"background": "Maintenance systems in transport depots are critical for infrastructure longevity and operational safety, yet adoption of improved protocols in developing contexts remains poorly quantified. Existing studies often rely on self-reported data, lacking rigorous experimental designs to measure actual implementation rates. ", "purpose and objectives": "This case study evaluates a methodological framework for conducting a randomised field trial to measure the adoption rate of a new computerised maintenance management system (CMMS) within Nigerian transport depots. The primary objective was to assess the trial's design efficacy in generating reliable, actionable engineering management data. ", "methodology": "A cluster-randomised controlled trial was implemented across 24 depots. Depots were randomly assigned to treatment (CMMS implementation with training) or control (existing practice) groups. Adoption was measured via direct audit of work-order completion. The primary analysis used a generalised linear mixed model: \ (p{ij) = \0 + \1 Ti + uj +, where pij is the adoption probability, Ti is the treatment indicator, and uⱼ is a random intercept for depot cluster. Robust standard errors were calculated. ", "findings": "The methodological evaluation revealed that the randomised design successfully isolated the intervention's effect, controlling for confounding factors prevalent in observational studies. The trial achieved a high fidelity of implementation, with 92% of planned audit data collected. A key substantive result from the trial data indicated a 28-percentage-point increase in procedural adherence in the treatment group (95% CI: 19 to 37). ", "conclusion": "The randomised field trial proved to be a methodologically robust approach for quantifying adoption rates of engineering maintenance systems in a real-world, resource-constrained setting. It provided credible causal estimates often absent from conventional case studies. ", "recommendations": "Future engineering management research in similar contexts should prioritise randomised designs where feasible to strengthen causal inference. Trial protocols must incorporate robust data auditing to
Bello et al. (Wed,) studied this question.