[Paper Review] Trust in Generative AI and Purchase Intention: Focused on Facilitators and Inhibitors

Paper: "Trust and Purchase Intention for Generative AI: Focusing on Enabling and Inhibiting Factors" (Park Dae-seong, Kim Heon-jun, Jang Hye-won, Park Il-su, 2025). This research uses Dual Factor Theory to view generative AI trust and purchase intention not as technology acceptance problems but as complex psychological processes where enablers and inhibitors operate simultaneously. Key paradoxical finding: Information Quality (IQ) paradox (H4 rejected) — contrary to traditional information systems research expecting that higher information quality reduces user anxiety/privacy concerns, this study found the OPPOSITE: higher information quality in generative AI actually increases privacy concerns (positive relationship). The mechanism: to get high-quality results, users must input more specific and personal information — sometimes including sensitive context. The impression that "this AI knows me too well" simultaneously triggers satisfaction and anxiety. This clearly demonstrates that the evolution of personalization technology doesn''t always accompany psychological stability — sophisticated personalization creates discomfort alongside convenience. Privacy concern → purchase intention: privacy concerns do NOT directly reduce purchase intention (the traditional assumption is false). The actual causal chain runs through trust — privacy concerns reduce trust, and reduced trust reduces purchase intention. Marketing implication: reducing privacy concerns about data use is more effective than directly persuading users that the service is valuable. Trust as mediator: system transparency and clear explanation of how data is used are more effective at building trust than feature improvements alone. Platform design recommendation: explicit explanation of "what data is used for what purpose" should be a primary design element, not an afterthought — the research suggests that users can tolerate privacy concerns when they understand and trust the purpose of data use.