Research in Personnel and Human Resources Management, Volume 29

RESEARCH IN PERSONNEL

AND HUMAN RESOURCES

MANAGEMENT

RESEARCH IN PERSONNELAND HUMAN RESOURCESMANAGEMENT

Series Editors: Hui Liao, Joseph J. Martocchio andAparna Joshi

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RESEARCH IN PERSONNEL AND HUMAN RESOURCESMANAGEMENT VOLUME 29

RESEARCH INPERSONNEL AND

HUMAN RESOURCESMANAGEMENT

EDITED BY

HUI LIAOUniversity of Maryland, USA

JOSEPH J. MARTOCCHIOUniversity of Illinois, USA

APARNA JOSHIUniversity of Illinois, USA

United Kingdom – North America – Japan

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CONTENTS

LIST OF CONTRIBUTORS vii

WORKPLACE SAFETY: A MULTILEVEL,INTERDISCIPLINARY PERSPECTIVE

Michael J. Burke and Sloane M. Signal 1

EXECUTIVE PAY AND FIRM PERFORMANCE:METHODOLOGICAL CONSIDERATIONS ANDFUTURE DIRECTIONS

Beth Florin, Kevin F. Hallock and Douglas Webber 49

A TIME-BASED PERSPECTIVE ON EMOTIONREGULATION IN EMOTIONAL-LABORPERFORMANCE

Michelle K. Duffy, Jason D. Shaw, Jenny M. Hooblerand Bennett J. Tepper

87

INSIGHTS FROM VOCATIONAL ANDCAREER DEVELOPMENTAL THEORIES:THEIR POTENTIAL CONTRIBUTIONS FORADVANCING THE UNDERSTANDING OFEMPLOYEE TURNOVER

Peter W. Hom, Frederick T. L. Leong andJuliya Golubovich

115

HOW DID YOU FIGURE THAT OUT? EMPLOYEELEARNING DURING SOCIALIZATION

Jaron Harvey, Anthony Wheeler,Jonathon R. B. Halbesleben and M. Ronald Buckley

167

v

COMPARING APPLES AND ORANGES:TOWARD A TYPOLOGY FOR ASSESSINGE-LEARNING EFFECTIVENESS

N. Sharon Hill and Karen Wouters 201

ABOUT THE AUTHORS 243

CONTENTSvi

LIST OF CONTRIBUTORS

M. Ronald Buckley Price College of Business, Division ofManagement, University of Oklahoma,Norman, OK, USA

Michael J. Burke Freeman School of Business, TulaneUniversity, New Orleans, LA, USA

Michelle K. Duffy Carlson School of Management, Universityof Minnesota, Minneapolis, MN, USA

Beth Florin Pearl Meyer & Partners, Southborough,MA, USA

Juliya Golubovich Department of Psychology, Michigan StateUniversity, East Lansing, MI, USA

Jonathon R. B.Halbesleben

Department of Management and Marketing,Culverhouse College of Commerce andBusiness Administration, University ofAlabama, 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 andBusiness Administration, University ofAlabama, Tuscaloosa, AL, USA

N. Sharon Hill School of Business, The George WashingtonUniversity, Washington, DC, USA

Peter W. Hom Department of Management, W.P. CareySchool of Business, Arizona State University,Tempe, AZ, USA

vii

Jenny M. Hoobler College of Business Administration,University of Illinois at Chicago, Chicago,IL, USA

Frederick T. L.Leong

Department of Psychology, Michigan StateUniversity, East Lansing, MI, USA

Jason D. Shaw Carlson School of Management, Universityof Minnesota, Minneapolis, MN, USA

Sloane M. Signal Freeman School of Business, TulaneUniversity, 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 ofBusiness Administration, University of RhodeIsland, Kingston, RI, USA

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

viii LIST OF CONTRIBUTORS

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 hasnot adopted a multilevel theoretical perspective that integrates theoreticalissues and findings from various disciplines. In this chapter, we integrateresearch on workplace safety from a variety of disciplines and fields todevelop a multilevel model of the processes that affect individual safetyperformance and safety and health outcomes. In doing so, we focus oncross-level linkages among national, organizational, and individual-levelvariables in relation to the exhibition of safe work behavior andoccurrence of individual-level accidents, injuries, illnesses, and diseases.Our modeling of workplace safety is intended to fill a theoretical gap inour understanding of how the multitude of individual differences andsituational factors interrelate across time to influence individual levelsafety behaviors and the consequences of these actions, and to encourageresearch to expand the limits of our knowledge.

Research in Personnel and Human Resources Management, Volume 29, 1–47

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ISSN: 0742-7301/doi:10.1108/S0742-7301(2010)0000029003

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dx.doi.org/10.1108/S0742-7301(2010)0000029003

An impressive body of literature on workplace safety that spans acrossdisciplines in medicine, public health, engineering, psychology, and businessis developing. Within these disciplines, researchers are often attending to thestudy of relations between variables at either the individual or situationallevel of analysis. With few exceptions (e.g., Wallace & Chen, 2006; Zohar &Luria, 2005), research to date has not adopted a multilevel theoreticalperspective to the study of workplace safety nor has it attempted to integratefindings from various disciplines. Recognizing that individuals and situationsare interdependent, we argue that adopting a multilevel perspective thatincorporates aspects of workplace safety from multiple disciplines is neededto fully understand the processes that lead to safety-related work behaviorand its consequences. In doing so, this chapter integrates research advanceswithin human resource management, organizational behavior, safetyengineering, and various fields of medicine and public health to focus oncross-level linkages among national, organizational, and individual-levelvariables in relation to the exhibition of safe work behavior and occurrenceof individual-level accidents, injuries, and illnesses. Our focus is not intendedto diminish the importance of emergent phenomena, processes, andoutcomes at higher levels of analysis such as at the work group andorganization levels, but rather our intent is to fill a theoretical gap in ourunderstanding of how the multitude of individual differences and situationalfactors interrelate across time to influence individual-level safety behaviorsand the consequences of these actions.

Hence, we have three goals for this chapter. First, we present a multilevelmodel of workplace safety in relation to individual-level outcomes. Here, weemphasize the study of construct domains where advances have been made inidentifying the appropriate taxonomic components or constructs relevant tothe study of individual-level outcomes. As such, our discussion is notexhaustive of potentially relevant constructs at each level of analysis. Second,we discuss how relationships between construct domains within the modelhave been studied and summarize what is known about these linkages.Throughout the discussion, our third goal is to discuss the limits of ourcurrent state of knowledge and practice and the types of research needed toexpand these limits.

A MULTILEVEL MODEL OF WORKPLACE SAFETY

A problem faced in the rapprochement of different disciplines and bodies ofliterature as they relate to workplace safety is where to begin in terms of a

MICHAEL J. BURKE AND SLOANE M. SIGNAL2

theoretical foundation. For our purposes, we begin with a discussion of safetyperformance, the safety-related actions or behaviors that workers exhibit inalmost all types of work to promote their safety and the safety and health ofothers. While disciplines vary considerably in terms of the relative emphasisplaced on the study of safe work behavior, all disciplines recognize the needfor safe work behavior in preventing or reducing negative outcomes such asinjuries and illnesses. For instance, researchers in industrial ergonomics mayfocus on the pacing of actions to avoid visual and muscular disturbances (e.g.,Salvendy, 1998; M. J. Smith, 1998), kinesthesiologists and occupationaltherapists study the biomechanics of postures and movements in efforts toreduce musculoskeletal disorders (e.g., Gagnon, 2003), applied psychologistsand human resource management researchers focus on the consistency orfrequency of actions taken to avoid accidents (e.g., Burke, Bradley, & Bowers,2003), community health researchers are often concerned with the percent oftime actions are engaged in so as to preclude the onset of disease (e.g., Forstet al., 2004; Mayer et al., 2007), and industrial hygienists frequently stress thecorrect 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 actionor behavior, the safety-related actions that workers engage in can beconceptualized relative to a core set of dimensions that apply across jobs andindustries, regardless of safety performance measurement considerationswithin any discipline.

At the broadest level, the safety-related actions that workers engage incan be placed into two broad content categories akin to notions of taskand contextual performance in the job performance literature (Motowidlo,Borman, & Schmit, 1997): safety compliance and safety participation,respectively. Safety compliance refers to generally mandated safetybehaviors, whereas safety participation refers to safety actions that aremore discretionary in nature (see Neal, Griffin, & Hart, 2000). In largepart, research on behavioral aspects of safety across disciplines has relied onthe notion of safety compliance and either explicitly or implicitly (via howsafety performance was measured) treated safety compliance as a unitaryfactor. Although research on safety participation is less frequent, thisliterature also explicitly or implicitly (e.g., Hofmann, Morgeson, & Gerras,2003) views safety participation as a unitary factor. These pointsare noteworthy, as Marchand, Simard, Carpentier-Roy, and Ouellet(1998) found that a two-factor model of safety performance (with factorsrelating to safety compliance and safety initiative) did not provide a goodfit to the data.

Workplace Safety: A Multilevel, Interdisciplinary Perspective 3

Given the literature on job performance, Marchand et al.’s (1998) factoranalytic findings are not surprising. The job performance literature hasproduced ample evidence that multiple dimensions underlie the analogousdomains 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 tonotions of safety compliance and safety participation, Burke, Sarpy, Tesluk,and Smith-Crowe (2002) confirmed, across 23 jobs, a grounded theoreticalmodel of general safety performance. Two of their confirmed factors, labeledusing personal protective equipment and engaging in work practices toreduce risk, would clearly fall within the domain of safety compliance;whereas the other two confirmed factors, communicating health and safetyinformation and exercising employee rights and responsibilities, are closer tothe notion of safety participation. Notably, these factors were confirmed forindividuals working in dyads, work groups, and teams. Together, conceptualand empirical research on the factor structure of behavioral safetyperformance would suggest that researchers across disciplinary boundariesmay benefit from attending to potentially useful construct distinctions in themeasurement and study of workers’ safety-related actions.

Another distinction in relation to safety performance, which is recognizedin the medical literature, is that of a work-around (Ash, Berg, & Coiera, 2004;Kobayashi, Fussell, Xiao, & Seagull, 2005). Work-arounds are actions toaddress a block or disruption in a work system that involve bypassing safetyprocedures or protocols and can be conceptually distinguished fromdeviance, errors, and mistakes in regard to motive (see Halbesleben,Wakefield, & Wakefield, 2008). That is, the motive is to complete the taskby getting around the block. Although the term shortcut has been usedinterchangeably with work-around, a shortcut is a specific case of a work-around intended to deal with a perceived time block. To date, themeasurement of work-arounds has been qualitatively approached (viaworkers’ responses to open-ended questions) within human factors research(e.g., Charlton, 2002). Additional research is needed to capture the behavioraldimensionality of work-arounds as an area of safety performance and toproduce measures that can assist in studying work-arounds in relation totheir 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 safetyperformance and its consequences as depicted in Fig. 1, we rely broadly onthe disciplinary literatures that consider behavioral aspects of workplacesafety. More specifically, at the individual level, we ground our modeling in


Fig.1.

AMultilevel

Model

ofWorkplace

Safety.Notes:

Solidlines

witharrowsrepresentexpecteddirecteffects.

Dashed

lines

witharrowsdesignate

expectedmoderation.Arced

lines

witharrowsrepresentfeedback

loops.


the applied psychology literature that identifies knowledge and motivationas proximal antecedents to performance and more stable individualdifferences as distal antecedents to performance. In the applied psychologyfield, the influence of distal antecedents on performance is often posited tobe mediated by either knowledge or motivation. At the organizational/group level of analysis, our theoretical modeling is, in large part, based ontheoretical advances within the domain of work climate. Further, at thislevel of analysis, we argue for an occupational safety conceptualization ofworkplace hazards that we believe offers considerable promise forreintroducing the study of objective workplace hazards into the humanresource management and applied psychology literatures. At the national/group level of analysis, we ground our work in research advances within thedomain of public health as well as theoretical notions of culture and valueswithin the management and organizational behavior literature.

Finally, in regard to the consequences of safety performance, referred to assafety outcomes, we focus on tangible events and results that are consideredwithin a variety of disciplines including illness and diseases discussed withinthe literature on occupational medicine. Furthermore, we incorporatenotions from the literature on education and learning to suggest howworkers reflect on and learn from perceived safety performance–outcomeassociations. In this sense, our model conceptualizes workplace safety as anongoing 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 complianceand safety participation at the national/regional, organizational/group, andindividual levels of analysis. While social, institutional, and legal considera-tions exist at a global level in relation to workplace safety (see Nuwayhid,2004), we begin our modeling of safety performance and its consequences atthe national/regional level of analysis. Our rationale is that at this level ofanalysis, one can more readily view and study the role of cultural values anddifferences between nations in regard to occupational safety and healthpolitical and research agendas. In adopting this approach, we recognize thatan understanding of safe work behavior and its consequences cannot bedivorced from cultural factors and the broader challenges of occupationalhealth and safety in both developing locations such as Bangladesh, Central


America, and South Africa (Ahasan, Mohiuddin, Vayrynen, Ironkannas, &Quddus, 1999; Joubert, 2002; Wesseling et al., 2002) and within regions ofdeveloped countries such as in the northern and southern states of the UnitedStates. (Richardson, Loomis, Bena, & Bailer, 2004). In addition, althoughone could develop arguments for other possible levels of analysis (i.e.,occupational, union, or industry levels), the three situational levels that weconsider comprise a more parsimonious and inclusive model than otherpossible situational breakdowns. That is, our theoretical arguments at eachsituational level apply across occupational, industrial, and union boundaries.

To unfold the discussion of our model in Fig. 1, we begin with apresentation of conceptual arguments for why cultural values influence thepolitical economy of nations and states and follow with a discussion ofseveral cross-level linkages between national/regional factors and organiza-tional/group-level factors. Subsequently, we discuss how key factorsinterrelate at the organization/group level, and how and why these factorswould be expected to affect processes at the individual level of analysis.Given the multitude of construct domains that we consider as antecedents ofsafety compliance and safety participation, we will refer to factors not onlyin terms of level of analysis but also with respect to their distance orexpected causal ordering in relation to these safety performance domains.In doing so, we discuss antecedents as distal and proximal to safetyperformance to assist in organizing the discussion of these factors.

Proximal antecedents will include safety motivation and safety knowl-edge. Safety motivation refers to one’s regulatory behavior primarily inrelation to exerting effort when on the job to enact safety behaviors;whereas, safety knowledge refers to an understanding of both safety-relatedfacts and procedures, and can be of an implicit or anticipatory nature (seeBroadbent, Fitzgerald, & Broadbent, 1986; Burke, Scheuer, & Meredith,2007; Gardner, Chmiel, & Wall, 1996). Other individual differences, whichare expected to affect safety performance (through safety motivation orsafety knowledge) as well as situational variables will be referred to as distalantecedents and will be defined below.

National/Regional Level Antecedents

Recently, several studies have examined how nations differ in regard toworkplace safety (e.g., Burke, Chan-Serafin, Salvador, Smith, & Sarpy,2008; Gyekye & Salminen, 2005; Havold, 2007; Infortunio, 2006). In largepart, these investigations have examined how cultural differences relate to


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 correlatedwith Hofstede’s (2001) cultural dimension of individualism/collectivism.As another example, Havold (2007) found that for workers from 10countries, 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 bivariateassociations between cultural dimensions (that reflect cultural values) andcriteria such as accident rates are notable, these types of studies do notnecessarily provide insight into the processes by which national cultureaffects aspects of workplace safety. Nevertheless, these studies have beenhelpful in pointing to the value of Hofstede’s cultural dimensions forconceptualizing the role of culture in workplace safety.

Although other researchers have developed taxonomies and frameworksfor describing cultural differences (e.g., Schwartz, 1999), considerableconceptual and empirical support has been generated for Hofstede’s culturaldimensions 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 fivedimensions to explain national cultural differences: power distance, whichdescribes the degree to which power is distributed equally; individualism/collectivism, which relates to how much an individual values his/her ownneeds (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 describesthe level of tolerance a society has for ambiguity; and long-term/short-termorientation, which refers to how much a society values thrift andperseverance (long-term) vs. fulfillment of social obligations (short-term).Several of these dimensions have important implications for understandinghow and why leaders and organizations within nations differ in theirorientation 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 workingconditions within organizations. Here, we refer to the political economy asthe political, economic, and legal systems of a country. More specifically,cultural values would be expected to drive the social justice orientation, thefiscal capacity of nations and states, and the capacity for labor to organize:


characteristics of the political economy that may significantly impact thesafety of working conditions within organizations. Our focus on culturalvalues as antecedents of aspects of the political economy is not intended toimply that the political economy of nations is a sole function of culturalvalues. The political, economic, and legal systems of nations have roots inhistorical/colonial ties, available natural resources, and international agree-ments to name just a few other antecedents (e.g., Golub, 1991; Jammeh &Delgado, 1991; Schad, 2001). Our focus on cultural values is meant tohighlight these ‘‘basic assumptions’’ of national culture as the primary driversof 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) shouldenjoy equal rights, responsibilities, and benefits (Turner, Pope, Ellis, &Carlson, 2009). In effect, the social justice orientation of a nation reflects theextent to which the equal rights of individuals are valued and individuals areresponsible for their actions. As such, the cultural dimensions of powerdistance, individualism/collectivism, and uncertainty avoidance would beexpected to directly affect the social justice orientation of nations.

In terms of a social justice orientation, differences can be observed acrossnations in institutions and legal considerations that relate to humanresources in general and workplace safety in particular (LaDou, 2002). At amore general level, many democratically oriented nations have laws thatrelate to equal employment opportunities. Moreover, in regard to safety,institutions within these types of nations often put into play researchagendas such as the National Occupational Research Agenda in the UnitedStates (National Institute for Occupational Safety and Health (NIOSH),2006) and engage in assessment practices that lead, through legislative andenforcement processes, to a focus on the rights of workers and safe workconditions. For instance, in a number of developed nations that emphasizeindividual rights, such as Canada, Germany, Sweden, and France,regulations have been formulated that guide not only human resourcepractices, but also workplace health and safety considerations (see Burke &Sarpy, 2003). A case in point is the United States where the OccupationalSafety and Health Administration (OSHA) has requirements and guidelinesthat 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 individualrights, but they also emphasize uniformity in adherence to these require-ments across organizations. The importance placed on standardization anduniformity in workplace safety practices and work conditions is undergirded


by the cultural value of uncertainty avoidance. These arguments form thebasis for the expected indirect effects, as specified in Fig. 1, of cultural valueson organizational policies and practices through an aspect of the politicaleconomy (i.e., social justice orientation) of nations.

Cultural values such as power distance and masculinity–femininity arealso expected to have meaningful influences on national wealth and,particularly, the distribution of wealth within nations (see Husted, 1999). Incomparison to cultures with low-power-distance orientation and lowermasculinity, cultures having higher power distance and masculinityorientations would be expected to have greater inequities in the distributionof wealth, where the majority of the wealth in the country would be underthe control of a select few. The role and size of government would also beexpected to be restricted in these cultures (Husted, 1999). As such, nationscharacterized by high vs. low power distance and masculinity would likelyhave less fiscal capacity and fewer regulatory bodies to focus on workplacesafety issues. To the extent that nations and states have the fiscal capacity toeffectively monitor and regulate occupational health and safety, organiza-tional policies and practices and the consequent safety climates oforganizations are likely to improve. Here, organizational safety climaterefers to work environment characteristics in relation to safety matters thataffect 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 thatlead to sanctions. On the other hand, high national or state debt mayimpede an entity’s ability to effectively monitor and regulate occupationalhealth and safety.

We caution that lower fiscal capacity alone may not explain declines inefforts to enforce safety-related laws and regulations. This point is apparentin gaps in organizational compliance with child labor laws and theconcurrent striking decline in child labor law enforcement activities, whichmay 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 caseof Nevada’s OSHA, which has allowed unsafe conditions to persist(Associated Press, 2009). Despite such cases, enforcement activities areexpected to be positively associated with improvements in organizationalsafety policies and practices and organizational safety climate (e.g., lowerhazardous orders violations defined as using a piece of equipmentprohibited by law), as organizations attempt to conform to governmentstandards and at the same time avoid sanctions.


Furthermore, cultural values would be expected to influence theorganizing capacity of labor. Traditionally, labor unions were founded onthe premise of protecting the rights of individual workers (Rosenberg, 2009),where valuing the equal rights of individuals would be considered animportant driver of unionization. However, in his analysis of hybridregimes, Robertson (2007) demonstrates that the premise of protecting therights of workers as a basis for unionization is specific to liberaldemocracies. In authoritarian regimes, unions are known to be used as ameans to suppress or control workers or where the ability to unionize at allhas been taken away from workers (Robertson, 2007). These considerationslead us to posit that nations higher in power-distance orientation would bemore likely to suppress the unionization of their labor forces or use laborunions as a means of control.

To the extent that labor can organize within a nation or state, we wouldexpect organizations to maintain sound organizational policies and practicesin relation to safety and more positive safety climates and safe workingconditions (e.g., reduce hazardous equipment violation). In effect, organizedlabor may force the maintenance of higher safety standards in efforts tosafeguard the health and well-being of its members. In the United States,this general expectation is consistent with research that shows thatunionized workplaces tend to be more compliant with Occupational Safetyand Health Act regulations (Weil, 2001). However, to the extent thatsocietal factors including prejudice impede workers’ efforts to organize forimprovements in work conditions (e.g., in the case of immigrant workers;Griffith, 1988), the work environment is likely to be less safe and associatedwith higher occupational injury rates (Dong & Platner, 2004; Richardsonet al., 2004).

In nations whose political economic climate favors industry over labor,we would also expect organizations to have potentially more hazardouswork environments (Loomis et al., 2009; Richardson et al., 2004). That is,when economic development is coupled with less attention to workplacesafety, workers are likely to face greater potential exposures to generalworkplace hazards (e.g., biological, chemical, radiological, and noise). Tiedto this point, in nations that have less developed economies, jobs will beconcentrated more in industries with higher levels of potentially severehazardous events and exposures (Koh & Chia, 1998). As a result, we wouldexpect the political economy of nations and states to directly affect thenature of workplace hazards and, indirectly, the occurrence of accidents,injuries, and illnesses/diseases. This expectation implies that, depending onthe political-economic climate of nations or regions within nations, negative


individual level safety outcomes and rates would differ across workers in thesame jobs and same industries.

Related to the above discussion, Holzberg (1981) has discussed howcultural values influence social stratification through the political economiesof nations. This point is relevant to our discussion, as such processes canoperate in a manner where different racial/ethnic or socioeconomic groupsare at times disproportionately exposed to hazardous work environments.The reader is also referred to Baum, Begin, Houweling, and Taylor (2009)and the Commission on the Social Determinants of Health (CSDH, 2008) formore general discussions of the social determinants of health inequitieswithin and across nations. For instance, within the United States, African-American men have higher cancer rates than White men and thesedifferences are, in part, attributed to differential exposures to hazardousoccupational conditions (see Briggs et al., 2003). As another example, in bothdeclining types of work (e.g., hand harvesting of row crops) and expandinglabor-intensive types of work (e.g., construction and landscaping) in thesouthern United States, a pattern has emerged where these types of work aredone by Hispanic workers who are experiencing some of the nation’s highestfatal occupational injury rates (Richardson et al., 2004). Also, in comparisonto non-Hispanic White men, African-American and Hispanic men miss morework days due to injury (Strong & Zimmerman, 2005). These developmentssignal a strong need, as discussed in detail below, to reconsider howeducational/development activities can take into account backgrounds ofworkers along with information on the severity of workplace hazards toenhance worker motivation to learn about and avoid such hazards (some ofwhich may seem quite benign, such as wood dust).

Together, we expect political/economic factors to have meaningful directeffects on organizational policies and practices in relation to safety andworkplace 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 andsafety outcomes such as fatal occupational injuries to be indirect throughthe environment (workplace hazards and safety climate) that workersexperience. To date, research has not been directed at addressing expectedcross-level mediated relationships involving factors at the national andorganizational levels of analysis as specified in Fig. 1.

Finally, as indicated in Fig. 1, we expect cultural values to directly affectleadership in organizations. As noted above, evidence indicates that culturalvalues, such as those focused on equality and respect for individual rights,


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

Organizational/Group-Level Antecedents

The above discussion recognized that elements of the national/regionalenvironment can have meaningful direct and indirect impacts on organiza-tional functioning insofar as the personal values of leaders and safetyclimate of the organization are concerned. As indicated in Fig. 1, at theorganization/group level of analysis, we expect societal values (e.g., justice)that become personalized over time as well as organizationally espousedvalues to underlie leadership, where leadership refers to the actions thatmanagers take to achieve organizational and group objectives. Organiza-tionally espoused values such as ‘‘Safety is Job One’’ or ‘‘Create and Sustaina Safe Work Environment’’ signal what is strategically important to leadersof organizations. To the extent that these values are enacted by leaders, theyguide the various organizational policies and practices (i.e., safety-relatedpolicies and production technology in the organization as well as humanresource management policies and practices in relation to workplace safety).Thus, we expect leader behavior to mediate the influence of cultural valuesand organizationally espoused values toward safety on the human resourceand safety-related policies of the organization.

Furthermore, the linkage between leadership and organizational or group-level climate has long been recognized in the literature (Lewin, Lippitt, &White, 1939). That is, leaders are directly or indirectly (as noted abovethrough the establishment of policies and procedures) instrumental inshaping and reinforcing an organizational climate for safety. However, if theleadership of an organization or group is deficient in terms of establishing apositive safety climate (e.g., in terms of supervisory support, work pressure,and reward practices), then a weak safety climate will result irrespective offormal policies. Further, if one were to view leadership behaviors and leader–member interactions along a continuum of support for the welfare or


concern of employees, then different forms of leadership (e.g., transforma-tional) would be expected to have stronger effects on safety climate incomparison to other forms of leader behavior (e.g., corrective leadership)(Zohar, 2003).

Noting that production technology and potential exposures to workplacehazards differ across departments of an organization, the possibility existsthat safety climate may also differ across these organizational units. Thispossibility is likely realized when top management beliefs about safety arenot uniformly conveyed to lower levels of management or when supervisorsof organizational subunits hold different beliefs about the importance ofsafety (Cox & Cox, 1991; Williamson, Feyer, Cairns, & Biancotti, 1997). Inparticular, when supervisors of work groups hold different beliefs about whois responsible for safety or attribute accidents to either internal or externalfactors, then they are likely to execute organization-level policies andprocedures differently, regardless of risks involved (Zohar, 2000; Zohar &Luria, 2004). Related to this point, we note that the safety climate dimensionof managerial support, which is included in almost all models of safetyclimate, refers to the support that employees’ immediate supervisor providesin carrying out their work. In this sense, our model in Fig. 1 leaves open thepossibility that organizational safety subclimates may be operating. Never-theless, the expected causal paths in the model would be expected to hold forpossible differences in organizational climates as well as for differences inorganizational safety subclimates.

Safety climates are also likely to vary across subunits of an organizationdue to the fact that members of different organizational subunits often facedifferent hazards or risks. For instance, nurses within a hospital emergencyroom may face exposure to harmful substances such as bloodbornepathogens; whereas nurses in a rehabilitation unit may confront situationsthat lead to overexertion. Notably, different social groups or units within anorganization can have different interpretations of risk, even when thepotential 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) havediscussed notions of ‘‘safety subcultures’’ and these groupings influence boththe perception of risk and safety-related behavior.

However, noting that perceptions of risk are socially constructed, webelieve that arguments in the literature to essentially abandon the study ofobjective risks in favor of the study of perceived risk (e.g., Morrow & Crum,1998; Pidgeon, 1991) unnecessarily promote a strict person-based approachto the study of risk. The weakness of this approach is apparent from the


considerable evidence showing that differences between individuals oftenlack 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 wouldnecessitate a cross-level or interactionist approach that explicitly measures ortakes into account the properties of potential hazardous events andexposures. We will return to this point in our discussion of educational/development activities.

Returning to our discussion of organizational safety climate, a number ofauthors have discussed how organizational climate might moderate relation-ships between individual difference variables (Brown, 1981; Tracey, Tannen-baum, & Kavanagh, 1995). Notably, a body of literature on climates-for-something suggests that organizational climate moderates relationshipsbetween individual difference variables to the extent that the organizationpromotes a strategically focused climate (i.e., a climate that is aligned withorganizational goals). This research is premised on the assumption thatindividuals strive to achieve an adaptive fit with their work environments. Asnoted by Smith-Crowe, Burke, and Landis (2003), one would expect that if anorganization has a climate supportive of safety, then workers will exert effortto act safely and transfer the knowledge that they have acquired througheducational/developmental experiences. On the other hand, in a comparableorganization without a climate focused on safety, one would expect a lowerrelationship between safety knowledge and safety performance as well asbetween safety motivation and safety performance because workers would notnecessarily be willing and able to exhibit acquired knowledge. This argumentapplies to both safety compliance and safety participation. The result beingthat we would expect safety climate to moderate relationships between safetyknowledge and safety performance and safety motivation and safetyperformance, with respect to both safety compliance and safety participationcriteria. Surprisingly, beyond Smith-Crowe et al. (2003), little research hasbeen directed at examining safety climate as a moderator of such relationshipswith no research to our knowledge on the degree to which safety climatemoderates safety motivation–safety performance relationships.

Although we did not emphasize a discussion of the expected direct effectsof human resource policies and safety-related policies on an organization’ssafety climate, these policies and associated practices (e.g., selectionpractices, training procedures, and safety meetings) are well recognized asimportant contributors to organizational safety climates (Neal & Griffin,


2004). Notably, within supply chains, Cantor (2008) discusses how humanresource practices as well as physical resources and production technologiescan enable organizations to better track and monitor activities and promotea strong safety climate. In an age of global sourcing practices, we note thatthe issue of workplace safety in organizational supply chains is anunderstudied area, where unsafe practices in one leg of the chain ororganization can have catastrophic adverse consequences in another part ofthe supply chain (see Cantor, 2008).

Distal Individual Level Antecedents

Psychological (Safety) ClimatePrior to discussing the role of employees’ perceptions of work environmentscharacteristics (i.e., psychological climate), we note key distinctions betweenorganizational climate and psychological climate. Organizational climatewas defined above as work environments characteristics in relation tomatters (here, safety) that affect members of the group or organization.Shared or aggregated perceptions of work environment characteristics mayserve as indicators of group or organizational climate (see James et al.,2008). While this point is widely recognized in the literature, what is notrecognized is that organizational climate need not be strictly operationally(and conceptually) defined in terms of shared perceptions of workenvironment characteristics. For instance, Burke et al. (2008) and Smith-Crowe et al. (2003) have discussed how subject matter expert judgments andcontent analyses of archival data may also serve as useful indicators orprovide meaningful descriptions of an organization’s climate. Furthermore,as discussed above within the domain of workplace safety, an organization’ssafety climate is primarily determined by leadership practices, humanresource policies and practices, safety-related policies and practices, andworkplace hazards. On the other hand, as discussed in more detail below,employees’ perceptions of work environment characteristics (psychologicalclimate) are conceptualized as personal value-based appraisals of workenvironment characteristics. This distinction between organizational andpsychological climates is nontrivial as meaningful and differential variationin climate scores (for similar dimensions) may exist at both levels of analysisand would be expected to have different implications for understanding therole of climate in individual safety performance and its consequences.Finally, we note that our reference to psychological safety climate, whichrefers to individual perceptions of characteristics of the work environment


in relation to safety, is distinct from Edmondson’s (1999) notion ofpsychological safety that relates to a team’s shared belief that members arefree to engage in risk taking.

In recent years, a number of studies have examined relationships at theindividual level of analysis between dimensions of climate, safety compliancefactors, safety participation factors, and accident involvement across a widerange of occupations and industries (see Clarke, 2006a). A general finding isthat the magnitude of safety climate–safety participation relationships isgreater than the magnitudes of the associations between safety climatedimensions 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 empiricallyaddressed the underlying mechanisms through which psychological climateperceptions affect safety performance and safety outcomes.

In an effort to advance our understanding of the linkages betweenpsychological climate and safety performance, we note that researchers haveoffered varied definitions and measures of safety climate at the individuallevel of analysis (see Flin, Mearns, O’Connor, & Bryden, 2000) with somedefinitions and measures being more narrowly focused on supervisorypractices (e.g., Zohar & Luria, 2004). In our view, conceptualizing safetyclimate (at either the individual or organizational level) with respect toclimate factors that have been confirmed across a wide range of occupationsand industries (i.e., means emphasis, goal emphasis, management support,etc.; see Burke, Borucki, & Kaufman, 2002; James et al., 2008) as well aswith respect to several more safety-specific issues that apply acrossorganizations (e.g., work pressure; see Neal & Griffin, 2004) provides acomprehensive, yet parsimonious definition of the factor space of workenvironment characteristics. Furthermore, we view psychological (safety)climate as an employee’s perception of these work environment character-istics that affect not only the employee’s personal well-being, but also thewell-being of relevant stakeholders (e.g., customers, suppliers, and thepublic). For instance, if we were studying safety climate in the nuclearhazardous waste industry, employees can provide useful information onaspects of the work environment (e.g., safe handling, storage, and disposalof nuclear waste) that not only affect them personally, but also theirperceptions of factors that might adversely affect the public (such as thefailure to maintain protective barriers in underground storage tanks, thetraining provided to materials handlers, and so on).

In adopting a multiple stakeholder perspective to the study of safetyclimate, we also view safety climate perceptions as being hierarchically


arranged. However, unlike other researchers who have posited a single,higher-order factor tied to employees perceptions of how the workenvironment affects their personal well-being (see Christian et al., 2009;Griffin & Neal, 2000; Neal & Griffin, 2004), we conceptualize the higher-order factors as employees’ assessments of well-being with respect torelevant stakeholder groups that they interact with in the organization’s taskenvironment. That is, to the extent that organizationally espoused valuesreflect concern for the safety and well-being of multiple stakeholders andorganizational practices reinforce these values, then we would argue thatemployees cognitively appraise their work environment with respect to theimpact of work environment characteristics on personal well-being as wellas with respect to the well-being of the other relevant stakeholder groups.Empirical support for a multiple stakeholder conceptualization to psycho-logical climate has been found in both business (see Burke, Borucki, &Hurley, 1992) and educational settings (see Vaslow, 1999), and discussed indetail relative to workplace safety (Burke et al., 2002).

A multiple stakeholder conceptualization of safety climate holds promisefor broadening the domain and measurement of safety climate and may leadto an improved understanding of the effects of safety-related work contextson individuals and groups other than employees. This point is important assafety contexts, perhaps more so than any other type of work environment,have the potential to affect the well-being (i.e., in the broadest psychologicaland physical sense) of employees, their families, customers/clients, and thepublic. One need not look far for striking cases of how work environmentcharacteristics that were initially studied in relation to occupational illnessesand disease quickly became major public health concerns (e.g., in the casesof silicosis, asbestosis, lead toxicity, and pesticide poisoning; Corn, 1992;Kipen, 1994; Nuwayhid, 2004; Rosner & Markowitz, 1991). We know verylittle about how workers perceive characteristics of work environments inrelation to safety and health of other stakeholders.

Studying safety climate from a multiple stakeholder perspective will alsobe important as the nature of work changes. While applied psychology andmanagement scholars have studied many aspects of the changing nature ofwork in regard to individual and group outcomes (see Ilgen & Pulakos,1999), virtually no research has been directed at examining how thechanging nature of work affects workers’ climate perceptions and, inparticular, their safety climate perceptions. Yet, changes in the nature ofsafety-related work often bring with them not only new productiontechnologies, which have new associated injury risks, but also potentialfor new exposures that could result in illnesses and diseases. These changes


may also have important implications for how workers perceive the safety oftheir work environments with respect to their well-being and the well-beingof others.

As an example of how the changing nature of work can potentially affectsafety climate, we will briefly discuss a few implications of the massive publicworks effort to repair the national highway system in the United States. Thischange from a focus on building highways to an emphasis on the repair ofhighways has brought with it a new method of repair, which uses large crewsto cut, break-up, and remove large blocks of concrete. This process results inthe generation of large amounts of dust and increased risk of silicosis forworkers (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 processessuch as epidemiological exposure assessments can provide valuableinformation related to potential work exposures of this nature (Ott, 1998),broader safety climate assessments (at either the individual or organiza-tional level of analysis) have the potential for identifying where moreimmediate actions and interventions could be initiated to safeguard variousconstituents. This is just one example where improved conceptualizationsand measures of safety climate from a multiple stakeholder perspective mayprovide valuable data for understanding safety-related outcomes.

PersonalityA substantial amount of empirical evidence exists concerning relationshipsamong personality characteristics, safety performance, and accident involve-ment (Christian et al., 2009; Clarke & Roberston, 2008). While many studieshave employed measures of general personality characteristics that couldbe classified within the Big 5 framework (e.g., conscientiousness, Geller,Roberts, & Gilmore, 1996; extraversion, Iverson & Erwin, 1997), research hasalso focused on several more specific aspects of personality (i.e., propensityfor risk taking, Frone, 1998; locus of control, Salminen & Klen, 1994).

In terms of safety performance and accident involvement, a number ofstudies have examined locus of control as a predictor (e.g., Brown, Wilis, &Prussia, 2000; Eklof, 2002; Hsu, Lee, Wu, & Takano, 2008; Rundmo, 2001).Locus of control is the extent to which an individual believes that events areunder his or her control as opposed to being the result of situational factors.Meta-analytic findings are consistent with the expectation that individualswho are higher in terms of internal control are more motivated to learnabout workplace safety, engage in higher levels of both safety participationand safety compliance, and have fewer accidents (see Christian et al., 2009).


Notably, preliminary evidence indicates that locus of control may be arelatively strong predictor of safety participation and a good predictor ofaccident involvement. These findings are consistent with the motivationalimplications of believing that one has control over events, and haveimportant implications for worker selection and training. However, moreresearch is needed before firm conclusions can be made about the relativeutility of locus of control as a predictor of safety participation vs. safetycompliance. Furthermore, research examining the expected indirect effect oflocus of control on safety performance and accident involvement throughsafety motivation is needed.

Conscientiousness has also received a fair amount of attention as apredictor of safety performance and accident involvement (Haaland, 2006).Those individuals who score high on this personality dimension are morelikely to be trustworthy and dependable, which would lead them to beingmotivated to engage in appropriate safety behavior and have fewer negativesafety outcomes. Despite this general expectation, conscientiousness has asomewhat modest (low) relationship with safety performance (see Christianet al., 2009). This result may be due to the fact that conscientiousness wouldbe expected to primarily affect safety performance and safety outcomesthrough safety motivation. Future research examining the extent to whichconscientiousness indirectly affects safety performance (with an emphasis onthe study of participatory behaviors) and safety outcomes would beinformative. To date, only a few studies have examined conscientiousness asan antecedent to safety participation (e.g., Geller, 1996).

In regard to accident involvement, neuroticism has been found to beconsistently 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 inneuroticism are more likely than those with lower levels of neuroticism toexperience negative affective states, which would lead them to have lowerlevels of safety participation, more lapses of attention, and be predisposed tomaking more mistakes. Although neuroticism has received a fair amount ofresearch attention relative to other personality characteristics in regard toaccident involvement, the available evidence would suggest that conscien-tiousness and locus of control play more central roles in the explication ofnegative safety outcomes.

Although several studies have examined the role of extraversion inaccident involvement and found it to have a low negative association (seeChristian et al., 2009), fewer studies have incorporated measures of opennessto experience and agreeableness. Individuals who are high in agreeableness


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 andfewer accidents. On the other hand, we do not have a strong basis forsuggesting how openness to experience will affect safety outcomes. Yet, tothe extent that elevated levels of openness to experience and extraversion arerelated to thrill seeking or the propensity to take risks (an amalgamation ofBig 5 traits; see Nicholson, Soane, Fenton-O’Creevy, & Willman, 2005), wewould expect safety motivation to decrease and consequent unsafe workbehavior to increase. In effect, the thrill seeking would be expected toundermine the desire or need to engage in safe work behavior.

Education/Development ExperiencesEducational and development experiences broadly defined are expected tolead to both the development of safety motivation and acquisition of safetyknowledge. 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 safetydomain focus heavily on the development of factual (often referred to asdeclarative) knowledge and procedural knowledge and skills related to usingpersonal protective equipment, engaging in work practices to reduce risk,communicating health and safety information, and exercising employeerights and responsibilities. From here onward, we will use the term ‘‘safetyknowledge’’ interchangeably with the concepts of declarative and proce-dural knowledge. In addition, educational activities can be directed at thedevelopment of worker attitudes and regulatory activities in efforts toenhance safety motivation (Ford & Tetrick, 2008). Thus, we would expectthese 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 ofa particular learning theory. For instance, the literature is replete withapplications of stage learning theories (e.g., Azizi et al., 2000), rein-forcement theory (e.g., Cooper, 2009; Lingard & Rowlinson, 1997), andprinciples of social and experiential learning theories (e.g., Lueveswanij,Nittayananta, & Robison, 2000) to safety knowledge development. The laterinterventions often employ more hands-on, experiential training methodssuch as role-plays, demonstrations with practice, and simulationsinvolving individuals, dyads, and teams. Along with the use of multipletraining methods in the delivery of training, many of the training programsbased on social and experiential learning theories emphasize individualized


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 therelative effectiveness of safety and health training methods according to theextent to which trainees participated in the learning process. Their meta-analytic findings were consistent with the theoretical argument that as themethod of safety and health training becomes more engaging (going frompassive, less engaging methods such as lecture to experiential-based, highlyengaging methods such as hands-on training that incorporate dialogue), theeffect of training is greater for knowledge acquisition, safety performance,and the reduction of accidents and injuries. Importantly, their findings pointto needed research on the usefulness of incorporating more active forms ofparticipation into traditionally structured safety training and developmentefforts including the commonly employed computer-based and distanceinstructional methods.

Burke et al.’s (2006) findings also suggest that the unbridled promotion ofreinforcement or operant theory as ‘‘Behavioral Safety’’ be tempered. Thereader is referred to Geller (1996) and McSween (2002) for discussions ofoperant theory that underlies moderately engaging feedback interventions.As discussed elsewhere (Burke et al., 2007; Cooper, 2009), applications ofoperant theory are most effective when work is relatively static (i.e., involvesprimarily routine actions) and where the intended target behaviors relate tosafety compliance. Also, as discussed by Olson and Winchester (2008), theworkplace literature on behavioral self-monitoring (BSM, another nameassociated with applications of operant theory) is theoretically unfocusedand has neglected relevant scholarly work.

A particular deficiency in the literature on educational efforts to developsafety knowledge and safety motivation is that we know little about learningconditions that promote dialogue and reflective thinking. Arguably, dialogueand reflection are critical elements of the learning process and efficacyformation (Burke, Holman, & Birdi, 2006; Gorsky & Caspi, 2005; Holman,2000a, 2000b). Dialogue involves discussion with others including virtualothers (interpersonal dialogue) or one’s self (intrapersonal dialogue), oftenwith respect to actions taken or considered. Dialogue is characterized bythought-provoking activities such as questioning, explaining, and evaluatingissues or problems at hand. Reflection is a systematic thought processconcerned with simplifying experience (i.e., thinking about contradictions,dilemmas, and possibilities). Practitioners and researchers alike couldconsider different forms of dialogue (e.g., inquiry, debate, argumentation,storytelling; see Cullen & Fein, 2005; Gorsky & Caspi, 2005) and different


structural considerations for promoting intrapersonal dialogue (e.g., self-instruction/review materials, tutorial sessions, website materials) andinterpersonal dialogue (e.g., actual and computer or web-based discussions;see Gorsky, Caspi, & Trumper, 2004). The form, structure, and instructionalactivities would likely be somewhat specific to the nature of the safetytraining intervention, the level of skill being acquired, and the size of thetraining group (Frederiksen, 1999; Gorsky, Caspi, & Trumper, 2006;McConnell, 1997). Nevertheless, research on the role of dialogue andreflection has considerable potential for advancing our understanding of howto optimally develop safety knowledge and safety motivation.

More recently, Burke et al. (2009) discussed how hazardous events andexposures might interact with developmental activities to influence safetymotivation and safety knowledge acquisition. Burke et al. (2009) posited thatfor hazardous events and exposures of an ominous nature (e.g., fires andexplosions, exposure to toxic chemicals, radiation, and human immunode-ficiency virus; see Mullet, Ciutad, & Riviere-Shafighi, 2004), the action,dialogue and considerable reflection that take place in highly engagingdevelopmental activities such as simulation training would be expected toengender a dread factor, a realization of the actual dangers and feelings ofdread. Furthermore, they argued that this realization and the experiencedfeelings and negative affect should play a primary role in motivatingindividuals to learn about how to avoid exposure to such hazards. However,the dread factor would not necessarily be produced in (a) highly engagingdevelopment activities targeted at hazardous events and exposures thattypically do not have severe injury potential (e.g., contact with objects andequipment, excessive physical effort, and repetitive bodily motion) or (b) inlesser engaging development activities (e.g., lectures), irrespective of the levelof hazard. Their theoretical arguments are consistent with that of manytheorists who have given affect a direct and primary role in motivatingbehavior, especially in regard to unpleasant feelings, which arguablymotivate action that people anticipate will avoid such feelings or associatedconsequences (see Schwartz & Clore, 1988; Slovic & Peters, 2006).Furthermore, their arguments are in line with social-cognitive perspectivesconcerning workers’ willingness to participate in safety interventions and thedevelopment 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 betweenobjective workplace hazards and educational/development activities in theformation of safety motivation and safety knowledge. That is, the above


arguments suggest that objective hazards and development activities of eithera formal or informal nature should jointly affect the motivation to engage insafe work behavior and the knowledge of how to do so. In effect, themotivational and learning benefits of more engaging education/developmentactivities should be enhanced when individuals face hazards of a particularlyominous nature, with no necessary enhanced benefits of more engagingdevelopmental activities when hazard severity is relatively low or when thedevelopmental activity is less engaging. However, we caution that whenchoosing educational/development activities, practitioners and researchersmay need to take into account the backgrounds of workers who may beconcentrated in particular types of hazardous work. For instance, indigenousfarm workers from Mexico and Guatemala who are working in the northwestUnited States are not of Hispanic or Latino descent and come from regionswith unique cultural and linguistic traditions that could affect what and howthey learn (Farquhar, Shadbeh, Samples, Ventura, & Goff, 2008).

Notably, the workplace hazard measurement system discussed in Burkeet al. (2009), which is based, in part, on the Bureau of Labor Statistics’Occupational Injury and Illness Classification System (OIICS) (also seeBiddle, 1998), hierarchically arranges workplace hazards to reflect theincreasing potential for severe illness, injury, or death due the hazardousevent or exposure. This workplace hazard measurement system permits thescoring of hazard event/exposure in terms of low and high severity hazards,and roughly corresponds to breaking the Bureau of Labor’s ranking system atthe midpoint of the OIICS hierarchy. Such a dichotomy highlights the pointat 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 ofhepatitis or HIV resulting from needle sticks within the fourth most severehazard category: exposure to harmful substances and environments). More-over, this workplace hazardous measurement system offers considerablepotential for reintroducing the study of objective hazards into the humanresource management and organizational behavior literature to illuminatehow workplace hazards interact with individual characteristics to affectworkplace safety.

Returning to a discussion of educational activities per se, several studieshave 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 schooleducation are up to three times more likely to have a work disability absence


than those with at least a high school diploma. Notably, these findings aremaintained when controlling for the type of work and number of hoursworked. These results imply that individual differences such as thosemodeled in Fig. 1 might be fruitfully investigated to further explicate therole of individual differences associated with educational attainment thatare impacting workplace safety for those with more vs. those with lessformal education. Finally, although various approaches have beenconsidered for imparting work-relevant safety knowledge to high schoolstudents and young workers (e.g., through vocational/technical education,skill standards, career clusters initiatives, and apprenticeships), we knowvery little about how these efforts affect safety performance and safetyoutcomes (see Schulte, Stephenson, Okun, Palassis, & Biddle, 2005).

Cognitive AbilitiesWhile general mental ability is well recognized as an antecedent to jobknowledge as specified in Fig. 1 (Colquitt, LePine, & Noe, 2000; Schmidt,Hunter, & Outerbridge, 1986), the study of cognitive abilities in safetyresearch has been largely delimited to the role of cognitive abilities inaccidents (e.g., Arthur, Barrett, & Alexander, 1991; Lawton & Parker, 1998;Wallace & Vodanovich, 2003). Here, research indicates that selectiveattention and cognitive failures are meaningful predictors of safetyperformance and accident involvement (Wallace & Vodanovich, 2003).Cognitive failure refers to a breakdown in cognitive functioning, whichresults in an error or mistake in task execution.

Cognitive failures have origins in the organization of work (e.g., 12-hourshifts in units with staffing shortages for nurses; see Smith, Folkard, Tucker, &Macdonald, 1998), sleep opportunities (Dawson & McCulloch, 2005), andrelatively stable individual differences. In regard to stable individualdifferences, some personality characteristics may predispose individuals tobeing more susceptible to experiencing cognitive failures than others (Wallace,Kass, & Stanny, 2002). While research has examined expected interactionsbetween cognitive failures and conscientiousness on accidents, examination ofhow the interactive effect of cognitive failure and personality characteristicssuch as conscientiousness operate through safety compliance on safetyoutcomes including accidents and near misses has not been made.

In addition, learning disabilities, a general term used to describe a varietyof information-processing problems, would be expected to directly relate tothe acquisition and retention of safety knowledge. Depending on the learningdisability including attention-deficit/hyperactivity disorder (ADHD), pro-blems may arise with respect to reading, memory, abstract reasoning, and


spatial orientation. As a result, individuals with such disabilities may, onaverage, acquire fewer job-relevant skills in comparison to those withoutsuch disabilities and be more likely to hold physically demanding and morehazardous jobs. While there is some evidence to support this assertion (seeMannuzza, Klein, Bessler, Malloy, & Hynes, 1997), the cognitive con-sequences of their disability alone would place them at greater risk ofworkplace injury and illness even for comparable jobs with people withoutsuch disabilities. In this sense, learning disabilities and ADHD can lead todifficulties reading instructions or remembering previously taught material(Schaeffer, 2004). Thus, we would expect learning disabilities to primarilyaffect safety outcomes such as injury indirectly through safety knowledgeand safety compliance (e.g., completing tasks in a required sequence).Indirect evidence for this proposed causal sequence comes from a large-scalestudy of workers with self-reported dyslexia (a learning disability character-ized by problems in reading, writing, and spelling) where there was an89% increase in work injury risk among workers with self-reporteddyslexia in comparison to workers reporting no learning disability (Breslin &Pole, 2009).

Although not depicted in Fig. 1, we would expect some learningdisabilities to interact with aspects of safety climate (e.g., time pressure orworkload demands) to affect safety performance and subsequent safetyoutcomes. That is, the expected negative effect of learning disabilities onsafety performance would be greater under more restrictive organizationalsafety climate conditions (e.g., high time pressure, high workload) thanunder less restrictive and more supportive safety climates. Decrements insafety performance would be expected to carry through in terms of increasesin workplace accidents, injuries, and near misses.

Physical Abilities and Physiological Aspects of WorkThe study of physical abilities and physiological aspects of safety-relatedwork has been approached in several ways with considerable research beinggenerated on the muscular nature of work (Smolander & Louhevaara, 1998),postures at work (Daltroy et al., 1997; Kuorinka, 1998), the ways the bodyproduces 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 thephysical-related predictors of safe work behavior and the design of workerand workplace interventions to reduce occupational injuries and illnesses(e.g., interventions to reduce the incidence of musculoskeletal problemsrelated to repetitive work or inappropriate postures). In large part, we would


expect physical abilities and physiological variables to directly relate to theactions that workers engage in, and indirectly through these actions (i.e.,safety performance) to occupational injuries and illnesses. This expectation issupported by findings that functional measures, such as balance, reactiontime, and isometric muscle strength, are related to performance on simulatedtasks (Davis, Dotson, & Santa Maria, 1982; Guralnik & Ferrucci, 2003) andthat increasing the physical capacity of workers can be beneficial in reducingtheir misfit with a work system (Genaidy, Karwowski, & Shoaf, 2002; Tuncelet al., 2008). In the case of non-exercise education/development effortsrelated to physical and physiological aspects of work, we expect theseinterventions to have direct impacts on workers’ safety knowledge especiallyof a procedural nature.

We note that flexibility and strength generally decline with increases inage (Brandon, Boyette, Lloyd, & Gaasch, 2004; Peate, Bates, Lunda,Francis, & Bellamy, 2007; Sherrington, Lord, & Finch, 2004). This generalfinding has important implications for understanding safety performanceand injury rates in occupations such as firefighting, where the execution ofjob tasks can require maximal physical performance (see Womack, Green, &Crouse, 2000). Furthermore, these findings suggest the need for continuedefforts to evaluate the efficacy of interventions to improve flexibility andcore strength (hip complex strength) among workers in highly demandingphysical work, especially where the work conditions are changing and notunder the worker’s control.

Finally, in relation to the physical characteristics of workers and theirhealth, little is known about the effects of ‘‘sickness presenteeism’’ (i.e.,attending work while sick) on safety performance and subsequent safetyoutcomes. Sickness presenteeism has been linked to risk of serious coronaryevents, theoretically as a result of the cumulative stress burden of workingwhile sick (Kivimaki et al., 2005). Yet, sickness presenteeism would beexpected to have more immediate impacts on safety performance especially inrelation to working with others and the effects on coworker health and safety.

Proximal Individual Level Antecedents

Safety KnowledgeConsistent with general models of performance (e.g., Campbell, 1990) andmore specific models of workplace safety (Neal & Griffin, 2004; Christianet al., 2009), safety knowledge is posited as a direct antecedent to safetyperformance. In particular, Burke and Sarpy (2003) discuss how and why


knowledge in four content areas is critical to engaging in safe workbehavior: fundamental knowledge and skills (e.g., related to usingpersonal protective equipment), recognition and awareness knowledge andskills (e.g., collecting information about workplace hazards), problem-solving skills (e.g., using union and management resources), and decision-making skills (e.g., negotiating effective health and safety contractlanguage). A fair amount of research indicates that safety knowledge inthese content areas has a relatively strong relationship with safetycompliance (see Christian et al., 2009). Importantly, empirical evidenceindicates that these relatively strong relationships between safety knowledgeand safety compliance are maintained across self and supervisory ratings ofjob performance as well as across industry and occupational boundaries(Burke, Sarpy et al., 2002; Griffin & Neal, 2000). In addition, safetyknowledge 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 knowledgedevelops and relates to safety compliance and safety participation, we knowrelatively little about how safety knowledge decays over time. Perhaps someof the best evidence related to declines in knowledge and skill comes fromsimulation research on healthcare education, where notable decays inpatient safety knowledge occurred from two to eight months (Laschingeret al., 2008). Related to this point, goal setting and feedback interventionshave been employed in the posttraining contexts to encourage retention andapplication of knowledge and skills in relation to more routine, house-keeping-type activities (see Hickman & Geller, 2003). These types ofinterventions have been effective in maintaining the display of routine, taskbehaviors across different types of work (Geller, 2001; Sulzer-Azaroff &Austin, 2000). However, little, if any, research attention has been given tothe role of dialogue and action-focused reflection in the maintenance ofknowledge and skills and particularly of an advanced procedural nature.This issue is important as severe injuries, illnesses, and fatalities occur moreoften in non-routine types of work (Kriebel, 1982; Peterson, 1998), wheremore complex skills are often required (Gardner et al., 1996). In addition,this issue of how best to promote the retention of safety knowledge isimportant due to the fact that efforts to maintain safety knowledge (viasafety training) are mandated for some occupations. To date, we rely moreon scientifically uninformed legislative/political processes and legislators todetermine when (in regard to timing/frequency) and how to maintain criticalsafety knowledge than on sound theoretical and empirical research bases forsuch recommendations.


Safety MotivationAlong with safety knowledge, safety motivation is posited as a directantecedent to safety performance. This expectation is consistent withrecognized models of workplace safety (Neal & Griffin, 2004; Christianet al., 2009). Given that safety participation is characterized as behavior thatis more volitional in nature, researchers have expected safety motivation tobe more strongly related to safety participation than to safety compliance.Some evidence, where participation motivation was measured, indicates thatmotivation is more strongly related to safety participation than to safetycompliance with safety knowledge being the primary determinant of safetycompliance (Griffin & Neal, 2000). Nevertheless, Christian et al.’s (2009)meta-analytic findings indicate that safety motivation is an importantantecedent of safety performance, where performance was primarilymeasured with respect to safety compliance.

As specified in Fig. 1, safety motivation would be expected to mediate therelationship among psychological work climate, personality, and education/development variables on safety performance. In regard to climate, thisgeneral expectation has been examined in several studies where climate andconscientiousness were expected to predict safety motivation, which in turnwas expected to relate to safety performance (Christian et al., 2009; Wallace &Chen, 2006). Notably, these expected mediated effects were found when safetymotivation was conceptualized and measured in a more general manner (seeChristian et al., 2009) as opposed to a more specific conceptualization andmeasurement (see Wallace & Chen, 2006, with respect to regulatory focustheory), and when climate was conceptualized and measured at the individualvs. group levels in these respective studies.

For the most part, research attention in the domain of workplace safetyhas been directed at how safety motivation and safety knowledge mediatethe effects of personality characteristics and climate variables on safetyperformance. This research has not examined mediation involving cognitiveabilities and education/development experiences as exogenous variables.Research incorporating constructs and measures from the latter domainswould inform the relative causal influence of distal antecedents of both acognitive and affective nature on safety performance. This point isimportant as safety motivation would be expected to primarily mediatethe effects of affectively oriented distal antecedents on safety participation;whereas safety knowledge would be expected to primarily mediate theeffects of more cognitively oriented distal antecedents on safety compliance.

More recent research points to the need to examine safety motivationin relation to two sometimes-competing performance goals: safety and


production. Although both types of goals are important in organizationalcontexts, understanding if and when employees focus on one type of goal atthe expense of the other type is important (given the consequence of errors ineither domain). Wallace and Chen (2006) refer to the focus on accomplishingmore tasks, more quickly as a promotion focus, and performing tasksaccurately and in accordance with one’s duties as a prevention focus (also seeForster, Higgins, & Bianco, 2003). Wallace and his colleagues’ research hasindicated that prevention and promotion foci relate positively and negativelyto safety and productivity, respectively, with task complexity serving as apossible moderator (Wallace, Little, & Shull, 2008). That is, when taskcomplexity is high, a promotion focus has been found to negatively relate tosafety and a prevention focus negatively to production. The extent to whichWallace et al.’s (2008) laboratory findings, which examined changes withinan experimental task, generalize to changes in work complexity within andacross 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 ofworkplace safety conceptualize safety-related behavior as a direct antecedentof accidents, near misses, injury, and illness (e.g., Neal & Griffin, 2004). Testsof structural equation models of workplace safety have lent strong support tothe conceptualization of safety behavior as direct antecedents to accidentsand injuries (see Christian et al., 2009; Paul & Maiti, 2007). Our modeling ofworkplace injuries, however, considers accidents as partially mediating thesafety performance–injury relationship. The rationale for the latter expectedpartial mediation is that some injuries result from accidents, but manyinjuries such as cumulative trauma, musculoskeletal, and knee injuries resultdirectly from work behavior (e.g., Alnaser, 2007; Brulin et al., 1998; Chenet al., 2004; Marras, Davis, Kirking, & Bertsche, 1999). That is, differentcombinations or repeated exposure to lifting, lowering, pushing, pulling,and carrying can precede injury in the absence of an accident. This discussionalso suggests that some physical work exposures of either a repetitiveor prolonged nature may interact with worker behavior to produceoccupational injuries and illnesses with delayed onset of symptoms (Cole,Ibrahim, & Shannon, 2005; Melchior et al., 2005). Furthermore, the indirectrelationship between safety performance and injuries through accidents is


important given that experiencing an accident-related injury may result inqualitatively different thinking about how to avoid the reoccurrence of suchan injury than might be expected from involvement in an accident withoutinjury or involvement in a near miss.

At the individual level of analysis, safety performance, often measured asa composite of safety participation and safety compliance items (e.g.,Barling et al., 2002), has been found to have moderate negative relationshipswith 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). Whiletoo few studies have included measures of accidents, near misses, andinjuries to judge possible differential relationships of these outcomes withsafety performance, limited evidence (see Probst, 2004) indicates that safetyperformance has a stronger relationship with near misses in comparison toother outcomes. This finding is not unexpected, as the base rate for nearmisses will tend to be greater than the rate of accidents and injuries.

Importantly, the literature on accident involvement and injury generallysupports our theoretical modeling of the role of distal (e.g., personality) andmore proximal antecedents (i.e., safety motivation and safety performance) tothese outcomes. For instance, Siu et al. (2003) found that negative affectivity(a Big 5 personality factor relating to emotional stability) had a strongindirect (through safety performance) effect on objectively measured workinjury for workers in two underground coal mines in India. As anotherexample, Probst and Brubaker (2001) found that safety motivation hadstrong indirect effects through safety compliance on separate measures ofworkplace accidents and injuries. Finally, Christian et al. (2009) presentedevidence supportive of expected indirect effects of psychological safetyclimate on accidents and injuries through safety knowledge, safety motiva-tion, and safety performance. In short, empirical research aimed at testingcausal models of workplace safety that incorporates distal and proximalantecedents of safety performance and the outcomes of safety-relatedbehavior is providing process insights in regard to workplace safety, butthis research is at an early stage.

Although we stressed above that causal modeling of accident involvementand injury would benefit from improved conceptualizations and measures ofsafety behavior in relation to confirmed safety performance constructs(Burke et al., 2002), we also believe that the same point holds to some extentfor accidents and injuries. First, accidents and injuries are frequentlymeasured via self-reports and often in terms of dichotomous items (e.g., theperson has or has not been in an accident or has or has not been injured),


which creates ambiguity in terms of the meaning of such measures. Second,when accidents are more objectively measured via the number of OSHA-recordable incidents they involve incidents that required more then basicfirst aid or resulted in lost work days due to injury. Depending on thepurpose of one’s investigation, self-reports or OSHA-recordable incidentsalone are likely deficient in terms of capturing the nature of accidents anddifferentiating more or less severe injuries. The nature of accidents (e.g.,accidents that do or do not require more than simple first aid) and injuryseverity (viewed in terms of financial, personal, and social costs) are related,yet research modeling accident/injury involvement has generally notattended to such distinctions. Notable exceptions that focus on thedistinction between OSHA- recordable (or Mine Safety and HealthAdministration-recordable) accidents and microaccidents that only requirebasic first aid are reported in Wallace and Chen (2006) and Zohar (2000,2002). Our understanding of the national, organizational, and individual-level antecedents of accident involvement and injury will likely improve tothe extent that we also improve conceptualizations and measures withinthese safety outcomes domains.

For the most part, the literature is silent in regard to estimatingrelationships between constructs within most domains in Fig. 1 andoccupational illnesses and diseases. This void is understandable given thatmany illnesses and diseases that have their origins in work such as cancer andlung disease tend to have long latencies before their onset. However, theliterature clearly recognizes the need for appropriate worker action to avoidexposures that may lead to occupational illnesses and diseases. In this sense,there is often some basis such as an epidemiological study to support the linkbetween the health protective actions that workers can take and the possiblereductions in risk associated with such exposures. To the extent that workersunderstand the association between such behaviors and health outcomes,their health protective actions are known to relate to perceptions of controlover potential exposures (see Arcury, Quandt, & Russell, 2002).

Numerous behavioral interventions have been examined, with the aim ofimproving workers’ motivation and knowledge for engaging in actions thatwill preclude exposure to substances and conditions that are known to relateto occupational illnesses and diseases. These interventions have beeneffective with respect to improving knowledge and behavior related tofoodborne 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


knowledge and safe work behavior, but also the reduction of symptomsthemselves as determined via clinical examinations (e.g., Held, Mygind,Wolff, Gyntelberg, & Anger, 2002, in the case of occupational contactdermatitis). Consistent with the above discussion, we believe that futureresearch directed toward examining a hypothesized interaction between thelevel of engagement of these types of behavioral interventions and thepotential severity of exposure (which in the case of many occupationalillnesses and diseases is high) on knowledge acquisition and safetyperformance would be informative. Along with engineering solutions,confirming such an expectation would have important practice implicationsrelated to conducting behavioral interventions to reduce risks associatedwith occupational illnesses and diseases.

Dialogue and Reflection

Unlike other models of workplace safety, we posit that dialogue (of eitherintrapersonal or interpersonal nature) and reflecting on one’s work-relatedsafety experiences are key elements in learning from these experiences.As discussed above, reflection is a systematic thought process concernedwith simplifying experience as well as a process involving thinking aboutcontradictions, dilemmas, and possibilities. In particular, intrapersonal and/or interpersonal dialogue subsequent to the experience of either positive ornegative consequences of safety-related behavior including a near miss islikely to engender concrete reflection in relation to actions taken and theconsequences of those actions. In regard to the severity of injury and illnessthat was or could have been experienced as a result of safety-relatedbehavior, this dialogue would likely focus on the dilemmas faced andpossibilities associated with these critical situations and actions taken.In considering the situational demands, this reflection might also take intoaccount 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’sknowledge and efficacy for handling future events (Burke, 2008). Thus, inFig. 1, we propose feedback loops from dialogue and reflection to safetyknowledge and safety motivation. Our expectation in regard to self-efficacyfor handling future events of a similar nature is that that self-efficacydevelops, in part, when individuals engage in extensive self-reflection ontheir experience and the adequacy of their thoughts and actions (Bandura,1986). In regard to safety knowledge development, our expectation is


consistent with arguments within action regulation theory and experientialtheories of learning that individuals learn from experiencing errors andthinking in terms of actions (Hacker, 2003; Heimbeck, Frese, Sonnentag, &Keith, 2003; Holman, Pavlica, & Thorpe, 1997). To the extent thatindividuals can independently or through other means be encouraged toengage in dialogue and reflection, especially subsequent to an accident or nearmiss, we would expect their procedural knowledge and skill for handling suchfuture events to improve. The reader is also referred to discussions in Teslukand Quigley (2003) and Hofmann and Stetzer (1998) for how an open, free-flowing discussion over safety issues may enhance learning from accidents,errors, and near misses (and to Edmondson, Dillon, & Roloff, 2008, for arelated discussion in teams).

In terms of organizational efforts to encourage individual learning fromaccidents, injuries, and near misses, several focused efforts could beconsidered. As an example, a structured after-action-review akin to theanalysis of critical incidents (Flanagan, 1954) might prove beneficial forgenerating dialogue and reflective thinking on the behalf of workers (Bairdet al., 1999). Relevant questions from an after-action-review might include:(a) What was the intent or purpose of the action, (b) What exactly occurredand why, (c) What lessons (in relation to organizational and personalcontributing factors) were learned, and (d) What future short-term andlonger-term actions and plans should be considered by management andworkers? Depending on the nature of the safety and health outcomes, Ellis,Mendel, and Nir’s (2006) work would suggest tailoring the after-action-review. That is, after successful events, their research would call for anemphasis on the discussion of incorrect actions, whereas, after unsuccessfulevents, their research supports a discussion of both correct and problematicactions. While Ellis and colleagues’ research is informative, research is neededto delineate how dialogue and reflection can be considered or encouragedwithin after-action-reviews or other types of interventions to optimallyenhance safety knowledge and safety motivation. The reader is referred toBurke et al. (2007) for a more detailed discussion of needed research ondialogical aspects of safety knowledge and motivation development.

CONCLUSION

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


individual safety performance and safety and health outcomes. Ourdiscussion highlighted what is known about key construct relationshipswithin and among national, organizational, and individual levels of analysisand identified future research directions for advancing our understanding ofworkplace safety. Importantly, our modeling of the situational- andindividual-level determinants of workplace safety reflected not only aninteractionist approach, but it also emphasized the social construction ofworkplace safety especially in relation to discussing how and why workerslearn and think about safety in and by action. Our emphasis on the socialconstruction of workplace safety also highlighted key social, political,economic, and cultural conditions that are expected within and amongnations to serve as important drivers of workplace safety.

Finally, to manage the scope of our review, we delimited our discussionsto key constructs and processes within and between the respective levels ofanalysis that over time would be expected to influence individual safetybehavior and outcomes. Although we delimited our scope in this manner,our perspective provides a basis for extending multilevel modeling ofworkplace safety to include processes and constructs that over time wouldaffect performance and safety-related outcomes at the organizational/groupand national/regional levels of analysis. In this sense, we hope that ourmultilevel, interdisciplinary perspective on workplace safety not onlyengenders research aimed at testing expected causal relationshipswithin the model, but it also encourages efforts to expand multilevelconceptualizations and efforts to study workplace safety at the organiza-tional/group and national/regional levels of analysis.

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between organization and group-level climates. Journal of Applied Psychology, 90(4),

616–628.


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 executivecompensation. In particular, we document main issues in the pay–performance debate and explain practical issues in setting pay as well asdata issues including how pay is disclosed and how that has changed overtime. We also provide a summary of the state of CEO pay levels and paymix in 2009 using a sample of over 2,000 companies and describe maindata sources for researchers. We also investigate what we believe to be atthe root of fundamental confusion in the literature across disciplines –methodological issues. In exploring methodological issues, we focus onempirical specifications, causality, fixed-effects, first-differencing, andinstrumental variable issues. We then discuss two important but not yetwell-explored areas, international issues, and compensation in non-profits.We conclude by examining a series of research areas where further workcan be done, within and across disciplines.




ISSN: 0742-7301/doi:10.1108/S0742-7301(2010)0000029004

49

dx.doi.org/10.1108/S0742-7301(2010)0000029004

dx.doi.org/10.1108/S0742-7301(2010)0000029004

Executive compensation is a topic that has interested researchers for nearlya century. Bearle and Means’s (1932) pioneering discussion of the moderncorporation, Robert’s (1956) first empirical study, Murphy’s (1985) firstdiscussion of considering panel data, Jensen and Murphy’s (1990) discussionof the way executives are paid, and Hall and Liebman’s (1998) study of thelink between wealth and firm performance and a steady stream of academicpapers since have focused on the link between pay of CEOs and corporateperformance. This subject is perhaps even more relevant today with thedramatic changes in the economy and the outcry from various constituentgroups to examine the pay of senior executive more closely. The subject hasinterested researchers from a variety of fields including Human ResourceManagement, Economics, Accounting, Finance, Law, Sociology, Psychology,and Industrial Relations. It is remarkable that, although hundreds of papershave been written on the subject, there is no real consensus on the relationshipbetween executive pay and firm performance. This is due, in part, to thediverse set of disciplines involved in the study, the wide variety of methodsused to investigate the main questions and the diversity in knowledge aboutthe institutions that matter in this area. This paper is an attempt to bridgethese gaps and consolidate some of the understanding of this important issue.

This paper reviews some of the literature, documents the current empiricalfacts, explains the data available, discusses pay and performance, discussesthe varied empirical methods and possible reasons for differences in‘‘results’’ across studies, identifies international issues, explores the currentregulatory environment, and considers avenues for future work in the area.The paper is almost exclusively focused on CEO pay, although there is somediscussion of the top executive team. Although it should be clear from thecontext, in most instances when we say ‘‘executive’’ we primarily arereferring to the CEO. The paper is not a comprehensive review of all of theliterature on executive compensation, nor is it a review of the pay-for-performance literature, and we apologize, in advance, to the authors whosepapers we have not discussed.1 However, this paper highlights certainstudies 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 anothersection, where we describe a series of important data issues. Part of the riseof the field has been due to public disclosure of executive compensation datain publicly traded firms in the United States. However, there have beensubstantive changes in disclosure over time, and we explore the implicationsof that for our work. We discuss types of executive compensation data andchanges over time and then go on to describe the specific sources most

BETH FLORIN ET AL.50

frequently used by academics and practitioners today. We finally discuss paylevels and pay mix across a set of 2,108 CEOs using data from 2009 proxystatements.

We then have a section on ‘‘practical’’ matters of setting executivecompensation and reasons why that may affect the pay-for-performancedebate. This includes a discussion of ‘‘proxy advisory services’’ and the rolethey play in setting executive compensation.

We then follow with a discussion of methodological issues, which we feelare a central part of this work. In fact, we feel that issues of methodologyare specifically important in this area of research since researchers acrossdisciplines (and within) use similar, yet distinctly different empirical models,and these can have profound implications for their findings. We feel thatthese considerations can help clarify past findings and perhaps suggest futuredirections. The particular functional form of the empirical specifications usedin the executive pay literature differ widely. We describe examples of eachand discuss implications of these models for interpreting pay-for-perfor-mance measures. In fact, we believe that a part of the problem in differinginterpretations of pay-for-performance results comes from problems inappropriately interpreting empirical models. This paper includes discussionof the ‘‘right’’ functional form, ‘‘fixed-effect’’ and ‘‘first-difference’’ models,instrumental variables and the implications for executive compensationresearch, and a section on thinking about ‘‘causality.’’ Next, we exploredifferent measures of firm performance including accounting measures,financial measures, subjective measures, and relative performance. We alsoinclude 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 describescompensation of senior managers in a variety of non-profit organizationsincluding non-profit hospitals, labor unions, and non-profits in general.Finally, we offer a concluding section where we summarize our work, discussvarious new government reforms (including say on pay and managing risk),and offer a plan for future work in the area.

A BRIEFHISTORYOF THE CEO PAY–PERFORMANCE

DEBATE, AND WHY IT ISN’T RESOLVED

Summarizing the massive literature on the CEO pay–performance debate isnot an easy task. However, in this section, we offer a brief summary of some

Executive Pay and Firm Performance 51

of the issues and findings over the past few decades. This will set the stagefor our later discussion of functional forms, empirical problems, and otherissues.

The study of executive compensation goes back at least to Roberts (1956)and even Bearle and Means (1932). There were also notable papers decadesago such as Masson (1971), Lewellen and Huntsman (1970), and Coughlinand Schmidt (1985) among others. Although the field really took off withthe 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 of461 executives at 71 firms over a number of years. But, rather thanestimating simple cross-sectional relationships (which showed no relation-ship between CEO pay and performance), Murphy (1985) introduced‘‘fixed-effects’’ models (described below) and found a strong relationshipbetween pay and performance. This empirical method was not novel ineconomics at the time, but it had not been applied to the CEO pay literatureand was an interesting and important advance for reasons we discuss below.Murphy (1985), documenting a relationship between pay and performancealso 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 paperusing first-difference methods (also described below). In that paper, theyfound that for every $1,000 increase in shareholder value (measured as achange in the market value of equity), CEO pay went up by $3.25. Theirinterpretation of this was that, although there was a relationship betweenpay and performance, the relationship was rather weak and could bestrengthened. In part, due to Jensen and Murphy’s work, and due to callsfrom practitioners, this leads to the extraordinary rise in the use of stockand stock options in executive compensation contracts. Options and stockbecame much more important components of executive pay packagesstarting in the early 1990s.2

Later in the 1990s, Hall and Liebman (1998) asked whether CEOs werepaid like ‘‘bureaucrats’’? They collected unique data on stock and stockoptions (that were at the time not more formally disclosed) and foundstronger relationships between pay and performance than found by Jensenand Murphy (1990), on the order of $5.29 for every $1,000 increase inshareholder wealth. They conclude that while this may still seem likequite a weak relationship, their work suggests that even small changes inperformance can have very large effects on the lifetime wealth of anexecutive.


More recently, Bebchuk and Fried (2004, 2006) wrote a provocative bookcalled ‘‘Pay Without Performance.’’ This book carefully articulates thedifference between the often-discussed ‘‘arm’s-length bargaining’’ frame-work and what they call the ‘‘managerial power’’ perspective, where, inessence, boards are captured by CEOs. They discuss many reasons why theythink the system for setting CEO pay needs reform.3

Answering the pay-for-performance debate in executive compensation isobviously a difficult question. There are many complications. For example,researchers use different data sources, companies have different compensa-tion and business strategies (even in the same industry), and there are manypotential factors that are not easily measured by academic researchers.However, one of the main reasons we think the debate has not yet beenresolved 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 confrontedresearchers who study executive compensation. This will include adiscussion of the types of compensation data, Securities and ExchangeCommission (SEC) rules changes on reporting compensation, and specificways firms are required to report today. We will also discuss major sourcesof data used by practitioners and academics. We will then go on to brieflydescribe pay levels and pay mix for CEOs across industries and firm sizesfrom 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 publishedon executive compensation is the availability of data. Data on executive payhave been widely available since 1992, when the SEC through new disclosurerules required firms to report on top officer compensation in a systematicway. However, the way data were reported was not, until very recently(a 2006 SEC change that we discuss below), entirely satisfying in terms ofreally understanding the way executives were paid at a point in time. Forexample, prior to 2006, we may have known an executive’s salary, bonus,value of stocks sold, and options exercised in a given year. However, thoseoptions and that stock may have been accumulated over many years so the


information reported in a given year was a combination of some compensa-tion for the most recent year (e.g., the salary) and stock and optionsaccumulated for perhaps as long as a decade earlier. This may be one of thereasons researchers have had trouble linking pay to performance – since theperformance may have been for a given year or the year prior while the paywas 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 togreat lengths to collect data on executive compensation prior to the mostrecent SEC change. Examples include Murphy (1985) – perhaps the first real‘‘classic’’ in the literature – Hall and Liebman (1998), and a more recenteffort by Frydman and Saks (2010). In the first case, Murphy (1985) collecteddata from 76 manufacturing firms (71 were used in the analysis sample) toinvestigate the relationship between pay and performance. In the second,Hall and Liebman (1998) collected detailed data on stock options (that werenot previously collected in one place) to demonstrate a relationship betweenownership in firms and firm performance. Frydman and Saks (2010) is anambitious example of careful data collection. In this paper, the authorsinvestigate executive compensation from a set of firms from 1936 to 2005.They find that the median real value of executive compensation was quite flatfrom the late 1940s through the 1970s, showing a weak overall link betweenCEO’s pay and firm’s growth. The collected the data by hand from companyproxy reports from 1936 through 1991 and then used ExecuComp (explainedin more detail below) for data beyond 1991.

In 2006, the SEC began requiring publicly traded firms to disclosecompensation for the CEO, Chief Financial Officer, and three other mosthighly paid Named Executive Officers (NEOs). The new guidelines bothclarified and standardized the elements of compensation as well as the timeperiod for reporting. Among the information firms are now required toreport are salary, bonus, nonequity incentive compensation, stock, stockoptions, changes in pension and nonqualified deferred compensation, andother compensation. It is worth being clearer about these seven ‘‘main’’components of compensation. Salary, of course, is the annual, fixed, andguaranteed compensation for the executive. Bonus and nonequity incentivecompensation are sometimes confused and, intuitively, both can beconsidered a type of ‘‘bonus.’’ Strictly speaking, the bonus as listed in thetable is formula-based pay beyond cash salary. On the other hand, nonequityincentive compensation can be both short- or long-term pay that is based onsome preset criteria (based on performance) whose outcome is uncertain.Stock compensation is the value of the stock granted over the prior year, as


of the time it is granted. Stock options represent the value of the optionsgranted over the prior year. Stock options pose a unique problem in valuingexecutive compensation contracts. The numbers included in firms’ proxystatement ‘‘summary compensation tables’’ are accounting-based numbersand 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 stockoptions from the stock option grant summary tables, which are also includedin firm proxy statements.4 Finally, ‘‘other’’ compensation refers to amountsof perquisites of $10,000 or more or to tax gross-ups, company contributionsfor security, private use of aircraft, financial planning, etc.

Table 1 is an example of a Summary Compensation Table for the GeneralElectric for 2009. Several features of the table are noteworthy. The table listscompensation 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 reasonfor listing more than five executives is the fact that some may have retired orotherwise left the firm during the year. Another (which is not the casefor GE) is the example of ‘‘co-CEOs’’. It is also clear from the table thatinformation is included for each of the last three years. This is the third yearsince the new SEC regulations came into place so is, therefore, the first timeoutsiders can see three years of compensation information all in the sameproxy statement. Table 1 also shows the seven different pay components thatare required to be reported for each executive.5 It is also interesting to seethat at GE, the CEO was not the highest paid executive (at least as reportedin the most recent proxy statement). In fact, as reported in Table 1, threeothers earned more than the CEO. Hallock and Torok (2010) report that of2,108 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, includingone-time signing bonus, larger than normal option grants (commonplacewhen hiring new executives) or severance, for example. In the case of GE inTable 1, the CEO received no bonus, option awards, or nonequity incentivepayout. All three executives who were paid more than the CEO had non-zerovalues for each of these elements of compensation. Finally, it is interesting tosee, in Table 1, the diversity of compensation across pay elements and to seethe 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 theperson- and firm-level that are now relatively widely used. The first,


Table

1.

ProxyStatementforGeneralElectricCompany(2008Summary

CompensationTable).

NameandPrincipal

Position

Year

Salary

1

($)

Bonus

($)

Stock

Awards2

($)

Option

Awards4

($)

Nonequity

IncentivePlan

Compensation

($)

Changein

Pension

Valueand

Nonqualified

Deferred

Compensation

Earnings5

($)

AllOther

Compensation6

($)

Total

($)

JeffreyR.Im

melt,

2008

3,300,000

06,860,3183

–0

3,563,466

372,819

14,096,603

ChairmanoftheBoard

andCEO

2007

3,300,000

5,800,000

9,802,3593

214,664

078,290

396,267

19,591,580

2006

3,300,000

5,000,000

7,404,2093

574,322

01,036,908

548,013

17,863,452

Keith

S.Sherin,

2008

1,500,000

2,550,000

2,987,493

1,597,537

2,555,300

2,503,541

288,718

13,982,589

ViceChairmanand

CFO

2007

1,354,167

3,000,000

3,076,095

1,714,833

01,281,453

275,400

10,701,948

2006

1,225,000

2,550,000

2,808,919

2,225,749

01,564,398

308,222

10,682,288

Michael

A.Neal,

2008

1,650,000

2,900,000

3,512,898

1,475,945

2,933,900

3,484,939

344,044

16,301,726

ViceChairman

2007

1,550,000

3,880,000

4,212,201

1,457,839

02,979,130

343,674

14,422,844

2006

1,400,000

3,300,000

3,906,929

1,759,672

03,032,927

294,872

13,694,400

JohnG.Rice,

2008

1,650,000

2,700,000

3,659,090

1,597,537

5,615,400

3,328,715

261,073

18,811,815

ViceChairman

2007

1,550,000

3,000,000

4,406,900

1,714,833

01,852,735

393,825

12,918,293

2006

1,400,000

2,550,000

4,122,437

2,225,749

02,183,677

335,866

12,817,729

BrackettB.Denniston,

2008

1,200,000

1,850,000

2,284,110

1,239,568

4,000,200

1,432,870

250,857

12,257,605

SeniorVicePresident,

GeneralCounseland

Secretary

David

R.Nissen,

2008

1,350,000

1,310,000

6,777,594

2,731,013

4,169,500

5,911,944

190,426

22,440,477

Form

erPresident&

CEO,GEMoney

RobertC.Wright,

2008

916,667

2,783,000

––

10,148,300

1,208,099

2,080,058

17,136,124

Form

erViceChairman

2007

2,750,000

7,590,000

1,943,665

1,303,005

01,072,075

1,314,005

15,972,750

2006

2,500,000

6,900,000

2,516,712

2,473,683

02,422,714

1,010,780

17,823,889

Note:Table

from

2009proxystatementofGeneralElectricCompany.

1Messrs.Sherin

andRicedeferredaportionoftheirsalaries

under

the2006ExecutiveDeferredSalary

Plan.They

werenotnamed

executives

atthetimethisplanwasinitiated.Theamountsare

alsopartoftheNonqualified

DeferredCompensationtableonpage30.In

addition,each

ofthenamed

executives

contributedaportionofhissalary

tothecompany’s401(k)savingsplan.

2Thiscolumnrepresentsthedollaramountsrecognized

forthe2007and2006fiscalyears

forthefairvalueofPSUsandRSUsgrantedin

those

years,aswellaspriorfiscalyears,in

accordance

withSFAS123R.Pursuantto

SEC

rules,theamountsshownexcludetheim

pact

ofestimated

forfeituresrelatedto

service-basedvestingconditions.ForRSUs,fairvalueiscalculatedusingtheclosingprice

ofGEstock

onthedate

ofgrant.


AsMr.Wrightiseligibleforretirement,thefairvalueofhisawardsthathavebeenheldformore

thanayearhavealreadybeenfullyexpensed.

Foradditionalinform

ation,referto

note23oftheGEfinan

cialstatem

entsin

theForm

10-K

fortheyearended

Decem

ber

31,2007,as

filedwith

theSEC.Forinform

ationonthevaluationassumptionswithrespectto

grants

madepriorto

2007,referto

thenote

onOther

Stock-R

elated

Inform

ationfortheGE

financialstatements

intheForm

10-K

fortherespectiveyear-end.Refer

tonote

3below

foradiscussionofthe

calculationofthefairvalueofPSUs.See

theGrants

ofPlan-BasedAwardstable

forinform

ationongrants

awarded

in2007.Theseamounts

reflectthecompany’saccountingexpense,anddonotcorrespondto

theactualvaluethatwillberealizedbythenamed

executives.

3Thisamountrepresents

thecompany’saccountingexpense

forPSUspursuantto

SFAS123R

andSEC

rules.It

reflects

theexpense

forall

previouslygrantedPSUs,

notonly

those

grantedin

2006or2007.Theactualvaluereceived

dependsonperform

ance:50%

ofthePSUs

convertsinto

GEstock

only

ifGE’scash

flowfrom

operatingactivities,adjusted

toexcludetheeffect

ofunusualevents,hasgrownanaverage

of10%

ormore

per

yearover

theperform

ance

period,and50%

convertsinto

GEstock

only

ifGE’stotalshareowner

return

meetsorexceeds

thatoftheS&P500over

theperform

ance

period.Accordingly,Mr.Im

meltmayreceive0%

,50%,or100%

ofeach

PSU

grant.Forexample,

asdescribed

intheCompensationDiscussionandAnalysisonpage17,Mr.Im

meltdid

notearn

50%,oratotalof215,000shares,from

the

PSUsgrantedto

him

inSeptember

2003andFebruary

2006because

thetotalshareowner

return

conditionwasnotmet.AlthoughthePSUs

notearned

byMr.Im

meltwerecancelled,therelatedaccountingexpense

of$4.3

millionhasbeendisclosedascompensationto

Mr.Im

melt

over

theperform

ance

period.In

measuringfairvalue,SFAS123R

distinguishes

betweenthePSU

vestingconditionrelatedto

thecompany’s

stock

price

andthenonstock

price-relatedperform

ance

condition.TherestrictionsonthePSUslapse

attheMDCC

meetingin

February

followingtheendoftheperform

ance

period.

4Thiscolumnrepresents

thedollaramounts

recognized

forthe2007and2006fiscalyears

forthefairvalueofstock

optionsgrantedin

those

years,aswellaspriorfiscalyears,in

accordance

withSFAS123R.Pursuantto

SECrules,theamountsshownexcludetheim

pact

ofestimated

forfeituresrelatedto

service-basedvestingconditions.AsMr.Wrightiseligibleforretirement,thefairvalueofhisawardsthathavebeenheld

formore

thanayearhavealreadybeenfullyexpensed.Forinform

ationonthevaluationassumptions,referto

note

23oftheGEfinancial

statementsin

theForm

10-K

fortheyearended

Decem

ber

31,2007,asfiledwiththeSEC.Forinform

ationonthevaluationassumptionswith

respectto

grantsmadepriorto

2007,referto

thenote

onOther

Stock-R

elatedInform

ationfortheGEfinancialstatementsin

theForm

10-K

fortherespectiveyear-end.See

theGrantsofPlan-BasedAwardstableforinform

ationonoptionsgrantedin

2007.Theseamountsreflectthe

company’saccountingexpense

anddonotcorrespondto

theactualvaluethatwillberealizedbythenamed

executives.

5This

columnrepresents

thesum

ofthechangein

pensionvalueandnonqualified

deferredcompensationearningsforeach

ofthenamed

executives.Thechangein

pensionvaluein

2007was$99,861,$1,221,780,$2,913,282,$1,759,575,and$488,342forMessrs.Im

melt,Sherin,

Neal,Rice,andWright,respectively.ThenegativevalueforMr.Im

meltwasprimarily

dueto

anincrease

inthediscountrate

usedto

calculate

thepresentvalueofhisbenefit,partiallyoffsetbyanadditionalyearofpensionaccrual.In

accordance

withSECrules,theamountincluded

in

thiscolumnrelatingto

thechangein

pensionvalueforMr.Im

meltis$0.See

thePensionBenefitstableonpage28foradditionalinform

ation,

includingthepresentvalueassumptionsusedin

thiscalculation.In

2007,theabove-market

earningsontheexecutivedeferredsalary

plansin

whichthenamed

executives

participatedwere$78,290,$59,673,$65,848,$93,160,and$583,733forMessrs.Im

melt,Sherin,Neal,Rice,

and

Wright,respectively.Above-market

earningsrepresentthedifference

betweenmarket

interestratesdetermined

pursuantto

SECrulesandthe

8.5%

to14%

interest

contingentlycredited

bythecompanyonsalary

deferredbythenamed

executives

under

variousexecutivedeferred

salary

plansin

effect

between1987and2007.See

Nonqualified

DeferredCompensationbeginningonpage29foradditionalinform

ation.

6See

theAllOther

Compensationtable

below

foradditionalinform

ation.


ExecuComp (Executive Compensation database) is produced by Standardand Poor’s Corporation and is surely the most widely used source of datafor research on executive pay by academics. This source has data availablefrom 1992 to present on the compensation of the top five highest paidemployees of U.S. publicly traded firms (who have managerial control) inroughly 1,500 firms per year. These firms include those listed in the Standardand Poor’s 500, the Standard and Poor’s SmallCap 600, and the Standardand Poor’s MidCap 400. The data source starts in 1992, which was (untilthree years ago) the last time there was a major change in executive paydisclosure rules.

Two other commercial executive pay sources are equilar.com andsalary.com. Each also provides comprehensive datasets of executivecompensation but have a larger focus on marketing to the for-profit firmand compensation consulting market. These sources are frequently used bycompensation design practitioners and consultants to help design executivepay plans (and to set comparison groups). Some academics are using datafrom 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 academicshave used ExecuComp in part due to inertia. It may also be due to costconsiderations. Equilar.com and salary.com provide many interface featuresfor, for example, making comparison groups easy and for presentationpurposes. 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 paydistributions.7 The data for this section are from salary.com and comprise2,108 publicly traded firms who reported executive compensation informa-tion 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 secondmeasure is ‘‘total compensation.’’ This is defined as the sum of salary, bonus,nonequity incentive, stock, stock options, change in pension and non-qualified deferred earnings, and other. Fig. 1 displays the median cashcompensation and total compensation for CEOs by industry for each of 22different industries. Notice the dramatic heterogeneity in compensation levelsfor the median CEO across industries. For example, the median CEO in


Fig. 1. CEO Compensation by Industry. Source: From Hallock and Torok (2010).

Data from salary.com


Commercial Banks earned about $581,000 in cash pay and $906,000 in totalcompensation. At the other extreme is the Food and Tobacco industry wherethe median CEO earned $2.28 million in cash compensation and $5.80million in total compensation. These statistics alone mask another level ofheterogeneity. Consider, for example, the Food and Tobacco industry(numbers not reported in tables or figures). There, the CEO at the 10thpercentile earned $575,000 in cash pay and $901,632 in total compensationbut the CEO at the 90th percentile of that industry earned $5.6 million incash and $14.9 million in total compensation (Table 2).

It may seem strange that Commercial Banks represent the industry withthe lowest paid median CEO. It must be kept in mind that these numbersdo not control for the size of the organization. In fact, there are a largenumber of Commercial Banks in the sample and many of them are quitesmall. Organization size (e.g., revenue and employees) is highly positivelycorrelated with the compensation of the senior leaders. Fig. 2 is a case inpoint. In this figure, the 2,108 companies are sorted by their level of annualrevenue. The smallest 10 percent are in decile 1, the next 10 percent indecile 2, and up to the largest 10 percent in decile 10. It is clear that themedian level of compensation rises monotonically with organization size.In particular, for the smallest 10 percent of companies (those with annualrevenues below $155 million, the median CEO earned $522,000 in cash payand $1.04 million in total compensation. This rises monotonically up to thelargest 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 largerdistribution. For example, for the largest 10 percent of companies, the CEOat the 10th percentile earned $4.2 million in total compensation but the CEOat the 90th percentile earned $25 million in total compensation.

Understanding the levels of pay for CEOs is interesting and important butmisses a more interesting and important part of executive compensation,how executives are paid. In particular, we now explore how executives arepaid across the seven components of compensation discussed above. Fig. 3shows a great deal of heterogeneity across compensation components byindustry. In fact, it is quite reasonable to expect diversity in compensationmix within industry. Fig. 4 reports the pay mix distribution on firm sizedeciles (the same deciles reported in Fig. 2). Notice that as the average firmgets larger a smaller fraction of the total compensation is paid in salary anda larger fraction is paid in stock and stock options. For example, for thesmallest 10 percent of companies, the fraction of total compensation paidin salary is 43.91 percent but for the largest 10 percent of companies, the


Table

2.

CEO

CompensationbyIndustry.

NCash

Compensation

TotalCompensation

10th

25th

Mean

Median

75th

90th

10th

25th

Mean

Median

75th

90th

Businessservices

88

500,000

683,542

1,352,478

1,071,563

1,637,340

2,493,708

686,254

1,199,744

3,438,851

2,232,751

5,430,194

7,282,921

Chem

icals

183

463,040

661,770

1,585,984

1,063,750

1,966,500

3,526,250

823,005

1,403,982

4,666,154

2,686,526

6,454,073

11,500,000

Commercialbanks

185

332,083

402,500

696,411

581,250

875,000

1,158,041

437,261

580,432

1,762,620

905,673

1,895,970

3,493,447

Commodities

85

678,501

961,299

1,867,734

1,524,583

2,434,725

3,284,000

1,102,336

1,686,500

5,099,530

3,606,402

6,353,118

10,800,000

Communications

78

654,711

780,000

1,585,526

1,137,002

1,732,500

3,577,845

819,035

1,518,311

4,293,980

2,849,066

5,615,136

11,900,000

Computerservices

137

370,000

555,024

1,220,244

875,308

1,375,350

2,671,250

589,629

1,132,758

3,285,691

1,939,824

3,845,512

7,676,077

Construction

26

833,750

1,000,000

2,100,333

1,353,457

2,386,318

5,005,000

1,705,216

2,252,217

5,297,176

4,349,201

7,411,561

11,000,000

Electronics

153

395,159

550,000

1,223,686

878,333

1,427,375

2,548,000

638,707

1,256,280

3,277,515

2,307,811

3,979,451

7,614,834

Energy

97

465,500

646,154

1,628,969

1,199,756

2,025,597

4,400,600

838,168

1,292,547

5,011,330

2,761,803

6,986,162

16,200,000

Financialservices

(nonbanks)

58

500,000

800,000

1,860,484

1,009,856

2,740,276

4,950,000

840,944

1,254,669

4,619,841

3,416,057

7,606,430

15,100,000

Foodandtobacco

50

575,000

1,147,342

2,922,108

2,277,237

4,046,612

5,614,329

901,632

2,025,222

6,946,929

5,804,652

9,575,570

14,900,000

Holdingcompanies

109

416,678

652,000

1,222,678

1,000,000

1,590,750

2,330,144

865,876

1,307,296

3,036,322

2,271,405

4,053,217

6,983,402

Industrialand

transportation

equipment

162

450,000

717,750

1,841,272

1,204,883

2,250,000

4,000,002

769,648

1,399,934

4,999,320

2,959,994

6,875,570

13,000,000

Insurance

89

612,435

894,072

1,993,162

1,375,000

2,839,000

4,100,000

941,741

1,830,011

5,683,356

3,269,665

7,325,265

14,900,000

Lumber

andpaper

40

602,077

788,987

1,373,753

1,124,018

1,692,627

2,691,200

936,277

1,393,705

3,588,637

3,313,487

5,167,598

6,929,589

Other

manufacturing

132

479,938

631,222

1,277,607

900,000

1,708,600

2,501,325

803,130

1,169,791

3,831,050

2,346,803

4,958,694

7,682,034

Other

services

91

500,000

800,000

1,308,878

1,003,167

1,600,000

2,163,333

836,708

1,192,087

3,530,677

2,459,920

4,387,239

6,765,583

Retailtrade

127

437,396

700,000

1,553,112

1,116,926

1,700,000

3,167,087

657,296

1,330,447

4,186,537

2,579,077

5,259,613

9,182,022

Textile

andapparel

20

654,647

852,332

1,505,563

1,325,500

2,292,103

3,477,798

984,558

1,450,150

3,268,828

2,053,119

6,072,780

8,106,963

Transportation

59

429,504

640,000

1,516,370

1,040,000

1,800,126

3,600,000

710,625

1,405,963

3,941,940

2,398,471

5,293,130

7,904,901

Utilities

86

548,035

850,962

1,694,643

1,287,665

2,050,000

3,457,587

874,643

1,800,794

4,551,456

3,217,168

6,571,790

10,700,000

Wholesale

trade(S)

53

605,000

891,033

1,774,548

1,450,059

2,075,000

2,841,266

1,185,812

1,629,529

3,575,034

2,888,031

4,674,634

6,929,124

Note:From

Hallock

andTorok(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 companiesearned 35.56 (21.55þ 14.01) percent of his or her total compensation instock and options. But the average CEO in the largest 10 percent ofcompanies earned 53.05 (24.81þ 28.24) percent of his or her totalcompensation in stock and stock options.

Now that we have set the stage for where the data come from and howCEOs are paid in the United States today, we turn to some of the practicalissues in setting CEO pay.

PRACTICAL ISSUES IN CEO PAY

In this section, we briefly describe some of the institutions that are importantin setting CEO and other senior executive compensation in the UnitedStates. In particular, we briefly mention the role of the Board of Directors,the Compensation Committee of the board, and the role of executivecompensation consultants. We go on to highlight some of what are known as

Fig. 2. CEO Compensation by Company Size. Source: From Hallock and Torok

(2010). Data from salary.com


Fig.3.

CEO

CompensationMix

byIndustry.Source:

From

Hallock

andTorok(2010).Data

from

salary.com


Fig.4.

CEO

CompensationMix

bySizeGroup.Source:

From

Hallock

andTorok(2010).Data

from

salary.com


‘‘proxy advisory services,’’ and the roles they play in executive compensationin 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 ofDirectors or the Compensation Committee of the Board, even thoughthese are the organizations that formally set pay of executives in publiclytraded companies.8 The Board is formally responsible for executivecompensation but most boards have a Compensation Committee (a subsetof 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 decilesof firm size have between 8 and 12 members. The median compensation ofBoard members from the top decile of firms in 2009 was $191,000 (Hallocket al., 2010).

Formally the Board is required to set compensation of the executive andexpected to be ‘‘independent.’’ Many Boards hire compensation consultantsto facilitate the development of compensation strategy and bring relevantdata to help frame pay decisions for CEOs and other top managers. Thesecompensation professionals consider the firm’s business strategy, compensa-tion 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 theUnited States for setting pay for executives. Bebchuk and Fried (2006) arguethat there is no ‘‘arms-length’’ negotiation between boards and the CEOover executive contracts. On the other hand, they argue that boards (andconsultants) are ‘‘captured’’ by CEOs who try to surround themselves withthose who advocate for higher pay or more favorable compensation mix.To ameliorate this potential conflict, some have argued that compensationconsultants to boards should be ‘‘independent.’’ An example of this is largemultipurpose HR consulting companies that provide executive compensa-tion consulting services, while at the same time providing others HR andbenefits administration and consulting services. Since the revenue to the HRconsulting firm from providing executive compensation consulting servicesmay be only a small fraction of the total revenue received from thatparticular firm, some argue that the executive compensation consultantsmay want to give the CEO higher pay in implicit exchange for the largevolume of other services provided to their company.


Proxy Advisory Services, Investors, and Executive Pay

Many ‘‘proxy advisory’’ services have emerged in the past decade includingRisk Metrics (formerly Institutional Shareholder Services), Glass Lewis, andGovernanceMetrics International. These organizations market and sell theirservices to smaller investors, firms, and institutional investors. Among theservices they provide is a type of ‘‘scorecard’’ by firm for thousands ofpublicly traded companies. They might, for example make recommenda-tions on votes before the company, provide information about directors andalso provide some information about executive compensation.

The kinds of information these organizations provide about executivecompensation is varied but quite limited. For example, in 2008, the typicalRisk Metrics report was on the order of 15 pages, one of which was devoted toexecutive compensation. This included a total of three charts showing (1) theCEO’s total compensation relative to a peer group median, (2) salary, bonus,and nonequity compensation of the CEO relative to the median of a peergroup, 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 perioddevoted one page to executive compensation. This included (1) a grade (A–F)and historical ‘‘compensation score,’’ (2) two charts comparing variouscomponents of compensation with sector groups, and (3) a chart on shareholderwealth and business performance. GovernanceMetrics International for thesame time period produced a three-page report for each firm that was entirelydevoted to executive compensation. This included some charts on pay levelsplotted against shareholder returns, comparisons to industry, charts on returnsand pay, relative to industry, a chart on pay mix, and a one-page narrative.

While we think these companies provide easily understood summaries toinvestors and firms with respect to CEO pay, firm performance, and com-parison groups, the analysis is extremely simple and largely reports numbersstraight out of individual firm proxy statements (except for some mediancomparisons by ‘‘industry’’). This serves as an example where the chasmbetween academic work on executive compensation and the practical world isenormous. Almost none of what has been learned in the past decades aboutexecutive compensation, pay, or performance is included in these sources.

METHODOLOGICAL ISSUES

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


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

Empirical Specifications

One of the difficulties in comparing the results of all papers in the executivepay literature is that very few of them estimate the exact same model. Whilethere does not appear to be a consensus on the ‘‘best’’ specification, there aremany commonalities that appear over and over again.

We begin our discussion with how compensation is actually defined. Themost popular definition is to use ‘‘total compensation’’ (see our discussionabove) as the dependent variable, but many studies also focused onoption (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 demonstratingtheir results are robust to multiple forms of pay. Clearly, it is natural toexpect certain forms of pay to be more strongly related to certain measuresof firm performance than others. For example, it is reasonable to expect thatstock and stock option compensation are more highly correlated with firmperformance than salary.

One important feature of a paper’s empirical specification is thefunctional form chosen to represent key variables. Specifically, the naturallog-transformation, which is used to deal with skewed data, is commonlyapplied to the dependent and many independent variables. While theinterpretation of coefficients is slightly less straightforward following a log-transformation, it is especially important for valid statistical inference whendealing with variables with a very skewed distribution such as compensationand sales data (Hallock, 1997 provided evidence that the log-transformationis important in executive compensation settings). Unfortunately, slightly lessthan half of the empirical papers we surveyed used the log-transformationon the dependent variable. This omission has the potential to seriously alterthe magnitude and interpretation of results.

One generally agreed-upon aspect is the need to control for a firm’s sizewhen estimating an executive’s pay. While not unanimous, nearly every study


we examined controls for size in some way. By far, the two most commonways to control for firm size were to use a measure of sales (revenue) or assetsheld by a firm. Another popular measure is the total number of employees.These three variables are commonly highly correlated, except in banks andother financial institutions, where assets are obviously substantially higherthan sales and employees, relative to many other industries.

When examining the link between pay and performance, a crucial choiceauthors must make is how exactly to define performance. There are anumber of accepted ways this is done, although the most convincing studiespresent the results for several different measures (Abowd, 1990). The mostcommon performance measure is a firm’s return on assets, followed by thereturn on common stock. Other measures include the return on equity,shareholder wealth, or firm profits. This point will be revisited in more detaillater 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 ofperformance. The intuition being that some industries naturally have veryvolatile performance indicators, likely weakening the pay–performance(observed) relationship because a high (or low) value may not necessarilybe a signal of the executive’s ability but rather a random shock. Studiesthat control for volatility generally use either the standard deviation(or variance) of performance (e.g., Garen, 1994) or the cumulativedistribution function (CDF) of performance (e.g., Garvey & Milbourn,2003; Dee, Lulseged, & Nowlin, 2005). The CDF appears to be the betteroption since it is a measure of the entire distribution of performance ratherthan just the spread.

One class of variables conspicuously absent from the majority of modelsis demographic information about the executive (Kostiuk, 1990; Hallock,1997; Bertrand & Mullainathan, 2001; Bertrand & Hallock, 2001 arenotable exceptions). Very few studies include standard control variablessuch as gender, race, or age. Age and tenure (and their squared terms) seemparticularly important to include in any sort of wage equation (as they arestandard practice dating back to the original Mincerian wage equations).However, whether due to data constraints or omission, these variablesappear in less than a fifth of the empirical studies we examined.

Another important feature of many papers (particularly those withoutfirm 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 governanceis positively related to the association between pay and performance.


While not all studies use panel data, and some that do don’t take fulladvantage of their panels, the papers that fully utilize panel data tend to bethe most convincing works. To begin our brief panel discussion, it seemsthat including industry fixed-effects are a bare minimum (this is true ina cross-sectional framework as well). Nearly every paper with firms indifferent industries either includes industry indicators or runs separateregressions for different industries. The most common panel data approachtaken 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 insufficientvariation within executive pay/performance measures. That said, the paper’sthat are able to employ a fixed-effects/differencing strategy are most likelyto come close to obtaining the best estimates of the ‘‘true’’ causal effectof performance on executive pay. We discuss this in more detail below in asection on causality.

In total, about half of the empirical papers we examined attempt tofully 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 ofempirical studies, with CEO/firm fixed-effects used by slightly less thana fifth of papers. A discussion of which of these approaches is moreappropriate (they are equivalent only in the case of two time periods) and isbeyond the scope of our project, and rests on distributional assumptions andserial correlation of the error term (Table 3).

To give the reader an idea of the relative frequency of the approaches toestimating executive compensation, we have compiled Table 4, which breaksdown the studies by type of model (fixed-effects, first-difference, etc.) andwhether the log-transformation was applied to the dependent variable.It should be noted that the 49 studies that appear in the table represent onlya sample of the executive pay literature (we believe a representative sampleof the top research), and there are undoubtedly a number of other papersthat 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 ofestablishing causality in empirical work, with specific emphasis on the CEOpay–performance problem. One of the reasons that there is such diversity in


Table

3.

CEO

CompensationbyRevenue.

Cash

Compensation

TotalCompensation

10th

25th

Mean

Median

75th

90th

10th

25th

Mean

Median

75th

90th

0–o116

326,774

396,000

616,804

521,703

765,290

975,849

449,349

676,779

1,382,037

1,034,623

1,631,245

2,500,199

116–o219

330,000

412,961

694,353

560,800

765,164

1,090,000

418,168

621,750

1,465,745

1,043,271

1,501,970

2,906,560

219–o360

385,467

500,000

827,883

700,625

967,500

1,418,027

521,101

807,011

1,787,558

1,309,888

2,138,803

3,562,378

360–o600

473,525

642,310

1,041,286

897,188

1,090,939

1,690,000

726,737

1,067,464

2,218,387

1,694,451

2,905,494

4,158,242

600–o912

530,173

690,000

1,163,253

948,147

1,334,500

1,741,250

854,755

1,484,095

2,729,881

2,118,466

3,234,420

4,677,025

912–o1371

638,230

838,235

1,452,193

1,223,614

1,723,000

2,399,341

1,078,276

1,681,375

3,246,883

2,648,624

4,031,500

6,461,061

1371–o2209

727,282

1,000,000

1,616,560

1,421,101

1,950,913

2,977,725

1,535,356

2,249,450

4,118,238

3,632,436

5,238,242

7,186,328

2209–o3974

800,000

1,031,279

2,037,876

1,703,739

2,440,838

3,750,000

1,567,596

2,664,599

5,434,129

4,662,380

6,702,930

9,479,940

3974–o9637

900,000

1,233,333

2,264,928

1,875,000

3,008,750

4,325,333

2,114,767

3,315,934

6,673,713

6,108,194

8,477,317

12,900,000

9637

1,000,000

1,694,000

3,334,802

3,025,857

4,468,336

7,016,500

4,223,182

6,478,689

11,600,000

11,300,000

16,100,000

25,000,000

Note:From

Hallock

andTorok(2010).Data

from

salary.com


Table

4.

BreakdownofEmpiricalResearchonExecutiveCompensation(Sample).

NoFE

Industry

FE

Firm

FE

CEO

FE

Diffin

DifforLagged

Term

Panel

A:Studiesthatdonotuse

alog-transform

ationonthedependentvariable

AntleandSmith(1986)

Aboody,Barth,and

Kaszmik

(2006)

ComprixandMuller

(2006)

Aggarw

alandSamwick

(1999b)

Aggarw

alandSamwick(1999a)

Baker

andHall(2004)

Core

etal.(1999)

Harford

andLi(2007)

Aggarw

alandSamwick

(2003)

Alm

azanet

al.(2005)

CarpenterandSanders

(2002)

Garen(1994)

Cichello

(2005)

AndersonandBizjak(2003)

Chen,Steiner,and

Whyte

(2006)

HartzellandStarks(2003)

Boschen

andSmith(1995)

Dee

etal.(2005)

Murphy(1985)

David

etal.(1998)

Frye,

Nelling,andWebb(2006)

Garvey

andMilbourn

(2003)

Girma,Thompson,andWright

(2007)

Hambrick

andFinkelstein(1995)

JensenandMurphy(1990)

LippertandMoore

(1994)

Rajgopal,Shevlin,andZamora

(2006)

Panel

B:Studiesthatuse

alog-transform

ationonthedependentvariable

Ang,Lauterbach,and

Vu(2003)

Chhaochhariaand

Grinstein(2009)

Bertrandand

Mullainathan(2001)

Abowd(1990)

Cosh

andHughes

(1997)

Core

andGuay(1999)

BebchukandGrinstein

(2005)

Becker

(2006)

Fattorusso,Skovoroda,

Buck,andBruce

(2007)

Cornettet

al.(2008)

CoughlinandSchmidt(1985)

HallandMurphy(2002)

CunatandGuadalupe

(2009)

GibbonsandMurphy(1990)

Hallock

(1997)

Gabaix

andLandier

(2008)

HallandKnox(2004)

Hallock

(2002)

HallandLiebman(1998)

Kostiuk(1990)

Kato

andKubo(2006)

Leonard

(1990)


the ‘‘answer’’ to the pay–performance problem is the diversity ofempirical methods and the dearth of papers that consider the issueof causality at all. For example, trying to consider causal relationships ina simple cross section using ordinary least squares (OLS) specifications isclearly impossible. Given the wide range of statistical approaches and resultsin the executive pay literature, we feel that having a basic notion of causalityis crucial for the reader to evaluate the relative merits of each paper.We have attempted to make this discussion as accessible as possible toreaders across fields.

The ultimate goal of most empirical research is to find the true causaleffect of some independent variable X on an outcome Y. In the rare case thatX is exogenously determined (randomly assigned), a simple regression ofY on X will give us the causal impact of X on Y. In the case of the pay-for-performance 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-unitchange in performance, and e is a random error term.

CEO Pay ¼ aþ bPerformanceþ � (1)

The reason this equation is never estimated is because in this model,we view performance as endogenous. By this, we mean that performancemeasures were not randomly assigned to executives, and that the sameprocess that determines performance may also be related to the process thatdetermines executive compensation (in a statistical sense, this means thatperformance is correlated with the error term).

A commonly discussed analogy is to think of the effect of schooling onfuture earnings (Griliches, 1976; Card, 1995). It is an accepted fact thatpeople with higher levels of schooling have higher incomes, but it is alsogenerally true that, on average, individuals with higher innate ability havehigher levels of schooling. So we must then ask how much of the increase inincome is due to increased schooling and how much is due to a higher innateability or some other unobserved variable related to schooling.

The most common way to address this issue is to estimate Eq. (2), wherewe have added a vector of control variables Z along with their associatedcoefficients d.

CEO Pay ¼ aþ bPerformanceþ Zdþ � (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 impacton an executive’s pay. In this case, for us to assign a causal interpretation to


the coefficient on performance, we are assuming that performance israndomly assigned after conditioning on the covariates in Z.

If performance cannot be considered exogenous, then the results weobtain will be considered correlational rather than causal. Hence, we couldsay that a one-unit increase in performance is on average associated witha b increase in compensation. We would, however, be unable to make thecausal claim that if we increased a performance measure by one unit, thenwe would observe an increase in pay of b. This is an important distinctionbecause it is possible to have a strong correlation between performance andpay, yet there is no causal link. Unfortunately, many authors in theexecutive pay literature fail to make this distinction, discussing their resultsin a causal context when they have not fully addressed potential endogeneityconcerns.

Establishing a causal link is not an easy task, and many will argue it isimpossible without a randomized experiment. There are several approaches,however, which can greatly improve the accuracy of the basic regressionestimates mentioned above.

First-Difference and Fixed-Effects

The essence of the first-difference and fixed-effect methods is to exploit thepanel nature of certain datasets, namely the fact that we may observethe same firm/executive different times. For instance, assume that there issome fixed but unobserved factor that is correlated with both executivecompensation and firm performance (this could be the executive’s ability,firm culture, etc.) denoted by g. If we observe many executives in periodst and t� 1, then assume that the true wage equations are as follows:

CEO Payt�1 ¼ aþ bPerformancet�1 þ Z t�1dþ gþ �t�1 (3)

CEO Payt ¼ aþ bPerformancet þ Z tdþ gþ �t (4)

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


algebraically identical. In panels with more than two years the two methodsdiffer, but the statistical assumptions underlying the difference betweenthese methods is beyond the scope of this chapter (although should be takenseriously by the researcher). Murphy’s (1985) classic is the first example ofthis method in the empirical CEO pay literature.

CEO Payt � CEO Payt�1 ¼ bðPerformancet � Performancet�1Þ

þ ðZ t � Z t�1Þdþ �t � �t�1(5)

CEO Pay ¼ aþ bPerformanceþ Zdþ Firmþ � (6)

It is always important to note in any model where the identificationis coming from (exactly which observations are contributing to whichestimates). In a standard OLS regression context, all observations contributeequally to the estimated parameters. However, in a first-difference/fixed-effect 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 notcontribute to the estimate of b. Practically, this is important because if thereis not much variation in a dataset, the estimates could be driven entirely bya 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 aset of industry dummy variables (assuming the dataset being used containsexecutives from more than one industry) is an absolute must. To not controlfor 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 donot believe the assumptions implicit in a fixed-effects framework (theseare discussed briefly in the section ‘‘Empirical Problems’’). In these cases,an instrumental variables (IV) framework can theoretically identify causaleffects. We will not go into the mechanics of IV, but the intention is tofind an exogenous variable that does not belong in the compensationequation 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 fromBertrand and Mullainathan (2001). In their study of oil companies, theyuse exogenous shocks to the price of oil to instrument for performance.


In this example, these shocks clearly do not belong in the compensationequation, but are highly correlated with performance indicators, makingthem an ideal instrument.

Empirical Problems

The two methods for establishing causality outlined above are by no meansa panacea and have many problems that must be addressed in practice.In fact, many researchers use these methods blindly and are lulled into a falsesense 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 thissetting. In an IV framework, strong instruments are often hard to find, andmust be rigorously justified in order to be accepted as valid.

When surveying the pay-for-performance, or any empirical literature forthat matter, it is important to keep this causal thought process in mind.Many studies will come to wildly different conclusions, for a variety ofdifferent reasons, and this line of thinking is key to discerning between thestudies you should believe and those you shouldn’t.

Performance Metrics

As mentioned before, a wide variety of measures, and categories ofmeasures, are used to proxy for performance throughout the executive payliterature. The main categories of performance metrics are accounting,economic/market, relative performance, and subjective. The most com-monly used measure, an accounting measure, is the after-tax return onassets (used by Bebchuk & Grinstein, 2005; Carpenter & Sanders, 2002;Chhaochharia & Grinstein, 2009; David, Kochhar, & Levitas, 1998 to namea few). This variable is preferable both because of its availability andstraightforward interpretation/construction.9

The next most common measure of performance falls under the marketcategory, shareholder return on common stock. Defined below,10 manyresearchers (such as Cosh & Hughes, 1997; Hall & Knox, 2004; Harford &Li, 2007) prefer this metric because it most directly measures the progressof the chief mission of the firm, to financially benefit its shareholders.Another accounting measure, the after-tax return on equity, is also widelyused (Hambrick & Finkelstein, 1995; Leonard, 1990).


As one might expect, the link between executive pay and performanceappears to be influenced somewhat by the performance metric used. Forinstance, Abowd (1990) found that the link was much stronger for marketmeasures than for accounting measures.

Another class of performance metrics deals with the possibility that firmsreward (or punish) their executives based on how the firm performs relativeto other comparable firms in the same industry. The most common way totest for the presence of relative performance evaluation is simply to controlfor the difference between a firm’s performance measure and the marketaverage for that same measure. Two of the papers that focus explicitly onthis type of performance metric are Antle and Smith (1986) and Gibbonsand Murphy (1990).

As mentioned earlier, some studies explore the link between pay andperformance using less quantitative and more subjective measures ofperformance. Denis, Hanouna, and Sarin (2006) find a positive link betweenallegations of securities fraud and executive stock options. In a similarlythemed paper, Efendi, Srivastava, and Swanson (2007) find a positivecorrelation between misstated financial statements and executive stockoptions 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 compensationand in executive compensation in particular. Part of the problem with thestudy of executive compensation and the pay-for-performance issue acrossnations is related to differences in disclosure and, therefore, availability ofdata across countries. There are two main issues we discuss here. First iswhy there may be differences in compensation across countries. Second, isthe state of the pay–performance literature using various within-countrydata 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 onthis issue. In a recent paper, Gabaix and Landier (2008) have some data


collected from the consulting company Towers Perrin on average levels ofpay for CEOs across various countries as well as average levels of companysize in those countries.

We have reproduced one of their figures as Fig. 5. The horizontal axis inthe figure is the average log company size in terms of company annualrevenue. The vertical axis is the average log total compensation for CEOs.The points represent the different countries. For example, it is clear from thefigure that CEOs in the United States are paid more, on average, than CEOsin 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. Thefigure also has an OLS regression line plotted through it. Intuitively thissuggests that if there is a ‘‘world’’ market for pay and if firm size is the onlyrelevant characteristic in predicting pay, then countries with points abovethe line have CEOs who are, on average, ‘‘over paid,’’ relative to theiraverage firm size and countries with points below the line have CEOs whoare, on average, ‘‘under paid,’’ relative to their average firms size.

This figure is interesting since it tells us several things. First, perhaps oneof the reasons CEOs in the United States are paid so much, relative to theircounterparts in other countries, is due to the fact that companies in theUnited 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.


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

International Pay and Performance within Countries

Several authors have tried to investigate the CEO pay–performancerelationship, or CEO pay more generally, in countries other than theUnited States. We highlight some of these studies here. In this section, wewill note studies from the UK, Germany, Japan, Sweden, and China.

The one country (other than the United States) where most work has beendone is the UK. In one paper, Conyon and Murphy (2000) find that CEOsare paid more in the US than the UK but much of that is due to stock-basedpay that has a stronger link to performance. Conyon and Sadler (2001)find that in the UK, the sensitivity of pay-for-performance increases withorganization levels. They go on to show a link between stock ownership andsubsequent performance. In a more recent paper, Fattorusso et al. (2007)use UK data and find little link between bonus and firm performance. Theyargue that bonuses are, therefore, in essence ‘‘guaranteed.’’ Finally, Girmaet 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 isa small pay–performance link.

In Germany, Edwards, Eggert, and Weichenrieder (2009) find that firmswith low concentrations of investor ownership have only a small linkbetween CEO pay and firm profits. However, those with highly concentratedownership have no link at all. Using some data from the UK and Germany,but in a mostly theoretical paper, Bruce, Buck, and Main (2005) discussregion-specific social norms, which others have argued are important tointernational compensation but very difficult to operationalize. In Japan,Kato and Kubo (2006) find a strong link between CEO pay and firmperformance using a ten-year panel , especially if using accounting measuresof performance. Becker (2006) finds that, in Sweden, incentives decreasewith CEO wealth. Finally, Firth, Fung, and Rui (2007) study China.

Clearly we need more research on the link between pay for CEOs and firmperformance internationally. Given the difficulties in matching data sourcesacross firms, the differences in disclosure requirements and tax rules across


countries, and vastly different social norms in some countries, this mayprove 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’’ isquite clear. On the other hand, in non-profit organizations, it is not alwaysclear what the ‘‘bottom line’’ mission of the organization really is. If wethought the ‘‘true’’ measure of performance for a particular non-profitwas ‘‘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 ofmanagers in non-profit organizations. Oster (1998) and Hallock (2002) bothshow that the strong link between firm size and executive pay that exists infor-profit firms is also present in non-profits. Additionally, Hallock (2002)points out that the revenue from government grants has no effect onexecutive compensation among non-profits once firm’s fixed-effects arecontrolled for. The same study also notes that a higher proportion ofcompensation at non-profits is in the form of benefits rather than salary.

As noted in both Oster (1998) and Hallock (2002), determinants of pay atnon-profits vary greatly by the type industry or charity. Examples includethe 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, andArnould (2005) examine the link between executive pay and profit-basedperformance measures following the introduction of health maintenanceorganizations (HMOs) into the market. They find that HMO penetrationstrengthened the link between pay and profit-based performance measures,and also increased the likelihood of turnover in less profitable non-profithospitals.

Some types of non-profits have more easily measurable performancemeasures. In their recent examination of the compensation structure for theheads of labor unions, Hallock and Klein (2009) find that the unionmembership and the wage of union members are strongly related to the payof the union executive.

Finally, Frye et al. (2006) use a matching strategy to compare firms thatare ‘‘socially responsible’’ to the more traditional for-profit firm. While theanalysis does not explicitly concern non-profits, the basic idea that some


firms may put more emphasis on performance measures not easily observablestill holds. Consistent with the non-profit literature, this study finds that‘‘socially responsible’’ firms (as measured by the Domini Social Index) havea much weaker link between pay and financial performance, and that optiongrants do not appear to induce the same risk-taking behavior in sociallyresponsible 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 executivecompensation. In particular, we have explored data issues including howpay is disclosed and how that has changed over time, a summary of the stateof CEO pay levels and pay mix in 2009 using a sample of over 2,000companies, described main data sources, documented main issues in thepay–performance debate and explained practical issues in setting pay.We also investigated what we believe to be at the root of fundamentalconfusion in the literature across disciplines – methodological issues. Inexploring 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-exploredareas, international issues, and compensation in non-profits.

We think there are several promising avenues for future work in executivecompensation. The dramatic advances in data availability, consistency, andquality will likely lead to less diversity in results in future studies. We hopethat researchers across fields take advantage of this. We also believe thata serious exchange of ideas on methods across (and within) disciplinesneeds to happen. We find it odd that researchers across disciplines have suchtrouble talking to, understand, or working with those outside their nicheareas. Additionally, we think that the walls between academic researchand practice should be brought down generally, but in the area of executivecompensation 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 andunexpected changes that could help us to better focus on issues of causalitywe mention above. In addition, certain legislation pending in Congressright now – including legislation related to ‘‘Say on Pay’’ could be veryinteresting. ‘‘Say on Pay’’ refers to the opportunity for a firm’s shareholders


to have an annual, nonbinding, vote on the compensation of the firms’senior executives. We suspect that study of this sort of proposal could leadto clever new research on executive compensation across disciplines. We alsobelieve 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’’ agree-ments are also gaining increased attention by practitioners and we suspectwill garner more attention from academics soon.

There is too little work on international issues in compensation and inexecutive compensation in particular. This is due, in part, to data availabilityproblems and in part due to the inherent difficulties in doing work acrosscountries and cultures. We hope that in the near future researchers willembrace the challenge of working on international issues.

Another area we feel is ripe for exploration and analysis by academicresearchers 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 willoccupy many of colleagues in the future.

This paper is focused on many areas of executive compensation includingthe pay-for-performance debate, the current issues, and the state of paytoday. But compensation internationally had a large focus on methodolo-gical issues and cross-disciplinary problems. Our hope is that this work willnot only help researchers focused on CEO pay and performance but alsomotivate researchers in other areas of Human Resources to explore outsidetheir 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 standardemployee 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 interestedreader 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.


6. Hallock and Olson (2010) provide a much more comprehensive description ofdata 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 firmA is a member of firm B’s board at the same time CEO of firm B is a member of firmA’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 helpfulsuggestions. We thank the Compensation Research Initiative at CornellUniversity for support.

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New York: Oxford University Press.


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 theoryof the effects of emotion regulation in emotional-labor performance.Drawing on Gross’s (1998a) process model, we argue that antecedent-and response-focused regulatory styles can be used to make differentialpredictions about outcomes such as performance, health, and antisocialbehavior and that these effects differ in shorter- and longer-time windows.We discuss the theoretical implications and address the strengths andlimitations of our approach.

Displays of task-appropriate emotions are particularly crucial facets ofperformance in occupations requiring employee–customer interaction andare the defining features of many jobs in the growing service economy(Bernhardt, Morris, Handcock, & Scott, 2001). The task conditions of suchjobs, the process of regulating emotion, and the emotional displays are often




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studied under the label of emotional labor (Beal, Trougakos, Weiss, &Green, 2006). The increasing importance of emotional labor is reflected notonly in its presence in today’s workplace, but also in the surge of researchinterest in understanding its consequences (e.g., Cote & Morgan, 2002;Grandey, Fisk, & Steiner, 2005). On one hand, researchers have found thatemotional labor not only negatively affects individuals’ physical and mentalhealth (e.g., Pugliesi, 1999), but also imposes staggering total societalcosts (e.g., Landy, 1992; Morris & Feldman, 1996). Some research has alsoshown 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, effectiveemotional displays have been often argued to relate positively to individualand organizational performance (e.g., Ashforth & Humphrey, 1993).

Although the past decade witnessed much progress, the emotional-labor literature has been beset by conceptual inconsistencies and anabsence of inquiry into the processes underlying the appropriate displayof emotions in business settings. Indeed, the conceptual literature has splitbetween 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 hascentered around what Beal et al. (2006) referred to as ‘‘affective delivery’’ orthe maintenance of required emotional displays at work. As those authorspointed out, ‘‘a detailed understanding of how employees successfullyregulate 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-laborliterature to more basic psychological theory on emotion regulation or the‘‘ways individuals influence which emotions they have, when they havethem, and how they experience and express these emotions’’ (Gross, 1999,p. 557). Regulation strategies can take various forms, but can be broadlycategorized as antecedent focused (regulating the emotion before it is fullyformed) or response focused (regulating the emotion after it is fully formed).She proposed that emotion-regulation research could serve to guide ourunderstanding of the regulatory processes underlying emotional-laborperformance. In this paper, we extend her work on emotion regulation inemotional-labor performance by developing a theory of the differentialeffects of emotion-regulation strategies on individual outcomes in worksettings. Our themes are individuals must regulate their emotions whenthey are confronted with emotion-labor tasks and required-display rules,regulatory styles play a role in determining important employee-related

MICHELLE K. DUFFY ET AL.88

outcomes, and these regulation processes have different consequences foremployee outcomes in different time windows.

Developing an emotion-regulation-based perspective on importantwork-related outcomes is important for several reasons. First, research hasshown that emotion regulation occurs commonly and daily. Thus, thequestion is not whether individuals regulate their emotions, but how theygo about doing so. We focus on how individuals regulate their emotions andthe implications these processes have for individuals and organizations.Following Grandey’s (2000) guiding work, we use basic psychologicaltheory on emotion regulation as a framework for understanding a widecross-section of individual outcomes (job performance, mental health,physical health, and the consequences of being targeted for and perpetratingaggression). Third, tremendous interest has grown recently in studyingthe consequences of emotional labor or the displays of task-appropriateemotions, especially in jobs in the growing service sector where thesedisplays 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 toexamining the consequences of emotion regulation across several importantwork-related outcomes, we incorporate the role of time and makedifferential predictions for different combinations of emotion-regulationstrategy and time frame. Second, we depart from extant literature, thepreponderance of which suggests that antecedent-focused emotion regula-tion produces better outcomes than response-focused emotion regulation,by arguing that the relative efficacy of antecedent- and response-focusedemotion regulation is time dependent. We distinguish between antecedent-and response-focused approaches across outcomes. Third, the study ofemotions in the organizational literature is nascent but rapidly growing,yet it has been hampered historically by what some see as the ‘‘intellectualdebasement of emotions in the workplace’’ (Muchinsky, 2000, p. 802). Ourapproach also differs from recent attempts to incorporate emotions into theorganizational literature in that we do not focus on the outcomes of specificemotions (e.g., Geddes & Callister’s, 2007 ambitious model of anger andantisocial behavior at work). Instead we focus on the processes individualsuse to regulate their emotions.

In the following sections, we (1) provide a foundation for our theorybuilding by briefly reviewing conceptual and definitional issues with respect toemotional labor; (2) review Gross’s (1998a, 1998b) process model of emotionregulation and Grandey’s (2000) application of it to the study of emotionallabor; (3) derive propositions concerning emotion regulation and short- and

Time and Emotion Regulation 89

long-term individual consequences in work settings; and (4) discuss theimplications 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, sheoffered a dramaturgical definition of emotional labor where she sawindividuals as managing emotions through surface- or deep-level acting.Surface-level actors manage their emotional expressions or displays; deep-levelactors ‘‘consciously modify feelings in order to express the desired emotion’’(Grandey, 2000, p. 96). She argued further that both surface-level actors anddeep-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-labormodel, which downplayed the role of emotion management and focusedalmost singularly on observable behaviors or emotional displays. Theseauthors suggested that many emotional displays are automatic or effortless,so emotional-labor performance may not manifest itself in stress-relatedoutcomes such as burnout and exhaustion. Rather, these authors argued,the primary outcome of effective emotional labor is task performance, andthis relationship should be positive to the extent that customers perceiveemotional displays as genuine. In a rather marked contrast, Morris andFeldman (1996) offered a third perspective by focusing not on emotionmanagement 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 thedissonance 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: theyare precursors to emotional labor, while effective displays of emotion are the


proximal outcomes of emotional labor. She proposed a return to emotionallabor’s roots – to a variation of Hochschild’s (1983) framework and the keyissue of emotion regulation. Defining emotional labor as the ‘‘process ofregulating both feelings and expressions for organizational goals,’’ Grandey(2000) used Gross’s (1998a, 1998b) emotion-regulation theory as a guidingframework for the study of emotional labor. She argued that understandingthe effects of emotional labor is more straightforward if viewed through thelens of the prolonged physiological and cognitive arousal that definesemotion 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, butit resides on a continuum of consciousness; some regulatory processes arenearly automatic and require little conscious processing (e.g., hidingdisappointment in a social context), while others require significant effortand involve higher-level conscious processing. As a number of authors havenoted, the process of emotion regulation extends well beyond the boundariesof emotional-labor performance; indeed it is a common, everyday process(Morris & Reilly, 1987).

Gross (1999) argued that the process of emotion regulation commenceswith exposure to and evaluation of environmental cues that then trigger orstimulate a set of response tendencies designed to shape emotional responses.When considered in a workplace context, these signals have much incommon with Morris and Feldman’s (1996) conceptualization of emotionallabor as a set of task characteristics. These tendencies can be broadlycategorized in terms of whether an individual attempts to adjust emotions bymodifying perceptions of the situation (or perhaps even the situation itself )or manipulates the emotional response after the emotion is fully formed andexperienced. At least five different emotion-regulatory strategies have beenidentified. Four of these strategies (situation selection, situation modification,attention deployment, and cognitive change) are referred to as antecedent-focused 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 thatare likely to have a strong emotional impact. Although these types ofregulation may occur frequently, they are less relevant for our purposesbecause, as Grandey (2000) pointed out, many workers, especially in jobs rife


with emotional-labor demands, have few alternatives for situation selectionor modification.

More appropriate for our purposes are the two antecedent-focusedapproaches that Gross (1999) labeled attentional deployment and cognitivechange. With attentional deployment, in a given emotion-eliciting situationan individual can choose to regulate emotions by redirecting attention fromthe cue and toward unrelated memories or events or by concentrating onsomething other than the current situational cue. Grandey (2000) offered theexample of a restaurant server who whistles arias to avoid being over-whelmed by negative or difficult customers. Cognitive change, in contrast,involves lessening the impact of a situational cue ‘‘either by changing howone thinks about the situation or about one’s capacity to manage thedemands it poses’’ (Gross, 1999, p. 560). Although these approaches havedifferences, the key issue with antecedent-focused emotion regulation is thatindividuals take action to lessen the impact of environmental cues beforethey fully affect the emotional experience and that the actions involvedeep-level modification of personal thoughts (attentional deployment) andexternal appraisals (cognitive change). These antecedent-focused approachesseem to share some construct space with Hochschild’s (1983) concept ofdeep-level acting.

The fifth type of emotion regulation is often referred to as response-focused emotion regulation; it involves influencing or altering responses afteremotions arise. The basic process underlying response-focused emotionregulation is suppression, such as hiding anger at a rude customer or calmingfrustration by breathing deeply and slowly (Gross, 1999). Suppression,therefore, ‘‘requires active inhibition of the emotion-expressive behaviorthat 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 alsofaking the emotional display (e.g., pretending to be happy when you areactually angry). Suppression seems to share some construct space withHochschild’s (1983) concept of surface-level acting.

Empirical research on these forms of emotion regulation has increased inrecent years. The implicit assumption in much of the empirical researchon emotion regulation is that individual- and work-related outcomes ofantecedent-focused regulation are more positive than outcomes of suppres-sion. Some evidence has supported this notion (Gross & John, 2003).Gross (2002), when discussing reappraisal as a form of antecedent-focusedregulation, argued that ‘‘efforts to down-regulate emotion throughreappraisal should alter the trajectory of the entire emotional response,


leading to lesser experiential, behavioral, and physiological responses’’(p. 283). In samples of US and French workers, Grandey et al. (2005) foundthat frequent suppression was strongly and positively related to emotionalexhaustion. Among a sample of employed college students, Cote andMorgan (2002) found that suppression of negative emotions was negativelyrelated to job satisfaction and positively related to intentions to quit. In anovel study of cheerleaders, Beal et al. (2006) found that participants whoperformed high levels of surface acting while experiencing negative emotionsreported higher levels of difficulty at maintaining required display rules.Two rare studies of deep acting (similar to the attentional deployment formof reappraisal) and surface acting (similar to suppression) also providedsupport for the positive effects of reappraisal and the negative effectsof suppression. Grandey (2003) found that surface acting was positivelyrelated to self-reports of emotional exhaustion and negatively related tocoworker ratings of affective delivery (e.g., warmth and friendliness), whiledeep acting was positively related to coworker ratings of affective delivery.Moreover, deep acting was not significantly related to self-reports ofemotional exhaustion. Among a convenience sample of workers in Canada,Brotheridge and Grandey (2002) found that surface acting was positivelyrelated to exhaustion and depersonalization and negatively related toperceptions of personal accomplishment. Consistent with Grandey’s (2003)results, deep acting was not related to exhaustion, but was positively relatedto perceptions of personal accomplishment.

When viewed in toto, initial findings have suggested fairly uniform effectsof antecedent- (positive) and response-focused (negative) emotion regula-tion on individual and workplace outcomes and, indeed, this is somewhatconsistent with experiment-based empirical literature in the basic psychol-ogy fields (e.g., Gross, 2001; Gross & John, 2003). But are the consequencesof emotion regulation always uniformly positive (antecedent focused) ornegative (response focused) in terms of individual outcomes in theworkplace? Although the advances in the past few years have beenconsequential, the literature has tended to view task conditions as uniformin terms of the emotional labor required – emotional labor is either requiredor not – and has tended to take short-term views of the dynamics of emotionregulation. We argue that much of the speculation concerning emotionregulation and outcomes implicitly confounds the effects of emotion regula-tion on short-term consequences with long-term consequences. Cote andMorgan’s (2002) study represented an initial attempt to study regulationprocesses over time, but the one-month time lag they examined may nothave been sufficient for differential effects to emerge. Gross (2001, 2002)


concluded his reviews of emotion-regulation research by suggesting thatresearchers explore the potential differential consequences of differentregulatory styles and pursue a research agenda focused on the ‘‘long-termconsequences of differing emotion regulation strategies’’ (p. 218). We arguebelow that emotion-regulation-based predictions may hold only in certaintime windows and only for certain outcomes. We focus on three aspectsof emotion regulation – attentional deployment, cognitive change, andsuppression – 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 emotionregulation in emotional-labor performance. Drawing on the definitionsdescribed above, we categorize work-related outcomes in terms of time anddraw distinctions or moderators with regard to task type where theorydictates. The range of outcome variables included in this conceptualizationshould be considered a subset of all possible outcomes influenced byemotional-labor contexts and the process of emotion regulation. In thissense, the proposed framework should be viewed as a point of departurerather than as an all-inclusive theoretical framework regarding the impactof emotion-regulation issues. We make two general assumptions in termsof the boundary conditions of our theory building. First, we assume thatenvironmental cues trigger the regulation of emotion in emotional-laborcontexts (Grandey, 2000). This triggering process can be a function of thework itself (e.g., the task characteristics that create emotional-laborsituations; Morris & Feldman, 1996), or of specific environmental events(e.g., interactions with angry customers; Rafaeli & Sutton, 1987, 1990), orboth (Sutton, 1991). Second, we use the notion of time in a general sense,making differential derivations and proposals for short and long timeframes, respectively. We do not intend to establish a specific window.Rather, we suggest that short-term propositions concern emotion-regulationstrategies in single emotional-labor events (e.g., interacting with a customerat a service desk). Long-term propositions concern the effects of emotion-regulation strategies in repeated emotional-labor events over time (e.g.,working in a customer-service role for a year). In the following sections wederive short-term predictions regarding attentional deployment, cognitive


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

Short-Term Consequences of Emotion Regulation

Job PerformanceIn an initial attempt to link emotion-regulation strategies to work-relatedoutcomes, Grandey (2000) proposed that individuals using antecedent-focused regulation would perform better in customer-service roles thanthose using suppression regulation. She argued that emotional suppressionmay relate to observers’ perceptions that the individual is ‘‘faking’’ or isdisingenuous. Alternatively, those engaging in attentional deployment orcognitive change have either reevaluated their emotional state and/orrecall other emotions to get through the moment (Gross, 1998a, 1998b).Accordingly, observers are less likely to detect their true feelings. On thesurface, these generalizations appear accurate, but a closer examination ofthe theoretical foundation of emotion regulation reveals that we can derivemore specific propositions.

First, in the emotional-labor context, the type of emotion regulation mayaffect job performance depending on how deeply one interacts with others.To illustrate, suppression, which requires that an individual inhibits agenerated emotion, may powerfully impact individuals (Gross, 1998a).In contrast, antecedent-focused regulation is a front-end approach in whichan individual interprets an emotional cue in unemotional terms before itis fully generated. The process of emotion suppression results in slightlyhigher levels of sympathetic activation (Gross & Levenson, 1997) than theprocess of emotion regulation through attentional deployment and cognitivechange; the activation difference may mean that the suppressor is moreengaged (e.g., energetic or on edge) in surface-level or shallow interactionsthan is the antecedent-focused regulator. Antecedent-focused regulation, bydefinition, means that the individual is more detached from the environ-mental or emotional cue (recall the example of the aria-whistling server),and observers may perceive them as being less engaged, and henceperforming 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-serviceemployee managing a drive-through window). Because of heightenedphysiological activation, these situations provide opportunities for higher


job performance among those engaging in suppression regulation comparedwith those using with antecedent-focused regulation.

In more interaction-rich interpersonal contexts, substantial benefits mayaccrue for job performance for those engaging in antecedent-focusedregulation. Interactions that last longer provide greater opportunities foremotional ‘‘leakage’’ (e.g., Ekman & Friesen, 1969) to betray the performerand also more opportunities for observers to detect that the employee isfaking emotions. By manipulating the environmental and emotional input,those engaging in attentional-deployment or cognitive-change forms ofregulation may be slightly less physiologically activated, but the payoffsinclude that they appear to be interacting more genuinely. The antecedent-focused process partly concerns perpetuating an illusion that the actualsituation is really something different (e.g., misperceiving that a situation isnot so ominous when it is actually threatening) such that the appraiser isconvinced that the expressed emotion is real. Taylor and Brown (1999)argued that illusions are a critical component of the ability to care for othersand are especially useful in threatening situations. In emotional-laborsituations where single interactions are deeper and last longer, this processmay be markedly advantageous in terms of job performance. To illustrate,some jobs requiring emotional labor are not particularly complex but requireprolonged interactions with others. For example, an auto salesperson and acustomer may haggle over the price of the car and trade-in for several hours.The time-exposure pressures may cause the salesperson to ‘‘leak’’ suppressednegative emotions, and ultimately reveal to the customer that the sales-person, as a suppression regulator, has been disingenuous. By way ofcontrast, the reappraisal regulator may be slightly less activated initially, butshould 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) willresult in better short-term job performance than antecedent-focusedemotion regulation (attentional deployment and cognitive change) insituations where interpersonal interactions are shallow and/or of shortduration.

Proposition 2. Antecedent-focused emotion regulation (attentionaldeployment and cognitive change) will result in better short-term jobperformance than response-focused emotion regulation (suppression) insituations where interpersonal interactions are deep and/or of longerduration.


Another important issue with regard to emotion regulation and short-term task performance concerns the level of cognitive activity needed intasks requiring emotional labor. A great deal of research has concernedthe loss of cognitive functioning during acute suppression episodes. Forexample, Baumeister, Bratslavsky, Muraven, and Tice (1998) found thatemotion regulation through suppression degrades performance on cognitiveassignments. The logic behind the finding is that individuals have a limitedsupply of resources for performing cognitive tasks; the challenge ofsuppressing a negative emotion exhausts some of the resources, leavingfew available for performing the task successfully. In addition, someresearchers have shown that suppression degrades memory, a key facet ofperformance on cognitively complex tasks (Richards & Gross, 1999). Theseauthors hypothesized and found that the conscious suppression of emotionsincreases self-focus and diminishes the ability to encode new information(e.g., Ellis & Ashbrook, 1988; Pyszczynski & Greenberg, 1987). Reappraisalregulation, by contrast, stops emotional cues before they consume valuablecognitive resources.

As mentioned before, many jobs that require emotional labor are notparticularly complex, but the implications of these arguments extend beyondjobs that demand constant emotional labor to those that require emotionregulation in only limited circumstances, for example, managing emotiveprocesses in periodic dealings with supervisors or working in teams and ontask forces charged with cognitive tasks. More mundanely, restaurantservers engaging in emotional suppression may perform less well on aspectsof their job that require more cognitive functioning (e.g., remembering thefood and drink orders from a large table). Thus, we suggest the followingproposition:

Proposition 3. Antecedent-focused emotion regulation (attentionaldeployment and cognitive change) will result in better short-term jobperformance than response-focused emotion regulation (suppression) inemotional-labor situations with high cognitive demands.

Mental HealthHaving an accurate perception of reality is often seen as a hallmark ofmental health and well-being, but considerable evidence has suggested thatindividuals may enjoy increased mental health by maintaining positiveillusions (Alloy & Ahrens, 1987; Taylor, 1983). As Taylor and Brownnoted, the happy person ‘‘appears to have the enviable capacity to distortreality’’ (1988, p. 204) to enhance their views of control and optimism.


The connections are clear between this literature base and reappraisal as anemotion-regulation style. In a manner similar to reappraisal, individualsmay protect their well-being by using filters that distort or modify negativityin the environment. Returning to the ‘‘whistling-arias’’ anecdote, individualswho use antecedent-focused (attentional deployment and cognitive change)emotion-regulation approaches create, in essence, positive illusionsabout situations so that they experience the emotion ‘‘in a direction thatenhances self-esteem, maintains beliefs in personal efficacy, and promotes anoptimistic view of the future’’ (Taylor & Brown, 1988, p. 204). Empiricalevidence 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 themtend to report lower subjective levels of that emotion (e.g., Gross, 1998a).Thus, we suggest the following proposition:

Proposition 4. Antecedent-focused emotion regulation (attentionaldeployment and cognitive change) will result in better short-term mentalhealth than response-focused emotion regulation (suppression).

Long-Term Consequences of Emotion Regulation

Job PerformanceSeveral theoretical perspectives posit that the long-term consequencesfor job performance will be more severe among those using response-focused rather than antecedent-focused emotion-regulation strategies.First, self-discrepancy theory (e.g., Higgins, Klein, & Strauman, 1985) holdsthat individuals are motivated to achieve a match between self-conceptand a number of personally relevant self-guides. Mismatches between theactual-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 toscripts can cause employees to deviate from their ought-self to anothersocially prescribed actual-self. Following the self-discrepancy model,individuals often suffer guilt and shame when they transgress or fail tomeet their self-imposed standards. When a salesperson suppresses negativeemotions 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 self-discrepancies to irritation, lethargy, and disinterest over time, all factors


that should reduce an employee’s ability to perform well on the job. Possiblyantecedent-focused regulation may also cause emotional laborers to displaydiminished performance over time, but following a self-discrepancyapproach, these negative effects on job performance in the long termshould be more pronounced under a suppression regulatory style.

A second theoretical issue concerns enhanced accessibility of emotionalcues over time. Polivy (1998) and others (e.g., Rachman, 1980) arguedthat emotions are a cue for appropriate behaviors and that repeatedinhibition or suppression may eventually produce a surge in target thoughtsduring the attempted suppression as well as behavioral excess overtime. This effect is particularly plausible for long-term job performance,because research has demonstrated that the surge in thoughts about theemotion may increase dramatically after lapses occur in mental control –either voluntarily (a work break, for example) or involuntarily (an acuteincrease in cognitive demands). Just as restrained dieters may suddenly losecontrol and overindulge, individuals regulating their response-focusedemotion may, over time, lose their strength to suppress emotions andultimately become incapable of doing so. Wegner and Schneider (1989)called this phenomenon the ‘‘rebound effect.’’ Repeated suppressionattempts and frequent relinquishment of mental control over time shouldweaken performance.

Proposition 5. Response-focused emotion regulation (suppression) willhave more severe negative effects on job performance over time thanantecedent-focused emotion regulation (attentional deployment andcognitive change).

Physical HealthAlthough emotion regulation in the form of suppression reduces theintensity of emotional displays, it does not exempt the person fromexperiencing the emotion physically and psychologically. Studies havesuggested that suppression increases physiological activation and elevatesindicators such as finger pulse, finger temperature, and skin conductance(Gross, 1998a; Gross & Levenson, 1997). Although these indicators may notbe hazardous in isolation, the cumulative effects of prolonged sympatheticactivation are related to a number of damaging health problems overtime (Krantz & Manuck, 1984). The physical consequences of emotionregulation – suppression in particular – can relate to cardiovascular andimmunological system malfunctioning (Schaubroeck & Jones, 2000). Eachtime the stress response is engaged, the immune system may be selectively


inhibited (O’Leary, 1990). In terms of cardiovascular reactions, emotionsuppression has been linked to increased sympathetic nervous systemactivation (including cardiovascular symptoms).

Proposition 6. Response-focused emotion regulation (suppression) willresult in more physical health problems over time than antecedent-focusedemotion regulation (attentional deployment and cognitive change).

Targeting of Antisocial BehaviorAs noted above, some evidence has indicated that antecedent-focusedregulation strategies (attention deployment and cognitive change) may bepreferable to suppression in terms of short-term mental health benefits andperhaps in terms of job performance as well. However, we propose that theemployee who must habitually use attentional deployment and cognitivechange may pay a significant cost in terms of being exposed to antisocialbehavior at work, such as social undermining (e.g., Duffy, Ganster, &Pagon, 2002; Duffy, Ganster, Shaw, Johnson, & Pagon, 2006) and abusivesupervision (e.g., Tepper, 2000; Tepper, Duffy, & Shaw, 2001). We suggesttwo primary reasons that antecedent-focused strategies may increasethe risk that an individual will be targeted for antisocial behavior at work.First, individuals who use attentional deployment (e.g., distraction) mayinadvertently signal would-be aggressors that the target individual may beeasily taken advantage of. As Marx, Heidt, and Gold (2005) pointed out,different forms of attentional deployment may help an individual stave offacute negative emotions associated with problematic situational cues, but‘‘others who note the observable signs of dissociation or altered conscious-ness may be quick to take advantage’’ (p. 80). Moreover, individuals whoare distracted and concentrating on things beyond the emotional-laborevent as ways of constricting emotional expression have been shown tosignal vulnerability for targeting (e.g., Luterek, Orsillo, & Marx, 2002).

Beyond attentional deployment’s attraction to aggressors, this avenue foremotional regulation may also interfere with an individual’s ability to processthreatening warnings in the environment. Emotions consist of responsetendencies that are meant to coordinate behavior in times of challenge ordanger. Gross (1998a, 1998b) suggested that one long-term consequence ofantecedent-focused regulation may be that one begins to deny importantfeatures of one’s environment through the use of unrealistic or inflexibleviews (e.g., insisting that ‘‘everything is okay’’ when it is not). Individuals canoften achieve attentional deployment through distraction or by focusingattention on nonemotional or cross-emotional aspects of the situation.


In addition, they can redirect their attention from the cue demanding higherlevels of concentration to another task, which consumes cognitive resourcesthat they could use to process the emotion fully. Both avenues for attentionaldeployment may impede the process for detecting and dealing with threat-relevant cues in the work context (Marx et al., 2005).

Our arguments concerning antecedent-focused regulation and workplacetargeting involve not only the threat-identification arguments above, but alsothe idea that attentional deployment and cognitive change can compromisedefense activation or reflex systems. The key interference point for defensivereflexes is during the postencounter stage – what emotion-regulationresearchers have called the emotion-soliciting situational cue. During thisstage, normal responses would include freezing, focusing attention on thecue, and considering the potential threat. This stage, which precedes themore commonly discussed ‘‘fight-or-flight’’ stage, is critical not only in termsof evaluating the situational cue but also in handling the situation. Ignoringsituational warnings or reevaluating one’s capacity to manage the situationinterferes with, and perhaps disables, this process. In essence, theseantecedent-focused strategies allow an individual to get through the momentbut may inadvertently result in a failure to utilize valuable informationthat the task or emotional cue contains. An individual using distraction tofend off a distressing emotion may be able to function, but may fail to seepotential hazards in the social environment ‘‘even under the circumstancesin which such arousal might be directly linked to such threat cues’’ (Marxet al., 2005, p. 81). Although unrelated to interpersonal deviance, researchexamining safety at work has linked distraction and proneness todistractibility as significant predictors of accidents (e.g., Hansen, 1989).

Likewise, an individual who engages in cognitive change while dealingwith irritating or hostile coworkers or customers may start to believethat the situation is ‘‘not so bad’’ or ‘‘under control,’’ when in reality thesituation should be activating defensive reflexes. These individuals,confronted with signs that others are bullying or aggressive, may ignorethe signals or reevaluate them as harmless. By ignoring their adaptiveemotion-based defenses they may become more vulnerable to future acts ofaggression by coworkers or customers. This risk may be heightened by thefact that these employees may also present themselves more passively insocial interactions (i.e., they do not feel a need to appear assertive becausethey do not perceive danger). Recent research has suggested that individualswho appear to be more passive and are low in self-determination are morelikely to report being victimized at work (e.g., Aquino, Grover, Bradfield &Allen, 1999).


In terms of response-focused emotion regulation, we expect that suppres-sion will not increase susceptibility in becoming a workplace target for twoprimary reasons. First, in response-focused regulation such as suppression,emotions are fully formed before they are modified. The formation of theemotion ensures that the key processing stage of the defense reflexes systemis not altered. Although the emotion is later modulated, emotion formationallows the appropriate defense mechanisms to be activated and thesituational cue processed correctly, which should aid in accurate threatdetection. In addition, Wegner’s (1994) ironic processing model suggeststhat suppression of thoughts and emotions involves two distinct mentalprocesses – an operating process that attempts to create the desired stateand a monitoring process that continuously supervises the suppression andsearches for lapses of mental control. Important in this view is that theprocesses underlying emotion suppression are attentional processes thatinitially orient the individual toward the stimulus or situational cue. In thecase of workplace stimuli, the suppression-processing model would suggestthat after attention is focused on the situational cue and the emotion isdeveloped, the choice to regulate through suppression starts an operatingprocess to bring desired emotions to the surface. After the operating phase isactivated, an unconscious monitoring process continues to scan for signsthat the system is working properly including ‘‘sensations and thoughts thatare inconsistent with the achievement of successful control’’ (Wegner, 1994,p. 38). Wegner (1994) concluded, ‘‘anything that is not the target of theoperating process, after all, indicates failure of the operating process andshould be monitored’’ (p. 40). It is reasonable, then, to expect that focusedregulation will lower the likelihood of victimization because not only areemotions fully formed before suppression, thereby activating defense reflexes,but the unconscious monitoring system will continue to scan for lapses ofmental control or other factors that may cause emotional control to fail.

Proposition 7. Antecedent-focused emotion regulation (attentionaldeployment and cognitive change) will relate to a higher risk of beingthe target of antisocial behavior at work relative to response-focusedemotion regulation (suppression).

Engaging in Antisocial Behavior at WorkAlthough the arguments above suggest that attentional deployment andcognitive change increase vulnerability, evidence has also suggested thatindividuals who habitually use response-focused regulation may be moreaggressive than those who use antecedent-focused approaches such as


attentional deployment and cognitive change. Constrained or suppressednegative emotion is often redirected or displaced toward less-powerful ormore-available targets (e.g., Marcus-Newhall, Pedersen, Carlson, & Miller,2000). The frustration hypothesis suggests that those individuals whosuppress their emotions will be more likely to express their frustrationthrough antisocial behaviors directed at others in the workplace. Theoryand empirical findings on displaced aggression are also consistent with thesearguments and further highlight that antisocial behavior can be triggered bya simple, daily event such as a happy dog jumping on the owner returningfrom work or a coworker in a break room commenting on an unrelatedtopic (Miller, Pedersen, Earleywine, & Pollock, 2003).

In terms of direct antisocial behavior, when emotions are suppressedduring emotional-labor work, emotional leakage may occur, especially whenthe social interactions are longer or deeper (e.g., Ekman & Friesen, 1969).Companies may have strict rules limiting emotional displays and prohibitingaggression toward customers. Fear of retaliation or punishment servesas a major constraining factor for the expression of negative emotions. Mostjobs that exert strong emotional-labor demands on employees (e.g., airportticketing agents, bar servers, social workers) require that they constrain theiremotions when they encounter hostile or abusive customers or clients. But itis also likely that suppressed workers will displace their aggression,expressing it toward coworkers or even further downstream toward othersoutside the workplace.

The ironic processing model noted above also shows that response-focusedregulation will increase the likelihood an individual will engage in antisocialbehavior at work. Recall that in the ironic processing model, an individualachieves mental control through an operating process that creates the desiredstate and a monitoring process that continuously supervises the suppressionand searches for lapses of mental control. As the irony label suggests, aconsequence of these mental processes is that the emotion and the precipitatingevent surrounding the emotions may actually become more accessible overtime (Polivy, 1998; Wegner & Schneider, 1989). Elevated accessibility maylower the threshold for future aggressive behavior in response to minorannoyances (Marcus-Newhall et al., 2000). Thus, individuals who suppressemotions at work may explode at seemingly trivial or minor triggers in amanner that seems incommensurate with the social interaction that precededthe reaction. Although evidence of these effects is sparse, Christoforou (2008)found among a sample of sales representatives that individuals using response-focused regulation strategies to deal with interactions from abusive customersengaged in higher levels of emotional deviance in the workplace.


In terms of antisocial work behavior, a value of antecedent-focusedregulation is that repeated regulation from attentional deployment andcognitive change is less likely to increase the risk of antisocial behaviordirected at individuals responsible for the emotion-triggering cue orindirectly at innocent coworkers, family members, and friends. Becausenegative emotions are not formed fully with ex ante regulation strategies,defensive reflexes such as fighting or fleeing and the mental control processescharged with operating- and monitoring-suppressed emotions are notactivated. In addition, the ironic processing system is circumvented, whichlessens the likelihood that the individual will easily recall the precipitatingevent, will extensively ruminate on the emotion and the cue, or willpervasively 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 theseideas can be found in a recent series of studies by Bushman, Bonacci,Pedersen, Vasquez, and Miller (2005). These authors found that comparedwith experimental participants who were distracted, participants whoruminated about a provocation were more likely to engage in displacedaggression after being exposed to a minor annoyance or trigger. Thus, wesuggest the following proposition:

Proposition 8. Response-focused emotion regulation (suppression) willrelate to a higher likelihood of engaging in direct antisocial behavior atwork and displaced aggression than the antecedent-focused emotionregulation (attentional deployment and cognitive change).

CONCLUSIONS AND FUTURE

RESEARCH DIRECTIONS

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


Our exposition is but a first attempt to delineate the relevant workplaceaftereffects of employed regulation strategies. It is impossible to point to oneemotion-management tactic as preferable in the absence of rich contextualand personal factors. Indeed, our framework highlights the positive andnegative aspects of both approaches to emotion regulation; neither isuniversally effective, neither is completely unsound. Given the expectationsand demands businesses are placing on the ever-increasing service sectorof the labor force, it has become crucial to know how, when, and whyindividuals use various emotional strategies (Tarvis, 1984). Moreover,further information regarding the relative benefits and detriments of variousemotion-regulation strategies would benefit managers and employees alike.

As Stone-Romero (1994) suggested, it is necessary to highlight theboundary conditions, both of the overall framework presented and of thespecific propositions offered. First, we make no differential predictionsregarding the type of emotion being regulated. Clearly, the type of emotion(e.g., disgust, sadness, anger, fear, enthusiasm, pride) may impact not onlythe 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 specificpropositions for the outcomes of the regulation of individual emotions. Wealso use the concept of time in a general sense (short term versus long term).We distinguish between single emotional-labor events (short term) versusthe same regulation strategy repeated over time (long-term). Although thisallows us to decipher inconsistent theory and research findings regardingemotion-regulation processes, future researchers should explore the timingissues our hypotheses capture. Perhaps research on behavioral spirals andadditional exploration of contagion effects would clarify the organizationalimpact 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 necessarybecause experimental psychology has conducted most emotion-regulationresearch; longitudinal field studies of emotion regulation in organizationalsettings are largely unavailable. Finally, we examine only consequences ofemotion regulation in the performance of emotional labor – the antecedentsremain largely unexplored. For instance, little is known about why peoplechoose to suppress rather than use attentional deployment, whether someindividuals oscillate between emotion-regulation strategies, and howfrequently they choose consciously between the two options.

At this point, we highlight the need for additional theory developmentand empirical research to test and extend the basic tenets of our theorizing.


The propositions delineated here are clearly testable, although not withoutchallenges. As noted above, much of the empirical testing of emotion-regulation theory tenets has been conducted in short-term laboratorysettings where emotion-regulation strategies have been manipulatedexperimentally. But the nature of emotion regulation and outcomes suchas performance, physical health, and antisocial behavior lend themselves tolongitudinal or multiwave studies. These work dynamics may play out inimmediate contexts or within certain interactions – for example, a customerdirectly targeting an employee – but in many cases will unfold over time.One-to-one correspondence will not appear between an employee engagingin attentional deployment or cognitive change and the outcomes of interest;rather our thesis suggests that these regulation approaches increase thelikelihood or probability that an individual will experience better or worseperformance, health, and antisocial-behavior-related outcomes over time.Similarly, even in longer time windows, we would expect that not everyincidence of suppression would result in direct or displaced antisocialbehavior, for example, but that repeated attempts to suppress negativeemotions would increase the likelihood of these outcomes. Gross (2001,2002) concluded his reviews of emotion-regulation research by suggestingthat researchers explore the potential differential consequences of differentregulatory styles and pursue a research agenda focused on the ‘‘long-termconsequences of differing emotion regulation strategies’’ (p. 218). The Coteand Morgan (2002) study represented an initial attempt to study regulationprocesses over time, but they used a one-month time lag, which may beinsufficient for differential effects to emerge. On the positive side, severalresearchers, including Cote and Morgan (2002) and Grandey et al. (2005),have developed and usefully employed measures of emotion regulation infield settings. Thus, the key challenges to overcome in terms of adequatetests of our propositions would seem to be designing and executing a studythat would allow these dynamic processes to unfold.

Woven throughout our analysis is the apparent critical need for examiningoutcomes of emotion regulation in field research where dynamics relatedto time are in play. As a number of authors (e.g., Goodman, Lawrence,Ancona, & Tushman, 2001) have pointed out, time has multiple meaningsand can be conceptualized in different ways. In terms of understanding thetemporal dynamics of emotion regulation, the problems are no less complex.Marks, Mathieu, and Zaccaro (2001), in their theory of time and teamprocesses, conceptualized team performance episodes as ‘‘distinguishableperiods of time over which performance accrues and feedback is available’’(p. 359). Emotion-regulation episodes could be similarly viewed as the time


between the situational cue that initiated the regulation process and the endof the regulated interaction or, perhaps, at the point when the emotion wasno longer being regulated. In longer-duration and interaction-rich inter-personal contexts, emotional leakage has more opportunity to emerge(e.g., Ekman & Friesen, 1969), and observers have more opportunity todetect whether and how individuals are regulating their emotions. Time in anemotion-regulation context can also be conceptualized as the mappingof 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 capturethis view of time by simply operationalizing it as job tenure. Because ourpropositions concerning long-term outcomes implicitly assume greater orlower likelihood of outcomes over time, it is reasonable to assume thatjob tenure moderates these relationships such that the relationships arestronger when job tenure is high. But, although convenient, job tenure as anoperationalization of recurring phases falls somewhat short of capturing thefull nature of the construct. Marks et al. (2001) pointed out that when time isconceptualized 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 episodesand the length of transition phases may differ markedly. Auto salespersonsmay face only one or two daily incidences that require them to regulateemotions, although, as noted, the incidences may last a relatively long time butbe alleviated by long transition or recovery phases in between. Othersalespersons such as call-center employees may have numerous, short, dailyinteractions that require emotion regulation and multiple, short-transitionperiods while they wait for their next call. The rhythms of these cycles aremarkedly different and likely have different implications for workplaceoutcomes, implications that in all likelihood will be masked by simple timemeasures such as tenure. Beyond these frequency and duration issues, someresearch has also suggested that the content of transition periods also affectsfuture behaviors. Harinck and De Dreu (2008) found that when theydistracted their experimental participants with another task during a break, theparticipants later reached higher-quality negotiated agreements, as comparedwith participants who continued to reflect on the ongoing negotiations duringthe break. Thus multiple periods of distracting transitions may diffuse orameliorate 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, timepassing, or time dragging; McGrath & Kelly, 1986), but also perceptions


about the novelty or originality of a given moment. It is possible to viewattentional deployment as a means of managing the passing of time.Grandey’s (2000) example of a server whistling arias to deploy attentionfrom negative or difficult customers might also be seen as way of passingtime. Aside from our arguments about threat detection and defensivereflexes 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) andothers, the long-term consequences of emotion-regulation strategies areessentially unexplored. We encourage future researchers to explore andextend our propositions by considering how and when these relationshipsstrengthen, weaken, or change in other ways across different views of time.

A final issue concerns the role of display rules in the emotion-regulationprocess. On one hand, display rules could be viewed as antecedents toemotion regulation and as useful in predicting what emotion-regulationstrategies individuals choose. In some organizations, display rules areimplicit and passed along through high-performance expectations (Zapf,2002). In others, display rules are formal, strict, and regulated by directsupervisors (e.g., Wilk & Moynihan, 2005). On the other hand, the strengthof organizational or supervisor-level display rules may change the nature ofthe relationships between emotion regulation and antisocial behavior. Forexample, Grandey et al. (2005) argued that response-focused regulationrelate positively to employee burnout, but they also argued and found thatthis relationship is stronger when individuals lack personal control over thesituation. Similarly, Wilk and Moynihan (2005) demonstrated that displayrules varied at the supervisor level, drained resources as they increased, andrelated positively to emotional exhaustion (see also Sutton, 1991). In termsof antisocial behavior at work, strict and regulated display rules are likely tosquelch certain forms of direct aggression that may result from emotion-regulation processes, but may increase the likelihood that response-focusedregulation will displace aggression toward coworkers or even outsidethe workplace. Understanding how display rules relate to different formsof emotion regulation but also shape antisocial responses to regulationstrategies are important areas for future research.

Implications for HRM Practice

Our theory building is helpful as an academic exercise, but it also haspractical implications. First, the effects of emotion-regulation strategies on


employees’ abilities to stave off accidents may be critical to occupationalhealth. In the United States, 21 million individuals work in occupationalsectors such as wholesale and retail trade where emotional laborpredominates (National Occupational Research Agenda, 2008), making thepotential for accidents and violence an important public health concern.If antecedent-focused strategies leave employees in high-risk workplacessuch as convenience stores and gas stations susceptible to social situationswhere others may harm them, perhaps training and awareness of moreappropriate regulation strategies would protect this at-risk population.Second, uncovering emotional-labor processes helps us understand thefactors that determine individual performance in jobs high in emotionallabor. Hochschild (1983) told of an airline that screens for ‘‘warmpersonalities’’ during interviews for flight attendants, explaining that, ingeneral, selling products and services involves ‘‘selling your personality.’’It is often assumed that being good at emotional labor stems from certainstable personality characteristics; organizations might screen for suchcharacteristics and select employees based on their emotional-labor skills.Yet our framework suggests that it is not personality that determinesindividual performance but the deployment of regulation strategies inresponse to emotion-eliciting situations. As such, perhaps high performancein these jobs comes from training or practice in situation assessment andregulation-strategy deployment.

In conclusion, while the current literature provides many interestinginsights about the role of emotion regulation in emotional-laborperformance, there is much more to be understood about the advant-ages and disadvantages of emotion regulation for individuals andorganizations. We provide this set of time-based propositions as a pointof departure for future research in this area and also as a roadmap forfuture theory-building and empirical testing. The study of emotionregulation in emotional-labor performance is fraught with challengesthat researchers will have to overcome, especially in terms of researchdesign and measurement. We hope, however, that this review willencourage investigators to take up these challenges and move the literatureforward.

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 vocationaland career psychology to further illuminate the turnover process.Prevailing theories about attrition have rarely integrated explanatoryconstructs from vocational research, though career (and job) choicesclearly have implications for employee affect and loyalty to a chosen jobin a career field. Despite remarkable inroads by new perspectives forexplaining turnover, career, and vocational formulations can nonethelessenrich these – and conventional – formulations about why incumbents stayor leave their jobs. To illustrate, vocational theories can help clarify whycertain shocks (critical events precipitating thoughts of leaving) driveattrition and what embeds incumbents. In particular, this chapter reviews




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

Theories and research on why and how employees sever employment tieshave 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 innovativetheories (e.g., Lee & Mitchell’s, 1994 unfolding model; macro-levelmodels 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 wellas new methodologies (latent growth modeling; Bentein, Vandenberg,Vandenberghe, & Stinglhamber, 2005) to promote understanding andprediction of organizational withdrawal. These recent developments arestimulating 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 unresolvedquestions about certain turnover phenomena.

To illustrate, though it is the most comprehensive formulation aboutproximal antecedents and processes leading to turnover thus yet conceived(Hom, 2010), the unfolding model fails to fully elucidate why various criticalevents (‘‘shocks’’) precipitate thoughts of leaving (Lee & Mitchell, 1994).For example, T. W. Lee, Mitchell, Wise, and Fireman (1996) observed thatsome nurses quit when they become pregnant, a shock that sets in motionpreexisting plans to exit for childbearing. They also noted that other nursesexit because their hospital switched from individualized to team-basedpatient care, a negative event violating personal career plans or goals. Whilespecifying that preexisting plans (to quit) and violations of career plansmediate shocks’ impact, this theory nevertheless overlooks why employeesadopt certain plans. Quite likely, employees react to the same critical eventsdifferently (not necessarily leaving) based on their assessment of how suchevents affect their personal plans (Sweeny, 2008). Other nurses facingpregnancies or team-based nursing would not necessarily resign if theyplan to work during motherhood or if this hospital policy fits theirprofessional image. Consequently, greater clarification of how differentindividuals appraise critical events is essential because critical events areprime movers of ‘‘nonaffective’’ turnover paths in the unfolding model that

PETER W. HOM ET AL.116

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 withdrawalphenomena remain unspecified or insufficient. To illustrate, empiricalresearch has long established that age and firm tenure are inverselyrelated to quits, such associations becoming styled facts in the turnoverliterature (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), thesedemographic characteristics deserve greater scholarly attention. After all,they often forecast attrition more reliably than common explanatoryconstructs (e.g., job satisfaction; Griffeth et al., 2000) and may reflect morefundamental but neglected distal causes (e.g., life stages). Further, manyscholars (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’sjob-hopping history, is a reliable signal of future quits. Though the premisethat ‘‘the best predictor of future behavior is past behavior’’ is deemed a basisfor 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-spancareer development theory, Holland’s career model, and social cognitivecareer theory (SCCT; Lent, Brown, & Hackett, 1994). These views have longdominated research on vocational choice and behavior (Leong & Barak,2001). Turnover investigators nonetheless have rarely capitalized oninsights and findings from this longstanding research stream. To addressthis oversight, our chapter demonstrates how these vocational modelscan further illuminate the reasons and manner by which incumbents vacatetheir jobs. Because certain SCCT constructs (e.g., newcomer self-efficacy)have been investigated by socialization scholars as turnover antecedents, wefurther elaborate and refine SCCT theory to extend existing socializationresearch on newcomer attrition.

DONALD SUPER’S DEVELOPMENTAL THEORY

Donald Super’s life-span career development theory (1953, 1990) is one ofthe pivotal theories in vocational psychology, which is embedded within a

Career Theories and Turnover 117

developmental framework. The main principle of Super’s theory, in contrastto other career theories, is the centrality of the development axis. In hismodel comprising ten propositions, Super posits that career developmentinvolves the ongoing implementation of the self-concept across life stages.He formulated career developmental stages that include a growthstage followed by exploration, establishment, maintenance, and declinestages. Within each of these developmental stages are substages that includedevelopmental tasks and challenges that individuals must meet andsurmount to realize their self-concept.

Life and Career Development Stages

According to Super’s (1953, 1990) theory, there are five life and careerdevelopment stages. These five stages are: (1) Growth stage (birth to 15),which is concerned with the development of the self-concept and one’scapacity, attitudes, interests, needs as they pertain to performance at schooland at home; (2) Exploration stage (ages 15–24), which produces tentativecareer/vocational choices and involves career exploration activities includingclass selection, work experiences, and hobbies; (3) Establishment stage (ages25–44), which entails entry into the workforce and the start of one’s officialcareer during which initial skill-building and stabilization in one’s workexperiences occur; (4) Maintenance stage (ages 45–64), which consists of anadjustment process to advance one’s position at work and to find continuingsatisfaction in work activities that one has mastered at an earlier stage; and(5) Decline stage (around age 65 onwards), which includes reduced outputand productivity in preparation for retirement. Super later reformulated thislast stage into a more positive ‘‘Readiness for retirement’’ stage.

Embedded within the major development stages, Super also articulatedcertain phases with specific developmental tasks that interact with anindividual’s experiences. Because our chapter focuses on turnover, we focuson phases within career development stages. Within the Exploration stage,the first phase involves Crystallization (ages 14–18) during which one isdeveloping and planning a tentative vocational goal. This is followed bya Specification phase (ages 18–21) where one firms up one’s vocational goaland moves on to the Implementation phase (ages 21–24), when one seeksand obtains the requisite education and training for the chosen career orvocation. The Establishment stage is comprised of two phases. During aStabilization phase (ages 24–35), individuals begin working and confirming


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

Career Constructs

Within this life-stage career development framework, Super conceptualizedseveral constructs that can advance turnover theory and research. The first isthe notion of the self-concept that is assessed by the Values Scale (VS). TheVS consists of 21 work values that help guide a person’s implementation ofhis or her self-concept. Attainment and implementation of these values in theworkplace give rise to job satisfaction and career growth and development.From a turnover point of view then, work environments that fail to provideopportunities for the satisfaction of employees’ values will motivate themto quit. For example, if strongly held values for autonomy, creativity, oradvancement are not satisfied within a particular work environment, thisvalue incongruence would likely stimulate turnover cognitions.

Career maturity is the next major construct developed within Super’stheory. His developmental model emphasizes one’s readiness to move on tothe next stage and hence the concept of career maturity is one of readiness totake on appropriate developmental tasks. Given differing maturity, differentpeople transition from school to work or from early career to mid-careerat differential speeds. According to Super, career maturity comprises thefollowing components: Awareness of the need to plan ahead; decision-making skills; knowledge and use of information resources; general careerinformation; general world of work information; and detailed informationabout occupations of preference. A student of Donald Super, John Crites(1973) went on to design a career maturity inventory (CMI) to measurecareer planfulness, career exploration, and readiness. While much voca-tional research has examined career maturity among late adolescents andcollege students, many implications of career maturity have yet to beexplored by turnover researchers among working adults.

Super (1953, 1990) conceptualized career maturity as a multidimensionalconstruct that represents a person’s readiness to cope with vocationallyrelated developmental tasks at a particular life-span period. Giving specialattention to adolescent development, Super noted that adolescents’ levelof career maturity evolves with growing awareness that they must makean occupational choice and their attitudes toward this task. During thisperiod, adolescents also develop competencies (i.e., knowledge, abilities, andskills) that facilitate career decisions and implementations, especially career


exploration and planning. In general, career maturity is often conceivedof as having both attitudinal and cognitive dimensions. In a stimulus–organism–response (S–O–R) paradigm for studying career maturity, theseattitudes and competencies serve as intervening variables (Savickas, 1985).Successful evolution of career maturity during adolescence is thus expectedto culminate in career choice crystallization and commitment.

In studying career maturity development, vocational researchers haveusually conceptualized change in quantitative terms, such as increases in theattitudinal and cognitive dimensions, which are operationalized differentlyin various measures of career maturity (Savickas, 1985). Whereas much ofthe research generated by Super and those following his paradigm havemostly focused on adolescents and college students (due to availability biasof counseling psychologists who were primarily academicians), we believethat the career maturity construct can be readily generalized into adulthoodto help clarify why employees stay or leave jobs.

Indeed, Savickas (1997, 2005) recently conceptualized the concept ofcareer adaptability – a construct akin to career maturity – to addressindividuals’ flexibility and maturity for coping with workplace challenges.Building upon Super and Knasel’s (1981) observation that adaptation is thecentral developmental challenge for adults, Savickas (2005) went on todevelop a career construction theory based on a constructivist perspective.Savickas (2005) viewed career construction as a series of attempts toimplement a self-concept in social roles, focusing attention on adaptationto a series of transitions from school to work, from job to job, and fromoccupation to occupation. In this theory of career construction, careeradaptability plays a central role in shaping the actual problem-solvingstrategies and coping behaviors of individuals in their work lives. This modelof career adaptability for adults thus parallels the career maturity modelfor adolescents. Within this model, Savickas proposed four dimensions ofadaptability: concern, control, curiosity, and confidence. The adaptiveindividual is (a) concerned about the vocational future, (b) exerts personalcontrol over his or her vocational future, (c) displays curiosity by exploringpossible selves and future scenarios, and (d) is confident about pursuing his orher aspiration. Therefore, increasing a client’s career adaptability is anessential goal of career counseling and career interventions.

Individuals lacking career adaptability might have difficulty managinginterpersonal relationships or meeting performance challenges in theworkplace. Therefore, career adaptability might be a useful predictor ofturnover cognitions and actual turnover. It is important to note, however,that the relationship between career adaptability and turnover may be more


complex. For example, Ito and Brotheridge (2005) examined the impact oforganizational support for career adaptability of employees in the form ofcareer information, advice, and encouragement. Unexpectedly, they foundthat career adaptability was ‘‘positively associated with both organizationalcommitment and intentions to leave, suggesting some unintended con-sequences for management approaches supporting career adaptability’’(p. 5). Apparently, promoting greater career adaptability among employeescan increase their ease of movement (March & Simon, 1958).

We return to describing Super’s theory and consider the ‘‘life careerrainbow.’’ In his later formulations, Super began examining the notion ofcareer salience or work centrality. He recognized that a career might not becentral to everyone and that its centrality may change over time as other liferoles compete for one’s attention and energies. Within this later programof research, he conceptualized the career rainbow in which an individualhas six life roles: worker, student, citizen, husband/wife, parent, and leisure.The rainbow concept presumes that work becomes central for people atthe middle stages of life but loses centrality due to competing demands offamily, leisure activities, and so forth. Outside the middle life stages, otherlife roles may become more central than work.

Career Developmental Assessment and Counseling Model

In 1992, Super and his colleagues articulated the Career DevelopmentalAssessment and Counseling (C-DAC) Model. C-DAC model emerged fromthe life span, life-space theory of careers and integrated key elements(i.e., the Life Career Rainbow, the Model of Importance, and the Model ofDeterminants) from Super’s theory. Therefore, the C-DAC model blendscomponents of differential, developmental, and personal construct theoriesinto one comprehensive career assessment and counseling system. Superrecommended that counselors implement the C-DAC model in a four-stepprocess (Super, Savickas, & Super, 1996). Step one consists of an initialinterview to identify a client’s presenting concerns. In the interview, thecounselor also reviews any available data from the client’s record anddevelops a preliminary counseling plan. At the same time, counselors mustunderstand the importance of work to the client relative to life roles in otherrealms (e.g., school, home and family, community, and leisure). Assessingthe client’s level of work role salience reveals whether further careerassessment and counseling will be meaningful (high career salience) or not(low career salience). Clients high in career salience show readiness to


maximally benefit from further career assessment. Clients low in careersalience 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 theclient’s career stage and concerns, and level of career maturity or careeradaptability. The counselor thus determines the client’s readiness for careerdecision-making activities, such as identifying and exploring occupationalinterests. In step three, the counselor helps clients to objectify theirinterests, abilities, and values. Finally, clients progress step four, whichinvolves subjective self-assessments that identify life themes and patterns(Super et al., 1996).

In formulating the C-DAC model, Super identified a comprehensive testbattery to provide counselors with flexibility in carrying out comprehensivecareer assessments. However, he emphasized four core measures for theC-DAC battery. The first is The Salience Inventory, a 170-item questionnairethat measures the extent to which individuals participate in, commit to, andexpect to realize values in five life roles: student, worker, citizen, homemaker(including spouse and parent), and leisure. The second core measure isthe Adult Career Concerns Inventory (ACCI; Super, Thompson, Lindeman,Jordaan, & Myers, 1988). Its 61 items assess planning attitudes, animportant dimension of career adaptability (Savickas, 1997). The ACCIconsists of 4 scales and 12 subscales that measure concerns related to careerstages and developmental tasks: Exploration (Crystallizing, Specifying,Implementing); Establishment (Stabilizing, Consolidating, Advancing);Maintenance (Holding, Updating, Innovating); and Disengagement(Decelerating, Retirement Planning, Retirement Living). The third coremeasure for the C-DAC battery is the Career Development Inventory (CDI;Super, Thompson, Lindeman, Jordaan, & Myers, 1979/1981). Its 120questions capture readiness for making educational and vocational choices.The CDI has two parts: (I) Career Orientation and (II) Knowledgeof Preferred Occupation. Part I includes four scales that assess CareerPlanning (CP), Career Exploration (CE), Career Decision Making (DM),and World-of-Work Information (WW). Part II contains one scalemeasuring Knowledge of Preferred Occupational Group (PO). Threecomposite scores result from summing individual scale scores are as follows:Career Development Attitudes combines CP and CE; Career DevelopmentKnowledge and Skills combines DM and WW; and Career OrientationTotal combines CDA and CDK. The final core measure Super specified wasthe VS, which includes items that assesses 21 intrinsic and extrinsic values


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., ‘‘bewhere employment is regular and secure’’).

Implications of Super’s Theory and Work forTurnover Theory and Research

In this section, we discuss how constructs and developmental processes(including vocational inventories designed to assess them) proposed bySuper can improve turnover understanding and prediction. Fundamentally,Super’s theoretical approach suggests a developmental trend in one’s careerand life and distinct developmentally linked tasks and challenges at differentstages. Each life and career developmental stage as articulated by Super maycreate a distinct set of forces that push or pull incumbents away from theircurrent jobs (Maertz & Griffeth, 2004). Following this developmental logic,the reasons why individuals quit likely vary by life stages. Along the careerdevelopmental axis in Super’s thinking, different developmental challengesand concerns at each stage can engender different motives for leaving at thestart of a career versus mid-career or toward the end of one’s career. Whileturnover researchers readily acknowledge that new and established employ-ees may quit for different reasons (Weller, Holtom, Matiaske, & Mellewigt,2009), they have not fully or explicitly capitalized on Super’s insightsfor identifying stage-dependent motives. Super’s perspective suggests thatrookies in the Exploration career stage (exploring various job options) mayexit 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 stageexit because they lack sufficient or timely promotional opportunities thatprovide a sense of career progress (Taylor, Audia, & Gupta, 1996).

To test this developmental viewpoint, turnover researchers can system-atically explore the impact of these developmental tasks and challenges withSuper’s Adult Career Concerns Inventory (ACCI). Using this inventory, theycan test the CDAC implication that a person at the Establishment stagewould more readily leave to advance his or her career prospects elsewhere,whereas someone at the Disengagement stage are more prone to leavedue to boredom or desire to participate in other life roles (by assuminganother less demanding job). Though stages are somewhat age-dependent,Super’s thinking also recognizes that people progress through career stagesat different rates. For example, some people explore various career optionsfar longer (such as women bearing and raising children before starting their


official careers) than the typical Exploration stage, whereas others may settleon a career at a relatively young age. If so, the ACCI would more preciselydiagnose turnover motives of people in the Exploration stage than wouldproxies based on job tenure or work history. After all, Super’s theory allowsfor the possibility of two individuals with the same length of employmentbelonging to different career stages.

Among his other conceptions, Super’s notion of self-concept implementa-tion can extend Lee and Mitchell’s (1994) unfolding model (more fullydescribed below) by defining the content of ‘‘matching scripts’’ (preexistingplans to quit) and ‘‘internal images’’ (i.e., career plans or goals, which path 2shocks might violate), increasing its precision for predicting when shocksevoke various withdrawal paths (shown in Fig. 1). According to Super,people prefer and choose vocations where they can fulfill values embodying

PersonalEvent

ActivatesMatching Script Quit

Path 1: Personal, Expected Shocks

Path 2: Negative Workplace Shocks

NegativeJob Event

Image ViolationOf Values Or Goals Quit

Path 3: Unsolicited Job Inquiry Shocks

Unsolicited Job Inquiry/Offer

•Compare with Current Job•May Pursue Other Jobs

Quit for Better Job

Path 4: Dissatisfaction-Induced Turnover

GradualJob Misfit

JobDissatisfaction

Compare Job Offersto Present Job

Seek OtherJobs Quit

Judgement of Misfit

Self-Concept Implementation Matching Script

•Self Concept Implementation-•RIASEC Vocational Fit

Career Images: Career Plans or Goals

CareerAdaptability

•Self-Concept Implementation•RIASEC Vocational Fit

Poor Vocational Fit

Fig. 1. How Career Constructs Influence the Unfolding Model.


their self-concepts. Vocational self-concepts may underpin matching scriptsand images and whether or not they can be implemented in a workplacemay activate various turnover paths. In T. W. Lee et al.’s (1996) study, forexample, only nurses who viewed themselves as primarily family caregiversrather than ‘‘worker bees’’ would formulate plans to quit when becomingpregnant (i.e., the personal shock allows them to implement their self-concept of motherhood in Fig. 1), while those nurses whose vocationalself-concepts were based on their capacity to deliver individualized patientcare would leave when the hospital introduced team-based nursing (i.e., thenegative workplace violates their self-concept implementation in Fig. 1).Further, Super’s construct explains why employees are responsive toanother shock in the unfolding model: unsolicited job inquiries. Accordingto Fig. 1, job incumbents may leave because they receive an unsolicited joboffer that permits them to better implement their self-concept.

Moreover, career maturity may help clarify why young workers (Griffethet al., 2000), new hires (Hom et al., 2008; Weller et al., 2009), and hobos(Hulin, Roznowski, & Hachiya, 1985) are exit-prone as they may poorlyprepare for jobs or make bad job choices. In contrast, higher career maturitymay account for why older people are more steadfast employees (Griffethet al., 2000) and for Booth, Francesconi, and Garcia-Serrano’s (1999)observation that British workers quit their fifth job less than they do theirfirst job. Conceivably, older employees’ experiences tackling developmentaltasks in advanced life stages or career stages helped them make wiser morejob choices, promoting their greater job stability (Wanous, 1992). Further,career maturity may explain quit decisions among older adults entering – orreentering – the workforce, such as women who raised children, formermembers of institutions (e.g., prison, military, priesthood; Ebaugh, 1988), orthose switching careers (who might return to school to learn new skills).They would undergo (or repeat) Exploration and Establishment stages fora new career later in life than younger people. Yet they may show greatercareer maturity about planning for and choosing jobs (improving joblongevity) as they have successfully surmounted development hurdles ofearlier life stages or career stages in a previous occupational field.

More research is warranted on the complex relationship betweencareer 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 satisfactionin jobs than their less adaptable counterparts, they also likely have greaterease of movement (Ito & Brotheridge, 2005). We thus suggest that careeradaptability can broaden the conceptual scope of ‘‘movement capital’’ – or


the human capital job incumbents possess that enhances job mobility(Trevor, 2001) – to encompass perceived control over one’s vocational plansand self-confidence to pursue one’s career goals. Although initially definedin terms of education and occupational skills (Trevor, 2001), movementcapital might also include career adaptability, which enable incumbentsto exit for better career opportunities elsewhere (Ito & Brotheridge,2005). Additionally, people high in career adaptability may attract moreunsolicited job offers as employers may believe that they can better managedynamic and wide-ranging challenges in the hypercompetitive globalmarketplace (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 theorganizational withdrawal process. Multiple competing life roles have rarelybeen examined in turnover research (Hom & Griffeth, 1995), though someauthors have alluded to work centrality (Mobley, 1982; Mobley, Griffeth,Hand, & Meglino, 1979). In particular, the life career rainbow may illuminatewomen’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 responsi-bilities (e.g., kinship responsibilities; Price & Mueller, 1981) can impel theirdepartures but fail to realize that such obligations change across life stages.When starting new careers in their 20s (Establishment stage), many womenbegin bearing children before their biological clock runs out (Booth et al.,1999). During this period of simultaneous childbearing and career establish-ment, young women face intense work – family conflict – especially inprofessional service firms (e.g., law and accounting firms; Dalton, Hill, &Ramsay, 1997) and research universities (Fox, 2005) that impose rigorous andfixed up-or-out promotion systems. Turnover experts have noted that suchinterrole conflict can drive quits (Hom &Kinicki, 2001) but have not formallyrecognized how such conflict varies across women’s life stages, waxing duringCareer Establishment (for young mothers beginning careers) and waningduring Career Maintenance (when children have grown up). Indeed, suchimplicit static views overstate motherhood as an ‘‘intrinsic’’ (or deterministic)explanation of women’s higher quit propensity (Fox, 2005). By the sametoken, turnover theorists cannot account for why some men take time offfrom work or switch to less challenging careers to spend more time withfamily or why some leave organizations to have a more balanced investmentof energy across multiple life roles (Hom & Kinicki, 2001; Hulin et al., 1985).In short, turnover scholars increasingly acknowledge that some leavers exitthe workforce but offer few theoretical explanations for nonemployment


destinations other than ‘‘search unemployment’’ ( job pursuits after leaving;T. H. Lee et al., 2008).

Further, Super’s developmental perspective suggests that turnover scholarsshould differentiate between life-cycle and career-stage effects. Though bothare age-dependent, they may exert differential influence on leaving. Usingchronological age and work experience as rough proxies for life and careerstages, respectively, Booth and associates (1999) observed that age and workhistory (e.g., date of entry into the labor market, number of previous part-time jobs) have distinct unique effects on turnover. As mentioned above,some people may begin a career – or a second one – later in life (perhaps theyraised children or retired from the military). As a result, 40-year-old newcareer entrants may have similar developmental challenges and concernsabout becoming established incumbents as 25-year-old entrants. None-theless, their different life stages can affect their progress through careerstages. Because their career spans are ‘‘truncated,’’ 40-year-old newcomersmay feel greater urgency to move through Career Establishment andMaintenance stages more quickly than 25-year olds, while more expertlynegotiating developmental tasks if they had transitioned through these careerstages before in a prior career. Forty-year-old women starting new careersafter motherhood may also move through career stages more rapidly andeasily than 25-year-old working mothers because they are liberated fromthe responsibilities of household formation. Such developmental speed maystrengthen their loyalty to employing institutions that offer timely careeropportunities (e.g., more rapid promotions; Taylor et al., 1996). At the sametime, mature rookies may face greater age discrimination (slower or denial ofpromotional or career opportunities), which would impede their develop-mental 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 vocationalpsychologists. Parsons lay the theoretical groundwork for this researchstream in 1909 by proposing a model of how individuals choose careers(Tracey & Rounds, 1993). In choosing an occupation, an individual assesseshis or her abilities, interests, goals, and means, analyzes what a particularoccupation requires and offers in return, and considers the analysisof the self in conjunction with the analysis of a potential occupation


(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 wouldserve as tools for those attempting to choose occupations as well as those inpositions to advise others making occupational choices (Tracey & Rounds,1993). Concomitantly, analysis of occupations has been made possible viapublically available vocational information. Sources include the OccupationalOutlook Handbook (U.S. Department of Labor, 2008) and the Dictionary ofOccupational Titles (U.S. Department of Labor, 1991). John Holland’s theoryof vocational choice, first introduced in 1959, provided a means of integratingself-assessment with occupational assessment.

Holland’s Central Theoretical Constructs

John Holland’s theory of careers, a seminal influence in vocationalpsychology, relates personality to career choice. One of its main attractionsis arguably its ability to convey a great deal of information via a fairlyparsimonious framework (Holland, 1996). Holland theorized that occupa-tional choice is influenced by an individual’s characteristics, includingpersonality, ability, and knowledge (Spokane, Luchetta, & Richwine, 2002).Holland proposed six personality types: realistic, investigative, artistic,social, enterprising, and conventional. These six personality types and theirhypothesized relationships to one another are summarized in what isreferred to as the RIASEC model. The model is presented visually as ahexagon. Any given individual is expected to exhibit aspects of the majorityor all of the six personality types, but in varying degree (Spokane et al.,2002). A letter code summarizes an individual’s combination of personalitytypes. The highest three letters correspond to an individual’s most dominantpersonality types. In the context of career advisement, these letters are usedto suggest careers that are appropriate given the individual’s personalityprofile. The organization of career environments along the same RIASECmodel as individuals’ personalities makes this matching possible.

Holland’s theory also features four dimensions: congruence, consistency,differentiation, and identity. Congruence constitutes how well alignedan individual’s personality is with his or her work environment (Spokaneet al., 2002). Given how obviously broad this conceptualization is, otherresearchers 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


(i.e., occupational specialty) congruence (Meir & Erez, 1981). Differentiationis the degree to which an individual’s interests are spread out or unified(Weinrach & Srebalus, 1990). Said differently, it reflects the extent to whichan individual resembles a single personality type and shows little similaritywith the other personality types (Meir, Esformes, & Friedland, 1994).Differentiation is calculated by taking the difference between the highestscore and the lowest score of the six (or highest three) types. Consistencyrefers to the strength of the relationship between an individual’sdominant two personality types (Spokane et al., 2002). Types that arepositioned next to each other along the hexagon are more consistent thantypes positioned farther apart. For example, investigative and artistic types,neighbors along a side of the hexagon, share the desire for intellectualstimulation (Furnham, 1994), while realistic and social types, which sitopposite one another on the hexagon, differ in terms of the former having anorientation toward things and the latter having an orientation toward people(Tracey & Rounds, 1997). Though we do not focus on the consistencydimension in what follows, we mention it here for the sake of completeness.

The final dimension of Holland’s theory, vocational identity, representshow well formed and stable an individual’s goals, interests, and talents are(Spokane et al., 2002). Before continuing on, we must distinguish Holland’sidentity dimension from the identity constructs based on social identitytheory (SIT; Tajfel & Turner, 1979) that are typically studied by career aswell as turnover theorists. As Grote and Raeder (2009) points out, identityin career research is used interchangeably with concepts like sense of self orself-concept. Turnover theorists typically split identity into personal identityand social identity with the former pertaining to personal attributes likedispositions and abilities and the latter having to do with group categoriessuch 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 organizationalidentity, or ‘‘perceived oneness with an organization’’ [Mael & Ashforth,1992, p. 103]) has been used to make and test predictions about a slewof organizational variables, with turnover being one of them. By contrast,Holland’s vocational identity construct – referring to the stability andcrystallization of vocational interests and goals – has not been thus appliedby turnover scholars (cf. Hom, 2010).

In addition to laying out the four dimensions described, Holland’s theoryoffers several basic premises. One premise is that individuals have apreference for environments that are conducive to the use of their abilitiesand skill sets, offer engaging problems and work roles, and permit


expression of their true attitudes and values (Weinrach & Srebalus, 1990).A second premise is that an individual’s behavior (e.g., career choice, jobchanges) is a product of the interaction of his or her personality andenvironment (Weinrach & Srebalus, 1990). The next premise says thatindividuals experience reinforcement and satisfaction when environ-ments they are in match their personalities. Reinforcing situations makebehavior (e.g., attendance) more stable (Spokane et al., 2002). The fourthpremise 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 adifferent, congruent work environment or adjust their outlook and behaviorto 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 usedbefore Holland promulgated his theory have been modified to incorporateHolland’s model (Tracey & Rounds, 1993). The SII, which requiresindividuals to report their degree of like or dislike for a large variety ofitems, proffers profiles for respondents at the end. Four sets of scales makeup this profile: general occupational themes, basic interest scales, occupa-tional scales, and personal styles scales. The general occupational themes arebased on Holland’s RIASEC model and inform the respondent about his orher work personality. The basic interest scales separate the aforementionedgeneral occupational themes into work, school, and leisure activities.The occupational scales direct the individual’s attention to occupations heor she is expected to fit well with based on having received the highest scorefor these occupations. The personal styles scales have to do with informa-tion 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), presentsrespondents with approximately 230 items that pertain to activities,competencies, occupational preferences, and abilities. Individuals indicatepreferred and disliked activities, activities in which they feel efficaciousand not, interesting and uninteresting occupations, and rate their skillsand abilities. The profile presented to the respondent shows a three-letterHolland code, which the individual can use to identify careers within aprovided list that match his or her interests and competencies.


Vocational Congruence: Methodological Issues and Moderators

Holland’s thesis that congruence between one’s interests and occupationshould lead to more satisfaction, success, and stability in the chosenoccupation has received much attention in the career-related literature(e.g., Chartrand & Walsh, 1999). Holland’s congruence dimension alreadyhas analogous constructs in the turnover literature. Rather than vocationalfit, Chatman (1991) examined how value congruency – or how closely anemployee’s values fit with those of the employer – induces loyalty. Mitchellet al. (2001b) also theorized a ‘‘fit’’ dimension in their model of why peoplestay in their jobs and defined it as ‘‘an employee’s perceived compatibility orcomfort with an organization and with his or her environment’’ (p. 1104).Their conceptualization is more encompassing than Holland’s; two types offit are considered: organizational fit and community fit. Yet Mitchell andLee’s (2001) description of on-the-job fit fails to explicitly specify vocationalinterest fit as an embedding force.

The extensive research on congruence has not, however, producedconclusive findings; researchers do not always find the anticipated linksbetween congruence and outcomes (e.g., Perdue, Reardon, & Peterson,2007). In their meta-analysis, Assouline and Meir (1987) found correlationsbetween congruence and satisfaction that range from �0.09 to 0.51, thoughreporting a mean corrected correlation of 0.21. Two later meta-analysesby Tranberg and colleagues (1993) and Tsabari and colleagues (2005)estimated correlations of 0.174 and 0.166, respectively. In short, congruenceis reliably, though modestly, correlated with satisfaction. We next reviewmethodological problems that may be responsible for such weak or modestrelationships. To capitalize on Holland’s theory as a framework for betterunderstanding job satisfaction, turnover researchers should design studiesthat minimize these problems’ influence to give a clearer picture of howcongruence affects satisfaction (and retention).

The lack of consistency and clarity in findings about congruence is partlyattributed to several methodological shortcomings (e.g., Chartrand &Walsh, 1999). Tracey (2007) proposes that self-selection may partly accountfor weak relationships between person–environment fit and outcomes. Thatis, individuals choosing to enter environments that initially fit their interestsfairly well and incongruent workers leaving their jobs would limit variancein congruence, attenuating its effects (Spokane, Meir, & Catalano, 2000;Tracey, 2007; Tsabari, Tziner, & Meir, 2005). Inadequate measurementof person–environment congruence – namely, poor assessments of people’spersonalities (or interests) and the environment – also weakens empirical


support (e.g., Chartrand & Walsh, 1999). Environmental measurement hasproven especially difficult. Ambiguity persists over which environmentallevels (e.g., social, cultural) (Furnham, 2001) should be measured andwhat the unit of analysis should be (e.g., job title, job tasks, job clusters)(Chartrand & Walsh, 1999). Thus, adequate methods of measuring theenvironment remain lacking (Chartrand & Walsh, 1999).

How job satisfaction is assessed can also impact the congruence–satisfaction relationship. The common use of an overall satisfaction scorecan obscure this relationship because an overall satisfaction scoreincorporates additional factors beyond how an individual feels about hisor her work activities (e.g., pay, coworkers; Chartrand & Walsh, 1999).Whether researchers measure actual fit (indirect fit indices based oncomparisons of separately assessed personal and environmental attributes;Kristof-Brown, Zimmerman, & Johnson, 2005) or perceived fit (direct assess-ments of compatibility; Mitchell & Lee, 2001) makes a difference as well.Because it refers to individuals’ perceptions of how well they fit their jobs ororganizations, perceived fit is likely more proximal to attitudes and behaviorthan is actual fit (Cable & DeRue, 2002; Mitchell & Lee, 2001). Sustainingthis view, Kristof-Brown and colleagues (2005) found in their meta-analysisthat perceived fit relates more strongly to outcomes (e.g., quit intentions, jobsatisfaction, organizational commitment) than does actual fit.

Apart from methodological influences, some researchers have identifiedmoderators that can affect relationships between interests-job fit and joboutcomes (Spokane et al., 2000; Tracey, 2007). In the career literature, muchwork has already been done to identify moderators. Group importancehas been shown to moderate the congruence–satisfaction relationship,such that the correlation between congruence and satisfaction is strongerwhen 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 averagejob tenure increased as jobs accumulated (Booth et al., 1999). Matureworkers stayed longer on their fifth job than on their first job. A meta-analysis by Tsabari et al. (2005) nonetheless concluded that the congruence–satisfaction relationship was actually stronger for those in the 20–30 agegroup than for those over 30. Their finding implies that congruence mattersless for older workers – that older individuals tend to be more satisfied thanyounger ones with their circumstances even if they fit the job less (whichaccords with the ‘‘role theory’’ of aging that self-integration, insight, andpositive psychosocial traits grow with age; Yang, 2008). In line with this,


Whitbourne (1986) documented age to be inversely related to identityflexibility, defined as ‘‘deliberate and informed comparison of one’s presentidentity commitments with other possibilities’’ (p. 164). In other words,older workers engage in less thinking about alternative commitments (andprospects of better job fit elsewhere). Further support for this idea comesfrom research on career stages, which suggests that earlier career stages arecharacterized by exploration of different options while later stages arecharacterized by stability (Brousseau, 1983).

Further, the time lag between assessments of vocational fit and outcomesmay moderate their relationships (Tracey, 2007). Vocational scholarsimplicitly define vocational congruence as a static construct, assumingstable or fixed personal and environmental attributes that can be capturedon one occasion. They typically assess individuals’ interests at one point intime and later compare these interests to the occupations (or academicmajors) these individuals choose. Such static research designs fail toconsider how congruence might change over time (Low & Rounds, 2007).Indeed, Holland’s theory posits that fit between the individual and his orher environment is dynamic and likely to change with experience andprogression into new stages of career (e.g., Brousseau, 1983). Furnham(2001) further elaborates on the dynamic nature of fit. Individuals change toadapt to their environments (e.g., altering personal work style) and canto some extent alter work environments (e.g., changing the way the job isdone) to better fit their needs. As a result of these adaptations, congruenceimproves over time. In support, some studies reveal that congruenceincreases over time (Meir & Navon, 1992; Tracey, Robbins, & Hofsess, 2005as cited in Tracey, 2007). That said, measuring fit at one point in time doesnot provide an indication of how that fit is likely to evolve, which someturnover scholars are acknowledging (Lee & Mitchell, 1994).

Further, Holland’s differentiation dimension, which relative to congruencehas received much less scholarly scrutiny (Meir et al., 1994), may impactassociations between interest-occupation congruence and outcomes. Lowdifferentiation (or more interest flexibility) may result in congruence beingless able to predict positive outcomes like job satisfaction. Conceivably,employees whose interests are less well formed (less differentiated) are betterable to deal with and more willingly grow into occupations that are imperfectfits with their primary interests, while those whose interests are well-definedmay be less tolerant and willing to adjust to occupations that are not goodfits (Meir et al., 1994). Darcy and Tracey (2003) sustain this moderatingeffect, reasoning that individuals with more flexible interests have a widerrange of likes than those with less flexible interests and thus are able to


substitute interests that an occupation cannot satisfy with other intereststhat it can satisfy. Those with less flexibility, however, are more likely toexperience negative outcomes if the chosen occupation does not offerinteresting activities because of their inability to substitute.

Attesting to this idea, Wessel, Ryan, and Oswald (2008) consideredadaptability, a construct which is akin to flexibility, as a moderator of theeffects of fit in the context of students’ selection of majors. Adaptability iswillingness and predisposition to adjust well to a changing environment.Among students who perceived low fit with their major, those who werehighly adaptable were more satisfied with their educational institution thanthose 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 beingsubject to change when individuals proactively change themselves or theirenvironment. Wessel et al.’s findings imply that adaptive individuals whomisfit their environment did something to adapt and to thereby remainsatisfied with their environment.

Finally, as suggested by Spokane (1985), vocational identity (which istypically measured via Holland, Daiger, and Power’s (1980) vocationalidentity scale) could prove to be another moderator of congruence–outcomerelationships; among those with a better sense of identity interest–occupational congruence should more strongly predict occupational out-comes. Healy and Mourton (1985) observed that for women college studentswhose interests matched their occupational choice, those high on vocationalidentity 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, usingjob satisfaction as the outcome. They did not find identity to moderatethe congruence–satisfaction relationship. We suggest, however, that furtherexamination of identity as a moderator is still warranted.

Alternatively, akin to congruence, vocational identity may be a predictor ofjob satisfaction in its own right. To illustrate, Carson and Mowsesian (1993)found that both congruence and vocational identity were significantlycorrelated with job satisfaction. The identity–satisfaction relationship(r ¼ 0.45) was actually stronger than the relationship between congruenceand satisfaction (r ¼ 0.18). Commenting on the robust relationshipbetween vocational identity and job satisfaction (e.g., r ¼ 0.70; Holland &Gottfredson, 1994), Holland (1997) placed particular emphasis on vocationalidentity when considering the interaction of individuals with their environ-ments. He described the construct as being applicable not just to individuals


but to work environments as well (‘‘environmental identity’’) as an organiza-tion 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 identityand an environment with a clear identity will make for more predictableinteraction of the two than will be the case for the interactions of individualsand environments with identities that are less well formed or clear.

Holland (1997) thus suggests that ‘‘Vocational Identity in conjunctionwith the rest of the theory provides a simple and plausible explanationof career stability or instability’’ (p. 173). Individuals with well-definedidentities have good understanding of what they want (e.g., their goals andinterests) and can offer (e.g., their talents and skills), which increases theirlikelihood of selecting jobs that fit their profile and of persisting with a jobsearch until finding a good match. Those with ill-defined identities, on theother hand, are more liable to choose poor-fitting work environments, andconsequently, to engage in job-hopping (Hom et al., 2008). Holland (1997)argues that some support for these predictions can be drawn from the strongrelationship 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 betweenvocational identity and job satisfaction could be inflated due to someoverlap between job satisfaction and what Holland et al.’s (1980) vocationalidentity scale measures. In summary, though concepts of person–environ-ment fit have been incorporated into some theories about attrition(Chatman, 1991; Mitchell & Lee, 2001), further theoretical gains arepossible based on elements of Holland’s theory. Going forward, turnoverresearchers need to address the methodological issues in how (vocational)fit is studied and consider moderators when examining how fit drives jobsatisfaction 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, wefurther describe how his model and measures of its constructs can enrichtheoretical perspectives and research on attrition.

Implications of Holland’s Theory for Turnover Theory and Research

Traditional Turnover ModelsIn this section, we summarize the insights Holland’s (1997) theoryoffers into causes of turnover and how the turnover process unfolds.


Traditional models of turnover assume job (dis)satisfaction to be a primedeterminant of turnover (e.g., Mobley et al., 1979; Price & Mueller, 1981,1986). In contrast to this prevailing preoccupation with proximal causes ofleaving, Holland’s theory points to poor vocational choices as a potentialdistal cause of job dissatisfaction (and turnover). Conversely, good (in termsof fit) vocational choices should result in positive work outcomes. Thoughempirical support for this prediction is mixed, methodological deficiencies(e.g., constrained congruence variance due to self-selection, poor environ-ment measures, global satisfaction indices) in tests and inadequate attentionto moderators likely understated evidence for congruence–outcome links(Assouline & Meir, 1987; Tranberg, Slane, & Ekberg, 1993; Tsabari et al.,2005). By addressing these methodological shortcomings (includingapplying Edwards’ (2002) approach for analyzing difference scores), webelieve that turnover investigators can more firmly demonstrate thatprior vocational choices can shape employees’ affect and loyalty to theirjob (attesting to Holland’s theory). By so doing, they can extendconventional withdrawal formulations that primarily scrutinize the effectsof job satisfaction: how it shapes quit decisions in combination withdeterminants, 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 broadarray of satisfaction causes, their attention to workplace causes leaves outmore distal causes of satisfaction (and leaving), such as vocational decisionsthat occur before job entry. All told, we contend that job satisfaction servesas the link between Holland’s theory (notably, vocational congruence), forwhich job satisfaction is a key outcome of interest, and many turnovertheories, for which job satisfaction is a key attrition driver.

The Unfolding ModelWhat is more, Holland’s formulation offers ways to refine newernontraditional turnover models (e.g., Lee & Mitchell, 1994; Mitchell &Lee, 2001), which downplay the centrality of job satisfaction proffered inolder schools of thought (Mobley et al., 1979; Price & Mueller, 1981, 1986).To illustrate how, we briefly summarize Lee and Mitchell’s (1994) unfoldingmodel. It proposes four pathways to turnover, three of which do notinvolve dissatisfaction (see Fig. 1). In the first three paths, jarring events(‘‘shocks’’) initiate the turnover process; in the fourth path, job dissatisfac-tion 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 agraduate-school admission or pregnancy, which activates a preexisting plan


(‘‘matching script’’) to quit (what Maertz & Campion, 2004 refer to as‘‘preplanned quits’’). For example, T. W. Lee et al. (1996) observed that anurse learning she was pregnant soon resigned; this shock triggered her planto opt out of the labor market. Negative workplace shocks activate anotherturnover path (Fig. 1, path 2) when they violate employees’ values or careergoals (known as ‘‘image violation’’). Employees determine whether or notthe shock can be integrated into their values or goals. If not, they exitwithout jobs in hand. T. W. Lee and associates (1996) gave the example ofa nurse leaving immediately when the hospital shifted from individualizedpatient care – her preferred nursing philosophy – to team-based nursing.

Unsolicited job offers or inquiries represent a third type of shockprompting a third withdrawal path (path 3 in Fig. 1). To illustrate, T. W. Leeet al. (1996) noticed that a nurse quit a hospital when a physician offered heranother position. Leavers taking this path are not unhappy with their currentjob; they simply prefer a better alternative. Finally, Lee and Mitchell (1994)designated a fourth path (path 4 in Fig. 1) to represent the conventionalwithdrawal path envisioned by traditional theorists (Hom & Kinicki, 2001;Price & Mueller, 1986; Steers & Mowday, 1981), in which dissatisfiedemployees pursue other jobs and exit when securing superior ones.

We argue that Holland’s theory can refine the unfolding model in severalways. Earlier we discussed how Holland’s views suggest additionalunderpinnings of job satisfaction, which can further explain the etiologybehind path 4 in the unfolding model as dissatisfaction is its prime mover.Though Mitchell et al. (2001a) estimated that only 37% of leavers takepath 4, T. H. Lee et al. (2008) recently determined that 60% of all leaversfrom a nationally representative sample left due to dissatisfaction. Fig. 1thus shows ‘‘poor vocational fit’’ as worsening job fit over time, whichin turn increases dissatisfaction and turnover path 4 in the unfolding model(Lee & Mitchell, 1994). Moreover, incorporating Holland’s idea thatindividuals have occupational orientations (i.e., realistic, investigative,artistic, social, enterprising, conventional) into the unfolding model canclarify why negative workplace shocks in path 3 motivate some employees toleave (i.e., shocks clash with their occupational orientation). To illustrate,business professors initially joining research-oriented business schoolsbecause they prefer investigative activities (the I in the RIASEC model)may face image violation when their colleges later stress ‘‘vocationaltraining’’ to attain higher MBA rankings (demanding and rewarding MBAteaching; S in the RIASEC model) (Morgeson & Nahrgang, 2008). Fig. 1thus specifies that violation of RIASEC vocational fit can engender turnoverpath 2. Likewise, Holland’s perspective can elucidate how unsolicited job


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

Job Embeddedness TheoryDeparting from earlier viewpoints (including the unfolding model) focusingon why people leave, Mitchell and colleagues (2001b) promulgated a novelperspective about why people stay. They came up with a new construct,called ‘‘job embeddedness,’’ to explain why some employees are lessinclined than others to leave. They proposed that certain forces embedemployees in their jobs: links (i.e., connections) to one’s organization andlarger community, perceived sacrifices (i.e., costs) of leaving, and fit (orcompatibility) with one’s organization and larger community (see Fig. 2).Links, sacrifices, and fit combine to cause embeddedness and can combinedifferently for different people but still result in equal levels of embedded-ness (Mitchell & Lee, 2001). Empirical tests find that embeddednessaccounts 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

JOB

EMBEDDEDNESSFIT

ON-THE-JOB

ON-THE-JOB

ON-THE-JOB

OFF-THE-JOB

OFF-THE-JOB

OFF-THE-JOB

LINKS

SACRIFICE

LINKS

FIT

SACRIFICE

VOCATIONAL FIT•RIASEC Match

VOCATIONALIDENTITY

VOCATIONAL DIFFERENTIATION

Fig. 2. How Career Constructs Influence Job Embeddedness.


embedding their coworkers (Felps et al., 2009), and mediates how high-commitment human resources management systems affect quit decisionsand firm commitment (Hom et al., 2009).

An obvious point of intersection between Holland’s theory and jobembeddedness is the organizational fit construct in Mitchell and Lee’s (2001)model. Embeddedness theorists describe this construct as a compilation ofvarious other fit constructs considered in previous literature. The broadernature 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, fitwith company culture, and satisfaction with the level of authority andresponsibility granted (Mitchell et al., 2001b). Though this conceptualizationof fit parallels Holland’s congruence construct, some elements of congruenceas described by Holland are arguably omitted. Measures about the matchbetween one’s career interests and job, ability to express one’s attitudesand values, and interest level in one’s job roles, may be usefully added.Indeed, other authors have long advocated for more comprehensiveconceptualization and measurement of person–environment fit, arguing thatconsideration of individual variability in the types of fit people find mostimportant can best elucidate how fit affects outcomes of interest (Piasentin &Chapman, 2006). Along with composition of more thorough measures oforganizational fit, expansion of the conceptual domain of the organizationalfit dimension to encompass vocational congruence is thus a viable directionfor further development of the embeddedness construct. That said, Fig. 2represents this proposition with a pathway from ‘‘RIASEC match’’ toon-the-job fit: Greater vocational congruence yields higher on-the-job fit.

Also missing from job embeddedness theory and research is considerationof individual differences that, given equal levels of links and sacrifices,may lead to the same level of fit being unequally embedding for differentindividuals. We contend that in this regard, Holland’s differentiationconstruct can make a theoretical contribution to job embeddedness theory.As we discussed above, persons with low differentiation of interests aremore willing to adapt to situations of low organizational fit. Adaptableindividuals whose vocational interests are flexible may ultimately experiencea high overall level of embeddedness despite an initial experience of loworganizational fit. Over time, these individuals may adjust things aboutthemselves or their environment so as to attain higher on-the-job fit. Forexample, Vandenberg and Nelson (1999) suggest that certain organizationalcultures may be amenable to employees taking action to eliminate a sourceof discomfort, such as asking to be transferred to a different supervisor.


Thus, embeddedness researchers might acknowledge the dynamic nature of(on-the-job) fit and assess the level of individuals’ job embeddedness at morethan one point in time. Turnover research will need to move beyond thestandard practice of surveying employees at one point in time and obtainingquit data at a second point in time because this static design misses thedynamic character of certain antecedents (e.g., fit) (Steel, 2002). Indeed,proponents of the unfolding model recognize that incorporating time intothe withdrawal process can resolve ambiguities about the relative durationof different withdrawal paths (e.g., time lapse between initial delibera-tions of quitting and its enactment) and can address potential switchingbetween decision paths over time (T. W. Lee et al., 1996; Lee, Mitchell,Holtom, McDaniel, & Hill, 1999). Fig. 2 thus specifies that vocationaldifferentiation 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 higheron-the-job (and overall) fit.

Finally, another individual difference variable that can enrich jobembeddedness thinking is vocational identity (Holland, 1997). Based onthe aforementioned rationale that persons with well-defined vocationalidentities can more effectively choose or secure jobs that match their careerprofile (Holland, 1997), we argue that such employees will have greateron-the-job fit. After all, empirical research reveals that such incumbents tendto feel higher job satisfaction (Carson & Mowsesian, 1993; Holland &Gottfredson, 1994). Consequently, Fig. 2 adds vocational identity asanother antecedent of on-the-job fit.

Future directions for nontraditional turnover models are already evident.Going beyond qualitative tests based on leavers’ retrospective accountsabout how shocks induced them to quit (T. W. Lee et al., 1996, 1999),Kammeyer-Mueller, Wanberg, Glomb, and Ahlburg (2005) furnishedadditional corroboration for the unfolding model by demonstrating thatshocks measured before turnover occurrence can predict future turnover.Embeddedness theorists encouraged research integrating the unfoldingmodel with the construct of job embeddedness, such as testing whether jobembeddedness 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 domake individuals more vulnerable to shocks they encounter. More than this,Holtom and associates (2008) prescribe applying social network methodol-ogy to capture more fully the impact of embedding links. Adding to thislaundry list of suggestions, we urge further refinements of the unfolding


model and embeddedness theory by incorporating the insights andmethodology 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) socialcognitive theory (SCT). These theoretical views promulgate self-efficacy – or‘‘beliefs in one’s capabilities to organize and execute the courses ofaction required to produce given attainments’’ (Bandura, 1997, p. 3) – asa fundamental determinant of self-regulating motivation and behavior.Self-efficacy beliefs can affect choices people make, their perseverance ingoal pursuits during setbacks, whether their thoughts facilitate performance,their experienced stress when coping with taxing stressors, and theirvulnerability to depression.

Hackett and Betz (1981) and Betz and Hackett (1981) initially appliedBandura’s early SCT theory to account for women’s career development.Compared with men, they claim that women fail to fully utilize theircapabilities, talents, and interests in vocational pursuits because their career-related self-efficacy expectations are lower (magnitude), weaker (strength ordurability), and less generalized (generality). In their view, such poor self-efficacy perceptions contribute to persistent and ubiquitous occupationalsegregation and pay inequity by discouraging women from entering male-dominated fields offering higher pay, status, and influence. Hackett and Betz(1981) also noted that the traditional socialization of women has denied themthe foundation on which to self-confidence to pursue occupations wheremen prevail (Bandura, 1977, 1997). Specifically, girls and young womenhave fewer opportunities to engage in traditionally masculine activities( performance accomplishments), see fewer female role models in a wide rangeof male careers (vicarious learning), and receive little encouragement topursue male-dominated careers (verbal persuasion). Lacking such self-efficacybases, women can also feel more anxiety when performing male sex-typedactivities (emotional arousal), which reinforces their sense of inefficacy. Initialtests of the Hackett–Betz formulation find that female undergraduates feelless efficacious about satisfying educational and performance requirementsfor stereotypically male jobs (e.g., engineers and lawyers) than do malestudents (Betz & Hackett, 1981) and that experimentally induced failure ona math task can weaken women’s feelings of task self-efficacy and interest(Hackett, Betz, O’Halloran, & Romac, 1990).


Later, Lent et al. (1994) expanded Hackett and Betz’s (1981) theory tofully incorporate Bandura’s (1991) other constructs and introduced threeinterrelated SCCT models corresponding to three career developmentstages: (1) formation of career interests; (2) academic and vocationalchoices; and (3) performance and persistence in educational and occupa-tional pursuits. Though domain content varies across stages, all modelsshare 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 tangiblerewards and self-evaluative outcomes (e.g., anticipated self-satisfaction)shape interests and goal-directed behaviors. Each model further presumesthat self-efficacy is a more potent behavioral determinant than are outcomeexpectations (Bandura, 1997). People refrain from performing an act if theydoubt that they can enact it even when they expect rewards from itsenactment (Lent et al., 1994). Conversely, a strong sense of personal efficacycan motivate individuals to perform behaviors even if they are uncertainabout the prospects of earning rewards. Indeed, a resilient sense of personalefficacy is most crucial for tackling difficult behaviors ‘‘because the roadto success is usually strewn with countless impediments’’ (Bandura, 1997,p. 126). Moreover, each SCCT model positions self-efficacy as the primebehavioral cause when the ‘‘quality of performance guarantees particularoutcomes’’ (Lent et al., 1994, p. 84). For such actions, self-efficacy directlyshapes expected outcomes because people perceiving that they can performthese behaviors know they will likely attain behavioral outcomes. Finally,self-efficacy beliefs can affect outcome values. For example, students whofeel inefficacious about their academic abilities devalue academic accom-plishments. We next describe how the SCCT perspective explains the threekey phases of career development.

Three-Stage SCCT Models

Vocational InterestsTo model how career interests arise, Lent and colleagues (1994) postulatethat children and adolescents begin developing a sense of efficacy aboutcertain career-relevant activities from doing them and observing others dothem. Through experience or vicarious learning, they also learn to expectcertain outcomes from such activities. More formally, Lent et al.’s interestmodel submits that self-efficacy perceptions and outcome expectations(especially anticipated self-satisfaction from meeting internal performance


standards; Bandura, 1982) together prompt the formation of ‘‘enduringinterests in activities’’ (Lent et al., 1994). This particular model embracesBandura’s (1997) view that ‘‘perceived efficacy creates interests throughengrossment in activities and the self-satisfactions derived from fulfillingpersonal challenges that lead to progressive mastery of occupationalactivities’’ (pp. 423–424). By comparison, vocational interests are stuntedwhen young people feel inefficacious about career-relevant activities orforesee few positive outcomes from them. Lent and associates (1994)further deduced that emergent career interests would in turn initiate thesetting of goals for greater exposure to career-related activities. Accordingly,more activity participation and practice can feed back and influenceSSCT determinants by increasing self-efficacy feelings (as one developscompetency) and expectations for positive outcomes (as one derives self-gratification from meeting internal standards). During their formative years,adolescents may continually repeat this spiraling cycle until their activityand career interests stabilize.

Vocational ChoiceThe second SCCT model by Lent and colleagues (1994) addressesdevelopment of career choices and entry once career interests are formed.Analogous to their interest model, they theorize that self-efficacy (aboutone’s success in a career rather than a particular activity), outcomeexpectations (about rewards available from a career), and goals (careerdecision) shape ‘‘choice actions,’’ such as actions to implement the careerchoice. Thus, individuals who feel confident about succeeding in a particularcareer, anticipate desirable career rewards, and have strong (preexisting)interests in activities associated with this career will form a goal (careerchoice) to pursue a particular career direction (choosing a vocation ordeclaring a college major). Strong intentions translate into actual careeractions, such as enrolling in a training program, selecting an academicmajor, or applying for a job in the occupational field.

Vocational ImplementationFor the career implementation stage, Lent et al.’s (1994) third SCCT modelseeks to explain career accomplishments (e.g., course grades) and persistence(e.g., stability of academic major). According to this model, individuals whobelieve that they can effectively perform a career-relevant task and expectrewards from successful performance would set more challenging perfor-mance goals. To illustrate, engineering students who feel confident that theycan achieve high grades in engineering courses and who expect rewarding


outcomes (e.g., more job opportunities) from stellar grades will establishhigh GPA goals. Higher goals then promote greater task performance ashard goals induce more effort and persistence. Finally, this model postulatesthat ability and past performance affect task performance directly as wellas indirectly via self-efficacy and outcome expectations. Sustaining thethree SCCT models, an early meta-analysis by Lent and colleagues (1994)documented that self-efficacy and outcome expectations positively andmoderately 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 andpersistence (Brown et al., 2008).

Though SCCT theorists primarily strive to explain students’ vocationaland academic choices, performance, and persistence, their viewpoints canalso help account for their workplace behaviors once they completedoccupational training. After all, Lent et al. (1994) proposed that an SCCTperspective can apply across the career life span and elucidate variouskinds of work adjustment (subsequent to schooling), such as occupationalsatisfaction and career change. Given its validity for predicting studentattrition from academic fields of study (Harvey & McMurray, 1994), weextend SCCT theory to deepen insight into why new employees leaveworkplaces. Our theoretical extension thus echoes Bandura’s (1997) assertionthat ‘‘the sense of efficacy that newcomers bring and further develop duringthe course of their occupational training at the beginning stage of theircareers 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 thetermination 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 theirinitial period of employment (Griffeth et al., 2000; Hom et al., 2008; Welleret al., 2009). They face special challenges when adjusting to new worksettings, such as coping with unexpected stressful events, mastering new jobrequirements, 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


entrants to the labor market (e.g., graduating students) because workplacerealities (e.g., close supervision, rare feedback, and organizational politics)differ so much from former school roles (Greenhaus, 1987). Given suchdemands, most scholars thus implicate assimilation maladjustment as a keydriver of newcomers’ premature departures (Allen, 2004; Griffeth & Hom,2001; Kammeyer-Meuller & Wanberg, 2003).

Despite an extensive body of findings about maladjustment causes andremedies (Allen, 2004; Ashforth, 2001; Chan & Schmitt, 2000; Wanous,1992), only a few studies examined how self-efficacy impacts newcomeradaptation. For example, socialization researchers report that beginningemployees with a strong sense of efficacy often redefine work roles to suittheir preferences (Ashforth & Saks, 2000), while those feeling less self-efficacious can most benefit from training or certain socialization tactics(Jones, 1986; Saks, 1995). All the same, these studies neglect pivotal SCCTconstructs, such as personal goals and outcome expectations (Lent et al.,1994), which likely codetermine socialization success along with self-efficacy (Bandura, 1997). Moreover, socialization experts (Jones, 1986;Saks, 1995) mainly attend to new hires’ beliefs about whether they canfulfill performance requirements, neglecting other kinds of demands crucialfor organizational assimilation (e.g., workgroup integration, managingstressors; Bandura, 1997). For instance, Saks (1995) only scrutinized newaccountants’ perceptions about efficacy for documenting audit proceduresand maintaining client relationships. Further, some authors investigatedgeneralized dispositions, such as desire for control (Ashford & Black,1996) or proactive personality (Kammeyer-Meuller & Wanberg, 2003). Bycomparison, SCCT theorists construe perceived self-efficacy as a state-likebelief about one’s capacity to perform in a particular domain of functioningand contend that such self-beliefs better explain for how one approaches andperforms 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 effectsand operationalized newcomer self-efficacy too narrowly (overlooking otherassimilation challenges) or too broadly (focusing on dispositional traitsrather than motivational states).

To further clarify the process by which newcomer self-efficacy sustainsjob survival, we extend Lent et al.’s (1994) framework to furnish a morethorough account of how self-perceptions about efficaciousness – acting inconcert with other SCCT constructs – facilitate newcomer socialization.Specifically, we adapt their SCCT model to focus on how newcomers copewith the stressful transition from outsider to insider status (Ashforth, 2001;


Hom et al., 1998; Wanous, 1992). After all, coping represents a ‘‘person’sconstantly changing cognitive and behavioral efforts to manage specificexternal and/or internal demands that are appraised as taxing or exceedingthe 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 thusasserts that newcomers’ confidence in surmounting assimilation obstacles(coping self-efficacy), their expectations about outcomes derived frommeeting these demands (coping outcome expectations), and the goals theyset to meet such demands (coping goals) promote their adaptation tounfamiliar work settings.

We also strengthen the explanatory power of Lent et al.’s (1994) model byconceptualizing SCCT constructs in terms of well-established socializationconcepts. In particular, our SCCT formulation specifies coping goals for thekey socialization tasks that many scholars have identified (Feldman, 1976).Thus, we maintain that newcomers would form separate coping goals tobecome 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 ‘‘copingstrategies’’ to represent how newcomers can manage assimilation difficulties.For instance, new hires might become more proficient in satisfying workrole requirements by changing work methods or soliciting advice fromothers (Feldman & Brett, 1983; Latack, 1986; Latack et al., 1995). OurSCCT coping perspective also recognizes that newcomers must regulateemotional distress because coping thinkers hold that coping successentails both effective stress management and problem-solving (Bandura,1997; Lazarus & Folkman, 1984; Sweeney, 2008). By modeling stressmanagement, we also acknowledge longstanding observations that new-comers feel ‘‘reality shock’’ – upon learning that the job does not live up toexpectations – 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 entryshock can invoke dysfunctional thoughts and emotions among newcomers,which can derail their adjustment.


In short, we adapt Lent et al.’s (1994) SCCT theory by integratingconcepts from the coping and socialization literatures to deepen insightinto why newcomers readily abandon jobs. By so doing, we derive a morethorough and precise account of how newcomers adjust to jobs and survivethe assimilation period. Fig. 3 depicts this integrated model. At the outset,we note that SCCT theorists prefer more fine-grained models for eachdomain of functioning (e.g., different self-efficacy beliefs for task masteryand social integration). But various domains are, however, combined tosimplify graphic depiction of this model. In what follows, we elaborate thelogic for the various pathways in this SCCT coping model of assimilation.

Coping Self-Efficacy - Coping GoalsOur SCCT framework posits that entering employees harbor self-beliefsabout 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 submitthat these beliefs shape the coping goals that are set and that guide actions(Latack et al., 1995; Lent et al., 1994). Specifically, beginning incumbentswho feel efficacious about their capabilities to overcome socializationhurdles would form stronger and more resilient goals, while those feelingless self-efficacious would set modest coping goals. To fully assimilate(Chan & Schmitt, 2000; Feldman, 1976; Hom et al., 1998), newcomers mustachieve multiple goals to cope with the challenges of performing job tasksproficiently (Kammeyer-Meuller & Wanberg, 2003), understanding rolerequirements (e.g., learning role expectations from their role set; Graen,1976), gaining collegial acceptance (Feldman, 1976), and managingdisruptive emotions aroused by entry shock (Bandura, 1997).

Following SCCT points of view (Bandura, 1997; Lent et al., 1994), ourmodel specifies that outcome expectations – anticipated outcomes forattaining coping goals – shape goal formation. That is, entry employees whoanticipate greater rewards (e.g., job security, teammate approval, health-care coverage) from goal attainment would then set higher coping goals(including becoming ‘‘insiders’’ sooner) than those looking forward to fewerrewards.

Coping Goals - Coping StrategiesThe current conceptualization submits that coping goals initiate variedcoping 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


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coping strategies according to the two functions that they fulfill: problemand stress management. In support, Folkman et al. (1986) noted that amultitude of distinct coping strategies (e.g., seeking social support, positivereappraisal) people use to handle stressful encounters in their day-to-daylives 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 toresolve problems or a means to manage emotional reactions. Further,Feldman and Brett (1983) observed that beginning employees cope with newtransitions by changing the environment (e.g., work longer hours, redefinethe job, get others to provide help, seek social support) or recalibratingaffective responses to the environment (e.g., overindulge in alcohol, repressawareness of stress).

Drawing from Lazarus and Folkman (1984) and Bandura (1997), ourtheory 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 (blockingout dysfunctional thoughts and emotions; Bandura, 1997). These copingstrategies are deployed to achieve specific coping goals and therebyconstitute the means by which newcomers address socialization challenges.To meet the task-mastery coping goal, new hires might redefine tasks andseek task advice from established incumbents (problem management) aswell as participate in cognitive reappraisal or constructive self-talk (stressmanagement; Manz & Neck, 1999) when facing frustrations or setbacks(Bandura, 1997).

Coping Efficacy - Stress Appraisal - EmotionsBecause how people interpret stressful events dictates their coping responses(Latack et al., 1995; Lazarus & Folkman, 1984; Sweeny, 2008), the currentmodel includes a sense-making mechanism in which newcomers firstinterpret the meaning of stressful events (e.g., hazing by coworkers) alongtwo dimensions. In primary appraisal, they must first decide if stressorspotentially threaten career plans or goals or represent opportunities toadvance careers (or competencies; Folkman et al., 1986). They next decidewhether or not they have the resources and ability to cope with threats orseize opportunities (secondary appraisal).

In our view, newcomers’ sense of personal efficacy also conditions theirstress appraisal. After all, how they construe stressful events depends on the‘‘match between perceived coping capabilities and potentially hurtful aspectsof the environment’’ says Bandura (1997, p. 140). Entering employees


perceiving that they can flourish in unfamiliar workplaces see opportunitiesfor growth and development, whereas those who believe otherwise mayregard socialization challenges as threatening. The latter in turn are prone toanxiety and dysfunctional thoughts (that fuel such anxiety), according toperspectives on depression and negative moods (Bandura, 1997; Beck, 1976;Burns, 1980). If extremely upset, some distressed newcomers may formcoping goals and execute coping strategies to manage dysfunctionalcognitions and affect to lessen their debilitating effects (Bandura, 1997).They might change their thoughts with cognitive reappraisal (e.g., reconstru-ing stressors as benign; Latack, 1986) or feelings with symptom management(e.g., alcohol consumption; Feldman & Brett, 1983).

Cognitive Control Efficacy as ModeratorWe further differentiate coping self-efficacy – perceived capacity to controlactual threats – from ‘‘cognitive control efficacy’’ – perceived capacity tocontrol intrusive disturbing cognitions (Bandura, 1997). Accordingly, ourmodel embraces Bandura’s (1997) contention that newcomers effectivelycope with stress by developing greater confidence that they can manageactual threats as well as manage how they think about them. ‘‘When peoplehave a strong sense of efficacy to control their own thinking, they are lessburdened by negative thoughts and experience a low level of anxiety,’’claims Bandura (1997, p. 149). We thus posit that cognitive control efficacydampens how stress appraisals arouse emotional distress. Persons avoidingexcessive worry and ruminations are less agitated by threats (Burns, 1980;Manz & Neck, 1999).

Socialization Sources of Self-EfficacyIn keeping with SCCT tradition (Hackett & Betz, 1981; Lent et al., 1994), wenext identify how employers’ assimilation methods, such as socializationtactics (Allen, 2004; Ashforth, 2001), cultivate self-efficacy development.In particular, formal tactics, which segregate newcomers from otheremployees during socialization, may foster self-efficacy. Segregated newhires can develop higher performance accomplishments because they canpractice new skills in safe training environments and acquire graduatedmastery experiences (Ashforth, 2001). Moreover, serial (which employsestablished incumbents as socialization agents) and collective (which groupsnewcomers and exposes them to common training) tactics may enhancenewcomer self-efficacy by way of vicarious learning. Newcomers observehow experienced incumbents perform tasks correctly and thereby may feelmore efficacious about their own ability to do the same. Investiture tactics


affirm newcomers’ incoming characteristics, while divestiture attempts tostrip away newcomers’ former identities (e.g., boot camp; Ashforth, 2001).When recruiting newcomers for their particular skills and abilities, firmsimplementing investiture communicate to recruits that they value them andbelieve them capable of meeting job demands. High expectations instill self-confidence in recruits. Finally, sequential (requiring recruits to follow a fixedprogression of steps before assuming roles) and fixed (setting a timetable forrole preparation) tactics alleviate anxiety. Beginning employees feel moreself-doubt when physiologically agitated by uncertainty, interpreting suchbodily states as indicative of weakened capabilities (e.g., fatigue duringathletic contests) to persist on tasks. Arousal, along with fear that arousalcan hinder performance, also erode self-efficacy (Bandura, 1997).

Feedback Loop via Causal AttributionsThis conceptual scheme introduces feedback loops as new employees mayrevise initial self-efficacy beliefs and outcome expectations based on earlyattributions about how well they initially performed socialization tasks andexperienced rewards. Bandura (1997) assert that people’s interpretationsabout feedback about task performance can modify self-beliefs aboutefficacy. Simply put, individuals who take credit for success (attributing highperformance to ability or effort) maintain – if not raise – beliefs about self-capabilities. Moreover, setbacks do not diminish self-efficacy when peoplemake effort attributions or assume that ability is cultivated through practicerather than innate. (Note that those making effort attributions may notfeel self-efficacious if they believe that they cannot repeat their high effort infuture performances). Further, experienced success (or failure) fulfillingsocialization requirements may modify newcomers’ expectations aboutoutcomes derived from task performance. Finally, individuals’ interpreta-tion of past performance can hinge on their current coping self-efficacy. Thisis because ‘‘people with a high sense of efficacy accept successes as indicantsof their capabilities but dismiss the diagnostic import of failures andattribute them to external impediments’’ (Bandura, 1997, pp. 154–155).

SCCT Constructs - Proximal Turnover AntecedentsWe next specify how our SCCT coping perspective dovetails with extantturnover models. Socialization theory and work regard job survival as apivotal assimilation outcome (Ashforth, 2001; Feldman, 1976; Saks, 1995)but rarely outline the process by which ineffective socialization engendersturnover (Kammeyer-Meuller & Wanberg, 2003). To address this oversight,we discuss how job embeddedness can mediate how SCCT constructs affect


newcomer retention (Hom et al., 1998; Weller et al., 2009). In ourperspective, newcomers who mastered job requirements, understood rolerequirements, secured collegial approval, and managed stress reactionsduring the adjustment period in turn become embedded through higherstronger fit, links, and sacrifice (Mitchell & Lee, 2001). Socialized newhires achieve on-the-job fit because they satisfy performance demandsof the new job (improving ability-job requirement fit) and internalizecorporate norms by way of social integration and role clarification (aligningvalues to those of the company; Chatman, 1991). By socially integratingwithin the workgroup, these newcomers also forge more – and stronger –links in the workplace (as well as social capital; Hom et al., 2009). Thoughrelatively new, assimilated newcomers also ‘‘experience a growing senseof sacrifice when considering leaving’’ (Weller et al., 2009, p. 1147), such asgiving up new mentors and friends and the ‘‘hard-won’’ status of survivingprobation (earning them associated perks, such as health and pensioncoverage) if they exit.

By comparison, on-the-job fit is low for poorly socialized newcomers asthey fail to meet performance standards or internalize corporate values(Mitchell & Lee, 2001; Weller et al., 2009). Newcomers who have troubleassimilating 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 muchsocial capital. As a result, they may incur little or no sacrifices upon leaving.Because unassimilated newcomers are less embedded in the job, they arethus more liable to quit according to Mitchell and Lee (2001).

All the same, job embeddedness – or its absence – cannot fully account forall 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 shocksduring 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 withcareer 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 observedthat job entrants whose pre-entry expectations are unrealistic (becauserecruiters misled them; Wanous, 1992) are more prone to endure ‘‘realityshock’’ and quickly exit (Hom et al., 1998). Consequently, our frameworkincludes shock-driven turnover as another path by which newcomers exit.


Implications of SCCT Theory for ExplainingHigher Minority and Female Quits

Though we propose a general framework to explain newcomer attrition, thisconceptualization can be adapted to reflect unique socialization problemsconfronting different populations. For example, Hom and associates (2008)documented higher corporate flight among female and minority incumbentsin managerial and professional fields where their identity groups have beenhistorically underrepresented (Hom & Griffeth, 1995; Roberson, 2004).Because most women and minorities exit corporate America during theirfirst few years of work, inadequate or incomplete organizational socializa-tion may underlie their higher turnover relative to white men (Hom et al.,2008). We thus extend our general formulation about newcomer coping toaddress their special adjustment challenges, offering a more comprehensiveexplanation about corporate flight among minorities and women.

Higher Coping DemandsFor women and minorities entering nontraditional workplaces or occupa-tional fields, social integration – or its absence – represents a prominentsocialization challenge because they, demographically dissimilar newcomers,may endure social isolation (due to tokenism), exclusion from informaldominant white-male networks (due to demographic dissimilarity; Eagly &Carli, 2007; Elvira & Cohen, 2001; Riordan, Schaffer, & Stewart, 2005), andhostility from peers or superiors (e.g., sexual or ethnic harassment; Berdahl& Moore, 2006; Laband & Lentz, 1998). Moreover, they may encountermore difficulty understanding and mastering new task requirements becausethey lack good mentors (due to supervisory bias or scarcity of mentorsof their own ethnicity or sex; Cottrell & Neuberg, 2005; Eagly & Karau,2002; Fiske, Cuddy, Glick, & Xu, 2002; Thomas, 2001) and receive lessinformation from colleagues (Forret & Dougherty, 2004; Ibarra, 1992).

Weaker Self-Efficacy SourcesBesides greater challenges, our SCCT formulation suggests that minoritiesand women may feel less efficacious about their ability to survive andflourish in white- or male-dominated workplaces. During their formativeyears, they may have experienced fewer sources on which to build strongself-beliefs that they can succeed in nontraditional career fields (Hackett &Betz, 1981; Lent et al., 2008). As youth, they knew few women or minorityprofessionals or managers, for example. Moreover, employing organiza-tions may fail to cultivate a robust sense of self-efficacy among them.


To illustrate, certain socialization tactics can disadvantage them. Forexample, firms applying collective socialization (grouping newcomers andorienting them together) may address primarily concerns of the majority ofnew 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 assignveterans from another sex or race to coach women and minorities (due toscarcity 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 dominantwhite male culture (Ely & Thomas, 2001), which devalues their culturalidentities and weakens their self-efficacy.

Along with discouraging socialization tactics, women and minorities faceother conditions, well-documented by diversity research (e.g., supervisorybias, impoverished job duties, glass ceilings; Eagly & Carli, 2007; Hom &Griffeth, 1995; Stroh, Brett, & Reilly, 1996) that diminish perceived self-efficacy to master socialization requirements. To illustrate, supervisors maynot see a good fit between minority and female newcomers’ attributes andjob requirements (Eagly & Carli, 2007; Heilman, 1983). Rather, they maybelieve that individual minorities and women possess traits stereotypical oftheir group, which do not resemble the requisite traits for occupations wherewhites or men predominate. As a result, superiors may act (or not act) inways that decrease women’s and minorities’ self-efficacy (Hom & Griffeth,1995). Expecting less of them, they may thus offer fewer chances forperformance accomplishments (relegating women and minorities mundanetasks or jobs), fewer occasions for vicarious learning (by personallydemonstrating how they would handle problems), and engage in less verbalpersuasion (to encourage women and minorities to tackle stretch assign-ments). Given stereotypes about occupational misfit, superiors may thus failto create the preconditions that promote self-efficacy among minority andfemale recruits (Bandura, 1997).

Stereotype ThreatOur SCCT viewpoint highlights how minorities and women enteringnontraditional vocational fields or workplaces may appraise threats posedby 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


identity group. When situated in workplaces that employ few members oftheir identity group (including leadership positions; Eagly & Carli, 2007;Hom et al., 2008), they may worry about corroborating implicit stereotypesthat those like them cannot succeed in white-male fields or settings. As aresult, minority and female newcomers feel performance anxiety, whichimpedes performance and induces them to vacate jobs where they cannotsucceed (Steele, 1997).

All told, an SCCT perspective can illuminate why minority and femalenewcomers more readily exit nontraditional jobs (e.g., managers, armyofficers; Payne & Huffman, 2005). With few exceptions (Stroh et al., 1996),turnover researchers have rarely scrutinized proximal or intermediarypsychological states behind minority and female exits (Roberson, 2004).Rather, they have focused on documenting reliable racial or genderdisparities 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 investigatinghow SCCT constructs mediate between disadvantageous working condi-tions and turnover can clarify how such conditions occasion greater femaleand minority attrition from male- or white-dominated workplaces. Indeed,mediation tests would add greater credibility to prevailing theories thatworkplace discrimination undergirds elevated minority and female corpo-rate flight (Hom et al., 2008) as they are often grounded in circumstantialevidence (Johnson & Neumark, 1997).

Moreover, SCCT theory might clarify why racial and gender differencesin turnover from nontraditional jobs vary across studies (Hom et al., 2008).For example, Lyness and Judiesch (2001) found no quit differences betweenmen and women managers from a financial services organization, whereasHom and associates (2008) observed that women professionals andmanagers from large firms representing various industries left at higherrates than their male counterparts. Women in the former study howeverrepresented 42% of the entire sample, whereas they constituted 28% of theworkforces sampled in the latter test. Quite likely, the sizeable representa-tion of women across the spectrum of jobs in Lyness and Judiesch’s (2001)study created (and reflected) conditions more hospitable to fosteringwomen’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 cangive task advice and social support. Given greater sources of self-efficacy,more women would feel efficacious and successfully assimilate, thereby


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

CONCLUDING REMARKS

Our brief review of three leading theories of career and vocationaldevelopment suggests that they hold great promise for elucidating why andhow people stay or leave. Currently, the dominant theoretical and empiricalapproaches focus on proximal antecedents to turnover, such as quitintentions, job attitudes (and their intermediate effects; Hom & Kinicki,2001), shocks (Lee & Mitchell, 1994; T. W. Lee et al., 1999), and jobembeddedness (certain dimensions such as fit and links; Mitchell et al.,2001b). Some turnover authors allude to distal causes, such as imageviolations, matching scripts (Lee &Mitchell, 1994), and centrality of nonworkvalues or roles (Mobley, 1982; Mobley et al., 1979). Complementing present-day theoretical advances, we suggest greater scholarly attention to morefundamental causes of attrition, such as the vocational and career constructsreviewed in this chapter. As we noted, these latter models can more preciselyanswer why image violations, matching scripts, or centrality of nonwork rolesmotivate turnover. In addition, as more constructs are transported fromvocational 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 andunderstanding do not always converge, though turnover scholars strivefor both goals (Maertz & Campion, 1998). Though current emphasis onpredictive accuracy can yield greater insight into turnover (cf., Lee &Mitchell, 1994), such orientation draws attention to immediate turnoverprecursors. More basic causes are thus overlooked, such as how otherwisesatisfying jobs can no longer embed incumbents when their career stageshifts over time or why many people exit the work force to pursue other liferoles, such as full-time parenting or education. Over the years, turnovertheorists increasingly recognize such forms of leaving (Hom & Kinicki,2001; Hulin et al., 1985; Lee & Mitchell, 1994) but their attempts atexplanation seem superficial. To address this theoretical void, we thusadvocate greater attention to distal constructs such as those formulated bycareer scholars. We hope that our discussion of the conceptual richness of


these career development models will encourage scholarly explorationsinto how career constructs can promote understanding – and perhapsprediction – of turnover.

ACKNOWLEDGMENT

We thank Neal Schmitt for his assistance in providing a review of our earlierdrafts.

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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 theprocess of organizational socialization. The relationship between learningand socialization is implicit in much of the existing socializationliterature. In an attempt to make this research more explicit, we suggesta theoretical approach to the actual learning processes that underlieworkers’ socialization experiences. In order to accomplish this, we reviewprevious work on socialization, information seeking and feedback seekingduring socialization, and learning. In doing so we describe the learningprocess that underlies socialization, highlighting the beginning of theprocess, the role of information during the process, and integrating threedifferent types of learning (planned, deutero, and meta) into the processof organizational socialization. In addition, we also discuss theimplications of these three types of learning during the process ofsocialization and directions in future research on the socialization process.




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Everyone who has ever been employed in an organization, whether on thefirst day or shortly thereafter, experiences that moment when he or shequestions 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 howthey are going to learn everything they need to know to survive inan organization. A number of questions may race through an employee’smind; ‘‘how will I learn all of the things I am supposed to do,’’ ‘‘how willI be evaluated,’’ ‘‘how do I get promoted,’’ or ‘‘how will I fit in with mycoworkers?’’ Success in a new job, however success is defined, hinges onemployees learning the ropes, and then using their knowledge to successfullynavigate new challenges that arise in organizational life. Upon initial entryinto an organization there is a burst of learning that helps employees todevelop needed work skills and abilities, along with basic group norms andvalues (Feldman, 1981). Following that momentary flurry of informationthere is a much longer period of adjustment and readjustment during whichemployees develop a greater understanding of what is really required tosucceed in the organization.

The process of answering these ‘‘how’’ questions, or the act of learninghow to be successful in a job is part of organizational socialization (referredto throughout as socialization). Socialization is the process throughwhich employees transition from being ‘‘outsiders’’ in the workplace tobeing ‘‘insiders’’ (Van Maanen & Schein, 1979). During this processemployees develop key attitudes, behaviors, and knowledge concerninghow 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 programsit may adopt (Allen, 2006; Van Maanen & Schein, 1979), and by employees’personalities and individual efforts to learn about their role in theorganization (Kammeyer-Mueller & Wanberg, 2003).

Early socialization research focused on developing models that explainedthe different stages of socialization (e.g., Feldman, 1981; Graen, 1976;Simpson, 1967; Van Maanen, 1975, 1976), which in turn explained thedevelopment of attitudinal outcomes such as job satisfaction, organizationalcommitment, and turnover intentions (Feldman, 1981; Van Maanen, 1975).More recent socialization research has explored the tactics implementedby organizations to facilitate the socialization process (Bauer et al., 1998;Cable & Parsons, 2001). These two streams of research seek to identifystages 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

JARON HARVEY ET AL.168

this process for the benefit of both individual and organization (Cable &Parsons, 2001).

During the development of socialization research, scholars have scruti-nized the experiences of new employees as a means to gain new insights intothis process. Some researchers have looked at the influence of newcomerproactivity (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 roleof 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 researcharea. However, while the study of socialization is vigorously active, one keyarea 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 theorganizational world and begin to inculcate and understand its traditions(Van Maanen & Schein, 1979). Stage models addressed learning tangentiallyby identifying what an employee could expect to learn at any given time inthe socialization process; with all of it culminating in an employee becomingnot only fully capable of carrying out the duties and functions of the job, butalso being enmeshed in the culture of the organization (cf., Schein, 1978).The research that has examined portions of the learning process during thesocialization 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 thatcan enhance or inhibit employee learning (Morrison & Brantner, 1992).

However, none of this has addressed the entire process through whichemployees learn. Individual learning occurs when people make changes intheir mental associations and their behavior based on information gatheredfrom personal experiences (Ormand, 1999). The process through whichemployees make sense of their surroundings likely has a significant impactupon how fully enmeshed in an organization an employee becomes throughsocialization. From social learning theory (Bandura, 1977) we know thatemployees make sense of their roles in an organization by learning fromthe situation, the individuals around them, and the formal organizationalpractices. As employees try to understand and adjust to a work situation,they will engage in different learning processes (e.g., Visser, 2007).

Socialization and Learning 169

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 theunderpinnings of workers’ socialization experiences.

Much of the extant socialization literature focuses on traditional notionsof planned learning. Planned learning refers to the development andmaintenance of learning systems that promote employee learning (Visser,2007). A focus on planned learning has specific limitations in understandingthe nature of socialization because it emphasizes established socializationprograms (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 inconsisten-cies that occur between individuals’ expectations and the actual consequencesof their actions (Argyris & Schon, 1974, 1996). Deutero-learning is a mode oflearning that is adaptive and unconscious (Bateson, 1972). As employeesinteract with coworkers and organizational systems they experiencedifferent consequences. It is from these consequences, combined with thesocial context that surrounds them, that employees learn how to adapt theirpersonal behavior (Bateson, 1972; Visser, 2003, 2007). In addition to plannedlearning, these two modes of learning provide researchers with a new lens toview the learning element of socialization.

By concentrating on learning processes, we have chosen not to focus uponwhere 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 directionthat may move socialization research forward by providing a clearer pictureof how the socialization process works. Our work extends the research onsocialization by focusing on the process that employees use to learn what ittakes to move from organizational outsider to insider. In doing this we seekto make three important contributions to the socialization literature.

First, we seek to expand the time frame that is normally considered asthe socialization experience. Typically, researchers have focused on thesocialization experience as occurring during the first 12–18 months of anemployee’s time with the organization (cf., Payne, Culbertson, Boswell, &Barger, 2008). We suggest that more important than a specific timeperiod are the boundary-crossing experiences (Schein, 1971) that individualsgo through during the course of a career. Drawing from a boundary-crossing typology, we argue that individuals may have a socializationexperience at any point in their career when boundaries are crossed.

Second, building on the initial trigger of boundary crossings, we useuncertainty reduction theory (Berger & Calabrese, 1975) and social


information processing (SIP) theory (Salancik & Pfeffer, 1978) to determinewhat important elements are part of the learning process during socializa-tion. Specifically, we describe how boundary crossings will increase workers’motivation to learn and influence both the sources and types of informa-tion available to workers. We also explore the role of tenure within anorganization on motivation to learn and the number of informationsources an employee will have. Based on motivation to learn, sources ofinformation, and types of information we then discuss about how each ofthese elements will influence the modes of planned, meta-, and deutero-learning during employees’ socialization experiences. The process describedhere proposes to explain what prompts employees’ socialization experiences,boundary crossings, and how these crossings influence the entire learningprocess that workers engage in throughout their socialization experience.Ultimately, we seek to further understand what influences learning duringthe socialization process, and how this learning, driven by motivation andthe sources and types of information available to employees, occurs; therebyexplaining the learning process that underlies the socialization experience ofworkers.

Third and finally, we offer recommendations for integrating learning intofuture socialization research. Specifically, we suggest exploring the relation-ships between different tactics of socialization and the different modes oflearning that we discussed in this paper. Additionally, because we suggestthat socialization experiences occur at multiple times during a career,not just when employees take new jobs, we suggest some avenues throughwhich the links between socialization experiences and the different modesof learning that individuals engage in can be explored. Finally, we alsodiscuss some of the possible measurement issues that researchers mayencounter as they move forward with this work.

HOW WE KNOW WHAT WE KNOW:

SOCIALIZATION AND LEARNING

To better understand the relationship between socialization and employeelearning we draw from existing research and theory in each of these areas todevelop an understanding of how learning occurs during a socializationexperience. We begin by briefly reviewing the general socialization literature.We then concentrate on important elements of learning in the socializationliterature; namely information and feedback seeking. Following this,


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

Learning the Ropes: A Socialization Overview

Any anxiety employees experience as they enter organizations is a functionof the uncertainty they are feeling. The more uncertainty individualsexperience, the greater their level of anxiety will be about the situation(Berger, 1979; Berger & Calabrese, 1975). Employees entering an organiza-tion take up identities that reflect their new job roles and organizationalsurroundings. This process of learning a new role, a new organizationalculture, and the accompanying requirements, and transitioning to thestatus of organizational insider is referred to as organizational socialization(Feldman, 1976). Through this process of learning about their new rolesand understanding organization expectations, employees come to a betterunderstanding of how to do their jobs and what they need to do to fit intothe organizational culture (Van Maanen & Schein, 1979). It is duringsocialization that new employees find out what the organization is like anddetermine whether they want to be a part of it. As employees learn abouttheir job and the organization, any anxiety felt as an outsider gives way tothe knowledge and certainty of becoming an insider.

As the socialization process commences, organizations employ differenttypes of socialization tactics to influence employees’ experiences (Saks &Ashforth, 1997; Van Maanen & Schein, 1979). Socialization tactics are themethods used by organizations to socialize their employees (Van Maanen &Schein, 1979). Van Maanen and Schein (1979) specified six differentsocialization tactics: collective-individual, formal-informal, sequential-variable, fixed-random, serial-disjunctive, and investiture-divestiture.Research suggests that these six different tactics exist on a continuum,


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 encouragethem to accept existing organizational norms. Organizations use individua-lized 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 responsiblefor themselves, pushes them to challenge the status quo, and tacklesituations themselves (Ashforth & Saks, 1996). Broadly speaking, then,organizations may apply an individual or institutional approach in thesocialization of employees (Jones, 1986; Van Maanen & Schein, 1979).Depending on the desired outcomes, some organizations may prefer tacticsthat allow for customization, while others may desire uniformity insocialization practices.

Each of the different socialization tactics focuses on a distinct aspect ofthe socialization experience (Van Maanen & Schein, 1979). Collective tacticsplace employees in groups during the socialization experience, whileindividualized tactics create unique learning opportunities for each employee.Formal tactics focus on separating newcomers from current employees, whileinformal tactics place the new person in an on-the-job training situation.Organizations using sequential tactics employ a predetermined sequence ofevents to provide employees with explicit information. On the other hand,there is no specific order or explicit information communicated to employeeswhen organizations use random tactics. Organizations that use fixed tacticsprovide precise information about each timetable for the assumption of arole, while variable tactics provide no indication about when employees willbe ready to assume the roles of their job. Serial tactics focus on using anexperienced organizational member to provide a role model for employeesto look to as they are socialized. Disjunctive tactics do not use a role modelduring the socialization of employees. Finally, to affirm the incoming identityand personal characteristics of employees, organizations will use investituretactics. Conversely, divestiture tactics concentrate on striping away thisidentity and these characteristics (Ashforth & Saks, 1996; Cable & Parsons,2001; Jones, 1986; Van Maanen & Schein, 1979).

Overall, then, socialization is the process by which employees becomefamiliar with new roles and organizational cultures (Van Maanen & Schein,1979). Organizations employ different tactics to socialize workers, andthrough these tactics, they can create either an institutionalized socializationexperience, individualized socialization experience, or a combination of the


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

Bits and Pieces: The Roles of Information andFeedback during Socialization Experiences

While little research has been conducted about the role of the actual learningprocess in socialization, a good deal of research has been done that is relatedto the learning process. The topics of information seeking and feedbackseeking (e.g., Ashford & Black, 1996; Kim, Cable, & Kim, 2005) havebeen 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 ofthe fundamental element of learning: information. Information is whatindividuals gather from their experiences, which then shape thoughts andbehaviors (Ormand, 1999). While none of these studies has focused on thecognitive processes of learning, they have concentrated on various aspects ofinformation, 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). Thefindings of these studies suggest how important information is to thesocialization experience.

Prior socialization research on information and feedback seeking hasfocused on studying these variables in the context of new hires (e.g., De Voset al., 2003). Some of this work has concentrated on specific populations ofnew hires, like trainees (Fedor, Rensvold, & Adams, 1992) and other workhas examined the mentor–protege relationship (Godshalk & Sosik, 2003).Most of these studies have looked at the role of information, either theseeking of it or the type of information, and its relationship with varioussocialization outcomes (e.g., De Vos et al., 2003; Fedor et al., 1992;Godshalk & Sosik, 2003). Because information plays a critical role in thelearning process, without it individuals would not have a reason to changetheir behavior (Ormand, 1999), it is only natural that it plays an importantrole in socialization research. In a general sense, during a socializationexperience, information aids workers in understanding what they should doto fulfill their role and be successful in their circumstances. Information alsoreduces an individual’s level of uncertainty about what types of behaviorsare appropriate in his or her new setting (Ashford & Black, 1996).


Feedback, which is a specific type of information, helps employees to knowwhat others think of them, so they can adjust their behavior in a way thathelps them obtain desired outcomes (Ashford & Black, 1996; Greenberger,Strasser, & Lee, 1988). Employee learning occurs as employees haveexperiences that provide both the information and feedback necessary forthem to more fully understand what it takes to survive in their new positionor new organization.

Early studies were unable to find a strong relationship between informationseeking and attitudinal outcomes such as job satisfaction (e.g., Ashford &Black, 1996; Bauer & Green, 1998). Later work, which integratedinformation seeking as an intermediate step between socialization tacticsand outcomes (e.g., Cooper-Thomas & Anderson, 2002) were more successfulin establishing that a significant relationship may exist between informationseeking and various socialization outcomes. In addition to searching for linksbetween information seeking and typical employment outcomes, researchershave also examined where employees look for information. Studies havefound that employees turn to different sources of information depending onwhat type of information they need (Burke & Bolf, 1986; De Vos et al., 2003;Morrison, 1993b; Ostroff & Kozlowski, 1992, 1993). For example, anengineer 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 herjob. Thus, sources and types of information are inextricably linked to oneanother. Other research has shown that workers have different methods foracquiring 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 acquiringinformation (Holder, 1996; Teboul, 1995). Additionally, there are manypositive 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 thatinformation seeking plays an important role in the process of socialization.

Information will always be useful for workers as they seek to reduce thegeneral uncertainty they have about an organization and their positionwithin it (Ashford & Black, 1996). However, employees who are concernedwith their individual performance and controlling personal outcomes willfocus on information that is specific to their performance; this type ofinformation is feedback (Greenberger et al., 1988). The research investigat-ing feedback seeking as part of the socialization process has found that it ispositively related to important attitudinal variables such as job satisfaction


and intention to turnover (Saks & Ashforth, 1997; Kammeyer-Mueller &Wanberg, 2003). One important antecedent of feedback seeking isperformance; specifically Fedor et al. (1992) found that a low performancewould increase the likelihood that trainees would seek feedback. Thus,feedback seeking is an important element of the socialization experience asworkers strive to learn what it takes to become accepted as a member of theorganization. As employees have socialization experiences, and find theywould like better outcomes (such as a higher level of performance or agreater understanding of the organizational culture) they are more likely toseek feedback.

Learning occurs as employees acquire knowledge, which changes the waythey think about things or how they behave (Ormand, 1999), and as theresearch on information and feedback seeking in socialization demonstratesmany employees actively seek out information that helps them to learn howto become more fully enmeshed in the organization. In a study of collegegraduates who had been employed less than a year, Morrison (1995) foundthat employees are more likely to seek out the information they need, ratherthan passively waiting for it to be given to them. This study, among severalothers, offers a different image of employees’ socialization experiences.Much of the early work on socialization assumed that employees were simplysponges that were passive during the socialization process and just absorbedwhatever information the organization passed to them (Miller & Jablin,1991). However, several studies have found that employees who proactivelyassert themselves during the socialization process have many positiveoutcomes (e.g., Gruman, Saks, & Zweig, 2006; Kim et al., 2005; Saks &Ashforth, 1996, 1997; Kammeyer-Mueller & Wanberg, 2003). Indeed, thebase of much of the research on information seeking and feedback seekingis employee proactivity. For example, in a study of new employees duringtheir first six months in the organization, Morrison (1993a) found a positiveassociation between the frequency of information seeking and the outcomesof job mastery, role clarity, and social integration. This work suggests that asemployees are proactive in their efforts to obtain information about theirroles in the organization and feedback about their performance, they aremore likely to obtain the status of organizational insider.

From prior work on information and feedback seeking in the socializationliterature, we can see that employees who have socialization experiences willseek out information to help them become a part of their organization (e.g.,Morrison, 1993a). Information plays a key role in the learning processbecause it helps workers adjust their thinking and behaviors, which can leadthem to be successful in their new position. As a result of previous work we


know what socialization is, and the key role that information plays duringemployee socialization experiences (i.e., the more information an employeehas the better his or her socialization experience). In the next section, wediscuss 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 oforganizational entry (cf., Feldman, 1976; Kammeyer-Mueller & Wanberg,2003; Van Maanen, 1975), socialization experiences can occur at otherpoints during an employee’s career (Ashforth et al., 2007; Van Maanen &Schein, 1979). In our discussion of boundary crossings, we seek toemphasize that boundaries can be crossed at any time during an employee’scareer. Consequently, socialization experiences can occur at any time andare not limited to just the time during organizational entry. Past researchsuggests that socialization occurs at boundary crossings or times oftransition, because it is during these occurrences that employees are mostreceptive to prompts about what he or she should be learning to becomemore fully entrenched in the organization (Ashforth et al., 2007; VanMaanen & Schein, 1979). Schein (1971) proposed that individuals crossvertical, horizontal, and inclusionary boundaries in the work environment.A vertical boundary crossing is most likely to occur in the context of apromotion, although demotions could have a similar impact. Horizontalcrossings may occur with a job transfer or any reorganization that shifts theemployee to a new department. The crossing of inclusionary boundaries ismarked by transitioning closer to the core of the organization where powerand decision-making capabilities are located; these crossings transform theemployee into more of an insider (Louis, 1980; O’Hara, Beehr, & Colarelli,1994; Schein, 1971). During each of these boundary crossings, there is agreater likelihood that individuals will be more open to socializationexperiences (Van Maanen & Schein, 1979). This is because they will besearching for anything that reveals what it takes to succeed in theorganization. The initial introduction to an organization is the most welldocumented and intensely scrutinized boundary crossing because it is asocialization 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 neverstops, and employees are continually learning what it takes to be a successfulcontributor to an organization.


Vertical Boundary CrossingsDuring the course of individuals’ careers, they are most likely to experiencevertical boundary crossings, which are ‘‘the increasing or decreasing’’ ofone’s level in the organizational hierarchy (Schein, 1971, p. 403) throughvarious promotions within the firm. As individuals ascend the organiza-tional ranks, they will acquire new job roles, interact with differentindividuals, and find their existing relationships altered. Conversely, if forsome reason employees are demoted, they will also experience a change injob roles and a change in their interactions with coworkers and supervisors.Organizational level events that may precipitate a vertical boundarycrossing are mergers, reorganizations, or even a layoff event. In the eventof a merger or reorganization, employees may be shifted around, givennew job roles, or placed in new supervisory positions. During a layoff anemployee could remain with the organization, but be reassigned to adifferent position. Each of these events may cause employees’ positions inthe organizational hierarchy to be changed. These same changes can occurfor employees who retain their jobs after a layoff. Additionally, researchsuggests that survivors of a layoff may have other types of adjustments thatare specific to the organization and coworkers because of the layoff(e.g., Brockner et al., 1994). The crossing of a vertical boundary creates aneed for individuals to learn or relearn what is expected of them and howthey fit in as insiders again.

Horizontal Boundary CrossingsIn the ever-changing work environment, employees may find themselvesshifting around to other positions, working in new teams, or otherwiseexperiencing different types of horizontal movements. Horizontal bound-aries are crossed when employees’ functions change or they are moved fromone division, or group, to another (Schein, 1971). There are several differentcareer 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 resultin employees learning new skills or competencies that are relevant to theirnew roles. Crossing a horizontal boundary may result in more than a simplechange in job roles; it may include special training or development that theemployee requires for broader experience within the organization (Schein,1971). In the case of an international assignment, individuals may beperforming the same job but need to learn a language or other skills to helpthem adjust (Black, Mendenhall, & Oddou, 1991). During the repatriationprocess, employees cross horizontal boundaries as they move geographic


areas, and often they find themselves facing a different job as a repatriate orother challenges as an outsider in their new position (Bolino, 2007). Someemployees may be part of management development programs that rotatethem through different functional areas of the organization. These typesof lateral movements can provide upending experiences that create a needfor employees to adjust their behavior to both new coworkers and a newwork environment. Indeed, employees who experience transfers within theorganization tend to seek more feedback (Kramer, 1993; Kramer & Noland,1999). Workers who are placed in new situations due to horizontalboundary crossings will need to learn about job duties, those around them,and whatever else it takes to become an insider.

Inclusionary Boundary CrossingsThe final type of boundary that individuals may cross during their time in theworkplace is the inclusionary boundary. This boundary represents the degreeto which other members of the organization trust and accept an employee(O’Hara et al., 1994; Schein, 1971). Crossing inclusionary boundaries may bethe true mark of an individual who is considered an organizational insider.Many of the career-changing events previously discussed are likely to causeemployees to cross this boundary because it may bring about changes in thesupervisors or coworkers surrounding an employee. While employees canwork toward crossing inclusionary boundaries, it is ultimately not up to themwhether 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 difficultto recognize than vertical or horizontal crossings, but O’Hara et al. (1994)note that shifts in power, access to sensitive information, and decision-making abilities are all likely to change during an inclusionary boundarycrossing. It is plausible that employees needing to cross inclusionaryboundaries must be perceived as trustworthy (Mayer, Davis, & Schoorman,1995) to develop the type of relationships that allow them to finally crossthis boundary. Mergers, reorganizations, job rotations, international assign-ments, working in ad hoc groups, and other similar movements can pushemployees across inclusionary boundaries because they are required to learnhow to interact with new personnel.

A good example of an inclusionary boundary crossing, which does notoccur 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 createa state of uncertainty for many employees. Although employees may havebeen insiders prior to a leadership change, this change could shift them to


outsider status again. Because of the increased frequency in organizationalturnover (Hom, Roberson, & Ellis, 2008), employees are likely to findthemselves crossing inclusionary boundaries several times during theircareers. We offer a guideline for boundary crossings in future research andnext discuss the consequences of crossing these boundaries in relation toworkers’ motivation to learn, information, and tenure.

Guideline 1. Researchers should seek to differentiate between types ofboundary 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 themin contact with new sources of information. To gain a better understandingof boundary crossings, and the effects of these crossings we turn touncertainty reduction theory (Berger, 1979) and SIP theory (Salanik &Pfeffer, 1978). Prior work in the socialization literature has used uncertaintyreduction theory to explain the behaviors of employees during socializa-tion (e.g., Kramer, 1994; Lester, 1987; Mignery, Rubin, & Gorden, 1995),because it focuses on the reaction of individuals when they experienceuncertainty, and it suggests what individuals will do to reduce theiruncertainty. We also turn to the SIP theory (Salancik & Pfeffer, 1978) todetermine how the work environment around employees is important duringthe socialization process. SIP theory holds that coworkers, other indivi-duals, and the workplace surrounding employees provides critical informa-tion for employees as they work to make sense of their situation (Jex & Britt,2008; Salancik & Pfeffer, 1978). Drawing from these two theories, we canfurther understand the role of learning in the socialization process.

Uncertainty Reduction TheoryUncertainty reduction theory proposes that individuals have an aversionto uncertainty and seek to gather information in an effort to reduce theiruncertainty. People seek to reduce uncertainty because it is associated withanxiety. Thus, the reduction of uncertainty is critical to lowering the levelsof anxiety that employees are feeling because of the situation they are in(Berger & Calabrese, 1975). People seek to reduce uncertainty for two mainreasons. First, they want to be able to predict the behavior of the individualsaround them. Knowing what others are going to do reduces the anxiety


associated with uncertainty. Second, employees want to be able to explainthe behavior of those around them. Knowing why people have behaved ina certain way reduces the anxiety individuals experience when interactingwith these people (Berger, 1979; Berger & Calabrese, 1975). Any time anemployee meets a new person or encounters an unknown situation thereis some uncertainty, because he or she is unable to predict or explain thebehavior of this new person or unknown situation. The greater amount ofuncertainty workers feel in their situation the more likely they are to takeactions that will reduce this uncertainty (Berger & Calabrese, 1975). Thus,individuals who experience new situations, such as when they enter anorganization, are motivated to reduce the amount of uncertainty thatsurrounds them (Saks & Ashforth, 1997). For this reason, we believe thatuncertainty reduction theory helps to explain the learning process that is thefoundation of the socialization experience.

Social Information Processing TheorySIP theory, developed in the late 1970s as a response to job enrichmentmodels of job satisfaction (i.e., Hackman & Oldham, 1976), proposes thatthe satisfaction of workers is influenced by the context of the situation andthe consequences of past choices (Salancik & Pfeffer, 1978). Fundamentally,this theory suggests that employees’ attitudes, behaviors, and beliefs areinfluenced by the work environment around them (Salancik & Pfeffer,1978). As such, coworkers, friends, and even the situation itself can influencean individual’s attitudes or behaviors at work. Workers who seek to makesense of their world will look for ‘‘salient, relevant, and credibleinformation’’ from the social environment around them (Zalesny & Ford,1990). Studies show that this information, which is gathered from the socialenvironment, influences the perceptions, attitudes, and beliefs about one’sself, job, and organization (Ibarra & Andrews, 1993). As such, we expectthat SIP theory will serve to guide us in understanding what is importantfor learning during socialization experiences.

Motivation to LearnReturning to boundary crossings, we know that each boundary-crossingexperience creates uncertainty for the employee. We suggest that this desireto reduce uncertainty will increase workers’ motivation to learn. Motivationto learn is a desire to engage in training, learn job-related content, and takepart in development activities (e.g., Carlson, Bozeman, Kacmar, Wright, &McMahan, 2000). Prior work links motivation to learn with certainindividual characteristics (Major, Turner, & Fletcher, 2006), but we also


posit that specific situations, such as boundary crossings, will increaseindividuals’ levels of motivation to learn. These boundary crossings promptthe beginning of the learning process, and because they increase levels ofuncertainty we believe that employees will be more motivated to learn; thuswe offer this guideline for future socialization research.

Guideline 2. Researchers should strive to measure employees’ motivationto learn in future socialization research.

InformationThe reduction of uncertainty is one of the primary reasons that individualsseek information during the socialization process (Miller & Jablin, 1991). Asemployees strive to become insiders in the organization by reducing the levelof uncertainty they feel about their position and the organization, SIP theorysuggests they feel they will seek to gain information from the environmentaround them (Zalesny & Ford, 1990). To find this information workers willbe able to turn to the new supervisors, coworkers, mentors, and writtendocuments (Miller & Jablin, 1991; Morrison, 1993b; Ostroff & Kozlowski,1993) that surround this new position. For example, employees who areengaged in an institutionalized socialization experience can turn to othercoworkers and ask questions that are relevant and important to gaininginformation about their job, work role, or other related content. Mentors canalso be a critical component of the socialization process because they arean important source of information for employees during socializationexperiences (cf. Ostroff & Kozlowski, 1992).

However, some employees may not always ask others when acquiringinformation, because they worry about how this might look to theirsupervisor or colleagues (Morrison, 1993b). Instead, they may simplyobserve the individuals, and strive to glean relevant information from theactions of these persons (Morrison, 1993a; Ostroff & Kozlowski, 1993), orturn to the written documents that are relevant to their position (Miller &Jablin, 1991). The sources of information available to a worker may bedetermined by the tactics the organization uses while socializing the worker.If an organization uses individual tactics and separates the employee fromhis or her coworkers (Cable & Parsons, 2001) he or she will not be able touse those coworkers as sources of information. Therefore, while boundarycrossings will generally provide new sources of information to employees,the socialization tactics used by an organization may also influence thenumber of sources available. For this reason, we suggest that future workdifferentiate between sources of information.


Guideline 3. Researchers should seek to differentiate between sources ofinformation in future socialization research.

Workers who have more sources of information will also have more typesof information available to them. Miller & Jablin (1991) have identifiedthree types of information that workers look for during a socializationexperience: referent, appraisal, and relational. Referent information helpsemployees know what is essential for them to perform well in their assignedroles (Hanser & Muchinsky, 1978; Miller & Jablin, 1991; Morrison, 1993b).The next information type, appraisal information, assists individuals inunderstanding if they are successful in their job performance (Miller &Jablin, 1991). Relational information is the final type of information, and itdeals with the relationships between employees and other organizationalmembers (Miller & Jablin, 1991). This type of information helps individualsto understand their standing within organizational groups, be it with theirboss, fellow coworkers, or subordinates. In these relationships, learningfrom context and nonverbal cues is critical to help employees read betweenthe lines and understand the true nature of the relationship. The differenttypes of information that an employee collects during the socializationprocess has an important influence on employee learning. Because SIPtheory proposes that worker attitudes, behaviors, and beliefs are shapedby the social environment surrounding them (Salancik & Pfeffer, 1978),we believe that these new sources of information and different types ofinformation are vital to employees as they navigate the socialization process,and we suggest the following.

Guideline 4. Researchers should seek to differentiate between types ofinformation in future socialization research.

TenureOne important factor that will influence the socialization process over thecareer of an employee is tenure, both tenure in the organization and tenurein a position. Uncertainty reduction theory suggests that time is importantto reduce the anxiety that people feel because of uncertainty (Berger, 1979).Over the course of time, individuals are able to gain knowledge aboutbehaviors, which helps to reduce uncertainty (Berger & Calabrese, 1975).If an individual remains in a single organization for an extended period, hisor her motivation to learn is likely to decrease because he or she will have abase of knowledge about how things work, which will reduce uncertaintywhen crossing boundaries in that organization. Indeed, the more familiar


individuals are with situations and the people around them the lessuncertainty they will feel (Berger, 1979). As employees spend more time inan organization, or more importantly a single position, their familiarity withboth the duties of the job and other employees increases. This results in areduced level of uncertainty, and we suggest that consequently the employeewill have less motivation to learn.

While tenure may diminish employees’ motivation to learn, it couldincrease the number of sources from which employees can get information.The more interactions people have and the more knowledge theygain, the more they will be able to reduce their uncertainty (Berger &Calabrese, 1975). The longer employees stay in a single organization, thegreater the number of colleagues they will have and the more interactionsthey will have had with these individuals. Employees will have more time toobserve, try new behaviors, and learn about the organizational policiesand procedures. The greater a worker’s familiarity with these sources, theless likely he or she is to experience uncertainty when faced with boundarycrossings. We expect that tenure in an organization will positively influencethe number of information sources to which workers have access, which willbe helpful to employees when crossing boundaries during a socializationexperience; therefore we offer the following guideline for future research.

Guideline 5. Future socialization research should seek to account fortenure interactions when employees are changing to new positions withinthe same organization.

The Learning Process: A Brief Review

As workers seek to gain insider status, they must learn how to behave intheir new workplace. Learning is the permanent change that occurs in anindividual’s cognitive associations or personal behaviors due to theexperiences he or she has (Ormand, 1999). The behavior of workers inorganizations is a function of employees’ cognitive processes and theenvironment around them (Davis & Luthans, 1980). The interaction betweenworkers’ cognitions and the situation around them, learning, is what shapeshow they behave in the workplace. To better understand the learning processthat employees experience during socialization we turn to social learningtheory (Bandura, 1977) to help us grasp how the learning process functionsin organizational settings. Social learning theory is a behavioral theory thatexplains how individuals learn in organizations. It incorporates some ofthe principles of operant conditioning (Skinner, 1969), along with the social


environment around employees. Thus, it addresses both the personalexperiences, which provide direct learning opportunities, along with thesocial context around the employees.

Social learning theory (Bandura, 1977) suggests that the learning process,which is a continuous reciprocal interaction of individuals’ thoughts, theirbehaviors, and their environmental situation, is the one of the best waysto explain the actions of individuals. Bandura labeled this process a triadicreciprocality; through this process individuals process information, learn,and then adjust their behavior accordingly (Bandura, 1977, 1986). It isimportant to point out that individuals do not simply react to theenvironment around them, nor are their actions determined solely by pastexperiences. Individuals consider the situation, and then using the informa-tion available from both the environment and past experience they useforethought in deciding how to behave (Bandura, 1986). According to sociallearning theory, workers can learn how to behave both through their owndirect experience and by observing the behavior of others (Bandura, 1986).

Both experiential and observational learning have important implicationsfor the socialization process, although each of these types of learning is verydifferent. Because social learning theory incorporates the principles oftraditional operant theories of behavior, which describes learning throughpersonal experience (e.g., Skinner, 1969), and it proposes that learning alsotakes place through the observation of others’ behavior (Bandura, 1977) it isespecially applicable to socialization experiences. Organizations can adoptan institutionalized or individualized approach, or some combination of thetwo, to the socialization of their employees. These different approaches willresult in both experiential and observational opportunities. If an organiza-tion uses informal socialization tactics that force employees to learn onthe job (Cable & Parsons, 2001) it is more likely that employees will beengaged in experiential learning. However, organizations that use serial andinvestiture tactics, which provide employees with role models to watch(Cable & Parsons, 2001), create situations where observational learning is akey part of the socialization experience. From these examples, we concludethat both observational and experiential learning are critical to learningduring socialization.

One of the strongest pieces of research to address the link betweensocialization and the actual process of learning was a study of British Armyrecruits that demonstrated the central role of the learning process in sociali-zation. Findings indicated that the relationship between socialization tacticsand outcomes, job satisfaction and organizational commitment, was fullymediated by the level of newcomer learning (Cooper-Thomas & Anderson,


2002). As we have discussed the learning process and socializa-tion experience are interrelated; this is because individuals need to learnin order to be socialized. However, whereas learning is about usinginformation acquired through different experiences and observations toinform future behaviors (Ormand, 1999), socialization focuses on employeesbecoming more familiar with the organization, their job, and those withwhom they interact (Van Maanen & Schein, 1979). Hence, the two processeshave different outcomes. Individuals learn based on things that they observeor experience, with the purpose of the learning being that they know how tobehave in a given situation (Bandura, 1986; Ormand, 1999). The socializa-tion experience focuses on integrating workers into their jobs by helping themto learn the ropes (Ashforth et al., 2007). We further suggest that the wayindividuals learn plays an important role in the socialization experience. Wenext focus on three different modes of learning to help us better understandhow 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 outsiderinto an insider, through becoming familiar with job roles and organizationalculture, employees who seek to accomplish this will seek to draw informationfrom both their experiences and observations. Central to our paper are thethree different modes of learning that help individuals to processinformation. In a reformulation of organizational learning types, necessi-tated by the ‘‘organizational learning jungle’’ (Huysman, 2000, p. 81), Visser(2007) offered a typology of three modes of learning used in organizationalsettings. These are planned, meta-, and deutero-learning. By considering eachof these learning processes, we can gain a better understanding ofsocialization experiences.

Planned LearningPlanned learning occurs when organizations create and maintain a system,routine, procedure, or structure with the specific purpose of inducingorganizational members to learn (Schon, 1971, 1975). Planned learning isthe type of learning that organizations rely on to create better employees.When an individual enters an organization, there are certain processesand rituals that he or she experiences. Many individuals spend their firstfew hours as newcomers in an organization, reading forms and filling outrequired paperwork. There may be hours or days of specified training about


how their job is properly preformed. The processes of orientation and otherforms of training are part of a broader effort to influence and teachemployees what it takes to become an insider in the organization (VanMaanen & Schein, 1979). By utilizing these types of systems organizationsrequire employees to engage in planned learning.

The most important element of planned learning is that it influencesemployee behavior (Visser, 2007). The incorporation of planned learningleads employees to alter their behavior in some way to better fulfill jobroles or fit into a work group. Planned learning is most likely to occurwhen organizations take an institutionalized approach to socialization. Forexample, both sequential and fixed tactics of socialization can facilitate toplanned learning. Employees are given explicit information about theactivities they will participate in when sequential tactics are employed andfixed tactics provide precise information about what will occur during eachstage of the socialization process (Ashforth & Saks, 1996; Van Maanen &Schein, 1979). Additionally, planned learning is the ideal way for gainingreferent information. This is because referent information lends itself tocommunication through an established routine or system that helps workersto know explicitly what is expected of them in their jobs. Other implicationsfor the use of planned learning during a socialization experience are that anorganization can adjust and adapt the socialization process based on theoutcomes, such as employee role mastery, to obtain the desired results.Planned learning has the potential to induce meta-learning among employeesbecause of the creation and maintenance of learning systems (Visser, 2007).Because of this, planned learning is a critical component for organizationsthat desire to influence employees during the socialization process and wesuggest the following:

Guideline 6. Researchers should theorize about the circumstances underwhich planned learning will be most effective during the socializationprocess. In contemplating these circumstances, they should focus on thedesired outcomes of the socialization process.

Meta-LearningThe next mode of learning that underlies socialization experiences is meta-learning. When a specific course of action fails to produce the desiredresults, 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 individualbegins to process the inconsistency between expectations and actual


consequences, searching for reasons why it may have occurred. Thislearning process results in two levels of inquiry, typically referred to assingle- and double-loop learning (Argyris, 2003; Argyris & Schon, 1978).The initial level of learning in the process of meta-learning, single-loop,involves reflection to better understand how to detect errors, and thoughtsabout how to effectively correct those errors (Argyris & Schon, 1974; Visser,2007). For example, if an individual finds himself or herself in the out-groupdue to consistent tardiness, there may be reflection on how this could havebeen anticipated. Upon reflection, the individual may remember that asupervisor and other coworkers made several comments about theimportance of being timely in this organization. Initially these commentswere simply brushed off, because the individual did not consider his or herlevel of tardiness to be relevant. In retrospect, the individual sees that his orher coworkers were making subtly important suggestions about how to fitin. Moving forward this individual plans to be aware that even seeminglyinconsequential remarks may have important implications. From thisexample, we can see that the individual focused on how the error could havebeen detected and how similar errors can be avoided in the future; this is thepurpose for first level of learning during meta-learning. Thus, such learningis essential as individuals work to understand their job and the culturearound them during socialization experiences.

The second level of learning concerns seeking ways to improve theunderstanding of the norms and values of the situation (Argyris, 2003;Crossan, 2003). Continuing the previous example, the employee may engagein double-loop learning by starting a conversation about why being on timeis so important, and perhaps if possible, some consideration of flextime bythe organization. An individualized approach to socialization (Jones, 1986) islikely to lead employees to engage in high levels of meta-learning. Because anindividualized approach is unstructured and unique for each employee, theywill need to reflect on both their experiences and observations in order tolearn what they need to do to become fully enmeshed in the organization. Insum then, meta-learning describes how individuals learn from the differencesbetween the actual and expected outcomes of the socialization process. Meta-learning will help individuals detect errors and aid them when looking forways to improve the understanding of the norms and values of a situation.

Meta-learning provides valuable insight into the learning process bydescribing how employees alter both their thoughts and behavior. It is aconscious process that involves employees stopping to think about theintended consequences of their actions (Argyris & Schon, 1996). Asemployees gain more information about different job roles, group processes,


or organizational attributes, they will enact that knowledge through theirbehavior. When those behaviors do not result in the anticipated results, theemployee will stop and think about what has happened. ‘‘Why has it notworked?’’ or ‘‘What did I miss?’’ are a few of the questions an individualmay ask. After some consideration, workers may make adjustments, beginexperimenting, and looking for ways to improve (Visser, 2007). If anorganization uses divestiture tactics to strip away the personal character-istics of employees (Van Maanen & Schein, 1979), the employees could usemeta-learning to process what this means for them as individuals and decidewhat types of behaviors they need to change. This type of learning may beespecially beneficial when workers are striving to master job-relatedcontent or negotiate group interactions as they endeavor to be accepted inthe organization. As discrepancies between workers’ expectations and theoutcomes occur, they will reflect on this appraisal information. By engagingin meta-learning with appraisal information, employees will be able todiscern what types of changes they need to take in order to correct theirperformance so they can obtain their desired outcomes. Thus, meta-learningwill be critical as employees seek to better understand their jobs, theorganizational culture, and how to succeed in their new position. Thus wesuggest the following guideline for future research.

Guideline 7. Researchers should theorize about how meta-learning can bebetter integrated into the socialization process. Researchers may wantto focus on ways to add specific moments for meta-learning into existingplanned-learning processes.

Deutero-LearningThe context that surrounds an employee provides a wealth of informationfor employees as they attempt to learn about their job and place in theorganization. Deutero-learning draws information from context-specificevents and nonverbal cues (Visser, 2007). At an unconscious level,individuals are constantly learning and absorbing knowledge about theorganization, acceptable forms of behavior, expectations, requirements,and other nuances of organizational life. Deutero-learning is based on twomain principles: all systems are capable of adaptation, in which learning isinherent, and learning occurs through an interaction (Bateson, 1963, 1972).First, all systems are capable of adaptation that requires learning. Allsystems, be they biological, social, ecological, or organizational, change overtime. This change occurs as the system adapts to the existing environment.Inherent in this adaptation is learning about what works and what does


not; with those who learn and adapt continuing their existence. The secondprinciple of deutero-learning is that learning occurs through an interaction.Individuals do not learn in isolation, there must be an interaction withsomething or someone in order for learning to occur (Bateson, 1972; Visser,2007). Employees learn by witnessing or experiencing event A in the contextof X. The interaction of A and X creates information, which then facilitateslearning. The results of this learning will be extended to similar situationswhere it will be maintained, or altered depending on the results of the newinteraction and the patterns of behavior that develop from it (Bateson, 1972).

Three main factors characterize deutero-learning. First, it is a continuousprocess of behavioral communication that is mostly unconscious (Haley,1963; Watzlawick, Bavelas, & Jackson, 1967) and can be both outwardand incidental in nature. Interactions occur continuously as individuals arearound one another and communicate with each other. Simple messagesprovide learning experiences by creating the context of a situation andthrough the elicitation of a response and nonverbal cues. We sometimesknow that we are learning and at other times we do not know we arelearning. The second feature of deutero-learning is that it cannot be overtlycontrolled. Attempts to control a deutero-learning situation create a newlearning process, because attempts to control result in a new context, whichthen creates a new interaction and from this interaction employees are able toextract new information (Visser, 2007). The final characteristic of deutero-learning is that it does not always lead to the improvement in the individualor organization; individuals sometimes learn counterproductive behaviors.While this type of learning can certainly occur in a fashion that leads to thebetterment of the individual or the organization, research has indicated thatcertain types of situations may result in negative outcomes for both theindividual 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 thatworkers having a socialization experience will engage in this learningprocess. For example, in the socialization setting, workers may be toldthat certain procedures are important; however, the tone, expression, andposture of the person communicating this message may convey that thisprocedure is actually unimportant. Individuals responsible for the socializa-tion of employees may unconsciously or consciously communicate nonverbalmessages that contradict the verbal message being communicated. Foremployees who are socialized through serial tactics, which provide anexperienced organizational member as a role model (Cable & Parsons, 2001),deutero-learning helps them to process this experience by using information


from both their experience and the context of the situation. Additionally,relational information, which aids individuals in knowing where they standin their relationships with other individuals (Miller & Jablin, 1991), is mostlikely to be acquired through deutero-learning. The workers’ relationshipstatus will most likely be communicated through subtle tones, posture, andexpressions of those they interact with; which will provide information forindividuals to process through deutero-learning.

Deutero-learning may also be particularly important for individualswho are worried about appearing incompetent because they ask too manyquestions (Ostroff & Kozlowski, 1992) or for socialization experiences thatoccur later in careers. Employees who are promoted, returning from aninternational assignment, or adapting to a change in leadership may rely ondeutero-learning to glean important details about how they can becomeorganizational insiders once again. Because this type of learning is notalways a conscious effort on the part of the learner (Bateson, 1972), it islikely to continue throughout individuals’ careers as they experience variousboundary crossings and move closer to being insiders. In many cases,deutero-learning helps employees to read the information between the linesas an organizational insider should, and for this reason we suggest thefollowing guideline:

Guideline 8. Researchers should theorize about ways in which deutero-learning may influence the socialization process. They may consider howthe environment around the employee will influence opportunities toengage in deutero-learning.

Each of these different modes of learning plays an important role duringthe socialization experience. These modes of learning are the culmination ofthe learning process that happens during socialization. As individuals crossboundaries, they are more motivated to learn and have more sources ofinformation. These three modes of learning are the processes by whichemployees take the information they have gained and use it to adjust andadapt their thoughts and behaviors.

SOCIALIZATION AND LEARNING:

THE NEVER-ENDING PROCESS

Organizational life is in constant flux. The environment around a firmchanges, innovations provide new opportunities, technology alters existing


jobs, and employees must cope with these changes the best they can. Inaddition to the outside pressures, influencing workers, there are personalchoices that alter career paths and create new and different situations inorganizational life. Employees may apply for promotions, be transferred todifferent positions, experience leadership changes, or switch organizationsall together. As individuals work through these different changes andadjust to new roles, jobs, and coworkers they continue to have socializa-tion experiences. This happens because they are making every effort tobecome, or regain their status as, organizational insiders. In this paper,we have sought to understand how the learning process, which underliessocialization works.

According to Fig. 1, there are several important elements to learningduring the socialization experience. We can see that workers in anorganization experience boundary crossings, or changes in their job, role,or people around them. Boundary crossings, which result in increases toemployees’ uncertainty and anxiety, then increase employees’ motivation tolearn. Additionally, boundary crossing also influences both the sources andtypes of information to which workers have access. However, tenure in acertain position or with the organization is likely to lower employees’ moti-vation to learn, because the more familiar individuals are with the peoplearound them and their situation the less uncertain they will feel because of it(Berger & Calabrese, 1975). For this reason, the longer an individual istenured in an organization the less uncertainty he or she will face whencrossing boundaries. On the other hand, tenure in an organization, and theassociated familiarity with other employees and the processes and practicesof the organization, will increase the number of sources from which workerscan acquire information. The increased motivation to learn, that results fromboundary crossings, drives workers to engage in learning, and the newlyacquired sources that provide a variety of information types, all of which

Fig. 1. The learning process during a socialization experience.


feed into the planned, meta-, and deutero- modes of learning. The processingof information through these three modes of learning is the final step in thelearning process, and leads employees to change what they think and howthey behave.

In taking a long-term view of the socialization process, we suggest thatindividuals will have multiple socialization experiences, and a learningview of socialization is applicable to an entire career rather than fororganizational entry only. The most common focus of socializationresearch is the point of organizational entry when a newcomer entersand adjusts to an organization (Ashforth et al., 2007; Louis, 1980). Thiscreates a constricted focus on a very limited period of time and a veryselect group of individuals. Ashforth et al. (2007) called this narrow focusinto question when they asked researchers to challenge some of thetraditional assumptions of socialization research. There appears to be aneed to expand the time period surrounding socialization research, so as torecognize different times in an employee’s career and the influences ofdifferent groups of employees. Using a learning perspective, we contendthat socialization has an overlooked temporal aspect. Individuals willlearn throughout their careers, and socialization experiences can occur atdifferent times during the course of a career; for instance they may happenafter promotions, transfers, and organizational changes. Thus, thesocialization experience does not occur a single point in time, but ratherthe multiple boundary crossings that occur during the course of anindividual’s career create many opportunities for this experience. Thesecrossings trigger the learning process so employees can regain their insiderstatus. When this happens, employees are motivated to learn and theybegin to draw from all their sources of information to learn what it is thatthey need to do next.

Conclusions and Directions for Future Research

The learning processes, which underlie the socialization experience, havebeen an implicit part of socialization from the beginning of research in thisarea (Van Maanen & Schein, 1979). In this paper, we have endeavoredto propose how the learning process occurs during socialization, and whatsome of the important elements of this process are. By examining thislearning process we have sought to better understand how boundarycrossings trigger the learning process, how it unfolds through motivation tolearn and the acquisition of information, and how three modes of learning


lead to the changes in thoughts and behaviors that indicate that learning hasoccurred (Ormand, 1999). This paper, and the uncovering of the learningprocesses that occur during socialization, suggests a series of importantdirections for future research.

The first direction is in clearly linking socialization tactics to differenttypes of information and modes of learning. As we have suggested, differenttypes of tactics and approaches to socialization lend themselves to providingdifferent 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 tosocialization, 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. Whenconsidering modes of learning, an institutionalized approach to socialization(Jones, 1986) is likely to have a stronger association with planned learningthan it is with meta-learning. Future research should seek to explore theserelationships to help organizations understand what types of informationworkers will receive, and how their employees will learn from differentsocialization tactics.

Second, by extending the temporal window of the socialization processto include the span of an entire career and focusing instead on boundarycrossings, we can see how individuals will have multiple socializationexperiences within a single organization. During an individual’s career span,individuals will experience myriad socialization events and may be moresusceptible to different learning modes during these multiple socializationevents. The way individuals learn during these socialization experiences alsohas important implications for understanding why certain outcomes occur.The model we propose integrates learning types into the socializationprocess, and we posit there are several outcomes of the socialization processare the result of these learning processes. With each boundary crossingemployees are motivated to learn, acquire different types of informationfrom sources, and learn from that the way they need to behave so they canreestablish themselves as insiders.

Third, in future work researchers need to address issues of measurementfor 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 ambiguityis an important outcome during socialization, and socialization programsare designed to reduce ambiguity, this may provide a starting point forscholars. In developing measures of planned and meta-learning researchers


may focus on how much an employee gains from organizational programsand how much they learn by stopping and thinking about the implicationsof their personal experiences. Gauging planned learning will help organiza-tions know how effective their socialization programs are. In measuringmodes of learning, deutero-learning presents the greatest difficulty, becauseit often happens unconsciously. For guidance in this area researchers canlook to the work of McClelland (1961). The needs that McClellanddescribed (e.g., Need for Power, Need for Achievement) were unobservable,and as such, measurement was problematic. To help capture the needs inquestion, ambiguous meaning thematic apperception tests where used(Holmstrom, Silber, & Karp, 1990). While such tests may not fully capturedeutero-learning they may provide a starting point for developing ameasurement of this mode of learning by providing some insight into theunconscious motivations, which drive our performance and are deemedimportant by an individual.

As further research provides a more complete picture of the learningprocess that underlies the socialization experiences of employees, there willbe important implications for organizations. By knowing how employeeslearn, what sets off the learning process, and what modes of learning areactive during socialization, organizations may be able refine their programsto help workers become more fully enmeshed in the organization. Byknowing that boundary crossings initiate the learning process, organizationscan be prepared to provide the information necessary to aid employeeslearning processes as they adapt to a new position or organization. Helpingemployees learn the ropes will not only lead to more satisfied workers, butmay 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 aframework for distinguishing between different e-learning programs, it isa challenge for researchers to compare their effectiveness or identifycharacteristics of e-learning that contribute to learning effectiveness.Based on general theories of learning, we develop a typology thatcompares e-learning programs in terms of the nature of the learninginteractions they provide for learners in three dimensions: degree ofinteraction, learner control of interactions, and informational value ofinteractions. The typology dimensions apply to learner–instructor,learner–learner, and learner–instructional material interactions. We alsodiscuss important theoretical implications of the typology. First, we showthe utility of the typology for comparing the effectiveness of differente-learning programs. Second, we apply the typology dimensions todevelop a theoretical framework for e-learning research that provides afoundation for examining factors that influence learning effectiveness inan e-learning program. The framework identifies e-learning programcharacteristics, learner characteristics, and contextual factors that impact




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learning effectiveness in different e-learning environments. It also showshow the typology dimensions align with learning goals to influencelearning effectiveness.

Organizations are using e-learning extensively for employee development(DeRouin, Fritzsche, & Salas, 2005). E-learning, which we define as the useof information and communication technologies (ICT) to deliver instructionto learners, offers many potential benefits to both organizations andemployees. The benefits to the organization include savings on travel andaccommodation for attending training, the ability to offer training to morepeople, greater consistency in training delivery, and improved tracking ofcourse completion and testing (Noe, 2005; Welsh, Wanberg, Brown, &Simmering, 2003). For employees, the benefits include greater flexibility overwhen and where training is completed, the potential to complete coursesegments for just-in-time training needs, and exposure to a wider offeringof courses (Noe, 2005; Welsh et al., 2003).

The growth in e-learning has been characterized as a revolution in trainingand development (Galagan & Drucker, 2000; Noe, 2005). Out of thisrevolution, a large number of different e-learning formats have emerged. Asdescribed by Welsh et al. (2003) in their review of the e-learning literature,e-learning applications can range from asynchronous e-learning, in whichlearners 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 asthe 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 integrategraphics, animation, video, and audio. To add to this complexity, manyorganizations use different combinations of e-learning formats and class-room 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 instruc-tion, or the effectiveness of different e-learning and blended learningprograms is akin to comparing apples and oranges. Given this, Smith andDillon (1999) argued that to facilitate empirical research in this area,researchers should first articulate the dimensions along which e-learningprograms vary. However, to our knowledge, there is currently nocomprehensive typology for comparing e-learning programs. Therefore, theprimary purpose of this chapter is to address this shortcoming in the extant

N. SHARON HILL AND KAREN WOUTERS202

literature by delineating dimensions of a typology that can be used tocharacterize different types of e-learning programs. Our typology isgrounded in theories of traditional learning that emphasize the critical roleof interactions between the learner and instructor, the learner and otherlearners, and the learner and instructional material for facilitating learning.We refer to these interactions as learning interactions and argue thate-learning programs can be differentiated by the extent to which they vary interms 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 theoreticalcontributions of the typology and its potential to advance e-learningresearch. First, we discuss the utility of the typology for comparing theeffectiveness of different e-learning, blended and classroom instructionformats. We show how the typology can help to explain current equivocalresearch findings in this area as well as stimulate new research directions.

Second, we apply the typology dimensions to develop a theoreticalframework for e-learning research that examines factors that influencelearning effectiveness in an e-learning program. In this framework, thedimensions of the typology act as mediating variables that link character-istics of e-learning programs to learning effectiveness. We describe how thetypology can be used to explain existing relationships found in the literaturebetween the characteristics of e-learning programs and learning effectivenessas well as to identify new e-learning program characteristics that the typologysuggests are worthy of attention. The framework also highlights learnercharacteristics and contextual factors that moderate the relationship betweenthe typology dimensions and learning effectiveness. We extend research inthis area by showing how the typology can be used to move beyond thefocus in the existing literature on individual characteristics and contextualfactors that are important for e-learning, in general, to also identify thosethat promote e-learning effectiveness in different types of e-learning environ-ments. Finally, although several scholars agree that for effective learning tooccur, the characteristics of an e-learning program should align with thelearning goals of the program, there is no clear agreement on the form thisalignment should take. We describe how the typology can be used toreconcile and extend different perspectives in this area.

The rest of the chapter proceeds as follows. We first define e-learning inorder to set the boundaries for the scope of learning programs addressed bythe typology. We then review existing typologies that distinguish betweendifferent e-learning programs, and discuss their limitations relative to the

Comparing Apples and Oranges 203

typology we develop in this chapter. Next, we review the major theories oflearning related to more traditional instruction that form the theoreticalfoundation for the typology. Drawing on these theories of learning ande-learning research, we develop the typology, which consists of threedimensions (degree of interaction, learner control of interactions, andinformational value of interactions). We discuss each dimension of thetypology in turn, including its theoretical basis and relationship toe-learning effectiveness. Having described the typology, we turn ourattention to its theoretical contributions, including as a foundation forcomparing the effectiveness of different e-learning programs and as a centralmechanism in a theoretical framework for identifying factors that influencee-learning effectiveness.

In summary, our goal is to make a significant contribution to thee-learning literature by developing a typology for distinguishing betweendifferent e-learning programs. The development of the typology relies onthe integration of several literature streams, including the training andeducational literature as well as the information systems, and organizationalbehavior 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) todeliver instruction to learners. Consistent with several existing definitionsin the literature, our definition focuses on technology-mediated learning asthe primary defining characteristic of e-learning. In other words, content istransmitted to the learner using technology, and technology is also used tofacilitate communication between the learner and any other participants inthe learning program (i.e., instructor and other learners). We also includein the e-learning definition all possible ICTs that can be used to deliverinstruction. 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 abroader 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 essentialfor developing a more comprehensive framework that applies to the widerange of e-learning applications currently found in organizations.


Our definition also includes both technology-mediated learning wherelearner, instructor, and fellow learners are spatially separated from eachother as well as technology-mediated self-study programs where there are noinstructor and other learners available. Several existing definitions makespecific assumptions regarding the presence of a human instructor and thatinstructor’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 toe-learning programs where there is a human instructor or other learners.

Between the two extremes of traditional classroom instruction and fullytechnology-mediated e-learning are different forms of blended learning.Blended learning has been defined as ‘‘training that combines traditionalclassroom sessions with e-learning and self-study’’ (Kovaleski, 2004, p. 35).Depending on the extent to which learning is technology-mediated, theblended learning program will be closer to the classroom instruction orthe e-learning end of the continuum we have described. Given the need fore-learning researchers to compare e-learning to blended and classroominstruction, the typology we develop can be applied to learning programsthat 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 usedto distinguish between different e-learning programs. We used an electronicsearch of EBSCO (Academic Search Premier, Business Search Premier,Education Research Complete, Eric, PsychArticles, Psychology andBehavioral Science Collection, and PsychInfo) and web searches performedwith Google Scholar to locate e-learning typologies in the literature from1985 to the present. We then supplemented these electronic searches withmanual searches of books on e-learning. Search terms included e-learning,online, web-based, distributed, distance, technology-mediated, and compu-ter-based learning/training/instruction. Our review of the literature revealedonly a few existing typologies. In this section, we briefly review these existingtypologies, and discuss their limitations relative to the typology we developin this chapter.

A first set of existing typologies distinguishes between different types ofe-learning based on the delivery medium. Specifically, Davidson-Shiversand Rasmussen (2006), De Volder (1996), and Taylor (2001) describedifferent generations of distance learning. Distance learning refers to the use


of different technologies to deliver instruction to learners who are spatiallyseparated from the instructor and other learners (Keegan, 1986). The firstgeneration of distance learning is the print-based model (i.e., correspon-dence education supported by distance instruction through writtenmessages), which is not consistent with our e-learning definition. Theremaining generations use ICT to deliver instruction to learners; hence, theyfall within our definition of e-learning. The second generation of distancelearning uses radio, telephone, and television in a nonintegrated form. Thethird generation uses computers and digital technologies to unite instructorsand learners and deliver the content of the course. The delivery medium inthe fourth and last generation of distance learning is the internet.

A second set of typologies differentiates between e-learning programsbased on specific characteristics of the technologies used to deliverinstruction and the spatial separation of the instructor and learners. Forexample, Aggarwal (2000) and Brewer, De Jonghe, and Stout (2001)developed a typology based on two dimensions: time (synchronous versusasynchronous) and place (same versus different). Traditional classroominstruction, where instructor and learner share the same space at the sametime and communicate in real time (synchronous), is at one extreme. At theother extreme, instructor and learners are at different locations andcommunicate 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 individuallearner, 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 dimensionsthat can be used to differentiate e-learning programs: type of interaction(human–human versus human–computer), information modality (the abilityto transmit verbal and/or nonverbal cues) and linearity (the extent to whichthe learner can navigate through the material).

Despite the intuitive appeal of focusing on the delivery media orunderlying technology characteristics to compare e-learning programs, sucha focus provides little information about the learning experience createdfor the learner when these features are combined in the context of differentinstructional designs. For example, the use of more synchronous technol-ogies, such as videoconferencing, provides the potential for high levels ofinteraction between the instructor and learners; however, depending on theinstructional design, such technologies might be used for one-way lecturingby the instructor (low level of interactivity) or for a highly interactiveexchange between the instructor and learners. In other words, the amount of


interaction that actually occurs depends on the instructional design andthe motivation of the instructor and other learners to participate in thediscussion.

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 andMayer (2003) and Holmes and Gardner (2006) distinguished between typesof e-learning based on the learning theory underlying the program’s design.(We include a more detailed discussion of learning theories in a later sectionof this chapter.) E-learning programs based on behavioral learning theory(e.g., ‘‘drill and practice’’ type programs) are characterized by demonstra-tions or examples and frequent practice with corrective feedback. A secondtype of e-learning based on information processing theory aims to presentthe information in a way that enhances the internal processes of acquiring,understanding, and retaining knowledge. A third type of e-learning drawsfrom the constructivist learning theory and focuses on making sense of thepresented material guided by the instructor and/or in collaboration withother learners.

By focusing primarily on the design philosophy underlying an e-learningprogram, and not the technology features, these last two typologies do notaccount for the fact that the use of different media and/or technologiescan create very different learning experiences in the context of a particulardesign. For example, different technologies facilitate different levels ofinteraction with other learners in a design based on constructivistlearning theory.

To address the shortcomings of these existing typologies, the typology wedevelop focuses on the learning experience created for the learner, ratherthan the specific features of the e-learning program in terms of technologyused or instructional design philosophy. In other words, we argue that it isultimately the type of learning experience created through the combinationof technology and instructional design choices that will influence learningeffectiveness, and not the specific program features per se. Related to ourapproach, there has been an ongoing debate in the educational literature asto whether it is the use of a particular delivery technology or the instruc-tional method that improves learning (Anderson & Elloumi, 2004; Carter,1996; Jonassen, Campbell, & Davidson, 1994; Richey, 2000). Instructionalmethods are defined as ‘‘any way to shape information that compensatesfor or supplants the cognitive processes necessary for achievement ormotivation’’ (Clark, 2001, p. 208). Two main protagonists in this discussion


have been Clark (1983) and Kozma (1991). Clark (1983) argued that anymedium, 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 onhow much of the learner’s cognitive work is performed by the medium. Tworecent meta-analyses (Bernard et al., 2004; Sitzmann, Kraiger, Stewart &Wisher, 2006) confirmed Clark’s position by showing that the instructionalmethods built into the program tend to take precedence over the technologyused to deliver instruction. Bernard et al. (2004) found that there wereinstances in which a distance learning group outperformed the traditionalclassroom instruction group, whereas in other instances the oppositeoccurred. The authors concluded that it is the instructional methods, such asthe feedback provided and the degree of learner engagement, independent ofthe medium, that determine learning effectiveness. Consistent with this,Sitzmann et al.’s (2006) findings indicated that web-based instruction andclassroom instruction were equally effective for teaching declarativeknowledge when the same instructional methods (e.g., the same level ofinteraction 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 arguethat both technological features and instructional methods shape thelearning experience. Further, these two factors combine with characteristicsof the instructor and other learners in the program to create differentlearning experiences for learners. These learning experiences vary in thenature of the learning interactions that occur, including interactions betweenthe learner and instructor, the learner and other learners, and the learnerand instructional material. Our focus on learning interactions is based onboth general and distance learning theories that predict that effectivelearning is facilitated by learning interactions. Next, we review these theoriesthat provide the foundation for our typology.

THEORETICAL FOUNDATION

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


instructional approaches, including classroom, blended, and e-learning.The major learning theories that have emerged over the past century includethe 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 canbe distinguished from each other in many different ways, for our currentpurpose, a meaningful way to compare the theories is to consider how theycharacterize the nature of interactions required for effective learning. In alllearning environments, interactions are the means by which information istransmitted and knowledge is constructed.

Proponents of behavioral theories, such as Skinner (1974) and Thorndike(1932), consider learning the uncritical absorption of objective knowledge andmodification of behavior. The instructor and instructional material facilitatelearning by providing the environmental stimuli for behavioral change tooccur, with the learner as a passive receiver of this stimuli (Leidner &Jarvenpaa, 1995). In other words, there is a one-way transfer of knowledgefrom the instructor and instructional material to the learner, with very littleinteractivity between the learner and these components of the program.

In contrast to behavioral theories, cognitive learning theories placemore emphasis on the learner as an active participant in the learningprocess. According to these theories, learning is a process in which thelearner constructs knowledge through continuous interaction with theother components of the learning program – instructor, other learners, andinstructional material (Salas et al., 2002). Cognitive constructivist theories,such as Mezirow’s (1991) theory of transformational learning, considerlearning a process of meaning construction that takes place in interactionwith the instructional material. From a social constructivist perspective(Wenger, 1998), learning is fundamentally a social activity taking placethrough interaction with other learners and the instructor. Finally, sociallearning theories, such as Bandura’s (1986) social cognitive theory, positthat individuals learn from observing others, and hence, interactions withothers are key.

Distance learning researchers have also built their theories around thecentral theme of interactions in the learning process. As mentioned earlier,the term distance learning has been used as a more general term to describeboth e-learning and other forms of learning that rely on alternative typesof technology (e.g., correspondence by mail), not just ICTs, to deliverinstruction to learners who are spatially separated from the instructor andother learners (Keegan, 1986). For example, Garrison (1989) maintainedthat any educational transaction is based on seeking understanding and


knowledge through dialogue and debate; therefore, it requires two-waycommunication between teacher and learner. In a learning setting wherelearner and instructor are separated from each other, and communicateusing technology, this process is compromised. In addition, Keegan (1986)argued that in distance learning the interactions between instructor andlearner that facilitate learning have to be artificially recreated in order forthe instruction to be effective. Finally, Moore (1991, 1993) argued that thereliance on technology-mediated communication creates a psychologicaland communication gap in the interactions that occur between the learnerand instructor, the learner and other learners, and the learner andinstructional material. According to Moore, the success of distance learningis determined by the extent to which this gap can be reduced.

In line with these theories of general learning and distance learning, webuild our typology around the central theme of interactions that occur in thelearning process between the learner and the different components of ane-learning program (i.e., instructor, other learners, and instructionalmaterial). We refer to these interactions as learning interactions. Consistentwith distance learning theories, we propose that the use of technology tomediate learning can significantly change the amount and nature of theseinteractions; hence, one way to distinguish between different e-learningprograms is to compare the nature of the learning interactions they createfor learners. Based on the theories reviewed above, we identify three types oflearning interactions: learner–instructor interactions, learner–learner inter-actions, and learner–instructional material interactions. Learner–instructorinteractions provide content, motivation, feedback, and dialogue betweeninstructor and learner. Learner–learner interactions involve the exchange ofinformation, ideas, and dialog between students about the course content.Finally, learner–instructional material interactions involve the process bywhich students obtain intellectual information from instructional material(Chen, 2001). Having discussed its theoretical foundation, we turn now to amore 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 thatthe range of different e-learning programs used in organizations createsignificant variation in the types of learning interactions they produce


for learners. This variation can be described in three dimensions: degreeof interaction, learner control of interactions, and informational value ofinteractions. Together these learning interaction dimensions form atypology for distinguishing between different e-learning programs. In thissection, we describe each learning interaction dimension, and also discuss itstheoretical basis. From a theoretical perspective, each dimension shouldfacilitate greater learning effectiveness. Learning effectiveness refers to thebenefits 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 eachdimension and learning effectiveness.

Degree of Interaction

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


Learner Control of Interactions

This dimension describes the extent to which a learner has control over theinteractions available in the e-learning program, such that the learner is ableto tailor the instruction to his or her individual needs. E-learning programsin which the sequencing of the content is structured in advance (e.g.,traditional lecture via videoconferencing) are situated at the lower end ofthis dimension. At the higher end of the learner control dimension aree-learning programs that allow learners to assess their own learning needsand adjust the program accordingly (e.g., web-based training providinghyperlinks to different knowledge sources).

In the existing literature, learner control most typically refers to controlof interaction with the instructional material (e.g., DeRouin et al., 2005).In this regard, several types of learner control and their impact on learningeffectiveness have been discussed, including learner control of sequence,pacing, content, context, method of presentation, task difficulty, andincentives (DeRouin et al., 2005). However, we propose that it is alsoimportant to consider learner control of interactions with the instructor andother learners. This is consistent with the broad definition of learner controlprovided in the literature as any instructional strategy in which learnersassume some form of control (DeRouin et al., 2005). For example, Wydra(1980) defined learner control as ‘‘a mode of instruction in which one ormore key instructional decisions are delegated to the learner’’ (p. 3). Reeves(1993) defined learner control as the degree to which an individual is givencontrol over various instructional features during a lesson or trainingprogram. New forms of technologies allow learners to control interactionswith the instructor and other learners. For example, learner control ofinteractions with the instructor is high when the e-learning course providesthe opportunity to chat with a content expert whenever the learner desires.This flexibility allows the type (i.e., instructor versus other learners) andamount of interaction to be tailored to meet learners’ needs (Keller, 1983;Milheim & Martin, 1991).

Several different theoretical perspectives have been used to explain thepositive impact of learner control on learning effectiveness (Milheim &Martin, 1991). From a motivational perspective, learner control shouldinfluence motivation to learn, which has been shown to increase learningeffectiveness (Klein et al., 2006). This occurs through several mechanisms(Milheim & Martin, 1991). First, learners’ needs are more likely to besatisfied when training is made more relevant to a learner by providing thelearner with greater control. Second, consistent with adult learning theory


(Knowles, 1990), learner control also satisfies a learner’s need to be self-directed. Finally, from an expectancy theory (Vroom, 1964) perspective,learner control increases a learner’s expectancy of success because thelearner is made to feel that learning outcomes are in his or her hands.

According to Milheim and Martin (1991), two additional theoreticalperspectives for understanding the impact of learner control on learningeffectiveness are attribution theory (Kelley, 1967) and information proces-sing theory (Gagne, 1985). Attribution theory states that learners seek tounderstand and explain why an event has occurred, and the explanationthey construct influences future action. By influencing a learner’s perceptionof the locus, stability, and controllability of learning, learner control canincrease the learner’s expectation of success, which will in turn producehigher levels of effort. Finally, information processing theory emphasizesthe internal processes that occur when training content is processed andretained. Learner control facilitates the process through which informationis coded for long-term memory by allowing the learner to organize informa-tion in a way that makes it personally meaningful. In summary, learnercontrol 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 muchopportunity for interaction an e-learning program offers, and how muchcontrol the learner has over those interactions, it is also important torecognize that not all learning interactions are created equal in terms of theirability to transfer information that is valuable for learning. Media richnesstheory (Daft & Lengel, 1986), a theory from communications research,provides a foundation for this dimension. A basic tenet of this theory isthat communication media differ in terms of richness or their ability toclarify ambiguity and facilitate understanding of communication messages.It further proposes that communication media can be placed on acontinuum of richness. For example, videoconferencing is richer than emailbecause it allows for both nonverbal and verbal communication. Emailcommunication lacks the body language and other nonverbal cues that helpto clarify the meaning of messages. In general, according to these theoreticalperspectives, less rich communication media are viewed as less effective forcommunication. Applying these theoretical arguments to e-learning suggests


that e-learning consisting of static text will transmit fewer cues to aidunderstanding of learning than e-learning programs in which multimedia isused 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 thatmedia richness was defined as a characteristic of the information carryingcapacity of the communication media; however, the construct of richnessalso applies to other types of information exchange, beyond directcommunications between one individual and another. For example, anindividual might post a presentation or engineering drawing to a website tobe viewed by others. Informational value is a more general term that appliesto all types of information exchanges, not just direct communicationsbetween individuals. Therefore, in an e-learning context, informationalvalue is relevant to learning interactions involving direct communicationwith an instructor and other learners as well as the presentation ofinformation to learners via the instructional material. This is consistent withour definition of e-learning as the use of both the information andcommunication technologies to deliver instruction to learners.

Kirkman and Mathieu’s (2005) construct of informational value focusedon the media used for communication and information exchange. In ane-learning context, we view informational value as a characteristic ofthe interaction that is shaped, not only by the media characteristics butalso by the extent to which that media is used by instructors and otherlearners to provide cues that enhance learning. For example, an interactionin which the instructor uses a richer communication media in a way that isvery expressive and provides a lot of nonverbal cues will create aninteraction of higher informational value than one in which an instructoruses the same media with a lower level of expressiveness. We discuss thisidea further in a later section in which we discuss the antecedents ofinformational value. Based on the theoretical perspectives above, interac-tions that are higher in informational value should facilitate greaterunderstanding on the part of the learner, and therefore enhance learningeffectiveness.

Taken together, the three learning interaction dimensions we have definedform 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, andclassroom instruction and (2) identifies factors that influence e-learningeffectiveness. We discuss these two theoretical implications in the sectionsthat follow.


COMPARING THE EFFECTIVENESS OF

DIFFERENT E-LEARNING PROGRAMS

The typology we have developed provides a means to compare the effectivenessof different e-learning programs. In this section, we discuss the utility of ourtypology for both deepening our understanding of existing research findings inthis area and identifying important new research directions. We argue that theequivocal results found when comparing the effectiveness of e-learning toclassroom 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 thetargeted e-learning programs. In this regard, the dimensions in our typologycan 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 effective-ness of e-learning, blended, and classroom instruction, it is necessary to movebeyond the technology per se and focus on how the technology is used withinthe context of the program’s instructional strategies (Bernard et al., 2004;Clark, 1983; Sitzmann et al., 2006). Our typology builds on this idea byproviding a means to compare e-learning programs that takes into accountall components of an e-learning program, including the technology, theinstructional design, and the other participants in the learning program(instructor and other learners). Our typology consists of three dimensionsalong which learning programs can differ. Rather than being tied to aparticular technology or instructional design feature, these dimensionsdescribe the learning experience created for the learner in terms of the natureof the interactions in which the learner engages while moving through theprogram. Further, because the framework is independent of any particulartechnology or instructional design strategy, it can be applied to learningprograms that consist entirely of classroom instruction as well as those thatare entirely technology-mediated. Hence, it can be used to facilitatecomparisons between learning programs that fall at any position along thecontinuum between these two extremes.

The utility of the typology for comparing the effectiveness of differentlearning formats is demonstrated by research that has compared learningprograms where all characteristics of the learning programs were the same,with the exception of differences related to one of the learning interactiondimensions in the typology. For example, Zhang (2005) conducted anexperiment that compared levels of student satisfaction and performance fora course that was delivered using three different instructional methods:classroom lecture, a videotape of that lecture, and a videotape of the lecture


in which students were allowed to control the sequencing of content and thepresentation format. Zhang found the highest level of student satisfactionand performance in the e-learning condition with learner control. Incontrast, Tutty and Klein (2008) found that students who were paired inlearning dyads performed better for an individual learning assignmentin the face-to-face condition compared to a computer-mediated program.Using follow-up surveys of the students and observation, the research teamexplained this result by the fact that students in the face-to-face conditionhad higher levels of interaction (degree of interaction) and also were able touse nonverbal and contextual cues to more effectively share informationwith their partners (informational value).

The research findings above demonstrate the importance of clearlyisolating where the learning programs that are being compared fall alongthe dimensions of our typology. The importance of this approach is alsoillustrated by considering research that has compared blended learning withclassroom instruction (for a review, see Klein et al., 2006). Researchers haveexplained their findings that blended learning is more effective by pointing tothe fact that blended learning incorporates the advantages of both classroomand e-learning (Klein et al., 2006; Sitzmann et al., 2006). For example,according to Sitzmann et al. (2006), ‘‘The instructional advantage of blendedlearning may be due to incorporating the benefits of personal interactiontypically found in CI [classroom instruction] and self study betweeninstructional meetings using the Web’’ (p. 646). These benefits of blendedlearning can be more precisely understood by comparing blended learningto classroom instruction using our typology. On the one hand, becauseblended learning programs involve some classroom instruction, they havethe potential to provide a high level of interaction and informational valueof learning interactions compared to pure e-learning. On the other hand, thee-learning component frequently allows the learner more control over thepace and depth of learning than traditional classroom learning. As a nextstep, researchers could use our typology to more precisely identify the typesof blended learning programs that are most effective. This is importantbecause within the continuum of blended learning, many differentcombinations of classroom instruction and e-learning are possible.

A THEORETICAL FRAMEWORK

FOR E-LEARNING RESEARCH

Another important theoretical contribution of the typology is its applicationto develop a theoretical framework for research that identifies factors that


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

E-learning Program Characteristics

Fig. 1 shows the characteristics of the e-learning program as antecedents ofthe typology dimensions. As shown in Fig. 1, we categorize these e-learningprogram characteristics according to the four potential components of ane-learning program: technology, instructional design, instructor, and otherlearners. With regard to the first two components, we argued earlier,

• Learner Characteristics

• Contextual Factors

• Learning Goals • Instructional design (e.g., learning model,accessibility of instructor and otherlearners)

• Technology (e.g., linearity, timedependency)

• Instructor (e.g., teaching style) and otherlearners (e.g., cognitive style, technicalexpertise)

• Instructional design (e.g., presentation ofinformation, one-way versus two-waycommunication, human-human versus human-computer interaction)

• Technology (e.g., information modality,human-human versus human-computerinteraction, one-way versus two-waycommunication, time dependency)

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

Learning Effectiveness

E-learning Program Characteristics

• Degree of interaction

Instructor

Other learners

Instructionalmaterials

Learning InteractionDimensions

• Informational value of interactions

Instructor

Other learners


• Learner control of interactions

Instructor

Other learners


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

• Technology (e.g., time dependency,perceived ease of use)

• Instructor (e.g., personality, teachingstyle) and other learners (e.g., cognitivestyle, technical expertise)

TYPOLOGY

Fig. 1. Theoretical Framework for E-learning Research.


following the dominant position taken in the design-technology discussionin the educational literature (Anderson & Elloumi, 2004; Carter, 1996;Jonassen et al., 1994), that both the technology used and instructionaldesign impact learning interactions. For example, interaction with fellowlearners is not possible without technology features that provide human–human interaction (e.g., chat). However, by the same token, the degreeof interaction will primarily be determined by design decisions, such asthose related to the learning model (behavioral versus constructivist), thenumber of participants (individual versus group assignments), and thefrequency of interactions.

The design and technology decisions provide the potential for certaintypes of interactions; however, the nature of the learning interactions in ane-learning program will also be influenced by how the instructor and otherlearners choose to apply this potential in their actual interactions. Forexample, an instructor who uses the opportunity to interact with learners viavideoconference to provide an animated and interactive presentation willprovide higher interactivity and informational value than an instructor thatdelivers a monotone, one-way delivery. Similarly, even though the programuses technology that allows learners to communicate with each other, andthe design allows for learner interaction, the extent to which the instructorpromotes and facilitates interaction between learners can play an importantrole in determining how much interaction actually occurs. Based on this,researchers have described the instructor as a major force in shaping thenature of the interactions that occur in an e-learning program (Webster &Hackley, 1997).

Below, we describe the e-learning characteristics that are antecedentsto the typology dimensions in our theoretical framework. While e-learningprograms differ in terms of many different characteristics, we believe thatthe typology helps to identify characteristics of e-learning that are likelyto be most impactful for learning. Our goal here is not to be exhaustive inlisting all possible e-learning characteristics, but to highlight some that arelikely to most influence our typology dimensions.

Antecedents of Degree of InteractionInstructional Design. The degree of interaction with the instructor, theother learners, and the instructional material is primarily determined by thedesign decisions made when developing the e-learning program (Moore,1993). Decisions regarding learning model, including requirements forinteraction and number of participants, will set boundary conditions forthe extent to which interaction can occur within the e-learning program.


First, with regard to the requirements for interaction, the cognitiveconstructivist view of learning (Dewey, 1938; Mezirow, 1991) emphasizesthat the learner takes responsibility for constructing meaning activelythrough dialogue with oneself and others. An instructional design basedon this perspective will pay more attention to building in opportunitiesfor interaction, such as live question/answer, discussion (Garrison, 1993)and collaborative problem-based learning opportunities, instead ofindividual assignments (Gorsky & Caspia, 2005). This is in contrast to abehaviorist orientation to learning (Skinner, 1974; Thorndike, 1932), whichunderscores the role of reinforcement by the external environment to ensurelearning, and translates into a program in which the learner does not playan active role in the learning process (Merriam & Caffarella, 1999).An example of a design characteristic that facilitates a high degree ofinteraction is the requirement for the learner to participate in groupactivities or submit questions on a discussion board. This is in contrast to e-learning programs that may have built-in opportunities for interaction, butdo not make these interactions a requirement, or a prerecorded lecture thatdoes not allow for interaction at all (Anderson & Garrison, 1995; Gorsky &Caspia, 2005).

Second, design decisions related to group size can influence the extent towhich learners interact with fellow learners and with the instructor (Chen &Willits, 1998). E-learning programs vary in the number of participants whocan interact simultaneously. Some programs do not allow interaction withother participants or are very limited in this regard (e.g., CD-ROM); othersallow large groups to participate (e.g., web-based application). Thee-learning program needs to allow at least two participants to interact witheach other, in order for interactions to take place. However, it is less clearhow large the group should be in order to optimize the level of interactivitywith fellow learners. Both the training and team literatures have shown thatgroup size has an effect on the extent to which groups interact (Arbaugh,2005; Cohen & Bailey, 1997). Larger groups may be less effective becauseof a decrease in involvement or participation by the individual members(Hare, 1981; McGrath, 1984). Also, the degree of interaction with theinstructor will diminish in larger groups (Gorsky & Caspia, 2005).

Technology. First, the time dependency of the program’s technology willdetermine the degree of interaction with the instructor and other learners.Time dependency captures the distinction between synchronous versusasynchronous technologies. Synchronous technologies allow for real-timeinteraction among learners and instructor, even if they are in different places


(e.g., tele- or videoconferencing system). Synchronous technologies increasethe likelihood of interactions with others (Lauzon & Moore, 1992; Proost,1998). The more synchronous technologies are used, the more learners willinteract with the instructor and fellow learners as they participate in theprogram at the same time. With asynchronous technologies, learners andinstructors are time independent and learners participate in the trainingprogram at their convenience. As a result, asynchronous technologiestypically have a lower level of interactivity.

Second, following the technology acceptance model (Davis, 1989), weexpect perceived ease of use of the technologies to positively influencelearners’ attitude toward the technologies. As a result, they will be morewilling to use the technologies to interact with the instructor, the otherlearners, and the instructional material. In line with this, research has shownthat the more computer software, email, world wide web, etc. are perceivedas easy to use, the more individuals make use of these technologies(Arbaugh, 2005).

Instructor and Other Learners. Several researchers have discussedcharacteristics of the instructor and other learners that have an impact onthe 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 thatinstructors characterized by a high degree of extraversion were morelikely 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 areparticipating in the program will influence the extent to which they areopen to and seek out opportunities for interaction with others. Two suchcharacteristics are cognitive style and expertise in using e-learningtechnologies. These individual characteristics mirror those described laterin this chapter as learner characteristics that facilitate learning in an e-learning environment with a high degree of interactivity. Here, it isimportant to note that certain cognitive styles have been associated withpreference for collaborative learning methods (for reviews, see Riding &Cheema, 1991; Smith, Murphy, & Mahoney, 2003), and expertise in using e-learning technologies has been positively associated with increasedparticipation in e-learning (Martins & Kellermanns, 2004). Hence, when


other learners have more collaborative learning styles and greater technicalexpertise, 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 isinfluenced by characteristics of the instructional design (e.g., learningmodel, including requirements for interaction and number of partici-pants), characteristics of the technology (e.g., time dependency andperceived ease of use), and characteristics of the instructor (e.g.,personality, teaching style) and other learners (e.g., cognitive style andtechnical expertise).

Antecedents of Learner Control of InteractionsInstructional Design. Instructional design decisions will also establishboundary conditions for the impact that technology as well ascharacteristics of the instructor and other learners have on the degree oflearner control of interactions. First, we expect the learning modelunderlying the e-learning program to have an effect on learner control. Aconstructivist orientation toward learning (Dewey, 1938; Mezirow, 1991)not only recognizes the critical importance of frequent interactions forlearning to occur but also that these interactions take place in anindividualized fashion while the learner is drawing his or her own lessonsfrom experience (Merriam & Caffarella, 1999). Compared to a behavioristapproach toward learning, the constructivist orientation is much more likelyto translate into a design that allows and encourages learners to exercisecontrol over their interactions with the instructor, other learners, andinstructional material. In contrast, the behaviorist approach will more likelytranslate 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 degreeof control the learner has over learner–instructor interactions, withsignificantly lower levels of learner control when the instructor is availableone hour per month versus seven days a week. Similarly, the accessibilityof other learners has a significant impact on the extent to which learnerscan 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 daysper week leads to more learner control of learner–learner interactions thangiving learners the opportunity to interact at limited times during theprogram (Gorsky & Caspia, 2005).


Technology. Within the constraints of design decisions that are made toenhance learner control, technology features will further impact thisdimension. First, we expect that the linearity (linear versus nonlinear) andthe time dependency (synchronous versus asynchronous) of the technologiesused will influence the learner control of leaner–instructional materialinteractions. Linearity describes technologies that only allow linearpredefined learning paths versus those that enable learners to customizetheir path through the program. With the exception of conferencingsystems (e.g., tele- and videoconferencing systems), most computer network-based technologies (e.g., web-based applications) allow a nonlinearpresentation of the content, and thus provide the flexibility to adaptthe learning path to individual learning needs and preferences (Proost,1998).

Whereas synchronous technologies increase the likelihood of interactionswith the instructor and fellow learners (as discussed above), they decreasethe likelihood of individualized interactions with the program. Themore synchronous technologies are used, the less learners will be able tocontrol 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 willmake the key decisions regarding the content, sequence, and pace oflearning. In contrast, asynchronous technologies provide the opportunityfor learning to be time independent and for the learner to participate at hisor her own pace. In summary, nonlinear and asynchronous technologies willincrease the potential that learners have to control their interactions with theinstructor, the other learners, and the instructional material.

Instructor and Other Learners. The control that learners have overinteractions with the instructor, the other learners, and the instructionalmaterial will also be determined by characteristics of the instructor andother learners. First, as argued by Anderson and Elloumi (2004), instructorsdiffer in their teaching style and skills for responding to diverse learnerneeds. Even given the constraints of the program’s instructional design,instructors vary in the extent to which they take a more instructor-led versusstudent-led approach to instruction. In a more instructor-led approach, anactive instructor teaches a passive learner and decides where to build inopportunities for interaction with others. In addition, the instructordetermines the pace, sequence, and methods of instruction (Anderson &Elloumi, 2004). This kind of learning reflects the goals of the instructor andignores the learner’s needs (Moore, 1973). In contrast, learner control of the


different learning interactions will increase when the instructor, within theconstraints 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), andcontent-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 whichlearners can control their interactions with each other are cognitive styleand expertise in using e-learning technologies. A learner’s control overinteractions with other learners requires that these other learners be willingto engage in interactions that suit the learner’s needs. As discussed above,we expect cognitive styles that relate to preference for learning collabora-tively with others and expertise in using e-learning technologies to have apositive effect on willingness to engage in learning interactions. Thus, whenother learners in an e-learning program have these characteristics, thisshould result in a learner having more control over interactions with theselearners.

Proposition 2. Learner control of the interactions in an e-learningprogram 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), andcharacteristics of the instructor (e.g., teaching style) and other learners(e.g., cognitive style and technical expertise).

Antecedents of Informational Value of InteractionsInstructional Design. The informational value of the interactions with theinstructor, the other learners, and the instructional material will be primarilydetermined by the program’s instructional design. The informational valueof the learner–instructional material interactions will depend on the designdecisions related to how content is presented. Whereas text is the simplestway to present information, additional features such as still images, graphics,images in motion, and sound can be added to increase the informationalvalue (Bell & Kozlowski, 2006). In addition, the informational value of thelearner–instructor and learner–learner interactions will be determined bythe type of communication allowed by the design. Specifically, a programdesign that allows for two-way communication will increase theinformational value of interactions with the instructor and other learners.Two-way communication allows the learner to receive feedback andseek clarification. This, in turn, enhances the informational value of the


interaction (Bell & Kozlowski, 2006). In contrast, if the e-learning program isdesigned to have only one-way communication, then informational value willbe diminished (Bell & Kozlowski, 2006).

Further, an e-learning program can be designed to allow for interactionsthat are human–human or human–computer. Communicating with otherpeople or working on assignments with other learners (i.e., human–humaninteractions) will result in higher informational value. This is becausesuch communication allows for detailed and immediate feedback and theexchange of nonverbal cues that enhance the meaning of the communica-tion. When learners only have the option to ask questions to an instructorproxy 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 alsoinfluence the informational value of the interactions. For example, thedesign might allow for human–human interactions; however, there are arange of technologies (e.g., email, chat, and phone) that can facilitate suchinteraction. Four characteristics of the technology that are relevant toinformational value are information modality (single versus multiple cues),human–human versus human–computer interaction, one-way versus two-way communication, and time dependency. Information modality refers tothe extent to which the technology allows for the communication of multiplecues and language variety (Proost, 1998). This dimension ranges fromhaving the potential to convey text only (single cue) to having the potentialto convey text in combination with graphics, pictures, sound, and nonverbalinformation (multiple cues). Human–human versus human–computerinteraction (Proost, 1998) refers to whether humans communicate withhumans, mediated by technology (e.g., in a virtual classroom orchat session) or humans communicate directly with the computer (e.g.,CD-ROM). Finally, technologies such as CD-ROM, video tape, audiotape, and DVD only allow one-way communication, whereas technologiessuch as interactive media and videoconferencing support two-waycommunication. In line with media richness theory (Daft & Lengel, 1986),we expect the information modality and time dependency of thetechnologies used to have a significant impact on the informational valueof the delivery and communication processes in an e-learning program.For example, a medium that only supports text-based cues and isasynchronous provides the learner with less rich information thansynchronous multimedia (Bell & Kozlowski, 2006). Also, as argued above,


whether the technology supports one-way versus two-way communicationor human–human versus human–computer interaction will also impact theinteraction’s informational value.

Instructor and Other Learners. A relevant concept with respect to theantecedents influencing the informational value of learner–instructor andlearner–learner interactions is immediacy behaviors. This concept wasfirst introduced by Mehrabian (1972) who defined it as behaviors thatincrease mutual sensory stimulation between two people and includes bothverbal and nonverbal behaviors. Examples of nonverbal immediacybehaviors are eye contact, facial expressions, gestures, and body position,whereas verbal immediacy involves behaviors such as using personalexamples, using humor, providing and inviting feedback, and addressingstudents by name (Gorham, 1988). We expect immediacy behaviors toinfluence the informational value of the learner–instructor and learner–learner interactions.

Proposition 3. The informational value of the interactions in an e-learningprogram 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), characteristicsof the technology (e.g., information modality, one-way versus two-waycommunication, human–human versus human–computer interaction, andtime 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 identifycharacteristics of e-learning programs that influence learning effectivenessthrough their influence on the typology dimensions. We now turn ourattention to how the typology dimensions influence learning effectiveness.As discussed earlier in our description of each learning interactiondimension, the dimensions are expected to positively influence learningeffectiveness. However, it is important to note that the typology describesthe potential for learning interactions that is available to the learner.As shown in Fig. 1, a number of factors can influence the extent towhich this potential translates into effective learning. These factors areshown as moderators in Fig. 1 and include learner characteristics,contextual factors, and learning goals. Next, we describe each of thesemoderators and discuss how our typology can help to advance researchrelated to each one.


Learner Characteristics

A good match between the characteristics of the learner and the learninginteraction opportunities offered by the e-learning program will enhancelearning effectiveness, both directly and through motivation to learn(Colquitt, LePine, & Noe, 2000). Motivation to learn describes a learner’sdesire to learn the content of the training program and has been shown toinfluence learning effectiveness (Colquitt et al., 2000). Motivation to learnmay be particularly important for e-learning because compared to moretraditional classroom instruction, e-learning requires learners to take greaterresponsibility for their own learning. Further, since many e-learning programsare completed over a longer period of time, there is a greater need for learnersto demonstrate persistence to complete an e-learning program.

With regard to the direct influence, learners whose characteristics makethem more able to cope with demands of the e-learning environment willperform better in that environment. Also, such learners tend to spend moretime in the e-learning program, which allows for greater exploration of theprogram content and more comprehensive practice, leading to greaterlearning effectiveness (Brown, 2001; Wang & Newlin, 2000). With regard tothe influence through motivation to learn, several theoretical perspectivessuggest that a match between individual characteristics and the e-learningprogram characteristics will influence motivation to learn (for a review, seeColquitt et al., 2000). For example, social cognitive theory (Bandura, 1986)proposes that one’s believe about one’s ability to execute a task will increasemotivation to perform that task. Learners whose characteristics are bettermatched to the program will be more confident of their ability to learnusing the e-learning program, and hence more motivated to learn. Similarly,based on the technology acceptance model (Davis, 1989), a match in learnercharacteristics is likely to influence perceived ease of use of the e-learningprogram, which according to this perspective, will influence motivation touse the program.

In their review of distance learning research, Salas et al. (2002) noted thatthere is a growing body of research to show that individual characteristicspredict distance learning outcomes. However, they cautioned that althoughsome individual characteristics will be important for all distance learningenvironments, ‘‘research has yet to identify the learner characteristics thatare important in specific DL [distance learning] environments’’ (p. 145).We agree that a shortcoming of most of the existing research in this area isthat researchers fail to specify the type of e-learning for which a particularindividual characteristic will be most important. Categorizing an e-learning


program according to the learning interaction dimensions we have definedhelps to identify which learner characteristics are important for e-learning ingeneral, and which will be important in specific e-learning environments.For example, individual characteristics related to a learner’s ability tomonitor his or her own learning process and make adjustments whereneeded will be most important for learning in an e-learning programcharacterized by the potential for a high degree of learner control. Hence,this individual characteristic varies in importance depending on where ane-learning program falls along this learning interaction dimension.

In this section, we provide examples of learner characteristics thatare likely to be important for all e-learning environments and those thatalign with specific dimensions of our typology. As with our discussion ofe-learning characteristics, our purpose is not to provide an exhaustive listof learner characteristics, but to illustrate the application of the typologyby focusing on some that are particularly germane to e-learning and to thedifferent typology dimensions.

Salas et al.’s (2002) review summarized learning characteristics that arelikely to facilitate learning effectiveness in most e-learning environments,regardless of where they fall on the dimensions of our typology. Theseinclude cognitive ability, learning self-efficacy, prior knowledge andexperience, and mastery goal orientation. In addition, since a definingcharacteristic of e-learning is the use of technology to deliver instruction,several researchers have focused on technical expertise as an importantindividual 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 mediatedusing technology, poor technology expertise is likely to impact the learner’sability to take advantage of any learning interactions that are available inthe e-learning program. Learners who lack technical expertise might notexplore the full degree of interactivity or learner control options available inthe program and may also be less receptive to the use of technology thatoffers higher informational value, since this technology typically has morecomplex technological features.

Another individual characteristic that is important across all learninginteraction dimensions is self-regulation. Self-regulation consists of strate-gies that individuals use in response to difficult or anticipated difficultiesin goal-directed action to guide their own behavior over time and acrosschanging circumstances (Kanfer & Heggestad, 1997). According to Kanferand Heggestad (1997) individuals vary in the extent to which they havethe skills to apply self-regulation strategies to deal with obstacles to goal


attainment. Individuals who are more self-regulating have a greatertendency to engage in motivation control strategies (e.g., self-goal settingand monitoring of progress toward goals and self-reinforcement) andemotion control strategies that protect on-task attention and preventdistracting emotional states (Kanfer & Heggestad, 1997). The use of suchself-regulation strategies has been shown to lead to improved performanceand more successful goal attainment (e.g., Creed, King, Hood, & McKenzie,2009; Porath & Bateman, 2006).

Self-regulation behaviors should ensure that learners are able tosuccessfully integrate e-learning into their everyday activities and maintaina high level of self-motivation to engage in e-learning. This will betterposition a learner to take full advantage of the degree of interactionsavailable in the e-learning program. For example, in an e-learning programin which learners communicate in an online discussion forum, a learner withpoor self-regulation skills may find less time to participate in and monitorthe discussion. As a result, the learner will be less engaged and derive lessbenefit from this aspect of the program. For similar reasons, learners whoare more effective at self-regulation will also be able to make better use ofthe learner control available in an e-learning program. Finally, with regardto informational value, programs that are low in informational value maybe less engaging and require additional effort on the part of the learner toderive meaning from the learning interactions. Learners that are moremotivated to persist with learning in the face of such obstacles are morelikely to expend the greater effort required to learn in such an environment.In line with these arguments, researchers have pointed to self-regulationduring the learning process as a critical success factor for learners in ane-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-learningenvironments, we also identify learner characteristics that align morespecifically with each typology dimension.

Learner Characteristics and Degree of InteractionWith regard to the degree of interaction dimension, individual differences indesire for interaction with others during the learning process are likely to beimportant. Differences in desire for interaction might stem from differencesin how information is processed during learning or differences in the needfor social interaction during the learning process. First, from a learning styleperspective, learners differ in the extent to which interaction with othersfacilitates their learning. For example, Sternberg’s (1997) theory of thinking


styles proposes that people differ in the cognitive mechanisms they use toorganize and govern tasks. Externals prefer to work through problemstogether 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 internalthinking style that describes people who prefer working alone. Internals findit disruptive to their concentration to interact with others while problemsolving or analyzing information. Given their preference for joint problemsolving and collaborative information processing, externals are likely tolearn less effectively in a learning environment with fewer opportunities forinteraction with others (i.e., low degree of interaction).

Research related to learning styles has also identified cognitive styledimensions 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 interactfrequently with other learners, as well as the instructor, as opposed tolearners who respond better to more independent and more individualizedapproaches. Underlying this cognitive style is a tendency to be sociable andsocially dependent. Related to this, Salas et al. (2002) suggested thatresearchers should strive to better understand how differences in social needsof learners affect learning effectiveness in a distance learning environment.Personality researchers have identified a number of individual differencesrelated to individuals’ need for social interaction, for example, Cheek andBuss’ (1981) sociability and Hill’s (1987) interpersonal orientation. Insummary, we argue that learners who have a greater desire for interactionwith others during the learning process will benefit more from programs thathave a high degree of learner–instructor and learner–learner interactions.

Learner Characteristics and Learner Control of InteractionsAn important learner characteristic for success in e-learning programs thatprovide a high degree of learner control is metacognition (for a review, seeDeRouin et al., 2005; Salas et al., 2002). Metacognition is concerned withhow the learner navigates and guides him- or herself within the trainingprogram and is a measure of the degree to which learners reflect on theirown learning process (Flavell, 1979). Individuals with greater metacognitiveskills are better able to monitor their progress, determine when they arehaving problems, and adjust their learning accordingly (Ford, Smith,Weissbein, Gully, & Salas, 1998; Schmidt & Ford, 2003). These types oflearners make better decisions about learning strategies and where to direct


their attention (Salas et al., 2002). As a result, metacognition has beenidentified as an important characteristic for learners in an environmentcharacterized 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 InteractionsLearner characteristics that align with the informational value dimensionof our typology are those related to learner preferences for how informationis presented and for certain modes of communication. These learnercharacteristics have received little attention in the existing literaturecompared to learner characteristics that align with the other twodimensions. However, research suggests that these differences do exist, andwe believe they are worthy of further research attention. For example,research related to learning preferences and readiness for online learning hasidentified a learning preference related to comfort with communicatingelectronically with other learners and an instructor (for a review, see Smith,2005). Smith (2000) analyzed learning preferences of 1,252 vocationalstudents and identified a difference related to comfort with learning usingtext or listening as opposed to nontextual interaction, including directexperience, observation, and practice. Learners who have a preference forricher communication modes will require higher levels of informationalvalue to facilitate their learning.

In addition, cross-cultural research has identified differences in commu-nication styles that could be relevant to this dimension (Hall, 1976). In low-context cultures, individuals communicate predominantly through explicitstatements in text and speech, whereas in high-context cultures individualsrely more heavily on contextual and nonverbal cues during communicationboth communicate and interpret the meaning of messages. Extending thisline of research, communication researchers have identified an individualdifference that aligns with these differences in communication style and thatdescribes the extent to which individuals are indirect in their communicationstyle, relying less on the content of the spoken or written word and moreon the nonverbal aspects (Holtgraves, 1997). Learners who are more indirectin their communication style are likely to find it more difficult to learn ine-learning programs with lower levels of informational value, which willnegatively impact learning effectiveness.

Proposition 4. Some learner characteristics will moderate the relationshipbetween all learning interaction dimensions and learning effectiveness(e.g., cognitive ability, learning self-efficacy, prior knowledge and experience,


mastery goal orientation, technical expertise, and self-regulation skills).Other learner characteristics will moderate the relationship betweenspecific learning interaction dimensions and learning effectiveness.For example, desire for interaction with others during the learningprocess will interact with degree of interaction, metacognition will interactwith learner control, and preference for richer communication modesand a more indirect communication style will interact with informationalvalue.

Contextual Factors

Since most e-learning research has taken place in educational, rather thanworkplace settings (DeRouin et al., 2005), contextual factors related toe-learning have received relatively little research attention. Yet, the limitedresearch in this area (e.g., Klein et al., 2006), as well as the practitionerliterature (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 whetherlearners engage in and complete e-learning programs. In Fig. 1, we includecontextual factors as important moderators of the relationships between thetypology dimensions and learning effectiveness. We argue that learningeffectiveness will be enhanced when characteristics of the learning contextalign with the learning interaction characteristics of the e-learning program.

Our argument for this relationship is based on a similar rationale to thatpresented for the moderating role of learner characteristics. First, contextualfactors can act as enablers or present barriers to engage in e-learning (Kleinet al., 2006; Martins & Kellermanns, 2004). In addition, the alignment ofcontextual factors with learning interaction characteristics can influencelearning effectiveness through motivation to learn. First, such an alignmentcan influence learners’ perception of how easy it is to engage in e-learning.As discussed above, this perception has been linked theoretically tomotivation to learn (e.g., social cognitive theory, Bandura, 1986; technologyacceptance model, Davis, 1989; and expectancy theory, Vroom, 1964).Second, this alignment can also increase motivation to learn by influencingperceptions of the utility of participating in e-learning. For example,expectancy theory (Vroom, 1964) suggests that when employees perceivethat there are benefits associated with participating in e-learning, theirmotivation to engage in e-learning will increase.

Traditional training research has focused on contextual factors suchas climate for transfer and opportunity to perform (Colquitt et al., 2000;


Noe, 2005) that influence learning outcomes. These factors will also beimportant in an e-learning environment; however, as with our discussion oflearner characteristics, we focus here on contextual factors that are likely tobe particularly germane to e-learning. Two contextual factors that wepropose will interact with all the typology dimensions to influence learningeffectiveness is the extent to which a learner has easy access to appropriatetechnology tools and technology support. E-learning programs that offermore interactivity, greater learner control, and increased informationalvalue will typically require the use of technologies with more complextechnical requirements. Hence, it is important for learners to have access toappropriate technologies and adequate support in using those technologies.Access to the technology required to effectively use an e-learning programand availability of technical support are related to a learner’s use of thesystem (Martins & Kellermanns, 2004). In addition, as noted above, theextent to which learners have the opportunity to engage in e-learningwithout distractions will influence the extent to which a high degree ofinteraction, high learner control, and high-informational value translatesinto learning effectiveness.

DeRouin, Fritzsche, and Salas (2004) reviewed several contextual factors,based on theories of motivation, which are relevant for increasing a learner’swillingness to engage with an e-learning program. These include supervisorsexpressing confidence in learners, providing them with encouragementand valued rewards for participation in e-learning, working with learnersto set difficult but attainable goals for learner performance as a result ofparticipating in an e-learning program, and monitoring learner progressagainst those goals.

Based on the arguments above, we propose that it is possible todifferentiate different work contexts in terms of their climate for e-learning.We define climate for e-learning as learners’ shared perceptions aboutcharacteristics of the work environment that facilitate e-learning in general,regardless of the type of e-learning program. Our definition is based on thedefinition of climate in the organizational literature. Climate has been definedas employees’ shared perception of the events, practices, and procedures aswell as the kind of behaviors that get rewarded, supported, and expected ina particular organization (Schneider, 1990). The relationship between climateand behavior has been demonstrated at different levels of analysis fordifferent 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 atmultiple levels of analysis. For example, organizations with strong climates


for e-learning will integrate e-learning into the organization’s performancemanagement system, promote the value of e-learning training, providetechnology to support different types of e-learning formats and technologicalsupport, provide workspaces free from distraction for completinge-learning, and monitor e-learning participation. In addition, at the work-group level, the existing literature has shown that managers play an importantrole in reducing barriers to engage in e-learning and have considerablediscretion in how organizational policies related to e-learning participation areimplemented at the workgroup level (Hequet & Johnson, 2003; AmericanSociety for Training and Development/The Masie Center, 2001).

In addition to factors that create a general climate for e-learning, relevantcontextual factors that specifically align with each typology dimension aredescribed below.

Contextual Factors and Degree of InteractionAn important contextual factor that aligns with the degree of interactiondimension is the extent to which the work context provides opportunitiesfor interaction to compensate for the lack of interaction available in ane-learning program. Without a high degree of interaction in an e-learningprogram, learners lack social support and feedback, which can lead tofeelings of isolation, uncertainty, and anxiety. This in turn can negativelyimpact learning effectiveness (Benbunan-Fich & Hiltz, 2003). Interactionswith managers or peers during the learning process can compensate for lackof interactivity in an e-learning program. For example, Moshinskie (2001)suggests that managers provide learners with opportunities to practiceand get feedback on what they are learning in the e-learning program.In addition, the organization can facilitate employees forming peer-learninggroups with other e-learners in the organization.

Contextual Factors and Learner ControlDeRouin et al. (2004) suggest that a specific aspect of the organizationalclimate that will help to facilitate learner control is the extent to which theorganization generally promotes employee participation, empowerment,and autonomy. In such a climate, employees will be more prepared andwilling to take control of their own learning because they are used to doingso as a natural part of their job.

Contextual Factors and Informational ValueAlthough we have already discussed the importance of providing learnersaccess to technology required to complete e-learning, an aspect of this that is


particularly important for the informational value dimension is the extentto which learners have access to technology that can support high-informational value interactions. For example, although an e-learningprogram might offer audio and video capability in addition to text, a learnermay be limited to text-based content because his/her computer equipmentcannot cope with the additional bandwidth required for the other richermedia options. Also, since programs with high informational value willtypically use more complex technologies (e.g., videoconferencing andaudiovisual), the need for more advanced technical support to address anyproblems encountered in using these technologies is particularly important(Martins & Kellermanns, 2004).

Proposition 5. Some contextual factors will moderate the relationshipbetween all learning interaction dimensions and learning effectiveness(i.e., climate for e-learning, including opportunity to engage in e-learningwithout 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 moderatethe relationship between specific learning interaction dimensions andlearning effectiveness. For example, supervisor and peer interactionduring the learning process will interact with degree of interaction,participative organizational climate will interact with learner control, andavailability of richer technology media and associated technical supportwill interact with informational value.

Learning Goals

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


Although several scholars agree on the need to align characteristics of ane-learning program with learning goals, there is less clarity on the exactnature of this match. Recently Bell and Kozlowski (2006) and Lowe andHolton (2005) developed theoretical models that address this question. Bothmodels make a distinction between different types of learning goals, whichrange 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, comprehen-sion, application, synthesis, and evaluation. Bell and Kozlowski (2006)distinguished between declarative, procedural, strategic, and adaptiveknowledge and skills. Both models show that different learning goals placedifferent demands on the e-learning instruction. Lowe and Holton (2005)discuss the match in terms of the instructional control and support providedin computer-based training. Bell and Kozlowski’s (2006) primary assump-tion 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, whenthe learner is asked to acquire declarative knowledge, a program with littleinteraction, low level of learner control, and with interactions with lowerinformational value is sufficient to attain targeted learning goals. First,a program-controlled learning experience will be more efficient to instructspecific well-defined concepts and facts (Lowe & Holton, 2005), as at thislevel one does not require learners to regulate their own learning process.Second, as stated by Bell and Kozlowski (2006), a text-based experience witha low level of interactivity will be most efficient to learn at the lowestlearning goal level. The authors based their statement on empirical evidenceshowing that richer interactions with more stimuli interfere with, rather thanstimulate, memorization. Finally, the authors argue that efforts to increasethe interactivity with the instructor and fellow learners, by for instanceproviding sophisticated ICTs, will not add much value to the learningprocess compared to the investments made.

In contrast, at the most complex level of knowledge and skills, where thelearner is asked to acquire more advanced problem-solving skills, thereneeds to be more interaction, with more learner control and higherinformational value. First, an e-learning experience in which the learner hascontrol over the program will be more effective to learn more advancedknowledge and skills. This is because this learning goal level requires thatlearners play an active role in their own learning process (Bell & Kozlowski,2006; Kolb, 1984) by adapting existing models, interpreting specific events,


etc. Second, more complex learning goals may require higher interactivitywith the instructor and fellow learners in order to exchange information andask for feedback (Garrison, 1993; Lowe & Holton, 2005). Finally, theinformational value provided in the interactions is important if moreadvanced knowledge and skills are the target. Learners need to acquirecoherent 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 researchrelated to matching learning goals to the e-learning experience provided tothe learner. To extend this research, researchers should examine the relativeimportance of each learning interaction dimension to different levels oflearning goals. This will allow e-learning program designers to make trade-offs 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 orincreasing the informational value of the program? We believe that these areimportant questions to address in future research in this area.

Proposition 6. E-learning effectiveness will increase when the character-istics of the learning interactions are matched to the level of learning goals.

CONCLUSION

E-learning holds much promise for helping organizations meet their futuretraining demands. However, to realize its full potential, there needs to be aclearer understanding of how different e-learning program configurationsinfluence learning outcomes. To this end, Smith and Dillon (1999) noted theimportance of clearly articulating the dimensions along which e-learningprograms differ. In response to this, we developed a typology based ongeneral theories of learning that compares e-learning programs accordingto the nature of the learning interactions they provide for learners. The threedimensions of the typology that describe important characteristics of thelearning interaction dimensions are degree of interaction, learner controlof interactions, and informational value of interactions. These dimensionsapply to interactions that occur between the learner and instructor, otherlearners, 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 theeffectiveness of different e-learning, blended learning, and classroom


instruction formats. The typology helps to reconcile existing research findingsas well as stimulate new research questions in this area. We believe that ourtypology is the first to provide a comprehensive foundation for comparing therange of different e-learning programs that are currently available, including acomparison of e-learning with classroom and blended instruction. Unlikeexisting typologies that are framed in terms of specific technology features ofthe e-learning program, our typology is flexible enough to apply to e-learningprograms at any point along the continuum from pure classroom instructionto instruction that is fully technology-mediated.

Second, based on the typology dimensions, we developed a theoreticalframework and related propositions that can be used to identify factors thatinfluence success in different types of e-learning environments (i.e., e-learningprograms located at different points along each of the typology dimensions).The framework identifies important characteristics of e-learning programsthat are antecedents to the typology dimensions, and therefore influencelearning effectiveness through these dimensions. It also identifies learnercharacteristics and contextual factors that moderate the relationshipsbetween the typology dimensions and learning effectiveness. Finally, itshows how the typology dimensions can be used to match different levels oflearning goals to the appropriate e-learning format.

Recent reviews of the traditional training and e-learning literature havecommented on the paucity of theory to guide e-learning research (Kleinet al., 2006; Welsh et al., 2003). This is in contrast to the progress that hasbeen made in both theoretical and empirical research in the traditionaltraining area (Salas & Cannon-Bowers, 2001). In summary, Salas andCannon-Bowers (2001, p. 483) noted that ‘‘A few researchers have begun toscratch the surfacey of this topic, but a science of distance learning andtraining needs to evolve.’’ We hope that our typology and theoreticalframework will help to advance the science of e-learning and stimulateresearch that can help organizations realize the full potential of theire-learning implementations.

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ABOUT THE AUTHORS

M. Ronald Buckley holds the JC Penney Company Chair of BusinessLeadership and is a professor of management and a professor of psychologyin the Michael F. Price College of Business at the University of Oklahoma.He earned his Ph.D. in industrial/organizational psychology from AuburnUniversity. 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 beenpublished in journals such as the Academy of Management Review,Personnel Psychology, Journal of Applied Psychology, OrganizationalBehavior and Human Decision Processes, and the Journal of Management.

Michael J. Burke is the Lawrence Martin Chair in Business at TulaneUniversity, and he holds adjunct appointments in Tulane’s Department ofPsychology and School of Public Health and Tropical Medicine. He receivedhis Ph.D. in psychology from Illinois Institute of Technology. He is a pastpresident of the Society for Industrial and Organizational Psychology and heserved as editor of Personnel Psychology. In 2006, he was awarded theDecade of Behavior Research Award for his research on workplace safetyfrom a federation of professional scientific associations. In 2009, ProfessorBurke completed a three-year term on the Safety and Occupational HealthStudy Section of the National Institute for Occupational Safety and Health.In the domain of workplace safety, his research interests focus on safetyclimate, safety and health training, and safety performance.

Michelle K. Duffy is a professor and the Board of Overseers Professor ofHRIR in the Carlson School of Management at the University ofMinnesota. She received a Ph.D. from the University of Arkansas. Herresearch interests include employee well-being, antisocial behaviors,emotions, and mood.

Beth Florin is Managing Director of Pearl Meyer and Partners and leads theSurvey and Employee Compensation Practice. She has specialized experiencein the design, development, and implementation of broad-based compensa-tion programs and total remuneration compensation surveys. She was a

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co-founder of Executive Alliance, a technology industry compensationconsultancy 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 resourcepositions at Data General Corporation. She is a graduate of the University ofFlorida and holds and M.S. in human resource management and researchmethodology from Cornell University. She is a member of the Dean’sAdvisory Council at the ILR School at Cornell and is a member of the Boardof the Compensation Research Initiative (CRI) at Cornell.

Juliya Golubovich is a student in the Graduate Program in OrganizationalPsychology at Michigan State University. She received her B.B.A. inindustrial/organizational psychology from Baruch College, City Universityof New York. Her primary research interests are in adverse impact againstminorities in testing, development of alternate test instruments, and non-cognitive predictors of performance.

Jonathon R. B. Halbesleben (Ph.D., University of Oklahoma) is theHealthSouth Chair of Health Care Management and associate professorin the Culverhouse College of Commerce and Business Administration atthe University of Alabama. His research interests include stress andburnout, work–family issues, and health care management. His work hasappeared 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 resourcestudies and director of the Compensation Research Initiative (CRI) at theILR School at Cornell University. He is also a research associate at theNational Bureau of Economic Research (NBER), a senior fellow forexecutive compensation, board compensation and board practices at TheConference Board and a member of the board of directors at WorldatWork.His current research includes projects on executive and director compensa-tion, the valuation of stock options and the design of compensation systems.He earned a B.A. in economics from the University of Massachusetts atAmherst and a Ph.D. in economics from Princeton University.

Jaron Harvey is an assist professor in the Culverhouse College of Commerceand Business Administration at the University of Alabama. He receivedhis Ph.D. from the University of Oklahoma. His research interests are inthe exchange relationships that exist between employers and employees.He is specifically interested in what creates these exchange relationships and

244 ABOUT THE AUTHORS

the exchanges that occur when employees go the extra mile for theiremployers.

N. Sharon Hill is an assistant professor at The George WashingtonUniversity School of Business. She received her Ph.D. in organizationalbehavior and human resources from University of Maryland, College Park.Dr. Hill’s research interests include technology-mediated work arrange-ments (e-learning and virtual teams) and organizational change. Theseinterests are motivated by her extensive corporate work experience prior toobtaining her Ph.D. Dr. Hill has presented her research at national andinternational conferences. Her work has appeared in OrganizationalBehavior and Human Decision Processes and the Journal of AppliedBehavioral Science, and has been recognized as a Best Paper by theAcademy of Management Organizational Development and ChangeDivision.

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 hasinvestigated theories of employee turnover in various occupations (Chinesemanagers, Swiss bankers, industrial salesmen, retail sales personnel,National Guardsmen, Mexican factory workers), designed realistic jobpreviews to reduce reality shock and early quits among new nurses andaccountants, and estimated the economic costs of turnover for mental healthagencies. He has authored scholarly articles in the Academy of ManagementJournal, the Journal of Applied Psychology, Organizational Behavior andHuman Decision Processes, and Personnel Psychology. He has authored twobooks entitled Employee Turnover and Retaining Valued Employees withRodger Griffeth. Dr. Hom serves on the editorial board for the Journal ofApplied Psychology.

Jenny M. Hoobler is an assistant professor of management in the College ofBusiness Administration at the University of Illinois at Chicago. Shereceived her Ph.D. from the University of Kentucky. She serves on theeditorial boards of the Journal of Organizational Behavior and Journal ofManagement Studies, and on the Executive Committee of the Academy ofManagement’s Human Resource Management Division. She has publishedin a variety of journals including the Academy of Management Journal andJournal of Applied Psychology. Her research focuses on supervisor–subordinate relationships, gender and diversity, and intersections betweenwork and non-work domains.

About the Authors 245

Frederick T. L. Leong is a professor of psychology at Michigan StateUniversity in the Industrial/Organizational and Clinical Psychologyprograms. He is also the director of the Consortium for MulticulturalPsychology Research at MSU. He has authored or co-authored over 130articles in various psychology journals, 80 book chapters, and also edited orco-edited 10 books. He is an editor-in-chief of the Encyclopedia ofCounseling (Sage Publications) and editor of the Division 45 Book Serieson Cultural, Racial and Ethnic Psychology. He is the founding editor of theAsian American Journal of Psychology. Dr. Leong is a fellow of theAmerican Psychological Association, Association for Psychological Science,Asian American Psychological Association and the International Academyfor Intercultural Research. His major research interests center aroundculture and mental health, cross-cultural psychotherapy (especially withAsians and Asian Americans), cultural and personality factors related tocareer choice, work adjustment, and occupational stress.

Jason D. Shaw is a professor and the Curtis L. Carlson Professor ofIndustrial Relations at the University of Minnesota. He received a Ph.D.from the University of Arkansas. His research interests include organiza-tional turnover, team effectiveness, pay systems, and personality.

Sloane M. Signal is a doctoral student in the A.B. Freeman School ofBusiness at Tulane University. Before enrolling in the Freeman School, sheserved as sequence coordinator and faculty for Advertising and PublicRelations at the Howard University John H. Johnson School of Commu-nications. From 2001 to 2005, Sloane was a member of the Journalism facultyat the University of Nebraska in Lincoln, where she co-authored The PeerReview of Teaching Portfolio Project as Scholarship Assessment in HigherEducation: An Advertising Curriculum Example. Her research interests includecommunicating 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. MackRobinson College of Business at Georgia State University. He received hisPh.D. in organizational psychology from the University of Miami. He is afellow of the American Psychological Association, Society of Industrial andOrganizational Psychology and the Southern Management Association. Hisresearch interests include leadership, employee well-being, and employeeperformance contributions. He currently serves on the editorial boards of

246 ABOUT THE AUTHORS

Academy of Management Journal, Journal of Applied Psychology, Journal ofOrganizational Behavior, and the Journal of Management.

Douglas Webber is a Ph.D. student in economics at Cornell University and isbeing funded on a National Science Foundation Fellowship. He is a memberof the Cornell Higher Education Research Institute (CHERI) and theCompensation Research Initiative (CRI) at Cornell. He is a graduate of theUniversity of Florida. His research interests are in compensation design andthe economics of education.

Anthony Wheeler is an associate professor of human resources managementin the Schmidt Labor Research Center and the College of BusinessAdministration at the University of Rhode Island. He completed hisundergraduate degree at the University of Maryland, College Park andearned both his masters and doctoral degrees at the University ofOklahoma; moreover, he is a certified senior professional in humanresources management (SPHR). His research interests include the influenceof HRM practices on person–environment fit and include examining issuesrelated to alternative staffing strategies. This research has lead to thepublication of several scholarly articles in outlets such as Journal ofManagement Education, Work & Stress, Leadership Quarterly, Journal ofOccupational and Health Psychology, Issues in Multilevel Research, Researchin Personnel and Human Resources Management, International Journal ofSelection and Assessment, Journal of Business Ethics, Journal of ManagerialIssues, and Journal of Business Logistics

Karen Wouters is a lecturer at the University of Maryland’s Robert H. SmithSchool of Business. Prior to joining the Smith School, she was a researchassociate at the Vlerick Management School, Belgium. She received herPh.D. in applied economic sciences from Ghent University, Belgium. Herresearch 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, vocationaltraining, and on-the-job learning and has presented her research at nationaland international conferences. One of her articles was given the 2002 HighlyCommended Award by Emerald Literati Club. In 2006 and 2008, shereceived the ‘‘Global Forum Best Paper’’ and the ‘‘Best Paper inManagement Development’’ from the Academy of Management.

About the Authors 247

Documents

Research in Personnel and Human Resources Management, Volume 29