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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”.
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”.
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