  Volunteer crowdsourcing platforms match volunteers with tasks which are often
recurring. To ensure completion of such tasks, platforms frequently use a lever
known as "adoption," which amounts to a commitment by the volunteer to
repeatedly perform the task. Despite reducing match uncertainty, high levels of
adoption can decrease the probability of forming new matches, which in turn can
suppress growth. We study how platforms should manage this trade-off. Our
research is motivated by a collaboration with Food Rescue U.S. (FRUS), a
volunteer-based food recovery organization active in over 30 locations. For
platforms such as FRUS, success crucially depends on volunteer engagement.
Consequently, effectively utilizing non-monetary levers, such as adoption, is
critical. Motivated by the volunteer management literature and our analysis of
FRUS data, we develop a model for two-sided markets which repeatedly match
volunteers with tasks. Our model incorporates match uncertainty as well as the
negative impact of failing to match on future engagement. We study the
platform's optimal policy for setting the adoption level to maximize the total
discounted number of matches. We fully characterize the optimal myopic policy
and show that it takes a simple form: depending on volunteer characteristics
and market thickness, either allow for full adoption or disallow adoption. In
the long run, we show that such a policy is either optimal or achieves a
constant-factor approximation. Our finding is robust to incorporating
heterogeneity in volunteer behavior. Our work sheds light on how two-sided
platforms need to carefully control the double-edged impacts that commitment
levers have on growth and engagement. A one-size-fits-all solution may not be
effective, as the optimal design crucially depends on the characteristics of
the volunteer population.
