Open Access
| Issue |
SHS Web Conf.
Volume 227, 2026
The 6th International Conference on Communication and Business (ICCB 2025)
|
|
|---|---|---|
| Article Number | 05004 | |
| Number of page(s) | 16 | |
| Section | Business, Technology, and Sustainable Innovation Ecosystems | |
| DOI | https://doi.org/10.1051/shsconf/202622705004 | |
| Published online | 09 January 2026 | |
- G. Enriquez et al., “Generative AI and composing: an intergenerational conversation among literacy scholars,” English Teach. Pract. Crit., vol. 23, no. 1, pp. 6–22, Dec. 2023, doi: 10.1108/ETPC-08-2023-0104. [Google Scholar]
- D. R. E. Cotton, P. A. Cotton, and J. R. Shipway, “Chatting and cheating: Ensuring academic integrity in the era of ChatGPT,” Innov. Educ. Teach. Int., vol. 61, no. 2, pp. 228–239, Mar. 2024, doi: 10.1080/14703297.2023.2190148. [Google Scholar]
- O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education – where are the educators?,” Int. J. Educ. Technol. High. Educ., vol. 16, no. 1, p. 39, 2019, doi: 10.1186/s41239-019-0171-0. [Google Scholar]
- D. Long and B. Magerko, “What is AI Literacy? Competencies and Design Considerations,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, in CHI ’20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 1–16. doi: 10.1145/3313831.3376727. [Google Scholar]
- J. Chung and S.-H. Jeong, “Exploring the perceptions of Chinese pre-service teachers on the integration of generative AI in English language teaching: Benefits, challenges, and educational implications,” Online J. Commun. Media Technol., vol. 14, p. e202457, Oct. 2024, doi: 10.30935/ojcmt/15266. [Google Scholar]
- Y. K. Dwivedi et al., “Opinion Paper: ‘So what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy,” Int. J. Inf. Manage., vol. 71, p. 102642, 2023, doi: https://doi.org/10.1016/j.ijinfomgt.2023.102642 [Google Scholar]
- R. Luckin, “Machine Learning and Human Intelligence: The future of education for the 21st century,” 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:69366349 [Google Scholar]
- F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Q., vol. 13, no. 3, pp. 319–340, Dec. 1989, doi: 10.2307/249008. [CrossRef] [Google Scholar]
- A. S. Al-Adwan, N. Li, A. Al-Adwan, G. A. Abbasi, N. A. Albelbisi, and A. Habibi, ““Extending the Technology Acceptance Model (TAM) to Predict University Students’ Intentions to Use Metaverse-Based Learning Platforms”,” Educ. Inf. Technol., vol. 28, no. 11, pp. 15381–15413, 2023, doi: 10.1007/s10639-023-11816-3. [Google Scholar]
- Uma Sekaran and Roger Bougie, “Research Method for Business Textbook (A Skill Building Approa),” United States John Wiley Sons Inc., 2016. [Google Scholar]
- J. Hair, J. Risher, M. Sarstedt, and C. Ringle, “When to use and how to report the results of PLS-SEM,” Eur. Bus. Rev., vol. 31, Dec. 2018, doi: 10.1108/EBR-11-2018-0203. [Google Scholar]
- S. Makridakis, The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms. 2017. [Google Scholar]
- Laura Kristen Allen and Panayiota Kendeou, “ED-AI Lit: An Interdisciplinary Framework for AI Literacy in Education,” Policy Insights from Behav. Brain Sci., vol. 11, no. 1, pp. 3–10, Dec. 2023, doi: 10.1177/23727322231220339. [Google Scholar]
- D. Cetindamar, K. Kitto, M. Wu, Y. Zhang, B. Abedin, and S. Knight, “Explicating AI Literacy of Employees at Digital Workplaces,” IEEE Trans. Eng. Manag., vol. PP, pp. 1–14, 2022, [Online]. Available: https://api.semanticscholar.org/CorpusID:245937059 [Google Scholar]
- M. Zhang, Y. Xu, and X. Wei, “AI-centered vs. Human-centered: Exploring Users’ Attitude toward AIGC in Varying Forms of Human-AI Collaboration. BT 28th Pacific Asia Conference on Information Systems, PACIS 2024, Ho Chi Minh City, Vietnam, July 1-5, 2024.,” 2024. [Online]. Available: https://aisel.aisnet.org/pacis2024/track01_aibussoc/track01_aibussoc/13 [Google Scholar]
- M. Mortensen, “Sneaking AI through the back door: constructing the identity of Capitol Hill rioters through social media images and facial recognition technologies,” Information, Commun. Soc., vol. 28, pp. 1–17, Jun. 2024, doi: 10.1080/1369118X.2024.2358164. [Google Scholar]
- L. Pan, H. Luo, and Q. Gu, “Incorporating AI Literacy and AI Anxiety Into TAM: Unraveling Chinese Scholars’ Behavioral Intentions Toward Adopting AI-Assisted Literature Reading,” IEEE Access, vol. PP, p. 1, Jan. 2025, doi: 10.1109/ACCESS.2025.3546572. [Google Scholar]
- Y. Wang, X. Guan, Y. Sun, H. Wang, and D. Chen, “The cognitive acceptance of generative AI image tools based on TPB-TAM model and multi-theory integration,” Adv. Des. Res., vol. 3, no. 1, pp. 38–54, 2025, doi: https://doi.org/10.1016/j.ijadr.2025.08.001 [Google Scholar]
- H. Bashir and R. Zhou, “Mental load, ChatGPT, and work-study balance: A TAM-based study of AI adoption by employees in non-traditional education,” Telemat. Informatics Reports, vol. 19, p. 100216, 2025, doi: https://doi.org/10.1016/j.teler.2025.100216. [Google Scholar]
- M. F. Shahzad, S. Xu, and I. Javed, “ChatGPT awareness, acceptance, and adoption in higher education: the role of trust as a cornerstone,” Int. J. Educ. Technol. High. Educ., vol. 21, no. 1, p. 46, 2024, doi: 10.1186/s41239-024-00478-x. [Google Scholar]
- A. Lu et al., “The roles of mobile app perceived usefulness and perceived ease of use in app-based Chinese and English learning flow and satisfaction,” Educ. Inf. Technol., vol. 27, Apr. 2022, https://doi.org/10.1007/s10639-022-11036-1 [Google Scholar]
- A. Bansah and D. Agyei, “Perceived convenience, usefulness, effectiveness and user acceptance of information technology: evaluating students’ experiences of a Learning Management System,” Technol. Pedagog. Educ., vol. 31, pp. 1–19, Jan. 2022, doi: 10.1080/1475939X.2022.2027267. [Google Scholar]
- S. Na, S. Heo, W. Choi, C. Kim, and S. W. Whang, “Artificial Intelligence (AI)-Based Technology Adoption in the Construction Industry: A Cross National Perspective Using the Technology Acceptance Model,” 2023. doi:10.3390/buildings13102518. [Google Scholar]
- N. A. Dahri et al., “Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study,” Heliyon, vol. 10, no. 8, p. e29317, 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e29317 [CrossRef] [Google Scholar]
- S. Jauk, D. Kramer, A. Avian, A. Berghold, W. Leodolter, and S. Schulz, “Technology Acceptance of a Machine Learning Algorithm Predicting Delirium in a Clinical Setting: a Mixed-Methods Study.,” J. Med. Syst., vol. 45, no. 4, p. 48, Mar. 2021, doi: 10.1007/s10916-021-01727-6. [Google Scholar]
- M. Sallam et al., “Assessing Health Students’ Attitudes and Usage of ChatGPT in Jordan: Validation Study,” JMIR Med. Educ., vol. 9, p. e48254, Aug. 2023, doi: 10.2196/48254. [Google Scholar]
- I. Ajzen, “The theory of planned behavior,” Organ. Behav. Hum. Decis. Process., vol. 50, no. 2, pp. 179–211, 1991, doi: https://doi.org/10.1016/0749-5978(91)90020-T [CrossRef] [Google Scholar]
- H. Han, L.-T. (Jane) Hsu, and C. Sheu, “Application of the Theory of Planned Behavior to green hotel choice: Testing the effect of environmental friendly activities,” Tour. Manag., vol. 31, no. 3, pp. 325–334, 2010, doi: https://doi.org/10.1016/j.tourman.2009.03.013 [Google Scholar]
- T. Valtonen, J. Kukkonen, S. Kontkanen, K. Sormunen, P. Dillon, and E. Sointu, “The impact of authentic learning experiences with ICT on pre-service teachers’ intentions to use ICT for teaching and learning,” Comput. Educ., vol. 81, pp. 49–58, 2015, doi: https://doi.org/10.1016/j.compedu.2014.09.008 [Google Scholar]
- K. Li, “Determinants of College Students’ Actual Use of AI-Based Systems: An Extension of the Technology Acceptance Model,” Sustainability, vol. 15, p. 5221, Mar. 2023, doi: 10.3390/su15065221. [Google Scholar]
- W. A. Alkhowaiter, “Use and behavioural intention of m-payment in GCC countries: Extending meta-UTAUT with trust and Islamic religiosity,” J. Innov. Knowl., vol. 7, no. 4, p. 100240, 2022, doi: https://doi.org/10.1016/j.jik.2022.100240. [Google Scholar]
- H. Latan, J. Hair, R. Noonan, and M. Sabol, “Introduction to the Partial Least Squares Path Modeling: Basic Concepts and Recent Methodological Enhancements,” 2023, pp. 3–21. doi: 10.1007/978-3-031-37772-3_1. [Google Scholar]
- S. Haryono, “Metode SEM untuk penelitian manajemen dengan AMOS LISREL PLS,” Luxima Metro Media, vol. 450, 2017. [Google Scholar]
- C. N. Prilop, D.-K. Mah, L. J. Jacobsen, R. R. Hansen, K. E. Weber, and F. Hoya, “Generative AI in teacher education: Educators’ perceptions of transformative potentials and the triadic nature of AI literacy explored through AI-enhanced methods,” Comput. Educ. Artif. Intell., vol. 9, p. 100471, 2025, doi: https://doi.org/10.1016/j.caeai.2025.100471. [Google Scholar]
- M. Al-Emran, V. Mezhuyev, and A. Kamaludin, “Technology Acceptance Model in M-learning context: A systematic review,” Comput. Educ., vol. 125, Jun. 2018, doi: 10.1016/j.compedu.2018.06.008. [Google Scholar]
- Y. Shaengchart, “A Conceptual Review of TAM and ChatGPT Usage Intentions Among Higher Education Students,” vol. 2, pp. 1–7, Sep. 2023. [Google Scholar]
- N. Fathema, D. Shannon, and M. Ross, “Expanding The Technology Acceptance Model (TAM) to Examine Faculty Use of Learning Management Systems (LMSs) In Higher Education Institutions,” J. Online Learn. Teach., vol. 11, pp. 210–233, Aug. 2015. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

