Social Influence in the Adoption of Low-Carbon Consumer Innovations

Charlie Wilson, Barnaby Andrews

Contact: charliewilson.academic@gmail.com

In this study we investigate social influence in the adoption of low-carbon innovations in mobility, food, homes and energy domains, using a unique dataset from an online survey of 6,000 adopters and non-adopters in the UK and Canada. Both adopters and non-adopters were asked standardised questions on the role of six information channels (friends, neighbours, social media, mass media, experts, descriptive norms) and four mechanisms of information diffusion: word-of-mouth (WoM), electronic WoM via social media (eWoM), peer or neighbourhood effects, and injunctive norms. We used groupwise comparisons (Mann-Whitney U tests, with effect sizes) to test hypotheses applying innovation adoption concepts applied to low-carbon consumer innovations. H1: All information channels and social influence mechanisms are more important to adopters than non-adopters. Adopters consistently scored all information channels and all mechanisms of information diffusion higher than non-adopters across the diverse set of 16 low-carbon innovations. We were unable to further test competing explanations: (1) causal - information about an innovation led to adoption; (2) reverse causal - adopters were more likely to find information salient if it was about an innovation they had adopted. H2: Peer effects are more influential on adoption for innovations which are physically or socially visible. From our set of 16 innovations, we identified a subgroup used in public settings (e.g., rooftop solar) and compared against a subgroup used in private with low social visibility (e.g., smart heating). We found mixed evidence that peer effects were stronger for more visible innovations, supporting the hypothesis in some cases (e.g., rooftop solar) but not others (e.g., electric vehicles). Access to infrastructure locally may mediate information diffusion through peer effects (e.g., charging infrastructure for electric vehicles). H3: Social media (eWoM) is more influential on adoption for innovations which are digital. We compared a subgroup of innovations using digital interfaces or online platforms (e.g., peer-to-peer carsharing) against a subgroup not requiring digital skills (e.g., solar storage systems). We found no evidence that social media was more influential for information diffusion for the digital innovations. Adopters with digital skills may rely on online information through web search and specialist websites rather than social media. H4: Injunctive social norms are more influential on adoption for innovations with strong social benefits. Low-carbon consumer innovations can support local economies (e.g., digital food hubs), social capital (e.g., ride-sharing), and healthy living (e.g., e-bikes). We identified a subgroup from our set of 16 innovations with clear social benefits (e.g., foodsharing apps) and compared against a subgroup with primarily private benefits (e.g., smart lighting). We found mixed evidence that injunctive social norms were stronger for innovations with clear social benefits. Our initial findings overall are that social influence in the adoption of low-carbon consumer innovations is in line with expectations in a general sense, but that innovations with specific characteristics (visible, digital, pro-social) do not privilege specific influence mechanisms. In work now underway, we are testing these findings further using multivariate models

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