Extracting and prioritizing knowledge risk components by considering the knowledge map

Abstract

Purpose This paper aims to identify knowledge risk components. The present research also tends to determine the relationship between knowledge risk components, the importance of each component and the final ranking of components. Design/methodology/approach This is applied research regarding type, as well as being a descriptive, analytical study regarding performance; it is done according to the opinion of experts. Library studies are applied to extract knowledge risk components. After extracting components, components were screened using the interview technique. In addition, network analytical process method has been used for considering the interrelationship of components and determining their values. Findings After making the required analysis and studies, a total of 17 knowledge risk components were identified in four clusters. The four clusters include knowledge cluster, knowledge map cluster, organization cluster and expert cluster. It is to be noted that the extracted components are prioritized in each cluster. In the regarded case study, different parts of the organization have been evaluated in terms of exposure to knowledge risk. Originality/value Identifying the knowledge risk components enables the organization for moving toward the implementation of the knowledge management system and informing the organization in connection with risk aversion. In fact, such components provide the chance for the organization to identify risks inherent in each department of any organization and develop the necessary measures to reduce the risk in risky areas.