Algorithmic control as a double-edged sword: Its relationship with service performance and work well-being

Bingqian Liang, Yixin Wang, Weiwei Huo, Mengli Song and Yi Shi

Journal of Business Research, 2025, vol. 189, issue C

Abstract: 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.

Keywords: Perceived algorithmic control; Work well-being; Service performance; Job embeddedness; Work anxiety; Pay satisfaction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:189:y:2025:i:c:s0148296325000220

DOI: 10.1016/j.jbusres.2025.115199

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