Trusting the AI, Crafting the Show: Algorithmic Dependence and Human–AI Collaboration in Variety-Show Post-Production Across Chinese OTT Platforms
Fang Wang, Yazhou Huang, Qianxun Jiang, Shaowei Chen · International Journal of Human-Computer Interaction · 2026
Chinese OTT variety-show post-production requires balancing creativity, technical execution, regulations, and platform algorithms under tight schedules. Drawing on UTAUT2, this study incorporates AI trust (AITR), AI anxiety (AIA), and algorithmic dependence (AD) in an explanatory sequential mixed-methods design. PLS-SEM of 443 responses shows that performance expectancy and effort expectancy enhance AITR; AITR promotes behavioral intention, which facilitates use behavior; use behavior then contributes to AD. Social influence and hedonic motivation increase behavioral intention, while facilitating conditions affect use behavior. AIA weakens the performance expectancy–AITR relationship but strengthens the effort expectancy–AITR relationship. Twenty interviews indicate AITR is grounded in interpretability, controllability, and verifiability, with controllability especially critical in fast-paced production. Trust and resistance may coexist when ease of use is perceived to threaten professional value. The study extends UTAUT2 beyond adoption by positioning AD as a post-adoption outcome and informs deployment compatible with human judgment, responsibility, and occupational sustainability.