How can recommender interfaces help users to adopt new behaviors? In the behavioral change literature, nudges and norms are studied to understand how to convince people to take action (e.g. towel re-use is boosted when stating that ‘75% of hotel guests’ do so), but what is advised is typically not personalized. Most recommender systems know what to recommend in a personalized way, but not much research has considered how to present such advice to help users to change their current habits. We examine the value of presenting normative messages (e.g. ‘75% of users do X’) based on actual user data in a personalized energy recommender interface called ‘Saving Aid’. In a study among 207 smart thermostat owners, we compared three different normative explanations (‘Global’, ‘Similar’, and ‘Expert’ norm rates) to a non-social baseline (‘kWh savings’). Although none of the norms increased the total number of chosen measures directly, we show evidence that the effect of norms seems to be mediated by the perceived feasibility of the measures. Moreover, how norms were presented (i.e. specific source, adoption rate) affected what measures were chosen within our Saving Aid interface.
What effect does feedback have on energy conservation? Comparing previous household usage, neighbourhood usage, and social norms in Japan
