Optimal dynamic pricing strategy for non-durable experience goods: the role of consumer AI learning
研究构建了体验品的动态定价模型，纳入产品感知质量和AI驱动的消费者学习，发现企业会根据AI信息级联和感知质量信号调整价格，AI学习显著影响定价和收益。
This study develops a dynamic pricing model for experiential products by explicitly integrating product perceived quality and AI-driven consumer learning. Several interesting and key conclusions are obtained. First, the results demonstrate that companies strategically adjust pricing in response to the strength of AI-enabled information cascades and the perceived quality signals after AI learning. Moreover, AI-driven consumer learning significantly shapes both optimal pricing and revenue. Specifically, consumer AI learning has a significant impact on both pricing and revenue gains. When the information cascade is in a positive state and the perceived quality indicator after AI learning is positive, it indicates strong market demand, and consumers are willing to accept higher-priced products, allowing companies to adjust prices upwards to increase revenue. In contrast, when the information cascade is in a positive state and the perceived quality indicator after AI learning is negative, it suggests weak market demand, and consumers are unwilling to pay higher prices for products, prompting companies to lower prices to stimulate demand. Furthermore, this study explores when demand fluctuations under consumer AI learning for various products increase, prices should be adjusted downward, highlighting the significant effect of demand changes under AI learning on pricing strategies when facing competition.