Document Type : Research Paper

Authors

1 Master of Business Administration, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran

2 Assistant Professor, Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran

3 Department of Industrial Management, Rasht branch, Islamic Azad University, Rasht, Guilan, Iran

Abstract

In today's competitive world, many manufacturing and service firms, including banks, have been forced to use new managerial approaches, and methods for assessing organizational performance. The Data Envelopment Analysis (DEA) approach is one of these approaches, which has been used to evaluate the performance of organizations since 1978. The purpose of this research is to use the technology super-efficiency in measuring the efficiency of bank branches (Case Study: Melli Bank of Gilan Province). In this research, the input-vector model of data envelopment analysis, in the form of constant return-to-equilibrium, and Anderson-Pearson (AP) superclass, have been used to measure the efficiency of branches 1, 2 and 3 degrees in the Melli Bank of Gilan Province, in 2015. In summary, the results showed that the average of the efficiency score of the surveyed branches in 2015 is 0.75. The grade of a branch of Golsar, Rasht, was introduced as the most efficient case, and Kish Shahr Bandar was introduced as the most inefficient branch, due to a score of 0.8, lower than the average of the total number of branches. The results showed that the most important factor in the weakness of cost management in inefficient branches is the cost of advertising and marketing, and the most weakness in the planning of the field of staff training of the inefficient branches.

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Main Subjects

Avkiran, N. K. (2011). Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks. Omega, 39(3), 323-334.
Azar, A., Zarei, M., Abadi, M.,  Moghbel baarz, A., & Khadivar, A. (2014) ."Bank Branch Efficiency Measurement with Network Data Envelopment Analysis (One of the Banks of Gilan Province)", Quarterly Journal of Monetary-Bank Research , 20, 285-305.
Bray, S., Caggiani, L., Dell’Orco, M., & Ottomanelli, M. (2014). Features selection based on fuzzy entropy for Data Envelopment Analysis applied to transport systems. Transportation Research Procedia, 3, 602-610.
Chan, V. L. (2006). Effects of deregulation on bank efficiency and productivity in Taiwan. Academia.
Chang, K. C., Lin, C. L., Cao, Y., & Lu, C. F. (2011). Evaluating branch efficiency of a Taiwanese bank using data envelopment analysis with an undesirable factor. African Journal of Business Management, 5(8), 3220.
Chang, T. P., Hu, J. L., Chou, R. Y., & Sun, L. (2012). The sources of bank productivity growth in China during 2002–2009: A disaggregation view. Journal of Banking & Finance, 36(7), 1997-2006.
Charnes , A., Cooper, W.W., Rhodes E., (1978). Measuring the efficiency of the decision making units, European Journal of Operational Research 2 (6) 429–444.
Chen, Y. C., Chiu, Y. H., Huang, C. W., & Tu, C. H. (2013). “The analysis of bank business performance and market risk—Applying Fuzzy DEA”. Economic Modelling, 32, 225-232.
Cooper, L. M. Seiford, K. Tone, (2002).” Data Envelopment Analysis (a text with comprehensive models, applications, references and DEA-Solver software)”, Kluwer Academic, Publishers, USA, Third Printing.            
Isazadeh, S., & Mazhari, M. (2017). "The Effect of Management Efficiency on Reducing Costs after the Consolidation of Banks in Iran" Journal of Applied Economic Studies, 6(21), 173-187.
Joo, S. J., Nixon, D., & Stoeberl, P. A. (2011). Benchmarking with data envelopment analysis: a return on asset perspective. Benchmarking: An International Journal, 18(4), 529-542.
Kao, C., & Liu, S. T. (2009). Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks. European Journal of Operational Research, 196(1), 312-322.
Khakia, A. R., Najafib, S. E. & Rashidia, S. (2012). “Improving efficiency of decision making units through BSC-DEA technique” Management Science Letters 2, 245–252.
Lee, H. S., & Zhu, J. (2012). Super-efficiency infeasibility and zero data in DEA. European Journal of Operational Research, 216(2), 429-433.
Mehregan, M.R. (2008)."Quantitative Models in Organizational Performance Evaluation: Data Envelopment Analysis",Tehran, Tehran University Press, Faculty of Publications.
Min, H., Min, H., Joo, S. J., & Kim, J. (2013). “Evaluating the financial performances of Korean luxury hotels using data envelopment analysis” International Journal of quality & reliability management, 25(4),74-86.
Moshkinroo, M., Mirzai, M., & Rezanejad, A. (2015)."Rating of Bank Branches Using Super Efficiency Models" May 31-May 1994, Eighth International Conference of Iranian Operations Research Association, Ferdowsi University of Mashhad, Mashhad, Iran, May 31, 2015.
Paradi, J. C., Rouatt, S., & Zhu, H. (2011). Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega, 39(1), 99-109.
Portela, M. C., & Thanassoulis, E. (2010). Malmquist-type indices in the presence of negative data: An application to bank branches. Journal of banking & Finance, 34(7), 1472-1483.
Rakhshan, S. A. & Alirezaei, M. R. (2014). "Ranking of decision-making units using a non-radial super Efficiency model", Industrial Management Department of Tehran University of Management, Department of Engineering, Faculty of Management, Tehran University, Tehran, Iran, 6(2), 136-101.
Roghanian, P., Rasli, A., & Gheysari, H. (2012). Productivity through effectiveness and efficiency in the banking industry. Procedia-Social and Behavioral Sciences, 40, 550-556.
Saboonchi, R., & Mousavi, S. M. (2016). "Efficiency Analysis and Prioritization of Lorestan Province Sports and Youth Departments, Using Data Envelopment Analysis". Contemporary Research in Sport Management, 6(11).
Sasikala, P. (2013). Research challenges and potential green technological applications in cloud computing. International Journal of Cloud Computing, 2(1), 1-19.
Shahabinejad, V., Shahabinejad H., & Sistani Badui, Y. (2015). "Measuring Performance, and Comparing Growth Growth of Melli Bank Branches in Kerman Province, Using Inclusive Data Analysis". Quarterly Journal of Financial and Economic Policies ,3(12), 124- 105.
Siriopoulos, C., & Tziogkidis, P. (2010). How do Greek banking institutions react after significant events?—A DEA approach. Omega, 38(5), 294-308.
Toloo, M. & Joshaghani, S. (2010)."Users Guide to GAMS with DEA ​​Models", Tehran, Academic Edition.
Vecchiola, C., Pandey, S., & Buyya, R. (2009). “High-performance cloud computing: A view of scientific applications”. In Pervasive Systems, Algorithms, and Networks (ISPAN), 2009 10th International Symposium on (pp. 4-16). IEEE..
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.
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(1), 378-389.