IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v288y2020i1d10.1007_s10479-019-03491-w.html
   My bibliography  Save this article

Cross-efficiency aggregation method based on prospect consensus process

Author

Listed:
  • Lei Chen

    (Fuzhou University)

  • Ying-Ming Wang

    (Fuzhou University
    Fuzhou University)

  • Yan Huang

    (Fuzhou University
    Fujian Agriculture and Forestry University)

Abstract

The arithmetic average method is usually adopted to aggregate cross-efficiency in traditional cross-efficiency methods. However, this method not only underestimates the importance of self-evaluation, but also ignores the subjective preference of decision-makers. This paper thus introduces prospect theory to describe the subjective preference of decision-makers in the aggregation process when they face gains and losses, then a new method is constructed to aggregate cross-efficiency. Based on the differences between the psychological expectations and aggregation results, the expectations are constantly adjusted until a consensus on aggregation results is reached. An aggregation result that is more acceptable to all decision-making units can then be obtained. Finally, the proposed method is applied to aggregate the cross-efficiency of 27 industrial robots to illustrate its effectiveness and convergence.

Suggested Citation

  • Lei Chen & Ying-Ming Wang & Yan Huang, 2020. "Cross-efficiency aggregation method based on prospect consensus process," Annals of Operations Research, Springer, vol. 288(1), pages 115-135, May.
    • Handle: RePEc:spr:annopr:v:288:y:2020:i:1:d:10.1007_s10479-019-03491-w
    DOI: 10.1007/s10479-019-03491-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-019-03491-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-019-03491-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Kao, Chiang & Liu, Shiang-Tai, 2019. "Cross efficiency measurement and decomposition in two basic network systems," Omega, Elsevier, vol. 83(C), pages 70-79.
    4. Amar Oukil & Srikrishna Madhumohan Govindaluri, 2017. "A systematic approach for ranking football players within an integrated DEA‐OWA framework," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 38(8), pages 1125-1136, December.
    5. 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.
    6. Liang Liang & Jie Wu & Wade D. Cook & Joe Zhu, 2008. "The DEA Game Cross-Efficiency Model and Its Nash Equilibrium," Operations Research, INFORMS, vol. 56(5), pages 1278-1288, October.
    7. Wu, Jie & Chu, Junfei & Sun, Jiasen & Zhu, Qingyuan, 2016. "DEA cross-efficiency evaluation based on Pareto improvement," European Journal of Operational Research, Elsevier, vol. 248(2), pages 571-579.
    8. Liu, Hui-hui & Song, Yao-yao & Yang, Guo-liang, 2019. "Cross-efficiency evaluation in data envelopment analysis based on prospect theory," European Journal of Operational Research, Elsevier, vol. 273(1), pages 364-375.
    9. Shiang-Tai Liu, 2018. "A DEA ranking method based on cross-efficiency intervals and signal-to-noise ratio," Annals of Operations Research, Springer, vol. 261(1), pages 207-232, February.
    10. James E. Smith & Canan Ulu, 2017. "Risk Aversion, Information Acquisition, and Technology Adoption," Operations Research, INFORMS, vol. 65(4), pages 1011-1028, August.
    11. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    12. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    13. Wang, Ying-Ming & Chin, Kwai-Sang, 2011. "The use of OWA operator weights for cross-efficiency aggregation," Omega, Elsevier, vol. 39(5), pages 493-503, October.
    14. Oral, Muhittin & Oukil, Amar & Malouin, Jean-Louis & Kettani, Ossama, 2014. "The appreciative democratic voice of DEA: A case of faculty academic performance evaluation," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 20-28.
    15. Hamza Bahaji, 2018. "Are employee stock option exercise decisions better explained through the prospect theory?," Annals of Operations Research, Springer, vol. 262(2), pages 335-359, March.
    16. Hédi Essid & Janet Ganouati & Stephane Vigeant, 2018. "A mean-maverick game cross-efficiency approach to portfolio selection: An application to Paris stock exchange," Post-Print hal-01916529, HAL.
    17. Balcombe, Kelvin & Bardsley, Nicholas & Dadzie, Sam & Fraser, Iain, 2019. "Estimating parametric loss aversion with prospect theory: Recognising and dealing with size dependence," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 106-119.
    18. Yang, Guo-liang & Yang, Jian-bo & Liu, Wen-bin & Li, Xiao-xuan, 2013. "Cross-efficiency aggregation in DEA models using the evidential-reasoning approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 393-404.
    19. Vipin, B. & Amit, R.K., 2019. "Describing decision bias in the newsvendor problem: A prospect theory model," Omega, Elsevier, vol. 82(C), pages 132-141.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hao Pan & Guo-liang Yang & Xiao-lei Chen & Yuan-yu Lou & Teng Wang & Zhong-cheng Guan, 2024. "Regret cross-efficiency evaluation using attitudinal entropy approach," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.
    2. Borrás, Fernando & Ruiz, José L. & Sirvent, Inmaculada, 2023. "Peer evaluation through cross-efficiency based on reference sets," Omega, Elsevier, vol. 114(C).
    3. Reza Fallahnejad & Mohammad Reza Mozaffari & Peter Fernandes Wanke & Yong Tan, 2024. "Nash Bargaining Game Enhanced Global Malmquist Productivity Index for Cross-Productivity Index," Games, MDPI, vol. 15(1), pages 1-21, January.
    4. Ganji, S.S. & Dehghani, Alireza & Ajirlu, Shahrouz Fathi, 2024. "Evaluation of intercity road passenger transportation using a novel double-frontier game-regret-cross-efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    5. Mostafa Davtalab-Olyaie & Hadis Mahmudi-Baram & Masoud Asgharian, 2023. "Measuring individual efficiency and unit influence in centrally managed systems," Annals of Operations Research, Springer, vol. 321(1), pages 139-164, February.
    6. Ganji, S.S. & Tirkolaee, Erfan Babaee & Jahed, Rasul, 2024. "Evaluating the performance of intercity road freight transport: Double-frontier parallel network cross-efficiency model," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    7. Feng Li & Han Wu & Qingyuan Zhu & Liang Liang & Gang Kou, 2021. "Data envelopment analysis cross efficiency evaluation with reciprocal behaviors," Annals of Operations Research, Springer, vol. 302(1), pages 173-210, July.
    8. Cui, Yuan & Pan, Hao & Huang, Yi-Di & Yang, Guo-liang, 2024. "How can sociological theories provide legitimacy to eco-efficiency evaluations? Embark on a journey toward understanding," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ganji, S.S. & Dehghani, Alireza & Ajirlu, Shahrouz Fathi, 2024. "Evaluation of intercity road passenger transportation using a novel double-frontier game-regret-cross-efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    2. Liu, Hui-hui & Song, Yao-yao & Liu, Xiao-xiao & Yang, Guo-liang, 2020. "Aggregating the DEA prospect cross-efficiency with an application to state key laboratories in China," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    3. Feng Li & Han Wu & Qingyuan Zhu & Liang Liang & Gang Kou, 2021. "Data envelopment analysis cross efficiency evaluation with reciprocal behaviors," Annals of Operations Research, Springer, vol. 302(1), pages 173-210, July.
    4. Cui, Yuan & Pan, Hao & Huang, Yi-Di & Yang, Guo-liang, 2024. "How can sociological theories provide legitimacy to eco-efficiency evaluations? Embark on a journey toward understanding," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    5. Shi, Hai-Liu & Chen, Sheng-Qun & Chen, Lei & Wang, Ying-Ming, 2021. "A neutral cross-efficiency evaluation method based on interval reference points in consideration of bounded rational behavior," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1098-1110.
    6. Yangxue Ning & Yan Zhang & Guoqiang Wang, 2023. "An Improved DEA Prospect Cross-Efficiency Evaluation Method and Its Application in Fund Performance Analysis," Mathematics, MDPI, vol. 11(3), pages 1-15, January.
    7. Davtalab-Olyaie, Mostafa & Asgharian, Masoud, 2021. "On Pareto-optimality in the cross-efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 288(1), pages 247-257.
    8. Oukil, Amar, 2020. "Exploiting value system multiplicity and preference voting for robust ranking," Omega, Elsevier, vol. 94(C).
    9. Hao Pan & Guo-liang Yang & Xiao-lei Chen & Yuan-yu Lou & Teng Wang & Zhong-cheng Guan, 2024. "Regret cross-efficiency evaluation using attitudinal entropy approach," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.
    10. 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.
    11. Ganji, S.S. & Tirkolaee, Erfan Babaee & Jahed, Rasul, 2024. "Evaluating the performance of intercity road freight transport: Double-frontier parallel network cross-efficiency model," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    12. Balk, Bert M. & (René) De Koster, M.B.M. & Kaps, Christian & Zofío, José L., 2021. "An evaluation of cross-efficiency methods: With an application to warehouse performance," Applied Mathematics and Computation, Elsevier, vol. 406(C).
    13. Kao, Chiang & Liu, Shiang-Tai, 2020. "A slacks-based measure model for calculating cross efficiency in data envelopment analysis," Omega, Elsevier, vol. 95(C).
    14. Zhiying Zhang & Huchang Liao, 2024. "A stochastic cross-efficiency DEA approach based on the prospect theory and its application in winner determination in public procurement tenders," Annals of Operations Research, Springer, vol. 341(1), pages 509-537, October.
    15. Liu, Yong-Jun & Yang, Guo-Sen & Zhang, Wei-Guo, 2024. "A novel regret-rejoice cross-efficiency approach for energy stock portfolio optimization," Omega, Elsevier, vol. 126(C).
    16. Borrás, Fernando & Ruiz, José L. & Sirvent, Inmaculada, 2023. "Peer evaluation through cross-efficiency based on reference sets," Omega, Elsevier, vol. 114(C).
    17. Liu, Hui-hui & Song, Yao-yao & Yang, Guo-liang, 2019. "Cross-efficiency evaluation in data envelopment analysis based on prospect theory," European Journal of Operational Research, Elsevier, vol. 273(1), pages 364-375.
    18. Shiang-Tai Liu & Yueh-Chiang Lee, 2021. "Fuzzy measures for fuzzy cross efficiency in data envelopment analysis," Annals of Operations Research, Springer, vol. 300(2), pages 369-398, May.
    19. Chen, Xiaolei & Guan, Zhongcheng & Yang, Guoliang & Pan, Hao & Xiong, Xi, 2024. "Evaluation of technology transfer performance for Chinese universities based on a dual-game cross-efficiency model," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    20. Chen, Lei & Wang, Ying-Ming, 2020. "DEA target setting approach within the cross efficiency framework," Omega, Elsevier, vol. 96(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:288:y:2020:i:1:d:10.1007_s10479-019-03491-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.