New Developments in Theoretical and Conceptual Approaches to Job Stress (Research in Occupational Stress and Well Being)

NEW DEVELOPMENTS IN THEORETICAL AND CONCEPTUAL APPROACHES TO JOB STRESS

RESEARCH IN OCCUPATIONAL STRESS AND WELL BEING Series Editors: Pamela L. Perrewe´ and Daniel C. Ganster Recent Volumes: Volume 1: Volume 2: Volume 3:

Exploring Theoretical Mechanisms and Perspectives Historical and Current Perspectives on Stress and Health

Volume 4: Volume 5:

Emotional and Physiological Processes and Positive Intervention Strategies Exploring Interpersonal Dynamics Employee Health. Coping and Methodologies

Volume 6: Volume 7:

Exploring the Work and Non-Work Interface Current Perspectives on Job-Stress Recovery

RESEARCH IN OCCUPATIONAL STRESS AND WELL BEING VOLUME 8

NEW DEVELOPMENTS IN THEORETICAL AND CONCEPTUAL APPROACHES TO JOB STRESS EDITED BY

PAMELA L. PERREWE´ Florida State University, USA

DANIEL C. GANSTER Colorado State University, USA

United Kingdom – North America – Japan India – Malaysia – China

Emerald Group Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2010 Copyright r 2010 Emerald Group Publishing Limited Reprints and permission service Contact: [email protected] No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. No responsibility is accepted for the accuracy of information contained in the text, illustrations or advertisements. The opinions expressed in these chapters are not necessarily those of the Editor or the publisher. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-84950-712-7 ISSN: 1479-3555

Awarded in recognition of Emerald’s production department’s adherence to quality systems and processes when preparing scholarly journals for print

CONTENTS LIST OF CONTRIBUTORS

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OVERVIEW

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OCCUPATIONAL STRESSORS AND JOB PERFORMANCE: AN UPDATED REVIEW AND RECOMMENDATIONS Christopher C. Rosen, Chu-Hsiang Chang, Emilija Djurdjevic and Erin Eatough THE SUCCESS RESOURCE MODEL OF JOB STRESS Simone Grebner, Achim Elfering and Norbert K. Semmer LOVING ONE’S JOB: CONSTRUCT DEVELOPMENT AND IMPLICATIONS FOR INDIVIDUAL WELL-BEING E. Kevin Kelloway, Michelle Inness, Julian Barling, Lori Francis and Nick Turner

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QUALITATIVE METHODS CAN ENRICH QUANTITATIVE RESEARCH ON OCCUPATIONAL STRESS: AN EXAMPLE FROM ONE OCCUPATIONAL GROUP Irvin Sam Schonfeld and Edwin Farrell

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FACING THE LIMITATIONS TO SELF-REPORTED WELL-BEING: INTEGRATING THE FACIAL EXPRESSION AND WELL-BEING LITERATURES Kevin J. Eschleman and Nathan A. Bowling

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KARASEK’S (1979) JOB DEMANDS-CONTROL MODEL: A SUMMARY OF CURRENT ISSUES AND RECOMMENDATIONS FOR FUTURE RESEARCH Jason Kain and Steve Jex ENGAGEMENT WITH INFORMATION AND COMMUNICATION TECHNOLOGY AND PSYCHOLOGICAL WELL-BEING Michael P. O’Driscoll, Paula Brough, Carolyn Timms and Sukanlaya Sawang

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INFORMATION AND COMMUNICATION TECHNOLOGY: IMPLICATIONS FOR JOB STRESS AND EMPLOYEE WELL-BEING Arla Day, Natasha Scott and E. Kevin Kelloway

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ABOUT THE AUTHORS

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LIST OF CONTRIBUTORS Julian Barling

Queen’s University, Kingston, Ontario, Canada

Nathan A. Bowling

Department of Psychology, Wright State University, Dayton, OH, USA

Paula Brough

Griffith University, Queensland, Australia

Chu-Hsiang Chang

Department of Environmental and Occupational Health, College of Public Health, University of South Florida, Tampa, FL, USA

Arla Day

Saint Mary’s University, Halifax, Nova Scotia, Canada

Emilija Djurdjevic

Department of Management, Sam M. Walton College of Business, University of Arkansas, Fayetteville, AR, USA

Erin Eatough

Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, USA

Achim Elfering

Department of Psychology, University of Bern, Bern, Switzerland

Kevin J. Eschleman

Department of Psychology, Wright State University, Dayton, OH, USA

Edwin Farrell

City College of the City University of New York, New York, NY, USA

Lori Francis

Saint Mary’s University, Halifax, Nova Scotia, Canada

Simone Grebner

Central Michigan University, Mount Pleasant, MI, USA vii

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Michelle Inness

University of Alberta, Edmonton, Alberta, Canada

Steve Jex

Department of Psychology, Bowling Green State University, Bowling Green, OH, USA

Jason Kain

American Institutes for Research, Georgetown, Washington, DC, USA

E. Kevin Kelloway

Saint Mary’s University, Halifax, Nova Scotia, Canada

Michael P. O’Driscoll

University of Waikato, Hamilton, New Zealand

Christopher C. Rosen

Department of Management, Sam M. Walton College of Business, University of Arkansas, Fayetteville, AR, USA

Sukanlaya Sawang

Queensland University of Technology, Brisbane, Australia

Irvin Sam Schonfeld

City College and the Graduate Center of the City University of New York, New York, NY, USA

Natasha Scott

Saint Mary’s University, Halifax, Nova Scotia, Canada

Norbert K. Semmer

Department of Psychology, University of Bern, Bern, Switzerland

Carolyn Timms

Griffith University, Queensland, Australia

Nick Turner

University of Manitoba, Winnipeg, Manitoba, Canada

OVERVIEW In our 8th volume of Research in Occupational Stress and Well Being, we offer eight chapters that examine theoretical, conceptual, and methodological advances to job stress research. Our lead chapter, by Christopher Rosen, Chu-Hsiang Chang, Emilija Djurdjevic, and Erin Eatough, provides a thorough review of conceptual and empirical research examining occupational stress and performance. They review and critique theories that help to explain the workplace stressor–performance relationship and they develop an eight-category taxonomy of workplace stressors. Finally, they evaluate how well contemporary research has dealt with limitations and weaknesses previously identified in earlier research. In the second chapter, Simone Grebner, Achim Elfering, and Norbert Semmer develop the Success Resource Model of Job Stress. Their model illustrates four dimensions of subjective occupational success: goal attainment, pro-social success, positive feedback, and career success. Success is argued to be an important resource that leads to a number of positive outcomes such as positive affect and emotions, health and well-being, learning, and energy. In the third chapter, E. Kevin Kelloway, Michelle Inness, Julian Barling, Lori Francis, and Nick Turner develop an intriguing construct they call ‘‘loving one’s job.’’ They examine three components of love of one’s job and then develop the theoretical and conceptual ties between truly loving your job and employee well-being. The fourth chapter, by Irvin Schonfeld and Edwin Farrell, challenges our sole focus on traditional empirical approaches to the study of job stress and examines how using qualitative methods can enrich our empirical work. Using extensive examples from research on job stress in teachers, they demonstrate how qualitative and quantitative methods support each other in occupational stress research. The fifth chapter, by Kevin Eschleman and Nathan Bowling, is a fascinating account of how and why it is important to examine facial expressions. They integrate the facial expression and occupational well-being literatures and argue that objective measures of facial expressions have distinct advantages over self-reports of occupational stress and well-being. ix

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In the sixth chapter, Jason Kain and Steve Jex review the conceptual and empirical literature on the popular job demands-control model. They examine critically the recent extensions of this model and offer a number of suggestions as to how to advance this theoretical perspective. The final two chapters examine the role of information and communication technology and employee stress and well-being. O’Driscall, Brough, Timms, and Sawang examine some key facilitators and barriers to user acceptance of and engagement with information technology and the subsequent associations with health and well-being. Using the job demands-resource perspective, Arla Day, Natasha Scott, and E. Kevin Kelloway develop a model depicting the role of information and communication technology on experienced strain, burnout, engagement, and performance. Together, these chapters offer insight into how we can improve and advance job stress research. These chapters challenge our traditional conceptual and methodological thinking and offer some exciting directions for future research. We hope you enjoy volume 8 of Research in Occupational Stress and Well Being. Pamela L. Perrewe´ Daniel C. Ganster Editors

OCCUPATIONAL STRESSORS AND JOB PERFORMANCE: AN UPDATED REVIEW AND RECOMMENDATIONS Christopher C. Rosen, Chu-Hsiang Chang, Emilija Djurdjevic and Erin Eatough ABSTRACT This chapter provides an updated review of research examining the relationship between occupational stressors and job performance. We begin by presenting an eight-category taxonomy of workplace stressors and we then review theories that explain the relationships between workplace stressors and job performance. The subsequent literature review is divided into two sections. In the first section, we present a summary of Jex’s (1998) review of research on the job stress–job performance relationship. In the second section, we provide an updated review of the literature, which includes studies that have been published since 1998. In this review, we evaluate how well the contemporary research has dealt with weaknesses and limitations previously identified in the literature, we identify and evaluate current trends, and we offer recommendations and directions for future research.

New Developments in Theoretical and Conceptual Approaches to Job Stress Research in Occupational Stress and Well Being, Volume 8, 1–60 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-3555/doi:10.1108/S1479-3555(2010)0000008004

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The relationship between work stressors and job performance has captivated researchers for over a century. Unfortunately, organizational scientists have had difficulty explaining why and how work stressors relate to job performance. A primary reason why it is not clear how job stress relates to performance is because there are a multitude of definitions for each of these constructs. Work stressors can refer to anything from role stressors to more acute, traumatic events such as observing the death of a coworker (Jex, 1998). As such, the relationship between work stress and job performance may be contingent upon the type of stressor examined and the nature of the criterion variable. One way to add clarity to this literature is by investigating how different forms of work stress relate to different measures of job performance. The last comprehensive summary of this literature was conducted by Jex (1998) over a decade ago. Since that time, over 100 papers have been published on this topic. Presently, we provide an updated review of research examining the work stressor–job performance relationship. In the following sections, we present a taxonomy for classifying the various stressors examined in the literature. We then review theories that have been used to describe relationships between work stressors and job performance and we present a two-part review of the literature. In the first section of the review, we summarize Jex’s (1998) discussion of research examining the stress– performance relationship. Next, we review stressor–performance research that has been published since 1998. Following these reviews, we (a) discuss how well research has addressed shortcomings in the literature that were noted by Jex (1998), (b) identify and critique emerging trends, and (c) present recommendations for future research.

TAXONOMY OF WORK STRESSORS Occupational stress refers to the process through which employees perceive, appraise, and respond to adverse or challenging job demands at work (Frese & Zapf, 1988). This definition is based on the stimulus–response approach (Jex, 1998) and distinguishes two specific elements of the stress process. The first element is the stressors, which are situational stimuli that require adaptive responses from employees. Strains, on the other hand, refer to a wide range of negative and harmful responses that employees may adopt when they encounter stressors. Strains can be emotional (e.g., anxiety, depression), physiological (e.g., problems with cardiovascular, biochemical, gastrointestinal, and musculoskeletal functioning), or behavioral (e.g.,

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substance abuse, smoking). More recent work on occupational stress has acknowledged that behavioral responses to stressors may include actions that are harmful for not only the individual employees, but also for the organization. For example, absenteeism, accidents, and turnover have been cited as behaviors associated with encountering stressors (Spector, 2008). In addition, studies have also explored how stressors may reduce positive employee outcomes, such as morale and performance (Cropanzano, Rupp, & Byrne, 2003; LePine, Podsakoff, & LePine, 2005). In the current review, employee performance is the focal strain response, and we summarize research examining its relationship with a wide array of stressors. We developed an eight-category taxonomy of work stressors that is based on both Jex’s (1998) categorization and a framework provided by the National Institute for Occupational Safety and Health (NIOSH, 1999). The first category is work role stressors, which include role ambiguity, role conflict, and role overload. Work roles refer to the set of responsibilities and authorities associated with a particular position (Jackson & Schuler, 1985). Role ambiguity occurs when employees are uncertain about what is expected of them, or are unsure about how they should fulfill their role expectations (Rizzo, House, & Lirtzman, 1970). Role conflict refers to when employees receive incompatible role expectations from different members of the organization (e.g., supervisor, coworker). Finally, role overload refers to when employees’ role expectations exceed the resources or time available to fulfill assigned responsibilities (Bacharach, Bamberger, & Conley, 1991). The second category is workload. Workload has been conceptualized as varying between objective information (e.g., number of widgets to produce) and subjective perceptions (Jex, 1998). Researchers (e.g., Spector & Jex, 1998) have also distinguished between quantitative workload (i.e., the amount of work to be completed) and qualitative workload (i.e., the difficulty of the tasks to be completed). The third category is situational constraints, which represent organizational factors (e.g., red tape, bureaucracy, faulty equipment, inaccurate information) that interfere with employees’ ability to complete work (Peters & O’Connor, 1980). The fourth category is job control. When employees have little autonomy to decide how and when to perform tasks, or are excluded from decision-making processes, this lack of control may elicit strain (Jex, 1998). The fifth category is social characteristics of the workplace. Jex (1998) and NIOSH (1999) both included interpersonal conflict at work as a major stressor among employees. We expand this category to include stressors derived from other interpersonal interactions, such as organizational politics and abusive supervision. The sixth category is career-related concerns (NIOSH, 1999).

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Job insecurity, underemployment, lack of learning and advancement opportunities, as well as work interference with nonwork domains (e.g., family) belong to this category. The seventh category is job conditions, which refer to the physical conditions in which employees must work in (e.g., temperature, noise, lighting), the nature of job tasks (e.g., emotional labor), and design of how tasks must be performed (e.g., break schedule, work hours, shiftwork). Finally, both Jex (1998) and NIOSH (1999) recognize the existence of acute stressors in the workplace. In contrast to the first seven stressor categories, which capture work characteristics that are continuously present, acute stressors (e.g., homicide in the workplace, natural disasters) are more episodic. In the current review, we include research examining acute stressors in the workplace as well as those that have been simulated in laboratory settings. Although these laboratory studies rely on manipulated stressors and more basic and simple performance indicators, they have the potential to provide insight into the stress process that is triggered by acute workplace stressors. As such, these studies may inform us about the mechanisms through which acute stressors influence performance.

PERFORMANCE CRITERIA Job performance can be broadly defined as behaviors engaged in by employees at work that are relevant to organizational goals (Campbell, 1994). Previous work has focused mostly on task performance, which assesses the extent to which individuals successfully perform tasks associated with their job description (Campbell, 1994). The current review includes assessment of more generally defined task performance, as well as indicators of task performance behaviors that are applicable for only certain jobs (e.g., customer satisfaction is often used to gauge task performance in the service industry). In addition to task performance, researchers have also identified other performance dimensions (e.g., Murphy, 1990; Organ, 1997), such as organizational citizenship behavior (OCB), which refers to discretionary behavior that benefits organizations by improving the social and psychological context in which the technical core of the organization operates (Borman & Motowidlo, 1993; Organ, 1997). OCBs differ from task performance in that the former is not formally prescribed by the job description whereas the latter is mandatory and OCBs are more generalizable across jobs; whereas task performance is more job-specific. Despite their discretionary nature, researchers have shown the important linkages

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between employee OCB performance and organizational profitability (Podsakoff, MacKenzie, Paine, & Bachrach, 2000). As such, task performance and OCB are both job behaviors that contribute positively to organizational effectiveness. More recently, the literature has considered work behaviors that are detrimental to organizational goals. Counterproductive work behavior (CWB) represents activities intended to harm organizations and/or various stakeholders of the organization, such as clients, coworkers, customers, and supervisors (Spector et al., 2006). CWB includes abuse or aggression against others, production deviance, sabotage, theft, and withdrawal behaviors such as lateness and absenteeism (Spector et al., 2006). Negative emotions (e.g., anger, anxiety, and frustration) have been proposed as the primary predictors of CWB (Spector & Fox, 2002). Consistent with this perspective, researchers have shown that stressful situations which induce these negative emotions are associated with CWB (e.g., Bruk-Lee & Spector, 2006). In addition to task performance, OCB and CWB, we extend the performance criterion space by including more basic cognitive functioning that underlies more complex behaviors. Indices of cognitive processes such as working memory, reaction times, and attention span are often assessed in laboratory settings to examine how stressors influence performance. We believe that these cognitive processes are important mechanisms that link stressors with on-the-job behaviors. Therefore, the current review includes these measures and summarizes how stressors in the experimental setting may alter participants cognitive functioning. Finally, organizations have increasingly relied on interdependent work units to complete tasks and achieve goals. As such, we decided to move beyond individual performance, and include team performance as another set of activities that may be associated with occupational stressors. We hope that by casting a wider net, we are able to better explain relationships among occupational stressors and performance.

THEORETICAL FRAMEWORKS RELATING STRESSORS TO PERFORMANCE Why should work stressors relate to job performance? A number of theories address this question. Some of these theories that have appeared in the occupational stress literature are based on existing motivational frameworks. For example, Beehr and Bhagat (1985) used Vroom’s (1964) VIE

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framework to explain the stressor–performance relationship. VIE theory suggests that motivation to perform depends on three factors: expectancy (viz., linkages between efforts and target behavior), instrumentality (viz., linkages between target behavior and desirable outcomes), and valence (viz., desirability of outcomes). Stressors may weaken the effort to performance relationships (i.e., expectancies), as employees who are dealing with more competing demands may experience more uncertainty regarding how to distribute their energy to achieve desired performance levels. They may also withhold efforts that would otherwise be devoted to performance in anticipation of using the reserved energy to cope with the stressors (McGrath, 1976). Beehr and Bhagat (1985) proposed that stressors may also damage performance to reward (i.e., instrumentality) relationships, as employees may be uncertain how their performance will be evaluated. Finally, stressors are also likely to reduce the valence of the outcomes, as successfully coping with stressors may represent a more attractive activity. Taken together, Beehr and Bhagat (1985) maintained that stressors reduce employee motivation to maintain or improve performance because they create uncertainties for employees. Other theories have emphasized the employee–organization exchange relationship as a key mechanism for understanding links between stressors and performance. Social exchange theory conceptualizes organizations as social marketplaces in which employees put forth time and effort for valued outcomes provided by the organization (Blau, 1964; Cropanzano, Howes, Grandey, & Toth, 1997; Cropanzano, Rupp, Mohler, & Schminke, 2001). According to this perspective, employees develop relationships based on both economic (e.g., effort for pay and promotion) and social exchanges (e.g., employees reciprocate organizational support by demonstrating organizational commitment and positive emotions) with their employers (Rhoades, Eisenberger, & Armeli, 2001; Shore, Tetrick, Lynch, & Barksdale, 2006). Employees develop these exchange relationships to the extent that they receive valued economic and socioemotional outcomes from their exchange partners (Cropanzano et al., 2001). Only when it is expected that organizations will provide employees with valued outcomes, and do so in a fair and consistent manner, do employees choose to remain in exchange relationships. According to Cropanzano et al. (2003), exchange relationship expectations are likely to be violated when employees experience emotional strain associated with work-related stressors. Emotional strain represents negative affective experiences, which may discount any benefits that employees receive from their organization. Moreover, emotional strain indicates that situational demands exceed employees’ coping and control

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resources (Karasek, 1998) and strain may, therefore, signal that inequity exists in exchange relationships (i.e., the presence of strain suggests that resources and support provided by the organization are disproportionately lower than employees’ efforts). In response, employees adjust their contributions downward to restore equity. A similar framework, Siegrist’s (2002) Effort–Reward Imbalance (ERI) model, also emphasizes reciprocal relationships between employees and organizations. Siegrist suggested that work environments are full of situational demands (e.g., time pressures and high workloads), which require coping efforts on the behalf of employees. In return for such efforts, organizations provide rewards (e.g., money, promotions, and approval). The ERI model stipulates that employees experience strain when their organization fails to reward coping efforts. In a series of studies, Siegrist and colleagues demonstrated that employees who expend high effort, but receive few rewards in return, experience higher levels of physiological strains, such as hypertension, symptoms of cardiovascular disease, and other physical complaints (e.g., Bosma, Peter, Siegrist, & Marmot, 1998; de Jonge, Bosma, Peter, & Siegrist, 2000; Dragano, Verde, & Siegrist, 2005; Peter & Siegrist, 1997). Additionally, ERIs are associated with elevated levels of anger (Smith, Roman, Dollard, Winefield, & Siegrist, 2005), reduced job satisfaction (de Jonge et al., 2000), and higher sickness-related absenteeism (Peter & Siegrist, 1997). The ERI model explains the general causes of strain, but Siegrist (1996, 2002) argued that the ERI model is most applicable for employees in highly constrained occupations, which make it difficult for employees to adjust coping efforts. For example, workers who have limited job mobility must continue to expend high levels of coping efforts, even when there is little hope of being rewarded for doing so. This implies that when it is possible to reduce their efforts in situations of low reward, employees may do so to eliminate imbalances in exchange relationships. This aspect of the ERI model parallels predictions based on social exchange theory. When describing stressor–performance relationships, researchers have also relied on theories that identify motivation as a function of the extent to which intrapersonal resources are depleted by stressors. For example, Conservation of Resources (COR) theory defines resources as ‘‘objects, personal characteristics, conditions, or energies that are valued by the individual’’ (Hobfoll, 1989, p. 516). According to COR, employees experience strain when there is potential or actual loss of resources, or when efforts to maximize resources do not result in adequate return (Hobfoll, 1998). In this case, when employees encounter stressors, their

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motivation level can be negatively affected. In addition, employees who experience loss of motivational resources are likely to reconsider how they expend the energy that is still available. In particular, they may change resource allocation strategies by devoting efforts to (a) protecting remaining resources and (b) accruing additional resources to help cope with situational demands (Halbesleben & Bowler, 2007). These changes in resource allocation strategies are associated with changes in motivation and related fluctuations in behavior (Halbesleben & Bowler, 2007). For example, Halbesleben and Wheeler (2006) found that employees experiencing organizational politics, a specific workplace stressor, demonstrated lower levels of task performance as a result of disengaging from their jobs to conserve mental resources. Moreover, Halbesleben and Bowler (2007) found that when dealing with stress, employees not only withheld efforts for task performance, they also expended additional efforts to help coworkers in order to develop new social ties. The authors suggested that these investments in developing social networks may have been aimed at expanding resources to help employees cope with future demands. The Job Demands-Resources (JD-R) model (Bakker & Demerouti, 2007) is a slightly different model that more explicitly describes the interplay between job demands, coping resources, and performance. According to Bakker and Demerouti (2007), job demands are aspects of the job (e.g., physical, psychological, social, organizational) that require sustained physical and/or psychological efforts. High job demands result in strains, which lead to reduced performance. Job resources are physical and psychological factors that are available for job performance, coping with job demands, and personal growth and development. An abundance of resources enhances motivation, which leads to better performance. However, when low resources are combined with high demands, employees’ motivation suffers substantially, and performance is reduced accordingly. Indeed, empirical studies provide evidence that job resources (e.g., autonomy, social support, development opportunities, and feedback) have unique effects on employee motivation (e.g., Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2007) and that motivation mediates the effects of job resources on employee performance (e.g., Bakker, Van Emmerik, & Van Ret, 2008; Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2009). The unique aspect of the JD-R model is that it articulates a dual-process model, such that job demands and resources have distinctive pathways through which they relate to performance (Bakker & Demerouti, 2007). Interestingly, the dual-process idea has also been advanced in recent studies examining hindrance versus challenge stressors (Boswell, Olson-Buchanan,

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& LePine, 2004; Cavanaugh, Boswell, Roehling, & Boudreau, 2000; LePine et al., 2005). This new framework suggests that stressors can be divided into two categories – hindrance stressors and challenge stressors – which have unique mechanisms linking them to performance. Hindrance stressors (i.e., constraints that impede employees’ work achievements and personal growth) are hypothesized to elicit strains, which lead to reduced performance. On the other hand, challenge stressors (i.e., obstacles to be overcome in order to achieve personal learning and development) are expected to enhance motivation and performance. Meta-analytic evidence supports the refinement of stressor categories and the proposed dual pathways to performance (e.g., LePine et al., 2005; Chang, Rosen, & Levy, 2009b). Thus, it appears that strains and motivation are important in explaining linkages between stressors and performance. Interestingly, this dual-process perspective presents a unique challenge when considering the nature of the relationships between stressors and performance. In particular, it is possible that some stressors may act as both hindrance and challenge stressors at the same time, in which case the stressor would have multiple, yet opposing, effects on performance. For example, high workload may serve as a challenge for employees and motivate them to put in more effort to meet work demands. On the other hand, high workload may also elicit negative emotional reactions and physical fatigue associated with overworking. As such, employees’ cognitive and emotional resources may be depleted by the stressor, which results in impaired performance. In this case, empirical studies may show a null bivariate relationship between workload and performance, but in reality this null relationship may be masking the complex mechanisms that link these two variables. Thus, the possible presence of countervailing causal pathways of stressors on performance further complicates the general question of how stressors relate to performance. Before we review studies examining relationships between stressors and performance, there are two points worth noting. First, the theoretical frameworks discussed in the preceding section assume a linear relationship between stressors and performance. However, alternative models exist that conceptualize a higher-order relationship between stressors and performance. For example, Yerkes and Dodson (1908) proposed the well-known Yerkes– Dodson law, stipulating that the relationship between one’s arousal level and performance should be quadratic, represented by an inverted U-shape. Since stressors are closely associated with physiological arousal level (Ganster & Schaubroeck, 1991), it has been argued that the relationship between stressors and performance would be best represented by an inverted U-shape,

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such that when employees experience an optimal level of stress, they are likely to perform the best. However, experiencing too little, or too much stress, is purportedly associated with lower performance. Unfortunately, very little empirical work has evaluated the accuracy of the Yerkes–Dodson law in the occupational stress literature (Ferris et al., 2006). Therefore, we focus on linear relationships between stressors and performance. The second characteristic shared by these theoretical frameworks is that they all assume that stressors are the antecedents of performance. Interestingly, recent work by Bolino and colleagues (e.g., Bolino & Turnley, 2005; Bolino, Turnley, Gilstrap, & Suazo, in press) indicates that engaging in extra-role performance or OCB can be a stressor for employees that is associated with strain responses. Moreover, Bolino et al. (in press) showed that, while pressure to perform OCB was predictive of OCB performance, this pressure was also positively related to employees’ turnover intentions. As such, it is possible that there may be reciprocal relationships between particular stressors and performance. We elaborate on this point further in the discussion that follows our review of the literature.

SUMMARY OF JEX’S (1998) REVIEW Role Stressors Of the six categories of stressors reviewed by Jex, role stressors received the most attention. Jex identified three major trends from studies examining role stressor–performance relationships. First, role ambiguity and role conflict demonstrated consistent, modest, negative relationships with performance, with role ambiguity being more strongly related to performance than role conflict and role ambiguity having stronger correlations with self-rated performance relative to performance ratings from other sources (Abramis, 1994; Jackson & Schuler, 1985). Second, the role overload–performance relationship received much less attention than the other two role stressors, and findings concerning this relationship were more inconsistent, with some showing a negative relationship (e.g., Jamal, 1984, 1985), and others showing no or positive effects (Beehr, Drexler, & Faulkner, 1997). Third, research focused primarily on a narrow definition of in-role performance as the outcome of role stressors, yet there was evidence that the nature and magnitude of the relationship between role stressors and performance may depend on the type of performance measured (e.g., Fried & Tiegs, 1995). Based on these general trends, Jex (1998) recommended that future

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researchers should (a) broaden the performance criteria, (b) consider methodological and theoretical issues associated with using different rating sources of performance (e.g., self versus supervisor), and (c) clearly identify how specific role stressors relate to particular dimensions of performance.

Workload Jex (1998) found that much of the workload research focused on relationships between work overload and health-related outcomes (e.g., Cooper & Marshall, 1976; Sparks, Cooper, Fried, & Shirom, 1997), rather than performance. Regarding performance, Jex drew three conclusions from his review. First, workload must be excessive to affect performance. Low-tomoderate workloads did not generally demonstrate negative effects on performance. Next, loss of sleep is the mechanism that links workload to performance, as research demonstrated that sleep deprivation and schedule shifts were associated with decreased arousal, which has the potential to lead to lower performance (Spurgeon & Harrington, 1989). Jex’s (1991) own research also supported these effects by demonstrating that sleep-deprived healthcare workers showed deficits on composite measure of performance consisting of unexplained absences, mistakes, and missing deadlines. Jex’s (1998) final conclusion was that the impact of work hours is contingent upon the complexity of the tasks being performed, such that long work hours were more negatively associated with performance when assigned tasks required sustained attention or vigilance, or were less complex (Beatty, Adhern, & Katz, 1977; Griffin, 1991). In addition to these conclusions, Jex (1998) also underscored the importance of considering sources of workload and performance ratings. In particular, Spector, Dwyer, and Jex (1988) found that self-reported measures of workload had a nonsignificant relationship with supervisor-rated job performance, whereas supervisorrated workload had a modest, positive relationship with performance. Therefore, Jex (1998) suggested that future researchers should consider how some of the sources of stressor and performance ratings affect observed relationships between these constructs.

Situational Constraints Jex (1998) argued that situational constraints should be the most logical stressor to be directly related to performance. However, empirical studies

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provided mixed support for this relationship. For example, laboratory studies (O’Connor, Peters, & Segovis, 1983; Peters, O’Connor, & Rudolf, 1980; Peters, Fisher, & O’Connor, 1982) demonstrated that situational constraints are associated with deficits in performance, but field research showed much more inconsistent results, with studies demonstrating modest (Steel & Mento, 1986) to weak (O’Connor et al., 1984) relationships between situational constraints and performance. Jex (1998) presented three explanations for these inconsistent findings. First, for situational constraints to inhibit performance, job tasks must be challenging, organizations must place high value on performance, and employees must not be able to attribute poor performance to external causes. Missing any of these conditions may suppress the effects of situational constraints on performance (Peters & O’Connor, 1988). This leads to the second explanation, which is that past studies were limited by their focus on in-role behaviors, without considering more discretionary activities. Indeed, empirical studies showed that relationships between situational constraints and OCB and CWB were as strong (Kruse, 1995), or stronger (Chen & Spector, 1992; Storms & Spector, 1987), than relationships between situational constraints and in-role behavior (Kruse, 1995; Chen & Spector, 1992; Storms & Spector, 1987). Jex’s (1998) final explanation for inconsistent findings across studies was based on Villanova’s (1996) research, which suggested that situational constraints and performance represent different levels of specificity. In particular, measures of situational constraints used in previous research assessed very general organizational conditions, whereas performance measures assessed much more specific behaviors. Therefore, Jex (1998) recommended that future researchers should consider specificity issues when examining situational constraint–performance relationships.

Perceived Control Job autonomy and participation in decision making (PDM) were the two measures of control discussed by Jex (1998). Meta-analyses showed that autonomy had weak (Fried & Ferris, 1987) to modest (Spector, 1986) relationships with performance. Spector’s (1986) meta-analysis also indicated a modest correlation between PDM and performance. The majority of studies included in these meta-analyses relied on supervisor ratings of overall effectiveness. Thus, Jex recommended that future researchers examine the relationship between perceived control and performance ratings from different sources and broader performance dimensions. In addition, the

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nature of PDM and autonomy, along with the design of these studies, precludes researchers from ruling out reverse causal explanations. In particular, managers may be willing to provide employees with more input into decisions and autonomy in how they do their jobs if they are strong performers. As such, Jex (1998) noted that the causal direction of the control–performance relationship deserves more attention. Finally, noting research (Spector, 1986) showing that perceived control is associated with feelings about one’s job and critical psychological states (Fried & Ferris, 1987), Jex (1998) recommended that researchers consider the role of specific mediators (i.e., psychological withdrawal) in linking perceived control to performance.

Interpersonal Conflict Relative to situational constraints, role stressors, perceived control, and workload, the relationship between interpersonal conflict and performance received much less attention prior to 1998. Therefore, Jex (1998) discussed evidence linking interpersonal conflict to criteria such as academic achievement (Barnes, Potter, & Fielder, 1983) and CWB (Chen & Spector, 1992). However, other studies showed little relationship between interpersonal conflict and performance (Spector, 1988). As such, Jex (1998) concluded that (a) the interpersonal conflict–performance relationship is under-researched and deserves more attention, (b) interpersonal conflict may be deleterious to performance because it has an impact on cognitive functioning (Barnes et al., 1983), and (c) interpersonal conflict may have its strongest effects on deviance.

Acute Stressors Relative to the other stressors included in Jex’s review, acute stressors are unique because they are episodic and not a constant part of the work environment. Jex (1998) summarized three studies (Beehr et al., 1997; Motowidlo, Packard, & Manning, 1986; Nowack & Hanson, 1983) that provided evidence for a negative relationship between acute stressors and job performance. Nowack and Hanson (1983) found that frequency of negative life events was negatively related to the performance of resident assistants in dormitories. Motowidlo et al. (1986) found that the frequency and intensity of acute, job-related stressful events were related to several

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dimensions of job performance for nurses (i.e., ratings of composure, warmth toward other nurses, tolerance with coworkers). Consistent with the effects of other stressors on performance, the acute stressors assessed by Motowidlo et al. demonstrated only modest effects on performance. Beehr et al. (1997) examined the effects of chronic and acute stressors encountered by door-to-door booksellers on job-specific performance dimensions. Results indicated that both chronic and acute stressors were related to the dollar value of sales, with chronic stressful job conditions having an effect almost twice as strong as acute stressors. These results suggest that acute stressors may not be as important to performance as stressors that are encountered on an ongoing basis. Overall, Jex (1998) concluded that (a) not many studies had examined the effects of acute stressors on performance, (b) constant stressors may have stronger effects on performance than more episodic ones, and (c) future research should focus additional attention on understanding, in relative terms, how acute and chronic stressors relate to performance.

CONCLUSIONS Jex drew four major conclusions about research examining the stressor– performance relationship. First, the relationship between stressors and performance is not strong. A possible reason for the observed small effect sizes was that there is a substantial amount of error in job performance measures. Nonetheless, Jex noted that the small effect sizes may be of practical significance, depending on the context in which they occur. Second, stressors may be more strongly related to behaviors not assessed on formal performance appraisals, such as CWB and OCB. Thus, Jex suggested that researchers consider broader conceptualizations of performance in the future. Third, the relationship between stressors and performance may be indirect, with cognitions, emotions, and psychological states acting as mediators. Therefore, Jex recommended that future researchers conduct longitudinal studies to examine the mediated effects of stressors on performance. Finally, Jex observed that the relationship between stressors and performance is complex, such that a number of factors (i.e., competence, demographic factors, personality traits, organizational commitment, and organizational support) interact with stressors to determine how they influence performance. In the following section, we review the literature that has been published since 1998 and discuss how these studies have addressed the issues highlighted by Jex.

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UPDATED LITERATURE REVIEW We utilized a revised taxonomy that has eight categories of work stressors (role stressors, workload, situational constraints, lack of control, social characteristics, career outcomes, job conditions, and acute stressors). We used the following keywords to identify work stress articles for this review: role stress, role ambiguity, role conflict, role overload, workload, qualitative workload, quantitative workload, situational constraints, bureaucracy, environmental uncertainty, hassles, organizational constraints, red tapes, perceived control, autonomy, challenge, empowerment, hindrance, opportunity, participative decision-making, perceived control, self-leadership, selfmanaged work teams, abusive supervision, aggression, bullying, incivility, interpersonal conflict, mistreatment, organizational justice, organizational politics, job insecurity, lack of advancement/promotion, lack of learning opportunities, overqualified, overskilled, underemployment, work–family conflict (WFC)/enhancement/facilitation/interference, emotional labor, extreme temperature, work schedule, exposure to toxin, shift, acute stressor, accident, mental arithmetic, violence, injury, public speaking. We crossed these keywords with the following keywords for performance: task performance, customer service, group decision quality, group decisionmaking, group performance, production, productivity, team performance, OCB, altruism, civic virtue, conscientiousness, courtesy, helping, individual initiative, interpersonal facilitation, loyalty, OCBI, OCBO, sportsmanship, safety performance, accuracy, behavior, memory, reaction time, vigilance. Once articles were obtained, we also reviewed their reference lists to ensure that no pertinent articles were excluded from our review. From this search, we identified over 125 studies to include in our updated literature review.

Role Stressors Since 1998, four meta-analyses (Eatough, Miloslavic, Chang, & Johnson, 2009; Gilboa, Shirom, Fried, & Cooper, 2008; O¨rtqvist & Wincent, 2006; Tubre & Collins, 2000) have examined the relationship between role stressors and performance. Three of these meta-analyses focused on task performance and one examined OCB as a correlate of role stressors. In terms of task performance, all three meta-analyses indicated that role ambiguity has a significant, moderate, negative relationship with in-role performance (Gilboa et al., 2008; O¨rtqvist & Wincent, 2006; Tubre & Collins, 2000). However, the results of these studies diverged with respect to the role conflict–task

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performance relationship. Tubre and Collins (2000) found that role conflict had a negligible, nonsignificant effect on performance, whereas Gilboa et al. (2008) and O¨rtqvist and Wincent’s (2006) estimated effect was slightly larger and statistically significant. Gilboa et al. (2008) attribute this discrepancy to the use of a larger sample, via the inclusion of additional studies, in their meta-analysis. Interestingly, this trend is opposite for OCB. Eatough et al. (2009) found that while role ambiguity had a significant, negative relationship with OCB, the negative relationship between role conflict and OCB was significantly stronger. Finally, meta-analytic results showed that role overload had weak and nonsignificant relationships with both task performance and OCB (Eatough et al., 2009; Gilboa et al., 2008; O¨rtqvist & Wincent, 2006). Therefore, at the bivariate level, these meta-analyses are consistent with Jex’s (1998) conclusion concerning the relationships between role stressors and performance. Some of these meta-analyses also considered between-study moderators of the effects of role stressors on performance. Tubre and Collins (2000) showed that role stressors had a larger, negative effect on the task performance of employees holding professional, technical, and managerial jobs relative to employees holding service, clerical, or sales jobs. Gilboa et al.’s (2008) results also indicated that role stressors had a greater impact on the performance of managers relative to non-managers. Similarly, rating source was found to moderate the effects of both role stressors on performance, such that role stressors were more strongly related to supervisor and peer ratings of performance relative to objective measures (Tubre & Collins, 2000), and role ambiguity was more strongly related to self-rated task performance than supervisor-rated or objective performance (Gilboa et al., 2008). Consistent with this finding, Eatough et al. (2009) found that role ambiguity and conflict had stronger associations with selfrated OCB relative to supervisor-rated OCB. These results were in accordance with Jex’s (1998) conclusion that role stressors demonstrate the strongest relationships with self-rated performance. Some researchers have discussed the implications of these findings, suggesting that self-rated performance may reflect both motivation to perform and actual performance, whereas other-rated performance captures only behaviors (Eatough et al., 2009). Since role stressors are likely to have stronger effects on behavioral intention (Jex, 1998), this tendency is manifested in the stronger relationship between role stressors and self-rated performance. Despite Jex’s (1998) recommendation, much less attention has been directed toward understanding the effects of role stressors on criteria other than task performance. Compared to the large number of effect sizes

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(k ¼ 114) summarized by Gilboa et al. (2008) and Eatough et al. (2009) meta-analysis summarized between 11 and 19 effect sizes for different role stressors and OCB relationships. As mentioned previously, Eatough et al. (2009) found moderate, negative relationships between OCB and role ambiguity and conflict. They also found that role ambiguity had a weaker relationship with OCB compared to task performance, whereas role conflict had a stronger relationship with OCB compared to task performance. Unfortunately, other than OCB, research has failed to examine relationships between role stressors and alternative measures of performance (e.g., CWB). Thus, more empirical work is needed to meaningfully compare effects of role stressors on different performance criteria. Following Jex’s recommendation, several studies published since 1998 examined the effects of role stressors on specific types of performance, with the most commonly examined outcome being self-reported salesperson performance. For the most part, findings were consistent with Jex’s review, demonstrating that role ambiguity had a stronger negative effect on salesperson performance relative to role conflict (Babakus, Cravens, Johnston, & Moncrief, 1999; Bhuian, Menguc, & Borsboom, 2005; Grant, Cravens, Low, & Moncrief, 2001; Low, Cravens, Grant, & Moncrief, 2001; Singh, 1998; Stamper & Johlke, 2003). A similar pattern of results was also found in studies examining the effects of role conflict and role ambiguity on self- (Babin & Boles, 1998; Chang & Chang, 2007; Saks & Ashforth, 2000; Somers, 2001) and supervisor ratings (Fried, Ben-David, Tiegs, Avital, & Yeverechyahu, 1998) of general performance. Also consistent with previous research, studies failed to demonstrate significant direct effects of role overload on self- (Jones, Chonko, Rangarajan, & Roberts, 2007) or supervisor ratings (Beehr, Jex, Stacy, & Murray, 2000) of general measures of performance. We located two studies that examined indirect effects of role stressors on performance. Babakus et al. (1999) tested a model in which emotional exhaustion was hypothesized to mediate the effects of role ambiguity and role conflict on sales employee performance. Results of this study failed to support the proposed mediation effects and indicated that only role ambiguity was related to performance. Bettencourt and Brown (2003) examined the indirect effects of role ambiguity and role conflict on customer-oriented boundary spanning behavior through job attitudes. Customer-oriented boundary spanning behaviors are activities that link the organization to its customers and include advocating the organization to outsiders, taking initiative to make suggestions for improving service delivery, and providing high quality service to customers. Bettencourt and

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Brown (2003) demonstrated that, in a sample of retail bank employees, work attitudes mediated the effects of role ambiguity and role conflict on customer-oriented boundary spanning behaviors, such that role stressors were related to less favorable attitudes, which were associated with fewer boundary spanning behaviors. Beyond direct and indirect effects, researchers have examined how characteristics of the situation (Beehr et al., 2000; Singh, 1998; Stamper & Johlke, 2003), work attitudes (Jex, Adams, Bachrach, & Sorenson, 2003), individual differences (Chang & Chang, 2007), and work stressors (Bhuian et al., 2005; Fried et al., 1998) interact to predict performance. These studies have provided mixed evidence for moderation. For example, Singh (1998) demonstrated that task variety, feedback, and participation buffer the effects of role conflict, role ambiguity, and role overload on salesperson performance; suggesting that job design can be leveraged to reduce the effects of role stressors on performance. Other studies failed to support hypotheses that higher levels of organizational (Stamper & Johlke, 2003) or coworker support (Beehr et al., 2003) attenuate the effects of role stress on performance. Interestingly, multiple studies provided evidence that role conflict and role ambiguity interact to predict supervisor ratings of both sales and general performance (Bhuian et al., 2005; Fried et al., 1998), demonstrating that performance is lowest when respondents report high levels of role ambiguity and role conflict. Finally, studies have shown that the moderating effect of the same factor may vary depending on the performance outcome. For example, Jex et al. (2003) found that the interaction between role ambiguity and affective commitment was not significant, but role conflict was negatively related to altruism when affective commitment was high; a pattern opposite to what was predicted. To conclude, meta-analyses and primary studies indicate that role ambiguity has modest, yet stronger effects on task performance and OCB relative to other role stressors; role conflict demonstrates weak, yet significant effects on task performance and OCB; and there is limited support for a relationship between role overload and performance. One explanation for the weak and nonsignificant effects found across studies is that previous research may have misspecified these relationships by focusing only on linear effects, which counters the Yerkes–Dodson law (1908). Thus, we call for additional research that explores theoretically derived nonlinear effects, including research examining how characteristics of individuals and situations affect role stressor–performance relationships and research that considers quadratic effects of role stressors.

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In addition, methodological issues, such as reliance on scales that have questionable measurement properties (see Gilboa et al., 2008), range restriction of samples (i.e., failure to sample people experiencing extremely high or extremely low levels of role stress), and differences across rating sources (e.g., self, peer, supervisor, and archival) may also explain weak relationships found in previous studies. Our review also indicated that few studies have examined role stressors in relation to alternative performance criteria other than task performance or OCB. Therefore, consistent with Jex’s (1998) recommendations, we call for researchers to focus attention on a broader range of performance criteria. At the same time, we also concur with Jex’s (1998) previous recommendation that researchers should consider how role stressors relate to specific work behaviors, as matching the specificity of role stressor measures and performance criteria is likely to result in stronger relationships and may potentially explain discrepancies found across studies.

Workload In recent years, researchers have examined relationships between different measures of workload and performance across both laboratory and field settings. Consistent with previous research (Spector, 1998; Beehr et al., 1997), these studies demonstrated that the workload–performance relationship is complex and is influenced by several intervening factors. For example, Jimmieson and Terry (1999) investigated how task characteristics interact with workload to influence objective (quantitative and qualitative) and subjective (self-ratings) measures of performance on an in-basket task. Workload was manipulated by providing participants with 10 min (high load) or 30 min (low load) to complete the in-basket task. In Study 1, the experimenters also manipulated the amount of behavioral control and the amount of procedural information communicated to participants. Results provided evidence for a three-way interaction in which process information had a positive effect on performance when control was low and workload was high. Thus, the authors concluded that process information had compensatory effects when workload was high and control was low. In Study 2, Jimmieson and Terry (1999) examined whether task complexity had an influence on relationships among workload, behavioral control, and procedural information. Results indicated that workload had main effects on quantitative and subjective measures of performance. However, Study 2 failed to replicate the three-way interaction between workload, control, and

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process information on performance. Nonetheless, these studies provide evidence that, when employees have less control, procedural information has the potential to buffer the effects of higher workload on task performance. Glaser, Tatum, Nebeker, Sorenson, and Aiello (1999) also specified a complex relationship between workload and performance by hypothesizing that perceived stress mediates the effects of workload on performance and that these effects are moderated by social support. In this study, which utilized a work simulation that occurred over a 10-day period, qualitative and quantitative workloads were manipulated. Participants were assigned to either a high (multiple goals) or low (single goal) qualitative workload condition and these conditions were crossed with high (high output on a data entry task) or low (low output on a data entry task) quantitative workload conditions. Perceived stress and social support were both assessed using a self-report questionnaire. Results indicated that stress mediates the effects of workload on performance. In addition, there was evidence for a three-way interaction between workload, support, and time in predicting stress, such that social support led to higher stress for those assigned to the high workload condition and this effect occurred only during the early stages of the study. Recently, Perry, Sheik-Nainar, Segall, Ma, and Kaber (2008) investigated the effects of physical workload on cognitive performance. This is an interesting study because many occupations, such as military, police, and firefighters, require employees to make quick decisions while also engaging in physically demanding tasks. In Perry et al.’s (2008) laboratory study, participants were assigned to one of three physical workload conditions (viz., standing, walking, and jogging) while they completed a military helicopter-loading simulation task. The simulation required participants to load helicopters to maximum weight capacity within an allotted time frame. Results of this study showed little effect of physical workload on participants’ performance of the cognitive task. In their post hoc explanation of the null findings, Perry et al. suggested that the physical workload may not have been strenuous enough and/or the task may have been too simple to interfere with performance. As such, there are opportunities for future research that considers the effects of more extreme forms of physical exertion on performance. Future research should consider the effects of physical exertion on a broader range of tasks, such as decision-making and creativity. Field studies have also examined moderators and mediators of the effects of workload on performance. Extending the JD-R model (Bakker & Demerouti, 2007), Dwyer and Fox (2006) examined how interactions

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between job resources (viz., control, training, support, task identification, task significance, skill variety, and feedback) and job demands (perceived amount of work and pace of work) predict the performance of call center employees, operationalized in terms of the duration of calls, the number of calls, and wait time before answering a call. Results indicated that the perceived amount of work was negatively related to performance (as indicated by longer calls, longer time that callers were left on hold, and fewer calls made). However, pace of work was not related to any of the performance indicators. In addition, training interacted with workload to predict performance (wait time and call duration) and pace interacted with feedback and control to predict wait time and call duration, respectively. Hence, there is evidence that job resources may offset the negative effects of workload on performance. Regarding mediation, Claessens, Van Eerde, Rutte, and Roe (2004) proposed and tested a demands-control model (Karasek, 1998) of time management utilizing a sample of engineers from the semiconductor industry. According to the hypothesized model, perceived time control mediates the effects of planning behavior, perceived workload, and job autonomy on self-ratings of performance. Results supported partial mediation, such that workload was associated with lower levels of productivity through its negative effects on perceptions of time control. However, these results should be interpreted with caution, as the finding did not meet multiple conditions for mediation (e.g., workload did not have a significant relationship with either the mediator or the dependent variable; see Baron & Kenny, 1986), and the authors did not provide tests of the significance of the indirect effects from workload to performance. Therefore, it is difficult to determine the extent to which the results supported the purported mediation effects. As such, we recommend that future studies attempt to replicate Claessen et al.’s (2004) findings and further explore the extent to which time control mediates the effects of workload on performance. Together, these studies have helped advance our knowledge of the workload–performance relationship by considering how situational conditions and individual perceptions work together to affect performance. These studies have provided evidence for the JD-R model by showing that resources attenuate the effects of elevated workloads on performance. However, because all individuals are unlikely to respond the same way to elevated workload, researchers should consider the effects of individual differences on these relationships. Moreover, many questions remain regarding how different measures of workload (e.g., hours, quantitative

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vs. qualitative workload) relate to task performance and alternative criteria. For example, the discretionary nature of OCB suggests that these activities may be more adversely affected by heavy workloads, relative to in-role performance. Unfortunately, this hypothesis has not been examined. Regarding CWB, the frustration–aggression hypothesis (Fox & Spector, 1999) suggests that CWB will increase with workload, as increased workload may hinder workers’ achievement of work goals, resulting in work frustration, a negative affective response that has been linked to CWB. In addition, workload may be positively related to CWB as employees who have more lax production requirements and low work demands may be more likely to loaf and engage in deviant behaviors to fill time on their jobs. Thus, future research should focus on individual differences, and alternative performance criteria for the workload–performance linkages.

Situational Constraints Jex’s (1998) review reported equivocal evidence for a relationship between situational constraints and performance. Since that time, empirical research has provided more consistent evidence linking situational constraints to inrole behavior. Specifically, Gilboa et al.’s (2008) meta-analysis indicated that situational constraints have effects on task performance that are comparable to those of role ambiguity, and these effects are similar across performance rating sources (viz., self vs. supervisor). In terms of specificity issues, Klein and Kim (1998) examined the effects of a job-specific measure of situational constraints for workers in retail/sales who worked within a goal-based incentive system. Klein and Kim first used a focus group to develop a jobspecific situational constraints scale that assessed factors that hinder sales goal attainment that are beyond employees’ control. Performance was assessed with an archival measure of sales per hour in dollars. Klein and Kim (1998) found that situational constraints had a moderate, negative relationship with performance. They also found that goal commitment mediated the effects of situational constraints on performance and the effects were moderated by leader–member exchange, such that goal commitment was more strongly related to the performance of members who had high quality relationship with their supervisors. We also located several studies that examined the effects of situational constraints on OCB and CWB. For example, Jex et al. (2003) examined the effects of organizational constraints (e.g., poor equipment, supplies, and interruptions) on supervisor ratings of altruism, one of five forms of OCB

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described by Podsakoff, Ahearne, and MacKenzie (1997). As expected, organizational constraints were negatively associated with altruism. This relationship was moderated by affective commitment, such that constraints were associated with less altruism for those who had lower affective commitment. Using a sample of employees from 14 Swiss hotels, Raub (2008) investigated the relationship between perceived centralization, an indicator that an organization is more bureaucratic, and helping and voice behaviors, both of which have been described as extra-role activities (Van Dyne & LePine, 1998). Results provided evidence that bureaucracy was negatively associated with both helping and voice behaviors. Thus, Jex et al. (2003) and Raub’s (2008) findings suggest that, in addition to having a negative effect on task performance, situational constraints may also affect OCB. Interestingly, a multi-wave study by Fay and Sonnentag (2002) provided evidence that, over time, organizational constraints (i.e., shortage defective equipment and tools, time pressure) may have a positive effect on certain OCB (i.e., personal initiative). Though this finding counters conventional wisdom, these results were consistent with control theory (Carver & Scheier, 1982) and the cybernetic model of stress (Edwards, 1992). This study serves as an example for how our knowledge of the stressor–performance relationship can be enhanced by considering how stressors relate differently to narrowly defined performance criteria, such as facets of OCB. In addition, it emphasizes the importance of examining relationships longitudinally. Unfortunately, this study did not examine other forms of OCB, therefore it was not possible to determine whether the effects of organizational constraints on different forms of OCB vary over time. Research has also examined relationships between situational constraints and workplace deviance. Spector and colleagues (Fox & Spector, 1999; Fox, Spector, & Miles, 2001; Penney & Spector, 2005) have provided evidence for a work frustration–aggression explanation of the effects of stressors, including organizational constraints, on CWB. These studies rely on the Dollard–Miller model (Dollard, Doob, Miller, Mowrer, & Sears, 1939) where frustration and negative emotions are expected to elicit aggression directed toward others (i.e., interpersonal aggression) and the organization (i.e., organizational aggression). Results of these studies (e.g., Fox & Spector, 1999; Fox et al., 2001; Penney & Spector, 2005) have shown that organizational constraints are associated with self-reported organizational and interpersonal deviance and that affective responses, such as frustration, play a role in mediating these effects. Consistent with other studies examining workplace deviance, a weakness of this research was its reliance

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on self-reported CWB (Penney & Spector, 2005 used peer ratings in addition to self reports, but they were not significant). As such, questions remain regarding whether situational constraints have effects on CWB that are comparable to their effects on other performance criteria measured by other sources. Taken together, empirical research since Jex’s (1998) review has provided stronger evidence supporting the negative effects of situational constraints on task performance, OCB, and CWB. Research has also shown that when situational constrains and performance is matched in terms of their specificity, they demonstrate stronger relationships. At the same time, recent research further underscores the importance of considering the source of performance ratings and the insights that may be gained by examining reciprocal relationships between constraints and performance over time.

Lack of Control Relatively few studies have focused on the direct relationship between perceived control and job performance. In fact, we located two studies published since 1998 that examined the effects of perceived control on performance (Sargent & Terry, 1998; Claessens et al., 2004). Sargent and Terry (1998) attempted to address shortcomings of Fox, Dwyer, and Ganster (1993) study, which failed to provide evidence supporting the interaction effect of job demands and control on performance. Sargent and Terry (1998) utilized a multidimensional measure of work control (viz., task control, decision control, and scheduling control) to explore the moderating effects of control on the relationship between various job demands and individual-level outcomes (viz., supervisor ratings of performance, job satisfaction, and depressive symptoms). Role ambiguity and role conflict were the focal job demands. The zero order correlations between demands and control variables were not significantly related to performance and none of the control variables buffered the effects of stressors on performance. However, task control buffered the effects of overload on depressive symptoms and ambiguity on job satisfaction. Thus, the results of this study did not provide evidence that control affects performance, but the results do contribute to the literature linking control to well-being. In Claessen et al.’s (2004) study, perceived control was identified as a mediator of the effects of workload, job autonomy, and planning behavior on self-reported performance, job satisfaction, and work strain. The authors reported that the results supported the proposed meditational role of

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perceived control over time. However, there is not enough information present in this study to determine whether an adequate test of mediation was performed. Therefore, additional research is necessary. Control can also take other forms, such as PDM, autonomy, and empowerment. Logan and Ganster (2007) conducted a study that examined how an empowerment intervention can improve performance in a trucking company. In this randomized field experiment, project managers for trucking units received an intervention that targeted control and selfefficacy beliefs. This intervention was aimed at increasing (a) overall perceptions of control, (b) self-determination beliefs (i.e., control over quality, pace, and scheduling), and (c) perceived control of maintenance (i.e., control over the maintenance of equipment). Results indicated that perceived maintenance control was improved by the intervention and this form of control was associated with improved performance as assessed by archival measures (viz., lower number of breakdowns and lower accident frequency). Thus, Logan and Ganster (2007) provided evidence that control beliefs can be affected by empowerment interventions and these beliefs have the potential to impact performance. Though Logan and Ganster (2007) provided some of the strongest evidence for the positive effects of empowerment on performance, several studies have provided additional evidence for the positive effects of empowerment on individual (Ahearne, Mathieu, & Rapp, 2005; Butts, Vandenberg, DeJoy, Schaffer, & Wilson, 2009; Chen, Kirkman, Kanfer, Allen, & Rosen, 2007; Chow, Lo, Sha, & Hong, 2006; Rapp, Ahearne, Mathieu, & Schillewaert, 2006) and team (Bogler & Somech, 2004; Chen et al., 2007; Chen & Klimoski, 2003; Kirkman & Rosen, 1999; Kirkman, Rosen, Tesluk, & Gibson, 2004; Seibert, Silver, & Randolph, 2004) performance. In addition, there is evidence that autonomy, which is the extent to which employees have latitude in carrying out job duties (Hackman & Oldham, 1980), is associated with OCB (Gellatly & Irving, 2001), supervisor evaluations of sales performance (Strain, 1999), team performance (Stewart, 2006), and team decision quality (Rico, Molleman, Sa´nchez-Manzanares, & Van der Vegt, 2007). These studies also indicate that autonomy has the potential to moderate the effects of personality (Gellatly & Irving, 2001) and team diversity conditions (Rico et al., 2007) on performance. Taken together, these results are consistent with Jex’s (1998) observation that perceived control has modest effects on performance. In accordance with Jex’s recommendation, research has expanded the criterion domain by examining how various measures of control (i.e., perceived control, empowerment, and autonomy) relate to criteria such as objective measures

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of performance, self and supervisor ratings of task performance, OCB, and indicators of team effectiveness. In addition, research has provided evidence, albeit indirectly, for the demands–control model, indicating that higher levels of control may offset the negative effects of demands on performance (Carton & Aiello, 2009). Unfortunately, the inclusion of various constructs under the perceived control category reflects a lack of theoretical and measurement precision. With the exception of Logan and Ganster (2007), studies examining empowerment have not focused explicitly on the effects of control, but rather have utilized variations of Spreitzer’s (1995) empowerment measure (which specifies that meaningfulness, impact, self-determination, and competence are indicators of empowerment) or examined leader empowerment behaviors in relation to performance. As such, future research should flesh out the specific dimensions of empowerment that are pertinent to individual and team performance, identify how these dimensions relate to control beliefs, and specify whether the effects of control beliefs on performance are direct, mediated, or moderated.

Interpersonal Demands Relatively little contemporary research has focused on the relationship between interpersonal conflict and in-role performance. However, several studies have shown that interpersonal conflict has a positive relationship with other forms of performance, particularly organizational deviance (Penney & Spector, 2002, 2005; Miles, Borman, Spector, & Fox, 2002). These studies have demonstrated moderate effect sizes between interpersonal conflict and self-reported aggression and deviance directed toward individuals and the organization. For example, Penney and Spector (2005) demonstrated that both self and peer-rated interpersonal conflict were associated with self and peer ratings of CWB. This study also provided evidence that negative affect moderates the effects of interpersonal conflict on CWB, such that people who are higher in negative affect are more likely to engage in CWB when conflict is high. In addition to workplace deviance, recent studies (Alper, Tjosvold, & Law, 2000; Jehn & Mannix, 2001; Stewart & Barrick, 2000) have also considered the effects of conflict on group performance. In a study of teams of part-time MBA students, Jehn and Mannix (2001) examined the effects of task, process, and relationship conflict on ratings of a final project. All three forms of conflict were negatively correlated with ratings on the final project,

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with task conflict showing the strongest effects. In addition, this study also showed that changes in different types of conflict over time may have effects on performance. Specifically, the authors observed that the best performing teams had low, but increasing process conflict over time; low relationship conflict, with a slight bump near the project deadline; and moderate task conflict midway through the study. These results show the importance of understanding how various types of conflict are differentially related to group performance over time. Stewart and Barrick (2000) proposed that conflict is an intra-team process that links team structure to performance. Using a sample of teams at manufacturing plants, Stewart and Barrick (2000) showed that conflict was negatively related to supervisor ratings of team performance and this relationship was stronger for teams working primarily on conceptual tasks (i.e., generating ideas, planning, choosing between alternatives, and negotiating conflicts) relative to teams focused on executing tasks. Thus, an important implication of this study is that the relationship between conflict and performance is contingent upon the type of work performed. Alper et al. (2000) investigated the role of team conflict efficacy (i.e., the belief by team members that their team can manage conflict situations), in mediating the effects of how teams approach conflict (in a cooperative vs. competitive style) on team performance. Cooperative approaches emphasize mutual goals, understanding others’ views, orientation toward mutual benefits, and tapping into different perspectives to find a solution that is satisfying to all parties. Competitive approaches focus on conflict as a win– lose situation and make use of pressure and intimidation to get others to conform. Alper et al.’s (2000) results demonstrated that cooperative approaches were positively related to conflict efficacy, whereas competitive approaches were negatively associated with efficacy. In turn, conflict efficacy was positively related to supervisor and team leader ratings of team performance. Together, these studies indicate that conflict has modest effects on team performance, and the effects vary depending on the types of conflict, the timing and nature of tasks, and strategies to resolve conflict. In addition to direct measures of interpersonal conflict, researchers have identified other aspects of the social context (e.g., interactional justice, organizational politics, workplace incivility, abusive supervision) that also reflect the quality of interpersonal interactions that employees experience when doing their jobs. Interactional justice refers to treatment that is received from agents of the organization when implementing organizational policies and procedures (Bies & Moag, 1986). Fair exchanges are marked by the demonstration of courtesy, respect, kindness, and consideration during

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interpersonal exchanges (Niehoff & Moorman, 1993). Meta-analyses (CohenCharash & Spector, 2001; Colquitt, Conlon, Wesson, Porter, & Ng, 2001) indicate that interactional justice is related to task performance and various measures of OCB. More recent empirical studies have also provided consistent evidence for relationships between interactional justice and self (Moliner, Martı´ nez-Tur, Ramos, Peiro´, & Cropanzano, 2008, Olkkonen, & Lipponen, 2006) and supervisor (Aryee, Budhwar, & Chen, 2002; Burton, Sablynski, & Sekiguchi, 2008; Karriker & Williams, 2009; Wong, Ngo, & Wong, 2006) ratings of task performance and OCB. Various mediators of these effects have been found, including engagement (Moliner et al., 2008), work–unit identification (Olkkonen, & Lipponen, 2006), organization– member, or leader–member exchange (Burton et al., 2008; Karriker & Williams, 2009), and supervisor trust (Aryee et al., 2002; Wong et al., 2006). Recently, two studies (Moliner et al., 2008; Yang & Diefendorff, 2009) examined stress-related mediators of the effects of interpersonal injustice on broadly defined performance criteria. Moliner et al. (2008) failed to provide evidence that burnout mediates the effect of interpersonal justice on performance. However, Yang and Diefendorff (2009) found that negative emotions fully mediated the effects of both supervisor and customer interpersonal injustice on CWB. While negative emotions explain the effects of interpersonal injustice on CWB, an alternative explanation for its effects on task performance and OCB is that employees reduce the time and effort that they put into their jobs to restore equity to their organizational exchange relationships (Cropanzano et al., 2001). Scant attention has been directed toward exploring these theoretical mechanisms; therefore future research needs to investigate the mechanisms that link interactional justice to performance. Organizational politics is another feature of the social context that has been identified as a source of stress which is detrimental to employee performance (Chang et al., 2009b). Theorists (Ferris, Russ, & Fandt, 1989; Vigoda, 2000a) have suggested that organizational politics are appraised as a threat because political activities interfere with employees’ ability to achieve desired career and personal outcomes. Politics are associated with heightened levels of interpersonal conflict, and political climates put demands on employees to engage in political behaviors to achieve their goals. Hence, performance deficits are often observed in employees who experience high levels of politics. Supporting this perspective, several studies have provided evidence that politics have direct (Vigoda, 2000a, 2000b; Maslyn & Fedor, 1998), indirect (Rosen, Levy, & Hall, 2006), and interactive effects (Chen & Fang, 2008; Hochwarter, Witt, & Kacmar, 2000;

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Kacmar, Bozeman, Carlson, & Anthony, 1999; Witt, 1998; Witt, Kacmar, Carlson, & Zivnuska, 2002; Zivnuska, Kacmar, Witt, Carlson, & Bratton, 2004) on in-role behaviors, OCB, and aggressive behaviors (Vigoda, 2002). Recently, Chang, Eatough, and Spector (2009a) conducted a meta-analysis of the outcomes of politics and demonstrated that employee perceptions of organizational politics had significant, negative relationships with supervisor-rated task performance and OCB. In addition, Chang et al. conducted a path analysis using meta-analytically derived coefficients and found that job anxiety mediated the effects of perceived politics on a multidimensional measure of performance. Thus, there is evidence that this interpersonal stressor negatively affects job performance. Two other social stressors that have become popular in the literature are workplace incivility and supervisor abuse. Workplace incivility refers to ‘‘relatively mild, rude, and discourteous behavior in the workplace’’ (Penney & Spector, 2005). It is generally a low intensity form of deviance that does not have a clear intention to harm the target (Andersson & Pearson, 1999), and has been compared to other daily hassles such as red tape (Cortina, Magley, Williams, & Langhout, 2001). As such, general cognitive theories of stress suggest that low-level, interpersonal mistreatment has a negative impact on psychosomatic well-being (Lazarus & Folkman, 1984) which leads to deficits in performance. Supporting this perspective, research has demonstrated that incivility impacts academic performance (Caza & Cortina, 2007), CWB (Penney & Spector, 2005), and organizational and individual effectiveness (Pearson & Porath, 2005). Similarly, abusive supervision is defined as ‘‘sustained forms of nonphysical hostility perpetrated by managers against their subordinates (e.g., loud outbursts, undermining, and belittling)’’ (Tepper, Henle, Lambert, Duffy, & Giacalone, 2008, p. 721). Abusive supervision is a chronic work stressor that drains employees’ coping resources (Tepper et al., 2008). Consistent with research examining other social stressors, there is evidence that experiencing abuse from a supervisor on an ongoing basis is associated with organizational deviance (Mitchell & Ambrose, 2007; Tepper et al., 2008, 2009; Thau, Bennett, Mitchell, & Marrs, 2009), aggression directed toward supervisors (Dupre, Inness, Connelly, Barling, & Hoption, 2006; Inness, Barling, & Turner, 2005), reducing OCB (Aryee, Chen, Sun, & Debrah, 2007), and lower performance ratings (Harris, Kacmar, & Zivnuska, 2007). As indicated by this review, increased research attention has been directed toward understanding the effects of conflict, and related interpersonal oriented stressors, on performance. These studies have provided fairly consistent evidence for the effects of these psychosocial stressors on

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performance. Unfortunately, little is known about the psychological processes that explain the stress-related aftermaths of interpersonal stressors. In addition, relatively little is known about the coping mechanisms that employees use to deal with these ongoing social stressors, as there have been no intervention studies on these topics. Moreover, it is not clear how much incremental variance these stressors explain in performance. Therefore, future research should attempt to clarify the mechanisms that link social stressors to performance. Specifically, tests of social exchange theory and JD-R based descriptions of how these interpersonal stressors affect performance are warranted, as research suggests that interpersonal stressors are detrimental to the development and maintenance of high quality employee–organization exchanges (Hall, Hochwarter, Ferris, & Bowen, 2004; Rosen, Chang, Johnson, & Levy, 2009) and the daily hassles associated with environments marked by interpersonal conflict are likely to draw down the coping resources available to employees for dealing with other situations.

Career Issues NIOSH (1999) added career concerns as a category of work stressors that includes job insecurity, underemployment, lack of advancement or promotion opportunities, lack of learning opportunities, as well as work interference with nonwork domains (e.g., family). Among these stressors, job insecurity and work–family issues have received the most attention in recent years. Job insecurity refers to an individual’s concern that a job will not exist in the future (Rosenblatt & Ruvio, 1996). Job insecurity has been described as a subjective phenomenon that is associated with how employees interpret their perceptions of the work environment (Hartley, Jacobson, Klandermans, & van vuuren, 1991). Job insecurity is perceived as threatening because it can have severe consequences by forcing people to make drastic and unexpected lifestyle changes. Previous research has shown that job insecurity is associated with less favorable attitudes and well-being (Sverke, Hellgren, & Na¨swall, 2002). However, the relationship between job insecurity and performance is less well understood. Though empirical research has shown that job insecurity has modest negative effects on self-reported task performance (De Cuyper & De Witte, 2006, 2007) and OCB (King, 2000), Sverke, Hellgren, and Na¨swall’s (2002) meta-analysis demonstrated a nonsignificant relationship between job insecurity and task performance. The authors concluded that

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this null effects size may reflect that the strength and direction of the job insecurity–performance relationship is dependent on the situation, such that job insecurity may be positively associated with performance in some contexts (i.e., when performance is a criterion for layoff decisions), but the relationship may be negative or nonsignificant in other situations (i.e., when tenure is a criterion for layoffs). Therefore, Sverke et al. (2002) emphasized the importance of examining the context when studying the job insecurity– performance relationship. Other studies have also provided evidence that the relationship between job insecurity and performance is quite complex. For example, Rosenblatt, Talmud, and Ruvio (1999) found that gender moderated the effects of job insecurity on performance, such that only female teachers’ performance was negatively affected by job insecurity. Similarly, Feather and Rauter’s (2004) results indicated that work status (i.e., permanent vs. contract) of teachers moderated the effects of job insecurity on OCB, such that job insecurity had a positive relationship with OCB of contract teachers, but was not related to the OCB of permanent teachers. One explanation for these findings is that contract teachers were hoping to gain permanent employment and, therefore, were more likely to go the extra-mile in an attempt to acquire a permanent contract. A final job insecurity study worthy of mention was conducted by Probst, Stewart, Gruys, and Tierney (2007). Across two studies, the authors examined relationships between job insecurity and productivity and creativity. The first study utilized an experimental design in which participants performed an editing task that approximated a real job. During the editing task, participants in the experimental condition were informed of pending layoffs. Results not only indicated that participants in the experimental condition increased productivity following the layoff announcement, but also demonstrated deficits in creative problem solving. In Study 2, employed participants completed a survey, which assessed job insecurity and CWB. After completing the survey, participants were asked to perform a creative problem-solving task. Although participants who reported experiencing higher levels of job insecurity performed worse on the problem-solving task, they also reported engaging in less CWB on the job. This research suggests that job insecurity has adverse affects on creativity, but may be beneficial to productivity. Together, these studies paint a complex picture of the job insecurity– performance relationship. They demonstrate both positive and negative relationships between job insecurity and performance and these effects may be contingent upon the type of performance that is measured. Therefore,

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future research should consider how the work context and characteristics of employees (i.e., job mobility) may explain why and when job insecurity is related to different types of performance. WFC is another career-related stressor that has received an increased amount of attention from researchers over the past decade. WFC deals with the pressures that are put on workers to satisfy competing work and family demands. Research examining the work–family interface has examined both family-to-work and work-to-family interference on individual outcomes. We focus on research that has investigated how family-to-work interference affects job performance. Prior to 1998, research provided mixed evidence for a relationship between WFC and performance, with some studies finding that WFC has a negative relationship with performance (Aryee, 1992; Frone, Yardley, & Markley, 1997) and others failing to support this linkage (Netemeyer, Boles, & McMurrian, 1996). These findings led Allen, Herst, Bruck, and Sutton (2000) to call for research that investigates other domains of performance in relation to WFC, as employees may be most likely to reduce discretionary behavior to accommodate demands from outside of work. Allen et al. (2000) also suggested that researchers use more objective measures of performance (e.g., sales volume) and supervisor ratings, as self-serving biases may limit the extent to which employees are willing to admit experiencing WFC and also simultaneously report lower levels of performance. In recent years, studies have attempted to address these issues. For example, Carlson, Witt, Zivnuska, Kacmar, and Grzywacz (2008) examined the relationship between a multi-faceted measure of WFC and supervisor-ratings of different facets of OCB. Results indicated that both time-based and strain-based family-towork conflict, defined as family demands requiring employees to take time off work and negative emotions spillover from family-to-work domain, were negatively associated with OCB directed at other individuals and the organization as a whole. Similarly, Witt and Carlson (2006) found a moderate, negative relationship between family-to-work conflict and supervisor-ratings of in-role job performance. This effect was moderated by both perceived organizational support and employee conscientiousness, such that WFC was more detrimental to the performance of highly conscientious employees and those who perceived lower levels of organizational support. Research (Demerouti, Taris, & Bakker, 2007; Kossek, Colquitt, & Noe, 2001) has also examined the effects of WFC on self-ratings of performance. Kossek et al. (2001) reported a small but significant relationship between WFC and self-rated task performance. On the other hand, Demerouti et al. (2007) found that WFC was negatively related to self-rated performance

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measured cross-sectionally, but unrelated to performance ratings reported one month later. Nonetheless, Demerouti et al.’s cross-lagged data provided some support for the COR model (Hobfoll, 1998). In particular, WFC was linked to recovery, recovery predicted concentration, and concentration predicted in-role performance. Together, this new wave of studies provides evidence that WFC has a negative impact on performance and that these effects are more pronounced for supervisor ratings of performance. This latter trend is different from what was observed for other stressors, such as role ambiguity and role conflict.

Job Conditions Job conditions, which include the physical environment, the psychosocial nature of job tasks, and characteristics of work schedules (e.g., shift work), have the potential to affect job performance by placing demands on employees’ attentional resources and thus distracting employees from performing job-related tasks. Regarding physical conditions, meta-analyses (Hancock, Ross, & Szalma, 2007; Pilcher, Nadler, & Busch, 2002) have provided evidence that thermal stressors (i.e., extreme heat and cold) affect performance. However, the magnitude of the effects varies across tasks. For example, Pilcher et al. (2002) found that cold temperatures had the greatest negative impact on reasoning, learning, and memory tasks; whereas hot temperatures had the greatest effects on attentional and perceptional tasks. Interestingly, temperature had the greatest effects on the performance of shorter duration (less than 2 h) tasks. This may be because individuals can successfully acclimatize to the temperature when they are exposed to it for longer duration. Once acclimatized, they physiologically adapt to thermal stress, and extreme temperature may therefore have smaller effects on performance. Pilcher et al. concluded that their results supported an inverted U-shaped relationship between temperature and performance on cognitive tasks, noting that temperatures above 901 Fahrenheit or below 501 Fahrenheit resulted in the greatest performance deficits. Hancock et al.’s (2007) meta-analysis provided similar findings, demonstrating that the effects of temperature varied across tasks. Their results showed that, relative to cognitive task performance, psychomotor and perceptual functioning were more affected by extreme temperatures. Thus, both meta-analyses demonstrated that task type, duration of task, and intensity of temperature are important to understanding how temperature affects performance and

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that extreme temperatures are distracting and interfere with attentional processes necessary to sustain performance. Several primary studies have also investigated moderators of the temperature–performance relationship. In a study examining the effects of cold temperatures on military-based cognitive tasks, Adam et al. (2008) found that physical exercise during exposure attenuates the negative effects of cold temperatures on cognitive performance. In another study involving a military sample, Radakovic et al. (2007) investigated the effects of acclimatization on the relationship between heat stress and both physiological and cognitive performance. In this study, soldiers who were not acclimatized demonstrated greater physiological stress, attention deficits, and more incorrect responses on a cognitive task after going through an exertional heat stress test; soldiers who were allowed to acclimate for 10 days showed almost no detrimental effects. Together, these studies provide evidence that physical activity and acclimatization may be useful strategies for reducing the effects of extreme temperatures on performance. In contrast to research examining the effects of thermal conditions on performance, few recent studies have examined how physical conditions influence performance. The only study we were able to locate was conducted by Ljungberg and Neely (2007) who examined the effects of noise and vibration on cognitive performance (e.g., short-term memory and grammatical reasoning) across two experiments. In Study 1, all participants were exposed to four conditions: (1) noise exposure, (2) vibration exposure, (3) combination exposure, and (4) control (i.e., no noise or vibration) on four separate days. The levels of noise and vibration were designed to simulate exposure to a forestry vehicle. Results indicated that subjective ratings of stress and cortisol levels increased when highly sensitive participants were exposed to noise. However, performance did not differ across conditions. The results of Study 2 were consistent with Study 1, failing to provide evidence for effects of noise and vibration on cognitive performance. The authors concluded that short exposures to noise and vibrations typical of the levels found in industrial vehicles affect subjective well-being but have only minimal effects on performance. However, they acknowledge that some people may be more sensitive to noise than others, and this individual difference may moderate the effects of noise exposure on performance. Another job condition that is relevant to performance is shiftwork, which refers to jobs that require employees to work rotating schedules. This type of work is stressful because workers often rotate rapidly through 24 h shifts, which interferes with sleep patterns and creates attentional deficits that are

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linked to reduced cognitive performance (Gold et al., 1992; Smith & Eastman, 2008). In addition, shiftwork requires workers to work during suboptimal (i.e., low-awareness) points of the diurnal cycle. We located two studies (Galy, Me´lan, & Cariou, 2008; Heuer, Spijkers, Kiesswetter, & Schmidtke, 1998) which have examined how different aspects of shiftwork (i.e., diurnal cycles and sleep deprivation) relate to performance. Galy et al. (2008) used a sample of shift workers from a nuclear power plant to investigate how diurnal cycles affect performance, measured with subjective and objective measures of cognitive functioning. Their results indicated that self-rated alertness varied according to a typical diurnal trend, wherein deficits were observed in the early morning hours. In addition to subjective alertness ratings, the authors found that time of day affected performance on non job-related cognitive tasks (i.e., reaction time, recall, and discrimination), such that a nocturnal drop was found for cognitive task scores. Heuer et al. (1998) performed a laboratory study that examined the effects of sleep loss on implicit and explicit sequence learning. Their results demonstrated that a night of sleep loss was related to deficits in automatic, skill-based learning behaviors, but did not have an impact on learning of tasks that required more explicit, consciously controlled performance. As such, this study provides evidence that sleep deprivation has the strongest effects on basic functional capabilities, which are ‘‘a major ingredient of complex professional tasks such as supervisory control of trouble shooting’’ (Heuer et al., 1998, p. 157). Together, these studies indicate that sleep deprivation has subtle, yet substantial effects on performance. Despite the evidence from these and other studies, additional research is necessary to verify these effects and to show that sleep deprivation is associated with job performance criteria, such as task performance, OCB, and CWB. Just as some jobs require shiftwork or exposure to extreme environmental conditions, other jobs make socioemotional demands on employees. For example, in helping, caring, and customer service jobs, employees are often required to follow display rules, which require employees to provide ‘‘service with a smile.’’ Display rules require employees to devote resources to regulating emotions so that emotional displays are appropriate and meet the demands of the job (Grandey & Brauberger, 2002). To modify affective displays in interpersonal interactions, employees must engage in either deep or surface acting. Deep acting refers to attempts to modify feelings to match required displays, whereas surface acting involves modifying outward displays without changing inner feelings. Deep-level acting is thought to have more positive outcomes and surface-level acting is considered to be somewhat detrimental to emotional well-being and performance, as it

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creates dissonance between what one displays outwardly and how one feels inwardly (Grandey, 2003). Field and laboratory studies have provided evidence that surface-level acting is more detrimental to work outcomes than deep acting. For example, Totterdell and Holman (2003) found that deep level (i.e., positive refocus and perspective taking) acting was positively associated with self-ratings of positive emotions and performance while surface acting (i.e., faking emotions) did not demonstrate these effects. In a study of university administrative assistants, Grandey (2003) found that surface acting was associated negatively with self and coworker ratings of affective delivery, which is the extent to which the target employee is sincere, enthusiastic, warm, friendly, and courteous during service encounters. Surface acting was also associated positively with self and coworker ratings of breaking character (i.e., revealing negative affective states to customers). In contrast to surface acting, deep acting demonstrated a positive relationship with affective delivery and had no relationship with breaking character. Surface acting was also related to emotional exhaustion, whereas deep acting was not. Together, these two studies (Grandey, 2003; Totterdell & Holman, 2003) show that internalization of emotional display rules affects how effectively employees perform customer service tasks. Research has also examined how emotion regulation affects other forms of performance. For example, Bechtoldt, Welk, Hartig, and Zapf (2007) examined linkages between surface and deep acting and workplace deviance. Results showed that surface acting had a positive relationship with interpersonal and organizational deviance, whereas relationships between deep acting and both forms of deviance were nonsignificant. Interestingly, this study showed that under certain conditions (low distributive justice, low dispositional self-control) deep acting is related to organizational deviance, which counters the conventional wisdom that deep acting is a beneficial form of regulating emotions (Brotherridge & Grandey, 2002). However, these results should be interpreted with caution, as this interaction was the only one of eight tested that was statistically significant, indicating that these results may have been due to chance. Together, these studies indicate that emotion regulation has the potential to affect customer service behaviors and organizational deviance. These studies, which represent research conducted in both laboratory and field settings, provide evidence that surface-level acting tends to have more harmful effects on employees, and organizations, relative to deep-level acting. Future research should, however, focus on examining the underlying processes that link emotion regulation to resource depletion and resulting performance deficits.

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Acute Stressors In contrast to the chronic stressors reviewed so far, acute stressors are more episodic and tend to have a higher level of intensity. Recent studies have shown that exposure to traumatic work events is associated with health and well-being outcomes, such as unhealthy coping behaviors (i.e., drinking to cope), depression, anxiety, and psychological distress (Bacharach, Bamberger, & Doveh, 2008; Bacharach & Bamberger, 2007). Unfortunately, due to their low base rate, acute job-related stressors have proven difficult to study in relation to job performance. Though most field studies have focused on exposure to acute stressors on employee well-being, we did locate a few recent studies that have examined the effects of acute stressors (e.g., exposure to assaults) on performance. Below, we review laboratory studies that have examined the effects of acute stressors on performance and then discuss available field research, which has examined this topic. A number of laboratory studies have examined the effects of traumatic events on the performance of police recruits. LeBlanc and colleagues (LeBlanc, Regehr, Jelley, & BArath, 2007; Regehr, LeBlanc, Jelley, & Barath, 2008) used a high fidelity police training simulation to examine the effects of acute stress on police officers. In these studies, police recruits participated in a simulation in which they were required to make decisions while responding to a 911 domestic violence call. In the simulation, an aggressive male answered the door and was initially uncooperative. After entering the residence, the officer encountered a nonresponsive female. The officer’s performance in the simulation was videotaped and then rated by three experts. LeBlanc et al. (2007) investigated the effects of previous exposure to traumatic events and self-reported traumatic stress symptoms that parallel the criteria for post-traumatic stress disorder (PTSD). Results demonstrated that neither prior exposure to police duty-related critical incidents, nor preexisting traumatic stress symptoms, were related to performance. As such, LeBlanc et al. (2007) concluded that police recruits with PTSD are no more likely to make errors in communication or judgment than others who do not report PTSD symptoms. Regerh et al. (2008) used the same performance criteria, but also examined physiological (heart rate and cortisol) and subjective (self-reported stress) measures of stress during the simulation. Results indicated that subjective anxiety increased during the simulation, but did not have a relationship with performance. In addition, Regerh et al. (2008) found that heart rate and cortisol levels increased during the simulation. Heart rate was not related to performance, but cortisol levels had a positive relationship with

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performance. The authors concluded that cortisol, in moderate levels, enhances performance by increasing arousal. In another study with police recruits in a high fidelity simulation, Shipley and Baranski (2002) examined how visuo-motor behavior rehearsal training (VBRT) could be used to enhance performance during an intense police situation. Shipley and Barinski’s (2002) simulation involved a routine traffic stop that turns into an armed confrontation. In this study, the treatment group received VBRT, which involved relaxation, imagery, and mental rehearsal exercises. Participants who received the training reported lower scores on a measure of state anxiety and demonstrated better performance on a simulated exchange of fire with an attacker. Thus, there is evidence that training attenuates the negative effects of acute stressors on performance. Researchers have also examined how limited duration; one-time events affect performance on cognitive tasks. Two laboratory studies (Oei, Everaerd, Elzinga, Van Well, & Bermond, 2006; Schoofs, PreuX, & Wolf, 2008) examined how exposure to acute social stress, as induced by the Trier Social Stress Test (TSST), affects physiological responses and working memory. The TSST has been used in the past to examine responses to psychobiological stress (Kirschbaum, Pirke, & Hellhammer, 1993). This test involves having participants give a public speech and then perform a cognitive task (such as one that involves mental arithmetic) in front of a committee. Schoofs et al. (2008) found that participants who completed the TSST showed elevated cortisol levels and demonstrated longer reaction times and lower accuracy on a cognitive task. They also found that the effects of the stress manipulation weakened over time (after 25 min performance began to return to baseline levels). In a similar study, Oei et al. (2006) examined the effects of the TSST manipulation on cortisol levels, working memory, and retrieval. Oei et al.’s (2006) findings indicated that stress impaired working memory performance at high, but not low loads and that high cortisol levels were associated with slower working memory performance at higher loads. Together, these two studies show that acute, social stress is associated with impaired cognitive functioning and related elevations in cortisol levels are associated with performance deficits. Several field studies have focused on how exposure to physical violence relates to performance of healthcare providers, with results demonstrating that assault experiences in nurses were negatively related to patient care quality and patient satisfaction (e.g., Arnetz & Arnetz, 2001; Lawoko, Soares, & Nolan, 2004). Specifically, Arnetz and Arnetz (2001) found that exposure to violence was a consistent predictor of patients’ ratings of quality of care and satisfaction with their treatment. Similarly, Lawoko et al. (2004)

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found that psychiatric nurses and psychiatrists’ own report of patient care were negatively related to assault experiences. However, other researchers found that exposure experiences were not related to nurses’ self-reported task performance (Yang, 2009). Research has also examined how exposure to physical assaults may be related to prevention performance (i.e., behaviors targeted at preventing future violence). Chang et al. (2009a) distinguished between prevention compliance, or following organizational policies designed to prevent violence, and prevention participation, which refers to putting in extra effort to engage in behaviors that will likely prevent assaults. They found that exposure to minor and major physical violence was related to both prevention compliance and prevention participation. Moreover, the results were consistent across self- and coworker-rated prevention performance. Thus, it appeared that acute stressors, in the form of being physically assaulted, may have implications for specific performance dimensions. To summarize, little research has examined the effects of acute stressors on performance in field settings. The studies that have examined this relationship have focused primarily on short-term effects, such as performance in a simulation or cognitive impairments, following exposure for shorter durations. It is clear that acute stress has short-term effects on cognition. However, results of police simulations indicate that acute stress has only limited effects on job performance and these effects could be remedied via training. Unfortunately, simulations of acute stressors may not be powerful enough to elicit similar cognitive and physiological responses as traumatic events such as 9/11, which has been shown to have long-term effects on the firefighters who were called to the World Trade Center that day (Bacharach et al., 2008;; Bacharach & Bamberger, 2007). As with many others stressors, ethical issues and other limitations make it difficult to assess long-range effects of traumatic events on performance. Nonetheless, these effects should be considered in the literature and, when possible, documented using qualitative methods, such as naturalistic observation techniques.

DISCUSSION AND FUTURE DIRECTION Summary of Current Review Consistent with Jex’s (1998) conclusions, our review indicates that across various categories, occupational stressors demonstrated small-to-moderate

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relationships with employee performance. Certain stressors showed stronger, more consistent main effects on performance (e.g., role ambiguity and situational constraints), while the effects of others (e.g., workload, interpersonal demands, and job insecurity) fluctuated depending on various characteristics of the situation and individual differences (e.g., Babakus et al., 1999; Glaser et al., 1999; Klein & Kim, 1998; Singh, 1998). These findings were consistent with Jex’s (1998) suggestion that modest effects observed in past research may be explained by various conditions that have the potential to accentuate or attenuate the relationship between stressors and performance. Our review also underscores the importance of continuing to examine how stressors relate to both task performance and extra-role activities, as research has shown that certain stressors (e.g., interpersonal demands and situational constraints) are more related to OCB and deviant behaviors than task performance. These findings are consistent with Jex’s (1998) speculation that discretionary behaviors may be more strongly influenced by stressors, as employees can more readily withdraw effort from discretionary activities without worrying about being penalized. As such, future research should continue to consider a more broadly defined performance criterion space that includes both general and job-specific measures of task performance, OCB, and CWB. We also observed several new developments in the literature. First, prior to 1998, the research provided only equivocal support for relationships between several stressors (i.e., situational constraints) and performance. More recent studies have provided strong evidence for associations between these stressors and task performance. Moreover, the aforementioned research examining the effects of stressors on alternative performance measures has shown that previous conclusions regarding the modest effects of stressors on performance may have been inaccurate, as these effects clearly vary depending on the performance criteria examined. In addition, we observed an increase in research examining topics, which were underresearched a decade ago. For example, several recent studies have focused on the implications of interpersonal conflict on performance with the results demonstrating fairly consistent evidence that these stressors affect effectiveness. Finally, by expanding Jex’s taxonomy to include additional categories that reflect the broader social context and career-related issues, we were able to identify several additional workplace stressors (e.g., organizational politics, justice, job insecurity) that have implications for employee performance.

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Trends in the Literature We identified five trends in the contemporary literature investigating relationships between occupational stressors and employee performance. First, researchers have expanded the criterion domain to include more discretionary behaviors, such as OCB and CWB, as outcomes of stressors. Indeed, there is evidence that some stressors (e.g., role conflict, situational strains, and interpersonal conflict) are more strongly related to extra-role behaviors than to task performance. Moreover, researchers have identified job-specific performance indicators that are particularly relevant in certain occupations. For example, patient care quality and patient satisfaction (Lawoko et al., 2004) were identified as unique performance indicators for nursing, whereas customer satisfaction (Grandey, 2003) has been used to assess service employees’ performance. Finally, closer attention has been directed toward more basic psychological or cognitive functioning as performance indicators, particularly in simulations conducted in laboratories. These studies provide insight into how occupational stressors affect a broad range of complex, job-related behaviors. A second trend in the contemporary literature is that more researchers are using meta-analysis to investigate relationships between stressors and performance. For example, four recent meta-analyses have examined relationships between role stressors and task performance or OCB (Eatough et al., 2009; Gilboa et al., 2008; O¨rtqvist & Wincent, 2006; Tubre & Collins, 2000), three meta-analyses have investigated relationships between thermal stress and performance (Dickerson & Kemeny, 2004; Hancock et al., 2007; Pilcher et al., 2002), and multiple meta-analyses have investigated the effects of workplace justice (Cohen-Charash & Spector, 2001; Colquitt et al., 2001) and organizational politics (Chang et al., 2009a; Miller, Rutherford, & Kolodinsky, 2008) on performance. These meta-analyses provide valuable information by quantitatively summarizing bivariate relationships between a particular stressor and performance and the estimated effect sizes are helpful because they overcome some of the errors associated with smaller or potentially biased samples (Schmidt, 1996). However, we caution others against over reliance on meta-analysis for two reasons. First, meta-analysis typically focuses only on bivariate relationships and it is, therefore, unable to account for more complex effects (e.g., moderation, indirect, or quadratic relationships). Second, meta-analysis often involves subjective judgments and coding (Nurius & Yeaton, 1987). Without clear documentation of how authors make these judgments, conclusions from meta-analysis need to be

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carefully interpreted (Bobko & Stone-Romero, 1998). Thus, while metaanalysis provides an estimate of the relationship between stressors and performance, primary studies are still needed to examine higher-order effects and/or more complicated, process-based models that link stressors to performance. A third trend is that while some researchers adhered to Jex’s (1998) recommendation to consider specificity issues by matching job-specific stressors to job-specific measures of performance, other researchers have moved toward a more aggregated approach. The former trend is evident in studies examining situational constraints (e.g., Klein & Kim, 1998), emotional labor (e.g., Grandey & Brauberger, 2002), and exposure to violence (e.g., Chang et al., 2009a). However, recent publications in highimpact journals have focused more on an aggregated approach, which examines how various groups of stressors relate to performance. For example, LePine et al. (2005) argued that role stressors, situational constraints, interpersonal conflicts, and organizational politics distract employees from devoting effort to performance and impede employees from achieving goals. Collectively, these stressors were aggregated into a single construct – hindrance stressors – because they are all linked to maladaptive emotional (e.g., anger, anxiety, depression), physical (e.g., physical symptoms), and motivational (e.g., withdrawal and reductions in performance) reactions (i.e., strains). LePine et al. (2005) further argued that pressure, time urgency, and workload should be combined into a single construct that represents ‘‘challenge stressors.’’ While these stressors may be associated with elevated strains, they are also likely to enhance employee motivation, and thus may have an overall positive effect on performance. Using this aggregation framework and meta-analytic estimates, LePine and colleagues (LePine et al., 2005; Podsakoff, LePine, & LePine, 2007) showed that as a group, hindrance stressors were associated with increased strains and reduced motivation, which led to lower performance and increases in withdrawal behavior. In contrast, challenge stressors were associated with increased strains and motivation and had a positive effect on performance and a negative relationship with withdrawal behavior. The dual mechanisms (i.e., strains and motivation) proposed by LePine et al. (2005) are in accordance with other models that link occupational stressors to performance (e.g., JD-R model; Bakker & Demerouti, 2007). Nonetheless, there are reasons to be cautious about research that uses an aggregation approach. For example, Gilboa et al. (2008) expressed concerns for the rigor of the procedure used for categorizing different stressors into hindrance versus challenge groups. Moreover, Chang et al. (2009b) showed that constructs that were previously classified as hindrance stressors by

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LePine et al. (2005) demonstrated different patterns of relationship with outcomes. Specifically, Chang et al. (2009a) showed that perceived organizational politics, role ambiguity, and role conflict were differentially related to performance and, when compared to the two role stressors, perceived politics worked through different mechanisms to affect performance. In particular, perceived politics had a stronger association with task performance than role conflict, and its relationship with OCB directed toward the organization was stronger than role stressors. Chang et al. also found that the relationship between politics and performance was fully mediated by strains (i.e., job anxiety and tension) and work attitudes (i.e., job satisfaction and affective commitment), with morale accounting for the stronger mediation effect. On the other hand, the relationship between role ambiguity and performance was equally mediated by strains and morale, and the relationship between role conflict and performance was only partially mediated by strains (Chang et al., 2009a). The evidence leads us to reiterate Jex’s (1998) original recommendation – that researchers should focus on relationships between specific stressors and specific performance dimensions. Doing so will allow us to identify unique associations between stressors and performance, and the mechanisms through which these associations may be explained. It also allows researchers to gain better understanding of how the interplay between various stressors relates to employee performance (Chang et al., 2009a). While a large number of contemporary studies have utilized meta-analysis or aggregation approaches to demonstrate relationships between stressors and performance, we located very few studies that examined the utility of interventions for reducing the effects of stressors on performance. Nonetheless, the intervention studies that we did locate provided interesting results that are supportive of stress interventions. For example, Logan and Ganster (2007) found that an empowerment intervention had positive effects on performance by enhancing employees’ perceived control. Similarly, Gajendran and Harrison’s (2007) review of empirical studies concluded that telecommuting policies have negative associations with the amount of WFC employees experience and telecommuting policies were also positively related to supervisor-rated performance. Arguably, interventions targeted at removal of stressors represent a primary prevention approach, and are likely to be the most effective (Barling, Kelloway, & Frone, 2005). Unfortunately, it is difficult to conduct well-designed, methodologically sound intervention research for a variety of practical and ethical reasons (Hurrell, 2005). However, it is imperative that researchers focus more on well-designed, theoretically driven intervention studies to determine how to control the

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causes and consequences of workplace stressors. Therefore, we call for researchers to devote more attention to intervention-based research. A final trend that we observed is that, with the exception of research examining job conditions, few studies utilized experimental design or were conducted in controlled laboratory settings. Unfortunately, without experimental studies that can clearly stipulate the causal order of stressors and performance, our understanding of the stressor–performance relationships remains incomplete. Knowledge of the causal linkages between stressors and employee performance are likely to enhance organizations’ willingness to invest in programs aimed at the removal of stressors, assistance with employee stress management, and mitigation of negative consequences of strains (Chang, Johnson, & Yang, 2007). As such, we recommend that future researchers adopt stronger designs that can shed light on causal relationships between occupational stressors. In addition to allowing researchers to make causal inferences, the results of well-designed experiments are also likely to provide guidance to researchers interested in evaluating the effectiveness of stress-related interventions.

Recommendations for Future Research Based on the current state of the literature, we propose four recommendations for future researchers. First, we recommend that researchers shift their focus from theory development to more precise tests of existing theory. In particular, researchers should begin by adopting consistent operational definitions of the elements of existing theories. This recommendation is not meant to single out a particular theoretical approach, because tests of most existing stress theories have suffered from many of these problems. However, the key components of Hobfoll’s (1989) COR model have been operationalized in a variety of ways, which are often not consistent across studies. One source of this problem is the lack of a clear definition for what constitutes ‘‘resources’’ considered in this model. Researchers have presented several different descriptions of this construct, making it difficult to evaluate the theory. Thus, we recommend that future studies adopt a more consistent approach to test existing theoretical frameworks. Researchers also need to focus on testing moderators and mediators derived from existing theory, as exploring intervening processes represents a ‘‘fundamental question in science’’ (Kenny, 2008, p. 354) which allows researchers to test and modify theoretical frameworks (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). Moreover, understanding why things

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occur provides the basis for organizational interventions to mitigate negative effects of organizational phenomena and helps identify boundary conditions for the theory being evaluated. Our second recommendation is that researchers should move beyond cross-sectional studies, and focus more on research that assesses stressors and outcomes over time. Although we recommended earlier that more experimental research is necessary, there are ethical and practical concerns that may preclude researchers from investigating the effects of certain stressors on performance. In those cases, longitudinal studies in which variables are measured at multiple time points may be particularly valuable. In addition to showing how stressors relate to performance over time, in terms of diminishing or increasing effects, longitudinal studies may help explain the reciprocal relationships that may exist between stressors and performance (e.g., Bolino & Turnley, 2005; Fay & Sonnentag, 2002). Our third recommendation is that researchers should consider the role of physiological responses as potential mediators of the effects of stressors on performance. A number of studies have explored how changes in cortisol levels relate to cognitive functioning, such as memory and reaction times (e.g., Ljungberg & Neely, 2007; Oi et al., 2006). Similarly, interpersonal conflict has been linked to increases in blood pressure (e.g., Salomon & Jagusztyn, 2008). There is evidence that exposure to stressors is also associated with higher muscle tension, as measured by surface electromyography (Krantz, Forsman, & Lundberg, 2004), which may be related to poorer performance in physical activities and more injuries. Though these physiological reactions are in and of themselves interesting, as they represent the health implications of exposure to occupational stressors, recent studies suggest that they may also explain linkages between stressors and performance. Thus, we recommend that future researchers take a more integrated perspective and consider how physiological responses have the potential to mediate the effects of stressors on performance. Finally, we recommend that researchers move beyond the focus on individual stressors and performance and explore these relationships at the group-level. In particular, recent studies have shown that individual-level stressors, such as interpersonal demands, have implications for group-level performance (Alper et al., 2000; Jehn & Mannix, 2001; Stewart & Barrick, 2000). There is also evidence that group-level factors affect individual-level outcomes. For example, organizational safety climate (i.e., employees’ shared perceptions about organizational policies, practices, and procedures regarding safety at the workplace) has been shown to act as a stressor for individual employees (Hemingway, 1999). Specifically, poor safety climate results in

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employees viewing the organization as unsupportive and indifferent to its workers’ safety and well-being and can result in strain responses (Siu, Phillips, Leung, 2004). Group behavioral norms represent another stressor that may have negative implications for group performance (e.g., Bolino et al., in press). These trends suggest that researchers should consider how (a) stressors at the individual level are linked to group-level performance, (b) group-level factors influence individual-level outcomes, and (c) group-level factors relate to group-level outcomes. By examining these cross-level and multi-level effects, occupational stress researchers will be better prepared to answer questions regarding how work stress relates to job performance.

CONCLUSION To conclude, much progress has been made in terms of understanding relationships between various work stressors and performance. Researchers have examined how several different categories of stressors relate to diverse performance criteria, studies have explored theoretically derived mediators and moderators of the effects of various stressors on performance, and meta-analyses have allowed scholars to cumulate findings across studies to derive more accurate estimates of effect sizes. However, several questions and research opportunities remain. First and foremost, more precise tests of theory are necessary to determine when, why, and how workplace stressors relate to individual, group, and organizational performance. Second, additional intervention studies are needed to determine how the effects of stressors on performance can be reduced or eliminated. Finally, occupational stress researchers need to further clarify what constitutes a workplace stressor, and scholars interested in conducting research in this domain should be aware of criterion related ‘‘problems’’ (Austin & Villanova, 1992) that may obfuscate our understanding of relationships between workplace stressors and job performance.

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THE SUCCESS RESOURCE MODEL OF JOB STRESS$ Simone Grebner, Achim Elfering and Norbert K. Semmer ABSTRACT New developments in concepts and approaches to job stress should incorporate all relevant types of resources that promote well-being and health. The success resource model of job stress conceptualizes subjective success as causal agents for employee well-being and health (Grebner, Elfering, & Semmer, 2008a). So far, very little is known about what kinds of work experiences are perceived as success. The success resource model defines four dimensions of subjective occupational success: goal attainment, pro-social success, positive feedback, and career success. The model assumes that subjective success is a resource because it is valued in its own right, triggers positive affect and emotions (e.g., pleasure, cf., Weiss & Cropanzano, 1996), helps to protect and gain other resources like selfefficacy (Hobfoll, 1998, 2001), has direct positive effects on well-being (e.g., job satisfaction, cf., Locke & Latham, 1990) and health (Carver & Scheier, 1999), facilitates learning (Frese & Zapf, 1994), and has an energizing (Locke & Latham, 1990, 2002) and attention-directing effect (Carver, 2003), which can promote recovery by promoting mental $

Majority of the work has been done at Central Michigan University.

New Developments in Theoretical and Conceptual Approaches to Job Stress Research in Occupational Stress and Well Being, Volume 8, 61–108 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-3555/doi:10.1108/S1479-3555(2010)0000008005

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detachment from work tasks in terms of absence of job-related rumination in leisure time (Sonnentag & Bayer, 2005). The model proposes that success is promoted by other resources like job control (Frese & Zapf, 1994) while job stressors, like hindrance stressors such as performance constraints and role ambiguity (LePine, Podsakoff, & LePine, 2005), can work against success (Frese & Zapf, 1994). The model assumes reciprocal direct effects of subjective success on well-being, health, and recovery (upward spiral), and a moderator effect of success on the stressor–strain relationship. The chapter discusses research evidence, measurement of subjective occupational success, value of the model for job stress interventions, future research requirements, and methodological concerns.

INTRODUCTION There is considerable agreement that theoretical and conceptual approaches to stress should identify and include all kinds of positive factors, which promote optimal human functioning, positive affect or emotions, wellbeing, and health (Schaufeli, 2004; Seligman, 2008). While there is no doubt that achievements define human functioning, the role of subjective achievements as causal agents for well-being and health has hardly been investigated. The success resource model of job stress (Grebner, Elfering, & Semmer, 2008b) provides a useful framework for understanding the role of subjectively evaluated achievements in the stress process. The core proposition is that subjectively experienced success at work promotes well-being and health. Moreover, the model posits that subjective success experiences can buffer the effect of job stressors on strain. In this chapter we focus on the relationship between subjective success and possible antecedents in terms of working conditions, personality characteristics, and outcomes in terms of well-being and health. First, we characterize the meaning of resources for well-being and health, focusing on job control as an example. Second, we describe why subjective occupational success is a resource in the job stress process. Third, we define subjective occupational success. Fourth, we delineate the importance of subjectivity of success for well-being and health. Fifth, we explain the conceptualization of success experiences as job-related events. Sixth, we describe dimensions and measurement of subjective occupational success.

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Seventh, we portray assumptions and empirical support of the success resource model of job stress. Eighth, we discuss implications of the model for job stress interventions. Finally, we specify future research requirements and methodological concerns.

THE MEANING OF RESOURCES FOR WELL-BEING AND HEALTH In this section we characterize the meaning of resources for well-being and health, focusing on job control as an example. Resources have served a central role in many theories of job stress (Cooper, 1998). Resources are, in general, beneficial for well-being and health (Kahn & Byosiere, 1992; Sonnentag & Frese, 2003; Theorell, 2003; Viswesvaran, Sanchez, & Fisher, 1999). Resources such as social support at work and job control are defined as factors in the job stress process that protect from detrimental immediate psychological (e.g., anger, anxiety), physiological (e.g., insufficient physiological recovery; McEwen, 1998), and behavioral responses to job stressors (e.g., performance decrements, errors, accidents; Elfering, Semmer, & Grebner, 2006). Availability of resources can protect from detrimental medium- and long-term consequences of job stress such as impaired wellbeing (e.g., job dissatisfaction, exhaustion, disengagement), negative health behavior (e.g., substance abuse; Liu, Wang, Zhan, & Shi, 2009), health problems (e.g., psychosomatic complaints), diseases (e.g., depression, musculoskeletal, cardiovascular, and infectious diseases), undesired attitudes (e.g., propensity to leave), and behaviors like absenteeism and turnover (Kahn & Byosiere, 1992; Sonnentag & Frese, 2003). Resources represent fulfillment of basic intrinsic motivational needs such as having control over one’s work situation (Elsass & Veiga, 1997; Greenberger & Strasser, 1986) and reaching one’s personal goals (Brunstein, 1993). Hence, lack of resources can directly lead to strain because frustration of a need may cause strain (Elsass & Veiga, 1997; Seligman, 1975). Possessing a variety of resources is mostly related to a higher status, which can protect from experiencing specific types of stressors that are typically related to a lower rank (e.g., poor working conditions, intimidation; Marmot, 2006; Sapolsky, 2005). Conversely, availability of resources is related to well-being (e.g., job satisfaction, work engagement) and health (Kahn & Byosiere, 1992; Sonnentag & Frese, 2003). Resources promote the belief that problems can

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be solved, goals can be reached, and stressful situations can be positively influenced or controlled or tolerated (Bandura, 1986). These beliefs, in turn, can influence primary appraisal of potentially stressful encounters (Lazarus & Folkman, 1984; e.g., ‘‘challenge’’ instead of ‘‘threat’’) or reduce perceived threat (e.g., self-efficacy, Bandura, 1986; see also Elsass & Veiga, 1997; Sutton & Kahn, 1987) by buffering the detrimental effect of stressors (Karasek, 1979). For example, job control can enable individuals to cope effectively by taking action against a stressor (e.g., terminating an unpleasant conversation with a client) or by selecting between stressors (e.g., wait on guest A instead of B, when both are unpleasant but one feels more competent for dealing with A). Effective coping that is also a resource can decrease stress responses (e.g., feelings of tension, anxiety, anger) and prevent detrimental long-term consequences of chronic exposure to job stressors (Latack & Havlovic, 1992; Perrez & Reicherts, 1992). While the buffering hypothesis of the job-demands-control model (Karasek, 1979) has not received consistent support (De Lange, Taris, Kompier, Houtman, & Bongers, 2003) a beneficial direct (main) effect of control on well-being and health is well supported (Kahn & Byosiere, 1992; Sonnentag & Frese, 2003). Furthermore, there is evidence that control may buffer the effects on stressors for people high in personal resources (e.g., self-efficacy; Meier, Semmer, Elfering, & Jacobshagen, 2008; Schaubroeck & Merritt, 1997). Finally, increasing job-related resources like job control is one of the most important measures for designing motivating and healthy working conditions (Parker & Wall, 1998), which enable employees to perform well and to stay healthy. For instance, employees working for managers who are more supportive of employee autonomy report higher levels of job satisfaction (Baard, Deci, & Ryan, 2004; Deci, Connell, & Ryan, 1989). Moreover, Humphrey, Nahrgang, and Morgeson (2007) report in a meta-analysis based on 259 studies that motivational job characteristics including job control (Hackman & Oldham, 1975) predict 25% of the variance in subjective performance, 34% in job satisfaction, and 24% in organizational commitment. Resources such as control are job-related resources, that is, they exist independent of the specific person involved (although the subjective experience of control may be different from control as assessed by other means). In addition to such job-related resources, it is important to consider resources that characterize the person (person-related resources), such as self-efficacy, self-esteem, internal locus of control, energy, etc. (Semmer & Meier, 2010). Except for being characteristics of the person rather than

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of working conditions, such resources have the same functions as do job-related resources. Sometimes both personal and job-related resources are necessary to achieve positive outcomes, as when job control buffers the effects of stressors on strain only for people high in self-efficacy or internal locus of control (Meier et al., 2008). Conceptualizing and examining the role of all relevant resources in the stress process is, therefore, of high importance in research on organizational stress. We argue that subjective occupational success is one of the resources that deserve more attention.

SUBJECTIVE SUCCESS: A NEGLECTED RESOURCE IN THE JOB STRESS PROCESS In this section we describe why subjective occupational success is a neglected resource in the job stress process. Resources can be defined as conditions (e.g., working conditions), personal characteristics (e.g., personality traits like optimism), goods, energies or objects, which are valued in their own right because they satisfy basic needs (e.g., control, goal attainment). Resources are valued because they promote the generation, attainment, and protection of other valued resources such as better working and living conditions, including higher levels of job control, job experience, approval, and higher self-esteem or status (conservation of resources theory; Hobfoll, 2001). Some job-related resources have been neglected mostly in favor of two types of (situation-related) resources that have been conceptualized in previous theoretical accounts on job stress, such as the demands-control model (Karasek, 1979), the demands-control-support model (Karasek & Theorell, 1990), and the Michigan framework for the study of stress in organizations (Kahn & Byosiere, 1992, p. 592). Theoretical accounts on job stress are summarized in Cooper (1998), Kahn and Byosiere (1992), and Sonnentag and Frese (2003). These two resources are (a) social support by supervisors and coworkers (Viswesvaran et al., 1999) and (b) job control (synonyms: job autonomy, decision latitude, decision authority, cf., Elsass & Veiga, 1997; Frese & Zapf, 1994; Karasek, 1979; Karasek & Theorell, 1990; Theorell, 2003). One reasons why job control is considered a core job characteristic in treatises on job design is that it motivates good performance and facilitates performing well (e.g., action theory, cf., Frese & Zapf, 1994;

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job characteristics model, cf., Hackman & Oldham, 1975; see also Parker & Wall, 1998). Research on job motivation, goals, and goal setting has emphasized the beneficial role of goal attainment and goal progress for wellbeing in terms of an antecedent (e.g., job satisfaction; Locke & Latham, 1990; see also Carver & Scheier, 1990; Diener & Lucas, 1999; Wiese, 2007). However, research on job stress has conceptualized achievement generally as a dependent variable (e.g., job strain in terms of performance decrements as consequence of chronic job stress, cf., Sonnentag & Frese, 2003; or individual or organizational performance as result of individual well-being, cf., happy-productive worker hypothesis, e.g., Judge, Thoresen, Bono, & Patton, 2001; Taris & Schreurs, 2009). To attain a better understanding of the job stress process the success resource model (Grebner et al., 2008b) conceptualizes subjective job-related success experiences, such as subjective goal attainment and perceived positive feedback, as resources in the job stress process in terms of causal agents for well-being and health. The model focuses on success as an antecedent of well-being and health while acknowledging beneficial reciprocal effects between subjective success and well-being and health in terms of an upward spiral (Hobfoll, 2001; Judge et al., 2001). Situationrelated resources (e.g., social support, job control) and personal resources (e.g., self-efficacy) are discussed as success promoting, whereas job stressors are conceptualized as hampering success (e.g., role ambiguity, cf., LePine, Podsakoff, & LePine, 2005; Frese & Zapf, 1994).

Why is Subjective Success a Resource? First, subjective success is an intrinsic need. Success, in terms of making progress toward personal goals, reaching task and career goals, pro-social goals, and receiving appreciation by others is a basic intrinsic need and, therefore, valued in it‘(Epstein, 1973, 1998; Hobfoll, 2001, 2002; McClelland, Atkinson, Clark, & Lowell, 1953). Hence, most people value success experiences such as achieving a difficult goal, getting a promotion, receiving a compliment by valued others, providing helpful advice, or successfully creating or maintaining valued social relationships. Second, subjective success leads to positive affect and emotions. Successes elicit positive affect and emotions like pleasure, happiness, interest, contentment, pride, and joy (cf., the broaden-and-build theory of positive emotions; Cohn, Fredrickson, Brown, Mikels, & Conway, 2009; Fredrickson, 2001), predict well-being (Carver & Scheier, 1990; Grebner et al., 2008a, 2008b;

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Locke & Latham, 1990; Wiese, 2007), and health (e.g., Carver & Scheier, 1999). The main reason for these effects is that fulfilled aspirations contribute substantially to satisfaction. Moreover, according to the affective events theory (Weiss & Cropanzano, 1996), subjective success experiences represent affective events, which lead to positive affect and emotions (see also Ilies, De Pater, & Judge, 2007). In addition, the stress-as-offense-to-self concept (Semmer, Jacobshagen, Meier, & Elfering, 2007) emphasizes that personal success and failure experiences are likely to have implications for the self-concept; they induce emotions such as pride or shame, which are emotions that have the self as reference (self-conscious emotions, cf., Tagney & Fisher, 1995). Third, subjective success helps in generating or protecting other resources, according to the broaden-and-build theory of positive emotions (Fredrickson, 1998). Success usually leads to positive affect, which, in turn, can ‘‘produce flourishing in terms of an upward spiral by broadening momentary thoughtaction repertoires’’ (Fredrickson & Branigan, 2001, p. 220). Pride, for example, produces broad-ranging thoughts and the ‘‘urge to share news of the achievement with others and to envision even greater achievements in the future’’ (Fredrickson & Branigan, 2001, p. 220). Moreover, success promotes other valued resources such as beliefs about one’s own capabilities to be able to solve problems and to produce desired levels of performance (self-efficacy; Bandura, 1982; Locke & Latham, 1990). According to Bandura (1994) the ‘‘most effective way of creating a strong sense of efficacy is through mastery experiences’’ because ‘‘successes build a robust belief in one’s personal efficacy’’ (p. 72). Therefore, subjective success experiences can have a motivating effect by boosting self-efficacy (Bandura, 1982) and can contribute positively to personality development. Enhanced self-efficacy, in turn, can lead to commitment to more difficult goals, increased effort, and persistence (Locke & Latham, 1990, 2002). Career success such as a promotion may help protect or broaden available resources such as autonomy, approval, social support by significant others, status, and financial resources, and may help to generate new resources (e.g., job-related knowledge, important business contacts). Fourth, subjective success can promote learning and personality development. Successes such as goal attainment and positive feedback by others can promote learning (Frese & Zapf, 1994) by cognitive processing of the experiences such as reflecting behaviors and strategies that led to goal attainment and learning which knowledge is most useful for efficient task accomplishment. Fifth, subjective success experiences can promote job performance, which is defined as meeting organizational goals (Sonnentag, 2002).

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Subjective success events might have an energizing effect in terms of motivating people to increase effort and persistence (Locke & Latham, 1990). Hence, subjective success can promote further good performance (Ilies & Judge, 2005; Locke & Latham, 2002). Sixth, subjective success can facilitate recovery from work demands. Beneficial effects of subjective success on psychological recovery (e.g., mental detachment from work; Grebner, Semmer, & Elfering, 2005; Sonnentag & Bayer, 2005) and physiological recovery (Linden, Earle, & Christenfeld, 1997) are plausible. Carver (2003) argues that positive emotions that are elicited by goal attainment indicate that one can attend to something else. Feeling pleasure about the result of job-related actions or about receiving appreciation signals that one was doing well, one’s actions were effective, and that the goal was attained. Hence, success can promote a readiness to attend to something other than work tasks in leisure. After experiencing job-related success, continuing thinking about work might happen in terms of a joyful reflection (savoring; Bryant & Veroff, 2007). Savoring success can promote recovery because positive emotions, which broaden situational thought–action repertoires (Fredrickson, 1998), are kept alive. According to the broaden-and-build theory of positive emotions, achievements serve to generate stress-resilience by serving the accumulation of momentary positive emotions (Cohn et al., 2009). Resilience includes the ability to bounce back quickly from negative emotions elicited by stressful encounters because positive emotions can have an ‘‘undoing effect’’ on negative emotions such as anxiety, sadness, and anger (Tugade & Fredrickson, 2004). Resilient individuals, for instance, switch off their minds after work quickly from stressful encounters at work and demonstrate faster cardiovascular recovery after exposure to a laboratory stressor (Tugade & Fredrickson, 2004). Moreover, success events such as goal attainment can increase the mental and physical resources of an individual by an immediate resource-restoration effect via positive emotions. Conversely, unresolved job-related problems and unattained work goals may lead to perseverative cognitions (e.g., worrying, rumination, cf., Brosschot, Pieper, & Thayer, 2005), which, in turn, lead to sustained physiological arousal and, therefore, insufficient physiological and psychological recovery after work. In the long run, insufficient recovery might lead to irritation, exhaustion, and disengagement; limited ability to perform; and ultimately to impaired health (allostatic load model; McEwen, 1998).

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DEFINITION OF SUBJECTIVE OCCUPATIONAL SUCCESS In the literature, ‘‘subjective success’’ is conceptualized rather heterogeneously. Some researchers summarize all types of situational resources (e.g., high levels of job control), personal resources (e.g., extraversion, selfefficacy, creativity), behaviors (e.g., organizational citizenship behavior), and outcomes (e.g., performance, job satisfaction, superior mental and physical health) under the umbrella term ‘‘success,’’ which are valued by individuals and the society (Lyubomirsky, King, & Diener, 2005a). We agree that all types of desirable conditions, outcomes, and behaviors can certainly be experienced as success. For instance, a life goal of many people is being happy (Emmons, 2003). However, we think that different concepts (e.g., working conditions, personality traits, work behaviors, wellbeing, and health) should be thoroughly untangled to avoid arbitrariness in the definition of success. For instance, a job-related concept of success should be more specific by discriminating success experiences from other concepts such as job or task characteristics (e.g., job autonomy; Hackman & Oldham, 1975). In the work domain, many researchers use the term ‘‘subjective success’’ interchangeably with well-being (e.g., career satisfaction and job satisfaction; Greenhaus, Parasuraman, & Wormley, 1990; Ng, Eby, Sorensen, & Feldman, 2005). Even though well-being, such as a high level of job satisfaction, can be experienced subjectively as a success, a conceptual overlap between independent variables (e.g., a promotion, positive feedback by a coworker) and dependent variables such as satisfaction (Heslin, 2005) should be avoided in order to prevent content-related redundancies in terms of conceptual overlap and inflated correlations between predictors and criteria. Hence, well-being should better be conceptualized as result of success (e.g., a therapist is satisfied because a therapy goal has been reached) or as an antecedent of success (e.g., happiness of a teacher leads to positive feedback by students) because ‘‘individual well-being and performance are distinct concepts’’ (Sonnentag, 2002, p. 407). Other researchers measured subjective success with items that capture both goal attainment and satisfaction (Wiese, Freund, & Baltes, 2000). The problem with such an operationalization is a conceptual overlap between the independent variable of goal attainment and the dependent variable of satisfaction, which can lead to inflated associations between the variables (Heslin, 2005).

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We define subjective occupational success as positive and meaningful work events that are related to work goals and one’s own working behavior and which are salient for the individual in terms of subjective goal attainment or reasonable goal progress (Grebner et al., 2008b). A variety of experiences can represent subjective success: (1) reaching milestones of processes such as acquiring an important project or (2) results in terms of actions (e.g., successfully settling a conflict between subordinates), (3) outcomes (e.g., a successful presentation or an excellent service), and (4) short-term consequences (e.g., positive feedback by a client), and longterm outcomes that are related to an individual’s work behavior (e.g., getting a promotion, improved working conditions such as receiving more autonomy or more interesting work tasks; Grebner et al., 2008b; Sonnentag, 2002).

SUBJECTIVITY OF SUCCESS Empirical psychology often actively discounted subjectivity (Ryan & Deci, 2008) because of the influence of third variables (e.g., self-enhancement, cf., Taylor & Sherman, 2008). Nonetheless, there is a growing interest in self-evaluation of performance. For instance, self-assessments are used for goal-negotiations (management-by-objectives; Drucker, 1954) and personneldevelopment (e.g., evaluation interviews, cf., Heidemeier & Moser, 2009). Moreover, self-evaluations are a basic constituent of self-management (Bandura, 1986) and self-leadership (Manz & Neck, 2004). Subjectivity of evaluation of achievements is a principal constituent of the success resource model. For attaching the meaning ‘‘success’’ to a positive and meaningful job-related event, individuals use personal values (Dyke & Murphy, 2006). For instance, a restaurant cook might value aesthetics very highly and might consider his or her own aesthetic criteria as essential for success. Moreover, people experience success when they meet personal goals. Hyvo¨nen, Feldt, Salmela-Aro, Kinnunen, and Ma¨kikangas (2009) differentiate, for instance, individual competence goals, personal progression goals, individual well-being goals, and job change goals. For example, a manager who strives for developing a valuable business network might experience success when he or she successfully establishes a useful business relationship. Subjectively experienced occupational success can correspond with objective success (e.g., other-ratings) or performance. However, subjective success is not necessarily identical with other ratings mainly because performance is defined as meeting organizational rather than personal goals

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(Sonnentag, 2002). In general, for several reasons there exists only a moderate correspondence between self- and other-ratings of achievements (e.g., meta-analysis by Heidemeier & Moser, 2009). Lack of self-other agreement can be explained by a tendency of self-enhancement and positive self-regard (Baumeister, 1993; Taylor & Sherman, 2008). Moreover, specific types of tasks, such as interpersonal tasks or working through others, make it difficult for others to assess achievement levels (Heidemeier & Moser, 2009). In addition, an employee can be successful in the eye of collaborators but not in subjective judgment (Grebner et al., 2008b). The reason might be that the individual has different or higher standards and aspirations. For example, an employee applied for a job and received a job offer. Coworkers are congratulatory when they learn about the job offer. However, the employee expected to achieve in negotiations a considerably higher salary and, therefore, does not experience the job offer as a real career success. Moreover, an individual can perceive him or herself to be successful while others do not perceive any success (Grebner et al., 2008b). Possible reasons can be that others have higher or different standards. For example, when a nurse pays close attention to a patient and receives positive feedback by the patient, the nurse might consider the care as successful. However, the supervisor might value mechanical care over attentive care and, therefore, does not interpret the positive feedback of a patient as success (Seligman, 2002). In addition, low-goal difficulty in relation to knowledge, skills, and abilities of an employee can explain disagreement in self-other ratings of achievements (Grebner et al., 2008b). For instance, when it is very easy to attain a goal or to receive positive feedback, an employee might not ascribe the meaning ‘‘success’’ to the event (Latham & Locke, 1991; Locke & Latham, 1990, 2002). Moreover, in many cases day-to-day goal progress and goal attainment or positive feedback might simply not be observable for others, for instance because an employee is working in the field (e.g., social worker, consultant) and does not communicate any and every positive event to coworkers or the supervisor. Altogether, subjectivity of success evaluations might be essential for predicting employee well-being and health because subjectivity ensures the congruence of personal goals and values with attaching the meaning ‘‘success’’ (Plaks & Stecher, 2007). Therefore, the success resource model emphasizes that people evaluate for themselves the degree to which they experience success. In line with the cognitive–emotional–motivational theory of emotion (Lazarus, 1991) and affective events theory (Weiss & Cropanzano, 1996) we posit that it is the subjective perception, appraisal (e.g., benefit),

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meaning, and interpretation of job-related events that determine individual emotional, cognitive, motivational, physiological, and behavioral responses to these events. Of course, subjective appraisals are shaped by common values and interpretative schemas of a given culture; therefore, some convergence between perceptions of success by different people is to be expected (cf., Semmer, McGrath, & Beehr, 2005). Nevertheless, it seems plausible that subjectively experienced success could be a better predictor of well-being and health than more objective indicators of success.

EVENT CHARACTER OF SUCCESS EXPERIENCES Is occupational status a subjective success experience? Achieved job-related rank or status (e.g., middle-manager, chief-secretary, team leader, selfemployed business consultant, senior researcher) or other enduring conditions, such as promotion prospects, can definitely be perceived as success. Nonetheless, people often quickly habituate to enduring conditions (e.g., major life changes) while they benefit from accumulated positive minor events by increases in subjective well-being (Mochon, Norton, & Ariely, 2008). Lasting conditions such as pay and rank, which are used as indicators of objective success (Abele & Wiese, 2008), usually do not strongly draw the attention in the long-term and become, therefore, less salient. The success resource model focuses on experiences with event character that are salient for job incumbents. Events like a promotion that leads to a higher rank (e.g., team leader) are more salient to the individual employee than enduring conditions like being a team leader. Salience is important because it draws the attention of the individual to the event, to concomitant positive emotions, and to self-enhancing cognitions. In addition, the salience of an event can trigger mental and emotional contemplation about the event. People who experience a success event attend to the content of the event and to positive emotions elicited by the event (savoring, defined as the capacity to enjoy life; Bryant & Veroff, 2007). Savoring can, for instance, include joyful reflection of success-relevant work strategies (e.g., ‘‘I allocated sufficient time’’), thoughts (e.g., ‘‘I thought I would make it’’), behaviors (e.g., ‘‘I communicated in a calm way’’), and emotions (e.g., ‘‘I felt confident’’). Hence, an important characteristic of subjective success experiences is their event character (Grebner, Elfering, Semmer, KaiserProbst, & Schlapbach, 2004; see also the affective events theory of Weiss & Cropanzano, 1996). Success experiences are proximal, positively evaluated job-related events that are appraised as beneficial and as having

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advantageous consequences. Therefore, success events elicit positive affect and emotions (e.g., joy), which, in turn, can affect behaviors (e.g., increased effort) and attitudes (e.g., increased affective commitment) and feed into peoples’ general well-being (e.g., happiness). Success events have a defined beginning and end. Hence, success experiences can be told and remembered as a story (e.g., ‘‘I made a good case for my project ideas’’). Therefore, subjective success events can be repeatedly recalled or communicated and used to trigger positive affect repeatedly from past accomplishments (Bryant & Veroff, 2007).

DIMENSIONS AND MEASUREMENT OF SUBJECTIVE SUCCESS The success resource model limits the definition of subjective occupational success to positive and meaningful job-related events that are related to one’s work goals and that are salient for the individual employee. The model differentiates four dimensions of subjective occupational success: goal attainment, pro-social success, positive feedback, and career success (see Fig. 1). Goal attainment and pro-social success are conceptualized as immediate types of subjective successes resulting from an individual’s effort.

Fig. 1.

Dimensions of Subjective Occupational Success.

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Goal attainment and pro-social success can lead to positive feedback. Moreover, goal attainment and pro-social success can contribute positively to career success. Thus, positive feedback and career success, in terms of acknowledgment of good performance by others, constitute possible consequences of immediate types of success (Grebner et al., 2008b). In order to better understand what people experience as a job-related success in terms of content, we conducted a critical incident interview study (Flanagan, 1954). We asked employees from a variety of occupations and organizations to report recently experienced job-related success (Grebner, Elfering, Achermann, Knecht, & Semmer, 2008a). We classified narratives of 195 success incidents reported by 57 employees into 14 categories using content analysis. Categories were summarized into four higher-order dimensions: goal attainment, pro-social success, positive feedback, and career success.

Subjective Goal Attainment The success resource model specifies subjective goal attainment as a core dimension of subjective occupational success (Grebner et al., 2008b). Subjective goal attainment is defined as reaching or exceeding predefined goals in terms of task performance (Sonnentag, 2002) or achievement of personal goals (Maier & Brunstein, 2001). These goals may match (redefined) organizational goals, but they also may differ from the expectations of the organization. Examples for personal goals are personal well-being goals (e.g., staying well), personal competence and achievement goals based on the individual’s own standards, power goals (e.g., becoming a project leader), personal affiliation goals (e.g., being a good teammate), and personal security goals (e.g., reaching a tenured position). An example of a personal goal that does not match organizational goals is establishing a business network that will be useful for future self-employment. The following types of subjective success events were classified as subjective goal attainment: attaining or exceeding goals, acting for one’s interest or ideas, and attaining a goal, despite adverse conditions (Grebner et al., 2008b).

Pro-Social Success Like goal attainment, pro-social success is an immediate type of subjective occupational success resulting from one’s effort. Pro-social success is defined

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as the desired result of behavior that aims at improving the situation of others (e.g., successfully motivating others). For instance, supervisory tasks usually include supporting subordinates and settling conflicts. Successfully achieving these tasks represents in-role pro-social success or task performance. However, pro-social success can also be based on voluntary helping behavior in terms of extra-role or contextual performance (e.g., organizational citizenship behavior; Organ, Podsakoff, & MacKenzie, 2006). Pro-social success can be related to organizational or personal values (e.g., supporting a good team climate) and goals (e.g., ‘‘helping others and let them know I care,’’ ‘‘be a good role model as a supervisor,’’ cf., Emmons, 2003). In our critical incidents study, the following types of events were classified as pro-social success: supporting others (e.g., providing coworkers with emotional or informational or tangible support), preventing negative outcomes for others (e.g., successfully settling conflicts), causing positive outcomes for others (e.g., successfully motivating others), and being asked for advice (Grebner et al., 2008b).

Positive Feedback Positive feedback is defined as appreciation of good performance for one’s work behavior (e.g., efficient) and quality of a work relationship (e.g., good teammate), or for personal attributes (e.g., dependable, trustworthy, joyous) and worth (e.g., highly valuable coworker). People reported experiencing positive feedback by others at work as success in our critical incident study (e.g., supervisor, coworkers, clients; Grebner et al., 2008b). The success resource model does not differentiate between performance-related and relationship-related positive feedback because either type of feedback might imply the other type of feedback or include both at the same time. For instance, when a worker receives the following compliment, ‘‘I enjoy working with you,’’ this can mean the feedback giver enjoys collaboration because of good working strategies and efficiency of the feedback receiver (performance-related feedback) or that the feedback receiver is, for instance, pleasant company (relationship-related feedback) or both at the same time. Positive feedback can occur as a result of successful pro-social behavior or job-related goal attainment (e.g., achieving good results, attaining or exceeding a goal) but is not limited to in-role or extra-role performance because the feedback can be related to the person as a whole. Not every immediate success is observed and acknowledged by others (London, 2003). For instance, positive feedback depends on the willingness of others to give

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positive feedback based on their personal attributes (e.g., extraversion) and preferences (e.g., sympathy). Moreover, positive feedback also depends on feedback-seeking behavior.

Career Success Career success is a further kind of acknowledgment of good performance. Career success is defined as attaining milestones in one’s career. The following types of success experiences were categorized as career success: promotions and career advancement such as being suggested for a promotion or receiving special training, the perception being innovative (e.g., developing a new concept for project management), outperforming others (e.g., selling more products than a coworker), and successfully making useful business contacts (Grebner et al., 2008b). Incidents related to career success were reported least frequently, as compared to other types of successes such as goal attainment, pro-social success, and positive feedback. This is, however, no surprise. For instance, being suggested for a promotion does not happen every day. Moreover, promotions are partly determined by beneficial situational circumstances (e.g., availability of mentoring; Singh, Ragins, & Tharenou, 2009). Furthermore, innovations are usually the result of long-term efforts (e.g., idea development, acquisition of funding, and complex problem solving). Outreaching performance of others is usually an outstanding event and difficult to reach when others are equally qualified or more experienced. Finally, successfully making useful business contacts usually happens at special occasions. The experience of career success depends on many factors. Immediate successes are certainly a possible antecedent of career success. However, various person-related factors (e.g., proactive work behavior) and situationrelated factors (e.g., job tenure, promotion possibilities, financial resources of the organization, organizational politics, labor market, supervisory and coworker support, chance events, etc.) may also play a role.

Measurement of Subjective Occupational Success We used the 14 categories of critical success incidents (Grebner et al., 2008a) to develop items for a German version of the Subjective Occupational Success Scales (SUCCESS, Grebner et al., 2008a, 2008b). Details of instrument development can be obtained from the first author. The SUCCESS is a

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16-item self-report instrument that captures goal attainment, pro-social success, positive feedback, and career success. The four qualitatively derived dimensions were quantitatively confirmed using principal components analysis (PCA). The four-factor structure was replicated using confirmatory factor analysis (CFA) in a further study. In both studies, all four success dimensions showed satisfactory internal consistency reliability (Grebner et al., 2008a, 2008b). The SUCCESS possesses criterion-related validity demonstrated by the relation of all four dimensions with well-being indicators in two studies. In addition, the instrument showed convergent validity with situation-related resources and discriminant validity with respect to job stressors. Subjective goal attainment is measured by three items (e.g., ‘‘I achieved good results’’), subjective pro-social success by six items (e.g., ‘‘I motivated others’’), perceived positive feedback by three items (e.g., ‘‘I received positive feedback by my supervisor’’), and subjective career success by four items (e.g., ‘‘I got a promotion’’; for a complete list of the items see the appendix). The instrument employs 7-point Likert scales. For goal attainment, prosocial success, and positive feedback the answer categories range from 1 (never) to 7 (all the time). For career success, answer categories range from 1 (does not apply at all) to 7 (does apply completely). Overall, the SUCCESS is a reliable and valid measure. Scales developed in German were translated by the first author and a native speaker into English. Hence, the SUCCESS is available in German and English. For the English version of the SUCCESS see the appendix. The instrument can be obtained in German from the first author.

MODEL ASSUMPTIONS AND SUPPORTING EVIDENCE Fig. 2 shows the success resource model. For the sake of clarity, several important and well-studied causal arrows of the job stress process (e.g., theoretical framework for the study of stress in organizations; Kahn & Byosiere, 1992, p. 592; Sonnentag & Frese, 2003) are included but not discussed or completely omitted in Fig. 2. For example, beneficial effects of situation- and person-related resources and the detrimental influence of job stressors on well-being and health are displayed but not described. For an overview see, for instance, Sonnentag and Frese (2003). In addition, several recursive effects, which are conceptualized by other researchers

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2b) Job-related resources e.g., Job control Social support

4)

Immediate Subjective Success Goal attainment

Job–related stressors Task-related stressors: e.g., Role ambiguity Role conflict Constraints

Person-related resources

Pro-social success 5)

e.g., Problem-focused coping Self-efficacy

Social stressors e.g., Workplace conflicts

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Affect & Well-being e.g., Joy Pride Pleasure Job satisfaction Career satisfaction Psychological recovery Exhaustion

Acknowledgement Success Positive feedback 6)

Career success

Health e.g., Psychosomatic complaints Physiological recovery

3)

Fig. 2.

The Success Resource Model of Job Stress.

(Bandura & Locke, 2003; Lyubomirsky et al., 2005a), are not shown except in the case of well-being and health to subjective success (dotted lines). Some of the linkages in the success resource model are empirically very well supported even though not all support is based on studies in the work domain. Other linkages are less well supported or have not been examined at all. Most research concerns the effects of goal attainment on well-being. For other dimensions of subjective success, evidence is sparse regarding assumptions of the success resource model. In particular, the hypothesized buffer effect of subjective success on the stressor–strain relationship (see arrow 3 in Fig. 2) is hardly investigated. In the following section we first discuss the assumptions of the success resource model. Second, we describe assumed mechanisms and available supporting evidence regarding arrows 1–3 in Fig. 2, focusing on relationships of subjective success with outcomes. Third, we briefly delineate possible mechanisms for long-term effects of success on outcomes. Finally, we discuss hypothesized effects of antecedents on subjective occupational success and supporting evidence.

Immediate Success is Positively Related to Acknowledgment Success The model posits that subjective success breeds subjective success: ‘‘immediate’’ types of subjective success such as subjective goal attainment

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and perceived pro-social success are positively related to possible consequential ‘‘acknowledgment success’’ in terms of positive feedback and career success (see Fig. 2, arrow 1). Using the SUCCESS we found moderate positive associations between both immediate types of subjective success with both types of acknowledgment success (Grebner et al., 2008a). The associations are far from perfect because, as elaborated, many other factors influence acknowledgment success beyond immediate successes.

Subjective Success is Positively Related to Well-Being and Health The model assumes a beneficial main effect of all success dimensions on positive affect and emotions, well-being, and health: Immediate and acknowledgment success are both positively related to well-being (e.g., joy, pride, job satisfaction, psychological recovery in terms of mental detachment from job-related matters in leisure time) and health, and negatively related to negative affect and emotions and impaired well-being (e.g., anxiety, feelings of tension, anger, exhaustion, feelings of resentment) and impaired health (e.g., psychosomatic complaints, insufficient physiological recovery after work) (see Fig. 2, arrow 2a). For instance, Olson and Zanna (1993) suggest that attitudes follow behavior (i.e., job satisfaction is a consequence of achievements). Also expectancy-based theories of motivation assume that satisfaction is a consequence from the intrinsic and extrinsic rewards tied to performance (Naylor, Pritchard, & Ilgen, 1980; Lawler & Porter, 1967; Vroom, 1964). Also Locke (1970) considers satisfaction as a consequence of performance. Moreover, Deci and Ryan (1985) state that satisfaction is a consequence of rewards that follow an individual’s behavior. Furthermore, positive affect, well-being, and health positively affect all types of subjective success (Lyubomirsky et al., 2005a) while negative affect, impaired well-being, and poor health negatively affect success (see Fig. 2, arrow 2b, dotted lines). Hence, the model posits a reciprocal relationship between subjective success and well-being (Diener, Suh, Lucas & Smith, 1999; Judge et al., 2001) and health while emphasizing the beneficial main effect on the outcomes. The model assumes strong effects from subjective success to affect, well-being, and health compared to weaker effects regarding the reverse causation. Evidence based on longitudinal research designs would allow testing for both types of effects simultaneously.

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Subjective Success Buffers the Impact of Job Stressors on Strain The success resource model hypothesizes that subjective success dimensions buffer the potential detrimental effect of job stressors on well-being and health (moderator effect) (see Fig. 2, arrow 3). First, success experiences might increase the belief in being able to perform well and, therefore, one’s primary appraisal of job stressors might be perceived as less threatening or harmful, or changed from threat to challenge. Second, success experiences trigger positive emotions and positive self-related cognitions. Therefore, employees are better able to focus on task-relevant cognitions and are less involved in task-irrelevant rumination about job stressors. For instance, positive feedback by the supervisor because of exceeding one’s goals might increase respect among coworkers because of status improvement and, therefore, offenses of coworkers might be less threatening and harmful. However, the possibility also exists that being preoccupied with one’s successes may detract attention from tasks and induce self-related behavior (Kluger & DeNisi, 1996), which would weaken this path.

Success Events Can Have Long-Term Effects Because of their event character, subjective success experiences (e.g., day-today goal attainment; Wiese, 2007) and their emotional or affective outcomes (e.g., contentment after making goal progress, pride after receiving positive performance-related feedback) are likely to be short-lived. Nevertheless, they may foster the development of personal resources (e.g., self-efficacy, knowledge, skills, experiences), which are more permanent. These resources outlast the transient emotional states that led to their acquisition’’ (Fredrickson, 2001, p. 220). These long-term effects are postulated to occur to the extent that successes are frequent, or very salient. For example, a promotion might elicit pride and permanently enhance the job incumbent’s person-related resources like self-efficacy and well-being (e.g., job satisfaction; Locke & Latham, 1990).

Goal Attainment Overall, consistent evidence showed that goal progress and goal attainment predict positive affect and emotions such as pleasure, joy, and pride. These emotions occur in various life domains, and they contribute to well-being

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(e.g., satisfaction and happiness, cf., Brunstein, 1993; Brunstein, Schultheiss, & Grassmann, 1998; Brunstein, Schultheiss, & Maier, 1999; Carver & Scheier, 1990; Diener & Lucas, 1999; Elliot, Sheldon, & Church, 1997; Emmons, 1986; Judge, Bono, Erez, & Locke, 2005; Koestner, Lekes, Powers, & Chicoine, 2002; Louro, Pieters, & Zeelenberg, 2007; Sheldon & Cooper, 2008; Smith, Ntoumanis, & Duda, 2007; Sirgy, 2006; Wiese, 2007; Yamaguchi & Halberstadt, 2008). People especially experience positive affect when they reach or exceed intrinsic goals (e.g., affiliation, doing interesting work) versus extrinsic goals (e.g., social recognition, status, money, cf., Deci & Ryan, 2008). Whereas short-term effects of goal attainment on positive affect and wellbeing are well supported, possible long-term effects have rarely been investigated. Lyubomirsky, Sheldon, and Schkade (2005b) suggested in their model of sustainable happiness that activities of people, in contrast to genetic factors or demographic factors like education, can lead to sustainable gain in well-being. For instance, Sheldon (2008) demonstrated in a longitudinal study that school-related goal attainment among freshmen can lead to increases in well-being, which could be maintained for three years after goals were attained. Enhanced well-being fosters setting of more self-concordant goals, which promotes future goal attainment and further increase in wellbeing (Sheldon & Houser-Marko, 2001). In addition, goal attainment can cause improvements in self-perception (e.g., higher level of perceived competence). Thus, Mu¨hlethaler, Jacobshagen, Ka¨lin, Grebner, and Semmer (forthcoming) could show that subjective success (assessed with a short version of the SUCCESS scale) predicted self-esteem, self-efficacy, and internal locus of control over one year, controlling for the baseline values of outcome variables. In the long run, goal attainment can result in improved life circumstances (e.g., better living situation; Sheldon, Kasser, Smith, & Shore, 2002). Research on goals in industrial/organizational psychology goes back to the earlier work of (Locke, 1968; see also Locke & Latham, 1990, 2002), who developed a goal-related model of job motivation and performance. Locke considers ‘‘goal success as leading to self-satisfaction’’ (Latham & Locke, 1991, p. 294). However, research on associations between subjective goal attainment and well-being and health indicators is still limited in the work domain, especially with regard to psychological recovery (e.g., mental detachment from job-related matters after work and over the weekend), indicators of impaired well-being (e.g., irritation, exhaustion, feelings of resentment), and health (e.g., psychosomatic complaints, physiological recovery). Subjective goal progress and goal achievement have been found to predict positive affect (Harris, Daniels, & Briner, 2003), job satisfaction,

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and commitment (Maier & Brunstein, 2001; Judge et al., 2005; Locke & Latham, 1990, 2002). Mento, Locke, and Klein (1992) showed that exceeding and attaining goals leads to high levels of job satisfaction. Confirming previous evidence (Locke & Latham, 1990, 2002; Wiese & Freund, 2005), we found that subjective goal attainment measured using the SUCCESS in two studies positively related to job satisfaction and work engagement and negatively related to exhaustion and resigned attitude toward one’s job (Grebner et al., 2008b), the latter being a resentful adaptation to suboptimal working conditions (Semmer & Meier, 2010). Therefore, evidence indeed indicates that goal attainment functions in the work domain in a way similar to other life domains. However, more research on the beneficial impact of subjective job-related goal attainment on well-being and health indicators is needed. In particular, sustainable beneficial effects of subjective goal attainment should be examined using longitudinal designs in order to confirm and expand the findings by Mu¨hlethaler et al. (forthcoming). Furthermore, the hypothesized buffering effect of subjective goal attainment on the stressor–strain relationship should be examined in future research.

Pro-Social Success In general, the intention to establish good and mutually gratifying relationships with other people (e.g., coworkers, supervisor) can be regarded as a basic need (Ryan & Deci, 2008). Helping others is positively related to positive affect (e.g., joy) and well-being such as happiness (Batson, 1990; Dovidio, Piliavin, Schroeder, & Penner, 2006; Dunn, Aknin, & Norton, 2008; Grant & Sonnentag, in press; Greenfield & Marks, 2004; Lyubomirsky et al., 2005b; Musick & Wilson, 2003; Organ et al., 2006; Otake, Shimai, Tanaka-Matsumi, Otsui, & Fredrickson, 2006; Williamson & Clark, 1989) because it ‘‘can make the helper feel good or feel better in case the helper is feeling bad’’ (Dovidio et al., 2006, p. 107). Emmons (2003) argues that trying to enhance the well-being of others (altruism; Haski-Leventhal, 2009) has an immediate positive effect on the promoters’ positive affect, wellbeing, and life satisfaction (see also Ackerman, Zuroff, & Moskowitz, 2000). While perceived social support by the receiver, in general, is beneficial for well-being and health (Viswesvaran et al., 1999), the role of pro-social success as a causal agent of helper well-being and health is limited, especially in the work domain. Grant and Sonnentag (in press) investigated the effect of providing social support to others on exhaustion. In one of their studies

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they found a negative correlation between perceived pro-social impact on others and exhaustion among professional fundraisers. Moreover, the experience of helping others at work buffered against the detrimental impact of negative task-evaluations (low intrinsic motivation) and negative coreself-evaluations on emotional exhaustion. The authors explain the protecting effect of helping others at work by an attention shift from one’s own negative task- and self-evaluations, and negative emotions to positive outcomes for others. Results were replicated in a second study among public sanitation employees (e.g., engineers, technicians, accountants). We found subjective pro-social success assessed by the SUCCESS to be positively related to job satisfaction, work engagement, and affective commitment in one study, and negatively related in another study to feelings of resentment (Grebner et al., 2008b). Overall, more research on the effects of successful helping behavior at work and on other aspects of pro-social success such as motivating others, settling conflicts, and preventing negative outcomes for others (e.g., layoffs) is needed. Main and buffer effects of pro-social success need to be replicated in further studies employing longitudinal designs to make causal associations more plausible and to investigate sustainable effects.

Positive Feedback Positive feedback signals what behavior and which outcomes are desired by others. In general, positive performance feedback, when it is informative and supportive of people’s autonomy, enhances intrinsic motivation that is characterized by pursuing activities because the activity itself is satisfying (Deci & Ryan, 2008). The impact of positive feedback on positive affect and emotions such as joy, pride, and pleasure has been emphasized by several researchers (Bandura & Locke, 2003; Kluger & DeNisi, 1996; Locke & Latham, 1990; Taylor, Fisher, & Ilgen, 1984). However, there exists little evidence about how positive feedback affects well-being and health indicators. In addition, positive feedback has not been conceptualized as a resource in the job stress process. Therefore, there is scarce evidence on the effects of positive job-related feedback on well-being and health indicators, including indicators of impaired well-being such as exhaustion and feelings of resentment (Geurts, Schaufeli, & Rutte, 1999). Moreover, possible beneficial effects of positive job-related feedback on psychological and physiological recovery from work demands are also rarely investigated. Positive job-related feedback measured using the SUCCESS correlated in

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two studies positively with job satisfaction and in one of the studies positively with work engagement and negatively with feelings of resentment and exhaustion (Grebner et al., 2008b). In general, more evidence on the effects of positive job-related feedback on well-being and health is needed. Main effects of feedback success need to be replicated in further studies employing longitudinal designs to make causal associations more plausible and to investigate sustainable effects. In addition, buffer effects of positive feedback on the job stressor–strain relationship should be examined in future research.

Subjective Career Success In general, there is an increasing interest in career success (Abele & Spurk, 2009; Abele & Wiese, 2008; Heslin, 2005; Ng et al., 2005; Seibert, Crant, & Kraimer, 1999; Wolff & Moser, 2009). Abele and Wiese (2008), similar to many other researchers (Ng et al., 2005; Seibert et al., 1999), differentiate between objective career success like pay, rank, and promotion, and subjective career success in terms of the individual’s own assessment of his or her career (e.g., job or career satisfaction). However, the success resource model conceptualizes positive psychological outcomes like career and job satisfaction as distinct from subjective success experiences. Career success is defined as events that represent attainment of important milestones in one’s career (e.g., making useful business contacts, acquiring an important customer; Grebner et al., 2008b). Our definition of career success does not include aspects of so-called objective success like pay and rank, whereas promotion, which is clearly an event, is included. However, in one study, Abele and Spurk (2009) used change of salary and status (which have event character) as predictors of career satisfaction and report positive cross-sectional associations after seven years of job experience. Using the SUCCESS, Grebner et al. (2008b) showed that subjective career success positively correlated with job satisfaction and work engagement, and negatively correlated with feelings of resentment. Altogether, there exists little evidence concerning effects of subjective career success, as we define it, on well-being, health, and recovery. Furthermore, to our knowledge, the hypothesized buffer effect of career success on the job stressor–strain relationship has not been examined so far. Altogether, subjective success dimensions measured by the SUCCESS predict a variety of well-being indicators. In line with the job demands

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resources model (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001) stronger effects were found for positive job-related well-being indicators.

Antecedents of Subjective Success In general, a large variety of macro-, meso-, and micro-level factors, including situational and person-related variables, can exert conducive or hindering influences on all types of subjective occupational success experiences. For instance, goal attainment of sales agents can be affected by macro-level factors such as the market situation. Pro-social success of a supervisor might depend on social skills (Huffcutt, Conway, Roth, & Stone, 2001). Positive feedback can be influenced by feedback-seeking behavior (Ashford & Cummings, 1983). Meso-level factors like organizational politics (Drory & Vigoda-Gadot, in press) and micro-level supervisory support can influence career success of an employee. Moreover, situation-related resources, job stressors, and attributes of goals (e.g., goal difficulty; Latham & Locke, 1991; Locke & Latham, 1990, 2002; Seijts, Latham, Tasa, & Latham, 2004) can affect subjective success. We limit the discussion of success-promoting and success-hindering factors to a few important stress-relevant variables. We discuss the role of job control, social support at work, problem-focused coping, and selfefficacy, which can promote success, and job stressors that can impede or even thwart success. The model posits that (Fig. 2, arrow 4) situation-related resources like job control and social support at work and (Fig. 2, arrow 5) person-related resources such as problem-focused coping and self-efficacy show a positive direct effect on subjective success. Job Control As discussed above, job control is the most investigated situationrelated resource in the job stress process. High job control enables one to make decisions that are tailored to requirements of specific situations (e.g., communicating directly) rather than simply following directions from others (e.g., using official communication channels). In general, job control allows handling one’s workload and making one’s work environment less threatening and more rewarding (Ganster, 1989; see also Bond & Bunce, 2003). High levels of job control can be helpful for goal attainment because the workers can, tailored to their own knowledge, skills, and abilities, define their goals or select between goals, which are defined by

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the supervisor. High levels of job control enable employees to modify goals set by others and make decisions according to situational circumstances (e.g., tight vs. ready market) and personal preferences (e.g., types of clients). For example, university teachers can define learning goals and teaching methods tailored to the knowledge of students. Job control implies that an employee can opt for specific ways of completing a task. Employee control over work schedules allows, for instance, solving complex problems in quiet times or taking the time to support coworkers or making useful business contacts. In general, job control can promote subjective success because situational aspects that are important for goal attainment and pro-social activities and career success can be controlled and influenced (Frese & Zapf, 1994). Job control can promote subjective success because control allows avoiding or ameliorating job stressors, which are conceptualized as success barriers (cf., action theory; Frese & Zapf, 1994). For instance, an employee can decide to work on tasks that require high concentration during times in which work interruptions are infrequent. In addition, job control can help one cope successfully with job stressors, which contributes to subjective success (Grebner et al., 2004). The success resource model assumes that job control exerts direct positive effects on goal attainment, pro-social success, and career success and an indirect positive effect on positive feedback via goal attainment and prosocial success. Research has shown that job control positively predicts performance (Bond & Bunce, 2003; Bond & Flaxman, 2006; meta-analysis by Humphrey et al., 2007). Moreover, our own studies demonstrated positive associations between job control and two dimensions of subjective success, goal attainment, and career success (Grebner et al., 2008b). Social Support Many successful individuals (e.g., researchers, artists, athletes) give credit for their achievements to the support of other people (e.g., colleagues, teachers, coaches; Feeney, 2004) in terms of being available, providing assistance and encouragement, facilitating problem resolution, and providing psychological and material resources needed to cope with stressful events (Cohen, 2004). In the literature on job stress, perceived social support (e.g., perceived availability of instrumental, informational, or emotional support provided by supervisor and coworkers if needed) has been investigated extensively with regard to a beneficial effect on well-being and health (Caplan, Cobb, French, van Harrison, & Pinneau, 1975; Kahn & Byosiere, 1992;

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Karasek & Theorell, 1990; Sonnentag & Frese, 2003). The main-effect model assumes that social support is beneficial ‘‘irrespective of whether one is under stress’’ (Cohen, 2004, p. 678; see also Viswesvaran et al., 1999). The success resource model posits that social support at work is directly useful for goal attainment and pro-social success, and career success. Furthermore, the model assumes an indirect positive effect of social support on positive feedback via goal attainment and pro-social success. Perceived support represents the belief that a competent other would listen, provide information, and intervene, when necessary. Social support includes, for instance, provision of needed resources, including direct helping (Caplan et al., 1975), mentoring (Ensher, Thomas, & Murphy, 2001), and giving comfort (Morgeson & Humphrey, 2006). Hence, social support can be helpful for goal attainment because informational and tangible support can be helpful or necessary for task completion and allow one to make direct goal progress. Social support might be most useful for goal attainment when job or task experience is limited, when tasks are novel, or when they are highly complex (Sonnentag, 2002). Moreover, social support can contribute positively to pro-social success by initiating a process of support reciprocation (Chiaburu & Harrison, 2008). In addition, social support can promote career success by direct help of a coworker with the acquisition of an important project or customer. Furthermore, support can be beneficial for emotion regulation (e.g., calming down) in stressful situations at work (Dahlen & Ryan, 2005), which is helpful for focusing on task-relevant cognitions. Available evidence supports the assumption that social support promotes success. For instance, Feeney (2004) demonstrated that highquality support for a relationship partner’s goal strivings is beneficial for the perceived likelihood of goal achievement. Armeli, Eisenberger, Fasolo, and Lynch (1998) reported a positive association between perceived organizational support and job performance among police patrol officers with strong socioemotional needs. AbuAlRub (2004) showed social support from coworkers positively related to self-rated job performance among nurses. A recent meta-analysis by Chiaburu and Harrison (2008) summarizing results of 161 studies demonstrated a beneficial effect of coworker support on individual effectiveness in terms of organizational citizenship behavior and task performance. Own studies showed perceived social support at work positively associated with goal attainment (Grebner et al., 2008b). Characteristics of the Individual A wide variety of characteristics of the individual can affect subjective success experiences (e.g., self-efficacy, extraversion, conscientiousness,

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optimism, perfectionism, problem-focused coping, etc.). Some individual characteristics are more relevant for subjective success experiences depending on the types of work tasks (e.g., extraversion might be especially important for jobs with client interactions), while other individual characteristics are important for success in general. We concentrate on two individual characteristics that play an important role in the job stress process (Schwarzer & Hallum, 2008) and are crucial for subjective success: self-efficacy (Bandura, 1986; Stajkovic & Luthans, 1998), and problemfocused coping (Lazarus & Folkman, 1984). Self-efficacy is one of the core constructs in social–cognitive theory (Bandura, 1997). Self-efficacy is defined as the belief in being capable of performing tasks and desired actions in relation to goals and standards, attaining goals, and mastering problem situations (Bandura, 1986). The success resource model emphasizes that self-efficacy promotes success because self-efficacy leads to higher goals, increased effort, and greater persistence (Locke & Latham, 1990).Moreover, the success resource model acknowledges reciprocal effects in terms of mutual beneficial effects of selfefficacy and success in terms of an upward spiral (e.g., social–cognitive theory, Bandura, 1986; see Mu¨hlethaler et al., forthcoming). Self-efficacy can play an important role for subjective success because people high in selfefficacy tend to take on challenges and invest sufficient effort. Also, individuals high in self-efficacy ‘‘set higher goals, are more committed to assigned goals, and find and use better task strategies to attain the goals’’ (Locke & Latham, 2002, p. 706). Furthermore, highly efficacious individuals perceive stressors as less threatening or harmful (Jex & Bliese, 1999; Sonnentag, 2002) and do not tend to cease their efforts or give up prematurely because of barriers (Bandura, 1982). Problem-focused coping is defined as cognitive and behavioral efforts to master, tolerate, minimize, or remove stressful events (Lazarus & Folkman, 1984; Skinner, Edge, Altman, & Sherwood, 2003). Problem-focused coping includes analyzing the problem situation, making and executing plans, taking direct action such as restructuring tasks, finding relevant information and sources of instrumental support, removing the cause of the problem (e.g., learning new skills), or forcing oneself to wait before acting (Carver, Scheier, & Weintraub, 1989). Problem-focused coping at work aims at removing barriers to goal attainment or directly at attaining work goals (Daniels, Harris, & Briner, 2002) such as dealing with customer complaints. Hence, the success resource model assumes that problem-focused coping promotes in particular immediate types of subjective success. Problemfocused coping can promote subjective occupational success experiences by

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successful problem solving (e.g., solving a conflict, removing situational constraints; Lazarus & Folkman, 1984; Lazarus, 1991). Moreover, problemfocused coping can help one calm down in stressful situations (Grebner et al., 2004), which can be helpful for focusing on task-relevant cognitions and actions. Supporting evidence has been reported, for example, by Forsythe and Compas (1987), Vitaliano, Dewolfe, Maiuro, Russo, and Katon (1990), Reicherts (1988), Perrez and Reicherts (1992), Reicherts and Pihet (2000), and Grebner et al. (2004). Daniels and Harris (2005) found in a diary study that problem-focused coping related to higher levels of goal attainment among hospital workers. In addition, Grebner (2009) showed problem-focused coping to be positively associated with goal attainment, pro-social success, and career success. Moreover, subjective success was found to mediate the problem-focused coping–well-being relationship. Job Stressors Hamper Subjective Success Finally, the success resource model hypothesizes that (Fig. 2, arrow 6) job stressors (in particular, hindrance stressors such as role ambiguity and workplace conflicts) negatively affect subjective success. Job stressors can delay, impede, or even thwart subjective success experiences like goal attainment or pro-social success (Frese & Zapf, 1994). In particular, hindrance stressors such as role ambiguity, organizational constraints, and red tape (LePine et al., 2005) are discussed as hindering goal attainment. The model conceptualizes reciprocal effects of well-being, health, and recovery on subjective success. Furthermore, the model hypothesizes a moderator effect of subjective success on the job stress–strain relationship. Success is assumed to buffer the detrimental impact of job stressors on well-being and health indicators because success leads to positive emotions, which can have an energizing effect and protective effect, and can lead to a reappraisal of job stressors (primary appraisal). When one has a higher level of motivation and energy, job stressors might appear less threatening and stress can be better tolerated. For instance, when a performance interview is coming up and an employee fears the conversation, a success experience such as acquiring an important customer or outperforming coworkers, can lead to a reappraisal from threat to challenge. Hindrance stressors include job stressors and social stressors. Job stressors, which include task-related stressors (e.g., work interruptions, organizational constraints, concentration demands, and role ambiguity; Frese & Zapf, 1994; Grebner et al., 2005) and social stressors (e.g., interpersonal conflicts, harassment) can affect subjective success negatively. Job stressors can delay, impede, or even thwart achievements because stressors

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can interfere with goal setting, planning activities, and task completion (Frese & Zapf, 1994). For instance, Fried, Shirom, Gilboa, and Cooper (2008) found in a meta-analysis among 22,000 individuals negative effects of work stressors on job performance. Social stressors like conflicts can produce tension, antagonism, and can distract from task completion. For instance, De Dreu and Weingart (2003) report negative associations between both task conflict and relationship conflict and team performance. Furthermore, coping with job stressors usually requires investment of additional effort and time (Frese & Zapf, 1994). The challenge–hindrance stressor framework by LePine et al. (2005) posits differential effects of two types of job stressors on motivation and performance, namely hindrance stressors (e.g., role ambiguity, red tape, hassles, and organizational constraints) and challenge stressors (e.g., time pressure). While both kinds of stressors are associated with strain, hindrance stressors lessen motivation and threaten personal goals, while challenge stressors (Cavanaugh, Boswell, Roehling, & Boudreau, 2000; LePine et al., 2005) promote motivation, goal attainment, and performance (LePine et al., 2005; Webster, Beehr, & Christiansen, in press). Hindrance stressors force an employee to invest extra time and additional effort to overcome barriers to task fulfillment. In general, hindrance stressors disturb the action process and compromise achievement of goals (Semmer & Meier, 2010). Hindrance stressors can decrease job motivation because ‘‘people are likely to believe that no reasonable level of effort will be adequate to meet these types of demands’’ (LePine et al., 2005, p. 766). Challenge stressors, on the other hand, support goal pursuit. LePine et al. (2005) found beneficial effects of challenge stressors on job performance, while hindrance stressors affected performance negatively. Grebner, Mauch, Zehnder, and Baumgarten (2008c) found time pressure to be positively related to career success, while role ambiguity and workplace conflicts were negatively related to goal attainment, positive feedback, and career success. The success resource model assumes that job stressors, in particular hindrance stressors and interpersonal conflicts, have a direct negative effect on goal attainment, pro-social success, positive feedback, and career success. For instance, organizational constraints such as insufficient information can delay goal attainment because extra time has to be invested to search for complete information. Interpersonal conflicts with the supervisor can impede positive feedback and career success (e.g., promotion). Role ambiguity (e.g., unclear responsibilities) can act as a barrier to goal attainment (Frese & Zapf, 1994), pro-social success, and career success.

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Nonetheless, there are reasons to discuss objections against the assumption that challenge stressors are, in general, beneficial for performance or success. Time pressure requires high speed of information processing, which, in turn, may require deviating from the original action plan and taking more risky actions (e.g., continuing driving while being fatigued). In addition, time pressure can thwart pro-social success because employees simply lack the necessary time for helping others. Furthermore, time pressure can impede goal attainment, for instance, when necessary information cannot be obtained within the available period. Moreover, overly hasty task conduct can lead to errors (e.g., planning errors, overlooking warning signs) and (fatal) accidents (Semmer, Ka¨lin, Elfering, & Tschan, 2008). Therefore, whether challenge stressors like time pressure are beneficial for success might depend heavily on the type of the work task and situational circumstances (e.g., type of client). In particular, for tasks that involve responsibility for the lives of people or tangible assets (e.g., airline pilots, engine drivers, truck drivers, surgeons, etc.) time pressure can lead to high costs. In this case, so-called challenge stressors might actually work as hindrance factors for success. On the other hand, obtaining success in spite of difficult circumstances (e.g., under time pressure) may be especially valuable.

IMPLICATIONS OF THE SUCCESS RESOURCE MODEL FOR JOB STRESS INTERVENTIONS The success resource model of job stress has significant implications for how individuals and organizations can strengthen individual resources and create motivating and healthy working conditions. Classical job stress interventions often aim at improving the coping skills of employees (Richardson & Rothstein, 2008). While improved coping skills can positively contribute to increased coping success, other types of job stress interventions should be used to enhance the frequency of success experiences. Both individual- and organization-targeted approaches are recommended (Semmer, 2006). At the individual level, cognitive–behavioral interventions can be used that focus on improving knowledge about subjective success, success promoting (e.g., job control), and hindrance factors (e.g., job stressors). Moreover, at this level skills should be improved (e.g., conflict-management, problem-focused coping, problem solving, and self-management including

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realistic goal setting, monitoring of goal progress, and self-reinforcement for goal attainment, and feedback-seeking behavior; Bowling, Beehr, & Grebner, 2010; Frayne & Gehringer, 2000; Linden, 2005; London, 2003; Richardson & Rothstein, 2008). Moreover, enhancing the quality and quantity of social support at work (Karasek & Theorell, 1990) can also increase subjective success at work. Organization-targeted job stress interventions, aimed at increasing the frequency of job-related subjective successes, include well-known stressand motivation-related job design measures (Hackman & Oldham, 1975; Parker & Wall, 1998; Parker & Ohly, 2008; Sonnentag & Frese, 2003). For instance, enhancement of job control (e.g., job enrichment, empowerment, self-managed work; Birdi et al., 2008) can be considered as the ‘‘silver bullet’’ of job design. Such positive changes can supplement interventions that change unreasonable goals and excessive workload and reduce hindrance stressors (e.g., role ambiguity, organizational constraints, and harassment).

FUTURE RESEARCH REQUIREMENTS AND METHODOLOGICAL CONCERNS Future research should test the success resource model of job stress using methodologically adequate research designs. In general, evidence on the success resource model is rather limited, and is mainly based on crosssectional designs. Therefore, the model should be tested by longitudinal full panel approaches that allow examining stability and time dynamics of subjective success, and causal directions of effects (Frese & Zapf, 1988; Zapf, Dormann, & Frese, 1996). Second, the model should be tested using multimethod approaches. Exclusive use of self-report for measuring both independent variables, like resources, and dependent variables such as job strain possibly leads to biased relationships due to correlated measurement errors (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003; Semmer, Grebner, & Elfering, 2004; Zapf et al., 1996), although this bias may have been overstated (Spector, 2006). For instance, self-report or self-observation of subjective success experiences using questionnaires or diaries can be combined with observation ratings of working conditions (Grebner et al., 2005) or physiological health indicators (e.g., changes in physiological stress systems such as the hypothalamic–pituitary–adrenal (HPA) axis, sympathetic-adrenal medullary (SAM) system, and cardiovascular system; Tugade & Fredrickson, 2004; Semmer et al., 2004). The advantage of a

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multimethod approach is that measurement errors of different methods can be assumed to be largely uncorrelated (Semmer et al., 2004). Third, the model should be tested combining macro- and micro-level approaches. The success resource model of job stress conceptualizes subjective success experiences as events. Hence, success experiences and outcomes should be measured using self-reports of chronic conditions (Grebner et al., 2008b) and event- or time-sampling approaches for measurement of single success events and situation-related outcomes (Grebner et al., 2004; Reicherts & Pihet, 2000). Antecedents of subjective success can be measured, for instance, using self-reports or observation ratings of chronic working conditions (Grebner et al., 2005) or situation-related measures of stressful (Grebner et al., 2004) or success-supporting events (e.g., experiences of supervisory support). Fourth, future research should identify relevant person-related variables that moderate the success-well-being/health relationship. Moderator variables may lead to a substantial amount of explained variance in subgroups, but reduce effects in the total sample. Therefore, these factors (e.g., demographic variables such as gender and age, industries and occupational groups, personality traits like optimism and perfectionism and goal attributes) should be considered as far as possible, which requires large and heterogeneous samples. Finally, goal attributes should be considered in future tests of the success resource model. A broad variety of goal attributes can influence the strength of the relationship between goal attainment and positive affect and well-being (Sirgy, 2006). For instance, attainment of both job-related approach goals and avoidance goals contribute to positive affect and well-being. Achievement of approach goals (e.g., receiving positive feedback) increases positive affect while attainment of avoidance goals (e.g., staying out of trouble) reduces the experience of negative affect (Gollwitzer, 1993). Meaningful and important goals (Oishi, Diener, Suh, & Lucas, 1999), high-level goals (Carver & Scheier, 1990), goals that are consistent with cultural norms (e.g., individualism), and goals associated with deprived needs (e.g., power, affiliation) increase ego involvement and, therefore, attainment of such goals leads to a strong increase in positive affect that, in turn, contributes to subjective well-being. In addition, because many employees work in teams with a high degree of interdependence, research is desirable that examines subjective success at the group level. A team-related variant of the SUCCESS could assess group-related goal attainment (e.g., ‘‘We reached our goals’’), group-related pro-social success (e.g., ‘‘We helped each other to succeed’’), and positive feedback targeted to a group as a whole (e.g., ‘‘We received positive feedback as a team,’’ cf., Grebner et al., 2008b).

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Finally, future research on subjective success should include development and evaluation of success-related interventions.

SUMMARY The success resource model (Grebner et al., 2008) emphasizes the importance of investigating goal attainment, pro-social success, positive feedback, and career success in research on job stress. Goal attainment and pro-social success represent immediate success resulting from job-related achievements. Positive feedback and career success are acknowledgments of good performance. The model conceptualizes subjective occupational success as a resource in the stress process because success is valued in its own right (Hobfoll, 2001); triggers positive affect and emotions (e.g., pride); helps generate, maintain, and protect other resources like status and beneficial working conditions (Hobfoll, 1998, 2001), affects well-being and health in a favorable manner (Carver & Scheier, 1990), boosts learning (Frese & Zapf, 1994); and has an energizing effect (Locke & Latham, 1990, 2002) that promotes recovery in times off work. Since subjective success is tied to one’s own effort, competence, and skills, success affirms the self, which renders it an especially important resource (Semmer, Grebner, & Elfering, 2010). The main proposition is that subjective success promotes well-being and health, including recovery from work demands. Moreover, the model posits that subjective success experiences at work can buffer detrimental effects of job stressors on strain. Situation- and person-related resources are assumed to promote success, while job stressors impede success. Although there exists supporting evidence for several aspects of the concept (e.g., beneficial effects of goal attainment on well-being; Diener & Lucas, 1999), evidence on the success resource model is limited. Available results support the main presumption that subjective success promotes different types of well-being, including job satisfaction, work engagement, affective commitment, and feelings of resentment and exhaustion. Also, subjective success fosters personal resources, such as self-efficacy and self-esteem (Mu¨hlethaler et al., forthcoming). Moreover, available evidence demonstrates the hypothesized beneficial effects of antecedents on subjective success including situationrelated resources like job control and social support, and person-related resources such as self-efficacy and problem-focused coping. Furthermore, job stressors, in particular, hindrance stressors (LePine et al., 2005), were found to impede subjective success. Little evidence is available on the proposed buffering effect of subjective success on detrimental effects of job

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stressors on well-being (Grant & Sonnentag, in press). Moreover, the beneficial effects of subjective success on psychological and physiological recovery are yet to be investigated. Hence, results should be replicated and the full model, including all success dimensions, antecedents, and all types of hypothesized outcomes, should be examined.

FINAL REMARKS ON POSSIBLE COSTS OF SUBJECTIVE SUCCESS Although the success resource model focuses on desirable consequences of success, we acknowledge that success can have costs. Of course, success has costs in terms of effort and time investment. Excessive work demands and unrealistic or competing goals (Locke, Smith, Erez, Chah, & Shaffer, 1994; Schmidt & Dolis, 2009) can require overinvestment of mental and physical resources (e.g., working overtime for an extended period) to reach goals. Even positively experienced investment of effort can lead to stress (e.g., fear of failure; Gaillard, 2001; Meijman & Mulder, 1998; Semmer et al., 2010) when individuals realize that resources are running low. Therefore, if mental, physical, and time resources are overused and recovery is insufficient, impaired well-being (e.g., irritation; Grebner et al., 2005), work-life imbalance (e.g., work–family conflict, cf., Bellavia & Frone, 2005), irreversibly limited ability to perform, and health problems can be side effects of success. In addition, success may have costs at the interpersonal level. Others might respond to an individual’s success by negative emotions (e.g., anger, envy) and counterproductive behavior, which may lead to ambivalence and discomfort regarding one’s own success (Henagan & Bedeian, 2009), even when the success implies positive affect and comfort (see also Cohen-Charash & Mueller, 2007; Fast & Chen, 2009; Heilman & Okimoto, 2007; Leach & Spears, 2008; Juola-Exline & Lobel, 1999).

ACKNOWLEDGMENTS Many colleagues, as well as students, have provided invaluable contributions to the empirical studies in this work. We cannot name them all, but we are especially indebted to the following people: Esther Achermann, Joy Baumgarten, Gilles Bigler, Manuel Cassina, Rahel Knecht, Ivo Mauch, and Co¨lestin Zehnder. Moreover, we would like to thank all our participants,

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and their supervisors and employers for active cooperation, administrative support, and continuing interest. We have profited from the work of, and discussions with, several colleagues, most notably Esther Achermann, Rahel Knecht, Sabine Sonnentag, and Dan Ganster.

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APPENDIX. THE SUBJECTIVE OCCUPATIONAL SUCCESS SCALES Instruction: ‘‘The following items apply to your recent experiences at work’’ Goal attainment (GA) GA1: I completed my tasks GA2: I achieved good results GA3: I attained goals/I made reasonable goal progress Pro-social success (PS) PS1: PS2: PS3: PS4: PS5: PS6:

I I I I I I

settled conflicts boosted the confidence of others helped others to succeed gave advice to others helped develop others motivated others

Positive feedback (PF) PF1: I received positive feedback by my supervisor PF2: I received positive feedback by coworkers PF3: I received positive feedback by subordinates Career success (CS) CS1: I made very useful work contacts CS2: I made some good career moves

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CS3: I was proactive in acquiring new customers/projects etc. CS4: I got a promotion/I received promotion prospects Answer categories for GA, PS, and PF: 1 ¼ never, 2 ¼ very infrequently, 3 ¼ quite infrequently, 4 ¼ sometimes, 5 ¼ quite frequently, 6 ¼ very frequently, 7 ¼ all the time. Answer categories for CS: 1 ¼ does not apply at all, 2 ¼ does hardly apply, 3 ¼ does apply a little bit, 4 ¼ does apply partly, 5 ¼ does apply somewhat, 6 ¼ does apply largely, 7 ¼ does apply completely. Source: SUCCESS (Grebner, Elfering, Achermann, Knecht, & Semmer, 2006).

LOVING ONE’S JOB: CONSTRUCT DEVELOPMENT AND IMPLICATIONS FOR INDIVIDUAL WELL-BEING E. Kevin Kelloway, Michelle Inness, Julian Barling, Lori Francis and Nick Turner ABSTRACT We introduce the construct of loving one’s job as an overlooked, but potentially informative, construct for organizational research. Following both empirical findings and theoretical developments in other domains we suggest that love of the job comprises a passion for the work itself, commitment to the employing organization, and high-quality intimate relationships with coworkers. We also suggest that love of the job is a taxonic rather than a dimensional construct – one either loves their job or does not. In addition, we propose that loving your job is on the whole beneficial to individual well-being. Within this broad context, however, we suggest that loving one’s job may buffer the effect of some stressors while at the same time increase vulnerability to others. These suggestions provide some initial direction for research focused on the love of one’s job.

New Developments in Theoretical and Conceptual Approaches to Job Stress Research in Occupational Stress and Well Being, Volume 8, 109–136 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-3555/doi:10.1108/S1479-3555(2010)0000008006

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Loving one’s job has been frequently identified in maxims about work as both a criterion for and a predictor of vocational success. As a criterion for success, individuals are advised to discover (Cassidy, 2000) or rediscover (Boyatzsis, McKee, & Goleman, 2002) their passion. People seeking career advice are told that loving their job is its own reward, and the adage attributed to Confucius ‘‘find a job you love and you’ll never work a day in your life,’’ expresses this sentiment well. As a predictor of success, loving a job has been tied to task and financial performance (Baum & Locke, 2004), as well as life happiness. Steve Jobs, Chief Executive Officer of Apple Computers, remarked in his address to Convocation at Stanford University: You’ve got to find what you love. And that is as true for your work as it is for your lovers. Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do. (Jobs, 2005)

Despite the centrality of the ‘‘love of one’s job’’ construct in the folklore of work, there has been little conceptual or empirical consideration of the nature of loving your job. Our goal in this chapter is to offer an initial conceptualization of the construct by drawing on the existing organizational literature. In doing so, we strive to achieve two major goals. First, we offer a definition of loving your job that is grounded in both theoretic analysis and empirical data. In doing so, we articulate three components of love of one’s job and detail a structure of love of one’s job. Second, we spell out the connections between ‘‘truly’’ loving your job and individual well-being.

DEFINING LOVE OF THE JOB In their review of the literature dealing with romantic love, Rempel and Burris (2005) defined love neither as a relationship, a form of behavior, nor an emotion. Rather, they conceptualized love as a ‘‘motivational state in which the goal is to preserve and promote the well-being of the valued object’’ (p. 299). Although it is clear that most of the available research in the literature on love has focused on interpersonal love, it is equally clear that there is nothing in the definition of love that precludes a focus on other targets. Indeed, Rempel and Burris (2005, p. 309) explicitly noted that their definition ‘‘allows love to be experienced toward any valued object.’’ Consistent with this definition, Ahuvia (2005) suggested that objects and activities are the focus of love as frequently as are people, and made reference to the possessions and activities loved by consumers. In a similar

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vein, Fournier (1998) had argued earlier that consumer brands can be likened to active relationship partners. Therefore, it is plausible that several valued objects can reasonably be considered objects of love. Notwithstanding this earlier speculation, whether a job can constitute such a valued object remains a critical question. We draw on several lines of evidence to answer in the affirmative. First, the historical record identifies paid employment as a central aspect of human experience throughout the development of civilization (Applebaum, 1984, 1992; Kelloway, Gallagher, & Barling, 2004; Pahl, 1988). Paid employment is associated with numerous manifest (e.g., pay) and latent (e.g., time structure) consequences (Jahoda, 1982) for the individual, and the absence of paid employment has been linked to deleterious consequences for individuals and society since at least the beginning of the Industrial Revolution (Burnett, 1994; Feather, 1990; Jahoda, 1982). Indeed, recent meta-analytic evidence (Paul & Moser, 2009) suggests that unemployment is related to several harmful mental health outcomes. Evidence gleaned from longitudinal studies and natural experiments points to a causal relationship between unemployment and distress. Second, the notion of a job lies at the intersection of work (i.e., purposive activity that is directed at the production of a valued good or service; Kelloway et al., 2004) and employment (i.e., the context in which work is performed; Kelloway et al., 2004). As a target for love, the ‘‘job’’ is conceptualized broadly and encompasses both the intrinsic (i.e., the work itself) and extrinsic (i.e., the context of the work) aspects of paid employment. Third, at a societal level, we often see holding a job as a terminal value that outweighs other considerations. For example, despite data suggesting that youth employment can result in a host of adverse consequences for young people, society (including both parents and children; Furnham & Thomas, 1984; Green, 1990; Greenberger & Steinberg, 1986; Mortimer, Finch, Dennehy, Lee, & Beebe, 1994; Phillips & Sandstrom, 1990) continues to value job experience and to encourage young people to obtain a ‘‘job’’ early in life (for a review see Kelloway & Barling, 1999). Finally, and of considerable import for the current presentation, there is clear evidence that individuals value their jobs, as is evident when they develop a sense of ownership over their jobs and even come to view the job as a form of valued property (Gordon & Lee, 1990; Kelloway, Barling, & Carroll, 1998). Thus, we suggest that there is sufficient evidence to conclude that a job constitutes a valued object, even a perhaps a possession (Gordon & Lee, 1990), that may be the focus of love. What is love in the context of ‘‘the job’’? Two qualitative studies suggested remarkably similar definitions of the experience of loving your

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job. Investigating the experiences of high-achieving women, (Richie, Fassinger, Linn, & Johnson, 1997, p. 139) noted that a large number of their respondents talked about loving their work. The researchers identified three dominant ‘‘themes’’ that characterized the participants’ career development, namely passion for the work, persistence, and connectedness. Similarly, after interviewing women who loved their jobs, Gordon (2006) also identified three themes characteristic of their accounts. First, each of the women interviewed took pleasure from her job activities. Second, each of the women felt good about her reasons for working. Finally, each of the women liked and at the very least respected the people with whom she worked. We suggest that these accounts parallel conceptualizations and empirical research on the components of interpersonal love. In his ‘‘triangular theory of love,’’ Sternberg (1986, 1987) suggests that interpersonal love consists of passion, commitment, and intimacy. In the context of interpersonal relationships, passion refers to the desire for union with another person and includes a variety of sources of motivation for such a union (Sternberg, 1986). These motivations may include, but are not limited to, sex, selfesteem, affiliation, nurturance, and self-actualization to name a few. According to Sternberg (1986), commitment consists of two fundamental choices. It is both a choice to select one particular person from the array of available alternatives, and a decision to maintain that relationship over time. The third dimension of Sternberg’s (1986) triangular theory of love is intimacy, and refers to the feelings of closeness, connectedness, or bondedness with another person. Intimacy promotes a desire to enhance another’s welfare, the experience of mutual happiness, having a positive regard for and being able to count on another person. Given that the dimensionality of passion, commitment, and intimacy components of love has been found to be robust across different samples (Falconi & Mullet, 2003; Lemieux & Hale, 2002), Sternberg’s tripartite model will provide the basis for the conceptualization of the love of one’s job. Specifically, we propose that love of one’s job comprises the experiences of passion for one’s work (Sagie & Koslowsky, 2000), affective commitment to the employing organization (Meyer & Allen, 1997), and a sense of intimacy with people at work (Brehm, Miller, Perlman, & Campbell, 2002). Consistent with Sternberg’s theory and the available evidence, our model of love of the job recognizes the nature and interrelatedness of the three components of passion, commitment, and intimacy, and in doing so we identify three separate foci: the work one does, the organization within which one works, and the people with whom one works. We suggest that

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recognition of the interrelatedness of passion, commitment, and intimacy gains increased importance in light of the observation that workers may love their work but dislike the conditions under which the work is performed (Kusnet, 2008). We begin by separately considering the three components inherent in our model of the love of one’s job and then turn to the question of how these components combine to form love of one’s job.

PASSION FELT FOR ONE’S WORK I never realized I was in love. And I was in love – I was in love with cookingy. And it was grueling work. Fantastic work. It’s basically 24 hours a day, 400 meals per serving, three and four times a day. – Executive Chef (Oprah Winfrey TV show, February 24, 2003).

In Sternberg’s (1986) triangular theory of love, passion involves an intense feeling of unity with and attraction for another person, and is motivated by several factors. In the context of one’s job, passion for one’s work may be construed as a unity with and attraction for one’s work, also motivated by several factors, many of which may overlap with interpersonal passion including motivations for self-esteem, self-actualization, and the nurturance of one’s career. The past few decades have seen considerable theorizing and research about people’s involvement in their work. Whether cast within the framework of job involvement (Kanungo, 1982), engagement (Harter, Schmidt, & Hayes, 2002), participation in decision making (Sagie & Koslowsky, 2000), or vigor (Shirom, 2003), the available data show that being more involved in one’s work generally results in higher levels of both productivity and well-being (Barling, Kelloway, & Iverson, 2003; Harter et al., 2002; Parker & Wall, 1998; Wall, Corbett, Martin, Clegg, & Jackson, 1990). However, individuals can feel more than involved in their work: they can be passionate, engaged, excited, or enthusiastic (Sirota, Mischkind, & Meltzer, 2005) about their work. Passion for one’s work goes well beyond most, if not all, current models that reflect different nuances of what might generically be called ‘‘job involvement.’’ Another job-related attitude that has been widely examined is that of job satisfaction (Spector, 1997). Job satisfaction describes how content individuals are with their jobs, and various aspects of their job (Spector, 1997) including their rate of pay, work responsibilities, variety of tasks, promotional opportunities, the work itself, and coworkers. Job satisfaction is considered to be an individual’s attitude toward one’s work experience,

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and as such, includes both cognitive and affective components (Wright, Cropanzano, & Bonett, 2007). Like job satisfaction, love of job is a positive emotional and attitudinal reaction to personal job-related experiences. However, the construct of job satisfaction (and dissatisfaction) does not capture the full range of affective responses to one’s work (Van Katwyk, Spector, Fox, & Kelloway, 2000), and conversations with workers (e.g., the executive chef cited above) provide anecdotal support for this suggestion. Indeed, despite advances toward understanding employee’s job-related attitudes that have been made in the literature, job satisfaction remains a relatively poor predictor of both individual well-being and job performance (Bond & Bunce, 2003; Iaffaldano & Muchinsky, 1985; Wright & Cropanzano, 2000). Several points regarding the present definition of passion for one’s work bear special focus. Notwithstanding the use of the term ‘‘passion,’’ there are no sexual undertones to this component in our model of the love of one’s job. Instead, we adopt Vallerand et al.’s (2003, p. 757) definition of passion as ‘‘a strong inclination toward an activity that people like, that they find important, and in which they invest time and energy.’’ This conceptualization is consistent with numerous formulations that suggest a motivational component to passion and link passion to achievement (Baum & Locke, 2004; Frijda, Mesquita, Sonnemans, & Van Goozen, 1991; Richie et al., 1997). Moreover, the use of the term ‘‘passion’’ in the context of one’s job conveys the intense longing for one’s work. Perhaps as is the case with romantic love, the longing for the object of the love of one’s job can best be understood from the consequences experienced when deprivation occurs. The absence of the focus of one’s passion (i.e., the specific work) would result in a negative psychological and psychosomatic state, as is evident from research on unemployment (Barling, 1990; Jahoda, 1982; McKee-Ryan, Song, Wanberg, & Kinicki, 2005; Paul & Moser, 2009). The depiction of passion as longing for one’s work is also consistent with Ashford, Lee, and Bobko’s (1989) model of job insecurity, in which one of the core characteristics is the threat of losing psychologically meaningful work (Barling & Kelloway, 1996). Finally, passion involves an affective component. Vallerand et al. (2003) distinguish between obsessive and harmonious passion, which differ in how the focal activity is integrated into personal identity. In brief, harmonious passion results from freely choosing and valuing an activity. Research shows that unlike obsessive passion, harmonious passion is associated with individuals’ hedonic and eudaimonic well-being (Philippe, Vallerand, &

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Lavigne, 2009). Reflective of harmonious passion, organizational theorists have identified job involvement as reflecting a psychological identification with one’s job (Rabinowitz & Hall, 1977; Saal, 1978) and as an indicator of context-specific mental health (Warr, 1987). In contrast, obsessive passion results from being forced or pressured to engage in an activity (Vallerand et al., 2007), possibly as a result of the contingencies involved. Reflective of obsessive passion, researchers have begun to document the negative correlates and outcomes associated with workaholism (Bonebright, Clay, & Ankenmann, 2000; Porter, 1996). Thus, in our model, a passion for the job comprises high levels of healthy engagement with, involvement in, and excitement stemming from the work itself. People who are passionate about their work will look forward to it upon waking each day, choose to engage in their work rather than other activities (e.g., recreation), voluntarily work overtime, and express considerable enjoyment and fulfillment when engaged in their work. This view is consistent with empirical research identifying the importance of employee engagement (Harter et al., 2002) and participation (Sagie & Koslowsky, 2000) for subsequent organizational outcomes.

COMMITMENT TO THE EMPLOYING ORGANIZATION Commitment is another of the three core dimensions of love identified by Sternberg (1986). In his view, commitment reflected a shift from the decision that one was in love in the short term to the intent to continue a particular relationship in the longer term. Similarly, Meyer and Herscovitch (2001) defined commitment as a force that binds an individual to a course of action. Moving beyond a simple ‘‘intent to remain’’ on a particular course of action, extensive research on organizational commitment (Meyer & Allen, 1997; Mowday, Steers, & Porter, 1979) has expanded this view to include possible bases for this intent. The most prominent conceptualization of organizational commitment is Meyer and Allen’s (1997) three-component model, which specifies that people can choose to remain with their organization because they want to (affective commitment), feel that they have to because of a lack of available alternatives (continuance commitment), or believe that they should (normative commitment; Meyer, Jackson, & Maltin, 2008). Based on their meta-analysis, Cooper-Hakim and Viswesvarin (2005) concluded that the

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various forms of commitment studied in organizational behavior in fact share a common, underlying psychological construct, with continuance commitment being one of the few exceptions (others are calculative and union commitment). Our model of love of one’s job explicitly focuses on affective commitment to the organization. Research findings show consistently that affective commitment is associated with several positive individual and organizational outcomes. Meyer, Stanley, Herscovitch, and Topolnytsky (2002) conducted a meta-analysis of the outcomes associated with different types of commitment. Consistent with studies reporting positive relationships between affective commitment and various outcomes associated with work attitudes and job performance (Meyer & Allen, 1997; Meyer, Paunonen, Gellatly, Goffin, & Jackson, 1989), they found that affective commitment was associated inversely with turnover and absenteeism, and positively with performance outcomes such as job performance and organizational citizenship. In contrast, continuance commitment is associated negatively with job performance (Cooper-Hakim & Viswesvarin, 2005; Meyer et al., 1989), and the consequences of normative commitment are moderate at best – a pattern that replicates findings from research on the nature and meaning of marital commitment (Adams & Jones, 1997; Johnson, Caughlin, & Huston, 1999). Thus, our model extends Sternberg’s original formulation in suggesting that the basis of commitment (i.e., affective commitment as opposed to normative or continuance commitment), rather than simply the decision to remain, is a critical component of loving one’s job. Equally important, affective commitment may be central to individuals’ well-being. In the same meta-analysis cited above, Meyer et al. (2002) found an inverse relationship between affective commitment and well-being, as reflected by lower levels of self-reported stress and work–family conflict. This is consistent with research suggesting that affective commitment is associated with lower levels of self-perceived stress at work (Begley & Czajka, 1993), and greater parental and community involvement (Kirchmeyer, 1992). Indeed, Warr (1987) went further than viewing wellbeing as an outcome of commitment, and identified affective commitment to the organization as a form of context-specific (i.e., work-related) well-being. One of the strongest correlates of affective commitment is that of job satisfaction (Meyer et al., 2002). It has been suggested by Meyer et al. (2002) that this correlation may be partially attributable to the fact that global measures of satisfaction often include items referencing the individual’s satisfaction with the organization as a whole. The relationships between individuals’ satisfaction with other aspects of work and commitment are far

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weaker. This may suggest that job satisfaction does not fully capture the intensity of emotional and attitudinal experiences that may best relate to one’s decision to remain affectively committed to their organization. In the present conceptualization, we propose that a person who is affectively committed to their organization is more likely to love their job.

INTIMACY WITH PEOPLE AT WORK I love my job so because I come into contact with so many wonderful people. – School janitor (Oprah Winfrey TV show, February 24, 2003).

Acknowledging the importance of high-quality personal relationships at work is by no means new. Their importance had been emphasized systematically as early as the results of the Hawthorne Electric Studies (Roethlisberger & Dickson, 1939) and the Tavistock Coal Mining Studies (Trist & Bamforth, 1951) – an era in which social needs were seen as dominating individuals’ motivations to work (Schein, 1980). Around the same time, Maslow drew specific attention to the need for love and belongingness in his need hierarchy (Maslow, 1944), which was influenced by his view of human degradation in the Second World War and his own personal experiences at work (Maslow, 1965). More recently, Baumeister and Leary (2000) suggested that forming social attachments is a fundamental human need that people will pursue under most social conditions and that feeling a sense of belongingness has strong effects on emotional patterns and cognitive processes. Various current streams of research (Gersick, Bartunek, & Dutton, 2000; Jehn & Shah, 1997) add to our understanding of the need for positive social relationships at work. Consistent with Sauter, Murphy, and Hurrell’s (1990) framework of the psychosocial factors that make up healthy work, a great deal of data support the suggestion that positive relationships with coworkers are associated with reduced strain (Beehr, Jex, Stacy, & Murray, 2000; Fry & Barker, 2002; Johnson & Hall, 1988; Koeske & Koeske, 1989), enhanced job satisfaction (Ducharme & Martin, 2000; Roxburgh, 1999), and improved performance (Barrick, Stewart, Neubert, & Mount, 1998). Certainly, data consistently shows the negative effects that accrue to well-being when belongingness needs are thwarted (Twenge, Baumeister, Tice, & Stucke, 2001; Twenge, Catanese, & Baumeister, 2003). The importance of relationships in the workplace is frequently acknowledged, but is not often the primary focus of research (Hodson, 1997). However, the available data suggest that the quality of relationships in the

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workplace is predictive of citizenship behaviors (Settoon & Mossholder, 2002), perceptions of the work environment (Klein, Conn, Smith, & Sorra, 2001), as well as group cohesion and job performance (Mullen & Copper, 1994). Moreover, the voluminous research findings on social support at work also show consistently that the various dimensions of social support (e.g., emotional, informational, instrumental, appraisal) are associated with increased productivity and individual well-being (Cohen & Wills, 1985; Frese, 1999). Interactions among coworkers can take many forms. Ferris et al. (2009) present an integrative, stage-based model of the development of work relationships. Initial interactions are likely to reflect aspects of instrumentality, affect, and respect. Across four stages, relationships expand and increase in intimacy to ultimately reflect qualities such as trust, loyalty, and flexibility, in addition to instrumentality, affect, and respect. The extent to which expectations are met influences progression in these stages, as individuals increasingly value the relationship in and of itself rather than as vehicle toward some end (Dwyer, Schurr, & Oh, 1987). Kram and Isabella’s (1985) categorization of peer relationships to some extent reflects the stage-based notion presented by Ferris et al. (2009). Peer relationships within organizations range from informational to collegial to special (Kram & Isabella, 1985). Information-based peer relationships are low-demand interactions in which both parties benefit from the sharing of information. Collegial peer relationships offer emotional support and feedback. They are characterized by moderate levels of trust and selfdisclosure. Special peer relationships are the most intimate, take the longest to develop, and are characterized by a high level of self-disclosure and self-expression. While all of these organizational relationships can offer career-enhancing functions, collegial and special peers also provide psychosocial support such as confirmation, emotional support, personal feedback, and friendship. Using the construct definition of the overall affective orientation derived from interacting with coworkers, Hain and Francis (2006) developed and tested a measure of coworker relationships. They found that positive coworker relationships directly influenced job satisfaction, which in turn was associated with improved emotional and physical well-being. Reflecting more intense workplace relationships, evidence suggests that workplace friendships contribute positively to the experience of work. The Gallup poll of employees across the world shows that a major characteristic of a great workplace is the opportunity to have a best friend at work (Based on Gallup Research (2009), What makes a good workplace?). Perceived

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opportunities for friendship at work have been directly associated with such positive outcomes as job satisfaction and job involvement (Riordan & Griffeth, 1995). Beyond friendship opportunities, the quality of workplace friendships also influences important outcomes. Friendship quality might be reflected in socializing outside of work, feeling that you can confide in a coworker, and trusting a coworker (Nielsen, Jex, & Adams, 2000). For example, high-quality relationships were positively associated with job satisfaction among some university faculty and staff (Winstead, Derlega, Montgomery, & Pilkington, 1995). Validation studies on the Workplace Friendship Scale, which incorporates both friendship quality and friendship opportunity, suggest that dimensions predict outcomes such as affective comments, job satisfaction, and turnover intentions (Nielsen et al., 2000) Beyond their influence on individual well-being and attitudes, positive coworker interactions also promote organizational functioning. Based on ethnographic observations, Hodson (1997) suggested that coworker relationships promote effective organizational functioning via four mechanisms. First, coworker relations promote occupational socialization; for example, apprentice models of entry into a field. Second, positive coworker relationships contribute to solidarity within an organization. Third, supportive coworker relationships help individuals in cases where they deem it necessary to resist those in authority. Last, coworkers help to affirm group identities, for instance by engaging in rituals surrounding events such as birthdays. Taken together, these functions make coworker relationships an important aspect of job satisfaction, positive relationships with management, and the sense of having meaningful work. Arguably, although intimacy within the context of a loving relationship includes constructive social interaction experienced as closeness and connectedness to another person, intimacy also goes further in terms of affective intensity. As Sternberg’s (1986) triangular theory of love suggests, intimacy includes the willingness to advance the well-being of the other, feelings of positive respect for the loved one, and cherishing the other’s place and role in one’s own life. Within the workplace, such intimacy would be apparent in relationships that enable employees to share salient workrelated and personal issues in confidence, reflecting a trust in others. Indeed, trust is an element of the special peer relationship as articulated by Kram and Isabella (1985) and high-quality workplace friendships as assessed by the Workplace Friendship Scale (2000). Trust is generally defined as ‘‘a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behavior of another’’ (Rousseau, Sitkin, Burt, & Camerer, 1998, p. 395). Trust is a primary

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dimension of relationship quality (Settoon & Mossholder, 2002), and a defining quality of positive relationships in the workplace (Pratt & Dirks, 2006). The available data suggest that trust plays an important role in the workplace influencing both well-being (Harvey, Kelloway, & Duncan-Leiper, 2003) and performance (Dirks, 1999). In studies of interpersonal relationships, trust is directly linked with love (Rempel, Holmes, & Zanna, 1985).

The Structure of Love Thus, we define love of one’s job as comprising passion for the work itself, affective commitment to the organization, and intimate relationships in the workplace. In the foregoing review, we have suggested that each of these individual components is, in itself, predictive of well-being. However, the fundamental premise underlying our model is that the love of one’s job is superior in a predictive sense to any of its three components alone. Testing this premise requires that one first specifies how these three components are combined – that is, how does one operationalize the construct of love of the job? We suggest that there are at least three viable alternatives: a common factor approach, an interactional approach, and a taxonic approach. Viewing the components as indicators of a common factor is consistent with the notion that each of them is focused on some aspects of work (the nature of the work as passion; the organization itself as commitment; and people at work as intimacy). In this sense, our operationalization of the three component model of the love of the job is similar to the way in which core self-evaluations (Judge, Bono, Erez, Locke, & Thoresen, 2002) comprising several different elements (i.e., self-esteem, generalized selfefficacy, locus of control, and emotional stability) can be viewed (Judge & Bono, 2001; Judge, Locke, Durham, & Kluger, 1998). Similarly, the construct of positive psychological capital (or ‘‘PsyCap,’’ defined as the sum of hope, optimism, resiliency, and self-efficacy) has been operationalized in this manner (Luthans, Youssef, & Avolio, 2006). However, it is possible that this approach fundamentally confounds the contribution of the individual components (i.e., passion, commitment, and intimacy) with the higher-order construct (love of the job). Moreover, an additive approach implies that a high level of one component (e.g., passion) may compensate for a low level of another (e.g., commitment). This additive compensatory approach does not reflect the original intent of Sternberg’s model that requires the presence of all three components to define what he terms as consummate love.

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A second possibility is that the most appropriate conceptualization comprises the interaction of intimacy, passion, and commitment, and that operationally it is the statistical interaction of the three elements that predicts variance in well-being, over and above any variance attributable to the individual components. This approach is consistent with Sternberg’s (1986) approach in which he identifies types of love defined in terms of low and high scores on each of the dimensions (e.g., being high on commitment and intimacy but low on passion is indicative of friendship). One advantage of this approach is that unlike the common factor approach described above, true love of the job requires high levels of passion, intimacy, and commitment. Nonetheless, operationalizing love of the job as a three-way interaction is a questionable choice as the detection and interpretation of such interactions is subject to a number of constraints (Dawson & Richter, 2006). Defining love of the job as a three-way interaction between the components may result in a model that is unlikely to ever be empirically validated as a result of the methodological difficulties in detecting interactions. Operationalizing love of the job as an interaction also introduces considerable complexity into the model that is not intended in our formulation. For example, Sternberg identifies eight different categories of relationship resulting from a 2 (passion)  2 (commitment)  2 (intimacy) taxonomy. However, rather than being interested in all possible combinations of the three constituent elements, we are really interested in only two groups of individuals: Those that love their jobs are defined as individuals who have passion for the work, commitment to the organization, and intimate relationships with coworkers, versus those who do not. Following this conceptual definition, we anticipate that love of the job would manifest empirically as an individual who scores ‘‘high’’ on measures representing these three constituents. Any other pattern (i.e., scoring low on all three dimensions or high on one dimension and low on the other two) constitute the group of individuals who, in our definition, do not love their job. In essence then, we suggest that love of the job is either present or not for each individual. This is a taxonic (Meehl, 1992, 1995) definition of love of the job comparable to the use of diagnostic categories in medical practice. Taxonic constructs are almost entirely overlooked in organizational research in favor of dimensional and continuous representations. Indeed, although the practice of dichotomizing continuous data was once well accepted, this procedure is now typically identified as a methodological error that biases statistical tests (MacCallum, Zang, Preacher, & Rucker, 2002). However,

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these same authors note that dichotomous representations may be appropriate when a series of well-conducted taxometric analyses support the taxonic nature of the construct. In essence, the recommendation is simply that the operationalizations of the construct should match the empirically verified latent structure of the construct (Ruscio & Ruscio, 2002). Techniques for identifying taxonic constructs are well-developed (Ruscio, Haslam, & Ruscio, 2006; Schmidt, Kotov, & Joiner, 2004), and provide an approach not often used in industrial/organizational psychology for operationalizing constructs. In the most common forms of taxometric analysis that follow from the seminal work of Meehl (1992, 1995), taxometric methods typically use a form of sliding cut kinetics in which the covariance between two variables is calculated and plotted at all possible levels of a third variable. Evidence for the existence of a taxonic construct is given by a sharp bend in the graph line at the point dividing the taxon and the complement. Although early implementation of the method was hampered by reliance on visual inspection of plot lines, more modern implementations rely on the calculation of fit indices to assess the validity of the taxonic model. Details of implementing the taxometric method can be found in other sources (Ruscio et al., 2006). However in relation to the current focus, we assert that love of one’s job is a taxonic construct – one either loves one’s job or does not. We suggest that this definition comprises an empirically verifiable hypothesis, and addressing the latent structure of the construct ‘‘love of one’s job’’ is a primary task for future research.

LOVE OF THE JOB AND WELL-BEING Love of the job comprises a combination of the amount of passion one has for the work itself, one’s degree of affective commitment to the employing organization, and the extent to which one has trusting intimate relationships with coworkers. We suggest that having true love of one’s job, that is simultaneously having high passion, high commitment, and high intimacy, has a beneficial effect on individual well-being. Two lines of evidence support our suggestion. First, there are data that show that each of the individual components of love are associated with well-being; in some cases, the findings are job specific. With respect to passion, harmonious passion (but not obsessive passion) is associated both with well-being and diverse aspects of performance

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(Philippe et al., 2009; Vallerand et al., 2007). Passion for one’s work is also associated with well-being, while an addictive relationship with one’s work is negatively associated with psychological well-being. Organizational commitment is also associated with well-being (Wiener, Muczyk, & Gable, 1987). That said, overcommitment to the organization is predictive of negative personal outcomes (Kinman & Jones, 2008). However, these findings are not uniform across different types of organizations or work contracts (Bernhard-Oettel, Cuyper, Berntson, & Isaakson, 2008). For example, crosssectional results show that being committed to one’s clients, but not one’s agency, is positively associated with well-being in the short term; however, if employees are reassigned to different clients, such commitment is negatively associated with well-being in the longer term (Galais & Moser, 2009). Intimacy is associated with lower psychological symptomatology (Schreiber, 2001). Specific to the work context, positive coworker relationships are associated with reduced job stress, strain, and burnout (Beehr et al., 2000; Johnson & Hall, 1988). Second, the positive association of emotions and well-being would suggest that it is beneficial for individuals to love their jobs. In particular, we suggest that loving one’s job enables an individual to reconceptualize many potential stressors in the workplace as challenges. In the context of romantic love, researchers have generally suggested that there are positive health consequences of being in a long-term romantic relationship (Berry & Worthington, 2001) – a suggestion that is consistent with the models specifying the health effects of positive emotions (Frederickson, 1998; see also Lazarus & Cohen-Charash, 2001). Esch and Stefano (2005) drew a clear link between love and positive emotions, suggesting that in doing so, the phenomenon becomes amenable to scientific enquiry. Certainly the available evidence would suggest strong associations between interpersonal love and health. For example, feelings of love or affection are associated with both improved cardiovascular response and positive endocrinal changes (Grewen, Anderson, Girdler, & Light, 2003; Light, Grewen, & Amico, 2005). We have described above how loving one’s job contributes to well-being. On the other hand, it is also possible that loving one’s job may make a person more vulnerable to stressors that threaten the loss of the beloved job. That is, individuals whose job security is threatened (e.g., by layoffs, restructuring) may suffer a compromise to their health (Ashford et al., 1989). Although the data suggest a generally positive health effect of being in love, others have noted the potential for love to be stressful in and of itself (Esch & Stefano, 2005). Considering the maintenance or dissolution of a relationship, some have pointed to both positive and negative experiences

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associated with being in love (Kemper & Bollogh, 1981), particularly noting that when a relationship is ‘‘going badly, a variety of health effects are reported’’ (Kemper & Bollogh, 1981, p. 84). Conservation of resources theory (Hobfoll, 1998) provides an explanation as to why those who love their jobs may be particularly vulnerable to stressors that threaten their work. According to conservation of resources theory, individuals work to obtain, maintain, and protect valued resources, any threat to which can be harmful to well-being. For those who love their job, their job content, colleagues, and organizations are valued resources that promote well-being. Indeed, recent findings suggest that access to these resources promotes positive change in individual well-being (Christie & Barling, 2009). In contrast, a threat to that job is in essence a threat to a multipronged set of valued resources that contribute substantially to health and well-being. Such a threat may lead to stress. We suggest that these findings parallel those in the broader organizational literature. For example, Schmidt (2007) describes two processes through which commitment is thought to moderate the effects of stressors on wellbeing. In one model, those who are more highly committed to the organization may experience the most negative effects if laid off (Mathieu & Zajac, 1990). In essence, the suggestion is that more highly committed employees are more vulnerable to the adverse effects of layoff decisions because their increased involvement in the workplace enhances their vulnerability. In the second model, commitment is hypothesized to buffer the effect of stressors on well-being (Begley & Czajka, 1993; Schmidt, 2007; Siu, 2002). In a similar vein, we postulate that individuals who love their jobs may be both buffered from and more vulnerable to stressors in the workplace. Love of the job is anticipated to act as a buffer against many of the day-to-day stressors entailed in working. Individuals who love their job are expected to be less affected by stressors such as role overload (Harvey et al., 2003). At the same time, individuals who love their job may be more vulnerable to stressors that threaten the existence of the relationship of the individual and the job. Threats to job security or potential changes in the nature of the job may be particularly difficult, and have negative consequences for the health of individuals who love their job.

AVENUES FOR FUTURE RESEARCH While the propositions advanced here await empirical scrutiny, the research agenda identified has the potential for continuing the move

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toward a more positive focus for organizational behavior, one that identifies and appreciates the intense positive emotions associated with the experience of work, and describes the plausible antecedents and outcomes of loving one’s job. Pursuing the structure, antecedents, and outcomes of the love of one’s job will provide a more balanced perspective in organizational behavior, and consistent with the goals of positive psychology, help to focus attention on positive, valued subjective human experiences that have the potential to buffer individuals from illness and enhance their well-being and personal fulfillment (Seligman & Csikszentmihalyi, 2000). Over and above identifying the nature and outcomes of the love of one’s job, our conceptualization of the love one’s job will enable us to confront other interesting questions, and we identify several such questions here. However, in doing so, we make no specific propositions in recognition of the more preliminary nature of these issues. First, despite widespread and long-standing beliefs that job dissatisfaction would predict serious illness, there is no compelling empirical support for this notion. We suggest that the failure to support this notion results from the lack of emotional depth and intensity that is reflected in current models and measurements of job dissatisfaction (Fitness & Fletcher, 1993), but could be captured more aptly in the hatred of one’s job. There is currently no focus on the nature or consequences of hating one’s job, but there are indications from research on close relationships that love and hate are separate emotions (Fitness & Fletcher, 1993). We would expect that the simple absence of the three core characteristics of the love of the job (passion, commitment, and intimacy) would not be sufficient to result in hating one’s job. Sternberg (2003) has proposed a conceptual model of hate that is derived from the triangular theory of love. His model of hate comprises several correlated components, namely disgust, devaluation and diminution, and anger or fear. Notwithstanding the absence of empirical evidence, the extent to which the model might provide the basis for an understanding of why people might hate their jobs is compelling. Following Fitness and Fletcher (1993), if hating one’s job is conceptually distinct from loving one’s job, a separate consideration of its nature, antecedents, and outcomes becomes warranted. Indeed, recent statistical developments (e.g., latent curve modeling; Christie & Barling, 2009) make it possible to subject questions about the dynamic development and consequences of the love of the job to empirical scrutiny. Second, a fundamental assumption underlying a social system that emphasizes monogamy is the idea that it is not possible to love more than

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one person simultaneously, raising two separate but related questions with regard to the love of one’s job. First, can individuals who hold more than one job simultaneously love them both equally and simultaneously? This is a critical question given the growing number of individuals who find themselves facing this situation (Barling & Gallagher, 1996). A second question is whether loving one’s job might limit or prevent the ability to love another individual, simultaneously and equally. Answers to questions such as these might provide the boundary conditions for the positive consequences of loving one’s job. Although we acknowledge that these scenarios might not be comparable given the nature of the love of one’s job and the relational nature of the love of another individual, we argue that these questions are relevant because the love of one’s job is based on a relationship between the individual and the job. If the love of one’s job is to have any meaning, might this imply that the love of one’s job is antithetical to loving someone simultaneously? There are two separate literatures that might provide some guidance in answering this question. First, given the central role of commitment within the love of one’s job construct, the long-standing debate whether dual commitment to company and union is at all feasible might be instructive. Results from studies on dual commitment have been inconsistent, showing a positive (Gottlieb & Kerr, 1950), a negative (Barling, Wade, & Fullagar, 1990), or no relationship (Sherer & Morishima, 1989) between commitment to the company and the union. The most appropriate conclusion from this research (Barling, Fullagar, & Kelloway, 1992) is that where there is no conflict between the focal organizations, dual commitment is possible; when conflict exists between the union and the company, unilateral commitment is most likely (Fullagar, Barling, & Christie, 1991). Second, the voluminous, empirically based literature of work–family conflict generated over more than two decades might also be instructive. Research shows consistently that work–family conflict affects the individuals concerned, those in close proximity to them (family members, coworkers), their organizations, and their communities (Bellavia & Frone, 2005). Embedded in the findings from these two different areas, and the research questions raised, is the assumption that the love of the job may under some circumstances have harmful effects. Yet lost in this literature are a set of findings suggesting that role accumulation may be good for one’s health, that is, well-being increases as the number of roles a person holds accumulates (Barnett & Hyde, 2001). Like the findings on dual commitment, data suggest that work and family roles may be conflictive, but do not necessarily need to be. Thus, while these issues await empirical evidence, we

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suggest that the love of one’s job need not preclude an individual from loving more than one job simultaneously, and in the same way loving more than one person is feasible. Third, the ideas presented so far are consistent with the notion that the love of one’s job is associated with well-being in a linear manner. In contrast, the possibility that the love of one’s job may be harmful to wellbeing would suggest that this relationship may be curvilinear in nature and ought to be investigated. Certainly notions that overcommitment can be detrimental to well-being (Kinman & Jones, 2008), or that individuals who love their jobs the most are especially vulnerable in the face of layoffs (Ashford et al., 1989) opens up a viable set of questions for future research. A fourth avenue for future research is to examine the nature of the relationship between job satisfaction and loving one’s job. Earlier we suggested that the love of one’s job will be related to but empirically separable from job satisfaction. We posit that the relationship between job satisfaction and the love of one’s job can best be characterized conceptually as existing on a continuum of affective intensity. In other words, job satisfaction would be a necessary, but insufficient condition for experiencing the love of the job. This conceptualization would have significant measurement implications, in that the most appropriate approach to modeling this relationship might not be to demonstrate the lack of a relationship between job satisfaction and love of the job, but to determine whether the data would fit a Guttman-type scale, reflecting a continuum of affective intensity. Fifth, people holding certain jobs (e.g., portfolio workers whose attachment to an organization would be tenuous by nature, lone workers, or employees engaged in dirty work) might be precluded from loving their job as opportunities for experiencing passion, commitment, and intimacy would be limited at best. Nonetheless, it is possible that opportunities for fulfilling the three components might come from unexpected sources (e.g., lone workers might experience intimate relationships with people external to their organization, such as customers or suppliers). Therefore, research focusing on such groups will be needed to test the boundaries of the model proposed here. This is critical if this model of the love of the job is not to be specific only to those situations compatible with a traditional organizational context. We save what is perhaps the most fundamental question for last, namely, what is the nature of the construct of the love of one’s job? Earlier, we suggested that at least three conceptualizations of the love of the job are possible, namely, a common factor approach, an interactional approach,

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and a taxonomic approach. While we initially favor a taxonomic approach, we acknowledge that the most appropriate answer to this question will be data based. A comprehensive approach to answering to this question will include tests of the nature of the construct as well as its predictive validity.

CONCLUSION The notion that people can and do love their job has largely escaped theoretical and empirical examination. A greater focus on the more intense positive emotions that individuals have toward their jobs offers a perspective that has the ability to trigger new ways of looking at the nature of work experiences, reinterpret existing research findings, and spark new research. Organizational researchers have the opportunity to help employees thrive and become personally fulfilled through their work experiences (Luthans, 2002). Given that most people spend much of their lives in some form of employment, a better understanding of the love of one’s job, its antecedents, and consequences has important implications for organizational functioning, as well as individuals’ well-being at work and in life.

ACKNOWLEDGMENTS We acknowledge with gratitude the Christie, Heather Dezan, Kate Dupre´, Adrienne Olnick, and Sean Tucker. Sciences and Humanities Research acknowledged.

comments of Tony Carroll, Amy Sandy Hershcovis, Colette Hoption, Financial support from the Social Council of Canada is gratefully

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Winstead, B. A., Derlega, V. J., Montgomery, M. J., & Pilkington, C. (1995). The quality of friendships at work and job satisfaction. Journal of Social and Personal Relationships, 12, 199–215. Wright, T. A., & Cropanzano, R. (2000). Psychological well-being and job satisfaction as predictors of job performance. Journal of Occupational Health Psychology, 5, 84–94. Wright, T. A., Cropanzano, R., & Bonett, D. (2007). The moderating role of employee positive well being on the relation between job satisfaction and job performance. Journal of Occupational Health Psychology, 12, 93–104.

QUALITATIVE METHODS CAN ENRICH QUANTITATIVE RESEARCH ON OCCUPATIONAL STRESS: AN EXAMPLE FROM ONE OCCUPATIONAL GROUP$ Irvin Sam Schonfeld and Edwin Farrell ABSTRACT The chapter examines the ways in which qualitative and quantitative methods support each other in research on occupational stress. Qualitative methods include eliciting from workers unconstrained descriptions of work experiences, careful first-hand observations of the workplace, and participant-observers describing ‘‘from the inside’’ a particular work experience. The chapter shows how qualitative research plays a role in (a) stimulating theory development, (b) generating hypotheses, (c) identifying heretofore researcher-neglected job stressors and coping responses, (d) explaining difficult-to-interpret quantitative findings, and (e) providing $

This chapter is an expansion of the paper, ‘‘Qualitative and Quantitative Methods in Occupational Stress Research’’ Professors Schonfeld and Farrell published in Rossi, A.M., Quick, J.C., & Perrewe´, P.L. (Eds.). (2009). Stress & quality of working life: The positive and the negative. Greenwich, CT: Information Age Publishing.

New Developments in Theoretical and Conceptual Approaches to Job Stress Research in Occupational Stress and Well Being, Volume 8, 137–197 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-3555/doi:10.1108/S1479-3555(2010)0000008007

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rich descriptions of stressful transactions. Extensive examples from research on job stress in teachers are used. The limitations of qualitative research, particularly in the area of verification, are also described.

QUALITATIVE RESEARCH ON OCCUPATIONAL STRESS CAN ENRICH QUANTITATIVE STRESS RESEARCH The purpose of this chapter is to advance the idea that qualitative methods and more highly controlled quantitative methods applied to occupationalstress research, together, compared to either methodology alone, can provide a clearer picture of the stress process. Plewis and Mason (2005) wrote that quantitative and qualitative methods represent ‘‘mutually informing’’ strands of research. Hugentobler, Israel, and Schurman (1992) underlined the view that every method has weaknesses, and that by applying manifold methods to the study of occupational stress, weaknesses in one method can be compensated for by strengths in other methods. They go on to show how qualitative and quantitative methods converged in identifying the sources of stress in workers in a manufacturing firm. Qualitative research, moreover, can be useful to quantitative researchers in instrument development (Blase, 1986; Brown et al., 1986; Schonfeld & Feinman, 2009). Qualitative methods, particularly methods associated with grounded theory (Glaser & Strauss, 1967), emphasize the emergence from data of theoretically important categories as well as hypotheses bearing on the relations among those categories. There is no dearth of literature on using multiple methods (Cresswell, 2003; Tashakkori & Teddlie, 2003). Smith (2006), justifying the application of multiple methodologies in educational research, pointed out that ‘‘any methodology has inherent deficiencies and fails to capture the chaos, complexity, and contextuality of applied fields such as education’’ (p. 458). We would add the applied field of occupationalstress research. Methods must fit the research questions. It is appropriate to use survey methods, for instance, when one wants to quantify variables in the occupational-stress context. To characterize descriptively the intensity of work-related stressors experienced by individual workers, however, qualitative methods may be profitably used (Jex, Adams, Elacqua, & Lux, 1997). There are at least three broad types of qualitative methods that have been employed in occupational-stress research (see Tables 1 and 2). The first, and most commonly used, method involves having members of

305 male and 109 female academics at a university in Wales

32 male police officers from two southern U.S. localities Included officers currently undercover, formerly undercover but now having routine duties, and never undercover and having routine duties

63 medical–surgical nurses and 67 critical-care nurses in a qualitative study 22 psychiatric nurses in quantitative study Not clear but probably predominantly female sample California

Arter (2008)

Bargagliotti and Trygstad (1987)

Sample

Leading sources of job stress were conducting research, time constraints, relationships with others, and financial difficulties

Key Findings

Qualitative interviews to identify stressors and designed to let categories of stressors emerge from the data Also collected quantitative data from standardized instruments

Colleague relationships were source of stress evident in qualitative study but not in quantitative study In quantitative study, difficulties with management were the most common stressors Difficult to compare psychiatric nurses to others because roles involve different types of nursing

Used an interview designed to Those currently undercover showed the most understand the phenomenology, deviance, defined as behavior that if or ‘‘lived experience,’’ of policing. discovered would lead to department Interview tied to Agnew’s (2001) sanctions strain theory Those formerly undercover showed a Police officers invited to serve as decrease in deviance from period covering ‘‘co-researchers’’ previous duties to new duties Unusual for purely qualitative Least deviance in the officers on routine study because author tests patrol hypotheses (regarding Agnew’s theory of stress)

Questionnaires containing openended questions about stressors as supplement to a quantitative study

Method

Qualitative Studies of Occupational Stress Involving a Variety of Workers with the Exception of Teachers.

Abouserie (1996)

Paper

Table 1.

Qualitative and Quantitative Research 139

10 Swedish women nurses

268 faculty and 74 student-affairs (S-A) staff members Sample was representative of faculty and S-A staff at Midwestern U.S. state university

Brown et al. (1986)

Sample

Billeter-Koponen and Frede´n (2005)

Paper

Key Findings

Qualitative component supplementing quantitative questionnaire study involving scales measuring job stress Qualitative component included open-ended questions on sources of job stress and coping

Qualitative results indicate that sources of stress included lack of time, problematic relationships, and certain job characteristics (e.g., red tape, committee work) Coping included self-care (e.g., relaxation and recreation) and direct action (e.g., time management and shedding responsibilities) Qualitative findings were consistent with quantitative findings, particularly in area of lack of time and problematic relationships

Semistructured interviews organized Stressors included colleagues absenting to allow categories of stressors to themselves, creating more work for nurses emerge who were present in terms of piling on tasks. Authors read nurse powerlessness into these conditions Strains included headaches, stomachaches, and lack of energy Presence of colleagues was important to wellbeing: ‘‘It is not the coffee, but the meeting others. One has to get energy. One is working much better’’ ‘‘Burnout was a mental coma. I could do nothing’’ (p. 24)

Method

Table 1. (Continued )

140 IRVIN SAM SCHONFELD AND EDWIN FARRELL

32 nurses in ward that was redesigned for more holistically delivered services and 75 nurses in control wards; Germany; 87.5% females

8 females nurses who worked in community mental health teams; U.K.

Bu¨ssing and Glaser (1999)

Carradice, Shankland, and Beail (2002)

Job stressors centered on lack of control and disrespect: 1. psychologists planned for residents but had unrealistic expectations and did not consider the ideas and experience of technicians; 2. administrators made decisions affecting technicians without technician input One of 4 supervisors was supportive; in that unit technicians were highly cohesive and had the best Cornell Medical Index scores Violence among residents was a stressor

Semistructured interview designed to elicit narratives about family caregivers, but caregivers themselves were not interviewed

The nurses indicated that caregiving gave rise to distress in the caregiver Technically not a study of occupational stress; more an assessment of nurses’ models of stress in family members who provided care to demented patients Some gaps in nurses’ understanding of caregiver stress By implication these gaps affect nurse efficacy

12 discussion groups The quantitative study had seemingly There was also a quantitative study contradictory results: nurses who worked Qualitative data were to illustrate the in the ‘‘holistic’’ wards experienced a meanings of quantitative findings reduction in stressors (time pressure, contradictory task goals, and ergonomic stressors) as a result of job redesign; however, emotional exhaustion and depersonalization were elevated Qualitative findings indicated that holistic system intensified nurses’ emotional work and interaction stress; no opportunity to withdraw from difficult patients; traditional wards had only piecemeal exposure to difficult patients

Interviews covered sources of 21 psychiatric technicians in 4 perceived work stresses, sources Southern California units that of satisfaction with work, and housed mentally retarded patients, support from work and nonwork whom a companion paper by sources Lundgren and Browner (1990) indicated were challenging to care Participant observation in each of the 4 units under study; authors for engaged in activities performed 12 techs were women by the technicians

Browner et al. (1987)

Qualitative and Quantitative Research 141

A ‘‘one-off’’ event was unanticipated, especially violent event; an epiphanal event had been experienced previously but now has acquired new meaning Officers who experienced one-off event were more motivated to recover and return to work; officers who experienced an epiphanal event were more pessimistic and more likely to want to leave job

35 English police officers 11 were female

Dick (2000)

Based on experiences of author as a counselor to police officers

Positive and negative pressures 5 supervisors and 5 administrators in Interviews using open-ended Some pressure viewed as helpful, causing questions U.K. sales office contributed to feeling of having one’s abilities stretched; ‘‘Can you think of a time at work first stage of a two-stage study; such pressure was reported to be when you felt under stress?’’ stage 2 was quantitatively stimulating ‘‘Can you tell me what happened and organized study how you managed to cope with it?’’ Coping seen as central to shaping stressful Gender distribution not clear experiences Qualitative results helped in development of items for coping scale used in stage 2

Dewe (1989)

Key Findings

21 directors were interviewed. Stressors included reduced funding, high Qualitative material came from the workload, understaffing, interpersonal interview, which included openconflict, and role ambiguity ended questions about stressors Coping included problem solving (e.g., time and structured probes about management, delegating), confrontation stressors (e.g., letting feelings out, expressing Interview also included questions anger), positive reappraisal (e.g., putting about coping events in perspective) All questionnaires also contained Support came from associates, mates, other quantitative measures administrators, and friends Quantitative findings indicate high levels of psychological symptoms relative to scale norms.

Method

43 California county directors of nursing 42 were women

Sample

Cohen (1989)

Paper

Table 1. (Continued ) 142 IRVIN SAM SCHONFELD AND EDWIN FARRELL

23 employees at a Swiss counseling agency 19 men

318 fourth-year U.K. medical students Sex distribution not mentioned

Elfering et al. (2005)

Firth and Morrison (1986)

Keenan and Newton’s (1985) SIR to ask about 1 stressful incident (excluding exams) in the last month Also asked about most liked and disliked aspects of work (gets at chronic stressors) Content analysis

Very similar to Grebner, Elfering, Semmer, Kaiser-Probst, and Schlapbach (2004). See below

Stressors included talking with psychiatric patients, effects of work on private life, and dealing with death Chronic stressors included feeling useless, relations with senior doctors, feeling inadequate

Employees experienced about 7 stressful work-related events per day Daily stressors came more from work than home Work stressors included interpersonal stressors, quantitative and qualitative overload, organizational problems such as lack of data backup Situational well-being after a daily stressor (ascertained qualitatively) was inversely related to the intensity of chronic stressors (measured quantitatively) Calming down after daily work stressor was directly related to job control

Officers often used palliative coping strategies Coping via rumination (here meaning dwelling on causes of stressful events) was especially evident in officers who experienced anger and depression Organizational values influenced the individual; example of ‘‘acting tough’’ in face of devastating stressor

Qualitative and Quantitative Research 143

No more than 26 Crown In-depth interviews with prosecutors ‘‘Qualitative overload’’ reflected in the prosecutors in Canadian province Observations of prosecutors indeterminacy and uncertainty connected (exact number not clear) Examination of documentary to many criminal cases Gender distribution not clear material Spillover of job stress with prosecutors showing difficulty leaving work difficulties behind at the end of the work day

Gomme and Hall (1995)

Stressors like workload and organizational Open-ended qualitative question constraints were universal included in survey to elicit from Other stressors like type of patient were only nurses the workplace condition a stressor in Israel perhaps because ‘‘Israeli each identified as most stressful or nurses were confronted with death and anxiety-provoking dying of young soldiers far more Responses were content analyzed frequently than nurses in the other countries’’ (p. 62)

1,442 nurses from U.S., U.K., Hungary, Italy, and Israel. More than 90% females

Number of themes emerged from data Burnt out psychiatrists showed more irritability Excessive work volume adversely affected them Perfectionistic behavior contributed to burnout Supportive relationships with managers were helpful Supportive family and friends were helpful

Key Findings

Glazer and Gyurak (2008)

Semistructured interview Used quantitative instrument to identify psychiatrists who were high and low in emotional exhaustion

Method

12 New Zealand psychiatrists; 6 were high in emotional exhaustion and 6 were low Sex distribution not mentioned

Sample

Fischer, Kumar, and Hatcher (2007)

Paper

Table 1. (Continued )

144 IRVIN SAM SCHONFELD AND EDWIN FARRELL

16 salespeople from the Midwestern U.S. 9 were men

80 Swiss apprentices 53 women and 27 men Employed in 5 occupations: nurses, cooks, salespersons, bank clerks, and technicians

Goodwin, Mayo, and Hill (1997)

Grebner et al. (2004)

Qualitative part is centerpiece integrated into ambitious quantitative daily diary study that assesses Ss on 7 days Qualitative description of stressors was part of paper-and-pencil pocket diary

In-depth, semistructured interviews regarding major sales loss and coping with such loss Interviews were ‘‘co-created’’ by interviewer and interviewee in order to cover themes in stress literature and have flexibility to follow topics brought up by salespeople

7.3 stressful events per person per week; more than 75% were work events Results consistent with the view that ‘‘the Swiss apprenticeship system prepares people rather well for their new role by extensive training’’ (p. 41) Overload and interpersonal stressors most commonly occurring work stressors Chronic job stressors (measured quantitatively) predicted the occurrence of daily stressors (ascertained qualitatively) Job control predicted calming down after a daily stressor Daily job stressors did not predict situational well-being when chronic job stressors were controlled

Intense loyalty to customers Money as scorecard to measure success Coping responses to major stressor, account loss, included mainly emotion-focused coping; emphasis on exercise and avoidance; little help-seeking Identified internalizers and externalizers among the responders to major sales loss Internalizers took loss personally; experienced intense emotions Externalizers were more likely to experience a rush of anger; internalizers, grief

Qualitative and Quantitative Research 145

59 U.K. prison workers 35 in focus groups Many distressing experiences including selfJobs included managers, health-care 24 interviewed harm among inmates assistants, nurses, prison officers Attempted to capture the ‘‘everyday Other stressors included high levels of role reality’’ of the participants Vast majority were women (Holmes, switching and absenteeism among April 23, 2009, personal coworkers communication) Supportiveness among staff but support was limited because of absenteeism Concerned that info. obtained in focus groups could have involved mimicry; one-to-one interviews served as a validity check. Info. from both sources dovetailed

Holmes and MacInnes (2003)

Using Keenan and Newton’s (1985) More stressors at work than at home SIR, each psychiatrist described a Violent patients were a stressor for psychiatrists at all levels of seniority stressful event that occurred in the last month Junior psychiatrists more often experienced Supplemented a quantitative study stressors in their personal lives (e.g., illness, loss) and patient-related stressors; for senior psychiatrists stressors more likely to include administrative problems Age and seniority-graded patterning of stressors. Balancing work and family life more of a stressor early in psychiatrists’ careers

106 U.K. psychiatrists of three seniority grades; about half were male

Stressors included keeping pace with heavy workload, upset at first experience at dissection, arrogant instructors Compared to those who did not report a stressor, those who did, had significantly higher scores on emotional disturbance

Key Findings

Guthrie, Tattan, Williams, Black, and Bacliocotti (1999)

Using Keenan and Newton’s (1985) SIR asked about 1 stressful incident (excluding exams) in the last month Also integrated a quantitative component in the form of scale measuring emotional disturbance with the qualitative data

Method

172 English first-year medical students, 51% males

Sample

Guthrie et al. (1995)

Paper

Table 1. (Continued ) 146 IRVIN SAM SCHONFELD AND EDWIN FARRELL

20 women nurses from U.S. operating room and general medical units

237 U.K. mental health social workers 61% were females

Hutchinson (1987)

Huxley et al. (2005)

Open-ended responses in a section of a quantitatively organized mail questionnaire/diary (1 week) study Qualitative data were analyzed thematically by a computer program

Participant observation in a variety of different units In-depth interview Developed level I and II codes for qualitative data Level I codes relied on the nurses words Level II codes merged categories from Level I Example of Level I expressions like ‘‘feeling angry,’’ ‘‘yelling,’’ and ‘‘feeling used’’ merging into Level II ‘‘catharsis’’

Social workers had high levels of psychological distress as per the quantitative part of the study The qualitative data suggest that the distress resulted from overwork, feeling intense pressure to work extra, burdensome paperwork, and time-consuming administrative work including government-mandated assessments

Self-care strategies emerged These included acting assertively, seeking resources, questioning, and setting limits

Themes that emerged from qualitative data Hugentobler et al. (1992) Michigan manufacturing plant with Semistructured individual, were consistent with quantitative results 1,080 employees in which a health ‘‘in-depth’’ interview covering a Major sources of stress in the plant: education intervention was being set of topic areas such as the interpersonal problems (e.g., lack of prepared nature of respondent’s job and cooperation); lack of timely information 42 employee’’key informants’’ its stressful aspects and feedback; lack of influence over interviewed Focus-group interviews eliciting important decisions; conflict between Unknown number of focus groups employee opinions and feelings quality and quantity in production with each group having 8–10 about past health education employees interventions and why they failed Value of multiple methods emphasized Gender distribution not clear Field observations Observation of committee meetings Supplemented by 3 waves of surveys

Qualitative and Quantitative Research 147

34 Canadian male and female managers who were experiencing role stress in a number of life roles

151 female clerical workers at U.S. university

Iwasaki, MacKay, and Ristock (2004)

Jex et al. (1997)

Male and female managers indicated personal relationships were a major source of stress although female managers were more ‘‘worried’’ about others Females were more likely to hold back feelings in mixed-sex group Females showed more stress issuing from home life and greater responsibility for caring for others

Workload (WL) pressures for all practitioners Burden of large amount of paperwork Dominating burden for rural doctors was requirement to handle great variety injuries and diseases because of the distance from a hospital Another element of the rural WL burden was the heavy on-call commitments

Meaning of work can affect stress symptoms Workers who emphasized boredom and negative work attitudes were more likely to experience stress symptoms than those who experienced work as meaningful

Key Findings

Qualitative measures consistent with Wrote descriptions of recent jobquantitative findings from the same study, related critical incidents that they suggesting that bias in the quantitative found stressful measures of role ambiguity, role conflict, Instructed to ‘‘think of a specific and interpersonal conflict was minimal incident that illustrates the degree Nevertheless, quantitative and qualitative of [stressor] you experience on measures should not be viewed as your job. Include all relevant interchangeable details such as y’’ (p. 232) Qualitative component an adjunct to quantitatively oriented study

3 focus groups: one all-female (n ¼ 12), one all-male (n ¼ 12), one half female and half male (n ¼ 10) Let deeper meanings emerge from transcripts

16 general practitioners (12 males), Semistructured interview including question about pressures of the job 14 nurses (all females), 9 practice Observation day in each practice to managers (8 females), and 14 verify info. obtained in interview administrative staff (all females) although not clear if blind to divided between rural and urban interview results practices that were about equal in Also had participants read and check size preliminary version of report on Scotland findings to identify discrepancies

Iversen, Farmer, and Hannaford (2002)

Qualitative interview with highly phenomenological, interpretive approach Meaning of work was the focus

Method

28 Danish workers in catering business where work was highly repetitive 24 were women

Sample

Isaksen (2000)

Paper

Table 1. (Continued ) 148 IRVIN SAM SCHONFELD AND EDWIN FARRELL

499 AIDS-care nurses in the 84% female U.S.

Kalichman, GueritaultChalvin, and Demi (2000)

In survey, there was an openended question asking nurses to write about one of the most stressful work experiences Also a quantitatively organized section that included quantitative coping scale and standard stressors. Integrated qualitative data into quantitative analyses

Intensive observation of 7 social work staff members, the interactions over 6 months executive director, SW supervisor, with participants in a variety fundraiser, and office manager at a of settings within work roles SW agency Checked observation notes with 8 women and 3 men participants U.S. Two in-depth interviews with each staff member

Kahn (1993)

Coping strategies varied by the nature of the stressful situation (e.g., death of a patient, staff conflict) Problem solving used more often in response to some workplace stressors (e.g., biohazards) In response to patient-care stressors nurses were more likely to use acceptance

Emergence of 8 caregiving dimensions: accessibility, inquiry, etc. From an organization level, patterns of caregiving could be supportive or depletive vis-a`-vis recipient Depleted coworker found to be at risk for burnout ‘‘Troubling patterns of interaction are generally overdetermined, locked into systems by multiple factors that render obsolete the simple language of single cause-effect relations’’ (p. 560)

Completed daily diary every day Qualitative findings indicate for men and when both members of the couple women, most key negative events were went to work or they spent more interpersonal than an hour together in the More negative events occurred at work than evening over the course of 3 weeks at home Participants asked to describe an incident that made them feel bad or good The study had a quantitative focus but also collected supplementary qualitative data

20 U.K. couples All college graduates

Jones and Fletcher (1996)

Qualitative and Quantitative Research 149

5 focus groups conducted with nurses from variety of subspecialties Questionnaire to assess satisfaction Sample of job leavers received an exit interview

Nine focus groups ‘‘The women [were] just as much farmers as their husbands’’ (T. Scharf, personal communication, April 21, 2008)

45 registered nurses in Karachi hospital with high turnover A second sample of nurses who were leaving their jobs (n unknown) No gender information but clues in paper suggest predominantly female sample

Khowaja, Merchant, and Hirani (2005)

Kidd, Scharf, and Veazie 70 Kentucky farmers age 55 and (1996) older Half female (T. Scharf, personal communication, April 21, 2008)

Method

798 young engineers Developed the Stress Incident Gender distribution unclear but given Record (SIR) era and other info. likely to be Wrote on stressful incidents predominantly male occurring in the last two weeks Instructed to ‘‘recall incident that made you feel anxious, annoyed, frustratedy’’ (p. 152)

Sample

Keenan and Newton (1985)

Paper

Table 1. (Continued )

Injury related to the way farmers prioritize safety decision-making and economic concerns Recommended that in disseminating safety knowledge, underline for farmers economic benefits of safety

Nurses often cited high workload (WL) as a contributor to dissatisfaction and turnover WL included having nurses perform nonnursing tasks such as removing linen and bringing water and tea Management disrespect was also important Satisfiers included safe working environment, opportunities for growth and advancement, advanced technology, and positive comments from patients and their families Most nurses in exit interview viewed salary as too low; by contrast, 93% of nurses in focus group viewed salary as satisfactory

Chief sources of stress included (a) job demands that waste time and (b) interpersonal conflict Interpersonal conflict included verbal aggression and covert hostility Predominant outcome was anger

Key Findings

150 IRVIN SAM SCHONFELD AND EDWIN FARRELL

50 U.K. people who in response to Semistructured interviews advertisements indicated that they were bullied or observed bullying at their workplaces 21 were men Teachers, factory workers, managers, secretaries, etc.

300 Florida faculty and support 143 in US and all in China completed Americans more likely to find lack of control staff quantitative component of study. a stressor. 286 university employees in China Keenan and Newton’s (1985) SIR Levels of interpersonal conflict about the Both samples had about 40% males supplement to quantitative same in the two countries but types of component of study conflict differed (in U.S. conflict was more Unlike most qualitative studies, direct and in China, more indirect) hypothesis-driven For Americans strains were more likely to be anger and frustration; for Chinese, anxiety

Lee (1998)

Liu, Spector, and Shi (2007)

Reactions to workplace bullying included nightmares Publicity about the workplace bullying and its wrongness were helpful to victims Euphemisms for bullying included ‘‘personality clash’’ Often bullying culminated in termination Fear of meeting bully outside of work

Semistructured interview; officer Stressors included difficult civilians, events asked to think of recent stressful with risk of physical harm to self or work event coworker, and death of a civilian Then answered standardized coping Type A officers used more active coping in items to assess coping with event. response to the events Analyses integrated quantitative and qualitative data

29 male and 2 female U.S. police officers

Kirmeyer and Diamond (1985)

Semistructured interview Some workers described stress as a stimulus, Inductive framework was computerwhile others, a stimulus-response relation driven content analysis of how Managers tended to describe stress as an workers conceptualized job stress individual response Those without management positions tended to describe stress as developing ‘‘from untenable job conditions’’ or in stimulusresponse terms Only small number believed organization had responsibility to manage stress; most believed that management of stress was up to the worker

45 U.K. residents who worked at a cross-section of jobs 20 were women

Kinman and Jones (2005)

Qualitative and Quantitative Research 151

Keenan and Newton’s (1985) SIR covering last 30 days; asked to describe reaction to stressor Also integrated a quantitative component of study into data analyses

207 U.S. graduate assistants 70% females

Mazzola, Jackson, Shockley, and Spector (2008)

Keenan and Newton’s (1985) SIR as supplement to quantitative component of study Unlike most qualitative studies, hypothesis-driven

Method

Semistructured interview that included questions on organizational changes and their impact

175 Florida university faculty and 161 support staff; 198 women Overlap with Florida participants in Liu, Spector, and Shi (2007).

Sample

Maki, Moore, Grunberg, 19 managers from west coast and Greenberg (2005) of U.S. 11 were women

Liu, Spector, and Shi (2008)

Paper

Table 1. (Continued )

Principal stressors included overload, interpersonal conflict, organizational constraints, and evaluation Stressors were related to emotional strain Quantitative analyses indicated that occurrence of any event was related to elevated physical symptoms Linked SIR reports of stressor to quantitative work stress scales Sample had lower stressor scale scores (e.g., interpersonal conflict) than published norms

Women were more likely to cry when having to inform employee about layoff; feeling shame after crying. Women showed greater emotional involvement with workers; men showed greater emotional suppression Women noted vast improvement in how women have been treated in the workplace Women showed greater reluctance to confront dismissal of their ideas Men felt greater pressure to advance in their careers, and this was stressful

Support staff had more conflict than faculty Women experienced more conflict than men Women had more strains Quantitative findings partly support qualitative findings

Key Findings

152 IRVIN SAM SCHONFELD AND EDWIN FARRELL

Structured and open-ended Documented distress in workers who saw interviews that capture work coworker(s) die in mining accident stressors and psychological distress Other stressors included underground Participant observation accidents (survivors suffering PTSD) and Quantitative component of the study ‘‘constant fear’’ of such accidents, physical included blood pressure (BP) demands of work, fear of underground measures. BP was not related to ‘‘demons,’’ disrespect from bosses, feelings about work exploitation, inadequate pay, bosses minimizing injury, medical staff not taking miners seriously Some veteran miners ‘‘numbed’’ to fear of accidents

813 male Black South African mineworkers

Modeling seen as neither glamorous nor well paid Models were subject to sharp competition and insult Great deal of rejection; great deal of standing Lack of privacy: having to change clothes in corridors Models often coped by attributing failure to get jobs to bad luck or by working on appearance Other kinds of coping included ‘‘strategic friendliness’’ and exchange such as buying an agent a gift

Men more likely to have problems with work relationships, particularly unfair criticism from the boss Women were more likely to be troubled by difficulty motivating subordinates whose performance did not meet standards Use of physical activity to cope Quantitative study indicated that females and males were about equally stressed

Molapo (2001)

In-depth critical incident (CI) interviews Quantitative study using survey to assess job stress and coping preceded by 6 months the CI interviews The 39 men and women in qualitative CI interview study were sampled from the 121 Participant observation Interviewed models

19 male and 20 female Canadian managers Theoretical sampling of managers about equally divided among occupants of low- and high-stress positions Quantitative study with 121 male and female managers

Mears and Finlay (2005) 15 female fashion models living in Atlanta

McDonald and Korabik (1991)

Qualitative and Quantitative Research 153

133 Florida women clerical Keenan and Newton’s (1985) SIR employees and 130 Indian women Augmented by questions that add clerical employees focus on women’s coping with the stressful work event and whom the women spoke to Content analysis of results Quantitative measures also in the study Given the size of the study, and unlike most qualitative research, an examination of the patterning of responses was hypothesis-driven

Narayanan, Menon, and Spector (1999a)

Focus groups Quantitative study with larger sample followed qualitative study

Method

104 U.S. nurses Predominantly female based on clues in text

Sample

Motowidlo, Packard, and Manning (1986)

Paper

Table 1. (Continued )

Interpersonal conflict was stressor in both places Otherwise different profiles of stressors with lack of clarity more common in India and overload in the U.S. In response to a stressor, more frustration/ annoyance/anger in the U.S. Acceptance/resignation in India Americans talked to coworkers more; Indians, to family

Stressors included work overload, uncooperative patients, negligent coworkers, difficulties with physicians Results of qualitative study helped to create self-report questionnaire for quantitative study conducted with much larger sample

Coping with fear of accidents by religious belief, reliance on traditional healers and ritual Downside of reliance on healers was interference with treatment for serious medical conditions (e.g., HIV) Support from family and friends

Key Findings

154 IRVIN SAM SCHONFELD AND EDWIN FARRELL

133 women clerical workers (same as in above study); 70 male and 54 female professors 79 male and 51 female retail sales employees All from Florida

Noonan et al. (2004)

17 high-achieving U.S. women having physical or sensory disabilities

Noblet and Gifford (2002) 32 Australian men who played Australian Rules Football professionally Sampling ensured inclusion of players with different levels of professional experience

Narayanan, Menon, and Spector (1999b) Stressors for clericals included lack of autonomy and interpersonal conflict For professors, interpersonal conflict and time wasting For sales employees, interpersonal conflict and time wasting Clericals coped by talking to coworkers and friends; professors, by direct action; sales people, by talking to coworkers and family

Semistructured interview Included questions specific to disabilities, career path, disability influences, stressors, coping Series of steps that begin with coding of data into concepts and then categorizing concepts into ‘‘increasingly comprehensive aggregates of categories’’ (p. 70)

Emergent model was centered on a dynamic self, which subsumes identity constructs such as disability identity and racial/ethnic identity The dynamic self embedded in contexts including family, opportunity structure, sociopolitical context, social support, disability impact (includes prejudice and discrimination), and the individual’s own attitudes toward work

Semistructured interview and focus Stressors included negative aspects of organizational system (e.g., autocratic groups leadership); performance worries (e.g., Authors compared results with pressure to perform); career development other studies of elite (but amateur) concerns (e.g., job insecurity); negative athletes to corroborate aspects of relationships (e.g., abusive interpretation of transcripts criticism); demanding nature of the work (e.g., long training sessions; injury); worknonwork interface (e.g., missing family and friends); post-football career uncertainty

Keenan and Newton’s (1985) SIR Describe emotional reaction to event How person handled event What support employee used, if any Content analysis The study, and unlike most qualitative research, was hypothesis-driven

Qualitative and Quantitative Research 155

Nurses interviewed and asked to describe a stressful event at work A quantitative component of the study described in Parkes (1984) used the stressful event elicited from the interview differently

150 U.K. student nurses, almost all women Plus an additional 56 from another intake group

Parkes (1985)

Stressors include communicating with dying patients and the death of patients. Problems arose when a patient died who was subject to a minor discourtesy Other stressors included interpersonal problems with supervisors and insecurities about own knowledge Insecurity helped by sensitive supervisors Two types of overload, pure workload and complexity of work

EMT argot was helpful in adjusting to the stressfulness of the job by distancing the EMT from injured person Humor was helpful in adjusting to stress Training served to frame injury objectively and distance the EMT from the gruesomeness of injury

Participant observation with author trained as EMT Rode with EMTs on calls Informal interviews with EMTs Immersion in work culture

22 emergency medical technicians in the Southwestern U.S. Sex of EMTs not specified but reader is led to believe they were mostly male

Key Findings

Palmer (1983)

Method Questionnaire that asked to describe Stressors included professional responsibility beyond competence; senior staff unfairly in own words one particularly critical, bullying, incompetent, or stressful work-related incident uncaring; intensity of work; conflicting that occurred in their new post demands; unexpected sudden death, Also asked how they coped sudden serious illness of patients In addition, there was a quantitative Hard emotional work dealing with death component Coping by means of talking to someone supportive Doctors who experienced incident beyond responsibility or competence had higher levels of distress

Sample

Paice, Rutter, Wetherell, 1435 U.K. doctors in the second Winder, and McManus half of their first year (2002) 787 women Given nature of sampling, the sample was fairly representative of population

Paper

Table 1. (Continued )

156 IRVIN SAM SCHONFELD AND EDWIN FARRELL

120 U.S workers from wide variety of jobs 56% males 25% of workers in arts and recreation; 20%, services; 15%, management and administration; 14%, sales

30 London mental health professionals drawn from earlier study (n ¼ 121) by Prosser et al. (1996) Gender distribution not clear

14 male and 11 female general practitioners and their partners; UK

Polanyi and Tompa (2004)

Reid et al. (1999)

Rout (1996)

Separate semistructured interviews for targeted GPs and spouses Covered stress related to job and coping

Semistructured interview Software facilitated textual analysis by creating indexing system for categorizing emergent themes Earlier quantitative study found that community mental health staff had higher levels of distress and emotional exhaustion than hospital-based staff This study to help understand earlier finding

Secondary data analysis of ‘‘Studs Terkel’’ type interviews of workers who provided rich descriptive monologues about their jobs Computer program organized coding

GPs experienced great time pressure Women GPs had more responsibilities for childcare and home; more conflict between work and home Husbands were not sufficiently supportive of the women given the dual burdens Wives of GPs experienced detachment in husbands High level of professional commitment subtracted from family life

Sources of satisfaction for hospital and community staff included contact with colleagues Ward nurses had little control over uncooperative patients, leading to negative mood Ward nurses complained of not having much of a therapeutic role Community staff had more patient responsibility; felt constant pressure and fear of patient crisis; fear of violence, personal safety

Identified dimension of work not found in demand-control and effort-reward imbalance models Dimension concerned meaning and purpose of work. Workers experienced distress when they believed purpose of their job was destructive; importance of feeling ethically at ease with one’s job Results also underlined importance of social interactions with clients and customers as well as with managers and coworkers

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One female Australian community nurse

148 group therapists in organizations and in private practice 58% women US

Shinn, Rosario, Mørch, and Chestnut (1984)

Sample

Severinsson (2003)

Paper

Key Findings

Questionnaire with open-ended questions about job stress and individual and organizational coping Closed-end items measuring psychological symptoms, somatic symptoms, alienation, job satisfaction

Stressors included workload, role conflict, lack of recognition, dealings with incompetent administrators, feeling inadequate in helping role Individual coping strategies included focusing on family, friends, hobbies; relaxing on weekends; building competence by attending workshops Agencies did little to help morale; some responses in this domain were tinged with bitterness Quantitative component indicated that Ss who worked for agencies showed more strain

Interview to produce narrative of Stressors included overwork in a frontline, the nurse’s professional and inner impoverished area and exposure to much life leading up to her becoming suffering burnt out Patients confided in her their personal sorrows that nurse held in confidence. That confidence became a burden Experienced headaches, exhaustion, and lowering of self-confidence Developed fear of making mistakes in caring for patients and need to leave nursing

Method

Table 1. (Continued )

158 IRVIN SAM SCHONFELD AND EDWIN FARRELL

Participant observation In-depth semistructured interviews

40 U.S. postsecondary instructors in correctional facilities 24 males

9 professional U.K. cricket batsmen All men

Tewksbury (1993)

Thelwell, Weston, and Greenlees (2007)

Semistructured interview

Semistructured interview designed to elicit narrative Transcription and computer-driven analysis of emergent themes

Taylor and Barling (2004) 20 registered rural Australian mental health nurses (5 males) Convenience sampling via snowballing; sought nurses experiencing carer fatigue

Stressors included perceptions of self (e.g., fear of failure, self-doubts); match-specific factors (e.g., respondent is last batsman); relationships with others (e.g., too much advice) Coping with stressors include self-talk (e.g., self-instructions) and support from teammates and others

Stressors involved instructors having to be extra careful interacting because of presence of violent felons Another stressor was increased likelihood that inmate would misinterpret kindness; intensified self-monitoring of speech Another stressor was having many weak students Satisfiers included feeling good about achievements (social compensations) and money earned

Stressors included: threat of job loss if one voices a complaint; high paperwork demands; emotional investment in patients who have chronic illness, and are not going to get better; some very disruptive patients; and doctors being dismissive and undervaluing nurses Reactions to the stressors include tiredness and insomnia Coping by setting boundaries, thinking about new career

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6 Swedish nurses (3 males) in an inpatient psychiatric ward

‘‘Narrative interview’’ in which nurse narrated a satisfying experience and an unsatisfying experience connected to a patient who harmed himself or herself

Burden of being on guard at all times because self-harm could be fatal, a burden compounded by patients who were manipulative and deceitful Experienced anger toward patients that nurses cannot release Felt troubled when there was lack of support from colleagues; felt confirmed when support was present Troubled by paradox of having to care for the self-harming patient without rewarding the patient with attention in the aftermath of the act of self-harm

19 thematic constructs emerged from the transcript of focus group interviews Qualitative results contributed to the development of 83 questionnaire items to be used in larger study of 787 miners 3 attributional factors emerged from quantitative study: time pressure/ performance pressure; management commitment; confidence in ability to deal with risk

Wilstrand, Lindgren, Gilje, and Olofsson (2007)

Miners distributed in 8 focus groups Concern was not actual risk-taking behavior but miners’ attributions regarding risk-taking

64 U.K. male miners participated in the first part of 2-part study, with 2nd part of study quantitatively organized

Key Findings

Weyman, Clarke, and Cox (2003)

Method

109 U.S. participants, mostly Field observations accompanying Humor used to distance self from others or correctional officers but including firefighters on 15 emergency feel superior to others; provide emotional firefighters and 911 operators response calls, shadowing relief; help with cognitive consistency (a Gender distribution not clear corrections officers, sitting in joke may put together unrelated or with 911 call-takers inconsistent matters) Ethnographic field interviews Humor served as an organizing force for In-depth formal interviews sense-making among workers in difficult Interviews aimed at obtaining worker jobs narratives and retrospective accounts of sense-making

Sample

Tracy, Myers, and Scott (2006)

Paper

Table 1. (Continued ) 160 IRVIN SAM SCHONFELD AND EDWIN FARRELL

High workload (WL) was emotionally and physically draining; feeling used up ‘‘Job tensions precipitated negative mood states which caused teachers to ‘neglect’ spouses and children’’ (p. 312) Excessive WL and poor quality of relationships with colleagues had detrimental effects

Farber (2000)

1 male high school teacher, 1 female Clinical case material high school teacher, and 1 female elementary school teacher; NY metropolitan area (Farber, April 2, 2008, personal communication)

Three subtypes of burnout: the worn-out, the classic (or frenetic), and the underchallenged subtypes

Teachers distressed by: inclusion of learners Engelbrecht, Oswald, 52 female and 3 male South African 10 teachers were administered a with short attention spans; limited contact Swart, and Eloff (2003) teachers having special education detailed interview, the purpose of with parents; children’s inappropriate children in their classes which was to closely examine social behavior, violent behavior; and perceived stressors associated with teachers’ lack of knowledge regarding having special education children managing the children in classes All teachers participated in quantitatively organized survey

2 qualitative studies: Teacher Personal Professional Life A. 80 teachers in urban Southeastern Inventory, an open-ended U.S. high school questionnaire designed to elicit B. 55 teachers in Iowa and Georgia from teachers effect of work on Gender distribution not clear. their personal lives

Blase and Pajak (1986)

Key Findings

Teacher Stress Inventory, a written Stressors included student discipline questionnaire, the purpose of problems, student apathy, low which was to identify, describe, achievement, and overload and illustrate meaning of stressors Exposure to stressors led to wasted instructional time, decline in teachers’ intellectual curiosity and enthusiasm, and increased distress

Method

392 U.S. teachers 67% females 38% taught in elementary school; 20%, middle school; 42%, high school

Sample

Qualitative Studies of Occupational Stress Bearing on Teachers.

Blase (1986)

Paper

Table 2.

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First-hand observation. Investigator situated herself in the staff room Recorded staff room conversations

Israeli high school having 138 teachers Quoted 55 teachers directly Descriptions of 110 teachers (includes the 55 above) Gender distribution not clear

Kainan (1994)

Interviews Observations Analysis of documents such as memos and quantitative data

Method

5-person focus groups, one-to-one interview sessions, questionnaires Purpose was to learn about effect of evolutionary theory on students, teachers’ feelings when writing and circulating evolution lessons, and identifying aspects of teaching evolution that made teachers uncomfortable

Sampled 6 urban schools (3 in a Northeastern U.S. city and 3 in Midwestern city) including elementary, middle, and high schools Numbers of teachers interviewed or observed was not clear nor was gender distribution

Sample

Griffith and Brem (2004) 15 biology teachers (8 women) from 6 Phoenix-area high schools and one middle school

Ginsberg, Schwartz, Olson, and Bennett (1987)

Paper

Table 2. (Continued )

Themes to emerge included teachers having to confront difficult students and a lack of appreciation. Teachers enjoyed complaining, ‘‘a general human phenomenon’’ (p. 286).

3 types of teachers emerged from qualitative data 1. Scientist teachers (deep love of science; no internal stress); 5 men, 0 women 2. Selective teachers (concern for community harmony led them to restrict content); 6 women, 1 man 3. Conflicted teachers (experienced internal and external pressure and worry about consequences); 2 women, 1 man Teachers lacked training in the social and personal implications of teaching evolution

Stressors included barriers to teaching (e.g., large volume of paperwork and other nonteaching roles) Student stressors included disrespectful behavior Security/safety a problem in school and neighborhood around school In schools with older students, problem of bigger and stronger students was more threatening

Key Findings

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644 British Columbia elementary and secondary school teachers No info. on gender distribution

8 male and 13 female urban U.S. high school teachers

Naylor (2001)

Parkay (1980)

Two qualitative interviews and observations Teachers were selected based on prior quantitative study

Survey with open-ended questions Methodology not well spelled out

Interview with open-ended questions

79 teachers from small Norwegian city 55% females

Mykletun (1985)

Some teachers showed cynical adaptation to the stress: ‘‘I have no standards. I give easy quizzes. I go over the test before the testy. I do what’s easiest on my nerves. But there’s not much in the way of rewards and satisfaction’’ (p. 457) Other teachers adapted by showing tolerance for the tumult and liking for the students; they had low levels of felt stress

3 broad categories of stressors: Teaching classes with heterogeneously placed students with special needs or deficient English; increased numbers of at-risk and disruptive children in classes; and unsupportive parents

Sources of satisfaction included: successful teaching and interactions with colleagues Job stress carried over to the home; observed in difficulties relaxing

As part of a larger quantitative study, Stressors included excessive paperwork linked to new educational initiatives, 2 open-ended questions, ‘‘What do inspections by government authority, and you find most stressful about your not having enough time job?’’ and ‘‘What are the main Reception teachers expressed dissatisfaction reasons for being satisfied/ with excessive formality in educational dissatisfied with your job?’’, were changes for the youngest children included in questionnaire At a time when a major education reform was initiated

Moriarty, Edmonds, 151 reception teachers (children Blatchford, and Martin age 5) and 208 first-year (2001) teachers Not indicated but likely to be principally female; U.K.

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Author’s own experiences as mathematics teacher in Brooklyn junior high school

74 NYC-area teachers (42 women) 252 NYC public school teachers (165 women) in first few years in profession

Case study of one female NYC high school teacher An adjunct to a report on a quantitatively organized study

More than 300 New York City area teachers on the job for less than 3 years More than 90% were women.

Schonfeld and Feinman (2009)

Schonfeld and Ruan (1991)

Schonfeld and Santiago (1994)

Sample

Sachar (1991)

Paper Everyday insult from some students Widespread student underachievement Safety a problem in and near school Disengaged principal; teachers obtained little help from other administrators Morale problems among faculty

Key Findings

As part of quantitatively organized study, the teachers were allowed to freely describe their working conditions

Interview of teacher

Themes to emerge from the data included feeling happy with the job; problems with administrators or colleagues/lack of support; serious classroom management difficulties; violence/lack of security/crime

Impact of stressors on psychological distress is not easily documented High levels of psychological distress antedated onset of stressors, suggesting plausibility of proneness to stressors as explanation for distress–stressor relation

CI study identified classroom management Qualitative, critical incident (CI) difficulties and violence as problems interview based on O’Driscoll and Example of teacher who would like help with Cooper (1994) for 74 teachers classroom management; however, asking Results of CI study used to construct for help would make teacher vulnerable to Teacher Daily Diary (TDD) for appearing incompetent, adversely affecting longitudinal, quantitative study job security involving the 252 teachers TDD study found high levels of classroom management problems in alternatively certified and, to a slightly lesser extent, traditionally certified teachers

Participant observer; own experiences and some observations of other teachers and administrators Author a journalist who obtained position as full-time teacher for one year

Method

Table 2. (Continued )

164 IRVIN SAM SCHONFELD AND EDWIN FARRELL

Younghusband (2008)

8 female Newfoundland (NF) teachers 12 female and 11 male NF teachers 169 female and 123 male additional NF teachers

Focus group for the 8 females 60–90 minute interviews of the 23 teachers Mail survey to the 292 teachers; survey included both qualitative and quantitative components

Interviews 10 male and 10 female former Des Moines elementary and secondary Field notes based on documents and conversations with the teachers 70 former teachers Selected from among 70 public school teachers who recently resigned

Steggerda (2003)

Interviews Asked for stories ‘‘they most often tell about their time at the urban schools’’

8 female and 4 male urban teachers who left the profession 7 worked in elementary schools, 1 in middle school, 4 in high schools From Massachusetts and Michigan (B. Smith, personal communication, April 2, 2008)

Smith and Smith (2006)

Qualitative data dovetailed with quantitative results that revealed high rate of exposure to abuse, threats, and violence Qualitative results underlined the anxiety and fear teachers felt as well as reluctance of administrators to take steps to support and protect teachers. Qualitative findings highlighted administrators who bullied and abused teachers Quantitative findings were consistent with the qualitative findings

Withdrawal from teaching as the result of: Unanticipated difficulties motivating students; Classroom management problems; Out-of-license assignments; Lack of support and respect from administrators; Exposure to violence

10 of 12 teachers told about violent incidents Examples included: a large 5th-grader pinning a pregnant teacher to the blackboard; and two groups of warring students who grabbed ground poles used to stake trees, swinging the poles at each other; throwing rocks and soda cans at each other

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occupational groups describe, in their own words, in writing or orally (including focus groups, which are, in effect, group interviews), their everyday work experiences. This type of method has been applied to a variety of occupational roles (Abouserie, 1996; Arter, 2008; Bargagliotti & Trygstad, 1987; Billeter-Koponen & Frede´n, 2005; Brown et al., 1986; Browner et al., 1987; Bu¨ssing & Glaser, 1999; Carradice et al., 2002; Cohen, 1989; Dewe, 1989; Dick, 2000; Elfering et al., 2005; Firth & Morrison, 1986; Fischer et al., 2007; Glazer & Gyurak, 2008; Gomme & Hall, 1995; Goodwin et al., 1997; Grebner et al., 2004; Guthrie et al., 1995, 1999; Holmes & MacInnes, 2003; Hugentobler et al., 1992; Hutchinson, 1987; Huxley et al., 2005; Isaksen, 2000; Iversen et al., 2002; Iwasaki et al., 2004; Jex et al., 1997; Jones & Fletcher, 1996; Kahn, 1993; Kalichman et al., 2000; Keenan & Newton, 1985; Khowaja et al., 2005; Kidd et al., 1996; Kinman & Jones, 2005; Kirmeyer & Diamond, 1985; Lee, 1998; Liu et al., 2007, 2008; McDonald & Korabik, 1991; Maki et al., 2005; Mazzola et al., 2008; Mears & Finlay, 2005; Molapo, 2001; Motowidlo et al., 1986; Narayanan et al., 1999a, 1999b; Noblet & Gifford, 2002; Noonan et al., 2004; Paice et al., 2002; Parkes, 1985; Polanyi & Tompa, 2004; Reid et al., 1999; Rout, 1996; Severinsson, 2003; Shinn et al., 1984; Taylor & Barling, 2004; Tewksbury, 1993; Thelwell et al., 2007; Tracy et al., 2006; Weyman et al., 2003; Wilstrand, Lindgren, Gilje, & Olofsson, 2007) including that of teachers (e.g., Blase, 1986; Blase & Pajak, 1986; Engelbrecht et al., 2003; Farber, 1991, 2000; Ginsberg et al., 1987; Griffith & Brem, 2004; Moriarty et al., 2001; Mykletun, 1985; Naylor, 2001; Parkay, 1980; Schonfeld & Feinman, 2009; Schonfeld & Ruan, 1991; Schonfeld & Santiago, 1994; Smith & Smith, 2006; Steggerda, 2003; Younghusband, 2008). In this type of qualitative research, workers’ descriptions of their working conditions are not constrained to fit the response alternatives found in structured interviews and questionnaires, the stock-in-trade of quantitatively oriented, occupationalstress investigators. The second method involves investigators who situate themselves in a workplace (without obtaining a position in the workplace), and observe, firsthand, workers on the job (Ginsberg et al., 1987; Gomme & Hall, 1995; Hugentobler et al., 1992; Iversen et al., 2002; Kahn, 1993; Kainan, 1994; Tracy et al., 2006). The third method involves participant observation. Here the researcher works at the kind of job that he or she intends to study, and describes elements of the occupational stress process ‘‘from the inside’’ (Browner et al., 1987; Hutchinson, 1987; Mears & Finlay, 2005; Molapo, 2001; Palmer, 1983; Tewksbury, 1993; see particularly Sachar, 1991). Sometimes the participant-observer obtains a partial work role that includes some but not all job tasks (Browner et al., 1987; C. H. Browner, personal

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communication, September 20, 2007). While this first-hand experience on the job provides an insider’s perspective, participant-observers, like the investigators in the second category, also closely observe other workers first hand. Although some investigators label as participant observation, scrutiny at close quarters without necessarily occupying the same occupational role as the workers under study (Gomme & Hall, 1995; Tracy et al., 2006), we do not. Qualitative research playing a direct role in hypothesis testing. Although not the focal concern of this chapter, it should be mentioned that 12 studies reviewed here (Arter, 2008; Elfering et al., 2005; Grebner et al., 2004; Guthrie et al., 1995; Jones & Fletcher, 1996; Kalichman et al., 2000; Kirmeyer & Diamond, 1985; Liu et al., 2007, 2008; Mazzola et al., 2008; Narayanan et al., 1999a, 1999b) contrast with the others. Although the 12 studies collected a substantial amount of qualitative data, these studies differ from the rest because the 12 were largely hypothesis-driven rather than hypothesisgenerating.1 Nine of the 12 employed ‘‘hybrid methodologies’’ (Mazzola, Schonfeld, & Spector, 2009) that coordinated qualitative and quantitative study components, and integrated into the same analyses both qualitative and quantitative data. The nine applied inferential statistical analyses (e.g., ANOVA) to variables developed from qualitative descriptions of work experiences and quantitative data from structured scales; one (Narayanan et al., 1999b), using chi-square statistics, assessed hypothesized relations among qualitatively ascertained variables; one (Liu et al., 2008) examined hypothesized relations in the qualitative data using log-linear modeling; and one (Arter, 2008) evaluated hypotheses without applying inferential statistics to the qualitative data. By contrast, the bulk of the studies cited in Tables 1 and 2 were more purely qualitative and exploratory, and principally examined qualitative data without the aid of inferential statistics.

A Quantitatively Oriented Approach to Measuring Stressful School Conditions Teaching is a particularly stressful occupation because the profession is built on a fundamental conflict, namely, the tension between the socializing agent and those being socialized (Mykletun, 1985). The examples to follow will show how qualitative research helps to add theoretical depth to findings obtained from a longitudinal study of new teachers. The qualitative research includes teachers’ descriptions of their jobs and a participant-observer’s description of her year as a junior high school math teacher as well as a Canadian interview and focus-group study.

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To describe how qualitative research was utilized in a research program devoted to teachers, we first briefly describe a series of quantitatively oriented studies and measurement concerns related to those studies. Within the framework of two cross-sectional studies of veteran teachers (Schonfeld, 1990, 1994) and one longitudinal study of newly appointed female teachers (Schonfeld, 1992a, 2001), one of us developed self-report instruments that were designed to assess teachers’ exposures to adverse working conditions. The occupational-stress scales had solid measurement characteristics. The alpha coefficients of scales measuring episodically occurring work events and ongoing job conditions were satisfactory. In the veteran- and newteacher samples, the occupational-stress scales were more highly related to each other than they were to nonwork stressors. In the longitudinal study of new teachers, workplace scales administered during the fall term demonstrated convergent and discriminant validity. The fall-term workplace measures were more highly related to spring-term depressive symptoms and job satisfaction four and a half months later than to summer, preemployment depressive symptoms and anticipatory levels of job satisfaction, measured four and a half months earlier (Schonfeld, 2000). Compared to other measures found in the occupational stress literature, the teacher stressor measures were relatively uncontaminated by negative affectivity, a personality trait thought to have the potential to affect the reporting of stressors (Brief, Burke, George, Robinson, & Webster, 1988), or by prior psychological distress (Schonfeld, 1992b, 1996). Like qualitatively oriented researchers, quantitatively oriented researchers are concerned with the richness and informativeness of the data they collect. Quantitatively oriented investigators have addressed the value and accuracy of both ‘‘objective’’ and self-report data, and have considered the best ways to ensure the validity of quantitative data (Frese & Zapf, 1994; Kasl, 1987). In view of these considerations, one of us secured official, objective data bearing on the quality of the workplaces of the new teachers who were employed in New York City public schools. The objective data included school-by-school rates of assaults, robberies, and sex offenses against teachers. One of the project’s aims was to link the official data, which were independent of the responses of the New York City participants in the longitudinal study, to various outcome measures, including depressive symptoms and job satisfaction. Interestingly, the objective data proved to be of little merit. An audit of the official data revealed widespread underreporting by administrators who were charged with officially recording and aggregating crimes occurring in the city’s schools (Dillon, 1994). The problem of underreporting violent incidents continues to occur in schools

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in New York City (Gootman, 2007) and across the United States (Schonfeld, 2006). Information obtained independently of audits is consistent with the view that there has been serious underreporting of violent incidents (Bloch, 1978; Sachar, 1991; Schonfeld, 1992b). This situation amounted to an instance in which the quality of the self-reported data that became part of the abovementioned episodic and ongoing stressor scales was superior to that of the so-called objective data. The longitudinal research on new teachers identified sizable mean differences in depressive symptoms and job satisfaction among new women teachers confronting different levels of adversity in working conditions (Schonfeld, 2001). Compared to their colleagues who worked in quieter circumstances, teachers who experienced high levels of episodic stressors (e.g., students acting aggressively or defiantly) were considerably more likely to show elevated depressive symptom levels and diminished job satisfaction. In addition, colleague and supervisor support were found to be a positive influence on job satisfaction. The findings were largely independent of the women’s (a) pre-employment symptom profiles, (b) anticipatory levels of job satisfaction measured prior to their entry into the teaching profession, and (c) stressors occurring outside of work.

Qualitative Data that Enrich the Quantitative Data As a supplement to the longitudinal study mentioned above (Schonfeld, 2001), the new teachers were given an opportunity to write, with no constraints, about their work experiences. As the longitudinal study progressed, hundreds of pages of the teachers’ written descriptions of their work lives accumulated. Given the labor required by the quantitative side of the research, a quantitatively oriented investigator may initially view qualitative research as an interested spectator; it is something best done by ethnographers who seek to describe diverse subcultures. By contrast, the research activities of a quantitative investigator are best devoted to scale construction, power analyses, the writing of computer programs to identify response sets, etc., in adherence to the methodological canons of quantitative research. How does one assess the reliability of workers’ characterizations of their phenomenal worlds? Despite the difficulties involved in ‘‘processing’’ the qualitative data, a reading of the teachers’ descriptions proved to be highly compelling and demanded a closer look.

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The qualitative data collected to supplement the quantitative research on new teachers provided a detailed examination of the transactions occurring in schools (Schonfeld & Santiago, 1994) as do qualitative data collected by Sachar (1991) and Younghusband (2008). These qualitative data vividly depicted the working conditions that gave rise to psychological distress in teachers. For example, a former public elementary school teacher, a participant in the longitudinal study, wrote (in future references, if we omit mentioning the study from which the quotation comes, we refer to the longitudinal study): I loved the teaching profession but because of my experience at P.S. xxx I doubt I’ll ever teach again. If I do, it will not be for the New York City Board of Education. My present job requires me to work many more hours and much harder but I am a much happier person. The stress caused by teaching a rough class is incredible. I used to come home crying every night.

Crying can be construed as a symptom of depression; it is captured in items on the Center for Epidemiologic Studies Depression Scale (Radloff, 1977) and the depression subscale of the SCL-90 (Derogatis, Lipman, & Covi, 1973). This teacher’s words and the words of many other teachers richly describe the human context to which the quantitative findings pertain. Consider the words of the following elementary school teacher (all teachers are public school teachers unless otherwise indicated): The students in my school are physically violent. It seems that fighting is the only solution to their problems. I was previously working in this school as a substitute teacher. It is discouraging and depressing to me to see that even first graders are fighting. There seems to be no love, friendship, or caring going on among the students.

Notice that she used the terms ‘‘discouraging’’ and ‘‘depressing’’ to describe how she felt about the student-to-student transactions she observed as part of her job. The longitudinal study found that teachers in the most dangerous, worst-run schools manifested high levels of depressive symptoms (Schonfeld, 2000). Consider the words of this female high school teacher who wrote to the first author in connection to an effort to follow a cohort into a fourth (and additional) year of teaching: This questionnaire is late getting to you because I didn’t want to fill it out while I was feeling depressed about the job. I kept waiting for it to pass. It usually does, but this has been a longer termed thing. I think this fourth-year, 37-year-old teacher is trying to accept that some things are probably not going to get easier anymore. It was so tough as a new teacher that [I thought] things could only get better.

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Also consider the fear in the next teacher and its impact on her health and life decisions: One of the worst classes I have is a fourth grade Gates class2 in which the children are around age 13. They are very rough children and I have to break up fights regularly. Last week as I was getting the children ready to be dismissed, an object which looked like a gun fell out of a child’s pocket. I was in a panic until the boy picked it up, turned it over and it was red and purple. In this class I would not have been surprised if it were a real gun. Weapons are constantly being taken away from children in this class. Also lately there has been a big security problem in the building. Several times intruders have entered the building. Last week children reported being threatened by a man with a knife and a gun. Since I have been teaching my health has declined. I am constantly sick with whatever the kids have and I have developed an ulcer-like condition. Last year I was perfectly healthy. I have decided that since I have the grades, in two years I will start law school.

Being a prekindergarten teacher does not provide immunity from classroom violence. Nor does it guarantee action by administrators. One prekindergarten teacher wrote: My supervisor was not helpful. She was daily informed of an insubordinate assistant teacher in my classroom. I was attacked by this person who is almost 100 lbs [heavier] than me and 10 inches taller than I am. The school is not standing behind me even though [administrators] told me this person is being put on probation due to insubordinate behavior in the classroom.

Participant-observer research, another form of qualitative research, also sheds light on teachers’ working conditions. Emily Sachar (1991), who had been a journalist, left her job at a newspaper to obtain a teaching position in one of New York City’s more chaotic schools, Walt Whitman Junior High School in Brooklyn. As a participant-observer, she wrote what amounts to an ethnographic account of one year in the life of a mathematics teacher. She described a high level of day-to-day verbal abuse, disrespect, and insult: My problems with Jimmy promptly worsened. By the third week, he had a ritual prank – raising his hand constantly to pose questions that had nothing to do with class work. I fell for the bait every time. His questions were tame enough at first. ‘‘Mrs. Sachar, could I get a drink? I’m gagging in my throat,’’ or ‘‘Mrs. Sachar, how about a night of no homework?’’ Their innocent tone did not last long. One day after waving his hand frantically, Jimmy asked, ‘‘Mrs. Sachar, where do babies come from?’’ Calmly I told him to ask his health instructor. Another day he tried, ‘‘Mrs. Sachar, do you like sex?y. Do you have orgasms, Mrs. Sachar?y Do you masturbate Mrs. Sachar?’’ (pp. 76, 77)3

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This student was not a rarity. A woman high school language arts teacher reported: The students are generally nasty, impolite, and non-cooperative. The result is that I feel that my health is suffering tremendously. I often feel confused and depressed. I just pray that all high schools are not this bad.

Consider this third-grade woman teacher. When I was first interviewed for this job my principal said the children were slow. I told him that I could deal with slow but not too many discipline problems. He assured me there were no discipline problems. However, I soon found out that 10 out of the 20 children in my class belong in special education for emotional problems as well as severe learning disabilities. [Administrators] have removed the top 7 children in my class so they can be in a more positive learning environment and are doing well. The remainder of the children consist of a child whose mother and two sisters died of AIDS, two self-destructive children, a child who sings whenever he feels like it, a child who likes to roll on the floor and quiet but resistant others who refuse to work. I have referred these children for special ed. (I am not a special ed teacher.) I feel more like a babysitter than a teacher and get little support past the removal of my high functioning students. I was told [administrators] expect results. I feel a lot of pressure because I still cannot control the room. Teachers who had these children say just close the door and survive. I really want to help these children. However, most come from such confusing backgrounds and I am not told very much by administration about their problems. I often feel confused and I’m sure the class senses this as well.

Another woman elementary school teacher wrote: My students have very short attention spans. They just will not behave. They will be quiet and well behaved for 5 minutes and then they are off again. In everything we do from reading to going down the stairs it takes us at least 10 minutes to quiet down. I try rewarding and praising good behavior but that doesn’t mean anything. Sometimes when I’m standing, trying very hard to teach a lesson, no one pays attention. I feel frustrated at least twice a day for the entire school week. I sometimes just want to quit with the behavior and lack of supplies in the school.

Although many fewer males than females were recruited to participate in the longitudinal study, male teachers described classroom management problems that rivaled those of female teachers. A male junior high school Spanish teacher wrote: My greatest problem is gaining and maintaining control of my students. Students are constantly getting out of their seats, calling out to each other and throwing paper in class. I admit I have lost control but I also believe that most students have very little respect for anyone. I feel that I am being left on my own to resolve my problems. When I did follow the recommendations of a [supervisor], I was told in effect that it’s my responsibility to discipline my class not theirs. I feel almost isolated and on most days I get home emotionally and physically drained.

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A woman elementary school teacher wrote: Presently a number of children have been transferred to my class. All of them have behavior problems. Fighting, name calling, swearing, and the inability to literally sit still for short periods of time remain problems for them.

The teacher went on to express worry that the newcomers will be a baleful influence on the behavior of the students who were already in her class. Violence and its threat are a problem for teachers and children. Sachar (1991) wrote: We were not officially informed of the gun incident until the monthly faculty conference on January 23rd [about three weeks after the incident occurred]. Then we learned that one student had been inches away from death in the accident. Winfield [the principal] told us that a twelve-year-old boy had brought a loaded gun to school, and that it had accidentally fired in class. The bullet tore a large hole through the coat of a girl standing next to him, then ricocheted off a desk. ‘‘If the girl had larger breasts, they would have been eliminated,’’ Winfield said, ‘‘and if she’d been turned in another direction, she’d probably be dead.’’ (p. 146)3

Despite the seriousness of the situation, the principal’s flippancy is evident. Violence was not a rare occurrence at Walt Whitman Junior High School. Sachar (1991) also wrote: This was only the first of a series of weapons incidents. In February, one dean told me, a sixth-grade girl hit another student over the head with a hammer and was suspended for five days. A few days later, another sixth-grader brought a custom-made .410-gauge shotgun to school, and was arrested. The boy had borrowed the weapon from his fourteen-year-old brother, a drug dealer, to scare another kid at school who was ‘‘giving him trouble.’’ A detective from the local precinct said that the boy showed no remorse: ‘‘He was quite callous, in fact.’’ (p. 146)3

Compounding the school’s problems, Sachar (1991) noted that many administrators were not forthcoming in helping the teachers tackle classroom management problems. She observed that administrators tended to squelch reports of school violence. The principal used to dress in such a way that parents visiting the school would mistake him for a member of the nonprofessional staff, and not think to stop to talk to him about their concerns. Many teachers in the longitudinal study reported that administrative support was absent. For example, this female junior high school language arts teacher reported: My supervisor has been totally nonexistent in my career to date. She has observed me twice since September – each time no longer than 5 minutes! She really has no idea what I’m doing (or not doing), except for the weekly set of plans I give her. No curriculum

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guidance, no support, no advice. I think it’s shameful that I am allowed to have virtual carte-blanche in my classroom especially since I am a first-year teacher.

In a similar vein, a female elementary school teacher complained that administrators in her school adhered to the view that ‘‘the child is precious’’ and that children should not be judged ‘‘without considering their race, socioeconomic [status], and gender.’’ However, she went on to note that administrators gave teachers ‘‘one tenth the consideration’’ given to students. She then commented sarcastically: ‘‘Perhaps I am ignorant but I view adults as important as children.’’ Another teacher, a woman who recently left teaching wrote, ‘‘The supervisor in my school has never praised me. She also has as little to do with me as possible.’’ Disrespect from administrators is compounded by administrative incompetence. A male junior high school language arts teacher complained that he was given a memo on Friday saying Monday’s classes would start later. When I got to school on Monday, classes started the regular time. Experienced teachers know to ignore this misinformation [that comes from administrators].

Consider the supervisory problem of this female high school math teacher: The person who puts stress in my work is my supervisor. She used to walk into my classroom at any time during the first 3 weeks of school to observe me or to give me things. From talking to other teachers in the department, it seemed that she did this with everybody. Anyway, I just didn’t like it. Also, I found out she hung around outside my classroom door. I don’t know what it meant. She just did it once. And I learned that she doesn’t mean what she says. For instance, she invited me to observe her teaching. When I went to her class, she asked me very coldly in front of the class: ‘‘May I help you?’’ And when I told her I came to observe her, she said, ‘‘Not today’’ and turned around to go to her desk. I felt insulted that she treated me that wayy. So, from now on I don’t worry about her and try to have as little contact as possible with her.

Qualitative material from Barry Farber (1991) in his book on teacher burnout depicts a young idealistic teacher working in an inner city school. Farber described her incessant problems controlling her class, the lack of help from an otherwise ‘‘caring’’ principal, and how ‘‘beat’’ she felt at the end of the day. Sachar (1991) also described the physical toll of the job including exhaustion and other bodily complaints. She wrote: I phoned this teacher on a Sunday to chat about the coming year and to gossip a bit about the school administration. ‘‘I’m in the midst of a diarrhea spell,’’ he said.

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‘‘What’s wrong? Did you eat something bad?’’ ‘‘You know what’s wrong,’’ my friend said. ‘‘I’ve got to go back there in two days.’’ This was a veteran teacher with a good reputation at Whitman, a man whose company I cherished during the year. Later the man reported ‘‘I feel helpless. You have a principal who says the school is great when the school stinks.’’ (p. 215)3.

Other teachers in Sachar’s school spoke of chronic depression. Consider the observation of this woman, a Brooklyn elementary school teacher: The children in my class have had behavior problems. Since I began to work, I have become sick with my nerves and have lost a lot of weight. I think that I would be much happier if I were to quit my job at this point.

The nervousness and weight loss are linked to her having to confront a difficult class in a high-need area, and suggest that she will quit her job, teacher retention being another casualty of exposure to highly problematic student behaviors (also see Ingersoll, 2003; Ingersoll & Smith, 2003). In fact, she moved to another school in a more middle-class area within a term. Teachers’ motivation to remain in the profession goes hand in hand with their experiencing high levels of psychological distress (Schonfeld, 2001).

Making Sense of Qualitative Data Given the wealth of descriptive material gathered from the new teachers in the longitudinal study, the project needed a method for categorizing the teachers’ writings. Brenner (2006) suggested an analytic framework for interview data consisting of five phases: transcription, description, analysis, interpretation, and display. Although she presented them as a linear progression, she emphasized that working with qualitative data is often a cyclical process. In this case, the transcription was relatively easy since the data were already written. For the qualitative data collected in the longitudinal study, a provisional set of themes emerged ‘‘naturally’’ from the new teachers’ writings according to a method described by Farrell (1990). The readers’ goal was to adhere to the principle that no preconceived theory guide this stage of the qualitative research, the readers following the groundbreaking dictum of Glaser and Strauss (1967) who advanced the view that theory arise from data. Of course, the thesis that important categories emerge from data is an ideal. Popper (1963) underlined the fact that ‘‘observation is always selective,’’ and

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that so much of what one observes is presupposed by a host of factors. Nonetheless, qualitative methods have a role to play in occupational-stress research. It should, of course, be noted that qualitative researchers dispute positivist social scientists on the role of methodology. Kirk and Miller (1986) maintained that quantitative definitions of reliability and validity are rarely appropriate to the way qualitative researchers work. They argued for a theoretical rather than an apparent validity. They were less charitable when discussing reliability, calling a single method of observation continually yielding an unvarying measurement a quixotic reliability. They advanced the idea of linking the two concepts while realizing that there are tradeoffs between them when conducting qualitative research. Qualitative researchers lean toward validity as the more important concept with experimental controls and triangulation to increase objectivity (cf., Goodwin et al., 1997; Holmes & MacInnes, 2003; Hugentobler et al., 1992; Kidd et al., 1996). Notwithstanding Kirk and Miller’s (1986) admonitions about reliability, Schonfeld and Santiago (1994) needed a way to make sense of hundreds of pages of teachers’ descriptions of their working conditions, descriptions that were collected as a supplement to the longitudinal study. After the initial content analysis, the two readers independently read through a series of about 75 writings, categorizing the writings by the provisionally agreedupon, ‘‘naturally emerging’’ set of themes mentioned above. After the readers examined their disagreements, they slightly altered the categorical scheme. The readers then proceeded to classify another series of about 75 descriptions using the revised scheme, checked how reliably they classified the writings, and made additional adjustments in the categorical scheme based on the location of disagreements. They blindly and incrementally refined the initial set of categories. With the final set of thematic categories, the pair of readers obtained coefficient kappas (Cohen, 1960) of 0.79 or greater for every category, indicating a satisfactory level of inter-rater agreement. All the teachers’ writings were reread and sorted on the basis of the final categorical scheme. With few exceptions (Elfering et al., 2005; Firth & Morrison, 1986; Glazer & Gyurak, 2008; Grebner et al., 2004; Isaksen, 2000; Keenan & Newton, 1985; Kidd et al., 1996; Kinman & Jones, 2005; McDonald & Korabik, 1991; Paice et al., 2002; Schonfeld & Feinman, 2009; Schonfeld & Santiago, 1994; Shinn et al., 1984) among the 81 qualitative studies of occupational stress that we reviewed (see Tables 1 and 2), most investigators neglected to apply kappa to assess the reliability of the categories that emerged from their

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data. Kappa should not be mistaken for percent agreement, a much weaker standard of reliability that has been used in some qualitative studies (Arter, 2008). Although validity checks have also been rare in qualitative, occupational stress research, they were sometimes carried out. Kidd et al. (1996) reported on a validity check that involved the successful application of their agricultural-stressor coding scheme, which they developed for one sample of farmers, to another farm sample. Goodwin et al. (1997) had interviewees read summaries of interviews to confirm the accuracy of the summaries; Noblet and Gifford (2002) and Arter (2008) had interpretations of the qualitative interview data corroborated by the interviewees. Goodwin et al. also solicited from interviewees’ interpretations and disconfirmations of ‘‘findings from previous interviews’’ as the interviews progressed. Iversen et al. (2002) had participants read a preliminary report in order to identify discrepant findings; none were identified and some participants noted that the analyses were very much consistent with their perceptions. Kahn (1993) had participants read a transcript of his observational field notes in order to check for accuracy. Other types of validity checks included having outside experts review transcripts and coding (Goodwin et al., 1997; Noblet & Gifford, 2002), using both interviews and focus groups to evaluate informational consistency (Holmes & MacInnes, 2003; Noblet & Gifford, 2002), having participants report on both stressful and satisfying experiences to help to assess for disconfirming conditions (Firth & Morrison, 1986; Jones & Fletcher, 1996; Moriarty et al., 2001; Wilstrand et al., 2007) and break response sets, cross-checking interview and observational data (Iversen et al., 2002), and cross-checking qualitative findings with quantitative results (Liu et al., 2008; Schonfeld & Santiago, 1994; Younghusband, 2008). Noblet and Gifford (2002), in their research on stress in professional athletes, compared their results to results of other studies of elite (but amateur) athletes, a kind of consistency check on sporting stress. Although most qualitative research is, by definition, interpretative (Erickson, 1986; Farrell, Pegero, Lindsey, & White, 1988; Rabinow & Sullivan, 1987), we suggest that some of the tools (e.g., kappa) employed by quantitative researchers can be used to strengthen qualitative research.

Four Themes Emerge from the Teacher Data Four major categories emerged from the new teachers’ descriptions: (a) interpersonal tensions and lack of support among colleagues/supervisors,

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(b) happiness with one’s job, (c) violence and other security problems, and (d) classroom management problems. Teachers’ descriptions sometimes reflected more than one theme. The themes illuminate problems with which quantitatively oriented occupational-stress researchers have grappled. The first two themes to emerge from the teachers’ writings accord with findings from the longitudinal study and with much of the quantitative research literature bearing on social support. Many new teachers described their distress when supervisors absented themselves from the supervisory role or when they obtained jobs in schools characterized by interpersonal tensions among the faculty members or between faculty and administrators. By contrast, when new teachers reported being happy with their jobs, they often described the importance to their well-being and success in managing a classroom, of good relationships with colleagues and supervisors. For example, a female fourth-grade Catholic-school teacher wrote: Where I work the teachers are very close. They help each other when help is needed. There is only one [other] teacher who is also teaching for the first time and we are close. We usually talk about school and our own personal life but we don’t do any recreation together.

Another woman who taught in a Catholic elementary school wrote: I believe that I do not have much stress to deal with because of the school I am working in. The principal and my colleagues made me feel welcome from the beginning. We have more of a family at school. I honestly could ask anyone for help.

Although some parochial schools offer clues for improving public schools (Bryk, Lee, & Holland, 1993), one of the Catholic-school teachers mentioned above went on to complain about the difficulties she experienced in making ends meet because her salary was considerably lower than that of her public school colleagues. In general, when teachers expressed satisfaction with their jobs, they tended to mention reliable colleagues and administrators who were available to help them (Schonfeld & Santiago, 1994). The examples of teachers who expressed satisfaction with their jobs are not limited to teachers in Catholic schools. Sometimes public school teachers expressed such satisfaction. Again, school administrators played an important role in the public school teachers’ satisfaction. A male elementary school teacher wrote: As a new teacher, I feel I am lucky to have landed a job in the school where I work. The main reason is that my supervisor (and mentor teacher) is very reliable and very, very cooperative and encouraging with me.

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This woman elementary school teacher wrote: I am extremely fortunate. My supervisors and administrators are very supportive. They go out of their way to help me when/if I need it. I have learned many things [during] my first year of teaching. Most important, though is that I can’t reach every child. I certainly try.

The theme of violence in the schools is particularly troubling. Violent and overly aggressive behavior has often been evidenced in qualitative research on teachers (Engelbrecht et al., 2003; Ginsberg et al., 1987; Sachar, 1991; Schonfeld & Feinman, 2009; Schonfeld & Santiago, 1994; Smith & Smith, 2006; Steggerda, 2003; Younghusband, 2008). Teachers reported on the personal consequences of having been victimized by violent students. Teachers also reported being affected by the prospect of violence even on occasions in which student violence did not occur. Bloch (1978) described a sample of 253 traumatized Los Angeles teachers referred for psychiatric evaluation in the aftermath of exposure to either physical violence or its threat. For many teachers, violence often seemed to be lurking. Bloch observed that ‘‘threats of a brutal attack were often more psychologically disabling than the actual event’’ (p. 1190). The picture is troubling enough to warrant public health concern. Lest the reader think that the problem of teachers being targets of verbally and physically assaultive behavior is concentrated in urban areas, such an assumption is wrong. Consider the example of Newfoundland teachers (Younghusband, 2008). With regard to verbally assaultive behavior, Younghusband reported that students commonly abused teachers, hurling at teachers derogatory comments including considerable profanity. Younghusband’s work underlined the extent to which teachers have been exposed to violence and its threat. One Newfoundland teacher reported: Recently a parent came to my school on two separate occasions and verbally and physically assaulted me. I was punched, yelled at continuously, kicked and threatened. I was told to leave the community or something.4

Another Newfoundland teacher related the following to Younghusband: I had to get my class out of the room while a student was tearing the place apart in anger. He struck several students as they were being removed. This occurs often, sometimes several times in a week. This child is as big as me.4

The following Newfoundland teacher expressed fear for her students and herself: A very disruptive student took a long pole (one to open windows with) and began swinging it at anyone he could strike. In fear of my own safety and especially the safety

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of my students I had to get everyone out of the classroom and leave the violent student in the room alone.4

Younghusband also found that many Newfoundland school administrators were unsupportive of teachers, failed to back teachers when irrationally angry parents bore down, and regarded teachers with contempt. Consider the observations of the following Newfoundland teacher: I was told by the principal: I was an idiot who did not deserve to teach, that I was a loser whose work was incomplete and total garbage, that as far as humans went I was a waste of time and energy and that if a grievance could be filed against someone for stupidity he would do so.4

Younghusband also obtained quantitative data from a survey she conducted of Newfoundland teachers. Her quantitative findings paralleled the results of her analyses of the qualitative data she collected. Qualitative findings from Massachusetts and Michigan (Smith & Smith, 2006) and Des Moines (Steggerda, 2003) are consistent with the results from New York City and Newfoundland. These qualitative findings dovetail with more extensive, quantitatively organized research showing the national dimensions of violence in schools (Schonfeld, 2006). Of course, the qualitative research shows the violence up close, and underscores the humanity of teachers caught in the aggressive tide. Smith and Smith (2006), for example, reported on a pregnant teacher who was pinned against the blackboard by ‘‘an exceptionally large fifth grader.’’ Apart from the violence, teachers described having students who were verbally, if not physically, assaultive (recall Sachar’s Jimmy). The disruption caused by the behavior of some children sabotaged lessons, causing teaching to proceed haltingly, in a stop-and-go manner, if at all. Thus, even if teachers did not become victims of violence, they had to be concerned about being targets of endemically disrespectful behavior that makes managing classrooms difficult. The qualitative findings just described suggest that if the qualitative and quantitative research traditions can be linked, a truer, more rounded picture can emerge of what it is like to work in a variety of school environments and the consequences those environments hold for teachers. The qualitative findings provide a context for the discovery (Reichenbach, 1951) of insights that contribute to a theory of job stress. Sachar’s (1991) participantobserver investigation, Younghusband’s (2008) focus groups and interview data, and Schonfeld and Santiago’s (1994) study of teachers’ descriptions of their jobs provide insights into why working in some schools may be normatively stressful. Although there are a number of different models of the stress process (Dohrenwend & Dohrenwend, 1981), a model of the stress

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process to emerge from the qualitative findings from both the longitudinal study and from the work of Sachar (1991) and Younghusband (2008) dovetails with Dohrenwend’s (1979) pathogenic-triad theory of stress. Dohrenwend (1979), in reviewing research on extreme situations, found that stressful life events can engender psychopathology in individuals in whom evidence of psychopathology had previously been absent. This is not to argue that teachers are in a position similar to that of combat infantry. Research, however, suggests that combinations of undesirable life events are particularly toxic when such events (a) are unanticipated, unscheduled, and outside the individual’s control; (b) lead to physical exhaustion; and (c) reduce social support. The elements of Dohrenwend’s (1979) theory of stress are well illustrated by the above examples. Clearly many teachers are affected by a dangerous level of violence in the schools that is a cause for anxiety. It is unlikely that academically trained individuals seeking entrance into a profession would foresee violence and endemically discourteous and disrespectful behaviors as everyday working conditions. Louis (1980) highlighted the demoralizing effect of the unrealistic expectations many new workers bring to their jobs. By contrast, among individuals entering the teaching profession only to work in the most chaotic and threatening schools, commonplace expectations regarding workplace safety and respect are not met (also see Steggerda, 2003). Qualitative findings of the longitudinal study, more than the quantitative results, underscore the shock and uncontrollability of teachers’ encounters with aggressive students (Schonfeld & Santiago, 1994; Smith & Smith, 2006), showing the applicability of hypotheses deriving from Dohrenwend’s (1979) theory of stress to teaching. Sachar’s (1991) participant-observer findings also highlight this sense of shock in encountering so much violence and disrespect as a normal and, too often, uncontrollable part of a work role. The sense of violence and shock is illustrated by an incident, this time occurring in the neighborhood of Sachar’s (1991) school, in which one Walt Whitman student, who began by bullying another Whitman student, set the other student on fire, severely burning, and almost killing, the victim. The appalling event brought to mind the words of the school’s namesake, ‘‘I mourn’d, and yet shall mourn with ever-returning spring.’’ Some of the above described qualitative findings highlight another element of the pathogenic triad. Although examples cited earlier suggest that exhaustion can accompany the job, such exhaustion does not betray ill conditioning on the part of the teacher incumbent. One new male teacher, who had contributed qualitative data to a pilot study, had been an intercollegiate trackman and cross-country runner. He obtained a job in a New York City junior high school in which only a small proportion of

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students read on grade level. He reported going to sleep just after he got home from work at about four o’clock in the afternoon. He attributed his fatigue to two sources: the energy he expended trying to maintain order within his classes and the piercing noise, as manifest in students’ loud talking and yelling, that permeated the school building throughout the day. One of the school’s deans, a former starter on a major college football team, evolved into a three-pack-a-day smoker. As mentioned earlier, teachers in the longitudinal study who reported satisfaction with work often indicated that collegial relations with coworkers and administrators contributed to that sense of satisfaction. By contrast, other beginning teachers who participated in the longitudinal study complained about being cut off from their more senior colleagues. They described administrators who rarely helped them develop the skills required to manage classrooms. Sachar (1991) described a principal who rarely helped new teachers adjust to the classroom, frequently isolating himself in his office, and a dean who seldom helped teachers with the violent students who were his responsibility to discipline. The principal’s lack of involvement continued for years after Sachar left the school, ending only when he was relieved of his job owing to his inaction over a case of sexual molestation (Steinberg, 1997). Events and conditions that deny the individual support are part of the pathogenic triad. Sachar’s (1991) insider’s description of an urban public school, Younghusband’s (2008) Newfoundland work, and qualitative data from the longitudinal study pointedly indicate that many of the difficulties teachers encounter come as a package, if not as a triad. One observes in the same school many troubled and violent students who block effective instruction for all students as well as imperil everyone’s safety, administrators who do not extend themselves to help teachers gain skill and competence, and a generally poorly managed, isolating, dirty, and noisy environment, a workplace from which teachers return home drained. Consistent with the longitudinal findings on new teachers (Schonfeld, 2001), the qualitative research paints a picture that suggests that some school environments are quite toxic to any teaching candidate with ordinary expectations about starting out in an honorable profession.

The Strengths and Limitations of Qualitative and Quantitative Research Qualitative research ordinarily will not help investigators test hypotheses derived from theory, nor of course is it meant to (exceptions are indicated

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in Table 1). The history of science, however, indicates that the strength of qualitative observation – we include uncontrolled, practical observation – is in theory development and hypothesis generation. We highlight four examples from diverse areas of medicine to underline this point. We chose medicine because of the value the research has had for human well-being. First, en route to mankind’s conquest of smallpox, what might be termed as qualitative observations, often made by ordinary people long before Jenner’s discovery of a vaccine, suggested the proto-hypothesis that inoculating susceptible individuals with small amounts of secretion from the pustules of affected individuals affords the inoculees immunity from the disease (Hopkins, 1983; Razzell, 1977). This experience contributed to the development of a theory of contagion, and helped undermine rival humoral theories of smallpox (Miller, 1957). Similarly, the experience of sailors dating back to the time of Francis Drake suggested that fresh fruit, particularly citrus fruit, prevents and cures scurvy (Carpenter, 1986). Carpenter (1986) showed that from the beginning of the seventeenth century, the men of the Hudson’s Bay Company kept scurvy to a minimum by sending small amounts of lime juice with its crews. We can call this an action hypothesis based on qualitative observational data. When fresh vegetables were unavailable, fresh game supplied by Hudson’s Bay hunters throughout the year, kept scurvy at bay. In the eighteenth and nineteenth centuries there were a number of ill-conceived theories of the disease (e.g., cold moist climates, potassium deficiencies) that led to ineffective treatments and preventive measures. Carpenter (1986) wrote that: It is a humbling moral to the story that, after all the attempts to apply new scientific concepts and hypotheses, the final solution came from rejection of theory and a return to the practical experience of previous centuries. [The nineteenth-century, Scottish physician Gilbert] Blane was one who had the necessary humility and could say: ‘‘Lemons and oranges y are the real specifics y [as] first ascertained and set in a clear light by Dr. Lind [in the eighteenth century]. Upon what principle their superior efficacy depends y I am at a loss to determine.’’ (p. 96)5

Later, highly controlled research, built upon the clues provided by earlier uncontrolled observation, linked vitamin C to the prevention of scurvy. The discovery of fluorides’ protective effects began with uncontrolled observations by dental practitioners who first described brown mottled tooth enamel in children living in a region of the Rocky Mountains (Black & McKay, 1916). Black and McKay (1916) believed they identified a new kind of dental pathology, noting the ‘‘general evil effect of the countenance of the individual’’ (p. 142). They observed that the amount of mottling was directly

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related to the age at which each child entered the region and that ‘‘as to caries, the teeth of these children compare favorably with those of other communities where endemic mottled enamel is unknown’’ (p. 145). More than ten years later the mottling was linked to the presence of fluorides in the drinking water as well as to a lower incidence of dental caries (Ainsworth, 1932). These early observations paved the way for controlled hypothesis-based research on the protection from dental caries fluorides afford (Ward & Miller, 1978). In psychiatry, uncontrolled, clinical observation first identified infantile autism (Kanner, 1943), a syndrome reflecting ‘‘the presence of markedly abnormal development in social interaction and communication and a markedly restricted repertoire of activity and interests’’ (American Psychiatric Association, 1994, p. 66). The syndrome is distinct from other debilitating mental disorders including schizophrenia. Kanner’s case study description of the syndrome has been well supported in the research literature (Rimland, 1964; Rutter & Schopler, 1979). Kanner’s description of the very-early developing and highly unusual behavior associated with the disorder suggested an organic cause (Rimland, 1964). These examples from the history of science emphasize, albeit in different contexts, an idea underlined by Kidd et al. (1996), namely, that ‘‘qualitative methods are preferred to quantitative methods when there is little information known about a phenomenon, the applicability of what is known has not been examined, or when there is reason to doubt the accepted knowledge about a given phenomenon’’ (p. 225; cf., Goodwin et al., 1997). However, when qualitative methods are employed in a field that has been well explored, it is likely that the theoretical insights that emerge from the data will make contact with existing theories. Qualitative methods, because of the freedom they give to respondents, also provide researchers leverage for overcoming preconceived ideas and cultural myths about stress at work (Firth & Morrison, 1986; Fischer et al., 2007). Bu¨ssing and Glaser (1999) demonstrated that qualitative methods that augment quantitative methods can help produce a cogent explanation of seemingly contradictory findings in quantitative data. Nurses who worked in redesigned, anti-Taylorist, ‘‘holistic’’ wards, with greater responsibility for fewer patients, experienced a reduction in stressors (time pressure, contradictory task goals, and ergonomic stressors) as a result of the job redesign; however, their levels of emotional exhaustion, surprisingly, were elevated compared to that of nurses in traditional wards. The qualitative findings indicated that the holistic nursing system led to an intensification of the nurses’ emotional work and interactional stress because they had no

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opportunity to withdraw from difficult patients. In traditional wards, because the work was more piecemeal, exposure to difficult patients was limited. Popper (1963) was right about the selective nature of observation. It is too unrealistic to hold to the view that theory will emerge from qualitative data untainted by the investigator’s prior exposure to existing theory and research findings. For example, in research on stressors affecting farmers, a coding scheme for stressors was based on a coding dictionary developed from the extant literature on agricultural stressors (Kidd et al., 1996). Blase (1986; Blase & Pajak, 1986) in his qualitative research on teachers found that work overload was a prominent stressor although the quantitatively oriented literature viewed overload this way in research antedating his. Despite adhering to the Glaser and Strauss’s (1967) canon of letting theoretically important categories emerge from data, Goodwin et al. (1997), in one of the methodologically soundest qualitative studies we reviewed, found emotion-focused coping strategies prominent among salespeople’s responses to major account loss, coping strategies long known to the quantitatively oriented investigators. Schonfeld and Santiago (1994) ‘‘took care to avoid imposing [existing theory]’’ on their data, and were aware that they should enter the qualitative phase of the research with open minds and let themes and theory emerge from the data (Glaser & Strauss, 1967). Schonfeld and Santiago were nonetheless aware of the existence of Dohrenwend’s (1979) pathogenic triad as well as other models of the stress process. There is thus an unavoidable tension in qualitative research. There are four other limitations to qualitative research. The first is the problem of reactivity. People who are observed sometimes change in response to the presence of an observer (Shai, 2002). The second limitation reflects Kasl’s (1978) observation, based on evidence from research on fighter pilots, air traffic controllers, and individuals in law enforcement, that workers’ self-reports on the stressfulness of a work role or the particular way in which the role is stressful may be less dependable than originally believed. For example, Kasl noted that when law enforcement personnel, a group with elevated risk of coronary disease, were questioned about job stressors affecting them, they were more likely to mention administrative duties and contacts with courts than life-threatening aspects of the job. Although Kasl applied the observation to quantitatively oriented job-stress research, the observation is, perhaps, more applicable to qualitative research that is dependent upon workers’ self-descriptions. Kasl (1978) recommended that investigators show caution with regard to accepting at face value workers’ self-reports on job stressors.

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The third is the concern that the researcher may overidentify with the workers being observed. The first author was once a mathematics teacher, and was concerned about the potential for his overidentifying with teachers, which would in turn affect his interpretation of the qualitative findings. One way to partly overcome such a limitation is to deploy multiple observers and multiple interpreters, and to subject hypotheses generated by qualitative data to rigorous testing using quantitative methods. The fourth is that the Glaser–Strauss enterprise has a Baconian cast. The vigorous hunt for data has no definable stopping point, leading to a piling up of facts (see Bacon, 1620/1960). Bertrand Russell (1945) warned that the Baconian idea that an ‘‘orderly arrangement of data would make the right hypothesis obvious’’ is ‘‘seldom the case’’ (p. 544). Russell went on to write that without some provisional hypothesis to help guide selection, the multiplication of facts can be baffling. The qualitative researcher must be cognizant of this problem. Qualitative research nonetheless is valuable, even in fields where much is already known. Insights from qualitative research can call attention to new ways of categorizing data when the data are relatively unstructured (Blase & Pajak, 1986). Even in well-trodden avenues of research, qualitative methods can provide surprising new ideas. Qualitative methods can identify important occupational stressors that research has overlooked. For example, incidents involving time wasting among engineers (Keenan & Newton, 1985), difficulties women managers have in motivating subordinates (McDonald & Korabik, 1991), and lack of meaning or ethics in work (Polanyi & Tompa, 2004) are stressors that previous research had missed. Qualitative research has helped to identify coping responses such as self-care activities in nurses (Hutchinson, 1987) that previous research had missed. Whether in well-studied areas or new areas of research, qualitative methods can help investigators understand the meaning and intensity of stressful incidents for workers (Dewe, 1989; Dick, 2000; Isaksen, 2000; Jex et al., 1997; Polanyi & Tompa, 2004; Steggerda, 2003), helping to lay a foundation for hypothesis testing and scale construction in quantitative research. It should be noted that both quantitative and qualitative data have been misinterpreted. Gould (1981) gives myriad examples of the former happening in his survey of the early research on human intelligence and race. An example of the latter error comes from Kanner (1943, 1949) who described the parents of autistic children as extremely cold and undemonstrative; in the popular press he went as far as to describe them as ‘‘just happening to defrost enough to produce a child’’ (The child is father, 1960, p. 78). Even if Kanner’s observations were accurate, quantitative research

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shows that the observations would only apply to Kanner’s clinical sample, and would be unrepresentative of the population of parents of autistic children.6 A good deal of theorizing followed Kanner’s papers suggesting that parental personality and behavior contributed to the etiology of the disorder (Cantwell, Baker, & Rutter, 1979; McAdoo & DeMeyer, 1979). Although the preponderance of evidence from rigorously designed, quantitatively organized studies is much more compatible with biological than psychological causal theories of autism (Dawson & Castelloe, 1992; Dawson & Osterling, 1997; Rutter & Schopler, 1979), an unfortunate effect of psychogenic theories that precipitated out of qualitative observational research is that of adding to the distress of parents of mentally disabled children, by falsely suggesting to the parents that their defective caregiving gave rise to their children’s disability (Rimland, 1964). This chapter advances the view that qualitative observation and quantitative methods in research on occupational stress help investigators push toward a common goal, namely, understanding, and doing something about, the stressors affecting workers. The history of scientific research teaches that uncontrolled, observational inquiry has contributed significantly to theories of the etiology of physical and mental disorder. Teachers’ and participant-observers’ descriptions of day-to-day work activities have contributed to theories of teacher stress. It is, however, important to emphasize the limits of both qualitative and quantitative research. Qualitative research should not substitute for appropriate quantitative methods of verification; qualitative research is ill suited for hypothesis testing. Consider the damage done by qualitative researchers (Bettelheim, 1967) who, on the basis of uncontrolled, clinicalobservational evidence, wrongly attributed autism to deviant parental behavior (see Pollak, 1997) or mistakenly attributed schizophrenia to ‘‘the severe warp and early rejection’’ of important figures such as the so-called ‘‘schizophrenogenic mother’’ (Fromm-Reichmann, 1948). Qualitative research can be helpful in contexts of discovery; quantitative research is more applicable to understanding measurable differences in discreet phenomena than to ‘‘thick descriptions’’ (Geertz, 1973) of workers in stressproducing settings. At the same time, we stress that it would be unfortunate to write off quantitative methods as a source of theoretical insight. Quantitative methods also play an important role in the context of discovery. For example, Trow (1957) pointed out that Durkheim’s (1897/1951) crude quantitative data, data that were far removed from the experiential context, added ‘‘much to our understanding of some of the most subtle and complex aspects of social life’’ (p. 35).

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The four themes that emerged from the examination of the qualitative data which the teacher studies produced were incorporated into research questions relevant to the analyses of the quantitative data generated by the longitudinal study (Schonfeld, 2001). Both the contexts of discovery and verification are essential to the research process (Reichenbach, 1951). We advance the view that in occupational-stress research, qualitative methods can be helpful in the context of discovery because such methods can contribute to (a) theory development, (b) hypothesis generation, (c) identification of stressors and coping responses researchers have previously missed, (d) explanations of difficult-to-interpret quantitative findings, and (e) rich descriptions of stressful transactions that humanize what quantitatively oriented researchers endeavor to study.

NOTES 1. We exclude from this brief discussion qualitative research that supplemented or accompanied a quantitatively oriented study (Schonfeld & Santiago, 1994) where (a) the qualitative data were examined separately and without the aid of inferential statistics and (b) the examination of the qualitative data was exploratory, and not hypothesis-driven. 2. Gates classes comprised students who were held back because of poor achievement. 3. The excerpts from Emily Sachar’s book Shut up and let the lady teach: A teacher’s year in a public school were quoted by permission of the publisher. 4. The excerpts from the paper by Lynda Younghusband were quoted by her permission. 5. The excerpt from Kenneth J. Carpenter’s book The history of scurvy and vitamin C was quoted by permission of the publisher. 6. Berkson’s fallacy, a principle from the highly quantitative field of epidemiology, indicates that if all potential research subjects are not equally likely to be incepted into a study sample, investigators will have difficulty concluding that an association, found in the sample, between a factor and a disorder applies to the population (Fleiss, 1981). The fallacy explains why it is often difficult to draw firm conclusions when studying factors associated with a disorder in clinical samples. Factors that propel potential research subjects into a clinical setting, where they may be recruited for a study, often differ from factors that increase individuals’ risk for a disorder. Studies of clinical samples may result in the investigator misidentifying factors that are associated with subjects’ arrival at a clinical setting as factors that increase subjects’ risk for a disorder. In the era of the Great Depression, it is likely that families that took their autistic children to see Kanner were mostly patrician in background. Their backgrounds could explain why the families could afford to visit Kanner (1943) at his Baltimore practice – many families traveled considerable distances – and may partly account for the coolness he observed in the parents of the affected children (cf., Wing, 1985).

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ACKNOWLEDGMENTS Preparation of the chapter was supported by NIOSH/CDC grants no. 1 01 OH02571-01 to -06 and PSC-CUNY Award Program grants nos. 667401, 668419, 669416, 661251, and 63593. We extend special notes of thanks to Joe Mazzola, Phillip Morgan, Sigmund Tobias, and George Schonfeld.

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Weyman, A., Clarke, D. D., & Cox, T. (2003). Developing a factor model of coal miners’ attributions on risk-taking at work. Work and Stress, 17, 306–320. Wilstrand, C., Lindgren, B.-M., Gilje, F., & Olofsson, B. (2007). Being burdened and balancing boundaries: A qualitative study of nurses’ experiences caring for patients who self-harm. Journal of Psychiatric and Mental Health Nursing, 14, 72–78. Wing, L. (1985). Autistic children: A guide for parents and professionals (2nd ed). New York: Brunner/Mazel. Younghusband, L. J. (2008). Violence in the classroom: The reality of a teacher’s workplace. Paper presented at the Work, Stress, and Health 2008 Conference, Washington, DC.

FACING THE LIMITATIONS TO SELF-REPORTED WELL-BEING: INTEGRATING THE FACIAL EXPRESSION AND WELL-BEING LITERATURES Kevin J. Eschleman and Nathan A. Bowling ABSTRACT Theorists, such as Darwin and Aristotle, have long argued that facial expressions communicate information about a person’s emotional state. Recently, validated coding strategies for facial expressions have been developed, which enable researchers to reliably assess a person’s affect. Although social, health, and clinical psychologists have regularly employed these objective measures of facial expressions (OMFE), occupational stress and well-being researchers are yet to benefit from this method. The subsequent chapter integrates the facial expression and occupational well-being literature. Specifically, we discuss the advantages of OMFE over self-reports and implications of OMFE for future research on occupational well-being.

New Developments in Theoretical and Conceptual Approaches to Job Stress Research in Occupational Stress and Well Being, Volume 8, 199–235 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-3555/doi:10.1108/S1479-3555(2010)0000008008

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Given the dynamic nature of job stress, affective reactions that occur during the stress process are an avenue for research and developments in stress theory (Perrewe´ & Zellars, 1999). Current assessments of affective reactions in the stress process are limited because they are predominately assessed through self-reports. To effectively test job stress theories that involve dynamic changes in affective reactions to job stressors researchers need to shift their conceptual approach to well-being by incorporating more objective measures of affect. More specifically, objective measures of facial expressions (OMFE) are a validated approach to assessing affective reactions and may lead to theoretical advancements in stress and wellbeing. In this chapter, we discuss common conceptualizations of well-being and the limitations of assessing well-being with self-report measures. We also introduce the facial expression coding, defend the validity of the objective assessment tool, and discuss the utility of OMFE in future stress and well-being research.

SELF-REPORTED WELL-BEING Conceptualization of Well-Being A common conceptualization of affective well-being is in the form of a hierarchical structure (Tellegen, Watson, & Clark, 1999). The lower levels of the hierarchy involve affective experiences that are more narrow and transient, whereas the top of the hierarchy includes affective experiences that are more broad and stable. For instance, mood and emotion are located at the lower levels of the hierarchy, whereas trait affect is located at the top. Mood and emotion differ in that mood is a broader concept than emotion. Mood is experienced with greater duration and more frequently (Gray & Watson, 2001). Emotions are intense and last up to a few minutes, whereas moods can last hours or days. In addition, mood and emotion differ in the experiences that trigger them. Emotions are triggered by specific stimuli or a defining moment, whereas mood is a summary of a person’s overall affective state (Watson & Clark, 1994). Mood and emotions are similar in that they refer to feeling states that can be broadly characterized as pleasant or unpleasant (i.e., positive or negative) and reflect what a person is experiencing internally (Parkinson, Totterdell, Briner, & Reynolds, 1996). In addition, it is believed that mood and emotion have common components, controlled by similar processes (Parkinson et al., 1996), and are both accompanied by a physiological response (Larsen, 2000).

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Because of these similarities, emotion and mood are often researched together using the broad label affect; referring to all mental states involving evaluative feelings (Parkinson et al., 1996). Ascending the hierarchical model (Tellegen et al., 1999), stable experiences of affect that lack a defining event are seen as personality traits. In sum, affective states include three common characteristics: (1) describe transient experiences, (2) include a subjective component, and (3) have both a psychological and a physiological element (Weiss, 2002).

Common Methods and Scales for Assessing Well-Being The assessment of employee well-being is heavily dependent on self-report. The use of self-reports to assess affect in the workplace was prevalent in the early stages of research in this area (e.g., Hersey, 1932) and is currently the method of choice among organizational psychologists (Weiss, 2002). Wellbeing in the late 1930s was primarily assessed using a job satisfaction scale (Weiss, 2002); more recently, this conceptualization has been recognized as being too narrow. As a result, numerous scales that address a broad range of issues dealing with well-being now exist. This is an effort to assess the more broadly defined construct of affective well-being. Although we cannot provide an exhaustive list of well-being scales currently in use, it is important to review some of the most common measures to demonstrate inherent flaws in the measurement of self-reported well-being. The assessment of well-being is heavily reliant on scales based on positive and negative affect (Weiss, 2002). Specifically, the Positive and Negative Affect Schedule (PANAS) is the most common scale used to assess an affective-oriented content domain (Gray & Watson, 2007). Previous measurement tools designed to assess an affective-oriented content domain often showed low reliability or poor convergent and discriminant validity when compared to the PANAS (Watson, Clark, & Tellegen, 1988). Depending on the researcher’s goals, the instructions for the PANAS can be modified to assess the affect that one is currently experiencing or has experienced during the ‘‘past few days,’’ ‘‘past week,’’ ‘‘past month,’’ ‘‘past year,’’ or ‘‘in general.’’ A respondent is presented emotions that vary in intensity and valence (e.g., bored, annoyed, ecstatic, and anxious) and are instructed to rate how often these emotions were experienced. Whereas instructions with shorter time frames (e.g., right now) are an assessment of mood, instructions with longer time frames (e.g., in general) assess dispositional or trait affect.

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Other scales include the Job-Related Affective Well-Being Scale (JAWS; Van Katwyk, Fox, Spector, & Kelloway, 2000) and the Physical Symptoms Inventory (Spector & Jex, 1998). The JAWS instructs employees to rate the amount to which any part of the job has made them feel one of thirty emotions over the past 30 days. In addition to psychological well-being, measures of physiological well-being are also common in occupational stress research, in which respondents indicate if they have experienced a minor physical symptom over the past 30 days (Spector & Jex, 1998). A recurring theme in the aforementioned scales is the requirement of the respondent to recall a psychological or physiological experience. Although we discuss the inherent flaws with self-report scales in more detail in the subsequent section of this chapter, researchers often attempt to overcome some of the limitations of self-reports in various ways. For example, Levenson and Gottman (1983, 1985) employed continuous self-reports in which participants first engaged in emotion eliciting activities and then watched a video replay of these activities. While watching these video replays of the activities, the participants used a dial to rate their recalled affect. Although this method enabled the researchers to assess the participant’s affective response without interrupting the activity, the selfreport of affect is still dependent on the participant reporting a memory of affect. Another attempt to improve self-reports of affect is done by having respondents report affect while they are undergoing an emotional event. This method is also flawed because the interruption may interfere with the affective experience to an unknown extent (Rosenberg & Ekman, 1994). In other words, stopping an activity to report current feelings changes the natural sequence of events and experiences and reduces generalizability of the experiment. The use of diaries is also a common method to assess wellbeing (e.g., Eid & Diener, 1999; Fleeson & Cantor, 1995; Tugade, Fredrickson, & Barrett, 2004). Although diaries rely less on memory of an affective experience than other methods, diaries are still dependent on selfreport of affect.

Potential Limitations of Self-Reports of Well-Being Although organizational researchers typically assess employee emotions using self-report questionnaires, there are several potential limitations associated with self-reports (for reviews of self-report research in organizations, see Podsakoff & Organ, 1986; Spector, 1994). Researchers who use self-report measures assume that participants know the answers to the

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questions being asked and are able and willing to respond accurately. However, there are reasons to believe that employees often provide inaccurate reports of their emotions. The limitations of self-reports include context effects, impression management/self-deception, problems associated with recalling past emotions, lack of awareness of one’s own emotions, common-method variance (CMV), and construct validity concerns. Context Effects Social scientists who conduct questionnaire research have long been aware of the potentially biasing influences of the context in which participants provide self-reported responses (Schwarz, 1999; Tourangeau & Rasinski, 1988). The nature of prior questionnaire content, for example, can impact responses to subsequent items (Bowling, Boss, Hammond, & Dorsey, 2009; Strack, Schwarz, & Schneidinger, 1985). Bowling et al. (2009), for example, found that the inclusion of content that reminded respondents of either positive or negative aspects of their jobs affected their subsequent responses to job satisfaction items. Other research has found that completing a selfreport measure of depression can influence subsequent reports of emotions (Mark, Sinclair, & Wellens, 1991). As a whole, the aforementioned findings demonstrate how context effects could potentially bias participants’ responses when self-report measures of affect are employed. Using OMFE to assess affect may be a viable means of overcoming this limitation. That is, because OMFE can be assessed unobtrusively, there is little fear that such a measure would ‘‘prime’’ participants’ memories and thus impact their responses to other measures. Impression Management and Self-Deception When using self-report measures to assess socially sensitive attitudes or behaviors (e.g., racial attitudes, sexual preferences, and illegal behavior), researchers are often concerned about participants providing responses that exaggerate their positive qualities or understate their negative qualities (Tourangeau & Smith, 1996). Such biased responding can be done intentionally and with the purpose of misleading the researcher, or it can be done unintentionally (Paulhus, 1984). When done intentionally, this behavior is referred to as impression management. When done unintentionally, it is referred to as self-deception. These biases may be particularly common when researchers assess selfreported employee affect. Commonly studied emotions include both positive affect (e.g., happiness, joy, and pride) and negative affect (e.g., anger, fear,

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and frustration). When self-report measures are used, participants may exaggerate the extent to which they experience positive emotions and understate the extent to which they experience negative emotions (Chen, Dai, Spector, & Jex, 1997). This may be particularly true when participants are concerned that their supervisors or co-workers will learn of their responses. The use of OMFE could help overcome the biasing effects of impression management and self-deception, however. Unlike self-report measures, OMFE can be used unobtrusively, and thus participants may not distort their results because they are unaware that a sensitive construct is being measured. Furthermore, there is extensive empirical evidence suggesting that people generally find it difficult to mislead trained observers by consciously manipulating their facial expressions (Ekman, Friesen, & O’Sullivan, 1988). Problems Recalling Past Emotions Researchers often ask participants to provide retrospective reports of past emotions. The PANAS (Watson et al., 1988) and the JAWS (Van Katwyk et al., 2000) scales are often used to assess emotions experienced during the past 30 days. There is reason to believe, however, that people may find it difficult to accurately report past emotions. Research examining retrospective reports of emotions has found that summary reports of past emotions are highly impacted by highly intense and recently experienced emotions and lack the sensitivity to recall the actual duration of an emotional experience (Robinson & Clore, 2002). Robinson and Clore (2002) argue that emotional experiences can be neither stored nor retrieved. Although similar emotions can be generated by mentally reconstructing an experience or situation (Wyer, Clore, & Isbell, 1999), this experience is a new emotion (Galin, 1994). In other words, when an employee recalls how they felt over an extended period of time, he or she will reconstruct an emotional experience that is at least slightly different. The inability to reconstruct the exact emotional experience is partly because a person’s emotion-related memory is similar to other forms of memory in that the ability to recall contextual details diminishes with time (Eich & Schooler, 2000). Retrospective reports include biases associated with both episodic and semantic memory. Episodic memory is the retrieval of specific moments from the past that are used to construct a similar emotional experience. Semantic memory is composed of generalized beliefs about emotions that would be present during a time frame. For example, imagine that an employee who is instructed to report her well-being at work over the past hour. Given the short time frame of the

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question, the employee will be able to engage in episodic memory recall in which she will remember specific situational cues to reconstruct the emotional experiences during the past hour. These cues could include remembering that there was not enough time to finish lunch or that the boss made a belittling remark. In addition, the employee may remember specific thoughts, such as recalling herself saying, ‘‘I feel tired.’’ If the time frame were increased from one hour to two weeks, the employee would shift from episodic memory recall to semantic. As a result, fewer contextual details will be remembered, and memories will be based on the decontextualized, symbolic beliefs. In other words, the employee will no longer extract specific memories about the workday, but rather rely on her belief of how she should have felt during that time. Demonstrating the effect of beliefs on retrospective reports of well-being, Mitchell, Thompson, Peterson, and Cronk (1997) found that employees’ retrospective reports of well-being after returning from a vacation were higher than reports of well-being while they were on vacation. Employees believed they were happier during vacation than they actually were. Although experience-sampling designs using self-report measures of current emotions (e.g., Ilies & Judge, 2002) are one approach to deal with the problems associated with recalling past emotions, we believe that the use of OMFE could also prove useful. For example, researchers could use video recordings of participants to assess participants’ facial expressions in realtime. Awareness of Emotions Researchers who use self-report measures assume that participants are aware of the emotions that they are experiencing. There is evidence, however, that this assumption may often be wrong. For example, there is growing evidence that emotional responses are partially implicit (i.e., they occur outside of one’s awareness) and that self-reported explicit emotions often correlate weakly with implicit emotions (Greenwald, McGhee, & Schwartz, 1998). Self-report measures therefore may miss an important aspect of emotions. We believe that OMFE may be useful for assessing implicit emotions. Our assumption is based on research showing that specific muscle movements in the face are an automatic (non-controlled) response (Ekman, Friesen, & Simons, 1985). Common-Method Variance CMV, which is variance attributable to the method one uses and not to the construct being assessed, is often cited as a limitation in studies that depend

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exclusively on self-report measures (for reviews of CMV, see Podsakoff, MacKenzie, Lee, & Podsakoff, 2003; Spector, 2006). CMV is often assumed to artificially inflate the observed relationships between variables assessed using self-reports, although it can also artificially attenuate observed relationships. For example, if self-reports were used to assess both abusive supervision and job satisfaction, many researchers would assume that the observed relationship between these two variables could be artificially inflated because both were assessed with the same method. Although there is extensive evidence that the problems generally associated with CMV are largely overstated, the widespread belief that CMV artificially inflates observed correlations persists (Spector, 2006). Fortunately, there are several approaches to combat CMV (Podsakoff et al., 2003; Semmer, Grebner, & Elfering, 2004; Spector & Brannick, 2009). One approach is to use a combination of both self-report and non-selfreport measures. A researcher concerned about CMV, for example, could examine the relationship between a predictor variable assessed with an objective measure and a criterion variable assessed using a self-report measure. Relevant to this chapter, self-report measures can be used to assess the predictors and consequences of employee emotions, and OMFE can be used to assess emotions.

VALIDITY OF FACIAL EXPRESSION ASSESSMENT Universality of Facial Expressions The assessment of affect through facial expression recognition is dependent on the consistent expression and recognition across contexts and cultures. In support of the theory of universality, researchers have found that raters from varying cultures consistently identify the same affective experience expressed in a face (Ekman, 1972; Izard, 1971). These studies provide evidence of a relationship between affect and facial expressions, but also that these expressions are consistently recognized across cultures. Although some researchers may disagree that humans express and recognize specific emotions across all cultures (e.g., Russell, 1994), several researchers have argued in favor of universality. In fact, six emotions are consistently found to be associated with the face in various cultures: happiness, sadness, surprise, fear, anger, and disgust (Boucher & Carlson, 1980; Ducci & Arcuri, 1982; Ekman, 1972; Ekman, Sorenson, & Friesen, 1969; Ekman et al., 1987; Izard, 1971; McAndrew, 1986; Niit & Valsiner, 1977). A meta-analysis on

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facial expression recognition supported the findings of six basic emotions, but also includes contempt as a universally recognized emotion (Elfenbein & Ambady, 2002). It should be noted that contempt had a lower inter-rater agreement than the aforementioned six emotions. Additional evidence for the universality of facial expressions can be found in more recent research of facial expressions in different cultures and among the visually impaired. Facial expressions have been used to identify emotional experiences of Olympic athletes from varying countries in the world (Matsumoto & Willingham, 2006). Similarly, Matsumoto and Willingham (2009) found no cultural differences in the spontaneous facial expression of emotion of Olympic and Paralympics athletes. In addition, spontaneous facial expression of emotion is found to be similar between blind and sighted athletes, indicating that these expressions are not observationally learned. Although some researchers have argued that the universality of the relationship between facial expression and emotion is no longer a debated topic in psychology (Matsumoto, 1990), reviews of cross-cultural research on facial expression recognition reveal that some inconsistencies in the findings exist (Russell, 1994). Russell (1994) found that emotion recognition accuracy will partially depend on the response format and the emotion labels used in a study. In addition, unpublished studies conducted in isolated and illiterate cultures by Ekman, Sorenson, and Friesen found that only happiness was accurately recognized (Russell, 1994). Although this is evidence against the theory of universality, it should be noted that the experimenters believe the results were likely influenced by the interaction between the participant and the translator (Sorenson, 1976). A more recent review of the association between emotion and facial expressions indicates that universality is at least partially accurate, but the influence of culture and context is undeniably present in both expression and recognition (Elfenbein & Ambady, 2002). It has also been found that the ability to recognize emotions increases as cultures and context become more similar (Elfenbein & Ambady, 2003). Variation between cultures in expression and recognition may be the result of three factors (Ekman, 1982). First, some gestures are culture specific and others are universal. Second, cultural norms regulate, mask, inhibit, or exaggerate natural facial expressions. Last, the causes or elicitations of emotion may vary across cultures. Although these factors are discussed in regard to cultural differences and between-person variability, they are also relevant in addressing the within-person variability in expressions (Matsumoto & Kupperbusch, 2001). In sum, there is evidence that expression and

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recognition of emotion are present in the face and that people have the innate ability to identify these expressions in photographs, but cultural and contextual variation is an undeniable limitation. The challenges posed by a person’s motivation to regulate, mask, inhibit, or exaggerate a natural facial expression has propelled researchers to develop a micro-level analysis of facial expressions and develop a standardized coding method.

Micro-Level Assessment of Facial Muscle Activation The relationship between facial muscle activity and affect is evidence that activity in the face reflects a person’s underlying emotions. This relationship enabled researchers to develop a facial coding method that overcomes several of the cultural and contextual concerns regarding facial expression recognition. A microanalytic approach to the association between facial expressions and affect led to the recognition of three pivotal muscular regions in the face (Ekman et al., 2002). These regions include the currugator, zygomatic, and orbicularis oculi muscles. The currugator muscle is located in the brow region. The zygomatic muscle is located in the cheek region. The orbicularis oculi muscle is located around the eye. Zygomatic activity is correlated with elicitation of positive emotions, whereas currugator activity is correlated with elicitation of negative emotions (e.g., Brown & Schwartz, 1980; Cacioppo & Petty, 1981; Sirota, Schwartz, & Kristeller, 1982). In addition, activity in the orbicularis oculi is associated with more accurate ratings of positive emotions when a person is attempting to mask the emotion and deceive the rater (Ekman et al., 1985). In other words, smiles displayed without activation in the orbicularis oculi are considered to be regulated facial expressions that do not coincide with positive emotional experiences. To measure muscular activity in the face, researchers rely on the assumption that a muscular region can be isolated despite the fact that facial muscles contain fibers that are interwoven (Cohn & Ekman, 2005). Under this assumption, research on facial activity has used several tools to code movement or measure electrical activity. The methods used to assess facial expressions at a microlevel include a computer-based facial imaging analysis, measurement of electrical muscular activity using electromyography (EMG) or a trained observer-based measurement (Ekman et al., 2002). Computer-based coding of facial expressions provides a digitized method of assessing facial activity. Although this method has been used in recent studies (e.g., Cohn & Schmidt, 2004; Dinges et al., 2005), several concerns with the method still

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exist and need to be addressed before the method can be consistently applied (Cohn & Ekman, 2005). More established methods of assessing facial expressions include EMG and trained observer-based coding. Facial EMG is a measurement of electrical activity in the skeletal muscles of the face (Fridlund & Cacioppo, 1986). EMGs have overcome reliability issues by following a standardized system to place an electrode on the appropriate muscular region. Validity concerns are also minimal because of good concurrent and predictive relationships with emotions recorded from both self-reports and observer-reports (e.g., Cacioppo, Martzke, Petty, & Tassinary, 1988; Cacioppo et al., 1992; Cohn, Schmidt, Gross, & Ekman, 2002). Although EMG can be a valuable measurement tool, several costs limit its application in research. Most notably, an EMG requires specialized equipment and a trained staff, electrodes can be intrusive and limit muscular activity, and it is difficult to apply in a naturalistic setting. To overcome these limitations without sacrificing the sensitivity of detecting muscle activation, observer-based coding methods have been developed (Ekman et al., 2002). Comparisons between manual coding and facial EMG have been rare (Cohn & Schmidt, 2004). One of the most common and valid observer-based coding methods is the facial action coding system (FACS; Ekman et al., 2002). The FACS is highly correlated with EMG readings (r ¼ .85) when muscle activity is assessed in people highly trained in activating specific muscles (Cohn & Ekman, 2005; Ekman, Schwartz, & Friesen, 1978). The FACS method has been shown to accurately predict an emotional experience (Ekman, Friesen, & Ancoli, 1980), distinguish when an affective change occurs (Ekman et al., 1985), differentiate between genuine and simulated expressions (Ekman, Hagar, & Friesen, 1981), and distinguish that spontaneous emotional experiences entail more intense movements and are less symmetrical than facial expressions that are requested and posed (Ekman et al., 1981). Although the FACS can be more easily applied to a naturalistic setting than EMG measurements and requires less advanced equipment, the time requirements limit the use of the FACS in research. The FACS requires approximately 100 hours to learn and between 1 minute and 10 hours to code facial activity in a 15-second period (Ekman & Friesen, 1978). However, the time requirements are less when analyzing photographs compared to video. The extensive time requirement of the FACS is easily understood by reviewing the complexity of the coding method. The FACS identifies 44 unique action units (AUs) or basic movements that the human face is capable of producing (see Table 1 for a description of each of the AUs). As a

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Table 1. AU

A Description of Action Units in the Facial Action Coding System. Name

Upper face AUs 1 2 4 5 6 7 43 45 46 70 71

Inner brow raise Outer brow raise Brow lowerer Upper lid raise Cheek raise Lids tight Eye closure Blink Wink Brows not visible Eyes not visible

Head positions 51 52 53 54 55 56 57 58

Turn left Turn right Head up Head down Tilt left Tilt right Forward Back

Eye positions 61 62 63 64 65 66

Eyes left Eyes right Eyes up Eyes down Walleye Cross-eye

Lip parting and jaw opening 25 Lips part 26 Jaw drop 27 Mouth stretch

AU

Name

Lower face AUs 9 10 11 12 13 14 15 16 17 18 20 22 23 24 28 72

Nose wrinkle Upper lip raiser Nasolabial furrow deepener Lip corner puller Sharp lip puller Dimpler Lip corner depressor Lower lip depress Chin raiser Lip pucker Lip stretch Lip funneler Lip tightener Lip presser Lips suck Lower face not visible

Miscellaneous AUs 8 19 21 29 30 31 32 33 34 35 36 37 38 39

Lips toward each other Tongue show Neck tightener Jaw thrust Jaw sideways Jaw clencher Bite Blow Puff Cheek suck Tongue bulge Lip wipe Nostril dilate Nostril compress

Source: Reprinted with permission from Ekman, Friesen, and Hagar (2002). Note: AU ¼ action unit.

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whole the AUs represent a comprehensive list of human facial movements. As shown in the table, AUs include movements of the lower and upper face, the eyes, lips, and jaws as well as head positions and miscellaneous movements. AU 4, for instance, involves lowering the eyebrows, and AU 12 involves raising the corner of the mouth. When coding for the presence of AUs, researchers often identify the intensity of the AU on a 5-point scale from ‘‘trace’’ to ‘‘maximum.’’ An emotion dictionary, which identifies the AUs that correspond to particular affective reactions, can then be used to translate the FACS scores into emotions (Ekman, Friesen, & Hagar, 2002). The combination of AU 6 and AU 12, for example, represents a Duchenne or genuinely felt smile and is indicative of positive emotion (Harker & Keltner, 2001). The presence of AU 12 with the absence of AU 6 represents a ‘‘fake’’ smile. This is indicative of a lack of positive emotion.

Genuine and Regulated Facial Expressions Advancements in coding strategies for facial expressions have enabled trained observers to distinguish between a genuine and regulated facial expression. An obstacle to OMFE is a person’s desire to regulate or mask their true feelings, similar to the role of impression management in selfreport scales. This limitation is not surprising because a person’s facial expressions are often used as a medium to convey information to others. As a result, an actor’s ability to regulate or mask a natural expression is a potential concern of OMFE. For example, imagine a high-stress occupation such as a soldier. Although a soldier is in a life-threatening occupation, it is not advantageous for him or her to display anxiety or fear. As a result, even when the soldier experiences a state of anxiety, the facial expression is unlikely to provide information of the underlying emotion to an untrained observer. A similar example can be imagined with professional athletes. Additionally, many occupations (e.g., customer service) require employees to regulate their emotions and express feelings that promote organizational goals and values (Hochschild, 1983). In short, employees are often motivated, for personal or organizational purposes, to regulate their facial expressions in an effort to hide their underlying feelings. Although regulated expressions may limit the ability to recognize affective experiences in the face, it is likely that the face will leak information (Ekman et al., 1988). In fact, raters have been successful in some cases in discriminating between posed and spontaneous expressions (e.g., Reuter-Lorenz & Davidson, 1981). Although early studies on the

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regulation of facial expressions found that raters were unable to consistently distinguish between true and false emotion (e.g., Greene, O’Hair, Cody, & Yen, 1985; Hemsley, 1977; Hocking & Leathers, 1980), these studies are limited in that raters only observed macrolevel indicators of expressions (e.g., smiles; Ekman et al., 1988). In fact, without microlevel assessment training, detecting a genuine expression is no better than chance (Frank, Ekman, & Friesen, 1993). Similar findings have been found in research examining the ability of employees in various occupations to distinguish between genuine and false emotions using facial expressions (Ekman & O’Sullivan, 1991). Specifically, Ekman and O’Sullivan (1991) found that only secret service employees were better than chance at identifying genuine emotions, whereas psychiatrists, federal polygraphers, lawyers, judges, police officers, and students were unsuccessful. An untrained observer is likely to use inaccurate cues that are based on social norms. Although untrained observers that rely on macrolevel indicators or inaccurate cues can severely reduce the validity of OMFE, a microlevel assessment of facial activity can overcome such limitations. A microlevel assessment of facial activity can focus on various muscular activation patterns in an attempt to identify genuine expressions. For instance, Fridlund (1988) found that asymmetry in facial actions is more likely in requested, specific facial actions or conversation than in responding to a joke or startle. In addition, Hess and Kleck (1990) used the apex, onset, and offset of muscular activity to distinguish between spontaneous and deliberate expressions. More recent research has detected genuine expressions by using a trained rater to focus activity in the obicularis oculi muscular region, rather than general observations of a smile (e.g., Ekman et al., 1988; Harker & Keltner, 2001; Matsumoto & Willingham, 2006, 2009). For example, Matsumoto and Willingham (2006, 2009) were able to identify genuine expressions in Olympic and Paralympics champions. The researchers found that although the athletes who finished 1st, 2nd, 3rd, or 4th showed general expressions of happiness, the athletes who finished 2nd and 4th expressed micro-level indicators of negative emotions. These findings are not surprising given that these athletes had just lost their most recent competitive event. In addition, the 2nd place winners are usually unhappy because they can easily imagine what could have been (i.e., ‘‘I could have gotten 1st place’’). On the contrary, the 3rd place winners can easily imagine themselves not being on the podium, and hence, they are filled with more positive emotions than negative. The underlying negative emotions detected in these studies would not likely be identified without the microlevel assessment of different muscular regions. Similarly, Harker and

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Keltner (2001) identified activity in the obicularis oculi in posed yearbook photographs. Activity in the obicularis oculi indicated the true emotional experience, rather than a regulated expression, and predicted well-being outcomes up to 30 years later. In sum, advancements in coding strategy have enabled trained raters to distinguish between genuine and regulated facial expressions; enabling researchers to assess true underlying emotion displayed in a person’s facial activity. The validity of OMFE is also demonstrated in a comparison with self-reports.

Comparison between OMFE and Self-Reports Although the limitations of self-reports of affect have been discussed, comparisons between OMFE and self-reports provide empirical evidence that OMFE can be used to overcome many of these concerns and are an accurate assessment of well-being. OMFE has been shown to moderately correlate with self-reported emotions with a range from .35 to .55 (Ekman et al., 1980). The moderate correlation between the two measures is evidence that the two measures have some empirical and conceptual overlap, but are assessing at least some distinct constructs. The differences between OMFE and self-reports may be because some people have greater difficulty expressing or identifying their state of well-being. As a result, OMFE are more sensitive to differences in affective states. For instance, whereas selfreports of well-being did not differ between patients with major depression, schizophrenics with blunted affect, non-blunted affect, and a normal control group, differences were found with OMFE (Berenbaum & Oltmanns, 1992). OMFE have also been shown to more accurately assess low intensity affective experiences than self-reports (Rosenberg & Ekman, 2005). Rosenberg and Ekman (2005) conducted an experiment in which they showed participants video clips that had been created to elicit varying emotions. In the first condition, participants watched the video twice. During the second viewing, participants reported when they experienced an emotional reaction. In a second condition, participants watched the video once and then a recording of themselves reacting to the video. The participants, in the second condition, stopped the recording each time they came to a moment in which they had an affective experience. Using OMFE, the researchers found that self-reports of an affective experience were given at the same time as high intensity facial expressions 87 percent of the time. Conversely, self-reports of an affective experience were given at the same time as low intensity facial expressions only 27 percent of the time.

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Rosenberg and Ekman’s findings indicate that OMFE provide similar results as self-reports for only high intensity emotions. The divergence for low intensity affect may indicate that self-reports are primarily assessing high intensity affect, whereas OMFE assess a greater range of emotions. This finding is not surprising given that common self-report affective wellbeing measures (e.g., PANAS) are an assessment of primarily high intensity affect (Barrett & Russell, 1999). Additional limitations of self-reports are also addressed using OMFE. In the subsequent section discussing areas of future research, we provide a more detailed description of the studies most relevant to occupational stress and well-being and discuss how OMFE can improve assessment and the theoretical underpinnings of well-being.

AREAS OF FUTURE RESEARCH Methodological Advancements Psychological and Physiological Well-Being The use of OMFE in assessing employee well-being is likely to provide several methodological advancements in assessing psychological and physiological well-being in the workplace. For instance, OMFE will address the concern regarding CMV, retrospective reports and memory distortion, and the effect of social desirability bias. In addition, OMFE can be used to help improve current self-report scales by enabling researchers to better distinguish between affect and cognition in well-being measures, most notably in job satisfaction measures. Job satisfaction scales are notoriously poor assessments of affective-oriented well-being (Brief & Roberson, 1989; Organ & Near, 1985). Because OMFE are not suspect to several cognitive processes (i.e., memory, social desirability, evaluative comparisons), OMFE is a predominately affective-oriented assessment of well-being. To overcome the concern for current self-report scales, OMFE can be used in lieu of or in conjunction with current scales in an effort to assess an employee’s affective response to the job. A second option is to include an OMFE in the development or identification of self-report scales that are more affective-oriented and closer to the intended job satisfaction construct. In the very least, OMFE will help researchers identify self-reports scales of psychological well-being that are most closely related to the intended construct being researched. In addition to psychological well-being, the use of OMFE may improve the assessment of physiological well-being. Self-reports of physiological well-being are subject to many of the same limitations as self-reports of

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psychological well-being. For instance, people have relatively poor access to their bodily reactions and have difficulty identifying subtle physiological responses. As a result, beliefs about situational factors are used to make inferences about their bodily processes (Pennebaker, 2000), which is one explanation of why people often overestimate prior pain (Rachman & Eyrl, 1989). Physiological strain may even be more problematic than psychological strain in some settings. For instance, imagine an employee that abuses a sick leave policy by claiming a physical injury or illness. Because physical ailments are likely to be perceived as an uncontrollable circumstance (Hebl & Kleck, 2002), supervisors may not question the validity of the excuse. In fact, organizational outcomes, such as hiring decisions, are more likely to be negatively influenced by psychological disabilities (i.e., depression) than physical disabilities (Hazer & Bedell, 2000). As a result, it is not a stretch to imagine employees abusing policy by over reporting physiological strain. Self-reports of physiological strain may also be severely under-reported in some occupations. Blue collar and manual labor employees such as factory workers and construction workers can potentially lose hours or their jobs if they do not reach physical standards. Professional athletes are also likely to under-report experiences of pain because their salary is highly dependent on their physical durability. It is also possible that it is socially unacceptable in these professions to acknowledge physical pain. Because of these potential concerns, predicting false claims, exaggeration, and under-reports of physiological strain is a research avenue that could benefit from OMFE. Health and clinical psychologists have used OMFE in an effort to overcome these limitations. Similar to emotions, people communicate pain in many ways (Craig & Prkachin, 1983), which include facial cues. Chronic back pain (Craig, Hyde, & Patrick, 1991), electric shock (Prkachin, 1992), blood pressure and heat rate (Lerner, Dahl, Hariri, & Taylor, 2007; Peters et al., 2003), and changes in coronary heart disease (Rosenberg et al., 2001) are associated with OMFE. In fact, OMFE coding of anger was a better predictor of changes in coronary heart disease than a well-established selfreport scale of hostility (Rosenberg et al., 2001). In addition, Rosenberg and colleagues found that regulated smiles distinguished between ischemic and nonischemics participants, with ischemics showing more false smiles. In other words, participants experiencing a physiological ailment attempted to mask their positive facial expressions. OMFE are also associated with endocrinological responses to stressors. For instance, participants with a psychological disorder that is often associated with endocrine disorders (i.e., schizophrenia) displayed

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consistently different facial expressions than participants not diagnosed with schizophrenia when an emotional stimulus was presented (Kohler et al., 2008). In a more direct assessment of the relationship between endocrinological responses and OMFE, Lerner et al. (2007) found a relationship between OMFE and cortisol reactions. Participants participated in a highstress task for five minutes. Greater expressions of fear were associated with higher cortisol levels, whereas greater expressions of anger and disgust were associated with lower levels. These findings were expected because fear is associated with thoughts of uncertainty and a lack of control over the environment, whereas anger and disgust are associated with thoughts of certainty and control (Smith & Ellsworth, 1985). It should be noted that researchers have had some difficulty identifying pain with OMFE when participants are instructed to hide or disguise their pain (Craig et al., 1991). Although several AUs (e.g., brow lower, cheek raise, lid tighten, upper lip raise) are associated with expressions of pain (Craig et al., 1991; LeResche & Dworkin, 1988) and present during faking and exaggeration (e.g., inner brow raise; Craig et al., 1991), it is likely that OMFE judges are more accurate in identifying exaggerated pain than deception (Craig, Prkachin, & Grunau, 1992). Nonetheless, OMFE could be extremely valuable in research pertaining to the validity of an employee’s self-report of physiological well-being. Longitudinal Models Although occupational stress and well-being researchers have emphasized the importance of longitudinal designs to more accurately test theoretical models (Zapf, Dorman, & Frese, 1996), collecting this data can be a difficult task. In an effort to assess change, researchers have relied on retrospective assessments of change in well-being. However, these reports drastically differ from the longitudinal assessment of well-being (Sprecher, 1999). The use of facial expressions and video recording devices can provide researchers with additional opportunities to assess well-being over time. Archival photographs and videos can now be used to detect well-being days, weeks, months, or even years in the past. A creative study design coded college yearbook photographs of participants who were in their 40s and 50s at the time of the study (Harker & Keltner, 2001). This design enabled Harker and Keltner to assess how affective experiences several decades prior can predict future well-being. Since this creative study design, several researchers have coded yearbook photographs (Hertenstein, Hansel, Butts, & Hile, 2009), used multiple archival photographs to assess affective development (Freese, Meland, & Irwin, 2008), or taken their own photographs to assess well-being

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14 months later (Bonanno, Keltner, Holen, & Horowitz, 1995). In sum, the use of facial expression coding will ease the arduous task of collecting longitudinal data. In fact, the coding system will enable researchers to become more creative in their study designs and searches for archival data. One can only guess how many times a person is photographed or recorded in a lifetime. In addition to assessing well-being over months or years, the ability to assess real-time emotions will enable researchers to design microlevel longitudinal designs that assess the process in which emotions emerge and change during a task or experience. Real-Time Emotions One of the most notable advancements in well-being research is the use of facial expressions to assess real-time emotions. This benefit is demonstrated in a study by Dinges et al. (2005), in which the researchers examined the anxiety levels during a high performance demand task (flight simulation). The researchers were interested in assessing how specific parts of the simulation would affect anxiety as well as how the overall experience would affect anxiety. Anxiety levels were assessed with a computer-based facial expression coding method. As a result of this method, the researchers were able to gain insight into which part of the task produces the most strain without relying on a retrospective report. In addition, a more accurate assessment of the total task effect was gained because the task was not intermittently stopped to collect self-reports.

Testing Theoretical Models Affect vs. Cognition The ability to assess real-time emotions also enables researchers to gather more detailed information about an emotional experience, which can then be used to test several theories. Specifically, facial expressions can be used to assess the amount of time it takes for an emotion to occur after a stimulus is presented (latency) and the duration of the emotion. For example, Ekman et al. (1985) examined reaction times to affective experiences using OMFE. The examination of whether a startle response to an affective stimulus should be considered an emotion would help clarify the long debate of whether affect or cognition comes first (Zajonc, 1980). Considering a startle as an emotion would support Zajonc’s claim that affect does not require a cognitive appraisal and refute the claim by Lazarus (1982) that a startle is a reflex instead of an emotion because cognition does not play a causal role.

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OMFE enabled Ekman et al. (1985) to record not only the emotion experienced but also the amount of time it took to experience each emotion. Participants were placed in a condition with an unanticipated startle, anticipated startle, inhibited startle, and a simulated startle. The OMFE was used to demonstrate that a startle reaction and emotions show very distinct facial movements. This finding refuted the claim that a startle was an extreme emotional version of surprise. The differences between a startle and surprise are much greater than other emotions that are claimed to be conceptually similar. In addition, the use of the OMFE enabled the researchers to provide evidence that the startle reaction is not an emotion. The reasoning for the claim was that a startle could not be completely inhibited by participants, the startle could not be reproduced with the same brief latency and duration, and distinct facial features were displayed during a startle reaction. Ekman et al.’s study provides evidence that OMFE can be used to distinguish minute changes in the face as well as estimate the latency for the onset of an emotion and the duration. The data in this study could not have been accurately assessed using self-report methods. Opponent Process Theory The empirical findings from Ekman et al. (1985) are an indication that OMFE can be applied to psychological theories that describe the onset and duration of emotional experiences. opponent process theory, for example, can be used to describe the initial emotional response (primary process) as well as the inhibitory responses (opponent process) that an individual automatically engages in (Bowling, Beehr, Wagner, & Libkuman, 2005; Landy, 1978). OMFE would allow researchers to test how fast affect emerges and how fast it fades. In addition, OMFE could be used to assess the strength of a person opponent process. Because researchers (Bowling et al., 2005) have suggested the integration of the opponent process theory into organizational research, OMFE will likely serve as a valuable tool in this process. In sum, OMFE will enable researchers to test questions that would previously require an EMG, but without removing the individual from a naturalistic setting. Transactional Theory of Stress and Coping The ability to assess real-time emotions will also enable researchers to more accurately integrate emotional experiences with cognitively oriented theoretical stress models. Perrewe´ and Zellars (1999) have introduced a stress model that integrates emotional experiences with Lazarus and Folkman’s (1984, 1987) transactional theory of stress and coping. The transactional model

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emphasizes the interaction between a person and the environment and involves two appraisal processes. The primary appraisal consists of the detection of a work stressor as well as the relevance and potential threat to well-being. If the individual perceives the stressor to be threatening and relevant, then he or she will engage in a secondary appraisal. The secondary appraisal is the evaluation of the resources available and the ability to cope with the stressor. In other words, the individual will determine how and whether a more positive environment can be created. In an effort to advance the transactional model, Perrewe´ and Zellars (1999) suggested that perceived causes of the felt stress and emotional experiences mediate the primary and secondary appraisals. Specifically, after the primary appraisal, an individual will perceive the cause of the stressor as internal or external and controllable or uncontrollable. These perceptions will then lead to specific affective responses (e.g., guilt shame, anger, and frustration), which in turn lead to the selection of a coping strategy (the secondary appraisal). For example, imagine an employee who is experiencing high levels of task overload that was caused by a lack of effort over the past several work days. This employee is likely to perceive the cause of the stressor to be internal and avoidable because if the employee had not slacked off (internal-controllable), he or she would not have been in this situation. As a result, this employee will experience feelings of guilt, which in turn could lead to a greater work effort (problem-focused coping). In a similar example, imagine that an employee is experiencing task overload because a coworker has been slacking off. The employee is likely to perceive this stressor to be caused externally (by the coworker) and avoidable (if the coworker had not voluntarily reduced their effort). As a result, the employee will likely experience anger and engage in an emotion-focused coping strategy (e.g., withdraw or cognitive reappraisal). The integration of emotion and the transactional models is not devoid of critiques and limitations; however, OMFE address several of the concerns. A primary concern for the integrated model is the use of self-reports for both the independent and the dependent variables (Frese & Zapf, 1999; Schaubroeck, 1999). As we have discussed, CMV can inflate an observed relationship. Additional concerns that will limit the ability of researchers to accurately specify the model is the reliance on retrospective reports of emotions, the difficulty to assess the complexities of an emotional experience in an organizational setting, and the possibility that emotions occur before attributions or after the coping behavior (Schaubroeck, 1999). Although the aforementioned limitations are valid concerns that can limit a researcher’s ability to accurately specify and test the integrated model,

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they can be overcome by OMFE. First, the concern for common-method bias is overcome by using an objective assessment of emotions. In fact, the use of OMFE will enable researchers to use subjective appraisals of stressors without an overdependence on self-reports. Second, the ability to assess real-time emotions using video recordings will avoid retrospective reports that are severely flawed. Video recordings of facial expressions will also overcome the concern regarding the complexities of an emotional experience. Specifically, OMFE can be used to assess the onset, duration, apex, and intensity of an emotional experience without the use of an EMG and the immediate awareness of the employee. Finally, the detailed information regarding the emotional experience will enable researchers to have a more accurate assessment of the onset of an emotion and whether it occurs before attributions, as a mediator between attributions and coping, or after the coping behavior. Given that multiple emotions can occur simultaneously and to varying degrees within the model (Perrewe´ & Zellars, 1999), it is expected that specification of the model will improve with the microlevel information provided by OMFE. In short, OMFE enable stress models that incorporate emotions and well-being to be more accurately specified and tested. Social Information Processing Theory Incorporating principles from social information processing theory (Salancik & Pfeffer, 1978) with OMFE can also advance research on how an employee determines or constructs their state of well-being. Salancik and Pfeffer (1978) describe how employees adapt attitudes, behavior, and beliefs to their social context. As a result, researchers can learn the most about an employee by studying the social environment in which behavior occurs. The social environment is used as a source of information or cue which employees use to interpret events. When cues regarding job stressors or characteristics are conflicting and ambiguous, employees depend on communication with others to derive perceptions of the work environment and their affective responses. In other words, employees convey information to each other using facial cues that are used to evaluate the environment. Although organizational psychologists have not incorporated facial reciprocity and OMFE, clinical psychology and social psychology researchers have used OMFE to assess interactive behavior patterns between two people (e.g., Steimer-Krause, Krause, & Wagner, 1990) or in response to photographs (e.g., Harker & Keltner, 2001). Photographs that were coded with an OMFE as expressing positive emotionality were positively

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associated with perceptions of approachability, acceptance, and trustworthiness of those individuals presented in the photographs (Harker & Keltner, 2001). A pertinent question to this situation is what information can supervisors convey to their employees by having a positive expression? Or, can a positive expression by a supervisor and coworkers result in a positive evaluation of a high stressor work environment? In addition, OMFE could be used in social information processing research to determine whether employees change their attitudes to be consistent with others in their environment. An employee who is surrounded by coworkers who are dissatisfied with their jobs and excessively gripe may alter his or her own perceptions of the workplace to coincide with the unhappy coworkers. Or, the employee may report negative feelings to conform to social norms, despite experiencing underlying feelings of satisfaction. This misrepresentation of emotions may even be done without the employee being aware (i.e., self deception), in which case OMFE will enable researchers to assess this degree of misrepresentation and gain a better understanding of the social influences in attitude formation, adjustment, and expression. In sum, OMFE will enable researchers to more accurately evaluate the social cues employees rely on to construct their perception of the work environment and their state of well-being.

Emotional Labor and Emotional Intelligence Although an employee’s state of well-being is dependent on the social cues in the environment, the emotional display may vary depending on the occupation. High emotional labor occupations require an employee to display emotions as part of their job and promote organizational or professional goals, also known as surface acting, as apposed to deep acting. Deep acting is when an employee modifies his or her emotional state to match the work requirements (Hochschild, 1983). Medical professionals, for example, are trained in bedside manner and are often required to display feelings of sympathy or joy to their patients. Career success for other professionals, such as actors, is almost solely dependent on their ability to convey emotions that appear to be authentic. Because facial expressions can be regulated by attenuating, amplifying, simulating, or masking the expression of emotion (Ekman et al., 1980), OMFE could be used as a training tool to help high emotional labor employees convey expressions that appear to be more authentic. Even when professional actors provided both an authentic emotional experience and acted an unfelt emotional

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experience, an OMFE was successful in identifying facial characteristic that differed between the two expressions (Gosselin, Kirouac, & Dore, 1995). To this end, OMFE could be valuable for training employees who are already highly skilled in emotion regulation. Emotional labor is also associated with employee well-being. Emotion regulation requires the use of a limited pool of energy resources (Richards & Gross, 1999) and is often found to be associated with stress-related physiological arousal (e.g., Butler et al., 2003), psychological strain (e.g., Schaubroeck & Jones, 2000), and performance on secondary tasks (Richards & Gross, 1999, 2000). In an effort to advance well-being and emotional labor research, OMFE can be used as a tool to assess the amount of emotional regulation taking place. Currently, to assess surface acting, researchers rely on self-report scales that ask employees to rate how often they engage in certain behaviors at work (e.g., I resist expressing my true feelings; Grandey, Fisk, & Steiner, 2005). In contrast, OMFE can enable a researcher to assess surface acting by coding the frequency of the behavior as well as the intensity of the regulation. In addition to training a person to regulate emotional displays, OMFE can be used to train employees to detect emotional states in customers and clients. Although OMFE require extensive training to successfully identify emotional expressions, some people have the ability to detect emotions using facial cues. In other words, some people have a high emotional intelligence and can identify the emotional states of other people. For instance, secret service agents are able to detect emotions using facial expressions at a probability greater than chance (Ekman & O’Sullivan, 1991). Future researchers should evaluate whether training in facial expression recognition can increase an employee’s emotional intelligence. Although the validity of OMFE coding is dependent on still video frames or photographs, it may be possible that some of the more prominent muscle movements can be identified during a social interaction and be used as a training tool. OMFE may also be used to design a facial expression recognition test. Greater ability to recognize facial expressions is positively associated with sales performance (Byron, Terrananova, & Nowicki, 2006). As a result, Byron and colleagues suggest the inclusion of a facial expression recognition test in selection batteries for interpersonal jobs. In sum, OMFE will not only improve the assessment of well-being but also serve as a valuable tool in training employees to properly regulate emotional expressions and to identify the emotional states of customers or clients.

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Affective-Oriented Personality Traits and Well-Being Research examining the effects of dispositions on well-being can also benefit from the use of OMFE. In fact, previous research has employed OMFE as both the predictor and criterion variables in the disposition – well-being relationship. Ruch (2005) found that participant extraversion predicted facial expressions displaying positive emotions and intensity of the expression. In addition, Ruch found that an increase in state extraversion levels by providing alcohol to the participants increased ratings of positive facial expressions. Overall, extroverts expressed more positive facial expressions than introverts when a positive stimulus was presented. OMFE have also been linked to structured interview ratings of types A and B personality (Chesney, Ekman, Friesen, Black, & Hecker, 1990). In more detail, type A and type B individuals differed in OMFE coding for glare and disgust, with type A individuals displaying higher scores for both facial expressions. In sum, OMFE are consistently associated with affectiveoriented dispositions. Although the aforementioned studies discuss the association between OMFE and dispositions, OMFE may also be a valid tool in the assessment of dispositions. Harker and Keltner (2001) conducted a longitudinal study on the relationship between college yearbook photographs and both personality traits and life outcomes up to 30 years later. An OMFE was used to examine whether a genuine positive expression (activation of the orbicularis oculi muscle region during a smile) would represent a disposition and predict future well-being. As expected, facial expressions in college yearbook photographs were associated with affiliation, competence, and negative emotionality decades later. Consequently, researchers have begun to use OMFE and photographs to assess a person’s affective-oriented disposition and subsequent well-being in effort to replicate and expand on Harker and Keltner’s findings (Freese et al., 2008; Hertenstein et al., 2009). Although Freese et al. (2008) found mixed results in an attempt to replicate previous findings, Hertenstein et al. (2009) found that authentic smiles in a photographs predicted divorce tendencies later in life. Although a single photograph has been used to predict criteria decades later (Harker & Keltner, 2001; Hertenstein et al., 2009), taking the average emotional state displayed in photographs throughout a person’s life is likely to be a better assessment of dispositional tendencies (Freese et al., 2008). In fact, using OMFE to code photographs for negative and positive emotions over an extended time frame (e.g., a 30-day period or a person’s entire adult life) will

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likely differentially predict affective-oriented criteria when compared to common dispositional scales that use similar time frames (e.g., the PANAS).

Stress Interventions In our discussion on social information processing theory, we described the role of social and environmental cues in the construction of an employee’s state of well-being. This effect also applies to organizational stress interventions. Interventions often involve a stressor and a well-being evaluation in which employees report which stressors are prevalent and the frequency of negative emotions. In addition, self-reports of stressors and strains are gathered after the intervention to evaluate the effectiveness of the program (for a review of stress intervention design see Newman & Beehr, 1979; Richardson & Rothstein, 2008). According to social information processing theory (Salancik & Pfeffer, 1978), self-reports of stressors and strains are likely to invoke mood or spurious effects. Employees who are satisfied with their jobs, for example, may be given a stressor evaluation form in which they are instructed to report the frequency of several stressors. Although the employee was originally satisfied with his or her job, the introduction of the scale has resulted in the stressors becoming the salient characteristic in the environment. In other words, the stressor scale has changed the perception of the employee. Similar effects can be expected when an employee is asked to complete questionnaires regarding strain. As a result, the use of OMFE to evaluate well-being for organizational interventions will lead to a more accurate assessment of effectiveness and avoid the ethical and costly concerns of invoking a negative emotional state. A similar suggestion is provided by Brief (1998) in which he contends that future organizational research could examine facial expressions of employees to determine affective responses to changes in organizational policy and restructuring. Brief’s suggestion for future research is based on the findings that EMG machines could be used to gauge attitudes to agreeable and disagreeable messages (Cacioppo & Petty, 1981). In sum, the evaluation of intervention effectiveness is likely to be most accurate if objective assessments of well-being are used. The decision concerning which employees to include in an intervention can be just as important as properly assessing the effectiveness. Although researchers have successfully employed self-reports to identify which individuals are most susceptible to stressors (e.g., Kobasa, 1979), OMFE may be a better predictor of treatment success. Support for this claim can be

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found in research pertaining to treatment interventions for individuals with affective disorders (Ekman, Matsumoto, & Friesen, 2005). Ekman and colleagues (2005) examined whether a psychological rating scale (i.e., brief psychiatric rating scale; BPRS; Overall & Gorham, 1962) would predict clinical improvement better than OMFE ratings of true positive emotions. Both measures were applied when the patients were admitted. The researchers found that contempt and false positive emotions (no activation in the orbicularis oculi) were better predictors of patient improvement than the BPRS. This study has significant implications for organizational research. For example, organizations attempting to improve employee well-being through policy changes may be able to predict the effectiveness of the intervention by assessing contempt and regulated expressions of happiness with OMFE.

Self-Conscious Emotions An area of organizational research that is neglected is the assessment of selfconscious emotions in the workplace. Self-conscious emotions entail selfawareness and the comparison of one’s action to standards and rules (Keltner & Buswell, 1996). Because these emotions place the individual in an unfavorable light, self-reports are likely an inappropriate method of assessment. In fact, because of the sensitive nature of self-conscious emotions, requiring an employee to reconstruct an emotional experience is possibly a violation of ethical standards and employee privacy. OMFE will enable researchers to explore these emotions without forcing employees to reconstruct a negative experience. OMFE researchers have been successful in developing coding strategies for self-conscious emotions. Keltner and Buswell (1996) found OMFE could be used to consistently identify distinct non-verbal displays of embarrassment. Specifically, embarrassed participants showed more smile controls, gazed downward for long durations, and first head movements or gaze shifts were in the left direction. In contrast, amused participants displayed more smiles and gazed to the right. With an objective assessment of self-conscious emotions, researchers will gain insight into the relationships between organizational events and employee reactions. We provide several suggestions for future researchers regarding selfconscious emotions. First, self-conscious emotions play central roles in socialization of an individual into a culture and his or her compliance to conventions, norms, and morals of the group (Lewis, 1993; Miller & Leary,

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1992). As a result, feelings of embarrassment are likely to moderate the relationship between engaging in counterproductive work behaviors and efforts to correct for the employee’s transgression. Some employees, for example, may experience embarrassment when committing a behavior that violates organizational norms. As a result, embarrassed employees may be more likely to attempt to rectify the situation (e.g., apologizing or doing extra work). In addition, Perrewe´ and Zellars (1999) emphasize the role of self-conscious emotions (e.g., shame) in their model of emotions, stress, and coping. Specifically, self-conscious emotions are likely to be a mediator between a stressor (attributed to internal causes and perceived as uncontrollable) and unproductive forms of emotion-focused coping (e.g., withdrawal behavior). Last, the use of the OMFE to code for self-conscious emotions may also aid in understanding behavioral and attitudinal responses to harassment. Limited research has examined reconciliation efforts by victims of harassment (e.g., Aquino, Tripp, & Bies, 2001, 2006). Employees experiencing self-conscious emotions are likely to have lower commitment and satisfaction and will be less likely to report the event or attempt to reconcile with the perpetrator. In addition, self-conscious emotions experienced by the perpetrator after harassing a fellow employee will likely predict future transgressions by the perpetrator. In sum, self-conscious emotions are an affective experience that has received little attention by well-being researchers and is highly susceptible to social desirability bias. OMFE can serve as a valuable tool in overcoming these limitations and enable researchers to explore the role of these sensitive emotions in an organizational context.

Practical Issues Researchers must address several practical concerns for OMFE to be a valid assessment tool. First, multiple coders are needed to estimate the inter-rater reliability. Although a second rater is needed to assess the validity of the coding method, a reliability estimate can be obtained by coding a small random sample of the participants (e.g., Harker & Keltner, 2001; Keltner, 1995). Second, coders should be blind to the experimental conditions and hypotheses (e.g., Lerner et al., 2007). Third, the resolution of the photographs can become a concern for the coders. However, Harker and Keltner’s (2001) coding was successful despite relying on photographs taken from college yearbooks over 40 years before the study. Given the date of the

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photographs, it is doubtful that the resolution of the photographs was high. Harker and Keltner were able to overcome this issue by enlarging the photograph to 5  7 inches. Next, if researchers are relying on archival photographs, the researcher should inquire about any modifications done to the photographs (e.g., photograph editing software to remove wrinkles). Photograph modifications, however, are not a concern for researchers collecting their own photographs. Finally, researchers employing OMFE should be aware of the advancements in coding strategies. Development of computer-imaging software to code facial movement is an ongoing process and may soon become a more common and efficient method to code facial movement (Movellan & Bartlett, 2005). Although advancements in computer-imaging software will save coding time, a user will still need to be a trained coder to accurately interpret the results. As computer-coding methods improve, new research avenues will be created. For instance, computer-coding may be used to improve the perception – action loop and increase a person’s engagement on computer tasks (Movellan & Bartlett, 2005). In more detail, computerimaging software may soon be advanced enough to detect a person’s affective state and adjust the computer task to promote more positive affective responses from the user. This adjustment is analogous to the way good teachers adjust their teaching strategies to get students more engaged. In an organizational context, an adaptive computer program may help improve employee engagement on a computerized training tutorial by adapting to the emotional state of the employee. In sum, future researchers should be aware of the advancements in coding strategies that are currently being developed. These advancements will likely enable researchers to pose new questions and test hypotheses that are not currently foreseeable.

SUMMARY Although we discussed the advantages of employing an objective measure of well-being over self-reports, we do not wish to suggest the abandonment of self-report. In fact, subjective assessments provide information about a person’s well-being that an OMFE cannot assess. Our goal for this chapter, rather, was to review a fertile area of facial expression research and suggest that organizational researchers consider OMFE in future research designs. The utility of OFME has already been identified by clinical, health, and social psychologists. In addition, the advancements in coding strategies have enabled psychologists to become trained to code basic AUs in less time

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(approximately 100 hours of training) and with more accuracy and detail. As a result, occupational stress and well-being researchers will be able to assess real-time emotions with precise assessments of the onset, apex, and offset of an emotional experience while in a naturalistic setting. The most interesting advantage of OMFE is the implications it will have for research design. With the ability to use video recording technology to assess employee emotions from archival data and without the immediate awareness of the employee, researchers will undoubtedly produce creative designs to address unanswered questions. In conclusion, the integration of the facial expression and occupational well-being literatures is well overdue. Occupational stress and well-being researchers will likely find OMFE to be an advantageous tool when assessing employee well-being.

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KARASEK’S (1979) JOB DEMANDSCONTROL MODEL: A SUMMARY OF CURRENT ISSUES AND RECOMMENDATIONS FOR FUTURE RESEARCH Jason Kain and Steve Jex ABSTRACT Karasek’s (1979) job demands-control model is one of the most widely studied models of occupational stress (de Lange, Taris, Kompier, Houtman, & Bongers, 2003). The key idea behind the job demandscontrol model is that control buffers the impact of job demands on strain and can help enhance employees’ job satisfaction with the opportunity to engage in challenging tasks and learn new skills (Karasek, 1979). Most research on the job demands-control has been inconsistent (de Lange et al., 2003; Van Der Deof & Maes, 1999), and the main reasons cited for this inconsistency are that different variables have been used to measure demands, control, and strain, not enough longitudinal research has been done, and the model does not take workers’ individual characteristics into account (Van Der Deof & Maes, 1999). To address these concerns, expansions have been made on the model such as integrating resources, self-efficacy, active coping, and social support into the model (Demerouti, New Developments in Theoretical and Conceptual Approaches to Job Stress Research in Occupational Stress and Well Being, Volume 8, 237–268 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-3555/doi:10.1108/S1479-3555(2010)0000008009

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Bakker, Nachreiner, & Schaufeli, 2001b; Johnson & Hall, 1988; Demerouti, Bakker, de Jonge, Janssen, & Schaufeli, 2001a; Landsbergis, Schnall, Deitz, Friedman, & Pickering, 1992). However, researchers have only been partially successful, and therefore, to continue reducing inconstencies, we recommend using longitudinal designs, both objective and subjective measures, a higher sample size, and a careful consideration of the types of demands and control that best match each other theoretically.

Throughout the years, many theoretical models of occupational stress have been proposed (see Jex & Yankelevich, 2008, for a review), while far fewer have been empirically tested. Of these models, none has been used as a theoretical foundation for research and been subjected to more empirical testing than Robert Karasek’s (1979) job demands-control model (de Lange, Taris, Kompier, Houtman, & Bongers, 2003). The popularity of the job demands-control model is most likely due to its simplicity, the ease with which it can be empirically tested, and the practical implications that can be gleaned from this model. The basic premise of the job demands-control model is that the most stressful or ‘‘high-strain’’ jobs are those in which employees are subjected to high levels of demands, yet at the same time have very little control over their work. A classic example of this type of job would be that of a production line worker. This type of worker might have a difficult production quota to meet, but at the same time have little if any control over the pace of the production line or how the product is produced. As the job demands-control model is an occupational stress model, it is designed to predict negative outcomes or strains. It is possible, however, to view the demands-control combination in a more positive light. Viewed from this perspective, the job demands-control model proposes that job control buffers the relationship between job demands and strain. In fact, Karasek (1979) proposed that high demand, high control, or ‘‘active’’ jobs help to enhance employees’ job satisfaction and provide the opportunity to engage in challenging tasks and learn new skills (Karasek, 1979). This is an aspect of the job demands-control model that for many years has largely been ignored, but more recently, researchers have begun to investigate it. After Karasek (1979) initially proposed the job demands-control model, most research supported the major premise of the model, namely, that demands are positively related to strain and that control is negatively to

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strain. Over the past 30 years, however, research has been quite inconsistent (de Lange et al., 2003; Van Der Deof & Maes, 1999) regarding the interaction between demands and control in predicting various types of strain. One of the major reasons for such inconsistency is that there is disagreement among researchers regarding the proper way to test the job demands-control model (de Lange et al., 2003). For many researchers, a proper test of the job demands-control requires a statistical interaction between job demands and job control (e.g., Spector, 1987). Such an interaction effect is illustrated in Table 1. As can be seen, job demands and strain are positively related only when job control is low. When job control is high, job demands and strain are unrelated. Karasek (1979; in de Lange et al., 2003), however, argued that a statistically significant interaction between job demands and job control is unnecessary to support the demands-control model. Specifically, Karasek argued that if job demands and job control each exert independent main effects on strain, this still supports that basic premise behind the model. Moreover, Karasek argued that reducing job demands and increasing control would have the effect of reducing strain even if no interaction is present. The other primary reasons cited for the inconsistent support for the job demands-control model are that different variables have been used to measure demands, control, and strain, and the model does not take workers’ individual characteristics into account (Van Der Deof & Maes, 1999). As there have been a wide variety of research studies attempting to reduce these inconsistencies, the purpose of the current chapter is to review and summarize this research and to make recommendations for future research. We begin the chapter by briefly defining the major components of the job demands-control model, followed by a review of empirical tests of the model. We then review results for different variables that have been used to measure demands, control, and strain and discuss why inconsistent measurements of these variables have led to mixed results. In the next part of the chapter, we review a modified model known as the job Table 1.

High control Low control

The Job Demands-Control Model. High Demands

Low Demands

‘‘Active jobs’’ ‘‘High-strain jobs’’

‘‘Low-strain jobs’’ ‘‘Passive jobs’’

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demands-resources model, which was designed to establish consistency in measurement by matching different combinations of demands, control, and resources to each other. We then discuss individual characteristics that have been found to influence support for the model and conclude by making recommendations for future research on the job demands-control model.

THE JOB DEMANDS-CONTROL MODEL Several years before, Karasek (1979) developed the job demands-control model, case studies of restaurant workers seemed to indicate that employees experienced the greatest job-related strain when they were under close supervision, yet at the same time were responsible for completing a large amount of work (Crozier, 1964; Drabek & Haas, 1969). However, occupational stress research at the time on the psychosocial effects of work-related strain primarily focused on either job decision latitude or the strain created by the environment (Holmes & Rahe, 1967; Kornhauser, 1965). In an effort to integrate these research streams and explain the case studies theoretically, Karasek (1979) developed the job demands-control model, which explains how levels of job demands and control can influence strain, job satisfaction, and learning. In the job demands-control model, job demands are measured as quantitative workload or role conflict (competing job-related role demands), while control is measured as the ability to make decisions about how to complete job tasks (also called job decision latitude), and strain is measured as physiological symptoms and cardiovascular disorder (Karasek, 1979). As illustrated in Table 1, the job demands-control model categorizes jobs into four types based on different combinations of job demands and control. According to the model, employees suffer the most physical symptoms in ‘‘high-strain’’ jobs, or jobs where employees experience high demands at work, and at the same time have little control over how to perform their tasks (Karasek, 1979). The primary reasons why ‘‘high-strain’’ jobs are so detrimental to health is that high demands and low control impede an individual’s ability to complete work in a specified time frame and perform their job as well as they would like. When people have too many tasks to perform and little control over how to perform them, they continually devote high amounts of cognitive resources to those tasks, which results in an elevated level of physiological arousal and increased cardiovascular and nervous system tension (Karasek, 1979). When they cannot get the work done, and the tension is not released, their bodies begin to run out of

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resources and their heart rate increases for a sustained amount of time, which results in physiological symptoms and illness (Karasek, 1979). Although the vast amount of research on the job demands-control model has focused on the effects of ‘‘high-strain’’ jobs, other combinations of job demands and job control are also of interest. For example, workers in jobs with high demands and high control, or ‘‘active jobs,’’ have greater satisfaction because they have intellectual demands that give them the opportunity to increase their competency, self-efficacy, skill development, and personal growth (Karasek, 1979; Karasek & Theorell, 1990). An example of this type of job would be business owner who works long hours, yet has a great deal of control over how he or she performs job tasks and decisions regarding the business. In addition, Karasek (1979) proposed that workers in ‘‘passive jobs,’’ or jobs with low demands and low control, have a gradual reduction of general problem-solving activity, boredom, and dissatisfaction due to the fact that constant repetition of a task results in a decreased capacity for intellectual challenge (Karasek, 1979). An example of a passive job would be night security guard. Such an individual has very few work demands (perhaps, other than staying awake) yet has very little say in how the job is performed. The dissatisfaction and strain associated with ‘‘passive jobs’’ is presumably due to boredom associated with such low-level job demands. The final type of job shown in Table 1 is ‘‘low demand, high control.’’ Most likely due to the rarity of such jobs, Karasek proposed no hypotheses about their effects on employees (Karasek, 1979). Karasek’s (1979) first test of the four categories used both cross-sectional and longitudinal data from Swedish workers and found that having higher demands and control led to greater satisfaction, having low demands and low control led to moderate levels of reported physical symptoms, and having high demands and low control led to high levels of reported physical symptoms.

RESEARCH ON THE JOB DEMANDS-CONTROL MODEL Since Karasek (1979) proposed the job demands-control model over 30 years ago, there have been numerous empirical tests; in fact, his model is by far the most widely tested occupational stress model. Despite the vast number of empirical tests, the research findings on the job demands-control model have been mixed (Demerouti, Bakker, de Jonge, Janssen, & Schaufeli, 2001a;

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Taris & Feij, 2004; Fox, Dwyer, & Ganster, 1993; Ganster, 1989; O’Driscoll & Beehr, 2000). The vast majority of the research has been done on the combination of high demands and low control, which has been split into two theoretical perspectives: an additive hypothesis that both high demands and little decision latitude influence job strain independently, and an interaction (buffer hypothesis) that low control strengthens the relationship between job demands and job strain (de Lange et al., 2003). The primary reason for this split is that early research supported the model, showing that an interaction between high workload (demands) and low control (decision latitude and skill discretion) led to higher levels of cardiovascular disorder, alcohol problems, intentions to quit, and depression (Bromet, Dew, Parkinson, & Schulberg, 1988; Karasek, 1979). Later research, however, failed to support the model finding that high workload and control independently predicted intent to quit and diastolic blood pressure; however, there were no significant interaction effects detected (Mcclenahan, Giles, & Mallett, 2007; Schnall et al., 1990; Taris & Feij, 2004). Although most research on the job demands-control model has supported the additive hypothesis (Van Der Deof & Maes, 1999), much less has provided support for the interactive or buffer hypothesis (Ganster, 1989; Van Der Deof & Maes, 1999; Fox et al., 1993). For example, Barnett and Brennan (1997) found that regardless of gender or socioeconomic status, after being in the same job for 1 year, doing increasingly busy, monotonous work, and having to work under time pressure with conflicting demands increased the amount of distress that employees feel; however, having monotonous work did not interact with lower amounts of pressure under time to influence strain. Mcclenahan et al. (2007), found in a sample of teachers that measures of work environment (representing demands, control, and support), burnout, psychological distress, and job satisfaction did not yield any two-way or three-way interactions that would indicate a buffer effect; however, main effects were found for all the demand and control variables on the health outcomes. Because so much research has been done on ‘‘high-strain’’ jobs, there have been multiple reviews attempting to summarize the findings (de Lange et al., 2003; Van Der Deof & Maes, 1999). Early reviews of the job demands-control model showed that only three interactions could be found between demands and control, and the significant relationships often existed in specific occupations such as health care; however, occupations such as clerical and police work are more likely to yield support for the additive hypothesis (Dollard & Winefield, 1988; Elsass & Veiga, 1997). Later reviews of the research done on the job demands-control model found that out of 31 studies conducted

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on psychological outcomes, 15 showed at least some support of a buffer hypothesis (studies with large amounts of males that take personality characteristics, type of organization, and hierarchical position into account), although the majority of the 15 studies provided only partial support (Van Der Deof & Maes, 1999). Additionally, of the nine studies that examined the model longitudinally, only two provided support for the buffering hypothesis (Van Der Deof & Maes, 1999). In a more recent review, de Lange et al. (2003) summarized the results of only methodologically rigorous tests of the job demands-control model. Specifically, these authors included only studies that were longitudinal, fully crossed lagged data with multiple time points, both objective and subjective data, and a wide variety of variables such as self-reported stress, sickness, cardiovascular disease, and lifestyle factors; 19 studies were identified, but only 8 found support for the ‘‘buffering’’ effect of demands on strain (de Lange et al., 2003). However, 12 studies reported main effects for demands and 9 studies reported main effects for control, providing a stronger argument for the additive hypothesis (de Lange et al., 2003). With respect to ‘‘high demand, high control’’ jobs, some research findings have shown that health care workers in this type of job are highly satisfied (de Jonge, Van Breukelen, Landeweerd, & Nijhuis, 1999; de Jonge, Dollard, Dormann, Le Blanc, & Houtman, 2000; Landsbergis, Schnall, Deitz, Friedman, & Pickering, 1992), Japanese workers holding this type of job have a lower mortality rate (Tsutsumi, Kayaba, Hirokawa, & Shizukiyo, 2006), young workers acquire the most new skills (de Witte, Verhofstady, & Omey, 2007), and that high demands and high control produce the highest level of self-efficacy, mastery, job involvement, and commitment (Demerouti et al., 2001a; Landsbergis et al., 1992). Other research, however, has been less supportive, showing that high job demands were detrimental even when combined with high control because they required too much attention from employees for them to focus on learning new tasks (Demerouti et al., 2001a; Taris & Feij, 2004). Research has also found that jobs with high demands and high control can lead to higher levels of work–family conflict because due to high demands, employees use their control to bring work home and neglect their family’s needs (Butler, Grzywacz, Bass, & Linney, 2005). The interaction between high demands and high control has also been found to lead to more satisfaction in certain professions such as health care, but not others such as warehouse work (de Jonge et al., 2000). Again, one of the primary reasons given for these inconsistent results is that Karasek (1979) referred to the outcome of ‘‘active jobs’’ in a wide variety of ways including learning and increased motivation,

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effective learning, feelings of mastery, and so on (Taris, Kompier, de Lange, Schaufelis, & Schreurs, 2003). Although there has been a great deal of research on high demand, low control jobs and, to a lesser extent high demand, high control jobs, there is a paucity of research on the other combinations of job demands and job control (low demands, high control; low demands, low control). Contrary to Karasek’s (1979) original research examining the interaction between low demands and high control, recent research has found that these jobs provide a higher amount of learning and self-efficacy than jobs with high demands and high control (Parker & Sprigg, 1999; Taris & Feij, 2004; Taris et al., 2003). The primary reason provided for this finding is that the lack of strain allows employees to focus on acquiring skills without the distraction of high work demands (Taris et al., 2003). This is likely the reason why new employees are often given reduced workloads or more limited assignments. Additionally, research has supported Karasek’s (1979) original research by finding that the combination of low demands and low control does not show higher levels of learning or personal accomplishment over time (Taris et al., 2003) and that employees in these types of jobs reported increased strain across a 2-year period (Taris & Feij, 2004).

MEASUREMENT PROBLEMS WITH THE JOB DEMANDS-CONTROL MODEL One of the major criticisms of research on the job demands-control model is that too many different types of measures have been used for demand, control, and strain (de Lange et al., 2003; Kristensen, 1995; Van Der Deof & Maes, 1999). As a result, it is difficult to compare results across studies. In Karasek’s (1979) original article, he states that researchers must distinguish between two types of variables in work environments: job demands and the discretion workers have to handle these demands. However, broad definitions were provided for how to measure these variables, such as job decision latitude consisting of two distinct constructs (skill discretion and decision authority), job demands consisting of both work overload and role conflict, and studies measuring variables differently (subjectively vs. objectively; Karasek, 1979). Additionally, a wide variety of measures of control have been used inconsistently, such as resource allocation, dealing with customers or the public, educational

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requirements for the job, organizing decisions, skill discretion, perceptions of scheduling, and task control (Cohen & Wills, 1985; Karasek, 1979; Sargent & Terry, 1998). Researchers have also used a wide variety of outcomes including job satisfaction, job involvement and commitment, efficacy and mastery, and job challenge (Holman & Wall, 2002; Van Yperen & Hagedoorn, 2003). A sample of the ways job demands, control, and strain have been measured is displayed in Table 2. The main reason all of these variables have been used is that researchers have used two theoretical frameworks to examine the job demands-control model: an epidemiological model, and a cognitive appraisal model. The epidemiological model attempts to link exposures to occupational conditions such as high work demands and technological demands to actual diseases such as coronary heart disease (Fox et al., 1993). The goal of this perspective is to identify risks on the job and make recommendations for broad policies of surveillance to reduce these factors. The cognitive appraisal paradigm is concerned with understanding the thought process that mediates the influence of work environments on mental and physical health. According to this framework, how people cognitively interpret

Table 2. A Sample of the Variety of Ways Demands, Control, and Strain are Measured. Study Citation

Demands

Karasek (1979)

Self-reported workload Role conflict

Fox et al. (1993)

Patient load Self-reported workload

Landsbergis (1988)

Self-reported workload Physical exertion Hazard exposure

Landsbergis et al. Self-reported workload (1998)

Control Autonomy Decision making

Strain

Exhaustion Depression Physical illness Task control Job satisfaction Scheduling control Illness Procedure and policy Somatic complaints control Blood pressure Salivary cortisol Decision latitude Job dissatisfaction Depression Physical symptoms Burnout Sleeping problems Decision latitude Smoking Alcohol use Lack of exercise

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situational demands determines psychological and physical well-being (Fox et al., 1993). Because of this inconsistent definition, researchers have yielded a wide variety of results. De Jonge et al. (1999, 2000), and Dollard, Winefield, Winefield, and de Jonge (2000), for example, all found that active jobs produced the highest amounts of job satisfaction. Parker and Sprigg (1999) and Holman and Wall (2002) also found that high demands and high control interacted to result in higher levels of self-efficacy and mastery. Additionally, research using primarily self-report measures of workload and weighted skill discretion and decision authority found that ‘‘active jobs’’ produced the greatest amount of active learning, problem solving, job satisfaction, internal locus of control, and job involvement (Landsbergis et al., 1992). Landsbergis et al. (1992) found that commitment and involvement were highest in ‘‘active jobs.’’ Other research has been less supportive, showing that high job demands were detrimental even when combined with high control because they required too much attention from employees for them to focus on learning new tasks (Demerouti et al., 2001a; Taris & Feij, 2004). Additionally, job involvement has been found to relate to higher levels of control regardless of the amount of demands employees were facing (de Jonge, Janssen, & van Breukelen, 1996). Jobs with high demands and high control have also been found to lead to higher levels of work–family conflict because due to high demands, employees use their control to bring work home and neglect their family’s needs (Butler et al., 2005). Other research has shown that there are main effects of demands and control on work–family conflict in that work–family conflict is higher when demands are high regardless of the amount of control employees have; however, control does moderately reduce work–family conflict independent of demands (Gronlund, 2007). The interaction between high demands and high control has also been found to lead to more satisfaction in certain professions such as health care, but not others such as warehouse work (de Jonge et al., 2000). Recent research has attempted to use multiple types of demands (cognitive decision, sensory demands, emotional demands, and risk demands), control (decision authority and skill discretion), and outcomes (mastery, health complaints, and job satisfaction), but also found mixed results. Specifically, it was found that quantitative and emotional demands best predicted job stress, subjective health complaints, and job satisfaction while mastery best predicted cognitive, emotional, and sensory demands; for an actual buffering effect, only skill discretion was shown to interact with all the demands to influence all of the different health outcomes

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(Mikkelsen, Ogaard, & Landsbergis, 2005). Other research examining selfrated health and incidence of heart disease (controlling for diet and exercise) has found evidence that workload and autonomy interact in a manner consistent with the job demands-control model to influence blood pressure, immune functioning, and coronary heart disease (Bishop et al., 2003; Sacker, Bartley, Frith, Fitzpatrick, & Marmot, 2001). In response to the wide variety of findings, early criticisms of the model indicated that more specific measures of demands, control, and strain; more dimensions of the model; and the lack of practical influence of the model all have led to the inconsistent findings (Kasl, 1996). Specifically, Jones, Bright, Searle, and Cooper (1998) suggested that expanding the model to include control over organizational factors such as scheduling, physical work hazards and technology, and job insecurity would create a more clear picture of the types of variables that interact with one another. Research suggests that the primary reason for inconsistent findings is that assessing a single construct of work control masks the buffering effect because there are many different types of control (scheduling, work pace, task control, and decision making), and only some of them may buffer the impact of job demands on strain (Sargent & Terry, 1998). In line with the stress matching hypothesis proposed by Cohen and Wills (1985), the idea that the type of control must match the nature of the demands to buffer against strain was tested as a way to resolve the inconsistent findings on the ‘‘buffering effect’’ (Sargent & Terry, 1998). Results of early research indicated that task sources of work control such as work pace and scheduling buffered the impact of job demands on strain much better than more peripheral types of control such as mobility and organizational decisions; specifically, task control buffered the impact of work overload on depression and also the impact of role ambiguity on job satisfaction (Sargent & Terry, 1998). Generally, research using measures targeting specific types of demands and control relevant to the samples being used (e.g., workload, role clarity, scheduling control, and decision making) as well as objective physical health outcomes such as cardiovascular health (blood pressure), cortisol levels, and physical symptoms has yielded the most support for the buffer hypothesis (Fox et al., 1993; Ganster, 1989; Johnson & Hall, 1988; Karasek, 1989; Kristensen, 1995; Landsbergis, 1988; Schnall, Landsbergis, & Baker, 1994). For example, the most comprehensive study finding support for the ‘‘buffering’’ effect (Fox et al., 1993) found that interactions between objective measures of workload and a wide variety of measures of control (scheduling of rest breaks, pacing, and arrangement of physical

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environment) predicted higher levels of blood pressure, cortisol, and illness. Dwyer and Ganster (1991) also found that objective measures of demands and control predicted absence, tardiness, and sickness and that subjective ones predicted job satisfaction. They measured job demands by using the results of a job analysis, which specifically focused these demands on the degree of vigilance employees have to show on the job. Wall, Jackson, Mullarkey, and Parker (1996) used both a specific measurement of job control focusing on the cognitive requirements of the manufacturing employees in their sample and a general measure of decision latitude and found interactions between job demands and the specific measure of control on job satisfaction, depression, and anxiety. Sacker et al. (2001) found that British working males between 20 and 64 years who self-reported working in a high demand, low control environment also reported higher amounts of chest pain lasting longer than a half hour and were found to have more medically documented heart attacks; these effects were found after controlling for adverse health behaviors such as poor diet, smoking, and a lack of exercise. Bishop et al. (2003) assessed blood pressure using an Accutracker II Blood Pressure Monitor and found that people who selfreported high demands and lower control at work had the highest blood pressures. Interactions among workers in their first job between work hours and autonomy also showed that autonomy buffered the impact of high work hours on job satisfaction and that high workload and autonomy influenced acquisition of new skills synergistically (de Witte et al., 2007). Additionally, research using all self-report measures of time urgent deadlines and weighted skill discretion and decision authority found support for the ‘‘buffering hypothesis’’ when predicting self-reported psychological outcomes such as anxiety, anger, job dissatisfaction, and physical symptoms (Landsbergis et al., 1992). Demerouti et al. (2001a) tested insurance agents and found that high work pace and sufficient time interacted with decision latitude in that high work pace and little available time predicted burnout and psychosomatic health complaints and high work pace and high decision latitude predicted job commitment and engagement. Similarly, Lang, Thomas, Bliese, and Adler (2007) used measures of demands, role clarity, strain, and performance measures specific to ROTC cadets and found that demands and role clarity interacted to predict psychological strain. Specifically, demands were positively related to psychological strain only under conditions of low role clarity, presumably because employees are able to handle greater demands when they understand what they are expected to do. Additionally, research on younger workers in environments with high

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demands and low control are less likely to be motivated to learn form their colleagues and supervisors (Taris & Feij, 2004).

LACK OF LONGITUDINAL RESEARCH ON THE JOB DEMANDS-CONTROL MODEL Another criticism of research testing the model is a dearth of longitudinal studies, which are the best way to research the model because to establish that high job demands result in strain, a causal relationship must be established between the two variables (de Lange et al., 2003). Longitudinal studies also allow for testing reciprocal relationships. For example, longitudinal designs can be used to assess both the influence of demands and control on strain and also the influence of strain on perceptions of the amount of demands and control in the work environment (de Lange et al., 2003). Additionally, longitudinal studies with multiple measurements limit interim effects such as job changes and maturation effects due to increased experience. However, despite this criticism, very few studies have collected multiple measurements over time (de Lange et al., 2003; Kristensen, 1995; Van Der Doef & Maes, 1999). Additionally, authors of the longitudinal studies that have been conducted have not justified the period between measurements, and those periods have varied widely. Daniels and Guppy (1994), for example, had a 1-month period between two measurements. Seven other studies used a 1-year period between two measurements (Bourbonnais, Comeau, & Vezina, 1999; Bromet et al., 1988; Carayon, 1992; Johnson et al., 1995). Other studies have put 3–4 years between measurements and have used three to four measurement periods (Kivimaki, Vahtera, Pentti, & Ferrie, 2000; Vahtera, Kivimaki, Pentti, & Theorell, 2000). The vast majority of these studies reported main effects of demands and control on strain, but no interaction between these variables (Bourbonnais et al., 1999; Carayon, 1992; Daniels & Guppy, 1994; Johnson et al., 1995; Kivimaki et al., 2000). One notable exception, however, is a study conducted by Ganster, Fox, and Dwyer (2001), which found that subjective and objective demands interacted to predict health care costs. Specifically, higher nurses’ patient load, contact hours, and subjective perceptions of work overload were more strongly related to health care costs when control was low (Ganster et al., 2001). Additionally, salivary cortisol measures were taken and accounted for 25% of the variance in health care costs (Ganster et al., 2001).

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EXTENSIONS OF THE JOB DEMANDS-CONTROL MODEL As was evident in the previous section, the empirical research on the job demands-control model has produced very mixed results. Given such mixed results, it is possible that the original job demands-control model as articulated by Karasek (1979) was overly simplistic. For example, one can think of many types of ‘‘job demands’’ that may or may not have a negative impact on employees. In addition, employees may have a great deal of control over some aspects of their work and very little over other aspects. Finally, it is possible that control buffers the effects of job demands for some types of people but not for others, that is, there are other variables that will either facilitate or suppress the demands–control interaction. Based on these concerns, researchers have recently developed a number of extensions of the job demands-control model. In this section, we summarize those that have received at least some level of empirical scrutiny. Job Demands-Resources Model To continue addressing the problem of using inconsistent demand, control, and strain variables, Demerouti et al. (2001b) created the job demandsresources model to broaden the model and test which variables specifically support a ‘‘buffering effect.’’ The definition of job demands was expanded to physical, social, or organizational aspects of a job that require continued physical and mental costs. Job demands include physical workload, time pressure, shift work, work to home conflict, and the physical environment of the workplace itself. Job resources are physical, psychological, social, and organizational characteristics that help people achieve work goals, reduce job demands, and increase personal growth and development. Resources can be external (organizational, participation in decision making, rewards, task variety, and social support) and internal (cognitive) (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001b). A final characteristic of the demandsresources model is that the dependent measures have been expanded. Rather than focusing on physical health, which has been the case in many tests of the job demands-control model, the job demands-resources model has also focused on psychological health, often in the form of burnout. According to the health impairment process of the job demandsresources model, high job demands and low resources are associated with job strain such as physical symptoms and burnout (Lewig, Xanthopoulou,

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Bakker, Dollard, & Metzer, 2007); however, having only high demands or lack of resources is associated with components of burnout. Specifically, having only high job demands is positively associated with exhaustion (being overextended by the emotional demands of work), and only having a lack of resources is associated with depersonalization (feeling detached) from work. The reason this happens is because when a person has too many demands, they need to expend extra energy and effort to compensate and become fatigued and ill when they cannot recover (Hockey, 1996). However, having a high amount of resources that help to make demands more predictable (role ambiguity and feedback) and more understandable (information and feedback from colleagues and supervisors) and having control over the demands (autonomy) buffer against the negative impact of demands on strain and job burnout. In general, the job demands-resources model has received more consistent support than the job demands-control model, and the primary reason is that empirical tests have been careful to match specific forms of resources with specific forms of demands in predicting strains. Social support, for example, fulfills the needs of wanting to relate to others and to seek out help in accomplishing tasks that are difficult. Autonomy, on the contrary, allows people to develop greater competency by having choices about how to accomplish work tasks. Additionally, research has found that combinations of resources can work together to buffer different forms of strain. Bakker, Demerouti, and Euwema (2005), for example, found that that autonomy, supervisor support, and feedback specifically buffered the impact of emotional demands such as complaints and impoliteness on burnout. Research has also shown that job demands such as a high workload, emotional demands, and work–home conflict influence in-role ( job-task) performance, which ultimately results in exhaustion. A lack of job resources resulting in lower levels of job engagement has also been shown to predict depression (Hakanen, Schaufeli, & Ahola, 2008). Recent research also indicates that for leaders having the demand of being in a centralized, hierarchical culture that limits their ability to make decisions and execute tasks leads to weaker innovation and long-range strategic planning, which ultimately results in higher levels of emotional exhaustion and turnover intentions (Knudsen, Ducharme, & Roman, 2009). When employees lack resources such as autonomy, development opportunities, and social support, their job goals are blocked, and they become frustrated and disengaged from work, which results in lower extra role (behaviors that help colleagues) performance (Bakker, Demerouti, & Verbeke, 2004). Additionally, people who are engaged at work but do not

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have resources will often proactively seek out other opportunities by moving to other companies that provide them with what they need (de Lange, de Witte, & Notelaers, 2008). However, for physical demands, only autonomy and social support are buffers, for work home interference autonomy, supervisor support, and feedback were buffers, and for work overload, only autonomy acted as a buffer (Bakker et al., 2005). The motivational process of the job demands-resources model builds on the Karasek’s (1979) ideas about ‘‘active jobs’’ by presenting the idea that when both demands and resources are high, employees are more engaged and innovative at work (Knudsen et al., 2009; Martin, Salanova, & Peiro, 2007). Essentially, employees need to adapt themselves to demands that increase their level of arousal by modifying their job environment. The reason that high demands and high resources result in engagement (positive, fulfilling work-related state of mind that leads to dedication), vigor (resilience, persistency, and willingness to invest effort into work), dedication (enthusiasm and inspiration), and innovation (introduction and application of new ideas, products, processes, and procedures in and organization that benefit it) is that they foster what Hackman and Oldham (1975) called a critical psychological state (meaningfulness) or a situation where the resources in the environment allow people to meet demands that result in personal growth and development. In other words, employees will be proactive about using their resources to meet demands, which will ultimately result in greater personal growth and development. Specifically, research has found that job resources such as feedback (Demerouti et al., 2001a, 2001b), social support, and autonomy/participative decision making (Bakker, Demerouti, de Boer, & Schaufeli, 2003a, 2003b) lead to greater affective commitment (a strong identification with the organization) and dedication (a sense of significance, enthusiasm, pride, and challenge), which ultimately leads to lower levels of turnover (Meyer, Stanley, Herscovitch, & Topolnytsky, 2002). These findings are robust across a wide variety of occupations, and work settings include call centers (de Lange et al., 2008), higher education (Bakker et al., 2003a, 2003b), leaders of addiction treatment organizations (Knudsen et al., 2009), dentists (Hakanen, Schaufeli, & Ahola, 2008), and even volunteer ambulance drivers (Lewig et al., 2007). Additionally, workers who obtain promotions in companies often do so because they have autonomy, which leads to greater engagement, or being engaged gives them a greater probability of being given autonomy (de Lange et al., 2008). One element that specifically influences these types of workers is connectedness with their organization, or performing work that is interesting and important, feeling appreciated

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and respected by the organization, and feeling connected to the organization’s values (Metzer, 2003). In general, having higher amounts of job resources leads to greater connectedness, which increases the likelihood that people will choose to continue working for an organization (Lewig et al., 2007). Research has also found that organizational resources (training, job autonomy, and technology) and work engagement predict service climate (shared perceptions of the practice procedures and behaviors that are rewarded and support by the organization in respect to customer service), which predicts employee performance and customer loyalty (Salanova, Agut, & Peiro, 2005).

‘‘Third Variable’’ Extensions When Karasek first developed the job demands-control model, he was rather vague on the role of situational influences or individual differences, but he did state that ‘‘It is certainly possible that worker’s personally affects his perception of decision latitude’’ (p. 290). Karasek also mentioned variables such as education, age, income, and urban vs. rural locations (Karasek, 1979) but was not specific as to what impact they would have on the demands–control interaction. Given the possible impact of individual differences on job decision latitude, one would presume that individual differences could also impact perceptions of job demands. It is also possible that the interaction between job demands and control could vary as a function of individual differences. Despite this possibility, early tests of the job demands-control model did not take individual difference variables into account, even as control variables. As a result, critics have pointed out that failing to take into account differences in ability, resources, and desire to master job requirements as the primary reasons that support for the model has been so inconsistent (Frese & Zapf, 1994; Kasl, 1996; Kristensen, 1995; Parkes, 1991; Van Der Deof & Maes, 1999). In response to this criticism, in more recent tests of the job demands-control model, researchers have begun to add variables to the model to account for individual differences. Johnson and Hall (1988), for example, found an interaction between job demands and control while predicting cardiovascular health only for people who reported receiving high amounts of social support. For those who reported low levels of social support, the influence of demands and control on cardiovascular problems was stronger.

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Based on these findings, Johnson and Hall (1988) referred to this extended job demands-control model, as the ‘‘iso-strain’’ model, which refers to the negative effects of working in isolation. Since Johnson and Hall’s study, the term ‘‘iso-stain’’ has been used frequently when referring to Karasek’s model. The primary reason social support attenuates the relationship between demands and cardiovascular health when people have high levels of control is that support offers employee a way to cope with the negative consequences of being in a ‘‘high-strain’’ job (Johnson & Hall, 1988). Additionally, research on nurses found that high psychological demands and low decision latitude result in higher levels of psychological distress; psychological demands and decision latitude both exerted additive effects on emotional exhaustion. Social support had a direct effect on psychological symptoms, but not with job strain (Bourbonnais et al., 1999). In a study that is not a direct test of the job demands-control model, but is highly related, Bliese and Castro (2000) also found that role clarity only moderated the relationship between job demands and psychological distress for military personnel reporting high levels of social support. No such interaction was found for military personnel reporting low levels of social support. Social support has also been shown to have a strong influence on the impact of jobs with high demands and high control, in that employees report greater levels of intrinsic motivation for high demands, high control jobs when the amount of social support they receive is high (Van Yperen & Hagedoorn, 2003). Additionally, it has been found that employees are more likely to take sick leave when they have high psychological and physical work demands, little opportunities to make decisions, and low amounts of social support from supervisors and colleagues (Vahtera et al., 2000). The primary reason that social support is necessary for higher levels of intrinsic motivation in high demands high control jobs is that social support increases employees’ confidence that they are a valued member of the organization, enhances their perceptions of relatedness, or connections to others, and improves their job performance (Van Yperen & Hagedoorn, 2003). Additionally, Saane, Mykletun, Dahl, Moen, and Tell (2005), found that self-reported demands, control, and support were each independently related to anxiety and depression. Although social support has been the primary ‘‘third variable’’ moderator of the demands–control interaction, a number of individual difference variables have also been explored although none have been researched extensively. Westman and Eden (1992), for example, found that the moderating effect of decision latitude on the job stressor–strain relationship

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depended on one’s position in the organization because employees who hold positions at a lower level are more impacted by a lack of decision latitude because of having fewer resources available to face the challenge of the demands. Another variable that would seem to have a great deal of promise is proactive personality. People who are highly proactive tend to search for opportunities in the environment, show initiative, take action, and persevere until they overcome any problems they may be facing (Parker & Sprigg, 1999). Parker and Sprigg (1999) showed that when these individuals are given autonomy in an environment with high demands, they use that autonomy to manage the demands, which ultimately limited the negative impact of job demands on their health. Proactive individuals also seek out opportunities and resources, which allows them to derive significantly more benefits from an ‘‘active jobs’’ with high demands and high control (Parker & Sprigg, 1999). Interestingly, however, when proactive employees do not have high demands and low control, their ability to use their proactive personality to manage high demands is constrained, ultimately resulting in high levels of strain. People who are not proactive, on the contrary, are less likely to act to reduce job demands, and therefore, they are less likely to learn and master new skills and suffer from high levels of job strain when they face high work demands regardless of how much control they are given (Parker & Sprigg, 1999). Another individual difference variable that has received some empirical scrutiny is coping. Although coping is not a dispositional variable, because choice of coping methods often depends on situational factors such as the stressor one is experiencing, there are still considerable differences between people with respect to type of coping methods used and the effectiveness of these methods. With respect to the job demands-control model, research has shown that active coping, or cognitively analyzing a situation and taking concrete action to solve or overcome problems, influences the relationship between demands, control, and strain (Ippolito, Adler, Thomas, Litz, & Holzl, 2005; Rijk, Le Blanc, & Schaufeli, 1998). Specifically, it has been found that job control attenuates the relationship between job demands and physical symptoms, as would be predicted by Karasek’s model, but only for people who are high in active coping. The reason that people who use active coping methods report lower levels of strain in a high demands, low control environment is that those who use active coping methods tend to utilize opportunities for job control to handle heavy job demands (Ippolito et al., 2005; Rijk et al., 1998). People who tend not to use active coping methods do not take opportunities to utilize control effectively (Rijk et al., 1998).

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Similar research findings were also reported for proactive employees, who use active coping, in that higher demands were associated with anxiety when control was low, but not when control was high (Parker & Sprigg, 1999). Relatedly, it has also been found that self-efficacy, or ‘‘a belief in one’s capability to mobilize the motivation, cognitive resources, and courses of action needed to meet given situational demands’’ (Bandura, 1997, p. 3), may also impact the demands–control interaction. The primary reason selfefficacy is critical to the demands-control model is because it influences people’s perceived ability to exercise control over the environment, and therefore, control acts as a buffer, but only for people who believe they can utilize the control to make themselves more effective at handling their demands (Litt, 1988; Salanova, Peiro, & Schaufeli, 2002; Schaubroeck & Merritt, 1997). People low in self-efficacy, on the contrary, experience enhanced distress and higher blood pressure when they are given control because they do not feel they can use it effectively (Litt, 1988; Schaubroeck & Merritt, 1997; Salanova et al., 2002). A final individual difference variable that has been found to influence the demands–control interaction is locus of control or the degree to which people believe they have control over reinforcements in their environment. Research has shown that locus of control may influence the buffering effect of control on the relationship between demands and anxiety as well as musculoskeletal pain (Daniels & Guppy, 1994; Meier, Semmer, Elfering, & Jacobshagen, 2008). Specifically, research has shown that people who do not believe they can control reinforcements in the environment (external locus of control) respond adversely when given the responsibility to plan and develop high demand work activities because they do not believe they have control over the outcome of the tasks (Daniels & Guppy, 1994; Meier et al., 2008). Research has also found that low social support and high strain leads to lower job satisfaction, the buffering hypothesis is strongest for people with an internal locus of control, and control has more benefit when social support is high for people with an internal locus of control. In other words, employees with the most job dissatisfaction have little support, low control, and an external locus (they do not believe they can control the environment) (Rodriguez, Bravo, & Peiro, 2001). In summary, a number of ‘‘third variables’’ have been proposed and tested as moderators of the interaction between job demands and job control. The most widely researched of these has been social support; so much so, in fact, that in recent treatments of the job demands-control model, researchers have often renamed it as the job demands-control-support model (Daniels, 1999; Mcclenahan et al., 2007; Rodriguez et al., 2001). Other variables that

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may moderate the job demands–control interaction include proactive personality, active coping, self-efficacy, and internal locus of control. Although these do represent a wide variety of individual difference variables, what they all seem to have in common is that they influence an employee’s beliefs regarding the desirability of job control or the employee’s perceived ability to utilize increased job control. Although more research on many of these third variables is obviously needed, it does seem evident that job control is not necessarily a stress buffer for everyone.

RECOMMENDATIONS FOR FUTURE RESEARCH Although there is no doubt that the job demands-control model is one of the most influential occupational stress models ever developed, it is also evident that the model needs further refinement. Additionally, a number of methodological issues need to be addressed in future research on the job demands-control model. In this final section, we discuss the most important theoretical and methodological issues that need to be addressed in future research on the job demands-control model.

Methodological Issues As testing the job demands-control model typically requires a test of the statistical interaction between job demands and control, researchers need to be aware of issues surrounding tests of statistical interactions (Aguinis & Stone-Romero, 1997). The most widely known issue is sample size. As we found in reviewing the literature on the job demands-control model, many studies did in fact utilize large sample sizes (although very few conducted a priori power analyses). Unfortunately, however, many tests of the job demands-control model have utilized relatively small sample sizes and therefore probably do not have adequate power to detect a statistical interaction. Thus, in future tests of the job demands-control model, we recommend that researchers take it upon themselves to use large sample sizes. In fact, this is particularly important because many recent studies have investigated the impact of third variables on the two-way job demands– control interaction. It is also important that gatekeepers of the publication process (e.g., journal editors and reviewers) require that future tests have adequate sample sizes.

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Other than having a small sample size, statistical power can be reduced by measurement error, multicollinearity, and setting an alpha level that is overly conservative (Aguinis & Stone-Romero, 1997). Of these three issues, researchers can likely have the most control over measurement error. In many instances, researchers ‘‘settle’’ for measures that barely meet the .70 criteria for reliability set by Nunnally and Bernstein (1994). We would urge researchers, however, to use measures with reliability estimates considerably higher than .70 if such measures are available. We would also recommend researchers to adopt an alpha level no lower than .05, and in accordance with the recommendations for moderated regression outlined by Aguinis and Stone-Romero (1997), consider raising their alpha levels beyond conventional levels (e.g., .10). A second methodological issue to be considered in future tests of the job demands-control model is the measurement of the major variables required to test the model (i.e., job demands, control, and strain). The most fundamental issue in this regard is the need for more uniformity in the measurement of demands, control, and strain. As has been evident in this chapter, researchers have used so many different measures of these variables that it is very difficult to draw firm conclusions about the validity of the job demands-control model. Another important measurement issue is whether to use self-reports of the major variables in the job demands-control model. Although many tests of the job demands-control model have utilized self-report measures of job demands and control, there have been studies that have used non-self-report measures (e.g., Fox et al., 1993). Furthermore, studies using non-self-report measures have generally supported the demand–control interaction. This is interesting, because in occupational stress research, effect sizes are typically much higher when self-report measures are used (see, e.g., Spector, Dwyer, & Jex, 1988). This also suggests that it is the actual level of job demands and control rather than the perceived level that is important in the context of the job demands-control model. Despite the importance of objectively measuring job demands and job control, it is often the case that for many jobs, it is quite difficult to ‘‘objectively’’ measure job demands and job control (Bommer, Johnson, Rich, Podsakoff, & Mackenzie, 1995). For university professors, for example, one could measure job demands by the number of courses taught or number of students one is advising. Job control could be assessed by examining policies regarding level of discretion regarding course times or number of office hours required. Although all of these would qualify as being somewhat ‘‘objective,’’ it is also the case that they would likely be

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somewhat deficient measures of job demands and job control (Bommer et al., 1995). To resolve this dilemma between objective and subjective measures, we recommend that researchers testing the job demands-control model incorporate multiple types of measures into their research. For example, self-reports of job demands and control can be supplemented by measure of these same constructs derived from job analysis data (e.g., Liu, Spector, & Jex, 2005). By including multiple measures, researchers can provide much more thorough tests of the job demands-control model and, more importantly, determine whether objective job conditions are more important than perceptions in testing the model. A third methodological issue in testing the job demands-control model is the use of longitudinal research designs. As stated earlier, studies that have used longitudinal research designs (see de Lange et al., 2003; Ganster et al., 2001) have generally been more supportive of the job demands-control model as compared to studies using cross-sectional designs. This may be due to the fact most strains, particularly those of a physical nature, represent accumulated effects that occur over a period. Thus, in future tests of the demands control model, we would encourage researchers to use longitudinal designs where possible. A final methodological recommendation in testing the job demandscontrol model is the use of experimental and quasi-experimental designs. When Karasek (1979) originally proposed the job demands-control model, he specifically implied that the model had important implications for job design or redesign in organizations. However, there is a dearth of research using experimental designs to manipulate the amount of demands and control in the working environment. Specifically, Perrewe and Ganster (1989) manipulated the amount of demands and control in a letter writing task and found that self-reported demands was more highly related to anxiety when control was low. Jackson (1983) conducted an experiment where one group in a hospital outpatient facility was given greater control by participating more in decisions, and another group was not, and found that the group give participation reported lower physiological strain. Most recently, Logan and Ganster (2005) conducted an intervention study to enhance manager’s perception of control and found that perceptions of control mediated the relationship between the intervention and the job satisfaction. Additionally, it was found that the intervention improved perceptions of control more for people who had supportive supervisors (Logan & Ganster, 2005). Given the success of experimental research, we would like to see more studies that evaluate interventions designed to either

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reduce job demands or increase job control in future research on the job demands-control model. Such studies would nicely complement the existing literature and provide for more methodologically rigorous tests of the job demands-control model.

Theoretical Issues and Practical Implications Given the research findings, many recommendations can be made for researching the job demands-control model in the future. de Lange et al. (2003), Kristensen (1995), and Van Der Doef and Maes (1999) brought together many of the theoretical criticisms of the model, which have motivated changes to the model to improve its practical value and research support. Specifically, these theoretical criticisms include more dimensions needed in the model, job decision latitude consists of two distinct constructs (skill discretion and decision authority) that cannot be combined theoretically, the model disregards individual differences, evidence indicates that there are additive and not synergistic effects between demands and control on strain, and the model does not include power structure at worksites. Methodologically, these studies point out that measurements are either objective or subjective, studies do not adequately describe specific occupational groups that experience different amounts of demands and control, or too many studies are cross-sectional. First, because past research using objective predictors (e.g., work hours) has provided more consistent support for the model with physiological variables as outcomes, research should examine physical symptoms and cardiovascular disorder as strain outcomes (Karasek, 1979). The studies conducted on the job demands-control model that have found significant results using psychological outcomes such as exhaustion and depression as outcomes also used subjective measures of workload and autonomy, and therefore, future researchers should take these findings into account. Second, Karasek and Theorell (1990) recommended testing physical job stressors and fear of unemployment and physical workload as job demands, but research has yet to test these ideas, and therefore, they should be examined in the future. Third, in accordance with the recommendations of Sargent and Terry (1998), the type of control should match the nature of the demands to buffer against strain. Early studies used the combined measure of job decision latitude and skill discretion to represent control, but many of those studies did not detect a buffering effect; when studies began matching specific types of control to specific types of demands, more support was

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found for the model (Sargent & Terry, 1998). For example, giving employees control over their pace of work and schedule may be a more effective buffer against workload than organizational decision making because it gives them more direct control over their workload (Sargent & Terry, 1998). Third, once enough third variables have been integrated into the model, further research needs to be done on interventions that optimize the amount of demands and control in employees’ environment. Currently, researchers have established that interventions designed to increase job control do seem to enhance satisfaction and performance (Bond & Bunce, 2001; Parker, Chmiel, & Wall, 1997; Wall, Kemp, Jackson, & Clegg, 1986). For example, after 4 years of implementing an initiative to increase the amount of decisions employees make, job satisfaction was found to be much higher in a chemical plant (Parker et al., 1997). Another intervention found that creating autonomous workgroups significantly improved intrinsic motivation and job satisfaction after 30 months (Wall et al., 1986). Also, research has found that using participative action research to change work environments can increase well-being and decrease absence (Bond & Bunce, 2001). Vertical loading, or interventions that give employees higher workloads, and autonomy have also been successfully implemented. Essentially, vertical loading interventions solicit more employee participation, which is supposed to enhance their satisfaction and productivity (Chung & Ross, 1977). Although these interventions are effective, they have not yet taken into account variables that have been added to the job demands-control model. Chung and Ross (1977) even admit that vertical job loading may not be appropriate for all employees because it likely has a stronger motivational value for employees who prefer challenge and have ability. For example, in the context of the demands-control model, individuals with lower selfefficacy experience enhanced distress and higher blood pressure when they are given more control, and therefore, researchers might want to design interventions to limit the control these people have rather than increase it (Litt, 1988; Schaubroeck & Merritt, 1997; Salanova et al., 2002). Finally, future research should take individual characteristics into account that have been found to interact with the job demands-control model, such as social support, active coping, self-efficacy, and external locus of control (Daniels & Guppy, 1994; Ippolito et al., 2005; Johnson & Hall, 1988; Schaubroeck & Merritt, 1997), and continue to do research on other variables that might influence the model, such as goal orientation (Van Yperen & Hagedoorn, 2003). For example, Van Yperen and Hagedoorn (2003) suggested integrating goal orientation into the model to account for people’s preferences for challenging tasks. Specifically, goal orientations

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explain the reason behind people’s achievement pursuits and why people prefer different amounts of challenge in their environment. Goal orientations can be situational or dispositional, and matching people’s dispositional goal orientation to the environment has been shown to enhance task enjoyment and performance (Chan & Mathieu, 2008; Harackiewicz & Sansone, 1991). Therefore, future research should examine whether different interactions of demands and control at work will create goal matching or mismatching situations. For example, in Karasek’s (1979) original model, he explains that when the demands exceed employees’ ability, they suffer from physical symptoms, and therefore, employees will likely suffer the most physical symptoms in environments where their goal orientation leads them to perceive a mismatch between their ability and the demands and control provided by their job. On the contrary, employees will likely be satisfied in conditions where their goal orientation leads them to perceive a match between their ability and the demands and control provided by their job.

CONCLUSION Although Karasek’s (1979) job demands-control model is one of the most widely researched models of workplace stress, support for the model has been inconsistent. The primary reasons provided for this inconsistency are that different variables have been used to measure demands, control, and strain, not enough longitudinal research is done and that workers’ individual characteristics are not taken into account (Van Der Deof & Maes, 1999). Researchers have attempted to reduce this inconsistency by either modifying or adding third variables to the model but have only been partially successful (Van Der Deof & Maes, 1999). To continue reducing inconsistent results, future research should use longitudinal designs, include both objective and subjective measures, have a higher sample size, and use careful consideration to ensure the measurements chosen to represent demands and control best match each other theoretically.

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ENGAGEMENT WITH INFORMATION AND COMMUNICATION TECHNOLOGY AND PSYCHOLOGICAL WELL-BEING Michael P. O’Driscoll, Paula Brough, Carolyn Timms and Sukanlaya Sawang ABSTRACT The impact of technology on the health and well-being of workers has been a topic of interest since computers and computerized technology were widely introduced in the 1980s. Of recent concern is the impact of rapid technological advances on individuals’ psychological well-being, especially due to advancements in mobile technology that have increased many workers’ accessibility and expected productivity. In this chapter we focus on the associations between occupational stress and technology, especially behavioral and psychological reactions. We discuss some key facilitators and barriers associated with users’ acceptance of and engagement with information and communication technology. We conclude with recommendations for ongoing research on managing occupational health and well-being in conjunction with technological advancements. New Developments in Theoretical and Conceptual Approaches to Job Stress Research in Occupational Stress and Well Being, Volume 8, 269–316 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-3555/doi:10.1108/S1479-3555(2010)0000008010

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INTRODUCTION It is widely recognized that technological changes, both at work and more broadly, have had an enormous impact on people’s lives and that much of this influence has been constructive and beneficial, for example, by developing a ‘‘mobile’’ workforce via telecommuting technologies. These technological advances have yielded positive benefits for individuals and their organizations. Nevertheless, there are also increasing concerns about the ‘‘dark side’’ of technologies and their negative impacts on levels of individual well-being. One major aim of ergonomics, for example, is to safeguard the physical and psychological health of workers by the most appropriate use of machinery and technology. A key element is the interaction between the worker and the technology: enhancing the ‘‘userfriendliness’’ of technology, improving worker performance, and minimizing the risks associated with the work environment. Specialized fields such as cognitive ergonomics focus on ensuring that the cognitive demands required to operate the technology do not overburden the user and that the technology complements the worker’s mental schema of how to perform the job. One of the issues confronting many workers today, however, is how technology has truncated their time, both in terms of their increased accessibility during nonwork hours and the increased speed at which work is now expected to be performed. Consequently, the relationship between psychological health and the implementation and usage of technology is now subject to detailed debate and research. This chapter provides an overview of the impact of technology on the psychological health and wellbeing of workers, with a specific focus on maintaining and enhancing their psychological and behavioral engagement with technology. The extent of technology utilized to assist us with our work and productivity has experienced exponential growth, especially over the last two decades. Recent reviews suggest that a large proportion of workers now use a computer in their job and that most workers (approximately half the global population) use a cell phone (O’Driscoll, Biron, & Cooper, 2009). Indeed, the pressure to keep up with new technology and software updates can be a significant source of stress in itself. The term techno-stress was coined in the 1980s to describe ‘‘the inability of an individual or organization to adapt to the introduction and operation of new technology’’ (Brod, 1982, p. 754). More recent definitions of techno-stress acknowledge the demands on employees to constantly renew their technical skills, to adapt to more complex technology/computer systems, and to increase their productivity (Wang, Shu, & Tu, 2008). One important consequence of

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computing technology is that it has enabled the boundaries between the work and nonwork domains to be increasingly blurred, such that many workers can now be virtually permanently connected to their work. Fenner and Renn (2004) referred to this additional labor as ‘‘technology-assisted supplemental work.’’ The consequences of this additional labor and the corresponding decrease in family and leisure time is, of course, a primary reason for the expansion in work-life balance research (see, for instance, Brough, O’Driscoll, Kalliath, Cooper, & Poelmans, 2009b). We begin this chapter by reviewing the known associations between occupational stress and technology. This includes a focus on techno-stress, and psychosocial reactions commonly associated with technology use, especially anxiety and frustration. We then discuss some of the major facilitators of workers’ acceptance of technology: attitudes toward technology, subjective norms concerning technology use, and perceived control over technology (mastery). From there we move to an overview of some barriers to individual engagement with technology: lack of control in purchasing and usage decision-making, technology that is complex and difficult to use, and problems with uncoordinated technologies. The chapter concludes with a brief discussion of the implications for the management of occupational health and well-being and identifies areas of interest for future research. The types of technology available to workers today are vast, and there are many issues surrounding their usage. For the purposes of this chapter, we limit our focus to issues that are directly relevant to well-being at work. There are some dimensions of technology usage, such as Internet addiction, which are more pertinent to the nonwork spheres of people’s lives, and therefore are not discussed here. Readers interested in detailed discussions of Internet addiction are instead directed to recent reviews by Beard (2009) and Kim and Davis (2009). Another major area which we do not address in detail is telecommuting or virtual work. There is a growing body of evidence attesting to the advantages and limitations of telecommuting and its direct consequences for psychological well-being (Novaco & Gonzalez, 2009). However, the issues associated with virtual work go beyond the utilization of technology per se and hence fall outside the purview of the present chapter. The literature on human–computer interactions also frequently refers to age and gender differences in levels of computer use, although inconsistent empirical evidence concerning these demographic variables is reported (see, e.g., Czaja et al., 2006). Given the aims of this chapter, we will not specifically examine demographic differences in any detail. Finally, as noted above, while we acknowledge the positive impact that technological advancements have had on well-being (at both the individual

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and societal level), in this chapter we focus primarily on negative effects, in particular the association between exposure to technology and occupational stress. Our approach is not intended to imply that negative effects are either more salient or more pervasive than the benefits of technology usage. Rather, we have adopted this perspective because we believe that efforts to enhance positive interactions between people and technologies require attention to factors that may undermine those interactions and hence lead to a deterioration of psychological health and well-being. We refer readers to the chapter in this volume by Day and Kelloway, which also discusses some important implications of modern technology for stress and well-being at work. Our chapter concludes with a summary of key factors affecting the impact of information and communication technology (ICT) on individual well-being and engagement, along with implications for the management of occupational health and well-being and some suggestions for ongoing research in this area.

UTILIZATION OF INFORMATION AND COMMUNICATION TECHNOLOGY As noted above, the concept of ‘‘technology’’ has various meanings and subsumes numerous constructs. In recent years the term ‘‘technology’’ has predominantly been associated with computers and other devices used by workers to communicate with each other, share information, and perform work tasks. In addition to computers, mobile phones, personal digital assistants (PDAs), pagers, and blackberries are all examples of technologies utilized to increase work flexibility, efficiency, and productivity. It is evident that these technologies have become an essential tool for job performance and organizational productivity across virtually all aspects of employment. Our understanding of the range of factors which can influence the complex interface between people and computers/other technologies is developing rapidly, but remains somewhat incomplete (Olson & Olson, 2003). Extensive usage of advanced computer-based technologies is relatively recent. Coovert and Thompson (2003) noted that ‘‘the 1960s marked the beginning of an era characterized by a growing reliance on sophisticated office technology such as photocopy machines and increasingly capable typewriters’’ (p. 221). Computers were introduced into workplaces (by IBM) in the early 1980s, along with the expression ‘‘user-friendly technology’’ (Coovert & Thompson, 2003). This expression signaled recognition that for

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technologies to be optimally effective, their design and implementation must be conducted in ways which take account of end-user cognitions, attitudes, competencies, and preferences. Recent research, which we review in this chapter, has built upon the theme of an optimal interface between the technology (both hardware and software) and the human operator. Mamaghani (2006) commented that prior to the 1990s the use of more ‘‘advanced’’ forms of computer technology was typically restricted to manufacturing and production companies. Several studies were conducted in the 1970s and 1980s to examine the psychosocial effects of advanced manufacturing technology (AMT). This research was often based on the job demands-control (JDC) model of work design enunciated by Karasek (1979). Mullarkey, Jackson, Wall, Wilson, and Grey-Taylor (1997), for example, investigated whether technological uncertainty and abstractness interact with two forms of personal control over work (method control and timing control) to predict levels of operator strain. Their findings did not support hypotheses derived from the JDC model, but they did identify interactions between technological uncertainty/abstractness and the pace of work. These authors therefore posited that a person–environment fit approach is useful when examining the impact of technology upon levels of occupational stress. Since the 1990s there has been a shift in focus to a wider range of work contexts in which advanced computer and related technologies have become integral to work roles and organizational functioning. Various labels have been used for these technologies, but often they are referred to collectively as information and communication technology or ICT. The power and versatility of ICTs have provided substantial benefits for individuals and organizations, as well as society more generally (Mamaghani, 2006). ICT enables individuals, teams, and organizations to gather, analyze, and distribute large amounts of information and data, and has increased ‘‘workers’ flexibility by creating mobile working practices and instant information transmission’’ (O’Driscoll et al., 2009, p. 106). A majority of workers are now wholly or partly reliant on ICT to perform their work effectively. The National Telecommunications and Information Administration reported, for instance, that 73% of US workers now use computers as part of their work (Lazar, Jones, & Shneiderman, 2006). Clearly the utilization of technology is now prevalent across a range of work settings and, in consideration of the current trends in global networking, this ICT usage is predicted to intensify in the near future. Nevertheless, despite the many advantages (especially for productivity and efficiency) of these advanced technologies, the potentially negative

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consequences of the technological imperative have also been discussed. The focus of this chapter is primarily on the impact of ICT on the psychosocial functioning of individual workers, and we discuss research that can be subsumed, in a general sense, under the rubric techno-stress. We begin this section with a discussion of the nature of techno-stress and its various elements, then turn to specific components such as anxiety, frustration, and psychological strain arising from the need to adapt to and effectively utilize ICT. Possible moderators (buffers) of the impact of techno-stress, such as self-efficacy and sense of control, are also discussed in our review of (a) factors which can facilitate worker engagement with ICT, and (b) barriers to engagement with technology.

TECHNO-STRESS Although, as noted above, the use of ICT has several distinct advantages and benefits for both workers and organizations, concerns have been raised about the potential negative impacts of ICT on the health and well-being of individuals. These concerns have led to research on the psychosocial impact of ICT. The expression techno-stress describes an array of negative reactions which individuals may experience when using ICT, particularly anxiety over one’s ability to use computers effectively, and physiological concomitants of this anxiety, including increased secretion of both adrenaline and noradrenaline. Arnetz and Wiholm (1997, p. 36), for example, described techno-stress as ‘‘the state of mental and physiological arousal observed in certain employees who are heavily dependent on computers in their work’’ and suggested that it occurs when ‘‘employees perceive there [sic] job as stimulating at the same time as they feel they do not quite master the necessary skills.’’ Reasons for the occurrence of techno-stress include the rapid pace of change in ICTs (i.e., constant adaptation to new systems), uncertainty about one’s ability to master the technology, and concern about how one’s capabilities will be viewed by other people. A parallel term that is less commonly cited is technophobia (i.e., the fear of technology). Technophobia is primarily caused by an individual’s acute anxiety over their ability to master the technology (Thorpe & Brosnan, 2007). Similarly, Thomee, Eklof, Gustafsson, Nilsson, and Hagberg (2007) referred to this anxiety as ICT stress, defined as a ‘‘condition brought on by interruptions at work, time pressure and technical problems in connection with ICT use’’ (p. 1301). ICT stress is therefore primarily associated with the situational factors that can impede a person’s ability to use ICT effectively, whereas

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techno-stress refers primarily to the psychological reactions to the technology itself. Nevertheless, there is considerable overlap between these concepts. Techno-stress covers a range of psychosocial reactions, including (as already mentioned) anxiety over the use and mastery of technology, frustration (due to utilization problems, software malfunctioning, and general inability to achieve anticipated outcomes), feelings of lack of selfefficacy, and depression. Individuals may also experience information overload (Bellotti, Ducheneaut, Howard, Smith, & Grinter, 2005), that is a feeling of being overwhelmed by the quantity of incoming information and the need to respond to it. Bellotti et al. suggested that experiences of information overload have risen exponentially with the increased use of email as a primary mechanism for communication. They reported that managers in particular are likely to experience information overload from email and frustration over an inability to monitor and manage multiple concurrent tasks for which they are responsible. Continuing overload can induce both anxieties over role performance and ultimately depression due to a sense of lack of accomplishment of important goals. Psychosocial reactions to ICT can be classified under three (fairly broad) categories: cognitive (i.e., beliefs about and perceptions of technology), affective (i.e., emotional responses), and behavioral (i.e., usage of or withdrawal from the technology). As noted by O’Driscoll et al. (2009), these categories are interconnected and interdependent, and clearly cognitive and affective responses are closely linked with behavioral reactions. In addition, many people report positive experiences when utilizing ICT, including increased flexibility, access to information, and ability to complete tasks more efficiently and effectively. We now turn to a more detailed review of the research that explores reactions to ICT and the factors that commonly predict techno-stress. Here we concentrate on the two affective components of techno-stress that have received most attention in the empirical literature: anxiety and frustration (Ragu-Nathan, Tarafdar, & Ragu-Nathan, 2008). Note that our aim is not to provide an exhaustive review of the extensive literature in this field, but rather to summarize and highlight major findings on techno-stress and the implications for enhancing levels of worker engagement with ICTs.

ICT Anxiety The pervasiveness of computers and other forms of ICT in workplaces in recent years has been accompanied by a wealth of research on the potential

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correlates of ICT usage, especially psychosocial factors associated with its use. Several research constructs have received attention but the most prominent is computer-related anxiety, which is also linked (in the longer term) with depression, although there has been relatively little investigation of depression per se. Anxiety over the use of ICTs is a somewhat controversial issue, and popular belief is that it is age-related and probably declining among younger generations. Nevertheless, Smith and Caputi (2007, p. 1482) observed that ‘‘one third of individuals within most populations experience computer anxiety to some degree’’ and ‘‘computer anxiety has been associated with the avoidance of, and resistance to, computer technology.’’ Thorpe and Brosnan (2007) suggested that there is no evidence of a decline in the prevalence of ICT anxiety. It is clear that, while ICT anxiety may not affect all workers, when it does exist its effects can be severely detrimental to psychological health and well-being. In some cases, computer anxiety may reach ‘‘clinical’’ levels and cognitions of computer-anxious individuals are not dissimilar from those of people with other phobias (Thorpe & Brosnan, 2007). Beckers and colleagues investigated the nature of computer-related anxiety in a series of studies. Although their research was conducted among university students rather than workers, their findings are pertinent for understanding the nature of anxiety associated with ICT usage and the correlates of this anxiety. Beckers and Schmidt (2001, p. 36) observed that computer anxiety primarily comprises four key elements: ‘‘(1) low confidence in one’s ability to use computers; (2) negative affective responses to them; (3) becoming aroused while using a computer or thinking about it; and (4) negative beliefs about the role of the computer in our lives’’. They also noted that there is scant evidence on how these factors interact with each other in influencing an individual’s overall emotional reactions to ICT usage. For example, it is possible that initial anxiety about using computers contributes to negative affective affections, but at the same time a generalized negative evaluation of ICT might also augment feelings of anxiety over its usage. In their initial research in the Netherlands, Beckers and Schmidt (2001) identified six major contributing factors to computer anxiety:      

Computer literacy Self-efficacy Physical arousal Affective reactions to computers Beliefs about computer benefits Beliefs concerning the dehumanizing effects of computers

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As anticipated, there were substantial interactions between these six factors. Computer literacy was strongly associated with both physical arousal and affective reactions, and self-efficacy contributed to increased computer literacy. Beckers and Schmidt concluded that ‘‘lack of confidence in using computers, disliking the machines, and displaying high levels of arousal seem to be at the core of the computer anxiety phenomenon’’ (p. 45). In follow-up investigations, Beckers and his colleagues further explored the nature of computer anxiety and its correlates. For example, Beckers and Schmidt (2003) examined the association between computer anxiety and level of experience with computers. Their findings demonstrated that anxiety is clearly linked with lower levels of ICT experience and self-rated computer efficacy. Feelings of mastery or control also contributed to reduced anxiety, as did having appropriate kinds of support from other people (especially those with ICT knowledge and skills). More recently, Beckers, Wicherts, and Schmidt (2007) conducted two studies to test whether computer anxiety displays properties of a ‘‘trait’’ or a ‘‘state.’’ Students completed questionnaires measuring computer anxiety, trait anxiety and state anxiety. Interestingly, computer anxiety was more closely aligned with trait anxiety than with state anxiety, which suggested to the authors that computer anxiety may reflect a more general underlying anxiety rather than one which is constrained to a particular situational stressor (computer use). Put another way, it appeared that negative mood (trait anxiety) was associated with computer anxiety, irrespective of the presence or absence of state anxiety. Beckers et al. concluded from these studies that computer anxiety may be more deeply embedded within overall trait anxiety than has been thought, which may undermine the efficacy of interventions which focus solely on training users in specific ICT skills. Several studies have confirmed the relevance of computer (or ICT) anxiety as a key factor in technology use, although the effects of anxiety are not always immediately apparent. For example, based on the ‘‘technology acceptance’’ model (which we describe later), Compeau, Higgins, and Huff (1999) predicted that low computer self-efficacy will be associated with higher levels of anxiety and reduced affect toward (i.e., liking for) computer use, which in turn will lead to less computer usage. Using a longitudinal design, Compeau et al. found that computer self-efficacy was strongly linked (in the predicted direction) with computer attitudes and computer anxiety one year later. Computer affect and anxiety were both significantly correlated with usage, although structural equations modeling suggested that the path between anxiety and usage (while negative as expected) was not statistically significant.

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Czaja et al. (2006) also explored the linkage between ICT anxiety and usage, in this case in respect of using the World Wide Web (WWW). Similar to Compeau et al. (1999), they predicted that computer efficacy would be associated with reduced computer anxiety. In addition, Czaja et al. (2006) hypothesized that anxiety, along with two forms of cognitive ability, would predict general computer use and engagement with the WWW. Their crosssectional study found that efficacy and cognitive ability were predictors of technology use. Anxiety did not significantly predict use per se, but it did show a negative link with the breadth of computer use; that is, respondents scoring higher on anxiety were less likely to engage with the WWW. A similar study was conducted by Joiner, Brosnan, Duffield, Gavin, and Maras (2007) involving university students in the UK and in Australia. Internet usage has become widespread in recent years, and these researchers explored the relationship between Internet identification, anxiety, and usage. Internet identification was defined as ‘‘the extent to which an individual’s self-concept is bound up with his or her perceived ability to use the Internet’’ (p. 1411). It was assumed that a high level of Internet identification would be associated with a desire to use the Internet effectively, which in turn would be related to actual use of the Internet. Overall, these expectations were confirmed by Joiner et al. Although the proportion of respondents displaying anxiety about Internet usage was relatively small, those who did report high anxiety were significantly less likely to identify with and use the technology. An alternative model of computer anxiety based on cognitive interference has been posited by Smith and Caputi (2007), who argued that unlike research on other forms of anxiety, studies of computer anxiety have typically not been based on clear theoretical models. Their cognitive interference model suggests that people with high computer anxiety will engage in a cycle of cognitive avoidance or withdrawal from usage, which will be accompanied by increased worry and self-deprecation (over an ability to master the technology). The most evident signs of this avoidance and withdrawal are engaging in distracting tasks and thinking about tasks that do not entail usage of ICT. This avoidance and psychological (and behavioral) withdrawal will further reinforce the person’s feelings of anxiety and reduce their capacity to engage effectively with the technology. A somewhat broader perspective on the role of affect has been outlined by Kay and Loverock (2008), who presented a typology of four emotional states (anger, anxiety, happiness, and sadness) associated with computerrelated tasks. An important aspect of this research is the clear differentiation between these four constructs, which have often been merged in previous

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studies. Kay and Loverock utilized 12 items to assess anger, anxiety, happiness, and sadness associated with learning a new software program. Their findings illustrated that the four constructs were interrelated, but distinct from each other. Furthermore, happiness correlated positively with positive affect and self-efficacy, and negatively with negative affectivity, while the three ‘‘negative’’ emotions (anger, anxiety, and sadness) showed a reverse pattern of relationships with other variables. An important and interesting side-issue referred to by the authors, although not incorporated into their research, is the dimensionality of anxiety. For instance, anxiety may take several different forms, including task performance anxiety, fear of social embarrassment, anxiety about dealing with any problems that may be encountered, and more general anxiety about new technology. Although it may be assumed that these forms of anxiety would be intercorrelated, they do not necessarily overlap and they may exhibit different connections with other variables, such as ICT usage and self-efficacy. Research has yet to explore these differential relationships. Although the above studies have examined possible effects of anxiety (and other affective reactions) on computer behaviors (e.g., usage), the factors which contribute to ICT anxiety have also been the subject of research in this field. Research among production companies in Norway by Mikkelsen, Ogaard, Lindoe, and Olsen (2002) explored the relationship between job characteristics and computer anxiety. These authors observed that research on computer anxiety has been somewhat dissociated from prevailing models of occupational health and well-being. For example, based on Karasek and Theorell’s (1990) job strain model, computer anxiety might be expected to arise from situations where workers feel high demands (i.e., pressure) from needing to utilize ICT effectively and a lack of control or mastery over the technology. Mikkelsen et al. found that demands per se were not strongly related to computer anxiety, but lack of decision authority (i.e., control) was a major contributor to high anxiety. They concluded that the ‘‘core problem in computer anxiety seemed to be the cognitive (and the physiological) response to computers and the perception of mastery of the technology’’ (p. 233). Interestingly, individual coping styles did not ameliorate the negative affects of anxiety. Another type of anxiety that may be associated with ICT, but is quite different from the manifestations described above, relates to workers’ perceptions of job insecurity. For many years concerns have frequently been expressed about the potential for new technologies to displace workers from their jobs or to downgrade their utilization of skills and knowledge acquired over their career. Although ICT can lead to the generation of new jobs, for individuals who do not feel confident with this technology there may be

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a very real threat to their job security. An illustration of research along these lines is a study conducted in a car component factory in Spain by Vieitez, Carcia, and Rodriguez (2001). These researchers found that a perception of threat to job security, due to the introduction of computerized technologies, was linked to both state and trait anxiety and depression among these factory workers, although not to overall work stress. One implication of their findings is that we need to look beyond specific computer anxiety to a broader range of potential effects, including perceptions that one’s job or career may be threatened by the introduction of ICT. In summary, there has been considerable assessment of levels of anxiety associated with ICT adoption and usage. Anxiety can arise due to concerns over one’s ability to effectively use the technology, over possible social embarrassment if the person feels that he or she is not mastering technology to the same extent as other people in their work context, and over the ability to deal with any technical difficulties or problems that may occur. Separate from, albeit related to, these forms of ICT anxiety is a feeling of job insecurity due to beliefs that the technology may ‘‘take over’’ the person’s job, hence making them redundant. Numerous studies have been conducted on these various manifestations of computer-related anxiety, and their association with computer attitudes and usage. Some studies have illustrated a direct relationship between heightened anxiety and reduced usage, whereas in others the relationship has been moderated by other factors. Overall, there is a clear connection between technological anxiety and other affective reactions, including feelings of strain, highlighting that this kind of anxiety is a significant factor in respect of workers’ psychological health and well-being.

Frustration with Technology Another psycho-social response to ICTs that has been observed among end users is frustration, often in relation to malfunctioning systems, inability to achieve desired goals, and lack of clear system messages (e.g. error messages). Anecdotal reports of frustrating experiences abound and suggest that negative encounters with ICT frequently produce this emotional outcome, but curiously there has been less empirical investigation of this reaction compared to computer-related anxiety. Nevertheless, the (limited amount of) evidence available consistently identifies the contributors to frustration and its impact on individuals’ psychological well-being and their engagement with technology.

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A theoretical model of computer-related frustration was proposed by Bessiere, Newhagen, Robinson, and Shneiderman (2006), who observed that frustration ‘‘has not been rigorously conceptualized as a factor in the study of the human–computer interface’’ (p. 941), despite the fact that it is a frequent complaint among computer users. Bessiere et al. defined frustration as an emotional response to circumstances that thwart a person’s ability to achieve their goals. In the case of ICTs, this can occur when (for example) programs do not perform as expected, the system ‘‘crashes,’’ there are excessive time delays in sending or retrieving information, or when required information or features are difficult to find or utilize. These and other occurrences can have a severe impact on the user’s performance and can generate high levels of arousal. According to Bessiere et al., arousal is the key component of frustration – too little or too much arousal inhibits optimal performance. Whether frustration will be transformed into other (more complex) emotional states will depend on the extent of control the individual feels they can exert over the situation. If individuals feel capable of resolving the problem themselves, or obtain timely assistance to resolve it, the sense of frustration may be acute but not develop into other emotions, such as anger, disappointment, disillusionment, and despair. In sum, ‘‘frustration per se may be maladaptive if no solution to the problem can be found or the path to the solution involves many obstacles’’ (Bessiere et al., 2006, p. 945). Similarly, a cycle of failure leading to frustration leading, in turn, to further failure can emerge, thereby increasing the levels of dysfunctional arousal and psychological strain experienced by the user. The computer frustration model presented by Bessiere et al. (2006) provides a valuable platform for research on the emotional response to technology. In addition to the situational factors mentioned above, Bessiere et al. also acknowledged the contribution of dispositional factors, such as negative affectivity and self-efficacy. Not all individuals react in the same way to technological problems and malfunctions. Some will respond by using problem-solving coping strategies, endeavoring to resolve the difficulty or seeking help to do so. Other users, especially those who have encountered problems previously, which they have felt incapable of resolving or who have high levels of dispositional negative affectivity, may adopt a more ‘‘resigned’’ or fatalistic approach to the situation. These individuals may experience higher levels of frustration and strain, due to a perceived lack of control over negative events and perceived inability to resolve them. In a test of their computer frustration model, Bessiere et al. (2006) examined the contribution of situational (technological) and dispositional

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(user) factors to user frustration and mood. Research participants worked on a project of their own, choosing on a computer for one hour, during which time they recorded any incident that frustrated them. Frustration was assessed at four points in time: immediately after an ‘‘incident’’ had occurred, across the entire session, immediately after the session, and anticipated frustration for the remainder of the day. Their findings illustrated that situational factors accounted for 1%–18% of the variance in frustration, whereas dispositional factors accounted for 8%–34% of variance in frustration. Mood, computer attitudes, and self-efficacy were key dispositional predictors of frustration levels. Overall, Bessiere et al. demonstrated that dispositional variables were more strongly linked than situational factors with computer frustration. The authors concluded that a user’s ‘‘ability to cope with computing technology appears to be a pervasive factor in how frustrated he or she becomes’’ (p. 958). Others studies have obtained complementary findings to those outlined above. For instance, Ceaparu, Lazar, Bessiere, Robinson, and Shneiderman (2004) employed a time-diary methodology to investigate users’ experiences. Participants were asked to log frustrating experiences as they occurred during a session of working with computers on their ‘‘usual’’ tasks and activities. Levels of mood and computer anxiety were collected prior to the session, along with computer experience levels and attitudes. A postsession survey was administered to gather data on mood following the session, overall frustration level, and the perceived impact of this frustration on the person’s day. The findings indicated that frustrating experiences were reasonably common and that those experienced during the working session were very similar to other frustrations they had encountered in previous computer usage. Around 33%–50% of the time spent at the computer was reported to be lost due to occurrences that induced frustration. The most commonly reported sources of frustration were error messages, lost network connections, long download times, and features that were difficult to find or use. Lazar et al. (2006) also used a time-diary methodology to explore frustration levels among computer users, although their study focused on actual workers rather than students. Users were asked to record frustrating experiences as they occurred in their normal working day. Email and word processing tasks induced the largest number of recorded frustrations, followed by web browsing problems, and users reported wasting around 40% of their (computer) time as a result of these experiences. The authors reported the average amount of time recorded as being lost per frustrating experience, which varied from 36 minutes for word processing to 105 minutes for software problems. Many individuals recorded multiple

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frustrating experiences in a single day. Frustration was associated with several different emotional reactions, including anger (with the technology and with themselves for not being able to master it), along with helplessness and/or resignation. In summary, it is evident that anxiety and frustration can both be potent sources of stress for ITC users, and each of these variables has been shown to have a negative impact on psychological well-being in the workplace. Continuing anxiety about one’s capabilities in mastering the technology and resultant job performance can ultimately cause depression for some individuals (Coovert & Thompson, 2003), while frustration can lead to psychological and behavioral withdrawal from using technology. Evidence suggests that these reactions derive from the combined effects of situational factors (for instance, the design and functioning of ICT systems) and personal factors (including attitudes toward computers and feelings of self-efficacy). These factors can function as either facilitators or inhibitors of workers’ engagement with the technology. To this point we have discussed some fundamental elements of psychological well-being associated with the adoption and implementation of ICT. The following sections of the chapter focus on some factors that may serve to either facilitate or inhibit workers’ psychological and behavioral engagement with technology. Our focus will be primarily on psycho-social issues, rather than purely technical aspects. One aim of these sections is to highlight practical steps, which may be undertaken to enhance worker engagement with technology and to reduce or remove potential barriers to its effective utilization.

FACILITATING ENGAGEMENT WITH ICT Modern workplaces require employees to successfully manage many different forms of ICT, including basic computing packages (such as word processing or data analysis programs), the Internet, email, and other applications. These technologies involve multitasking skills and at least some practical understanding of the technology. As noted earlier, effective utilization of ICTs benefits organizations via potential gains in efficiency and productivity and enables work to become more collaborative and transparent. Nevertheless, the technological advantages afforded by ICT will not be fully realized unless employees are able and willing to use these systems. Previous research has indicated that over 50% of organizations encounter employee resistance toward new technology implementation

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(Venkatesh, Morris, Davis, & Davis, 2003). It is crucial, therefore, to examine the processes that underlie user acceptance of technology, in terms of both facilitating variables and inhibitors. In this section we review some of the key facilitators of engagement with ICTs. Much of the research on the key facilitators of ICT engagement has derived from a general perspective referred to as the ‘‘technology acceptance model’’ (TAM; Davis, 1989), which describes the relationships between users’ perceptions of the usefulness of the technology, its perceived ease of use, and their acceptance of it. The TAM is based on earlier social– psychological theories including the theory of reasoned action (TRA) developed by Fishbein and Ajzen (1975). The TRA suggests that behavior is directly influenced by one’s behavioral intentions, which in turn are preceded by attitudes toward the activity and subjective norms concerning its desirability. For instance, if a person believes that using a blackberry or other ICT device will provide greater accessibility to important work-related information, and also that other people (such as one’s supervisor) also favor the usage of this technology, he or she will probably develop a motivation and behavioral intention to use ICT and will ultimately engage with this technology. In addition to the above, Fishbein and Ajzen (1975) also differentiated affect from cognition and conation. Whereas cognition refers to the individual’s knowledge and beliefs about the technology, affect denotes the person’s feelings about and evaluation of the technology, and conation reflects their behavioral intentions. These concepts have been valuable in distinguishing between various components of the process-linking attitudes to behavior. Finally, subjective norms also play a key role in the TRA and refer to the person’s beliefs about other people’s opinions of the technology and desirability of using it. For instance, if one’s supervisor and work colleagues are strong advocates for using a particular form of ICT, their norms will exert pressure on the individual to conform. Subjective norms can have a direct impact on behavioral intentions because the person is motivated to conform with others’ beliefs, does not wish to be perceived as incompetent or as a ‘‘technological luddite,’’ or may feel that their work performance may be compared unfavorably with others’ performance because they have not mastered the technology. The TRA has been widely adopted as an explanation of engagement with ICT, especially through its application in the TAM (described below), although one limitation is that it does not take into account the notion of perceived control over technology, which has been found to have a significant impact on the extent to which workers engage with ICT.

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To overcome this constraint, Ajzen (1985) developed the theory of planned behavior (TPB), which directly incorporated perceptions of control as predictors of intentions to use technology, along with subjective norms and attitudes toward technology. As we outline below, perceptions of and beliefs about the extent of control one can exert over technology play a critical role in the acceptance and use of ICT, and reflects a significant modification in the theoretical discussions.

Technology Acceptance Model The TAM (Davis, 1989) built upon concepts inherent within both the TRA and the TPB, focusing specifically on individual acceptance or rejection of technology (see Fig. 1). The TAM has been applied in numerous studies of attitudes toward, and usage of, computerized technologies (for a metaanalysis, see Ma & Liu, 2004). Davis (1989, p. 320) suggested that two important factors in the TAM are (1) perceived usefulness of the technology, that is, ‘‘a belief that using the new system will increase performance’’ and (2) perceived ease of use, ‘‘the degree to which a person believes that using a particular system would be effortless.’’ Later conceptualizations (see, e.g., Compeau et al., 1999) have modified the latter component from ‘‘effortless’’ to ‘‘manageable,’’ but these two factors are still recognized as being critical to user engagement with any technology. The TAM extends concepts that had been developed in the earlier attitude models and applies them specifically to technology acceptance and usage. For instance, Venkatesh and Davis (2000) collected longitudinal data to examine the effects of social influence and cognitive processes on user acceptance (see Fig. 1). The theoretical model was confirmed across four organizations and three measurement points (i.e., preimplementation, one month postimplementation, and three months postimplementation), and the longitudinal design of the research enabled the authors to examine changes over time. For instance, they found that respondents relied less on social information (i.e., social influence) as they gained experience with the technology over time, but continued to judge the utility of the system on the basis of potential status benefits associated with its use, such as whether it enhanced their feelings of achievement and recognition from others at work. In contrast, the effects of cognitive processes (concerning the demonstrability of results and ease of use) remained significant over time. Venkatesh and Davis concluded that ‘‘user acceptance of information technology in the workplace remains a complex, elusive, yet extremely important phenomenon’’ (p. 200).

Fig. 1. The Technology Acceptance Model. Source: Reprinted from Venkatesh and Davis (2000), with permission from the Institute of Operations Research and Management Sciences (INFORMS), 7240 Parkway Drive, Suite 300, Hanover, MD 21076, USA.

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In a more recent paper, Venkatesh and Bala (2008) have provided an updated version of the TAM, which expanded the range of contributors to the perceived ease of use of technology. The updated TAM incorporates variables such as perceived self-efficacy, perceptions of external control, and computer anxiety, which are discussed in more detail below, as well as perceived enjoyment of technology, as potential antecedents of perceived ease of use. Venkatesh and Bala argued that the revised TAM provides a model that can be used as a lever in research on the effects of technology implementation. Based on the TAM and other perspectives concerning technology implementation, research has focused on a range of factors that may facilitate the adoption and utilization of ICTs. These factors can be grouped into three broad, albeit overlapping, categories: personal/dispositional factors, organizational factors, and characteristics of the technology per se. Given the focus of the present chapter on psycho-social issues, we will not address the technical features, even though these clearly have a substantial bearing on the adoption and ease of use of ICTs. Our attention will be limited to some key personal and organizational factors that have been shown to be relevant to users’ attitudes toward and usage of ICTs. For a review of the effects of specific technological aspects (including ergonomic issues), see Coovert, Thompson, and Craiger (2005).

Dispositional Facilitators of Technology Engagement A number of dispositional variables have been associated with the competent use of ICTs. The most prominent of these dispositional variables is self-efficacy, which is closely related to perceptions of control and mastery over the technology, as well as general self-esteem (Hair, Renaud, & Ramsay, 2007). Several studies have investigated the facilitating role of computer self-efficacy, which refers to the person’s belief that they have the capability and resources to successfully utilize the technology, to overcome any obstacles that might impede their performance, and to deal with problems encountered in the use of ICT. Computer self-efficacy has also been found to be a significant predictor of workers’ reactions to the implementation of new technology. For example, Compeau et al. (1999) examined predictions from the TAM and found that initial computer self-efficacy strongly predicted later usage and feelings (i.e., affect) about computer use, and was negatively linked with computer anxiety. Self-efficacy was also associated with greater expectations concerning outcomes, including both

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performance-related outcomes and personal benefits from using the technology. Other studies have obtained comparable findings. For instance, Salanova, Grau, Cifre, and Llorens (2000) presented Spanish data illustrating the moderating role played by self-efficacy in facilitating both attitudes and behaviors relating to computer usage and to psychological well-being. Salanova et al. found that computer self-efficacy was positively associated with frequency of computer usage and with computer training. In addition, there was a training times self-efficacy interaction effect, such that computer self-efficacy reduced the negative effects of training on burnout (emotional exhaustion and cynicism). Salanova et al. suggested that enhancing levels of computer self-efficacy is an important prelude to the provision of complex training programs, which in themselves may induce high levels of strain among workers. Similarly, Downey and McMurtrey (2007) observed that general computer self-efficacy reflects an overall belief in one’s ability to effectively manage ICT, rather than just specific elements of ICT. People with high general computer self-efficacy are more likely to engage with new forms of both hardware and software (programs). In their study, specific self-efficacy served as a stronger predictor of computer competence, but a global selfefficacy measure was a better predictor of anxiety over computer use and affect (attitudes) toward computers. Agarwal, Sambamurthy, and Stair (2000) also noted that general beliefs about computer self-efficacy may be predictive of specific beliefs about one’s ability to manage a particular program or equipment. That is, a generalized feeling of self-efficacy may be a key antecedent to perceptions of how much cognitive effort will be required to use an ICT, in addition to accounting for perceptions of competence. An alternative conceptualization of the role of computer self-efficacy is illustrated in a study conducted by Thatcher and Perrewe (2002), who examined this variable as an outcome (rather than a predictor) of computer anxiety and personal innovativeness (PI). In their research conducted with university students, anxiety about the use of computers was linked with low computer self-efficacy, whereas innovativeness was associated with feelings of greater self-efficacy. Hence computer self-efficacy appears to function both as a predictor (of usage, performance, and affect), and also as a consequence of anxiety and other factors. A related factor that can also affect users’ reactions to ICT is perceptions of mastery or perceived control over the technology. Feelings of control over

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technology may arise from either a sense of personal competence or from receiving technical and other forms of support which enhance control (Sawang, Unsworth, & Sorbello, 2007). Nevertheless, perceived control has been closely aligned with computer self-efficacy, and there is consistent evidence that computer mastery and self-efficacy are strongly related (Beas & Salanova, 2006). Perceived control (or mastery) of ICT can have a substantial influence on psychological health and well-being, and is associated with reduced levels of anxiety and depression and increased levels of satisfaction with the ICT (O’Driscoll et al., 2009). The research findings summarized above have implications for the design of interventions to increase workers’ skills and competency, and to enhance their well-being. PI is another dispositional factor, which has been linked with attitudes toward ICT and usage or performance. Agarwal and Prasad (1998) commented that an inclination to innovate has long been recognized as an important determinant of the adoption of (new) technologies, but was not clearly operationalized in previous research. Agarwal and Prasad defined PI as ‘‘the willingness of an individual to try out any new information technology’’ (p. 206), and suggested that this construct could be valuable in identifying individuals who are most likely to adopt new systems. A corollary is that people scoring high on PI would be expected to experience less anxiety and tension when using unfamiliar technologies, and hence potentially less strain resulting from less-than-optimal experiences. Evidence confirming this expectation was reported by Thatcher and Perrewe (2002), who found that PI was associated with low levels of computer anxiety and high levels of computer self-efficacy. Agarwal and Prasad, however, found no significant direct effects of PI on intentions to use a new technology (in their case, the WWW), although it did significantly moderate the relationship between perceived technological compatibility (with work style and requirements) and intentions to use the WWW. From the above summary of empirical research, it is evident that dispositional factors such as self-efficacy (and more broadly, self-esteem) and PI may function as significant facilitators of engagement with new technologies, and can also be related to workers’ psychological health and well-being when they interface with ICT. Clearly further research is needed to explore the exact mechanisms by which these (and other) dispositional variables can affect user reactions, both short- and long-term, but there is widespread recognition of their potential importance and contribution to the ability and willingness of people to utilize ICTs.

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Organizational Facilitators of Technology Engagement Organizational factors also play a role in either facilitating or inhibiting worker adoption and use of ICT. Later we discuss some of the ways in which these factors can operate as ‘‘road blocks,’’ but here we focus on organizational factors that have been found to enhance attitudes toward and usage of the technology, along with workers’ well-being. Perhaps the two most prominent of these organizational-level factors are (a) training and (b) support for ICT users. The literature in this field has also described the impact which organizational culture and climate can exert on worker attitudes and behaviors. We next discuss each of these areas in turn. The impact of training on workers’ reactions has been the subject of debate and research for many years. Early training efforts tended to focus predominantly on improving specific skills, such as word processing, managing different software programs, and identifying and resolving technical (mostly software) problems when they occurred. That is, the emphasis was mainly on skill development and problem-solving, rather than on broader issues such as enhancement of self-efficacy and anxiety reduction. More recently, however, there have been calls for broader-based training, which incorporates not only specific skills and knowledge, but also an awareness of the psycho-social variables that impinge upon the human–computer interface. There has also been growing recognition that training needs to be ongoing and tailored to individuals’ needs and preferred modi operandi. Despite an increased awareness in organizations and among ICT professionals of the need for training that takes account of psycho-social factors, including those discussed here, research reports mixed results on the effectiveness of training efforts to enhance workers’ ICT knowledge and skills and to reduce their negative reactions (such as anxiety and frustration). In fact there has been relatively little empirical exploration of the effectiveness of such training programs, even though various researchers have advocated for more systematic training programs to meet user needs (see, e.g., Agarwal et al., 2000; Llorens, Salanova, & Grau, 2003; Mikkelsen et al., 2002). An example of this line of research is a study conducted by Beas and Salanova (2006) on the effects of computer-aided technology training. Beas and Salanova explored the relationship between computer self-efficacy and psychological well-being among ICT workers in Spain. They suggested that an important component of technological training is ensuring that users enhance their perceptions of self-efficacy and mastery of the technology. This may be more important than learning specific skills or acquiring

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knowledge about specific applications (programs). Beas and Salanova found that simply increasing the number of training hours or the number of courses attended did not necessarily yield more positive outcomes in terms of increased self-efficacy and self-confidence in using computers, or workers’ judgments about achieving their objectives. However, when workers displayed favorable attitudes toward computer use, there was a positive correlation between training hours and professional self-confidence. In contrast, when users displayed initially unfavorable attitudes, the correlation between training and self-confidence was negative. Beas and Salanova suggested that this finding may be attributable to the negative views held toward the training by individuals who were indisposed to computer usage; that is, they may not believe that the training will be of any value, and perhaps therefore do not engage with it fully. If this is the case, a major implication is that it is important to focus on user attitudes (and self-efficacy) both prior to and during training sessions. There have also been suggestions that older workers may have less capacity to learn computer-based skills, may need slower-paced learning, and may experience greater anxiety and concerns about using ICT to perform tasks which previously they have carried out in other ways (e.g., manually; Czaja et al., 2006). Although age differences in ICT use and performance have not been universally established, it is likely that older workers may require different training approaches to their younger counterparts who are more familiar with ICT. Therefore, it is critical to be aware of the varied outcomes of training for different users and to consider individual differences in (for example) perceptions of ICT, selfefficacy, and what has sometimes been referred to as the ‘‘digital divide,’’ which separates those who are comfortable with using ICT (and other technologies) from those who feel less at home with these technologies (Bessiere et al., 2006; Czaja et al., 2006). Along with appropriate training, the provision of ongoing technical and social support has also been demonstrated to be a facilitator of workers’ engagement with ICT. There is, of course, a long history of research on the role of social support in alleviating work-related stress and enhancing workers’ well-being in work contexts, but attention to this variable in research on computer attitudes has been less systematic and extensive, although it is recognized as an important contributor to users’ experiences with ICT. O’Driscoll et al. (2009), for example, observed that ‘‘there would appear to be insufficient cross-fertilization between technology implementation and the literature of stress management, especially the role of social support in enabling individuals to develop more effective coping strategies’’ (p. 125).

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Support for ICT users can come from various sources and take a range of different forms. Clearly appropriate kinds and levels of technical support represent a sine qua non for the adoption and effective implementation of ICT. The provision of technical support has been widely discussed in the literature (see, e.g., Peansupap & Walker, 2006; Vonk, Geertman, & Schot, 2007) and hence will not be addressed here. One other highly salient source of support, which has been emphasized in the literature, is support from top management. Clearly it is critical for senior managers in an organization to adopt positive views on the implementation and utilization of technology, and to model its use. For instance, case studies carried out by Subramanian and Lacity (1997) on the implementation of client/server systems in the UK illustrated that when top management was supportive of ICT, it was more likely to be implemented effectively. Senior managers can provide both direct support (for instance, financial and other resources, including training) to enhance effective ICT implementation, but also more indirect forms of support, such as allowing individuals time to experiment with the technology and to devote their time to learning its functions and applications without feeling a pressure to increase performance levels during the period of learning and skill acquisition. Senior managers can also ensure that other types and sources of support are available, including the availability of ICT professionals who can assist other workers. The importance of managerial support as a ‘‘driver’’ of technology usage was also confirmed in research conducted by Lee, Kim, and Kim (2006) in Korea. These researchers compared the relative contributions of several factors to the successful introduction of an enterprise-wide knowledgemanagement system. Criterion variables assessed in their survey included employee perceptions of a ‘‘learning orientation’’ (i.e., willingness to experiment and learn new approaches), trust among organizational members, and employee commitment to knowledge management. Support from top management was operationalized as presentation of clear vision of knowledge management, understanding of knowledge management, frequency of mentoring, and manager involvement in knowledge-management activities. Lee et al. found that, while the provision of IT support was the most critical predictor of outcomes, top management support was also a moderately strong predictor of both trust and employee commitment, although it did not significantly predict learning orientations. Management support also displayed indirect effects, via trust and commitment, on levels of knowledge sharing and knowledge quality, which were important criterion variables in respect of the implementation of the knowledgemanagement system.

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The role of senior management support in the implementation and usage of new technology was also demonstrated by Vonk et al. (2007), who conducted focus group interviews with employees of regional planning organizations in the Netherlands. At the time of this research the organizations were adopting a new geo-information technology system to assist with their regional planning. Vonk et al. explored the factors perceived to increase employee engagement with this technology. Their research identified a range of manager behaviors that might either facilitate or inhibit progress toward adopting the technology, including manager awareness of the existence and potential of the geo-information technology, provision of opportunities for innovation, and taking a ‘‘learning organization’’ perspective in order to promote the adoption and implementation of the technology. Vonk et al. also concluded that an innovation ‘‘champion’’ is an important ingredient in fostering the promotion of new technology. Similar conclusions were derived by Peansupap and Walker (2006) from their investigation of ICT diffusion initiatives in an Australian construction industry. Peansupap and Walker developed what they referred to as ‘‘driver and barrier’’ models of ICT innovation, which suggested that change in technologies ‘‘requires intense management interventions to facilitate a supportive workplace environment that strongly links personal and organizational resource investment with demonstrated outcome benefits’’ (p. 364). In addition to technical support, senior management support was reported by their respondents to make a significant contribution to their ability to make effective use of ICT innovations. Like Vonk et al. (2007), Peansupap and Walker argued that leadership by a ‘‘champion’’ is needed to convince other potential users of the benefits of the technology. Along with support from management, social support and practical assistance from one’s work colleagues also play a major role in users’ engagement with ICT and their reactions to it. How well individuals adapt to new ICT systems may be substantially determined by the availability and use of support from their work colleagues. For instance, Bruque, Moyano, and Eisenberg (2008) identified the importance of informational networks and supportive networks to workers as they adjust to new technologies. This distinction mirrors the literature on social support, which also commonly distinguishes between information support and emotional support (see, e.g., Brough & Pears, 2004; Viswesvaran, Sanchez, & Fisher, 1999). Bruque et al. observed that the size of a worker’s supportive network and the density of their informational network each predicted their adaptation to an IT-induced change. They concluded that ‘‘social support may serve as a buffer against the undesired psychological outcomes experienced during

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change and thus improve adaptation’’ and that ‘‘an informational network made up of strong ties facilitates the exchange of information (and knowledge) among individuals in the network’’ (p. 195). Evidence obtained in this research indicated that social and informational support from work colleagues may be even more critical than the level of IT-related education the person has acquired. In addition to their direct impact on individual users’ adaptation and experiences with ICT, support from manager, coworkers, and others (e.g., technical personnel) can also contribute to an organizational climate that promotes and reinforces the utilization of ICTs. Climate researchers (James et al., 2008) have noted that rather than focusing solely on a unidimensional concept of organizational climate, it is important to examine different climate dimensions, including a climate for technological innovation and change. It has long been recognized that an organizational climate that fosters technological change is an important ingredient in the facilitation of worker reactions to ICT and their consequent well-being. Zammuto and O’Connor (1992) reviewed the role played by organizational culture in the successful adoption of AMTs, suggesting that dimensions of culture, such as the degree of emphasis placed on flexibility control, make a substantial contribution to the process of adoption and implementation of AMTs. Specifically, they proposed that organizations that emphasize control-oriented values are more likely to experience failure in the implementation of AMTs, due to an inability to cope with the complexities and uncertainties of AMT. In contrast, organizations that place more value on flexibility should be able to reap the benefits of AMTs, given that flexibility is a key element of AMT success. Although culture and climate are not synonymous constructs, there is a considerable overlap in their impact on individual workers’ behaviors and experiences. For instance, Kopelman, Brief, and Guzzo (1990) developed a theoretical model that illustrates the interconnectivity between culture and climate, and how the core values of an organization contribute to employee perceptions of organizational climate via their link with human resource practices. With respect to ICT, there are clear associations between the organization’s values concerning the adoption of this technology and the support provided to workers in their efforts to utilize ICT and their experiences of the technology. In other words, the impact of organizational values on worker reactions is mediated by their perceptions of the climate for technology usage that operates in their organization. As noted above, senior management support for technology, training programs, and technical support contributes to an organizational climate

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that promotes and facilitates ICT adoption and assists workers to interface effectively with the technology (Peansupap & Walker, 2006). Therefore, these factors can exert not only a direct impact on worker experiences, but also an indirect effect via their contribution to a climate that is either favorable or unfavorable toward technology. Lee et al. (2006) proposed that ‘‘climate maturity’’ develops from ‘‘knowledge management in terms of learning orientation, trust among organizational members, and employees’ commitment to knowledge management’’ (p. 52), and suggested that climate maturity is a key determinant of knowledge sharing and the quality of information shared by organizational members. In their research, employee commitment to knowledge management via ICTs was the most significant contributor to knowledge sharing and quality, but trust also played a substantial role. Lee et al. concluded that the development of climate maturity is important for effective knowledge management. Finally, Wang et al. (2008) conducted a study in China, which demonstrated that two dimensions of climate (centralization of control and innovation) were significant predictors of techno-stress among a diverse sample of employees. Workers in organizations that encouraged greater centralization and innovation reported higher levels of techno-stress. In explaining this result the authors suggested that centralization of control may suppress individual creativity and enthusiasm for using ICT. While a greater emphasis on innovation may exacerbate techno-stress due to high levels of competition between employees, with those who are less computer literate feeling inferior to their more experienced counterparts. The latter finding would appear to contradict the suggestion above that a climate for innovation can make a positive contribution to worker usage of technology and their well-being. It may be that an innovation climate can have both positive and negative effects on worker reactions to ICT, and that the nature of these effects is more complex than is frequently depicted in the climate literature. Further exploration of the multidimensional impacts of organizational climate is required to elucidate the potential outcomes (at different levels) of climate components. In conclusion, in this section we have reviewed several factors that have been found to make a significant contribution to the facilitation of ICT implementation and adoption, and particularly to workers’ reactions to and experiences with ICTs. These factors are typically derived from the TAM and range from personal (dispositional) variables such as levels of self-efficacy, mastery, and PI, to organizational variables including top management support, support from work colleagues, training, and whether the organizational climate promotes and fosters ICT utilization.

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Clearly, other factors that have not been discussed here, especially technical support, are also pertinent to the facilitation of engagement with ICT. The overarching theme emerging from research on workers’ engagement is that managers and change agents cannot make simplistic assumptions about the implementation of technology and that they need to recognize and investigate psycho-social as well as technical issues when adopting new technologies. In addition to the above potential facilitators of ICT engagement, it is critical to be aware of potential ‘‘barriers’’ to this engagement. We now turn to some factors that have been demonstrated to impede worker engagement with ICT.

BARRIERS TO ENGAGEMENT WITH ICT In the previous sections we reviewed some of the main factors that may promote the usage of ICT by workers. In this section we examine some variables that can function as deterrents to engagement with technology. Clearly, the absence of the facilitating factors described above, such as selfefficacy, supportive management, and a climate that rewards technology usage, can create barriers to engagement. In addition, however, there are several other factors that can have detrimental effects on workers’ attitudes and responses to technology, and to technological change. Again our focus will be primarily on psycho-social factors that influence worker reactions. Over the past 20 years or so, there has been considerable research into these potential deterrents to usage. We do not aim to provide an exhaustive review of this literature, but rather to highlight some of the key findings and to give illustrations of the types of research conducted. Earlier in this chapter we discussed the phenomenon of techno-stress, one dimension of which is techno-phobia (Thorpe & Brosnan, 2007), which refers to a dysfunctional level of fear and anxiety concerning the use of computers and other electronic devices, leading to a reluctance to engage with ICT. Techno-stress and techno-phobia can arise due to complicated interfaces that are not ‘‘user-friendly,’’ along with a culture/climate within the organization that does not recognize the diversity of knowledge, skills, and abilities with respect to technology usage. Rather than focusing on presumed deficits among users (e.g., due to age- or gender-related issues), the discussion below illustrates that it is imperative to examine the ways in which ICTs are set up and implemented in organizations as potential barriers to worker engagement. User accounts of ICT failures are important to take into account, including perceptions of constantly-changing systems

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and software, difficulties associated with learning and mastering complex programs, and technical faults which can lead to frustration and a perceived lack of achievement (O’Driscoll et al., 2009). De Palmer (2001) described the ‘‘tinker factor,’’ which reflects a tendency on the part of ICT professionals to prefer to manipulate technology than to relate to people. Similarly, Oudshoorn, Romes, and Stienstra (2004) discussed the apparent inability of ICT designers to envisage the actual needs of end users, and that software is often designed to suit the expertize, interests, and ambitions of the designers. This mismatch can lead to considerable user frustration and may be a fundamental obstacle to people’s engagement with ICT. For instance, workers can be thwarted in their efforts to achieve ICT mastery by the additional cognitive load required to operate the technology (O’Driscoll et al., 2009). Social cognitive theory (Bandura, 2001) argued that a central need for people is a sense of agency or mastery over what is required of them. Salanova, Peiro, and Schaufeli (2002), for example, demonstrated that a sense of mastery (self-efficacy) is central to workers’ ability to engage with technology. On the other hand, perceptions of incompetence at work can contribute to the development of worker burnout (Cherniss, 1993) and hence disengagement from work. Technology changes that create cognitive overload potentially exacerbate perceptions of incompetence in people, and can have serious implications for their mental health and well-being (Sonnentag & Bayer, 2005). Theories such as TRA, TPB, and TAM previously discussed in this chapter also consider the importance of social influence (subjective norms) in regard to employee acceptance of technology (Davis, 1989; Venkatesh & Davis, 2000). Similarly, Jian (2007) discussed employee resistance to technology within a social constructionist framework, suggesting that people who lack autonomy may try to achieve some level of control at work by avoiding usage of new technologies. The most prominent barriers to engagement with ICT are discussed below under four headings: (1) mandatory imposition of ICT within the workplace, (2) technological complexity and difficulty in use, (3) problems with coordinating different technologies, and (4) blurring boundaries between work and nonwork lives.

Mandatory Imposition of ICT within the Workplace Venkatesh et al. (2003) found that the mandatory introduction of ICT tended to reduce worker acceptance and future use of the technology.

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This is consistent with Damodaran and Ophert’s (2000) observation that whenever ICT is imposed upon people, organizational change and productivity are impeded rather than fostered. Damodaran and Olphert concluded that ‘‘soft’’ issues (trust, work relationships, autonomy, and communication) were pivotal to acceptance of innovative change in the workplace. They also noted that enforced knowledge sharing, such as happens when organizations institute shared file-storage systems and shared drives, can generate distrust and suspicion, leading to resistance from employees in taking up the new system. For instance, if employees perceive that their work environment is competitive, they may well prefer not to place their own files in a shared storage system. Similarly, Jian (2007) observed that preexisting tensions within an organization, which are likely to arise if there is excessive competition between individuals or groups, can create a climate that is not conducive to the introduction of new ICTs. Jian outlined three prominent tensions that emerged from interviews with US employees of an information and technology service provider. These major tensions were (1) conflict between espoused management values of empowering employees versus mandated technology practices, (2) disjunction between a model that presupposed integration between processes versus a perceived disintegration in relationships between work groups, and (3) conflict between enterprise-wide and local practices. Each of these tensions created resistance from end users of the system, including withdrawal from usage, which obviously is an undesirable end point. Jian’s analysis illustrated that tensions exist not just in relation to the nature of the technology itself, but also in terms of the existing organizational climate and culture, as well as the fit between the technology and the climate/culture, which we discussed earlier. Inadequate communication and consultation between management and workers over the introduction of new ICT or changes in ICT systems can also function as major barriers to worker engagement with the technology. Kim and Mauborgne (1998) suggested that organizations need to develop a consultative culture and climate where employee needs are understood and satisfied. According to Kim and Mauborgne, it is important that employees are provided with explanations as to why decisions (such as those involved in purchasing and mandating technology) have been made. In addition, provision of a clear rationale for decisions is a signal to employees that they are respected by management, and that their intellectual and other contributions are valued. Lack of respect for employee values, goals, and preferences may be seen by workers as a breach of their psychological contract with the organization, leading to a perception that

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organizational decision-making processes are unfair or unjust (Miller, 2001). Such perceptions will also contribute to feelings of resistance to change and potential withdrawal from usage of new technologies. This potential barrier to engagement with ICT reflects issues emerging from the more general literature on communication within organizational settings, and highlights the importance of developing relationships based upon trust. For instance, Rousseau (1995) illustrated how the psychological contract forms an essential ingredient underpinning reciprocity between employers and employees, and that contract breaches or violations have a significant impact on workers’ feelings of loyalty and commitment to their organization, as well as engagement with their job. Miller (2001) observed that, while people may not always be able to put into words exactly what they want from their relationship with the organization, they immediately recognize when their rights have been violated. Resistance to ICT developments can reflect lack of employee trust in management and perceptions of contract breach. Given the increasing centrality of ICT for the work of many individuals, failure to engage with the technology has serious repercussions for their overall job performance, as well as their own feelings of satisfaction and achievement (Coovert, Walvoord, Stilson, & Prewett, 2009). Successful implementation of technology is therefore highly dependent upon communication and the climate/culture of the organization. Leonardi (2009) illustrated this point via a qualitative investigation of the poor takeup of computer simulation technology purchased by a major automotive company. Leonardi found that a lack of accurate (and adequate) communication within the company, rather than features of the technology itself, was responsible for a perception that the technology was not an efficient tool. This led to employees preferring to retain outdated methodologies and ultimately the failure of the planned organizational change. Leonardi’s research is supported by that of Babin, Tricot, and Marine (2009), who noted that many people requiring assistance in using new technology do not seek expert help, but rather are more strongly influenced by the assistance of managers and colleagues. If the latter are uninformed or lack expertize and the ability to communicate effectively, it can exacerbate difficulties in adopting ICT. In addition, those making decisions to purchase ICT may lack the expertize to choose appropriately. England and Stewart (2007) found that implementation of ICT in the Australian and New Zealand health sector could be compromised by managers who were uninformed about the applicability of ICTs, and hence were resistant to the idea that ICT could be applied within clinical health settings. Gal and Berente (2008) obtained data, which indicated that ICT can be framed within organizations in a centered,

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temporally bounded, and individually focused way that will inevitably undermine its successful implementation. Accordingly, people interpret their experience with ICT in the light of their previous experience with technology and within the context of their organizational culture. The context in which technology is introduced is generally more influential in regard to its successful implementation than the specific features and capacity of the technology itself (Gal & Berente, 2008). It is therefore apparent that successful ICT implementation in the workplace is significantly influenced by the existing organizational culture and climate. If organizations are desirous of having employees who engage with ICT promising substantial productivity gains, they need to provide leadership that engenders trust and ensures that avenues are in place for two-way communication within the workplace.

Technological Complexity and Difficulty of Use A second barrier to engagement with ICT, which has been widely discussed in the literature, is the perception that it is complex and difficult to use effectively (Beers, Boshuizen, Kirschner, Gijselaers, & Westendorp, 2008). This factor is a central element in the TAM described earlier. There are many anecdotal accounts of people’s negative experiences in their endeavors to master the technology. Frequently reported issues include time lost due to unclear error messages, unpredictable delays in program reaction times, poorly designed interfaces that may be difficult to comprehend and utilize, unduly long download times, features that are difficult to identify and locate, and lost connections (Ceaparu et al., 2004). There is consistent evidence that these difficulties induce user frustration, which has a substantial impact on both work productivity and workers’ emotional states. ICT which is effectively designed and implemented will enhance people’s job performance, reducing the need for repetitive tasks and enabling them to devote their cognitive energy to more productive and creative tasks. However, this promise of ‘‘freeing up’’ worker time and energy from routine, mechanical work and speeding up task performance has not always been realized in practice, and the gap between promise and reality has often been attributed to software packages and programs that have not been designed with end-user needs and capabilities in mind. One very salient variable in this regard is cognitive load, which refers to the pressures and demands placed on individuals’ cognitive functioning by the complexities of the software. Oudshoorn et al. (2004) offered a pertinent

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example of the introduction of a poorly designed software system created for use in businesses and homes in Amsterdam. The system was intended to be accessible and easy to use but in reality was extremely complex. The software contained six distinct programs, each with a different interface; the help menu was not readily accessible to users, who therefore had to navigate their way through the myriad procedures by trial and error, and it contained numerous special effects not operating in older computers. These and other features created substantial difficulties for the users, resulting in less–thanoptimal utilization of the technology. According to Oudshoorn et al., these complications derived from the goals of the designers, who aimed to put their own stamp on the program design. For instance, the designers assumed that users’ knowledge and equipment would be equivalent to their own and that users would share the designers’ fascination with the new technology. Consequently, the actual functions the software was intended to perform were lost in ‘‘flashy’’ features that did not contribute to the purpose for which it was commissioned. In summary, the impracticalities of the program and the resultant cognitive demands on users created substantial user frustration and, in many cases, abandonment of the system. As Coovert et al. (2005, p. 316) commented, ‘‘technology works best when it complements rather than dictates workers’ jobs’’. In the above scenario, clearly there was a distinct lack of complementarity between user needs and the design of the ICT system. Similarly, Damodaran and Ophert (2000) observed that the reason for failure of a new electronic information system in a large multinational company was a lack of user-friendliness. Problems arose due to the system’s unnecessary complexity, a premature ‘roll-out’, inadequate user support and training, and inappropriate document management functionality. These ICT problems together with the system’s mandatory and ‘top-down’ introduction guaranteed its failure. Damodaran and Olphert suggested that motivation for the expensive exercise that eventually resulted in failure was a technology ‘push’ within the organization as a result of a techno-centric approach on the part of management. In other words, a perceived urgency on the part of management to be seen as supportive of ICT innovation resulted in an inappropriate choice of software and some degree of alienation of the workforce. Among concerns raised by employees in Damodaran and Ophert’s (2000) research was the suspicion that management was seeking to gain access to their knowledge in order to make them expendable. This is reminiscent of Kim and Mauborgne’s (1998) prediction that management failure to ask employees for input, to provide an opportunity to propose suggestions and

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raise questions about decisions, would be characterized by hoarding of ideas, foot-dragging, and other counterproductive activities. Damodaran and Olphert observed that the findings of the study provide the basis for tailoring the change management process to address explicitly the need to achieve the change to a culture of trust which was perceived as critical for the success of [the ICT project] within the company. Trust in the system will be affected by concerns for job security particularly if sharing information is seen to reduce the value of an individual to the company. In addition, trusting that others will value, respect, and wisely use shared know-how, will determine how willingly individuals relinquish information which they currently store in personal files (p. 411).

Problems with Coordinating Different Technologies Stephens (2007) commented that most research does not consider the fact that ICT use at work often involves a mix of ICTs. People are exposed concurrently to auditory, visual, and textual modalities. This could be seen as an important benefit of more advanced ICTs in that follow-up strategies such as emails and phone calls can help to ensure that tasks are completed efficiently and effectively. However, Ferran and Watts (2008) warned that it is important to remember that ICT does not replace human contact. They found that people process communication provided in videoconferences (involving audio and visual images) differently to how they process information gained in face-to-face meetings. Interpersonal contact entails individuals making use of heuristic cues and judgments based on their impression of how likeable the speaker is, often more than the quality of argument presented. Ferran and Watts concluded that the cognitive load required for processing videoconference information is far higher than that needed for face-to-face communication. This has resonance with the work of Freeman (2002), who noted that ICT-mediated communication has not produced a wide dispersal of telecommuting jobs: ‘‘one presumed reason for this is that much important information, be it business, or scientific, or technological, is tacit, requiring human interaction to be effectively transmitted’’ (p. 295). This highlights the point that using the technology itself, in addition to complex cognitive processing, can add to intrinsic cognitive load and lead to quite different outcomes than would be achieved through face-to-face interactions with colleagues. Indeed Beers et al. (2008) commented that: ‘‘tools may cause unwanted side-effects that are extraneous to the goals of those tools in terms of extra effort on the part of

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the participants’’ (p. 325), suggesting that cognitive load considerations must be factored into considerations of ICT change within the workplace. Ragu-Nathan et al. (2008) asserted that ‘‘empirical research on ICTrelated stressors for end users is practically non-existent’’ (p. 421). However, a wealth of such work has been conducted within the realm of human factors, specifically within domains where the consequences of human error may be life-threatening. Modern hospital operating theaters, for example, represent settings where professional teams rely on combinations of ICT that were not available in traditional theaters. The introduction of ICT to operating theaters has introduced two new possibilities for error: (1) discriminating between multiple alarms and (2) tailoring the information provided by the alarm to the stage of the operation (Seagull & Sanderson, 2001; Watson, Sanderson, & Russell, 2004). The first problem involves a proliferation of alarms and the fact that they are difficult to discriminate between. Surgical team members have to decipher the problem by looking up from their work and locating the appropriate visual display. According to Seagull and Sanderson, ‘‘practitioners are regularly confronted with arrays of technologies that were introduced in piecemeal fashion and were not necessarily designed to operate in conjunction with each other’’ (pp. 66–67). Concerning the second possible source of error, Seagull and Sanderson (2001) warned about the distractive potential of alarms monitoring patients’ vital signs at certain phases of surgical procedures. For example, they observed surgical teams ignoring the apnea alarm (which monitors patient breathing) 48% of the time. According to Seagull and Sanderson, this particular alarm is very annoying and redundant during the intubation and emergence phases and only instructive during the maintenance phase of surgical procedures. Watson et al. (2004) suggested that some anesthesiologists opt to silence annoying alarms in the interest of reducing operating theatre noise and reducing strain on working memory. However, Watson et al. noted that such a practice constitutes a ‘‘two-edged sword’’ that could well ‘‘reduce the number of nuisance alarms at the cost of possibly missing informative alarms’’ (p. 279). In addition, according to Watson et al., people can forget to turn silenced alarms back on, which is especially dangerous when low-risk patients are followed by those at higher risk. In response to this non-use of some surgical ICTs, the utility of audio displays providing continuous information about patients’ vital signs has been investigated, along with the impact these displays have on the peripheral awareness of the surgical teams and their effectiveness (Sanderson et al., 2008; Watson & Sanderson, 2007). Refinements to auditory alarms are currently being tested that include, for example, continuous sound patterns that

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provide information in the periphery of team members’ awareness. Changes in the pattern of a continuous background sound would alert staff to the existence of a problem and also provide information about the nature of the problem. This example illustrates why it is important that ICT designers are fully cognizant of the application of their design and also consult with end users of the ICT in order to ensure users’ future engagement with technology and that the technology fulfils the purpose for which it was designed. Effective utilization of complex technologies rests heavily upon coordination and different technological applications being complementary with each other, rather than in conflict (Stephens, 2007).

Blurring Boundaries between Work and Nonwork Life A final barrier to worker engagement with ICT, which we will discuss here, is that it may be perceived as exacerbating inter-role conflict between work responsibilities and family (and other nonwork) commitments. Although, as we discussed earlier in this chapter, technological innovations have clearly increased the flexibility with which work can be performed, reducing both time and place constraints, the potential downside of this increased flexibility is that work may encroach upon people’s private lives. For instance, telework (which is based almost entirely on optimal use of ICTs) may eliminate stress due to commuting and juggling work and family commitments, but it also has been found to induce other stressors that can offset the advantages of this form of working. Research findings on the positive versus negative outcomes of telework are, however, quite inconsistent. Although some studies have reported a reduction in work–family conflict among teleworkers, others have obtained evidence for increased conflict between work and family lives (Brough, O’Driscoll, & Kalliath, 2007; Coovert et al., 2009). Another form of ICT that affects an even greater proportion of workers is email, which has become an inevitable part of everyday life as well as being an integral component of many people’s jobs. Ramsay, Hair, and Renaud (2008) noted that emails can contribute to increased strain in the workplace in two ways: (1) the sheer number of emails often does not equate with improved organizational communication because of information overload and (2) people find it difficult to return to task after being interrupted by emails. We would add a third dimension to email communication: Because of constant connectivity, emails traverse traditional boundaries between work and home, which means that people may be denied important ‘‘downtime’’ from their work.

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Ragu-Nathan et al. (2008) commented that emails can present individuals with more information than they can handle efficiently. For instance, workers can feel overwhelmed with the sheer volume of organizational information that appears in their inbox. Jackson, Dawson, and Wilson (2003) found that most employees in a large organization felt compelled to respond to the arrival of an email message within 6 s. This had serious implications in that workers were interrupted and workflow was disrupted. In addition, according to Ragu-Nathan et al. (2008), employees are exposed to work-life balance pressures resulting from expanding multiple applications of ICT. Employees are often still connected in their leisure time by means of the Internet or wireless email devices, as well as (increasingly) cellphones, and can therefore be easily accessible to work demands, leading to a feeling that it is impossible to achieve ‘‘down-time’’ from their work. An example of this was demonstrated by Osterlund and Robson (2009), who reported that academic teaching assistants, who are usually employed on a casual basis and who are often postgraduate students with heavy study loads, complained of being overextended by undergraduate students’ email access to them in nonwork time, with attendant expectations that such issues would be addressed almost immediately. Pentland and Feldman (2007) also commented that, because computers and mobile phones have applications for both work and entertainment via a complex array of ICTs, boundaries between work and the personal lives of individuals are inevitably merged, as illustrated by Osterlund and Robson’s (2009) research. In a psychological sense this has serious implications for the mental health and well-being of individuals. According to Sonnentag (2005), people need some time during the day to have their attention fully occupied by something other than work concerns. Sonnentag warned that when individuals are not able to achieve ‘‘psychological detachment,’’ they may experience loss of functionality in their work and even develop aspects of burnout, such as cynicism toward their organization and work colleagues. ICTs may therefore contribute in significant ways to workers’ feelings of being overstretched, with potentially serious consequences for their mental health. The above issues do not suggest that use of email (or other electronic communication mechanisms, such as cellphones and blackberries) should be abandoned, but rather that they need to be monitored and managed effectively, by both the individual worker and by management initiatives designed to ensure their optimal usage and to minimize potentially negative effects. With respect to work-life balance, one suggestion for reducing conflict between work and family commitments is to use a strategy of

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deliberate segmentation, in particular to deliberately separate work activities from home life (Boswell & Olson-Buchanan, 2007; Brough, O’Driscoll, & Biggs, 2009a). This may entail people deciding not to access work-related emails during their family and leisure time; such a decision would need support from organizational management, so that they do not create expectations that employees would be ‘‘on call’’ during nonwork time. In the above section we have illustrated some, although clearly not all, factors which may function as barriers to worker engagement with ICT. We have focused our discussion on four key issues that are associated with the use of ICTs, which may impinge upon individuals, both at work and at home, and hence influence their attitudes toward ICT usage and their adoption of the technology. As we noted at the commencement of this section, our treatment is not intended to be exhaustive, and clearly there are many other factors that may affect individuals’ reactions to ICT. We have selected the above four factors because they are, in our view, critical for the effective implementation of new technologies. In summary, it is important that ICTs: (1) provide people with the capacity to complete their work more easily (Coovert et al., 2005), (2) allow workers to be more constructive or creative in their job by reducing the need for individuals to undertake routine, repetitive tasks (Beers et al., 2008), (3) provide more intuitively responsive information that addresses user needs (Seagull & Sanderson, 2001), and (4) do not intrude on people’s nonwork lives to the detriment of their other (nonwork related) commitments, interests, and sources of enjoyment and fulfillment (Osterlund & Robson, 2009; Sonnentag, 2005). Almost 20 years ago, Orlikowski (1992) pointed out that ICT gains its meaning only through manipulation by humans. Because technology requires human agency in order to be a successful tool, individuals can choose not to use it. Reasons for this decision have a great deal to do with the social context in which the ICT is placed and whether it adds to intrinsic cognitive load, therefore increasing task demands. Hence, theoretical models explaining employee resistance to the introduction of ICT have moved from descriptions of reluctant users of technology or lacking in technology self-efficacy (Agarwal & Karahanna, 2000), to a more inclusive exploration of the social context in which technology is implemented. In relation to social context, this section of the current chapter has raised two important points in relation to the use of ICTs within organizations: (1) how ICT is introduced and (2) matching the design features of the technology to end-user needs, capabilities, and modi operandi.

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IMPLICATIONS FOR MANAGING TECHNOLOGICAL ENGAGEMENT AND PSYCHOLOGICAL WELL-BEING This chapter, along with Day and Kelloway (2010), has outlined some of the possible consequences of ICT on the psychological health and well-being of individual users. While acknowledging the myriad benefits and advantages of ICTs in terms of more effective and efficient job performance, as well as the potential for increased flexibility with respect to where and when work is carried out, there are also several negative outcomes that may arise for people using ICTs. In particular, we have discussed anxiety over and frustration with technology usage, along with cognitive overload, as issues of major concern and potential contributors to techno-stress. From the research presented here it is evident that these factors can exert a substantial impact on users’ willingness to engage with the technology as well as their psychological well-being. As a corollary, clearly it is imperative to develop strategies to optimize both worker productivity and psychological wellbeing. To conclude this chapter, we briefly offer some recommendations for enhancing technological engagement and, consequently, workers’ health and well-being. One fairly obvious strategy for increasing the effective utilization of ICT is to endeavor to minimize design features that induce negative behavioral and emotional reactions on the part of end users. For instance, we have referred to some prominent software features that have been reported as creating frustration among users, including long delays in downloading, inaccessibility of certain program features, and uninterpretable error messages. Clearly, designing systems that are more ‘‘user friendly’’ would be a major strategy for increasing worker engagement with ICT. Another complementary approach is the provision of appropriate and timely training in the use of software packages. Unfortunately, as noted earlier, training programs are not always conducted systematically (Agarwal et al., 2000). One major obstacle appears to be that training programs frequently do not address user needs and/or are not set up to enable users to learn at their own pace and time (Llorens et al., 2003). To be optimally effective, training providers need to understand user competency levels and feelings of self-efficacy with regards to learning new technological skills. For instance, in the study we reported earlier, Beas and Salanova (2006) argued that the development of computer self-efficacy and a sense of mastery over technology is perhaps even more important than learning how to use specific

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programs, and hence trainers may need to invest time and energy into the enhancement of overall computer self-efficacy among users, rather than focusing exclusively on developing knowledge about specific software programs. The more general approach to training is compatible with the concept of personal control, which has been implicated as a critical determinant of user reactions to, and engagement with, technology (Bessiere et al., 2006). Consideration must be given to individual differences in the adaptiveness to technology. For instance, although not reviewed here in detail, some studies have demonstrated age-related differences in the ability to adapt to new technologies, although the evidence is not consistent (Czaja et al., 2006). Perhaps more directly relevant are differences in levels of experience with these technologies. A ‘‘one-size-fits-all’’ approach to ICT training is unlikely to generate universal benefits; rather, it is important to gauge individuals’ levels of competence and experience and to design training programs that match their knowledge and skill levels. Furthermore, it needs to be recognized that learning is a continuous process, although not necessarily linear, and that different people will progress at different rates and have distinct learning trajectories. We have also discussed the importance of various forms of social support. Technical support is clearly a vital contributor to skill development. As with training, the kind of support provided to users must be geared toward their needs and their capabilities. A frequently-raised concern about technical support is that those providing it may not fully grasp the individual’s needs or their ability to take on board the information and advice being provided, engage in ‘‘techno-speak’’ which is virtually incomprehensible to end users, and provide solutions at a fast pace (Bruque et al., 2008). The commonality of such complaints illustrates that technical support persons need to learn how to communicate on the end-user’s wavelength and to assist in a manner which is constructive and helpful to the person, rather than following a predetermined script which may be comprehensible to them but not to others. Other forms and sources of support can also make a substantial contribution to enhancing the technological engagement of workers, and hence their feelings of well-being. Earlier we reviewed research suggesting that support from top management is necessary to ensure that workers are encouraged to utilize new technologies and that a supportive environment (climate) is developed within the organization. It is clear that, especially when new technology is being introduced, support from top management (including role modeling) is essential. For example, the important role

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played by top management in establishing a climate for technical innovation has been demonstrated (Peansupap & Walker, 2006), although managers also need to be alert to the potential negative effects of imposing technology on organizational members (Venkatesh et al., 2003). Rather, a collaborative and consultative approach has been found to be most efficacious in addressing any worker concerns and anxieties about the implementation of new technologies (Kim & Mauborgne, 1998). Management also plays a critical role in conveying organizational expectations regarding technology usage. There are several areas where these expectations impinge upon individual workers’ experiences. Two of them, which were mentioned above, are the use of emails and teleworking. With respect to the former, although email communication has become increasingly prevalent in a wide range of organizational settings, different expectations may exist that can influence the frequency and manner of email usage. As observed by Bellotti et al. (2005) and by Hair et al. (2007), email usage has escalated in recent years and is now a primary mode of communication within (and between) organizations. However, these (and other) observers have reflected on the potential misuse of emails, including the risk that they may dominate people’s work lives. It is incumbent on management, therefore, to establish parameters for email usage, as well as to lay out protocols for email communications, which do not contain the nonverbal cues critical for effective transmission of information. Similarly, teleworking has been shown to have a significant impact on workers’ psychological and physical well-being (see, e.g., Lundberg & Lindfors, 2002) and represents another area where management can exert control over its parameters. Specifically, managerial expectations about worker availability (via technology) can have a significant impact on the extent to which workers feel pressured to be ‘‘on call’’ and their ability to maintain a healthy level of work-life balance (Brough et al., 2009a). It is incumbent on managers to develop realistic expectations concerning teleworker availability and to communicate these expectations clearly to their workforce. Finally under this heading, support from colleagues and coworkers is another major component of technology acceptance and engagement. Babin et al. (2009) and Leonardi (2009) have illustrated that effective communication within the organization and a supportive work climate can have more impact on workers’ reactions to ICT than specific features of the technology. Support from colleagues can help with the development and maintenance of a collaborative work environment where individuals perceive that others are willing to assist them when difficulties arise, and

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that it is appropriate to seek help from one’s work colleagues to resolve the specific problems encountered. For this to occur, development of a climate of trust is essential.

CONCLUSION In conclusion, in this chapter we have offered an overview of some key contributors to worker engagement with ICT, and their relationships with psychological health and well-being. We began with a discussion of techno-stress, a phenomenon attributed to the rapid development of new technologies and people’s psychological reactions to these technologies. This was followed by an outline of two key reactions – technological anxiety and frustration – which have been frequently identified among workers, along with feelings of information overload that can arise from increased accessibility to large amounts of information and the concomitant difficulty of distinguishing between information which is relevant and that which is peripheral to one’s work performance. Investigations of techno-stress and its components have typically examined relatively short-term reactions to technological change, and more research is required to investigate longer-term affective and behavioral reactions, especially issues such as exogenous depression that may (as suggested earlier) occur when a person experiences ongoing anxiety and frustration in using ICT. These longerterm effects may be especially debilitating and damaging to a person’s subjective health and well-being, and may diminish their ability to engage with technology and to utilize it optimally in their job performance. Exploration of longer-term impacts of ICT needs to be a priority for future research. The major sections of our chapter described some key facilitators and inhibitors of worker engagement with technology, and the influence that these factors may exert on worker psychological well-being. Although these factors all make a substantial contribution, our discussion was avowedly selective, and we refer readers to other reviews, including Day and Kelloway (2010). The issues focused in this chapter highlight possible interventions that may be implemented to enhance both technological engagement and workers’ well-being, especially training, support, and development of an organizational culture and climate fostering technological change, at the same time recognizing the needs of individual workers and the importance of sustaining their well-being. In light of the issues presented in this chapter, it is imperative that systematic efforts are made by both management and individual workers to ensure that new ICTs are successfully implemented

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and that user well-being is a paramount consideration in the adoption and utilization of ICT.

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INFORMATION AND COMMUNICATION TECHNOLOGY: IMPLICATIONS FOR JOB STRESS AND EMPLOYEE WELL-BEING Arla Day, Natasha Scott and E. Kevin Kelloway ABSTRACT In this chapter, we use the job demands–resources (JD-R) model (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001) and the transactional model of stress (Lazarus & Folkman, 1984) to provide a theoretical framework with which to examine information and communication technology (ICT) as both a demand and a resource. We review specific characteristics of ICT that may either increase or decrease employee stress and well-being. Specifically, we examine the extent that ICT increases accessibility of workers and access to information, the extent to which it improves communication and control over one’s job and life, and the extent to which it is used to monitor employees or provide feedback. Finally, we examine the organizational, job, and individual factors that may mitigate or exacerbate the impact of ICT demands on individual outcomes.

New Developments in Theoretical and Conceptual Approaches to Job Stress Research in Occupational Stress and Well Being, Volume 8, 317–350 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-3555/doi:10.1108/S1479-3555(2010)0000008011

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Technology is neither good nor bad–nor is it neutral. – Kranzberg’s First Law of Technology (Kranzberg, 1986)

There has been a fundamental shift in the way in which work is completed and the nature of employee interactions due to the increasing use of information and communication technology (ICT) in the workplace (Ragu-Nathan, Tarafdar, & Ragu-Nathan, 2008). ICT is defined as any technology or device that has the capacity to acquire, store, process, or transmit information (Steinmueller, 2000) and can include personal computers, the Internet, mobile communication devices, and email. As implied by Kranzberg’s (1986) first law of technology, the rapid adoption of ICT in organizations can be expected to substantially alter the individual experience of work; although they are wide ranging, these changes are neither entirely for the better nor for the worse. Lowry and Moskos (2005) described ICT in the workplace as a ‘‘doubleedged’’ sword because it is not homogenous in either its uses or its impact on employees. Even though ICT can be used to make work more efficient and employees’ lives better, the demands associated with its use may create additional problems for employees (Coovert & Thompson, 2003; Korunka & Vitouch, 1999; Morgan, Morgan, & Hall, 2000). ICT may have a positive impact on people by increasing access to information (Migliarese & Paolucci, 1995), allowing greater flexibility (Standen, Daniels, & Lamond, 1999), improving efficiency, and increasing communication (Dewett & Jones, 2001). However, ICT may also create increased demands and stress in the workplace by creating expectations of greater productivity (Wang, Shu, & Tu, 2008) and accessibility (Tarafdar, Tu, Ragu-Nathan, & Ragu-Nathan, 2007), as well as creating technical ‘‘glitches’’ (Coovert & Thompson, 2003). Many people work with some form of ICT (Seppa¨la¨, 2001; Porter & Kakabadse, 2006). In fact, a recent report by the US Department of Labor indicated that 55.5% of employees require the use of a computer for their job and approximately two of every five employees use the Internet or email for work purposes (Bureau of Labor Statistics, 2005). As the reliance on ICT to perform work-related duties continues to grow and more advanced forms of ICT are adopted in the workplace, it becomes increasingly important to understand the effects that ICT have on individual employees’ well-being and performance. Therefore, within this chapter, we examine the theoretical and methodological developments associated with the widespread adoption of ICT in the workplace. We also review specific aspects of ICT that may have either positive or negative effects on individual

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outcomes. We use the job demands–resources (JD-R) model (Bakker & Demerouti, 2007; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001) and the transactional model of stress (Lazarus & Folkman, 1984) to provide a theoretical framework with which to examine ICT as both a demand and a resource. Finally, we examine the conditions that may facilitate employee perceptions of ICT as a demand and the conditions that may encourage employees to perceive ICT as a resource.

JOB DEMANDS–RESOURCES MODEL The JD-R model (Demerouti et al., 2001) is a contemporary work stress model that can be used to explain how positive and negative health and work outcomes can be the product of various aspects of the working environment. This model posits that employees are exposed to physical, psychological, social, and organizational aspects of the working environment that can be categorized as either demands or resources (Bakker & Demerouti, 2007; Demerouti et al., 2001). Job demands refer to any aspects of the job (e.g., workload, time pressure, emotionally taxing social interactions, loud noises) that require extended physical or psychological effort on the part of the employee and that are associated with increased physical and psychological costs (e.g., fatigue, exhaustion; Bakker & Demerouti, 2007; Demerouti et al., 2001). Conversely, the work environment also includes a number of physical, psychological, social, and organizational aspects (e.g., job control, social support, task variety, and compensation) of the job that encourage employee health and productivity. These job resources assist employees with the completion of their work, reduce the burden of job demands, and can promote personal growth and development (Bakker & Demerouti, 2007). Thus, the basic premise of the JD-R model is that working conditions can act as either demands or resources for employees, with job demands depleting employees’ physical and psychological reserves and job resources motivating and engaging employees, which may also produce a buffering effect against experiencing high demands.

TRANSACTIONAL MODEL OF STRESS Job demands as conceptualized in the JD-R model are comparable to the conceptualization of job stressors within the transactional model of stress (Lazarus & Folkman, 1984), such that they may be perceived as stressful

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and result in negative personal outcomes. However, the transactional model also emphasizes that not every individual will react the same to a potential stressful event (Lazarus & Folkman, 1984). The extent that working conditions (including ICT) are perceived as taxing or exceeding individuals’ resources will determine the extent that they view ICT as being negative or harmful, and consequently, have the potential to create strain in an individual (cf. Lazarus & Folkman, 1984; Pratt & Barling, 1988). Forms of strain can manifest in terms of various psychological, behavioral, and physical symptoms (Bartone, Ursano, Wright, & Ingraham, 1989; Jex & Beehr, 1991). Moreover, according to this model, certain individual or external factors may moderate or buffer the relationship, such that these factors can help protect employees against stressors or workplace demands by reducing their potential negative effects. Therefore, stressors and demands may be similar in their definition and, when perceived negatively, produce negative strain outcomes. However, if factors in the work environment are viewed positively (i.e., as resources), positive outcomes (e.g., well-being; engagement) may result. ICT is regarded as an integral part of the working conditions employees face every day while they perform their jobs. Therefore, ICT in the workplace may be viewed as either a demand/stressor for employees or a resource that motivates employees to be more engaged and productive in their work. However, it is unclear under what conditions ICT would be perceived as a demand rather than a resource. Therefore, we will examine the link between general ICT use and strain, and we will explore the specific characteristics of ICT that may influence whether it is perceived either as a demand or as a resource. To build upon these theoretical models in the context of ICT, we discuss the outcomes in more broad terms to include employee performance as well as employee strain and well-being. Thus, the focus of this chapter is on employees’ well-being and performance as a result of work specific ICT, and not on the antecedents of employee use or acceptance of technology as outlined in the technology acceptance model (TAM) literature. We refer readers to the chapter in this volume by O’Driscoll, Brough, Timms, and Sawang for an overview the TAM literature.

ICT USE AND STRAIN The anecdotal reports and preliminary evidence suggest that use of ICT is related to increased stress and strain outcomes. For example, a recent exploratory study found that individuals who have high exposure to ICTs

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(e.g., computers and cell phones) are also more likely to experience symptoms of prolonged stress and depression one-year later (Thome´e, Eklo¨f, Gustaffson, Nilsson, & Hagberg, 2007). Furthermore, those individuals who sent or received a high volume of short messages (e.g., MSN or Yahoo messenger) were also more likely to experience symptoms of prolonged stress one-year later (Thome´e et al., 2007). Porter and Kakabadse (2006) conducted a qualitative analysis of manager’s comments about the use of technology at work. Many managers reported feeling stressed by technology, describing it as ‘‘an irritant’’ and ‘‘a nuisance’’ (p. 550). Similarly, Wood (2001) cited a survey by Concord Communications Inc., indicating that 80% of Canadian managers and executives reported stress due to expectations to implement new ICT. Furthermore, it has been argued that employees who work in jobs with high exposure to ICT are more likely to experience technostress and psychosomatic symptoms such as mental fatigue, headaches, moodiness, and difficulty concentrating (Arnetz & Wiholm, 1997). The compilation of these research findings suggests that individuals may perceive their interaction with (i.e., use of) ICT as a potential stressor in the workplace. Using the transactional model of stress (Lazarus & Folkman, 1984) as a framework, we would expect employees’ ability to cope with ICT to influence their strain outcomes. Thus, the more problems or hassles an employee experiences while using ICT, the more taxing this interaction is likely to become, resulting in the employee perceiving their interaction with ICT in the workplace as stressful, and may increase employee strain. As employees’ exposure to and interactions with new forms of ICT increase, it is plausible to expect that the number of minor problems or hassles employees experience while using them may also increase. General hassles are defined as critical and regular demands placed on an individual (DeLongis, Coyne, Dakof, Folkman, & Lazarus, 1982). Although hassles may be viewed as inconsequential in predicting employee health outcomes, they tend to be associated with increased strain (Cooper, Kirkcaldy, & Brown, 1994) and decreased psychological health (Gruen, Folkman, & Lazarus, 1988). Much of the early research examining ICT as a potential source of stress (i.e., demand) concentrated on hassles with using computers (Hudiburg, 1989a, 1989b, 1992), and the anxiety and stress levels associated with computer use (Hudiburg, 1995). On the basis of the daily hassles literature, Hudiburg (1989a) defined a computer hassle as an experience resulting from the interactions with a computer that may be perceived by individuals as stressful. The research by Hudiburg and his colleagues (Hudiburg, 1989a, 1989b, 1992; Hudiburg, Pashaj, & Wolfe, 1999) on computer hassles can be extended to include hassles experienced using all forms of ICT.

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Problems and Hassles Associated with Using ICT There are four types of ICT hassles that employees may experience while using ICT at work: (1) experiencing ICT malfunctions, (2) using multiple ICTs that are incompatible with each other, (3) experiencing increased demands because of ICT security precautions, and (4) expectations to continually update skills for new ICT. (1) ICT malfunctions. The quality of ICT systems (e.g., user-friendliness, ability to handle employees tasks) within the workplace has been found to influence employees’ attitudes and perceptions of ICT (Ouadahi, 2008). The early research by Hudiburg, Pashaj, and Wolfe (1999) indicated that increased computer hassles are associated with increased somatic complaints and anxiety in computer users. Hudiburg (1995) suggested that technology that is not functioning properly may be frustrating to employees and may increase their feelings of stress. One manifestation of this stress and frustration may be abuse of technology (e.g., hitting or throwing ICT equipment). For example, Wood (2001) reported that 83% of corporate ICT managers witnessed abuse of computer equipment. Other research has also indicated that computer hassles are associated with lower well-being (Hudiburg, Ahrens, & Jones, 1994; Hudiburg, Brown, & Jones, 1993). (2) Incompatible technologies. Related to the concept of general breakdowns in technology is the issue of difficulties due to incompatible technologies. Recent research examining teacher stress and the use of technology in the classroom found that teachers reported that one of the main causes of stress was the usability of the technology, such as the number of errors they encountered and the compatibility of various technologies (Al-Fudail & Mellar, 2008). Furthermore, Ragu-Nathan et al. (2008) reported that the majority of off-the-shelf ICT applications required some form of adaptation to specific workplaces and that even after the modification; employees are still likely to experience problems with the applications such as documentation errors and programming errors. (3) Security demands. Over the past few years, there has been an increased focus on computer and Internet security. Because the Internet is accessible to anyone regardless of their global location, there is a threat that information that is sent via the Internet will be ‘‘lost, stolen, corrupted, or misused’’ (Longstaff et al., 1997, p. 231). Furthermore, there is a risk that computer viruses can be contracted through Internet downloads or unsolicited emails (i.e., spam; Bissett & Shipton, 2000). Passwords are one means by which we attempt to keep electronic information secure; however,

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because so many programs and websites require passwords, they have become a source of stress. Brown, Bracken, Zoccoli, and Douglas (2004) found that students have an average of 4.45 different passwords that they are required to use for an average of 8.18 functions. Over 30% of respondents periodically forget their passwords and 22.5% have mixed up their passwords. Accordingly, there may be a burden associated with remembering different passwords and possibly not being able to gain access to important information or programs because of failure to recall correct passwords. Employees may experience these three types of ICT hassles (i.e., malfunctions, incompatibilities, and security demands) regardless of their familiarity with and skills using the particular form of ICT. When new, unfamiliar ICT is introduced into the workplace, employees may face an additional hassle or burden of having to continuously learn how to use the new or updated ICT. (4) Expectations for continuous learning. Given that a change in work practices can influence mood, physical well-being, and work performance (Stewart & Barling, 1996), technology may also be perceived as stressful because it introduces rapid change into the work life of the employee. Because implementing new technology requires employees to attend training sessions, learn new software, and incorporate the new technology into their work, it is plausible that this continuous learning process (i.e., being required to continuously update one’s technical skills) may result in employee stress. Having to keep up with ICT upgrades and technological advances requires continuous updating of employees’ technical skills (Wang et al., 2008). Continuous ICT change can result in the frustrating experience of having to learn a new technology when the employee is still in the process of mastering the current technology (Korunka, Zauchner, & Weiss, 1997). Indeed, research suggests that the implementation of new technology can increase work demands (Korunka, Huemer, Litschauer, & Karetta, 1996), employee frustration (Zorn, 2002), and perceived stress (Korunka et al., 1997). Wood (2001) also noted that exaggerated expectations regarding people’s ability to master extremely complicated technologies might lead to negative feelings toward technology in general. Thus far, employee stress and subsequent strain outcomes associated with ICT has only been considered in the context of employees’ interaction with ICTs and the extent that certain properties of the ICT creates hassles and stress for the employee. However, this view of how ICT affects employees is rather simplistic. In determining the full impact of employee–ICT interactions on employees’ stress and strain, it is important to examine

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not only the hassles associated with using ICT but also the underlying purpose of the interaction with ICT (e.g., either to create more flexibility in their workplace or to be constantly accessible to colleagues and supervisors, even during non-working hours). Ultimately, it is the underlying purpose for using the ICT that will determine whether employees view it as a demand (i.e., stressor) or a resource.

ICTS AS DEMANDS AND RESOURCES On the basis of the general definition of ICT and the specifics of the JD-R stress and transactional stress models, we define workplace ICT demands as any ICT factor or process at work involving some type of storing, transmitting, or processing technology (e.g., computer programs) or device (e.g., computer, cell phone) that have the potential to be perceived as stressful by workers. Although there is some evidence suggesting the negative individual effects of ICT use, the question remains as to exactly how ICT may create strain outcomes. There has been much research demonstrating that specific job stressors are associated with negative personal and organizational outcomes (Baba, Jamal, & Tourigny, 1998; Fox, Dwyer, & Ganster, 1993; Jones, Flynn, & Kelloway, 1995; Kelloway & Day, 2005; Sauter & Murphy, 1995). Using the general work stress literature and the JD-R model, we have identified several technology factors that may require employees to extend physical and/or psychological effort. Conversely, we define ICT resources as any ICT factor or process at work involving some type of storing, transmitting, or processing technology (e.g., computer programs) or device (computer, cell phone) that assist employees with the completion of their work, reduce the burden of job demands, or that promote personal growth and development. Although much of the research focus to date has been on how ICT may act as demands or stressors, certain characteristics of ICT may enhance employee satisfaction, well-being, and productivity. ICT can increase employees’ control over when and where they complete their work (Teo, Lim, & Wai, 1998), enhance their ability to problem solve by increasing their access to information (Morgan et al., 2000), improve efficiency (Dewett & Jones, 2001), and increase the communication between employees and their colleagues, managers, and subordinates (Zaccaro & Bader, 2003). Therefore, on the basis of the previous literature on ICT in the workplace, and the definitions of ICT demands and ICT resources, we have identified

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five characteristics of ICT that can be viewed as either a demand or a resource. That is, the impact of ICT depends on the extent to which it (1) increases or decreases accessibility of workers, (2) improves or restricts access to information, (3) improves or worsens communication, (4) is used to monitor employees’ performance or to provide employee improvement feedback, and (5) increases or decreases control over one’s work and home life. How ICT functions in these five categories impacts whether it is perceived as a demand or a resource.

Accessibility and Availability ICT can make employees more accessible to others at work and can allow work and the workplace to be more available to the employee. The portability of various technologies (e.g., lap-top computers, Blackberries, iPhones) and easy access to ICT functions (e.g., email, text, and voice messages) enable employees to continue working after leaving the office for the day (Porter & Kakabadse, 2006). This ease of access may have both positive and negative effects. Availability as a Demand Technology has been found to facilitate the ‘‘spillover’’ of work to the family domain (Rosen & Weil, 1997), which may result in decreased employee well-being (Kinnunen, Feldt, Geurts, & Pulkkinen, 2006). Furthermore, co-workers and clients may expect employees to be available and accessible outside of work hours (Porter & Kakabadse, 2006). This feeling of constantly ‘being connected’ can lead employees to perceive that they are never free from technology as well as the workplace (Ragu-Nathan et al., 2008). In an interview study, examining the use of mobile work phones, Lowry and Moskos (2005) found that some of the most common reports were that work cell phones ‘‘tethered’’ workers to the workplace and that they were ‘‘bodily appendages.’’ For example, one employee commented, ‘‘I have my work mobile phone next to me at the dinner table, but I shouldn’t. It’s like part of my body. Blasted thing!’’ (p. 8). These pressures may prevent employees from mentally detaching from work, which may result in increased stress and strain outcomes. Indeed, Sonnentag and Bayer (2005) reported that inability to mentally detach from work was associated with increased fatigue and negative mood.

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Availability as a Resource Conversely, having increased access to the workplace may allow employees greater flexibility and freedom, in terms of where and when they do their job. For example, employees who are often on-call for work (but who do not have to physically be at work) have reported that ICT devices such as cell phones actually increases the quality of their home life (Lowry & Moskos, 2005). Cell phones allow employees who were previously required to stay at home while on-call for work the freedom to plan family outings during their on-call hours. Similarly, managers report ICTs allow them the freedom to be accessible to new trainees if there are any problems while they are away from the workplace (Lowry & Moskos, 2005). For some individuals, knowing they can contact other employees easily allows them to feel more secure in their work roles and their ability to complete work tasks (Lowry & Moskos, 2005).

Access to Information Just as ICT can increase access between the workplace and employees, it can also increase employees’ access to information, which may have either positive or negative effects on employees. Increased information may lead to information overload and increased workload, or it may decrease workload by providing easier access to information and reducing time and travel to get information (e.g., reduce travel either to the workplace or to other sites housing required information). Access to Information as a Demand Although the initial intent of ICT was to increase and improve communication of information, ICT may also be a catalyst for stress by producing information and work overload. In addition to strain due to increased pressures to be accessible, much research has shown that increased demands at work can lead to decreased job satisfaction and increased strain (Day & Livingstone, 2001; Kelloway & Barling, 1991; for a review see Beehr, 2005). Therefore, the extent that ICT contributes to increasing one’s workload may be associated with increased employee strain. For example, although continual advances and upgrades to ICTs allow workers to complete their work more easily and quickly, it also can place greater productivity expectations on employees (Wang et al., 2008). Being overloaded with emails and having increasing demands from dealing with technology can create ‘‘technostress’’ (Rosen & Weil, 1997; Wood, 2001; Zuckerman, 1987). Porter and Kakabadse (2006) conducted interviews and

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focus groups with professionals from across the United States, the United Kingdom, and Germany and found that ICT largely encouraged and enabled working excess hours. Participants reported that ICT caused them to spend more time on the job than was required, to sacrifice family and life activities, and to persistently think about ICT-related work activities (e.g., emails) when away from work. Furthermore, A˚borg and Billing (2003) found that introducing electronic document handling systems to the workplace increased employee workload, decreased the amount of control employees had over their work, and increased reports of health problems (i.e., musculoskeletal disorders and stress-related mental and somatic symptoms). Access to Information as a Resource By definition, part of the purpose of ICT is to increase access to information and to create more effective information and communication transfer. ICT can increase access to information by improving employees’ ability to store and process information (Migliarese & Paolucci, 1995). ICTs such as email and work-related web pages have increased employees’ ability to create and share information with colleagues, clients, and supervisors. ICT enables employees to perform at a higher level by increasing their abilities to efficiently and effectively gather and analyze information (Dewett & Jones, 2001). Specifically, the advent of the Internet as an information source has revolutionized the way that employees collect data. Information gathering, fact checking, accessing directories, and ordering supplies are all workrelated activities made easier by having access to the Internet (Stevens, Williams, & Smith, 2000). Employees who have access to the Internet report having access to more information sources; as a result, they spend less of their time at work on information-gathering tasks and are able to work more efficiently (Stevens et al., 2000). This type of information efficiencies (i.e., the cost and time savings associated with ICT improved work performance) may also expand employees’ role within the organization (Dewett & Jones, 2001). Research has also indicated that increased employee information (i.e., empowerment structures) is associated with decreased job tension, increased work effectiveness, and increased psychological empowerment (Laschinger, Finegan, Shamian, & Wilk, 2001; Laschinger, Wong, McMahon, & Kaufmann, 1999).

Communication A common reason for introducing new ICT into the workplace is to create more effective and efficient ways for employees to communicate with other

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employees, clients, and suppliers. However, ICT may be a demand in that it creates ineffective communication and/or allows an opportunity for disrespectful or aggressive communication practices. Communication as Demand Although the intent of using ICT within the workplace is often to create effective mechanisms to communicate to others, it often becomes a conduit for miscommunication (unintentional or ineffective miscommunication; Ramirez, Walther, Burgoon, & Sunnafrank, 2002). ICT-assisted communication can also allow greater dissemination (and greater impact) of intentionally harmful or aggressive communications (intentional cyberaggression; Weatherbee & Kelloway, 2006). (1) Ineffective communication. Although computer-mediated communication such as email may improve the frequency and ease of communication, it may also lead to an increased probability of miscommunication (Ramirez et al., 2002). Given that email and text messages provide limited information regarding the tone or intonation of the message, it is not surprising that this form of communication has the greatest margin of error among all types of communication (e.g., face-to-face, phone calls; Rainey, 2000). Miscommunication resulting from email and text messages may lead to frustration and increased stress. Markus (1994) surveyed individuals regarding their email communication and concluded that approximately 72% of individuals received emails that angered them because of miscommunication and 36% of individuals indicated that they had misinterpreted emails. These types of miscommunications can lead to unintentional interpersonal conflict and stressful situations. There are also a number of situations in which ICTs are used to communicate information for the purposes of intentionally causing interpersonal conflict. (2) Cyber-aggression. ICTs are increasingly used to perpetrate acts of harm or aggression toward others. Labeled as ‘‘cyber-aggression’’ (Weatherbee & Kelloway, 2006), this form of aggressive behavior is similar to other forms of aggression in that it can be broadly defined as behaviors occurring in the workplace that are intended to harm others (e.g., Schat & Kelloway, 2005). However, the use of technology to enact these behaviors introduces at least three significant differences in the construct. First, most organizations provide widespread access to ICT. In effect, this means that members of the organization (and potentially those outside the organization) have direct electronic access to all other organizational members (Sproull, 1994) allowing for the potential mass distribution of harmful or aggressive messages (Weatherbee & Kelloway, 2006). Second, as

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a result of wide-spread access, the use of technology allows for the direct distribution of aggressive messages across organizational boundaries (Flynn & Khan, 2003). Finally, the capability when using email to ‘‘Bcc,’’ or ‘‘blind copy’’ to others, including organizational superiors, introduces the possibility of escalating conflict by bringing in third parties to witness aspects of the exchange without the knowledge of the other party (Weatherbee & Kelloway, 2006). Perhaps the most common form of cyber-aggression is the use of email to transmit harmful or otherwise inappropriate messages (Trombly & Holohan, 2000). Flaming, for example, is the intentional use of uncivil, often profane and intentionally insulting language typically sent through email (Siegel, Dubrovsky, Kiesler, & McGuire, 1986). Other forms of aggressive behavior have also involved email communication. The use of email for the transmission of sexually explicit images, for example, has been seen as a form of harassment (Solomon, 1999). The use of bulletin boards, blogs, or emails to express hostile or derogatory views of others is increasingly common within organizations (Rosman, 2002) and is frequently used as a form of ‘‘good-bye’’ from terminated employees (Post & Brown, 2003). Recipients of such messages frequently experience anger, negative affect (Alonzo & Aiken, 2002) and impaired task perception and decision making (Martin, Hiesel, & Valencic, 2001). The combination of these two responses may trigger a hasty negative response thereby initiating an electronic ‘‘incivility spiral’’ (Anderson & Pearson, 1999). Within a group, these behaviors can lead to polarization (Sia, Tan, & Wei, 2002), with subsequent negative consequences for the organizations (Rosman, 2002).

Communication as a Resource Compared to face-to-face communication, electronic-mediated communication has been shown to augment the total amount of communication within organizations (Dewett & Jones, 2001). Research evidence suggests that ICTs such as voice mail and email can enhance communication, collaboration, and information sharing among employees, supervisors, and subordinates (Lind & Zmud, 1995; Pickering & King, 1995; Zaccaro & Bader, 2003). As a result of increased communication and information sharing, ICT can decrease the amount of work ambiguity that employees experience. Furthermore, electronic communication devices (e.g., email) and ICT applications such as Groupware can help employees overcome common barriers to group work such as timing issues and communication by

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assisting group members’ interactions (Migliarese & Paolucci, 1995; Raghuram, Garud, Wiesenfeld, & Gupta, 2001). Dewett and Jones (2001) suggested that the most important benefit of ICT use within organizations is the improved ability to enable employees to link and coordinate with colleagues from across roles and departments within and external to the organization. Specifically, ICT can enable employees to coordinate their work tasks more effectively. For example, group decision support systems (GDSS) allow the coordination of work tasks, enables communication, and fosters cooperation among employees (Migliarese & Paolucci, 1995). ICT can be a particularly important resource for employees in organizations where supervisors, coworkers, and subordinates do not work out of the same location. Specifically, wireless and mobile ICT are particularly helpful in situations where employees work at different locations and at different times (e.g., construction sites). With multiple work site situations, ICT enables real-time information and knowledge sharing, thus increasing the ease in which work is accomplished and improving work performance (Nielsen & Koseoglu, 2007). For example, if the continuation of work at one site requires the decision approval from a supervisor who is located at the head office, electronic communication devices allow the immediate contact of the supervisor. Furthermore, there are times when problems arise at work sites in which the site employees are unable to solve. The uses of electronic communication devices in these situations allow for real-time consultation with supervisors or colleagues off-location to identify possible solutions, resulting in minimum disruption to the work process. Moreover, communication can be viewed as a mechanism for colleagues to provide social support, which has been associated with decreased employee burnout (Miller, Ellis, Zook, & Lyles, 1990).

Electronic Monitoring Over the past decade, ICT has been introduced as a means of employee monitoring. For example, employers may record the speed and accuracy of keystrokes, record telephone calls, monitor emails, and Internet use, observe employees using webcams, or view employees’ desktops through a remote computer (Miller & Weckert, 2000; Mishra & Crampton, 1998; Stanton & Weiss, 2000). In addition to monitoring employees while in their workspace, ICTs such as electronic badges and camera surveillance also allow

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organizations to monitor employees’ behaviors when they are away from their desk (Coovert & Thompson, 2003; Stanton & Weiss, 2000). Although organizations may view the use of ICT for employee monitoring purposes as helping to facilitate the provision of performance evaluations and rewards (Miller & Weckert, 2000), employees may view its use as a lack of trust and perceive the organization as controlling, thus leading to increased stress. Electronic Monitoring as a Demand Theory and research suggest that electronic performance monitoring results in employee stress and strain outcomes such as anxiety, depression, health complaints, anger, and fatigue (Amick & Smith, 1992; Lund, 1992; Schleifer & Shell, 1992; Smith, Carayon, Sanders, Lim, & LeGrande, 1992) especially if employees believe that the information gleaned from monitoring will result in negative repercussions (Stanton & Weiss, 2000). Electronic performance monitoring plausibly influences employee perceptions of job control, job demands, and social support, which may ultimately lead to increased employee stress. Furthermore, Levy (1994) found that employee monitoring could result in stressful work conditions by increasing employee perceptions of workload and social isolation and contributing to fear of job loss and lack of job control. Increased psychological stress and low employee morale may also result from the infringement of one’s personal space and privacy associated with security monitoring (e.g., Coovert & Thompson, 2003; Fairweather, 1999). Electronic Monitoring as a Resource Although electronic monitoring is typically discussed as a means of control or close supervision, and therefore is considered a demand, a closer reading of the literature suggests that the implementation of monitoring practices has also been associated with more positive effects. For example, in a laboratory study Aiello and Kolb (1995) reported a social facilitation effect whereby the task performance of highly skilled individuals entering data was enhanced by the implementation of electronic performance monitoring. Although monitoring was also associated with stress in this study, monitoring at the group (as opposed to individual) level was associated with less stress. Consistent with the social facilitation framework, Davidson and Henderson (2000) showed that in a similar task, electronic monitoring was associated with increased positive mood when participants were performing a simple task. These results suggest that the manner in which electronic performance monitoring is implemented is an important determinant of its outcomes. This conclusion is echoed by Adler and

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Tompkins (1997) in their review of the electronic monitoring literature. They suggested that electronic monitoring is likely to result in positive results when (a) employees are involved in the design and implementation of electronic monitoring systems and procedures, (b) monitoring is focused on performance-related activities, and (c) monitoring results in supportive feedback to employees.

ICT Control This type of input and involvement is also reflected in the construct of control. The extent to which employees feel that they have control over how and when they use ICT to assist with the completion of their work will impact their view of ICT as either a demand or a resource. Individuals who do not have control over their access, and use of, ICT may experience increase stress. Conversely, individuals who have control over ICT will have greater flexibility in their work and more opportunities for work-life balance. Lack of Control as a Demand Research has consistently found that actual or perceived lack of job control is associated with increased strain (Barling & Kelloway, 1996; Day & Jreige, 2002; Dwyer & Ganster, 1991; Elass & Veiga, 1997; Jonge, Dollard, Doramann, LeBlanc, & Houtman, 2000; Lui, Spector, & Jex, 2005, Piotrkowski, Cohen, & Coray, 1992; Spector & Jex, 1991). People who perceive they do not have control over their environment tend to experience higher levels of strain (Dwyer & Ganster, 1991). Therefore, a lack of control over one’s use of ICT (in terms of how and when it can be used) may also have similar negative effects. In fact, Hair, Renaud, and Ramsay (2007) found that lack of control over email and one’s general accessibility to others was related to having a stressed orientation toward email. That is, those who felt they had a lack of control over their communication with others felt the need to respond to emails immediately, which resulted in feelings of stress. Furthermore, Coovert and Thompson (2003) suggested that technological malfunctioning and breakdowns can also negatively influence feelings of control that may result in stress and anxiety for the user. Finally, employees who are encouraged to participate in the implementation of new technology (i.e., have some control over the process of introducing new technology) tend to experience less strain than those who are not involved (Coovert & Thompson, 2003).

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Control as a Resource Conversely, increased control over access to, choice of, and use of ICT in the workplace can provide more flexibility to employees and may be perceived as a resource and be associated with positive individual outcomes. In general, the literature on job control has demonstrated that greater work schedule flexibility results in lower levels of perceived stress, increased employee morale, autonomy, and job satisfaction (Sparks, Faragher, & Cooper, 2001). Employees who have the ability to control when electronic performance monitoring systems are activated report a greater sense of control and higher levels of performance (Stanton & Barnes-Farrell, 1996). Standen et al. (1999) suggested that having control over ICT that permits telework provides employees with more control over their work schedule than other common work accommodations (i.e., part-time work, flextime). Similarly, ICT can allow greater flexibility for workers in terms of where they are able to complete their work. For example, having access to virtual private network technology allows workers to access their work computers from home, and email and cell phones allow employers to stay in contact with employees when they do work outside of the office. Having access to this type of technology gives employees more opportunities to balance their work responsibilities with their home responsibilities. Having options such as telecommuting, virtual meetings, and teleconferences, employees can avoid the commute to the workplace and use ICT to complete important work, while spending more time with their family. Improved work–life balance is one of many potential advantages for employees who have control over their access to ICT. Standen et al. (1999) suggested that employees who have more flexibility and control over their work schedules due to the use of ICT are more likely to report improved general quality of life (e.g., more access to leisure activities), improved employee psychological functioning at work, improved work performance, decreased time-based role conflict, and increased family support. These positive effects of ICT-assisted work may also improve employee health and well-being. Thus far, we have described five characteristics of ICT that depending on the function of ICT within the workplace can assist with the completion of work and increase employee well-being or impede on the work process and be viewed as an additional source of stress. Although ICT demands can increase employee stress and levels of strain, various factors may mitigate the negative effects of ICT demands.

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MODERATORS OF THE ICT DEMANDS–STRAIN RELATIONSHIP A number of organizational, job, and individual characteristics may moderate the relationship between ICT demands and employee strain, such that they buffer the demand–strain relationship or exacerbate it. For example, having control over one’s job may help alleviate the negative impact of ICT demands, by providing the employee with more options for managing the demands. Similarly, the manner in which the organization supports and encourages the use of ICT can buffer the demand–strain relationship. In addition, previous research has identified individual characteristics of the employee that may influence the extent that ICT demands produce negative employee outcomes. Job-Related and Organizational Moderators Job Control Providing employees more control over various aspects of their jobs, such as increasing decision-making capabilities and work autonomy, may allow employees to exert more influence over potentially stress-provoking areas of their work life (Dwyer & Ganster, 1991), such as ICT demands. Employees who have a high degree of perceived control over their job tend to experience decreased stress and strain outcomes (Boswell, Olson-Buchanan, & LePine, 2004) and increased job and life satisfaction (Day & Jreige, 2002). In addition to the direct benefits of reducing strain, job control has been found to moderate the relationship between stressors and employee outcomes (e.g., blood pressure, Fox et al., 1993; emotional exhaustion, Day, Sibley, Scott, Tallon, & Ackroyd-Stolarz, 2009). Therefore, it is plausible that providing employees with more control over their job could also buffer the negative effects of ICT demands. Organizational Support There are a large number of studies indicating that organizational support is related to increased employee satisfaction (Patrick & Laschinger, 2006; Rhoades and Eisenberger, 2002) and positive mood (Rhoades & Eisenberger, 2002). Moreover, organizational support has been shown to buffer the negative impact of several organizational stressors such as workplace aggression (Schat & Kelloway, 2003) and family–work conflict (Witt & Carlson, 2006), both of which can be discussed in terms of ICT demands

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(i.e., ineffective communication and constant accessibility). Therefore, it is plausible that organizational support of employees’ use of ICTs could also buffer the negative impact of the ICT demands described earlier. There are a number of specific ways that organizations can support their employees’ use of ICT at work. For example, training employees in the current technologies required to do their job organizations may reduce stress and strain experienced by employees. Salanova and Schaufeli (2000) found that an individual’s attitude toward technology mediates the relationship between ICT exposure and employee burnout. Specifically, high exposure to technology leads to more positive appraisals of technology, which in turn lead to decreased symptoms of burnout (i.e., employees were less cynical and had higher self-confidence and sense of goal attainment). Korunka and Vitouch (1999) also found that employees who were involved in the implementation of new technologies and who are properly trained (e.g., received qualifications and more than eight hours training) to use the technology experience less stress, strain, and dissatisfaction. Beas and Salanova (2006) suggested that training could be used to boost the selfefficacy of employees and ultimately reduce their perceived stress and strain. In addition to individual training, organizations can offer support to employees by providing them with organizational-based technical support. The amount of technical support provided to employees may minimize perceived stress. Good technical assistance may lead to fewer technical problems, enhanced productivity (Ragu-Nathan et al., 2008), and increased employee well-being. Finally, organizational recognition of ICT skills can help employees feel appreciated for the work they do in mastering new technology.

Individual Characteristics as Moderators In addition to these job-specific and organizational factors that can buffer the negative effects of ICT demands, there may also be a number of individual characteristics that influence the extent that ICT demands are perceived as stressful and lead to increased strain. However, little research has examined the direct or moderating impact of individual characteristics on ICT stress in the workplace. Much of the previous ICT research involving individual differences has focused on individual’s acceptance and usage of technology, including their attitudes toward it (see the O’Driscoll et al. chapter for an overview). However, it is also important to examine whether these characteristics can exacerbate or

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mitigate the impact of technology on employees stress, well-being, and performance. Despite the lack of research that has examined individual characteristics and ICT-related stress in the workplace, the TAM and ICT usage literature has identified societal and individual factors that impact one’s use of technology (e.g., Venkatesh & Davis, 2000). When examining ICT in an employment situation, in which ICT use may not be discretionary, ‘‘ICT usage’’ may not be the appropriate criterion. However, this literature provides a good basis for developing a research agenda to identify individual characteristics that impact the relationship between ICT demands and employee well-being and performance. For example, in addition to demographic variables (e.g., age and gender), having the required skills to use ICT, being more proficient at using ICT, and having a positive attitude toward technology all may be ‘‘enabling factors’’ when using new technologies (Selwyn, 2003, p. 103). Therefore, we review the literature on the direct and moderating impact of (1) age and gender, (2) self-efficacy, (3) neuroticism and anxiety, (4) flexibility and adaptability, and (5) openness to experience and personal innovativeness. Age and Gender Some research has found that older individuals perceive ICT as more difficult to use (Burton-Jones & Hubona, 2005). However, the few studies on ICT stress and age have been mixed. For example, Hudiberg and Necessary (1996) found that age is not related to computer-related stress; However, Ragu-Nathan et al. (2008) found that older individuals experienced lower technostress. They suggested that perhaps older workers are more able to handle high stress in general. Gender differences have been found not only in the usage of technology but also in their experience of ICT. Compared to women, men tend to report that software is easier to use (Gefen & Straub, 1997) and they are more likely to use computers at work (Venkatesh & Morris, 2000). Although several studies found that women tend to report higher anxiety using technology than do men (Igbaria & Chakrabarti, 1990; Rosen & Maguire, 1990; Whitley, 1997), Ragu-Nathan et al. (2008) found that men experienced more technostress than women. Self-Efficacy Perceived self-efficacy involves people’s belief regarding their ability to reach a desired outcome (Bandura, 1997). As such, individuals with high selfefficacy are more confident in their ability to effectively manage situations

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and will be less inclined to view situations as stressful. Although research has shown that general self-efficacy is an important moderator in the stress process (Grau, Salanova, & Peiro´, 2001; Jex & Bliese, 1999; Salanova, Grau, Cifre, & Llorens, 2000), much less research has examined the potential buffering effects of self-efficacy on the relationship between ICT demands and individual strain. Moreover, to better understand the relationship between self-efficacy and ICT demands and strain, it may be worthwhile to look at specific selfefficacy beliefs pertaining to ICT. For instance, Salanova, Grau, and Martı´ nez (2006) have suggested that the moderating effects of self-efficacy are best understood when efficacy beliefs are specific to the area of interest. In this regard, computer self-efficacy has been conceptualized as a multilevel construct including a general computer self-efficacy (GCSE) component, which describes efficacy beliefs that span across all computer domains, and an application-specific self-efficacy (ASSE), which describes efficacy beliefs that are specific to an application or system (Marakas, Yi, & Johnson, 1998). Research suggests that high GCSE can help decrease burnout, depression, anxiety (Beas & Salanova, 2006) and anger (Wilfong, 2006) associated with computer use. Not surprisingly, individuals with high levels of ASSE tend to perform better than individuals with low ASSE (Johnson, 2005). Furthermore, some research has indicated that computer self-efficacy is associated with lower anxiety and computer-phobia (Compeau & Higgins, 1995). Salanova et al. (2000) conducted one of the few studies to investigate the moderating role of self-efficacy in the relationship between ICT demands and negative health outcomes. They found GCSE moderated the relationship between forms of technology exposure (i.e., increased frequency of computer training) and burnout (Salanova et al., 2000). Specifically, individuals who received additional computer training and who had high GCSE showed a decrease in exhaustion and cynicism. Conversely, individuals who received additional computer training and who had low GCSE reported increased levels of exhaustion and cynicism. Neuroticism and Trait Anxiety Previous research has demonstrated a moderating effect of neuroticism in the relationship between organizational stressors (e.g., conflict, alienation) and job stress (Nasurdin, Ramayah, & Kumaresan, 2005). Korukonda (2005) found that neuroticism was positively correlated with technophobia. Therefore, we may also expect neuroticism to exacerbate the relationship between ICT demands and strain outcomes. That is, given a high level of

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demands (e.g., hassles, overload), individuals with high levels of neuroticism may experience higher strain symptoms compared to individuals with low levels of neuroticism. Related to the concept of neuroticism is trait anxiety, and more relevant to the ICT literature, ICT anxiety. ICT anxiety is defined as anxiety about the implications of using ICTs (Thatcher & Perrewe´, 2002) and is an immediate physical reaction to imaginary or actual ICT use (Bozionelos, 2001; Ragu-Nathan et al., 2008). Anxiety is related to self-efficacy, and as such, it is expected to play a similar role to self-efficacy in the ICT demandnegative outcome relationship. Doronina (1995) identified various forms of specific ICT anxiety. For example, individuals may experience specific anxiety from fears of breaking the technology, from being ignorant of the technology, or of various perceived health threats from using the technology. More recently, conceptualizations of ICT anxiety have included a ‘‘time panic’’ component (Wang et al., 2008), which includes a fear of not completing the ICT-dependent task in the allotted time. Similar to the effects of low efficacy, the underlying fear of using technology may exacerbate the experience of some ICT demands and thus result in increased strain. For instance, the ever-increasing complexity of ICT is often perceived as a demand because employees associate increased complexity with increased workload (Ragu-Nathan et al., 2008). An employee who experiences high ICT anxiety may perceive this demand as more stressful than an employee with low levels of ICT anxiety, due to their fear of using technology. As a result, individuals with high ICT anxiety are likely to experience more negative health outcomes. It is likely that the time-panic component of ICT anxiety also may exacerbate the negative effects of some ICT demands. Specifically, individuals with high ICT anxiety may perceive the demand of employee monitoring as particularly stressful and as a result experience more negative health outcomes than someone with low ICT anxiety. Adaptability and Flexibility Employees’ level of flexibility or adaptability to changes due to technology may be another important moderating construct to consider. Pulakos, Arad, Donovan, and Plamondon (2000) developed a taxonomy of adaptive job performance. Three of their adaptive performance facets may be relevant when introducing new ICT: solving problems creatively, dealing with uncertain situations, and learning. They suggested that ‘‘cognitively oriented constructs may be important in predicting adaptive performance dimensions’’ such as learning new technologies (p. 622). Therefore,

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individual levels of adaptability and flexibility may be important to the ICT stress model. Openness to Experience and Personal Innovativeness Openness to experience is a well-studied personality trait that is related to flexibility and adaptability. Although Ouadahi (2008) argued that few studies have examined the relationship between openness and ICT use and acceptance, it is feasible that openness to learning and new experiences may be an important predictor. In one of the few studies in this area, Korukonda (2005) found that openness to experience was negatively correlated with technophobia. Surprisingly, however, Hudiburg et al. (1999) found that openness to experience was positively correlated with computer hassles. Related to openness to experience is the construct of personal innovativeness in information technology, which is defined as ‘‘the willingness of an individual to try out any new information technology’’ (Agarwal & Prasad, 1998, p. 383). Similar to openness, it is conceptualized as a stable trait; however, it is situation-specific in that it pertains only to ICT. An employee’s openness and willingness to try new ICTs and to ‘‘play around’’ with them may be an especially important characteristic for helping employees cope with the lack of control they may feel over the new ICT.

DISCUSSION AND AGENDA FOR FUTURE RESEARCH It is projected that the use of ICT in the workplace will only continue to increase in the coming years (Coovert & Thompson, 2003). New applications of technology are continuously being adopted in a wide variety of occupational settings. Technology is being used to facilitate learning in schools (Crisp, Lewis, & Robertson, 2006), to communicate with patients in the medical realm (Cartwright, Gibbon, McDermott, & Bor, 2005; Egan, Chenoweth, & McAuliffe, 2006; Goodyear-Smith, Wearn, Everts, Huggard, & Halliwell, 2005), and to deliver mental health interventions (Chester & Glass, 2006). Consequently, it seems likely that the use of ICT in the workplace will only continue to increase in the coming years. In light of the potential negative outcomes associated with the use of ICT, organizations may want to better understand the impact of ICT on employees, and consequently, implement practices that facilitate the use and application of existing and new technologies.

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Agenda for Future Research Despite the increasingly rapid adoption of ICT in organizations, research on the positive and negative consequences of technology use is in many respects in its infancy. Therefore, our predominant suggestion is for more research on the individual and organizational outcomes associated with ICT. Moreover, several specific avenues of research are likely to prove most fruitful in examining the effects of ICT in the workplace. First, as is apparent from the preceding review, we believe that the JD-R provides an appropriate framework for the examination of such effects. Most importantly, the JD-R framework directs our attention to both the positive (resources) and negative (demand) aspects of ICT. Adopting this framework moves us beyond the simplistic consideration of the ‘‘stress’’ associated with ICT to the consideration of the specific features of ICT that may be harmful or beneficial. No matter what the outcome of behavioral research, it is unlikely that we will convince the world to halt the adoption of ICT. Researchers can have the greatest impact on organizational practices by identifying the specific features of ICT use that minimize harm and maximize the individual and organizational benefits of ICT. We suggest that the JD-R framework provides the theoretical basis for research to move in that direction. On the basis of the JD-R model, we have identified several aspects of ICT that may be perceived as a demand. A lack of control over ICT and being obliged to stay connected during non-work times may increase strain. Strain may also arise if the use of ICT creates more work for employees or introduces communication problems (i.e., simple ineffective communication to more intentional misuse of ICT through cyber-aggression). Finally, the rapid pace of ICT advancements introduces increased expectations for continuously learning how to use new ICT, and the increased potential for encountering problems and hassles while using ICT has the potential to result in negative employee outcomes. In addition to the individual interactions with ICT, the manner in which organizations choose to use ICT (e.g., employee monitoring) can also produce demands on employees. To the extent that researchers have become interested in the use of ICT in organizations, they have overwhelmingly focused on the potential negative effects of adopting technology. We have suggested that ICT may also be seen as a resource, but this claim rests on a less well-established empirical base than does the claim that ICT may be seen as a demand. Nonetheless, we have identified several aspects of ICT that may act as resources. ICT may

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ICT FACTORS • Hassles Incompatibility Security Learning expectations • Accessibility/Availability On-call 24/7 Flexibility in workplace/scheduling • Access to information Information overload Improved access to information Decreased workload • Communication Ineffective communication Cyberaggression Improved communication, collaboration, & information access Decreased workload • Electronic Monitoring Evaluative Electronic Monitoring Feedback • Amount of control Increased accessibility/workload Increased flexibility

Fig. 1.

ORGANIZATIONAL & JOB FACTORS • Organizational support • Control OUTCOMES PERCEPTION

• Strain • Burnout • Engagement • Performance

• Demands • Resources

INDIVIDUAL FACTORS • Age & Gender • General efficacy • ICT efficacy • Neuroticism & Trait Anxiety • Flexibility & Adaptability • Openness to experience & Personal Innovativeness

ICT Model Based on the Transactional Model of Stress and the Job Demands–Resources Model.

help to increase control and flexibility of work. It may also increase access to information and improve communication and collaboration among employees. Finally, both organizational support and general autonomy at work may buffer the negative effects of ICT demands on employee strain outcomes. We have summarized the proposed relationships among the ICT factors and employee outcomes in Fig. 1. The second general avenue of research focuses on methodological issues; most of the extant research has relied on cross-sectional data. Given the exploratory nature of much of this research, such reliance is predictable and perhaps even desirable. However, we suggest it is now time to move beyond a sole reliance on cross-sectional data to a consideration of longitudinal relationships (Rosel & Plewis, 2008; Zapf, Dormann, & Frese, 1996). This focus on methodology is important because individual experiences of ICT may change dramatically over time. Technological innovations that were novel less than 20 years ago (e.g., cell phones, GPS) are now part and parcel of everyday life. The available data suggest that the adoption of technology might be particularly stressful at first (Porter & Kakabadse, 2006) until users become accustomed to the new way of working. In terms of the JD-R, this pattern suggests that what might be initially considered a ‘‘demand’’ may become a resource as individuals learn to use and adapt to new technology.

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Understanding this trajectory is only possible through longitudinal data and is a worthwhile objective for future research. In focusing on longitudinal data, it may also be useful to consider the possibility of non-linear relationships or trajectories. Although rarely considered in organizational research, we suggest that it is important to consider non-linear relationships for at least two reasons. First, as implied earlier the role and impact of ICT may be substantially altered simply as a function of experience, and it will be important to understand those dynamics. Second, with successive generations of ICT communication becomes faster and information more easily accessible. Although our technological capacities increase geometrically, we are not convinced that the same increase will be manifested in our ability to respond to increased technological demands. Identifying the boundary points beyond which technological innovation impairs our ability to communicate and work with information is, we suggest, an important role for social science and organizational researchers. The last avenue of research addresses advances in technology. We note that much of the extant research has emerged in reaction to the adoption of technology (i.e., after concerns regarding ICT usage have begun to emerge). We suggest that there is considerable value in researchers focusing on the leading edges of ICT to prospectively predict potential effects. Identifying emergent technology may be somewhat speculative but is not impossible (see Coovert & Thompson, 2001 for a discussion of emergent technology). Moreover, the adaptation of a well-established framework such as the JD-R many enable researchers to predict the most likely consequences of adopting new ICT drawing on the extant empirical literature.

Concluding Remarks Kranzberg’s (1986) first law states that technology is neither good nor bad nor neutral. In the current context, we suggest that Kranzberg’s law can be interpreted as saying that technology has both positive and negative consequences for individuals and organizations. We have suggested that this view is consistent with the propositions of the job demands– resources model and that it is now time to move past a simple view of technology as a stressor to a more nuanced view specifying the conditions under which ICT comprises a demand or a resource. It is our hope that such a consideration will lead the more effective adoption of technology in organizations.

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ABOUT THE AUTHORS Julian Barling received his PhD in 1979 from the University of the Witwatersrand (South Africa) and is currently associate dean with responsibility for the graduate and research programs. Julian is the author/editor of several books, including Employment, Stress and Family Functioning (1990, Wiley) and The Psychology of Workplace Safety (1999, APA). He is senior editor of the Handbook of Work Stress (2005, Sage) and the Handbook of Organizational Behavior (2008, Sage), and he is the author of well over 150 research articles and book chapters. Julian was formerly the editor of the Journal of Occupational Health Psychology. In 2002, Julian received the National Post’s ‘‘Leaders in Business Education’’ award and Queen’s University’s Award for Excellence in Graduate Student Supervision in 2008. He is a fellow of the Royal Society of Canada, SIOP, APS, and the European Academy of Occupational Health Psychology. He is currently involved in research on leadership, work stress, and workplace aggression. Nathan A. Bowling, who earned a PhD in industrial and organizational psychology from Central Michigan University in 2005, is an assistant professor at Wright State University. His research interests include job attitudes, counterproductive and deviant work behaviors, and occupational stress. Nathan’s research has been published in top scholarly journals, including the Journal of Applied Psychology, Journal of Occupational and Organizational Psychology, Journal of Occupational Health Psychology, and Journal of Vocational Behavior. Paula Brough, PhD, is associate professor in the School of Psychology, Griffith University, Australia, and director of the Social and Organizational Psychology Research Unit. Paula’s research encompasses the evaluation and enhancement of occupational psychological health, with specific interests in occupational stress, coping, and work-life balance. Specifically, Paula’s research focuses on two main categories: (1) reducing experiences of occupational stress within the high-stress industries and (2) enhancing individual health and organizational performance. Paula has produced over 50 publications describing her research primarily with the police, emergency services, and corrections industries. In 2009, this work was condensed into a 351

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book published by Edward Elgar: Workplace Psychological Health. Paula is a member of journal editorial boards, serves as an academic reviewer, and regularly presents her work to both academic and industry audiences. Chu-Hsiang Chang is an assistant professor in the Department of Environmental and Occupational Health at the University of South Florida. She received her PhD in Industrial/Organizational Psychology from the University of Akron. Her research interests include occupational stress, workplace violence, organizational politics, leadership, and employee motivation. She has published papers in journals such as Psychological Bulletin, Academy of Management Journal, Organizational Behavior and Human Decision Processes, and Work & Stress. Arla Day received her PhD from University of Waterloo in 1996. She is the Canada research chair and professor of Industrial/Organizational Psychology at Saint Mary’s University and a fellow of the Canadian Psychological Association. Arla is a founding member of two research and community outreach centers: The CN Centre for Occupational Health and Safety and the Centre for Leadership Excellence, and she chairs the Nova Scotia chapter of the APA’s Psychological Healthy Workplace Program committee. Arla has authored many articles, presentations, and chapters on occupational stress, employee well-being, work-life balance, emotional intelligence, stress interventions, and healthy workplaces. Emilija Djurdjevic is a PhD student in the Management department of the Sam M. Walton College of Business at the University of Arkansas. Her current research interests include leadership, organizational politics, decision-making, and work stress. Erin Eatough is a doctoral student in Industrial/Organizational Psychology with a concentration in Occupational Health Psychology at the University of South Florida. She is interested in how occupational stressors impact psychological and physical health. She also studies the relationships between job stress and performance outcomes, such as safety performance. She has published her research on hormonal responses to stress in Psychoneuroendocrinology and she has presented her work at a variety of academic conferences such as the Society for Industrial/Organizational Psychology, Work, Stress, and Health, and the International Society of Psychoneuroendocrinology. Achim Elfering is an associate professor in the Department of Work and Organizational Psychology, University of Bern. In 1993, he graduated with a Masters degree from the University of Wuerzburg, Germany. In 1997, he

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received his PhD at the University of Frankfurt, Germany. Much of his work addresses work stress, occupational back pain, and safety at work. This research has been internationally recognized by the Spine Journal Young Investigator Research Award in 2001. Furthermore, Achim Elfering is interested in positive work experiences, job satisfaction, and work socialization. Achim Elfering is affiliated with the Swiss Centre of Competence in Research on Affective Sciences. Achim Elfering is involved in research, university teaching, and teaching and consulting outside the university. Kevin J. Eschleman is a PhD candidate at Wright State University. His research interests include personality, occupational stress and well-being, and the role of stereotypes and prejudice in the workplace. Kevin is under the advisement of Dr. Nathan Bowling and has worked on research projects published in the Journal of Applied Psychology, Journal of Occupational Health Psychology, Work & Stress, and Journal of Occupational and Organizational Psychology. Edwin Farrell is Professor Emeritus of Education at the City College of New York. His books, Hanging in and Dropping Out and Self and School Success, offer a qualitative researcher’s analysis of the lives and stresses of urban adolescents. Adel Assal and Farrell’s ethnography, ‘‘Attempts to make meaning of terror’’ (Anthropology and Education Quarterly) was based on data collected during the Lebanese civil war. Farrell’s latest publication was IN/ACTION, a novel (Virgilius Press). He received his EdD from the Laboratory for Human Development of Harvard University. Lori Francis has a PhD in Industrial/Organizational Psychology from the University of Guelph. Lori is an associate professor in the Department of Psychology at Saint Mary’s University in Halifax, Nova Scotia. Dr. Francis has broad research interests in occupational health psychology including work stress, aggression, and workplace fairness. Dr. Francis is a member of the CN Centre for Occupational Health and Safety and serves on the Board of Directors for the Nova Scotia Health Research Foundation. She is also an active member of the Nova Scotia Psychologically Healthy Workplace Awards program. Simone Grebner is professor of Applied Psychology within the School of Applied Psychology at the University of Applied Sciences Northwestern Switzerland. She studied in Wurzburg (Germany) and received her PhD in 2002 in Work and Organizational Psychology from the University of Bern (Switzerland). She previously held appointments at the Federal Institute of

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Technology (Zurich, Switzerland), the University of Munich (Germany), the University of Fribourg (Switzerland), the University of Bern (Switzerland), and at Central Michigan University (USA). Her current research interests focus on chronic and situation-related job stressors and resources, coping with job stress, evaluation of stress- and self-management trainings, and physiological stress responses. One of her specific current interests is the nature and effects of subjective success experiences for the employee. Simone Grebner is involved in research, university teaching, and teaching and consulting outside the university. Michelle Inness is an assistant professor at the University of Alberta School of Business. Broadly speaking, her research focuses on well-being at work and includes topics such as workplace aggression, workplace safety, justice, emotional labor, and most recently, the love of one’s job. Her research has been published in journals such as the Journal of Applied Psychology and the Journal of Business Ethics. She has an interest in using unique research designs and conducting research that bridges knowledge and perspective from different disciplines, as illustrated in her work on love of one’s job. Dr. Steve Jex is currently associate professor of Industrial/Organizational Psychology at Bowling Green State University and guest scientist at the National Institute for Occupational Safety and Health (NIOSH). He has also held faculty positions at Central Michigan University and the University of Wisconsin Oshkosh and a guest scientist appoint with Walter Reed Army Institute of Research. Dr. Jex received his PhD in Industrial/ Organizational Psychology from the University of South Florida and has spent most of his postdoctoral career conducting research on occupational stress. His research has appeared in a number of scholarly journals including Journal of Applied Psychology, Journal of Organizational Behavior, Journal of Occupational Health Psychology, Journal of Applied Social Psychology, and Work & Stress. He also serves on three editorial board, and has been associate editor of Journal of Occupational and Organizational Psychology. In addition to his research and editorial activities, Dr. Jex is the author of two books: Stress and Job Performance: Theory, Research, and Implications for Managerial Practice and Organizational Psychology: A ScientistPractitioner Approach. Jason Kain is in his fourth year of graduate school at Bowling Green State University. His research specialization is occupational health, and he has researched a wide variety of topics including incivility, recovery from work, and intrusions at work. He also does research examining the relationship

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between theories of work stress and motivation. Jason has first authored a grant, has 2 book chapter publications, 4 articles under review, and 10 conference presentations. Jason has also been involved with variety of applied Industrial/Organizational Psychology projects including evaluating a scholarship fund, doing a culture assessment, and additionally, Jason has had two internships in Industrial/Organizational Psychology: one at Human Resources Research Organization (HumRRO) in Minneapolis, MN, and one at the Walter Reed Army Institute for Research (WRAIR) in Silver Spring, MD. E. Kevin Kelloway received his PhD from Queen’s University in 1991 and is the Canada Research Chair in Occupational Health Psychology as well as the Director of the CN Centre for Occupational Health and Safety at Saint Mary’s University. An active researcher he authored over 100 articles and book chapters in addition to authoring/editing 10 books. He is a fellow of the Society for Industrial/Organizational Psychology and of the Association for Psychological Science. His current research interests include the role of leaders in occupational health and safety as well as issues related to workplace violence. Michael P. O’Driscoll, PhD, is professor of Psychology at the University of Waikato, Hamilton, New Zealand, where he has taught courses in organizational psychology since 1981 and convenes the post-graduate program in organizational psychology. He has a PhD in Psychology from the Flinders University of South Australia. His primary research interests are in the fields of job-related stress, coping and psychological well-being, and work-life balance. More generally, he is interested in work attitudes and behaviors and the relationship between work and health. He has published empirical and applied journal articles on these and other topics in organizational psychology and is co-author of 6 books and around 30 book chapters. He has served on the editorial boards of several academic journals and was editor of the New Zealand Journal of Psychology (2001– 2006). He has provided consulting services to a number of organizations, with a particular focus on work and well-being. Christopher C. Rosen is an assistant professor in the Sam M. Walton College of Business at the University of Arkansas. He received his PhD in Industrial/Organizational Psychology from the University of Akron. His current research interests include organizational politics, employee–organization exchange relationships, organizational justice, personality and individual differences, and feedback processes in organizations. His

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publications have appeared in journals such as Academy of Management Journal, Journal of Applied Psychology, Journal of Organizational Behavior, and Organizational Behavior and Human Decision Processes. Sukanlaya Sawang earned her MA (Experimental Psychology) at the University of Central Oklahoma, USA, and PhD (Innovation Management) at Queensland University of Technology, Australia. Upon her completion of doctoral studies, she joined the School of Psychology at Griffith University in Australia as a postdoctoral research fellow and project manager. Dr. Sawang is currently a lecturer in the School of Management, Faculty of Business, at Queensland University of Technology. Her research focuses on cross-cultural studies that include occupational stress and well-being, work engagement, technology, and innovation implementation. Dr Sawang’s research has been published in academic journals in both psychology and management disciplines, such as Applied Psychology: An International Review, International Organizational Behavior, and International Journal of Cross Cultural Management. Her journal article was also selected for the Emerald Management Reviews Citation of Excellence Award. Irvin Sam Schonfeld is a professor of Psychology at the City College (CCNY) and the Graduate Center of the City University of New York. He completed his doctoral work in psychology at the City University’s Graduate Center and earned a postdoctoral degree in epidemiology at Columbia University. Professor Schonfeld has published in the Journal of Occupational Health Psychology, Genetic, Social, and General Psychology Monographs, Journal of Abnormal Child Psychology, the Archives of General Psychiatry, Developmental Psychology, Child Development, and Organizational Research Methods. His research interests include occupational stress in teachers, youth psychopathology, the psychology of teaching evolution, and applied statistics. He has also written a memoir, Not Quite Paradise, about growing up in a New York City housing project. Natasha Scott is a part-time faculty member and PhD candidate in Industrial/Organizational Psychology at Saint Mary’s University. She specializes in occupational health and safety research and has experience conducting applied research in various industries including construction, offshore oil, and healthcare. She has particular knowledge of safety culture/ climate, patient safety, and occupational stress. Natasha is a member of the CN Centre for Occupational Health and Safety, a center of research excellence at Saint Mary’s University. She is also a member of the Nova Scotia Psychologically Healthy Workplace Program Committee.

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Norbert K. Semmer is professor of the Psychology of Work and Organizations at the University of Bern, Switzerland. He studied psychology in Regensburg (Germany), Groningen (The Netherlands), and Berlin (Germany) and received his PhD from the Technical University of Berlin in 1983. His major interests refer to (1) stress at work and its implications for health and productivity; (2) efficiency in work behavior: its characteristics and its training; and (3) human error and its implications for quality and safety. His current work focuses on the concept of Stress as Offense to Self, largely within the context of the Swiss Centre for Affective Sciences. He is member of the editorial boards of several journals in Work Psychology (e.g., European Journal of Work and Organizational psychology) and Occupational Health (e.g., Journal of Occupational Health Psychology, Scandinavian Journal of Work, and Environment and Health). Norbert is affiliated with the Swiss Centre of Competence in Research on Affective Sciences. Norbert is involved in teaching at the university, research, and teaching/consulting outside the University. Carolyn Timms, BA(Ed) BPsych (Hons), PhD, is a postdoctoral research fellow on Work-Life Balance research project led by Associate Professor Paula Brough at Griffith University. Before her academic career, Carolyn had a rewarding career as a high school teacher. Her previous experience as an employee has provided her an appreciation of the importance of researching the topography of workplace relationships. In addition, while completing her PhD, Carolyn was employed as a Senior Research Officer on the ARC-funded ‘‘Girls and ICT’’ project. This experience confirmed her love of academic research. She has published journal articles and book chapters in the Educational and Organizational Psychology domains. Most recently, she received a highly commended award from the Emerald Literati Awards for Excellence for a paper that combined both areas of her expertise. Nick Turner (PhD, Sheffield) is associate dean (research) at the Asper School of Business, University of Manitoba, Winnipeg, Canada. His research interests lie in the area of occupational health psychology, with a primary focus on the psychosocial predictors of psychological and physical well-being at work. His current projects include socio-moral dimensions of transformational leadership and the social construction of workplace safety. His research has appeared in Journal of Applied Psychology, Journal of Occupational Health Psychology, and Journal of Business Ethics.