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Justice for the Crowd: Organizational Justice and Turnover in Crowd-Based Labor

Author

Listed:
  • Xiaochuan Song

    (Business Department, Misericordia University, Dallas, PA 18612, USA)

  • Graham H. Lowman

    (Michael A. Leven School of Management, Entrepreneurship and Hospitality, Kennesaw State University, Kennesaw, GA 30144, USA)

  • Peter Harms

    (Department of Management, University of Alabama, Tuscaloosa, AL 35487, USA)

Abstract

Crowd-based labor has been widely implemented to solve human resource shortages cost-effectively and creatively. However, while investigations into the benefits of crowd-based labor for organizations exist, our understanding of how crowd-based labor practices influence crowd-based worker justice perceptions and worker turnover is notably underdeveloped. To address this issue, we review the extant literature concerning crowd-based labor platforms and propose a conceptual model detailing the relationship between justice perceptions and turnover within the crowd-based work context. Furthermore, we identify antecedents and moderators of justice perceptions that are specific to the crowd-based work context, as well as identify two forms of crowd-based turnover as a result of justice violations: requester and platform turnover. In doing so, we provide a novel conceptual model for advancing nascent research on crowd-based worker perceptions and turnover.

Suggested Citation

  • Xiaochuan Song & Graham H. Lowman & Peter Harms, 2020. "Justice for the Crowd: Organizational Justice and Turnover in Crowd-Based Labor," Administrative Sciences, MDPI, vol. 10(4), pages 1-37, November.
    • Handle: RePEc:gam:jadmsc:v:10:y:2020:i:4:p:93-:d:449590
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    References listed on IDEAS

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