Abstract
Despite the expectation of various advantages, university-industry research collaboration (UIC), a relationship between two different worlds, often faces serious conflicts. The performance of UIC depends on the research partners’ strategies and institutional designs through which they seek to mitigate these conflicts and increase partner incentives. We pay special attention to the role of the university intellectual property (IP) policy, formally introduced to Japan in 2003, as the basis of UIC contracts and empirically examine its impact on the performance of UIC projects, considering the factors in firms’ participation in UIC. We argue that the university IP policy that is equitable in sharing revenue and royalty from innovative outcomes and applied flexibly according to the partner’s needs may contribute to improving project performance by enhancing the commitment of firms, and we test our hypotheses using a sample of Japanese firms obtained from our original survey. The estimation results support the hypotheses, although the mediation via the firm’s commitment only partially explains the relationship between the university IP policy and UIC performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
These numbers do not include commissioned (contracted) R&D projects and technology licensing. National universities include national graduate institutes and junior colleges. Public organizations and local authorities count as private firms. This statistic comprises only the formal projects that have been reported to MEXT by national universities since 1983.
According to Japan Science and Technology Agency (JST) (2011), there were 46 officially acknowledged TLOs in September 2010, of which 38 were affiliated with national or public universities. By March 2009, 22 % of all universities (and 81% of national universities) had established IP Centers, and the number of patents applications from Japanese universities had increased 4 times from 2,462 in 2003 to 9,869 in 2007 (MEXT 2009).
Based on case studies featuring European biotechnology research consortia, Foray and Steinmueller (2003) investigate how the governance rule of IP rights affects research productivity, suggesting that any uniform rule on IP rights reduces productivity.
In Japan, IP policies may differ across universities according to their conditions. Each university independently determines its own IP policy, which is more or less flexibly applied to individual cases, in accordance with legal and administrative rules.
In fact, our interviews on university partners (professors) who were involved in UIC reveal that they are not interested in university IP policy and the contents of UIC contract.
Moreover, some of the interview partners selected from among the survey respondents mentioned that they looked for optimal university partners (professors) rather than partner universities.
This survey was conducted as part of an international comparison among four countries, including Japan. The original English version of the questionnaire was translated into Japanese and other local languages.
We identified 1,761 firms from diverse biotechnology-related industries, 3,520 firms from diverse microelectronics-related industries, and 4,037 firms classified as part of the software industry; we then added 564 firms from the JBA.
Although several respondents did not provide their affiliation and name, we could match their responses with TSR database by using an identification number printed in an unnoticeable way in a corner of the response envelope.
We cannot use partner university’s characteristics in this estimation model, because such information is not available for the firms that did not conduct UIC during the given period. Instead, we attempt to control for the availability of potential university partners in the region.
We follow the mediation criteria in Baron and Kenny (1986).
The UIC projects may include more than one university and/or firms and research institutes (research consortia). The other variables for the partner university refer to the most important partner if there is more than one university in the UIC project.
9 % of respondents did not provide information on their technological fields. These firms were eventually dropped in the empirical estimation.
The conditions of mediation are defined as follows (See Baron and Kenny (1986) for details): (1) an independent variable is significantly correlated with a dependent variable; (2) the same independent variable is significantly associated with the mediator variable; (3) the mediator variable is significantly correlated with the dependent variable even if the focal independent variable is included together in the model, whereby the effect of the independent variable on the dependent variable should be lower.
We also conduct Sobel-Goodman Mediation Tests for the estimation results in Table 2. The proportion of the direct effect on products that is mediated is 0.14 for Specification (3) and 0.10 for Specification (4), indicating partial mediation regarding commitment.
This result may also be attributed to measurement errors due to subjective evaluation of research capability. However, even if this variable should be liable to measurement errors, we could still control for the research capability of partner universities complementarily with the national university dummy: In Japan, top universities according to university rankings are all national universities represented by the former imperial universities, whereas most universities are private.
However, these studies examine the effect of proximity on firm performance, whereas we examine its effect on project performance. Thus, we cannot directly compare our results with those of the previous studies. As far as we know, few empirical studies have directly investigated the effect of geographical proximity to a research partner on UIC project outcomes. An exception is Mora-Valentin et al. (2004), who do not find a significant effect of geographical proximity to a research partner on project outcomes.
In a similar vein, Zucker and Darby (2001) find no evidence of geographically localized knowledge spillovers in Japan by analyzing biotech patents.
In an unreported estimation, we include the education variables for both top managers and top researchers to find that only the dummy for PhD 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 sometimes be the same person. The result would then be subject to significant multicollinearity.
Eom and Lee (2010) obtain the result (from Korean firms) that neither firm size nor R&D intensity has a significant effect on UIC participation.
The authors are grateful to an anonymous referee of this journal for pointing out the possibility of the common method bias.
Podsakoff et al. (2003) provide a detailed and critical review of the potential sources and mechanisms of common method biases and some techniques for controlling them.
As mentioned before, several respondents preferred anonymity and did not provide their affiliation and name, but in fact we could identify them using printed numbers on the response envelope.
If we find that the mean values of the relevant variables for anonymous respondents are constantly lower than those for another group, we regard these differences as “consistent.”
References
Acs, Z. J., Anselin, L., & Varga, A. (2002). Patents and innovation counts as measures of regional production of new knowledge. Research Policy, 31, 1069–1085.
Aghion, P., & Tirole, J. (1994). The management of innovation. Quarterly Journal of Economics, 109, 1185–1209.
Anderson, T. R., Daim, T. U., & Lovoie, F. F. (2007). Measuring the efficiency of university technology transfer. Technovation, 27, 306–318.
Anselin, L., Varga, A., & Acs, Z. (1997). Local geographical spillovers between university research and high technology innovations. Journal of Urban Economics, 42, 422–448.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.
Bekkers, R. & Bodas Freitas, I. M. (2010). Catalysts and barriers: Factors that affect the performance of university-industry collaborations. Conference paper presented at the 13th International Schumpeter Society Conference in Aalborg, Denmark, June 21–24, 2010.
Brief, A. P., Burke, M. J., George, J. M., Robinson, B. S., & Webster, J. (1988). Should negative affectivity remain an unmeasured variable in the study of job stress? Journal of Applied Psychology, 73, 193–198.
Bruneel, J., D’Este, P., & Salter, A. (2010). Investigating the factors that diminish the barriers to university-industry collaboration. Research Policy, 39, 858–868.
Caldera, A., & Debande, O. (2010). Performance of Spanish universities in technology transfer: An empirical analysis. Research Policy, 39, 1160–1173.
Chapple, W., Lockett, A., Siegel, D. S., & Wright, M. (2005). Assessing the relative performance of U.K. university technology transfer offices: Parametric and non-parametric evidence. Research Policy, 34, 369–384.
Chen, P. Y., & Spector, P. E. (1991). Negative affectivity as the underlying cause of correlations between stressors and strains. Journal of Applied Psychology, 76, 398–407.
Cohen, W. M., & Levinthal, D. A. (1989). Innovation and learning: Two faces of R&D. Economic Journal, 99, 569–596.
Das, T. K., & Teng, B. S. (1998). Between trust and control: Developing confidence in partner cooperation in alliances. Academy of Management Review, 23, 491–512.
Debackere, K., & Veugelers, R. (2005). The role of academic technology transfer organizations in improving science industry links. Research Policy, 34, 321–342.
Eom, B. Y., & Lee, K. (2010). Determinants of industry-academy linkages and their impact on firm performance: The case of Korea as a latecomer in knowledge industrialization. Research Policy, 39, 625–639.
Fontana, R., Geuna, A., & Matt, M. (2006). Factors affecting university–industry R&D projects: The importance of searching, screening, and signaling. Research Policy, 35, 309–323.
Foray, D., & Steinmueller, E. (2003). On the economics of R&D and technological collaborations: Insights and results from the project Colline. Economics of Innovation and New Technology, 12, 77–91.
Friedman, J., & Silberman, J. (2003). University technology transfer: Do incentives, management, and location matter? Journal of Technology Transfer, 28, 17–30.
Fritsch, M., & Lucas, R. (2001). Determinants of R&D cooperation: Evidence from Spanish manufacturing firms. International Journal of Industrial Organization, 26, 113–136.
Furman, J. L., Kyle, M. K., Cockburn, I., & Henderson, R. M. (2010). Public and private spillovers, location, and the productivity of pharmaceutical research. Annales d’Economie et de Statistique, 79(80), 167–190.
George, G., Zahra, S. A., & Wood, D. R. (2002). The effects of business-university alliances on innovative output and financial performance: A study of publicly traded biotechnology companies. Journal of Business Venturing, 17, 577–609.
Grilli, L. (2009). Direct public subsidies and the productivity of high-tech start-ups: Exploring the governmental “build efficiency” function. Paper presented at the 36th Annual Conference of EARIE (European Association for Research in Industrial Economics) in Ljubljana, Slovenia, in September 2009.
Hall, B., Link, A. N., & Scott, J. T. (2000). Universities as research partners. Review of Economics and Statistics, 85, 485–491.
Hall, B., Link, A. N., & Scott, J. T. (2001). Barriers inhibiting industry from partnering with universities: Evidence from the Advanced Technology Program. Journal of Technology Transfer, 26, 87–98.
Heckman, J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.
Hemmert, M., Okamuro, H., Bstieler, L., & Ruth, K. (2008). An inquiry into the status and nature of university-industry research collaborations in Japan and Korea. Hitotsubashi Journal of Economics, 49(2), 163–180.
Hemmert, M., Bstieler, L., Ruth, K., & Okamuro, H. (2009). Examining trust formation in university-industry research collaborations: A comparison between East Asian and Western countries. Paper presented at the 2009 Annual Meeting of the Academy of International Business (AIB) in San Diego, USA, in June 2009.
Jaffe, A. B. (1989). Real effects of academic research. American Economic Review, 79, 957–970.
Japan Science and Technology Agency (JST) (2011). Sangakukan Renkei Databook 2010–2011 [Industry-University Cooperation Databook 2010–2011] http://sangakukan.jp/, in Japanese.
Lerner, J., & Merges, R. P. (1998). The control of technology alliances: An empirical analysis of the biotechnology industry. Journal of Industrial Economics, 46, 125–156.
Link, A. N., & Scott, J. T. (2005). Universities as partners in U.S. research joint ventures. Research Policy, 34, 385–393.
Lopez, A. (2008). Determinants of R&D cooperation: Evidence from Spanish manufacturing firms. International Journal of Industrial Organization, 26, 113–136.
Macho-Stadler, I., Martinez-Giralt, X., & Perez-Castrillo, J. D. (2007). Licensing of university inventions: The role of technology transfer office. International Journal of Industrial Organization, 25, 483–510.
Meyer-Krahmer, F., & Schmoch, U. (1998). Science-based technologies: University- industry interactions in four fields. Research Policy, 27, 835–851.
Ministry of Education, Culture, Sport, Science and Technology (MEXT) (2009). Heisei 20 Nendo Daigakutouniokeru Sangaku Renkeitou Jisshi Joukyounitsuite [The State of UIC in 2008]. http://www.mext.go.jp/a_menu/shinkou/sangaku/1282374.htm, in Japanese.
Mohnen, P., & Hoareau, C. (2003). What type of enterprise forges close links with universities and government labs? Evidence from CIS 2. Managerial and Decision Economics, 24, 133–145.
Morandi, V. (2011). The management of industry-university joint research projects: How do partners coordinate and control R&D activities? Journal of Technology Transfer. doi:10.1007/s10961-011-9228-5.
Mora-Valentin, E. M., Montoro-Sanchez, A., & Guerras-Martin, L. A. (2004). Determining factors in the success of R&D cooperative agreements between firms and research organizations. Research Policy, 33, 17–40.
Motohashi, K. (2005). University–industry collaborations in Japan: The role of new technology-based firms in transforming the national innovation system. Research Policy, 34, 583–594.
Motohashi, K., & Yun, X. (2007). China’s innovation system reform and growing industry and science linkages. Research Policy, 36, 1251–1260.
National Institute of Science and Technology Policy (2003). Sangaku Renkei 1983-2001 [UIC 1983-2001]. NISTEP Research Material No. 96, in Japanese.
Nishimura, J., & Okamuro, H. (2011). R&D productivity and the organization of cluster policy: An empirical evaluation of the Industrial Cluster Project in Japan. Journal of Technology Transfer, 36, 117–144.
Okamuro, H. (2007). Determinants of successful R&D cooperation in Japanese small businesses: The impact of organizational and contractual characteristics. Research Policy, 36, 1529–1544.
Okamuro, H., Kato, M., & Honjo, Y. (2011). Determinants of R&D cooperation in Japanese start-ups. Research Policy, 40, 728–738.
Owen-Smith, J., & Powell, W. W. (2004). Knowledge networks as channels and conduits: The effects of spillovers in the Boston biotechnology community. Organization Science, 15, 5–21.
Perkmann, M., & Walsh, K. (2007). University-industry relationships and open innovation: Towards a research agenda. International Journal of Management Review, 9, 259–280.
Podsakoff, Ph. M., MacKenzie, S. B., Lee, J. L., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903.
Rondé, P., & Hussler, C. (2005). Innovations in regions: What does really matter? Research Policy, 34, 1150–1172.
Siegel, D. S., Waldman, D., & Link, A. (2003). Assessing the impact of organizational practices on the relative productivity of university technology transfer offices: An exploratory study. Research Policy, 32, 27–48.
Siegel, D. S., Veugelers, R., & Wright, M. (2007). Technology transfer offices and commercialization of university intellectual property: Performance and policy implications. Oxford Review of Economic Policy, 23, 640–660.
Thursby, J. G., & Kemp, S. (2002). Growth and productive efficiency of university intellectual property licensing. Research Policy, 31, 109–124.
Thursby, J. G., & Thursby, M. C. (2002). Who is selling the ivory tower? Sources of growth in university licensing. Management Science, 48, 90–104.
Thursby, J. G., Jensen, R., & Thursby, M. C. (2001). Objectives, characteristics, and outcomes of university licensing: A survey of major U.S. universities. Journal of Technology Transfer, 26, 59–72.
Veugelers, R., & Cassiman, B. (2005). R&D cooperation between firms and universities: Some empirical evidence from Belgian manufacturing. International Journal of Industrial Organization, 23, 355–379.
Woolgar, L. (2007). New institutional policies for university-industry links in Japan. Research Policy, 36, 1261–1274.
Zucker, L. G., & Darby, M. R. (2001). Capturing technological opportunity via Japan’s star scientists: Evidence from Japanese firms’ biotech patents and products. Journal of Technology Transfer, 26, 37–58.
Acknowledgments
The authors are grateful for the financial support obtained from the Volkswagen Foundation 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, at the Research in Entrepreneurship and Small Business (RENT) XXIV Conference in Maastricht, the Netherlands, in November 2010, and at the Intellectual Property Seminar of National Graduate Institute for Policy Studies in Tokyo, Japan, in December 2011. The authors appreciate the valuable comments and suggestions from the participants of these conferences, including Martin Hemmert, Keun Lee, and Reinhilde Veugelers, and an anonymous referee and an editor of this journal. Any errors or omissions are the authors’ own.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix 1: Variable construction of partner and project characteristics
(1) flexible_ip: evaluation score for the following question (1 = do not agree—7 = fully agree):
University intellectual property policies were sufficiently flexible to meet our firm’s needs.
(2) equitable_ip: evaluation score for the following question (1 = do not agree—7 = fully agree):
University intellectual property policies were equitable in revenue and royalty sharing.
(3) close_relationship: mean value of the evaluation scores for the following questions:
Prior to this UI research collaboration,
–how close was your relationship with the university partner? (1 = very lose—7 = very close)
–how could the history of your company and this university partner be characterized (1 = volatile—7 = stable)
–how could the personal relationships between individuals of your company and individuals of this university partner be described (1 = non-existent—7 = close and established).
(4) research capability: evaluation score for 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 (1 = fully disagree—7 = fully agree)?
–We believed they were scientifically leading in their field.
(5) commitment: evaluation score for the following question:
Please evaluate the communication and interaction with the (most important) university research partner in the partnership (1 = strongly disagree—7 = strongly agree).
We were very committed to this university partner.
(6) unpre_mkt: evaluation score for the following question:
The market surrounding the research collaboration was very predictable and easy to forecast versus unpredictable and hard to anticipate (1 = strongly disagree—7 = strongly agree).
(7) unpre_tech: evaluation score for the following question:
The technological developments surrounding the research collaboration were predictable versus unpredictable and hard to anticipate (1 = strongly disagree—7 = strongly agree).
(8) uic_success: evaluation score for the following question:
How successful would you rate joint R&D projects with universities in general prior to this UI partnership (1 = generally unsuccessful—7 = generally successful)?
Appendix 2
See Table 6.
Appendix 3: Statistical remedy of the common method bias
Podsakoff et al. (2003) provide several statistical remedies for the common method bias, among which we employ a simple technique for partial correlation procedure to check the robustness of our estimation results. We include a proxy for the source of the common method bias, such as social desirability, in the estimation model (Brief et al. 1988; Chen and Spector 1991). By applying this method, the source of the common method bias can be controlled out of the estimation.
Among the several factors in the common method bias, we focus on social desirability, as discussed. We use the variable uic_success, subjective evaluation of past UIC experiences, as a proxy for social desirability because people tend to exaggerate successful experiences, especially when they know that such experiences are regarded as socially desirable and are thus highly evaluated. The self-evaluation of the past UIC experience may also be related to the potential of future UIC success: The firms with positive experience in UIC are likely to be successful in future UIC. However, when controlling for the past relationship with the university partner (close_relationship), we may regard this variable as representing social desirability to some extent.
Table 7 summarizes the estimation results with the variable of social desirability (uic_success). We estimate here only the second step (OLS) because the inverse Mills ratios were not significant in all models, as shown in Table 2. The variable uic_success has a positive and significant effect on products, but not on patents. The effects of the university IP policy variables on UIC performance do not significantly change after including uic_success in the models.
Rights and permissions
About this article
Cite this article
Okamuro, H., Nishimura, J. Impact of university intellectual property policy on the performance of university-industry research collaboration. J Technol Transf 38, 273–301 (2013). https://doi.org/10.1007/s10961-012-9253-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10961-012-9253-z