Toward Sociotechnical Awareness: Unpacking Generative AI Anxiety and Its Impact on Deep Engagement in the Post-Adoption Stage
Ke Zhao, Li Li, Wenyi Chen, H Liu · International Journal of Human-Computer Interaction · 2026
Amid the accelerating diffusion of generative AI (GenAI), this study investigates generative AI anxiety and its impact on deep engagement in the post-adoption stage. Employing a sequential mixed-methods design in China from October to November 2025, we first proposed the construct of deep use intention and utilized interviews (N = 16) via purposive sampling and procedural grounded theory to map a multidimensional landscape of anxiety toward GenAI among young adults, revealing a shift in the formation mechanism from sociotechnical blindness to sociotechnical awareness. Subsequently, we conducted a national survey (N = 899) employing random and snowball sampling. Data analysis via partial least squares structural equation modeling (PLS-SEM) using SmartPLS 4 revealed that: (1) deep use intention was primarily driven by factors other than AI anxiety; (2) while aggregate AI anxiety exerted no significant effect on deep use intention, its internal dimensions exhibited divergent impacts; and (3) AI literacy was validated as a necessary threshold for deep use intention but failed to moderate the impact of AI anxiety, reflecting a potential homogenization of psychological responses. This study advances the understanding of user behavior and the potential AI outcomes divide in the post-adoption stage.