建立知識流程觀模式協助群體知識支援

Abstract

在知識密集的工作環境中，有效地提供工作者所需的知識文件，以協助其工作執行，是知識管理領域中的重要議題。從知識需求的角度分析，知識流代表個別與群體知識工作者在執行工作時，其知識需求與知識參考行為的脈絡。組織運用知識流，可有系統的將工作者的知識需求作精確的表示，亦可有效地藉此運作組織的知識支援體系。然而，在群體合作的環境下，不同工作者依其任務特性或扮演角色的不同，常有不同的知識需求。目前已知的知識流研究，大多只提供單一知識流讓工作者參考，並未考量知識流在團隊合作中的適用性。 本研究提出『知識流程觀』模式，以有效改善知識流研究之不足。此一知識流程觀模式，以知識流為基礎，將工作特性及個別角色納入考量，使不同的工作者對同一知識流可有不同的虛擬知識流來滿足其知識需求。 首先，以知識本體論作為知識流中知識節點抽象化的基礎，來建構基礎知識流，從而系統性的表達工作者的知識需求。 在基礎知識流之上，本研究建構知識流程觀模式並進行理論探討。知識流程觀主要是將基礎知識流中的部分知識節點，依照工作特性的知識需求，進行知識概念的歸納抽象化，以產生虛擬知識節點，並進而產生符合工作者知識需求的虛擬知識流。 為了探討工作者在不同角色時的知識需求，本研究亦提出，『以角色為基礎的知識流程觀』模式，利用角色與知識節點的相關度來產生虛擬知識節點，及分析角色所需知識概念層級與工作應有知識概念層級來推算角色知識需求。 知識流程觀與虛擬知識流是一個創新概念與理論模式，不但可擴展知識流的研究理論，對於組織的知識管理，特別是合作型知識支援的推展具有創新與實務的貢獻。

In knowledge-intensive working environments, workers need task-relevant knowledge and documents to support their task performance. Thus, how to effectively fulfill workers’ knowledge-needs is an important issue in realizing knowledge management in organizations. From a knowledge-needs perspective, a knowledge flow (KF) represents a flow of individual’s or group members’ knowledge-needs and referencing behavior of codified knowledge in conducting tasks. The flow has been utilized to facilitate organizational knowledge support by illustrating workers’ knowledge-needs systematically and precisely. However, conventional knowledge-flow models cannot work well in cooperative teams, which team members usually have diverse knowledge-needs in terms of task functions and roles. The reason is that those conventional models only provide one single view to all participants and do not reflect individual knowledge-needs in teams. Hence, the novel concepts and theoretical model of knowledge flow view (KFV) are proposed in this dissertation. The KFV model builds virtual knowledge flows derived from a base KF to provide abstracted knowledge to serve different workers’ knowledge-needs from task function and role perspectives. This dissertation uses domain ontology as the base of knowledge node abstraction. Hence, base knowledge flows are built to represent workers’ knowledge-needs systematically. Based on the base knowledge flows, a theoretical model of KFV is investigated and developed for discovering virtual knowledge nodes and virtual knowledgeflows. The KFV model abstracts the knowledge nodes of partial base knowledge flow to generate virtual knowledge nodes according to task functions, through knowledge concept induction and generalization. In addition, this dissertation proposes a role-based KFV model to investigate different knowledge-needs of distinct roles. The model exploits the relevance degrees between roles and knowledge nodes to derive virtual knowledge nodes and analyzes roles’ required knowledge concept level and operation required knowledge concept level to derive knowledge concepts of virtual knowledge nodes. The models of KFV and the concept of virtual knowledge flow are innovative, which extends the scope of knowledge flow research and enhances the efficiency of cooperative knowledge support in organizations.