Problem definition: Volunteer crowdsourcing platforms match volunteers with tasks that 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 more than 30 locations. For platforms such as FRUS, effectively using nonmonetary levers, such as adoption, is critical. Methodology/results: Motivated by the volunteer management literature and our analysis of FRUS data, we develop a model for two-sided markets that repeatedly match volunteers with tasks. We study the platform’s optimal policy for setting the adoption level to maximize the total discounted number of matches. When market participants are homogeneous, we fully characterize the optimal myopic policy and show that it takes a simple extreme 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. We further extend our analysis to settings with heterogeneity and find that the structure of the optimal myopic policy remains the same if volunteers are heterogeneous. However, if tasks are heterogeneous, it can be optimal to only allow adoption for the harder-to-match tasks. Managerial implications: 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. Setting a misguided adoption level may result in marketplace decay. At the same time, a one-size-fits-all solution may not be effective, as the optimal design crucially depends on the characteristics of the volunteer population.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2020.0426 .

中文翻译：

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对志愿者众包平台的承诺：对增长和参与的影响

问题定义：志愿者众包平台为志愿者匹配经常重复的任务。为了确保完成此类任务，平台经常使用称为“采用”的杠杆，这相当于志愿者承诺重复执行任务。尽管减少了匹配的不确定性，但高水平的采用会降低形成新匹配的可能性，从而抑制增长。我们研究平台应如何管理这种权衡。我们的研究是由与美国食品救援 (FRUS) 的合作推动的，这是一个活跃在 30 多个地点的志愿者食品回收组织。对于 FRUS 等平台来说，有效利用非货币杠杆（例如采用）至关重要。方法/结果：在志愿者管理文献和我们对 FRUS 数据分析的推动下，我们开发了一个双边市场模型，该模型反复将志愿者与任务进行匹配。我们研究了平台的最佳策略，用于设置采用级别，以最大化匹配的总折扣数量。当市场参与者同质时，我们充分描述了最优的短视政策，并表明它采用一种简单的极端形式：根据志愿者特征和市场厚度，要么允许完全采用，要么不允许采用。从长远来看，我们证明这样的政策要么是最优的，要么实现了常数因子近似。我们进一步将分析扩展到具有异质性的环境，并发现如果志愿者是异质性的，最佳短视政策的结构保持不变。但是，如果任务是异构的，则最好只允许采用难以匹配的任务。管理影响：我们的工作揭示了双边平台需要如何谨慎控制承诺杠杆对增长和参与度的双刃影响。设置错误的采用水平可能会导致市场衰退。与此同时，一刀切的解决方案可能并不有效，因为最佳设计很大程度上取决于志愿者群体的特征。补充材料：在线附录可在 https://doi.org/ 上找到。 10.1287/msom.2020.0426。