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

Open this paper in CoChat — free account Open PDF

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.

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

Crossref · 10.1080/10447318.2024.2310354

Cites

76 of 77 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Retracted
Evaluating 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
Common method biases in behavioral research: A critical review of the literature and recommended remedies
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
Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification
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
What drives mobile commerce?
Wu, J.-H., & Wang, S.-C. (2005) · Information & Management · doi:10.1016/j.im.2004.07.001 · Cited by 2,284
Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit
Lee, M.-C. (2009) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2008.11.006 · Cited by 2,037

Show all 76 references

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

Sorted by citations

AI hallucination: towards a comprehensive classification of distorted information in artificial intelligence-generated content
Sun, Y., Sheng, D., Zhou, Z., & Wu, Y. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03811-x · Cited by 327
Understanding Behavioral Intention to Use of Air Quality Monitoring Solutions with Emphasis on Technology Readiness
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
Integrating AIGC with design: dependence, application, and evolution - a systematic literature review
Wu, J., Cai, Y., Sun, T., Ma, K., & Lu, C. (2024) · Journal of Engineering Design · doi:10.1080/09544828.2024.2362587 · Cited by 106
The influence of generative artificial intelligence on creative cognition of design students: a chain mediation model of self-efficacy and anxiety
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
The mediating role of attitude towards the technology in shaping artificial intelligence usage among professionals
Emon, M. M. H., & Khan, T. (2025) · Telematics and Informatics Reports · doi:10.1016/j.teler.2025.100188 · Cited by 99
The effect of trust on user adoption of AI-generated content
Zhou, T., & Lu, H. (2024) · The Electronic Library · doi:10.1108/el-08-2024-0244 · Cited by 70
Investigating the factors influencing users’ adoption of artificial intelligence health assistants based on an extended UTAUT model
Su, J., Wang, Y., Liu, H., Zhang, Z., Wang, Z., & Li, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-01897-0 · Cited by 68
Determinants of behavioral intentions and their impact on student performance in the use of AI technology in higher education in Indonesia: A SEM-PLS analysis based on TPB, UTAUT, and TAM frameworks
Nurtanto, M., Nawanksari, S., Sutrisno, V. L. P., Syahrudin, H., Kholifah, N., Rohmantoro, D., Utami, I. S., Mutohhari, F., & Hamid, M. A. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101638 · Cited by 73
Determinants of Student Adoption of Generative AI in Higher Education
Aldreabi, H., Dahdoul, N. K. S., Alhur, M., Alzboun, N., & Alsalhi, N. R. (2025) · The Electronic Journal of e-Learning · doi:10.34190/ejel.23.1.3599 · Cited by 72
How the Human-Like Characteristics of AI Assistants Affect Employee Creativity: A Social Network Ties Perspective
Zhang, X., Yu, P., Ma, L., & Liang, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2379719 · Cited by 53
Driving Factors of Generative AI Adoption in New Product Development Teams from a UTAUT Perspective
Xia, Y., & Chen, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2375686 · Cited by 58
The power of human-like virtual-influencer-generated content: Impact on consumers’ willingness to follow and purchase intentions
Rungruangjit, W., Mongkol, K., Piriyakul, I., & Charoenpornpanichkul, K. (2024) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2024.100523 · Cited by 55
Digital transformation in emerging markets: Adoption dynamics of AI image generation in marketing practices
Emon, M. M. H. (2025) · Telematics and Informatics Reports · doi:10.1016/j.teler.2025.100267 · Cited by 47
When Usefulness Fuels Fear: The Paradox of Generative AI Dependence and the Mitigating Role of AI Literacy
Liu, J. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2544006 · Cited by 43
Artificial intelligence, social influence, and AI anxiety: analyzing the intentions of science doctoral students to use ChatGPT with PLS-SEM
Uludağ, F., Kılıç, E., & Çelik, H. E. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05641-x · Cited by 41
Research on designers’ behavioral intention toward Artificial Intelligence-Aided Design: integrating the Theory of Planned Behavior and the Technology Acceptance Model
Jiao, J., & Cao, X. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1450717 · Cited by 37
Technology Acceptance and Innovation Diffusion: Are Users More Inclined Toward AIGC-Assisted Design?
Jiang, Q., Zhang, J., Wang, P.-H., & Zhang, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2408513 · Cited by 38
A Systematic Review of User Attitudes Toward GenAI: Influencing Factors and Industry Perspectives
Chen, J., Wei, X., Xie, Q., Hu, A., Qiao, Y., Wan, R., & Liu, Y. (2025) · Journal of Intelligence · doi:10.3390/jintelligence13070078 · Cited by 41
Examining Generative AI User Intermittent Discontinuance from a C-A-C Perspective
Zhou, T., & Zhang, C. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2376370 · Cited by 35
Association between Generative AI self-efficacy and Generative AI acceptance: The mediating role of Generative AI trust and the moderating role of Generative AI risk perception
Zhang, G., & Yu, T. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105791 · Cited by 34
What motivates academics in Egypt toward generative AI tools? An integrated model of TAM, SCT, UTAUT2, perceived ethics, and academic integrity
Eldakar, M. A. M., Shehata, A. M. K., & Ammar, A. S. A. (2025) · Information Development · doi:10.1177/02666669251314859 · Cited by 33
The potential of AI tools in shaping digital consumers’ behavior: investigating e-commerce engagement of Chinese Generation Z
Pu, L., Radics, R. I., Umar, M., Jeremiah, F., & Quan, Z. (2025) · Asia Pacific Journal of Marketing and Logistics · doi:10.1108/apjml-08-2024-1048 · Cited by 31
A study on students' behavioural intention and use behaviour of artificial intelligence-generated content in physical education: Employing an extended the unified theory of acceptance and use of technology model
Wu, Q., Li, S., Xin, S., Hou, Q., & Li, P. (2025) · Journal of Hospitality Leisure Sport & Tourism Education · doi:10.1016/j.jhlste.2025.100547 · Cited by 29
Blockchain-based food traceability system and pro-environmental consumption: A moderated mediation model of technology anxiety and trust in organic food product
Duong, C. D., Tran, T. V. H., Nguyen, T. H., Ngo, T. V. N., & Vu, T. N. (2024) · Digital Business · doi:10.1016/j.digbus.2024.100095 · Cited by 28
Exploring the acceptance of generative artificial intelligence-assisted learning and design creation among students in art design specialties: based on the extended TAM model
Zhu, Z., Ren, Y., & Shen, A. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13551-3 · Cited by 30
Fostering Continuous Innovation in Creative Education: A Multi-Path Configurational Analysis of Continuous Collaboration with AIGC in Chinese ACG Educational Contexts
Huangfu, J., Li, R., Xu, J., & Pan, Y. (2024) · Sustainability · doi:10.3390/su17010144 · Cited by 25
From algorithm aversion to AI dependence: Deskilling, upskilling, and emerging addictions in the GenAI age
Kim, T., Usman, U., Garvey, A. M., & Duhachek, A. (2025) · Consumer Psychology Review · doi:10.1002/arcp.70008 · Cited by 33
Gen AI Literacy and Acceptance Among EFL Teachers: An Exploration Through the UTAUT Model and Self‐Determination Theory
Jin, H., & Rui, Y. (2025) · European Journal of Education · doi:10.1111/ejed.70364 · Cited by 18
Unveiling the Complexity of Designers’ Intention to Use Generative AI in Corporate Product Design: A Grounded Theory and fsQCA
Li, H., Liu, Y., Guo, Q., Shi, M., Zhang, P., & Kim, S. B. (2025) · Systems · doi:10.3390/systems13040275 · Cited by 19
Adapting to generative AI: Examining the users' coping strategies of generative AI image systems
Lee, C. T., Shen, Y., Wang, C.-H., & Hung, H.-Y. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124193 · Cited by 17
From Seeking to Sharing: Pathways of Environmental Risk Information on Social Media and the Roles of Outcome Expectations, Efficacy, and AI-Generated Content
Wang, W., & Gong, H. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2443247 · Cited by 17
Factors influencing the adoption of generative AI in education: A systematic review, proposed framework and future research agenda
Tan, Q. (2025) · British Educational Research Journal · doi:10.1002/berj.70059 · Cited by 19
A Study of Factors Influencing Chinese Design Students’ Adoption of AIGC Tools
Wang, X., Li, M., Yao, Y., & Zhu, X. (2025) · IEEE Access · doi:10.1109/access.2025.3582599 · Cited by 15
How anthropomorphic AI features affect music students’ acceptance: a study among Chinese undergraduates
Tan, J., Anyanwu, C. C., Liu, Y., & Suryanti, S. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1649747 · Cited by 15
Factors influencing digital media designers’ subscription to premium versions of AI drawing tools through a mixed methods study
Hu, L., Chen, M., & Ding, N. (2025) · Scientific Reports · doi:10.1038/s41598-025-99924-7 · Cited by 15
Factors influencing older adults’ adoption of AI voice assistants: extending the UTAUT model
Li, H., & Wei, X. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1618689 · Cited by 14
Explore the Fashion Industry’s Behavioral Intention to Use Artificial Intelligence Generated Content Tools Based on the UTAUT Model
Li, X., Shen, L., & Ren, X. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2432759 · Cited by 18
Determinants of consumers’ intentions to use smart home devices from the perspective of perceived value: A mixed SEM, NCA, and fsQCA study
Zhang, K., & Cheng, X. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104399 · Cited by 14
Empowering Firms With AI-Generated Content: Strategic Approaches to R&D and Advertising in the Era of Generative AI
Li, L., Li, J., Islam, N., Zhang, Z., & Behl, A. (2025) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2025.3552142 · Cited by 13
FinTech Edge: Utility Computing & Artificial Intelligence Technologies for Smart Financial Acquisition & Blockchain in the Financial Industries
Singh, S. K., Sarkar, T., Mangla, M., Rakhra, M., Singh, A., & Jairath, K. (2024) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i61595.2024.10828908 · Cited by 12
The design industry in the AI era: how AI awareness and AI literacy influence the innovative work behavior of Chinese generation Y designers
Ding, N., Chen, M., & Hu, L. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105650 · Cited by 11
Is the impact of artificial intelligence generation tools on improving art education positive or negative? Perspectives of professors and students
Zhang, R., Lee, H., Wu, R., Yang, W., & Pan, Y. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2490173 · Cited by 11
Investigating users’ acceptance of AI-based creativity support tools: an empirical study from China’s creative industries
Liu, J., Zou, J., Zhang, J., & Teng, J. (2025) · Current Psychology · doi:10.1007/s12144-025-08116-z · Cited by 11
Configurational effects of personal innovativeness, self-efficacy, and perceived risk on AI adoption in media students
Lan, Y., Liu, S., Chen, H., & Xia, L. (2026) · Scientific Reports · doi:10.1038/s41598-026-36538-7 · Cited by 12
Determinants of university students’ attitudes towards smart devices in the smart campus environment
Wei, B., Zhuo, Y., Zeng, H., Hong, H., & Liu, H. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05853-1 · Cited by 10
Adopting AR wayfinding in heritage tourism: extending the UTAUT in cultural contexts
Wang, F., Peng, X., Wang, Y., Fan, W., Wang, K., & Du, Y. (2025) · npj Heritage Science · doi:10.1038/s40494-025-02069-z · Cited by 10
AI Integration in Education: A Correlational Study on Attitudes, Perceptions and Anxiety Among Pre-Service Teachers
Devanadera, A. C. (2025) · LatIA · doi:10.62486/latia2025249 · Cited by 11
Understanding graphic designers’ usage behavior of generative artificial intelligence tools
Qiu, T., Yang, D., Zeng, H., & Chen, X. (2024) · Kybernetes · doi:10.1108/k-05-2024-1159 · Cited by 10
Research on the factors affecting the adoption of health short videos by the college students in China: unification based on TAM and UTAUT model
Han, P., Liu, S., Zhang, D., Li, X., & Li, X. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1547402 · Cited by 9
The cognitive acceptance of generative AI image tools based on TPB-TAM model and multi-theory integration
Wang, Y., Guan, X., Sun, Y., Wang, H., & Chen, D. (2025) · Advanced Design Research · doi:10.1016/j.ijadr.2025.08.001 · Cited by 9
Evaluating the efficacy of AI-Generated advertising: A cross-national analysis of customer responses on brand perceptions and customer engagement with evidence from Vietnam and Australia
Nguyen, K. M., Phan, T. M., Tran, Y. N. N., Nguyen, A. T., Nguyen, T. L. N., Hoang, G. H., Trần, T. T., & Nguyen, N. T. (2026) · Journal of Global Scholars of Marketing Science · doi:10.1080/21639159.2026.2617659 · Cited by 9
To acknowledge or conceal: an exploratory study on designers' self-determination factors and attitudes toward artificial intelligence participation in their works
Jiang, Q., Qian, J., & Zang, Y. (2024) · Kybernetes · doi:10.1108/k-02-2024-0471 · Cited by 10
Bridging Religiosity and Digital Competence: The Mediating Role of Digital Ethics in Higher Education
Naufalin, L. R., Indrawati, C. D. S., Indriayu, M., & Noviani, L. (2026) · Munaddhomah Jurnal Manajemen Pendidikan Islam · doi:10.31538/munaddhomah.v7i1.2455 · Cited by 8
A two-step structural equation modeling and explainable machine learning framework for understanding university students’ adoption of generative AI: balancing intrinsic motivations and perceived risks
Zeng, D., Xu, X., Zhu, T., Li, Y., & Li, Q. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1743722 · Cited by 7
The impact of personality traits and AI literacy on the adoption intentions of AI among design faculty in Chinese higher education
Ding, N., Chen, M., & Hu, L. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105709 · Cited by 7
Study of adoption of AI-generated health information in video platforms using SEM and fsQCA
Wu, Q., Liu, X., & Zhong, R. (2025) · Aslib Journal of Information Management · doi:10.1108/ajim-01-2025-0037 · Cited by 7
Decoding electric vehicle adoption intention in densely populated cities: An integrated hybrid SEM–ANN approach
Chang, R., Chanda, R. C., Zadeh, A. V., & Hanifah, H. M. (2025) · Energy Strategy Reviews · doi:10.1016/j.esr.2025.101964 · Cited by 7
Smart hospitality: What drives consumers to accept AI services?
Azhar, M., Akhtar, M. J., Siddiqui, M. S., Khan, Z., & Khan, F. A. (2025) · Journal of Hospitality and Tourism Insights · doi:10.1108/jhti-04-2025-0492 · Cited by 8
How personal anticipations and perceived gratifications influence continuous use intention toward AI-driven chatbots? Moderating roles of perceived innovativeness and active involvement
Liu, Y., Zhang, K., Zhang, Y., Pang, H., & Liu, B. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106526 · Cited by 7
A comparative analysis of generative AI adoption among design professionals in China and the United Kingdom: a UTAUT perspective
Fang, C., Zhang, M., Khiatani, P. V., Lin, H., Liu, W., & Wang, S. J. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-06796-x · Cited by 9
Exploring the formation of learning burnout among college students in AI context: a serial mediation mechanism of AI dependence and addiction based on I-PACE model
Lan, Y., Liu, S., & Chen, H. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2026.1756441 · Cited by 7
Exploring the Determinants of Generative Artificial Intelligence Use Intention: A Mixed Methods Study
Cheng, M., Chong, H., Xu, Y., & Wu, H. (2026) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-17763 · Cited by 7
How Do Short-Form Video Creators Select Media Platforms? Research Based on the Extended UTAUT Model
Liang, J., Wang, J., & Yao, K. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2422762 · Cited by 8
The role of AI face-swapping technology anxiety in negative eWOM contagion: SEM and fsQCA findings
Xie, X.-Z., & Duan, Q.-T. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05222-y · Cited by 5
Impacts of AI on Human Designers: A Systematic Literature Review
Sinlapanuntakul, P., & Zachry, M. R. (2025) · IEEE Transactions on Professional Communication · doi:10.1109/tpc.2025.3588655 · Cited by 6
A study on factors influencing music enthusiast’s behavioral intention to use generative AI for music composition based on an expanded technology acceptance model
Zhou, Y., Wang, Y., & Su, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-28532-2 · Cited by 6
Trust, risk, and usability: examining students' use behaviour of generative artificial intelligence tools in higher education
Park, E. K. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2619983 · Cited by 7
Can Artificial Intelligence-Generated Sponsored Vlogs Trigger Online Shopping? Exploring the Impact on Consumer Purchase Intentions
Liu, Q., Lian, Z., & Osman, L. H. (2025) · Journal of Promotion Management · doi:10.1080/10496491.2025.2513323 · Cited by 6
How Differential Risk Perception Influences Users’ Positive Information Behaviour Toward AIGC Technologies: An Analysis Based on the Cognitive-Affective-Conative (CAC) Model
Yang, Z., & Jiang, Y. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2526582 · Cited by 6
Why do users continue to use GCAI? A mixed-methods study
Xiong, L., Zhou, Y., & Hua, Y. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103309 · Cited by 5
Deciphering the Mind of the CEO: Is Artificial Intelligence a Valuable Investment in Customer Acquisition?
Maldonado-Canca, L.-A., Cabrera-Sánchez, J.-P., & Molinillo, S. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2470287 · Cited by 7
Exploring Chinese university design students’ continuance intention to use generative AI: a two‑stage SEM–ANN analysis
Bao, Q., Wang, B., Liang, N., & Wang, J. (2026) · Education and Information Technologies · doi:10.1007/s10639-025-13872-3 · Cited by 5
Emotionally Resonant Branding: The Role of AI in Synthesising Dynamic Brand Images for Artists in the Music Industry
Liyanage, K. P. T. L. W., & Balalle, H. (2024) · Open Journal of Applied Sciences · doi:10.4236/ojapps.2024.149175 · Cited by 4
The Impact of User Experience on Continuous Usage Intention for AI-Generated Digital Music Platforms: Examining the Mediating Roles of Emotional Attachment and Technology Self-Efficacy
Liu, Z. (2025) · SAGE Open · doi:10.1177/21582440251406713 · Cited by 4
Examining factors affecting the acceptance of AI-powered creativity support tools among the industrial design community in China
Zou, J., Kamarudin, K. M., Abidin, S. Z., Zhang, J., & Liu, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-33100-9 · Cited by 5
Revealing the Role of Technostress in the Continuous Intention of Using AIGC Among Designers: An Investigation Based on UTAUT and TTF Models
Feng, L., & Hu, W. (2025) · SAGE Open · doi:10.1177/21582440251382501 · Cited by 5
The effects of responsiveness, perceived warmth, and anthropomorphism on university students' use of conversational AI for learning support: a chain mediation analysis based on S-O-R framework
Lan, Y., Liu, S., & Chen, H. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1769964 · Cited by 5
Tourists’ Mindsets and the Adoption of ChatGPT for Travel Services: A Moderated-Mediation Model of Self-Efficacy, Anxiety, and Social Influence
Hamdy, A., Zhang, J., Peng, S., Agag, G., & Eid, R. (2026) · Journal of Travel Research · doi:10.1177/00472875261450095 · Cited by 4
The psychological mechanism and heterogeneity of art design students’ intention to use AI: an empirical analysis based on an integrated framework
Jiang, C., Hui, B., Sun, W., Li, J., Wang, X., & Wang, R. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1835335 · Cited by 4
Factors influencing the intention to use artificial intelligence for online advertising on social networks
Giang, N. T. P., Duy, N. B. P., Anh, N. H. T., Giang, D. N. T., Triet, D. M., & Tan, T. D. (2025) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2025416 · Cited by 4
Factors Influencing Designers' Adoption of AI-Generated Content Tools: Task Characteristics, Technology Characteristics
Lai, S., Wu, Z., Zhou, J., & Chen, Y. (2025) · International Conference on Big Data, Information and Computer Network (BDICN) · doi:10.1145/3727353.3727430 · Cited by 4
Trusting the black box: Adapting a multidimensional measure of trust in generative AI
Alon, L., & Levkovich, I. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100295 · Cited by 4
Exploring the Moderating Effect of Price on the Relationship Between Intention and Use of Voice Assistants
Báscones, G. G. U., Carrión-Bósquez, N., Veas-González, I. A., García-Umaña, A., Meneses, L. J. U., Guerra-Regalado, W. F., Donoso, A. P. V., & Arias, M. A. S. (2025) · IEEE Access · doi:10.1109/access.2025.3598009 · Cited by 3
Pre-Service and In-Service Teachers’ Perspectives on Artificial Intelligence in Education: Insights from Physical Education and Classroom Practice in the Southern Philippines
Tejada, L. G. (2025) · LatIA · doi:10.62486/latia2025248 · Cited by 3
The Application of Generative Artificial Intelligence in Design-Based Elementary Education: A Mixed-Methods Study and Dynamic Scaffolding Model Construction Based on Color Composition Courses
Ling, J., Chen, J., Zhong, C., Zhang, H., Ma, Z., Huang, N., & Ou-yang, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93429-2_4 · Cited by 3
DM-CAM: A novel product design method combining diffusion models and class activation mapping for enhanced user-driven design
Wang, Z., Ping, Y., Lu, Z., Wang, J., & You, F. (2025) · Journal of Engineering Design · doi:10.1080/09544828.2025.2541521 · Cited by 2
Service innovation through generative AI art platforms: the MIND affordances
Lee, C. T., & Shen, Y. (2025) · Service Industries Journal · doi:10.1080/02642069.2025.2587593 · Cited by 2
The double-edged sword of AI literacy: Functional differentiation of anxiety and motivational regulation in the GenAI context
Xu, T., Wu, K., Wang, D., & Wang, W. (2026) · Learning and Motivation · doi:10.1016/j.lmot.2026.102276 · Cited by 2
Factors influencing user adoption of AI painting tools based on the AIDUA model
Li, Y., Siek, H. L., & Guo, J. (2026) · Scientific Reports · doi:10.1038/s41598-026-48130-0 · Cited by 2
Beyond One-Size-Fits-All: Profiling Designers’ Adoption of Generative Artificial Intelligence in Professional Design Practice
Fang, C., Guo, J., Khiatani, P. V., Lin, H., Zhang, B., Yenigün, E. Ö., & Wang, S. G. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2658255 · Cited by 3
Exploring the usage demands of AIGC functions among Chinese researchers: A study based on the KANO model
Xie, Z., Li, W., & Yu, W. B. (2025) · Information Development · doi:10.1177/02666669241313369 · Cited by 2
Self-citation
Gender differences in creative workers’ general attitudes toward artificial intelligence painting tools
Yuanxia, L. (2025) · AI & Society · doi:10.1007/s00146-025-02307-9 · Cited by 2
Exploring factors influencing ehealth applications adoption in Malaysia: an integrated techno-humanist model (THM)
Lim, S. S., Zadeh, A. V., Hanifah, H. M., & Nikbin, D. (2025) · The Electronic Library · doi:10.1108/el-12-2024-0392 · Cited by 2
Artificial Intelligence and the Evolving Landscape of Legal Accountability
Mansoor, S. I. U., Kausar, H., Khan, M. S. B., & Yadav, G. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9576-9.ch003 · Cited by 1
Acceptance of Artificial Intelligence in the Classroom: Transforming Pedagogical Approaches and Student Learning in Morocco
Bouziane, A., & Bouziane, K. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-95627-0_17 · Cited by 1
Chinese Designers’ Attitudes Toward the Disclosure of Using Artificial Intelligence Design Tools: Combining Technology Readiness and Self-Determination Theory
Deng, L., Shi, M., Zhang, M., & Wei, W. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2508303 · Cited by 1
AI in Art Industry: Will it be Accepted in Creative Industries and Change the Market?
Yuri, A., Juwitasary, H., & Syahchari, D. H. (2025) · IEEE Symposium on Industrial Electronics & Applications (ISIEA) · doi:10.1109/isiea65768.2025.11138371 · Cited by 1
Can human values influence metaverse technology? The case of augmented reality smart glasses adoption
Udo, G. G., & Bagchi, K. K. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105535 · Cited by 1
Exploring AI Chatbot Adoption for Knowledge Workers: A Unified Theory of Acceptance and Use of Technology Extension
Bianchini, F., Braccini, A. M., Luzi, F. D., Macrì, M., & Mecella, M. (2025) · Cureus Journal of Computer Science. · doi:10.7759/s44389-025-07459-9 · Cited by 1

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: hybrid
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
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

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks