Analyzing the efficiency of bank branches via novel weighted stochastic imprecise data envelopment analysis
RAIRO. Operations Research, Tome 55 (2021) no. 3, pp. 1559-1578

As of 21st century, the terms of efficiency and productivity have become notions which dwells on both business and academic world more frequently compared to past. It is known that it is hard to increase the efficiency and productivity of both production and service systems. In this study, the efficiency analysis of the branches of a bank was conducted. Furthermore, a Weighted Stochastic Imprecise Data Envelopment Analysis (WSIDEA), which is a new approach developed based on Data Envelopment Analysis (DEA), was proposed. Efficiency levels and results of decision-making units were examined according to the proposed new method. Additionally, six different DEA model results are obtained. The results of the six different DEA model and the proposed “WSIDEA” model were compared in terms of efficiency level of decision-making units, and the differences between them were examined.

Sensitivity of the inefficient units were also examined. On the other hand, unrealistic efficiency levels created by traditional methods for branches were also analyzed. Apart from all these sensitivity analyses, the sensitivity of the data set used in the analysis is scrutinized.

DOI : 10.1051/ro/2021067
Classification : 90C08
Keywords: Data Envelopment Analysis, Banking, Weighted Stochastic Imprecise Data Envelopment Analysis, Sensitivity Analysis
@article{RO_2021__55_3_1559_0,
     author = {Aydin, Nezir and Yurdakul, G\"okhan},
     title = {Analyzing the efficiency of bank branches \protect\emph{via} novel weighted stochastic imprecise data envelopment analysis},
     journal = {RAIRO. Operations Research},
     pages = {1559--1578},
     year = {2021},
     publisher = {EDP-Sciences},
     volume = {55},
     number = {3},
     doi = {10.1051/ro/2021067},
     mrnumber = {4270856},
     zbl = {1471.91628},
     language = {en},
     url = {https://www.numdam.org/articles/10.1051/ro/2021067/}
}
TY  - JOUR
AU  - Aydin, Nezir
AU  - Yurdakul, Gökhan
TI  - Analyzing the efficiency of bank branches via novel weighted stochastic imprecise data envelopment analysis
JO  - RAIRO. Operations Research
PY  - 2021
SP  - 1559
EP  - 1578
VL  - 55
IS  - 3
PB  - EDP-Sciences
UR  - https://www.numdam.org/articles/10.1051/ro/2021067/
DO  - 10.1051/ro/2021067
LA  - en
ID  - RO_2021__55_3_1559_0
ER  - 
%0 Journal Article
%A Aydin, Nezir
%A Yurdakul, Gökhan
%T Analyzing the efficiency of bank branches via novel weighted stochastic imprecise data envelopment analysis
%J RAIRO. Operations Research
%D 2021
%P 1559-1578
%V 55
%N 3
%I EDP-Sciences
%U https://www.numdam.org/articles/10.1051/ro/2021067/
%R 10.1051/ro/2021067
%G en
%F RO_2021__55_3_1559_0
Aydin, Nezir; Yurdakul, Gökhan. Analyzing the efficiency of bank branches via novel weighted stochastic imprecise data envelopment analysis. RAIRO. Operations Research, Tome 55 (2021) no. 3, pp. 1559-1578. doi: 10.1051/ro/2021067

[1] R. G. Amin and M. Toloo, Finding the most efficient DMUs in DEA: an improved integrated model. Comput. Ind. Eng. 52 (2007) 71–77. | DOI

[2] P. Andersen and N. Petersen, A procedure for ranking efficient units in data envelopment analysis. Manage. Sci. 30 (1993) 1261–1264. | Zbl | DOI

[3] K. D. Atalay and A. Apaydin, Şans KisitliStokastik Programlama Problemlerinin Deterministik Eşitlikleri. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi 1 (2011) 1–18.

[4] M. Azadi and R. F. Saen, A new chance-constrained data envelopment analysis for selection third-party reverse logistics providers in the existence of dual role factors. Expert Syst. App. 38 (2011) 12231–12236. | DOI

[5] R. D. Banker, A. Charnes and W. W. Cooper, Some models for estimating tecnical and scale inefficiencies in data envelopment analysis. Manege. Sci. 30 (1984) 1078–1092. | Zbl | DOI

[6] Y. Bian and F. Yang, Resource and environment efficiency analysis of provinces in Chine: a DEA approach based on Shannon’s entropy. Energy Policy 38 (2010) 1909–1917. | DOI

[7] J. Borda, Memoire sur les Electin au Scrutin. Histoire de l’Academie Royale des Sciences 1781, Paris 12 (1784).

[8] J. E. Boscá, V. Liern, A. Martínez and R. Sala, Increasing offensive or defensive efficiency? An analysis of Italian and Spanish football. Omega 37 (2009) 63–78. | DOI

[9] S. Çakir and S. Perçin, AB Ülkelerinde Bütünleşik Entropi Ağirlik-Topsis Yöntemleriyle AR-GE performansinin Ölçülmesi. Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 32 (2013) 77–95.

[10] A. Charnes and W. W. Cooper, Chance-constrained programming. Manage. Sci. 6 (1959) 73–79. | MR | Zbl | DOI

[11] A. Charnes, W. W. Cooper and E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1978) 429–444. | MR | Zbl | DOI

[12] X. Chen and C.-C. Lu, The impact of the macroeconomic factors in the bank efficiency: evidence from the Chinese city banks. North Am. J. Econ. Finance 55 (2020) 101294. | DOI

[13] Y. Chen, W. D. Cook, N. Li and J. Zhu, Additive efficiency decomposition in two-stage DEA. Eur. J. Oper. Res. 196 (2009) 1170–1176. | Zbl | DOI

[14] A. Chitnis and O. Vaidya, Performance assessment of tennis players: application of DEA. Soc. Behav. Sci. 133 (2014) 74–83. | DOI

[15] W. D. Cook and J. Zhu, Classifying inputs and outputs in data envelopment analysis. Eur. J. Oper. Res. 180 (2007) 692–699. | Zbl | DOI

[16] W. W. Cooper, H. Deng, Z. Huang and S. X. Li, Chance constrained programming approaches to congestion in stochastic data envelopment analysis. Eur. J. Oper. Res. 155 (2004) 487–501. | MR | Zbl | DOI

[17] W. Cooper, J. L. Ruiz and I. Sirvent, Selecting non-zero weights to evaluate effectiveness of basketball players with DEA. Eur. J. Oper. Res. 195 (2009) 563–574. | Zbl | DOI

[18] Ö. Cosgun and G. Yurdakul, Performance evaluation of an apparel retailer’s stores by using stochastic imprecise DEA. J. Mult.-Valued Logic Soft Comput. 34 (2020) 59–75.

[19] M. N. Coşkun, A. H. Çermikli, H. O. Eruygur, F. Öztürk, İ. Tokatlioğlu, G. Aykaç and T. Dağlaroğlu, Türkiye’de Bankacilik Sektörü Piyasa Yapisi, Firma Davranişlarive Rekabet Analizi. Türkiye Bankalar Birliği, İstanbul (2012).

[20] J. De Gregorio and P. E. Guidotti, Financial development and economic growth. World Dev. 23 (1995) 433–448. | DOI

[21] E. Deliktaş and M. Balcilar, A comparative analysis of productivity growth, catch-up, and convergence in transition economies. Emerg. Markets Finance Trade 41 (2005) 6–28. | DOI

[22] E. Demireli and A. Y. Özdemir, Seçilmiş Avrupa Ülkeleride Makroekonomik Performans Ölçümü: Şans KisitliVeri Zarflama Analizi ile Bir Uygulama. Dumlupinar Üniversitesi Sosyal bilimler Dergisi 37 (2013) 303–330.

[23] T. Ertay, D. Ruan and U. R. Tuzkaya, Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems. Inf. Sci. 176 (2006) 237–262. | DOI

[24] R. Farzipoor Saen, Suppliers selection in the presence of both cardinal and ordinal data. Eur. J. Oper. Res. 183 (2007) 741–747. | Zbl | DOI

[25] D. A. Grigorian and V. Manole, Determinants of commercial bank performance in transition: An application of data envelopment analysis. Comp. Econ. Stud. 48 (2006) 497–522. | DOI

[26] P. Guo and H. Tanaka, Fuzzy DEA: a perceptual evaluation method. Fuzzy Set Syst. 119 (2001) 149–160. | MR | DOI

[27] B. Hsiao, C.-C. Chern and C.-R. Chiu, Perdormance evaluation with entropy based weighted Russel measure in data envelopment analysis. Expert Syst. App. 38 (2011) 9965–9972. | DOI

[28] D. Ju-Long, Control Problems of Grey Systems. Syst. Control Lett. 1 (1982) 288–294. | MR | Zbl | DOI

[29] C. Kao and S.-N. Hwang, Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. Eur. J. Oper. Res. 185 (2008) 418–429. | Zbl | DOI

[30] K. Kayalidere and S. Kargin, Çimento ve Tekstil Sektöründe Etkinlik Çalişmasive Veri Zarflama Analizi. Doküz Eylül Üniversitesi Sosyal Bilimler Dergisi 6 (2004) 196–219.

[31] M. Khodabakhshi, A super-efficiency model based on improved outputs in data envelopment analysis. Appl. Math. Comput. 184 (2007) 695–703. | MR | Zbl

[32] Kpmg, Bankacilik: Sektörel Bakiş. KPMG, İstanbul (2019).

[33] K. C. Land, C. K. Lovell and S. Thore, Chance-constrained Data Envelopment Analysis. Manage. Decis. Econ. 14 (1993) 541–554. | DOI

[34] H. F. Lewis, K. A. Lock and T. R. Sexton, Organizational capability, efficiency, and effectiveness in Major League Baseball: 1901–2002. Eur. J. Oper. Res. 197 (2009) 731–740. | Zbl | DOI

[35] J. S. Liu, L. Y. Lu and W.-M. Lu, Research fronts in data envelopment analysis. Omega 58 (2016) 33–45. | DOI

[36] R. Mansour and C. El Moussawi, Efficiency, technical progress and productivity of Arab banks: a non-parametric approach. Q. Rev. Econ. Finance 75 (2020) 191–208. | DOI

[37] E. D. Mecit and İ. Alp, A new restricted model using correlation coefficients as an alternative to cross-efficiency evaluation in Data Envelopment Analysis. Hacettepe J. Math. Stat. 41 (2012) 321–335. | MR | Zbl

[38] O. B. Olesen and N. C. Petersen, Stochastic Data Envelopment Analysis: a review. Eur. J. Oper. Res. 251 (2015) 1–20. | MR | Zbl

[39] A. D. Ross, K. Kuzu and W. Li, Exploring supplier performance risk and buyer’s role using chance-constraint data envelopment analysis. Eur. J. Oper. Res. 250 (2015) 1–13. | MR | Zbl

[40] A. P. S. Rubem and L. C. Brandão, Multiple criteria data envelopment analysis – An application to UEFA EURO 2012. Proc. Comput. Sci. 55 (2015) 186–195. | DOI

Cité par Sources :