The Influence of AI-Based Product Recommendation Systems on Impulse Buying in Mobile Commerce

Abstract

The article investigates how AI based product recommendation systems shape impulse buying in mobile commerce, focusing on when personalized suggestions intensify unplanned purchasing. It asks how recommendation features and mobile interface cues are conceptualized as stimuli, which psychological mechanisms link them to impulsive outcomes, and which contextual or consumer factors condition these effects. Using a systematic literature review of peer reviewed studies published between 2018 and 2022, the study extracts data on theoretical frameworks, mobile settings, recommendation designs, measures of impulse buying, and reported mediators and moderators. The synthesis shows that AI based recommendations typically raise perceived relevance, reduce search effort, and guide attention, which strengthens positive affect, perceived value, flow, and urge to buy, especially in hedonic and socially rich mobile environments. Evidence also highlights moderating roles of demographic characteristics, social influence, and privacy concerns, while revealing heavy reliance on cross sectional self-report designs. The review identifies key gaps in causal evidence, algorithmic transparency, and ethical considerations around AI mediated impulse buying.