Manufacturing plants in Kenya have adopted various systems to improve productivity and efficiency, but there is a need for methodological evaluation of these adoption rates. Multilevel regression analysis was employed to analyse data from manufacturing plants in Kenya, stratifying by both organisational (level-2) and process-level factors (level-1). The study aimed at identifying significant predictors of system adoption using mixed-effects models with robust standard errors to account for within-cluster correlation. The multilevel regression analysis revealed that organisational size had a significant positive effect on the adoption rate, indicating that larger plants were more likely to adopt new systems. Additionally, process-level factors such as training and maintenance resources also played a critical role in system adoptions. This study provides a methodological evaluation of multilevel regression analysis for measuring manufacturing plant system adoption rates in Kenya. The findings suggest that organisational size and resource availability are key determinants of system adoption, offering insights for policymakers and practitioners in the Kenyan manufacturing sector. Future research should consider extending this model to other sectors within Kenya and exploring additional contextual factors influencing system adoptions. multilevel regression analysis, manufacturing plants, system adoption, productivity improvement, mixed-effects models The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Ochieng et al. (2013) studied this question.