A Structural Model of Factors Influencing AI Adaptation and SME Performance in Bangkok and Nonthaburi, Thailand

Information from the 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. HighlightsThis 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.

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Related topics: Digital Transformation in Industry · AI in Service Interactions · Technology Adoption and User Behaviour