Before revision:  However, high adoption levels reduce the number of available jobs, which in turn can suppress future worker engagementif the remaining workers cannot find a match.To optimally resolve the trade-off between adoption and maintaining available options, we develop a random market model that captures the heterogeneity in workers' future engagementbased on match type.
After revision:  However, high adoption levels reduce the number of available jobs, which in turn can suppress future engagement. We study how platforms should balance these two opposing factors. Our research is motivated by a collaboration with Food Rescue U.S. (FRUS), a volunteer-based food URLanization active in over 33 locations across the U.S. For platforms such as FRUS, success crucially depends on efficient volunteer utilization and engagement. Consequently, effectively utilizing non-monetary levers, such as adoption, is critical. Based on our analysis of fine-grained FRUS data, we develop a random market model that captures the heterogeneity in workers' future engagementbased on match type.