Exploring the Factors Influencing AI Adoption in the Vietnamese Garment Industry: The Role of Technological Readiness and Environmental Pressure

Artificial intelligence (AI) is increasingly discussed as a technology that can support operational improvement, production planning, quality control, and competitiveness in manufacturing industries. In the Vietnamese garment industry, however, evidence on the factors associated with AI adoption remains limited. This study examines the determinants of AI adoption in Vietnam's garment sector using the Technology–Organisation–Environment (TOE) framework. The research focuses on three explanatory dimensions: technological context, organisational context, and environmental context. Data were collected through a survey of 301 valid respondents working in the garment industry and were analysed using partial least squares structural equation modelling. The measurement model was assessed through indicator reliability, internal consistency reliability, convergent validity, discriminant validity, and collinearity statistics. The structural model results show that technological context has a positive and statistically significant relationship with AI adoption. Environmental context also has a positive and statistically significant relationship with AI adoption and shows the strongest path coefficient among the three TOE dimensions. In contrast, organisational context has a positive but statistically non-significant relationship with AI adoption at the conventional 5% significance level. The model explains 60.6% of the variance in AI adoption, indicating a meaningful level of explanatory power. The findings suggest that technological readiness and external environmental pressures are more influential than internal organisational conditions in explaining AI adoption in the surveyed context. The study contributes to the application of the TOE framework in the Vietnamese garment industry and provides practical implications for firms seeking to strengthen technology infrastructure, respond to market requirements, and develop more appropriate strategies for AI implementation.