Research in Personnel and Human Resources Management, Volume 29

RESEARCH IN PERSONNEL AND HUMAN RESOURCES MANAGEMENT RESEARCH IN PERSONNEL AND HUMAN RESOURCES MANAGEMENT Series Edit...

Author: Hui Liao

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RESEARCH IN PERSONNEL AND HUMAN RESOURCES MANAGEMENT

RESEARCH IN PERSONNEL AND HUMAN RESOURCES MANAGEMENT Series Editors: Hui Liao, Joseph J. Martocchio and Aparna Joshi Recent Volumes: Volumes 1–10:

Edited by Kendrith M. Rowland and Gerald R. Ferris

Volumes 11–20: Edited by Gerald R. Ferris Supplement 1:

International Human Resources Management – Edited by Albert Nedd

Supplement 2:

International Human Resources Management – Edited by James B. Shaw and John E. Beck

Supplement 3:

International Human Resources Management – Edited by James B. Shaw, Paul S. Kirkbridge and Kendrith M. Rowland

Supplement 4:

International Human Resources Management in the Twenty-First Century – Edited by Patrick M. Wright, Lee D. Dyer, John W. Boudreau and George T. Milkovich

Volume 21:

Edited by Joseph J. Martocchio and Gina Ferris

Volume 22:

Edited by Joseph J. Martocchio and Gerald R. Ferris

Volumes 23–27: Edited by Joseph J. Martocchio Volume 28:

Edited by Joseph J. Martocchio and Hui Liao

RESEARCH IN PERSONNEL AND HUMAN RESOURCES MANAGEMENT VOLUME 29

RESEARCH IN PERSONNEL AND HUMAN RESOURCES MANAGEMENT EDITED BY

HUI LIAO University of Maryland, USA

JOSEPH J. MARTOCCHIO University of Illinois, USA

APARNA JOSHI University of Illinois, 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: 978-0-85724-125-2 ISSN: 0742-7301 (Series)

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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WORKPLACE SAFETY: A MULTILEVEL, INTERDISCIPLINARY PERSPECTIVE Michael J. Burke and Sloane M. Signal EXECUTIVE PAY AND FIRM PERFORMANCE: METHODOLOGICAL CONSIDERATIONS AND FUTURE DIRECTIONS Beth Florin, Kevin F. Hallock and Douglas Webber A TIME-BASED PERSPECTIVE ON EMOTION REGULATION IN EMOTIONAL-LABOR PERFORMANCE Michelle K. Duffy, Jason D. Shaw, Jenny M. Hoobler and Bennett J. Tepper INSIGHTS FROM VOCATIONAL AND CAREER DEVELOPMENTAL THEORIES: THEIR POTENTIAL CONTRIBUTIONS FOR ADVANCING THE UNDERSTANDING OF EMPLOYEE TURNOVER Peter W. Hom, Frederick T. L. Leong and Juliya Golubovich HOW DID YOU FIGURE THAT OUT? EMPLOYEE LEARNING DURING SOCIALIZATION Jaron Harvey, Anthony Wheeler, Jonathon R. B. Halbesleben and M. Ronald Buckley

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CONTENTS

COMPARING APPLES AND ORANGES: TOWARD A TYPOLOGY FOR ASSESSING E-LEARNING EFFECTIVENESS N. Sharon Hill and Karen Wouters

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ABOUT THE AUTHORS

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LIST OF CONTRIBUTORS M. Ronald Buckley

Price College of Business, Division of Management, University of Oklahoma, Norman, OK, USA

Michael J. Burke

Freeman School of Business, Tulane University, New Orleans, LA, USA

Michelle K. Duffy

Carlson School of Management, University of Minnesota, Minneapolis, MN, USA

Beth Florin

Pearl Meyer & Partners, Southborough, MA, USA

Juliya Golubovich

Department of Psychology, Michigan State University, East Lansing, MI, USA

Jonathon R. B. Halbesleben

Department of Management and Marketing, Culverhouse College of Commerce and Business Administration, University of Alabama, Tuscaloosa, AL, USA

Kevin F. Hallock

Cornell University and NBER, ILR School, Ithaca, NY, USA

Jaron Harvey

Department of Management and Marketing, Culverhouse College of Commerce and Business Administration, University of Alabama, Tuscaloosa, AL, USA

N. Sharon Hill

School of Business, The George Washington University, Washington, DC, USA

Peter W. Hom

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

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LIST OF CONTRIBUTORS

Jenny M. Hoobler

College of Business Administration, University of Illinois at Chicago, Chicago, IL, USA

Frederick T. L. Leong

Department of Psychology, Michigan State University, East Lansing, MI, USA

Jason D. Shaw

Carlson School of Management, University of Minnesota, Minneapolis, MN, USA

Sloane M. Signal

Freeman School of Business, Tulane University, New Orleans, LA, USA

Bennett J. Tepper

J. Mack Robinson College of Business, George State University, Atlanta, GA, USA

Douglas Webber

Cornell University, ILR School, Ithaca, NY, USA

Anthony Wheeler

Schmidt Labor Research Center, College of Business Administration, University of Rhode Island, Kingston, RI, USA

Karen Wouters

Robert H. Smith School of Business, University of Maryland, College Park, MD, USA

WORKPLACE SAFETY: A MULTILEVEL, INTERDISCIPLINARY PERSPECTIVE Michael J. Burke and Sloane M. Signal ABSTRACT While research on workplace safety spans across disciplines in medicine, public health, engineering, psychology, and business, research to date has not adopted a multilevel theoretical perspective that integrates theoretical issues and findings from various disciplines. In this chapter, we integrate research on workplace safety from a variety of disciplines and fields to develop a multilevel model of the processes that affect individual safety performance and safety and health outcomes. In doing so, we focus on cross-level linkages among national, organizational, and individual-level variables in relation to the exhibition of safe work behavior and occurrence of individual-level accidents, injuries, illnesses, and diseases. Our modeling of workplace safety is intended to fill a theoretical gap in our understanding of how the multitude of individual differences and situational factors interrelate across time to influence individual level safety behaviors and the consequences of these actions, and to encourage research to expand the limits of our knowledge.

Research in Personnel and Human Resources Management, Volume 29, 1–47 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0742-7301/doi:10.1108/S0742-7301(2010)0000029003

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MICHAEL J. BURKE AND SLOANE M. SIGNAL

An impressive body of literature on workplace safety that spans across disciplines in medicine, public health, engineering, psychology, and business is developing. Within these disciplines, researchers are often attending to the study of relations between variables at either the individual or situational level of analysis. With few exceptions (e.g., Wallace & Chen, 2006; Zohar & Luria, 2005), research to date has not adopted a multilevel theoretical perspective to the study of workplace safety nor has it attempted to integrate findings from various disciplines. Recognizing that individuals and situations are interdependent, we argue that adopting a multilevel perspective that incorporates aspects of workplace safety from multiple disciplines is needed to fully understand the processes that lead to safety-related work behavior and its consequences. In doing so, this chapter integrates research advances within human resource management, organizational behavior, safety engineering, and various fields of medicine and public health to focus on cross-level linkages among national, organizational, and individual-level variables in relation to the exhibition of safe work behavior and occurrence of individual-level accidents, injuries, and illnesses. Our focus is not intended to diminish the importance of emergent phenomena, processes, and outcomes at higher levels of analysis such as at the work group and organization levels, but rather our intent is to fill a theoretical gap in our understanding of how the multitude of individual differences and situational factors interrelate across time to influence individual-level safety behaviors and the consequences of these actions. Hence, we have three goals for this chapter. First, we present a multilevel model of workplace safety in relation to individual-level outcomes. Here, we emphasize the study of construct domains where advances have been made in identifying the appropriate taxonomic components or constructs relevant to the study of individual-level outcomes. As such, our discussion is not exhaustive of potentially relevant constructs at each level of analysis. Second, we discuss how relationships between construct domains within the model have been studied and summarize what is known about these linkages. Throughout the discussion, our third goal is to discuss the limits of our current state of knowledge and practice and the types of research needed to expand these limits.

A MULTILEVEL MODEL OF WORKPLACE SAFETY A problem faced in the rapprochement of different disciplines and bodies of literature as they relate to workplace safety is where to begin in terms of a

Workplace Safety: A Multilevel, Interdisciplinary Perspective

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theoretical foundation. For our purposes, we begin with a discussion of safety performance, the safety-related actions or behaviors that workers exhibit in almost all types of work to promote their safety and the safety and health of others. While disciplines vary considerably in terms of the relative emphasis placed on the study of safe work behavior, all disciplines recognize the need for safe work behavior in preventing or reducing negative outcomes such as injuries and illnesses. For instance, researchers in industrial ergonomics may focus on the pacing of actions to avoid visual and muscular disturbances (e.g., Salvendy, 1998; M. J. Smith, 1998), kinesthesiologists and occupational therapists study the biomechanics of postures and movements in efforts to reduce musculoskeletal disorders (e.g., Gagnon, 2003), applied psychologists and human resource management researchers focus on the consistency or frequency of actions taken to avoid accidents (e.g., Burke, Bradley, & Bowers, 2003), community health researchers are often concerned with the percent of time actions are engaged in so as to preclude the onset of disease (e.g., Forst et al., 2004; Mayer et al., 2007), and industrial hygienists frequently stress the correct sequencing of actions to avoid exposure to toxic substances (e.g., Perry & Layde, 2003). Although disciplines within business, psychology, engineering, medicine, and public health focus on different aspects of action or behavior, the safety-related actions that workers engage in can be conceptualized relative to a core set of dimensions that apply across jobs and industries, regardless of safety performance measurement considerations within any discipline. At the broadest level, the safety-related actions that workers engage in can be placed into two broad content categories akin to notions of task and contextual performance in the job performance literature (Motowidlo, Borman, & Schmit, 1997): safety compliance and safety participation, respectively. Safety compliance refers to generally mandated safety behaviors, whereas safety participation refers to safety actions that are more discretionary in nature (see Neal, Griffin, & Hart, 2000). In large part, research on behavioral aspects of safety across disciplines has relied on the notion of safety compliance and either explicitly or implicitly (via how safety performance was measured) treated safety compliance as a unitary factor. Although research on safety participation is less frequent, this literature also explicitly or implicitly (e.g., Hofmann, Morgeson, & Gerras, 2003) views safety participation as a unitary factor. These points are noteworthy, as Marchand, Simard, Carpentier-Roy, and Ouellet (1998) found that a two-factor model of safety performance (with factors relating to safety compliance and safety initiative) did not provide a good fit to the data.

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Given the literature on job performance, Marchand et al.’s (1998) factor analytic findings are not surprising. The job performance literature has produced ample evidence that multiple dimensions underlie the analogous domains of task (requisite) and contextual (discretionary) work behaviors (e.g., Campbell, McHenry, & Wise, 1990; Motowidlo et al., 1997). Supporting the multidimensionality of behavioral factors in regard to notions of safety compliance and safety participation, Burke, Sarpy, Tesluk, and Smith-Crowe (2002) confirmed, across 23 jobs, a grounded theoretical model of general safety performance. Two of their confirmed factors, labeled using personal protective equipment and engaging in work practices to reduce risk, would clearly fall within the domain of safety compliance; whereas the other two confirmed factors, communicating health and safety information and exercising employee rights and responsibilities, are closer to the notion of safety participation. Notably, these factors were confirmed for individuals working in dyads, work groups, and teams. Together, conceptual and empirical research on the factor structure of behavioral safety performance would suggest that researchers across disciplinary boundaries may benefit from attending to potentially useful construct distinctions in the measurement and study of workers’ safety-related actions. Another distinction in relation to safety performance, which is recognized in the medical literature, is that of a work-around (Ash, Berg, & Coiera, 2004; Kobayashi, Fussell, Xiao, & Seagull, 2005). Work-arounds are actions to address a block or disruption in a work system that involve bypassing safety procedures or protocols and can be conceptually distinguished from deviance, errors, and mistakes in regard to motive (see Halbesleben, Wakefield, & Wakefield, 2008). That is, the motive is to complete the task by getting around the block. Although the term shortcut has been used interchangeably with work-around, a shortcut is a specific case of a workaround intended to deal with a perceived time block. To date, the measurement of work-arounds has been qualitatively approached (via workers’ responses to open-ended questions) within human factors research (e.g., Charlton, 2002). Additional research is needed to capture the behavioral dimensionality of work-arounds as an area of safety performance and to produce measures that can assist in studying work-arounds in relation to their antecedents and consequences in a model such as one shown in Fig. 1. To develop the multilevel model of the processes through which national/ regional, organizational/group, and individual-level factors affect safety performance and its consequences as depicted in Fig. 1, we rely broadly on the disciplinary literatures that consider behavioral aspects of workplace safety. More specifically, at the individual level, we ground our modeling in

Fig. 1.

A Multilevel Model of Workplace Safety. Notes: Solid lines with arrows represent expected direct effects. Dashed lines with arrows designate expected moderation. Arced lines with arrows represent feedback loops.

Workplace Safety: A Multilevel, Interdisciplinary Perspective 5

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the applied psychology literature that identifies knowledge and motivation as proximal antecedents to performance and more stable individual differences as distal antecedents to performance. In the applied psychology field, the influence of distal antecedents on performance is often posited to be mediated by either knowledge or motivation. At the organizational/ group level of analysis, our theoretical modeling is, in large part, based on theoretical advances within the domain of work climate. Further, at this level of analysis, we argue for an occupational safety conceptualization of workplace hazards that we believe offers considerable promise for reintroducing the study of objective workplace hazards into the human resource management and applied psychology literatures. At the national/ group level of analysis, we ground our work in research advances within the domain of public health as well as theoretical notions of culture and values within the management and organizational behavior literature. Finally, in regard to the consequences of safety performance, referred to as safety outcomes, we focus on tangible events and results that are considered within a variety of disciplines including illness and diseases discussed within the literature on occupational medicine. Furthermore, we incorporate notions from the literature on education and learning to suggest how workers reflect on and learn from perceived safety performance–outcome associations. In this sense, our model conceptualizes workplace safety as an ongoing process, where workers learn and think in and by action.

ANTECEDENTS OF SAFETY COMPLIANCE AND SAFETY PARTICIPATION In terms of situational factors, we classified antecedents of safety compliance and safety participation at the national/regional, organizational/group, and individual levels of analysis. While social, institutional, and legal considerations exist at a global level in relation to workplace safety (see Nuwayhid, 2004), we begin our modeling of safety performance and its consequences at the national/regional level of analysis. Our rationale is that at this level of analysis, one can more readily view and study the role of cultural values and differences between nations in regard to occupational safety and health political and research agendas. In adopting this approach, we recognize that an understanding of safe work behavior and its consequences cannot be divorced from cultural factors and the broader challenges of occupational health and safety in both developing locations such as Bangladesh, Central


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America, and South Africa (Ahasan, Mohiuddin, Vayrynen, Ironkannas, & Quddus, 1999; Joubert, 2002; Wesseling et al., 2002) and within regions of developed countries such as in the northern and southern states of the United States. (Richardson, Loomis, Bena, & Bailer, 2004). In addition, although one could develop arguments for other possible levels of analysis (i.e., occupational, union, or industry levels), the three situational levels that we consider comprise a more parsimonious and inclusive model than other possible situational breakdowns. That is, our theoretical arguments at each situational level apply across occupational, industrial, and union boundaries. To unfold the discussion of our model in Fig. 1, we begin with a presentation of conceptual arguments for why cultural values influence the political economy of nations and states and follow with a discussion of several cross-level linkages between national/regional factors and organizational/group-level factors. Subsequently, we discuss how key factors interrelate at the organization/group level, and how and why these factors would be expected to affect processes at the individual level of analysis. Given the multitude of construct domains that we consider as antecedents of safety compliance and safety participation, we will refer to factors not only in terms of level of analysis but also with respect to their distance or expected causal ordering in relation to these safety performance domains. In doing so, we discuss antecedents as distal and proximal to safety performance to assist in organizing the discussion of these factors. Proximal antecedents will include safety motivation and safety knowledge. Safety motivation refers to one’s regulatory behavior primarily in relation to exerting effort when on the job to enact safety behaviors; whereas, safety knowledge refers to an understanding of both safety-related facts and procedures, and can be of an implicit or anticipatory nature (see Broadbent, Fitzgerald, & Broadbent, 1986; Burke, Scheuer, & Meredith, 2007; Gardner, Chmiel, & Wall, 1996). Other individual differences, which are expected to affect safety performance (through safety motivation or safety knowledge) as well as situational variables will be referred to as distal antecedents and will be defined below.

National/Regional Level Antecedents Recently, several studies have examined how nations differ in regard to workplace safety (e.g., Burke, Chan-Serafin, Salvador, Smith, & Sarpy, 2008; Gyekye & Salminen, 2005; Ha˚vold, 2007; Infortunio, 2006). In large part, these investigations have examined how cultural differences relate to

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employees’ perceptions of workplace safety and accident involvement (i.e., fatal accident rates and perceived responsibility for accident causation) across nations. For instance, in a study with data collected for 43 countries, Infortunio (2006) found that fatal accident rates were negatively correlated with Hofstede’s (2001) cultural dimension of individualism/collectivism. As another example, Ha˚vold (2007) found that for workers from 10 countries, Hofstede’s cultural dimensions of power distance, individualism, and uncertainty avoidance were positively related to aggregated workers’ perceptions of workplace safety (safety climate). While evidence of bivariate associations between cultural dimensions (that reflect cultural values) and criteria such as accident rates are notable, these types of studies do not necessarily provide insight into the processes by which national culture affects aspects of workplace safety. Nevertheless, these studies have been helpful in pointing to the value of Hofstede’s cultural dimensions for conceptualizing the role of culture in workplace safety. Although other researchers have developed taxonomies and frameworks for describing cultural differences (e.g., Schwartz, 1999), considerable conceptual and empirical support has been generated for Hofstede’s cultural dimensions including the stability of national scores on these dimensions (see Hofstede & McCrae, 2004; Søndergaard, 1994). Hofstede’s (1980, 1991, 2001) work and research based on his taxonomy has focused on five dimensions to explain national cultural differences: power distance, which describes the degree to which power is distributed equally; individualism/ collectivism, which relates to how much an individual values his/her own needs (individualistic) vs. those of a group (collectivistic); masculinity/ femininity, which refers to the distribution of masculine values (assertive) vs. feminine values (modest and caring); uncertainty avoidance, which describes the level of tolerance a society has for ambiguity; and long-term/short-term orientation, which refers to how much a society values thrift and perseverance (long-term) vs. fulfillment of social obligations (short-term). Several of these dimensions have important implications for understanding how and why leaders and organizations within nations differ in their orientation toward workplace safety. At the national and regional levels (e.g., state or territory) within nations, cultural values would be expected to underlie several aspects of the political/ economy that, in turn, would be expected to influence safety-related working conditions within organizations. Here, we refer to the political economy as the political, economic, and legal systems of a country. More specifically, cultural values would be expected to drive the social justice orientation, the fiscal capacity of nations and states, and the capacity for labor to organize:


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characteristics of the political economy that may significantly impact the safety of working conditions within organizations. Our focus on cultural values as antecedents of aspects of the political economy is not intended to imply that the political economy of nations is a sole function of cultural values. The political, economic, and legal systems of nations have roots in historical/colonial ties, available natural resources, and international agreements to name just a few other antecedents (e.g., Golub, 1991; Jammeh & Delgado, 1991; Schad, 2001). Our focus on cultural values is meant to highlight these ‘‘basic assumptions’’ of national culture as the primary drivers of aspects of the political economy that relate to workplace safety. Social justice rests on the premise that all individuals belonging to a society (a nation or region within a nation for the purposes of this chapter) should enjoy equal rights, responsibilities, and benefits (Turner, Pope, Ellis, & Carlson, 2009). In effect, the social justice orientation of a nation reflects the extent to which the equal rights of individuals are valued and individuals are responsible for their actions. As such, the cultural dimensions of power distance, individualism/collectivism, and uncertainty avoidance would be expected to directly affect the social justice orientation of nations. In terms of a social justice orientation, differences can be observed across nations in institutions and legal considerations that relate to human resources in general and workplace safety in particular (LaDou, 2002). At a more general level, many democratically oriented nations have laws that relate to equal employment opportunities. Moreover, in regard to safety, institutions within these types of nations often put into play research agendas such as the National Occupational Research Agenda in the United States (National Institute for Occupational Safety and Health (NIOSH), 2006) and engage in assessment practices that lead, through legislative and enforcement processes, to a focus on the rights of workers and safe work conditions. For instance, in a number of developed nations that emphasize individual rights, such as Canada, Germany, Sweden, and France, regulations have been formulated that guide not only human resource practices, but also workplace health and safety considerations (see Burke & Sarpy, 2003). A case in point is the United States where the Occupational Safety and Health Administration (OSHA) has requirements and guidelines that relate to the maintenance of safe work conditions and the development (in terms of training) and protection of individual workers (OSHA, 1998). Notably, the OSHA guidelines not only reflect a concern for individual rights, but they also emphasize uniformity in adherence to these requirements across organizations. The importance placed on standardization and uniformity in workplace safety practices and work conditions is undergirded

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by the cultural value of uncertainty avoidance. These arguments form the basis for the expected indirect effects, as specified in Fig. 1, of cultural values on organizational policies and practices through an aspect of the political economy (i.e., social justice orientation) of nations. Cultural values such as power distance and masculinity–femininity are also expected to have meaningful influences on national wealth and, particularly, the distribution of wealth within nations (see Husted, 1999). In comparison to cultures with low-power-distance orientation and lower masculinity, cultures having higher power distance and masculinity orientations would be expected to have greater inequities in the distribution of wealth, where the majority of the wealth in the country would be under the control of a select few. The role and size of government would also be expected to be restricted in these cultures (Husted, 1999). As such, nations characterized by high vs. low power distance and masculinity would likely have less fiscal capacity and fewer regulatory bodies to focus on workplace safety issues. To the extent that nations and states have the fiscal capacity to effectively monitor and regulate occupational health and safety, organizational policies and practices and the consequent safety climates of organizations are likely to improve. Here, organizational safety climate refers to work environment characteristics in relation to safety matters that affect members of the group or organization. In line with this expectation, Pfeffer and Salancik (1978) argued that in states with high fiscal capacity, organizations respond to regulatory uncertainty by reducing conditions that lead to sanctions. On the other hand, high national or state debt may impede an entity’s ability to effectively monitor and regulate occupational health and safety. We caution that lower fiscal capacity alone may not explain declines in efforts to enforce safety-related laws and regulations. This point is apparent in gaps in organizational compliance with child labor laws and the concurrent striking decline in child labor law enforcement activities, which may reflect changing enforcement policy as much as fiscal considerations (see Rauscher, Runyan, Schulman, & Bowling, 2008). At the regional level, another example is the lax and questionable enforcement policies in the case of Nevada’s OSHA, which has allowed unsafe conditions to persist (Associated Press, 2009). Despite such cases, enforcement activities are expected to be positively associated with improvements in organizational safety policies and practices and organizational safety climate (e.g., lower hazardous orders violations defined as using a piece of equipment prohibited by law), as organizations attempt to conform to government standards and at the same time avoid sanctions.


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Furthermore, cultural values would be expected to influence the organizing capacity of labor. Traditionally, labor unions were founded on the premise of protecting the rights of individual workers (Rosenberg, 2009), where valuing the equal rights of individuals would be considered an important driver of unionization. However, in his analysis of hybrid regimes, Robertson (2007) demonstrates that the premise of protecting the rights of workers as a basis for unionization is specific to liberal democracies. In authoritarian regimes, unions are known to be used as a means to suppress or control workers or where the ability to unionize at all has been taken away from workers (Robertson, 2007). These considerations lead us to posit that nations higher in power-distance orientation would be more likely to suppress the unionization of their labor forces or use labor unions as a means of control. To the extent that labor can organize within a nation or state, we would expect organizations to maintain sound organizational policies and practices in relation to safety and more positive safety climates and safe working conditions (e.g., reduce hazardous equipment violation). In effect, organized labor may force the maintenance of higher safety standards in efforts to safeguard the health and well-being of its members. In the United States, this general expectation is consistent with research that shows that unionized workplaces tend to be more compliant with Occupational Safety and Health Act regulations (Weil, 2001). However, to the extent that societal factors including prejudice impede workers’ efforts to organize for improvements in work conditions (e.g., in the case of immigrant workers; Griffith, 1988), the work environment is likely to be less safe and associated with higher occupational injury rates (Dong & Platner, 2004; Richardson et al., 2004). In nations whose political economic climate favors industry over labor, we would also expect organizations to have potentially more hazardous work environments (Loomis et al., 2009; Richardson et al., 2004). That is, when economic development is coupled with less attention to workplace safety, workers are likely to face greater potential exposures to general workplace hazards (e.g., biological, chemical, radiological, and noise). Tied to this point, in nations that have less developed economies, jobs will be concentrated more in industries with higher levels of potentially severe hazardous events and exposures (Koh & Chia, 1998). As a result, we would expect the political economy of nations and states to directly affect the nature of workplace hazards and, indirectly, the occurrence of accidents, injuries, and illnesses/diseases. This expectation implies that, depending on the political-economic climate of nations or regions within nations, negative

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individual level safety outcomes and rates would differ across workers in the same jobs and same industries. Related to the above discussion, Holzberg (1981) has discussed how cultural values influence social stratification through the political economies of nations. This point is relevant to our discussion, as such processes can operate in a manner where different racial/ethnic or socioeconomic groups are at times disproportionately exposed to hazardous work environments. The reader is also referred to Baum, Be`gin, Houweling, and Taylor (2009) and the Commission on the Social Determinants of Health (CSDH, 2008) for more general discussions of the social determinants of health inequities within and across nations. For instance, within the United States, AfricanAmerican men have higher cancer rates than White men and these differences are, in part, attributed to differential exposures to hazardous occupational conditions (see Briggs et al., 2003). As another example, in both declining types of work (e.g., hand harvesting of row crops) and expanding labor-intensive types of work (e.g., construction and landscaping) in the southern United States, a pattern has emerged where these types of work are done by Hispanic workers who are experiencing some of the nation’s highest fatal occupational injury rates (Richardson et al., 2004). Also, in comparison to non-Hispanic White men, African-American and Hispanic men miss more work days due to injury (Strong & Zimmerman, 2005). These developments signal a strong need, as discussed in detail below, to reconsider how educational/development activities can take into account backgrounds of workers along with information on the severity of workplace hazards to enhance worker motivation to learn about and avoid such hazards (some of which may seem quite benign, such as wood dust). Together, we expect political/economic factors to have meaningful direct effects on organizational policies and practices in relation to safety and workplace hazards that are present in these organizations. Furthermore, while some research has examined direct relationships between political/ economic factors and occupational injury rates (e.g., Loomis et al., 2009), we view the effect of political/economic factors on safety performance and safety outcomes such as fatal occupational injuries to be indirect through the environment (workplace hazards and safety climate) that workers experience. To date, research has not been directed at addressing expected cross-level mediated relationships involving factors at the national and organizational levels of analysis as specified in Fig. 1. Finally, as indicated in Fig. 1, we expect cultural values to directly affect leadership in organizations. As noted above, evidence indicates that cultural values, such as those focused on equality and respect for individual rights,


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become personalized in the sense that they remain relatively stable over time. In addition, Hofstede’s cultural dimensions, in particular those focused on individualism and masculinity, have been shown to have a greater effect on workers’ perceptions of the ethicality of actions within organizations than ethicality as specified in company codes of conduct (Arnold, Bernardi, Neidermeyer, & Schmee, 2007). As such, we expect cultural values to affect organizational safety climate indirectly through the actions of organizational leaders embedded within those nations. We will comment further on this expectation below.

Organizational/Group-Level Antecedents The above discussion recognized that elements of the national/regional environment can have meaningful direct and indirect impacts on organizational functioning insofar as the personal values of leaders and safety climate of the organization are concerned. As indicated in Fig. 1, at the organization/group level of analysis, we expect societal values (e.g., justice) that become personalized over time as well as organizationally espoused values to underlie leadership, where leadership refers to the actions that managers take to achieve organizational and group objectives. Organizationally espoused values such as ‘‘Safety is Job One’’ or ‘‘Create and Sustain a Safe Work Environment’’ signal what is strategically important to leaders of organizations. To the extent that these values are enacted by leaders, they guide the various organizational policies and practices (i.e., safety-related policies and production technology in the organization as well as human resource management policies and practices in relation to workplace safety). Thus, we expect leader behavior to mediate the influence of cultural values and organizationally espoused values toward safety on the human resource and safety-related policies of the organization. Furthermore, the linkage between leadership and organizational or grouplevel climate has long been recognized in the literature (Lewin, Lippitt, & White, 1939). That is, leaders are directly or indirectly (as noted above through the establishment of policies and procedures) instrumental in shaping and reinforcing an organizational climate for safety. However, if the leadership of an organization or group is deficient in terms of establishing a positive safety climate (e.g., in terms of supervisory support, work pressure, and reward practices), then a weak safety climate will result irrespective of formal policies. Further, if one were to view leadership behaviors and leader– member interactions along a continuum of support for the welfare or

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concern of employees, then different forms of leadership (e.g., transformational) would be expected to have stronger effects on safety climate in comparison to other forms of leader behavior (e.g., corrective leadership) (Zohar, 2003). Noting that production technology and potential exposures to workplace hazards differ across departments of an organization, the possibility exists that safety climate may also differ across these organizational units. This possibility is likely realized when top management beliefs about safety are not uniformly conveyed to lower levels of management or when supervisors of organizational subunits hold different beliefs about the importance of safety (Cox & Cox, 1991; Williamson, Feyer, Cairns, & Biancotti, 1997). In particular, when supervisors of work groups hold different beliefs about who is responsible for safety or attribute accidents to either internal or external factors, then they are likely to execute organization-level policies and procedures differently, regardless of risks involved (Zohar, 2000; Zohar & Luria, 2004). Related to this point, we note that the safety climate dimension of managerial support, which is included in almost all models of safety climate, refers to the support that employees’ immediate supervisor provides in carrying out their work. In this sense, our model in Fig. 1 leaves open the possibility that organizational safety subclimates may be operating. Nevertheless, the expected causal paths in the model would be expected to hold for possible differences in organizational climates as well as for differences in organizational safety subclimates. Safety climates are also likely to vary across subunits of an organization due to the fact that members of different organizational subunits often face different hazards or risks. For instance, nurses within a hospital emergency room may face exposure to harmful substances such as bloodborne pathogens; whereas nurses in a rehabilitation unit may confront situations that lead to overexertion. Notably, different social groups or units within an organization can have different interpretations of risk, even when the potential hazardous event or exposure is the same (Weyman & Clarke, 2003). In fact, Pidgeon (1991) and others (e.g., Fleming, Flin, Mearns, & Gordon, 1998; Perez-Floriano & Gonzalez, 2007; Rundmo, 1996) have discussed notions of ‘‘safety subcultures’’ and these groupings influence both the perception of risk and safety-related behavior. However, noting that perceptions of risk are socially constructed, we believe that arguments in the literature to essentially abandon the study of objective risks in favor of the study of perceived risk (e.g., Morrow & Crum, 1998; Pidgeon, 1991) unnecessarily promote a strict person-based approach to the study of risk. The weakness of this approach is apparent from the


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considerable evidence showing that differences between individuals often lack cross-situational consistency (Hattrup & Jackson, 1996; Mischel, 1990). If, in fact, risk perception is socially constructed, a point that we agree with, then understanding how risk perceptions and safety-relevant motivations (e.g., motivation to learn about and avoid hazardous events or exposures) develop in situ is of paramount concern. Yet, such investigations would necessitate a cross-level or interactionist approach that explicitly measures or takes into account the properties of potential hazardous events and exposures. We will return to this point in our discussion of educational/ development activities. Returning to our discussion of organizational safety climate, a number of authors have discussed how organizational climate might moderate relationships between individual difference variables (Brown, 1981; Tracey, Tannenbaum, & Kavanagh, 1995). Notably, a body of literature on climates-forsomething suggests that organizational climate moderates relationships between individual difference variables to the extent that the organization promotes a strategically focused climate (i.e., a climate that is aligned with organizational goals). This research is premised on the assumption that individuals strive to achieve an adaptive fit with their work environments. As noted by Smith-Crowe, Burke, and Landis (2003), one would expect that if an organization has a climate supportive of safety, then workers will exert effort to act safely and transfer the knowledge that they have acquired through educational/developmental experiences. On the other hand, in a comparable organization without a climate focused on safety, one would expect a lower relationship between safety knowledge and safety performance as well as between safety motivation and safety performance because workers would not necessarily be willing and able to exhibit acquired knowledge. This argument applies to both safety compliance and safety participation. The result being that we would expect safety climate to moderate relationships between safety knowledge and safety performance and safety motivation and safety performance, with respect to both safety compliance and safety participation criteria. Surprisingly, beyond Smith-Crowe et al. (2003), little research has been directed at examining safety climate as a moderator of such relationships with no research to our knowledge on the degree to which safety climate moderates safety motivation–safety performance relationships. Although we did not emphasize a discussion of the expected direct effects of human resource policies and safety-related policies on an organization’s safety climate, these policies and associated practices (e.g., selection practices, training procedures, and safety meetings) are well recognized as important contributors to organizational safety climates (Neal & Griffin,

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2004). Notably, within supply chains, Cantor (2008) discusses how human resource practices as well as physical resources and production technologies can enable organizations to better track and monitor activities and promote a strong safety climate. In an age of global sourcing practices, we note that the issue of workplace safety in organizational supply chains is an understudied area, where unsafe practices in one leg of the chain or organization can have catastrophic adverse consequences in another part of the supply chain (see Cantor, 2008).

Distal Individual Level Antecedents Psychological (Safety) Climate Prior to discussing the role of employees’ perceptions of work environments characteristics (i.e., psychological climate), we note key distinctions between organizational climate and psychological climate. Organizational climate was defined above as work environments characteristics in relation to matters (here, safety) that affect members of the group or organization. Shared or aggregated perceptions of work environment characteristics may serve as indicators of group or organizational climate (see James et al., 2008). While this point is widely recognized in the literature, what is not recognized is that organizational climate need not be strictly operationally (and conceptually) defined in terms of shared perceptions of work environment characteristics. For instance, Burke et al. (2008) and SmithCrowe et al. (2003) have discussed how subject matter expert judgments and content analyses of archival data may also serve as useful indicators or provide meaningful descriptions of an organization’s climate. Furthermore, as discussed above within the domain of workplace safety, an organization’s safety climate is primarily determined by leadership practices, human resource policies and practices, safety-related policies and practices, and workplace hazards. On the other hand, as discussed in more detail below, employees’ perceptions of work environment characteristics (psychological climate) are conceptualized as personal value-based appraisals of work environment characteristics. This distinction between organizational and psychological climates is nontrivial as meaningful and differential variation in climate scores (for similar dimensions) may exist at both levels of analysis and would be expected to have different implications for understanding the role of climate in individual safety performance and its consequences. Finally, we note that our reference to psychological safety climate, which refers to individual perceptions of characteristics of the work environment


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in relation to safety, is distinct from Edmondson’s (1999) notion of psychological safety that relates to a team’s shared belief that members are free to engage in risk taking. In recent years, a number of studies have examined relationships at the individual level of analysis between dimensions of climate, safety compliance factors, safety participation factors, and accident involvement across a wide range of occupations and industries (see Clarke, 2006a). A general finding is that the magnitude of safety climate–safety participation relationships is greater than the magnitudes of the associations between safety climate dimensions and safety compliance dimensions (Christian, Bradley, Wallace, & Burke, 2009; Clarke, 2006a). However, with a few exceptions (Christian et al., 2009; Griffin & Neal, 2000), researchers have not conceptually or empirically addressed the underlying mechanisms through which psychological climate perceptions affect safety performance and safety outcomes. In an effort to advance our understanding of the linkages between psychological climate and safety performance, we note that researchers have offered varied definitions and measures of safety climate at the individual level of analysis (see Flin, Mearns, O’Connor, & Bryden, 2000) with some definitions and measures being more narrowly focused on supervisory practices (e.g., Zohar & Luria, 2004). In our view, conceptualizing safety climate (at either the individual or organizational level) with respect to climate factors that have been confirmed across a wide range of occupations and industries (i.e., means emphasis, goal emphasis, management support, etc.; see Burke, Borucki, & Kaufman, 2002; James et al., 2008) as well as with respect to several more safety-specific issues that apply across organizations (e.g., work pressure; see Neal & Griffin, 2004) provides a comprehensive, yet parsimonious definition of the factor space of work environment characteristics. Furthermore, we view psychological (safety) climate as an employee’s perception of these work environment characteristics that affect not only the employee’s personal well-being, but also the well-being of relevant stakeholders (e.g., customers, suppliers, and the public). For instance, if we were studying safety climate in the nuclear hazardous waste industry, employees can provide useful information on aspects of the work environment (e.g., safe handling, storage, and disposal of nuclear waste) that not only affect them personally, but also their perceptions of factors that might adversely affect the public (such as the failure to maintain protective barriers in underground storage tanks, the training provided to materials handlers, and so on). In adopting a multiple stakeholder perspective to the study of safety climate, we also view safety climate perceptions as being hierarchically

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arranged. However, unlike other researchers who have posited a single, higher-order factor tied to employees perceptions of how the work environment affects their personal well-being (see Christian et al., 2009; Griffin & Neal, 2000; Neal & Griffin, 2004), we conceptualize the higherorder factors as employees’ assessments of well-being with respect to relevant stakeholder groups that they interact with in the organization’s task environment. That is, to the extent that organizationally espoused values reflect concern for the safety and well-being of multiple stakeholders and organizational practices reinforce these values, then we would argue that employees cognitively appraise their work environment with respect to the impact of work environment characteristics on personal well-being as well as with respect to the well-being of the other relevant stakeholder groups. Empirical support for a multiple stakeholder conceptualization to psychological climate has been found in both business (see Burke, Borucki, & Hurley, 1992) and educational settings (see Vaslow, 1999), and discussed in detail relative to workplace safety (Burke et al., 2002). A multiple stakeholder conceptualization of safety climate holds promise for broadening the domain and measurement of safety climate and may lead to an improved understanding of the effects of safety-related work contexts on individuals and groups other than employees. This point is important as safety contexts, perhaps more so than any other type of work environment, have the potential to affect the well-being (i.e., in the broadest psychological and physical sense) of employees, their families, customers/clients, and the public. One need not look far for striking cases of how work environment characteristics that were initially studied in relation to occupational illnesses and disease quickly became major public health concerns (e.g., in the cases of silicosis, asbestosis, lead toxicity, and pesticide poisoning; Corn, 1992; Kipen, 1994; Nuwayhid, 2004; Rosner & Markowitz, 1991). We know very little about how workers perceive characteristics of work environments in relation to safety and health of other stakeholders. Studying safety climate from a multiple stakeholder perspective will also be important as the nature of work changes. While applied psychology and management scholars have studied many aspects of the changing nature of work in regard to individual and group outcomes (see Ilgen & Pulakos, 1999), virtually no research has been directed at examining how the changing nature of work affects workers’ climate perceptions and, in particular, their safety climate perceptions. Yet, changes in the nature of safety-related work often bring with them not only new production technologies, which have new associated injury risks, but also potential for new exposures that could result in illnesses and diseases. These changes


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may also have important implications for how workers perceive the safety of their work environments with respect to their well-being and the well-being of others. As an example of how the changing nature of work can potentially affect safety climate, we will briefly discuss a few implications of the massive public works effort to repair the national highway system in the United States. This change from a focus on building highways to an emphasis on the repair of highways has brought with it a new method of repair, which uses large crews to cut, break-up, and remove large blocks of concrete. This process results in the generation of large amounts of dust and increased risk of silicosis for workers (Valiante, Schill, Rosenman, & Socie, 2004). Silicosis is a disabling, nonreversible lung disease (NIOSH, 2002). Given the context of this work, the threat is also a public health concern. While health-monitoring processes such as epidemiological exposure assessments can provide valuable information related to potential work exposures of this nature (Ott, 1998), broader safety climate assessments (at either the individual or organizational level of analysis) have the potential for identifying where more immediate actions and interventions could be initiated to safeguard various constituents. This is just one example where improved conceptualizations and measures of safety climate from a multiple stakeholder perspective may provide valuable data for understanding safety-related outcomes. Personality A substantial amount of empirical evidence exists concerning relationships among personality characteristics, safety performance, and accident involvement (Christian et al., 2009; Clarke & Roberston, 2008). While many studies have employed measures of general personality characteristics that could be classified within the Big 5 framework (e.g., conscientiousness, Geller, Roberts, & Gilmore, 1996; extraversion, Iverson & Erwin, 1997), research has also focused on several more specific aspects of personality (i.e., propensity for risk taking, Frone, 1998; locus of control, Salminen & Klen, 1994). In terms of safety performance and accident involvement, a number of studies have examined locus of control as a predictor (e.g., Brown, Wilis, & Prussia, 2000; Eklo¨f, 2002; Hsu, Lee, Wu, & Takano, 2008; Rundmo, 2001). Locus of control is the extent to which an individual believes that events are under his or her control as opposed to being the result of situational factors. Meta-analytic findings are consistent with the expectation that individuals who are higher in terms of internal control are more motivated to learn about workplace safety, engage in higher levels of both safety participation and safety compliance, and have fewer accidents (see Christian et al., 2009).

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Notably, preliminary evidence indicates that locus of control may be a relatively strong predictor of safety participation and a good predictor of accident involvement. These findings are consistent with the motivational implications of believing that one has control over events, and have important implications for worker selection and training. However, more research is needed before firm conclusions can be made about the relative utility of locus of control as a predictor of safety participation vs. safety compliance. Furthermore, research examining the expected indirect effect of locus of control on safety performance and accident involvement through safety motivation is needed. Conscientiousness has also received a fair amount of attention as a predictor of safety performance and accident involvement (Haaland, 2006). Those individuals who score high on this personality dimension are more likely to be trustworthy and dependable, which would lead them to being motivated to engage in appropriate safety behavior and have fewer negative safety outcomes. Despite this general expectation, conscientiousness has a somewhat modest (low) relationship with safety performance (see Christian et al., 2009). This result may be due to the fact that conscientiousness would be expected to primarily affect safety performance and safety outcomes through safety motivation. Future research examining the extent to which conscientiousness indirectly affects safety performance (with an emphasis on the study of participatory behaviors) and safety outcomes would be informative. To date, only a few studies have examined conscientiousness as an antecedent to safety participation (e.g., Geller, 1996). In regard to accident involvement, neuroticism has been found to be consistently related, albeit in a low positive manner, to accident involvement (e.g., Davids & Mahoney, 1957; Frone, 1998; Hansen, 1989; Salminen, Klen, & Ojanen, 1999). The general reasoning is that individuals higher in neuroticism are more likely than those with lower levels of neuroticism to experience negative affective states, which would lead them to have lower levels of safety participation, more lapses of attention, and be predisposed to making more mistakes. Although neuroticism has received a fair amount of research attention relative to other personality characteristics in regard to accident involvement, the available evidence would suggest that conscientiousness and locus of control play more central roles in the explication of negative safety outcomes. Although several studies have examined the role of extraversion in accident involvement and found it to have a low negative association (see Christian et al., 2009), fewer studies have incorporated measures of openness to experience and agreeableness. Individuals who are high in agreeableness


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are more friendly, good natured, and likely to conform to social norms. These characteristics should lead to higher levels of safety motivation and, consequently, higher levels of safety participation and safety compliance and fewer accidents. On the other hand, we do not have a strong basis for suggesting how openness to experience will affect safety outcomes. Yet, to the extent that elevated levels of openness to experience and extraversion are related to thrill seeking or the propensity to take risks (an amalgamation of Big 5 traits; see Nicholson, Soane, Fenton-O’Creevy, & Willman, 2005), we would expect safety motivation to decrease and consequent unsafe work behavior to increase. In effect, the thrill seeking would be expected to undermine the desire or need to engage in safe work behavior. Education/Development Experiences Educational and development experiences broadly defined are expected to lead to both the development of safety motivation and acquisition of safety knowledge. Formal on-the-job (Baird, Holland, & Deacon, 1999; Seibert, 1999) as well as informal or off-site (Curwick, Reeb-Whitaker, & Connon, 2003; Marsick & Watkins, 1997) educational activities within the safety domain focus heavily on the development of factual (often referred to as declarative) knowledge and procedural knowledge and skills related to using personal protective equipment, engaging in work practices to reduce risk, communicating health and safety information, and exercising employee rights and responsibilities. From here onward, we will use the term ‘‘safety knowledge’’ interchangeably with the concepts of declarative and procedural knowledge. In addition, educational activities can be directed at the development of worker attitudes and regulatory activities in efforts to enhance safety motivation (Ford & Tetrick, 2008). Thus, we would expect these educational experiences to directly relate to safety motivation. The primary means for developing safety knowledge is formal training (Colligan & Cohen, 2004), which is occasionally guided by the application of a particular learning theory. For instance, the literature is replete with applications of stage learning theories (e.g., Azizi et al., 2000), reinforcement theory (e.g., Cooper, 2009; Lingard & Rowlinson, 1997), and principles of social and experiential learning theories (e.g., Lueveswanij, Nittayananta, & Robison, 2000) to safety knowledge development. The later interventions often employ more hands-on, experiential training methods such as role-plays, demonstrations with practice, and simulations involving individuals, dyads, and teams. Along with the use of multiple training methods in the delivery of training, many of the training programs based on social and experiential learning theories emphasize individualized

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feedback and dialogue in small groups (e.g., Luskin, Somers, Wooding, & Levenstein, 1992). In 2006, Burke et al. reported on a meta-analysis that examined the relative effectiveness of safety and health training methods according to the extent to which trainees participated in the learning process. Their metaanalytic findings were consistent with the theoretical argument that as the method of safety and health training becomes more engaging (going from passive, less engaging methods such as lecture to experiential-based, highly engaging methods such as hands-on training that incorporate dialogue), the effect of training is greater for knowledge acquisition, safety performance, and the reduction of accidents and injuries. Importantly, their findings point to needed research on the usefulness of incorporating more active forms of participation into traditionally structured safety training and development efforts including the commonly employed computer-based and distance instructional methods. Burke et al.’s (2006) findings also suggest that the unbridled promotion of reinforcement or operant theory as ‘‘Behavioral Safety’’ be tempered. The reader is referred to Geller (1996) and McSween (2002) for discussions of operant theory that underlies moderately engaging feedback interventions. As discussed elsewhere (Burke et al., 2007; Cooper, 2009), applications of operant theory are most effective when work is relatively static (i.e., involves primarily routine actions) and where the intended target behaviors relate to safety compliance. Also, as discussed by Olson and Winchester (2008), the workplace literature on behavioral self-monitoring (BSM, another name associated with applications of operant theory) is theoretically unfocused and has neglected relevant scholarly work. A particular deficiency in the literature on educational efforts to develop safety knowledge and safety motivation is that we know little about learning conditions that promote dialogue and reflective thinking. Arguably, dialogue and reflection are critical elements of the learning process and efficacy formation (Burke, Holman, & Birdi, 2006; Gorsky & Caspi, 2005; Holman, 2000a, 2000b). Dialogue involves discussion with others including virtual others (interpersonal dialogue) or one’s self (intrapersonal dialogue), often with respect to actions taken or considered. Dialogue is characterized by thought-provoking activities such as questioning, explaining, and evaluating issues or problems at hand. Reflection is a systematic thought process concerned with simplifying experience (i.e., thinking about contradictions, dilemmas, and possibilities). Practitioners and researchers alike could consider different forms of dialogue (e.g., inquiry, debate, argumentation, storytelling; see Cullen & Fein, 2005; Gorsky & Caspi, 2005) and different


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structural considerations for promoting intrapersonal dialogue (e.g., selfinstruction/review materials, tutorial sessions, website materials) and interpersonal dialogue (e.g., actual and computer or web-based discussions; see Gorsky, Caspi, & Trumper, 2004). The form, structure, and instructional activities would likely be somewhat specific to the nature of the safety training intervention, the level of skill being acquired, and the size of the training group (Frederiksen, 1999; Gorsky, Caspi, & Trumper, 2006; McConnell, 1997). Nevertheless, research on the role of dialogue and reflection has considerable potential for advancing our understanding of how to optimally develop safety knowledge and safety motivation. More recently, Burke et al. (2009) discussed how hazardous events and exposures might interact with developmental activities to influence safety motivation and safety knowledge acquisition. Burke et al. (2009) posited that for hazardous events and exposures of an ominous nature (e.g., fires and explosions, exposure to toxic chemicals, radiation, and human immunodeficiency virus; see Mullet, Ciutad, & Riviere-Shafighi, 2004), the action, dialogue and considerable reflection that take place in highly engaging developmental activities such as simulation training would be expected to engender a dread factor, a realization of the actual dangers and feelings of dread. Furthermore, they argued that this realization and the experienced feelings and negative affect should play a primary role in motivating individuals to learn about how to avoid exposure to such hazards. However, the dread factor would not necessarily be produced in (a) highly engaging development activities targeted at hazardous events and exposures that typically do not have severe injury potential (e.g., contact with objects and equipment, excessive physical effort, and repetitive bodily motion) or (b) in lesser engaging development activities (e.g., lectures), irrespective of the level of hazard. Their theoretical arguments are consistent with that of many theorists who have given affect a direct and primary role in motivating behavior, especially in regard to unpleasant feelings, which arguably motivate action that people anticipate will avoid such feelings or associated consequences (see Schwartz & Clore, 1988; Slovic & Peters, 2006). Furthermore, their arguments are in line with social-cognitive perspectives concerning workers’ willingness to participate in safety interventions and the development of safety motivation (see Cree & Kelloway, 1997; Floyd, Prentice-Dunn, & Rogers, 2000; Ford & Tetrick, 2008; Goldberg, Dar-El, & Rubin, 1991). The above arguments are the basis for the expected interaction between objective workplace hazards and educational/development activities in the formation of safety motivation and safety knowledge. That is, the above

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arguments suggest that objective hazards and development activities of either a formal or informal nature should jointly affect the motivation to engage in safe work behavior and the knowledge of how to do so. In effect, the motivational and learning benefits of more engaging education/development activities should be enhanced when individuals face hazards of a particularly ominous nature, with no necessary enhanced benefits of more engaging developmental activities when hazard severity is relatively low or when the developmental activity is less engaging. However, we caution that when choosing educational/development activities, practitioners and researchers may need to take into account the backgrounds of workers who may be concentrated in particular types of hazardous work. For instance, indigenous farm workers from Mexico and Guatemala who are working in the northwest United States are not of Hispanic or Latino descent and come from regions with unique cultural and linguistic traditions that could affect what and how they learn (Farquhar, Shadbeh, Samples, Ventura, & Goff, 2008). Notably, the workplace hazard measurement system discussed in Burke et al. (2009), which is based, in part, on the Bureau of Labor Statistics’ Occupational Injury and Illness Classification System (OIICS) (also see Biddle, 1998), hierarchically arranges workplace hazards to reflect the increasing potential for severe illness, injury, or death due the hazardous event or exposure. This workplace hazard measurement system permits the scoring of hazard event/exposure in terms of low and high severity hazards, and roughly corresponds to breaking the Bureau of Labor’s ranking system at the midpoint of the OIICS hierarchy. Such a dichotomy highlights the point at which the consequences of hazards go from being less severe (e.g., slips, overexertion, repetitive motion within the third most severe hazard category: bodily reaction and exertion) to being more severe (e.g., the contraction of hepatitis or HIV resulting from needle sticks within the fourth most severe hazard category: exposure to harmful substances and environments). Moreover, this workplace hazardous measurement system offers considerable potential for reintroducing the study of objective hazards into the human resource management and organizational behavior literature to illuminate how workplace hazards interact with individual characteristics to affect workplace safety. Returning to a discussion of educational activities per se, several studies have pointed to educational status (i.e., educational level, school attendance, high school drop out) as a predictor of work injuries (e.g., Breslin, 2008; Breslin et al., 2007). These studies, several of which are longitudinal, indicate that young workers (aged 16–24) with less than a high school education are up to three times more likely to have a work disability absence


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than those with at least a high school diploma. Notably, these findings are maintained when controlling for the type of work and number of hours worked. These results imply that individual differences such as those modeled in Fig. 1 might be fruitfully investigated to further explicate the role of individual differences associated with educational attainment that are impacting workplace safety for those with more vs. those with less formal education. Finally, although various approaches have been considered for imparting work-relevant safety knowledge to high school students and young workers (e.g., through vocational/technical education, skill standards, career clusters initiatives, and apprenticeships), we know very little about how these efforts affect safety performance and safety outcomes (see Schulte, Stephenson, Okun, Palassis, & Biddle, 2005). Cognitive Abilities While general mental ability is well recognized as an antecedent to job knowledge as specified in Fig. 1 (Colquitt, LePine, & Noe, 2000; Schmidt, Hunter, & Outerbridge, 1986), the study of cognitive abilities in safety research has been largely delimited to the role of cognitive abilities in accidents (e.g., Arthur, Barrett, & Alexander, 1991; Lawton & Parker, 1998; Wallace & Vodanovich, 2003). Here, research indicates that selective attention and cognitive failures are meaningful predictors of safety performance and accident involvement (Wallace & Vodanovich, 2003). Cognitive failure refers to a breakdown in cognitive functioning, which results in an error or mistake in task execution. Cognitive failures have origins in the organization of work (e.g., 12-hour shifts in units with staffing shortages for nurses; see Smith, Folkard, Tucker, & Macdonald, 1998), sleep opportunities (Dawson & McCulloch, 2005), and relatively stable individual differences. In regard to stable individual differences, some personality characteristics may predispose individuals to being more susceptible to experiencing cognitive failures than others (Wallace, Kass, & Stanny, 2002). While research has examined expected interactions between cognitive failures and conscientiousness on accidents, examination of how the interactive effect of cognitive failure and personality characteristics such as conscientiousness operate through safety compliance on safety outcomes including accidents and near misses has not been made. In addition, learning disabilities, a general term used to describe a variety of information-processing problems, would be expected to directly relate to the acquisition and retention of safety knowledge. Depending on the learning disability including attention-deficit/hyperactivity disorder (ADHD), problems may arise with respect to reading, memory, abstract reasoning, and

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spatial orientation. As a result, individuals with such disabilities may, on average, acquire fewer job-relevant skills in comparison to those without such disabilities and be more likely to hold physically demanding and more hazardous jobs. While there is some evidence to support this assertion (see Mannuzza, Klein, Bessler, Malloy, & Hynes, 1997), the cognitive consequences of their disability alone would place them at greater risk of workplace injury and illness even for comparable jobs with people without such disabilities. In this sense, learning disabilities and ADHD can lead to difficulties reading instructions or remembering previously taught material (Schaeffer, 2004). Thus, we would expect learning disabilities to primarily affect safety outcomes such as injury indirectly through safety knowledge and safety compliance (e.g., completing tasks in a required sequence). Indirect evidence for this proposed causal sequence comes from a large-scale study of workers with self-reported dyslexia (a learning disability characterized by problems in reading, writing, and spelling) where there was an 89% increase in work injury risk among workers with self-reported dyslexia in comparison to workers reporting no learning disability (Breslin & Pole, 2009). Although not depicted in Fig. 1, we would expect some learning disabilities to interact with aspects of safety climate (e.g., time pressure or workload demands) to affect safety performance and subsequent safety outcomes. That is, the expected negative effect of learning disabilities on safety performance would be greater under more restrictive organizational safety climate conditions (e.g., high time pressure, high workload) than under less restrictive and more supportive safety climates. Decrements in safety performance would be expected to carry through in terms of increases in workplace accidents, injuries, and near misses. Physical Abilities and Physiological Aspects of Work The study of physical abilities and physiological aspects of safety-related work has been approached in several ways with considerable research being generated on the muscular nature of work (Smolander & Louhevaara, 1998), postures at work (Daltroy et al., 1997; Kuorinka, 1998), the ways the body produces force and generates movement (Darby, 1998; Hultman, Nordin, & Ortengren, 1984), and the role of fatigue (Robb, Sultana, Ameratunga, & Jackson, 2008). These efforts have contributed to an understanding of the physical-related predictors of safe work behavior and the design of worker and workplace interventions to reduce occupational injuries and illnesses (e.g., interventions to reduce the incidence of musculoskeletal problems related to repetitive work or inappropriate postures). In large part, we would


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expect physical abilities and physiological variables to directly relate to the actions that workers engage in, and indirectly through these actions (i.e., safety performance) to occupational injuries and illnesses. This expectation is supported by findings that functional measures, such as balance, reaction time, and isometric muscle strength, are related to performance on simulated tasks (Davis, Dotson, & Santa Maria, 1982; Guralnik & Ferrucci, 2003) and that increasing the physical capacity of workers can be beneficial in reducing their misfit with a work system (Genaidy, Karwowski, & Shoaf, 2002; Tuncel et al., 2008). In the case of non-exercise education/development efforts related to physical and physiological aspects of work, we expect these interventions to have direct impacts on workers’ safety knowledge especially of a procedural nature. We note that flexibility and strength generally decline with increases in age (Brandon, Boyette, Lloyd, & Gaasch, 2004; Peate, Bates, Lunda, Francis, & Bellamy, 2007; Sherrington, Lord, & Finch, 2004). This general finding has important implications for understanding safety performance and injury rates in occupations such as firefighting, where the execution of job tasks can require maximal physical performance (see Womack, Green, & Crouse, 2000). Furthermore, these findings suggest the need for continued efforts to evaluate the efficacy of interventions to improve flexibility and core strength (hip complex strength) among workers in highly demanding physical work, especially where the work conditions are changing and not under the worker’s control. Finally, in relation to the physical characteristics of workers and their health, little is known about the effects of ‘‘sickness presenteeism’’ (i.e., attending work while sick) on safety performance and subsequent safety outcomes. Sickness presenteeism has been linked to risk of serious coronary events, theoretically as a result of the cumulative stress burden of working while sick (Kivima¨ki et al., 2005). Yet, sickness presenteeism would be expected to have more immediate impacts on safety performance especially in relation to working with others and the effects on coworker health and safety.

Proximal Individual Level Antecedents Safety Knowledge Consistent with general models of performance (e.g., Campbell, 1990) and more specific models of workplace safety (Neal & Griffin, 2004; Christian et al., 2009), safety knowledge is posited as a direct antecedent to safety performance. In particular, Burke and Sarpy (2003) discuss how and why

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knowledge in four content areas is critical to engaging in safe work behavior: fundamental knowledge and skills (e.g., related to using personal protective equipment), recognition and awareness knowledge and skills (e.g., collecting information about workplace hazards), problemsolving skills (e.g., using union and management resources), and decisionmaking skills (e.g., negotiating effective health and safety contract language). A fair amount of research indicates that safety knowledge in these content areas has a relatively strong relationship with safety compliance (see Christian et al., 2009). Importantly, empirical evidence indicates that these relatively strong relationships between safety knowledge and safety compliance are maintained across self and supervisory ratings of job performance as well as across industry and occupational boundaries (Burke, Sarpy et al., 2002; Griffin & Neal, 2000). In addition, safety knowledge has been found to be a strong predictor of safety participation (Christian et al., 2009). While we have a reasonably good understanding of how safety knowledge develops and relates to safety compliance and safety participation, we know relatively little about how safety knowledge decays over time. Perhaps some of the best evidence related to declines in knowledge and skill comes from simulation research on healthcare education, where notable decays in patient safety knowledge occurred from two to eight months (Laschinger et al., 2008). Related to this point, goal setting and feedback interventions have been employed in the posttraining contexts to encourage retention and application of knowledge and skills in relation to more routine, housekeeping-type activities (see Hickman & Geller, 2003). These types of interventions have been effective in maintaining the display of routine, task behaviors across different types of work (Geller, 2001; Sulzer-Azaroff & Austin, 2000). However, little, if any, research attention has been given to the role of dialogue and action-focused reflection in the maintenance of knowledge and skills and particularly of an advanced procedural nature. This issue is important as severe injuries, illnesses, and fatalities occur more often in non-routine types of work (Kriebel, 1982; Peterson, 1998), where more complex skills are often required (Gardner et al., 1996). In addition, this issue of how best to promote the retention of safety knowledge is important due to the fact that efforts to maintain safety knowledge (via safety training) are mandated for some occupations. To date, we rely more on scientifically uninformed legislative/political processes and legislators to determine when (in regard to timing/frequency) and how to maintain critical safety knowledge than on sound theoretical and empirical research bases for such recommendations.


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Safety Motivation Along with safety knowledge, safety motivation is posited as a direct antecedent to safety performance. This expectation is consistent with recognized models of workplace safety (Neal & Griffin, 2004; Christian et al., 2009). Given that safety participation is characterized as behavior that is more volitional in nature, researchers have expected safety motivation to be more strongly related to safety participation than to safety compliance. Some evidence, where participation motivation was measured, indicates that motivation is more strongly related to safety participation than to safety compliance with safety knowledge being the primary determinant of safety compliance (Griffin & Neal, 2000). Nevertheless, Christian et al.’s (2009) meta-analytic findings indicate that safety motivation is an important antecedent of safety performance, where performance was primarily measured with respect to safety compliance. As specified in Fig. 1, safety motivation would be expected to mediate the relationship among psychological work climate, personality, and education/ development variables on safety performance. In regard to climate, this general expectation has been examined in several studies where climate and conscientiousness were expected to predict safety motivation, which in turn was expected to relate to safety performance (Christian et al., 2009; Wallace & Chen, 2006). Notably, these expected mediated effects were found when safety motivation was conceptualized and measured in a more general manner (see Christian et al., 2009) as opposed to a more specific conceptualization and measurement (see Wallace & Chen, 2006, with respect to regulatory focus theory), and when climate was conceptualized and measured at the individual vs. group levels in these respective studies. For the most part, research attention in the domain of workplace safety has been directed at how safety motivation and safety knowledge mediate the effects of personality characteristics and climate variables on safety performance. This research has not examined mediation involving cognitive abilities and education/development experiences as exogenous variables. Research incorporating constructs and measures from the latter domains would inform the relative causal influence of distal antecedents of both a cognitive and affective nature on safety performance. This point is important as safety motivation would be expected to primarily mediate the effects of affectively oriented distal antecedents on safety participation; whereas safety knowledge would be expected to primarily mediate the effects of more cognitively oriented distal antecedents on safety compliance. More recent research points to the need to examine safety motivation in relation to two sometimes-competing performance goals: safety and

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production. Although both types of goals are important in organizational contexts, understanding if and when employees focus on one type of goal at the expense of the other type is important (given the consequence of errors in either domain). Wallace and Chen (2006) refer to the focus on accomplishing more tasks, more quickly as a promotion focus, and performing tasks accurately and in accordance with one’s duties as a prevention focus (also see Fo¨rster, Higgins, & Bianco, 2003). Wallace and his colleagues’ research has indicated that prevention and promotion foci relate positively and negatively to safety and productivity, respectively, with task complexity serving as a possible moderator (Wallace, Little, & Shull, 2008). That is, when task complexity is high, a promotion focus has been found to negatively relate to safety and a prevention focus negatively to production. The extent to which Wallace et al.’s (2008) laboratory findings, which examined changes within an experimental task, generalize to changes in work complexity within and across jobs, are needed future research directions.

CONSEQUENCES OF SAFETY PERFORMANCE Accidents, Near Misses, Illness/Disease, and Injury With a few exceptions (e.g., Barling, Loughlin, & Kelloway, 2002), models of workplace safety conceptualize safety-related behavior as a direct antecedent of accidents, near misses, injury, and illness (e.g., Neal & Griffin, 2004). Tests of structural equation models of workplace safety have lent strong support to the conceptualization of safety behavior as direct antecedents to accidents and injuries (see Christian et al., 2009; Paul & Maiti, 2007). Our modeling of workplace injuries, however, considers accidents as partially mediating the safety performance–injury relationship. The rationale for the latter expected partial mediation is that some injuries result from accidents, but many injuries such as cumulative trauma, musculoskeletal, and knee injuries result directly from work behavior (e.g., Alnaser, 2007; Brulin et al., 1998; Chen et al., 2004; Marras, Davis, Kirking, & Bertsche, 1999). That is, different combinations or repeated exposure to lifting, lowering, pushing, pulling, and carrying can precede injury in the absence of an accident. This discussion also suggests that some physical work exposures of either a repetitive or prolonged nature may interact with worker behavior to produce occupational injuries and illnesses with delayed onset of symptoms (Cole, Ibrahim, & Shannon, 2005; Melchior et al., 2005). Furthermore, the indirect relationship between safety performance and injuries through accidents is


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important given that experiencing an accident-related injury may result in qualitatively different thinking about how to avoid the reoccurrence of such an injury than might be expected from involvement in an accident without injury or involvement in a near miss. At the individual level of analysis, safety performance, often measured as a composite of safety participation and safety compliance items (e.g., Barling et al., 2002), has been found to have moderate negative relationships with accidents, near misses, and injuries (Clarke, 2006b; Hayes, Perander, Smecko, & Trask, 1998; Hofmann & Morgeson, 1999; Paul & Maiti, 2007; Probst, 2004; Probst & Brubaker, 2001; Siu, Phillips, & Leung, 2003). While too few studies have included measures of accidents, near misses, and injuries to judge possible differential relationships of these outcomes with safety performance, limited evidence (see Probst, 2004) indicates that safety performance has a stronger relationship with near misses in comparison to other outcomes. This finding is not unexpected, as the base rate for near misses will tend to be greater than the rate of accidents and injuries. Importantly, the literature on accident involvement and injury generally supports our theoretical modeling of the role of distal (e.g., personality) and more proximal antecedents (i.e., safety motivation and safety performance) to these outcomes. For instance, Siu et al. (2003) found that negative affectivity (a Big 5 personality factor relating to emotional stability) had a strong indirect (through safety performance) effect on objectively measured work injury for workers in two underground coal mines in India. As another example, Probst and Brubaker (2001) found that safety motivation had strong indirect effects through safety compliance on separate measures of workplace accidents and injuries. Finally, Christian et al. (2009) presented evidence supportive of expected indirect effects of psychological safety climate on accidents and injuries through safety knowledge, safety motivation, and safety performance. In short, empirical research aimed at testing causal models of workplace safety that incorporates distal and proximal antecedents of safety performance and the outcomes of safety-related behavior is providing process insights in regard to workplace safety, but this research is at an early stage. Although we stressed above that causal modeling of accident involvement and injury would benefit from improved conceptualizations and measures of safety behavior in relation to confirmed safety performance constructs (Burke et al., 2002), we also believe that the same point holds to some extent for accidents and injuries. First, accidents and injuries are frequently measured via self-reports and often in terms of dichotomous items (e.g., the person has or has not been in an accident or has or has not been injured),

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which creates ambiguity in terms of the meaning of such measures. Second, when accidents are more objectively measured via the number of OSHArecordable incidents they involve incidents that required more then basic first aid or resulted in lost work days due to injury. Depending on the purpose of one’s investigation, self-reports or OSHA-recordable incidents alone are likely deficient in terms of capturing the nature of accidents and differentiating more or less severe injuries. The nature of accidents (e.g., accidents that do or do not require more than simple first aid) and injury severity (viewed in terms of financial, personal, and social costs) are related, yet research modeling accident/injury involvement has generally not attended to such distinctions. Notable exceptions that focus on the distinction between OSHA- recordable (or Mine Safety and Health Administration-recordable) accidents and microaccidents that only require basic first aid are reported in Wallace and Chen (2006) and Zohar (2000, 2002). Our understanding of the national, organizational, and individuallevel antecedents of accident involvement and injury will likely improve to the extent that we also improve conceptualizations and measures within these safety outcomes domains. For the most part, the literature is silent in regard to estimating relationships between constructs within most domains in Fig. 1 and occupational illnesses and diseases. This void is understandable given that many illnesses and diseases that have their origins in work such as cancer and lung disease tend to have long latencies before their onset. However, the literature clearly recognizes the need for appropriate worker action to avoid exposures that may lead to occupational illnesses and diseases. In this sense, there is often some basis such as an epidemiological study to support the link between the health protective actions that workers can take and the possible reductions in risk associated with such exposures. To the extent that workers understand the association between such behaviors and health outcomes, their health protective actions are known to relate to perceptions of control over potential exposures (see Arcury, Quandt, & Russell, 2002). Numerous behavioral interventions have been examined, with the aim of improving workers’ motivation and knowledge for engaging in actions that will preclude exposure to substances and conditions that are known to relate to occupational illnesses and diseases. These interventions have been effective with respect to improving knowledge and behavior related to foodborne illness (Finch & Daniel, 2005), HIV infection (Wu et al., 2002), noise-induced hearing loss (Lusk et al., 2003), and respiratory disease (Acosta, Chapman, Bigelow, Kennedy, & Buchan, 2005) to name just a few. In some cases, the interventions have led to not only improvements in


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knowledge and safe work behavior, but also the reduction of symptoms themselves as determined via clinical examinations (e.g., Held, Mygind, Wolff, Gyntelberg, & Anger, 2002, in the case of occupational contact dermatitis). Consistent with the above discussion, we believe that future research directed toward examining a hypothesized interaction between the level of engagement of these types of behavioral interventions and the potential severity of exposure (which in the case of many occupational illnesses and diseases is high) on knowledge acquisition and safety performance would be informative. Along with engineering solutions, confirming such an expectation would have important practice implications related to conducting behavioral interventions to reduce risks associated with occupational illnesses and diseases.

Dialogue and Reflection Unlike other models of workplace safety, we posit that dialogue (of either intrapersonal or interpersonal nature) and reflecting on one’s work-related safety experiences are key elements in learning from these experiences. As discussed above, reflection is a systematic thought process concerned with simplifying experience as well as a process involving thinking about contradictions, dilemmas, and possibilities. In particular, intrapersonal and/ or interpersonal dialogue subsequent to the experience of either positive or negative consequences of safety-related behavior including a near miss is likely to engender concrete reflection in relation to actions taken and the consequences of those actions. In regard to the severity of injury and illness that was or could have been experienced as a result of safety-related behavior, this dialogue would likely focus on the dilemmas faced and possibilities associated with these critical situations and actions taken. In considering the situational demands, this reflection might also take into account information on leader behavior and safety climate considerations. Importantly, dialogue and reflection in regard to critical situations, actions taken, and their consequences would be expected to enhance one’s knowledge and efficacy for handling future events (Burke, 2008). Thus, in Fig. 1, we propose feedback loops from dialogue and reflection to safety knowledge and safety motivation. Our expectation in regard to self-efficacy for handling future events of a similar nature is that that self-efficacy develops, in part, when individuals engage in extensive self-reflection on their experience and the adequacy of their thoughts and actions (Bandura, 1986). In regard to safety knowledge development, our expectation is

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consistent with arguments within action regulation theory and experiential theories of learning that individuals learn from experiencing errors and thinking in terms of actions (Hacker, 2003; Heimbeck, Frese, Sonnentag, & Keith, 2003; Holman, Pavlica, & Thorpe, 1997). To the extent that individuals can independently or through other means be encouraged to engage in dialogue and reflection, especially subsequent to an accident or near miss, we would expect their procedural knowledge and skill for handling such future events to improve. The reader is also referred to discussions in Tesluk and Quigley (2003) and Hofmann and Stetzer (1998) for how an open, freeflowing discussion over safety issues may enhance learning from accidents, errors, and near misses (and to Edmondson, Dillon, & Roloff, 2008, for a related discussion in teams). In terms of organizational efforts to encourage individual learning from accidents, injuries, and near misses, several focused efforts could be considered. As an example, a structured after-action-review akin to the analysis of critical incidents (Flanagan, 1954) might prove beneficial for generating dialogue and reflective thinking on the behalf of workers (Baird et al., 1999). Relevant questions from an after-action-review might include: (a) What was the intent or purpose of the action, (b) What exactly occurred and why, (c) What lessons (in relation to organizational and personal contributing factors) were learned, and (d) What future short-term and longer-term actions and plans should be considered by management and workers? Depending on the nature of the safety and health outcomes, Ellis, Mendel, and Nir’s (2006) work would suggest tailoring the after-actionreview. That is, after successful events, their research would call for an emphasis on the discussion of incorrect actions, whereas, after unsuccessful events, their research supports a discussion of both correct and problematic actions. While Ellis and colleagues’ research is informative, research is needed to delineate how dialogue and reflection can be considered or encouraged within after-action-reviews or other types of interventions to optimally enhance safety knowledge and safety motivation. The reader is referred to Burke et al. (2007) for a more detailed discussion of needed research on dialogical aspects of safety knowledge and motivation development.

CONCLUSION In this chapter, we integrated research on workplace safety from a variety of disciplines and fields within business, engineering, psychology, public health, and medicine to develop a multilevel model of the processes that affect


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individual safety performance and safety and health outcomes. Our discussion highlighted what is known about key construct relationships within and among national, organizational, and individual levels of analysis and identified future research directions for advancing our understanding of workplace safety. Importantly, our modeling of the situational- and individual-level determinants of workplace safety reflected not only an interactionist approach, but it also emphasized the social construction of workplace safety especially in relation to discussing how and why workers learn and think about safety in and by action. Our emphasis on the social construction of workplace safety also highlighted key social, political, economic, and cultural conditions that are expected within and among nations to serve as important drivers of workplace safety. Finally, to manage the scope of our review, we delimited our discussions to key constructs and processes within and between the respective levels of analysis that over time would be expected to influence individual safety behavior and outcomes. Although we delimited our scope in this manner, our perspective provides a basis for extending multilevel modeling of workplace safety to include processes and constructs that over time would affect performance and safety-related outcomes at the organizational/group and national/regional levels of analysis. In this sense, we hope that our multilevel, interdisciplinary perspective on workplace safety not only engenders research aimed at testing expected causal relationships within the model, but it also encourages efforts to expand multilevel conceptualizations and efforts to study workplace safety at the organizational/group and national/regional levels of analysis.

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EXECUTIVE PAY AND FIRM PERFORMANCE: METHODOLOGICAL CONSIDERATIONS AND FUTURE DIRECTIONS Beth Florin, Kevin F. Hallock and Douglas Webber ABSTRACT This paper is an investigation of the pay-for-performance link in executive compensation. In particular, we document main issues in the pay– performance debate and explain practical issues in setting pay as well as data issues including how pay is disclosed and how that has changed over time. We also provide a summary of the state of CEO pay levels and pay mix in 2009 using a sample of over 2,000 companies and describe main data sources for researchers. We also investigate what we believe to be at the root of fundamental confusion in the literature across disciplines – methodological issues. In exploring methodological issues, we focus on empirical specifications, causality, fixed-effects, first-differencing, and instrumental variable issues. We then discuss two important but not yet well-explored areas, international issues, and compensation in non-profits. We conclude by examining a series of research areas where further work can be done, within and across disciplines.


49

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Executive compensation is a topic that has interested researchers for nearly a century. Bearle and Means’s (1932) pioneering discussion of the modern corporation, Robert’s (1956) first empirical study, Murphy’s (1985) first discussion of considering panel data, Jensen and Murphy’s (1990) discussion of the way executives are paid, and Hall and Liebman’s (1998) study of the link between wealth and firm performance and a steady stream of academic papers since have focused on the link between pay of CEOs and corporate performance. This subject is perhaps even more relevant today with the dramatic changes in the economy and the outcry from various constituent groups to examine the pay of senior executive more closely. The subject has interested researchers from a variety of fields including Human Resource Management, Economics, Accounting, Finance, Law, Sociology, Psychology, and Industrial Relations. It is remarkable that, although hundreds of papers have been written on the subject, there is no real consensus on the relationship between executive pay and firm performance. This is due, in part, to the diverse set of disciplines involved in the study, the wide variety of methods used to investigate the main questions and the diversity in knowledge about the institutions that matter in this area. This paper is an attempt to bridge these gaps and consolidate some of the understanding of this important issue. This paper reviews some of the literature, documents the current empirical facts, explains the data available, discusses pay and performance, discusses the varied empirical methods and possible reasons for differences in ‘‘results’’ across studies, identifies international issues, explores the current regulatory environment, and considers avenues for future work in the area. The paper is almost exclusively focused on CEO pay, although there is some discussion of the top executive team. Although it should be clear from the context, in most instances when we say ‘‘executive’’ we primarily are referring to the CEO. The paper is not a comprehensive review of all of the literature on executive compensation, nor is it a review of the pay-forperformance literature, and we apologize, in advance, to the authors whose papers we have not discussed.1 However, this paper highlights certain studies that are examples of the issues we explore. In the next section, we describe the CEO pay-for-performance debate and, in general terms, why it has not yet been resolved. Following that is another section, where we describe a series of important data issues. Part of the rise of the field has been due to public disclosure of executive compensation data in publicly traded firms in the United States. However, there have been substantive changes in disclosure over time, and we explore the implications of that for our work. We discuss types of executive compensation data and changes over time and then go on to describe the specific sources most

Executive Pay and Firm Performance

51

frequently used by academics and practitioners today. We finally discuss pay levels and pay mix across a set of 2,108 CEOs using data from 2009 proxy statements. We then have a section on ‘‘practical’’ matters of setting executive compensation and reasons why that may affect the pay-for-performance debate. This includes a discussion of ‘‘proxy advisory services’’ and the role they play in setting executive compensation. We then follow with a discussion of methodological issues, which we feel are a central part of this work. In fact, we feel that issues of methodology are specifically important in this area of research since researchers across disciplines (and within) use similar, yet distinctly different empirical models, and these can have profound implications for their findings. We feel that these considerations can help clarify past findings and perhaps suggest future directions. The particular functional form of the empirical specifications used in the executive pay literature differ widely. We describe examples of each and discuss implications of these models for interpreting pay-for-performance measures. In fact, we believe that a part of the problem in differing interpretations of pay-for-performance results comes from problems in appropriately interpreting empirical models. This paper includes discussion of the ‘‘right’’ functional form, ‘‘fixed-effect’’ and ‘‘first-difference’’ models, instrumental variables and the implications for executive compensation research, and a section on thinking about ‘‘causality.’’ Next, we explore different measures of firm performance including accounting measures, financial measures, subjective measures, and relative performance. We also include a discussion of the little-studied area of international compensation. Executive compensation in non-profit organizations is also briefly discussed. As difficult as it may be to consider the pay of managers in for-profit firms, at least it is clear that there is a ‘‘bottom’’ line. This section describes compensation of senior managers in a variety of non-profit organizations including non-profit hospitals, labor unions, and non-profits in general. Finally, we offer a concluding section where we summarize our work, discuss various new government reforms (including say on pay and managing risk), and offer a plan for future work in the area.

A BRIEF HISTORY OF THE CEO PAY–PERFORMANCE DEBATE, AND WHY IT ISN’T RESOLVED Summarizing the massive literature on the CEO pay–performance debate is not an easy task. However, in this section, we offer a brief summary of some

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of the issues and findings over the past few decades. This will set the stage for our later discussion of functional forms, empirical problems, and other issues. The study of executive compensation goes back at least to Roberts (1956) and even Bearle and Means (1932). There were also notable papers decades ago such as Masson (1971), Lewellen and Huntsman (1970), and Coughlin and Schmidt (1985) among others. Although the field really took off with the availability and use of better data (both in terms of quantity and quality) and Murphy’s (1985) landmark study. Murphy (1985) collected data on the compensation and performance of 461 executives at 71 firms over a number of years. But, rather than estimating simple cross-sectional relationships (which showed no relationship between CEO pay and performance), Murphy (1985) introduced ‘‘fixed-effects’’ models (described below) and found a strong relationship between pay and performance. This empirical method was not novel in economics at the time, but it had not been applied to the CEO pay literature and was an interesting and important advance for reasons we discuss below. Murphy (1985), documenting a relationship between pay and performance also wrote a paper in the Harvard Business Review at the time stating that ‘‘CEOs are worth every nickel they can get.’’ Five years later Jensen and Murphy (1990) wrote an important paper using first-difference methods (also described below). In that paper, they found that for every $1,000 increase in shareholder value (measured as a change in the market value of equity), CEO pay went up by $3.25. Their interpretation of this was that, although there was a relationship between pay and performance, the relationship was rather weak and could be strengthened. In part, due to Jensen and Murphy’s work, and due to calls from practitioners, this leads to the extraordinary rise in the use of stock and stock options in executive compensation contracts. Options and stock became much more important components of executive pay packages starting in the early 1990s.2 Later in the 1990s, Hall and Liebman (1998) asked whether CEOs were paid like ‘‘bureaucrats’’? They collected unique data on stock and stock options (that were at the time not more formally disclosed) and found stronger relationships between pay and performance than found by Jensen and Murphy (1990), on the order of $5.29 for every $1,000 increase in shareholder wealth. They conclude that while this may still seem like quite a weak relationship, their work suggests that even small changes in performance can have very large effects on the lifetime wealth of an executive.


53

More recently, Bebchuk and Fried (2004, 2006) wrote a provocative book called ‘‘Pay Without Performance.’’ This book carefully articulates the difference between the often-discussed ‘‘arm’s-length bargaining’’ framework and what they call the ‘‘managerial power’’ perspective, where, in essence, boards are captured by CEOs. They discuss many reasons why they think the system for setting CEO pay needs reform.3 Answering the pay-for-performance debate in executive compensation is obviously a difficult question. There are many complications. For example, researchers use different data sources, companies have different compensation and business strategies (even in the same industry), and there are many potential factors that are not easily measured by academic researchers. However, one of the main reasons we think the debate has not yet been resolved is methodological issues that we explore in the rest of this paper. But, first we turn to some practical, institutional issues.

DATA ISSUES In this section, we will outline several data issues that have confronted researchers who study executive compensation. This will include a discussion of the types of compensation data, Securities and Exchange Commission (SEC) rules changes on reporting compensation, and specific ways firms are required to report today. We will also discuss major sources of data used by practitioners and academics. We will then go on to briefly describe pay levels and pay mix for CEOs across industries and firm sizes from a set of 2,108 companies.

Types of Executive Compensation Data and Changes Over Time One of the reasons for the meteoric rise in the number of papers published on executive compensation is the availability of data. Data on executive pay have been widely available since 1992, when the SEC through new disclosure rules required firms to report on top officer compensation in a systematic way. However, the way data were reported was not, until very recently (a 2006 SEC change that we discuss below), entirely satisfying in terms of really understanding the way executives were paid at a point in time. For example, prior to 2006, we may have known an executive’s salary, bonus, value of stocks sold, and options exercised in a given year. However, those options and that stock may have been accumulated over many years so the

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BETH FLORIN ET AL.

information reported in a given year was a combination of some compensation for the most recent year (e.g., the salary) and stock and options accumulated for perhaps as long as a decade earlier. This may be one of the reasons researchers have had trouble linking pay to performance – since the performance may have been for a given year or the year prior while the pay was from a hybrid of data that may have covered a large number of years. We discuss this problem with the pay–performance relationship below. Of course, there have been exceptional cases of authors who have gone to great lengths to collect data on executive compensation prior to the most recent SEC change. Examples include Murphy (1985) – perhaps the first real ‘‘classic’’ in the literature – Hall and Liebman (1998), and a more recent effort by Frydman and Saks (2010). In the first case, Murphy (1985) collected data from 76 manufacturing firms (71 were used in the analysis sample) to investigate the relationship between pay and performance. In the second, Hall and Liebman (1998) collected detailed data on stock options (that were not previously collected in one place) to demonstrate a relationship between ownership in firms and firm performance. Frydman and Saks (2010) is an ambitious example of careful data collection. In this paper, the authors investigate executive compensation from a set of firms from 1936 to 2005. They find that the median real value of executive compensation was quite flat from the late 1940s through the 1970s, showing a weak overall link between CEO’s pay and firm’s growth. The collected the data by hand from company proxy reports from 1936 through 1991 and then used ExecuComp (explained in more detail below) for data beyond 1991. In 2006, the SEC began requiring publicly traded firms to disclose compensation for the CEO, Chief Financial Officer, and three other most highly paid Named Executive Officers (NEOs). The new guidelines both clarified and standardized the elements of compensation as well as the time period for reporting. Among the information firms are now required to report are salary, bonus, nonequity incentive compensation, stock, stock options, changes in pension and nonqualified deferred compensation, and other compensation. It is worth being clearer about these seven ‘‘main’’ components of compensation. Salary, of course, is the annual, fixed, and guaranteed compensation for the executive. Bonus and nonequity incentive compensation are sometimes confused and, intuitively, both can be considered a type of ‘‘bonus.’’ Strictly speaking, the bonus as listed in the table is formula-based pay beyond cash salary. On the other hand, nonequity incentive compensation can be both short- or long-term pay that is based on some preset criteria (based on performance) whose outcome is uncertain. Stock compensation is the value of the stock granted over the prior year, as


55

of the time it is granted. Stock options represent the value of the options granted over the prior year. Stock options pose a unique problem in valuing executive compensation contracts. The numbers included in firms’ proxy statement ‘‘summary compensation tables’’ are accounting-based numbers and do not necessarily reflect the value of the options at the time of the grant. Therefore, we recommend and most researchers use the value of stock options from the stock option grant summary tables, which are also included in firm proxy statements.4 Finally, ‘‘other’’ compensation refers to amounts of perquisites of $10,000 or more or to tax gross-ups, company contributions for security, private use of aircraft, financial planning, etc. Table 1 is an example of a Summary Compensation Table for the General Electric for 2009. Several features of the table are noteworthy. The table lists compensation for the CEO, CFO, and five other executives. As noted above, firms are required to list the CEO, CFO, and at least three others. One reason for listing more than five executives is the fact that some may have retired or otherwise left the firm during the year. Another (which is not the case for GE) is the example of ‘‘co-CEOs’’. It is also clear from the table that information is included for each of the last three years. This is the third year since the new SEC regulations came into place so is, therefore, the first time outsiders can see three years of compensation information all in the same proxy statement. Table 1 also shows the seven different pay components that are required to be reported for each executive.5 It is also interesting to see that at GE, the CEO was not the highest paid executive (at least as reported in the most recent proxy statement). In fact, as reported in Table 1, three others earned more than the CEO. Hallock and Torok (2010) report that of 2,108 firms they studied, in only 81% CEO was the highest paid executive. There are many reasons why the CEO may not be the highest paid, including one-time signing bonus, larger than normal option grants (commonplace when hiring new executives) or severance, for example. In the case of GE in Table 1, the CEO received no bonus, option awards, or nonequity incentive payout. All three executives who were paid more than the CEO had non-zero values for each of these elements of compensation. Finally, it is interesting to see, in Table 1, the diversity of compensation across pay elements and to see the diversity of pay within the top management team.

Main Data Sources Used by Academics and Practitioners There are three major commercial data sources on executive pay at the person- and firm-level that are now relatively widely used. The first,

1,350,000

916,667 2,750,000 2,500,000

2008 2007 2006 2008 2007 2006 2008 2007 2006 2008 2007 2006 2008

2008

2008 2007 2006

Jeffrey R. Immelt, Chairman of the Board and CEO Keith S. Sherin, Vice Chairman and CFO Michael A. Neal, Vice Chairman

Stock Awards2 ($)

2,783,000 7,590,000 6,900,000

1,310,000

– 214,664 574,322 1,597,537 1,714,833 2,225,749 1,475,945 1,457,839 1,759,672 1,597,537 1,714,833 2,225,749 1,239,568

– – 1,943,665 1,303,005 2,516,712 2,473,683

10,148,300 0 0

4,169,500

0 0 0 2,555,300 0 0 2,933,900 0 0 5,615,400 0 0 4,000,200

Option Nonequity Awards4 Incentive Plan ($) Compensation ($)

6,777,594 2,731,013

0 6,860,3183 5,800,000 9,802,3593 5,000,000 7,404,2093 2,550,000 2,987,493 3,000,000 3,076,095 2,550,000 2,808,919 2,900,000 3,512,898 3,880,000 4,212,201 3,300,000 3,906,929 2,700,000 3,659,090 3,000,000 4,406,900 2,550,000 4,122,437 1,850,000 2,284,110

Bonus ($)

1,208,099 1,072,075 2,422,714

5,911,944

3,563,466 78,290 1,036,908 2,503,541 1,281,453 1,564,398 3,484,939 2,979,130 3,032,927 3,328,715 1,852,735 2,183,677 1,432,870

Change in Pension Value and Nonqualified Deferred Compensation Earnings5 ($)

2,080,058 1,314,005 1,010,780

190,426

372,819 396,267 548,013 288,718 275,400 308,222 344,044 343,674 294,872 261,073 393,825 335,866 250,857

All Other Compensation6 ($)

17,136,124 15,972,750 17,823,889

22,440,477

14,096,603 19,591,580 17,863,452 13,982,589 10,701,948 10,682,288 16,301,726 14,422,844 13,694,400 18,811,815 12,918,293 12,817,729 12,257,605

Total ($)

Note: Table from 2009 proxy statement of General Electric Company. 1 Messrs. Sherin and Rice deferred a portion of their salaries under the 2006 Executive Deferred Salary Plan. They were not named executives at the time this plan was initiated. The amounts are also part of the Nonqualified Deferred Compensation table on page 30. In addition, each of the named executives contributed a portion of his salary to the company’s 401 (k) savings plan. 2 This column represents the dollar amounts recognized for the 2007 and 2006 fiscal years for the fair value of PSUs and RSUs granted in those years, as well as prior fiscal years, in accordance with SFAS 123R. Pursuant to SEC rules, the amounts shown exclude the impact of estimated forfeitures related to service-based vesting conditions. For RSUs, fair value is calculated using the closing price of GE stock on the date of grant.

Brackett B. Denniston, Senior Vice President, General Counsel and Secretary David R. Nissen, Former President & CEO, GE Money Robert C. Wright, Former Vice Chairman

John G. Rice, Vice Chairman

3,300,000 3,300,000 3,300,000 1,500,000 1,354,167 1,225,000 1,650,000 1,550,000 1,400,000 1,650,000 1,550,000 1,400,000 1,200,000

Year

Salary1 ($)

Proxy Statement for General Electric Company (2008 Summary Compensation Table).

Name and Principal Position

Table 1. 56 BETH FLORIN ET AL.

As Mr. Wright is eligible for retirement, the fair value of his awards that have been held for more than a year have already been fully expensed. For additional information, refer to note 23 of the GE financial statements in the Form 10-K for the year ended December 31, 2007, as filed with the SEC. For information on the valuation assumptions with respect to grants made prior to 2007, refer to the note on Other Stock-Related Information for the GE financial statements in the Form 10-K for the respective year-end. Refer to note 3 below for a discussion of the calculation of the fair value of PSUs. See the Grants of Plan-Based Awards table for information on grants awarded in 2007. These amounts reflect the company’s accounting expense, and do not correspond to the actual value that will be realized by the named executives. 3 This amount represents the company’s accounting expense for PSUs pursuant to SFAS 123R and SEC rules. It reflects the expense for all previously granted PSUs, not only those granted in 2006 or 2007. The actual value received depends on performance: 50% of the PSUs converts into GE stock only if GE’s cash flow from operating activities, adjusted to exclude the effect of unusual events, has grown an average of 10% or more per year over the performance period, and 50% converts into GE stock only if GE’s total shareowner return meets or exceeds that of the S&P 500 over the performance period. Accordingly, Mr. Immelt may receive 0%, 50%, or 100% of each PSU grant. For example, as described in the Compensation Discussion and Analysis on page 17, Mr. Immelt did not earn 50%, or a total of 215,000 shares, from the PSUs granted to him in September 2003 and February 2006 because the total shareowner return condition was not met. Although the PSUs not earned by Mr. Immelt were cancelled, the related accounting expense of $4.3 million has been disclosed as compensation to Mr. Immelt over the performance period. In measuring fair value, SFAS 123R distinguishes between the PSU vesting condition related to the company’s stock price and the nonstock price-related performance condition. The restrictions on the PSUs lapse at the MDCC meeting in February following the end of the performance period. 4 This column represents the dollar amounts recognized for the 2007 and 2006 fiscal years for the fair value of stock options granted in those years, as well as prior fiscal years, in accordance with SFAS 123R. Pursuant to SEC rules, the amounts shown exclude the impact of estimated forfeitures related to service-based vesting conditions. As Mr. Wright is eligible for retirement, the fair value of his awards that have been held for more than a year have already been fully expensed. For information on the valuation assumptions, refer to note 23 of the GE financial statements in the Form 10-K for the year ended December 31, 2007, as filed with the SEC. For information on the valuation assumptions with respect to grants made prior to 2007, refer to the note on Other Stock-Related Information for the GE financial statements in the Form 10-K for the respective year-end. See the Grants of Plan-Based Awards table for information on options granted in 2007. These amounts reflect the company’s accounting expense and do not correspond to the actual value that will be realized by the named executives. 5 This column represents the sum of the change in pension value and nonqualified deferred compensation earnings for each of the named executives. The change in pension value in 2007 was $99,861, $1,221,780, $2,913,282, $1,759,575, and $488,342 for Messrs. Immelt, Sherin, Neal, Rice, and Wright, respectively. The negative value for Mr. Immelt was primarily due to an increase in the discount rate used to calculate the present value of his benefit, partially offset by an additional year of pension accrual. In accordance with SEC rules, the amount included in this column relating to the change in pension value for Mr. Immelt is $0. See the Pension Benefits table on page 28 for additional information, including the present value assumptions used in this calculation. In 2007, the above-market earnings on the executive deferred salary plans in which the named executives participated were $78,290, $59,673, $65,848, $93,160, and $583,733 for Messrs. Immelt, Sherin, Neal, Rice, and Wright, respectively. Above-market earnings represent the difference between market interest rates determined pursuant to SEC rules and the 8.5% to 14% interest contingently credited by the company on salary deferred by the named executives under various executive deferred salary plans in effect between 1987 and 2007. See Nonqualified Deferred Compensation beginning on page 29 for additional information. 6 See the All Other Compensation table below for additional information.

Executive Pay and Firm Performance 57

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ExecuComp (Executive Compensation database) is produced by Standard and Poor’s Corporation and is surely the most widely used source of data for research on executive pay by academics. This source has data available from 1992 to present on the compensation of the top five highest paid employees of U.S. publicly traded firms (who have managerial control) in roughly 1,500 firms per year. These firms include those listed in the Standard and Poor’s 500, the Standard and Poor’s SmallCap 600, and the Standard and Poor’s MidCap 400. The data source starts in 1992, which was (until three years ago) the last time there was a major change in executive pay disclosure rules. Two other commercial executive pay sources are equilar.com and salary.com. Each also provides comprehensive datasets of executive compensation but have a larger focus on marketing to the for-profit firm and compensation consulting market. These sources are frequently used by compensation design practitioners and consultants to help design executive pay plans (and to set comparison groups). Some academics are using data from these sources but they are much more widely used by practitioners.6 The three data sources fundamentally report the same basic information. equilar.com and salary.com started after ExecuComp and perhaps academics have used ExecuComp in part due to inertia. It may also be due to cost considerations. Equilar.com and salary.com provide many interface features for, for example, making comparison groups easy and for presentation purposes. Most academic don’t need these features of their products.

Pay Levels and Pay Mix across Industries and Size Groups in 2009 This section is designed to set the basic context for the kinds of pay levels, mix (types of pay across different components of compensation), and pay distributions.7 The data for this section are from salary.com and comprise 2,108 publicly traded firms who reported executive compensation information in their proxy statements as of June 2009. Fig. 1 displays two measures of compensation. The first is defined as ‘‘cash’’ and is the sum of salary, bonus, and nonequity incentive. The second measure is ‘‘total compensation.’’ This is defined as the sum of salary, bonus, nonequity incentive, stock, stock options, change in pension and nonqualified deferred earnings, and other. Fig. 1 displays the median cash compensation and total compensation for CEOs by industry for each of 22 different industries. Notice the dramatic heterogeneity in compensation levels for the median CEO across industries. For example, the median CEO in


Fig. 1.

59

CEO Compensation by Industry. Source: From Hallock and Torok (2010). Data from salary.com

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BETH FLORIN ET AL.

Commercial Banks earned about $581,000 in cash pay and $906,000 in total compensation. At the other extreme is the Food and Tobacco industry where the median CEO earned $2.28 million in cash compensation and $5.80 million in total compensation. These statistics alone mask another level of heterogeneity. Consider, for example, the Food and Tobacco industry (numbers not reported in tables or figures). There, the CEO at the 10th percentile earned $575,000 in cash pay and $901,632 in total compensation but the CEO at the 90th percentile of that industry earned $5.6 million in cash and $14.9 million in total compensation (Table 2). It may seem strange that Commercial Banks represent the industry with the lowest paid median CEO. It must be kept in mind that these numbers do not control for the size of the organization. In fact, there are a large number of Commercial Banks in the sample and many of them are quite small. Organization size (e.g., revenue and employees) is highly positively correlated with the compensation of the senior leaders. Fig. 2 is a case in point. In this figure, the 2,108 companies are sorted by their level of annual revenue. The smallest 10 percent are in decile 1, the next 10 percent in decile 2, and up to the largest 10 percent in decile 10. It is clear that the median level of compensation rises monotonically with organization size. In particular, for the smallest 10 percent of companies (those with annual revenues below $155 million, the median CEO earned $522,000 in cash pay and $1.04 million in total compensation. This rises monotonically up to the largest 10 percent of firms (those with annual revenues above $9.6 billion) where the median CEO earned $3.03 million in cash compensation and $11.3 million in total compensation. Again, the median masks the larger distribution. For example, for the largest 10 percent of companies, the CEO at the 10th percentile earned $4.2 million in total compensation but the CEO at the 90th percentile earned $25 million in total compensation. Understanding the levels of pay for CEOs is interesting and important but misses a more interesting and important part of executive compensation, how executives are paid. In particular, we now explore how executives are paid across the seven components of compensation discussed above. Fig. 3 shows a great deal of heterogeneity across compensation components by industry. In fact, it is quite reasonable to expect diversity in compensation mix within industry. Fig. 4 reports the pay mix distribution on firm size deciles (the same deciles reported in Fig. 2). Notice that as the average firm gets larger a smaller fraction of the total compensation is paid in salary and a larger fraction is paid in stock and stock options. For example, for the smallest 10 percent of companies, the fraction of total compensation paid in salary is 43.91 percent but for the largest 10 percent of companies, the

500,000 463,040 332,083 678,501 654,711 370,000 833,750 395,159 465,500 500,000

575,000 416,678 450,000

612,435 602,077 479,938 500,000 437,396 654,647 429,504 548,035 605,000

88 183 185 85 78 137 26 153 97 58

50 109 162

89 40 132 91 127 20 59 86 53

10th

1,352,478 1,585,984 696,411 1,867,734 1,585,526 1,220,244 2,100,333 1,223,686 1,628,969 1,860,484

Mean 1,071,563 1,063,750 581,250 1,524,583 1,137,002 875,308 1,353,457 878,333 1,199,756 1,009,856

Median 1,637,340 1,966,500 875,000 2,434,725 1,732,500 1,375,350 2,386,318 1,427,375 2,025,597 2,740,276

75th

894,072 788,987 631,222 800,000 700,000 852,332 640,000 850,962 891,033

1,993,162 1,373,753 1,277,607 1,308,878 1,553,112 1,505,563 1,516,370 1,694,643 1,774,548

1,375,000 1,124,018 900,000 1,003,167 1,116,926 1,325,500 1,040,000 1,287,665 1,450,059

2,839,000 1,692,627 1,708,600 1,600,000 1,700,000 2,292,103 1,800,126 2,050,000 2,075,000

10th

25th

Mean

Median

Total Compensation 75th

90th

4,100,000 941,741 2,691,200 936,277 2,501,325 803,130 2,163,333 836,708 3,167,087 657,296 3,477,798 984,558 3,600,000 710,625 3,457,587 874,643 2,841,266 1,185,812

1,830,011 1,393,705 1,169,791 1,192,087 1,330,447 1,450,150 1,405,963 1,800,794 1,629,529

5,683,356 3,588,637 3,831,050 3,530,677 4,186,537 3,268,828 3,941,940 4,551,456 3,575,034

3,269,665 3,313,487 2,346,803 2,459,920 2,579,077 2,053,119 2,398,471 3,217,168 2,888,031

7,325,265 14,900,000 5,167,598 6,929,589 4,958,694 7,682,034 4,387,239 6,765,583 5,259,613 9,182,022 6,072,780 8,106,963 5,293,130 7,904,901 6,571,790 10,700,000 4,674,634 6,929,124

901,632 2,025,222 6,946,929 5,804,652 9,575,570 14,900,000 865,876 1,307,296 3,036,322 2,271,405 4,053,217 6,983,402 769,648 1,399,934 4,999,320 2,959,994 6,875,570 13,000,000

2,493,708 686,254 1,199,744 3,438,851 2,232,751 5,430,194 7,282,921 3,526,250 823,005 1,403,982 4,666,154 2,686,526 6,454,073 11,500,000 1,158,041 437,261 580,432 1,762,620 905,673 1,895,970 3,493,447 3,284,000 1,102,336 1,686,500 5,099,530 3,606,402 6,353,118 10,800,000 3,577,845 819,035 1,518,311 4,293,980 2,849,066 5,615,136 11,900,000 2,671,250 589,629 1,132,758 3,285,691 1,939,824 3,845,512 7,676,077 5,005,000 1,705,216 2,252,217 5,297,176 4,349,201 7,411,561 11,000,000 2,548,000 638,707 1,256,280 3,277,515 2,307,811 3,979,451 7,614,834 4,400,600 838,168 1,292,547 5,011,330 2,761,803 6,986,162 16,200,000 4,950,000 840,944 1,254,669 4,619,841 3,416,057 7,606,430 15,100,000

90th

1,147,342 2,922,108 2,277,237 4,046,612 5,614,329 652,000 1,222,678 1,000,000 1,590,750 2,330,144 717,750 1,841,272 1,204,883 2,250,000 4,000,002

683,542 661,770 402,500 961,299 780,000 555,024 1,000,000 550,000 646,154 800,000

25th

Cash Compensation

CEO Compensation by Industry.

Note: From Hallock and Torok (2010). Data from salary.com

Business services Chemicals Commercial banks Commodities Communications Computer services Construction Electronics Energy Financial services (nonbanks) Food and tobacco Holding companies Industrial and transportation equipment Insurance Lumber and paper Other manufacturing Other services Retail trade Textile and apparel Transportation Utilities Wholesale trade (S)

N

Table 2.


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Fig. 2.

BETH FLORIN ET AL.

CEO Compensation by Company Size. Source: From Hallock and Torok (2010). Data from salary.com

fraction of total compensation paid in salary is only 13.5 percent. Conversely, the average CEO in the smallest 10 percent of companies earned 35.56 (21.55 þ 14.01) percent of his or her total compensation in stock and options. But the average CEO in the largest 10 percent of companies earned 53.05 (24.81 þ 28.24) percent of his or her total compensation in stock and stock options. Now that we have set the stage for where the data come from and how CEOs are paid in the United States today, we turn to some of the practical issues in setting CEO pay.

PRACTICAL ISSUES IN CEO PAY In this section, we briefly describe some of the institutions that are important in setting CEO and other senior executive compensation in the United States. In particular, we briefly mention the role of the Board of Directors, the Compensation Committee of the board, and the role of executive compensation consultants. We go on to highlight some of what are known as

Fig. 3.

CEO Compensation Mix by Industry. Source: From Hallock and Torok (2010). Data from salary.com


Fig. 4.

CEO Compensation Mix by Size Group. Source: From Hallock and Torok (2010). Data from salary.com

64 BETH FLORIN ET AL.


65

‘‘proxy advisory services,’’ and the roles they play in executive compensation in the United States today.

What Happens in Board Rooms, and How Pay is Set Very little academic work formally discusses the role of the Board of Directors or the Compensation Committee of the Board, even though these are the organizations that formally set pay of executives in publicly traded companies.8 The Board is formally responsible for executive compensation but most boards have a Compensation Committee (a subset of the board) who set pay of the CEO and his or her top team. Hallock, Tonello, and Torok (2010) show that the median Boards across 10 deciles of firm size have between 8 and 12 members. The median compensation of Board members from the top decile of firms in 2009 was $191,000 (Hallock et al., 2010). Formally the Board is required to set compensation of the executive and expected to be ‘‘independent.’’ Many Boards hire compensation consultants to facilitate the development of compensation strategy and bring relevant data to help frame pay decisions for CEOs and other top managers. These compensation professionals consider the firm’s business strategy, compensation strategy, industry, organization size, composition of pay of ‘‘like’’ firms, and make recommendations to the board. Many in the press, government, and academia (most notably Bebchuk & Fried, 2006) have been critical of the formal system that exists in the United States for setting pay for executives. Bebchuk and Fried (2006) argue that there is no ‘‘arms-length’’ negotiation between boards and the CEO over executive contracts. On the other hand, they argue that boards (and consultants) are ‘‘captured’’ by CEOs who try to surround themselves with those who advocate for higher pay or more favorable compensation mix. To ameliorate this potential conflict, some have argued that compensation consultants to boards should be ‘‘independent.’’ An example of this is large multipurpose HR consulting companies that provide executive compensation consulting services, while at the same time providing others HR and benefits administration and consulting services. Since the revenue to the HR consulting firm from providing executive compensation consulting services may be only a small fraction of the total revenue received from that particular firm, some argue that the executive compensation consultants may want to give the CEO higher pay in implicit exchange for the large volume of other services provided to their company.

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Proxy Advisory Services, Investors, and Executive Pay Many ‘‘proxy advisory’’ services have emerged in the past decade including Risk Metrics (formerly Institutional Shareholder Services), Glass Lewis, and GovernanceMetrics International. These organizations market and sell their services to smaller investors, firms, and institutional investors. Among the services they provide is a type of ‘‘scorecard’’ by firm for thousands of publicly traded companies. They might, for example make recommendations on votes before the company, provide information about directors and also provide some information about executive compensation. The kinds of information these organizations provide about executive compensation is varied but quite limited. For example, in 2008, the typical Risk Metrics report was on the order of 15 pages, one of which was devoted to executive compensation. This included a total of three charts showing (1) the CEO’s total compensation relative to a peer group median, (2) salary, bonus, and nonequity compensation of the CEO relative to the median of a peer group, and (3) stock and option awards of the CEO relative to a peer group. A comparable 10-page report from Glass Lewis for the same time period devoted one page to executive compensation. This included (1) a grade (A–F) and historical ‘‘compensation score,’’ (2) two charts comparing various components of compensation with sector groups, and (3) a chart on shareholder wealth and business performance. GovernanceMetrics International for the same time period produced a three-page report for each firm that was entirely devoted to executive compensation. This included some charts on pay levels plotted against shareholder returns, comparisons to industry, charts on returns and pay, relative to industry, a chart on pay mix, and a one-page narrative. While we think these companies provide easily understood summaries to investors and firms with respect to CEO pay, firm performance, and comparison groups, the analysis is extremely simple and largely reports numbers straight out of individual firm proxy statements (except for some median comparisons by ‘‘industry’’). This serves as an example where the chasm between academic work on executive compensation and the practical world is enormous. Almost none of what has been learned in the past decades about executive compensation, pay, or performance is included in these sources.

METHODOLOGICAL ISSUES In this section, we explore two main issues. First, there have been a wide variety of empirical specifications in diverse research on pay and


67

performance for CEOs of publicly traded firms. We explore the diverse set of specification and how one might make different interpretations of results depending on specification used. Second, we discuss the issue of causality in empirical research on the pay–performance relationship for CEOs. Our intent is to help consolidate understanding and hopefully help researchers synthesize the diverse results.

Empirical Specifications One of the difficulties in comparing the results of all papers in the executive pay literature is that very few of them estimate the exact same model. While there does not appear to be a consensus on the ‘‘best’’ specification, there are many commonalities that appear over and over again. We begin our discussion with how compensation is actually defined. The most popular definition is to use ‘‘total compensation’’ (see our discussion above) as the dependent variable, but many studies also focused on option (Almazan, Hartzell, & Starks, 2005), bonus (Fattorusso, Skovoroda, Buck, & Bruce, 2007), or basic cash compensation (Comprix & Muller, 2006). One desirable trend in many papers is the analysis of several models, defining executive compensation differently each time, and demonstrating their results are robust to multiple forms of pay. Clearly, it is natural to expect certain forms of pay to be more strongly related to certain measures of firm performance than others. For example, it is reasonable to expect that stock and stock option compensation are more highly correlated with firm performance than salary. One important feature of a paper’s empirical specification is the functional form chosen to represent key variables. Specifically, the natural log-transformation, which is used to deal with skewed data, is commonly applied to the dependent and many independent variables. While the interpretation of coefficients is slightly less straightforward following a logtransformation, it is especially important for valid statistical inference when dealing with variables with a very skewed distribution such as compensation and sales data (Hallock, 1997 provided evidence that the log-transformation is important in executive compensation settings). Unfortunately, slightly less than half of the empirical papers we surveyed used the log-transformation on the dependent variable. This omission has the potential to seriously alter the magnitude and interpretation of results. One generally agreed-upon aspect is the need to control for a firm’s size when estimating an executive’s pay. While not unanimous, nearly every study

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we examined controls for size in some way. By far, the two most common ways to control for firm size were to use a measure of sales (revenue) or assets held by a firm. Another popular measure is the total number of employees. These three variables are commonly highly correlated, except in banks and other financial institutions, where assets are obviously substantially higher than sales and employees, relative to many other industries. When examining the link between pay and performance, a crucial choice authors must make is how exactly to define performance. There are a number of accepted ways this is done, although the most convincing studies present the results for several different measures (Abowd, 1990). The most common performance measure is a firm’s return on assets, followed by the return on common stock. Other measures include the return on equity, shareholder wealth, or firm profits. This point will be revisited in more detail later in this section. One important factor when looking at the effect of performance on pay, which only about a third of studies control for, is the variability of performance. The intuition being that some industries naturally have very volatile performance indicators, likely weakening the pay–performance (observed) relationship because a high (or low) value may not necessarily be a signal of the executive’s ability but rather a random shock. Studies that control for volatility generally use either the standard deviation (or variance) of performance (e.g., Garen, 1994) or the cumulative distribution function (CDF) of performance (e.g., Garvey & Milbourn, 2003; Dee, Lulseged, & Nowlin, 2005). The CDF appears to be the better option since it is a measure of the entire distribution of performance rather than just the spread. One class of variables conspicuously absent from the majority of models is demographic information about the executive (Kostiuk, 1990; Hallock, 1997; Bertrand & Mullainathan, 2001; Bertrand & Hallock, 2001 are notable exceptions). Very few studies include standard control variables such as gender, race, or age. Age and tenure (and their squared terms) seem particularly important to include in any sort of wage equation (as they are standard practice dating back to the original Mincerian wage equations). However, whether due to data constraints or omission, these variables appear in less than a fifth of the empirical studies we examined. Another important feature of many papers (particularly those without firm fixed-effects) is the inclusion of some measure of corporate governance (such as Core, Holthausen, & Larcker, 1999; Cornett, Marcus, & Tehranian, 2008). Evidence suggests that the strength of a firm’s corporate governance is positively related to the association between pay and performance.


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While not all studies use panel data, and some that do don’t take full advantage of their panels, the papers that fully utilize panel data tend to be the most convincing works. To begin our brief panel discussion, it seems that including industry fixed-effects are a bare minimum (this is true in a cross-sectional framework as well). Nearly every paper with firms in different industries either includes industry indicators or runs separate regressions for different industries. The most common panel data approach taken by studies in this literature is a first-difference approach (Boschen & Smith, 1995; Anderson & Bizjak, 2003; Becker, 2006 just to name a few), with relatively few papers using a firm/executive fixed-effect strategy (such as Aggarwal & Samwick, 1999a, 1999b, 2003; Cichello, 2005; Murphy, 1985). In many cases, this is likely due to a lack of data or insufficient variation within executive pay/performance measures. That said, the paper’s that are able to employ a fixed-effects/differencing strategy are most likely to come close to obtaining the best estimates of the ‘‘true’’ causal effect of performance on executive pay. We discuss this in more detail below in a section on causality. In total, about half of the empirical papers we examined attempt to fully exploit the panel nature of their data to varying degrees of success. The first-difference model was used by about a third of our sample of empirical studies, with CEO/firm fixed-effects used by slightly less than a fifth of papers. A discussion of which of these approaches is more appropriate (they are equivalent only in the case of two time periods) and is beyond the scope of our project, and rests on distributional assumptions and serial correlation of the error term (Table 3). To give the reader an idea of the relative frequency of the approaches to estimating executive compensation, we have compiled Table 4, which breaks down the studies by type of model (fixed-effects, first-difference, etc.) and whether the log-transformation was applied to the dependent variable. It should be noted that the 49 studies that appear in the table represent only a sample of the executive pay literature (we believe a representative sample of the top research), and there are undoubtedly a number of other papers that could appear in the table but are not included.

Thinking More Seriously about Causality The following section is meant as a brief introduction to the problem of establishing causality in empirical work, with specific emphasis on the CEO pay–performance problem. One of the reasons that there is such diversity in

396,000 412,961 500,000 642,310 690,000 838,235 1,000,000 1,031,279 1,233,333 1,694,000

25th

616,804 694,353 827,883 1,041,286 1,163,253 1,452,193 1,616,560 2,037,876 2,264,928 3,334,802

Mean 521,703 560,800 700,625 897,188 948,147 1,223,614 1,421,101 1,703,739 1,875,000 3,025,857

Median 765,290 765,164 967,500 1,090,939 1,334,500 1,723,000 1,950,913 2,440,838 3,008,750 4,468,336

75th 975,849 1,090,000 1,418,027 1,690,000 1,741,250 2,399,341 2,977,725 3,750,000 4,325,333 7,016,500

90th 449,349 418,168 521,101 726,737 854,755 1,078,276 1,535,356 1,567,596 2,114,767 4,223,182

10th

Mean

Median

Total Compensation 75th

90th

676,779 1,382,037 1,034,623 1,631,245 2,500,199 621,750 1,465,745 1,043,271 1,501,970 2,906,560 807,011 1,787,558 1,309,888 2,138,803 3,562,378 1,067,464 2,218,387 1,694,451 2,905,494 4,158,242 1,484,095 2,729,881 2,118,466 3,234,420 4,677,025 1,681,375 3,246,883 2,648,624 4,031,500 6,461,061 2,249,450 4,118,238 3,632,436 5,238,242 7,186,328 2,664,599 5,434,129 4,662,380 6,702,930 9,479,940 3,315,934 6,673,713 6,108,194 8,477,317 12,900,000 6,478,689 11,600,000 11,300,000 16,100,000 25,000,000

25th

CEO Compensation by Revenue.

Note: From Hallock and Torok (2010). Data from salary.com

0–o116 326,774 116–o219 330,000 219–o360 385,467 360–o600 473,525 600–o912 530,173 912–o1371 638,230 1371–o2209 727,282 2209–o3974 800,000 3974–o9637 900,000 9637 1,000,000

10th

Cash Compensation

Table 3.

70 BETH FLORIN ET AL.

Industry FE

Firm FE

Garen (1994)

Panel B: Studies that use a log-transformation on the dependent variable Ang, Lauterbach, and Chhaochharia and Bertrand and Vu (2003) Grinstein (2009) Mullainathan (2001) Cosh and Hughes Core and Guay (1999) Bebchuk and Grinstein (1997) (2005) Fattorusso, Skovoroda, Cornett et al. (2008) Buck, and Bruce (2007) Hall and Murphy (2002) Cunat and Guadalupe (2009) Hallock (1997) Gabaix and Landier (2008) Hallock (2002) Kostiuk (1990)

Carpenter and Sanders (2002) Chen, Steiner, and Whyte (2006) Dee et al. (2005)

Anderson and Bizjak (2003)

Almazan et al. (2005)

Aggarwal and Samwick (1999a)

Diff in Diff or Lagged Term

Murphy (1985)

Hall and Liebman (1998) Kato and Kubo (2006) Leonard (1990)

Hall and Knox (2004)

Gibbons and Murphy (1990)

Coughlin and Schmidt (1985)

Becker (2006)

Abowd (1990)

David et al. (1998) Frye, Nelling, and Webb (2006) Garvey and Milbourn (2003) Girma, Thompson, and Wright (2007) Hambrick and Finkelstein (1995) Jensen and Murphy (1990) Lippert and Moore (1994) Rajgopal, Shevlin, and Zamora (2006)

Hartzell and Starks (2003) Boschen and Smith (1995)

Aggarwal and Samwick (1999b) Aggarwal and Samwick (2003) Cichello (2005)

CEO FE

Breakdown of Empirical Research on Executive Compensation (Sample).

Panel A: Studies that do not use a log-transformation on the dependent variable Antle and Smith (1986) Aboody, Barth, and Comprix and Muller Kaszmik (2006) (2006) Baker and Hall (2004) Core et al. (1999) Harford and Li (2007)

No FE

Table 4. Executive Pay and Firm Performance 71

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BETH FLORIN ET AL.

the ‘‘answer’’ to the pay–performance problem is the diversity of empirical methods and the dearth of papers that consider the issue of causality at all. For example, trying to consider causal relationships in a simple cross section using ordinary least squares (OLS) specifications is clearly impossible. Given the wide range of statistical approaches and results in the executive pay literature, we feel that having a basic notion of causality is crucial for the reader to evaluate the relative merits of each paper. We have attempted to make this discussion as accessible as possible to readers across fields. The ultimate goal of most empirical research is to find the true causal effect of some independent variable X on an outcome Y. In the rare case that X is exogenously determined (randomly assigned), a simple regression of Y on X will give us the causal impact of X on Y. In the case of the pay-forperformance literature, this would be the equivalent of estimating Eq. (1): where a is a constant, b is the change in pay associated with a one-unit change in performance, and e is a random error term. CEO Pay ¼ a þ bPerformance þ

(1)

The reason this equation is never estimated is because in this model, we view performance as endogenous. By this, we mean that performance measures were not randomly assigned to executives, and that the same process that determines performance may also be related to the process that determines executive compensation (in a statistical sense, this means that performance is correlated with the error term). A commonly discussed analogy is to think of the effect of schooling on future earnings (Griliches, 1976; Card, 1995). It is an accepted fact that people with higher levels of schooling have higher incomes, but it is also generally true that, on average, individuals with higher innate ability have higher levels of schooling. So we must then ask how much of the increase in income is due to increased schooling and how much is due to a higher innate ability or some other unobserved variable related to schooling. The most common way to address this issue is to estimate Eq. (2), where we have added a vector of control variables Z along with their associated coefficients d. CEO Pay ¼ a þ bPerformance þ Zd þ

(2)

These control variables may include measures such as the size of the firm, the age of the executive, or any other factors that we believe has an impact on an executive’s pay. In this case, for us to assign a causal interpretation to


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the coefficient on performance, we are assuming that performance is randomly assigned after conditioning on the covariates in Z. If performance cannot be considered exogenous, then the results we obtain will be considered correlational rather than causal. Hence, we could say that a one-unit increase in performance is on average associated with a b increase in compensation. We would, however, be unable to make the causal claim that if we increased a performance measure by one unit, then we would observe an increase in pay of b. This is an important distinction because it is possible to have a strong correlation between performance and pay, yet there is no causal link. Unfortunately, many authors in the executive pay literature fail to make this distinction, discussing their results in a causal context when they have not fully addressed potential endogeneity concerns. Establishing a causal link is not an easy task, and many will argue it is impossible without a randomized experiment. There are several approaches, however, which can greatly improve the accuracy of the basic regression estimates mentioned above.

First-Difference and Fixed-Effects The essence of the first-difference and fixed-effect methods is to exploit the panel nature of certain datasets, namely the fact that we may observe the same firm/executive different times. For instance, assume that there is some fixed but unobserved factor that is correlated with both executive compensation and firm performance (this could be the executive’s ability, firm culture, etc.) denoted by g. If we observe many executives in periods t and t 1, then assume that the true wage equations are as follows: CEO Payt1 ¼ a þ bPerformancet1 þ Z t1 d þ g þ t1

(3)

CEO Payt ¼ a þ bPerformancet þ Z t d þ g þ t

(4)

Subtracting Eq. (3) from Eq. (4) will then give us Eq. (5), which we can estimate since all variables in Eq. (5) are observed, and an unbiased estimate of b. This is known as the first-difference method for obvious reasons. The most famous example of this in the empirical executive compensation literature is Jensen and Murphy (1990). The fixed-effect method is illustrated in Eq. (6), where instead of taking the difference between two periods we include a dummy variable for each CEO/firm in the sample. Interestingly, in the two-period case, the first-difference and fixed-effect methods are

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BETH FLORIN ET AL.

algebraically identical. In panels with more than two years the two methods differ, but the statistical assumptions underlying the difference between these methods is beyond the scope of this chapter (although should be taken seriously by the researcher). Murphy’s (1985) classic is the first example of this method in the empirical CEO pay literature. CEO Payt CEO Payt1 ¼ bðPerformancet Performancet1 Þ þ ðZ t Z t1 Þd þ t t1

(5)

CEO Pay ¼ a þ bPerformance þ Zd þ Firm þ

(6)

It is always important to note in any model where the identification is coming from (exactly which observations are contributing to which estimates). In a standard OLS regression context, all observations contribute equally to the estimated parameters. However, in a first-difference/fixedeffect context, the parameters are estimated based on changes within a firm. In other words, if a firm’s performance does not change, then it will not contribute to the estimate of b. Practically, this is important because if there is not much variation in a dataset, the estimates could be driven entirely by a small number of firms, or could even be the result of random noise. While it is not always possible to control for firm fixed-effects, including a set of industry dummy variables (assuming the dataset being used contains executives from more than one industry) is an absolute must. To not control for industry would implicitly assume that, conditional on the covariates, firms in different industries have the exact same pay structure.

Instrumental Variables Sometimes researchers do not have panel data available to them, or do not believe the assumptions implicit in a fixed-effects framework (these are discussed briefly in the section ‘‘Empirical Problems’’). In these cases, an instrumental variables (IV) framework can theoretically identify causal effects. We will not go into the mechanics of IV, but the intention is to find an exogenous variable that does not belong in the compensation equation correlated with the potentially endogenous variable of interest (performance), and identify b from this exogenous variation. The best example in the executive compensation literature is from Bertrand and Mullainathan (2001). In their study of oil companies, they use exogenous shocks to the price of oil to instrument for performance.


75

In this example, these shocks clearly do not belong in the compensation equation, but are highly correlated with performance indicators, making them an ideal instrument.

Empirical Problems The two methods for establishing causality outlined above are by no means a panacea and have many problems that must be addressed in practice. In fact, many researchers use these methods blindly and are lulled into a false sense of security that all problems have been solved via use of these methods. Clearly that is not the case. For a first-difference/fixed-effects approach, having a large dataset is crucial, since multicollinearity is exacerbated in this setting. In an IV framework, strong instruments are often hard to find, and must be rigorously justified in order to be accepted as valid. When surveying the pay-for-performance, or any empirical literature for that matter, it is important to keep this causal thought process in mind. Many studies will come to wildly different conclusions, for a variety of different reasons, and this line of thinking is key to discerning between the studies you should believe and those you shouldn’t.

Performance Metrics As mentioned before, a wide variety of measures, and categories of measures, are used to proxy for performance throughout the executive pay literature. The main categories of performance metrics are accounting, economic/market, relative performance, and subjective. The most commonly used measure, an accounting measure, is the after-tax return on assets (used by Bebchuk & Grinstein, 2005; Carpenter & Sanders, 2002; Chhaochharia & Grinstein, 2009; David, Kochhar, & Levitas, 1998 to name a few). This variable is preferable both because of its availability and straightforward interpretation/construction.9 The next most common measure of performance falls under the market category, shareholder return on common stock. Defined below,10 many researchers (such as Cosh & Hughes, 1997; Hall & Knox, 2004; Harford & Li, 2007) prefer this metric because it most directly measures the progress of the chief mission of the firm, to financially benefit its shareholders. Another accounting measure, the after-tax return on equity, is also widely used (Hambrick & Finkelstein, 1995; Leonard, 1990).

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As one might expect, the link between executive pay and performance appears to be influenced somewhat by the performance metric used. For instance, Abowd (1990) found that the link was much stronger for market measures than for accounting measures. Another class of performance metrics deals with the possibility that firms reward (or punish) their executives based on how the firm performs relative to other comparable firms in the same industry. The most common way to test for the presence of relative performance evaluation is simply to control for the difference between a firm’s performance measure and the market average for that same measure. Two of the papers that focus explicitly on this type of performance metric are Antle and Smith (1986) and Gibbons and Murphy (1990). As mentioned earlier, some studies explore the link between pay and performance using less quantitative and more subjective measures of performance. Denis, Hanouna, and Sarin (2006) find a positive link between allegations of securities fraud and executive stock options. In a similarly themed paper, Efendi, Srivastava, and Swanson (2007) find a positive correlation between misstated financial statements and executive stock options which were ‘‘in the money.’’ Finally, McGuire, Dow, and Argheyd (2003) find that executive compensation is unrelated to various measures of ‘‘social performance.’’

INTERNATIONAL ISSUES Generally, very little is known about international issues in compensation and in executive compensation in particular. Part of the problem with the study of executive compensation and the pay-for-performance issue across nations is related to differences in disclosure and, therefore, availability of data across countries. There are two main issues we discuss here. First is why there may be differences in compensation across countries. Second, is the state of the pay–performance literature using various within-country data sources.

CEO Pay Differences across Countries Making CEO pay comparisons across countries is extremely difficult. Fortunately there is one source that we know of that sheds some light on this issue. In a recent paper, Gabaix and Landier (2008) have some data


77

collected from the consulting company Towers Perrin on average levels of pay for CEOs across various countries as well as average levels of company size in those countries. We have reproduced one of their figures as Fig. 5. The horizontal axis in the figure is the average log company size in terms of company annual revenue. The vertical axis is the average log total compensation for CEOs. The points represent the different countries. For example, it is clear from the figure that CEOs in the United States are paid more, on average, than CEOs in any other country. At the same time, firms in the United States are larger (in terms of annual revenue) than firms in any of the other countries. The figure also has an OLS regression line plotted through it. Intuitively this suggests that if there is a ‘‘world’’ market for pay and if firm size is the only relevant characteristic in predicting pay, then countries with points above the line have CEOs who are, on average, ‘‘over paid,’’ relative to their average firm size and countries with points below the line have CEOs who are, on average, ‘‘under paid,’’ relative to their average firms size. This figure is interesting since it tells us several things. First, perhaps one of the reasons CEOs in the United States are paid so much, relative to their counterparts in other countries, is due to the fact that companies in the United States are so large.11 Second, even though firm size is one important

Fig. 5. CEO Compensation versus Firm Size across Countries. Source: From Gabaix and Landier (2008). With permission from MIT Press Journals.

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predictor of CEO pay (e.g., Rosen, 1992), firm size doesn’t completely explain why CEOs in the United States are paid so much more than elsewhere. We should note that the data for this figure were provided by one Human Resource consulting firm and their clients may not be representative of the universe of firms.

International Pay and Performance within Countries Several authors have tried to investigate the CEO pay–performance relationship, or CEO pay more generally, in countries other than the United States. We highlight some of these studies here. In this section, we will note studies from the UK, Germany, Japan, Sweden, and China. The one country (other than the United States) where most work has been done is the UK. In one paper, Conyon and Murphy (2000) find that CEOs are paid more in the US than the UK but much of that is due to stock-based pay that has a stronger link to performance. Conyon and Sadler (2001) find that in the UK, the sensitivity of pay-for-performance increases with organization levels. They go on to show a link between stock ownership and subsequent performance. In a more recent paper, Fattorusso et al. (2007) use UK data and find little link between bonus and firm performance. They argue that bonuses are, therefore, in essence ‘‘guaranteed.’’ Finally, Girma et al. (2007) find little link between CEO pay and performance, on average, in the UK. However, they find that for firms with many employees, there is a small pay–performance link. In Germany, Edwards, Eggert, and Weichenrieder (2009) find that firms with low concentrations of investor ownership have only a small link between CEO pay and firm profits. However, those with highly concentrated ownership have no link at all. Using some data from the UK and Germany, but in a mostly theoretical paper, Bruce, Buck, and Main (2005) discuss region-specific social norms, which others have argued are important to international compensation but very difficult to operationalize. In Japan, Kato and Kubo (2006) find a strong link between CEO pay and firm performance using a ten-year panel , especially if using accounting measures of performance. Becker (2006) finds that, in Sweden, incentives decrease with CEO wealth. Finally, Firth, Fung, and Rui (2007) study China. Clearly we need more research on the link between pay for CEOs and firm performance internationally. Given the difficulties in matching data sources across firms, the differences in disclosure requirements and tax rules across


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countries, and vastly different social norms in some countries, this may prove to be difficult yet interesting work.

OTHER ORGANIZATIONAL FORMS Studying executive compensation in the for-profit world is clearly difficult, as this paper has stressed. However, in for-profit firms the ‘‘bottom line’’ is quite clear. On the other hand, in non-profit organizations, it is not always clear what the ‘‘bottom line’’ mission of the organization really is. If we thought the ‘‘true’’ measure of performance for a particular non-profit was ‘‘alleviating poverty’’ or ‘‘helping those in need’’ or ‘‘caring’’ or ‘‘trustworthiness,’’ how would we measure this? A series of recent papers have begun to investigate the compensation of managers in non-profit organizations. Oster (1998) and Hallock (2002) both show that the strong link between firm size and executive pay that exists in for-profit firms is also present in non-profits. Additionally, Hallock (2002) points out that the revenue from government grants has no effect on executive compensation among non-profits once firm’s fixed-effects are controlled for. The same study also notes that a higher proportion of compensation at non-profits is in the form of benefits rather than salary. As noted in both Oster (1998) and Hallock (2002), determinants of pay at non-profits vary greatly by the type industry or charity. Examples include the nursing home industry as studied by Weisbrod and Schlesinger (1986), or universities that are examined by Ehrenberg, Cheslock, and Epifantseva (2001). Looking specifically at non-profit hospitals, Bertrand, Hallock, and Arnould (2005) examine the link between executive pay and profit-based performance measures following the introduction of health maintenance organizations (HMOs) into the market. They find that HMO penetration strengthened the link between pay and profit-based performance measures, and also increased the likelihood of turnover in less profitable non-profit hospitals. Some types of non-profits have more easily measurable performance measures. In their recent examination of the compensation structure for the heads of labor unions, Hallock and Klein (2009) find that the union membership and the wage of union members are strongly related to the pay of the union executive. Finally, Frye et al. (2006) use a matching strategy to compare firms that are ‘‘socially responsible’’ to the more traditional for-profit firm. While the analysis does not explicitly concern non-profits, the basic idea that some

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firms may put more emphasis on performance measures not easily observable still holds. Consistent with the non-profit literature, this study finds that ‘‘socially responsible’’ firms (as measured by the Domini Social Index) have a much weaker link between pay and financial performance, and that option grants do not appear to induce the same risk-taking behavior in socially responsible firms that is observed in nonsocially responsible firms.

CONCLUDING COMMENTS AND RESEARCH QUESTIONS FOR THE FUTURE This paper is an investigation of the pay-for-performance link in executive compensation. In particular, we have explored data issues including how pay is disclosed and how that has changed over time, a summary of the state of CEO pay levels and pay mix in 2009 using a sample of over 2,000 companies, described main data sources, documented main issues in the pay–performance debate and explained practical issues in setting pay. We also investigated what we believe to be at the root of fundamental confusion in the literature across disciplines – methodological issues. In exploring methodological issues, we focused on empirical specifications, causality, fixed-effects, first-differencing, and instrumental variables issues. We ended with a discussion of two important but not yet well-explored areas, international issues, and compensation in non-profits. We think there are several promising avenues for future work in executive compensation. The dramatic advances in data availability, consistency, and quality will likely lead to less diversity in results in future studies. We hope that researchers across fields take advantage of this. We also believe that a serious exchange of ideas on methods across (and within) disciplines needs to happen. We find it odd that researchers across disciplines have such trouble talking to, understand, or working with those outside their niche areas. Additionally, we think that the walls between academic research and practice should be brought down generally, but in the area of executive compensation in particular. There are also a host of regulatory issues on the horizon, and we suspect (hope!) that some will lead to the potential for interesting exogenous and unexpected changes that could help us to better focus on issues of causality we mention above. In addition, certain legislation pending in Congress right now – including legislation related to ‘‘Say on Pay’’ could be very interesting. ‘‘Say on Pay’’ refers to the opportunity for a firm’s shareholders


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to have an annual, nonbinding, vote on the compensation of the firms’ senior executives. We suspect that study of this sort of proposal could lead to clever new research on executive compensation across disciplines. We also believe more work on issues of corporate governance would be investigated, including work on the separation of the CEO and Chair of the Board. The difficult issue of severance and so-called ‘‘change-in-control’’ agreements are also gaining increased attention by practitioners and we suspect will garner more attention from academics soon. There is too little work on international issues in compensation and in executive compensation in particular. This is due, in part, to data availability problems and in part due to the inherent difficulties in doing work across countries and cultures. We hope that in the near future researchers will embrace the challenge of working on international issues. Another area we feel is ripe for exploration and analysis by academic researchers is that of ‘‘risk.’’ While there has been some work in this area, the recent financial crisis has focused this discussion much more sharply. We expect analysis focusing on risk, risk-adjusted pay, and the like will occupy many of colleagues in the future. This paper is focused on many areas of executive compensation including the pay-for-performance debate, the current issues, and the state of pay today. But compensation internationally had a large focus on methodological issues and cross-disciplinary problems. Our hope is that this work will not only help researchers focused on CEO pay and performance but also motivate researchers in other areas of Human Resources to explore outside their own discipline and work more closely with those from other fields.

NOTES 1. See for example Devers, Cannella, Reilly, and Yoder (2007) and Murphy (1999) for recent reviews. 2. One other reason for the dramatic increase in the use of options could have been due to the accounting treatment of the options. Until recently, most standard employee options did not have to count as an expense in the company balance sheet. 3. See Kay and Van Putten (2007) for a rejoinder written by experienced practitioners in the field. 4. The subject of how to value stock options for executives and other employees is an interested issue for debate. We do not focus on it in this paper. The interested reader can find discussion of this in Lambert, Larcker, and Verrecchia (1991), Hall and Murphy (2002), and Hallock and Olson (2010). 5. Recall that for most studies, researchers don’t use the stock options data from this table but use the stock option grants table that appears later in proxy statements.

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6. Hallock and Olson (2010) provide a much more comprehensive description of data sources for research on executive compensation and employee stock options. 7. Substantially more detail than provided in this section can be found in Hallock and Torok (2010). This section is based on that work. 8. There are some exceptions including Hallock (1997, 1999) who investigates the relationships between ‘‘reciprocally interlocking boards of directors’’ (CEO of firm A is a member of firm B’s board at the same time CEO of firm B is a member of firm A’s board) and executive compensation. 9. Defined as the net income plus interest, divided by the average total assets over the previous year (adjusted for the corporate tax rate). 10. Calendar-year return (dividends plus capital gains) per share of common stock. 11. Note that the axes are in logarithms so a step from 3 to 4 is, for example, substantially smaller than a step from 7 to 8.

ACKNOWLEDGMENT We are grateful to Sherrilyn Billger and Catherine McLean for helpful suggestions. We thank the Compensation Research Initiative at Cornell University for support.

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A TIME-BASED PERSPECTIVE ON EMOTION REGULATION IN EMOTIONAL-LABOR PERFORMANCE Michelle K. Duffy, Jason D. Shaw, Jenny M. Hoobler and Bennett J. Tepper ABSTRACT We extend emotional-labor research by developing a time-based theory of the effects of emotion regulation in emotional-labor performance. Drawing on Gross’s (1998a) process model, we argue that antecedentand response-focused regulatory styles can be used to make differential predictions about outcomes such as performance, health, and antisocial behavior and that these effects differ in shorter- and longer-time windows. We discuss the theoretical implications and address the strengths and limitations of our approach.

Displays of task-appropriate emotions are particularly crucial facets of performance in occupations requiring employee–customer interaction and are the defining features of many jobs in the growing service economy (Bernhardt, Morris, Handcock, & Scott, 2001). The task conditions of such jobs, the process of regulating emotion, and the emotional displays are often Research in Personnel and Human Resources Management, Volume 29, 87–113 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0742-7301/doi:10.1108/S0742-7301(2010)0000029005

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studied under the label of emotional labor (Beal, Trougakos, Weiss, & Green, 2006). The increasing importance of emotional labor is reflected not only in its presence in today’s workplace, but also in the surge of research interest in understanding its consequences (e.g., Coˆte´ & Morgan, 2002; Grandey, Fisk, & Steiner, 2005). On one hand, researchers have found that emotional labor not only negatively affects individuals’ physical and mental health (e.g., Pugliesi, 1999), but also imposes staggering total societal costs (e.g., Landy, 1992; Morris & Feldman, 1996). Some research has also shown occupation-level wage penalties for performing emotional labor, especially in jobs lower in other forms of cognitive complexity (e.g., Glomb, Kammeyer-Mueller, & Rotundo, 2004). On the other hand, effective emotional displays have been often argued to relate positively to individual and organizational performance (e.g., Ashforth & Humphrey, 1993). Although the past decade witnessed much progress, the emotionallabor literature has been beset by conceptual inconsistencies and an absence of inquiry into the processes underlying the appropriate display of emotions in business settings. Indeed, the conceptual literature has split between focusing on the task characteristics that elicit emotional labor (e.g., Morris & Feldman, 1996) and the appropriate display of emotions (e.g., Ashforth & Humphrey, 1993), while the empirical literature has centered around what Beal et al. (2006) referred to as ‘‘affective delivery’’ or the maintenance of required emotional displays at work. As those authors pointed out, ‘‘a detailed understanding of how employees successfully regulate their emotional expressions at work seems necessary’’ (p. 1053). We take steps in this direction in this paper. In her ambitious essay, Grandey (2000) linked the emotional-labor literature to more basic psychological theory on emotion regulation or the ‘‘ways individuals influence which emotions they have, when they have them, and how they experience and express these emotions’’ (Gross, 1999, p. 557). Regulation strategies can take various forms, but can be broadly categorized as antecedent focused (regulating the emotion before it is fully formed) or response focused (regulating the emotion after it is fully formed). She proposed that emotion-regulation research could serve to guide our understanding of the regulatory processes underlying emotional-labor performance. In this paper, we extend her work on emotion regulation in emotional-labor performance by developing a theory of the differential effects of emotion-regulation strategies on individual outcomes in work settings. Our themes are individuals must regulate their emotions when they are confronted with emotion-labor tasks and required-display rules, regulatory styles play a role in determining important employee-related

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outcomes, and these regulation processes have different consequences for employee outcomes in different time windows. Developing an emotion-regulation-based perspective on important work-related outcomes is important for several reasons. First, research has shown that emotion regulation occurs commonly and daily. Thus, the question is not whether individuals regulate their emotions, but how they go about doing so. We focus on how individuals regulate their emotions and the implications these processes have for individuals and organizations. Following Grandey’s (2000) guiding work, we use basic psychological theory on emotion regulation as a framework for understanding a wide cross-section of individual outcomes (job performance, mental health, physical health, and the consequences of being targeted for and perpetrating aggression). Third, tremendous interest has grown recently in studying the consequences of emotional labor or the displays of task-appropriate emotions, especially in jobs in the growing service sector where these displays are defining features (Beal et al., 2006; Brotheridge & Grandey, 2002; Glomb, et al., 2004; Grandey, 2003; Morris & Feldman, 1996). Our approach is unique in three different ways. First, in addition to examining the consequences of emotion regulation across several important work-related outcomes, we incorporate the role of time and make differential predictions for different combinations of emotion-regulation strategy and time frame. Second, we depart from extant literature, the preponderance of which suggests that antecedent-focused emotion regulation produces better outcomes than response-focused emotion regulation, by arguing that the relative efficacy of antecedent- and response-focused emotion regulation is time dependent. We distinguish between antecedentand response-focused approaches across outcomes. Third, the study of emotions in the organizational literature is nascent but rapidly growing, yet it has been hampered historically by what some see as the ‘‘intellectual debasement of emotions in the workplace’’ (Muchinsky, 2000, p. 802). Our approach also differs from recent attempts to incorporate emotions into the organizational literature in that we do not focus on the outcomes of specific emotions (e.g., Geddes & Callister’s, 2007 ambitious model of anger and antisocial behavior at work). Instead we focus on the processes individuals use to regulate their emotions. In the following sections, we (1) provide a foundation for our theory building by briefly reviewing conceptual and definitional issues with respect to emotional labor; (2) review Gross’s (1998a, 1998b) process model of emotion regulation and Grandey’s (2000) application of it to the study of emotional labor; (3) derive propositions concerning emotion regulation and short- and

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long-term individual consequences in work settings; and (4) discuss the implications of the framework for future theory, research, and practice.

EMOTIONAL LABOR AND THE EMOTION-REGULATION PROCESS Perspectives on Emotional Labor The study of emotional labor is rooted in Hochschild’s (1979, 1983) sociological perspective on the management of feelings in public situations. Drawing on Goffman’s (1959) influential work on self-presentation, she offered a dramaturgical definition of emotional labor where she saw individuals as managing emotions through surface- or deep-level acting. Surface-level actors manage their emotional expressions or displays; deep-level actors ‘‘consciously modify feelings in order to express the desired emotion’’ (Grandey, 2000, p. 96). She argued further that both surface-level actors and deep-level actors find the acting to be effortful and distasteful; as a result, emotional labor should relate positively to emotional exhaustion and strain. In contrast, Ashforth and Humphrey (1993) proposed the emotional-labor model, which downplayed the role of emotion management and focused almost singularly on observable behaviors or emotional displays. These authors suggested that many emotional displays are automatic or effortless, so emotional-labor performance may not manifest itself in stress-related outcomes such as burnout and exhaustion. Rather, these authors argued, the primary outcome of effective emotional labor is task performance, and this relationship should be positive to the extent that customers perceive emotional displays as genuine. In a rather marked contrast, Morris and Feldman (1996) offered a third perspective by focusing not on emotion management or the effectiveness of displays, but on task characteristics. They argued that emotional labor can be defined by the frequency, intensity, duration, and variety of required emotional displays, as well as the dissonance these tasks create. In her synthesis and extension of these founding works, Grandey (2000) argued that the Ashforth and Humphrey (1993) and Morris and Feldman (1996) frameworks described parts of the emotional-labor process effectively, but not necessarily the construct of emotional labor itself. In particular, task characteristics are factors that create emotional-labor situations: they are precursors to emotional labor, while effective displays of emotion are the


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proximal outcomes of emotional labor. She proposed a return to emotional labor’s roots – to a variation of Hochschild’s (1983) framework and the key issue of emotion regulation. Defining emotional labor as the ‘‘process of regulating both feelings and expressions for organizational goals,’’ Grandey (2000) used Gross’s (1998a, 1998b) emotion-regulation theory as a guiding framework for the study of emotional labor. She argued that understanding the effects of emotional labor is more straightforward if viewed through the lens of the prolonged physiological and cognitive arousal that defines emotion as well as the processes and strategies used to regulate this arousal. We adopt Grandey’s (2000) definition of emotional labor in this paper.

The Emotion-Regulation Framework Emotion regulation is typically characterized as a conscious process, but it resides on a continuum of consciousness; some regulatory processes are nearly automatic and require little conscious processing (e.g., hiding disappointment in a social context), while others require significant effort and involve higher-level conscious processing. As a number of authors have noted, the process of emotion regulation extends well beyond the boundaries of emotional-labor performance; indeed it is a common, everyday process (Morris & Reilly, 1987). Gross (1999) argued that the process of emotion regulation commences with exposure to and evaluation of environmental cues that then trigger or stimulate a set of response tendencies designed to shape emotional responses. When considered in a workplace context, these signals have much in common with Morris and Feldman’s (1996) conceptualization of emotional labor as a set of task characteristics. These tendencies can be broadly categorized in terms of whether an individual attempts to adjust emotions by modifying perceptions of the situation (or perhaps even the situation itself ) or manipulates the emotional response after the emotion is fully formed and experienced. At least five different emotion-regulatory strategies have been identified. Four of these strategies (situation selection, situation modification, attention deployment, and cognitive change) are referred to as antecedentfocused approaches because they occur in advance of full emotion generation (Gross, 1998a). Situation selection and modification involve ‘‘niche picking’’ (Scarr & McCartney, 1983) or avoiding certain people or situations that are likely to have a strong emotional impact. Although these types of regulation may occur frequently, they are less relevant for our purposes because, as Grandey (2000) pointed out, many workers, especially in jobs rife

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with emotional-labor demands, have few alternatives for situation selection or modification. More appropriate for our purposes are the two antecedent-focused approaches that Gross (1999) labeled attentional deployment and cognitive change. With attentional deployment, in a given emotion-eliciting situation an individual can choose to regulate emotions by redirecting attention from the cue and toward unrelated memories or events or by concentrating on something other than the current situational cue. Grandey (2000) offered the example of a restaurant server who whistles arias to avoid being overwhelmed by negative or difficult customers. Cognitive change, in contrast, involves lessening the impact of a situational cue ‘‘either by changing how one thinks about the situation or about one’s capacity to manage the demands it poses’’ (Gross, 1999, p. 560). Although these approaches have differences, the key issue with antecedent-focused emotion regulation is that individuals take action to lessen the impact of environmental cues before they fully affect the emotional experience and that the actions involve deep-level modification of personal thoughts (attentional deployment) and external appraisals (cognitive change). These antecedent-focused approaches seem to share some construct space with Hochschild’s (1983) concept of deep-level acting. The fifth type of emotion regulation is often referred to as responsefocused emotion regulation; it involves influencing or altering responses after emotions arise. The basic process underlying response-focused emotion regulation is suppression, such as hiding anger at a rude customer or calming frustration by breathing deeply and slowly (Gross, 1999). Suppression, therefore, ‘‘requires active inhibition of the emotion-expressive behavior that is generated as the emotion unfolds’’ (Gross, 2001, p. 216). Grandey (2000) noted that suppression can involve not only adjusting the intensity (e.g., acting very happy when you are only moderately happy) but also faking the emotional display (e.g., pretending to be happy when you are actually angry). Suppression seems to share some construct space with Hochschild’s (1983) concept of surface-level acting. Empirical research on these forms of emotion regulation has increased in recent years. The implicit assumption in much of the empirical research on emotion regulation is that individual- and work-related outcomes of antecedent-focused regulation are more positive than outcomes of suppression. Some evidence has supported this notion (Gross & John, 2003). Gross (2002), when discussing reappraisal as a form of antecedent-focused regulation, argued that ‘‘efforts to down-regulate emotion through reappraisal should alter the trajectory of the entire emotional response,


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leading to lesser experiential, behavioral, and physiological responses’’ (p. 283). In samples of US and French workers, Grandey et al. (2005) found that frequent suppression was strongly and positively related to emotional exhaustion. Among a sample of employed college students, Coˆte´ and Morgan (2002) found that suppression of negative emotions was negatively related to job satisfaction and positively related to intentions to quit. In a novel study of cheerleaders, Beal et al. (2006) found that participants who performed high levels of surface acting while experiencing negative emotions reported higher levels of difficulty at maintaining required display rules. Two rare studies of deep acting (similar to the attentional deployment form of reappraisal) and surface acting (similar to suppression) also provided support for the positive effects of reappraisal and the negative effects of suppression. Grandey (2003) found that surface acting was positively related to self-reports of emotional exhaustion and negatively related to coworker ratings of affective delivery (e.g., warmth and friendliness), while deep acting was positively related to coworker ratings of affective delivery. Moreover, deep acting was not significantly related to self-reports of emotional exhaustion. Among a convenience sample of workers in Canada, Brotheridge and Grandey (2002) found that surface acting was positively related to exhaustion and depersonalization and negatively related to perceptions of personal accomplishment. Consistent with Grandey’s (2003) results, deep acting was not related to exhaustion, but was positively related to perceptions of personal accomplishment. When viewed in toto, initial findings have suggested fairly uniform effects of antecedent- (positive) and response-focused (negative) emotion regulation on individual and workplace outcomes and, indeed, this is somewhat consistent with experiment-based empirical literature in the basic psychology fields (e.g., Gross, 2001; Gross & John, 2003). But are the consequences of emotion regulation always uniformly positive (antecedent focused) or negative (response focused) in terms of individual outcomes in the workplace? Although the advances in the past few years have been consequential, the literature has tended to view task conditions as uniform in terms of the emotional labor required – emotional labor is either required or not – and has tended to take short-term views of the dynamics of emotion regulation. We argue that much of the speculation concerning emotion regulation and outcomes implicitly confounds the effects of emotion regulation on short-term consequences with long-term consequences. Coˆte´ and Morgan’s (2002) study represented an initial attempt to study regulation processes over time, but the one-month time lag they examined may not have been sufficient for differential effects to emerge. Gross (2001, 2002)

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concluded his reviews of emotion-regulation research by suggesting that researchers explore the potential differential consequences of different regulatory styles and pursue a research agenda focused on the ‘‘long-term consequences of differing emotion regulation strategies’’ (p. 218). We argue below that emotion-regulation-based predictions may hold only in certain time windows and only for certain outcomes. We focus on three aspects of emotion regulation – attentional deployment, cognitive change, and suppression – and in the following sections, elucidate our predictions.

WORK-RELATED OUTCOMES OF EMOTION REGULATION IN EMOTIONAL-LABOR PERFORMANCE In this section, we derive propositions for the consequences of emotion regulation in emotional-labor performance. Drawing on the definitions described above, we categorize work-related outcomes in terms of time and draw distinctions or moderators with regard to task type where theory dictates. The range of outcome variables included in this conceptualization should be considered a subset of all possible outcomes influenced by emotional-labor contexts and the process of emotion regulation. In this sense, the proposed framework should be viewed as a point of departure rather than as an all-inclusive theoretical framework regarding the impact of emotion-regulation issues. We make two general assumptions in terms of the boundary conditions of our theory building. First, we assume that environmental cues trigger the regulation of emotion in emotional-labor contexts (Grandey, 2000). This triggering process can be a function of the work itself (e.g., the task characteristics that create emotional-labor situations; Morris & Feldman, 1996), or of specific environmental events (e.g., interactions with angry customers; Rafaeli & Sutton, 1987, 1990), or both (Sutton, 1991). Second, we use the notion of time in a general sense, making differential derivations and proposals for short and long time frames, respectively. We do not intend to establish a specific window. Rather, we suggest that short-term propositions concern emotion-regulation strategies in single emotional-labor events (e.g., interacting with a customer at a service desk). Long-term propositions concern the effects of emotionregulation strategies in repeated emotional-labor events over time (e.g., working in a customer-service role for a year). In the following sections we derive short-term predictions regarding attentional deployment, cognitive


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change, and suppression in terms of job performance and mental health, and long-term predictions in terms of job performance, physical health, and exposure to accidents, abusive situations, and aggression.

Short-Term Consequences of Emotion Regulation Job Performance In an initial attempt to link emotion-regulation strategies to work-related outcomes, Grandey (2000) proposed that individuals using antecedentfocused regulation would perform better in customer-service roles than those using suppression regulation. She argued that emotional suppression may relate to observers’ perceptions that the individual is ‘‘faking’’ or is disingenuous. Alternatively, those engaging in attentional deployment or cognitive change have either reevaluated their emotional state and/or recall other emotions to get through the moment (Gross, 1998a, 1998b). Accordingly, observers are less likely to detect their true feelings. On the surface, these generalizations appear accurate, but a closer examination of the theoretical foundation of emotion regulation reveals that we can derive more specific propositions. First, in the emotional-labor context, the type of emotion regulation may affect job performance depending on how deeply one interacts with others. To illustrate, suppression, which requires that an individual inhibits a generated emotion, may powerfully impact individuals (Gross, 1998a). In contrast, antecedent-focused regulation is a front-end approach in which an individual interprets an emotional cue in unemotional terms before it is fully generated. The process of emotion suppression results in slightly higher levels of sympathetic activation (Gross & Levenson, 1997) than the process of emotion regulation through attentional deployment and cognitive change; the activation difference may mean that the suppressor is more engaged (e.g., energetic or on edge) in surface-level or shallow interactions than is the antecedent-focused regulator. Antecedent-focused regulation, by definition, means that the individual is more detached from the environmental or emotional cue (recall the example of the aria-whistling server), and observers may perceive them as being less engaged, and hence performing more poorly. In many occupations rife with emotional labor, the level of interpersonal interaction with others is often quite shallow (e.g., a receptionist directing traffic in a busy office, or a food-service employee managing a drive-through window). Because of heightened physiological activation, these situations provide opportunities for higher

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job performance among those engaging in suppression regulation compared with those using with antecedent-focused regulation. In more interaction-rich interpersonal contexts, substantial benefits may accrue for job performance for those engaging in antecedent-focused regulation. Interactions that last longer provide greater opportunities for emotional ‘‘leakage’’ (e.g., Ekman & Friesen, 1969) to betray the performer and also more opportunities for observers to detect that the employee is faking emotions. By manipulating the environmental and emotional input, those engaging in attentional-deployment or cognitive-change forms of regulation may be slightly less physiologically activated, but the payoffs include that they appear to be interacting more genuinely. The antecedentfocused process partly concerns perpetuating an illusion that the actual situation is really something different (e.g., misperceiving that a situation is not so ominous when it is actually threatening) such that the appraiser is convinced that the expressed emotion is real. Taylor and Brown (1999) argued that illusions are a critical component of the ability to care for others and are especially useful in threatening situations. In emotional-labor situations where single interactions are deeper and last longer, this process may be markedly advantageous in terms of job performance. To illustrate, some jobs requiring emotional labor are not particularly complex but require prolonged interactions with others. For example, an auto salesperson and a customer may haggle over the price of the car and trade-in for several hours. The time-exposure pressures may cause the salesperson to ‘‘leak’’ suppressed negative emotions, and ultimately reveal to the customer that the salesperson, as a suppression regulator, has been disingenuous. By way of contrast, the reappraisal regulator may be slightly less activated initially, but should be capable of successfully interacting with the client for a longer time. These arguments are summarized in the following propositions: Proposition 1. Response-focused emotion regulation (suppression) will result in better short-term job performance than antecedent-focused emotion regulation (attentional deployment and cognitive change) in situations where interpersonal interactions are shallow and/or of short duration. Proposition 2. Antecedent-focused emotion regulation (attentional deployment and cognitive change) will result in better short-term job performance than response-focused emotion regulation (suppression) in situations where interpersonal interactions are deep and/or of longer duration.


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Another important issue with regard to emotion regulation and shortterm task performance concerns the level of cognitive activity needed in tasks requiring emotional labor. A great deal of research has concerned the loss of cognitive functioning during acute suppression episodes. For example, Baumeister, Bratslavsky, Muraven, and Tice (1998) found that emotion regulation through suppression degrades performance on cognitive assignments. The logic behind the finding is that individuals have a limited supply of resources for performing cognitive tasks; the challenge of suppressing a negative emotion exhausts some of the resources, leaving few available for performing the task successfully. In addition, some researchers have shown that suppression degrades memory, a key facet of performance on cognitively complex tasks (Richards & Gross, 1999). These authors hypothesized and found that the conscious suppression of emotions increases self-focus and diminishes the ability to encode new information (e.g., Ellis & Ashbrook, 1988; Pyszczynski & Greenberg, 1987). Reappraisal regulation, by contrast, stops emotional cues before they consume valuable cognitive resources. As mentioned before, many jobs that require emotional labor are not particularly complex, but the implications of these arguments extend beyond jobs that demand constant emotional labor to those that require emotion regulation in only limited circumstances, for example, managing emotive processes in periodic dealings with supervisors or working in teams and on task forces charged with cognitive tasks. More mundanely, restaurant servers engaging in emotional suppression may perform less well on aspects of their job that require more cognitive functioning (e.g., remembering the food and drink orders from a large table). Thus, we suggest the following proposition: Proposition 3. Antecedent-focused emotion regulation (attentional deployment and cognitive change) will result in better short-term job performance than response-focused emotion regulation (suppression) in emotional-labor situations with high cognitive demands. Mental Health Having an accurate perception of reality is often seen as a hallmark of mental health and well-being, but considerable evidence has suggested that individuals may enjoy increased mental health by maintaining positive illusions (Alloy & Ahrens, 1987; Taylor, 1983). As Taylor and Brown noted, the happy person ‘‘appears to have the enviable capacity to distort reality’’ (1988, p. 204) to enhance their views of control and optimism.

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The connections are clear between this literature base and reappraisal as an emotion-regulation style. In a manner similar to reappraisal, individuals may protect their well-being by using filters that distort or modify negativity in the environment. Returning to the ‘‘whistling-arias’’ anecdote, individuals who use antecedent-focused (attentional deployment and cognitive change) emotion-regulation approaches create, in essence, positive illusions about situations so that they experience the emotion ‘‘in a direction that enhances self-esteem, maintains beliefs in personal efficacy, and promotes an optimistic view of the future’’ (Taylor & Brown, 1988, p. 204). Empirical evidence has suggested that at least in the short-term, antecedent-focused (attentional deployment and cognitive change) approaches are effective; individuals who are asked to reappraise emotions rather than suppress them tend to report lower subjective levels of that emotion (e.g., Gross, 1998a). Thus, we suggest the following proposition: Proposition 4. Antecedent-focused emotion regulation (attentional deployment and cognitive change) will result in better short-term mental health than response-focused emotion regulation (suppression).

Long-Term Consequences of Emotion Regulation Job Performance Several theoretical perspectives posit that the long-term consequences for job performance will be more severe among those using responsefocused rather than antecedent-focused emotion-regulation strategies. First, self-discrepancy theory (e.g., Higgins, Klein, & Strauman, 1985) holds that individuals are motivated to achieve a match between self-concept and a number of personally relevant self-guides. Mismatches between the actual-self (the attributes you believe you possess) and the ought-self (the attributes you believe you should possess) can cause discomfort, guilt, and the tendency toward self-punishment (Higgins, 1987). Conformance to scripts can cause employees to deviate from their ought-self to another socially prescribed actual-self. Following the self-discrepancy model, individuals often suffer guilt and shame when they transgress or fail to meet their self-imposed standards. When a salesperson suppresses negative emotions and pretends to be ‘‘chipper’’ as if the ‘‘customer is always right,’’ negative outcomes may surface. Beyond these outcomes, Higgins (1987) reviewed a substantial body of evidence that related these forms of selfdiscrepancies to irritation, lethargy, and disinterest over time, all factors


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that should reduce an employee’s ability to perform well on the job. Possibly antecedent-focused regulation may also cause emotional laborers to display diminished performance over time, but following a self-discrepancy approach, these negative effects on job performance in the long term should be more pronounced under a suppression regulatory style. A second theoretical issue concerns enhanced accessibility of emotional cues over time. Polivy (1998) and others (e.g., Rachman, 1980) argued that emotions are a cue for appropriate behaviors and that repeated inhibition or suppression may eventually produce a surge in target thoughts during the attempted suppression as well as behavioral excess over time. This effect is particularly plausible for long-term job performance, because research has demonstrated that the surge in thoughts about the emotion may increase dramatically after lapses occur in mental control – either voluntarily (a work break, for example) or involuntarily (an acute increase in cognitive demands). Just as restrained dieters may suddenly lose control and overindulge, individuals regulating their response-focused emotion may, over time, lose their strength to suppress emotions and ultimately become incapable of doing so. Wegner and Schneider (1989) called this phenomenon the ‘‘rebound effect.’’ Repeated suppression attempts and frequent relinquishment of mental control over time should weaken performance. Proposition 5. Response-focused emotion regulation (suppression) will have more severe negative effects on job performance over time than antecedent-focused emotion regulation (attentional deployment and cognitive change). Physical Health Although emotion regulation in the form of suppression reduces the intensity of emotional displays, it does not exempt the person from experiencing the emotion physically and psychologically. Studies have suggested that suppression increases physiological activation and elevates indicators such as finger pulse, finger temperature, and skin conductance (Gross, 1998a; Gross & Levenson, 1997). Although these indicators may not be hazardous in isolation, the cumulative effects of prolonged sympathetic activation are related to a number of damaging health problems over time (Krantz & Manuck, 1984). The physical consequences of emotion regulation – suppression in particular – can relate to cardiovascular and immunological system malfunctioning (Schaubroeck & Jones, 2000). Each time the stress response is engaged, the immune system may be selectively

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inhibited (O’Leary, 1990). In terms of cardiovascular reactions, emotion suppression has been linked to increased sympathetic nervous system activation (including cardiovascular symptoms). Proposition 6. Response-focused emotion regulation (suppression) will result in more physical health problems over time than antecedent-focused emotion regulation (attentional deployment and cognitive change). Targeting of Antisocial Behavior As noted above, some evidence has indicated that antecedent-focused regulation strategies (attention deployment and cognitive change) may be preferable to suppression in terms of short-term mental health benefits and perhaps in terms of job performance as well. However, we propose that the employee who must habitually use attentional deployment and cognitive change may pay a significant cost in terms of being exposed to antisocial behavior at work, such as social undermining (e.g., Duffy, Ganster, & Pagon, 2002; Duffy, Ganster, Shaw, Johnson, & Pagon, 2006) and abusive supervision (e.g., Tepper, 2000; Tepper, Duffy, & Shaw, 2001). We suggest two primary reasons that antecedent-focused strategies may increase the risk that an individual will be targeted for antisocial behavior at work. First, individuals who use attentional deployment (e.g., distraction) may inadvertently signal would-be aggressors that the target individual may be easily taken advantage of. As Marx, Heidt, and Gold (2005) pointed out, different forms of attentional deployment may help an individual stave off acute negative emotions associated with problematic situational cues, but ‘‘others who note the observable signs of dissociation or altered consciousness may be quick to take advantage’’ (p. 80). Moreover, individuals who are distracted and concentrating on things beyond the emotional-labor event as ways of constricting emotional expression have been shown to signal vulnerability for targeting (e.g., Luterek, Orsillo, & Marx, 2002). Beyond attentional deployment’s attraction to aggressors, this avenue for emotional regulation may also interfere with an individual’s ability to process threatening warnings in the environment. Emotions consist of response tendencies that are meant to coordinate behavior in times of challenge or danger. Gross (1998a, 1998b) suggested that one long-term consequence of antecedent-focused regulation may be that one begins to deny important features of one’s environment through the use of unrealistic or inflexible views (e.g., insisting that ‘‘everything is okay’’ when it is not). Individuals can often achieve attentional deployment through distraction or by focusing attention on nonemotional or cross-emotional aspects of the situation.


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In addition, they can redirect their attention from the cue demanding higher levels of concentration to another task, which consumes cognitive resources that they could use to process the emotion fully. Both avenues for attentional deployment may impede the process for detecting and dealing with threatrelevant cues in the work context (Marx et al., 2005). Our arguments concerning antecedent-focused regulation and workplace targeting involve not only the threat-identification arguments above, but also the idea that attentional deployment and cognitive change can compromise defense activation or reflex systems. The key interference point for defensive reflexes is during the postencounter stage – what emotion-regulation researchers have called the emotion-soliciting situational cue. During this stage, normal responses would include freezing, focusing attention on the cue, and considering the potential threat. This stage, which precedes the more commonly discussed ‘‘fight-or-flight’’ stage, is critical not only in terms of evaluating the situational cue but also in handling the situation. Ignoring situational warnings or reevaluating one’s capacity to manage the situation interferes with, and perhaps disables, this process. In essence, these antecedent-focused strategies allow an individual to get through the moment but may inadvertently result in a failure to utilize valuable information that the task or emotional cue contains. An individual using distraction to fend off a distressing emotion may be able to function, but may fail to see potential hazards in the social environment ‘‘even under the circumstances in which such arousal might be directly linked to such threat cues’’ (Marx et al., 2005, p. 81). Although unrelated to interpersonal deviance, research examining safety at work has linked distraction and proneness to distractibility as significant predictors of accidents (e.g., Hansen, 1989). Likewise, an individual who engages in cognitive change while dealing with irritating or hostile coworkers or customers may start to believe that the situation is ‘‘not so bad’’ or ‘‘under control,’’ when in reality the situation should be activating defensive reflexes. These individuals, confronted with signs that others are bullying or aggressive, may ignore the signals or reevaluate them as harmless. By ignoring their adaptive emotion-based defenses they may become more vulnerable to future acts of aggression by coworkers or customers. This risk may be heightened by the fact that these employees may also present themselves more passively in social interactions (i.e., they do not feel a need to appear assertive because they do not perceive danger). Recent research has suggested that individuals who appear to be more passive and are low in self-determination are more likely to report being victimized at work (e.g., Aquino, Grover, Bradfield & Allen, 1999).

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In terms of response-focused emotion regulation, we expect that suppression will not increase susceptibility in becoming a workplace target for two primary reasons. First, in response-focused regulation such as suppression, emotions are fully formed before they are modified. The formation of the emotion ensures that the key processing stage of the defense reflexes system is not altered. Although the emotion is later modulated, emotion formation allows the appropriate defense mechanisms to be activated and the situational cue processed correctly, which should aid in accurate threat detection. In addition, Wegner’s (1994) ironic processing model suggests that suppression of thoughts and emotions involves two distinct mental processes – an operating process that attempts to create the desired state and a monitoring process that continuously supervises the suppression and searches for lapses of mental control. Important in this view is that the processes underlying emotion suppression are attentional processes that initially orient the individual toward the stimulus or situational cue. In the case of workplace stimuli, the suppression-processing model would suggest that after attention is focused on the situational cue and the emotion is developed, the choice to regulate through suppression starts an operating process to bring desired emotions to the surface. After the operating phase is activated, an unconscious monitoring process continues to scan for signs that the system is working properly including ‘‘sensations and thoughts that are inconsistent with the achievement of successful control’’ (Wegner, 1994, p. 38). Wegner (1994) concluded, ‘‘anything that is not the target of the operating process, after all, indicates failure of the operating process and should be monitored’’ (p. 40). It is reasonable, then, to expect that focused regulation will lower the likelihood of victimization because not only are emotions fully formed before suppression, thereby activating defense reflexes, but the unconscious monitoring system will continue to scan for lapses of mental control or other factors that may cause emotional control to fail. Proposition 7. Antecedent-focused emotion regulation (attentional deployment and cognitive change) will relate to a higher risk of being the target of antisocial behavior at work relative to response-focused emotion regulation (suppression). Engaging in Antisocial Behavior at Work Although the arguments above suggest that attentional deployment and cognitive change increase vulnerability, evidence has also suggested that individuals who habitually use response-focused regulation may be more aggressive than those who use antecedent-focused approaches such as


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attentional deployment and cognitive change. Constrained or suppressed negative emotion is often redirected or displaced toward less-powerful or more-available targets (e.g., Marcus-Newhall, Pedersen, Carlson, & Miller, 2000). The frustration hypothesis suggests that those individuals who suppress their emotions will be more likely to express their frustration through antisocial behaviors directed at others in the workplace. Theory and empirical findings on displaced aggression are also consistent with these arguments and further highlight that antisocial behavior can be triggered by a simple, daily event such as a happy dog jumping on the owner returning from work or a coworker in a break room commenting on an unrelated topic (Miller, Pedersen, Earleywine, & Pollock, 2003). In terms of direct antisocial behavior, when emotions are suppressed during emotional-labor work, emotional leakage may occur, especially when the social interactions are longer or deeper (e.g., Ekman & Friesen, 1969). Companies may have strict rules limiting emotional displays and prohibiting aggression toward customers. Fear of retaliation or punishment serves as a major constraining factor for the expression of negative emotions. Most jobs that exert strong emotional-labor demands on employees (e.g., airport ticketing agents, bar servers, social workers) require that they constrain their emotions when they encounter hostile or abusive customers or clients. But it is also likely that suppressed workers will displace their aggression, expressing it toward coworkers or even further downstream toward others outside the workplace. The ironic processing model noted above also shows that response-focused regulation will increase the likelihood an individual will engage in antisocial behavior at work. Recall that in the ironic processing model, an individual achieves mental control through an operating process that creates the desired state and a monitoring process that continuously supervises the suppression and searches for lapses of mental control. As the irony label suggests, a consequence of these mental processes is that the emotion and the precipitating event surrounding the emotions may actually become more accessible over time (Polivy, 1998; Wegner & Schneider, 1989). Elevated accessibility may lower the threshold for future aggressive behavior in response to minor annoyances (Marcus-Newhall et al., 2000). Thus, individuals who suppress emotions at work may explode at seemingly trivial or minor triggers in a manner that seems incommensurate with the social interaction that preceded the reaction. Although evidence of these effects is sparse, Christoforou (2008) found among a sample of sales representatives that individuals using responsefocused regulation strategies to deal with interactions from abusive customers engaged in higher levels of emotional deviance in the workplace.

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In terms of antisocial work behavior, a value of antecedent-focused regulation is that repeated regulation from attentional deployment and cognitive change is less likely to increase the risk of antisocial behavior directed at individuals responsible for the emotion-triggering cue or indirectly at innocent coworkers, family members, and friends. Because negative emotions are not formed fully with ex ante regulation strategies, defensive reflexes such as fighting or fleeing and the mental control processes charged with operating- and monitoring-suppressed emotions are not activated. In addition, the ironic processing system is circumvented, which lessens the likelihood that the individual will easily recall the precipitating event, will extensively ruminate on the emotion and the cue, or will pervasively feel frustration associated with attempts to suppress the emotion. Attentional deployment and cognitive change should be less likely, then, to cause direct and displaced antisocial behavior. Indirect support for these ideas can be found in a recent series of studies by Bushman, Bonacci, Pedersen, Vasquez, and Miller (2005). These authors found that compared with experimental participants who were distracted, participants who ruminated about a provocation were more likely to engage in displaced aggression after being exposed to a minor annoyance or trigger. Thus, we suggest the following proposition: Proposition 8. Response-focused emotion regulation (suppression) will relate to a higher likelihood of engaging in direct antisocial behavior at work and displaced aggression than the antecedent-focused emotion regulation (attentional deployment and cognitive change).

CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS The concept of emotional labor has received increasing attention in the organizational literature in recent years, but the process of emotion regulation and its consequences in the workplace have rarely been explored. Research in psychology disciplines has confirmed the importance of emotion regulation in assessing the behavior, attitudes, and well-being of individuals, but inconsistencies have marred the findings. In this paper, we propose the factor of time (short term versus long term) as critical, and typically missing, in conceptualization of emotion regulation. We then develop an initial framework for predicting time-based work-related consequences of regulation processes.


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Our exposition is but a first attempt to delineate the relevant workplace aftereffects of employed regulation strategies. It is impossible to point to one emotion-management tactic as preferable in the absence of rich contextual and personal factors. Indeed, our framework highlights the positive and negative aspects of both approaches to emotion regulation; neither is universally effective, neither is completely unsound. Given the expectations and demands businesses are placing on the ever-increasing service sector of the labor force, it has become crucial to know how, when, and why individuals use various emotional strategies (Tarvis, 1984). Moreover, further information regarding the relative benefits and detriments of various emotion-regulation strategies would benefit managers and employees alike. As Stone-Romero (1994) suggested, it is necessary to highlight the boundary conditions, both of the overall framework presented and of the specific propositions offered. First, we make no differential predictions regarding the type of emotion being regulated. Clearly, the type of emotion (e.g., disgust, sadness, anger, fear, enthusiasm, pride) may impact not only the choice of emotion-regulation strategies but also their consequences. Research on the asymmetry of positive and negative social interactions (e.g., Duffy et al., 2002) may help future researchers to develop specific propositions for the outcomes of the regulation of individual emotions. We also use the concept of time in a general sense (short term versus long term). We distinguish between single emotional-labor events (short term) versus the same regulation strategy repeated over time (long-term). Although this allows us to decipher inconsistent theory and research findings regarding emotion-regulation processes, future researchers should explore the timing issues our hypotheses capture. Perhaps research on behavioral spirals and additional exploration of contagion effects would clarify the organizational impact of short- and long-term regulations. We also rely on the results of experimental research and, to some extent, other conceptual work, to develop our propositions. This is necessary because experimental psychology has conducted most emotion-regulation research; longitudinal field studies of emotion regulation in organizational settings are largely unavailable. Finally, we examine only consequences of emotion regulation in the performance of emotional labor – the antecedents remain largely unexplored. For instance, little is known about why people choose to suppress rather than use attentional deployment, whether some individuals oscillate between emotion-regulation strategies, and how frequently they choose consciously between the two options. At this point, we highlight the need for additional theory development and empirical research to test and extend the basic tenets of our theorizing.

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The propositions delineated here are clearly testable, although not without challenges. As noted above, much of the empirical testing of emotionregulation theory tenets has been conducted in short-term laboratory settings where emotion-regulation strategies have been manipulated experimentally. But the nature of emotion regulation and outcomes such as performance, physical health, and antisocial behavior lend themselves to longitudinal or multiwave studies. These work dynamics may play out in immediate contexts or within certain interactions – for example, a customer directly targeting an employee – but in many cases will unfold over time. One-to-one correspondence will not appear between an employee engaging in attentional deployment or cognitive change and the outcomes of interest; rather our thesis suggests that these regulation approaches increase the likelihood or probability that an individual will experience better or worse performance, health, and antisocial-behavior-related outcomes over time. Similarly, even in longer time windows, we would expect that not every incidence of suppression would result in direct or displaced antisocial behavior, for example, but that repeated attempts to suppress negative emotions would increase the likelihood of these outcomes. Gross (2001, 2002) concluded his reviews of emotion-regulation research by suggesting that researchers explore the potential differential consequences of different regulatory styles and pursue a research agenda focused on the ‘‘long-term consequences of differing emotion regulation strategies’’ (p. 218). The Coˆte´ and Morgan (2002) study represented an initial attempt to study regulation processes over time, but they used a one-month time lag, which may be insufficient for differential effects to emerge. On the positive side, several researchers, including Coˆte´ and Morgan (2002) and Grandey et al. (2005), have developed and usefully employed measures of emotion regulation in field settings. Thus, the key challenges to overcome in terms of adequate tests of our propositions would seem to be designing and executing a study that would allow these dynamic processes to unfold. Woven throughout our analysis is the apparent critical need for examining outcomes of emotion regulation in field research where dynamics related to time are in play. As a number of authors (e.g., Goodman, Lawrence, Ancona, & Tushman, 2001) have pointed out, time has multiple meanings and can be conceptualized in different ways. In terms of understanding the temporal dynamics of emotion regulation, the problems are no less complex. Marks, Mathieu, and Zaccaro (2001), in their theory of time and team processes, conceptualized team performance episodes as ‘‘distinguishable periods of time over which performance accrues and feedback is available’’ (p. 359). Emotion-regulation episodes could be similarly viewed as the time


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between the situational cue that initiated the regulation process and the end of the regulated interaction or, perhaps, at the point when the emotion was no longer being regulated. In longer-duration and interaction-rich interpersonal contexts, emotional leakage has more opportunity to emerge (e.g., Ekman & Friesen, 1969), and observers have more opportunity to detect whether and how individuals are regulating their emotions. Time in an emotion-regulation context can also be conceptualized as the mapping of multiple regulation events in terms of cycles, frequency, and rhythm (Ancona, Okhuysen, & Perlow, 2001) – a conceptualization that Marks et al. (2001) labeled a recurring phase model of time. It may be possible to capture this view of time by simply operationalizing it as job tenure. Because our propositions concerning long-term outcomes implicitly assume greater or lower likelihood of outcomes over time, it is reasonable to assume that job tenure moderates these relationships such that the relationships are stronger when job tenure is high. But, although convenient, job tenure as an operationalization of recurring phases falls somewhat short of capturing the full nature of the construct. Marks et al. (2001) pointed out that when time is conceptualized as a series of episodes, it can be deconstructed into action – antecedent- or response-focused regulating in our case – and transition phases. Across jobs, the frequency and duration of the emotion-regulation episodes and the length of transition phases may differ markedly. Auto salespersons may face only one or two daily incidences that require them to regulate emotions, although, as noted, the incidences may last a relatively long time but be alleviated by long transition or recovery phases in between. Other salespersons such as call-center employees may have numerous, short, daily interactions that require emotion regulation and multiple, short-transition periods while they wait for their next call. The rhythms of these cycles are markedly different and likely have different implications for workplace outcomes, implications that in all likelihood will be masked by simple time measures such as tenure. Beyond these frequency and duration issues, some research has also suggested that the content of transition periods also affects future behaviors. Harinck and De Dreu (2008) found that when they distracted their experimental participants with another task during a break, the participants later reached higher-quality negotiated agreements, as compared with participants who continued to reflect on the ongoing negotiations during the break. Thus multiple periods of distracting transitions may diffuse or ameliorate the effects of response-focused regulation and aggression. A third way to view time concerns regulators’ temporal perceptions, which can range from perceptions of time passage (e.g., time flying, time passing, or time dragging; McGrath & Kelly, 1986), but also perceptions

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about the novelty or originality of a given moment. It is possible to view attentional deployment as a means of managing the passing of time. Grandey’s (2000) example of a server whistling arias to deploy attention from negative or difficult customers might also be seen as way of passing time. Aside from our arguments about threat detection and defensive reflexes above, attentional deployment may also change individuals’ ‘‘understanding and knowledge about time acquired through the senses’’ (Ancona et al., 2001, p. 519). In general, as noted by Gross (1999) and others, the long-term consequences of emotion-regulation strategies are essentially unexplored. We encourage future researchers to explore and extend our propositions by considering how and when these relationships strengthen, weaken, or change in other ways across different views of time. A final issue concerns the role of display rules in the emotion-regulation process. On one hand, display rules could be viewed as antecedents to emotion regulation and as useful in predicting what emotion-regulation strategies individuals choose. In some organizations, display rules are implicit and passed along through high-performance expectations (Zapf, 2002). In others, display rules are formal, strict, and regulated by direct supervisors (e.g., Wilk & Moynihan, 2005). On the other hand, the strength of organizational or supervisor-level display rules may change the nature of the relationships between emotion regulation and antisocial behavior. For example, Grandey et al. (2005) argued that response-focused regulation relate positively to employee burnout, but they also argued and found that this relationship is stronger when individuals lack personal control over the situation. Similarly, Wilk and Moynihan (2005) demonstrated that display rules varied at the supervisor level, drained resources as they increased, and related positively to emotional exhaustion (see also Sutton, 1991). In terms of antisocial behavior at work, strict and regulated display rules are likely to squelch certain forms of direct aggression that may result from emotionregulation processes, but may increase the likelihood that response-focused regulation will displace aggression toward coworkers or even outside the workplace. Understanding how display rules relate to different forms of emotion regulation but also shape antisocial responses to regulation strategies are important areas for future research.

Implications for HRM Practice Our theory building is helpful as an academic exercise, but it also has practical implications. First, the effects of emotion-regulation strategies on


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employees’ abilities to stave off accidents may be critical to occupational health. In the United States, 21 million individuals work in occupational sectors such as wholesale and retail trade where emotional labor predominates (National Occupational Research Agenda, 2008), making the potential for accidents and violence an important public health concern. If antecedent-focused strategies leave employees in high-risk workplaces such as convenience stores and gas stations susceptible to social situations where others may harm them, perhaps training and awareness of more appropriate regulation strategies would protect this at-risk population. Second, uncovering emotional-labor processes helps us understand the factors that determine individual performance in jobs high in emotional labor. Hochschild (1983) told of an airline that screens for ‘‘warm personalities’’ during interviews for flight attendants, explaining that, in general, selling products and services involves ‘‘selling your personality.’’ It is often assumed that being good at emotional labor stems from certain stable personality characteristics; organizations might screen for such characteristics and select employees based on their emotional-labor skills. Yet our framework suggests that it is not personality that determines individual performance but the deployment of regulation strategies in response to emotion-eliciting situations. As such, perhaps high performance in these jobs comes from training or practice in situation assessment and regulation-strategy deployment. In conclusion, while the current literature provides many interesting insights about the role of emotion regulation in emotional-labor performance, there is much more to be understood about the advantages and disadvantages of emotion regulation for individuals and organizations. We provide this set of time-based propositions as a point of departure for future research in this area and also as a roadmap for future theory-building and empirical testing. The study of emotion regulation in emotional-labor performance is fraught with challenges that researchers will have to overcome, especially in terms of research design and measurement. We hope, however, that this review will encourage investigators to take up these challenges and move the literature forward.

ACKNOWLEDGMENT The authors thank Jacquelyn Thompson for editorial assistance.

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INSIGHTS FROM VOCATIONAL AND CAREER DEVELOPMENTAL THEORIES: THEIR POTENTIAL CONTRIBUTIONS FOR ADVANCING THE UNDERSTANDING OF EMPLOYEE TURNOVER Peter W. Hom, Frederick T. L. Leong and Juliya Golubovich ABSTRACT This chapter applies three of the most prominent theories in vocational and career psychology to further illuminate the turnover process. Prevailing theories about attrition have rarely integrated explanatory constructs from vocational research, though career (and job) choices clearly have implications for employee affect and loyalty to a chosen job in a career field. Despite remarkable inroads by new perspectives for explaining turnover, career, and vocational formulations can nonetheless enrich these – and conventional – formulations about why incumbents stay or leave their jobs. To illustrate, vocational theories can help clarify why certain shocks (critical events precipitating thoughts of leaving) drive attrition and what embeds incumbents. In particular, this chapter reviews Research in Personnel and Human Resources Management, Volume 29, 115–165 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0742-7301/doi:10.1108/S0742-7301(2010)0000029006

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Super’s life-span career theory, Holland’s career model, and social cognitive career theory and describes how they can fill in theoretical gaps in the understanding of organizational withdrawal.

Theories and research on why and how employees sever employment ties have undergone a renaissance in modern times (Holtom, Mitchell, Lee, & Eberly, 2008; Hom, 2010). After ‘‘fallow’’ years during the late 20th century (O’Reilly, 1991), turnover scholars began introducing a host of innovative theories (e.g., Lee & Mitchell’s, 1994 unfolding model; macro-level models of aggregate quit rates; Kacmar, Andrews, Rooy, Steilberg, & Cerrone, 2006) and constructs ( job embeddedness, Mitchell, Holtom, Lee, Sablynski, & Erez, 2001b; movement capital, Trevor, 2001) as well as new methodologies (latent growth modeling; Bentein, Vandenberg, Vandenberghe, & Stinglhamber, 2005) to promote understanding and prediction of organizational withdrawal. These recent developments are stimulating considerable rethinking and empirical inquiry (Holtom et al., 2008; Hom, 2010). Despite such theoretical and methodological advances, however, there remain major gaps in turnover perspectives and unresolved questions about certain turnover phenomena. To illustrate, though it is the most comprehensive formulation about proximal antecedents and processes leading to turnover thus yet conceived (Hom, 2010), the unfolding model fails to fully elucidate why various critical events (‘‘shocks’’) precipitate thoughts of leaving (Lee & Mitchell, 1994). For example, T. W. Lee, Mitchell, Wise, and Fireman (1996) observed that some nurses quit when they become pregnant, a shock that sets in motion preexisting plans to exit for childbearing. They also noted that other nurses exit because their hospital switched from individualized to team-based patient care, a negative event violating personal career plans or goals. While specifying that preexisting plans (to quit) and violations of career plans mediate shocks’ impact, this theory nevertheless overlooks why employees adopt certain plans. Quite likely, employees react to the same critical events differently (not necessarily leaving) based on their assessment of how such events affect their personal plans (Sweeny, 2008). Other nurses facing pregnancies or team-based nursing would not necessarily resign if they plan to work during motherhood or if this hospital policy fits their professional image. Consequently, greater clarification of how different individuals appraise critical events is essential because critical events are prime movers of ‘‘nonaffective’’ turnover paths in the unfolding model that

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many if not most leavers follow (Holtom, Mitchell, Lee, & Inderrieden, 2005; T. H. Lee, Gerhart, Weller, & Trevor, 2008; Mitchell, Holtom, & Lee, 2001a). Similarly, theoretical accounts of many well-documented withdrawal phenomena remain unspecified or insufficient. To illustrate, empirical research has long established that age and firm tenure are inversely related to quits, such associations becoming styled facts in the turnover literature (Griffeth, Hom, & Gaertner, 2000; Hom, Roberson, & Ellis, 2008). While often treated as control variables or proxies for proximal antecedents (Mitchell et al., 2001b; Mobley, Horner, & Hollingsworth, 1978), these demographic characteristics deserve greater scholarly attention. After all, they often forecast attrition more reliably than common explanatory constructs (e.g., job satisfaction; Griffeth et al., 2000) and may reflect more fundamental but neglected distal causes (e.g., life stages). Further, many scholars (Judge & Watanabe, 1995) and practitioners (Khatri, Fern, & Budhwar, 2001) believe that the ‘‘Hobo’’ syndrome (Ghiselli, 1973; Hulin, Rosnowski, & Hachiya, 1985; Maertz & Campion, 2004), an individual’s job-hopping history, is a reliable signal of future quits. Though the premise that ‘‘the best predictor of future behavior is past behavior’’ is deemed a basis for the hobo syndrome (Griffeth & Hom, 2001), prediction is not, however, explanation. In short, what explains hobos’ wanderlust? To enrich turnover perspectives and clarify certain turnover phenomena, we draw on theory and research from career and vocational psychology. Three of the most prominent theories in this discipline are Super’s life-span career development theory, Holland’s career model, and social cognitive career theory (SCCT; Lent, Brown, & Hackett, 1994). These views have long dominated research on vocational choice and behavior (Leong & Barak, 2001). Turnover investigators nonetheless have rarely capitalized on insights and findings from this longstanding research stream. To address this oversight, our chapter demonstrates how these vocational models can further illuminate the reasons and manner by which incumbents vacate their jobs. Because certain SCCT constructs (e.g., newcomer self-efficacy) have been investigated by socialization scholars as turnover antecedents, we further elaborate and refine SCCT theory to extend existing socialization research on newcomer attrition.

DONALD SUPER’S DEVELOPMENTAL THEORY Donald Super’s life-span career development theory (1953, 1990) is one of the pivotal theories in vocational psychology, which is embedded within a

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developmental framework. The main principle of Super’s theory, in contrast to other career theories, is the centrality of the development axis. In his model comprising ten propositions, Super posits that career development involves the ongoing implementation of the self-concept across life stages. He formulated career developmental stages that include a growth stage followed by exploration, establishment, maintenance, and decline stages. Within each of these developmental stages are substages that include developmental tasks and challenges that individuals must meet and surmount to realize their self-concept.

Life and Career Development Stages According to Super’s (1953, 1990) theory, there are five life and career development stages. These five stages are: (1) Growth stage (birth to 15), which is concerned with the development of the self-concept and one’s capacity, attitudes, interests, needs as they pertain to performance at school and at home; (2) Exploration stage (ages 15–24), which produces tentative career/vocational choices and involves career exploration activities including class selection, work experiences, and hobbies; (3) Establishment stage (ages 25–44), which entails entry into the workforce and the start of one’s official career during which initial skill-building and stabilization in one’s work experiences occur; (4) Maintenance stage (ages 45–64), which consists of an adjustment process to advance one’s position at work and to find continuing satisfaction in work activities that one has mastered at an earlier stage; and (5) Decline stage (around age 65 onwards), which includes reduced output and productivity in preparation for retirement. Super later reformulated this last stage into a more positive ‘‘Readiness for retirement’’ stage. Embedded within the major development stages, Super also articulated certain phases with specific developmental tasks that interact with an individual’s experiences. Because our chapter focuses on turnover, we focus on phases within career development stages. Within the Exploration stage, the first phase involves Crystallization (ages 14–18) during which one is developing and planning a tentative vocational goal. This is followed by a Specification phase (ages 18–21) where one firms up one’s vocational goal and moves on to the Implementation phase (ages 21–24), when one seeks and obtains the requisite education and training for the chosen career or vocation. The Establishment stage is comprised of two phases. During a Stabilization phase (ages 24–35), individuals begin working and confirming

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their career choices, while being primarily focused on career advancement during a Consolidation phase (ages 35þ).

Career Constructs Within this life-stage career development framework, Super conceptualized several constructs that can advance turnover theory and research. The first is the notion of the self-concept that is assessed by the Values Scale (VS). The VS consists of 21 work values that help guide a person’s implementation of his or her self-concept. Attainment and implementation of these values in the workplace give rise to job satisfaction and career growth and development. From a turnover point of view then, work environments that fail to provide opportunities for the satisfaction of employees’ values will motivate them to quit. For example, if strongly held values for autonomy, creativity, or advancement are not satisfied within a particular work environment, this value incongruence would likely stimulate turnover cognitions. Career maturity is the next major construct developed within Super’s theory. His developmental model emphasizes one’s readiness to move on to the next stage and hence the concept of career maturity is one of readiness to take on appropriate developmental tasks. Given differing maturity, different people transition from school to work or from early career to mid-career at differential speeds. According to Super, career maturity comprises the following components: Awareness of the need to plan ahead; decisionmaking skills; knowledge and use of information resources; general career information; general world of work information; and detailed information about occupations of preference. A student of Donald Super, John Crites (1973) went on to design a career maturity inventory (CMI) to measure career planfulness, career exploration, and readiness. While much vocational research has examined career maturity among late adolescents and college students, many implications of career maturity have yet to be explored by turnover researchers among working adults. Super (1953, 1990) conceptualized career maturity as a multidimensional construct that represents a person’s readiness to cope with vocationally related developmental tasks at a particular life-span period. Giving special attention to adolescent development, Super noted that adolescents’ level of career maturity evolves with growing awareness that they must make an occupational choice and their attitudes toward this task. During this period, adolescents also develop competencies (i.e., knowledge, abilities, and skills) that facilitate career decisions and implementations, especially career

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exploration and planning. In general, career maturity is often conceived of as having both attitudinal and cognitive dimensions. In a stimulus– organism–response (S–O–R) paradigm for studying career maturity, these attitudes and competencies serve as intervening variables (Savickas, 1985). Successful evolution of career maturity during adolescence is thus expected to culminate in career choice crystallization and commitment. In studying career maturity development, vocational researchers have usually conceptualized change in quantitative terms, such as increases in the attitudinal and cognitive dimensions, which are operationalized differently in various measures of career maturity (Savickas, 1985). Whereas much of the research generated by Super and those following his paradigm have mostly focused on adolescents and college students (due to availability bias of counseling psychologists who were primarily academicians), we believe that the career maturity construct can be readily generalized into adulthood to help clarify why employees stay or leave jobs. Indeed, Savickas (1997, 2005) recently conceptualized the concept of career adaptability – a construct akin to career maturity – to address individuals’ flexibility and maturity for coping with workplace challenges. Building upon Super and Knasel’s (1981) observation that adaptation is the central developmental challenge for adults, Savickas (2005) went on to develop a career construction theory based on a constructivist perspective. Savickas (2005) viewed career construction as a series of attempts to implement a self-concept in social roles, focusing attention on adaptation to a series of transitions from school to work, from job to job, and from occupation to occupation. In this theory of career construction, career adaptability plays a central role in shaping the actual problem-solving strategies and coping behaviors of individuals in their work lives. This model of career adaptability for adults thus parallels the career maturity model for adolescents. Within this model, Savickas proposed four dimensions of adaptability: concern, control, curiosity, and confidence. The adaptive individual is (a) concerned about the vocational future, (b) exerts personal control over his or her vocational future, (c) displays curiosity by exploring possible selves and future scenarios, and (d) is confident about pursuing his or her aspiration. Therefore, increasing a client’s career adaptability is an essential goal of career counseling and career interventions. Individuals lacking career adaptability might have difficulty managing interpersonal relationships or meeting performance challenges in the workplace. Therefore, career adaptability might be a useful predictor of turnover cognitions and actual turnover. It is important to note, however, that the relationship between career adaptability and turnover may be more


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complex. For example, Ito and Brotheridge (2005) examined the impact of organizational support for career adaptability of employees in the form of career information, advice, and encouragement. Unexpectedly, they found that career adaptability was ‘‘positively associated with both organizational commitment and intentions to leave, suggesting some unintended consequences for management approaches supporting career adaptability’’ (p. 5). Apparently, promoting greater career adaptability among employees can increase their ease of movement (March & Simon, 1958). We return to describing Super’s theory and consider the ‘‘life career rainbow.’’ In his later formulations, Super began examining the notion of career salience or work centrality. He recognized that a career might not be central to everyone and that its centrality may change over time as other life roles compete for one’s attention and energies. Within this later program of research, he conceptualized the career rainbow in which an individual has six life roles: worker, student, citizen, husband/wife, parent, and leisure. The rainbow concept presumes that work becomes central for people at the middle stages of life but loses centrality due to competing demands of family, leisure activities, and so forth. Outside the middle life stages, other life roles may become more central than work.

Career Developmental Assessment and Counseling Model In 1992, Super and his colleagues articulated the Career Developmental Assessment and Counseling (C-DAC) Model. C-DAC model emerged from the life span, life-space theory of careers and integrated key elements (i.e., the Life Career Rainbow, the Model of Importance, and the Model of Determinants) from Super’s theory. Therefore, the C-DAC model blends components of differential, developmental, and personal construct theories into one comprehensive career assessment and counseling system. Super recommended that counselors implement the C-DAC model in a four-step process (Super, Savickas, & Super, 1996). Step one consists of an initial interview to identify a client’s presenting concerns. In the interview, the counselor also reviews any available data from the client’s record and develops a preliminary counseling plan. At the same time, counselors must understand the importance of work to the client relative to life roles in other realms (e.g., school, home and family, community, and leisure). Assessing the client’s level of work role salience reveals whether further career assessment and counseling will be meaningful (high career salience) or not (low career salience). Clients high in career salience show readiness to

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maximally benefit from further career assessment. Clients low in career salience may, depending on their unique life status, need help either (a) orienting to the world-of-work prior to further assessment or (b) exploring and preparing for other life roles. In the second step, the counselor administers instruments to measure the client’s career stage and concerns, and level of career maturity or career adaptability. The counselor thus determines the client’s readiness for career decision-making activities, such as identifying and exploring occupational interests. In step three, the counselor helps clients to objectify their interests, abilities, and values. Finally, clients progress step four, which involves subjective self-assessments that identify life themes and patterns (Super et al., 1996). In formulating the C-DAC model, Super identified a comprehensive test battery to provide counselors with flexibility in carrying out comprehensive career assessments. However, he emphasized four core measures for the C-DAC battery. The first is The Salience Inventory, a 170-item questionnaire that measures the extent to which individuals participate in, commit to, and expect to realize values in five life roles: student, worker, citizen, homemaker (including spouse and parent), and leisure. The second core measure is the Adult Career Concerns Inventory (ACCI; Super, Thompson, Lindeman, Jordaan, & Myers, 1988). Its 61 items assess planning attitudes, an important dimension of career adaptability (Savickas, 1997). The ACCI consists of 4 scales and 12 subscales that measure concerns related to career stages and developmental tasks: Exploration (Crystallizing, Specifying, Implementing); Establishment (Stabilizing, Consolidating, Advancing); Maintenance (Holding, Updating, Innovating); and Disengagement (Decelerating, Retirement Planning, Retirement Living). The third core measure for the C-DAC battery is the Career Development Inventory (CDI; Super, Thompson, Lindeman, Jordaan, & Myers, 1979/1981). Its 120 questions capture readiness for making educational and vocational choices. The CDI has two parts: (I) Career Orientation and (II) Knowledge of Preferred Occupation. Part I includes four scales that assess Career Planning (CP), Career Exploration (CE), Career Decision Making (DM), and World-of-Work Information (WW). Part II contains one scale measuring Knowledge of Preferred Occupational Group (PO). Three composite scores result from summing individual scale scores are as follows: Career Development Attitudes combines CP and CE; Career Development Knowledge and Skills combines DM and WW; and Career Orientation Total combines CDA and CDK. The final core measure Super specified was the VS, which includes items that assesses 21 intrinsic and extrinsic values


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people seek in life. The VS assesses values such as Ability Utilization (e.g., ‘‘use all my skills and knowledge’’) and Economic Security (e.g., ‘‘be where employment is regular and secure’’).

Implications of Super’s Theory and Work for Turnover Theory and Research In this section, we discuss how constructs and developmental processes (including vocational inventories designed to assess them) proposed by Super can improve turnover understanding and prediction. Fundamentally, Super’s theoretical approach suggests a developmental trend in one’s career and life and distinct developmentally linked tasks and challenges at different stages. Each life and career developmental stage as articulated by Super may create a distinct set of forces that push or pull incumbents away from their current jobs (Maertz & Griffeth, 2004). Following this developmental logic, the reasons why individuals quit likely vary by life stages. Along the career developmental axis in Super’s thinking, different developmental challenges and concerns at each stage can engender different motives for leaving at the start of a career versus mid-career or toward the end of one’s career. While turnover researchers readily acknowledge that new and established employees may quit for different reasons (Weller, Holtom, Matiaske, & Mellewigt, 2009), they have not fully or explicitly capitalized on Super’s insights for identifying stage-dependent motives. Super’s perspective suggests that rookies in the Exploration career stage (exploring various job options) may exit because they do not find that they are a good fit for a particular job (Hom et al., 2008), whereas veterans at the Establishment career stage exit because they lack sufficient or timely promotional opportunities that provide a sense of career progress (Taylor, Audia, & Gupta, 1996). To test this developmental viewpoint, turnover researchers can systematically explore the impact of these developmental tasks and challenges with Super’s Adult Career Concerns Inventory (ACCI). Using this inventory, they can test the CDAC implication that a person at the Establishment stage would more readily leave to advance his or her career prospects elsewhere, whereas someone at the Disengagement stage are more prone to leave due to boredom or desire to participate in other life roles (by assuming another less demanding job). Though stages are somewhat age-dependent, Super’s thinking also recognizes that people progress through career stages at different rates. For example, some people explore various career options far longer (such as women bearing and raising children before starting their

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official careers) than the typical Exploration stage, whereas others may settle on a career at a relatively young age. If so, the ACCI would more precisely diagnose turnover motives of people in the Exploration stage than would proxies based on job tenure or work history. After all, Super’s theory allows for the possibility of two individuals with the same length of employment belonging to different career stages. Among his other conceptions, Super’s notion of self-concept implementation can extend Lee and Mitchell’s (1994) unfolding model (more fully described below) by defining the content of ‘‘matching scripts’’ (preexisting plans to quit) and ‘‘internal images’’ (i.e., career plans or goals, which path 2 shocks might violate), increasing its precision for predicting when shocks evoke various withdrawal paths (shown in Fig. 1). According to Super, people prefer and choose vocations where they can fulfill values embodying

Path 1: Personal, Expected Shocks Activates Matching Script

Personal Event

Quit

Matching Script

Self-Concept Implementation

Path 2: Negative Workplace Shocks Negative Job Event

Image Violation Of Values Or Goals

•Self -Concept Implementation •RIASEC Vocational Fit

Judgement of Misfit

Quit

Career Images: Career Plans or Goals

Path 3: Unsolicited Job Inquiry Shocks Unsolicited Job Inquiry/Offer

•Compare with Current Job •May Pursue Other Jobs

Career Adaptability

Quit for Better Job

•Self-Concept Implementation •RIASEC Vocational Fit

Path 4: Dissatisfaction-Induced Turnover Gradual Job Misfit

Job Dissatisfaction

Seek Other Jobs

Compare Job Offers to Present Job

Poor Vocational Fit

Fig. 1.

How Career Constructs Influence the Unfolding Model.

Quit


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their self-concepts. Vocational self-concepts may underpin matching scripts and images and whether or not they can be implemented in a workplace may activate various turnover paths. In T. W. Lee et al.’s (1996) study, for example, only nurses who viewed themselves as primarily family caregivers rather than ‘‘worker bees’’ would formulate plans to quit when becoming pregnant (i.e., the personal shock allows them to implement their selfconcept of motherhood in Fig. 1), while those nurses whose vocational self-concepts were based on their capacity to deliver individualized patient care would leave when the hospital introduced team-based nursing (i.e., the negative workplace violates their self-concept implementation in Fig. 1). Further, Super’s construct explains why employees are responsive to another shock in the unfolding model: unsolicited job inquiries. According to Fig. 1, job incumbents may leave because they receive an unsolicited job offer that permits them to better implement their self-concept. Moreover, career maturity may help clarify why young workers (Griffeth et al., 2000), new hires (Hom et al., 2008; Weller et al., 2009), and hobos (Hulin, Roznowski, & Hachiya, 1985) are exit-prone as they may poorly prepare for jobs or make bad job choices. In contrast, higher career maturity may account for why older people are more steadfast employees (Griffeth et al., 2000) and for Booth, Francesconi, and Garcia-Serrano’s (1999) observation that British workers quit their fifth job less than they do their first job. Conceivably, older employees’ experiences tackling developmental tasks in advanced life stages or career stages helped them make wiser more job choices, promoting their greater job stability (Wanous, 1992). Further, career maturity may explain quit decisions among older adults entering – or reentering – the workforce, such as women who raised children, former members of institutions (e.g., prison, military, priesthood; Ebaugh, 1988), or those switching careers (who might return to school to learn new skills). They would undergo (or repeat) Exploration and Establishment stages for a new career later in life than younger people. Yet they may show greater career maturity about planning for and choosing jobs (improving job longevity) as they have successfully surmounted development hurdles of earlier life stages or career stages in a previous occupational field. More research is warranted on the complex relationship between career adaptability and turnover uncovered by Ito and Brotheridge (2005). While reducing desirability of movement (March & Simon, 1958) because adaptable employees may find greater success and satisfaction in jobs than their less adaptable counterparts, they also likely have greater ease of movement (Ito & Brotheridge, 2005). We thus suggest that career adaptability can broaden the conceptual scope of ‘‘movement capital’’ – or

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the human capital job incumbents possess that enhances job mobility (Trevor, 2001) – to encompass perceived control over one’s vocational plans and self-confidence to pursue one’s career goals. Although initially defined in terms of education and occupational skills (Trevor, 2001), movement capital might also include career adaptability, which enable incumbents to exit for better career opportunities elsewhere (Ito & Brotheridge, 2005). Additionally, people high in career adaptability may attract more unsolicited job offers as employers may believe that they can better manage dynamic and wide-ranging challenges in the hypercompetitive global marketplace (see Fig. 1). Further, Super’s thoughts about the life career rainbow and how career – or work – salience fluctuates across life stages can further explicate the organizational withdrawal process. Multiple competing life roles have rarely been examined in turnover research (Hom & Griffeth, 1995), though some authors have alluded to work centrality (Mobley, 1982; Mobley, Griffeth, Hand, & Meglino, 1979). In particular, the life career rainbow may illuminate women’s higher attrition in female-dominated jobs (e.g., nursing; Hom & Griffeth, 1991) and male-dominated fields or workplaces (Hom et al., 2008). Turnover scholars often recognize how women’s greater domestic responsibilities (e.g., kinship responsibilities; Price & Mueller, 1981) can impel their departures but fail to realize that such obligations change across life stages. When starting new careers in their 20s (Establishment stage), many women begin bearing children before their biological clock runs out (Booth et al., 1999). During this period of simultaneous childbearing and career establishment, young women face intense work – family conflict – especially in professional service firms (e.g., law and accounting firms; Dalton, Hill, & Ramsay, 1997) and research universities (Fox, 2005) that impose rigorous and fixed up-or-out promotion systems. Turnover experts have noted that such interrole conflict can drive quits (Hom & Kinicki, 2001) but have not formally recognized how such conflict varies across women’s life stages, waxing during Career Establishment (for young mothers beginning careers) and waning during Career Maintenance (when children have grown up). Indeed, such implicit static views overstate motherhood as an ‘‘intrinsic’’ (or deterministic) explanation of women’s higher quit propensity (Fox, 2005). By the same token, turnover theorists cannot account for why some men take time off from work or switch to less challenging careers to spend more time with family or why some leave organizations to have a more balanced investment of energy across multiple life roles (Hom & Kinicki, 2001; Hulin et al., 1985). In short, turnover scholars increasingly acknowledge that some leavers exit the workforce but offer few theoretical explanations for nonemployment


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destinations other than ‘‘search unemployment’’ ( job pursuits after leaving; T. H. Lee et al., 2008). Further, Super’s developmental perspective suggests that turnover scholars should differentiate between life-cycle and career-stage effects. Though both are age-dependent, they may exert differential influence on leaving. Using chronological age and work experience as rough proxies for life and career stages, respectively, Booth and associates (1999) observed that age and work history (e.g., date of entry into the labor market, number of previous parttime jobs) have distinct unique effects on turnover. As mentioned above, some people may begin a career – or a second one – later in life (perhaps they raised children or retired from the military). As a result, 40-year-old new career entrants may have similar developmental challenges and concerns about becoming established incumbents as 25-year-old entrants. Nonetheless, their different life stages can affect their progress through career stages. Because their career spans are ‘‘truncated,’’ 40-year-old newcomers may feel greater urgency to move through Career Establishment and Maintenance stages more quickly than 25-year olds, while more expertly negotiating developmental tasks if they had transitioned through these career stages before in a prior career. Forty-year-old women starting new careers after motherhood may also move through career stages more rapidly and easily than 25-year-old working mothers because they are liberated from the responsibilities of household formation. Such developmental speed may strengthen their loyalty to employing institutions that offer timely career opportunities (e.g., more rapid promotions; Taylor et al., 1996). At the same time, mature rookies may face greater age discrimination (slower or denial of promotional or career opportunities), which would impede their developmental progress and thus inspire them to quit (Johnson & Neumark, 1997).

JOHN HOLLAND’S PERSON–ENVIRONMENT THEORY Career choice constitutes a major stream of inquiry for vocational psychologists. Parsons lay the theoretical groundwork for this research stream in 1909 by proposing a model of how individuals choose careers (Tracey & Rounds, 1993). In choosing an occupation, an individual assesses his or her abilities, interests, goals, and means, analyzes what a particular occupation requires and offers in return, and considers the analysis of the self in conjunction with the analysis of a potential occupation

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(Parsons, 1909). Expecting vocational interests to play a role in choice of, satisfaction with, and performance in occupations, vocational researchers (e.g., Kuder, 1939; Strong, 1943) constructed interest inventories that would serve as tools for those attempting to choose occupations as well as those in positions to advise others making occupational choices (Tracey & Rounds, 1993). Concomitantly, analysis of occupations has been made possible via publically available vocational information. Sources include the Occupational Outlook Handbook (U.S. Department of Labor, 2008) and the Dictionary of Occupational Titles (U.S. Department of Labor, 1991). John Holland’s theory of vocational choice, first introduced in 1959, provided a means of integrating self-assessment with occupational assessment.

Holland’s Central Theoretical Constructs John Holland’s theory of careers, a seminal influence in vocational psychology, relates personality to career choice. One of its main attractions is arguably its ability to convey a great deal of information via a fairly parsimonious framework (Holland, 1996). Holland theorized that occupational choice is influenced by an individual’s characteristics, including personality, ability, and knowledge (Spokane, Luchetta, & Richwine, 2002). Holland proposed six personality types: realistic, investigative, artistic, social, enterprising, and conventional. These six personality types and their hypothesized relationships to one another are summarized in what is referred to as the RIASEC model. The model is presented visually as a hexagon. Any given individual is expected to exhibit aspects of the majority or all of the six personality types, but in varying degree (Spokane et al., 2002). A letter code summarizes an individual’s combination of personality types. The highest three letters correspond to an individual’s most dominant personality types. In the context of career advisement, these letters are used to suggest careers that are appropriate given the individual’s personality profile. The organization of career environments along the same RIASEC model as individuals’ personalities makes this matching possible. Holland’s theory also features four dimensions: congruence, consistency, differentiation, and identity. Congruence constitutes how well aligned an individual’s personality is with his or her work environment (Spokane et al., 2002). Given how obviously broad this conceptualization is, other researchers have reasoned that several types of congruence may exist, including avocational congruence (Meir, Melamed, & Abu-Freha, 1990), skill-utilization congruence (Meir et al., 1990), and within occupational


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(i.e., occupational specialty) congruence (Meir & Erez, 1981). Differentiation is the degree to which an individual’s interests are spread out or unified (Weinrach & Srebalus, 1990). Said differently, it reflects the extent to which an individual resembles a single personality type and shows little similarity with the other personality types (Meir, Esformes, & Friedland, 1994). Differentiation is calculated by taking the difference between the highest score and the lowest score of the six (or highest three) types. Consistency refers to the strength of the relationship between an individual’s dominant two personality types (Spokane et al., 2002). Types that are positioned next to each other along the hexagon are more consistent than types positioned farther apart. For example, investigative and artistic types, neighbors along a side of the hexagon, share the desire for intellectual stimulation (Furnham, 1994), while realistic and social types, which sit opposite one another on the hexagon, differ in terms of the former having an orientation toward things and the latter having an orientation toward people (Tracey & Rounds, 1997). Though we do not focus on the consistency dimension in what follows, we mention it here for the sake of completeness. The final dimension of Holland’s theory, vocational identity, represents how well formed and stable an individual’s goals, interests, and talents are (Spokane et al., 2002). Before continuing on, we must distinguish Holland’s identity dimension from the identity constructs based on social identity theory (SIT; Tajfel & Turner, 1979) that are typically studied by career as well as turnover theorists. As Grote and Raeder (2009) points out, identity in career research is used interchangeably with concepts like sense of self or self-concept. Turnover theorists typically split identity into personal identity and social identity with the former pertaining to personal attributes like dispositions and abilities and the latter having to do with group categories such as ethnicity, gender, and company that individuals may identify with (Mael & Ashforth, 1995). As Randsley de Moura and colleagues (2008) point out in their literature review, SIT (e.g., the construct of organizational identity, or ‘‘perceived oneness with an organization’’ [Mael & Ashforth, 1992, p. 103]) has been used to make and test predictions about a slew of organizational variables, with turnover being one of them. By contrast, Holland’s vocational identity construct – referring to the stability and crystallization of vocational interests and goals – has not been thus applied by turnover scholars (cf. Hom, 2010). In addition to laying out the four dimensions described, Holland’s theory offers several basic premises. One premise is that individuals have a preference for environments that are conducive to the use of their abilities and skill sets, offer engaging problems and work roles, and permit

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expression of their true attitudes and values (Weinrach & Srebalus, 1990). A second premise is that an individual’s behavior (e.g., career choice, job changes) is a product of the interaction of his or her personality and environment (Weinrach & Srebalus, 1990). The next premise says that individuals experience reinforcement and satisfaction when environments they are in match their personalities. Reinforcing situations make behavior (e.g., attendance) more stable (Spokane et al., 2002). The fourth premise addresses situations of an environment-personality mismatch. Mismatches stimulate behavioral change in order to remove incongruence (i.e., mismatch). In such circumstances, individuals may search for a different, congruent work environment or adjust their outlook and behavior to fit their current environment (Spokane et al., 2002).

Vocational-Interest Measurement and Occupational Classification Vocational interest inventories, like the Strong Interest Inventory (SII) and Kuder General Interest Survey, that existed and were widely used before Holland promulgated his theory have been modified to incorporate Holland’s model (Tracey & Rounds, 1993). The SII, which requires individuals to report their degree of like or dislike for a large variety of items, proffers profiles for respondents at the end. Four sets of scales make up this profile: general occupational themes, basic interest scales, occupational scales, and personal styles scales. The general occupational themes are based on Holland’s RIASEC model and inform the respondent about his or her work personality. The basic interest scales separate the aforementioned general occupational themes into work, school, and leisure activities. The occupational scales direct the individual’s attention to occupations he or she is expected to fit well with based on having received the highest score for these occupations. The personal styles scales have to do with information about the individual’s style in various domains (e.g., work, learning, leadership, and team participation). Holland’s own inventory, the Self-Directed Search (SDS), presents respondents with approximately 230 items that pertain to activities, competencies, occupational preferences, and abilities. Individuals indicate preferred and disliked activities, activities in which they feel efficacious and not, interesting and uninteresting occupations, and rate their skills and abilities. The profile presented to the respondent shows a three-letter Holland code, which the individual can use to identify careers within a provided list that match his or her interests and competencies.


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Vocational Congruence: Methodological Issues and Moderators Holland’s thesis that congruence between one’s interests and occupation should lead to more satisfaction, success, and stability in the chosen occupation has received much attention in the career-related literature (e.g., Chartrand & Walsh, 1999). Holland’s congruence dimension already has analogous constructs in the turnover literature. Rather than vocational fit, Chatman (1991) examined how value congruency – or how closely an employee’s values fit with those of the employer – induces loyalty. Mitchell et al. (2001b) also theorized a ‘‘fit’’ dimension in their model of why people stay in their jobs and defined it as ‘‘an employee’s perceived compatibility or comfort with an organization and with his or her environment’’ (p. 1104). Their conceptualization is more encompassing than Holland’s; two types of fit are considered: organizational fit and community fit. Yet Mitchell and Lee’s (2001) description of on-the-job fit fails to explicitly specify vocational interest fit as an embedding force. The extensive research on congruence has not, however, produced conclusive findings; researchers do not always find the anticipated links between congruence and outcomes (e.g., Perdue, Reardon, & Peterson, 2007). In their meta-analysis, Assouline and Meir (1987) found correlations between congruence and satisfaction that range from 0.09 to 0.51, though reporting a mean corrected correlation of 0.21. Two later meta-analyses by Tranberg and colleagues (1993) and Tsabari and colleagues (2005) estimated correlations of 0.174 and 0.166, respectively. In short, congruence is reliably, though modestly, correlated with satisfaction. We next review methodological problems that may be responsible for such weak or modest relationships. To capitalize on Holland’s theory as a framework for better understanding job satisfaction, turnover researchers should design studies that minimize these problems’ influence to give a clearer picture of how congruence affects satisfaction (and retention). The lack of consistency and clarity in findings about congruence is partly attributed to several methodological shortcomings (e.g., Chartrand & Walsh, 1999). Tracey (2007) proposes that self-selection may partly account for weak relationships between person–environment fit and outcomes. That is, individuals choosing to enter environments that initially fit their interests fairly well and incongruent workers leaving their jobs would limit variance in congruence, attenuating its effects (Spokane, Meir, & Catalano, 2000; Tracey, 2007; Tsabari, Tziner, & Meir, 2005). Inadequate measurement of person–environment congruence – namely, poor assessments of people’s personalities (or interests) and the environment – also weakens empirical

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support (e.g., Chartrand & Walsh, 1999). Environmental measurement has proven especially difficult. Ambiguity persists over which environmental levels (e.g., social, cultural) (Furnham, 2001) should be measured and what the unit of analysis should be (e.g., job title, job tasks, job clusters) (Chartrand & Walsh, 1999). Thus, adequate methods of measuring the environment remain lacking (Chartrand & Walsh, 1999). How job satisfaction is assessed can also impact the congruence– satisfaction relationship. The common use of an overall satisfaction score can obscure this relationship because an overall satisfaction score incorporates additional factors beyond how an individual feels about his or her work activities (e.g., pay, coworkers; Chartrand & Walsh, 1999). Whether researchers measure actual fit (indirect fit indices based on comparisons of separately assessed personal and environmental attributes; Kristof-Brown, Zimmerman, & Johnson, 2005) or perceived fit (direct assessments of compatibility; Mitchell & Lee, 2001) makes a difference as well. Because it refers to individuals’ perceptions of how well they fit their jobs or organizations, perceived fit is likely more proximal to attitudes and behavior than is actual fit (Cable & DeRue, 2002; Mitchell & Lee, 2001). Sustaining this view, Kristof-Brown and colleagues (2005) found in their meta-analysis that perceived fit relates more strongly to outcomes (e.g., quit intentions, job satisfaction, organizational commitment) than does actual fit. Apart from methodological influences, some researchers have identified moderators that can affect relationships between interests-job fit and job outcomes (Spokane et al., 2000; Tracey, 2007). In the career literature, much work has already been done to identify moderators. Group importance has been shown to moderate the congruence–satisfaction relationship, such that the correlation between congruence and satisfaction is stronger when a group is important to an individual (Meir, Keinan, & Segal, 1986; Meir, Hadas, & Noyfeld, 1997; Meir & Green-Eppel, 1999; Meir, Tziner, & Glazner, 1997). Age – or life stage – can moderate congruence effects as well. A longitudinal study of the work lives of British workers found that average job tenure increased as jobs accumulated (Booth et al., 1999). Mature workers stayed longer on their fifth job than on their first job. A metaanalysis by Tsabari et al. (2005) nonetheless concluded that the congruence– satisfaction relationship was actually stronger for those in the 20–30 age group than for those over 30. Their finding implies that congruence matters less for older workers – that older individuals tend to be more satisfied than younger ones with their circumstances even if they fit the job less (which accords with the ‘‘role theory’’ of aging that self-integration, insight, and positive psychosocial traits grow with age; Yang, 2008). In line with this,


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Whitbourne (1986) documented age to be inversely related to identity flexibility, defined as ‘‘deliberate and informed comparison of one’s present identity commitments with other possibilities’’ (p. 164). In other words, older workers engage in less thinking about alternative commitments (and prospects of better job fit elsewhere). Further support for this idea comes from research on career stages, which suggests that earlier career stages are characterized by exploration of different options while later stages are characterized by stability (Brousseau, 1983). Further, the time lag between assessments of vocational fit and outcomes may moderate their relationships (Tracey, 2007). Vocational scholars implicitly define vocational congruence as a static construct, assuming stable or fixed personal and environmental attributes that can be captured on one occasion. They typically assess individuals’ interests at one point in time and later compare these interests to the occupations (or academic majors) these individuals choose. Such static research designs fail to consider how congruence might change over time (Low & Rounds, 2007). Indeed, Holland’s theory posits that fit between the individual and his or her environment is dynamic and likely to change with experience and progression into new stages of career (e.g., Brousseau, 1983). Furnham (2001) further elaborates on the dynamic nature of fit. Individuals change to adapt to their environments (e.g., altering personal work style) and can to some extent alter work environments (e.g., changing the way the job is done) to better fit their needs. As a result of these adaptations, congruence improves over time. In support, some studies reveal that congruence increases over time (Meir & Navon, 1992; Tracey, Robbins, & Hofsess, 2005 as cited in Tracey, 2007). That said, measuring fit at one point in time does not provide an indication of how that fit is likely to evolve, which some turnover scholars are acknowledging (Lee & Mitchell, 1994). Further, Holland’s differentiation dimension, which relative to congruence has received much less scholarly scrutiny (Meir et al., 1994), may impact associations between interest-occupation congruence and outcomes. Low differentiation (or more interest flexibility) may result in congruence being less able to predict positive outcomes like job satisfaction. Conceivably, employees whose interests are less well formed (less differentiated) are better able to deal with and more willingly grow into occupations that are imperfect fits with their primary interests, while those whose interests are well-defined may be less tolerant and willing to adjust to occupations that are not good fits (Meir et al., 1994). Darcy and Tracey (2003) sustain this moderating effect, reasoning that individuals with more flexible interests have a wider range of likes than those with less flexible interests and thus are able to

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substitute interests that an occupation cannot satisfy with other interests that it can satisfy. Those with less flexibility, however, are more likely to experience negative outcomes if the chosen occupation does not offer interesting activities because of their inability to substitute. Attesting to this idea, Wessel, Ryan, and Oswald (2008) considered adaptability, a construct which is akin to flexibility, as a moderator of the effects of fit in the context of students’ selection of majors. Adaptability is willingness and predisposition to adjust well to a changing environment. Among students who perceived low fit with their major, those who were highly adaptable were more satisfied with their educational institution than those who were low on adaptability. For students with high perceived fit, adaptability level did not make much difference in institutional satisfaction. This finding is consonant with Furnham’s (2001) discussion of fit as being subject to change when individuals proactively change themselves or their environment. Wessel et al.’s findings imply that adaptive individuals who misfit their environment did something to adapt and to thereby remain satisfied with their environment. Finally, as suggested by Spokane (1985), vocational identity (which is typically measured via Holland, Daiger, and Power’s (1980) vocational identity scale) could prove to be another moderator of congruence–outcome relationships; among those with a better sense of identity interest– occupational congruence should more strongly predict occupational outcomes. Healy and Mourton (1985) observed that for women college students whose interests matched their occupational choice, those high on vocational identity were more decided and had more general career information. (No relationships were found for male students). Carson and Mowsesian (1993) tested Spokane’s (1985) idea with a sample of employed adults, using job satisfaction as the outcome. They did not find identity to moderate the congruence–satisfaction relationship. We suggest, however, that further examination of identity as a moderator is still warranted. Alternatively, akin to congruence, vocational identity may be a predictor of job satisfaction in its own right. To illustrate, Carson and Mowsesian (1993) found that both congruence and vocational identity were significantly correlated with job satisfaction. The identity–satisfaction relationship (r ¼ 0.45) was actually stronger than the relationship between congruence and satisfaction (r ¼ 0.18). Commenting on the robust relationship between vocational identity and job satisfaction (e.g., r ¼ 0.70; Holland & Gottfredson, 1994), Holland (1997) placed particular emphasis on vocational identity when considering the interaction of individuals with their environments. He described the construct as being applicable not just to individuals


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but to work environments as well (‘‘environmental identity’’) as an organization can vary in how clear and temporally stable goals, tasks, and rewards are. He predicted that the interaction of an individual with a well-formed identity and an environment with a clear identity will make for more predictable interaction of the two than will be the case for the interactions of individuals and environments with identities that are less well formed or clear. Holland (1997) thus suggests that ‘‘Vocational Identity in conjunction with the rest of the theory provides a simple and plausible explanation of career stability or instability’’ (p. 173). Individuals with well-defined identities have good understanding of what they want (e.g., their goals and interests) and can offer (e.g., their talents and skills), which increases their likelihood of selecting jobs that fit their profile and of persisting with a job search until finding a good match. Those with ill-defined identities, on the other hand, are more liable to choose poor-fitting work environments, and consequently, to engage in job-hopping (Hom et al., 2008). Holland (1997) argues that some support for these predictions can be drawn from the strong relationship between vocational identity and job satisfaction, since job (dis)satisfaction is a well-established antecedent of voluntary turnover. We must note, however, that issues of measurement creep up here as well. Carson and Mowsesian (1993) suggest that the relationship between vocational identity and job satisfaction could be inflated due to some overlap between job satisfaction and what Holland et al.’s (1980) vocational identity scale measures. In summary, though concepts of person–environment fit have been incorporated into some theories about attrition (Chatman, 1991; Mitchell & Lee, 2001), further theoretical gains are possible based on elements of Holland’s theory. Going forward, turnover researchers need to address the methodological issues in how (vocational) fit is studied and consider moderators when examining how fit drives job satisfaction and the withdrawal process (Hom & Kinicki, 2001; Lee & Mitchell, 1994; Mobley et al., 1979; Price & Mueller, 1986; Rusbult & Farrell, 1983) to best capitalize on Holland’s theory. In what follows, we further describe how his model and measures of its constructs can enrich theoretical perspectives and research on attrition.

Implications of Holland’s Theory for Turnover Theory and Research Traditional Turnover Models In this section, we summarize the insights Holland’s (1997) theory offers into causes of turnover and how the turnover process unfolds.

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Traditional models of turnover assume job (dis)satisfaction to be a prime determinant of turnover (e.g., Mobley et al., 1979; Price & Mueller, 1981, 1986). In contrast to this prevailing preoccupation with proximal causes of leaving, Holland’s theory points to poor vocational choices as a potential distal cause of job dissatisfaction (and turnover). Conversely, good (in terms of fit) vocational choices should result in positive work outcomes. Though empirical support for this prediction is mixed, methodological deficiencies (e.g., constrained congruence variance due to self-selection, poor environment measures, global satisfaction indices) in tests and inadequate attention to moderators likely understated evidence for congruence–outcome links (Assouline & Meir, 1987; Tranberg, Slane, & Ekberg, 1993; Tsabari et al., 2005). By addressing these methodological shortcomings (including applying Edwards’ (2002) approach for analyzing difference scores), we believe that turnover investigators can more firmly demonstrate that prior vocational choices can shape employees’ affect and loyalty to their job (attesting to Holland’s theory). By so doing, they can extend conventional withdrawal formulations that primarily scrutinize the effects of job satisfaction: how it shapes quit decisions in combination with determinants, or how mediators convey its influence onto quitting (Hom & Kinicki, 2001; Lee & Mitchell, 1994; Rusbult & Farrell, 1983; Steers & Mowday, 1981). Though Price and Mueller (1981, 1986) specified a broad array of satisfaction causes, their attention to workplace causes leaves out more distal causes of satisfaction (and leaving), such as vocational decisions that occur before job entry. All told, we contend that job satisfaction serves as the link between Holland’s theory (notably, vocational congruence), for which job satisfaction is a key outcome of interest, and many turnover theories, for which job satisfaction is a key attrition driver. The Unfolding Model What is more, Holland’s formulation offers ways to refine newer nontraditional turnover models (e.g., Lee & Mitchell, 1994; Mitchell & Lee, 2001), which downplay the centrality of job satisfaction proffered in older schools of thought (Mobley et al., 1979; Price & Mueller, 1981, 1986). To illustrate how, we briefly summarize Lee and Mitchell’s (1994) unfolding model. It proposes four pathways to turnover, three of which do not involve dissatisfaction (see Fig. 1). In the first three paths, jarring events (‘‘shocks’’) initiate the turnover process; in the fourth path, job dissatisfaction is the catalyst, as in conventional models of turnover. In one path (Fig. 1, path 1), the shock is typically a personal, nonwork event, such as a graduate-school admission or pregnancy, which activates a preexisting plan


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(‘‘matching script’’) to quit (what Maertz & Campion, 2004 refer to as ‘‘preplanned quits’’). For example, T. W. Lee et al. (1996) observed that a nurse learning she was pregnant soon resigned; this shock triggered her plan to opt out of the labor market. Negative workplace shocks activate another turnover path (Fig. 1, path 2) when they violate employees’ values or career goals (known as ‘‘image violation’’). Employees determine whether or not the shock can be integrated into their values or goals. If not, they exit without jobs in hand. T. W. Lee and associates (1996) gave the example of a nurse leaving immediately when the hospital shifted from individualized patient care – her preferred nursing philosophy – to team-based nursing. Unsolicited job offers or inquiries represent a third type of shock prompting a third withdrawal path (path 3 in Fig. 1). To illustrate, T. W. Lee et al. (1996) noticed that a nurse quit a hospital when a physician offered her another position. Leavers taking this path are not unhappy with their current job; they simply prefer a better alternative. Finally, Lee and Mitchell (1994) designated a fourth path (path 4 in Fig. 1) to represent the conventional withdrawal path envisioned by traditional theorists (Hom & Kinicki, 2001; Price & Mueller, 1986; Steers & Mowday, 1981), in which dissatisfied employees pursue other jobs and exit when securing superior ones. We argue that Holland’s theory can refine the unfolding model in several ways. Earlier we discussed how Holland’s views suggest additional underpinnings of job satisfaction, which can further explain the etiology behind path 4 in the unfolding model as dissatisfaction is its prime mover. Though Mitchell et al. (2001a) estimated that only 37% of leavers take path 4, T. H. Lee et al. (2008) recently determined that 60% of all leavers from a nationally representative sample left due to dissatisfaction. Fig. 1 thus shows ‘‘poor vocational fit’’ as worsening job fit over time, which in turn increases dissatisfaction and turnover path 4 in the unfolding model (Lee & Mitchell, 1994). Moreover, incorporating Holland’s idea that individuals have occupational orientations (i.e., realistic, investigative, artistic, social, enterprising, conventional) into the unfolding model can clarify why negative workplace shocks in path 3 motivate some employees to leave (i.e., shocks clash with their occupational orientation). To illustrate, business professors initially joining research-oriented business schools because they prefer investigative activities (the I in the RIASEC model) may face image violation when their colleges later stress ‘‘vocational training’’ to attain higher MBA rankings (demanding and rewarding MBA teaching; S in the RIASEC model) (Morgeson & Nahrgang, 2008). Fig. 1 thus specifies that violation of RIASEC vocational fit can engender turnover path 2. Likewise, Holland’s perspective can elucidate how unsolicited job

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offers (path 3 shocks) impel departures. Using the same running example, I-oriented business faculty may abandon their academic posts because other universities recruit them away with the lure of superior opportunities to fulfill ‘‘I’’ preferences (e.g., supervising PhD candidates rather than teaching MBA students). In short, alternative jobs represent better matches for their occupational orientation. Fig. 1 thus shows an influence of ‘‘RIASEC vocational fit’’ on employees’ consideration of alternative job options that offer better fit with occupational preferences.

Job Embeddedness Theory Departing from earlier viewpoints (including the unfolding model) focusing on why people leave, Mitchell and colleagues (2001b) promulgated a novel perspective about why people stay. They came up with a new construct, called ‘‘job embeddedness,’’ to explain why some employees are less inclined than others to leave. They proposed that certain forces embed employees in their jobs: links (i.e., connections) to one’s organization and larger community, perceived sacrifices (i.e., costs) of leaving, and fit (or compatibility) with one’s organization and larger community (see Fig. 2). Links, sacrifices, and fit combine to cause embeddedness and can combine differently for different people but still result in equal levels of embeddedness (Mitchell & Lee, 2001). Empirical tests find that embeddedness accounts for unique turnover variance beyond attitudes and alternatives (Mitchell et al., 2001b), encourages higher performance and citizenship (Lee, Mitchell, Sablynski, Burton, & Holtom, 2004), retains employees by

VOCATIONAL IDENTITY

VOCATIONAL FIT • RIASEC Match

ON-THE-JOB LINKS

ON-THE-JOB FIT

OFF-THE-JOB LINKS

JOB EMBEDDEDNESS

OFF-THE-JOB FIT

VOCATIONAL DIFFERENTIATION ON-THE-JOB SACRIFICE

Fig. 2.

OFF-THE-JOB SACRIFICE

How Career Constructs Influence Job Embeddedness.


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embedding their coworkers (Felps et al., 2009), and mediates how highcommitment human resources management systems affect quit decisions and firm commitment (Hom et al., 2009). An obvious point of intersection between Holland’s theory and job embeddedness is the organizational fit construct in Mitchell and Lee’s (2001) model. Embeddedness theorists describe this construct as a compilation of various other fit constructs considered in previous literature. The broader nature of their construct is reflected in the items used to assess on-the-job fit. Items ask about liking of work group members, similarity to coworkers, utilization of skills and talents on the job, quality of match with company, fit with company culture, and satisfaction with the level of authority and responsibility granted (Mitchell et al., 2001b). Though this conceptualization of fit parallels Holland’s congruence construct, some elements of congruence as described by Holland are arguably omitted. Measures about the match between one’s career interests and job, ability to express one’s attitudes and values, and interest level in one’s job roles, may be usefully added. Indeed, other authors have long advocated for more comprehensive conceptualization and measurement of person–environment fit, arguing that consideration of individual variability in the types of fit people find most important can best elucidate how fit affects outcomes of interest (Piasentin & Chapman, 2006). Along with composition of more thorough measures of organizational fit, expansion of the conceptual domain of the organizational fit dimension to encompass vocational congruence is thus a viable direction for further development of the embeddedness construct. That said, Fig. 2 represents this proposition with a pathway from ‘‘RIASEC match’’ to on-the-job fit: Greater vocational congruence yields higher on-the-job fit. Also missing from job embeddedness theory and research is consideration of individual differences that, given equal levels of links and sacrifices, may lead to the same level of fit being unequally embedding for different individuals. We contend that in this regard, Holland’s differentiation construct can make a theoretical contribution to job embeddedness theory. As we discussed above, persons with low differentiation of interests are more willing to adapt to situations of low organizational fit. Adaptable individuals whose vocational interests are flexible may ultimately experience a high overall level of embeddedness despite an initial experience of low organizational fit. Over time, these individuals may adjust things about themselves or their environment so as to attain higher on-the-job fit. For example, Vandenberg and Nelson (1999) suggest that certain organizational cultures may be amenable to employees taking action to eliminate a source of discomfort, such as asking to be transferred to a different supervisor.

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Thus, embeddedness researchers might acknowledge the dynamic nature of (on-the-job) fit and assess the level of individuals’ job embeddedness at more than one point in time. Turnover research will need to move beyond the standard practice of surveying employees at one point in time and obtaining quit data at a second point in time because this static design misses the dynamic character of certain antecedents (e.g., fit) (Steel, 2002). Indeed, proponents of the unfolding model recognize that incorporating time into the withdrawal process can resolve ambiguities about the relative duration of different withdrawal paths (e.g., time lapse between initial deliberations of quitting and its enactment) and can address potential switching between decision paths over time (T. W. Lee et al., 1996; Lee, Mitchell, Holtom, McDaniel, & Hill, 1999). Fig. 2 thus specifies that vocational differentiation can moderate the influence of vocational fit on on-the-job fit. Vocational congruence might be initially low (as well as on-the-job fit) but those low on vocational differentiation may proactively develop higher on-the-job (and overall) fit. Finally, another individual difference variable that can enrich job embeddedness thinking is vocational identity (Holland, 1997). Based on the aforementioned rationale that persons with well-defined vocational identities can more effectively choose or secure jobs that match their career profile (Holland, 1997), we argue that such employees will have greater on-the-job fit. After all, empirical research reveals that such incumbents tend to feel higher job satisfaction (Carson & Mowsesian, 1993; Holland & Gottfredson, 1994). Consequently, Fig. 2 adds vocational identity as another antecedent of on-the-job fit. Future directions for nontraditional turnover models are already evident. Going beyond qualitative tests based on leavers’ retrospective accounts about how shocks induced them to quit (T. W. Lee et al., 1996, 1999), Kammeyer-Mueller, Wanberg, Glomb, and Ahlburg (2005) furnished additional corroboration for the unfolding model by demonstrating that shocks measured before turnover occurrence can predict future turnover. Embeddedness theorists encouraged research integrating the unfolding model with the construct of job embeddedness, such as testing whether job embeddedness moderates the effects of shocks (Holtom & Inderrieden, 2006; Mitchell & Lee, 2001). In that light, Burton, Holtom, Sablynski, Mitchell, and Lee (2010) recently showed that low levels of job embeddedness do make individuals more vulnerable to shocks they encounter. More than this, Holtom and associates (2008) prescribe applying social network methodology to capture more fully the impact of embedding links. Adding to this laundry list of suggestions, we urge further refinements of the unfolding


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model and embeddedness theory by incorporating the insights and methodology of Holland’s approach to vocational congruence.

SOCIAL COGNITIVE CAREER THEORY SCCT is a predominant formulation for explaining career interests, choice, and preparation based on Bandura’s (1977, 1982, 1989, 1991) social cognitive theory (SCT). These theoretical views promulgate self-efficacy – or ‘‘beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments’’ (Bandura, 1997, p. 3) – as a fundamental determinant of self-regulating motivation and behavior. Self-efficacy beliefs can affect choices people make, their perseverance in goal pursuits during setbacks, whether their thoughts facilitate performance, their experienced stress when coping with taxing stressors, and their vulnerability to depression. Hackett and Betz (1981) and Betz and Hackett (1981) initially applied Bandura’s early SCT theory to account for women’s career development. Compared with men, they claim that women fail to fully utilize their capabilities, talents, and interests in vocational pursuits because their careerrelated self-efficacy expectations are lower (magnitude), weaker (strength or durability), and less generalized (generality). In their view, such poor selfefficacy perceptions contribute to persistent and ubiquitous occupational segregation and pay inequity by discouraging women from entering maledominated fields offering higher pay, status, and influence. Hackett and Betz (1981) also noted that the traditional socialization of women has denied them the foundation on which to self-confidence to pursue occupations where men prevail (Bandura, 1977, 1997). Specifically, girls and young women have fewer opportunities to engage in traditionally masculine activities ( performance accomplishments), see fewer female role models in a wide range of male careers (vicarious learning), and receive little encouragement to pursue male-dominated careers (verbal persuasion). Lacking such self-efficacy bases, women can also feel more anxiety when performing male sex-typed activities (emotional arousal), which reinforces their sense of inefficacy. Initial tests of the Hackett–Betz formulation find that female undergraduates feel less efficacious about satisfying educational and performance requirements for stereotypically male jobs (e.g., engineers and lawyers) than do male students (Betz & Hackett, 1981) and that experimentally induced failure on a math task can weaken women’s feelings of task self-efficacy and interest (Hackett, Betz, O’Halloran, & Romac, 1990).

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Later, Lent et al. (1994) expanded Hackett and Betz’s (1981) theory to fully incorporate Bandura’s (1991) other constructs and introduced three interrelated SCCT models corresponding to three career development stages: (1) formation of career interests; (2) academic and vocational choices; and (3) performance and persistence in educational and occupational pursuits. Though domain content varies across stages, all models share Bandura’s (1991) core constructs: self-efficacy, outcome expectations (anticipated behavioral outcomes), outcome values, and goals. Specifically, each model posits that anticipated outcomes of behavior, such as tangible rewards and self-evaluative outcomes (e.g., anticipated self-satisfaction) shape interests and goal-directed behaviors. Each model further presumes that self-efficacy is a more potent behavioral determinant than are outcome expectations (Bandura, 1997). People refrain from performing an act if they doubt that they can enact it even when they expect rewards from its enactment (Lent et al., 1994). Conversely, a strong sense of personal efficacy can motivate individuals to perform behaviors even if they are uncertain about the prospects of earning rewards. Indeed, a resilient sense of personal efficacy is most crucial for tackling difficult behaviors ‘‘because the road to success is usually strewn with countless impediments’’ (Bandura, 1997, p. 126). Moreover, each SCCT model positions self-efficacy as the prime behavioral cause when the ‘‘quality of performance guarantees particular outcomes’’ (Lent et al., 1994, p. 84). For such actions, self-efficacy directly shapes expected outcomes because people perceiving that they can perform these behaviors know they will likely attain behavioral outcomes. Finally, self-efficacy beliefs can affect outcome values. For example, students who feel inefficacious about their academic abilities devalue academic accomplishments. We next describe how the SCCT perspective explains the three key phases of career development.

Three-Stage SCCT Models Vocational Interests To model how career interests arise, Lent and colleagues (1994) postulate that children and adolescents begin developing a sense of efficacy about certain career-relevant activities from doing them and observing others do them. Through experience or vicarious learning, they also learn to expect certain outcomes from such activities. More formally, Lent et al.’s interest model submits that self-efficacy perceptions and outcome expectations (especially anticipated self-satisfaction from meeting internal performance


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standards; Bandura, 1982) together prompt the formation of ‘‘enduring interests in activities’’ (Lent et al., 1994). This particular model embraces Bandura’s (1997) view that ‘‘perceived efficacy creates interests through engrossment in activities and the self-satisfactions derived from fulfilling personal challenges that lead to progressive mastery of occupational activities’’ (pp. 423–424). By comparison, vocational interests are stunted when young people feel inefficacious about career-relevant activities or foresee few positive outcomes from them. Lent and associates (1994) further deduced that emergent career interests would in turn initiate the setting of goals for greater exposure to career-related activities. Accordingly, more activity participation and practice can feed back and influence SSCT determinants by increasing self-efficacy feelings (as one develops competency) and expectations for positive outcomes (as one derives selfgratification from meeting internal standards). During their formative years, adolescents may continually repeat this spiraling cycle until their activity and career interests stabilize. Vocational Choice The second SCCT model by Lent and colleagues (1994) addresses development of career choices and entry once career interests are formed. Analogous to their interest model, they theorize that self-efficacy (about one’s success in a career rather than a particular activity), outcome expectations (about rewards available from a career), and goals (career decision) shape ‘‘choice actions,’’ such as actions to implement the career choice. Thus, individuals who feel confident about succeeding in a particular career, anticipate desirable career rewards, and have strong (preexisting) interests in activities associated with this career will form a goal (career choice) to pursue a particular career direction (choosing a vocation or declaring a college major). Strong intentions translate into actual career actions, such as enrolling in a training program, selecting an academic major, or applying for a job in the occupational field. Vocational Implementation For the career implementation stage, Lent et al.’s (1994) third SCCT model seeks to explain career accomplishments (e.g., course grades) and persistence (e.g., stability of academic major). According to this model, individuals who believe that they can effectively perform a career-relevant task and expect rewards from successful performance would set more challenging performance goals. To illustrate, engineering students who feel confident that they can achieve high grades in engineering courses and who expect rewarding

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outcomes (e.g., more job opportunities) from stellar grades will establish high GPA goals. Higher goals then promote greater task performance as hard goals induce more effort and persistence. Finally, this model postulates that ability and past performance affect task performance directly as well as indirectly via self-efficacy and outcome expectations. Sustaining the three SCCT models, an early meta-analysis by Lent and colleagues (1994) documented that self-efficacy and outcome expectations positively and moderately correlate with vocational interests, choice, and performance. Later research also corroborated these models (Betz & Hackett, 2006), establishing their validity for predicting academic interests and choice goals (Lent, Lopez, Lopez, & Sheu, 2008) as well as academic performance and persistence (Brown et al., 2008). Though SCCT theorists primarily strive to explain students’ vocational and academic choices, performance, and persistence, their viewpoints can also help account for their workplace behaviors once they completed occupational training. After all, Lent et al. (1994) proposed that an SCCT perspective can apply across the career life span and elucidate various kinds of work adjustment (subsequent to schooling), such as occupational satisfaction and career change. Given its validity for predicting student attrition from academic fields of study (Harvey & McMurray, 1994), we extend SCCT theory to deepen insight into why new employees leave workplaces. Our theoretical extension thus echoes Bandura’s (1997) assertion that ‘‘the sense of efficacy that newcomers bring and further develop during the course of their occupational training at the beginning stage of their careers contributes to the success of [the] socialization process’’ (p. 446). In the section below, we adapt Lent et al.’s (1994) SCCT perspective – especially their performance model – to advance understanding of the termination process among the most exit-prone segment of the workforce: newcomers (Hom et al., 2008).

SCCT Model of Newcomer Coping and Attrition Job attrition is primarily concentrated among new hires during their initial period of employment (Griffeth et al., 2000; Hom et al., 2008; Weller et al., 2009). They face special challenges when adjusting to new work settings, such as coping with unexpected stressful events, mastering new job requirements, and earning coworker acceptance (Ashforth, 2001; Feldman, 1976; Hom, Griffeth, Palich, & Bracker, 1998; Kammeyer-Meuller & Wanberg, 2003). Such challenges are more daunting if they are also new


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entrants to the labor market (e.g., graduating students) because workplace realities (e.g., close supervision, rare feedback, and organizational politics) differ so much from former school roles (Greenhaus, 1987). Given such demands, most scholars thus implicate assimilation maladjustment as a key driver of newcomers’ premature departures (Allen, 2004; Griffeth & Hom, 2001; Kammeyer-Meuller & Wanberg, 2003). Despite an extensive body of findings about maladjustment causes and remedies (Allen, 2004; Ashforth, 2001; Chan & Schmitt, 2000; Wanous, 1992), only a few studies examined how self-efficacy impacts newcomer adaptation. For example, socialization researchers report that beginning employees with a strong sense of efficacy often redefine work roles to suit their preferences (Ashforth & Saks, 2000), while those feeling less selfefficacious can most benefit from training or certain socialization tactics (Jones, 1986; Saks, 1995). All the same, these studies neglect pivotal SCCT constructs, such as personal goals and outcome expectations (Lent et al., 1994), which likely codetermine socialization success along with selfefficacy (Bandura, 1997). Moreover, socialization experts (Jones, 1986; Saks, 1995) mainly attend to new hires’ beliefs about whether they can fulfill performance requirements, neglecting other kinds of demands crucial for organizational assimilation (e.g., workgroup integration, managing stressors; Bandura, 1997). For instance, Saks (1995) only scrutinized new accountants’ perceptions about efficacy for documenting audit procedures and maintaining client relationships. Further, some authors investigated generalized dispositions, such as desire for control (Ashford & Black, 1996) or proactive personality (Kammeyer-Meuller & Wanberg, 2003). By comparison, SCCT theorists construe perceived self-efficacy as a state-like belief about one’s capacity to perform in a particular domain of functioning and contend that such self-beliefs better explain for how one approaches and performs tasks than personality-like traits (Bandura, 1997). In summary, socialization tests omitted complimentary constructs put forth by Lent et al. (1994) that may reinforce or condition how newcomer self-efficacy’s effects and operationalized newcomer self-efficacy too narrowly (overlooking other assimilation challenges) or too broadly (focusing on dispositional traits rather than motivational states). To further clarify the process by which newcomer self-efficacy sustains job survival, we extend Lent et al.’s (1994) framework to furnish a more thorough account of how self-perceptions about efficaciousness – acting in concert with other SCCT constructs – facilitate newcomer socialization. Specifically, we adapt their SCCT model to focus on how newcomers cope with the stressful transition from outsider to insider status (Ashforth, 2001;

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Hom et al., 1998; Wanous, 1992). After all, coping represents a ‘‘person’s constantly changing cognitive and behavioral efforts to manage specific external and/or internal demands that are appraised as taxing or exceeding the person’s resources’’ (Folkman, Lazarus, Dunkel-Schetter, DeLongis, & Gruen, 1986, p. 993). We draw from Lazarus and Folkman’s (1984) framework about how people cope with stress to adapt Lent et al.’s (1994) constructs to the domain of coping. Our SCCT model of coping thus asserts that newcomers’ confidence in surmounting assimilation obstacles (coping self-efficacy), their expectations about outcomes derived from meeting these demands (coping outcome expectations), and the goals they set to meet such demands (coping goals) promote their adaptation to unfamiliar work settings. We also strengthen the explanatory power of Lent et al.’s (1994) model by conceptualizing SCCT constructs in terms of well-established socialization concepts. In particular, our SCCT formulation specifies coping goals for the key socialization tasks that many scholars have identified (Feldman, 1976). Thus, we maintain that newcomers would form separate coping goals to become effective performers and socially integrated within work groups (Chan & Schmitt, 2000; Feldman, 1976; Kammeyer-Meuller & Wanberg, 2003). Similarly, our model specifies how different socialization tactics (Jones, 1986) can furnish the bases for developing (coping) self-efficacy, noting how these tactics embody the diverse self-efficacy underpinnings (e.g., vicarious learning) noted by Bandura (1997) and SCCT theories. From theory and research on coping with workplace stress (Latack, Kinicki, & Prussia, 1995), our reformulated SCCT model subsumes ‘‘coping strategies’’ to represent how newcomers can manage assimilation difficulties. For instance, new hires might become more proficient in satisfying work role requirements by changing work methods or soliciting advice from others (Feldman & Brett, 1983; Latack, 1986; Latack et al., 1995). Our SCCT coping perspective also recognizes that newcomers must regulate emotional distress because coping thinkers hold that coping success entails both effective stress management and problem-solving (Bandura, 1997; Lazarus & Folkman, 1984; Sweeney, 2008). By modeling stress management, we also acknowledge longstanding observations that newcomers feel ‘‘reality shock’’ – upon learning that the job does not live up to expectations – and anxiety when they enter novel workplaces (Hom et al., 1998; Kramer, 1974; Meglino, DeNisi, Youngblood, & Williams, 1988; Wanous, 1992). As a result, our theoretical approach asserts that entry shock can invoke dysfunctional thoughts and emotions among newcomers, which can derail their adjustment.


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In short, we adapt Lent et al.’s (1994) SCCT theory by integrating concepts from the coping and socialization literatures to deepen insight into why newcomers readily abandon jobs. By so doing, we derive a more thorough and precise account of how newcomers adjust to jobs and survive the assimilation period. Fig. 3 depicts this integrated model. At the outset, we note that SCCT theorists prefer more fine-grained models for each domain of functioning (e.g., different self-efficacy beliefs for task mastery and social integration). But various domains are, however, combined to simplify graphic depiction of this model. In what follows, we elaborate the logic for the various pathways in this SCCT coping model of assimilation. Coping Self-Efficacy - Coping Goals Our SCCT framework posits that entering employees harbor self-beliefs about their capacity to perform new jobs (‘‘coping self-efficacy,’’ Saks, 1995), given the vital role that self-efficacy plays in newcomer adjustment (Ashforth, 2001). Consistent with coping and SCCT thinking, we submit that these beliefs shape the coping goals that are set and that guide actions (Latack et al., 1995; Lent et al., 1994). Specifically, beginning incumbents who feel efficacious about their capabilities to overcome socialization hurdles would form stronger and more resilient goals, while those feeling less self-efficacious would set modest coping goals. To fully assimilate (Chan & Schmitt, 2000; Feldman, 1976; Hom et al., 1998), newcomers must achieve multiple goals to cope with the challenges of performing job tasks proficiently (Kammeyer-Meuller & Wanberg, 2003), understanding role requirements (e.g., learning role expectations from their role set; Graen, 1976), gaining collegial acceptance (Feldman, 1976), and managing disruptive emotions aroused by entry shock (Bandura, 1997). Following SCCT points of view (Bandura, 1997; Lent et al., 1994), our model specifies that outcome expectations – anticipated outcomes for attaining coping goals – shape goal formation. That is, entry employees who anticipate greater rewards (e.g., job security, teammate approval, healthcare coverage) from goal attainment would then set higher coping goals (including becoming ‘‘insiders’’ sooner) than those looking forward to fewer rewards. Coping Goals - Coping Strategies The current conceptualization submits that coping goals initiate varied coping strategies for each domain of socialization, consistent with SCCT (Lent et al., 1994) and coping (Bandura, 1997; Latack et al., 1995) models. We nonetheless follow Lazarus and Folkman’s (1984) scheme for classifying

Fig. 3.

•Encounter Unexpected Conditions •Reality Shock •Face Uncertainty

Transition to New Workplace

Cognitive Control Efficacy

•Fit •Links •Sacrifice

Shock-Driven Turnover Path

•Problem Management •Work Long Hours •Redefine Job •Seek Task Information •Seek Social Support •Change Work Methods •Stress Management •Cognitive Reappraisal •Constructive Self-Talk •Functional Thinking •Symptom Management •Recreation •Exercise •Family Activities

Coping Strategies

Meeting Socialization Challenges

Expanded Social Cognitive Career Theory of Newcomer Adaptation.

•Anxiety Arousal •Stress Reactions

Emotional States

•Task Mastery •Role Clarification •Social Integration •Manage Disruptive Thoughts & Emotions

Coping Goals

•Internal Attributions for Success •Effort Attributions for Setbacks •Believe Ability is Acquired •Believe Effort can be Repeated

Causal Attributions Of Performance

•Perceived Threat or Opportunity •Secondary Appraisal Can I Cope?

Primary Appraisal

•Self-Regulatory Skills

Ability/Skills

•Performance Accomplishments •Internships •Prior Job Experience •On-the-Job Training •Formal Socialization Tactics •Vicarious Learning Outcome •Serial Socialization Tactics Expectations •Collective Socialization Tactics •Verbal Persuasion •Investiture Socialization Tactics •Emotional Arousal Coping Efficacy •Sequential & Fixed •Perceived Coping Socialization Tactics Efficacy •Orientation

Sources of Efficacy

Job Embeddedness

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coping strategies according to the two functions that they fulfill: problem and stress management. In support, Folkman et al. (1986) noted that a multitude of distinct coping strategies (e.g., seeking social support, positive reappraisal) people use to handle stressful encounters in their day-to-day lives either serve to regulate stressful emotions or modify stressors (or both). Likewise, Latack (1986) and Latack et al. (1995) interpreted employees’ various ways for coping with job stress as embodying a proactive strategy to resolve problems or a means to manage emotional reactions. Further, Feldman and Brett (1983) observed that beginning employees cope with new transitions by changing the environment (e.g., work longer hours, redefine the job, get others to provide help, seek social support) or recalibrating affective responses to the environment (e.g., overindulge in alcohol, repress awareness of stress). Drawing from Lazarus and Folkman (1984) and Bandura (1997), our theory thus classifies coping strategies according to problem management (actions to handle socialization demands, such as working long hours; Feldman & Brett, 1983; Morrison, 2002) or stress management (blocking out dysfunctional thoughts and emotions; Bandura, 1997). These coping strategies are deployed to achieve specific coping goals and thereby constitute the means by which newcomers address socialization challenges. To meet the task-mastery coping goal, new hires might redefine tasks and seek task advice from established incumbents (problem management) as well as participate in cognitive reappraisal or constructive self-talk (stress management; Manz & Neck, 1999) when facing frustrations or setbacks (Bandura, 1997). Coping Efficacy - Stress Appraisal - Emotions Because how people interpret stressful events dictates their coping responses (Latack et al., 1995; Lazarus & Folkman, 1984; Sweeny, 2008), the current model includes a sense-making mechanism in which newcomers first interpret the meaning of stressful events (e.g., hazing by coworkers) along two dimensions. In primary appraisal, they must first decide if stressors potentially threaten career plans or goals or represent opportunities to advance careers (or competencies; Folkman et al., 1986). They next decide whether or not they have the resources and ability to cope with threats or seize opportunities (secondary appraisal). In our view, newcomers’ sense of personal efficacy also conditions their stress appraisal. After all, how they construe stressful events depends on the ‘‘match between perceived coping capabilities and potentially hurtful aspects of the environment’’ says Bandura (1997, p. 140). Entering employees

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perceiving that they can flourish in unfamiliar workplaces see opportunities for growth and development, whereas those who believe otherwise may regard socialization challenges as threatening. The latter in turn are prone to anxiety and dysfunctional thoughts (that fuel such anxiety), according to perspectives on depression and negative moods (Bandura, 1997; Beck, 1976; Burns, 1980). If extremely upset, some distressed newcomers may form coping goals and execute coping strategies to manage dysfunctional cognitions and affect to lessen their debilitating effects (Bandura, 1997). They might change their thoughts with cognitive reappraisal (e.g., reconstruing stressors as benign; Latack, 1986) or feelings with symptom management (e.g., alcohol consumption; Feldman & Brett, 1983). Cognitive Control Efficacy as Moderator We further differentiate coping self-efficacy – perceived capacity to control actual threats – from ‘‘cognitive control efficacy’’ – perceived capacity to control intrusive disturbing cognitions (Bandura, 1997). Accordingly, our model embraces Bandura’s (1997) contention that newcomers effectively cope with stress by developing greater confidence that they can manage actual threats as well as manage how they think about them. ‘‘When people have a strong sense of efficacy to control their own thinking, they are less burdened by negative thoughts and experience a low level of anxiety,’’ claims Bandura (1997, p. 149). We thus posit that cognitive control efficacy dampens how stress appraisals arouse emotional distress. Persons avoiding excessive worry and ruminations are less agitated by threats (Burns, 1980; Manz & Neck, 1999). Socialization Sources of Self-Efficacy In keeping with SCCT tradition (Hackett & Betz, 1981; Lent et al., 1994), we next identify how employers’ assimilation methods, such as socialization tactics (Allen, 2004; Ashforth, 2001), cultivate self-efficacy development. In particular, formal tactics, which segregate newcomers from other employees during socialization, may foster self-efficacy. Segregated new hires can develop higher performance accomplishments because they can practice new skills in safe training environments and acquire graduated mastery experiences (Ashforth, 2001). Moreover, serial (which employs established incumbents as socialization agents) and collective (which groups newcomers and exposes them to common training) tactics may enhance newcomer self-efficacy by way of vicarious learning. Newcomers observe how experienced incumbents perform tasks correctly and thereby may feel more efficacious about their own ability to do the same. Investiture tactics


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affirm newcomers’ incoming characteristics, while divestiture attempts to strip away newcomers’ former identities (e.g., boot camp; Ashforth, 2001). When recruiting newcomers for their particular skills and abilities, firms implementing investiture communicate to recruits that they value them and believe them capable of meeting job demands. High expectations instill selfconfidence in recruits. Finally, sequential (requiring recruits to follow a fixed progression of steps before assuming roles) and fixed (setting a timetable for role preparation) tactics alleviate anxiety. Beginning employees feel more self-doubt when physiologically agitated by uncertainty, interpreting such bodily states as indicative of weakened capabilities (e.g., fatigue during athletic contests) to persist on tasks. Arousal, along with fear that arousal can hinder performance, also erode self-efficacy (Bandura, 1997). Feedback Loop via Causal Attributions This conceptual scheme introduces feedback loops as new employees may revise initial self-efficacy beliefs and outcome expectations based on early attributions about how well they initially performed socialization tasks and experienced rewards. Bandura (1997) assert that people’s interpretations about feedback about task performance can modify self-beliefs about efficacy. Simply put, individuals who take credit for success (attributing high performance to ability or effort) maintain – if not raise – beliefs about selfcapabilities. Moreover, setbacks do not diminish self-efficacy when people make effort attributions or assume that ability is cultivated through practice rather than innate. (Note that those making effort attributions may not feel self-efficacious if they believe that they cannot repeat their high effort in future performances). Further, experienced success (or failure) fulfilling socialization requirements may modify newcomers’ expectations about outcomes derived from task performance. Finally, individuals’ interpretation of past performance can hinge on their current coping self-efficacy. This is because ‘‘people with a high sense of efficacy accept successes as indicants of their capabilities but dismiss the diagnostic import of failures and attribute them to external impediments’’ (Bandura, 1997, pp. 154–155). SCCT Constructs - Proximal Turnover Antecedents We next specify how our SCCT coping perspective dovetails with extant turnover models. Socialization theory and work regard job survival as a pivotal assimilation outcome (Ashforth, 2001; Feldman, 1976; Saks, 1995) but rarely outline the process by which ineffective socialization engenders turnover (Kammeyer-Meuller & Wanberg, 2003). To address this oversight, we discuss how job embeddedness can mediate how SCCT constructs affect

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newcomer retention (Hom et al., 1998; Weller et al., 2009). In our perspective, newcomers who mastered job requirements, understood role requirements, secured collegial approval, and managed stress reactions during the adjustment period in turn become embedded through higher stronger fit, links, and sacrifice (Mitchell & Lee, 2001). Socialized new hires achieve on-the-job fit because they satisfy performance demands of the new job (improving ability-job requirement fit) and internalize corporate norms by way of social integration and role clarification (aligning values to those of the company; Chatman, 1991). By socially integrating within the workgroup, these newcomers also forge more – and stronger – links in the workplace (as well as social capital; Hom et al., 2009). Though relatively new, assimilated newcomers also ‘‘experience a growing sense of sacrifice when considering leaving’’ (Weller et al., 2009, p. 1147), such as giving up new mentors and friends and the ‘‘hard-won’’ status of surviving probation (earning them associated perks, such as health and pension coverage) if they exit. By comparison, on-the-job fit is low for poorly socialized newcomers as they fail to meet performance standards or internalize corporate values (Mitchell & Lee, 2001; Weller et al., 2009). Newcomers who have trouble assimilating also tend to be excluded from organizational social networks, which deprive them of embedding links. Foundering during socialization, they may also not accumulate as many corporate perks nor develop much social capital. As a result, they may incur little or no sacrifices upon leaving. Because unassimilated newcomers are less embedded in the job, they are thus more liable to quit according to Mitchell and Lee (2001). All the same, job embeddedness – or its absence – cannot fully account for all newcomer attrition. Some beginning employees quit soon after job entry – well before they can (gradually) learn if they are good fits with the job (Weller et al., 2009). Rather, these leavers may likely encounter shocks during early employment (Lee & Mitchell, 1994). Weller and colleagues (2009) note that ‘‘new employees who are going through a role transition (outsider to insider) are more likely to experience organizational shocks’’ (p. 1148). If they experience negative workplace shocks that conflict with career values, goals, or plans (Lee & Mitchell, 1994), they may readily leave – even without job offers in hand (taking turnover path 2; T. W. Lee et al., 1996, 1999). Upholding this thesis, socialization investigators often observed that job entrants whose pre-entry expectations are unrealistic (because recruiters misled them; Wanous, 1992) are more prone to endure ‘‘reality shock’’ and quickly exit (Hom et al., 1998). Consequently, our framework includes shock-driven turnover as another path by which newcomers exit.


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Implications of SCCT Theory for Explaining Higher Minority and Female Quits Though we propose a general framework to explain newcomer attrition, this conceptualization can be adapted to reflect unique socialization problems confronting different populations. For example, Hom and associates (2008) documented higher corporate flight among female and minority incumbents in managerial and professional fields where their identity groups have been historically underrepresented (Hom & Griffeth, 1995; Roberson, 2004). Because most women and minorities exit corporate America during their first few years of work, inadequate or incomplete organizational socialization may underlie their higher turnover relative to white men (Hom et al., 2008). We thus extend our general formulation about newcomer coping to address their special adjustment challenges, offering a more comprehensive explanation about corporate flight among minorities and women. Higher Coping Demands For women and minorities entering nontraditional workplaces or occupational fields, social integration – or its absence – represents a prominent socialization challenge because they, demographically dissimilar newcomers, may endure social isolation (due to tokenism), exclusion from informal dominant white-male networks (due to demographic dissimilarity; Eagly & Carli, 2007; Elvira & Cohen, 2001; Riordan, Schaffer, & Stewart, 2005), and hostility from peers or superiors (e.g., sexual or ethnic harassment; Berdahl & Moore, 2006; Laband & Lentz, 1998). Moreover, they may encounter more difficulty understanding and mastering new task requirements because they lack good mentors (due to supervisory bias or scarcity of mentors of their own ethnicity or sex; Cottrell & Neuberg, 2005; Eagly & Karau, 2002; Fiske, Cuddy, Glick, & Xu, 2002; Thomas, 2001) and receive less information from colleagues (Forret & Dougherty, 2004; Ibarra, 1992). Weaker Self-Efficacy Sources Besides greater challenges, our SCCT formulation suggests that minorities and women may feel less efficacious about their ability to survive and flourish in white- or male-dominated workplaces. During their formative years, they may have experienced fewer sources on which to build strong self-beliefs that they can succeed in nontraditional career fields (Hackett & Betz, 1981; Lent et al., 2008). As youth, they knew few women or minority professionals or managers, for example. Moreover, employing organizations may fail to cultivate a robust sense of self-efficacy among them.

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To illustrate, certain socialization tactics can disadvantage them. For example, firms applying collective socialization (grouping newcomers and orienting them together) may address primarily concerns of the majority of new hires (white men), omitting unique concerns of minorities or women (e.g., forming productive relationships with white-male mentors; Thomas, 2001). Along these lines, employers deploying serial tactics may assign veterans from another sex or race to coach women and minorities (due to scarcity of female and minority mentors; Elvira & Cohen, 2001; Ibarra, 1992), who provide ‘‘marginal mentoring’’ (Ragins, Cotton, & Miller, 2000). Further, firms using divestiture tactics try to strip away newcomers’ incoming identities to reconstruct a new corporate identity (Ashforth, 2001). This approach forces women and minorities to assimilate to the dominant white male culture (Ely & Thomas, 2001), which devalues their cultural identities and weakens their self-efficacy. Along with discouraging socialization tactics, women and minorities face other conditions, well-documented by diversity research (e.g., supervisory bias, impoverished job duties, glass ceilings; Eagly & Carli, 2007; Hom & Griffeth, 1995; Stroh, Brett, & Reilly, 1996) that diminish perceived selfefficacy to master socialization requirements. To illustrate, supervisors may not see a good fit between minority and female newcomers’ attributes and job requirements (Eagly & Carli, 2007; Heilman, 1983). Rather, they may believe that individual minorities and women possess traits stereotypical of their group, which do not resemble the requisite traits for occupations where whites or men predominate. As a result, superiors may act (or not act) in ways that decrease women’s and minorities’ self-efficacy (Hom & Griffeth, 1995). Expecting less of them, they may thus offer fewer chances for performance accomplishments (relegating women and minorities mundane tasks or jobs), fewer occasions for vicarious learning (by personally demonstrating how they would handle problems), and engage in less verbal persuasion (to encourage women and minorities to tackle stretch assignments). Given stereotypes about occupational misfit, superiors may thus fail to create the preconditions that promote self-efficacy among minority and female recruits (Bandura, 1997). Stereotype Threat Our SCCT viewpoint highlights how minorities and women entering nontraditional vocational fields or workplaces may appraise threats posed by role transitions differently – or more severely – than do white men. Specifically, they are prone to ‘‘stereotype threat’’ (Roberson & Kulik, 2007), feeling anxiety about confirming negative stereotypes about their


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identity group. When situated in workplaces that employ few members of their identity group (including leadership positions; Eagly & Carli, 2007; Hom et al., 2008), they may worry about corroborating implicit stereotypes that those like them cannot succeed in white-male fields or settings. As a result, minority and female newcomers feel performance anxiety, which impedes performance and induces them to vacate jobs where they cannot succeed (Steele, 1997). All told, an SCCT perspective can illuminate why minority and female newcomers more readily exit nontraditional jobs (e.g., managers, army officers; Payne & Huffman, 2005). With few exceptions (Stroh et al., 1996), turnover researchers have rarely scrutinized proximal or intermediary psychological states behind minority and female exits (Roberson, 2004). Rather, they have focused on documenting reliable racial or gender disparities in attrition or relating disparate workplace conditions (e.g., pay, promotions) to differential attrition (Daniels, 2004; Greenhaus, Collins, Singh, & Parasuraman, 1997; Hewlett & Luce, 2005; Lyness & Judiesch, 2001; Lyness & Thompson, 1997; Rosin & Korabik, 1995). Yet investigating how SCCT constructs mediate between disadvantageous working conditions and turnover can clarify how such conditions occasion greater female and minority attrition from male- or white-dominated workplaces. Indeed, mediation tests would add greater credibility to prevailing theories that workplace discrimination undergirds elevated minority and female corporate flight (Hom et al., 2008) as they are often grounded in circumstantial evidence (Johnson & Neumark, 1997). Moreover, SCCT theory might clarify why racial and gender differences in turnover from nontraditional jobs vary across studies (Hom et al., 2008). For example, Lyness and Judiesch (2001) found no quit differences between men and women managers from a financial services organization, whereas Hom and associates (2008) observed that women professionals and managers from large firms representing various industries left at higher rates than their male counterparts. Women in the former study however represented 42% of the entire sample, whereas they constituted 28% of the workforces sampled in the latter test. Quite likely, the sizeable representation of women across the spectrum of jobs in Lyness and Judiesch’s (2001) study created (and reflected) conditions more hospitable to fostering women’s self-efficacy and job survival. In their financial services firm, women could find more female mentors who can ‘‘show them the ropes’’ about how to succeed and more welcoming mixed-gender networks that can give task advice and social support. Given greater sources of self-efficacy, more women would feel efficacious and successfully assimilate, thereby

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staying as long as their male counterparts. By contrast, women in Hom and associates’ (2008) study felt less efficacious as they lacked more favorable workplace conditions for building self-efficacy. Their sample of corporate women (especially short-tenure ones) were poorly socialized into workplaces and thus more likely to quit than men.

CONCLUDING REMARKS Our brief review of three leading theories of career and vocational development suggests that they hold great promise for elucidating why and how people stay or leave. Currently, the dominant theoretical and empirical approaches focus on proximal antecedents to turnover, such as quit intentions, job attitudes (and their intermediate effects; Hom & Kinicki, 2001), shocks (Lee & Mitchell, 1994; T. W. Lee et al., 1999), and job embeddedness (certain dimensions such as fit and links; Mitchell et al., 2001b). Some turnover authors allude to distal causes, such as image violations, matching scripts (Lee & Mitchell, 1994), and centrality of nonwork values or roles (Mobley, 1982; Mobley et al., 1979). Complementing presentday theoretical advances, we suggest greater scholarly attention to more fundamental causes of attrition, such as the vocational and career constructs reviewed in this chapter. As we noted, these latter models can more precisely answer why image violations, matching scripts, or centrality of nonwork roles motivate turnover. In addition, as more constructs are transported from vocational and career theories and empirically linked to turnover research, we can build more comprehensive models within the turnover field. In closing, we contend that the twin scientific goals of prediction and understanding do not always converge, though turnover scholars strive for both goals (Maertz & Campion, 1998). Though current emphasis on predictive accuracy can yield greater insight into turnover (cf., Lee & Mitchell, 1994), such orientation draws attention to immediate turnover precursors. More basic causes are thus overlooked, such as how otherwise satisfying jobs can no longer embed incumbents when their career stage shifts over time or why many people exit the work force to pursue other life roles, such as full-time parenting or education. Over the years, turnover theorists increasingly recognize such forms of leaving (Hom & Kinicki, 2001; Hulin et al., 1985; Lee & Mitchell, 1994) but their attempts at explanation seem superficial. To address this theoretical void, we thus advocate greater attention to distal constructs such as those formulated by career scholars. We hope that our discussion of the conceptual richness of

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these career development models will encourage scholarly explorations into how career constructs can promote understanding – and perhaps prediction – of turnover.

ACKNOWLEDGMENT We thank Neal Schmitt for his assistance in providing a review of our earlier drafts.

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HOW DID YOU FIGURE THAT OUT? EMPLOYEE LEARNING DURING SOCIALIZATION Jaron Harvey, Anthony Wheeler, Jonathon R. B. Halbesleben and M. Ronald Buckley ABSTRACT In this paper, we suggest a contemporary view of learning during the process of organizational socialization. The relationship between learning and socialization is implicit in much of the existing socialization literature. In an attempt to make this research more explicit, we suggest a theoretical approach to the actual learning processes that underlie workers’ socialization experiences. In order to accomplish this, we review previous work on socialization, information seeking and feedback seeking during socialization, and learning. In doing so we describe the learning process that underlies socialization, highlighting the beginning of the process, the role of information during the process, and integrating three different types of learning (planned, deutero, and meta) into the process of organizational socialization. In addition, we also discuss the implications of these three types of learning during the process of socialization and directions in future research on the socialization process.


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Everyone who has ever been employed in an organization, whether on the first day or shortly thereafter, experiences that moment when he or she questions if he or she will succeed in the job or within that organization. Be it on the first day, or shortly thereafter, employees want to know how they are going to learn everything they need to know to survive in an organization. A number of questions may race through an employee’s mind; ‘‘how will I learn all of the things I am supposed to do,’’ ‘‘how will I be evaluated,’’ ‘‘how do I get promoted,’’ or ‘‘how will I fit in with my coworkers?’’ Success in a new job, however success is defined, hinges on employees learning the ropes, and then using their knowledge to successfully navigate new challenges that arise in organizational life. Upon initial entry into an organization there is a burst of learning that helps employees to develop needed work skills and abilities, along with basic group norms and values (Feldman, 1981). Following that momentary flurry of information there is a much longer period of adjustment and readjustment during which employees develop a greater understanding of what is really required to succeed in the organization. The process of answering these ‘‘how’’ questions, or the act of learning how to be successful in a job is part of organizational socialization (referred to throughout as socialization). Socialization is the process through which employees transition from being ‘‘outsiders’’ in the workplace to being ‘‘insiders’’ (Van Maanen & Schein, 1979). During this process employees develop key attitudes, behaviors, and knowledge concerning how to successfully function as a member of the organization (Bauer, Morrison, & Callister, 1998). This process, fundamentally a learning process, is influenced both by the organization, through different tactics or programs it may adopt (Allen, 2006; Van Maanen & Schein, 1979), and by employees’ personalities and individual efforts to learn about their role in the organization (Kammeyer-Mueller & Wanberg, 2003). Early socialization research focused on developing models that explained the different stages of socialization (e.g., Feldman, 1981; Graen, 1976; Simpson, 1967; Van Maanen, 1975, 1976), which in turn explained the development of attitudinal outcomes such as job satisfaction, organizational commitment, and turnover intentions (Feldman, 1981; Van Maanen, 1975). More recent socialization research has explored the tactics implemented by organizations to facilitate the socialization process (Bauer et al., 1998; Cable & Parsons, 2001). These two streams of research seek to identify stages of socialization (e.g. Feldman, 1976; Van Maanen & Schein, 1979), link these stages to important organizational and employee outcomes (Fisher, 1986), and determine the best way for organizations to influence

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this process for the benefit of both individual and organization (Cable & Parsons, 2001). During the development of socialization research, scholars have scrutinized the experiences of new employees as a means to gain new insights into this process. Some researchers have looked at the influence of newcomer proactivity (Chan & Schmitt, 2000; Kammeyer-Mueller & Wanberg, 2003), control (Ashforth & Saks, 2000), and involvement (Bauer & Green, 1994) during the socialization experience, while others have examined the role of information and feedback seeking during these experiences (Bauer & Green, 1998; Morrison, 1993a, 1993b; Ostroff & Kozlowski, 1992, 1993). Recent reviews (e.g. Ashforth, Sluss, & Harrison, 2007; Bauer et al., 1998) and meta-analyses (Bauer, Bodner, Erdogan, Truxillo, & Tucker, 2007; Saks, Uggerslev, & Fassina, 2007) attest to the developed nature of this research area. However, while the study of socialization is vigorously active, one key area of the socialization process has yet to receive much scholarly scrutiny: the learning process that occurs during socialization. The role of learning in socialization has been evident from the beginning, because it is implied that employees must be learning as they see the organizational world and begin to inculcate and understand its traditions (Van Maanen & Schein, 1979). Stage models addressed learning tangentially by identifying what an employee could expect to learn at any given time in the socialization process; with all of it culminating in an employee becoming not only fully capable of carrying out the duties and functions of the job, but also being enmeshed in the culture of the organization (cf., Schein, 1978). The research that has examined portions of the learning process during the socialization experience has focused on information acquisition (Ostroff & Kozlowski, 1993), sources of information (Ostroff & Kozlowski, 1992), content of information (Burke & Bolf, 1986), and some of the factors that can enhance or inhibit employee learning (Morrison & Brantner, 1992). However, none of this has addressed the entire process through which employees learn. Individual learning occurs when people make changes in their mental associations and their behavior based on information gathered from personal experiences (Ormand, 1999). The process through which employees make sense of their surroundings likely has a significant impact upon how fully enmeshed in an organization an employee becomes through socialization. From social learning theory (Bandura, 1977) we know that employees make sense of their roles in an organization by learning from the situation, the individuals around them, and the formal organizational practices. As employees try to understand and adjust to a work situation, they will engage in different learning processes (e.g., Visser, 2007).

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In this paper, we focus on three specific modes of learning: planned, meta-, and deutero-learning. Each of these modes of learning is a part of the underpinnings of workers’ socialization experiences. Much of the extant socialization literature focuses on traditional notions of planned learning. Planned learning refers to the development and maintenance of learning systems that promote employee learning (Visser, 2007). A focus on planned learning has specific limitations in understanding the nature of socialization because it emphasizes established socialization programs (e.g., Klein & Weaver, 2000) and established group behaviors (e.g., Chen, 2005). To address these limitations, in addition to planned learning, we consider the other two modes of learning described by Visser (2007): meta- and deutero-learning. Meta-learning is the processing of inconsistencies that occur between individuals’ expectations and the actual consequences of their actions (Argyris & Scho¨n, 1974, 1996). Deutero-learning is a mode of learning that is adaptive and unconscious (Bateson, 1972). As employees interact with coworkers and organizational systems they experience different consequences. It is from these consequences, combined with the social context that surrounds them, that employees learn how to adapt their personal behavior (Bateson, 1972; Visser, 2003, 2007). In addition to planned learning, these two modes of learning provide researchers with a new lens to view the learning element of socialization. By concentrating on learning processes, we have chosen not to focus upon where socialization research has been, which others have established (e.g. Ashforth et al., 2007; Bauer et al., 2007; Saks et al., 2007), but on a direction that may move socialization research forward by providing a clearer picture of how the socialization process works. Our work extends the research on socialization by focusing on the process that employees use to learn what it takes to move from organizational outsider to insider. In doing this we seek to make three important contributions to the socialization literature. First, we seek to expand the time frame that is normally considered as the socialization experience. Typically, researchers have focused on the socialization experience as occurring during the first 12–18 months of an employee’s time with the organization (cf., Payne, Culbertson, Boswell, & Barger, 2008). We suggest that more important than a specific time period are the boundary-crossing experiences (Schein, 1971) that individuals go through during the course of a career. Drawing from a boundarycrossing typology, we argue that individuals may have a socialization experience at any point in their career when boundaries are crossed. Second, building on the initial trigger of boundary crossings, we use uncertainty reduction theory (Berger & Calabrese, 1975) and social


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information processing (SIP) theory (Salancik & Pfeffer, 1978) to determine what important elements are part of the learning process during socialization. Specifically, we describe how boundary crossings will increase workers’ motivation to learn and influence both the sources and types of information available to workers. We also explore the role of tenure within an organization on motivation to learn and the number of information sources an employee will have. Based on motivation to learn, sources of information, and types of information we then discuss about how each of these elements will influence the modes of planned, meta-, and deuterolearning during employees’ socialization experiences. The process described here proposes to explain what prompts employees’ socialization experiences, boundary crossings, and how these crossings influence the entire learning process that workers engage in throughout their socialization experience. Ultimately, we seek to further understand what influences learning during the socialization process, and how this learning, driven by motivation and the sources and types of information available to employees, occurs; thereby explaining the learning process that underlies the socialization experience of workers. Third and finally, we offer recommendations for integrating learning into future socialization research. Specifically, we suggest exploring the relationships between different tactics of socialization and the different modes of learning that we discussed in this paper. Additionally, because we suggest that socialization experiences occur at multiple times during a career, not just when employees take new jobs, we suggest some avenues through which the links between socialization experiences and the different modes of learning that individuals engage in can be explored. Finally, we also discuss some of the possible measurement issues that researchers may encounter as they move forward with this work.

HOW WE KNOW WHAT WE KNOW: SOCIALIZATION AND LEARNING To better understand the relationship between socialization and employee learning we draw from existing research and theory in each of these areas to develop an understanding of how learning occurs during a socialization experience. We begin by briefly reviewing the general socialization literature. We then concentrate on important elements of learning in the socialization literature; namely information and feedback seeking. Following this,

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we discuss boundary crossings. In this section, we describe what boundary crossings are and why they serve as a trigger for the learning process during socialization experiences. Next, using uncertainty reduction theory (Berger, 1979) and SIP theory (Salancik & Pfeffer, 1978), we consider how boundary crossings increase employees’ motivation to learn, as well as the number of information sources available to employees, and we also discuss the role tenure may play during this learning process. Finally, we draw from the learning literature to discuss the role of learning in organizations, paying particular attention to social learning theory (Bandura, 1977). We then focus on three modes of learning, planned, meta-, and deutero-learning and their role of processing information that employees gather during the socialization experience.

Learning the Ropes: A Socialization Overview Any anxiety employees experience as they enter organizations is a function of the uncertainty they are feeling. The more uncertainty individuals experience, the greater their level of anxiety will be about the situation (Berger, 1979; Berger & Calabrese, 1975). Employees entering an organization take up identities that reflect their new job roles and organizational surroundings. This process of learning a new role, a new organizational culture, and the accompanying requirements, and transitioning to the status of organizational insider is referred to as organizational socialization (Feldman, 1976). Through this process of learning about their new roles and understanding organization expectations, employees come to a better understanding of how to do their jobs and what they need to do to fit into the organizational culture (Van Maanen & Schein, 1979). It is during socialization that new employees find out what the organization is like and determine whether they want to be a part of it. As employees learn about their job and the organization, any anxiety felt as an outsider gives way to the knowledge and certainty of becoming an insider. As the socialization process commences, organizations employ different types of socialization tactics to influence employees’ experiences (Saks & Ashforth, 1997; Van Maanen & Schein, 1979). Socialization tactics are the methods used by organizations to socialize their employees (Van Maanen & Schein, 1979). Van Maanen and Schein (1979) specified six different socialization tactics: collective-individual, formal-informal, sequentialvariable, fixed-random, serial-disjunctive, and investiture-divestiture. Research suggests that these six different tactics exist on a continuum,


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which ranges from institutionalized to individualized (Bauer et al., 1998; Jones, 1986). Institutionalized tactics (e.g., collective, formal, sequential, fixed, serial, and investiture) reflect an organized program of socialization, designed to familiarize employees with the organization and encourage them to accept existing organizational norms. Organizations use individualized tactics (e.g., individual, informal, variable, random, disjunctive, and divestiture) in the absence of a structured socialization program. Individualized tactics place workers in situations where they are responsible for themselves, pushes them to challenge the status quo, and tackle situations themselves (Ashforth & Saks, 1996). Broadly speaking, then, organizations may apply an individual or institutional approach in the socialization of employees (Jones, 1986; Van Maanen & Schein, 1979). Depending on the desired outcomes, some organizations may prefer tactics that allow for customization, while others may desire uniformity in socialization practices. Each of the different socialization tactics focuses on a distinct aspect of the socialization experience (Van Maanen & Schein, 1979). Collective tactics place employees in groups during the socialization experience, while individualized tactics create unique learning opportunities for each employee. Formal tactics focus on separating newcomers from current employees, while informal tactics place the new person in an on-the-job training situation. Organizations using sequential tactics employ a predetermined sequence of events to provide employees with explicit information. On the other hand, there is no specific order or explicit information communicated to employees when organizations use random tactics. Organizations that use fixed tactics provide precise information about each timetable for the assumption of a role, while variable tactics provide no indication about when employees will be ready to assume the roles of their job. Serial tactics focus on using an experienced organizational member to provide a role model for employees to look to as they are socialized. Disjunctive tactics do not use a role model during the socialization of employees. Finally, to affirm the incoming identity and personal characteristics of employees, organizations will use investiture tactics. Conversely, divestiture tactics concentrate on striping away this identity and these characteristics (Ashforth & Saks, 1996; Cable & Parsons, 2001; Jones, 1986; Van Maanen & Schein, 1979). Overall, then, socialization is the process by which employees become familiar with new roles and organizational cultures (Van Maanen & Schein, 1979). Organizations employ different tactics to socialize workers, and through these tactics, they can create either an institutionalized socialization experience, individualized socialization experience, or a combination of the

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two. In the next section, we focus on research about the role of information and feedback seeking during the socialization experience.

Bits and Pieces: The Roles of Information and Feedback during Socialization Experiences While little research has been conducted about the role of the actual learning process in socialization, a good deal of research has been done that is related to the learning process. The topics of information seeking and feedback seeking (e.g., Ashford & Black, 1996; Kim, Cable, & Kim, 2005) have been at the center of several investigations of the socialization process (cf., De Vos, Buyens, & Shalk, 2003; Kim et al., 2005; Morrison & Brantner, 1992). These studies are important in understanding the learning process, which underlies socialization experiences, because they focus on one of the fundamental element of learning: information. Information is what individuals gather from their experiences, which then shape thoughts and behaviors (Ormand, 1999). While none of these studies has focused on the cognitive processes of learning, they have concentrated on various aspects of information, such as sources of information, the acquisition of information, and the context around individuals providing information (e.g., Burke & Bolf, 1986; Morrison & Brantner, 1992; Godshalk & Sosik, 2003). The findings of these studies suggest how important information is to the socialization experience. Prior socialization research on information and feedback seeking has focused on studying these variables in the context of new hires (e.g., De Vos et al., 2003). Some of this work has concentrated on specific populations of new hires, like trainees (Fedor, Rensvold, & Adams, 1992) and other work has examined the mentor–prote´ge´ relationship (Godshalk & Sosik, 2003). Most of these studies have looked at the role of information, either the seeking of it or the type of information, and its relationship with various socialization outcomes (e.g., De Vos et al., 2003; Fedor et al., 1992; Godshalk & Sosik, 2003). Because information plays a critical role in the learning process, without it individuals would not have a reason to change their behavior (Ormand, 1999), it is only natural that it plays an important role in socialization research. In a general sense, during a socialization experience, information aids workers in understanding what they should do to fulfill their role and be successful in their circumstances. Information also reduces an individual’s level of uncertainty about what types of behaviors are appropriate in his or her new setting (Ashford & Black, 1996).


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Feedback, which is a specific type of information, helps employees to know what others think of them, so they can adjust their behavior in a way that helps them obtain desired outcomes (Ashford & Black, 1996; Greenberger, Strasser, & Lee, 1988). Employee learning occurs as employees have experiences that provide both the information and feedback necessary for them to more fully understand what it takes to survive in their new position or new organization. Early studies were unable to find a strong relationship between information seeking and attitudinal outcomes such as job satisfaction (e.g., Ashford & Black, 1996; Bauer & Green, 1998). Later work, which integrated information seeking as an intermediate step between socialization tactics and outcomes (e.g., Cooper-Thomas & Anderson, 2002) were more successful in establishing that a significant relationship may exist between information seeking and various socialization outcomes. In addition to searching for links between information seeking and typical employment outcomes, researchers have also examined where employees look for information. Studies have found that employees turn to different sources of information depending on what type of information they need (Burke & Bolf, 1986; De Vos et al., 2003; Morrison, 1993b; Ostroff & Kozlowski, 1992, 1993). For example, an engineer is more likely to turn to a fellow engineer rather than a manager, when he or she needs help with understanding a technical part of his or her job. Thus, sources and types of information are inextricably linked to one another. Other research has shown that workers have different methods for acquiring information, such as direct questioning or observation (Burke & Bolf, 1986; Major, Kozlowski, Chao, & Gardner, 1995; Teboul, 1995). Studies have also shown that there is a social cost associated with acquiring information (Holder, 1996; Teboul, 1995). Additionally, there are many positive outcomes, such as increased role clarity, job mastery, job satisfaction, and organizational commitment, associated with information seeking (e.g., Cooper-Thomas & Anderson, 2002; Finkelstein, Kulas, & Dages, 2003; Holder, 1996; Saks & Ashforth, 1997). From these findings, it is clear that information seeking plays an important role in the process of socialization. Information will always be useful for workers as they seek to reduce the general uncertainty they have about an organization and their position within it (Ashford & Black, 1996). However, employees who are concerned with their individual performance and controlling personal outcomes will focus on information that is specific to their performance; this type of information is feedback (Greenberger et al., 1988). The research investigating feedback seeking as part of the socialization process has found that it is positively related to important attitudinal variables such as job satisfaction

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and intention to turnover (Saks & Ashforth, 1997; Kammeyer-Mueller & Wanberg, 2003). One important antecedent of feedback seeking is performance; specifically Fedor et al. (1992) found that a low performance would increase the likelihood that trainees would seek feedback. Thus, feedback seeking is an important element of the socialization experience as workers strive to learn what it takes to become accepted as a member of the organization. As employees have socialization experiences, and find they would like better outcomes (such as a higher level of performance or a greater understanding of the organizational culture) they are more likely to seek feedback. Learning occurs as employees acquire knowledge, which changes the way they think about things or how they behave (Ormand, 1999), and as the research on information and feedback seeking in socialization demonstrates many employees actively seek out information that helps them to learn how to become more fully enmeshed in the organization. In a study of college graduates who had been employed less than a year, Morrison (1995) found that employees are more likely to seek out the information they need, rather than passively waiting for it to be given to them. This study, among several others, offers a different image of employees’ socialization experiences. Much of the early work on socialization assumed that employees were simply sponges that were passive during the socialization process and just absorbed whatever information the organization passed to them (Miller & Jablin, 1991). However, several studies have found that employees who proactively assert themselves during the socialization process have many positive outcomes (e.g., Gruman, Saks, & Zweig, 2006; Kim et al., 2005; Saks & Ashforth, 1996, 1997; Kammeyer-Mueller & Wanberg, 2003). Indeed, the base of much of the research on information seeking and feedback seeking is employee proactivity. For example, in a study of new employees during their first six months in the organization, Morrison (1993a) found a positive association between the frequency of information seeking and the outcomes of job mastery, role clarity, and social integration. This work suggests that as employees are proactive in their efforts to obtain information about their roles in the organization and feedback about their performance, they are more likely to obtain the status of organizational insider. From prior work on information and feedback seeking in the socialization literature, we can see that employees who have socialization experiences will seek out information to help them become a part of their organization (e.g., Morrison, 1993a). Information plays a key role in the learning process because it helps workers adjust their thinking and behaviors, which can lead them to be successful in their new position. As a result of previous work we


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know what socialization is, and the key role that information plays during employee socialization experiences (i.e., the more information an employee has the better his or her socialization experience). In the next section, we discuss boundary crossings and the role they play in socialization experiences.

The Trigger of Socialization Experiences: Boundary Crossings While most socialization research has focused on the initial point of organizational entry (cf., Feldman, 1976; Kammeyer-Mueller & Wanberg, 2003; Van Maanen, 1975), socialization experiences can occur at other points during an employee’s career (Ashforth et al., 2007; Van Maanen & Schein, 1979). In our discussion of boundary crossings, we seek to emphasize that boundaries can be crossed at any time during an employee’s career. Consequently, socialization experiences can occur at any time and are not limited to just the time during organizational entry. Past research suggests that socialization occurs at boundary crossings or times of transition, because it is during these occurrences that employees are most receptive to prompts about what he or she should be learning to become more fully entrenched in the organization (Ashforth et al., 2007; Van Maanen & Schein, 1979). Schein (1971) proposed that individuals cross vertical, horizontal, and inclusionary boundaries in the work environment. A vertical boundary crossing is most likely to occur in the context of a promotion, although demotions could have a similar impact. Horizontal crossings may occur with a job transfer or any reorganization that shifts the employee to a new department. The crossing of inclusionary boundaries is marked by transitioning closer to the core of the organization where power and decision-making capabilities are located; these crossings transform the employee into more of an insider (Louis, 1980; O’Hara, Beehr, & Colarelli, 1994; Schein, 1971). During each of these boundary crossings, there is a greater likelihood that individuals will be more open to socialization experiences (Van Maanen & Schein, 1979). This is because they will be searching for anything that reveals what it takes to succeed in the organization. The initial introduction to an organization is the most well documented and intensely scrutinized boundary crossing because it is a socialization experience involving the crossing of all three boundaries (Louis, 1980); however, boundaries can be crossed at any time in employees’ careers. For this reason, we contend that the socialization process never stops, and employees are continually learning what it takes to be a successful contributor to an organization.

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Vertical Boundary Crossings During the course of individuals’ careers, they are most likely to experience vertical boundary crossings, which are ‘‘the increasing or decreasing’’ of one’s level in the organizational hierarchy (Schein, 1971, p. 403) through various promotions within the firm. As individuals ascend the organizational ranks, they will acquire new job roles, interact with different individuals, and find their existing relationships altered. Conversely, if for some reason employees are demoted, they will also experience a change in job roles and a change in their interactions with coworkers and supervisors. Organizational level events that may precipitate a vertical boundary crossing are mergers, reorganizations, or even a layoff event. In the event of a merger or reorganization, employees may be shifted around, given new job roles, or placed in new supervisory positions. During a layoff an employee could remain with the organization, but be reassigned to a different position. Each of these events may cause employees’ positions in the organizational hierarchy to be changed. These same changes can occur for employees who retain their jobs after a layoff. Additionally, research suggests that survivors of a layoff may have other types of adjustments that are specific to the organization and coworkers because of the layoff (e.g., Brockner et al., 1994). The crossing of a vertical boundary creates a need for individuals to learn or relearn what is expected of them and how they fit in as insiders again. Horizontal Boundary Crossings In the ever-changing work environment, employees may find themselves shifting around to other positions, working in new teams, or otherwise experiencing different types of horizontal movements. Horizontal boundaries are crossed when employees’ functions change or they are moved from one division, or group, to another (Schein, 1971). There are several different career events that may cause employees to cross horizontal boundaries: reorganization, international assignments, repatriation, special assignments, rotations to other work units, and so forth. Each of these events may result in employees learning new skills or competencies that are relevant to their new roles. Crossing a horizontal boundary may result in more than a simple change in job roles; it may include special training or development that the employee requires for broader experience within the organization (Schein, 1971). In the case of an international assignment, individuals may be performing the same job but need to learn a language or other skills to help them adjust (Black, Mendenhall, & Oddou, 1991). During the repatriation process, employees cross horizontal boundaries as they move geographic


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areas, and often they find themselves facing a different job as a repatriate or other challenges as an outsider in their new position (Bolino, 2007). Some employees may be part of management development programs that rotate them through different functional areas of the organization. These types of lateral movements can provide upending experiences that create a need for employees to adjust their behavior to both new coworkers and a new work environment. Indeed, employees who experience transfers within the organization tend to seek more feedback (Kramer, 1993; Kramer & Noland, 1999). Workers who are placed in new situations due to horizontal boundary crossings will need to learn about job duties, those around them, and whatever else it takes to become an insider. Inclusionary Boundary Crossings The final type of boundary that individuals may cross during their time in the workplace is the inclusionary boundary. This boundary represents the degree to which other members of the organization trust and accept an employee (O’Hara et al., 1994; Schein, 1971). Crossing inclusionary boundaries may be the true mark of an individual who is considered an organizational insider. Many of the career-changing events previously discussed are likely to cause employees to cross this boundary because it may bring about changes in the supervisors or coworkers surrounding an employee. While employees can work toward crossing inclusionary boundaries, it is ultimately not up to them whether they will cross this boundary or not; it is up to those around them (O’Hara et al., 1994). Inclusionary boundary crossings can be more difficult to recognize than vertical or horizontal crossings, but O’Hara et al. (1994) note that shifts in power, access to sensitive information, and decisionmaking abilities are all likely to change during an inclusionary boundary crossing. It is plausible that employees needing to cross inclusionary boundaries must be perceived as trustworthy (Mayer, Davis, & Schoorman, 1995) to develop the type of relationships that allow them to finally cross this boundary. Mergers, reorganizations, job rotations, international assignments, working in ad hoc groups, and other similar movements can push employees across inclusionary boundaries because they are required to learn how to interact with new personnel. A good example of an inclusionary boundary crossing, which does not occur in conjunction with any vertical or horizontal boundary crossings, is a change in leadership. A change in leadership may cause a shift in power, access to sensitive information, and decision making, which will create a state of uncertainty for many employees. Although employees may have been insiders prior to a leadership change, this change could shift them to

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outsider status again. Because of the increased frequency in organizational turnover (Hom, Roberson, & Ellis, 2008), employees are likely to find themselves crossing inclusionary boundaries several times during their careers. We offer a guideline for boundary crossings in future research and next discuss the consequences of crossing these boundaries in relation to workers’ motivation to learn, information, and tenure. Guideline 1. Researchers should seek to differentiate between types of boundary crossings in future socialization research.

On the Other Side: Motivation, Information, and Tenure We suggest that crossing boundaries will have two outcomes for workers: first, it will influence their motivation to learn and second, it will place them in contact with new sources of information. To gain a better understanding of boundary crossings, and the effects of these crossings we turn to uncertainty reduction theory (Berger, 1979) and SIP theory (Salanik & Pfeffer, 1978). Prior work in the socialization literature has used uncertainty reduction theory to explain the behaviors of employees during socialization (e.g., Kramer, 1994; Lester, 1987; Mignery, Rubin, & Gorden, 1995), because it focuses on the reaction of individuals when they experience uncertainty, and it suggests what individuals will do to reduce their uncertainty. We also turn to the SIP theory (Salancik & Pfeffer, 1978) to determine how the work environment around employees is important during the socialization process. SIP theory holds that coworkers, other individuals, and the workplace surrounding employees provides critical information for employees as they work to make sense of their situation (Jex & Britt, 2008; Salancik & Pfeffer, 1978). Drawing from these two theories, we can further understand the role of learning in the socialization process. Uncertainty Reduction Theory Uncertainty reduction theory proposes that individuals have an aversion to uncertainty and seek to gather information in an effort to reduce their uncertainty. People seek to reduce uncertainty because it is associated with anxiety. Thus, the reduction of uncertainty is critical to lowering the levels of anxiety that employees are feeling because of the situation they are in (Berger & Calabrese, 1975). People seek to reduce uncertainty for two main reasons. First, they want to be able to predict the behavior of the individuals around them. Knowing what others are going to do reduces the anxiety


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associated with uncertainty. Second, employees want to be able to explain the behavior of those around them. Knowing why people have behaved in a certain way reduces the anxiety individuals experience when interacting with these people (Berger, 1979; Berger & Calabrese, 1975). Any time an employee meets a new person or encounters an unknown situation there is some uncertainty, because he or she is unable to predict or explain the behavior of this new person or unknown situation. The greater amount of uncertainty workers feel in their situation the more likely they are to take actions that will reduce this uncertainty (Berger & Calabrese, 1975). Thus, individuals who experience new situations, such as when they enter an organization, are motivated to reduce the amount of uncertainty that surrounds them (Saks & Ashforth, 1997). For this reason, we believe that uncertainty reduction theory helps to explain the learning process that is the foundation of the socialization experience. Social Information Processing Theory SIP theory, developed in the late 1970s as a response to job enrichment models of job satisfaction (i.e., Hackman & Oldham, 1976), proposes that the satisfaction of workers is influenced by the context of the situation and the consequences of past choices (Salancik & Pfeffer, 1978). Fundamentally, this theory suggests that employees’ attitudes, behaviors, and beliefs are influenced by the work environment around them (Salancik & Pfeffer, 1978). As such, coworkers, friends, and even the situation itself can influence an individual’s attitudes or behaviors at work. Workers who seek to make sense of their world will look for ‘‘salient, relevant, and credible information’’ from the social environment around them (Zalesny & Ford, 1990). Studies show that this information, which is gathered from the social environment, influences the perceptions, attitudes, and beliefs about one’s self, job, and organization (Ibarra & Andrews, 1993). As such, we expect that SIP theory will serve to guide us in understanding what is important for learning during socialization experiences. Motivation to Learn Returning to boundary crossings, we know that each boundary-crossing experience creates uncertainty for the employee. We suggest that this desire to reduce uncertainty will increase workers’ motivation to learn. Motivation to learn is a desire to engage in training, learn job-related content, and take part in development activities (e.g., Carlson, Bozeman, Kacmar, Wright, & McMahan, 2000). Prior work links motivation to learn with certain individual characteristics (Major, Turner, & Fletcher, 2006), but we also

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posit that specific situations, such as boundary crossings, will increase individuals’ levels of motivation to learn. These boundary crossings prompt the beginning of the learning process, and because they increase levels of uncertainty we believe that employees will be more motivated to learn; thus we offer this guideline for future socialization research. Guideline 2. Researchers should strive to measure employees’ motivation to learn in future socialization research. Information The reduction of uncertainty is one of the primary reasons that individuals seek information during the socialization process (Miller & Jablin, 1991). As employees strive to become insiders in the organization by reducing the level of uncertainty they feel about their position and the organization, SIP theory suggests they feel they will seek to gain information from the environment around them (Zalesny & Ford, 1990). To find this information workers will be able to turn to the new supervisors, coworkers, mentors, and written documents (Miller & Jablin, 1991; Morrison, 1993b; Ostroff & Kozlowski, 1993) that surround this new position. For example, employees who are engaged in an institutionalized socialization experience can turn to other coworkers and ask questions that are relevant and important to gaining information about their job, work role, or other related content. Mentors can also be a critical component of the socialization process because they are an important source of information for employees during socialization experiences (cf. Ostroff & Kozlowski, 1992). However, some employees may not always ask others when acquiring information, because they worry about how this might look to their supervisor or colleagues (Morrison, 1993b). Instead, they may simply observe the individuals, and strive to glean relevant information from the actions of these persons (Morrison, 1993a; Ostroff & Kozlowski, 1993), or turn to the written documents that are relevant to their position (Miller & Jablin, 1991). The sources of information available to a worker may be determined by the tactics the organization uses while socializing the worker. If an organization uses individual tactics and separates the employee from his or her coworkers (Cable & Parsons, 2001) he or she will not be able to use those coworkers as sources of information. Therefore, while boundary crossings will generally provide new sources of information to employees, the socialization tactics used by an organization may also influence the number of sources available. For this reason, we suggest that future work differentiate between sources of information.


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Guideline 3. Researchers should seek to differentiate between sources of information in future socialization research. Workers who have more sources of information will also have more types of information available to them. Miller & Jablin (1991) have identified three types of information that workers look for during a socialization experience: referent, appraisal, and relational. Referent information helps employees know what is essential for them to perform well in their assigned roles (Hanser & Muchinsky, 1978; Miller & Jablin, 1991; Morrison, 1993b). The next information type, appraisal information, assists individuals in understanding if they are successful in their job performance (Miller & Jablin, 1991). Relational information is the final type of information, and it deals with the relationships between employees and other organizational members (Miller & Jablin, 1991). This type of information helps individuals to understand their standing within organizational groups, be it with their boss, fellow coworkers, or subordinates. In these relationships, learning from context and nonverbal cues is critical to help employees read between the lines and understand the true nature of the relationship. The different types of information that an employee collects during the socialization process has an important influence on employee learning. Because SIP theory proposes that worker attitudes, behaviors, and beliefs are shaped by the social environment surrounding them (Salancik & Pfeffer, 1978), we believe that these new sources of information and different types of information are vital to employees as they navigate the socialization process, and we suggest the following. Guideline 4. Researchers should seek to differentiate between types of information in future socialization research. Tenure One important factor that will influence the socialization process over the career of an employee is tenure, both tenure in the organization and tenure in a position. Uncertainty reduction theory suggests that time is important to reduce the anxiety that people feel because of uncertainty (Berger, 1979). Over the course of time, individuals are able to gain knowledge about behaviors, which helps to reduce uncertainty (Berger & Calabrese, 1975). If an individual remains in a single organization for an extended period, his or her motivation to learn is likely to decrease because he or she will have a base of knowledge about how things work, which will reduce uncertainty when crossing boundaries in that organization. Indeed, the more familiar

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individuals are with situations and the people around them the less uncertainty they will feel (Berger, 1979). As employees spend more time in an organization, or more importantly a single position, their familiarity with both the duties of the job and other employees increases. This results in a reduced level of uncertainty, and we suggest that consequently the employee will have less motivation to learn. While tenure may diminish employees’ motivation to learn, it could increase the number of sources from which employees can get information. The more interactions people have and the more knowledge they gain, the more they will be able to reduce their uncertainty (Berger & Calabrese, 1975). The longer employees stay in a single organization, the greater the number of colleagues they will have and the more interactions they will have had with these individuals. Employees will have more time to observe, try new behaviors, and learn about the organizational policies and procedures. The greater a worker’s familiarity with these sources, the less likely he or she is to experience uncertainty when faced with boundary crossings. We expect that tenure in an organization will positively influence the number of information sources to which workers have access, which will be helpful to employees when crossing boundaries during a socialization experience; therefore we offer the following guideline for future research. Guideline 5. Future socialization research should seek to account for tenure interactions when employees are changing to new positions within the same organization. The Learning Process: A Brief Review As workers seek to gain insider status, they must learn how to behave in their new workplace. Learning is the permanent change that occurs in an individual’s cognitive associations or personal behaviors due to the experiences he or she has (Ormand, 1999). The behavior of workers in organizations is a function of employees’ cognitive processes and the environment around them (Davis & Luthans, 1980). The interaction between workers’ cognitions and the situation around them, learning, is what shapes how they behave in the workplace. To better understand the learning process that employees experience during socialization we turn to social learning theory (Bandura, 1977) to help us grasp how the learning process functions in organizational settings. Social learning theory is a behavioral theory that explains how individuals learn in organizations. It incorporates some of the principles of operant conditioning (Skinner, 1969), along with the social


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environment around employees. Thus, it addresses both the personal experiences, which provide direct learning opportunities, along with the social context around the employees. Social learning theory (Bandura, 1977) suggests that the learning process, which is a continuous reciprocal interaction of individuals’ thoughts, their behaviors, and their environmental situation, is the one of the best ways to explain the actions of individuals. Bandura labeled this process a triadic reciprocality; through this process individuals process information, learn, and then adjust their behavior accordingly (Bandura, 1977, 1986). It is important to point out that individuals do not simply react to the environment around them, nor are their actions determined solely by past experiences. Individuals consider the situation, and then using the information available from both the environment and past experience they use forethought in deciding how to behave (Bandura, 1986). According to social learning theory, workers can learn how to behave both through their own direct experience and by observing the behavior of others (Bandura, 1986). Both experiential and observational learning have important implications for the socialization process, although each of these types of learning is very different. Because social learning theory incorporates the principles of traditional operant theories of behavior, which describes learning through personal experience (e.g., Skinner, 1969), and it proposes that learning also takes place through the observation of others’ behavior (Bandura, 1977) it is especially applicable to socialization experiences. Organizations can adopt an institutionalized or individualized approach, or some combination of the two, to the socialization of their employees. These different approaches will result in both experiential and observational opportunities. If an organization uses informal socialization tactics that force employees to learn on the job (Cable & Parsons, 2001) it is more likely that employees will be engaged in experiential learning. However, organizations that use serial and investiture tactics, which provide employees with role models to watch (Cable & Parsons, 2001), create situations where observational learning is a key part of the socialization experience. From these examples, we conclude that both observational and experiential learning are critical to learning during socialization. One of the strongest pieces of research to address the link between socialization and the actual process of learning was a study of British Army recruits that demonstrated the central role of the learning process in socialization. Findings indicated that the relationship between socialization tactics and outcomes, job satisfaction and organizational commitment, was fully mediated by the level of newcomer learning (Cooper-Thomas & Anderson,

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2002). As we have discussed the learning process and socialization experience are interrelated; this is because individuals need to learn in order to be socialized. However, whereas learning is about using information acquired through different experiences and observations to inform future behaviors (Ormand, 1999), socialization focuses on employees becoming more familiar with the organization, their job, and those with whom they interact (Van Maanen & Schein, 1979). Hence, the two processes have different outcomes. Individuals learn based on things that they observe or experience, with the purpose of the learning being that they know how to behave in a given situation (Bandura, 1986; Ormand, 1999). The socialization experience focuses on integrating workers into their jobs by helping them to learn the ropes (Ashforth et al., 2007). We further suggest that the way individuals learn plays an important role in the socialization experience. We next focus on three different modes of learning to help us better understand how individuals make sense of their new roles in organizational settings.

How the Wheels Turn: Types of Learning Because the goal of the socialization experience is to change from an outsider into an insider, through becoming familiar with job roles and organizational culture, employees who seek to accomplish this will seek to draw information from both their experiences and observations. Central to our paper are the three different modes of learning that help individuals to process information. In a reformulation of organizational learning types, necessitated by the ‘‘organizational learning jungle’’ (Huysman, 2000, p. 81), Visser (2007) offered a typology of three modes of learning used in organizational settings. These are planned, meta-, and deutero-learning. By considering each of these learning processes, we can gain a better understanding of socialization experiences. Planned Learning Planned learning occurs when organizations create and maintain a system, routine, procedure, or structure with the specific purpose of inducing organizational members to learn (Scho¨n, 1971, 1975). Planned learning is the type of learning that organizations rely on to create better employees. When an individual enters an organization, there are certain processes and rituals that he or she experiences. Many individuals spend their first few hours as newcomers in an organization, reading forms and filling out required paperwork. There may be hours or days of specified training about


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how their job is properly preformed. The processes of orientation and other forms of training are part of a broader effort to influence and teach employees what it takes to become an insider in the organization (Van Maanen & Schein, 1979). By utilizing these types of systems organizations require employees to engage in planned learning. The most important element of planned learning is that it influences employee behavior (Visser, 2007). The incorporation of planned learning leads employees to alter their behavior in some way to better fulfill job roles or fit into a work group. Planned learning is most likely to occur when organizations take an institutionalized approach to socialization. For example, both sequential and fixed tactics of socialization can facilitate to planned learning. Employees are given explicit information about the activities they will participate in when sequential tactics are employed and fixed tactics provide precise information about what will occur during each stage of the socialization process (Ashforth & Saks, 1996; Van Maanen & Schein, 1979). Additionally, planned learning is the ideal way for gaining referent information. This is because referent information lends itself to communication through an established routine or system that helps workers to know explicitly what is expected of them in their jobs. Other implications for the use of planned learning during a socialization experience are that an organization can adjust and adapt the socialization process based on the outcomes, such as employee role mastery, to obtain the desired results. Planned learning has the potential to induce meta-learning among employees because of the creation and maintenance of learning systems (Visser, 2007). Because of this, planned learning is a critical component for organizations that desire to influence employees during the socialization process and we suggest the following: Guideline 6. Researchers should theorize about the circumstances under which planned learning will be most effective during the socialization process. In contemplating these circumstances, they should focus on the desired outcomes of the socialization process. Meta-Learning The next mode of learning that underlies socialization experiences is metalearning. When a specific course of action fails to produce the desired results, individuals will try to learn what went wrong. This discrepancy, between the desired and actual results, is where the learning process begins (Visser, 2007). The process of meta-learning occurs when an individual begins to process the inconsistency between expectations and actual

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consequences, searching for reasons why it may have occurred. This learning process results in two levels of inquiry, typically referred to as single- and double-loop learning (Argyris, 2003; Argyris & Scho¨n, 1978). The initial level of learning in the process of meta-learning, single-loop, involves reflection to better understand how to detect errors, and thoughts about how to effectively correct those errors (Argyris & Scho¨n, 1974; Visser, 2007). For example, if an individual finds himself or herself in the out-group due to consistent tardiness, there may be reflection on how this could have been anticipated. Upon reflection, the individual may remember that a supervisor and other coworkers made several comments about the importance of being timely in this organization. Initially these comments were simply brushed off, because the individual did not consider his or her level of tardiness to be relevant. In retrospect, the individual sees that his or her coworkers were making subtly important suggestions about how to fit in. Moving forward this individual plans to be aware that even seemingly inconsequential remarks may have important implications. From this example, we can see that the individual focused on how the error could have been detected and how similar errors can be avoided in the future; this is the purpose for first level of learning during meta-learning. Thus, such learning is essential as individuals work to understand their job and the culture around them during socialization experiences. The second level of learning concerns seeking ways to improve the understanding of the norms and values of the situation (Argyris, 2003; Crossan, 2003). Continuing the previous example, the employee may engage in double-loop learning by starting a conversation about why being on time is so important, and perhaps if possible, some consideration of flextime by the organization. An individualized approach to socialization (Jones, 1986) is likely to lead employees to engage in high levels of meta-learning. Because an individualized approach is unstructured and unique for each employee, they will need to reflect on both their experiences and observations in order to learn what they need to do to become fully enmeshed in the organization. In sum then, meta-learning describes how individuals learn from the differences between the actual and expected outcomes of the socialization process. Metalearning will help individuals detect errors and aid them when looking for ways to improve the understanding of the norms and values of a situation. Meta-learning provides valuable insight into the learning process by describing how employees alter both their thoughts and behavior. It is a conscious process that involves employees stopping to think about the intended consequences of their actions (Argyris & Scho¨n, 1996). As employees gain more information about different job roles, group processes,


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or organizational attributes, they will enact that knowledge through their behavior. When those behaviors do not result in the anticipated results, the employee will stop and think about what has happened. ‘‘Why has it not worked?’’ or ‘‘What did I miss?’’ are a few of the questions an individual may ask. After some consideration, workers may make adjustments, begin experimenting, and looking for ways to improve (Visser, 2007). If an organization uses divestiture tactics to strip away the personal characteristics of employees (Van Maanen & Schein, 1979), the employees could use meta-learning to process what this means for them as individuals and decide what types of behaviors they need to change. This type of learning may be especially beneficial when workers are striving to master job-related content or negotiate group interactions as they endeavor to be accepted in the organization. As discrepancies between workers’ expectations and the outcomes occur, they will reflect on this appraisal information. By engaging in meta-learning with appraisal information, employees will be able to discern what types of changes they need to take in order to correct their performance so they can obtain their desired outcomes. Thus, meta-learning will be critical as employees seek to better understand their jobs, the organizational culture, and how to succeed in their new position. Thus we suggest the following guideline for future research. Guideline 7. Researchers should theorize about how meta-learning can be better integrated into the socialization process. Researchers may want to focus on ways to add specific moments for meta-learning into existing planned-learning processes. Deutero-Learning The context that surrounds an employee provides a wealth of information for employees as they attempt to learn about their job and place in the organization. Deutero-learning draws information from context-specific events and nonverbal cues (Visser, 2007). At an unconscious level, individuals are constantly learning and absorbing knowledge about the organization, acceptable forms of behavior, expectations, requirements, and other nuances of organizational life. Deutero-learning is based on two main principles: all systems are capable of adaptation, in which learning is inherent, and learning occurs through an interaction (Bateson, 1963, 1972). First, all systems are capable of adaptation that requires learning. All systems, be they biological, social, ecological, or organizational, change over time. This change occurs as the system adapts to the existing environment. Inherent in this adaptation is learning about what works and what does

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not; with those who learn and adapt continuing their existence. The second principle of deutero-learning is that learning occurs through an interaction. Individuals do not learn in isolation, there must be an interaction with something or someone in order for learning to occur (Bateson, 1972; Visser, 2007). Employees learn by witnessing or experiencing event A in the context of X. The interaction of A and X creates information, which then facilitates learning. The results of this learning will be extended to similar situations where it will be maintained, or altered depending on the results of the new interaction and the patterns of behavior that develop from it (Bateson, 1972). Three main factors characterize deutero-learning. First, it is a continuous process of behavioral communication that is mostly unconscious (Haley, 1963; Watzlawick, Bavelas, & Jackson, 1967) and can be both outward and incidental in nature. Interactions occur continuously as individuals are around one another and communicate with each other. Simple messages provide learning experiences by creating the context of a situation and through the elicitation of a response and nonverbal cues. We sometimes know that we are learning and at other times we do not know we are learning. The second feature of deutero-learning is that it cannot be overtly controlled. Attempts to control a deutero-learning situation create a new learning process, because attempts to control result in a new context, which then creates a new interaction and from this interaction employees are able to extract new information (Visser, 2007). The final characteristic of deuterolearning is that it does not always lead to the improvement in the individual or organization; individuals sometimes learn counterproductive behaviors. While this type of learning can certainly occur in a fashion that leads to the betterment of the individual or the organization, research has indicated that certain types of situations may result in negative outcomes for both the individual and the organization (Bateson, 1972; Dopson & Neumann, 1998; Haley, 1963; Smith, 1976; Watzlawick, Bavelas, & Jackson, 1967). Because deutero-learning is constantly occurring, it is inevitable that workers having a socialization experience will engage in this learning process. For example, in the socialization setting, workers may be told that certain procedures are important; however, the tone, expression, and posture of the person communicating this message may convey that this procedure is actually unimportant. Individuals responsible for the socialization of employees may unconsciously or consciously communicate nonverbal messages that contradict the verbal message being communicated. For employees who are socialized through serial tactics, which provide an experienced organizational member as a role model (Cable & Parsons, 2001), deutero-learning helps them to process this experience by using information


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from both their experience and the context of the situation. Additionally, relational information, which aids individuals in knowing where they stand in their relationships with other individuals (Miller & Jablin, 1991), is most likely to be acquired through deutero-learning. The workers’ relationship status will most likely be communicated through subtle tones, posture, and expressions of those they interact with; which will provide information for individuals to process through deutero-learning. Deutero-learning may also be particularly important for individuals who are worried about appearing incompetent because they ask too many questions (Ostroff & Kozlowski, 1992) or for socialization experiences that occur later in careers. Employees who are promoted, returning from an international assignment, or adapting to a change in leadership may rely on deutero-learning to glean important details about how they can become organizational insiders once again. Because this type of learning is not always a conscious effort on the part of the learner (Bateson, 1972), it is likely to continue throughout individuals’ careers as they experience various boundary crossings and move closer to being insiders. In many cases, deutero-learning helps employees to read the information between the lines as an organizational insider should, and for this reason we suggest the following guideline: Guideline 8. Researchers should theorize about ways in which deuterolearning may influence the socialization process. They may consider how the environment around the employee will influence opportunities to engage in deutero-learning. Each of these different modes of learning plays an important role during the socialization experience. These modes of learning are the culmination of the learning process that happens during socialization. As individuals cross boundaries, they are more motivated to learn and have more sources of information. These three modes of learning are the processes by which employees take the information they have gained and use it to adjust and adapt their thoughts and behaviors.

SOCIALIZATION AND LEARNING: THE NEVER-ENDING PROCESS Organizational life is in constant flux. The environment around a firm changes, innovations provide new opportunities, technology alters existing

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jobs, and employees must cope with these changes the best they can. In addition to the outside pressures, influencing workers, there are personal choices that alter career paths and create new and different situations in organizational life. Employees may apply for promotions, be transferred to different positions, experience leadership changes, or switch organizations all together. As individuals work through these different changes and adjust to new roles, jobs, and coworkers they continue to have socialization experiences. This happens because they are making every effort to become, or regain their status as, organizational insiders. In this paper, we have sought to understand how the learning process, which underlies socialization works. According to Fig. 1, there are several important elements to learning during the socialization experience. We can see that workers in an organization experience boundary crossings, or changes in their job, role, or people around them. Boundary crossings, which result in increases to employees’ uncertainty and anxiety, then increase employees’ motivation to learn. Additionally, boundary crossing also influences both the sources and types of information to which workers have access. However, tenure in a certain position or with the organization is likely to lower employees’ motivation to learn, because the more familiar individuals are with the people around them and their situation the less uncertain they will feel because of it (Berger & Calabrese, 1975). For this reason, the longer an individual is tenured in an organization the less uncertainty he or she will face when crossing boundaries. On the other hand, tenure in an organization, and the associated familiarity with other employees and the processes and practices of the organization, will increase the number of sources from which workers can acquire information. The increased motivation to learn, that results from boundary crossings, drives workers to engage in learning, and the newly acquired sources that provide a variety of information types, all of which

Fig. 1.

The learning process during a socialization experience.


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feed into the planned, meta-, and deutero- modes of learning. The processing of information through these three modes of learning is the final step in the learning process, and leads employees to change what they think and how they behave. In taking a long-term view of the socialization process, we suggest that individuals will have multiple socialization experiences, and a learning view of socialization is applicable to an entire career rather than for organizational entry only. The most common focus of socialization research is the point of organizational entry when a newcomer enters and adjusts to an organization (Ashforth et al., 2007; Louis, 1980). This creates a constricted focus on a very limited period of time and a very select group of individuals. Ashforth et al. (2007) called this narrow focus into question when they asked researchers to challenge some of the traditional assumptions of socialization research. There appears to be a need to expand the time period surrounding socialization research, so as to recognize different times in an employee’s career and the influences of different groups of employees. Using a learning perspective, we contend that socialization has an overlooked temporal aspect. Individuals will learn throughout their careers, and socialization experiences can occur at different times during the course of a career; for instance they may happen after promotions, transfers, and organizational changes. Thus, the socialization experience does not occur a single point in time, but rather the multiple boundary crossings that occur during the course of an individual’s career create many opportunities for this experience. These crossings trigger the learning process so employees can regain their insider status. When this happens, employees are motivated to learn and they begin to draw from all their sources of information to learn what it is that they need to do next.

Conclusions and Directions for Future Research The learning processes, which underlie the socialization experience, have been an implicit part of socialization from the beginning of research in this area (Van Maanen & Schein, 1979). In this paper, we have endeavored to propose how the learning process occurs during socialization, and what some of the important elements of this process are. By examining this learning process we have sought to better understand how boundary crossings trigger the learning process, how it unfolds through motivation to learn and the acquisition of information, and how three modes of learning

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lead to the changes in thoughts and behaviors that indicate that learning has occurred (Ormand, 1999). This paper, and the uncovering of the learning processes that occur during socialization, suggests a series of important directions for future research. The first direction is in clearly linking socialization tactics to different types of information and modes of learning. As we have suggested, different types of tactics and approaches to socialization lend themselves to providing different types of information and engaging different modes of learning. Some tactics are more likely to be linked with certain types of information. Employees in organizations that have an individualized approach to socialization, which separates employees from each other (Cable & Parsons, 2001), are less likely to receive relational information (Miller & Jablin, 1991) about their relationships with other employees and the organization. When considering modes of learning, an institutionalized approach to socialization (Jones, 1986) is likely to have a stronger association with planned learning than it is with meta-learning. Future research should seek to explore these relationships to help organizations understand what types of information workers will receive, and how their employees will learn from different socialization tactics. Second, by extending the temporal window of the socialization process to include the span of an entire career and focusing instead on boundary crossings, we can see how individuals will have multiple socialization experiences within a single organization. During an individual’s career span, individuals will experience myriad socialization events and may be more susceptible to different learning modes during these multiple socialization events. The way individuals learn during these socialization experiences also has important implications for understanding why certain outcomes occur. The model we propose integrates learning types into the socialization process, and we posit there are several outcomes of the socialization process are the result of these learning processes. With each boundary crossing employees are motivated to learn, acquire different types of information from sources, and learn from that the way they need to behave so they can reestablish themselves as insiders. Third, in future work researchers need to address issues of measurement for these different modes of learning. For both planned and meta-learning, prior work that has focused on role ambiguity (e.g., Gruman et al., 2006; Holder, 1996) may be particularly helpful. Because reducing role ambiguity is an important outcome during socialization, and socialization programs are designed to reduce ambiguity, this may provide a starting point for scholars. In developing measures of planned and meta-learning researchers

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may focus on how much an employee gains from organizational programs and how much they learn by stopping and thinking about the implications of their personal experiences. Gauging planned learning will help organizations know how effective their socialization programs are. In measuring modes of learning, deutero-learning presents the greatest difficulty, because it often happens unconsciously. For guidance in this area researchers can look to the work of McClelland (1961). The needs that McClelland described (e.g., Need for Power, Need for Achievement) were unobservable, and as such, measurement was problematic. To help capture the needs in question, ambiguous meaning thematic apperception tests where used (Holmstrom, Silber, & Karp, 1990). While such tests may not fully capture deutero-learning they may provide a starting point for developing a measurement of this mode of learning by providing some insight into the unconscious motivations, which drive our performance and are deemed important by an individual. As further research provides a more complete picture of the learning process that underlies the socialization experiences of employees, there will be important implications for organizations. By knowing how employees learn, what sets off the learning process, and what modes of learning are active during socialization, organizations may be able refine their programs to help workers become more fully enmeshed in the organization. By knowing that boundary crossings initiate the learning process, organizations can be prepared to provide the information necessary to aid employees learning processes as they adapt to a new position or organization. Helping employees learn the ropes will not only lead to more satisfied workers, but may also lead to a more effective organization.

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COMPARING APPLES AND ORANGES: TOWARD A TYPOLOGY FOR ASSESSING E-LEARNING EFFECTIVENESS N. Sharon Hill and Karen Wouters ABSTRACT E-learning programs exist in a wide variety of formats. Without a framework for distinguishing between different e-learning programs, it is a challenge for researchers to compare their effectiveness or identify characteristics of e-learning that contribute to learning effectiveness. Based on general theories of learning, we develop a typology that compares e-learning programs in terms of the nature of the learning interactions they provide for learners in three dimensions: degree of interaction, learner control of interactions, and informational value of interactions. The typology dimensions apply to learner–instructor, learner–learner, and learner–instructional material interactions. We also discuss important theoretical implications of the typology. First, we show the utility of the typology for comparing the effectiveness of different e-learning programs. Second, we apply the typology dimensions to develop a theoretical framework for e-learning research that provides a foundation for examining factors that influence learning effectiveness in an e-learning program. The framework identifies e-learning program characteristics, learner characteristics, and contextual factors that impact Research in Personnel and Human Resources Management, Volume 29, 201–242 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0742-7301/doi:10.1108/S0742-7301(2010)0000029008

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learning effectiveness in different e-learning environments. It also shows how the typology dimensions align with learning goals to influence learning effectiveness.

Organizations are using e-learning extensively for employee development (DeRouin, Fritzsche, & Salas, 2005). E-learning, which we define as the use of information and communication technologies (ICT) to deliver instruction to learners, offers many potential benefits to both organizations and employees. The benefits to the organization include savings on travel and accommodation for attending training, the ability to offer training to more people, greater consistency in training delivery, and improved tracking of course completion and testing (Noe, 2005; Welsh, Wanberg, Brown, & Simmering, 2003). For employees, the benefits include greater flexibility over when and where training is completed, the potential to complete course segments for just-in-time training needs, and exposure to a wider offering of courses (Noe, 2005; Welsh et al., 2003). The growth in e-learning has been characterized as a revolution in training and development (Galagan & Drucker, 2000; Noe, 2005). Out of this revolution, a large number of different e-learning formats have emerged. As described by Welsh et al. (2003) in their review of the e-learning literature, e-learning applications can range from asynchronous e-learning, in which learners work completely at their own pace, to synchronous e-learning that is ‘‘live’’ and requires that learners be at their computers at the same time as the instructor and other learners participating in the e-learning program. In addition, a wide range of technologies are used to support e-learning, resulting in a large variety of delivery formats. These range from static, primarily text-based content to more sophisticated programs that integrate graphics, animation, video, and audio. To add to this complexity, many organizations use different combinations of e-learning formats and classroom instruction to form blended learning. As a result of the wide variety of e-learning and blended formats, comparing the effectiveness of e-learning to traditional classroom instruction, or the effectiveness of different e-learning and blended learning programs is akin to comparing apples and oranges. Given this, Smith and Dillon (1999) argued that to facilitate empirical research in this area, researchers should first articulate the dimensions along which e-learning programs vary. However, to our knowledge, there is currently no comprehensive typology for comparing e-learning programs. Therefore, the primary purpose of this chapter is to address this shortcoming in the extant

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literature by delineating dimensions of a typology that can be used to characterize different types of e-learning programs. Our typology is grounded in theories of traditional learning that emphasize the critical role of interactions between the learner and instructor, the learner and other learners, and the learner and instructional material for facilitating learning. We refer to these interactions as learning interactions and argue that e-learning programs can be differentiated by the extent to which they vary in terms of three key learning interaction characteristics: degree of interaction, learner control of interactions, and informational value of interactions. These characteristics form the three dimensions of our typology. Another purpose of this chapter is to highlight important theoretical contributions of the typology and its potential to advance e-learning research. First, we discuss the utility of the typology for comparing the effectiveness of different e-learning, blended and classroom instruction formats. We show how the typology can help to explain current equivocal research findings in this area as well as stimulate new research directions. Second, we apply the typology dimensions to develop a theoretical framework for e-learning research that examines factors that influence learning effectiveness in an e-learning program. In this framework, the dimensions of the typology act as mediating variables that link characteristics of e-learning programs to learning effectiveness. We describe how the typology can be used to explain existing relationships found in the literature between the characteristics of e-learning programs and learning effectiveness as well as to identify new e-learning program characteristics that the typology suggests are worthy of attention. The framework also highlights learner characteristics and contextual factors that moderate the relationship between the typology dimensions and learning effectiveness. We extend research in this area by showing how the typology can be used to move beyond the focus in the existing literature on individual characteristics and contextual factors that are important for e-learning, in general, to also identify those that promote e-learning effectiveness in different types of e-learning environments. Finally, although several scholars agree that for effective learning to occur, the characteristics of an e-learning program should align with the learning goals of the program, there is no clear agreement on the form this alignment should take. We describe how the typology can be used to reconcile and extend different perspectives in this area. The rest of the chapter proceeds as follows. We first define e-learning in order to set the boundaries for the scope of learning programs addressed by the typology. We then review existing typologies that distinguish between different e-learning programs, and discuss their limitations relative to the

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typology we develop in this chapter. Next, we review the major theories of learning related to more traditional instruction that form the theoretical foundation for the typology. Drawing on these theories of learning and e-learning research, we develop the typology, which consists of three dimensions (degree of interaction, learner control of interactions, and informational value of interactions). We discuss each dimension of the typology in turn, including its theoretical basis and relationship to e-learning effectiveness. Having described the typology, we turn our attention to its theoretical contributions, including as a foundation for comparing the effectiveness of different e-learning programs and as a central mechanism in a theoretical framework for identifying factors that influence e-learning effectiveness. In summary, our goal is to make a significant contribution to the e-learning literature by developing a typology for distinguishing between different e-learning programs. The development of the typology relies on the integration of several literature streams, including the training and educational literature as well as the information systems, and organizational behavior literature.

E-LEARNING DEFINITION We define e-learning as the use of ICTs (e.g., internet, intranet, CD-ROM, interactive TV, teleconferencing, computer-conferencing, and chat) to deliver instruction to learners. Consistent with several existing definitions in the literature, our definition focuses on technology-mediated learning as the primary defining characteristic of e-learning. In other words, content is transmitted to the learner using technology, and technology is also used to facilitate communication between the learner and any other participants in the learning program (i.e., instructor and other learners). We also include in the e-learning definition all possible ICTs that can be used to deliver instruction. Some researchers have defined e-learning more narrowly, focusing on the use of computer-based technologies only (e.g., Lowe & Holton, 2005; Welsh et al., 2003). Our definition is consistent with others (e.g., Kaplan-Leiserson, 2002; DeRouin et al., 2005) who have offered a broader definition that incorporates the use of computer-based technologies (e.g., internet, intranet/extranet, and CD-ROM) as well as other ICTs (e.g., audio, video, and TV). We believe this broader definition is essential for developing a more comprehensive framework that applies to the wide range of e-learning applications currently found in organizations.


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Our definition also includes both technology-mediated learning where learner, instructor, and fellow learners are spatially separated from each other as well as technology-mediated self-study programs where there are no instructor and other learners available. Several existing definitions make specific assumptions regarding the presence of a human instructor and that instructor’s spatial separation from the learner (e.g., Klein, Noe, & Wang, 2006); however, consistent with others (e.g., Bell & Kozlowski, 2002; DeRouin et al., 2005; Welsh et al., 2003), our definition is not limited to e-learning programs where there is a human instructor or other learners. Between the two extremes of traditional classroom instruction and fully technology-mediated e-learning are different forms of blended learning. Blended learning has been defined as ‘‘training that combines traditional classroom sessions with e-learning and self-study’’ (Kovaleski, 2004, p. 35). Depending on the extent to which learning is technology-mediated, the blended learning program will be closer to the classroom instruction or the e-learning end of the continuum we have described. Given the need for e-learning researchers to compare e-learning to blended and classroom instruction, the typology we develop can be applied to learning programs that fall at all positions on the classroom instruction–e-learning continuum.

REVIEW OF EXISTING E-LEARNING TYPOLOGIES We performed a search to identify existing typologies that have been used to distinguish between different e-learning programs. We used an electronic search of EBSCO (Academic Search Premier, Business Search Premier, Education Research Complete, Eric, PsychArticles, Psychology and Behavioral Science Collection, and PsychInfo) and web searches performed with Google Scholar to locate e-learning typologies in the literature from 1985 to the present. We then supplemented these electronic searches with manual searches of books on e-learning. Search terms included e-learning, online, web-based, distributed, distance, technology-mediated, and computer-based learning/training/instruction. Our review of the literature revealed only a few existing typologies. In this section, we briefly review these existing typologies, and discuss their limitations relative to the typology we develop in this chapter. A first set of existing typologies distinguishes between different types of e-learning based on the delivery medium. Specifically, Davidson-Shivers and Rasmussen (2006), De Volder (1996), and Taylor (2001) describe different generations of distance learning. Distance learning refers to the use

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of different technologies to deliver instruction to learners who are spatially separated from the instructor and other learners (Keegan, 1986). The first generation of distance learning is the print-based model (i.e., correspondence education supported by distance instruction through written messages), which is not consistent with our e-learning definition. The remaining generations use ICT to deliver instruction to learners; hence, they fall within our definition of e-learning. The second generation of distance learning uses radio, telephone, and television in a nonintegrated form. The third generation uses computers and digital technologies to unite instructors and learners and deliver the content of the course. The delivery medium in the fourth and last generation of distance learning is the internet. A second set of typologies differentiates between e-learning programs based on specific characteristics of the technologies used to deliver instruction and the spatial separation of the instructor and learners. For example, Aggarwal (2000) and Brewer, De Jonghe, and Stout (2001) developed a typology based on two dimensions: time (synchronous versus asynchronous) and place (same versus different). Traditional classroom instruction, where instructor and learner share the same space at the same time and communicate in real time (synchronous), is at one extreme. At the other extreme, instructor and learners are at different locations and communicate with a time lag (asynchronous). Hedberg, Brown, and Arrighi (1997) added a third dimension, group size (individual versus group), to these two dimensions. Some delivery media are targeted to an individual learner, whereas other media allow learners to work in group (e.g., groupware tools). Dillemans, Lowyck, Van der Perre, Claeys, and Elen (1998) and Proost (1998) identified three additional technology dimensions that can be used to differentiate e-learning programs: type of interaction (human–human versus human–computer), information modality (the ability to transmit verbal and/or nonverbal cues) and linearity (the extent to which the learner can navigate through the material). Despite the intuitive appeal of focusing on the delivery media or underlying technology characteristics to compare e-learning programs, such a focus provides little information about the learning experience created for the learner when these features are combined in the context of different instructional designs. For example, the use of more synchronous technologies, such as videoconferencing, provides the potential for high levels of interaction between the instructor and learners; however, depending on the instructional design, such technologies might be used for one-way lecturing by the instructor (low level of interactivity) or for a highly interactive exchange between the instructor and learners. In other words, the amount of


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interaction that actually occurs depends on the instructional design and the motivation of the instructor and other learners to participate in the discussion. We also found two typologies that do not focus on the technology used, but on aspects of the program’s instructional design. Horton and Horton (2003) made a distinction between instructor- and learner-led e-learning, referring to the difference in control over the learning process. Clark and Mayer (2003) and Holmes and Gardner (2006) distinguished between types of e-learning based on the learning theory underlying the program’s design. (We include a more detailed discussion of learning theories in a later section of this chapter.) E-learning programs based on behavioral learning theory (e.g., ‘‘drill and practice’’ type programs) are characterized by demonstrations or examples and frequent practice with corrective feedback. A second type of e-learning based on information processing theory aims to present the information in a way that enhances the internal processes of acquiring, understanding, and retaining knowledge. A third type of e-learning draws from the constructivist learning theory and focuses on making sense of the presented material guided by the instructor and/or in collaboration with other learners. By focusing primarily on the design philosophy underlying an e-learning program, and not the technology features, these last two typologies do not account for the fact that the use of different media and/or technologies can create very different learning experiences in the context of a particular design. For example, different technologies facilitate different levels of interaction with other learners in a design based on constructivist learning theory. To address the shortcomings of these existing typologies, the typology we develop focuses on the learning experience created for the learner, rather than the specific features of the e-learning program in terms of technology used or instructional design philosophy. In other words, we argue that it is ultimately the type of learning experience created through the combination of technology and instructional design choices that will influence learning effectiveness, and not the specific program features per se. Related to our approach, there has been an ongoing debate in the educational literature as to whether it is the use of a particular delivery technology or the instructional method that improves learning (Anderson & Elloumi, 2004; Carter, 1996; Jonassen, Campbell, & Davidson, 1994; Richey, 2000). Instructional methods are defined as ‘‘any way to shape information that compensates for or supplants the cognitive processes necessary for achievement or motivation’’ (Clark, 2001, p. 208). Two main protagonists in this discussion

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have been Clark (1983) and Kozma (1991). Clark (1983) argued that any medium, appropriately applied, can be used to provide quality instruction, while Kozma (1991) argued that media attributes alone influence learning, and the effectiveness of a medium to provide quality instruction depends on how much of the learner’s cognitive work is performed by the medium. Two recent meta-analyses (Bernard et al., 2004; Sitzmann, Kraiger, Stewart & Wisher, 2006) confirmed Clark’s position by showing that the instructional methods built into the program tend to take precedence over the technology used to deliver instruction. Bernard et al. (2004) found that there were instances in which a distance learning group outperformed the traditional classroom instruction group, whereas in other instances the opposite occurred. The authors concluded that it is the instructional methods, such as the feedback provided and the degree of learner engagement, independent of the medium, that determine learning effectiveness. Consistent with this, Sitzmann et al.’s (2006) findings indicated that web-based instruction and classroom instruction were equally effective for teaching declarative knowledge when the same instructional methods (e.g., the same level of interaction with the learner and the same level of learner control) were used. Consistent with the dominant position in the distance education literature (Anderson & Elloumi, 2004; Carter, 1996; Jonassen et al., 1994), we argue that both technological features and instructional methods shape the learning experience. Further, these two factors combine with characteristics of the instructor and other learners in the program to create different learning experiences for learners. These learning experiences vary in the nature of the learning interactions that occur, including interactions between the learner and instructor, the learner and other learners, and the learner and instructional material. Our focus on learning interactions is based on both general and distance learning theories that predict that effective learning is facilitated by learning interactions. Next, we review these theories that provide the foundation for our typology.

THEORETICAL FOUNDATION As noted by Salas, Kosarzycki, Burke, Fiore, and Stone (2002), there is currently no theory that predicts e-learning effectiveness. However, general theories of learning provide a useful starting point for developing an e-learning typology. Furthermore, since researchers frequently seek to compare e-learning with traditional classroom instruction, a typology that is based on more general learning theories should apply to the full range of


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instructional approaches, including classroom, blended, and e-learning. The major learning theories that have emerged over the past century include the behaviorist, cognitive, constructivist, and social learning theories (Leidner & Jarvenpaa, 1995; Merriam & Caffarella, 1999; Salas et al., 2002). Although, these theories differ in their major assumptions and can be distinguished from each other in many different ways, for our current purpose, a meaningful way to compare the theories is to consider how they characterize the nature of interactions required for effective learning. In all learning environments, interactions are the means by which information is transmitted and knowledge is constructed. Proponents of behavioral theories, such as Skinner (1974) and Thorndike (1932), consider learning the uncritical absorption of objective knowledge and modification of behavior. The instructor and instructional material facilitate learning by providing the environmental stimuli for behavioral change to occur, with the learner as a passive receiver of this stimuli (Leidner & Jarvenpaa, 1995). In other words, there is a one-way transfer of knowledge from the instructor and instructional material to the learner, with very little interactivity between the learner and these components of the program. In contrast to behavioral theories, cognitive learning theories place more emphasis on the learner as an active participant in the learning process. According to these theories, learning is a process in which the learner constructs knowledge through continuous interaction with the other components of the learning program – instructor, other learners, and instructional material (Salas et al., 2002). Cognitive constructivist theories, such as Mezirow’s (1991) theory of transformational learning, consider learning a process of meaning construction that takes place in interaction with the instructional material. From a social constructivist perspective (Wenger, 1998), learning is fundamentally a social activity taking place through interaction with other learners and the instructor. Finally, social learning theories, such as Bandura’s (1986) social cognitive theory, posit that individuals learn from observing others, and hence, interactions with others are key. Distance learning researchers have also built their theories around the central theme of interactions in the learning process. As mentioned earlier, the term distance learning has been used as a more general term to describe both e-learning and other forms of learning that rely on alternative types of technology (e.g., correspondence by mail), not just ICTs, to deliver instruction to learners who are spatially separated from the instructor and other learners (Keegan, 1986). For example, Garrison (1989) maintained that any educational transaction is based on seeking understanding and

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knowledge through dialogue and debate; therefore, it requires two-way communication between teacher and learner. In a learning setting where learner and instructor are separated from each other, and communicate using technology, this process is compromised. In addition, Keegan (1986) argued that in distance learning the interactions between instructor and learner that facilitate learning have to be artificially recreated in order for the instruction to be effective. Finally, Moore (1991, 1993) argued that the reliance on technology-mediated communication creates a psychological and communication gap in the interactions that occur between the learner and instructor, the learner and other learners, and the learner and instructional material. According to Moore, the success of distance learning is determined by the extent to which this gap can be reduced. In line with these theories of general learning and distance learning, we build our typology around the central theme of interactions that occur in the learning process between the learner and the different components of an e-learning program (i.e., instructor, other learners, and instructional material). We refer to these interactions as learning interactions. Consistent with distance learning theories, we propose that the use of technology to mediate learning can significantly change the amount and nature of these interactions; hence, one way to distinguish between different e-learning programs is to compare the nature of the learning interactions they create for learners. Based on the theories reviewed above, we identify three types of learning interactions: learner–instructor interactions, learner–learner interactions, and learner–instructional material interactions. Learner–instructor interactions provide content, motivation, feedback, and dialogue between instructor and learner. Learner–learner interactions involve the exchange of information, ideas, and dialog between students about the course content. Finally, learner–instructional material interactions involve the process by which students obtain intellectual information from instructional material (Chen, 2001). Having discussed its theoretical foundation, we turn now to a more detailed description of each dimension of the typology.

THREE-DIMENSIONAL TYPOLOGY OF LEARNING INTERACTIONS Based on the theories reviewed in the previous section, we argue that the range of different e-learning programs used in organizations create significant variation in the types of learning interactions they produce


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for learners. This variation can be described in three dimensions: degree of interaction, learner control of interactions, and informational value of interactions. Together these learning interaction dimensions form a typology for distinguishing between different e-learning programs. In this section, we describe each learning interaction dimension, and also discuss its theoretical basis. From a theoretical perspective, each dimension should facilitate greater learning effectiveness. Learning effectiveness refers to the benefits to learners that result from participating in the e-learning program, for example, increased knowledge and skills, or new behaviors (Noe, 2005). However, as we discuss in a later section, there are other factors (e.g., learner characteristics) that moderate the relationship between each dimension and learning effectiveness.

Degree of Interaction This dimension describes the extent to which the e-learning program provides the learner with opportunities for interaction (i.e., the amount of interactivity) with the instructor, the other learners, and the instructional material. The importance of this dimension is shown by our discussion in the previous section of learning theories (e.g., social cognitive theory, constructivist learning theories) that emphasize the critical role of interactions for effective learning. Degree of learner–instructor and degree of learner–learner interaction refer respectively to the extent to which a learner is able to interact with an instructor and with other learners in the program (Moore, 1991, 1993). Interactions with an instructor, in which the learner receives direction and guidance, have been shown to increase learning effectiveness (Lemak, Shin, Reed, & Montgomery, 2005; Najjar, 1996). Similarly, interactions with other learners, for example, through technologymediated group learning, have also been positively associated with learning effectiveness (Arbaugh, 2005; Lou, Abrami, & Apollonia, 2001). Finally, learning programs also vary in the degree of interaction built into the instructional material. For instance, degree of interaction with the instructional material is high when the learner has to solve a problem through question and answer and is regularly prompted with test questions. Research shows that increased interaction with the instructional material provides the learner with more practice and feedback and leads to more time spent on task, which positively impacts learning (for a review, see Brown, 2001). In summary, learner–instructor, learner–learner, and learner– instructional material interactions should facilitate the learning process.

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Learner Control of Interactions This dimension describes the extent to which a learner has control over the interactions available in the e-learning program, such that the learner is able to tailor the instruction to his or her individual needs. E-learning programs in which the sequencing of the content is structured in advance (e.g., traditional lecture via videoconferencing) are situated at the lower end of this dimension. At the higher end of the learner control dimension are e-learning programs that allow learners to assess their own learning needs and adjust the program accordingly (e.g., web-based training providing hyperlinks to different knowledge sources). In the existing literature, learner control most typically refers to control of interaction with the instructional material (e.g., DeRouin et al., 2005). In this regard, several types of learner control and their impact on learning effectiveness have been discussed, including learner control of sequence, pacing, content, context, method of presentation, task difficulty, and incentives (DeRouin et al., 2005). However, we propose that it is also important to consider learner control of interactions with the instructor and other learners. This is consistent with the broad definition of learner control provided in the literature as any instructional strategy in which learners assume some form of control (DeRouin et al., 2005). For example, Wydra (1980) defined learner control as ‘‘a mode of instruction in which one or more key instructional decisions are delegated to the learner’’ (p. 3). Reeves (1993) defined learner control as the degree to which an individual is given control over various instructional features during a lesson or training program. New forms of technologies allow learners to control interactions with the instructor and other learners. For example, learner control of interactions with the instructor is high when the e-learning course provides the opportunity to chat with a content expert whenever the learner desires. This flexibility allows the type (i.e., instructor versus other learners) and amount of interaction to be tailored to meet learners’ needs (Keller, 1983; Milheim & Martin, 1991). Several different theoretical perspectives have been used to explain the positive impact of learner control on learning effectiveness (Milheim & Martin, 1991). From a motivational perspective, learner control should influence motivation to learn, which has been shown to increase learning effectiveness (Klein et al., 2006). This occurs through several mechanisms (Milheim & Martin, 1991). First, learners’ needs are more likely to be satisfied when training is made more relevant to a learner by providing the learner with greater control. Second, consistent with adult learning theory


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(Knowles, 1990), learner control also satisfies a learner’s need to be selfdirected. Finally, from an expectancy theory (Vroom, 1964) perspective, learner control increases a learner’s expectancy of success because the learner is made to feel that learning outcomes are in his or her hands. According to Milheim and Martin (1991), two additional theoretical perspectives for understanding the impact of learner control on learning effectiveness are attribution theory (Kelley, 1967) and information processing theory (Gagne´, 1985). Attribution theory states that learners seek to understand and explain why an event has occurred, and the explanation they construct influences future action. By influencing a learner’s perception of the locus, stability, and controllability of learning, learner control can increase the learner’s expectation of success, which will in turn produce higher levels of effort. Finally, information processing theory emphasizes the internal processes that occur when training content is processed and retained. Learner control facilitates the process through which information is coded for long-term memory by allowing the learner to organize information in a way that makes it personally meaningful. In summary, learner control of interactions should positively impact the learning process.

Informational Value of Interactions Informational value is the extent to which, within the different interactions, communication or data are transmitted that are valuable for learning (Kirkman & Mathieu, 2005). In addition to considering how much opportunity for interaction an e-learning program offers, and how much control the learner has over those interactions, it is also important to recognize that not all learning interactions are created equal in terms of their ability to transfer information that is valuable for learning. Media richness theory (Daft & Lengel, 1986), a theory from communications research, provides a foundation for this dimension. A basic tenet of this theory is that communication media differ in terms of richness or their ability to clarify ambiguity and facilitate understanding of communication messages. It further proposes that communication media can be placed on a continuum of richness. For example, videoconferencing is richer than email because it allows for both nonverbal and verbal communication. Email communication lacks the body language and other nonverbal cues that help to clarify the meaning of messages. In general, according to these theoretical perspectives, less rich communication media are viewed as less effective for communication. Applying these theoretical arguments to e-learning suggests

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that e-learning consisting of static text will transmit fewer cues to aid understanding of learning than e-learning programs in which multimedia is used to create a richer learning experience. Based on the construct of media richness, Kirkman and Mathieu (2005) defined a new construct: informational value. They argued that media richness was defined as a characteristic of the information carrying capacity of the communication media; however, the construct of richness also applies to other types of information exchange, beyond direct communications between one individual and another. For example, an individual might post a presentation or engineering drawing to a website to be viewed by others. Informational value is a more general term that applies to all types of information exchanges, not just direct communications between individuals. Therefore, in an e-learning context, informational value is relevant to learning interactions involving direct communication with an instructor and other learners as well as the presentation of information to learners via the instructional material. This is consistent with our definition of e-learning as the use of both the information and communication technologies to deliver instruction to learners. Kirkman and Mathieu’s (2005) construct of informational value focused on the media used for communication and information exchange. In an e-learning context, we view informational value as a characteristic of the interaction that is shaped, not only by the media characteristics but also by the extent to which that media is used by instructors and other learners to provide cues that enhance learning. For example, an interaction in which the instructor uses a richer communication media in a way that is very expressive and provides a lot of nonverbal cues will create an interaction of higher informational value than one in which an instructor uses the same media with a lower level of expressiveness. We discuss this idea further in a later section in which we discuss the antecedents of informational value. Based on the theoretical perspectives above, interactions that are higher in informational value should facilitate greater understanding on the part of the learner, and therefore enhance learning effectiveness. Taken together, the three learning interaction dimensions we have defined form a typology for distinguishing between different e-learning programs. The typology has important theoretical implications for research that (1) compares the effectiveness of different e-learning, blended learning, and classroom instruction and (2) identifies factors that influence e-learning effectiveness. We discuss these two theoretical implications in the sections that follow.


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COMPARING THE EFFECTIVENESS OF DIFFERENT E-LEARNING PROGRAMS The typology we have developed provides a means to compare the effectiveness of different e-learning programs. In this section, we discuss the utility of our typology for both deepening our understanding of existing research findings in this area and identifying important new research directions. We argue that the equivocal results found when comparing the effectiveness of e-learning to classroom instruction (for reviews, see DeRouin et al., 2005; Welsh et al., 2003) is in large part due to the lack of specificity regarding the characteristics of the targeted e-learning programs. In this regard, the dimensions in our typology can shed light on these equivocal findings. As discussed in our review of existing typologies in an earlier section, existing research has already recognized that when comparing the effectiveness of e-learning, blended, and classroom instruction, it is necessary to move beyond the technology per se and focus on how the technology is used within the context of the program’s instructional strategies (Bernard et al., 2004; Clark, 1983; Sitzmann et al., 2006). Our typology builds on this idea by providing a means to compare e-learning programs that takes into account all components of an e-learning program, including the technology, the instructional design, and the other participants in the learning program (instructor and other learners). Our typology consists of three dimensions along which learning programs can differ. Rather than being tied to a particular technology or instructional design feature, these dimensions describe the learning experience created for the learner in terms of the nature of the interactions in which the learner engages while moving through the program. Further, because the framework is independent of any particular technology or instructional design strategy, it can be applied to learning programs that consist entirely of classroom instruction as well as those that are entirely technology-mediated. Hence, it can be used to facilitate comparisons between learning programs that fall at any position along the continuum between these two extremes. The utility of the typology for comparing the effectiveness of different learning formats is demonstrated by research that has compared learning programs where all characteristics of the learning programs were the same, with the exception of differences related to one of the learning interaction dimensions in the typology. For example, Zhang (2005) conducted an experiment that compared levels of student satisfaction and performance for a course that was delivered using three different instructional methods: classroom lecture, a videotape of that lecture, and a videotape of the lecture

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in which students were allowed to control the sequencing of content and the presentation format. Zhang found the highest level of student satisfaction and performance in the e-learning condition with learner control. In contrast, Tutty and Klein (2008) found that students who were paired in learning dyads performed better for an individual learning assignment in the face-to-face condition compared to a computer-mediated program. Using follow-up surveys of the students and observation, the research team explained this result by the fact that students in the face-to-face condition had higher levels of interaction (degree of interaction) and also were able to use nonverbal and contextual cues to more effectively share information with their partners (informational value). The research findings above demonstrate the importance of clearly isolating where the learning programs that are being compared fall along the dimensions of our typology. The importance of this approach is also illustrated by considering research that has compared blended learning with classroom instruction (for a review, see Klein et al., 2006). Researchers have explained their findings that blended learning is more effective by pointing to the fact that blended learning incorporates the advantages of both classroom and e-learning (Klein et al., 2006; Sitzmann et al., 2006). For example, according to Sitzmann et al. (2006), ‘‘The instructional advantage of blended learning may be due to incorporating the benefits of personal interaction typically found in CI [classroom instruction] and self study between instructional meetings using the Web’’ (p. 646). These benefits of blended learning can be more precisely understood by comparing blended learning to classroom instruction using our typology. On the one hand, because blended learning programs involve some classroom instruction, they have the potential to provide a high level of interaction and informational value of learning interactions compared to pure e-learning. On the other hand, the e-learning component frequently allows the learner more control over the pace and depth of learning than traditional classroom learning. As a next step, researchers could use our typology to more precisely identify the types of blended learning programs that are most effective. This is important because within the continuum of blended learning, many different combinations of classroom instruction and e-learning are possible.

A THEORETICAL FRAMEWORK FOR E-LEARNING RESEARCH Another important theoretical contribution of the typology is its application to develop a theoretical framework for research that identifies factors that

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E-learning Program Characteristics

TYPOLOGY

• Instructional design (e.g., learning model, including requirements for interaction and number of participants)

Learning Interaction Dimensions

• Technology (e.g., time dependency, perceived ease of use) • Instructor (e.g., personality, teaching style) and other learners (e.g., cognitive style, technical expertise) • Instructional design (e.g., learning model, accessibility of instructor and other learners) • Technology (e.g., linearity, time dependency) • Instructor (e.g., teaching style) and other learners (e.g., cognitive style, technical expertise) • Instructional design (e.g., presentation of information, one-way versus two-way communication, human-human versus human-computer interaction) • Technology (e.g., information modality, human-human versus human-computer interaction, one-way versus two-way communication, time dependency)

• Degree of interaction Instructor Other learners Instructional materials

• Learner Characteristics • Contextual Factors • Learning Goals

• Learner control of interactions Instructor Other learners Instructional materials

Learning Effectiveness

• Informational value of interactions Instructor Other learners Instructional materials

• Instructor and other learners (e.g., immediacy behaviors)

Fig. 1.

Theoretical Framework for E-learning Research.

influence e-learning effectiveness (see Fig. 1). In this framework, the learning interaction dimensions of the typology are central mediating mechanisms that explain the relationship between characteristics of an e-learning program and learning effectiveness. This theoretical framework facilitates research related to factors that influence e-learning effectiveness in four categories: e-learning program characteristics, learner characteristics, contextual factors, and learning goals. Next, we describe each component of this framework and its relationship to the dimensions of our typology. We also develop propositions to guide e-learning research related to each component of the framework.

E-learning Program Characteristics Fig. 1 shows the characteristics of the e-learning program as antecedents of the typology dimensions. As shown in Fig. 1, we categorize these e-learning program characteristics according to the four potential components of an e-learning program: technology, instructional design, instructor, and other learners. With regard to the first two components, we argued earlier,

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following the dominant position taken in the design-technology discussion in the educational literature (Anderson & Elloumi, 2004; Carter, 1996; Jonassen et al., 1994), that both the technology used and instructional design impact learning interactions. For example, interaction with fellow learners is not possible without technology features that provide human– human interaction (e.g., chat). However, by the same token, the degree of interaction will primarily be determined by design decisions, such as those related to the learning model (behavioral versus constructivist), the number of participants (individual versus group assignments), and the frequency of interactions. The design and technology decisions provide the potential for certain types of interactions; however, the nature of the learning interactions in an e-learning program will also be influenced by how the instructor and other learners choose to apply this potential in their actual interactions. For example, an instructor who uses the opportunity to interact with learners via videoconference to provide an animated and interactive presentation will provide higher interactivity and informational value than an instructor that delivers a monotone, one-way delivery. Similarly, even though the program uses technology that allows learners to communicate with each other, and the design allows for learner interaction, the extent to which the instructor promotes and facilitates interaction between learners can play an important role in determining how much interaction actually occurs. Based on this, researchers have described the instructor as a major force in shaping the nature of the interactions that occur in an e-learning program (Webster & Hackley, 1997). Below, we describe the e-learning characteristics that are antecedents to the typology dimensions in our theoretical framework. While e-learning programs differ in terms of many different characteristics, we believe that the typology helps to identify characteristics of e-learning that are likely to be most impactful for learning. Our goal here is not to be exhaustive in listing all possible e-learning characteristics, but to highlight some that are likely to most influence our typology dimensions. Antecedents of Degree of Interaction Instructional Design. The degree of interaction with the instructor, the other learners, and the instructional material is primarily determined by the design decisions made when developing the e-learning program (Moore, 1993). Decisions regarding learning model, including requirements for interaction and number of participants, will set boundary conditions for the extent to which interaction can occur within the e-learning program.


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First, with regard to the requirements for interaction, the cognitive constructivist view of learning (Dewey, 1938; Mezirow, 1991) emphasizes that the learner takes responsibility for constructing meaning actively through dialogue with oneself and others. An instructional design based on this perspective will pay more attention to building in opportunities for interaction, such as live question/answer, discussion (Garrison, 1993) and collaborative problem-based learning opportunities, instead of individual assignments (Gorsky & Caspia, 2005). This is in contrast to a behaviorist orientation to learning (Skinner, 1974; Thorndike, 1932), which underscores the role of reinforcement by the external environment to ensure learning, and translates into a program in which the learner does not play an active role in the learning process (Merriam & Caffarella, 1999). An example of a design characteristic that facilitates a high degree of interaction is the requirement for the learner to participate in group activities or submit questions on a discussion board. This is in contrast to elearning programs that may have built-in opportunities for interaction, but do not make these interactions a requirement, or a prerecorded lecture that does not allow for interaction at all (Anderson & Garrison, 1995; Gorsky & Caspia, 2005). Second, design decisions related to group size can influence the extent to which learners interact with fellow learners and with the instructor (Chen & Willits, 1998). E-learning programs vary in the number of participants who can interact simultaneously. Some programs do not allow interaction with other participants or are very limited in this regard (e.g., CD-ROM); others allow large groups to participate (e.g., web-based application). The e-learning program needs to allow at least two participants to interact with each other, in order for interactions to take place. However, it is less clear how large the group should be in order to optimize the level of interactivity with fellow learners. Both the training and team literatures have shown that group size has an effect on the extent to which groups interact (Arbaugh, 2005; Cohen & Bailey, 1997). Larger groups may be less effective because of a decrease in involvement or participation by the individual members (Hare, 1981; McGrath, 1984). Also, the degree of interaction with the instructor will diminish in larger groups (Gorsky & Caspia, 2005). Technology. First, the time dependency of the program’s technology will determine the degree of interaction with the instructor and other learners. Time dependency captures the distinction between synchronous versus asynchronous technologies. Synchronous technologies allow for real-time interaction among learners and instructor, even if they are in different places

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(e.g., tele- or videoconferencing system). Synchronous technologies increase the likelihood of interactions with others (Lauzon & Moore, 1992; Proost, 1998). The more synchronous technologies are used, the more learners will interact with the instructor and fellow learners as they participate in the program at the same time. With asynchronous technologies, learners and instructors are time independent and learners participate in the training program at their convenience. As a result, asynchronous technologies typically have a lower level of interactivity. Second, following the technology acceptance model (Davis, 1989), we expect perceived ease of use of the technologies to positively influence learners’ attitude toward the technologies. As a result, they will be more willing to use the technologies to interact with the instructor, the other learners, and the instructional material. In line with this, research has shown that the more computer software, email, world wide web, etc. are perceived as easy to use, the more individuals make use of these technologies (Arbaugh, 2005). Instructor and Other Learners. Several researchers have discussed characteristics of the instructor and other learners that have an impact on the degree to which they interact with a learner in the learning process (Gorsky & Caspia, 2005). Instructors’ personality traits and teaching style, for instance, play a critical role in creating and maintaining learner– instructor interaction (Moore, 1993). Chan’s (2002) study revealed that instructors characterized by a high degree of extraversion were more likely to interact with their students in a distance learning environment. Webster and Hackley (1997) found that a more interactive teaching style (encouraging learners to interact) was positively related to learners’ involvement and participation in the learning process. We further expect that characteristics of the other learners who are participating in the program will influence the extent to which they are open to and seek out opportunities for interaction with others. Two such characteristics are cognitive style and expertise in using e-learning technologies. These individual characteristics mirror those described later in this chapter as learner characteristics that facilitate learning in an elearning environment with a high degree of interactivity. Here, it is important to note that certain cognitive styles have been associated with preference for collaborative learning methods (for reviews, see Riding & Cheema, 1991; Smith, Murphy, & Mahoney, 2003), and expertise in using elearning technologies has been positively associated with increased participation in e-learning (Martins & Kellermanns, 2004). Hence, when


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other learners have more collaborative learning styles and greater technical expertise, they will tend to seek out more interactions with a learner, resulting in a higher degree of learner–learner interaction. Proposition 1. The degree of interaction in an e-learning program is influenced by characteristics of the instructional design (e.g., learning model, including requirements for interaction and number of participants), characteristics of the technology (e.g., time dependency and perceived ease of use), and characteristics of the instructor (e.g., personality, teaching style) and other learners (e.g., cognitive style and technical expertise). Antecedents of Learner Control of Interactions Instructional Design. Instructional design decisions will also establish boundary conditions for the impact that technology as well as characteristics of the instructor and other learners have on the degree of learner control of interactions. First, we expect the learning model underlying the e-learning program to have an effect on learner control. A constructivist orientation toward learning (Dewey, 1938; Mezirow, 1991) not only recognizes the critical importance of frequent interactions for learning to occur but also that these interactions take place in an individualized fashion while the learner is drawing his or her own lessons from experience (Merriam & Caffarella, 1999). Compared to a behaviorist approach toward learning, the constructivist orientation is much more likely to translate into a design that allows and encourages learners to exercise control over their interactions with the instructor, other learners, and instructional material. In contrast, the behaviorist approach will more likely translate into a program- or instructor-led e-learning program. Second, following Chen (2001) and Gorsky, Caspia, and Tuvi-Arad (2004), we expect the accessibility of the instructor to determine the degree of control the learner has over learner–instructor interactions, with significantly lower levels of learner control when the instructor is available one hour per month versus seven days a week. Similarly, the accessibility of other learners has a significant impact on the extent to which learners can chose to interact with each other (Gorsky, Caspia, & Trumper, 2004). For example, providing the learner with other learners’ contact details (e.g., email addresses) and providing access to other students seven days per week leads to more learner control of learner–learner interactions than giving learners the opportunity to interact at limited times during the program (Gorsky & Caspia, 2005).

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Technology. Within the constraints of design decisions that are made to enhance learner control, technology features will further impact this dimension. First, we expect that the linearity (linear versus nonlinear) and the time dependency (synchronous versus asynchronous) of the technologies used will influence the learner control of leaner–instructional material interactions. Linearity describes technologies that only allow linear predefined learning paths versus those that enable learners to customize their path through the program. With the exception of conferencing systems (e.g., tele- and videoconferencing systems), most computer networkbased technologies (e.g., web-based applications) allow a nonlinear presentation of the content, and thus provide the flexibility to adapt the learning path to individual learning needs and preferences (Proost, 1998). Whereas synchronous technologies increase the likelihood of interactions with the instructor and fellow learners (as discussed above), they decrease the likelihood of individualized interactions with the program. The more synchronous technologies are used, the less learners will be able to control their own learning process (Hedberg et al., 1997; Proost, 1998). In particular, when synchronous technologies are used for content delivery (and not mere communication), it is more likely that the instructor will make the key decisions regarding the content, sequence, and pace of learning. In contrast, asynchronous technologies provide the opportunity for learning to be time independent and for the learner to participate at his or her own pace. In summary, nonlinear and asynchronous technologies will increase the potential that learners have to control their interactions with the instructor, the other learners, and the instructional material. Instructor and Other Learners. The control that learners have over interactions with the instructor, the other learners, and the instructional material will also be determined by characteristics of the instructor and other learners. First, as argued by Anderson and Elloumi (2004), instructors differ in their teaching style and skills for responding to diverse learner needs. Even given the constraints of the program’s instructional design, instructors vary in the extent to which they take a more instructor-led versus student-led approach to instruction. In a more instructor-led approach, an active instructor teaches a passive learner and decides where to build in opportunities for interaction with others. In addition, the instructor determines the pace, sequence, and methods of instruction (Anderson & Elloumi, 2004). This kind of learning reflects the goals of the instructor and ignores the learner’s needs (Moore, 1973). In contrast, learner control of the


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different learning interactions will increase when the instructor, within the constraints of the program’s design, takes a learner-centered (i.e., supporting individualized learning activities), community-centered (i.e., encouraging collaborative and individual interactions in many formats), and content-centered (i.e., providing direct access to vast libraries of content) approach (Anderson & Elloumi, 2004; Whipp & Chiarelli, 2004). Learner characteristics that will have an impact on the extent to which learners can control their interactions with each other are cognitive style and expertise in using e-learning technologies. A learner’s control over interactions with other learners requires that these other learners be willing to engage in interactions that suit the learner’s needs. As discussed above, we expect cognitive styles that relate to preference for learning collaboratively with others and expertise in using e-learning technologies to have a positive effect on willingness to engage in learning interactions. Thus, when other learners in an e-learning program have these characteristics, this should result in a learner having more control over interactions with these learners. Proposition 2. Learner control of the interactions in an e-learning program is influenced by characteristics of the instructional design (e.g., learning model and accessibility of the instructor and other learners), characteristics of the technology (e.g., linearity and time dependency), and characteristics of the instructor (e.g., teaching style) and other learners (e.g., cognitive style and technical expertise). Antecedents of Informational Value of Interactions Instructional Design. The informational value of the interactions with the instructor, the other learners, and the instructional material will be primarily determined by the program’s instructional design. The informational value of the learner–instructional material interactions will depend on the design decisions related to how content is presented. Whereas text is the simplest way to present information, additional features such as still images, graphics, images in motion, and sound can be added to increase the informational value (Bell & Kozlowski, 2006). In addition, the informational value of the learner–instructor and learner–learner interactions will be determined by the type of communication allowed by the design. Specifically, a program design that allows for two-way communication will increase the informational value of interactions with the instructor and other learners. Two-way communication allows the learner to receive feedback and seek clarification. This, in turn, enhances the informational value of the

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interaction (Bell & Kozlowski, 2006). In contrast, if the e-learning program is designed to have only one-way communication, then informational value will be diminished (Bell & Kozlowski, 2006). Further, an e-learning program can be designed to allow for interactions that are human–human or human–computer. Communicating with other people or working on assignments with other learners (i.e., human–human interactions) will result in higher informational value. This is because such communication allows for detailed and immediate feedback and the exchange of nonverbal cues that enhance the meaning of the communication. When learners only have the option to ask questions to an instructor proxy or e-tutor (i.e., human–computer interactions: Maruping & Agarwal, 2004; Proost, 1998), informational value is lower.

Technology. The technology used to implement design decisions will also influence the informational value of the interactions. For example, the design might allow for human–human interactions; however, there are a range of technologies (e.g., email, chat, and phone) that can facilitate such interaction. Four characteristics of the technology that are relevant to informational value are information modality (single versus multiple cues), human–human versus human–computer interaction, one-way versus twoway communication, and time dependency. Information modality refers to the extent to which the technology allows for the communication of multiple cues and language variety (Proost, 1998). This dimension ranges from having the potential to convey text only (single cue) to having the potential to convey text in combination with graphics, pictures, sound, and nonverbal information (multiple cues). Human–human versus human–computer interaction (Proost, 1998) refers to whether humans communicate with humans, mediated by technology (e.g., in a virtual classroom or chat session) or humans communicate directly with the computer (e.g., CD-ROM). Finally, technologies such as CD-ROM, video tape, audio tape, and DVD only allow one-way communication, whereas technologies such as interactive media and videoconferencing support two-way communication. In line with media richness theory (Daft & Lengel, 1986), we expect the information modality and time dependency of the technologies used to have a significant impact on the informational value of the delivery and communication processes in an e-learning program. For example, a medium that only supports text-based cues and is asynchronous provides the learner with less rich information than synchronous multimedia (Bell & Kozlowski, 2006). Also, as argued above,


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whether the technology supports one-way versus two-way communication or human–human versus human–computer interaction will also impact the interaction’s informational value. Instructor and Other Learners. A relevant concept with respect to the antecedents influencing the informational value of learner–instructor and learner–learner interactions is immediacy behaviors. This concept was first introduced by Mehrabian (1972) who defined it as behaviors that increase mutual sensory stimulation between two people and includes both verbal and nonverbal behaviors. Examples of nonverbal immediacy behaviors are eye contact, facial expressions, gestures, and body position, whereas verbal immediacy involves behaviors such as using personal examples, using humor, providing and inviting feedback, and addressing students by name (Gorham, 1988). We expect immediacy behaviors to influence the informational value of the learner–instructor and learner– learner interactions. Proposition 3. The informational value of the interactions in an e-learning program is influenced by characteristics of the instructional design (e.g., presentation of information, one-way versus two-way communication, and human–human versus human–computer interaction), characteristics of the technology (e.g., information modality, one-way versus two-way communication, human–human versus human–computer interaction, and time dependency), and characteristics of the instructor and other learners (e.g., immediacy behaviors). So far, we have considered how our typology can be used to identify characteristics of e-learning programs that influence learning effectiveness through their influence on the typology dimensions. We now turn our attention to how the typology dimensions influence learning effectiveness. As discussed earlier in our description of each learning interaction dimension, the dimensions are expected to positively influence learning effectiveness. However, it is important to note that the typology describes the potential for learning interactions that is available to the learner. As shown in Fig. 1, a number of factors can influence the extent to which this potential translates into effective learning. These factors are shown as moderators in Fig. 1 and include learner characteristics, contextual factors, and learning goals. Next, we describe each of these moderators and discuss how our typology can help to advance research related to each one.

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Learner Characteristics A good match between the characteristics of the learner and the learning interaction opportunities offered by the e-learning program will enhance learning effectiveness, both directly and through motivation to learn (Colquitt, LePine, & Noe, 2000). Motivation to learn describes a learner’s desire to learn the content of the training program and has been shown to influence learning effectiveness (Colquitt et al., 2000). Motivation to learn may be particularly important for e-learning because compared to more traditional classroom instruction, e-learning requires learners to take greater responsibility for their own learning. Further, since many e-learning programs are completed over a longer period of time, there is a greater need for learners to demonstrate persistence to complete an e-learning program. With regard to the direct influence, learners whose characteristics make them more able to cope with demands of the e-learning environment will perform better in that environment. Also, such learners tend to spend more time in the e-learning program, which allows for greater exploration of the program content and more comprehensive practice, leading to greater learning effectiveness (Brown, 2001; Wang & Newlin, 2000). With regard to the influence through motivation to learn, several theoretical perspectives suggest that a match between individual characteristics and the e-learning program characteristics will influence motivation to learn (for a review, see Colquitt et al., 2000). For example, social cognitive theory (Bandura, 1986) proposes that one’s believe about one’s ability to execute a task will increase motivation to perform that task. Learners whose characteristics are better matched to the program will be more confident of their ability to learn using the e-learning program, and hence more motivated to learn. Similarly, based on the technology acceptance model (Davis, 1989), a match in learner characteristics is likely to influence perceived ease of use of the e-learning program, which according to this perspective, will influence motivation to use the program. In their review of distance learning research, Salas et al. (2002) noted that there is a growing body of research to show that individual characteristics predict distance learning outcomes. However, they cautioned that although some individual characteristics will be important for all distance learning environments, ‘‘research has yet to identify the learner characteristics that are important in specific DL [distance learning] environments’’ (p. 145). We agree that a shortcoming of most of the existing research in this area is that researchers fail to specify the type of e-learning for which a particular individual characteristic will be most important. Categorizing an e-learning


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program according to the learning interaction dimensions we have defined helps to identify which learner characteristics are important for e-learning in general, and which will be important in specific e-learning environments. For example, individual characteristics related to a learner’s ability to monitor his or her own learning process and make adjustments where needed will be most important for learning in an e-learning program characterized by the potential for a high degree of learner control. Hence, this individual characteristic varies in importance depending on where an e-learning program falls along this learning interaction dimension. In this section, we provide examples of learner characteristics that are likely to be important for all e-learning environments and those that align with specific dimensions of our typology. As with our discussion of e-learning characteristics, our purpose is not to provide an exhaustive list of learner characteristics, but to illustrate the application of the typology by focusing on some that are particularly germane to e-learning and to the different typology dimensions. Salas et al.’s (2002) review summarized learning characteristics that are likely to facilitate learning effectiveness in most e-learning environments, regardless of where they fall on the dimensions of our typology. These include cognitive ability, learning self-efficacy, prior knowledge and experience, and mastery goal orientation. In addition, since a defining characteristic of e-learning is the use of technology to deliver instruction, several researchers have focused on technical expertise as an important individual characteristic in an e-learning environment, (Brown, 2001; Hill, Smith, & Mann, 1987; Lowe & Holton, 2005; Martins & Kellermanns, 2004; Thompson & Lynch, 2003). Since e-learning interactions are mediated using technology, poor technology expertise is likely to impact the learner’s ability to take advantage of any learning interactions that are available in the e-learning program. Learners who lack technical expertise might not explore the full degree of interactivity or learner control options available in the program and may also be less receptive to the use of technology that offers higher informational value, since this technology typically has more complex technological features. Another individual characteristic that is important across all learning interaction dimensions is self-regulation. Self-regulation consists of strategies that individuals use in response to difficult or anticipated difficulties in goal-directed action to guide their own behavior over time and across changing circumstances (Kanfer & Heggestad, 1997). According to Kanfer and Heggestad (1997) individuals vary in the extent to which they have the skills to apply self-regulation strategies to deal with obstacles to goal

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attainment. Individuals who are more self-regulating have a greater tendency to engage in motivation control strategies (e.g., self-goal setting and monitoring of progress toward goals and self-reinforcement) and emotion control strategies that protect on-task attention and prevent distracting emotional states (Kanfer & Heggestad, 1997). The use of such self-regulation strategies has been shown to lead to improved performance and more successful goal attainment (e.g., Creed, King, Hood, & McKenzie, 2009; Porath & Bateman, 2006). Self-regulation behaviors should ensure that learners are able to successfully integrate e-learning into their everyday activities and maintain a high level of self-motivation to engage in e-learning. This will better position a learner to take full advantage of the degree of interactions available in the e-learning program. For example, in an e-learning program in which learners communicate in an online discussion forum, a learner with poor self-regulation skills may find less time to participate in and monitor the discussion. As a result, the learner will be less engaged and derive less benefit from this aspect of the program. For similar reasons, learners who are more effective at self-regulation will also be able to make better use of the learner control available in an e-learning program. Finally, with regard to informational value, programs that are low in informational value may be less engaging and require additional effort on the part of the learner to derive meaning from the learning interactions. Learners that are more motivated to persist with learning in the face of such obstacles are more likely to expend the greater effort required to learn in such an environment. In line with these arguments, researchers have pointed to self-regulation during the learning process as a critical success factor for learners in an e-learning environment (for reviews, see Salas et al., 2002; Smith, 2005; Smith et al., 2003). In addition to learner characteristics that are important for all e-learning environments, we also identify learner characteristics that align more specifically with each typology dimension. Learner Characteristics and Degree of Interaction With regard to the degree of interaction dimension, individual differences in desire for interaction with others during the learning process are likely to be important. Differences in desire for interaction might stem from differences in how information is processed during learning or differences in the need for social interaction during the learning process. First, from a learning style perspective, learners differ in the extent to which interaction with others facilitates their learning. For example, Sternberg’s (1997) theory of thinking


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styles proposes that people differ in the cognitive mechanisms they use to organize and govern tasks. Externals prefer to work through problems together with others and prefer approaches, such as group brainstorming, where solutions evolve through collaborative problem solving (cf. Workman, Kahnweiler, & Bommer, 2003). In contrast, Sternberg defined an internal thinking style that describes people who prefer working alone. Internals find it disruptive to their concentration to interact with others while problem solving or analyzing information. Given their preference for joint problem solving and collaborative information processing, externals are likely to learn less effectively in a learning environment with fewer opportunities for interaction with others (i.e., low degree of interaction). Research related to learning styles has also identified cognitive style dimensions that encompass preference for collaborative learning methods (for reviews, see Riding & Cheema, 1991; Smith et al., 2003). For example, the Wholist cognitive style dimension identified by Riding and Cheema (1991) describes learners who prefer to learn in groups and to interact frequently with other learners, as well as the instructor, as opposed to learners who respond better to more independent and more individualized approaches. Underlying this cognitive style is a tendency to be sociable and socially dependent. Related to this, Salas et al. (2002) suggested that researchers should strive to better understand how differences in social needs of learners affect learning effectiveness in a distance learning environment. Personality researchers have identified a number of individual differences related to individuals’ need for social interaction, for example, Cheek and Buss’ (1981) sociability and Hill’s (1987) interpersonal orientation. In summary, we argue that learners who have a greater desire for interaction with others during the learning process will benefit more from programs that have a high degree of learner–instructor and learner–learner interactions. Learner Characteristics and Learner Control of Interactions An important learner characteristic for success in e-learning programs that provide a high degree of learner control is metacognition (for a review, see DeRouin et al., 2005; Salas et al., 2002). Metacognition is concerned with how the learner navigates and guides him- or herself within the training program and is a measure of the degree to which learners reflect on their own learning process (Flavell, 1979). Individuals with greater metacognitive skills are better able to monitor their progress, determine when they are having problems, and adjust their learning accordingly (Ford, Smith, Weissbein, Gully, & Salas, 1998; Schmidt & Ford, 2003). These types of learners make better decisions about learning strategies and where to direct

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their attention (Salas et al., 2002). As a result, metacognition has been identified as an important characteristic for learners in an environment characterized by a high degree of learner control (for reviews, see Lowe & Holton, 2005; Salas et al., 2002; Smith, 2005; Smith et al., 2003). Learner Characteristics and Informational Value of Interactions Learner characteristics that align with the informational value dimension of our typology are those related to learner preferences for how information is presented and for certain modes of communication. These learner characteristics have received little attention in the existing literature compared to learner characteristics that align with the other two dimensions. However, research suggests that these differences do exist, and we believe they are worthy of further research attention. For example, research related to learning preferences and readiness for online learning has identified a learning preference related to comfort with communicating electronically with other learners and an instructor (for a review, see Smith, 2005). Smith (2000) analyzed learning preferences of 1,252 vocational students and identified a difference related to comfort with learning using text or listening as opposed to nontextual interaction, including direct experience, observation, and practice. Learners who have a preference for richer communication modes will require higher levels of informational value to facilitate their learning. In addition, cross-cultural research has identified differences in communication styles that could be relevant to this dimension (Hall, 1976). In lowcontext cultures, individuals communicate predominantly through explicit statements in text and speech, whereas in high-context cultures individuals rely more heavily on contextual and nonverbal cues during communication both communicate and interpret the meaning of messages. Extending this line of research, communication researchers have identified an individual difference that aligns with these differences in communication style and that describes the extent to which individuals are indirect in their communication style, relying less on the content of the spoken or written word and more on the nonverbal aspects (Holtgraves, 1997). Learners who are more indirect in their communication style are likely to find it more difficult to learn in e-learning programs with lower levels of informational value, which will negatively impact learning effectiveness. Proposition 4. Some learner characteristics will moderate the relationship between all learning interaction dimensions and learning effectiveness (e.g., cognitive ability, learning self-efficacy, prior knowledge and experience,

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mastery goal orientation, technical expertise, and self-regulation skills). Other learner characteristics will moderate the relationship between specific learning interaction dimensions and learning effectiveness. For example, desire for interaction with others during the learning process will interact with degree of interaction, metacognition will interact with learner control, and preference for richer communication modes and a more indirect communication style will interact with informational value. Contextual Factors Since most e-learning research has taken place in educational, rather than workplace settings (DeRouin et al., 2005), contextual factors related to e-learning have received relatively little research attention. Yet, the limited research in this area (e.g., Klein et al., 2006), as well as the practitioner literature (Frankola, 2001; Hequet & Johnson, 2003; Moshinskie, 2001; American Society for Training and Development/The Masie Center, 2001), suggests that contextual factors can have a significant impact on whether learners engage in and complete e-learning programs. In Fig. 1, we include contextual factors as important moderators of the relationships between the typology dimensions and learning effectiveness. We argue that learning effectiveness will be enhanced when characteristics of the learning context align with the learning interaction characteristics of the e-learning program. Our argument for this relationship is based on a similar rationale to that presented for the moderating role of learner characteristics. First, contextual factors can act as enablers or present barriers to engage in e-learning (Klein et al., 2006; Martins & Kellermanns, 2004). In addition, the alignment of contextual factors with learning interaction characteristics can influence learning effectiveness through motivation to learn. First, such an alignment can influence learners’ perception of how easy it is to engage in e-learning. As discussed above, this perception has been linked theoretically to motivation to learn (e.g., social cognitive theory, Bandura, 1986; technology acceptance model, Davis, 1989; and expectancy theory, Vroom, 1964). Second, this alignment can also increase motivation to learn by influencing perceptions of the utility of participating in e-learning. For example, expectancy theory (Vroom, 1964) suggests that when employees perceive that there are benefits associated with participating in e-learning, their motivation to engage in e-learning will increase. Traditional training research has focused on contextual factors such as climate for transfer and opportunity to perform (Colquitt et al., 2000;

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Noe, 2005) that influence learning outcomes. These factors will also be important in an e-learning environment; however, as with our discussion of learner characteristics, we focus here on contextual factors that are likely to be particularly germane to e-learning. Two contextual factors that we propose will interact with all the typology dimensions to influence learning effectiveness is the extent to which a learner has easy access to appropriate technology tools and technology support. E-learning programs that offer more interactivity, greater learner control, and increased informational value will typically require the use of technologies with more complex technical requirements. Hence, it is important for learners to have access to appropriate technologies and adequate support in using those technologies. Access to the technology required to effectively use an e-learning program and availability of technical support are related to a learner’s use of the system (Martins & Kellermanns, 2004). In addition, as noted above, the extent to which learners have the opportunity to engage in e-learning without distractions will influence the extent to which a high degree of interaction, high learner control, and high-informational value translates into learning effectiveness. DeRouin, Fritzsche, and Salas (2004) reviewed several contextual factors, based on theories of motivation, which are relevant for increasing a learner’s willingness to engage with an e-learning program. These include supervisors expressing confidence in learners, providing them with encouragement and valued rewards for participation in e-learning, working with learners to set difficult but attainable goals for learner performance as a result of participating in an e-learning program, and monitoring learner progress against those goals. Based on the arguments above, we propose that it is possible to differentiate different work contexts in terms of their climate for e-learning. We define climate for e-learning as learners’ shared perceptions about characteristics of the work environment that facilitate e-learning in general, regardless of the type of e-learning program. Our definition is based on the definition of climate in the organizational literature. Climate has been defined as employees’ shared perception of the events, practices, and procedures as well as the kind of behaviors that get rewarded, supported, and expected in a particular organization (Schneider, 1990). The relationship between climate and behavior has been demonstrated at different levels of analysis for different elements of the work setting (e.g., Colquitt, Noe, & Jackson, 2002; Hofmann, Morgeson, & Gerras, 2003; Simons & Roberson, 2003). It is possible to conceptualize and measure climate for e-learning at multiple levels of analysis. For example, organizations with strong climates


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for e-learning will integrate e-learning into the organization’s performance management system, promote the value of e-learning training, provide technology to support different types of e-learning formats and technological support, provide workspaces free from distraction for completing e-learning, and monitor e-learning participation. In addition, at the workgroup level, the existing literature has shown that managers play an important role in reducing barriers to engage in e-learning and have considerable discretion in how organizational policies related to e-learning participation are implemented at the workgroup level (Hequet & Johnson, 2003; American Society for Training and Development/The Masie Center, 2001). In addition to factors that create a general climate for e-learning, relevant contextual factors that specifically align with each typology dimension are described below. Contextual Factors and Degree of Interaction An important contextual factor that aligns with the degree of interaction dimension is the extent to which the work context provides opportunities for interaction to compensate for the lack of interaction available in an e-learning program. Without a high degree of interaction in an e-learning program, learners lack social support and feedback, which can lead to feelings of isolation, uncertainty, and anxiety. This in turn can negatively impact learning effectiveness (Benbunan-Fich & Hiltz, 2003). Interactions with managers or peers during the learning process can compensate for lack of interactivity in an e-learning program. For example, Moshinskie (2001) suggests that managers provide learners with opportunities to practice and get feedback on what they are learning in the e-learning program. In addition, the organization can facilitate employees forming peer-learning groups with other e-learners in the organization. Contextual Factors and Learner Control DeRouin et al. (2004) suggest that a specific aspect of the organizational climate that will help to facilitate learner control is the extent to which the organization generally promotes employee participation, empowerment, and autonomy. In such a climate, employees will be more prepared and willing to take control of their own learning because they are used to doing so as a natural part of their job. Contextual Factors and Informational Value Although we have already discussed the importance of providing learners access to technology required to complete e-learning, an aspect of this that is

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particularly important for the informational value dimension is the extent to which learners have access to technology that can support highinformational value interactions. For example, although an e-learning program might offer audio and video capability in addition to text, a learner may be limited to text-based content because his/her computer equipment cannot cope with the additional bandwidth required for the other richer media options. Also, since programs with high informational value will typically use more complex technologies (e.g., videoconferencing and audiovisual), the need for more advanced technical support to address any problems encountered in using these technologies is particularly important (Martins & Kellermanns, 2004). Proposition 5. Some contextual factors will moderate the relationship between all learning interaction dimensions and learning effectiveness (i.e., climate for e-learning, including opportunity to engage in e-learning without distraction, access to appropriate technology tools and support, rewards for e-learning participation, setting e-learning performance goals, and supervisor encouragement). Other contextual factors will moderate the relationship between specific learning interaction dimensions and learning effectiveness. For example, supervisor and peer interaction during the learning process will interact with degree of interaction, participative organizational climate will interact with learner control, and availability of richer technology media and associated technical support will interact with informational value.

Learning Goals There is general agreement in the traditional training literature that desired learning outcomes are more likely to be attained by aligning characteristics of an e-learning program with the targeted learning goals (e.g. Gagne´, 1985; Noe, 2005). Similarly, scholars in the distance and technology-based education literature have underscored the importance of such alignment for learning effectiveness (e.g. Kozma, 1991; Moore, 1993). For example, Kozma (1991) stated that whether or not a technology-based program makes a difference in learning depends on how the program corresponds to the particular learning task, because tasks vary in the demands they place on the learner. Consistent with this existing research, we include learning goals as a moderator in the relationship between learning interaction dimensions and learning effectiveness.


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Although several scholars agree on the need to align characteristics of an e-learning program with learning goals, there is less clarity on the exact nature of this match. Recently Bell and Kozlowski (2006) and Lowe and Holton (2005) developed theoretical models that address this question. Both models make a distinction between different types of learning goals, which range from basic level to advanced knowledge and skills. Lowe and Holton (2005) based their model on Bloom, Engelhart, Furst, Hill, and Krathwohl’s (1956) classification consisting of five categories: knowledge, comprehension, application, synthesis, and evaluation. Bell and Kozlowski (2006) distinguished between declarative, procedural, strategic, and adaptive knowledge and skills. Both models show that different learning goals place different demands on the e-learning instruction. Lowe and Holton (2005) discuss the match in terms of the instructional control and support provided in computer-based training. Bell and Kozlowski’s (2006) primary assumption is that as the complexity of the knowledge and skills targeted increases, the richness of the distributed learning experience in terms of content, immersion, interactivity, and communication must also increase. Translated to our model, this suggests that at the most basic level, when the learner is asked to acquire declarative knowledge, a program with little interaction, low level of learner control, and with interactions with lower informational value is sufficient to attain targeted learning goals. First, a program-controlled learning experience will be more efficient to instruct specific well-defined concepts and facts (Lowe & Holton, 2005), as at this level one does not require learners to regulate their own learning process. Second, as stated by Bell and Kozlowski (2006), a text-based experience with a low level of interactivity will be most efficient to learn at the lowest learning goal level. The authors based their statement on empirical evidence showing that richer interactions with more stimuli interfere with, rather than stimulate, memorization. Finally, the authors argue that efforts to increase the interactivity with the instructor and fellow learners, by for instance providing sophisticated ICTs, will not add much value to the learning process compared to the investments made. In contrast, at the most complex level of knowledge and skills, where the learner is asked to acquire more advanced problem-solving skills, there needs to be more interaction, with more learner control and higher informational value. First, an e-learning experience in which the learner has control over the program will be more effective to learn more advanced knowledge and skills. This is because this learning goal level requires that learners play an active role in their own learning process (Bell & Kozlowski, 2006; Kolb, 1984) by adapting existing models, interpreting specific events,

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etc. Second, more complex learning goals may require higher interactivity with the instructor and fellow learners in order to exchange information and ask for feedback (Garrison, 1993; Lowe & Holton, 2005). Finally, the informational value provided in the interactions is important if more advanced knowledge and skills are the target. Learners need to acquire coherent mental models and situational awareness to reach this target, and this can be facilitated by providing multiple data and contextual cues (Bell & Kozlowski, 2006). We believe that our typology is a useful way to integrate existing research related to matching learning goals to the e-learning experience provided to the learner. To extend this research, researchers should examine the relative importance of each learning interaction dimension to different levels of learning goals. This will allow e-learning program designers to make tradeoffs in the design process. For example, given a more complex learning goal, is it best to allocate resources to providing greater levels of learner control or increasing the informational value of the program? We believe that these are important questions to address in future research in this area. Proposition 6. E-learning effectiveness will increase when the characteristics of the learning interactions are matched to the level of learning goals.

CONCLUSION E-learning holds much promise for helping organizations meet their future training demands. However, to realize its full potential, there needs to be a clearer understanding of how different e-learning program configurations influence learning outcomes. To this end, Smith and Dillon (1999) noted the importance of clearly articulating the dimensions along which e-learning programs differ. In response to this, we developed a typology based on general theories of learning that compares e-learning programs according to the nature of the learning interactions they provide for learners. The three dimensions of the typology that describe important characteristics of the learning interaction dimensions are degree of interaction, learner control of interactions, and informational value of interactions. These dimensions apply to interactions that occur between the learner and instructor, other learners, and instructional material. We highlighted several important theoretical contributions of the typology. First, we discussed how the typology can be used in research to compare the effectiveness of different e-learning, blended learning, and classroom

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instruction formats. The typology helps to reconcile existing research findings as well as stimulate new research questions in this area. We believe that our typology is the first to provide a comprehensive foundation for comparing the range of different e-learning programs that are currently available, including a comparison of e-learning with classroom and blended instruction. Unlike existing typologies that are framed in terms of specific technology features of the e-learning program, our typology is flexible enough to apply to e-learning programs at any point along the continuum from pure classroom instruction to instruction that is fully technology-mediated. Second, based on the typology dimensions, we developed a theoretical framework and related propositions that can be used to identify factors that influence success in different types of e-learning environments (i.e., e-learning programs located at different points along each of the typology dimensions). The framework identifies important characteristics of e-learning programs that are antecedents to the typology dimensions, and therefore influence learning effectiveness through these dimensions. It also identifies learner characteristics and contextual factors that moderate the relationships between the typology dimensions and learning effectiveness. Finally, it shows how the typology dimensions can be used to match different levels of learning goals to the appropriate e-learning format. Recent reviews of the traditional training and e-learning literature have commented on the paucity of theory to guide e-learning research (Klein et al., 2006; Welsh et al., 2003). This is in contrast to the progress that has been made in both theoretical and empirical research in the traditional training area (Salas & Cannon-Bowers, 2001). In summary, Salas and Cannon-Bowers (2001, p. 483) noted that ‘‘A few researchers have begun to scratch the surface y of this topic, but a science of distance learning and training needs to evolve.’’ We hope that our typology and theoretical framework will help to advance the science of e-learning and stimulate research that can help organizations realize the full potential of their e-learning implementations.

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ABOUT THE AUTHORS M. Ronald Buckley holds the JC Penney Company Chair of Business Leadership and is a professor of management and a professor of psychology in the Michael F. Price College of Business at the University of Oklahoma. He earned his Ph.D. in industrial/organizational psychology from Auburn University. His research interests include, among others, work motivation, racial and gender issues in performance evaluation, business ethics, interview issues, and organizational socialization. His work has been published in journals such as the Academy of Management Review, Personnel Psychology, Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, and the Journal of Management. Michael J. Burke is the Lawrence Martin Chair in Business at Tulane University, and he holds adjunct appointments in Tulane’s Department of Psychology and School of Public Health and Tropical Medicine. He received his Ph.D. in psychology from Illinois Institute of Technology. He is a past president of the Society for Industrial and Organizational Psychology and he served as editor of Personnel Psychology. In 2006, he was awarded the Decade of Behavior Research Award for his research on workplace safety from a federation of professional scientific associations. In 2009, Professor Burke completed a three-year term on the Safety and Occupational Health Study Section of the National Institute for Occupational Safety and Health. In the domain of workplace safety, his research interests focus on safety climate, safety and health training, and safety performance. Michelle K. Duffy is a professor and the Board of Overseers Professor of HRIR in the Carlson School of Management at the University of Minnesota. She received a Ph.D. from the University of Arkansas. Her research interests include employee well-being, antisocial behaviors, emotions, and mood. Beth Florin is Managing Director of Pearl Meyer and Partners and leads the Survey and Employee Compensation Practice. She has specialized experience in the design, development, and implementation of broad-based compensation programs and total remuneration compensation surveys. She was a 243

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co-founder of Executive Alliance, a technology industry compensation consultancy that was acquired by Clark Consulting in 2001. Prior to that, she was a senior human resource consultant with William M. Mercer, Incorporated’s High Tech Compensation Practice and held human resource positions at Data General Corporation. She is a graduate of the University of Florida and holds and M.S. in human resource management and research methodology from Cornell University. She is a member of the Dean’s Advisory Council at the ILR School at Cornell and is a member of the Board of the Compensation Research Initiative (CRI) at Cornell. Juliya Golubovich is a student in the Graduate Program in Organizational Psychology at Michigan State University. She received her B.B.A. in industrial/organizational psychology from Baruch College, City University of New York. Her primary research interests are in adverse impact against minorities in testing, development of alternate test instruments, and noncognitive predictors of performance. Jonathon R. B. Halbesleben (Ph.D., University of Oklahoma) is the HealthSouth Chair of Health Care Management and associate professor in the Culverhouse College of Commerce and Business Administration at the University of Alabama. His research interests include stress and burnout, work–family issues, and health care management. His work has appeared in such journal outlets as the Journal of Applied Psychology, Journal of Management, Academy of Management Learning and Education, and Research in Personnel and Human Resource Management. Kevin F. Hallock is a professor of labor economics and of human resource studies and director of the Compensation Research Initiative (CRI) at the ILR School at Cornell University. He is also a research associate at the National Bureau of Economic Research (NBER), a senior fellow for executive compensation, board compensation and board practices at The Conference Board and a member of the board of directors at WorldatWork. His current research includes projects on executive and director compensation, the valuation of stock options and the design of compensation systems. He earned a B.A. in economics from the University of Massachusetts at Amherst and a Ph.D. in economics from Princeton University. Jaron Harvey is an assist professor in the Culverhouse College of Commerce and Business Administration at the University of Alabama. He received his Ph.D. from the University of Oklahoma. His research interests are in the exchange relationships that exist between employers and employees. He is specifically interested in what creates these exchange relationships and

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the exchanges that occur when employees go the extra mile for their employers. N. Sharon Hill is an assistant professor at The George Washington University School of Business. She received her Ph.D. in organizational behavior and human resources from University of Maryland, College Park. Dr. Hill’s research interests include technology-mediated work arrangements (e-learning and virtual teams) and organizational change. These interests are motivated by her extensive corporate work experience prior to obtaining her Ph.D. Dr. Hill has presented her research at national and international conferences. Her work has appeared in Organizational Behavior and Human Decision Processes and the Journal of Applied Behavioral Science, and has been recognized as a Best Paper by the Academy of Management Organizational Development and Change Division. Peter W. Hom is a professor of management at Arizona State University (Tempe, AZ). He received his Ph.D. from the University of Illinois (Champaign-Urbana) in industrial/organizational psychology. Dr. Hom has investigated theories of employee turnover in various occupations (Chinese managers, Swiss bankers, industrial salesmen, retail sales personnel, National Guardsmen, Mexican factory workers), designed realistic job previews to reduce reality shock and early quits among new nurses and accountants, and estimated the economic costs of turnover for mental health agencies. He has authored scholarly articles in the Academy of Management Journal, the Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, and Personnel Psychology. He has authored two books entitled Employee Turnover and Retaining Valued Employees with Rodger Griffeth. Dr. Hom serves on the editorial board for the Journal of Applied Psychology. Jenny M. Hoobler is an assistant professor of management in the College of Business Administration at the University of Illinois at Chicago. She received her Ph.D. from the University of Kentucky. She serves on the editorial boards of the Journal of Organizational Behavior and Journal of Management Studies, and on the Executive Committee of the Academy of Management’s Human Resource Management Division. She has published in a variety of journals including the Academy of Management Journal and Journal of Applied Psychology. Her research focuses on supervisor– subordinate relationships, gender and diversity, and intersections between work and non-work domains.

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Frederick T. L. Leong is a professor of psychology at Michigan State University in the Industrial/Organizational and Clinical Psychology programs. He is also the director of the Consortium for Multicultural Psychology Research at MSU. He has authored or co-authored over 130 articles in various psychology journals, 80 book chapters, and also edited or co-edited 10 books. He is an editor-in-chief of the Encyclopedia of Counseling (Sage Publications) and editor of the Division 45 Book Series on Cultural, Racial and Ethnic Psychology. He is the founding editor of the Asian American Journal of Psychology. Dr. Leong is a fellow of the American Psychological Association, Association for Psychological Science, Asian American Psychological Association and the International Academy for Intercultural Research. His major research interests center around culture and mental health, cross-cultural psychotherapy (especially with Asians and Asian Americans), cultural and personality factors related to career choice, work adjustment, and occupational stress. Jason D. Shaw is a professor and the Curtis L. Carlson Professor of Industrial Relations at the University of Minnesota. He received a Ph.D. from the University of Arkansas. His research interests include organizational turnover, team effectiveness, pay systems, and personality. Sloane M. Signal is a doctoral student in the A.B. Freeman School of Business at Tulane University. Before enrolling in the Freeman School, she served as sequence coordinator and faculty for Advertising and Public Relations at the Howard University John H. Johnson School of Communications. From 2001 to 2005, Sloane was a member of the Journalism faculty at the University of Nebraska in Lincoln, where she co-authored The Peer Review of Teaching Portfolio Project as Scholarship Assessment in Higher Education: An Advertising Curriculum Example. Her research interests include communicating across cultures both inside and outside of the United States, diversity and multiculturalism in the workplace and the role of acculturation, and the scholarship of teaching and learning. Bennett J. Tepper is a professor of managerial sciences in the J. Mack Robinson College of Business at Georgia State University. He received his Ph.D. in organizational psychology from the University of Miami. He is a fellow of the American Psychological Association, Society of Industrial and Organizational Psychology and the Southern Management Association. His research interests include leadership, employee well-being, and employee performance contributions. He currently serves on the editorial boards of

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Academy of Management Journal, Journal of Applied Psychology, Journal of Organizational Behavior, and the Journal of Management. Douglas Webber is a Ph.D. student in economics at Cornell University and is being funded on a National Science Foundation Fellowship. He is a member of the Cornell Higher Education Research Institute (CHERI) and the Compensation Research Initiative (CRI) at Cornell. He is a graduate of the University of Florida. His research interests are in compensation design and the economics of education. Anthony Wheeler is an associate professor of human resources management in the Schmidt Labor Research Center and the College of Business Administration at the University of Rhode Island. He completed his undergraduate degree at the University of Maryland, College Park and earned both his masters and doctoral degrees at the University of Oklahoma; moreover, he is a certified senior professional in human resources management (SPHR). His research interests include the influence of HRM practices on person–environment fit and include examining issues related to alternative staffing strategies. This research has lead to the publication of several scholarly articles in outlets such as Journal of Management Education, Work & Stress, Leadership Quarterly, Journal of Occupational and Health Psychology, Issues in Multilevel Research, Research in Personnel and Human Resources Management, International Journal of Selection and Assessment, Journal of Business Ethics, Journal of Managerial Issues, and Journal of Business Logistics Karen Wouters is a lecturer at the University of Maryland’s Robert H. Smith School of Business. Prior to joining the Smith School, she was a research associate at the Vlerick Management School, Belgium. She received her Ph.D. in applied economic sciences from Ghent University, Belgium. Her research interests are primarily in the areas of leadership development, executive coaching, learning from on-the-job experiences and e-learning. Dr. Wouters has written articles in the areas of e-learning, vocational training, and on-the-job learning and has presented her research at national and international conferences. One of her articles was given the 2002 Highly Commended Award by Emerald Literati Club. In 2006 and 2008, she received the ‘‘Global Forum Best Paper’’ and the ‘‘Best Paper in Management Development’’ from the Academy of Management.

Research in Personnel and Human Resources Management, Volume 29

Research in Personnel and Human Resources Management, Volume 25 (Research in Personnel and Human Resources Management)

Research in Personnel and Human Resources Management