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Journal of Business Venturing 38 (2023) 106272
0883-9026/© 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Job burnout and work engagement in entrepreneurs: How the
psychological utility of entrepreneurship drives
healthy engagement
Martin Obschonka
a
,
*
, Ignacio Pavez
b
, Teemu Kautonen
b
,
c
, Ewald Kibler
d
,
Katariina Salmela-Aro
e
, Joakim Wincent
f
,
g
a
Amsterdam Business School, University of Amsterdam, Netherlands
b
Facultad de Economía y Negocios, Universidad del Desarrollo, Chile
c
United Arab Emirates University, UAE
d
Aalto University School of Business, Entrepreneurship, Finland
e
Helsinki University, Department of Psychology, Finland
f
Hanken School of Economics, Finland
g
University of St Gallen, Switzerland
ARTICLE INFO
Keywords:
Entrepreneurship
Psychological utility
Well-being
Job burnout
Work engagement
Work recovery
Job demands
Job resources
Psychological capital
Solo entrepreneurs
ABSTRACT
What is the real value of entrepreneurship? We propose a framework of psychological utility by
integrating Job Demands-Resources (JD-R) theory with a recovery approach from a personal
agency perspective. We hypothesize that personal agency together with the positive JD-R pattern
of entrepreneurship generates outstanding psychological utility, which maintains and rewards a
healthy, strong work engagement that spills over to off-work time. This benets entrepreneurs,
but also their businesses reliant on strong work engagement that avoids burnout. We validate our
framework by means of panel data comprising four waves (348 entrepreneurs and 1002 em-
ployees), where we also analyze different types of entrepreneurs.
1. Introduction
Since the outset of entrepreneurship research, the wider positive contributions of entrepreneurs to the economy have been
frequently highlighted (e.g., regarding job creation, innovation, industry evolution, or growth; see Audretsch, 1995; Haltiwanger et al.,
2013; Schumpeter, 1934). However, the value of entrepreneurship may also lie in benets that accrue to the individual entrepreneurs
themselves—the individual payoff to entrepreneurship as personal utility (Baumol, 1990; Douglas and Shepherd, 2002; L´
evesque
et al., 2002; Van Praag and Versloot, 2007). Despite its prominent role particularly in economics, personal utility is still often referred
to as a black box (Kahneman et al., 1997; Kahneman and Thaler, 1991; Kaplan and Schulhofer-Wohl, 2018).
This black box of personal utility in the context of entrepreneurship is important to unpick since such utility should have partic-
ularly important implications for entrepreneurs' persisting motivation and personal well-being as well as their businesses.
* Corresponding author.
E-mail addresses: m.obschonka@uva.nl (M. Obschonka), ignaciopavez@udd.cl (I. Pavez), teemu.kautonen@uaeu.ac.ae (T. Kautonen), ewald.
kibler@aalto. (E. Kibler), katariina.salmela-aro@helsinki. (K. Salmela-Aro), joakim.wincent@hanken., joakim.vincent@unisg.ch (J. Wincent).
Contents lists available at ScienceDirect
Journal of Business Venturing
journal homepage: www.elsevier.com/locate/jbusvent
https://doi.org/10.1016/j.jbusvent.2022.106272
Received 24 June 2021; Received in revised form 15 November 2022; Accepted 17 November 2022
Journal of Business Venturing 38 (2023) 106272
2
Entrepreneurs might not only accrue such benets as passive recipients, but also proactively, as intentional agents in their own positive
development (Bandura, 1989; Heckhausen and Schulz, 1995; Lerner and Busch-Rossnagel, 1981), where entrepreneurship is an in-
strument to maximize the utility of work via personal agency (Douglas and Shepherd, 2000; Frese, 2009). Hence, to the extent that
individuals are motivated to maximize and realize their own utility via personal agency (Simon, 2000), we argue that entrepreneurs,
compared to non-entrepreneurs, derive outstanding personal utility
1
proactively from their work. The actual personal utility of
entrepreneurial work has been subject to ongoing debate, particularly with respect to underlying psychological processes (Benz and
Frey, 2008; Douglas and Shepherd, 2000; L´
evesque et al., 2002; Monsen et al., 2010; Shane, 2008; Van Praag and Versloot, 2007).
Two often used perspectives to approach personal utility are the economic versus the psychological lens (Kaplan and Schulhofer-
Wohl, 2018). On the one hand, economic (or nancial) personal utility focuses on the economic value of work. Some entrepreneurship
studies have shown, however, that “the majority of entrepreneurs would earn higher incomes as wage employees” (Van Praag and
Versloot, 2007, p. 377; but for a critical view, see also Åstebro and Chen, 2014), suggesting that the economic benets of entrepre-
neurship are often not much better (or perhaps even less) than in comparable employed work. Thus, entrepreneurship might offer
other types of personal utility, otherwise entrepreneurship as a career choice and everyday work role should be rather less popular and
personally satisfying than it is (Benz and Frey, 2008).
On the other hand, to go beyond such ‘simple utility’ (Monsen et al., 2010), entrepreneurship scholars have begun to focus on non-
economic (non-nancial) personal utility—a phenomenon we can summarize as psychological utility. Compared to the traditional
economic model (Robinson, 1962), newer interpretations of the utility concept seek to integrate latest insights from psychology to
derive a more psychologically realistic understanding of utility, which also considers the subjectivity and complexity of individual
psychological processes, and thus the boundaries of rationality (Kahneman and Thaler, 1991; Kahneman et al., 1997; Simon, 2000).
While integrating psychological insights into utility models has been highlighted as one of the “most compelling and inuential”
(Loewenstein, 1999, p. 315) contributions of psychology to economics, contemporary utility-focused entrepreneurship research has
yet to witness a deeper integration of recent psychological insights.
Applying a focus on positive potential and outcomes for an entrepreneur, psychological utility can be understood, conceptualized,
and researched from a positive psychology perspective—“a science of positive subjective experience, positive individual traits, and
positive institutions [that] promises to improve quality of life and prevent […] pathologies” (Seligman and Csikszentmihalyi, 2000, p.
5). From this perspective, and consistent with previous discussions in entrepreneurship research, we dene the psychological utility of
entrepreneurship as the entrepreneur's positive subjective experience (while avoiding negative psychological outcomes) as return to their own
work engagement, positive traits, and a positive institutional environment, and in contrast to non-entrepreneurial work. In other words, such
utility covers not only the mere psychological rewards and benets of own entrepreneurship (Kaplan and Schulhofer-Wohl, 2018), but
places them into perspective in comparison to own, on-going investments into an entrepreneurial career (the psychological payoffs to
this investment), also as juxtaposed to other career options and types of work (e.g., employed work; Goethner et al., 2012).
Highlighting such psychological utility that contrasts a more traditional understanding of utility is something that urgently has
been called for in the entrepreneurship literature. In fact, a growing body of conceptual and empirical work has emphasized outcome-
oriented psychological factors, such as job satisfaction or happiness (e.g., Stephan, 2018; Wiklund et al., 2019; Van Praag and Versloot,
2007) as well as expected/anticipated utility shaping the choice for an entrepreneurial career (Douglas and Shepherd, 2000, 2002).
Despite this research interest, our knowledge of the complex psychological utility processes (e.g., with respect to the mechanisms
leading to such experienced outcomes, and how entrepreneurs can actually maximize psychological utility) remains limited. A notable
exemption is Benz and Frey's (2008) work on procedural utility—the utility entrepreneurs derive from the process of decision
making—and how instrumental outcomes are generated in entrepreneurship (see also Frey et al., 2004).
Here we address the experienced psychological utility of entrepreneurship and the underlying processes that indicate how entre-
preneurs maximize such utility. Consistent with our denition of psychological utility, we develop a testable model, combining a stress
and motivation model, the Job Demands-Resources (JD-R) theory (Bakker et al., 2014; Demerouti et al., 2001), with a work recovery
approach (Bennett et al., 2018; Sonnentag et al., 2022). It is important to note, however, that we do not claim this to be the only way of
advancing our understanding of the psychological utility of entrepreneurship, which, by its very nature, should be highly complex.
2
At
its core this approach enables us to investigate an intriguing psychological mechanism, that is, how entrepreneurs seem unconcerned
by burnout risks or their strong work engagement. We show that this mechanism may form the core for our understanding of the high
psychological utility that entrepreneurs proactively derive from work.
Entrepreneurship scholars have recently called for conceptual and empirical research that captures the complexity of motivation
and stress processes specic to entrepreneurial work (Nikolaev et al., 2020; Stephan, 2018; Wiklund et al., 2019), also with an eye to
the practical implications — asking, for example, how entrepreneurs can avoid negative, and maximize positive, psychological work
outcomes. There has also been a recent call to investigate eudaimonic well-being in entrepreneurs (e.g., as compared to hedonic well-
being), which emphasizes personal agency and related “multiple facets of well-being such as purposeful engagement, realization of
personal potential, autonomy, mastery, quality ties to others, and self-acceptance” (Ryff, 2019, p. 647). The model we develop is based
upon key characteristics of how entrepreneurs can avoid negative, and maximize positive, psychological outcomes.
1
In this paper, utility is understood as the actually experienced utility while working as an entrepreneur, as opposed to expected future utility,
relevant for example for occupational choice processes (Kahneman et al., 1997).
2
A common emphasis in economic research is that utility is a black box, rendering this a phenomenon that is rather complex and difcult to
directly measure and test (Kahneman et al., 1997; Kahneman and Thaler, 1991; Robinson, 1962). With the integration of testable, concrete psy-
chological models and concepts it becomes possible to break this complexity down (Simon, 2000). We regard our study as one such example.
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
3
We present original panel survey data comprising four waves collected at two-month intervals (348 entrepreneurs and 1002
employees). Our results conrm the notion that entrepreneurship offers better psychological utility than employed work because it
represents a more positive JD-R pattern (positive, energizing motivation activation pattern outweighing a negative, depleting stress
process). We also nd that entrepreneurs show higher work engagement than employees, interpretable as an agentic utility maxi-
mization process (investments in a positive JD-R job setting that offers outstanding utility). In the same vein, entrepreneurs indeed
engage less in traditional off-work recovery via psychological detachment, which might place them at risk for burnout; yet they are
protected by the positive JD-R pattern and also conserve maximized psychological utility derived from entrepreneurial work (by
avoiding specic costs). Finally, we establish that, within the entrepreneurial population, it is the solo entrepreneurs that particularly
benet from high psychological utility.
Taken together, our ndings advance scholarly knowledge in three ways. First, our overall contribution proffers new theoretical
and empirical insights into the complex psychological utility of entrepreneurship and its underlying mechanisms, and how entre-
preneurs can, and do, actually maximize such utility offered by their work—an important research gap in contemporary entrepre-
neurship research, as stressed above. The second (nested) type of contributions represents specic insights into a psychological
mechanism of entrepreneurial stress, motivation, and recovery. We provide novel and robust empirical evidence on the scope and
prevalence of work engagement and job burnout in entrepreneurs, as well as its underlying mechanisms (as compared to employed
individuals). By combining JD-R theory with the recovery approach to illuminate how motivational and stress processes operate
holistically in entrepreneurs we also contribute to the small but growing research on the role of recovery in preventing ill-being and
promoting well-being in entrepreneurs (Wach et al., 2021; Williamson et al., 2021). Finally, our third type of contribution lies in a
more nuanced understanding of the heterogeneity of entrepreneurs and its implications for psychological utility (such as solo en-
trepreneurs vs. employer entrepreneurs, but also novice vs. serial entrepreneurs, young vs. mature rms, and rms of different sizes).
Although this ‘heterogeneity challenge’ has been frequently highlighted in entrepreneurship research, it nevertheless remains but
seldomly addressed by means of study designs that attempt to account for it (Davidsson, 2016).
2. Theoretical framework and hypotheses development
We begin with our psychological utility framework (Fig. 1), which guided us to develop the concrete hypotheses that break down
stress, motivation, and recovery processes, and which together are indicative of psychological utility (Fig. 2). In combination these
hypotheses explain why, and how, entrepreneurs maintain a healthy high work engagement and derive outstanding psychological
utility.
2.1. Personal agency and engagement as the epitome of entrepreneurship: maximizing utility or risk for negative outcomes?
One foundation of our utility framework draws from the personal agency paradigm in psychology (Bandura, 1989; Heckhausen and
Schulz, 1995), which emphasizes that individuals typically seek and thrive on personal control and mastery in challenging task en-
vironments (such as entrepreneurship), thereby in turn resulting in better performance of these challenging tasks. Successful entre-
preneurship relies on the personal agency of an individual entrepreneur—the strong and persistent motivation to engage in their daily
work, be it to start, run, or grow a business, and the individual freedom and autonomy to make own choices and regulate own
entrepreneurial actions (Frese, 2009; Shane et al., 2003). Entrepreneurs typically need to engage in a wide variety of work tasks
(Lazear, 2005), while often being psychologically ‘on alert’ on a continual basis to stay in control of concrete operational issues and
deal with uncertainty, risk, and contingency, even while remaining open to new opportunities (Tang et al., 2012). Hence, it is not
surprising that a myriad of entrepreneurship theories and studies highlight the role of personal agency (e.g., Frese, 2009; Newman
et al., 2019; Shane et al., 2003).
3
In addition, and in light of the importance of such a continual engagement in a complex task
environment, scholars have emphasized energizing processes as essential components for entrepreneurs to be able to function psy-
chologically and to be ‘in control’ (Stephan, 2018; Shir and Ryff, 2021; Wiklund et al., 2019), as shown for example in prior research on
entrepreneurial passion (Cardon et al., 2009; Stroe et al., 2018).
Debate has ensued, however, over whether personal agency and the strong and persistent engagement—fueled by motivation
processes—could also come at signicant psychological costs for entrepreneurs, such as high stress/burnout (Baron et al., 2016; Fernet
et al., 2016; Hessels et al., 2017; Kollmann et al., 2019; see also Lerman et al., 2021). In the occupational health literature, over-
engagement and strong, persisting work passion have been linked to the risk of burnout, for instance due to conict with other life
activities and a lack of recovery (Sonnentag et al., 2022; Sonnentag and Fritz, 2015; Vallerand et al., 2010). Since such work
engagement requires signicant self-regulation, the psychological resources driving such self-regulation deplete over time (Muraven
and Baumeister, 2000), thereby potentially impeding entrepreneurs' ability to function psychologically (Nikolaev et al., 2020).
Moreover, the detrimental effect of stressors that might be prevalent in entrepreneurial work (Lerman et al., 2021; Stephan, 2018) may
be amplied through strong and persistent work engagement, particularly if recovery processes are impaired (Sonnentag and Fritz,
2015). Indeed, some studies indicate that “entrepreneurs have stressful jobs—high work demands that require intense effort and
concentration” (Stephan, 2018, p. 296; see also Lerman et al., 2021; Palmer et al., 2021; Wei et al., 2015). Work and occupational
health experts describe entrepreneurs' working conditions as “characterized by long working hours and the potential for stress and
3
However, to the best of our knowledge such an agency perspective to date has not been prominently applied in psychological utility-focused
entrepreneurship research.
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
4
health-related issues” (OECD, 2017, p. 110).
However, a very different story is told by research indicating that entrepreneurs experience less, and not more, stress than non-
entrepreneurs (Baron et al., 2016; Hessels et al., 2017). Here we build on this positive view, by stressing that personal agency in
entrepreneurship does not only drive the entrepreneurial process as the ‘engine room’, but is also a mechanism by which entrepreneurs
maximize psychological utility: the personal agency of entrepreneurs as the freedom and intrinsic driver to reap, protect, and maximize
psychological utility offered by their job. These two positive roles of personal agency in entrepreneurs (i.e., the ‘engine room’ effect
benecial for their businesses, and the utility maximization benecial for themselves) represent two sides of the same coin, possibly
also reinforcing each other. For example, more work engagement to build, run, and grow the business can be accompanied by more
psychological utility,
4
which in turn incentivizes continued strong work engagement.
Guided by our agency-based psychological utility framework (Fig. 1), we identify two pathways by which personal agency is linked
to psychological utility in entrepreneurs. First, entrepreneurs' personal agency drives their continuous work engagement in the context
of the specic demands and resources associated with their job. Second, it also fuels their psychological engagement during off-work
time (associated with a lack of off-work recovery) in a sense that it protects psychological utility, as explained in the following.
Our guiding framework (see Fig. 1) represents the overall, novel theoretical rationale for our study, from which we derive a
concrete research model that combines a motivation and stress theory (JD-R theory) with a recovery approach. It is this research
model, in its totality, that enables submitting our utility framework to a concrete empirical examination. While our guiding framework
represents the overall, novel theoretical rationale for our study, with its holistic emphasis on the integration of motivation, stress, and
recovery factors and mechanisms, in the following we also deem it important to launch our hypotheses development with a discussion
of the JD-R theory and the work recovery approach. This is also pertinent because particularly the JD-R theory itself has yet to be
introduced properly in the entrepreneurship literature.
5
2.2. The Job Demands-Resources (JD-R) theory
JD-R theory is usually understood as an integrated theory of occupational motivation and stress (Bakker et al., 2014). With its dual
focus on avoiding negative outcomes (e.g., burnout) and promoting positive ones (such as energized, healthy work engagement), JD-R
theory cleaves to the logic of positive psychology to address psychological utility (Seligman and Csikszentmihalyi, 2000; see also
Sweetman and Luthans, 2010), and has become one of the most inuential theories relating to job performance, motivation, and well-
and ill-being at work (Bakker et al., 2014).
To date, JD-R theory has been applied in numerous occupational health and performance studies. For example, currently JD-R
theory helps to explain, describe, and make predictions about employee burnout, work engagement, and various aspects of job per-
formance (e.g., efciency and productivity), motivational outcomes (e.g., learning and proactive behavior), and health-related out-
comes (e.g., depression and anxiety disorders, and absenteeism due to illness) (Bakker et al., 2014; Bakker and Demerouti, 2017).
There is clear evidence from prior occupational health research on the need to conceptualize and research motivation and stress
processes in an integrative (and at the same time parsimonious) manner (Bakker and Demerouti, 2017). A myriad of research has
Fig. 1. Psychological utility framework: How entrepreneurs proactively derive psychological utility via high work engagement (investing
engagement in work offering a positive, energizing, and rewarding JD-R prole job), which also spills over to non-work time.
4
Due to the personal investments (=high work engagement) in a job setting that represents a positive JD-R pattern, and because of positive
spillover processes of healthy high engagement to non-work time (see Fig. 1).
5
At rst glance, it might seem that such an isolated focus on JD-R theory and the work recovery approach in the following sections distracts from
the psychological utility focus of this study. We deem it essential, however, to provide these separate overviews as this allows readers to understand
our framework and derived research model better.
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
5
shown that such a dual-process view can help uncover the complex drivers of motivation and stress (as well as well- and ill-being)
specic to certain occupations and types of jobs, such as those in which strong work engagement entails an elevated threat of
burnout, e.g., for nurses (Van der Colff and Rothmann, 2009), teachers (Hakanen et al., 2006), or police ofcers (Hu et al., 2017).
Previous entrepreneurship research has already targeted individual (yet isolated) components of JD-R theory, such as job demands
(e.g., Lerman et al., 2021), job resources (e.g., Hessels et al., 2017), and personal resources (e.g., Baron et al., 2016), yet there is a lack
of both an integrative model and an integrative empirical test. Integrating such determinants and processes is vital for reecting the
complexity of well-being and motivation (Nikolaev et al., 2020). Moreover, this also enables researchers to examine the additional
variance in the outcome variables that is explained by each determinant and process (Kautonen et al., 2015).
2.2.1. Job burnout and work engagement as central outcomes
Two central outcomes in JD-R theory are job burnout and work engagement. Job burnout is dened as “a prolonged response to
chronic emotional and interpersonal stressors on the job” (Maslach, 2003, p. 189) and is expressed by feelings of exhaustion, cynicism,
and a sense of inefcacy (Maslach et al., 2001). Exhaustion is characterized by strain and overtaxing from work; cynicism is described
as a loss of interest and distal attitude to work (e.g., not appreciating work as meaningful); and feelings of inefcacy are often
Fig. 2. Research model: hypothesized effects with direct paths (A) and mediation paths (B).
Note: Entrepreneurship (being an entrepreneur, as opposed to being an employee) does not have a direct effect on burnout (A), but this is due to the
positive indirect effect of work recovery and the negative indirect effect of job and personal resources (B). The dashed line represents a null effect;
the solid lines represent a positive (+) or negative (−) effect.
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
6
characterized by a sense of incompetence (Schaufeli et al., 2002). Burnout can affect both physical and mental health, with far-
reaching consequences for individuals. Workers with high levels of job burnout tend to be physically, mentally, and emotionally
exhausted when performing their tasks; hence, they are less effective at accomplishing their goals and show lower levels of well-being
and job performance (Bakker et al., 2008; Taris, 2006). The WHO (2019) acknowledges burnout in the 11th Revision of the Inter-
national Classication of Diseases (ICD-11) as an occupational phenomenon (rather than medical condition). However, is job burnout
a commonplace phenomenon in entrepreneurs? The notion that burnout levels do differ across occupations has been well supported in
the literature (Schutte et al., 2000). While job burnout has become an important topic in business research over the past decades
(Cordes and Dougherty, 1993), burnout among entrepreneurs has been studied less exhaustively than in the context of other occu-
pations (M¨
akiniemi et al., 2020; Wei et al., 2015); and the limited number of studies present mixed and inconclusive evidence. In our
model we dene the absence of burnout (e.g., despite continuously high work engagement) as a necessary condition for psychological
utility.
Work engagement, in turn, is described as a positive, fullling work-focused mental state. Thus, such a focus on work engagement
follows a positive psychology perspective (Sweetman and Luthans, 2010). Cardinal indicators of work engagement are high levels of
vigor, dedication, and absorption (Bakker, 2011; Schaufeli et al., 2006). Vigor refers to high levels of energy and mental resilience
while working, as well as the desire to invest effort in the task at hand and persist even when facing difculties. Dedication describes a
sense of high signicance, challenge, inspiration, enthusiasm, and pride in one's work, which leads to a greater commitment to task
performance and the overall role played in an organization. Absorption is characterized as being fully concentrated and happily
engrossed in one's work; time passes quickly, and it may be difcult to disconnect from the task being performed (Schaufeli et al., 2002;
Toth-Kiraly et al., 2021). Work engagement helps individuals to be goal-oriented, to focus on the task at hand, and to bring more
energy and enthusiasm to the job, thereby typically enabling them to perform better and achieve higher levels of well-being compared
with individuals who are less engaged (Hopstaken et al., 2015). Very high work engagement can also have negative consequences; for
example, over-engagement and workaholism can lead to psychological ill-being in at-risk occupations and job settings (Sonnentag and
Fritz, 2015; Toth-Kiraly et al., 2021). Therefore, the presence of healthy work engagement contributes to psychological utility in our
model.
2.2.2. Job demands and resources, and associated stress and motivation processes
JD-R theory sets its main focus on two types of job characteristics that are deemed to shape and maintain job burnout and work
engagement in characteristic ways: job demands and job resources (Bakker and Demerouti, 2017). Job demands are dened as “those
physical, social, or organizational aspects of the job that require sustained physical or mental effort and are therefore associated with
certain physiological and psychological costs” (Demerouti et al., 2001, p. 501). Job demands are not necessarily negative, but when
many of them exist simultaneously and substantial effort is required to meet them, they may turn into stressors in certain circumstances
(Bakker et al., 2010). Examples of job demands are high work pressure stemming from activities that demand a high level of cognition
or emotionally exacting interaction with people (e.g., colleagues, patients, or customers). Job resources, on the other hand, refer to
“those physical, psychological, social, or organizational aspects of the job that may do any of the following: (a) be functional in
achieving work goals; (b) reduce job demands and the associated physiological and psychological costs; (c) stimulate personal growth
and development” (Demerouti et al., 2001, p. 501). Examples of job resources are job control/autonomy, participation in decision-
making, performance feedback, supervisor support, task variety, and opportunities for growth.
Job demands and resources activate two distinct and independent processes: (health-impairment/depleting) stress processes, and
(energizing) motivational processes (Bakker et al., 2014; Bakker and Demerouti, 2017). In JD-R theory, job demands are strong
predictors of job burnout (i.e., stress processes), whereas job resources are strong predictors of work engagement (i.e., motivational
processes) (Demerouti et al., 2001). These pathways produce unique outcomes related to either stress or motivational processes
(Bakker and Demerouti, 2017).
In addition to job demands and job resources, the JD-R model also incorporates personal resources as an important component that
can also have an impact on both job burnout and work engagement (Bakker et al., 2014). Personal resources broadly refer to people's
psychological capital (Baron et al., 2016) and their beliefs about “their ability to successfully control and have an impact on their
environment” (Bakker, 2011, p. 266). Prior studies found that personal resources positively relate to desired outcomes, such as
motivation, goal-setting, job performance, and life satisfaction (Judge et al., 2004; Youssef and Luthans, 2007). As such, JD-R theory
suggests that personal resources (e.g., positive self-evaluations such as optimism or self-efcacy) can play a role similar to that of job
resources, meaning that they have a direct, positive effect on work engagement and are able to buffer the undesirable impact of
negative job demands on stress (Bakker and Demerouti, 2017).
2.3. The work recovery approach
Entrepreneurship scholars have lately begun to highlight work recovery among entrepreneurs (Wach et al., 2021; Williamson et al.,
2021), yet this has yet to be integrated into stress and motivation models (such as JD-R theory). It is also crucial to address the
fundamental question of which type of recovery is actually useful for entrepreneurs. So far, some entrepreneurship studies appear to
indicate that entrepreneurs require particular support to realize recovery experiences, although it is still fairly unclear just how
stressful entrepreneurial work typically is and what kind of value off-work recovery, for instance, would actually have for
entrepreneurs.
In the occupational health literature, work recovery is recognized as an important process that helps to avoid ill-being in high
engagement and stressful jobs by replenishing internal resources necessary for restoring an individual's mental, physical, and
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
7
emotional energy (Bennett et al., 2018; Sonnentag et al., 2022; Sonnentag and Fritz, 2015). To date little is known about how such
work recovery operates concretely in the context of entrepreneurial stress (Stephan, 2018; Williamson et al., 2021); and this research
problem is pertinent because an entrepreneur's drive to maintain strong work engagement over long periods, coupled with limited
opportunities for work recovery, can come at the cost of burnout or workaholism (Gorgievski et al., 2010; Stroe et al., 2018; Toth-
Kiraly et al., 2021; Wach et al., 2021).
Consequently, a better understanding of the combined experience of job demands, job and personal resources, and work recovery is
necessary to advance our knowledge of the relationship between stress and well-being in entrepreneurs. Here, we essentially develop
and test an opposing process model
6
of entrepreneurship and burnout, where high work engagement (driven by better job and personal
resources), coupled with low recovery from work (the ‘always on alert’/‘switched-on’ pattern) subjects entrepreneurs to the risk of
burnout due to a lack of winding down, yet better job and personal resources actually serve to protect them from burnout. These
mechanisms represent opposing processes, where the former increases burnout while the latter decreases it (see also Murayama and
Elliot, 2012; MacKinnon et al., 2007). This subsequently leads us to assume that entrepreneurs might not show burnout levels that
differ from those of employees, although entrepreneurs are critically at risk of burnout.
Work recovery is dened as diversionary, relaxation or mastery-oriented experiences “during which individual functional systems
that have been called upon during a stressful experience return to their prestressor levels” (Sonnentag and Fritz, 2007, p. 205).
Diversionary experiences refer to psychologically disengaging from the job and imply the cessation of thinking about job-related
problems, or mentally switching off from work; in other words, to detach from work psychologically after the daily work is done,
also called ‘off-work recovery’ (Sonnentag et al., 2022; Sonnentag and Fritz, 2015). Relaxation experiences are associated with leisure
activities (e.g., meditation or recreational walks), where the body can reach a state of low physiological and psychological activation
and increased levels of positive affect (Boyatzis et al., 2021; Richardson and Rothstein, 2008; Sianoja et al., 2018). Mastery-oriented
experiences refer to challenging and/or learning experiences that occur outside of the job (Sonnentag and Fritz, 2007). As highlighted
above, work recovery helps employees gain and replenish internal resources and, thus, restore their mental, physical, and emotional
energy (Richardson and Rothstein, 2008; Sonnentag and Fritz, 2015; Wach et al., 2021). Therefore, work recovery is essential to
sustain workers' well-being and performance over time (Bennett et al., 2018; Williamson et al., 2021).
Intriguingly, entrepreneurs and their entrepreneurial work are often described as adhering to an ‘always on alert’ pattern, which
could impair proper recovery processes, thereby affecting the vital psychological detachment from work during leisure time (Wach
et al., 2021). Such psychological detachment from work has received much attention in occupational health research in employees due
to its potential protection against psychological ill-being (e.g., in research on mindfulness and related recreational well-being stra-
tegies such as yoga; see Pascoe et al., 2017). Recent research has begun to devote more attention to the role of work recovery in the
specic context of entrepreneurship, but it remains unclear whether entrepreneurs require (and proactively seek) recovery processes
that are similar to those of employees.
In the following sections, we elaborate a set of hypotheses aimed at explaining work engagement and job burnout in entrepreneurs
versus employees, and also between solo entrepreneurs versus entrepreneurs who employ others. As noted earlier, these hypotheses
form our research model (Fig. 2), which we derive from our guiding framework of psychological utility (Fig. 1). In this model, we
essentially interpret entrepreneurial work as a positive JD-R prole of demands and resources with a primacy of energizing motivation
activation over depleting stress activation. Related to this are implications for off-work recovery, particularly benets and costs, and
thus its usefulness and prevalence in entrepreneurs. The central components of our theorizing are also depicted in Table 1. The nal
research model, in its holistic entirety, subsequently reveals the unique psychological utility of entrepreneurship.
2.4. Mechanisms behind entrepreneurial burnout
We begin by focusing on the single mechanisms that shape burnout levels. Drawing from JD-R theory and the recovery approach,
and building on existing research in the occupational health and entrepreneurship literature, we postulate an opposing process model
of entrepreneurship and burnout that contains specic mediation mechanisms, as explicated in the following.
First, as also illustrated in Table 1, we expect that entrepreneurs engage less in off-work recovery (e.g., psychological detachment
after daily work) than employees do. As stressed above, it has become a predominant view in occupational health research (which of
course mostly focuses on employees) to see such psychological detachment after daily work as a key mechanism to prevent high stress
and burnout (Sonnentag et al., 2022; Sonnentag and Fritz, 2007). From this stress perspective, one could argue that lower levels of such
off-work recovery would put entrepreneurs at risk of burnout. As discussed earlier, entrepreneurship scholars have recently begun to
emphasize such a lack of recovery from potentially stressful entrepreneurial work as a major challenge for the well-being and pro-
ductivity of entrepreneurs (e.g., Wach et al., 2021; Williamson et al., 2021; see also Kollmann et al., 2019). The often-cited ‘always on
alert’/‘always switched-on’ pattern of entrepreneurial work, alongside many entrepreneurs' ambitions to remain engaged with their
entrepreneurial endeavors even after ofcial working hours, can be expected to result in less recovery.
Interestingly, from a personal agency perspective, this lack of off-work recovery might also have to do with certain psychological
costs that entrepreneurs anticipate and, therefore, purposefully avoid (although such recovery might be useful from a stress
perspective) (see also Table 1). Staying in control, despite the entrepreneurial uncertainty and (shifting) challenges often faced by
entrepreneurs, has been described as a key psychological driver in entrepreneurs (Sarasvathy, 2009), which is consistent with agency
6
Hypothesizing and testing this type of opposing mediation model is still rather new to entrepreneurship, compared to other elds, where it is
more established (e.g., Murayama and Elliot, 2012; O'Rourke and MacKinnon, 2018).
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
8
theories (Bandura, 1989; Heckhausen and Schulz, 1995; Lerner and Busch-Rossnagel, 1981). To stay in control, as the ‘pilot in the
plane’ (Sarasvathy, 2009), entrepreneurs need to concentrate on a wide variety of job tasks and decision-making processes (Lazear,
2005), which are often not merely routines but responses to new situations. Hence, as noted earlier, this requires a certain ‘switched
on’ mental state (Tang et al., 2012). Completely switching off after the daily work is done, and then switching on again the next day,
could arguably produce an elevated cognitive load for entrepreneurs who need to oversee a complex set of tasks (Sweller, 1988). Since
such entrepreneurial work task and decision-making processes often do not represent simple routines but require complex, entre-
preneurial cognition and continuous adaptations to changing circumstances, from a cognitive load and motivational perspective it is
actually benecial to stay switched on to a certain degree after work. This ultimately lowers cognitive load and also helps entre-
preneurs feel more in control (the captain in the plane on a ight is still the captain, even during recovery breaks). Nevertheless, it is
central for our hypothesized model that, from a stress perspective, entrepreneurs might be more at risk of burnout if they avoid off-
work recovery (Sonnentag et al., 2022)—and hence we model it as a risk factor (an indirect effect in Fig. 2 that would increase burnout
in entrepreneurs).
Second, while less recovery from work might place entrepreneurs at risk for burnout, we also hypothesize that an important
protective mechanism is to be found in entrepreneurs' job and personal resources. In regard to job resources, research has established
that entrepreneurs enjoy high levels of job autonomy, full participation in decision-making, and direct feedback and reward for their
decisions and actions (Mill´
an et al., 2013; Nordenmark et al., 2012). Job autonomy as a central job resource of entrepreneurs has been
linked to lower stress levels in entrepreneurs (Hessels et al., 2017), and this is also regarded as increasing well-being (e.g., happiness) in
entrepreneurs (Benz and Frey, 2008). In regard to personal resources, entrepreneurship scholars have highlighted the role of per-
sonality traits shared by many entrepreneurs and individuals who are attracted to entrepreneurship, traits that inuence how one
interprets and copes with stressors (Baron et al., 2016). Hence, self-selection could be at work in this context, implying that individuals
who opt for entrepreneurship have certain psychological capacities (including personal resources) that predispose them to dealing
more effectively with work-related stress. Here, prior research has shown that entrepreneurs exhibit more optimism than other in-
dividuals when dealing with complex or uncertain situations (Hmieleski and Baron, 2009), and their positive attitudes to self-efcacy
are often considerably higher (Newman et al., 2019). Baron et al. (2016) have revealed higher levels of psychological capital in en-
trepreneurs than in other populations, and the authors speculate that such dispositions enable entrepreneurs to tolerate or manage the
adverse effects of stress more efciently—in other words, entrepreneurs might be less stressed than non-entrepreneurs.
Third, it is important to note that JD-R theory also indicates a positive relationship between job demands and job burnout. While
entrepreneurial work is often assumed to entail particularly taxing job demands and stressors (OECD, 2017; Stephan, 2018), the
evidence does not provide a clear picture of the differences in job demands between entrepreneurs and employees. For example,
although prior research indicates that entrepreneurs tend to report longer working hours (Nordenmark et al., 2012), their subjective
perception of job demands can be lower than that of wage workers (Parslow et al., 2004; Prottas and Thompson, 2006). Hessels et al.
(2017. p. 183) have concluded that “there is little indication, however, that on average the level of job demand is different for the self-
employed than for wage workers.” For these reasons we did not develop a hypothesis for the possible mediating effect of job demands
Table 1
Summary of assumed JD-R patterns and the related role of off-work recovery.
JD-R pattern Implication for dual process (stress
vs. motivation) and resulting
activation pattern
Off-work recovery
Psychological benets Psychological costs Prevalence
Entrepreneurial
work
‘Positive’ JD-R
pattern
a
(Gain focus:
individual can
prioritize
maximization of
psychological utility)
Amplied
motivation
process
(Energized
high
engagement)
Muted stress
process
(Better
protected from
stress)
Primacy of
(healthy)
energizing
motivation
activation
Low
(Few benets given the
primacy of positive
motivation activation that
counterbalances burnout
risks)
High
(Switching off-on costs,
loss of control, etc.)
Low
(Costs higher
than
benets)
Employed work ‘Negative’ JD-R
pattern
b
(Loss focus: individual
has to prioritize
avoiding negative
outcomes)
Amplied
stress process
(Higher risk for
burnout)
Muted
motivation
process
(Less
energized, low
engagement)
Primacy of
(unhealthy)
depleting stress
activation
High
(Important to
counterbalance primacy of
negative stress activation =
actual recovery benets)
Low
(Less disruption of
positive activation,
lower switching off-on
costs, less loss of
control, etc.)
High
(Benets
higher than
costs)
a
‘Positive’ stands for a more favorable pattern of elevated job and personal resources, with average (non-elevated) job demands (=primacy of
motivation activation, according to JD-R theory).
b
‘Negative’ stands for a less favorable pattern of lower job and personal resources, with average job demands (=primacy of stress activation,
according to JD-R theory).
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
9
on job burnout.
Since we assume the existence of opposing processes—one that puts entrepreneurs at risk, and one that protects them—we
consequently assume that entrepreneurs' burnout levels do not differ from those of employees. In other words, while there might be no
statistically signicant relationship between entrepreneurship and burnout, important dynamics behind this link are ‘masked’. As
highlighted by MacKinnon et al. (2007, p. 602), “there are several examples in which an overall X to Y relation may be nonsignicant,
yet mediation exists.” Hence, such opposing process models contribute to a better understanding of relationships and the complex
dynamics that underlie them (Murayama and Elliot, 2012), as for instance has been the case in showing the effectiveness and
mechanisms of psychological interventions in the absence of a given intervention's direct statistical effect (O'Rourke and MacKinnon,
2018).
Hypothesis 1. There are opposing mediation effects between being an entrepreneur vs. being employed on the one side, and burnout
on the other (H1a, b vs. H1c).
7
H1a. Entrepreneurs show higher levels of job resources – and job resources have a negative effect on burnout.
H1b. Entrepreneurs show higher levels of personal resources – and personal resources have a negative effect on burnout.
H1c. Entrepreneurs show lower levels of work recovery – and work recovery has a negative effect on burnout.
2.5. Mechanisms behind entrepreneurial engagement
Based on JD-R theory as part of our agency-based utility approach, we assume that entrepreneurs show higher levels of work
engagement than employed individuals due to their (energizing) higher job and personal resources, and as a utility maximization
process given the positive utility pattern offered by entrepreneurial work. First, compared with salaried individuals, individuals who
choose to be entrepreneurs usually rate highly in several psychological traits that have been found to predict work engagement across
occupations, such as achievement-related traits (e.g., self-efcacy, internal locus of control, and need for achievement), resilience, and
general positive dispositions, such as optimism and hope (Simmons and Lovegrove, 2005; Sweetman and Luthans, 2010). Second,
entrepreneurs might benet in particular from a core job resource that is undeniably associated with entrepreneurial work: job au-
tonomy and control (e.g., the ability to set priorities and to select and organize personal work tasks) (Mill´
an et al., 2020; Nordenmark
et al., 2012; Prottas and Thompson, 2006). Having more autonomy and control at work allows entrepreneurs to organize their daily
tasks and schedule, and to craft their job, as they please, thereby attaining a more meaningful and motivational work experience (and
deriving what Benz and Frey (2008) have termed ‘procedural utility’). In addition, the variety of tasks that entrepreneurs need to
perform, as well as the creative character of their work, makes their job content richer in comparison with many other occupations;
furthermore, this also helps to activate their intrinsic motivation and, thus, personal engagement levels (Bakker and Demerouti, 2017;
Gagn´
e and Deci, 2005; Gorgievski et al., 2010). We did not expect an effect of work recovery on work engagement, following previous
recovery research (Kinnunen et al., 2011).
Hypothesis 2. Entrepreneurs experience higher work engagement levels than employed individuals because they benet from (a)
higher levels of engagement—enhancing job resources, and (b) higher levels of engagement—enhancing personal resources.
Taking Hypotheses 1 and 2 together we thus assume that entrepreneurs, compared to employed individuals, experience the more
positive JD-R pattern in terms of demands and resources (as also illustrated in Table 1). This boosts their high, healthy engagement,
while avoiding negative outcomes (burnout)—two hallmarks of psychological utility as dened from a positive psychology
perspective.
2.6. Differences between solo entrepreneurs and employer entrepreneurs
Our framework also allows us to make a more specic assumption for entrepreneurial subpopulations. Following earlier work
indicating that stress, motivation, and recovery processes may differ between entrepreneurial groups (Baron et al., 2016; Bennett et al.,
2021; Hessels et al., 2017; Stephan, 2018; Wach et al., 2021), we also developed assumptions when comparing solo entrepreneurs with
employer entrepreneurs (i.e., those employing others in their businesses). Existing evidence that compares these two types of entre-
preneurs is not clear in terms of the effect on stress, work engagement, and work recovery that arises from hiring employees. For
example, Hessels et al. (2017) found that solo entrepreneurs show lower levels of stress compared to employer entrepreneurs, although
other studies suggest that this effect is not consistent across samples of entrepreneurs (Fernet et al., 2016; Torr`
es et al., 2021). Similar
results are found for work engagement and work recovery (Baron et al., 2016; Stephan, 2018; Williamson et al., 2021).
Here we assume that solo entrepreneurs might be able to derive the most psychological utility from their work, due to their
particularly positive JD-R pattern. In other words, they might represent a prime example of the entrepreneurial utility mechanisms
illustrated in Table 1. Specically, we assume that solo entrepreneurs report perceiving less burnout because they face less stressful job
demands than other entrepreneurs do. First, the businesses of entrepreneurs with employees tend to be larger, more complex, and more
7
Note that this assumption that this opposing mediation leads to non-signicant differences in job burnout levels between entrepreneurs and
employed individuals is not part of the actual hypothesis because we do not actually test such a null hypothesis in our analyses. It is merely our
logical conclusion if opposing mediation (Hypothesis 1) is indeed present (MacKinnon, Fairchild & Fritz, 2007; see also Murayama and Elliot, 2012).
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
10
ambitious than those of solo entrepreneurs, which increases the number and variety of both tasks and working relationships (e.g.,
employees, providers, and clients). In addition, the process of growing a business increases the number of demands (e.g., higher
number of clients and suppliers, and higher administrative and legal requirements) with which employer entrepreneurs need to deal,
thereby reducing their capacity to inuence (or control) what happens in their work environment and, thus, lowering their perception
of job autonomy, in comparison to being a solo entrepreneur (Dijkhuizen et al., 2016; Fernet et al., 2016; M¨
akiniemi et al., 2020).
Second, running a larger and more complex business implies dealing with a higher workload (e.g., increased administrative, coor-
dinating, or coaching activities), more diverse and psychologically demanding tasks (e.g., attracting and retaining clients, and securing
nancing), and higher work and emotional pressure arising from supervisory tasks and direct responsibility for employees' emotional
and economic well-being (Fernet et al., 2016; Lazear, 2005; Prottas and Thompson, 2006), all of which are factors associated with
higher levels of stress (Lerman et al., 2021; M¨
akiniemi et al., 2020; Wei et al., 2015).
Hypothesis 3. Solo entrepreneurs experience lower job burnout levels than employer entrepreneurs because they face lower levels of
burnout—enhancing job demands while maintaining high engagement levels.
3. Methods
3.1. Data
We used original panel survey data comprising four waves collected at two-month intervals using the proprietary Bilendi Panel in
the United Kingdom in 2016 (www.bilendi.co.uk) (see also Kibler et al., 2019). We chose a two-month interval for the survey waves
because we judged this to be sufciently long to reduce common method bias (the respondent being unlikely to be biased by their
responses to the previous wave) and improve causal inference, while at the same time being short enough to facilitate low attrition
rates: Bilendi recommended conducting multi-wave studies within a timeframe of about six months, so as to ensure that the majority of
the respondents stayed with the panel throughout the study.
Bilendi recruits panelists from a range of online sources, including partnerships, advertisements, social media, sponsored links, and
referral programs. The recruitment of panelists is permission-based, ensures the absence of duplicate entries, and pays particular
attention to representativeness in terms of the panel's regional, social, and age distribution. We compared the panel's gender, age, and
occupation distribution with UK national statistics from the same year. While women are over-represented in the panel in comparison
to national statistics (57 % vs. 50 %), the age distribution is very similar in different groups within the 25-to-65-year age bracket used
in our survey (e.g., 23 % and 30 % of Bilendi panelists are 25–34 and 35–49 years old, respectively, compared to 23 % and 34 %
nationally). Self-employed individuals are under-represented in the panel: the self-employment rate in the panel is 8 %, compared to
15 % nationally. Bilendi panelists receive loyalty points that can be exchanged for a wide range of goods or services, or a small cash
incentive for participating in surveys.
The cover letter introducing the questionnaire clearly identied the study as ‘academic research’, acknowledged the names and
afliations of the research team, and assured respondents of their anonymity. The primary target group of the survey comprised 525
individuals identied by the service provider's database as ‘entrepreneurs’. We also drew a random sample of 3000 individuals from
the general adult population (between 25 and 65 years of age) included in the database. At the beginning of the survey we screened out
those who were neither entrepreneurs nor employed in a commercial enterprise. To reduce heterogeneity and improve comparability
with the focal sample of entrepreneurs, we restricted the employee sample to commercial enterprises. The research agency applied
weighting procedures in data collection from the random sample to ensure that the nal sample of employees we received through this
procedure was representative of UK wage and salary employees in this age bracket, and that non-response bias was not an issue.
At baseline, 1085 employees and 414 entrepreneurs completed the questionnaire; the number of missing values or patterned re-
sponses was not excessive. The retention rates among entrepreneurs surveyed at baseline were 93 % in Wave 2, 64 % in Wave 3, and 54
% in Wave 4. Corresponding rates for employees were 97 %, 65 %, and 54 %. In each wave we ensured that the entrepreneurs were
operating the same business and that the employees were in the same jobs as at baseline. Our analytic sample consists of those who
participated in two or more waves (348 entrepreneurs and 1002 employees). This is because we use lagged variables as predictors to
improve causal inference and reduce common method bias.
By using means comparisons, we found that entrepreneurs lost to follow-up were likelier to have lower levels of small adminis-
trative tasks that hinder the progress of their main work, and higher levels of role overload. They were also younger than those who
participated in two or more waves. Among the employees, those who opted out of the survey after Wave 1 were on average likelier to
be younger and have a higher level of education than employees in the analytic sample. The remaining characteristics included in our
analysis were similar, regardless of attrition status. However, given that only 66 entrepreneurs and 83 employees did not participate in
at least Wave 2 in addition to the baseline, the effects of attrition should not be overstated.
Although specically targeting those 525 individuals known to be entrepreneurs make the data non-representative as a whole,
having a sizable sample of entrepreneurs should ensure the reliability of the tests of our hypotheses. Due to the oversampling of
entrepreneurs, we ran additional tests of non-response bias and representativeness for the nal sample of entrepreneurs with the UK
population of entrepreneurs. For this purpose, we conducted an archival analysis (Rogelberg and Stanton, 2007) by comparing the
entrepreneur respondents' personal (gender, age) and rm characteristics (region, industry, number of employees) with those of the
UK small-business population using data from the Ofce for National Statistics (2016a, 2016b).
The age density curves for entrepreneurs in the UK and those in our sample look similar: the density in the under-65 age group
increases until individuals' mid-40s, thereafter declining with each additional year of age. The gender distribution in our sample is,
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
11
however, markedly different from the population: whereas only 21 % of UK entrepreneurs are women, the respective percentage in our
sample is 43 %. Hence, we control for the effect of gender in our analysis. The regional distribution of the sample is very similar to the
national distribution: London and the South East of England account for approximately one third of the sample, followed by the
Midlands (15 %) and North West (11 %). Approximately one third of the sampled entrepreneurs operate in the service sector, which is
similar to the population statistics. The major industry difference is the lower share of rms in the manufacturing and construction
sectors in the sample compared to the population, and a higher share of rms classied as ‘other’ in our sample. This is most likely due
to our survey using fewer categories than those listed in national statistics, thus leading more respondents to not nding an industry
description that they felt was suitable for their business. The size distribution of the sampled businesses shows 92 % micro-enterprises
(fewer than 10 employees), which is close to the national gure of 96 %. However, our sample contains fewer solo entrepreneurs (57
%) in comparison to the population (76 %). We take this into account by estimating separate models for entrepreneurs with and
without employees.
3.2. Measures
3.2.1. Dependent variables
Job burnout was operationalized as emotional exhaustion from work by using a ve-item scale, anchored at very rarely (1) and very
often (5), by Maslach et al. (1996). Sample items include “I feel emotionally drained from my work”, and “I feel tired when I get up in
the morning and have to face another working day”. Note that often job burnout is not only operationalized via such exhaustion but
also via cynicism and inefcacy (Maslach et al., 2001). However, it is widely established that exhaustion is the cardinal indicator of
burnout—especially from a stress perspective (Maslach et al., 2001).
Work engagement was measured with seven items adapted from the Utrecht Work Engagement Scale UWES-9 (Schaufeli et al.,
2006). Sample items include “When I work, I feel full of energy”, and “I am enthusiastic about my work”. The items for both scales were
measured on a ve-point rating scale anchored at very rarely (1) and very often (5).
Although exhaustion and engagement have been associated with negative and positive work outcomes, respectively, prior studies
have not included the entrepreneurial context. In order to ensure the nomological validity of our dependent variables, we examined
their correlations with the logical consequences of being either exhausted or engaged with work: life satisfaction, work satisfaction, and
self-reported progress toward main work goals in the last 2 months. All three variables were measured with single items on a percent scale
from 0 to 100 %. As expected, exhaustion correlates negatively (Pearson correlation coefcients with life satisfaction, work satis-
faction, and performance are −0.38, −0.48, and −0.31, respectively; all p <0.001) and engagement correlates positively (0.40, 0.64,
and 0.40; all p <0.001) with these outcomes.
3.2.2. Independent variables
3.2.2.1. Entrepreneurial status. Following Hessels et al. (2017), we examined two types of entrepreneurial status variables. First, we
used one indicator variable to capture whether the person is an entrepreneur (coded as 1; vs. an employee in a commercial enterprise,
coded as 0). Second, we also distinguished between solo entrepreneurs (operating a business with no employees other than themselves;
coded as 1) and employer entrepreneurs (operating a business with employees; coded as 0).
3.2.3. Mediators
3.2.3.1. Job resources. We focused on personal autonomy at work as a key job characteristic in the entrepreneurial stress process
identied in prior research (e.g., Kibler et al., 2019). We operationalized autonomy in terms of both work tasks and working hours, and
measured this characteristic by using six items adapted from Semmer et al. (1999), including “How much can you inuence the work
tasks you undertake”, “To what extent can you decide how to accomplish a work task”, and “To what extent can you independently
schedule a work day”. The scale items were measured on a ve-point rating scale anchored at never (1) and always (5).
3.2.3.2. Personal resources. Following Baron et al. (2016), we measured psychological capital with the 12-item Psychological Capital
Questionnaire (PCQ-12), which comprises the sub-dimensions of hope, optimism, resilience, and self-efcacy, and whose validity and
reliability have been established in several prior studies (Avey et al., 2010). If the wording of the original items was aimed at em-
ployees, the respective item's wording was adjusted accordingly for the respondents who identied themselves as entrepreneurs.
Sample items include “Currently, I am meeting the work goals that I have set for myself” (hope), “I can get through difcult times at
work because I have experienced difculty before” (resilience), “I feel condent presenting my work in meetings with important
stakeholders” (self-efcacy/entrepreneurs), “I feel condent representing my work area in meetings with management” (self-efcacy/
employees), and “I always look on the bright side regarding my work” (optimism). Responses could be given on a ve-point rating scale
anchored at disagree strongly (1) and agree strongly (5).
3.2.3.3. Job demands. We measured a list of ve job demands, two of which capture potential hindrance demands: the extent to which
the accomplishment of the main work tasks is hindered by small administrative tasks (administrative task hindrance), and the degree of
ambiguity felt by an individual about their work role (role ambiguity). Administrative task hindrance was measured with a variable
capturing how often interviewees felt that progress in their main work was hindered by necessary minor administrative tasks. We used
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
12
six categories from very rarely or never to almost daily. Role ambiguity was measured with a four-item scale adapted from Rizzo et al.
(1970), including “Clear, planned goals and objectives exist for my work”, and “I know what my responsibilities are”. The items were
measured on a ve-point rating scale anchored at disagree strongly (1) and agree strongly (5). We reversed the item scores to create a
scale of role ambiguity.
The remaining three job demands constitute potential challenge demands: the degree to which role overload and time pressure were
experienced in work, as well as the extent of the total workload. Role overload was measured with two items adapted from Beehr et al.
(1976) (see also Beehr et al., 2000): “It often seems there is too much work for one person to do”, and “The performance standards set
for my work are too high”. The items were measured on a ve-point rating scale anchored at disagree strongly (1) and agree strongly (5).
Time pressure was captured with three items adapted from Semmer et al. (1999): “How often are you under time pressure at work”,
“How often do you end up working longer than intended”, and “How often do you have to work at high speed”. The items were
measured on a ve-point rating scale anchored at very rarely/never (1) and very often (5). Workload was measured by asking the
respondent to indicate the number of hours they worked per week over the last two weeks.
3.2.3.4. Work recovery. We operationalized work recovery with a four-item scale of psychological job detachment after work, adapted
from Sonnentag and Fritz (2007) (e.g., “I forget about work”, “I distance myself from work”). This variable measures how respondents
detach mentally from their daily work (e.g., during leisure time), which is often considered an essential protective mechanism in stress
processes (Sonnentag & Bayer, 2005). This was measured on a ve-point rating scale anchored at disagree strongly (1) and agree strongly
(5).
3.2.4. Control variables
We controlled for gender, age, and education. We also controlled for the number of vacation days taken by respondents in the
preceding two months (Bloom et al., 2009). We use a logarithmic transformation of the number of vacation days to correct for
skewedness.
3.3. Analysis strategy
We chose to use structural equation modeling (SEM) for two reasons. First, almost all our variables consist of multi-item scales, and
modeling these as latent variables allowed us to account for measurement error. Second, our hypotheses imply multiple mediation
effects that are best tested by computing indirect effects in the SEM framework (Williams et al., 2009). Our model uses lagged pre-
dictors to improve causal inference and reduce common method bias; the dependent variables are from Wave t, while the mediators are
from Wave t-1. The dependent variables and mediators are stable over time across the four survey waves: one-way analysis of variance
(ANOVA) tests did not reveal signicant means differences for any of these variables. The independent variables (entrepreneur/
employed, and solo entrepreneur/entrepreneur with employees) and the control variables gender, age, and higher education degree
are time invariant in our modeling context. The control variable that captures the number of vacation days in the preceding two
months is taken from Wave t, because a recent vacation is likelier to inuence present job burnout than a vacation taken several months
ago (Bloom et al., 2009). Because our model can include multiple observations per respondent, we report cluster-robust standard errors
in the structural models to account for the non-independence of two or more observations from the same respondent. The entire
analysis was carried out with the software package Stata 15.
4. Results
4.1. Measurement model
Our SEM estimation strategy followed the two-step approach recommended by Anderson and Gerbing (1988), the initial step of
which is to assess the dimensionality, reliability, and validity of the measurement scales prior to estimating the structural equations.
We started by running an exploratory principal components analysis to identify whether the individual items constituting the mea-
surement scales load satisfactorily on their intended factors. Since the loadings were low-factor, we decided to discard two items from
the PSQ-12 scale: one relating to hope (“If I had a problem at work, I could think of many ways to solve it”) and one belonging to the
resilience sub-dimension (“I can work effectively alone if I have to”). This still leaves two or three items for each sub-dimension within
the psychological capital scale.
Next, we performed a conrmatory factor analysis (CFA). Because psychological capital comprises four sub-dimensions, we
modeled it as a second-order latent variable; the latent variable ‘psychological capital’ predicts the latent variables ‘hope, resilience,
self-efcacy, and optimism’, which in turn are associated with the respective observed variables. All other constructs were modeled as
rst-order latent variables. We ran the CFA separately for each survey wave. The standardized factor loadings were all signicant at the
p <0.001 level, and the t of the model with the data in each wave was satisfactory (Hu and Bentler, 1999); the comparative t index
(CFI) scores range from 0.948 to 0.951, the standardized root mean squared residual (SRMR) index values range from 0.048 to 0.051,
and the root mean square error of approximation (RMSEA) scores range from 0.043 to 0.045.
Table A1 in the appendix reports the latent variable correlations in the CFA model alongside the Cronbach's alphas, composite
reliabilities, and square roots of the average variance-extracted (AVE) scores for each factor. The Cronbach's alphas and composite
reliabilities of all constructs exceed the recommended threshold level of 0.7, suggesting satisfactory reliability (Chin, 1998; Nunnally,
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
13
1978). Because the square root of each construct's AVE exceeds its correlation with the other latent variables, we concluded that the
measurement model has good discriminant validity (Fornell and Larcker, 1981). While the values reported in Table A1 are based on the
CFA for Wave 1, the results in all other waves are very similar. Furthermore, we estimated varying specications of the CFA model,
such as one factor explaining all items or the items of constructs with high latent variable correlations loading on a single factor. In
every specication tested, the t of the alternative model was signicantly worse than in the original model, where all items load on
their theoretically specied factors.
4.2. Descriptive statistics
Following Hessels et al. (2017), Table 2 displays the means and standard deviations and Table 3 the inter-correlations of all
variables included in the analyses.
4.3. Structural model
4.3.1. Hypothesis tests
Table 4 displays the results for the models testing the effect on job burnout (exhaustion) and work engagement of being an
entrepreneur, as compared with being an employee, whereas Table 5 presents the results for being a solo entrepreneur, as compared
with being an employer entrepreneur. To facilitate comparability with Hessels et al. (2017) and the presentation's readability, the
results tables omit the paths from the group dummies (entrepreneur vs. employee, and solo entrepreneur vs. employer entrepreneur)
and the demographic control variables on the individual mediators, even though those paths are included in the structural model.
Instead, following Hessels et al. (2017), we integrated the indirect effects in the main results tables as tests of the mediation hy-
potheses. In addition to the regression tables, Fig. 3 provides an overview of the signicant coefcients for the paths from the inde-
pendent group variables to the mediators, and from the mediators to the dependent variables.
Model 1 in Table 4 reveals that entrepreneurs have lower levels of job burnout than employees (whereas our initial conceptual
model shown in Fig. 1 would have assumed a non-signicant relationship). The coefcient of the entrepreneur dummy is negative and
signicant at the p <0.01 level. The other regressions depicted in Table 4 examine the mediation effects behind this direct effect by
rst testing the effects of personal and job resources (Model 2), then the effect of work recovery (Model 3), followed by the effect of job
demands (Model 4), and nally all of these together (Model 5). The results indicate some support for Hypothesis 1a: job resource
‘autonomy’ is a signicant mediator of the effect on job burnout of being an entrepreneur in comparison to being an employee.
However, psychological capital as a personal resource is not a signicant mediator, and the same applies to work recovery (although
entrepreneurs show signicantly less recovery from their daily work than do employees; see Table 2). Thus, Hypotheses 1b and 1c are
not supported.
The results in Table 4 also provide some support for Hypothesis 2, which addresses work engagement as the dependent variable.
The coefcient of the entrepreneur dummy on engagement in Model 1 is positive and signicant at the p <0.001 level—as expected,
Table 2
Descriptive statistics.
(1) Employees (2)
Entrepreneurs
(1)–(2) (3) Employer
entrepreneurs
(4) Solo
entrepreneurs
(3)–(4)
Mean SD Mean SD Mean SD Mean SD
Dependent variables
Job burnout (Exhaustion) 2.72 1.07 2.55 0.99 0.17*** 2.72 1.04 2.43 0.94 0.29***
Work engagement 3.31 0.90 3.70 0.78 −0.39*** 3.73 0.84 3.68 0.74 0.05
Job/personal resources
Autonomy 3.47 0.83 4.30 0.70 −0.83*** 4.20 0.73 4.36 0.67 −0.16***
Psychological capital 3.71 0.62 3.69 0.66 0.02 3.86 0.66 3.57 0.64 0.29***
Job demands
Administrative tasks 4.01 1.56 3.76 1.49 0.25*** 3.88 1.48 3.68 1.49 0.20*
Role ambiguity 2.25 0.75 1.98 0.69 0.27*** 1.98 0.72 1.98 0.66 0.00
Role overload 2.97 0.98 2.93 1.00 0.04 3.23 1.00 2.70 0.94 0.53***
Time pressure 3.38 1.04 3.15 1.01 0.23*** 3.41 0.95 2.95 1.00 0.46***
Workload 45.11 16.39 41.66 18.97 3.45*** 47.66 19.41 37.24 17.37 10.42***
Work recovery
Psychological detachment 3.37 0.97 2.97 0.98 0.40*** 3.01 1.03 2.94 0.94 0.07
Control variables
Female (base: male) 0.37 0.43 −0.06*** 0.42 0.45 −0.03
Age 45.79 47.24 10.65 −1.44*** 44.37 11.31 49.35 9.61 −4.98***
Higher education (base: no higher education) 0.38 0.46 −0.08*** 0.45 0.46 −0.01
Number of vacation days in last 2 months 3.49 4.70 3.81 6.12 −0.32 3.63 5.86 3.93 6.31 −0.30
Observations 3276 1137 482 655
Individuals 1020 361 155 206
Note: Statistical signicance tests of mean differences are based on t-tests for continuous and chi-squared tests for categorical variables. *, **, and ***
indicate statistical signicance at p <0.05, 0.01, and 0.001 levels, respectively.
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
14
Table 3
Correlations.
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.
1. Entrepreneurs (base: employee) 1
2. Solo entrepreneur (base: employer entrepreneur) – 1
3. Job burnout (exhaustion) −0.07* −0.15* 1
4. Work engagement 0.19* −0.03 −0.37* 1
5. Job resources (autonomy) 0.41* 0.11* −0.25* 0.4* 1
6. Work recovery (detachment) −0.17* −0.04 −0.18 −0.03 −0.03 1
7. Personal resources (psychological capital) −0.03 −0.22* −0.26 0.53* 0.44* 0.14* 1
Job demands
8. Administrative tasks −0.06* −0.05 0.32* −0.09* −0.02 −0.15 −0.06* 1
9. Role ambiguity −0.15* 0.00 0.30* −0.54* −0.47* −0.10 −0.60* 0.15* 1
10. Role overload −0.01 −0.26* 0.45* −0.13* −0.13* −0.17* −0.07* 0.30* 0.16* 1
11. Time pressure −0.09* −0.23* 0.42* −0.04* −0.07* −0.19* 0.01 0.52* 0.10* 0.42* 1
12. Workload −0.10* −0.27* 0.13 0.02 −0.01 −0.09* 0.06* 0.14* 0.04* 0.13 0.20* 1
13. Female 0.06* 0.03 0.04* 0.04* 0.01 −0.04* −0.03 0.03 −0.03 −0.00 0.02 −0.05* 1
14. Age 0.06* 0.23* −0.13* 0.10* 0.11* 0.05* 0.07* −0.05* −0.11* −0.15* −0.10 0.01 −0.24* 1
15. Higher education 0.07* 0.00 0.01 0.02 0.03 −0.09* 0.03 0.06* 0.03 0.07* 0.03 −0.08* 0.06* −0.21* 1
16. Number of vacation days
a
−0.04* 0.02 −0.04* 0.02 −0.01 0.08* 0.03 0.02 −0.01 −0.03 −0.03 −0.05* −0.00 0.04* 0.05*
Notes: The second column is based entirely on the entrepreneur sub-sample. All remaining columns are based on the full sample. Pearson product-moment correlation coefcients were used.
*
p <0.05 (two-tailed).
a
Logarithmic transformation was used in the correlation analysis. Note that the second column in this table uses only the entrepreneur sample because it displays the correlations of being an employer
entrepreneur compared with being a solo entrepreneur, together with the dependent variables, mediators, and controls.
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
15
Table 4
Structural model: the effect of being an entrepreneur in comparison to being an employee.
(1) (2) (3) (4) (5)
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Entrepreneur (base:
employee)
−0.07**
(0.02)
0.19***
(0.02)
−0.01
(0.03)
0.11***
(0.02)
−0.12***
(0.03)
0.19***
(0.03)
−0.02
(0.02)
0.13***
(0.02)
−0.01
(0.03)
0.09***
(0.02)
Job/personal resources
Job autonomy
t-1
−0.18***
(0.04)
0.24***
(0.03)
−0.11**
(0.03)
0.15***
(0.03)
Psychological capital
t-1
−0.25***
(0.04)
0.53***
(0.03)
−0.19***
(0.03)
0.39***
(0.04)
Job demands
Administrative tasks
t-1
0.10***
(0.03)
−0.01
(0.02)
0.11***
(0.03)
−0.02
(0.02)
Role ambiguity
t-1
0.22***
(0.03)
−0.50***
(0.02)
0.07*
(0.03)
−0.19***
(0.04)
Role overload
t-1
0.40***
(0.03)
−0.13***
(0.03)
0.38***
(0.03)
0.03
(0.03)
Time pressure
t-1
0.30***
(0.03)
0.02
(0.03)
0.30***
(0.03)
0.12***
(0.03)
Workload
t-1
0.02
(0.02)
0.07***
(0.02)
0.03
(0.02)
0.03
(0.02)
Work recovery
Psychological detachment
t-
1
−0.21***
(0.03)
−0.00
(0.03)
−0.05
(0.03)
−0.14***
(0.03)
Control variables
Female 0.01
(0.03)
0.05*
(0.03)
0.01
(0.03)
0.04
(0.02)
0.00
(0.03)
0.04
(0.03)
0.04
(0.02)
0.02
(0.02)
0.03
(0.02)
0.04*
(0.02)
Age −0.14***
(0.03)
0.12***
(0.03)
−0.11***
(0.03)
0.05**
(0.02)
−0.13***
(0.03)
0.11
(0.03)
−0.01
(0.02)
0.02
(0.02)
−0.02
(0.02)
0.03
(0.02)
Higher education −0.01
(0.03)
0.02
(0.03)
−0.00
(0.03)
0.01
(0.02)
−0.03
(0.03)
0.03
(0.03)
−0.05*
(0.02)
0.06*
(0.02)
−0.04
(0.02)
0.01
(0.02)
Number of vacation days
a
−0.04*
(0.02)
0.02
(0.02)
−0.05
(0.02)
0.01
(0.02)
−0.05*
(0.02)
0.02
(0.02)
−0.05*
(0.02)
0.02
(0.02)
−0.04*
(0.02)
0.01
(0.02)
Indirect effect of being an
entrepreneur vs.
employee
Total −0.16***
(0.04)
0.17***
(0.04)
0.09***
(0.02)
0.00
(0.01)
−0.15**
(0.05)
0.13***
(0.03)
−0.18**
(0.06)
0.21***
(0.04)
Via job autonomy −0.08***
(0.02)
0.10***
(0.01)
−0.04**
(0.01)
0.06***
(0.01)
Via psychological capital 0.00
(0.01)
−0.01
(0.02)
0.00
(0.01)
−0.01
(0.01)
(continued on next page)
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
16
Table 4 (continued )
(1) (2) (3) (4) (5)
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Via administrative tasks −0.01*
(0.00)
0.00
(0.00)
−0.01*
(0.00)
0.00
(0.00)
Via role ambiguity −0.03***
(0.01)
0.07***
(0.01)
−0.01*
(0.00)
0.03***
(0.01)
Via role overload −0.00
(0.01)
0.00
(0.00)
−0.00
(0.01)
−0.00
(0.00)
Via time pressure −0.03**
(0.01)
−0.00
(0.00)
−0.03**
(0.01)
−0.01*
(0.01)
Via workload −0.00
(0.00)
−0.01*
(0.00)
−0.00
(0.00)
−0.00
(0.00)
Via psychological
detachment
0.09***
(0.02)
0.00
(0.01)
0.01
(0.01)
0.03***
(0.01)
Observations 4413 2928 2928 2928 2928
Individuals 1381 1350 1350 1350 1350
R-squared 0.05 0.03 0.12 0.38 0.07 0.05 0.33 0.31 0.33 0.41
Notes: Smaller sample sizes in Models 2–4 are due to the use of lagged variables and missing values on the mediators of 31 individuals. Maximum-likelihood estimates were made. Standardized coefcients
with cluster-robust standard errors are in parentheses. *, **, and *** denote statistical signicance at the 5 %, 1 %, and 0.1 % levels (two-tailed), respectively. Both dependent variables are from Wave t,
whereas all time-variant predictors are from Wave t-1.
a
Logarithmic transformation was used in the analysis.
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
17
Table 5
Structural model: the effect of being a solo entrepreneur in comparison to being an employer entrepreneur.
(1) (2) (3) (4) (5)
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Solo entrepreneur (base:
employer
entrepreneur)
−0.12*
(0.05)
−0.04
(0.05)
−0.11
(0.06)
0.04
(0.05)
−0.09
(0.06)
−0.06
(0.06)
0.10
(0.05)
−0.05
(0.05)
0.08
(0.05)
0.01
(0.05)
Job/personal resources
Job autonomy
t-1
−0.12
(0.06)
0.20***
(0.05)
−0.08
(0.06)
0.13**
(0.05)
Psychological capital
t-1
−0.13*
(0.06)
0.49***
(0.07)
−0.14*
(0.06)
0.43***
(0.07)
Job demands
Administrative tasks
t-1
0.14*
(0.06)
−0.10*
(0.05)
0.13*
(0.06)
−0.09
(0.04)
Role ambiguity
t-1
0.11*
(0.04)
−0.45***
(0.04)
−0.01
(0.05)
−0.18**
(0.06)
Role overload
t-1
0.49***
(9.06)
−0.20**
(0.06)
0.47***
(0.06)
−0.19***
(0.05)
Time pressure
t-1
0.20*
(0.09)
0.26***
(0.06)
0.24**
(0.08)
0.23***
(0.05)
Workload
t-1
0.06
(0.05)
0.04
(0.05)
0.07
(0.05)
−0.01
(0.04)
Work recovery
Psychological
detachment
t-1
−0.04
(0.06)
−0.15**
(0.06)
0.04
(0.05)
−0.22***
(0.05)
Control variables
Female 0.10
(0.05)
0.03
(0.05)
0.08
(0.06)
0.10*
(0.05)
0.10
(0.06)
0.01
(0.06)
0.09*
(0.05)
0.02
(0.05)
0.07
(0.05)
0.07
(0.05)
Age −0.18***
(0.05)
0.07
(0.06)
−0.18**
(0.06)
0.04
(0.05)
−0.19**
(0.05)
0.05
(0.06)
−0.01
(0.05)
0.00
(0.05)
−0.01
(0.05)
0.01
(0.05)
Higher education 0.03
(0.05)
0.02
(0.05)
0.04
(0.05)
0.03
(0.04)
0.05
(0.05)
0.03
(0.06)
0.02
(0.04)
0.04
(0.04)
0.02
(0.04)
0.03
(0.04)
Number of vacation
days
a
−0.08*
(0.03)
0.04
(0.04)
−0.06
(0.04)
0.01
(0.03)
−0.06
(0.04)
0.06
(0.04)
−0.06
(0.04)
0.06
(0.04)
−0.05
(0.04)
0.04
(0.04)
Indirect effects of being a
solo entrepreneur
Total indirect effect 0.03
(0.04)
−0.12*
(0.05)
0.00
(0.01)
0.01
(0.01)
−0.35***
(0.09)
0.01
(0.05)
−0.31**
(0.10)
−0.08
(0.06)
Via job autonomy −0.01
(0.01)
0.02
(0.01)
−0.01
(0.01)
0.01
(0.01)
Via psychological capital 0.03
(0.01)
−0.10**
(0.03)
0.03*
(0.01)
−0.09**
(0.03)
(continued on next page)
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
18
Table 5 (continued )
(1) (2) (3) (4) (5)
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Via administrative tasks −0.00
(0.01)
0.00
(0.01)
−0.00
(0.01)
0.00
(0.01)
Via role ambiguity −0.00
(0.01)
0.01
(0.03)
0.00
(0.00)
0.01
(0.01)
Via role overload −0.12***
(0.03)
0.05**
(0.02)
−0.12***
(0.03)
0.05**
(0.02)
Via time pressure −0.04
(0.02)
−0.05**
(0.02)
−0.05*
(0.02)
−0.05**
(0.02)
Via workload −0.02
(0.01)
−0.01
(0.01)
−0.02
(0.01)
0.00
(0.01)
Via psychological
detachment
0.00
(0.00)
0.01
(0.01)
−0.00
(0.00)
0.01
(0.01)
Observations 1137 745 745 745 745
Individuals 361 348 348 348 348
R-squared 0.08 0.01 0.12 0.27 0.09 0.03 0.36 0.32 0.38 0.36
Notes: Smaller sample sizes in Models 2–4 are due to the use of lagged variables and missing values on the mediators of 13 individuals. Maximum-likelihood estimates were made. Standardized coefcients
with cluster-robust standard errors are in parentheses. *, **, and *** denote statistical signicance at the 5 %, 1 %, and 0.1 % levels (two-tailed), respectively. Both dependent variables are from Wave t,
whereas all time-variant predictors are from Wave t-1.
a
Logarithmic transformation was used in the analysis.
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
19
entrepreneurs are indeed more engaged. Moreover, we found support for Hypothesis 2a, suggesting that the ‘autonomy’ job resource is
a signicant mediator of the effect on work engagement of being an entrepreneur, in comparison to being an employee, yet the data do
not conrm a mediating effect of the ‘psychological capital’ personal resource as expected in Hypothesis 2b. In addition, we nd that
work recovery is a signicant mediator in this context.
The rst model in Table 5 examines Hypothesis 3: whether solo entrepreneurs have lower levels of job burnout than employer
entrepreneurs. The coefcient of the solo entrepreneur dummy is indeed negative and signicant at the p <0.05 level in Model 1.
Moreover, we found that the ‘role overload’ and ‘time pressure at work’ job demands do indeed signicantly mediate the effect of being
a solo entrepreneur on job burnout. This supports Hypothesis 3.
Furthermore, our analyses identied several unexpected mediation effects, which we will discuss in the Discussion and implications
section.
4.3.2. Sensitivity analysis
We estimated additional models to examine the robustness of our results for different classications of employees and entrepre-
neurs. The full sensitivity analysis is reported in the Appendix. In terms of entrepreneur-employee comparisons, we compared en-
trepreneurs with corporate entrepreneurs, and employer entrepreneurs with salaried managers. In each of these comparisons, we
sought to reduce the heterogeneity of the groups being compared. Aside from minor differences, the substantive results of these
comparisons are very similar to those in Model 5 in Table 4. Furthermore, we compared additional groups within the entrepreneur
subsample: novice vs. serial entrepreneurs; owners of young vs. mature rms; and solo entrepreneurs vs. entrepreneurs who employ
1–4 people or those who employ 5 or more people. We did not nd any signicant differences between novice and serial entrepreneurs
in terms of our model. We found that entrepreneurs in young rms reported lower levels of work engagement than their counterparts in
mature rms. Moreover, we found similar differences between the solo entrepreneurs and entrepreneurs with 1–4 employees, as shown
in Model 5 in Table 5. At the same time, we did not nd any signicant differences between employer entrepreneurs with 1–4 or 5 or
more employees. Thus, in terms of predicting job burnout and work engagement in entrepreneurs, the relevant difference lies in
whether or not those individuals have employees, while the number of employees itself does not matter. This is an important nding,
as one could argue that rm size (and not solo vs. employer entrepreneurs) plays a key role in stress and motivation processes.
5. Discussion and implications
An entrepreneur's healthy, continual work engagement is widely regarded as a key ingredient in the entrepreneurial process
(Schumpeter, 1934; Shane et al., 2003). At the same time, debate exists in the literature of whether (and why) entrepreneurship is
associated with major psychological costs of such work engagement (e.g., high stress or mental health impairments; Lerman et al.,
2020; Stephan, 2018; see also Baron et al., 2016; Hessels et al., 2017). A rather different perspective, however, would ask whether
strong work engagement in entrepreneurship is associated with more positive personal outcomes, such as outstanding psychological
utility, while avoiding negative outcomes (Benz and Frey, 2008; Douglas and Shepherd, 2000; L´
evesque et al., 2002; Monsen et al.,
Fig. 3. Regression coefcients for the indirect pathways.
Note: The coefcients in italics refer to the regression analysis comparing solo vs. employer entrepreneurs (Table 5), and the other coefcients to the
regression comparing self-employed with employees (Table 4). Only indirect effects are depicted (Table 4 shows one direct effect—from entre-
preneur to work engagement). *, **, and *** denote statistical signicance at the 5 %, 1 %, and 0.1 % levels, respectively.
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
20
2010; Van Praag and Versloot, 2007). The present study addressed this positive perspective by developing and testing a research model
focusing on stress, motivation, and recovery processes. This was used not only to predict concrete job burnout (strain and overtaxing
from work) and work engagement (the positive, fullling work-focused mental state), but also, from a holistic view, to ultimately
validate our framework of psychological utility that asks how entrepreneurs are empowered to maximize utility through their personal
agency.
Utility is a complex phenomenon, as also becomes evident in the theoretical and empirical complexity of our research model. We
combined theories and approaches from occupational health psychology in a novel way to develop and test a theoretical model
specically designed for entrepreneurship (“from theories for employees to a theory of entrepreneurial work”, Stephan, 2018, p. 308).
While previous studies have focused either on entrepreneurial stress (Hessels et al., 2017; Wei et al., 2015), work engagement
(M¨
akiniemi et al., 2020; Shir et al., 2019; Stephan, 2018), or work recovery (e.g., Wach et al., 2021; Williamson et al., 2021), our study
develops a more holistic and integrated understanding. We have indeed been able to explain substantially larger shares of the variance
in our outcome variables (e.g., in our burnout variable) than, for example, previous entrepreneurship studies predicting stress levels (e.
g., Baron et al., 2016; Hessels et al., 2017). We summarize our core results as follows:
5.1. Psychological utility of entrepreneurship
We started with a novel theoretical framework (Fig. 1) that explains why entrepreneurship, compared to paid employment, offers
outstanding psychological utility, nding this to be due to a positive JD-R pattern, but also because it better enables entrepreneurs to
derive and maximize their psychological utility proactively as part of their own agency and personal mastery—both during and after
‘ofcial’ working hours (see also Table 1). While there is no clear evidence in the literature that the main value of entrepreneurship (for
the average entrepreneur) would lie in outstanding economic utility (Åstebro and Chen, 2014; Van Praag and Versloot, 2007), evidence
is now mounting that it offers outstanding psychological utility, something that entrepreneurs can control and thrive on, and which
intrinsically motivates them (Frey et al., 2004). In other words, how entrepreneurs engage in their work (which is also essential for the
success of their business) revolves around this non-economic value of entrepreneurship—and this is a key message of our study. It was
particularly our holistic focus that enabled us to arrive at these conclusions. Interestingly, our study also postulates and conrms some
of the central processes involved in why entrepreneurs tend to show better utility outcomes (such as higher job satisfaction) than non-
entrepreneurs (Benz and Frey, 2008; Stephan, 2018; Van Praag and Versloot, 2007). While it remains a matter of debate as to why
exactly entrepreneurs are often more satised with their work, we have here added a novel theoretical perspective.
Beyond these broader implications for utility, testing our research model has also delivered more specic insights into stress,
motivation, and recovery mechanisms in entrepreneurs, all of which are distinct research areas in contemporary entrepreneurship
research. Hence, we deem it important to discuss these individual implications in greater detail.
5.2. Results referring to stress, motivation, and recovery processes
First, our implications for stress research are the following: With our data we found no support for an underlying assumption in the
literature that entrepreneurship could often lead to toxic stress levels, discovering instead that entrepreneurs on average show less job
burnout than employed individuals; and solo entrepreneurs in particular exhibit the lowest risk among all entrepreneurs. These
ndings add to those prior studies indicating that entrepreneurs experience less negative work stress than non-entrepreneurs (Baron
et al., 2016; Hessels et al., 2017), and it specically challenges those studies that have found entrepreneurs to be more stressed and in
particular need for regular work recovery.
Our Hypothesis 1 stipulated that opposing mediation (MacKinnon, Fairchild & Fritz, 2007; Murayama and Elliot, 2012) operates
within the conceptual link between entrepreneurship and burnout. On the one hand, entrepreneurs might be better protected from
burnout due to better resources—namely, job and personal resources. We proved this mechanism to be true in our sample, yet only for
job resources (autonomy) and not for personal resources (psychological capital).
8
Other than expected, we also found that job demands
mediated the link between entrepreneurship and burnout: entrepreneurs experienced fewer job demands that were predictive of
burnout in our analysis (e.g., administrative tasks, role ambiguity, and time pressure). This nding, again, contradicts previous as-
sumptions in the literature on the presence of particularly stressful job demands in entrepreneurs. It underscores our general
assumption that entrepreneurship, on average, represents a rather positive JD-R pattern (Table 1).
On the other hand, however, entrepreneurs might be at risk of burnout due to a continuous lack of daily recovery from work (in a
high engagement job). While we indeed found that entrepreneurs report less psychological detachment from their daily work in
comparison to employees, this did not turn out to be a valid mediator—work recovery had no effect on burnout in our analysis. In other
words, although entrepreneurs appear to recover less well from their daily work, factors highlighted in the JD-R model, such as job
resources and job demands, are more important for their (lower) burnout levels. Given that we found that entrepreneurs face less
stressful job demands even as they benet from more job autonomy, it could be assumed that they may need less recovery than
employees do, for example. Moreover, recent research in occupational health psychology indicates that, in the context of high
autonomous work motivation, such psychological detachment from work is less useful (Olafsen and Bentzen, 2020). Finally, a further
reason for this reduced off-work recovery could, as discussed earlier, lie in (as yet unknown) potential psychological costs, such as
8
Intriguingly, our study reveals that entrepreneurs do not differ in psychological capital from non-entrepreneurs, but that they might use this
(similar) psychological capital in different ways (e.g., to derive more psychological utility; see Fig. 1).
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
21
switching off-on costs and associated loss of control (Table 1). In other words, entrepreneurs might actually have an intrinsic interest in
avoiding a complete psychological detachment from their daily work. This has important implications for work recovery research and
interventions in the wider domain of entrepreneurship (Wach et al., 2021; Williamson et al., 2021).
Beyond this, our study also signicantly contributes to the wider literature on job burnout (Cordes and Dougherty, 1993; Bakker
and Demerouti, 2017). We add to the debates by asking, for example, whether strong motivation and engagement at work could have a
dark side in terms of burnout; and how engaged, passionate individuals can be protected from such risks while retaining their
engagement (e.g., Vallerand et al., 2010). We also illustrate that a comparison of entrepreneurs with non-entrepreneurs generates
important new insights for the broader JD-R and work recovery literatures, for example in terms of how job demands and resources
may operate in comparison to recovery processes (Bennett et al., 2018; Kinnunen et al., 2011). From a JD-R and work recovery
perspective, entrepreneurship forms a particularly interesting study context (compared to other ‘occupations’ and types of jobs) for
delving into the combination of energizing processes that serve to maintain high work engagement while generating protective factors
that avoid ill-being such as burnout. For instance, these insights could be applied to job redesign and job crafting for employed workers
at risk (e.g., nurses, teachers, or police ofcers).
Second, our study delivers concrete implications for research on entrepreneurial motivation. The data showed that entrepreneurs
are more engaged (an obvious nding, although it has yet to be demonstrated clearly in entrepreneurship research), and that job
resources (autonomy), yet not personal resources (psychological capital), mediated this relationship. This reects the motivation
process described in JD-R theory, and highlights the role of job resources as a motivational driver of entrepreneurship, so far largely
overlooked in models of entrepreneurial motivation (e.g., Shane et al., 2003).
While other studies point to the potentially negative dynamics of entrepreneurial over-engagement (e.g., Stroe et al., 2018), the
present analysis indicates that entrepreneurs, in general, might not show toxic over-engagement (and overburdened passion) for their
work, but instead a rather healthy strong work engagement (fueled by their personal agency and rewarded by psychological utility).
Interestingly, and contrary to expectations, work recovery also turned out to be a valid mediator. While previous studies in occupa-
tional health research (Kinnunen et al., 2011) found no effect of psychological detachment from work on work engagement, our
present analysis conrms precisely such an effect.
Third, our results add further insights into the recovery processes relevant for entrepreneurs. Our study is the rst to show that
entrepreneurs indeed engage less in off-work recovery (for the potential reasons discussed above). While this might place them at risk
for toxic stress, it actually seems rather adaptive. This challenges the broad consensus in occupational health psychology that holds
that off-work recovery via psychological detachment from work is one of the most important and effective recovery processes (Son-
nentag et al., 2022). At least for entrepreneurs, this simply might not apply. Entrepreneurs might benet from other types of recov-
ery—for example, they might enjoy more freedom than employees in arranging sufcient at-work recovery (Chan et al., 2022), for
instance via job crafting (Zhang and Parker, 2019). We may also ask whether entrepreneurs use a form of recovery after their daily
work other than psychological detachment, e.g., those physical or social leisure activities that would still allow them to be attached
mentally to their entrepreneurial work by staying switched-on. Future research might also nd it worthwhile to test a potential dark
side of a lack of off-work recovery in entrepreneurs, for example addressing workaholism and entrepreneurial addiction and whether
such a lack of recovery is linked to these negative habits (Spivack and McKelvie, 2018). We speculate, however, that, on average, the
positive psychological utility effects largely outweigh such risks, unless the entrepreneur is in critical circumstances (e.g., inevitable
business failure and unrealistic, high work engagement despite such inevitable failure as a sign of negative workaholism and addiction;
here disengagement and more work recovery might be necessary).
5.3. Solo entrepreneurship: the psychologically healthiest form of entrepreneurship
We also took a closer look at various types of entrepreneurs. We found that solo entrepreneurs can benet from a particularly
positive pattern of job demands and resources—as a consequence, this form of entrepreneurship may indeed be regarded as the most
psychologically healthy. We call to mind that solo entrepreneurs showed high work engagement levels, as do employer entrepreneurs;
hence, while having a similarly high work engagement level, the former face even fewer burnout manifestations than the latter type of
entrepreneurs (due to the positive JD-R pattern with high job resources and low job demands).
Finally, in a return to our utility focus, if psychological utility as conceptualized in our study is indeed an important driver of
entrepreneurship, our results indicate that this is particularly true for solo entrepreneurs. Hence, we can speculate that a main driver of
why solo entrepreneurs run, and persist with, their businesses is precisely this exceptional psychological utility, which might also
compensate them for less economic utility compared to running a bigger business with employees. Nevertheless, this better psycho-
logical utility could be an impediment to future business growth since individuals typically imbue avoiding a loss of existing benets,
such as experienced utility (Kahneman et al., 1997), with considerable value. This loss aversion (or endowment effect/status quo bias;
Kahneman et al., 1991) might be particularly powerful in the context of risky choices (such as potential transition from solo to
employer entrepreneurship). Hence, while psychological utility might come with a great deal of positive outcomes, a reference point
that frames their future decision-making may be set at the moment entrepreneurs actually experience this utility (Kahneman et al.,
1991). Previous entrepreneurship research indicates that such beliefs and expectations concerning the effect of future growth on well-
being and stress levels shape entrepreneurs' attitude toward growth more than economic expectations (Wiklund et al., 2003). We thus
ask: Does solo entrepreneurship represent a ‘utility gap’ where solo entrepreneurs refrain from business growth because of loss
aversion? And, can an increase in personal resources (e.g., psychological capital), as identied in our study, empower entrepreneurs to
take the plunge and grow the business, despite such utility loss and potential stressors also for their (future) employees (Wiklund et al.,
2003)?
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
22
5.4. Practical implications
JD-R studies are well-suited to delivering a host of practical implications, such as those related to potential job redesign, where an
organization changes job demands and resources to make them more favorable for employees, or job crafting, where individual
employees actively change the design of their jobs (see Bakker and Demerouti, 2017; Zhang and Parker, 2019). Such research would
also serve to inform interventions and training, for example for those groups especially at risk of burnout or low motivation.
This notwithstanding, the present study's fairly positive ndings in terms of stress and motivation in entrepreneurs reveal the lack
of a strong necessity for such improvements in the real world—at least when compared to employees. Nevertheless, our study hints at
potential risk factors present in entrepreneurs that may take time to unfold, such as the identied habitual lack of off-work recovery
(which could become problematic at critical moments of personal business failure or macro-level crises for example) or the growing job
demands and decreasing job resources that appear when transitioning from being a solo entrepreneur to running a larger business with
employees (which could also amplify issues resulting from lack of work recovery during critical periods). Entrepreneurs should be
encouraged to be prepared to seek adequate work recovery, if required (Wach et al., 2021; Williamson et al., 2021). Future research
should also take a closer look at the effect of active job-crafting in entrepreneurs in terms of their stress and motivation proc-
esses—something which has recently been included in the JD-R model (Bakker and Demerouti, 2017). This could also help inferring
additional practical implications in this regard.
Finally, entrepreneurs growing their business by employing staff could also consider our implications for employed work. As shown
in our study, employed individuals are at greater risk of burnout due to the more negative JD-R pattern; and proper off-work recovery
is clearly vital. Hence, entrepreneurs could not only employ job-crafting for their own work, but should also empower their employees
to do likewise.
5.5. Limitations
Our study has several limitations. First, we conducted our research prior to the COVID-19 pandemic, and it stands to reason that
such a major disruptive crisis affects stress and motivational processes, for example by being particularly detrimental to solo entre-
preneurs (Block et al., 2020). Hence, whereas our study established that solo entrepreneurs show an especially positive psychological
pattern in general, this could be essentially reversed during a major crisis, and early indications exist in the literature that entre-
preneurs have faced stronger burnout during the COVID-19 period than before the crisis (Torr`
es et al., 2021). Our model and results
could help explain such an apparent increase in burnout among entrepreneurs. Due to the crisis entrepreneurs may face two new risk
factors: more work demands and economics risks, and the habitually low recovery from work that is associated with low psychological
detachment from the fresh challenges connected to their work in times of major crisis. In other words, the latent risk factor of low
recovery might shift to become a manifest risk during a crisis.
Second, our study did not examine the micro-processes shaping stress and work engagement across daily cycles (e.g., Wach et al.,
2021). We focused on long-term patterns because they proved to be particularly important for personal and business outcomes, yet
future research could also target potential gain/loss spirals (over both the short and long term).
Third, we neither studied potential interaction effects, for instance between resources and demands (Bakker and Demerouti, 2017),
nor did we control for income (Baron et al., 2016). We acknowledge that recent versions of JD-R theory assume additional, more
complex relationships than those considered in our research model, such as the direct effect of job burnout on work engagement or
additional interaction effects between job demands, on the one hand, and job resources and personal resources on the other (Bakker
and Demerouti, 2017; Bakker et al., 2014).
Fourth, in our data collection we focused solely on exhaustion as the cardinal indicator of job burnout, choosing not to study other
dimensions of job burnout, such as cynicism and inefcacy (Maslach et al., 2001). Future research that builds on our model and results
could easily incorporate these additional dimensions.
Lastly, we did not study ultimate well-being and job performance outcomes. For example, JD-R theory predicts that work
engagement and burnout critically shape entrepreneurs' job performance. This notwithstanding, as reported above, we did nd the
expected correlations between job burnout and work engagement on the one side, and indicators of well-being and job performance on
the other (life satisfaction, work satisfaction, and self-reported progress toward main work goals in the past two months). This is fully
in line with our utility framework, where entrepreneurs' personal agency drives positive psychological outcomes (in addition to
positive business outcomes that, in turn, also contribute to these psychological outcomes).
6. Conclusion
In wider public debate, yet also in scholarly discourse, entrepreneurs are often depicted as a type of ‘gold hunters’, who use their
personal agency to hunt down and embrace the next opportunity, thereby generating and exploiting new economic activity (Shane,
2008). From a positive psychology perspective, however, this picture needs to be revised. Entrepreneurs not only mine economic,
relatively uncertain utility, but are also miners of forms of relatively certain psychological utility.
9
What is more, the latter might often
be even more crucial as the driver for entrepreneurship, given that many entrepreneurs might earn more as employees, and that
9
Psychological utility is relatively certain because it is deeply embedded in the fundamental nature of entrepreneurial work (e.g., the positive JD-
R pattern, as shown in our study).
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
23
striving for control is a fundamental human motive.
While our study also makes novel contributions to the specic stress, motivation, and recovery literatures, as well as to the solo
entrepreneurship literature, we locate our main contribution in the revival and reinterpretation of utility as a key concept in the
entrepreneurship literature. Entrepreneurs' psychological utility, and how they mine it, might play a more essential and complex role
for their functioning and decision-making, but also for related business outcomes, than previously thought. In a way, this form of
intrinsic self-interest
10
reminds us of what Adam Smith (1776) referred to as the invisible hand in the economy. From this macro
perspective, the collective maximization of psychological utility (when all mine their own 'gold of positive psychology') not only
promises personal benet in the entrepreneurial sector but also, more broadly, to the development of healthy, motivated, and well-
rewarded entrepreneurs running their businesses, collectively generating broader social and economic benets.
CRediT authorship contribution statement
Martin Obschonka: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – Original Draft, Writing
Review & Editing, Visualization, Supervision, Project administration
Ignacio Pavez: Conceptualization, Investigation, Writing – Original Draft, Writing Review & Editing
Teemu Kautonen: Conceptualization, Methodology, Software, Validation, Formal analysis, Writing – Original Draft, Writing
Review & Editing
Ewald Kibler: Conceptualization, Methodology, Validation, Writing – Original Draft, Writing Review & Editing, Supervision,
Project administration
Katariina Salmela-Aro: Conceptualization, Supervision, Project administration
Joakim Wincent: Supervision, Project administration, Funding acquisition
Data availability
The data that has been used is condential.
Acknowledgments
The authors are grateful for input from Clara Heissler and Dieter Zapf regarding some of the measurements used in this study.
Appendix A
Table A1
Latent variable correlations, Cronbach's alphas, composite reliabilities (CR), and square roots of the average variance extracted (AVE, diagonal axis,
italicized)
a
.
Alpha CR Job
burnout
Work
Engagement
Job resources
(Autonomy)
Work recovery
(Detachment)
Personal resources
(Psychological
capital)
Job demands
(Role
ambiguity)
Job
demands
(Role
overload)
Job
demands
(Time
pressure)
Job burnout
(exhaustion)
0.93 0.92 0.83
Work engagement 0.92 0.92 −0.42 0.79
Job resources
(autonomy)
0.87 0.87 −0.28 0.53 0.72
Work recovery
(detachment)
0.91 0.91 −0.18 −0.07 −0.07 0.85
Personal resources
(psychological
capital)
0.89 0.92 −0.29 0.66 0.52 0.13 0.86
Job demands
Role ambiguity 0.82 0.90 0.31 −0.56 −0.51 −0.06 −0.60 0.84
Role overload 0.72 0.74 0.57 −0.13 −0.13 −0.20 −0.10 0.17 0.77
Time pressure 0.75 0.75 0.53 −0.03 −0.09 −0.26 −0.00 0.11 0.56 0.78
a
Note that this table excludes single-item measures of job demands because the quantities reported are only applicable to multi-item measures.
10
In a similar vein, Shane et al. (2003) refer to entrepreneurs' egoistic passion, with ego as a central motive for entrepreneurship.
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
24
Sensitivity analyses
Table A2 displays four models comparing entrepreneurs with employees. Model 1 is the same as Model 4 in Table 4: it compares all
entrepreneurs with all employees in our data. This model is included for ease of comparability of the additional analyses with the main
model.
Model 2 compares all entrepreneurs with corporate entrepreneurs. The latter were identied by the loose criterion of employees
who are engaged in the launch of new products, services, processes, or lines/units of business. The main effect of being an independent
entrepreneur on engagement has the same magnitude as in Model 1: independent entrepreneurs are more engaged with their work
than corporate entrepreneurs. There is no direct effect on burnout in either of these models. The remaining effects are similar between
Models 1 and 2, with the exception that the signicance levels are generally lower in Model 2, owing most likely to the smaller sample
used in its estimation. One difference worth noting is that psychological capital negatively mediates the effect on engagement in Model
2: independent entrepreneurs report lower levels of psychological capital than their corporate counterparts, and this negatively affects
their engagement.
Model 3 contrasts employer entrepreneurs with salaried managers. As in the case of Model 2, the effects in Model 3 are similar to
those in Model 1, albeit weaker (possibly owing to the smaller sample size in Model 3) and, hence, sometimes not statistically
signicant.
Model 4 compares solo entrepreneurs and employer entrepreneurs with all employees. The direct effects of being a solo entre-
preneur or employer entrepreneur are almost the same: the coefcients have the same signs, signicance, and magnitude as in Model 1
for all entrepreneurs. Thus, being a solo or employer entrepreneur is associated with a higher level of work engagement than being an
employee, yet there is no signicant effect on job burnout. Notably, there is no difference between the coefcients of solo and employer
entrepreneurs. However, the indirect effects show two differences to Model 1: the mediating effects of solo and employer entrepreneur
via psychological capital and role overload are signicant in Model 4, whereas those effects are non-signicant in Model 1.
Interestingly, the effects have opposite signs: the effect of being a solo entrepreneur on job burnout is positively mediated by
psychological capital and negatively mediated by role overload, whereas the mediations are negative and positive, respectively, in the
case of employer entrepreneurs. In contrast, the effect of being a solo entrepreneur on work engagement is negatively mediated by
psychological capital and positively mediated by role overload, while these mediation effects are positive and negative, respectively,
for employer entrepreneurs.
In summary, the additional analyses overall support the conclusions drawn from the main analyses in our study.
Table A2
Additional structural models comparing entrepreneurs with employees.
(1) All entrepreneurs vs. all
employees
(2) All entrepreneurs vs.
corporate entrepreneurs
(3) Employer entrepreneurs vs.
managers
(4) Solo/employer
entrepreneurs vs. all
employees
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Entrepreneur (base:
employee)
−0.01
(0.03)
0.09***
(0.02)
Entrepreneur (base:
corporate
entrepreneur)
0.02
(0.04)
0.09*
(0.04)
Employer
entrepreneur
(base: managers)
0.01
(0.03)
0.07*
(0.03)
Solo/employer
entrepreneurs
(base: employee)
Solo entrepreneur −0.01
(0.02)
0.09***
(0.02)
Employer
entrepreneur
−0.01
(0.02)
0.08***
(0.02)
Job/personal
resources
Job autonomy
t-1
−0.11**
(0.03)
0.15***
(0.03)
−0.09
(0.05)
0.10*
(0.04)
−0.09*
(0.04)
0.16***
(0.04)
−0.10**
(0.03)
0.16***
(0.03)
Psychological
capital
t-1
−0.19***
(0.03)
0.39***
(0.04)
−0.15**
(0.05)
0.40***
(0.06)
−0.05
(0.04)
0.23***
(0.06)
−0.14***
(0.03)
0.34***
(0.03)
Job demands
Administrative
tasks
t-1
0.11***
(0.03)
−0.02
(0.02)
0.18***
(0.04)
−0.08*
(0.04)
0.19***
(0.04)
−0.07*
(0.03)
0.10***
(0.03)
−0.04
(0.02)
Role ambiguity
t-1
0.07*
(0.03)
−0.19***
(0.04)
0.05
(0.05)
−0.27***
(0.06)
0.16***
(0.04)
−0.31***
(0.05)
0.11***
(0.03)
−0.28***
(0.03)
Role overload
t-1
(continued on next page)
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
25
Table A2 (continued )
(1) All entrepreneurs vs. all
employees
(2) All entrepreneurs vs.
corporate entrepreneurs
(3) Employer entrepreneurs vs.
managers
(4) Solo/employer
entrepreneurs vs. all
employees
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
0.38***
(0.03)
0.03
(0.03)
0.45***
(0.05)
−0.11**
(0.04)
0.34***
(0.04)
−0.00
(0.04)
0.31***
(0.02)
−0.05*
(0.02)
Time pressure
t-1
0.30***
(0.03)
0.12***
(0.03)
0.22**
(0.07)
0.18***
(0.05)
0.17***
(0.04)
0.04
(0.04)
0.24***
(0.03)
0.02
(0.02)
Workload
t-1
0.03
(0.02)
0.03
(0.02)
0.05
(0.03)
0.02
(0.03)
0.00
(0.03)
0.06
(0.03)
0.03
(0.02)
0.03
(0.02)
Work recovery
Psychological
detachment
t-1
−0.05
(0.03)
−0.14***
(0.03)
−0.01
(0.04)
−0.13**
(0.04)
−0.02
(0.04)
−0.09*
(0.04)
−0.05*
(0.03)
−0.11***
(0.02)
Control variables
Female 0.03
(0.02)
0.04*
(0.02)
0.05
(0.04)
0.05
(0.03)
0.03
(0.03)
0.06
(0.03)
0.02
(0.02)
0.03
(0.02)
Age −0.02
(0.02)
0.03
(0.02)
−0.02
(0.04)
0.05
(0.04)
−0.05
(0.03)
0.04
(0.03)
−0.03
(0.02)
0.02
(0.02)
Higher education −0.04
(0.02)
0.01
(0.02)
−0.02
(0.03)
0.01
(0.03)
−0.03
(0.03)
0.03
(0.03)
−0.03
(0.02)
0.02
(0.02)
Number of vacation
days
a
−0.04*
(0.02)
0.01
(0.02)
−0.05
(0.03)
0.02
(0.03)
−0.02
(0.03)
0.03
(0.03)
−0.04
(0.02)
0.02
(0.02)
Indirect effect of being
an entrepreneur
vs. employee
Total −0.18**
(0.06)
0.21***
(0.04)
−0.19**
(0.07)
0.06
(0.06)
−0.08
(0.06)
0.13*
(0.05)
Via job autonomy −0.04**
(0.01)
0.06***
(0.01)
−0.03
(0.02)
0.04*
(0.02)
−0.02*
(0.01)
0.04**
(0.01)
Via psychological
capital
0.00
(0.01)
−0.01
(0.01)
0.02
(0.01)
−0.04*
(0.02)
0.00
(0.00)
−0.01
(0.01)
Via administrative
tasks
−0.01*
(0.00)
0.00
(0.00)
−0.02*
(0.01)
0.01
(0.01)
−0.03**
(0.01)
0.01
(0.01)
Via role ambiguity −0.01*
(0.00)
0.03***
(0.01)
−0.01
(0.01)
0.05**
(0.02)
−0.01
(0.01)
0.03*
(0.01)
Via role overload −0.00
(0.01)
−0.00
(0.00)
−0.03
(0.02)
0.01
(0.01)
0.03
(0.01)
−0.00
(0.00)
Via time pressure −0.03**
(0.01)
−0.01*
(0.01)
−0.03*
(0.01)
−0.02*
(0.01)
−0.01
(0.01)
−0.00
(0.00)
Via workload −0.00
(0.00)
−0.00
(0.00)
−0.01
(0.00)
−0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
Via psychological
detachment
0.01
(0.01)
0.03***
(0.01)
0.00
(0.01)
0.02*
(0.01)
0.00
(0.00)
0.01
(0.01)
Indirect effect of being
a solo
entrepreneur vs.
employee
Total −0.23***
(0.06)
0.14**
(0.05)
Via job autonomy −0.03**
(0.01)
0.05***
(0.01)
Via psychological
capital
0.02**
(0.01)
−0.04***
(0.01)
Via administrative
tasks
−0.01*
(0.00)
0.00
(0.00)
Via role ambiguity −0.01**
(0.00)
0.03***
(0.01)
Via role overload −0.02**
(0.01)
0.00
(0.00)
Via time pressure −0.03***
(0.01)
−0.00
(0.00)
Via workload −0.00
(0.00)
−0.01
(0.00)
Via psychological
detachment
0.01
(0.00)
0.02***
(0.00)
Indirect effect of being
an employer
entrepreneur vs.
employee
(continued on next page)
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
26
Table A2 (continued )
(1) All entrepreneurs vs. all
employees
(2) All entrepreneurs vs.
corporate entrepreneurs
(3) Employer entrepreneurs vs.
managers
(4) Solo/employer
entrepreneurs vs. all
employees
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Total −0.04
(0.06)
0.26***
(0.05)
Via job autonomy −0.02**
(0.01)
0.04***
(0.01)
Via psychological
capital
−0.01*
(0.00)
0.02*
(0.01)
Via administrative
tasks
−0.00
(0.00)
0.00
(0.00)
Via role ambiguity −0.01**
(0.00)
0.03***
(0.01)
Via role overload 0.02**
(0.01)
−0.00
(0.00)
Via time pressure 0.00
(0.01)
0.00
(0.00)
Via workload 0.00
(0.00)
0.00
(0.00)
Via psychological
detachment
0.01
(0.00)
0.01**
(0.00)
Observations 2928 1199 1204 2928
Individuals 1350 622 553 1350
R-squared 0.33 0.41 0.24 0.23 0.24 0.23 0.23 0.29
Notes: Maximum-likelihood estimates. Standardized coefcients with cluster-robust standard errors in parentheses. *, **, and *** denote statistical
signicance at the 5 %, 1 %, and 0.1 % levels (two-tailed), respectively. Both dependent variables are from Wave t, whereas all time-variant predictors
are from Wave t-1.
a
Logarithmic transformation was used in the analysis.
Table A3 presents comparisons between different sub-groups within the entrepreneur sample. Again, Model 1 is the same as Model
4 in Table 5 and is included here to facilitate comparisons.
Model 2 compares novice and serial entrepreneurs. For the novice entrepreneurs, the current business is their rst start-up, whereas
the serial entrepreneurs have started one or more businesses in the past. Because neither the direct effects of the novice/serial
entrepreneur variable nor any of the mediation effects are statistically signicant, we can only conclude that there are no differences
between novice and serial entrepreneurs in our study. We also ran the model by excluding rms aged 10 years or more, and another
one limited to rms younger than 5 years. The rationale for this was based on the expectation of the effect of novice entrepreneurs
being more prominent among young rms. However, the results remained the same, which supports the above conclusion of the
insignicance of the novice/serial difference in the current context.
Model 3 compares young (under 5 years) and mature (5 years or more) rms. Interestingly, entrepreneurs running young rms
experience less job burnout than owners of mature rms. While there are no signicant mediation effects pertaining to burnout, the
total mediated effect of running a young versus a mature rm on work engagement is negative. The mediation occurs via psychological
capital and role ambiguity.
Model 4 expands upon Model 1 by dividing employer entrepreneurs into two groups: those with 1–4 employees (base category),
and those with 5 or more employees. The direct effects of this extended rm size variable on job burnout and work engagement are
non-signicant. The mediated effects are shown in Table A4. Regarding job burnout, the only signicant differences lie in the
mediating effects of role overload and time pressure: solo entrepreneurs report less burnout because they experience less role overload
and less time pressure, as compared to entrepreneurs who have 1–4 employees. The differences between entrepreneurs who employ 5
or more people compared to those with 1–4 employees are non-signicant. In terms of work engagement, the engagement of solo
entrepreneurs compared to entrepreneurs with 1–4 employees is positively affected by the lower levels of role overload, while it is
negatively impacted by lower levels of psychological capital and, interestingly, by the lower levels of time pressure. Again, the dif-
ferences between entrepreneurs who employ 1–4 versus 5 or more people are not signicant. Therefore, for predicting the levels of job
burnout and work engagement, the relevant threshold in our data is whether the entrepreneur has employees or not, whereas the size
of the rm itself does not matter.
Table A3
Additional structural models with alternative categorizations of entrepreneurs (and their rms).
(1) Solo entrepreneurs vs.
employer entrepreneurs
(2) Novice vs. serial
entrepreneurs
(3) Young vs. mature rms (4) Different rm sizes
(continued on next page)
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
27
Table A3 (continued )
(1) Solo entrepreneurs vs.
employer entrepreneurs
(2) Novice vs. serial
entrepreneurs
(3) Young vs. mature rms (4) Different rm sizes
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Solo entrepreneur
(base: employer
entrepreneur)
0.08
(0.05)
0.01
(0.05)
Novice entrepreneur
(base: serial
entrepreneur)
0.01
(0.04)
0.01
(0.04)
Young rm (base:
mature rm)
−0.08*
(0.04)
0.03
(0.04)
Firm size (base: 1–4
employees)
Solo entrepreneur 0.03
(0.06)
0.03
(0.05)
5 or more
employees
0.01
(0.05)
0.06
(0.05)
Job/personal
resources
Job autonomy
t-1
−0.08
(0.06)
0.13**
(0.05)
−0.07
(0.05)
0.11*
(0.05)
−0.08
(0.05)
0.11*
(0.05)
−0.07
(0.05)
0.11*
(0.05)
Psychological
capital
t-1
−0.14*
(0.06)
0.43***
(0.07)
−0.12*
(0.05)
0.36***
(0.07)
−0.12*
(0.05)
0.36***
(0.07)
−0.11*
(0.05)
0.35***
(0.07)
Job demands
Administrative
tasks
t-1
0.13*
(0.06)
−0.09
(0.04)
0.11*
(0.05)
−0.09*
(0.04)
0.11*
(0.05)
−0.09*
(0.04)
0.11*
(0.05)
−0.09*
(0.04)
Role ambiguity
t-1
−0.01
(0.05)
−0.18**
(0.06)
0.01
(0.05)
−0.21***
(0.06)
0.02
(0.05)
−0.21***
(0.06)
0.01
(0.05)
−0.21***
(0.06)
Role overload
t-1
0.47***
(0.06)
−0.19***
(0.05)
0.34***
(0.05)
−0.12**
(0.04)
0.33***
(0.05)
−0.12**
(0.04)
0.34***
(0.05)
−0.13**
(0.04)
Time pressure
t-1
0.24**
(0.08)
0.23***
(0.05)
0.19***
(0.05)
0.18***
(0.04)
0.19***
(0.05)
0.18***
(0.04)
0.19***
(0.05)
0.18***
(0.04)
Workload
t-1
0.07
(0.05)
−0.01
(0.04)
0.06
(0.04)
−0.01
(0.04)
0.05
(0.04)
−0.01
(0.04)
0.06
(0.04)
−0.01
(0.04)
Work recovery
Psychological
detachment
t-1
0.04
(0.05)
−0.22***
(0.05)
0.02
(0.04)
−0.19***
(0.04)
0.02
(0.04)
−0.19***
(0.04)
0.02
(0.04)
−0.19***
(0.04)
Control variables
Female 0.07
(0.05)
0.07
(0.05)
0.08
(0.05)
0.07
(0.05)
0.08
(0.05)
0.07
(0.05)
0.08
(0.05)
0.08
(0.05)
Age −0.01
(0.05)
0.01
(0.05)
−0.06
(0.05)
0.00
(0.04)
−0.07
(0.05)
0.01
(0.04)
−0.06
(0.04)
0.01
(0.04)
Higher education 0.02
(0.04)
0.03
(0.04)
0.03
(0.04)
0.04
(0.04)
0.04
(0.04)
0.03
(0.04)
0.03
(0.04)
0.03
(0.04)
Number of vacation
days
a
−0.05
(0.04)
0.04
(0.04)
−0.05
(0.03)
0.06
(0.03)
−0.05
(0.03)
0.06
(0.03)
−0.05
(0.03)
0.06
(0.03)
Indirect effect of
being an
entrepreneur vs.
employee
Total −0.31**
(0.10)
−0.08
(0.06)
−0.11
(0.06)
0.06
(0.06)
−0.04
(0.07)
−0.14*
(0.07)
Via job autonomy −0.01
(0.01)
0.01
(0.01)
−0.00
(0.00)
0.01
(0.01)
0.00
(0.00)
−0.01
(0.01)
Via psychological
capital
0.03*
(0.01)
−0.09**
(0.03)
−0.00
(0.01)
0.01
(0.02)
0.01
(0.01)
−0.04*
(0.02)
Via administrative
tasks
−0.00
(0.01)
0.00
(0.01)
−0.01
(0.01)
0.01
(0.01)
−0.01
(0.01)
0.00
(0.01)
Via role ambiguity 0.00
(0.00)
0.01
(0.01)
−0.00
(0.00)
0.02
(0.01)
0.00
(0.01)
−0.03*
(0.01)
Via role overload −0.12***
(0.03)
0.05**
(0.02)
−0.03
(0.02)
0.01
(0.01)
−0.02
(0.02)
0.01
(0.01)
Via time pressure −0.05*
(0.02)
−0.05**
(0.02)
−0.01
(0.01)
−0.01
(0.01)
−0.01
(0.01)
−0.01
(0.01)
Via workload −0.02
(0.01)
0.00
(0.01)
−0.00
(0.00)
−0.00
(0.00)
−0.00
(0.00)
0.00
(0.00)
(continued on next page)
M. Obschonka et al.
Journal of Business Venturing 38 (2023) 106272
28
Table A3 (continued )
(1) Solo entrepreneurs vs.
employer entrepreneurs
(2) Novice vs. serial
entrepreneurs
(3) Young vs. mature rms (4) Different rm sizes
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Job burnout
(exhaustion)
Work
engagement
Via psychological
detachment
−0.00
(0.00)
0.01
(0.01)
0.00
(0.00)
−0.00
(0.01)
0.01
(0.01)
−0.00
(0.01)
Observations 745 745 745
Individuals 348 348 348
R-squared 0.38 0.36 0.25 0.28 0.26 0.28 0.25 0.28
Notes: Maximum-likelihood estimates. Standardized coefcients with cluster-robust standard errors in parentheses. *, **, and *** denote statistical
signicance at the 5 %, 1 %, and 0.1 % levels (two-tailed), respectively. Both dependent variables are from Wave t, whereas all time-variant predictors
are from Wave t-1.
a
Logarithmic transformation was used in the analysis.
Table A4
Indirect effects of different rm sizes on job burnout and work engagement.
Mediator Job burnout (exhaustion) Work engagement
Solo entrepreneur (vs. 1–4
employees)
5+employees (vs. 1–4
employees)
Solo entrepreneur (vs. 1–4
employees)
5+employees vs. 1–4
employees
Total
Job/personal resources
Via job autonomy −0.01
(0.01)
−0.00
(0.01)
0.01
(0.01)
0.00
(0.01)
Via psychological capital 0.02
(0.01)
−0.01
(0.01)
−0.05*
(0.03)
0.04
(0.02)
Job demands
Via administrative tasks −0.00
(0.01)
0.00
(0.01)
0.00
(0.01)
−0.00
(0.01)
Via role ambiguity −0.00
(0.00)
−0.00
(0.00)
0.00
(0.01)
0.10
(0.01)
Via role overload −0.06**
(0.02)
0.02
(0.02)
0.02*
(0.01)
−0.01
(0.01)
Via time pressure −0.03*
(0.01)
0.01
(0.01)
−0.03*
(0.01)
0.01
(0.01)
Via workload −0.02
(0.01)
0.00
(0.00)
0.00
(0.01)
−0.00
(0.00)
Via psychological
detachment
0.00
(0.00)
0.00
(0.01)
−0.01
(0.01)
−0.02
(0.01)
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