Artificial intelligence recommendation systems and consumer purchase intention: A systematic literature review and research agenda

Abstract

Artificial intelligence recommendation systems now shape how consumers discover and evaluate products online, yet research on their influence on purchase intention remains scattered across theories, contexts and methods. This study presents a systematic literature review following the Scientific Procedures and Rationales for Systematic Literature Reviews protocol. A Scopus search covering January 2015 to March 2026 returned 1,779 records, which were screened to 397 and then to a final sample of 112 peer reviewed journal articles. The sample was analysed through bibliometric mapping in VOSviewer and Biblioshiny, including keyword co-occurrence, thematic maps and thematic evolution, and through the Theory, Context, Characteristics and Methodology (TCCM) framework. The literature draws mainly on the Stimulus Organism Response model, the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, and trust theory. Most studies report that personalization, recommendation quality, transparency, explainability and trust are associated with stronger purchase intention, while privacy concerns, perceived manipulation and algorithmic bias weaken acceptance. Research is concentrated in e commerce and technologically advanced markets and relies mainly on cross sectional surveys and structural equation modelling. The review identifies gaps in explainable and ethical AI, consumer resistance, emerging market and cross-cultural evidence, and longitudinal and experimental designs, and it proposes a structured agenda for future research. Findings concern intentions rather than observed purchasing and reflect a single database.