Arranz, Carlos F.A. ORCID: https://orcid.org/0000-0002-6866-0684 (2024) A system dynamics approach to modelling eco‐innovation drivers in companies: understanding complex interactions using machine learning. Business Strategy and the Environment. pp. 1-24. ISSN 0964-4733 (Print), 1099-0836 (Online) (doi:10.1002/bse.3704)

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Abstract

This paper examines the effect of drivers in the development of eco-innovation from a system dynamics perspective. While previous literature has made important contributions in identifying factors that influence the development of eco-innovations, there remains limited understanding of how these drivers act and interact in promoting its development. Therefore, there is a need to develop a framework of relationships and drivers that encourage and support eco-innovation in companies. This paper develops an integrated framework encompassing key internal, market and governmental factors and their complex interactions using principles of system dynamics and machine learning to address this gap. The research questions how these drivers interact in a dynamic and non-linear manner to influence the development of eco-innovation in companies and how can these interactions be effectively modelled and understood, considering the complexities of sustainable business practices and the limitations of traditional linear approaches. We empirically test these questions by using the Spanish Technological Innovation Panel database. The findings demonstrate that eco-innovation is not solely driven by isolated factors; instead, it emerges from the complex interplay between internal capabilities, governmental policies and market dynamics. By emphasising the synergistic effects of these drivers, the research offers a nuanced understanding of their systemic interactions. Furthermore, our analysis highlights the varying efficiency levels of different drivers, underscoring the pivotal role of environmental corporate policies and the strategic allocation of financial resources. In contrast, cooperation, market forces and regulations exhibit lower efficiency in driving eco-innovation processes. These insights not only advance theoretical knowledge but also provide valuable guidance for businesses and policymakers, offering a more holistic approach to fostering sustainable innovation.

Item Type:	Article
Uncontrolled Keywords:	eco-innovation; environmental innovation; system dynamics; dynamic modelling; machine learning; artificial neural networks; interaction effects; drivers; feedback loops; complex systems
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HD Industries. Land use. Labor Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Business Greenwich Business School > Networks and Urban Systems Centre (NUSC)
Last Modified:	02 Dec 2024 15:55
URI:	http://gala.gre.ac.uk/id/eprint/45899

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