CI-DEA: A Way to Improve the Discriminatory Power of DEA—Using the Example of the Efficiency Assessment of the Digitalization in the Life of the Generation 50+

Abstract

Data envelopment analysis (DEA) is a popular and universal method for examining the efficiency with which decision-making units (DMUs) transform multiple inputs into multiple outputs. However, DEA has its limitations, one of them being its decreasing discriminatory power when the number of analyzed DMUs is insufficient or when there are too many variables (inputs/outputs) describing them. When resigning from any of the variables is impossible or undesired, or when the number of units cannot be increased, CI-DEA, a method proposed in this article, proves to be helpful. It consists of replacing the inputs and/or outputs of the studied DMUs with a smaller number of composite indicators. The aggregation of variables is not based on subjective decisions of the analyst, but depends solely on correlations that exist among variables. The construction of the CI-DEA model makes the interpretation of the results unambiguous and easy. The reliability of the results obtained with CI-DEA have been confirmed by extensive simulation studies performed under conditions of predetermined real-efficiency of DMUs. The usefulness of CI-DEA on real data has been demonstrated on the example of the efficiency assessment of the digitalization in the life of the Generation 50+ in 32 European countries.