Customer Experience in AI-Driven E-Commerce: An Empirical Model of Drivers and Strategic Outcomes
Year: 2026
Type: article
Study design: Observational Study
Abstract: As AI-powered e-commerce platforms grow more capable of predicting customer wants, a critical question remains unexplored: what makes customers perceive these experiences positively? The rapid integration of artificial intelligence (AI) into e-commerce platforms is reshaping how customers search for, evaluate, and experience digital services. However, empirical research has not kept pace with clarifying which platform-level factors most effectively shape customer experience (CX) in AI-driven environments. This study validated the Trust, Autonomy, Personalization, and Customer Engagement (TAPE) framework as a comprehensive set of CX drivers in intelligent commerce. Using survey data from 400 active e-commerce users, we employed a multi-stage approach combining exploratory factor analysis, confirmatory factor analysis, and covariance-based structural equation modeling (SEM) with bootstrapped mediation testing. All four TAPE drivers demonstrated significant positive reflective associations with CX, with personalization and engagement emerging as the strongest contributors. CX was strongly associated with customer satisfaction, loyalty, and brand equity, and mediated the effects of all four dimensions on these strategic outcomes, with model comparison evidence supporting full mediation. The study contributes theoretically by integrating and empirically validating four established CX dimensions within the AI-enabled e-commerce context, and by demonstrating the central mediating role of CX in converting intelligent platform features into user-perceived strategic value. Managerially, the TAPE framework provides actionable guidance for designing transparent, adaptive, and engaging AI-driven customer journeys that enhance both experience quality and long-term brand outcomes.
Source: Information
DOI: 10.3390/info17050414
Authors: Srinivas Kumar Mittameedi, Varun Dogra
Institution: Lovely Professional University
Language: English
Topic: AI in Service Interactions
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7158370178