Examining factors affecting the acceptance of AI-powered creativity support tools among the industrial design community in China
The emergence of artificial intelligence (AI)-powered creativity support tools (CSTs) is recently transforming the creative design industry. Yet within the specific industrial design field, such tools are neither widespread nor well-tailored to unique needs of the community. This gap makes the indus...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Nature Research
2025
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/123105/1/123105.pdf http://psasir.upm.edu.my/id/eprint/123105/ https://www.nature.com/articles/s41598-025-33100-9?error=cookies_not_supported&code=987358bc-3b05-4c49-b023-1696c8e21d0a |
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| Summary: | The emergence of artificial intelligence (AI)-powered creativity support tools (CSTs) is recently transforming the creative design industry. Yet within the specific industrial design field, such tools are neither widespread nor well-tailored to unique needs of the community. This gap makes the industrial design community’s acceptance of AI-CSTs uncertain. To address the issue, this study explored key factors influencing the acceptance of AI-CSTs by extending the unified theory of acceptance and use of technology (UTAUT) model. The authors used a structural equation modeling approach to carried out an empirical study. Data were collected through a questionnaire survey with 515 industrial design stakeholders in China. The results indicated that technology optimism and personal innovativeness positively affected performance expectancy. Interactivity and facilitating conditions were positive determinants of effort expectancy. The variable “perceived risk” was constructed by three first-order components, namely ethical risk, privacy risk and output risk. Finally, intention to use was significantly affected by performance expectancy, effort expectancy, price value, and perceived risk. Based on the theoretical findings, we presented general AI-CST promotion strategies and specific AI-CST optimization strategies for the industrial design community. This study contributed to a deeper understanding of designers’ behavioral intentions toward AI-CSTs and provides actionable insights for stakeholders to improve system usability, risk control, and technology fit in creative domains. |
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