Speaker
Description
Dairy technology adoption offers an important pathway for improving smallholder productivity, household income, and food security in sub-Saharan Africa. This study modelled the drivers of dairy technology adoption among dairy value chain actors in Uasin Gishu County, Kenya. An explanatory sequential mixed-methods design involved 355 participants from Ainabkoi, Kapseret, and Moiben sub-counties. Quantitative data were analysed using descriptive statistics, inferential tests, and Partial Least Squares Structural Equation Modelling with 5,000 bootstrap resamples in SmartPLS 4. Qualitative data from seven Key Informant Interviews and five Focus Group Discussions were analysed thematically. Human Capital emerged as the strongest predictor of Technology Use Level (β = 0.415, p < 0.001), followed by Information Source (β = 0.188, p < 0.01) and Financial Capital (β = 0.165, p < 0.05). Cooperative membership produced the largest difference in Technology Use Level (Cohen’s d = 0.699, p < 0.001), highlighting cooperatives as key institutions for training, information access, service delivery, and technology uptake. Technology Use Level was the strongest predictor of Economic Effectiveness (β = 0.481, p < 0.001; R² = 0.354), identifying sustained use as the main pathway linking adoption to economic benefits. Technology Suitability was the strongest predictor of Future Adoption Intention (β = 0.368, p < 0.001), indicating farmers preferred technologies aligned with their production systems and resource conditions. Mediation analysis showed Technology Use Level transmitted the effect of Financial Capital on Economic Effectiveness (Z = 4.248, p < 0.001) and Human Capital on Adoption Intention (Z = 2.581, p = 0.010). The findings identify knowledge and skills, reliable information, financial capacity, cooperative support, sustained use, and technology suitability as the main drivers of dairy technology adoption. Policies should prioritise farmer training, cooperative strengthening, accessible finance, responsive extension, and context-appropriate technology delivery.