Abstract
{ "background": "Municipal infrastructure asset management systems (AMS) are promoted to enhance the sustainability of public works in developing nations, yet their adoption by local government engineers remains inconsistent and poorly understood.", "purpose and objectives": "This case study aimed to evaluate the efficacy of a structured training and support intervention on the adoption rates of a digital AMS among municipal engineering teams, and to identify key barriers to sustained use.", "methodology": "A randomised field trial was conducted with 42 municipal engineering departments. Treatment groups received a tailored training programme and technical support, while control groups received only basic system access. Adoption was measured via system logins and data entry completeness over a defined period. The primary analysis used a logistic regression model: $\\logit(pi) = \\beta0 + \\beta1 Ti + \\beta' Xi + \\epsiloni$, where $pi$ is the probability of consistent use, $Ti$ is the treatment indicator, and $X_i$ is a vector of covariates. Robust standard errors were clustered at the municipal level.", "findings": "The intervention significantly increased the probability of consistent AMS use by 35 percentage points (95% CI: 22 to 48). However, adoption in the treatment group plateaued after the initial support period, with a key thematic barrier being the perceived misalignment between system data requirements and existing, paper-based workflow approvals.", "conclusion": "While targeted training demonstrably accelerates initial adoption of engineering asset management systems, sustained engagement requires deeper integration with entrenched bureaucratic processes.", "recommendations": "Future AMS implementations should co-design data entry protocols with finance and administration units to align with existing approval chains. Pilot programmes should include a phased withdrawal of support to identify and address workflow discontinuities.", "key words": "asset management, infrastructure, adoption, randomised trial, municipal engineering, Ghana", "contribution statement": "This study provides the first experimental evidence on the causal effect of training interventions on digital AMS adoption