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Performance measurement with classification information: an enhanced additive DEA model

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  • Bala, Kamel
  • Cook, Wade D.

Abstract

This paper presents an improved measurement tool for evaluating performance of branches within a major Canadian bank. While there have been numerous previous studies of performance in the banking industry, particularly at the branch level, this study is different in a very significant way: specifically two kinds of data are used to develop the model. The first type of data is that related to standard transactions, available from any bank; such have formed the basis of numerous previous studies. The second type of data, obtained from the site studied, is classification information, based on branch consultant/expert judgment as to good and poor performance of branches. The purpose herein is to present a modified version of an existing benchmarking model, data envelopment analysis (DEA), and to show how this tool is applied in the banking industry. The mechanism used herein to incorporate expert knowledge within the DEA framework is to first apply a discriminant or classification tool, to quantify the functional relation that best captures the expert's mental model for performance. The outcome of this first phase is an orientation of variables to aid in the definition of inputs and outputs. The resulting orientation then defines the DEA model that makes up the second phase of the model.

Suggested Citation

  • Bala, Kamel & Cook, Wade D., 2003. "Performance measurement with classification information: an enhanced additive DEA model," Omega, Elsevier, vol. 31(6), pages 439-450, December.
    • Handle: RePEc:eee:jomega:v:31:y:2003:i:6:p:439-450
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    References listed on IDEAS

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    1. Schaffnit, Claire & Rosen, Dan & Paradi, Joseph C., 1997. "Best practice analysis of bank branches: An application of DEA in a large Canadian bank," European Journal of Operational Research, Elsevier, vol. 98(2), pages 269-289, April.
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    5. Cook, Wade D. & Hababou, Moez, 2001. "Sales performance measurement in bank branches," Omega, Elsevier, vol. 29(4), pages 299-307, August.
    6. H. David Sherman & George Ladino, 1995. "Managing Bank Productivity Using Data Envelopment Analysis (DEA)," Interfaces, INFORMS, vol. 25(2), pages 60-73, April.
    7. Wade Cook & Moez Hababou & Hans Tuenter, 2000. "Multicomponent Efficiency Measurement and Shared Inputs in Data Envelopment Analysis: An Application to Sales and Service Performance in Bank Branches," Journal of Productivity Analysis, Springer, vol. 14(3), pages 209-224, November.
    8. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
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    Citations

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    Cited by:

    1. Jesús Pastor & C. Lovell & Henry Tulkens, 2006. "Evaluating the financial performance of bank branches," Annals of Operations Research, Springer, vol. 145(1), pages 321-337, July.
    2. Edirisinghe, N.C.P. & Zhang, X., 2010. "Input/output selection in DEA under expert information, with application to financial markets," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1669-1678, December.
    3. Shimshak, Daniel G. & Lenard, Melanie L. & Klimberg, Ronald K., 2009. "Incorporating quality into data envelopment analysis of nursing home performance: A case study," Omega, Elsevier, vol. 37(3), pages 672-685, June.
    4. Cook, Wade D. & Bala, Kamel, 2007. "Performance measurement and classification data in DEA: Input-oriented model," Omega, Elsevier, vol. 35(1), pages 39-52, February.
    5. Paradi, Joseph C. & Rouatt, Stephen & Zhu, Haiyan, 2011. "Two-stage evaluation of bank branch efficiency using data envelopment analysis," Omega, Elsevier, vol. 39(1), pages 99-109, January.
    6. G. Tohidi & F. Matroud, 2017. "A new non-oriented model for classifying flexible measures in DEA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1019-1029, September.
    7. Sohrab Kordrostami & Soheila Seyedboveir & Behrouz Daneshian & Alireza Amirteimoori, 2018. "Efficiency measurement in dynamic two-stage network structures with flexible intermediate materials," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 28(3), pages 63-76.
    8. Dan Li & Yanfeng Li & Yeming Gong & Jiawei Yang, 2021. "Estimation of bank performance from multiple perspectives: an alternative solution to the deposit dilemma," Journal of Productivity Analysis, Springer, vol. 56(2), pages 151-170, December.
    9. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    10. Liu, W.B. & Zhang, D.Q. & Meng, W. & Li, X.X. & Xu, F., 2011. "A study of DEA models without explicit inputs," Omega, Elsevier, vol. 39(5), pages 472-480, October.
    11. Gutiérrez-Nieto, Begoña & Serrano-Cinca, Carlos & Mar Molinero, Cecilio, 2007. "Microfinance institutions and efficiency," Omega, Elsevier, vol. 35(2), pages 131-142, April.
    12. Dejene Mamo Bekana (MBA) & Asres Abitie (MBA), 2012. "Evaluation Of Financial Performance Of Banking Enterprises; The Case Of Construction And Business Bank Of Ethiopia," Revista Tinerilor Economisti (The Young Economists Journal), University of Craiova, Faculty of Economics and Business Administration, vol. 1(18), pages 82-102, April.
    13. Joseph Paradi & Sandra Vela & Haiyan Zhu, 2010. "Adjusting for cultural differences, a new DEA model applied to a merged bank," Journal of Productivity Analysis, Springer, vol. 33(2), pages 109-123, April.
    14. Rita Shakouri & Maziar Salahi & Sohrab Kordrostami & Jie Wu, 2019. "Flexible measure in the presence of the partial input to output impacts process," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 29(3), pages 77-98.
    15. Chambers, Robert G., 2023. "Dual structures for the additive DEA model," European Journal of Operational Research, Elsevier, vol. 307(2), pages 984-989.
    16. Asmild, Mette & Bogetoft, Peter & Leth Hougaard, Jens, 2013. "Rationalising inefficiency: Staff utilisation in branches of a large Canadian bank," Omega, Elsevier, vol. 41(1), pages 80-87.
    17. Paradi, Joseph C. & Zhu, Haiyan, 2013. "A survey on bank branch efficiency and performance research with data envelopment analysis," Omega, Elsevier, vol. 41(1), pages 61-79.
    18. Cook, Wade D. & Zhu, Joe, 2007. "Classifying inputs and outputs in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 180(2), pages 692-699, July.
    19. Z Yang, 2009. "Assessing the performance of Canadian bank branches using data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 771-780, June.

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