A design theory for observational crowdsourcing

观察众包的设计理论

• 批准号: RGPIN-2020-04916
• 负责人: Parsons, Jeffrey
• 金额: $ 2.55万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746417
• 关键词: design; theory; observational; crowdsourcing

Crowdsourcing, in which activities normally undertaken inside an organization are expanded to engage ordinary people in contributing to activities of an organization, has become increasingly popular with the development of online software platforms that support these activities. Crowdsourcing involving data collection (data crowdsourcing) is one of the most important forms, as it makes it possible to collect data on a scale that is otherwise impractical for many organizations. Most data crowdsourcing efforts to date have focused on simple and well-defined data collection tasks (microtasks). However, a new and more powerful form of data crowdsourcing has emerged, requiring data collection for complex, ill-defined and evolving uses (observational crowdsourcing). However, despite major differences in the nature of the task and the contributors, the primary approach for designing software platforms for observational crowdsourcing currently mirrors that of microtask crowdsourcing. This limits the extent to which observational crowdsourcing can be used effectively. We propose to develop and test a design theory for observational crowdsourcing. This design theory will be expressed in the form of design principles that link features of a software platform with the distinct characteristics of observational crowdsourcing projects. These principles will be derived and justified based on core underlying theories that connect design features to desired outcomes. This theory will then be used to design a new crowdsourcing platform that will host observational crowdsourcing projects. In this process, specific design choices will be made following design guidelines extracted from the theory. The platform and design theory will be evaluated by assessing the quality of data collected. Key expected outcomes are that the platform will enable the collection of accurate, precise, and diverse data, which will be useful to the consumers of observational crowdsourcing data. The significance and impact of this work are two-fold. First, the platform will support observational crowdsourcing projects in Canada and beyond. Such projects stand to benefit project sponsors and other data consumers, while engaging data contributors in contributing to real societal challenges. Second, the proposed design principles will be available to be adapted and used by other developers of observational crowdsourcing and related technologies.

众包（将通常在组织内部进行的活动扩展到让普通人为组织的活动做出贡献）随着支持这些活动的在线软件平台的开发而变得越来越受欢迎。涉及数据收集的众包（数据众包）是最重要的形式之一，因为它使得收集数据成为可能，而这对于许多组织来说是不切实际的。迄今为止，大多数数据众包工作都集中在简单且定义明确的数据收集任务（微任务）上。然而，一种新的、更强大的数据众包形式已经出现，需要为复杂、不明确和不断发展的用途收集数据（观察众包）。然而，尽管任务的性质和贡献者存在重大差异，但目前设计观察众包软件平台的主要方法反映了微任务众包的方法。这限制了观察众包的有效使用程度。我们建议开发和测试观察众包的设计理论。这种设计理论将以设计原则的形式表达，将软件平台的特征与观察性众包项目的独特特征联系起来。这些原则将根据将设计特征与期望结果联系起来的核心基础理论来推导和论证。然后，该理论将用于设计一个新的众包平台，该平台将托管观察性众包项目。在此过程中，将根据从理论中提取的设计指南做出具体的设计选择。 将通过评估收集的数据的质量来评估平台和设计理论。主要预期成果是该平台将能够收集准确、精确和多样化的数据，这对观测众包数据的消费者将很有用。 这项工作的意义和影响是双重的。首先，该平台将支持加拿大及其他地区的观察众包项目。此类项目将使项目发起人和其他数据消费者受益，同时让数据贡献者为真正的社会挑战做出贡献。其次，所提出的设计原则将可供观测众包和相关技术的其他开发商进行调整和使用。