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Ranking bank branches using DEA and multivariate regression models

Author

Listed:
  • Reza Kiani Mavi
  • Reza Farzipoor Saen
  • Neda Kiani Mavi
  • Sina Saeid Taleshi
  • Zeinab Rezaei Majd

Abstract

Service companies continually seek improved methods to measure the performance of their organisations because they are committed to improve efficiency and effectiveness in their operating units. Managers generally regard conventional methods inadequate. DEA has proven itself to be both theoretically sound framework for performance measurement and an acceptable method by those being measured. This paper assesses bank branches efficiency using DEA technique and multivariate regression techniques. Here, we proposed two multivariate regression models. In model (1), we used the exact data and in model (2), we used weighted data for fitting the regression equation. Weights were attributed to input variables based on group analytic hierarchy process. The efficiency of this approach is tested with application in bank branches. According to the results, weighted multivariate regression model has more advantages over conventional methodologies. LINGO software is used for obtaining efficiency scores in DEA.

Suggested Citation

  • Reza Kiani Mavi & Reza Farzipoor Saen & Neda Kiani Mavi & Sina Saeid Taleshi & Zeinab Rezaei Majd, 2015. "Ranking bank branches using DEA and multivariate regression models," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 24(3), pages 245-261.
    • Handle: RePEc:ids:ijores:v:24:y:2015:i:3:p:245-261
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    Cited by:

    1. Carlo Drago & Loris Di Nallo & Maria Lucetta Russotto, 2023. "Social Sustainability in European Banks: A Machine Learning Approach using Interval- Based Composite Indicators," Working Papers 2023.13, Fondazione Eni Enrico Mattei.

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