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Slemp, G. R., & Vella-Brodrick, D. A., (2013). The job crafting questionnaire: A new scale to measure the
extent to which employees engage in job crafting. International Journal of Wellbeing, 3(2), 126-146.
doi:10.5502/ijw.v3i2.1
Gavin R. Slemp
Monash University
gavin.slemp@unimelb.edu.au
Copyright belongs to the author(s)
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ARTICLE
The job crafting questionnaire: A new scale to measure
the extent to which employees engage in job crafting
Gavin R. Slemp · Dianne A. Vella-Brodrick
Abstract: 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.
Keywords: job crafting, task crafting, relational crafting, cognitive crafting, scale development,
wellbeing
1. Introduction
Practitioners are frequently briefed with the task of enhancing employee satisfaction,
wellbeing, and performance. Although some interventions have successfully improved
contextual or job characteristics (Kluger & DeNisi, 1996; Parker, Chmiel & Wall, 1997; Wall,
Kemp, Jackson & Clegg, 1986), an alternative avenue is to focus on behaviour-based change
(e.g., Black, 2001; Seligman, Steen, Park & Peterson, 2005). A focus on employee characteristics
such as behaviour or cognitions is promising not only because it can yield important individual
outcomes related to wellbeing, but also because such characteristics benefit organisations (e.g.,
Harter, Schmidt, & Keyes, 2003; Hodges & Clifton, 2004). Job crafting is a promising yet
relatively unexplored approach that, potentially, employees can use to heighten their job
satisfaction and wellbeing (Wrzesniewski & Dutton, 2001).
Job crafting is described as the ways in which employees take an active role in initiating
changes to the physical, cognitive, or social features of their jobs. It is an informal process that
workers use to shape their work practice so that it aligns with their idiosyncratic interests and
values. In this way, job crafting is a form of proactive behaviour, driven by employees rather
than management (Grant & Ashford, 2008). In their original conceptualisation of the construct,
Wrzesniewski and Dutton (2001) argued for the existence of three forms of job crafting. Task
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crafting refers to initiating changes in the number or type of activities one completes on the job
(e.g.,introducingnewtasksthatbettersuitone’sskillsorinterests).Relationalcraftinginvolves
exercising discretion about whom one interacts with at work (e.g., making friends with people
with similar skills or interests). Cognitive crafting is distinct from task and relational crafting in
thatitinvolvesalteringhowone‘sees’one’sjob, with the view to making it more personally
meaningful(e.g.,makinganefforttorecognisetheeffectone’sworkhas onthesuccessof the
organisation or community). In initiating task,relational, and cognitive changes to one’s job
boundaries, the meaning of the job and the identity of the employee also change accordingly.
Job crafting shows promise as an effective workplace intervention because it requires
employees to adopt an active role in shaping their work experience. It recognises that although
employees are typically not able to redesign their jobs, there will be opportunities in the context
of almost any job where employees can initiate changes to tasks, interactions, or ways they
think about their work to make it more personally meaningful or enjoyable. Job crafting, then,
can be applied across a variety of roles with different levels of seniority and degrees of
autonomy (Berg, Wrzesniewski, & Dutton, 2010; Wrzesniewski & Dutton, 2001), and hence it is
plausible that even in the most restricted and routine jobs employees are able to initiate
changes to influence their work experience. The literature also attests to the organisational
benefits of employee proactive behaviour. Studies have shown, for example, that proactive
employees display better performance, progress their careers at a faster rate, and are generally
paid more (Grant, Parker, & Collins, 2009; Seibert, Kraimer, & Crant, 2001; Thompson, 2005;
Van Scotter, Motowildo, & Cross, 2000).
Despite job crafting being a promising basis for workplace interventions, it has received
surprisingly little research attention. This gap in the literature might stem from the fact that,
until recently, few measures of the construct were available. Indeed, with few exceptions, the
vast majority of the research on job crafting has been qualitative or theoretical in nature (e.g.,
Berg, Grant, & Johnson, 2010; Berg, Wrzesniewski, & Dutton, 2010; Fried, Grant, Levi, Hadani
& Slowik, 2007; Lyons, 2008; Wrzesniewski & Dutton, 2001) and there remains an important
need to assess empirically the relationships between job crafting and other employee outcomes.
1.1 Previous efforts to develop a measure of job crafting
Although there have been some efforts to develop measures of job crafting, their contexts are
generally limited. Ghitulescu (2006) and Leana, Appelbaum, and Shevchuk (2009), for example,
developed measures of job crafting that were highly specific to their populations of interest—
manufacturers and teachers, respectively—and hence contain items specifically targeted
towards these two occupation groups. Although rigorously constructed and useful for their
respective populations, these scales are not appropriate for empirical research with more
general working populations. This includes those employees from the regular private or public
sectors, whose jobs traditionally involve a high degree of autonomy and hence considerable
scope for implementing job-crafting behaviours.
Only recently has a more general scale for job crafting been published. This scale,
developed by Tims, Bakker, and Derks (2012), consists of four dimensions representing four
different types of job crafting: increasing social job resources, increasing structural job
resources, increasing challenging job demands, and decreasing hindering job demands. In this
way, similar to their previous work (e.g., Tims & Bakker, 2010), these authors frame their
conceptualisation of job crafting within the Job Demands-Resources (JD-R) model (Bakker &
Demerouti, 2007; Demerouti, Bakker, Nachreiner, & Schaufeli, 2000, 2001), which posits that job
characteristics can be categorised into two opposing classes: job demands and job resources. Job
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demands consist of those physical, social, or organisational aspects of jobs that require
sustained mental and physical effort, and are thus associated with psychological costs such as
burnout and exhaustion. Examples of job demands include work-load and time pressures
(Demerouti et al., 2000). Job resources are those physical, social or organisational characteristics
of jobs that aid the achievement of work goals or stimulate personal growth or development
(Demerouti et al., 2001). Examples of job resources are performance feedback and task variety
(Demerouti et al., 2000). Job resources are therefore an important buffer to the psychological
costs associated with job demands (Bakker, Demerouti, & Euwema, 2005; Bakker, Hakanen,
Demerouti & Xanthopoulou, 2007). Tims et al. (2012) suggest that job crafting reflects the
changes that employees make to balance their job demands and job resources with their
personal needs and abilities. Framed within the JD-R model, then, job crafting is a process by
which employees seek to maximise their job resources and minimise their job demands.
1.2 The importance of cognitive crafting
Tims et al. (2012) made a practical and creative contribution by framing their job crafting scale
within the JD-R model and, indeed, many types of job crafting behaviours are attempts to
increase job resources and decrease job demands. Moreover, this scale has since been used and
adapted for further research by Petrou, Demerouti, Peeters, Schaufeli, and Hetland (2012) and
Nielsen and Abildgaard (2012). However, we argue that a measure of job crafting that directly
addresses the cognitive component of job crafting is also needed. This is because crafting
cognitions about work is an important way in which individuals can shape their work
experience (Wrzesniewski & Dutton, 2001). It also permits another avenue from which to exert
someinfluenceoverone’sjobandmaysuitparticulartypesofjobsoremployees.Moreover,it
allows employees to appreciate the broader effects of their work and to recognise the value that
their job may hold in their life.
Cognitive crafting is perhaps the facet of job crafting that aligns most closely to “work
identity”, which is essentially how people define or perceive themselves at work (Bartel &
Dutton, 2001; Wrzesniewski & Dutton, 2001). According to Wrzesniewski and Dutton (2001), a
large part of one’s work identity is cognitive, in that it helps people realise a more global
conception of themself at work, where they can make claims about what work is and what it is
not. While one’s work identity cannot be changed at will, employees can make claims about
who they are as employees and why their work matters. These claims form the identity that
each employee creates for himself or herself at work and ultimately changes the personal
meaning that is reflected in their work more generally. Wrzesniewski and Dutton (2001) cite a
hypothetical scenario about physicians who alter the way in which they cognitively frame their
job. Physicians, as providers of health services, can view their work in several ways. For
example, they might frame work about healing people into heightened states of positive
physical wellbeing. Alternatively, they might frame work about acting upon illness, disease, or
injury to merely keep people alive and functioning with the technology and equipment
available to them. Through cognitive crafting, employees can alter the way in which they see
their work in order to obtain a more positive work identity, and ultimately derive an enhanced
level of meaning and purpose from their work. It is our view that a measure of job crafting
needs to include this important component of job crafting.
Although some items of the Tims et al. (2012) scale are focussed on reducing the
psychological and emotional costs of hindering job demands (e.g., “I make sure my work is
mentallylessintense”;“Itrytoensuremyworkisemotionallylessintense”),itremainsunclear
whether these items refer to employee behaviour or employee cognitions. For example,
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employees could make their work emotionally less intense by changing their workplace
behaviours (e.g., working on projects that are less emotionally draining; seeking more help
from others), or in contrast, by changing their cognitions (e.g., thinking about how one’s job
gives value to one’slifeasawhole;thinkingabouttheaspectsofone’sjobthatareemotionally
rewarding). It is important for a scale of job crafting to assess the cognitive component of the
construct as doing so will enable researchers to investigate the full range of antecedents and
consequences for each dimension. It will also allow researchers to examine several more
specific questions about job crafting. For example, a new scale will allow researchers to
investigate whether the cognitive component of job crafting explains as much variance in
important employee outcomes as the other, more behavioural, components of task and
relational crafting. It may also shed light on where certain types of job crafting fit in temporal
sequence. It is possible, for example, that cognitive crafting precedes the more behavioural
attempts to craft work, perhaps because cognitive crafting may be implemented more quickly
and with less discretionary effort than the more behavioural activities of relational and task
crafting. Finally, it is currently unknown whether all three forms of job crafting need to be
demonstrated in order to produce lasting changes in employee outcomes. A new scale which
includes clear dimensions on all three forms will allow scholars to examine these important
research questions.
1.3 Aim and hypotheses
Although job crafting is a conceptually appealing concept on which to design employee-based
interventions, until recently there has been little effort to establish a quantitative measure of the
construct that can be used in psychological research. Only recently have findings begun to
emerge that suggest job crafting is an important predictor of important employee outcomes,
such as work engagement, cynicism, employability, performance ratings, and job satisfaction
(Nielsen & Abildgaard, 2012; Petrou et al., 2012; Tims et al., 2012). Beyond these studies
however, there has been a dearth of research into the empirical relationships between job
crafting and employee outcomes. There has been even less research examining the relationship
between cognitive crafting and employee outcomes. The aim of this study is therefore to
develop the Job Crafting Questionnaire (JCQ). The JCQ is designed to measure the original
types of activities that represented job crafting and is hence consistent with Wrzesniewski and
Dutton’s(2001)originalmodelofjobcraftingthatincludestask,relational,andcognitiveforms
of job crafting. These three types of activities represent three distinct yet meaningful ways in
which employees can shape their work experience. Thus, it was hypothesised:
Hypothesis 1: The JCQ items load on three dimensions that represent task, relational, and
cognitive forms of job crafting, and this model will fit the data better than will a single-factor
model.
Another aim of the present study was to examine the convergent validity of the JCQ by
correlating the job-crafting dimensions with other theoretically related constructs. As job
crafting has been described as a form of discretionary behaviour that is driven by the employee
rather than by management (e.g., Grant & Ashford, 2008), it was anticipated that all dimensions
of the JCQ would be positively correlated with other self-initiated proactive behaviours that
employees can exhibit at work to enhance their enjoyment or performance. Thus, it was
hypothesised:
Hypothesis 2:Thereis apositiverelationshipbetweentheJCQand employees’tendencyto
engage in organisational citizenship behaviour (OCB) – a form of discretionary behaviour that
promotes the effective functioning of the organisation (Organ, 1988). This prediction was made,
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as similar to OCB, job crafting is a form of discretionary behaviour that employees initiate at
work to change their work experience.
Hypothesis 3: There is a positive relationship between the JCQ and employees’ strengths’
use.Thispredictionismadeasusingone’sstrengthsatworkcouldpotentiallybeconsidereda
special form of task crafting, whereby employees select those tasks in which they are more
skilled, experienced, or for which they hold more natural talent. Hence, it is likely that
employees who use their strengths at work are also likely to see themselves as active job
crafters.
Hypothesis 4: There is a positive relationship between the JCQ and setting intrinsically
motivated (i.e., self-concordant; Sheldon & Elliot, 1999) work-related goals. This prediction is
made because intrinsically motivated goals are those that are consistent with employees’
inherent interests and values. Job-crafting activities are initiated so employees can make subtle
changes to their roles in order to enhance these intrinsic work qualities. Thus, employees who
are motivated by the intrinsic enjoyment and satisfaction that their work brings are likely to
engage in job crafting, which is a method by which employees have the potential to enhance
these intrinsic features of their job by ultimately making their work more consistent with their
personal interests, skills, and desires.
Given that job crafting is a form of self-initiated behaviour that employees use to make their
work more meaningful and enjoyable, it was further hypothesised that the JCQ would be
related to other work-specific emotions and cognitions. Hence, it was hypothesised:
Hypothesis 5: There is a positive relationship between the JCQ and the constructs of
employee job satisfaction, work contentment, work enthusiasm, and work-specific positive
affect.
Hypothesis 6: For the same reason it was hypothesised that the JCQ is negatively related
to work-specific negative affect.
2. Method
2.1 Participants
Data from a sample of 334 employees were included in the quantitative analysis, which
involved both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) of the
scale items. This sample was recruited through various means, including social networking
sites, online discussion forums, and through staff email and newsletters of organisations that
had agreed to invite their staff to participate. All participants were at least 18 years of age and
were in paid employment. The invitations directed participants to an explanatory statement
that contained a link to the questionnaires. Participation in this study was voluntary.
Because the JCQ was a part of a larger battery of psychological questionnaires, many
participants dropped out after having completed the items related to job crafting, thus limiting
the demographics information to 253 participants in total (75.7%). These complete cases were
used in the convergent analyses, where the complete data set was needed. T-tests revealed that
there were no mean differences with respect to any of the study variables between the complete
andmissingdatasets(allp’s >.05),suggestingthat themissingdatawere missingatrandom
(Little & Rubin, 2002). Of the complete cases, more than half were female (66.8%) and the mean
age was 41.94 (SD = 11.38). The majority worked full-time (76.4%), and on average participants
worked 38.02 hours per week. Most employees worked in education (68.0%), followed by
banking and financial services (6.4%), and healthcare (6.0%). The mean income was AUD76,371
per annum, and the mean years of education was 17.60 (SD = 3.56).
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2.2 Scale construction
The questions were developed to measure the extent to which employees engaged in the types
of activities that were consistentwithWrzesniewskiandDutton’s(2001)originalmodelofjob-
crafting that consisted of task, relational, and cognitive forms of crafting. Most items were
original but four items were adapted from Leana et al. (2009), who developed a measure of job
crafting specifically for teachers in education settings. Their scale consisted of the task and
relational forms of crafting (at both the individual and group level), but omitted the cognitive
form of crafting. Only those items that were adaptable to more general working environments
were selected from this scale, and were altered for appropriate use with more general working
samples by removing any reference to education or classroom-based environments. These
items provided theoretically consistent examples of ways in which employees might engage in
task or relational crafting at work and were hence incorporated into the present study. All
items that were developed to measure the extent to which employees engage in cognitive
crafting in the present study were original.
By reviewing the extant literature on what constituted the types of activities that
represented job crafting, as well as examining the existing measures of job crafting, a
preliminary set of 27 items was developed and administered to a separate sample of 23
working adults for qualitative analysis. These participants were known to the researcher and
provided feedback about items they deemed to be clear and thus which should be retained, and
also items they deemed to be confusing and which should be either eliminated or reworded.
They also provided feedback about whether each item made sense within a general working
context. Based on this analysis, a final set of 21 items was retained for the EFA and CFA
components of the study. Upon consultation with the participants who provided feedback, four
of these 21 items were also reworded to enhance clarity and relevance to suit more general
working samples. The final set of 21 items consisted of seven items for each of task, relational,
and cognitive forms of job crafting.
The job-crafting questionnaire was introduced with the following statement: “Employees
are frequently presented with opportunities to make their work more engaging and fulfilling.
These opportunities might be as simple as making subtle changes to your work tasks to
increase your enjoyment, creating opportunities to connect with more people at work, or
simply trying to view your job in a new way to make it more purposeful. While some jobs will
provide more of these opportunities than others, there will be situations in all jobs where one
can make subtle changes to make it more engaging and fulfilling.” Participants were then
instructed to indicate the extent to which they engaged in each job-crafting behaviour or
cognition on a Likert-type scale from 1 (hardly ever) to 6 (very often).
2.3 Procedure
Once the preliminary set of 21 items was developed and adjusted based on participant
feedback, it was administered to a working sample for quantitative analysis. The majority of
the sample was invited to participate through the organisation for which they worked. These
organisations consisted of a large Australian university, a large Australian banking and finance
company, and a large Australian health insurance company. In each case, an organisational
representative sent an email to the employees inviting staff to participate. It was made known
to participants that they could choose not to participate and that their managers would never
gain access to their responses. The remaining participants were recruited through
advertisements on social networking sites and online discussion forums. All participants were
offered the choice to enter a lottery to win an 8GB iPod touch as an incentive. The initial email
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or advertisement contained a link to the study explanatory statement, which then directed
participants to the questionnaires. The set of questionnaires was counterbalanced to ensure that
the order of presentation of each questionnaire was not the same for the entire sample.
2.4 Overview of statistical analyses
Analyses were conducted in four steps. First, an EFA was conducted on the scale items.
Following this a CFA was undertaken. The internal consistency, as well as the convergent
validity of the scale were then examined. The methods used in the four steps are described in
detail below.
Step 1: Exploratory factor analysis. In the first stage, an EFA was conducted to determine a
workable factor structure. Of the total 334 participants, a sub-sample of 151 participants was
randomly selected using the randomisation function of SPSS 19. An EFA with maximum
likelihood extraction was then conducted on this sub-sample to determine the factor structure
of the 21 job-crafting items. Due to previous literature indicating a threshold loading of .40
(Gorsuch, 1983), items that that did not meet this cutoff, as well as items that cross-loaded on
multiple factors, were dropped one at a time. This process was repeated until the solution
showed a simple structure (Thurstone, 1947), and all items met the inclusion criteria.
Step 2: Confirmatory factor analysis. Using AMOS 19 (Arbuckle, 2010), a CFA was
subsequently conducted on the remaining 183 participants of the total sample to determine
whether the factor structure required modification. The CFA was used to confirm the
exploratory model, and if possible, to refine the model using a separate sample of participants.
CFA is a form of structural equation modelling that is used to determine the goodness of fit
between a hypothesised factor structure and the sample data. Decisions concerning whether or
not to add a path in the model are determined by a combination of logical, theoretical and
empirical indications. Modification indices are the empirical indicators used by AMOS to
suggest paths that will improve the fit of the model. This often involves allowing the error
terms of various items in the model to be correlated. However, it was determined a priori that
in the effort to keep the model theory driven rather than empirically driven, a more
theoretically justifiable procedure was to exclude problematic items (Levine, Hullett, Turner &
Lapinski, 2006). Problematic items were defined as those with highly correlated error terms
and/or those which loaded on the wrong factor. Further, not permitting correlations between
error terms increases the chances that the factor structure will replicate across samples (Byrne,
2010).
In the CFA, the factor loading of one indicator variable to each latent variable was fixed to
1.0. This established the metric of each latent variable. Correlations were allowed between the
pairs of latent variables in the model, as theoretically, different types of job-crafting behaviours
should be related to each other. Correlations between other variables were fixed to 0.0.
To assess model fit, we followed the recommendation of Marsh, Balla, and Hau (1996) by
using multiple fit indices. Moreover, as per the recommendations of Jaccard and Wan (1996), a
range of fit indices across different classes of fit indices was used. Hence, five indices guided
our assessment of model fit: chi square/df ratio (χ2/df), the Non Normed Fit Index (NNFI;
Tucker & Lewis, 1973), the Comparative Fit Index (CFI; Bentler, 1990), the Incremental Fit Index
(Bentler & Bonnet, 1980), and the Root Mean Square Error of Approximation (RMSEA; Browne
& Cudeck, 1993). Values of .90 for the NNFI and IFI (Byrne, 1994) indicate good fit. Although
the recommended CFI values range from .90 to .95, generally values close to or approaching .95
are more accepted as indicating good fit (Hu & Bentler, 1999). The χ2/df ratio provides an
estimate of model fit that is less sensitive to sample size than the regular chi square index.
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Althoughthereisnoclearguidelinefortheχ2/dfratio,valuesfrom2(Ullman,2007)toashigh
as 5 (Wheaton, Muthen, Alwin & Summers, 1977) have been recommended as appropriate cut-
offs. A value of 3 is another guideline (Bollen, 1989; Kline, 2005), and this was the value selected
to ensure consistency with previous job-crafting research (e.g., Tims et al., 2012). The RMSEA
takes into account the error of approximation in the population and tests how well the model
would fit the population covariance matrix if it were available (Byrne, 2010). Values less than
.08 indicate reasonable fit (Browne & Cudeck, 1993), and values less than .05 indicate a good fit
(Stieger, 1990). Values greater than 1.0 should lead to model rejection (Browne & Cudeck, 1993;
MacCallum, Browne, & Sugawara, 1996). The chi-square test statistic was not used as an index
of model fit because it is likely to reject a good fitting model due to trivial differences between
the correlations and the covariances in the observed and predicted matrices (Meyers, Gamst &
Guarino, 2006).
Step 3: Reliability analysis. Internal consistency was assessed by computing Cronbach’s
alphas for the job-crafting dimensions, as well as the total scale. These estimates were
calculated before and after the factor analysis stage where items were dropped. Although alpha
estimates provide limited practical information about a measure when used in isolation, when
used in combination with EFA and CFA they can be useful in supporting the reliability of a
scale after its multi-dimensionality has been confirmed (Levine et al., 2006).
Step 4: Convergent analyses. To assess convergent validity, the JCQ was correlated with
other constructs with which it should theoretically be related. The measures that were used in
these analyses are detailed in the following section.
2.5 Measures
Job crafting. Job crafting was measured with the final JCQ developed in this study (see
Appendix). The complete measure consisted of 15 items and participants indicate the frequency
with which they have engaged in each job-crafting activity from 1 (hardly ever) to 6 (very
often).
Strengths use. The extent to which participants used their strengths was assessed with
GovindjiandLinley’s(2007) 14-itemStrengthsUse Scale.Anexampleitemis “Myworkgives
me lots of opportunities to use my strengths”. Participants indicate the extent to which they
agree with each statement from 1 (strongly disagree) to 7 (strongly agree). These authors
reportedaCronbach’salphaof.95.Anequivalent reliability (.95)wasfoundwiththecurrent
study’sdataset.Govindji and Linley (2007) found the items to load on a single 'strengths use'
factor. Moreover, the scale correlated moderately to strongly with self-efficacy (.63), self-esteem
(.56), subjective wellbeing (.51), psychological wellbeing (.56), and subjective vitality (.45),
supporting its validity.
Intrinsic goal striving. Participants were asked to list two work-related goals and we then
used the same method as Emmons (1986), as well as Sheldon and colleagues (e.g., Sheldon &
Elliot, 1999; Sheldon & Lyubomirsky, 2006), to calculate the extent to which these goals were
intrinsically motivated. This procedure requests participants to list a work-related goal and
subsequently rate whether it is pursued for external motivations (pursued to please others or
for rewards), introjected motivations (striving to avoid guilt or self-criticism), identified
motivation (pursued due to internal values or beliefs) and intrinsic motivation (pursued due to
the intrinsic enjoyment and satisfaction from the task or goal itself). Participants rated the
extent to which both goals were pursued for each of the four reasons by responding on a seven-
point scale from 1 (not at all for this reason) to 7 (completely for this reason). As in past
research (e.g., Sheldon & Elliot, 1999; Sheldon & Houser-Marko, 2001; Sheldon & Lyubomirsky,
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2006) an intrinsic motivation score was then calculated by averaging the intrinsic and identified
ratings, and subtracting the averaged external and introjected ratings for each goal. This scale
hadsatisfactoryreliabilitywithaCronbach’salphaof.74forthecurrentstudy’sdataset.
Organisational citizenship behaviour (OCB). OCB was assessed with the 13-item Podsakoff,
Ahearne, and MacKenzie (1997) scale, which measures the helping, civic virtue, and
sportsmanshipcomponentsofOCBs.Anexampleitemis“Ihelpoutothersiftheyfallbehind
intheirwork”.Participantsrespondfrom1(stronglydisagree) to 5 (strongly agree). Podsakoff
et al. (1997) reported alpha coefficients of .95, .96, and .88 for the three components respectively.
Thefullscalealphacoefficientusingthecurrentstudy’sdataislowerbutstillsatisfactory(.79).
Podsakoff et al. (1997) also showed the measure predicted work group performance, thus
lendingsomesupportforthescale’svalidity.
Job satisfaction. The Michigan Organizational Assessment Questionnaire (Cammann,
Fichman, Jenkins & Klesh, 1979) was used to measure jobsatisfaction.Anexampleitemis“All
in all, I am satisfied with my job”, and participants respond from 1 (strongly disagree) to 7
(stronglyagree).Cammannetal.(1979)reportedaCronbach’salpha of .77andinthepresent
study it was .90. Moreover, Bruck, Allen and Spector (2002) showed that scores on the job
satisfaction scale can be predicted from work-family conflict.
Affective wellbeing. Affective wellbeing was measured with the Warr (1990) affective
wellbeing scales. Six descriptor words were used to describe the anxiety-contentment axis (e.g.,
“Relaxed”forPositiveAffect,“Tense”forNegativeAffect)andthedepression-enthusiasm axis
(“Cheerful” for Positive Affect, “Miserable” for Negative Affect) of affective wellbeing.
Participants indicated the frequency with which they had experienced each emotion at work on
a 6-point scale from 1 (never) to 6 (all of the time). The scale had high internal consistency, with
Cronbach’s alphas of .90 for the anxiety-contentment axis and .91 for the depression-
enthusiasm axis. Warr (1990) found that contentment was positively related to job satisfaction
and motivation (.21 and .20, respectively) and negatively related to work overload and distress
(-.40 and -.46, respectively). Similarly, enthusiasm was positively related to job satisfaction and
motivation (both .40), and negatively related to task repetition and distress (-.22 and -.39
respectively),supportingthescale’svalidity.
Warr’s(1990) affective wellbeing scaleswere also used to measure work-specific positive
affect (WSPA) and negative affect (WSNA). WSPA and WSNA were measured by calculating
an average score for the six items thatreflected both PA and NA in Warr’s (1990) affective
wellbeing measure. This scale also had high internal consistency,withCronbach’salphasof.92
and .93 for WSPA and WSNA, respectively.
3. Results
3.1 Exploratory factor analysis (N = 150)
EFA with maximum likelihood extraction and oblique rotation in SPSS 19 was used to
determine if the factor structure of the 21 items was consistent with the original model of job
crafting (Wrzesniewski & Dutton, 2001). One case was missing most of its data for the job-
crafting items. This case was dropped listwise, leaving data from 150 participants for the
analysis. The remainder of the missing values for each item was very low (0.0% to 2.0%), and
multiple imputation methods (three imputations with SPSS) were used to estimate these values
(Little & Rubin, 2002).
Prior to performing the EFA, the suitability of data for factor analysis was assessed.
Inspection of the correlation matrix revealed many coefficients of .3 and above. The Kaiser-
Meyer-Oklin value was .89, exceeding the recommended value of .6 (Kaiser, 1970, 1974).
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Bartlett’s Test of Sphericity was statistically significant, supporting the factorability of the
correlation matrix (Bartlett, 1954).
Maximum likelihood extraction revealed the presence of three factors with eigenvalues
exceeding 1. These factors explained 40.45% (eigenvalue = 8.96), 8.58% (eigenvalue = 2.31), and
7.19% (eigenvalue = 1.79) of the variance respectively. Figure 1 shows the scree plot and a break
after the third factor.
Figure 1. Scree plot showing a break after the third factor
Aninspectionof the screeplotrevealedabreakafter thethirdfactor,andCatell’s(1966) scree
test indicated a three-factor solution for further investigation. This was further supported by a
parallel analysis, which showed three factors with eigenvalues exceeding the corresponding
criterion values for a randomly generated data matrix of equivalent size (21 variables × 150
cases).
The three-factor solution explained a total of 56.23% of the variance. To aid in the
interpretation of these three factors, direct oblimin rotation was performed. The rotated factor
solution resembled a simple structure, with all three factors showing several strong loadings.
Those items that exhibited a cross loading or loaded greater than .35 on the wrong factor were
deleted. Due to previous literature suggesting a threshold for factor loadings of .40 (Gorsuch,
1983), items that did not meet this cutoff were dropped. On this basis, two of the items for
cognitive crafting were deleted. Another EFA was performed and a solution consisting of 19
Factor Number
Eigenvalue
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items was retained, with a clear simple structure present in the data (Thurstone, 1947). These
data are presented in Table 1. There were moderate to strong correlations between the three
factors (from .42 to .57), supporting the use of oblique rotation.
Table 1: Items, means, standard deviations, and factor loadings of the three-factor Job
Crafting Questionnaire
Factor
Item
M
SD
1
2
3
Task Crafting
1
Introduce new approaches to improve your work*
3.94
1.48
.75
2
Change the scope or types of tasks that you complete at work
3.54
1.47
.92
3
Introduce new work tasks that better suit your skills or
interests
3.42
1.47
.86
4
Choose to take on additional tasks at work
4.12
1.34
.58
5
Give preference to work tasks that suit your skills or interests
4.09
1.39
.59
6
Change the way you do your job to make it more enjoyable
for yourself*
3.73
1.39
.74
7
Change minor procedures that you think are not productive*
3.91
1.35
.66
Cognitive Crafting
8
Think about how your job gives your life purpose
3.69
1.46
.87
9
Remind yourself about the significance your work has for the
success of the organisation
3.48
1.41
.66
10
Remind yourself of the importance of your work for the
broader community
3.45
1.53
.81
11
Think about the ways in which your work positively impacts
your life
3.66
1.43
.85
12
Reflect on the role your job has for your overall well-being
3.96
1.33
.69
Relational Crafting
13
Engage in networking activities to establish more
relationships
3.68
1.48
.45
14
Make an effort to get to know people well at work
4.24
1.24
.77
15
Organise or attend work related social functions
3.39
1.56
.77
16
Organise special events in the workplace (e.g., celebrating a
co-worker's birthday)*
3.16
1.61
.82
17
Introduce yourself to co-workers, customers, or clients you
have not met
3.95
1.37
.65
18
Choose to mentor new employees (officially or unofficially)
3.48
1.51
.58
19
Make friends with people at work who have similar skills or
interests
4.09
1.33
.62
* indicates items that were adapted or taken from Leana, Appelbaum, and Shevchuk (2009).
Taken together, the results of the EFA support a three-factor solution, with seven items loading
on each of task and relational crafting, and five items loading on cognitive crafting.
3.2 Confirmatory factor analysis (N = 180)
In order to examine if the three-factor solution fits the data best in the second sample, CFA was
conducted using AMOS 19 (Arbuckle, 2010). As structural equation modelling requires a
complete data set for each case (Byrne, 2010), it was determined a priori to drop any cases that
were missing more than 5% of the items for the questionnaire. This approach led to three cases
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being excluded from the analysis, leaving data from 180 participants. The remainder of the
missing values for each item was very low (0.0% to 2.2%), and multiple imputation methods
(three imputations with SPSS) were used to estimate these values (Little & Rubin, 2002).
CFA was performed initially on the 19-item scale, which indicated a reasonably poor fit to
thedata (χ2/df =2.44, CFI = .89, NNFI = .88, IFI = .89,RMSEA = .09).Moreover, the RMSEA
confidence interval was above the upper bound limit of .08 (Byrne, 2010). The modification
indices suggested that two task-crafting items (items 6 and 7 from Table 1) correlated with the
wrong factor. A relational-crafting item (item 17 from Table 1) correlated with the wrong factor,
while another relational-crafting item (item 13 from Table 1) was both poorly correlated with
the relational-crafting latent variable and the error term was correlated with several error terms
for items that loaded on the cognitive and task-crafting latent variables. On this basis, these
four items were dropped, which left 15 items for the analysis: five for each latent variable.
Another CFA was conducted which indicated that the fit of the model was substantially
improved. The fit indices indicated a model that fit the data well, and are presented in the top
row of Table 2.
Table 2: Confirmatory factor analysis of the three-factor Job Crafting Questionnaire (N = 180)
Model
χ2
df
χ2/df
CFI
NNFI
IFI
RMSEA
Three factor model
149.01
87
1.71
.96
.95
.96
.06
One factor model
551.28
90
6.13
.68
.63
.68
.17
Note: χ2/df = normed chi square, CFI = comparative fit index; NNFI = non normed fit index; IFI =
incremental fit index; RMSEA = root mean square error of approximation. The final scale consists of 15
items: 5 for each job-crafting factor.
As can be observed in Table 2, the hypothesised three-factor model was tested against a single-
factor model due to the possibility that job crafting is a uni-dimensional construct. For example,
it is possible that the fact employees initiate changes to their work (uni-dimensional model) is
more salient than the types of changes (hypothesised multi-dimensional model) employees
initiate at work. Table 2 shows that the three-factor model fit the observed data better than the
alternative one-factor model, supporting Hypothesis 1. The NNFI and IFI were both above .90,
the CFI was greater than .95, and the normed chi square was less than 3. The RMSEA was also
small (.06), with the confidence intervals within the range suggesting acceptable fit (lower
bound = .05, upper bound = .08). All fit indices support a three-factor model. Moreover, all
items loaded significantly and strongly on their respective latent variables, with standardised
loadings ranging from .56 to .89 (all p’s < .001). Standardised parameterestimates indicated
moderate to strong correlations between the latent variables: Task crafting-Relational crafting
(.54), Relational crafting-Cognitive crafting (.74), and Task crafting-Cognitive crafting (.80).
3.3 Reliability analyses
Internal consistency statistics are presented in Table 3 (below). The Cronbach’s alphas of the
three sub-scales were all well above the recommended threshold of .70 (Nunnally & Bernstein,
1994). Before items were dropped, the scale reliabilities were .90, .89, .86, and .94 for task,
cognitive, relational, and total job crafting, respectively. As can be observed in Table 3, after the
items were dropped through the CFA process, these reliabilities were lowered slightly, though
not substantially.
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Table 3: Reliability estimates for task, cognitive, relational, and total job crafting
Scale
Number
of items
Cronbach's
alpha
Task Crafting
5
.87
Cognitive Crafting
5
.89
Relational Crafting
5
.83
Total Job Crafting
15
.91
Note: N = 334
3.4 Convergent validity
To examine the convergent validity of the new scale, the job crafting sub-scales and total scale
were correlated with other variables with which they should be theoretically related. These
correlations are presented in Table 4. Composite scores were calculated by adding the scores
for each construct and dividing by the total number of items.
Table 4: Correlations between the dimensions of job crafting with job satisfaction, intrinsic
goal strivings (work), strengths use, OCB, work contentment, work enthusiasm, work-
related positive affect, and work related negative affect
Construct
1
2
3
4
5
6
7
8
9
10
11
1. Task Crafting
2. Cognitive Crafting
.52**
3. Relational Crafting
.42**
.53**
4. Job Crafting Total
.81**
.83**
.77**
5. Strengths Use
.43**
.39**
.36**
.49**
6. Intrinsic Goal Setting
(work)
.20**
.32**
.30**
.34**
.40**
7. OCB
.40**
.33**
.41**
.47**
.35**
.22**
8. Job Satisfaction
.38**
.45**
.21**
.43**
.41**
.30**
.24**
9. Work Contentment
.29**
.26**
.13*
.28**
.24**
.25**
.14*
.62**
10. Work Enthusiasm
.45**
.42**
.26**
.47**
.40**
.38**
.29*
.75**
.76**
11. WSPA
.40**
.40**
.27**
.45**
.37**
.31**
.27**
.66**
.83**
.86**
12. WSNA
-.25**
-.23**
-.11
-.26**
-.25**
-.30**
-.14*
-.67**
-.86**
-.84**
-.64**
Note: N = 250; OCB = Organisational Citizenship Behaviour; WSPA = Work-Specific Positive Affect;
WSNA = Work-Specific Negative Affect.
* p <.05
** p <.01
It was predicted that all dimensions of the JCQ would exhibit positive correlations with similar
behaviourally based indices of strengths use, intrinsic goal strivings at work, and OCB. As
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expected, all of these correlations were significant and in the expected positive direction. It was
also predicted that the dimensions of job crafting would be positively related with job
satisfaction, work contentment, work enthusiasm, and WSPA. These correlations were also
significant and positive. Finally, it was predicted that the dimensions of job crafting would be
negatively correlated with WSNA. Although the relationship between relational crafting and
WSNA did not reach statistical significance, it was in the expected negative direction. All other
correlations were significant and negative, though the strength of these relationships was
generally weaker than the relationships between job-crafting and proactive behaviours and
positive states.
4. Discussion
The aim of the present study was to develop and validate the JCQ, which can be used in
psychological research to assess the extent to which individuals engage in job-crafting
activities.AshypothesisedandconsistentwithWrzesniewskiandDutton’s(2001)modelofjob
crafting, the present study showed the job-crafting items to load on the three dimensions of
task crafting, relational crafting, and cognitive crafting. The EFA and CFA both revealed a
three-factor structure that reflects each dimension of job crafting, though the CFA worked best
when problematic items were dropped from the measure. Hence, all three forms of job crafting
indicate different processes through which employees can take active roles in shaping their
experience of work.
Also as hypothesised, the JCQ correlated in the hypothesised directions with other scales
selected based on their theoretical association with job crafting. Thus, the JCQ dimensions
exhibited positive correlations with other proactive-behaviour-based assessments such as
strengths use, intrinsic goal setting at work, and OCB. The scale was also positively correlated
with job satisfaction, work contentment, work enthusiasm, and WSPA, and negatively
correlatedwithWSNA.Allcorrelationssupportthemeasure’sconvergentvalidity.Itisworth
noting, however, that the JCQ exhibited weak, though generally significant, relationships with
WSNA. It is possible, then, that job crafting holds a weaker influence on negative states than it
does on positive states, probably because job-crafting activities are directed at enhancing the
enjoyment and satisfaction employees attain from their work. Hence, it is plausible that job-
crafting activities are used primarily by mentally healthy employees to enhance their work
satisfaction and enjoyment rather than by dissatisfied or unhappy employees to lift themselves
into states where their dissatisfaction, unhappiness, or other negative experiences are less
intense. Job crafting, then, might be a useful strategy in enhancing the mental health and
happiness of those people thought to be languishing (Keyes, 2002, 2003, 2007)—that is, people
who neither suffer from mental illness nor experience positive mental health. It is these people
who are often overlooked in psychological research (Keyes, 2003) and efforts to enhance their
wellbeing will be a welcome addition to the literature. Further research is needed to confirm
these findings using measures of other work-related negative states such as intention to leave,
stress, exhaustion, or burnout.
The JCQ differed from existing measures of job crafting in three important ways. First,
items were worded in a way that was relevant and meaningful for the general adult working
population, rather than for specific working groups, occupations, or industries of interest. This
allows the measure to be used in research involving a range of occupations, organisational
contexts, or industries where scope exists for implementing job-crafting activities. Second, the
JCQ showed that cognitive-crafting items loaded on a separate construct to the other more
behavioural features of task and relational crafting. This suggests that cognitive crafting—the
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processes through which employees frame their perception of their job in a more positive and
meaningful light—forms a significant part of what constitutes job crafting. The JCQ hence
aligns with the original three-component model of job crafting put forward by Wrzesniewski
and Dutton (2001). This is important because, as argued by Wrzesniewski and Dutton,
employee cognitions are an important component of what composes the experiences of a job.
Employees can shape these cognitions, and in so doing, shape the way in which they approach
and experience their work. Moreover, cognitions about work form an important part of our
work identity (Wrzesniewski & Dutton, 2001) and crafting cognitions is a method by which
employees can shape the way they define or perceive themselves at work. Through cognitive
crafting, employees have the capacity to adopt a more positive and meaningful view of their
work, which may ultimately have corresponding influences on employee wellbeing, turnover,
or engagement. Although these relationships were not tested here, the JCQ opens these
questions to empirical inquiry. Third, the JCQ is quite brief in terms of its number of items.
Still, it retains equally notable factorial validity, convergent validity, and reliability statistics as
previous measures. Researchers constrained for time may find it useful to assess job crafting
using a more efficient measure, such as the JCQ, than those developed previously. The fact that
the measure fits without allowing error terms to correlate also increases the probability that it
will hold up across different working populations.
There are several implications of this study for the progression of job-crafting research.
First, an alternative general scale can now be used to assess the extent to which employees craft
their jobs. Due to its consistency with the original model of job crafting conceptualised by
Wrzesniewski and Dutton (2001), it will allow researchers to assess the relationships between
all three types of job crafting and different employee outcomes. Hence, the full range of
antecedents and consequences of each dimension of job crafting can now be explored. Second,
there is to our knowledge no present research that has explored whether the three forms of job
crafting affect workplace outcomes, and similarly, there is no theory about the underlying
mechanisms that explain how they might affect these outcomes. The JCQ will allow researchers
to address these gaps by providing them with a statistically validated tool to progress job-
crafting research, and ultimately, establish a sound theory as to how the dimensions of job
crafting affect work outcomes. Finally, given the positive relationships between job crafting and
the employee outcomes presented in Table 4, the JCQ may provide HR practitioners a useful
tool to measure the extent to which their staff engage in job crafting and hence develop
programs that enhance their employees’ ability to engage in these activities that potentially
impact proactive behaviour or wellbeing.
The current findings should be interpreted in light of some limitations. First, the sample
wasquitehomogenousintermsofparticipants’education,nationality,andincome,probably
because most participants worked either in education (68%) or the corporate sector in
Australia. The average years’ education was 17.60, which is well above the length of time
required to obtain a secondary education. Moreover, the average income was quite high,
indeed higher than the average contemporary working income in most countries. This negates
the generalisability of the findings to more diverse groups of workers, including, for example,
the blue-collar sector and employees from diverse cultural groups. Another limitation is that
the sample was not large enough to conduct an invariance test to determine whether the factor
structure of the scale is sustainable across the wider adult working population. Invariance tests
from different employee populations, such as blue-collar workers or employees working in
different cultures would further elucidate how these employees craft their jobs to enhance the
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experience of work. An invariance test will also allow researchers to further confirm the factor
structure of the measure and cross-validate it in a separate sample beyond corporate Australia.
In conclusion, the JCQ fits a three-factor structure, supported by the results of both EFA
and CFA. The total scale, as well as its individual dimensions, have demonstrated high internal
consistency reliability. In addition, the measure correlates in theoretically expected directions
with other similar, previously validated measures, thus supporting its convergent validity.
Therefore, it is anticipated that the JCQ can be used to further progress job-crafting research. At
the same time, further assessments should continue with diverse samples to provide
cumulative and substantial psychometric evidence for this new measure. Ultimately, with the
development of a theoretically based, practical, and psychometrically sound measure of job
crafting, more information about the efficacy and applied utility of job-crafting interventions
can be gained to improve the quality of employees’ work life.
Acknowledgments
The authors would like to thank Dr Simon Albrecht and Dr Simon Moss of Monash University, who
offered valuable statistical insight and advice that greatly assisted the research.
Authors
Gavin R. Slemp
Monash University
gavin.slemp@unimelb.edu.au
Dianne A. Vella-Brodrick
University of Melbourne and Monash University
Publishing Timeline
Received 19 March 2013
Accepted 1 August 2013
Published 7 October 2013
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Appendix:The Job Crafting Questionnaire (JCQ)
Employees are frequently presented with opportunities to make their work more engaging and
fulfilling. These opportunities might be as simple as making subtle changes to your work tasks
to increase your enjoyment, creating opportunities to connect with more people at work, or
simply trying to view your job in a new way to make it more purposeful. While some jobs will
provide more of these opportunities than others, there will be situations in all jobs where one
can make subtle changes to make it more engaging and fulfilling.
Please indicate the extent to which you engage in the following behaviours using the following
scale: 1 = Hardly Ever, to 6 = Very Often. (Note: 'Very Often' means as often as possible in your
workplace)
1. Introduce new approaches to improve your work*
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
2. Change the scope or types of tasks that you complete at work
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
3. Introduce new work tasks that you think better suit your skills or interests
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
4. Choose to take on additional tasks at work
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
5. Give preference to work tasks that suit your skills or interests
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
6. Think about how your job gives your life purpose
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
7. Remind yourself about the significance your work has for the success of the
organisation
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
8. Remind yourself of the importance of your work for the broader community
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
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9. Think about the ways in which your work positively impacts your life
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
10. Reflect on the role your job has for your overall well-being
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
11. Make an effort to get to know people well at work
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
12. Organise or attend work related social functions
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
13. Organise special events in the workplace (e.g., celebrating a co-worker's birthday)*
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
14. Choose to mentor new employees (officially or unofficially)
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
15. Make friends with people at work who have similar skills or interests
1 (Hardly Ever) 2 3 4 5 6 (Very Often)
Note: Items 1 to 5 reflect task crafting, items 5 to 10 reflect cognitive crafting, and items 11 to 15 reflect
relational crafting.
*indicates items that were adapted or taken from Leana, Appelbaum, & Shevchuk (2009).
