DRAFT

Regional Engagement or Knowledge Commercialization?
A Comparison of University Faculty Norms in the U.S. and Europe
Harvey Goldstein**, Edward Bergman*, and Gunther Maier*
Abstract
The expansion of universities‟ missions to include the support of regional economic
development has led to conflicts between traditional norms of open science and the norms ofentrepreneurialism, as well as placing university faculty in situations of potential conflict ofinterest. We posit that there are important differences between how universities support regional
economic development in terms of leading to normative and ethical conflicts. Using data from
two independent samples of U.S. and European faculty, we explore and compare faculty attitudes
towards regional engagement and knowledge commercialization using factor analysis. The
results show that U.S. faculty make a clear distinction between the appropriateness of university
regional engagement, on the one hand, and knowledge commercialization, on the other.
European faculty view regional engagement and knowledge commercialization along the same
spectrum in terms of appropriateness. At the same time, attitudes of faculty in the U.S. and inEurope reveal independent commitment to the norms of open science and avoidance of situationsof conflicts of interest.

Keywords: universities, academic entrepreneurship, knowledge commercialization, engagement,
open science, conflicts of interest

JEL Classification: O31, O32, O38, O17** MODUL University-Vienna* Vienna University of Economics and Business11. Introduction
The „entrepreneurial turn‟ of universities has a number of faces. Universities are now
widely perceived, and expected, to be important assets and actors in helping regions become andremain competitive in the globalized, knowledge-based economy. Indeed, the well-known,
traditional tripartite mission of public U.S. research universities, of teaching, research, and public
service, has now become a four-part mission with the addition of economic development. Whilestate legislatures may use a subtle set of sticks and carrots for universities to become engaged in
activities to promote economic development, there is also a sense of social responsibility byuniversity officials and research faculty to be engaged in economic development, in exchange forthe privileges and benefits they receive as both organizations and as individual researchers. At
the same time, almost all research universities in the US, both public and private, have been
motivated to become more involved in the commercialization of knowledge in order to diversify
research funding sources, to more generally improve their revenue picture and endowments, andto retain and attract entrepreneurially inclined faculty and graduate students (Goldstein,
Bergman, Maier 2012).
In Europe, until quite recently, the principal mission of universities was teaching, whileknowledge transfer functions and responsibility for basic science were shared between
academies of science and national research institutions. Hence most universities remained quite
distant from both deliberate applications of knowledge, including knowledge commercialization,
and a commitment to assist in the economic development of their respective regions. A numberof factors have changed that since the 1990s, including: greater autonomy and flexibility given tostate-supported universities from national ministries; a more open and competitive environment
among European universities for recruiting academic talent, securing external research funding,2and prestige; and increased awareness of the need for the EU to play „catch-up‟ by leveraging allof its knowledge assets in order to more successfully compete in the global, knowledge-based
economy.
With new missions added to universities‟ responsibilities, however, it should not be
unexpected that there will be tensions and conflicts in how universities adapt to the additionalroles in terms of resource allocations, rules and regulations, and norms of academic behavior.
This should be the case in universities on both continents, though the nature of these tensions androle conflicts would be different between the U.S. and Europe because of the different histories
and traditions of their respective higher education systems. Many policy officials view
university engagement in assisting economic development, on the one hand, and universities‟
commercialization of knowledge as a continuum of highly overlapping activities. We
hypothesize that these activities are quite distinct in terms of the institutional and individual
norms that guide attitudes and behavior within universities, and their perceived appropriateness.
Yet we also posit that these norms will differ between faculty in the U.S. and faculty in Europe,for the reasons sketched above, and hence faculty attitudes in Europe towards the appropriaterole of universities will differ from faculty attitudes in the U.S.
We hypothesize that U.S. faculty are less likely than European faculty to perceive
regional engagement and knowledge commercialization as two roles on the same spectrum.
More specifically, we expect U.S. faculty to view knowledge commercialization as a threat to theviolation of the Mertonian norms of open science (Merton 1973) and to pose greater possibilityof serious conflicts of interest, but that regional engagement is not perceived as posing suchthreats and is nearly consensually approved. In Europe, because both of the new roles ofuniversities‟ regional engagement and knowledge commercialization were added at the same3

time, faculty are less likely to perceive them as different in terms of appropriateness and lesslikely to see either as a threat to the (arguably, less strongly embedded) norms of open scienceor posing serious ethical conflicts of interest.
Using web-based attitudinal surveys of university faculty in the US and in the EU
countries, we explore the structure of attitudes towards these two faces of academic
entrepreneurship. The results will shed light on the prospects of universities being able to act
entrepreneurially and at the same time preserve a set of norms that are valuable, if not necessary,for universities to be able to continue their important fiduciary role given to them by society,seeking truth.
The next section provides a brief review of the relevant literature. In section three wedescribe the study population, data collection procedures, measures and the analytic techniques
used. Section four presents the separate results of our empirical analysis and hypothesis tests for
the U.S and European data.

We compare and contrast the results in section 5 and in section 6

we conclude the paper with the key findings and a discussion of some of their implications foruniversities continuing on the entrepreneurial path after making the „turn‟.

2. Literature review
There is by now a large extant literature on academic entrepreneurship. A comprehensive
literature review is found in Rothaermel, Agung and Jiang (2007). This literature spans both
positive and normative dimensions of universities engaging in patenting and other forms ofcommercialization, including the opportunities and threats posed by the „entrepreneurial turn‟(e.g., Etzkowitz, Webster and Terra, 2000; Bok 2003), the impacts of intellectual property laws
and regulations on university technology transfer activities (e.g., Mowery et al. 2001; Murray4

2006; Litan, Mitchell, and Reedy 2007), the productivity and effectiveness of university
technology transfer offices (Thursby and Kemp 2002; Siegel et al. 2004), and motivations for,and explanations of, entrepreneurial behaviour within the academy (e.g, Owen-Smith and Powell2001; Stuart and Ding 2006).
There is also a fairly large literature on the emergence and growth of an entrepreneurial
culture within universities, with implications for norms that govern or guide behaviour as well as
institutional policies and priorities. Etzkowitz et al. (2000) have argued that the traditional
norms governing or guiding behaviour within universities will (and should) change to adapt tothe entrepreneurial turn. Clark (1998, 2003) and Davies (2001) use a broader concept ofacademic entrepreneurship and suggest the behaviour of some universities to adapt and adjust to
an altered set of external demands and even to take advantage of new opportunities such as
greater autonomy does not necessarily imply erosion of the hallmark of institutions of higher
education as places of open and free inquiry. Yet one of the most oft-discussed potential impactsof the entrepreneurial turn is whether it has lead to an erosion of the norms of open science.
Analyzing survey data of about 700 natural scientists in Japan, Shibayama (2012) concluded thatthe norm of making „practical‟ contributions, and the norms of open science are determined
independently. In other words, they are not perceived to be inherently conflictual, leaving open
the possibility that academic entrepreneurship can be promoted by universities withoutcompromising the norms of open science. Ambos et al. (2008) consider the institutional,organizational, and individual attributes that allow university researchers to reconcile theconflicting demands (and norms) of academic research and commercialization and thus behave„ambidextrously‟.

5

While the attitudes of faculty and other university-based researchers actively involved inknowledge commercialization have been studied (e.g., Blumenthal et al.1996; Louis et al. 2001),
there have been relatively few attempts to systematically gauge the attitudes of a broad range ofuniversity faculty towards the university‟s „entrepreneurial turn‟, whether they are actuallyengaged in commercialization activities or not, with the exception of Lee (1996).
Lee surveyed faculty in 115 research universities in the US from nine different
disciplinary groupings in the natural sciences, engineering, and the social sciences. They wereasked questions about whether they approved of changes in evaluative standards of faculty
performance with respect to weighing user-oriented research and patentable inventions, andwhether they were in agreement with a variety of university roles involving industry
collaboration. The results of Lee‟s study were that: (1) a large majority of faculty respondents
were in favour of changes in the criteria for evaluating faculty performance by giving weight to„user-oriented research‟ and patentable inventions and this represented an increase from the
1980s; (2) a majority of respondents said they agreed with their universities actively participatingin local and regional development, facilitating commercialization of university-based research,and encouraging faculty to engage in consulting for private firms; but (3) a majority did notsupport their universities providing start-up assistance or make equity investments in private
firms. Lee‟s 1996 study suggests that while there is broad (and growing) acceptance of some
aspects of the „entrepreneurial turn‟, there are other activities or roles – that pose the greatest
perceived threats to the „core values of the research university‟ (Lee 1996, p. 860) – that are
opposed by a significant portion of faculty members.
In part as an alternative response to some of the same pressures that have driven
universities to become actively involved in knowledge commercialization, many universities in6the US have rededicated themselves to the ideal of „public engagement‟. With a long tradition
rooted in the land-grant colleges and universities activity of cooperative extension, perhaps best
exemplified in the „Wisconsin idea‟ (Ward 1992), the engaged university represents:“ . . . the partnership of university knowledge and resources with those of the public andprivate sectors to enrich scholarship, research, and creative activity; enhance curriculum,
teaching, and learning; prepare educated, engaged citizens; strengthen democratic values
and civic responsibility; address critical societal issues; and contribute to the public
good.” (CIC Committee on Engagement 2005)
Even within the domain of university-industry collaboration, there are activities that fallwithin the sphere of engagement rather than commercialization. These include consulting, joint
research, and training, in contrast to patenting and licensing and spin-off activities (D‟Este andPatel 2007).
University engagement has had a different history and focus in Europe. Recent
expansions of the EU call for much greater standardization of study programs, recognition ofequivalent degrees, mobility of faculty and students, and uniform practices that have benefitted
the US higher education system for many decades.i At the same time, major changes ingovernance were underway, with national university systems granting greater autonomy,
budgetary discretion, and a general shift from regulation- to performance-based management
practices (CHEPS 2006; Estermann and Nokkala, 2009). Aghion et al. (2007, pp. 6, 7) haveargued that sufficient funds should be supplied to a subset of Europe‟s research universities,
along with greater budgetary and administrative autonomy, to leverage such funding effectively;
attention to greater academic mobility and less endogamy should also be given. Aghion, et al.
7(2009) argue further that the structural changes most capable of stimulating the academic output
of all public European universities in the Shanghai Top 500 are autonomy (institution-levelbudgeting, purchasing, hiring, salary scales) and competition (research funds, faculty, students),
including the administrative procedures to implement these reforms. They argue both are needed
simultaneously and have their greatest effects in countries whose university systems operate
close to knowledge and innovation frontiers.
The decentralization of authority and policymaking has steadily shifted oversight
generally from ministerial and parliamentary to external bodies (e.g., EU-wide accreditationgroups) and greater stakeholder oversight, including local governments, civil society and theeconomy. Europe‟s steady „endogenization of universities‟ into the fabric of its social andeconomic life may be expected to increase simultaneously the number and variety of
stakeholders and the demands placed upon university academics. New stakeholders expect
academics to be responsive to needs that lie beyond the classic education and research functionsof universities. Accordingly, so-called third-tier, third-stream or „public service or engagement‟
obligations are growing in importance in the EU, such that academic units and faculties are
expected to contribute to societal problem-solving by extending their missions beyond theconfines of classical university concerns.
This extension of the European university‟s mission implies engagement in areas thatwere once the responsibility of other institutions. Traditional divisions of labor relegatedexclusively to the university faculties their principal teaching and knowledge transfer functions,plus sharing with academies of science and national research bodies a responsibility for basic
research. Most universities remained quite apart from the deliberate application of knowledge,
8which was conducted within ministries and departments or by business, although individual
faculties might exercise their „professor‟s privilege‟ in transferring their specific researchfindings to the market. University faculties are now being drawn into all these functions at
different rates and mixtures, often to permit joint and more cost-effective progress toward
economic and social objectives, but also in response to commercial opportunities (Bergman2009).
As university involvement in academic entrepreneurship has clearly increased over thelast twenty years, so have the attitudes, for and against, changed. These attitudes, we suspect are
more complex, because there are a number of different impacts generated by academic
entrepreneurship, and whose valuations vary among actors. For example, many faculty may
believe that technology-based start-ups by university staff are appropriate because they canenhance the innovativeness and hence the competitiveness of the regional economy. On theother hand, faculty may perceive that the same activities can also lead to thorny conflicts ofinterest or dilute the quality of more basic, scholarly research (Goldstein 2010).

3. Study Populations, Data Collection, Measures, and Analytic Techniques
The study population for the U.S. data set consists of faculty from six selected disciplines
from all research universities. A random sample of 71 universities stratified by public landgrant, public non-land grant, and private, was drawn from the population of research universities
in the US in the „Very high‟ and „High‟ research intensive categories.ii The resulting sample isshown in Table 1.
[Table 1 about here]

9The six disciplines are biological sciences, physics, computer science, chemical engineering,
economics, and history. These disciplines were selected based upon their: ubiquity among
research universities, variation in the approaches to inquiry and knowledge production using the
Stokes (1997) typology as adapted by Bergman (2009), and variation in the likelihood ofopportunities for faculty to produce research that has potential for commercialization.iii Withineach of the six academic departments in the 71 research universities, one tenured or tenure-track
faculty member was randomly selected from each academic rank: assistant, associate, and full
professor, plus the department chairperson. The web page of each department was used toprovide the full listing of tenured and tenure-track faculty from which the particular faculty
members were drawn for the final sample. A total of 1,611 faculty members were sent webbased questionnaires in January 2007, of which 84 were returned as undeliverable. After several
follow-ups to non-respondents, we ended up with 369 usable responses for an effective response
rate of 24.1 percent.
A set of fourteen attitudinal questions was included in the survey questionnaire. Faculty
were asked to indicate on a five point Likert-scale if they: strongly agree (coded 5), agree (4),neither agree nor disagree (3), disagree (2), or strongly disagree (1) with each statement.
The questions are intended to either directly reveal attitudes towards at least two different
faces of academic entrepreneurship – university engagement and knowledge commercialization - or to reveal underlying beliefs that are considered to affect attitudes. The beliefs include thedegree of commitment to the norms of open science, and the degree of commitment to theseparation of personal financial interest from the roles of faculty members as researchers andteachers. We briefly discuss each of the fourteen questions in terms of how we interpret the10responses in relationship to the concepts of regional engagement, knowledge commercialization,
norms of open science, and conflicts of interest.
Q1-RED. My university, in addition to its basic functions of teaching and research,should be actively and directly involved in assisting state and regional economic development.
This provides a direct way to measure acceptance of the idea that universities have a larger
societal obligation beyond the traditional missions of teaching and advancement of knowledge,
and specifically to help improve regional economic conditions by bringing to bear knowledge
and expertise. This role does not necessarily exclude knowledge commercialization as a means
to improve regional economic development, but it is broader and implies an institutional
commitment to social responsibility in return for receiving public resources.
Q2_TA. My university should encourage and reward faculty for providing technicaland/or managerial assistance to existing business organizations located in the region or state.
Providing technical or managerial assistance to existing regional businesses is a more specificmeans of universities assisting regional economic development, but compared to the broader rolein question 1, the „public good‟ dimension of this role is given up since the beneficiaries areindividual businesses.
Q3_Comm. My university should be actively involved in the commercialization ofuniversity-based academic research. This question very directly probes the respondent‟s views
that there is a legitimate mission of the modern research university is to pro-actively assist andencourage university researchers to commercialize their research when there is a potential
market.
Q4_Startup. My university should provide start-up assistance for technology-oriented
firms that grow out of university-based research. Providing assistance to start-ups that grew out
11

of university research is a direct form of knowledge commercialization and with the university
possibly having a financial interest in helping such start-ups.
Q5_Equity. My university should make equity investments in technology-oriented startup businesses that grow out of university-based research. This activity explicitly includes auniversity financial interest in the generation of technology-based start-up businesses as a formof knowledge commercialization. It places the university in the role of a venture capitalist
typically where the university‟s investments are high risks.
Q6_ProprR. My university should encourage and reward faculty to engage in useroriented, proprietary research with industry funding. This university policy is aimed atincreasing research funding from private industry sources. Seeking new and under-exploitedsources of research funding helps universities move up in the National Science Foundation
rankings and to incidentally enhance their prestige. The policy described focuses on conducting
applied research, which can be construed as a form of engagement, but it is also proprietary,which can mean restrictions on the dissemination of research results. .
Q7_Patent. My university should reward faculty who produce a patentable invention atleast the same amount of credit as a peer-reviewed article when making tenure and promotion
decisions. This is another policy aimed at giving „weight‟ to a form of knowledgecommercialization – patentable inventions – as part of the reward structure for faculty
performance and productivity. The policy enlarges the scope of what has been understood to bescholarly achievement in most research universities, so as to create incentives for junior faculty
to engage in commercialization.
Q8_PeerR. Knowledge creation is best measured by scholarly, peer-review publications.
This statement is intended to indicate the respondents‟ views on one of the norms of open
12

science, peer review. It also identifies distinctions in views about the concept of knowledge:
restricted to traditional, basic, scholarly knowledge, on the one hand, or inclusive of useroriented knowledge.
Q9_FreeEx. Unfettered inquiry and the free exchange of ideas is important in my work.
This statement also focuses on the degree of commitment to the central norms of open science.
It counterposes any restrictions being placed on what is researched and on the dissemination of
results.
Q10_Threat. The increasing emphasis within many universities for commercializing
university research threatens the quality of basic, scholarly research. This expresses a
perception that one of the major harms of commercialization within universities is a substitutionof effort and resources from basic, „scientific‟ research, to user-oriented research, and leading toa long-term loss in the production of cutting-edge scholarship.
Q11_Consult. A full-time faculty member on average spends more than one day per
week consulting. This scenario poses a situation where the respondent may feel there is a
conflict-of interest introduced between personal financial interest – expressed as spending morethan the „customary‟ time for consulting -- and meeting the teaching, research, service, and otherobligations of a faculty member and university employee.
Q12_Delay. It is legitimate for scholarly findings are delayed for circulation and peer
review for six months in order to benefit the private industry funding source. This statement is
specifically about restrictions on dissemination of research results, and generally about the
respondent‟s degree of commitment to the norms of open science.
Q13_SCoI. A faculty member supervises a graduate student’s dissertation researchwhen the research is funded by a private company and the faculty member has a financial
13

interest in that company. The scenario poses another possible conflict-of-interest situation,
between the financial interest of the faculty member and the role of mentor and supervisor for the
training of future scholars.
Q14_FCoI. A faculty member has a research contract with a company in which thefaculty member has a financial interest. This is a variant of the scenario in Q13. Since it does
not involve the role of supervising graduate students, however, it focuses just on the potential
harm of the faculty member slanting, and/or restricting dissemination, of his/her own research
results when there is a financial interest involved.
The faculty sample for the EU data set is drawn from all universities in the top 500
Shanghai rankings for 19 EU countries (except in Austria and Switzerland where all universities
were included). Because European universities tend not be not structured with the sameprofessorial ranks found in the U.S., the sampling procedure within the respective
departments/institutes was also slightly different from that used in the U.S. Here the director ofthe department/institute was selected for the sample along with two additional faculty listed on
the webpage. The responses to the questionnaire also allowed us to know whether the
respondent had a permanent contract (equivalent to full or associate professor in U.S.
universities) or a time-specified contract (equivalent to an assistant professor). Questionnaires
were sent in winter of 2009. There were 1,798 valid responses representing an 18 percent
response rate. Response rates did not vary significantly by discipline, by they did vary bycountry, from lows of 12 to 14 percent (Czech, Spain, UK) to highs of 27-30 percent (Finland,
Slovenia, Italy). The number and distribution of EU faculty respondents by country is shown in
Table 2.
(Table 2 about here)
14

The questions soliciting attitudes about university regional engagement, knowledgecommercialization, the norms of open science, and the threat of conflicts of interest fromacademic entrepreneurship were the same as on the questionnaire administered to the U.S.
faculty sample, with the exception that two of the questions were not included in thequestionnaire administered to EU faculty.iv
Descriptive statistics for the results of both the U.S. and EU responses have been reported
in Goldstein, Bergman, and Maier (2012), as have the results of estimated ordered logit models
to explain the variation in attitudes towards university activities of regional engagement andknowledge commercialization. In this paper we employ and report on the application of factor
analysis to explore the underlying relationships of attitudes among the different faces ofacademic entrepreneurship and how these attitudes are related to belief in the norms of open
science and perception that academic entrepreneurship poses conflicts of interest for university
faculty. Factor analyses with a varimax rotation are performed separately on the U.S and EU
attitudinal variables described above. For factors with eigenvalues 1.0 or greater, we thenestimate average factor scores on categories of faculty respondents by academic discipline, by
ranking of university, and by regional economic condition.

4. Factor analysis of attitudes towards the multiple faces of academic entrepreneurship
To examine the relationships among attitudes towards the different dimensions ofacademic entrepreneurship we employ factor analysis. Factor analysis allows us to view theunderlying multivariate structure of correlations among the variables by conveying the variationfrom the original set of variables into a more parsimonious set of new variables, or factors. The
original variables entered in the analysis are the responses to the fourteen questions listed above
15

(twelve questions in the case of the EU data). We specifically are interested in what therelationship is between faculty attitudes towards university engagement, on the one hand, andknowledge commercialization on the other. But we also are able to investigate to what extent thenorms of open science and perception of ethical conflicts of interest are associated withcommercialization.
The questions that reveal attitudes towards university engagement are Q1_RED and
Q2_TA; questions that reveal attitudes towards knowledge commercialization are Q3_Comm,
Q4_Startup, Q5_Equity, Q7_Patent and Q10_Threat; questions that reveal attitudes towards thenorms of open science are Q6_Propr, Q8_PeerR, Q9_FreeEx and Q12_Delay; and questions thatreveal attitudes towards potential conflicts of interest are Q11_Consult, Q13_SCoI, and
Q14_FCoI. Our working hypotheses are that (i) attitudes towards university engagement andknowledge commercialization are largely independent; (ii) adherence to the norms of open
science will be inconsistent with positive attitudes towards knowledge commercialization; (iii)
negative attitudes towards knowledge commercialization are associated with disapproval of
behaviour that potentially poses conflicts of interest among roles for faculty members.
After using a varimax rotation, the factor loadings for the U.S. data are shown in Table 3
and for the EU data in Table 4 (the full correlation matrices are shown in Appendix Tables 1 and
2).v(Tables 3 and 4 about here)

The results for the U.S. show there are five distinct factors that have eigenvalues above
the threshold of 1.0, collectively explaining 67.1 percent of the total variation in the data. Theinterpretation of the factors based upon the loadings are the following:16Factor 1, with high positive loadings on Q3_Comm, Q4_Startup, and Q5_Equity (andsomewhat less on Q6_Propr) and a negative loading on Q10_Threat, establishes broad
agreement concerning the appropriateness of knowledge commercialization.
Factor 2, with high positive loadings on Q1_RED, Q2_TA, Q7_Patent, and (somewhatless) Q6_Propr, finds university engagement, broadly understood, to be appropriate.

It

combines approval of faculty who conduct user-oriented proprietary research and receive
reward and credit from their university for the development of inventions (patenting) withefforts of the university to assist regional economic development and provide technical ormanagerial assistance to regional firms.
Factor 3, combining Q13_SCoI and Q14FCoI, reveals disapproval of situations in which
private research funding might have a corrupting influence on a faculty member‟sobligations to mentoring students and to conduct and produce „objective‟ research.
Factor 4, with high positive loadings on Q8_PeerR and Q9_FreeEx, indicates agreementwith the norms of open science.
Factor 5, loading positively on Q11_Consult and Q12_Delay, indicates the recognition
that conflicts of interest may arise between, on the one hand, being a university employee
and a member of a wider scholarly community, and on the other, acting legitimately as a
private entrepreneur.
The results of the analysis for the EU data show three factors with eigenvalues at or above
the threshold of 1.0, collectively taking into account 55.5 percent of the total variation.
Factor 1 is interpreted as agreement of the appropriateness of both university regional
engagement (Q1_RED and Q2_TA) and knowledge commercialization (Q3_Comm,17

Q4_Startup, Q5_Equity, Q10_Threat) along with applied research (Q6_PropR andQ7_Patent).
Factor 2, with positive loadings on Q11_Consult, Q12_Delay, and Q13_StdCoI, indicatesattitudes about the appropriateness of situations in which there are actual or potential
conflicts of interest between faculty members responsibilities to their universities andtheir wider scholarly communities, and their personal financial and entrepreneurialinterests.
Factor 3 is a singular factor (Q8_PeerR) that focuses on positive attitudes towards one of
the norms of open science, of the importance of increasing the validity of research results
by subjecting them to peer review.
Factor scores are the normalized values for the new variables (factors) for each observation.vi
The mean factor scores for specific categories of faculty respondents are shown in Tables 5-7(U.S. data) and Tables 8-11 (EU data). The factor scores allow us to identify which categories of
respondents are likely to hold maximal approval or disapproval views on each of the respective
factors.
For the U.S. faculty, there are clear and statistically significant differences in mean scoresamong the disciplines on all of the five factors, among categories of university rankings on factor
2 (only), and among categories of regional economic condition only on factor 3.
Faculty in computer science are much more likely to approve of knowledgecommercialization, and faculty in history are much more likely to disapprove,compared to the other disciplines;
Differences in attitudes towards universities being involved in regional engagement,
across disciplines are less significant than for other factors, in the sense it is closer to18

being accepted consensually; it is interesting to point out that economists -- who
potentially could contribute the most -- also disapprove of regional engagement themost, by far, among the six disciplines;
Conflicts of interest situations are least likely, by far, to be accepted by faculty inhistory, while such conflicts tend to be most acceptable to faculty in engineering andto a lesser extent in physics and computer science;
Commitment to the norms of open science is strongest in history and biology, andweakest in economics and computer science;
Conflicts of interest between faculty as university employees and entrepreneurs(factor 5) are most acceptable to those in biology and physics, and least acceptable tofaculty in economics and history.
There are significant differences only on factor 2 (university regional engagement)
across categories of university rankings
There is no significant variation in faculty attitudes towards either knowledgecommercialization or regional engagement across types of regional economic
conditions;
Attitudes towards conflict-of-interest situations involving company-funded researchfrom companies in which the faculty member has a financial interest (factor 3) aresignificantly different across regional economic conditions; such situations areapproved much less by faculty in universities located in low UE regions compared toother regions;Though the overall differences across categories are not significant, the strength ofcommitment to the norms of open science is highest in regions of lowest
19unemployment, while acceptance of being in situations of conflicts of interest
between a faculty member‟s obligations to his/her university and scholarly
community, and his/her private entrepreneurial interest is significantly lower inregions with the lowest UE rates.
[Tables 5, 6, and 7 about here]
For EU faculty, there is significant variation in the scores for all three factors acrossacademic disciplines and for factors 1 and 2 across university ranking categories. What is mostclear, though, is the absence of variation in scores on any of the factors by regional economic
condition. Specifically,
Faculty in engineering and computer science, predictably, have the highest approvalfor knowledge commercialization with regional engagement (factor 1) while facultyin history have the lowest;
Faculty in engineering and economics have the highest acceptance of being insituations of conflicts of interest involving academic entrepreneurship (factor 2) whilefaculty from history have the lowest;
Faculty from chemical engineering and biology have the greatest commitment to thenorm of peer review as a dimension of open science (factor 3) while faculty from
history have the lowest; at first the result for historians might seem counterintuitive,
but it is consistent with a tradition of many European historians not regarding theirdiscipline as a scientific one, and hence the importance of peer review is not asembedded in disciplinary practice.
The higher the university ranking, the less approving are faculty of knowledgecommercialization/regional engagement;
20

The higher the university ranking, the lower the approval of situations of conflict ofinterest.
[Tables 8, 9, and 10 about here]

5. Comparing U.S. and EU Results
The results show a number of expected similarities in the structure of attitudes towards
academic entrepreneurship, but also some rather important differences. First, and perhaps mostsalient of all, the appropriateness of knowledge commercialization and regional engagement are
viewed as distinctly different by U.S. respondents, but EU faculty do not differentiate these
activities in terms of their approval (or not). Rather, EU faculty view them as activities on thesame spectrum, with a certain amount of blending. This differentiation between the U.S. and EU
respondents is understandable from the point of view that in the U.S. there has been a history andtradition of university engagement in regional development that easily predates knowledgecommercialization, whereas in the EU these two activities were adopted at about the same timeand for many of the same reasons.
Second, attitudes towards situations that pose potential conflict-of-interest and theviolation of norms of open science are differentiated from attitudes towards knowledgecommercialization in both U.S. and EU results. The implication here is that faculty cansimultaneously have both positive attitudes towards knowledge commercialization and regard
situations that can potentially occur with commercialization as inappropriate as well as maintaincommitment to the norms of open science. We interpret this as a feeling that, “Yes these (bad)things (from knowledge commercialization) could potentially happen, but I (or my colleagues)
would not allow those to occur.”21

There are differences here, however, between the U.S. and EU results. For EU
respondents, concerns with potential conflicts-of-interest are resolved in one factor. For U.S.
respondents, however, attitudes towards conflict-of-interest are more subtly differentiated: one
factor is focused focus on the potential threat of research results and student advising being
tainted (corrupted) by a faculty member having financial ties to private research sources, while
another involves the potential conflict between acting entrepreneurially and obligations to one‟s
employer and to one‟s community of scholars.
Third, the „scores‟ on all of the factors vary significantly across academic discipline, inboth the U.S. and EU cases. Generally speaking, faculty for both the U.S. and the EU in the
Pasteur disciplines – computer science and engineering – were more approving of knowledgecommercialization, while faculty in the humanities (history) were the least approving. Whether
that stemmed from ideology or from less opportunity personally to engage in commercialization
cannot be discerned from the results.
Fourth, regional economic conditions do not matter much in accounting for variations inattitudes towards academic entrepreneurship on either side of the ocean. We would expect, otherthings equal, that faculty in universities located in regions with greater levels of economic
distress would be more inclined to especially accept university regional engagement, but also
knowledge commercialization along with greater tolerance for situations posing conflicts-ofinterest and violations of the norms of open science. But only in the U.S. on factor 3 – conflictof interest situations involving receiving research funding from a company in which the faculty
member had a personal financial interest – are there significant differences among types ofregions by economic well-being.

22

Fifth, in setting out to compare faculty attitudes in the U.S. with those in EU research
universities, we have implicitly treated the EU as relatively homogeneous in terms of national
systems of higher education, Yet we are aware that within the EU bloc there are importantdifferences in the historical and institutional roles of universities across countries, Accordingly,
we have examined whether factor scores on the three significant factors vary among different
groups of relatively homogeneous countries of the EU.
Using four macro-regions (Nordic countries, countries bordering the Mediterranean, theten countries of the former Communist bloc (the EU-10), and the remaining EU „core‟) analysis
of variance tests show significance differences in the mean factor scores among the four macroregions on all three factors (see Table 11). Faculty in universities located in the countries
bordering the Mediterranean and to a lesser extent the former Communist bloc countries weremore approving of knowledge commercialization cum engagement and less concerned bypotential conflicts of interest involving commercialization compared to faculty in the Nordic
countries or in the EU core. This pattern correlates with the widely acknowledged North-Southand West-East divides in terms of socioeconomic development within Europe and suggests thatthe imperatives of „catch-up‟ at the national level outweigh traditional views of the priorities
among university missions. That faculty in the Nordic and Mediterranean countries havesignificantly stronger agreement with the importance of peer review as a norm of open science,compared to faculty in the EU-10 (the EU core mean is close to zero), suggests to us that thedegree of adherence to the norms of open science may be more related to variation in recent
national investments and upgrading of the quality of research universities than to the overalllevel of national economic development. This is only speculative, however, and deserves further
empirical investigation. As we had suspected, an analysis of variance test for differences in the23

factor scores across the four Census regions of the U.S. showed no significant differences on anyof the five factors, indicating a high degree of national integration of systems of higher education
within the U.S., despite that a majority of universities are funded and regulated by the individual
50 states.
[Table 11 about here]
6. So What (Does it Mean)?
We have analyzed the relationships among attitudes towards multiple dimensions ofacademic entrepreneurship held by faculty in US and European universities. Both knowledgecommercialization and „engagement are „on the rise‟; they each represent ways that universities
can demonstrate their responsiveness to external demands, as well as ways that the institutions
can enhance their revenue (in the case of commercialization) and their attractiveness toentrepreneurially-minded faculty and graduate students who seek non-academic outlets for theirscholarly and professional work. But still there are important differences. While university
engagement has a long and generally noble tradition in the U.S., going back to the Morrill Act of1865 that initiated the establishment of land-grant universities, knowledge commercialization isrelatively new and poses (to many) a putative threat to the widely-held norms of open scienceand involves situations rife with conflicts of interest. In EU universities, regional engagement
has not been a traditional activity of universities, and this helps to explain why faculty
respondents do not separate or make a clear distinction between engagement andcommercialization.
Somewhat surprising to us is that the attitude towards university engagement appears not
to be motivated primarily as commitment to work for the common or public good, but rather toengage for engagement‟s sake, to extend the beneficiaries of their knowledge transmission and24

expertise beyond the ivory tower. In the case of the U.S., the evidence for this comes from thehigh loading of favourable attitudes towards conducting user-oriented, proprietary research andwith patenting on factor 2, the regional engagement factor. In the EU case, it is the combining ofregional engagement with commercialization along with patenting.
The results shed some new light on why faculty may approve or disapprove of different
dimensions of academic entrepreneurship. Attitudes towards knowledge commercialization arebound up with one‟s degree of commitment to the norms of open science and to the risks one
perceives of conflicts of interest, but the evidence suggests that these risks are not considered
likely by those who have favourable views towards commercialization. Otherwise it is difficult
to explain how one can approve of commercialization and strongly adhere to the norms of open
science, and disapprove of situations posing conflicts of interest.
Overall, the results reinforce the importance of universities in both the U.S. and inEurope, as they proceed further down the path of entrepreneurialism, of being able to safeguard
long-cherished norms of open science and taking steps to enforce conflict of interest policies, notrelaxing them.

25References
Aghion P, Dewatripont M, Hoxby C, Mas-Colell A, Sapir A (2007), „Why reform Europe‟suniversities?‟ Bruegelpolicybrief 4, 1–8
Aghion P, Dewatripont M, Hoxby C, Mas-Colell A, Sapir A (2009) The governance andperformance of research universities: Evidence from Europe and the US. Working paper 14851,
National Bureau of Economic Research, Cambridge, MA
Ambos, T.C., K.Makela, J. Birkinshaw, and P. D‟Este (2008), „When does university research
get commercialized? Creating ambidexterity in research institutions, Journal of Management
Studies 45, 1424-1447.
Bergman, E.M. (2009), „Marshall‟s dilemma: intangible assets and European universities‟,
IAREG Working Paper 1.3.e, p. 29.
_310 311..33
Bergman, E.M. (2010). “Knowledge links between European universities and firms:
A review”, Papers in Regional Science 89 (2), 311-333.P in Regional Science
Blumenthal, D., E.G. Campbell, N. Causino, and K.S. Louis (1996), „Participation of life-sciencefaculty in research relationships with industry,‟ New England Journal of Medicine 335, 17341739.

Bok, D. (2003), Universities in the Marketplace: The Commercialization of Higher Education.
Princeton, US: Princeton University Press.

Carnegie Foundation for the Advancement of Teaching (2006)
www.carnegiefoundation.org/classification
CHEPS (2006), „The extent and impact of higher education governance reform across Europe:
Part four, governance reform survey results,‟ Center for Higher Education Policy Studies,
University of Twente.
26

CIC Committee on Engagement (2005). Resource Guide and Recommendations for Defining andBenchmarking Engagement. Champaign, US: Committee on Institutional Cooperation.
Clark, B. (1998), „The entrepreneurial university: demand and response,” Tertiary Education andManagement 4 (1), 5-16.
Clark, B. (2001), „The entrepreneurial university: new foundations for collegiality, autonomy,
and achievement,” Higher Education Management 13 (2), 9-24.
Davies, J.L. (2001), „The emergence of entrepreneurial cultures in European universities,‟
Higher Education Management 13(2), 25-43.
D‟Este, P. and P. Patel (2007), “University-industry linkages in the UK: what are the factors
underlying the variety of interactions with industry?” Research Policy 36, 1295-1313.

Estermann T, Nokkala T (2009) University autonomy in Europe I: Exploratory study. Brussels
European University Association, Brussels
Etzkowitz, H., A. Webster, C. Gebhardt and B.R.C. Terra (2000), „The future of the university
and the university of the future: evolution of ivory tower to entrepreneurial paradigm,‟ Research
Policy 29, 313-330.
Goldstein, H.A. (2010), „The „entrepreneurial turn‟ and regional economic development missionof universities,‟ Annals of Regional Science 43(1), 83-109.
Goldstein, H.A., E. Bergman, and G. Maier (2012), „University mission creep? Comparing EU
and US faculty views of university involvement in regional economic development andcommercialization‟, Annals of Regional Science (available on-line).

27

Lee, Y.S. (1996), „Technology transfer and the research university: a search for the boundaries ofuniversity-industry collaboration,‟ Research Policy 25, 843-863.
Litan, R.E., L. Mitchell, and E.J. Reedy (2007), „Commercializing university innovations: abetter way‟, NBER Working Paper (April), Cambridge, US: National Bureau of Economic
Research.

Louis, K.S., L.M. Jones, M.S. Anderson, D. Blumenthal, and E.G. Campbell (2001),„Entrepreneurship, secrecy, and productivity: a comparison of clinical and non-clinical life
sciences faculty‟, Journal of Technology Transfer 26, 233-245.

Merton, R.K. (1973). The Sociology of Science. Chicago, US: University of Chicago Press.
Mowery, D., R. Nelson, B. Sampat, and A. Ziedonis (2001), „The growth of patenting andlicensing by U.S. universities: an assessment of the effects of the Bayh-Dole Act of 1980‟,
Research Policy 30, 99-119.
Murray, F. (2006), „The oncomouse that roared: resistance and accommodation to patenting inacademic science‟, Unpublished manuscript, MIT Sloan School of Management, February.
Owen-Smith, J. and W.W. Powell (2001), „Careers and contradictions: faculty responses to thetransformation of knowledge and its uses in the life sciences‟, Research in the Sociology of Work10, 109-140.
Rothaermel, F.T., S.D. Agung and L. Jiang (2007), „University entrepreneurship: a taxonomy of
the literature‟, Industrial and Corporate Change 16 (4), 691-791.
Shibayama, S. (2012), „Conflict between entrepreneurship and open science, and the transition ofscientific norms‟, Journal of Technology Transfer 37 (4), 508-531.

28

Siegel, D. S., D.A. Waldman, L.E. Atwater and A.N. Link (2004), „Toward a model of theeffective transfer of scientific knowledge from academicians to practitioners: qualitative
evidence from the commercialization of university technologies‟, Journal of Engineering &Technology Management 21 (1-2), 115-142.
Stokes, D.E. (1997), Pasteur’s Quadrant: Basic Science and Technological Innovation.
Washington, DC: Brookings Institution Press.
Stuart, T. and W.W. Ding (2006), „When do scientists become entrepreneurs? The social
structural antecedents of commercial activity in the academic life sciences‟, American Journal of
Sociology 112 (1), 97-144.
Thursby, J.G. and S. Kemp (2002), „Growth and productive efficiency of university intellectual
property licensing‟, Research Policy 31, 109-124.
Ward D. (1992), „Serving the State: the Wisconsin idea revisited‟, Educational Record (Spring):
12-17.

29

Table 1: Sample of Research Universities: U.S.
Research Intensity*

Public Land-Grant

Public Non Land Grant

PrivateTotal

Very high

13

13

16

42

High

7

14

8

29

Total

20

27

24

71

*Based upon Carnegie Foundation for the Advancement of Teaching (2006), Classification of Institutions of Higher Education

Table 2: Distribution of EU Faculty Respondents by Country
Country

Frequency (%)

Country

Frequency (%)

Austria *118 (6.6)

Hungary26 (1.5)

Belgium

56 (3.1)

Ireland

26 (1.5)

Switzerland*

125 (7.0)

Italy

117 (6.5)

Czech Republic

12 (0.7)

Netherlands

161 (9.0)

Germany

514 (28.6)

Poland21 (1.2)

Spain

62 (3.5)

Portugal

16 (0.9)

Finland33 (1.8)

Sweden49 (2.7)

France138 (7.7)

Slovenia

8 (0.4)

Greece16 (0.9)

U.K.

*Oversampled to include all universities30

229 (12.7)

Table 5: Factor Scores by Academic Discipline
U.S. Data
Discipline

FactorScore 1***

FactorScore 2*

FactorScore 3***

FactorScore 4***

FactorScore 5***BiologyN = 66

Mean(Std dev).181(0.964).778(0.977)-.092(1.028).195(0.847)-.352(0.859)Comp Sci
N = 57

Mean(Std dev).526(1.15).140(.988).163(1.06)-.216(1.028).077(1.04)

Economics
N = 62

Mean(Std dev)-.052(.818)-.317(1.18).103(.924)-.354(1.18).195(1.07)Engineering Mean
N = 51(Std dev).084(1.04).035(.959).400(1.10).016(.958).118(.977)

History
N = 76

Mean(Std dev)-.611(.750)-.062(.886)-.539(.798).210(.896).194(.867)

Physics
N = 57

Mean(Std dev).060(.936).164(.959).192(.864).082(.983)-.245(1.10)***Significant @0.01*Significant @0.10

31

Table 6: Factor Scores by University Ranking
U.S. Data
University Shanghai
Ranking

Factor Score
1

Factor Score
2***

Factor Score
3

Factor Score
4

Factor Score
51-50
N = 102

Mean(Std dev).098(1.02)-.512(1.02)-.070(.947)-.040(1.11)-.079(.995)

51-200N = 89Mean(Std dev).000(1.00).142(.901)-.037(1.04).126(.906)-.010(1.06)

201-400
N = 95

Mean(Std dev)-.169(.970).160(.942).108(.980).028(1.03)-.006(.941)401 +N = 83

Mean(Std dev).072(1.00).294(.918).002(1.04)-.117(.910).114(1.01)*** Significant @ 0.01

Table 7: Factor Scores by Regional UE Rate
U.S. Data
Regional UE Rate
Category

Factor Score
1

Factor Score
2

Factor Score
3*

Factor Score
4

Factor Score
5

Very low
N = 70

Mean(Std dev).018(.953).181(.908)-.266(.976).115(.909)-.105(1.01)Low
N = 124

Mean(Std dev).010(1.07)-.088(1.12).113(1.02)-.134(1.16).017(.997)

High
N = 113

Mean(Std dev).022(1.01)-.059(.931)-.014(.959).068(.933)-.035(.953)

Very highN = 62

Mean(Std dev)-.081(.903).080(.962).099(1.03).013(.849).148(1.08)*Significant @ 0.10

32

Table 3: Rotated Component Matrix
U.S. Data

Component
1

2

3

4

5

Q1_RED

.192

.750

.162

.151

-.015

Q2_TA

,291

,775

,131

,044

,095

Q3_Comm

.707

.310

.253

-.029

-.076

Q4_Startup

.848

.201

.113

.013

.119

Q5_Equity

.845

.130

-.013

-.002

.152

Q6_ProprR

.435

.540

.164

-.138

.047

Q7_Patent

.077

.700

-.029

-.284

.086

Q8_PeerR

.058

-.253

.068

.798

-.062

Q9_FreeEx

-.125

.182

-.214

.729

-.101

Q10_Threat

-.525

-.200

-.341

.344

.271

Q11_Consult

.064

.091

-.001

-.099

.858

Q12_Delay

.077

.062

.437

-.059

.540

Q13_StdCoI

.086

.147

.867

-.056

.047

Q14_FCoI

.185

.087

.803

-.057

.087

Extraction method: principal component analysis
Rotation method: Varimax with Kaiser normalization

33

Table 4: Rotated Component Matrix
EU Data

Variable

Component
1

2

3

Q1_RED

.697

.211

-.024

Q4_Startup

.756

.035

.069

Q5_Equity

.653

-.013

,215

Q6_ProprR

.740

.289

-.093

Q2_TA

.742

.252

-.129

Q3_Comm

.735

.176

.090

Q7_Patent

.591

.127

-.291

Q8_PeerR

-.010

-.030

.938

Q10_Threat

-.584

-.213

.177

Q11_Consult

.091

.733

-.083

Q12_Delay

.203

.642

-.038

Q13_StdCOI

.149

.674

.045

Extraction method: Principal component analysis
Rotation method: Varimax with Kaiser Normalization

34

Table 8: Factor Scores by Academic Discipline
EU Data
Discipline

Factor Score 1***

Factor Score 2***

Factor Score 3***

BiologyN = 413

Mean(Std dev)-.030(.989)-.009(.954).159(.916)

Comp ScienceN = 302

Mean(Std dev).185(1.00).076(1.01).029(1.01)

Economics
N = 213

Mean(Std dev)-.072(.984)

.184
(.97)

.100
(1.07)

Chem Engr
N = 59Mean(Std dev).253(.991).461(1.09).227(.957)

History
N = 192

Mean(Std dev)-.269(.988)-.322(.957)-.503(1.11)

Physics
N = 474

Mean(Std dev).017(.994)-.049(1.01).061(.920)***Significant @ 0.01

35

Table 9: Factor Scores by Shanghai Rankings
EU Data
University Shanghai
Ranking Category

Factor Score 1***

Factor Score 2***

Factor Score 3

1-50
N = 154

Mean(Std dev)-.130(1.06)-.127(1.03).126(.928)

51-200N = 651

Mean(Std dev)-.089(.984)-.087(.995)-.039(1.05)

201-400
N = 540

Mean(Std dev).071(1.02).086(.997)-.020(.998)401 +N = 307

Mean(Std dev).129(.942).097(.981).054(.927)***Significant @ 0.01

Table 10: Factor Scores by Regional UE Rate Category
EU Data
Regional UE Category

Factor Score 1

Factor Score 2

Factor Score 3

Very Low UE
N = 51

Mean(Std dev).067(.862)-.005(1.02).077(1.16)

Low UEN = 972

Mean(Std dev).021(.991).001(.987).042(.978)High UEN = 405

Mean(Std dev)-.079(1.06)-.008(1.01)-.083(1.04)

Very High UE
N = 224

Mean(Std dev).038(.956).011(1.03)-.050(.970)

36

Table 11 Factor Scores by EU Macro-Region
EU Macro-Region

Factor Score 1***

Factor Score 2***

Factor Score 3***

EU CoreN = 1129

Mean(Std dev)-0.059(0.994)-0.083(0.985)0.044(1.03)

Mediterranean
N = 323

Mean(Std dev)

0.223(1.02)

0.269(1.03)

0.126(0.888)

Nordic
N = 145

Mean(Std dev)-0.081(0.949)-0.100(0.973)0.136(0.928)EU-10
N = 55

Mean(Std dev)

0.106(0.940)0.380(0.869)-0.191(0.991)*** Significant @ 0.01

37

Appendix Table 1: Bivariate Correlations, U.S. Data

Q1
RED

Q1
RED

Q2
TA

Q3
Com

Q4
Start

Q5
EquityQ6
ProprR

Q7
Patent

Q8
PeerR

Q9
FreeEx

Q10
Threat

Q11

Q12
Delay

Q13
StCOI

Q14
FacCOI

1.000

Pr
Q2
TA

.616

Pr

0.00

Q3
Comm

.385

.424

Pr
Q4
Startup

0.00

0.00

.374

.467

.599

Pr

0.00

0.00

0.00

Q5
Equity

.288

.370

.520

.677

Pr

0.00

0.00

0.00

0.00

Q6
ProprR

.358

.511

.500

.412

.422

Pr

0.00

0.00

0.00

0.00

0.00

Q7
Patent

.290

.376

.292

.234

.241

.357

1.000

1.000

1.000

1.000

1.000

1.000

Pr

0.00

0.00

0.00

0.00

0.00

0.00

Q8
PeerR

-0.04

-.099

0.044

0.025

-0.011

-.086

-.212

1.000

Pr

0.348

0.021

0.302

0.566

0.806

0.044

0.00

Q9
Free Ex

0.031

-0.06

-.140

-.148

-.091

-.145

-.085

.229

1.000

Pr

0.48

0.17

0.001

0.001

0.04

0.001

0.05

0.00

Q10
Threat

-.281

-.313

-.539

-.413

-.384

-.419

-.225

.137

.230

1.000

Pr

0.00

0.00

0.00

0.00

.00

0.00

0.00

0.001

0.00

Q11
Consult

0.074

.166

0.066

.121

.160

.128

.104

-.173

-0.054-0.027

Pr

0.09

0.00

0.13

0.005

0.00

0.003

0.02

0.00

0.22

0.53

Q12
Delay

.194

.206

.254

.231

.151

.285

.127

-0.04

-.220

-.191

.221

Pr

0.00

0.00

0.00

0.00

0.001

0.00

0.004

0.36

0.00

0.00

0.00

Q13
StdCOI

.189

.233

.310

.221

.180

.295

.108

-0.039

-.193

-.328

.113

.352

Pr

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.38

0.00

0.00

0.009

0.00

Q14
FacCOI

.215

.242

.320

.294

.271

.274

.124

-0.033

-.163

-.348

.129

.282

.636

Pr

0.00

0.00

0.00

0.00

0.00

0.00

0.004

0.45

0.00

0.00

0.003

0.00

0.00

38

1.000

1.000

1.000

1.000

39

Appendix Table 2: Bivariate Correlations, EU Data
Q1
RED
Q1
RED
Pr
Q2
TA
Pr
Q3
Comm
Pr
Q4
Startup

Q2
TA

Q3
Comm

Q4
Startup

Q5
EquityQ6
ProprR

Q7
Patent

Q8
PeerR

Q10
ThreatQ11
Consult

Q12
Delay

Q13
StCOI

1.000

.539

1.000

0.00
.441

.514

1.000

0.00

0.00

.490

.496

.479

Pr

0.00

0.00

0.00

Q5
Equity

.389

.349

.403

.438

Pr

0.00

0.00

0.00

0.00

Q6
ProprR

.489

.690

.527

.495

.399

1.000

1.000

1.000

Pr

0.00

0.00

0.00

0.00

0.00

Q7
Patent

.375

.434

.377

.355

.302

.423

1.000

Pr

0.00

0.00

0.00

0.00

0.00

0.00

Q8
PeerR

-.036

-.072

.036

-.012

.052

-.069

-.137

Pr

0.14

0.03

0.143

0.63

0.03

0.00

0.00

Q10
Threat

-.404

-.414

-.484

-.340

-.239

-.453

-.314

-.082

Pr

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.001

Q11
Consult

.226

.282

.195

.120

.102

.265

.170

-.078

-.184

Pr

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.001

0.00

Q12
Delay

.273

.255

.253

.196

.168

.277

.252

-.069

-.224

.269

1.000

1.000

1.000

1.000

Pr

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.004

0.00

0.00

Q13
StdCOI

.192

.221

.216

.194

.135

.288

.160

-.052

-.201

.252

.249

Pr

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.032

0.00

0.00

0.00

40

1.000

NOTESi

The following discussion of EU faculty engagement draws upon Bergman (2010).

ii

Public, land-grant universities are a subset of public universities originally created by legislation of the U.S.
Congress (Morill Acts of 1862 and 1890). They historically had the special mission of teaching the ‘practicalsubjects of agriculture, applied science, and engineering, and specializing in research related to the needs of thestates’ agricultural and industrial sectors. In general there is designated one land grant university per state.
The Carnegie Foundation for the Advancement of Teaching has a widely used classification system of all
institutions of higher education in the U.S. Their latest classification has three categories of doctoral-granting
universities. Doctoral-granting are those that awarded a minimum of 20 doctorate degrees in 2003-04. Within‘Doctoral granting’ are three sub-categories: Research university/Very high intensity (RU/VH); Research
university/High intensity (RU/H); and doctoral granting (DRU). Assignment of an institution to one of these subcategories is based upon a set of multiple indicators of the amount of research activity that occurs within theinstitution. Based upon the Carnegie classification in 2006, there were 96 RU/VH institutions, 103 RU/H
institutions, and 87 DRU institutions.
iii

Biology and physics fit within Stokes’ ‘Bohr disciplines’, and computer science and chemical engineering are‘Pasteur disciplines’. Extending Stokes’ typology, economics and political science can be considered ‘Northdisciplines’, as they provide the theoretical and conceptual backing for institutional arrangements (Bergman 2009).
English and history are not considered scientific disciplines.
iv

The two questions not included on the EU faculty instrument were: “Do you agree or disagree with the following
statement, unfettered inquiry and the free exchange if ideas is important in my work”, and “To what extent do you
agree or disagree that the following is appropriate: a faculty member has a research contract with a company inwhich the faculty member has a financial interest.”v

We conducted factor analyses using equamax, and quartimax rotations to test the robustness of the results to the
choice of rotation algorithms. The differences in results were negligible. We also conducted the analysis with datafrom only the Pasteur and Bohr disciplines, and the results were substantially different.
vi

Because factors are linear combinations of original variables with different scales, factor scores are normalizedwith means = 0.0 and the standard deviations = 1.041