考虑消费者反展厅行为的定价与人工智能投入策略

Pricing and AI Investment Strategies Considering Consumer Webrooming Behavior

摘要: 随着全渠道零售的深入发展，消费者反展厅行为(即线上搜集信息、线下完成购买)日益普遍，这对线上线下商家的定价策略带来深刻影响。与此同时，人工智能技术在电商平台的应用日益广泛，是企业优化用户体验、降低信息不对称的重要工具。本文基于博弈论模型，在考虑消费者渠道偏好异质性和线上搜索成本的基础上，分析消费者出现反展厅行为时，电商企业的最优定价与人工智能技术投资策略。研究发现，产品不匹配价值折损、渠道适配成本和旅行成本共同影响双渠道的价格博弈与利润分配。人工智能投入水平随产品不匹配价值折损变化呈非线性调整。因此，企业应结合产品特性和消费者行为建立动态定价与技术投入调整机制。

Abstract: With the in-depth development of omnichannel retail, consumer webrooming behavior (i.e., gathering information online and completing purchases offline) has become increasingly prevalent, exerting a profound impact on the pricing strategies of online and offline merchants. Simultaneously, the application of artificial intelligence technology in e-commerce platforms is becoming increasingly widespread, serving as a crucial tool for businesses to optimize user experience and reduce information asymmetry. Based on a game theory model, this paper analyzes the optimal pricing and AI technology investment strategies of e-commerce enterprises under consumer webrooming behavior, taking into account the heterogeneity of consumer channel preferences and online search costs. The study reveals that product mismatch value depreciation, channel adaptation costs, and travel costs jointly influence the price game and profit distribution across dual channels. The level of AI investment adjusts nonlinearly in response to changes in product mismatch value loss. Consequently, enterprises should establish a dynamic pricing and technology investment adjustment mechanism based on product characteristics and consumer behavior.