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

Xiaochuan Song, Graham H. Lowman and Peter Harms
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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

Administrative Sciences, 2020, vol. 10, issue 4, 1-37

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.

Keywords: crowd-based labor; organizational justice; compensation; evaluation; communication (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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