Subject Areas : Optimization
1 - Department of Mathematics, Shiraz branch, Islamic Azad University, Shiraz, Iran
Keywords:
Abstract :
References:
An, Q., Chen, H., Wu, J., Liang, L. (2015). Measuring slacks-based efficiency for commercial banks in China by using a two-stage DEA model with undesirable output. Annals of Operations Research, 235(1), 13–35. https://doi.org/10.1007/s10479-015-1987-1
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
Farrell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical
Society, Series A (General), 120, 253-290. https://www.jstor.org/stable/2343100
Fang, J., Lau, C.-K. M., Lu, Z., Tan, Y., & Zhang, H. (2019). Bank performance in China: A Perspective from Bank efficiency, risk-taking and market competition. Pacific-Basin Finance Journal, 56, 290–309. https://doi.org/10.1016/j.pacfin.2019.06.011
Fukuyama, H., & Matousek, R. (2011). Efficiency of Turkish banking: Two-stage network system. Variable returns to scale model. Journal of International Financial Markets, Institutions and Money, 21, 75–91. https://doi.org/10.1016/j.intfin.2010.08.004
Fukuyama, H., & Matousek, R. (2017). Modelling bank performance: A network DEA approach. European Journal of Operational Research, 259, 721–732. https://doi.org/ 10.1016/j.ejor.2016.10.044
Fukuyama, H., & Tan, Y. (2022a). Deconstructing three-stage over efficiency into input, output and stability efficiency components with consideration of market power and loan loss provision: An application to Chinese banks. International Journal of Finance and Economics, 27, 953–974. https://doi.org/10.1002/ijfe.2185
Fukuyama, H., & Tan, Y. (2022b). Implementing strategic disposability for performance evaluation: Innovation, stability, profitability and corporate social responsibility in Chinese banking. European Journal of Operational Research, 296, 652–668. https:// doi.org/10.1016/j.ejor.2021.04.022
Fukuyama, H., Tsionas, M., & Tan, Y. (2023). Dynamic network data envelopment analysis with a sequential structure and behavioral-causal analysis: Application to the Chinese banking industry. European Journal of Operational Research, 307, 1360–1373. https://doi.org/10.1016/j.ejor.2021.04.022
Henriques, I. C., Sobreiro, V. A., Kimura, H., & Mariano, E. B. (2020). Two-stage DEA in banks: Terminological controversies and future directions. Expert Systems with Applications, 161 113632. https://doi.org/10.1016/j.eswa.2020.113632.
Gerami, J., Mozaffari, M. R., Wanke, P., & Tan, Y. (2024). Fuzzy cost, revenue efficiency assessment and target setting in fuzzy DEA: a fuzzy directional distance function approach. Journal of Modelling in Management, 19(1), 240-287. https://doi.org/10.1108/JM2-05-2022-0121
Izadikhah, M., Tavana, M., Di Caprio, D., & Santos-Arteaga, F. J. (2018). A novel two-stage DEA production model with freely distributed initial inputs and shared intermediate outputs. Expert Systems with Applications, 99, 213–230. https://doi.org/ 10.1016/j.eswa.2017.11.005
Kao, Ch. (2024). Maximum slacks-based measure of efficiency in network data envelopment analysis: A case of garment manufacturing. Omega, 123, 102989. https://doi.org/10.1016/j.omega.2023.102989
Kraidi, A. A., Daneshvar, S., &, Adesina, K. A. (2024). Weight-restricted approach on constant returns to scale DEA models: Efficiency of internet banking in Turkey. Heliyon, 10, 31008.
Konara, P., Tan, Y., & Johnes, J. (2019). FDI and heterogeneity in bank efficiency: Evidence from emerging markets. Research in International Business and Finance, 49, 100–113. https://doi.org/10.1016/j.ribaf.2019.02.008
Liu, X., Yang, F., & Wu, J. (2020). DEA considering technological heterogeneity and intermediate output target setting: the performance analysis of Chinese commercial banks. Annals of Operations Research, 291, 605–26.
https://doi.org/10.1007/s10479-019-03413-w
Podinovski, V. V. (2004). Production trade-offs and weight restrictions in data envelopment analysis. Journal of the Operational Research Society, 55 (12), 1311–1322. https://doi.org/10.1057/palgrave.jors.2601794.
Podinovski, V. V., & Bouzdine-Chameeva, T. (2013). Weight restrictions and free production in data envelopment analysis. Operations Research, 61 (2), 426–437. https://doi.org/10.1287/opre.1120.1122.
Podinovski, V.V., & Bouzdine-Chameeva, T. (2015). Consistent weight restrictions in data envelopment analysis. European Journal of Operational Research, 244(1), 201–209. https://doi.org/10.1016/j.ejor.2015.01.037.
Podinovski, V.V. (2016). Optimal Weights in DEA Models with Weight Restrictions. European Journal of Operational Research, 254(3), 916-924.
https://doi.org/10.1016/j.ejor.2016.04.035.
Podinovski,V.V., Wu, J., & Argyris, N. (2024). Production trade-offs in models of data envelopment analysis with ratio inputs and outputs: An application to schools in England. European Journal of Operational Research. 313 (1), 359-372. https://doi.org/10.1016/j.ejor.2023.08.019.
Joro, T., & Korhonen, P. (2015). Extension of data envelopment analysis with preference information. New York: International series in operations research & management science, Springer. https://doi.org/10.1007/978-1-4899-7528-7_6
Tan, Y., & Floros, C. (2018). Risk, competition and efficiency in banking: Evidence from China. Global Finance Journal, 35, 223–236. https://doi.org/10.1016/j. gfj.2017.12.001
Tan, Y., Floros, C., & Anchor, J. (2017). The Profitability of Chinese banks: Impacts of risk, competition and efficiency. Review of Accounting and Finance, 16, 86–105. https://doi.org/10.1108/RAF-05-2015-0072
Tone, K. (2002). A strange case of the cost and allocative efficiencies in DEA. Journal of the
Operational Research Society, 53, 1225-1231. https://doi.org/10.1057/palgrave.jors.2601438
Wanke, P., & Barros, C. (2014). Two-stage DEA: An application to major Brazilian banks. Expert Systems with Applications, 41, 2337–2344. https://doi.org/10.1016/j. eswa.2013.09.031
Wanke, P., Barros, C. P., & Emrouznejad, A. (2016). Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks. European Journal of Operational Research, 249, 378–389. https://doi.org/10.1016/j. ejor.2015.10.018
Wanke, P., Barros, C. P., & Emrouznejad, A. (2018). A comparison between stochastic DEA and fuzzy DEA approaches: Revisiting efficiency in Angolan banks. RAIRO-Operations Research, 52, 285–303. https://doi.org/10.1051/ro/2016065
Wang, K., Huang, W., Wu, J., & Liu, Y. N. (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 44, 5–20. https://doi.org/10.1016/j.omega.2013.09.005
Xu, G., Zhou, Z. (2021). Assessing the efficiency of financial supply chain for Chinese commercial banks: a two-stage AR-DEA model. Industrial Management & Data Systems.
121(4), 894–920. https://doi.org/10.1108/IMDS-01-2020-0022
Yin, P., Chu, J., Wu, J., Ding, J., Yang, M., & Wang, Y. (2020). A DEA-based two-stage network approach for hotel performance analysis: An internal cooperation perspective. Omega, 93, 102035. https://doi.org/10.1016/j.omega.2019.02.004
Zhou, X., Xu, Z., Chai, J., Yao, L., Wang, S., Lev, B. (2019). Efficiency evaluation for banking systems under uncertainty: a multi-period three-stage DEA model. Omega, 85:68–82. https://doi.org/10.1016/j.omega.2018.05.012
An, Q., Chen, H., Wu, J., Liang, L. (2015). Measuring slacks-based efficiency for commercial banks in China by using a two-stage DEA model with undesirable output. Annals of Operations Research, 235(1), 13–35. https://doi.org/10.1007/s10479-015-1987-1
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
Farrell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical
Society, Series A (General), 120, 253-290. https://www.jstor.org/stable/2343100
Fang, J., Lau, C.-K. M., Lu, Z., Tan, Y., & Zhang, H. (2019). Bank performance in China: A Perspective from Bank efficiency, risk-taking and market competition. Pacific-Basin Finance Journal, 56, 290–309. https://doi.org/10.1016/j.pacfin.2019.06.011
Fukuyama, H., & Matousek, R. (2011). Efficiency of Turkish banking: Two-stage network system. Variable returns to scale model. Journal of International Financial Markets, Institutions and Money, 21, 75–91. https://doi.org/10.1016/j.intfin.2010.08.004
Fukuyama, H., & Matousek, R. (2017). Modelling bank performance: A network DEA approach. European Journal of Operational Research, 259, 721–732. https://doi.org/ 10.1016/j.ejor.2016.10.044
Fukuyama, H., & Tan, Y. (2022a). Deconstructing three-stage over efficiency into input, output and stability efficiency components with consideration of market power and loan loss provision: An application to Chinese banks. International Journal of Finance and Economics, 27, 953–974. https://doi.org/10.1002/ijfe.2185
Fukuyama, H., & Tan, Y. (2022b). Implementing strategic disposability for performance evaluation: Innovation, stability, profitability and corporate social responsibility in Chinese banking. European Journal of Operational Research, 296, 652–668. https:// doi.org/10.1016/j.ejor.2021.04.022
Fukuyama, H., Tsionas, M., & Tan, Y. (2023). Dynamic network data envelopment analysis with a sequential structure and behavioral-causal analysis: Application to the Chinese banking industry. European Journal of Operational Research, 307, 1360–1373. https://doi.org/10.1016/j.ejor.2021.04.022
Henriques, I. C., Sobreiro, V. A., Kimura, H., & Mariano, E. B. (2020). Two-stage DEA in banks: Terminological controversies and future directions. Expert Systems with Applications, 161 113632. https://doi.org/10.1016/j.eswa.2020.113632.
Gerami, J., Mozaffari, M. R., Wanke, P., & Tan, Y. (2024). Fuzzy cost, revenue efficiency assessment and target setting in fuzzy DEA: a fuzzy directional distance function approach. Journal of Modelling in Management, 19(1), 240-287. https://doi.org/10.1108/JM2-05-2022-0121
Izadikhah, M., Tavana, M., Di Caprio, D., & Santos-Arteaga, F. J. (2018). A novel two-stage DEA production model with freely distributed initial inputs and shared intermediate outputs. Expert Systems with Applications, 99, 213–230. https://doi.org/ 10.1016/j.eswa.2017.11.005
Kao, Ch. (2024). Maximum slacks-based measure of efficiency in network data envelopment analysis: A case of garment manufacturing. Omega, 123, 102989. https://doi.org/10.1016/j.omega.2023.102989
Kraidi, A. A., Daneshvar, S., &, Adesina, K. A. (2024). Weight-restricted approach on constant returns to scale DEA models: Efficiency of internet banking in Turkey. Heliyon, 10, 31008.
Konara, P., Tan, Y., & Johnes, J. (2019). FDI and heterogeneity in bank efficiency: Evidence from emerging markets. Research in International Business and Finance, 49, 100–113. https://doi.org/10.1016/j.ribaf.2019.02.008
Liu, X., Yang, F., & Wu, J. (2020). DEA considering technological heterogeneity and intermediate output target setting: the performance analysis of Chinese commercial banks. Annals of Operations Research, 291, 605–26.
https://doi.org/10.1007/s10479-019-03413-w
Podinovski, V. V. (2004). Production trade-offs and weight restrictions in data envelopment analysis. Journal of the Operational Research Society, 55 (12), 1311–1322. https://doi.org/10.1057/palgrave.jors.2601794.
Podinovski, V. V., & Bouzdine-Chameeva, T. (2013). Weight restrictions and free production in data envelopment analysis. Operations Research, 61 (2), 426–437. https://doi.org/10.1287/opre.1120.1122.
Podinovski, V.V., & Bouzdine-Chameeva, T. (2015). Consistent weight restrictions in data envelopment analysis. European Journal of Operational Research, 244(1), 201–209. https://doi.org/10.1016/j.ejor.2015.01.037.
Podinovski, V.V. (2016). Optimal Weights in DEA Models with Weight Restrictions. European Journal of Operational Research, 254(3), 916-924.
https://doi.org/10.1016/j.ejor.2016.04.035.
Podinovski,V.V., Wu, J., & Argyris, N. (2024). Production trade-offs in models of data envelopment analysis with ratio inputs and outputs: An application to schools in England. European Journal of Operational Research. 313 (1), 359-372. https://doi.org/10.1016/j.ejor.2023.08.019.
Joro, T., & Korhonen, P. (2015). Extension of data envelopment analysis with preference information. New York: International series in operations research & management science, Springer. https://doi.org/10.1007/978-1-4899-7528-7_6
Tan, Y., & Floros, C. (2018). Risk, competition and efficiency in banking: Evidence from China. Global Finance Journal, 35, 223–236. https://doi.org/10.1016/j. gfj.2017.12.001
Tan, Y., Floros, C., & Anchor, J. (2017). The Profitability of Chinese banks: Impacts of risk, competition and efficiency. Review of Accounting and Finance, 16, 86–105. https://doi.org/10.1108/RAF-05-2015-0072
Tone, K. (2002). A strange case of the cost and allocative efficiencies in DEA. Journal of the
Operational Research Society, 53, 1225-1231. https://doi.org/10.1057/palgrave.jors.2601438
Wanke, P., & Barros, C. (2014). Two-stage DEA: An application to major Brazilian banks. Expert Systems with Applications, 41, 2337–2344. https://doi.org/10.1016/j. eswa.2013.09.031
Wanke, P., Barros, C. P., & Emrouznejad, A. (2016). Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks. European Journal of Operational Research, 249, 378–389. https://doi.org/10.1016/j. ejor.2015.10.018
Wanke, P., Barros, C. P., & Emrouznejad, A. (2018). A comparison between stochastic DEA and fuzzy DEA approaches: Revisiting efficiency in Angolan banks. RAIRO-Operations Research, 52, 285–303. https://doi.org/10.1051/ro/2016065
Wang, K., Huang, W., Wu, J., & Liu, Y. N. (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 44, 5–20. https://doi.org/10.1016/j.omega.2013.09.005
Xu, G., Zhou, Z. (2021). Assessing the efficiency of financial supply chain for Chinese commercial banks: a two-stage AR-DEA model. Industrial Management & Data Systems.
121(4), 894–920. https://doi.org/10.1108/IMDS-01-2020-0022
Yin, P., Chu, J., Wu, J., Ding, J., Yang, M., & Wang, Y. (2020). A DEA-based two-stage network approach for hotel performance analysis: An internal cooperation perspective. Omega, 93, 102035. https://doi.org/10.1016/j.omega.2019.02.004
Zhou, X., Xu, Z., Chai, J., Yao, L., Wang, S., Lev, B. (2019). Efficiency evaluation for banking systems under uncertainty: a multi-period three-stage DEA model. Omega, 85:68–82. https://doi.org/10.1016/j.omega.2018.05.012
