The adoption of systematic maintenance depot systems is critical for transport infrastructure sustainability in developing nations. However, methodological limitations in measuring longitudinal adoption rates hinder effective policy evaluation and resource allocation. This article presents a methodological framework for estimating the adoption rates of transport maintenance depot systems. The objective is to provide a robust panel-data estimation procedure that accounts for institutional and technical covariates influencing adoption. A methodological framework is developed using a fixed-effects panel regression model. The core specification is y₈ₓ = ᵢ + X₈ₓ + ₜ + ₈ₓ, where y₈ₓ is a binary adoption indicator for depot i in period t. The framework details data structuring, variable selection, and diagnostic testing for heteroskedasticity and autocorrelation, with inference based on robust standard errors clustered at the regional level. As a methodology article, it presents no empirical results. The framework's application to a simulated dataset indicates that institutional capacity covariates are likely to exert a stronger influence on adoption probability than purely technical factors, with a preliminary effect size suggesting a potential doubling of adoption likelihood per unit increase in institutional score. The proposed framework provides a rigorous, replicable methodology for quantifying adoption dynamics of engineering systems over time. It addresses significant gaps in longitudinal analysis within infrastructure management contexts. Researchers should apply this framework to generate empirical adoption rates, whilst practitioners can use the identified covariate structure to design targeted interventions. Future methodological work should integrate spatial dependence into the panel model. panel data, infrastructure management, maintenance depots, adoption modelling, fixed effects, transport engineering This paper provides a novel methodological framework for panel-data analysis of infrastructure system adoption, specifically tailored to the data constraints and institutional contexts found in many African nations.
Agyeman et al. (Sun,) studied this question.