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Exposing the ideal combination of endogenous¿exogenous drivers for companies' ecoinnovative orientation: Results from machine-learning methods

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Exposing the ideal combination of endogenous¿exogenous drivers for companies' ecoinnovative orientation: Results from machine-learning methods

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Peiró Signes, A.; Segarra-Oña, M.; Trull-Dominguez, O.; Sánchez-Planelles, J. (2022). Exposing the ideal combination of endogenous¿exogenous drivers for companies' ecoinnovative orientation: Results from machine-learning methods. Socio-Economic Planning Sciences. 79:1-15. https://doi.org/10.1016/j.seps.2021.101145

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/180858

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Title: Exposing the ideal combination of endogenous¿exogenous drivers for companies' ecoinnovative orientation: Results from machine-learning methods
Author: Peiró Signes, Angel Segarra-Oña, Marival Trull-Dominguez, Oscar Sánchez-Planelles, Joaquín
UPV Unit: Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses
Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Issued date:
Embargo end date: 2024-02-02
Abstract:
[EN] This study provides an XGBoost model to characterize the environmental orientation of innovative firms. This novel approach, using state-of-the-art machine learning methodologies and multiple recognized drivers of ...[+]
Subjects: Eco-innovation , Drivers , Innovative firms , Machine learning
Copyrigths: Embargado
Source:
Socio-Economic Planning Sciences. (issn: 0038-0121 )
DOI: 10.1016/j.seps.2021.101145
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.seps.2021.101145
Type: Artículo

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