Content uploaded by Zorana Ivcevic Pringle
Author content
All content in this area was uploaded by Zorana Ivcevic Pringle on Apr 29, 2019
Content may be subject to copyright.
Career Development International
Highly engaged but burned out: intra-individual profiles in the US workforce
Julia Moeller, Zorana Ivcevic, Arielle E. White, Jochen I. Menges, Marc A. Brackett,
Article information:
To cite this document:
Julia Moeller, Zorana Ivcevic, Arielle E. White, Jochen I. Menges, Marc A. Brackett, (2018) "Highly
engaged but burned out: intra-individual profiles in the US workforce", Career Development
International, https://doi.org/10.1108/CDI-12-2016-0215
Permanent link to this document:
https://doi.org/10.1108/CDI-12-2016-0215
Downloaded on: 29 January 2018, At: 01:41 (PT)
References: this document contains references to 66 other documents.
To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 12 times since 2018*
Access to this document was granted through an Emerald subscription provided by
Token:Eprints:SN45N4MQKBYXRCMKFRR7:
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald
for Authors service information about how to choose which publication to write for and submission
guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as
well as providing an extensive range of online products and additional customer resources and
services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the
Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for
digital archive preservation.
*Related content and download information correct at time of download.
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
Highly engaged but burned out:
intra-individual profiles in
the US workforce
Julia Moeller
Yale Child Study Center and Yale Center for Emotional Intelligence,
Yale University, New Haven, Connecticut, USA and
Department of Education, University of Leipzig, Leipzig, Germany
Zorana Ivcevic and Arielle E. White
Yale Child Study Center and Yale Center for Emotional Intelligence,
Yale University, New Haven, Connecticut, USA
Jochen I. Menges
WHU –Otto Beisheim School of Management, Düsseldorf, Germany, and
Marc A. Brackett
Yale Child Study Center and Yale Center for Emotional Intelligence,
Yale University, New Haven, Connecticut, USA
Abstract
Purpose –The purpose of this paper is to use the job demands-resources model to investigate
intra-individual engagement-burnout profiles, and demands-resources profiles.
Design/methodology/approach –ArepresentativesampleoftheUSworkforce was surveyed online. Latent
profile analysis (LPA) and configural frequency analysis examined intra-individual profiles and their inter-relations.
Findings –A negative inter-individual correlation between engagement and burnout suggested that burnout
tends to be lower when engagement is high, but intra-individual analyses identified both aligned
engagement-burnout profiles (high, moderate, and low on both variables), and discrepant profiles
(high engagement –low burnout; high burnout –low engagement). High engagement and burnout
co-occurred in 18.8 percent of workers. These workers reported strong mixed (positive and negative) emotions
and intended to leave their organization. Another LPA identified three demands-resources profiles: low
demands –low resources, but moderate self-efficacy, low workload and bureaucracy demands but moderate
information processing demands –high resources, and high demands –high resources. Workers with high
engagement –high burnout profiles often reported high demands –high resources profiles. In contrast,
workers with high engagement –low burnout profiles often reported profiles of high resources, moderate
information processing demands, and low other demands.
Originality/value –This study examined the intersection of intra-individual engagement-burnout profiles
and demands-resources profiles. Previous studies examined only one of these sides or relied on inter-individual
analyses. Interestingly, many employees appear to be optimally engaged while they are burned-out and
considering to leave their jobs. Demands and resources facets were distinguished in the LPA, revealing that
some demands were associated with resources and engagement.
Keywords Burnout, Configural frequency analysis, Dark side of engagement, Demands-resources model,
Intra-individual analyses, Latent profiles
Paper type Research paper
1. Introduction
Work engagement drives employees’productivity and well-being, and is therefore
considered a desirable, optimal form of work motivation (e.g. Bakker and Demerouti, 2007;
Gorgievski-Duijvesteijn and Bakker, 2010). Consistent findings of positive associations
between work engagement, desirable employee characteristics, and work outcomes have
Career Development International
©EmeraldPublishingLimited
1362-0436
DOI 10.1108/CDI-12-2016-0215
Received 4 December 20 16
Revised 30 May 2017
20 September 2017
Accepted 26 September 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1362-0436.htm
The study was conducted at the Yale Center for Emotional Intelligence but completed after the first
author became an Assistant Professor at the University of Leipzig.
Intra-
individual
profiles in the
US workforce
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
lead to the conclusion that highly engaged employees were flourishing and thriving
(Bakker and Sanz-Vergel, 2013).
On the other hand, high work motivation may result in exhaustion and health impairment,
particularly in the presence of high work demands and time pressure (Bakker et al., 2007;
Crawford et al., 2010; Virtanen et al., 2012). Extremes of such exhausting engagement are
phenomena such as workaholism (Gorgievski-Duijvesteijn and Bakker, 2010) and karoshi
(sudden death due to overwork; Ishiyama and Kitayama, 1994; Okudaira, 2004).
Recent studies suggest that engagement and exhaustion are experienced together in large
groups of high school students (Tuominen-Soini and Salmela-Aro, 2014; Salmela-Aro, Moeller,
Schneider, Spicer, and Lavonen, 2016). Likewise, high demands and resources co-occur in
substantial groups of employees (Van den Broeck et al.,2012).However,littleisknownaboutthe
relations of engagement-burnout profiles to demands-resources profiles, and about the
prevalence of each pattern in the adult workforce. The current study examined the intersection
of intra-individual engagement-burnout profiles with demands-resources profiles in a
representative sample of US employees.
1.1 Engagement and burnout: representing two motivational pathways
Work engagement includes physical, cognitive, and emotional aspects (Kahn, 1990) and is
described as a positive, fulfilling, work-related state of mind characterized by vigor,
dedication, and absorption (Schaufeli et al., 2002). Engagement is part of one of two
motivational pathways described by the job demands-resources model ( JD-R) (Bakker and
Demerouti, 2007): the engagement pathway states that job and personal resources (such as
social support and autonomy) lead to engagement, which in turn predicts desired outcomes
such as work performance (Halbesleben and Wheeler, 2008; Schaufeli et al., 2006), business
unit performance (Harter et al., 2002), client satisfaction (Salanova et al., 2005), and safe
working behavior (Nahrgang et al., 2011).
The second pathway described by the JD-R model is the strain pathway. It states that job
demands (such as work pressure and emotional demands) predict burnout (defined as
exhaustion, cynicism/indifference and decreased productivity). Burnout in turn predicts
negative job and health consequences including turnover intentions and health impairments
(Bakker and Demerouti, 2007).
1.2 Interactions between the pathways: co-occurring demands and resources
Many studies have found negative correlations between engagement and burnout and
between demands and resources (e.g. González-Romá et al., 2006; Schaufeli et al., 2008;
Demerouti, Bakker, De Jonge, Janssen, and Schaufeli, 2001). Although this suggests that
overall, demands and burnout tend to be low when resources and engagement are high,
and vice versa, the engagement and strain pathways are not mutually exclusive[1]:
interactions between demands and resources suggest that high demands and resources
may occur together and that such a pattern has a particularly strong impact on
engagement. High resources also have been found to buffer against the negative effects of
high demands (Bakker et al., 2007; Hakanen et al., 2005). What is more, not all demands
have detrimental effects on engagement: a meta-analysis found that demands perceived as
challenges predicted engagement, whereas demands perceived as obstructions or
threats predicted burnout (Crawford et al., 2010). Particularly time pressure demands
predicted engagement.
Intra-individual cluster analyses of demands and resources identified four clusters:
“demanding (high demands, low resources), resourceful (low demands, high resources),
poor (low demands and low resources) and rich (high demands and high resources) jobs”
(Van den Broeck et al., 2012, p. 691). In all of these clusters, engagement was high
(above the midpoint on a scale from 1 –never to 6 –always) and burnout was low
CDI
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
(below the midpoint of the same response scale). Nevertheless, demanding jobs were
characterized by the highest burnout ranks and the lowest engagement ranks, whereas
resourceful jobs were characterized by the lowest burnout and high resources.
A limitation of the above study is that only z-scores (ranks), but not raw scores of
demands and resources were reported, which may distort the shape and meaning of
profiles in cluster analyses ( Moeller, 2015). Although Van den Broeck et al. (2012)
distinguished between three different demands and three different resources, they
collapsed these facets into composite scores of overall demands and overall resources in
the cluster analysis. The current study builds on their approach but distinguishes between
different facets of demands and resources in the cluster analysis.
1.3 Person-oriented studies in the JD-R literature
The interactions among elements of the strain and engagement pathways suggest that
beneficial and harmful work experiences co-occur in some individuals. However, it is not
clear how many workers experience intra-individual engagement-burnout profiles, and how
these profiles differ on work outcomes.
Commonly employed inter-individual methods only allow for conclusions at the population
level (Molenaar, 2004; Reitzle, 2013). This is problematic in workplaces where there is a need
for individualized feedback and support. Person-centered, intra-individual analyses can
address these limitations. Existing literature using such methods has mostly addressed facets
of engagement and burnout. In a longitudinal study of Finnish managers, Mäkikangas et al.
(2012) concluded that dedication (engagement facet) and cynicism (burnout facet) represented
opposites with a strong negative relationship, but vigor (another engagement facet) and
exhaustion ( burnout facet) may occur together. Similarly, Mäkikangas et al. (2014) found in a
diary study on Finnish employees that moderate levels of vigor and exhaustion were
experienced together on some days and by some employees. In another person-oriented study,
Innanen et al. (2014) identified two profiles of engagement, burnout, and workaholism among
Finnish university students: one beneficial profile of high engagement and relatively low
burnout and workaholism, and a second, less beneficial profile of high workaholism and
burnout. Despite high burnout and workaholism, the latter profile displayed moderate (above
scale midpoint) levels of the engagement facet of absorption (while the other engagement
facets dedication and energy were low in this profile).
The current study draws its hypotheses and methodological approach most directly from
research on intra-individual profiles of engagement and burnout in high schools. Examining
intra-individual profiles of engagement and burnout, Tuominen-Soini and Salmela-Aro (2014)
and Salmela-Aro, Moeller, Schneider, Spicer, and Lavonen, (2016) found that between one-
fourth and one-third of all students experienced high levels of both engagement and burnout.
Such engaged-exhausted individuals displayed at the same time desirable and undesirable
characteristics (desirable: high achievement, valuing school highly; undesirable: relatively high
stress and depressive symptoms; see Tuominen-Soini and Salmela-Aro, 2014). In the long run,
engaged-exhausted students were more likely to move into the disengaged group over six
years than their peers who had high engagement and low burnout (Tuominen-Soini and
Salmela-Aro, 2014). Both studies differed from other approaches (such as Mäkikangas et al.,
2012, 2014) in that they examined all three components of engagement (energy, dedication, and
absorption), and all three components of burnout (affective, cognitive, and behavioral) that are
often discussed in the respective literatures. The current study applied the same method and
draws its assumptions (particularly RQ3) directly from the studies by Tuominen-Soini and
Salmela-Aro (2014), as well as Salmela-Aro, Moeller, Schneider, Spicer, and Lavonen, (2016).
Together, these studies suggest that engagement and burnout may be experienced together
by some individuals. However, the relations between engagement-burnout profiles and
demands-resources profiles are unclear because previous person-oriented studies either
Intra-
individual
profiles in the
US workforce
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
examined engagement-burnout profiles, or demands-resources profiles, but not their possible
interaction. Another limitation is that most person-oriented studies on engagement and burnout
in workplaces were conducted in just two countries, Finland (Mäkikangas et al.,2012,2014;
Innanen et al., 2014) or the Netherlands (Demerouti Bakker, De Jonge, Janssen, and Schaufeli,
2001), and mostly in relatively small convenience samples. It is therefore unclear to what extent
these profiles and their prevalence are generalizable to US participants.
1.4 The present study
This study employs person-oriented analyses based on the JD-R model. We tested the
prevalences of engagement-burnout profiles as well as demands-resources profiles in a
representative sample of the US workforce. By identifying these profiles, it becomes possible
to offer a richer description of the lived experience and offer more useful information to
managers as they consider new job descriptions and ways to motivate and support workers.
We examined how demands-resources profiles were associated with engagement-burnout
profiles, while previous studies examined either engagement-burnout profiles or
demands-resources profiles, but not their intra-individual intersections.
Hypotheses
RQ1. Are engagement and burnout negatively correlated? We expected a negative inter-
individual correlation between engagement and burnout, as reported previously
(e.g. Schaufeli et al., 2008; Demerouti Bakker, De Jonge, Janssen, and Schaufeli, 2001).
RQ2. Which intra-individual engagement-burnout profiles can be identified, and what is
the prevalence of different profiles?
We expected profiles with discrepant levels of engagement and burnout (one variable high while
the other is low) as well as profiles with aligned engagement and burnout (both variables high or
low; Tuominen-Soini and Salmela-Aro, 2014; Salmela-Aro, Moeller, Schneider, Spicer, and
Lavonen, 2016). Specifically, we expected one profile of aligned high engagement –high burnout
(“engaged-exhausted”), one with high engagement –low burnout (“engaged”), one with high
burnout –low engagement (“burned out”)andan“apathetic”profile (low engagement –low
burnout; Kahn, 1990; Salmela-Aro, Muotka, Alho, Hakkarainen, and Lonka, 2016; Stock, 2015):
RQ3. How do engagement-burnout profiles differ in outcomes?
Consistent with the engagement pathway described in the J-DR model (e.g. Bakker and
Demerouti, 2007; Demerouti, Bakker, Nachreiner, and Schaufeli, 2001), we hypothesized that
engaged and engaged-exhaused profiles are associated with desirable job outcomes
(positive emotions, skill acquisition). In contrast, based on the strain pathway (Bakker and
Demerouti, 2007), we expected burnout and engaged-exhausted profiles to show high levels
of undesirable job outcomes (negative emotions, turnover intentions). The “apathetic”group
(low burnout –low engagement) was expected to display low positive and negative
emotions and low skill acquisition:
RQ4. Which intra-individual profiles of demands and resources can be identified, and
how frequent are different profiles?
Based on findings and labels by Van den Broeck et al. (2012), we expected four profiles of
demands and resources: “demanding jobs”(high demands –low resources), “resourceful
jobs”(low demands –high resources), “poor jobs”( low demands –low resources) and
“rich jobs”(high demands –high resources) jobs:
RQ5. How do demands-resources profiles relate to engagement-burnout profiles?
We expected the patterns as presented in Table I.
CDI
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
2. Methods
2.1 Data collection procedures
Participants were recruited through the survey provider Qualtrics. To recruited a
demographically representative sample, Qualtrics using quota that reflected representative
distributions of gender, geographical region, race/ethnicity, and age in the US workforce,
according to the US Bureau of Labor Statistics (2016). Participants completed the surveys online.
2.2 Sample
In total, 1,085 US employees were surveyed. Because the study aimed at investigating
workplace experiences, only adults older than 18 years who lived in the USA and worked
more than 30 hours per week were surveyed. The sample consisted of 53.6 percent male
participants, 46.2 percent female and 0.3 percent reported “other”gender identities. Data were
available from all 50 US states. The sample was 78.9 percent white/Caucasian, 10.6 percent
black/African-American, 4.3 percent Asian/Asian-American, 1.9 percent Biracial or
Multiracial, 1.0 percent American Natives or Alaska Natives, 0.5 percent Native Hawaiian
or other Pacific Islanders, and 4.3 percent reported other identities (multiple answers were
allowed). Furthermore, 10.8 percent identified as Hispanic. Participants were on average
40.4 years old (SD ¼14.0, min ¼18; max ¼74). The average subjective socio-economic status
rating was 6.04 (SD ¼2.35); measured with a scale of 0 (worst-off ) to 10 (best-off), based on
Ostrove et al. (2000).
2.3 Measures
Engagement and burnout. Engagement, burnout, demands, and resources were assessed
with self-report scales ranging from 1 (never/almost never) to 6 (always/almost always).
Engagement was assessed with items developed by Rich et al. (2010). Originally, the
measure had three subscales: physical, cognitive, and affective engagement. We administered
two items for each of these three facets, selecting items that had factor loadings of β⩾0.79 in
the two samples reported by Rich et al. (2010) (e.g. “I strive as hard as I can to complete my
job”and “I feel energetic at my job”). A confirmatory factor analysis supported a model with
three first-order factors (representing the three expected subscales of physical, cognitive and
affective engagement; χ
2
(df ) ¼24.295(6); p-value ( χ
2
)¼0.000; CFI ¼0.996; TLI ¼0.991;
RMSEA ¼0.054; 90% CI ¼0.033-0.078; SRMR ¼0.013). These first-order factors were
strongly correlated (r
phys.emo
¼0.76; r
emo.cogn
¼0.74; r
phys.cogn
¼0.99), which is why we
collapsed them into one overall engagement score for the following analyses.
Engagement-burnout profiles
Demands-resources profiles
High
engagement –
low burnout
(“engaged”)
High engagement –
high burnout
(“engaged-
exhausted”)
Low
engagement –
high burnout
(“burned-out”)
Low
engagement –
low burnout
(“apathetic”)
Low demands –high
resources (resourceful jobs)
+−−
High demands –high
resources (rich jobs)
+
High demands –low
resources (demanding jobs)
−+
Low demands –low
resources (poor jobs)
−− +
Table I.
Expected most
frequent combinations
(+) and least frequent
combinations (−) of
demands-resources
profiles (rows) by
engagement-burnout
profiles (columns)
Intra-
individual
profiles in the
US workforce
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
Burnout was assessed with the ten-item short version of the burnout measure
(Malach-Pines, 2005; e.g. How often do you experience the following at work?:
“Disappointed with people,”“Physically weak/sickly”). The CFA showed multiple residual
correlations in line with previous findings (Malach-Pines, 2005) and an acceptable fit after
including these residual correlations in the model ( χ
2
(df ) ¼190.654(28); p-value
(χ
2
)¼0.000; CFI ¼0.984; TLI ¼0.974; RMSEA ¼0.076; 90% CI ¼0.066-0.086;
SRMR ¼0.018).
Demands and resources. Since a recent meta-analysis found that challenging task-related
demands correlated with engagement, while demands hindering the workflow correlated
with burnout (Crawford et al., 2010), we aimed to capture diverse demands: workload
(general demand), information processing demand (task-related, potentially challenging),
and cumbersome bureaucracy (task-hindering demand). Items were created for the purpose
of this study, based on a review of measures for demands, resources, and workplace climate
(e.g. Rothmann et al., 2006; Kirby et al., 2003; Clark et al., 2000).
Workload was assessed with three items (e.g. “I have too much work to do;”response scale
1¼strongly disagree-6 ¼strongly agree). Information processing demands were assessed with
four items (e.g. “I have to concentrate all the time to watch for things going wrong;”response
scale 1 ¼never/almost never-6 ¼always/almost always), adapted from Morgeson and
Humphrey (2006). Cumbersome bureaucracy was assessed with three items (e.g. “Paperwork
slows me down;”response scale 1¼never/almost never-6 ¼always/almost always).
We aimed to assess diverse resources: rewards and recognitions (general work resource),
supervisor support (inter-personal resource), and self-efficacy (intra-personal resource).
Supervisor support was assessed with four items (e.g. “My supervisor provides me the
support I need to do my job well;”response scale: 1 ¼never/almost never-6 ¼always/almost
always). Rewards and recognition were measured with three items asking about
compensation, opportunities to get raises, and general recognition for success
(e.g. “I am compensated well for my work;”response scale 1 ¼strongly disagree-
6¼strongly agree). Self-efficacy was assessed with three items (e.g. “I have the skills I need
to do my job well;”response scale 1 ¼never/almost never-6 ¼always/almost always).
Outcomes. As work outcomes, we assessed positive and negative emotions, skill
acquisition, and turnover intentions.
Positive and negative emotions were assessed with 11 items from the positive and
negative affect schedule (PANAS-X) (Watson and Clark, 1999). Positive emotions were
measured with the items confident, enthusiastic, happy, inspired, interested, and proud.
Negative emotions were assessed with the items afraid, angry, tired, guilty, and disgusted.
Participants were asked to rate how often they experienced these emotions at work on a
scale from 0 (never) to 100 (always).
Skill acquisition was assessed with the items “How many skills have you acquired at this
job that you could put on your resume?”and “How many accomplishments did you have in
this job that you could put on your resume (e.g. developed products, publications, etc.)?”
(response scale: 0 ¼none to 4 ¼four or more).
Turnover intentions were measured with six items adapted from scales by Colarelli
(1984) and Wayne et al. (1997), e.g., “If an opportunity presented itself, I would pursue
another job;”response scale: 1 ¼strongly disagree to 6 ¼strongly agree.
3. Analyses and results
To facilitate comparisons of mean scores across measures, all measures were brought to the
same metric by transformation to a scale ranging from 0 to 1, using the proportion of
maximum scaling method (see Little, 2013). Table II shows means, standard deviations, and
internal consistencies for all applied measures.
CDI
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
3.1 Are engagement and burnout negatively correlated? (RQ1)
As in previous studies (e.g. Schaufeli et al., 2008), engagement and burnout were negatively
correlated across individuals (r¼−0.13*). However, the scatterplot (Figure 1) shows that
high engagement occurs often in combination with high burnout, but also often with low
burnout (Table III).
3.2 Which intra-individual engagement-burnout profiles are experienced in the US
workforce, and how often? (RQ2)
To identify groups of individuals with distinct engagement-burnout profiles, latent profile
analyses (LPA) were conducted, using Mplus and the robust estimator MLR. The indicators in
these LPA were engagement and burnout (entered as manifest variables). Models with two,
three, four, five, and six profiles were estimated and compared with each other based on
MSD α
Engagement 0.72 0.231 0.92
Burnout 0.37 0.278 0.96
Demands
Workload 0.55 0.265 0.70
Information processing demands 0.68 0.231 0.85
Cumbersome bureaucracy 0.45 0.278 0.84
Resources
Supervisor support 0.63 0.296 0.95
Rewards and recognition 0.61 0.269 0.83
Self-efficacy 0.77 0.216 0.85
Outcomes
Positive emotions 0.63 0.254 0.92
Negative emotions 0.36 0.242 0.83
Skill acquisition 0.62 0.308 0.77
Turnover intentions 0.43 0.281 0.87
Table II.
Descriptives
1.0
0.8
0.6
0.6
Engagement
Burnout
0.8 1.0
0.4
0.4
0.2
0.2
0.0
0.0
Figure 1.
Scatter plot of
engagement and
burnout scores
Intra-
individual
profiles in the
US workforce
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
criteria of interpretability, parsimony, and problem-free estimation. The final model was
chosen using the following criteria: replicated log likelihood; models with smaller AIC, BIC,
CAIC, and AWE (model fit and parsimony indicators) were preferred over models with larger
values; and the Bayes factor and correct model probability (see Masyn, 2013) were used to
identify the best model among the set of estimated models. Finally, a model was considered
the most parsimonious if models with more profiles did not change any of the conclusions.
The Lo-Mendell-Rubin likelihood ratio test and the bootstrapped likelihood ratio test were
used to discard models that did not fit the data better than a more parsimonious model.
The model fit indices were somewhat inconclusive because different indices supported
different models as the best fitting solution. The AIC, BIC, and BIC-based fit indices
(CAIC, AWE, correct model probability) supported the model with the highest number of
profiles. The Bayes factor supported none of these models. In contrast, the indicators of
parsimony (VLMR and LRT test) supported the models with three and five profiles. We chose
the five-profile model as final solution for the three following reasons: it replicated the expected
profiles shown in studies on engagement and burnout profiles among high school students
(Salmela-Aro, Moeller, Schneider, Spicer, and Lavonen, 2016; Tuominen-Soini and
Salmela-Aro, 2014), it was one of the two models supported by the VLMR and LRT tests,
and within this pair, it was the only model that showed the expected and theoretically
interesting but small profile of individuals with low scores of engagement and burnout (profile
4), and it was the model with the highest entropy, meaning the overall classification quality of
individuals to profiles was best for this model (Table IV).
The final five-profile model included two profiles with strong differences between the
engagement and burnout scores (the engaged and the burned-out profile 43.3 percent of
individuals), and three profiles with aligned engagement and burnout (both low, moderate,
or high; 56.7 percent of individuals).
The most frequent profile (41.1 percent of individuals) represented employees with
high engagement and low burnout (engaged profile). The opposite profile of low
2 3 4 5 6 7 8 9 10 11 12
1. Engagement −0.13** 0.21** 0.54** 0.12** 0.47** 0.45** 0.57** 0.47** −0.19** 0.44** −0.07*
2. Burnout 0.29** 0.11** 0.47** −0.21** −0.17** −0.11** −0.28** 0.56** −0.06* 0.56**
Demands
3. Workload 0.48** 0.50** 0.21** 0.38** 0.10** 0.18** 0.15** 0.17** 0.33*
4. Information
processing
demands 0.33** 0.34** 0.35** 0.42** 0.29** 0.05 0.41** 0.13**
5. Cumbersome
bureaucracy 0.10** 0.15** −0.03 0.03 0.28** 0.08** 0.44**
Resources
6. Supervisor
support 0.66** 0.39** 0.55** −0.22** 0.32** −0.19**
7. Rewards and
recognition 0.33** 0.52** −0.24** 0.33** −0.13**
8. Self-efficacy 0.29** −0.12** 0.36** −0.01
Work outcomes
9. Positive emotions −0.16** 0.33** −0.23**
10. Negative
emotions −0.09** 0.38**
11. Skill acquisition −0.08*
12. Turnover
intentions
Notes: *po0.05; **po0.01
Table III.
Zero-order correlations
among all
study variables
CDI
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
engagement and high burnout ( burned-out profile) was very rare (1.8 percent of the
sample). A third group experienced high levels of both engagement and burnout
(highly engaged-exhausted profile; 18.8 percent), while another group reported moderate
levels of engagement and burnout (moderately engaged-exhausted profile; 35.5 percent).
There also was a small group with very low levels of both engagement and burnout
(apathetic profile, 2.4 percent) (Figure 2).
3.3 How do engagement-burnout profiles differ in distal outcomes? (RQ3)
The groups of individuals with distinct engagement-burnout profiles differed in their
average levels of positive and negative emotions, skill acquisition and turnover intentions.
The omnibus tests for overall differences among these groups (between-subjects effects)
were all significant with large effect sizes (see Table V ).
Engaged individuals: individuals in the engaged group reported the highest average
levels of positive emotions and the highest skill acquisition. In contrast, negative emotions
and turnover intentions were rather low for these individuals.
Burned-out individuals were the opposite of the engaged individuals, as they reported
the highest levels of negative emotions, high turnover intentions, the lowest levels of
positive emotions, and low skill acquisition.
Engaged-exhausted individuals: the moderately engaged-exhausted individuals
reported moderate levels of demands, resources, positive and negative emotions,
No. of
profiles
Log L AIC BIC Bayes factor Correct model
probability
CAIC
2−3,443.796 6,901.592 6,936.381 2.1E-42 1.1E-85 6,915.78
3−3,337.384 6,694.768 6,744.465 1.2E-22 5.2E-44 6,715.04
4−3,276.43 6,578.861 6,643.468 2.8E-16 4.5E-22 6,605.21
5−3,230.154 6,492.307 6,571.824 1.6E-06 1.6E-06 6,524.74
6−3,206.357 6,450.715 6,545.141 3.5E-10 0.999998394 6,489.23
No. of
profiles
AWE VLMR
test
LRT test Parametric
bootstrapped
likelihood
ratio test
Entropy Profile sizes
2 6,950.97 0.000 0.000 0.000 0.727 68.5%; 31.5%
3 6,765.31 0.000 0.000 0.000 0.753 47.1%; 32.3%; 20.6%
4 6,670.56 0.152 0.161 0.000 0.828 41.1%; 37.7%; 17.3%; 3.9%
5 6,605.17 0.000 0.000 0.000 0.855 41.4%; 35.5%; 18.8%; 2.4%; 1.8%
6 6,584.74 0.051 0.058 0.000 0.807 41.7%; 23.2%; 18.0%; 12.8%; 2.6%; 1.6%
Table IV.
Latent profile analysis
fit indices for
engagement and
burnout
0
0.2
0.4
0.6
0.8
1
Engaged
(41.4%)
Moderately
engaged-exhausted
(35.5%)
Highly
engaged-exhausted
(18.8%)
Apathetic
(2.4%)
Burned-out
(1.8%)
Burnout Engagement
Figure 2.
Mean scores of
engagement and
burnout by profile
in the final model
(profiles ordered
by size)
Intra-
individual
profiles in the
US workforce
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
skill acquisition, and turnover intentions. The highly engaged-exhausted individuals
experienced high levels of all these variables.
Apathetic individuals reported moderate levels of positive and negative emotions.
The interpretation of this profile as apathetic individuals was supported by these individuals’
very low levels of skill acquisition. Turnover intentions were also low in this profile.
3.4 What combinations of demands and resources are observed within individuals? (RQ4)
In the LPA on demands and resources, a model with three profiles fitted the data best,
according to the parsimony criterion, VLMR test and LRT test (see Table VI and Figure 4).
The indicators based on the log likelihood would have supported models with more profiles,
but a four-profile model only added yet another profile with aligned (low) levels of demands
and resources, which did not contribute novel insights beyond the information conveyed by
the three-profile model.
To interpret the profiles, we kept the labels suggested by Van den Broeck et al. (2012).
As Figure 3 shows, the first of these profiles (39.9 percent) was characterized by the lowest
demands and lowest resources among all profiles, although information processing
demands and self-efficacy resources were still above the scale midpoint. This resembled the
group called “poor jobs”by Van den Broeck et al. (2012).
The second profile (26.1 percent) was characterized by high levels of all resources,
relatively low workload and low cumbersome bureaucracy, but moderate information
M(SD) MANOVA
Profile 1
engaged
(41.4%)
Profile 2
moderately
engaged-
exhausted (35.5%)
Profile 3
highly engaged-
exhausted
(18.8%)
Profile 4
apathetic
(2.4%)
Profile 5
burned-out
(1.8%) Fη
2
Engagement 0.88 (0.11 0.53 (0.12) 0.82 (0.12) 0.10 (0.10) 0.19 (0.13) 719.581 0.754
Burnout 0.15 (0.13) 0.39 (0.16) 0.78 (0.14) 0.12 (0.14) 0.78 (0.17) 648.170 0.734
Outcomes
Positive emotions 0.75 (0.20) 0.52 (0.23) 0.62 (0.27) 0.53 (0.27) 0.33 (0.30) 60.740 0.192
Negative emotions 0.24 (0.20) 0.38 (0.19) 0.55 (0.26) 0.49 (0.27) 0.58 (0.29) 78.198 0.235
Skill acquisition 0.72 (0.29) 0.54 (0.29) 0.66 (0.28) 0.22 (0.26) 0.38 (0.32) 31.228 0.118
Turnover
intentions 0.30 (0.28) 0.46 (0.21) 0.69 (0.20) 0.27 (0.22) 0.62 (0.30) 84.388 0.265
Notes: For pairwise comparisons, see Table A1. All tests for between subjects effects were significant
po0.000; df
between
¼4; df
within
¼937
Table V.
Differences between
profiles in distal
outcomes (M, SD,
and MANOVA)
No. of
profiles
Log L AIC BIC Bayes factor Correct model
probability
CAIC
2 89.162 −140.324 −45.898 0.00 8.2137E-221 −101.81
3 285.395 −518.79 −389.575 0.00 3.4918E-146 −466.09
4 481.351 −896.702 −732.699 0.00 1.12585E-71 −829.81
5 669.11 −1258.22 −1059.429 0.00 1.00 −1177.14
No. of
profiles
AWE VLMR
test
LRT test Parametric
bootstrapped
likelihood ratio test
Entropy Profile sizes
2−6.3 0.0000 0.0000 0.0000 0.786 38.1%; 61.9%
3−335.39 0.0000 0.0000 0.0000 0.770 39.9%; 26.1%; 34.0%
4−663.92 0.0620 0.0647 0.0000 0.812 23.2%; 6.0%; 25.2%; 45.6%
5−976.06 0.0053 0.0057 0.0000 0.857 3.9%; 10.9%; 21.5%; 22.9%, 40.7%
Table VI.
Latent profile analysis
fit indices for the
demands-resources
profiles
CDI
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
processing demands. Thus, it seems that information processing demands act more like the
resources and less like the other demands (workload and cumbersome bureaucracy).
This suggests that it is crucial to distinguish between different facets of demands when
examining the links between demands and engagement (see Crawford et al., 2010).
This profile resembled the group called “resourceful jobs”by Van den Broeck et al. (2012).
In the third profile (34.0 percent), all demands and
resources were relatively high (resembling the “rich jobs”profile described by Van den
Broeck et al., 2012).
3.5 How do demands-resources profiles relate to engagement-burnout profiles? (RQ5)
In a next step, we examined associations of the demands-resources profiles with the previously
described engagement-burnout profiles. For this purpose, we compared the proportions of the
three demands-resources profiles in each engagement-burnout profile and used a configural
frequency analysis (ConFA) (see Lienert, 1969; von Eye, 1990) to test whether each profile
combination was more (or less) frequent than would be expected if there was no relation
between the engagement-burnout and the demands-resources profiles. The ConFA was
conducted in R (RStudio, version 1.0.136, package “cfa;”Mair and Funke, 2017). The results are
displayed in Figure 4 and Table AII.
The frequencies of the three demands-resources profiles differed strongly between the
engagement-burnout groups (see Figure 4). Most strikingly, 100 percent of the apathetic
individuals belonged to the “poor job”profile (low demands –low resources except for
moderate self-efficacy). This constellation was a“type”according to the ConFA, meaning it was
significantly more frequent than expected if there was no relation between these profile groups.
Similarly, 84.2 percent workers in the burned-out group displayed a “poor job”profile
(low demands –low resources, except for moderate self-efficacy), and 15.8 percent belonged
to the “rich jobs”group (high demands –high resources).
In contrast, 64.0 percent of the highly engaged-exhausted individuals reported a
“demanding jobs”profile (high demands –low resources). This constellation was a “type,”
meaning more frequentthan we would expect if there was no relation between the two groups,
according to the ConFA. In total, 32.0 percent of the highly engaged-exhausted individuals
belonged to the “poor”group (low demands –low resources, but moderate self-efficacy),
and this combination was an “antitype,”i.e., less frequent than expected by chance. In total,
4 percent of the engaged-exhausted individuals belonged to the “resourceful jobs”profile
(low demands, except for moderate information processing demands –high resources).
0
0.2
0.4
0.6
0.8
1Workload
Information processing
demands
Cumbersome bureaucracy
Self-efficacy
Rewards and recognitions
Supervisor support
Resourceful jobs
(26.1%)
Rich jobs
(34.0%)
Poor jobs
(39.9%)
Figure 3.
Profiles of demands
and resources
Intra-
individual
profiles in the
US workforce
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
Among the moderately engaged-exhausted individuals, a relatively large number of
individuals reported a profile of “poor jobs”(low demands –low resources but moderate
self-efficacy; 61.4 percent), a “type,”according to the ConFA. The other moderately
engaged-exhausted individuals reported either “resourceful jobs”(high demand –resources;
24.6 percent), or “resourceful jobs”(low demands but moderate information processing
demands –high resources; 14.0 percent).
A particular characteristic of the engaged group was the high proportion of individuals
who reported experiencing a “resourceful job”(low demands except for moderate
information processing demands –high resources; 49.2 percent), which was a “type,”
meaning a constellation significantly more frequent than expected by chance, according to
the ConFA. Another 31.3 percent of individuals in the engaged group reported “rich jobs”
(high demands –high resources), and 19.5 percent reported “poor jobs”(low demands –low
resources, but moderate self-efficacy), which was an “antitype,”significantly less frequent
than we would expect if there was no relation between these groups.
4. Discussion
This study investigated intra-individual profiles of work engagement and burnout, as well
as profiles of demands and resources, in a representative sample of 1,085 US workers.
Although engagement and burnout were negatively correlated across individuals (RQ1),
they were also aligned (both high, moderate, or low) in more than half the sample (RQ2).
Almost one out of five workers reported high levels of both engagement and burnout, and
these engaged-exhausted workers also reported co-occurring high levels of positive and
negative emotions, as well as strong turnover intentions combined with high skill
acquisition (RQ3).
Three demands-resources profiles were identified (RQ4) and associated with
engagement-burnout profiles (RQ5). Interestingly, information processing demands were
relatively high in all profiles, even when other demands such as workload and cumbersome
bureaucracy were low, in line with Crawford et al.’s (2010) distinction between engaging and
hindering demands. Low demands and resources were typical for the apathetic and
burned-out engagement-burnout profiles, while high demands and low resources were more
20%
61%
32%
100%
84%
49%
14%
4%
31% 25%
64%
16%
0%
20%
40%
60%
80%
100%
Engaged
(41.4%)
Moderately
engaged-exhausted
(35.5%)
Highly
engaged-exhausted
(18.8%)
Apathetic
(2.4%)
Burned-out
(1.8%)
“Rich jobs”: high demands, high resources
“Resourceful jobs”: moderate information processing, otherwise low demands, high resources
“Poor jobs”: lowest demands, lowest resources, but moderate self-efficacy
Figure 4.
Proportions of
demands-resources
profiles (y-axis) within
engagement-burnout
profiles (x-axis)
CDI
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
frequent in the engaged profile. The engaged-exhausted profile (high levels of engagement
and burnout) also showed frequent co-occurrences of high demands and resources (RQ5).
These results indicate that high work engagement can be a double-edged sword for some
employees, as it is associated with beneficial experiences and outcomes when burnout
symptoms are low, but with mixed feelings and combinations of desired and undesired
outcomes when burnout symptoms are high. Workers who experienced high engagement
together with high burnout were particularly likely to experience a combination of high
demands and high resources (RQ5). This is in line with the interaction effects that have been
reported in inter-individual studies on engagement, where high demands fostered
engagement as long as resources were high, while high resources buffered against the
negative effects of job demands (Bakker et al., 2005, 2007; Hakanen et al., 2005).
4.1 Theoretical implications
Previous studies have emphasized the negative association between engagement and
burnout (Byrne et al., 2016) and some studies even concluded that engagement and burnout
were –at least in part –opposite poles of a joint dimension (González-Romá et al., 2006;
Demerouti et al., 2010, for a critical discussion see Byrne et al., 2016 and the recent special
issue by Schaufeli and De Witte, 2017). In contrast, our findings suggest that the structure of
engagement and burnout differs between individuals, meaning there are groups of
individuals accounting for negative correlations (e.g. the “engaged”and the “burned out”
groups), and other individuals driving a positive correlation (e.g. the “apathetic”and the
“engaged-exhausted”groups). That the relation between engagement and burnout can
differ between individuals is in line with the findings by Mäkikangas et al. (2012, 2014).
Highly engaged workers are not necessarily the employees managers do not need to worry
about, because engagement might not be the purely desirable form of motivation as which it
is sometimes portrayed (Bakker and Schaufeli, 2008). Since this “darker side”of engagement
is not visible unless intra-individual co-occurrences with burnout are examined, future
studies should assess engagement and burnout jointly and combine the classic
inter-individual analyses with intra-individual approaches.
This study points to potential downsides of attributes generally considered beneficial or
positive, similar to recent research on the dark side of motivation and positive emotions
(Gruber et al., 2011; Kashdan and Biswas-Diener, 2014; Moeller et al., 2015, under review;
Oettingen, 2015; Pekrun et al., 2002; Vallerand et al., 2003). For example, the motivational
construct of passion, which is similar to engagement, has been found to have both positive
(harmonious) and negative (obsessive) components (Vallerand et al., 2003), which can
co-occur within individuals (Moeller et al., 2015). Likewise, positive emotions such as interest
and happiness were found to co-occur with negative experiences such as stress and anxiety
(Gruber et al., 2011; Moeller et al., 2018; Pekrun et al., 2002). Together, these findings suggest
that the beneficial and potentially harmful motivational and emotional processes are often
intertwined within individuals, which makes it necessary to assess both sides in joint intra-
individual frameworks.
Intra-individual profile analysis also revealed that workload, cumbersome bureaucracy,
and information processing demands differed in their relationship to resources and
engagement (see Figures 3, 4, and Table III), which is in line with a previous
(inter-individual) meta-analysis (Crawford et al., 2010). However, unlike in previous
inter-individual studies, it was not the time pressure or workload that accounted for this
association between demands and engagement, but the requirements to fully concentrate on
the task at hand, direct undivided attention to the task, and think quickly in order to prevent
problems from arising (i.e. the aspects of information processing demands). There is a need
for replications and systematic comparisons of different demands and their intra-individual
associations with resources and engagement in future studies.
Intra-
individual
profiles in the
US workforce
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
Due to the representative sample of this study in terms of gender, age, region, industry
and ethnicity in the US workforce, the prevalences of profiles described in this study may be
generalizable for the working US population. Fortunately, a large group of US workers
(41.4 percent of our participants) is mainly engaged and not burned-out. The small numbers
of burned-out individuals and apathetic individuals (together 3.2 percent) also are
comforting. Concerning, however, is the finding that many engaged employees suffer of
stress and burnout symptoms, which may be the beginning of pathway leading into
disengagement (Tuominen-Soini and Salmela-Aro, 2014).
4.2 Limitations
A limitation is the rather exploratory nature of LPA, which bears the risk of sample-specific
findings. There is a need for systematic replications to support the generalizability of these
findings across demographics and other factors that might influence the results. Although
we examined a large and demographically representative sample of employees in the US
workforce, we cannot conclude that the same shape and prevalence of profiles could be
expected for all domains. For instance, there might be more engaged-exhausted employees
in highly competitive work environments where workers do not receive or do not dare to use
opportunities to recover or maintain their resources. Since domains already differ in their
average engagement and burnout rates (e.g. Carod-Artal and Vázquez-Cabrera, 2013),
it would be interesting to find out whether they also differ in regard to the shape and
prevalences of engagement-burnout profiles.
Since two profiles (the burned-out and the apathetic groups) were rather small, the
findings related to these groups need to be replicated in a larger sample. We included these
small profiles in our final model because we had expected to find these groups, they showed
the expected outcomes, and previous research shows that burnout is a highly relevant and
worrisome problem for those few who experience it (Hapke et al., 2012). Not including this
profile in the final model, therefore, would have left out important information about the
most vulnerable workers.
Although the presented results of aligned levels of engagement and burnout are similar
to those observed in educational studies, it is possible that they might have been affected by
an acquiescence response style. Future research should apply validation scales (“lie scales”)
that would help to control for such response styles.
4.3 Directions for future research
Many new questions arise from the present study: What are the short- and long-term
consequences of experiencing high levels of engagement and burnout together?
Do engaged-exhausted workers feel the beneficial and aversive aspects of motivation and
strain in the same situations, or one after another during the day? How sustainable is
exhausting motivation in the long run? How many engaged-exhausted workers transit into
a more manifest burnout group or back into the mainly engaged group? What can be done
to prevent further burnout manifestation for these workers at risk?
To answer these questions, future studies should employ situational measures of
engagement and burnout, as they have been suggested in the work literature (Bakker and
Bal, 2010; Petrou et al., 2012), as well as in the education literature (Moeller et al., 2017;
Salmela-Aro, Moeller, Schneider, Spicer, and Lavonen, 2016). Such situational assessments
can now be administered through participants’phones and then combined with information
collected by the phones’sensors, such as location, movement/physical activity, recovery/
night inactivity, etc. Using this technology for the study of situational fluctuation in
engagement and work stress would give exciting new directions to further studies.
Another question for future studies is why engagement and burnout co-occurred in some
individuals but not in others. While demands-resources profiles seem to play a role, a part of
CDI
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
the engaged and the engaged-exhausted workers experienced similar demands-resources
profiles (e.g. high demands –high resources). More research is needed to understand why
the same demands-resources experiences lead to different engagement-burnout
constellations for different individuals, and which other factors predict co-occurrences
among beneficial and harmful work experiences. Particularly important are the questions of
how the engaged-exhausted profile develops and what kind of support workers need to
prevent the transitions into burnout, depression, and turnover like those found in the high
school context (Tuominen-Soini and Salmela-Aro, 2014). Longitudinal studies of workers’
transitions between profiles of engagement and burnout are needed to answer these
questions. These longitudinal studies should apply repeated in-the-moment measures of
demands, resources, engagement and exhaustion (experience sampling), assessed in
multiple waves (e.g. during one week at T1 and another week six months later at T2),
to provide information on both the moment-to-moment fluctuation, long-term stability, and
prospective predictions of outcomes by preceding engagement-burnout profiles.
Furthermore, intervention studies could help to determine how organizations, managers,
and colleagues can support employees to maintain and renew their resources in ways that
allow them to cope with the stress and exhaustion that even the most motivated individuals
tend to experience after long periods of hard work.
In summary, this study points at crucial challenges for supervisors and organizations.
Nearly half of all employees were moderately to highly engaged in their work but also
exhausted and ready to leave their organizations. This should give managers much to think
about. Meeting the needs of these employees can support employees’well-being, as well as
organizational productivity. Understanding the profiles of engagement and burnout may
help supervisors and organizational leaders to identify employees who are motivated but
also at risk for burnout and turnover, and in turn address these employees’needs to make
sure they continue to thrive and contribute to their organization’s productivity.
Note
1. For a detailed discussion about the dependence vs independence of engagement with burnout,
see the recent special issue “Burnout and work engagement: dual unity?”by Schaufeli and
DeWitte (2017).
References
Asparouhov, T. and Muthén, B. (2014), “Auxiliary variables in mixture modeling: using the BCH
method in Mplus to estimate a distal outcome model and an arbitrary secondary model”,
Mplus Web Notes, Vol. 21 No. 2, pp. 1-22, available at: www.statmodel.com/examples/webnotes/
webnote21.pdf
Bakker, A.B. and Bal, M.P. (2010), “Weekly work engagement and performance: a study among starting
teachers”,Journal of Occupational and Organizational Psychology, Vol. 83 No. 1, pp. 189-206.
Bakker, A.B. and Demerouti, E. (2007), “The job demands-resources model: state of the art”,Journal of
Managerial Psychology, Vol. 22 No. 3, pp. 309-328.
Bakker, A.B. and Sanz-Vergel, A.I. (2013), “Weekly work engagement and flourishing: the role of
hindrance and challenge job demands”,Journal of Vocational Behavior, Vol. 82 No. 3,
pp. 397-409.
Bakker, A.B. and Schaufeli, W.B. (2008), “Positive organizational behavior: engaged employees in
flourishing organizations”,Journal of Organizational Behavior, Vol. 29 No. 2, pp. 147-154.
Bakker, A.B., Demerouti, E. and Euwema, M.C. (2005), “Job resources buffer the impact of job demands
on burnout”,Journal of Occupational Health Psychology, Vol. 10 No. 2, pp. 170-180.
Intra-
individual
profiles in the
US workforce
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
Bakker, A.B., Hakanen, J.J., Demerouti, E. and Xanthopoulou, D. (2007), “Job resources boost work
engagement, particularly when job demands are high”,Journal of Educational Psychology,
Vol. 99 No. 2, pp. 274-284.
Byrne, Z.S., Peters, J.M. and Weston, J.W. (2016), “The struggle with employee engagement: measures
and construct clarification using five samples”,Journal of Applied Psychology, Vol. 101 No. 9,
pp. 1201-1227.
Carod-Artal, F.J. and Vázquez-Cabrera, C. (2013), “Burnout syndrome in an international setting”,
in Bährer-Kohler, S. (Ed.), Burnout for Experts, Springer, New York, NY, pp. 15-35.
Clark, D.A., Clark, P.F., Day, D. and Shea, D. (2000), “The relationship between health care reform and
nurses’interest in union representation: the role of workplace climate”,Journal of Professional
Nursing, Vol. 16 No. 2, pp. 92-96.
Colarelli, S.M. (1984), “Methods of communication and mediating processes in realistic job previews”,
Journal of Applied Psychology, Vol. 69, pp. 633-642.
Crawford, E.R., LePine, J.A. and Rich, B.L. (2010), “Linking job demands and resources to employee
engagement and burnout: a theoretical extension and meta-analytic test”,Journal of Applied
Psychology, Vol. 95 No. 5, pp. 834-848.
Demerouti, E., Mostert, K. and Bakker, A.B. (2010), “Burnout and work engagement: a thorough
investigation of the independency of both constructs”,Journal of Occupational Health
Psychology, Vol. 15 No. 3, p. 209.
Demerouti, E., Bakker, A.B., Nachreiner, F. and Schaufeli, W.B. (2001), “The job demands-resources
model of burnout”,Journal of Applied Psycholology, Vol. 86 No. 3, pp. 499-512.
Demerouti, E., Bakker, A.B., De Jonge, J., Janssen, P.P. and Schaufeli, W.B. (2001), “Burnout and
engagement at work as a function of demands and control”,Scandinavian Journal of Work,
Environment & Health, Vol. 27 No. 4, pp. 279-286.
González-Romá, V., Schaufeli, W.B., Bakker, A.B. and Lloret, S. (2006), “Burnout and work engagement:
independent factors or opposite poles?”,Journal of Vocational Behavior, Vol. 68 No. 1, pp. 165-174.
Gorgievski-Duijvesteijn, M. and Bakker, A. (2010), “Passion for work: work engagement versus
workaholism”, in Albrecht, S.L. (Ed.), Handbook of Employee Engagement: Perspectives, Issues,
Research and Practice, New Horizons in Management, Cheltenham, pp. 264-271.
Gruber, J., Mauss, I.B. and Tamir, M. (2011), “A dark side of happiness? How, when, and why happiness
is not always good”,Perspectives on Psychological Science, Vol. 6 No. 3, pp. 222-233.
Hakanen, J.J., Bakker, A.B. and Demerouti, E. (2005), “How dentists cope with their job demands and
stay engaged: the moderating role of job resources”,European Journal of Oral Sciences, Vol. 113
No. 6, pp. 479-487.
Halbesleben, J.R. and Wheeler, A.R. (2008), “The relative roles of engagement and embeddedness
in predicting job performance and intention to leave”,Work & Stress,Vol.22No.3,
pp. 242-256.
Hapke, U., Maske, U., Busch, M., Schlack, R. and Scheidt-Nave, C. (2012), “Stress, Schlafstörungen,
Depressionen und Burn-out: Wie belastet sind wir. (Stress, sleeping disorders, depression and
burnout: how strained are we?)”,Bundesgesundheitsblatt, Vol. 55, pp. 987-988, available at: www.
bioy-shop.de/media/pdf/34/a2/d2/Studie-zur-Gesundheit-Erwachsener-in-Deutschland.pdf
Harter, J.K., Schmidt, F.L. and Hayes, T.L. (2002), “Business-unit-level relationship between employee
satisfaction, employee engagement, and business outcomes: a meta-analysis”,Journal of Applied
Psychology, Vol. 87 No. 2, pp. 268-279.
Innanen, H., Tolvanen, A. and Salmela-Aro, K. (2014), “Burnout, work engagement and workaholism
among highly educated employees: profiles, antecedents and outcomes”,Burnout Research,
Vol. 1 No. 1, pp. 38-49.
Ishiyama, F.I. and Kitayama, A. (1994), “Overwork and career-centered self-validation among the
Japanese: psychosocial issues and counseling implications”,International Journal for the
Advancement of Counseling, Vol. 17 No. 3, pp. 167-182.
CDI
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
Kahn, W.A. (1990), “Psychological conditions of personal engagement and disengagement at work”,
Academy of Management Journal, Vol. 33 No. 4, pp. 692-724.
Kashdan, T. and Biswas-Diener, R. (2014), The Upside of Your Dark Side: Why Being Your Whole
Self-not Just Your “Good”Self-drives Success and Fulfillment, Penguin, New York, NY.
Kirby, J.R., Delva, M.D., Knapper, C. and Birtwhistle, R.V. (2003), “Development of the approaches to
work and workplace climate questionnaires for physicians”,Evaluation & the Health
Professions, Vol. 26 No. 1, pp. 104-121.
Lienert, G.A. (1969), “Die Konfigurationsfrequenzanalyse als Klassifikationsmethode in der klinischen
Psychologie”(Configural frequency analysis as a classification method in clinical psychology)”,
in Irle, M. (Ed.), Bericht über den 26. Kongress der Deutschen Gesellschaft für Psychologie in
Tübingen 1968, Hogrefe, Göttingen, pp. 244-253.
Little, T.D. (2013), Longitudinal Structural Equation Modeling (Methodology in the Social Sciences),
Guilford Press, New York, NY.
Mair, P. and Funke, S. (2017), “cfa: configural frequency analysis (CFA). R package version 0.10-0”,
available at: https://CRAN.R-project.org/package=cfa (accessed September 19, 2017).
Mäkikangas, A., Feldt, T., Kinnunen, U. and Tolvanen, A. (2012), “Do low burnout and high work
engagement always go hand in hand? Investigation of the energy and identification dimensions
in longitudinal data”,Anxiety, Stress & Coping, Vol. 25 No. 1, pp. 93-116.
Mäkikangas, A., Kinnunen, S., Rantanen, J., Mauno, S., Tolvanen, A. and Bakker, A.B. (2014),
“Association between vigor and exhaustion during the workweek: a person-centered approach
to daily assessments”,Anxiety, Stress, & Coping, Vol. 5 No. 27, pp. 555-575.
Malach-Pines, A. (2005), “The burnout measure, short version”,International Journal of Stress
Management, Vol. 12 No. 1, pp. 78-88.
Masyn, K.E. (2013), “Latent class analysis and finite mixture modelling”, in Little, T.D. (Ed.),
The Oxford Handbook of Quantitative Methods in Psychology, Oxford University Press,
New York, NY, pp. 551-611.
Moeller, J. (2015), “A word on standardization in longitudinal studies: don’t”,Frontiers in
Psychology, Vol. 6, available at: www.ncbi.nlm.nih.gov/pmc/articles/PMC4569815/pdf/fpsyg-
06-01389.pdf
Moeller, J., Keiner, M. and Grassinger, R. (2015), “Two sides of the same coin: do the dual types of
passion describe distinct subgroups of individuals”,Journal for Person-Oriented Research, Vol. 1
No. 3, pp. 131-150.
Moeller, J., Ivcevic, Z., White, A. and Brackett, M.A. (2018), “Mixed emotions: network analyses of intra-
individual Co-occurrences within and across situations”,Manuscript Accepted for Publication
at Emotion, available at: https://osf.io/b4cxt/
Moeller, J., Spicer, J., Salmela-Aro, K. and Schneider, B. (2017), “Advances in the research on situation-
specific and contextual aspects of student engagement”, in Schoon, I. and Silbereisen, R.K. (Eds),
Pathways to Adulthood. Educational Opportunities, Motivation and Attainment in Times of
Social Change, UCL IOE Press, London, pp. 119-136.
Molenaar, P.C. (2004), “A manifesto on psychology as idiographic science: bringing the person back
into scientific psychology, this time forever”,Measurement, Vol. 2 No. 4, pp. 201-218.
Morgeson, F.P. and Humphrey, S.E. (2006), “The work design questionnaire (WDQ): developing and
validating a comprehensive measure for assessing job design and the nature of work”,Journal of
Applied Psychology, Vol. 91 No. 6, pp. 1321-1339.
Nahrgang, J.D., Morgeson, F.P. and Hofmann, D.A. (2011), “Safety at work: a meta-analytic
investigation of the link between job demands, job resources, burnout, engagement, and safety
outcomes”,Journal of Applied Psychology, Vol. 96 No. 1, pp. 1-24.
Oettingen, G. (2015), Rethinking Positive Thinking: Inside the New Science of Motivation, Current,
New York, NY.
Intra-
individual
profiles in the
US workforce
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
Okudaira, M. (2004), “Karoshi (death from overwork) from a medical point of view”,Japan Medical
Association Journal, Vol. 47 No. 5, pp. 205-210.
Ostrove, J.M., Adler, N.E., Kuppermann, M. and Washington, A.E. (2000), “Objective and subjective
assessments of socioeconomic status and their relationship to self-rated health in an ethnically
diverse sample of pregnant women”,Health Psychology, Vol. 19 No. 6, pp. 613-618.
Pekrun, R., Goetz, T., Titz, W. and Perry, R.P. (2002), “Academic emotions in students’self-regulated
learning and achievement: a program of qualitative and quantitative research”,Educational
Psychologist, Vol. 37 No. 2, pp. 91-105.
Petrou, P., Demerouti, E., Peeters, M.C., Schaufeli, W.B. and Hetland, J. (2012), “Crafting a job on a daily
basis: contextual correlates and the link to work engagement”,Journal of Organizational
Behavior, Vol. 33 No. 8, pp. 1120-1141.
Reitzle, M. (2013), “Introduction: doubts and insights concerning variable-and person-oriented approaches
to human development”,European Journal of Developmental Psychology, Vol. 10 No. 1, pp. 1-8.
Rich, B.L., Lepine, J.A. and Crawford, E.R. (2010), “Job engagement: antecedents and effects on job
performance”,Academy of Management Journal, Vol. 53 No. 3, pp. 617-635.
Rothmann, S., Mostert, K. and Strydom, M. (2006), “A psychometric evaluation of the job demands-
resources scale in South Africa”,South African Journal of Industrial Psychology, Vol. 32 No. 4,
pp. 76-86.
Salanova, M., Agut, S. and Peiró, J.M. (2005), “Linking organizational resources and work engagement
to employee performance and customer loyalty: the mediation of service climate”,Journal of
Applied Psychology, Vol. 90 No. 60, pp. 1217-1227.
Salmela-Aro, K., Moeller, J., Schneider, B., Spicer, J. and Lavonen, J. (2016), “Integrating the light and
dark sides of student engagement using person-oriented and situation-specific approaches”,
Learning and Instruction, Vol. 43, pp. 61-70, available at: www.sciencedirect.com/science/article/
pii/S0959475216300019
Salmela-Aro, K., Muotka, J., Alho, K., Hakkarainen, K. and Lonka, K. (2016), “School burnout and
engagement profiles among digital nativesinFinland:aperson-orientedapproach”,European
Journal of Developmental Psychology,Vol.13No.6,pp.704-718,doi:10.1080/17405629.2015.1107542.
Schaufeli, W.B. and De Witte, H. (2017), “Burnout and work engagement: dual unity?”,Burnout
Research, Vol. 5, pp. 1-60, available at: www.sciencedirect.com/science/journal/22130586/5
Schaufeli, W.B., Taris, T.W. and Bakker, A.B. (2006), “Dr. Jekyll or Mr. Hyde? On the differences
between work engagement and workaholism”, in Burke, R.J. (Ed.), New Horizons in
Management. Research Companion to Working Time and Work Addiction, Edward Elgar
Publishing, Cheltenham, pp. 193-217, doi: 10.4337/9781847202833.00018.
Schaufeli, W.B., Taris, T.W. and Van Rhenen, W. (2008), “Workaholism, burnout, and work
engagement: three of a kind or three different kinds of employee well-being?”,Applied
Psychology, Vol. 57 No. 2, pp. 173-203.
Schaufeli, W.B., Salanova, M., González-Romá, V. and Bakker, A.B. (2002), “The measurement of
engagement and burnout: a two sample confirmatory factor analytic approach”,Journal of
Happiness Studies, Vol. 3 No. 1, pp. 71-92.
Stock, R.M. (2015), “Is boreout a threat to frontline employees’innovative work behavior?”,Journal of
Product Innovation Management, Vol. 32 No. 4, pp. 574-592.
Tuominen-Soini, H. and Salmela-Aro, K. (2014), “Schoolwork engagement and burnout among Finnish
high school students and young adults: profiles, progressions, and educational outcomes”,
Developmental Psychology, Vol. 50 No. 3, pp. 649-662.
US Bureau of Labor Statistics (2016), “Employment status of the civilian population by race, sex, and
age”, available at: www.bls.gov/news.release/empsit.t02.htm (accessed September 19, 2017).
Vallerand, R.J., Blanchard, C., Mageau, G.A., Koestner, R., Ratelle, C., Léonard, M., Gagné, M. and
Marsolais, J. (2003), “Les passions de l’ame: on obsessive and harmonious passion”,Journal of
Personality and Social Psychology, Vol. 85 No. 4, pp. 756-767.
CDI
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
Van den Broeck, A., De Cuyper, N., Luyckx, K. and De Witte, H. (2012), “Employees’job demands-
resources profiles, burnout and work engagement: a person-centred examination”,Economic
and Industrial Democracy, Vol. 33 No. 4, pp. 691-706.
Virtanen, M., Stansfeld, S.A., Fuhrer, R., Ferrie, J.E. and Kivimäki, M. (2012), “Overtime work as a
predictor of major depressive episode: a 5-year follow-up of the Whitehall II study”,PloS One,
Vol. 7 No. 1.
von Eye, A. (1990), Introduction to Configural Frequency Analysis. The Search for Types and Antitypes
in Cross-classification, Cambridge University Press, New York, NY.
Watson, D. and Clark, L.A. (1999), “The PANAS-X: manual for the positive and negative affect
schedule-expanded form”, Iowa Research, available at: http://ir.uiowa.edu/cgi/viewcontent.cgi?
article=1011&context=psychology_pubs (accessed December 3, 2016).
Wayne, S.J., Shore, L.M. and Liden, R.C. (1997), “Perceived organizational support and leader -member
exchange: a social exchange perspective”,Academy of Management Journal, Vol. 40, pp. 82-111.
Further reading
Muthén, L.K. and Muthén, B.O. (1998-2015), Mplus User’s Guide, 7th ed., Muthén and Muthén,
Los Angeles, CA.
Appendix
Profile Profile Profile Profile Profile Profile Profile Profile Profile Profile
1 vs 2 1 vs 3 1 vs 4 1 vs 5 2 vs 3 2 vs 4 2 vs 5 3 vs 4 3 vs 5 4 vs 5
Resources
Rewards and recognitions 0.000 0.000 0.000 0.000 0.000 0.039 0.000 0.000 0.000 0.282
Supervisor support 0.000 0.000 0.000 0.000 0.000 0.027 0.000 0.000 0.000 0.290
Self-efficacy 0.000 0.000 0.000 0.000 0.000 0.880 0.000 0.018 0.000 0.000
Demands
Workload 0.133 0.000 0.373 0.000 0.000 0.608 0.000 0.002 0.000 0.043
Cumbersome bureaucracy 0.001 0.000 0.753 0.000 0.000 0.192 0.000 0.000 0.000 0.035
Information processing
demands
0.000 0.011 0.001 0.000 0.000 0.285 0.000 0.000 0.000 0.001
Work outcomes
PANAS positive 0.000 0.000 0.000 0.000 0.000 0.022 0.526 0.000 0.129 0.024
PANAS negative 0.000 0.000 0.000 0.000 0.000 0.004 0.049 0.679 0.276 0.306
Skill acquisition 0.000 0.013 0.000 0.000 0.000 0.068 0.000 0.000 0.000 0.090
Turnover intentions 0.000 0.000 0.000 0.394 0.000 0.013 0.000 0.473 0.000 0.000
Notes: Profile 1 ¼engaged; profile 2 ¼moderately engaged-exhausted; profile 3 ¼highly engaged-
exhausted; profile 4 ¼disengaged; profile 5¼burned-out
Source: BCH method; Asparouhov and Muthén (2014)
Table A1.
p-values for pairwise
comparisons between
engagement-burnout
profiles in distal
outcomes
Intra-
individual
profiles in the
US workforce
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
Corresponding author
Julia Moeller can be contacted at: julia.moeller@uni-leipzig.de
Engagement-burnout profiles
Demands-resources
profiles Engaged
Moderately
engaged-exhausted
Highly engaged-
exhausted Disengaged Burned-out
Low demands –
low resources
86 (19.5%) (Antitype) 232 (61.4%) (Type) 64 (32.0%) 26 (100%) (Type) 16 (84.2%)
Low demands –
high resources
217 (49.2%) (Type) 53 (14.0%) 8 (4.0%) (Antitype) 0 (0.0%) 0 (0.0%)
High demands –
high resources
138 (31.3%) 93 (24.6%) 128 (64.0%) (Type) 0 (0.0%) 3 (15.8%)
Notes: “Type”means that the cell was significantly more frequent than we would expect if there was no relationship
between the two profiles, according to the ConFa; “Antitype”means that the cell was significantly less frequent than we
would expect if there was no relationship between the two profiles. Absolute frequencies, percentages within columns, and
results of the configural frequency analysis
Table A2.
Frequencies
of demands-resources
profiles within
engagement-burnout
profiles
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
CDI
Downloaded by 217.227.35.14 At 01:41 29 January 2018 (PT)
