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dc.creatorBarrios Aguirre, Fernando
dc.creatorMora Malagón, Sandra Yaneth
dc.creatorAmado Piñeros, Martha Isabel
dc.creatorGutiérrez Bernal, Luis Gabriel
dc.date2022-12-27
dc.identifierhttps://www.jotmi.org/index.php/GT/article/view/4050
dc.identifier10.4067/S0718-27242022000400040
dc.descriptionThis 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.en-US
dc.formatapplication/pdf
dc.languageeng
dc.publisherFacultad de Economía y Negocios, Universidad Alberto Hurtadoen-US
dc.relationhttps://www.jotmi.org/index.php/GT/article/view/4050/1462
dc.rightsCopyright (c) 2022 Fernando Barrios Aguirre, Sandra Yaneth Mora Malagón, Martha Isabel Amado Piñeros, Luis Gabriel Gutiérrez Bernalen-US
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0en-US
dc.sourceJournal of Technology Management & Innovation; Vol. 17 No. 4 (2022); 40-47en-US
dc.sourceJournal of Technology Management & Innovation; Vol. 17 Núm. 4 (2022); 40-47es-ES
dc.source0718-2724
dc.subjectKnowledge Networksen-US
dc.subjectinnovative performanceen-US
dc.subjectneural networksen-US
dc.titleNetworks, obstacles, and resources for innovative performance: An analysis via neural networks for prediction in the manufacturing industryen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeArtículo revisado por paresen-US


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