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AI-Driven Personalization and Consumer Trust in E-Commerce: A Synthesis of Contemporary Evidence

Author(s) Nidhi Goel
Country India
Abstract AI now runs most of the personalization machinery in e-commerce — the recommendation systems, the chatbots, the interfaces that affects consumer product identification, evaluation and purchase. But the research findings on the relationship between trust and personalisation remains contradictory. Some studies found that it boosts purchase intention and loyalty by a wide margin. Others find the opposite of it that people get suspicious and worry about privacy they trust the platform less once personalization starts feeling intrusive or hard to explain. This paper pulls together 24 recent studies, including empirical work, conceptual papers, and one bibliometric review, on AI personalization, recommendation systems, and consumer trust. Instead of going study by study, it synthesises them to see where they actually agree and where they don't. The pattern that emerges is that transparency, accuracy, and perceived value tend to build trust whereas Privacy worries, over-personalization, and cultural or religious mismatch tend to break it down. And on one specific point, whether transparency helps or hurts persuasion, the studies contradict each other outright. The paper ends by looking at fairness, accountability, and governance questions in AI-driven personalization, and also suggest future research directions.
Keywords Artificial Intelligence, Personalization, Consumer Trust, Recommendation Systems, E-commerce, Privacy Paradox
Published In Volume 7, Issue 7, July 2026
Published On 2026-07-29
DOI https://doi.org/10.70528/IJLRP.v7.i7.2333
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