Số 219 | Tháng 6/2024
Ứng dụng khung S-O-R và mô hình UTAUT2 trong nghiên cứu ý định tiếp tục sử dụng chatbot AI của sinh viên tại Thành phố Hồ Chí Minh
Trần Dục Thức, Nguyễn Minh Vy
Tóm tắt:
Mục tiêu của nghiên cứu này là đánh giá sự tác động của các nhân tố đến ý định tiếp tục sử dụng chatbot AI mà cụ thể ChatGPT của sinh viên tại Thành phố Hồ Chí Minh (TP. HCM). Một điểm mới của nghiên cứu thể hiện qua việc sử dụng khung Kích thích – Sinh vật – Phản ứng (S-O-R) để điều chỉnh mô hình UTAUT2 cho phù hợp với bối cảnh nghiên cứu, trong đó ảnh hưởng xã hội đóng vai trò (S), bốn biến (kỳ vọng về hiệu quả; kỳ vọng nỗ lực; điều kiện thuận lợi; giá trị về chi phí) đóng vai trò (O), biến phụ thuộc ý định tiếp tục sử dụng (BI) đóng vai trò (R), ngoài ra còn có hai biến (động lực thụ hưởng và thói quen) đóng vai trò là biến độc lập. Kết quả phân tích mô hình cấu trúc SEM bằng SmartPLs 4.0 với dữ liệu từ 200 phiếu khảo sát trên google forms cho thấy, tất cả các biến (một biến S, bốn biến O và hai biến độc lập) đều tác động cùng chiều lên biến phụ thuộc R, ngoài ra biến S còn có tác động cùng chiều đến bốn biến O. Như vậy, 11 giả thuyết nghiên cứu đề xuất đều được chấp nhận. Kết quả nghiên cứu này là cơ sở để tiến hành các nghiên cứu liên quan tiếp theo nhằm thúc đẩy sinh viên tích cực tiếp tục sử dụng chatbot AI.
Tài liệu tham khảo:
- Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS quarterly, 227-247.
- Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS quarterly, 665-694.
- Al-Emran, M., AlQudah, A. A., Abbasi, G. A., Al-Sharafi, M. A., & Iranmanesh, M. (2024). Determinants of using AI-based chatbots for knowledge sharing: Evidence from PLS-SEM and fuzzy sets (fsQCA). IEEE Transactions on Engineering Management, 71, 4985-4999. https://doi.org/10.1109/TEM.2023.3237789
- Anderson, K. C., Knight, D. K., Pookulangara, S., & Josiam, B. (2014). Influence of hedonic and utilitarian motivations on retailer loyalty and purchase intention: a facebook perspective. Journal of Retailing and Consumer Services, 21(5), 773-779.
- Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to casual modeling: personal computer adoption ans use as an Illustration.
- Bilro, R. G., Loureiro, S. M. C., & Ali, F. (2018). The role of website stimuli of experience on engagement and brand advocacy. Journal of Hospitality and Tourism Technology, 9(2), 204-222. https://doi.org/10.1108/JHTT-12-2017-0136.
- Brown, L. G. (1990). Convenience in services marketing. Journal of Services Marketing, 4(1), 53-59. https://doi.org/10.1108/EUM0000000002505.
- Cao, H. T., Huynh, C. B., Cao, L. (2023). Integrating ChatGPT into Online Education System in Vietnam: Opportunities and Challenges, EdArXiv Preprints.
- Chang, C.-C., Yan, C.-F., & Tseng, J.-S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5). https://doi.org/10.14742/ajet.818.
- Chiang, C. F., & Jang, S. S. (2007). The effects of perceived price and brand image on value and purchase intention: Leisure travelers' attitudes toward online hotel booking. Journal of Hospitality & Leisure Marketing, 15(3), 49-69.
- Dai, Q., & Cheng, K. (2022). What drives the adoption of agricultural green production technologies? An extension of TAM in agriculture. Sustainability, 14(21), 14457. https://doi.org/10.3390/su142114457.
- Dao, X. Q., Le, N. B, Vo, T. D., P, X. D., Ngo, B. B. (2023). Can ChatGPT pass the Vietnamese National High School Graduation Examination? https://doi.org/10.48550/arxiv.2306.09170.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008.
- Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982.
- Gallarza, M. G., & Saura, I. G. (2006). Value dimensions, perceived value, satisfaction and loyalty: an investigation of university students’ travel behaviour. Tourism management, 27(3), 437-452. https://doi.org/10.1016/j.tourman.2004.12.002.
- Guo, J., Hao, H., Wang, M., & Liu, Z. (2022). An empirical study on consumers' willingness to buy agricultural products online and its influencing factors. Journal of Cleaner Production, 336, 130403. https://doi.org/10.1016/j.jclepro.2022.130403.
- 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. https://doi.org/10.1007/978-3-030-80519-7.
- Hsu, C.-L., & Lin, J. C.-C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & management, 45(1), 65-74. https://doi.org/10.1016/j.im.2007.11.001.
- Hsu, C.-L., & Lu, H.-P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & management, 41(7), 853-868. https://doi.org/10.1016/j.im.2003.08.014.
- Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information & Management, 48(1), 1-8.
- Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Research note—two competing perspectives on automatic use: A theoretical and empirical comparison. Information systems research, 16(4), 418-432.
- Kim, Y. A., & Srivastava, J. (2007). Impact of social influence in e-commerce decision making. Proceedings of the ninth international conference on Electronic commerce.
- Lauring, J. O., Pelowski, M., Forster, M., Gondan, M., Ptito, M., & Kupers, R. (2016). Well, if they like it... Effects of social groups’ ratings and price information on the appreciation of art. Psychology of Aesthetics, Creativity, and the Arts, 10(3), 344. https://doi.org/10.1037/aca0000063.
- Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 705-737.
- Lyu, J., & Zhang, J. (2021). An empirical study into consumer acceptance of dockless bikes sharing system based on TAM. Sustainability, 13(4), 1831. https://doi.org/10.3390/su13041831.
- Mehrabian, A., & Russell, J. A. (1974). The basic emotional impact of environments. Perceptual and motor skills, 38(1), 283-301. https://doi.org/10.2466/pms.1974.38.1.283.
- Menon, D., & Shilpa, K. (2023). Chatting with ChatGPT: Analyzing the factors influencing users' intention to use Open AI's ChatGPT using the UTAUT model. Heliyon, 9(11), Article e20962. https://doi.org/10.1016/j.heliyon.2023.e20962.
- Nath, R., Bhal, K. T., & Kapoor, G. T. (2013). Factors influencing IT adoption by bank employees: An extended TAM approach. Vikalpa, 38(4), 83-96. https://doi.org/10.1177/0256090920130406.
- Nguyen, T. B., Tran, T. L. A., Tran, T. T. H., Le, T. T., Tran, P. N. H., & Nguyen, M. H. (2022). Factors influencing continuance intention of online shopping of generation Y and Z during the new normal in Vietnam. Cogent
- Nguyen, T., & Cao, L. (2023). The Impact of ChatGPT on Vietnamese Education.https://doi.org/10.35542/osf.io/sb2qv.
- Peng, C., & Kim, Y. G. (2014). Application of the stimuli-organism-response (SOR) framework to online shopping behavior. Journal of Internet Commerce, 13(3-4), 159-176. https://doi.org/10.1080/15332861.2014.944437.
- Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2021). Social Isolation and Acceptance of the Learning Management System (LMS) in the time of COVID-19 Pandemic: An Expansion of the UTAUT Model. Journal of Educational Computing Research, 59(2), 183-208. https://doi.org/10.1177/0735633120960421.
- Rice, R. E., Grant, A. E., Schmitz, J., & Torobin, J. (1990). Individual and network influences on the adoption and perceived outcomes of electronic messaging. Social networks, 12(1), 27-55. https://doi.org/10.1016/0378-8733(90)90021-Z.
- See, G.-T., & Goh, Y.-N. (2019). Tourists' intention to visit heritage hotels at George Town World Heritage Site. Journal of Heritage Tourism, 14(1), 33-48. https://doi.org/10.1080/1743873X.2018.1458853.
- Shoufan, A. 2023. Exploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey. IEEE Access, 11, 38805-38818. https://doi.org/10.1109/ACCESS.2023.3268224.
- Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS quarterly, 125-143. https://doi.org/10.2307/249443.
- Trần Quang Quý (2024). TP. HCM khẳng định vị thế trung tâm kinh tế lớn của cả nước. Nhân Dân. https://nhandan.vn/thanh-pho-ho-chi-minh-khang-dinh-vi-the-trung-tam-kinh-te-lon-cua-ca-nuoc-post806598.html.
- Tran, T. B. Y., Nguyen, N., Greenland, S., & Saleem, M. A. (2023). Unrestricted tobacco marketing prompts young adults to smoke in an emerging market: a study of emotional responses. Journal of Strategic Marketing, 1-15. https://doi.org/10.1080/0965254X.2023.2268667 .
- Tran, T. D., Pham, T. V., Nguyen, P. C. T., Louis, L. T., & Le, N. N. T. (2024). A Case Study of Decision-Making Towards Using Online Food Distribution Services After Covid-19 In Vietnam. Journal of Distribution Science, 22(3), 33–47. https://doi.org/10.15722/JDS.22.03.202403.33.
- Tuan Mansor, T. M., Mohamad Ariff, A., Hashim, H. A., & Ngah, A. H. (2022). External whistleblowing intentions of auditors: a perspective based on stimulus–organism–response theory. Corporate Governance: The International Journal of Business in Society, 22(4), 871-897. https://doi.org/10.1108/CG-03-2021-0116.
- 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.
- 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.
- Wen, C., Prybutok, V. R., & Xu, C. (2011). An integrated model for customer online repurchase intention. Journal of Computer information systems, 52(1), 14-23. https://doi.org/10.1080/08874417.2011.11645518.
- Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136.
- Xie, S., & Madni, G. R. (2023). Impact of social media on young generation’s green consumption behavior through subjective norms and perceived green value. Sustainability, 15(4), 3739.
- Zhai, X. (2023). ChatGPT user experience: Implications for education. AI4STEM Education Center. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4312418.
Applying the Stimulus - Organism - Response Framework and the Unified Theory of Acceptance and Use of Technology 2 Model to Investigate Studying Students’ Intention to Continue Using AI Chatbots in Ho Chi Minh City
Abstract:
This research aims to discuss the factors that influence the intention of students in Ho Chi Minh City to continue using AI chatbots, especially GPT chat. Besides, the novelty of the research is also shown through the use of the Stimulus - Organism - Response (S-O-R) framework to adapt the unified theory of acceptance and use of technology 2 (UTAUT2) model to suit the research context, including social influence plays a role (S); four variables (including performance expectations, effort expectations, favourable conditions and cost value) play the roles (O); the dependent variable of intention to continue using (BI) plays a role (R); in addition, there are two variables (enjoyment motivation and habits) that act as independent variables. The results of analyzing the SEM structural model using SmartPLs 4.0 with data from 200 surveys on Google Forms show that all variables (one S variable, four O variables and two independent variables) positively impact the dependent variable R. Moreover, variable S also positively impacts four variables O. Thus, the eleven proposed research hypotheses are all accepted. This research result is the basis for further related research to motivate students to use AI chatbots actively.