The Impact of AI on Self-learning Capabilities of Employees in SMEs
A Case Study in Ho Chi Minh City, Vietnam
DOI:
https://doi.org/10.25120/jre.4.2.2024.4176Keywords:
Artificial intelligence, SMEs, Ho Chi Minh City, Workforce Development, Technology Acceptance Model (TAM), Self-Directed LearningAbstract
This study investigates the impact of Artificial Intelligence (AI) on self-directed learning and critical thinking among employees in Small and Medium Enterprises (SMEs) in Ho Chi Minh City, Vietnam. A mixed-methods research approach was employed, combining a quantitative survey of 305 employees across various industries and qualitative data from 15 in-depth interviews with managers and staff. Structural Equation Modeling (SEM) was used to analyze the relationships between AI access, employee attitudes, organizational support, digital literacy, and self-learning outcomes. Qualitative analysis provided additional insights into contextual factors influencing AI adoption. The findings highlight that AI significantly enhances self-directed learning when SMEs offer structured training programs and technological resources. Employees with strong critical thinking skills effectively utilize AI tools for evidence-based decision-making and analytical tasks. However, barriers such as disparities in digital literacy, inconsistent AI adoption strategies, and insufficient organizational support hinder optimal outcomes. Organizational support emerged as a key enabler, with employees receiving adequate training reporting improved learning and skill development. This study extends the Self-Directed Learning Theory (SDL) and Technology Acceptance Model (TAM) by identifying mediating roles of organizational and individual factors. Practical recommendations include fostering digital literacy, critical thinking, and AI-supportive organizational cultures to optimize workforce development.
References
Ajlouni, A. O., Wahba, F. A.-A., & Almahaireh, A. S. (2023). Students’ attitudes towards using ChatGPT as a learning tool: The case of the University of Jordan. International Journal of Interactive Mobile Technologies, 17(18), 99–117. https://doi.org/10.3991/ijim.v17i18.41753 DOI: https://doi.org/10.3991/ijim.v17i18.41753
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411 DOI: https://doi.org/10.1037//0033-2909.103.3.411
Bagozzi, R. P., & Foxall, G. R. (1996). Construct validity and measurement in organizational research: A critical review. Organizational Research Methods, 1(1), 45-87. https://doi.org/10.1177/109442819600100103
Bagozzi, R. P., & Foxall, G. R. (1996). Construct validity and measurement in organizational research: A critical review. Organizational Research Methods, 1(1), 45-87. DOI: https://doi.org/10.1177/109442819800100104
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage.
Schumacker, R. E., & Lomax, R. G. (1996). A beginner’s guide to structural equation modeling. Lawrence Erlbaum Associates.
Churchill, G. A. (1995). Marketing research: Methodological foundations (6th ed.). Dryden Press.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
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: https://doi.org/10.2307/249008
Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71(3), 500-507. https://doi.org/10.1037/0021-9010.71.3.500 DOI: https://doi.org/10.1037//0021-9010.71.3.500
Facione, P. A. (1990). Critical thinking: A statement of expert consensus for purposes of educational assessment and instruction. California Academic Press.
Giraud, L., Zaher, A., Hernandez, S., & Ariss, A. (2022). The impacts of artificial intelligence on managerial skills. Journal of Management & Governance, 33(1), 102-120. https://doi.org/10.1080/12460125.2022.2069537 DOI: https://doi.org/10.1080/12460125.2022.2069537
Gu, R., Xi, Z., Lin, B., & Ji, Y. (2022). Teacher-guided autonomous learning enabled by artificial intelligence empowered remote experiment platform. Proceedings of the 2022 IEEE Global Engineering Education Conference (EDUCON), 52537, 9766531. https://doi.org/10.1109/EDUCON52537.2022.9766531 DOI: https://doi.org/10.1109/EDUCON52537.2022.9766531
Hakiki, M., Fadli, R., Samala, A. D., Fricticarani, A., Dayurni, P., Rahmadani, K., Astiti, A. D., & Sabir, A. (2023). Exploring the impact of using Chat-GPT on student learning outcomes in technology learning: The comprehensive experiment. Advances in Mobile Learning Educational Research, 3(2), 859-872. https://doi.org/10.25082/AMLER.2023.02.013 DOI: https://doi.org/10.25082/AMLER.2023.02.013
Hulland, J., Chow, Y. H., & Lam, S. (1996). Use of causal models in marketing research: A review. International Journal of Research in Marketing, 13(2), 181-197. https://doi.org/10.1016/0167-8116(96)00002-X DOI: https://doi.org/10.1016/0167-8116(96)00002-X
Jia, X.-H., & Tu, J.-C. (2024). Towards a new conceptual model of AI-enhanced learning for college students: The roles of artificial intelligence capabilities, general self-efficacy, learning motivation, and critical thinking awareness. Systems, 12(3), Article 74. https://doi.org/10.3390/systems12030074 DOI: https://doi.org/10.3390/systems12030074
Jumani, A., Laghari, A., Narwani, K., & David, S. (2021). Examining the present and future integrated role of artificial intelligence in the business: A survey study on corporate sector. https://doi.org/10.4236/jcc.2021.91008 DOI: https://doi.org/10.4236/jcc.2021.91008
Kataria, R. (2023). Factors influencing students' intention to adopt and use ChatGPT in higher education: A study in the Vietnamese context. International Journal of Advanced Research, 12(4), 157-166. https://doi.org/10.1007/s10639-023-12333-z DOI: https://doi.org/10.1007/s10639-023-12333-z
Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. Association Press.
Kshetri, N. (2020). Artificial intelligence in human resource management in the global south. AMCIS 2020 Proceedings. https://aisel.aisnet.org/amcis2020/org_transformation_is/org_transformation_is/27
Luong, N. M., Nguyen, N. T., Dinh, V. T., Truong, D. T., & Nguyen, T. H. (2024). Digital transformation in Vietnam: A case study of Hanoi SMEs. International Journal of Advanced and Applied Sciences, 11(4), 207-215. https://doi.org/10.21833/ijaas.2024.04.022 DOI: https://doi.org/10.21833/ijaas.2024.04.022
Nicolas, J., Pitaro, N. L., Vogel, B., & Mehran, R. (2023). Artificial intelligence – Advisory or adversary? International Cardiology Review, 12(4), 567-590. https://doi.org/10.15420/icr.2022.22 DOI: https://doi.org/10.15420/icr.2022.22
Rožman, M., Oreški, D., & Tominc, P. (2022). Integrating artificial intelligence into a talent management model to increase the work engagement and performance of enterprises. Frontiers in Psychology, 13(2), 1014434. https://doi.org/10.3389/fpsyg.2022.1014434 DOI: https://doi.org/10.3389/fpsyg.2022.1014434
Schwab, K. (2017). The Fourth Industrial Revolution. Currency.
Steenkamp, J.-B. E. M., & Van Trijp, H. C. M. (1991). The use of LISREL in validating marketing constructs. International Journal of Research in Marketing, 8(4), 283-299. https://doi.org/10.1016/0167-8116(91)90027-5 DOI: https://doi.org/10.1016/0167-8116(91)90027-5
Viktor, M., Anna, K., & Olga, M. (2021). Development of a model for evaluating the effectiveness of innovative startups based on information cycles and using neural networks. Indonesian Journal of Electrical Engineering and Computer Science, 23(1), 396-404. https://doi.org/10.11591/ijeecs.v23.i1.pp396-404 DOI: https://doi.org/10.11591/ijeecs.v23.i1.pp396-404
Xu, G., Xue, M., & Zhao, J. (2023). The relationship of artificial intelligence opportunity perception and employee workplace well-being: A moderated mediation model. International Journal of Environmental Research and Public Health, 20(3), Article 1974. https://doi.org/10.3390/ijerph20031974 DOI: https://doi.org/10.3390/ijerph20031974
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Hang Truong Thi Le, Thong Nguyen Ngoc, Phong Tran Vu, Tai Le Tan

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially
Anyone using the work must attribute the work to the original creator. The licence allows copying and distribution of the original work but no adaptations or modified versions of the work may be distributed.
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.