ABSTRACT

This study examines how the use of algorithmic control within gig economy platforms relates to the well-being and behavior of workers. Specifically, we explore how two different forms of algorithmic control—gatekeeping and guiding—correspond with (positive) challenge technostressors and (negative) threat technostressors experienced by Uber drivers. We also examine the moderating impact of algorithmic control transparency on these relationships, as well as the outcomes of technostressors in terms of continuance intentions and workaround use. Based on a survey of 621 U.S.-based Uber drivers, we find that gatekeeping and guiding algorithmic control positively relate to both challenge and threat technostressors. The study bridges the literature on control and technostress by conceptualizing algorithmic control as a condition that puts workers under stress. This stress is found to contribute to important behavioral consequences pertaining to both continuance intentions and workaround use. Findings from our work suggest that gig economy organizations can use algorithmic control to enhance challenge technostressors for their workers, thereby contributing to the cultivation of a more committed workforce. Furthermore, we find evidence disputing the assumption that algorithmic control transparency can mitigate the negative effects of threat technostressors.

摘要

本研究探讨了在零工经济平台中算法控制的使用如何与工人的福祉和行为相关联。具体来说，我们探讨了两种不同形式的算法控制——把关和引导——如何与优步司机经历的（积极的）挑战技术压力源和（消极的）威胁技术压力源相对应。我们还研究了算法控制透明度对这些关系的调节影响，以及技术压力源在持续意图和变通方法使用方面的结果。根据对 621 名美国 Uber 司机的调查，我们发现把关和引导算法控制与挑战和威胁技术压力源正相关。该研究通过将算法控制概念化为使工人处于压力之下的条件，将控制和技术压力的文献联系起来。发现这种压力会导致与持续意图和变通方法使用有关的重要行为后果。我们的工作结果表明，零工经济组织可以使用算法控制来增强其员工的挑战技术压力，从而有助于培养更忠诚的员工队伍。此外，我们发现证据质疑算法控制透明度可以减轻威胁技术压力源的负面影响的假设。我们的工作结果表明，零工经济组织可以使用算法控制来增强其员工的挑战技术压力，从而有助于培养更忠诚的员工队伍。此外，我们发现证据质疑算法控制透明度可以减轻威胁技术压力源的负面影响的假设。我们的工作结果表明，零工经济组织可以使用算法控制来增强其员工的挑战技术压力，从而有助于培养更忠诚的员工队伍。此外，我们发现证据质疑算法控制透明度可以减轻威胁技术压力源的负面影响的假设。