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Frontier projection and efficiency decomposition in two-stage processes with slacks-based measures

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  • Chen, Ya
  • Li, Yongjun
  • Liang, Liang
  • Salo, Ahti
  • Wu, Huaqing

Abstract

In the prior literature on measuring the efficiency of two-stage processes, there are both radial and non-radial methods of efficiency measurement. In some cases, non-radial methods which allow all inputs, intermediate measures and outputs to change non-proportionally are more appropriate than radial methods, but they do not ensure stage efficiency or allow for the efficiency decomposition of two-stage processes. Based on slacks-based measure (SBM), this paper develops both envelopment-based and multiplier-based models to obtain simultaneously both the frontier projection and the efficiency decomposition. Specifically, we propose the variable intermediate measures SBM (VSBM) model to evaluate the system efficiency of two-stage processes and consider the following three properties of the VSBM model: 1) we derive the efficient DEA frontier projection based on the VSBM model; 2) we address potential conflicts in this model with respect to the intermediate measures; 3) we prove that the system inefficiency is equivalent to the sum of inefficiencies of the two stages. Furthermore, we derive the efficiency decomposition of two-stage processes based on the dual of the VSBM model. Finally, we apply our proposed approach to real data of US commercial banks, and extend our approach to settings in which the assumption of variable returns to scale (VRS) holds or there are more general network structures.

Suggested Citation

  • Chen, Ya & Li, Yongjun & Liang, Liang & Salo, Ahti & Wu, Huaqing, 2016. "Frontier projection and efficiency decomposition in two-stage processes with slacks-based measures," European Journal of Operational Research, Elsevier, vol. 250(2), pages 543-554.
  • Handle: RePEc:eee:ejores:v:250:y:2016:i:2:p:543-554
    DOI: 10.1016/j.ejor.2015.09.031
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    4. Ang, Sheng & Liu, Pei & Yang, Feng, 2020. "Intra-Organizational and inter-organizational resource allocation in two-stage network systems," Omega, Elsevier, vol. 91(C).
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    7. Chen, Ya & Pan, Yongbin & Wang, Mengyuan & Ding, Tao & Zhou, Zhixiang & Wang, Ke, 2023. "How do industrial sectors contribute to carbon peaking and carbon neutrality goals? A heterogeneous energy efficiency analysis for Beijing," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 67-80.
    8. Kao, Chiang, 2017. "Efficiency measurement and frontier projection identification for general two-stage systems in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 261(2), pages 679-689.
    9. Kottas, Angelos T. & Bozoudis, Michail N. & Madas, Michael A., 2020. "Turbofan aero-engine efficiency evaluation: An integrated approach using VSBM two-stage network DEA," Omega, Elsevier, vol. 92(C).
    10. Victor John M. Cantor & Kim Leng Poh, 2020. "Efficiency measurement for general network systems: a slacks-based measure model," Journal of Productivity Analysis, Springer, vol. 54(1), pages 43-57, August.
    11. Huaqing Wu & Jingyu Yang & Wensheng Wu & Ya Chen, 2023. "Interest rate liberalization and bank efficiency: A DEA analysis of Chinese commercial banks," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 467-498, June.
    12. M.V. Leonov, 2021. "Review of Modern Approaches for Assessing the Effectiveness of Banking," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 20(2), pages 294-326.
    13. Xiaohong Liu & Feng Yang & Jie Wu, 2020. "DEA considering technological heterogeneity and intermediate output target setting: the performance analysis of Chinese commercial banks," Annals of Operations Research, Springer, vol. 291(1), pages 605-626, August.
    14. Lim, Dong-Joon & Kim, Moon-Su, 2022. "Measuring dynamic efficiency with variable time lag effects," Omega, Elsevier, vol. 108(C).
    15. Ya Chen & Justin Wang & Joe Zhu & H. David Sherman & Shin-Yi Chou, 2019. "How the Great Recession affects performance: a case of Pennsylvania hospitals using DEA," Annals of Operations Research, Springer, vol. 278(1), pages 77-99, July.
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    17. Jomthanachai, Suriyan & Wong, Wai-Peng & Soh, Keng-Lin & Lim, Chee-Peng, 2022. "A global trade supply chain vulnerability in COVID-19 pandemic: An assessment metric of risk and resilience-based efficiency of CoDEA method," Research in Transportation Economics, Elsevier, vol. 93(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. Patrizii, Vincenzo, 2020. "On network two stages variable returns to scale Dea models," Omega, Elsevier, vol. 97(C).
    20. Mahdiloo, Mahdi & Toloo, Mehdi & Duong, Thach-Thao & Farzipoor Saen, Reza & Tatham, Peter, 2018. "Integrated data envelopment analysis: Linear vs. nonlinear model," European Journal of Operational Research, Elsevier, vol. 268(1), pages 255-267.
    21. 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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