We study the design of a decentralized platform in which workers and jobs repeatedly match, and their future engagement with the platform depends on whether they successfully find a match . The platform offers two types of matches to workers: an " adopted match " which entails repeatedly matching with the same job or a one-time match.Due to randomness in match compatibility, adoption seems favorable as it reduces uncertaintyin matching. 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. Our analysis reveals that the optimal policy for maximizing the matching in a single period is either full or no adoption. For sufficiently thick markets, we show that the optimal single-period policy is also optimal for maximizing the total discounted number of matches . In thinner markets, even though a static policy of full or no adoption can be suboptimal,  it achieves a constant-factor approximation where the factor improves with market thickness  .
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 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 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 .
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We study the design of a decentralized platform in which workers and jobs repeatedly match, and their future engagement with the platform depends on whether they successfully find a match . The platform offers two types of matches to workers: an
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
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adopted match
adoption.
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which entails repeatedly matching with the same job or a one-time match.Due to randomness in match compatibility, adoption seems favorable as it reduces uncertaintyin matching. However, high adoption levels reduce the number of available jobs,
Despite being effective in reducing match uncertainty, high levels of adoption reduce match availability for volunteers
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worker engagementif the remaining workers cannot find a match.To optimally resolve the trade-off between adoption and maintaining available options,
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,
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random market model that captures the heterogeneity in workers' future engagementbased on match type.
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.
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policy for maximizing the matching in a single period
myopic policy
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the optimal single-period
such a myopic
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for maximizing the total discounted number of matches
in the long run
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we show
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. 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