CÁC YẾU TỐ ẢNH HƯỞNG ĐẾN Ý ĐỊNH VÀ HÀNH VI SỬ DỤNG TRÍ TUỆ NHÂN TẠO TẠO SINH CỦA NHÂN VIÊN MARKETING TỰ DO TẠI THÀNH PHỐ HỒ CHÍ MINH: MỞ RỘNG MÔ HÌNH UTAUT2

DOI 10.54404/jts.2026.216v.04
Từ khóa: UTAUT2 GenAI trí tuệ nhân tạo nhân viên marketing tự do

Tóm tắt

Nghiên cứu này khám phá các yếu tố ảnh hưởng đến ý định và hành vi sử dụng trí tuệ nhân tạo tạo sinh (Generative Artificial Intelligence - GenAI) của nhân viên marketing tự do tại TP.HCM, dựa trên mô hình UTAUT2 mở rộng với ba biến: niềm tin vào năng lực bản thân, sự tin cậy và quan ngại quyền riêng tư. Dữ liệu khảo sát trực tuyến từ 522 nhân viên được phân tích bằng mô hình cấu trúc tuyến tính bình phương tối thiểu riêng phần (PLS-SEM). Kết quả xác nhận toàn bộ các giả thuyết. Cụ thể, thói quen, kỳ vọng hiệu quả, ảnh hưởng xã hội, sự tin cậy và niềm tin vào năng lực bản thân tác động tích cực đến ý định và hành vi ứng dụng GenAI. Ngược lại, quan ngại quyền riêng tư tạo thành rào cản lớn, tác động tiêu cực đến quá trình tiếp nhận. Ý định sử dụng vẫn là yếu tố dự báo mạnh mẽ nhất đối với hành vi thực tế. Nghiên cứu củng cố năng lực giải thích của UTAUT2 đối với công nghệ tạo sinh, làm rõ vai trò thiết yếu của nhận thức rủi ro và tín nhiệm. Kết quả cung cấp cơ sở khoa học để lực lượng lao động tự do, các nhà phát triển và hoạch định chính sách thúc đẩy ứng dụng GenAI hiệu quả và an toàn.

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Tài liệu tham khảo

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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: 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.
DOI: doi.org/10.1007/978-3-031-48057-7_18

Tác giả

Nguyễn Nam Phong Trường Đại học Tài chính - Marketing Đặng Văn Mỹ Trường Đại học Tài chính - Marketing Nguyễn Thanh Hùng Trường Đại học Tài chính - Marketing
CÁC YẾU TỐ ẢNH HƯỞNG ĐẾN Ý ĐỊNH VÀ HÀNH VI SỬ DỤNG TRÍ TUỆ NHÂN TẠO TẠO SINH CỦA NHÂN VIÊN MARKETING TỰ DO TẠI THÀNH PHỐ HỒ CHÍ MINH: MỞ RỘNG MÔ HÌNH UTAUT2

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