Knowledge Management for Open Innovation: Bayesian Networks through Machine Learning

Abstract

Knowledge management within organizations allows to support a global business strategy and represents a systemic and organized attempt to use knowledge within an organization to improve its performance. The objective of this research is to study and analyze knowledge management through Bayesian networks with machine learning techniques, for which a model is made to identify and quantify the various factors that affect the correct management of knowledge in an organization, allowing you to generate value. As a case study, a technology-based services company in Mexico City is analyzed. The evidence found shows the optimal and non-optimal management of knowledge management, and its various factors, through the causality of the variables, allowing us to more adequately capture the interrelationship to manage it. The results show that the most relevant factors for having adequate knowledge management are information management, relational capital, intellectual capital, quality and risk management, and technology assimilation.