{ "background": "The adoption of advanced process-control systems (PCS) is critical for enhancing efficiency and quality in civil engineering projects. In many developing economies, however, the rate and determinants of this technological transition are poorly quantified, hindering effective policy and industry strategy.", "purpose and objectives": "This study develops and applies a novel Bayesian hierarchical model to evaluate the adoption rates and key influencing factors of PCS within the civil engineering sector of a West African nation, providing a robust methodological framework for technological diffusion analysis.", "methodology": "A longitudinal dataset of engineering firms was analysed using a Bayesian hierarchical logistic model. The core adoption probability for firm i at time t was modelled as $\\text{logit}(p{it}) = \\alpha{j[i]} + \\beta X{it}$, where $\\alphaj \\sim \\text{Normal}(\\mu\\alpha, \\sigma\\alpha^2)$ represents random effects for firm sub-sector j, and $X_{it}$ are firm-level covariates. Posterior distributions were estimated using Markov chain Monte Carlo sampling.", "findings": "The model estimates a substantial increase in adoption probability, with the posterior mean for the overall adoption rate rising from 0.18 (95% credible interval: 0.14–0.23) to 0.67 (0.60–0.73) over the study period. Firm size and engagement in public-private partnerships showed strongly positive associations with adoption, with posterior probabilities exceeding 0.99.", "conclusion": "The adoption of process-control systems has accelerated significantly, though diffusion remains uneven across firm types. The Bayesian hierarchical approach successfully quantified this trajectory and its drivers, accounting for sectoral heterogeneity.", "recommendations": "Industry bodies should promote knowledge-sharing networks targeting small and medium-sized enterprises. Policymakers are advised to integrate technology adoption criteria into public infrastructure procurement processes to incentivise wider uptake.", "key words": "technological diffusion, Bayesian statistics, logistic regression,