The integration of advanced manufacturing technologies in Senegalese industrial settings has shown varying levels of adoption across different sectors and plants. A Bayesian hierarchical model was employed to analyse data from multiple factories. The model accounts for both plant-specific variability and aggregate-level trends, providing nuanced insights into adoption dynamics. The analysis revealed a significant variation in adoption rates among plants (e. g. , adoption ranged between 20% and 85%), with factors such as initial investment costs and local technological infrastructure being key determinants of uptake. This study underscores the importance of considering both aggregate and specific plant-level data when evaluating technology diffusion. The Bayesian hierarchical model offers a robust framework for understanding adoption patterns in complex systems. Manufacturing plants should prioritise transparent communication about benefits and cost implications to facilitate smoother integration of new technologies. Policymakers could also consider regional support programmes to enhance accessibility and reduce barriers. Bayesian Hierarchical Model, Adoption Rates, Manufacturing Systems, Senegal The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Ndiaye et al. (Tue,) studied this question.
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