طراحی مدل ارزیابی عملکرد دو‌سطحی پارک‌های علم‌و‌فناوری با استفاده از تحلیل پوششی داده‌ها

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی و مالی، دانشکده مدیریت و حسابداری، پردیس فارابی دانشگاه تهران، قم، ایران

2 استاد، گروه مدیرت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران

3 استادیار، گروه مدیریت دولتی و امور عمومی، دانشکده مدیریت و حسابداری، پردیس فارابی دانشگاه تهران، قم، ایران

چکیده

در عصر اقتصاد دانش‌بنیان، یکی‌ از مهم‌ترین نهادهای تأثیرگذار بر رشد این اقتصاد ارزیابی عملکرد است. هدف این پژوهش ارائه مدلی جهت ارزیابی کارایی پارک‌های علم‌وفناوری و شرکت‌های مستقر در آن‌ها با توجه به مختصات جغرافیایی-اقتصادی ایران است. در این پژوهش، ابتدا پژوهشگران با استفاده از مبانی نظری و نظر خبرگان متغیرهای مورداستفاده برای ارزیابی عملکرد پارک‌های علم‌وفناوری را شناسایی نمودند. به‌علاوه برای بالا بردن روایی مدل از توپولوژی‌های متفاوت متغیرها استفاده شد تا بهترین ارتباط میان این متغیرها و همچنین بالاترین سطح کارایی واحدهای تصمیم‌گیری شناسایی شود.
از طرف دیگر، مدل تحلیل پوششی داده‌های دوسطحی استوار برای ارزیابی عملکرد پارک انتخاب شد. مدل ریاضی با توجه به ماهیت مسئله و همچنین بسط موری و بنکر توسعه داده شد. این تغییرات موجب توسعه مدل دوسطحی استوار تحلیل پوششی داده‌ها با متغیر غیراختیاری شد. درنهایت، کارایی پارک‌های علم‌وفناوری و واحدهای مستقر در دو مدل، بدون استفاده از متغیر غیراختیاری و با استفاده از متغیر اختیاری تشکیل و نتایج آن‌ها موردمقایسه قرار گرفت. بر اساس نتایج متغیر‌های غیراختیاری درصد تولید ناخالص هر استان و طول عمر پارک به‌عنوان متغیر میانجی شناسایی شدند.

کلیدواژه‌ها


عنوان مقاله [English]

Designing Multilevel Assessment model to evaluate science and technology parks using DEA

نویسندگان [English]

  • mahdi nikneshan 1
  • Adel Azar 2
  • said hossion akhavan alavi 3
1 industrial management, university of Tehran
2 Professor, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
3 Assistant Professor, Faculty of Public Administration & Management, Farabi collage, Tehran University, Qom, Iran
چکیده [English]

In knowledge base economy, performance evaluation of STPs as one of the most important entities in this economy is vital. In this research, we used literature review and industries expert interviews to determine what criteria are necessary to assess STPs. different topologies was used to assess the validity of the model and best model was chosen through highest reliability criteria. By comparison we made sure that selected criteria are reliably aligned with sience and technology parks objectives. On mathematical side we used robust multi-level DEA to assess STPs performances. We developed a Multi-level DEA with Non-Discretionary using banker's extension. Applying the developed model and the original model we assessed the efficiency of Iranian STPs.Then we compared the results for these two model on efficiency of DMUs, and their respecting weights using regression analysis. Based on the results two moderator variables (Park maturity and percentage of gross production in state) variable was identified and their impact on model and efficiency of DMUs was evaluated as high.

کلیدواژه‌ها [English]

  • Science and technology parks
  • Multi-level Assessment
  • Robust DEA
  • Non-Discretionary Factors

[1]       Azar, a., et al., Designing of working Groups Performance Evaluation Model: Multi level DEA Approach. Organizational Resources Management Researchs, 2012. 2(3): p. 1-22.

[2]       McCarthy, I.P., et al., A typology of university research park strategies: What parks do and why it matters. Journal of Engineering and Technology Management, 2018. 47: p. 110-122.

[3]       Monck, C. and K. Peters, science park as an instrument of regional competitiveness: measuring success and impact, in ISAP. 2009.

[4]       D. Banker, R. and R. C. Morey, Efficiency Analysis for Exogenously Fixed Inputs and Outputs. Vol. 34. 1986. 513-521.

[5]       Lindelöf, P. and H. Löfsten, Science Park Location and New Technology-Based Firms in Sweden – Implications for Strategy and Performance. Small Business Economics, 2003. 20(3): p. 245-258.

[6]       Lindelöf, P. and H. Löfsten, Proximity as a Resource Base for Competitive Advantage: University–Industry Links for Technology Transfer. The Journal of Technology Transfer, 2004. 29(3): p. 311-326.

[7]       Löfsten, H. and P. Lindelöf, Science Parks and the growth of new technology-based firms—academic-industry links, innovation and markets. Research policy, 2002. 31(6): p. 859-876.

[8]       Löfsten, H. and P. Lindelöf, Determinants for an entrepreneurial milieu: Science Parks and business policy in growing firms. Technovation, 2003. 23(1): p. 51-64.

[9]       Löfsten, H. and P. Lindelöf, R&D networks and product innovation patterns—academic and non-academic new technology-based firms on Science Parks. Technovation, 2005. 25(9): p. 1025-1037.

[10]    Westhead, P. and D.J. Storey, Links between higher education institutions and high technology firms. Omega, 1995. 23(4): p. 345-360.

[11]    Siegel, D.S., P. Westhead, and M. Wright, Assessing the impact of university science parks on research productivity: exploratory firm-level evidence from the United Kingdom. International Journal of Industrial Organization, 2003. 21(9): p. 1357-1369.

[12]    Colombo, M.G. and M. Delmastro, How effective are technology incubators? Research Policy, 2002. 31(7): p. 1103-1122.

[13]    EIB, PLAN AND MANAGE A SCIENCE PARK IN THE MEDITERRANEAN GUIDEBOOK FOR DECISION MAKERS. 2010, Europiean Investment Bank.

[14]    Ringlever, J., Assessment of Technology Parks: a University case, in ndustrial Engineering and Management. 2012, Twente: Enschede, the Netherlands.

[15]    Angulo-Guerrero, M.J., S. Pérez-Moreno, and I.M. Abad-Guerrero, How economic freedom affects opportunity and necessity entrepreneurship in the OECD countries. Journal of Business Research, 2017. 73: p. 30-37.

[16]    Dabrowska, J., Measuring the success of science parks: performance monitoring and evaluation, in XXVIII IASP World Conference on Science and Technology Parks, 2011. 2012, IASP.

[17]    Liberati, D., M. Marinucci, and G.M. Tanzi, Science and technology parks in Italy: main features and analysis of their effects on the firms hosted. The Journal of Technology Transfer, 2016. 41(4): p. 694-729.

[18]    Mansour, A.M.H. and L. Kanso, Science park implementation–A proposal for merging research and industry in developing Arab countries. HBRC journal, 2018. 14(3): p. 357-367.

[19]    Cheba, K. and J. Hołub-Iwan, How to measure the effectiveness of technology parks? The case of Poland. 2014.

[20]    Lin, C.-L. and G.-H. Tzeng, A value-created system of science (technology) park by using DEMATEL. Vol. 36. 2009. 9683-9697.

[21]    Soenarso, W.S., D. Nugraha, and E. Listyaningrum, Development of Science and Technology Park (STP) in Indonesia to Support Innovation-Based Regional Economy: Concept and Early Stage Development. World Technopolis Review, 2013. 2(1): p. 32-42.

[22]    M’Chirgui, Z., et al., University technology commercialization through new venture projects: an assessment of the French regional incubator program. The Journal of Technology Transfer, 2016: p. 1-19.

[23]    van Weele, M., F.J. van Rijnsoever, and F. Nauta, You can't always get what you want: How entrepreneur's perceived resource needs affect the incubator's assertiveness. Technovation, 2017. 59: p. 18-33.

[24]    Albahari, A., et al., The influence of Science and Technology Park characteristics on firms' innovation results. Papers in Regional Science, 2018. 97(2): p. 253-279.

[25]    Jarunee, W., Technology auditing and risk management of technology incubators/science parks. World Journal of Entrepreneurship, Management and Sustainable Development, 2017. 13(1): p. 44-56.

[26]    Soenarso, W., D. Nugraha, and E. Listyaningrum, Development of Science and Technology Park (STP) in Indonesia to Support Innovation-Based Regional Economy: Concept and Early Stage Development. Vol. 2. 2013.

[27]    Ramírez-Alesón, M. and M. Fernández-Olmos, Unravelling the effects of Science Parks on the innovation performance of NTBFs. The Journal of Technology Transfer, 2018. 43(2): p. 482-505.

[28]    Mian, S., W. Lamine, and A. Fayolle, Technology Business Incubation: An overview of the state of knowledge. Technovation, 2016. 50-51(Supplement C): p. 1-12.

[29]    Sun, C.C., Evaluating and benchmarking productive performances of six industries in Taiwan Hsin Chu Industrial Science Park. Vol. 38. 2011. 2195-2205.

[30]    Van Dierdonck, R., K. Debackere, and B. Engelen, University-industry relationships: How does the Belgian academic community feel about it? Research Policy, 1990. 19(6): p. 551-566.

[31]    Monck, C., Performance monitoring and evaluation, in UKSPA conference proceedings. 2010

[32]    Mansour, A.M.H. and L. Kanso, Science park implementation – A proposal for merging research and industry in developing Arab countries. HBRC Journal, 2017.

[33]    Rubin, T.H., T.H. Aas, and A. Stead, Knowledge flow in Technological Business Incubators: Evidence from Australia and Israel. Technovation, 2015. 41-42: p. 11-24.

[34]    van Weele, M., F.J. van Rijnsoever, and F. Nauta, You can't always get what you want: How entrepreneur's perceived resource needs affect the incubator's assertiveness. Technovation, 2017. 59(Supplement C): p. 18-33.

[35]    Cook, W.D., et al., Hierarchies and Groups in DEA. Journal of Productivity Analysis, 1998. 10(2): p. 177-198.

[36]    Cook, W.D. and R.H. Green, Evaluating power plant efficiency: a hierarchical model. Computers & Operations Research, 2005. 32(4): p. 813-823.

[37]    Azizi, H., A. Amirteimoori, and S. Kordrostami, Measurement of the worst practice of decision-making units: Incorporating both undesirable outputs and non-discretionary inputs into imprecise DEA. Modern Research in Decision Making, 2018. 3(2): p. 197-222.