"background": "The adoption of advanced manufacturing systems in developing economies is a critical driver of industrial productivity. However, rigorous, evidence-based evaluations of adoption rates and causal impacts within the African context remain scarce, with many studies relying on descriptive or correlational analyses. ", "purpose and objectives": "This study aims to provide a robust methodological evaluation of manufacturing systems adoption. Its primary objective is to quantify the causal effect of targeted intervention programmes on the uptake of integrated computer-aided manufacturing (ICAM) systems using a quasi-experimental design. ", "methodology": "A comparative, longitudinal study was conducted, analysing panel data from a treatment group of plants participating in a national industrial modernisation scheme and a matched control group. The core impact was estimated using a difference-in-differences model: Y{it = \0 + \1 (\) + \ + \ +, where Y₈ₓ is the adoption index for plant i in period t. Inference was based on cluster-robust standard errors at the plant level. ", "findings": "The analysis indicates a statistically significant positive treatment effect. Plants under the intervention scheme demonstrated a 34% higher aggregate adoption rate of ICAM systems compared to the control group (95% CI: 22% to 46%). The integration of production planning and control modules showed the most substantial increase. ", "conclusion": "The quasi-experimental design confirms that structured intervention programmes can significantly accelerate the adoption of advanced manufacturing systems. The findings underscore the importance of policy-supported mechanisms for technological upgrading. ", "recommendations": "Policymakers should design and scale intervention schemes with clear technical support components. Plant managers should prioritise the implementation of integrated planning modules as a foundational step. Future research should incorporate lifecycle cost analysis. ", "key words": "manufacturing systems, technology adoption, quasi-experimental design, difference-in-differences, industrial
Okonkwo et al. (Wed,) studied this question.
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