{ "background": "The adoption of advanced process-control systems (PCS) in industrial and infrastructure projects is a critical driver of efficiency and quality. However, robust quantitative methods for evaluating the causal impact of policy and market interventions on adoption rates in emerging economies are underdeveloped.", "purpose and objectives": "This case study aims to develop and apply a quasi-experimental econometric model to measure the causal effect of a national industrial modernisation policy on PCS adoption within the country's engineering sector.", "methodology": "A difference-in-differences (DiD) model is employed, using a panel dataset of engineering firms. Treated firms were those eligible for the policy's capital allowance scheme, with a matched control group of ineligible firms. The core model is $Y{it} = \\alpha + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\cdot \\text{Post}t) + \\epsilon{it}$, where $Y_{it}$ is a binary adoption indicator. Inference is based on cluster-robust standard errors at the firm level.", "findings": "The policy intervention had a statistically significant positive effect on adoption. The DiD estimator, $\\delta$, was 0.18 (95% CI: 0.12, 0.24), indicating an 18-percentage-point increase in the likelihood of PCS adoption among treated firms relative to the control group.", "conclusion": "The national policy was effective in accelerating technological uptake. The DiD approach provides a credible methodological framework for isolating causal effects in technology adoption studies within engineering contexts.", "recommendations": "Policymakers should consider extending similar capital incentive schemes to small and medium enterprises. Future evaluations of engineering technology programmes should incorporate quasi-experimental designs to strengthen evidence for decision-making.", "key words": "process-control systems, technology adoption, difference-in-differences, causal inference, industrial policy, engineering