Discovering role‐based virtual knowledge flows for organizational knowledge support

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 KFV model enhances the efficiency of KF usage, as well as the effectiveness of knowledge sharing and knowledge support in organizations.