Algorithmic control as a double-edged sword: Its relationship with service performance and work well-being
研究开发了感知算法控制量表，发现算法控制通过增加工作嵌入和工作焦虑，对服务绩效和工作幸福感产生双重影响，且效果受薪酬满意度调节。
Despite the disruptive changes introduced by algorithmic control in app-work platforms, research on its effects on app workers’ service performance and work well-being remains fragmented and inconsistent. Firstly, due to the lack of an established scale for perceived algorithmic control, we develop and validate one based on three rational control mechanisms and the input-behavior/process-output framework in Study 1. This validated scale, encompassing instructive guidance, process monitoring, and evaluation feedback, lays the foundation for subsequent empirical investigation. In Study 2, we build upon the job demands-resources perspective to hypothesize that perceived algorithmic control leads to increased job embeddedness and work anxiety. These, in turn, are expected to have both beneficial and detrimental impacts on service performance and work well-being. The indirect effects are dependent on the level of pay satisfaction. Our model is supported by findings from a multisource, three-wave study involving 359 app workers. Implications of our findings are discussed.