A modified DEA-based approach for selecting preferred benchmarks in social networks

In recent years, social network analysis (SNA) has been combined with data envelopment analysis (DEA) to select benchmarks and fully rank decision-making-units (DMUs). However, such methods fail to identify the most suitable benchmarks for the group. On one hand, they may incorrectly identify an efficient DMU as a suitable benchmark for the group. On the other hand, they fail to recognise any inefficient DMU as a benchmark for the group. To address such limitations occurred in the conventional DEA-based SNA approaches, this study proposes a modified DEA-SNA method. The new approach selects preferred benchmarks from the perspective of the entire DMU group considering both efficient DMUs and inefficient DMUs as candidates. The proposed method can identify efficient DMUs that obtain little endorsement from inefficient DMUs and inefficient DMUs that could be good benchmarks because they are endorsed by many DMUs with worse performance. The benchmarking information helps decision-makers identify suitable benchmarks, especially for those units whose efficiency scores are too low to learn from the industry leaders. Two examples are used to illustrate the practicability and superiority of our proposed method.