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Interpersonal Deviance, Organizational Deviance, and Their CommonCorrelates: A Review and Meta-AnalysisChristopher M. Berry, Deniz S. Ones, and Paul R. Sackett

University of Minnesota, Twin Cities Campus

Interpersonal deviance (ID) and organizational deviance (OD) are highly correlated (R. S. Dalal, 2005). This,together with other empirical and theoretical evidence, calls into question the separability of ID and OD. Asa further investigation into their separability, relationships among ID, OD, and their common correlates weremeta-analyzed. ID and OD were highly correlated ( .62) but had differential relationships with key BigFive variables and organizational citizenship behaviors, which lends support to the separability of ID and OD.Whether the R. J. Bennett and S. L. Robinson (2000) instrument was used moderated some relationships. IDand OD exhibited their strongest (negative) relationships with organizational citizenship, Agreeableness,Conscientiousness, and Emotional Stability. Correlations with organizational justice were small to moderate,and correlations with demographic variables were generally negligible.

Keywords: counterproductive work behavior, workplace deviance, interpersonal deviance, organizationaldeviance

It has become popular in the workplace deviance literature tomake a distinction between interpersonal deviance (ID), whichencompasses deviant behaviors targeted toward individuals (e.g.,violence, gossip, theft from coworkers), and organizational devi-ance (OD), which encompasses deviant behaviors targeted towardthe organization (e.g., intentionally working slowly, damagingcompany property, sharing confidential company information),and to treat these as separate behavioral families. Although thisdichotomy originally arose from the multidimensional scalingstudy by Robinson and Bennett (1995), the case can be made thatmuch of its popularity stems from the development, validation, andpublication of a public-domain self-report measure of workplacedeviance that includes ID and OD subscales (Bennett & Robinson,2000). Indeed, the vast majority of research using the ID–ODdistinction uses the Bennett and Robinson (2000) self-report mea-sure or some variant of it. Despite the intuitive appeal of thedistinction, though, the foundation for it came mostly from em-ployees’ perceptions of the similarity of deviant workplace behav-iors (e.g., Gruys & Sackett, 2003; Robinson & Bennett, 1995)instead of from the more crucial evidence of actual covariationbetween deviant workplace behaviors (Ones & Viswesvaran,2003; Sackett & DeVore, 2002). Recent meta-analytic evidence(Dalal, 2005) addressing the actual covariation between ID andOD found that the two dimensions correlated highly ( r .70,

corrected for unreliability), calling into question whether theID–OD distinction is a meaningful one (at least as currentlymeasured). Therefore, a critical examination of the evidence forand against the ID–OD distinction is warranted. The followingsections of this article review the literature on the structure of workplace deviance, examine the empirical evidence for and

against the ID–OD distinction, and then propose a series of meta-analyses of relationships that ID and OD have with a common setof correlates to address questions about the construct validity of the ID–OD distinction.

Structure of Workplace Deviance

There have been many different perspectives on the structure of workplace deviance. One conceptualization is to view the deviancedomain as characterized by an overall deviance construct, withspecific deviant behavior domains (e.g., theft, lateness, harass-

ment; each commonly measured in terms of frequency of occur-rence) loading to differing degrees on an overall construct. Thepolar opposite of the single construct position is to view eachdeviant behavior domain as a discrete construct. Differentiating IDand OD constitutes a position between these two extremes. Aperspective differentiating between ID and OD creates behavioralfamilies larger than each specific behavior but smaller than anoverall deviance construct. If ID and OD relate differently tovarious behaviors of interest in organizations, they lend more focusand specificity to the study and prediction of such behaviorsbeyond the overall construct approach yet are more parsimoniousthan the approach that assumes separate constructs for each be-havior, which makes them potentially useful for the developmentof more systematic and integrative theories of workplace deviance.Whether facets such as ID and OD add to our understanding of deviance beyond the overall or separate construct approaches is inmany ways ultimately an empirical question.

An early attempt to group deviant behaviors into broader be-havioral categories was that by Hollinger (1986; Hollinger &Clark, 1982a, 1982b, 1983a, 1983b), who developed a two-category framework for the interrelationships of deviant behaviors.The first category of behaviors was labeled property deviance andreferred to organization-targeted acts and misuse of employerassets. The second category of behaviors was labeled productiondeviance and referred to violating norms about how work shouldbe carried out.

Christopher M. Berry, Deniz S. Ones, and Paul R. Sackett, Departmentof Psychology, University of Minnesota, Twin Cities Campus.

Correspondence concerning this article should be addressed to Christo-pher M. Berry, 75 East River Road, Minneapolis, MN 55455. E-mail:[email protected]

Journal of Applied Psychology Copyright 2007 by the American Psychological Association2007, Vol. ● , No. ● , 000– 000 0021-9010/07/$12.00 DOI: 10.1037/0021-9010. ● .● .000

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Robinson and Bennett (1995), noticing that Hollinger’s (1986;Hollinger & Clark, 1982a, 1982b, 1983a, 1983b) framework failedto address deviant behaviors of an interpersonal nature (e.g., sexualharassment and physical aggression), broadened the taxonomyand, using multidimensional scaling, produced a two-dimensionalsolution. The first dimension reflected a continuum of behaviors

ranging from relatively minor acts to more serious ones (severitydimension). The second dimension reflected a continuum indicat-ing the extent to which deviant behaviors were interpersonal andharmful to individuals versus noninterpersonal and harmful to theorganization (target dimension).

Crossing the two dimensions identified by Robinson and Ben-nett (1995) yielded four quadrants of deviant behaviors. Onequadrant contained serious and organizationally harmful behaviors(labeled property deviance ). A second quadrant contained rela-tively minor but still organizationally harmful behaviors (labeled production deviance ). A third quadrant contained relatively minorand interpersonally harmful behaviors (labeled political deviance ).The fourth quadrant contained serious and interpersonally harmfulbehaviors (labeled personal aggression ).

On the basis of Robinson and Bennett’s (1995) work, Bennettand Robinson (2000) developed and validated a self-report instru-ment of workplace deviance. This instrument contained two sub-scales based on the Robinson and Bennett (1995) target dimension:one subscale measuring ID, and one measuring OD. Confirmatoryfactor analyses were offered in support of the two-factor model of deviance, and evidence of convergent and discriminant validitywas presented.

Thus, there have been many conceptualizations of the structureof deviance. Dimensions reflecting distinctions among targets of the behavior, severity of the behavior, and task relevance (e.g.,Gruys & Sackett, 2003) of the behavior have been empiricallyidentified. Behavioral categories reflecting property deviance, pro-

duction deviance, political deviance, personal aggression, ID, OD,and many more specific categories (e.g., theft, attendance, drugand alcohol use) have been identified. Sackett and DeVore (2002)suggested a hierarchical model, with a general deviance factor atthe top, several group factors (e.g., ID and OD) below the generalfactor, and specific behavior domains (e.g., theft, attendance, drugand alcohol use) below these group factors.

Probably the most popular way the structure of deviance iscurrently conceptualized is the ID–OD factor model proposed byBennett and Robinson (2000). Although the ID–OD distinction hasenjoyed some support (e.g., Bennett & Robinson, 2000; Gruys &Sackett, 2003; Robinson & Bennett, 1995), so have other concep-tualizations. This calls into question why the ID–OD factor modelis currently so popular compared with all other models. Onepossibility is that its popularity arose in great part because of theease of measurement resulting from the existence of a publicdomain self-report instrument measuring ID and OD (i.e., Bennett& Robinson, 2000). Therefore, an examination of the evidence forand against the ID–OD distinction is warranted. The followingsection of this article provides such an examination.

Evidence for and Against the Distinction BetweenID and OD

When one is making the case for a distinction between twoconstructs (e.g., ID and OD), three lines of evidence are the most

common and compelling. First, for two constructs to be considereddistinct, they must not be too highly correlated (e.g., Campbell &Fiske, 1959). This calls into question what exactly is meant by “toohighly correlated.” It is difficult to point to one value as thisthreshold, but we posit that when the correlation between twoconstructs approaches values commonly agreed on as acceptable

for reliability coefficients (e.g., r .70 and higher), the distinc-tiveness of the two constructs becomes questionable. Mostly on thebasis of self-report measures of ID and OD, Dalal’s (2005) meta-analysis estimated that the two constructs have a corrected corre-lation of .70. This magnitude of correlation calls into question theseparability of ID and OD but is not by itself conclusive evidence.

The second commonly presented line of evidence for the dis-tinctiveness of two constructs is whether factor analyses of theircontent domains result in interpretable two-factor solutions. Atleast three studies (Bennett & Robinson, 2000; Lee & Allen, 2002;Sackett, Berry, Wiemann, & Laczo, 2005) have addressed theID–OD distinction by testing it against an overall deviance con-struct using factor analyses of deviance measures. Lee and Allen(2002) reported finding inadequate fit for the two-factor ID–ODmodel, whereas Bennett and Robinson (2000) and Sackett et al.(2005) reported adequate fit, but each of these three studies useddifferent cutoffs for determining adequate fit. Although there isdebate about what can be called adequate fit of a factor model(Maruyama, 1998), authors have suggested cutoff values for atleast two fit indexes used in all three studies: the normed fit index(.90; Bentler & Bonett, 1980) and the comparative fit index (.95;Hu & Bentler, 1999). According to these fit criteria, ID–ODtwo-factor models did not fit adequately in Bennett and Robinson(2000) or Lee and Allen (2002) but did fit adequately in Sackett etal. (2005). One possibility for this disparity is that Sackett et al.(2005) used a much larger sample size ( N 900) than did Bennettand Robinson (2000; N 143) or Lee and Allen (2002; N 155).

One cannot know for sure, though, whether sampling error is at theroot of differences between these specific factor-analytic results.Thus, it appears that the factor-analytic evidence for the ID–ODdistinction is inconclusive.

The final commonly presented line of evidence for the distinc-tiveness of two constructs is data demonstrating differential cor-relations with other constructs. Such evidence for self-report mea-sures of ID and OD was presented in the original validation studyof Bennett and Robinson’s (2000) measure. Bennett and Robinson(2000) listed observed correlations between ID and 17 variablesand between OD and the same 17 variables. If ID and OD scalesmeasure distinct constructs, they should show differing patterns of correlations with variables for which there is a theoretical basis forexpecting differences. Bennett and Robinson (2000) found thatvariables that possessed a more organizational orientation, such asproperty and production deviance (Hollinger & Clark, 1982b,1983a, 1983b), psychological and physical withdrawal (Lehman &Simpson, 1992), and the conscientiousness dimension of organi-zational citizenship behavior (OCB; Podsakoff, McKenzie, Moor-man, & Fetter, 1990), did indeed correlate more strongly with OD.Also, antagonistic work behaviors (Lehman & Simpson, 1992),Machiavellianism (Christie & Geis, 1970), and the courtesy di-mension of OCB (Podsakoff et al., 1990), which are composedmostly of interpersonal target behaviors, correlated more stronglywith ID. Therefore, the evidence from Bennett and Robinson(2000) supports the distinction between the ID and OD scales.

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None of these correlations was corrected for statistical artifacts,though. Also, as with any single study with a moderate samplesize, sampling error could have affected the relative magnitudes of these correlations. Thus, meta-analyses of such correlations, cor-recting for statistical artifacts, would be most useful. There isprecedence for using meta-analysis to test the validity of distinc-

tions between constructs, as this approach has been used in theOCB (LePine, Erez, & Johnson, 2002) and organizational justice(Cohen-Charash & Spector, 2001; Colquitt, Conlon, Wesson, Por-ter, & Ng, 2001) literatures.

There have been a number of meta-analyses in the devianceliterature (e.g., Cohen-Charash & Spector, 2001; Colquitt et al.,2001; Dalal, 2005; Lau, Au, & Ho, 2003; Salgado, 2002). With theexception of Dalal (2005), though, none of these previous meta-analyses has made a distinction between ID and OD. Thus, of theseprevious meta-analyses, only Dalal (2005) could even attempt todraw conclusions about relationships at the ID–OD facet level.Examining the viability of separating ID and OD was not thepurpose of Dalal’s (2005) meta-analysis, though, and Dalal (2005)

only meta-analyzed correlations among ID, OD, and some OCBvariables. There are many important correlates of ID and OD otherthan OCB, and comparisons of all of these correlates’ relationshipswith ID versus OD are needed if one is to best be able to drawconclusions about the viability of the ID–OD distinction.

Research Questions

The previous section of this article demonstrates that there arestill a number of pieces missing in the construct validity puzzle of the ID–OD distinction. Therefore, a series of meta-analyses werecompleted to help fill in some of the missing pieces. The presentmeta-analyses had three general purposes. Their main purpose was

to test the convergent and discriminant validity evidence for IDand OD by determining whether ID and OD have differentialrelationships with a common set of correlates. This focus onID–OD correlations with a large set of variables is the key way thepresent meta-analyses go beyond existing meta-analyses in thedeviance domain.

Furthermore, whereas previous meta-analyses examined rela-tionships between deviance and other constructs in a piecemealfashion (e.g., Dalal, 2005, only examined OCB–deviance; Sal-gado, 2002, only examined Big Five–deviance), the present studymeta-analyzes relationships among ID, OD, and every variablewith which ID and OD have been correlated in at least threestudies. This makes the present meta-analyses the most ambitiouseffort to date to further understanding of the nomological net of workplace deviance and affords them one more key advantageover the previous meta-analyses. That is, the present meta-analysesmake exclusive use of aggregate deviance criteria (i.e., multi-itemmeasures of ID and OD), whereas previous meta-analyses exam-ining similar relationships to those in the present study differedamong themselves in the degree to which they incorporated sam-ples using aggregate criteria versus narrower individual behavioralcriteria (e.g., theft, lateness, absenteeism), which confounds com-parisons among previous meta-analyses. Using a common level of aggregate measures of deviance makes comparisons of relation-ships (e.g., deviance’s relationships with Big Five vs. organiza-tional justice vs. OCB) more interpretable.

The second general purpose of the present study is to further testthe discriminant validity evidence for ID and OD by updating thecorrelation between ID and OD reported in Dalal (2005). Dalal’s(2005) estimate is updated in that the present study uses a largernumber of independent samples and more than double the totalsample size of Dalal (2005) to establish the most stable estimate of

the ID–OD relationship to date.The third general purpose of the present meta-analyses is to deter-

mine whether theBennett and Robinson (2000) measureof workplacedeviance is comparable to other existing deviance self-report mea-sures. Devianceresearch using theID–OD distinction has mostly usedthe Bennett and Robinson (2000) self-report measure. Other research-ers have developed other self-report scales using this ID–OD distinc-tion (e.g., Laczo, 2002; Marcus, Schuler, Quell, & Humpfner, 2002),although we know of only one study (Sackett et al., 2005) in whichparticipants responded to both the Bennett and Robinson (2000)measure and a second, different self-report measure of workplacedeviance. Sackett et al. (2005) administered to participants both theBennett and Robinson (2000) measure and an unpublished self-reportdeviance measure constructed by Laczo (2002). The ID scales in thetwo different deviance measures correlated .70 ( r 1.00, correctedfor unreliability) with each other, whereas the OD scales in the twodeviance measures correlated .68 ( r .98, corrected for unreliability).Although the evidence from Sackett et al. (2005) provided support forthe comparability of these deviance measures, these findings cannotnecessarily be extended beyond their sample or to measures other thanLaczo’s (2002). Therefore, it is still possible that other measures mayexhibit different patterns of relationships than the Bennett and Rob-inson (2000) measure. Thus, the final purpose of the present meta-analyses is to examine the previously unaddressed question of whether type of deviance scale (Bennett & Robinson, 2000, vs. otherscales) moderates relationships among ID, OD, and their commoncorrelates. Answering each of the three main research questions of the

present meta-analyses is crucial in determining the viability of theID–OD distinction (at least as it is currently measured).

Method

Literature Search

A sixfold approach was used to identify articles that mightcontain useful coefficients. First, a keyword search of the Psy-cINFO database was performed (keywords were antisocial behav-ior , counterproductive behavior , counterproductive work behav-io r , counterproductivity , dysfunctional work behavior ,interpersonal deviance , noncompliant behavior , organizationaldeviance , organizational misbehavior , organizational retaliationbehavior , organization-motivated aggression , workplace aggres-sion , workplace deviance , and workplace deviant behavior ). Sec-ond, the Social Sciences Citation Index was used to identify anyarticles that cited either Bennett and Robinson (2000) or Robinsonand Bennett (1995). Third, manual searches of Academy of Man-agement Journal , International Journal of Selection and Assess-ment , Journal of Applied Psychology , Journal of Organizational Behavior , and Personnel Psychology were carried out for the years1999 (the year before the publication of Bennett & Robinson,2000) onward. Fourth, manual searches of the conference pro-grams for the Society for Industrial and Organizational Psychologyand Academy of Management conferences were carried out for the

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years 1998 onward. Fifth, the reference sections of located primarystudies and any pertinent meta-analyses (Cohen-Charash & Spec-tor, 2001; Colquitt et al., 2001; Dalal, 2005; Lau et al., 2003;Salgado, 2002) were examined for relevant citations. Sixth, prom-inent researchers in the organizational deviance and counterpro-ductive work behavior areas were contacted with requests for any

unpublished work.Studies that contained enough information to extract (a) a correla-

tion between ID and OD, (b) a correlation between ID and some othervariable, or (c) a correlation between OD and some other variablewere initially included. This resulted in 31 studies, from which 38independent samples containing 449 correlations were drawn (therewere so many correlations per sample because each sample might listcorrelations between ID, OD, and any number of variables, all of which were coded). Of these, we retained only samples reporting acorrelation between a variable and ID or OD for which there were atleast three independent samples within the entire meta-analytic data-base reporting a correlation between that variableand ID and OD (i.e.,if Variable X and ID were correlated in three samples but Variable X

and OD were correlated in only two samples, neither of these rela-tionships was meta-analyzed). There is a precedent for this three-sample cutoff, as this was the decision rule used in Cohen-Charashand Spector (2001), a similarly designed meta-analysis in the organi-zational justice domain. This resulted in a final database of 30 articles,from which 37 independent samples containing 298 coefficients weredrawn (within each variable no sample contributed more than onecoefficient, but across variables each sample might have contributedmore). Twenty variables were identified that met the inclusion criteria(see Table 1 for a listing and definitions of these variables).

Procedure

For each study, the correlation among ID, OD, and the othervariable was coded. In addition, the workplace deviance measureused (Bennett & Robinson, 2000, vs. some other measure) andwhether deviance was measured via self-report were coded for useas moderators. 1 Christopher M. Berry independently coded allstudies. To provide a check on the accuracy of coding, Deniz S.Ones independently coded a subset of 8 studies containing 143coefficients, and Paul R. Sackett independently coded a subset of 8 different studies containing 86 coefficients. Agreement betweenBerry and Ones was 94% (99% between Berry and Sackett) forcorrelations, 100% (100%) for deviance scale used, and 100%(100%) for whether deviance was measured via self-report. Alldisagreements were minor and resolved via discussion as needed.

Formulas provided by Hunter andSchmidt (1990,p. 274) were firstused to correct all gender and minority status point-biserial correla-tions to what the correlations would be if sample sizes for gender andminority–majority subgroups were equal. The Hunter–Schmidt Meta-Analysis Program (Schmidt & Le, 2004) computer software was thenused to arrive at meta-analytic estimates of the mean correlations andvariability of relationships among ID, OD, and the other variables.Correlations were corrected for sampling error and for unreliability inboth variables using alpha coefficients (when applicable). Not enoughinformation was available in primary studies to make individualstatistical artifact corrections, so the artifact distribution method wasused. See Table 2 for the artifact distributions used in the presentmeta-analyses and their sources.

Some studies used supervisor or peer report to measure ID orOD instead of self-report. There was concern that supervisor orpeer reports might differ systematically from self-reports, perhapsbecause of halo error (Dalal, 2005; Sackett et al. 2005), problemswith objectivity of self-report, or low detection rates of deviance.Therefore, we carried out all analyses both using all studies and

excluding those studies that did not use self-report.The variability of the corrected correlations across studies was

also examined. Hunter and Schmidt (1990) suggested that if lessthan 75% of the variability in correlations across studies is ac-counted for by statistical artifacts, moderators likely exist. Mod-erator analyses were carried out for all relationships in which eachof the following was true: Artifacts did not account for 75% of thevariability, the absolute magnitude of corrected variability ( SD )was still large enough to suspect moderators, and enough infor-mation and enough samples existed for meaningful moderatoranalyses. The same meta-analytic procedures were used as weredescribed above for the full-sample meta-analyses.

ResultsMeta-analytic results are reported in Table 3. In no case did the

exclusion of nonself-report samples significantly change any of themeta-analytic estimates (see values in parentheses in Table 3).First, the correlation between ID and OD was calculated ( .62,corrected for sampling error and unreliability). This value wasslightly lower than Dalal’s (2005) corrected estimate of .70. Itshould be mentioned that the mean observed ID–OD correlationswere the same (.52) in the present study and in Dalal (2005).Therefore, we can explain the difference by concluding that thepresent study corrected the mean observed correlation using higherreliability estimates than Dalal (2005). The higher reliability for IDand OD found in the present study was expected, as we made

exclusive use of aggregate measures of ID and OD, whereas Dalal(2005) often used narrower behavioral measures of deviance (e.g.,withdrawal, substance abuse) and classified these as ID or OD.Regardless, the magnitude of the correlation between ID and ODwas still large enough in both studies to call into question theseparability of ID and OD.

Second, meta-analyses of the common correlates of ID and ODwere completed. In many cases, ID and OD had very similarrelationships with their common correlates. For instance, correla-tions for ID and OD with age, Emotional Stability, Openness toExperience, and procedural justice were all very similar. Therewere also a number of striking differences in correlations of ID andOD with many variables. We draw particular attention to Agree-ableness, Conscientiousness, and each of the OCB variables.Agreeableness correlated .14 stronger with ID than with OD.Conscientiousness, conversely, correlated .19 greater with ODthan with ID. Finally, OD correlated between .07 and .27 stronger

1 There was an attempt to code for other useful moderators of relation-ships (e.g., gender composition of the sample, tenure of the sample). Notenough information was reported in primary studies to examine suchmoderators, however. Although this is unfortunate, we feel that the mostimportant potential moderators of relationships among ID, OD, and othervariables at this point in the construct validation process of self-reportdeviance scales is the specific deviance scale used (Bennett & Robinson,2000, vs. some other) and whether that deviance scale used self-report.


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with OCB variables than did ID. Therefore, although they hadsome very similar correlations with a number of common corre-lates, it appears that ID and OD did have quite different correla-tions with some personality traits and with OCB, which lendssupport to the separability of ID and OD.

In addition to shedding light on whether ID and OD are sepa-rable, the meta-analyses in this article also contribute to furtherunderstanding of the nomological net of ID and OD. For instance,ID and OD were much more strongly correlated with the Big Fivepersonality dimensions of Agreeableness, Conscientiousness, andEmotional Stability ( .23 to .46) than with Extraversion orOpenness to Experience ( .09 to .02); this echoes previous find-ings in the deviance literature (e.g., Ones & Viswesvaran, 2001).

Furthermore, that Agreeableness correlated more strongly with IDand Conscientiousness correlated more strongly with OD makesconceptual sense, as Agreeableness is a more interpersonally ori-ented trait, whereas Conscientiousness is less so. The fact thatEmotional Stability correlated similarly with ID and OD makessense, as Emotional Stability does not possess a strong interper-sonal versus organizational component.

OCB variables also exhibited moderate to strong negative cor-relations with ID and OD, ranging from .20 to .47. That ODcorrelated more strongly with OCB conscientious initiative andOCB organizational support was expected, given that these typesof OCB do not have strong interpersonal components. The findingsfor overall OCB and OCB personal support were not as easily

Table 1 Definitions for Each Variable Meta-Analyzed

Variable Definition

Deviance

Interpersonal deviance Deviant behaviors in which employees engage that are targeted toward individuals (e.g., violence, gossip, theftfrom coworkers).Organizational deviance Deviant behaviors in which employees engage that are targeted toward the organization (e.g., working slowly,

damaging company property, sharing confidential company information).

Big Five

Emotional Stability The Big Five personality trait generally reflecting the degree to which a person is secure, is calm, has lowanxiety, and has low emotionality.

Extraversion The Big Five personality trait generally reflecting the degree to which a person is sociable, assertive, talkative,ambitious, and energetic.

Openness to Experience The Big Five personality trait generally reflecting the degree to which a person is curious, intelligent,imaginative, and independent.

Agreeableness The Big Five personality trait generally reflecting the degree to which a person is likable, easy to get alongwith, and friendly.

Conscientiousness The Big Five personality trait generally reflecting the degree to which a person is hard working, dependable,and detail oriented.

Organizational citizenship behaviors

Organizational citizenship behavior Work behaviors that support the broader organizational, social, and psychological environment of anorganization.

Conscientious initiative A subset of behaviors that generally focus on doing more than is expected in one’s job and persisting withenthusiasm and effort.

Organizational support A subset of behaviors that generally focus on supporting organizational objectives and following rules andprocedures.

Personal support A subset of behaviors that generally focus on helping other employees, being courteous, and beingcooperative.

Organizational justice

Distributive justice A type of organizational justice perception focusing on the degree to which an employee feels the allocationof outcomes or rewards was fair.

Interactional justice A type of organizational justice perception focusing on the degree to which an employee feels he or she hasbeen treated sensitively and is respected.Interpersonal justice A type of organizational justice perception focusing on the degree to which an employee feels communication

from the organization has been personal and respectful.Procedural justice A type of organizational justice perception focusing on the degree to which an employee feels the process by

which rewards are distributed or decisions are made was fair.

Demographics

Age Age (in years) of the participant filling out the deviance measure.Gender 0 female, 1 maleMinority status 0 White, 1 not white (is a racial–ethnic minority member)Tenure The number of years an employee has been employed by an organization.Work experience The number of years an employee has been working full-time jobs.




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interpretable. It was expected that personal support would have astronger relationship with ID than with OD, because personalsupport is composed of interpersonally oriented behaviors. Also,as overall OCB is made up of both interpersonally and organiza-tionally oriented behaviors, it was not expected that there would beso much stronger a relationship with OD than with ID.

Organizational justice variables exhibited low to moderate neg-ative correlations with ID and OD ( .07 to .25). Distributive andinterpersonal justice generally had the weakest correlations with

ID and OD (although ID correlated .19 with interpersonal justice,which makes sense given the interpersonal component of interper-sonal justice). Interactional and procedural justice, though, hadmoderate negative correlations with ID and OD, with neitherexhibiting particularly different magnitudes of correlation with IDor OD. It was not necessarily clear why procedural justice percep-tions had stronger correlations with ID and OD than did distribu-tive justice perceptions. For interactional versus interpersonal jus-t ice, the disparity might have been due to the typicalconceptualization of interpersonal justice as a component of inter-actional justice, although this is only conjecture.

Demographic variables 2 did not have particularly strong correla-tions with either ID or OD. Age had a small negative correlation withID and OD, being male was slightly positively correlated with ID andOD, and work experience and tenure generally had small negativecorrelations with ID and OD (work experience actually had a mod-erate correlation with OD, but this was based on only three samples).

Finally, the moderator analyses (see Table 4) addressed the ques-tion of whether relationships differed according to whether the Ben-nett and Robinson (2000) instrument was used to measureID and OD.Enough information and samples in each moderator level were avail-able to test this for only five variables. 3 In each of the relationships forwhich we performed moderator analyses, before we tested for mod-erators, 75% of the variability in correlations was not accounted for.First, type of measure did not moderate the relationship between IDand OD; the relationship among gender, ID, and OD; or the relation-

ship among age, ID, and OD. For OCB organizational support andOCB personal support, however, correlations were much higher whensome measure other than Bennett and Robinson’s (2000) was used.For OCB organizational support, the relative magnitudes of correla-tions with ID and OD were similar regardless of whether the Bennettand Robinson (2000) measure was used. For OCB personal support,though, the difference between correlations with ID and OD nearlydisappeared when some measure other than Bennett and Robinson’s(2000) was used. Therefore, although many relationships were simi-

lar, there is some evidence that the Bennett and Robinson (2000)instrument differs from other self-report deviance measures, at least inits relationships with OCB.

Discussion

Summary of Findings

The present meta-analyses provide support for the usefulness of separating self-report workplace deviance scales into ID and ODdimensions. Although ID and OD did exhibit similar relationships

2 Minority status (White vs. not White) was reported in a number of studies, with being White having a small positive correlation with both IDand OD (k s 5 and 7; s .07 and .09, SD s .09 and .10, respectively).Such results are difficult to interpret, as they do not differentiate amongminority subgroups. We urge researchers to report correlations for eachracial subgroup in a sample. Although we recognize it is often the case thatthere are very limited numbers of minorities in a given sample, suchcorrelations are of value for subsequent meta-analyses.

3 There was not enough information for moderator analyses on OCBconscientious initiative or overall OCB because many primary studies onlyincluded measures of one of the OCB facets. Therefore, the OCB personalsupport correlations were not necessarily drawn from the same samples aswere the OCB organizational support or OCB conscientious initiativecorrelations.

Table 2 Reliability Distributions for Artifact Corrections

Variable r xx SD N k Source

DevianceID .84 .07 6,878 26 Present study

OD .82 .07 6,080 22 Present studyBig FiveEmotional Stability .78 .11 370 Viswesvaran & Ones (2000)Extraversion .78 .09 307 Viswesvaran & Ones (2000)Openness .73 .12 251 Viswesvaran & Ones (2000)Agreeableness .75 .11 123 Viswesvaran & Ones (2000)Conscientiousness .78 .10 307 Viswesvaran & Ones (2000)

OCB .79 16,455 47 Dalal (2005)OS .74 5,607 23 Dalal (2005)CI .73 .06 1,412 3 Present studyPS .73 5,864 24 Dalal (2005)

Organizational justiceDistributive justice .91 66 Hauenstein et al. (2001)Interactional justice .90 .06 668 3 Present studyInterpersonal justice .89 .04 645 3 Present studyProcedural justice .91 66 Hauenstein et al. (2001)

Note. These reliability coefficients are all alphas. ID interpersonal deviance; OD organizational deviance;NR not reported; OCB organizational citizenship behavior; OS organizational support; CI consci-entious initiative; PS personal support. Blank cells indicate that data were not reported.


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Table 3 Meta-Analytic Results: Relationships Among ID, OD, and Their Common Correlates

Variable N k r m SDr SD CV10 CV90 % var

DevianceID–OD 10,104 27 .52 .10 .62 .11 .49 .76 15.4

(7,090) (25) (.50) (.11) (.61) (.12) (.45) (.76) (15.7)Big FiveEmotional Stability–ID 2,842 10 .20 .11 .24 .12 .39 .09 26.0

(2,318) (7) ( .22) (.10) ( .27) (.11) ( .40) ( .13) (26.6)Emotional Stability–OD 2,300 7 .19 .11 .23 .12 .39 .08 22.4Extraversion–ID 2,360 8 .02 .11 .02 .11 .12 .17 28.2

(1,836) (5) (.02) (.09) (.03) (.10) ( .10) (.15) (31.0)Extraversion–OD 1,836 5 .07 .12 .09 .14 .26 .09 17.9Openness–ID 2,360 8 .07 .05 .09 .00 .09 .09 100.0

(1,836) (5) ( .07) (.05) ( .08) (.00) ( .08) ( .08) (100.0)Openness–OD 1,772 5 .03 .07 .04 .06 .12 .04 53.8Agreeableness–ID 3,336 10 .36 .09 .46 .10 .58 .33 27.5

(2,934) (8) ( .35) (.09) ( .44) (.10) ( .57) ( .32) (25.7)Agreeableness–OD 2,934 8 .25 .08 .32 .08 .42 .21 35.2Conscientiousness–ID 3,458 11 .19 .12 .23 .13 .40 .06 20.0

(2,934) (8) ( .20) (.08) ( .24) (.08) ( .34) ( .14) (37.2)Conscientiousness–OD 2,934 8 .34 .08 .42 .08 .53 .32 32.2

OCBOCB–ID 2,725 8 .17 .13 .21 .14 .39 .03 17.0OCB–OD 2,725 8 .36 .10 .44 .11 .58 .31 23.2OCB (CI)–ID 2,020 5 .15 .13 .20 .15 .39 .00 14.2OCB (CI)–OD 2,020 5 .36 .08 .47 .09 .59 .36 27.5OCB (OS)–ID 3,253 8 .19 .11 .24 .13 .41 .08 18.3OCB (OS)–OD 3,253 8 .36 .13 .46 .16 .66 .25 10.7OCB (PS)–ID 3,386 9 .24 .15 .31 .18 .54 .09 10.7OCB (PS)–OD 3,643 10 .30 .11 .38 .12 .54 .23 20.4

(3,386) (9) ( .28) (.10) ( .37) (.11) ( .51) ( .22) (22.2)Organizational justice

Distributive justice–ID 1,089 5 .12 .04 .13 .00 .13 .13 100.0(832) (4) ( .10) (.04) ( .12) (.00) ( .12) ( .12) (100.0)

Distributive justice–OD 1,089 5 .10 .06 .12 .00 .12 .12 100.0(832) (4) ( .12) (.07) ( .13) (.00) ( .13) ( .13) (100.0)

Interactional justice–ID 1,208 6 .22 .13 .25 .13 .41 .08 26.6

(951) (5) ( .25) (.13) ( .29) (.12) ( .44) ( .13) (28.4)Interactional justice–OD 1,190 6 .18 .09 .21 .07 .29 .25 56.5(933) (5) ( .22) (.06) ( .25) (.00) ( .25) ( .25) (100.0)

Interpersonal justice–ID 1,242 4 .17 .16 .19 .17 .41 .02 12.2Interpersonal justice–OD 1,242 4 .06 .06 .07 .04 .12 .02 76.5Procedural justice–ID 1,542 7 .19 .07 .21 .02 .24 .19 93.6

(1,285) (6) ( .20) (.07) ( .23) (.01) ( .24) ( .22)Procedural justice–OD 1,542 7 .18 .09 .21 .07 .30 .13 55.3

(1,285) (6) ( .22) (.05) ( .25) (.00) ( .25) ( .25)Demographics

Age–ID 6,249 14 .05 .07 .06 .06 .13 .02 52.9(2,967) (9) ( .09) (.08) ( .10) (.05) ( .17) ( .04) (58.8)

Age–OD 5,928 12 .09 .09 .10 .08 .21 .00 25.1(2,914) (10) ( .12) (.12) ( .13) (.11) ( .28) (.01) (23.2)

Gender–ID a 6,250 14 .14 .07 .15 .06 .07 .23 52.2(2,968) (10) (.17) (.07) (.19) (.03) (.14) (.23) (75.5)

Gender–OD a 5,929 12 .11 .09 .12 .08 .02 .22 23.4(2,915) (10) (.10) (.12) (.11) (.11) ( .03) (.26) (23.7)

Tenure–ID 2,211 7 .01 .03 .01 .00 .01 .01 100.0Tenure–OD 2,710 9 .07 .07 .08 .05 .14 .01 60.5

(2,453) (8) ( .06) (.07) ( .07) (.05) ( .13) ( .01) (64.0)Work experience–ID 794 3 .10 .02 .11 .00 .11 .11 100.0Work experience–OD 783 3 .22 .05 .25 .00 .25 .25 100.0

Note. Values in parentheses are estimates excluding samples using nonself-report of deviance Gender: Female 0, Male 1. r m meansample-size-weighted correlation; CV 10 and CV 90 10% and 90% credibility values, respectively; % var percentage of variance attributable to artifactsID interpersonal deviance; OD organizational deviance; OCB organizational citizenship behavior; CI conscientiousness initiative; OS organizational support; PS personal support.a These point-biserial correlations were corrected to account for uneven sample sizes in each subcategory (e.g., uneven sample sizes for men vs. women).




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with a number of variables, they had clear, differential relation-ships with the Big Five personality dimensions of Agreeablenessand Conscientiousness and with most OCB variables. Agreeable-ness, Conscientiousness, and OCB are important variables onwhich to see differential correlations. Agreeableness and Consci-entiousness are two of the three personality traits that underlieintegrity tests (Marcus & Schuler, 2004; Ones, 1993; Ones &Viswesvaran, 2001) and thus likely play a key role in determiningworkplace deviance. In addition, the relationship between OCBand workplace deviance has been a common and sometimes hotlydebated research topic (e.g., Bennett & Stamper, 2002; Dalal,2005; Kelloway, Loughlin, Barling, & Nault, 2002; Miles, Bor-man, Spector, & Fox, 2002; Sackett et al., 2005), which testifies tothe importance of OCB–deviance relationships.

However, for many variables the similarity in relationships withID and OD was striking. For instance, the third Big Five person-ality variable that underlies integrity tests, Emotional Stability, hadvirtually identical relationships with ID and OD, as did many otherwidely researched variables (e.g., age; gender; tenure; and percep-tions of distributive, interactional, and procedural justice). ID andOD were also relatively strongly correlated with each other. There-fore, it seems that, for some purposes, creating an overall work-place deviance composite would be justified.

The present meta-analyses also further understanding of thenomological net of ID and OD by simultaneously examining therelationships among ID; OD; and Big Five personality, OCB,

organizational justice, and demographic variables, using the sameaggregate deviance criteria in each independent sample. Becausethe same deviance criteria were used for all meta-analyses, it isappropriate to compare magnitudes of relationships that ID andOD had with each of these classes of variables. The strongestcorrelates of ID and OD were personality and OCB variables.Considerably below these correlations, in terms of magnitude,were correlations with organizational justice variables. Demo-graphic variables had only very weak correlations with ID and OD.These four classes of variables reside at different conceptual levelsin relation to deviance, though, and these different conceptuallevels may confound comparisons of magnitudes of correlation. 4

That is, Big Five and justice variables are probably best concep-

tualized as (personality and situational perception, respectively)antecedents of deviance, whereas OCB is probably best concep-tualized as a behavioral construct at a similar conceptual level todeviance. Categorizing the conceptual level of demographic vari-ables in relation to deviance is more difficult, so we simply refer

4 We recognize that there are other key differences between our meta-analysesand previousmeta-analyses thatmight havecontributed to differencesbetween estimates, and we have outlined these in the Comments column of Table 5. We by no means assert that the level at which deviance was measuredis the only reason for differences between meta-analyses’ estimates, but we dofeel it is an important point of difference between the meta-analyses.

Table 4 Moderator Analysis Results: Effects of Deviance Measure Used on Relationships

Variable N k r m SDr SD CV10 CV90 % var

DevianceID–OD (BR) 4,751 17 .49 .12 .59 .13 .43 .76 14.9

(4,494) (16) (.48) (.12) (.58) (.13) (.42) (.74) (15.8)ID–OD (other) 5,353 10 .54 .07 .65 .07 .56 .75 18.4Demographics

Age–ID (BR) 2,117 8 .06 .09 .06 .07 .15 .03 47.8(1,593) (5) ( .07) (.08) ( .07) (.06) ( .15) (.01) (47.3)

Age–ID (Other) 4,132 6 .05 .06 .05 .05 .12 .01 100.0Age–OD (BR) 1,593 5 .08 .07 .09 .05 .16 .02 55.8Age–OD (Other) 4,335 7 .10 .09 .11 .09 .22 .01 18.5

(1,321) (5) ( .16) (.15) ( .18) (.15) ( .37) (.01) (16.5)Gender–ID (BR) a 2,117 8 .19 .10 .20 .08 .10 .31 37.1

(1,593) (5) (.18) (.08) (.19) (.06) (.12) (.27) (49.4)Gender–ID (other) a 4,133 6 .11 .04 .13 .02 .10 .15 100.0Gender–OD (BR) a 1,593 5 .10 .12 .12 .12 .03 .26 21.2Gender–OD (other) a 4,336 7 .11 .07 .13 .07 .04 .21 25.7

(1,322) (5) (.10) (.12) (.11) (.11) ( .03) (.25) (100.0)OCB

OCB (OS)–ID (BR) 1,906 4 .13 .01 .16 .00 .16 .16 100.0OCB (OS)–ID (other) 1,347 4 .29 .12 .37 .14 .54 .19 17.1OCB (OS)–OD (BR) 1,906 4 .28 .05 .36 .04 .41 .30 63.5OCB (OS)–OD (other) 1,347 4 .46 .13 .60 .16 .80 .39 10.1OCB (PS)–ID (BR) 2,039 5 .15 .07 .19 .07 .28 .11 44.1OCB (PS)–ID (other) 1,347 4 .38 .12 .49 .14 .67 .31 14.7OCB (PS)–OD (BR) 2,039 5 .22 .04 .28 .00 .28 .28 100.0OCB (PS)–OD (other) 1,604 5 .40 .07 .51 .07 .61 .42 41.6

(1,347) (4) ( .39) (.08) ( .50) (.08) ( .60) ( .40) (37.6)

Note. Values in parentheses are estimates excluding samples using nonself report of deviance. r m mean sample-size-weighted correlation; CV 10 andCV90 10% and 90% credibility values, respectively; % var percentage of variance attributable to artifacts; ID interpersonal deviance; OD organizational deviance; BR Bennett and Robinson (2000) measure of workplace deviance used; Other some measure other than the Bennett andRobinson (2000) measure was used; OCB organizational citizenship behavior; OS organizational support; PS personal support.a These point-biserial correlations were corrected to account for uneven sample sizes in each subcategory (e.g., uneven sample sizes for men vs. women).


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Table 5Comparisons of Estimates From Previous Meta-Analyses With Those of the Present Meta-Analyses

VariablePrevious meta-analysis’s

estimatesPresent meta-analyses’

estimate Comments

Deviance

ID–OD .70 (Dalal, 2005) .62 Dalal’s (2005) estimate was based on fewer samples(k 20) and less than half ( N 4,136) the totalsample size of the present study. Present studymakes exclusive use of aggregate deviancemeasures.

Big Five

Emotional Stability–deviance .06 (Salgado, 2002) ID .24, OD .23 Present study makes exclusive use of aggregatedeviance measures.

Extraversion–deviance .01 (Salgado, 2002) ID .02, OD .09 Present study makes exclusive use of Big Fivemeasures.

Openness–deviance .14 (Salgado, 2002) ID .09, OD .04Agreeableness–deviance .20 (Salgado, 2002) ID .46, OD .32Conscientiousness–deviance .26 (Salgado, 2002) ID .23, OD .42

OCB

OCB (PS)–Deviance ID .11, OD .16 (Dalal,2005)

ID .31, OD .38 Present study makes exclusive use of aggregatedeviance measures.

OCB (OS)–Deviance ID .17, OD .33 (Dalal,2005)

ID .24, OD .46 Bennett and Robinson’s (2000) measure, whichmoderates OCB relationships, was used in lessthan half of Dalal’s (2005) articles, according toreferences, although it is unclear exactly howoften it was used.

Organizational justice

Distributive justice–deviance .30 (Colquitt et al., 2001) ID .13, OD .12 Present study makes exclusive use of aggregatedeviance measures.

Interpersonal justice–deviance .35 (Colquitt et al., 2001) ID .19, OD .07 Colquitt et al.’s (2001) estimates were based onlarger numbers of samples than were the present

study’s.Procedural justice–deviance .31 (Colquitt et al., 2001) ID .21, OD .21Distributive justice–deviance .24 (Cohen-Charash &

Spector, 2001)ID .13, OD .12 Cohen-Charash and Spector’s (2001) estimates were

based on smaller numbers of samples than thepresent study’s.

Procedural justice–deviance .29 (Cohen-Charash &Spector, 2001)

ID .21, OD .21 Present study makes exclusive use of aggregatedeviance measures.

Demographics

Age–deviance Theft .21,production deviance .33,Lateness .20,Absence .11(Lau et al., 2003)

ID .06, OD .10 Lau et al.’s (2003) estimates were generally basedon small numbers of samples.

Lau et al. (2003) did not correct for uneven gendersplits.

Present study makes exclusive use of aggregatedeviance measures.

Gender–deviance Lateness .04,Absence .10,Alcohol abuse .10(Lau et al., 2003)

ID .15, OD .12

Tenure–deviance Theft .12,Lateness .13,Absence .13(Lau et al., 2003)

ID .01, OD .08

Note. All estimates reported in this table were corrected for unreliability in both variables, except for estimates from Salgado (2002; these were alsocorrected for range restriction in personality variables) and Cohen-Charash and Spector (2001; these are uncorrected, sample-size weighted mean estimates).ID interpersonal deviance; OD organizational deviance; OCB organizational citizenship behavior; PS personal support; OS organizationalsupport.




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of organizational justice perceptions (e.g., Robinson & Greenberg,1998). This might have been further fueled by the results of Salgado’s (2002) meta-analysis, in which the magnitudes of rela-tionships of overall workplace deviance with personality variableswere relatively low, especially when compared with correlationsbetween organizational justice and deviance reported in Colquitt et

al. (2001) and Cohen-Charash and Spector (2001). Comparisons of the magnitudes of Big Five versus justice relationships from theseprevious meta-analyses are tenuous, though, because there arepossible differences in criteria between the meta-analyses. Theresults of the present meta-analyses, which examine both Big Fiveand justice relationships using the same aggregate ID and ODcriteria, contradict the findings and assertions from previous work.Thus, it is useful to compare directly our meta-analyses withCohen-Charash and Spector’s (2001), Colquitt et al.’s (2001), andSalgado’s (2002) meta-analyses. For the sake of comparabilitywith these meta-analyses’ overall workplace deviance estimates,composite theory formulas (e.g., Ghiselli, Campbell, & Zedeck,1981, pp. 163–164) can be used to estimate the correlation be-tween each of the present study’s variables and an overall work-place deviance measure (composite of ID and OD scale scores).

Table 6 shows these values for Big Five personality and orga-nizational justice variables in the present study, using the sameoverall aggregate deviance criterion. The key message from theanalyses reported in Table 6 is that, when the same overall aggre-gated deviance criterion was used, personality variables such asConscientiousness, Agreeableness, and Emotional Stability hadgenerally higher correlations with overall workplace deviance thandid organizational justice variables. Still, some organizational jus-tice variables did have appreciable effects on workplace deviance.Furthermore, previous meta-analyses have demonstrated that whennarrower deviance criteria are used, justice–deviance relationshipscan approach the magnitudes of personality–deviance relation-

ships. Either way, given the combined evidence from the presentmeta-analyses and previous ones, such as Ones, Viswesvaran, andSchmidt’s (1993) integrity-testing meta-analysis, the assertion that justice is more important in predicting deviance than personalityappears to be false. Therefore, perhaps a perspective that includesindependent, additive main effects for both personality traits and

justice variables as well as an interactionist perspective, wherebypersonality and justice perceptions interact to predict deviance,may be most profitable (e.g., Bies & Tripp, 2005; Greenberg,2002), although the present meta-analyses were not able to testthis. Future deviance research will likely benefit from such anintegrative approach wherein multiple theoretical perspectives on

deviance are taken into account (Fox & Spector, 2005).

Practical Implications

The results of the present meta-analyses also have implicationsfor practice. That is, evidence is beginning to mount that ID andOD, despite being related, are different phenomena. Thus, organi-zational interventions aimed at one cannot be assumed to havecomparable effects on the other. For instance, Conscientiousness iscommonly used in the selection of applicants for hire. The resultsof the present meta-analyses suggest that hiring employees on thebasis of Conscientiousness will have a stronger impact on levels of OD than on levels of ID. Similarly, efforts by organizations toengender climates more conducive to OCB may have strongereffects on OD than on ID. Conversely, reviewing organizationalpolicies and procedures in an effort to reduce feelings of injusticeamong employees might be expected to have similar effects onlevels of ID and OD.

Furthermore, results suggest that if organizations are concernedwith levels of employee deviance, it may be most useful to enacta multipronged strategy of selection for personality traits corre-lated with deviance along with an effort to engender a climateconducive to OCB. It may also be useful for organizations toreview policies or episodes that lead to feelings of injustice amongemployees. Much less useful would be policies by which certaindemographic subsets (e.g., new or young employees) are moni-tored more closely.

Finally, the support for Sackett and DeVore’s (2002) conceptu-alization of deviance as a hierarchical construct has implicationsfor practice. The results of the present meta-analyses demonstratethe usefulness of both overall and ID–OD facet levels of deviance,although the relative merits of these levels of deviance versusnarrower levels have not been directly assessed. Previous meta-analyses, though, have established the usefulness of conceptualiz-ing deviance at the narrower behavioral categories, such as theftand lateness. Comparisons between our meta-analyses and previ-ous meta-analyses demonstrate that the level at which deviance ismeasured affects the relationships that it has with other constructs.Therefore, if organizations wish to realize the greatest potentialfrom deviance-reducing interventions, they should be very carefuland explicit about exactly what behaviors they want to reduce. Thekey is in realizing the level of the hierarchical deviance constructat which one wants to enact change and using a predictor that isappropriately matched for that level of specificity (Ones & Vi-swesvaran, 1996).

Additional Issues, Limitations, and Implications for Future Research

A number of additional issues in the present research alsowarrant discussion and suggest further directions for future re-search. First, the number of studies used in some of the meta-analyses was relatively small. The sample sizes in each of the

Table 6 Estimates of the Correlations Between an Overall Workplace Deviance Composite and Big Five Personality and Organizational Justice Variables

Variable Estimated a

Big Five personalityEmotional Stability .26Extraversion .03Openness to Experience .08Agreeableness .44Conscientiousness .35

Organizational justiceDistributive justice .14Interactional justice .26Interpersonal justice .14Procedural justice .23

a Each of these estimates was calculated via composite theory formulasfrom Ghiselli et al. (1981, pp. 163–164).


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meta-analyses (other than moderator analyses) ranged from 783 to10,104, with an average sample size of 2,865. Furthermore, these

meta-analyses go far beyond any single study in expanding ourunderstanding of the relationship among ID, OD, and their com-mon correlates. Additionally, because multiple venues for unpub-lished work were mined for primary studies, these meta-analysesshould be relatively comprehensive in terms of the empirical work in this domain.

Second, it should be mentioned that not enough information wasincluded in studies to correct for range restriction. One can imag-ine that some of these samples (many of which were composed of current employees) might have been restricted on deviance eitherthrough hiring practices that directly or indirectly screened outthose prone to deviance or through attrition mechanisms wherebythose engaging in deviance were more likely to have been fired.Although this might have suppressed the absolute magnitude of correlations, this should not have had an appreciable effect on therelative magnitude of correlations that ID and OD had with acommon set of correlates unless it were the case that those engag-ing in ID were more likely to be excluded than those engaging inOD (or vice versa). Likewise, the relative magnitudes of correla-tions of ID and OD with personality, OCB, justice, and demo-graphic variables would only have been affected if range restric-tion differentially influenced these different sets of variables. Wecan think of no theoretical or empirical work suggesting that suchmight have been the case, so the lack of range restriction correc-tions is not a major threat to the general conclusions from thepresent meta-analyses.

Third, one could imagine that employees were more willing toreport some types of deviance than others. For example, perhaps

employees felt more comfortable admitting to acts in which thevictim was an impersonal organization (OD) than admitting to actsin which the victim was a human being (ID). If some suchreporting mechanism truly existed, this differential willingness toadmit might have caused the disparity in correlations among ID,OD, and their common correlates. This is unlikely to have been acause of the findings in the present meta-analyses, though, becauseof two key points. First, both ID and OD had sizable relationshipswith at least some variables (e.g., ID correlated .46 with Agree-ableness, and OD correlated .47 with OCB conscientious initia-tive), which argues against reduced variance from some unwill-ingness to admit to one type of deviance being so severe as toprevent finding relationships with any variables. Second, neitherID nor OD exhibited systematically lower correlations with othervariables than the other, which would be expected if variance wasbeing restricted on one because of differential willingness to admit.Therefore, differential willingness to report certain types of devi-ance is not seen as a potential weakness of the present study.

Fourth, although the magnitudes of correlations reported in thisstudy represent very useful information, readers should take cau-tion when interpreting these magnitudes. These correlations weremostly based on self-reports of ID and OD and should not beconfused with validities for predicting objective, verifiable devi-ance criteria. For instance, at first glance it may appear that ourestimates of Agreeableness’s and Conscientiousness’s validitiesexceed even the commonly cited meta-analytic estimate of .32 for

Table 7Comparison of Correlations Based on Nonself-Report Deviance Measures With Those Based onSelf-Report Deviance Measures

Variable

Self-report Nonself-report

k N k N

DevianceID–OD .61 25 7,090 .67 2 3,014

Big FiveEmotional Stability–ID .27 7 2,318 .14 3 524Extraversion–ID .03 5 1,836 .01 3 524Openness–ID .08 5 1,836 .11 3 524Agreeableness–ID .44 8 2,934 .57 3 524Conscientiousness–ID .24 8 2,934 .17 3 524

OCBOCB (PS)–OD .37 9 3,386 .58 1 257

Organizational justiceDistributive justice–ID .12 4 832 .18 1 257Distributive justice–OD .13 4 832 .07 1 257Interactional justice–ID .29 5 951 .10 1 257Interactional justice–OD .25 5 933 .05 1 257

Procedural justice–ID .23 6 1,285 .10 1 257Procedural justice–OD .25 6 1,285 .02 1 257Demographics

Age–ID .10 9 2,967 .01 4 3,282Age–OD .13 10 2,914 .08 2 3,014Gender–ID .19 10 2,968 .12 4 3,282Gender–OD .11 10 2,915 .13 2 3,014Tenure–OD .07 8 2,453 .18 1 257

Note. Rhos were corrected for sampling error (when there was more than one sample) and unreliability viaalphas in both measures (where appropriate). Gender correlations were also corrected for uneven gender splits.ID interpersonal deviance; OD organizational deviance; OCB organizational citizenship behavior; PSpersonal support.


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integrity tests in predicting counterproductive work behaviors(Ones et al., 1993). Ones et al.’s estimate was based completely onobjective, verifiable deviance criteria, though, and is not directlycomparable to our mostly self-report estimates. More appropriatelycomparable estimates from Ones et al. would be those for admis-sions criteria, which ranged from .23 to .99, depending on the type

of integrity test and sample used. Given such high magnitudes, itis unfortunate that so little research has been done on relationshipsamong ID, OD, and compound traits such as integrity (only twosuch studies were identified in this study’s literature search).Clearly, this is a need for future research.

This also leads us to mention that one caveat to the results of thepresent meta-analyses is that they were largely based on self-reports of deviance, meaning that it remains to be seen whetherthese relationships hold for different deviance criteria. Some evi-dence is provided in that the inclusion of samples in whichnonself-report deviance measures were used did not greatly affectpooled meta-analytic estimates. One alternative explanation,though, is that this was simply because there were so few nonself-report samples, so the law of averages worked to keep estimatesstable. To test this, exploratory meta-analyses were carried outwith only nonself-report samples (see Table 7). Although many of these analyses were difficult to interpret because of the possibilityof sampling error, the general pattern of results was that relation-ships did not differ to a great degree when nonself-report was used(average absolute difference of .10 when nonself-report was used).Additionally, self-report correlations in Table 7 correlated .89 withnonself-report correlations. This is not to say there were no note-worthy differences. Correlations between ID and Emotional Sta-bility, Agreeableness, and Conscientiousness were moderately dif-ferent when nonself-report was used. Furthermore, it should benoted that, for the sake of comparability of comparisons, alphareliability coefficients were used to correct correlations in nonself-

report samples. Because each of the nonself-report samples incor-porated a single judge completing ID and OD scales about some-one, corrections using interrater reliability coefficients might beconsidered (e.g., Schmidt, Viswesvaran, & Ones, 2000), whichcould potentially change results.

Conclusions

The present meta-analyses provide the clearest and most com-prehensive picture to date of the relationship among ID, OD, andtheir common correlates. ID and OD scales were relatively highlycorrelated with each other and had similar relationships with manyvariables but also had differential relationships with key variables,such as Conscientiousness, Agreeableness, and OCB. When thesefindings are taken with evidence from previous work examiningthe relationship between ID and OD and the factor structure of workplace deviance scales, the viability of separate ID and ODscales becomes more apparent, as does the formation of overallworkplace deviance composites. Furthermore, ID and OD scaleshad their strongest relationships with Big Five personality andOCB, moderate relationships with organizational justice percep-tions, and little to no relationship with most demographic vari-ables. Such findings provide important theoretical contributions tomultiple literatures within industrial/organizational psychology,including the workplace deviance–counterproductive work behav-ior, OCB, personality, and organizational justice fields.

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Received November 23, 2005Revision received March 31, 2006

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