Knowledge Conversion and Transfer: A Mathematical Interpretation

Abstract

This research proposes a novel graph-theoretic model to mathematically quantify the efficiency of tacit knowledge transfer within organizational networks. Drawing upon Nonaka and Takeuchi's SECI model, we represent individuals as nodes and their interactions (e.g., mentorship, collaboration, observation) as edges, weighted by frequency or quality of interaction. The model introduces metrics for "tacit knowledge reachability" and "conversion potential," allowing for the identification of optimal pathways and bottlenecks in knowledge flow. Case studies from professional service firms will provide empirical data to validate the model's predictive power for organizational learning and innovation.