The UTAUT model has been extensively applied in fields like information technology and education, but its application in the agricultural sector, regarding cold chain technology adoption among vegetable producers, remains scarce. Despite its potential to reduce significant post-harvest losses and improve food security, the adoption of CCT remains limited in low-resource agricultural settings. Using data collected from 87 vegetable producers and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM), the study examines the influence of performance expectancy, effort expectancy, social influence, and facilitating conditions on behavioral intention. Data from 87 vegetable producers who used the technology was collected to test the hypothesized model. The model explained 58.9% of the variance in adoption intention, with performance expectancy (β = 0.491, p ≤ 0.000), social influence (β = 0.211, p ≤ 0.05), and facilitating conditions (β = 0.206, p ≤ 0.05) emerging as significant predictors. The Effort expectancy, while positively perceived, did not show a significant effect, suggesting that ease of use is secondary to perceived utility. The findings underscore the importance of performance-driven messaging, peer influence, and supportive infrastructure in scaling agro-technologies. In conclusion, Vegetable producers indicate willingness to accept, adopt and use the technology; it is recommended that the training on the operation of the technology should be taken into account. This research contributes to the technology adoption literature in agriculture and informs policy and practice aimed at enhancing food system resilience and achieving sustainable development outcomes.