"background": "The adoption of advanced process-control systems is critical for enhancing industrial efficiency and productivity. In many developing economies, however, systematic evaluation of the factors influencing this adoption is lacking, hindering targeted interventions and policy formulation. ", "purpose and objectives": "This paper develops and applies a novel Bayesian hierarchical model to quantify the adoption rates of process-control systems within the nation's industrial sector and to identify the key technical and organisational determinants driving this uptake. ", "methodology": "A structured survey was administered to a stratified sample of industrial firms. The core statistical model is a Bayesian hierarchical logistic regression: (p{ij) = \ + \ Xij, where pij is the probability of adoption for firm i in sector j, \ \ (\\, \\) are sector-level intercepts, and \ represents coefficients for firm-level covariates X. Posterior distributions were estimated using Hamiltonian Monte Carlo. ", "findings": "The model reveals a low overall adoption probability of 0. 23 (95% credible interval: 0. 18 to 0. 29). Sector-level variability was significant (\\ = 1. 2), with the food processing sector showing the highest adoption propensity. Firm size and prior investment in staff technical training were the most influential positive predictors. ", "conclusion": "Adoption of advanced control systems remains limited and highly heterogeneous across industrial sectors. The Bayesian hierarchical framework effectively captures this multi-level variability, providing a more nuanced understanding than traditional regression approaches. ", "recommendations": "Policy initiatives should prioritise sector-specific support programmes, with a focus on incentivising workforce development and technical training. Future research should integrate longitudinal data to model adoption pathways over time. ", "key words": "Bayesian statistics, hierarchical modelling, industrial automation, technology adoption, developing economies", "contribution statement": "This paper introduces a novel
Asante et al. (Mon,) studied this question.