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Discovering role-based virtual knowledge flows for organizational knowledge support
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AI
DOI：10.1016/j.dss.2012.11.018.png)
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In knowledge-intensive work environments, workers need task-relevant knowledge and documents to support the execution of tasks. A knowledge flow (KF) represents an individual's or group's knowledge-needs and referencing behavior of codified knowledge during the performance of organizational tasks. Through knowledge flows, organizations can provide workers with task-relevant knowledge to satisfy their knowledge-needs. In teamwork environments, knowledge workers with different roles and task functions usually have diverse knowledge-needs, but conventional KF models cannot satisfy such needs. In a previous work, we proposed a novel concept and theoretical model called Knowledge Flow View (KFV). Based on workers diverse knowledge-needs, the KFV model abstracts knowledge nodes of partial KFs and generates virtual knowledge nodes through a knowledge concept generalization procedure. However, the KFV model did not consider the diverse knowledge-needs of workers who play different roles in a team. Therefore, in this work, we propose a role-based KFV model that discovers role-based virtual knowledge flows to satisfy the knowledge-needs of different roles. First, we analyze the level of knowledge required by workers to fulfill various roles. Then, we develop role-based knowledge flow abstraction methods that generate appropriate virtual knowledge nodes to provide sufficient knowledge for each role. The proposed role-based ITV model enhances the efficiency of KF usage, as well as the effectiveness of knowledge sharing and knowledge support in organizations. (c) 2013 Elsevier B.V. All rights reserved.
Keyword:
Knowledge flow
Knowledge flow view
Knowledge support
Knowledge management
Role
Ontology
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IF:
6.8
论文数:
3.8K
被引数:
1.5W
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