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A comparative analysis of generative AI adoption among design professionals in China and the United Kingdom: a UTAUT perspective
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  • Published: 25 February 2026

A comparative analysis of generative AI adoption among design professionals in China and the United Kingdom: a UTAUT perspective

  • Cong Fang1,
  • Mingyuan Zhang1,
  • Paul Vinod Khiatani2,
  • Huan Lin3,
  • Wei Liu4 &
  • …
  • Stephen Jia Wang1,5 

Humanities and Social Sciences Communications , Article number:  (2026) Cite this article

  • 2432 Accesses

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Information systems and information technology
  • Science, technology and society

Abstract

Generative Artificial Intelligence (GenAI) is rapidly transforming the design industry, offering new capabilities to enhance creativity and streamline design processes. However, there is still limited understanding of how design professionals in different countries and technological environments view and use these tools, especially given the uneven global development of GenAI. Based on the Unified Theory of Acceptance and Use of Technology framework (UTAUT), this study aims to investigate the factors that influence the adoption of GenAI tools among design professionals in China and the United Kingdom (UK). Overall, 607 responses (233 from China and 374 from the UK) were collected from a cross-national survey. The results showed that in both China and the UK, performance expectations, social influence, and resistance bias had a strong effect on designers’ intentions to use GenAI tools. Trust was important in the UK but not in China, while access to technology and related resources was a stronger moderating factor in China. These findings highlight the role of technological contexts in shaping how professionals adopt GenAI in design work. By providing insights into these differences, this study contributes to the understanding of how GenAI can be adopted more effectively by design professionals in different regions and how these differences may influence the future global competitive landscape of GenAI in design.

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Data availability

The survey instrument and variable codebook detailing variable definitions, coding schemes, and data transformations are provided as Supplementary Materials accompanying this article. The raw survey data are not publicly available because they contain sensitive information related to participants’ professional roles and organisational contexts (including company location and work type), which creates a risk of indirect identification and would breach participant confidentiality agreements. An anonymised, processed dataset supporting the findings of this study is archived on the Open Science Framework and is available upon reasonable request from the corresponding author, subject to ethical approval and applicable data protection requirements.

References

  • Ajzen I (1985) From intentions to actions: a theory of planned behavior. In: Kuhl J, Beckmann J (eds) Action control: from cognition to behavior. Springer Berlin Heidelberg, p 1–39. https://doi.org/10.1007/978-3-642-69746-3_2

  • Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211. https://doi.org/10.1016/0749-5978(91)90020-T

    Google Scholar 

  • Ajzen I, Fishbein M (1980) Understanding attitudes and predicting social behavior (Paperback ed.). Prentice Hall

  • Al Hadwer A, Tavana M, Gillis D, Rezania D (2021) A systematic review of organizational factors impacting cloud-based technology adoption using technology-organization-environment framework. Internet Things 15: 100407. https://doi.org/10.1016/j.iot.2021.100407

    Google Scholar 

  • Aldreabi H, Dahdoul NKS, Alhur M, Alzboun N, Alsalhi NR (2025) Determinants of Student Adoption of Generative AI in Higher Education. Electron J e-Learn 23(1):15–33. https://doi.org/10.34190/ejel.23.1.3599

  • Al-kfairy M (2025) Strategic integration of generative AI in organizational settings: applications, challenges and adoption requirements. IEEE Eng Manag Rv e1–14. https://doi.org/10.1109/EMR.2025.3534034

  • Al-kfairy M, Mustafa D, Kshetri N, Insiew M, Alfandi O (2024) Ethical challenges and solutions of generative AI: an interdisciplinary perspective. Informatics 11(3):58

    Google Scholar 

  • Al-Sharafi MA, Al-Emran M, Arpaci I, Iahad NA, AlQudah AA, Iranmanesh M, Al-Qaysi N (2023) Generation Z use of artificial intelligence products and its impact on environmental sustainability: A cross-cultural comparison. Comput Hum Behav 143: 107708. https://doi.org/10.1016/j.chb.2023.107708

    Google Scholar 

  • Alonso-Almeida MdM, Giglio C, Lazzolino G (2024) A cross-country analysis of decision-making factors influencing tourists’ shift towards circular destinations in EU-27. Socio Econ Plan Sci 94: 101955. https://doi.org/10.1016/j.seps.2024.101955

    Google Scholar 

  • Alshehri M, Drew S, Alhussain T, Alghamdi R (2012) The impact of trust on e-government services acceptance: a study of users’ perceptions by applying UTAUT model. Int J Technol Diffus 3(2):50–61

    Google Scholar 

  • Anantrasirichai N, Bull D (2022) Artificial intelligence in the creative industries: a review. Artif Intell Rev 1–68

  • Andreasen MM, Hansen CT, Cash P (2015) Conceptual design. Springer International Publishing Switzerland. https://doi.org/10.1007/978-3-319-19839-2_2

  • Andrews JE, Ward H, Yoon J (2021) UTAUT as a model for understanding intention to adopt AI and related technologies among librarians. J Acad Libr 47(6):102437. https://doi.org/10.1016/j.acalib.2021.102437

    Google Scholar 

  • Bach TA, Khan A, Hallock H, Beltrão G, Sousa S (2024) A systematic literature review of user trust in AI-enabled systems: an HCI perspective. Int J Hum Compu Interact 40(5):1251–1266. https://doi.org/10.1080/10447318.2022.2138826

    Google Scholar 

  • Bandura A, Walters RH (1977) Social learning theory (Vol. 1). Prentice hall Englewood Cliffs, NJ

  • Bentler P (1987) Practical issues in structural modeling. Common Problems/Proper Solutions: Avoiding Error in Survery Research/Sage

  • Bordas A, Le Masson P, Thomas M, Weil B (2024) What is generative in generative artificial intelligence? A design-based perspective. Res Eng Des 35(4):427–443. https://doi.org/10.1007/s00163-024-00441-x

    Google Scholar 

  • Breward C, Fisher, F, Wood, G, Spaces, & Places: British D (2015) British design: tradition and modernity after 1948. Bloomsbury Academic

  • Bridge G, Armstrong B, Reynolds C, Wang C, Schmidt X, Kause A, Ffoulkes C, Krawczyk C, Miller G, Serjeant S, Oakden L (2021) Engaging citizens in sustainability research: comparing survey recruitment and responses between Facebook, Twitter and qualtrics. Br Food J 123(9):3116–3132. https://doi.org/10.1108/BFJ-06-2020-0498

    Google Scholar 

  • Brislin RW (1970) Back-translation for cross-cultural research. J Cross Cult Psychol 1(3):185–216. https://doi.org/10.1177/135910457000100301

    Google Scholar 

  • Budhathoki T, Zirar A, Njoya ET, Timsina A (2024) ChatGPT adoption and anxiety: a cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Stud High Educ 49(5):831–846. https://doi.org/10.1080/03075079.2024.2333937

  • Cabrera-Sánchez J-P, Villarejo-Ramos ÁF, Liébana-Cabanillas F, Shaikh AA (2021) Identifying relevant segments of AI applications adopters – Expanding the UTAUT2’s variables. Telemat Inform 58: 101529. https://doi.org/10.1016/j.tele.2020.101529

    Google Scholar 

  • Camburn B, He Y, Raviselvam S, Luo J, Wood K (2020) Machine learning-based design concept evaluation. J Mech Des 142(3). https://doi.org/10.1115/1.4045126

  • Cao Y, Li S, Liu Y, Yan Z, Dai Y, Yu PS, 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

  • Capraro V, Lentsch A, Acemoglu D, Akgun S, Akhmedova A, Bilancini E, Bonnefon J-F, Brañas-Garza P, Butera L, Douglas KM, Everett JAC, Gigerenzer G, Greenhow C, Hashimoto DA, Holt-Lunstad J, Jetten J, Johnson S, Kunz WH, Longoni C,…Viale R (2024) The impact of generative artificial intelligence on socioeconomic inequalities and policy making. PNAS Nexus 3(6). https://doi.org/10.1093/pnasnexus/pgae191

  • Cetinic E, She J (2022) Understanding and creating art with AI: review and outlook. ACM Trans. Multimedia Comput. Commun. Appl. 18(2), https://doi.org/10.1145/3475799

  • Chen P, Wu Y, Li Z, Zhang H, Zhou M, Yao J, You W, Sun L (2025) GPSdesign: integrating generative AI with problem-solution co-evolution network to support product conceptual design. Int J Human Comput Interact 1–21. https://doi.org/10.1080/10447318.2025.2453003

  • Cheung CMK, Lee MKO (2010) A theoretical model of intentional social action in online social networks. Decis Support Syst 49(1):24–30. https://doi.org/10.1016/j.dss.2009.12.006

    Google Scholar 

  • Cheung GW, Cooper-Thomas HD, Lau RS, Wang LC (2024) Reporting reliability, convergent and discriminant validity with structural equation modeling: a review and best-practice recommendations. Asia Pac J Manag 41(2):745–783. https://doi.org/10.1007/s10490-023-09871-y

    Google Scholar 

  • Cheung R, Vogel D (2013) Predicting user acceptance of collaborative technologies: an extension of the technology acceptance model for e-learning. Comput Educ 63:160–175. https://doi.org/10.1016/j.compedu.2012.12.003

    Google Scholar 

  • Choi JK, Ji YG (2015) Investigating the importance of trust on adopting an autonomous vehicle. Int J Hum Comput Interact 31(10):692–702. https://doi.org/10.1080/10447318.2015.1070549

    Google Scholar 

  • Clunas C (1997) Art in China. Oxford University Press, USA

  • Council D (2022) Design economy: people, places and economy value. designcouncil.org.uk

  • Creswell JW, Creswell JD (2017) Research design: qualitative, quantitative, and mixed methods approaches. Sage Publications

  • Cumming E, Kaplan W (1991) The arts and crafts movement. Thames and Hudson, London

  • Dash G, Paul J (2021) CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technol Forecast Soc Change 173: 121092. https://doi.org/10.1016/j.techfore.2021.121092

    Google Scholar 

  • Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340. https://doi.org/10.2307/249008

    Google Scholar 

  • Davis RL, Wambsganss T, Jiang W, Kim KG, Käser T, Dillenbourg P (2024) Fashioning creative expertise with generative AI: graphical interfaces for design space exploration better support ideation than text prompts. Proceedings of the CHI conference on human factors in computing systems, Honolulu, HI, USA

  • DiMaggio PJ, Powell WW (1983) The iron cage revisited: institutional isomorphism and collective rationality in organizational fields. Am Sociol Rev 48(2):147–160

    Google Scholar 

  • Du Y, Li T, Gao C (2023) Why do designers in various fields have different attitude and behavioral intention towards AI painting tools? An extended UTAUT model. Procedia Comput Sci 221:1519–1526. https://doi.org/10.1016/j.procs.2023.08.010

    Google Scholar 

  • Duan P, Warner J, Li Y, Hartmann B (2024) Generating automatic feedback on UI mockups with large language models. Proceedings of the CHI conference on human factors in computing systems, Honolulu, HI, USA. https://doi.org/10.1145/3613904.3642782

  • Duester E (2024) Digital art work and AI: a new paradigm for work in the contemporary art sector in China [Original Research]. Eur J Cult Manag Policy 14:2024. https://doi.org/10.3389/ejcmp.2024.12470

    Google Scholar 

  • Dwivedi YK, Rana NP, Jeyaraj A, Clement M, Williams MD (2019) Re-examining the unified theory of acceptance and use of technology (UTAUT): towards a revised theoretical model. Inf Syst Front 21(3):719–734. https://doi.org/10.1007/s10796-017-9774-y

    Google Scholar 

  • Eckhardt A, Laumer S, Weitzel T (2009) Who Influences Whom? Analyzing workplace referents’ social influence on it adoption and non-adoption. J Inf Technol 24(1):11–24. https://doi.org/10.1057/jit.2008.31

    Google Scholar 

  • Emhmed S, Al-Sanjary OI, Jaharadak AA, Aldulaimi SH, HazimAlkawaz M (2021) Technical and organizational facilitating conditions — the antecedent factors and impact on the intention to use ERP system in Libyan universities. 2021 IEEE 17th International Colloquium on Signal Processing & Its Applications (CSPA)

  • Fang C, Liu W, Lin H, Qi Y, Tian X, Huang Y, Wang SJ (2024) A user-centred collective system design approach for Smart Product-Service Systems: a case study on fitness product design. Des J 27(3):410–432. https://doi.org/10.1080/14606925.2024.2304412

    Google Scholar 

  • Fang C, Zhu Y, Fang L, Long Y, Lin H, Cong Y, Wang SJ (2025) Generative AI-enhanced human-AI collaborative conceptual design: a systematic literature review. Des Stud 97: 101300. https://doi.org/10.1016/j.destud.2025.101300

    Google Scholar 

  • Faraon M, Rönkkö K, Milrad M, Tsui E (2025) International perspectives on artificial intelligence in higher education: An explorative study of students’ intention to use ChatGPT across the Nordic countries and the USA. Educ Inf Technol. https://doi.org/10.1007/s10639-025-13492-x

  • Faruk LID, Rohan R, Ninrutsirikun U, Pal D (2023) University students’ acceptance and usage of generative AI (ChatGPT) from a psycho-technical perspective. Proceedings of the 13th international conference on advances in information technology, Bangkok, Thailand. https://doi.org/10.1145/3628454.3629552

  • Feng S, Ma S, Wang H, Kong D, Chen C (2024) MUD: towards a large-scale and noise-filtered UI dataset for modern style UI modeling. Proceedings of the CHI conference on human factors in computing systems, Honolulu, HI, USA. https://doi.org/10.1145/3613904.3642350

  • Florén H, Frishammar J (2012) From preliminary ideas to corroborated product definitions: Managing the front end of new product development. Calif Manag Rev 54(4):20–43. https://doi.org/10.1525/cmr.2012.54.4.20

    Google Scholar 

  • Fortune (2024) Fortune China. fortunechina.com

  • Frich J, MacDonald Vermeulen L, Remy C, Biskjaer MM, Dalsgaard P (2019) Mapping the landscape of creativity support tools in HCI. Proceedings of the 2019 CHI conference on human factors in computing systems

  • Fui-Hoon Nah F, Zheng R, Cai J, Siau K, Chen L (2023) Generative AI and ChatGPT: applications, challenges, and AI-human collaboration. J Inf Technol Case Appl Res 25(3):277–304. https://doi.org/10.1080/15228053.2023.2233814

    Google Scholar 

  • Gansser OA, Reich CS (2021) A new acceptance model for artificial intelligence with extensions to UTAUT2: an empirical study in three segments of application. Technol Soc 65: 101535. https://doi.org/10.1016/j.techsoc.2021.101535

    Google Scholar 

  • Garson GD (2016) Partial least squares. Regression and structural equation models. In: Statistical Publishing Associates

  • Gelbrich K, Sattler B (2014) Anxiety, crowding, and time pressure in public self-service technology acceptance. J Serv Mark 28(1):82–94. https://doi.org/10.1108/JSM-02-2012-0051

    Google Scholar 

  • Gmeiner F, Yang H, Yao L, Holstein K, Martelaro N (2023) Exploring challenges and opportunities to support designers in learning to co-create with AI-based manufacturing design tools. Proceedings of the 2023 CHI conference on human factors in computing systems, https://doi.org/10.1145/3544548.3580999

  • Gold AH, Malhotra A, Segars AH (2001) Knowledge management: an organizational capabilities perspective. J Manag Inf Syst 18(1):185–214. https://doi.org/10.1080/07421222.2001.11045669

    Google Scholar 

  • Grassini S, Aasen ML, Møgelvang A (2024) Understanding university students’ acceptance of ChatGPT: insights from the UTAUT2 model. Appl Artif Intell 38(1):2371168. https://doi.org/10.1080/08839514.2024.2371168

    Google Scholar 

  • Guo S, Jin Z, Sun F, Li J, Li Z, Shi Y, Cao N (2021) Vinci: an intelligent graphic design system for generating advertising posters. Proceedings of the 2021 CHI conference on human factors in computing systems

  • Guo X, Huang Z, Liu Y, Zhao W, Yu Z (2023) Harnessing multi-domain knowledge for user-centric product conceptual design. J Comput Inf Sci Eng 23(6):060807. https://doi.org/10.1115/1.4062456

    Google Scholar 

  • Hair JF (2009) Multivariate data analysis

  • Hair JF (2014) A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications

  • Hair JF, Hult GTM, Ringle CM, Sarstedt M (2022) A primer on partial least squares structural equation modeling (PLS-SEM), 3rd edn. SAGE Publications, Inc

  • Hair JF Jr, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Ray S (2021) Partial least squares structural equation modeling (PLS-SEM) using R: a workbook. Springer International Publishing Cham

  • Han Y, Moghaddam M (2021) Analysis of sentiment expressions for user-centered design. Expert Syst Appl 171: 114604. https://doi.org/10.1016/j.eswa.2021.114604

    Google Scholar 

  • Haoran X, Shuyao C, Zhang Y (2023) Magical brush: a symbol-based modern chinese painting system for novices. Proceedings of the 2023 CHI conference on human factors in computing systems, Hamburg, Germany. https://doi.org/10.1145/3544548.3581429

  • Henseler J, Ringle CM, Sarstedt M (2016) Testing measurement invariance of composites using partial least squares. Int Mark Rev 33(3):405–431

    Google Scholar 

  • Hine E, Floridi L (2024) Artificial intelligence with American values and Chinese characteristics: a comparative analysis of American and Chinese governmental AI policies. AI Soc 39(1):257–278. https://doi.org/10.1007/s00146-022-01499-8

    Google Scholar 

  • Hofstede G (1984) Culture’s consequences: international differences in work-related values (Vol. 5). Sage

  • Hofstede G (2001) Culture’s consequences: comparing values, behaviors, institutions and organizations across nations. International Educational and Professional

  • Holzmann P, Schwarz EJ, Audretsch DB (2020) Understanding the determinants of novel technology adoption among teachers: the case of 3D printing. J Technol Transf 45(1):259–275. https://doi.org/10.1007/s10961-018-9693-1

    Google Scholar 

  • Hou Y, Yang M, Cui H, Wang L, Xu J, Zeng W (2024) C2Ideas: supporting creative interior color design ideation with a large language model. Proceedings of the CHI conference on human factors in computing systems. Honolulu, HI, USA. https://doi.org/10.1145/3613904.3642224

  • Huang R, Lin H, Chen C, Zhang K, Zeng W (2024) PlantoGraphy: incorporating iterative design process into generative artificial intelligence for landscape rendering. Proceedings of the CHI conference on human factors in computing systems, Honolulu, HI, USA. https://doi.org/10.1145/3613904.3642824

  • Huang Z, Quan K, Chan J, MacNeil S (2023) CausalMapper: challenging designers to think in systems with causal maps and large language model. Proceedings of the 15th conference on creativity and cognition, virtual event, USA. https://doi.org/10.1145/3591196.3596818

  • IBM (2020) IBM SPSS Statistics for Windows. In (Version 27.0) IBM Corp

  • Inie N, Falk J, Tanimoto S (2023) Designing participatory AI: creative professionals’ worries and expectations about generative AI. Extended abstracts of the 2023 CHI conference on human factors in computing systems, Hamburg, Germany. https://doi.org/10.1145/3544549.3585657

  • Jeary L, Gajjar D (2024) Artificial intelligence and new technology in creative industries. UK Parliament. https://post.parliament.uk/artificial-intelligence-and-new-technology-in-creative-industries/

  • Jing Q, Zhou T, Tsang Y, Chen L, Sun L, Zhen Y, Du Y (2023) Layout generation for various scenarios in mobile shopping applications. Proceedings of the 2023 CHI conference on human factors in computing systems, Hamburg, Germany. https://doi.org/10.1145/3544548.3581446

  • John D, Joel Klinger, Juan Mateos-Garcia, Stathoulopoulos K (2020) The art in the artificial, London. Creative Industries Policy and Evidence Centre and Nesta. https://pec.ac.uk/research-reports/the-art-in-the-artificial

  • Kariv D, Giglio C, Corvello V (2024) Fostering Entrepreneurial intentions: exploring the interplay of education and endogenous factors. Int Entrep Manag J 21(1):17. https://doi.org/10.1007/s11365-024-01020-1

    Google Scholar 

  • Kaufman JC, Sternberg RJ (2006) The international handbook of creativity. Cambridge University Press

  • Keane M (2013) Creative industries in China art, design and media. Polity Press

  • Kim H-W, Kankanhalli A (2009) Investigating user resistance to information systems implementation: a status quo bias perspective. MIS Q 33(3):567–582. https://doi.org/10.2307/20650309

    Google Scholar 

  • Kim J, Giroux M, Lee JC (2021) When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychol Mark 38(7):1140–1155. https://doi.org/10.1002/mar.21498

    Google Scholar 

  • Kim YJ, Choi JH, Fotso GMN (2024) Medical professionals’ adoption of AI-based medical devices: UTAUT model with trust mediation. J Open Innov Technol, Mark, Complex 10(1):100220. https://doi.org/10.1016/j.joitmc.2024.100220

    Google Scholar 

  • Kock N (2015) Common method bias in PLS-SEM: a full collinearity assessment approach. Int J Collab 11(4):1–10

    Google Scholar 

  • Köse DB (2023) Can cat videos harm your relationships? Hedonic and utilitarian content as technological antecedents of phubbing. Comput Hum Behav 149: 107964. https://doi.org/10.1016/j.chb.2023.107964

    Google Scholar 

  • Kwak Y, Seo YH, Ahn J-W (2022) Nursing students’ intent to use AI-based healthcare technology: path analysis using the unified theory of acceptance and use of technology. Nurse Educ Today 119: 105541. https://doi.org/10.1016/j.nedt.2022.105541

    Google Scholar 

  • Kwon E, Huang F, Goucher-Lambert K (2022) Enabling multi-modal search for inspirational design stimuli using deep learning. Artif Intell Eng Des Anal Manuf 36: e22. https://doi.org/10.1017/S0890060422000130

    Google Scholar 

  • Lee BC, Chung J (2024) An empirical investigation of the impact of ChatGPT on creativity. Nat Hum Behav. https://doi.org/10.1038/s41562-024-01953-1

  • Lehdonvirta V, Wú B, Hawkins Z (2024) Compute North vs. Compute South: the uneven possibilities of compute-based AI governance around the globe. Proceedings of the AAAI/ACM conference on AI, ethics, and society

  • Li H, Liu Y, Guo Q, Shi M, Zhang P, Kim S (2025) Unveiling the complexity of designers’ intention to use generative AI in corporate product design: a grounded theory and fsQCA. Systems 13(4):275

    Google Scholar 

  • Li J, Cao H, Lin L, Hou Y, Zhu R, Ali AE (2024) User experience design professionals’ perceptions of generative artificial intelligence. Proceedings of the CHI conference on human factors in computing systems. Honolulu, HI, USA. https://doi.org/10.1145/3613904.3642114

  • Li W (2024) A study on factors influencing designers’ behavioral intention in using AI-generated content for assisted design: perceived anxiety, perceived risk, and UTAUT. Int J Hum Comput Interact 1–14. https://doi.org/10.1080/10447318.2024.2310354

  • Liang H, Xue Y (2009) Avoidance of information technology threats: a theoretical perspective. MIS Q 33(1):71–90. https://doi.org/10.2307/20650279

    Google Scholar 

  • Liao J, Hansen P, Chai C (2020) A framework of artificial intelligence augmented design support. Hum Comput Interact 35(5-6):511–544. https://doi.org/10.1080/07370024.2020.1733576

    Google Scholar 

  • Lin DC-E, Martelaro N (2024) Jigsaw: supporting designers to prototype multimodal applications by chaining AI foundation models. Proceedings of the CHI conference on human factors in computing systems, Honolulu, HI, USA

  • Lin H, Jiang X, Deng X, Bian Z, Fang C, Zhu Y (2024) Comparing AIGC and traditional idea generation methods: evaluating their impact on creativity in the product design ideation phase. Think Skills Creativ 54: 101649. https://doi.org/10.1016/j.tsc.2024.101649

    Google Scholar 

  • Liu B (2024) Age discrimination in Chinese internet workplace. J Educ Humanit Soc Sci 27:172–180

    Google Scholar 

  • Liu Y, Bakici T (2019) Enterprise social media usage: the motives and the moderating role of public social media experience. Comput Hum Behav 101:163–172. https://doi.org/10.1016/j.chb.2019.07.029

    Google Scholar 

  • Liu Y-LE, Huang Y-M (2024) Exploring the perceptions and continuance intention of AI-based text-to-image technology in supporting design ideation. Int J Hum Comput Interact 41(1):1–13. https://doi.org/10.1080/10447318.2024.2311975

    Google Scholar 

  • Liu J, Zou J, Zhang J, Teng J (2025) Investigating users’ acceptance of AI-based creativity support tools: an empirical study from China’s creative industries. Curr Psychol 44(16):13933–13950. https://doi.org/10.1007/s12144-025-08116-z

  • Lopez CE, Miller SR, Tucker CS (2019) Exploring biases between human and machine generated designs. J Mech Des 141(2):021104. https://doi.org/10.1115/1.4041857

    Google Scholar 

  • Lords HO (2018) AI in the UK: ready, willing and able?

  • Lu J, Yu C-S, Liu C (2005) Facilitating conditions, wireless trust and adoption intention. J Comput Inf Syst 46(1):17–24. https://doi.org/10.1080/08874417.2005.11645865

    Google Scholar 

  • Maican CI, Sumedrea S, Tecau A, Nichifor E, Chitu IB, Lixandroiu R, Bratucu G (2023) Factors influencing the behavioural intention to use AI-generated images in business: a UTAUT2 perspective with moderators. J Organ End Use Comput 35(1):1–32. https://doi.org/10.4018/JOEUC.330019

    Google Scholar 

  • Martins C, Oliveira T, Popovič A (2014) Understanding the Internet banking adoption: a unified theory of acceptance and use of technology and perceived risk application. Int J Inf Manag 34(1):1–13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002

    Google Scholar 

  • Mathieson K, Peacock E, Chin WW (2001) Extending the technology acceptance model: the influence of perceived user resources. SIGMIS Database 32(3):86–112. https://doi.org/10.1145/506724.506730

    Google Scholar 

  • Mayer RC, Davis JH, Schoorman FD (1995) An integrative model of organizational trust. Acad Manag Rev 20(3):709–734. https://doi.org/10.5465/amr.1995.9508080335

    Google Scholar 

  • McCorduck P, Cfe C (2004) Machines who think: a personal inquiry into the history and prospects of artificial intelligence. AK Peters/CRC Press

  • Mcknight DH, Carter M, Thatcher JB, Clay PF (2011) Trust in a specific technology: an investigation of its components and measures. ACM Trans Manag Inf Syst 2(2), https://doi.org/10.1145/1985347.1985353

  • Mensah IK, Khan MK, Pratt CB (2024) The moderating influence of government support as the major environmental context on SMEs’ adoption of social media systems—from the technology-organization-environment perspective. J Knowl Econ. https://doi.org/10.1007/s13132-024-02440-8

  • Migliorini S (2024) China’s interim measures on generative AI: origin, content and significance. Comput Law Secur Rev 53: 105985. https://doi.org/10.1016/j.clsr.2024.105985

    Google Scholar 

  • Millet K, Buehler F, Du G, Kokkoris MD (2023) Defending humankind: anthropocentric bias in the appreciation of AI art. Comput Hum Behav 143: 107707. https://doi.org/10.1016/j.chb.2023.107707

    Google Scholar 

  • Nightingale SJ, Farid H (2022) AI-synthesized faces are indistinguishable from real faces and more trustworthy. Proc Natl Acad Sci USA 119(8):e2120481119. https://doi.org/10.1073/pnas.2120481119

    Google Scholar 

  • OECD (2024) OECD AI Principles. https://www.oecd.org/en/topics/ai-principles.html

  • Omrani N, Rivieccio G, Fiore U, Schiavone F, Agreda SG (2022) To trust or not to trust? An assessment of trust in AI-based systems: concerns, ethics and contexts. Technol Forecast Soc Change 181: 121763. https://doi.org/10.1016/j.techfore.2022.121763

    Google Scholar 

  • OpenAI (2024) OpenAI API - Supported Countries and Territories. https://help.openai.com/en/articles/5347006-openai-api-supported-countries-and-territories

  • Pantano E, Rese A, Baier D (2017) Enhancing the online decision-making process by using augmented reality: a two country comparison of youth markets. J Retail Consum Serv 38:81–95. https://doi.org/10.1016/j.jretconser.2017.05.011

    Google Scholar 

  • Peñarroja V, Sánchez J, Gamero N, Orengo V, Zornoza AM (2019) The influence of organisational facilitating conditions and technology acceptance factors on the effectiveness of virtual communities of practice. Behav Inf Technol 38(8):845–857. https://doi.org/10.1080/0144929X.2018.1564070

    Google Scholar 

  • Premkumar G (2003) A meta-analysis of research on information technology implementation in small business. J Organ Comput Electron Commer 13(2):91–121. https://doi.org/10.1207/S15327744JOCE1302_2

    Google Scholar 

  • Quispel A, Maes A, Schilperoord J (2015) Graph and chart aesthetics for experts and laymen in design: the role of familiarity and perceived ease of use. Inf Vis 15(3):238–252. https://doi.org/10.1177/1473871615606478

    Google Scholar 

  • Ram S, Sheth JN (1989) Consumer resistance to innovations: the marketing problem and its solutions. J Consum Mark 6(2):5–14. https://doi.org/10.1108/EUM0000000002542

    Google Scholar 

  • Rane N, Choudhary SP, Rane J (2024) Acceptance of artificial intelligence: key factors, challenges, and implementation strategies. J Appl Artif Intell 5(2):50–70. https://doi.org/10.48185/jaai.v5i2.1017

    Google Scholar 

  • Rezwana J, Maher ML (2022) Designing creative AI partners with COFI: a framework for modeling interaction in human-AI co-creative systems. ACM Trans Comput Hum Interact https://doi.org/10.1145/3519026

  • Ringle CM, Wende S, Becker J-M (2024) SmartPLS 4. In https://www.smartpls.com/

  • Rogers EM, Singhal A, Quinlan MM (2014) Diffusion of innovations. In An integrated approach to communication theory and research. Routledge, p 432–448

  • Saghafian M, Laumann K, Skogstad MR (2021) Stagewise overview of issues influencing organizational technology adoption and use [Review]. Front Psychol 12. https://doi.org/10.3389/fpsyg.2021.630145

  • Scott WR (2013) Institutions and organizations: ideas, interests, and identities. Sage publications

  • Seeber I, Bittner E, Briggs RO, de Vreede T, de Vreede G-J, Elkins A, Maier R, Merz AB, Oeste-Reiß S, Randrup N, Schwabe G, Söllner M (2020) Machines as teammates: a research agenda on AI in team collaboration. Inf Manag 57(2):103174. https://doi.org/10.1016/j.im.2019.103174

    Google Scholar 

  • Sheppard BH, Hartwick J, Warshaw PR (1988) The Theory of reasoned action: a meta-analysis of past research with recommendations for modifications and future research. J Consum Res 15(3):325–343. https://doi.org/10.1086/209170

    Google Scholar 

  • Sheppard M, Vibert C (2019) Re-examining the relationship between ease of use and usefulness for the net generation. Educ Inf Technol 24(5):3205–3218. https://doi.org/10.1007/s10639-019-09916-0

    Google Scholar 

  • Shi Y, Gao T, Jiao X, Cao N (2023) Understanding design collaboration between designers and artificial intelligence: a systematic literature review. Proc ACM Hum Comput Interact 7(CSCW2), https://doi.org/10.1145/3610217

  • Shin D, Wang LL, Hsieh G (2024) From paper to card: transforming design implications with generative AI. Proceedings of the 2024 CHI conference on human factors in computing systems, Honolulu, HI, USA. https://doi.org/10.1145/3613904.3642266

  • Siangliulue P, Chan J, Dow SP, Gajos KZ (2016) IdeaHound: improving large-scale collaborative ideation with crowd-powered real-time semantic modeling. Proceedings of the 29th annual symposium on user interface software and technology

  • Srinivasan R, Uchino K (2021) Biases in generative art: a causal look from the lens of art history. Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, virtual event, Canada. https://doi.org/10.1145/3442188.3445869

  • Srite M, Karahanna E (2006) The role of espoused national cultural values in technology acceptance. MIS Q 30(3):679–704. https://doi.org/10.2307/25148745

    Google Scholar 

  • Straub D, Keil M, Brenner W (1997) Testing the technology acceptance model across cultures: a three country study. Inf Manag 33(1):1–11. https://doi.org/10.1016/S0378-7206(97)00026-8

    Google Scholar 

  • Takaffoli M, Li S, Mäkelä V (2024) Generative AI in user experience design and research: how do UX practitioners, teams, and companies use GenAI in industry? Proceedings of the 2024 ACM designing interactive systems conference, Copenhagen, Denmark. https://doi.org/10.1145/3643834.3660720

  • Talukder M, Quazi A (2011) The impact of social influence on individuals’ adoption of innovation. J Organ Comput Electron Commer 21(2):111–135. https://doi.org/10.1080/10919392.2011.564483

    Google Scholar 

  • Tan L, Luhrs M (2024) Using generative AI midjourney to enhance divergent and convergent thinking in an architect’s creative design process. Design J 1–23. https://doi.org/10.1080/14606925.2024.2353479

  • Tang X, Yu K, Yu W (2022) The impact of firm’s unethical behavior in investment decisions among young investors in China. Psychol Res Behav Manag 15(null):3427–3443. https://doi.org/10.2147/PRBM.S384377

    Google Scholar 

  • Tang Y, Zhang N, Ciancia M, Wang Z (2024) Exploring the impact of AI-generated image tools on professional and non-professional users in the art and design fields companion. Publication of the 2024 conference on computer-supported cooperative work and social computing, San Jose, Costa Rica. https://doi.org/10.1145/3678884.3681890

  • Utterback JM (1971) The process of technological innovation within the firm. Acad Manag J 14(1):75–88

    Google Scholar 

  • Vannoy SA, Palvia P (2010) The social influence model of technology adoption. Commun ACM 53(6):149–153. https://doi.org/10.1145/1743546.1743585

    Google Scholar 

  • Venkatesh V (2022) Adoption and use of AI tools: a research agenda grounded in UTAUT. Ann Oper Res 308(1):641–652. https://doi.org/10.1007/s10479-020-03918-9

    Google Scholar 

  • Venkatesh V, Brown SA, Maruping LM, Bala H (2008) Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Q 32(3):483–502. https://doi.org/10.2307/25148853

    Google Scholar 

  • Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540

    Google Scholar 

  • Venkatesh V, Thong JY, Xu X (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q 157–178

  • Wan Q, Lu Z (2023a) GANCollage: a GAN-driven digital mood board to facilitate ideation in creativity support. Proceedings of the 2023 ACM designing interactive systems conference, Pittsburgh, PA, USA. https://doi.org/10.1145/3563657.3596072

  • Wan Q, Lu Z (2023b) Investigating semantically-enhanced exploration of GAN latent space via a digital mood board. Extended Abstracts of the 2023 CHI conference on human factors in computing systems, Hamburg, Germany. https://doi.org/10.1145/3544549.3585740

  • Wang J, Yuan X, Hu S, Lu Z (2025) AI vs. human paintings? Deciphering public interactions and perceptions towards AI-generated paintings on TikTok. Int J Hum Comput Interact 1–24. https://doi.org/10.1080/10447318.2025.2531284

  • Wenger E (1998) Communities of practice: learning, meaning, and identity. Cambridge University Press. https://doi.org/10.1017/CBO9780511803932

  • West SG (1995) Structural equation models with nonnormal variables: Problems and remedies. Structural equation modeling: concepts, issues and applications/Sage

  • Williams MD, Rana NP, Dwivedi YK (2015) The unified theory of acceptance and use of technology (UTAUT): a literature review. J Enterp Inf Manag 28(3):443–488. https://doi.org/10.1108/JEIM-09-2014-0088

    Google Scholar 

  • Wu I-L, Chen J-L (2014) A stage-based diffusion of IT innovation and the BSC performance impact: a moderator of technology–organization–environment. Technol Forecast Soc Change 88:76–90. https://doi.org/10.1016/j.techfore.2014.06.015

    Google Scholar 

  • Xia Y, Chen Y (2024) Driving factors of generative AI adoption in new product development teams from a UTAUT perspective. Int J Hum Comput Interact 1–22. https://doi.org/10.1080/10447318.2024.2375686

  • Xiang W, Zhu H, Lou S, Chen X, Pan Z, Jin Y, Chen S, Sun L (2024) SimUser: generating usability feedback by simulating various users interacting with mobile applications. Proceedings of the CHI conference on human factors in computing systems, Honolulu, HI, USA. https://doi.org/10.1145/3613904.3642481

  • Xiao LY, Fraser TC, Nielsen RKL, Newall PWS (2024) Loot boxes, gambling-related risk factors, and mental health in Mainland China: a large-scale survey. Addictive Behav 148: 107860. https://doi.org/10.1016/j.addbeh.2023.107860

    Google Scholar 

  • Xiong Y, Shi Y, Pu Q, Liu N (2024) More trust or more risk? User acceptance of artificial intelligence virtual assistant. Hum Factors Ergon Manuf Serv Ind 34(3):190–205. https://doi.org/10.1002/hfm.21020

    Google Scholar 

  • Xu M (2025) Interaction between students and artificial intelligence in the context of creative potential development. Interact Learn Environ 1–16. https://doi.org/10.1080/10494820.2025.2465439

  • Yakubu MN, David N, Abubakar NH (2025) Students’ behavioural intention to use content generative AI for learning and research: a UTAUT theoretical perspective. Educ Inf Technol. https://doi.org/10.1007/s10639-025-13441-8

  • Yan Z, Yang C, Liang Q, Chen XA (2023) XCreation: a graph-based crossmodal generative creativity support tool. Proceedings of the 36th annual ACM symposium on user interface software and technology, San Francisco, CA, USA. https://doi.org/10.1145/3586183.3606826

  • Yang Q, Feng S, Zhao T, Kalantari S (2023) Co-design with myself: a brain-computer interface design tool that predicts live emotion to enhance metacognitive monitoring of designers. Extended abstracts of the 2023 CHI conference on human factors in computing systems, Hamburg, Germany. https://doi.org/10.1145/3544549.3585701

  • Ye T, Xue J, He M, Gu J, Lin H, Xu B, Cheng Y (2019) Psychosocial factors affecting artificial intelligence adoption in health care in China: cross-sectional study. J Med Internet Res 21(10):e14316. https://doi.org/10.2196/14316

    Google Scholar 

  • Yin M, Han B, Ryu S, Hua M (2023) Acceptance of generative AI in the creative industry: examining the role of AI anxiety in the UTAUT2 model. HCI International 2023 – Late Breaking Papers, Cham

  • Yin M, Jiang S, Niu X (2024) Can AI really help? The double-edged sword effect of AI assistant on employees’ innovation behavior. Comput Hum Behav 150: 107987. https://doi.org/10.1016/j.chb.2023.107987

    Google Scholar 

  • Yoon H, Oh C, Jun S (2024) How can I trust AI?: Extending a UXer-AI collaboration process in the early stages. Extended abstracts of the 2024 CHI conference on human factors in computing systems. https://doi.org/10.1145/3613905.3650879

  • Yuan C, Marion T, Moghaddam M (2021) Leveraging end-user data for enhanced design concept evaluation: a multimodal deep regression model. J Mech Des 144. https://doi.org/10.1115/1.4052366

  • Yun H (2025) China’s data sovereignty and security: implications for global digital borders and governance. Chin Political Sci Rev 10(2):178–203. https://doi.org/10.1007/s41111-024-00269-9

    Google Scholar 

  • Zhang G, Chong L, Kotovsky K, Cagan J (2023) Trust in an AI versus a Human teammate: the effects of teammate identity and performance on human-AI cooperation. Comput Hum Behav 139:107536. https://doi.org/10.1016/j.chb.2022.107536

    Google Scholar 

  • Zhang L, Fang C, Lin H, Liang G, Luo S (2025) Factors influencing attitudes and behavioral intentions toward GenAI in creative collaboration: a cross-cultural comparison via a hybrid multistage approach. Think Skills Creat 102020. https://doi.org/10.1016/j.tsc.2025.102020

  • Zhang L, Fang C, Wang SJ, Wang Y, Luo S (2025) Students’ attitudes and sentiments toward AI-generated images: deep learning-based social media text mining. Interact Learn Environ 1–26. https://doi.org/10.1080/10494820.2025.2545964

  • Zhang M, Cheng Z, Shiu STR, Liang J, Fang C, Ma Z, Fang L, Wang SJ (2023) Towards human-centred AI-co-creation: a three-level framework for effective collaboration between human and AI computer supported cooperative work and social computing, Minneapolis, MN, USA. https://doi.org/10.1145/3584931.3607008

  • Zhang M, Wang Z (2024) Running head: the fusion of Chinese traditional culture and modern design the fusion of Chinese traditional culture and modern design: exploring balance and conflict resolution in a globalized context. Libr Prog Libr Sci Inf Technol Comput 44(3)

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Acknowledgements

This study was funded by the Hong Kong Polytechnic University’s Research Centre for Future (Caring) Mobility (P0042701), University's Strategic Importance Project (P0036851), SD/COMP Joint Research Scheme (P0042739), Collaborative Research with World-leading Research Groups (P0039528) and the National Natural Science Foundation of China (62302263). During the preparation of this work, the authors only used ChatGPT 4.0 to improve language readability and proofreading. No AI was used to generate any content or conduct any part of the research.

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Authors and Affiliations

  1. School of Design, The Hong Kong Polytechnic University, Hong Kong SAR, China

    Cong Fang, Mingyuan Zhang & Stephen Jia Wang

  2. Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China

    Paul Vinod Khiatani

  3. Key Laboratory of Air-driven Equipment Technology of Zhejiang Province, Quzhou University, Quzhou, China

    Huan Lin

  4. King’s College London, London, UK

    Wei Liu

  5. Laboratory for Artificial Intelligence in Design, Hong Kong SAR, China

    Stephen Jia Wang

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  6. Stephen Jia Wang
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Contributions

Conceptualisation, CF and PVK; Data curation, MYZ and CF; Methodology, CF, PVK and SW; Validation, CF and HL; Software, MYZ; Writing—original draft preparation, CF, MYZ and PVK; Writing—review and editing, CF, PVK, HL and SW; Supervision, WL and SW; Funding acquisition, SW.

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Correspondence to Stephen Jia Wang.

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Ethics approval

All procedures in this study involving human participants were conducted in accordance with institutional and national ethical standards and with the 1964 Helsinki Declaration and its later amendments. Ethical approval for this study was obtained prior to data collection from the Ethical Review Board of The Hong Kong Polytechnic University (Reference No. HSEARS20240315013; approval date: 25 March 2024). The approval covered the full study protocol and its scope. All recruitment procedures, informed consent processes, and data collection activities were centrally designed and administered by the research team at The Hong Kong Polytechnic University. The study was conducted under the auspices of a single institution and did not involve site-based data collection, local institutional collaborators, or local investigators in the host countries. In accordance with the Declaration of Helsinki, the study protocol, including the survey instrument, recruitment strategy, informed consent procedures, and data management plan, was reviewed and approved by the above-named ethics committee before the research commenced. All participants provided informed consent prior to participation.

Informed consent

Informed consent was obtained from all participants before data collection. Participants were provided with the standard online information sheet issued by The Hong Kong Polytechnic University, which described the study purpose, procedures, potential risks, and participant rights. They indicated their consent electronically before accessing the questionnaire. Consent was obtained between April and August 2024.

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Fang, C., Zhang, M., Khiatani, P.V. et al. A comparative analysis of generative AI adoption among design professionals in China and the United Kingdom: a UTAUT perspective. Humanit Soc Sci Commun (2026). https://doi.org/10.1057/s41599-026-06796-x

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  • Received: 25 January 2025

  • Accepted: 16 February 2026

  • Published: 25 February 2026

  • DOI: https://doi.org/10.1057/s41599-026-06796-x

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