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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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    6. Svetlana V. Ratner & Artem M. Shaposhnikov & Andrey V. Lychev, 2023. "Network DEA and Its Applications (2017–2022): A Systematic Literature Review," Mathematics, MDPI, vol. 11(9), pages 1-24, May.
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    8. 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.
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