AI-Driven Personalization and Purchase Intention: The Mediating Role of Algorithmic Fairness Perception

Abstract

Artificial intelligence (AI)-driven personalization has revolutionized digital commerce by enabling platforms to provide highly tailored recommendations, customized content, and adaptive shopping experiences. Although prior research has established that AI personalization positively influences consumer responses through dimensions such as trust, usefulness, and satisfaction, there has been limited exploration of how consumers assess the fairness of algorithmic decision-making prior to forming purchase intentions. Furthermore, few studies have integrated AI-driven personalization, brand experience, perceived value, and social proof into a cohesive framework that elucidates consumer behavior through the lens of algorithmic fairness perception. To address this gap, the present study examines the mediating role of algorithmic fairness perception in elucidating how AI-enabled marketing stimuli affect purchase intention. A quantitative cross-sectional survey was conducted involving 400 consumers with experience using AI-enabled e-commerce platforms, and the proposed model was analyzed utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that AI-driven personalization, brand experience, perceived value, and social proof significantly enhance algorithmic fairness perception. Additionally, AI-driven personalization, brand experience, perceived value, and algorithmic fairness perception substantially increase purchase intention, while social proof does not exhibit a significant direct effect. Nevertheless, algorithmic fairness perception serves as a significant mediator in the relationships between all antecedent variables and purchase intention. The originality of this study lies in positioning algorithmic fairness perception as the central psychological mechanism that translates AI-enabled marketing strategies into consumer purchase intention within a Stimulus–Organism–Response (SOR) framework. These findings contribute to the growing body of literature on AI marketing by shifting the analytical focus from technological effectiveness to ethical algorithmic evaluation, offering practical guidance for the development of transparent, responsible, and consumer-oriented AI systems that promote sustainable customer relationships.