How do internal, market and institutional factors affect the development of eco-innovation in firms?

This paper investigates how drivers affect the development of eco-innovation in firms. Our research classifies the eco-innovation drivers in three categories: internal factors, market factors, and institutional factors. Using a sample with 9172 firms from the Spanish Innovation Survey Panel, we stud...

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Bibliographic Details
Published in:Journal of cleaner production 2021-05, Vol.297, p.126692, Article 126692
Main Authors: Arranz, Nieves, Arguello, Nohemi Lopez, Fernández de Arroyabe, Juan Carlos
Format: Article
Language:English
Subjects:
Artificial neural networks
Drivers
Eco-innovation
Methodological approach
Regression
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Summary:This paper investigates how drivers affect the development of eco-innovation in firms. Our research classifies the eco-innovation drivers in three categories: internal factors, market factors, and institutional factors. Using a sample with 9172 firms from the Spanish Innovation Survey Panel, we study the impact of eco-innovation drivers for energy and environmental efficiency objectives. This research utilizes a combination of two methods: Ordinal Logit Regression Models and Artificial Neural Networks. The results allow us to compare the impact of each variable. From a methodological point of view, this approach allows overcoming the difficulties of performing a regression analysis, mainly due to the low levels of explained variance and the problem of comparing the regression coefficients obtained. From the Artificial Neural Networks analysis, it is observed that the factor that most affects the eco-innovation is the previous experiences in eco-innovation, compared to variables such as external financing or innovation capabilities, which have a very small impact. These results may have important repercussions from the point of view of developing environmental incentive policies. •This paper investigates how drivers affect the development of eco-innovation in firms.•This research utilizes a combination of Ordinal Logit Regression Models and Artificial Neural Networks.•The factor that most affects eco-innovation is the previous experiences in eco-innovation.•These results may have important repercussions from the point of view of developing environmental incentive policies.
ISSN:0959-6526
1879-1786
DOI:10.1016/j.jclepro.2021.126692