Bayesian Hierarchical Model for Measuring Adoption Rates in Senegalese Manufacturing Plants Systems

The adoption of advanced manufacturing systems in Senegalese agricultural settings is crucial for enhancing productivity and sustainability. A Bayesian hierarchical model was applied to analyse data from multiple plants across different regions in Senegal. This approach accounts for both plant-specific and regional variability. The analysis revealed a significant difference (p < 0.05) in adoption rates between large-scale and small-scale operations, suggesting that scale is a critical determinant of system uptake. This study provides insights into the factors affecting the diffusion of advanced manufacturing systems in Senegal’s agricultural sector. Further research should explore targeted interventions to increase adoption among smaller-scale operations. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.