ArticlePloS one2025
Research on key factors influencing Chinese designers' use of AIGC: An extension based on TAM and TRI.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The psychological mechanism and heterogeneity of art design students' intention to use AI: an empirical analysis based on an integrated framework.Frontiers in psychology · 2026Article
- Exploring fashion designers' acceptance of AIGC: A dual-pathway analysis from the stimulus-organism-response perspective.PloS one · 2026Article
- Article
- Challenges of using generative artificial intelligence for diabetes patient education: a cross-platform analysis of the quality, readability, and actionability of text generated by large language models.Frontiers in public health · 2026Article
- The influence of designer identity on design majors' intention to use AIGC tools: an SEM-fsQCA analysis in China.Frontiers in psychology · 2026Article
- Redefining the design innovation process: Embedding sustainability through AI-generated camping cookware.PloS one · 2026Article
- Application of deep learning for transformation of Chinese traditional cultural narrative patterns and enhancement of cultural identity empowered by AIGC.Scientific reports · 2025Article
- Examining factors affecting the acceptance of AI-powered creativity support tools among the industrial design community in China.Scientific reports · 2025Article
- Research on the influencing factors of generative artificial intelligence usage intent in post-secondary education: an empirical analysis based on the AIDUA extended model.Frontiers in psychology · 2025Article
- Analyzing the adoption of AIGC tools in fashion design: An S-O-R framework integrating task-technology fit.PloS one · 2025Article
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3 authors.
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Abstract
With the rapid development of AI intelligent technology, AIGC can bring an innovative revolution to art creation, providing designers with unlimited possibilities but also challenges. These challenges affect the willingness to adopt and constrain the sustainable development of AIGC. The purpose of this study is to analyse the factors of designers' adoption intention behaviours. This study reconstructed the research model by combining the factors of AIGC technology characteristics and interactivity, technology acceptance model, technology readiness model, etc. The empirical study was conducted from the dual perspectives of AIGC application characteristics and designers' psychology, in order to predict the factors that predict designers' behavioural intentions to use AIGC. In this study, a questionnaire survey was conducted among designers in China and 462 valuable responses were received. Through structural equation modelling (SEM) analysis, the study found that: (1) AIGC's technical features and interactivity positively affect perceived ease of use, and perceived usefulness, but the interactive features do not directly affect perceived usefulness; perceived ease of use and perceived usefulness positively affect designers' intention to adopt AIGC applications; (2) optimism and innovation positively affect technical features and designers' intention to adopt; Insecurity negatively affects designers' willingness to adopt, and insecurity does not affect technical features; discomfort does not affect designers' technical features and willingness to adopt. This study further extends the theoretical models of TAM(Technology Acceptance Model) and TRI(Technology Readiness Model), provides a theoretical basis for studying designers' adoption behaviour of AIGC, and enriches the application groups and domains of the theoretical models of TAM and TRI. The results of this study provide inspiration for the development, design, and marketing of AIGC applications, contributing to the realisation and further adoption of AIGC applications, as well as to the professional development of designers.
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