AI 프라이버시 우려가 사용자의 개인화 서비스 수용 의지에 미치는 영향 연구

초록

Purpose – This research aims to examine the relationship between privacy concerns and the willingness to accept AI-powered personalized services, investigating the mediating roles of perceived risk and trust, as well as the moderating effects of perceived institutional environment and corporate transparency. Design/Methodology/Approach – Based on privacy calcul us theory, a theoretical model was constructed and empirically tested using data collected via a questionnaire survey from 332 Iinternet users with experience in AI personalized services. The data were analyzed using Structural Equation Modeling (SEM), bootstrap analysis, and multiple regression analysis. Findings – The results revealed that (1) privacy concerns directly and negatively impact acceptance willingness; (2) perceived risk and trust serve as significant partial mediators; and (3) corporate transparency significantly weakens the negative relationship between privacy concerns and acceptance willingness, while the moderating effect of perceived institutional environment is not significant. The study also confirms the existence of the “privacy paradox” in the AI context. Research Implications – The findings provide empirical support for privacy calculus theory and offer valuable insights for businesses. Enhancing corporate transparency can effectively mitigate the negative impact of privacy concerns. For researchers, this study validates a nuanced mechanism behind user decision-making in AI services. For practitioners, it highlights strategic levers to balance personalized service delivery with user privacy protection. The use of multiple robust statistical methods strengthens the reliability of the conclusions.

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