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Models for measuring and benchmarking olympics achievements

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  • Li, Yongjun
  • Liang, Liang
  • Chen, Yao
  • Morita, Hiroshi

Abstract

As demonstrated in several recent studies, data envelopment analysis (DEA) is a useful tool for evaluating and comparing the performance of nations competing in the Olympic Games. Assurance regions (ARs) have been used to further refine the DEA results. These AR DEA models assume that ARs apply uniformly across all nations. Such models can have shortcomings in the sense that different nations may impose different ARs, as nations may value gold, silver, and bronze medals differently. This paper extends previous DEA studies by incorporating multiple sets of nation-specific ARs into the DEA. By doing so, we establish fair models for measuring and benchmarking the performance of nations at six summer Olympic Games.

Suggested Citation

  • Li, Yongjun & Liang, Liang & Chen, Yao & Morita, Hiroshi, 2008. "Models for measuring and benchmarking olympics achievements," Omega, Elsevier, vol. 36(6), pages 933-940, December.
    • Handle: RePEc:eee:jomega:v:36:y:2008:i:6:p:933-940
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    Cited by:

    1. Sekitani, Kazuyuki & Zhao, Yu, 2021. "Performance benchmarking of achievements in the Olympics: An application of Data Envelopment Analysis with restricted multipliers," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1202-1212.
    2. Calzada-Infante, Laura & Lozano, Sebastián, 2016. "Analysing Olympic Games through dominance networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1215-1230.
    3. Wang, John & Yan, Ruiliang & Hollister, Kimberly & Zhu, Dan, 2008. "A historic review of management science research in China," Omega, Elsevier, vol. 36(6), pages 919-932, December.
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    6. Magdalena Szydełko, 2022. "Ocena klasyfikacji i typologii benchmarkingu z wykorzystaniem warunków poprawności podziału logicznego pojęć," Nowoczesne Systemy Zarządzania. Modern Management Systems, Military University of Technology, Faculty of Security, Logistics and Management, Institute of Organization and Management, issue 4, pages 79-90.
    7. Li, Yongjun & Lei, Xiyang & Dai, Qianzhi & Liang, Liang, 2015. "Performance evaluation of participating nations at the 2012 London Summer Olympics by a two-stage data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 243(3), pages 964-973.
    8. Pedro Garcia‐del‐Barrio & Carlos Gomez‐Gonzalez & José Manuel Sánchez‐Santos, 2020. "Popularity and Visibility Appraisals for Computing Olympic Medal Rankings," Social Science Quarterly, Southwestern Social Science Association, vol. 101(5), pages 2137-2157, September.
    9. M. Flegl & L. A. Andrade, 2018. "Measuring countries’ performance at the Summer Olympic Games in Rio 2016," OPSEARCH, Springer;Operational Research Society of India, vol. 55(3), pages 823-846, November.
    10. Botti, Laurent & Briec, Walter & Cliquet, Gérard, 2009. "Plural forms versus franchise and company-owned systems: A DEA approach of hotel chain performance," Omega, Elsevier, vol. 37(3), pages 566-578, June.
    11. Tsoi, Ka Ho & Loo, Becky P.Y., 2021. "Cutting the loss: International benchmarking of a sustainable ferry business model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 167-188.
    12. Plácido Moreno & Sebastián Lozano, 2014. "A network DEA assessment of team efficiency in the NBA," Annals of Operations Research, Springer, vol. 214(1), pages 99-124, March.
    13. Sebastián Lozano & Gabriel Villa, 2023. "Multiobjective centralized DEA approach to Tokyo 2020 Olympic Games," Annals of Operations Research, Springer, vol. 322(2), pages 879-919, March.
    14. Josef Jablonsky, 2018. "Ranking of countries in sporting events using two-stage data envelopment analysis models: a case of Summer Olympic Games 2016," 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. 26(4), pages 951-966, December.
    15. Qi Wang & Tobias Jeppsson, 2022. "Identifying benchmark units for research management and evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7557-7574, December.
    16. Natalia R. Potoczek, 2021. "The use of process benchmarking in the water industry to introduce changes in the digitization of the company’s value chain," Journal of Entrepreneurship, Management and Innovation, Fundacja Upowszechniająca Wiedzę i Naukę "Cognitione", vol. 17(4), pages 51-89.
    17. Jarmila Horváthová & Martina Mokrišová & Mária Vrábliková, 2021. "Benchmarking—A Way of Finding Risk Factors in Business Performance," JRFM, MDPI, vol. 14(5), pages 1-17, May.
    18. Xiyang Lei & Yongjun Li & Qiwei Xie & Liang Liang, 2015. "Measuring Olympics achievements based on a parallel DEA approach," Annals of Operations Research, Springer, vol. 226(1), pages 379-396, March.
    19. Yang, Min & Li, Yongjun & Chen, Ya & Liang, Liang, 2014. "An equilibrium efficiency frontier data envelopment analysis approach for evaluating decision-making units with fixed-sum outputs," European Journal of Operational Research, Elsevier, vol. 239(2), pages 479-489.
    20. Partovi, Fariborz Y., 2011. "Corporate philanthropic selection using data envelopment analysis," Omega, Elsevier, vol. 39(5), pages 522-527, October.
    21. Villa, G. & Lozano, S., 2016. "Assessing the scoring efficiency of a football match," European Journal of Operational Research, Elsevier, vol. 255(2), pages 559-569.
    22. Wu, Jie & Liang, Liang & Chen, Yao, 2009. "DEA game cross-efficiency approach to Olympic rankings," Omega, Elsevier, vol. 37(4), pages 909-918, August.

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