Dataset for Analysis of the Impact of a Targeted Personalized AI System on Purchase Intention

Abstract—In today’s Industry 5.0 digital world, targeted personalised artificial intelligence (AI) systems have emerged as a critical infrastructure resource for e-commerce platforms looking to maximise consumer choice structures and reduce information overload. As digital marketplaces become more competitive, it is critical to understand the behavioural dynamics influenced by AI recommendations. This study combines the core components of the DeLone and McLean Information Systems Success Model, specifically Information Quality and System Quality, with modern concepts of perceived usefulness, trust, and user satisfaction to assess their combined structural influence on customer purchase intention. Using a quantitative explanatory research approach, empirical data was acquired via a cross-sectional web survey from an active demography of Indonesian e-marketplace consumers living in the region, Indonesia. The proposed conceptual framework and its eight underlying structural hypotheses were empirically verified and confirmed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with the SmartPLS 4.0 software tool. The analytical results demonstrate that high-quality AI system qualities greatly improve users’ perceived usefulness and platform trust, which serve as important predecessors to increased user pleasure. Greatly influence final buying intentions. The results provide a significant theoretical contribution to the literature on consumer behaviour and very practical implications for system engineers and digital marketers. The findings provide stakeholders with the opportunity to proactively improve transaction conversion rates, foster long-term platform trust and optimise the design of algorithmic recommendations to meet the diverse needs of modern digital customers.

Keywords—Personalized AI System, DeLone & McLean, Trust, User Satisfaction, Purchase Intention.