Creative and innovative performance: a meta-analysis of relationships with task, citizenship, and counterproductive job performance dimensions

Abstract

Many studies have examined creative and innovative performance (CIP)–task performance, CIP–organizational citizenship behaviour (OCB), and CIP–counterproductive work behaviour (CWB) relationships in order to differentiate CIP from these established job performance dimensions. However, the overlap between CIP and these performance dimensions is still not clear due to mixed findings evident in the literature. To address this issue, we conducted a comprehensive meta-analytic review of empirical research into CIP–task performance, CIP–OCB, and CIP–CWB relationships derived from 39 studies and 40 independent samples. Overall, CIP was positively related to task performance (ρ = .55) and OCB (ρ = .56) and negatively related to CWB (ρ = −.23). We did not observe evidence suggesting that CIP measurement, rating source, OCB target, or CWB type moderated these relationships. Implications of our findings and directions for future research are discussed.

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European Journal of Work and Organizational Psychology
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Creative and innovative performance: a meta-
analysis of relationships with task, citizenship, and
counterproductive job performance dimensions
Michael B. Harari, Angela C. Reaves & Chockalingam Viswesvaran
To cite this article: Michael B. Harari, Angela C. Reaves & Chockalingam Viswesvaran
(2016) Creative and innovative performance: a meta-analysis of relationships with task,
citizenship, and counterproductive job performance dimensions, European Journal of Work
and Organizational Psychology, 25:4, 495-511, DOI: 10.1080/1359432X.2015.1134491
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Creative and innovative performance: a meta-analysis of relationships with task, citizenship,
and counterproductive job performance dimensions
Michael B. Harari
a
*, Angela C. Reaves
b
and Chockalingam Viswesvaran
b
a
Department of Management, Florida Atlantic University, Boca Raton, FL, USA;
b
Department of Psychology, Florida International
University, Miami, FL, USA
(Received 25 May 2015; accepted 16 December 2015)
Many studies have examined creative and innovative performance (CIP)–task performance, CIP–organizational citizenship
behaviour (OCB), and CIP–counterproductive work behaviour (CWB) relationships in order to differentiate CIP from these
established job performance dimensions. However, the overlap between CIP and these performance dimensions is still not
clear due to mixed findings evident in the literature. To address this issue, we conducted a comprehensive meta-analytic
review of empirical research into CIP–task performance, CIP–OCB, and CIP–CWB relationships derived from 39 studies
and 40 independent samples. Overall, CIP was positively related to task performance (ρ= .55) and OCB (ρ= .56) and
negatively related to CWB (ρ=−.23). We did not observe evidence suggesting that CIP measurement, rating source, OCB
target, or CWB type moderated these relationships. Implications of our findings and directions for future research are
discussed.
Keywords: Innovation; creativity; creative and innovative performance; job performance
Individual job performance—behaviours engaged in or
outcomes brought about by employees that contribute to
the functioning of organizations—is one of the most
central constructs in Industrial, Work, and
Organizational (IWO) Psychology and Human
Resource Management (Campbell & Wiernik, 2015).
Considerable work has been conducted in order to
explicate the dimensionality of job performance and
this remains an important issue (see Viswesvaran &
Ones, 2000 for a review). While ubiquitous job perfor-
mance dimensions, such as task performance, organiza-
tional citizenship behaviours (OCBs), and
counterproductive work behaviours (CWBs), have been
identified in the literature, as the nature of work con-
tinues to change, new job performance dimensions are
introduced. For example, due to the dynamic nature of
jobs in the modern workplace, research has explicated
adaptive performance as a critical dimension of indivi-
dual job performance (Pulakos, Arad, Donovan, &
Plamondon, 2000). Similarly, due to the emergence of
the knowledge economy and the importance of knowl-
edge sharing for organizational effectiveness, knowl-
edge transfer has been described as an important
dimension of job performance (Harari, Jain, & Joseph,
2014). In much the same way, due to the importance of
individual creativity and innovation for the success of
modern organizations (Eisenhardt & Tabrizi, 1995;
Geroski, Machin, & Van Reenen, 1993; Gong, Zhou,
& Chang, 2013), research has explicated creativity and
innovation as critical dimensions of individual job per-
formance (e.g., Oldham & Cummings, 1996;Tierney&
Farmer, 2002).
Individual creative and innovative performance (CIP)
has been taking a more prominent role in models of job
performance and the construct has been the focus of a
great deal of research. Many studies have investigated
interventions (e.g., selection, job design) that can impact
individual CIP (e.g., Pace & Brannick, 2010; Zhou, 1998).
When novel dimensions of job performance are intro-
duced in the literature, understanding their relationships
with existing dimensions of job performance is a critical
issue. Estimates of these relationships are necessary for
empirically differentiating the performance dimensions
and for advancing our understanding of the structure of
individual job performance (Hoffman, Blair, Meriac, &
Woehr, 2007; Viswesvaran, Schmidt, & Ones, 2005).
Understanding CIP’s relationship with task performance,
OCB, and CWB is particularly important, given their
ubiquity in the literature, and is the focus of the present
study.
Given the importance of this issue, many studies have
examined CIP–task performance, CIP–OCB, and CIP–
*Corresponding author. Email: mharari@fau.edu
European Journal of Work and Organizational Psychology, 2016
Vol. 25, No. 4, 495–511, http://dx.doi.org/10.1080/1359432X.2015.1134491
© 2016 Informa UK Limited, trading as Taylor & Francis Group

CWB relationships. However, there is great disparity in
findings across these studies and it is therefore not possi-
ble to draw firm conclusions from the literature. In the
present study, we use meta-analysis to synthesize findings
across studies, providing a summary of the state of the
literature and point estimates of population correlations.
Such an approach has been useful in many other studies
for understanding this important issue of performance
dimension intercorrelations. For example, meta-analyses
have been conducted to synthesize relationships between
task performance and OCB (Hoffman et al., 2007), task
performance and CWB (Carpenter & Berry, in press), and
OCB and CWB (Dalal, 2005).
Obtaining robust point estimates of the population
correlations (e.g., CIP–task performance) allows us to
test whether the confidence intervals do not include 1.0,
suggesting that ratings of CIP and the other performance
dimensions are empirically distinct. Before investigating
whether the different dimensions of performance (e.g.,
CIP, task performance, OCB) have differential validity
(or unique distinct patterns of correlations with external
variables), we need to establish that the corrected correla-
tions are significantly different from 1.0. Thus, establish-
ing robust point estimates of the correlations between
performance dimensions is a necessary first step before
embarking on a campaign to discover different patterns of
correlations with external variables.
As an example, for many decades we believed that
peers and supervisors were rating different constructs
when they assessed different performance dimensions
(e.g., compliance). Viswesvaran, Schmidt, and Ones
(2002) showed that when correlations between supervi-
sory and peer ratings of a dimension (e.g., compliance)
were corrected for intersupervisor and interpeer reliability,
the corrected correlations were largely not statistically
different from 1.0—suggesting that peers and supervisors
were typically rating the same dimension, although they
may be sampling different behaviours from that same
construct domain. Theoretically, it is one thing to say
that peers and supervisors are rating two different con-
structs and another thing to say that they are sampling
different samples of behaviour from the same construct
domain. The same issue is relevant here. While CIP is
considered distinct from existing performance dimensions,
research is needed to test this notion by first establishing
that the CIP–performance dimension relationships differ
from 1.0. Assuming these correlations do differ from 1.0,
subsequent research can then advance this literature by
examining differential validity between the dimensions.
Considering the growing interest in CIP, a meta-ana-
lysis into CIP–task performance, CIP–OCB, and CIP–
CWB relationships is needed. In conducting such a
meta-analysis, we respond to several calls in the literature
for meta-analyses into the correlates of CIP (Anderson, De
Dreu, & Nijstad, 2004; Anderson & King, 1993). In the
following sections, we discuss CIP in greater depth.
Following this, we review the dimensions of task perfor-
mance, OCB, and CWB and discuss potential mechanisms
linking CIP to these dimensions.
Creative and innovative performance
Changes to the competitive landscape of many organiza-
tions have led to creativity and innovation being consid-
ered key variables in the organizational sciences with
implications for firm performance (Anderson, Potocnik,
& Zhou, 2014). Creativity refers specifically to idea gen-
eration, while innovation refers to idea generation and
implementation (Anderson et al., 2014; Hulsheger,
Anderson, & Salgado, 2009). While the role of creativity
and innovation as determinants of organizational perfor-
mance is most immediately relevant in organizations that
introduce innovative products into the marketplace, the
importance of creativity and innovation spans across jobs
and organizations (Zhou, 2008). Indeed, across work
environments, developing or altering products, processes,
or procedures has the potential to improve efficiencies,
reduce waste, enhance operational outcomes, and impact
the bottom line. Therefore, creativity and innovation can
contribute to the effectiveness of most (if not all) organi-
zations and jobs (Eisenhardt & Tabrizi, 1995; Geroski
et al., 1993).
As a job performance dimension, CIP encompasses
creative and innovative behaviours and outcomes (e.g.,
introducing new ideas into the work environment in a
systematic way; generating original solutions for pro-
blems; Janssen, 2000)versus creative and innovative
processes that result in those behaviours and outcomes
(Campbell & Wiernik, 2015; Zhang & Bartol, 2010b).
Oldham and Cummings (1996) summarized that defini-
tions of CIP focus on “the product or outcome of a
product development process”(p. 608). Zhang and
Bartol (2010b) noted that CIP “refers to creative out-
comes”(p. 862). Janssen (2000)defined CIP as “the
intentional creation, introduction, and application of
new ideas within a work role, group or organization”
(p. 288). Thus, CIP refers to the proficiency with which
employees generate and implement novel ideas in the
workplace (see also Scott & Bruce, 1994), while crea-
tive and innovative processes—methods that result in
creative ideas, such as problem identification and infor-
mation searching—are conceptualized as predictors
(Zhang & Bartol, 2010a).
In the literature under study, CIP has been operationa-
lized as either creative performance (i.e., idea generation)
or innovative performance (i.e., idea generation and
implementation). In order to provide a fine-grained analy-
sis of CIP–performance dimension correlations, where
possible, we conduct all analyses using: (a) all measures
(i.e., creative performance or innovative performance), (b)
496 M.B. Harari et al.

innovative performance only, and (c) creative performance
only.
CIP and job performance dimensions
While CIP has been operationalized as a novel and unique
dimension of job performance in the literature, empirical
evidence is needed to differentiate CIP from other dimen-
sions of performance. Two factors contribute to the over-
lap between ratings of different performance dimensions: a
general factor of job performance and halo error (Cooper,
1981; Viswesvaran et al., 2005). Both of these are impor-
tant considerations here. A general factor of job perfor-
mance represents substantive overlap between the
dimensions of job performance. This can occur as a result
of shared determinants between the performance dimen-
sions. Halo error is a psychological process that occurs
when raters hold a particular impression of a ratee that
influences their performance ratings similarly across
dimensions (Thorndike, 1920). With this in mind, in this
section, we review the interactionist model of CIP as an
overarching theoretical framework guiding our study. In
addition, we review cognitive information processing
models of performance evaluation. Note that this discus-
sion is meant to provide a broad framework for our study.
In subsequent sections, we discuss more specific mechan-
isms linking CIP to each of the individual performance
dimensions.
The interactionist model of CIP (Woodman, Sawyer,
& Griffin, 1993) proposes that individual-level CIP is a
function of complex person-by-situation interactions.
Specifically, cognitive (e.g., cognitive ability, cognitive
styles, knowledge) and non-cognitive (e.g., personality,
motivation) individual differences account for CIP and
these effects are moderated by social influences (e.g.,
social facilitation, social rewards) and contextual influ-
ences (e.g., task and time constraints). Many individual
characteristics highlighted as CIP predictors in the inter-
actionist model are valid predictors of other dimensions of
job performance. For example, cognitive predictors of CIP
such as cognitive ability and job knowledge are predictors
of task performance as well (Kuncel, Hezlett, & Ones,
2004; Salgado, Anderson, Moscoso, Bertua, & de Fruyt,
2003; Schmidt & Hunter, 1998). Cognitive ability is also
linked to CWBs (Dilchert, Ones, Davis, & Rostow, 2007).
Research also supports that an internal locus of control,
which is specified in the interactionist model as a predictor
of CIP, predicts OCB (Motowidlo & Van Scotter, 1994).
CIP and OCB are also both predicted by prosocial motiva-
tion (Grant & Berry, 2011; Grant & Mayer, 2009).
Because CIP, in part, is a function of individual differ-
ences that are also determinants of task performance,
OCB, and CWB, we would expect overlap between rat-
ings of these dimensions. That is, a general factor of job
performance could account for overlap between ratings of
CIP and the other performance dimensions (Viswesvaran
et al., 2005). In addition to a general factor of job perfor-
mance, halo error can account for inflated covariation
between ratings of different performance dimensions if
they are obtained from the same rater.
One theoretical framework for understanding halo
error is the cognitive information processing perspective
of performance evaluation (Cooper, 1981). Such models
focus on the cognitive processes involved in rating job
performance and how they could result in rating errors
such as halo. Several such models exist (e.g., Borman,
1978; DeNisi, Cafferty, & Meglino, 1984; Feldman, 1981)
and, while there are differences between them, they share
the following basic cognitive processes involved in rating
performance: observation, encoding, storage, retrieval, and
integration.
Cooper (1981) reviewed how errors involving several
of these processes could result in halo. For example, when
observing performance, an undersampling of ratee beha-
viour can result in halo error because a lack of actual
performance observations would require raters to rely on
overall impressions when rating performance. When
encoding performance observations, raters are likely to
use idiosyncratic factors and available organization
themes, which can result in halo error. Finally, when
these encoded observations are stored in memory, ineffi-
ciencies and loss of detail over time can result in mem-
ories that are saturated by a general impression. In short,
all these processes result in a global evaluation (idiosyn-
cratic to that rater) of the ratee that influences ratings of
that ratee in all specificdimensions. The effects of halo
can be summarized as follows. Between-rater reliabilities
and between-rater inter-dimension correlations are reduced
by halo error, whereas within-rater reliabilities and within-
rater inter-dimension correlations are inflated by halo
error. The psychological process underlying halo error—
idiosyncratic rater impressions of each ratee—is symme-
trical: It inflates all within-rater inter-dimension correla-
tions and deflates all between-rater inter-dimension
correlations in comparison to values that would be
observed in the absence of halo error (Viswesvaran
et al., 2005).
Beyond the role of a general factor of job performance
and halo error, causal relationships between job perfor-
mance dimensions are possible (MacKenzie, Podsakoff, &
Ahearne, 1998; Viswesvaran, 2002). Competing theoreti-
cal perspectives exist that predict different relationships
between CIP and the different performance dimensions
and, due to discrepant findings in the literature, these
relationships are not clear. As noted earlier, a great deal
of research has been conducted in order to explicate the
dimensionality of job performance, with many multidi-
mensional models proposed (Viswesvaran & Ones,
2000). This vast literature has resulted in three broad,
ubiquitous dimensions of job performance that are
European Journal of Work and Organizational Psychology 497

applicable across jobs: task performance, OCBs, and
CWBs. We discuss each in the following sections.
Task performance
Task performance speaks to the proficiency with which
employees carry out the core requirements of their jobs,
such as those tasks that are specified in a job description
(Motowidlo, Borman, & Schmit, 1997; Murphy, 1989).
Task performance constitutes behaviours that contribute
directly or indirectly to the technical core of the organiza-
tion. Creative and innovative behaviours by individuals
should enhance task performance as the behaviours are
introduced to solve a workplace issue (Walberg & Stariha,
1992). Drawing on expectancy theory (Vroom, 1964),
Yuan and Woodman (2010) proposed that perceived per-
formance improvements were a determinant of CIP—that
is, employees would be more likely to engage in creative
and innovative behaviours if they believed that doing so
would positively impact their task performance. Results
supported this contention. Other research also suggests
that employees engage in CIP in order to facilitate perfor-
mance in core work-related tasks, for example, by devel-
oping and implementing new methods for carrying out
tasks or by modifying existing procedures (Gong,
Huang, & Farh, 2009). Thus, there is likely to be a
positive relationship between CIP and task performance.
However, a competing perspective exists. As refer-
enced earlier, CIP occurs as a result of creative processes,
such as idea generation (Zang & Bartol, 2010a,2010b).
Engaging in creative processes needed for superior CIP
can detract from individual task performance. Attention
capacity theory (Kahneman, 1973) suggests that indivi-
duals possess finite attentional capacity and cognitive
resources. As a result, employees are not able to apply
equivalent resources towards competing demands faced at
work. An emphasis placed on superior creative perfor-
mance could detract from task performance, while an
emphasis on superior task performance could detract
from creative performance (Scott, 1995). Thus, even
though CIP might be engaged in with the ultimate aim
of improving task performance, employees might have
less attention and fewer cognitive resources available to
perform core work tasks after engaging in the processes
needed for CIP. This could lead to a smaller or null
relationship between CIP and task performance (though
not likely a negative relationship).
The extant literature has estimated the CIP–task per-
formance relationship as varying between .17 and .75.
While we can conclude from the existing literature that
the relationship is positive, it is still not clear exactly what
the relationship is and if it differs significantly from 1.0.
As we discuss here, this is an important issue. An
observed CIP–task performance correlation of .17 with
the two dimensions measured with a coefficient alpha of
.80 on a sample of N= 170 (Janssen & VanYperen, 2004)
indicates that the estimated true score correlation does not
include 1.0 in its 95% confidence interval—suggesting the
two dimensions are distinct and have the potential for
differential validity. On the other hand, an observed
CIP–task performance correlation of .75 with the two
dimensions measured with a coefficient alpha of .80 on a
sample of N= 171 (Oldham & Cummings, 1996) indicates
that the estimated true score correlation does include 1.0 in
its 95% confidence interval—suggesting the two dimen-
sions are not distinct and unlikely to have the potential for
differential validity. If we were to use an inter-rater relia-
bility estimate of .52 (Viswesvaran, Ones, & Schmidt,
1996; Viswesvaran, Ones, Schmidt, Le, & Oh, 2014)
instead of a coefficient alpha of .80, the potential for
overlap between ratings of CIP and task performance
appears even greater. Furthermore, to test theoretical mod-
els that include CIP and task performance with meta-
analysed correlation matrices (cf. Viswesvaran & Ones,
1995), it is essential to estimate the exact magnitude of
this relationship. A point estimate of the true score corre-
lation is also required for utility analysis (selection or
training that focuses on CIP and task performance). It
does not suffice to say the relationship is positive. Thus,
the purpose of our study is to advance cumulative knowl-
edge with respect to the CIP–task performance relation-
ship, going beyond the conclusion that it is positive,
towards an understanding of the actual magnitude of the
relationship.
Organizational citizenship behaviours
While early research into the content domain of job per-
formance focused squarely on core requirements, research
began to take the perspective that job performance entailed
more than task performance alone. This thinking gave rise
to research into OCBs—discretionary behaviours that,
while not formally recognized as constituting performance
in a given job, nonetheless contribute to the functioning of
organizations (Borman & Motowidlo, 1993; Organ, 1997).
OCB is multidimensional. While several dimensional
models exist, the most broadly applicable distinction con-
cerns the target of the OCB: individuals versus the orga-
nization. OCB-I refers to extra-role behaviours directed
towards individuals in the workplace, while OCB-O refers
to extra-role behaviours directed towards the organization
itself.
OCB is often conceptualized as a predictor of CIP
(e.g., Xerri & Brunetto, 2013). OCB is an important
means by which employees build social capital (i.e.,
strong interpersonal relationships shared among employ-
ees; Bolino, Turnley, & Bloodgood, 2002). This is impor-
tant for CIP for a few reasons. First, social capital
facilitates the development of shared perspectives among
employees. Drawing on motivated information processing
498 M.B. Harari et al.

theory (e.g., Kunda, 1990), Grant and Berry (2011) argued
that perspective taking facilitates CIP because those who
can take the perspective of others are more likely to
identify issues that can be addressed by creative and
innovative solutions. Social capital is also necessary for
idea implementation. As noted in Janssen (2000), “the
innovation process consists of idea promotion to potential
allies. That is, once a worker has generated an idea, he or
she has to engage in social activities to find friends, back-
ers, and sponsors surrounding an idea, or to build a coali-
tion of supporters who provide the necessary power
behind it”(p. 288). In fact, Tsai and Ghoshal (1998)
found empirical support for the effect of social capital on
innovation in organizations. Also along these lines,
Grodal, Nelson, and Siino (2015) found that innovative
work by individuals is predicated on help seeking and help
giving. In modern complex organizations, the information
for successful performance is distributed across indivi-
duals and co-operation is required for initiating new pro-
cedures. We therefore expect a positive relationship
between ratings of OCB and CIP.
Counterproductive work behaviours
Research has also recognized that employees can engage
in behaviours on the job that detract from effective func-
tioning of the organization, referred to as CWBs. CWB
encompasses a wide range of behaviours that have been
examined in the literature for some time, including theft,
aggression, and withdrawal (Viswesvaran & Ones, 2000).
Note that CWB is not merely the opposite end of the same
continuum as OCB (Dalal, 2005). Indeed, the low end of
OCB reflects the absence of discretionary behaviours that
contribute to the effectiveness of the organization, rather
than the presence of behaviours that run counter to the
organization’s goals.
CWB is multidimensional (cf. Robinson & Bennett,
1995); however, our ability to examine CIP–CWB rela-
tionships at the dimension level is limited by the avail-
ability of different CWB dimensions examined in the
relevant literature. Nonetheless, we are able to draw the
following distinction between types of CWB: deviance—
behaviours that violate organizational norms in such a
manner as to impair the well-being of the organization or
its members (e.g., counterproductive interpersonal beha-
viours, theft, loafing)—and withdrawal (e.g., absenteeism,
tardiness, turnover intentions; Carpenter & Berry, in
press).
Innovation requires generating support from others for
creative ideas in the workplace (Janssen, 2000).
Particularly important are relationships with one’s super-
visors. As noted in Klein and Knight (2005)“the decision
to adopt and implement an innovation is typically made by
those higher in the hierarchy than the innovation’s targeted
user”(p. 244). Withdrawal and deviant behaviours should
have a negative impact on employees’relationships with
their superiors. For example, research suggests that orga-
nizational records of absenteeism are negatively associated
with supervisory ratings of employee effort and interper-
sonal behaviour (Viswesvaran, 2002). An inability to gain
the support of one’s supervisors due to CWB would be
detrimental to CIP. CIP and CWB should therefore be
negatively related.
Alternatively, as noted in Anderson et al. (2014),
CIP can disrupt organizational norms and the effect of
CIP on organizational members might not be uniformly
positive. For example, CIP can have the effect of influ-
encing established organizational hierarchies and can be
viewed by some organizational members as an interfer-
ence with their established work roles (Adolfsson,
Smide, Gregeby, Fernstrom, & Wikblad, 2004). Thus,
others can perceive employee CIP as disruptive and
problematic and as having the effect of harming others
within the organization. This could result in a positive
CIP–CWB relationship. CIP can also influence CWB
when creative and innovative behaviours have the effect
of bypassing procedures that ensure safety by introdu-
cing what are termed in the literature as workaround
behaviours (Burke, Sarpy, Tesluk, & Smith-Crowe,
2002; Reason, Parker, & Lawton, 1998). Thus, the
direction of the CIP–CWB relationship is unclear.
Moderators
The primary focus of the present study is to differentiate
ratings of CIP from ratings of task performance, OCB, and
CWB by estimating population correlations between these
variables using psychometric meta-analysis. As reviewed
earlier, we will assess measurement characteristics of the
performance dimensions as moderators. Specifically, we
will compute population correlations separately by mea-
surement of CIP (i.e., all measures, innovative perfor-
mance, creative performance), OCB (i.e., overall, OCB-I,
OCB-O), and CWB (i.e., overall, deviance, withdrawal).
Outside of these measurement characteristics, as our focus
is on performance ratings, rater level variables are relevant
as well. In particular, our focus is on rating source. Based
on the availability of ratings provided by different sources
in the relevant literature, we examine effects separately for
the following groups of raters: overall (i.e., across all
raters), self, other (i.e., supervisors, subordinates, peers),
and supervisors. Insufficient studies were available to
examine the effects for subordinate and peer ratings in
isolation.
While we do not form any specific hypotheses for the
effect of rating source on the relationships examined here,
it is an important consideration for research into perfor-
mance measurement. For example, Viswesvaran et al.
(2005) found that the effect of halo error on performance
dimension intercorrelations was stronger for peers as
European Journal of Work and Organizational Psychology 499

compared to supervisors (this study did not include self-
ratings). Sackett (2002) argued that supervisors, peers, and
subordinates might base ratings of CWB to a greater
degree on general impressions of ratees versus observa-
tions of behaviours because, in many cases, employees
engage in CWBs in a discrete manner. Thus, other ratings
of CWB should be influenced by halo error to a greater
extent than self-ratings and thus, the CIP–CWB correla-
tion could be larger for other raters than for self-raters.
The same has been argued for OCBs (Schnake, 1991).
Indeed, Dalal (2005) found that the OCB–CWB relation-
ship was significantly stronger when assessed via super-
visory versus self-ratings. Therefore, we explore if the
CIP–task performance, CIP–OCB, and CIP–CWB rela-
tionships are moderated by rating source.
Summary
Investigating how CIP relates to task performance, OCB,
and CWB advances our understanding of CIP as a job
performance dimension and illuminates theories of indivi-
dual job performance and organizational effectiveness.
The current empirical literature is scattered and seemingly
contradictory and there are competing predictions for
these relationships. By cumulating results across studies
using psychometric meta-analytic methods (Schmidt &
Hunter, 2014), we can estimate the sample-size-weighted
correlations between the performance dimensions across
studies. We examine the role of rating source, measure-
ment of CIP, OCB target (i.e., OCB-I vs. OCB-O), and
type of CWB (i.e., deviance vs. withdrawal) as
moderators.
Method
Literature search
To identify studies for inclusion in the meta-analysis, we
searched PsychInfo, ABI/Inform, and Google Scholar. We
searched for articles that included, anywhere in the text,
either creativ* or innovat* and any of the following: job
performance, task performance, in-role performance,
organizational citizenship behavior, organizational citi-
zenship behaviour, contextual performance, contextual
behavior, contextual behaviour, extrarole behavior,
extra-role behavior, extra role behavior, extrarole beha-
viour, extra-role behaviour, extra role behaviour, counter-
productive work behavior, counterproductive work
behaviour, organizational deviance, interpersonal
deviance, work* deviance, sabotage, theft, absenteeism,
lateness, tardiness, bullying, turnover,orcounterproduc-
tivity. This search returned a total of 877 unique studies.
Based on a review of the abstracts of these 877 studies, we
identified 74 as being potentially relevant for the present
study.
Inclusion criteria and coding scheme
To be included in the present analysis, the study had to
report a zero-order correlation between ratings of CIP and
task performance, OCB, or CWB. Studies had to be con-
ducted using field samples of employees. CIP had to be
measured as a behaviour rather than as an individual
difference (e.g., creative personality), consistent with the
definition offered earlier in the paper. Since our aim was to
distinguish between ratings of CIP and the three perfor-
mance dimensions, only studies where the performance
dimensions were rated by the same rater were included.
Studies that utilized multisource data (e.g., Janssen &
Giebels, 2013) and objective measures of performance
(e.g., Martinaityte & Sacramento, 2013) were excluded.
A total of 39 studies (and 40 independent samples) met
these criteria and were included in the meta-analysis.
Studies were coded for sample size, correlations, relia-
bility (i.e., coefficient alphas), and rating source by the
first two authors. We also coded whether CIP was mea-
sured as creative performance or innovative performance,
whether OCB was directed towards individuals (OCB-I)
or the organization (OCB-O), and the type of CWB (i.e.,
deviance, withdrawal). Finally, we coded the industry in
which the primary study was conducted for descriptive
purposes. Where multiple measures of the same construct
were reported, we formed composites using the methods
outlined in Schmidt and Hunter (2014). We estimated
composite reliability using the formulas provided in
Mosier (1943). Agreement between the two authors was
95%. Discrepancies were resolved between the authors
and therefore, agreement was ultimately 100%.
Descriptive information from studies included in the
meta-analysis is reported in Table 1.
Analyses
We conducted our analyses using the psychometric meta-
analysis procedures outlined in Schmidt and Hunter
(2014). Consistent with the recommendations in Schmidt
and Hunter (2014), we used random effects models. For
each meta-analytic cumulation (i.e., CIP–task perfor-
mance, CIP–OCB, CIP–CWB), we computed the sample
size-weighted observed correlation. Corrections for mea-
surement error in CIP and other job performance dimen-
sion scores were made using artefact distributions
comprised of coefficient alphas derived from the studies
included in the meta-analysis (see Table 2). Using the
artefact distributions, we calculated sample size-weighted
corrected correlations as well as their associated sample
size-weighted standard deviations. We estimated 80%
credibility intervals as well as 95% confidence intervals
(using the methods outlined in Viswesvaran et al., 2002)
around each estimate of ρ. In order to conclude that ratings
of CIP and the other performance dimensions are
500 M.B. Harari et al.

Table 1. Descriptive information of studies included in meta-analysis.
Study CIP N Task OCB OCB-I OCB-O CWB Withdrawal Deviance Rating source Industry
Alge, Ballinger, Tangirala, and Oakley (2006) IP 303 0.46 0.45 0.40 Other Variety
Anthony (2012) IP 98 0.63 Supervisor Variety
Aryee, Walumbwa, Zhou, and Hartnell (2012) IP 193 0.50 Supervisor Telecommunications
Binnewies, Sonnentag, and Mojza (2009) IP 358 0.42 0.32 Self Non-profit
Cheung (2011) IP 252 0.41 Supervisor Manufacturing
Chughtai and Buckley (2011) IP 168 0.30 Self Research centre
Eder (2007) CP 269 0.58 0.81 Supervisor Variety
Eschleman, Madsen, Alarcon, and Barelka (2014) IP 341 0.58 0.50 0.55 Self Variety
Eschleman et al. (2014) IP 92 −0.07 −0.21 0.09 Other Military
Fluegge (2008) CP 205 0.57 0.73 Supervisor Variety
Gilson (2000) IP 686 −0.21 Self Variety
Gong et al. (2009) IP 200 0.73 Supervisor Insurance
Hirst, Van Knippenberg, Zhou, Zhu, and Tsai (in press) CP 317 0.56 Supervisor Variety
Hu, Kaplan, Wei, and Vega (2014) IP 140 0.51 0.59 Supervisor Technology
Janssen and Huang (2008) IP 157 0.63 0.59 Supervisor Bank
Janssen and Van Yperen (2004) IP 170 0.17 Supervisor Energy supplier
Kahya (2009) CP 143 0.44 0.49 0.45 0.52 Supervisor Manufacturing
Karatepe, Kilic, and Isiksel (2008) IP 170 0.00 Self Hotel
Lassk and Shepherd (2013) IP 460 0.58 Self Health and beauty
Lee, Tan, and Javalgi (2010) IP 497 0.59 Supervisor Hospital
Liden, Wayne, Liao, and Meuser (2014) IP 952 0.43 Supervisor Restaurant
Lu, Zhou, and Leung (2011) IP 166 0.42 Supervisor Variety
Luksyte (2011) IP 215 −0.04 −0.01 −0.04 Self Community college
Miao, Newman, and Lamb (2012) IP 322 0.49 Supervisor Manufacturing
Moss and Ritossa (2007) IP 263 0.67 Supervisor Government
Ng and Feldman (2009) CP 162 0.44 0.58 −0.25 Peer Variety
Oldham and Cummings (1996) IP 171 0.75 Supervisor Manufacturing
Pace and Brannick (2010) CP 83 0.48 Supervisor Variety
Raja and Johns (2010) IP 383 0.42 0.51 0.42 0.42 Peer Variety
Rank, Nelson, Allen, and Xu (2009) IP 152 0.50 Supervisor Variety
Reaves (2015) IP 299 0.29 0.48 Self Variety
Tse and Chui (2014) IP 250 0.06 0.03 0.08 Supervisor Banking
Wang, Begley, Hui, and Lee (2012) IP 176 0.54 0.47 0.51 Supervisor Electronics
Wang, Chiang, Tsai, Lin, and Cheng (2013) IP 261 0.51 0.57 0.52 0.46 Supervisor Technology
Wang and Ma (2013) IP 210 −0.49 Self
Xerri and Brunetto (2013) IP 210 0.20 0.19 0.12 Self Hospital
Yu & Frankel (2013) IP 206 0.42 Supervisor Bank
Zhang & Bartol (2010b) IP 367 0.33 Supervisor Technology
Zhang, Lepine, Buckman, and Wei (2014) IP 339 0.57 0.70 0.57 0.73 −0.23 Supervisor Variety
Zhen and Aryee (2007) IP 171 0.38 Supervisor Manufacturing
Note: CIP–performance correlations appear below performance dimensions.
CP, creative performance. IP, innovative performance.
European Journal of Work and Organizational Psychology 501

empirically distinct, it would be necessary that the 95%
confidence intervals around each estimate of ρdo not
reach 1.00. We also calculated the percentage of variance
in observed correlations accounted for by statistical arte-
facts (%Var). This value is useful for identifying the pre-
sence of moderator variables. Specifically, consistent with
the 75% rule of thumb, where statistical artefacts
accounted for less than 75% of the variance in observed
correlations, we concluded that moderators of the relation-
ship are likely present (Schmidt & Hunter, 2014).
We repeated our analyses for each level of each mod-
erator examined. Differences between relationships at each
level of each moderator were examined by assessing the
overlap in their 95% confidence intervals. If the confi-
dence intervals did not overlap, we concluded that the
relationships were significantly different. In determining
the number of studies needed to be included in each meta-
analytic calculation, we implemented a frequently used
rule of thumb; we required at least three studies be avail-
able (Viswesvaran et al., 2002).
Results
Results of the CIP–task performance meta-analysis are
reported in Table 3. The table is divided into three sec-
tions. The top section reports a meta-analysis of
correlations between CIP and task performance where all
CIP measures (i.e., innovative performance and creative
performance) were included, the middle section reports
results of analyses involving innovative performance
only, and the bottom section reports results of analyses
involving creative performance only. When reviewing
these three sections of the table, it is apparent that relation-
ships do not vary substantively between these three sets of
analyses. We therefore limit our in-text review of results to
the top part of the table where both measures of innovative
performance and creative performance were included.
Overall, the CIP–task performance relationship was
large in magnitude, positive, and non-zero (ρ= .55).
While we did detect substantial variability in population
effect sizes underlying this effect (as evident by the width
of the 80% credibility intervals), the value was always
greater than 0, suggesting that CIP and task performance
are positively related across samples. The 95% confidence
intervals around this estimate of rho ranged from .50 to
.60, supporting a distinction between ratings of CIP and
task performance (as the confidence intervals do not
extend to 1.00). We did not observe evidence suggesting
that rating source moderated the CIP–task performance
relationship, as the estimates of ρwere similar in magni-
tude and not significantly different from one another
across ratings sources. The CIP–task performance
Table 2. Reliability distributions for the different performance dimensions.
CIP IP CP Task OCB OCB-I OCB-O CWB Deviance Withdrawal
Overall .90 (.06) .90 (.07) .94 (.03) .86 (.09) .87 (.09) .84 (.06) .83 (.08) .89 (.03) .89 (.06) .88 (.02)
Self .88 (.07) .88 (.07) –.84 (.08) .87 (.09) .86 (.10) .83 (.11) .87 (.02) ––
Other .91 (.06) .90 (.06) .94 (.03) .86 (.09) .87 (.10) .83 (.06) .83 (.08) –– –
Supervisor .92 (.05) .92 (.05) .95 (.04) .86 (.09) .89 (.03) .84 (.06) .85 (.06) –– –
Note: Mean frequency-weighted alphas are reported in cells with standard deviations in parentheses.
CIP, all measures; IP, innovative performance; CP, creative performance.
Table 3. Meta-analysis of CIP–task performance relationships.
KN r ρσ
ρ
%Var CV
L
CV
U
CI
L
CI
U
All measures
Overall 28 7660 0.49 0.55 0.12 22.28% 0.40 0.71 0.50 0.60
Self 4 1285 0.43 0.50 0.13 17.74% 0.34 0.67 0.36 0.64
Other 24 6375 0.50 0.56 0.11 24.32% 0.42 0.71 0.51 0.61
Supervisor 22 5830 0.50 0.57 0.12 22.21% 0.42 0.72 0.51 0.63
Innovative performance
Overall 22 6481 0.48 0.55 0.13 19.26% 0.38 0.71 0.49 0.61
Self 4 1285 0.43 0.51 0.13 17.83% 0.34 0.67 0.37 0.65
Other 18 5196 0.49 0.56 0.12 20.31% 0.40 0.71 0.50 0.62
Supervisor 17 4813 0.50 0.56 0.13 18.66% 0.40 0.72 0.49 0.63
Creative performance
Other 6 1179 0.53 0.59 0.00 100.00% 0.59 0.59 0.54 0.64
Supervisor 5 1017 0.54 0.61 0.00 100.00% 0.61 0.61 0.56 0.66
Note: K, number of independent samples included in analysis; N, pooled sample size; r, observed sample size-weighted correlation; ρ, sample size-
weighted corrected correlation; σ
ρ
, sample size-weighted standard deviation of corrected correlations; %Var, percentage of variance accounted for in
correlations by statistical artefacts (i.e., sampling error, measurement error); CV, 80% credibility intervals; CI, 95% confidence intervals.
502 M.B. Harari et al.

relationship was ρ= .50 for self-ratings, ρ= .56 for other
ratings, and ρ= .57 for supervisor ratings. While there was
a great degree of variability left in these estimates once
accounting for statistical artefacts (i.e., sampling error,
measurement error), suggesting the likely presence of
moderator variables, results indicated that these values
are always positive across populations. From these results,
we concluded that CIP is positively related to task
performance.
At this point, we do note one unique finding with
respect to the creative performance–task performance rela-
tionship reported in the bottom third of Table 3. When
focusing only on creative performance, we found mean
correlations of similar magnitude as just discussed, but
once accounting for the effect of statistical artefacts, our
results indicated no variability in effect sizes across stu-
dies. That is, our analysis suggested that no moderators
influence the creative performance–task performance rela-
tionship and that creative performance appears to share a
much more consistent relationship with task performance
than does innovative performance.
Tab l e 4 reports the results of the meta-analysis of CIP–
OCB relationships. Similar to the analyses involving task
performance just discussed, Table 4 is broken into three
sections: analyses of correlations involving all CIP mea-
sures appear at the top, those for measures of innovative
performance appear in the middle, and those for measures
of creative performance appear on the bottom. The results
across sets of analyses are consistent and we therefore
limit our in-text review of results to the overall analyses
reported in the top portion of the table.
Overall, we estimated a population correlation
between CIP and OCB of ρ= .56. Results indicated that
multiple population correlations likely underlie this value
(i.e., that the relationship is influenced by moderators), as
only 9.26% of the variability in correlations could be
accounted for by statistical artefacts. However, our results
indicated that, across populations, the CIP–OCB correla-
tion was positive, as our 80% credibility intervals ranged
from .29 to .83. The value of ρestimated in our analyses
was also non-zero, as the 95% confidence intervals (.46 to
.66) excluded zero. Note also that the 95% confidence
intervals excluded 1.00 and our analyses therefore also
indicated a distinction between ratings of CIP and OCB.
Further, the relationship estimated across rating sources
did not vary by the target of the OCB—the population
correlation estimated for OCB-I was ρ= .46 and was
ρ= .49 for OCB-O. The 95% confidence intervals around
these estimates overlapped. The CIP–OCB relationship
did not vary by rating source and accounting for rating
source as a moderator did not appreciably reduce the
variability in our estimated correlations. For self-ratings,
Table 4. Meta-analysis of CIP–OCB relationships.
KN r ρσ
ρ
%Var CV
L
CV
U
CI
L
CI
U
All measures
Overall 19 4352 0.50 0.56 0.21 9.26% 0.29 0.83 0.46 0.66
OCB-I 12 2821 0.40 0.46 0.21 9.42% 0.20 0.72 0.34 0.58
OCB-O 10 2498 0.42 0.49 0.22 8.27% 0.21 0.77 0.35 0.63
Self 4 1208 0.41 0.47 0.15 14.89% 0.29 0.66 0.31 0.63
Other 15 3144 0.53 0.59 0.22 8.92% 0.31 0.87 0.47 0.71
OCB-I 10 2270 0.41 0.47 0.22 8.79% 0.19 0.74 0.33 0.61
OCB-O 8 1947 0.43 0.50 0.21 8.65% 0.23 0.78 0.34 0.66
Supervisor 11 2204 0.56 0.62 0.22 6.00% 0.34 0.90 0.48 0.76
OCB-I 7 1492 0.43 0.49 0.20 9.61% 0.23 0.75 0.33 0.65
OCB-O 5 1169 0.47 0.54 0.25 5.68% 0.22 0.86 0.31 0.77
Innovative performance
Overall 15 3573 0.46 0.52 0.20 10.31% 0.26 0.77 0.41 0.63
OCB-I 11 2678 0.40 0.46 0.21 8.75% 0.19 0.73 0.32 0.60
OCB-O 9 2355 0.42 0.48 0.23 7.68% 0.20 0.77 0.33 0.63
Self 4 1208 0.41 0.48 0.15 15.45% 0.29 0.67 0.32 0.64
Other 11 2365 0.48 0.54 0.22 9.39% 0.26 0.82 0.41 0.67
OCB-I 9 2127 0.40 0.47 0.22 8.05% 0.18 0.75 0.31 0.63
OCB-O 9 2355 0.42 0.48 0.22 7.67% 0.19 0.77 0.33 0.63
Supervisor 8 1587 0.51 0.56 0.22 7.06% 0.28 0.84 0.40 0.72
OCB-I 6 1349 0.43 0.49 0.21 8.32% 0.22 0.77 0.31 0.67
OCB-O 4 1026 0.47 0.53 0.27 4.69% 0.19 0.87 0.26 0.80
Creative performance
Other 4 779 0.68 0.75 0.12 21.08% 0.60 0.91 0.62 0.88
Supervisor 3 617 0.71 0.79 0.13 12.15% 0.63 0.96 0.63 0.95
Note: K, number of independent samples included in analysis; N, pooled sample size; r, observed sample size-weighted correlation; ρ, sample size-
weighted corrected correlation; σ
ρ
, sample size-weighted standard deviation of corrected correlations; %Var, percentage of variance accounted for in
correlations by statistical artefacts (i.e., sampling error, measurement error); CV, 80% credibility intervals; CI, 95% confidence intervals.
European Journal of Work and Organizational Psychology 503

we estimated a population correlation of ρ= .47. It was
not possible to assess OCB target as a moderator. For
other ratings, we estimated a population correlation of
ρ= .59. The value was similar for OCB-I (ρ= .47) and
OCB-O (ρ= .50). For supervisory ratings, the estimated
population correlation was ρ= .62 and this value also did
not differ by OCB target (ρ= .49 and .54 for OCB-I vs.
OCB-O, respectively). All of the 95% confidence intervals
around all of the estimates reviewed here overlapped.
Therefore, we did not observe evidence suggesting that
the CIP–OCB relationship varied by rating source or by
the target of the OCB. In each of these analyses, there was
a great degree of heterogeneity in population correlations
suggesting the presence of additional moderators.
Nonetheless, inspection of the 80% credibility intervals
indicated that all of these estimates were greater than
zero across populations. Thus, while the magnitude of
the effect does likely differ as a result of unaccounted
for moderators, we concluded that CIP and OCB are
positively related.
In terms of our analyses involving only measures of
creative performance (reported in the bottom section of
Table 4), we did estimate population creative perfor-
mance–OCB correlations that were larger in magnitude
than those observed in our earlier analyses. These analyses
could only be carried out for other raters and among those
other rater studies, four included supervisors. The popula-
tion correlation estimated for other raters was ρ= .75 and
when including supervisor raters only, the correlation was
ρ= .79. Based on an inspection of the 95% confidence
intervals, these values were not significantly different from
the lower point estimates derived from studies including
innovative performance measures.
Finally, results of the meta-analysis of CIP–CWB cor-
relations are reported in Table 5. We calculated this rela-
tionship using (a) all measures (i.e., innovative
performance and creative performance; reported in the
top half of Table 5) and (b) innovative performance only
(reported in the bottom half of Table 4). It was not possi-
ble to conduct analyses involving creative performance
measures only. Our findings were parallel across analyses,
and therefore we review findings from our analyses that
included measures of both innovative performance and
creative performance. Overall, we estimated a population
correlation of ρ=−.23. The 80% credibility intervals
(−.40 to −.07) excluded zero and thus, we concluded that
CIP and CWB are negatively related across populations.
Further, the 95% confidence intervals around the estimate
of ρranged from −.34 to −.12. Thus, we can conclude that
the estimated CIP–CWB relationship is non-zero (as the
confidence intervals did not include zero) and that raters
do distinguish between CIP and CWB (as the confidence
intervals did not include −1.00). This relationship did not
vary significantly between types of CWB, as the relation-
ship for withdrawal and deviance was ρ=−.22 and
ρ=−.20, respectively. Two studies included in these
analyses used raters classified as “other,”while the
remaining four used self-ratings. When analysing results
for self-ratings only, findings were consistent with the
overall analyses (i.e., ρ=−.23).
Discussion
As the nature of work continues to change, so will con-
ceptualizations of job performance. With research increas-
ingly recognizing the importance of creativity and
innovation for the success of modern organizations, so
too has creative and innovative performance been recog-
nized as a critical dimension of individual job performance
(e.g., Janssen & Van Yperen, 2004; Oldham & Cummings,
1996). Despite the research accumulated thus far into the
relationship between CIP and established dimensions of
individual job performance, firm conclusions concerning
the magnitude and direction of these relationships could
not be reached due to mixed findings in the literature.
Responding to calls in the literature for the use of meta-
analysis to clarify the state of the creativity and innovation
literature (e.g., Anderson et al., 2004; Anderson & King,
1993), we used meta-analysis to synthesize CIP–task
Table 5. Meta-analysis of CIP–CWB relationships.
KN r ρσ
ρ
%Var CV
L
CV
U
CI
L
CI
U
All measures
Overall 6 1782 −0.21 −0.23 0.13 18.42% −0.40 −0.07 −0.34 −0.12
Withdrawal 4 1281 −0.19 −0.22 0.17 11.73% −0.43 −0.01 −0.40 −0.04
Deviance 3 716 −0.18 −0.20 0.07 49.08% −0.29 −0.11 −0.31 −0.09
Self 4 1281 −0.20 −0.23 0.16 12.57% −0.44 −0.02 −0.40 −0.06
Innovative performance
Overall 5 1620 −0.21 −0.23 0.14 15.58% −0.41 −0.05 −0.36 −0.10
Self 4 1281 −0.20 −0.23 0.16 12.58% −0.44 −0.02 −0.40 −0.06
Note: K, number of independent samples included in analysis; N, pooled sample size; r, observed sample size-weighted correlation; ρ, sample size-
weighted corrected correlation; σ
ρ
, sample size-weighted standard deviation of corrected correlations; %Var, percentage of variance accounted for in
correlations by statistical artefacts (i.e., sampling error, measurement error); CV, 80% credibility intervals; CI, 95% confidence intervals.
504 M.B. Harari et al.

performance, CIP–OCB, and CIP–CWB relationships
across 39 studies that spanned nearly two decades.
Results indicated that CIP was related to the other
performance dimensions. However, while these perfor-
mance dimensions were meaningfully related to one
another, the overlap was not so great as to suggest that
CIP was redundant with any of these constructs. Indeed, in
no instance did the 95% confidence intervals around each
estimate of ρoverlap with 1.00. Our analyses supported
the conceptual distinction made between CIP and all three
performance dimensions (i.e., task performance, OCB,
CWB) by all raters (self, others) included in our analyses.
In order to ground our findings more clearly within the
existing literature into the intercorrelations among perfor-
mance dimensions, we constructed a true score correlation
matrix between the four performance dimensions exam-
ined in the present study, reported in Table 6. The correla-
tions involving CIP were derived from the present study.
The correlation between OCB and task performance was
reported in Hoffman et al. (2007). The task performance–
CWB and OCB–CWB correlations were reported in
Carpenter and Berry (in press). The Carpenter and Berry
meta-analysis is useful for our purposes because, much
like our meta-analysis, it included the CWB sub-dimen-
sions of deviance and withdrawal. The true score task
performance–deviance and task performance–withdrawal
correlations were ρ= .09 and .02, respectively (averaged
to ρ= .06) and the true score OCB–deviance and OCB–
withdrawal correlations were ρ=−.27 and −.17, respec-
tively (averaged to ρ=−.22).
Among the correlations included in Table 6, the largest
relationship is between task performance and OCB
(ρ= .74). The relationships observed in the present study
between CIP and both task performance and OCB were
much smaller; ρ= .55 and .56, respectively. The relation-
ship between CIP and CWB is virtually identical to the
relationship between OCB and CWB (ρ=−.23 vs. −.22),
while the relationship between task performance and
CWB is much smaller in magnitude (ρ= .06). Overall,
as would be expected, the performance dimensions are
intercorrelated (Viswesvaran et al., 2005). However, the
correlations involving CIP are not so large as to suggest
that these dimensions are redundant. Indeed, the correla-
tions involving CIP are all smaller than the task perfor-
mance–OCB correlation. The pattern of correlations
reported here is consistent with our initial conclusions
that an empirical distinction between CIP and these other
performance dimensions is evident.
Our findings have implications for our understanding
of creative and innovative behaviours as dimensions of job
performance. First, with the emerging research highlight-
ing the importance of creativity and innovation as deter-
minants of organizational performance (Gong et al.,
2013), understanding the determinants of CIP is a critical
pursuit in the literature. The moderate overlap between
CIP and both task performance and OCB suggests that
there are likely many similar determinants of performance
on each of these dimensions, as suggested by interactionist
model (Woodman et al., 1993). For example, research
suggests that cognitive ability, an established predictor of
task performance, is also correlated with creativity in
organizations (Kuncel et al., 2004). However, there also
exists substantial variability in CIP that is not shared with
task performance, OCB, and CWB. While CIP likely
shares predictors with other dimensions, it is also likely
that unique predictors exist that predict CIP but not task
performance, OCB, or CWB. As an example, Pace and
Brannick (2010) found that a contextualized openness to
experience scale predicted creative performance but not
task performance. More research is needed into the deter-
minants of CIP, including dispositional, attitudinal, demo-
graphic, and organizational variables.
The results of our study have implications for future
meta-analyses involving predictors and outcomes of CIP.
In order to test comprehensive measurement and path
models involving individual job performance, the inter-
correlations between different performance dimensions are
needed (Viswesvaran & Ones, 1995). The same approach
is used to test outcomes of performance dimensions. For
example, Podsakoff, Whiting, Podsakoff, and Blume
(2009) used meta-analysis to test the incremental validity
of OCBs for predicting individual- and organizational-
level outcomes beyond task performance—an analysis
that required a true score point estimate of the task per-
formance–OCB relationship. As research into the out-
comes of CIP continues to accumulate, our estimates
would facilitate a similar test of incremental prediction
of individual- and organizational-level outcomes by CIP
beyond what can be accounted for by task performance,
OCB, and CWB.
While creativity and innovation in organizations are
associated with favourable outcomes, the predominant
focus in the literature on productive outcomes has been
referred to as a pro-innovation bias (Kimberly, 1981),
based on the notion that research has emphasized how
innovation contributes towards, while ignoring how it
might detract from, organizational and individual
Table 6. True score correlation matrix between CIP, task per-
formance, OCB, and CWB.
123
1 CIP
2 Task performance .55
a
3 OCB .56
a
.74
b
4 CWB −.23
a
.06
c
−.22
c
Note:
a
Value derived from current study.
b
Value derived from Hoffman et al. (2007).
c
Value derived from Carpenter and Berry (in press).
European Journal of Work and Organizational Psychology 505

effectiveness. The lack of research into counterproductive
aspects of creativity and innovation has led to an innova-
tion maximization fallacy (Anderson et al., 2014); the
belief that creativity and innovation are always a good
thing and that more is always better. This is potentially
problematic because, as noted in Anderson et al. (2014),
“creativity and innovation are often experienced as dis-
ruptive events, do not always benefit all parties affected,
may be initiated in response to distress-related stimuli, and
excessive innovation may be counter-productive to other
aspects of individual, team, or organizational perfor-
mance”(p. 1320). Our study has implications for our
understanding of the pro-innovation bias.
First, in the job performance literature, it is apparent
that research has paid greater attention to productive (e.g.,
task performance, OCB) versus counterproductive (e.g.,
CWB) correlates of CIP. Our analysis of the CIP–task
performance relationship included 28 correlations and
our analysis of the CIP–OCB relationship included 19
correlations, while our analysis of the CIP–CWB relation-
ship included only 6 correlations. Thus, consistent with
the contention of the pro-innovation bias, the literature
into the relationships between CIP and job performance
dimensions has in fact focused on productive correlates to
a much greater degree than counterproductive correlates.
However, our findings were contrary to the notion that
CIP is detrimental in terms of individual effectiveness.
Indeed, we found strong evidence to suggest that CIP is
consistently positively related to productive performance
dimensions and consistently negatively related to counter-
productive performance dimensions. This is not to say,
however, that CIP (or creativity and innovation in organi-
zations more broadly defined) is associated with only
productive correlates and outcomes. Indeed, many studies
suggest that, under some circumstances, creativity and
innovation are associated with negative outcomes (e.g.,
Gong et al., 2013).
Limitations and future directions
While we discussed literature earlier suggesting that CIP is
a relevant performance dimension across industries and
jobs, the extent to which CIP–performance dimension
relationships vary across industries and jobs is not clear.
For example, it is possible that in creative industries and
jobs, such as technology or research and development, the
CIP–task performance relationship would be higher
because CIP could be perceived by raters as a core
requirement of the job. As can be seen in Table 1, our
database included studies carried out in a wide variety of
industries and since very few studies were conducted in
the same or similar industries, it was generally not possi-
ble to carry out comparative analyses. However, it was
possible to compare the CIP–task performance relation-
ship between manufacturing (k= 5) and technology
companies (k= 3), where one might hypothesize that the
relationship would be stronger in the latter as compared to
the former. However, this was not the case, as the cor-
rected sample size-weighted mean correlations estimated
in each subsample did not differ significantly from one
another. In manufacturing industries, the relationship was
ρ= .55 (95% CI = .43 to .67) and in technology industries,
the relationship was ρ= .49 (95% CI = .37 to .61). Thus,
we present some preliminary evidence that the CIP–task
performance relationship might not vary by industry.
However, we note that this is a comparison only between
two industries. We recommend that future research exam-
ine this issue across different industries and jobs for the
different performance dimensions.
Our interest in the present study was to understand the
differentiation between ratings of CIP and task perfor-
mance, OCB, and CWB, requiring a reliance on single
source data. For example, correlations between CIP and
these other dimensions where different raters rated each
performance dimensions would not reflect the effect of
halo error, which was an important factor in our study.
Our study demonstrated that raters do differentiate
between CIP and these other performance dimensions;
that there is an empirical distinction. However, it is also
useful to understand relationships between ratings of per-
formance dimensions with halo error factored out
(Scullen, Mount, & Goff, 2000). By using multisource
rating data, Viswesvaran et al. (2005) were able to factor
the effect of halo error out of performance dimension
intercorrelations, finding evidence of a general factor of
job performance. At present, it is not clear how much of
the overlap between CIP and the three performance
dimensions examined here was due to halo error versus
a general factor of job performance. While the focus of
our study was same source performance ratings, future
research can separate these two competing sources of
variance by using multisource data.
Another important point is that causal relationships
between CIP and the other performance dimensions are
possible, as noted earlier in the paper. While we did
discuss some potential mechanisms linking CIP to the
other performance dimensions, testing these causal rela-
tionships was not the focus of our study and more robust
research designs are needed to address this issue. Most
useful would be studies using both multisource and multi-
wave data collections. Also, as our study emphasized the
extent to which raters differentiate between different per-
formance dimensions, consistent with other research into
this issue (e.g., Dalal, 2005), relevant moderators included
rating source and sub-dimensions of the various criterion
measures (e.g., OCB-I vs. OCB-O). When examining
causal relationships, other moderators might be more rele-
vant. Jobs differ in the extent to which creativity or inno-
vation is conceptualized as a core requirement of the job.
Creativity requirements might moderate the relationship
506 M.B. Harari et al.

between CIP and task performance, such that the relation-
ship is stronger when creativity requirements are high.
One’s position in the organization’s hierarchy might be
relevant as well, as it is those in higher levels of the
hierarchy who have more latitude for implementing crea-
tive ideas (Klein & Knight, 2005). The relationship
between OCB and CIP could be weaker for employees
at higher hierarchical levels because, with formal decision-
making authority, they are less likely to need social capital
to gain support for creative ideas before they can be
implemented. In short, more robust research designs are
needed to test causal relationships between CIP and other
performance dimensions and moderators of these
relationships.
In order to differentiate ratings of CIP from those of
task performance, OCB, and CWB, we focused on asses-
sing the correlations between ratings of these different
performance dimensions. However, other lines of evidence
can be used to address this issue as well. Specifically,
ratings of these different performance dimensions could
also be differentiated by examining their relationships
with external variables. Similar patterns of relationships
would suggest similarities in the performance dimensions
while different patterns of relationships would further
support an empirical distinction between the dimensions
(Nunnally, 1978). Once a sufficient body of literature
emerges examining external correlates of CIP, research
can use meta-analysis to synthesize these findings and
compare relationships involving CIP to those involving
these other performance dimensions. This research would
be very useful in building on our findings and further
highlighting the similarities and differences between CIP
and existing performance dimensions.
We examined the intercorrelations among the perfor-
mance dimensions when they were all assessed at the
same point in time. Longitudinal research should address
whether these intercorrelations vary over time. There are
two aspects to investigating this dynamicity of relations.
First, we need to examine whether CIP at time 1 is related
to OCB and task performance at time 2 (and how they
vary as the time interval between time 1 and time 2
changes). This form of dynamicity can be affected by
situational characteristics such as job and organizational
features. Second, an alternate form of dynamicity is
whether the intercorrelations among CIP, task perfor-
mance, OCB, and CWB all measured at the same time
vary as a function of the employee’s tenure. Both forms of
dynamicity have theoretical and practical implications for
our field.
Some of our analyses were based on as few as three
primary studies. This is potentially problematic when it
comes to interpreting indices of variability in correlations
across samples due to the effect of second-order sampling
error (Schmidt & Hunter, 2014). When a small number of
correlations are included in analyses, it is possible that
they cluster closely together or vary widely from one
another only by chance; a large sample of correlations is
needed for a more accurate determination of variability.
However, this is not a problem when it comes to inter-
preting the observed and population correlations. Even
when based on only a small number of studies, sample
size-weighted correlations provide a more stable estimate
of the correlation than can be obtained from any of the
primary studies included in the analysis (Valentine, Pigott,
& Rothstein, 2010). For example, the fewest number of
correlations (i.e., 3) were used to calculate the supervisor-
rated creative performance–OCB relationship and overall
CIP–deviance relationship. In these cases, while the num-
ber of independent correlations included was 3, the esti-
mated correlations were based on total sample sizes of 617
and 716, respectively. Thus, these point estimates are
based on much larger sample sizes than any primary
study, are less affected by sampling error than any esti-
mates available in a primary study, and provide the best
estimate of these relationships available.
Conclusion
As the generation and implementation of novel ideas has
become central for the success of modern organizations,
CIP has emerged as a crucial dimension of individual job
performance. However, the literature assessing the rela-
tionships between CIP and other performance dimensions
was scattered and contradictory. In the present study, we
synthesized this literature using psychometric meta-analy-
sis. Our results suggested that, across samples, CIP was
positively related to task performance and OCB and nega-
tively related to CWB. Importantly, the relationships
between CIP and these other job performance dimensions
were not so large as to suggest that there is no empirical
distinction between them. Indeed, CIP appears to add to
our understanding of the structure individual job
performance.
Disclosure statement
No potential conflict of interest was reported by the authors.
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