A Structural Model of Factors Influencing AI Adaptation and SME Performance in Bangkok and Nonthaburi, Thailand
Main Article Content
Abstract
Artificial intelligence (AI) adoption plays a critical role in enhancing SME performance in Bangkok and Nonthaburi, Thailand. Based on the extended Technology–Organization–Environment (TOE) framework, this study investigates how technological readiness, organizational capability, and environmental pressures influence AI adaptation and performance outcomes. Using simple random sampling, data were collected from 360 SME owners and managers and analyzed through Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). The model demonstrated good fit (χ²/df = 1.94, CFI = 0.95, TLI = 0.94, RMSEA = 0.053, SRMR = 0.044). Technology (β = 0.36, p < 0.001), Organization (β = 0.42, p < 0.001), and Environmental factors (β = 0.29, p < 0.001) significantly influenced AI adaptation. AI adaptation strongly affected SME performance (β = 0.67, p < 0.001) and mediated the relationships between contextual factors and performance outcomes. SME performance was measured consistently with the SEM indicators (SME1–SME6): productivity, cost efficiency, product quality, market expansion, profitability, and long-term sustainability. The findings confirm that strengthening technological, organizational, and environmental readiness enhances AI-driven performance among Thai SMEs.
Highlights
This study provides empirical evidence on how Technology, Organization, and Environment (TOE) factors shape AI Adaptation and subsequently enhance SME performance in Bangkok and Nonthaburi, demonstrating that organizational readiness exerts the strongest influence on AI integration, technological readiness significantly facilitates both adoption and performance outcomes, and environmental pressures positively drive adoption behavior; the validated CFA and SEM models confirm robust measurement and structural relationships, showing that AI Adaptation substantially mediates the effects of TOE factors on revenue growth, productivity, and employment gains, offering policy-relevant insights for strengthening SME capabilities and accelerating AI-driven economic growth in Thailand’s metropolitan region.
Article Details
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Copyright: CC BY-NC-ND 4.0
References
Abu Bakar, M. R., Razali, N. M., Ishak, K. K., Ismail, M. N., & Sembok, T. M. T. (2024). Adoption of industry 4.0 with cloud computing as a mediator: Evaluation using TOE framework for SMEs. JOIV: International Journal on Informatics Visualization, 8(2), 554-563. http://dx.doi.org/10.62527/joiv.8.2.2205
Alka’awneh, S. M. N., Abdul-Halim, H., & Saad, N. H. M. (2025). A review of Diffusion of Innovations Theory (DOI) and Technology, Organization, and Environment framework (TOE) in the adoption of artificial intelligence. International journal of academic research in business and social sciences, 15(3), 2118- 2129.
Arroyabe, M. F., Arranz, C. F., de Arroyabe, I. F., & de Arroyabe, J. C. F. (2024). Analyzing AI adaptation in European SMEs: A study of digital capabilities, innovation, and external environment. Technological Society, 79, 102733.
Badghish, S., & Soomro, Y. A. (2023). Artificial intelligence adoption by SMEs to achieve sustainable business performance – Application of Technology Organization Environment framework. Sustainability, 16(5), 1864.
Bangkok Bank. (2025, March). Thailand economic outlook and SME performance report 2024–2025 [PDF]. Bangkok Bank. Retrieved from https://www.bangkokbank.com/th TH/-/media/1cfe1a9cf9a445169845ee686e64c158.ashx
Baker, J. (2011). The technology–organization–environment framework. Information Systems Theory: Explaining and Predicting Our Digital Society. 1, 231-245.
Chiew, K. Z., & Cheah, J. S. S. (2025). Reassessing ICT adoption among Malaysian SMEs: A post COVID 19 thematic literature review using TOE framework. Journal of the Community Development in Asia, 8(2), 249 265.
Chummee, P., & Khammadee, P. (2025). An investigation of metaverse adoption for SMEs in Bangkok through the Technology-Organization Environment (TOE) framework. Asian Social and Educational Innovation Journal, 1(2), 20-38.
Chummee, Pattarapon. (2025). Confirmatory factor analysis of technology, organization and environment on artificial intelligence adoption for small and medium-sized enterprises in the metropolitan area. Asian Social and Educational Innovation Journal, 1(1), 1-10.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). Multivariate data analysis (9th eds.). Hampshire, United Kingdom: Cengage Learning.
Herrera, F. (2025). Artificial intelligence adoption in SMEs: Survey based on TOE–DOI framework, primary methodology and challenges. Applied Sciences, 15(12), 6465.
Kalleparambil, S. A., & Akoum, M. (2025). AI Organisational Readiness for SMEs: A Tailored Model for AI Adaptation Success. Journal of Information & Knowledge Management, 24(06), 2550076.
Nagel, L., Seo, Y., & Dutta, D. (2024). Environmental readiness and digital transformation: Exploring AI Adaptation in small and medium enterprises. Sustainability, 16(5), 1864.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd eds.). New York, USA.: McGraw-Hill.
OECD. (2021). The digital transformation of SMEs. OECD Publishing. Retrieved from https://www.oecd.org/industry/smes/the-digital-transformation-of-smes_ec3163f5.pdf
Office of Small and Medium Enterprise Promotion (OSMEP). (2023). Situation report of MSMEs in Thailand 2023[PDF]. OSMEP. Retrieved from https://old.sme.go.th/upload/mod_download/download 20230904173524.pdf
Pasukree, R., Plubplueng, T., Phencharoenkit, R., & Vanpetch, P. (2025). Factors affecting artificial intelligence technology adoption among small and medium enterprises in Samphanthawong District, Bangkok. The EUrASEANs: Journal on Global Socio-Economic Dynamics, 4(53), 254-270.
Prakash, C. (2025). Evaluating the TOE framework for technology adoption: A systematic review of its strengths and limitations. International Journal on Recent and Innovation Trends in Computing and Communication, 13(1), 76-82.
Rawashdeh, A., Bakhit, M., & Abaalkhail, L. (2022). Determinants of artificial intelligence adoption in SMEs: The mediating role of accounting automation. International Journal of Accounting & Information Management, 30(4), 510-529.
Ruenphongphun, P., & Khumtha, S. (2025). Determinants of AI and machine learning technology acceptance in business decision-making among Thai SMEs: Evidence from Chachoengsao Province. Procedia of Multidisciplinary Research, 5(1), 42-58.
Reuters. (2024). Google to invest $1 billion in Thai data center, cloud infrastructure. Reuters. Retrieved from https://www.reuters.com/technology/google-invest-1-billion-thai-data-centre-cloud-infrastructure-2024 09 30/
Rogers, E. M. (2003). Diffusion of innovations (5th eds.). New York, USA.: Free Press.
Sánchez, E., Calderón, R., & Herrera, F. (2025). Artificial intelligence adoption in SMEs: Survey based on TOE–DOI framework, primary methodology and challenges. Applied Sciences, 15(12), 6465.
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.
Thailand Development Research Institute (TDRI). (2025, April). SMEs need ecosystems, not just support: Policy paper on inclusive digital economy growth [Policy brief]. TDRI. Retrieved from https://www.tdri.or.th/en/2025/10/smes-need-ecosystems-not-just-support/
Verma, U. S., Somjai, S., & Rusmee, A. (2021). Organizational characteristics influencing AI Adaptation in Thai SMEs: The mediating role of marketing mix. Psychology and Education, 58(5), 5352-5358.
Vongurai, R. (2025). Examining organizational and human drivers of artificial intelligence adoption for enhancing operational performance in Thai technology enterprises. The Journal of Behavioral Science, 20(2), 1-14.
