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Accepted for publication in Journal of Technology Transfer
Why do academics engage with industry? The entrepreneurial university and
individual motivations
Pablo D’Este1, Markus Perkmann2
1INGENIO
CSIC-UPV
Spanish Council for Scientific Research – Polytechnic University of Valencia
Ciudad Politecnica de la Innovacion - Edif 8E 4
Camino de Vera s/n
46022 Valencia
T: +34 963 877 007 ext: 77048
E: pdeste@ingenio.upv.es
2Imperial College London
Business School
South Kensington Campus
London SW7 2AZ
T: +44 207 59 41955
F: +44 20 7594 5915
E: m.perkmann@imperial.ac.uk
Abstract
The debate on the entrepreneurial university has raised questions about what motivates
academic scientists to engage with industry. This paper provides evidence based on
survey data for a large sample of UK investigators in the physical and engineering
sciences. The results suggest that most academics engage with industry to further their
research rather than to commercialize their knowledge. However, there are differences
in terms of the channels of engagement. Patenting and spin-off company formation are
motivated exclusively by commercialization whilst joint research, contract research and
consulting are strongly informed by research-related motives. We conclude that policy
should refrain from overly focusing on monetary incentives for industry engagement
and consider a broader range of incentives for promoting interaction between academia
and industry.
Keywords: University-industry relations – joint research – collaborative research –
commercialization – entrepreneurial university - motivation
JEL: I23, O32
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1. Introduction
The ‘entrepreneurial university’ is in vogue (Etzkowitz 2003; Slaughter and Leslie
1997). Proponents of the entrepreneurial university claim that universities are being
transformed from ivory towers to engines of economic growth (Florida and Cohen
1999; Feller 1990). In similar vein, others argue that universities and industry are
converging towards a hybrid order where the differences between scholarly and
commercial logics are becoming blurred (Owen-Smith 2003). Policy-makers in a
number of countries are promoting such developments by encouraging collaboration
between universities and industry (Mowery and Nelson 2004). Implicit in many
accounts of the entrepreneurial university is the assumption that academic researchers
engage with industry in order to commercialize their knowledge. For this reason, policy-
makers provide monetary incentives to academics to facilitate their commercial
involvement (Lach and Schankerman 2008; Link and Siegel 2005).
In this paper, we investigate whether the idea of the entrepreneurial university is
reflected in academic researchers’ motivations. The purpose is to present evidence on
the motivational drivers underpinning various forms of engagement with industry,
including informal collaboration as well as more formal engagement via patenting and
academic entrepreneurship. We present results from a large scale survey of physical and
engineering faculty at UK universities. We find, first, that commercialization ranks as
the least important motivation for engaging with industry while research-related reasons
dominate. Thus, it would seem that academics engage with industry mainly to support
their academic research activities. Second, we find that the academics’ motivations
differ depending on the channel of engagement. We examine classic technology transfer
mechanisms, including patenting and spin-off companies, as well as collaborative and
informal modes of interaction, including joint research, contract research and
consulting. While patenting and spin-off founding are motivated by commercialization,
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collaboration is dominated by research-related motivations, including learning from
industry and fund-raising.
Our analysis contributes to the debate on the entrepreneurial university by shedding
light on its micro-foundations (Jain et al. 2009). Understanding the individual
motivational drivers for university-industry relations is important for judging the
ultimate organizational and societal implications of the entrepreneurial university
(Siegel et al. 2007). Our discussion suggests that undue policy emphasis on
commercialization obscures the fact that industry engagement often generates
considerable benefits for academic research. We conclude that, given academics’
motivations, to talk of convergence between scholarship and commerce may be
premature, although interaction between these realms continues to be mutually
beneficial.
Our work also contributes to the emerging body of literature on informal and
collaborative modes of university-industry interaction (Link et al. 2007; Grimpe and
Fier in press). While previous research has often focused on more easily measurable
interactions such as patenting, licensing and academic entrepreneurship, collaboration
has remained in the background, with some exceptions (Meyer-Krahmer and Schmoch
1998; Ponomariov 2008; Martinelli et al. 2008; Perkmann and Walsh 2007). To help fill
this gap, we explore the drivers of informal interaction, and how they differ from
interactions underpinned by intellectual property transfer, and academic
entrepreneurship. Our contribution is important both conceptually and practically
because collaborations constitute the majority of university-industry interactions.
The paper is structured as follows. Drawing on existing work, we outline the debate on
the entrepreneurial university and show how it raises questions on academics’
motivation to engage with industry. We present survey data from a sample of UK
academics, which enable us to investigate their reasons for engagement with industry
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and, specifically, whether different channels of engagement are underpinned by
different motivations. We conclude by discussing the implications of our results against
the context of the existing literature, and deriving some policy conclusions.
2. Conceptual considerations
2.1. The entrepreneurial university
Universities are increasingly being called upon to contribute to economic development
and competitiveness (Feller 1990) and policy-makers have put in place initiatives aimed
at increasing the rate of commercialization of university technology. Notably, policy-
makers implemented laws that provide commercialization incentives to universities by
granting them ownership of intellectual property arising from their research. Examples
comprise the 1980 Bayh-Dole Act in the US and similar legislation in other countries
(Mowery and Sampat 2005; Valentin and Jensen 2007). Other policies encourage
universities and firms to engage in partnerships and personnel exchange, for instance
via university-industry centers or science parks (Adams et al. 2001; Siegel and Zervos
2002; Hall et al. 2000; Siegel et al. 2003). Finally, a third type of initiative seeks to
build universities’ knowledge transfer capabilities by supporting recruitment and
training of technology transfer staff (Woolgar 2007; Kirby 2006).
While the jury on the effectiveness of some of these policies is still out (Mowery and
Nelson 2004), various trends indicate a growing ambition among universities to respond
to the call for a greater role in technology development, demonstrated by an increasing
propensity among universities to patent (Nelson 2001; Stiglitz and Wallsten 1999),
increased revenues derived from university licensing (Thursby et al. 2001), increasing
numbers of university researchers engaging in academic entrepreneurship (Shane 2005),
and the diffusion of technology transfer offices, industry collaboration support offices
and science parks (Siegel et al. 2003).
The growing involvement of universities in technology transfer and commercialization
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raises questions about their nature and mission (McKelvey and Holmén 2009).
Advocates of the ‘triple helix’ theory claim that universities have embraced economic
and social development as a new mission, in addition to their traditional missions of
teaching and research (Etzkowitz 1998). In accepting this new task, universities are said
to become part of a coherent system that includes industry and government and
underpins innovation and economic progress (Etzkowitz and Leydesdorff 2000).
Implicit in this view is that the role of academics is shifting. Rather than concentrating
on ‘blue-skies’ research, academics are seen increasingly to be eager to bridge the
worlds of science and technology, in an entrepreneurial way, by commercializing the
technologies that emerge from their research (Clark 1998; Shane 2004; Etzkowitz
2003).
By actively engaging in technology development, universities are demonstrating
ambidexterity in their ability to produce both scientific knowledge and technology
outputs (Ambos et al. 2008). For instance, in rapidly developing areas such as
biotechnology, ‘star scientists’ excel both as academic researchers and academic
entrepreneurs (Zucker and Darby 1996). In an analysis of the publishing and patenting
activities of research-intensive US universities, Owen-Smith (2003) finds a convergence
towards a ‘hybrid system’, linking scientific and technological success. Specifically, he
shows that academic success drives technological invention while advantages in
technological invention are driven by organizational learning relating to procedures and
organizational arrangements for identifying, protecting and managing IP. Over time,
positive feedback loops between the two realms lead to a hybrid order where the best
universities excel in both scientific research and technology commercialization (Owen-
Smith 2003).
Critics have responded by underlining the potentially detrimental effects of
‘entrepreneurial science’ on the long-term production of scientific knowledge, voicing
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fears that academic science is being instrumentalized and even manipulated by industry
(Noble 1977; Slaughter and Leslie 1997; Krimsky 2003). Many universities appear to
have become ‘knowledge businesses’ which are focused not so much on generating
public goods for national audiences but providing services to specific stakeholders
(McKelvey and Holmén 2009; Vallas and Kleinman 2008). The perceived risks include
a shift from basic research towards more applied topics and less academic freedom
(Blumenthal et al. 1986; Behrens and Gray 2001), lower levels of research productivity
among academics (Agrawal and Henderson 2002) and a slowing-down of open
knowledge diffusion (Nelson 2004; Rosell and Agrawal 2009; Murray and Stern 2007).
2.2 Informal collaboration with industry
Existing work investigating the features of the entrepreneurial university has primarily
focused on academic researchers’ engagement in patenting, licensing and academic
entrepreneurship (Phan and Siegel 2006; Rothaermel et al. 2007). However, interactions
between universities and industry take multiple forms, with interaction channels ranging
from inter-organizational relationships (e.g. joint research or contract research) to spin-
off companies, to IP transfer including patenting and licensing (Carayol 2003; D'Este
and Patel 2007; Bonaccorsi and Piccaluga 1994; Schartinger et al. 2002; Cohen et al.
2002; Bercovitz and Feldman 2006).
Among these channels, engagement in collaboration is far more frequent than
engagement in patenting and academic entrepreneurship (D'Este and Patel 2007;
Perkmann and Walsh 2007). There are three main forms of collaboration. Collaborative
(or joint) research refers to formal collaborative arrangements aimed at cooperation on
R&D projects (Hall et al. 2001). In many cases, the content of this research can be
considered ‘pre-competitive’, and these projects are often subsidized by public funding.
Contract research, on the other hand, refers to research that is directly commercially
relevant to firms and, therefore, is usually ineligible for public support. Contract
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research is explicitly commissioned by firms and the work is usually more applied than
in collaborative research arrangements (Van Looy et al. 2004). Finally, consulting refers
to research or advisory services provided by individual academic researchers to their
industry clients (Perkmann and Walsh 2008). Consulting projects are typically
commissioned directly by the industry partner and the income derived from them often
accrues to individuals although it can be channeled through university research accounts
to support research. Some of the above types of collaboration have been referred to as
‘informal’ collaboration (Link et al. 2007) even though most of these arrangements tend
to be formalized via contracts. In this paper, therefore, we use the terms ‘collaboration’
or ‘collaborative forms of interaction’ to include collaborative research, contract
research and consulting.
Collaboration is not only more frequently used than IP transfer and academic
entrepreneurship but it also tends to be more highly valued. Research suggests, for
instance, that the role of IP transfer in transferring knowledge is modest (Agrawal and
Henderson 2002). In many cases, faculty do not disclose inventions to their university,
and hence these are unaccounted for by studies focused on IP (Siegel et al. 2003).
Roessner (1993), drawing on survey evidence relating to different interaction channels,
finds that US research and development (R&D) executives place the highest value on
contract research, followed by co-operative research while they see licensing as less
relevant. Similarly, according to the Carnegie Mellon Survey, US R&D executives
regard consulting, contract research and joint research as more relevant channels than
licensing (Cohen et al. 2002). These finding are confirmed by a number of other studies
(Klevorick et al. 1995; Mansfield 1991; Pavitt 1991; Agrawal and Henderson 2002;
Schartinger et al. 2002).
Having established the empirical significance of collaborative forms of interaction, the
question arises how they relate to the idea of the entrepreneurial university. On one
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hand, it could be argued that collaborative forms of engagement constitute just another,
less formalized form of technology transfer that is governed by dynamics similar to
patenting and academic entrepreneurship. Increasing collaborative involvement may
therefore be consistent with a scenario where academic researchers adopt industrial
logics and become active participants in technology development and
commercialization (Etzkowitz 1998).
On the other, collaboration may be governed by a logic that differs from the idealized
norms of the entrepreneurial university. In this scenario, collaboration would be
informed by the traditional values of the scientific system as elaborated by Merton
(1973) and Polanyi (2000 [1962]). Collaborative engagement with industry may benefit
academics’ research activities by establishing relationships with knowledge users and
mobilizing resources that complement public research funding. In many disciplines,
interaction between the producers of scientific knowledge and producers of technology
underlies the progress of both science and technology in a recursive way (Rosenberg
1982). Even though science may not be immediately applied, it is often nevertheless
inspired by practical considerations and hence benefits from interactive contact with
technology producers (Stokes 1997). Benefits from industry cooperation include
securing funds for graduate students, accessing laboratory equipment, gaining insights
applicable to academic research, and supplementing research monies (Mansfield 1995;
Murray 2002).
In this paper, we seek clarification on the nature of collaboration by exploring academic
researchers’ motivation to engage in it, as compared with the more overtly commercial
forms of entrepreneurial behavior.
2.3. Why do academics engage with industry?
Universities are professional bureaucracies whose members are relatively free to pursue
activities that they believe are in the overall interests of the organization (Mintzberg
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1983). Contrary to teaching, engaging with industry constitutes discretionary behavior
for academics. Many universities have formal policies for encouraging their academic
staff to seek industry assignments for a specified share of their time (Perkmann and
Walsh 2008). Royalty sharing policies at many universities provide incentives for the
disclosure of inventions to the university administration (Bercovitz and Feldman 2008)
and subsequent participation of inventors in product development efforts via spin-off
companies or licensing (Lowe 2006).
Deployment of these incentive mechanisms presupposes that academic researchers
respond to financial incentives tied to successful commercialization of their ideas
(Jensen and Thursby 2001). This logic is implicit in life cycle theories that maintain that
junior researchers focus on building reputation in academia while later in their careers
they capitalize on their expertise by reaching out to industry (Stephan and Levin 1992;
Zuckerman and Merton 1972). A qualitative study by Owen-Smith and Powell (2001)
provides some support for the idea that academics are attracted by monetary profit. The
authors find that in the life sciences – where patents have higher monetary value –
researchers patent to enhance their incomes. In the physical sciences, on the other hand,
patenting is less attractive because of lower monetary pay-offs and therefore is pursued
primarily to develop relationships with firms, access equipment or exploit other
research-related opportunities (Owen-Smith and Powell 2001).
However, other contributions suggest that working with industry is not necessarily
underpinned by entrepreneurial intentions in the sense of responding to economic
opportunities. Bercovitz and Feldman (2008) find that faculty members’ compliance
with entrepreneurial behavior can be substantial or symbolic. Only under certain
conditions – e.g. presence of local entrepreneurial norms - do academics engage in
substantial entrepreneurial behavior as opposed to superficial compliance. A study of
German academic researchers demonstrated that researchers engage in patenting not for
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personal profit but to signal their achievements and gain reputation amongst their
academic and industry-related communities (Göktepe-Hulten and Mahagaonkar 2009).
Research on attitudes to academic entrepreneurship present a differentiated picture.
Data on US universities indicate that most academics, particularly in engineering and
the applied sciences, are keen on technology transfer activities, but less so on overly
commercial schemes such as start-up assistance to new technology firms, and equity
investment (Lee 1996). Faculty in high ranked institutions are less in favor of academic
entrepreneurship than academics at lower tier universities. The main concern of
academics is that industry involvement might restrict academic freedom, i.e. the ability
to pursue curiosity-driven research without having to consider commercial gain (Lee
1996). However, academics appear to draw boundaries between the forms of industry
engagement they see as legitimate, and others that they view as overly commercial (Lee
1996). In any case, academics express significant support for industry collaboration
particularly when it is related to their research (Lee 2000). A meta-study shows that
academic researchers’ attitudes to financial ties with industry sponsors are largely
positive, especially when funding is indirectly related to their research, disclosure is
agreed upfront, and ideas are freely publicized (Glaser and Bero 2005). A study of
German academic researchers in four disciplines suggests that acquiring additional
research funds and learning from industry constitute the main motives for engaging with
industry (Meyer-Krahmer and Schmoch 1998).
Our review of the literature on academics’ motivation for engaging with industry
reveals discordance between two groups of authors. While a first group emphasize
academics’ utility-maximizing commercialization behavior, others find that academics
operate in a strongly institutionalized environment sporting science-specific norms and
values. In the view of the former group, academics collaborate with industry to pursue
commercialization while the latter believes that, rather than being entrepreneurs,
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academics collaborate with industry primarily to support their research. Our goal in this
paper is to help clarify which of the above views is accurate and which type of
collaboration is driven by commercialization behavior and research-driven behavior,
respectively.
We present results from a unique dataset, collected from physical and engineering
science faculty at UK universities, which is distinct in two ways. First, instead of
providing evidence on academics’ attitudes, we present data on academics’ motivation
to engage in actual collaboration. Previous, attitudinal, studies provide respondents’
views about industry engagement, but do not connect them with actual collaboration
(Lee 1996; Glaser and Bero 2005). Second, we have motivational data on a whole range
of different forms of interaction, allowing us to draw a comparison between the classic
modes of commercialization (patenting, academic entrepreneurship) and more informal
collaboration modes. Many existing studies provide evidence only on specific types of
academic industry involvement, with a number of contributions investigating
academics’ motives for engaging in patenting (Owen-Smith and Powell 2001; Moutinho
et al. 2007; Baldini et al. 2007).
3. Data and main variables
3.1. Sample and data collection
Our data are derived from a large-scale survey of university researchers aimed at
obtaining information on their interactions with industry. The sample was compiled
from the record of research grants holders from the UK’s Engineering and Physical
Sciences Research Council (EPSRC) between 1999 and 2003. The EPSRC provides
research funding mainly to university-based investigators based on applications
submitted in response to open calls. It distributes 20-25% of the total UK public science
budget. The EPSRC actively encourages partnerships between researchers and the
potential users and beneficiaries of research, such as industry, government, National
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Health Service (NHS) trusts and non-profit organizations. Almost 45% of EPSRC-
funded projects involve partnerships with industry or other stakeholders.
To ensure our sample was representative of the population of researchers in the physical
and engineering sciences, we excluded disciplines whose researchers might be likely to
apply to other research councils. The ten disciplines considered in our study are:
Chemical Engineering; Chemistry; Civil Engineering; Computer Science; Electrical and
Electronic Engineering; General Engineering; Mathematics; Mechanical, Aeronautics
and Manufacturing Engineering; Metallurgy and Materials; and Physics. The sample
includes 4,337 researchers, corresponding to approximately 42% of the population of
active researchers in our target disciplines.1
The survey was administered by post in 2004 and generated 1,528 valid questionnaires,
a response rate of 35.2%. Our tests for response bias indicate that there are no
statistically significant differences among response rates across scientific disciplines.2
However, there are statistically significant differences with respect to certain individual
characteristics, including the proportion of respondents and non-respondents holding
collaborative grants over the period 1991-2003 (57% and 53% for respondents and non-
respondents, respectively), and being a professor (44% and 39% for respondents and
non-respondents, respectively). Overall, though, response rate biases are relatively
minor and unlikely to affect the results.
The questionnaire contained questions on various aspects of industry engagement.3 Our
analysis is based on two sets of information: a) the frequency of engagement with
industry through five channels and b) the respondents’ rationales for engagement with
1 According to data from the UK 2001 Research Assessment Exercise (RAE)
2 Response rates (number of valid returned questionnaires relative to population surveyed) by discipline:
Chemical Engineering, 35.6%; Chemistry, 35.9%; Civil Engineering, 35.5%; Computer Science, 30.2%;
Electrical & Electronic Engineering, 34.7%; General Engineering, 39.7%; Mathematics, 38.4%;
Mechanical, Aeronautical & Manufacturing Engineering, 36.9%; Metallurgy & Materials, 34.2%; and
Physics, 32.7%.
3 See D’Este and Patel (2007) for a detailed description.
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industry. We analyzed our data via ordered logit regressions, using engagement in
various types of channels as the dependent variables.
3.2. Dependent, explanatory and control variables
Dependent variables
We consider five dependent variables, each representing frequency of industry
engagement via a specific channel: joint research agreement, contract research
agreement, consulting, spin-off company establishment, and patenting. Respondents
were asked: ‘How frequently were you engaged in the following types of activity in the
calendar years 2002 and 2003?’ They were given a choice of five intervals: 0, once or
twice, 3 to 5 times, 6 to 9 times, and 10 times or more.4 Based on responses, and given
that activity was strongly concentrated in the first two interval categories, we defined
our dependent variables as ranging between 0 and 2, 0 if the researcher had no
involvement for a type of activity, 1 for one or two instances, and 2 if the researcher
engaged three or more times in an activity (see descriptive statistics in Table A1 in the
Appendix). There is little overlap among these channels, while there is positive and
significant bivariate correlation between each pair; Spearman correlation coefficients
range from 0.12 to 0.34. Since our dependent variables are discrete and ordered, we use
ordered logit models for our estimations.
The three channels with the highest proportion of researchers engaging at least once are:
contract research, joint research, and consulting. More than 50% of respondents
indicated using each of these channels at least once in the period analyzed.
Explanatory variables
Academics’ motivations for engaging with industry constituted our explanatory
variables. We built them from the responses to the following question in the survey:
4 However, for patents, respondents were requested to report the actual number of patent applications.
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‘Please rank the following reasons for your involvement in interactions with industry
according to their importance’ (see Table 1). Respondents were asked to score the
importance of each item on a five-point Likert scale, ranging from ‘not important’ (1) to
‘extremely important’ (5). We carried out a factor analysis (principal component
analysis - PCA) on the 12 items to determine whether they corresponded to more
general, underlying rationales for engagement with industry. We then used these factors
– which we called ‘motivations’ – as explanatory variables (see Table A2 in the
Appendix for descriptive statistics).
Specifically, we regressed each of the dependent variables on the extent to which
respondents assessed each motivation as important. We measured the importance
attributed to a specific motivation by taking the average score of respondents’
assessment of the importance of the single incentive items that composed each
motivation. For instance, if one factor comprised four items, the average score refers to
the average of these four incentive items. Since each item in the questionnaire was
ranked on a five-point Likert scale, our measure for each motivation ranges between 1
and 5; the higher the number, the higher the importance attached to a specific
motivation.
Control variables
We used a number of control variables reflecting the characteristics of individual
university researchers and their organizational environments. We aimed to control for
individual experience and career-stage effects through the following variables: a) extent
of previous involvement with industry, measured by number of joint publications with
industry in the period 1995-2000, and average value of collaborative EPSRC grants
(i.e. with industry) obtained by the researcher between 1995 and 2001;5 and b)
researcher’s age and academic status (i.e. whether the researcher is a professor or not).
5 Both variables log transformed.
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Our organization-level control variables include the impact of department size, the
composition of departmental research funding, and research quality of the institution.
Previous research shows that these organizational characteristics could have an impact
on the extent to which researchers engage with industry (Belkhodja and Landry 2007;
Schartinger et al. 2002; Tornquist and Kallsen 1994; Feldman et al. 2002). We
considered the following variables: a) size of the department to which the researcher is
affiliated (measured by average number of full-time equivalent staff for the period
1998/99-2000/01); b) volume of research funding at department level, including volume
of research income from contracts with industry per member of staff, and volume of
research income from public sources per member of staff over the same period (both
indicators refer to the period 1998/99-2000/01);6 and c) departmental research quality
proxied by the 2001 UK RAE rating. We use dummy variables to identify departments
with the highest score (5*) and departments ranked lower than five, using point five as
the reference category.7 Finally, we include scientific discipline and regional dummies
to control for differences across scientific fields and geographic location in terms of
researchers’ propensities to engage with industry. Some of information underpinning
the control variables is from non-survey sources, such as records of previous
collaborative grants, joint publications, or RAE research rankings, in order to alleviate
some common method bias.
3.3. Control for selection bias
Only respondents reporting engagement with industry (1,088 individuals - 71% of
1,528) were asked about their motivations. Because this risks introducing selection bias
6 Data on department finances and staff numbers are from www.hesa.ac.uk. Variables for industry and
public research funding, and number of staff, were computed at department level as averages for the
academic years 1998-99 and 2000-01. Public research funding refers to funding for research from any of
the UK research councils. Finance data are in £’000. All variables log transformed.
7 The choice of these three categories is based on the fact that the reference category accounts for a large
proportion of departments: three categories produces a more even distribution of departments.
Information on UK RAE 2001 is from: www.hero.ac.uk.
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since we do not account for why researchers decide to engage with industry, we use a
two-stage regression model, drawing on Manning et al. (1987). In the first stage, we ran
a logit model with the dependent variable for whether a researcher engaged with
industry or not. We included five control variables to capture perceived barriers to
engaging with industry, and some individual and departmental features; information
was available from all 1,528 respondents for all these variables.8
From this model we calculated the predicted probability for each individual to engage
with industry. We then ran a second stage model for individuals who engaged at least
once, but controlled for selection bias by including the predicted probabilities of
interaction from the first stage model (variable name: prob.). In the second stage, we
used frequency of engagement in the various channels as defined above (section 3.2) as
dependent variables, in ordered logit regressions.
4. Results
4.1. Taxonomy of motivations for engaging with industry
Table 1 presents descriptive results for the different incentive items, broken down by
discipline, to indicate the proportion of respondents assessing an item as very or
extremely important (i.e. scores of 4 or 5).
--------------------------------
Insert Table 1 about here
--------------------------------
Two issues emerged. First, there is significant variation in terms of which incentive
items researchers deem to be important. While 74.5% of researchers rated ‘applicability
8 The 5 variables related to barriers are dichotomous variables which take the value 1 if the respondent
assessed the barriers as very, or extremely important. The 5 barriers are: absence of established
procedures to collaborate with industry; nature of my research not aligned with industry interests or
needs; potential conflicts with industry regarding royalty payments from patents or other IP rights; short
term orientation of industry research; and rules and regulations imposed by university or government
funding agency. The results of the first-stage logistic regressions are available on request.
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of research’ as highly important, only 11.1% rated ‘seeking IP rights’ similarly. Also,
‘access to personal income’ was considered important by only 16% of academics,
indicating that pecuniary gains were far less significant than other reasons for working
with industry.
Second, there was variation across disciplines, with some notable differences such as
those between engineering, and chemistry, computer science, mathematics and physics.
Across the engineering fields, there are few statistical differences in terms of incentives
ranked by researchers as important.9 Significantly fewer researchers in mathematics and
chemistry assessed items as important compared to the overall sample. Computer
scientists and physicists occupied an intermediate position, since for approximately half
of the items, proportions were not statistically different from those prevailing in the
engineering fields.
A factor analysis conducted on the 12 items resulted in four factors (Table 2). The first
comprises five items, all related to expectations related to learning opportunities from
engagement with industry. We labeled this ‘learning’ motivation. The second factor,
which we labeled ‘access to in-kind resources’, reflects keenness to access resources,
such as materials, research expertise and equipment. The third factor is related to
expectations about ‘accessing funding’ for research. The fourth factor, which we labeled
‘commercialization’, reflects expectations of personal economic returns (PCA results
are reported in Table A3 in the Appendix).
--------------------------------
Insert Table 2 about here
--------------------------------
A first evaluation of these results reveals that three motivations, i.e. learning, access to-
9 The two items where there were significant differences across engineering fields are: ‘feedback from
industry’ and ‘access to equipment’.
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in-kind resources, and access to funding, are related to supporting academics’ research
and only commercialization is related to deriving economic benefit from the research.
We look at the implications of this finding in the discussion section.
4.2. Relationship between types of motivation and channels of interaction
Having identified four independent motivations for academics to engage with industry,
we conducted a regression analysis to examine the impact of these motivations on
engaging in different channels of interactions.
Table 3 presents the results for the relationship between frequency of interaction via
five channels, and researchers’ ranking of the importance of the four motivations. We
find that motivations have a distinct influence on the frequency of interactions across
engagement channels. The learning motivation is positively associated with higher
frequencies of industry engagement across several channels, i.e. joint research, contract
research and consulting, all of which are based on relationships involving personal
contacts with industry partners.
--------------------------------
Insert Table 3 about here
--------------------------------
Commercialization as a main motivation is positively associated with spin-off company
activity, consulting and patenting, but shows no significant relationship with frequency
of engagement in any of the other channels. Researchers who regard access to funding
as particularly important engage more frequently in joint research, contract research and
to some degree, consulting, although this last is only weakly significant. In contrast,
high importance of access to in-kind resources has a negative effect on the frequency of
engagement in contract research, consulting, spin-offs and patenting, and no significant
impact on joint research.
Finally, with respect to our control variables, these results show that, ceteris paribus,
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experience in collaborative research increases the probability of more frequent
collaboration via several channels. While being a professor has a positive impact on
engagement frequency (with the exception of spin-off company activity), being a young
researcher has a positive impact on the frequency of engagement in joint research and
consulting. Researchers in lower-rated research departments tend to do more consulting
compared to researchers in high ranked departments, while researchers in departments
with higher ratios of per capita research income from industry are particularly likely to
engage in more frequent contract research. We also found some variation across
disciplines. For instance, while chemists are less likely to engage in contract research
and consulting compared to mechanical engineers, they are more likely to patent.
To confirm the robustness of our results, we conducted analyses using different
constructions for the dependent variables. For instance, we devised dichotomous
dependent variables and ran probit and logit regressions. The results are similar to those
in Table 3. Also, since interaction via one channel may not be independent of activity
via another, we conducted multivariate probit analysis to capture possible
interdependencies among different channels, based on the STATA routine proposed by
Cappellari and Jenkins (2003). Table A4 in the Appendix reports the results for the
multivariate probit model, which are in line with those in Table 3.
As our information is drawn from a survey, the results do not provide ultimate answers
about the direction of causation. However, conceptually, we would argue that it is more
likely that motivation determines the frequency of engagement than vice versa.
5. Discussion
In this paper, we investigate what motivates academics to engage with industry using
both informal collaboration and formal models of interaction. We identified four main
motivations: (i) commercialization (commercial exploitation of technology or
knowledge); (ii) learning (informing academic research through engagement with
20
industry); (iii) access to funding (complementing public research monies with funding
from industry); and (iv) access to in-kind resources (using industry-provided equipment,
materials and data for research).
Three of these factors are research-related; only one is related to an intention to be
entrepreneurial. In fact, our results suggest that most academics engage with industry in
order to further their own research, either through learning or through access to funds
and other resources. In addition, commercialization on average was ranked lowest by
our survey respondents (Appendix Table A2).
While the desire to raise funds for research is intuitively appealing, the learning
motivation requires clarification. The items related to the ‘learning’ motivation refer to
the expected benefits from gaining new insights, receiving feedback on research, and
accessing new knowledge through engagement with industry. These benefits are likely
to arise from an important yet often under-appreciated aspect of public research, i.e.
backward linkages from applied technology. For instance, resolving problems that occur
in technology development can lead to follow-on research activities, inform academic
research agendas and in some cases even lead to new scientific disciplines (Rosenberg
1982). Mansfield (1995) observes that the problems that many academics choose to
work on are often inspired by their consulting activities. Also, a significant share of
basic public research is associated with ‘Pasteur’s quadrant’, i.e. is driven by the pursuit
of basic understanding and considerations of use (Stokes 1997). Much research in
biotechnology, computer science, aeronautical engineering and other disciplines
conforms to the Pasteur logic. It involves an intrinsic affinity between academic and
industry research, which has implications for academics’ motivations for choosing to
interact with industry. Thus, whenever researchers engage in research that is driven by
considerations of both basic understanding and use, the ‘learning-based’ logic for
interaction is likely to be prevalent.
21
We also find that engagement in different forms of interaction is underpinned by
varying motivations. Academics motivated by learning frequently engage in joint
research, contract research and consulting, while motivations related to
commercialization of research lead to engagement in activities such as patenting, spin-
offs and consulting. It should be borne in mind, however, that patenting and
involvement in spin-off companies are relatively rare compared to involvement in
collaborative forms of interaction. Only around 17% of the respondents who engaged
with industry participated in spin-off companies, and approximately 30% reported filing
patents.
The channels of engagement underpinned by research-related motivations, particularly
learning and access to funding, are all based on direct collaboration with industry
partners, which suggests that academic research interests benefit most from highly
interactive, ‘bench-level’ relationships with industry users. The fact that ‘access to in-
kind resources’ is negatively related to most forms of interaction requires further
comment. As joint research is not affected by this relationship, it appears that,
particularly the more commercial forms of interaction are rarely directly conducive to
carrying out academic research. For instance, data derived from consultancy work or
contract research may not be sufficiently novel for publication. However, these direct
effects tend to be outweighed by indirect benefits, such as learning and access to
research funding. Learning is an indirect benefit in that industry projects may not lead
directly to novel scientific outputs, but may lead to new research problems and learning
about new industrial applications (Perkmann and Walsh 2009). Access to funding is
also an indirect benefit as it may facilitate economies of scale and retention of staff at
university laboratories.
It would appear from our results that there is a tension between commercialization and
research-related motivations. While patenting and spin-off involvement are driven by
22
commercialization, the more collaborative forms of interaction are driven by research-
related motivations, but not commercialization. For patenting and spin-off involvement,
our results confirm the basic premise of the entrepreneurial university. Academics
engage in these activities because they are interested in deriving personal pay-offs from
the commercialization of their knowledge and technologies. However, they do not
appear to derive significant research-related benefits from this entrepreneurial behavior.
The reverse applies to collaborative forms of interaction: the motivations for joint
research and contract research are clearly research-driven and commercialization plays
no role.
Consulting is an exception to this pattern in that it is driven by both commercialization
and research-related motivations. Consulting is ‘polyvalent’ as it allows academics to
pursue personal income in an entrepreneurial manner (Louis et al. 1989), and to build
personal relationships with industry practitioners and learn about industry problems and
applications. Provision of consultancy, therefore, would be attractive for researchers
who are driven by learning motivations (Mansfield 1995; Murray 2002). Thus,
consulting may constitute the ‘boundary’ to university-industry collaboration (Lee
1996) in the sense that it marks the limits to what constitutes research-relevant
involvement with industry. So, while joint research, contract research and consulting are
conducive to academic output, involvement in patenting and academic entrepreneurship
may not generate similar complementarities with research.
In terms of policy, our results suggest a cautious approach to undifferentiated attempts
to promote the entrepreneurial university. Many policy measures emphasize
commercialization as the central mechanism for rendering university knowledge
relevant to economy and society. These include the Bayh-Dole Act in the US and
similar legislative initiatives in other countries, as well as governments’ attempts to
increase ‘third stream engagement’ in universities through subsidies for technology
23
transfer offices (Mowery and Sampat 2005; Czarnitzki et al. 2009). Data on disclosures,
patenting, licensing and spin-offs are often used as metrics for assessing universities’
technology transfer efforts. These types of policy measures are based on the principle
that universities seek to protect their IP and exploit it in the industry market place. As
the proceeds from the commercialization of IP are usually shared between the university
and the individual academic inventor(s), the financial incentive is seen as encouraging
academic involvement in technology transfer (Lach and Schankerman 2008).
If, on the other hand, academics engage with industry mainly to further their research,
reliance on academics’ entrepreneurial behavior appears misplaced. This is reinforced
by the fact that the intention of policy-makers is not necessarily to maximize
universities’ income, but rather to make technology available to firms and society at
large. Also, universities’ efforts to reap significant income from commercialization are
generally unsuccessful as the proceeds from licensing are usually decimated by the costs
of patenting and maintaining technology transfer offices (Thursby et al. 2001). This
means universities should be encouraged not to privilege a narrow remit of technology
transfer offices as champions of IP protection and incubators for spin-offs as this kind of
interaction might be misaligned with most academics’ motivations for working with
industry. As our results show, academics generally view collaborative engagement with
industry as beneficial to their research and, given that industry pays for much of this
interaction, it could be assumed that industry partners also judge it to be useful
(Gulbrandsen and Slipersæter 2007). Universities therefore should integrate their
monetary incentive schemes for commercialization with general policies enabling and
encouraging collaboration with industry more generally. Conceptually, the ultimate
implication of our findings is that – in the setting of university-industry relations – the
locus of economic opportunity recognition will in most cases lie with industry partners
commissioning contract research and consulting rather than academic researchers
24
pursuing academic entrepreneurship. This means most – but not all – academics are
motivated by finding solutions to interesting problems rather than pursuing economic
opportunities.
Our paper has some limitations, raising questions for future research. The data for our
analysis are drawn from the physical and engineering sciences only. The life sciences
are generally characterized by a high intensity of university-industry relationships
(Powell et al. 1996) and responses from life science researchers could provide a
different picture of the motivations underpinning IP transfer. However, results from the
large body of existing research on the life sciences may have been generalized too
readily and further research should investigate other disciplines. Furthermore, our
results need to be validated by research using alternative approaches to sampling. As
our sample was constructed from the records of academics who received government
grants, there may be a bias towards particularly successful and/or comparatively senior
researchers and against researchers who may have received industry funding only.
Another avenue for further research is to examine the effects of different channels of
interaction for the direction and quality of research conducted by academic researchers.
Our results suggest that the impact on academic research of industry engagement may
differ according to the motivations driving interactions. When academics work with
industry primarily to further their research, negative impacts on the direction of their
research or on their research productivity will be arguably less likely. This holds
particularly when academics are motivated by learning and access to resources. Our
data suggest that this type of collaboration is less likely to result in immediately
commercially relevant outputs, such as patents and spin-offs. At the same time,
however, in the longer term, engagement in relationship-intensive collaboration with
companies might enhance academic research output and generate university benefits via
better research evaluations and higher levels of funding. Future research should seek to
25
provide more informed judgment on the potential benefits and drawbacks associated
with the different channels of engagement with industry used by academic researchers.
6. Conclusion
Our results suggest the vision of entrepreneurial university fails to neatly capture the
complex nature of academic researchers’ interactions with industry. Rather than a
‘hybrid order’ in which universities and industry converge to become common drivers
of technological and economic development, most academic researchers are keen to
retain their autonomy by ensuring that collaborative work with industry is conducive to
– or at least compatible with – their research activity. This suggests that, for
universities, the benefits of university-industry collaboration are best attained by cross-
fertilization rather encouraging academics to become economic entrepreneurs.
Collaboration is fruitful when it facilitates or contributes to both industry applications
and academic research. Such collaboration retains the distinctiveness of the realms of
scholarship and industry, but enables connections via interactive links that allow
academic input to commercial problems and promotion of new ideas and new problems
for university research (Rosenberg 1982; Stokes 1997). Announcements of the
entrepreneurial university may therefore be premature and based on an overstated
generalization of insights from the life sciences (see e.g. Owen-Smith and Powell 2001).
Our analysis of the physical and engineering sciences provides a useful corrective in
this respect and simultaneously alleviates many of the fears voiced by some observers
relating to the alleged ‘sell-out’ affecting universities. As opposed to a ‘sell-out’, we
found strong evidence that universities managed to retain their distinct identity as
organizations governed by the ‘republic of science’.
Acknowledgements: We thank Virginia Acha, Thomas Astebro, Charles Baden-Fuller,
Kate Bishop, Isabel Bodas de Araújo Freitas, Maryann Feldman, Roberto Fontana
26
Patrick Llerena, Ammon Salter, Naohiro Shichijo, Valentina Tartari, Finn Valentin,
Jaider Vega-Jurado, John Walsh, Kathryn Walsh for helpful comments. The usual
disclaimer applies. Previous versions of the paper were presented at the Triple Helix
Conference (16-18 May 2007, Singapore), the AIM workshop ‘Exploring & Mapping
University-Industry Relationships’ (21 May 2007, London) and the DIME plenary
session at the DRUID Summer Conference (17-19 June 2009, Copenhagen). The
authors acknowledge support from the Innovation and Productivity Grand Challenge
(IPGC), an initiative of the Advanced Institute of Management Research (AIM) funded
by the UK’s Engineering and Physical Sciences Research Council (EP/C534239/1).
Markus Perkmann acknowledges funding from the Economic and Social Research
Council via an AIM Practices Fellowship (RES-331-27-0063).
27
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32
Tables and Figures
Table 1. Proportion of respondents who assessed incentive items as very or extremely
important (4 or 5 on five-point Likert scale)
CHNG CHEM CIVG COMP ELEC GENG MATH MENG MANG PHYS Total
1. Applicability of research 71.9 64.9 72.2 78.5 78.8 79.0 68.8 80.7 80.0 73.1 74.5
2. Research Income from industry 75.9 79.3 79.5 65.1 80.6 71.4 54.8 76.5 79.0 69.9 74.4
3. Research Income from Gov. 71.4 58.6 76.8 75.0 79.0 76.5 62.9 75.8 80.0 61.2 70.8
4. Information on industry problem 77.2 53.7 79.5 65.7 70.3 75.3 66.2 78.9 70.5 54.8 67.8
5. Feedback from industry 42.1 44.2 62.0 53.3 67.1 58.6 36.5 58.0 49.2 46.2 52.7
6. Information on industry research 41.1 36.3 50.7 41.3 52.1 45.5 32.8 51.6 52.5 47.1 45.0
7. Access to materials 43.1 30.8 50.0 41.5 48.6 43.9 20.6 45.7 47.5 43.1 41.2
8. Becoming part of a network 42.6 29.9 45.8 24.5 33.6 38.3 27.4 41.9 38.6 32.7 34.8
9. Access to research expertise 34.5 29.6 22.2 31.4 35.4 30.6 22.6 34.8 33.9 35.0
31.5
10. Access to equipment 19.3 22.2 18.3 10.4 35.7 25.5 9.7 23.0 15.5 25.2
22.0
11. Source of personal income 17.2 12.2 15.5 31.1 15.7 11.1 26.2 11.2 15.3 15.8 16.1
12. Seeking IPR 8.8 13.3 10.0 5.8 11.3 12.4 3.2 9.9 5.2 21.6 11.0
Number of observations* 58 209 73 107 146 100 66 163 62 104 1088
Notes: Abbreviations: CHNG, Chemical Engineering; CHEM, Chemistry; CIVG; Civil Engineering;
COMP, Computer Science; ELEC, Electrical and Electronic Engineering; GENG, General Engineering;
MATH, Mathematics; MENG, Mechanical, Aeronautical and Manufacturing Engineering, MANG;
Materials and Metallurgy; PHYS, Physics.
* The total number of observations slightly varies across items due to missing responses.
33
Table 2: Summary of factor analysis results
Motivational items Motivation
Source of personal income
Seeking IPRs
Commercialization
Information on industry problems
Feedback from industry
Information on industry research
Applicability of research
Becoming part of a network
Learning
Access to materials
Access to research expertise
Access to equipment
Access to in-kind resources
Research income from industry
Research income from Gov.
Access to funding
34
Table 3.Relationship between frequency of interaction and motivations
Ordered Logistic Regressions. Dependent variables: frequency of engagement in five channels
Joint
Research Contract
Research Consulting Spin-offs Patents
Commercialization 0.020 0.042 0.559 *** 0.488 *** 0.758 ***
(0.074) (0.071) (0.077) (0.102) (0.087)
Learning 0.265 *** 0.276 *** 0.177 * 0.197 -0.019
(0.088) (0.093) (0.094) (0.139) (0.109)
Funding Resources 0.129 * 0.299 *** 0.133 * 0.039 0.037
(0.068) (0.076) (0.075) (0.106) (0.092)
In-kind Resources 0.091 -0.231 *** -0.196 ** -0.349 *** -0.204 **
(0.068) (0.071) (0.077) (0.108) (0.085)
N. Joint publ. (ln) 0.102 0.178 * 0.083 0.275 ** 0.195 *
(0.099) (0.098) (0.098) (0.130) (0.107)
N. Collab. Gr. (ln) 0.059 * 0.041 0.059 * 0.099 ** 0.032
(0.033) (0.033) (0.034) (0.047) (0.037)
Age -0.021 ** -0.003 -0.018 ** 0.009 0.007
(0.009) (0.009) (0.008) (0.012) (0.010)
Professor status 0.581 *** 0.441 ** 0.438 ** 0.407 * 0.291
(0.183) (0.177) (0.173) (0.243) (0.205)
Industry inc/staff (ln) 0.164 0.305 ** -0.088 0.064 0.227
(0.126) (0.122) (0.134) (0.175) (0.149)
Public inc/staff (ln) -0.234 * 0.122 -0.039 -0.097 0.299 *
(0.141) (0.142) (0.162) (0.216) (0.181)
Dept. staff (ln) 0.027 0.129 0.192 0.025 0.005
(0.139) (0.144) (0.154) (0.187) (0.165)
RAE 2001 Low -0.037 0.276 0.363 ** -0.077 0.149
(0.179) (0.183) (0.185) (0.267) (0.210)
RAE 2001 High -0.085 -0.052 0.163 0.119 -0.119
(0.182) (0.191) (0.195) (0.269) (0.223)
Chemistry --- -0.644 *** -0.681 *** --- 0.799 ***
(0.242) (0.251) (0.285)
Civil Engineering -0.717 *** --- --- --- ---
(0.278)
Computer Science --- -1.223 *** -1.482 *** --- ---
(0.296) (0.332)
Electric & Electronic Eng. --- --- -0.995 *** --- 0.909 ***
(0.255) (0.296)
General Engineering --- -0.641 ** --- --- 0.669 **
(0.271) (0.321)
Mathematics -0.849 ** --- --- --- ---
(0.399)
Physics --- -0.747 ** -0.976 ** --- ---
(0.369) (0.387)
Prob. 0.688 0.505 1.439 ** -0.298 1.435 **
(0.584) (0.573) (0.579) (0.849) (0.677)
Threshold / Cut point 1 0.986 2.393 ** 2.745 *** 3.444 *** 5.418 ***
Region dummies Included Included Included Included Included
Number of observations 960 964 966 964 959
Log Likelihood -918.2 -902.0 -870.9 -469.7 -671.6
Restricted Log Likelihood -973.3 -991.6 -975.9 -511.4 -765.6
Pseudo R2 Nagelkerke 0.13 0.19 0.23 0.13 0.22
Note: Two tailed t-test: * p < 0.10; ** p < 0.05; *** p < 0.01. Robust standard errors between brackets. For
discipline dummy variables, only significant coefficients are shown in the table.
35
Appendix
Table A1: Descriptive statistics for dependent variables
Dependent Variables Average St.
Dev. Min. Max. % Observations
Category ‘0’ % Obs.
Category ‘1’ % Obs.
Category ‘2’ Number
valid Obs.
1. Joint Research 0.79 0.70 0 2 37.2 47.1 15.8 1079
2. Contract Res. 0.85 0.70 0 2 33.5 48.5 18.1 1085
3. Consulting 0.68 0.71 0 2 46.6 38.8 14.6 1087
4. Spin-offs 0.19 0.43 0 2 82.9 15.3 1.8 1085
5. Patenting 0.29 0.56 0 2 68.9 23.7 7.4 1079
36
Table A2: Descriptive statistics and correlation matrix for explanatory and control variables
Ave. St. Dev. Min. Max. 1 2 3 4 5 6 7 8 9 10 11 12
1. Commercialization 2.04 0.91 1 5
2. Learning 3.50 0.85 1 5 0.24
3.Funding resources 3.97 0.94 1 5 0.13 0.28
4. In-kind resources 2.78 1.06 1 5 0.19 0.48 0.22
5. Ln Joint Pub 0.52 0.73 0.0 3.81 0.01 0.02 0.11 0.04
6. Ln Coll. Grant 2.86 2.47 0.0 7.60 0.02 0.06 0.15 0.09 0.07
7. Age 45.9 9.86 27 75 -0.05 0.04 0.05 -0.01 0.16 0.27
8. Professor 0.53 0.50 0.0 1 -0.02 -0.02 0.09 -0.03 0.22 0.26 0.59
9. Indu. Inc./staff 1.61 0.78 0.0 3.53 -0.04 0.05 0.13 0.02 0.12 0.11 0.01 -0.01
10. Pub. Inc./staff 2.61 0.70 0.0 4.33 -0.01 -0.08 -0.01 0.02 0.14 0.11 -0.01 0.05 0.34
11. Ln Staff 4.22 0.68 2.07 5.53 0.01 -0.04 -0.02 0.01 0.09 0.07 0.01 0.02 0.42 0.37
12. Low RAE 0.31 0.46 0 1 0.02 0.07 0.07 0.02 -0.04 -0.09 -0.01 -0.06 -0.12 -0.36 -0.41
13. High RAE 0.30 0.46 0 1 -0.01 -0.05 -0.03 0.01 0.07 0.08 0.06 0.07 0.20 0.36 0.43 -0.43
Correlation coefficients significant at the 0.05 level, in bold.
37
Table A3: Factor analysis results: Incentives for interacting with industry
Mean St. Dev. Factor 1 Factor 2 Factor 3 Factor 4
Source of personal income 2.04 1.25 -0.032 -0.079 -0.001 0.896
Seeking IPRs 2.05 1.11 0.324 0.340 0.105 0.521
Information on industry problems 3.87 1.07 0.800 0.079 0.160 -0.033
Feedback from industry 3.41 1.19 0.721 0.220 0.080 0.081
Information on industry research 3.26 1.21 0.656 0.303 0.216 0.012
Applicability of research 3.99 1.05 0.764 0.044 -0.015 0.075
Becoming part of a network 2.94 1.21 0.625 0.288 0.016 0.064
Research income from industry 4.01 1.12 0.064 -0.001 0.831 0.178
Research income from government 3.93 1.16 0.159 0.172 0.772 -0.121
Access to materials 3.03 1.35 0.193 0.735 0.047 0.020
Access to research expertise 2.83 1.23 0.254 0.812 0.011 -0.036
Access to equipment 2.48 1.48 0.127 0.821 0.155 0.082
Rotation sums of squared loadings 2.82 2.26 1.40 1.15
Proportion of variance explained (%) 23.48 18.81 11.69 9.55
Cumulative proportion of variance explained (%) 23.48 42.29 53.98 63.53
38
Table A4: Relationship between frequency of interaction and motivations
Results of multivariate probit analysis. Dependent variables are dichotomous taking the
value of 1 if the degree of engagement is above the median for a given engagement
channel (and 0 otherwise)
Joint
Research
(3 times or more)
Contract
Research
(3 times or more)
Consulting
(3 times or more) Spin-offs
(at least once) Patents
(at least once)
Commercialisation -0.008 0.036 0.281 *** 0.288 *** 0.451 ***
(0.062) (0.062) (0.062) (0.055) (0.054)
Learning 0.219 *** 0.163 ** 0.152 * 0.086 -0.005
(0.077) (0.076) (0.080) (0.071) (0.066)
Funding Resources 0.096 0.225 *** 0.001 0.019 0.062
(0.066) (0.067) (0.066) (0.059) (0.055)
In-kind Resources 0.048 -0.137 ** -0.146 ** -0.195 *** -0.113 **
(0.058) (0.058) (0.061) (0.056) (0.050)
N. Joint publ. (ln) 0.121 0.097 0.057 0.116 0.100
(0.077) (0.077) (0.082) (0.075) (0.067)
N. Collab. Gr. (ln) 0.066 ** 0.033 0.047 0.053 ** 0.009
(0.028) (0.028) (0.030) (0.027) (0.024)
Age -0.018 ** -0.002 -0.021 *** 0.004 0.004
(0.007) (0.007) (0.008) (0.007) (0.006)
Professor status 0.372 ** 0.283 * 0.322 ** 0.201 0.124
(0.146) (0.146) (0.155) (0.139) (0.126)
Industry inc/staff (ln) 0.089 0.189 * -0.041 0.017 0.064
(0.101) (0.107) (0.109) (0.097) (0.090)
Public inc/staff (ln) -0.119 0.114 -0.117 -0.081 0.145
(0.113) (0.126) (0.117) (0.106) (0.106)
Dept. staff (ln) 0.038 0.184 -0.019 -0.032 -0.004
(0.109) (0.114) (0.114) (0.106) (0.097)
RAE 2001 Low 0.049 0.205 0.241 -0.084 0.054
(0.147) (0.149) (0.156) (0.142) (0.128)
RAE 2001 High 0.035 0.109 0.405 ** 0.091 -0.032
(0.153) (0.154) (0.164) (0.147) (0.135)
Prob. (formal interaction) -0.001 0.336 0.906 0.039 0.869 *
(0.553) (0.573) (0.656) (0.488) (0.455)
Intercept -1.753 ** -3.879 *** -1.366 -1.686 ** -3.022 ***
Reg. & Discipline dummies Included Included Included Included Included
Rho1 Rho2 Rho3 Rho4
Rho2 0.435 (0.064)
Rho3 0.349 (0.072) 0.389 (0.068)
Rho4 0.374 (0.067) 0.176 (0.075) 0.082 (0.076)
Rho5 0.257 (0.065) 0.207 (0.066) 0.086 (0.072) 0.551 (0.052)
Observations 945
LL -1894.5
LL0 -1989.8
Wald χ2(160) 433.4
Note: Two tailed t-test: * p < 0.10; ** p < 0.05; *** p < 0.01. Standard errors between brackets. All regressions
include discipline dummies.
39
