{ "background": "The performance of transport infrastructure in many developing nations is critically dependent on the efficacy of maintenance depot systems. However, evaluating the adoption and effectiveness of such systems remains methodologically challenging, often relying on aggregated metrics that mask regional and operational heterogeneity.", "purpose and objectives": "This article presents a novel Bayesian hierarchical modelling framework designed to rigorously evaluate the adoption rates of transport depot maintenance systems. The objective is to provide a robust methodological tool that quantifies adoption while accounting for multi-level variability inherent in national infrastructure networks.", "methodology": "A Bayesian hierarchical model is formulated, explicitly modelling adoption probability at the depot level, nested within regional administrative zones. The core model is specified as $\\text{logit}(p{ij}) = \\alpha + \\beta X{ij} + uj$, where $p{ij}$ is the adoption probability for depot $i$ in zone $j$, $X{ij}$ are depot-level covariates, and $uj \\sim N(0, \\sigma^2u)$ are zone-level random effects. Inference uses Markov Chain Monte Carlo simulation with weakly informative priors.", "findings": "The methodological application, using illustrative data, demonstrates the framework's capacity to produce nuanced estimates. For instance, the model successfully differentiates between a high posterior probability of adoption in one zone (estimated mean 0.85, 95% credible interval 0.78 to 0.91) and significantly lower adoption in another, which aggregated analysis would overlook. The zone-level random effects variance was estimated with substantial precision ($\\sigmau = 0.52$, 95% CrI: 0.41, 0.67).", "conclusion": "The proposed Bayesian hierarchical framework offers a statistically rigorous methodology for evaluating infrastructure system adoption. It moves beyond simplistic national averages to deliver a granular, evidence-based assessment tool for engineers and policymakers.", "recommendations": "It is recommended that transport authorities and researchers apply this modelling framework to generate spatially and administratively disag