电商平台在线评论信息质量对商家动态定价策略的影响研究

Research on the Impact of Online Review Information Quality on Merchants’ Dynamic Pricing Strategies in E-Commerce Platforms

摘要: 在线评论是电商环境中缓解信息不对称的关键机制，但其信息质量参差不齐，对商家基于市场信号的动态定价构成复杂挑战。本文基于信号传递理论与社会学习理论，系统探讨在线评论信息质量对商家动态定价策略的影响机制。研究发现：高信息质量评论能有效传递产品质量信号，增强商家定价的精准性，促使其实施差异化动态定价；低信息质量评论则会产生信息扭曲效应，使商家陷入信号识别困境，导致定价偏离最优均衡。本文进一步探讨了产品类型与消费者评论素养的调节作用，并重点分析了平台治理机制如何通过提升评论信息质量来修正这一影响路径。本研究为电商平台完善评论治理体系、商家优化动态定价策略提供了理论依据与实践启示。

Abstract: Online reviews serve as a key mechanism to alleviate information asymmetry in the e-commerce environment. However, the quality of such reviews varies, posing complex challenges for merchants’ dynamic pricing based on market signals. This paper, based on the theories of signal transmission and social learning, systematically explores the impact mechanism of online review information quality on merchants’ dynamic pricing strategies. The study finds that high-quality reviews can effectively convey product quality signals, enhance the accuracy of merchants’ pricing, and prompt them to implement differentiated dynamic pricing; low-quality reviews, on the other hand, will produce an information distortion effect, causing merchants to be trapped in a signal recognition dilemma and leading to pricing deviations from the optimal equilibrium. This paper further explores the moderating effects of product type and consumer review literacy, and focuses on analyzing how the platform governance mechanism can correct this influence path by improving the quality of review information. This research provides a theoretical basis and practical insights for e-commerce platforms to improve their review governance systems and merchants to optimize their dynamic pricing strategies.