Edinburgh Napier University

  This paper introduces a novel geometric approach to modeling the dynamics of knowledge transfer. By integrating geometric probability models with knowledge entity interactions represented in multigeometric spaces, we predict knowledge transfer effectiveness and efficiency. We leverage concepts from Riemannian geometry, particularly closed geodesics, to model optimal pathways of knowledge flow within organizational networks. Our methodology employs a dual-space representation, combining Euclidean and hyperbolic embeddings to capture both hierarchical and non-hierarchical relationships among knowledge entities. We detail the application of these models in various scenarios, including educational and organizational settings, and validate them through empirical data. The integration of geometric concepts with knowledge transfer theories offers a powerful toolset for organizations to optimize their knowledge transfer processes, identify potential bottlenecks, and implement targeted interventions. Our approach demonstrates significant potential to enhance predictive accuracy in understanding and improving knowledge dissemination dynamics.