A Study on Factors Influencing Designers’ Behavioral Intention in Using AI-Generated Content for Assisted Design: Perceived Anxiety, Perceived Risk, and UTAUT
Open accessAbstract
This study aims to comprehensively understand the intention to use Artificial Intelligence Generated Assistance in Design Tools (AIGC) among design students and practitioners, along with its influencing factors.Utilizing Smart-PLS software and Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, we constructed a comprehensive research model.Based on 404 valid questionnaire responses, we systematically analyzed the underlying mechanisms of designers' attitudes towards AIGC tools.The sample encompasses diverse schools and levels of professional experience, ensuring the wide applicability of research outcomes.In the data analysis process, professional statistical analysis methods, including path analysis and standardized path coefficients, were employed to ensure a profound exploration of research questions.The results indicate that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly positively influence the willingness to use AIGC tools, while perceived anxiety and perceived risk exert negative impacts.This study, by integrating traditional and novel factors, provides crucial theoretical and practical guidance for the actual application of AIGC technology in the design field, offering profound insights for the future development and education of design technology.
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AI outputs may drift away from the intended message.
“which supports the need for human-led refinement because AI outputs may drift away from the intended message [7].”
From Human-AI Co-Creation in Visual Communication Design: A Framework and Empirical Study (2026) · cites this paper as [7]
The Introduction describes generating images from textual descriptions, while §2.2 discusses designers’ perceived risks, chiefly ethical, copyright, and privacy concerns. Neither passage says AI outputs may depart from an intended message. Searches for intended message, deviation from design intent, and outputs inconsistent with prompts found no supporting passage in our copy; page numbers were not available. (LLM, conf=0.75)
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- Authors
- Li, W.
- Affiliations
- Loughborough University (GB)
- Corresponding author
- Li, Weiyi
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2024-03-06
- Volume
- 41
- Issue
- 2
- Pages
- 1064-1077
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W4392757147
- Cited by
- 167
- References
- 76
- Citations by year
- 2026: 73 · 2025: 74 · 2024: 20
- FWCI
- 62.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Human-Computer Interaction (publishedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- AI-generated content, perceived anxiety, risk perception, UTAUT, design students, design professionals, performance expectancy, effort expectancy, social influence, facilitating conditions, design education
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