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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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7. Summary and future research

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7.   Summary and future research

Performance evaluation, traditionally influenced by the paradigm of the homo oeconomicus and based on simple, aggregate, and short-term financial measures, has been called to major changes: (i) to accept the idea of a decision maker with limited capacity and a non-optimizing behavior and (ii) to consider additional non-financial performance criteria. The first change requires a movement from a normative to a descriptive performance evaluation, consistent with the advances made in BAR and also in BOM and BOR. The second change offers a number of benefits, but also some challenges, e.g., the data aggregation of non-financial data. One alternative for coping with the aggregation challenge is offered by DEA, which aggregates financial and non-financial performance criteria into a single efficiency score. This instrument, despite being highly accepted in the academic world, is still not broadly used in the management area.

Based on the definition of Little (1970/2004) about the requirements of a management tool, it has been hypothesized that DEA has two main limitations to overcome in order to be more easily accepted by the management. The first one is of normative character and states that DEA is not always robust from a managerial perspective, e.g., when the efficiency score of one DMU is maximized by ignoring a factor in which the DMU under analysis has a comparative weakness (zero-value weight). The second drawback is from a descriptive character and it refers to the question whether DEA results...

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