RVRS Efficiency and Scale Adjustment Decisions of Tourism Enterprises Under COVID-19
针对COVID-19对数据包络分析凸性假设的挑战，引入非凸正则可变规模报酬前沿分析中国上市旅游企业效率（2017-2023），发现最优规模调整可减少高达69.17%的损失，且约三分之一企业在2021年仍处于规模报酬递增阶段。
The sudden and recurrent nature of COVID-19 challenges the convexity assumption in data envelopment analysis (DEA) and may bias efficiency estimates and crisis response strategies. To address this bias, this study first introduces the non-convex regular variable returns to scale (RVRS) frontier to analyze Chinese listed tourism enterprises efficiency (2017–2023). We evaluate the efficiency evolution and explore how scale adjustment decisions can mitigate the losses caused by the pandemic. Results indicate that while COVID-19 significantly diminished tourism enterprise efficiency, optimal scale adjustments could reduce losses by up to 69.17%. Heterogeneity analysis reveals that large enterprises and travel agency benefited most from scale adjustment. Notably, approximately one-third of enterprises in 2021 were still operating under increasing returns to scale, suggesting that optimal responses to the pandemic were not uniformly contractionary. This study contributes by demonstrating how the RVRS framework improves measurement accuracy and supports more effective, non-linear crisis management decisions.