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References
1.
Abed, S. S. S. (2024). Understanding the Determinants of Using Government AI-Chatbots by Citizens in Saudi Arabia: International Journal of Electronic Government Research, 20(1), 1-20.
2.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T.
3.
Alhwaiti, M. (2023). Acceptance of Artificial Intelligence Application in the Post-Covid Era and Its Impact on Faculty Members’ Occupational Well-being and Teaching Self Efficacy: A Path Analysis Using the UTAUT 2 Model. Applied Artificial Intelligence, 37(1), 2175110.
4.
Ali, M. S. M., Wasel, K. Z. A., & Abdelhamid, A. M. M. (2024). Generative AI and Media Content Creation: Investigating the Factors Shaping User Acceptance in the Arab Gulf States. Journalism and Media, 5(4), 1624-1645. https://doi.org/10.3390/journalmedia5040101.
5.
AlMuhanna, N., Hall, W., & Millard, D. E. (2023). Fear of the dark: A cross-cultural study into how perceptions of antisocial behaviour impact the acceptance and use of Twitter. Behaviour & Information Technology, 42(8), 1180-1193. https://doi.org/10.1080/0144929X.2022.2064766.
6.
Alzaidi, M. S., & Agag, G. (2022). The role of trust and privacy concerns in using social media for e-retail services: The moderating role of COVID-19. Journal of Retailing and Consumer Services, 68, 103042. https://doi.org/10.1016/j.jretconser.2022.103042.
7.
Brandtzaeg, P. B., & Følstad, A. (2018). Chatbots: Changing user needs and motivations. Interactions, 25(5), 38-43. https://doi.org/10.1145/3236669.
8.
Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (2024). ChatGPT adoption and anxiety: A cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education, 49(5), 831–846. https://doi.org/10.1080/03075079.2024.2333937.
9.
Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201, 123247. https://doi.org/10.1016/j.techfore.2024.123247.
10.
Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. https://doi.org/10.1016/j.technovation.2021.102312.
11.
Cao, Z., & Peng, L. (2025). An empirical study of factors influencing usage intention for generative artificial intelligence products: A case study of China. Journal of Information Science, 51(6), 1513–1528. https://doi.org/10.1177/01655515241297329.
12.
Chauhan, S., & Jaiswal, M. (2016). Determinants of acceptance of ERP software training in business schools: Empirical investigation using UTAUT model. The International Journal of Management Education, 14(3), 248–262. https://doi.org/10.1016/j.ijme.2016.05.005.
13.
Chen, D., Liu, W., & Liu, X. (2024). What drives college students to use AI for L2 learning? Modeling the roles of self-efficacy, anxiety, and attitude based on an extended technology acceptance model. Acta Psychologica, 249, 104442. https://doi.org/10.1016/j.actpsy.2024.104442.
14.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.
15.
Collie, R. J., Martin, A. J., & Gasevic, D. (2024). Teachers’ generative AI self-efficacy, valuing, and integration at work: Examining job resources and demands. Computers and Education: Artificial Intelligence, 7, 100333. https://doi.org/10.1016/j.caeai.2024.100333.
16.
Compeau, D. R., & Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688.
17.
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.
18.
Decision Lab & MMA. (2025). The current state of AI in Vietnam’s marketing landscape. Retrieved from Decision Lab report. https://www.decisionlab.co/blog/how-businesses-are-leveraging-ai-to-redefine-marketing.
19.
Dinev, T., & Hart, P. (2005). Internet Privacy Concerns and Social Awareness as Determinants of Intention to Transact. International Journal of Electronic Commerce, 10(2), 7-29.
20.
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. Education and Information Technologies, 30(13), 17835-17880.
21.
Fast Company. (2024). Thanks to AI, we’re in the golden age of freelancing. Retrieved from https://www.fastcompany.com/91116345/thanks-to-ai-were-in-the-golden-age-of-freelancing
22.
Fishbein, M., & Ajzen, I. (1975). Beliefs, attitude, intention, and behavior: An introduction to theory and research.
23.
Gajić, T., Vukolić, D., Bugarčić, J., Đoković, F., Spasojević, A., Knežević, S., Đorđević Boljanović, J., Glišić, S., Matović, S., & Dávid, L. D. (2024). The Adoption of Artificial Intelligence in Serbian Hospitality: A Potential Path to Sustainable Practice. Sustainability, 16(8), 3172.
24.
Glikson, E. and Woolley, A.W. (2020) Human Trust in Artificial Intelligence: Review of Empirical Research. Academy of Management Annals, 14, 627-660.
25.
Gupta, B., Dasgupta, S., & Gupta, A. (2008). Adoption of ICT in a government organization in a developing country: An empirical study. The Journal of Strategic Information Systems, 17(2), 140-154.
26.
Gursoy, D., Li, Y., & Song, H. (2023). ChatGPT and the hospitality and tourism industry: An overview of current trends and future research directions. Journal of Hospitality Marketing & Management, 32(5), 579-592. https://doi.org/10.1080/19368623.2023.2211993.
27.
Habibi, A., Muhaimin, M., Danibao, B. K., Wibowo, Y. G., Wahyuni, S., & Octavia, A. (2023). ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, 100190. https://doi.org/10.1016/j.caeai.2023.100190.
28.
Hair, J. F., Hauff, S., Hult, G. T. M., Richter, N. F., Ringle, C. M., & Sarstedt, M. (2017). Partial Least Squares Strukturgleichungsmodellierung: Eine anwendungsorientierte Einführung. Verlag Franz Vahlen GmbH. https://doi.org/10.15358/9783800653614.
29.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202.
30.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46(1-2), 1-12. https://doi.org/10.1016/j.lrp.2013.01.001.
31.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
32.
Hao, Z., Miao, E., & Yan, M. (2021). Research on School Principals’ Willingness to Adopt Artificial Intelligence Education and Related Influencing Factors. 2021 Tenth International Conference of Educational Innovation through Technology (EITT), 356-361.
33.
Hazzan-Bishara, A., Kol, O., & Levy, S. (2025). The factors affecting teachers’ adoption of AI technologies: A unified model of external and internal determinants. Education and Information Technologies, 30(11), 15043-15069. https://doi.org/10.1007/s10639-025-13393-z.
34.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8.
35.
Hilken, T., De Ruyter, K., Chylinski, M., Mahr, D., & Keeling, D. I. (2017). Augmenting the eye of the beholder: Exploring the strategic potential of augmented reality to enhance online service experiences. Journal of the Academy of Marketing Science, 45(6), 884-905.
36.
Hock, C., Ringle, C. M., & Sarstedt, M. (2010). Management of multi-purpose stadiums: Importance and performance measurement of service interfaces. International Journal of Services Technology and Management, 14(2/3), 188. https://doi.org/10.1504/IJSTM.2010.034327.
37.
Hussain, M., Mollik, A. T., Johns, R., & Rahman, M. S. (2019). M-payment adoption for bottom of pyramid segment: An empirical investigation. International Journal of Bank Marketing, 37(1), 362-381.
38.
Kong, S. C., Yang, Y., & Hou, C. (2024). Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7, 100328.
39.
Liébana-Cabanillas, F., Kalinic, Z., Muñoz-Leiva, F., & Higueras-Castillo, E. (2024). Biometric m-payment systems: A multi-analytical approach to determining use intention. Information & Management, 61(2), 103907. https://doi.org/10.1016/j.im.2023.103907.
40.
Magni, D., Del Gaudio, G., Papa, A., & Della Corte, V. (2024). Digital humanism and artificial intelligence: The role of emotions beyond the human–machine interaction in Society 5.0. Journal of Management History, 30(2), 195-218. https://doi.org/10.1108/JMH-12-2022-0084
41.
Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet Users’ Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model. Information Systems Research, 15(4), 336-355.
42.
McKinsey & Company (2023). The economic potential of generative AI: The next productivity frontier. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#business-and-society.
43.
McKinsey & Company (2024). The state of AI in early 2024: Gen AI adoption spikes and starts to generate value. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2024.
44.
Mensah, I. K., & Zhao, T. (2024). Factors driving the acceptance of COVID-19 pandemic mobile contact tracing apps: The influence of security and privacy concerns. Heliyon, 10(20), e39086.
45.
Milne, G. R., & Boza, M.-E. (1999). Trust and concern in consumers’ perceptions of marketing information management practices. Journal of Interactive Marketing, 13(1), 5-24.
46.
Moorthy, K., Johanthan, S., Tham, C., Xuan, K. X., Yan, L. L., Xunda, T., & Sim, T. C. (2019). Behavioural Intention to Use Mobile Apps by Gen Y in Malaysia. Journal of Information, 5(1), 1-15.
47.
Mustofa, R. H., Kuncoro, T. G., Atmono, D., Hermawan, H. D., & Sukirman. (2025). Extending the technology acceptance model: The role of subjective norms, ethics, and trust in AI tool adoption among students. Computers and Education: Artificial Intelligence, 8, 100379.
48.
Nan, D., Sun, S., Zhang, S., Zhao, X., & Kim, J. H. (2025). Analyzing behavioral intentions toward Generative Artificial Intelligence: The case of ChatGPT. Universal Access in the Information Society, 24(1), 885-895. https://doi.org/10.1007/s10209-024-01116-z.
49.
Norizan, A. R., & Mohamad Zamri, M. F. (2024). Visual Generative AI Tools Acceptance by Multimedia and Animation University Students. 2024 International Visualization, Informatics and Technology Conference (IVIT), 101-107.
50.
Phang, I. G., & Kong, Y. Z. (2024). Exploring the influence of technical and sensory factors on Malaysians’ intention to adopt virtual tours in heritage travel. Journal of Hospitality and Tourism Insights, 7(3), 1313–1329. https://doi.org/10.1108/JHTI-04-2023-0281.
51.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of Method Bias in Social Science Research and Recommendations on How to Control It. Annual Review of Psychology, 63(1), 539–569. https://doi.org/10.1146/annurev-psych-120710-100452.
52.
Qin, H., Zhu, Y., Jiang, Y., Luo, S., & Huang, C. (2024). Examining the impact of personalization and carefulness in AI-generated health advice: Trust, adoption, and insights in online healthcare consultations experiments. Technology in Society, 79, 102726.
53.
Rafi, M., Aitken, J. M., Fatah, T. D., & Mailangkay, A. (2024). Analyzing The Impact of Generative AI on IT Employee Performance. 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT), 1-7. https://doi.org/10.1109/ICCIT62134.2024.10701171.
54.
Ramo, R. M., Alshaher, A. A., & Al-Fakhry, N. A. (2022). The Effect of Using Artificial Intelligence on Learning Performance in Iraq: The Dual Factor Theory Perspective. Ingénierie Des Systèmes d Information, 27(2), 255-265. https://doi.org/10.18280/isi.270209.
55.
Romero-Rodríguez, J.-M., Ramírez-Montoya, M.-S., Buenestado-Fernández, M., & Lara-Lara, F. (2023). Use of ChatGPT at University as a Tool for Complex Thinking: Students’ Perceived Usefulness. Journal of New Approaches in Educational Research, 12(2), 323-339.
56.
Salesforce. (2024). New Research: 60% of Marketers Say Generative AI will Transform Their Role, But Worry About Accuracy. Retrieved from https://www.salesforce.com/news/stories/generative-ai-for-marketing-research/.
57.
Schroeder, T., Haug, M., & Gewald, H. (2022). Data Privacy Concerns Using mHealth Apps and Smart Speakers: Comparative Interview Study Among Mature Adults. JMIR Formative Research, 6(6), e28025. https://doi.org/10.2196/28025.
58.
Sergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V. (2025). Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2. Contemporary Educational Technology, 17(2), ep571. https://doi.org/10.30935/cedtech/16039.
59.
Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information Privacy: Measuring Individuals’ Concerns About Organizational Practices1. MIS Quarterly, 20(2), 167-196.
60.
Sun, X.-F. (2026). Exploring key factors influencing urban air transport Acceptance: A trust and Risk-Embedded UTAUT2 framework. Transportation Research Part F: Traffic Psychology and Behaviour, 116, 103448. https://doi.org/10.1016/j.trf.2025.103448.
61.
Supianto, Widyaningrum, R., Wulandari, F., Zainudin, M., Athiyallah, A., & Rizqa, M. (2024). Exploring the factors affecting ChatGPT acceptance among university students. Multidisciplinary Science Journal, 6(12), 2024273.
62.
Thủ tướng Chính phủ. (2021). Quyết định 127/QĐ-TTg: Phê duyệt Chiến lược quốc gia về nghiên cứu, phát triển và ứng dụng trí tuệ nhân tạo đến năm 2030. https://vbpl.vn/TW/Pages/vbpqen-toanvan.aspx?ItemID=14940
63.
Uzir, M. U. H., Al Halbusi, H., Lim, R., Jerin, I., Abdul Hamid, A. B., Ramayah, T., & Haque, A. (2021). Applied Artificial Intelligence and user satisfaction: Smartwatch usage for healthcare in Bangladesh during COVID-19. Technology in Society, 67, 101780.
64.
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273-315.
65.
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.
66.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward A Unified View1. MIS Quarterly, 27(3), 425-478.
67.
Venkatesh, V., Thong, J., Hong Kong University of Science and Technology, Xu, X., & The Hong Kong Polytechnic University. (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.
68.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology1. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412.
69.
Weglarz, D., Pla-Garcia, C., & Jiménez-Zarco, A. I. (2025). Acceptance of Generative AI in the Creative Industry: Examining the role of Brand Recognition and Trust in the AI adoption. Retos, 15(29), 90-27. https://doi.org/10.17163/ret.n29.2025.01.
70.
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. In H. Degen, S. Ntoa, & A. Moallem (Eds.), HCI International 2023 - Late Breaking Papers (Vol. 14059, pp. 288-310). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-48057-7_18.
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