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

Ángel Peiró-Signes, Marival Segarra-Oña, Óscar Trull-Domínguez and Joaquín Sánchez-Planelles

Socio-Economic Planning Sciences, 2022, vol. 79, issue C

Abstract: 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 eco-innovation, provides solid empirical support for the understanding of the mechanisms that are crucial for firms' transition to a low-carbon economy. Although many drivers have been considered to affect firms’ eco-innovation, our feature selection process using the BorutaShap algorithm demonstrates that few aspects are truly relevant. Furthermore, analyzing a tree surrogate of the final model, our study explores the different paths or combinations of aspects that consistently lead to a specific eco-innovation orientation. The accuracy of the model and the large and complete spectrum of innovative companies in the sample contribute to the generalizability of the results.

Keywords: Eco-innovation; Drivers; Innovative firms; Machine learning (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:79:y:2022:i:c:s0038012121001373

DOI: 10.1016/j.seps.2021.101145

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