Examining the performance impacts of Artificial Intelligence (AI) in Online Retailing: An Empirical Analysis

Abstract

Artificial Intelligence (AI) has emerged as a transformative force reshaping industries worldwide, with retailing experiencing particularly profound change. Yet, alongside its potential, AI raises concerns about trust, ethics, and data privacy, creating uncertainty about its real-world impact. While prior research has often relied on surveys or self-reported data, limited attention has been given to objective performance outcomes in live digital environments. This thesis addresses this gap by examining how AI integration influences the performance of online retailers using the De-Lone and McLean Information Systems (IS) Success Model. The model’s constructs—system quality, information quality, service quality, user satisfaction, use, and net benefits—offer a multi-dimensional framework for evaluating the effectiveness of AI-enabled systems. The study draws on a dataset of 500 North American online retailers. AI adoption indicators were identified through a semi-automated keyword detection method combining Google’s Programma-ble Search Engine with an AI-related terminology framework. These indicators were integrated into a structural model, with confirmatory factor analysis (CFA) and structural equation modeling (SEM) conducted in SmartPLS 4 to test relationships among the constructs. Findings demonstrate that AI-driven system quality is the strongest driver of satisfaction and use. System use emerged as the most powerful predictor of net benefits, emphasizing that value de-pends on active engagement with AI features. User satisfaction also functioned as an important mediator linking technical capabilities to performance outcomes. In contrast, AI-enabled service quality showed no significant effect, while information quality produced mixed results, moderated by organizational maturity and contextual complexity. The study contributes theoretically by validating and extending the DeLone and McLean model in the context of AI-integrated retailing and highlighting conditional effects of service and information quality. Practically, it underscores the need for retailers to prioritize usability, customer experience, and adoption strategies alongside technical deployment. Overall, the research advances understand-ing of how AI shapes digital commerce, offering an evidence-based view of its impact on system success, customer trust, and organizational performance.