Sharing Economy, Platforms and Crowds
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Paper Number
1331
Paper Type
Completed
Description
While digital labor platforms market entrepreneurship to workers in the sharing economy, their algorithmic management (AM) also has negative consequences. Earlier studies suggest that workers’ perceived lack of information about the AM practices negatively affects their control over their work and their feelings towards the organizations. Yet, besides first evidence that an increased information transparency leads to higher worker cooperation, there is a dearth of research on the effects of providing high levels of information about the applied AM criteria and the assigned jobs on workers’ intention to stay with the organizations. Drawing on principal-agent theory, organizational algorithmic transparency and work autonomy, I perform an experimental ridehailing study addressing this gap. Providing high levels of the two information types separately leads to higher staying intentions, whereas combining high levels of both information types shows no significant effect. The study contributes to the research stream about digital labor platforms using AM.
Recommended Citation
Göttel, Vincent, "Sharing Algorithmic Management Information in the Sharing Economy – Effects on Workers’ Intention to Stay with the Organization" (2021). ICIS 2021 Proceedings. 3.
https://aisel.aisnet.org/icis2021/sharing_econ/sharing_econ/3
Sharing Algorithmic Management Information in the Sharing Economy – Effects on Workers’ Intention to Stay with the Organization
While digital labor platforms market entrepreneurship to workers in the sharing economy, their algorithmic management (AM) also has negative consequences. Earlier studies suggest that workers’ perceived lack of information about the AM practices negatively affects their control over their work and their feelings towards the organizations. Yet, besides first evidence that an increased information transparency leads to higher worker cooperation, there is a dearth of research on the effects of providing high levels of information about the applied AM criteria and the assigned jobs on workers’ intention to stay with the organizations. Drawing on principal-agent theory, organizational algorithmic transparency and work autonomy, I perform an experimental ridehailing study addressing this gap. Providing high levels of the two information types separately leads to higher staying intentions, whereas combining high levels of both information types shows no significant effect. The study contributes to the research stream about digital labor platforms using AM.
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09-Crowds