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A learning ladder toward efficiency: Proposing network-based stepwise benchmark selection

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  • Ghahraman, Abaghan
  • Prior, Diego

Abstract

Starting from the knowledge-based view of efficiency improvement, we propose a network-based approach to find the optimal stepwise benchmarking paths toward the efficiency frontier. The approach treats the Data Envelopment Analysis system as a network of teaching and learning firms and calculates the overall shortest paths taking into account both input endowment similarity and the efficiency gap covered in each step. In addition, based on network centrality concepts, the method discriminates between efficient and intermediate units, and highlights possible outliers or specialized units. As a real-world example, the method is applied to a network of Canadian bank branches and practical implications are discussed.

Suggested Citation

  • Ghahraman, Abaghan & Prior, Diego, 2016. "A learning ladder toward efficiency: Proposing network-based stepwise benchmark selection," Omega, Elsevier, vol. 63(C), pages 83-93.
    • Handle: RePEc:eee:jomega:v:63:y:2016:i:c:p:83-93
    DOI: 10.1016/j.omega.2015.10.004
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    References listed on IDEAS

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    Cited by:

    1. Monge, Juan F. & Ruiz, José L., 2023. "Setting closer targets based on non-dominated convex combinations of Pareto-efficient units: A bi-level linear programming approach in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1084-1096.
    2. Laura Calzada-Infante & Sebastián Lozano, 2022. "Computing multiperiod efficiency using dominance networks," Annals of Operations Research, Springer, vol. 309(1), pages 37-57, February.
    3. Zhou, Xiaoyang & Xu, Zhongwen & Chai, Jian & Yao, Liming & Wang, Shouyang & Lev, Benjamin, 2019. "Efficiency evaluation for banking systems under uncertainty: A multi-period three-stage DEA model," Omega, Elsevier, vol. 85(C), pages 68-82.
    4. Khaled Belahcène & Vincent Mousseau & Wassila Ouerdane & Marc Pirlot & Olivier Sobrie, 2023. "Multiple criteria sorting models and methods. Part II: theoretical results and general issues," 4OR, Springer, vol. 21(2), pages 181-204, June.
    5. Akber Aman Shah & Desheng Wu & Vladmir Korotkov, 2019. "Are Sustainable Banks Efficient and Productive? A Data Envelopment Analysis and the Malmquist Productivity Index Analysis," Sustainability, MDPI, vol. 11(8), pages 1-19, April.
    6. Kadziński, Miłosz & Stamenković, Mladen & Uniejewski, Maciej, 2022. "Stepwise benchmarking for multiple criteria sorting," Omega, Elsevier, vol. 108(C).
    7. Gobbo, Simone Cristina de Oliveira & Mariano, Enzo Barberio & Gobbo Jr., José Alcides, 2021. "Combining social network and data envelopment analysis: A proposal for a Selection Employment Contracts Effectiveness index in healthcare network applications," Omega, Elsevier, vol. 103(C).
    8. Lozano, Sebastián & Calzada-Infante, Laura, 2018. "Computing gradient-based stepwise benchmarking paths," Omega, Elsevier, vol. 81(C), pages 195-207.
    9. Ramón, Nuria & Ruiz, José L. & Sirvent, Inmaculada, 2020. "Cross-benchmarking for performance evaluation: Looking across best practices of different peer groups using DEA," Omega, Elsevier, vol. 92(C).
    10. Petrović, Marijana & Bojković, Nataša & Stamenković, Mladen & Anić, Ivan, 2018. "Supporting performance appraisal in ELECTRE based stepwise benchmarking model," Omega, Elsevier, vol. 78(C), pages 237-251.

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