A Randomised Field Trial Methodology for Evaluating the Adoption of Industrial Machinery Fleet Systems in Uganda

{ "background": "The adoption of advanced industrial machinery fleet systems in developing economies is critical for infrastructure development, yet robust methodologies for evaluating their uptake are lacking. Existing studies often rely on retrospective surveys or case studies, which are prone to selection bias and do not establish causal relationships between intervention and adoption.", "purpose and objectives": "This article presents a novel methodological framework for conducting a randomised field trial to rigorously measure the causal effect of a targeted support programme on the adoption rates of modern fleet management systems among Ugandan construction and mining enterprises.", "methodology": "The proposed methodology employs a cluster-randomised controlled trial design. Eligible firms are randomised into treatment and control groups. The treatment group receives a bundled intervention comprising technical training, access to fleet telematics software, and initial maintenance support. Adoption is measured via a primary outcome of software utilisation intensity, collected through system logs, and a secondary outcome of self-reported practice change. The causal effect is estimated using a linear mixed model: $Y{ij} = \\beta0 + \\beta1 T{ij} + \\gamma X{ij} + uj + \\epsilon{ij}$, where $uj$ represents cluster-level random effects. Inference will be based on 95% confidence intervals with robust standard errors.", "findings": "As a methodology article, this paper presents no empirical results from the trial's application. However, the detailed protocol anticipates a primary analysis direction, hypothesising that the intervention will increase the mean monthly software utilisation rate by a minimum of 15 percentage points compared to the control group.", "conclusion": "The outlined methodology provides a rigorous, transparent, and replicable framework for generating high-quality evidence on technology adoption drivers within the structural engineering sector in Uganda and similar contexts.", "recommendations": "Researchers and policymakers should employ such experimental designs to move beyond correlational evidence. Future applications should consider longer-term follow-up to assess sustainability and incorporate cost-benefit analysis into the trial design.", "key words": "randomised controlled trial, technology adoption