Measuring the Efficiency of Turkish State Universities Based on a Two-Stage DEA Model

Year 2021, , 1210 - 1220, 01.12.2021

Emre Koçak , H. Hasan Örkcü

https://doi.org/10.35378/gujs.801115

Cited By: 2

Abstract

According to the efficiency scores obtained by Data Envelopment Analysis (DEA), the main problem for inefficient decision-making units (DMU) is the factors that cause inefficiency. To deal with this problem, various studies have been handled, such as decomposing the total efficiency score, and two-stage DEA which can divide the whole process into two parts has been developed. In this study, independent models where the whole process is called a black box and related models where the series relationship is established in the whole process are discussed comparatively. These models are used to measure graduate education performances and scientific and technological research competency performances of state universities in Turkey. When the overall performances of universities are examined; Gebze Technical, Hacettepe, İstanbul Technical, İzmir Institute of Technology and Middle East Technical have been determined as efficient universities both in terms of overall performances of universities and in terms of independent model and related model. İstanbul, Ankara, Boğaziçi and Gazi, have been

determined as efficient universities only in terms of independent model. Furthermore, this study is consistent with the research universities determined by the Council of Higher Education through certain criteria.

Keywords

Two-Stage Data Envelopment Analysis, Graduate Education, Scientific and Technological Research Competency

References

• [1] Kuah, C. T., Wong, K. Y., “Efficiency assessment of universities through data envelopment analysis”, Procedia Computer Science, 3: 499-506, (2011).
• [2] Liang, L., Li, Z. Q., Cook, W. D., Zhu, J., “Data envelopment analysis efficiency in two-stage networks with feedback”, IIE Transactions, 43(5): 309-322, (2011).
• [3] Lee, B. L., Worthington, A. C., “A network DEA quantity and quality-orientated production model: An application to Australian university research services”, Omega, 60: 26-33, (2016).
• [4] Xiong, X., Yang, G. L., Guan, Z. C., “Assessing R&D efficiency using a two-stage dynamic DEA model: A case study of research institutes in the Chinese Academy of Sciences”, Journal of Informetrics, 12(3): 784-805, (2018).
• [5] Yang, G. L., Fukuyama, H., Song, Y. Y., “Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model”, Journal of Informetrics, 12(1): 10-30, (2018).
• [6] Charnes, A., Cooper, W. W., Rhodes, E., “Measuring the efficiency of decision making units”, European Journal of Operational Research, 2(6): 429-444, (1978).
• [7] Banker, R. D., Charnes, A., Cooper, W. W., “Some models for estimating technical and scale inefficiencies in data envelopment analysis”, Management Science, 30(9): 1078-1092, (1984).
• [8] Byrnes, P., Färe, R., Grosskopf, S., “Measuring productive efficiency: an application to Illinois strip mines”, Management Science, 30(6): 671-681, (1984).
• [9] Kao, C., “Some properties of Pareto efficiency under the framework of data envelopment analysis”, International Journal of Systems Science, 26(9): 1549-1558, (1995).
• [10] Färe, R., Grosskopf, S., “Productivity and intermediate products: A frontier approach”, Economics Letters, 50(1): 65-70, (1996).
• [11] Seiford, L. M., Zhu, J., “Profitability and marketability of the top 55 US commercial banks” Management Science, 45(9): 1270-1288, (1999).
• [12] Kao, C., “Network data envelopment analysis”. Switzerland: Springer International Publishing, (2017).
• [13] Kao, C., Hwang, S. N., “Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan”, European Journal of Operational Research, 185(1): 418-429, (2008).