AIGC (AI-Generated Content) represents a novel approach to content production and is regarded as a powerful engine for enhancing productivity and human creativity. In recent years, AIGC products have garnered significant attention across various sectors. While AIGC brings convenience to human life, users may develop algorithm aversion toward AIGC products due to stereotypes. This study explores the impact mechanism of task type (utilitarian vs. hedonic) on users' willingness to adopt AIGC products, focusing on the mediating roles of perceived usefulness and perceived ease of use, as well as the moderating effect of anthropomorphism. The findings provide theoretical and practical implications for the contextualized design of AIGC products and user behavior guidance. Integrating the Task-Technology Fit (TTF) theory and the Technology Acceptance Model (TAM), this study constructs a theoretical framework where task type serves as the independent variable, perceived usefulness as the mediating variable, and anthropomorphism as the moderating variable. Employing a scenario-based experimental method, the study simulates utilitarian and hedonic task scenarios and derives the following conclusions:(1) Task type significantly influences users' willingness to adopt AIGC products. Compared to hedonic tasks, users exhibit a higher willingness to use AIGC products when facing utilitarian tasks.(2) Perceived usefulness and perceived ease of use mediate the relationship between task type and usage intention.(3) Anthropomorphism moderates the relationship between task type and perceived usefulness: Under high anthropomorphism, hedonic tasks exert a stronger positive effect on perceived usefulness, whereas utilitarian tasks are more readily accepted by users under low anthropomorphism. By addressing differences in task contexts, this study fills the research gap regarding the heterogeneity of task scenarios. It also provides insights for enterprises to optimize AIGC product functionality and interaction design across different use contexts.

AIGC, Usage Intention, TTF, TAM