The adoption rates of power-distribution equipment in Kenya are influenced by a variety of factors including socioeconomic conditions and technological advancements. A Bayesian hierarchical model was employed to analyse data from multiple districts in Kenya. The model accounts for spatial heterogeneity and incorporates district-specific covariates to estimate adoption rates with uncertainty quantification. The analysis revealed significant variation in the adoption rates across different districts, indicating that local conditions play a crucial role in determining equipment uptake. This study demonstrates the effectiveness of Bayesian hierarchical models for assessing the deployment patterns of power-distribution equipment, offering policymakers actionable insights to optimise resource allocation. Policymakers should consider district-specific factors when implementing new power-distribution equipment strategies, thereby enhancing overall adoption rates and efficiency. Bayesian Hierarchical Model, Power-Distribution Equipment, Adoption Rates, Kenya The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.