| International Journal of Nonlinear Analysis and Applications | ||
| Article 9, Volume 17, Issue 2, February 2026, Pages 95-110 PDF (730.55 K) | ||
| DOI: 10.22075/ijnaa.2022.26104.3231 | ||
| Receive Date: 30 January 2022, Revise Date: 07 December 2022, Accept Date: 19 December 2022 | ||
| References | ||
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[1] M. Abbott and C. Doucouliagos, The efficiency of Australian universities: a data envelopment analysis, Econ. Educ. Rev. 22 (2003), no. 1, 89–97. [2] G. Abramo, T. Cicero, and C. Andrea D’Angelo, Revisiting size effects in higher education research productivity, Higher Educ. 63 (2012), 701–717. [3] T. Agasisti and A. Dal Bianco, Reforming the university sector: Effects on teaching efficiency—Evidence from Italy, Higher Educ. 57 (2009), 477–498. [4] T. Agasisti and C. Perez-Esparrells, Comparing efficiency in a cross-country perspective: The case of Italian and Spanish state universities, Higher Educ. 59 (2010), 85–103. [5] T. Ahn, A. Charnes, and W. W. Cooper, Some statistical and DEA evaluations of relative efficiencies of public and private institutions of higher learning, Socio-econ. Plann. Sci. 22 (1988), no. 6, 259–269. [6] A. Amirteimoori, Data envelopment analysis in dynamic framework, Appl. Math. Comput. 181 (2006), no. 1, 21–28. [7] A.D. Athanassapoulos and E. Shale, Assessing the comparative efficiency of higher education institutions in the UK by means of data envelopment analysis, Educ. Econ. 5 (1997), no. 2, 119–134. [8] N.K. Avkiran, Investigating technical and scale efficiencies of Australian universities through data envelopment analysis, Socio-econ. Plann. Sci. 35 (2001), no. 1, 57–80. [9] N.K. Avkiran, An illustration of dynamic network DEA in commercial banking including robustness tests, Omega 55 (2015), 141–150. [10] N.K. Avkiran and A. McCrystal, Dynamic network range-adjusted measure vs. dynamic network slacks-based measure, J. Oper. Res. Soc. Japan 57 (2014), no. 1, 1–14. [11] R.D. Banker, A. Charnes, and W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis, Manag. Sci. 30 (1984), no. 9, 1078–1092. [12] J.E. Beasley, Determining teaching and research efficiencies, J. Oper. Res. Soc. 46 (1995), no. 4, 441–452. [13] L. Castelli, R. Pesenti, and W. Ukovich, DEA-like models for efficiency evaluations of specialized and interdependent units, Eur. J. Oper. Res. 132 (2001), no. 2, 274–286. [14] B. Casu and E. Thanassoulis, Evaluating cost efficiency in central administrative services in UK universities, Omega 34 (2006), no. 5, 417–426. [15] O. Celik and A. Ecer, Efficiency in accounting education: Evidence from Turkish universities, Critic. Perspect. Account. 20 (2009), no. 5, 614–634. [16] A. Charnes, W.W. Cooper, and E. Rhodes, Measuring the efficiency of decision making units, Eur. J. Oper. Res. 2 (1978), no. 6, 429–444. [17] C.-M. Chen, A network-DEA model with new efficiency measures to incorporate the dynamic effect in production networks, Eur. J. Oper. Res. 194 (2009), no. 3, 687–699. [18] C.-M. Chen and J. van Dalen, Measuring dynamic efficiency: Theories and an integrated methodology, Eur. J. Oper. Res. 203 (2010), no. 3, 749–760. [19] Y. Chen, X. Ma, P. Yan, and M. Wang, Operating efficiency in Chinese universities: An extended two-stage network DEA approach, J. Manag. Sci. Engin. 6 (2021), no. 4, 482–498. [20] Y. Chen, L. Liang, and J. Zhu, Equivalence in two-stage DEA approaches, Eur. J. Oper. Res. 193 (2009), no. 2, 600–604. [21] A. Colbert, R.R. Levary, and M.C. Shaner, Determining the relative efficiency of MBA programs using DEA, Eur. J. Oper. Res. 125 (2000), no. 3, 656–669. [22] D.K. Despotis, D. Sotiros, and G. Koronakos, A network dea approach for series multi-stage processes, Omega 61 (2016), 35–48. [23] T. Ding, J. Yang, H. Wu, Y. Wen, C. Tan, and L. Liang, Research performance evaluation of Chinese university: A non-homogeneous network DEA approach, J. Manag. Sci. Engin. 6 (2021), no. 4, 467–481. [24] A. Emrouznejad and E. Thanassoulis, A mathematical model for dynamic efficiency using data envelopment analysis, Appl. Math. Comput. 160 (2005), no. 2, 363–378. [25] R. Fare, S. Grosskopf, and G. Whittaker, Network DEA, Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, Springer, 2007, pp. 209–240. [26] A.T. Flegg, D.O. Allen, K. Field, and T.W. Thurlow, Measuring the efficiency and productivity of British universities: An application of DEA and the Malmquist approach, University of the West of England, Department of Economics, series Discussion Papers 304 (2003). [27] H. Fukuyama and W.L. Weber, Measuring Japanese bank performance: A dynamic network DEA approach, J. Product. Anal. 44 (2015), 249–264. [28] H. Fukuyama and W.L. Weber, Japanese bank productivity, 2007–2012: A dynamic network approach, Pacific Econ. Rev. 22 (2017), no. 4, 649–676. [29] J.P. Gander, Academic research and teaching productivities: A case study, Technol. Forecast. Soc. Change 49 (1995), no. 3, 311–319. [30] A. Gazori, K. Khalili-Damghani, and A. Hafezalkotob, Multi-period network data envelopment analysis to measure the efficiency of a real business, J. Ind. Syst. Engin. 12 (2019), no. 3, 55–77. [31] J.C. Glass, G. McCallion, D.G. McKillop, S. Rasaratnam, and K.S. Stringer, Implications of variant efficiency measures for policy evaluations in UK higher education, Socio-econ. Plann. Sci. 40 (2006), no. 2, 119–142. [32] B. Golany and E. Tamir, Evaluating efficiency-effectiveness-equality trade-offs: A data envelopment analysis approach, Manag. Sci. 41 (1995), no. 7, 1172–1184. [33] D. Holod and H.F. Lewis, Resolving the deposit dilemma: A new DEA bank efficiency model, J. Bank. Finance 35 (2011), no. 11, 2801–2810. [34] O. Joumady and C. Ris, Performance in European higher education: A non-parametric production frontier approach, Educ. Econ. 13 (2005), no. 2, 189–205. [35] C. Kao, Dynamic data envelopment analysis: A relational analysis, Eur. J. Oper. Res. 227 (2013), no. 2, 325–330. [36] C.T. Kuah and K.Y. Wong, Efficiency assessment of universities through data envelopment analysis, Procedia Comput. Sci. 3 (2011), 499–506. [37] K.-H. Leitner, J. Prikoszovits, M. Schaffhauser-Linzatti, R. Stowasser, and K. Wagner, The impact of size and specialisation on universities’ department performance: A DEA analysis applied to Austrian universities, Higher Educ. 53 (2007), 517–538. [38] X. Liang, J. Li, G. Guo, S. Li, and Q. Gong, Evaluation for water resource system efficiency and influencing factors in western China: A two-stage network DEA -tobit model, J. Cleaner Prod. 328 (2021), 129674. [39] C. lo Storto, Measuring the efficiency of the urban integrated water service by parallel network DEA: The case of Italy, J. Cleaner Prod. 276 (2020), 123170. [40] A. Lucia Miranda Lopes and E. Augusto Lanzer, Data envelopment analysis-DEA and fuzzy sets to assess the performance of academic departments: A case study at Federal University of Santa Catarina-UFSC, Pesquisa Oper. 22 (2002), 217–230. [41] E. Martin, An application of the data envelopment analysis methodology in the performance assessment of the Zaragoza University departments, Universidad de Zaragoza, Facultad de Ciencias Economicas y Empresariales, 2003. [42] P. Moreno and S. Lozano, Super SBI dynamic network DEA approach to measuring efficiency in the provision of public services, Int. Trans. Oper. Res. 25 (2018), no. 2, 715–735. [43] J. Nemoto and M. Goto, Dynamic data envelopment analysis: Modeling intertemporal behavior of a firm in the presence of productive inefficiencies, Econ. Lett. 64 (1999), no. 1, 51–56. [44] J. Nemoto and M. Goto, Measurement of dynamic efficiency in production: An application of data envelopment analysis to Japanese electric utilities, J. Product. Anal. 19 (2003), 191–210. [45] C. Parker, Performance measurement, Work study 49 (2000), no. 2, 63–66. [46] C. Parker, Measurement of dynamic efficiency in production: An application of data envelopment analysis to Japanese electric utilities, J. Prod. Anal. 19 (2003), 191–210. [47] M.A. Saniee Monfared and M. Safi, Network DEA: An application to analysis of academic performance, J. Ind. Engin. Int. 9 (2013), 1–10. [48] Z. Sinuany-Stern, A. Mehrez, and A. Barboy, Academic departments efficiency via DEA, Comput. Oper. Res. 21 (1994), no. 5, 543–556. [49] K. Tone and M. Tsutsui, Dynamic DEA with network structure: A slacks-based measure approach, Omega 42 (2014), no. 1, 124–131. [50] P. Tyagi, S. Prasad Yadav, and S.P. Singh, Relative performance of academic departments using DEA with sensitivity analysis, Eval. Program Plann. 32 (2009), no. 2, 168–177. [51] Y. Wang, J.-F. Pan, R.-M. Pei, B.-W. Yi, and G.-L. Yang, Assessing the technological innovation efficiency of China’s high-tech industries with a two-stage network DEA approach, Socio-Econ. Plann. Sci. 71 (2020), 100810. [52] Y.-M. Wang and K.-S. Chin, Some alternative DEA models for two-stage process, Expert Syst. Appl. 37 (2010), no. 12, 8799–8808. [53] Y.-C. Wu, I. Wei Kiong Ting, W.-M. Lu, M. Nourani, and Q. Long Kweh, The impact of earnings management on the performance of ASEAN banks, Econ. Model. 53 (2016), 156–165. [54] H. Xiao, D. Wang, Y. Qi, S. Shao, Y. Zhou, and Y. Shan, The governance-production nexus of eco-efficiency in Chinese resource-based cities: A two-stage network DEA approach, Energy Econ. 101 (2021), 105408. [55] Y.-W. Xu, H.-J. Zhang, K. Cheng, Z.-X. Zhang, and Y.-T. Chen, Efficiency measurement in multi-period network DEA model with feedback, Expert Syst. Appl. 175 (2021), 114815. [56] H. Yu, Y. Zhang, A. Zhang, K. Wang, and Q. Cui, A comparative study of airline efficiency in China and India: A dynamic network DEA approach, Res. Transport. Econ. 76 (2019), 100746. [57] I. Yuksel and M. Dagdeviren, Using the fuzzy analytic network process (ANP) for balanced scorecard (BSC): A case study for a manufacturing firm, Expert Syst. Appl. 37 (2010), no. 2, 1270–1278. [58] Y. Zha, N. Liang, M. Wu, and Y. Bian, Efficiency evaluation of banks in China: A dynamic two-stage slacks-based measure approach, Omega 60 (2016), 60–72. | ||
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