Knowledge Transfer Prediction Mechanics

Fascia, Michael

Abstract

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.

Citation

Fascia, M. (2024). Knowledge Transfer Prediction Mechanics. Journal of Strategy, Operations & Economics, 8(2), Article 2. https://doi.org/10.0708/JSOE.2024946720

• Journal Article Type: Article
• Acceptance Date: May 10, 2024
• Online Publication Date: Jul 8, 2024
• Publication Date: 2024
• Deposit Date: Jul 8, 2024
• Electronic ISSN: 2396-8826
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 2
• Article Number: 2
• DOI: https://doi.org/10.0708/JSOE.2024946720
• Keywords: Geometric probability models, Knowledge transfer prediction, Riemannian geometry, Closed geodesics ,Multi-geometric embeddings, Organizational networks, Knowledge flow optimization, Euclidean and hyperbolic spaces
• Publisher URL: http://www.soejournal.com/index.php/soe/article/view/61