Context-dependent DEA

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this chapter, the context-dependent DEA is discussed. Since a product can appear attractive in comparison with a contextual of less attractive or unattractive alternatives, the performance of firms can be influenced by the context. For an example, twenty-three Tokyo public libraries are considered and a context-dependent DEA proposed by Chen et al. (2005) is discussed. The attractiveness of each library on a particular performance level in comparison with other libraries are measured. Libraries are classified on several empirical efficient frontiers, where each frontier is used to evaluate the attractiveness. The performance of the technically efficient libraries changes as the technically inefficient libraries change their performance. The context-dependent DEA also represents another view to differentiate the performance of efficient DMUs. When DMUs in a particular level are observed as having the same performance, the attractiveness measure lets us discriminate the “equal performance” based upon the third option or the same particular evaluation context. We also develop the VBA procedure to measure the attractiveness with just one click.

Original languageEnglish (US)
Title of host publicationInternational Series in Operations Research and Management Science
PublisherSpringer New York LLC
Pages289-301
Number of pages13
DOIs
StatePublished - Jan 1 2018

Publication series

NameInternational Series in Operations Research and Management Science
Volume269
ISSN (Print)0884-8289

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Strategy and Management
  • Management Science and Operations Research
  • Applied Mathematics

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  • Cite this

    Khezrimotlagh, D., & Chen, Y. (2018). Context-dependent DEA. In International Series in Operations Research and Management Science (pp. 289-301). (International Series in Operations Research and Management Science; Vol. 269). Springer New York LLC. https://doi.org/10.1007/978-3-319-76345-3_10