Article
Nonparametric frontier analysis with multiple constituencies
Journal of the Operational Research Society
(2005)
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
Disciplines
Publication Date
2005
DOI
https://doi.org/10.1057/palgrave.jors.2601816
Citation Information
M L Bougnol, J L Dula, D Retzlaff-Roberts and Keith Womer. "Nonparametric frontier analysis with multiple constituencies" Journal of the Operational Research Society Vol. 56 (2005) p. 252 - 266 Available at: http://works.bepress.com/keith-womer/35/