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

Abstract—In the contemporary Industry 5.0 digital environment, targeted personalised artificial intelligence (AI) systems have emerged as an essential infrastructure resource for e-commerce platforms seeking to optimise consumer choice architectures and mitigate information overload. As digital marketplaces become increasingly competitive, understanding the behavioural dynamics driven by AI recommendations is crucial. This study integrates the core components of the DeLone & McLean Information Systems Success Model specifically Information Quality and System Quality with contemporary notions of perceived usefulness, trust, and user satisfaction to evaluate their combined structural influence on customer purchase intention. Employing a quantitative explanatory research design, empirical data was collected through a cross-sectional web survey from an active demographic of Indonesian e-marketplace consumers residing in the region, Indonesia. The proposed conceptual framework and its eight underlying structural hypotheses were empirically tested and validated using Partial Least Squares Structural Equation Modelling (PLS-SEM) via the SmartPLS 4.0 software application. The analytical results confirm that high-quality AI system attributes significantly enhance users' perceived usefulness and platform trust, acting as critical precursors to elevated user satisfaction. Ultimately, these interconnected psychological responses robustly drive final purchase intentions. The findings provide comprehensive theoretical contributions to consumer behaviour literature and deliver highly practical insights for system engineers and digital marketers. By leveraging these results, stakeholders can strategically improve transaction conversion rates, foster long-term platform trust, and optimise algorithmic recommendation designs to satisfy the distinct preferences of modern digital consumers. Keywords—Personalized AI System, DeLone & McLean, Trust, User Satisfaction, Purchase Intention.