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.
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Morita, H., Zhu, J. (2007). Context-Dependent Data Envelopment Analysis and its Use. In: Zhu, J., Cook, W.D. (eds) Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-71607-7_13
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DOI: https://doi.org/10.1007/978-0-387-71607-7_13
Publisher Name: Springer, Boston, MA
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