ARTIFICIAL INTELLIGENCE DRIVEN CUSTOMER ENGAGEMENT IN ONLINE RETAIL

Abstract

The integration of artificial intelligence (AI) into e-commerce has profoundly transformed consumer engagement methods; nonetheless, its total impact on the customer experience remains complex and multifaceted. This study employs a quantitative research methodology to examine the fundamental factors that affect customer perceptions of AI-driven engagement and its effect on satisfaction. A structured online survey was administered to a cohort of 300 seasoned internet users. Exploratory Factor Analysis (EFA) of 25 Likert-scale statements identified a robust four-factor structure: Perceived Value & Personalization (PVP), Privacy & Transparency Apprehension (PTA), Relational Decoupling (RD), and Service Efficiency & Reliability (SER), collectively accounting for 58.4% of the total variance. A multivariate regression analysis indicated that all four variables were significant predictors of overall customer satisfaction (R² = .506, p < .001). Specifically, PVP (β = .398) and SER (β = .311) had positive influences, but PTA (β = -.242) and RD (β = -.187) shown large negative effects. Additionally, a mediation analysis indicated that Relational Decoupling partially mediates the adverse relationship between privacy concerns and satisfaction (indirect effect: 0.10, 95% CI [0.05, 0.16]). The findings suggest that although AI's functional benefits are strong motivators for happiness, ethical and relational issues significantly diminish their impact. This research provides a validated paradigm for online retail, emphasizing that an effective AI approach must achieve a balance of technological efficiency, clear data regulations, and human-centered design to foster trust and enduring loyalty.