In knowledge-intensive crowdsourcing (KI-C), estimating proper participants is an important way to ensure tasks crowdsourcing outcomes. Participants’ attributes (PAs) act as the main decision factors which are viewed as criteria for evaluating and estimating potential participants. Actually, multiple interdependent PAs have effect on participant estimation. It is an initial and vital work in estimating participants in KI-C to identify those PAs and measure their relationships. Consequently, this study first identifies PAs for participant estimation in KI-C by integrating PAs presented in the related academic studies and some practical KI-C sites. Subsequently, this study develops an integrated 2-tuple linguistic method and decision making trial and evaluation laboratory method to describe and measure causal relationships of the identified PAs. Identification of PAs would offer a common list of criteria for participant estimation in KI-C and aid to enrich studies in this field. Additionally, measurement of the PAs’ relationships through causality and prominence can assist requesters and managers of KI-C sites to understand and deal with those PAs in practical.

在知识密集型众包（KI-C）中，估计合适的参与者是确保任务众包结果的重要方法。参与者的属性（PA）是主要的决策因素，被视为评估和估计潜在参与者的标准。实际上，多个相互依赖的PA会对参与者的估计产生影响。评估KI-C参与者以识别那些PA并衡量其关系是一项至关重要的工作。因此，本研究首先识别功率放大器用于KI-C参与者估计通过集成功率放大器在相关的学术研究和一些实用的KI-C网站中介绍。随后，本研究开发了一种集成的二元组语言方法以及决策试验和评估实验室方法，以描述和衡量已识别PA的因果关系。对PA的鉴定将为KI-C中的参与者评估提供一个通用的标准列表，并有助于丰富该领域的研究。此外，通过因果关系和突出程度来评估PA之间的关系可以帮助KI-C站点的请求者和管理者实际理解和处理这些PA。