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Context-dependent performance standards in DEA

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Abstract

Data envelopment analysis (DEA) is a mathematical approach to measuring the relative efficiency of peer decision making units (DMUs). It is particularly useful where no a priori information on the tradeoffs or relations among various performance measures is available. However, it is very desirable if “evaluation standards,” when they can be established, be incorporated into DEA performance evaluation. This is especially important when service operations are under investigation, because service standards are generally difficult to establish. The approaches that have been developed to incorporate evaluation standards into DEA, as reported in the literature, have tended to be rather indirect, focusing primarily on the multipliers in DEA models. This paper introduces a new way of building performance standards directly into the DEA structure when context-dependent activity matrixes exist for different classes of DMUs. For example, two sets of branches, whose transaction times are known to be different from each other, usually have two different activity matrixes. We develop a procedure so that a set of standard DMUs can be generated and incorporated directly into the DEA analysis. The proposed approach is applied to a sample of 100 branches of a major Canadian bank where different sets of time standards exist for three distinct groups of branches.

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References

  • Ali, A. I., & Seiford, L. M. (1993). Computational accuracy and infinitesimals in data envelopment analysis. INFOR, 31(4), 290–297.

    Google Scholar 

  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.

    Article  Google Scholar 

  • Barr, R. S., & Siems, T. F. (1994). Predicting bank failure using DEA to quantify management quality. Federal Reserve Bank of Dallas Financial Industry Studies, 1, 1–31.

    Google Scholar 

  • Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: international survey and directions for future research. European Journal of Operational Research, 98(2), 175–212.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., Golany, B., Seiford, L. M., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics, 30(1/2), 91–107.

    Article  Google Scholar 

  • Chen, Y., Morita, H., & Zhu, J. (2003). Multiplier bounds in DEA via strong complementary slackness condition solution. International Journal of Production Economics, 86(1), 11–19.

    Article  Google Scholar 

  • Cook, W. D., & Hababou, M. (2001). Sales performance measurement in bank branches. OMEGA, 29, 299–307.

    Article  Google Scholar 

  • Cook, W. D., & Zhu, J. (2005). Building performance standards into data envelopment analysis structures. IIE Transactions, 37, 267–275.

    Article  Google Scholar 

  • Cook, W. D., & Zhu, J. (2006). Incorporating multi-process performance standards into the DEA framework. Operations Research, 54(4), 656–665.

    Article  Google Scholar 

  • Cook, W. D., Hababou, M., & Tuenter, H. (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, 14, 209–224.

    Article  Google Scholar 

  • Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: a comprehensive text with models, applications, references. Boston: Kluwer Academic.

    Google Scholar 

  • Golany, B., & Roll, Y. (1994). Incorporating standards via data envelopment analysis. In A. Charnes, W. W. Cooper, A. Y. Lewin, & L. M. Seiford (Eds.), Data envelopment analysis: theory, methodology, and applications. Boston: Kluwer Academic.

    Google Scholar 

  • Sherman, H. D., & Gold, F. (1985). Bank branch operating efficiency: evaluation with data envelopment analysis. Journal of Banking and Finance, 9(2), 297–315.

    Article  Google Scholar 

  • Thompson, R. G., Langemeier, L. N., Lee, C. T., Lee, E., & Thrall, R. M. (1990). The role of multiplier bounds in efficiency analysis with application to Kansas farming. Journal of Econometrics, 46, 93–108.

    Article  Google Scholar 

  • Zhu, J. (2003). Quantitative models for performance evaluation and benchmarking: DEA with spreadsheets and DEA Excel solver. Boston: Kluwer Academic.

    Google Scholar 

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Correspondence to Wade D. Cook.

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Cook, W.D., Zhu, J. Context-dependent performance standards in DEA. Ann Oper Res 173, 163–175 (2010). https://doi.org/10.1007/s10479-008-0421-3

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