Impact of AI-Driven Personalization on Consumer Buying Behaviour in E-Commerce

Abstract

The rapid development of Artificial Intelligence (AI) has significantly transformed the e-commerce sector by enabling businesses to understand consumer preferences and provide highly personalized shopping experiences. AI-driven personalization uses technologies such as machine learning, recommendation systems, predictive analytics, chatbots, personalized advertisements, and customized product displays to match products and services with individual consumer preferences. The present study examines the impact of AI-driven personalization on consumer buying behaviour in e-commerce. The study focuses on major dimensions of AI personalization, including personalized product recommendations, customized promotional offers, personalized content, AI-powered customer assistance, and predictive product suggestions. It further examines the influence of perceived relevance, perceived usefulness, consumer trust, privacy concerns, and perceived intrusiveness on consumer buying behaviour. The study proposes a conceptual framework based on the Stimulus–Organism–Response (S-O-R) framework and Technology Acceptance Model (TAM). The paper argues that relevant and useful AI personalization can reduce consumers' search effort, improve shopping convenience, enhance engagement, and positively influence purchase intention. However, excessive personalization, privacy concerns, lack of transparency, and perceived manipulation may negatively affect consumer trust and buying behaviour. The study provides implications for e-commerce businesses regarding responsible AI implementation, transparency, privacy protection, and consumer-centric personalization.