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Comprehensive performance evaluation of banking branches: A three-stage slacks-based measure (SBM) data envelopment analysis

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  • Mahmoudabadi, Mohammad Zarei
  • Emrouznejad, Ali

Abstract

The productivity of the banks in any country is a key factor in the growth and development of that country's economy. Recently, the evaluation and improvement of the productivity of the banking industry has been taken into much consideration in Iran. Data Envelopment Analysis (DEA) is a comprehensive and accepted approach for assessing the performance of banking industry. Although extensive studies have been done on banking industry using standard DEA models, they are, in fact, they ignore the internal structure of bank performance. Since the overall operational process of the banking system is made up of several partial processes, network DEA models are used to take into account all the internal components of the process and the coherence of the whole process. This is also done as the evaluation of the efficiency of partial processes helps to identify the sources of inefficiency of the overall banking system. In the present study, a network Slacks-Based Measure (SBM) DEA model is used in which the efficiency of the overall system is equal to the weighted average of the efficiency of the individual stages. The main advantage of this model is its ability to provide better efficiency criteria, calculate the weight of each stages separately, and simultaneously evaluate the mediator variables as both input and output. Finally, the comprehensive performance evaluation of banking industry is designed in three divisions, namely, production, intermediation, and social welfare approach. The model is applied to simultaneously evaluate operational efficiency, service effectiveness, and social effectiveness for 37 branches of one of the largest commercial banks in Iran.

Suggested Citation

  • Mahmoudabadi, Mohammad Zarei & Emrouznejad, Ali, 2019. "Comprehensive performance evaluation of banking branches: A three-stage slacks-based measure (SBM) data envelopment analysis," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 359-376.
    • Handle: RePEc:eee:reveco:v:64:y:2019:i:c:p:359-376
    DOI: 10.1016/j.iref.2019.08.001
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    3. Jinkai Li & Jingjing Ma & Wei Wei, 2020. "Analysis and Evaluation of the Regional Characteristics of Carbon Emission Efficiency for China," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    4. Mushtaq Taleb & Ruzelan Khalid & Ali Emrouznejad & Razamin Ramli, 2023. "Environmental efficiency under weak disposability: an improved super efficiency data envelopment analysis model with application for assessment of port operations considering NetZero," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6627-6656, July.
    5. Imad Bou-Hamad & Abdel Latef Anouze & Ibrahim H. Osman, 2022. "A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information," Annals of Operations Research, Springer, vol. 308(1), pages 63-92, January.
    6. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
    7. Shi, Xiao & Wang, Libo & Emrouznejad, Ali, 2023. "Performance evaluation of Chinese commercial banks by an improved slacks-based DEA model," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    8. Hashem Omrani & Arash Alizadeh & Ali Emrouznejad & Zeynab Oveysi, 2023. "A novel best‐worst‐method two‐stage data envelopment analysis model considering decision makers' preferences: An application in bank branches evaluation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3593-3610, October.
    9. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    10. Zhang, Dongyang & Mohsin, Muhammad & Rasheed, Abdul Khaliq & Chang, Youngho & Taghizadeh-Hesary, Farhad, 2021. "Public spending and green economic growth in BRI region: Mediating role of green finance," Energy Policy, Elsevier, vol. 153(C).
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    12. Khosro Soleimani-Chamkhorami & Saeid Ghobadi, 2021. "Cost-efficiency under inter-temporal dependence," Annals of Operations Research, Springer, vol. 302(1), pages 289-312, July.

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