Networks, obstacles, and resources for innovative performance: An analysis via neural networks for prediction in the manufacturing industry

Authors

  • Fernando Barrios Aguirre School of Management, Business, and Economics Sciences, University Foundation of the Andean Area, Bogotá, Colombia https://orcid.org/0000-0001-9577-3329
  • Sandra Yaneth Mora Malagón School of Economic, University of the Andes, Bogota, Colombia
  • Martha Isabel Amado Piñeros School of Management, Business, and Economics Sciences, University Foundation of the Andean Area, Bogotá, Colombia
  • Luis Gabriel Gutiérrez Bernal School of Management, Business, and Economics Sciences, University Foundation of the Andean Area, Bogotá, Colombia

Keywords:

Knowledge Networks, innovative performance, neural networks

Abstract

This document aims to predict the level of innovation in manufacturing companies in Colombia between 2017-2018. A forecasting mechanism for innovation performance has been constructed using Neural Networks (NNs). This model considers the objectives of innovation, obstacles to innovation, knowledge networks, and technical information of each one of the firms. Results show that demand push, vertical sources, financial obstacles, and qualified personnel are the most important variables in predicting innovative performance. Our empirical analysis uses firm-level innovation survey data from the EDIT (Encuesta de Desarrollo e Innovación Tecnológica in Spanish, Technological Development, and Innovation Survey in English) for Colombia for the years 2017-2018.

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Published

2022-12-27

How to Cite

Barrios Aguirre, F., Mora Malagón, S. Y. ., Amado Piñeros, M. I., & Gutiérrez Bernal, L. G. (2022). Networks, obstacles, and resources for innovative performance: An analysis via neural networks for prediction in the manufacturing industry. Journal of Technology Management &Amp; Innovation, 17(4), 40–47. Retrieved from https://www.jotmi.org/index.php/GT/article/view/4050

Issue

Section

Research Articles