Diffusion of eco-innovation through inter-firm network targeting: An agent-based model. (10th February 2022)

• Record Type: Journal Article
• Authors: Ramkumar, Shyaam; Mueller, Matthias; Pyka, Andreas; Squazzoni, Flaminio
• Is Part Of: Journal of cleaner production. Volume 335(2022)
• Publication Date: 2022-02-10
• Subjects: Eco-innovation -- Diffusion -- Agent-based modelling -- Inter-firm networks -- Targeting strategies; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.130298
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: To address increasingly urgent global environmental challenges, there is a need for rapid and widespread adoption of eco-innovations. Previous research has shown that targeting key initial adopters using network-based strategies is an effective way to increase the adoption and diffusion of innovations; however, these models have not specifically been applied to the diffusion of eco-innovations through inter-firm networks. Therefore, this paper develops an agent-based model that tests various network-based targeting strategies on generated inter-firm networks with different parameters to understand mechanisms that could boost and accelerate the adoption and diffusion of eco-innovations. The results show that targeting firms with high degree or high influence could more quickly diffuse incremental eco-innovations, while targeting neighbors of adopters is a better strategy for more radical eco-innovations with greater barriers to adoption. This has implications for policymakers, companies, and startups who wish to accelerate eco-innovation adoption and diffusion to create positive societal impacts. Highlights: Targeting key adopters is an effective way to diffuse the adoption of innovations. We apply diffusion of innovation theories and agent-based models to eco-innovations. We extend these models to study the effect of network-based targeting on diffusion. We find specific network-based strategies more effectively diffuse eco-innovations. We conclude with implications for policymakers, companies, and startups.