Understanding the adoption of AI-generated deepfake content on social media: An extended Information Acceptance Model (IACM) approach

The rapid rise of AI-generated deepfake content (AGDC) is transforming digital entertainment and communication, yet how users cognitively and affectively evaluate and adopt such synthetic media, particularly in emerging digital markets, remains insufficiently understood. Addressing this gap, this study examines how Vietnamese Generation Z evaluates and adopts AGDC on social media platforms by extending the Information Acceptance Model (IACM). The proposed framework integrates cognitive factors (perceived information usefulness, quality, credibility, and information needs) and affective factors (perceived enjoyment and uncertainty) to explain their effects on attitude, trust, and subsequently information adoption. Data collected from 715 respondents were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that perceived usefulness, information quality, information needs, and enjoyment significantly strengthen both attitude and trust, while credibility enhances trust but does not significantly influence attitude. In contrast, perceived uncertainty negatively affects both attitude and trust. Attitude and trust jointly explain 54.3% of the variance in AGDC adoption intention, highlighting the importance of both evaluative and relational mechanisms in synthetic media adoption. Notably, information usefulness emerged as the strongest predictor of attitude, whereas information needs had the strongest effect on trust. Theoretically, this study advances prior IACM research by incorporating affective mechanisms into the model and demonstrating its applicability to AI-generated deepfake content. By providing empirical evidence from a rapidly digitalizing context, the findings refine the cognitive–affective structure of IACM and extend its relevance to emerging forms of AI-mediated information environments. The findings also offer practical insights for designing transparent and engaging AI-generated media that encourage informed and responsible user engagement.