Evaluation of the Dynamic Performance of Banking Industry Using NDEA in the Presence of Undesirable Intertemporal Intermediate Products (Case study: Tejarat Bank)
الموضوعات : فصلنامه ریاضیMohammad Reza Razmkhah 1 , Amin Mostafaee 2 , Saber Saati 3 , Maryam Shoar 4
1 - Department of Industrial Management, North Tehran Branch, Islamic Azad University, Tehran, Iran.
2 - Department of Mathematics, North Tehran Branch, Islamic Azad University, Tehran, Iran.
3 - Department of Mathematics, North Tehran Branch, Islamic Azad University, Tehran, Iran.
4 - Department of Mathematics, North Tehran Branch, Islamic Azad University, Tehran, Iran.
الکلمات المفتاحية: Efficiency, Network Data envelopment analysis, Slack based measure, Banking industry.,
ملخص المقالة :
Performance evaluation has become an unavoidable necessity for the long-term survival of organizations. In particular, efficiency measurement plays a central role in performance evaluation. The banking industry is considered as one of the most important economic sectors, and hence, one of the most important challenges in efficiency evaluation using Data Envelopment Analysis (DEA) is the selection of input-output factors. Since banking industry have complex and multi-stage process, traditional models that follow a simple black box structure cannot be used to evaluate the efficiency of banks. This study develops a slack based method for evaluating the dynamic efficiencies of banks with a network structure. The main aim of this study is to develop a dynamic two-stage DEA model for evaluating the efficiencies of 55 Tejarat bank branches in the presence of undesirable intertemporal intermediates. The results show that the proposed model measures the overall and stage efficiencies more accurately and reveals the source of branch inefficiency. The presented model can be developed straightforwardly to other network structures and applications.
1-Altoona’s, Y. Liu, M-h, Molneux, P. Seth, R. (2000), Efficiency and risk in Japanese banking, journal of banking and finance,31, p 0950 – 0931.
2-Chang, Y., Fare, R., and Grosskopf, S. (1997).Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3):229–240.
3-Charnes, A., Cooper, W. W., & Rhodes, E. (1978), “Measuring the efficiency of decision-making units”. European Journal of Operational Research, 2, 429-444.
4-Chao-Chung Kang, Cheng-Min Feng, Ping-Fung Chou, Wann-Ming Wey, Haider A. Khan Mixed network DEA models with shared resources for measuring and decomposing performance of public transportation systems January (2023) Other articles in Research in Transportation Business & Management Volume 46.
5-Cooper, W., Seaford, L. M., Tone, K., (2007). Data Envelopment Analysis a Comprehensive Text with Models Applications References and DEA Solver Software, 2nded, section 7, 219-223.
6-Despotis, K., D., Koronakos, G., Sotiros, D. (2016).Composition versus decomposition in two-stage network DEA: a reverse approach. Journal of Productivity Analysis, 45, 71-87.
7-Fukuyama, H., & Matousek, R. (2017).Modeling Bank Performance: A Network DEA Approach, European Journal of Operational Research, 259, 721-732.
8-Haicheng Xu, Yingjie Zheng, Yanling Li, Xingbo Xu, Yaqi Xie;(2023).Operational management efficiency and club convergence of Chinese state-owned toll road companies: A three-stage SBM-DEA model, Research in Transportation Business & Management January, 100915.
9-Holed, D., Lewis, H. F. (2011). Resolving the deposit dilemma: A new DEA bank efficiency model Journal of Banking and Finance, 35, 2811.
10-Jorge Antunes, Abdollah Hadi-Vencheh, Ali Jamshidi, Yong Tan, Peter Wanke,2024, Cost efficiency of Chinese banks: Evidence from DEA and MLP-SSRP analysis. Expert Systems with Applications Volume 237, Part A, 1 March 2024, 121432.
11-Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192, 949-962.
12-Kao, C. (2013). Dynamic data envelopment analysis: A relational analysis European Journal of Operational Research.
13-Kao, C. (2014). Efficiency decomposition for general multi-stage systems in data envelopment analysis. European Journal of Operational Research, 232, 117-124.
14-Kao,C.(2017).Dynamic Systems Network Data Envelopment Analysis, International Series in Operations Research & Management Science, 241, chapter 17, p p.419-431418-429.
15-Kao, C. (2019). Inefficiency identification for closed series production systems. European Journal of Operational Research, 275(2), 599-607.
16-Kao, C. (2018). A classification of slacks-based efficiency measures in network data envelopment analysis with an analysis of the properties possessed. European Journal of Operational Research, 270(3), 1109-1121.
17-Kao, C. (2024). Maximum slacks-based measure of efficiency in network data envelopment analysis: A case of garment manufacturing, Omega Volume 123, February 2024, 102989.
18-Kao, C., Hwang, S. N. (2010). Efficiency measurement for network systems: IT impact on firm performance. Decision Support Systems, 48, 437-446.
19-Kao, C., Liu, S. T. (2014). Multi-period efficiency measurement in data envelopment analysis: the case of Taiwanese commercial banks. Omega, 47, 90-98.
20-Kao, C., Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185, 418-429.
21-Lee, Chien-Chiang; Wenjie Ni; Xiaoming Zhang, FinTech development and commercial bank efficiency in China, Global Finance Journal, August 2023, 100850
22- Liu, J. S., Lu, L. Y., Lu, W. M. & Lin, B. j. (2013). A survey of DEA applications. Omega, 41, 893-912.
23-Mehdi Toloo; Tone, k; Mohammad Izadikhah;(2023). Selecting slacks-based data envelopment analysis models, European Journal of Operational Research Volume 308, Issue 3 Pages 949-1396.
24- Seaford, L. M. and Zhu, J. (2002).Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1):16–20.
25-Tone, k., & Tsutsui, M. (2010).Dynamic DEA: A slacks-based measure approach. Omega, 38, 3-4.
26-Tone, k., & Tsutsui, M. (2014).Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42, 124-131.
27-Tsung-Sheng Chang, Kaoru Tone, Chen-Hui Wu; Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation; European Journal of Operational Research 291(2021) 766-781.
28-Whacker, J. G. (1998).A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16(4), 361-385.
29-Xiao Shi, Ali Emrouznejad, Wenqi Yu, Overall efficiency of operational process with undesirable outputs containing both series and parallel processes: A SBM network DEA model the Expert Systems with Applications 15 September 2021, 115062.