IMPACT OF AI-POWERED PERSONALIZED RECOMMENDATIONS ON CONSUMER PURCHASE INTENTION IN E-COMMERCE PLATFORMS

Artificial Intelligence (AI)-powered personalized recommendation systems have become critical tools for enhancing consumer engagement and shaping purchase decisions within e-commerce environments. These systems leverage algorithms and data analytics to tailor product suggestions based on consumer behavior patterns, preferences, and past interactions. This conceptual review synthesizes existing academic and industry research to evaluate how AI-based personalization influences consumer purchase intention—defined as the likelihood that a consumer will buy a product. Key mechanisms through which AI recommendations impact intention include increased relevance of offerings, improved decision-making efficiency, and enhanced consumer satisfaction. While personalization can improve perceived usefulness, it also raises concerns related to privacy, algorithmic transparency, and consumer trust. This paper reviews literature across disciplines—marketing, information systems, consumer psychology, and data ethics—to identify the primary drivers and barriers of AI-based recommendation effectiveness. The review highlights research gaps such as the moderating roles of consumer privacy concerns, perceived personalization value, and demographic differences. A conceptual framework is proposed to illustrate the relationships between personalization attributes, mediating consumer perceptions, and purchase intention outcomes. Strategic, managerial, and policy implications are discussed, emphasizing the need for ethical design practices, transparent data usage policies, and consumer education. The study concludes by suggesting avenues for future research, including experimental designs to validate framework paths and cross-cultural investigations. This paper offers a comprehensive understanding for scholars and practitioners seeking to optimize recommendation systems while balancing consumer trust and business performance.