AI 개인화 서비스에서 대학생의 개인정보 보호 규제 인식이 만족도에 미치는 영향: 지각된 유용성의 매개와 프라이버시 민감성의 조절효과

As AI-powered personalization rapidly expands, users increasingly face a trade-off between convenience and privacy anxiety. In this context, understanding whether privacy regulations function as a signal of institutional trust has become a timely and essential question. This study investigates the effects of perceived privacy regulation on users’ perceived usefulness and personalization satisfaction, while examining the mediating role of perceived usefulness and the moderating role of privacy sensitivity. A survey was conducted among 312 university students in Gyeonggi Province who had experience using AI programs such as ChatGPT and Sora. Data were analyzed using SPSS 29.0 and PROCESS Macro. The results revealed that perceived privacy regulation had a significant positive effect on both perceived usefulness (β = .552, p < .01) and personalization satisfaction (β = .568, p < .01). Moreover, perceived usefulness partially mediated the relationship between privacy regulation and satisfaction, while privacy sensitivity moderated the relationship&#8212;indicating that users with higher sensitivity were more responsive to regulatory cues. These findings highlight that privacy regulation is not merely a legal safeguard but a cognitive trust mechanism that enhances users’ acceptance and satisfaction with AI services. The study extends the Privacy Calculus Theory by integrating institutional trust and individual differences, proposing an AI trust governance model that bridges regulatory assurance and user psychology.