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

References

• Abou-Shouk, M., & Soliman, M. (2021). The impact of gamification adoption intention on brand awareness and loyalty in tourism: The mediating effect of customer engagement. Journal of Destination Marketing & Management, 20, 100559. https://doi.org/10.1016/j.jdmm.2021.100559
   Web of Science ®Google Scholar
• Al Qeisi, K. I., & Al-Abdallah, G. M. (2014). Website design and usage behaviour: An application of the UTAUT model for internet banking in UK. International Journal of Marketing Studies, 6(1), 75. https://doi.org/10.5539/ijms.v6n1p75
   Google Scholar
• Anouze, A. L. M., & Alamro, A. S. (2019). Factors affecting intention to use e-banking in Jordan. International Journal of Bank Marketing, 38(1), 86–112. https://doi.org/10.1108/IJBM-10-2018-0271
   Web of Science ®Google Scholar
• Bae, S. Y., & Chang, P.-J. (2021). The effect of coronavirus disease-19 (COVID-19) risk perception on behavioural intention towards ‘intact’ tourism in South Korea during the first wave of the pandemic (March 2020). Current Issues in Tourism, 24(7), 1017–1035. https://doi.org/10.1080/13683500.2020.1798895
   Web of Science ®Google Scholar
• Bardeen, J. R., Fergus, T. A., & Orcutt, H. K. (2013). Experiential avoidance as a moderator of the relationship between anxiety sensitivity and perceived stress. Behavior Therapy, 44(3), 459–469. https://doi.org/10.1016/j.beth.2013.04.001
   PubMed Web of Science ®Google Scholar
• Beaudry, A. and A. Pinsonneault (2005). "Understanding user responses to information technology: A coping model of user adaptation." MIS Quarterly, 29(3): 493–524. https://doi.org/10.2307/25148693
   Web of Science ®Google Scholar
• Bekaert, G., & Harvey, C. R. (2003). Emerging markets finance. Journal of Empirical Finance, 10(1–2), 3–55. https://doi.org/10.1016/S0927-5398(02)00054-3
   Google Scholar
• Bendary, N., & I. Al-Sahouly. (2018). Exploring the extension of unified theory of acceptance and use of technology, UTAUT2, factors effect on perceived usefulness and ease of use on mobile commerce in Egypt. Journal of Business and Retail Management Research, 12(2).
   Google Scholar
• Birch, A., & Irvine, V. (2009). Preservice teachers’ acceptance of ICT integration in the classroom: Applying the UTAUT model. Educational Media International, 46(4), 295–315. https://doi.org/10.1080/09523980903387506
   Google Scholar
• Bobrow, D. G., & Whalen, J. (2002). Community knowledge sharing in practice: The Eureka story. Reflections, 4(2), 47–59. https://doi.org/10.1162/152417302762251336
   Google Scholar
• Burns, W. J., Peters, E., & Slovic, P. (2012). Risk perception and the economic crisis: A longitudinal study of the trajectory of perceived risk. Risk Analysis, 32(4), 659–677. https://doi.org/10.1111/j.1539-6924.2011.01733.x
   PubMed Web of Science ®Google Scholar
• 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.
   Google Scholar
• Celik, H. (2016). Customer online shopping anxiety within the Unified Theory of Acceptance and Use Technology (UTAUT) framework. Asia Pacific Journal of Marketing and Logistics, 28(2). https://doi.org/10.1108/APJML-05-2015-0077
   Web of Science ®Google Scholar
• Cetinic, E., & She, J. (2022). Understanding and creating art with AI: Review and outlook. ACM Transactions on Multimedia Computing, Communications, and Applications, 18(2), 1–22. https://doi.org/10.1145/3475799
   Web of Science ®Google Scholar
• Chan, R., Troshani, I., Rao Hill, S., & Hoffmann, A. (2022). Towards an understanding of consumers’ FinTech adoption: The case of open banking. International Journal of Bank Marketing, 40(4), 886–917. https://doi.org/10.1108/IJBM-08-2021-0397
   Web of Science ®Google Scholar
• Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. The Winners, 13(2), 10–114. https://doi.org/10.21512/tw.v13i2.656
   Google Scholar
• Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652
   PubMed Web of Science ®Google Scholar
• Chen, C., Fu, J., & Lyu, L. (2023). A pathway towards responsible AI generated content. arXiv preprint arXiv:2303.01325.
   Google Scholar
• 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.
   Google Scholar
• Chen, S.-C., & Chen, H.-H. (2009). The empirical study of customer satisfaction and continued behavioural intention towards self-service banking: Technology readiness as an antecedent. International Journal of Electronic Finance, 3(1), 64–76. https://doi.org/10.1504/IJEF.2009.024270
   Google Scholar
• Cheng, K. H., & Tsai, C. C. (2020). Students’ motivational beliefs and strategies, perceived immersion and attitudes towards science learning with immersive virtual reality: A partial least squares analysis. British Journal of Educational Technology, 51(6), 2140–2159. https://doi.org/10.1111/bjet.12956
   Web of Science ®Google Scholar
• Cheung, C. M., & Lee, M. K. (2010). A theoretical model of intentional social action in online social networks. Decision Support Systems, 49(1), 24–30. https://doi.org/10.1016/j.dss.2009.12.006
   Web of Science ®Google Scholar
• Chiu, C.-Y., Chen, C.-L., & Chen, S. (2022). Broadband mobile applications’ adoption by SMEs in Taiwan—a multi-perspective study of determinants. Applied Sciences, 12(14), 7002. https://doi.org/10.3390/app12147002
   Google Scholar
• Chuang, H. (2016). Factors influencing behavioral intention of wearable symbiotic devices–case study of the MI band. Soochow Journal of Economics and Business, 93, 1–24.
   Google Scholar
• Dahlman, C. J., & Westphal, L. E. (1981). The meaning of technological mastery in relation to transfer of technology. The Annals of the American Academy of Political and Social Science, 458(1), 12–26. https://doi.org/10.1177/000271628145800102
   Google Scholar
• Dave, A., Saxena, A., & Jha, A. (2023). Understanding user comfort and expectations in AI-based systems. https://doi.org/10.21203/rs.3.rs-3135320/v1
   Google Scholar
• de Sena Abrahão, R., Moriguchi, S. N., & Andrade, D. F. (2016). Intention of adoption of mobile payment: An analysis in the light of the unified theory of acceptance and use of technology (UTAUT). RAI Revista de Administração e Inovação, 13(3), 221–230. https://doi.org/10.1016/j.rai.2016.06.003
   Google Scholar
• Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance: The processing efficiency theory. Cognition & Emotion, 6(6), 409–434. https://doi.org/10.1080/02699939208409696
   Web of Science ®Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
   Web of Science ®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 [Paper presentation]. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany. https://doi.org/10.1145/3544548.3580999
   Google Scholar
• Goldschmidt, G. (1995). The designer as a team of one. Design Studies, 16(2), 189–209. https://doi.org/10.1016/0142-694X(94)00009-3
   Google Scholar
• Grupe, D. W., & Nitschke, J. B. (2013). Uncertainty and anticipation in anxiety: An integrated neurobiological and psychological perspective. Nature Reviews-Neuroscience, 14(7), 488–501. https://doi.org/10.1038/nrn3524
   PubMed Web of Science ®Google Scholar
• Habibi, A., Yusop, F. D., & Razak, R. A. (2020). The role of TPACK in affecting pre-service language teachers’ ICT integration during teaching practices: Indonesian context. Education and Information Technologies, 25(3), 1929–1949. https://doi.org/10.1007/s10639-019-10040-2
   Web of Science ®Google Scholar
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2009). Análise multivariada de dados Bookman editora.
   Google Scholar
• Hair Jr, J., Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
   Google Scholar
• Hamilton, M. A., Rouse, R. A., & Rouse, J. (1994). Dentist communication and patient utilization of dental services: Anxiety inhibition and competence enhancement effects. Health Communication, 6(2), 137–158. https://doi.org/10.1207/s15327027hc0602_4
   Google Scholar
• Heron, K. E., & Smyth, J. M. (2010). Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behaviour treatments. British Journal of Health Psychology, 15(Pt 1), 1–39. https://doi.org/10.1348/135910709X466063
   PubMed Web of Science ®Google Scholar
• Hinds, P. J. (1999). The curse of expertise: The effects of expertise and debiasing methods on prediction of novice performance. Journal of Experimental Psychology: Applied, 5(2), 205–221. https://doi.org/10.1037/1076-898X.5.2.205
   Web of Science ®Google Scholar
• Hong, J.-W., & Curran, N. M. (2019). Artificial intelligence, artists, and art: Attitudes toward artwork produced by humans vs. artificial intelligence. ACM Transactions on Multimedia Computing, Communications, and Applications, 15(2s), 1–16. https://doi.org/10.1145/3326337
   Google Scholar
• Hu, L-t., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037//1082-989X.3.4.424
   Web of Science ®Google Scholar
• Hussain, S., Guangju, W., Jafar, R. M. S., Ilyas, Z., Mustafa, G., & Jianzhou, Y. (2018). Consumers’ online information adoption behavior: Motives and antecedents of electronic word of mouth communications. Computers in Human Behavior, 80(C), 22–32. https://doi.org/10.1016/j.chb.2017.09.019
   Web of Science ®Google Scholar
• Jiao, H., Qian, L., Liu, T., & Ma, L. (2021). Why do people support online crowdfunding charities? A case study from China. Frontiers in Psychology, 12, 582508. https://doi.org/10.3389/fpsyg.2021.582508
   PubMed Web of Science ®Google Scholar
• 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.
   Google Scholar
• Kumar, J. A., Bervell, B., Annamalai, N., & Osman, S. (2020). Behavioral intention to use mobile learning: Evaluating the role of self-efficacy, subjective norm, and WhatsApp use habit. IEEE Access, 8, 208058–208074. https://doi.org/10.1109/ACCESS.2020.3037925
   Web of Science ®Google Scholar
• Lang, C. (2018). Perceived risks and enjoyment of access-based consumption: Identifying barriers and motivations to fashion renting. Fashion and Textiles, 5(1), 1–18. https://doi.org/10.1186/s40691-018-0139-z
   Google Scholar
• Lawson, B. (2006). How designers think: The design process demystified. Routledge.
   Google Scholar
• Lee, J.-H., & Song, C.-H. (2013). Effects of trust and perceived risk on user acceptance of a new technology service. Social Behavior and Personality, 41(4), 587–597. https://doi.org/10.2224/sbp.2013.41.4.587
   Web of Science ®Google Scholar
• Lee, M.-C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141. https://doi.org/10.1016/j.elerap.2008.11.006
   Web of Science ®Google Scholar
• Lin, H. C., Ho, C. F., & Yang, H. (2022). Understanding adoption of artificial intelligence-enabled language e-learning system: An empirical study of UTAUT model. International Journal of Mobile Learning and Organisation, 16(1), 74–94. https://doi.org/10.1504/IJMLO.2022.119966
   Google Scholar
• Liu, X. (2020). Artistic reflection on artificial intelligence digital painting. Journal of Physics: Conference Series, 1648(3), 032125. https://doi.org/10.1088/1742-6596/1648/3/032125
   Google Scholar
• Lovreglio, R., Ronchi, E., & Nilsson, D. (2016). An evacuation decision model based on perceived risk, social influence and behavioural uncertainty. Simulation Modelling Practice and Theory, 66, 226–242. https://doi.org/10.1016/j.simpat.2016.03.006
   Web of Science ®Google Scholar
• Lu, H.-P., & Yang, Y.-W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34, 323–332. https://doi.org/10.1016/j.chb.2013.10.020
   Web of Science ®Google Scholar
• Maillet, É., Mathieu, L., & Sicotte, C. (2015). Modeling factors explaining the acceptance, actual use and satisfaction of nurses using an electronic patient record in acute care settings: An extension of the UTAUT. International Journal of Medical Informatics, 84(1), 36–47. https://doi.org/10.1016/j.ijmedinf.2014.09.004
   PubMed Web of Science ®Google Scholar
• Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. https://doi.org/10.1038/nature14236
   PubMed Web of Science ®Google Scholar
• Moorthy, K., Chun T'ing, L., Ming, K. S., Ping, C. C., Ping, L. Y., Joe, L. Q., & Jie, W. Y. (2019). Behavioral intention to adopt digital library by the undergraduates. International Information & Library Review, 51(2), 128–144. https://doi.org/10.1080/10572317.2018.1463049
   Google Scholar
• 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
   Google Scholar
• Paredes, M. R., Apaolaza, V., Fernandez-Robin, C., Hartmann, P., & Yañez-Martinez, D. (2021). The impact of the COVID-19 pandemic on subjective mental well-being: The interplay of perceived threat, future anxiety and resilience. Personality and Individual Differences, 170, 110455. https://doi.org/10.1016/j.paid.2020.110455
   PubMed Web of Science ®Google Scholar
• Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144
   Web of Science ®Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Pratama, A. R. (2021). Fun first, useful later: Mobile learning acceptance among secondary school students in Indonesia. Education and Information Technologies, 26(2), 1737–1753. https://doi.org/10.1007/s10639-020-10334-w
   Web of Science ®Google Scholar
• 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
   Google Scholar
• Rezvani, S., Heidari, S., Roustapisheh, N., & Dokhanian, S. (2022). The effectiveness of system quality, habit, and effort expectation on library application use intention: The mediating role of perceived usefulness, perceived ease of use, and user satisfaction. International Journal of Business Information Systems, 1(1), 1. https://doi.org/10.1504/IJBIS.2022.10049515
   Google Scholar
• Roskes, M., De Dreu, C. K. W., & Nijstad, B. A. (2012). Necessity is the mother of invention: Avoidance motivation stimulates creativity through cognitive effort. Journal of Personality and Social Psychology, 103(2), 242–256. https://doi.org/10.1037/a0028442
   PubMed Web of Science ®Google Scholar
• Russell, S. (2016). Rationality and intelligence: A brief update. Fundamental issues of artificial intelligence (vol 1132, pp. 2016). Pearson.
   Google Scholar
• Sarstedt, M., Ringle, C.M., & Hair, J.F. (2022). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, A. Vomberg (Eds.), Handbook of market research. Springer. https://doi.org/10.1007/978-3-319-57413-4_15
   Google Scholar
• Shiferaw, K. B., Mengiste, S. A., Gullslett, M. K., Zeleke, A. A., Tilahun, B., Tebeje, T., Wondimu, R., Desalegn, S., & Mehari, E. A. (2021). Healthcare providers’ acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: An extended UTAUT model. PLOS One, 16(4), e0250220. https://doi.org/10.1371/journal.pone.0250220
   PubMed Web of Science ®Google Scholar
• Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling consumers’ adoption intentions of remote mobile payments in the United Kingdom: Extending UTAUT with innovativeness, risk, and trust. Psychology & Marketing, 32(8), 860–873. https://doi.org/10.1002/mar.20823
   Web of Science ®Google Scholar
• Strohmaier, D., Zeng, J., & Hafeez, M. (2019). Trust, distrust, and crowdfunding: A study on perceptions of institutional mechanisms. Telematics and Informatics, 43, 101252. https://doi.org/10.1016/j.tele.2019.101252
   Web of Science ®Google Scholar
• Tao, W., Gao, S., & Yuan, Y. (2023). Boundary crossing: An experimental study of individual perceptions toward AIGC. Frontiers in Psychology, 14, 1185880. https://doi.org/10.3389/fpsyg.2023.1185880
   PubMed Web of Science ®Google Scholar
• Thanigan, J., Reddy, S. N., Sethuraman, P., & Rajesh, J. I. (2021). Understanding consumer acceptance of M-Wallet apps: The role of perceived value, perceived credibility, and technology anxiety. Journal of Electronic Commerce in Organizations, 19(1), 65–91. https://doi.org/10.4018/JECO.2021010104
   Web of Science ®Google Scholar
• Venkatesh, V. (2022). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308(1–2), 641–652. https://doi.org/10.1007/s10479-020-03918-9
   Web of Science ®Google Scholar
• Venkatesh, Morris, Davis, Davis. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3): 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting and Social Change, 126, 207–216. https://doi.org/10.1016/j.techfore.2017.08.013
   Web of Science ®Google Scholar
• Wang, Y., & Dong, Y. (2023). AIGC assisted generation craft based on dialogue interface. Highlights in Science, Engineering and Technology, 57, 242–246. https://doi.org/10.54097/hset.v57i.10008
   Google Scholar
• Wijaya, T. T., Cao, Y., Bernard, M., Rahmadi, I. F., Lavicza, Z., & Surjono, H. D. (2022). Factors influencing microgame adoption among secondary school mathematics teachers supported by structural equation modelling-based research. Frontiers in Psychology, 13, 952549. https://doi.org/10.3389/fpsyg.2022.952549
   PubMed Web of Science ®Google Scholar
• Wrycza, S., Marcinkowski, B., & Gajda, D. (2017). The enriched UTAUT model for the acceptance of software engineering tools in academic education. Information Systems Management, 34(1), 38–49. https://doi.org/10.1080/10580530.2017.1254446
   Web of Science ®Google Scholar
• Wu, C.-H., & Chen, S.-C. (2015). Understanding the relationships of critical factors to Facebook educational usage intention. Internet Research, 25(2), 262–278. https://doi.org/10.1108/IntR-11-2013-0232
   Web of Science ®Google Scholar
• Wu, J., Gan, W., Chen, Z., Wan, S., & Lin, H. (2023). AI-Generated Content (AIGC): A survey. arXiv preprint arXiv:2304.06632
   Google Scholar
• Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719–729. https://doi.org/10.1016/j.im.2004.07.001
   Web of Science ®Google Scholar
• Wu, S. (2020). Retracted: Development of graphic design based on artificial intelligence. Journal of Physics: Conference Series, 1533(3), 032022. https://doi.org/10.1088/1742-6596/1533/3/032022
   Google Scholar
• Xu, M., Du, H., Niyato, D., Kang, J., Xiong, Z., Mao, S., Han, Z., Jamalipour, A., Kim, D. I., & Leung, V. (2023). Unleashing the power of edge-cloud generative AI in mobile networks: A survey of AIGC services. arXiv preprint arXiv:2303.16129. https://doi.org/10.1109/COMST.2024.3353265
   Google Scholar
• Zhang, W. (2020). A study on the user acceptance model of artificial intelligence music based on UTAUT. Journal of the Korea Society of Computer and Information, 25(6), 25–33. https://doi.org/10.9708/jksci.2020.25.06.025
   Google Scholar
• Zhang, C., Zhang, C., Li, C., Qiao, Y., Zheng, S., Dam, S. K., Zhang, M., Kim, J. U., Kim, S. T., & Choi, J. (2023). One small step for generative AI, one giant leap for AGI: A complete survey on ChatGPT in AIGC era. arXiv preprint arXiv:2304.06488. https://doi.org/10.13140/RG.2.2.24789.70883
   Google Scholar
• Zou, C., Li, P., & Jin, L. (2022). Integrating smartphones in EFL classrooms: Students’ satisfaction and perceived learning performance. Education and Information Technologies, 27(9), 12667–12688. https://doi.org/10.1007/s10639-022-11103-7
   Web of Science ®Google Scholar