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Combining bootstrap data envelopment analysis with social networks for rank discrimination and suitable potential benchmarks

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  • Kang, Hee Jay
  • Kim, Changhee
  • Choi, Kanghwa

Abstract

Many studies have attempted to overcome the inherent relativity of data envelopment analysis (DEA), which gives rise to the existence of undiscriminated decision-making units (DMUs) or the changes in the reference relationships depending on how the DMUs are grouped. This study presents a new and intuitive algorithm based on the bootstrapping method for generating a network that reflects both the DMUs’ original reference relationships and their potential benchmarks in a way that does not violate DEA's theoretical and practical premises while enabling a fuller ranking of the DMUs. An in-depth discussion regarding the definition of two types of potential benchmarks that can be used to rank DMUs flexibly is provided to highlight the superiority of the proposed algorithm. The application of our proposed algorithm delivers significant advantages over existing network approach models and opens new possibilities for utilizing the bootstrap DEA method.

Suggested Citation

  • Kang, Hee Jay & Kim, Changhee & Choi, Kanghwa, 2024. "Combining bootstrap data envelopment analysis with social networks for rank discrimination and suitable potential benchmarks," European Journal of Operational Research, Elsevier, vol. 312(1), pages 283-297.
    • Handle: RePEc:eee:ejores:v:312:y:2024:i:1:p:283-297
    DOI: 10.1016/j.ejor.2023.06.038
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