Context-Emergent Turnover (CET) Theory: A Theory of Collective Turnover

CONTEXT-EMERGENT TURNOVER (CET)THEORY: A THEORY OF

COLLECTIVE TURNOVER

ANTHONY J. NYBERGROBERT E. PLOYHART

University of South Carolina

We develop a theory of collective turnover that emphasizes its microfoundationorigins and unit-level consequences. Collective turnover is the quantity and quality ofdepletion of employee knowledge, skills, abilities, and other characteristics (KSAOs)from the unit, meaning that it is the collective loss of unit KSAOs. We ground ourtheory of collective turnover within the nomological network of human capital re-sources and resource-based theory. We then use this framework to examine thedynamic relationships between collective turnover and human capital resources andtheir effects on unit performance. We also describe the moderating impact of context(climate and environmental complexity) and time. Thus, we present context-emergentturnover (CET) theory.

After thousands of studies on individual turn-over, scholars are increasingly turning their at-tention to the study of collective turnover—the“aggregate levels of employee departures thatoccur within groups, work units, or organiza-tions” (Hausknecht & Trevor, 2011: 353). Such at-tention is understandable given the growingrecognition that collective turnover can have im-portant consequences for organizational pro-ductivity, performance, and—potentially—com-petitive advantage (Hancock, Allen, Bosco,McDaniel, & Pierce, in press; Hausknecht & Hol-werda, in press; Hausknecht & Trevor, 2011; Park& Shaw, 2011; Shaw, 2011). Further, the conse-quences of collective turnover may be quite dif-ferent from the consequences of individual turn-over (Dess & Shaw, 2001; Shaw, Duffy, Johnson, &Lockhart, 2005). The paramount importance ofunderstanding collective turnover is high-lighted in the strategic human resource man-agement (HRM) literature, where collective turn-over is seen as the primary mechanism drivinghuman capital depletion (Gardner, Wright, &Moynihan, 2011; Lepak & Shaw, 2008). Thus, ex-amining collective turnover is vital for under-standing how it affects a firm’s ability to usehuman capital to achieve competitive advan-tage (Delery & Shaw, 2001).

Yet despite the growth of empirical collectiveturnover research, most theory continues to bebased on individual-level conceptualizations(Hausknecht & Trevor, 2011; Shaw, 2011). We still

lack a comprehensive, unifying framework forunderstanding what collective turnover is, aswell as its effects on unit performance. Dess andShaw (2001) highlighted important differencesbetween collective and individual turnover, yetmore than a decade later their calls to developcollective-level turnover theory have gonemostly unheeded. Recently, Hausknecht andTrevor (2011) noted that most collective turnoverresearch still relies on individual-level theoriesand assumptions, despite the potential for col-lective turnover’s consequences to be multipli-catively greater than assumed at the individuallevel because of disruption and coordinationlosses that cannot be observed at the individuallevel. They state that this research “lacks a rig-orous analysis of its major antecedents and con-sequences, as well as its key emergent themesand implications” (2011: 353), and that “collec-tive level theory has been absent from much ofthe [collective turnover] research reviewed here”(2011: 379).

In this article we address this deficiency bydeveloping a theory of collective turnover. Weground this theory in the recognition that collec-tive turnover is the aggregate quantity andquality of employee knowledge, skills, abilities,and other characteristics (KSAOs) depleted fromthe unit. Functionally, then, collective turnoveris the depletion of human capital resources. Thetheory developed here has three broad theoret-ical contributions. First, it describes the emer-

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gent nature of collective turnover, and it identi-fies its consequences and relationships withinthe nomological network of human capital re-sources as defined within the strategic HRM lit-erature (e.g., Lepak, Liao, Chung, & Harden,2006) and the resource-based view’s (RBV’s)treatment of intangible resources (e.g., Coff &Kryscynski, 2011). Consequently, we explore theemergence process of collective turnover andemphasize the microfoundations of resource de-pletion. Second, the theory develops the dy-namic and reciprocal nature of collective turn-over and human capital resources by focusingattention on the timing and flows of collectiveturnover. Finally, the theory explicates how con-text, conceptualized in terms of climate and en-vironmental complexity, influences collectiveturnover and its consequences. Thus, we presentcontext-emergent turnover (CET) theory.

The implications of these theoretical contribu-tions help explain previous research inconsis-tencies using a holistic but parsimonious theoryand generate future research prospects by offer-ing guidelines for exploring the influences andconsequences of collective turnover.1 These im-plications (1) explain the nomological networkof collective turnover; (2) identify when individ-ual and collective turnover effects are likely tobe similar and when they are likely to be differ-ent; (3) identify when collective turnover willhave positive, negative, or no consequences onunit2 performance; and (4) provide a foundationfor conceptualizing collective turnover and,thus, help evaluate the validity of collectiveturnover measures.

We first summarize existing turnover re-search, noting the dominance of individual-level studies and the recent extension to thecollective level. Our review is focused and brief

because much of this research has recently beenreviewed (i.e., Hausknecht & Trevor, 2011; Shaw,2011). We then introduce CET theory to explainthe nature of collective turnover and identify itsrelationships to human capital resourcesthrough the RBV lens.

THE NEED FOR COLLECTIVETURNOVER THEORY

Turnover research has primarily focused onpredicting individual turnover (for reviews seeGriffeth, Hom, & Gaertner, 2000, and Holtom,Mitchell, Lee, & Eberly, 2008). Such studieslargely examine psychological antecedents toturnover that culminate in a stay-leave decision(e.g., Hom, Caranikas-Walker, Prussia, & Grif-feth, 1992; Mobley, 1982). Recent advancementsin turnover research (e.g., the unfolding model ofturnover) have emphasized phenomena that areexternal to the employee, such as shocks (Lee &Mitchell, 1994), localized unemployment rates(e.g., Gerhart, 1990; Hulin, Roznowski, &Hachiya, 1985; Trevor, 2001), and organizationalinfluences such as pay growth (e.g., Nyberg,2010; Trevor, Gerhart, & Boudreau, 1997).

During the past decade, interest in collectiveturnover has been growing (e.g., Hausknecht &Holwerda, in press; McElroy, Morrow, & Rude,2001; Shaw, Delery, Jenkins, & Gupta, 1998;Shaw, Gupta, & Delery, 2005; Takeuchi, Mari-nova, Lepak, & Liu, 2005). Researchers have ex-amined relationships between collective turn-over and sales growth (e.g., Batt, 2002), netperformance (e.g., Glebbeek & Bax, 2004), effi-ciency (e.g., Kacmar, Andrews, Van Rooy, Steil-berg, & Cerrone, 2006), productivity (e.g., Shaw,Gupta, & Delery, 2005), and various moderatorsof the collective turnover–unit performance rela-tionship, such as percentage of newcomers (e.g.,Hausknecht, Trevor, & Howard, 2009) and laborsegment (e.g., Siebert & Zubanov, 2009). In a fewstudies researchers have also examined ante-cedents to collective turnover, such as coworkerdemographics (e.g., Sacco & Schmitt, 2005), co-worker embeddedness (e.g., Felps et al., 2009),and downsizing (Trevor & Nyberg, 2008).

It is common for collective turnover research-ers to generalize individual-level theory andfindings to the unit level (Hausknecht & Trevor,2011). At first glance this seems reasonable, be-cause in many collective turnover studies re-searchers sum individual turnover to determine

1 We briefly consider antecedents, but our primary focusis on consequences. This is for four reasons. First, CET the-ory, which is firmly grounded in the RBV literature, lendsitself to focusing on unit consequences more than the ante-cedents of collective turnover. Second, CET theory has clearapplications for future research and practical opportunitiesfor units to mitigate the assumed negative consequences ofturnover. Third, space constraints do not allow us to considerall potential antecedents along with consequences. Fourth,most collective turnover studies have examined conse-quences; thus, this is the area that can benefit the most froma unifying framework.

2 We use the term unit to signify collective levels of em-ployees (e.g., groups, departments, organizations, etc.).

110 JanuaryAcademy of Management Review

collective turnover rates (e.g., Batt, 2002; Kacmaret al., 2006; Morrow & McElroy, 2007). Hence, ifthe measurement is a simple sum of individual-level turnover, then one might assume that in-dividual theory could also be used to explaincollective turnover. However, this approach ispotentially fallacious (Rousseau, 1985). Multi-level theory identifies when higher- and lower-level phenomena are likely to be isomorphic(the degree to which higher- and lower-levelphenomena are identical) and how higher-levelphenomena emerge from lower-level phenom-ena. Kozlowski and Klein (2000) described emer-gent phenomena as collective constructs thatoriginate from the behavior, affect, and cogni-tion of individuals. The nature of interactionsamong unit members, and the context withinwhich they interact, is what contributes to theemergence of collective phenomena from lower-level origins. Bliese (2000) noted that total iso-morphism is rare; partial isomorphism is morecommon and suggests that lower- and higher-level constructs have similarities but also differ-ences. For example, climate is the shared per-ception of what is important and rewarded bythe organization (James & Jones, 1974; Reichers& Schneider, 1990; Schneider, 1987) and is dis-tinct from its individual-level origins. Similarly,it is inappropriate to generalize individual-levelturnover findings to collective turnover becausewhen emergent phenomena are partially iso-morphic, generalizations across levels are notwarranted (Kozlowski & Klein, 2000; Rousseau,1985; Schwab, 1991).

Collective turnover is an emergent phenome-non that is only partially isomorphic with indi-vidual turnover. Although collective turnoveroriginates from individual turnover, it is differ-ent conceptually and empirically. Conceptually,individual turnover is based on an employee’sdecision to stay or leave (for involuntary turn-over, it is based on a supervisor’s decision).When an employee leaves, that employee takesall of his or her knowledge, skills, expertise, andother contributions (e.g., performance). Individ-ual turnover consequences are traditionallyconsidered from the basis of replacement costsand lost individual productivity; context and so-cial relationships are rarely considered (Dess &Shaw, 2001). In contrast, collective turnover in-volves aggregated individual turnover deci-sions and may result in less or more detrimentalconsequences than simple replacement costs.

These losses will depend on employee KSAOs,context, social relationships, and employeeroles in the unit (Shaw, 2011).

Therefore, the consequences of collective turn-over are potentially much greater than thoserecognized at the individual level (Hausknecht& Holwerda, in press; Hausknecht & Trevor, 2011;Shaw, 2011). Empirical research reinforces thepartial isomorphism expectation that the twoconstructs are not identical. For instance, indi-vidual withdrawal behavior has different deter-minants and consequences than collective with-drawal behavior (Hulin, 1991). Similarly, Shaw,Duffy, Johnson, and Lockhart (2005) found thatsocial capital losses explain declines in unitperformance above and beyond what would beexpected from turnover rates and that these aremore pronounced when rates are low.

Because individual and collective turnoverare only partially isomorphic, generalizing indi-vidual-level turnover assumptions and theoriescan fail to uncover the processes that explaincollective turnover’s nature and relationshipswith other constructs. Hausknecht and Trevor(2011) have illustrated one example of this prob-lem: individual-level turnover theory suggeststhat through replacing poorer performers invol-untary turnover is positively related to perfor-mance. However, a collective turnover perspec-tive suggests that involuntary turnover may benegatively related to performance because ofcoordination disruptions and erosions of cli-mate. Attempts to reconcile these findings areessentially cross-level fallacies that mix andmatch theory and findings from multiple levels.Another example is the potential benefits of vol-untary turnover (Dalton, Todor, & Krackhardt,1982; Hollenbeck & Williams, 1986). The argu-ment is that voluntary turnover is good if re-placements are higher quality or if new ideasare brought to the unit. This seems reasonablewhen considering a single replacement; how-ever, at the collective level it is likely to dependon many factors, a few being the employee’srole, relationships, and institutional knowledgeand the time needed to find replacements.

Thus, collective and individual turnover con-structs are different theoretically and empiri-cally and have different antecedents and conse-quences. Generalizing individual-level theoriesof turnover to the collective level is unlikely tobe appropriate; instead, new theory must be de-veloped (a point made forcefully by Hausknecht

2013 111Nyberg and Ployhart

& Trevor, 2011). A theory of collective turnovermust account for its “microfoundations” (Felin &Hesterly, 2007; Gerhart, 2005), often described asunderstanding individual-level phenomena inthe context of firm-level theorizing (Foss, 2011).Because collective turnover is the depletion ofhuman capital resources, it is necessary to em-bed a theory of collective turnover within humancapital resource theory (Ployhart & Moliterno,2011; Wright & McMahan, 2011). In human capi-tal resource theory, emergence, time, and con-text are vital (Bowen & Ostroff, 2004; Lepak et al.,2006; Ployhart & Moliterno, 2011). Consequently,understanding collective turnover requires un-derstanding emergence, time, and context. Wenext develop such a theory.

CET THEORY

In this section we develop key propositions forCET theory. Our fundamental points are thatcollective turnover emerges from individualKSAOs, collective turnover contains qualitativeand quantitative components, and collectiveturnover represents the depletion of human cap-ital resources. These points, in turn, have rami-fications for collective turnover’s relationshipswith human capital. Consequently, an under-standing of collective turnover must comethrough embedding it within a nomological net-work of human capital resources.3

The Nature of Collective Turnover

Collective turnover defined. Collective turn-over is the quantity and quality of KSAO deple-tion from the unit. Collective turnover is derivedfrom the KSAOs of employees who leave theunit, thus emphasizing the microfoundationsand cross-level nature of collective turnover. Bydefining collective turnover as emerging fromKSAOs, we explicitly recognize that it has atemporal component and that it is affected bycontext, particularly climate and environmentalcomplexity (Kozlowski & Klein, 2000). As a result,CET explains why collective turnover conse-

quences frequently will be greater than individ-ual consequences.

CET theory further breaks from traditional re-search by proposing that collective turnovercontains both quantitative and qualitative com-ponents that change over time. Quantitativecomponents represent the rate of unit turnover—that is, the percentage of employees who leavethe unit. Qualitative components represent thetypes of KSAOs lost (e.g., cognitive ability) andthe degree of competence or quality of thoseKSAOs (e.g., high versus low cognitive ability).Thus, collective turnover represents the propor-tion of employee depletion and these employ-ees’ cumulative KSAOs. Qualitative aspects ofcollective turnover are typically ignored in ex-isting research, but the quantitative componentis, by itself, deficient because it negates a com-plete understanding of collective turnover’sKSAO microfoundations. Stated differently, ifcollective turnover emerges from individualKSAOs, then neglecting to consider thoseKSAOs will cause us to miss what is perhaps themost important element of collective turnover.As will be seen, quality is generally expected tohave greater impact on unit performance thanquantity.

Collective turnover is distinct from humancapital resources. As the aggregate loss ofKSAOs, collective turnover is functionally thedepletion of human capital resources. This is asimple but vitally important perspective, be-cause it means that an understanding of collec-tive turnover must be embedded within the no-mological network of human capital resourcesand RBV. One cannot fully appreciate the nu-ances of collective turnover, its meaning, its de-terminants, and its consequences without un-derstanding its relationship to human capitalresources—and vice versa (Nyberg, Moliterno,Hale, & Lepak, in press). Thus, embedding col-lective turnover within resource-based theorymore richly conceptualizes both collective turn-over and human capital resources and builds oncalls to study their interrelationship (Dess &Shaw, 2001; Hausknecht & Trevor, 2011; Shaw,2011). Collective turnover may result in either anet positive or net negative change in the valueof the human capital resource, depending on thequantity and quality of the human capital de-pleted. For example, losing a few low-qualityemployees may increase the value of the humancapital resource, leading to improved unit per-

3 We note that CET theory is not specific to involuntary orvoluntary turnover. Our propositions encompass both be-cause CET theory is intended to explain the nature andeffects of both voluntary and involuntary collective turnover.After describing the theory, we revisit the role of voluntaryand involuntary turnover within CET theory.


formance, whereas losing high-quality employ-ees may decrease the value of the human capi-tal resources and hurt unit performance.Furthermore, the timing of these losses maylead to profound differences. For instance, los-ing one employee per month for a year mayhave a very different impact on the unit thanlosing twelve employees in one month.

Hence, understanding the meaning of collec-tive turnover requires understanding its rela-tionships with human capital resources, which,in turn, requires a grounding in resource-basedtheory (Molloy, Chadwick, Ployhart, & Golden,2011; Nyberg et al., in press). Human capital re-sources are intangible unit-level constructs thatcan possess the characteristics needed to influ-ence unit performance and competitive advan-tage (Hatch & Dyer, 2004; Wright, Dunford, &Snell, 2001; Wright & McMahan, 1992; Wright,McMahan, & McWilliams, 1994). Although hu-man capital resources exist at the unit level,they are based on the aggregation of individualKSAOs such that human capital resourcesemerge from individual-level KSAOs (Ployhart &Moliterno, 2011; Wright & McMahan, 2011).4 Theextent to which emergence occurs depends onthe nature of member interaction (Kozlowski &Klein, 2000). First, environmental complexityrepresents the amount of coordination requiredby the unit’s task demands (Van de Ven, Del-becq, & Koenig, 1976). Second, climate repre-sents the degree to which unit members have ashared sense of what the unit expects, rewards,and supports (Bowen & Ostroff, 2004; Schneider,Bowen, Ehrhart, & Holcombe, 2000; Schneider &Reichers, 1983). Climate influences how mem-bers interact, communicate, trust, and shareknowledge; hence, it is a conduit through whichhuman capital resources are connected toKSAOs (Kozlowski & Ilgen, 2006; Schneider et al.,2000). Thus, human capital resources emergefrom KSAOs as a function of environmental com-plexity demands and a supportive climate.

Positioning collective turnover within re-source-based theory helps clarify the similari-ties and differences between collective turnoverand human capital. In terms of similarities, both

are unit-level phenomena that emerge from theKSAOs of employees within the unit. Both alsoshare qualitative components (e.g., high or lowlevels of knowledge), and both fluctuate overtime and exist, in theory, indefinitely (Molloy etal., 2011). Yet collective turnover and humancapital resources are profoundly different. Theyare not mirror images of each other, and collec-tive turnover is not simply the “reverse” of hu-man capital resource emergence. Rather, theyare distinct conceptually and empirically. First,and perhaps most fundamental, they have dif-ferent roles within the nomological network ofintangible resources. Human capital resources,on the one hand, should enhance unit perfor-mance because they are strategically valuableand “controlled by a firm,” enabling it “to con-ceive of and implement strategies that improveits efficiency and effectiveness” (Barney, 1991:101). Collective turnover, on the other hand,rarely helps implement or conceive of a unit’sstrategy (e.g., Delery & Shaw, 2001). Organiza-tions try to develop strategies for reducing turn-over, but the emphasis is generally on reducingthe erosion of valuable human capital resourcesrather than leveraging collective turnover fororganizational profit. Thus, for organizations,the most meaningful ramification of collectiveturnover is primarily the extent to which it de-pletes human capital resources and affects unitperformance.5

Second, collective turnover contains a quanti-tative component that is usually independent ofthe quantitative component of human capitalresources. Quantity within human capital re-sources is not usually considered but is essen-tially the “base rate” or total number of unitemployees. In contrast, collective turnoverquantity represents the erosion (reduction) fromthis base rate. This can be seen in the typicalcollective turnover rate metric, which is thenumber of employees who leave divided by thetotal number of employees (i.e., the denominatoris the base rate; Hausknecht & Trevor, 2011).Collective turnover and human capital resource

4 Alternative conceptualizations of human capital re-sources exist (e.g., as individual investment decisions ineconomics), but we limit our discussion to the resource-based and strategic HRM treatment of human capitalresources.

5 Strategic choices to remove underperforming employeescan occur, such as when a coaching staff is fired after alosing season, but even in this situation the longer-termemphasis is on accumulating higher-quality human capitalresources. Downsizing may be a notable exception (seeTrevor & Nyberg, 2008, for a detailed discussion of this topic),but considering it in detail is beyond the scope of this article.


quantities are independent if there are replace-ments. Suppose a unit loses 40 out of 100 em-ployees but replaces them immediately. In thissituation the quantitative component of collec-tive turnover is 40 percent while the quantitativecomponent of human capital is 100 percent be-cause human capital remains at (or quickly re-turns to) full capacity. In fact, with immediatereplacement, collective turnover rates can fluc-tuate up and down without having a discernibleeffect on the quantitative component of humancapital. Only if one assumes the situation wherecollective turnover losses are not replaced willthe quantitative components of collective turn-over and human capital be related in a deter-ministic manner. And in this situation a bizarreconsequence can theoretically occur such thathuman capital disappears if collective turnoveris 100 percent.

Third, collective turnover and human capitalresources are qualitatively different. One obvi-ous difference is that the unit’s human capital isbased on the KSAOs of employees who remain,whereas collective turnover is based on theKSAOs of employees who have left; hence, thecomposition of the two constructs differs interms of employees and their KSAOs. For exam-ple, if very high-quality employees leave theunit, then the KSAO quality of collective turn-over will be greater than the KSAO quality of thehuman capital resource. Even though both col-lective turnover and human capital resourcesoriginate in KSAOs, they are based on theKSAOs of different people (i.e., those still em-ployed versus those no longer employed). If oneholds replacements constant, then collectiveturnover must represent very different qualitiesof KSAOs than human capital resources. Fur-thermore, employees remaining in the unit arelikely to have a different performance composi-tion than those departing the unit (Nyberg, 2010;Shaw, 2011).

Finally, collective turnover and human capitalresources are likely to have different anteced-ents, since the practices and policies that con-tribute to human capital resource accumulationdiffer from those that contribute to resource de-pletion (i.e., collective turnover; Sirmon, Hitt, &Ireland, 2007; Takeuchi, Tesluk, Yun, & Lepak,2005). Indeed, this is the logic underlying high-performance work systems and the desire tobundle complementary practices so as to simul-taneously achieve different HR goals, such as

increasing human capital accumulation whilereducing collective turnover (Becker & Huselid,2006; Huselid, 1995; Sirmon et al., 2007). Evenwhen antecedents are shared by both collectiveturnover and human capital resources, the ef-fects of the antecedents are likely to be different.For example, recruiting likely has a strongereffect on accumulation than retention, and cli-mate likely has a stronger effect on retentionthan accumulation (Lepak et al., 2006; Schnei-der, 1987).6

Thus, even though collective turnover repre-sents the depletion of human capital resourcesand is embedded within the RBV, there are pro-found conceptual differences between collectiveturnover and human capital resources. They arerelated but distinct constructs, not unlike posi-tive and negative affectivity or procedural anddistributive justice. As implied above, one mayhave high or low rates of collective turnover andhigh or low qualities of turnover while indepen-dently having high or low quantities and qual-ities of human capital. It is only if one assumesthere are no replacements that human capitalresources and collective turnover will have aquantitatively deterministic relationship, buteven in this situation the relationship will bequalitatively independent. Finally, as we willsee, the differences between collective turnoverand human capital resources are even more pro-nounced when we examine their consequences.

Postulate 1: Collective turnover is thequantity and quality of KSAO deple-tion from the unit.

Postulate 2: Collective turnover is dis-tinct from human capital resources.

Postulate 3: The quantitative andqualitative components of collectiveturnover independently reflect humancapital resource depletion.

6 It is beyond the scope of this article to discuss anteced-ents in detail. However, research in strategy points out thatsome practices are more useful for accumulating resourcesthan for stopping the depletion of resources (Eisenhardt &Martin, 2000). Even at the individual level one may find aKSAO related to job performance (e.g., cognitive ability) buta different construct (e.g., job satisfaction) related to turnover(Maltarich, Nyberg, & Reilly, 2010).


The Collective Turnover–Human CapitalResource Nomological Network

CET theory breaks from existing research bynot equating collective turnover exclusivelywith negative unit performance consequences.By recognizing quantitative and qualitativecomponents, we gain a greater understanding ofwhy relationships between collective turnoverand unit performance differ in magnitude anddirection. Embedding collective turnover withinresource-based theory and human capital re-sources further helps to identify how and whycollective turnover impacts unit performancevia direct, mediating, and moderating effects.

Figure 1 provides an organizing frameworkthat identifies four paths through which collec-tive turnover influences the unit’s performance.Path a in Figure 1 shows the direct effect ofcollective turnover on unit performance. Path bshows the indirect (mediated) influence that col-lective turnover has on unit performance via itsinfluence on the human capital resource. Path cshows the moderating effect that collective turn-over has on the human capital resource–unitperformance relationship. Path d shows themoderating effect that collective turnover has onthe human capital resource emergence process.

At first glance some of these four paths mayseem intuitive. However, most turnover researchhas not explicitly considered the various rela-tionships between collective turnover and hu-man capital suggested in Figure 1. Thus, eventhese straightforward relationships may helpreorient and direct future research. Furthermore,these relationships quickly become complexwhen time and context are considered. For ex-ample, in Path b the timing of measurement isimportant such that human capital resourcesmust be assessed after collective turnover. Thus,time and climate (contextual influences) perme-ate the entire system of relationships such thatthe strength of the relationships may be affectedby time and climate. Going forward, we firstdiscuss each of the specific paths and then insubsequent sections turn our attention to theinfluence of time and context (e.g., climate).

Finally, before developing the specific pathsin Figure 1, we summarize why collective turn-over or human capital resources may influenceunit performance and competitive advantage.Unit performance is usually assessed in terms ofaccounting (e.g., controllable profit), financial(e.g., sales), and market-based (e.g., market-share, customer satisfaction) metrics, as well as

FIGURE 1CET Theory Consequencesa

Unit performanceHuman capital

Collectiveturnover

KSAOs

(b)

(c)

(a)

(d)

aAlthough not shown, it is assumed that time and climate influence the relationships in the entire figure.


sustained competitive advantage (see Crook,Todd, Combs, Woehr, & Ketchen, 2011, and Rich-ard, Devinney, Yip, & Johnson, 2009, for distinc-tions between different types of unit perfor-mance). These unit performance outcomes areaffected by many factors, but those most rele-vant here involve the productivity, efficiency,and coordination of employees. Human capitalresources are generally expected to positivelyinfluence these processes (Crook et al., 2011; Ny-berg et al., in press). In contrast, collective turn-over is generally expected to negatively impactthese processes (Hancock et al., in press;Hausknecht et al., 2009; Kacmar et al., 2006).More generally, research suggests that collec-tive turnover decreases group efficiency bymaking coordination difficult, reducing proso-cial behavior, and reducing communication(Kozlowski & Ilgen, 2006; Lepak et al., 2006). How-ever, as described below, in some cases collec-tive turnover may have positive effects, such aswhen subversive members leave. Importantly,only productivity losses are observable at theindividual level; coordination and efficiencylosses can only be detected at higher levels.

Direct effects. In most existing collective turn-over research, scholars have examined the di-rect effects of collective turnover rates on unitperformance (Hausknecht & Trevor, 2011). Thesedirect relationships are usually expected to benegative. In contrast, CET theory proposes thatthe extent to which collective turnover producesnegative, no, or positive effects on unit perfor-mance is determined by the quantity and qual-ity of KSAOs lost. Consider four collective turn-over scenarios. First, when collective turnover isof low quantity and low quality, it is unlikelythat collective turnover will produce a strongeffect on unit performance. Even if there is adetectable effect, the relationship may actuallybe positive because the unit is losing low-quality employees. Second, when collectiveturnover is of low quantity and high quality, theconsequences may be stronger and slightly tomoderately negative. Although good employeeswill be lost, the losses may not be at a rate thatwill cause great disruption. Third, when collec-tive turnover is of high quantity and low quality,the consequences may be negative and moder-ately strong. Although the lost employees are oflower competence, the high rate of loss maycreate performance disruptions that also erodethe unit’s climate and coordination. Fourth,

when collective turnover is of high quantity andhigh quality, the effects are likely to be stronglynegative and severe. In this scenario organiza-tions must rely on the performance of the re-maining lower-quality employees. In such a sce-nario, even if the unit replaces employeesquickly, the erosion of climate is likely to lead toreduced productivity and efficiency. Thus, thequantity and quality of collective turnover mayinteract to determine the magnitude and direc-tion of collective turnover’s consequences. Thegenerally negative relationship between quan-tity and unit performance is likely to be evenmore negative when the loss involves high-quality versus low-quality employees. This di-rect effect is depicted by Path a in Figure 1.

Proposition 1: The quality and quan-tity mix of collective turnover will de-termine the direction and strength ofthe collective turnover–unit perfor-mance relationship.

Mediating effects. Collective turnover canalso impact unit performance indirectly by in-fluencing the human capital resource. The RBVpredicts that valuable resources can influenceunit performance through strategy enactment orthrough enabling the development of strategy(Barney, 1991). Because collective turnover is thedepletion of human capital resources, collectiveturnover necessarily alters that resource and,consequently, unit performance. This influenceresults from the reduction in the stock and qual-ity of the human capital resource.

In terms of stock, a critical mass of valuablehuman capital must be present for it to influenceunit performance (Dierickx & Cool, 1989). If thereis no critical mass of valuable human capital,then human capital resources cannot influenceunit performance, even if the stock of capitalpresent is of high quality. For example, serviceorientation is a valuable human capital re-source in many retail units, but a store with toofew employees, even if those employees arehighly service oriented, will not be able to pro-vide adequate customer service (Ployhart,Weekley, & Ramsey, 2009). The precise criticalmass threshold likely differs according to manyfactors, such as industry, environmental com-plexity, and competitive environment. More im-portant, the relationship between a valuable hu-man capital resource and unit performance isaffected by the stock of the KSAOs within the


unit. Collective turnover can erode the stock ofhuman capital resources, possibly even deplet-ing this stock to the point of falling below acritical mass and, hence, influencing unit per-formance through changing the human capitalresource.

The degree to which collective turnover influ-ences unit performance via the human capitalresource also depends on the specific quality ofKSAOs lost. The depletion of human capital re-sources will be more impactful if collective turn-over involves employees with high-qualityKSAOs. Consider two identical units with iden-tical human capital resources and identicalrates of turnover. If collective turnover were sim-ply a rate, then both units would see the samehuman capital stock erosion resulting in thesame impact on unit outcomes. However, if UnitA loses higher-quality employees than Unit B,the negative effects of human capital changeson unit outcomes may be greater for Unit A.Alternatively, if Unit A has a higher turnoverrate but loses lower-quality employees, the in-direct negative effect of turnover on perfor-mance may be weaker for Unit A than Unit B. Butif the critical mass of human capital is alreadydiminished, then losing even moderate-qualityemployees can be damaging (and more damag-ing than if the unit were fully staffed). Finally,the opposite effect can occur where collectiveturnover indirectly influences unit performanceby enhancing the human capital resource. Forinstance, if collective turnover involves onlylow-quality KSAOs or those employees who aredisrupting the unit’s climate (e.g., destroyingtrust), then collective turnover can increase unitperformance through strengthening the humancapital resource. As such, the quality of collec-tive turnover may be more important than therate of turnover. These mediated relationshipsare depicted in Path b in Figure 1.

Proposition 2: The quality and quan-tity mix of collective turnover will in-directly influence unit performancethrough its effects on the human cap-ital resource.

Moderating effects. It is also the case thatcollective turnover can moderate the humancapital resource–unit performance relationship.High levels of collective turnover may weakenthe human capital–unit performance relation-ship by interfering with coordination, diverting

attention toward training for new employees, orcreating additional responsibilities for remain-ing employees. For example, collective turnoverdisrupts communication, the shared and tacitunderstanding of each other’s behavior, and theability to adapt and coordinate effectively(George & Bettenhausen, 1990). Collective turn-over may further erode a climate of trust, be-cause such a climate takes time to develop(Colquitt, Scott, & LePine, 2007). In contrast, col-lective turnover can strengthen a climate of trustif disruptive unit members leave and the unitbecomes more harmonious and cohesive,thereby promoting greater efficiency (Dalton &Todor, 1979), which will enhance the human cap-ital resource–unit performance relationship. Incombination, it is likely that high levels of col-lective turnover will create greater disruption,thus decreasing the human capital unit perfor-mance relationship more so than will lower lev-els of collective turnover. This moderating effectis depicted in Path c in Figure 1.

Proposition 3: Collective turnover willmoderate the human capital resource–unit performance relationship such thathigh levels of collective turnover willlikely weaken the relationship betweenthe unit’s human capital resource andthe unit’s performance.

Moderating human capital resource emer-gence. Collective turnover (quality and quantity)may also indirectly deplete human capital re-sources and, hence, may influence the humancapital resource–unit performance relationshipby affecting the process of human capital re-source emergence. This influence on emergenceis most likely to occur through changes to theunit’s climate. A supportive climate facilitateshuman capital resource emergence (cf. Ployhart& Moliterno, 2011), and to the extent collectiveturnover erodes a supportive climate, humancapital resource emergence will be weakened.When employees leave a unit, all of their con-tributions leave, including their relationshipswith other employees and their contributions tothe unit’s climate. High levels of collective turn-over will increase barriers to communicationand diminish the likelihood that employees willinvest in each other because of a reduced expec-tation of relational stability, further impedinghuman capital resource emergence. Thus, by af-fecting unit climate, collective turnover may


also affect the process of human capital re-source emergence.

As described, the nature of interactionsamong unit members and the context withinwhich they interact (described below) are whatcontribute to the emergence of collective phe-nomena from lower-level origins. Thus, theemergence process from KSAOs to the unit’s hu-man capital resource is also influenced by therelationship among individual employees withthose KSAOs. Hence, collective turnover, to thedegree that it changes those relationships, canalso influence the emergence process. For ex-ample, if collective turnover reduces the numberof employees within the unit, those who remainwill need to interact in different ways, which, inturn, may influence the quality of the unit’s hu-man capital. This moderating effect on the hu-man capital resource emergence is depicted inPath d in Figure 1.

Proposition 4: Collective turnover willinfluence the process of human capi-tal resource emergence.

Next, we expand the relationships shown inFigure 1 to consider how they are affected bytime and context. To keep the presentation man-ageable, we focus our attention on the direct(Path a) and moderating (Path c) relationships inFigure 1, because these are likely to be of thegreatest immediate interest. However, the logicof the propositions that follow extends naturallyto the mediated (Path b) and emergence-moderating (Path d) relationship shown in Fig-ure 1.

The Role of Time

Flows. Realizing that collective turnover de-pletes human capital resources implies a tem-poral dynamic between the two constructs thatis not observable at the individual level. As anintangible resource, human capital does nothave a predetermined life span—in theory, itcan continue indefinitely as members who leaveare replaced with new members (Molloy et al.,2011). Furthermore, the stock and quality of in-tangible resources, like human capital, can fluc-tuate over time because of changes in collectiveturnover, recruitment, and selection (Dierickx &Cool, 1989; Schneider, 1987; Sirmon et al., 2007).Consequently, understanding the flow (both inand out) of human capital resources is critical

and requires a longitudinal perspective, whichleads to greater awareness of the role of re-placements in the consequences of collectiveturnover.

This process can be described by adaptingDierickx and Cool’s (1989) bathtub metaphor. Atub needs a sufficient amount of water at a suf-ficient temperature to effectively clean a person.The amount of water in the tub is its stock, andthe temperature of the water is its quality. Waterflows in and out of the tub; it can be accumu-lated (opening the tap) or depleted (opening thedrain) to influence the stock. Similarly, there is aflow to human capital. Both the number of peo-ple within a unit (level of water) and the qualityof the human capital (water temperature)change over time. Management needs to controlthe acquisition and development of human cap-ital to offset erosion due to collective turnover(e.g., Schneider, 1987; Sirmon et al., 2007). This, inturn, means that collective turnover may not al-ways weaken the human capital–unit perfor-mance relationship. For example, a unit with astrong inflow of human capital of similar (orbetter) quality to that leaving the unit may pro-duce a situation where collective turnoverdoes not affect the human capital resource–unitperformance relationship—it may even enhanceit. Alternatively, if human capital is depletedfaster than it can be replaced, or if the quality ofhuman capital lost is greater than the quality ofhuman capital gained by replacement, then col-lective turnover will weaken the human capitalresource–unit performance relationship. Usingthe bathtub metaphor, the former instance oc-curs when the water drains faster than it isreplaced; the latter instance occurs when thetemperature of the water from the tap is colderthan the water being drained from the tub. Hu-man capital resources and collective turnoverare related dynamically and reciprocally overtime, and change in one may influence changein the other.

Proposition 5: The relationship be-tween human capital resources andcollective turnover is dynamic overtime. Flows of human capital re-sources into the unit will affect thequality and consequences of collec-tive turnover flows, and vice versa.

Although collective turnover depletes humancapital, it also allows for replacement. That is,


the direction of the discrepancy between collec-tive turnover and human capital accumulationalso influences collective turnover’s conse-quences. The relative difference in inflows ver-sus outflows is more important than simply theexistence of change (Dierickx & Cool, 1989), andresearchers need to account for replacementswhen evaluating turnover effects (e.g., Haus-knecht & Holwerda, in press; Trevor & Nyberg,2008). For instance, assuming equal rates, it is adifferent situation when the quality of collectiveturnover outflows is greater than the quality ofhuman capital inflows, compared with the op-posite scenario (Subramaniam & Youndt, 2005).Human capital resource erosion occurs whencollective turnover exceeds replacement capitalinflows; expansion occurs when human capitalinflows exceed collective turnover. Thus, thediscrepancy (erosion or expansion) between hu-man capital inflows and collective turnover out-flows has both a direct effect on unit perfor-mance and a moderating effect on the humancapital–unit performance relationship.

Proposition 6a: The direction of thediscrepancy between human capitalresource inflows and outflows (collec-tive turnover) will influence the direc-tion and magnitude of the collectiveturnover–unit performance relation-ship. Erosion will have a negative ef-fect on unit performance, whereas ex-pansion will have a positive effect.

Proposition 6b: The direction of thediscrepancy between human capitalresource inflows and outflows (collec-tive turnover) will influence the mag-nitude of the moderating effect of col-lective turnover on the human capitalresource–unit performance relation-ship. Erosion will reduce the effect ofhuman capital on unit performance,whereas expansion will increase theeffect.

Timing of collective turnover. Thinking of col-lective turnover in terms of flows leads to therealization that the strongest influence on thehuman capital–unit performance relationshipmay not be the absolute level of collective turn-over but, rather, whether the rate and/or qualityof collective turnover is increasing or decreas-ing over time (Hausknecht & Holwerda, in press).

When constructs are dynamically related, levelsof one influence levels of the other (Dierickx &Cool, 1989), and looking at cross-sectional rela-tionships divorced from the trajectory of the con-structs can be severely misleading (see Maxwell& Cole, 2007, for empirical illustrations). For ex-ample, Ployhart et al. (2009) found that changesin human capital resources were more stronglyrelated to financial performance metrics thanthe stock of human capital resources. Thechanges in collective turnover may morestrongly affect the dynamic human capital re-sources–unit performance relationship than theabsolute level of collective turnover at a givenpoint in time (i.e., its stock).

The rate and timing of change are also impor-tant to consider. For example, suppose the col-lective turnover of Unit A is greater than thecollective turnover of Unit B. Assuming all elseis equal, one would expect the collective turn-over of Unit A to be more detrimental. However,if Unit A’s collective turnover is decreasing rap-idly while Unit B’s collective turnover is increas-ing, the detrimental effects of Unit B’s turnovermay actually be worse than Unit A’s, eventhough Unit A has a higher collective turnoverrate. Similarly, a fast erosion of human capitalresources is likely to be more disruptive to co-workers and the unit’s climate than a slow ero-sion. For example, one may expect different uniteffects on performance and climate when oneemployee leaves per month for a year versuswhen twelve employees leave in one month,especially if the size of the group is small. Theoutcomes of these differently timed events willlikely depend on a number of factors, includingthe size of the unit, the industry, the employeeswho remain, the events triggering the depar-tures, and the expectations that the remainingemployees have for the future. Hausknecht andHolwerda (in press) discuss issues of timing insome detail.

One reason the rate or timing of collectiveturnover changes is likely to be more impactfulthan an absolute amount is because turnoverlikely contributes to a “climate of turnover” anda contagion effect on those who remain (Felps etal., 2009).7 For example, observing other mem-bers of one’s unit leave is likely to produce ashock to one’s employment, contributing to visu-

7 We thank an anonymous reviewer for this suggestion.


alizing multiple pathways to leaving (Lee &Mitchell, 1994). This may be especially true fornew employees who are not fully embedded.Fast increases in turnover also disrupt commu-nication and performance, requiring remainingemployees to take on additional responsibilitieswhile newer employees are learning the job andtheir role (e.g., Kacmar et al., 2006; Shaw, Duffy,Johnson, & Lockhart, 2005). Finally, increasingcollective turnover rates may weaken the jobembeddedness of the remaining employees byweakening their links to others within the firm.This could, in turn, reduce the remaining em-ployees’ perceptions of fit and the expected sac-rifices that might occur if they were to leave(Lee, Mitchell, Sablynski, Burton, & Hol-tom, 2004).

Proposition 7: The timing and rate ofcollective turnover change will have astronger influence on the dynamic hu-man capital resource–unit perfor-mance relationship than the absolutelevel of collective turnover.

The Role of Context

Incorporating context provides a major breakfrom individual-level turnover predictions andfindings. We conceptualize context in two ways,reflecting the social (climate) and technical (en-vironmental complexity) features of organiza-tions (Katz & Kahn, 1978).8

Climate. Climate is a shared sense of whatthe unit rewards, supports, and considers impor-tant (Ostroff, Kinicki, & Tamkins, 2003; Reichers& Schneider, 1990). Climate influences the atti-tudes, cognitions, and behaviors of those withinthe unit (Bowen & Ostroff, 2004; Ostroff, 1993;Ostroff et al., 2003). It is therefore a form of con-text because it is a unit-level construct mediat-ing the relationship between objective unitcharacteristics, policies, practices, and proce-dures and employees’ shared psychological re-actions (Bowen & Ostroff, 2004; Campbell, Dun-nette, Lawler, & Weick, 1970). As a form ofcontextual influence, climate affects collectiveturnover and human capital resources in numer-ous ways. It affects who is attracted to, selectedby, and remains in the unit (Schneider, 1987).

Climate also affects collective turnover (Carr,Schmidt, Ford, & DeShon, 2003) and human cap-ital resource emergence (Ployhart, Weekley, &Baughman, 2006; Schneider, Smith, Taylor, &Fleenor, 1998). The relationships among climate,collective turnover, and human capital re-sources are also likely to be reciprocal, sinceclimate will change as a result of collectiveturnover and human capital resource accumula-tions (Ostroff et al., 2003; Schneider, 1987).

It is beyond the scope of this article to con-sider all such interrelationships implied in Fig-ure 1, so we emphasize the most novel of thesethat is consistent with our focus on understand-ing collective turnover’s consequences. Specifi-cally, we posit that climate will moderate thecollective turnover–unit performance relation-ship (Path a in Figure 1). Climate may offsetcollective turnover’s effects on performance. Forexample, a strong climate for teamwork maylead members to expand their work roles andcover the tasks and responsibilities of thoseleaving the unit. Similarly, a strong climate mayhelp maintain job satisfaction and commitmentin the face of collective turnover and, hence,reduce the likelihood of future collective turn-over. However, climate may also exacerbate col-lective turnover’s effects on unit performance.For example, losing higher-quality employeesmay lead to a turnover contagion effect (Felps etal., 2009), such as can occur on executive teamswhen a CEO suddenly leaves (Shen & Cannella,2002). Likewise, to the extent that climate per-ceptions are consistent with the unit’s strategicfocus (e.g., Schneider et al., 2000) and collectiveturnover occurs for those individuals with theKSAOs most valuable for that strategic focus,the loss of valuable employees will be magni-fied by the erosion of climate.

Proposition 8: Climate will moderatethe collective turnover–unit perfor-mance relationship.

Environmental complexity. Environmentalcomplexity is the nature of interconnections andinterdependence required by unit task demands(Bell & Kozlowski, 2002; Van de Ven et al., 1976).Environmental complexity lies on a continuumfrom simple to complex. Increasing environmen-tal complexity demands greater reciprocal inter-action, two-way communication, stronger socialnetworks, synchronized coordination, and more

8 We thank an anonymous reviewer for encouraging us tomore deeply examine climate’s role.


interdependence among members (Bell & Koz-lowski, 2002). There are four workflow structuresalong the environmental complexity continuum(Van de Ven et al., 1976). Pooled structures occurwhen collective performance is the simple sumof individual performance contributions. Inten-sive workflow structures occur when collectiveperformance depends on distributed expertiseand high collaboration among team members.Falling between pooled and intensive workflowstructures are sequential and reciprocal work-flow structures, with the latter being more com-plex than the former. Figure 2 provides a visualsummary of environmental complexity. Thegreater the environmental complexity, thegreater collective turnover’s direct effect on unitperformance and the greater its moderating ef-fect on the human capital resource–unit perfor-mance relationship.

First, environmental complexity influences(moderates) the direct effect of collective turn-over on unit performance. In general, greaterenvironmental complexity is associated withgreater collective turnover consequences, be-cause more complex environments require more

synchronous member interaction, coordination,communication, and adaptation (Hackman, 1987;Kozlowski & Ilgen, 2006; Marks, Mathieu, &Zaccaro, 2001). Consider the consequences ofcollective turnover between pooled and inten-sive workflow structures (i.e., very simple ver-sus very complex environments). In the pooledworkflow structure, tasks are largely indepen-dent, there is no need for coordination, and thehuman capital resource is simply the sum ofindividual contributions (an example might bea box store, such as Wal-Mart, or drivers in atrucking company). In the intensive workflowstructure, tasks are interdependent, requiringhighly synchronized interaction and communi-cation, and the human capital resource is thecombination of member KSAOs and relation-ships (an example might be a surgery team ora team-based manufacturing organization,such as Toyota). Collective turnover will bemuch more damaging in the intensive work-flow structure (e.g., where members must relyon each other) because there is greater disrup-tion of member communication and coordina-tion (Shaw et al., 2005).

FIGURE 2The Effect of Environmental Complexity on Collective Turnover

Types of workflow structure

Pooled Sequential Reciprocal Intensive

Individual-collective isomorphism

Greater Lesser

Consequences of turnover

Lesser Greater

Multilevel comparison of turnover’s consequences

Individual = collective Individual < collective

Simple Environmental complexity Complex

i j k i j k i k

j

i j k

Sum


Proposition 9: Environmental com-plexity will moderate the collectiveturnover–unit performance relation-ship. As environmental complexity in-creases, the direct effect of collectiveturnover on unit performance will be-come stronger.

Environmental complexity also interactswith collective turnover to moderate the hu-man capital resource–unit performance rela-tionship. We noted earlier how, depending onthe specific combination of quantitative andqualitative components, collective turnovercan strengthen or weaken the human capitalresource–unit performance relationship. Thismoderating effect is further compounded byenvironmental complexity.

Collective turnover produces a stronger mod-erating effect on the human capital resource–unit performance relationship as environmentalcomplexity increases, because in highly com-plex environments units must have diverse yetcomplementary types of human capital to effec-tively accomplish the unit’s task (Bell, 2007; Koz-lowski, Gully, Nason, & Smith, 1999; Steiner,1972). Unit-specific human capital, the proximalhuman capital determinant of unit performance(Hatch & Dyer, 2004), becomes intertwinedwithin unit members and within a particulartask context, which facilitates member pacing,synchronization, and coordination (Liebeskind,1996). Therefore, when collective turnover oc-curs, complementary forms of human capital arelost, and replacements need sufficient time formembers to adapt and learn each other’s rou-tines (Marks et al., 2001). For example, the retailindustry experiences enormous (often over 50percent) annual turnover without a fatal effecton profitability because in many retail indus-tries employees can be replaced relatively eas-ily, and the change in personnel may not affectefficiency as greatly as it would in a more com-plex environment. On the other hand, withinintensive contexts, collective turnover can ne-gate the effects of human capital resources onunit performance. For instance, if astronautssuffered similarly high levels of collective turn-over, it could make mission completion unten-able because of the time it takes to train andassimilate astronauts into situations whereclose coordination is needed among teammembers.

Proposition 10: There is a three-wayinteraction among collective turnover,environmental complexity, and hu-man capital resources. The moderat-ing effect of collective turnover on thehuman capital resource–unit perfor-mance relationship will increase asenvironmental complexity increases.

For both Propositions 9 and 10, it is also likelythat various combinations of quantitative andqualitative components of collective turnoverwill produce different consequences in differenttypes of environmental complexity. High rates ofcollective turnover or loss of high-quality em-ployees is likely to always be problematic, butthe magnitude may be greater as environmentalcomplexity increases. For example, losing high-quality employees may be relatively unimport-ant within a pooled workflow, but the same turn-over may be fatal in an intensive workflow.Similarly, losing low-quality employees may nothave as much effect in a pooled workflow as inan intensive workflow. As discussed, this isbecause of the highly coordinative nature of in-tensive task demands and because collectiveturnover has greater consequences when envi-ronmental complexity increases. Indeed, re-search suggests that unit performance in inten-sive task environments is often determined bythe lowest-ability members (e.g., Steiner, 1972);hence, removing these employees may help unitperformance. For instance, LePine, Hollenbeck,Ilgen, and Hedlund (1997) found that the “weak-est link” on a team negatively influenced theteam’s performance. Thus, in situations wherecollective turnover removes the weakest per-formers, unit performance may improve.

Proposition 11a: The quantitative andqualitative components of collectiveturnover will interact with environ-mental complexity to influence the di-rect effects of collective turnover onunit performance.

Proposition 11b: The quantitative andqualitative components of collectiveturnover will interact with environ-mental complexity to influence themoderating effects of collective turn-over on the human capital resource–unit performance relationship.


In concluding our CET theory discussion, werevisit the idea that prompted the need for atheory of collective turnover: the partial iso-morphism of collective and individual turn-over. By understanding the role of context, it ispossible to identify conditions where collec-tive and individual turnover are likely to pro-duce similar consequences and where theyare likely to be different. Only in low-complex-ity environments (pooled workforce structure),in a single point in time, should collective andindividual turnover produce very similar con-sequences, because in this situation individ-ual KSAOs and turnover are simply pooled orsummed to the unit level (see Figure 2). Hence,given that members are independent, individ-ual-level turnover consequences may be sim-ilar to the unit level (although not identical,and we maintain that the conceptual meaningof collective and individual turnover likewisediffers). However, as environmental complex-ity increases and/or as time is considered, in-dividual turnover underestimates collectiveturnover effects. This occurs because of theinteractive and synchronous nature of theunit’s environmental complexity, and, hence,the losses that occur because of turnoverare not simply KSAOs and performance butalso losses of knowledge within a social net-work (Shaw et al., 2005). The more the unit’sperformance depends on member coordina-tion, the greater the consequences of collec-tive turnover will be, but such interdepen-dence cannot be observed or detected at theindividual level or in a single time period(Dess & Shaw, 2001). For instance, in serviceenvironments where workers need to rely oneach other, increased turnover rates lead topoorer communication within groups, result-ing in lower customer satisfaction (Kacmar etal., 2006).

Proposition 12a: As environmentalcomplexity increases, individual-levelturnover consequences increasinglywill underestimate collective turnoverconsequences.

Proposition 12b: As observation peri-ods increase, individual-level turn-over consequences increasingly willunderestimate collective turnoverconsequences.

INVOLUNTARY AND VOLUNTARYTURNOVER AS SPECIAL CASES OF

COLLECTIVE TURNOVER

Voluntary and involuntary turnover representtwo distinct constructs of individual-level turn-over (McElroy et al., 2001; Shaw et al., 1998). Atthe individual level, voluntary turnover is con-sidered more essential than involuntary turn-over (e.g., Trevor et al., 1997; Williams & Living-stone, 1994) because units have less control overvoluntary turnover than involuntary turnover(Dalton et al., 1982). Hence, most individual-levelresearch focuses on the predictors of voluntaryturnover, and these two constructs are usuallyconsidered independently of each other (Mal-tarich et al., 2010).

However, within CET theory we do not distin-guish between voluntary and involuntary turn-over. Collective turnover is the depletion of hu-man capital resources. As such, the reason forthe turnover is not as relevant in terms of howhuman capital influences unit performance;what matters for collective turnover is the quan-tity and quality of human capital depletion. Thisis an important difference because it providesthe opportunity to generate a more parsimoni-ous explanation of collective turnover. For ex-ample, it is likely that a major difference be-tween involuntary and voluntary turnover is thequalitative component captured in CET theorysuch that involuntary turnover reflects lower-quality KSAOs while voluntary turnover reflectshigher-quality KSAOs.9 If true, then CET incor-porates both involuntary and voluntary turnoverinto a more concise theoretical framework andfurther allows precise predictions to be made forwhen the two types of turnover will be similarand different.

We recognize that distinctions between invol-untary and voluntary turnover remain importantat the individual level. Furthermore, distinctionsbetween the causes of involuntary and volun-tary collective turnover may contribute to under-standing the causes and consequences of cli-mate erosion and turnover contagion. Eachturnover type may also have different influenceson later turnover. For example, high voluntarycollective turnover may beget more voluntaryturnover because of the shock of employees’ de-parture, which may, in turn, cause the remain-

9 We thank an anonymous reviewer for this suggestion.


ing employees to reconsider their role with theunit (Lee & Mitchell, 1994). Voluntary turnovermay also reduce job embeddedness if it is well-liked employees who are leaving (Lee et al.,2004). In contrast, involuntary collective turnover(e.g., when disruptive members are forced out)may reduce subsequent voluntary turnoveramong the remaining higher-performing em-ployees and positively affect unit performance(Nyberg, 2010). Therefore, we expect differencesbetween involuntary and voluntary turnover tomatter, but what matters most for understandingthe effects of collective turnover is the quantityand quality of human capital resource deple-tion. This assumption is obviously one of con-siderable scholarly interest and should betested with empirical research.

IMPLICATIONS

CET theory ties turnover constructs togetheracross levels of analysis to form a holistic modelof collective turnover. The theory posits a simplebut profound observation: collective turnover isthe quantity and quality of KSAO depletion fromthe unit. As a form of human capital resourcedepletion, collective turnover contains bothquantitative and qualitative components. De-pending on the specific combination of quantityand quality, collective turnover may producepositive or negative effects on unit performance.Collective turnover may also strengthen orweaken the human capital resource–unit perfor-mance relationship. CET theory incorporatestime by showing how human capital resourcesand collective turnover are related dynamicallyand reciprocally. Additionally, CET theoryshows that as environmental complexity in-creases, the consequences of collective turnoverbecome greater and diverge more from individ-ual-level turnover effects.

Further, CET theory shows how the nature andconsequences of collective turnover cannot bedivorced from the nomological network of hu-man capital resources. Embedding collectiveturnover in this nomological network helps toexplain inconsistencies in the empirical litera-ture, including when collective turnover willhave positive, negative, or no relationship withunit performance and when collective turnoverwill weaken or strengthen the human capital–unit performance relationship. Moreover, collec-tive turnover does more than deplete human

capital; it also erodes climate and disrupts unitcoordination and communication (Dess & Shaw,2001). Through embedding collective turnoverwithin the nomological network of human capi-tal resources, CET theory offers a number ofimplications for understanding past researchand directing future research.

Understanding Past Research

CET theory offers a means to explain discrep-ant findings in the extant turnover literature. Forexample, individual-level turnover researchsuggests that involuntary turnover positively af-fects performance, but collective turnover re-search suggests that all turnover is negativelyrelated to unit performance (Hausknecht &Trevor, 2011). At the individual level, involuntaryturnover is assumed to result in poor performers’being replaced by higher performers, but even iftrue, this ignores collective phenomena and pro-cesses. For example, CET theory suggests thatcollective turnover may (1) be detrimental to cli-mate and disruptive to human capital resourceemergence (Proposition 4), (2) lower the stock ofhuman capital resources to the point of dimin-ishing a human capital effect (Proposition 2),and (3) affect unit performance differentially de-pending on the flows of the human capital re-source in and out of the unit (Propositions 5, 6a,and 6b), as well as the timing of the turnover(Proposition 7). Thus, CET theory explains whyinvoluntary collective turnover’s impact will dif-fer from individual-level findings; one reason isthat collective turnover disrupts unit coordina-tion, and such disruptions cannot be observed ordetected at the individual level.

Another example concerns potential benefitsof moderate voluntary turnover (Dalton et al.,1982; Hollenbeck & Williams, 1986). At the indi-vidual level, it is expected that voluntary turn-over is good if replacements are higher qualityor if new ideas are brought to the unit. This isreasonable when considering an individual re-placement; however, at the collective level, itlikely depends on many other factors, includingthe quality and quantity of the mix of collectiveturnover (Propositions 1 and 2), the flow of col-lective turnover (Propositions 5, 6a, and 6b), theunit’s climate (Proposition 8), and the environ-mental complexity of the unit (Propositions 9, 10,11a, and 11b). Thus, CET theory explains whenand why collective turnover’s impact can be


positive or negative; the replacement quality ofhuman capital is only one small part of a totalstory that cannot be observed or detected at theindividual level.

Directions for Future Research

Going forward, CET theory raises many ques-tions that should be tested to examine its valid-ity and to extend its contribution. For example,what is the relative importance of quality versusquantity in explaining collective turnover ef-fects, and does this relative importance changeover time or by context? It will also be useful toempirically examine the role of context (climateand environmental complexity). For example,does climate act as a buffer or magnifier ofcollective turnover’s effects on unit perfor-mance? Rigorous empirical testing of the predic-tions specified in this article will be necessaryto garner confidence in the theory.

CET theory also opens several new researchdirections. One immediate and potentially far-reaching implication is for scholars to study theeffects of human capital and turnover jointly.For too long human capital and turnover havebeen studied in relative isolation, as thoughthey are independent phenomena. When viewedfrom resource-based or human capital perspec-tives, human capital resources and collectiveturnover are clearly distinct yet related, and tostudy one while neglecting the other seems lesslikely to illuminate the nature of both. Althoughsome human capital resource studies control forturnover and many turnover studies control forelements of human capital, researchers shouldstudy both phenomena simultaneously (e.g.,Kacmar et al., 2006), particularly their dynamicand reciprocal relationship over time (e.g.,Sacco & Schmitt, 2005; Van Iddekinge et al.,2009). Examining the dynamic relationships be-tween human capital resources and collectiveturnover is vital for understanding collectiveturnover’s consequences. We would further addthat research should refine CET by consideringrelationships between generic and specific hu-man capital resources and collective turnover.For example, collective turnover may morestrongly moderate the specific human capital–unit performance relationship than the generichuman capital–unit performance relationship.

Another area of study is the several likelyantecedents of collective turnover. First, climate

may be the most proximal antecedent of collec-tive turnover (Ostroff et al., 2003). This is impor-tant (and parsimonious) because it means thatorganizational and HR policies, practices, andprocedures may (at least partially) influence col-lective turnover indirectly through climate. Sec-ond, researchers should examine the cross-levelantecedents of individual turnover to see howthey relate to the emergence of collective turn-over. Climate will be a likely influence, but sotoo will economic factors (e.g., unemploymentrates) and psychological factors (e.g., commit-ment). Turnover research has largely been con-ducted within level, either individual or collec-tive, and more could be understood about bothby testing cross-level turnover models.

Studies should also investigate the relation-ship between KSAOs and individual perfor-mance within CET theory. In the interest ofspace and parsimony, and consistent with re-source-based theory and human capital re-source emergence (Coff & Kryscynski, 2011; Ploy-hart & Moliterno, 2011), we did not elaborate onthe role of individual performance in CET the-ory. Empirical relationships between KSAOsand individual performance are moderate(Schmidt & Hunter, 1998), and the two constructdomains are not identical. Thus, KSAOs andperformance have different roles within re-source-based theory and CET theory. For in-stance, while intelligence is correlated with in-dividual performance (Schmidt & Hunter, 1998),performance depends on additional features,such as motivation and opportunity (Campbellet al., 1970; Nyberg, Fulmer, Gerhart, & Carpen-ter, 2010; Vroom, 1964). Future research shouldexamine the potential ramifications of exclud-ing “individual” performance from CET theory. Itwill also be useful to examine social capital’srole in relation to specific KSAOs, because so-cial and human capital are likely linked (Lepak& Shaw, 2008) since collective turnover can di-minish social capital (Dess & Shaw, 2001), andthese diminished unit-level relationships canalso lead to additional collective turnover, thusfurther damaging unit performance.

CET theory differences in various contextsshould also be explored. Collective turnoverwithin one level (e.g., small groups and teams orlower-skill jobs) may have both different ante-cedents and different consequences than collec-tive turnover at other levels (e.g., organization orhigher-skill jobs). This raises two potential is-


sues. First, collective turnover may have differ-ent characteristics at two different unit levels(e.g., when considered at the team versus theorganization level). Second, bundling multiplelevels (or jobs) together when investigating col-lective turnover may lead to erroneous conclu-sions. That is, researchers may be wise to con-sider jobs or bundles of jobs with similarcomplexity levels when conducting collectiveturnover research because it could be that com-plexity is a driving force in determining the con-sequences of collective turnover. If this is true,then combining jobs or levels of different com-plexities may mask consequences. For example,if there is a minimal effect of collective turnoverin lower-complexity jobs but a strong effect ofcollective turnover in more complex jobs, bun-dling them may underestimate the importanceof collective turnover in the more complex jobsand overestimate the effect of collective turn-over in less complex jobs. Moving in this direc-tion, Trevor and Nyberg (2008) examined exemptand nonexempt employees separately to ex-plore the ramifications of downsizing on subse-quent collective voluntary turnover for these twodistinct groups. However, a more nuanced focuson the quality of employees leaving or the spe-cific jobs (and the relevance of those specificjobs to the unit) will provide a greater under-standing of collective turnover’s impact on theunit’s performance.

Finally, in future research there should be amore fine-grained consideration of time and thetiming of collective turnover and human capitalresource replacements. We focused broadly onthe concept of resource and collective turnoverstocks and flows, but clearly when collectiveturnover occurs may also be important. For ex-ample, Hausknecht et al. (2009) found that turn-over was more disruptive when there was agreater concentration of newcomers, suggestingthat the timing of collective turnover may affectits consequences. One may also consider theconsequences of the time needed to fill a va-cancy. Our discussion of flows and human cap-ital resource erosion versus expansion assumedreplacements could be found quickly, but thismay oftentimes be untenable for key positions.Indeed, long delays in finding adequate re-placements may further magnify collective turn-over’s consequences. Thus, researchers shouldalso consider the duration of vacancies or wherein the unit’s development cycle collective turn-

over occurs. Furthermore, the timing of collectiveturnover is likely to have different consequencesdepending on environmental complexity such thattiming matters more as complexity increases.

Measurement and Analysis Implications

CET theory offers a means to provide a morecomplete understanding of collective turnoverand, consequently, has several measurementramifications. Collective turnover is primarilymeasured using rates, which is effectively thesum of the individual turnover decisions(Hausknecht & Trevor, 2011; Shaw, 2011). How-ever, at the collective level, attention to howturnover impacts unit performance may be bet-ter served by focusing on the quality and quan-tity of the employees who collectively leave, be-cause the loss of KSAO competence (quality) islikely to be more important than a simple turn-over rate, and the qualitative component bettercaptures the nature of KSAO depletion from hu-man capital resources.

Rather than only operationalizing collectiveturnover as a rate, researchers should, whenfeasible, consider collective turnover in terms ofquantity and quality. That is, for a given KSAOtype or composite of KSAOs, collective turnoverinvolves two important dimensions: rate andquality. KSAO types may range from cognitive(e.g., ability, knowledge, skill) to noncognitive(e.g., personality, interests, values; see Ployhart& Moliterno, 2011). Consequently, future re-search should explore measures of collectiveturnover that capture both dimensions whilepaying attention to the type of KSAO being mea-sured. One approach is to develop a weightedcomposite of collective turnover, where theKSAO scores of those who quit are weighted bytheir sample size. Another approach is to extendexisting rate metrics. For example, for a specifictype of KSAO, measures may be constructed thatoperationalize rate and quality, such as [(num-ber quit � KSAO scores of those who quit)/(totalnumber in unit)]. A final approach is to measurethe KSAOs of interest, similar to Shaw et al.(2005) in their study of social networks and col-lective turnover (see also Hausknecht & Hol-werda, in press, for further discussion of this is-sue).

In terms of data and analysis, there are sev-eral ways to provide a richer operationalizationof collective turnover within the human capital


nomological network. For example, if KSAOscores are available for employees, and if it ispossible to identify who is still employed andwho has left, one may create two variables. Thehuman capital resource variable can be createdby aggregating individual KSAOs to the unitlevel for those individuals still employed (asdone in Ployhart et al., 2009). The collective turn-over variable can be created by using the aboveoperationalization—specifically, collective turn-over � �1 * [(number quit � KSAO scores ofthose who quit)/(total number in unit)]. Thesetwo variables can then be used independentlyand/or interactively to examine unit perfor-mance consequences. The weighting by �1 forcollective turnover represents the depletion orloss of human capital resources. Because theKSAO scores of human capital and collectiveturnover are based on different people, the cor-relation between these two variables will likelybe considerably less than 1 and negative.

These are just examples, and it is beyond thescope of this article to develop or propose spe-cific measures. However, the possibilities af-forded by CET theory offer many directions fordeveloping and evaluating future collectiveturnover measures (see also Hausknecht & Hol-werda, in press). That said, it will not always bepossible to measure quantity and quality in asingle study. For example, it may not be possi-ble to measure the KSAOs of interest, or one mayneed to rely on proxy measures (e.g., GPA) ratherthan individual KSAO scores (intelligence). Itmay not even be possible to obtain measures ofproxies. Testing CET theory will require re-searchers to follow methodologies similar tomultilevel strategic HRM studies, such as Shawet al. (2005), Takeuchi, Tesluk, Yun, and Lepak(2005), Nishii, Lepak, and Schneider (2008), andPloyhart et al. (2009). Such studies are challeng-ing but not impossible. When such rich, multi-level data collections are not possible, we be-lieve there is still value in studies measuring asingle component of collective turnover, but au-thors should take additional lengths to at leasttheorize and conceptualize the latent nature oftheir collective turnover measure (e.g., whattypes of KSAOs are likely captured in the turn-over rate measure). Similarly, if a researcheronly has access to a single type of KSAO, itwould be helpful to the readers if the researcherwould consider the potential consequences in

the study’s limitations and directions for futureresearch.

CONCLUSION

Despite the organizational ramifications ofturnover, most turnover research remains at theindividual level (Holtom et al., 2008), and collec-tive turnover research is stinted by a lack ofcollective turnover theory (Hausknecht & Trevor,2011; Shaw, 2011). This article addresses thisdeficiency by developing a theory of collectiveturnover. CET theory defines collective turnoveras the quantity and quality of KSAO depletionfrom the unit. We ground collective turnover the-ory within the RBV, thus proposing that collec-tive turnover is inherently the depletion of hu-man capital resources. Consequently, CETtheory emphasizes the emergent and dynamicnature of collective turnover and its reciprocalrelationship with human capital resources overtime. CET theory also integrates contextual (cli-mate and environmental complexity) influenceson collective turnover. In sum, CET is a theory ofcontext-emergent turnover and posits that, as aform of human capital resource depletion, col-lective turnover contains both quantitative andqualitative components.

Developing a holistic theory of collective turn-over expands our ability to explore collectiveturnover antecedents and consequences inways that have not been fully recognized. CETtheory provides a framework for theorizingabout collective turnover, explaining past em-pirical collective turnover inconsistencies, andaltering the focus of collective turnover from anexclusive examination of individual leavers toan expanded focus on human capital resourcedepletion. Collective turnover is of great theo-retical and practical interest, and it is our hopethat CET theory facilitates a greater under-standing of this important organizationalphenomenon.

REFERENCES

Barney, J. B. 1991. Firm resources and sustained competitiveadvantage. Journal of Management, 17: 99–120.

Batt, R. 2002. Managing customer services: Human resourcepractices, quit rates, and sales growth. Academy ofManagement Journal, 45: 587–597.

Becker, B., & Huselid, M. 2006. Strategic human resourcesmanagement: Where do we go from here? Journal ofManagement, 32: 898–925.


Bell, B. S., & Kozlowski, S. W. J. 2002. A typology of virtualteams: Implications for effective leadership. Group &Organization Management, 27: 14–49.

Bell, S. T. 2007. Deep-level composition variables as predic-tors of team performance: A meta-analysis. Journal ofApplied Psychology, 92: 395–415.

Bliese, P. 2000. Within-group agreement, non-independence,and reliability: Implications for data aggregation andanalysis. In K. J. Klein & S. W. Kozloski (Eds.), Multileveltheory, research, and methods in organizations: Founda-tions, extensions, and new directions: 349–381. San Fran-cisco: Jossey-Bass.

Bowen, D. E., & Ostroff, C. 2004. Understanding HRM–firmperformance linkages: The role of the strength of theHRM system. Academy of Management Review, 29: 203–221.

Campbell, J. J., Dunnette, M. D., Lawler, E. E., & Weick, K. E.1970. Managerial behavior, performance, and effective-ness. New York: McGraw-Hill.

Carr, J. Z., Schmidt, A. M., Ford, J. K., & DeShon, R. P. 2003.Climate perceptions matter: A meta-analytic path anal-ysis relating molar climate, cognitive and affectivestates, and individual level work outcomes. Journal ofApplied Psychology, 88: 605–619.

Coff, R., & Kryscynski, D. 2011. Drilling for micro-foundationsof human capital based competitive advantages. Jour-nal of Management, 37: 1429–1448.

Colquitt, J. A., Scott, B. A., & LePine, J. A. 2007. Trust, trust-worthiness, and trust propensity: A meta-analytic test oftheir unique relationships with risk taking and job per-formance. Journal of Applied Psychology, 92: 909–927.

Crook, T. R., Todd, S. Y., Combs, J. G., Woehr, D. J., & Ketchen,D. J., Jr. 2011. Does human capital matter? A meta-analysis of the relationship between human capital andfirm performance. Journal of Applied Psychology, 96:443–456.

Dalton, D. R., & Todor, W. D. 1979. Turnover turned over: Anexpanded and positive perspective. Academy of Man-agement Review, 4: 225–235.

Dalton, D. R., Todor, W. D., & Krackhardt, D. M. 1982. Turnoveroverstated: A functional taxonomy. Academy of Man-agement Review, 7: 117–123.

Delery, J., & Shaw, J. 2001. The strategic management ofpeople in work organizations: Review, synthesis, andextension. Research in Personnel and Human ResourcesManagement, 20: 165–198.

Dess, G. G., & Shaw, J. D. 2001. Voluntary turnover, socialcapital, and organizational performance. Academy ofManagement Review, 26: 446.

Dierickx, I., & Cool, K. 1989. Asset stock accumulation andsustainability of competitive advantage. ManagementScience, 35: 1504–1511.

Eisenhardt, K. M., & Martin, J. A. 2000. Dynamic capabilities:What are they? Strategic Management Journal, 21: 1105–1121.

Felin, T., & Hesterly, W. S. 2007. The knowledge-based view,nested heterogeneity, and new value creation: Philo-

sophical considerations on the locus of knowledge.Academy of Management Review, 32: 195–218.

Felps, W., Mitchell, T. R., Hekman, D. R., Lee, T. W., Holtom,B. C., & Harman, W. S. 2009. Turnover contagion: Howcoworkers’ job embeddedness and job search behaviorsinfluence quitting. Academy of Management Journal, 52:545–561.

Foss, N. J. 2011. Micro-foundations for the resource-basedview? Journal of Management, 37: 376–395.

Gardner, T. M., Wright, P. M., & Moynihan, L. M. 2011. Theimpact of motivation, empowerment, and skill enhanc-ing practices on aggregate voluntary turnover: The me-diating effect of collective affective commitment. Per-sonnel Psychology, 64: 315–350.

George, J. M., & Bettenhausen, K. 1990. Understanding proso-cial behavior, sales performance, and turnover: A group-level analysis in a service context. Journal of AppliedPsychology, 75: 698–709.

Gerhart, B. 1990. Voluntary turnover and alternative job op-portunities. Journal of Applied Psychology, 75: 467–476.

Gerhart, B. 2005. Human resources and business perfor-mance: Findings, unanswered questions, and an alter-native approach. Management Revue, 16: 174–185.

Glebbeek, A. C., & Bax, E. H. 2004. Is high employee turnoverreally harmful? An empirical test using company re-cords. Academy of Management Journal, 47: 277–286.

Griffeth, R. W., Hom, P. W., & Gaertner, S. 2000. A meta-analysis of antecedents and correlates of employeeturnover: Update, moderator tests, and research impli-cations for the next millennium. Journal of Management,26: 463–488.

Hackman, J. R. 1987. The design of work teams. In J. Lorsch(Ed.), Handbook of organizational behavior: 315–342.Englewood Cliffs, NJ: Prentice-Hall.

Hancock, J. I., Allen, D. G., Bosco, F. A., McDaniel, K. M., &Pierce, C. A. In press. Meta-analytic review of employeeturnover as a predictor of firm performance. Journal ofManagement.

Hatch, N. W., & Dyer, J. H. 2004. Human capital and learningas a source of sustainable competitive advantage. Stra-tegic Management Journal, 25: 1155–1178.

Hausknecht, J. P., & Holwerda, J. A. In press. When doesemployee turnover matter? Dynamic member configura-tions, productive capacity, and collective performance.Organization Science.

Hausknecht, J. P., & Trevor, C. O. 2011. Collective turnover at thegroup, unit, and organizational levels: Evidence, issues,and implications. Journal of Management, 37: 352–388.

Hausknecht, J. P., Trevor, C. O., & Howard, M. J. 2009. Unit-level voluntary turnover rates and customer servicequality: Implications of group cohesiveness, newcomerconcentration, and size. Journal of Applied Psychology,94: 1068–1075.

Hollenbeck, J. R., & Williams, C. R. 1986. Turnover function-ality versus turnover frequency: A note on work attitudesand organizational effectiveness. Journal of AppliedPsychology, 71: 606–611.


Holtom, B. C., Mitchell, T. R., Lee, T. W., & Eberly, M. B. 2008.Turnover and retention research: A glance at the past, acloser review of the present, and a venture into thefuture. Academy of Management Annals, 2: 231–274.

Hom, P. W., Caranikas-Walker, F., Prussia, G. E., & Griffeth,R. W. 1992. A meta-analytical structural equations anal-ysis of a model of employee turnover. Journal of AppliedPsychology, 77: 890–909.

Hulin, C. L. 1991. Adaptation, persistence, and commitmentin organizations. In M. D. Dunnette & L. M. Hough (Eds.),Handbook of industrial and organizational psychology:445–505. Palo Alto, CA: Consulting Psychologists Press.

Hulin, C. L., Roznowski, M., & Hachiya, D. 1985. Alternativeopportunities and withdrawal decisions: Empirical andtheoretical discrepancies and an integration. Psycho-logical Bulletin, 97: 233–250.

Huselid, M. 1995. The impact of human resource manage-ment practices on turnover, productivity, and corporatefinancial performance. Academy of Management Jour-nal, 38: 635–672.

James, L. R., & Jones, A. P. 1974. Organizational climate: Areview of theory and research. Psychological Bulletin,81: 1096–1112.

Kacmar, K. M., Andrews, M. C., Van Rooy, D. L., Steilberg,R. C., & Cerrone, S. 2006. Sure everyone can be replaced. . . but at what cost? Turnover as a predictor of unit-levelperformance. Academy of Management Journal, 49: 133–144.

Katz, D., & Kahn, R. 1978. The social psychology of organiza-tions (2nd ed.). Hoboken, NJ: Wiley.

Kozlowski, S., & Klein, K. 2000. A multilevel approach totheory and research in organizations: Contextual, tem-poral, and emergent processes. In K. J. Klein & S. W.Kozlowski (Eds.), Multilevel theory, research, and meth-ods in organizations: Foundations, extensions, and newdirections: 3–90. San Francisco: Jossey-Bass.

Kozlowski, S. W. J., Gully, S. M., Nason, E. R., & Smith, E. M.1999. Developing adaptive teams: A theory of compila-tion and performance across levels and time. In D. R.Ilgen & E. D. Pulakos (Eds.), The changing nature of workperformance: Implications for staffing, personnel ac-tions, and development: 240–292. San Francisco: Jossey-Bass.

Kozlowski, S. W. J., & Ilgen, D. R. 2006. Enhancing the effec-tiveness of work groups and teams. Psychological Sci-ence in the Public Interest, 7: 77–124.

Lee, T., & Mitchell, T. 1994. An alternative approach: Theunfolding model of voluntary employee turnover. Acad-emy of Management Review, 19: 51–89.

Lee, T., Mitchell, T., Sablynski, C., Burton, J., & Holtom, B.2004. The effects of job embeddedness on organizationalcitizenship, job performance, volitional absences, andvoluntary turnover. Academy of Management Journal,47: 711–722.

Lepak, D. P., Liao, H., Chung, Y., & Harden, E. E. 2006. Aconceptual review of human resource management sys-tems in strategic human resource management re-

search. Research in Personnel and Human ResourcesManagement, 25: 217–271.

Lepak, D. P., & Shaw, J. D. 2008. Strategic HRM in NorthAmerica: Looking to the future. International Journal ofHuman Resource Management, 19: 1486–1499.

LePine, J. A., Hollenbeck, J. R., Ilgen, D. R., & Hedlund, J. 1997.The effects of individual differences on the performanceof hierarchical decision making teams: Much more thanG. Journal of Applied Psychology, 82: 803–811.

Liebeskind, J. P. 1996. Knowledge, strategy, and the theory ofthe firm. Strategic Management Journal, 17: 93–107.

Maltarich, M. A., Nyberg, A. J., & Reilly, G. 2010. A conceptualand empirical analysis of the cognitive ability–voluntary turnover relationship. Journal of Applied Psy-chology, 95: 1058–1070.

Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. 2001. A temporallybased framework and taxonomy of team processes.Academy of Management Review, 26: 356–376.

Maxwell, S. E., & Cole, D. A. 2007. Bias in cross-sectionalanalyses of longitudinal mediation. PsychologicalMethods, 12: 23–44.

McElroy, J. C., Morrow, P. C., & Rude, S. N. 2001. Turnover andorganizational performance: A comparative analysis ofthe effects of voluntary, involuntary, and reduction-in-force turnover. Journal of Applied Psychology, 86: 1294–1299.

Mobley, W. H. 1982. Employee turnover. Reading, MA: Addi-son-Wesley.

Molloy, J., Chadwick, C., Ployhart, R. E., & Golden, S. 2011.Making “intangibles” tangible: A cross-disciplinary re-view and multilevel framework for future research. Jour-nal of Management, 37: 1299–1315.

Morrow, P. C., & McElroy, J. C. 2007. Efficiency as a mediatorin turnover-organizational performance relations. Hu-man Relations, 60: 827–849.

Nishii, L., Lepak, D., & Schneider, B. 2008. Employee attribu-tions of the “why” of HR practices: Their effects on em-ployee attitudes and behaviors, and customer satisfac-tion. Personnel Psychology, 61: 503–545.

Nyberg, A. J. 2010. Retaining your high performers: Moderatorsof the performance–job satisfaction–voluntary turnover re-lationship. Journal of Applied Psychology, 95: 440–453.

Nyberg, A. J., Fulmer, I. S., Gerhart, B., & Carpenter, M. A.2010. Agency theory revisited: CEO return and share-holder interest alignment. Academy of ManagementJournal, 53: 1029–1049.

Nyberg, A. J., Moliterno, T. P., Hale, D., Jr., & Lepak, D. P. Inpress. Resource-based perspectives on unit-level hu-man capital: A review and integration. Journal of Man-agement.

Ostroff, C. 1993. The effects of climate and personal influ-ences on individual behavior and attitudes in organiza-tions. Organizational Behavior and Human DecisionProcesses, 56: 56–90.

Ostroff, C., Kinicki, A. J., & Tamkins, M. M. 2003. Organiza-tional culture and climate. Handbook of psychology:565–593. New York: Wiley.


Park, T. Y., & Shaw, J. D. 2011. Turnover rates and organiza-tional performance: A meta-analysis. Paper presented atthe annual meeting of the Academy of Management.San Antonio, TX.

Ployhart, R., Weekley, J., & Baughman, K. 2006. The structureand function of human capital emergence: A multilevelexamination of the attraction-selection-attrition model.Academy of Management Journal, 49: 661–677.

Ployhart, R., Weekley, J., & Ramsey, J. 2009. The consequencesof human resource stocks and flows: A longitudinal exam-ination of unit service orientation and unit effectiveness.Academy of Management Journal, 52: 996–1015.

Ployhart, R. E., & Moliterno, T. P. 2011. Emergence of thehuman capital resource: A multilevel model. Academyof Management Review, 36: 127–150.

Reichers, A. E., & Schneider, B. 1990. Climate and culture: Anevolution of constructs. In B. Schneider (Ed.), Organiza-tional climate and culture: 5–39. San Francisco: Jossey-Bass.

Richard, P. J., Devinney, T. M., Yip, G. S., & Johnson, G.2009. Measuring organizational performance: Towardmethodological best practice. Journal of Management,35: 718 – 804.

Rousseau, D. M. 1985. Issues of level in organizational re-search: Multi-level and cross-level perspectives. Re-search in Organizational Behavior, 7: 1–37.

Sacco, J. M., & Schmitt, N. 2005. A dynamic multilevel modelof demographic diversity and misfit effects. Journal ofApplied Psychology, 90: 203–231.

Schmidt, F., & Hunter, J. 1998. The validity and utility ofselection methods in personnel psychology: Practicaland theoretical implications of 85 years of research find-ings. Psychological Bulletin, 124: 262–274.

Schneider, B. 1987. The people make the place. PersonnelPsychology, 40: 437–453.

Schneider, B., Bowen, D. E., Ehrhart, M. G., & Holcombe, K. M.2000. The climate for service: Evolution of a construct. InN. M. Ashkanasy, C. Wilderom, & M. F. Peterson (Eds.),Handbook of organizational culture and climate: 21–36.Thousand Oaks, CA: Sage.

Schneider, B., & Reichers, A. E. 1983. On the etiology ofclimates. Personnel Psychology, 36: 19–39.

Schneider, B., Smith, D. B., Taylor, S., & Fleenor, J. 1998.Personality and organizations: A test of the homogeneityof personality hypothesis. Journal of Applied Psychol-ogy, 83: 462.

Schwab, D. P. 1991. Contextual variables in employee per-formance-turnover relationships. Academy of Manage-ment Journal, 34: 966–975.

Shaw, J. D. 2011. Turnover rates and organizational perfor-mance. Organizational Psychology Review, 1: 187–213.

Shaw, J. D., Delery, J. E., Jenkins, G. D., Jr., & Gupta, N. 1998.An organizational-level analysis of voluntary and in-voluntary turnover. Academy of Management Journal,41: 511–525.

Shaw, J. D., Duffy, M. K., Johnson, J. J., & Lockhart, D. 2005.Turnover, social capital losses, and performance. Acad-emy of Management Journal, 48: 594–606.

Shaw, J. D., Gupta, N., & Delery, J. E. 2005. Alternative con-ceptualizations of the relationship between voluntaryturnover and organizational performance. Academy ofManagement Journal, 48: 50–68.

Shen, W., & Cannella, A. A. 2002. Power dynamics within topmanagement and their impacts on CEO dismissal fol-lowed by inside succession. Academy of ManagementJournal, 45: 1195–1206.

Siebert, W. S., & Zubanov, N. 2009. Searching for the optimallevel of employee turnover: A study of a large U.K. retailorganization. Academy of Management Journal, 52: 294–313.

Sirmon, D. G., Hitt, M. A., & Ireland, R. D. 2007. Managing firmresources in dynamic environments to create value:Looking inside the black box. Academy of ManagementReview, 32: 273–292.

Steiner, I. D. 1972. Group process and productivity. New York:Academic Press.

Subramaniam, M., & Youndt, M. A. 2005. The influence ofintellectual capital on the types of innovative capabili-ties. Academy of Management Journal, 48: 450–463.

Takeuchi, R., Marinova, S. V., Lepak, D. P., & Liu, W. 2005.A model of expatriate withdrawal-related outcomes:Decision making from a dualistic adjustment perspec-tive. Human Resource Management Review, 15: 119 –138.

Takeuchi, R., Tesluk, P. E., Yun, S., & Lepak, D. P. 2005. Anintegrative view of international experience. Academyof Management Journal, 48: 85–100.

Trevor, C. O. 2001. Interactions among actual ease-of-movement determinants and job satisfaction in theprediction of voluntary turnover. Academy of Manage-ment Journal, 44: 621– 638.

Trevor, C. O., Gerhart, B., & Boudreau, J. W. 1997. Volun-tary turnover and job performance: Curvilinearityand the moderating influences of salary growthand promotions. Journal of Applied Psychology, 82:44 – 61.

Trevor, C. O., & Nyberg, A. J. 2008. Keeping your headcountwhen all about you are losing theirs: Downsizing,voluntary turnover rates, and the moderating role ofHR practices. Academy of Management Journal, 51:259 –276.

Van de Ven, A. H., Delbecq, A. L., & Koenig, K., Jr. 1976.Determinants of coordination modes within organiza-tions. American Sociological Review, 41: 322–338.

Van Iddekinge, C. H., Ferris, G. R., Perrewé, P. L., Perryman,A. A., Blass, F. R., & Heetderks, T. D. 2009. Effects ofselection and training on unit-level performance overtime: A latent growth modeling approach. Journal ofApplied Psychology, 94: 829–843.

Vroom, V. H. 1964. Work and motivation. New York: Wiley.

Williams, C. R., & Livingstone, L. P. 1994. Another look atthe relationship between performance and voluntary


turnover. Academy of Management Journal, 37: 269 –298.

Wright, P. M., Dunford, B. B., & Snell, S. A. 2001. Humanresources and the resource based view of the firm. Jour-nal of Management, 27: 701–721.

Wright, P. M., & McMahan, G. C. 1992. Theoretical perspec-tives for strategic human resource management. Journalof Management, 18: 295–320.

Wright, P. M., & McMahan, G. C. 2011. Exploring humancapital: Putting human back into strategic human re-source management. Human Resource ManagementJournal, 21: 93.

Wright, P. M., McMahan, G. C., & McWilliams, A. 1994. Hu-man resources and sustained competitive advantage: Aresource-based perspective. International Journal of Hu-man Resource Management, 5: 301–326.

Anthony J. Nyberg ([email protected]) is an associate professor in theDarla Moore School of Business at the University of South Carolina. He received hisPh.D. from the University of Wisconsin–Madison. His research focuses on strategichuman resource management, human capital, compensation, and employee move-ment.

Robert E. Ployhart ([email protected]) is the Bank of America Professor of Busi-ness Administration at the Darla Moore School of Business, University of SouthCarolina. He received his Ph.D. from Michigan State University. His primary interestsinclude human capital, staffing, recruitment, and advanced statistical methods.


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Context-Emergent Turnover (CET) Theory: A Theory of Collective Turnover