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This is the authors’ final peer reviewed (post print) version of the item published as: Albrecht, Simon L. 2012, The influence of job, team and organizational level resources on employee well-being, engagement, commitment and extra-role performance : test of a model, International journal of manpower, vol. 33, no. 7, Special Issue: Changing work environments and employee wellbeing, pp. 840-853. Available from Deakin Research Online: http://hdl.handle.net/10536/DRO/DU:30049852 Reproduced with the kind permission of the copyright owner Copyright: 2012, Emerald
The influence of job, team and organizational level resources on employee well‐being, engagement,
commitment and extra‐role performance Test of a model
Simon L. Albrecht
School of Psychology, Deakin University, Melbourne, Australia
Abstract Purpose – Worker well‐being continues to be fundamental to the study of work and a primary
consideration for how organizations can achieve competitive advantage and sustainable and ethical
work practices (Cartwright and Holmes; Harter, Schmidt and Keyes; Wright and Cropanzano). The
science and practice of employee engagement, a key indicator of employee well‐being, continues to
evolve with ongoing incremental refinements to existing models and measures. This study aims to
elaborate the Job Demands‐Resources model of work engagement (Bakker and Demerouti) by
examining how organizational, team and job level factors interrelate to influence engagement and
well‐being and downstream outcome variables such as affective commitment and extra‐role
behaviour.
Design/methodology/approach – Structural equations modelling of survey data obtained from
3,437 employees of a large multi‐national mining company was used to test the important direct and
indirect influence of organizational focused resources (a culture of fairness and support), team
focused resources (team climate) and job level resources (career development, autonomy,
supervisor support, and role clarity) on employee well‐being, engagement, extra‐role behaviour and
organizational commitment.
Findings – The fit of the proposed measurement and structural models met criterion levels and the
structural model accounted for sizable proportions of the variance in engagement/wellbeing (66
percent), extra‐role‐behaviour (52 percent) and commitment (69 percent).
Research limitations/implications – Study limitations (e.g. cross‐sectional research design) and
future opportunities are outlined.
Originality/value – The study demonstrates important extensions to the Job Demands‐Resources
model and provides researchers and practitioners with a simple but powerful motivational
framework, a suite of measures, and a map of their inter‐relationships which can be used to help
understand, develop and manage employee well‐being and engagement and their outcomes.
Keywords Employee well‐being, Engagement, Job resources, JD‐R, Commitment, Extra‐role
behaviour, Organizational behaviour, Employees behaviour
Paper type Research paper
Introduction Researchers have provided clear evidence that the experience of work can have both positive and
negative influences on the health and well‐being of individual workers (Warr, 1999). Consequently,
employee well‐being remains fundamental to the study of work and a primary consideration for how
organizations can achieve competitive advantage and sustainable and ethical work practices
(Cartwright and Holmes, 2006; Wright and Cropanzano, 2007). Job demands such as role conflict,
role ambiguity and work‐overload have consistently been shown to lead to adverse employee
psychological outcomes such as anxiety, depressive symptoms, ill‐health and negative work‐family
spill‐over (Bakker et al., 2003; Quick et al., 1986; Soderfeldt et al., 2000). In contrast job resources
such as autonomy, skill utilization, professional development and social support have consistently
been shown to be related to functional individual well‐being related outcomes such as engagement,
job satisfaction and health (Halbesleben, 2010). While Warr (1990) some 20 years ago suggested
that researchers and practitioners need to broaden their focus from stress to also include a focus on
employee well‐being, more recently there has been a proliferation of research into positive well‐
being related constructs such as engagement (Bakker and Demerouti, 2008), passion (Gorgievski and
Bakker, 2010), thriving (Cameron, 2010), flourishing (Seligman, 2011) and flow (Csikszentmihalyi,
1990).
So how do the constructs of well‐being and engagement relate or overlap? Schaufeli et al. (2008)
explicitly conceptualized employee engagement as a form of well‐being. Schaufeli et al. argued that
the concept of work engagement “emerged from burnout research in an attempt to cover the entire
spectrum running from employee unwell‐being (burnout) to employee well‐being” (p. 176).
Similarly, Harter et al. (2002) argued that within the broad category of employee well‐being,
engagement is associated with more frequent experiences of positive affect, which then lead to
“the efficient application of work, employee retention, creativity and ultimately business outcomes”
(pp. 1‐2). Given that both engagement and employee well‐being are characterized, at least in part, in
terms of positive affective states such as enthusiasm, happiness, interest and vigor, the conceptual
association between engagement as an indicator of employee well‐being has been well established
(Bakker and Oerlemans, 2011). The present research focuses on identifying the relationships
between job‐, team‐ and organizational‐level predictors of the positive psychological construct of
well‐being, operationalized in terms of employee engagement.
Employee engagement remains a hot topic within both the academic and practitioner domains. The
field continues to grow and to grow rapidly. Macey et al. (2009) recently noted that “rarely has a
term [y] resonated as strongly with business executives as employee engagement has in recent
years” (p. xv). Similarly, the body of scholarly work focused on work engagement continues to
flourish and the number of books (e.g. Albrecht, 2010; Bakker and Leiter, 2010; Salanova and
Schaufeli, 2009), research papers, conference papers and conference presentations emanating from
the academic domain continues to grow. As previously mentioned, the conceptual overlap between
employee well‐being and employee engagement is well established (Bakker and Oerlemans, 2011).
Despite the enormous advances in understanding how best to conceptualize, measure and manage
engagement, recent research and reviews of the state of play of employee engagement (e.g. Bakker
et al., 2011; Crawford et al., 2010) have identified a number of issues yet to be fully resolved. For
example there are still unresolved issues about how best to conceptualize and measure
engagement. Additionally, more research is needed to ascertain the influence that organizational‐
and team‐level variables such as organizational culture, organizational climate and team climate
exert on employee engagement (Albrecht, 2010).
With respect to how best conceptualize engagement, Schaufeli and colleagues’ defined work
engagement as “a positive, fulfilling, work related state of mind that is characterized by vigor,
dedication, and absorption” (Schaufeli et al., 2002, p. 74). Engaged employees experience well‐
being related positive emotions such joy and enthusiasm. Although Schaufeli et al.’s definition
and measure remain widely accepted in the academic domain, there may be additional dimensions
of engagement not fully encompassed by their conceptualization. Macey et al. (2009), for example,
argued in support of a definition of engagement that, amongst other things, more explicitly
acknowledges an alignment between work effort and organizational goals. More generally, Seligman
(2011) argued that, in addition to engagement, positive emotion, relationships, meaning and
achievement are core constituents of psychological well‐being.
A broad range of models, theories and frameworks have been invoked to understand and explain
the importance, emergence and maintenance of employee engagement and employee well‐being.
Such theories and models include conservation of resources theory (Hobfoll, 1989); self‐
determination theory (Deci and Ryan, 1985); social exchange theory (Blau, 1964); social identity
theory (Tajfel, 1974); role theory (Kahn, 1990); broaden‐and‐build theory of positive emotion
(Fredrickson, 2001); job characteristics theory (Hackman and Oldham, 1980); the circumplex
model of affect (Russell, 1980, 2003); and the job demands‐resources model (JD‐R, Bakker and
Demerouti, 2007, 2008). The JD‐R model, the most widely cited and widely used theoretical model in
the engagement literature, shows how job resources (e.g. autonomy, feedback, support) and
personal resources (e.g. self‐efficacy, optimism, resilience) directly influence work engagement,
which in turn influences outcomes such as commitment, in‐role performance, extra‐role
performance, creativity and financial outcomes. Furthermore, the JD‐R explains how job demands
(e.g. workload, time pressure) moderate the relationship between job resources and engagement
such that the motivational influence of resources on engagement is enhanced when employees
experience demands as being moderately high, high or “challenging” (e.g. Hakanen et al., 2005). The
present research focuses on the motivational, engagement and well‐being components of the JD‐R
and looks to determine what drives engagement and well‐being and the relationship engagement
and well‐being has with key attitudinal and behavioural outcomes.
Recent meta‐analyses and qualitative reviews have helped identify the strongest and most
reliable predictors of engagement (e.g. Crawford et al., 2010; Christian and Slaughter, 2007;
Halbesleben, 2010; Mauno et al., 2010; Simpson, 2008). Halbesleben’s (2010) meta‐analysis,
consistent with the JD‐R (Bakker and Demerouti, 2007), showed that feedback, autonomy, social
support and organizational climate are consistently associated with engagement or particular facets
of engagement. Crawford et al.’s meta‐ analysis identified work role fit, job variety, rewards and
recognition, recovery and opportunities for development as reliable predictors of engagement.
Warr (1990), along with many other researchers, has found similar job characteristics (e.g. role
clarity) to be predictive of work‐related well‐being. Halbesleben’s meta‐analysis also showed that
personal resources (e.g. self‐efficacy and optimism) are strongly related to engagement.
Overall, although researchers have identified a broad range of predictors of engagement and well‐
being, opportunities remain to organize such theoretically and empirically derived key drivers in
an overarching framework or taxonomy. The JD‐R (Bakker and Demerouti, 2007, 2008) has to date
provided such an integrating framework, integrating earlier models (e.g. demand‐control model
Karasek, 1979; Karasek and Theorell, 1990) and providing a flexible yet robust account of how job
factors may be salient across a broad range of job and organizational contexts (Bakker et al., 2011).
Albrecht (2010), however, suggested elaborating the JD‐R framework by recognizing and including
more “distal” organizational‐level resources such as senior leadership support, clarity of
organizational vision, organizational climate, organizational support and supportive HRM policies.
Although Halbesleben (2010) identified organizational climate as an antecedent of engagement
and Xanthopoulou et al. (2009) identified that “team atmosphere” influences engagement, job‐
resources have primarily been assessed with more “proximal” job‐related variables such as
autonomy, job feedback, skill utilization, supervisor support and opportunities for career
development (e.g. Schaufeli et al., 2009). The explicit recognition of organizational‐ and team‐level
resources might also prove helpful to researchers and practitioners interested in differentially
focusing resources toward the development of employee engagement, employee well‐being,
positive attitudes (e.g. organizational commitment) and performance (e.g. extra‐role performance).
In support of this claim, Richardson and West (2010) suggested that a supportive
organizational context which reinforces the effectiveness of team‐based structures will help
ensure the alignment of team and organizational values, and therefore facilitate effective
leadership and employee engagement. The modeling proposed in Figure 1 therefore shows
organizational‐ and team‐level resources influencing engagement. Furthermore, and consistent with
meta‐analytic evidence (see Parker et al., 2003), organizational‐level and team‐level variables are
modeled to directly influence organizational‐level outcomes such as organizational commitment
and extra‐role behaviour in addition to their influence being partially mediated by engagement.
Additionally, given the relative advantages of higher‐order modeling of constructs over first‐
order individual constructs (Newman et al., 2010), Figure 1 models a number of job resources
subsumed under a higher order factor. Higher order modeling also has the advantage of
potentially capturing synergistic effects beyond the additive effects of the individual first‐order
factors (Chadwick, 2010; Combs et al., 2006).
At a somewhat broader level, another unresolved issue or challenge in the engagement
literature centres on the research‐practice divide. Both scientists and practitioners have suggested
that the research and the practice of engagement are, in large part, progressing along different
paths. Macey and Schneider (2008), for example, noted that “scholars and practitioners think and
speak about engagement in different ways” (p. 76). As an example, many organizations use
proprietal or self‐developed measures of engagement which have not been peer‐reviewed and
which have limited or no supporting psychometric evidence. Additionally, many practitioner surveys
focus on engagement scores per se and do not focus on establishing causal relations among the full
array of constructs measured. To help bridge the science‐ practice divide, academics might usefully
help practitioners analyse data derived from practitioner developed measures with reference to
validated models such as the JD‐R (Bakker and Demerouti, 2007, 2008).
To sum up, the aim of the present research was to test a model showing how organizational‐, team‐
and job‐level resources directly and indirectly influence employee engagement as an indicator of
job‐related well‐being and to model their impact on extra‐role performance and organizational
commitment (see Figure 1). The research further aimed to cross‐reference the findings with a
well‐established model of work engagement, and to thereby help ascertain the ecological
validity of existing engagement‐related models, theories and research. More specifically the study
aimed to identify how the “job resources” component of the JD‐R might be reconceptualized to
additionally reflect the influence of organizational‐ and team‐level experiences of work.
Figure 1. Organizational, team and job resources influencing engagement/well‐being, commitment
and extra‐ role performance
Note: All parameter estimates significant at p ≤ 0.001; percentage variance explained shown in
brackets; items and errors not shown for ease of representation; no correlated errors included in the
modelling
Method
Participants and procedure Participants were recruited from a large multi‐national mining company. All employees were invited
to anonymously complete a hard‐copy questionnaire and then mail their responses in a self‐
addressed envelope to an independent data management company. The data management
company provided the researcher with a compiled data file which was used to provide the client
organization with a report. Of the total 4,182 employees within the company, 3,515 usable
responses were received (response rate ¼ 73 per cent). Respondents worked across a range of
roles (e.g. operator, process
technician, administrative support, manager, senior manager) in one of 15 major work
sites across Australasia. A total of 46 per cent of respondents were female and 54 per cent were
male. The age of participants ranged between 18‐19 years (1 per cent), 20‐29 years (16 per cent), 30‐
39 years (29 per cent), 40‐49 years (33 per cent), 50‐59 years (18 per cent) and 60 and above (3 per
cent). Tenure ranged between less than one year (13 per cent), one to five years (32 per cent), six to
ten years (14 per cent) and more than ten years (41 per cent).
Measures Measures were adapted from pre‐existing published academic scales, adapted from commercially
developed scales previously used by the client organization, or developed new by a panel of internal
HR/OD consultants and managers from the client organization in consultation with the researcher.
Responses to 42 items selected to measure organizational culture of support, team climate,
supervisor coaching, career development, autonomy, engagement, extra‐role behaviour and
organizational commitment were included in the analyses. All items were rated on a five‐point Likert
scale ranging from 1 ¼ “strongly disagree” to 5 ¼ “strongly agree”. A “Don’t Know/Not Applicable”
response option was also provided. Given that the items were adapted or developed for the present
analysis and no pre‐existing validity and reliability information is available, the psychometric
properties of the measures needed to be established. Evidence in support of the validity and
reliability of the measures is described in the results section.
The measure of organizational culture was designed to assess the extent to which employees
perceived a culture of openness, fairness and support. Example items included: “[organization] has a
culture where people can challenge the usual way of doing things” and “[organization] has good
workplace policies to support me when I have non‐work related issues”. Team climate was assessed
with items such as: “There is a strong sense of teamwork amongst employees in my [unit]” and
“Differing opinions are openly discussed in making decisions in my team”. The job resources of
supervisory coaching, career development, role clarity and autonomy were assessed with items such
as: “my leader coaches me on improving my skills”, “there are sufficient opportunities for me to
receive training to increase my suitability for a better job”, “I understand how my work helps my site
achieve its goals” and “I have the authority to carry out my work tasks”. Engagement, as an indicator
of well‐being, was measured with four items, including “I am enthusiastic about the work I do’ and I
am motivated to do the best work possible”. Extra‐role behaviour was assessed with items including
“I work beyond what is required to help [organization] succeed” and “I often look for ways to do my
job better”. Commitment was assessed with items such as “I strongly believe in the goals and
objectives of [organization]” and “I feel a strong sense of belonging at [organization]”.
Analyses As a first step in the analyses, given that the set of adapted or developed items had not been
previously validated, exploratory factor analysis was conducted on a randomly selected sub‐sample
of responses in order to identify structure within the data and to determine the highest loading
items on the constructs of interest (see author for details). Nine clearly interpretable factors
emerged and the three to five highest loading items on each of these factors were retained. Next,
and in line with Anderson and Gerbing’s (1988) recommendations, the dimensionality of alternative
measurement models was more rigorously tested with confirmatory factor analysis (CFA) using
AMOS 18 and the raw data as the input. Model fit was assessed with reference to a range of
recommended “fit indices” (Marsh et al., 1996). After calculating descriptive, reliability and
correlation statistics for all variables, the viability of modelling job resources as a higher order
construct was assessed with reference to the target coefficient 2 (TC2, Marsh, 1987). Finally the
proposed structural relations between the constructs (see Figure 2) were evaluated using structural
equations modelling.
Results
Measurement model CFA of the full measurement model consisting of 36 items measuring organizational culture, team
climate, supervisor coaching, career development, role clarity, autonomy, engagement, extra‐role
behaviour and organizational commitment was conducted with the 3,437 cases having no missing
Figure 2 Parameter estimates (standardized) for proposed model
Note: All parameter estimates significant at p ≤ 0.001; percentage variance explained shown in
brackets; items and errors not shown for ease of representation; no correlated errors included in the
modelling
Table I.
Means, standard deviations, Cronbach’s a’s (on diagonal in italics) and correlations between the
modelled variables
Notes: n ¼ 3,437; **significant at the po0.01 level, (two‐tailed)
data on any of the variables of interest. The RMSEA (0.043), RMSEA 90 per cent confidence interval
(0.042‐0.044), the CFI (0.949) and the TLI (0.942) suggested the model had good fit to the data. All
standardized factor loadings were significant at po0.001 and ranged between 0.543 and 0.898.
Means, SDs, internal consistencies (Cronbach’s as) and correlations among all studied variables are
presented in Table I. All but one of the reliabilities exceeded the generally accepted criterion of 0.70
(Nunnally, 1978) and the bivariate correlations were consistent with the proposed relationships.
As previously mentioned, the viability of modelling job resources as a higher order construct (a
higher order “job resources” factor explaining the covariance among the first factors of supervisory
coaching, career development, role clarity and autonomy), was assessed with reference to the TC2
(Marsh, 1987). The TC2 of 0.92 exceeded the recommended criterion value of 0.90 (Marsh, 1987),
thus suggesting the appropriateness of the higher‐order modelling. Furthermore, the loadings of
each first‐order factor on the higher order factor (ranging from 0.54 to 0.77) exceeded the
recommended criterion of 0.50 (Kline, 1998). Additionally, χ2 difference tests (Anderson and
Gerbing, 1988), conducted to assess the discriminant validity of the higher order job resource factor
in relation to the organizational culture and the team climate variables, indicated that the constructs
were significantly distinct (Δχ2 (df,1) ¼ 1,391.1; Δχ2 (df,1) ¼ 1,747.5, respectively). Finally, the
CFA of the overall measurement model which included the higher order modelling of “job
resources” and the organizational, team and the outcome variables yielded a good fitting model:
RMSEA (0.048), RMSEA 90 per cent confidence interval (0.047‐0.050), the CFI (0.954) and the TLI
(0.948). All standardized factor loadings were significant at po0.001, ranging between 0.482 and
0.901.
Having established a defensible measurement model, the analysis proceeded to test the fit of the
proposed structural model (see Figure 2). The estimation of the proposed model yielded acceptable
fit indices: χ2 =5,150.283, df=580, χ2 ratio= 8.874, GFI = 0.919, AGFI = 0.907, TLI = 0.929, CFI = 0.934,
RMSEA = 0.048, RMSEA 90 per cent CI = 0.047‐0.049. All of the proposed parameter estimates were
statistically significant and the model explained a sizable proportion of the variance in engagement
(66 per cent), team culture (45 per cent), higher order job resources (82 per cent), extra‐role
behaviour (52 per cent) and organizational commitment (69 per cent). Beyond the significant direct
effects shown in Figure 2, the results of bias corrected bootstrap procedures available in AMOS
revealed significant indirect effects. For example, the standardized indirect effect from
organizational culture through team and job resources to engagement was 0.554 (confidence
interval 0.544‐0.708, p = 0.001). The indirect effect from team resources through job resources to
engagement was 0.441 (p = 0.002). Organizational resources, team resources and higher order job
resources all exerted significant indirect effects through engagement on extra‐role behaviour and
organizational commitment.
Discussion The research aimed to test an expanded model of the JD‐R model (Bakker and Demerouti, 2008)
showing how organizational, team and job resources directly and indirectly influence engagement
as an indicator of employee well‐being and the downstream attitudes of commitment and extra‐role
behaviour. The results showed that, as proposed: organizational culture was directly and positively
associated with team resources, job resources, engagement, commitment and extra‐role behaviour;
team climate was positively associated with engagement and job resources; the job resources of
supervisory coaching, career development, role alignment and autonomy, modelled as part of a
higher factor, were positively associated with engagement; engagement was directly associated with
commitment and extra‐role performance; organizational culture, team climate and job resources
exerted significant indirect effects on commitment and extra‐role behaviour; and the final structural
model achieved a satisfactory level of overall fit and explained sizable amounts of variance in the
variables included in the model.
The pattern of findings support the motivational processes proposed in the JD‐R model (Bakker and
Demerouti, 2008), suggesting that the provision of job resources (i.e. supervisory coaching, career
development, role clarity and autonomy) can serve to intrinsically motivate employees and result in
increased positive affect, commitment and positive attitudes towards work. The identification of role
clarity (incorporating role alignment with organizational goals) as a job resource, adds to the list of
job resources previously identified in the engagement literature. While conceptually similar
dimensions have been previously acknowledged in the engagement literature (e.g. Macey and
Schneider, 2008) the current study is the first to explicitly recognize and measure role clarity within
the JD‐R framework.
The results also extend the JD‐R, engagement and well‐being literature by suggesting that job
resources can be conceptualized and operationalized as a higher‐order factor. The successful
modelling of job resources as a higher order factor facilitates the efficient empirical testing of the
motivational processes described by the JD‐R. Additional research is warranted to test the possibility
of extending the higher order factor to include additional previously identified job‐level resources
such as performance feedback and co‐worker support (Halbesleben, 2010).
Importantly, the results also showed that organizational‐ and team‐level resources exert a direct
influence on engagement and well‐being and hence have direct input into the motivational
processes proposed in the JD‐R. Although the direct effects of organizational and team culture on
engagement were not as strong as job resources, some of their direct influence on engagement was
no doubt diluted through their direct influence job‐level resources. Both organizational climate and
team climate were shown to have relatively strong associations with job resources. Additionally,
both organizational resources and team resources were shown to have significant indirect effects on
engagement through their influence on job resources. The results therefore serve to extend the JD‐R
by suggesting that organizational‐ and team‐level resources are additional and distinct resources
which inter‐relate with job‐level resources as a “system” of resources to influence engagement.
Consistent with calls to more fully recognize the social context of work engagement (Leiter and
Bakker, 2010), consideration of additional organizational factors such as clarity of organizational
goals (Patterson et al., 2005), communication of vision (Griffin et al., 2010) and strategic alignment
(O’Reilly et al., 2010), as more distal organizational‐ and team‐level resources likely to influence
employee engagement, is warranted. Further research aimed at identifying if such variables can be
modelled as part of a higher order organizational resources factor or a higher order team resources
factor is also warranted.
Overall, the results suggest that to motivate and engage employees, and thereby contribute to
employee well‐being, performance and commitment, organizations should create open, supportive
and fair organizational and team cultures, and ensure jobs are clearly aligned with organizational
goals and have appropriate levels of autonomy, support and career development opportunities.
More generally, the results suggest that the JD‐R model could usefully be elaborated to explicitly
include organizational‐ and team‐level resources and to operationalize job resources as a higher
order factor. Such elaborations need to be modelled within the context of the full JD‐R where
demands and individual resources are also included.
Practical implications The results of the current study have several practical implications. Given the direct effects found for
job resources on employee engagement and well‐being, organizations might usefully look at
implementing a range of job‐level training and development programs aimed at setting systems and
supports to more widely, deeply and effectively embed discretion and decision‐making authority,
supervisory coaching and support, role clarity and career development within the organizational
context. More widely, such systems and supports might include establishing senior management’s
active commitment to the establishment of more participative work cultures, climates and practices;
job re‐design; broad spectrum training and development focused on engagement principles and
practices; and the redesign of supervisor performance criteria to incorporate the effective
management of participation and autonomy. Regular administration of organizational climate and
engagement surveys and multi‐rater feedback processes might provide useful indicators of the
extent to which such processes are being effectively implemented. Similarly, interventions aimed
directly at developing employee engagement should be implemented. There is an increasing
literature describing how to intervene to develop engagement at the level of the individual, the job
and the organization (e.g. Schaufeli and Salanova, 2010).
Limitations While the present research has provided new insights into the relationships between job resources,
organizational resources, team resources, engagement, extra‐role behaviour and commitment,
some limitations need to be acknowledged. Although rigorous confirmatory and structural modelling
technologies were used, the cross‐sectional data does not enable the determination of causal
relations. Longitudinal analyses, preferably drawn over three time periods, would enable much
stronger claims to be made about causality and potential reciprocality of influence among the
variables. Multi‐level analysis, determining the extent that different teams, business units and
organizational units account for variance in the relationships identified, is also warranted.
Additionally, given that all of the data were collected through self‐report procedures, the usual
caveats around “common method variance” apply. However, given that the measurement model
demonstrated acceptable fit to the data, and given that the correlations between the measured
constructs were moderate and varied quite considerably, the issue of common method variance
appears not to be overly problematic. Another important limitation centers on the generalizability of
the findings. All of the participants were employed in the resources sector. It would be useful in
future studies to obtain data from various additional types of organizations and industry sectors to
enable comparison of the results.
Conclusion There has been increasing and widespread academic and practitioner interest in understanding
positive organizational constructs such as engagement and well‐being. While job resources have
been found to significantly influence engagement and well‐being, the contributions of contextual
and team‐level resources in the motivational processes implicit in the JD‐R model have yet to be fully
explored. The results of the present study showed that beyond the provision of job‐level resources,
organizational‐ and team‐level resources are also key motivational constructs which help explain
how greater levels of engagement and well‐being can be generated. Additional job resources (e.g.
job involvement) and additional up‐stream organizational and team climate factors (e.g. vision
clarity, psychological safety) could also be assessed for their direct and indirect impact on job
resources and engagement (Bakker et al., 2011).
As a final observation and call for action, the results presented derive from a collaborative applied
research project involving internal organizational practitioner perspectives and an academic
perspective. The analyses help show how collaboration and communication between academics and
HR, OD and operational staff can help bridge the scientist‐practitioner divide (Anderson et al., 2001)
using practical models such as the JD‐R (Bakker and Demerouti, 2007, 2008). The expanded JD‐R
model, as shown in Figure 2, maps a number of potential research opportunities within the broader
ongoing engagement and employee well‐being research agenda.
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About the author Simon L. Albrecht is a Senior Lecturer within the School of Psychology at Deakin University,
Melbourne, Australia. His teaching, research and practice interests are in the areas of employee
engagement, organisational climate, organisational development, leadership development, teams,
training, positive psychology, and organisational politics. He has published in numerous international
journals, has edited an internationally recognized Handbook on Employee Engagement, has
presented at numerous international conferences, and has numerous book chapters in print. Simon
L. Albrecht can be contacted at: [email protected]