Designing A Mixed System of Network DEA for Evaluating the Efficiency of Branches of Commercial Banks in Iran

Subject Areas : Multi-Criteria Decision Analysis and its Application in Financial Management

Sajad  Akbari ¹ (Ph.D. student of Industrial Engineering, Kish International Campus, University of Tehran)
Jafar  heydari ² (School of Industrial and Systems Engineering, College of Engineering, University of Tehran,Tehran)
Mohammadali  Keramati ³ (Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
Abbas  Keramati ⁴ (School of Industrial and Systems Engineering, College of Engineering, University of Tehran, and Ted Rogers School of Information Technology Management, Ryerson University, Toronto, ON, Canada)

Received: 2019-01-07 Accepted : 2019-02-23 Published : 2019-03-01

Keywords: network, Efficiency, Data envelopment analysis, Performance Evaluation, Banking industry,

Abstract :

One of the most important applications of data envelopment analysis tech-nique is measuring the efficiency of bank branches. Performance measure-ment in the banking industry is important for several groups, including bank managers, customers, investors, and shareholders. The purpose of this study is to examine and design a mixed structure to measure the efficiency of branches of Iranian banks according to their policies. In order to obtain the efficiency of the structure divisions proposed in this study, a slack-based NDEA model was selected to solve its mathematical model. The study sam-ple consists of 31 branches of a large commercial bank in Iran. The ad-vantage of this research to previous studies is that the result will be more realistic considering the inputs and outputs consistent with Iran's banking conditions.

References:

[1]         Paradi JC, Rouatt S, Zhu H. Two-stage evaluation of bank branch efficiency using data envelopment analysis, Omega, 2011, 39(1), P.99-109. Doi:10.1016/j.omega.2010.04.002.

[2]         Wang K, Huang W, Wu J, Liu Y-N. Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 2014, 44, P.5-20. Doi:10.1016/j.omega.2013.09.005.

[3]         Huang C-W, Chiu Y-H, Lin C-H, Liu H-H. Using a hybrid system DEA model to analyze the influence  of authomatic banking service on commercial bank’s efficiency, J. Oper Res Soc Japan, 2012, 55(4), P. 209-224. Doi: 10.15807/jorsj.55.209.

[4]         Kao C. Network data envelopment analysis: A review. Eur. J. Oper. Res., 2014, 239 (1), P.1-16. Doi:10.1016/j.ejor.2014.02.039.

[5]         Kao C. Network Data Envelopment Analysis: Foundations and Extensions. 240. Springer; 2017. Doi:10.1007/978-3-319-31718-2_7.

[6]         Färe, R., Grosskopf S. Network DEA, Springer, Boston,2000, Doi:10.1007/978-0-387-71607-7_12.

[7]         Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units., Eur. J. Oper. Res. 1978, 2(6), P.429-444. Doi:10.1016/0377-2217(78)90138-8.

[8]         Benston GJ. Branch banking and economies of scale, J. Finance, 1965, 20(2), P.312-331. Doi: 10.1111/j .1540-6261.1965.tb00212.x.

[9]         Greenbaum SI. Competition and Efficiency in the Banking System--Empirical Research and Its Policy Implications, J Polit Econ., 1967, 75(4) , P.461-479.Doi:10.1086/259318.

[10]       Avkiran NK. Opening the black box of efficiency analysis: an illustration with UAE banks. Omega, 2009, 37(4), P.930-941. Doi:10.1016/j.omega.2008.08.001.

[11]       Lin T-Y, Chiu S-H. Using independent component analysis and network DEA to improve bank performance evaluation. Econ Model, 2013, 32, P.608-616.Doi:10.1016/j.econmod.2013.03.003.

[12]       Tone K, Tsutsui M. Network DEA : A slacks-based measure approach, Eur. J. Oper. Res., 2009, 197(1), P.243-252.Doi:10.1016/j.ejor.2008.05.027.

[13]       Goyal J, Aggarwal A. Efficiency and technology gaps in Indian banking sector: Application of meta-frontier directional distance function DEA approach. J. Financ. Data, Sci., 2018, 10, P.1-17. Doi:/10.10 16/j.jfds.2018.08.002.

[14]       Wanke P, Maredza A, Gupta R. Merger and acquisitions in South African banking: A network DEA model. Res Int Bus Financ. 2017,41,P.362-376. Doi:10.1016/j.ribaf.2017.04.055.

[15]       Abreu ES De, Kimura H, Sobreiro VA. What Is Going On with Studies on Banking Efficiency? Res. Int. Bus. Financ., 2019, 47(C), P. 195-219..Doi:10.1016/j.ribaf.2018.07.010.

[16]       Fernandes FDS, Stasinakis C, Bardarova V. Two-stage DEA-Truncated Regression: Application in banking efficiency and financial development. Expert Syst Appl, 2018, 96, P.284-301. Doi:10.1016/j.es wa.2017.12.010.

[17]       Zhou X, Xu Z, Chai J, Yao L, Wang S, Lev B. Efficiency evaluation for banking systems under uncerta-inty: A multi-period three-stage DEA model. Omega. 2018,(accepted) Doi:10.1016/j.omega.201 8.05.012.

[18]       Keramati A, Azadeh MA, Mehran-Gohar M, Afshari-Mofrad M. Investigating the effects of information technology investment on bank performance: considering the role of environmental dynamism and strategy. Int J Appl Decis Sci., 2012, 5(1), P.32-46. Doi: 0.1504/IJADS.2012.044945.

[19]       Ebrahimnejad A, Tavana M, Lotfi FH, Shahverdi R, Yousefpour M. A three-stage data envelopment analysis model with application to banking industry. Measurement. 2014, 49, P.308-319. Doi:10.1016/j. measurement.2013.11.043.

[20]       Jahanshahloo GR, Amirteimoori AR, Kordrostami S. Multi-component performance, progress and regress measurement and shared inputs and outputs in DEA for panel data: an application in com-mercial bank branches. Appl Math Comput., 2004, 151(1), P.1-16. Doi:10.1016/S0096-3003(03)00318-7.

[21]       Amirteimoori A, Nashtaei RA. The role of time in multi-component efficiency analysis: An application. Appl Math Comput. 2006, 177(1),P.1-17. Doi:https://doi.org/10.1016/j.amc.2005.10.029.

[22]       Izadikhah M. Improving the Banks Shareholder Long Term Values by Using Data Envelopment Analysis Model. Adv Math Financ Appl. 2018, 3(2), P.27-41. Doi:10.22034/AMFA.2018.540829.

[23]       Izadikhah M, Tavana M, Di Caprio, D., A novel two-stage DEA production model with freely distributed initial inputs and shared intermediate outputs. Expert Syst Appl., 2018, 99, P. 213-230. Doi:10.1016/j.eswa.2017. 11.005.

[24]       Tavana M, Izadikhah M, Di Caprio, D., Saen RF. A New Dynamic Range Directional Measure for Two-Stage Data Envelopment Analysis Models with Negative Data. Comput Ind Eng. 2018, 115, P. 427-448, Doi:10.1016/j .cie.2017.11.024.

[25]   Esfandiar M, Saremi M, Nia HJ. Assessment of the Efficiency of Banks Accepted in Tehran Stock Exchange Using the Data Envelopment Analysis Technique. Adv Math Financ Appl. 2018, 3(2), P.1-11. Doi:10.22034/AMFA.2018.540815.

[26]     Kao C. Efficiency decomposition in network data envelopment analysis : A relational model. Eur J Oper Res. 2009, 192, P.949-962. Doi:10.1016/j.ejor.2007.10.008.