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Nonparametric frontier analysis with multiple constituencies


  • M-L Bougnol

    () (Western Michigan University)

  • J H Dulá

    (University of Mississippi)

  • D Retzlaff-Roberts

    (University of South Alabama)

  • N K Womer

    (University of Mississippi)


Abstract We introduce a methodology for generalizing Data Envelopment Analysis (DEA) to incorporate the role and impact of constituencies in the classification of the model's attributes. Constituencies determine whether entities' attributes in a DEA study are treated as desirable or undesirable. This extension of DEA is the basis for a methodology to answer questions that arise such as: Which constituencies find what entities efficient? Which entities are in the efficient frontier for a specified constituency? and What benchmarking prescriptions apply to inefficient entities for a given constituency? Constituencies allow new applications for DEA analyses of public projects to determine their impact on voters and marketing studies where a product defined by multiple attributes is analysed with respect to diverse markets, are two examples of the type of application for the new methodology. We introduce a DEA LP especially formulated for this new framework with many desirable properties. The new methodology is motivated and validated with a cost–benefit analysis application for a public project.

Suggested Citation

  • M-L Bougnol & J H Dulá & D Retzlaff-Roberts & N K Womer, 2005. "Nonparametric frontier analysis with multiple constituencies," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(3), pages 252-266, March.
  • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:3:d:10.1057_palgrave.jors.2601816
    DOI: 10.1057/palgrave.jors.2601816

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    References listed on IDEAS

    1. Caporaletti, L. E. & Dulá, J. H. & Womer, N. K., 1999. "Performance evaluation based on multiple attributes with nonparametric frontiers," Omega, Elsevier, vol. 27(6), pages 637-645, December.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Russell G. Thompson & F. D. Singleton & Robert M. Thrall & Barton A. Smith, 1986. "Comparative Site Evaluations for Locating a High-Energy Physics Lab in Texas," Interfaces, INFORMS, vol. 16(6), pages 35-49, December.
    4. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
    5. 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.
    6. Scheel, Holger, 2001. "Undesirable outputs in efficiency valuations," European Journal of Operational Research, Elsevier, vol. 132(2), pages 400-410, July.
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