The Role of AI-Driven Recommendation Satisfaction in Repurchase Intention: A PRISMA-Based Systematic Review of E-Commerce Studies

Abstract

This study aims to systematically review the relationship between user satisfaction with artificial intelligence (AI)-based recommendation systems and repurchase intention on e-commerce platforms. As competition in the digital commerce sector intensifies, understanding how AI-driven personalization shapes consumer loyalty has become increasingly critical. This study employed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, in which an initial search using keywords “AI recommendation”,” customer satisfaction”, and “repurchase intention” on the Scopus database yielded 400 documents published between 2020 and 2025. Following a multi-stage screening process including removal of non-eligible document types, evaluation of title and abstract relevance, and full-text accessibility checks, 40 articles were ultimately included for analysis. Findings consistently demonstrate that customer satisfaction functions as the dominant mediating variable between AI recommendation quality and repurchase intention. Personalized recommendation systems reduce users' information overload, while AI-powered chatbots with empathetic and proactive strategies further enhance satisfaction. The effectiveness of AI recommendations in driving repurchase intention is contextual and moderated by demographic factors such as gender, age, and digital literacy. These findings extend existing consumer behavior theory by integrating user experience as a critical dimension in assessing AI system effectiveness on e-commerce platforms.