Résumés
Abstract
This study proposes a hypothetical model combining the unified theory of acceptance and use of technology (UTAUT) with self-determination theory (SDT) to explore design professionals’ behavioral intentions to use artificial intelligence (AI) tools. Moreover, it incorporates job replacement (JR) as a moderating role. Chinese-speaking design professionals in regions influenced by Confucian culture were surveyed. An analysis of 565 valid cases with AMOS (Analysis of Moment Structures) supported the structural model hypothesis. The model explains 52.1% of the variance in behavioral intention to use (BIU), proving its effectiveness in explaining these variances. The results further validate the importance of performance expectancy (PE) over effort expectancy (EE) in influencing BIU. Additionally, it has been shown that the impact on intrinsic motivation (IM) and extrinsic motivation (EM) can be either amplified or diminished by anxiety about JR. For individuals experiencing higher levels of JR anxiety, there is a marked increase in IM. They may perceive adopting AI tools as an opportunity to enhance their skills and job security. Conversely, this anxiety also significantly boosts EM, as the potential for improved efficiency and productivity with AI use becomes a compelling incentive. These findings suggest new paths for academic researchers to explore the psychological impacts of AI on design professionals’ roles. For practitioners, especially in human resources and organizational development, understanding these dynamics can guide the creation of training programs that address job replacement anxiety.
Keywords:
- unified theory of acceptance and use of technology,
- UTAUT,
- self-determination theory,
- generative artificial intelligence,
- GenAI,
- job replacement,
- performance expectancy
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Biographical note
Hsi-Hsun Yang received his Ph.D. degree in the Graduate School of Engineering Science and Technology from the National Yunlin University of Science and Technology (YunTech) in 2009. He is now an Associate Professor of the Department of Digital Media Design, National Yunlin University of Science and Technology. He has utilized VR, digital game, game-based learning, MOOC, and AI applications in education or board games to investigate issues with a focus on students' learning motivation and behaviors.
Bibliography
- Aktan, M. E., Turhan, Z., & Dolu, I. (2022). Attitudes and perspectives towards the preferences for artificial intelligence in psychotherapy. Computers in Human Behavior, 133, Article 107273. https://doi.org/10.1016/j.chb.2022.107273
- Alpert, R., & Haber, R. N. (1960). Anxiety in academic achievement situations. The Journal of Abnormal and Social Psychology, 61(2), 207–215. https://doi.org/10.1037/h0045464
- Amabile, T. M. (1993). Motivational synergy: Toward new conceptualizations of intrinsic and extrinsic motivation in the workplace. Human Resource Management Review, 3(3), 185–201. https://doi.org/10.1016/1053-4822(93)90012-S
- Appleby, C. (2023, March 7). Will colleges ban ChatGPT? BestColleges. https://www.bestcolleges.com/news/will-colleges-ban-chatgpt/
- Barbara, M. B. (1998). Structural equation modeling with Lisrel, Prelis, and Simplis: Basic concepts, applications, and programming. Lawrence Erlbaum Associates Publishers.
- Briggs, N. E. (2006). Estimation of the standard error and confidence interval of the indirect effect in multiple mediator models [Doctoral dissertation, Ohio State University].
- Chou, Y. H. D., Li, T. Y. D., & Ho, C. T. B. (2018). Factors influencing the adoption of mobile commerce in Taiwan. International Journal of Mobile Communications, 16(2), 117–134. https://doi.org/10.1504/IJMC.2018.089754
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
- Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of Research in Personality, 19(2), 109–134. https://doi.org/10.1016/0092-6566(85)90023-6
- Deci, E. L., Ryan, R. M., Gagné, M., Leone, D. R., Usunov, J., & Kornazheva, B. P. (2001). Need satisfaction, motivation, and well-being in the work organizations of a former Eastern Bloc country: A cross-cultural study of self-determination. Personality and Social Psychology Bulletin, 27(8), 930–942. https://doi.org/10.1177/014616720127800
- Donnermann, M., Lein, M., Messingschlager, T., Riedmann, A., Schaper, P., Steinhaeusser, S., & Lugrin, B. (2021). Social robots and gamification for technology supported learning: An empirical study on engagement and motivation. Computers in Human Behavior, 121, Article 106792. https://doi.org/10.1016/j.chb.2021.106792
- 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 Computer Science, 221, 1519–1526. https://doi.org/10.1016/j.procs.2023.08.010
- Duong, C. D., Vu, T. N., & Ngo, T. V. N. (2023). Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. The International Journal of Management Education, 21(3), Article 100883. https://doi.org/10.1016/j.ijme.2023.100883
- Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., Carter, L., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
- Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21, 719–734. https://doi.org/10.1007/s10796-017-9774-y
- Engel, J. F., Blackwell, R. D., & Miniard, P. W. (1995). Consumer behavior (8th ed.). Dryden Press.
- Fan, W., Williams, C. M., & Wolters, C. A. (2012). Parental involvement in predicting school motivation: Similar and differential effects across ethnic groups. The Journal of Educational Research, 105(1), 21–35. https://doi.org/10.1080/00220671.2010.515625
- Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
- Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.1177/002224378101800313
- Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
- Gessl, A. S., Schlögl, S., & Mevenkamp, N. (2019). On the perceptions and acceptance of artificially intelligent robotics and the psychology of the future elderly. Behaviour & Information Technology, 38(11), 1068–1087. https://doi.org/10.1080/0144929X.2019.1566499
- Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
- Hars, A., & Ou, S. (2014). Working for free? Motivations for participating in open-source projects. International Journal of Electronic Commerce, 6(3), 25–39. https://doi.org/10.1080/10864415.2002.11044241
- Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
- Huang, C. L., & Haried, P. (2020). An evaluation of uncertainty and anticipatory anxiety impacts on technology use. International Journal of Human–Computer Interaction, 36(7), 641–649. https://doi.org/10.1080/10447318.2019.1672410
- King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003
- Kline, T. J. B. (2005). Psychological testing: A practical approach to design and evaluation. Sage Publications. https://doi.org/10.4135/9781483385693
- Korzyński, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., Wach, K., & Ziemba, E. (2023). Generative artificial intelligence as a new context for management theories: Analysis of ChatGPT. Central European Management Journal, 31(1), 3–13. https://doi.org/10.1108/CEMJ-02-2023-0091
- Lawler, E. E., & Porter, L. W. (1967). The effect of performance on job satisfaction. Industrial Relations, 7(1), 20–28. https://doi.org/10.1111/j.1468-232X.1967.tb01060.x
- Lin, C. P., & Bhattacherjee, A. (2008). Elucidating individual intention to use interactive information technologies: The role of network externalities. International Journal of Electronic Commerce, 13(1), 85–108. https://doi.org/10.2753/JEC1086-4415130103
- Maduku, D. K., Mpinganjira, M., Rana, N. P., Thusi, P., Ledikwe, A., & Mkhize, N. H. B. (2023). Assessing customer passion, commitment, and word-of-mouth intentions in digital assistant usage: The moderating role of technology anxiety. Journal of Retailing and Consumer Services, 71, Article 103208. https://doi.org/10.1016/j.jretconser.2022.103208
- Morosan, C., & DeFranco, A. (2016). It’s about time: Revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, 53, 17–29. https://doi.org/10.1016/j.ijhm.2015.11.003
- Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile Internet. Computers and Education Open, 2, Article 100041. https://doi.org/10.1016/j.caeo.2021.100041
- Oliver, R. L. (1974). Expectancy theory predictions of salesmen’s performance. Journal of Marketing Research, 11(3), 243–253. https://doi.org/10.1177/002224377401100302
- Overmier, J. B., & Lawry, J. A. (1979). Pavlovian conditioning and the mediator of behavior. Psychology of Learning and Motivation, 13, 1–55. https://doi.org/10.1016/S0079-7421(08)60080-8
- Piniel, K., & Csizér, K. (2013). L2 motivation, anxiety and self-efficacy: The interrelationship of individual variables in the secondary school context. Studies in Second Language Learning and Teaching, 3(4), 523–550. https://doi.org/10.14746/ssllt.2013.3.4.5
- Rahman, M. S., Sabbir, M. M., Zhang, J., Moral, I. H., & Hossain, G. M. S. (2022). Examining students’ intention to use ChatGPT: Does trust matter? Australasian Journal of Educational Technology, 39(6), 51–71. https://doi.org/10.14742/ajet.8956
- Rotter, J. B., Chance, J. E., & Pharses, E. J. (1972). Applications of a social learning theory of personality. Holt Rinehart & Winston.
- Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020
- Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864
- Shahsavar, Y., & Choudhury, A. (2023). User intentions to use ChatGPT for self-diagnosis and health-related purposes: Cross-sectional survey study. JMIR Human Factors, 10, Article e47564. https://doi.org/10.2196/47564
- Shek, D. T. L., & Yu, L. (2014). Confirmatory factor analysis using AMOS: A demonstration. International Journal on Disability and Human Development, 13(2), 191–204. https://doi.org/10.1515/ijdhd-2014-0305
- Teo, T., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the unified theory of acceptance and use of technology. Interactive Learning Environments, 22(1), 51–66. https://doi.org/10.1080/10494820.2011.641674
- Tyagi, P. K. (1985). Relative importance of key job dimensions and leadership behaviors in motivating salesperson work performance. Journal of Marketing, 49(3), 76–86. https://doi.org/10.1177/00222429850490030
- van Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838–852. https://doi.org/10.1016/j.compedu.2006.09.001
- Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
- Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
- Venkatesh, V., Thong, J., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428
- Vogels, E. A. (2023, May 24). A majority of Americans have heard of ChatGPT, but few have tried it themselves. Pew Research Center. https://www.pewresearch.org/short-reads/2023/05/24/a-majority-of-americans-have-heard-of-chatgpt-but-few-have-tried-it-themselves/
- Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887
- Wang, Y. M., Wei, C. L., Lin, H. H., Wang, S. C., & Wang, Y. S. (2022). What drives students’ AI learning behavior: A perspective of AI anxiety. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2022.2153147
- Whittaker, T. A., & Schumacker, R. E. (2022). A beginner’s guide to structural equation modeling (3rd ed.). Routledge. https://doi.org/10.4324/9781003044017
- Williams, J., & MacKinnon, D. P. (2008). Resampling and distribution of the product methods for testing indirect effects in complex models. Structural Equation Modeling, 15(1), 23–51. https://doi.org/10.1080/10705510701758166
- Yoo, S. J., Han, S. H., & Huang, W. (2012). The roles of intrinsic motivators and extrinsic motivators in promoting e-learning in the workplace: A case from South Korea. Computers in Human Behavior, 28(3), 942–950. https://doi.org/10.1016/j.chb.2011.12.015
- Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, Article 100025. https://doi.org/10.1016/j.caeai.2021.100025
- Zhao, Q., Chen, C. D., Cheng, H. W., & Wang, J. L. (2018). Determinants of live streamers’ continuance broadcasting intentions on Twitch: A self-determination theory perspective. Telematics and Informatics, 35(2), 406–420. https://doi.org/10.1016/j.tele.2017.12.018
- Zhu, Q. (2020). Ethics, society, and technology: A Confucian role ethics perspective. Technology in Society, 63, Article 101424. https://doi.org/10.1016/j.techsoc.2020.101424