Detecting Critical Input Measure Of Regional Rural Banks In India Through Multifaceted Super-Efficiency Model
Keywords:
Tangible Banking Activities, Efficiency Score, Multiple Performance Measures, Regional Rural Bank.Abstract
The transition with the well-argued restructuring of the financial sector that was initiated globally over the last few years has affected the functioning of not only the banking industry in India but also the efficiency and effectiveness of Regional Rural Banks with a diverse range of decision issues. Since the events associated with everyday decision tradeoffs among the multiple variables the RRB units can filter out costly errors before they do any damage and can influence the performance, so deciding with the multiple measures, the managerial prerequisites are to identify and utilize different influential inputs by pursuing the best-practiced resource regenerating process to put the operating unit in a stronger position. Ruling out all other possibilities than the chosen one can best be identified with the relative magnitudes of different inputs and outputs. Using last year published data of all the forty-five RRBs, operating all over the country, the study drill down to identify the decisive one from the three inputs namely non-performing assets, operating expenditure, and interest costs on a bundle of tangible activities covering net profit, number of branches, and total business, as these are more regular in banking operation. The best practice rank based on efficiency score is evaluated to see whether a change in the value of one or a set of decisive factors significantly affects the performance ranking between the banking units under study. The study provided two key interesting indications: first, there is almost adequate uniformity in the performance grades between the RRB units with a clear signal that the non-performing assetsare not the only decisive value driver to change the unsatisfying performance score of the banking units under study, except five decision-making units, and second, only one- fourth of the RRB units are more fulfilling in achieving the best practice status with a set of decisive variables
JEL classification N5; O 44; Q56
.
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Minghua, C., & Rui, W. (2017). Monetary policy and bank risk-taking: Evidence from emerging economies. Emerging Market Review. Vol. 31. Issue: June, 116-140.
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Xiaoyang, Z. Z., & Benjamin, L. (2019). Efficiency evaluation for banking systems under uncertainty: A multi-period three-stage DEA model . Omega, Vol. 85, June, 68-82.
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