Knowledge Management for Open Innovation: Bayesian Networks through Machine Learning

Terán-Bustamante, Antonia; Martínez Velasco, Antonieta Teodora; Dávila-Aragón, Griselda

doi:10.3390/joitmc7010040

Knowledge Management for Open Innovation: Bayesian Networks through Machine Learning

Journal

Journal of Open Innovation: Technology, Market, and Complexity

ISSN

2199-8531

Date Issued

2021

Author(s)

Type

Resource Types::text::journal::journal article

DOI

10.3390/joitmc7010040

URL

https://scripta.up.edu.mx/handle/20.500.12552/1776

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.