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.

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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

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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 mission1. 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 paradigm3? 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.

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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

5 This 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.

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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

6 Pavitt (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.

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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:

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.

iiiiii SIZEPOLICYRESEARCHTYPEY εγζδβα +++++=

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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

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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).

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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 Etzkowitz7 (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.

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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”.

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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.

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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.

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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.

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

Universities. Mainly, MIT and Stanford.

They comment that a model needs to include support in venture capital and surrogate entrepreneurs (external drivers). Therefore, they present 5 schemes to include people and capital in different stages of the entrepreneurial process.

This work considers "four principal groups which are involved in the spin-off process: the technology originator, the entrepreneur, the R&D organization itself, and the venture investor" (1996: 26).

Di Gregorio and Shane 2001 This paper takes data from 101 US universities.

These scholars found that "Intellectual eminence and the policy of making equity investments in TLO start-ups and maintaining a low investor's share of royalties increases new firms’ formation".

This model benefits the contact between novelty knowledge and 'smart' capital; I could probably say that it suggests a low participation of the research institution in the development of the business.

Bercovitz and Feldman 2003 Grounded in Medical schools from Duke University and John Hopkins University.

This study suggests that "the adoption of initiatives like technology transfer is a function of the norms at the institutions where the individual trained; the observed behavior of their chairman and the observed behavior of similar individuals".

These scholars focus their study on the incidence of the institutional culture and personal training in order to accept new challenges.

Lockett, Wright and Franklin 2003 Grounded in a survey from 57 UK Universities, but later, it takes a subgroup of the 10 best spin-off performance universities.

This study separates drivers of universities spin-off activities. They find that universities with "clearer strategies towards" spin-offs, the use of surrogate entrepreneurs and to possess expertise and networks improve spin-off performance.

This model emphasises on attitude and policies. However, when these scholars choose the most successful universities in spin-off activity, 9 out of 10 were traditional research universities. Therefore we could suggest that this factor is not neutral.

Continue in the next page.

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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

concentration of brains, an entrepreneurial culture and an infrastructure supportive of high tech and entrepreneurial activity".

This model is based in high capabilities in knowledge production and endogenous entrepreneurial attitude and skills.

Landry, Amara and Rherrad 2006 This study considers a database of 1554 university researchers funded by the Natural Sciences and Engineering Research Council of Canada.

More than a model this study searches for drivers for spin-off activity among academics. It proposes as main drivers: traditional funding of university research and university-industry partnership grants. Also, this study finds that "the degree of novelty of research knowledge increases the likelihood of spin-off creation" (2006: 1611).

This study finds a link between traditional and lower applied research with spin-off creation that could be considered contrary to the traditional paradigm in public policies (the applied research is more connected with innovation).

O'Shea, Allen., Morse, O'Gorman and Roche

2007 MIT They consider a model that integrates 4 dimensions: science and engineering resources, the quality of research faculty, supporting organizational mechanism and policies, and entrepreneurial culture.

This model relieves the endogenous capabilities and takes as a base the resources produced from the research activity.

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.

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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 Cambridge12.

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

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

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

Type

Number of students 2004/05

Number of subjects 2004/05

RAE 2001 Research

funding (QR) 2004/05 £

PhD Awarded (2004/05)

CMG Mean 13,974 19.63 3.35 1,493,566 28Mode - 22 - - -Std. Deviation 4,020 3.94 0.47 1,587,330 18.47

1994 group

Mean 11,450 18.79 4.58 13,235,671 166Mode - 15 - - -Std. Deviation 3,074 3.52 0.25 3,019,396 39.751

Russell group

Mean 19,794 24.53 4.88 43,503,491 468Mode - 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

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

pattern.

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

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

Reach out to Business and the Community (HEROBaC)

Funding to support activities to improve linkages between Universities and their communities

£20 million per year allocated to provide funding for the establishment of activities such as corporate liaison offices

1999 University Challenge Fund (UCF)

Seed investments to help commercialisation of university IPR

£45 million was allocated in 1999 with 15 seed funds being set-up and £15 million in 2001

1999 Science Enterprise Challenge (SEC)

Teaching of entrepreneurship to support the commercialisation of science and technology

SEC was initially provided with £28.9 million in 1999 for up to 13 centres

2001 Higher Education Innovation Fund (HEIF)

Single, long term commitment to a stream of funding to “support universities’ potential to act as drivers of knowledge in the economy”

HEIF was launched in 2001 to bring together a number of previously independently administered third 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 3,029Patent granted 188 234* 199 371* 463Licensed non-software 238 306 324* 507* 1,246Spin-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

Figure 6: Evolution in the UK production of technology transfer elements, 1999-2004

0

200

400

600

800

1,000

1,200

1,400

1999/00 2000/01 2001/02 2002/03 2003/04

Period

Resu

lts

Patent grantedLicense non-softwareSpin-offs

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

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 AUTM survey

UK HEIs, HE BCI Survey

Number of institutions 165 164Research expenditure Industrial (£000s) 1,551,410 186,771Research expenditure Public (£000s) 14,102,984 2,400,052Total research expenditure (£000s) 21,296,961 3,633,283New patents granted 3,450 463Patents per £10 million research expenditure 1.6 1.3IP income from licensing, other and spin-off sales (£000s) 632,061 38,234Licence income as percentage of total research expenditure 3.0% 1.1%Spin-off companies formed 348 167Research £ 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

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

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

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

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

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

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%.

15 This sample is taken because Pavitt (2001) suggests that this movement (spin-off and entrepreneurial universities) would be understood better showing only some faculties: engineering, chemical and life science.

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:

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.

iiiiii SIZEPOLICYRESEARCHTYPEY εγζδβα +++++=

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

performance divided in 20’tiles

Figure 12: Average of spin-off per 20’tiles

201918171615131211973

20

15

10

5

0

Cou

nt

201918171615131211973

6.00

4.00

2.00

0.00

Mea

n Sp

in-o

ffs

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).

50% 50%

36

Figure 13: Spin-offs descriptive statistic and test of normality

Descriptive Statistic/Variable Sample of 70 universities Sample of 35 better

performance

Spin-offs LN Spin-offs Spin-offs LN Spin-offs Valid 70 52 35 35Mean 1.9500 0.6645 3.5429 1.1655Std. Deviation 1.9948 0.8397 1.6377 0.4555Skewness 1.071 -0.342 1.139 -0.024Std. Error of Skewness 0.287 0.330 0.398 0.398Kurtosis 0.904 -1.048 2.100 -0.560Std. Error of Kurtosis 0.566 0.650 0.778 0.778Z Skewness 3.732 -1.036 2.862 -0.060Z 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

Normality test Kolmogorov-Smirnov(a) Shapiro-Wilk 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 0.002Sample 35 Spin-offs 0.139 35 0.085 0.912 35 0.009

LN Spin-offs 0.107 35 0.200(*) 0.964 35 0.301a 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

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

Type

Number of students 2004/05

Number of subjects 2004/05*

RAE 2001** Research

funds 2004/05 £

PhD Granted (2004/05)

CMG Mean 13,974 19.63 3.35 3,510,880 28Mode - 22 - - -Std. Deviation 4,020 3.94 0.47 2,546,212 18.47

1994 group

Mean 11,450 18.79 4.58 25,117,530 166Mode - 15 - - -Std. Deviation 3,074 3.52 0.25 9,674,461 39.751

Russell group

Mean 19,794 24.53 4.88 97,236,890 468Mode - 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

universities, the spin-off performance has a

considerable difference. Means comparison

shows a difference between Russell group

and 1994 group and CMG. 1994 and CMG

have the same mean. However in the last

Figure 16: Type v Spin-off 2002/04 per year

Russell group1994 groupCMG

4

3

2

1

0

Source: HEFCE (2006)comparison, I assumed a normal distribution of LN Spin-offs.

38

Figure 17: ANOVA test means comparison between types of universities in spin-off performance

ANOVA LN Spin-offs

Sum of 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

-.42492 .23889 .216 -1.0280 .1782-1.19215* .23272 .000 -1.7796 -.6047

.42492 .23889 .216 -.1782 1.0280-.76723* .22897 .006 -1.3453 -.18921.19215* .23272 .000 .6047 1.7796.76723* .22897 .006 .1892 1.3453

-.42492 .23889 .244 -1.0171 .1673-1.19215* .23272 .000 -1.7690 -.6153

.42492 .23889 .244 -.1673 1.0171-.76723* .22897 .005 -1.3348 -.19961.19215* .23272 .000 .6153 1.7690.76723* .22897 .005 .1996 1.3348

(J) Typology1994 groupRussell groupCMGRussell groupCMG1994 group1994 groupRussell groupCMGRussell groupCMG1994 group

(I) TypologyCMG

1994 group

Russell group

CMG

1994 group

Russell group

Scheffe

Bonferroni

MeanDifference

(I-J) Std. Error Sig. Lower Bound Upper Bound95% Confidence Interval

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 LN Spin-offs

Sum of Squares Df Mean Square F Sig.

Entrepreneurial Between Groups 0.955 2 0.477 2.505 0.098Within Groups 6.099 32 0.191 Total 7.054 34

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

University Group/Research variables

PhD Granted per 000’s students

(2004/05)

Research Total Funds per 000’s

students (2004/05)

Research Total Funds per 000’s

students (2004/05)

All Faculties Entrepreneurial

Total sample Number 70 70 70Mean 11.7 2,163.70 4,140.79Std. Deviation 11.1 2,789.49 4,404.20

CMG Number 32 32 32Mean 2.1 240.32 396.72Std. Deviation 1.3 154.85 376.31

1994 group Number 19 19 19Mean 15.3 2,264.02 5,302.76Std. Deviation 4.5 902.51 3,461.76

Russell group Number 19 19 19Mean 24.3 5,302.76 8,632.64Std. Deviation 10.2 3,461.76 3,862.86

Entrepreneurial sample Number 35 35 35Mean 18.4 3,692.11 6,759.91Std. Deviation 10.8 3,202.46 4,246.16

CMG Number 5 5 5Mean 3.4 350.99 615.93Std. Deviation 2.4 214.63 609.22

1994 group Number 11 11 11Mean 15.0 2,428.76 6,317.92Std. Deviation 4.4 1,006.99 2,922.60

Russell group Number 19 19 19Mean 24.3 5,302.76 8,632.64Std. 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

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 0.000a Lilliefors Significance Correction

Figure 22: Normality test Skewness and Kurtosis for research variables Total sample

Total sample PhD per 000's

RT per 000's

students

RTFunds per 000's Faculty

LN RT per 000's

students

LN PhD 000's

LN RT per 000's faculty

student

Skewness 1.168 2.460 1.100 -.308 -.217 -.434Std. Error of Skewness 0.287 0.287 0.287 0.287 0.289 0.293Kurtosis 1.503 7.677 0.881 -.852 -1.510 -1.210Std. Error of Kurtosis 0.566 0.566 0.566 0.566 0.570 0.578Z Skewness 4.070 8.571 3.833 -1.073 -0.751 -1.481Z 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

Entrepreneurial sample PhD per 000's

RT per 000's

students

RTFunds per 000's Faculty

LN RT per 000's

students

LN PhD 000's

LN RT per 000's faculty

student

Skewness 0.977 1.956 0.729 -1.094 -1.678 -1.822Std. Error of Skewness 0.398 0.398 0.398 0.398 0.398 0.398Kurtosis 1.656 4.502 1.074 1.404 3.271 2.980Std. Error of Kurtosis 0.778 0.778 0.778 0.778 0.778 0.778Z Skewness 2.455 4.915 1.832 -2.749 -4.216 -4.578Z 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

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

students, total sample

Figure 25: Distribution of LN PhD each 000’s students,

total sample

10.008.006.004.002.00

12.5

10.0

7.5

5.0

2.5

0.0

Freq

uenc

y

Mean =6.7574�Std. Dev. =1.5656�

N =70

LN RT per 000's students

4.002.000.00

12.5

10.0

7.5

5.0

2.5

0.0

Freq

uenc

yMean =1.8787�

Std. Dev. =1.22271�N =69

LN PhD 000's

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.

42

Figure 26: Distribution of RT per 000’s students,

entrepreneurial sample

Figure 27: Distribution of PhD each 000’s students,

entrepreneurial sample

20000.0015000.0010000.005000.000.00

14

12

10

8

6

4

2

0

Freq

uenc

y

Mean =3692.1094�Std. Dev. =3202.45643�

N =35

RT per 000's students

60.0050.0040.0030.0020.0010.000.00

10

8

6

4

2

0

Freq

uenc

y

Mean =18.3999�Std. Dev. =10.83307�

N =35

PhD per 000's

RESEARCH looks at an important variable for spin-off activity because I recognise an important

difference between Total sample and Entrepreneurial sample in this variable (almost 50%), and LN

Histograms -which represents frequencies in research activity- show two curves, probably teaching

universities and research universities. The graph of the entrepreneurial sample shows only one curve:

research universities. When I apply correlation: Pearson and Kendall’s test, I obtain a Sig. correlation

between variables.

43

Figure 28: Correlation between LN Spin-offs and LN Research funds 000’s students

Correlation LN Spin-offs

LN RT per 000's

students

Pearson LN Spin-offs Pearson Correlation 1 0.591(**)

Sig. (2-tailed) 0.000 N 52 52

LN RT per 000's students Pearson Correlation 0.591(**) 1 Sig. (2-tailed) 0.000 N 52 70

Kendall’s LN Spin-offs Correlation Coefficient 1.000 0.442(**)

Sig. (2-tailed) . 0.000 N 52 52

LN RT per 000's students Correlation Coefficient 0.442(**) 1.000 Sig. (2-tailed) 0.000 . N 52 70

** Correlation is significant at the 0.01 level (2-tailed).

Figure 29: Scatter-plot LN Spin-offs and LN Research funds 000’s

10.009.008.007.006.005.004.00

2.50

2.00

1.50

1.00

0.50

0.00

-0.50

-1.00

LNSp

inof

fs

R Sq Linear = 0.349

44

Figure 30: Correlation between LN Spin-offs and LN PhD 000’s students

Correlation LN Spin-offs

LN PhD 000's

Pearson

LN Spin-offs Pearson Correlation 1 0.558(**) Sig. (2-tailed) 0.000 N 52 52

LN PhD 000's Pearson Correlation 0.558(**) 1 Sig. (2-tailed) 0.000 N 52 69

Kendall’s

LN Spin-offs Correlation Coefficient 1.000 0.390(**) Sig. (2-tailed) . 0.000 N 52 52

LN RT per 000's students Correlation Coefficient 0.390(**) 1.000 Sig. (2-tailed) 0.000 . N 52 69

** Correlation is significant at the 0.01 level (2-tailed).

Figure 31: Scatter-plot LN Spin-offs and LN PhD 000’s

4.002.000.00

2.50

2.00

1.50

1.00

0.50

0.00

-0.50

-1.00

LNSp

inof

fs

R Sq Linear = 0.312

This correlation between enterprise production and research could have different explanations. First,

research could be the raw material to produce new business. Probably this is the most important

explanation and influence in spin-off activity. But, there are relationships which could explain this

effect. First, Universities with more postgraduate programmes have more mature students and

entrepreneurial activity could require mature entrepreneurs. Second, research funds allow more

academic hours for other activities (different from teaching). Third, research is associated with

45

prestige and capital flow towards research universities. Finally, Research funds are resources, and

business activities are easier for the richest.

Figure 32 shows total research funds and number of incubators per 4’til and group of universities. It

is interesting to see that spin-off activity could have scale economies because last 4’til are more

efficient for Research funds and incubators than 3rd 4’til. When I compare groups of universities, the

Russell group seems to be less efficient. However, these comparisons could be partial because I do

not measure ‘quality’ (impact, value added) or ‘survival’ (How many companies achieve incomes) of

the spin-off. If I consider that sometimes the cost of the research is associated with the acquisition

of expensive instruments – for example a special microscope for nanotechnology issues- I could say

that the investment in research enables other types of innovation, more sophisticated disclosures. If

this is the main factor in successful spin-off activity it could suggest that this type of knowledge

transfer is an elite issue and that more than entrepreneurial policy needs elements of strategic

investment decisions in research.

Figure 32: Comparative efficiency among universities according Spin-off performance and university groups.

Spin-off RT funds Efficiency Incubators Efficiency 1st 4'tiles 0.00 23,033 0 11 0 2nd 4'tiles 12.50 181,080 14,486 9 0.72 3rd 4'tiles 42.00 887,395 21,128 14 0.33 4th 4'tiles 82.00 1,293,240 15,771 13 0.16

Average 17,471 0.34 CMG 26.50 39,734 1,499 20 0.75 1994 34.00 477,233 14,036 14 0.41 Russell group 76.00 1,847,501 24,309 13 0.17

46

3.2.4. POLICY

POLICY is a vector of key variables accounting for policy-related indicators regarding spin-off

management. POLICY contains management and attitude issues. Probably, this vector summarizes

‘entrepreneurial’ culture. I use two dummy variables to describe the effect of entrepreneurship tools

(ET) such as incubator and seed funds. In addition, I will use three scale variables which describe

different aspects of “entrepreneurial” attitude: number of staff in business and community affairs

(Staff B&C, only with private sector); years of IP office which could be connected with experience

and learning process in entrepreneurial issues for the university; and percentage of industry finance

in research (over total funds) because this is a indicator of linkages with the private sector and I

could suppose that a more positive attitude toward industry could be connected with entrepreneurial

capabilities. Figure 33 shows the use of ET by the groups of universities. Russell group shows a

more intensive use of ET, in addition it has biggest staff B&C. On the other hand, 1994 universities

show a lower staff B&C.

Figure 33: Entrepreneurship tools (ET) in average per University group

Entrepreneurship tools CMG 1994 Russell On-campus incubators partner HEI HEI How many with own incubator* 20/32 14/19 13/19 Other incubators in the locality partner partner partner Science park accommodation partner partner partner Entrepreneurship training HEI HEI HEI Seed corn investment HEI HEI both How many with own seed fund 15/32 15/19 16/19 Venture capital partner partner partner Business advice both both both Staff Business and Community 24 17 33 Years IP Office (cut 2005) 10 14 14

Source: Grounded in HEFCE (2006) * It is difficult to assets its impact because there are different tools that replace incubators and its use is massive

Although some scholars argue that it is difficult to evaluate these types of entrepreneurship skills (ex.

Pavitt, 2001), they do appear to be a strategic ‘must’16 because almost all universities have these. The

total sample shows 67% of universities are owners of incubator and/or seed funds, and in the

16 This sentence is used in business language as a thing which is an obligation to participate in a specific industry more than a strategic decision. In this case to have an incubator or seed funds seems an obligation to participate in spin-offs business.

47

Entrepreneurial sample this reaches 79%. In addition, universities offer partner incubators or funds,

and some universities consider that their laboratories and other office are incubators. Figures 34 and

35 show the differences between Universities 4’tiles from a lower use of entrepreneurial tools to a

higher use (HEFCE, 2006)17. In the general sample graphically I could assume impact, but in the case

of the 1994 group and the Russell group, this impact does not exist. I suggest that these groups have

massive use of these tools, so their effect is less clear. But, in the CMG group there are differences in

use 4’tiles which use more entrepreneurial tools improve their performance. Therefore I could

assume that in terms of strategy the use of these tools is a “must”.

Figure 34: Spin-offs v ET use Figure 35: Spin-offs v ET use CMG

4321

3

2.5

2

1.5

1

0.5

0

4321

2

1.5

1

0.5

0

:

Figure 36: Spin-offs v ET use 1994 group Figure 37: Spin-offs v ET use Russell group

4321

2.5

2

1.5

1

0.5

0

:

4321

5

4

3

2

1

0

:

17 The indicator used to rank in 4’tiles was elaborated assigning values to nominal survey answers: 0 if the answer is not have the ET; 1 if it has a partner; 2 if the HEI is the owner of the ET; and 3 if it has own ET and a partner. The average of these values is an indicator called INTENSITY of USE ET. Universities which have more devices have a higher value. This indicator is grounded in HEFCE (2006). (a more extends explanation in Appendix 3)

48

3.2.4.2. Experience

Another variable which I want to observe is experience, because I could assume that spin-off

performance depends on the experience and learning that the entrepreneurial staff have. Figures 38,

39, 40 and 41 show graphically internal department for commercialization maturity in different

universities (x’s axis) against spin-off performance (y’s axis). The variable was obtained from

HEFCE (2006)18. The graphs do not show a relationship between time and performance. This does

not necessarily mean that experience does not have impact, but means that possibly there are other

variables more powerful which drive spin-offs creation.

Figure 38:Spin-offs v years IP office general Figure 39: Spin-offs v years IP office CMG

36353423212018171615141312987654310

4

2

0

201817161513129876541

4

2

0

:

Figure 40: Spin-offs v years IP office 1994 group

Figure 41: Spin-offs v years IP office Russell group

363523212018161514765430

4

2

0

:

353420181716151385431

8

6

4

2

:

18 I took the question Q13 -When was the internal department established? The survey relation internal department with the IP office

49

In the total and entrepreneurial sample, this variable shows proximity to a normal distribution thus I

use LN Experience. The histogram shown in Figure 44 shows this proximity.

Figure 42: Experience and LN Experience descriptive statistic and Skewness and Kurtosis Test for normality

Sample 70 35

Variable Experience LN Experience Experience LN

Experience Number 70 69 35 34 Mean 11.90 2.22 13.49 2.34 Std. Deviation 8.400 0.814 10.030 0.851 Skewness 1.037 -0.705 0.888 -0.610 Std. Error of Skewness 0.287 0.289 0.398 0.403 Kurtosis 1.096 0.462 0.214 0.066 Std. Error of Kurtosis 0.566 0.570 0.778 0.788 Z Skewness 3.61 -2.44 2.23 -1.51 Z Kurtosis 1.39 0.90 0.52 0.29

Figure 43: Test for normality Experience and LN Experience

Kolmogorov-Smirnov(a) Shapiro-Wilk

Sample 70 Statistic df Sig. Statistic df Sig. Experience 0.145 70 0.001 0.894 70 0.000

LN Experience 0.172 69 0.000 0.928 69 0.001

Sample 35 Statistic df Sig. Statistic df Sig. Experience 0.139 34 0.095 0.884 34 0.002 LN Experience 0.194 34 0.002 0.934 34 0.041

a Lilliefors Significance Correction

Figure 44: Histogram of LN Experience Figure 45: Q-Q plot LN Experience

4.002.000.00

LN Experience

20

15

10

5

0

Freq

uenc

y

Mean =2.2179�Std. Dev. =0.81447�

N =69

Histogram

420

Observed Value

2

0

-2

Expe

cted

Nor

mal

Normal Q-Q Plot of LN Experience

50

3.2.4.3. Staff

Staff is a variable that indicates importance of business and community activities for the university

because is data which show the number of people who are contracted by the University for these

Activities. The variable was obtained from HEFCE (2006)19.

Figure 46: Staff and LN Staff descriptive statistic and Skewness and Kurtosis Test for normality

Sample Total Entrepreneurial Number 70 70 35 35 Mean 24.46 2.8788 26.74 2.9755Std. Deviation 22.714 0.81304 26.148 0.76193Skewness 2.538 -0.262 2.692 0.473Std. Error of Skewness 0.287 0.287 0.398 0.398Kurtosis 8.169 1.575 8.572 0.225Std. Error of Kurtosis 0.566 0.566 0.778 0.778Z Skewness 8.84 -0.91 6.76 1.19Z Kurtosis 3.80 1.67 3.32 0.54Minimum 1 0.00 4 1.39Maximum 134 4.90 134 4.90

Figure 47: Test for normality Staff and LN Staff

Kolmogorov-Smirnov(a) Shapiro-Wilk Total Statistic df Sig. Statistic df Sig. Staff 0.206 70 0.000 0.735 70 0.000 LN Staff 0.096 70 0.176 0.973 70 0.141 Entrepreneurial Statistic df Sig. Statistic df Sig. Staff 0.217 35 0.000 0.692 35 0.000 LN Staff 0.152 35 0.041 0.965 35 0.311

a Lilliefors Significance Correction

I will use LN Staff in future tests because this variable shows a Sig. normal distribution in both cases.

Figure 48 tries to correlate LN Spin-offs and LN Staff but the correlation is not Sig. because the

likelihood of beta is 0 is higher than 5% (both test Pearson and Kendall’s). Scatter/dot between LN

Spin-off and LN Staff considering different groups of universities shows the weak correlation among

these variables. This could be affected by the same factors which affect experience.

19 This data was obtained from Q9 How many of your institution's staff are employed in a dedicated Business and Community function (Full-time equivalents)? -Engaging with commercial partners.

51

Figure 48: Correlation between LN Spin-offs and LN Staff, Total sample

Pearson test LNstaff LNspinoff

LNstaff Pearson Correlation 1 0.252 Sig. (2-tailed) 0.072 N 70 52 LNspinoff Pearson Correlation 0.252 1 Sig. (2-tailed) 0.072 N 52 52 Kendall's tau_b LNstaff LNspinoff

LNstaff Correlation Coefficient 1.000 0.192 Sig. (2-tailed) . 0.057 N 70 52

LNspinoff Correlation Coefficient 0.192 1.000 Sig. (2-tailed) 0.057 . N 52 52

Figure 49: Scatter/dot LN spin-off- LNstaff Figure 50: Scatter/dot LN spin-off- LN staff CMG

4.002.00

2.50

2.00

1.50

1.00

0.50

0.00

-0.50

-1.00

R Sq Linear = 0.063

4.504.003.503.002.502.001.501.00

2.00

1.50

1.00

0.50

0.00

-0.50

-1.00

:

R Sq Linear = 3.449E-4

Figure 51: Scatter/dot LN spin-off- LN staff 1994 group

Figure 52: Scatter/dot LN spin-off- LN staff Russell group

4.003.503.002.502.001.501.00

1.50

1.00

0.50

0.00

-0.50

-1.00

:

R Sq Linear = 0.096

5.004.504.003.503.002.502.001.50

2.00

1.50

1.00

0.50

:

R Sq Linear = 0.027

52

3.2.4.4. University – Industry linkages

This variable tries to measure of the proximity or importance for the university of University-

Industry linkages. I reflect this through industry research funds compared with the total funds, a

percentage of the total. This variable is grounded in HESA (2006) information. Figures 53 and 54

show the descriptive statistic and normality test.

Figure 53: Percentage of Research from industry over Total Research Funds and its LN, descriptive statistic and

Skewness and Kurtosis Test for normality

Total sample RIndTot percentage LN RindRtotal

RIndTot percentage

Faculty

LN RindRtotal Faculty

Number 70 67 67 65Mean 7.7464 1.9152 11.5827 2.1667Std. Deviation 4.84773 0.62957 12.17251 0.77473Skewness 0.838 -0.503 3.641 0.150Std. Error of Skewness 0.287 0.293 0.293 0.297Kurtosis 0.871 0.127 17.927 0.599Std. Error of Kurtosis 0.566 0.578 0.578 0.586Z Skewness 2.92 -1.72 12.43 0.51Z Kurtosis 1.24 0.47 5.57 1.01

Figure 54: Test for normality Percentage of Research from industry over Total Research Funds and its LN

Kolmogorov-Smirnov(a) Shapiro-Wilk Statistic df Sig. Statistic df Sig.

RIndTot 00’s 0.085 67 0.200(*) 0.948 67 0.007LN RindRtotal 0.091 65 0.200(*) 0.975 65 0.210RIndTot 00’s Faculty 0.202 67 0.000 0.657 67 0.000LN RindRtotal Faculty 0.062 65 0.200(*) 0.990 65 0.887

* This is a lower bound of the true significance. a Lilliefors Significance Correction

I accept that these distributions are normally distributed. However, in the case of the entrepreneurial

faculties’ variable it is necessary to use LN. However as correlation tests are not Sig. then I can not

assume that a closer relationship between university and industry could drive a successful spin-off

strategy.

53

Figure 55: Correlation between LN Spin-offs and LN Percentage of Research from industry over Total Research

Funds, Total sample

Pearson LN Spin-offs LN RindRtotal

LN Spin-offs Pearson Correlation 1 0.012 Sig. (2-tailed) 0.933 N 52 51

LN RindRtotal Pearson Correlation 0.012 1 Sig. (2-tailed) 0.933 N 51 67

Kendall's tau_b LN Spin-offs LN RindRtotal

LN Spin-offs Correlation Coefficient 1.000 -.003 Sig. (2-tailed) . 0.974 N 52 51

LN RindRtotal Correlation Coefficient -.003 1.000 Sig. (2-tailed) 0.974 . N 51 67

Figure 56: Scatter/dot LN spin-off- LN Percentage of Research from industry over Total Research Funds

3.002.502.001.501.000.50

2.50

2.00

1.50

1.00

0.50

0.00

-0.50

-1.00

LNSp

inof

fs

R Sq Linear = 1.444E-4

54

3.2.4.5. Consulting

Finally, I add a contrast with the variable consulting. This is the income from consultancies which is

obtained by each university; I took this data from HEFCE (2006). This was transformed to represent

an income by each thousand of students. Figures 57 and 58 shows descriptive statistics and normality

test.

Figure 57: Consulting income per year and its LN, descriptive statistic and Skewness and Kurtosis Test for normality.

Total and Entrepreneurial samples

Total sample Entrepreneurial

Consulting Income £000's LN Consulting Consulting

Income £000's LN Consulting

Number 70 64 35 31Mean 1,181.80 6.00 1,171.45 6.10Std. Deviation 1,954.94 1.81 1,676.71 1.78Skewness 2.789 -0.531 1.914 -0.465Std. Error of Skewness 0.287 0.299 0.398 0.421Kurtosis 8.507 -0.096 3.713 -0.455Std. Error of Kurtosis 0.566 0.590 0.778 0.821Z Skewness 9.72 -1.78 4.81 -1.10Z Kurtosis 3.88 0.40 2.18 0.74

Figure 58: Test for normality Consulting income per year and its LN. Total and Entrepreneurial samples

Kolmogorov-Smirnov(a) Shapiro-Wilk

Statistic df Sig. Statistic df Sig. Consulting Income £000's 0.268 64 0.000 0.643 64 0.000LN Consulting 0.105 64 0.076 0.968 64 0.093Consulting Income £000's 0.296 31 0.000 0.757 31 0.000LN Consulting 0.122 31 0.200(*) 0.954 31 0.198

a Lilliefors Significance Correction

This variable is normally distributed for LN consulting income in both cases. Figure 59 shows that

this variable is not correlated with spin-off activity. In addition, the graph in the Figure 60 does not

show a relationship.

55

Figure 59: Correlation between LN Spin-offs and LN Consulting income per year, Total sample

Pearson LN Spin-offs LN RindRtotal Faculty

LN Spin-offs Pearson Correlation 1 -0.047 Sig. (2-tailed) 0.745 N 52 50

LN RindRtotal Faculty Pearson Correlation -0.047 1 Sig. (2-tailed) 0.745 N 50 65

Kendall's tau_b LN Spin-offs LN RindRtotal Faculty

LN Spin-offs Correlation Coefficient 1.000 -0.016 Sig. (2-tailed) . 0.873 N 52 50

LN RindRtotal Faculty Correlation Coefficient -0.016 1.000 Sig. (2-tailed) 0.873 . N 50 65

Figure 60: Scatter/dot LN spin-off- LN Consulting income per year, Total sample

10.008.006.004.002.00

2.50

2.00

1.50

1.00

0.50

0.00

-0.50

-1.00

LNSp

inof

fs

R Sq Linear = 5.592E-4

56

3.2.4.6. Size

This variable is used to control by size the regression. In the next Figures, size is normally distributed

for all samples. There is also a strong correlation with LN Spin-offs. Therefore it is necessary to take

this variable and in addition, use other quantities variables which are not affected by size.

Figure 61: Number of students and its LN, descriptive statistic and Skewness and Kurtosis Test for normality. Total

and Entrepreneurial samples, included entrepreneurial faculties

Total sample Students 000's Student 000’s Faculty

LN Students 000's

LN Students 000’s Faculty

Number 70 70 70 70Mean 14.87 3.385 2.642 1.081Std. Deviation 5.19 1.677 0.346 0.573Skewness 0.802 0.942 -0.125 -0.906Std. Error of Skewness 0.287 0.287 0.287 0.287Kurtosis 1.166 2.435 -0.400 1.110Std. Error of Kurtosis 0.566 0.566 0.566 0.566Z Skewness 2.79 3.28 -0.44 -3.16Z Kurtosis 1.44 2.07 0.84 1.40Minimum 6.63 0.48 1.89 -0.74Maximum 32.53 10.04 3.48 2.31

Entrepreneurial sample Students 000's Students faculty 000's

LN Students 000's

LN Students 000's Faculty

Number 35 35 35 35Mean 16.753 4.12 2.764 1.322Std. Deviation 5.556 1.71 0.341 0.479Skewness 0.676 1.037 -0.343 -1.266Std. Error of Skewness 0.398 0.398 0.398 0.398Kurtosis 0.894 3.370 0.217 3.264Std. Error of Kurtosis 0.778 0.778 0.778 0.778Z Skewness 1.70 2.61 -0.86 -3.18Z Kurtosis 1.07 2.08 0.53 2.05Minimum 6.63 0.75 1.89 -0.29Maximum 32.53 10.04 3.48 2.31

With this control, I am cutting part of the effect of TYPE which is controlled by RESEARCH, also.

Hence, TYPE will mainly measure possible effects of influence, reputation and political influence.

57

Figure 62: Test for normality number of students and its LN. Total and Entrepreneurial samples, included

entrepreneurial faculties

Total sample Kolmogorov-Smirnov(a) Shapiro-Wilk

Statistic df Sig. Statistic df Sig. Students 000's 0.082 70 0.200(*) 0.955 70 0.013Students faculty 000's 0.067 70 0.200(*) 0.947 70 0.005LN Students 000's 0.102 70 0.067 0.985 70 0.548LN Students 000's Faculty 0.120 70 0.015 0.945 70 0.004

Entrepreneurial sample Kolmogorov-Smirnov(a) Shapiro-Wilk

Statistic df Sig. Statistic df Sig. Students 000's 0.083 35 0.200(*) 0.967 35 0.358Students faculty 000's 0.116 35 0.200(*) 0.930 35 0.028LN Students 000's 0.118 35 0.200(*) 0.983 35 0.842LN Students 000's Faculty 0.163 35 0.020 0.904 35 0.005

* This is a lower bound of the true significance. a Lilliefors Significance Correction

Figure 63: Correlation between LN Spin-offs and number of students 000’s, Total sample

Pearson LN Spin-offs Students faculty 000's

LN Spin-offs Pearson Correlation 1 0.521(**) Sig. (2-tailed) 0.000 N 52 52

Students faculty 000's Pearson Correlation 0.521(**) 1 Sig. (2-tailed) 0.000 N 52 70

Kendall's tau_b LN Spin-offs Students faculty 000's

LN Spin-offs Correlation Coefficient 1.000 0.409(**) Sig. (2-tailed) . 0.000 N 52 52

Students faculty 000's Correlation Coefficient 0.409(**) 1.000 Sig. (2-tailed) 0.000 . N 52 70

** Correlation is significant at the 0.01 level (2-tailed).

Figure 64: Scatter/dot LN spin-off- Number of students 000’s, Total sample

35.0030.0025.0020.0015.0010.005.00

2.50

2.00

1.50

1.00

0.50

0.00

-0.50

-1.00

LNSp

inof

fs

R Sq Linear = 0.215

58

3.3. Regression analysis

I use the SPSS program to analyse our sample. First I apply a regression over all the variables

described against LN spin-offs (dependent variable) and I use the number of students and research

for all the university departments. Following this, I try with the “Stepwise” mode in SPSS, and search

for variables which enable me to explain in a better way the relationship between LN Spin-offs and

my independent variables. Next, I apply the variables selected in a regression model, in the mode

“Enter”.

Figure 65 explains the search within the Total sample, the first four regressions were applied over all

the university departments, and the last three considered only entrepreneurial faculties. Among these

regressions I consider that the regression 3 represent the best model, because it explains 46% of the

effect and it has statistic significance.

Model 3 for Total sample LN Spin-offs = -1.997 + 0.283 LN RT Funds 000’s + 0.169 Number of students 000’s

Figure 66 apply regression over the entrepreneurial sample (35). In this case, I repeat the same model

and order used for the Total sample. Among these regressions, I choose the regression number 6

because it presents the best combination between significance of the ANOVA test (0.1% that is

lower than 5%) and R2 (28%).

Model 6 for Entrepreneurial sample LN Spin-offs = 0.586 + 0.141 Number of students 000’s Entrepreneurial faculties

Finally, I apply regression analysis for each group of universities, but I did not obtain regression with

significance in the CMG and the 1994 groups. In the case of the Russell group, I obtained an

acceptable level of significance in a model which considers LN Staff. Probably, when I use regression

within groups the homogeneity of the sample and a loss of information for spin-off performance (in

the case of CMG and 1994 group) do not allow the use of regression analysis.

Figure 65: Regression analysis for Total sample of UK universities (70), depend variable LN spin-offs

Total sample Depend variable LN Spin-offs Regressions NUMBER 1 2 3 4 5 6* 7 ANOVA 0.016 0.000 0.000 0.020 0.029 0.000 0.000

R2 0.466 0.349 0.460 0.402 0.448 0.318 0.451 F statistic 2.615 26.847 20.867 2.610 2.362 23.273 20.125 Probability Variables Beta Sig. Beta Sig. Beta Sig. Beta Sig. Beta Sig. Beta Sig. Beta Sig. CONSTANT -2.724 0.067 -1.947 0.003 -1.997 0.000 -0.621 0.443 -1.343 0.448 0.309 0.014 -2.075 0.000 TYPE 1994 group dummy -0.800 0.245 0.348 0.259 -0.588 0.429 Russell group dummy -0.757 0.430 0.961 0.007 -0.275 0.762 0.973 0.000 RESEARCH LN PhD awarded per 000's students 0.221 0.529 0.292 0.337 LN Research Total Funds 000's students 0.373 0.196 0.359 0.000 0.283 0.000 0.251 0.000 LN RT Funds 000's entrepreneurial faculties 0.158 0.580 POLICY Incubator dummy 0.142 0.593 0.213 0.429 0.055 0.836 Seed funds dummy 0.004 0.990 0.084 0.781 -0.129 0.688 LN Experience -0.016 0.920 0.040 0.800 -0.028 0.850 LN Staff 0.019 0.915 0.142 0.394 0.062 0.726 LN Research funding by industry % -0.113 0.630 -0.198 0.384 LN Research funding by industry % EF -0.037 0.859 LN Consulting income 0.015 0.856 0.020 0.787 -0.009 0.900 SIZE Number of students 000's 0.048 0.150 0.169 0.003 0.025 0.425 Number of students 000's EF 0.138 0.106 0.0002 0.000

* This regression works with a dummy variable

60

Figure 66: Regression analysis for Entrepreneurial sample of UK universities (70), depend variable LN spin-offs

Entrepreneurial sample Depend variable LN Spin-offs Regressions NUMBER 1 2 3 4* 5 6 7 ANOVA 0.086 0.101 0.051 0.030 0.074 0.001 0.011

R2 0.556 0.133 0.169 0.135 0.566 0.280 0.307 F statistic 2.047 2.464 3.265 5.153 2.135 12.836 4.426 Probability Variables Beta Sig. Beta Sig. Beta Sig. Beta Sig. Beta Sig. Beta Sig. Beta Sig. CONSTANT -1.069 0.484 0.221 0.933 0.005 1.277 0.000 -0.003 0.998 0.586 0.002 0.558 0.122 TYPE 1994 group dummy -1.310 0.012 -0.326 0.047 -0.355 0.030 -1.046 0.039 Russell group dummy -1.359 0.070 -0.968 0.099 RESEARCH LN PhD awarded per 000's students 0.221 0.490 0.335 0.134 LN Research Total Funds 000's students 0.263 0.397 0.065 0.373 LN RT Funds 000's entrepreneurial faculties 0.041 0.830 POLICY Incubator dummy 0.244 0.228 0.204 0.301 Seed funds dummy 0.178 0.451 0.162 0.487 LN Experience -0.153 0.158 -0.134 0.183 -0.033 0.697 LN Staff 0.220 0.168 0.112 0.258 0.187 0.225 0.027 0.786 LN Research funding by industry % 0.028 0.873 LN Research funding by industry % EF 0.049 0.744 LN Consulting income -0.040 0.524 -0.020 0.682 SIZE Number of students 000's 0.019 0.464 0.260 0.062 Number of students 000's EF 0.068 0.296 0.141 0.001 0.143 0.004

* This regression works with a dummy variable

61

Figure 67: Regression analysis for different university groups of UK universities (70), depend variable LN spin-offs

University groups Depend variable LN Spin-offs Regressions Type of University CMG 1994 RUSSELL NUMBER 1* 2 (stepwise) 3* 4 (stepwise) 5 6 (stepwise) ANOVA 0.061 0.637 0.261 0.045

R2 0.999 0.591 0.768 0.274 F statistic 161.956 0.801 1.836 4.901 Variables Beta Sig. Beta Sig. Beta Sig. Beta Sig. Beta Sig. Beta Sig. CONSTANT -2.398 0.159 2.049 0.801 -4.851 0.293 0.296 0.536 RESEARCH LN PhD awarded per 000's students 1.088 0.049 -2.020 0.245 -1.574 0.150 LN Research Total Funds 000's students -3.910 0.070 0.987 0.254 1.509 0.104 LN RT Funds 000's entrepreneurial faculties 3.467 0.064 POLICY Incubator dummy -0.159 0.269 -0.228 0.771 0.687 0.089 Seed funds dummy -1.597 0.054 -0.395 0.651 -0.325 0.502 LN Experience 2.647 0.026 0.004 0.991 -0.289 0.136 LN Staff -1.429 0.028 0.605 0.414 0.646 0.085 0.331 0.045 LN Research funding by industry % -3.341 0.046 0.025 0.954 -0.243 0.493 LN Research funding by industry % EF 2.177 0.057 LN Consulting income -0.006 0.919 -0.515 0.098 -0.325 0.066 SIZE Number of students 000's 0.403 0.031 -0.139 0.476 -0.031 0.395 Number of students 000's EF -0.001 0.079

* This regression show with correlation, eigenvalue and condition index.

4. Discussion

Now, I will analyse and sum up the different topics which could be discussed regarding the review of

the previous chapters. I will propose some questions and then, I discuss each issue with the

information from Chapters 1, 2 and 3.

From Chapter 1 I identified the disagreement among scholars about features of the changes in the

university. While some scholars – such as Clark (1998) and Leydersdorff and Etzkowitz (1998) - say

that the university is entering a new stage of development, other scholars, such as Martin (2003) and

Pavitt (2003), maintain that this change is only the evolution of the traditional university. In the

literature review I proposed that both paradigms could be merged because although Martin (2003)

and Pavitt (2003) are right that the main representatives of ‘Entrepreneurial’ universities are ‘classical’

universities, these universities have undergone a cultural change i.e. the opening up of the sources of

funding and changes in their relationship with the community. Chapter 2 shows the example of UK

third stream policies and it is possible to recognise the relationship between paradigms and public

policies. An evolutionary model that explains the evolution of the policies emerges from the review

of the Blair government third stream policies.

The exercise undertaken in Chapter 3 demonstrates that in the first stage, research is the main factor

which explains a better spin-off performance. Second, the size of the ‘Entrepreneurial’ departments

– such as chemical, engineering and life science faculties - is the independent variable that better

explains spin-off performance among ‘Entrepreneurial’ universities. Finally, among large research

universities the difference in performance can be explained by the number of staff in business and

community activities.

Now, I will propose some questions to analyse and sum up the information.

63

Is there a new pattern of university that makes better technology transfer? Is there a new type of university, the

‘entrepreneurial’ university?

Probably, the most appropriate assumption is that there are two groups of universities: Research

universities such as the ‘Russell’ group and the ‘1994’ group, and teaching universities like the CMG

universities. A second assumption is that research universities face increasing demands from the

government and industry, and they need to extend their sources of funds into a more complex

structure of financing – Clark (1998) suggests that this aspect is central for ‘Entrepreneurial’

universities.

The first answer is: The research university has the best performance in third stream activities. The

second answer is: More than a new university, the concept is that there are new attitudes and

capabilities which are necessary to acquire in order to allow the research universities to compete in

the knowledge economy. However, the universities must maintain their quality in research activity in

order to generate disclosures which are the key for successful third stream activities.

Is the Merton’s academic culture dead? Does the university need a big cultural change?

Probably ‘yes’ is the best answer because an ‘Entrepreneurial’ university is an active member of the

community more than an isolated ‘temple’ of knowledge.

Pavitt (2003) suggests that the change of attitude that the ‘Entrepreneurial’ university encourages

may only be necessary in ‘Entrepreneurial’ faculties. Nevertheless, several scholars in the paper

review argue that a change of attitude in the university authorities and the academic community is

necessary, and also an improvement in research capabilities in ‘Entrepreneurial’ faculties. Therefore,

although Spin-off activity is important in a few faculties, the support and behaviour of the university

authorities and the academic community seems to be important also (Bercovitz and Feldman, 2003).

My analysis of UK universities found a correlation between the use of ‘Entrepreneurial’ tools and

success in third stream activities. When I separated ‘Entrepreneurial’ sample (35 of best

performance), only 11 universities from ‘1994’ group were considered. explained by an attitude

64

difference. Thus, I could say that ‘Entrepreneurial’ universities first need a change in attitude hence

the second answer could be ‘yes’, too.

When I worked with the ‘Entrepreneurial’ sample of 35 universities, the main driver is the size of the

‘Entrepreneurial’ faculties. Therefore, Pavitt’s suggestion (2003) is supported for this evidence.

Are the entrepreneurial tools a new part of the core in the new model of university? What is the role of the

entrepreneurial tools in the academic structure?

The answer to the first question would be negative. Incubators, technology transfer office and funds

seem to be peripheral elements for a third stream strategy. In addition, the authorities’ attitudes,

quality of research, importance of the ‘Entrepreneurial’ faculties seem the aim of a third mission

strategy. There is much evidence for this in the literature review and in the results of the UK case

analysis.

The role of commercialization tools could be defined as links between the knowledge production of

the university and different stakeholders such as venture capital funds, the business community,

government, etc.

Have the third stream public policies been successful in UK? Are they important?

According to Figure 5 I could say that the UK third stream policies have increased the production of

disclosures, patents and licences. However, when I presented the results of the policies in our

analysis, this variable is not significant. This effect could be because we used a ‘cut’ in one period

(2003-2004) and in order to see the effect of the policy it is necessary to consider a ‘time series’.

If I consider the speeches of the Prime Minister on the UK third stream policies, I could suppose an

evolution of the government evaluation about these policies. Blair (2002) seems satisfied with the

effects of the policies – “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” (Blair, 2002).

However, in 2007, Blair (2007) criticises his previous policies and supports an increase in

65

investments in research – “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” (Blair, 2007).

Although I could say that third stream public policies are important in commercialization outputs

such as patents and licences, they are not enough to have a good third mission strategy. In addition,

it is necessary to consider policies that improve academic attitudes and more investment in research

of ‘global’ quality.

Are spin-offs the best way to collaborate with the development?

Spin-off production is a very expensive strategy. Figure 5 shows that many universities in UK have

chosen a licence strategy rather than spin-offs. It is possible for this option to be explained by the

cost of spin-offs in terms of investments and time in comparison with licences. Universities which

have the largest budget for research prefer spin-off production because there is evidence that these

enterprises produce significantly more returns that licences and patents (Shane, 2004).

Explanations for this correlation between the highest budget and spin-offs strategy could be

explained by two considerations: first, universities with the largest investment in research can make

more exclusive innovation because they have exclusive equipment which enables them to offer

disclosures that are more difficult to imitate on the markets therefore they are more valuable.

Second, these universities have more staff and infrastructure; these both support spin-offs.

Although spin-offs seem to be a very good element with which to collaborate with regional

development, the best evidence of success occurs in the richest ‘global’ universities such as MIT,

Stanford, Cambridge and Oxford. It is unrealistic to expect that any region can have this type of

university. Therefore, according to their universities and industries, regions must study which is their

best technology transfer strategy.

66

Is it possible to have a country benchmark with the information obtained in this study?

Although the models derived from the regression analysis cannot be directly used in other countries,

it is possible to build a benchmark model using the typology of universities which are used in this

study. For example, I could assume that a developing country has five ‘1994’ group universities and

the rest of the universities are polytechnics. With this assumption it is possible to assume cost and

results of a third stream strategy. In this sense, policy makers could evaluate country performance

and policy stage of development assuming an evolutionary model in third stream activities.

What is the paradigm?

I now return to the first question: what is the paradigm that must guide policy makers’ actions? I

found that the university which offers the best support for third stream activities can be describes as:

- A university concerned about the quantity and the quality of the research that is

produced.

- A university interested in a regional economy that has a positive attitude toward third

stream activities.

- A university that has strong ‘Entrepreneurial’ faculties: chemical, engineering and life

science faculties.

- A university that allocates resources for technology transfer: human resources, capital and

infrastructure.

It is perhaps a mistake for the policy makers to have a paradigm linked with business skills as driver

where the sources of spin-offs could be the MBA20 programmes and the business schools. The

paradigm that is closest to spin-off production is a university that has programmes which are strong

in science and technology.

20 Master in Business Administration

67

5. Conclusions

In this dissertation, the questions are the following: what is the paradigm that drives the policy

decisions in higher education third stream public policies? Is a university that manages more business

skills? Or is it an institution which concentrates on knowledge production? In this thesis I assume

that the initial vision or paradigm used by the governments is a key element in the policy design

because this idea determines where the government invests funds and this decision generates

changes into the relationship between university-industry-government and strengthens or weakens

some areas into the university structure for example: research activities.

Chapter 1 is a literature review about scholars’ opinions of the changes in the university in the last

ten years. My thesis is that the differences among academics could be narrowed down to two options

for public policies. One option, the ‘Entrepreneurial’ university, argues that universities are

undergoing a great change and a new pattern of university is being born. This position encourages

the proactive link between university, government and industry in a triple helix model to develop the

economy. In this current line of thought the spin-offs are considered the most sophisticated type of

technology transfer and the ideal goal for an ‘Entrepreneurial’ university. On the other hand, several

scholars argue that these ideas about a new pattern and the intromission of the government and

industry are not innocuous and could have a negative effect in the research activity. They argue that

ultimately, research is the real root of the economic development because most ‘Entrepreneurial’

universities are the ‘classical’ large research universities – such as Cambridge - which produce the

majority of the spin-off in the university system. Chapter 2 reviews the UK case of universities and

third stream policies. I use the UK case because the British university system is a world leader in

terms of its policies and it has different types of universities (large research universities, medium and

small research universities and ex polytechnics) which can also be found in the majority of the

systems in the world. In addition, during the last decade, the Labour Government has applied

aggressive policies in third stream activities. Finally, the UK system of policy evaluation provides

good quality information that enables to carry out a complete analysis. In the last part of this

68

Chapter, I proposed a hypothesis for spin-off production which will be analysed in the following

Chapter. Chapter 3 develops an analysis of seventy UK universities which represent three types of

universities: large research universities represented by the ‘Russell’ group, medium and small research

intensive universities represented by the ‘1994’ group and teaching universities represented by the

Campaign for Mainstream universities (CMG). I used spin-offs as a dependent variable and four

independent variables: type of university, research, policies and size. Size was considered a control

variable. The Chapter develops the descriptive statistics for each variable and in the last part there is

a regression analysis. Chapter 4 analyses the different information obtained in the previous chapters.

I sorted this data using several questions which emerge from the development of the dissertation.

Reviewing the information, I could suggest that it would be necessary to merge both paradigms –

‘Entrepreneurial’ and ‘Evolution of the Classical’ university- to build a good vision about what type

of university drives economic development. On the one hand, a change of attitude in academic

culture seems to be the first condition for achieving a better university performance in third stream

activities and this is a central point of the ‘Entrepreneurial’ university theory. On the other hand, the

evidence shows that the research university is mainly responsible for the disclosures, patents, licences

and finally spin-offs which are the vehicles that improve the regional economy. In Chapter 1, I

collected information about models; the majority of these combine both paradigms. It is remarkable

that the best examples of ‘Entrepreneurial’ universities are research intensive universities such as

MIT and Stanford. In Chapter 2, I conclude that UK policy has evolved in the last 10 years.

Although UK policy seemed close to the ‘Entrepreneurial’ paradigm, the recent speeches made by

the Prime Minister seem closer to the second paradigm. However, again, it is possible to interpret

this change as an evolution in public policy. In a first stage, it impulses a change of the academic

culture but in a second stage, policy invests in knowledge production that can create comparative

advantages for the economy. From Chapter 3, I was able to conclude that there is a separation of the

universities into two types: research and teaching universities, the research universities are the

producer of the spin-offs. It is possible to conclude that research and size are the unique factors that

69

explain in a general sample the spin-off production. However, policy, i.e. the variable which groups

the measures taken by the university authorities to improve third stream activities, seems a ‘must’ in

the third mission strategy because in a general analysis these policies have improved the quantity of

patents and licences in the UK example21 . Specifically, when I analysed a separate a group of

‘Entrepreneurial’ universities the activity of the faculties -like engineering, chemical and life science-

explain the spin-off production better. Finally, among ‘Russell’ group the variable that could explain

performance is the number of staff in business and community activities. Thus, I conclude from

different sources - general literature review, a revision of the UK case, and statistical analysis of an

important group of the UK universities - that both paradigms work better together. I attempt to

identify a model which would best drive public policies for third stream activities through

universities. If I sum up the conclusions in a model that explains spin-off activity, it should have

research as the basis. Next, the model must consider research activity in ‘Entrepreneurial’ faculties.

Finally, I include policies as a ‘must’ in the model. Research, ‘Entrepreneurial’ faculties and staff in

business and community activities produce first disclosures, next, patents, and finally as the most

sophisticated element, spin-offs. Figure 68 shows the model graphically.

Figure 68: Model for Spin-off production

21 Although when I analyse policies individually these seem not have impact for example: experience, staff and other. But, UK third stream policies are much extended that is why it is possible that the statistics do not find a strong relationship between policies and spin-off considering individual cases.

70

The main policy implications that I have obtained from this dissertation are: i) Research is the base in

third stream actions, so a government that is trying to improve the economy through the university

must identify the research capabilities in its university system. ii) It may be that, good public

intervention must consider an evolutionary model which considers different stages in the

intervention; probably an ‘Entrepreneurial’ attitude is the first condition which is necessary in order

to identify the research universities to invest in the third mission. The features of the

‘Entrepreneurial’ faculties are the next indicator of spin-off activity. Third stream policies must be

considered a ‘must’ into the spin-off strategy, tools such as: technology transfer office, incubators

and other elements help to build a university programme that improves its contribution to the

economy. iii) The evidence about the cost of the most successful spin-off activity shows that this

could be a very expensive strategy and perhaps it is not a good option for the developing regions.

Finally, I should point out that the analysis does not separate the effect of the investment resources.

The funds spent on research greatly surpass the funds spent in commercialization activities.

Therefore the question is whether it is possible to obtain better results when the policy focuses its

attention on another strategy, probably not focused on universities and research. Thus, I found a

relationship between expenses on research investment and success in spin-off activity. This

relationship can be explained because the research that gives disclosure is carried out not only with

creativity – it may not have relationship with the cost - it also has a relationship with exclusive and

expensive equipment used by the academics that produce a market differentiated by entry barriers22.

What is the paradigm? This question may sound irrelevant for people who work in the US university

system; however, as Pavitt (2001) warned, this is not obvious in other countries. When I reviewed

the UK case, I assumed that the policy had evolved, but it is also possible to assume that, there was a

change in paradigm, moving from the entrepreneurial paradigm to the research one. The UK system

has resilience; however in other countries where the university structure is weaker, (for example in

22 Concept developed by Michael Porter in his 5 strategic forces that explain a differential of income by the difficulty to compete for external organizations.

71

developing countries) it could destroy the value in their research system to follow the

‘Entrepreneurial’ policy that seems to be possible only for millionaires. Here, Martin’s evolutionary

model and Pavitt’s suggestions emerge as important considerations for the policy makers who must

avoid pursuing false dreams.

72

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Appendix 1: University groups

Russell Group 1994 Group1 University of Birmingham 1 University of Bath 2 University of Bristol 2 Birkbeck, University of London 3 University of Cambridge 3 Durham University 4 Cardiff University 4 University of East Anglia 5 University of Edinburgh 5 University of Essex 6 University of Glasgow 6 University of Exeter 7 Imperial College London 7 Goldsmiths College, University of London8 King’s College London 8 Lancaster University 9 University of Leeds 9 University of Leicester 10 University of Liverpool 10 Loughborough University 11 London School of Economics 11 Queen Mary, University of London12 University of Manchester 12 University of Reading 13 Newcastle University 13 Royal Holloway, University of London14 University of Nottingham 14 School of Oriental and African Studies15 The Queen’s University of Belfast 15 University of Saint Andrews 16 University of Oxford 16 University of Surrey 17 University of Sheffield 17 University of Sussex 18 University of Southampton 18 University of York 19 University College London 20 University of Warwick

Campaigning for Mainstream Universities1 University of Abertay Dundee 21 University of Northampton 2 Anglia Ruskin University 22 University of Paisley 3 Bath Spa University 23 Roehampton University 4 University of Luton 24 Southampton Solent University 5 University of Bolton 25 Staffordshire University 6 University of Wales Institute, Cardiff 26 University of Sunderland 7 University of Central England, Birmingham 27 University of Teesside 8 University of Central Lancashire 28 Thames Valley University 9 Coventry University 29 University of Westminster 10 University of Derby 30 University of Wolverhampton 11 University of East London 12 University of Glamorgan 13 Glasgow Caledonian University 14 University of Greenwich 15 Kingston University 16 Leeds Metropolitan University 17 London Metropolitan University 18 London South Bank University 19 Middlesex University 20 Napier University