英语翻译Peer-Based Computer-Supported KNOWLEDGE REFINEMENT an Em

英语翻译
Peer-Based Computer-Supported KNOWLEDGE REFINEMENT an Empirical Investigation
Knowledge management (KM) repository-based systems (such as those involving “best practices” and “lessons learned” applications) are generally costly to operate since they require expert judgment to determine which knowledge submissions are to be included in the system and refined to make them as efficacious as possible.Empirical evidence in cognitive psychology suggests that experts may not be needed for such refinement when the knowledge consumers are nonexperts.The knowledge “distance” between experts and nonexperts may indeed weaken expert-centric knowledge refinement.In addition,peer judgments delivered to nonexpert end users may substitute well for expert judgments.
Multiple peer judgments,especially when provided through computer-supported knowledge refinement systems,may be much less costly and just as good or perhaps even better than expert judgments,since peers are likely to think more like nonexpert users.A computer-support system is helpful for facilitating peer-based knowledge refinement,since more peers than experts are probably required for peer-based refinement to be effective.Here,we present the results of an experimental study we conducted and a corporate application that confirms our hypothesized equality or superiority of peer-based knowledge refinement compared to expert-centric knowledge refinement.We make no attempt to compare the actual costs of using individual experts vs.multiple peers; we address only the issue of comparing the quality of the results of the two options.
Knowledge repositories are KM systems that play a crucial role in extending organizational memory,preventing organizational forgetfulness,and leveraging competitive advantage.When an organization solicits knowledge from its employees,it must evaluate the submitted knowledge and refine it into a form that is most useful for system users.A traditional assumption in the knowledge-refinement process suggests that experts “provide a framework for evaluation and incorporating new experiences and information”.

基于对等计算机支持知识精炼实证研究
通常代价高昂的操作,因为它们需要专家判断来确定哪些知识意见书并将其包括在系统中改进以使其尽可能为有效的知识管理 (KM) 基于存储库的系统 （例如,只包括"最佳做法"和"教训"应用程序）.认知心理学中的实证证据表明专家可能不需要这种精制的知识消费者时 nonexperts.知识专家和 nonexperts 之间的"距离"事实上可能会削弱以专家为中心的知识求精.此外,对等判决传递到 nonexpert 的最终用户很好的专家判断代替.
多个的对等判断尤其是通过计算机支持的知识求精的系统提供可能较成本高昂,而且正如好或比专家的判断也许更好,因为对等点很可能认为更像 nonexpert 的用户.计算机支持系统是有助于促进基于对等的知识细化的起更多的同行比专家可能需要基于对等的细化是有效的.在这里,我们提出我们所进行的实验研究和企业应用程序,确认我们的虚拟的平等的结果或基于对等的知识求精以专家为中心的知识求精相比的优势.我们不会尝试进行比较的使用与多个对等点的个别专家实际成本,我们处理只比较的两个选项结果的质量问题.
发挥至关重要的作用,在扩展组织记忆、 防止组织遗忘,和利用竞争优势的知识管理系统知识存储库.当组织索取其员工从知识时,它必须评估提交的知识,并优化该到最有用的系统用户的窗体.一个传统的假设,知识精制过程中建议专家"提供一个框架的评价,并融入新的经验和信息".