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Exploring Knowledge Filtering Processes in Electronic Networks of Practice

机译:在实践电子网络中探索知识过滤过程

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Electronic networks of practice (ENPs) have become an important mechanism for knowledge exchange among loosely connected individuals who share common knowledge interests. While prior research has explored factors that influence knowledge contribution in such networks, less is understood about the process by which individuals evaluate and ultimately adopt knowledge from ENPs. This study examines the process of knowledge filtering in online ENP forums. Drawing from dual process and information-evaluation theories, we hypothesize that performance on a knowledge-filtering task will be influenced by the constancy and directionality of search patterns employed by knowledge seekers. Hypotheses are tested in an experiment that utilized an eye tracker to record gaze data from professional software developers using an experimental ENP forum. By combining information-evaluation and dual process theory perspectives, our results deepen the insights offered in extant information-processing literature by showing that higher filtering accuracy is associated with (a) constant evaluation of some types of information attributes (solution content) but not others (peripheral cues), and (b) increasing attribute-based processing over time.
机译:实践电子网络(ENP)已成为共享共同知识兴趣的松散个人之间进行知识交换的重要机制。尽管先前的研究已经探索了影响此类网络中知识贡献的因素,但人们对个人评估和最终采用ENP知识的过程了解得很少。本研究考察了在线ENP论坛中知识过滤的过程。基于双重过程和信息评估理论,我们假设知识过滤任务的绩效将受到知识寻求者所采用的搜索模式的稳定性和方向性的影响。假设在一个实验中进行了测试,该实验利用眼动仪通过实验性ENP论坛记录了来自专业软件开发人员的凝视数据。通过结合信息评估和双重过程理论的观点,我们的结果通过显示更高的过滤精度与(a)不断评估某些类型的信息属性(解决方案内容)相关,而加深了现有信息处理文献中提供的见解。 (外围提示),以及(b)随着时间的推移增加基于属性的处理。

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