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Context-Dependent Data Envelopment Analysis and its Use

In: Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis

Author

Listed:
  • Hiroshi Morita

    (Osaka University)

  • Joe Zhu

    (Worcester Polytechnic Institute)

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 is 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 the attractiveness and the progress when DMUs exhibiting poorer and better performance are chosen as the evaluation context, respectively. This chapter also presents a slack-based context-dependent DEA approach. In DEA, nonzero input and output slacks are very likely to present after the radial efficiency score improvement. The slack-based context-dependent DEA allows us to fully evaluate the inefficiency in a DMU’s performance.

Suggested Citation

  • Hiroshi Morita & Joe Zhu, 2007. "Context-Dependent Data Envelopment Analysis and its Use," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 241-259, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-71607-7_13
    DOI: 10.1007/978-0-387-71607-7_13
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    Cited by:

    1. Pawel Dobrzanski & Sebastian Bobowski, 2020. "The Efficiency of R&D Expenditures in ASEAN Countries," Sustainability, MDPI, vol. 12(7), pages 1-26, March.

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