Context-Dependent Data Envelopment Analysis and its Use

TLDR: This chapter presents a slack-based context-dependent DEA approach that allows us to fully evaluate the inefficiency in a DMU’s performance.

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.