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International Entrepreneurship and Management Journal (2024) 20:607–629
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The entrepreneurial intention oftop athletes—does
resilience lead theway?
Kathrin M.Steinbrink1 · CelineStröhle1
Accepted: 22 March 2023 / Published online: 18 April 2023
© The Author(s) 2023
Abstract
Some jobs have a higher level of challenges and adversities. Individuals pursuing
these jobs learn how to react to challenges and build up resilience. Within this study,
we concentrated on the potential career path of top athletes as entrepreneurs, who
are both expected to have a higher level of resilience than non-athletes. The purpose
of this research was to examine if resilience is a determining factor on entrepre-
neurial intention and if the model based on the Theory of Planned Behavior (TPB)
can be applied in general and for specific groups with a high level of resilience. To
address the research questions, we collected data from a sample of 195 top athletes
and 142 non-athletes. First, the level of resilience and entrepreneurial intention were
compared with an analysis of variance (ANOVA). Subsequently, the structural equa-
tion model tested the influence of resilience on entrepreneurial intention, mediated
by the TPB, first for the whole sample and then as a multigroup comparison for
both groups. Resilience had an indirect influence on entrepreneurial intention, medi-
ated by the explaining factors of the TPB (personal attitude, subjective norm, per-
ceived behavioral control). The multigroup comparison revealed a difference in the
influence of perceived behavioral control on entrepreneurial intention between top
athletes and non-athletes. Based on these results, this research added further knowl-
edge to the field of entrepreneurial intention by examining the specific role of resil-
ience necessary for careers as top athletes and entrepreneurs. It also contributes by
researching the specific group of top athletes compared to non-athletes and extrapo-
lating recommendations in entrepreneurship education for both groups, as creating
athletes’ awareness of potential overconfidence or implementing resilience training
in education for non-athletes.
Keywords Athlete entrepreneurship· Theory of planned behavior· Career transition·
Structural equation modeling· Multigroup comparison· Top athletes· Resilience
* Kathrin M. Steinbrink
k.steinbrink@uni-hohenheim.de
1 Institute ofMarketing & Management Entrepreneurship Research Group (570c), University
ofHohenheim, Wollgrasweg 49, D-70599Stuttgart, Germany
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Introduction
Some professions are curtailed, and individuals are at some point, sometimes even
unforeseeable, forced into occupational re-orientation. This limitation affects profes-
sional athletes for different reasons, such as declining performance due to aging,
accidents, illness, or personal reasons. Research on the topic of sports entrepreneur-
ship increase progressively over the last years (González-Serrano, Jones, & Llanos-
Contrera, 2019). Evidence suggests that entrepreneurship is a popular second-career
option for professional athletes (Kenny, 2015), who seem well-equipped for this
career (Steinbrink etal., 2020).
When considering the person-job fit theory, with a positive assessment of a job
environment being a fit between a person’s abilities and a job’s demands (Kristof,
1996), an entrepreneurial career for former top athletes seems even more likely.
Success as an athlete often translates into success as an entrepreneur (Bernes etal.,
2009). Both experience and personality influence the entrepreneurial intention (EI)
of athletes (e.g., Ardichvili etal., 2003; Kerr etal., 2018). Entrepreneurs face the
risk of failure in general and operate daily in a changing environment, dealing with
uncertainty and incomplete information (Ayala & Manzano, 2014). This experience
is similar to an athlete’s (Fletcher & Hanton, 2003). Within the context of profes-
sional sports, stressors range from daily demands to major life events (Sarkar &
Fletcher, 2013) and can be classified into three categories: competitive performance
(e.g., performance expectations, loss of form, rivalry), organizational (e.g., finances,
interpersonal conflicts), and personal stressors (e.g., social contacts, injury; Fletcher
& Sarkar, 2012). One crucial aspect both jobs have in common is resilience.
Prior research has shown that resilience helps entrepreneurs overcome adversity
(D’andria et al., 2018) and achieve career success (Salisu etal., 2020). However,
researchers have called for more research on personality traits in the context of sports
entrepreneurship (Ratten & Tajeddini, 2019). Although numerous studies on the resil-
ience of athletes can be found (Galli & Gonzalez, 2015), most research focuses on the
current situation of being a sports student (Gonzalez et al., 2016), coach (Sarkar &
Hilton, 2020), or athlete (Belem etal., 2014; Brown etal., 2015). In their meta-analysis,
Korber and McNaughton (2018) identified six research streams within the discussion
of entrepreneurship and resilience, e.g., antecedents of entrepreneurial resilience (as
traits or characteristics) or resilience as a determinant of EI. Resilience influences
entrepreneurial intention in different contexts, such as adverse political (Bullough
etal., 2014) or economic situations (Bullough & Renko, 2013). Additionally, the posi-
tive relationship between sports and EI was examined in sports students (González-
Serrano etal., 2018a; Naia etal., 2017; Teixeira & Forte, 2017), but no research was
found on top athletes. Korber and McNaughton (2018) stated that more research is
needed to understand the multiple dimensions of entrepreneurial resilience.
Resilience in both research fields, sports and work, has been comprehensively
researched within an interdisciplinary meta-analysis of over 52 studies (Bryan etal.,
2019). Nevertheless, no study has combined resilience as a result of previous expe-
rience as an athlete and the transition of that skill into a new field of work. This
study aimed to widen the scope of this field and research resilience as a gained skill
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that can be transferred for further career options after a sports career. In this case,
the influence of resilience on entrepreneurial intention, or the willingness to start a
firm, was researched in general and in top athletes. Furthermore, we contributed to
the discussion on the Theory of planned behavior (TPB) in two ways. The TPB is a
psychological theory stating that the three components, personal attitude (PA), sub-
jective norms (SN), and perceived behavioral control (PBC), together explain with
high accuracy an individual’s behavioral intention (Ajzen, 1991). First, the influ-
ence of an additional variable within the TPB (resilience on intention, mediated by
PA, SN, and PBC) was tested. Subsequently, the model was researched within the
environment of professional sports with its specific adversities and stressors.
In summary, this research sought to determine if resilience is a defining factor
of entrepreneurial intention if the Theory of Planned Behavior (TPB) mediates this
relationship, and if the model can be applied in general or for specific groups with a
high level of resilience on a homogenous sample of top athletes.
Theoretical framework
Resilience andtheperson–job fit
Sarkar and Fletcher (2013) pointed out that resilience is based on the presence of
adversity and positive adaptation. Resilience is conceptualized as a personality trait
(e.g., Ayala & Manzano, 2010; Shin etal., 2012) but also as a process that is able to
change over time (e.g., Brewer & Hewstone, 2004; Luthar etal., 2000). This change-
able process includes that resilience varies contextually (depending on the situation)
and temporally (during a specific situation and as a lifespan process) (Bonanno etal.,
2010; Hobfoll, 1989). In their grounded theory on the resilience of Olympic champi-
ons, Fletcher and Sarkar (2012) combined both perspectives (trait and process) and
suggested an influence of numerous psychological factors on the relationship between
stress and resilience. According to Fletcher and Sarkar (2012, p. 675), we understand
resilience as "the role of mental processes and behavior in promoting personal assets
and protecting an individual from the potential negative effect of stressors."
Athletes build up emotional capital during their career, supporting them to over-
come obstacles and hurdles (Ratten, 2015). Dirmanchi and Khanjani (2019) found
a significant difference in resilience between athletes and non-athletes with spinal
cord injuries. Galli and Vealey (2008) found that high-level athletes faced adver-
sities and experienced negative psychological effects but also developed a range
of coping strategies to deal with those situations. As a result, athletes experienced
growth and improvement, underlying the developmental process of resilience within
sports. Considering resilience as a changing and learnable skill (Gu & Day, 2007;
Luthar etal., 2000), we expected a higher level of resilience in top athletes who used
to be confronted with stressors and hypothesized that:
H1a: the level of resilience is higher in top athletes than in non-athletes.
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The investment in human capital affects the motivation towards an entrepreneur-
ial career but is influenced by culture (Pinzón etal., 2021). Do Paço etal. (2015)
examined the entrepreneurial intention of girls attending a business school compared
to boys attending a sports school without entrepreneurship education. The authors
concluded that other factors influencing EI have to be considered. According to the
person-job fit theory, jobs with suitable demands for a person’s abilities are compat-
ible (Kristof, 1996); jobs that fit are expected to be assessed positively by an indi-
vidual. Entrepreneurs face the risk of failure in general and operate daily business
in a changing environment, dealing with uncertainty and incomplete information
(Ayala & Manzano, 2014). Specific psychological characteristics are expected of
entrepreneurs, as they have chosen a path containing risks and adversities (Bulmash,
2016). Based on the person-job fit and Steinbrink etal.’s (2020) findings, athletes
are expected to consider entrepreneurship a suitable career option. In agreement
with Pellegrini etal. (2020), who identified different reasons for a higher entrepre-
neurial intention in athletes within their literature review, it was hypothesized that:
H1b: the level of EI is higher in top athletes than in non-athletes.
Theory ofplanned behavior
In general, intention can be defined as "a person’s readiness to perform a [given] behav-
ior" (Ajzen, 2011, p. 1122). More specifically, the entrepreneurial intention is under-
stood as an individual’s conscious awareness and determination to create a new venture.
In the perspective of Ajzen’s (1991) theory, the intention to create a new venture can
be considered the best predictor for the actual venture creation. Explaining the entre-
preneurial process and, therefore, the intention with only personality variables is highly
complex. In a previous study, individual and situational variables showed poor predic-
tive validity and explanatory power (Krueger etal., 2000). Therefore, a mediating role
of variables explaining entrepreneurial intention is suggested (Munir etal., 2019). A
widely used and validated model predicting entrepreneurial intention is TPB, which
was applied here following Ajzen (1991). Within this model, the entrepreneurial inten-
tion is based on the personal attitude towards entrepreneurship, the perceived behavio-
ral control, and the subjective norm (Ajzen, 1991).
Personal attitude (PA) reflects the individual’s (favorable or unfavorable) evaluation
of the behavior (Ajzen, 1991) or is their personal attitude towards entrepreneurship.
Subjective norms (SN) reflect the social components within the TPB. This term refers
to the perceived normative beliefs of the individual’s social reference group regard-
ing whether to engage in the behavior (here entrepreneurship) or not (Ajzen, 1991).
The social reference group can be family and friends. However, in the case of athletes,
trainers, sponsors, media, and the public can also be perceived as a reference group
generating social pressure to perform (Hayes etal., 2020). The role of the subjective
norm within the TPB is unequivocal as several studies found no significant relation-
ship between SN and EI (e.g., Autio etal., 2001; Krueger etal., 2000).
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Perceived behavioral control The concepts of perceived behavioral control (PBC),
perceived feasibility (Shapero & Sokol, 1982), and self-efficacy (Bandura, 1977) are
similar (Dissanayake, 2013). PBC refers to the individual’s belief in being able to
perform the behavior, and in addition includes the perception of an individual’s con-
trol of the behavior (Ajzen, 1991). In this context, PBC is the individual’s belief in
being able to start a firm and volitionally control the circumstances. The more indi-
viduals feel capable of an activity, the more they are involved in and committed to
achieving that activity (Bandura, 1991).
In line with previous studies (e.g., Kautonen etal., 2015), we expected PA, SN, and
PBC to be antecedents of EI. Therefore, we hypothesized that:
H2: (a) personal attitude, (b) subjective norm, and (c) perceived behavioral
control have a positive effect on entrepreneurial intention.
Integration ofresilience intheTPB
Korber and Naughton (2018) examined the relationship between resilience and
entrepreneurship, where EI represents one of the six identified research directions.
It is expected that a person with a high level of resilience might consider entrepre-
neurship as a career path to fulfill the demand of facing stressors/adversities with
the skill of resilience. Thus, based on the person-job fit theory and considering the
three explaining factors of EI according to the TPB, a person with a high level of
resilience should have a positive attitude towards entrepreneurship. This also applies
to the social perspective; resilient individuals are perceived to be able to work under
pressure (Gould etal., 2002). This belief in the perception by the social reference
group is expected to lead to a positive influence on resilience in the SN. Stress toler-
ance has been found to be positively related to perceived behavioral control (Ahmed
etal., 2019), leading to the hypothesis that.
H3: resilience has a positive effect on (a) attitudes towards entrepreneurship,
(b) subjective norms, and (c) perceived behavioral control.
Jin (2017) studied the effect of psychological capital on entrepreneurial intention
and found resilience to be positively and significantly related to intention but did not
consider the framework of the TPB. The mediating effect of TPB variables between
psychological, cultural, and socioeconomic variables and entrepreneurial inten-
tion has been confirmed in several studies (e.g., Ahmed etal., 2019; Entrialgo &
Iglesias, 2016; Gorgievski etal., 2018; Munir etal., 2019). Hlatywayo etal. (2017)
found resilience to be the only psychological capital construct that added significant
value to the prediction of entrepreneurial intention in university graduates. In line
with the TPB, it was hypothesized that.
H4: the relationship between resilience and entrepreneurial intention is mediated
by (a) attitudes towards entrepreneurship, (b) subjective norms, and (c) perceived
behavioral control.
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Multigroup comparison
A positive adaption to adversity and resilience-building starts in early childhood
and continues by belonging to different communities (Clauss-Ehlers, 2008; Waller,
2001), such as sports teams. Life as an entrepreneur is as highly demanding as it
is for athletes. Hisrich etal. (2005) highlighted financial, psychological, and social
risks in their definition of entrepreneurship. Applying the categories of Fletcher and
Sarkar (2012) to entrepreneurs, competitive performance stressors can be market-
related, e.g., market shares. Organizational stressors are highly relevant for entrepre-
neurs, e.g., uncertainty concerning income. Personal stressors might, for example,
be personal health issues due to entrepreneurial stress (Cardon & Patel, 2015).
As previously mentioned, a higher level of resilience is expected for athletes,
influencing resilience for an entrepreneurial intention (Hlatywayo et al., 2017).
Therefore, considering the framework of TPB, we hypothesized that:
H5: the effect size of resilience on (a) PA, (b) SN, and (c) PBC is greater in top
athletes than in non-athletes.
H6: the effect size of (a) PA, (b) SN, and (c) PBC on EI is greater in top athletes
than in non-athletes.
Methodology
Data collection andsample
Data were collected between June and August 2021 via an online survey of 337 peo-
ple in Germany(Table1). Of the participants, 195 were coded as top athletes, and
142 were coded as non-athletes (control group). Based on Steinbrink etal. (2020),
interviewees were classified as top athletes by answering "(1) the frequency of train-
ing and participation in competitions with a focus on winning, and [either] (2a) the
participation in high-level international competitions, [or] (2b) the affiliation to a
squad" with yes (p. 866). Two respondents were deleted, answering (1) with no and
both (2a) and (2b) with yes. Profession was also considered; if an athlete’s main paid
occupation was pursuing a sport, he/she was also classified as a top athlete. There-
fore, homogeneity concerning the personal relevance of sport and a high timely
focus on sports within the life situation is assumed for the here defined top athletes.
The average age was 25.35years (26.01 for top athletes, 24.87 for non-athletes), and
in sum, 67.06% were female (131 top athletes, 95 non-athletes), and 32.94% were
male (64 top athletes, 47 non-athletes). Participation was voluntary, and to ensure
confidentiality, all questionnaires were anonymous.
Measures
A 10-item short version of the original CD-RISC survey by Connor and Davidson
(2003) was developed by Campbell-Sills and Stein (2007) to measure the multidi-
mensional construct of resilience and is widely used within the research fields of
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sport and entrepreneurship (e.g., Salisu etal., 2020; Schippers etal., 2019). Further-
more, a study across cricket players found the short version more suitable (Gucciardi
etal., 2011). The instrument uses 10 items, e.g. the “tend to bounce back after ill-
ness or hardship” or whether the persons asked “can stay focused under pressure”
or “think of self as a strong person”, rated on a scale from 0 (not true at all) to 4
(true nearly all the time) (Campbell-Sills & Stein, 2007, p. 1025). As the question-
naire was conducted in German, the German translation by Sarubin etal. (2015) was
applied. With an αCronbach of 0.90 for 25 items and 0.84 for ten items, the internal
consistency of both versions in the German language was confirmed. The reliability
was also tested with a test–retest measure and confirmed for both versions (Sarubin
etal., 2015). The survey length was reduced by choosing the short version for an
increased response rate.
The entrepreneurial intention questionnaire (EIQ), developed and validated by
Liñán and Chen (2009), is a widely used questionnaire measuring entrepreneurial
intention (e.g., Al-Jubari et al., 2019; Hassan et al., 2020; Krasniqi etal., 2019).
Already used and validated in the research of resilience (González-López etal.,
2019), the EIQ was applied within this study. To measure entrepreneurial intention,
6 items were asked (e.g. “I am determined to create a firm in the future”); measuring
the antecedents, the instrument included 5 items to measure personal attitude (e.g.
“A career as entrepreneur is attractive for me”), 3 items to measure subjective norm
(e.g. “If you decided to create a firm, would your close family approve of that deci-
sion?”, and 6 items to measure perceived behavioral control (e.g. “I know how to
develop an entrepreneurial project”) (Liñán & Chen, 2009, p. 612 f.).
As control variables, entrepreneurial background and experience (both dichotomous)
were integrated into the model. Prior research found a positive relationship between
entrepreneurial background and entrepreneurial intention (Feder & Niţu-Antonie,
2017). The entrepreneurial background was defined here by knowing an entrepre-
neur (in the family or social environment). Another aspect positively influencing the
explaining factors of the TPB is the entrepreneurial experience (Miralles etal., 2016).
Therefore, we explicitly asked about entrepreneurial experiences. Conscious of the sim-
plification, we followed Farmer etal. (2011) to evaluate theoretical or practical experi-
ences of entrepreneurship as a binary variable (yes or no) prior to the survey.
To prevent distortion and reduce the possibility of an alternative explanation for the
results (Becker, 2005; Schmitt & Klimoski, 1991), control variables were included as
influencing the TPB, in addition to the exogenous variable of resilience. As some studies
Table 1 Sample characteristics
top athlete non-athlete sum
absolute in % absolute in % absolute in %
n195 100,00 142 100,00 337 100,00
m64 32,82 47 33,10 111 32,94
w131 67,18 95 66,90 226 67,06
average age 24,87 26,01 25,35
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explained the direct influence on entrepreneurial intention (e.g., Altinay et al., 2012;
Garaika etal., 2019; Rasli etal., 2013) and others via the TPB (Fini etal., 2012; Miralles
etal., 2016; Zhang etal., 2014), this study included all possible paths for initial testing
on controls.
Data analysis
All questions were mandatory to ensure no missing values. First, the data were checked
for normality with Cook’s Distance using SPSS. No outliers were identified, as no
value exceeded 0.57. The critical value was 1 (Norušis, 2006).
An analysis of variance (ANOVA) was executed in SPSS to first check for dif-
ferences in top athletes’ resilience and entrepreneurial intention compared to non-
athletes (H1a–b). Second, confirmatory factor analysis (CFA) was conducted to
validate the convergent and discriminant validity of the measurement model. The
measurement model contained the factors and correlations between the latent varia-
bles of the model. Subsequently, the structural model was built, and H2 and H3 were
tested with the maximum likelihood method. The bootstrap procedure was applied
to test the mediation (H4a–c) (Cheung & Lau, 2008). For testing H5, a multigroup
comparison was conducted to identify differences between athletes and non-athletes,
which were categorized as dichotomous variables.
Results
Analysis ofvariance (ANOVA)
To check for differences between top athletes and non-athletes, an ANOVA was con-
ducted in SPSS. Following Fischer and Milfont (2010), the variables were z standard-
ized. The results (Table2) showed significant differences in R and EI between the groups
of top athletes and non-athletes (FR[1,335] = 42,363, p = 0.000; FEI[1,335] = 19,314,
p = 0.000). As shown in Fig.1, there was a greater difference between top athletes and
non-athletes for R than for EI. Therefore, hypotheses 1a and 1b were supported.
Table 2 Results of the ANOVA of resilience and entrepreneurial intention between top athletes and non-athletes
SS sum of squares, df degrees of freedom, MS mean square
SS df MS F Sig
Resilience Between groups 37.719 1 37.719 42.363 0.000
Within groups 298.281 335 0.890
Total 336.000 336
Entrepreneurial
intention Between groups 18.316 1 18.316 19.314 0.000
Within groups 317.684 335 0.948
Total 336.000 336
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Structural equation modeling
Common method bias
Harman’s single-factor test (Harman, 1976) for common method bias was performed
with SPSS 25. 42.45% of the variance was explained by loading all variables on a sin-
gle factor. Common method bias is expected if more than 50% of the variance can be
explained (Podsakoff etal., 2003). Additionally, common method bias was checked
with AMOS, showing a very poor model fit (χ2 = 4600,678, p = 0.000, CFI = 0.403,
GFI = 0.275, AGFI = 0.206, RMSEA = 0.217, SRMR = 2021, PCLOSE = 0.000)
(Biraglia & Kadile, 2017; Kumar & Shukla, 2019). Therefore, common method bias
was expected not to be an issue in this study.
Measurement model analysis
Due to improvable model fit, covariances between the error terms were added; two
items (R5, PBC1) were removed due to low loadings, and after checking for residual
covariances, R3 and R7 were also removed. Model fit indices can be classified into
absolute, incremental, and parsimony fit indices (Hair etal., 2019). According to
Hair etal. (2019), at least the χ2 with the associated degrees of freedom (df) and one
fit index of each category should be displayed to report the model fit. Lower val-
ues are desirable for badness-of-fit indices (χ2, RMSEA, SRMR) as they measure
error or deviation. In contrast, goodness-of-fit indices (CFI, TLI, AGFI) range from
0 to 1, and values < 0.9 are considered acceptable (Malhotra, 2010). The adjusted
measurement model showed a satisfactory fit for all three categories of model fit
(χ2 = 5483.932, df = 274, CMIN/df = 1,766, RMSEA = 0.048, SRMR = 0.0366,
CFI = 0.972, TLI = 0.967, AGFI = 0.875, PNFI = 0.791).
-0,50-0,40 -0,30-0,20 -0,100,000,100,200,3
00
,40
Entrepreneurial Intention (z-standardized) Resilience (z-standardized)
topathlete
non-athlete
Fig. 1 Level of Resilience and Entrepreneurial Intention for top athletes and non-athletes
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Construct validity
Construct validity was assessed by convergent, discriminant, and nomological validity
(Hair etal., 2019). For checking the convergent validity, the average variance extracted
(AVE) is a common method for covariance-based models (dos Santos & Cirillo, 2021).
The AVE for PA, SN, PBC, and EI was above the threshold of 0.5 (AVEPA = 0.741,
AVESN = 0.531, AVEPBC = 0.917, AVEEI = 0.845) (Fornell & Larcker, 1981). As Malhotra
(2010) argued, AVE is often too strict, and other criteria, such as composite reliability
(CR), are also reliable. The slight deviation of AVER = 0.497 could be considered sufficient
considering that the CRR = 0.830 exceeds the minimum for CR > 0.7 (Hair et al., 2019).
Table3 shows the results of the average variance extracted and the composite reliability.
The discriminant validity was assessed by comparing the square root of AVE
with the correlations between the constructs (Fornell & Larcker, 1981). In Table4,
the square roots of AVE are presented in the diagonals, showing higher values com-
pared to the correlations presented below them. The significant positive correlations
between the constructs support the nomological validity (Hair etal., 2019).
Table 3 Results of the average variance extracted and composite reliability
Construct Item Loading Composite
reliability Average variance
extracted
Resilience R1 0.641 0.83 0.497
R2 0.798
R6 0.707
R9 0.741
R10 0.622
Personal Attitude PA1 0.723 0.934 0.741
PA2 0.904
PA3 0.849
PA4 0.894
PA5 0.918
Subjective Norm SN1 0.769 0.767 0.531
SN2 0.841
SN3 0.541
Perceived behavioral control PBC2 0.868 0.917 0.689
PBC3 0.855
PBC4 0.894
PBC5 0.918
PBC6 0.761
Entrepreneurial intention EI1 0.846 0.97 0.845
EI2 0.938
EI3 0.914
EI4 0.965
EI5 0.916
EI6 0.931
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All path coefficients leading from the latent factors on the items were statistically
significant (p < 0.001), and the standardized regression weights ranged from 0.539
(SN3) to 0.965 (EI4).
Based on the statistics, the model can be considered reliable and valid (Hair etal., 2019).
Structural model analysis
The structural model was built based on the hypothesized paths. The maximum likeli-
hood method was used to test H2(a–c) and H3(a–c). Following a recursive method, at
each iteration, the path with the lowest t-statistic was removed until all paths showed
a significance of p < 0.05 (Liñán & Chen, 2009), except for the hypothesized paths.
There was a significant positive relationship between PA and EI and between
PBC and EI; therefore, H2a and H2c were supported. H2b was rejected, as there was
a very small negative effect size from SN on EI. The positive effect from R on all
three antecedents of the TPB was confirmed with a high level of probability. Thus,
H3(a–c) was supported.
Table5 also presents the results of testing for mediation between R and EI. The
total indirect effects of the mediated paths were significant and positive for the
mediation of PA and PBC, supporting H4a and H4c. However, the construct of SN
was not significant, and therefore, H4b was rejected. In addition, the direct effect
between R and EI was not significant. The relationship between R and EI was com-
pletely explained by full mediation via PA and PBC.
This model explained 74.4% of the variance in entrepreneurial intention. Figure2
shows the structural model with standardized estimates of the hypothesized paths for
the whole sample, the top athletes and non-athletes.
Comparing top athletes tonon‑athletes
After validating the suggested model in general and in consideration of the differences
in the means of R and EI, the relationship within the model was compared between top
athletes and non-athletes. The multigroup test was also a test on mediation. The mod-
erating variable was the dichotomous variable of top athlete versus non-athlete.
Table 4 Square root of AVE and correlations between the constructs testing discriminant validity
The diagonal numbers in italic are the square root of the AVE values
CR composite reliability, AVE average variance extracted, MSV maximum shared variance,R resilience,
PA personal attitude, SN subjective norm, PBC perceived behavioral control,EI entrepreneurial intention
*** Correlation is significant at the 0.001 level
Variable CR AVE MSV RPA SN PBC EI
R0.83 0.497 0.28 0.705
PA 0.934 0.741 0.739 0.421*** 0.861
SN 0.767 0.531 0.185 0.431*** 0.291*** 0.728
PBC 0.917 0.689 0.478 0.529*** 0.666*** 0.338*** 0.83
EI 0.97 0.845 0.739 0.356*** 0.860*** 0.266*** 0.692*** 0.919
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Table6 shows the effect sizes and p-values of both groups. An overall chi-square
difference test over the whole model detected a difference in the model for top ath-
letes versus non-athletes (χ2 = 53,217, df = 30, p-value = 0.006). A significant dif-
ference was observed between the two groups for at least one path. Assessing mul-
tigroup differences with CR has been criticized because it only compares one path
for both groups and does not consider the other paths within the model (Klesel etal.,
2019). Therefore, a chi-square difference test was conducted for all paths to deter-
mine which relationships differed significantly (Byrne, 2004).
Byrne and Stewart (2006) suggested the ΔCFI-method and the chi-square differ-
ence test to test factorial invariance. The CFI of the model without constraints was
Table 5 Hypothesis with standardized estimates, p-value, and results of the hypothesized paths, includ-
ing model fit indices
S.E. estimated standard error, C.R. composite reliability, R resilience, PA personal attitude, SN subjective
norm, PBC perceived behavioral control, EI entrepreneurial intention
Path Stand. Estimate S.E. C.R. p-value Findings
H2a PA → EI 0.746 0.050 17.500 0.000 Supported
H2b SN → EI -0.023 0.067 -0.630 0.523 Rejected
H2c PBC → EI 0.235 0.042 6.193 0.000 Supported
H3a R → PA 0.331 0.135 5.626 0.000 Supported
H3b R → SN 0.414 0.100 6.011 0.000 Supported
H3c R → PBC 0.387 0.134 6.904 0.000 Supported
Indirect path Stand. Estimate Lower Upper p-value Findings
H4a R → PA → EI 0.264 0.496 0.986 0.001 Supported
H4b R →SN → EI 0.005 -0.058 0.08 0.808 Rejected
H4c R →PBC →EI 0.119 0.207 0.473 0.000 Supported
R2EI = 0.744
Model fit χ2 = 554.383, df = 281, CMIN/df = 1.973, RMSEA = 0.054, SRMR = 0.0797,
CFI = 0.963, TLI = 0.958, AGFI = 0.963, PNFI = 0.803
Fig. 2 Standardized estimates of the hypothesized paths for the whole sample, top athletes and non-athletes
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0.952. When constraining the path from resilience to the antecedents of the TPB, the
CFI remained 0.952. When constraining the paths within the TPB (PA → EI, SN → EI,
PBC → EI), the CFI decreased to 0.951. Although that difference seems marginal, the
model fit was reduced when equally constraining the TPB for top and non-athletes.
As a second method to examine differences in the paths, Byrne and Stewart (2006)
suggested the chi-square difference test to constrain each path individually. Table7
shows the results of the chi-square difference test, including the results of the hypoth-
esized paths. As indicated by the ΔCFI, the difference between the groups for the rela-
tionship between R and PA, SN, and PBC was not significant. Therefore, H5(a–c) was
rejected. The significant difference between top athletes and non-athletes, as shown
by the overall χ2 test and suggested by the ΔCFI test, was found for PBC → EI. Thus,
H6a and H6b were also rejected, and H6c was supported.
Discussion andtheoretical implications
Explaining theentrepreneurial intention ofathletes
The role of the subjective norm within the TPB is controversial. Some studies have
found a significant direct relation between SN and EI (e.g., Moriano etal., 2012;
Tong etal., 2011), whereas others have not (e.g., González-Serrano etal., 2018b;
Table 6 Multigroup comparison
with standardized estimates
and p-value for top athletes and
non-athletes, including model
fit indices
Path Group Standardized
Estimate SE C.R. p-value
PA →EI Top athlete 0.728 0.065 13.718 0.000
Non-athlete 0.766 0.075 10.677 0.000
SN → EI Top athlete 0.031 0.079 0.758 0.000
Non-athlete -0.065 0.102 -0.996 0.319
PBC → EI Top athlete 0.290 0.056 6.074 0.000
Non-athlete 0.159 0.064 2.523 0.012
R → PA Top athlete 0.215 0.242 2.511 0.012
Non-athlete 0.374 0.179 4.221 0.000
R → SN Top athlete 0.389 0.166 4.291 0.000
Non-athlete 0.495 0.145 4.607 0.000
R → PBC Top athlete 0.322 0.244 3.944 0.000
Non-athlete 0.447 0.179 5.211 0.000
Top athlete
R2EI = 0.790
Model fit χ2 = 454.659, df = 281, CMIN/df = 1.618,
RMSEA = 0.056, SRMR = 0.0746,
CFI = 0.957, TLI = 0.951, AGFI = 0.816, PNFI = 0.775
Non-athlete
R2EI = 0.667
Model fit χ2 = 457.018, df = 281, CMIN/df = 1.626,
RMSEA = 0.067, SRMR = 0.1107,
CFI = 0.945, TLI = 0.937, AGFI = 0.765, PNFI = 0.753
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Liñán & Chen, 2009). Focusing on sport science students, a significant positive rela-
tionship of PA and PBC was observed on EI (Gonzalez-Serrano etal., 2018b; Naia
etal., 2017), but no relationship (Gonzalez-Serrano etal., 2018b) or a weak negative
relationship at a low level of significance (Naia etal., 2017) was observed between
SN and EI. A possible explanation might be the different contexts in which the TPB
was applied (Krueger etal., 2000). A meta-analysis of social entrepreneurship inten-
tion found the subjective norm significant over 31 studies (Zaremohzzabieh etal.,
2019). Conversely, Kachkar and Djafri (2021) found SN not significantly influencing
the intention of refugees, indicating that the opinion of the refugee community did
not determine their intention to participate in microenterprise support programs. As
shown in Fig.2, the whole sample and the non-athletes failed in significance. For top
athletes, a weak but significant relation was identified. A possible reason is that ath-
letes have an additional social reference group through media and their huge network,
which they gained during their active careers (Ratten & Miragaia, 2020). Athletes
might feel a high pressure from that extended social group, which leads to the higher
importance of the other’s opinion when forming the entrepreneurial intention.
Ajzen (1991) demonstrated that the extent to which PBC influences intention var-
ies across situations, stating that "the addition of perceived behavioral control should
become increasingly useful as volitional control over behavior decreases" (p. 185).
Control beliefs are expected to be influenced by experiences and reduce the per-
ceived adversity of a subsequent situation (Su etal., 2021). Karimi etal. (2014) found
differences in the relationship between perceived behavioral control and intention
based on culture. The argument of low uncertainty avoidance for Iranians (meaning
being less afraid in uncertain situations and having a higher tolerance for ambiguity)
compared to other countries (Karimi etal., 2014) can be transferred to the context
of professional sport. A higher risk propensity is required and confirmed for profes-
sional athletes by prior research (Steinbrink etal., 2020). Therefore, top athletes are
expected to feel more capable of facing adversities and coping with the uncertainties
of the entrepreneurial path. Furthermore, athletes who exhibit a high sense of inter-
nal control or those that are less controlled by their environment are able to maintain
low stress levels (Holden etal., 2019). Athletes exhibiting high levels of self-efficacy
and self-confidence are expected to believe in their abilities and athletic performance
Table 7 Hypothesis with results of the chi-square difference test including the results of the hypothesized paths
Hypothesis Model description χ2df Δχ2Δdf Sign. Findings
Unconstrained 911.818 562
Fully constrained 965.035 592 53.217 30 0.000
H5a R → PA constrained 912.046 563 0.228 1 n.s Rejected
H5b R → SN constrained 911.855 563 0.037 1 n.s Rejected
H5c R → PBC constrained 911.826 563 0.008 1 n.s Rejected
H6a PA → EI constrained 912.491 563 0.673 1 n.s Rejected
H6b SN → EI constrained 913.300 563 1.482 1 n.s Rejected
H6c PBC → EI constrained 914.816 563 2.998 1 p < 0,1 Supported
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(Besharat & Pourbohlool, 2011; Fletcher & Sarkar, 2012). Boyd etal. (2021) identi-
fied indicators showing that athletes have a strong belief in their skills for entrepre-
neurship and gained them within their sports careers. Therefore, the strong relation
between PBC and EI can be explained for athletes.
The role ofresilience
In addition, Korber and McNaughton (2018) concluded that resilience might reduce
the fear of failure and lead to the entrepreneurial engagement of overconfident entre-
preneurs. Our results showed a higher level of resilience for top athletes compared
to non-athletes and a positive relationship between PBC and EI. Compared to non-
athletes, this influence was found to be significantly stronger, indicating that top ath-
letes were highly influenced in their intention by the level of perceived control over
a situation. Therefore, top athletes are expected to be highly confident in their ability
to control a situation, such as an entrepreneurial event. Entrepreneurship education
has to increase the awareness of risks and potential obstacles to prevent top athletes
from being overconfident and making irrational, risky entrepreneurial decisions.
Another indicator that resilience explained EI was the explained variance. The
meta-analysis by Armitage and Conner (2001) analyzed 185 studies using the TPB to
explain behavior and intention, showing 29 to 39% of the explained variance. Looking
at the specific context of entrepreneurial intentions, the TPB can explain up to 59%
of the variance (Kautonen etal., 2015). Zhao etal. (2010) calculated an R2 = 0.36 for
the big five personality traits (openness to experience, conscientiousness, extraver-
sion, agreeableness, neuroticism), explaining the entrepreneurial intention. Liñán and
Chen (2009) tested different demographic and human capital variables on the ante-
cedents of entrepreneurial intention within the TPB. With the variables of gender,
role model (personally knowing an entrepreneur), self-employment experience, and
work experience, the antecedents achieved R2PA = 0.192, R2SN = 0.152 R2PBC = 0.177,
and R2EI = 0.555. Therefore, 55.5% of the variance in entrepreneurial intention and
17,7% in PBC were explained by the model Liñán and Chen (2009). The model
applied in this study explained 32.1% more of the variance in PBC. Therefore, the
relevance of resilience is very high for explaining the perceived behavioral control
concerning an entrepreneurial event. The R2 of this study reached R2EI = 0.744 for
all participants, R2EI, NO = 0.667 for non-athletes, and R2EI,TA = 0.790 for top athletes.
Thus, the explained variance for the entrepreneurial intention of top athletes was
79.0% in the model. This value, being 12.3% higher than for non-athletes, indicated
that the expected model implies highly relevant explaining factors for top athletes.
Practical implications
The great model fit for the overall sample and the group of top athletes could lead
to the conclusion that the model based on resilience explained the entrepreneurial
intention for top athletes but not exclusively. Considering the results of ANOVA,
which showed that both resilience and EI were greater for top athletes, we expected
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the model to work well for people with a high level of resilience, notwithstand-
ing how the level of resilience was gained. No significant difference was observed
between top athletes and non-athletes in the relationship between resilience and PA,
SN, and PBC, supporting this presumption. Therefore, all individuals with a high
level of resilience, whether gained through competitive sport or other adverse expe-
riences, such as illness or loss, had a positive relationship with the explaining fac-
tors of EI within this study. By strengthening the awareness of resilience and help-
ing people to discover their potential for resilient behavior, their attitude towards
entrepreneurship, perceived behavioral control, and normative beliefs about entre-
preneurship can be strengthened, leading to a higher entrepreneurial intention. Fur-
thermore, as the strength of the relationship between PBC and intention was very
high, the level of perceived controllability over an entrepreneurial event should be
enhanced to strengthen entrepreneurial intention. As a learnable skill, resilience
training should be considered a part of entrepreneurship education for non-athletes.
The need for a more interdisciplinary approach in sports education (Ratten &
Jones, 2018) and especially the need for entrepreneurship education of sports stu-
dents (Jones & Jones, 2014) and athletes was pointed out in prior research. The same
was found in this study concerning the group of top athletes. With a potentially high
level of confidence and fearlessness (Korber & McNaughton, 2018), entrepreneurial
risks could be taken carelessly by top athletes. A high level of risk can lead to great
success but can also result in failure (Georgiana-Delia, 2013). Motivation towards
an entrepreneurial career is needed to support top athletes in their career transition.
However, understanding and managing risks should also be considered.
Sport associations can use the study’s findings to support athletes on an individual
basis as well as leverage the associations’ success with internal projects on innova-
tion, supported by athletes as intrapreneurs. Furthermore, the findings are highly rel-
evant for investors, as they invest rather in the entrepreneur than products or busi-
ness plans (Mason & Stark, 2004). As entrepreneurship has a high relevance for a
country’s economic success (Wennekers & Thurik, 1999), policy should be aware of
the study’s findings and make leverage of that asset by promoting athletes to an entre-
preneurial career and providing suitable policy interventions as funding requirements
(Ratten & Miragaia, 2020).
Limitations andfuture research
Intention was found to be the best predictor of actual behavior, which are both con-
sidered in the full TPB (Ajzen, 1991). Kautonen etal. (2015) criticized the scar-
city of research on actual entrepreneurial behavior. Additionally, within this study,
entrepreneurial intentions were the best approximation for understanding the career
transition process of athletes. Future research on the influence of resilience on an
entrepreneurial career should further develop this study’s findings and include entre-
preneurial action. Further longitudinal studies to research the actual entrepreneurial
behavior of top athletes should also be undertaken.
Within the multigroup comparison, the results should be interpreted with caution
based on existing limitations. The parsimony fit indices measure the fit compared to
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its complexity (Hair etal., 2019). A simpler model with fewer variables or estimated
parameter paths is suggested to improve parsimony fit (Hair etal., 2019). A remarkable
difference in the parsimony fit (see AGFI in Table6) was identified within the multi-
group comparison. The absolute fit indices indicate how well a model fits the sample
data (Hair etal., 2019). The difference in SRMR was striking. The eligibility of the
model can be confirmed for athletes but has to be further explored for non-athletes.
Future research might look at other contexts promoting resilience, such as other
job profiles with specific stressors leading to resilience (e.g., army; Lee etal., 2013)
or personal stressors (e.g., illness or victims of domestic abuse; Anderson etal.,
2012) and their influence on entrepreneurial intention. Furthermore, not only ath-
lete entrepreneurship as a second career option should be researched in more deep-
ness. Also, interpreneurial activities of athletes (Jones etal., 2020) within the sports
industry, e.g. within associations or clubs, should be considered. Thinking about
training methods and competition results, resilience and entrepreneurial intention
can also be drivers towards success that should be content of future research.
Conclusion
Resilience is considered a learnable skill that athletes develop by permanently facing
adversities affecting their sports and private lives. Compared to the reference group,
the level of resilience and entrepreneurial intention was higher for top athletes. Over-
all, this study confirmed that the TPB includes resilience as an additional influencing
factor, both in general and for the specific group of top athletes. In addition to con-
tributing to the research field of athlete entrepreneurship, this study also adds knowl-
edge to the discussion of the TPB, especially concerning the relationship between
PBC and EI that differs under the perspective of resilience. Practical implications
underline specific requirements of entrepreneurship education for athletes. Resilience
and its advantages are not exclusive to athletes as different kinds of adverse events
can foster resilience (Seery etal., 2010). In the case of athletes, adversities are con-
spicuously present. Therefore, athletes should be aware of their function as role mod-
els and discuss their success stories after failure to motivate non-athletes to take risks,
fail, and try again, aiming to build a high competence of resilience.
Funding Open Access funding enabled and organized by Projekt DEAL.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
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