Commitment on Volunteer Crowdsourcing Platforms: Implications for Growth and Engagement

TL;DR

This paper analyzes how volunteer crowdsourcing platforms can optimally manage commitment levels to balance task completion and platform growth, using a model informed by real-world data from Food Rescue U.S.

Contribution

It develops a theoretical model for two-sided markets that incorporates match uncertainty and volunteer engagement, providing a simple, optimal policy for setting adoption levels.

Findings

Optimal policy depends on volunteer characteristics and market size.

Allowing full adoption or disallowing it can be optimal based on context.

The policy is robust to volunteer heterogeneity.

Abstract

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.