Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/233144
Authors: 
Wojcik, Victoria
Dyckhoff, Harald
Clermont, Marcel
Year of Publication: 
2019
Citation: 
[Journal:] Business Research [ISSN:] 2198-2627 [Volume:] 12 [Year:] 2019 [Issue:] 2 [Pages:] 559-595
Abstract: 
After 40 years of research with thousands of application-oriented scientific papers, empirical evidence that data envelopment analysis (DEA) has really improved the practice of performance measurement and benchmarking in real-life non-production contexts is rare. The main reason for this deficit may be that DEA is founded on the concepts of production theory such as production possibility set or returns to scale. These concepts can hardly be applied to pure multiple-criteria evaluation problems, which are often attempted to be solved using DEA. This paper systematically investigates strengths and weaknesses of DEA in the exemplary case of welfare evaluation using real data on 27 countries of the European Union. We analyze and explain the differences in the results of various frequently used DEA models for two different, but strongly connected sets of welfare indicators, thereby demonstrating the pitfalls, which often arise in the application of DEA, as well as some approaches for avoiding them.
Subjects: 
Benchmarking
Performance measurement
Data envelopment analysis application
Model choice and building
Indicator selection
Welfare evaluation
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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