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Abstract

This paper examines the effect of founders' financial education on the quality of financial information provided to investors as well as on the perceived capabilities of the founding team. The examinations are based on a unique sample of 130 international student-run start-ups taking part in the Jacobs Startup Competition. This paper finds that founding teams with at least one member having a financial education background provide more useful and specific financial information in their business plans. However, the readability of this information is lower than that of teams with no such educational background. Moreover, the results suggest that investors regard founding teams comprised of at least one team member with financial education as more capable and competent. This effect stems from investors screening of team members' biographic information rather than from indirect effects resulting from higher quality financial reporting in business plans. The findings contribute to the existing entrepreneurship education literature by providing empirical evidence that accounting and finance courses should be core elements of entrepreneurship curricula.
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FINANCIAL EDUCATION OF FOUNDERS, IS IT IMPORTANT?
A CASE STUDY OF JACOBS STARTUP COMPETITION
Andreas Seebeck, Euro-FH University of Applied Sciences, Doberaner Weg 20, 22143 Hamburg;
andreas.seebeck@fau.de
Robin M. Wolter, Jacobs University Bremen and Main Organizer of Jacobs Startup Competition, Campus Ring 1,
28759 Bremen; R.Wolter@jacobs-university.de
Abstract
This paper examines the effect of founders’ financial education on the quality of financial
information provided to investors as well as on the perceived capabilities of the founding team.
The examinations are based on a unique sample of 130 international student-run start-ups taking
part in the Jacobs Startup Competition. This paper finds that founding teams with at least one
member having a financial education background provide more useful and specific financial
information in their business plans. However, the readability of this information is lower than that
of teams with no such educational background. Moreover, the results suggest that investors regard
founding teams comprised of at least one team member with financial education as more capable
and competent. This effect stems from investors screening of team members’ biographic
information rather than from indirect effects resulting from higher quality financial reporting in
business plans. The findings contribute to the existing entrepreneurship education literature by
providing empirical evidence that accounting and finance courses should be core elements of
entrepreneurship curricula.
Keywords:
Financial education, entrepreneurship education, start-up competition, business plan, founding
team, textual analysis
Acknowledgements:
We thank Tilo Halaszovich and Hauke Laackmann for their helpful comments on an earlier version
of this paper. In addition, we thank Jacobs Startup Competition (JSC) organising committee for
providing the data analysed in this paper.
Biographical notes: Andreas Seebeck is Professor of Business Administration in particular
Accounting and the dean of the study programme Entrepreneurship and Innovation at Euro-FH
University of Applied Sciences in Hamburg. He is also lecturer at Jacobs University Bremen. He
received his doctorate from the FAU Erlangen-Nuremberg. His research focus lies on accounting,
digital innovations, and ethics. He has published on these topics in such journals as Journal of
Accounting and Organizational Change and Journal of Business Ethics.
Robin Wolter studied International Business Administration at the Jacobs University Bremen and
was a teaching assistant at the Department of Business & Economics. From 2018 to 2020, he was
the main organizer of the Jacobs Startup Competition (JSC). After his graduation, he started his
professional career at KPMG in Hamburg.
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1 Introduction
Universities face the challenging task of developing curricula that provide students with the
knowledge required for their future career intentions. For entrepreneurship education, this means
that universities aim to instil the skills, attitudes, and characteristics into students that are needed
to implement their own business ideas in highly uncertain environments (Koivumaa and Puhakka,
2013; Siddiqui and Alaraifi, 2019). In line with universities’ intentions, students enter
entrepreneurship programmes in order to get better equipped with the tools that allow for the
successful development in entrepreneurial ecosystems (e.g., Kurniawan et al., 2019; Stamboulis
and Barlas, 2014). This raises the question of what types of courses and topics should ideally be
covered in entrepreneurship programmes.
Particularly, the integration of accounting and finance courses in entrepreneurship
programmes remains a matter of controversy. Opponents argue that accounting and finance
knowledge is of minor relevance for founders who should focus on the implementation and
development of their core businesses and rather outsource accounting activities to experts
(Aronsson, 2004). In contrast, proponents argue that accounting and finance courses should be at
the heart of entrepreneurship curricula (Samkin et al., 2012) as financial education plays a key role
in providing founders with the skills needed to direct investment and grow their business, and thus
achieve entrepreneurial success (Nwaigburu and Eneogwe, 2013; Nwokike, 2013; Wise, 2013;
Byun et al., 2018).
This paper contributes to the discussion above by providing evidence on how financial
education affects the quality of financial information in business plans. Moreover, it sheds light on
the effect that founders’ financial education has on investors’ perceptions of team capabilities,
3
including an examination of potential transmission mechanisms of higher quality financial
reporting in business plans on perceived team capabilities.
This study finds the following: first, founding teams with at least one member having a
financial education background provide more useful financial information in their business plans.
According to the findings of innovative computer-aided textual analysis, this financial information
is more specific, but less readable. Next, it finds that potential investors perceive founding teams
which include financially educated founders as more capable and competent. The results reveal
that this positive effect stems from investors’ screening of team members’ biographic information
rather than from indirect effects resulting from higher quality financial reporting in business plans.
The results are robust to a battery of robustness checks.
The findings of this paper contribute to existing entrepreneurship education literature in
several ways and have various practical implications for universities, students, founders, and
investors. First, they are of high interest for universities in the process of developing
entrepreneurship curricula, as they indicate that accounting and finance courses play a crucial role
in entrepreneurship education and thus should not be discounted in entrepreneurship programmes.
The unique study design allows to trace the positive effects of founders’ financial education
background back to university education, as participation in the Jacobs Startup Competition (JSC)
is strictly limited to student-run start-ups. Therefore, it reduces the risk of omitted variables such
as founders’ work experiences in accounting. Such biases are common when examining the effects
of study programme choices on graduates’ skills and attitudes.
Second, the findings are particularly interesting for prospective and current
entrepreneurship students when making their university and course decisions and assembling their
founding teams. Enrolling in entrepreneurship programmes that offer accounting and finance
4
courses is of great advantage for future founders, since the financial education gained through
these courses can result in a greater awareness of the inherent financial risks in the entrepreneurial
ecosystem and a higher ability to address them. The unique data derived from JSC’s uniform
evaluation sheets generates interesting insights into investors’ decision-making, which normally
stay hidden, as investors’ evaluations of team capabilities are typically not publicly available.
Third, the findings are of interest to investors who strive to improve their start-up
evaluation models. Investors tend to use their own evaluation schemes, indicating that existing
models are not fully descriptive of the determinants that contribute to their investment decisions
(Shafi, 2019). This paper responds to the call for research on more realistic evaluation models
(Köhn, 2018), identifying founders’ financial education as an important criterion.
The remainder of this study is structured as follows: section 2 provides background
information on JSC and the relevance of financial education for founders. In section 3, the
hypotheses are developed. Section 4 presents the research methodology. In section 5, the empirical
results are presented and discussed. In section 6, the conclusion and implications are presented.
2 Background
2.1 The Jacobs Startup Competition (JSC)
The Jacobs Startup Competition (JSC) is a student-run annual competition at Jacobs University,
Bremen. It provides a platform for student entrepreneurs to pitch their business ideas and to get in
touch with venture capitalists, angel investors, incubators, accelerators, and other founders.
Participation is limited to early-stage start-ups with an internationally scalable business idea and
less than $25,000 annual revenue. Participants come from 39 countries worldwide and are enrolled
at 64 different universities. The students’ home universities range from well-known institutions
5
such as Harvard University, Oxford University, and University of Cambridge to lesser-known
institutions. From a research perspective, JSC constitutes a unique setting that allows for insights
into founders’ diverse educational backgrounds.
There are three stages of the competition. In stage one, all applicants submit an executive
summary of their business plan, which is evaluated by mentors, i.e., venture capitalists, angel
investors, and business representatives, who typically have founding experiences themselves. The
mentors use a uniform evaluation form to assess the start-ups. In stage two, the best teams hand in
more detailed documents, including their pitch decks. Founders are guided through their business
plans by questions about their start-up’s value proposition, pricing and sales strategy, financing
plans, and their visions for the future. In stage three, the best candidates are invited to pitch their
idea in front of a jury consisting of experienced individuals from both academia and the industry.
Most jury members represent the mindset of typical venture capitalists, i.e., private equity investors
who decide to provide capital to companies exhibiting high growth potential in exchange for equity
stake. However, some represent the mindset of angel investors, i.e., high-net-worth-individuals
who decide to invest their private wealth. All jury members use a uniform evaluation form to assess
the start-ups. Evaluation criteria include, inter alia, the distinctiveness of the unique selling point,
the achievability of financial projections, and the portrayal of the team’s capabilities.
1
The two-day annual final events take place on the international Jacobs University campus
in Bremen. Alongside the final pitches, many expert talks and workshops given by successful start-
up founders and entrepreneurship professionals take place. Student founders get the opportunity
to meet inspiring entrepreneurs from all over the world and network with established accelerators
1
For reasons of competition, the exact evaluation criteria are subject to confidentiality.
6
and investors. The teams that proceed to the final get the chance to win a monetary reward, as well
as access to other valuable resources.
2.2 The relevance of founders’ financial education
Financial education is an opportunity for the economic development of the individual (Bull and
Willard, 1993; Johannisson, 2011). The Organisation for Economic Cooperation and Development
(OECD, 2005, p.13) defines financial education as: “The process by which financial
consumers/investors improve their understanding of financial products and concepts and, through
information, instruction, and/or objective advice, develop the skills and confidence to become
more aware of financial risks and opportunities to make informed choices, to know where to go
for help, and to take other effective actions to improve their financial well-being.” All these skills
and attitudes are important characteristics for entrepreneurs.
Consequently, prior literature finds that financial education is an effective mechanism for
improving financial behaviour of both consumers and entrepreneurs (Hastings et al., 2013;
Fernandes et al., 2014; Zinman, 2015). Further, financial education has a strong positive impact
on financial literacy (e.g., Altman, 2012; Samkin et al., 2012; Kaiser and Menkhoff, 2017).
Financial literacy refers to understanding basic financial attitudes and services, saving and
spending money, understanding nancial records, and the awareness of financial risks (Wise,
2013). People with high financial literacy are more likely to accumulate wealth and manage it
effectively (Hilgert et al., 2003; Stango and Zinman, 2009; Gale and Levine, 2010; Meoli et al.,
2020).
Financial education can take place both at school and at university, as well as in non-
educational settings such as at the workplace, counselling, and community-based programmes
(Gale and Levine, 2010). The evidence of a positive and a substantial impact on financial literacy
7
is ambiguous for all the aforementioned approaches. However, classroom financial education is
found to effectively increase financial knowledge in many studies (Bernheim et al., 2001; Gutter
et al., 2008; Kaiser and Menkhoff, 2017).
Several studies have pointed out the great importance of financial information in
entrepreneurial ecosystems, especially when seeking external funding (e.g., Cassar, 2009;
Fleming, 2009; Wise, 2013). For instance, Fleming (2009) finds that increased quantifications of
earnings forecasts have positive effects on investors’ screening judgments, and Wise (2013)
highlights the signalling function of providing financial information. The high relevance of
financial information, in combination with the positive effects of financial education on founders’
financial literacy and behaviour, calls for a manifestation of accounting and finance courses in
entrepreneurship education. However, recent studies show that they feature rarely in such curricula
(Siddiqui and Alaraifi, 2019; Abad-Segura and González-Zamar, 2019).
Whilst several papers describe accounting and finance classes as one of many aspects of
entrepreneurship education (e.g., Jusoh et al., 2011; Katz et al., 2016), only a few papers focus on
their particular relevance. Nwokike (2013) concludes that accounting skills are essential for
entrepreneurship education as they prepare students for the sustainable growth of their business.
Nwaigburu and Eneogwe (2013) emphasise that entrepreneurs with basic accounting knowledge
are more likely to be successful in their start-up projects. Overall, these studies point to the crucial
importance of financial education for entrepreneurs. In line with this view, Siddiqui and Alaraifi
(2019) argue for more entrepreneurial accounting and finance components in entrepreneurship
curricula.
8
3 Development of hypotheses
There are three main reasons why founders’ financial education is likely to positively affect the
quality of financial information provided to external parties. First, financially knowledgeable
founders have a greater awareness of the benefits of providing verifiable financial information
when seeking funding (Köhn, 2018). Entrepreneurial ecosystems are characterised by high
information asymmetries and uncertainty (Pelz, 2019). Prior literature finds that a detailed
reporting of financial information to external users can lead to a reduction of ambiguity and
information asymmetry (Huang, 2017; Kaya and Seebeck, 2019; Lerman, 2020; Seebeck and
Vetter, 2021). Founders with a financial education background, ex ante, know about the relevance
of financial reporting. Consequently, the quality of financial information provided in their business
plans is likely to be higher.
Second, founders with a financial education background likely have a greater ability to
present the most important financial information in a clearer way and in the right context (Allee
and Yohn, 2009). Accounting and finance classes typically provide students with skills such as
analysing and preparing financial records, budgeting, and controlling. Hence, the quality of
financial information in business plans is likely to be higher for founding teams with financially
knowledgeable members.
Third, providing easy-to-grasp and objectifiable information, such as quantifications of
scaling opportunities, is challenging. Start-ups’ growth is accompanied by increasing complexities
of firm structures and business models. It demands that firms prepare documentation outlining
relevant financial information, which objectively captures complex structures and can be used for
performance measurement internally and externally (Cassar, 2009; Wise, 2013). Financially
9
educated founders are likely to have greater confidence in their ability to respond to internal and
external information demands and thus provide superior information.
To summarise, financial education likely results in founders’ greater awareness of the
inherent financial risks in the entrepreneurial ecosystem as well as a greater ability and confidence
to address external users’ financial information needs. However, there can also be negative effects
of financial education on the quality of information provided to users. Founders with a financial
education background may not make optimal use of the scarce space in business plans by putting
too much emphasis on financial aspects. Thereby, they may neglect other relevant information
regarding their business models, strategies, and customer needs, which are essential to interpreting
the data. Moreover, financial education may lead to less readable information due to the greater
extent of the inherently more complex financial information (Loughran and Mcdonald, 2014).
Overall, this paper assumes that founders with a financial education background tend to
provide higher quality financial information in their business plans. Thus, the first hypothesis, in
the alternative form, is stated as follows:
H1 Founders’ financial education background is positively associated with the quality of
financial information provided in business plans.
Prior literature identifies financial literacy to be a crucial skill for founders (e.g., Allee and
Yohn, 2009; Davila et al., 2009; Fleming, 2009; Nwaigburu and Eneogwe, 2013). One important
way of fostering financial literacy is through financial education (Altman, 2012; Samkin et al.,
2012). It allows founders to better understand the nature of business operations and the underlying
structures (Davila and Oyon, 2009; Brinckmann et al., 2011; Audretsch et al., 2019). Hence, it can
be expected that investors view the founding teams as more capable if they have at least one
member who is financially knowledgeable. Aside from the direct effects of financial education on
10
perceived team capabilities, it is also possible that higher quality financial information in business
plans produced by financially knowledgeable founders (as predicted by hypothesis one) may have
a positive effect on investors’ perceptions of the team.
However, there are several potential reasons why founders’ financial education may not be
positively or even negatively related to perceived team capabilities. First, financial education may
result in increased moral hazard concerns and thus higher agency costs. For instance, founders
may have incentives to use financial resources for other purposes than those which reflect the
investors’ best interests (Allee and Yohn, 2009; Cumming et al., 2019). Greater financial
knowledge comes alongside amplified knowledge of how-to window-dress figures and obfuscate
adverse information. Therefore, a profound financial education background can increase distrust
of information validity. Second, investors expect founding teams to have increased expertise in the
core disciplines in which the start-up operates, which allows for better strategic planning (Henneke
and Lüthje, 2007; Köhn, 2018). The average team size in early-stage start-ups amounts to three
founding members (Backes-Gellner et al., 2015; Kaiser and Müller, 2015). Team members with a
strong accounting and finance background potentially lack in-depth product and industry expertise.
Third, investors generally look for creative founders that are able to perform in rapid changing
environments (e.g., Maxwell et al., 2011; Davis et al., 2017). For instance, the findings from Davis
et al. (2017) indicate that start-up performance is positively related to creativity. However, the
persisting stereotype accountant is “someone who is single-mindedly preoccupied with precision
and form, methodical and conservative, and a boring joyless character” (Friedman and Lyne, 2001,
p.1). Strong founders’ accounting backgrounds can thus result in a presumed lack of creativity
(Florin et al., 2007).
11
Overall, the ex-ante relationship between founders’ financial education backgrounds and
investors’ perception of team capabilities remains unclear. On the one hand, investors are likely to
appreciate financial skills when assessing the teams’ capabilities by screening biographic
information. In addition, higher quality financial information in business plans, which is likely
driven by financial education, as described in hypothesis one, may positively affect perceived team
capabilities. On the other hand, there are several reasons, as described above, why there may be
no or even a negative effect of financial education on the perception of team capabilities. The
second hypothesis, in the alternative form, is stated as follows:
H2 Founders’ financial education background is associated with perceived team capabilities.
4 Data and methodology
The empirical analyses in this paper are based on a unique sample of 130 international student-run
start-ups that took part in the Jacobs Startup Competitions (JSC) from 2018 to 2020. The members
of the founding teams are enrolled in 64 universities located in 39 countries worldwide. The
analyses of hypothesis one are based on structured executive summaries of business plans. The
analyses of hypothesis two are based on aggregate evaluations of the participating teams’
characteristics, which are derived from 279 individual evaluation sheets.
2
According to JSC participation conditions, participants must be enrolled in a university
programme when applying. Consequently, founders in the setting generally do not have extensive
extracurricular experiences such as comprehensive job experience. Hence, the dataset allows to
draw direct inferences about the influence of accounting and finance courses on financial
information provided in business plans and on the perceived capabilities of the founding team.
2
Multiple evaluations from different jury members for the same business plan are aggregated to mean values, resulting
in 130 observations.
12
Thereby, the research design reduces the risk of omitted variables, which is often prevalent in
studies examining the effects of study programme choices on graduates’ skills and attitudes. Table
1 displays the composition of the dataset
.
Table 1: Description of the dataset
Year % N
2018
3
4
.
6
45
2019
42.3
55
2020
23.
1
30
100.0
130
Notes: This table presents the sample used for the examination of hypotheses
one
and
two
.
For the examination of the first hypothesis, the following logit regression model is used:
  
        !  " #   $
 %&
Eq. 1
The dependent variable is quality of reporting (REP_QUALITY), which is measured in
three different ways: first, by the usefulness of financial information provided in the business plan.
This measure assesses the perceived quality of the financial information provided
(QUAL_PERCEIV). In contrast to the following two alternative quality measures, the usefulness
of information is determined by the user’s subjective expectations.
Second, by the specificity of financial information provided in the business plan
(SPECIFICITY). In line with recent accounting studies (e.g., Hope et al., 2016; Seebeck and Kaya,
2021), specificity is measured using innovative text mining tools. More precisely, the Stanford
Named Entity Recognizer (NER) with seven clusters, including 1) locations, 2) organisations, 3)
13
dates, 4) money values, 5) persons, 6) percentages, and 7) time indications, is used.
3
This approach
allows for the objective quantification of the provided financial information. SPECIFICITY equals
one if the founders provide above-median specificity of financial information, and zero otherwise.
Third, by the readability of the financial information provided in business plans
(READABILITY)
4
. The BOG index by StyleWriter is used to measure readability. It is widely
utilised in recent scientific literature to measure the readability of financial information (Bonsall
et al., 2017; Blanco et al., 2020; Hasan, 2020; Seebeck and Kaya, 2021). The BOG index improves
standard readability formulae such as the Flesch–Kincaid readability tests and the Gunning fog
index, by measuring readability with a graded word list rather than simply using a syllable count,
or word length. It further rates the readability of a document according to document type, the
writing task, and the audience, using the following formula:
'( )*+)*,)'( -./0'( 1 )2
Eq. 2
where the Sentence BOG captures a scaled average sentence length. The Word BOG incorporates
a multitude of style issues in plain English, such as the usage of the passive voice and superfluous
words as well as the word difficulty. Pep is a measure to account for good writing by analysing
the choice of words and the names used.
The independent variable in the regression model is an indicator variable describing the
financial education background of the founding teams gained through university accounting and
finance courses (FIN_EDU). If at least one team member has a profound accounting and finance
education background, the variable is coded as one, and zero otherwise.
3
Specificity is measured using the Stanford NER tool version 4.2.0 (Finkel et al., 2005).
4
Readability is measured using StyleWriter 4.43 by Editor Software (UK) Ltd.
14
The following control variables are used: level of education (GRAD), sex ratio (SEX), team
size (SIZE), type of organisation (TYPE), and number of words used to describe financial
information (LENGTH). GRAD considers the founders’ level of education, differentiating
between undergraduate and postgraduate founders. According to Jones (2011; 2013)
entrepreneurship education varies significantly between the undergraduate and postgraduate
levels. Further, Jafari-Sadeghi et al. (2020) find that a higher level of education increases
entrepreneurial competency and exhibits a positive influence on business creation. SEX is the ratio
of male to female team members, controlling for diversity of the founding team. The gender of
team members has been found to have an influence on financial reporting decisions (Francis et al.,
2015), financial reporting quality (Labelle et al., 2010), and credit access of start-ups (Henderson
et al., 2015). SIZE controls for the number of team members, as bigger teams are more likely to
have greater expertise and more resources to prepare their business plans likely resulting in higher
reporting quality. TYPE distinguishes between different types of organisations, including B2B,
B2C, and B2B2C. This control variable is based on Morris et al. (2005). It is used to describe the
business model, which influences business plan structure and financial reporting (Nielsen and
Roslender, 2015). Finally, LENGTH captures the length of the financial information provided in
the business plans. Prior studies find that the text length influences the reporting quality. For
instance, longer answers are likely to feature more specific words, but also tend to be less readable
due to their more complex sentence structures (Seebeck and Kaya, 2021). LENGTH values are
winsorised at the 5% level and logarithmised. Table 2 provides a description of all variables.
For the examination of the second hypothesis, the following two-stage least-squares
regression model is used:
15
Stage 1: 
          !
" #   $  %&
Eq. 3
Stage 2: 344     
      
" !  $ #   %
Eq. 4
In both stages of the regression, the control variables GRAD, SEX, SIZE, and TYPE
remain unchanged from hypothesis one. For the first stage of the model, the dependent variable is
the reporting quality of financial information (REP_QUALITY), as used in the examination of
hypothesis one. In the second stage of the regression model, the impact of financial reporting
quality in business plans on perceived team capabilities (JDMT_TEAM) is estimated. Team
evaluations are based on a list of three criteria regarding the team roles, collaboration, and
decision-making capabilities. Jury members evaluate the team capabilities based on the business
plans and supporting documents, including the founders’ CVs. In additional analyses, the jury
members’ assessment of the financial capabilities of the team (JDMT_FIN) is used as the
dependent variable.
The number of words used to describe financial information in the business plan
(LENGTH) is used as an instrumental variable. To ascertain the validity of the instrumental
variable, a number of diagnostic tests are performed. Their outcomes indicate that the instrument
chosen is highly significant (p-value < 0.01, F-test 10.55)
5
. LENGTH is expected to be correlated
with the quality of financial reporting in business plans, but uncorrelated with the error term.
Hence, it has no clear causal link to investors’ perceptions of team capabilities. In this vein,
LENGTH increases REP_QUALITY, and thus the likelihood that investors perceive the team
capabilities as high.
5
Wooldridge’s (1995) test of over-identification yielded a robust score χ2 of 2.43 with a corresponding p-value of
0.0518.
16
Table 2: Variable definitions
Variable description
Dependent variables
REP_QUALITY
QUAL_PERCEIV
1
Usefulness of financial information provided in executive summaries of
business plans;
M
edian split
[1= above
-
median; 0= below
-
median]
SPECIFICITY
1
Specificity of financial information provided in business plans measured
by the Stanford Named Entity Recognizer (NER) with seven clusters
including 1) locations, 2) organisations, 3) dates, 4) money values, 5)
persons, 6) percentages, and 7) time indications;
M
edian split [1= ab
ove
-
median; 0= below
-
median]
READABILITY
1
Readability of financial information provided in executive summaries of
business plans measured by the BOG-Index;
M
edian split [1= above
-
median; 0= below
-
median]
TEAM_CAP
JDMT_TEAM
2
Average jury members’ evaluation of team capabilities derived from
279 individual evaluation sheets containing a uniform list of criteria
[in percent]
JDMT_FIN
2
Average jury members’ evaluation of financial capabilities derived from
279 individual evaluation sheets containing a uniform list of criteria
[in percent]
Independent variable
FIN_EDU Financial education of founding team measured by the existence of at
least one team member with a university financial education
background;
[1= fin
.
e
du
.
background
; 0= no
fin
.
edu
.
b
ackground
]
Control variables
GRAD
3
Founding team’s level of education [1= undergraduate; 2= postgraduate;
3= mixed]
SEX
3
Founding team’s sex ratio [female over male]
SIZE
3
Size of founding team [discrete]
TYPE
3
Typology of start-up’s operating industry [1= B2B; 2= B2C; 3=
B2B2C]
LENGTH
4
Number of words used to describe financial information in the business
plan [logarithmic
, winsori
s
ed at the 5 % level
]
Notes:
1
QUAL_PERCEIV, SPECIFICITY and READABILITY are the dependent variables in
the regression model used to examine hypothesis one and in the first stage of the 2SLS
regression model used to examine hypothesis two.
2
JDMT_TEAM and JDMT_FIN are the dependent variables in the second stage of the 2SLS
regression model used to examine hypothesis two.
3
These variables are used as control variables
in the examination of hypotheses one and two.
4
LENGTH is used as a control variable in the
examination of hypothesis one and as an instrumental variable in the first stage of the 2SLS
regression model for hypothesis two.
17
5 Findings
Descriptive statistics and Pearson correlations
Table 3 presents the descriptive statistics. The average founding team size is three to four founders,
with a minimum of one and a maximum of twelve team members. This is in line with earlier studies
(e.g., Backes-Gellner et al., 2015; Kaiser and Müller, 2015). In 43.5 percent of the teams, at least
one team member has a financial education background. Similar to the findings of Ughetto et al.
(2019), women are under-represented in the founding teams, with a mean sex ratio of only 15.6
percent. The mean evaluation of team capabilities is 66 percent, ranging from 20 percent to 100
percent.
Table 3: Descriptive statistics
Variables N Mean Std.
Dev.
Min 1
st
2
nd
3
rd
Max
Dependent variables
QUAL_PERCEIV
130
0.496
0.495
0.000
0.000
0.000
1.000
1.000
SPECIFICITY
130
1.
123
1.141
0.000
0.000
1.000
2.000
5
.000
READABILITY
130
9.640
3.241
1.250
7.662
9.750
11.9
8
20.15
JDMT_TEAM
130
0.
660
0.
173
0.200
0.554
0.671
0.770
0
.
975
JDMT_FIN
130
0.
508
0.
195
0.
1
50
0.400
0.501
0.650
0.900
Independent variable
FIN_
EDU
130
0.435
0.498
0.000
0.000
0.000
1.000
1.000
Control variables
GRAD
130
2.160
0.918
1.000
1.000
2.000
3.000
4.000
SEX 130 0.159 0.207 0.000 0.000 0.000 0.333 1.000
SIZE
130
3.351
1.617
1.000
2.000
3.000
4.000
12.00
TYPE
130
2.015
0.690
1.000
2.000
2.000
3.000
4.000
LENGTH
130
77.99
53.0
8
26
.00
38.25
66.50
115.0
215.0
Notes: This table presents the descriptive statistics for the sample used to examine hypotheses one and two. All
variables are defined in Table 2. The descriptive statistics for LENGTH are presented as absolute numbers
(winsorised) for easier interpretation. The regression analyses, however, are based on their natural logarithms.
18
Table 4 presents the Pearson’s correlation coefficients for all variables. The analysis provides
initial evidence for a positive association between financial education background and the dependent
variables QUAL_PERCEIV (0.145, p<0.1) and SPECIFICITY (0.149, p<0.1). However, there is a
negative association between FIN_EDU and READABILITY (-0.159, p<0.1). Correlation
coefficients between control variables are low (<0.6), indicating no risk of multicollinearity. It also
provides initial evidence in favour of hypothesis two, indicating a positive association between
FIN_EDU and perceived team capabilities (0.343, p<0.01), as well as perceived financing capabilities
(0.268, p<0.01). Moreover, there is a strong correlation of LENGTH and each of the three REP-
QUALITY variables (i.e., QUAL_PERCEIV, SPECIFICITY, and READABILITY), which are used
in the first stage of the 2SLS regression to examine hypothesis two, but no correlation between
LENGTH and JDMT_TEAM (JDMT_FIN).
Table 4: Pearson’s correlations between all variables
(1a) (1b) (1c) (2a) (2b) (3) (4) (5) (6) (7) (8)
QUAL_PERCEIV (1a)
1
SPECIFICITY (1b) 0.163
0.063
1
READABILITY (1c) -0.045
0.610
0.093
0.291
1
JDMT_TEAM (2a) 0.082
0.353
0.029
0.742
0.017
0.848
1
JDMT_FIN (2b) 0.090
0.306
0.005
0.949
-0.024
0.787
0.506
0.001
1
FIN_EDU (3)
0.1
4
5
0.0
89
0.1
49
0.0
89
-
0.1
59
0.0
73
0.
269
0.000
0.2
97
0.002
1
GRAD (4) 0.037
0.672
0.056
0.523
-0.066
0.457
-0.008
0.925
-0.020
0.890
0.054
0.539
1
SEX (5) -0.039
0.664
-0.126
0.153
0.042
0.637
0.013
0.877
0.192
0.029
-0.039
0.664
0.225
0.010
1
SIZE (6) 0.012
0.895
0.025
0.775
0.060
0.497
0.276
0.002
0.167
0.058
0.012
0.895
0.265
0.003
0.156
0.076
1
TYPE (7) 0.034
0.700
0.055
0.534
-0.033
0.707
-0.004
0.966
-0.098
0.269
0.034
0.700
0.039
0.559
0.009
0.294
0.048
0.586
1
LENGTH (8) 0.238
0.
036
0.244
0.052
-0.173
0.0
42
0.172
0.
3
1
8
0.317
0.
4
1
0
-0.021
0.1
53
-0.100
0.
38
9
-0.261
0.
298
0.002
0.
071
0.012
0.
65
3
1
Notes: This table presents the Pearson’s correlations and their two-tailed p-values for the main variables. Coefficients in bold
present statistical significance at the 10 percent level. All variables are defined in Table 2. Correlation coefficients using
Cramer’s V provide qualitatively the same results.
19
Results for hypothesis one
Table 5 presents the results for the examination of the first hypothesis. The coefficient for
FIN_EDU in Model 1 is 0.847 (p<0.05). The marginal effect is 20.0, which means that the
probability of providing highly useful financial information in business plans increases by 20
percentage points for a team that includes at least one financially educated team member, ceteris
paribus.
6
In Model 2, the coefficient for FIN_EDU is 0.903 (p<0.05), indicating that founders with
a financial education background also present more specific financial information in their business
plans. Both findings are in line with the first hypothesis stating that financial education of founders
is positively associated with the quality of financial information provided in business plans. They
suggest that students with a financial education background have a greater awareness of investors’
demands and show greater ability to respond to them.
However, in Model 3, a negative coefficient for FIN_EDU (-1.142; p<0.05) is shown,
indicating that the readability of information provided in the business plans is lower for founding
teams with a financial education background. Whilst this finding is in line with Loughran and
McDonald (2014) stating that financial disclosures typically incorporate a relatively higher
percentage of complex words, it raises the question of how the more specific but less readable
financial information disclosed by teams with a financial education background influences the
perception of founding team capabilities, which is examined in hypothesis two.
Finally, Table 5 provides the results of the goodness-of-fit test for the fitted model. First,
the Pearson χ2 goodness-of-fit test shows that the model fits reasonably well. However, detailed
test statistics indicate that the number of covariate patterns is close to the number of observations,
making the applicability of the Pearson χ2 test questionable, but not necessarily inappropriate.
6
The odds ratio is 2.323. Thus, the odds are 2.323 times higher that financially educated teams provide highly useful
financial information in business plans.
20
Next, following Hosmer, Lemeshow, and Sturdivant (2013), data was regrouped by ordering the
predicted probabilities and then ten nearly equal-sized groups were formed, resulting in a Hosmer-
Lemeshow χ2 value of 10.19 on 8 df., with a p-value of 0.287 for Model 1. Overall, the goodness-
of-fit tests provide no evidence of lack of fit.
Table 5: Results from logistic regression for hypothesis one
Dependent Variable
Model 1 (logit)
Model 2 (logit)
Model
3 (logit)
QUAL_PERCEIV
SPECIFICITY READABILITY
FIN_EDU
0.847**
(0.407)
0.903**
(0.463)
-1.142**
(0.435)
GRAD
-0.057
(0.
216
)
0.329
(0.250)
-0.135
(0.220)
SEX
-1.339*
(0.670)
-2.726*
(1.209)
1.161
(0.995)
SIZE
-0.071
(
0.120
)
-0.013
(0.139)
0.207
(0.122)
TYPE
0.298
(0.262)
0.168
(0.285)
-0.145
(0.260)
LENGTH 0.099**
(0.037)
0.009**
(0.004)
-0.042**
(0.018)
Constant
-0.430
(0.
561
)
-2.531***
(0.934)
0.219
(0.809)
Observations 130 130 130
Pearson χ
2
128.24 126.45 128.03
Prob >
χ
2
0.30
9
0.346
0.31
5
Hosmer
-
Lemeshow
χ
2
1
0
.1
9
1
0
.
1
4
1
0
.
0
3
Prob >
χ
2
0.287
0.29
4
0.252
Notes: This table shows the regression coefficients for logit regressions of
QUAL_PERCEIV, SPECIFICITY, and READABILITY on FIN_EDU and control
variables. The standard deviations are displayed in parentheses. Model fit is
determined based on Pearson and Hosmer-Lemeshow χ²-values for binary models
reported at p<0.05. All variables are defined in Table 2. *, **, and *** indicates
significance at the 0.01, 0.05, and 0.10 levels, respectively.
Table 6 shows the results of a series of additional tests. In Panel A of Table 6, the
results of an OLS regression analyses are presented. Firstly, in Model 4, an ordinary least square
21
(OLS) regression model with the same dependent and independent variables as presented in Eq. 1
is used, yielding qualitatively similar results.
7
This OLS regression model is also used to assess
the variance inflation factors (VIFs) of all variables used in the examinations. The mean VIF is
1.052, the maximum VIF is 1.062. As all VIFs are below 5, there is no reason to assume
multicollinearity. Next, the table shows OLS regression results for continuous dependent variables
for SPECIFICITY (Model 5) and READABILITY (Model 6).
8
Overall, the results provide
additional evidence in support of hypothesis one.
Moreover, in further additional analyses another control variable is added to the basic
regression model which controls for context financial literacy on country-level (CONTEXT).
9
In
line with prior studies (e.g., Bruhn and Zia, 2011; Meoli et al., 2020), higher financial literacy of
founders driven by context financial literacy may also have an influence on financial reporting in
business plans, as student entrepreneurs who are ex ante more financially literate are likely to
provide higher quality financial reporting. The results presented in Model 5 indicate that context
financial literacy has no influence on financial reporting in business plans. However, the
coefficient for FIN_EDU remains positive and significant when adding the additional control
variables, supporting the initial findings.
10
Thirdly, in Model 6, year-fixed effects are included to
control for factors changing each year that are common to all business plans for a given year. The
coefficient for FIN_EDU is positive and significant on a 10 percent level.
Overall, the findings support hypothesis one stating that financial education of founders
results in higher quality of financial information provided in business plans.
7
In further robustness checks, a probit regression model is used. The results remain qualitatively the same (results are
untabulated).
8
The two variables were logarithmised to adjust for the relative sensitivity of the data.
9
Country-level financial literacy is based on the Global Financial Literacy Survey by Klapper et al. (2015).
10
Results are qualitatively the same when using SPECIFICITY and READABILITY as dependent variables.
22
Table 6: Additional OLS regression analyses for hypothesis one (Panel A)
Dependent Variable
Model 4 (OLS)
Model 5 (OLS)
Model
6 (OLS)
QUAL_PERCEIV
VIF
1
SPECIFICITY
cont
1
READABILITY
cont
1
FIN_EDU
0.166**
(0.060)
1.050 0.114*
(0.063)
0.389**
(0.
179
)
GRAD
0.058
(0.048)
1.056 0.031
(0.023)
0.073
(0.094)
SEX
-0.460*
(0.207)
1.051 -0.140
(0.986)
-0.129
(0.399)
SIZE
-0.002
(0.029)
1.048 -0.009
(0.013)
0.004
(0.005)
TYPE
-0.031
(0.058)
1.015 0.014
(0.028)
0.326*
(0.335)
LENGTH 0.002**
(0.001)
1.062 0.060**
(0.026)
0.183*
(0.085)
Constant 0.397**
(0.029)
-0.064
(0.103)
1.767
(0.462)
Observations
130
130
130
Model fit
2
0.146
0.142
0.1
76
Mean VIF
1.052
1.052
1.052
Notes: This table shows regression coefficients for linear regressions of QUAL_PERCEIV,
SPECIFICITY, and READABILITY on FIN_EDU and control variables. The standard
deviations are displayed in parentheses.
1
SPECIFICITY
cont
(Model 7) and READABILITY
cont
(Model 8) are continuous variables.
2
Model fit is determined based on R²-values. All variables
are defined in Table 2. *, **, and *** indicates significance at the 0.01, 0.05, and 0.10 levels,
respectively.
23
Table 6: Additional robustness analyses for hypothesis one (Panel B)
Dependent Variable
Model 7(logit)
Model
8 (fixed
-
effects)
2
QUAL_PERCEIV
QUAL_PERCEIV
FIN_EDU
0.898**
(0.
463
)
0.171*
(0.091)
GRAD
0.329
(0.2
50
)
0.056
(0.049)
SEX
-1.727
(
1.210
)
-0.465*
(0.209)
SIZE
-0.154
(0.
140
)
0.000
(0.029)
TYPE
0.175
(0.2
87
)
0.033
(0.058)
LENGTH 0.009**
(0.004)
0.002**
(0.001)
CONTEXT
1
0.002
(0.01
2
)
Constant
-2.641**
(
1.103
)
-0.048
(0.195)
Observations
130
130
Model fit
3
11.258
0.102
Year FE
Not
included
Included
Notes: This table shows regression coefficients for a log regression
and
fixed-
effects regression of QUAL_PERCEIV on FIN_EDU and control
variables. The standard deviations are displayed in parentheses.
1
In Model
7, the additional control variable CONTEXT is included.
2
In Model 8,
fixed
effects are included.
3
Model fit is determined based on Hosmer-
Lemeshow
χ²-values for Model 7 and on R²-
values for Model 8. All variables are
defined in Table 2. *, **, and *** indicates
significance at the 0.01, 0.05,
and 0.10 levels, respectively.
24
Results for hypothesis two
Table 7 presents the results of the two-stage least-square (2SLS) instrumental variable regression
of investors’ perceptions of team capabilities on financial reporting quality in business plans and
control variables. The first stage of the regression analyses in Models 9–11 reveal that the
coefficient of LENGTH is significant for all three REP_QUALITY measures, implying that the
instrumental variable is associated with quality of financial reporting in business plans. While there
is a positive association between LENGTH and QUAL_PERCEIV and LENGTH and
SPECIFICITY, the association between LENGTH and READABILITY is negative.
In the second stage of the 2SLS regression, the coefficient of financial reporting quality as
measured by QUAL_PERCEIV, SPECIFICITY and READABILITY is not significant, indicating
that financial reporting quality in business plans is not related to perceived team capabilities. The
findings indicate that there are no significant transmission effects that higher financial reporting
quality has on investors’ perceived team capabilities. However, given that only the best business
plans from the start-up competition, which made it to the JSC finals, are included in the sample, it
cannot be ruled out that there is a positive effect of financial reporting in business plans on
perceived team capabilities when considering all participating teams.
11
The coefficient for FIN_EDU is significant in both stages of the regression models 9 and
10. For instance, the coefficient of FIN_EDU in Model 9 is 0.166 (p<0.05) in stage one and 0.085
(p<0.05) in stage two, indicating that founders’ financial education background has a positive
effect on the usefulness of financial information provided in business plans as well as on perceived
team capabilities. The results for specificity are similar. Thus, in accordance with prior studies
11
Data availability (i.e., team evaluations) is limited to teams that took part in the JSC finals, not allowing for
examinations of the effects of financial reporting quality in business plans of non-finalists on perceived team
capabilities.
25
(e.g., Davila et al., 2009, Fleming, 2009, and Allee and Yohn, 2009), the findings suggest that
founders’ financial education background is a crucial component of the skillset needed to succeed
in the entrepreneurial ecosystem.
Interestingly, having a financial education background has a negative effect on readability
of financial information provided in business plans (stage 1), but a positive effect on perceived
team capabilities. This finding provides further evidence that the positive effect in the latter does
not stem from higher financial reporting quality. As jury members assess the teams based on
various information including the founders’ CVs, it seems likely that the screening of biographic
information of the founders is the key driver.
In robustness checks, perceived team capabilities (JDMT_TEAM) are replaced by
perceived financial capabilities (JDMT_FIN). The results are qualitatively the same for both the
first and second stage and can be interpreted as confirming evidence of the validity of the model
(results are untabulated). Additional tests further revealed that founding teams without a financial
education background do not identify their missing accounting knowledge as a critical weakness
point for the future success of their business (results are untabulated). This in in line with the notion
that founders’ missing financial education background results in a lack of awareness of investors’
demands of useful and specific financial information.
Overall, the results support the second hypothesis stating that founders’ financial education
positively affects perceived team capabilities. This positive effect of financial education cannot be
attributed to higher quality financial reporting in business plans.
26
Table 7: Results from logistic regression for hypothesis two
Dependent Variable
Model
9
(2SLS)
Model 1
0
(2SLS)
Model
1
1
(2SLS)
1
st
stage 2
nd
stage 1
st
stage 2
nd
stage 1
st
stage 2
nd
stage
QUAL
PERCEIV
JDMT
TEAM
SPECI-
FICITY
JDMT
TEAM
READ-
ABILITY
JDMT
TEAM
REP_QUALITY
0.303
(0.286)
0.231
(0.195)
-1.288
(3.250)
FIN_EDU
0.166**
(0.060)
0.085**
(0.038)
0.113*
(0.069)
0.080**
(0.041)
-0.105**
(0.0041)
0.039*
(0.008)
GRAD
0.058
(0.048)
0.013*
(0.002)
-0.031
(0.0022)
-0.028
(0.024)
-0.035
(0.022)
-0.104
(0.227)
SEX
-0.460*
(0.207)
0.169
(0.120)
-0.183*
(0.097)
0.218*
(0.131)
0.199**
(0.09
0
)
0.377
(0.729)
SIZE
-0.002
(0.029)
0.061*
(0.014)
0.005
(0.013)
0.020*
(0.028)
0.007
(0.013)
0.050
(0.088)
TYPE -0.031
(0.058)
-0.013
(0.030)
-0.007
(0.027)
-0.017
(0.028)
-0.006
(0.026)
0.020
(0.106)
LENGTH 0.002**
(0.001)
0.001***
(0.000)
-0.041**
(0.020)
Constant
0.397**
(0.029)
0.422**
(0.170)
0.525***
(0.075)
0.539***
(0.082)
0.546***
(0.077)
1.270
(1.758)
Observations
130
130
130
130
130
130
Model fit
1
0.
146
0.1
40
0.
181
Wald test
χ2
17.18
21.46
13.03
Prob >
χ2
0.009
0.002
0.031
Notes: This table presents the results of instrumental variable regressions of perceived team
capabilities on financial reporting quality in business plans (i.e., QUAL_PERCEIV, SPECIFICITY,
and READABILITY), and on control variables. The standard deviations are presented in
parentheses.
1
Model fit is determined based on adjusted R²-values. All variables are defined in Table
2. ***, **, * in
dicate significance at the 0.01, 0.05, and 0.10 levels, respectively.
27
6 Conclusion and discussion
Entrepreneurship education has attracted great interest among researchers (Kolvereid and Moen,
1997; Matlay and Carey, 2007; Solomon, 2007; Berglund and Holmgren, 2013; Fellnhofer and
Kraus, 2015). Motivated by recent literature which finds that entrepreneurial accounting and
finance are only rarely covered in many entrepreneurship programmes, although desired by
numerous students, this study aims at understanding whether accounting and finance courses
should be considered core elements of entrepreneurship education. It provides initial empirical
evidence that founders’ financial education positively affects the quality of financial information
provided in business plans. Moreover, financial education results in higher perceived team
capabilities which are directly driven by better evaluations of team member skills based on
biographic profiles rather than indirectly through superior financial reporting practices in business
plans. Overall, the results of the paper support the view that universities should include accounting
and finance courses in their entrepreneurship curricula to better equip entrepreneurship students
with the necessary tools and mindsets to excel in the entrepreneurial ecosystem.
This paper also demonstrates that university-based start-up competitions, such as JSC, are
an important element for entrepreneurial education. This is not only because they allow students
to apply their knowledge in a simulated business world, but also because they generate useful
research data that enable researchers to examine how to best optimise entrepreneurship education.
The paper has a number of limitations. First, it is limited to the valuation of jury members
in the Jacobs Startup Competition (JSC). In the real business world, investors may make their
investment decisions differently, depending upon their individual valuation models, existing
investment portfolios, environmental conditions, and personal beliefs. Second, the results are
limited to student entrepreneurs without significant work experience. Graduates’ operational
28
experience in accounting, finance, and management positions can potentially compensate for
missing university accounting education. However, the JSC sample reduces the risk of omitted
variables typically prevalent in concurrent studies, demonstrating a link between university
accounting and finance education and the quality of financial information provided by the founders
as well as their perceived capabilities.
Third, the study is limited to the external accounting perspective by examining financial
information provided to investors and their judgement of team capabilities. Future studies may
deal with the association between financial education, internal management and control systems,
and entrepreneurial success. For instance, profound accounting and finance knowledge allows for
enhanced monitoring mechanisms and facilitates the identification of bottlenecks in the framework
of management accounting (Cassar, 2009). Future studies may also examine how an educational
background in other business disciplines affects entrepreneurial success, the quality of business
plans, and perceived capabilities of the founding teams.
29
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