How Transparency and Value Affect Platform Workers’ Workaround Use and Continuance Intention

Abstract

Online labor platforms use algorithmic management systems and algorithmic control to automate management tasks traditionally performed by humans. However, these platforms face a control-performance dilemma: while granting workers control over tasks increases satisfaction, lacking algorithmic control of workers’ behavior impedes the system’s performance. Although workers’ reactions to algorithmic control, such as using workarounds or leaving the platform, are well understood, the underlying causes remain unclear. Addressing this shortcoming, we tested our proposed research model using survey data. We apply agency theory to examine how transparency and value impact workaround use and continuance intention. Our study reveals that transparency reduces workaround use while its impact is moderated by value, which also increases continuance intention. However, we could not confirm a significant relationship between workaround use and continuance intention. We address this by introducing the concepts of benevolent and malevolent workarounds, prompting further discussion on workarounds in the context of algorithmic management.