Online labor platforms (OLPs) can use algorithms along two dimensions: matching and control. While previous research has paid considerable attention to how OLPs optimize matching and accommodate market needs, OLPs can also employ algorithms to monitor and tightly control platform work. In this paper, we examine the nature of platform work on OLPs, and the role of algorithmic management in organizing how such work is conducted. Using a qualitative study of Uber drivers’ perceptions, supplemented by interviews with Uber executives and engineers, we present a grounded theory that captures the algorithmic management of work on OLPs. In the context of both algorithmic matching and algorithmic control, platform workers experience tensions relating to work execution, compensation, and belonging. We show that these tensions trigger market-like and organization-like response behaviors by platform workers. Our research contributes to the emerging literature on OLPs.

中文翻译：

---

在线劳动力平台工作的算法管理：当匹配遇到控制

在线劳动力平台（OLP）可以使用两个维度的算法：匹配和控制。虽然之前的研究已经相当关注 OLP 如何优化匹配和适应市场需求，但 OLP 还可以使用算法来监控和严格控制平台工作。在本文中，我们研究了 OLP 平台工作的性质，以及算法管理在组织此类工作如何进行中的作用。通过对 Uber 司机感知的定性研究，辅以对 Uber 高管和工程师的采访，我们提出了一个扎根的理论，该理论捕捉了 OLP 工作的算法管理。在算法匹配和算法控制的背景下，平台工作人员会经历与工作执行、薪酬和归属感相关的紧张局势。我们表明，这些紧张局势引发了平台工作者的类似市场和类似组织的响应行为。我们的研究为新兴的 OLP 文献做出了贡献。