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A classification of slacks-based efficiency measures in network data envelopment analysis with an analysis of the properties possessed

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  • Kao, Chiang

Abstract

The slacks-based measure (SBM) is a data envelopment analysis (DEA) technique that has been widely used in measuring the efficiency of a system treated as a whole unit. When the internal structure of a system is considered, different ways of modeling the intermediate products that link the component divisions based on the production possibility set (PPS) have been proposed. The types of PPSs can be classified as independent, relational, and cooperative, and the efficiency of the system can be measured from the viewpoint of either outside peers or inside managers for different purposes. This paper shows that models corresponding to the independent type do not properly describe the relationships between the divisions, and are not suitable for measuring the efficiency of network systems. Models corresponding to the relational type may cause a waste of the intermediate products in the system, and are not suitable for internal evaluations, while they are more persuasive for external evaluations. Those corresponding to the cooperative type are appropriate for both external and internal evaluations, and are able to obtain comparable results. An example is used to illustrate the characteristics of the efficiencies measured from the models corresponding to different types of PPSs.

Suggested Citation

  • Kao, Chiang, 2018. "A classification of slacks-based efficiency measures in network data envelopment analysis with an analysis of the properties possessed," European Journal of Operational Research, Elsevier, vol. 270(3), pages 1109-1121.
    • Handle: RePEc:eee:ejores:v:270:y:2018:i:3:p:1109-1121
    DOI: 10.1016/j.ejor.2018.04.036
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    Cited by:

    1. Zhang, Ruchuan & Wei, Qian & Li, Aijun & Ren, LiYing, 2022. "Measuring efficiency and technology inequality of China's electricity generation and transmission system: A new approach of network Data Envelopment Analysis prospect cross-efficiency models," Energy, Elsevier, vol. 246(C).
    2. Ouyang, Wendi & Yang, Jian-bo, 2020. "The network energy and environment efficiency analysis of 27 OECD countries: A multiplicative network DEA model," Energy, Elsevier, vol. 197(C).
    3. Yu, Ming-Miin & Nguyen, Minh-Anh Thi, 2023. "Productivity changes of Asia-Pacific airlines: A Malmquist productivity index approach for a two-stage dynamic system," Omega, Elsevier, vol. 115(C).
    4. Liu, Dan & Zhang, Jiahuang & Yu, Ming-Miin, 2023. "Decomposing airline profit inefficiency in NDEA through the non-competitive Nerlovian profit inefficiency model," Journal of Air Transport Management, Elsevier, vol. 107(C).
    5. Xiao, Huijuan & Wang, Daoping & Qi, Yu & Shao, Shuai & Zhou, Ya & Shan, Yuli, 2021. "The governance-production nexus of eco-efficiency in Chinese resource-based cities: A two-stage network DEA approach," Energy Economics, Elsevier, vol. 101(C).
    6. Liu, Haiyue & Zhang, Ruchuan & Zhou, Li & Li, Aijun, 2023. "Evaluating the financial performance of companies from the perspective of fund procurement and application: New strategy cross efficiency network data envelopment analysis models," Energy, Elsevier, vol. 269(C).
    7. Ang, Sheng & Liu, Pei & Yang, Feng, 2020. "Intra-Organizational and inter-organizational resource allocation in two-stage network systems," Omega, Elsevier, vol. 91(C).
    8. Ming-Miin Yu & Li-Hsueh Chen, 2020. "A meta-frontier network data envelopment analysis approach for the measurement of technological bias with network production structure," Annals of Operations Research, Springer, vol. 287(1), pages 495-514, April.
    9. Chen, Shanshan & Zhang, Ruchuan & Li, Peiwen & Li, Aijun, 2023. "How to improve the performance of China's energy-transport-economy-environment system: An analysis based on new strategy parallel-series input-output data envelopment analysis models," Energy, Elsevier, vol. 281(C).
    10. Kao, Chiang, 2019. "Inefficiency identification for closed series production systems," European Journal of Operational Research, Elsevier, vol. 275(2), pages 599-607.
    11. H. Pierre Hsieh & Yueh‐Cheng Wu & Wen‐Min Lu & Yao‐Chieh Chen, 2020. "Assessing and ranking the innovation ability and business performance of global companies in the aerospace and defense industry," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(6), pages 952-963, September.
    12. Gerami, Javad & Mozaffari, Mohammad Reza & Wanke, Peter F. & Correa, Henrique L., 2022. "Improving information reliability of non-radial value efficiency analysis: An additive slacks based measure approach," European Journal of Operational Research, Elsevier, vol. 298(3), pages 967-978.
    13. Qian Long Kweh & Wen-Min Lu & Fengyi Lin & Yung-Jr Deng, 2022. "Impact of research and development tax credits on the innovation and operational efficiencies of Internet of things companies in Taiwan," Annals of Operations Research, Springer, vol. 315(2), pages 1217-1241, August.
    14. Lozano, Sebastián & Khezri, Somayeh, 2021. "Network DEA smallest improvement approach," Omega, Elsevier, vol. 98(C).
    15. Mohammad Izadikhah & Elnaz Azadi & Majid Azadi & Reza Farzipoor Saen & Mehdi Toloo, 2022. "Developing a new chance constrained NDEA model to measure performance of sustainable supply chains," Annals of Operations Research, Springer, vol. 316(2), pages 1319-1347, September.
    16. Yu, Ming-Miin & Rakshit, Ipsita, 2023. "Assessing the dynamic efficiency and technology gap of airports under different ownerships: A union dynamic NDEA approach," Omega, Elsevier, vol. 119(C).
    17. Yu, Ming-Miin & Chen, Li-Hsueh, 2023. "Productivity change of airlines: A global total factor productivity index with network structure," Journal of Air Transport Management, Elsevier, vol. 109(C).
    18. Kao, Chiang, 2020. "Decomposition of slacks-based efficiency measures in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 283(2), pages 588-600.
    19. Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.
    20. Chen, Kun & Zhu, Joe, 2020. "Additive slacks-based measure: Computational strategy and extension to network DEA," Omega, Elsevier, vol. 91(C).

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