Understanding the challenges affecting food-sharing apps’ usage: insights using a text-mining and interpretable machine learning approach

Food waste is a serious problem affecting societies and contributing to climate change. About one-third of all food produced globally is wasted, while millions of people remain food insecure. Food-sharing apps attempt to simultaneously address ‘hunger’ and ‘food waste’ at the community level. Though highly beneficial, these apps experience low usage. Existing studies have explored multiple challenges affecting food-sharing usage, but are constrained by limited data and narrow geographical focus. To address this gap, this study analyzes online user reviews from top food-sharing apps operating globally. A unique approach of analyzing text data with interpretable machine learning (IML) tools is utilized. Eight challenges affecting food-sharing app usage are obtained using the topic modeling approach. Further, the review scores representing user experience (UX) are assessed for their dependence on each challenge using the document-topic matrix and machine learning (ML) procedures. Tree-based ML algorithms, namely regression tree, bagging, random forest, boosting, and Bayesian additive regression tree are employed. The best-performing algorithm is then complemented with IML tools such as accumulated local effects and partial dependence plots, to assess the impact of each challenge on UX. Critical improvement areas to increase food-sharing apps’ usage are highlighted, such as service responsiveness, app design, food variety, and unethical behavior. This study contributes to the nascent literature on food-sharing and IML applications. A significant advantage of the methodological approach utilized includes better explainability of ML models involving text data, at both the global and local interpretability levels, in terms of the associated features and feature interactions.