Economic versus belief-based models: Shedding light on the adoption of novel green technologies. (February 2017) - Girod, Bastien; Mayer, Sebastian; Nägele, Florian
Abstract: Understanding the determinants for the adoption of novel green consumer technologies is important to effectively foster their diffusion. Energy and environmental science literature often takes an approach based on economic variables such as objectively measureable household and technology characteristics. Increasingly, also subjective variables based on personal belief are considered. On the basis of a survey about the intention to adopt an exemplary novel green consumer technology (intelligent thermostats), we contribute to the clarification of the explanatory power of these two approaches. We first compare the economic model to the belief-based model and second, investigate how beliefs about the green technology are influenced by personal environmental norms and innovativeness. Our evaluation shows that the belief-based model explains considerably higher variance in the intention to adopt. Thereby the perceived hedonic satisfaction, usefulness, habit and facilitating conditions reveal as key determinants. Moreover, environmental norms show lower impact than personal innovativeness. In the discussion we consolidate these findings and point to the risk of omitted variable bias when selectively including belief-based variables in adoption models. Our findings suggest that policies can effectively accelerate the early market diffusion of green consumer technologies by incentivizing retailers to introduce and market such technologies. Highlights: Adoption of a green consumer technology (energy-saving thermostats) is evaluated. Subjective beliefs about the technology show higher impact than objective measures. Key beliefs relate to pleasure, usefulness, habits and facilitating conditions. Personal innovativeness is more relevant for adoption than environmental norms. Isolated use of belief-based adoption determinants can lead to omitted variable bias.