GENERATIVE AI INTEGRATION AND CORE BUSINESS FUNCTIONS: EVIDENCE FROM VIETNAMESE SMALL AND MEDIUM-SIZED ENTERPRISES
DOI:
https://doi.org/10.59266/houjs.2026.1446Keywords:
core business functions, GenAI integration, SMEs, TOE framework, VietnamAbstract
This article identifies the determinants affecting the integration of Generative Artificial Intelligence (GenAI) into core business functions of small and medium-sized enterprises (SMEs) in Vietnam. Grounded in the Technology-Organization-Environment (TOE) framework, the research used a cross-sectional survey of 202 directors, managers, and senior decision-makers from 20 SMEs across Hanoi, Hai Phong, Da Nang, Nha Trang, and Ho Chi Minh City. Confirmatory Factor Analysis and Structural Equation Modeling were conducted in R (lavaan, maximum-likelihood estimation), with 5,000-resample bootstrapping to test mediating effects. The measurement model showed that technological, organizational, and environmental factors have direct effects on GenAI adoption and indirect effects on CBF. Recommendations are proposed on strengthening organizational readiness, technological infrastructure, environmental responsiveness, and performance measurement.
References
Abrokwah-Larbi, K., & Awuku-Larbi, Y. (2024). The impact of artificial intelligence in marketing on the performance of business organizations: Evidence from SMEs in an emerging economy. Journal of Entrepreneurship in Emerging Economies, 16(4), 1090-1117. https://doi.org/10.1108/JEEE-07-2022-0207
Badghish, S., & Soomro, Y. A. (2024). Artificial intelligence adoption by SMEs to achieve sustainable business performance: Application of technology-organization-environment framework. Sustainability, 16(5), Article 1864. https://doi.org/10.3390/su16051864
Benbya, H., Strich, F., & Tamm, T. (2024). Navigating the promises and perils of generative artificial intelligence for knowledge and creative work. Journal of the Association for Information Systems, 25(1), 23-36. https://doi.org/10.17705/1jais.00861
Chatterjee, S., Chaudhuri, R., Vrontis, D., & Basile, G. (2022). Digital transformation and entrepreneurship process in SMEs of India: A moderating role of adoption of AI-CRM capability and planning strategy. Journal of Strategy and Management, 15(3), 416-433. https://doi.org/10.1108/JSMA-02-2021-0049
Chatterjee, S., Chaudhuri, R., Vrontis, D., & Giovando, G. (2023). Digital workplace and organization performance: Moderating role of digital leadership capability. Journal of Innovation & Knowledge, 8(2), Article 100334. https://doi.org/10.1016/j.jik.2023.100334
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez- Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), Article 100899. https://doi.org/10.1016/j.hrmr.2022.100899
Chu, B. Q., & Nguyen, T. K. D. (2025). Artificial intelligence applications in e-commerce: Opportunities, challenges, and solutions for businesses on the Shopee.vn platform. Journal of Science, Hanoi Open University, 5A, 332-344. https://doi.org/10.59266/houjs.2025.954
Chuma, E. L., & de Oliveira, G. G. (2023). Generative AI for business decision-making: A case of ChatGPT. Management Science and Business Decisions, 3(1), 5-11. https://doi.org/10.52812/msbd.63
Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2022). Intention to use analytical artificial intelligence (AI) in services: The effect of technology readiness and awareness. Journal of Service Management, 33(2), 293-320. https://doi.org/10.1108/JOSM-10-2020-0378
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate data analysis (8th ed.). Cengage Learning.
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. https://doi.org/10.1108/EBR-11-2018-0203
Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Jatobá, M. N., Ferreira, J. J., Fernandes, P. O., & Teixeira, J. P. (2023). Intelligent human resources for the adoption of artificial intelligence: A systematic literature review. Journal of Organizational Change Management, 36(7), 1099-1124. https://doi.org/10.1108/JOCM-03-2022-0075
Lutfi, A., Alsyouf, A., Almaiah, M. A., Alrawad, M., Abdo, A. A. K., Al- Khasawneh, A. L., Ibrahim, N., & Saad, M. (2022). Factors influencing the adoption of big data analytics in the digital transformation era: Case study of Jordanian SMEs. Sustainability, 14(3), Article 1802. https://doi.org/10.3390/su14031802
Merhi, M. I., & Harfouche, A. (2024). Enablers of artificial intelligence adoption and implementation in production systems. International Journal of Production Research, 62(15), 5457-5471. https://doi.org/10.1080/00207543.2023.2167014
Mogaji, E., Viglia, G., Srivastava, P., & Dwivedi, Y. K. (2024). Is it the end of the technology acceptance model in the era of generative artificial intelligence? International Journal of Contemporary Hospitality Management, 36(10), 3324-3339. https://doi.org/10.1108/IJCHM-08-2023-1271
Nguyen, V. P. (2022). The critical factors impacting artificial intelligence applications adoption in Vietnam: A structural equation modeling analysis. Economies, 10(6), Article 129. https:// doi.org/10.3390/economies10060129
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269-275. https://doi.org/10.1016/j.ijresmar.2023.03.001
Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., & Jones, P. (2025). The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management, 63(3), 1297-1331. https://doi.org/10.1080/00472778.2024.2379999
Tominc, P., Oreški, D., Čančer, V., & Rožman, M. (2024). Statistically significant differences in AI support levels for project management between SMEs and large enterprises. AI, 5(1), 136-157. https://doi.org/10.3390/ai5010008
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
