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Context-dependent data envelopment analysis--Measuring attractiveness and progress

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  • Seiford, Lawrence M.
  • Zhu, Joe

Abstract

Data envelopment analysis (DEA) is a methodology for identifying the efficient frontier of decision making units (DMUs). Context-dependent DEA refers to a DEA approach where a set of DMUs are evaluated against a particular evaluation context. Each evaluation context represents an efficient frontier composed by DMUs in a specific performance level. The context-dependent DEA measures (i) the attractiveness when DMUs exhibiting poorer performance are chosen as the evaluation context, and (ii) the progress when DMUs exhibiting better performance are chosen as the evaluation context. The current paper extends the context-dependent DEA by incorporating value judgment into the attractiveness and progress measures. The method is applied to measuring the attractiveness of 32 computer printers. It is shown that the attractive measure helps (i) customers to select the best option, and (ii) printer manufacturers to identify the potential competitors.

Suggested Citation

  • Seiford, Lawrence M. & Zhu, Joe, 2003. "Context-dependent data envelopment analysis--Measuring attractiveness and progress," Omega, Elsevier, vol. 31(5), pages 397-408, October.
    • Handle: RePEc:eee:jomega:v:31:y:2003:i:5:p:397-408
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    References listed on IDEAS

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    2. Doyle, JR & Green, RH, 1991. "Comparing products using data envelopment analysis," Omega, Elsevier, vol. 19(6), pages 631-638.
    3. Thompson, Russell G. & Langemeier, Larry N. & Lee, Chih-Tah & Lee, Euntaik & Thrall, Robert M., 1990. "The role of multiplier bounds in efficiency analysis with application to Kansas farming," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 93-108.
    4. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    5. Amos Tversky & Itamar Simonson, 1993. "Context-Dependent Preferences," Management Science, INFORMS, vol. 39(10), pages 1179-1189, October.
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