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The antecedents of green innovation performance: A model of learning and capabilities

Open access
Albort-Morant, Gema, Leal-Millán, Antonio, Cepeda-Carrión, Gabriel·2016·Journal of Business Research

Abstract

Environmental management and green practices have a narrow linkage with firm innovativeness. Companies that are pioneers in green innovation strategies might reach and sustain competitive advantages. Thus, successful green innovation performance (GIP) helps firms to achieve greater efficiency as well as to establish and strengthen their core competences. This study focuses on the dynamic capabilities (DC) and ordinary capabilities (OC) like antecedents of GIP, and the relationship between these constructs. Proposing a mediation model to analyze both direct and indirect relationships, this study applies variance-based structural equation modeling through a partial least squares to a sample of 112 firms from the Spanish automotive components' manufacturing sector. The results suggest that both the direct effect and indirect effect of capabilities (DC and OC) on GIP are positive and significant, and improve the prediction of firm's GIP. Furthermore, the structural model supports that DC influence GIP by reconfiguring relationship-learning capabilities (a type of OC).

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Metadata

Authors
Albort-Morant, G., Leal-Millán, A., & Cepeda-Carrión, G.
Venue
Journal of Business Research
Publisher
Elsevier BV
Published
2016-11
Volume
69
Issue
11
Pages
4912-4917
ISSN
0148-2963 (ISSN-L)
OpenAlex
W2345425456
Cited by
665
References
9
FWCI
54.27
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: The antecedents of green innovation performance: A model of learning and capabilities. https://app.cochat.ai/papers/10.1016/j.jbusres.2016.04.052
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Albort-Morant, G., Leal-Millán, A., & Cepeda-Carrión, G. (2016). The antecedents of green innovation performance: A model of learning and capabilities. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2016.04.052

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.