Multi-Dimensional Predictors of Sharing Platform Growth: A Machine Learning Study

Abstract

Hyperlocal sharing platforms are gaining traction as a sustainable alternative for distributing resources, particularly in the case of food sharing, where success relies heavily on community engagement and local interactions. Despite their growing popularity, there is little understanding of what contributes to the growth of sharing platforms and their evolution over time. In collaboration with the UK’s largest food sharing platform, this study explores over five million anonymised sharing instances across 312 districts in England over a 51-month period. Building on prior literature, we empirically examine platform growth using temporal network structures, user behavioural variation, and local demographic and environmental characteristics, to model user acquisition and retention. Using a machine learning approach, we utilise SHAP variable importance to determine the most impactful attributes for platform growth and identified key predictors of the proliferation of food sharing platforms. This includes the distribution of super-users, the presence of active volunteers, and the formation of structured communities. Our findings demonstrate how simultaneously combining data from users, networks and geographical dimensions provides a more useful explanation of growth than any isolated disciplinary theory. The results contribute to the theorisation of growth in sharing platforms, offering managerial insights that support the development and sustainability of food sharing networks within the sharing economy.