Bayesian hierarchical models are increasingly used for analysing adoption rates in complex systems such as process-control systems (PCS). PCS play a crucial role in urban resilience by managing resources efficiently and ensuring sustainable development. In Ethiopia, there is a need to evaluate the effectiveness of these systems through robust statistical methods. The methodology involves collecting data from multiple sites across Ethiopia, employing a Bayesian hierarchical model with Markov Chain Monte Carlo (MCMC) techniques to estimate adoption rates. Sensitivity analyses are performed to assess the robustness of the model under different conditions. A key finding is that adoption rates vary significantly between urban and rural areas in Ethiopia, influenced by factors such as socio-economic status and access to information technology infrastructure. The Bayesian hierarchical model provides a nuanced understanding of these variations. The application of the Bayesian hierarchical model has provided valuable insights into the adoption dynamics of PCS in Ethiopian cities, which can inform future policy interventions aimed at enhancing system efficiency and urban resilience. Policy makers should consider regional-specific factors when implementing PCS to ensure higher adoption rates and better outcomes. Future research could explore longer-term impacts and broader geographical scales. Bayesian hierarchical model, process-control systems, adoption rates, Ethiopia, urban resilience The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Wondimu et al. (Fri,) studied this question.
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