Evolving missions and university entrepreneurship: academic spin-offs and graduate start-ups in the entrepreneurial society
Evolving missions and university entrepreneurship: academic spin-offs and graduate start-ups in the entrepreneurial society
Chiara Marzocchi 0 1 2 3
Fumi Kitagawa 0 1 2 3
Mabel S a´nchez-Barrioluengo 0 1 2 3
JEL Classification I 0 1 2 3
0 University of Edinburgh Business School , Edinburgh , UK
1 Manchester Institute of Innovation Research, The University of Manchester , Manchester , UK
2 & Chiara Marzocchi
3 Unit of Human Capital and Employment, Joint Research Centre, European Commission , Ispra , Italy
A recent call has urged to broaden the conceptualization of university entrepreneurship in order to appreciate the heterogeneity of contexts and actors involved in the process of entrepreneurial creation. A gap still persists in the understanding of the variety of ventures generated by different academic stakeholders, and the relationships between these entrepreneurial developments and university missions, namely, teaching and research. This paper addresses this particular gap by looking at how university teaching and research activities influence universities' entrepreneurial ventures such as academic spin-offs and graduate start-ups. Empirically, we analyse the English higher education sector, drawing on institutional data at the university level. First, we explore the ways in which teaching and research activities are configured, and secondly, we examine how such configurations relate to academic spin-offs and graduate start-ups across different universities over time. Our findings suggest, first, that the evolution of USOs and graduate start-ups exhibit two different pathways over time; and second, that teaching and research both affect entrepreneurial ventures but their effect is different.
Academic entrepreneurship spin-offs; Teaching; Research
The role of the university has evolved over time in response to the forces shaping economic
. A number of studies on academic entrepreneurship and
(Rothaermel et al. 2007)
has acknowledged the high potential of
academic entrepreneurial mechanisms as beneficial to the economy and society in general
(Feldman and Desrochers 2004; Shane 2004; O’Shea et al. 2005; Wright et al. 2007)
However, the role of the university in the entrepreneurial society has been portrayed in a
much broader way
and urges to build a wider perspective reflecting the
changing roles of universities in order to ‘‘embrace greater variety in the extent and nature
of academic entrepreneurship’’
(Siegel and Wright 2015, p 584)
The recent call to rethink academic entrepreneurship derives from the recognition of an
undue policy and research interest placed on business ventures linked to the
commercialization of research and intellectual property (IP) protection
(D’Este and Patel 2007;
Grimaldi et al. 2011)
. There is also an acknowledged need to extend the understanding of
the different contexts of entrepreneurship, actors and mechanisms that facilitate a wide
range of venture creation
(Leitch et al. 2012; Siegel and Wright 2015)
. Most of the
literature tends to focus on research by academic staff, neglecting the role that teaching
exerts on business creation
(Guerrero and Urbano 2012)
. Moreover, a broader range of
entrepreneurial ventures is emerging from higher education institutions (HEIs) and
different stakeholders, notably students and alumni, are becoming more embedded in the
process of generation of entrepreneurial opportunities
(Libecap 2005; Hsu et al. 2007;
Mars et al. 2008; Shah and Pahnke 2014; Link et al. 2014)
. However, whilst studies suggest
that more consideration should be given to both the growing scale and economic relevance
of students’ start-ups, empirical findings investigating these processes in relation to the
organizational contexts of universities remain limited
(e.g. Astebro et al. 2012; Guerrero
et al. 2016; Bergmann et al. 2016; Beyhan and Findik 2017)
Gaps still persist on both the understanding of the variety of ventures generated by
different academic stakeholders, and the relationships between these entrepreneurial
developments and universities’ missions, namely, teaching and research. This paper
addresses this particular gap by looking at how the institutional contexts of university
activities, those inherently related to teaching and research, influence university
entrepreneurial ventures, focusing on university spin-offs and graduate start-ups.
Before academics and students start a new business, the university provides a range of
support mechanisms to develop entrepreneurial ideas, improve strategic planning and gain
access to new resources. The university environment itself provides the context where
academics and students can access to knowledge through research and teaching, including
research projects, scientific publications, research supervisions, and teaching provisions
ranging from academic curricula to more specific forms of entrepreneurship education and
training. The aim of this work is to improve our understanding of how activities related to
teaching and research affect the diversity of entrepreneurial ventures generated at the
university level, and asking ultimately whether or not universities with different
organization of research and teaching activities should pursue the same goals in terms of business
ventures. In order to investigate this further, firstly, we explore the ways in which teaching
and research activities are configured within the university, and secondly, we examine how
such configurations explain different entrepreneurial outcomes across HEIs. Empirically,
we analyse the UK higher education sector, more specifically we focus on the population of
English HEIs drawing on longitudinal data on both university spin-offs (USOs) and
The rest of the paper is structured as follows: the next section provides a review of the
literature and introduces the theoretical approaches underlying our hypotheses. Section 3
presents backgrounds of the study, data and methodology. The results of the data analysis
are discussed in Sects. 4 and 5 reflects on the implications and limitations of our findings
and identifies directions for future research.
2 Theoretical framework and hypotheses
2.1 Scope of university entrepreneurship
Over the last three decades, a broad range of literature surrounding the concepts of
‘‘university entrepreneurship’’ and ‘‘academic entrepreneurship’’
(e.g. Rothaermel et al.
2007; Siegel et al. 2007a, b; Wright 2014)
has developed, where the focus has been mostly
on spin-offs (USOs) based on university intellectual property. The relevance of activities
encompassing the commercialization of research results and the protection and exploitation
of intellectual property emanating from universities has been acknowledged as major
policy and research drivers towards the promotion of innovation and economic growth
Geuna and Muscio 2009; Mowery and Sampat 2005; Bercovitz and Feldman 2006)
Spinoffs represent a crucial dimension of university entrepreneurship. They involve the
development of a business opportunity based on novel technology emerging from
(Markman et Al. 2008)
. As such, they reflect a specific entrepreneurial
outcome directly linked to the research capacity by university staff to transfer benefits from
research to the wider society
(Rasmussen et Al. 2011)
. Several contributions have
examined the variety of factors that influence both the generation and success factors of
spin-offs activity. These includes national policies, institutional support in the form of
specialized and dedicated infrastructures such as incubators, as well as types and
composition of entrepreneurial teams
(e.g. Siegel et al. 2003; Shane 2004; Lockett et al. 2005;
Link and Scott 2005; Niosi 2006; Siegel et al. 2007a, b; Damsgaard and Thursby 2013)
In particular, studies focusing on organizational factors indicate the significance of the
entrepreneurial role of technology transfer offices (TTOs), as a key for the success of
(e.g. Goldstein 2010; Siegel et al. 2003)
. These include TTOs’ expertise and
networking capacity, and their ability to recognize opportunities and organize equity
ownership for the spin-offs
(Lockett and Wright 2005; O’Shea et al. 2005)
. At the same time,
authors have pointed out that economic returns from spin-offs are indeed small and mostly
sorted across few institutions (Heher 2006) and that the probability to generate financially
rewarding forms of entrepreneurship from technology transfer activities of research alone
is on average fairly low
(Lester 2005; Mowery et al. 2001; Harrison and Leitch 2010)
Indeed, although spin-offs convey an important measure of entrepreneurial output of
academic research, reflections on HEIs entrepreneurial capacity should not be confined to
(Grimaldi et al. 2011)
The recent shift in perspective on the role of universities has expanded the focus from
the analysis of the instruments available to generate entrepreneurship from
commercialization of research (such as spin-offs and licensing) to a broader scope and capacity of
universities entrepreneurship. Much attention has been devoted to the role of training as an
engine to foster entrepreneurial culture and drive forward socio-economic development
(Kuratko 2005; European Commission 2006)
. The role higher education plays in
determining the chances of individual graduates’ self-employment and new venture creation has
been acknowledged in a number of entrepreneurship and education literature
2004; Be´chard and Gre´goire 2005; Kuratko 2005; Fayolle 2006, 2013)
. Growing policy
evidence suggests a positive association between the magnitude of students’
entrepreneurship and the performance of the regional and national economy as the number
of HEIs using their initiatives to stimulate graduate enterprise and entrepreneurship
(European Commission 2012, 2015; GEM 2012)
However, it has been pointed out that the literature on graduate entrepreneurship often
neglects the contextual nature of such activities
(Greene and Saridakis 2008)
studies are beginning to shed light on the contextual characteristics driving entrepreneurial
choices by highlighting different organizational, institutional and regional contexts that
influence graduate start-ups and venture creation
(Astebro et al. 2012; Leitch et al. 2012;
Dodd and Hynes 2012; Bergmann et al. 2016; Walter and Dohse 2012)
organizational capabilities, resources and ultimately the knowledge generated by the
contextual characteristics associated to research and teaching, affect the entrepreneurial
competences of graduate entrepreneurs, and determine the institutional capacity to
generate student ventures (Beyhan and Findik 2017). Shah and Pahnke (2014, p. 782) identify
two key dimensions of knowledge which contribute to university entrepreneurship. One is
innovative knowledge which provides the understanding of a particular technology and
serves as the basis for commercial opportunities, while the other is entrepreneurial
knowledge and provides an understanding of the entrepreneurial processes and networks
from which to draw resources and expertise, including those gained by students while
pursuing their education. Building on this, with an aim to probe the influence of teaching
and research on university entrepreneurial activities, we hypothesize that:
Both teaching and research activities exert an influence on entrepreneurial ventures.
2.2 University context and entrepreneurship
According to the different practices and contextual characteristics they adopt, universities
can be considered as hybrid organizations where competing demands and institutional
logics coexist and lead to heterogeneous pathways and outcomes
(Powell and Colyvas
2008; Ocasio and Radoynovska 2016)
. Universities have historically balanced the
combination of such pathways in order to adapt and respond to external pressures and
accomplish their multiple missions. As a result, at the individual university level,
universities scale and combine their activities to reflect broader higher education dominant
logics associated with teaching and research missions respectively.
This conceptualization closely relates to recent developments on the entrepreneurial
(Guerrero and Urbano 2012; Abreu et al. 2016)
, where perceived
interlinkages between teaching, research, and knowledge exchange activities interact and
nurture academic entrepreneurship
(Abreu and Grinevich 2013; Healey et al. 2014; Siegel
and Wright 2015)
. Many studies have looked at entrepreneurial ventures differentiating
between research-led and teaching-led universities. For instance,
Abreu et al. (2016)
analyses the entrepreneurial practices by academic staff and show that teaching-led
universities engage better at the local level and in less formal types of ventures, while
research-led HEIs perform better internationally and in more traditional commercialisation
activities. It is also suggested that teaching-led universities exert a capacity to promote
academic entrepreneurship and technology clusters
(Calzonetti et al. 2012; Braunerhjelm
and Helgesson 2006)
due to their proactive leadership in regional capacity building and
networking, rather than on ‘‘pushing’’ innovations via the formal
knowledgecommercialization routes (Abreu et al. 2016).
Greene and Saridakis (2008)
showed that in
the UK pre-1992 universities (generally research focussed) are more likely to be positively
associated with initial self-employment of their graduates.
cluster analysis of UK universities based on research performance indicators, identifying
two clusters of high and low research-intensive universities.
Abreu et al. (2009)
knowledge exchange performance of the Russell Group Universities, other established
universities formed before 1992, post-1992 universities and others.
These studies inform our understanding of the variety across HEIs, but they tend to
adopt a restrictive perspective on what teaching and research capacity entails and how
those affect HEIs. First, they tend to apply a dichotomous categorization of research-led
versus teaching-led universities not considering the dynamic changes of universities over
(Sa´nchez-Barrioluengo et al. 2016)
. Second, the studies above either underplay the
role of teaching in relation to graduates’ business ventures or do not consider such ventures
focussing exclusively on staff entrepreneurial outcomes.
Naturally, universities’ teaching and research activities and the entrepreneurial ventures
they generate are further conditioned by other factors. Notably, universities with different
organizational heritage play different roles, reflecting diverse institutional priorities,
cultures and governance structures, and also a different mix of discipline areas and research
(Perkmann et al. 2011; Hewitt-Dundas 2012; Abreu and Grinevich 2013; Abreu
et al. 2016)
. They are influenced by the university strategies and reward systems (Huyghe
and Knockaert 2015); but also by the disciplinary composition and the research quality of
(Abreu et al. 2016)
. It is argued that organizations with the capacity to pursue
entrepreneurial strategies are in the positions to systematically recognize and exploit
(Eisenhardt et al. 2000)
as they define and set specific actions
and outcomes to reach their objectives
(Ireland et al. 2009)
Studies have found that the availability of resources (stock of technologies and skilled
staff), incentives and rewards systems, business development competences and the ability
to access external finances and networks are the main factors that facilitate the formation of
spin-off companies in universities
(Lockett et al. 2003; Ismail et al. 2010; Lockett et al.
. These entrepreneurial opportunities rest on the knowledge generated at universities
(Shah and Pahnke 2014). As well as considering the knowledge from research related
resources (research endowments), there is a need to broaden the conceptual understanding
of the links between knowledge, skills and networks gained by students while pursuing
their education provisions (teaching endowments).
We use the term ‘endowment’ to identify the combination of contextual characteristics
related to teaching and research, and representing the value that universities place on them
as part of their institutional mission. The institutional context, as defined by the unique
combination of teaching and research endowments, including the structures and practices
at different institutional environments in response to HEIs’ teaching and research missions,
affect the capacity to pursue different entrepreneurial ventures. HEIs driven by research
reflect logics linked to competitive research grants, and collaborative research activities
with industry and their entrepreneurial scope will arguably favour business ventures
protecting the governance of science-generated knowledge, e.g.: licensing of technology and
spin offs creation
(Colyvas and Powell 2007)
. Following this, we hypothesize that:
H2 The effect of teaching and research endowments on business ventures is different,
H2a Greater availability of knowledge from research activities (research endowments)
positively influences spin-offs but not graduate start-ups.
H2b Greater availability of knowledge from teaching activities (teaching endowments)
positively influences graduates start-ups but not spin-offs.
3 Background, data and methodology
The following sections provide some background on the higher education sector and
university entrepreneurship policies in the UK and England and then move to describe the
sources of data as well as main variables and statistical methods used for the analysis.
3.1 Higher education institutions and university entrepreneurship policies in England and the UK
In the UK, higher education policy is a devolved matter across England, Scotland, Wales
and Northern Ireland. Given the devolution of the Higher Education sector and the related
differences in sources of funding and policy initiatives across England, Scotland, Wales
and Northern Ireland, this paper focuses on the higher education sector in England alone.
The Higher Education Funding Council for England (HEFCE) is the main funding body
for the English HEIs and provides resources to support universities’ knowledge exchange
and entrepreneurship activities
(Kitagawa and Lightowler 2013; Rossi and Rosli 2015)
England, the Higher Education Funding Council (HEFCE) has funded ‘third stream’
initiatives since the late 1990s, initially through the Higher Education Reach Out to Business
and the Community initiative (HEROBC) and since 2001 through the Higher Education
Innovation Fund (HEIF). Over the last decade, the government aimed to support the
development of the long-term institutional strategies for entrepreneurial activities by
providing a stable stream of funding for the universities.
The UK government policy has emphasised the links between universities and
economic development since the late 1990s
. The creation of academic spin-off
firms was particularly promoted after the Lambert Review of Business-University
by addressing issues of commercialization and institutional
constraints on Intellectual Property Rights (IPRs). There are also growing concerns that the
focus of universities and of policy-makers has been on the number, rather than on the
quality and commercial viability, of these university spin-off firms
(Harrison and Leitch
Since the early 2000s, a number of initiatives have been implemented at the UK level to
stimulate university enterprise and entrepreneurship in a broader sense
contribute to the development of entrepreneurial and enterprising staff, students and
. The UK government has particularly focused on encouraging
more graduates to pursue entrepreneurial career paths
(HM Treasury and BERR 2008;
; and creating a joined approach across industry, government and HEIs to
respond to societal and economic challenges and to develop entrepreneurial environments
within and beyond HEIs (CIHE/NCGE/NESTA 2008). Examples include initiatives
designed to increase the number of students undertaking entrepreneurship modules
(Enterprise in Higher Education initiative), the allocation of resources to establish twelve
regional Science Enterprise Challenge Centres across the UK funded by the Office of
Science and Technology in early 2000s.
3.2 Data and sources of information
This analysis is drawn on a longitudinal database that combines evidence from the Higher
Education Business Community Interaction (HE-BCI) Survey and other data from the
Higher Education Statistical Agency (HESA). The aggregate information covers the full
population of English universities (N = 163) for six academic years
(from 2008–2009 to
Since 2000, the HE–BCI annually collects administrative data from the UK HEIs on
the income generated through university-led knowledge exchange activities, ranging
from the commercialization of new knowledge, to the delivery of professional training,
consultancy and other activities with a direct social benefit
(HEFCE 2015; Smith 2015)
The data has been collected by the HESA as part of the Finance Statistics Return (FSR),
the main source of financial information on the total activities of all UK HEIs (HEFCE
2017). In England, the Funding Council uses certain elements of the HE–BCI returns as
part of the funding formulae that determines the allocation of the Higher Education
Innovation Fund (HEIF) for each university. The HE–BCI collects information on
universities’ entrepreneurial venture outcomes, including university spin-offs (USOs) and
graduate start-ups. In addition to the financial data, the HE–BCI survey collects
information on structures and support mechanisms available at HEIs for knowledge exchange
activities. Interpretation of the HE–BCI data needs some caution due to problems of
internal consistency particularly for the first editions of the survey (1999–2000 to
2003–2004). Despite this, it is acknowledged that the HE–BCI provides the most
complete and extensive longitudinal data on university venture activities at the national
level in the UK
Our database draws on relatively recent sets of data from the HE-BCI (2008/
2009–2013/2014), which is then complemented by further information on universities’
characteristics drawn from other data available from the Higher Education Statistical
Agency (HESA). These include data regarding students (undergraduates and
postgraduates), as well as staff composition and distribution in relation to research and teaching
allocation of their time.
3.2.1 Dependent variables: university spin-offs and graduate start-ups
Our dependent variables represent two types of university entrepreneurial venture:
academic spin-offs (USOs) and graduate start-ups. The HE-BCI provides definitions for both
academic spin-offs and graduate start-ups in order to assure consistency and homogeneity
across the data submitted by the single universities
. USOs are companies
setup to exploit IPR originated from within the HEI and where the HEI continues to have
some ownership. Graduate start-ups include all new business started by recent graduates
(within 2 years after the graduation) regardless of where any IPR resides, but only where
there has been formal business and/or enterprise support from the HEI. Also, before being
officially included in the HE-BCI statistics, graduate start-ups must have achieved legal
registration with the tax office (e.g. with HM revenue and customs).
Figure 1 shows the different patterns of these entrepreneurial outcomes. Specifically,
the figure illustrates the evolution of new and active USOs and graduate start-ups returned
by English universities between the academic year 2008/2009 and 2013/2014.
The evolution of the two types of entrepreneurial ventures indicates that different
dynamics have been taking place during the time period considered. USOs have registered
a contraction (both in terms of growth rate1: -11.5%; and number of active firms: -0.8%),
whilst graduate start-ups have been consistently increasing (15.8% of new firms; and 16%
of active firms).
In order to distinguish between a short and long-term effect of teaching and research
endowments on entrepreneurial ventures, for the purpose of the analysis we collated data
by individual institutions on: (1) total number of new companies created within the
reporting period (Creation) as a proxy of an entrepreneurial output; and (2) the current
turnover of all active firms (£000 s) (turnover) as a proxy of an entrepreneurial outcome.
1 The average annual growth rate is calculated by dividing the slope by the income. The slope is determined
by the regression line formed by the matrix corresponding to the years of study 2008/09-2013/14 and
number of spin-offs and start-ups raised by the universities.
3.2.2 Independent variables: teaching and research endowments
Our two main independent variables relate to the teaching and research characteristics that
define respectively teaching and research endowments. These endowments represent the
combination of different activities related to teaching and research and thus they are treated
not as discrete but as a combined spectrum. We define teaching and research endowments
according to a set of variables capturing both university resources and university activities.
In terms of resources we include both teaching and research funding allocation (e.g.: how
much funding the individual university receives given the number of enrolled students, and
the amount of funding the university obtains from competitive research), as well as the
number of staff. In terms of university activities we include information on undergraduate
and postgraduate students and the results of the research excellence framework (REF) as a
measure of the assessment in the quality of research. A detailed definition of all the
variables included is presented in Table 1.
In order to prove the adequacy and the uniqueness of the proposed variables capturing
teaching and research endowments, we run factor analyses for each year of the time period
considered. Specifically, we use a principal components analysis with a Varimax rotation
(with Kaiser normalization), and extract factors at the 1.0 or greater eigenvalue level. The
results of the rotated matrix are robust and stable across all the years analysed
2008/2009 to AY 2013/2014)
and confirm that research and teaching activities combine in
two main factors, corresponding to teaching and research endowments (Table 2). We use
the factor scores from the factor analyses for each particular year to express our main
independent variables representing Teaching (TE) and Research (RE) endowments across
universities in England. Teaching endowments reflect universities activities strongly
associated to teaching logics, and the internal consistency is mostly related to teaching
funding and undergraduate students. Conversely, research endowments seem to be mostly
loaded by activities related to research and particularly by research funding and staff in
3.2.3 Control variables
We select a range of control variables to reduce the observed heterogeneity across the
sample and minimize bias in the results. We select activities at the university level
supporting entrepreneurship, trying to capture institutional level characteristics that determine
differences in the chances to develop entrepreneurial ventures across universities.
Specifically we include information about the specialization of the university, the
availability of infrastructures for entrepreneurial activities, the existence of specific
entrepreneurship training activities, the availability of a strategic business plan and a
specific variable to control for university size using transparent approach to costing
(TRAC) groups. All of them are explained below.
University specialization Specialization by areas and subject domains at individual HEIs
has a strong impact on the heterogeneity of the higher education system, not only at the
European and national level, but also in terms of differences between institutions
Vught 1996, 2008)
. Adopting a similar approach to Rossi (2009), we use a Herfindahl–
Hirschman index to analyse the subject specialisation of universities in England. The index
represents the concentration of area specializations within HEIs. In order to determine such
areas we use information on the Cost centres per HEIs showing the share of staff (full-time
Varimax rotation and Kaiser Normalization applied. Rotated matrix presents values of factors loadings
above 0.5. TE and RE refer to teaching and research endowments respectively
equivalent) per field using nine cost centres.2 Cost centres group staff members to specific
discipline area avoiding double counting of staff allocations. As such, considering the
number of full-time equivalent staff per Centre group (xi), the index provides a measure of
the magnitude of the disciplinary area within the university:
The information on the size of the cost centres within the single university is then linearly
added in order to calculate a measure of the university specialization as:
2 The nine Cost centre considered are: Medicine, dentistry and health; Agriculture, forestry and veterinary
science; Biological, mathematical and physical sciences; Engineering and technology; Architecture and
planning; Administrative, business and social studies; Humanities and language based studies and
archaeology; Design, creative and performing arts; Education.
This index takes values between 0 and 1, with values closer to zero representing a much
diversified portfolio (or low concentration) of area specialisations within the university.
Entrepreneurial infrastructures The existence of internal structures such as TTOs or
incubators has been recognised as important assets for commercialization activities
and is considered critical organizational factor
(Siegel et al. 2003)
as expression of the university’s capacity and likelihood to promote entrepreneurship. In
order to control for the availability of these infrastructures, we create a dummy variable
with the value 1 if the university has incubators (on-campus or in the locality) and/or a
scientific park accommodation and 0 otherwise. On-campus/other incubators are defined as
small office areas used as launch-pads for business ideas from students, staff and alumni
that provide a mentoring environment and easy access to facilities. Similarly, science park
accommodations include high-specification, purpose built accommodations for start-ups or
expanding companies, aimed at scientific research, technology, environmental,
engineering, ICT and other knowledge sectors. This information is extracted from the Part A of the
Entrepreneurship training We capture the availability of entrepreneurship training
activities available in English universities by drawing on the data available in the Part A of
the HE-BCI survey. Specifically, we look at whether or not universities offer support for
USOs and graduate start-ups through entrepreneurship training, either provided by the HEI
or in collaboration with a partner organization. It should be noted that this is bespoke
training for entrepreneurs and is different from the entrepreneurship education provided as
part of the regular curriculum. Using this information we build a dummy variable with the
value 1 if the university reports offering such extra-curricular training activities related to
entrepreneurship and 0 otherwise.
Strategic business plan We consider the strategic business plans developed by HEIs and
collect information about commercialization and engagement activities as a signal of the
university’s capacity to define and foster its entrepreneurial strategy. Using information
from the Part A of the HE-BCI survey, we create a dummy variable taking the value of 1 if
the university has developed a strategic plan for business support and 0 otherwise.
Size We control for a potential size effect, e.g.: if larger universities tend to show a
greater number of entrepreneurial ventures because of a greater availability of resources to
invest in entrepreneurial activities. In order to control for the HEI’s size, we use
information collected via the TRAC Groups
. TRAC Groups are based on
‘‘transparent approach to costing’’ criteria and cluster universities according to financial
information comparing research income to the total income generated by the HEI per
academic year. This provides a framework that ‘‘facilitates the production of high quality,
comparable information on the cost of activities across the UK HE sector’’
TRAC Groups are adjusted periodically in order to reflect changes in universities’ funding
streams’ capacities. The last TRAC return information for the period 2013–2014 (with
references to income data of 2012–2013) identifies the following Peer Groups:
According to the Table 3 we generated a categorical variable, sorting universities within
the corresponding TRAC group.
* Research income is defined as the funding council recurrent research grant plus the total research grants
and contracts returned in the HESA Finance Statistics Return (FSR)
Active spin-offs and graduate start-ups To further prevent the size of the university to
affect the results of the analysis, we control for the number of active spin-offs and graduate
start-ups in order to reduce the bias which a greater availability of research or teaching
capacity will have on the likelihood to generate more spin-offs and graduate start-ups
3.3 Statistical methods
In order to capture the factors affecting the entrepreneurial outcomes of universities, we
measure USOs and graduate start-ups in terms of creation and turnover. The first indicator
(creation) is a count variable. Given the attention drawn by the empirical literature to the
risks associated to analyse such variables, our preferred specification is a Poisson model for
(Wooldridge 2010; Cameron and Trivedi 2001)
. Our choice takes into account
the advantages of Poisson over Negative binomial models, particularly that Poisson models
provide consistent estimates of the coefficients of interest even when the underlying
distribution of the dependent variable is not Poisson
(Gourie´roux et al. 1984; Wooldridge
. Moreover, even in a longitudinal framework as the one considered here, Poisson
estimators have been shown to be robust to a number of misspecifications, such as:
overdispersion (it can be accommodated by using robust standard errors); the presence of an
excessive number of zeros; and dependence over time
(Bertanha and Moser 2016)
into account that our dependent variable is zero-inflated, we use robust standard errors
clustered. The turnover variable is a quantitative measure left-skewed. In order to obtain
non-skewed variables, we apply logarithms and then estimate our results using a fixed
effects Ordinary Least Squares regression with robust standard errors.
4.1 Descriptive results
Our descriptive results focused on the evolution of USOs and graduate start-ups over time
in terms of creation and turnover are presented in Fig. 2. In the period considered the
creation of USOs shows lower figures than those of graduate start-ups. Specifically, on
average only 0.82 and 0.55 new USOs were created in AY 2008/2009 and AY 2013/2014
respectively, compared to an average of 11.1 and 25 new graduate start-ups in the same
two periods considered. With respect to turnover however, USOs figures are significantly
higher than those of graduate start-ups. In this case, while the average turnover from
spinoffs is more than £2000 thousand, the monetary output of graduate start-ups locates around
£650 thousands (average values for independent variables are available Table 4).
In relation to the independent variables included in our analysis, we can see that on
average English HEIs present a specialisation index of 0.34 that highlights that academic
staff is fairly equally distributed across subjects. Also, on average English HEIs appear to
be inclined to adopt entrepreneurial mechanisms, with 68.6% of the universities showing to
offer entrepreneurial infrastructures; 77.8% providing bespoke training related to
entrepreneurship; and 80% having included a business plan as part of their strategy. In
addition, there is an important difference between the number of active USOs and start-ups
in the studied period with less than 5 ventures in the first case and more than 35 in the
second one (on average).
4.2 Regression results
Table 5 presents the results of our specifications looking at both the number of new
companies created per year (Creation: Column I) and the turnover generated by all active
firms (Turnover: Column II) across USOs and graduate start-ups. Considering that teaching
and research endowments will not have an immediate effect on entrepreneurial ventures,
we lag the impact of the two main independent variables in the model of 1 year.
Overall, the results support our hypotheses. Research and teaching endowments have an
effect on both USOs and graduate start-ups (H1) with some existing differences on their
impact depending on the type of venture considered (H2). Endowments in research
significantly support USOs creation but have no effect on new graduate start-ups (Columns I).
The availability of infrastructures such as incubators or science parks affects the creation of
USOs and has no impact on graduate start-ups. University’s specialisation by area domain
sorts a negative effect on spin-offs creation reflecting that for spin-offs variety of
knowledge domains rather than specialisation might improve the chances to generate new
ventures. Finally, the provision of bespoke entrepreneurship training by the university
negatively affects the creation of new graduate start-ups, but has no effect on USOs. There
are possible different interpretations on this result. Specific entrepreneurship training may
give graduates better understanding of the risks and challenges of starting up, which may
discourage their actual start-up activities. Academic USOs may have access to other forms
of bespoke support mechanisms provided by other organisations such as TTOs.
In relation to turnover (Column II), both graduate start-ups and academic spin-offs are
negatively affected by research endowments. However, universities’ endowments in
teaching positively influence the turnover of graduate start-ups, while negatively affecting
returns for spin-offs. The effect of university specialization by area domain is very
9 7 . 5 3
D .08 ,03 .93 04 .98 .99 .27 81 81 80 .05 .45
S 2 1 3 5 0 0 0 7 7 7 1 8
significant and negative for both the entrepreneurial venture activities. The strategic
business orientation of the university positively affects both USOs and graduate start-ups.
University infrastructures and entrepreneurship education however are relevant and
positively affect only graduate start-ups but have no effect on USOs’ turnover. This may
suggest that the availability of specific infrastructures (such as incubators) and enduring
entrepreneurship training is more relevant for graduate ventures. Finally, the number of
current active spin-offs and start-ups positively affect turnover for both types of ventures.
In sum, the effect of teaching endowments positively relates to the returns generated by
graduate start-ups but it is negative for turnover. Conversely, the impact of research
endowment is more diverse. Research endowments at the university level have no effect on
the chances to generate new student ventures, and negatively impact on successful financial
returns by graduate start-ups. On the other hand, research endowments do positively relate
to the creation of spin-offs ventures, but its effect becomes negative once linked to
5 Conclusions and discussion
In recent years, an increasing recognition has been placed on the broadened scope and
nature of academic entrepreneurship, and the diverse ways that universities engage in these
(Siegel and Wright 2015)
. Furthermore, the paradigm shift towards the
‘‘entrepreneurial society’’ is noted where the university needs to design new structures to
ameliorate tensions arising between traditional research and teaching, and different forms
of entrepreneurial endeavours in order to enhance ‘‘entrepreneurship capital’’
2014, p. 313)
in a variety of institutional contexts. In this light, this paper aimed to
Robust Standard errors; * p \ .1; ** p \ .05; *** p \ .01; Column (I): Poisson regression (clustered ID);
Column (II): OLS (robust SE). The 1-year lag introduced in the regressions limits the analysis to the period
between 2009/2010 and 2012/2013
contribute to the understanding of the broadened scope and nature of academic
entrepreneurship and investigated the ways in which the configuration of universities’
institutional endowments of research and teaching affect diverse entrepreneurial ventures.
Universities have different sets of resources and capabilities depending on their
organizational characteristics. They combine these assets partly as path dependent processes
and partly as a response to a number of external factors—such as government policy,
research funding, student market, and societal needs. In this paper, we empirically
investigated the scope of academic entrepreneurship in terms of its links to research and
teaching activities. Our results suggest, first, that the evolution of USOs and graduate
startups exhibit two different pathways over time; and second, that both endowments in
teaching and research affect entrepreneurial ventures but their effect is different.
Related to the first point, our results suggest that while the creation of USOs has been
constant during the last years, there has been a considerable growth in the generation of
graduate start-ups. Our empirical analysis shows the existence of two distinctive logics
(manifested by teaching and research endowments) emerging across the English higher
education sector over time. In relation to our hypotheses, both research and teaching play a
role in determining the chances to create or grow an entrepreneurial venture. Particularly,
stronger research endowments generate and nurture principally USOs creation but have a
negative impact on the growth (turnover) of both spin-offs and graduate start-ups. On the
other hand, teaching endowments seem to affect positively graduate start-ups turnover but
negatively USOs’. Thus, in universities where the emphasis has been historically placed on
acquiring and strengthening research endowments, the entrepreneurial behaviour will
favour the generation of academic spin-offs in the short-term. Conversely, HEIs
traditionally focused on teaching-related activities are better positioned to enable the success of
student start-ups instead of academic spin-offs. Our results support the idea that the
entrepreneurial knowledge required to promote business ventures is informed by both
teaching and research activities
(Shah and Pahnke 2014)
. However, we only partially
support findings by
Beyhan and Findik (2017)
on the role of research in influencing
students’ ventures, and particularly by discussing separately the contribution of teaching
and research endowments, we found that research endowments do not foster graduate
This study is an attempt to contribute to the empirical understanding of the diversity and
complexity of academic entrepreneurial behaviour at the system level
(see also, Kitagawa
et al. 2016; Sa´nchez-Barrioluengo et al. 2016)
. The topic relates to HEIs’ resource
allocation decisions and has wider implications on the strategic resource management of
universities, including reflections for higher education policy makers and funders alike. At
the national higher education system level, our findings emphasize that universities are
diverse. Individual HEIs generate different types of academic entrepreneurial ventures,
conditioned by their strengths, historical contexts and external environments. Further
questions remain with regards to institutional management and strategic choices: do
universities have to choose between graduate start-ups and academic spin-offs activities? Is
there a trade-off between these? And in what ways effective support mechanisms can be
built to enhance a combination of teaching and research endowments to benefit both these
activities? This in turn draws attention to the policy question of whether or not all
universities should aspire to develop the same kind of academic entrepreneurship. At the
university level, tensions may occur between strategic decisions regarding the types and
levels of support for different academic entrepreneurship. As these competing value sets
co-exist within the university, they also affect the shape and dynamics of the university’s
different missions and entrepreneurial outcomes.
It should be noted that university missions are interdependent—when particular
emphasis is put on one mission that could affect, reinforce or decrease the others
. Individual level data on entrepreneurial activities would complement
the picture depicted in this paper, providing better understanding of the internal
characteristics and micro-processes of university entrepreneurship, as well as how individual
actors—both academics and graduates—respond and evolve in relation to different
organizational contexts, heterogeneous knowledge and capabilities creation processes
et al. 2008)
There are a number of limitations in this study. Due to the nature of the institutional data
available in the HE-BCI, we did not look into the disciplinary differences of academic
entrepreneurship activities. Better understanding on the internal differences between types
of university entrepreneurship; such types in diverse organisational contexts (e.g.
disciplinary differences) and resources (such as internal support mechanisms and access to
external investment) are required.
Finally, as well as within universities, it should be noted that competing values and
drivers exist within graduate start-ups and USOs as organizations. Most USOs are market
driven in nature, whilst their growth strategies and entrepreneurial characteristics may
change over time. Their links and relationships with the university also changes. In the case
of graduate start-ups, some may be driven by socially motivated objectives while others
may be commercially driven. Universities may support student start-ups within the
teaching and learning contexts, whilst the nature of the student venture may change over
the time. More broadly, the implications for longitudinal strategies of sustainable and
successful venture creation and the effectiveness of the support mechanisms provided by
HEIs, need to be further understood in the light of the different contribution of teaching
and research activities to academic entrepreneurship.
Acknowledgements Preliminary drafts of this paper were presented at the following workshop and
conferences: ‘‘Academic Entrepreneurship and Knowledge and Technology Transfer: How do they relate to
Research, Teaching, and Universities as Organization?’’ University of Kassel, Germany, (April, 2016); the
EURKIND Conference in Valencia, Spain (June 2016); and Regional Innovation Policies Conference in
Cardiff, the UK, (November 2016). We are grateful to the participants who provided comments and
suggestions. We also wish to thank both the anonymous reviewers and the editor for the helpful comments
provided. The views expressed in this paper are purely those of the authors and may not in any
circumstances be regarded as stating an official position of the European Commission.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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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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