The impact of digital start-up founders’ higher education on reaching equity investment milestones
The impact of digital start-up founders' higher education on reaching equity investment milestones
Daniel Ratzinger 0 1 2
Simon Mosey 0 1 2
Kevin Amess 0 1 2
Andrew Greenman 0 1 2
JEL Classification L 0 1 2
0 Nottingham University Business School, University of Nottingham , Nottingham , UK
1 Haydn Green Institute for Innovation and Entrepreneurship, Nottingham University Business School, University of Nottingham , Nottingham , UK
2 & Daniel Ratzinger
This paper builds on human capital theory to assess the importance of formal education among graduate entrepreneurs. Using a sample of 4953 digital start-ups the paper evaluates the impact of start-up founding teams' higher education on the probability of securing equity investment and subsequent exit for investors. The main findings are: (1), teams with a founder that has a technical education are less likely to remain self-financed and are more likely to secure equity investment and to exit, but the impact of technical education declines with higher level degrees, (2) teams with a founder that has doctoral level business education are less likely to remain self-financed and have a higher probability of securing equity investment, while undergraduate and postgraduate business education have no significant effect, and (3) teams with a founder that has an undergraduate general education (arts and humanities) are less likely to remain self-financed and are more likely to secure equity investment and exit while postgraduate and doctoral general education have no significant effect on securing equity investment and exit. The findings enhance our understanding of factors that influence digital start-ups achieving equity milestones by showing the heterogeneous influence of different types of higher education, and therefore human capital, on new ventures achieving equity milestones. The results suggest that researchers and policy-makers should extend their consideration of universities entrepreneurial activity to include the development of human capital.
University qualifications economy
M13 J24 I23
Human capital Equity investments
High growth start-ups are considered to be a significant driver of economic growth and
researchers and policy-makers alike are concerned with understanding their emergence and
(Grilli and Murtinu 2014; Lerner 2010; Mason and Brown 2014)
. Digital start-ups
are of particular interest as they are expected ‘‘to deliver more value and wealth to more
consumers and citizens more broadly than any economic development since the Industrial
(Dean et al. 2012, p. 5)
. Fundamental to the creation and growth of digital
start-ups is the successful exploitation of new knowledge
(Audretsch et al. 2016; De
. A significant repository of that knowledge is to be found within university
departments of computer science and business schools (Kollmann 2006). Yet, the process
by which such knowledge is transferred to digital start-ups remains underexplored.
Universities, through their research and teaching, are recognized to be vital contributors
to the growth of technology industries
(Belitski and Desai 2016)
, with particular attention
being paid to their entrepreneurial activity. Although entrepreneurial practices in both
teaching and research
, have been recognized to have a positive economic
(Guerrero et al. 2015)
, studies have historically focused upon the
commercialization of intellectual property created from university research activities as a primary
mechanism for knowledge transfer
(Siegel and Wright 2015)
. These activities, however,
represent a ‘‘distinct minority of all academic interactions with external organizations’’
(Hughes and Kitson 2012, p. 734)
and the broader role of entrepreneurial universities is
being recognized, with an increasing focus upon other entrepreneurial individuals beside
. For instance, the deployment of creative problems skills by
graduates, gained through the teaching practices of the traditional faculties of a university
have been argued to be vital for innovation and economic development
(Philpott et al.
. More recently, attention has therefore shifted towards the knowledge transfer
contribution of university students, particularly those who show an interest in
entrepreneurship as a postgraduate career option
(Fayolle and Gailly 2015)
. Studies have
considered the economic impact and employment contribution that graduates make
through creating start-ups
(Siegel and Wright 2015)
, yet how the knowledge is transferred
from universities to these graduate entrepreneurs has received rather less attention
2014; Nelson 2012)
We propose that a focus upon the human capital that universities contribute to graduate
entrepreneurs could therefore yield new insights. Education is generally argued to be
amongst the most crucial investments in human capital
and the factor that
also has the most direct effect
. Through teaching, universities could
therefore be seen as major contributors to the skillsets of start-up founders. Human capital
is widely recognized in entrepreneurship research
and has also been linked
to economic growth
(Calcagnini et al. 2016)
, but few studies have focused on the impact of
universities on the performance of start-ups through the human capital they contribute
. This may be in part due to the contradictory findings across different
studies. Some authors argue that human capital provided through education provides a
consistent and positive effect upon start-up performance
. Others suggest that
there may be an upper limit to the benefits of education and that even customized
programs, such as entrepreneurship degrees, may increase entrepreneurial engagement but do
not show any discernible impact upon venture performance
(Davidsson and Honig 2003)
We aim to reconcile this debate by considering digital start-ups, a type of venture that are
arguably the most reliant upon knowledge based assets
(Dean et al. 2012)
, and unpick the
relative effects of different types of higher education upon the performance of their
A challenge in considering the performance of such early stage and private businesses is
the absence of traditional financial information. One approach is to adopt investment
milestones as proxy performance measures
. This could be via angel
(Kerr et al. 2014)
or venture capital investments
(Colombo and Grilli 2010;
Mason and Brown 2014)
. Although it is generally very difficult for start-ups to secure such
equity investments (Van Osnabrugge 2000), those that do so are more likely to achieve
(Colombo and Grilli 2010)
From an investment point of view, successful start-up growth trajectories can be
therefore captured by observing the securing of an equity investment, followed by an
acquisition or initial public offering in order to liquidize the equity holders’ investments
(DeTienne 2010; Hegde and Tumlinson 2014)
Regardless of the performance measures used, prior research on digital start-ups has
tended to neglect the impact of human capital due to a focus upon firm level attributes
(Hayter et al. 2016)
. We address this gap through assessing the impact of higher education
on digital technology entrepreneurs’ ability to reach the equity investment milestones of
funding and exit. With a focus on universities as educators, this study therefore explores
their impact on the digital economy in terms of their human capital contribution to start-up
founding teams through formal higher education.
In the following section, we review contemporary work within human capital theory to
build hypotheses relating specific types of higher education to digital start-up performance.
Next, we describe the methodology used and then present a summary of our findings. We
conclude this article with a discussion of the findings and provide suggestions for further
2 Human capital theory
With knowledge recognized to be a core asset of start-ups in the digital economy
human capital theory is an appropriate approach to evaluate the impact of
higher education upon that knowledge. A human capital approach is widely utilized in
and has also been used to explain economic
(Calcagnini et al. 2016)
. Human capital theory assumes that an individual’s
performance outcome is related to their skill and knowledge levels
(Martin et al. 2013)
that increased performance and productivity levels can be expected with increased human
. By contrast, personal traits are not considered as human capital
because they cannot be transferred or developed over time. Within entrepreneurship,
human capital has been identified as a much more significant performance indicator than
(Wright et al. 2007)
. Entrepreneurs with higher levels of human capital have
been found to be more likely to identify entrepreneurial opportunities
Honig 2003; Shane 2000)
. In addition, it has been proposed that human capital has a
positive influence on venture performance, although it has also been noted that related
empirical evidence is inconclusive
(Davidsson and Honig 2003; Marvel and Lumpkin
Eesley and Roberts (2012)
disentangled human capital into the two
concepts of innate talent and the accumulation of entrepreneurial experience. They
highlighted that rather than there being a general positive impact of human capital upon venture
performance, using this finer grained distinction show that the relative importance of
experience versus talent changes with the context (i.e., when the current market or
technology is familiar, experience dominates and vice versa). We therefore propose to take a
similar approach to untangle the effect of human capital upon digital start-ups
performance. If we focus upon the specific types of human capital provided by higher education
then we should be able to tease out the impact of education in different disciplines and at
different levels of study, when controlling for other factors.
In more general studies, formal education has generally been found to have an influence
upon engaging in entrepreneurship, but when it comes to the success of subsequent
entrepreneurial activities, when controlling for previous start-up experience, education was
not seen to have any significant impact
(Davidsson and Honig 2003)
. It was concluded, that
‘‘even the most specific type of explicit human capital, formal education as provided by
business classes, only succeeded in increasing the pace of gestation activities, not in
affecting critical outcomes’’
(Davidsson and Honig 2003, p. 322)
By contrast, research focusing upon the digital economy
technology industry overall
, have shown human capital in the form of
business and technical knowledge to increase start-up performance.
A confounding effect which may reconcile these inconsistent findings is that many
startups consist of multiple founders who work as a team. While research investigating the
human component of entrepreneurship initially focused on individual entrepreneurs, with
the maturing of the research area a shift towards entrepreneurial teams is observed
et al. 2006)
. Teams may confer benefits, especially within knowledge-based industries, and
it has been argued that entrepreneurial teams, rather than individual entrepreneurs, more
commonly drive the venture creation process. Moreover, start-up investors in particular
have noted concerns regarding the skill sets and expertise held by the whole founding team
(Kamm et al. 1990)
. For example, while specific multidisciplinary skill sets of computer
science and business skills required for an effective digital start-up in the digital economy
may be difficult to obtain on an individual level, such mixed skill sets can
be acquired within a founding team
(Stuetzer et al. 2012)
For this study, a team is therefore defined as ‘‘two or more individuals who jointly
establish a business in which they have an equity (financial) interest’’
(Kamm et al. 1990,
and, to enable a comparative analysis, single founders are also considered as a
oneperson team in our analysis. As human capital has an influence on performance at an
(Martin et al. 2013)
, the composition of the founding team is arguably
influential upon the performance of the start-up. Although it has been suggested that
diversity in a team can have a positive impact on performance, homogeneous teams with a
shared cognition are commonly seen in academic spinouts, leading to suboptimal outcomes
(Vanaelst et al. 2006)
. The underperformance of teams with members that have similar
backgrounds has also been confirmed in an earlier study on high-tech spin-offs
and Moray 2004)
Ruef et al. (2003)
concluded that, although a rational team formation
based on the highest achievable outcome could be expected in the specific case of
competitive start-up founding teams, team formation is commonly driven by familiarity and
trust rather than functional performance.
These counterfactual findings suggest that graduate founders are most likely to build
successful ventures if they have a skillset that is matched to the needs of the specific
(Kollmann 2006; Oakey 2012)
. Therefore, for the digital sector we propose
that founders who have studied business or technical education at university (such as
computer science or engineering), when controlling for other factors, should outperform
those that have not:
Hypothesis 1 Start-up founders with higher business education, or higher technical
education, have a higher probability of reaching investment milestones.
Building upon this more granular approach, human capital theory has also been
criticized due to common assumption that ‘‘more human capital is always better’’ and that
over-investments are therefore not being considered
(Davidsson and Honig 2003, p. 305)
Indeed, as discussed by
Marvel and Lumpkin (2007)
, certain types of human capital may
not be as desirable as others for entrepreneurship. For instance, formal education and deep
knowledge in an area have been found to be more significant for enabling radical
innovation than broad knowledge across a number of areas. However, the inverse relationship
has been found to enable incremental innovation
(Marvel and Lumpkin 2007)
In this vein, we propose that a specific human capital configuration that consists of
formal higher education in technical and business related disciplines thereby meeting the
specific knowledge needs of the digital economy
should have a higher
impact on the probability of reaching investment milestones than more general education,
which we define as higher education in non-technical and non-business disciplines (such as
the arts, humanities and social sciences):
Hypothesis 2a Higher business education has a greater impact on the probability of
reaching investment milestones than more general higher education.
Hypothesis 2b Higher technical education has a greater impact on the probability of
reaching investment milestones than more general higher education.
Previous studies have relied on the number of years of any type of formal education as a
measure of human capital
(Marvel and Lumpkin 2007)
, although this approach has been
as it reduces the multi-faceted concept of human capital to a
. It has been highlighted, that research needs to move
beyond such single measures for human capital
(McGuirk et al. 2015)
. Particularly at a
higher education level, the multi-faceted nature becomes evident with an increase in degree
levels that shows a shift from practice to research. While the undergraduate and
postgraduate level are mainly focused on generally applicable learning outcomes such as
problem solving skills and communication, the most advanced level of doctorates is more
generally concerned with developing those skills for specific application within the
(Bologna Working Group on Qualifications Frameworks 2005)
would suggest a non-linear relationship between education level and venture performance,
with the possibility of potential upper limits to the value of human capital gained through
formal education for venture performance
(Davidsson and Honig 2003)
. This is contested
, who suggests consistent and linear increases in performance with
concomitant increases in technical ability and business acumen. Consequently, we propose the
following hypothesis to assess the impact of specific higher education degree levels upon
Hypothesis 3 Increasing university degree levels lead to increasing probability of
reaching investment milestones.
3 Research methodology
3.1 Data source
We use a novel data source that is crowd-sourced, operated by CrunchBase and published
under the Creative Commons Attribution License (www.crunchbase.com). CrunchBase’s
mission is ‘‘to make information about the startup world available to everyone and
maintainable by anyone’’ and therefore its data is not just created and maintained by AOL
employees (particularly by people working for TechCrunch.com) but also by the general
public. Anyone who accepts the terms and conditions can register on CrunchBase to get
access to the dataset. In the context of this project, participants are companies and people
whose information is stored in the CrunchBase database.
CrunchBase offers a unique opportunity to explore technology entrepreneurship on a
large scale with records on more than 270,000 people that are linked to more than 220,000
companies. It has also been demonstrated that CrunchBase data is particularly reliable
when it comes to financial data
(Block and Sandner 2011)
. The crowd-sourced information,
which is moderated before being accepted, is a comprehensive source of data on start-ups,
entrepreneurs, and key milestones (e.g. investments and achieved exits). This enables a
between-group analysis of graduate and non-graduate founders that have secured an equity
investment or that have exited their start-up.
3.2 Data sample
The data sample used in this study has been retrieved from CrunchBase between 1 May
2014 and 10 May 2014. It has subsequently been filtered for start-ups that have at least one
valid person with a job role containing one of the following three keywords to capture
start-up founders only: owner, founder, founding. While CrunchBase provides various
information about people and companies, some information that is vital for this study was
missing. The gender of each founder is determined with an algorithm utilizing a database
containing 134,138 first names that was developed for this study, with data openly
published by various national statistics agencies. Based on the total number of occurrences of a
particular name for a specific gender, a gender probability measure is calculated for every
first name found in the dataset.
Furthermore, each founder’s degree(s) are recorded in a semi-structured manner. While
degree subject, degree level, and institution are stored separately, the information in these
fields is unstructured text that required processing prior so that quantitative techniques
could be employed. This required a mixture of named entity disambiguation and named
entity recognition analysis in order to identify the information held within data points and
to further categorize the information. Algorithms for pre-processing the data points are
developed that link to the following additional data sources, particularly in order to
overcome named entity recognition as well as named entity disambiguation challenges:
Microsoft Bing, Wikipedia and Google Maps as knowledge bases and uClassify.com for
natural language processing. Figure 1 shows the data model entity relationship.
We obtain information on more than 220,000 start-ups stored on Crunchbase. We then
filter and clean the data in the following ways. First, we selected start-ups founded in the
last 25 years. Second, digital start-ups were selected from the following categories on
Crunchbase: advertising; e-commerce; enterprise software and services; games and video;
hardware; mobile; network hosting; search; security; software; web; messaging; and
analytics. Third, the data points are cleaned and categorized. Fourth, only start-ups where
the founder provided complete information on their Crunchbase profile and a distinctively
identified degree subject are used in the analysis. As a consequence of these processes 4953
digital start-ups are included in our data set. The characteristics of the final sample are
extensively tested by comparing it with existing studies, including data from the Global
Entrepreneurship Monitor, gender comparisons
(Coleman and Robb 2009)
and a pilot
study conducted previously
(Ratzinger et al. 2013)
. Overall, on a descriptive level the
CrunchBase data reveals similar results to other studies when investigating higher
education levels of start-up founders.
Although extensive work has been conducted to test the robustness and reliability of the
data sample, the limitations need to be recognized. Issues have been controlled to the best
of the authors’ knowledge, but the fact remains that the original data sample is
crowdsourced and is focused on many other data points in addition to human capital. In addition,
the choice of control variables was limited by the data source.
3.3 Empirical strategy
We aim to test the hypotheses stated in the prior section and determine and quantify the
impact of higher education qualifications on digital start-ups achieving two investment
milestones: funding and exit. These investment milestones are binary outcomes that are
ordered and therefore lend themselves to being analyzed using an ordered probit model,
which assumes a preferred order of outcomes
(Halab´ı and Lussier 2014)
. The estimating
equation is specified as follows:
sf ¼ b0EDUCATIONf þ b1Zf þ ui
where subscript f (f = 1, …, F) denotes the fth start-up and F denotes the total number of
start-ups in the cross section; s represents the dependent variable that identifies the
investment milestones that identify three different states for start-ups on an ordinal scale:
self-sustaining (0); funding (1); and exit (2). EDUCATION represents the following
dichotomous education variables: higher technical education; higher business education;
and general higher education. For the more detailed analysis required for testing
Hypothesis 3, each of the education variables is further distinguished between the different
degree levels: undergraduate; masters; and doctorate. Z represents a vector of control
variables capturing the following start-up characteristics and founder demographics:
startup age; number of cofounders; and founders’ gender. In addition, a scale measures the
proportion of founders in a team who have obtained their degree from the same university.
Founders prior start-up experience is captured in a variable with the following scale: 0
(self-sustained/non-funded start-up), 1 (funded start-up) to 2 (exited start-up).
3.4 Descriptive statistics
Ordered probit estimates of the determinants of digital start-ups achieving investment
milestones are reported in Table 3. The results show that technical education, business
education, general education are statistically significant determinants of digital start-ups
achieving investment milestones. The control variables that are found to be statistically
significant determinants of digital start-ups achieving investment milestones are: gender
and cofounders attending the same university. The results are obtained from estimation of
ordered probit regressions and the estimated marginal effects, conditional on the mean, for
each variable used in the regressions.
Reported in Table 4 are the marginal effects, which quantify the impact of determinants
on achieving investment milestones. The marginal effects figures represent the impact of
the probabilities of outcomes. Here, we focus on those variables relevant to hypotheses 1
and 2. The probability of being self-sustained is 9, 4, and 5 percentage points lower for
cofounders with technical, business, and general degrees, respectively. In contrast the
probability of being funded is 7, 3, and 4 percentage points higher if the co-founders have a
technical, business, and general degree, respectively. Finally, the probability of exiting is 3,
1, and 2 percentage points higher for co-founders with technical, business, and general
We now provide brief account of the control variables. Co-founders attending the same
university, our proxy for social capital, are less likely to be self-sustained and an increased
likelihood of being funded and of exiting. Founding teams prior experience of being
selfsustained increases the likelihood of the current venture being self-sustained by 16
M. E. marginal effect
***, **, and * indicate significance at the 1, 5, and 10% level, respectively
percentage points while reducing the likelihood of achieving funding and exit milestones.
In contrast, prior experience of achieving funding and exit milestones decreases the current
venture’s likelihood of being self-sustained but increases its likelihood of achieving
funding and exit milestones. Both, start-up age and number of cofounders have shown to
positively influence the probabilities of funding or exiting.
In order to test Hypothesis 3 an ordered probit model containing specific degree level
attributes is estimated and the results are reported in Table 5. Results show that different
levels of technical higher education (i.e. undergraduate, postgraduate and doctorate
degrees) are all statistically significant determinants of achieving investment milestones. In
contrast, not all levels of business and general higher education impact on investment
milestones. Doctorate level business education is significant at the 5% level while
undergraduate business education is significant at the 10% level.
In order to determine the influence on various specific milestones, marginal effects have
been calculated and are reported in Table 6. We first comment on the impact of technical
education. Undergraduate, postgraduate and doctoral technical education all have a
negative impact, significant at the 1% level, on the likelihood of a venture being financially
self-sustained. In contrast, the different levels of technical education all have a statistically
significant positive impact on the probability of achieving funding and exit milestones. The
likelihood of being funded increases by 6 percentage points for postgraduate and doctoral
Table 5 The impact of specific
higher education on reaching
investment milestones (ordered
technical education while the probability of achieving exit increases by 3 percentage points
for postgraduate education. The other impacts of technical education are more modest.
The marginal effects in Table 6 shows that not all levels of business education have a
statistically significant effect on the probability of achieving investment milestones.
Doctoral business education reduces the likelihood of being self-sustained by 23
percentage points and the probability of being funded by 13 percentage points. No level of
business education had an impact on achieving the exit milestone better than the 10% level
Table 6 shows that only undergraduate general education had a statistically significant
impact on achieving investment milestones. The probability of being self-sustained is 8
percentage points lower while the probability of achieving funding and exit increases by 5
percentage points and 2 percentage points, respectively.
M. E. marginal effect
***,**, * Significance at the 1, 5, and 10% level, respectively
Davidsson and Honig (2003)
did not observe a relationship between business
education and the performance of ventures, this study suggests that the probability of
securing funding increases by 3 percentage points if someone in the cofounding team has
obtained a business degree. While the specific content of the studied subjects is unclear in
this study, the results are more in line with the results presented by
Tan and Ng (2006)
have identified entrepreneurship education as a factor that increases an entrepreneur’s
confidence to participate in high growth businesses.
The expectations that start-ups in the digital space benefit most from technical
education, which can be seen as industry specific human capital, has been confirmed. Technical
education increases the probability of receiving funding by 7 percentage points and the
probability of exiting a business by 3 percentage points. The importance of such industry
specific human capital is also reflected in the increase of probabilities on each technical
degree level. With both, technical and business education being significant factors in
increasing the probabilities of reaching investment milestones, the importance of balanced
skill sets, as highlighted by
Stuetzer et al. (2012)
, can be confirmed. Hypothesis 1 can
therefore be accepted.
When discussing balanced skill sets, not only industry specific human capital, in the
form of technical education and entrepreneurship specific human capital in the form of
(e.g. Kollmann 2006; Oakey 2012)
, should be considered. As can be
seen in this specific industry, although all start-ups can be expected to have a technical
element in their business model in common, opportunities that are being exploited may be
found in completely unrelated industries. Besides the technical element, the range of
industries that digital start-ups operate within varies highly and while this study has
insufficient data to distinguish between each specific type of education that may be
relevant, we capture general education as all non-technical and non-business subjects. The
contribution of such general higher education in addition to the expected business and
technical knowledge needed in a digital start-up is an unexpected finding, with general
higher education showing an increase in the probability of receiving funding by 4
percentage points and in the probability of exiting the business by 2 percentage points. This is
remarkable in that it shows a higher impact than business education. In addition, although
higher technical education demonstrates the greatest impact on the probability of reaching
both investment milestones, general higher education shows an impact that is greater by 1
percentage point than higher business education for each investment milestone. Although
some teams may not possess the recommended technical and business skills
2006; Oakey 2012)
, it could be assumed that this advanced knowledge in a specific field,
and the transferable skills obtained during their studies at a degree level
Group on Qualifications Frameworks 2005)
, give them a competitive advantage in
exploiting an opportunity that they have identified
(Davidsson and Honig 2003; Marvel and
. Hypothesis 2a is therefore rejected while hypothesis 2b is accepted, which
suggests that for digital start-ups, technical education is preeminent (Marvel and Lumpkin
The analysis of the separate degree levels has revealed that higher levels do not
necessarily lead to higher performance. While some of the degree levels have not revealed any
significant results, technical education at a doctorate level increases the probability of
exiting a start-up by 1 percentage point less than at a postgraduate level. While higher
technical education had a greater impact than higher business education overall, by far the
largest impact on a specific degree level has been observed for business education at a
doctorate level. This further emphasizes the importance of distinguishing between higher
degree levels as opposed to measuring education in number of years or as a dichotomous
variable. We therefore contribute to the debate on upper limits of human capital by
highlighting the value of moving beyond a single measure
(McGuirk et al. 2015)
the multiple dimensions of human capital
, and of education in general
(Bologna Working Group on Qualifications Frameworks 2005)
. Hypothesis 3 is
consequently rejected, which confirms the importance of specific human capital configurations
as opposed to overall high levels of human capital
(Marvel and Lumpkin 2007)
The results of this analysis contribute to the commonly raised but under-investigated
issue of over-investments in human capital
(Davidsson and Honig 2003)
. While the
investment in human capital by means of education and training are academically well
, the role of universities is being increasingly challenged in the
(e.g. Nathan et al. 2012)
. However, the results of this study suggest that
universities as educators potentially play a significant role. Higher education increases the
probabilities of receiving funding or exiting a business significantly. This study has
illustrated the universities’ impact by linking their primary mission of teaching to their
more contemporary mission of economic development (Etzkowitz and Leydesdorff 2000).
With higher educated founding teams indicating increased probabilities of reaching
milestones closely related to high growth venture performance, a correlation to economic
impact is evident. This is notable as teaching is an activity that has not traditionally been
evaluated in economic terms
(Philpott et al. 2011)
This study makes a novel contribution to the human capital literature, by analyzing the
impact of higher education on start-ups in an industry sector of growing importance.
Although the digital sector is increasingly questioning the role of universities
et al. 2012)
, the results have indicated that universities have a considerable influence upon
digital start-ups performance. Through making a finer grained examination of the effect of
higher education we show that increased formal business and technical education within
founding teams increases the probability of reaching investment milestones for digital
start-ups. In addition, and counterintuitively, general higher education in the arts,
humanities or social sciences also provide performance benefits.
By focusing upon the context of the digital economy, this research also contributes by
examining the impact of specific human capital
(Jayawarna et al. 2011)
, in the form of
education developed at university and broken down by subject area, on the distinct
performance measures of probability and timing of reaching start-up investment milestones
(Unger et al. 2011)
. Crucially, this research moves beyond a single measure for human
(McGuirk et al. 2015)
by examining the multi-dimensional nature
the education variable. Consequently, other than previously thought
(Criaco et al. 2014;
Jayawarna et al. 2011; Milla´n et al. 2014; Oakey 2012; Schultz 1961)
, the findings of this
research indicate that human capital, in the form of higher education, has a heterogeneous
influence on high growth start-up performance in the form of reaching equity investment
milestones. We therefore reconcile the debate surrounding the potential negative effects of
overinvestments in human capital
(Davidsson and Honig 2003)
. Although technical
degrees are seen to be valuable for graduate founders, overinvestment in technical
education produces lower returns in the digital sector.
The analysis has been conducted on a relatively large dataset that was readily available.
In line with previous suggestions
(Gao et al. 2016)
, the value in using such a dataset has
been demonstrated with the findings of this research. Therefore, attention has also been
drawn to the potential of using existing datasets in entrepreneurship research, although it
needs to be recognized that the results are limited by the nature of the crowd sourced
dataset. In addition, the statistical analysis in this study has been conducted on each
estimation coefficient individually. Further research should consider the analysis of
combined factors in order to investigate the effect of a combination of human capital
7 Managerial implications
For policy makers and practitioners, the results can act as guidance for understanding the
impact of human capital on start-up performance. Attention is also drawn to the importance
of including students from a variety of disciplines in entrepreneurship education programs
such as the Summer of Student Innovation organized by
, which is aimed at all
students in UK higher and further education, or the Digital Futures Young Entrepreneurs
Scheme organized by the University of Nottingham, which is aimed at postgraduate and
(Digital Economy Network 2015)
. Practitioners should recognize
that higher education has an impact on digital start-ups reaching major investment
milestones. Of particular note is the finding that the impact varies based on studied subject area
and degree level. Broadly speaking, practitioners, such as investors, should be actively
seeking teams with mixed skillsets, as they show the strongest relative performance in
reaching major start-up investment milestones. Whereas the existing literature is
predominantly focused on inculcating industry-relevant skills in technology and business
(Kollmann 2006, p. 334)
, practitioners need to take notice that general higher education in
subjects other than technology and business can also have a positive impact. Generally, as
a practitioner, it is therefore worth examining the educational background of digital
startup founders and its applicability to the specific start-up in more detail.
Considering educational background, practitioners should also be aware of the potential
pitfalls that seem to be associated with deep subject knowledge, such as that obtained
through a doctorate degree. The heterogeneous nature of human capital observed in this
research project can lead to adverse effects on the performance in terms of reaching
With clear economic impact, indirectly resulting from higher education, policy makers
should also rethink the way the output of the entrepreneurial university is being measured.
This research has taken a first step to demonstrate the viability of using previously
underutilized open datasets for quantifying indirect academic entrepreneurship.
8 Limitations and future research
With the analyses conducted in this study, the probabilities of achieving specific outcomes
have been investigated. Whilst an increase in probabilities to reach specific stages can
certainly be understood as a positive sign, timing is arguably of equal importance
et al. 2014)
. However, at this stage, the speed at which those outcomes are achieved
remains unknown, which could be of interest to policy makers concerned about economic
growth as well as private equity investors. Therefore, to add another dimension to this
research, further work could focus on the timing of reaching specific investment
milestones. This research has taken a first step towards investigating the impact of the start-up
founding teams’ human capital configuration over time
(Mun˜ oz-Bullon et al. 2015)
the findings of this exploratory research can act as the basis for future investigations.
However, it needs to be highlighted that this research generalized higher education in the
data analysis, and consequently did not measure the effect of specific universities on digital
start-ups. We hope that the findings of this research will help guide future studies to
address such limitations.
Acknowledgements This work was supported by the Engineering and Physical Sciences Research Council
(grant numbers EP/G037574/1, EP/G065802/1) through the Horizon Centre for Doctoral Training at the
University of Nottingham and the RCUK’s Horizon Digital Economy Research Institute. The authors would
like to thank the special issue editors and reviewers for their constructive efforts in helping to develop this
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
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