博弈论视角下直播电商定价策略与消费者决策互动研究

Research on Pricing Strategy and Consumer Decision Interaction in Live-Streaming E-Commerce from the Perspective of Game Theory

摘要: 直播电商的爆发式增长使“主播–商家–消费者”三方利益博弈日益复杂，定价策略作为核心利益分配机制，直接影响三方决策与平台生态可持续性。本文基于斯塔克尔伯格博弈理论，构建主播主导的三方博弈模型，融合Logit模型刻画消费者异质性选择行为，系统分析主播佣金比例、产品质量对商家供货定价、消费者购买决策的互动影响机制。通过数值仿真验证模型有效性，研究发现：主播佣金与商家定价呈倒“U”型关系，佣金比例超过35%后会显著抑制商家定价积极性；产品质量对消费者选择的影响系数是价格敏感系数的1.8倍，高质量产品可容忍15%~20%的溢价空间；三方均衡定价策略下，平台整体转化率较随机定价提升，商家利润与消费者剩余也有不同程度增长。研究结论为直播电商三方主体的定价决策与平台规则优化提供了理论支撑与实践参考。

Abstract: The explosive growth of live-streaming e-commerce has made the tripartite interest game among streamers, merchants, and consumers increasingly complex. As a core mechanism for interest distribution, pricing strategies directly influence the decisions of all three parties and the sustainability of the platform ecosystem. Based on the Stackelberg game theory, this paper constructs a streamer-led tripartite game model, which incorporates the Logit model to characterize consumers’ heterogeneous choice behaviors, and systematically analyzes the interactive impact mechanisms of streamers’ commission rates and product quality on merchants’ supply pricing and consumers’ purchase decisions. The validity of the model is verified through numerical simulation. The findings reveal that: There is an inverted U-shaped relationship between streamers’ commissions and merchants’ pricing, and merchants’ pricing enthusiasm is significantly inhibited when the commission rate exceeds 35%; The impact coefficient of product quality on consumer choice is 1.8 times that of the price sensitivity coefficient, and high-quality products can tolerate a premium range of 15%~20%; Under the tripartite equilibrium pricing strategy, the overall platform conversion rate improves compared with random pricing, and merchants’ profits and consumer surplus increase to varying degrees. The conclusions of this study provide theoretical support and practical references for the pricing decisions of the three parties in live-streaming e-commerce and the optimization of platform rules.