Abstract
Innovation is a key driver of competitiveness and productivity in today’s market. In this scenario, Knowledge is seen as a company’s key asset and has become the primary competitive tool for many businesses. However, An efficient knowledge management must face diverse challenges such as the knowledge leakage and the poor coordination of work teams. To address these issues, experts in knowledge management must support organizations to come up with solutions and answers. However, in many cases, the precision and ambiguity of their concepts are not the most appropriate. This article describes a method for the diagnosis and initial assessment of knowledge management. The proposed method uses machine-learning techniques to analyze different aspects and conditions associated with knowledge transfer. Initially, we present a literature review of the common problems in knowledge management. Later, the proposed method and its respective application are exposed. The validation of this method was carried out using data from a group of software companies, and the analysis of the results was performed using Support Vector Machine (SVM).
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Jurado, J.L., CastañoTrochez, A., Ordoñez, H., Ordoñez, A. (2020). Knowledge Transfer in Software Companies Based on Machine Learning. In: Mejia, J., Muñoz, M., Rocha, Á., A. Calvo-Manzano, J. (eds) Trends and Applications in Software Engineering. CIMPS 2019. Advances in Intelligent Systems and Computing, vol 1071. Springer, Cham. https://doi.org/10.1007/978-3-030-33547-2_11
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DOI: https://doi.org/10.1007/978-3-030-33547-2_11
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