AIGC technology adoption in Yixing Zisha ceramic design: An extended TAM model analysis
List of Authors
  • Pan Shimin , Rusmadiah Anwar

Keyword
  • AIGC; Artificial Intelligence; TAM Model; Yixing Zisha; Ceramic Design

Abstract
  • This study examines the willingness of the Yixing Zisha design industry to adopt Artificial Intelligence Generated Content (AIGC) technology, using the extended Technology Acceptance Model (TAM) as a theoretical framework. The impact of critical factors such as self-efficacy, resource quality, subjective norms, and convenience on the perceived usability and usefulness of AIGC technology was analyzed through a questionnaire survey of 600 industry practitioners. Focusing on the impact of perceived ease of use and usefulness of AIGC technology on industry adoption and the factors influencing it, the study explores the psychological mechanisms of technology acceptance, taking into account the specific context and needs of the industry. This study provides theoretical and strategic guidance for the effective adoption and application of AIGC technology in the Yixing Zisha design industry, and the results are valuable for understanding the importance of integrating traditional art and modern technology in a broader context, which can help promote technological innovation and sustainable development in related industries.

Reference
  • 1. State Council. (2006). Notice on the publication of the first batch of national intangible cultural heritage list. Retrieved from https://www.gov.cn/zwgk/2006-06/02/content_297946.htm

    2. Convention for the Protection of Intangible Cultural Heritage. (2006). Gazette of the Standing Committee of the National People's Congress of the People's Republic of China, (2), 138-145.

    3. Lu, Z., Song, X., & Jin, Y. (2023). The current status and development of intelligent design under AIGC technology trends. Packaging Engineering, 24, 18-33. https://doi.org/10.19554/j.cnki.1001-3563.2023.24.003

    4. Lyu, Y., Wang, X., Lin, R., & Wu, J. (2022). Communication in human-AI co-creation. Applied Science, 12, 11312. https://doi.org/10.3390/app122211312

    5. Lin, C.-Y., & Xu, N. (2022). Exploring the factors influencing the intention to use AI robotic architects in architectural design through the extended TAM model. Technology Analysis & Strategic Management, 34, 349-362. https://doi.org/10.1080/09537325.2021.1900808

    6. Shen, S., Chen, Y., Hua, M., & Ye, M. (2023). Measuring Designers' Use of Midjourney Based on the Technology Acceptance Model. Presented at Shanghai Jiao Tong University, Shanghai, China. https://doi.org/10.21606/iasdr.2023.794

    7. Dou, J. H., Zhang, B. R., & Qian, X. S. (2023). A review of research on artificial intelligence empowering the field of cultural heritage: A visualization analysis based on CiteSpace. Packaging Engineering, 44(14), 1-20. https://doi.org/10.19554/j.cnki.1001-3563.2023.14.001

    8. Chai, J. X., & Ding, H. X. (2023). AIGC and the design of arts and crafts. Shanghai Arts and Crafts, 2023(03), 75-77.

    9. Cao, J. Y. (2023). Application of AIGC technology in Chinese traditional art style animation. Daguan (Forum), 2023(10), 72-74.

    10. Wang, Y. F., Gong, X. L., Zhu, H. Q., & Li, G. L. (2023). Research on creative design of ceramics under AIGC technology. Ceramics Science and Art, 57(10), 84-87. https://doi.org/10.13212/j.cnki.csa.2023.10.083

    11. Chen, L. F., Xiang, A. L., & Shen, Y. (2023). The road to integration: Opportunities and challenges of AIGC in the field of Chinese art and design. Chinese Art, 2023(05), 36-44.

    12. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research[M]. MA: Addison-Wesley.

    13. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior[A]. Action-control: From cognition to behavior[C]. Heidelberg: Springer.

    14. Ajzen, I. (1987). Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology[A]. Advances in experimental social psychology[C]. New York: Academic Press, 1987.https://doi.org/10.1016/S0065-2601(08)60411-6

    15. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results[D]. MIT Sloan School of Management, Cambridge.

    16. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS Quarterly, 1989, (3). DOI: 10.2307/249008

    17. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four ongitudinal field studies[J]. Management Science, 2000, (2).https://doi.org/10.1287/mnsc.46.2.186.11926

    18. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning[J]. Journal of Educational Technology & Society, 2009, (3)

    19. Wani, I. A., & Mehraj, H. K. (2014). Total quality management in education: An analysis[J]. International Journal of Humanities and Social Science Invention, 2014, (6).

    20. Venkatesh, V. & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions[J]. Decision Sciences, 2008, (2). https://doi.org/10.1111/j.1540-5915.2008.00192.x

    21. Teo, T. (2011). Factors influencing teachers’intention to use technology: Model development and test[J]. Computers & Education, 2011, (4). https://doi.org/10.1016/j.compedu.2011.06.008

    22. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models[J]. Information Systems Research, 1995, (2).https://doi.org/10.1287/isre.6.2.144

    23. Almaiah, M. A., Jalil, M. A., & Man, M. (2016). Extending the TAM to examine the effects of quality features on mobile learning acceptance[J]. Journal of Computers in Education, 2016, (4).https://doi.org/10.1007/s40692-016-0074-1