IMPACT OF ARTIFICIAL INTELLIGENCE ON CUSTOMER SATISFACTION IN ONLINE SHOPPING

Abstract

This study empirically examines the impact of Artificial Intelligence (AI) deployment on customer satisfaction within the online shopping sector. Driven by rapid digital transformations, e-commerce firms leverage AI architecture such as personalized recommendations, machine-learning-based fraud prevention, interactive chatbots, and predictive offers—to enhance user experiences. Using a convenience sampling framework, primary data was gathered from 150 online shopping consumers via a structured 5-point Likert scale questionnaire. Instrument stability was verified using a Cronbach’s Alpha reliability check, which yielded a strong internal consistency score of 0.864. Inferential analyses, including Pearson Bivariate Correlation and Multiple Linear Regression, were applied to map the directional relationships between variables. The Pearson analysis revealed a strong, positive relationship (r = 0.782) between AI service quality and customer satisfaction. Multiple regression testing indicated that automated payment security (b = 0.345) and algorithmic product recommendations (b = 0.312) exert the highest statistical influence on user satisfaction, while AI chat bots (b = 0.215) provide a smaller, though still significant, contribution. Hypothesis testing resulted in the rejection of the null hypothesis (H0), confirming that AI integrations significantly improve consumer satisfaction. The study concludes with actionable strategies for e-commerce platforms to optimize their front-end interface and back-end security infrastructure.