Unveiling Knowledge Structures Within Organizations: A Novel Network Weighting Framework

Organizations increasingly recognize the pivotal role of knowledge and relationships in driving effective communication, collaboration, and innovation. However, existing approaches for knowledge-based social network analysis often rely on intrusive or labor-intensive data collection methods, which restricts their practical application. This study presents a novel framework for the generation and scrutiny of knowledge networks within organizations. Unlike the existing methods, our proposed approach leverages readily available administrative data, obviating the need for intrusive employee monitoring. This feature enables continuous organizational monitoring of intellectual capital and aids in predicting the ramifications of future staffing changes. Furthermore, our novel adaptable network weighting method provides a nuanced view of the knowledge and relational dynamics that are often not detected by traditional approaches. By utilizing flow-based centrality metrics, the model captures the emergent structural properties that may otherwise be overlooked. Thus the proposed framework offers a holistic, flexible, and efficient tool for mapping and understanding organizational knowledge dynamics.