{ "background": "Municipal infrastructure asset management (IAM) systems are critical for sustainable service delivery, yet their adoption across municipalities remains inconsistent and poorly measured. Current evaluation relies on self-reported surveys, which lack rigour in establishing causal effects of intervention programmes.", "purpose and objectives": "This article presents a novel methodological framework for rigorously evaluating the causal impact of IAM capacity-building interventions on adoption rates within a municipal context. The primary objective is to detail a protocol for a randomised field trial (RFT) designed for this setting.", "methodology": "The proposed RFT methodology clusters municipalities by key characteristics before random assignment to treatment (a structured IAM implementation support programme) or control groups. Adoption is measured via a composite index derived from asset register audits and process compliance checks. The causal effect is estimated using a linear mixed model: $Y{ij} = \\beta0 + \\beta1 T{ij} + \\gamma X{ij} + uj + \\epsilon{ij}$, where $uj$ are cluster random effects. Inference uses cluster-robust standard errors.", "findings": "As a methodology article, this paper presents no empirical results from a completed trial. However, a pilot simulation based on historical data indicates the design is sufficiently powered to detect a minimum 15-percentage-point increase in the mean adoption index, with a 95% confidence interval excluding a null effect of less than 5 points.", "conclusion": "The outlined RFT provides a robust, evidence-based framework for quantifying the efficacy of IAM interventions, moving beyond anecdotal assessment. It addresses a significant methodological gap in infrastructure management evaluation.", "recommendations": "Researchers and municipal engineering practitioners should employ experimental designs to test asset management support programmes. Funding bodies should mandate such rigorous evaluation to ensure value and identify scalable interventions.", "key words": "asset management, randomised controlled trial, infrastructure, municipal engineering, evaluation methodology, causal inference", "contribution statement": "This paper provides the first detailed experimental protocol