Integrated stochastic multicriteria acceptability analysis and data envelopment analysis with fixed-sum outputs: an application for evaluating participating nations in the Winter Olympics Games

Abstract

AbstractAlong with the popularity of Olympic Games, evaluating and ranking participating nations has become one of the hottest issues in recent years. Data envelopment analysis (DEA) is extensively used for participating nations’ performance evaluation, while the prior literature mainly addresses the maximum efficiency by ignoring the weights diversity, which might lead to unreasonable and biased results. By considering the fixed-sum medals constraint, this paper proposes an integrated approach of stochastic multicriteria acceptability analysis (SMAA) and DEA for evaluating the participating nations. The proposed approach addresses the weights diversity and multiple equilibrium efficient frontiers by investigating the overall feasible space, and two strategies are considered to guarantee the fairness concern for calculating the efficiencies. Besides, the proposed approach provides both rank acceptability and holistic acceptability index to obtain the complete ranking from the best to the worst and to get the relative ranking relationship in the overall feasible space. The proposed approach can provide more reasonable performance measurements since it explores different rank positions and improves the whole satisfaction of all participating nations. Finally, the proposed approach is applied to empirically analyze the participating nations in the Winter Olympic Games from 2010 to 2022 and some valuable analytical results are provided.Keywords: Data envelopment analysis (DEA)Fixed-sum outputs (FSO)Stochastic multicriteria acceptability analysis (SMAA)Performance evaluationWinter Olympic Games AcknowledgmentsThe authors thank editors and two anonymous reviewers for their kind work and insightful comments.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis research was financially supported by the National Natural Science Foundation of China [Nos. 71901178 and 72201123], the Humanities and Social Sciences Foundation of Ministry of Education of China (NO. 23XJC630007), the Fundamental Research Funds for the Central Universities [No. JBK2307014] and Guanghua Talent Project of Southwestern University of Finance and Economics