In South Africa, industrial machinery fleets play a crucial role in various sectors such as manufacturing, mining, and construction. The study employed multilevel regression analysis to analyse data from multiple levels (industrial sectors and individual fleet units) to understand factors influencing adoption rates. Multilevel regression revealed that sector-specific characteristics significantly influenced machinery adoption, with manufacturing having the highest adoption rate at 75% compared to mining at 60% and construction at 55%. The multilevel regression analysis provided insights into the factors affecting machinery adoption rates across different sectors in South Africa. Further research should explore inter-sectoral collaboration for shared resources and standardised training programmes to enhance machinery utilization efficiency. Industrial Machinery, Adoption Rates, Multilevel Regression Analysis, Sector-Specific Factors The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Khumalo et al. (Wed,) studied this question.
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