"background": "The adoption of advanced process-control systems in industrial and infrastructure projects is critical for efficiency and safety. However, in many developing economies, the rate and determinants of this adoption are poorly quantified, hindering effective policy and technical support. ", "purpose and objectives": "This study aims to methodologically evaluate the adoption of process-control systems and to estimate adoption rates and their key drivers using a longitudinal dataset from Uganda. ", "methodology": "A panel-data econometric model was employed, using a unique, firm-level dataset. The core specification was a fixed-effects logit model: Adoption{it = \ + \1 TechCapit + \2 CostAccessit + \ + \ +, where \ and \ represent firm and year fixed effects. Inference was based on cluster-robust standard errors. ", "findings": "A positive and statistically significant relationship was found between technical capacity and adoption likelihood (p < 0. 01). The model predicts that a one-standard-deviation increase in technical capacity score increases the probability of adoption by approximately 18 percentage points (95% CI: 12 to 24). Access to capital, while positive, showed a weaker and less consistent effect. ", "conclusion": "The methodological framework provides a robust tool for tracking technological uptake. The findings strongly indicate that building technical expertise is a more decisive lever for accelerating adoption than improving financial access alone within the studied context. ", "recommendations": "Industry associations and policymakers should prioritise long-term technical skills development and knowledge-transfer programmes. Future research should integrate more granular data on system types and performance outcomes. ", "key words": "process control, technology adoption, panel data, fixed effects, industrial automation, developing economies", "contribution statement": "This paper provides a novel panel-data estimation framework and presents the first longitudinal, firm-level evidence on the drivers of process-control system adoption in the region. "
Akello et al. (2020) studied this question.