Efficiency Analysis of Science and Technology Parks Using Data Envelopment Analysis: Evidence from Turkey

Year 2021, Volume: 24 Issue: 4, 1667 - 1674, 01.12.2021

Necla Arslan Önder Belgin

https://doi.org/10.2339/politeknik.649833

Cited By: 1

Abstract

Research and Development (R&D) and innovation have a significant impact on the competitiveness of countries. Science and Technology Parks (STPs) are an important component of R&D and innovation ecosystems of countries and they aim to increase the university-industry collaboration. This study addresses the efficiency analysis of STPs in Turkey using Data Envelopment Analysis (DEA). For this purpose, an input-oriented DEA model is used to obtain efficiency scores of STPs and 5 of 22 STPs are found to be efficient. After that, to examine the strong and weak areas of STPs six additional Data Envelopment Analysis (DEA) models are considered. According to these models, STPs exhibits lower performance in the efficiency of revenue and patents. Finally, STPs are clustered based on efficiency scores as Marketers, Researchers and Low-performers using K-means clustering and we made suggestions for each cluster. The motivation of this study is contributing to policies for increasing the performance and the impact of the STPs in Turkey.

Keywords

Science and technology parks, data envelopment analysis, efficiency, cluster analysis

References

• Hobbs, K.G., Link, A.N. and Scott, J.T., “Science and technology parks: an annotated and analytical literature review”, Journal of Technology Transfer, 42: 957–76, (2017).
• Hu, J-L., Yeh, F-Y. and Chang, I-T., “Industrial park efficiency in Taiwan”, Journal of Information and Optimization Sciences, 30(1): 63-86, (2009).
• Hu, J-L., Han, T-F., Yeh, F-Y. and Lu, C-L., “Efficiency of science and technology industrial parks in China”, Journal of Management Research, 10(3): 151-166, (2010).
• Farrell, M. J., “The measurement of productive efficiency”, Journal of the Royal Statistical Society, 120: 253-290, (1957).
• Charnes, A., Cooper, W. W. and Rhodes, E., “Measuring the efficiency of decision-making units”, European Journal of Operational Research, 2: 429-444, (1978).
• Banker R.D., Charnes, A. and Cooper, W.W., “Some models for estimating technical and scale inefficiencies in data envelopment analysis”, Management Science, 30(9): 1078-1092, (1984).
• Hirschberg, J.G. and Lye, J.N., “Clustering in data envelopment analysis using bootstrapped efficiency scores”, Papers 800, Department of Economics, Melbourne, (2001).
• Sherman, H.D. and Zhu, J., “Service productivity management: Improving service performance using data Envelopment analysis (DEA)”, Springer, New York, (2006).
• Lemos, C.A.A., Lins, M.P.E. and Ebecken, N.F.F., “DEA implementation and clustering analysis using the K-means algorithm”, Data Mining VI, WIT Press, Brazil, (2005).
• Bougnol M-L., Dula, J.H., EstellitaLins, M.P. and Moreira da Silva, A.C., “Enhancing standard performance practices with DEA”, Omega, 38(1–2): 33-45, (2010).
• Maghyereh, A. I. and Awartani, B., “The effect of market structure, regulation, and risk on banks efficiency: Evidence from the Gulf cooperation council countries”, Journal of Economic Studies, 41(3): 405-430, (2014).
• Cook, W.D., Seiford, L.M., “Data envelopment analysis (DEA) – thirty years on”, European Journal of Operations Research, 192(1): 1–17, (2009).
• Liu, J.S., Lu, L.Y.Y., Lu, W.-M., Lin, B.J.Y., “Data envelopment analysis 1978–2010: a citation-based literature survey”. Omega, 41(1): 3–15, (2013).
• Liu, J.S., Lu, L.Y.Y., Lu, W.-M., Lin, B.J.Y., “A survey of DEA applications”, Omega, 41(5): 893–902, (2013).
• Fraley, C. and Raftery, A.E., “Model-based clustering, discriminant analysis, and density estimation”, Journal of the American Statistical Association, 97(458): 611-631, (2002).
• Alpaydin, E., “Introduction to machine learning”, The MIT Press, London, (2014).
• Jain, A., Murty, M. and Flynn, P. “Data clustering: A review”, ACM Computing Surveys, 31: 264-323, (1999).
• Han B, Liu L. and Omiecinski E., “NEAT: Road network aware trajectory clustering”, Proceedings of the 32nd IEEE International Conference on Distributed Computing Systems, Macau, China, 142–151, (2012).
• Vesanto, J., and Alhoniemi, E., “Clustering of the self-organizing map”, IEEE Transactions on Neural Networks, 11(3): 586-600, (2000).
• Buhmann, J. and Kühnel, H., “Complexity optimized data clustering by competitive neural network”, Neural Computation, 5(1): 75-88, (1993).
• Jain, A.K., “Data clustering: 50 years beyond K-means”, Pattern Recognition Letters, 31: 651-666, (2010).
• MacQueen, J.B. ‘Some methods for classification and analysis of multivariate observations’, Proceedings of 5th Berkley Symposium on Mathematical Statistics and Probability, I, California, 281-297, (1967).
• Mao, J. and Jain, A.K., “A self-organizing network for hyper-ellipsoidal clustering (HEC)”, Transactions on Neural Networks, 7(1): 16-29, (1996).
• Hung, S.-W., Wang, A.P., “Entrepreneurs with Glamour? DEA Performance Characterization of High-Tech and Older-Established Industries”, Economic Modelling, 29: 1146–1153, (2012).