Acceptance of Generative AI in the Creative Industry: Examining the Role of AI Anxiety in the UTAUT2 Model
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14059 LNCS, Page: 288-310
2023
- 8Citations
- 58Captures
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Conference Paper Description
With the boosting entrenchment of Generative artificial intelligence (AI) across the creative markets, little is explored around the opinions of those who are within the influenced industries. How well professionals in the creative domains are viewing and embracing this newly emerged technology awaits verification. Using a survey method, this study shed light on the underpinning factors that could predict professionals’ acceptance and usage intention of Generative AI under the status quo. By integrating the expanded Unified Theory of Acceptance and Use of Technology (UTAUT2) model, the study incorporates the dimension of AI anxiety into the framework. Regression analyses reveal that acceptance and usage intention of Generative AI can be predicted by factors including performance expectancy, social influence, hedonic motivation, habit, and AI anxiety, while effort expectancy, facility conditions, and price value cannot predict users’ intention yet at current situations. The study shows the importance of the emotional attitudes of users and provides stakeholders with insights to develop Generative AI products to better fit the adaptability of users. Findings suggest that people who are actively involved in the creative and cultural economies favour using Generative AI, even when undergoing AI learning anxiety. Participants with a relatively higher level of education perform with more resilience and stability when faced with AI-related situations, as they are less possible to withdraw from future usage though undergoing the fear of Generative AI products, and they appear to less addictively rely on Generative AI tools despite all the merits.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85178606124&origin=inward; http://dx.doi.org/10.1007/978-3-031-48057-7_18; https://link.springer.com/10.1007/978-3-031-48057-7_18; https://dx.doi.org/10.1007/978-3-031-48057-7_18; https://link.springer.com/chapter/10.1007/978-3-031-48057-7_18
Springer Science and Business Media LLC
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