Developing a Scalable Dynamic Norm Menu-Based Intervention to Reduce Meat Consumption

Abstract

How can we curb the current norm of unsustainable levels of meat consumption? Research on dynamic norms finds that learning that others are starting to eat less meat can inspire people to follow suit. Across four field experiments, we test efforts to scale dynamic-norm messages by incorporating them into restaurant and web-based menus. Studies 1–3 find increases in vegetarian orders when dynamic norms are included in menus (1–2.5 percentage points), although this effect does not always reach statistical significance and varies across populations and analytic models. In Study 4, dynamic norms significantly reduced vegetarian orders. These results raise two critical questions. First, where and with whom should a dynamic norm message reduce meat consumption? Our field data and past theory point to non-high socioeconomic contexts, and contexts where the reference group of people who have changed is meaningful to consumers. Second, how can the treatment be strengthened? Over five online experiments, we find that the visibility of the messages can be greatly improved, and more relatable norm referents can be selected. Although impacts on food orders appear modest, the minimal costs of scaling menu-based dynamic norm messages and the possibility of improving effect sizes make this a promising approach.