AI-Based Dynamic Pricing Strategies and Perceived Price Fairness in Digital Platforms

Abstract

This article examines how artificial intelligence based dynamic pricing on digital platforms shapes perceived price fairness and subsequent customer responses. The study focuses on revenue management practices in electronic commerce, ride hailing, online travel, and accommodation sharing services where machine learning models continuously adjust prices based on behavioural and contextual data. Using a systematic literature review, the article synthesises peer reviewed evidence on the design of algorithmic pricing, the role of personalization intensity and transparency, and the psychological mechanisms that connect price perceptions with trust, satisfaction, and loyalty. The findings show that artificial intelligence based pricing can enhance revenue and capacity utilisation, but that opaque and highly personalized prices often generate perceptions of unfairness, privacy concern, and feelings of betrayal, which weaken platform relationships. The review concludes that dynamic pricing strategies must treat fairness as a central design constraint and align pricing logic and communication with evolving expectations of contractual fairness on digital platforms and with emerging regulatory and ethical standards.