university spinoff, innovation, entrepreneurship, policy

1. Dissertation
Identifying successful patterns in spin-off activities among UK Universities:
What is the paradigm?
Dr. Martin Meyer/Dr. Pablo D’Este
Tutors
Inti Nunez
MSc PPSTI 2006/07
SPRU, University of Sussex
Supported by the Programme Alβan, The European Union Programme of High Level
Scholarships for Latin America. Scholarship N° E06M100213CL.

2. 2
INDEX
Introduction
1. A theoretical framework of third stream activities, public policies and spin-offs
1.1. University and development
1.2. The “Entrepreneurial” University
1.3. The Evolution of the models
2. The UK case, Universities and Typologies
2.1. The UK University System
2.2. Types of Universities
2.3. UK Higher Education public policies: the evolution of a paradigm
2.4. Building an hypothesis: Type of University, Research, and Policies
3. Methodology and empirical evidence
3.1. The equation
3.2. Data
3.3. Comparisons
4. Analyses and results
5. Conclusions
References

3. 3
The truth is that what's most important for us is that Harvard stays great
and gets even greater and remains the best university in the world and a
major magnet of attraction. And if Harvard succeeds in doing that, we will
be fine.
Lawrence Summer (2004) commenting on the Mayor of Boston’s.
Former President, Harvard University.
Introduction
In recent years many governments have encouraged their university systems, to attempt to add
another mission to the university model: the third mission 1 . The third mission or third stream
activities are actions which have as their objective the transmission of knowledge in the economic
system2. However, this stimulation has been expressed in different ways in the last ten years. My
essay attempts to identify patterns or guidelines in these public policies because sometimes
government actions are developing based from different academic ideas. On the one hand, I find the
academic notion of a new type of university, the entrepreneurial university. On the other hand, some
scholars warn that the traditional university values are the main responsibility of the creation of
knowledge. What, then, is the paradigm 3 ? What are we looking for when we put the university
system under pressure to do more on an entrepreneurial level?
In this essay I will investigate these paradigms and the evolution of the third stream public policies
from the last ten year period in the UK (1998-2007), that is, the period between the internet bubble
and the final phase of Blair’s government. Although I can identify earlier third stream activities4, I
focus my attention on this period - after 1998 - because from this period it is possible to recognise a
break in the university system; Geuna (1998) calls this ‘the institutional reconfiguration’. I selected
the UK case because the types of Universities present in the UK can be grouped into representative
1 This is the third mission because historically the university has two missions: the provision of teaching and the
conducting of research.
2 Mollas-Gallart et al. (2002) propose a categorization of activities and some indicators that enable us to identify the
actions and results.
3 Paradigms are archetypes or set of practices (Kuhn, 1962) which define models or patterns of answers in a particular
period of time. I use this word to explain that the answer of the policy makers in a particular period of time can be
influenced by previous models of answer or ‘paradigms’.
4 Martin (2003) gives a complete history about third stream activities in the university system history.

4. 4
typologies which could serve as a good example of evolution for the third stream public policies
around the world. In this line, Blair’s policies concerning the University system represent the change
of paradigms in the ten year period.
As Summer’s comments indicate5, I recognise a tension between two paradigms. First, the paradigm
of a university that is closer with community service, business creation and regional links. Second,
Merton’s paradigm represented by the ‘classical’ university that defences the independence in the
knowledge creation. I could say that within the UK case, an example of the first paradigm is when
the government began to be more active in stimulating links between university and industry with
the creation of “third stream” activities policies in 1998 (HE White Paper 1998, cited by Hiscooks,
2005: 2) and the creation of the “Entrepreneurial university” concept based on the works of Clark
(Clark, 1998; Clark, 2004) and Leydersdorff and Etzkowitz (1998). The second paradigm
corresponds to fortify the world class university such as the US system of Ivy League universities
which focus its scope in the university capabilities to create new knowledge. I could identify this idea
with the Martin’s understanding about the evolution of the social contract that has its origins in the
late XIX century, and took force after Vannevar Bush’s claims, where the process of entry into third
stream activities is the natural evolution of this contract, affecting all university patterns, but is not
particularly distinctive in one species (Martin, 2003). This paradigm involves the evolution of the
university role as an elite institution in the creation of new knowledge, an institution concerned with
the future of society, where classical research universities are the champions. I could call this
paradigm ‘The evolution of the classical university’. This difference between paradigms would create
a problem between ‘the followers of university-industry links’ and ‘the defenders of research
tradition’. As Pavitt (2001) warns, a partial understanding of how the US innovation system
5This sentence, which begins our essay, assumes that there are two positions between the president of Harvard and the
Mayor of the Boston City. The university president asks about possible links between the university and the city, but the
Mayor’s answer was that the best business for the city is that the university remains the best university in a traditional
sense.

5. 5
functions6 could produce biases in policy makers’ ideas about the importance of the basic research
system.
This essay argues that it is necessary to link together both lines of thought; I have been able to find
evidence of change in attitude and management structure within universities which enable a better
“entrepreneurial” performance. However, the deeper roots of this new structure are the most
traditional universities and their research capabilities. I argue that in order to understand the
paradigm about the “entrepreneurial” university (and prepare a university strategy in third stream
activities); we must first begin to understand the research system and the university structure. Next,
policies that stimulate university-industry links and the use of entrepreneurial tools such as
specialised human resources, incubators and funds, could be used successfully.
The main structure of this dissertation begins with a theoretical framework. In the first chapter, I
develop a framework to understand the evolution of the university system, third stream activities,
and specifically, spin-off activity. In this part, our main question is whether the “entrepreneurial”
university is a new institution, in particular, Etzkowitz’s idea about the “Entrepreneurial University”,
which suggests the creation of a new university and a new strategy in the internal behaviour of these
institutions, where the idea is a change in the drivers of success (Etzkowitz et al., 2000: 314). Also, I
review other literature and discuss examples of the traditional university pattern of evolution, and
contrast both theories, locating conflict points and evidence. In addition, I will examine in more
depth “entrepreneurial” university literature and case studies of regional-university economic growth.
I will look for and identify models and understand the evolution of this idea and its drivers.
In the second chapter, I will examine the UK case and its characteristics. I consider its history and
describe briefly the different types of university. In the next part of this chapter I will describe the
evolution of the third stream public policies and its paradigms. Finally, I will propose a hypothesis
6Pavitt (2001) suggests that giving the US national innovation system (NIS) as an example is complex because the
expenses involved in R&D are very important and it is often not possible to measure this because the US NIS is very
complex.

6. 6
for the relationship between spin-off creation and the university, which I be developed in the next
chapter.
In the third chapter, I will try to develop a model that explains the success of third mission
implementation and will present a proposal to understand the “entrepreneurial” university effect, this
proposal or model consider three drivers: type of university, research and policies. I will use
regressions to find the drivers and will work with the following equation:
Yi = α + β TYPEi + δRESEARCH i + ζPOLICYi + γSIZE i + ε i
The dependent variable will be ‘spin-offs’ because it represents the most sophisticated type of
technology transfer activity, therefore it will be a good indicator of success. The variable ‘Type’ has
been chosen because it is a vector that sums up: history, political influence, reputation and probably
capabilities as to add human resources of excellence. The variable ‘Research’ was selected because
this vector has been considered as of lower importance by “new” university followers, however
Pavitt’s suggestion argues that it is a crucial element to develop a successful entrepreneurial strategy.
In our model, ‘Research’ is considered as the production engine of disclosures, patents and after
licenses or spin-offs. Therefore, research is the basis which explains the contribution that universities
make to the economy. The variables ‘Policies’ are management decisions which are arrived at by the
university authorities in order to improve the ‘entrepreneurial’ output. I consider this variable –
Policies- because I will show that there are ‘packages’ of policies which must be added if we want to
achieve a successful “entrepreneurial” strategy, involving specialised staff, an IP office, incubators
and seed capital. However, if these ‘packages’ are not coherent with the last two factors – type of
university and research -, they cannot create spin-offs alone. ‘Size’ is a variable which enables me to
demonstrate that the effect of research on large, traditional universities consists of more than only
size.

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In Chapter 4, I will analyse and comment on the results and evidence which was obtained in the
previous chapters in order to attempt to demonstrate that university typology, intensity of research
activity and entrepreneurship policies are spin-off main drivers. I will organise the chapter in terms
of the questions and answers which emerge from the previous data.
Finally in my conclusions, if I can demonstrate that the model grounded in research as base,
university type and third stream policies explains the success in spin-off activity, I could suggest that
public policies, which seek regional economic growth through universities, must focus in more depth
on research capabilities and third stream policies in classical research universities, rather than purely
create business links.
Key words: Types of Universities, third stream public policies, paradigms and spin-offs.

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1. Theoretical framework about universities and development; the entrepreneurial universities
and spin-off creation models
In this chapter I will aim to take up a position within an overall theoretical framework. Specifically, I
will identify some differences among scholars about the nature of the new university pattern that is
emerging, and identify several specific differences in the paradigms. To start, I will examine
Etzkowitz’s idea about the “Entrepreneurial University”. His studies suggest the creation of a new
university and a new strategy in the internal behaviour of these institutions, where the idea is a
change in the indicators of success. He argues that in addition to the two historical missions of the
university; to deliver teaching and to carry out research, it is necessary to add another mission which
considers the extension activities that try to achieve an impact in its economic, regional context
(Etzkowitz et al. 2000: 314). The objective in this third mission is to create a new university strategy
which is closer to the problems of society and to direct the university toward making a practical
contribution in its region. Etzkowitz’s idea could suggest that a university with more links to its
community and better commercialization activities correspond to the ideal university in terms of
regional development. However, other scholars argue that this idea of a ‘new’ university could be a
mistake because, empirically, the traditional university is the highest contributor in patents, licences
and spin-offs which are the basis for the development. This idea takes as its ideal of university the
‘classical’ model. This model can be explained in terms of Martin’s understanding (Martin, 2003)
about of the ‘classical’ university evolution from the social contract that has its origins in the last part
of XIX century, and it is reinforced after Vannevar Bush claims (where the process of entrance in
third stream activities is the natural evolution of this contract). Martin (2003) argues that the
evolution of this contract affecting all the university patterns, but it is not a particular distinctive in
one specie (Martin, 2003) Thus, Martin remarks that this process must be understood as the
evolution of a system more than the creation of a new institution. This paradigm means the
evolution of the university role as an elite institution in the creation of new knowledge which is the
basis for all the missions: teaching, research and extension. The ideal institution in this current of

9. 9
thought is one concerned about the society’s future and its best examples can be found among
classical research universities such as the US system of Ivy League universities or the Russell group in
UK. These two paradigms, Etzkotwitz’s new university and the evolution of the traditional research
university, seem to be quite different. However I believe that the new paradigm of the successful
university has something of both.
The origin of the entrepreneurial universities is related to two issues. First, society demands a
university that participates in its community, and that is concerned with the importance of
knowledge in the ‘knowledge economy’. Second, various governments are using active policies which
encourage the University system to link with the business community and diversify universities
sources of funding. These two issues transformed the traditional university model, in the sense that
universities require new capabilities to respond to these new demands. Thus we could have agreed
with “entrepreneurial model” that focuses its attention in a change of the traditional academic culture
and considers, as a successful university model, a university with strong links with its community.
However, the success in third stream activities is linked to a special type of university. The
‘Entrepreneurial’ model requires elements of the second paradigm (the evolution of the traditional
university) such as the academic organisation and knowledge creation structure from the medieval,
classical research-intensive university.
The theoretical framework begins by explaining the theories about the university and its participation
in economic regional development. Next, I will review the triple helix theory, the ‘Entrepreneurial’
university and the debates about this theory. Finally in this chapter, I will describe the evolution in
terms of the models which can explain spin-off performance. Although success could be signified by
different variables, I will take spin-off activity as an indicator of success for third stream activities,
considering that spin-offs can also be an indicator correlated with patents, licensing and spill over
creation and these activities has been correlated with regional economic development (Roberts, E.
and Malone, D. 1996: 17).

10. 10
1.1. University and development: the last sixty years
In the last sixty years universities have undergone changes (Martin, 2003: 7), for example: their role
in the economic structure, quantity of students, topics and subjects, etc. I will focus on the period
after Vannevar Bush’s claims (NSF, 1945), where there is a break (an inflexion) point in the
investment in technology. Figure 1 shows this increase.
Figure 1: Evolution of funding in US system of innovation 1953-2002
Source: Steinmuller, 2007 grounded in NSF, 2004 (Division of Science Resources Statistics, National
Patterns of R&D Resources, annual series. See appendix table 4-3 and 4-4
Bush proposed a simple model where “(the) government put money into the basic research end of
the chain, out from the other end of the chain would eventually come benefits in terms of wealth,
health and national security”; (Martin, 2003: 9). Martin (2003: 9) summarise the main characteristics
of this social contract in three ways: i) “it implied a high level of autonomy for science, ii) Decisions
about allocation of resources were left to the peer review system, and iii) this system assumed “that
basic research was best done in universities” (Martin, 2003: 9). Bush’s model promises that the
investment in science and technology at universities is the base for future economic development.
Thus, the universities add another role to those of teaching and creating knowledge that is to
collaborate in regional development.

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Although this new mission has undergone changes in the last decades, its importance has been
maintained; Wolfe (2003: 93) quoting an article in The Economist said:
The University acts ‘not just as a creator of knowledge, a trainer of young minds and a transmitter of
culture, but also as a major agent of economic growth: the knowledge factory, as it were, at the centre
of knowledge economy’
However, in the late 80´s several scholars suggested that this contract had been replaced by a new
revised social contract; (Martin, 2003: 10, quoting Guston, 2000). Geuna (1998: 49) suggests
“Probably, we are now entering a fifth phase that can be called the institutional reconfiguration of
the university”. In addition, Martin argues that “governments now expected more specific benefits in
return for continued investments in scientific research and in universities”. Thus, although the
university achieves a central position in this new scheme of development, it also loses some of
freedom because the government, which invests more funds in research, seeks to improve the
control over its investments. This relationship of university-government is at the core of my work
because if the government follows the third stream policies from the ‘Entrepreneurial’ paradigm and
expects a great change in the university structure, it implies following policies that affect and
transform the traditional university scheme. However, is it necessary to change the traditional
university pattern? Did the government influence improve the university performance in third stream
activities? What is the paradigm in university typology that builds better public policies? Pavitt (2001;
2003: 91) warns that a generalisation about the importance of ‘Entrepreneurial’ concepts such as
applied research – and particularly in grounding policies in the example from the US case - would
construct an incorrect paradigm and this could drive policies which ‘destroy value’ inside the
university system. In addition, he says that some myths about the comparison between the US and
European system can be easily refuted (Pavitt, 2001: 771). Thus, I have some scholars that defend
the importance of the traditional scheme of basic knowledge production who understand the
‘reconfiguration’ process (Geuna, 1998) as an evolution of the traditional system, and in this case the
basis of this evolution would be the research universities.

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However, there is another group of scholars who propose a great change in the university and a
strong public intervention over the funding system. They put as an ideal a new pattern of university,
the ‘Entrepreneurial University’.
1.2. The ‘Entrepreneurial’ University
In 1998, two works mentioned the birth of “The entrepreneurial university”. First, Clark in his book
“Creating Entrepreneurial Universities: Organizational Pathways of Transformation” presents five key elements
which define this new university:
A diversified funding base; a strengthened steering core; an expanded outreach periphery; a
stimulated academic heartland; and an integrated entrepreneurial culture” (Clark, 2004: 2).
Second, Leydesdorff and Etzkowitz 7 (1998) argue that “universities take on entrepreneurial tasks
such as marketing knowledge and creating companies, while developing an academic dimension”.
Hence, these scholars propose a new model of university that participates actively in its regional
development context and they accept the influence over the university of the government and
industry. This could be considered a difference in regard to Vannevar Bush’s social contract and
Merton’s ideas about the independence of the academic community.
I found several essays that configured a new idea about the role and independence of the university
and they draw on the ‘Entrepreneurial’ paradigm which is linked with economic development by the
government. Clark (2004: 1) emphasises on ‘attitude’; he believes that the modern university needs to
create proactive capabilities to understand its context better. Thus the classical scheme of
maintaining the traditional academic culture might be not sustainable. In addition, Etzkowitz (2002)
thinks that a modern university needs to create capabilities as an incubator, in a scheme that is more
aggressive about commercialization from the university and its academics. In other works
(Etzkotwitz and Klofsten, 2005; Etzkotwitz, 2006), he encourages the achievement of a triple helix
7 However, Bercovitz and Feldmann (2006: 175) said “Etzkowitz (1983) has coined the phrase entrepreneurial
universities to describe the series of changes that reflect the more active role universities have taken in promoting direct
and active transfer of academic research”. Then, they suggest that the origin of the name is Etzkowitz, 1983.

13. 13
model – government–industry–university – which develops an innovation region through “an
‘assisted linear model’ for translation of research results with commercial potential into either
existing firms or start ups” (Etzkotwitz, 2006: 310). I could summarise these ideas as: distrust in the
traditional academic culture; an acceptance of the intervention of the government and industry in the
university; and a great expectation of more proactive technology transfer methods, specifically spin-
offs.
Thus for the government, the spin-off activity could be an indicator of success for its third stream
policies. But, what is the importance of the spin-off as an indicator? Shane (2004) argues that the
spin-off activity is the most sophisticated method of technology transfer and “Governments…are
devoting increasing amounts of money to universities, with the goal of turning them into engines of
economic growth through spin-off8 company formation” (Shane, 2004: 1). He said that:
Spin-off are valuable in at least five ways: they enhance local economy development; they are useful
for commercializing university technologies; they help universities with their major missions of
research and teaching; they are disproportionately high performing companies; and they generate
more income for universities than licensing to established companies. (Shane, 2004: 20)
However, Shane (2004) recognises that there are few academic studies that explain the impact of
spin-off in economic development (Shane, 2004: 2). He points out that it is an activity concentrated
in few academic institutions, and that is why it is not generalised (Shane, 2004: 67). Next, Pavitt’s
warning about problems when the policy makers generalise from these assumptions could be
considered. In addition, some scholars suggest that there is a relationship between traditional
research universities such as Cambridge or Oxford and spin-off activity (Landry et al., 2006: 1612;
Lockett et al., 2003: 190; Yencken and Gillin, 2002). Landry et al. (2006: 1612) said “More
specifically, the results of this study suggest that Etzkotwitz’s entrepreneurial university could not
exist without the resources and capabilities of the traditional university”. Therefore, I can recognise a
gap between paradigms which could generate a problem in higher education policy design. This
difference between paradigms separates two currents of thought – ‘Entrepreneurial’ and ‘Classical’ –
8 Shane (2004: 4) defined Spin-off as “a new company founded to exploit a piece of intellectual property created in an
academic institution”.

14. 14
however, Mueller (2006) suggests that one mix of both theories could explain more effectively the
impact of the university in the economic development:
i) A well developed regional knowledge stock is a crucial determinant of regional economic
performance…The evidence suggests that both basic and applied research promotes growth…ii)
Regions with a higher level of entrepreneurship experience greater economic performance…iii)
Universities are a source of innovations: the more firms draw from knowledge generated at
universities, the more those regions experience economic growth. (Muller, 2006: 1506)
In the next part we describe the evolution of the models that stimulate and explain spin-off activity. I
will attempt to discover a pattern of evolution of these models over the last ten year period.

15. 15
1.3. The Evolution of the models
I will review several cases and models that explain spin-off creation. What are the drivers? What are
the examples considered? I will present two cases from MIT - to begin and to finish - which is
considered the main example of an ‘Entrepreneurial’ university around the world (Roberts et al.
1996; O’Shea et al. 2007). Figure 2 shows a summary of models for spin-off activity. All the cases
consider in research as important in some way within the models and drivers. It is especially
important in the last case, where MIT’s model is based in internal capabilities and this model
emphasises research quality and quantity as the base that explains spin-off creation. Comparing the
last with the first model where external conditions such as offers of funds and surrogate
entrepreneurs are considered external factors, I could assume that the models have migrated (or
evolved) from the externals conditions to internal capabilities such as research and culture.
Reviewing the year 2003 I find two cases where the attitude and culture appear as central elements in
achieving good performance in spin-off creation. Nevertheless, these essays consider very particular
examples: simply put, they take successful universities in third stream activities, so it is not possible
to generalise from their conclusions. I could summarise the evolution as: in a first stage, external
factors seem important as surrogate entrepreneurs and ‘smart’9 capital. Next, endogenous culture and
attitude is central in technology transfer models. Finally, I consider that drivers evolved toward a
research-based model. Probably, this evolution of the models could be explained because in the first
stage, venture capital and entrepreneurs could be a restriction factor in the US and research is not
considered as a restrictive condition. However in the second stage, where the policies provide more
entrepreneurial external factors, it may be that the internal culture in several universities affects the
spin-off performance. Finally, in the last period, having capital, entrepreneurs and staff which are
provided by the policies that stimulate entrepreneurial attitude, the generation of disclosures –
therefore research - would act as a restriction. However, some of these studies – cases shown in
Figure 2 – consider time series that does not explain this ‘evolution’. Thus, I have two possible ways
9 This is a name for investments that consider not only money because these add business capabilities, too. Examples
could be venture capital, angel investors and seed funds.

16. 16
to explain the changes in the models of spin-off creation in the last ten years. First, the changes
could be explained by the evolution in the restriction condition. Second, the scholars were affected
by prejudices, in a first stage close to the year 1998; ‘Entrepreneurial’ ideas seemed to explain the
spin-off’s creation. In the last period, the ‘classical’ paradigm close to research universities was
reinforced by the empirical evidence.
In the next chapter, I will review the UK case. This case considers the history of the UK higher
education system; the existence of different types of universities; and the evolution of public policies
for third stream activities during the period of Tony Blair’s Government.

17. Figure 2: Evolution of the spin-off creation models 1996 - 2007
Authors and Title Year Data Model and/or Drivers Comments
Roberts and Malone 1996 Grounded in US elite They comment that a model needs to This work considers "four principal
Universities. Mainly, MIT and include support in venture capital and groups which are involved in the spin-
Stanford. surrogate entrepreneurs (external off process: the technology originator,
drivers). Therefore, they present 5 the entrepreneur, the R&D
schemes to include people and capital organization itself, and the venture
in different stages of the investor" (1996: 26).
entrepreneurial process.
Di Gregorio and Shane 2001 This paper takes data from These scholars found that "Intellectual This model benefits the contact
101 US universities. eminence and the policy of making between novelty knowledge and 'smart'
equity investments in TLO start-ups capital; I could probably say that it
and maintaining a low investor's share suggests a low participation of the
of royalties increases new firms’ research institution in the development
formation". of the business.
Bercovitz and Feldman 2003 Grounded in Medical schools This study suggests that "the adoption These scholars focus their study on the
from Duke University and of initiatives like technology transfer is incidence of the institutional culture
John Hopkins University. a function of the norms at the and personal training in order to accept
institutions where the individual new challenges.
trained; the observed behavior of their
chairman and the observed behavior of
similar individuals".
Lockett, Wright and Franklin 2003 Grounded in a survey from This study separates drivers of This model emphasises on attitude and
57 UK Universities, but later, universities spin-off activities. They policies. However, when these scholars
it takes a subgroup of the 10 find that universities with "clearer choose the most successful universities
best spin-off performance strategies towards" spin-offs, the use of in spin-off activity, 9 out of 10 were
universities. surrogate entrepreneurs and to possess traditional research universities.
expertise and networks improve spin- Therefore we could suggest that this
off performance. factor is not neutral.
Continue in the next page.

18. 18
Figure 2: Evolution of the spin-off creation models 1996 - 2007
Authors and Title Year Data Model and/or Drivers Comments
Adams 2005 Stanford University The author sum up his drivers in "a This model is based in high capabilities
concentration of brains, an in knowledge production and
entrepreneurial culture and an endogenous entrepreneurial attitude
infrastructure supportive of high tech and skills.
and entrepreneurial activity".
Landry, Amara and Rherrad 2006 This study considers a More than a model this study searches This study finds a link between
database of 1554 university for drivers for spin-off activity among traditional and lower applied research
researchers funded by the academics. It proposes as main drivers: with spin-off creation that could be
Natural Sciences and traditional funding of university considered contrary to the traditional
Engineering Research research and university-industry paradigm in public policies (the applied
Council of Canada. partnership grants. Also, this study research is more connected with
finds that "the degree of novelty of innovation).
research knowledge increases the
likelihood of spin-off creation" (2006:
1611).
O'Shea, Allen., Morse, 2007 MIT They consider a model that integrates 4 This model relieves the endogenous
O'Gorman and Roche dimensions: science and engineering capabilities and takes as a base the
resources, the quality of research resources produced from the research
faculty, supporting organizational activity.
mechanism and policies, and
entrepreneurial culture.

19. 2. The UK case. Universities and Typologies10
In this chapter we review the UK university system and its types of universities, how this structure
was generated and the different features among groups. We will describe types because in our model
this division enables us to separate behaviour by groups. Too, we will show the evolution of the third
stream public policies in UK and the paradigms above these policies. Finally, we will propose a
model which explains the performance in spin-off creation.
2.1. The UK Higher Education system
The UK higher education system considers 166 institutions among universities and colleges11 which
receive £12.8 billion in funding per year (HEFCE, 2005). Too, we could add that universities
conform a economy sector “In 1999–2000 they generated directly and indirectly over £34.8 billion of
output and over 562,000 full time equivalent jobs throughout the economy” ( – ,2003 :10); it is a
global leader in research, for example UK universities have produced 44 Nobel prize winners, and 13
per cent of the world’s most highly cited academic publications ( – , 2003: 10). In addition, British
higher education is an export product and example around the world: “In 1962–63 there were 28,000
overseas students in Great Britain…; by 2001–02 there were about 225,000…” ( – , 2003: 10). We
could add that UK has 29 universities among the best 200 in the world, a system which is only
exceeded by the US higher education system (THES, 2006).
Geuna (1998: 49) shows us that there was a period of ‘expansion and diversification’ of the university
system between “the end of the Second World War” and “the end of the 1970´s” which is
emphasised by Shattock (1996: 24) who maintains that the UK University system is a creation of the
last 50 years:
10 This chapter is grounded in “A study in University typologies: Is there a strong relation between typology and
performance in University Spin-off? A review from the UK experience”. It was prepared as Term Paper for the course
Political Economy for Science Policy which was taught for Dr. Aldo Geuna (SPRU, 2007). Although we add information
the main structure conserve its features. We prefer not quoted this previous work because we could lose the original
sources.
11 HEFCE (2005: 3) gives a number of 112 universities.

20. 20
Over 50 years Britain has moved from a position where it had 18 universities but no university system
to one where it has 105 universities and a very clearly defined university system.
The government and its policies have played an important role in this ‘expansion and diversification’.
For example, creating several research universities “founded in the 1950s and 1960” (HEFCE, 2005:
3) and enabling the change of status for the polytechnics under the Further and Higher Education
Act 1992. Geuna (1998: 67) suggests that there were four main drivers which impulse these
expansion policies: the process of specialization and expansion in the research activity, the successful
use of scientific discoveries, new demands for more skills by the industry and government and social
pressure for a democratization of the university, and the strong economic growth of the post war
period. Martin (2003: 23) adds that this expansion in other countries like Central and Eastern Europe
countries could be associated to process of privatization and the diversification process to new
business models that are enabled by “the development of information and communication
technologies” (Martin, 2003: 22). Therefore, we could consider that Shattock’s description for UK
system has a connexion with one global process. Thus, we could take the UK case as a global
example.
2.2. Types of universities
In this part we try to establish a typology of universities that facilitate to recognise the drivers in
spin-off creation and too possibly it enables us to create a useful model for benchmark. Different
scholars maintain that the age of creation can be a good base to establish a university typology
(Shattock, 1996; Geuna, 1998; Martin, 2003). Then we will use the historical classification to separate
three types of universities: classical or medieval large research universities, regional or small and
medium research universities and ‘teaching’ universities.
A first type of university is the medieval large research university like Oxford and Cambridge 12 .
Martin (2003: 7) maintains that, from the long-term historical perspective, the construction of the
12 HEFCE (2005: 3) mentions that these universities “date from the 12th and 13th centuries”.

21. 21
university is dependent on different social contracts13: The first social contract was the medieval
university14 , where the university is considered a space for the knowledge. Geuna (1998: 54) say
about the medieval university that:
(The students) were involved in the elaboration and transmission of a peculiar good: knowledge…It
was a community with internal cohesion, articulated organisation and a corporate personality. It was a
moral and legal entity enjoying a degree of independence from external powers and capable of
continuity through time.
Then, the original organization was centred in the knowledge production and had independence for
this. Martin (2003) describes this type of university as ‘classical’ university which “might be termed
the pure or ‘immaculate’ conception, the purpose of the university is education and knowledge ‘for
its own sake’” (Martin, 2003: 14). This type is represented in the UK System by ‘the Russell group’
which groups twenty prestigious and ancient universities. It is considered a research-led UK
universities group established in 1994 to represent their interests. In 2004/5, they accounted for 65%
of UK Universities' research grant and contract income and 56% of all doctorates awarded in UK
(HESA, 2006).
We will consider a second group that was born around the middle of the XIX century, small and
medium research universities or land-grant universities which had a more specialised (narrow)
subjects or influence areas as ‘regional’ universities than medieval type. Martin (2003: 19) says:
In the United State, in the case of the land-grant universities, ‘the social contract’ embodied in the
1862 Morrill Act involved giving them land in return for supporting the development of agriculture
and the mechanic arts (Martin quoted this from Rosenberg and Nelson 1994: 325)
In the case of UK, ‘1994 group’ could be considered a represent of this type of universities. It was
founded as an answer to Russell group which is composed of ‘smaller research-intensive universities'
with an international reputation like Sussex and Surrey universities. This group is conformed by
universities created around 1950s and 1960s where the government looked for to create new
13 This social contract it interprets the demands of the society and influences in the government actions. That’s why it
finishes modelling the university model.
14 We work with the three social contracts suggested by Martin (2003), but Geuna divides the history of the university in
five phases.

22. 22
universities that will explore new interdisciplinary subjects. However, the core of these universities
continues being research.
Finally, the last group is ‘teaching’ universities which were born as a necessity of the government to
expand the number of the professionals in the economic structure. In UK the group that represents
this type of university is ‘Campaign for Mainstream Universities’ (CMG) or ‘the Coalition of Modern
Universities’. Martin (2003: 23) says “there will be specialized universities, in particular teaching-only
institutions although those may be ones that prove most vulnerable to new entrants from the
commercial sector”. Geuna (1998: 71) describes these three groups as:
The consequence has been a polarisation of the system into three main groups. At the top are almost
exclusively the pre-war universities. They have a higher status, more rights and privileges, and wider
sources of funds. These high status universities are the sites where much of the top scientific research
is carried out. A second group is composed of the majority of the new universities and some of the
PSIs. They are characterised by a lower status and lower funds, but they have rights and privileges
similar to the pre-war university. They are involved in mainly technical research usually applied and
oriented to regional needs. Finally, at the lowest level are the groups of vocational PSIs that
exclusively undertake teaching responsibilities.
Having this three groups we can describe the main characteristics in UK grounded in: ‘Russell group’,
‘1994 group’ and CMG. Figure 3 shows a summary of characteristics.
Figure 3: Main characteristics among UK types of universities
Number of Number of Research PhD
Type
students subjects RAE 2001 funding (QR) Awarded
2004/05 2004/05 2004/05 £ (2004/05)
CMG Mean 13,974 19.63 3.35 1,493,566 28
Mode - 22 - - -
Std. Deviation 4,020 3.94 0.47 1,587,330 18.47
1994 Mean 11,450 18.79 4.58 13,235,671 166
group Mode - 15 - - -
Std. Deviation 3,074 3.52 0.25 3,019,396 39.751
Russell Mean 19,794 24.53 4.88 43,503,491 468
group Mode - 24 4.73 - -
Std. Deviation 4,88 3.69 0.25 20,304,357 186.33
Source: Based in HESA (2006) and HERO (2001)
The importance of this typology is which enables to connect these models with the performance in
technology transfer activities like patents, licences and spin-offs and these types of universities are

23. 23
recognised around the world then it is possible to develop a benchmark scheme beginning from this
pattern.

24. 24
2.3. UK Higher Education public policies and the evolution of the paradigm
In the knowledge economy, entrepreneurial universities will be as important as
entrepreneurial businesses, the one fostering the other. Universities are wealth
creators in their own right: in the value they add through their teaching at home; in
the revenue, commitment and goodwill for the UK they generate from overseas
students, a market we need to exploit as ambitiously as possible; and in their
research and development, of incalculable impact to the economy at large. We look
to you, and to our other leading universities, not only as the guardians of
traditions of humane learning on which your reputation depends; but also as one
of our key global industries of the future, able to give the UK a decisive competitive
advantage within Europe and beyond in the 21st century economy.
Tony Blair (1999), Prime Minister of UK
But more general initiatives too are helping lead to a major cultural change in
higher education. A recent survey showed that in 1999-2000, 199 companies were
spun off from our universities, compared with 70 a year on average in the previous
five years.
Tony Blair (2002), Prime Minister of UK
Funding per student had fallen by over 20 per cent in real terms in the previous five
years. We were not producing enough graduates to respond to global competition.
Teaching quality had suffered. So had research. Expansion had been done
on the cheap.
Tony Blair (2007), Prime Minister of UK
We will take the Laborist government of Tony Blair to look for the drivers of its policies in third
stream activities. How the previous quotes from the prime minister show we could assume that the
paradigms which support these policies were changing. But, these changes were produced by wrong
paradigms or did they have a logic connexion with the evolution of the problems? This could have
policy implications because one model -the last paradigm mentioned by Blair (2007)- seems to define
the traditional research as the base of the strategy. On the other hand, the first speech of Blair (1999)
seems to attack the ‘classical’ university. Thus, we take two models. One ‘static’ model which
assumes that ‘classical’ university was always the responsible to produce new knowledge which is the
base of the development, and a second model that assumes a ‘dynamic’ model with stages: first, the
policies broke the traditional close culture of medieval universities. Next, the policy adds third stream
skills within the university scheme and connects it with the other corpus involved in regional
development. Finally, it is necessary to invest in traditional research to create more disclosures. We
will review the third stream UK policies searching keys to discover which is the paradigm?

25. 25
Hiscooks (2005: 2) argues that with the beginning of the new Labour government in 1997 the “new
mission for UK universities was embodied” and transformed into public policies. He adds:
The funding for these activities, known in the UK as ‘third stream funding’ has risen steadily year on
year from the £20 million allocated in 1998, £45 million in 1999, to a target of £150 million by 2010.
The public policy to improve third stream activities took corpus in one series of programmes
showed in the next Figure.
Figure 4: UK Programmes to support third stream activities 1998 – 2001
Year Initiative Purpose Details
1998 Higher education Funding to support activities to £20 million per year allocated to
Reach out to improve linkages between provide funding for the establishment
Business and the Universities and their communities of activities such as corporate liaison
Community offices
(HEROBaC)
1999 University Seed investments to help £45 million was allocated in 1999 with
Challenge Fund commercialisation of university IPR 15 seed funds being set-up and £15
(UCF) million in 2001
1999 Science Enterprise Teaching of entrepreneurship to SEC was initially provided with £28.9
Challenge (SEC) support the commercialisation of million in 1999 for up to 13 centres
science and technology
2001 Higher Education Single, long term commitment to a HEIF was launched in 2001 to bring
Innovation Fund stream of funding to “support together a number of previously
(HEIF) universities’ potential to act as drivers independently administered third
of knowledge in the economy” stream funding sources. This was then
extended in 2004 with a further £185
million awarded
Source: Hiscooks (2005: 3)
So gradually, UK universities began to add tools for this third mission. In terms of entrepreneurship,
they added incubators, science parks, funds, and other. The results of these policies can be seeing in
the next Figure, where we can verify the increase in the quantity of disclosures. Figure 6 shows
graphically it. However, the spin-off activity decreased in the last 3 years, this could be an effect of
competence between spin-offs and licenses.
Figure 5: Indicators of entrepreneurial activity for UK Universities, 1999-2004
Tech Transfer results/Year 1999/00 2000/01 2001/02 2002/03 2003/04
Disclosures 1,912 2,159 2,478 2,710 3,029
Patent granted 188 234* 199 371* 463
Licensed non-software 238 306 324* 507* 1,246
Spin-offs 203 248 213 197 167
Source: HEFCE (2002), HEFCE (2003), HEFCE (2004), HEFCE (2005) and HEFCE (2006)
* This data present differences between surveys

26. 26
Figure 6: Evolution in the UK production of technology transfer elements, 1999-2004
1,400
1,200
1,000
Patent granted
Results
800
License non-software
600
Spin-offs
400
200
0
1999/00 2000/01 2001/02 2002/03 2003/04
Period
The model used by the UK policy makers is represented in the next Figure. They search to stimulate
disclosures which are the raw material for patents. Patents would be the key in the technology
transfer process because they originate licenses and spin-offs.
Figure 7: A simplified Research Exploitation Process
Source: HEFCE (2005: 18)

27. 27
Although, the Figures show a successful policy, why is the UK government unconformed with its
results? (Blair, 2007) This could be explained by the constant comparison with the US performance
(Pavitt, 2001). The next Figure shows this comparison where research expenditure (private and
public) has big gap between US and UK. In this Figure UK seems more efficient, however it could
be not precise because the quality of Spin-off –employees that can generate or the value in the initial
public offering (IPO)- could be higher. The disclosures can be connected with the exclusivity of the
technology and this can be connected with the research expenditure, then the UK government sees
this ‘efficiency’ like a gap more than an advantage.
Figure 8: Commercialisation activity in the UK and US, 2003-04
US universities UK HEIs, HE
AUTM survey BCI Survey
Number of institutions 165 164
Research expenditure Industrial (£000s) 1,551,410 186,771
Research expenditure Public (£000s) 14,102,984 2,400,052
Total research expenditure (£000s) 21,296,961 3,633,283
New patents granted 3,450 463
Patents per £10 million research expenditure 1.6 1.3
IP income from licensing, other and spin-off sales (£000s) 632,061 38,234
Licence income as percentage of total research expenditure 3.0% 1.1%
Spin-off companies formed 348 167
Research £ expenditure per spin-off (£000s) 61,198 21,756
Source of US data: AUTM Financial Year 2003 report
Source of UK data: HESA FSR 2003-04 and HE-BCI survey 2003-04
In our ‘dynamic’ model and how Pavitt (2001) suggests, first, the countries have the paradigm about
problems in the academic culture and links between university and industry. But finally, when they
have policies that improve these two elements, the core of the problem is the size and strengths of
the research system, and this is a huge problem because when we compare the expenses in
commercialization activities and research, third stream policies are cheaper in compares with
research.

28. 28
2.4. Building a Proposition: Type of University, Research, and Policies
In this part we will propose a model which summarise the drivers to consider in spin-off
performance. This model will be tested in the next chapter with statistics tools. The Figure 9 shows
the conceptual model. Research in ‘entrepreneurial’ faculties -like engineering, chemical and life
science- is considered the base of this model because occupied a 50% of the graphic representation.
In addition, we add the type of university because this research must be conducted above a type of
organization that provides connexions, prestige and others. Probably, an important consideration is
to open the sources of finance because could be a key to have resources that enable free hours for
other activities in the academic staff. We think that ‘research’ universities are the ‘business’ model for
spin-offs against the ‘modern’ universities or ‘teaching’ universities. Finally, we add the policies
which support the networks, infrastructure and finance resources to improve the disclosures. In our
model the disclosure production is the base for patents and licences and spin-offs are the most
sophisticated elements in technology transfer from the universities as Shane (2004) suggests.
Figure 9: Model proposal
We will give more details about which are the meanings for each part of the model.

29. 29
2.4.1. Type of University
This variable considers the main characteristics of the university and it divides only in three groups:
large research universities, medium research universities and teaching universities. The model
suggests that the type is an important consideration to know the capabilities of the university to
produce disclosures.
2.4.2. Research
This variable considers the characteristics of the research within the university. It not only consider
quantity and quality, too it could include attitude, tradition and culture.
2.4.3. Policies
Finally, this variable considers all the university policies that support the third stream activities.
Mainly, it is measures of management that are took by the university authorities to improve the spin-
offs.
Thus, our model depends of: the structure and mission of the university; the nature of the research
activity; and the management decisions that search to improve the spin-off production.

30. 30
3. Methodology and empirical evidence
In this chapter I present some empirical evidence in order to test the proposition outlined in the
previous section, this is, that the main driver of spin-offs generation at the university level is the
research capacity. In other words, research is a necessary condition for the sustained generation of
spin-offs at university level. From this central proposition, I develop an empirical analysis using a
sample of seventy universities in the UK that represent three groups: (i) polytechnics or modern
universities called Campaign for Mainstream Universities (CMG), which represents medium and
large teaching universities; (ii) medium research intensive universities identified as “1994 group”,
which represent a type of regional universities; and (iii) large research universities called “Russell
group”, which represent national universities with a broad mission and typically old history. The data
base was built from HESA (2006), HEFCE (2006) and HERO (2001).
Next, I look at the relationships between a spin-off as a dependent variable, and type of university
(TYPE), research performance (RESEARCH) and policy (POLICY) as independent variables. The
central model is represented by an equation. First I will explain the equation; each variable will be
presented with descriptive statistics and some basic statistical tools. The variables TYPE and part of
POLICY will be tried as dummy variables, TYPE distinguishes “Russell group” and “1994 group”
with two dummies, and POLICY distinguishes the presence of incubators and seed funds.
RESEARCH summarise three variables: research funding, PhDs as proportion of the number of
students and university RAE. POLICY adds number of staff in business and community activities
and years of the IP office. After this, I will apply a regression analysis that seeks to connect a
dependent variable with independent variables. The regression analysis will be applied over a sample
of 70 universities. In addition, I will apply a second regression over 35 universities. I will compare
groups with universities with an ‘entrepreneurial’ positive attitude (50% more productive in spin-
off’s production). Thus, I hope to clean the effect of institutions that do not present a constant
performance in this activity where their imprecise strategies could affect the regression analysis.

31. 31
If I verify that the main drivers are: typology, research, and policies, I could say that the elite
university of research is the main entrepreneurial university and HE public policies must focus their
efforts on research and academic culture adaptation (the business skills are a commodity).

32. 32
3.1. Data
I take a group of 70 UK Universities which represent three types of universities: Large research
universities; small and medium research universities and modern universities; and teaching
universities or polytechnics. The first criterion of selection was taken from the existing group among
UK Universities. The large research universities group was taken from “Russell group” because this
group represents large elite universities in UK. The small and medium research universities group
was taken from “1994 group” which represents new research universities smaller than the first
group. Teaching universities were taken from Campaign from mainstream universities (CMG) or
modern universities group, because this group prioritizes the first university mission over research.
The second criterion was to eliminate ‘outsiders’ cases among groups, mainly by size and research
behaviour. I eliminated the London School of Economics in the Russell group; Goldsmith college,
Birkbeck College and the School of Oriental and African Studies in the 1994 group; finally I
eliminated Bath Spa University, the University of Bolton and the University of Abertay Dundee in
the CMG. I also eliminated from the sample Staffordshire University because its spin-off data was
incoherent concerning the level of activity and results of the previous years. Finally London
Metropolitan University was eliminated because in some tables of information this university does
not have enough data.
In order to complete the sample I add some universities which have similar characteristics to these
three groups. I added the University of Kent, the University of Strathcycle, the University of Dundee
and the University of Ulster to the “1994 group” and Brunel University, the University of
Huddersfield, the University of Brighton, Northumbria University, the University of Salford,
Liverpool John Moores University and the University of Portsmouth in the CMG group. With these
inclusions I completed a sample of 70 universities: 19 large research universities, 19 small and
medium research universities and 32 teaching universities.

33. 33
Figure 10: Main features of the sample used
UK HE Institutions Total HE UK Sample 70 % Sample 35 %
Number 168 70 42% 35 21%
Students 1.647.116 1.040.797 63% 586.355 36%
Total Funds Research 2.883.900 2.437.082 85% 2.180.635 76%
Entrepreneurial faculties Total HE UK Sample 70 % Sample 35 %
Students 333.893 236.964 71% 144.309 43%
Total Funds Research 1.244.267 1.087.611 87% 973.297 78%
I will then use a selection of 35 ‘better performance in spin-off activity’ universities. This sample
includes only 5 CMG universities (5/32, 16%), 11 ‘1994’ universities (11/19, 58%) and all ‘Russell
group’ (19/19, 100%). Another sub-sample which I will use is the numbers of students and research
for specific entrepreneurial faculties15 within the original 70 sample and 35 entrepreneurial sample.
For the variable ‘students’ I take the following faculties: Pharmacy and pharmacology, biosciences,
chemistry, physics, general engineering, chemical engineering, mineral metallurgy and materials
engineering, civil engineering, electrical electronic and computer engineering, mechanical aero and
production engineering, other technologies and Information technology and systems sciences and
computer software engineering (HESA, 2006). For the variable ‘research’, I take the same faculties
but this list does not include other technologies (HESA, 2006). Thus, I am taking 63% of the UK
students and 85% of the research funds, and in the second sample 36% of the students and 76% of
the research funds. This could be a sign of the importance of research activity because when I reduce
the sample to 50%, research only decreases by 9%.
This sample is taken because Pavitt (2001) suggests that this movement (spin-off and entrepreneurial universities)
15
would be understood better showing only some faculties: engineering, chemical and life science.

34. 34
3.2. The equation
I ground our proposition in a model which could be shown as an equation. This section shows this
equation and describes each part of it:
Yi = α + βTYPEi + δRESEARCH i + ζPOLICYi + γSIZEi + ε i
Where:
The Dependent variable is SPIN-OFFS. TYPE is a vector of dummy variables reflecting types of
universities, I will use three groups: ‘Russell group’ (19), ‘1994 group’ (19), and CMG universities
(32). RESEARCH is another variable vector compressing research performance: quality, relative
importance of research programmes on the student structure and funding. POLICY is a vector of
key variables accounting for policy-related indicators regarding spin-off management. I use variables
that represent resources, importance and experience linked to third stream activities. Probably,
POLICY contains culture and attitude issues because this summarises the management decisions
about the university’s entrepreneurial strategy. This vector could summarize ‘entrepreneurial culture’.
SIZE is a variable that controls by size. Finally, εi is a normally distributed error assumed iid.
3.2.1. SPIN-OFFS: The dependent variable
I take the spin-offs data from HEFCE (2006), this survey presents data for periods 2002/03 and
2003/04. In 2002/03 and 2003/04 the UK universities produced 197 and 167 spin-offs, my sample
of universities (70) was responsible for 72% and 63%. I will take the data as an average of two
periods: 2002/03 and 2003/04. I prefer to minimise the impact of a particular year in the sample.
Spin-offs, as we saw in the last chapter, are the most sophisticated product in the technology transfer
value chain (Shane, 2004), that is why I will suppose it is the best measure for mature third stream
activities. Thus, I am considering the strategy patents-license as a lower “entrepreneurial” possibility.

35. 35
HEFCE (2001) defined spin-off as:
Spin-offs are enterprises, in which an HEI or HEI employee(s) possesses equity stakes, which have
been created by the HEI or its employees to enable the commercial exploitation of knowledge arising
from academic research. Other ‘start-up’ companies may be formed by HEI staff or students without
the direct application of HEI-owned intellectual property.
I take both types of spin-offs HEI ownership and supported by the HEI but without equity. The
next figure shows that the average spin-off per year in our sample reaches 1.95. However, this
sample has 18 universities which do not present spin-off activity; in this case the mode value is 0. If I
filter the values that show the lower spin-off activity and choose a filter for LN higher than 0, I
separate the sample into a second more entrepreneurial group with 35 universities. It is important to
warn that spin-off is a very concentrated activity in a few universities. The next Figures show a
division into twenty parts based on spin-off performance 50% of the universities present a
performance lower than 1 spin-off per year and only 20% present a performance superior than 4
spin-off per year (groups 17, 18, 19 and 20).
Figure 11: Frequency of universities according spin-off Figure 12: Average of spin-off per 20’tiles
performance divided in 20’tiles
20
50% 6.00
50%
15
Mean Spin-offs
Count
4.00
10
5 2.00
0
0.00
3 7 9 11 12 13 15 16 17 18 19 20
3 7 9 11 12 13 15 16 17 18 19 20
Source: Grounded in HEFCE (2006) Source: Grounded in HEFCE (2006)
Figures 13 and 14 show the descriptive statistic for spin-off activity (for both samples).

36. 36
Figure 13: Spin-offs descriptive statistic and test of normality
Sample of 35 better
Sample of 70 universities
Descriptive Statistic/Variable performance
Spin-offs LN Spin-offs Spin-offs LN Spin-offs
Valid 70 52 35 35
Mean 1.9500 0.6645 3.5429 1.1655
Std. Deviation 1.9948 0.8397 1.6377 0.4555
Skewness 1.071 -0.342 1.139 -0.024
Std. Error of Skewness 0.287 0.330 0.398 0.398
Kurtosis 0.904 -1.048 2.100 -0.560
Std. Error of Kurtosis 0.566 0.650 0.778 0.778
Z Skewness 3.732 -1.036 2.862 -0.060
Z Kurtosis 1.264 1.270 1.643 0.848
Source: Based in HEFCE (2006)
Figure 14: Tests of Normality Spin-offs and LN Spin-offs
Kolmogorov-Smirnov(a) Shapiro-Wilk
Normality test
Statistic Df Sig. Statistic df Sig.
Sample 70 Spin-offs 0.183 70 0.000 0.871 70 0.000
LN Spin-offs 0.140 52 0.013 0.922 52 0.002
Sample 35 Spin-offs 0.139 35 0.085 0.912 35 0.009
LN Spin-offs 0.107 35 0.200(*) 0.964 35 0.301
a Lilliefors Significance Correction
Source: Based in HEFCE (2006)
I could say that Spin-off activity has a normal distribution among 50% of the universities which
present a more active ‘entrepreneurial’ performance. When I apply LN over Spin-offs, according to
Skewness and Kurtosis, I could assume that LN Spin-off in both groups are normally distributed;
however, Kolmorov-Smirnov test for the first group does not present a significant value. On the
other hand, Shapiro-Wilk presents normal distribution for the second group. Taking this first
comparison, I need to know what the factor which produces this difference is. I will research three
factors. First, this difference could be produced by political influence or prestige, this variable is
TYPE. Second, it could be an effect of research funding and attitude about knowledge production:
spin-offs depend on research as a raw material. Third, differences among types of universities could
be explained by the use of entrepreneurship tools and culture.

37. 37
3.2.2. TYPE
This vector is worked with dummies variables. I consider the CMG group as basis, and then this
group -within both dummy variables- has the value 0. It is supposed that this group has a lower level
in spin-off activity. Second, I consider “1994 group” as a dummy then this group uses number 1 and
the other groups 0. After, in the next variable, the Russell group takes the value 0, and other groups
0. Using this method I can obtain the effect of the typology over spin-off activity. The next figure
shows the main features of each type.
Figure 15: Types of universities main characteristics
Number of Number of Research
Type PhD Granted
students subjects RAE 2001** funds
(2004/05)
2004/05 2004/05* 2004/05 £
CMG Mean 13,974 19.63 3.35 3,510,880 28
Mode - 22 - - -
Std. Deviation 4,020 3.94 0.47 2,546,212 18.47
1994 Mean 11,450 18.79 4.58 25,117,530 166
group Mode - 15 - - -
Std. Deviation 3,074 3.52 0.25 9,674,461 39.751
Russell Mean 19,794 24.53 4.88 97,236,890 468
group Mode - 24 4.73 - -
Std. Deviation 4,88 3.69 0.25 47,718,797 186.33
Sources: HESA (2006) and HERO (2001)
* This number was obtained from HESA (2006) with the departments that presented students
** This number was obtained from HERO (2001) taking a weighed average among faculties’ RAE and scholars
Figure 16 shows that according type of Figure 16: Type v Spin-off 2002/04 per
year
universities, the spin-off performance has a 4
considerable difference. Means comparison 3
2
shows a difference between Russell group
1
and 1994 group and CMG. 1994 and CMG
0
CMG 1994 group Russell group
have the same mean. However in the last
Source: HEFCE (2006)
comparison, I assumed a normal distribution of LN Spin-offs.

38. 38
Figure 17: ANOVA test means comparison between types of universities in spin-off performance
ANOVA Sum of
LN Spin-offs Squares df Mean Square F Sig.
Between Groups 12.910 2 6.455 13.722 0.000
Within Groups 23.049 49 0.470
Total 35.959 51
Figure 18: Multiple comparisons test between types of universities in spin-off performance
Multiple Comparisons
Dependent Variable: LN Spin-offs
Mean
Difference 95% Confidence Interval
(I) Typology (J) Typology (I-J) Std. Error Sig. Lower Bound Upper Bound
Scheffe CMG 1994 group -.42492 .23889 .216 -1.0280 .1782
Russell group -1.19215* .23272 .000 -1.7796 -.6047
1994 group CMG .42492 .23889 .216 -.1782 1.0280
Russell group -.76723* .22897 .006 -1.3453 -.1892
Russell group CMG 1.19215* .23272 .000 .6047 1.7796
1994 group .76723* .22897 .006 .1892 1.3453
Bonferroni CMG 1994 group -.42492 .23889 .244 -1.0171 .1673
Russell group -1.19215* .23272 .000 -1.7690 -.6153
1994 group CMG .42492 .23889 .244 -.1673 1.0171
Russell group -.76723* .22897 .005 -1.3348 -.1996
Russell group CMG 1.19215* .23272 .000 .6153 1.7690
1994 group .76723* .22897 .005 .1996 1.3348
*. The mean difference is significant at the .05 level.
When I compare the second sample among universities that produce spin-offs (35), I do not find
any difference between means and in this case, LN Spin-offs is normally distributed. Therefore, I
could think that universities which decide to implement entrepreneurship issues do not show any
difference in performance.
Figure 19: ANOVA test means comparison between types of universities in spin-off performance, partial sample with
better performance in spin-off activity
ANOVA Sum of
LN Spin-offs Squares Df Mean Square F Sig.
Between Groups 0.955 2 0.477 2.505 0.098
Entrepreneurial Within Groups 6.099 32 0.191
Total 7.054 34

39. 39
3.2.3. RESEARCH
This vector is worked with two variables: Students awarded with a PhD per year per one thousand
students (total university students), and total research funds per one thousand students. Thus, I take
a variable which shows the importance of research in the students’ structure and the university
capabilities to support research with resources. Both variables are controlled by size. Figure 20 shows
these variables divided into two samples: total (70) and entrepreneurial (35), and we add the values
for entrepreneurial faculties in these two samples.
Figure 20: Characteristics of the sample in research variables
Research Total Research Total
PhD Granted per
Funds per 000’s Funds per 000’s
000’s students
students students
University Group/Research variables (2004/05)
(2004/05) (2004/05)
All Faculties Entrepreneurial
Number 70 70 70
Total sample Mean 11.7 2,163.70 4,140.79
Std. Deviation 11.1 2,789.49 4,404.20
Number 32 32 32
CMG Mean 2.1 240.32 396.72
Std. Deviation 1.3 154.85 376.31
Number 19 19 19
1994 group Mean 15.3 2,264.02 5,302.76
Std. Deviation 4.5 902.51 3,461.76
Number 19 19 19
Russell group Mean 24.3 5,302.76 8,632.64
Std. Deviation 10.2 3,461.76 3,862.86
Number 35 35 35
Entrepreneurial sample Mean 18.4 3,692.11 6,759.91
Std. Deviation 10.8 3,202.46 4,246.16
Number 5 5 5
CMG Mean 3.4 350.99 615.93
Std. Deviation 2.4 214.63 609.22
Number 11 11 11
1994 group Mean 15.0 2,428.76 6,317.92
Std. Deviation 4.4 1,006.99 2,922.60
Number 19 19 19
Russell group Mean 24.3 5,302.76 8,632.64
Std. Deviation 10.2 3,461.76 3,862.86
This Figure shows important differences (almost 50%) in research variables between total and
entrepreneurial sample explained by the lower presence of CMG universities (more than by a
difference within groups).

40. 40
Figure 21: Normality Test for Research variables, Samples 70 and 35
Kolmogorov-Smirnov(a) Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
PhD per 000’s 0.192 70 0.000 0.852 70 0.000
Research 000’s student 0.218 67 0.000 0.725 67 0.000
LN PhD 000's 0.186 69 0.000 0.893 69 0.000
LN Research student 0.170 67 0.000 0.926 67 0.001
Research 000's Faculty 0.180 67 0.000 0.861 67 0.000
LN Research Faculty 0.185 67 0.000 0.904 67 0.000
PhD per 000's 0.165 35 0.016 0.915 35 0.010
Research 000’s student 0.242 35 0.000 0.797 35 0.000
LN PhD 000's 0.236 35 0.000 0.826 35 0.000
LN Research student 0.196 35 0.002 0.897 35 0.003
Research 000's Faculty 0.166 35 0.016 0.946 35 0.083
LN Research Faculty 0.254 35 0.000 0.777 35 0.000
a Lilliefors Significance Correction
Figure 22: Normality test Skewness and Kurtosis for research variables Total sample
RT per RTFunds LN RT per LN RT per
PhD per LN PhD
Total sample 000's per 000's 000's 000's faculty
000's 000's
students Faculty students student
Skewness 1.168 2.460 1.100 -.308 -.217 -.434
Std. Error of Skewness 0.287 0.287 0.287 0.287 0.289 0.293
Kurtosis 1.503 7.677 0.881 -.852 -1.510 -1.210
Std. Error of Kurtosis 0.566 0.566 0.566 0.566 0.570 0.578
Z Skewness 4.070 8.571 3.833 -1.073 -0.751 -1.481
Z Kurtosis 1.630 3.683 1.248 1.227 1.628 1.447
Figure 23: Normality test Skewness and Kurtosis for research variables Entrepreneurial sample
RT per RTFunds LN RT per LN RT per
PhD per LN PhD
Entrepreneurial sample 000's per 000's 000's 000's faculty
000's 000's
students Faculty students student
Skewness 0.977 1.956 0.729 -1.094 -1.678 -1.822
Std. Error of Skewness 0.398 0.398 0.398 0.398 0.398 0.398
Kurtosis 1.656 4.502 1.074 1.404 3.271 2.980
Std. Error of Kurtosis 0.778 0.778 0.778 0.778 0.778 0.778
Z Skewness 2.455 4.915 1.832 -2.749 -4.216 -4.578
Z Kurtosis 1.459 2.406 1.175 1.343 2.050 1.957
Considering these tests, I could work with LN of research variables for the Total sample because
Skewness and Kurtosis show a possibility of normal distribution and histograms (Figure 24 and

41. 41
Figure 25 shows that these groups could be a normal distribution but there are two groups –possibly,
teaching universities and research universities-) I consider that it is possible to assume normal
distribution and apply tests with this evidence.
Figure 24: Distribution of LN RT per 000’s Figure 25: Distribution of LN PhD each 000’s students,
students, total sample total sample
LN RT per 000's students LN PhD 000's
12.5 12.5
10.0
10.0
Frequency
Frequency
7.5
7.5
5.0
5.0
2.5
2.5
Mean =6.7574
Std. Dev. =1.5656 Mean =1.8787
0.0
N =70 Std. Dev. =1.22271
0.0
N =69
2.00 4.00 6.00 8.00 10.00
0.00 2.00 4.00
In the case of the entrepreneurial sample, I accept a normal distribution for the variables:
• PhD per 000’s students; because the histogram shows a close distribution with normality and
Skewness and Kurtosis are closed with the Sig. values (between -1.96 and 1.96).
• LN research total funds per each thousand students; the same previous argument.
• Research Total funds for entrepreneurial faculties because all the tests show normal
distribution.