Input/Output Variables Selection in Data Envelopment Analysis: A Shannon Entropy Approach

The purpose of this study is to provide an efficient method for the selection of input-output indicators in the data envelopment analysis (DEA) approach, in order to improve the discriminatory power of the DEA method in the evaluation process and performance analysis of homogeneous decision-making units (DMUs) in the presence of negative values and data. For this purpose, the Shannon entropy technique is used as one of the most important methods for determining the weight of indicators. Moreover, due to the presence of negative data in some indicators, the range directional measure (RDM) model is used as the basic model of the research. Finally, to demonstrate the applicability of the proposed approach, the food and beverage industry has been selected from the Tehran stock exchange (TSE) as a case study, and data related to 15 stocks have been extracted from this industry. The numerical and experimental results indicate the efficacy of the hybrid data envelopment analysis-Shannon entropy (DEASE) approach to evaluate stocks under negative data. Furthermore, the discriminatory power of the proposed DEASE approach is greater than that of a classical DEA model.

Automated summary

The purpose of this study is to provide an efficient method using the DEA approach and Shannon entropy technique to select input-output indicators in order to improve the evaluation and performance analysis of decision-making units in the presence of negative values and data. The study shows that the proposed DEASE approach is effective and has greater discriminatory power than the classic DEA model in evaluating stocks.