Global COE Hi-Stat Discussion Paper Series 189

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

Impact of University Intellectual Property Policy on
the Performance ofUniversity-Industry Research Collaboration

Hi-Stat Discussion Paper

Hiroyuki Okamuro
Junichi Nishimura

May 2011

Hi-Stat
Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan
http://gcoe.ier.hit-u.ac.jp

Impact of University Intellectual Property Policy on the Performance ofUniversity-Industry Research Collaboration
Hiroyuki Okamuroa* and Junichi Nishimurab
Abstract
Despite various expected advantages, university-industry research collaboration (UIC), a
relationship between two different worlds, often faces serious difficulties. Thus, theperformance of UIC depends on the research partners’ strategies to bridge the gaps between
them according to the institutional environment. In Japan, UIC has developed rapidly since
the late 1990s based on drastic institutional changes regarding universities. We pay special
attention to the role of the university intellectual property (IP) policy introduced after 2003
and empirically examine its impact on the performance of UIC projects. A clear andequitable IP policy that can be applied flexibly to the needs of partners would be optimal fora UIC to be efficiently managed. Otherwise, the project might face serious conflicts ofinterests and low incentive for cooperation. Using a sample of Japanese firms from ouroriginal survey, we find that the IP policy of partner universities indeed has a positive andsignificant impact on various performances of UIC projects, controlling for firm and projectcharacteristics and considering potential selection bias from UIC participation.
Keywords: university; intellectual property policy; research collaboration; project
performance; JapanJEL Classification Code: D23, L24, O32, O34* Corresponding authoraHitotsubashi University, Graduate School of Economics, Kunitachi, Tokyo 186-8601

Japan; e-mail: okamuro@econ.hit-u.ac.jp; phone: +81-42-5808792; fax: +81-42-5808882.
b

Hitotsubashi University, Institute of Innovation Research, Kunitachi, Tokyo 186-8603

Japan

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1. Introduction
The open innovation system, especially university-industry research collaboration (hereafter
UIC), has been attracting considerable attention worldwide. Through UIC, researchers atuniversities can access research funds and benefit from the ideas and expertise of private
firms, while the latter can absorb and utilize the advanced scientific knowledge created atuniversities.
However, participation in a UIC as such does not necessarily guarantee its success. UIC
is a complex relationship between extremely different worlds. Researchers in academia andprivate business often have considerably different interests, objectives, constraints, andincentives. Such disparities may cause serious conflicts, misunderstanding, and distrust
between the partners of UIC, and thus lower the satisfaction of the participants and decrease
the effectiveness of UIC (Das and Teng 1998; Grilli and Milano 2009). Therefore, how tomanage a UIC project by bridging different cultures should be a key consideration for the
success of UIC (Mora-Valentin et al. 2004), even as the strategies for managing UIC are
constrained by the institutional environment.
In Japan, UIC has increased and intensified rapidly since the late 1990s. The recent
development of UIC in Japan follows some major changes in the national innovation system,
especially in the university system (Hemmert et al. 2008; Woolgar 2007). Under the Scienceand Technology Basic Plan launched in 1996, the Ministry of Education, Culture, Sport,
Science and Technology (MEXT) has promoted the establishment of Technology Licensing
Organizations (TLO) since 1998 and Intellectual Property (IP) Centers since 2003 in
Japanese universities. In addition to some other legal and institutional changes, Japanese
universities have been activating their IP policy as one of their most important strategies.
According to MEXT (2009), 58% of all universities (92% of national universities) drew up
an IP policy by 2008, the year of our survey. Therefore, in this paper, we pay special
attention to the university IP policy as an advantage of using recent Japanese data.
The IP policy of the partner university may affect the UIC performance by determining
the basic rules of UIC with regard to the management of existing and generated IP. A clear
and equitable IP policy that can be flexibly applied to the needs of partners would be
optimal for a UIC to be efficiently managed. Otherwise, the project might face serious
conflicts of interests and low incentive for cooperation.
Thus, by focusing on the IP policy of partner universities, we empirically investigate
the determinants of UIC performance in Japan using original survey data. In this paper, UIC
is defined as project-based collaboration in research and development (R&D) between
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universities and private companies aiming at the generation or transfer of new technologies,
products, or processes. Unlike most previous studies, we integrate the analyses of both UIC
participation and performance by using Heckman’s two-step procedure to check and control
for possible sample selection bias from UIC participation. We examine the effects of firm
and top manager’s characteristics in the first step, and then those of firm and projectcharacteristics (focusing on the partner university’s IP policy) in the second step.
The remainder of this paper is organized as follows: We provide a brief review ofprevious literature in Section 2 and the development of UIC in Japan in Section 3. The
conceptual framework and the hypotheses are presented in Section 4. In Section 5, we
explain the empirical methodology and present the data, and discuss the empirical results in
Section 6. Finally, Section 7 concludes this paper.
2. Literature review
Several studies have thus far investigated the effectiveness of technology transfer byuniversities or technology transfer offices (TTOs) (Anderson et al. 2007; Caldera and
Debande 2010; Chapple et al. 2005; Debackere and Veugelers 2005; Friedmand andSilberman 2003; Macho-Stadler et al. 2007; Siegel et al. 2003; Siegel et al. 2007; Thursby
et al. 2001; Thursby and Kemp 2002; Thursby and Thursby 2002).
Siegel et al. (2007) summarize recent empirical studies on university TTOs and the key
factors for their performance, reporting that the performance of technology transfer isaffected by university characteristics, including ownership (public or private), academic
quality, local conditions of high-tech demand, and the design of licensing contract as well as
TTO characteristics such as size and age. More recently, Caldera and Debande (2010)
examined how TTO characteristics affect university’s performance of technology transfer,controlling for the nature and type of technology transfer and academic quality. Using thesurvey data on technology transfer activities of 52 universities in Spain over 2001–2005,
they find that university’s rules on the conflicts of interests between academic teaching
responsibilities and external activities have a positive effect on R&D contracts, licenses, or
spin-off creation. In sum, previous empirical studies suggest the importance of university IP
policy for the performance of technology transfer from universities. However, the effects ofuniversity IP policy on the performance of collaborative R&D between university andindustry have never been explicitly addressed.
Most studies of UIC performance examine the effect of engaging in UIC as such andfind that UIC participation directly or indirectly increases R&D productivity of participating3

firms, measured by the number of patent applications (e.g., George et al. 2002; Zucker andDarby 2001). A recent study by Eom and Lee (2010) demonstrates that UIC participation in
Korea improves both firm and innovation performance. However, few studies empiricallyinvestigate how project and partner characteristics affect UIC performance. Okamuro(2007) analyzes how project characteristics affect technological and commercial success ofinter-firm R&D cooperation, but not of UIC. Hemmert et al. (2009) examine the effect ofproject characteristics on trust formation in UIC, but not on project performance.
Characteristics of UIC projects may change and develop as they proceed. However,existing studies do not explicitly consider the development of UIC projects, except for
Mora-Valentin et al. (2004), who examine the effects of project characteristics on theperformance of cooperative R&D, distinguishing between contextual factors (initialconditions) and organizational factors (how the project is organized and managed). They
provide an empirical model that integrates various factors from several previous studies inmanagement science and find that both contextual and organizational factors affect UIC
performance. However, their model comprises only these factors and does not include otherbasic project or partner characteristics.
Most previous studies investigate the determinants of either participation or
performance of UIC, thus addressing these research issues separately. However, empiricalresults of UIC performance can be subject to latent sample selection bias, unless theprobability of UIC participation is randomly given (Eom and Lee 2010). Therefore, weshould check and control for possible influence of the determinants of UIC participation on
UIC performance. Eom and Lee (2010) employ Heckman’s two-step procedure to cope withsample selection bias, but in the second step (performance function), they focus only on
firm characteristics.
Thus, we propose and employ a two-step procedure to integrate the estimations of thedeterminants of UIC participation and UIC performance, including the characteristics of the
top manager and the firm in the first step, and project and partner characteristics (including
the partner university’s IP policy) in the second step. Most previous studies use firm-sidedata for empirical analysis and neglect university-side data. With rapid changes in theuniversity system, the strategy of the partner university with regard to intellectual property(IP) policy is particularly important, especially in the Japanese context.
Finally, previous empirical studies on UIC, including the studies on the determinants of
UIC participation, concentrate on Western countries, especially in Europe, and few studies
on UIC have been carried out in Japan and in East Asia (Eom and Lee 2010; Hemmert et al.
2008; Motohashi 2005; Okamuro et al. 2011), despite rapid development of UIC in recent
4years.
Thus, our study has the following major contributions to the literature: First, we focus
on project and partner characteristics as the determinants of UIC performance rather than
examining the effect of UIC participation itself. More specifically, we shed light on the roleof the university IP policy, which is important in the context of rapid changes in theuniversity system. Second, by using Heckman’s two-step procedure, we check and control
for potential sample selection bias of participating in UIC. Finally, this is a pioneering
empirical research on the determinants of both the participation in and performance of the
UIC in Japan.
3. Development of UIC in Japan
In this section, we will sketch the development and institutional background of UIC in
Japan. Formal and informal UIC has been active for more than 100 years 1, encompassing alarge part of the history of Japanese universities. Therefore, it would be too simplistic toassume that UIC is underdeveloped in Japan or lags far behind the United States and Europe.
However, UIC was legally constrained after World War II as a public reaction against thecooperation with the defense industry during the wartime and became even more inactive
after the 1960s 2.
Although MEXT changed its university policy to permit UIC during the 1980s, thecrucial moment in the establishment of a science and technology policy came in 1995 with
the Science and Technology Basic Law. Two important initiatives aimed at promoting UIC
were taken under the first Science and Technology Basic Plan (1996–2000): “The act topromote technology transfer from universities” in 1998—the so-called TLO Act—which
financially supports the Technology Licensing Organizations (TLOs) at universities, and“the special act to regenerate industrial vitality” in 1999, which enabled national
universities to retain IP rights based on government research funds (the Japanese version of
the U.S. Bayh-Dole). According to the Japan Science and Technology Agency (JST) (2011),
there were 46 officially acknowledged TLOs in September 2010, of which 38 wereaffiliated to national or public universities.
Under the second Science and Technology Basic Plan (2001–2005), the university
1

Baba and Goto (eds.) (2007) provide several important examples of UIC in Japan from the 1880s
to the 1950s.
2
Even during the period when formal UIC was inactive, close informal relationships were in place.
These informal relationships were formed and maintained through the placement of students(Branscomb et al., 1999).

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system in Japan experienced further drastic changes. In 2003, MEXT started to set up IP
Centers at universities to manage and improve the use of intellectual property. By March
2009, 22% of all universities (81% of national universities) established IP Centers, and thenumber of patents applied by Japanese universities increased four times from 2,462 in 2003
to 9,869 in 2007 (MEXT 2009). In 2004, national universities were transformed into more
independent university corporations. In 2005, the Ministry of Economy, Trade, and Industry(METI) achieved its goal to create 1,000 academic spin-offs within five years. It is also
noteworthy that the constraints on the faculty of national universities to work for private
firms have been relaxed systematically since 2000. In order to promote UIC at the regional
level, METI launched the Industrial Cluster Project in 2001, followed by MEXT’sIntellectual Cluster Project in 2002 3.
Supported by these policies, the number of R&D collaborations between national
universities and private firms etc. increased remarkably from 56 in 1983 to 2,568 in 1998
and to 14,303 in 2008 (MEXT 2009; National Institute of Science and Technology Policy2003) 4 . The number of joint R&D projects involving private and public universities(prefectural and municipal) amounted to 3,335 in 2008, so that national universities
accounted for the majority of UIC projects (81%).
It is noteworthy that small firms play an important and increasing role in R&D
collaboration with national universities 5: their share in the joint R&D projects with national
universities has consistently increased, from 13% in the mid-1980s (1983–1986) to 40% at
the beginning of the 21st century (2001–2004) 6.
4. Conceptual framework and hypotheses
Previous empirical studies concentrate on the effect of UIC participation as suchand not on the determinants of UIC performance. Focusing on inter-firm R&D cooperation,
Okamuro (2007) argues and demonstrates that contractual and organizational characteristics

3

See Nishimura and Okamuro (2011a, 2011b) for more details of these cluster projects.
These numbers do not include commissioned (contracted) research projects. National universities
include national graduate institutes and junior colleges. Public organizations and local authorities
count among private firms etc. This statistic counts only the formal projects reported to MEXT fromeach national university since 1983.
5
According to the Small and Medium Enterprise Basic Law revised in 1999, small businesses in the
manufacturing sector are defined as those with less than 300 employees or a capital of 300 millionyen.
6
In calculating the share of small businesses, the number of joint R&D projects with private firms(excluding public organizations and local authorities) is used as the denominator.
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of cooperative projects significantly affect technological and commercial success of joint
R&D. With regard to UIC, the organization and management of the project may be evenmore crucial for project performance compared to inter-firm cooperation, because UIC is apartnership between completely different types of organizations with different objectives,interests, administration, and incentive systems. To bridge the gap between such different
worlds and to achieve high performance in UIC, project and partner characteristics that
promote sufficient information sharing, efficient coordination, and mutual trust are essential(Barnes et al. 2002; Hemmert et al. 2009; Mora-Valentin et al. 2004).
Specifically, the IP policy of the partner university may significantly affect UIC
performance. This is a particularly important and interesting issue for Japan, where
universities rapidly establish TLOs and IP Centers to make efficient use of their patents andto smooth or promote UICs, as mentioned before. The university IP policy determines thebasic rules of UIC with regard to the management of existing and generated IP 7. A clear andequitable IP policy that can be applied flexibly to the needs of partners would be optimal fora UIC to be efficiently managed. Otherwise, the project might face serious conflicts ofinterests and low incentive for cooperation. Hence, we present the first hypothesis asfollows:
H1: Clear, equitable, and flexible IP policy of the partner university positively affects UIC
performance.
Moreover, if the IP policy of the partner university affects UIC performance by promoting
effective coordination among project members, as explained above, the effect of theuniversity IP policy on UIC performance is expected to be mediated by the effectiveness ofcoordination in the project or the efforts for effective coordination. By measuring theeffectiveness of coordination in UIC based on the levels of communication quality andfirm’s commitment, we postulate the second hypothesis as follows:
H2: The effect of the partner university’s IP policy on UIC performance is mediated by thelevel of communication quality in and firm’s commitment to the project.
We may regard the IP policy of the university as exogenous. The IP policy of a university isadministratively determined at the head office or the IP Center, conditioned by government
7

In Japan, IP policy may differ across universities according to their conditions. Each university
can quite flexibly determine its IP policy in accordance with certain legal and administrative rules.

7policies, and managed by IP managers who are dispatched by MEXT or directly hired byuniversity administration. Even professors cannot determine or influence the IP policy of
their own universities, especially at national universities, where even the most distinguishedresearchers are treated equally with less productive and refutable colleagues. With regard to
the decision by firm’s managers, we assume that they select university partners according totheir research capability and research matching, but not according to the university IP policy.
Therefore, the university IP policy can be regarded as exogenous and independent of any
strategies of the UIC partners 8.
5. Empirical strategy
5.1. Empirical models and variables
We estimate the determinants of UIC participation and UIC performance in an
integrated way, using Heckman’s two-step procedure (cf. Eom and Lee 2010). In this way,
we can also control for potential sample selection bias with regard to UIC participation(Heckman 1979). A firm with higher R&D capability is more likely to be involved in UIC(Cohen and Levinthal 1989; Fontana et al. 2006; Lopez 2008). Therefore, in the first step,
we estimate the probability of UIC engagement using a probit model with the whole sample
to examine what type of firm is willing to collaborate with universities. In the second step,
we investigate the effect of the university IP policy on UIC performance by OLS with a
sub-sample of UIC participants, controlling for sample selection bias. We also control forseveral project characteristics following Mora-Valentin et al. (2004).
5.1.1. First step: Determinants of UIC participation
We estimate a probit model to analyze the determinants of UIC participation. The
empirical specification is as follows:.
The dependent variable is a UIC dummy (uic), which takes on the value of one if a firm
engaged in and finished a UIC project during the preceding three years, and zero otherwise.

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Hall et al. (2001) point out that IP issues are major barriers for firms to cooperate with universities.
Therefore, we cannot completely exclude the possibility of sample selection bias in the sense thatfailed attempts of UIC due to poor or unfavorable IP policy are not observable and thus not includedin the sample. Unfortunately, we cannot control for such selection bias due to data constraints.

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We use top managers’ education and firm characteristics as independent variables.
Top managers’ education
Focusing on start-up firms, Okamuro et al. (2011) show that top managers’ humancapital, including their graduate education, significantly affects firms’ propensity tocooperate with universities and public research institutes. They argue that especially forsmall start-ups, top managers’ human capital proxies extensive network and high capabilityof the firms, and signals their capability to potential partners, thereby allowing the firms tofind university partners more easily. Therefore, we assume that firms that have top
managers with higher education level are more likely to participate in UIC.
We distinguish between different educational levels and backgrounds using five dummy
variables: (1) graduates of junior high schools or high schools (who do not have university
degrees), (2) bachelors in the human or social sciences (univ_soc), (3) bachelors in thenatural sciences (univ_nat), (4) those with graduate degrees in the human or social sciences(grad_soc), and (5) those with graduate degrees in the natural sciences (grad_nat). We use
the first group as the baseline reference in our model. If top managers’ educational level isimportant for UIC participation, we can expect positive signs for the coefficients of
univ_soc, univ_nat, grad_soc, and grad_nat. We may also expect an education in the naturalsciences to have a stronger effect on UIC participation than that in the human or social
sciences, because top managers with a natural science background may have lower barriers
to UIC.
Firm characteristics
We use dummies for firm age (age), number of employees (emp), R&D ratio to sales(rd_ratio), and firms’ technology dummies as the variables of firm characteristics variables.
Firm age (age) may be a proxy for accumulated experience and network formation. The
number of employees (emp) is a measure of firm size. R&D ratio to sales is regarded as
R&D intensity. Dummy variables of technology fields, that is, biotechnology (d_bio),
microelectronics (d_micro), and software (d_soft, reference group), are included in themodel to control for technological opportunities. According to the previous literature(Fritsch and Lukas 2001; Mohnen and Hoareau 2003), we postulate that age, emp, andrd_ratio have positive effects on UIC participation.
5.1.2. Second step: Determinants of UIC performance
We employ OLS to examine the success factors of UIC. In order to control for sample
9selection bias, we incorporate in the model the inverse Mills ratio, which is calculated in the
probit model. Our empirical model is described as follows:
UIC performance = f (partner characteristics, project characteristics, firm
characteristics, inverse Mills ratio).
We use four dependent variables on UIC performance obtained from the survey data. We
asked the respondent firms for their subjective evaluation of UIC performance using a
7-point Likert scale (where 1 = do not agree at all and 7 = strongly agree) with regard to theextent to which they agree with the following statements: (1) the results and benefits of thisUIC met our expectations (expected_results), and (2) the UIC was a technical success(tech_success). We also asked them to categorize the concrete technological outcomes they
could achieve as a direct result of UIC, using a 7-point Likert scale (where 1 = none and 7 =lot) regarding (1) new patent applications (patents) and (2) new products developed(products).
The independent variables comprise partner, project, and firm characteristics.
Partner characteristics
We pay special attention to the IP policy of the partner university, considering therecent emergence and development of IP policies at Japanese universities. We measure thefirm’s evaluation of the partner university’s IP policy with regard to clearness, equitability(in sharing revenue and royalty), and flexibility to meet the needs of partners, using 7-point
Likert scales (Appendix 1) and construct the variable univ_ip as the average value of thesethree items (later we use these items separately). According to Hypothesis 1, we expect thecoefficient of this variable to be positive. We also include the national university dummy(national_univ) to control for the differences among UICs with national, other public, andprivate universities. Further, we use the firm’s evaluation of the research capability of its
partner university regarding the level of scientific research and specific knowledge(research_capability) to control for the partner’s competence. This variable is measured bya single questionnaire item using a 7-point Likert scale (Appendix 1). Finally, the distanceto the university partner (distance) is included in the model, which is a categorical variable
that takes one of the following six values: 1 = less than 10 km, 2 = 10–20 km, 3= 21–50 km,
4 = 51–100 km, 5 = 101–500 km, and 6 = more than 500 km.
Project characteristics
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With regard to project characteristics, we use two types of ex ante relationship factorsthat are determined prior to the UIC and two kinds of ex post relationship factors that are
determined after the beginning of UIC, following Mora-Valentin et al. (2004).
The former variables of ex ante factors comprise (1) the closeness of the relationshipwith the university partners before the UIC (close_relationship) and (2) the extent ofcontractual safeguards on performance obligation, project schedules and budget, dataprotection, profit sharing, etc., regarding UIC (contract). Both of them are measured using
7-point Likert scales. We created this variable by calculating the mean of evaluation scoresof eight related questionnaire items, while the variable close_relationship is derived from asingle item in the survey (Appendix 1). We expect these variables to have positive effects on
UIC performance.
The latter variables of ex post factors include (1) communication quality with regard totimeliness, reliability, and completeness of information exchange in UIC (commuquality)and (2) strength of commitment by the participating firm (commitment). These variables areconstructed by a subjective evaluation based on 7-point Likert scales (Appendix 1).
According to Hypothesis 2, the effect of university IP policy (univ_ip) is mediated by thesevariables; we expect that, when they are included in the model, the coefficients of univ_ip
become much smaller and less significant. We also expect them to have positive effects on
UIC performance.
The survey collected further information on project membership, project duration,technological orientation, public subsidy for UIC, and uncertainty surrounding the UIC
project. As a variable of membership structure, we include in the model the number ofparticipants (firms, universities, and public research institutes) (number_participants) 9. In
addition to project duration (proyr), the technological orientation of the UIC may also be
related with the technological success of UIC. Thus, from the survey data, we create dummy
variables of technological orientation in three categories (basic research, applied research,and development), among which the first one is regarded as the baseline reference. In order
to control for the effect of public subsidy, we use the dummy variable subsidy, which takeson the value of one if the UIC received public funds. Finally, we control for market andtechnology unpredictability surrounding the UIC project using the variables unpre_mkt andunpre_tech. These variables are measured by 7-point Likert scales (Appendix 1).

9

UIC projects may include more than one university and/or other firms and research institutes(research consortia). The other variables about the partner university refer to the most importantpartner if there is more than one university in the UIC project.

11

Firm characteristics
Finally, we use the same variables of firm characteristics (age, emp, rd_ratio, d_bio,
and d_micro) as in the probit model in the second-step OLS. Firm age (age) may be
regarded as business experience and network formation. Firm size (emp) is a proxy fororganizational capability, and R&D ratio (rd_ratio) is a measure of R&D capability or
absorptive capability. We predict that these firm characteristics positively contribute to UIC
performance. UIC performance may differ across technology fields, which we consider withdummy variables of technological fields (d_bio and d_micro).
5.2. Data source and sample
Our empirical analyses are based on original survey data. We conducted a postal survey
in summer 2008 among 9,882 firms in Japan with 20 or more employees in the fields ofbiotechnology, microelectronics, and software, and obtained 1,732 responses (17.5%). We
selected these three technology fields as representing major science-based industries inwhich UIC is especially important (Meyer-Krahmer and Schmoch 1998). Our sample firms
were extracted from the company database of Tokyo Shoko Research (TSR) and the
directory of the Japan Bioindustry Association (JBA).
Among the respondents, 1,560 firms could in fact be classified into the above threetechnological fields, and of them, 264 (17%) completed UIC during the preceding three
years (the sub-sample for the second-step analysis). The basic statistics of the dependentand independent variables are summarized in Table 1.
[Table 1 around here]
Among the variables of UIC performance, those of satisfaction have relatively higher meanscores (4.9 for expected_results and 5.1 for tech_success), but those of more concrete
outcomes have lower scores (3.5 for patents and 4.1 for products). With regard to partner
characteristics, the mean score for univ_ip is 5.0, suggesting that the IP policy of the partner
university is quite positively evaluated by the respondents; 83% of the (most important)partner universities are national ones. On average, 4.6 member organizations are involved ina UIC project, which comes to an end within 28 months on average. 20% of the projects
target basic research, and 55% of the projects obtained public subsidy for R&D.
On average, the sample firms are 22 years old and have 51 employees. They are12classified into biotechnology (23%), microelectronics (20%), and software (48%) 10. The
average ratio of R&D expenditure to sales (R&D intensity) is 4.6%; 63% of top managersof the sample firms obtained undergraduate education (up to bachelor degree), while 10%
have master or doctor degrees.
6. Estimation results
In this section, we first present the results of the second-step estimation of thedeterminants of UIC performance, focusing on the impact of the university IP policy, before
reporting the results of the first-step estimation on the determinants of UIC participation.
6.1. Second step: UIC performance
Table 2 shows the estimation results of the second-step OLS regarding UIC
performance with regression coefficients and robust standard errors. We have four
dependent variables: expected_results, tech_success, patents, and products. Specification(1) does not include ex post project characteristics (commuquality and commitment), while
Specification (2) includes both of them to check the mediation effects.
[Table 2 around here]
First, univ_ip has positive and significant effects on all dependent variables inSpecification (1). This result confirms H1. Indeed, the IP policy of the partner university
matters much for the success of a UIC project. A clear and equitable IP policy that can be
flexibly applied according to the needs of participating firms contributes to UIC
performance. When the variables of project coordination (commuquality and commitment)
are included in the estimation, Specification (2), the coefficients of univ_ip are still
significant, but much smaller with lower significance levels than those of Specification (1).
This result supports H2 and shows that the efforts of project coordination mediate the effectof the university IP policy on UIC performance. The IP policy of the partner university loses
its importance after controlling for the measures of coordination during the UIC. Therefore,
the effect of IP policy on UIC performance may be largely attributed to the improvement of
coordination in the project.
109% of the respondents did not provide information on their technological fields. These firms were
eventually dropped in the empirical estimation.

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We confirm positive and significant effects of the university IP policy on UIC
performance even after controlling for improved project coordination, but we are unable toexplain this remaining effect. We believe that universities with a favorable IP policy may
also have a well-managed organization as well as transparent and favorable rules related to
UIC (for example, regarding faculty’s obligation, graduate course education, or donation),
which could contribute to UIC performance.
The national university dummy (national_univ) has a weak but positive and significant
effect on UIC performance except for patent application (patents) in Specification (1). The
effect of national universities is no more significant after controlling for project
coordination—Specification (2). The research capability of the partner university (researchcapability) has positive and significant effects on expected_results and tech_success.
Distance to the partner university (distance) affects only tech_success, but positively,
suggesting that research collaboration with a university in a distant region is more likely to
be evaluated as a technological success than that with a nearby university.
The result that distance, and not proximity to the partner, leads to higher
technological satisfaction is contrary to the findings of most previous studies (Acs et al.
2002; Anselin et al. 1997; Furman et al. 2010; Jaffe 1989; Owen-Smith and Powell 2004;
Rondé and Hussler 2005) 11, but consistent with Japanese studies such as Nishimura andOkamuro (2011a) 12. We may attribute this result to a better matching: The farther theuniversity partners are searched for, the higher the probability that an optimal partner can befound, because an optimal research partner is not always located nearby. We may argue that
this matching effect exceeds the advantage of proximity, even after controlling for theevaluation of research capability of the partner.
With regard to project characteristics, we find that the results differ across thedependent variables. The strength of ex ante ties (close_relationship) has no significant
effect on all measures of UIC performance except for tech_success in Specification (1). The
strength of contractual safeguards (contract) has a positive but weak effect on
expected_results and tech_success. In contrast to these ex ante project characteristics, ex
post characteristics (commu_quality and commitment) show a positive and significant
11

However, these studies examine the effect of proximity on firm performance, whereas we examine
the effect on project performance. Thus, we cannot directly compare our results with those of theprevious studies. As far as we know, few empirical studies directly investigate the effect ofgeographical proximity to a research partner in UIC on project outcomes. An exception is
Mora-Valentin et al. (2004), who do not find significant effect of geographical proximity to a
research partner on project outcomes.
12
In a similar vein, Zucker and Darby (2001) find no evidence of geographically localized
knowledge spillovers in Japan by analyzing biotech patents.

14

impact on UIC performance except for patent application (patents). Thus, ex post
characteristics of UIC projects are in general more important for UIC performance than ex
ante characteristics (initial conditions). Besides, as mentioned above, we found that these ex
post variables mediate the effect of IP policy of the partner university (univ_ip).
It is also noteworthy that most of the other project and firm characteristics have no or
little effect on UIC performance except for project duration and firm size. We again observe
some differences among the dependent variables: patent application (patent) is significantly
associated with several variables of these project and firm characteristics, while
expected_results is not correlated with them.
The mean variance inflation factor (VIF) is sufficiently low (1.62 for Specification (1)and 1.70 for Specification (2)), so that we may exclude the multicollinearity problem. The
inverse Mills ratio is not significant except for expected_results. Thus, we confirm andcorrect sample selection bias only for this dependent variable. However, the results of thefirst step are also worth reporting, because sample selection bias is confirmed for a part ofthe estimations.
6.2. First step: UIC participation
Table 3 shows the empirical results in the first step on the determinants of UIC
participation. In the probit model, we include top managers’ education and the firm
characteristics. We show the marginal effect of each variable on the probability of UIC
participation.
[Table 3 around here]
First, we find that top managers’ education is strongly related to the propensity of UIC
participation. All variables—univ_soc, univ_nat, grad_soc, and grad_nat—have asignificantly positive impact on UIC participation 13. In addition, the marginal effects ofgrad_soc and grad_nat are larger than those of univ_soc and univ_nat. This means that afirm managed by a top manager with a higher educational level is more likely to engage in
UIC. However, our results also suggest that top managers with university degrees in thehuman or social sciences are by no means less willing to cooperate with universities
13

In an unreported estimation, we include education variables of both top managers and top
researchers to find that only the dummy for Ph.D. in natural science has a significant impact on UIC
participation. Since our sample consists of SMEs, the top manager and the top researcher of a firm
may be the same person in several cases. Then the result would be subject to significant
multicollinearity.

15

compared to those with degrees in the natural sciences. This is probably because top
managers with a natural science background tend to prefer in-house R&D to UIC because of
the “not-invented-here” (NIH) syndrome.
Second, with regard to firm characteristics, the R&D ratio to sales (rd_ratio) has apositive and significant impact on UIC participation. Therefore, as we indeed expected, afirm with a higher R&D capability is more likely to participate in UIC. Unexpectedly,
however, we find that firm size (emp) has no significant impact on UIC participation, whichmeans that small size in itself is not a barrier to UIC.
Finally, we find that the propensity of UIC participation significantly differs acrosstechnological fields: Biotech and microelectronics firms are more willing to conduct UIC
than software firms. This suggests that technological opportunities matter for engaging in
UIC.
7. Concluding remarks
The aim of this paper was to investigate the impact of university IP policy on UIC
performance with original survey data from Japanese firms, controlling for potential sample
selection bias using Heckman’s two-step procedure. The estimation results show that the IP
policy of partner universities regarding clearness, equitability, and flexibility indeed matters,
but its impact on UIC performance is mediated by the quality of communication between
the partners and the level of the firm’s commitment to the project. We also found that someother project characteristics significantly affect UIC performance, although the effects differ
across the dependent variables and thus depend on how we define UIC performance.
Contrary to the results of most previous literature, the distance to the partner is positively
correlated with UIC performance. We further found that firms’ R&D intensity and top
managers’ education level are important determinants of UIC participation, while firm size
has no influence after controlling for these variables.
This paper has some major contributions to the literature. First, it sheds light on theeffect of the university IP policy on successful UIC, which is important in the context of
rapid changes in the university system. Second, it examines the determinants of both UIC
participation and UIC performance in an integrative framework and hence checks andcontrols for sample selection bias in performance estimation. Third, it reveals the role of top
managers’ education as an important factor of UIC participation. Fourth and last, it focuseson UIC in Japan, on which few empirical studies have been carried out despite theincreasing attention and importance of UIC in Japan in recent years.
16However, as with most previous studies, our study is based on survey data from the firmside, so that we cannot consider the factors of university researchers. A matched-data
analysis using survey data from both sides of UIC would be desirable for further research.
Specifically, we measure the characteristics of university IP policy by subjective andretrospective evaluations by partner firms. A more objective measurement should be
discussed. Further, the IP policy and other project or partner characteristics are measured as
one-shot evaluations. Considering the recent development of university IP policy, future
research should address its dynamics more explicitly. Lastly, we cannot exclude thepossibility of sample selection bias in the sense that failed attempts of UIC due to poor or
unfavorable IP policy of potential partner universities are not observable. We should collectinformation about failed partnerships to cope with this problem.
Despite such weakness, our findings have the following important implications for
managers. First, in searching for a research partner among universities, managers should
also look at the IP policy of the universities. Further, initial conditions of the projectsignificantly affect UIC performance. Specifically, it is important to find an optimal partnereven if located in a distant area. However, our findings also show how to manage
cooperation right during the project may be more important for performance than the initial
settings. We also find that firm characteristics and project size do not significantly affect
UIC performance. Therefore, how to manage a project is more important for UIC
performance than who (which firm) engages in the project. Even if small businesses have
limited R&D resources, they can successfully cooperate in R&D with universities withappropriate project management expertise.
Regarding policy implications, our results suggest that it is essential for successful UIC
to establish a clear, equitable, and flexibly applicable IP policy. Moreover, given thenegative effect of closeness to the partner on UIC performance, it is important for policy
makers to encourage and support partner search in UIC in wider geographic areas.

Acknowledgments
The authors are grateful for the financial support obtained from the Volkswagen Foundation17

in Germany for this research project. Early versions of this paper were presented at the
Global Conference on University-Industry Collaboration in Seoul, Korea, in April 2010 andat the Research in Entrepreneurship and Small Business (RENT) XXIV Conference in
Maastricht, the Netherlands, in November 2010. The authors appreciate the valuablecomments and suggestions from the participants of these conferences, including Martin
Hemmert, Keun Lee, and Reinhilde Veugelers.

18

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22

Appendix 1: Variable construction of partner and project characteristics
univ_ip: mean of the evaluation scores of the following items:
Please evaluate how clearly the partner university’s policy on intellectual property rights
was defined (where 1 = do not agree and 7 = fully agree).
University intellectual property policies were clear and easily understood.
University intellectual property policies were sufficiently flexible to meet our firm’s needs.
University intellectual property policies were equitable in revenue and royalty sharing.
close_relationship: the evaluation score of the following question:
Prior to this UI research collaboration, how close was your relationship with the university
partner (where 1 = not at all close and 7 = very close)?
research capability: the evaluation score of the following question:
How did your company evaluate the research capability of your partner university before
you entered into a UI research collaboration with it (where 1 = fully disagree and 7 = fully
agree)?
We believed they were scientifically leading in their field.
contract: mean of the evaluation scores of the following items:
How clearly were the following issues defined at the beginning of the research collaboration(where 1 = there was no mutual understanding and 7 = the mutual understanding wasexactly defined in a written document)?
Roles and responsibilities of each partner
Performance obligations of each partner
Project schedules (timing and deadlines)
Project budget (how it should be used and checked)
Data/secrecy protection, publication of the findings
Profit sharing from new products and processes, etc.
Legal procedures in case a partner does not fulfill his role or obligation
Procedures in case of unexpected events
commuquality: mean of the evaluation scores of the following items:
Overall, the communication between our firm’s and the university partner’s representatives
23

was (1–7).
untimely/timely
inaccurate/accurate [you can rely on it]
incomplete/complete
commitment: the evaluation score of the following item:
Please evaluate the communication and interaction with the university research partner inthe partnership (if there was more than one partner, please refer to the most important
university research partner; 1 = strongly disagree… 7 = strongly agree).
We were very committed to this university partner.
unpre_mkt, unpre_tech: the evaluation scores of the following items, respectively
Please respond regarding the extent of market and technological uncertainties surrounding
this UI R&D collaboration (1–7).
The market surrounding the research collaboration was very predictable and easy to forecast
versus unpredictable and hard to anticipate.
The technological developments surrounding the research collaboration werepredictable versus hard to anticipate, or unpredictable.

24

Table 1: Basic statistics of the variables
Variables

Obs

Mean

Std. Dev.

Min

Max

uic

1560

0.17

0.38

0

1

expected_results

255

4.86

1.63

1

7

tech_success

253

5.08

1.66

1

7

patents

256

3.45

2.32

1

7

products

256

4.09

2.14

1

7

univ_ip

253

4.95

1.64

1

7

national_univ

260

0.83

0.38

0

1

research_capability

254

5.16

1.63

1

7

distance

255

3.21

1.77

1

6

close_relationship

213

5.13

1.75

1

7

contract

256

4.70

1.37

1

7

commuquality

259

5.33

1.31

1

7

commitment

251

4.43

1.83

1

7

number_participants

244

4.62

4.74

2

48

Dependent var.

Independent var.

log (proyr )

250

3.35

0.65

0.69

4.79

basic research

258

0.20

0.40

0

1

applied research

258

0.41

0.49

0

1

development

258

0.39

0.49

0

1

subsidy

256

0.55

0.50

0

1

unpre_mkt

253

4.64

1.70

1

7

unpre_tech

252

4.70

1.58

1

7

log (age )

1549

3.09

0.72

0.69

4.71

log (emp )

1525

3.93

0.96

0

7.91

rd_ratio

1437

4.63

12.74

0

100

d_bio

1560

0.23

0.42

0

1

d_micro

1560

0.20

0.40

0

1

d_soft

1560

0.48

0.50

0

1

univ_soc

1441

0.30

0.46

0

1

univ_nat

1441

0.33

0.47

0

1

grad_soc

1441

0.01

0.12

0

1

grad_nat

1441

0.09

0.28

0

1

Firm characteristics

Top managers' education

25

Table 2: Determinants of UIC performance (second step)
expected_results

univ_ip
national_univ
research_capability
distance
close_relationshipcontract

(1)

(2)

(1)

(2)

(1)

(2)

(1)

(2)

0.127*

0.290***

0.167**

0.333***

0.282**

0.220**

0.178*

0.081

0.067

0.070

0.071

0.115

0.127

0.110

0.105

0.710*

0.270

0.626**

0.361－0.267－0.4711.034**

0.839

0.371

0.383

0.320

0.327

0.519

0.565

0.525

0.554

0.136*

0.062

0.159**

0.114*

0.158

0.125

0.046

0.023

0.079

0.076

0.068

0.065

0.111

0.116

0.113

0.114

0.101

0.094

0.146***

0.124**

0.058

0.070

0.087

0.057

0.066

0.063

0.057

0.054

0.093

0.095

0.095

0.093

0.105

0.025

0.153**

0.073－0.104－0.118

0.106

0.009

0.074

0.073

0.064

0.063

0.105

0.111

0.106

0.109

0.169*

0.147*

0.141*

0.087－0.006－0.0060.028－0.030

0.094

0.089

0.081

0.077

0.134

0.136

0.135

0.133

commitmentlog(proyr)appliedresearchdevelopment
subsidy
unpre_mkt
unpre_techlog(age)log(emp)rd_ratio
d_bio
d_microconstant

products

0.287***

commuquality

number_participants

patents

tech_success0.468***

0.312***

0.189

0.111

0.096

0.168

0.273*
0.165

0.097*

0.162***－0.0450.184*

0.056

0.057

0.102

0.100

0.012

0.011

0.043

0.039

－0.090－0.088－0.039－0.050

0.040

0.037

0.034

0.032

0.056

0.057

0.057

0.056

0.056

0.153

0.204

0.318*

0.987***

0.985***

0.430

0.621**

0.192

0.191

0.165

0.165

0.272

0.294

0.275

0.288

0.230

0.375

0.667**

0.743***

0.938*

1.019**

0.121

0.173

0.341

0.321

0.293

0.276

0.482

0.491

0.488

0.481

0.121

0.313

0.232

0.331

－0.238－0.162

0.662

0.629

0.344

0.326

0.294

0.280

0.483

0.495

0.489

0.485

0.012

0.055

－0.235－0.2270.719*

0.762**－0.153－0.132

0.271

0.253

0.234

0.218

0.383

0.386

0.388

0.378

－0.0210.017－0.083－0.042－0.179－0.176

0.001

0.057

0.085

0.080

0.073

0.069

0.120

0.123

0.122

0.121

－0.047－0.049

0.023

0.038

－0.049－0.073－0.124－0.135

0.085

0.080

0.074

0.070

0.120

0.123

0.122

0.121

0.001

－0.045－0.028－0.010－0.462－0.515*－0.282－0.166

0.217

0.214

0.173

0.169

0.282

0.294

0.285

0.288

－0.258*－0.224－0.242**－0.247**0.448**

0.497**－0.234－0.262

0.155

0.150

0.124

0.119

0.200

0.204

0.201

0.2

－0.023－0.021－0.012－0.012

0.012

0.016

－0.032*－0.028

0.015

0.014

0.012

0.011

0.019

0.019

0.019

0.018

－0.214－0.537

0.411

0.126

1.648**

1.697**

0.575

0.360

0.653

0.643

0.518

0.505

0.819

0.851

0.827

0.832

－0.114－0.351

0.566

0.353

－0.052－0.033

0.604

0.364

0.514

0.497

0.410

0.392

0.650

0.662

0.656

0.648

4.039*

2.973

1.207

0.200

－1.359－1.960

2.314

0.609

2.337

2.331

1.874

1.854

2.997

3.176

3.025

3.109

Uncensored obs

127

125

127

125

128

126

128

126

Censored obs

1112

1112

1112

1112

1112

1112

1112

1112

Wald chi2 test

75.85***

111.83***146.27***191.86***61.39***

58.79***

37.75***

46.03***－1.398*－1.457*－0.479－0.564－0.439－0.220－0.252－0.117

0.822

0.902

0.747

0.717

1.199

1.227

1.209

1.201

1.62

1.70

1.62

1.70

1.62

1.70

1.62

1.70

Mills
Mean VIF

Note 1: Level of significance: *** 1%, ** 5%, * 10%
Note 2: Standard errors are indicated in italics

26

Table 3: Determinants of UIC participation (first step)
Marginal effect

Robust S.E.

univ_soc

0.064**

0.030

univ_nat0.079***

0.029

grad_soc

0.358***

0.129

grad_nat0.088**

0.050

log (age)－0.0200.014

log (emp)

0.006

0.010

rd_ratio

0.004**

0.002

d_bio

0.179***

0.034

d_micro0.156***

0.033

Top managers' education

Firm characteristicsN
Log pseudolikelihood

1309－494.155
Wald chi2(9) = 86.23 (p=0.000)

Note: Level of significance: *** 1%, ** 5%, * 10%27