A Note On Data-Driven Actor-Differentiation And Sdgs 2 And 12: Insights From A Food-Sharing App

Using a dataset of 8,907 users of OLIO, a sharing-economy app designed to facilitate peer-to-peer food sharing and redistribution in response to UN SDGs 2 and 12, this study investigates the extent to which actor-differentiation between community volunteers and regular users is possible, and a useful means for quantifying the efficacy of volunteer-based community activation for start-ups. Specifically, we find that food-collection volunteers have substantially greater unique interactions and item listing volumes. We also find that volunteers have eight times more contact with other users and 17 times higher listing volumes. Volunteers also have conversations four times sooner after listing an item for sharing, and faster transaction rates. We conduct this research against a backdrop of increased interest in data-driven research in entrepreneurship and innovation and with reference to opportunities to advance the operationalization of SDGs via innovation mobilization.