Impact of AI-Based Recommendation Systems on Consumer Purchase Behaviour in E-Commerce

Artificial Intelligence has quietly become the backbone of modern e-commerce platforms. From the products we see on homepages to the items suggested at checkout, AI-driven recommendation systems shape much of our online shopping experience. While prior research often measures their effectiveness through technical metrics such as click-through rates and conversions, this paper shifts the focus toward the psychological dimension of consumer decision-making. Drawing upon existing literature in marketing, behavioural economics, and information systems, this study develops a conceptual framework examining how recommendation accuracy, perceived usefulness, trust, and perceived intrusiveness interact to influence purchase behaviour. The paper argues that AI recommendations do not affect consumers in a purely linear manner. Instead, their influence depends heavily on how users interpret the system—whether as a supportive assistant simplifying choices or as a manipulative mechanism driven by profit motives. This research highlights the dual nature of AI personalization: it reduces cognitive effort and enhances purchase intention, yet may simultaneously trigger privacy concerns and psychological resistance