Incorporating social context and co-evolution in an innovation diffusion model--with an application to cleaner vehicles

Existing diffusion models have proven less suitable for the analysis of environmental innovations, such as hybrid vehicles, which emerge in a context of changing social appraisal and regulatory support. In this paper, we offer an agent-based analysis of innovation diffusion which is better suitable for those cases. We explore future scenarios of car engine technology with support of a simulation model. In the model, actor behavior is modeled explicitly on the basis of actor frames and the consequent appraisal of technology options by potential adopters and by suppliers. The model is analytically novel in that it incorporates five feedback loops: interactive learning between suppliers and users, scale and learning economies, endogenous taste formation among consumers, and social learning (attribution of meaning). In the model, consumer decisions influence each other and the supply of innovation by shaping R&D and investment decisions of suppliers. The model is applied to explore future diffusion scenarios of clean vehicles, though it starts with simulating recent historic trends, including the quick diffusion of direct fuel injection systems and slow diffusion of electric vehicles in the last 20 years. It is shown that the success of hybrid electric vehicles critically depends on suppliers shifting towards HEV development and production (resulting in greater supply of HEV models) and on consumers valuing hybrid electric vehicles more than environmentally improved diesel vehicles