Visual Generative AI Tools Acceptance by Multimedia and Animation University Students

This study investigates the factors influencing multimedia and animation students’ behavioral intention regarding visual generative AI tools, using a modified Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The significance of the study resembles the need to look into potential for adopting AI image, video or animation generative tools in related industry trends. This research is pivotal in ensuring that the integration of AI technologies into these sectors is both effective and beneficial. The findings reveal that performance expectancy, effort expectancy, facilitating condition, hedonic motivation, and personal innovativeness significantly positively impact students' behavioral intentions to adopt these tools. In contrast, social influence, perceived risk, price value, and habit do not significantly influence their intentions. Additionally, a strong relationship between behavioral intention and actual use behavior underscores the importance of fostering positive student’s intentions to drive technology adoption. These results provide theoretical insights and practical implications for developers and marketers to enhance the design, functionality, and promotional strategies of visual generative AI tools.