Volunteer crowdsourcing platforms , such as food URLanizations, match volunteers with tasks which are often recurring. To ensure completion of such tasks, platforms frequently use a commitment lever known as "adoption . " Despite being effective in reducing match uncertainty, high levels of adoption reduce match availability for volunteers 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 locationsacross 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 model for a repeated two-sided matching market consisting of tasks (prearranged donations) and volunteers . Our model incorporates the uncertainty in match compatibility 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. Our analysis reveals that the optimal myopic policy is either full or no adoption. For sufficiently thick markets , we show that such a myopic policy is also optimal in the long run. In thinner markets, even though a static policy of full or no adoption can be suboptimal, we show it achieves a constant-factor approximation where the factor improves with market thickness. Using our analytical and empirical results, we revisit the current design of the FRUS platform and make location-specific policy recommendations. More broadly, our work sheds light on how other two-sided platforms can control the double-edged impacts that commitment levers have on growth and engagement  .
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 URLanization 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 .
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future engagement
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balance these two opposing factors
manage this trade-off
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33 locationsacross the U. S.
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efficient volunteer utilization and
volunteer
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Based on
Motivated by the volunteer management literature and
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fine-grained
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matching market consisting of tasks (prearranged donations) and volunteers
markets which repeatedly match volunteers with tasks
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the uncertainty in match compatibility
match uncertainty
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is either full or no adoption. For sufficiently thick markets
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
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myopic policy is also optimal in the long run. In thinner markets, even though a static policy of full or no adoption can be suboptimal, we show it
policy is either optimal or
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where the factor improves with market thickness. Using our analytical and empirical results, we revisit the current design of the FRUS platform and make location-specific policy recommendations. More broadly, our
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need to carefully
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. A one-size-fits-all solution may not be effective, as the optimal design crucially depends on the characteristics of the volunteer population