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Identifying managerial groups in a large Canadian bank branch network with a DEA approach

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  • Paradi, Joseph C.
  • Zhu, Haiyan
  • Edelstein, Barak

Abstract

This paper develops a new grouping approach in using data envelopment analysis as a framework to identify management groups and group performance leaders. The management group relies on the fact that branches grouped together present similar managerial preferences over performance goals and resource deployments due to internal or external market forces. This grouping approach can help a firm to create continuous improvement opportunities with effectively promoting the best managerial practices within groups, given similar operating characteristics. This approach’s grouping power and rationality is examined in the context of a large Canadian bank with about 1000 branches. The advantages of this new grouping approach are further verified through comparisons with the results obtained from the traditional clustering algorithm and the collaborating Bank’s “community type and population size” grouping criteria.

Suggested Citation

  • Paradi, Joseph C. & Zhu, Haiyan & Edelstein, Barak, 2012. "Identifying managerial groups in a large Canadian bank branch network with a DEA approach," European Journal of Operational Research, Elsevier, vol. 219(1), pages 178-187.
    • Handle: RePEc:eee:ejores:v:219:y:2012:i:1:p:178-187
    DOI: 10.1016/j.ejor.2011.12.022
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    Cited by:

    1. Sakouvogui Kekoura & Shaik Saleem & Addey Kwame Asiam, 2020. "Cluster-Adjusted DEA Efficiency in the presence of Heterogeneity: An Application to Banking Sector," Open Economics, De Gruyter, vol. 3(1), pages 50-69, January.
    2. Kutlu, Levent & Mamatzakis, Emmanuel & Tsionas, Mike G., 2022. "A principal–agent approach for estimating firm efficiency: Revealing bank managerial behavior," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    3. Yung‐ho Chiu & Tai‐Yu Lin & Tzu‐Han Chang & Yi‐Nuo Lin & Shih‐Yung Chiu, 2021. "Prevaluating efficiency gains from potential mergers and acquisitions in the financial industry with the Resample Past–Present–Future data envelopment analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(2), pages 369-384, March.
    4. Mihăiță-Cosmin M. POPOVICI, 2013. "A Survey On Bank Efficiency Research With Data Envelopment Analysis And Stochastic Frontier Analysis," SEA - Practical Application of Science, Romanian Foundation for Business Intelligence, Editorial Department, issue 1, pages 134-142, June.
    5. Mohamed Dia & Amirmohsen Golmohammadi & Pawoumodom M. Takouda, 2020. "Relative Efficiency of Canadian Banks: A Three-Stage Network Bootstrap DEA," JRFM, MDPI, vol. 13(4), pages 1-25, April.
    6. Kok Fong See & Yong He, 2015. "Determinants of Technical Efficiency in Chinese Banking: A Double Bootstrap Data Envelopment Analysis Approach," Global Economic Review, Taylor & Francis Journals, vol. 44(3), pages 286-307, September.
    7. repec:cmj:journl:y:2013:i:27:popovicimc is not listed on IDEAS
    8. Pavlos Almanidis & Mustafa U. Karakaplan & Levent Kutlu, 2019. "A dynamic stochastic frontier model with threshold effects: U.S. bank size and efficiency," Journal of Productivity Analysis, Springer, vol. 52(1), pages 69-84, December.
    9. Herrera-Restrepo, Oscar & Triantis, Konstantinos, 2019. "Enterprise design through complex adaptive systems and efficiency measurement," European Journal of Operational Research, Elsevier, vol. 278(2), pages 481-497.

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