"background": "The sustainable provision of safe drinking water remains a critical engineering challenge in many regions. Understanding the drivers behind the adoption of different water treatment technologies is essential for effective infrastructure planning and policy. ", "purpose and objectives": "This study aims to methodologically evaluate and compare the factors influencing adoption rates for various centralised and decentralised water treatment systems. The objective is to identify the most significant technical, socio-economic, and institutional predictors of successful implementation. ", "methodology": "A comparative study employing a three-level hierarchical linear model. The model, specified as ijk = \0 + u{0j + v0k + \ Xijk +, where j and k denote district and region levels, was fitted to a novel, longitudinally compiled national dataset. Inference was based on robust standard errors to account for heteroskedasticity. ", "findings": "Community-managed decentralised systems showed a 24% higher mean adoption rate (95% CI: 18% to 30%) compared to centrally managed utilities when controlling for infrastructure age. A key theme was the paramount importance of local technical capacity, which had a stronger positive effect than initial capital investment alone. ", "conclusion": "The diagnostic analysis demonstrates that adoption is not merely a function of technology selection but is profoundly shaped by multi-level governance structures and local operational expertise. Comparative performance is contingent on these contextual factors. ", "recommendations": "Engineering project planning must integrate explicit capacity-building modules. Policy should prioritise funding for training and spare parts supply chains alongside physical infrastructure. Future system designs should favour modularity to enhance local manageability. ", "key words": "water treatment systems, adoption rates, multilevel regression, infrastructure diagnostics, hierarchical linear model, Ethiopia", "contribution statement": "This paper provides a novel methodological framework for the comparative diagnostics of engineering infrastructure adoption, introducing a multilevel model that explicitly partitions variance between household,
Asfaw et al. (Fri,) studied this question.