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

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.