Trust-Based Consumer Responses to Ai Recommendations: Examining Transparency, Purchase Intention, and Digital Skepticism in E-Commerce

Abstract

Artificial intelligence (AI) has transformed e-commerce by enabling recommendations. However, AI recommendations do not necessarily lead to purchasing decisions. This study examines the influence of AI Recommendation Transparency on Purchase Intention, with Trust as a mediating mechanism and Digital Skepticism as a moderating condition. A cross-sectional survey was conducted among 168 undergraduate students at Universitas Mohammad Husni Thamrin, Jakarta, who had experience using AI-based recommendations. The relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results show that AI Recommendation Transparency significantly influences Trust (β = 0.705, p < 0.001), while its effect on Purchase Intention is not significant (β = 0.104, p > 0.05). Trust significantly influences Purchase Intention (β = 0.610, p < 0.001), indicating its role in translating transparency into purchasing decisions. Digital Skepticism does not significantly moderate the relationship between AI Recommendation Transparency and Purchase Intention (β = 0.062, p > 0.05), but significantly moderates the relationship between Trust and Purchase Intention (β = 0.149, p < 0.05). These findings indicate that transparency alone is insufficient to generate purchase intention; its value emerges when it builds consumer trust. The study highlights the importance of explainable, credible, and responsible AI systems.