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Abstract

Job crafting is a form of proactive work behavior that involves employees actively changing the (perceived) characteristics of their jobs, including behaviors aimed at increasing challenging and decreasing hindering job demands, as well as those directed at increasing structural and social job resources (Tims & Bakker, 2010). Research on job crafting has rapidly increased over the past decade, but findings have thus far not been quantitatively synthesized. We first integrate job crafting as conceptualized by Tims and Bakker (2010) with a more general theoretical model of proactive work behavior. Then, we present a meta-analysis (K = 122 independent samples representing N = 35,670 workers) of relationships between job crafting behaviors and their various antecedents and work outcomes derived from our model. We consider both overall and dimension-level job crafting relationships. Notably, overall job crafting was found to be strongly associated with proactive personality (rc = .543), promotion regulatory focus (rc = .509), and work engagement (rc = .450). Differential results emerged when considering specific job crafting dimensions. For example, increasing challenging job demands was associated with other-rated work performance (rc = .422), whereas decreasing hindering job demands was related to turnover intentions (rc = .235). Beyond these zero-order relationships, a meta-analytic confirmatory factor analysis provide support for the operationalization of overall job crafting based upon the proposed dimensions, with the exception of decreasing hindering demands. Additionally, results of meta-analytic relative weights analyses speak to the unique relationships of all four job crafting dimensions with different work outcomes.
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Job Crafting: A Meta-Analysis of Relationships with Individual Differences, Job Characteristics,
and Work Outcomes
Cort W. Rudolph
Ian M. Katz
Kristi N. Lavigne
Saint Louis University
Hannes Zacher
University of Leipzig and Queensland University of Technology
Recommended Citation:
Rudolph. C.W., Katz, I.M., Lavigne, K.N., & Zacher, H. (2017, In Press). Job crafting: a meta-analysis
of relationships with individual differences, job characteristics, and work outcomes.
Journal of Vocational Behavior.
Author Note
Cort W. Rudolph, Ian M. Katz, and Kristi N. Lavigne, Department of Psychology, Saint
Louis University, St. Louis, MO (USA). Hannes Zacher, Institute of Psychology, University of
Leipzig, Leipzig (Germany) and School of Management, Queensland University of Technology,
Brisbane (Australia). Correspondence concerning this article should be addressed to Cort W.
Rudolph, Saint Louis University, Morrissey Hall 2827 St. Louis, MO (USA), 63103,
rudolphc@slu.edu, +1(314) 977-7299 Portions of this work were presented at the 32nd Annual
Conference of the Society for Industrial and Organizational Psychology, Orlando FL (USA).!
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Abstract
Job crafting is a form of proactive work behavior that involves employees actively changing the
(perceived) characteristics of their jobs, including behaviors aimed at increasing challenging and
decreasing hindering job demands, as well as those directed at increasing structural and social
job resources (Tims & Bakker, 2010). Research on job crafting has rapidly increased over the
past decade, but findings have thus far not been quantitatively synthesized. We first integrate job
crafting as conceptualized by Tims and Bakker (2010) with a more general theoretical model of
proactive work behavior. Then, we present a meta-analysis (K = 122 independent samples
representing N = 35,670 workers) of relationships between job crafting behaviors and their
various antecedents and work outcomes derived from our model. We consider both overall and
dimension-level job crafting relationships. Notably, overall job crafting was found to be strongly
associated with proactive personality (rc = .543), promotion regulatory focus (rc = .509), and
work engagement (rc = .450). Differential results emerged when considering specific job crafting
dimensions. For example, increasing challenging job demands was associated with other-rated
work performance (rc = .422), whereas decreasing hindering job demands was related to turnover
intentions (rc = .235). Beyond these zero-order relationships, a meta-analytic confirmatory factor
analysis provide support for the operationalization of overall job crafting based upon the
proposed dimensions, with the exception of decreasing hindering demands. Additionally, results
of meta-analytic relative weights analyses speak to the unique relationships of all four job
crafting dimensions with different work outcomes.
Keywords: job crafting; job demands; job resources; meta-analysis; review
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1. Introduction
Job crafting is a specific form of proactive work behavior that involves employees
actively changing the (perceived) characteristics of their jobs (Tims & Bakker, 2010;
Wrzesniewski & Dutton, 2001). As job crafting is initiated by employees themselves, it has been
described as an individualized, bottom-up, and proactive approach to job re-design, compared to
top-down and “one-size-fits-all” approaches that are initiated by the organization (Demerouti &
Bakker, 2014; Grant & Parker, 2009; Parker, 2014; Parker & Ohly, 2008). Cumulative evidence
suggests that there is appreciable variability in relationships between discrete job characteristics
and employee outcomes such as job satisfaction and performance (Fried & Ferris, 1987;
Humphrey, Nahrgang, & Morgeson, 2007). Thus, in times of rapid organizational change, job
crafting may constitute a promising alternative to traditional job re-design approaches.
Researchers have argued that even in stable work environments and in jobs with low autonomy,
employees are able to make some changes to their job demands and resources (Petrou,
Demerouti, & Schaufeli, 2016).
Job crafting is not a new concept. Nearly 30 years ago, Kulik, Oldham, and Hackman
(1987, p. 292) noted that “Another strategy for work redesign is a participative change process,
in which jobholders are actively involved in determining what changes will be made in their jobs
to improve the match with their own needs and skills […] employees may on occasion redesign
their jobs on their own initiative -- either with or without management assent and cooperation.”
Despite its longstanding definition, research concerning the antecedents, consequences, and
correlates of job crafting has only increased over the past decade. This increase is due in large
part to the publication of an article by Tims and Bakker (2010) that, based on the job demands-
resources (JD-R) model (Bakker & Demerouti, 2016; Bakker & Demerouti, 2007; Demerouti et
al., 2001), positioned job crafting as a theoretically important mechanism linking characteristics
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of the work environment to work outcomes. The JD-R model is a comprehensive theoretical
framework for understanding how job design elements influence occupational well-being and
work performance. The model describes how job demands and resources influence motivation-
enhancing (e.g., work engagement) and strain-enhancing (e.g., exhaustion) processes and work
performance. Accordingly, job crafting serves as an important link between work motivation and
the cultivation of both job and personal resources that, in turn, help increase person-job fit
(Bakker & Demerouti, 2016). To operationalize job crafting in terms of the JD-R model, Tims,
Bakker, and Derks (2012) published a widely-used scale designed to measure job crafting in
terms of proactive behaviors that employees engage in to increase challenging and to decrease
hindering job demands, as well as to increase structural and social job resources.
In this study, we present the results of a meta-analysis conducted to integrate extant
research on job crafting as conceptualized by Tims and Bakker (2010). To organize this effort,
we present a conceptual model (Figure 1) that extends existing theorizing by positioning job
crafting within well-established models of proactive work behavior (in particular, Bindl &
Parker, 2011, but also Crant, 2000; Frese & Fay, 2001; Grant & Ashford, 2008; and Parker,
Bindl, & Strauss, 2010). This model also builds upon recent theorizing on the antecedents and
outcomes of job crafting (Demerouti, 2014; Wang, Demerouti, & Bakker, 2017) as a means of
guiding hypotheses and corresponding empirical tests of the associations between job crafting
and a range of relevant antecedents and outcomes. Our overarching goals and contributions here
were three-fold, and address current needs that broadly characterize this literature.
First, applying our model, we meta-analytically synthesize relationships of job crafting
with individual differences, job characteristics, and individual-level work outcomes (Figure 1).
Second, because research based on Tims and Bakker’s (2010) conceptualization has adopted
different operationalizations of job crafting (e.g., Tims, et al., 2012; Petrou, Demerouti, Peeters,
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Schaufeli, & Hetland, 2012), we considered job crafting in two different ways. On the one hand,
we considered relationships with the four specific job crafting dimensions outlined by Tims and
Bakker (2010), namely increasing challenging and decreasing hindering job demands, as well as
increasing structural and social job resources. On the other hand, acknowledging that research
has also conceptualized job crafting as a composite aggregation of these four dimensions (e.g.,!
Akkermans & Tims, 2016;!Bell & Njolli, 2016; Ingusci et al., 2016; Tims, Derks, & Bakker,
2016), we provide meta-analytic evidence for the interrelationships among the four forms of job
crafting. We also conduct a meta-analytic confirmatory factor analysis (CFA) of these
relationships, and consider relationships of “overall” job crafting with associated antecedents and
outcomes. Building upon the latter point, we additionally offer a series of moderator analyses,
which address whether combining various job crafting dimensions yields substantially different
conclusions regarding such effects. By addressing this goal, we aim to provide some necessary
clarity surrounding the operation of overall versus dimension-level conceptualizations of job
crafting.
Third, we more closely examine the role of dimension-specific forms of job crafting by
decomposing the relative contribution of each of the four dimensions as predictors of work
outcomes via meta-analytic relative weights analyses. This goal specifically addresses the need
to understand the unique relationships between each job crafting dimension and the outcomes
considered within our integrative model. In summary, our study contributes to an enhanced
understanding of the nature of the job crafting construct by a) applying methods of quantitative
synthesis to test the associations implied by an integrative model, b) investigating how these
associations vary as a function of how job crafting is conceptualized, and c) exploring the
differential relationships that job crafting dimensions have with work outcomes.
2. Job Crafting: Conceptualization and Measurement
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Job crafting can be differentiated conceptually from other proactive work behavior
constructs, such as personal initiative, taking charge, and voice (Parker & Collins, 2010; Tornau
& Frese, 2013, 2015), in that it is specifically directed at changing the (perceived) characteristics
of one’s job (Demerouti & Bakker, 2014). While other forms of proactive behavior may result in
changes to one’s job characteristics, the underlying intentions of these behaviors are more
broadly focused (Frese, Garst, & Fay, 2007). Additionally, although our meta-analysis
particularly focuses on the most commonly used approach to job crafting that has been offered
by Tims and Bakker (2010) and Tims et al. (2012), a number of alternative conceptualizations
and measures of job crafting do exist (see Table 1 for a summary).
Job crafting was first formally defined by Wrzesniewski and Dutton (2001) as “the
physical and cognitive changes individuals make in the task or relational boundaries of their
work” (p. 179) and “the action employees take to shape, mold, and redefine their jobs” (p. 180).
Within this definition, physical changes refer to actual alterations of job characteristics, cognitive
changes involve psychological redefinitions and reinterpretations of job characteristics without
actual changes, and relational boundary changes entail altering the quantity or quality of
workplace relationships. Wrzesniewski and Dutton suggest that employees are motivated to
engage in job crafting to fulfill basic psychological needs for autonomy, positive self-image, and
relatedness (cf. Ryan & Deci, 2000). To this end, job crafting leads to changes in employees’
identity and perceived meaning of work which, in turn, lead to greater job satisfaction and
performance. Job crafting influences identity development because it helps increase the fit
between employees’ views and definitions of themselves and their work. Wrzesniewski and
Dutton (2001) also argue that job crafting influences employees’ understanding of the purpose of
their work (i.e., perceived meaning), because their job characteristics become more aligned with
their individual abilities and needs.
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Recently, researchers have developed measurement instruments to capture the
dimensions of job crafting (i.e., physical, cognitive, and relational) proposed by Wrzesniewski
and Dutton (2001) (e.g., Ghitulescu, 2006; Niessen et al., 2016; Slemp & Vella-Brodrick, 2013;
see Table 1). In parallel, a number of researchers have proposed alternative conceptualizations of
job crafting (e.g., Berg et al., 2010; Leana et al., 2009; see Table 1). The most widely known and
adopted theoretical model was developed by Tims and Bakker (2010), who define job crafting as
a form of proactive behavior that involves employees initiating changes in their (actual or
perceived) job demands and resources to increase the fit between these job characteristics and
their personal abilities and needs. Increased person-job fit, in turn, should lead to higher job
satisfaction, work engagement, and perceived meaningfulness of work. Further grounding job
crafting within the larger JD-R framework (e.g., Bakker & Demerouti, 2007; Demerouti, Bakker,
Nachreiner, & Schaufeli, 2001), Bakker (2011) updated this general model to suggest that job
crafting completes an in transitu feedback loop linking work engagement and performance to
enhanced job and personal resources.
Based upon the theory offered by Tims and Bakker (2010), Tims et al. (2012) suggested
that job crafting consists of four dimensions: Increasing challenging job demands involves
performing behaviors such as asking for more responsibilities and volunteering for special
projects. Decreasing hindering job demands entails performing behaviors that aim to minimize
physical, cognitive, and emotional demands, such as reducing workload and work-family
conflict. Increasing structural job resources includes performing behaviors that aim to increase
the autonomy, skill variety, and other motivational characteristics of the job. Finally, increasing
social job resources entails asking for feedback as well as advice and support from supervisors
and colleagues.
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On the basis of factor-analytic evidence, Petrou and colleagues (2012) collapsed two of
the dimensions in Tims and Bakker’s (2010) conceptualization -- increasing structural and social
job resources -- into one increasing job resources dimension and only differentiated between
three types of job crafting. Likewise, Nielsen and Abildgaard (2012) developed a comparable,
but much less widely used job crafting scale for blue-collar workers that additionally includes the
dimensions “decreasing social job demands” and “increasing quantitative job demands.” Of note,
in our meta-analysis, we focus on the four dimensions of job crafting originally proposed and
represented in the measurement model offered by Tims and colleagues (2012), because these
dimensions directly map onto the widely accepted and studied theoretical model offered by Tims
and Bakker (2010) and because this scale is the most commonly used in the literature.
While these dimensions of job crafting are often considered independently in the
literature, research has also aggregated scores across these dimensions to represent overall job
crafting (e.g., Akkermans & Tims, 2016;!Bell & Njolli, 2016; Ingusci et al., 2016; Tims, Derks,
& Bakker, 2016). Indeed, this operationalization suggests that different related dimensions of job
crafting reflect a latent, higher-order or composite job crafting construct. Considering various
theoretical models of job crafting (e.g., Tims & Bakker, 2010; Bakker, 2011), this overall
conceptualization is consistent with the idea that job crafting represents the orchestration of
related proactive behaviors that are jointly enacted and represent striving towards enhanced
person-environment fit.
Next, we will introduce an integrated theoretical model that explains links between
various antecedents and consequences of job crafting. Prevailing theoretical models have
variously positioned job crafting within the implied action-phase sequence of the JD-R (e.g.,
Tims & Bakker, 2010; Bakker, 2011), meaning that several constructs can be conceptualized
both as antecedents and outcomes of job crafting. For instance, work engagement is likely to
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influence and to be influenced by job crafting (Bakker, 2010; Lu, Wang, Lu, Du, & Bakker,
2014). Given the non-experimental nature of the research considered by our meta-analysis, we
necessarily consider individual differences, job characteristics, and work outcomes to be
correlates rather than causal antecedents or outcomes of job crafting (see Figure 1). However,
we can consider theoretically grounded, empirically supported arguments for the classification of
these variables as antecedents or outcomes of job crafting on the basis of theory and research.
3. Integrative Theoretical Model of Job Crafting
3.1. Job Crafting as a Form of Proactive Behavior
There has recently been increasing interest in identifying different forms of proactive
work behaviors (Parker & Bindl, 2017), and empirical models have established a differentiated
nomological network of related proactivity constructs (e.g., personal initiative, taking charge,
voice; see Tornau & Frese, 2013). Moreover, a number of theoretical advancements have
proposed various antecedents and outcomes of proactive behavior (e.g., Bindl & Parker, 2011,
Crant, 2000; Grant & Ashford, 2008). Job crafting involves proactive changes that employees
make to balance their job demands and resources with their personal capacities and needs.
However, job crafting has yet to be formally integrated into these more general models of
proactivity (e.g., Bindl & Parker, 2011). Two recent theoretical advancements put forward by
Demerouti (2014) and Wang, Demerouti, and Bakker (2017) hint at a variety of personal (e.g.,
proactive personality; general self-efficacy) and contextual antecedents (e.g., job resources, such
as job autonomy; job demands, such as workload), as well as positive (e.g., work engagement;
work performance) and negative (e.g., job strain) outcomes associated with job crafting.
These complimentary models of job crafting antecedents and outcomes very closely map
onto more general models of proactive work behavior found in the literature. For example, an
early model by Crant (2000) specifies individual differences (e.g., proactive personality, self-
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efficacy) and contextual factors (e.g., norms, social support) as predictors of proactive work
behavior, and work performance and attitudes as outcomes. Frese and Fay (2001) offer a similar
model that focuses specifically on personal initiative, but that also specifies environmental and
person-level antecedents (i.e., personality; knowledge, skills, and abilities). Grant and Ashford
(2008) extend these models to include additional situational (e.g., ambiguity) and dispositional
(e.g., big five personality) antecedents to proactive work behavior. More comprehensive
extensions of these models by Bindl, Parker, and colleagues (Bindl & Parker 2011; Parker,
Bindl, & Strauss, 2010) specify distal individual differences (e.g., demographics, personality)
and situational antecedents (e.g., job design), proximal individual difference antecedents (e.g.,
goals, affect), as well as individual work performance, career, and well-being outcomes.
Likewise, the job crafting models offered by Demerouti (2014) and Wang et al. (2017)
are consistent with Tornau and Frese’s (2013) integrative and empirically supported proactivity
framework, which specifies both distal and proximal antecedents of proactive work behaviors,
including personality and job characteristics, as well as work outcome variables, including job
satisfaction and work performance. Thus, the conceptual model that serves as our theoretical
framework in this study is grounded within the integration of these various perspectives on job
crafting and proactive work behavior (see Figure 1). We next elaborate on this model and more
directly explain the nature of the proposed links tested in our meta-analysis. For reasons of
parsimony, we took a theory-driven and affirmative approach to justify our hypotheses, meaning
that we do not additionally justify why we do not expect certain relationships.
3.2. Job Crafting and Individual Differences
Consistent with general models of proactivity (e.g., Bindl & Parker, 2011; Frese & Fay,
2001; Grant & Ashford, 2008), our integrative model of job crafting presented in Figure 1
suggests that various personality characteristics and beliefs serve as individual difference
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antecedents of job crafting. In our meta-analysis, we examine relationships between job crafting
and the traits included within the five-factor model of personality, as well as proactive
personality, promotion and prevention regulatory focus, and general self-efficacy.
The five factor model of personality (i.e., the "Big Five"; Digman, 1990) is defined by
trait conscientiousness, extraversion, neuroticism (i.e., low emotional stability), agreeableness,
and openness to experience. In their dynamic model of proactivity, Grant and Ashford (2008)
suggest that certain big five traits (i.e., conscientiousness, neuroticism, and openness to
experience) are important for the development of proactive work behaviors (see also Wu & Li,
2017). Expanding upon these predictions, we expect that all of the big five traits are related to
overall job crafting (see Bell & Njoli, 2016). Considering specific job crafting dimensions,
conscientious employees are task-oriented and persistent. Considering the latter, Grant and
Ashford (2008) emphasize the importance of persistence for the sustained enactment of proactive
work behavior. Conscientiousness should thus facilitate increasing challenging job demands,
decreasing hindering job demands, and increasing structural job resources. Extraverted
employees are outgoing and sociable, and are adept at managing social interactions (Aspendorf
& Wilpers, 1998). As such, Wu and Li (2017) suggest that extraversion is particularly important
for facilitating proactive behavior in relational contexts, thus higher extraversion should be
associated with higher levels of increasing social job resources. Emotionally stable employees
(i.e., those low in neuroticism) cope well with stressors, which should facilitate decreasing
hindering job demands. Moreover, more emotionally stable people tend to experience positive
emotions and have higher self-confidence (Judge, Locke, & Durham, 1997), which may support
the conviction to successfully influence change (Morrison & Phelps, 1999). Agreeable
employees are friendly and team-oriented which, similar to extraversion, should enable
increasing social job resources. Finally, employees with high levels of openness to experience
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are curious and creative, which may support the information-collection stages of proactive action
processes (see Frese & Fay, 2001). Thus, openness should relate positively to increasing
challenging and decreasing hindering job demands.
Considering proactive personality, employees who are generally more proactive are more
likely to engage in all forms of job crafting because they tend to show high levels of initiative,
identify opportunities, overcome barriers, and persevere until they reach their goals (Crant, Hu,
& Jiang, 2017; Bakker, Tims, & Derks, 2012). In a domain-general sense, self-efficacy refers to
people’s confidence in their ability to accomplish a given task successfully (Bandura, 2000). We
expect that general self-efficacy is positively related to increasing challenging demands and
increasing structural and social job resources, given that employees with high general self-
efficacy set ambitious goals for themselves, persist during goal pursuit, and use better strategies
to accomplish their goals (Bandura, 2000; Kanten, 2014).
According to regulatory focus theory (Higgins, 1997), people with higher levels of
promotion focus concentrate on their hopes, accomplishments, and gains while pursuing their
goals, whereas people with higher levels of prevention focus concentrate on safety,
responsibilities, and avoiding losses. We expect that employees with higher levels of promotion
focus show greater levels of increasing challenging demands and increasing structural and social
resources than employees with a lower levels of promotion focus. Moreover, we predict that
employees with higher levels of prevention focus will engage more in reducing hindering
demands and less in other forms of job crafting than employees with lower levels of prevention
focus. Research suggests that those with higher levels of promotion focus are more likely take
steps to improve their person-job fit in general, whereas employees with higher levels of
prevention focus are more focused on preventing negative outcomes (e.g., low performance,
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negative evaluations) by reducing hindering job demands (Bipp & Demerouti, 2015; Petrou &
Demerouti, 2015; Wang et al., 2017).
3.3. Job Crafting and Job Characteristics
Our integrative model of job crafting also considers job autonomy and workload as
specific job characteristics. The inclusion of such job characteristics in our model mirrors past
models of proactivity, which have positioned job characteristics as important antecedents to
proactive work behaviors (e.g., Bindl & Parker, 2011; Grant & Ashford, 2008; Ohly & Schmitt,
2017). Consistent with the predictions of these models, we expect that job autonomy is positively
related to overall job crafting. Additionally, we expect positive relationships between job
autonomy and both increasing challenging job demands and increasing structural and social job
resources, because job autonomy provides employees with opportunities and necessary
information to make changes to their job characteristics based on their individual abilities and
needs (Lyons, 2008; Tims, Bakker, & Derks, 2013). Moreover, we predict that workload is
positively related to decreasing hindering demands as well as increasing structural and social job
resources, because employees who experience a higher workload will be motivated to reduce the
demands that are placed on them and to seek resources that help them manage these demands
(Bakker & Demerouti, 2016).
3.4. Job Crafting and Work Outcomes
Our integrative model of job crafting delineates important work outcomes that are
consistent with the general model of proactive behavior by Bindl and Parker (2011; i.e., job
performance and well-being). Likewise, consistent with Crant’s (2000) model of proactive
behavior, we consider job attitudes as outcomes of job crafting. Recent qualitative literature
reviews have suggested that job crafting is associated with a variety of favorable work outcomes
(Demerouti & Bakker, 2014; Wang et al., 2017). In their model of proactive work behavior,
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Bindl and Parker (2011) argue that proactive behavior is positively associated with job
performance and well-being. Expanding upon this model, we meta-analytically review
associations between job crafting and job attitudes (i.e., job satisfaction, turnover intentions),
indicators of occupational well-being (i.e., work engagement, job strain), and work performance
(i.e., self- and other-rated task performance, contextual performance) to gain a better
understanding of these potential consequences of job crafting.
3.4.1. Job attitudes. We expect that job crafting is positively related to job satisfaction
and negatively related to turnover intentions. One’s attitude toward their job should result in part
from improvements in job characteristics and perceived person-job fit due to engaging in job
crafting (Edwards & Shipp, 2007; Wang et al., 2017). In particular, we predict that increasing
challenging job demands and increasing structural and social job resources (but not decreasing
hindering job demands) are positively related to favorable job attitudes (i.e., higher job
satisfaction and lower turnover intentions).
3.4.2. Occupational well-being. Work engagement and job strain are important
indicators of occupational well-being and are integral to the JD-R (Demerouti et al., 2001).
Similar to job attitudes, work engagement and job strain should be influenced by job crafting via
improved job characteristics and perceived person-job fit. Previous research indeed suggests that
job crafting leads to improved employee well-being (Petrou et al., 2012; Petrou, Demerouti, &
Schaufeli, 2015; Tims, et al., 2013) and that these relationships are mediated by enhanced
person-job fit (Chen, Yen, & Tsai, 2014). We expect that increasing challenging job demands
and increasing structural and social job resources (but not decreasing hindering job demands) are
positively related to occupational well-being.
3.4.3. Work performance. Finally, we predict that job crafting, and particularly
increasing challenging job demands and increasing structural and social job resources, are
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positively related to both self-rated and other-rated work performance and contextual
performance (Borman & Motowidlo, 1997; Rotundo & Sackett, 2002). These job crafting
behaviors should be positively related to work performance and contextual performance because
they improve person-job fit which, in turn, facilitates performance (Edwards, Caplan, &
Harrison, 1998; Kristof-Brown, Zimmerman, & Johnson, 2005). Several recent studies have
found that job crafting is positively related to work performance (e.g., Bakker et al., 2012;
Demerouti et al., 2015; Gordon, Demerouti, Le Blanc, & Bipp, 2015; Tims et al. 2015b).
However, findings regarding the relationship between job crafting and contextual performance
are somewhat mixed. For instance, Gordon and colleagues (2015) found that decreasing
hindering job demands was negatively related to contextual performance, whereas Tims et al.
(2015b) found a non-significant effect of decreasing hindering job demands on contextual
performance. Using meta-analytic methods, we aim to better understand the relationships
between job crafting dimensions and different forms of performance.
3.5. Descriptive Relationships with Demographic and Employment Characteristics
Finally, our integrative model of job crafting specifies several demographic and
employment characteristics. To be consistent with past meta-analytic models of proactivity
constructs (Tornau & Frese, 2013) we conceptualize these relationships here as descriptive,
rather than as substantive in nature. Moreover, because Bindl and Parker’s (2011) model of
proactivity specifies such demographic and employment characteristics as distal antecedents of
proactive work behaviors, we likewise consider these variables here. We examine descriptive
relationships between job crafting and five commonly assessed demographic and employment
characteristics: chronological age, gender, education, tenure, and work hours. Even though job
crafting theories (e.g., Demerouti, 2014; Tims & Bakker, 2010, Wang et al., 2017) do not make
specific predictions regarding these characteristics, it is still useful to understand the nature of
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such relationships, not only for comparing findings with research on other forms of proactive
behavior (Tornau & Frese, 2013) and the planning of future research endeavors, but also for the
continued development of enhanced theories of job crafting. To this end, it would be helpful to
know whether job crafting is more or less common among younger or older employees, male or
female employees, more or less educated employees, employees with shorter or longer tenure,
and employees working fewer or more hours.
Some theorizing exists to support specific relationships between job crafting and
demographic and employment characteristics. Based on human capital theory (Becker, 1975), it
could be argued that older employees, as well as those with longer tenure and higher levels of
education may have greater accumulated job and general knowledge and thus are in a better
position to craft their jobs compared to younger employees and those with shorter tenure and
lower levels of education. Based upon action regulation theory, it could also be argued that older
and more experienced employees (i.e., relative to younger and less experienced employees) are
more likely to have developed cognitive routines in their work that are detrimental to behavioral
changes like job crafting (Zacher, Hacker, & Frese, 2016).
Research concerning gender differences in job crafting is somewhat equivocal. For
example, Petrou, Demerouti, and Xanthopoulou (2016) find that overall, men are more likely to
engage in job crafting than women, whereas Van Hoof and Van Hooft (2014) find the opposite.
There is also evidence for variability in this effect at the job crafting dimension level. For
example, Van Hoof and Van Hooft (2014) report that women were more likely than men to craft
via increasing structural job resources and increasing challenging job demands. Career
development research has found that women may be afforded fewer challenging work
experiences than their male counterparts (e.g., Lyness & Thompson, 2000; Ohlott et al., 1994;
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Van Velsor and Hughes, 1990). Because the pursuit of challenging job assignments is an
important perquisite for career advancement in many cases (Mainiero, 1994; Ragins et al., 1998),
job crafting may represent a particularly important opportunity for women to proactively manage
their career progression. While formally untested, the results of our meta-analysis will shed light
on these possibilities and provide directions for future research and theorizing.
4. Method
4.1. Inclusion Criteria
As part of our efforts, we conducted two separate meta-analyses (i.e., a primary meta-
analysis of overall job crafting and specific job crafting dimension relationships, and an ancillary
meta-analysis of interrelations among the specific job crafting dimensions). Thus, we established
two sets of a priori inclusion/exclusion criteria. For the primary analysis, we set six inclusion
criteria to guide our literature searches. First, to qualify for inclusion, articles must have
measured job crafting in terms of either (a) increasing structural job resources, increasing social
job resources, increasing challenging job demands, or decreasing hindering job demands via the
instrument developed by Tims et al. (2012) or a related instrument that likewise captures these
dimensions of increasing and decreasing job demands and resources (e.g., Petrou et al., 2012;
Nielsen & Abildgaard, 2012), or (b) overall job crafting via an aggregation of two or more of
these dimensions.
Studies adopting alternative measurement instruments (see Table 1) were excluded from
our analysis when such scales could not be readily mapped onto Tims and Bakker’s (2010)
theoretical framework and its related measurement model and associated scale items (i.e., Tims
et al., 2012). Across all of our literature search efforts and attempts to obtain unpublished data
and pre-press manuscripts, no studies measured multiple dimension of job crafting that
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simultaneously map onto the Tims et al. (2012) and the Wrzesniewski and Dutton (2001)
conceptualizations of job crafting. This is important to note, because this “gap” makes a direct
empirical comparison between these alternative job crafting theories via meta-analytic synthesis
impossible at this point in time.
In terms of conceptualizing overall job crafting in our analyses, we either coded such
relationships directly from studies that reported job crafting as a composite score (e.g., Tims et
al., 2016) or we computed a composite across available crafting dimension correlations using
Hunter and Schmidt’s (2004) composite formulae to represent overall job crafting relationships.
This first inclusion criterion led to the exclusion of review articles (e.g., Demerouti, 2014;
Demerouti & Bakker, 2014; Nielsen, 2013), studies adopting exclusively qualitative
methodologies, and studies that investigated non-work forms of crafting (e.g., leisure crafting,
Petrou & Bakker, 2016).
Second, in addition to measuring job crafting in some capacity, at least one of the
individual differences, job characteristics, work outcomes, or demographic variables from our
integrative job crafting models must also have been measured (see Figure 1). Thus, studies that
only considered the psychometric qualities of job crafting scales were excluded from this
primary analysis, as were studies that did not measure at least one of the relevant antecedent or
outcome variables (e.g., Akin, Sarıçam, Kaya, & Demir, 2014).
Third, to avoid double counting (i.e., to maintain sample independence), we excluded
studies in which authors clearly used the same dataset and reported the same correlations in more
than one published study, unless different outcomes were clearly considered in both studies (e.g.,
Tims et al., 2013, and Tims, Bakker, & Derks, 2015a, both use the same sample; however,
different outcome variables are reported in each study, and overlapping job crafting relationships
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were only coded from one study; similar overlapping samples are also present in both Petrou &
Demerouti, 2015, and Petrou, Demerouti, & Xanthopoulou, 2016).
Related to this, in cases where both theses/dissertations and published versions of these
theses/dissertations were obtained via our literature searches, the study with more information
(i.e., a greater number of relevant job crafting relationships) was coded (e.g., Study 1 from
Chapter Six of Petrou’s, 2013 dissertation contains more complete information than the resulting
publication, Petrou et al., 2016). Similarly, in the case of one master’s thesis (Hekkert-Koning,
2014), the sample used and the relationships reported completely overlapped with a more recent
published work. Thus, we opted to code only the published work (Brenninkmeijer & Hekkert-
Koning, 2015) and excluded the thesis. Studies reporting results in languages other than English
were translated using translation software.
Consistent with meta-analytic best practices (e.g., Cooper, Hedges, & Valentine, 2009;
Higgins & Green, 2011), we sought to actively include unpublished master’s theses and doctoral
dissertations in our meta-analysis (K = 41). In some universities, groups of bachelor’s and
master’s students work together in “thesis circles” to complete such projects. In such cases, we
coded non-overlapping relationships that were unique to each individual study to ensure sample
independence. Such studies are noted in the references with the superscript “TC” and a number
representing thesis circle membership (e.g., TC1).
Fourth, whenever longitudinal analyses were reported, we coded relationships based on
time-one data for complete panel designs (e.g., Vogt, Hakanen, Brauchli, Jenny, & Bauer, 2016),
and between job crafting and relevant correlates at other time points for incomplete panel designs
(e.g., Nielsen & Abildgaard, 2012). Fifth, when an article reported results obtained from multiple
independent samples, each sample was included separately in the meta-analysis (e.g., De Beer,
Tims, & Bakker, 2016; Nielsen, Sanz-Vergel, Munego-Rodriguez, & Mirkko, in press).
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Finally, diary research has shown that employees engage in job crafting on a daily basis
(Demerouti et al., 2015; Petrou et al., 2012; Tims, Bakker, & Derks, 2014). However, the
number of studies using such designs is still small relative to others (i.e., we identified K = 12
diary studies). To be consistent with our operationalization of job crafting, we considered data at
the between-person level of analysis only (i.e., within-person data aggregated to the between-
person level) from such studies.
For the ancillary meta-analysis of dimension-level intercorrelations, we adopted an
additional inclusion criterion. Specifically, to facilitate meta-analytic regression modelling and
relative weights analysis, we additionally needed to quantify the strength of the intercorrelations
between individual dimensions of job crafting. For this analysis, we only considered studies that
measured all four job crafting dimensions included in the Tims et al. (2012) job crafting scale.
Indeed, a number of studies excluded certain dimensions of the job crafting scale (e.g.,
Berdicchia, Nicolli, & Masino, 2016; Gordon, Demerouti, Le Blanc, & Bipp, 2015) and were
consequently omitted from this dimension-level analysis.
4.2. Literature Search
All literature searching was done between April 1, 2016 and June 20, 2016, with
supplementary literature searches conducted in December 2016 to support a revision effort. We
initially searched the electronic database, Google Scholar, with follow-up searches conducted
using EBSCOHost, Emerald, JSTOR, ProQuest, PsycINFO, ScienceDirect, and Web of Science.
All searches used the keyword “job crafting” as well as the individual dimensions of job crafting
as defined by Tims et al. (2012; i.e., “increasing structural job resources,” “decreasing hindering
job demands,” “increasing social job resources,” and “increasing challenging job demands”). For
each subsequent database, we noted all non-redundant articles (i.e., uniquely identified articles
not overlapping with previous searches).
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Additionally, we conducted forward searches to find studies citing the original Tims and
Bakker (2010) theory development paper, along with the Tims et al. (2012) job crafting scale
development paper. To locate additional literature, we examined the reference lists of all
retrieved articles and conducted systematic forward searches of studies that cited each retrieved
article. This search process yielded an initial set of over 500 references. In a second step, based
on our inclusion criteria, we selected all relevant quantitative-empirical studies on job crafting
from these initial references by carefully examining the abstract, methods, and results of each
article. We also conducted searches within the conference programs of the Academy of
Management (years 2012 to 2015), the Society for Industrial and Organizational Psychology
(years 2012 to 2016), and the European Association for Work and Organizational Psychology
(years 2011, 2013, 2015), and we contacted all authors whose abstracts mentioned job crafting.
Finally, to obtain unpublished data and in-press articles, we contacted researchers who have
published previously on job crafting, and we requested articles using professional mailing lists
and website postings. For one such unpublished dataset (Akkermans & Tims, 2016), a published
article was noted while this manuscript was under review (Akkermans & Tims, 2017).
Additionally, we searched for pre-press “online first” articles via various relevant journal
websites (e.g., Journal of Management, Journal of Organizational Behavior, Journal of
Vocational Behavior, Human Relations).
In total, our meta-analytic database contained 1,429 effect sizes coded from K = 108
sources representing K = 122 independent samples and a total of N = 35,670 workers. Our
ancillary meta-analysis of the intercorrelations between the Tims et al. (2012) job crafting
dimensions was based upon a total of K = 42 independent samples, representing a subset of N =
13,440 workers. All included studies are marked with an asterisk (i.e., *) in the reference list.
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While coding, we took efforts to contact authors to clarify information (e.g., the dummy
coding pattern of gender) or missing data (e.g., scale reliabilities; intercorrelations among job
crafting dimensions to facilitate composite formation). In most cases, such issues were quickly
and easily clarified (i.e., in only K = 4 cases, we were unable to receive the required information
for inclusion, and such studies were subsequently excluded). We additionally excluded one study
that reported untenable correlations (i.e., Rofcanin, Berber, Koch, & Sevinc, 2015).
4.3. Measures of Constructs
4.3.1. Included relationships. We meta-analyzed relationships between overall job
crafting, job crafting dimensions, and the set of a priori identified individual differences, job
characteristics, and work outcomes (Figure 1). Consistent with past research and best
methodological practices, we included such relationships in our meta-analytic models in cases
where these relationships were represented in at least three (K 3) independent samples that
measured each of the four dimensions of job crafting that were originally included in the Tims et
al. (2012) scale. As Valentine, Pigott, and Rothstein (2010) note, even when K = 2, meta-analysis
is superior to other means of synthesis (e.g., the “cognitive algebra” by which one tries to
mentally integrate findings across studies). Moreover, a number of previous meta-analyses in the
organizational sciences have adopted this K 3 criterion (e.g., Eby, Allen, Evans, Ng, &
DuBois, 2008; Choi, Oh, & Colbert, 2014; Kirca et al., 2012; Meyer, Stanley, Herscovitch, &
Topolnytsky, 2002; Viswesvaran et al., 2002; Viswesvaran & Ones, 1995). Ultimately only 2%
(i.e., 3) of the 115 effect sizes reported in our meta-analysis are based on K= 3 studies; the
average number of studies included across these 115 estimates is approximately K = 12. Initially,
we set out to code relationships that eventually did not meet our minimum K 3 criterion for
each of the four Tims et al. (2012) dimensions (e.g., individual differences, including positive
and negative affect and psychological capital; job characteristics, including skill variety and
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feedback, and work outcomes, including counterproductive work behaviors and organizational
commitment). Supplemental analyses and results characterizing these data are available from the
first author.
When overlapping variables were not available in at least three samples, we made efforts
to combine relevant variables into a typology of synthetic construct groupings. This was the case
for three work outcomes investigated here: 1) other-rated task performance was composed of
supervisor, peer, and customer-rated task performance, 2) job strain was composed of burnout
and emotional exhaustion, and 3) contextual performance was composed of self- and other-rated
organizational citizenship behaviors and self-rated contextual performance. It should be noted
that when coding effect sizes for demographic characteristics, age and tenure were
conceptualized chronologically (i.e., in years). Gender was operationalized as dummy coded
categories, such that higher values were indicative of females (i.e., 0 = Male, 1 = Female).
Education was operationalized such that higher scores indicate higher levels of educational
attainment. Additionally, tenure was considered in terms of both job (e.g., Berdicchia, 2015) and
organizational (e.g., Peeters, Arts, & Demerouti, 2016) tenure.
4.3.2. Composite and dimension-level job crafting. For overall job crafting,
relationships were either directly coded from primary studies (i.e., those reporting aggregated job
crafting scores; e.g., Solberg & Wong, 2016; Tims et al., 2016) or were combined from
dimension-level relationships using composite formulae from Hunter and Schmidt (2004). In
both cases, these relationships represent the association between a combination of job crafting
dimensions and a given correlate.
With respect to relationships at the dimension level, we coded heterogeneous job crafting
dimensions that most directly map onto the theoretical model offered by Tims and Bakker (2010)
and the corresponding Tims et al. (2012) measure of job crafting. The scaling of job crafting
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offered by Petrou et al. (2012) combines the two resource-based crafting dimensions into one
larger homogenous cluster of job crafting behavior (i.e., increasing job resources). As such, we
did not code such homogenous clusters. However, because the items included in this cluster do
generally map onto those of the Tims et al. (2012) scale, these dimensions were considered when
computing composite correlations to represent overall job crafting relationships.
4.4. General Overview of Meta-Analytic Procedures!
Following an exhaustive literature search, each of the first three authors independently
coded approximately one-third of all studies applying the a priori determined inclusion criteria
outlined above. Coding correlations and reliabilities directly from primary studies does not
require subjective judgements (Cooper, 1998; 2009; Hunter & Schmidt, 2004; Whetzel &
McDaniel, 1988). Indeed, initial calibration coding on a random subset of 10 studies yielded
perfect agreement among the three coders. Nonetheless, the coding team met for weekly coding
calibration meetings, during which each study was individually considered by the team and any
disagreements encountered were discussed until agreement was reached via consensus.
We corrected observed correlations for sampling and measurement error, and combined
effect size estimates using Hunter and Schmidt’s (2004) random-effects procedure. First, we
corrected for sampling error by calculating sample size-weighted correlations. Second, where
possible (i.e., for multi-item scales), we corrected for the lack of perfect reliability, as it is well-
established that unreliability attenuates zero-order correlations (Hunter & Schmidt, 2004).
Artifact distributions were used for cases in which a study did not report the reliability estimate
for a given construct (Hunter & Schmidt, 2004).
In addition to the sample-size weighted correlation (r) and the sample size-weighted and
reliability-corrected correlation (rc), we report the 95% confidence interval and the 80%
credibility interval for rc, as well as the variance attributable to statistical artifacts (% var). A
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sample size-weighted and reliability-corrected correlation is considered statistically significant
when its associated confidence interval does not include zero. If a credibility interval includes
zero, moderators are likely present (Geyskens, Krishnan, Steenkamp, & Cunha, 2009).
Alternatively, the 75% rule can be applied (i.e., a moderator is likely to be present when the
percentage variance accounted for by statistical artifacts is < 75%, see Hunter & Schmidt, 2004).
4.5. Meta-Analytic Confirmatory Factor Analysis
Given the conceptualization of overall job crafting in primary research, consideration of
the latent structure of job crafting bears some attention. The use of overall job crafting here and
in past research begs the question, to what extent is this an accurate representation of the job
crafting construct? Our meta-analysis of intercorrelations among Tims et al. (2012) dimensions
can partially speak to this concern. We subjected these correlations to a CFA, specified as a
single latent variable representing overall job crafting, with each of the four Tims et al. (2012)
dimensions loading onto this factor (see Figure 2).
4.6. Meta-Analytic Multiple Regression Models
Estimating bivariate dimension-level job crafting and work outcome relationships is
important for understanding how job crafting operates at the dimension level; however, to more
closely address the unique and relative contribution of Tims et al.’s (2012) job crafting
dimensions to the prediction of work outcomes, we also ran a series of meta-analytic multiple
regression models. While it is unlikely that the substantive conclusions drawn from such models
depend upon corrections for statistical artifacts (e.g., attenuation due to measurement error;
Michel, Viswesvaran, & Thomas, 2011), some have cautioned against this practice (LeBreton,
Scherer, & James, 2014). To be more conservative in our estimates of these effect, we ran these
models using uncorrected (i.e., sample size-weighted but not reliability-corrected) meta-analytic
esimates. Moreover, as suggested by Viswesvaran and Ones (1995), the sample size for each
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regression model was the harmonic mean of the sample size across the relevant correlations
considered. For such models, each work outcome was regressed simultaneously onto all four
Tims et al. (2012) job crafting dimenions.
When the predictors included in a regression model are correlated, the relative
contribution of each predictor to the model R2 cannot be accurately determined by examining the
regression weights alone (LeBreton, Ployhart, & Ladd, 2004). To determine the relative
contribution of each of the job crafting dimensions to the prediction of each work outcome, we
conducted a relative weights analysis (Johnson, 2000). We repeated these analyses for each work
outcome of interest. Relative weights analysis produces two types of coefficients – relative
weights and rescaled relative weights. Relative weights reflect the proportion of variance
explained in an outcome that is attributed to each of the predictor variables (e.g., any given job
crafting dimension), while the rescaled relative weights reflect the percentage of predicted
variance that is accounted for by each predictor variable (i.e., calculated by dividing the relative
weights by the model R2; LeBreton, Hargis, Griepentrog, Oswald, & Ployhart, 2007). !
4.7. Publication Status Sensitivity Analyses
Consistent with best practices for conducting meta-analysis (e.g., Cooper, Hedges, &
Valentine, 2009; Hunter & Schmidt, 2004), we took active steps to locate and obtain as many
unpublished data sources as possible. Despite these efforts, the possibility of the so-called “file-
drawer” problem (Rosenthal, 1979) could still result in publication bias and unduly affect the
results presented here. Conversely, it might be argued that the consideration of a relatively large
number of unpublished studies might itself present a systematic artifact that abides further
consideration. We conducted two separate sensitivity analyses to address the influence of
publication status on our conclusions. To ensure a reasonable distribution of published and
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unpublished studies, we considered only the three highest K outcomes from our primary analysis
(i.e., work engagement, job satisfaction, and self-rated work performance) for both analyses.
The first sensitivity analysis directly addresses the possibility of publication bias.
Publication bias occurs when the results observed from primary studies (i.e., those that are
readily available to review) systematically differ from the results in the population of all possible
primary studies (McDaniel, Rothstein, & Whetzel, 2006). To address this, we used trim-and-fill
procedures to examine the extent to which “missing” studies would change the conclusions
drawn here. The trim-and-fill method is a funnel plot symmetry approach, which both identifies
and corrects for publication bias (see Duval & Tweedie, 1998; 2000).
Whereas the first sensitivity analysis addresses whether the exclusion of unpublished data
could affect our conclusions, the second sensitivity analysis considered whether the inclusion of
unpublished data has a commensurate influence. To address this, we employed cumulative meta-
analysis. In cumulative meta-analysis, studies are sorted by a variable of interest (i.e., in this
case, publication status). Then, a series of iterative meta-analyses are conducted, each adding one
additional effect size at a time. We ordered publication status into blocks as 1 = published
studies, 2 = unpublished conference papers, 3 = unpublished data, and 4 = unpublished theses or
dissertations, which were then sequentially entered into a cumulative meta-analysis.
Such cumulative results can be examined for evidence of what McDaniel (2009) calls
“drift.” Meta-analytic results from the studies “first” entered into the cumulative analysis
represent estimates of the population mean from published studies. Meta-analytic results from
later stages of this iterative process represent those from the addition of unpublished studies to a
distribution that already contains published studies. If unpublished studies are somehow biasing
the conclusions, the cumulative results will “drift” in either a more positive or a more negative
direction (i.e., reflecting the direction of this bias) as unpublished studies are added.
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4.8. Overall Job Crafting Moderator Analysis
We did not hypothesize nor model substantive moderators of job crafting relationships
for two reasons. First, tests of moderators must be firmly grounded within theory and, in contrast
to assumptions about main effects, existing theories of job crafting inconsistently delineate the
role and operation of substantive moderators (e.g., Demerouti, 2014; Tims & Bakker, 2010).
Second, for the few cases where there is a delineation of theoretically justifiable moderators,
there is an inconsistent and relatively diffuse representation of such moderators within primary
studies. As such, we offer tests of homogeneity as evidence for future research to consider
conditional effects that may influence the strength of relationships between job crafting, its
dimensions, and the other variables considered here.
Although substantive moderators could not be addressed here, we did consider an
important methodological moderator representing the construction of overall job crafting. While
a growing number of studies consider overall job crafting as an aggregation of the four Tims et
al. (2012) job crafting dimensions, there is also speculation that the decreasing hindering job
demands dimensions represents a unique withdrawal (e.g., Demerouti, 2014; Tims et al., 2013)
or prevention-focused form of job crafting (e.g., Lichtenhaler, 2016) separate from the other
three dimensions. Our relative weights analyses can speak to the unique relationships that each
of these job crafting dimensions have with the outcomes considered in Figure 1. However, it is
likewise important to ascertain what influence (if any) the differential inclusion of these
dimensions has on the overall construal of job crafting. Whereas our meta-analytic CFA can
speak to the tenability of this overall operationalization from a factor structure perspective, the
aim of this moderator analysis is to determine how the construction of overall job crafting affects
its relationship with relevant antecedents and outcomes.
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When coding overall job crafting for our primary analyses, we additionally coded the
composition of overall job crafting as either 1 = “excludes decreasing hindering job demands”
and 2 = “includes decreasing hindering job demands,” based upon the way that job crafting was
operationalized and measured within each primary study. Thus, we considered the inclusion or
exclusions of decreasing hindering job demands across primary studies to be a natural
independent manipulation, rather than a synthetic and dependent construal.
For this analysis, we considered any effects that contained at least K = 2 for each
subgroup (i.e., to facilitate a comparison of least two studies each that either includes or excludes
the decreasing hindering job demands dimension). Consistent with best practices (Cooper,
Hedges, & Valentine, 2009), we used weighted least squares regression models to estimate these
conditional effects. As with our primary meta-analytic framework, we used Hunter-Schmidt
random effects estimators and sample-size weighting for effect sizes. To be most conservative,
we considered raw (i.e., uncorrected for predictor and criterion unreliability) correlations as
inputs in these models.
We report omnibus Q-statistics and associated inferential tests for significant moderator
effects along with I2 estimates. The I² statistic is an expression of the inconsistency of studies’
results that indexes the percentage of variation across studies that is due to heterogeneity
(interpreted as the proportion of the total variation among effect sizes that is due to systematic
differences between effect sizes rather than by chance alone; see Higgins and Thompson, 2002;
Higgins et al., 2003; Shadish & Haddock, 2009; pp. 263). Higher I² (i.e., those closer to 100%)
suggest that a larger proportion of residual heterogeneity remains unaccounted for after
moderators are modelled (e.g., values between 50% and 100% suggest “substantial” to
“considerable” levels of heterogeneity, see Higgins & Green, 2011). Likewise, following the
suggestions of Hunter and Schmidt (1990), we report 95% confidence intervals for each
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subgroup effect size to allow for direct comparisons between different levels of the moderator.
Non-overlapping 95% confidence intervals suggest that moderator subgroups are statistically
different from one another (p < .05).
5. Results
We first summarize the results of our meta-analytic CFA, and then turn to our primary
meta-analysis of zero-order effects and relative weights analyses. Finally, we discuss sensitivity
and moderator analyses. Given the number of relationships we have considered, the summary of
the zero-order effects focuses only on relationships with overall job crafting; we then expand
upon notable dimension-level relationships in our discussion. Complete zero-order meta-analytic
results of job crafting and its dimensions are summarized in Tables 2-5. Table 2 summarizes
findings for individual differences, Tables 3 and 4 summarize findings for job characteristics and
work outcomes, respectively, and Table 5 summarizes finding for demographic variables. All
effects summarized below are statistically significant (p < .05), except where noted.
5.1. Testing the Latent Structure of Job Crafting
We specified a one-factor CFA model, in which all four Tims et al. (2012) job crafting
dimensions loaded onto a single latent factor representing overall job crafting (see Table 6 &
Figure 2). The fit of this model was satisfactory (χ2(2) = 241.70, p < .05, CFI = .97, TLI = .90,
RMSEA = .09, SRMR = .04). Whereas the pattern of meta-analytic intercorrelations (see Table
6) suggest that the Tims et al. (2012) job crafting dimensions exhibit a generally positive
manifold (Spearman, 1904), a low standardized factor loading was observed between the
decreasing hindering job demands dimension and the latent job crafting factor. While fit of this
model to the data would suggest that these multiple forms of job crafting can be represented as
an aggregate score, these results also hint that some caution should be exercised when construing
job crafting as an “overall” construct (Akkermans & Tims, 2016;!Bell & Njolli, 2016; Ingusci et
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al., 2016; Tims, Derks, & Bakker, 2016). Notwithstanding this finding, however, we further
consider evidence for the operation of decreasing hindering job demands both in terms of its
predictive capacity and in terms of its construction, below.
5.2. Testing the Integrative Model of Job Crafting
5.2.1. Job crafting and individual differences. Considering big five personality
dimensions, we find positive relationships between overall job crafting and agreeableness, (rc =
.272), conscientiousness (rc = .200), extraversion (rc = .224), and openness to experience (rc =
.218). In contrast, neuroticism is unrelated to overall job crafting. Proactive personality is
positively related to overall job crafting (rc = .543), as is general self-efficacy (rc = 395).
Considering regulatory focus, we find that promotion focus is positively related to overall job
crafting (rc = .509) and that prevention focus is likewise positively related to overall job crafting
(rc = .157)
5.2.2. Job crafting and job characteristics. Job autonomy (rc = .279) and workload (rc
= .164) are both positively related to overall job crafting.
5.2.3. Job crafting and work outcomes. Job satisfaction is positively related to overall
job crafting (rc = .288); however, overall job crafting was not significantly related to turnover
intentions.
5.2.4. Occupational well-being. Work engagement is positively related to overall job
crafting (rc = .450), while job strain is negatively related to overall job crafting (rc = -.125).
5.2.5 Work Performance. Both self-rated work performance (rc = .274) and other-rated
work performance are positively related to overall job crafting (rc = .184). Additionally,
contextual performance is positively related to overall job crafting (rc = .314).
5.2.6. Demographic and Employment Characteristics. Considering chronological age,
a negative relationship is observed with overall job crafting (rc = -.100). Likewise, tenure is
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weakly and negatively related to overall job crafting (rc = -.105). Additionally, we find small,
positive relationships between overall job crafting and gender (rc = .027), education (rc = .110),
and work hours (rc = .098). !
5.3. Summary of Meta-Analytic Multiple Regression Models
Below, we report the results of the relative weights analyses summarizing the relative
contribution of each of the Tims et al. (2012) job crafting dimensions to the prediction of work
outcomes. Note that full results of these models are reported in Table 7, and only the most
important predictors (i.e., in terms of absolute magnitude of variance explained) are summarized
below.
5.3.1. Job attitudes. As a set, job crafting explained R2 = 14% of the variance in job
satisfaction. Relative weights analysis indicated that increasing structural job resources
accounted for 54.72% of this explained variance. With respect to turnover intentions, our
analysis revealed that R2 = 6% of the variance in turnover intentions can be attributed to these
four job crafting dimensions. At the dimension level, decreasing hindering job demands
accounted for 69.57% of the variance explained in turnover intentions.
5.3.2. Occupational well-being. Considering work engagement, job crafting dimensions
accounted for R2 = 29% of the variance in work engagement, and increasing structural job
resources explained 58.74% of this total variability. The set of job crafting dimensions accounted
for R2 = 3% of the variance in job strain and decreasing hindering job demands explains 42.05%
of this effect.
5.3.3. Work performance. Considering self-rated job performance, job crafting
dimensions explained R2 = 12% of the variance in self-rated job performance. The relative
weights analysis showed that increasing structural job resources is the most important crafting
dimension in terms of self-rated job performance, accounting for 66.44% of the variance
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explained across all four crafting dimensions. As a set, the four job crafting dimensions
accounted for R2 = 11% of the variance in other-rated job performance. Of the four dimensions,
increasing challenging job demands accounted for 67.83% of this variance explained. With
respect to contextual performance, job crafting explained R2 = 21% of the variance. Increasing
structural job resources accounted for 61.72% of this effect.
5.4. Publication Status Sensitivity Analyses
5.4.1. Trim-and-fill models. Trim-and-fill estimates for job satisfaction (rTF = .254) and
self-rated work performance (rTF = .243) were quite consistent with the raw zero-order estimates
found in Table 4. However, there was some evidence that publication bias may be affecting the
estimates of the work engagement parameter. Indeed, the trim-and-fill estimate was somewhat
higher than the zero-order effect (rTF = 0.440 versus r = .401), and the estimated number of
missing studies was K = 13. Some caution must be taken when interpreting these findings, as this
method has been noted to perform poorly (Terrin, 2003; Peters, 2007) when there is substantial
between-study heterogeneity (i.e., as observed for work engagement: % var = 15.08; I2 =
88.28%).
5.4.2. Cumulative meta-analysis. The total number of studies considered in the
cumulative meta-analysis of work engagement was K = 60. Initially, K = 25 published studies
were entered iteratively, followed sequentially by K = 35 unpublished data sources. The
observed cumulative effect after the initial entry of all published studies was rpub= 0.390 [95%
CI: 0.315; 0.466], whereas the observed overall cumulative effect including both published and
unpublished studies was rtotal = .401 [95% CI: 0.358; 0.445].
Likewise, the total number of studies considered in the cumulative meta-analysis of job
satisfaction was K = 20. Initially, K = 8 published studies were entered iteratively, followed
sequentially by K = 12 unpublished data sources. The observed cumulative effect after the initial
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entry of all published studies was rpub= 0.283 [95% CI: 0.182l 0.385], whereas the observed
overall cumulative effect including both published and unpublished studies was rtotal = .254 [95%
CI: 0.173; 0.334].
Finally, the total number of studies considered in the cumulative meta-analysis of self-
rated work performance was K = 27. Initially, K = 12 published studies were entered iteratively,
followed sequentially by K = 15 unpublished data sources. The observed cumulative effect after
the initial entry of all published studies was rpub= 0.249 [95% CI: 0.172; 0.327], whereas the
observed overall cumulative effect including both published and unpublished studies was rtotal =
.233 [95% CI: 0.163; 0.303]. Across all three cumulative meta-analyses, the overlapping
confidence intervals between blocks of published and unpublished studies suggests no evidence
of so-called “drifts” (McDaniel, 2009) associated with publication status.
5.5. Results of Overall Job Crafting Moderator Analysis
In terms of formal tests of moderation, for all three of the antecedents (i.e., proactive
personality, general self-efficacy, and job autonomy) and one of the outcomes (i.e., other-rated
work performance), there was no evidence to suggest that the inclusion or exclusion of the
decreasing hindering job demands dimension substantially changed the strength of the observed
effects. The relationship for work engagement (QM(1) = 9.237, p = .002, I2 = 83.95%) was,
however, significantly lower with the inclusion of decreasing hindering job demands, rinclude =
.365 [95% CI: 0.323; 0.406], than without, rexclude = .512 [95% CI: 0.427; 0.598]. Likewise, the
relationship for self-rated work performance (QM(1) = 4.029, p = 0.045, I2 = 70.90%) was
significantly lower with the inclusion of decreasing hindering job demands, rinclude = .200 [95%
CI: 0.134; 0.266], than without, rexclude = .348 [95% CI: 0.219; 0.478].
6. Discussion
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The main aims of this study were to integrate research on job crafting as conceptualized
by Tims and Bakker (2010) and Tims et al., (2012) by quantitatively synthesizing empirical
findings on relevant antecedents and outcomes of job crafting, investigating how these
associations vary as a function of how job crafting is conceptualized, and to examinine the
relative importance of different job crafting dimensions for predicting work outcomes. In the
following sections, we summarize and interpret our findings, discuss relevant limitations, suggest
implications for theory, and outline directions for future research.
6.1. Summary and Interpretation of Findings
Consistent with Bindl and Parker’s (2011) general model of proactive behavior, we
hypothesized that overall job crafting and some of its dimensions would be associated with
certain personality characteristics and beliefs. We found meaningful relationships between
overall job crafting and agreeableness, conscientiousness, extraversion, openness to experience,
proactive personality, general self-efficacy, and promotion and prevention regulatory focus. The
results for personality and general self-efficacy largely overlap with previous meta-analytic
findings for other forms of proactive behavior (i.e., personal initiative, taking charge, and voice;
Tornau & Frese, 2013). For instance, Tornau and Frese (2013) also found that conscientiousness,
extraversion, openness, and agreeableness were positively, and neuroticism was negatively
related to personal initiative. Findings for the job crafting dimensions were largely as expected
too, but there were also some unexpected results. For example, consistent with expectations, we
found that conscientiousness related positively to increasing structural job resources and
increasing challenging job demands. In contrast, associations between conscientiousness and
increasing social job resources and decreasing hindering demands were relatively small, and in
the case of the latter, negative.
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As expected, extraversion was positively related to increasing social job resources and,
contrary to expectations, also to increasing structural job resources and increasing challenging
job demands. One potential explanation for the latter findings is that extraversion has underlying
elements of assertiveness, which is an important antecedent of proactivity (Major, Turner, &
Fletcher, 2006). Also counter to expectations, we observed a positive relationship between
neuroticism and decreasing hindering job demands, suggesting that less emotionally stable
employees show greater efforts to reduce hindering demands than more emotionally stable
employees. Neuroticism was further negatively related to increasing structural job resources and
increasing challenging job demands. As hypothesized, agreeableness related positively with
increasing social job resources, but also with the other job crafting dimensions in Tims and
Bakker’s (2010) model, including decreasing hindering job demands. Openness was positively
related to increasing structural job resources and increasing challenging job demands, but it was
also unexpectedly negatively related to decreasing hindering job demands. Perhaps the
underlying curiosity and creativity inherent in people with high openness to experience direct
their attention towards more productive expressions of job crafting (Demerouti et al., 2015).
All job crafting dimensions except for decreasing hindering job demands were positively
and meaningfully associated with proactive personality, general self-efficacy, and promotion
focus. These findings were consistent with expectations based upon the job crafting literature and
the proactivity literature more generally (e.g., Parker et al., 2011), which suggest that these traits
are associated with higher levels of job crafting behavior. In contrast, decreasing hindering job
demands had weak and negative relationships with these traits. It may be that employees with
high levels of proactive personality, general self-efficacy, and promotion focus, direct more
attention to “growth-oriented” job crafting behaviors than on decreasing hindering demands. We
predicted and found a positive relationship between prevention focus and decreasing hindering
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demands. Unexpectedly, prevention focus was also weakly yet positively related to increasing
social job resources and increasing challenging job demands. With respect to the latter finding, it
may be that the concern for security and safety that characterizes people with a higher prevention
focus leads them to enact socially-focused forms of job crafting.
Consistent with the general model of proactive behavior (Bindl & Parker, 2011), we
further considered job characteristics as antecedents of job crafting. We found that overall job
crafting and all crafting dimensions -- except for decreasing hindering job demands -- were
positively related to job autonomy and workload. Consistently, Tornau and Frese (2013) found
that job autonomy was positively associated with personal initiative, however this study did not
examine workload. We expected most of these positive relationships based on proactivity and
job crafting theories; however, the positive association between workload and increasing
challenging job demands as well as the negative associations between job autonomy and
decreasing hindering job demands were contrary to our expectations. Future research should
explore why employees with a high workload would further increase their challenging job
demands, and why higher levels of job autonomy might prevent employees from decreasing
hindering demands. To the former point, the concept of active jobs (Karasek & Theorell, 1990)
may be particularly relevant. Additionally, some have speculated that engaging in job crafting
behaviors that decrease hindering job demands may signal withdrawal from work (e.g.,
Demerouti, 2014; Tims et al., 2013). Thus, such relationships may actually reflect a positive
process (e.g., job characteristics facilitating more adaptive crafting behaviors targeted at
increasing resources and challenging job demands).
Findings for job crafting and work outcomes were largely as expected based upon
theoretical considerations and consistent with meta-analytic findings for other forms of proactive
behavior (Tornau & Frese, 2013). Overall job crafting and its dimensions were positively related
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to job satisfaction, work engagement, self- and other-rated work performance, and contextual
performance. Considering the relative weights analysis for dimension-level relationships,
increasing structural job resources accounted for the most variance across these outcomes, with
the exception of other-rated job performance which had the strongest associations with
increasing challenging job demands. More generally, these relationships are consistent with
theory suggesting that job crafting leads to improved person-job fit which, in turn, positively
impacts job attitudes, occupational well-being, and different forms of job performance.
Similarly, Tornau and Frese (2013) found that personal initiative was positively associated with
job satisfaction and task performance. Again, the only job crafting dimension for which we
found negative or non-significant associations with these favorable outcomes was decreasing
hindering job demands.
Overall job crafting was negatively related job strain and not significantly related to
turnover intentions; specific job crafting dimensions related differentially to these work
outcomes as reflected in both the zero-order analyses and the meta-analytic multiple regression
models. As expected, increasing structural job resources and increasing challenging job demands
related negatively to job strain and turnover intention. Further underscoring the argument that
decreasing hindering job demands reflects withdrawal behaviors, we found a positive
relationship between decreasing hindering job demands and job strain. However, it appears from
the relative weights analyses that increasing structural job resources and increasing challenging
job demands may serve to offset this negative influence. Likewise, we found a positive
relationship between decreasing hindering job demands and turnover intentions. While our
regression models and relative weights analyses suggest that job crafting as a set did not explain
much of the variance in turnover intentions, most of the variance that was explained could be
attributed to decreasing hindering job demands. We also observed non-significant relationships
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between increasing social job resources and turnover intentions, and a small yet significant
negative relationship between increasing social job resources and job strain. These findings
contribute to enhanced theorizing regarding proactive behavior, as researchers have so far
neglected associations with occupational well-being and withdrawal (Parker & Bindl, 2017).
The general model of proactive behavior (Bindl & Parker, 2011) also considers
demographic and employment characteristics as antecedents. In our study, overall job crafting
was negatively related to age and tenure, and positively related to education and number of work
hours. Regarding gender, women reported higher levels of job crafting than men. In contrast,
Tornau and Frese (2013) found positive associations of personal initiative with age and
education, somewhat higher personal initiative among men compared to women, and a non-
significant association with tenure. The strongest negative relationships with age and tenure
emerged for increasing social job resources, whereas the other relationships were weak or non-
significant. One explanation for negative relationships of age and tenure with increasing social
job resources may be that older workers already have established work routines and networks
which they can rely on for social support and, thus, they may not need to further increase their
social resources (Zacher et al., 2016). Further research is needed that directly investigates the
roles of age and tenure for job crafting. For instance, more research should address why age and
tenure may relate differentially to various forms of job crafting, whether or not age and tenure
serve as boundary conditions for the effects of job crafting on various work outcomes, and --
from a social normative and age-role perspective -- whether or not job crafting is viewed
differently (i.e., as actions perceived by others) for younger and older workers (Kooij, Tims, &
Kanfer, 2015; Zacher & Kooij, 2017).
There were small yet significant gender differences observed for increasing structural and
social job resources. In both cases, the direction of these effects suggests that women engage in
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job crafting to a greater extent then men. Consistent with a human capital argument (Becker,
1975), we find positive relationships between education and all job crafting dimensions except
for decreasing hindering demands, which was negatively related to education. This might suggest
that higher levels of education facilitate the accumulation of job knowledge and expertise which,
in turn, facilitate job crafting. Finally, number of work hours was associated with overall
crafting, and increasing structural job resources and challenging job demands, suggesting that
those workers who spend additional time at work are more likely to obtain job resources such as
autonomy and challenging job demands such as new projects. Alternatively, it may also be
possible that engaging in these job crafting behaviors leads to an increase in the number of work
hours.
Evidence gleaned across several analyses calls into question the role of decreasing
hindering job demands in tandem with the other dimensions of job crafting outlined by Tims and
Bakker (2010; Tims et al., 2012). The CFA model specified on the basis of meta-analytically
derived intercorrelations between these job crafting dimensions had a satisfactory fit, but also
suggested a very small factor loading and a small amount of variance explained between a
general factor of job crafting and the decreasing hindering job demands dimension. While this
factor analytic evidence seems damning in-and-of-itself, it also bears noting that for only two
variables (i.e., work engagement and self-rated work performance) did we observe evidence that
the inclusion of this dimension in the overall conceptualization of job crafting appreciably
changes the strength of the job crafting relationship. These findings have interesting implications
for the idea of “construct drift” (Nichols, 2006). Specifically, the adoption of alternative
operationalizations of job crafting that differ from the one originally proposed by Tims et al.
(2012) may lead to very different conclusions regarding these outcomes. Likewise, evidence
from both the zero-order meta-analysis and the relative weights analyses suggests that there are
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unique patterns of correlations and predictive relationships associated with decreasing hindering
job demands. For example, the zero-order analyses suggest significant relationships with
neuroticism and prevention regulatory focus, whereas our relative weights analysis suggests that
decreasing hindering job demands is particularly relevant for the prediction of turnover
intentions and job strain.
In summary, our meta-analytic findings on overall job crafting are largely consistent with
propositions of the general model of proactive behavior (Bindl & Parker, 2011) and relevant
research on other forms of proactive behavior, such as personal initiative (Tornau & Frese,
2013). Our findings regarding the associations between overall job crafting and the antecedents
and outcomes considered here seem to suggest that overall job crafting is similar to other forms
of proactive behavior. However, differential results observed across job crafting dimensions and
the results of the CFA and relative weights analyses suggest that job crafting is not necessarily a
homogeneous construct. Indeed, specific job crafting dimensions are differentially associated
with both antecedents and work outcomes. In particular, the decreasing hindering demands
dimension appears to differ markedly from the other three job crafting dimensions, and this
observation deserves further attention in future research. The latter finding is also relevant to the
development of enhanced theoretical models of proactivity. Indeed, past syntheses of proactivity
constructs (Tornau & Frese, 2013) have not considered the possibly negative implications of
proactive work behaviors, making our contribution to this literature particularly important.
6.2. Theoretical Implications
In developing and testing our theoretical model, we focused attention on the integration
of job crafting as conceptualized by Tims and Bakker (2010) with more general models of
proactive work behavior. This integration should support future research concerning job crafting
and inspire the redevelopment of theoretical models of proactive work behavior. Another
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important point to consider here is the integration of the various conceptualizations of job
crafting behaviors that have been proposed in the literature (see Table 1). Although our meta-
analysis could not address this integration empirically, we can offer some reasoned guidance to
support future efforts directed at this concern.
At its core (i.e., as it is understood most generally both by Wrzesniewski & Dutton, 2001,
and Tims & Bakker, 2010), job crafting refers to various proactive efforts enacted to enhance
person-environment fit (see also Wang, et al., 2017). Person-environment fit has been the focus
of proactive work behavior research for some time. For example, Parker and Collins (2010)
differentiated three higher-order categories of proactive work behaviors that vary in the type of
change (i.e., “proactive goals”) individuals seek to bring about (i.e., proactive person-
environment behavior, proactive work behavior, and proactive strategic behavior). Most relevant
to the present discussion, proactive person-environment fit behavior refers to those actions that
aim to achieve enhanced fit between one’s personal attributes and that of the work environment.
Parker and Bindl (2017) further suggest that job crafting is a specific proactive strategy to bolster
supplies-values fit at work (i.e., the extent to which one’s work environment supplies the
attributes that one values; Edwards, 2008; see also Ashford & Black, 1996 for a corollary
argument). Curiously, little empirical research to date has directly examined the relationship
between person-environment fit and job crafting (Lu, Wang, Lu, Du, & Bakker, 2014; Niessen,
Weseler, & Kostova, 2016).
If a “bridge” were to be built between the models of job crafting offered by
Wrzesniewski and Dutton (2001) and Tims and Bakker (2010), person-environment fit could
thus serve as an important theoretical linkage between these two perspective and various models
of proactive work behavior outlined here. In a more fine-grained sense, certain dimensions of job
crafting from each model could be mapped onto one another. For example, while somewhat
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distinct, increasing social job resources as defined by Tims and Bakker (2010) could be argued to
reflect attempts at relational crafting as defined by Wrzesniewski and Dutton (2001). Likewise,
increasing challenging job demands and decreasing hindering job demands map onto the general
idea of task crafting insomuch as changing task boundaries means that employees modify the
quantity (i.e., number) or quality (i.e., content) of their job tasks.
Despite these clear content overlaps, linkages between cognitive crafting as defined by
Wrzesniewski and Dutton (2001) and those dimensions outlined by Tims and Bakker (2010) are
less clear. Moreover, each of these proposed relationships requires thorough empirical
consideration before a differentiated nomological network of job crafting behaviors and their
outcomes can be established. We hope that these propositions serve as a call for more research
on such an integrative perspective on job crafting. One additional benefit of such an integration
across job crafting models may be to build a stronger empirical case for the role that job crafting
plays for those long-term outcomes that could not be considered in our meta-analysis, but that
are associated with the cultivation of meaning at work (e.g., Berg, Dutton, & Wrzesniewski,
2013). We would argue that this represents a “missing piece” that is vital to fully realizing the
integration of these two literatures.
6.3. Limitations and Future Research
Meta-analysis can be generally criticized on several fronts. For example, some would
argue that meta-analysis inappropriately combines and summarizes divergent relationships. We
have addressed this issue here by focusing our efforts not only on an overall conceptualization of
job crafting but also on dimensions of job crafting that are based on widely accepted theoretical
models. Additionally, some would criticize meta-analysis for either being reflective of the
quality of the research that is available in the literature or dependent upon significant findings
that have been published (i.e., the so-called “file drawer” problem). With respect to the former
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issue of study quality, we did exclude one study that was deemed to report untenable job crafting
effects. Considering the latter issue of the file drawer problem more directly, we took
comprehensive steps to locate and include unpublished data sources in our analyses, which
should preclude such a criticism from being levied against this work. Our publication status
sensitivity analysis and trim-and-fill analyses generally supports this conclusion.
We acknowledge four additional limitations to the generalizability of our results that bear
further consideration and elaboration as part of future research efforts. First, the focus of our
meta-analysis was on the bivariate associations between job crafting and a variety of individual
differences, job characteristics, and work outcomes that a) map onto theory and b) are most
representative of the job crafting literature at this point in time. However, more complex
relationships between job crafting and such variables must be considered in future investigations.
For example, future research needs to examine mediators of these relationships, such as objective
and subjective person-job fit (Gordon et al., 2015). Speaking to this idea, Oldham and Hackman
(2010) questioned whether the beneficial outcomes of job crafting derive from actual changes in
job characteristics or from being involved in job crafting activities. Indeed, the primary studies
considered here cannot tease apart such reciprocal causal processes. However, future research
must endeavor to do so. Related to this, future research must consider various boundary
conditions that facilitate/mitigate job crafting. We would argue that this is a concern for job
crafting research as well as theory, which has inconsistently represented the role of moderators.
Across job crafting models (e.g., Demerouti, 2014; Wang et al., 2017), only the model by Tims
and Bakker (2010) specifies possible moderators (i.e., work characteristics and personality). In
the broader literature on proactivity, Bindl and Parker (2011) offer that situational judgment,
affect, and values are important moderators of the influence of proactive behavior on work
outcomes.
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Considering further the notion that crafting may have reciprocal relationships with certain
variables, more research is needed on the cyclical effects of job crafting, job characteristics, and
work outcomes. For instance, work engagement, person-environment fit, and leader-member
exchange quality are likely to be both predictors and outcomes of job crafting (Bakker, 2011;
Schaufeli, Bakker, & Van Rhenen, 2009; Wang et al., 2017). Non-experimental research cannot
demonstrate causal relationships, and any meta-analysis of such non-experimental studies is
likewise unable to do so. As such, an additional limitation of the present research is that all
primary works considered herein are correlational in nature and, except for a relatively small
subset of multi-wave panel studies and daily-diary, are cross-sectional/single time point and/or
comprised of self-reports of work behaviors, including job crafting. While experimentally
manipulating job crafting may be unrealistic, organizational interventions designed to enhance
job crafting are within reason. So far, however, there is only very limited research on job crafting
interventions (Demerouti & Bakker, 2014; Gordon et al., 2013; Van Mersbergen, 2012).
A second limitation to note is that a majority of research to-date has focused on positive
outcomes rather than dysfunctional consequences of job crafting. Our analysis does suggest that
decreasing hindering job demands is associated with higher turnover intentions and higher job
strain, however relatively few studies have investigated these relationships and their causal
direction is unclear. Indeed, there is the possibility that job crafting facilitates the introduction of
certain inefficiencies into work processes, and the discretionary nature of job crafting --
particularly task and relational focused actions -- may lead to conflict among team members
(Demerouti et al., 2015; Oldham & Hackman, 2010).
Third, beyond the within-person conceptualization of job crafting that is adopted by daily
diary studies, job crafting is generally understood at the between-person level of analysis. As job
crafting dictates some degree of discretion in the enactment of one’s job role (e.g., through the
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enhancement of resources and through the enlargement and contraction of job demands) it is
necessary to take a more nuanced multilevel perspective on job crafting. Indeed, job crafting
must be understood at the individual level, but also as manifestations at the team and
organizational levels (Tims et al., 2013), as well as in terms of cross-level job crafting (Leana et
al., 2009) and collaborative job crafting (McClelland, Leach, Clegg, & McGowan, 2014). At
present, our meta-analysis cannot address such multilevel effects as primary studies have yet to
widely adopt such operationalizations.
Finally, further research is needed on job crafting and time, including intraindividual
variation in job crafting over short durations and longer-term longitudinal research. Existing
daily diary studies on job crafting have shown that job crafting varies substantially both within
and between individuals (Demerouti et al., 2015; Petrou et al., 2012). Moreover, longitudinal
studies on job crafting (e.g., Tims et al., 2015) suggest that job crafting has longer-term effects
on work performance. To this general idea, we were not able to include several immediate and
long-term work outcomes of job crafting suggested by Wang et al. (2017) in the current meta-
analysis. These outcomes deserve further primary research attention, including needs
satisfaction, work meaning and identity, health, psychological ownership, employability, and
organizational effectiveness.
7. Conclusion
We conceptually integrated job crafting into a general model of proactive behavior and
conducted a comprehensive meta-analytic study of the relationships between job crafting and its
associated dimensions with various individual differences, job characteristics, and work
outcomes. Generally, we found that relationships for overall job crafting were similar to those
found in studies on other forms of proactive behavior, whereas more differentiated results
emerged when considering the four job crafting dimensions. Specifically, decreasing hindering
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demands seems to be less reflective of the overall job crafting construct and differently
associated with antecedents and outcomes than the other three job crafting dimensions. Based
upon these findings, future research should be cautious about the use of aggregate job crafting
scores in this way. Likewise, a more complete “unpacking” of the adaptive and
counterproductive implications of decreasing hindering job demands is warranted on the basis of
these results. In sum, our findings suggest that job crafting is associated with individual
differences and job characteristics, and that job crafting, in turn, is related to employees’ job
attitudes, occupational well-being, and different forms of work performance.
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Table 1
Conceptualizations and Measures of Job Crafting
Citation
Examples of Job Crafting Behaviors and/or Relevant Scale Items
Wrzesniewski & Dutton, 2001
Taking control over job tasks; modifying the quantity (i.e., number) or
quality (i.e., content) of job tasks.
Altering the quantity or quality of workplace relationships.
Psychological redefinitions and reinterpretations of job characteristics.
Ghitulescu (2006)
“How often do you teach concepts in small steps that are more
manageable for some students in your math classes?”
“In an average month, about how many times do you talk to
administrators about math instruction?”
“My job is very significant and important the results of my work are
likely to significantly affect the lives or well-being of other people.”
Leana, Appelbaum, & Shevchuk (2009)
“How often do you introduce new approaches on your own to improve
your work in the classroom?”
“How often do you decide together with your coworkers to change
minor work procedures that you think are not productive (such as lunch
time or transition routines)?”
Tims & Bakker (2010)
Adding job tasks; volunteering for interesting project groups; taking
over tasks from their supervisor.
Asking colleagues to help them with their tasks; reducing the number of
interactions with demanding customers or colleagues.
Seeking social support; enhancing job autonomy.
Berg, Grant, & Johnson (2010)
Task Expanding (e.g., highlighting assigned tasks); Job Expanding
(e.g., adding tasks); Role Reframing (e.g., altering role perceptions).
Vicarious Experiencing; Hobby Experiencing
Volman (2011)
“I, by myself, made work more challenging.”
“I, by myself, ask advice from my co-workers to solve difficulties in
my job.”
Tims, Bakker, & Derks (2012)
“I try to develop my capabilities.”
“I ask my supervisor to coach me.”
“When an interesting project comes along, I offer myself proactively as
a project co-worker.”
“I try to ensure that I do not have to make many difficult decisions at
work.”
Nielsen & Abildgaard (2012)
“When a new task comes up, I sign up for it.”
“I try to avoid emotionally challenging situations with my customers.”
JOB CRAFTING META-ANALYSIS
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“I ask for feedback on my performance from customers.”
“When there isn’t much to do, I offer my help to colleagues.”
“I ensure that my work is the least burdening/straining.”
Petrou, et al. (2012)
“I ask others for feedback on my job performance.”
“I ask for more tasks if I finish my work.”
“I try to ensure that my work is emotionally less intense.”
Slemp & Vella-Brodrick (2013)
“Change the scope or types of tasks that you complete at work.”
“Engage in networking activities to establish more relationship.”
“Think about how your job gives you purpose.”
Niessen, Weseler, & Kostova (2016)
“…I concentrate on specific tasks.”
“…I usually limit the amount of time I spend with people I do not get
along well with, and only contact them for things that are absolutely
necessary.”
“…I try to look upon the tasks and responsibilities I have at work as
having a deeper meaning than is readily apparent.”
Lichtenthaler & Fischbach (2016)
Identical items to Tims, Bakker, & Derks (2012) "increasing"
dimensions.
Identical items to Tims, Bakker, & Derks (2012) "decreasing"
dimension.
JOB CRAFTING META-ANALYSIS
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Table 2
Summary of Meta-Analytic Relationships: Individual Differences as Correlates of Job Crafting
Job Crafting Correlate
Type of Job Crafting
K
N
r
rc
SDrc
CIL
CIU
%Var
CVL
CVU
Big Five Agreeableness
Overall Job Crafting
5
2,944
0.198
0.272
0.220
0.224
0.319
13.580
-0.010
0.554
Structural
5
2,944
0.279
0.404
0.112
0.356
0.453
51.772
0.260
0.548
Social
5
2,944
0.089
0.130
0.227
0.077
0.182
8.292
-0.162
0.421
Challenging
5
2,944
0.169
0.246
0.126
0.195
0.298
31.421
0.085
0.408
Hindering
5
2,944
0.105
0.154
0.157
0.101
0.206
17.222
-0.047
0.355
Big Five Conscientiousness
Overall Job Crafting
5
2,944
0.154
0.200
0.086
0.154
0.246
28.590
0.090
0.310
Structural
5
2,944
0.208
0.285
0.066
0.237
0.332
43.954
0.200
0.370
Social
5
2,944
0.017
0.024
0.051
-0.026
0.074
55.717
-0.042
0.089
Challenging
5
2,944
0.114
0.157
0.079
0.108
0.206
34.599
0.056
0.258
Hindering
5
2,944
-0.043
-0.060
0.118
-0.110
-0.010
19.237
-0.211
0.091
Big Five Extraversion
Overall Job Crafting
6
3,075
0.194
0.224
0.176
0.185
0.263
7.329
-0.001
0.450
Structural
6
3,075
0.169
0.205
0.101
0.163
0.247
21.512
0.075
0.335
Social
6
3,075
0.142
0.176
0.149
0.133
0.219
11.809
-0.015
0.367
Challenging
6
3,075
0.246
0.302
0.088
0.261
0.342
25.548
0.189
0.414
Hindering
6
3,075
-0.022
-0.028
0.175
-0.071
0.016
8.974
-0.252
0.197
Big Five Neuroticism
Overall Job Crafting
7
3,566
-0.017
-0.021
0.116
-0.062
0.019
18.510
-0.170
0.127
Structural
7
3,566
-0.100
-0.132
0.147
-0.175
-0.090
14.411
-0.321
0.056
Social
7
3,566
-0.003
-0.004
0.042
-0.048
0.040
66.871
-0.058
0.050
Challenging
7
3,566
-0.046
-0.061
0.144
-0.104
-0.018
14.369
-0.246
0.123
Hindering
6
3,075
0.115
0.155
0.199
0.108
0.202
8.846
-0.101
0.410
Big Five Openness
Overall Job Crafting
5
2,944
0.174
0.218
0.225
0.174
0.262
6.287
-0.070
0.506
Structural
5
2,944
0.267
0.352
0.187
0.308
0.396
12.612
0.113
0.591
Social
5
2,944
0.044
0.059
0.175
0.011
0.107
9.133
-0.166
0.284
Challenging
5
2,944
0.200
0.266
0.181
0.220
0.313
11.121
0.035
0.498
Hindering
5
2,944
-0.075
-0.100
0.084
-0.148
-0.052
31.167
-0.208
0.008
Proactive Personality
Overall Job Crafting
12
4,189
0.474
0.543
0.036
0.516
0.570
69.974
0.497
0.590
Structural
10
4,434
0.518
0.631
0.000
0.605
0.657
100.000
0.605
0.657
Social
10
4,434
0.186
0.225
0.042
0.191
0.259
63.517
0.171
0.279
Challenging
11
4,636
0.526
0.639
0.105
0.614
0.664
17.330
0.505
0.773
Hindering
8
3,656
-0.045
-0.054
0.139
-0.093
-0.015
14.279
-0.231
0.124
General Self-Efficacy
Overall Job Crafting
12
2,418
0.335
0.395
0.157
0.353
0.437
20.212
0.194
0.596
Structural
3
1,238
0.436
0.542
0.084
0.486
0.598
31.290
0.435
0.649
Social
4
1,511
0.150
0.193
0.086
0.129
0.256
37.751
0.082
0.304
Challenging
6
1,568
0.334
0.432
0.085
0.375
0.489
53.518
0.324
0.540
JOB CRAFTING META-ANALYSIS
!
!
79
Hindering
6
1,768
-0.015
-0.019
0.161
-0.077
0.040
17.382
-0.224
0.187
Promotion Focus
Overall Job Crafting
12
3,225
0.432
0.509
0.040
0.476
0.542
71.774
0.458
0.561
Structural
6
1,349
0.379
0.468
0.062
0.412
0.525
58.519
0.389
0.548
Social
9
2,193
0.356
0.445
0.052
0.399
0.491
65.805
0.378
0.512
Challenging
11
2,784
0.435
0.550
0.093
0.512
0.588
36.262
0.431
0.669
Hindering
12
3,225
0.021
0.026
0.095
-0.017
0.070
40.066
-0.095
0.148
Prevention Focus
Overall Job Crafting
11
3,138
0.129
0.157
0.123
0.115
0.199
25.271
-0.001
0.315
Structural
5
1,262
0.014
0.018
0.000
-0.054
0.090
100.000
-0.054
0.090
Social
8
2,106
0.057
0.072
0.131
0.018
0.127
26.566
-0.096
0.240
Challenging
10
2,697
0.073
0.095
0.114
0.046
0.144
32.529
-0.051
0.241
Hindering
11
3,138
0.117
0.152
0.102
0.107
0.197
35.890
0.021
0.284
Note. K = cumulative number of studies; N = cumulative sample size; r = sample-sized weighted correlation; rc = sample size-weighted and reliability-corrected
correlation; SDrc = standard deviation of rc; CI = 95% confidence interval for rc; CV = 80% credibility interval for rc; %Var = variance attributable to statistical
artifacts (sampling error & unreliability); Social = Increasing Social Job Resources; Structural = Increasing Structural Job Resources; Challenging =Increasing
Challenging Job Demands; Hindering = Decreasing Hindering Job Demands.
JOB CRAFTING META-ANALYSIS
!
!
80
Table 3
Summary of Meta-Analytic Relationships: Job Characteristics as Correlates of Job Crafting
Job Crafting Correlate
Type of Job Crafting
K
N
r
rc
SDrc
CIL
CIU
%Var
CVL
CVU
Job Autonomy
Overall Job Crafting
25
8,805
0.240
0.279
0.083
0.256
0.302
34.531
0.173
0.385
Structural
14
5,644
0.369
0.456
0.101
0.428
0.484
23.948
0.327
0.586
Social
16
5,957
0.098
0.121
0.078
0.090
0.152
40.408
0.022
0.220
Challenging
22
7,722
0.261
0.322
0.110
0.296
0.347
25.469
0.181
0.463
Hindering
18
6,714
-0.060
-0.076
0.100
-0.106
-0.046
29.898
-0.204
0.052
Workload
Overall Job Crafting
12
2,878
0.144
0.164
0.088
0.123
0.205
40.601
0.052
0.276
Structural
4
716
0.162
0.195
0.140
0.109
0.282
28.461
0.016
0.375
Social
5
918
0.143
0.179
0.090
0.099
0.259
51.579
0.064
0.294
Challenging
11
2,288
0.143
0.181
0.128
0.130
0.233
32.267
0.018
0.345
Hindering
12
2,494
-0.001
-0.002
0.134
-0.052
0.048
30.266
-0.173
0.170
Note. K = cumulative number of studies; N = cumulative sample size; r = sample-sized weighted correlation; rc = sample size-weighted and reliability-corrected
correlation; SDrc = standard deviation of rc; CI = 95% confidence interval for rc; CV = 80% credibility interval for rc; %var = variance attributable to statistical
artifacts (sampling error & unreliability); Social = Increasing Social Job Resources; Structural = Increasing Structural Job Resources; Challenging =Increasing
Challenging Job Demands; Hindering = Decreasing Hindering Job Demands.
JOB CRAFTING META-ANALYSIS
!
!
81
Table 4
Summary of Meta-Analytic Relationships: Work Outcomes as Correlates of Job Crafting
Job Crafting Correlate
Type of Job Crafting
K
N
r
rc
SDrc
CIL
CIU
%Var
CVL
CVU
Job Satisfaction
General Job Crafting
20
6,599
0.254
0.288
0.115
0.262
0.314
21.500
0.140
0.436
Structural
9
4,694
0.338
0.398
0.077
0.368
0.428
28.804
0.299
0.496
Social
17
6,163
0.207
0.248
0.123
0.219
0.277
19.883
0.090
0.406
Challenging
16
5,762
0.253
0.312
0.086
0.282
0.341
37.084
0.202
0.421
Hindering
17
5,658
-0.099
-0.124
0.204
-0.156
-0.091
10.197
-0.385
0.138
Turnover Intentions
General Job Crafting
4
2,429
-0.019
-0.021
0.151
-0.066
0.023
8.236
-0.215
0.173
Structural
4
2,429
-0.133
-0.158
0.029
-0.204
-0.111
73.522
-0.194
-0.121
Social
3
2,240
-0.019
-0.022
0.015
-0.070
0.026
88.310
-0.042
-0.002
Challenging
4
2,429
-0.075
-0.091
0.106
-0.139
-0.043
17.480
-0.227
0.045
Hindering
3
2,240
0.202
0.235
0.023
0.189
0.282
77.232
0.207
0.264
Work Engagement
General Job Crafting
60
21,635
0.401
0.450
0.135
0.438
0.463
15.075
0.277
0.623
Structural
32
12,814
0.500
0.591
0.074
0.576
0.607
36.526
0.496
0.687
Social
39
14,535
0.297
0.352
0.123
0.334
0.370
19.461
0.194
0.510
Challenging
48
16,412
0.380
0.454
0.133
0.438
0.470
20.438
0.284
0.625
Hindering
42
12,258
-0.074
-0.090
0.159
-0.112
-0.069
16.787
-0.294
0.114
Job Strain
Overall Job Crafting
18
7,654
-0.108
-0.125
0.135
-0.151
-0.100
14.767
-0.298
0.047
Structural
9
3,342
-0.129
-0.157
0.159
-0.198
-0.117
13.434
-0.361
0.046
Social
10
3,726
-0.039
-0.046
0.092
-0.085
-0.008
31.447
-0.165
0.072
Challenging
15
5,425
-0.115
-0.140
0.126
-0.172
-0.108
20.050
-0.302
0.022
Hindering
16
5,631
0.119
0.150
0.168
0.118
0.183
13.752
-0.065
0.366
Self Rated Performance
Overall Job Crafting
27
7,770
0.233
0.274
0.125
0.249
0.299
22.496
0.113
0.435
Structural
14
5,664
0.324
0.400
0.057
0.371
0.428
50.618
0.326
0.473
Social
16
6,125
0.106
0.133
0.119
0.102
0.165
22.507
-0.019
0.286
Challenging
23
7,300
0.243
0.310
0.126
0.283
0.338
25.105
0.149
0.472
Hindering
20
5,804
-0.055
-0.070
0.176
-0.103
-0.037
15.330
-0.296
0.155
Other Rated Performance
Overall Job Crafting
7
1,024
0.158
0.184
0.174
0.115
0.254
22.827
-0.039
0.408
Structural
4
543
0.212
0.276
0.000
0.171
0.381
100.000
0.171
0.381
Social
6
930
0.167
0.211
0.118
0.132
0.291
42.111
0.061
0.362
Challenging
5
659
0.319
0.422
0.119
0.331
0.513
45.610
0.269
0.574
Hindering
4
721
-0.010
-0.013
0.189
-0.108
0.082
20.961
-0.255
0.229
Contextual Performance
Overall Job Crafting
12
3,689
0.262
0.314
0.172
0.278
0.351
13.405
0.095
0.534
Structural
4
2,318
0.417
0.506
0.024
0.465
0.547
78.813
0.475
0.537
Social
6
2,782
0.125
0.152
0.000
0.107
0.196
100.000
0.107
0.196
Challenging
10
3,360
0.322
0.429
0.161
0.389
0.469
19.730
0.223
0.635
JOB CRAFTING META-ANALYSIS
!
!
82
Hindering
10
3,360
-0.121
-0.161
0.083
-0.205
-0.117
43.541
-0.267
-0.055
Note. K = cumulative number of studies; N = cumulative sample size; r = sample-sized weighted correlation; rc = sample size-weighted and reliability-corrected
correlation; SDrc = standard deviation of rc; CI = 95% confidence interval for rc; CV = 80% credibility interval for rc; %var = variance attributable to statistical
artifacts (sampling error & unreliability); Social = Increasing Social Job Resources; Structural = Increasing Structural Job Resources; Challenging =Increasing
Challenging Job Demands; Hindering = Decreasing Hindering Job Demands; Self Rated Performance = self rated work performance; Other Rated Performance =
other rated work performance.
JOB CRAFTING META-ANALYSIS
!
!
83
Table 5
Summary of Meta-Analytic Relationships: Demographics as Correlates of Job Crafting
Job Crafting Correlate
Type of Job Crafting
K
N
r
rc
SDrc
CIL
CIU
%Var
CVL
CVU
Age
Overall Job Crafting
50
14,469
-0.092
-0.100
0.095
-0.118
-0.083
31.369
-0.222
0.022
Structural
21
6,593
0.028
0.033
0.051
0.005
0.060
62.178
-0.032
0.098
Social
26
7,370
-0.167
-0.194
0.122
-0.220
-0.168
24.346
-0.351
-0.037
Challenging
33
9,347
-0.037
-0.043
0.145
-0.066
-0.019
18.468
-0.228
0.143
Hindering
29
8,470
-0.033
-0.039
0.126
-0.064
-0.014
22.678
-0.201
0.123
Tenure
Overall Job Crafting
19
5,705
-0.095
-0.105
0.069
-0.133
-0.076
45.907
-0.193
-0.016
Structural
8
3,453
-0.025
-0.029
0.088
-0.067
0.009
27.679
-0.142
0.084
Social
8
3,453
-0.138
-0.158
0.119
-0.196
-0.121
17.602
-0.310
-0.006
Challenging
10
3,618
-0.083
-0.092
0.072
-0.128
-0.056
39.128
-0.185
0.001
Hindering
9
3,112
-0.056
-0.063
0.049
-0.103
-0.024
60.431
-0.127
0.000
Gender
Overall Job Crafting
32
10,781
0.025
0.027
0.058
0.006
0.048
51.142
-0.047
0.101
Structural
13
4,022
0.062
0.070
0.000
0.035
0.105
100.000
0.035
0.105
Social
18
4,799
0.069
0.080
0.033
0.047
0.112
82.202
0.038
0.122
Challenging
23
6,657
0.021
0.024
0.099
-0.004
0.051
31.632
-0.103
0.151
Hindering
23
6,707
-0.020
-0.023
0.064
-0.051
0.005
53.107
-0.105
0.059
Education
Overall Job Crafting
23
5,785
0.100
0.110
0.112
0.082
0.138
28.128
-0.034
0.253
Structural
9
3,502
0.098
0.116
0.097
0.077
0.154
28.401
-0.009
0.240
Social
13
4,006
0.113
0.131
0.081
0.095
0.166
40.714
0.027
0.234
Challenging
13
4,038
0.123
0.145
0.152
0.109
0.181
16.556
-0.050
0.340
Hindering
14
3,735
-0.052
-0.061
0.025
-0.099
-0.024
89.487
-0.093
-0.029
Work Hours
Overall Job Crafting
8
1,764
0.088
0.098
0.091
0.046
0.150
40.307
-0.019
0.215
Structural
5
995
0.092
0.106
0.058
0.035
0.176
65.749
0.031
0.180
Social
5
995
-0.011
-0.012
0.031
-0.083
0.058
87.170
-0.052
0.027
Challenging
4
856
0.154
0.171
0.132
0.098
0.244
24.168
0.002
0.340
Hindering
4
778
-0.052
-0.060
0.000
-0.140
0.020
100.000
-0.140
0.020
Note. K = cumulative number of studies; N = cumulative sample size; r = sample-sized weighted correlation; rc = sample size-weighted and reliability-corrected
correlation; SDrc = standard deviation of rc; CI = 95% confidence interval for rc; CV = 80% credibility interval for rc; %var = variance attributable to statistical
artifacts (sampling error & unreliability); Social = Increasing Social Job Resources; Structural = Increasing Structural Job Resources; Challenging =Increasing
Challenging Job Demands; Hindering = Decreasing Hindering Job Demands.
JOB CRAFTING META-ANALYSIS
!
!
84
Table 6
Meta-Analysis of Inter-Relationship Between Job Crafting Dimensions
Job Crafting Relationship
K
N
r
rc
CIL
CIU
%Var
CVL
CVU
Social-Structural
42
13,440
0.306
0.398
0.378
0.418
18.811
0.211
0.585
Social-Challenging
42
13,440
0.390
0.507
0.489
0.526
31.598
0.369
0.646
Social-Hindering
42
13,440
0.133
0.174
0.152
0.196
12.375
-0.075
0.424
Structural-Challenging
42
13,440
0.521
0.671
0.655
0.687
23.220
0.510
0.832
Structural-Hindering
42
13,440
0.005
0.006
-0.016
0.028
22.198
-0.168
0.181
Challenging-Hindering
42
13,440
0.020
0.026
0.004
0.048
15.769
-0.189
0.242
Note. K = cumulative number of studies; N = cumulative sample size; r = sample-sized weighted correlation; rc = sample size-weighted and reliability-corrected
correlation; SDrc = standard deviation of rc; CI = 95% confidence interval for rc; CV = 80% credibility interval for rc; %var = variance attributable to statistical
artifacts (sampling error & unreliability). Social = Increasing Social Job Resources; Structural = Increasing Structural Job Resources; Challenging =Increasing
Challenging Job Demands; Hindering = Decreasing Hindering Job Demands
JOB CRAFTING META-ANALYSIS
!
!
85
Table 7
Relative Weights Analysis
!!
!!
!!
!!
!!
!!
!!
!!
Job Satisfaction
Dimension
B
SEB
t-value
p
RW
%R2
Social
0.113
0.011
10.169
<.001
0.024
16.635
R2 = .14
Structural
0.266
0.012
22.470
<.001
0.078
54.724
F = 356.50, p < .05
Challenging
0.073
0.012
5.933
<.001
0.029
20.266
!!
Hindering
-0.117
0.010
-11.537
<.001
0.012
8.375
Turnover Intentions
Dimension
B
SEB
t-value
p
RW
%R2
Social
-0.003
0.016
-0.159
0.874
0.001
1.009
R2 = .06
Structural
-0.127
0.017
-7.530
<.001
0.015
24.654
F = 72.25, p < .05
Challenging
-0.012
0.017
-0.682
0.495
0.003
4.772
!!
Hindering
0.203
0.014
14.097
<.001
0.041
69.565
Work Engagement
Dimension
B
SEB
t-value
p
RW
%R2
Social
0.142
0.008
17.805
<.001
0.046
15.599
R2 = .29
Structural
0.394
0.009
46.158
<.001
0.172
58.744
F = 1220.12, p < .05
Challenging
0.121
0.009
13.762
<.001
0.067
22.953
!!
Hindering
-0.097
0.007
-13.350
<.001
0.008
2.705
Job Strain
Dimension
B
SEB
t-value
p
RW
%R2
Social
0.000
0.013
0.036
0.971
0.001
2.351
R2 = .03
Structural
-0.094
0.014
-6.896
<.001
0.011
32.604
F = 64.63, p < .05
Challenging
-0.069
0.014
-4.870
<.001
0.008
22.994
!!
Hindering
0.121
0.012
10.372
<.001
0.014
42.051
Self-Rated Performance
Dimension
B
SEB
t-value
p
RW
%R2
Social
-0.011
0.011
-1.051
0.293
0.004
3.519
R2 = .12
Structural
0.272
0.012
23.606
<.001
0.077
66.435
F = 305.07, p < .05
Challenging
0.107
0.012
8.961
<.001
0.032
27.165
!!
Hindering
-0.057
0.010
-5.787
<.001
0.003
2.881
Other-Rated Performance
Dimension
B
SEB
t-value
p
RW
%R2
Social
0.047
0.026
1.780
0.075
0.013
11.956
R2 = .11
Structural
0.056
0.028
2.003
0.045
0.021
19.875
F = 47.48, p < .05
Challenging
0.272
0.029
9.392
<.001
0.072
67.830
!!
Hindering
-0.022
0.024
-0.916
0.360
0.000
0.339
Contextual Performance
Dimension
B
SEB
t-value
p
RW
%R2
Social
-0.024
0.013
-1.825
0.068
0.006
2.939
R2 = .21
Structural
0.345
0.014
24.146
<.001
0.127
61.719
F = 351.92 p < .05
Challenging
0.154
0.015
10.465
<.001
0.057
27.857
!!
Hindering
-0.123
0.012
-10.052
<.001
0.015
7.485
Note. B = regression weight, SEB = standard error for B; RW = raw relative weight; %R2 = Rescaled raw relative
weight as a percent of total variance explained by model. Social = Increasing Social Job Resources; Structural =
Increasing Structural Job Resources; Challenging =Increasing Challenging Job Demands; Hindering = Decreasing
Hindering Job Demands
JOB CRAFTING META-ANALYSIS
!
!
86
Figure 1
Conceptual Model & Overview of Relationships Investigated in Meta-Analysis
JOB CRAFTING META-ANALYSIS
!
!
87
Figure 2
Summary of Standardized Factor Loadings for Tims et al. (2012) Job Crafting Confirmatory Factor Analysis
Note. χ2=241.70, p < .05, CFI = .97, TLI = .90, RMSEA = .09, SRMR = .04
Overall Job
Crafting
Increasing
Social Job
Resources
R2= .232
Increasing
Structural Job
Resource
R2= .411
Increasing
Challenging
Job Demands
R2= .657
Decreasing
Hindering Job
Demands
R2= .002
.482* .641* .811* .047*
... Despite often confirmed positive effects (see Rudolph et al., 2017 for a metaanalysis), job crafting studies have also indicated that self-initiated changes in one's job are not always beneficial for employees (e.g., Dubbelt et al., 2019;Lichtenthaler & Fischbach, 2019). Crafting social resources can be resource-consuming as it is a form of proactive behavior at work and requires effort, resources and energy (Grant & Parker, 2009). ...
... For example, by asking supervisors for feedback on a completed task, employees receive information about their work behavior that they can use for future work tasks and their personal development at work. Many studies have shown that crafting social resources is positively associated with work engagement (e.g., Lichtenthaler & Fischbach, 2019;Rudolph et al., 2017). We therefore propose the following hypothesis: ...
... Work engagement represents an important indicator of occupational well-being and should therefore be increased in employees. Previous research demonstrated that crafting social resources promotes engaged employees (e.g., Rudolph et al., 2017). However, our study results have implications for the use of crafting social resources and its impact on work engagement as an indicator of occupational well-being. ...
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Crafting social resources is a job crafting strategy that implies changing one’s social job resources to improve person-job fit and work-related well-being. Previous research has mostly assumed a resource-generating nature of crafting social resources and investigated the linear positive effects of this job crafting strategy on, for example, work engagement. Considering that crafting social resources can also be resource-consuming, in this paper, we referred to conservation of resources theory and resource allocation theory and proposed a curvilinear, U-shaped relationship between crafting social resources and work engagement. We further predicted that exhaustion would moderate this curvilinear relationship. To test our hypotheses, a two-wave study with 233 employees was conducted. Consistent with our assumptions, compared with a low or high level, a moderate (i.e., occasional) level of crafting social job resources was associated with a lower level of work engagement three months later. Furthermore, exhaustion acted as a moderator insomuch that a low level of exhaustion mitigated the detrimental effect of crafting social resources at a moderate level on work engagement. Accordingly, the findings showed that crafting social resources is not always beneficial and can impair employees’ work engagement, especially for exhausted employees.
... Work outcomes have been defined by Rudolph et al. (2017) as the process in which individual feedback or knowledge on the extent to which an individual has been successful in their work roles is evident. This can be identified from the information obtained from production rates or even customer satisfaction scores. ...
Article
Background and purpose: The purpose of this study is to investigate the relationship between core job characteristics (CJC) and personal work outcomes (OUT), as well as the roles of experienced meaningfulness of work (EMW) and experienced responsibility for outcomes of work (EROW) in mediating the CJC–OUT relationship. Specifically, this study attempts to examine the effectiveness of CJC in improving EMW and EROW and to shed light on the roles of EMW and EROW in enhancing the OUT of employees in the Northern Cyprus hotel sector. Methods: This study adopted a quantitative approach to collect and analyze the data from 420 tourism stakeholders in Northern Cyprus hotel sector. A partial least squares (PLS) technique using Smart-PLS was applied to test the direct relationships within the research model and determine any mediating effects. Results: The analysis revealed strong support for meaningfulness of work and experienced responsibility for outcomes of work acting as partial mediators in the relationship between core job characteristics and personal work outcomes. Moreover, core job characteristics was found to have a reasonable direct effect on personal work outcomes, experienced meaningfulness of work, and experienced responsibility for outcomes of work. Conclusion: The current study points to the importance of including experienced meaningfulness of work and experienced responsibility for outcomes of work as mediating variables to understand better the relationship between core job characteristics and Personal work outcomes. Several theoretical and practical implications are included before pinpointing the directions of potential future studies that makeup on the evidence-based argument regarding the results of this study. Lastly, top management in hotel sector would benefit from job redesign because the results demonstrated that the core job characteristics have a positive effect on their work outcomes.
... Prior research on crafting outside the job While research on job crafting is plentiful (for reviews and meta-analyses: Rudolph et al., 2017;Lichtenthaler and Fischbach, 2019;Zhang and Parker, 2019;Lazazzara et al., 2020), research on crafting outside the work context is scarce. The two primary constructs relevant here are leisure crafting and home crafting. ...
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Shaping off-job life is becoming increasingly important for workers to increase and maintain their optimal functioning (i.e., feeling and performing well). Proactively shaping the job domain (referred to as job crafting) has been extensively studied, but crafting in the off-job domain has received markedly less research attention. Based on the Integrative Needs Model of Crafting, needs-based off-job crafting is defined as workers’ proactive and self-initiated changes in their off-job lives, which target psychological needs satisfaction. Off-job crafting is posited as a possible means for workers to fulfill their needs and enhance well-being and performance over time. We developed a new scale to measure off-job crafting and examined its relationships to optimal functioning in different work contexts in different regions around the world (the United States, Germany, Austria, Switzerland, Finland, Japan, and the United Kingdom). Furthermore, we examined the criterion, convergent, incremental, discriminant, and structural validity evidence of the Needs-based Off-job Crafting Scale using multiple methods (longitudinal and cross-sectional survey studies, an “example generation”-task). The results showed that off-job crafting was related to optimal functioning over time, especially in the off-job domain but also in the job domain. Moreover, the novel off-job crafting scale had good convergent and discriminant validity, internal consistency, and test–retest reliability. To conclude, our series of studies in various countries show that off-job crafting can enhance optimal functioning in different life domains and support people in performing their duties sustainably. Therefore, shaping off-job life may be beneficial in an intensified and continually changing and challenging working life.
... Job crafting has been referred to as the self-initiated behaviors that employees take to shape, mold, and change their jobs (Wrzesniewski and Dutton, 2001;Tims and Bakker, 2010;Tims et al., 2012;Zhang and Parker, 2019). Crafting can help satisfying psychological needs and exhibits favorable outcomes, such as employee performance and well-being (for a review see Rudolph et al., 2017;Zhang and Parker, 2019;Mäkikangas and Schaufeli, 2021). Wrzesniewski and Dutton (2001) initially described this concept as a social constructivist approach that refers to "the physical and cognitive changes individuals make in the task or relational boundaries of their work" (p. ...
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Ongoing developments, such as digitalization, increased the interference of the work and nonwork life domains, urging many to continuously manage engagement in respective domains. The COVID-19 pandemic and subsequent home-office regulations further boosted the need for employees to find a good work-nonwork balance, thereby optimizing their health and well-being. Consequently, proactive individual-level crafting strategies for balancing work with other relevant life domains were becoming increasingly important. However, these strategies received insufficient attention in previous research despite their potential relevance for satisfying psychological needs, such as psychological detachment. We addressed this research gap by introducing a new scale measuring crafting for a work-nonwork balance and examining its relevance in job-and life satisfaction, work engagement, subjective vitality, family role and job performance, boundary management and self-rated work-nonwork balance. The Work-Nonwork Balance Crafting Scale was validated in five countries (Austria, Finland, Germany, Japan, and Switzerland), encompassing data from a heterogeneous sample of more than 4,200 employees. In study 1, exploratory factor analysis revealed a two-factorial scale structure. Confirmatory factor analysis, test for measurement invariance, and convergent validity were provided in study 2. Replication of confirmatory factor analysis, incremental and criterion validity of the Work-Nonwork Balance Crafting Scale for job and life satisfaction were assessed in study 3. Study 4 displayed criterion validity, test–retest reliability, testing measurement invariance, and applicability of the scale across work cultures. Finally, study 5 delivered evidence for the Work-Nonwork Balance Crafting Scale in predicting work-nonwork balance. The novel Work-Nonwork Balance Crafting Scale captured crafting for the challenging balance between work and nonwork and performed well across several different working cultures in increasingly digitalized societies. Both researchers and practitioners may use this tool to assess crafting efforts to balance both life domains and to study relationships with outcomes relevant to employee health and well-being.
... More specifically, there seems to be a gap between how many I-O psychologists view themselves politically (i.e., their personal political orientation) and how some of their research approaches could be classified from a critical I-O psychology perspective (see Islam & Sanderson, 2022). For instance, an I-O psychologist might see herself as politically left-wing, but she may conduct research on individual difference predictors of job crafting (i.e., actively fitting the job to one's abilities and needs; Rudolph et al., 2017). From a critical perspective, this approach has been characterized as being consistent with a neoliberal agenda, because it is assumed to suggest that the main responsibility to craft one's job (and, thus, also the responsibility for the positive outcomes of job crafting, such as improved well-being) is placed upon the individual employee and not, as with traditional work design, on the employer or broader social and economic conditions (Bal & Dóci, 2018). ...
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Researchers and practitioners have become increasingly interested in the role of political orientation in the workplace. Importantly, people do not always agree with other members of their profession when it comes to politics. However, the effects of such person-occupation political orientation misfit on people’s work-related attitudes remain unclear. According to the social identity perspective, person-occupation political orientation misfit is likely to lead to the experience of identity threat which, in turn, should negatively impact people’s occupational identification. To address this idea empirically, the goal of this study was to examine the influence of different political depictions of the field of industrial and organizational (I-O) psychology (i.e., as generally neoliberal, left-wing, pluralistic, or neutral) on I-O psychologists’ occupational identification, depending on their personal political orientation (i.e., more or less liberal vs. conservative). Specifically, we hypothesized that experiencing person-occupation political orientation misfit would reduce occupational identification. Results of an experiment (n = 800 I-O psychology academics and practitioners) provided some support for this hypothesis, suggesting specifically that person-occupation political orientation misfit might alienate people with a more conservative political orientation from their occupation.
... According to Wrzesniewski and Dutton (2001), employees are motivated to engage in these various forms of job crafting for a number of reasons, including a desire to gain autonomy over their job in order to ultimately avoid negative outcomes such as alienation and boredom at work. Metaanalytic evidence supports the idea that job crafting is effective at enhancing employees' work experiences, linking job crafting to many positive benefits such as increased job satisfaction, organizational commitment, and job performance (both selfand other-related), as well as decreased job strain (Rudolph et al., 2017). Further, and particularly germane to the current study, a growing number of studies indicate that job crafting may serve as a valuable strategy that employees can use to increase perceptions of person-job fit (Kooij et al., 2017;Lu et al., 2014), and in turn foster positive feelings about the meaningfulness of their work (Tims et al., 2016). ...
Chapter
Gesundheitsförderliche Arbeitsgestaltung wurde lange Zeit als Top-down-Prozess betrachtet, der von den Organisationen eingeleitet und durchgeführt wird. Die zunehmende Dynamik und Autonomie von Beschäftigten tragen allerdings dazu bei, dass Top-down-Maßnahmen nicht mehr ausreichen, um den sich rasch wandelnden und hoch individualisierten Arbeitskontexten zu begegnen. Vielmehr sind vermehrt die Arbeitnehmerinnen und Arbeitnehmer selbst gefragt, proaktiv mit zur Gestaltung und Aufrechterhaltung einer gesunden, produktiven und motivierenden Arbeit beizutragen, indem sie selbstinitiiert die vorgefundenen Arbeitsaufgaben und Arbeitsbedingungen an die eigenen Bedürfnisse anpassen. Studien zeigen, dass dieses sogenannte Job Crafting mit positiven Konsequenzen für die Gesundheit und die Leistung einhergeht. Entsprechend scheint es lohnenswert im Sinne der betrieblichen Gesundheitsförderung, Job Crafting zu unterstützen und zu fördern. Der vorliegende Beitrag befasst sich mit den Fragen, ob und wie Organisationen Job Crafting bei den Beschäftigten ermöglichen und fördern können. Es werden mögliche Ansätze von Organisationen zur Unterstützung des Job-Crafting-Verhaltens diskutiert: (a) Gestaltung von Arbeitsbedingungen, die das Job Crafting von Beschäftigten ermöglichen und erleichtern, (b) Förderung von Job Crafting durch Führungskräfte und (c) das Trainieren von Job Crafting mittels spezieller Übungen. Ein Überblick über die Wirksamkeit der in der Literatur vorhandenen Job-Crafting-Interventionen und Empfehlungen zur Umsetzung einer wirksamen Intervention wird erarbeitet.
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The successful learning and professional development of student teachers in field experiences depend on the conditions at the individual practicum school. As a consequence, researchers have investigated the relevance of various contextual factors (e.g. mentoring) and ways of improving them (e.g. mentor training). Whether and to what avail student teachers take an active role in adapting the conditions of field experiences to their needs has, however, received scant attention. In applying the concept of job crafting to field experiences during initial teacher education, the present study examines whether student teachers engage in activities to increase resources and control demands during practical phases. How job crafting can be predicted and whether such behaviour is related to higher rates of job satisfaction, engagement, and learning gains are investigated by surveying 132 student teachers at three measurement intervals (beginning, middle, and end) of a 14-week practical phase. The results indicate moderate to high rates of job crafting amongst student teachers. Just as job crafting was significantly predicted by contextual and individual factors, it predicted student teachers' job satisfaction, teacher engagement and learning gains. The findings support the relevance of a research focus on proactive student teacher behaviour in field experiences.
Article
The association between job crafting and work engagement (WE) has not been much explored in the Indian context. To address this, the current study was undertaken on a group of knowledge workforce from Indian industries. Data were collected from 297 respondents that included junior-, mid- and senior-level employees. It was found that seeking social resources predicts WE as well as organizational commitment amongst the Indian knowledge workers via person–job fit. The study contributes to the literature by exploring the relationship between proactively seeking social resources that shape relationships at work and achieves WE, furthering organizational commitment. It helps reaffirm the independent nature of the job crafting dimension in a collectivistic society. Supervisors can cultivate proactive crafting in establishments to boost and promote an engaged workforce. Bearing in mind the inferences Human Resource Development (HRD) managers ought to make optimum use of human assets by nurturing constructive psychological states and leveraging individual proactivity at work. Limitations and future directions have been discussed.
Article
Building on Agency Theory and Job Characteristics Theory, this study examines how the autonomy of work interacts with individual proactivity and jointly enhances hotel frontline employees’ self-affirmation and performance. Using a longitudinal research design of three-wave data collection, the findings of this study suggested that the autonomy of work enhances employees’ perceived self-efficacy and sense of personal control. Although the perceived sense of control did not lead to employees’ organizational citizenship behaviors (OCBs), self-efficacy can facilitate employees’ OCBs directed toward both internal and external customers. In addition, the autonomy of work’s influence on employees’ perceived self-efficacy and sense of control was stronger among employees with relatively proactive personalities. The study adds empirical evidence to Agency Theory and Job Characteristics Theory and supports the importance of autonomy at the workplace as a necessary factor to encourage employees’ OCBs.
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The results of meta-analytic ( MA ) and validity generalization ( VG ) studies continue to be impressive. In contrast to earlier findings that capped the variance accounted for in job performance at roughly 16%, many recent studies suggest that a single predictor variable can account for between 16 and 36% of the variance in some aspect of job performance. This article argues that this “enhancement” in variance accounted for is often attributable not to improvements in science but to a dumbing down of the standards for the values of statistics used in correction equations. With rare exceptions, applied researchers have suspended judgment about what is and is not an acceptable threshold for criterion reliability in their quest for higher validities. We demonstrate a statistical dysfunction that is a direct result of using low criterion reliabilities in corrections for attenuation. Corrections typically applied to a single predictor in a VG study are instead applied to multiple predictors. A multiple correlation analysis is then conducted on corrected validity coefficients. It is shown that the corrections often used in single predictor studies yield a squared multiple correlation that appears suspect. Basically, the multiple predictor study exposes the tenuous statistical foundation of using abjectly low criterion reliabilities in single predictor VG studies. Recommendations for restoring scientific integrity to the meta-analyses that permeate industrial–organizational (I–O) psychology are offered.
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We aimed to explore the mediating role of perceived organizational support (POS) on the relationship between job crafting and job satisfaction, which is considered as an important outcome for the development of well-being at work. Participants were 263 teachers from public schools in the South of Italy. Results indicated that POS fully mediated the relationship between job crafting and job satisfaction. Implications for management educational practice and limitations of the study are discussed.
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Empirical research on employee job crafting is scarce, probably because until recently scales with which the construct can be reliably and validly measured were not available. Although a general scale has recently been developed, the cognitive component of job crafting was omitted. The aim of the present study was to address this gap by developing and validating the 15-item Job Crafting Questionnaire (JCQ). The sample consisted of 334 employees who completed a battery of questionnaires, including the JCQ. Exploratory and confirmatory factor analyses both supported a three-factor structure that reflected the task, relational, and cognitive forms of job crafting originally presented by Wrzesniewski and Dutton (2001). Convergent analyses showed the JCQ correlated positively with indices of proactive behaviour (i.e., organisational citizenship behaviour, strengths use, and self-concordant goal setting), and positive work functioning (i.e., job satisfaction, work contentment, work enthusiasm, and positive affect). These analyses also showed the measure correlated inversely with negative affect. Reliability analyses indicated the measure has high internal consistency. Together, the analyses supported the reliability and validity of the JCQ and it shows good promise as a measure to progress research on job crafting.