Commitment on Volunteer Crowdsourcing Platforms: Implications for Growth and Engagement

Irene Lo, Vahideh Manshadi, Scott Rodilitz, Ali Shameli
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Abstract

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 .
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志愿者众包平台上的承诺:对增长和参与的影响
问题定义:志愿者众包平台将志愿者与经常重复性的任务相匹配。为确保此类任务的完成,平台经常使用一种被称为 "采纳 "的杠杆,即志愿者承诺重复执行任务。尽管可以降低匹配的不确定性,但高水平的采用率会降低形成新匹配的概率,这反过来又会抑制增长。我们研究了平台应如何管理这种权衡。我们的研究源于与美国食品救援组织(FRUS)的合作,这是一个以志愿者为基础的食品回收组织,活跃在 30 多个地区。对于像 FRUS 这样的平台来说,有效利用领养等非货币杠杆至关重要。方法/结果:受志愿者管理文献和 FRUS 数据分析的启发,我们建立了一个重复匹配志愿者与任务的双面市场模型。我们研究了平台设置采用水平的最优政策,以最大化总匹配数的折现。当市场参与者是同质时,我们完全描述了最优近视政策的特征,并表明它采取了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,要么实现了恒定系数近似。我们进一步将分析扩展到异质性环境,发现如果志愿者是异质性的,最优近视政策的结构保持不变。然而,如果任务是异质的,那么只允许采用较难匹配的任务可能是最优的。管理意义:我们的研究揭示了双向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。设定错误的采用水平可能会导致市场衰退。同时,"一刀切 "的解决方案可能并不有效,因为最佳设计在很大程度上取决于志愿者群体的特征:在线附录见 https://doi.org/10.1287/msom.2020.0426 。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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