A Meta-Analysis of the Relationship Between General Mental Ability andNontask Performance

Erik Gonzalez-Mul and Michael K. MountUniversity of Iowa

In-Sue OhTemple University

Although one of the most well-established research findings in industrialorganizational psychology isthat general mental ability (GMA) is a strong and generalizable predictor of job performance, thismeta-analytically derived conclusion is based largely on measures of task or overall performance. Theprimary purpose of this study is to address a void in the research literature by conducting a meta-analysisto determine the direction and magnitude of the correlation of GMA with 2 dimensions of nontaskperformance: counterproductive work behaviors (CWB) and organizational citizenship behaviors (OCB).Overall, the results show that the true-score correlation between GMA and CWB is essentially 0 (.02,k 35), although rating source of CWB moderates this relationship. The true-score correlation betweenGMA and OCB is positive but modest in magnitude (.23, k 43). The 2nd purpose of this study is toconduct meta-analytic relative weight analyses to determine the relative importance of GMA and thefive-factor model (FFM) of personality traits in predicting nontask and task performance criteria. Resultsindicate that, collectively, the FFM traits are substantially more important for CWB than GMA, that theFFM traits are roughly equal in importance to GMA for OCB, and that GMA is substantially moreimportant for task and overall job performance than the FFM traits. Implications of these findings for thedevelopment of optimal selection systems and the development of comprehensive theories of jobperformance are discussed along with study limitation and future research directions.

Keywords: general mental ability, organizational citizenship behavior, counterproductive work behavior

There is broad scientific consensus that general mental ability(GMA) plays an integral role in success at work, in ones career,and in life in general. As 52 prominent social science researchersconcluded, GMA is strongly related, probably more so than anyother single measurable human trait, to many important educa-tional, occupational, economic, and social outcomes (L. S. Got-tfredson, 1997a, p. 14). In fact, meta-analytic research has shownthat GMA correlates above .50 with occupational level attained,performance within ones chosen occupation, and performance intraining programs (Ree & Earles, 1992; Salgado et al., 2003).Considering the cumulative validity evidence regarding GMA,Scherbaum, Goldstein, Yusko, Ryan, and Hanges (2012) stated

that the robust, well-established findings regarding the GMAperformance relationship is the major contribution of theindustrialorganizational psychology field to the study of intelli-gence.

Despite the overwhelming evidence that GMA plays a criticalrole in success in the workplace, most evidence regarding thevalidity of GMA is based on criterion measures of task perfor-mance or overall performance (e.g., Hunter, 1986; Salgado et al.,2003). To be clear, task performance is an important facet of jobperformance because it consists of behaviors that contribute to theproduction of a good or the provision of a service (Rotundo &Sackett, 2002, p. 67). However, in the past decade researchers haveidentified an expanded domain of job performance that includestwo nontask performance components, counterproductive workbehaviors (CWB) and organizational citizenship behaviors (OCB),in addition to task performance (Borman & Motowidlo, 1993;Dalal, 2005; Lievens, Conway, & De Corte, 2008; Organ & Ryan,1995; Robinson & Bennett, 1995; Rotundo & Sackett, 2002).These two nontask performance components are critical becausethe broad sets of behaviors associated with OCB and CWB caninfluence the success (or failure) of organizations and can have astrong positive (or negative) effect on the welfare of individuals inthe organizations. In their Annual Review of Psychology article,Sackett and Lievens (2008) identified the expanded criterion do-main as one of the major developments in the past decade that canlead to improved personnel selection (see also Hough & Oswald,2000).

The emergence of the expanded domain of job performanceprovides the impetus for the present study because it highlights a

This article was published Online First August 18, 2014.Erik Gonzalez-Mul and Michael K. Mount, Department of Manage-

ment and Organizations, Henry B. Tippie College of Business, Universityof Iowa; In-Sue Oh, Department of Human Resource Management, FoxSchool of Business, Temple University.

An earlier version of this article was presented at the 2013 Academy ofManagement Conference in Orlando, Florida. We would like to thankFrank Schmidt, Amy Colbert, Ernest OBoyle, Chris Berry, and BenPostlethwaite for their constructive feedback on earlier drafts of this article.We would also like to thank Sharon Parker for her helpful recommenda-tions and developmental feedback throughout the revision process.

Correspondence concerning this article should be addressed to ErikGonzalez-Mul, Department of Management and Organizations, Henry B.Tippie College of Business, University of Iowa, W361 Pappajohn BusinessBuilding, Iowa City, IA 52242-1994. E-mail: erik-gonzalez-mule@uiowa.edu

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Journal of Applied Psychology 2014 American Psychological Association2014, Vol. 99, No. 6, 12221243 0021-9010/14/$12.00 http://dx.doi.org/10.1037/a0037547

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gap in the applied psychology literature. Despite the widely heldbelief that GMA is the best single predictor of job performance,our knowledge and understanding of this relationship is incom-plete because we do not know the answers to very basic questions,such as, do more intelligent people engage in more (or less)counterproductive work behaviors that are harmful to the organi-zation and its members, and do more intelligent people engage inmore (or less) citizenship behaviors that promote the functioningof the organization? As Dilchert, Ones, Davis, and Rostow (2007,p. 625) stated: The cognitive abilityCWB link deserves moreattention than it has received in industrial and organizationalpsychology so far. This sentiment was echoed by scholars withregard to OCB (e.g., LePine & Van Dyne, 2001; Salgado, 1999).

Accordingly, the first purpose of this study is to address thesevoids in the literature by conducting a meta-analysis that examinesthe direction and magnitude of the relationship between GMA andthe two dimensions of nontask performance: CWB and OCB. Thesecond purpose draws on the idea that nontask behaviors areinfluenced less by ones cognitive ability and more by onesvolitional and motivational factors, and therefore are more likely tobe predicted by personality traits (e.g., Borman & Motowidlo,1993). For example, in their Annual Review of Psychology articleBorman, Hanson, and Hedge (1997) stated that it appears thatability best predicts technical proficiencyrelated criteria and per-sonality best predicts such criterion domains as teamwork, inter-personal effectiveness, and contextual performance (p. 330). Toour knowledge, this assertion has not been tested meta-analytically. Accordingly, we conducted relative weight (RW)analyses (Johnson, 2000) based on meta-analytic evidence to de-termine the relative contribution of the five-factor model (FFM) ofpersonality traits and GMA in predicting CWB, OCB, task per-formance, and overall job performance. Thus, the present study isthe first to compare the relative importance of the FFM and GMAin predicting task and nontask performance criteria.

We believe the findings of our study may have importantimplications for both selection practice and theory. First, meta-analytic estimates of the relationship between GMA and nontaskbehaviors will help practitioners understand how their selectionsystem will fare with respect to their criteria of choice. This isparticularly important given the more dynamic and social nature oftodays workplace where such behaviors have become increas-ingly important (e.g., team systems, innovation focus). Second, theresults of the current study will help scholars formulate morecomprehensive theories of performance that account for the mul-tidimensional nature of job performance criteria via cognitive andnoncognitive predictor constructs. For example, if the results showthat GMA is related to both nontask performance components, itwill provide further evidence of the importance and ubiquity ofGMA as a selection instrument. On the other hand, if the resultsshow that GMA is unrelated (or is only weakly related) to bothnontask components, at a minimum, it calls into question thewidely held belief in the field that GMA is the single best predictorof job performance (e.g., F. L. Schmidt, 2002). Such findingswould suggest that theories of job performance should be revisedto reflect the differential relations of GMA with task versus non-task performance components.

The remainder of this article is organized as follows. First, wediscuss and define the two dimensions of the nontask performancedomain (CWB and OCB) and review relevant research pertaining

to each. Then we formulate our hypotheses pertaining to theexpected magnitude and direction of the relationships of GMAwith nontask performance. Third, we discuss possible moderatorsof the relationship of GMA with nontask performance. Last, wediscuss the relative importance of GMA and the FFM personalitytraits in predicting nontask performance.

Theoretical Perspectives and Research Hypotheses

Dimensions of Nontask Performance

CWB are intentional behaviors that violate organizational normsand are contrary to the legitimate interests of the organization andits members (Bennett & Robinson, 2000; Gruys & Sackett, 2003).CWB were originally subsumed by contextual performance (Bor-man & Motowidlo, 1993), but more recent models of job perfor-mance (e.g., Rotundo & Sackett, 2002) have consistently shownthat they constitute a third factor of job performance or an impor-tant dimension of nontask performance (Bennett & Robinson,2000; Dalal, 2005; Sackett, Berry, Wiemann, & Laczo, 2006;Spector, Bauer, & Fox, 2010). Robinson and Bennetts (1995)seminal work distinguished between two types of CWB accordingto their target: organizational and interpersonal deviance. Organi-zational deviance (CWB-O) consists of behaviors targeted at theorganization and the task, such as theft, sabotage, or shirking.Interpersonal deviance (CWB-I) consists of behaviors targeted atother organizational members, such as yelling, insulting, or takingcredit for others work (Bennett & Robinson, 2000). Empiricalevidence shows that there are enormous personal and organiza-tional costs associated with CWB. For example, CWB can causepersonal suffering and discomfort such as decreased well-beingand satisfaction, as well as increased stress and depression (Bowl-ing & Beehr, 2006; Spector & Fox, 2005). Additionally, CWB cancause financial costs to organizations through behaviors such astheft and sabotage, shirking, increased absenteeism and turnover(among both offenders and victims), which result in a direct loss ofbillions of dollars of financial losses for organizations (Berry,Carpenter, & Barratt, 2012; Burke, Tomlinson, & Cooper, 2011;Dunlop & Lee, 2004).

OCB, on the other hand, are individual behaviors that [are]discretionary, not directly or explicitly recognized by the for-mal reward system, and in the aggregate promote the efficientand effective functioning of the organization (Organ, Podsa-koff, & Mackenzie, 2006, p. 8). OCB have been shown tocontribute to individual- and organizational-level effectiveness,making them a desirable set of behaviors for employees toengage in (Organ et al., 2006; Parker, Williams, & Turner,2006; Podsakoff, Whiting, Podsakoff, & Blume, 2009). As withCWB, OCB researchers (e.g., Organ et al., 2006) delineatedOCB according to whether they are directed at the organization(OCB-O) or other individuals (OCB-I) and, more recently,whether they are change-oriented (OCB-CH; Chiaburu, Oh,Berry, Li, & Gardner, 2011; Choi, 2007). OCB-O consists ofbehaviors such as volunteering for overtime and job dedication,whereas OCB-I consists of behaviors such as helping, courtesy,and interpersonal facilitation (Chiaburu et al., 2011). OCB-CHincludes a broad class of positive, proactive behaviors, such asvoice, creativity, and adaptive performance (Griffin, Neal, &Parker, 2007; Parker & Collins, 2010). At its core, individuals

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1223GMA AND NONTASK PERFORMANCE

engaging in OCB-CH contribute to heightened organizationalperformance by proactively suggesting new ideas and findingmore efficient and novel ways to accomplish tasks. There arecommonalities among CWB and OCB dimensions, as CWB-O(e.g., theft), OCB-O (e.g., job dedication) and OCB-CH (e.g.,personal initiative) represent behaviors that are more directlyrelated (in the case of CWB-O, antithetically) to the technicalcore of work and thus more directly related to task performance.On the other hand, the interpersonal dimensions of CWB-I (e.g.,racial slurs) and OCB-I (e.g., helping) are more directly relatedto the social context of work and less directly related to taskperformance.

Relationship of GMA With CWBNumerous studies in the criminology literature have argued that

GMA has an inhibitory effect on CWB (e.g., Dilchert et al.,2007; M. R. Gottfredson & Hirschi, 1990; Jensen, 1998; Marcus,Wagner, Poole, Powell, & Carswell, 2009; Moffitt & Silva, 1988).According to this hypothesis, high-GMA individuals are betterable to reason and learn, and therefore better evaluate all thepossible consequences of their actions. As OToole (1990, p. 220)stated, People with low intelligence may have a poorer ability toassess risks and, consequently take more poor risks . . . underconditions that a more intelligent person would avoid. In worksettings a high-GMA individual might shy away from shirking atwork or insulting coworkers because he or she better anticipatesthe possible negative long-term consequences (i.e., disciplinaryaction, damaged relationship with coworkers) of the behavior andknows that those far outweigh the possible short-term benefits ofthe deviant behavior. In line with the inhibitory effect, researchsuggests that cognitive ability is associated with the ability to delaygratification, a lack of which is associated with greater propensityto engage in delinquent or deviant behavior (Jensen, 1998; Ter-man, 1916).

A related explanation pertains to moral reasoning. Jensen (1998)points out that intelligence is related to all forms of reasoning, andadults of low GMA do not have the same developmental level ofmoral reasoning that is attained by adults of average and higherGMA. As such, higher GMA individuals will have a better senseof the inherent wrongness of their behaviors than lower GMAindividuals. Jensen also suggests that individuals with low GMAwill experience less success and more failure, which leads tofrustration, alienation, rejection of commonly accepted socialnorms, and verbal and physical aggression. That is, low GMA maybe one of the root causes to the frustrationaggression cycle, whichsuggests a negative relationship between GMA and CWB (Fox &Spector, 1999).

At the core of these perspectives is that high-GMA individualshave a greater ability to reason, learn, and solve problems. Thiscapacity has numerous positive benefits that result in less frequentengagement in CWB: better anticipation of the possible negativeconsequences of CWB, greater ability to suppress or delay grati-fication, superior moral reasoning, and less likelihood of fallingvictim of the vicious frustrationaggression cycle. Most likely,these mechanisms operate in concert and lead to a negative rela-tionship between GMA and CWB whereby smarter people engagein fewer CWB. In support of this contention, Dilchert et al. (2007)found that GMA has a negative relationship with the frequency of

organizational and interpersonal CWB based on organizationalrecords of formally recorded incidents among police officers. Assuch, we hypothesized the following:

Hypothesis 1: GMA will be negatively related to CWB.

Relationship of GMA With OCBThe greater ability of high-GMA individuals to reason, learn,

and solve problems may also explain the potential positive rela-tionship between GMA and OCB. That is, higher GMA individ-uals have a better understanding of the moral reasons (e.g., goingbeyond ones prescribed duties to help others is the right thing todo) and positive consequences (e.g., receiving recognition andrewards, positive feeling of self-worth) of engaging in more OCB(Podsakoff et al., 2009). In addition, Motowidlo, Borman, andSchmit (1997) derived a theory of individual differences in workperformance based on F. L. Schmidt and Hunters (e.g., Hunter,1986; F. L. Schmidt, Hunter, & Outerbridge, 1986) as well asBorman, Hanson, Oppler, and Pulakoss (1992) work. Their theorypostulates that GMA will be positively related to OCB because ofits effect on contextual knowledge, defined as knowledge of thefacts, principles, and procedures for effective action in situationsthat call for helping and cooperating with others; endorsing, sup-porting, and defending organizational objectives; persisting despitedifficult obstacles; and volunteering (Motowidlo et al., 1997; p.80). This suggests that contextual job knowledge plays an impor-tant role in the link between GMA and OCB, like task-related jobknowledge plays a key role in the link between GMA and taskperformance (Hunter, 1986; F. L. Schmidt & Hunter, 2004). Ex-amples of contextual knowledge include knowing how to makesuggestions to improve organizational functioning without con-flicting with supervisors, knowing how to calm an upset coworker,knowing how to work productively with difficult peers, knowinghow to project a favorable image of the organization, and so forth(Motowidlo et al., 1997). Because contextual knowledge requiresthe ability to learn, reason, and solve problems in various settingsinvolving other individuals and organizational policies (Ct &Miners, 2006), we would expect a positive relationship betweenGMA and OCB. Therefore, we hypothesized the following:

Hypothesis 2: GMA will be positively related to OCB.

Magnitude of the Relationships of GMA WithNontask Performance

Although we expect that GMA will predict nontask performance(negatively for CWB and positively for OCB), there are theoreticalreasons to believe that the relationship between GMA and nontaskperformance will be modest in magnitude compared to the rela-tionship between GMA and task performance. Over 50 years ago,Cronbach (1960) coined the term construct fidelity to refer to thenature and quality of information yielded by a measuring device.From a theoretical perspective, the construct fidelity principleposits that the predictive validity of a construct will depend on howwell it aligns with criteria in terms of the underlying constructsbeing assessed (Arthur & Villado, 2008; Campbell, 1990; Sackett& Lievens, 2008). This is relevant in the present study becausethere are fundamental differences in the nature of the informationassessed by GMA (and hence what criterion behaviors it will

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1224 GONZALEZ-MUL, MOUNT, AND OH

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predict) and the nature of information captured by nontask perfor-mance criteria such as CWB and OCB (and hence what constructswill predict them).

In applying the construct fidelity principle to personnel selec-tion, Borman and Motowidlo (1993) distinguished between twotypes of predictors that they labeled can-do and will-do. GMAis a can-do predictor because, as discussed earlier, it influencestask performance mostly through ones cognitive capacity to ac-quire, process, and apply information (e.g., Hunter, 1986; F. L.Schmidt et al., 1986). As such, criterion measures like task per-formance measures that are influenced strongly by the acquisitionand application of job-related information have greater fidelitywith GMA, and therefore will be predicted well by cognitiveability measures. In contrast, the two nontask performance criteriaare voluntary, intentional, and motivated behaviors. Consequently,they are more likely to be predicted by will-do predictors, such asthe FFM personality traits, which influence individuals motiva-tion and willful intentions to engage voluntarily in particularbehaviors. Empirical findings corroborate this logic, as personalitytraits have been shown to influence behavior through mediatingmechanisms that capture ones motivation and self-regulatory pro-cesses such as effort, goal-setting, and discretion (e.g., Barrick,Mount, & Strauss, 1993; Judge & Ilies, 2002; Mount, Ilies, &Johnson, 2006).

Hypothesis 3: The magnitude of the correlation between GMAand nontask performance will be modest and smaller in mag-nitude compared to the GMAtask performance relationship.

Relative Importance of GMA and Personality inPredicting Nontask Performance

Despite the can-do versus will-do distinction, it is important tonote that task performance is also influenced by personality. Thisis similar in nature to our argument that nontask performance isalso influenced by GMA. Namely, in the same way that taskperformance is influenced by motivation and self-regulation, it islikely that both OCB and CWB are influenced at least to somedegree by (contextual) knowledge acquisition. For example, someaspects of contextual knowledge are relevant to nontask perfor-mance behaviors, such as knowing when and how to help individ-uals (OCB) or knowing that a given behavior is morally wrong orharmful to ones career if caught (CWB). However, compared totask performance behaviors that require job-specific knowledge(e.g., facts, principles, concepts), OCB and CWB have substan-tially less construct fidelity with GMA because contextual knowl-edge may be less determined by GMA than job-specific knowl-edge because of its increased social and interpersonal focus(Chiaburu et al., 2011; Morgeson, Reider, & Campion, 2005).Therefore, we argue that whereas task performance behaviors areinfluenced largely by can-do factors and less so by will-do factors,the opposite is true for nontask performance behaviors, which areinfluenced more by will-do factors and less by can-do factors. Onthe basis of the preceding logic, we hypothesized the following:

Hypothesis 4: GMA will be relatively less important in pre-dicting CWB and OCB than the FFM traits, collectively.

Hypothesis 5: GMA will be relatively more important inpredicting task performance than the FFM traits, collectively.

To test these arguments, we gauge the relative importance ofGMA and the FFM personality traits in predicting CWB and OCB.To do so, we conduct RW analyses (Johnson, 2000) based onmeta-analytic evidence to determine the relative contribution ofthe FFM and GMA in predicting CWB, OCB, task performance(specific job performance dimensions), and a composite of jobperformance (overall job performance). The use of RW analyses(Johnson, 2000) is warranted due to the moderate to strong true-score correlations among the FFM traits (Mount, Barrick,Scullen, & Rounds, 2005). Relative weights broadly represent theaverage contribution of a predictor to the total R2, net of the otherpredictors, which provides an intuitive index of relative impor-tance among predictors (Johnson, 2000).

Moderators for the Relationship Between GMA andNontask Performance

In addition to our expectations regarding the overall relationshipand relative importance of GMA with nontask performance, weinvestigate the effects of three theoretically derived moderators:target of the nontask performance behaviors (other people at work,the organization, or change oriented), rating source, and job com-plexity. We also investigate relevant methodological moderators(e.g., publication status, GMA scales used), which are discussed inthe Method section.

Target of CWB and OCB. Based on the construct fidelityprinciple discussed earlier, the distinction between organization-ally based and interpersonally targeted CWB and OCB is note-worthy because it is possible that GMA has differential relation-ships with these two types of nontask behaviors. It is wellestablished that GMA is a major driver of task performance, andtherefore it follows that GMA should have a stronger negativerelationship with CWB-O than with CWB-I, and a stronger posi-tive relationship with OCB-O and OCB-CH than with OCB-Ibecause the former have a correspondence with task behaviors. Forexample, organizationally targeted CWB (e.g., theft) and OCB(e.g., job dedication) represent behaviors more directly related (inthe case of CWB-O, antithetically) to task performance. In the caseof OCB-CH, proactive behaviors require higher order cognitiveprocessing than the other two types of OCB, because it involvesanticipating the needs of the organization, as well as identifyingareas for improvement, and suggesting ways to meet the organi-zations needs or suggesting ways to improve the organization(Grant & Ashford, 2008). Thus, due to the proactive and task-based nature of OCB-CH, it is possible that it will have a strongercorrelation with GMA than OCB-I. On the other hand, becauseCWB-I (e.g., racial slurs) and OCB-I (e.g., helping) have a socialand interpersonal context focus, their relationship with GMA willbe much weaker given the lack of construct fidelity.

With respect to CWB, an alternative argument is that higherGMA individuals are more cognizant that CWB-I are more ob-servable/detectable, because by definition they involve intentionalnegative behaviors directed toward other individuals (Oh, Charlier,Mount, & Berry, 2014). As such, higher GMA individuals aremore likely to avoid engaging in CWB-I in order to avoid sanc-tions (including lower overall job performance ratings) at workthan lower GMA individuals. This could lead to a stronger nega-tive relationship between GMA and CWB-I than between GMAand CWB-O. Given this, it is unclear which relationship will be

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1225GMA AND NONTASK PERFORMANCE

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more negative, the GMACWB-O relationship or the GMACWB-I relationship. Because of the difficulty in predicting whichtarget has a stronger relationship with GMA, we examine thisresearch question in an exploratory manner.

CWB rating source. Because there were only three studies inour database that used self-reported OCB measures (we report theresults of the studies to be thorough for informational purposes),we examine the moderating effect of rating source for CWB only.The most frequently used method of collecting CWB is via self-report primarily because individuals are in the best position toreport on the frequency of their own CWB (particularly targeted atthe organization; Berry et al., 2012; Berry, Ones, & Sackett, 2007;Raver & Nishii, 2010). A recent meta-analysis provided supportfor this logic, as individuals actually self-report engaging in more(over one third of a standard deviation) CWB than observers reportthem engaging in ( .35; Berry et al., 2012). However, scholarshave argued that high-GMA individuals will be less likely toself-report their transgressions because they better understand thepossible negative consequences of doing so (Dilchert et al., 2007).Thus, the negative GMACWB relationship that we hypothesizedmay be even stronger when CWB are measured via self-reportscompared to other sources and methods.

In addition, non-self-report ratings of CWB could lead to dif-ferent relationships with CWB. When individuals engage in CWB,they typically do so with the explicit purpose of getting away withit (e.g., theft, falsifying an expense report, shirking, using drugsor alcohol on the job). Therefore, it is highly unlikely that the truefrequency of CWB engaged in by the individual will be fullyobserved (or detected) by others, regardless of their perspective(peers, subordinates bosses, or objective records). Further, as Mof-fitt and Silva (1988) discuss in their differential detection hypoth-esis, high-GMA individuals may be more likely to engage indeviant behavior without being caught because of their superiorproblem-solving skills. Moreover, given that high-GMA individ-uals are likely to be better performers, the GMACWB relation-ship based on supervisor ratings may reflect a halo effect wherebyCWB represent poor task performance, which leads to an inflatednegative relationship between GMA and CWB. Similarly, objec-tive records of CWB suffer from their own form of deficiencybecause they represent only major CWB that have been detectedand formally documented. As follows from the above discussion,it is difficult to predict whether or in which direction the relation-ship between GMA and CWB will vary across different CWBrating sources and methods. Thus, we will examine this moderat-ing effect in an exploratory manner.

Job complexity. Although research has shown the validity ofGMA in predicting task performance to be relatively stable acrosssituational contexts, one moderator identified as affecting theGMAtask performance relationship is job complexity. Researchshows that the validity of GMA, although relatively high for alljobs, is even higher for more complex jobs compared to lesscomplex jobs (F. L. Schmidt & Hunter, 2004; F. L. Schmidt,Shaffer, & Oh, 2008). This is because more complex jobs requirehigh levels of information processing and problem-solving skillsand broader domains of knowledge related to the job. Further,individuals in more complex jobs (e.g., professors, scientists, en-gineers) are typically less constrained by situational factors (e.g.,high autonomy, more flexible work schedules). This represents aweak situation where individual differences (e.g., GMA, person-

ality traits) are more freely expressed in their behavior. However,as discussed above, although we believe nontask performance isinfluenced by GMA, the major driver of nontask performance islikely motivation and self-regulatory mechanisms that are primar-ily determined by personality traits. Nonetheless, it is possible thatthe aforementioned argument could apply to OCB and CWB. Inthe current study, therefore, we examine this notion in an explor-atory fashion.1

Method

Literature Search

We employed five strategies to identify all available publishedand unpublished articles that might supply pertinent effect sizes.First, we searched the PsycINFO, Web of Knowledge, and Dis-sertation Abstracts International databases for articles containingkeywords associated with GMA, such as cognitive ability, intelli-gence, general mental ability, and g factor, coupled with keywordsassociated with CWB and OCB, such as counterproductive behav-ior, counterproductive work behavior, antisocial behavior, disrup-tive behavior, counterproductivity, delinquent behavior, deviance,interpersonal deviance, noncompliant behavior, organizationaldeviance, retaliation, rule compliance, theft, reprimands, griev-ances, workplace deviance, helping, interpersonal facilitation, jobdedication, extra-role behaviors, pro-social behavior, organiza-tional citizenship behaviors, creativity (creative performance), in-novation (innovative behavior), proactive behavior (performance),adaptive performance, voice, taking charge, personal initiative,and contextual performance, either in the abstract or article key-words. Second, we used Google Scholar to identify all the articlesthat cited Bennett and Robinson (2000), Robinson and Bennett(1995), Motowidlo et al. (1997), and Borman and Motowidlo(1993), as well as the articles found in Step 1. These articles werethen searched to identify any pertinent coefficients. Third, wemanually searched all relevant major journals, such as the Journalof Applied Psychology, Academy of Management Journal, Person-nel Psychology, Journal of Management, Journal of Organiza-tional Behavior, International Journal of Selection and Assess-ment, and Personality and Individual Differences, published from1995 to 2013. Fourth, we searched the conference programs for theSociety for Industrial and Organizational Psychology and Acad-emy of Management conferences for any pertinent articles. Fifth,we consulted the reference sections of meta-analyses conducted onCWB and OCB (e.g., Berry et al., 2007; Chiaburu et al., 2011;Dalal, 2005; Salgado, 2002).

Inclusion and Exclusion CriteriaTo be included in the current meta-analysis, primary studies had

to meet the following criteria. First, we retained primary studiesthat contained a correlation or other statistics (e.g., univariate t, F)that could be converted into a correlation coefficient betweenGMA and either CWB or OCB. Second, we also included onlysamples where participants were adults who were employed at the

1 We would like to thank an anonymous reviewer for suggesting thisresearch question.

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time of data collection. Third, only test (not self-report) measuresof GMA were considered, and fewer g-loaded perceptual abilities(i.e., civil service exams common in police selection) were ex-cluded from the current meta-analysis. Fourth, we only includedprimary studies that measured naturally occurring CWB (OCB) inwork settings as opposed to primary studies based on contrivedCWB (OCB) lab tasks. Finally, given that early OCB measureswere often contaminated with CWB content (Dalal, 2005), weincluded only those studies whose OCB measure could be distin-guished from lack of CWB and only those studies whose criteriaclearly fit our definitions of CWB and OCB. These search tech-niques and decision criteria yielded 35 independent samples of theGMACWB correlation encompassing 12,074 individuals. Ofthese, 23 were published in peer-reviewed academic journals, onewas an unpublished raw data set, nine were doctoral disserta-tions, one was a military technical report, and one was aconference presentation. For the GMAOCB meta-analyses, wewere able to identify 43 independent samples encompassing12,507 individuals. Of these, 29 were published in peer-reviewed academic journals, eight were dissertations, five werefrom masters theses, and one was a conference presentation.All the studies we included are reported in Appendices A(CWB) and B (OCB), and studies that were considered butultimately excluded are reported in Appendix C.

Meta-Analytic ProceduresFor each primary study, we coded or computed the correlation

between GMA and CWB and/or OCB. In addition, we coded thecriterions rating source, occupation and job complexity of thesample, publication status, target of the criterion if not clearlyspecified in the study (e.g., organizational deviance vs. interper-sonal deviance), measure used for the criterion (e.g., Bennett &Robinson, 2000), and measure used for GMA (e.g., the WonderlicPersonnel Test). Because of the high number of studies on theGMACWB relationship conducted in both military and policesettings, we coded and included these categories as moderators.CWB and OCB measures from many of the primary studies couldnot be coded according to their target because the measures inthose studies mixed both the interpersonal and organizationaltargets and combined measures different in target. Further, manyof the samples were mixed in terms of their jobs and occupationsor did not provide sufficient information about the samples, despiteour effort to contact the authors of those studies. Therefore, thefirst and third authors holistically categorized the level of jobcomplexity for each sample according to all the available infor-mation in the article into either low-, medium-, or high-complexitycategories (see Le et al., 2011); the interrater agreement was 91%.Any remaining discrepancies were resolved through a series ofdiscussions. In terms of coding other information necessary fordata-analysis, the first author coded all the primary studies, and thethird author independently randomly double-checked 40% of theprimary studies for accuracy. The agreement rate was very high(Cohens .98); all discrepancies involved subjective judgmentcalls such as whether reliability estimates reported based on testmanuals should be coded (we decided to use them) and whichsample size should be coded if only the sample size range wasreported (we decided to use the lowest sample size to be conser-vative).

We used the Hunter and Schmidt random-effects meta-analysismethod to synthesize correlation coefficients across the primarystudies (Hunter & Schmidt, 2004; F. L. Schmidt, Oh, & Hayes,2009). Because most primary studies reported reliability estimates,we used individual correction methods (VG6 module; F. L.Schmidt & Le, 2004). Because of recent criticism levied towardthis method (Erez, Bloom, & Wells, 1996; Kish-Gephart,Harrison, & Trevio, 2010; LePine, Erez, & Johnson, 2002), wealso report the meta-analytic population effect size estimates andaccompanying confidence intervals (CIs) computed using Erez etal.s (1996) random-effects method, where individual study corre-lations are treated as Level 1 variables and moderators as Level 2variables in a hierarchical linear modeling (HLM) framework(Raudenbush, Bryk, & Congdon, 2004).2 The substantive conclu-sions across the two methods were nearly identical. Correlationsreported in primary studies were corrected for measurement errorin both the independent and dependent variables using local reli-ability (in most cases, coefficient alpha, and in a few cases,testretest reliability) reported in the primary studies. For primarystudies that did not report the reliability for GMA, we used thereliability estimate provided by the test manual. The mean reli-ability for GMA across the primary studies is .86 (SD .09, k 77). For studies reporting objective CWB measures (i.e., counts ofincidences of CWB), we used the reliability estimate of .83 cal-culated by Dilchert et al. (2007). This is likely a conservativeestimate, as many of the studies with an objective record criterionincluded in the meta-analysis utilized a single count of the numberof grievances filed against an officer. Some primary studies thatdid not use objective records also did not report a reliabilityestimate. For these studies, we imputed the average criterionreliability (.82 [SD .07, k 16] for CWB, .89 [SD .07, k 39] for OCB).

Further, we corrected the correlations for indirect range restric-tion in order to generalize our results to the general applicantpopulation. We used the ux value of .63 meta-analytically derivedby F. L. Schmidt et al. (2008), as the standard deviation ratio of theGMA of applicants to incumbents was not available in any of theprimary studies. For those studies that reported different dimen-sions of GMA (i.e., verbal, quantitative) and their individualrelationships with CWB and OCB, we computed the compositecorrelation. We followed the same procedure for studies reportingspecific dimensions of CWB or OCB. If the composite could notbe determined (i.e., no intercorrelations between dimensions weregiven), we used the average.

Finally, we also separately computed and reported (see Appen-dix D) true-score correlations corrected for measurement error inthe criterion measure using a meta-analytic interrater reliability of.53 (instead of local/alpha reliability) for supervisor ratings ofCWB and OCB in order to be fully comparable with prior meta-analyses that we chose to use in the RW analyses mentioned above(Berry et al., 2012; Chiaburu et al., 2011; Hurtz & Donovan, 2000;F. L. Schmidt et al., 2008).

We examined the standard error of the mean true-score corre-lations by computing their 95% CIs to determine if the estimatedtrue-score correlation differs from 0. To gauge the degree of a

2 We would like to thank an anonymous reviewer for suggesting theseanalyses.

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1227GMA AND NONTASK PERFORMANCE

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moderating effect, we took several steps. First, we computed the80% credibility interval (CrI) and the between-studies variance(2) for each overall correlation (e.g., GMACWB, GMAOCB).A CV that includes 0 and a significant between-studies varianceindicates that moderator effects are likely (Erez et al., 1996;Hunter & Schmidt, 2004). Second, we computed correlations andassociated 95% CIs in different moderator categories. We exam-ined if the 95% CIs around two true-score correlations in a cate-gory (e.g., published vs. unpublished) overlap. Complete overlapsuggests the difference between two true-score correlations is fullyartifactual due to second-order sampling error and that there is nomeaningful moderating effect. In contrast, no overlap suggests thedifference between two true-score correlations is nonartifactualand that there is a meaningful moderating effect. Third, we alsoexamined all moderators simultaneously while accounting for theintercorrelations between moderators using a regression-basedmethod. To do this, we transformed individual correlations intoFishers Z and regressed them on the proposed moderators in HLMwherein each study was weighted by the inverse of the samplingerror variance (Erez et al., 1996; Steel & Kammeyer-Mueller,

2002). If the HLM regression weight (B) associated with a givenmoderator was significant, it can be interpreted as a significantmoderating effect.

Results

Tables 1 and 2 summarize the meta-analytic results for therelationship of GMA with CWB and OCB, respectively. To beconsistent with prior meta-analytically derived correlations that weuse in our RW analyses, we refer to Hunter and Schmidt correctedcorrelation coefficients in our description of results, but the resultsusing Erez et al.s (1996) method are also presented in Tables 1and 2. None of our substantive conclusions differed across meth-ods. Table 3 presents regression results for the moderators.

Relationships Between GMA and CWBAs shown in the first row of Table 1, the overall true-score

correlation between GMA and CWB was essentially 0 ( .02).Further, its 95% CI as well as 80% CrI included 0 (95% CI [.09,

Table 1Correlation Between General Mental Ability (GMA) and Counterproductive Work Behaviors (CWB) and Moderator Analyses

Variable k N

HunterSchmidts method Erez et al.s method

r SDr SD 95% CI 80% CrI 95% CI

GMACWB 35 12,074 .02 .10 .02 .18 [.09 .04] [.25 .21] .03 [.10 .04]CWB rating source

Self-rated 19 6,700 .03 .08 .05 .13 [.01 .11] [.13 .21] .06 [.01 .14]Non-self-rated 16 5,374 .08 .09 .11a .17 [.20 .02] [.34 .11] .15 [.28 .02]

Objective record 12 4,696 .08 .09 .12 .16 [.17 .06] [.33 .09] .17 [.31 .02]Supervisor rated 4 678 .04 .09 .08a .14 [.24 .09] [.26 .11] .09 [.27 .10]

Target of CWBCWB-O 7 1,854 .11 .12 .20 .17 [.34 .07] [.42 .01] .14 [.43 .19]CWB-I 4 1,462 .03 .10 .09 .18 [.27 .10] [.32 .14] .02 [.25 .22]

Job complexityLow 13 3,925 .03 .09 .04 .15 [.05 .13] [.15 .23] .08 [.02 .17]Medium 18 6,537 .05 .10 .07 .18 [.16 .02] [.31 .16] .11 [.25 .04]High 4 1,612 .00 .09 .01 .15 [.17 .15] [.21 .18] .00 [.21 .21]

Publication statusPublished 23 8,307 .01 .11 .01 .19 [.08 .06] [.25 .23] .00 [.15 .14]Unpublished 12 3,767 .04 .08 .06 .13 [.14 .02] [.22 .10] .09 [.20 .02]

GMA assessmentWPT 12 2,776 .04 .12 .04 .22 [.17 .09] [.32 .24] .04 [.22 .14]Other 23 9,298 .01 .09 .02 .16 [.09 .05] [.22 .18] .03 [.11 .06]

CWB assessmentBennett and Robinson 7 1,003 .00 .10 .03 .17 [.19 .12] [.26 .19] .02 [.18 .15]Other 29 11,071 .02 .10 .02 .17 [.09 .04] [.25 .20] .03 [.12 .05]

MilitaryMilitary 10 5,200 .00 .07 .00 .11 [.08 .07] [.15 .14] .00 [.11 .11]Nonmilitary 25 6,874 .03 .12 .04 .22 [.12 .03] [.33 .24] .05 [.15 .06]

PolicePolice 10 3,758 .09 .10 .13 .18 [.25 .02] [.37 .10] .18 [.34 .02]Nonpolice 25 8,316 .01 .08 .02 .13 [.03 .08] [.15 .19] .03 [.03 .10]

Note. CWB-O CWB directed at the organization; CWB-I CWB directed at individuals; WPT Wonderlic Personnel Test; k number ofstatistically independent samples; N total sample size; r sample-size-weighted mean correlation; SDr sample-size-weighted observed standarddeviation of correlations; mean true-score correlation corrected for indirect range restriction on the predictor measure and measurement error in thepredictor and criterion measures; SD standard deviation of true-score correlations corrected for indirect range restriction on the predictor measure andmeasurement error in both the predictor and criterion measures; CI confidence interval around the mean true-score correlation; CrI credibility interval;2 estimate of between-studies variance for the GMACWB relationship .002 (p .05).a The true-score correlations corrected for indirect range restriction on the predictor measure and measurement error in both the predictor (using localreliability) and criterion (using the interrater reliability of .53 for single supervisor ratings and .83 for objective records) measures are .12 (input to relativeweight analyses in Table 4) and .14 from top to bottom.

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1228 GONZALEZ-MUL, MOUNT, AND OH

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.04]; 80% CrI [.25, .21]), and the between-studies variance (.002,p .05) was significant. These results failed to provide support forHypothesis 1. Next, as shown in Table 3, we investigated theeffects of our substantive moderators (e.g., rating source, jobcomplexity) as well as some methodological moderators (e.g.,publication status, occupation of the sample, and type of scale usedto assess GMA and CWB) by regressing the correlations from ourdatabase on our moderators and weighting them by the inverse ofthe sampling error variance. We should note that we were unableto evaluate the target moderator in this manner because manystudies did not provide the necessary information (i.e., reported anoverall CWB as opposed to CWB-O or CWB-I), and some studiesmeasured CWB-O, CWB-I, and overall CWBthus, one valuecould not be assigned to each sample. We also report results for allmoderator categories in Table 1.

The correlation between GMA and CWB, as shown in Table1, was small and positive when CWB were self-rated and the95% CI included 0 ( .05; 95% CI [.01, .11]). The corre-lation was negative and small when CWB were non-self-ratedand the 95% CI did not include 0 ( .11; 95% CI[.20, .02]). This is consistent with the regression resultsshown in Table 3 (B .12, p .05). It is important to notethat the 95% CIs of the estimates did not overlap. Further, asshown in Table 1, the correlations gleaned from different non-self-rating sources (objective records vs. supervisory ratings)were not significantly different from one anotherobjective .12, 95% CI [.17, .06]; supervisor .08,95% CI [.24, .09]). Thus, our results indicate that the rela-

tionship between GMA and CWB is moderated by the source ofthe CWB, with self-ratings and non-self-ratings (including bothsupervisor ratings and objective records) yielding correlationsthat were different from each other.

Second, as shown in Tables 1 and 3, the GMACWB corre-lation in police samples was negative ( .13; 95% CI[.25, .02]), compared to a correlation of .02 (95% CI [.03,.08]) in nonpolice samples. Although their 95% CIs overlappedonly slightly and the regression indicated a significant moder-ating effect (B .12, p .05), these results should beinterpreted with caution because the police samples utilizedobjective records exclusively as measures of CWB.

As shown in Table 1, we did not detect any meaningfulmoderating effects in terms of the target of CWB (CWB-O vs.CWB-I), job complexity, publication status, GMA measures,and military versus nonmilitary samples. However, as shown inTable 3, the regression showed that the military versus nonmil-itary samples moderator was significant (B .11, p .05)when entered with the other moderators into the regression; inmilitary samples, the relationship is 0, but it was slightlynegative in nonmilitary settings. Given the small difference ineffect size, it should be interpreted with caution, but it maysuggest that in strong situations like the military, the GMACWB relationship is close to 0.

Relationships Between GMA and OCBAs shown in the first row of Table 2, the overall correlation

between GMA and OCB was positive and moderate in magnitude

Table 2Correlation Between General Mental Ability (GMA) and Organizational Citizenship Behaviors (OCB) and Moderator Analyses

Variable k N

HunterSchmidts method Erez et al.s method

r SDr SD 95% CI 80% CrI 95% CI

GMAOCB 43 12,507 .13 .17 .23 .17 [.18 .29] [.01 .45] .29 [.21 .37]OCB rating source

Self-rated 7 2,103 .11 .15 .19 .18 [.05 .33] [.04 .42] .29 [.06 .49]Supervisor-rated 36 10,404 .14 .17 .24a .17 [.18 .30] [.03 .46] .29 [.04 .50]

Target of OCBOCB-O 9 4,328 .10 .14 .18 .15 [.08 .29] [.01 .38] .32 [.10 .51]OCB-I 11 5,161 .09 .12 .16 .14 [.08 .25] [.01 .33] .25 [.12 .37]OCB-CH 14 5,169 .14 .17 .24 .17 [.15 .33] [.02 .46] .33 [.13 .50]

Job ComplexityLow 19 5,541 .13 .17 .24 .16 [.16 .32] [.03 .45] .35 [.24 .45]Medium 20 6,693 .13 .17 .23 .17 [.15 .31] [.01 .46] .27 [.11 .42]High 4 273 .07 .16 .13 .23 [.12 .38] [.16 .42] .11 [.19 .39]

Publication statusPublished 29 7,667 .14 .18 .25 .19 [.18 .32] [.00 .49] .24 [.16 .32]Unpublished 14 4,840 .12 .15 .22 .14 [.14 .29] [.04 .39] .32 [.18 .45]

GMA assessmentWPT 15 2,345 .13 .19 .22 .23 [.10 .35] [.08 .52] .30 [.12 .47]Other 28 10,162 .13 .16 .24 .15 [.18 .30] [.04 .43] .31 [.21 .40]

Note. OCB-O OCB directed at the organization; OCB-I OCB directed at individuals; OCB-CH change-oriented OCB; WPT WonderlicPersonnel Test; k number of statistically independent samples; N total sample size; r sample-size-weighted mean correlation; SDr sample-size-weighted observed standard deviation of correlations; mean true-score correlation corrected for indirect range restriction on the predictormeasure and measurement error in both the predictor and criterion measures; SD standard deviation of true-score correlations corrected for indirect rangerestriction on the predictor measure and measurement error in both the predictor and criterion measures; CI confidence interval around the meantrue-score correlation; CrI 80% credibility interval; 2 estimate of between-studies variance for the GMA-OCB relationship .006 (p .001).a The true-score correlation corrected for indirect range restriction on the predictor measure and measurement error in both the predictor (using localreliability) and criterion (using the interrater reliability of .53 for single supervisor ratings) measures is .31 (input to relative weight analyses in Table 4).

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1229GMA AND NONTASK PERFORMANCE

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( .23; 95% CI [.18, .29]), thereby providing support forHypothesis 2. However, the 80% CrI was very wide (80% CrI [.01,.45]), and the between-studies variance (.004, p .05) was sig-nificant, suggesting the presence of moderators. As such, wefollowed the same procedure described above to evaluate the effectof our a priori specified moderators (OCB target, job complexity),

as well as the same moderators that we evaluated in the GMACWB relationship to determine if they affect the GMAOCBrelationship. As was the case with GMACWB, we were unable toinclude the target moderator in the regression analysis. As shownin Tables 2 and 3, all of the 95% CIs of our hypothesized mod-erators overlapped and none of the moderator coefficients weresignificant. This suggests that the GMAOCB relationship ishighly generalizable across all examined moderator classes. Insum, the correlations between GMA and the two nontask perfor-mance criteria (CWB .02 and OCB .23) were muchsmaller than the correlation between GMA and task performance( .69; F. L. Schmidt et al., 2008), providing support forHypothesis 3.

Relative Importance of the FFM and GMAOne of the major purposes of this study is to determine the

relative importance of GMA and the FFM in predicting CWB andOCB, and compare the results to those obtained in previous meta-analytic studies for task performance and overall job performance.As described in the Method section, the footnote of Table 4, andAppendix D, we took two steps to make our meta-analytic esti-mates comparable to the other elements in the matrix. First, weonly included non-self-report criterion measures for the GMAOCB and GMACWB true-score correlations corrected for mea-surement error in both the measures and range restriction on thepredictor measure. Second, we corrected these estimates with thesame interrater reliability estimate of .53 used by the FFMOCB,FFMCWB, FFMtask performance, and GMAtask performancemeta-analyses included in our matrix from Berry et al. (2012),Chiaburu et al. (2011), F. L. Schmidt et al. (2008; reanalysis ofHunter, 1986), and Hurtz and Donovan (2000), respectively. Be-

Table 3Omnibus Moderator Hierarchical Linear ModelRegression Results

Coefficient (B)Moderator CWB OCB

Rating source .12 .05Publication status .05 .10WPT .01 .03Complexity .00 .12B&R .10Military .11Police .12

Note. Two hierarchical linear model regression results are reported to-gether. In each regression (i.e., counterproductive work behaviors [CWB],organizational citizenship behaviors [OCB]), all moderators are enteredsimultaneously. For rating source, 1 nonself, 0 self. For publicationstatus, 1 published, 0 unpublished. For the Wonderlic Personnel Test(WPT), 1 used WPT, 0 other general mental ability measure used. Forjob complexity, 1 low, 2 medium, 3 high. For the Bennett andRobinson (B&R) scale, 1 used B&R, 0 other CWB measure used. Formilitary, 1 military sample, 0 other. For police, 1 police sample,0 other. The moderating effects of the behavioral target (e.g., CWB-Ovs. CWB-I; OCB-O vs. OCB-I vs. OCB-CH; see Table 2 for definitions)could not be tested here because only a few primary studies provided thenecessary information. p .05.

Table 4Relative Weight Analysis of the Five-Factor Model (FFM) and General Mental Ability (GMA)Predicting Counterproductive Work Behaviors (CWB), Organizational Citizenship Behaviors(OCB), Task Performance, and a Job Performance Composite

Predictor

CWBa(reverse coded) OCBa

Taskperformancea

Jobperformancecompositeb

RW %RW RW %RW RW %RW RW %RW

Emotional stability .007 4 .005 3 .006 1 .005 2Extraversion .018 12 .003 2 .007 1 .001 0Openness/intellect .028 19 .024 16 .021 4 .005 2Agreeableness .052 35 .012 8 .003 1 .023 9Conscientiousness .032 21 .024 17 .024 4 .040 16GMA .013 9 .079 53 .499 89 .176 71All FFM traitsc .137 91 .070 47 .061 11 .073 29

Total R2 .149 .149 .561 .249RGMA over FFM2 .015 .073 .527 .177RFFM over GMA2 .135 .053 .085 .073

Note. The meta-analytic input matrix is presented in Appendix D. We reversed correlations involving CWBbefore conducting relative weight analyses to ease interpretation. RW relative weight (Johnson, 2000);%RW percentage of relative weight calculated by dividing individual relative weights by their sum (total R2)and multiplying by 100 (RWs add up to R2 and %RWs add up to 100%, respectively); RGMA over FFM2 changein R2 due to adding GMA to the FFM; RFFM over GMA2 change in R2 due to adding the FFM to GMA.a We used meta-analytic results only based on non-self-report CWB (reverse coded), OCB, and task performance(see Appendix D for more details). b This is a composite of CWB (reversed coded), OCB, and task perfor-mance (see Appendix D for more details). c This is the sum of RWs (and %RWs) of all FFM traits.

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1230 GONZALEZ-MUL, MOUNT, AND OH

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cause part of the purpose of the RW analyses was to compute anoverall job performance composite from task performance, OCB,and CWB, we needed meta-analytic estimates of these intercorre-lations from non-self-report sources so that they are more directlycompatible with the other correlations in our matrix. To ourknowledge, there is no meta-analysis between specifically non-self-rated task performance and CWB in the literature. Therefore,we conducted this meta-analysis (k 10, N 3,752, r .49, .56, 95% CI [.66, .45]).3 See Appendix D for the fullmeta-analytic correlation matrix used in the RW analyses alongwith details of the correlations sources. Note that we used theGMAnontask performance correlations derived with the HunterSchmidt method to conduct the RW analyses. We also reversed allthe correlations with CWB to aid interpretation before conductingRW analyses.

As shown in Table 4, GMA (RW .013; %RW 9) accountedfor 9% of the explained variance in CWB, whereas the FFM(RW .137; %RW 91) accounted for 91% of the explainedvariance in CWB. In addition, GMA (RW .079; %RW 53)and the FFM (RW .070; %RW 47) each explained approx-imately half of the explained variance in OCB. Therefore, Hypoth-esis 4 was only supported in the case of CWB and not OCB.Further, GMA accounted for 89% of the explained variance in taskperformance, whereas the FFM accounted for only 11% of theexplained variance in task performance, providing support forHypothesis 5. When job performance is operationalized as a com-posite of task performance, OCB, and CWB, results showed GMA(RW .176; %RW 71) accounted for over twice as muchexplained variance as the FFM (RW .073; %RW 29). Further,as shown in the bottom of Table 4, hierarchical multiple regressionanalyses (RGMA over FFM2 and RFFM over GMA2 ) provided virtually thesame results as the corresponding RW results. As an additional testof the differential prediction of task versus nontask performanceby GMA, we created a database of our GMACWB and GMAOCB correlations in addition to seven meta-analytically derivedGMAtask performance correlations from F. L. Schmidt et al.(2008). We then dummy-coded the different criteria with taskperformance as the referent and regressed the correlations on thedummy codes. These analyses showed that the GMACWB andGMAOCB correlations are significantly different from theGMAtask performance relationships.4

DiscussionThe general conclusion in the industrialorganizational psy-

chology literature is that GMA is the best single predictor of jobperformance. As F. L. Schmidt (2002, p. 207) stated, the purelyempirical research evidence in I/O psychology showing a stronglink between [GMA] and job performance is so massive that thereis no basis for questioning the validity of [GMA] as a predictor ofjob performance. Yet, most of the cumulative knowledge aboutthe validity of GMA is based on the criterion of task performance,which raises questions about whether GMA is a valid predictor ofnontask performance. As a result, our major goal in this study wasto respond to the long overdue call by Salgado (1999) to furtherdevelop cumulative knowledge regarding the relationship of GMAwith job performance by expanding the criterion space to includenontask performance such as OCB and CWB. As F. L. Schmidtand Kaplan (1971; see Rotundo & Sackett, 2002; Viswesvaran &

Ones, 2000) suggested, it is beneficial to understand the relation-ships of GMA with specific job performance dimensions (such asCWB and OCB) for enhancing our theoretical understanding ofboth GMA and work behaviors.

The results of the meta-analyses revealed that the relationshipsbetween GMA and nontask performance criteria are modest, es-pecially relative to the strong relationship between GMA and taskperformance. First, counter to our expectations, the omnibus true-score correlation between GMA and CWB, overall, is essentially 0( .02), although it is modestly negative ( .11) whenCWB are measured by non-self-report methods (e.g., supervisorsor objective records). This finding calls into question the charac-terization by some that GMA is an all-purpose tool that can beused to solve any kind of problem including delinquency (L. S.Gottfredson, 1997b; Jensen, 1998). However, this finding requiresfurther explanation, which we discuss later. Second and in linewith our expectations, the omnibus true-score correlation betweenGMA and OCB is positive but moderate in magnitude ( .23),which shows that more intelligent people have a tendency to bemore helpful to coworkers and more likely to do more than the jobrequires. Third, the meta-analytic RW and regression analysesshowed that the FFM is substantially more important than GMA inpredicting CWB and that the FFM and GMA are about equal inpredicting OCB. These findings provide mixed support for Bor-man et al.s (1993, 1997) theory as well as our hypotheses. Asexpected, results showed that GMA is substantially more impor-tant than the FFM for task performance and, to a lesser extent,overall job performance. These findings have several implicationsfor both theory and practice.

Theoretical ImplicationsThe overall, null true-score correlation between GMA and CWB

runs contrary to our predictions derived from the inhibitory effectfrom the criminology literature. One explanation, albeit specula-tive, is that the inhibitory effect has limited applicability to work-ing adults. Sociologists postulate that the inhibitory effect of GMAbegins to manifest itself in adolescencewell before individualsenter the workforce (Walsh & Ellis, 2003). The implication of thisis that if GMA acts as an inhibitory mechanism, many low-GMAindividuals may begin a criminal career early in their lives (e.g.,M. R. Gottfredson & Hirschi, 1990) and be less likely (or less able)to be employed later when they become adults. That is, it seemsthat the inhibitory effect-based explanation is more suitable fordelinquent behaviors among adolescents, not necessarily CWBamong working adults (behaviors that can happen in adulthoodwhen people are at work).

Accordingly, in order to more completely understand the GMACWB relationship, it is helpful to consider other important aspectsof the employment context. One such aspect is that for individualsto be sanctioned for engaging in CWB, they must be detected byother individuals at work. This means that the operational measureof CWB actually is the extent to which the individual has beendetected engaging in CWB, not necessarily the actual frequency of

3 Full results of the task performanceCWB meta-analysis are availablefrom the first author.

4 We would like to thank an anonymous reviewer for suggesting theseanalyses. The full results are available from the first author on request.

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1231GMA AND NONTASK PERFORMANCE

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doing so. Relatedly, Moffitt and Silvas (1988) differential detec-tion hypothesis posits that more intelligent individuals do notnecessarily engage in fewer CWB but rather are better able toavoid being caught engaging in CWB by using their superiorproblem-solving ability to skirt any organizational monitoringsystems. That is, the differential detection hypothesis suggests thatthese differences in the detection rate then manifest themselves ina negative overall correlation between GMA and the frequency ofdetected CWB, although low-GMA individuals do not actuallyengage in less deviance at work than high-GMA individuals.Consistent with this hypothesis, the true-score correlation of GMAwith non-self-rated CWB (supervisor, archival) is modest yetnegative at .11 (its 95% CI excludes 0), but that between GMAand self-rated CWB is essentially 0 (its 95% CI includes 0).Although this effect is relatively small, it means that smarterpeople are seen by others as engaging in fewer CWB despite therebeing no difference in the way smart versus less smart individualsreport the frequency of their own deviant behavior.

A plausible alternative explanation is that, as a dimension of jobperformance, ratings of CWB are influenced by an overall, latentjob performance construct such that when supervisors (or others)rate CWB, they are influenced by the individuals overall level ofperformance, which tends to be higher for smart people. In otherwords, this explanation would suggest that the negative correlationobserved between GMA and non-self-rated CWB may be artifac-tually influenced by halo error. Although the present study cannotdefinitively answer whether this is the case, it is informative toexamine the GMACWB relationship gleaned from formal per-sonnel records versus supervisor ratings. The magnitude of thetrue-score correlation for personnel records was similar to that forsupervisor ratings (.12 vs. .08), and their 95% CIs fully over-lapped. Compared to supervisor ratings of CWB, personnel re-cords of counterproductivity are less likely to be influenced byhalo error because they are often reported by a variety of sources(e.g., coworkers, customer complaints), usually document specificinfractions the individual in question committed, and are usuallyreported at a different time than an evaluation of performance.Therefore, the explanation whereby ratings of CWB are due tohalo error seems less plausible. Overall, we believe that our resultsare consistent with the differential detection hypothesis.

The result that GMA is moderately correlated with OCB sup-ports our hypotheses. Further, the finding that the FFM traits,collectively, are about equal in importance to GMA in predictingOCB provides mixed support for Borman and Motowidlo (1993)and Motowidlo et al.s (1997) theory. On the one hand, in accor-dance with their theory, the FFM performs much better in com-parison to GMA when considering nontask as opposed to taskperformance, and on the other hand, their theory stipulates that theFFM will be a stronger predictor than GMA for nontask behaviors,which we found not to be the case. We also found that the modesttrue-score correlation of GMA with OCB was similar in magnitudeto that of the correlations for the individual FFM traits (Chiaburuet al., 2011). Overall, this is consistent with the idea that higherGMA individuals are better able to acquire and apply contextualjob knowledge, and this leads to more helping and volunteeringbehaviors (Motowidlo et al., 1997). Consistent with the theory andour hypothesis, this correlation is also substantially lower than thatbetween GMA and task performance. The GMAOCB relation-ship was not moderated by any of the moderators we investigated.

Considering both the magnitude and direction of the GMACWB and GMAOCB relationships, as well as the relative impor-tance of GMA and the FFM in predicting task performance, ourfindings provided partial support for the construct fidelity principle(Cronbach, 1960). Namely, CWB are influenced primarily bywill-do predictors, task performance is influenced primarily bycan-do factors, and OCB are influenced by both will-do and can-dofactors. This finding corroborates previous arguments that CWBand OCB are related yet distinct constructs (Dalal, 2005) andaugments the existing nomological nets of CWB and OCB.

From a personnel selection standpoint, it is important to knowthe relative importance of GMA and FFM for overall job perfor-mance (F. L. Schmidt & Kaplan, 1971). Our finding shows thatwhen overall performance is operationalized as a composite oftask, OCB, and CWB, GMA is the strongest predictor, as it isabout 2 times more important than the FFM. Nonetheless, theresults showed that the FFM was more important in predictingoverall job performance (when it explicitly includes nontask per-formance measures) than previous studies have shown (e.g., F. L.Schmidt et al., 2008). Overall these findings provide essentialinformation for theories of job performance by estimating therelationship between GMA and two nontask performance criteriaand showing the relative importance of GMA and the FFM traitsin predicting both task and nontask performance criteria.

Finally, when interpreting the present findings, it is noteworthythat the results are based on self-reports of the FFM traits, whichcan be biased due to faking, particularly in high-stakes settings(e.g., employment; Morgeson et al., 2007). Recent meta-analyticevidence presented by Oh, Wang, and Mount (2011) has shownthat observer ratings of the FFM are substantially more valid thanthe corresponding self-reports. Therefore, we conducted additionalanalyses whereby we simply replaced the composite correlationsbetween self-reports of the FFM and overall job performance inAppendix D (input to the RW and regression analyses in Table 4)with corresponding meta-analytic correlations between single ob-server ratings of the FFM and overall job performance based onOh et al. (2011). The results of the RW analysis changed dramat-ically. We found that the FFM traits, collectively, are somewhatmore important than GMA (%RW 55 and 45, respectively) inpredicting the overall performance composite (detailed results areavailable from the first author upon request). This clearly showsthat when predicting overall performance, the FFM traits, whenmeasured via non-self-report methods, are substantially more im-portant relative to GMA than previously thought.

Practical ImplicationsAlthough GMA is the single best individual difference predictor

of task performance, it appears to have only small utility inpredicting CWB and moderate usefulness in predicting OCB com-pared to task performance. However, despite these limitations, theresults of the current study could prove to be quite useful forpractical purposes. For example, the finding that there is littledifference between high- and low-GMA individuals in self-reported CWB, yet high-GMA individuals are reported by othersources to engage in the behavior less frequently than low-GMAindividuals, should signal to managers that it is important tomonitor their smart, presumably high-performing employees forcounterproductive behaviors just as they would other employees,

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1232 GONZALEZ-MUL, MOUNT, AND OH

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as it would be erroneous to assume that smart people engage infewer CWB. With regard to OCB, the findings further affirm theubiquity of GMA as a selection instrument. Although the relation-ship is modest, when an organization uses GMA to select individ-uals, it is likely to select individuals who are likely to engage insatisfactory task performance and OCB as well. In addition, theresults of this study suggest the equally critical role of noncogni-tive as well as cognitive predictors in a selection battery. Practi-tioners wishing to select employees with a selection battery thatoptimizes prediction of all three dimensions of job performancewould be well served by using noncognitive predictors, such as theFFM, in addition to GMA.

Limitations and Future Research DirectionsAs with all meta-analytic studies, the current study was limited

by the extent of the current research literature. First, we identified35 correlations between GMA and CWB and 43 correlationsbetween GMA and OCB that met our inclusion criteria. Somemoderator analyses could be conducted and reliably interpretedwith this number of studies, but there were some moderators thathad only a handful of studies. It is likely that the relationshipbetween GMA and nontask behaviors is quite complex and thereare other situational moderators and/or mediators of the relation-ship. For example, it could be that CWB that are more calculativein nature (e.g., falsifying an expense report) are more stronglyrelated to GMA, whereas those CWB more spontaneous in nature(e.g., yelling at a coworker) are more strongly related to person-ality. Similarly, nontask performance behaviors that are moreimpactful (e.g., suggestions to the organization that result in majorimprovements) may be more strongly related to GMA as opposedto the frequency counts frequently studied in nontask performanceresearch. This latter point also suggests that GMA could moderatethe relationship between OCB and job performance, with higherGMA individuals better able (versus more willing) to engage inOCB that directly benefit the organization (e.g., OCBCH).5 Fu-ture primary research should explore this possibility.

Further, because of a lack of primary studies, we were unable totest plausible mediators (e.g., delayed gratification, contextualknowledge) to more fully examine why GMA might relate tonontask performance. A recent primary study found initial supportfor the premise that contextual knowledge is one of critical medi-ators of the GMAOCB relationship (Bergman, Donovan, Dras-gow, Overton, & Henning, 2008), yet this stream of research is inits infancy, and future research is needed to explore the role ofknowledge in the GMAOCB performance relationship. We werealso unable to locate any studies examining the role of any of ourproposed mediators on CWB in a work setting. Thus, it is oursincere hope that more primary studies will be conducted on themediating mechanisms between GMA and nontask performanceand that a future meta-analysis will ultimately test the theoreticalrelationships we propose in their entirety.

Second, another limitation stems from our use of extant meta-analytic estimates to derive our regression equations. One suchissue is that the correlations available in the literature for GMAand task performance ratings are likely contaminated with OCBand CWB. As Rotundo and Sackett (2002) showed, managerslikely use many different sources of information to derive em-ployee performance ratings. As such, if ratings of task perfor-

mance subsume ratings of OCB and CWB, it could be that thevalidity of GMA in predicting task performance is actually under-estimated because the relationship of GMA with either CWB orOCB is substantially lower than that between GMA and taskperformance. Future research should explore these possibilities, aswe had difficulty locating meta-analytic evidence for the GMA(pure) task performance relationship. Relatedly, our relative im-portance analyses were confined to those variables for which wecould locate meta-analytic relationships with nontask performance.One variable that has enjoyed a recent surge in scholarly interest isemotional intelligence, which relates to ones ability to functioneffectively in ones social environment at work (Joseph & New-man, 2010). It is likely that this variable predicts nontask perfor-mance and operates within the can-do versus will-do predictordistinction, yet it is unknown how important it is in predictingnontask criteria relative to GMA or the FFM. Future researchshould examine this question.6

Third, as shown in Table 4, GMA and the FFM in combinationexplained a substantial amount of variance in task performance,but explained substantially less for CWB and OCB. Given thatperformance is a function of ability, motivation, and opportunity,we may need to further explore each of these factors and theirinteractions. For example, researchers have commonly viewedCWB as the consequence of the violation of a social exchangecontract (e.g., Blau, 1964; Colbert, Mount, Harter, Witt, & Barrick,2004; Dalal, 2005) and stressful conditions (Fox & Spector, 1999;Spector & Fox, 2005; Spector et al., 2010). For example, assumingthat high-GMA individuals are more likely to be high-performingemployees, they may engage in CWB if they perceive an asym-metric social exchange contract, as in the case where their orga-nization does not provide them with adequate rewards or advance-ment opportunities. On the other hand, low-GMA individuals maybe generally less satisfied with and feel more stress from theirwork because low-GMA individuals are more likely to have lesscomplex and less fulfilling jobs or to be low performers whoobtain fewer rewards (e.g., Judge, Klinger, Simon, 2010), and,thus, may disengage or perform work poorly as opposed to engag-ing in CWB for revenge motives (frustrationaggression;Spector & Fox, 2005). Future research should explore the differentmotives underlying nontask behaviors for low- or high-GMAindividuals.

Fourth, it is now widely accepted that OCB has three compo-nents: OCB-I, OCB-O, and OCB-CH (Chiaburu et al., 2011).However, as an anonymous reviewer suggested, it may be possibleto apply this typology to the domain of CWB. A recently devel-oped construct titled prosocial rule-breaking behavior, definedas any instance where an employee intentionally violates a formalorganizational policy, regulation or prohibition with the primaryintention of promoting the welfare of the organization or one of itsstakeholders (Morrison, 2006, p. 6) could be a type of change-oriented CWB (CWB-CH). Smart or socially adroit people may bebetter at identifying and doing behaviors of this type in a moreacceptable manner (Oh et al., 2014). Unfortunately, we wereunable to locate any relevant primary study that measured GMA

5 We would like to thank Sharon Parker for suggesting this.6 We would like to thank an anonymous reviewer for suggesting this

future stream of research.

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and prosocial rule-breaking behavior. However, we believe that itis a fruitful research avenue to further conceptualize and study theconstruct of change-oriented CWB.

Finally, although we found some support for the differentialdetection hypothesis, we cannot definitively conclude that it is whythere is a negative correlation between GMA and non-self-ratedCWB. A fruitful avenue of future research might involve experi-mental studies aimed at understanding whether high-GMA peopleare better able to conceal their CWB from others.

Summary and ConclusionThe purpose of this meta-analytic study was to enhance our

understanding of the way GMA predicts job performance criteriaby expanding the criterion space to include two nontask perfor-mance criteria: OCB and CWB. Our results show that GMA is aweak predictor of CWB and is a moderately useful predictor ofOCB. Additional results also show that GMA is a less importantpredictor of CWB than the FFM and roughly equivalent with theFFM when predicting OCB. This finding augments the evidencethat CWB and OCB are related yet distinct from each other.Overall, these results address a void in the industrialorganizational psychology literature by providing essential infor-mation about the way GMA relates to the three major domains ofjob performance. We hope that this information aids scholars inrefining existing theories of job performance and in informingrelevant personnel selection practices.

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