The adoption rates of new transport maintenance technologies in Tanzanian depots have been a subject of interest due to their potential impact on vehicle reliability and operational efficiency. A Bayesian hierarchical model was employed to analyse data from multiple depots. The model accounts for both within-depot and depot variations in adoption rates. The analysis revealed a significant variation (direction: higher) in adoption rates among depots, with proportions ranging from 40% to 75%, indicating varied levels of technology adaptation. Bayesian hierarchical modelling provided nuanced insights into the factors driving technology uptake, offering a robust framework for future studies and policy formulation. Future research should consider integrating additional variables such as depot size and regional economic conditions to refine model accuracy. Bayesian Hierarchical Model, Adoption Rates, Tanzanian Transport Maintenance Depots, Technology Uptake The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Chituwo et al. (Tue,) studied this question.
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