How do algorithms influence platform workers’ behavior? Relationship between algorithmic autonomy support and platform workers’ stress and incivility

Abstract

Purpose Drawing upon the needs–supplies fit theory, this study aims to examine how the expected and perceived algorithmic autonomy support (AAS) influences platform workers’ work stress and incivility, with a focus on the moderating role of self-direction. Design/methodology/approach Using data collected from 422 platform workers in China through multiple waves, the model is tested through polynomial regression and response surface analysis. Findings The results indicate that a mismatch between expected and perceived AAS is associated with higher levels of incivility among platform workers, and this relationship is mediated by work stress. These findings are particularly salient when self-direction is emphasized. Practical implications This study suggests that a universally high level of AAS may not necessarily reduce workers’ stress and incivility. Instead, it is important for platforms to ensure that their workers’ expectations of autonomy support are met and for workers to be given the space to exercise self-direction. Originality/value Previous studies have highlighted the need to pay attention to workers in mobile and ambiguous environments, and this study adds to this literature by focusing specifically on platform organizations and workplaces. This study provides valuable insights into the relationship between AAS, work stress and platform workers’ incivility.