Long-Term Functionality of Rural Water Services in Developing Countries: A System Dynamics Approach to Understanding the Dynamic Interaction of Factors

Abstract

Research has shown that sustainability of rural water infrastructure in developing countries is largely affected by the dynamic and systemic interactions of technical, social, financial, institutional, and environmental factors that can lead to premature water system failure. This research employs system dynamics modeling, which uses feedback mechanisms to understand how these factors interact dynamically to influence long-term rural water system functionality. To do this, the research first identified and aggregated key factors from the literature, then asked water sector experts to indicate the polarity and strength between factors through Delphi and cross impact survey questionnaires, and finally used system dynamics modeling to identify and prioritize feedback mechanisms. The resulting model identified 101 feedback mechanisms that were dominated primarily by three- and four-factor mechanisms that contained some combination of the factors: Water System Functionality, Community, Financial, Government, Management, and Technology, implying these factors were the most influential on long-term functionality. These feedback mechanisms were then scored and prioritized, with the most dominant feedback mechanism identified as Water System Functionality-Community-Finance-Management. This study showcases a way for practitioners to better understand the complexities inherent in rural water development using expert opinion and indicates the need for future research in rural water service sustainability that investigates the dynamic interaction of factors in different contexts.

Using Causal Loop Diagramming to Explore the Drivers of the Sustained Functionality of Rural Water Services in Timor-Leste

“…A similar set of feedback mechanisms can be found to exist in a recent study on community-led water service management programs in Central America by Davis [39], where community satisfaction and payment were found to either enable or prohibit project success. In agreement with Willetts' feedback structure, in a study using the input from water sector experts, Walters and Javernick-Will [40] found that the most influential feedback loop included water system functionality, community financial support and effective management. Thus, a unifying aspect shared between these aforementioned feedback loops identified in the literature, and one which we will maintain within our dynamic hypothesis of CBM-service dynamics, appears to be the importance of community satisfaction and involvement as a driver of payment for the proper maintenance of the water system.…”

Can Sanitary Surveys Replace Water Quality Testing? Evidence from Kisii, Kenya

“…Future research should focus on weighting the individual elements of a sanitary survey inspection to identify whether some elements have a higher contribution to risk. Such weighting could use an expert opinion or tools such as decision trees or system dynamics approaches to understand the interactions between hazards [25,26,27]. …”

Understanding Rural Water Services as a Complex System: An Assessment of Key Factors as Potential Leverage Points for Improved Service Sustainability

“…A study protocol for the workshops and data collection was reviewed and approved by the University of Colorado's Institutional Review Board (IRB # 17-0292) and all participants gave their informed consent for inclusion before they participated in the workshops. Building upon participatory system dynamic methods [55][56][57][58], this work used a 'Factor Mapping' workshop format to elicit knowledge about factor interaction and dynamics as developed by Walters et al [59][60][61][62]. The GMB approach provided a structured process for engaging knowledgeable stakeholders in discussions around complex problems [46,63,64].…”

“…Next, the CLD is analyzed using Vensim System Dynamics software to identify all possible unique feedback loops that begin and end at the factor of interest (e.g., Service Sustainability) [74]. Finally, to be able to infer which of the feedback loops are most likely to drive system behavior, each loop is quantified by averaging the strength of all the influences between each factor in the loop [59] (Equation (1)). This creates a 'normalized score' for each feedback loop.…”