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Cited by 183Technology Adoption and User BehaviourDigital Media and Visual ArtInnovative Human-Technology Interaction

The DOI matches its Crossref record; 76 of the 77 references it cites were found in a registry or an index, and 1 are not checked yet.

A Study on Factors Influencing Designers’ Behavioral Intention in Using AI-Generated Content for Assisted Design: Perceived Anxiety, Perceived Risk, and UTAUT

Open access
Li, Weiyi·2025·International Journal of Human–Computer Interaction
DOI 10.1080/10447318.2024.2310354
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Abstract

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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Research works printed but not linked: 8

  1. Cao Y. Li S. Liu Y. Yan Z. Dai Y. Yu P. S. & Sun L. (2023). A comprehensive survey of AI-Generated Content (AIGC): A history of generative AI from GAN to ChatGPT. arXiv preprint arXiv:2303.04226. · arXiv
  2. Chen C. Fu J. & Lyu L. (2023). A pathway towards responsible AI generated content. arXiv preprint arXiv:2303.01325. · arXiv
  3. Chen C. Wu Z. Lai Y. Ou W. Liao T. & Zheng Z. (2023). Challenges and remedies to privacy and security in AIGC: Exploring the potential of privacy computing blockchain and beyond. arXiv preprint arXiv:2306.00419. · arXiv
  4. 10.1037//1082-989X.3.4.424 · DOI · Paper page
  5. Kandoth, S., & Shekhar, S. K. (2022). Social influence and intention to use AI: The role of personal innovativeness and perceived trust using the parallel mediation model. Forum Scientiae Oeconomia, 10(3), 131–150.
  6. Nayak, R. D. (2014). Anxiety and mental health of software professionals and mechanical professionals. International Journal of Humanities and Social Science Invention, 3(2), 52–56. https://www.researchgate.net/profile/Ramyashilpa-Nayak/publication/346486234_Anxiety_and_Mental_Health_of_Software_Professionals_and_Mechanical_Professionals/links/5fdb229492851c13fe90c6bb/Anxiety-and-Mental-Health-of-Software-Professionals-and-Mechanical-Professionals.pdf
  7. Reddy, M. D. M., Basha, M. S. M., Hari, M. M. C., & Penchalaiah, M. N. (2021). Dall-e: Creating images from text. UGC Care Group I Journal, 8(14), 71–75. https://www.journal-dogorangsang.in/no_1_NECG_21/14.pdf
  8. Wu J. Gan W. Chen Z. Wan S. & Lin H. (2023). AI-Generated Content (AIGC): A survey. arXiv preprint arXiv:2304.06632 · arXiv

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Metadata

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
DOI
10.1080/10447318.2024.2310354
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
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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