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 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
RETRACTED: Development of Graphic Design Based on Artificial Intelligence
Wu, S. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1533/3/032022 · Cited by 14
RetractedEvaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
Anxiety and Performance: The Processing Efficiency Theory
Eysenck, M. W., & Calvo, M. G. (1992) · Cognition & Emotion · doi:10.1080/02699939208409696 · Cited by 2,661
Wu, J.-H., & Wang, S.-C. (2005) · Information & Management · doi:10.1016/j.im.2004.07.001 · Cited by 2,284
Lee, M.-C. (2009) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2008.11.006 · Cited by 2,037
Research works printed but not linked: 8
- 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
- Chen C. Fu J. & Lyu L. (2023). A pathway towards responsible AI generated content. arXiv preprint arXiv:2303.01325. · arXiv
- 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
- 10.1037//1082-989X.3.4.424 · DOI · Paper page
- 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.
- 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
- 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
- Wu J. Gan W. Chen Z. Wan S. & Lin H. (2023). AI-Generated Content (AIGC): A survey. arXiv preprint arXiv:2304.06632 · arXiv
Cited by
Sorted by citations
Sun, Y., Sheng, D., Zhou, Z., & Wu, Y. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03811-x · Cited by 327
Aini, Q., Manongga, D., Rahardja, U., Sembiring, I., & Li, Y.-M. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2357860 · Cited by 181
Wu, J., Cai, Y., Sun, T., Ma, K., & Lu, C. (2024) · Journal of Engineering Design · doi:10.1080/09544828.2024.2362587 · Cited by 106
Hwang, Y., & Wu, Y. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1455015 · Cited by 104
Examining generative AI user addiction from a C-A-C perspective
Zhou, T., & Zhang, C. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102653 · Cited by 112
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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
