AI Powered Retail Pricing Transparency Effects on Consumer Trust and Purchase Intentions in US Algorithmic Pricing Systems

Abstract

The rapid integration of artificial intelligence into retail pricing systems has transformed how firms determine, personalize, and communicate prices to consumers across digital marketplaces in the United States. Algorithmic pricing models leverage large-scale behavioral data, predictive analytics, and machine learning optimization to dynamically adjust prices in real time, enabling retailers to maximize revenue efficiency and market responsiveness. While these systems enhance operational precision, they simultaneously introduce growing concerns regarding pricing transparency, perceived fairness, consumer privacy, and algorithmic accountability. This review paper examines the relationship between AI-driven pricing transparency and consumer behavioral outcomes, with particular emphasis on trust formation and purchase intentions within algorithmically mediated retail environments. Drawing from interdisciplinary literature spanning marketing analytics, behavioral economics, information systems, and digital ethics, the study synthesizes evidence on how disclosure practices, explainability mechanisms, and perceived data usage influence consumer responses to personalized pricing. The review further evaluates psychological reactions to price discrimination, fairness perceptions, and privacy trade-offs associated with AI-enabled personalization. Regulatory developments and emerging governance frameworks in U.S. retail markets are analyzed to understand how policy interventions shape transparency expectations and platform accountability. The paper\ identifies key moderating variables, including consumer awareness, algorithm literacy, brand reputation, and contextual transparency cues, that determine whether AI pricing enhances or erodes trust. By integrating technological, behavioral, and regulatory perspectives, this study proposes a conceptual framework linking transparency design strategies to sustainable consumer engagement outcomes. The findings aim to guide retailers, policymakers, and system designers toward responsible algorithmic pricing practices that balance innovation with ethical consumer protection and long-term market trust.