Nonparametric frontier analysis with multiple constituencies

Bougnol M-L (), J H Dulá, D Retzlaff-Roberts and Norman Womer
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Bougnol M-L: Western Michigan University
J H Dulá: University of Mississippi
D Retzlaff-Roberts: University of South Alabama

Journal of the Operational Research Society, 2005, vol. 56, issue 3, 252-266

Abstract: 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.

Keywords: nonparametric efficient frontiers; data envelopment analysis (DEA); linear programming; convex analysis (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (2)

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DOI: 10.1057/palgrave.jors.2601816

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