企業與大學的產學合作能夠讓國家獲得持續不斷的創新動力。但是如何評估產學合作的績效少有人探討，更少有研究同時考量企業與大學之間的知識與資源流通，分析產學合作的績效。大學與企業產學合作績效探討，若只針對大學或企業進行評估，則無法一窺全貌，因各項產學合作的投入、產出之資訊未必完整揭露，因此，使用傳統網路資料包絡法評估績效，亦有其限制。為解決前述問題，本研究定義一可分析不明確數值之模糊網絡資料包絡分析法規劃，將可以(1)以大學的觀點來評估效率；(2)分析內部生產活動，並可以了解由於生產率降低，對產出造成的影響；(3)解決評估此產學合作網路組成的投入、產出之資訊揭露不完整的問題。本研究以我國科技大學與企業間的產學合作資料進行實證研究。實證研究之結果，可作為改善產學合作的績效之依據外，也可以做為大學推動產學合作策略訂定的參考。

The University-Industry cooperation between enterprises and universities enables the country to obtain continuous innovation momentum. However, how to evaluate the performance of University-Industry cooperation is rarely discussed. Few studies consider the flow of knowledge and resources between enterprises and universities to analyze the performance of University-Industry cooperation. If only make an evaluation to university or business, the research into the University-Industry cooperation performance only gives a glimpse of the picture. Because of the input or output from the University-Industry cooperation information may not be completely exposed. Therefore, there is a limit to performance using the traditional network data envelopment to evaluate the performance of University-Industry collaboration. In order to solve the above problems, this study defines a fuzzy network data envelopment analysis method that can analyze ambiguous values, which can be (1) to evaluate the efficiency from a university perspective (2) to analyze internal production activities, and to understand that due to lower productivity (3) to solve the problem of incomplete disclosure of the input and output for the University-Industry cooperation network. This study has been based on empirical research on the University-Industry cooperation between enterprises and universities. The results of empirical research can serve as a basis for improving the performance of University-Industry cooperation as well as a reference for universities to promote University-Industry cooperation.

Adler, N., Friedman, L., & Sinuany-Stern, Z. (2002). Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research, 140(2), 249-265.

Albats, E., Fiegenbaum, I., & Cunningham, J. A. (2018). A micro level study of university industry collaborative lifecycle key performance indicators. The Journal of Technology Transfer, 43(2), 389-431.

Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science, 39(10), 1261-1264.

Andrade, R., Fernandes, G., & Tereso, A. (2016). Benefits Management in University-Industry R&D Collaborative Projects: A Review on Benefits and Success Factors. Procedia Computer Science, 100, 921-927.

Baker, R., & Talluri, S. (1997). A closer look at the use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 32(1), 101-108.

Bercovitz, J., & Feldman, M. (2008). Academic entrepreneurs: Organizational change at the individual level. Organization Science, 19(1), 69-89.

Brady, S. R. (2015). Utilizing and adapting the Delphi method for use in qualitative research. International Journal of Qualitative Methods, 14(5), 1-6.

Breese, R. (2012). Benefits realisation management: Panacea or false dawn? International Journal of Project Management, 30(3), 341-351.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.

Chen, C., & Yan, H. (2011). Network DEA model for supply chain performance evaluation. European journal of operational research, 213(1), 147-155.

Council, N. R. (2000). Research Teams and Partnerships: Trends in the Chemical Sciences, Report of a Workshop. Washinton D.C., USA: National Academies Press.

D'Este, P., Guy, F., & Iammarino, S. (2012). Shaping the formation of university–industry research collaborations: what type of proximity does really matter? Journal of economic geography, 13(4), 537-558.

Daniel, D. R. (1961). Management information crisis. Harvard business review, 39(5), 111-121.

Ecker, P. S., & Staples, K. (1997). Collaborative Conflict And The Future: Academic-In D Ustrial Alliances And Adaptations. Computers and technical communication: pedagogical and programmatic perspectives, 3, 375.

Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 1(34), 35-49.

Feller, I. (2005). A historical perspective on government-university partnerships to enhance entrepreneurship and economic development. Economic development through entrepreneurship: Government, university and business linkages, 6-28.

Filieri, R., McNally, R. C., O'Dwyer, M., & O'Malley, L. (2014). Structural social capital evolution and knowledge transfer: Evidence from an Irish pharmaceutical network. Industrial Marketing Management, 43(3), 429-440.

Galagedera, D. U., Roshdi, I., Fukuyama, H., & Zhu, J. (2018). A new network DEA model for mutual fund performance appraisal: An application to US equity mutual funds. Omega, 77, 168-179.

Grimaldi, R., & Von Tunzelmann, N. (2002). Assessing collaborative, pre‐competitive R&D projects: the case of the UK LINK scheme. R&D Management, 32(2), 165-173.

Guimón, J. (2013). Promoting University-Industry Collaboration in Developing Countries. The Innovation Policy Platform: Policy Brief, 1(3), 12.
Gunasekaran, A., Irani, Z., Choy, K.-L., Filippi, L., & Papadopoulos, T. (2015). Performance measures and metrics in outsourcing decisions: A review for research and applications. International Journal of Production Economics, 161, 153-166.

Gupta, S., & Maltz, E. (2015). Interdependency, dynamism, and variety (IDV) network modeling to explain knowledge diffusion at the fuzzy front-end of innovation. Journal of Business Research, 68(11), 2434-2442.

Hagedoorn, J., Link, A. N., & Vonortas, N. S. (2000). Research partnerships 1. Research Policy, 29(4-5), 567-586.

Hinojosa, M., Lozano, S., & Mármol, A. (2017). DEA production games with fuzzy output prices. Fuzzy Optimization and Decision Making, 1-19.

Kaklauskas, A., Banaitis, A., Ferreira, F. A. F., Ferreira, J. J. M., Amaratunga, D., Lepkova, N., Banaitiene˙, N. (2018). An Evaluation System for University–Industry Partnership Sustainability: Enhancing Options for Entrepreneurial Universities. Sustainability, 10(1), 1-17.

Kao, H.-Y., Chan, C.-Y., & Wu, D.-J. (2014). A multi-objective programming method for solving network DEA. Applied Soft Computing, 24, 406-413.

Kerssensvan Drongelen, I., Nixon, B., & Pearson, A. (2000). Performance measurement in industrial R&D. International Journal of Management Reviews, 2(2), 111-143.

Lee, E. S., & Li, R. J. (1993). Fuzzy multiple objective programming and compromise programming with Pareto optimum. Fuzzy sets and Systems, 53(3), 275-288.

Link, A. N., Siegel, D. S., & Bozeman, B. (2007). An empirical analysis of the propensity of academics to engage in informal university technology transfer. Industrial and corporate change, 16(4), 641-655.

Louis, K. S., Blumenthal, D., Gluck, M. E., & Stoto, M. A. (1989). Entrepreneurs in academe: An exploration of behaviors among life scientists. Administrative Science Quarterly, 110-131.
Mansfield, E. (1995). Academic research underlying industrial innovations: sources, characteristics, and financing. The review of Economics and Statistics, 55-65.

Marotta, D., Mark, M., Blom, A., & Thorn, K. (2007). Human Capital And University-Industry Linkage's Role In Fostering Firm Innovation: An Empirical Study Of Chile And Colombia. World Bank Publications Policy Research Working Paper,4443 , 2-41.

Moed, H. F. (2006). Citation Analysis In Research Evaluation. Netherlands. Springer Science & Business Media.

Mone, E. M., & London, M. (2018). Employee Engagement Through Effective Performance Management: A Practical Guide for Manager. London, England: Routledge.

Murry Jr, J. W., & Hammons, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423-436.

Newberg, J. A., & Dunn, R. L. (2002). Keeping Secrets In The Campus Lab: Law, Values And Rules Of Engagement For Industry‐University R&D Partnerships. American Business Law Journal, 39(2), 187-240.

Niesten, E., & Jolink, A. (2015). The impact of alliance management capabilities on alliance attributes and performance: a literature review. International Journal of Management Reviews, 17(1), 69-100.

Norcross, J. C., Hedges, M., & Prochaska, J. O. (2002). The face of 2010: A Delphi poll on the future of psychotherapy. Professional Psychology: Research and Practice, 33(3), 316-322.

Perkmann, M., Neely, A., & Walsh, K. (2011). How should firms evaluate success in university–industry alliances? A performance measurement system. R&D Management, 41(2), 202-216.

Perkmann, M., & Walsh, K. (2007). University–industry relationships and open innovation: Towards a research agenda. International Journal of Management Reviews, 9(4), 259-280.

Perkmann, M., & Walsh, K. (2009). The two faces of collaboration: impacts of university-industry relations on public research. Industrial and corporate change, 18(6), 1033-1065.

Perrini, F., & Tencati, A. (2006). Sustainability and stakeholder management: the need for new corporate performance evaluation and reporting systems. Business Strategy and the Environment, 15(5), 296-308.

Rosenberg, N. (1992). Scientific instrumentation and university research. Research Policy, 21(4), 381-390.

Rouyendegh, B. D., & Erol, S. (2010). The DEA–FUZZY ANP department ranking model applied in Iran Amirkabir University. Acta Polytechnica Hungarica, 7(4), 103-114.

Sekayi, D., & Kennedy, A. (2017). Qualitative Delphi Method: A Four Round Process with a Worked Example. The Qualitative Report, 22(10), 2755-2763.

Sinuany-Stern, Z., & Friedman, L. (1998). DEA and the discriminant analysis of ratios for ranking units. European Journal of Operational Research, 111(3), 470-478.

Tavana, M., Khalili-Damghani, K., Arteaga, F. J. S., Mahmoudi, R., & Hafezalkotob, A. (2018). Efficiency decomposition and measurement in two-stage fuzzy DEA models using a bargaining game approach. Computers & Industrial Engineering, 118, 394-408.

Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European journal of operational research, 197(1), 243-252.

Torgersen, A. M., Førsund, F. R., & Kittelsen, S. A. (1996). Slack-adjusted efficiency measures and ranking of efficient units. Journal of Productivity Analysis, 7(4), 379-398.
Tzeng, G.-H., & Huang, J.-J. (2013). Fuzzy multiple objective decision making. New York, USA: Chapman and Hall/CRC.

Van Dooren, W., Bouckaert, G., & Halligan, J. (2015). Performance management in the public sector: Routledge Routledge Masters in Public Management.New York, USA.

van Fenema, P. C., & Keers, B. M. (2018). Interorganizational Performance Management: A Co‐evolutionary Model. International Journal of Management Reviews, 20(3), 772-799.

Veugelers, R. (2012). Which policy instruments to induce clean innovating? Research Policy, 41(10), 1770-1778.

Voytek, K. P., Lellock, K. L., & Schmit, M. A. (2004). Developing performance metrics for science and technology programs: the case of the manufacturing extension partnership program. Economic Development Quarterly, 18(2), 174-185.

Wang, J. (2016). Knowledge creation in collaboration networks: Effects of tie configuration. Research Policy, 45(1), 68-80.

Wilkinson, T. J., & Kannan, V. R. (2013). Strategic Management in the 21st Century. Volume 3. Oxford, England.

Yang, C., & Liu, H.-M. (2012). Managerial efficiency in Taiwan bank branches: A network DEA. Economic Modelling, 29(2), 450-461.

Yu, J.-R., Tzeng, Y.-C., Tzeng, G.-H., Yu, T.-Y., & Sheu, H.-J. (2004). A fuzzy multiple objective programming to DEA with imprecise data. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12(05), 591-600.

Zeleny, M., & Cochrane, J. (1982). Multiple criteria decision making McGraw-Hill New York, 34. 1011-1022.

Zhang, L., & Luo, Y. (2016). Evaluation of input output efficiency in higher education based on data envelope analysis. International Journal of Database Theory and Application, 9(5), 221-230.