This study examines the influence of social network members (versus strangers) on sustainable food consumption choices to investigate how social influence can challenge the status quo in unsustainable consumption practices. We hypothesized that changes to individual consumption practices could be achieved by revealing ‘invisible’ descriptive and injunctive social norms. We further hypothesized that it matters who reveals these norms, meaning that social network members expressing their norms will have a stronger influence on other’s consumption choices than if these norms are expressed by strangers. We tested these hypotheses in a field experiment (N = 134), where participants discussed previous sustainable food consumption (revealing descriptive norms) and its importance (revealing injunctive norms) with either a stranger or social network member. We measured actual sustainable food consumption through the extent to which participants chose organic over non-organic consumables during the debrief. Findings showed that revealed injunctive norms significantly influenced food consumption, more so than revealed descriptive norms. We also found that this influence was stronger for social network members compared to strangers. Implications and further research directions in relation to how social networks can be used to evoke sustainable social change are discussed.

From labels to carbon offsets, various interventions to reduce emissions from air travel have been discussed. If implemented, their effects have been negligible. This is exacerbated by ever-increasing numbers of air travellers. This paper argues that patterns of air travel behaviour are extremely diverse and that empirical findings for predictors are very mixed. Research also points to promising multi-dimensional constructs, which might explain the diverse patterns of air travel. This paper has three aims: (1) investigate the explanatory power of multi-dimensional factors, namely quality of life preferences, lifestyles and geographical context; (2) in addition to norms, values and perceived behavioural control; (3) all in relation to diverse travel patterns, flyers/non-flyers, for short/middle- and long-distance personal air travel. We control for socio-demographic factors. We estimate zero-inflated Poisson regression models, utilising data from 4235 Swiss participants collected in 2017. For short/middle-distance personal air travel, lifestyle, geographical context, personal norms, values, age and income are significant predictors. In contrast, long-distance personal air travel is explained by quality of life preferences and gender. These different explanatory patterns for short/middle- vs. long-distance flights highlight the need to differentiate interventions and trigger points to govern air travel behaviour.

Leisure travel is often highly car-based. In Switzerland, the research area of our study, more than 60% of all kilometers traveled for leisure purposes are traveled by car, and an increase is predicted. As traveling by car comes with many negative side-effects, a more sustainable transportation system calls for a shift to less car-based forms of (leisure) travel. This paper reports an online choice experiment, testing the effectiveness of two financial and three non-financial treatments to reduce car-based leisure travel, all which are currently being discussed in the research and political domains in Switzerland. Additionally, we control for a wide range of determinants proven to be relevant for mode choice. We asked 737 participants to imagine they are visiting family or friends living a short distance (5 km) and longer-distance (100 km) away and to report their mode choice for such trips. Findings show the tested treatments will likely have a limited effect on reducing car-based leisure travel. Results also show that car use is motivated by different reasons than public transport or bike usage. Car users mainly want to travel fast and see car travel as the only viable option. Public transport and bike users, however, attach more importance to enjoyment. Specifically, the availability of a car, bike or public transport card is significant for the mode choice and habitual use. To unfreeze existing lock-ins, we conclude that research needs to look at reasons for why drivers believe there is no alternative.

The availability of big data allows a wide range of predictive analyses that could inform policies for promoting sustainable behaviors. While providing great predictive power, adopted models fall short in explaining the underlying mechanisms of behavior. However, predictive analyses can be enhanced by complementary theory-based inferential analyses, guiding tailored policy design to focus on relevant response mechanisms. This paper illustrates the complementary value of multidisciplinary inferential models in informing large predictive models. We focus on Structural Equation Modeling, an approach suitable for a holistic examination of different pathways and hypotheses from multiple disciplines. Drawing on an interdisciplinary theoretical framework we develop an empirically tractable model and apply it to a sample of household data from Switzerland. The model focuses on the relationships that delineate the underlying mechanisms for energy consumption behaviors in the case of private transportation. The results are discussed in light of possible contributions to policies aiming at the promotion of sustainable travel behavior as well as data requirements for analyses relying on big data.