, while perceived intensity had a strong negative association with well-being. Emotional labor did not mediate the relationship between monitoring and well-being in the form hypothesized, although it was related to these two factors. Work context (job control, problem solving demand, supervisory support) did not mediate the relationship between monitoring and well-being, but job control and supervisory support did moderate the relationship between perceived intensity and well-being. Relative to other study variables, perceived intensity showed stronger associations with emotional exhaustion, while job control and supervisory support tended to show stronger

1 Institute

of Work Psychology, University of Sheffield, Sheffield S10 2TN, United Kingdom. all correspondence to David Holman, Institute of Work Psychology, University of Sheffield, Sheffield S10 2TN, United Kingdom; e-mail: [email protected]. The support of the Economic and Social Research Council (ESRC) (U.K.) is gratefully acknowledged. The work was part of the programme of the ESRC Centre for Organization and Innovation.

2 Address

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Holman, Chissick, and Totterdell

associations with depression and job satisfaction. Implications for theory, practice, and future research are discussed. KEY WORDS: performance monitoring; emotional labor; well-being; stress; call centers; customer service.

A wide range of job, organizational, and environmental factors has been identified as antecedents of affect and well-being at work. One organizational factor that has received less attention than most is performance monitoring. Performance monitoring can be defined as those practices that involve “the observation, examination, or recording of employee work related behaviors (or all of these), with and without technological assistance” (Stanton, 2000, p. 87). To its advocates, performance monitoring enables the organization to monitor and improve employee performance, to reduce costs and to ensure customer satisfaction (Alder, 1998; Chalykoff & Kochan, 1989). Employees are thought to benefit because they can receive accurate and timely feedback, have their performance recognized and assessed fairly, improve their performance and develop new skills (Grant & Higgins, 1989). It has also been suggested that well-being is improved by, for example, deriving satisfaction from the knowledge of one’s improved performance and from being better able to cope with work demands (Aiello & Shao, 1993; Bandura, 1997; Hackman and Oldham, 1976; Stanton, 2000). To its critics, performance monitoring is intrinsically threatening to employees because the information gained may adversely affect employees’ remuneration or their relationship with coworkers (Alder, 1998). Monitoring is also considered to intensify employees’ workload and to increase the level of work demands (Smith, Carayon, Sanders, Lim, & LeGrande, 1992). The threat of monitoring and the high level of demand are thought to impact negatively on employee well-being. There are, however, relatively few studies of performance monitoring as an antecedent of affect and well-being, although existing studies do suggest a link to employee stress (Aiello & Kolb, 1995; Chalykoff & Kochan, 1989; Smith et al., 1992). This lack of research seems surprising given that performance monitoring is widely used by organizations. Indeed, in 1992 it was estimated that 26 million U.S. workers were monitored electronically3 (Nussbaum, 1992; Ross, 1992) and that between 1990 and 1992 approximately 70,000 U.S. companies spent $500 million on monitoring software (Bylinsky, 1991; Halpern, 1992). Moreover, it is likely that the number of employees being monitored has increased in the last 10 years. One reason for this is the greater use and availability of technologies that make performance monitoring easier and more viable (e.g., monitoring software, just-intime systems, total quality management systems, Waterson et al., 1997). Another

3 This does not include those monitored through nonelectronic means and thus the overall total is likely

to be higher.

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reason is the growth of call centers.4 In call centers, performance monitoring occurs through the continuous electronic monitoring of quantitative performance indicators such as length of call, number of calls, and amount of time logged on and off the system. In addition, a call can be listened to or recorded remotely (with or without the employee’s knowledge) in order to assess its quality. Performance monitoring is thus a highly prominent and pervasive feature of everyday life in call centers. Indeed, the pervasiveness of performance monitoring in call centers has led to them being labelled as “electronic panopticans” (Fernie and Metcalf, 1998); it also makes call centers an excellent location in which to study performance monitoring. Despite the prominence and pervasiveness of performance monitoring in call centers, very little research has been conducted in to its effects (Chalykoff & Kochan, 1989, Smith et al., 1992). Furthermore, only a handful of studies have focused on how different aspects of performance monitoring might relate to wellbeing (Stanton, 2000), and these studies have generally examined the content of monitoring (e.g., type, frequency, feedback processes) rather than other aspects of monitoring (e.g., its purpose or perceived intensity). Research has also tended to use global measures of stress (e.g., feeling stressed or not, Aiello & Kolb, 1995) or measures of satisfaction (Chalykoff & Kochan, 1989). Few studies have used well-validated measures of well-being that reflect the diverse ways in which affect can be experienced, for example, anxiety, depression, or emotional exhaustion (Smith et al., 1992). Another weakness of the performance monitoring literature is that field studies have rarely, if ever, addressed the mechanisms that might link performance monitoring to well-being. Finally, there have been few attempts to assess the joint effects of monitoring and contextual factors (e.g., job control, job demand, social support) on well-being or to assess the importance of monitoring relative to other contextual factors (Carayon, 1994). The purpose of the present study was to address these issues. Specifically, this study had the following four aims. The first aim was to examine the relationship between the nature, purpose and intensity of performance monitoring, and various measures of employee well-being in a call center environment. The second aim was to examine one mechanism that might mediate the relationship between performance monitoring and well-being in a service context, namely, emotional labor. The third aim was to examine the joint effects of performance monitoring and contextual variables on employee well-being. The final aim was to examine the relative importance of performance monitoring and contextual variables with regard to well-being. 4 For example, call centers currently employ approximately 2% of the U.K. working population, which

is a rise from 1% in the mid 1990’s (Fernie & Metcalf, 1998). Although call centers vary in size and purpose, central to all call centers are information technologies that integrate call management systems with VDU technologies and customer data bases. This enables incoming and outgoing calls to be easily distributed to available staff, as well as enabling information (e.g., customers’ details) to be instantly accessed or inputted (or both).

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PERFORMANCE MONITORING As noted, performance monitoring involves the observation, examination, and recording of employee work behaviors (Stanton, 2000). Within organizations it also normally involves feedback processes—although feedback is not necessarily an aspect of monitoring. Performance monitoring has also been conceptualized as existing in both “traditional” and “electronic” forms.5 Traditional forms, such as direct observation, listening to calls, work sampling, and self-report, tend to be episodic and collect both qualitative and quantitative data. Electronic performance monitoring involves the automatic and remote collection of quantitative data (e.g., key strokes, call times). It also permits the continuous monitoring of performance. Traditional and electronic forms of performance monitoring vary according to a range of characteristics (Carayon, 1993; Stanton, 2000). These characteristics can be clustered into two main groups, namely, content and purpose. The “content” of performance monitoring covers the more “objective” qualities of the monitoring process. It includes: frequency (e.g., is it continuous or episodic, its regularity); feedback (e.g., how data is fed back, how often it is fed back); performance criteria (e.g., qualitative, quantitative, clarity); source (e.g., who or what collects the data); and target (e.g., is monitoring at an individual or group level, which task is monitored). The “purpose” of performance monitoring covers the uses to which the performance data is put. For example, is the data used for punitive purposes, developmental purposes, or to inform reward and payment decisions? In addition to content and purpose, performance monitoring research has highlighted a third factor, “monitoring cognitions” (Stanton, 2000). This factor covers employee perceptions of monitoring and includes attitudes toward monitoring (e.g., is it an invasion of privacy), assessments of its fairness and whether the monitoring system is trusted (Chalykoff & Kochan, 1989; Niehoff & Moorman, 1993). The perceived intensity of monitoring has also been suggested as an important monitoring cognition, but as yet it has not been studied empirically (Stanton, 2000). PERFORMANCE MONITORING AND WELL-BEING In laboratory and field studies, monitored employees (or participants) are generally found to have higher levels of stress and dissatisfaction than nonmonitored employees (Aiello et al., 1991; Aiello & Kolb; 1995, David & Henderson, 2000; Irving, Higgins, & Safeyeni, 1986).6 In one of the more comprehensive field studies, Smith et al. (1992) compared monitored and nonmonitored employees. Monitored employees reported higher levels of boredom, depression, anxiety, anger, and fatigue. 5 While 6 One

not ideal labels, we keep them as they are used by others in this field (e.g., Stanton, 2000). exception is the study by Nebeker and Tatum (1993), which found no difference.

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Other studies have focused on the relationship between various characteristics of performance monitoring and well-being. Carayon (1994), in a reanalysis of the Smith et al. (1992) data, focused on 14 performance monitoring characteristics. Of the 14 measures, only two had a consistent relationship with employee stress. First, if employees thought that performance monitoring presented a good picture of their work to management, it was negatively associated with depression, anger, fatigue, and job dissatisfaction. Second, if an employee’s performance data was compared to other employees’ data, there was a positive association with depression, anger, and fatigue. It is interesting to note that anxiety was not associated with any performance monitoring characteristic. In a study of performance monitoring in a call center, Chalykoff and Kochan (1989) found a positive relationship between job satisfaction and the immediacy of feedback, clear rating criteria and whether the feedback was positive. Like Carayon (1994), they too found no relationship with the frequency of monitoring. They also found no relationship to attitudes about monitoring, such as whether it was an invasion of privacy or a good tool if used properly. From the above it is apparent that, while being monitored is more stressful than not being monitored, there is little evidence to indicate exactly what it is about being monitored that makes it so much more stressful than not being monitored. The strongest evidence suggests that feedback can have positive effects on well-being. This contradicts any suggestion that monitoring produces only negative effects. Given the paucity of studies on performance monitoring (especially in call centers), more research is needed on how different aspects of performance monitoring relate to well being. In particular, research is needed that focuses on a wide range of performance monitoring characteristics, for example, its content, purpose, and monitoring cognitions. It is also important to examine whether performance monitoring affects particular aspects of well-being or has more global effects. For example, Carayon’s finding that performance monitoring was unrelated to anxiety suggests it might only affect particular aspects of well-being (Carayon, 1994). It is not possible at present to make reliable assertions as to whether monitoring has specific or global effects. This is because previous research has tended to use global measures of stress (i.e., whether the individual feels stressed or not) or measures of satisfaction (Aiello & Kolb, 1995; Chalykoff & Kochan, 1989). This implies that, as in the studies by Smith et al. (1992) and Carayon (1994), the diversity of emotional experience should be measured. Thus, the first aim of this study is to examine the relationship between different aspects of employee well-being and the content of performance monitoring, the purpose of performance monitoring and monitoring cognitions. How might the different aspects of performance monitoring affect well-being? With regard to the content of performance monitoring, two rival hypotheses exist. The first hypothesis asserts that well-being may be improved by those aspects of the content of performance monitoring which pertain directly to the development of performance (e.g., the feedback process, the clarity of performance criteria). The performance-related content of performance is thought to increase an employee’s

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knowledge of his or her performance and to help improve the employee’s skills and performance. Greater knowledge of one’s performance can reduce anxiety about how one is viewed by the organization. The improvement in skills and performance may improve a person’s ability to cope with work demands, and this results in reduced anxiety and less emotional exhaustion (Jenkins & Maslach, 1994; Karasek & Theorell, 1990). The second hypothesis asserts that performance monitoring is intrinsically threatening, because the information gained may affect remuneration and promotion decisions or affect an employee’s relationship with the employee’s colleagues or supervisor (or both). The fear of evaluation may also heighten sensitivity to feedback and damage feelings of self-worth (Smith, Carayon, & Miezio, 1986). Monitoring is also seen to intensify workload (as increased surveillance means that the employee has less opportunity to reduce his or her work rate) and increase cognitive demand as it is an additional factor to be considered by the employee (Smith et al., 1992). The threat of monitoring and the high level of demand are thought to impact negatively on employee well-being. As current evidence seems to support the first explanation, the first hypothesis of this study is: Hypothesis 1: The performance-related content of performance monitoring will be positively associated with well-being. Performance monitoring can be used for a number of reasons and although no research has been conducted on the purposes of monitoring (Stanton, 2000), it is likely that when an employee perceives the purposes of monitoring to be beneficial (e.g., developmental, ensuring correct standards of service) rather than nonbeneficial (e.g., punitive), then performance monitoring will be positively associated with well-being. Indeed, beneficial purposes may reduce the likelihood of monitoring being thought of as threatening and anxiety provoking. The second hypothesis is: Hypothesis 2: When the purpose of monitoring is perceived as beneficial, it will be positively associated with well-being. A range of employee monitoring cognitions could be studied, for example, fairness, trust, and attitudes. In this study we decided to focus on intensity as it had not been studied before. When monitoring is perceived as intense (i.e., that there is no escape and that it is pervasive), individuals will feel under greater pressure and perceive work demands to be higher. As higher levels of work pressure and demands have generally been shown to be associated with lower levels of wellbeing (Karasek & Theorell, 1990), the following hypothesis is proposed: Hypothesis 3: The intensity of monitoring will be negatively associated with wellbeing.

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EMOTIONAL LABOR AND CALL MONITORING In service organizations, employees are generally required to manage their emotional expression toward customers (Hochschild, 1983). In particular, employees are expected to display the appropriate emotional expression, whether it be a display of positive emotion (e.g., “smiling down the phone” in call centers, Belt, Richardson, & Webster, 1999) or negative emotion (e.g., anger in bill collectors, Sutton, 1991). The emotional display of employees is seen to be an intrinsic and important aspect of the service provided, and the effort involved in managing one’s emotions in exchange for a wage has been labelled “emotional labor” (Hochschild, 1983, p. 7). For the organization, emotional labor has a number of potential benefits, such as improved customer service, customer retention and increased sales. For the employee, studies generally show that the effects of emotional labor can be positive and negative (although a few studies show no effects, e.g., Zerbe, 2000). For example, Zapf, Vogt, Seifert, Mertini, and Isic (1999) found that the requirement to express positive emotions was associated with feelings of both personal accomplishment and emotional exhaustion. Similarly, Schaubroeck and Jones (2000) found that the requirement to express positive emotions was associated with symptoms of ill health, while Parkinson (1991) found that more emotionally expressive hairdressers received bigger tips. Although the relationships between emotional labor and well-being are complex, one aspect of emotional labor, called emotional dissonance, has been consistently associated with lower well-being (Hochschild, 1983; Zapf et al., 1999). Emotional dissonance occurs when there is a discrepancy between what a person is required to express by the organization and what they feel. In response to this, the individual can either display his or her “true” emotions or try to ensure that the emotions displayed are in line with organizational requirements. If they choose the latter option, two modes of emotional regulation may be deployed, surface acting or deep acting (Hochschild, 1983). Surface acting involves the display of required emotions but there is no attempt to actually feel or experience those emotions. For example, an employee may “paste” a smile on her face even though she is feeling unhappy. Thus, inherent in surface acting is a continued discrepancy between displayed and felt emotions. In contrast, workers who perform “deep acting” endeavor to feel the required emotions as a means of creating an appropriate display of emotion (Brotheridge & Lee, 1998). To perform deep acting, an employee can adopt various strategies, such as attention deployment (think about events that call up emotions needed), and cognitive change (reappraise the situation so its emotional impact is lessened, Grandey, 2000). The effort involved in managing one’s emotions is thought to decrease wellbeing. Indeed, high levels of emotional management have been linked to emotional exhaustion, a feeling of being used up, worn out and irritable in one’s personal interactions (Brotheridge & Lee, 1998; Brotheridge & Grandey, 2002; Maslach & Jackson, 1981). Furthermore, being in a state of dissonance can in itself be anxiety

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provoking (Carver, Lawrence, & Scheier, 1995), while surface acting may stifle personal expression and this may be experienced as unpleasant and dissatisfying (Rutter & Fielding, 1988). Continued and sustained feelings of dissonance and surface acting (particularly faking and feeling false) may also result in depression, as the feeling of “being a fake” and being false may damage one’s self worth (Bandura, 1997). Alternatively, deep acting may reduce dissonance and, therefore, anxiety. Performance monitoring is one means through which organizational requirements are enforced. Performance monitoring may reduce the range of emotions displayed by employees (by specifying what emotions can be displayed) and increase the level of dissonance. This increase in dissonance and its regulation using surface and deep acting may then affect well-being. This chain of reasoning suggests that emotional labor will mediate the relationship between performance monitoring and well-being. The following hypothesis is therefore proposed: Hypothesis 4: Emotional labor will mediate the relationship between performance monitoring and well-being. WORK CONTEXT, PERFORMANCE MONITORING, AND WELL-BEING In separate reviews of the performance monitoring literature, Smith and Amick (1989) and Carayon (1993) suggest that job control, job demand, and social support are important factors to consider when examining the relationship between work context, performance monitoring and well-being. Carayon (1993) argues that, while performance monitoring will have direct effects on well-being, its effects on well-being will also be indirect and mediated by the work context (e.g., job design and supervisory style) (Rousseau, 1978). According to this idea, performance monitoring indirectly affects well-being by simplifying work, reducing job control (Schleifer, 1990; Smith & Amick, 1989; Smith et al., 1992; Stanton & Barnes Farrell, 1996) and increasing work demands (Smith et al., 1992). Smith, Carayon, and Miezio (1986) also found that the introduction of performance monitoring produces a stricter and more coercive style of supervision. These changes in work context are then seen to impact negatively on employee well-being. The following hypothesis is therefore proposed. Hypothesis 5: Job control, job demands, and supervisory support will mediate the relationship between performance monitoring and well-being. Carayon (1993) also argues that the work context will moderate the relationship between performance monitoring and well-being. In particular, it is expected that job control, job demands, and supervisory support will affect the impact of the intensity of monitoring. When job control and supervisory support are high they will act as buffers, reducing the negative impact of intensity on well-being. Job demand will act as a “multiplier.” When job demand is high, it will increase

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the negative impact of intensity on well-being. With regard to the content and purpose of monitoring, the supervisor could affect the content and purpose of performance monitoring by, for example, deciding to use it for punitive purposes or to increase the level of feedback. As such, when supervisory support is high it will increase the impact that the content and purpose of monitoring have on well-being. On the basis of the above, the following hypotheses are proposed: Hypothesis 6: Job control, job demands, and supervisory support will moderate the relationship between the intensity of monitoring and well-being. Hypothesis 7: Supervisory support will moderate the relationship between the content and purpose of call monitoring and well-being. Carayon (1994) also examined the effects of performance monitoring characteristics on well-being relative to other work context variables such as workload demand, variation in workload, and job control. He found that workload demand and job control tended to have much stronger relationships to well-being than performance monitoring characteristics. However, this research was not conducted in a call center environment where performance monitoring is more pervasive. In such a context, performance monitoring may have a much stronger relationship with well-being relative to other work context variables. The following research question was therefore set. Research Question 1: What are the relative effects of performance monitoring characteristics and work context variables on well-being in a call center environment? METHOD Sample and Procedure The present research was conducted in two call centers, “Mortgage-call” and “Loan-call,” that were part of a U.K. bank. The data for this study was collected as part of a larger longitudinal investigation into employee attitudes and well-being in call centers. The research was conducted mainly by a questionnaire administered on site. Prior to the survey administration, interviews with four Call Center Agents (CSAs) and two supervisors concerning call monitoring were conducted and the information obtained was used in the questionnaire design. It was stressed that participation in the research was entirely voluntary and confidentiality was guaranteed. The survey took place during normal working hours and questionnaires were returned to the researcher on site. A total of 347 questionnaires were returned by CSAs. This represented a response rate of 79%. Of the total sample, 70.6% were female and 29.4% were male. The mean age was 32.3 years (range 19–57). The average job tenure was 28 months, and average time with the company was 45 months.

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Description of Cases and Performance Monitoring Procedures At both sites, CSAs spent about 80–90% of their time answering incoming calls that were mainly from external customers. The remaining time was spent in team meetings and off-line administration. At Mortgage-call, a CSA’s job entailed taking incoming calls from customers, bank branches, and solicitors concerning mortgage enquiries. At “Loan-call,” CSAs took incoming calls regarding loan applications and queries. CSAs were also expected to sell loan insurance. CSAs were monitored similarly at both sites, although there were small differences. At both sites, continuous electronic performance monitoring (via a management information system) was used to collect quantitative performance data on logging-in and out times, average call time, average time unavailable, and average call rate. This information was circulated to CSAs on a daily basis. Episodic traditional monitoring, namely listening to calls, was conducted in three ways. Firstly, “side-by-side,” whereby the supervisor sits next to the CSA while they take a call. Secondly, “remote” monitoring, where the supervisor sits separately from the CSA, often out of sight, and listens to the CSA’s calls without the agent’s knowledge. Thirdly, calls may be randomly taped and then listened to by the supervisor on a later occasion. The dominant method used was remote monitoring. The supervisor graded the content of the call according to a specific guideline. At Mortgage-call a guideline of eight areas, such as “greeting,” “identify and analyse customer needs,” and “professionalism,” was used. To obtain an “exceed” rating with regard to professionalism, CSAs had to, for example, “demonstrate interest in helping the customer,” “have a pleasant, friendly, approachable, and professional tone,” offer “sincere apologies” and be “patient with difficult and unresponsive customers.” At Loan-call, CSAs had to follow a call-guide that indicated what should ideally be said to the customer. The call-guide was split into 12 sections (e.g., introduction, gathering information, questioning) and included recommendations about emotional display (e.g., shows empathy). CSAs received feedback relating to calls during “coaching” sessions. Employees were meant to have their calls listened to from three to six times a month, although in practice it was some times less than this. Monitoring information made up 65% of the CSA’s Performance Management plans. Performance management reviews took place once a quarter and determined promotion and pay levels. Measures Performance Monitoring The content, purpose, and intensity of performance monitoring were measured. The content of performance monitoring was measured using five items and the items pertained directly to those aspects of the content of monitoring that are

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performance-related. For this reason the scale is referred to as the “performancerelated content of monitoring.” The five items were partly based on items used by Chalykoff and Kochan (1989) and included four questions on traditional performance monitoring. This covered the frequency of call monitoring, clarity of performance criteria, the immediacy of feedback and whether the feedback was positive. Another question covering the frequency of feedback received from continuous electronic monitoring was added. Both traditional and electronic forms of monitoring were covered and this is in keeping with recommendations by Lund (1992). A 5-point scale was used (Not at all to A great deal; α = 0.75). The purpose of monitoring was a newly constructed 3-item scale (sample items, “The purpose of call monitoring is to ensure I provide the correct level of customer service,” “The purpose of call monitoring is to punish me rather than develop me” [reverse scored]). For reasons of clarity we will refer to this scale as “beneficial-purpose of monitoring.” The perceived intensity of performance monitoring was a 5-item scale covering the intensity of both the electronic and traditional forms of monitoring (sample items, “Call monitoring, e.g., remote, side by side, at work is too intense” and “Monitoring of statistics increases the pressure I feel under.” For purpose and intensity of call monitoring, a 5-point scale was used (Strongly disagree to Strongly agree) and the reliabilities were α = 0.74 and α = 0.88 respectively. Emotional Labor With regard to emotional labor, emotional dissonance, surface acting, and deep acting were measured. Emotional dissonance was measured using a newly constructed 3-item scale (sample items; “I often feel there is a discrepancy between what I feel and the emotions I am required to express to customers” and “There’s no difference between the emotions I’m expected to express to customers and how I feel” [reverse scored]). A 5-point scale was used (Strongly disagree to Strongly agree). Surface acting and deep acting were measured using Brotheridge and Lee’s 3-item scales (Brotheridge & Lee, 1998). A sample item for surface acting was “In order to do your job effectively, how often do you fake a good mood?” A sample item for deep acting was “how often do you try to actually experience the emotions you display to customers?” Both items used a 5-point scale (Not at all to A great deal). The reliabilities for emotional dissonance, surface acting, and deep acting were, respectively, α = 0.76, α = 0.85, and α = 0.90. Job Context Adapted versions of the Jackson, Wall, Martin, and Davids’ scales were used to measure job control and job demand (Jackson, Wall, Martin, & David, 1993). Items were reworded where necessary to reflect a call center environment. Specifically, job control was measured by a 5-item scale (sample item, “Can you vary

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how you talk with customers?”; α = 0.80). Job demand was measured by the problem-solving demand scale and had five items (sample item, “Do you have to solve problems which have no obvious correct answer?”; α = 0.77). Both scales used a 5-point scale (Not at all to A great deal). Supervisor support was based on a measure used by Axtell et al. (2000) and consisted of six items relating to the quality of supervisory style (e.g., “Does your supervisor discuss and solve problems with you?”; α = 0.79). Well-Being Four measures of job-related well-being were used, the first being “Intensity of Emotional Exhaustion” (Maslach & Jackson, 1981). Space limitations meant that only the four items that loaded most highly on “Emotional Exhaustion” in Maslach and Jackson’s study were used (Maslach & Jackson, 1981). A sample item was “Feel emotionally drained from your work.” A 7-point scale was used (Very Mildly to Very Strongly; α = .93). Anxiety and Depression were measured using six item scales developed by Warr (1990). Both scales asked about feelings experienced at work over the last month. Feelings on the anxiety scale included tense, worried, calm, and relaxed. Feelings on the depression scale included miserable, depressed, and optimistic. A 5-point scale was used (Never to All of the time) and the reliabilities were α = 0.83 and α = 0.84 respectively. Job satisfaction was measured using Warr, Cook, and Wall’s scale containing 15 items for example, physical work conditions, job security, recognition for good work, and amount of responsibility (Warr, Cook, & Wall, 1979). A sample item was “How satisfied are you with your colleagues.” A 7-point scale (Extremely dissatisfied to Extremely satisfied) was used (α = 0.87). It is recognized that job satisfaction, as an attitude, has both affective and cognitive components (Eagly & Chaiken, 1993; Fisher, 2000; Weiss & Cropanzano, 1996). It was included in this study as we were interested in its affective component and in order to facilitate comparison with previous research. RESULTS Zero-order correlations and means of the main study variables are shown in Table I. Focusing on correlations between performance monitoring variables, it is evident that the performance-related content of monitoring was unrelated to intensity of monitoring. The performance-related content of monitoring was, however, positively related to the beneficial-purpose of monitoring (r = .30, p < .01), while the beneficial-purpose of monitoring was negatively related to intensity (r = −.42, p < .01). The sizes of the correlations between the monitoring variables suggests that they are separate constructs. Of the correlations between the emotional labor variables, deep acting was related to surface acting (r = .37, p < .01) but not

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0.75 −.08 0.88 −.09 0.75 .00 0.76

2.63 2.56 3.51 3.67

−.09

.38∗∗∗

.53∗∗∗

−.05 .09

−.08 −.17∗∗

−.03

.09

.07 .27∗∗∗

.39∗∗∗

—

.37∗∗∗ .36∗∗∗

—

8

.32∗∗∗

.27∗∗∗

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.31∗∗∗

.35∗∗∗

.11∗

.06

— .14∗∗

9

.02

−.20∗∗∗ −.04 .18∗∗∗ .05

.29∗∗∗ −.05

.28∗∗∗

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−.25∗∗∗

−.11∗

.41∗∗∗ −.29∗∗∗ −.33∗∗∗ −.28

.10 .35∗∗∗

.19∗∗∗

.25∗∗∗

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7

−.59∗∗∗

−.37∗∗∗

—

11

.67∗∗∗

—

12

—

13

−.26∗∗∗

— .03

14

—

15

.55∗∗∗ −.24∗∗∗ −.33∗∗∗ .17∗∗ .11

−.17∗∗∗ −.17∗∗∗ −.27∗∗∗ −.26∗∗ .22∗∗∗ −.15∗∗ .13∗∗ .04

.58

.67

−.39

—

10

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< .05. ∗∗ p < .01. ∗∗∗ p < .001.

.01

.04

0.73 −.02

2.78

−.15∗∗

.04 −.18∗∗∗

−.13∗

−.16∗∗

−.42∗∗

—

6

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.05

.05 .08

−.09

.14∗

0.81

4.41

−.07

−.08

−.07

.02 .06 −.12* −.07

1.00 −.02 1.74 .01

2.30 3.19

−.02 .02

1.04 −.18∗∗∗

2.67

−.02

.08

.02

.30∗∗∗

5

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−.06

0.66 −.04

3.94

—

4

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−.02

.08

−.04

.03

0.84

3.37

3

—

—

2

−.22∗∗∗

27.38 25.83

8.69 — 0.46 −.28∗∗∗

1

.40∗∗∗

32.22 0.29

SD


∗p

1. Age (years) 2. Gender (male = 1) 3. Job tenure (months) 4. Content of performance monitoring 5. Purpose of performance monitoring 6. Intensity of performance monitoring 7. Emotional dissonance 8. Surface acting 9. Deep acting 10. Emotional exhaustion 11. Job satisfaction 12. Job-related anxiety 13. Job-related depression 14. Job control 15. Problem solving demand 16. Supervisor support

M

Table I. Means, Standard Deviations, and Correlations Between the Main Study Variables (N = 347)

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Table II. Results of Hierarchical Regressions: The Effect of Performance Monitoring on WellBeing (N = 347) Emotional exhaustion

All regressions Step 1: Control Regression 1 Step 2: Content of monitoring Regression 2 Step 2: Purpose of monitoring Regression 3 Step 2: Intensity of monitoring ∗∗p

Depression

Job satisfaction

1R 2

β

1R 2

Anxiety β

1R 2

β

1R 2

β

.00

—

.01

—

.03

—

.04

—

.13∗∗∗

.37 .42

.02∗∗

.00

−.06 .01

−.10

.03∗∗

−.17 .01

−.10 .06∗∗∗ −.24 .18∗∗∗

.13∗∗∗

.38

.12∗∗∗

.36

.12∗∗∗

−.15

.36

.13∗∗∗

−.37

< .01. ∗∗∗p < .001.

emotional dissonance. Emotional dissonance and surface acting were positively correlated (r = .39, p < .01). The sizes of the correlations between the emotional labor variables suggests that they are separate constructs. Relationship of Performance Monitoring to Well-Being To test the first three hypotheses, a series of hierarchical regressions were conducted for each of the well-being measures. At step one, control variables were entered, namely, site (dummy coded), age, gender, and job tenure. The same control variables were used in all subsequent analyses. At step two, the appropriate performance monitoring variable was entered. The results are shown in Table II. From Table II, it can be seen that the performance-related content of performance monitoring was unrelated to emotional exhaustion and anxiety, negatively associated with depression (β = −.15, p < .01) and positively associated with job satisfaction (β = .37, p < .01). Hypothesis 1, that the performancerelated content of call monitoring will be positively associated with well-being, was partially confirmed. Hypothesis 2, that when the purpose of monitoring is beneficial it will be positively associated with well-being, and Hypothesis 3, that the intensity of monitoring will be negatively associated with well-being, were generally confirmed. Specifically, the beneficial-purpose of monitoring was negatively associated with emotional exhaustion and depression (β = −.17, p < .01, and β = −.24, p < .01 respectively), but positively associated with job satisfaction (β = .42, p < .01). The intensity of monitoring was associated with all the measures of well-being. Intensity was positively associated with emotional exhaustion, anxiety, and depression (respectively, β = .38, β = .36, β = .36, all p < .01) and negatively associated with job satisfaction (β = −.37, p < .01). It is also worth noting that the level of association between well-being and intensity was consistently higher than for the performance-related content or beneficial-purpose of

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monitoring variables. The intensity of monitoring accounted for 12–13% of the remaining variance, after the control variables had been entered, for each of the well-being measures. Relationship of Call Monitoring to Emotional Labor and Well-Being Hypothesis 4 stated that emotional labor (manifest as dissonance followed by surface or deep acting) would mediate the relationship between performance monitoring and well-being. Following Baron and Kenny (1986), the test for mediation was carried out in two stages. The first stage examined whether the following conditions exist: the independent variable must affect the mediator, the independent variable must affect the dependent variable, and the mediator must affect the dependent variable. These initial conditions must be met if a mediation can be said to exit. These conditions were not met with regard to deep acting and most of the other variables, and with regard to the performance-related content of monitoring and both emotional dissonance and surface acting. Deep acting and the performance-related content of monitoring were not included in further mediation analyses. The second stage involved the test for mediation. Thus, for each of the well-being measures, control variables were entered at step one, followed by the appropriate monitoring measure at step two. At step three, emotional dissonance was entered and at step four, surface acting. A mediated effect is shown when the effect of the independent variable on the dependent variable is nonsignificant when the effects of the mediator(s) are partialled out. We found no mediation effects, as the performance monitoring variables were still significantly related to the dependent variable, indicating that they had direct effects on well-being unmediated by emotional labor. However, we were still interested to explore the separate relationships between performance monitoring and emotional labor (manifest as dissonance followed by surface or deep acting), and emotional labor and well-being. Two further sets of hierarchical regressions were therefore conducted. The first set of regressions examined whether dissonance mediated the relationship between performance monitoring and surface acting. To test this, surface acting was the dependent variable and control variables were entered at step one. At step two the appropriate monitoring variable was entered (purpose or intensity), followed by emotional dissonance at step three. The results in Table III show that when emotional dissonance was entered, the relationship between performance monitoring and surface acting became nonsignificant. Emotional dissonance thus mediates the relationship between the beneficial-purpose and intensity of monitoring and surface acting. The second additional set of regressions found that surface acting did not mediate the relationship between dissonance and well-being, and further regression analyses confirmed that emotional dissonance and surface acting had direct effects on well-being.

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Holman, Chissick, and Totterdell Table III. Results of Hierarchical Regressions: Emotional Dissonance Mediating Between Performance Monitoring and Surface Acting (N = 347) β at each step

Regression 1 Step 1: Controls Step 2: Purpose of monitoring Step 3: Emotional dissonance R2 1R 2 Regression 2 Step 1: Controls Step 2: Intensity of monitoring Step 3: Emotional dissonance R2 1R 2

1

2

3

—

— −.14∗∗

— −.08 .36∗∗∗ .22∗∗ .12∗∗

.08∗∗ — — .08 —

.10∗∗ .02 — .19 .11∗∗ .04

— .09 .35∗∗∗ .22∗∗∗ .11

Note. Regression 1: Surface acting as dependent and purpose of monitoring as independent variable. Regression 2: Surface acting as dependent and intensity of monitoring as independent variable. ∗∗ p < .01. ∗∗∗ p < .001.

Relationship of Work Context, Performance Monitoring, and Well-Being Hypothesis 5 stated that job control, job demands, and supervisory support will mediate the relationship between performance monitoring and well-being. To test this a series of hierarchical regressions were conducted with each of the well-being meaures as the dependent variable. At step one, control variables were entered, at step two, the appropriate performance monitoring variable was entered, and at step three, the appropriate work context variable (i.e., job control, problem solving demand, supervisor support) was entered. Hypothesis 5 was not confirmed as no mediation effects were found. Further analysis also revealed that performance monitoring did not mediate the relationship between work context and well-being. Hypotheses 6 and 7 were concerned with the moderating effects of the work context on the relationship between performance monitoring and well-being. To test these hypotheses, a series of hierarchical moderated regressions were conducted with each well-being measure as the dependent variable. All variables were standardized prior to analysis. At step one, control variables were entered, followed by the appropriate performance monitoring variable at step two and the appropriate work context variable at step three. Finally, at step four, the appropriate crossproduct term was entered (i.e., performance monitoring variable × work context variable). A consistent and significant pattern of interactions was found, but only with regard to the intensity of monitoring and two work context measures, namely, job control and supervisor support. Only these results are shown in Table IV.

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Table IV. Results of Hierarchical Moderated Regressions: The Moderating Effect of Job Control and Supervisor Support on the Relationship Between the Intensity of Performance Monitoring and Well-Being (N = 347) Emotional exhaustion

All regressions Step 1: Controls Step 2: Intensity of monitoring Regression 1 Step 3: Job control Step 4: Interaction term Regression 2 Step 3: Supervisor support Step 4: Interaction term ∗p

Anxiety

1R 2

β

1R 2

.00 .13∗∗∗

— .01 .38 .12∗∗∗

β

Depression

Job satisfaction

1R 2

1R 2

β

— .04 .36 .13∗∗∗

— −.37

.02∗∗ −.16 .04∗∗∗ −.23 .08∗∗∗ −.33 .03∗∗∗ .04∗∗∗ −.71 .03∗∗ −.58 .01∗ −.41 .02∗∗

.20 .46

.02∗∗ .01∗

.49 .13

−.16 .02∗∗ −.52 .02∗∗

— .03 .36 .12∗∗∗

β

−.15 .06∗∗∗ −.26 .21∗∗∗ −.58 .02∗∗∗ −.69 .00

< .05. ∗∗ p < .01. ∗∗∗ p < .001.

Table IV shows that there were significant interactions between job control and the intensity of monitoring with regard to emotional exhaustion ( p < .01), anxiety ( p < .01), depression ( p < .05), and job satisfaction ( p < .01). There were also significant interactions between supervisor support and the intensity of monitoring with regard to emotional exhaustion ( p < .05), anxiety ( p < .01), and depression ( p < .01). When plotted the interactions were in the form predicted. For example, from Fig. 1 it can be seen that when the intensity of monitoring was high, it was associated with low levels of anxiety when job control was high. When job control was low, high intensity was associated with high levels of anxiety. Overall, the results from the moderated regression analysis offer partial support for Hypothesis 6. Job control and supervisor support may thus act to buffer the effects of the intensity of monitoring on well-being. Hypothesis 7 was not confirmed as

Fig. 1. Job-related anxiety as a function of method control and intensity of monitoring.

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Table V. Stepwise Hierarchical Regressions of Effects of Performance Monitoring Characteristics and Work Context Variables on Well-Being (N = 347) Step 2 variables entered stepwise

1R 2

β

.13∗∗∗

.37 −.17 .15 −.15

Emotional exhaustion

1. 2. 3. 4.

Intensity of performance monitoring Supervisor support Prob-solving demands Job control

.03∗∗ .02∗∗ .02∗∗

Anxiety

1. 2. 3. 4.

Intensity of performance monitoring Job control Supervisor support Purpose of performance monitoring

.12∗∗∗ .04∗∗∗ .02∗∗ .01∗∗

Depression

1. Supervisor support 2. Job control 3. Intensity of performance monitoring

.13∗∗∗ .08∗∗∗ .03∗∗∗

.35 −.23 −.14 −.13 −.36 −.32 .20

Job satisfaction

1. 2. 3. 4. 5.

.32∗∗∗ .04∗∗∗ .02∗∗∗ .02∗∗∗ .01∗

.57 .21 −.15 −.14 .12

∗p

Supervisor support Purpose of performance monitoring Job control Prob-solving demands Content of performance monitoring

< .05. ∗∗p < .01. ∗∗∗p < .001.

supervisor support did not moderate the relationship between the performancerelated content and beneficial-purpose of call monitoring and well-being. To examine the relative effects of performance monitoring characteristics and work context variables on well-being, a series of stepwise regressions were conducted with each well-being measure as the dependent variable. At step one, control variables were entered. At step two, all the performance monitoring and work context variables were entered stepwise. From Table V, it is evident that the intensity of monitoring has relatively higher associations with emotional exhaustion (β = .37, p < .01) and anxiety (β = .35, p < .01) than other performance monitoring variables and work context variables. Supervisor support has higher relative levels of association with depression (β = −.36, p < .01) and job satisfaction (β = .57, p < .01) than the other variables. Overall, supervisor support and job control exhibit a more consistent and relatively higher level of association with the well-being measures. DISCUSSION The findings of this study show that performance monitoring in call centers is an important antecedent of well-being and emotional labor, although the impact of performance monitoring is not uniform. Thus, the various aspects of performance monitoring had a positive and negative relationship with well-being. In particular, and in line with previous work by Chalykoff and Kochan (1989) and Carayon (1994), the performance-related content of monitoring was associated with low

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depression and high job satisfaction but was unrelated to emotional exhaustion and anxiety. This indicates that when clear performance criteria are developed and when positive feedback is given regularly, the monitoring system will be associated with greater well-being. The beneficial-purpose of monitoring was also associated with low emotional exhaustion, low depression and high job satisfaction. Taken together, these findings suggest that monitoring can play a role in improving well-being when it is seen to be part of a broader system aimed at improving employees’ skills and abilities. It is the employee’s increased ability to cope with demand that produces the improvements in well-being. The results do not support the argument that monitoring is an intrinsically threatening and anxiety-provoking event. Although the performance-related content and beneficial-purpose of monitoring are positively associated with well-being, the perceived intensity of monitoring had a strong negative association with all four measures of well-being. This negative relationship may be caused by the perceived intensity of the monitoring process encouraging employees to focus inward on the effectiveness of their actions. This may be beneficial in some circumstances, but it also means that greater effort and attention is given to tasks that may normally be performed effortlessly. The increase in efforts to regulate behavior (as evidenced by the positive relationship between intensity and surface acting) means that more cognitive resources will be devoted to the task at hand. Over time this may cause cognitive resources to become depleted more rapidly, and it is this depletion of cognitive resources that has been linked to higher anxiety and depression (Kuhl, 1992). The finding that the intensity of monitoring accounts for much of the negative effects of monitoring on well-being addresses the fact that, although previous research has found that monitored employees reported worse well-being than nonmonitored employees, it did not single out the performance monitoring characteristics that caused this lower well-being. Indeed, the most consistent evidence pointed to those characteristics that had positive effects on well-being. This study has thus demonstrated that it is the perceived intensity of monitoring that accounts for its negative effects. One issue arising from this is why do some people perceive intensity to be higher than others? This study would seem to suggest that it is not necessarily due to the frequency of monitoring, as there was no relationship between the content and intensity of monitoring. Other individual, job, and organizational factors may therefore play a part. For example, individual differences such as self-efficacy (Bandura, 1997), personality and affectivity (Weiss & Cropanzano, 1996), as well as differences in the perception of the nature and type of performance criteria measured (i.e., some may be seen to be more intrusive) may play a role. A further point worth raising is that the performance-related content of monitoring has specific effects on well-being. Thus, according to Warr’s typology of employee well-being (Warr, 1996), the performance-related content of monitoring tends to be associated with pleasurable and aroused states of well-being (e.g., enthusiastic, cheerful, happy) rather than unpleasurable aroused states (e.g.,

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anxious, tense) or unpleasurable unaroused states (e.g., gloomy, fatigued, sad). The beneficial-purpose and intensity of monitoring had more global affects on wellbeing. However, it can also be noted that, from the examination of relative effects, the intensity of monitoring was more highly associated with unpleasant aroused states (e.g., anxiety) and unpleasant unaroused states (e.g., emotional exhaustion). The second main finding of this paper is that performance monitoring can be an antecedent of emotional labor (i.e., manifest as emotional dissonance followed by surface acting or deep acting) in call centers. Thus, emotional dissonance was found to mediate the relationship between the beneficial purpose of monitoring and surface acting. Of particular interest here are the reasons why a beneficial purpose of monitoring may act to reduce emotional dissonance. One explanation is that a beneficial purpose may lower an employee’s anxiety about being punished, or it may increase their enthusiasm. In either case, the effect should lead to a reduction in emotional dissonance and subsequent efforts to regulate emotion. Emotional dissonance also mediated the relationship between the intensity of monitoring and surface acting. This might be explained by the fact that the perception of intense monitoring may make the person more aware of their emotional states and, consequentially, of any dissonance that occurs. In keeping with earlier research, emotional dissonance and surface acting were also found to be directly and negatively associated with well-being (Zapf et al., 1999). Thus, while the relationship between performance monitoring, emotional labor, and well-being was not in the hypothesized form, emotional dissonance and surface acting do seem to play a role in this relationship. However, it should be noted that, against expectations, deep acting was unrelated to emotional dissonance. A possible reason for this is that deep acting is a response to emotional dissonance that then reduces emotional dissonance, which has the effect of cancelling out any relationship that might be found when general levels of emotional dissonance are measured. This points to the difficulty of studying emotional labor, particularly through survey-based methods (Grandey, 2000). Indeed, research is clearly needed that measures both the dissonance felt before attempts to regulate emotion and the dissonance felt after attempts to regulate emotion. This might be best achieved through experience sampling methodologies (Totterdell & Parkinson, 1999; Weiss, Nicholas, & Daus, 1999). An examination of how the work context affected the relationship between performance monitoring and well-being revealed only small interaction effects and no mediated effects. In particular, job control and supervisor were found to play a small role in buffering the impact of the perceived intensity of performance monitoring on well-being. One surprising finding was that supervisor support neither mediated nor moderated the relationship between the content and purpose of monitoring and well-being. The reasons for this are unclear, but it does indicate that, in this context, variation within the performance-related content and the beneficial purpose of monitoring are probably caused by other factors such as departmental

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policies and culture. For example, departmental policies may strongly dictate the content of performance monitoring in a call center and the supervisor’s style of management may therefore have little effect on it. With regard to relative effects, the intensity of monitoring appears to have stronger effects on emotional exhaustion and anxiety than the other work context variables or performance monitoring variables that were included in this study. In contrast, supervisor support had a higher level of association with well-being than the performance-related content and beneficial-purpose of monitoring. This is in line with Carayon’s study (Carayon, 1994) that found that the work context variables had higher associations with well-being than content-based performance monitoring variables (it must be noted that this study did not examine the intensity of monitoring). The strong relationship between the perceived intensity of monitoring and well-being has an important practical implication, namely, that every effort should be made to reduce the perception that monitoring is intense. It is possible that the perception of intensity is linked to the number and type of performance measures used. Lowering the number and changing the type of performance measures may reduce the perception that every aspect of behavior is monitored and decrease the monitoring system’s pervasiveness. It might be argued, however, that any reduction in the number of criteria may adversely affect the effectiveness of the performance appraisal process. In response, it can be argued that feelings of intensity may result in the performance appraisal criteria being devalued; and criteria need to be valued if they are to be of use. Thus, removing nonessential performance criteria should reduce intensity and improve the performance appraisal process. Job control should also be increased by reducing restrictions on what an employee can say (this is particularly pertinent to call centers that use scripts) and by involving employees in the design of the monitoring system (Chalykoff & Kochan, 1989). A further practical implication is that the monitoring system should involve frequent and positive feedback and be based on clear performance criteria. Moreover, performance monitoring should be recognized as being part of a system that aims to develop employees’ skills and performance and be designed so that it is closely linked to other support and development practices such as performance appraisal and coaching. By linking monitoring to these practices, the likelihood of monitoring being accepted, and its positive impact on well-being, should increase. The finding that supervisors can play a role in reducing the negative effects of intensity on well-being implies that they should be given training on conducting performance appraisals. The need to invest in the training of supervisors is acutely important in call centers, where CSAs are often promoted from within to this role. This can lead to a situation where new supervisors have to deal with sensitive issues (such as giving feedback on performance) under demanding conditions, but are relatively inexperienced and ill equipped to cope with such tasks.

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Although the present study has a number of relevant findings, it is appropriate to highlight several limitations. The first is that the study is cross-sectional and the direction of causality cannot be confirmed. Second, common method variance presents a potential source of invalidity to substantive interpretation. However, while such biases can cause self-ratings to be associated with self-reported outcomes, they would act against differential effects of the kind observed. A tendency to respond positively or negatively would result in associations among all the variables but not in certain performance monitoring variables being positively related to well-being and others negatively associated. Only demand characteristics of a very sophisticated kind would artifactually create such effects. The use of selfratings might also be seen as problematic. Third, questions can be raised about the extent to which these findings will generalize to organizations other than call centers. The prominence of performance monitoring in call centers may exacerbate the strength of relationships found. In other organizations the effects of performance monitoring may be much lower. Future research would clearly benefit from being longitudinal and multimethod. Equally important is the need to ground future research in this area in theories of emotional regulation (e.g., Gross, 1998), theories that specify the structure, causes and consequences of affective experiences at work (Weiss & Cropanzano, 1996) and in theories that posit alternative mechanisms (Karasek & Theorell, 1990). Thus, emotional labor is only one way that emotions might be regulated at work in response to performance monitoring. Performance monitoring may affect a whole host of regulatory strategies and these need to be addressed (Parkinson & Totterdell, 1999). The causes of emotions at work could be strengthened by studying a wider variety of “content” performance monitoring characteristics (e.g., source, target, the inclusion of positive and negative forms of feedback) and “monitoring cognitions” (e.g., trust, fairness; Stanton, 2000). The individual, job, and organizational factors that affect the perception of performance monitoring intensity require further study. Another important mechanism that can be referred to as “the performance and skill development” mechanism that is, how improvements in skill increase employees’ ability to cope with work demands and the effects this has on self-efficacy and well-being (Bandura, 1997; Jenkins & Maslach, 1994; Karasek & Theorell, 1990). Future research should also examine the real-time consequences (e.g., affect, customer service, emotional displays) of monitoring using diary methods or similar (Totterdell & Parkinson, 1999; Weiss, Nicholas, & Daus, 1999). In summary, this study has further illuminated the relationship between performance monitoring, work context, and well-being. In particular, it has shown that performance monitoring as an important antecedent of well-being and one that has both a positive and negative impact on well-being. However, the exact mechanisms by which this occurs requires further research. This study has also demonstrated that the work context can moderate the relationship between the intensity of monitoring and well-being, although the effect of work context may be relatively small. In all, this suggests that it is critical for practitioners, especially

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in a call center environment, to situate performance monitoring within a broader system of employee development and to ensure that the monitoring system is not viewed as intense. REFERENCES Aiello, J. R., DeNisi, A. S., Kirkoff, K., Shao, Y., Lund, M. A., & Chomiak, A. A. (1991). The impact of feedback and individual/group monitoring on work effort. Paper presented at the American Psychological Society, Washington. Aiello, J. R., & Kolb, K. J. (1995). Electronic performance monitoring and social context: Impact on productivity and stress. Journal of Applied Psychology, 80, 339–353. Aiello, J. R., & Shao, Y. (1993). Electronic performance monitoring and stress: The role of feedback and goal setting. In M. J. Smith & G. Salavendy (Eds.), Human–computer interaction: Applications and case studies (pp. 1011–1016). Amsterdam: Elsevier Science. Aiello, J. R., & Svec, C. M. (1993). Computer monitoring and work performance: Extending the social facilitation framework to electronic presence. Journal of Applied Social Psychology, 23, 537– 548. Alder, G. S. (1998). Ethical issues in electronic performance monitoring: A consideration of deontological and teleological perspectives. Journal of Business Ethics, 17, 729–743. Axtell, C., Holman, D., Wall, T., Waterson, P., Harrington, E., & Unsworth, K. (2000). Shopfloor innovation: Facilitating the suggestion and implementation of ideas. Journal of Occupational and Organisational Psychology, 73, 265–285. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman. Baron, R. B., & 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. Belt, V., Richardson, R., & Webster, J. (1999, March 19). Smiling down the phone: Women’s work in Telephone Call Centres. Call Centre Conference, London School of Economics. Brotheridge, C. M., & Grandey, A. A. (2002). Emotional labor and burnout: Comparing two perspectives of “people work.” Journal of Vocational Behavior, 60, 17–39. Brotheridge, C. M., & Lee, R. T. (1998, August). On the dimensionality of emotional labour: Development of an emotional labour scale. Paper presented at the First Conference on Emotions in Organisational Life, San Diego, CA. Bylinsky, G. (1991, November 4). How companies spy on employees. Fortune, pp. 131–140. Carayon, P. (1993). Effects of electronic performance monitoring on job design and worker stress: Review of the literature and conceptual model. Human Factors, 35, 385–395. Carayon, P. (1994). Effects of electronic performance monitoring on job design and worker stress: Results of two studies. International Journal of Human–Computer Interaction, 6, 177– 190. Carver, C. S., Lawrence, J. W., & Scheier, M. F. (1995). A control-process perspective on the origins of affect. In L. L. Leonard & A. Tesser (Eds.), Striving and feeling (pp. 11–52), Madwah: Erlbaum. Chalykoff, J., & Kochan, T. (1989). Computer-aided monitoring: Its influence on employee job satisfaction and turnover. Personnel Psychology, 42, 807–834. David, R., & Henderson, R. (2000). Electronic performance monitoring: A laboratory investigation of the influence of monitoring and difficulty on task performance, mood state, and self reported stress levels. Journal of Applied Social Psychology, 30, 906–920. Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. New York: Harcourt Brace Jovanovich. Fernie, S., & Metcalf, D. (1998). (Not) Hanging on the telephone: Payment systems in the new sweatshops. Centrepiece, 3, 7–11. Fisher, C. D. (2000). Moods and emotions while working: Missing pieces of job satisfaction? Journal of Organizational Behavior, 21, 185–202. Grandey, A. A. (2000). Emotion regulation in the workplace: A new way to conceptualise emotional labour. Journal of Occupational Health, 5, 95–110. Grant, R. A., & Higgins, C. A. (1989). Computerised performance monitors: Factors affecting acceptance. IEEE Transactions on Engineering Management, 38, 306–314.

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