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Input–output substitutability and strongly monotonic p-norm least distance DEA measures

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  • Fukuyama, Hirofumi
  • Maeda, Yasunobu
  • Sekitani, Kazuyuki
  • Shi, Jianming

Abstract

In DEA, there are two frameworks for efficiency assessment and targeting: the greatest and the least distance framework. The greatest distance framework provides us with the efficient targets that are determined by the farthest projections to the assessed decision making unit via maximization of the p-norm relative to either the strongly efficient frontier or the weakly efficient frontier. Non-radial measures belonging to the class of greatest distance measures are the slacks-based measure (SBM) and the range-adjusted measure (RAM). Whereas these greatest distance measures have traditionally been utilized because of their computational ease, least distance projections are quite often more appropriate than greatest distance projections from the perspective of managers of decision-making units because closer efficient targets may be attained with less effort. In spite of this desirable feature of the least distance framework, the least distance (in) efficiency versions of the additive measure, SBM and RAM do not even satisfy weak monotonicity. In this study, therefore, we introduce and investigate least distance p-norm inefficiency measures that satisfy strong monotonicity over the strongly efficient frontier. In order to develop these measures, we extend a free disposable set and introduce a tradeoff set that implements input–output substitutability.

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  • Fukuyama, Hirofumi & Maeda, Yasunobu & Sekitani, Kazuyuki & Shi, Jianming, 2014. "Input–output substitutability and strongly monotonic p-norm least distance DEA measures," European Journal of Operational Research, Elsevier, vol. 237(3), pages 997-1007.
  • Handle: RePEc:eee:ejores:v:237:y:2014:i:3:p:997-1007
    DOI: 10.1016/j.ejor.2014.02.033
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    Cited by:

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    2. Aparicio, Juan & Cordero, Jose M. & Pastor, Jesus T., 2017. "The determination of the least distance to the strongly efficient frontier in Data Envelopment Analysis oriented models: Modelling and computational aspects," Omega, Elsevier, vol. 71(C), pages 1-10.
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    8. Juan Aparicio & Magdalena Kapelko & Bernhard Mahlberg & Jose L. Sainz-Pardo, 2017. "Measuring input-specific productivity change based on the principle of least action," Journal of Productivity Analysis, Springer, vol. 47(1), pages 17-31, February.
    9. Tone, Kaoru & Chang, Tsung-Sheng & Wu, Chen-Hui, 2020. "Handling negative data in slacks-based measure data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 282(3), pages 926-935.
    10. Ruiz, José L. & Sirvent, Inmaculada, 2019. "Performance evaluation through DEA benchmarking adjusted to goals," Omega, Elsevier, vol. 87(C), pages 150-157.
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    13. Aparicio, Juan & Cordero, Jose M. & Gonzalez, Martin & Lopez-Espin, Jose J., 2018. "Using non-radial DEA to assess school efficiency in a cross-country perspective: An empirical analysis of OECD countries," Omega, Elsevier, vol. 79(C), pages 9-20.
    14. Kao, Chiang, 2022. "A maximum slacks-based measure of efficiency for closed series production systems," Omega, Elsevier, vol. 106(C).
    15. Kao, Chiang, 2022. "Closest targets in the slacks-based measure of efficiency for production units with multi-period data," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1042-1054.
    16. Ando, Kazutoshi & Minamide, Masato & Sekitani, Kazuyuki & Shi, Jianming, 2017. "Monotonicity of minimum distance inefficiency measures for Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 260(1), pages 232-243.
    17. Henriques, C.O. & Chavez, J.M. & Gouveia, M.C. & Marcenaro-Gutierrez, O.D., 2022. "Efficiency of secondary schools in Ecuador: A value based DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    18. Aparicio, Juan & Borras, Fernando & Pastor, Jesus T. & Vidal, Fernando, 2015. "Measuring and decomposing firm׳s revenue and cost efficiency: The Russell measures revisited," International Journal of Production Economics, Elsevier, vol. 165(C), pages 19-28.
    19. Lozano, Sebastián & Khezri, Somayeh, 2021. "Network DEA smallest improvement approach," Omega, Elsevier, vol. 98(C).
    20. 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.
    21. Liu, Wenbin & Zhou, Zhongbao & Ma, Chaoqun & Liu, Debin & Shen, Wanfang, 2015. "Two-stage DEA models with undesirable input-intermediate-outputs," Omega, Elsevier, vol. 56(C), pages 74-87.
    22. Cook, Wade D. & Ruiz, José L. & Sirvent, Inmaculada & Zhu, Joe, 2017. "Within-group common benchmarking using DEA," European Journal of Operational Research, Elsevier, vol. 256(3), pages 901-910.
    23. Somayeh Razipour-GhalehJough & Farhad Hosseinzadeh Lotfi & Gholamreza Jahanshahloo & Mohsen Rostamy-malkhalifeh & Hamid Sharafi, 2020. "Finding closest target for bank branches in the presence of weight restrictions using data envelopment analysis," Annals of Operations Research, Springer, vol. 288(2), pages 755-787, May.
    24. Zhu, Qingyuan & Wu, Jie & Ji, Xiang & Li, Feng, 2018. "A simple MILP to determine closest targets in non-oriented DEA model satisfying strong monotonicity," Omega, Elsevier, vol. 79(C), pages 1-8.

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