Decoding user disclosure intentions in generative AI: Exploring the influence of system attributes on trust and privacy concerns within the extended technology acceptance model

2026/01/28 by Jing Niu, Bilal Mazhar, Yilin Ren +1 · 1 voice
Social Sciences · Computer Science · #Ethics and Social Impacts of AI #Privacy, Security, and Data Protection #AI in Service Interactions

paper · pdf · doi:10.5817/cp2026-1-5

openalex publication_date 2026/01/28 · openalex created_date 2026/01/29 · openalex updated_date 2026/07/17

Abstract

As generative artificial intelligence becomes more prevalent, understanding the factors that influence users' willingness to disclose personal information to these platforms is crucial. This study explores how Perceived Autonomy and Perceived Empathy in generative AI platforms influence users’ Behavioral Intent to Disclose personal information. Using a sample of 1,034 generative AI users in China, Structural Equation Modeling (SEM) was employed to analyze the relationships between autonomy, empathy, trust, privacy concerns, and disclosure intentions. The results show that both Perceived Autonomy and Perceived Empathy positively influence Behavioral Intent to Disclose personal information on generative AI platforms. These relationships are positively mediated by Trust in Generative AI and negatively mediated by Privacy Concerns, highlighting the role of trust in enhancing disclosure and privacy concerns in reducing it. These findings align with the Extended Technology Acceptance Model, which emphasizes the role of trust and privacy as external factors in shaping user behavior. This study provides valuable insights into how AI system attributes influence disclosure decisions, offering guidance for the design of more trustworthy and privacy-sensitive generative AI platforms.

Citations

• A new criterion for assessing discriminant validity in variance-based structural equation modeling
• Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
• The promise and challenges of generative AI in education
• An empirical study of factors influencing usage intention for generative artificial intelligence products: A case study of China
• Toward an empathy-based trust in human-otheroid relations
• Generative AI in Marketing and Principles for Ethical Design and Deployment
• Trust in AI: Progress, Challenges, and Future Directions
• Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systems
• RAI Guidelines: Method for Generating Responsible AI Guidelines Grounded in Regulations and Usable by (Non-)Technical Roles
• Trust and ethics in AI
• How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research
• Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model
• Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity
• ‘Okay google, what about my privacy?’: User's privacy perceptions and acceptance of voice based digital assistants

Discussions

• 5️⃣ Decoding user disclosure intentions in generative AI: Exploring the influence of system attributes on trust and privacy concerns within the extended technology acceptance model ✍ Jing Niu, Bilal M [bsky, 0 points, 1 comments]