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Start with the discovery: Improving capacity factors analysis with the appreciative inquiry approach

机译：从发现入手：通过欣赏式查询方法改进容量因素分析

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摘要

Several decision-aid frameworks attempt to provide an optimal solution to the complex, challenging problem of delivering water supply and sanitation services to communities in need. However, a failure in proper needs assessment or handoff causes systemic failure of an installed system. Capacity factors analysis (CFA) is one such framework that focuses on personalized technology-specific alternative recommendations; and it faces similar challenges. While helpful in many ways, designers using CFA still make critical assumptions with regards to community's expressed needs and only passively include the community members in the design process. Appreciative inquiry (AI) is introduced as a means to bridge this gap and increase community empowerment. The AI approach is a four-phase process: discovery, dream, design, and destiny. The process starts with asking community members about their strengths and capabilities, follows with their vision of the community, creates a space for collaborative design, and ends with implementation. A service-learning experience in Tshapasha is provided to demonstrate AI's benefits. The results are compared to a CFA-focused study of Tshapasha from 2011.
机译：几个决策辅助框架试图为向需要的社区提供供水和卫生服务的复杂而具有挑战性的问题提供最佳解决方案。但是，正确的需求评估或切换失败会导致已安装系统出现系统性故障。容量因子分析（CFA）是这样一种框架，其重点是针对特定于技术的个性化替代建议。并且面临着类似的挑战。尽管使用CFA的设计师有很多帮助，但他们仍会根据社区表达的需求做出关键性的假设，并且仅在设计过程中被动地将社区成员包括在内。引入鉴赏性查询（AI）作为弥合这种差距并增强社区权能的一种手段。人工智能方法包括四个阶段：发现，梦想，设计和命运。该过程从询问社区成员的优势和能力开始，接着是对社区的愿景，为协作设计创造空间，最后是实施。提供了在Tshapasha的服务学习经验，以证明AI的好处。将结果与自2011年起针对CFA的Tshapasha的研究进行了比较。

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