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Data Envelopment Analysis: From Normative to Descriptive Performance Evaluation

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Nadia Vazquez Novoa

The question of modern performance evaluation has been extensively discussed in the literature, leading to a call for models including non-optimizing behaviors of decision makers and non-financial performance criteria. A promising management instrument is data envelopment analysis (DEA), which enables the aggregation of financial and non-financial indicators into a single measure. This work contributes to a better understanding of DEA from two perspectives: (i) it offers a normative solution to the zero-value weight problem and (ii) it provides the first experimental results on behavioral DEA based on an original taxonomy of cognitive biases related to performance evaluation. Behavioral DEA is a completely new research area which yields plenty of research opportunities.

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6. The decoy effect in relative performance evaluation and the debiasing role of DEA

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6.   The decoy effect in relative performance evaluation and the debiasing role of DEA835

The decoy effect is a special kind of context effect, which refers to the influence that contextual variables have on decision making. Overwhelming evidence exists demonstrating that context is likely to influence performance evaluation.836 For example, evaluators’ preferences are biased not only by the past performance of the DMU, but also by the performance of other DMUs under analysis.837 With regard to the latter aspect, the decoy effect implies that the inclusion of a dominated alternative – the decoy – can influence the choice between the non-dominated alternatives. Concretely, the probability of preferring the target alternative, which is the non-dominated option that is most similar to the decoy, may increase. The existence of this effect has repeatedly been confirmed by research on consumer behavior, showing that customers tend to prefer the target alternative.838

The research settings used for studying consumer behavior strongly resemble relative performance evaluation cases where alternatives are compared on a utility function level. Furthermore, these research settings are constructed in a way that they differentiate between non-dominated (i.e., efficient) and dominated (i.e., inefficient) units like in the context of DEA. Against this background, two questions arise: (i) does the decoy effect also occur in cases where the relative performance of alternatives is evaluated, and (ii) to what extent can the application of DEA – namely the incorporation of respective efficiency scores and the mention of existing slacks – act...

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