Abstract
Gongbi painting artists’ acceptance of Artificial Intelligence Generated Content (AIGC) through an extended Technology Acceptance Model (TAM). Using mixed methods (204 questionnaires and 15 interviews), we explored the effects of cultural adaptability, creative subjectivity, and ethical risk. Findings show that cultural adaptability significantly increases perceived usefulness and ease of use, while ethical risk negatively impacts adoption. Creative subjectivity strongly promotes willingness to use AIGC. However, perceived usefulness and ease of use do not directly affect intention. Results highlight the need for culturally sensitive AI design that balances tradition and innovation, while addressing ethical concerns.
Keywords
Generative Artificial Intelligence (AIGC); TAM Modelling; Cultural Adaptability; Creative Subjectivity
DOI
https://doi.org/10.21606/iasdr.2025.397
Citation
Mao, Y.,and Kong, A.(2025) Gongbi Artists' Acceptance of AIGC: An Extended TAM Analysis with Qualitative and Quantitative Data, in Chang, C.-Y., Chen, C.-H., & Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.397
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 3 - Design, Art & Technology
Included in
Gongbi Artists' Acceptance of AIGC: An Extended TAM Analysis with Qualitative and Quantitative Data
Gongbi painting artists’ acceptance of Artificial Intelligence Generated Content (AIGC) through an extended Technology Acceptance Model (TAM). Using mixed methods (204 questionnaires and 15 interviews), we explored the effects of cultural adaptability, creative subjectivity, and ethical risk. Findings show that cultural adaptability significantly increases perceived usefulness and ease of use, while ethical risk negatively impacts adoption. Creative subjectivity strongly promotes willingness to use AIGC. However, perceived usefulness and ease of use do not directly affect intention. Results highlight the need for culturally sensitive AI design that balances tradition and innovation, while addressing ethical concerns.