Conceptualizing Internet Privacy Concern Constructs for Generative Artificial Intelligence: An Analysis of CFIP, IUIPC, and Related IPC Models

Generative Artificial Intelligence (GenAI) technologies have experienced rapid adoption despite growing concerns surrounding privacy, security, and user trust. This study examines perceptions of Generative Artificial Intelligence (GenAI) and behavioral intent through the lens of established information privacy and trust frameworks. Drawing from validated constructs in CFIP, IUIPC, Van Slyke et al.’s privacy and trust framework, and Koohang et al.’s social media privacy model, a survey was administered through Amazon Mechanical Turk with 52 valid respondents and evaluated with structural equation modeling using SmartPLS. Results indicate that the models adapted from Koohang et al. (2022) and Van Slyke et al. (2006) demonstrated the strongest explanatory power for Behavioral Intent, accounting for 46.2% and 59.6% of variance respectively. Across all models containing trust constructs, trust demonstrated a statistically significant positive relationship with behavioral intent toward GenAI. These findings suggest that while privacy concerns remain relevant, trust may play a more central role in shaping the user’s willingness to engage with Generative AI systems. The study contributes to emerging research on Generative AI adoption, privacy perceptions, and trust formation while identifying opportunities for future refinement of AI privacy-related theories and models.