Abstract
Data envelopment analysis (DEA) is a nonparametric technique for determining the efficiency of a homogeneous set of decision-making units (DMUs). There are two common problems with traditional DEA. First, traditional DEA fails to adequately distinguish the efficiency DMUs. Second, the DMUs within the same industry are non-homogeneous. This study aims to develop a system-ranking-efficiency model to solve the problems of non-homogeneity and efficiency ranking for DMUs in the same group. The proposed system-rankingefficiency model is based on the concept of boundary change and considers the efficiency DMUs with the greatest influence on the boundary as the most important and, thus, as the ones that should have the highest efficiency ranking. The model is applied in the Taiwan securities industry, in which it was found to successfully rank all the DMUs.
Highlights
Based on the literature, Data envelopment analysis (DEA) provides a basis for the efficiency ranking of decision-making units (DMUs)
The present study describes a systemranking-efficiency model that aims to address the problems of non-homogeneity and efficiency ranking for the DMUs in an industry
The present study focuses on the Taiwan securities industry, considers the non-homogeneity problem existing in the industrial samples, and studies the super-efficiency model in non-homogeneous systems
Summary
Data envelopment analysis (DEA) provides a basis for the efficiency ranking of decision-making units (DMUs). There are two common problems with DEA. When the efficiency value for several DMUs is 1, DEA fails to adequately distinguish among these efficiency DMUs. Second, the DMUs within an industry can be in different systems frontiers; when DEA is adopted, the performance assessment cannot be carried out for the whole industry. Some approaches for differentiation need to be applied. The present study describes a systemranking-efficiency model that aims to address the problems of non-homogeneity and efficiency ranking for the DMUs in an industry
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