Journal of Computational Analysis and Applications VOL. 33, NO. 2, 2024 649 Abinesh R.C et al 649-655 The Impact of AI-Driven Personalization on Customer Satisfaction in E-Commerce: Balancing Technology, Transparency, and Control Abinesh R.C 1 , Rhytheema Dulloo 2 1 MBA Scholar, School of Management, Hindustan Institute of Technology and Science, Chennai, India 2 Assistant Professor (S.G), School of Management, Hindustan Institute of Technology and Science, Chennai, India Email: dulloo.rhytheema@gmail.com 2 Received: 05.03.2024 Revised : 10.04.2024 Accepted: 20.05.2024 ABSTRACT The rapid advancements in artificial intelligence (AI) have revolutionized the way businesses interact with their customers. One key area of AI-driven innovation is personalization, where algorithms analyze customer data to deliver tailored experiences. This study investigates the impact of AI-driven personalization on customer satisfaction in e-commerce.Using a sample of 502 customers from an e- commerce platform, the research examines the relationships between AI-driven personalization, recommendation algorithm transparency, customer control over personalization, and overall customer satisfaction. A quantitative cross-sectional design is employed, utilizing validated scales and multiple linear regression analysis. Results indicate that AI-driven personalization has the strongest positive effect on customer satisfaction (β = 0.45, p < 0.001), followed by recommendation algorithm transparency (β = 0.23, p < 0.001) and customer control over personalization (β = 0.16, p < 0.01). The model explains 49% of the variance in customer satisfaction.These findings underscore the importance of implementing sophisticated AI-driven personalization strategies while maintaining transparency and offering customers control over their personalized experiences. The study contributes to the growing body of literature on AI applications in e-commerce and provides practical implications for businesses seeking to enhance customer satisfaction through personalization technologies. Keywords: Artificial Intelligence (AI), e-commerce, customer satisfaction, innovation, personalization, recommendation algorithm transparency. INTRODUCTION In the rapidly evolving landscape of e-commerce, artificial intelligence (AI) has emerged as a transformative force, reshaping the way businesses interact with their customers. Among the myriad applications of AI, personalization stands out as a key driver of customer engagement and satisfaction. By leveraging sophisticated algorithms to analyze vast amounts of customer data, businesses can now offer tailored experiences that cater to individual preferences and needs.The impact of AI-driven personalization on customer satisfaction in e-commerce is a topic of growing importance, as businesses strive to balance the benefits of advanced technology with concerns about transparency and user control. As consumers become increasingly aware of data collection and usage practices, there is a pressing need to understand how personalization efforts affect their overall satisfaction.This study aims to investigate the complex relationships between AI-driven personalization, recommendation algorithm transparency, customer control over personalization, and overall customer satisfaction in the e-commerce context. By examining these factors, we seek to contribute to the growing body of literature on AI applications in e- commerce and provide actionable insights for businesses looking to enhance their personalization strategies. Our research addresses the following key questions: 1. To what extent does AI-driven personalization impact customer satisfaction in e-commerce? 2. How does the transparency of recommendation algorithms influence customer perceptions and satisfaction? 3. What role does customer control over personalization play in shaping overall satisfaction? As AI continues to advance and shape the future of e-commerce, understanding the complex interplay between personalization, transparency, and customer control becomes increasingly crucial.This research