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Target intermediate products setting in a two-stage system with fairness concern

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  • An, Qingxian
  • Chen, Haoxun
  • Xiong, Beibei
  • Wu, Jie
  • Liang, Liang

Abstract

In a two-stage system with two divisions connected in series, fairly setting the target outputs for the first stage or equivalently the target inputs for the second stage is critical, in order to ensure that the two stages have incentives to collaborate with each other to achieve the best performance of the whole system. Data envelopment analysis (DEA) as a non-parametric approach for efficiency evaluation of multi-input, multi-output systems has drawn a lot of attention. Recently, many two-stage DEA models were developed for studying the internal structures of two-stage systems. However, there was no work studying fair setting of the target intermediate products (or intermediate measures) although unreasonable setting will result in unfairness to the two stages because setting higher (fewer) intermediate measures means that the first (second) stage must make more efforts to achieve the overall production plan. In this paper, a new DEA model taking account of fairness in the setting of the intermediate products is proposed, where the fairness is interpreted based on Nash bargaining game model, in which the two stages negotiate their target efficiencies in the two-stage system based on their individual efficiencies. This approach is illustrated by an empirical application to insurance companies.

Suggested Citation

  • An, Qingxian & Chen, Haoxun & Xiong, Beibei & Wu, Jie & Liang, Liang, 2017. "Target intermediate products setting in a two-stage system with fairness concern," Omega, Elsevier, vol. 73(C), pages 49-59.
  • Handle: RePEc:eee:jomega:v:73:y:2017:i:c:p:49-59
    DOI: 10.1016/j.omega.2016.12.005
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    3. Meng, Fanyong & Xiong, Beibei, 2021. "Logical efficiency decomposition for general two-stage systems in view of cross efficiency," European Journal of Operational Research, Elsevier, vol. 294(2), pages 622-632.
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    5. Chang, Tsung-Sheng & Lin, Ji-Gang & Ouenniche, Jamal, 2023. "DEA-based Nash bargaining approach to merger target selection," European Journal of Operational Research, Elsevier, vol. 305(2), pages 930-945.
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    7. Zhang, Linyan & Chen, Kun, 2019. "Hierarchical network systems: An application to high-technology industry in China," Omega, Elsevier, vol. 82(C), pages 118-131.
    8. Min Yang & Qingxian An & Tao Ding & Pengzhen Yin & Liang Liang, 2019. "Carbon emission allocation in China based on gradually efficiency improvement and emission reduction planning principle," Annals of Operations Research, Springer, vol. 278(1), pages 123-139, July.
    9. Lozano, S. & Hinojosa, M.A. & Mármol, A.M., 2019. "Extending the bargaining approach to DEA target setting," Omega, Elsevier, vol. 85(C), pages 94-102.
    10. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    11. Qingxian An & Fanyong Meng & Beibei Xiong & Zongrun Wang & Xiaohong Chen, 2020. "Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach," Annals of Operations Research, Springer, vol. 290(1), pages 707-729, July.
    12. Khoveyni, Mohammad & Fukuyama, Hirofumi & Eslami, Robabeh & Yang, Guo-liang, 2019. "Variations effect of intermediate products on the second stage in two-stage processes," Omega, Elsevier, vol. 85(C), pages 35-48.
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    14. Koronakos, Gregory & Sotiros, Dimitris & Despotis, Dimitris K. & Kritikos, Manolis N., 2022. "Fair efficiency decomposition in network DEA: A compromise programming approach," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).

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