A Study on the Factors Influencing Young People's Choice to Use AI Tools for Creative Activities

DOI: https://doi.org/10.62517/jnme.202610502

Author(s)

Xingyuan Fang

Affiliation(s)

Zhejiang Wanli University, Finance, Ningbo, Zhejiang, China

Abstract

Based on the Technology Acceptance Model (TAM), the Theory of Planned Behaviour (TPB) and the Theory of Social Norms, this study constructs a three-dimensional integrated 'technology–society–individual' model to investigate the key psychological and social factors driving young people's use of AI image and video generation tools. A total of 107 valid questionnaires were collected, and SPSS 27.0 was utilised to conduct descriptive statistics and multiple linear regression analyses. The findings revealed that respondents' perceived usefulness of AI tools had the highest mean score (5.51), whilst their behavioural intention had a mean of 5.26; however, perceived risk (5.22) and technology anxiety (4.87) were also at medium-to-high levels. The regression model indicated that professional background had a significant effect on usage frequency (β = 0.312, p = 0.020), whilst the direct effects of the various psychological variables on usage frequency were not statistically significant. The findings suggest that, in the context of AI creative tools, adoption behaviour is not only a product of psychological cognition but also the result of 'practical embedding'.

Keywords

AI Creative Tools; Technology Acceptance Model; Theory of Planned Behaviour; Usage Behaviour; Influencing Factors

References

[1] Manovich, L. (2023). “AI and the future of visual culture”. Bloomsbury. [2] China Internet Network Information Centre. (2025). The 53rd “Statistical Report on the Development of the Internet in China”. [3] China Youth Daily. (2026a). Over 90 per cent of university students surveyed regard AI as an 'all-round study companion'. [4] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. “MIS Quarterly”, 13(3), 319–340. [5] Ajzen, I. (1991). The theory of planned behaviour. “Organisational Behaviour and Human Decision Processes”, 50(2), 179–211. [6] Rogers, E. M. (1995). “Diffusion of innovations” (4th ed.). Free Press. [7] Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. “International Journal of Human-Computer Studies”, 59(4), 451–474. [8] Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. “MIS Quarterly”, 27(3), 425–478. [9] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. “Journal of Applied Social Psychology”, 22(14), 1111–1132. [10] Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. “MIS Quarterly”, 24(4), 665–694. [11] “China Youth Daily”. (2026b). When AI can answer everything, how should universities adapt 'intelligently'? [12] Chi, O. H., Jia, S., Li, Y., & Gursoy, D. (2023). Developing a formative scale to measure consumers' trust in AI service robots. “International Journal of Hospitality Management”, 112, 103426.