对志愿者众包平台的承诺：对增长和参与的影响
Commitment on Volunteer Crowdsourcing Platforms: Implications for Growth and Engagement
摘要：
志愿者众包平台将志愿者与经常重复的任务相匹配。为了确保完成这些任务，平台经常使用一种称为“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，这反过来会抑制增长。我们研究平台应该如何管理这种权衡。我们的研究是由与食品救援美国（FRUS），一个志愿者为基础的食品回收组织活跃在30多个地点的合作动机。对于像FRUS这样的平台来说，成功关键取决于志愿者的参与。因此，有效地利用非货币杠杆，如采用，是至关重要的。出于志愿者管理文献和我们对FRUS数据的分析，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相匹配。我们的模型结合了匹配的不确定性以及未能匹配对未来参与的负面影响。我们研究了平台的最优策略，用于设置采用水平，以最大限度地提高匹配的总折扣数。我们充分描述了最优近视政策，并表明它采取了一种简单的形式：根据志愿者的特点和市场厚度，要么允许完全采用或不允许采用。从长远来看，我们表明，这样的政策是最佳的或实现了常数因子近似。我们的发现是强大的，将异质性志愿者行为。我们的工作揭示了双边平台需要如何小心控制承诺杠杆对增长和参与的双刃剑影响。“一刀切”的解决办法可能并不有效，因为最佳设计在很大程度上取决于志愿人员群体的特点。
英文摘要：
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.
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