Micro-entrepreneurs, Dependent Contractors, and Instaserfs: Understanding Online Labor Platform Workforces

Abstract

The rapidly growing number of people who find work via online labor platforms are not employees, nor do they necessarily fit traditional conceptualizations of independent contractors, freelancers, or the selfemployed. The ambiguous nature of their employment status and its implications for worker well-being have attracted substantial controversy, but to date most empirical research in this area has focused on the market efficiency of a single platform rather than on workers themselves and related human resource management issues. Research progress will require understanding how online labor platform work differs from other types of nonstandard employment arrangements, as well as critical differences across different labor platform firms in how work and workers are managed. This paper proposes a conceptual classification framework to facilitate research on the attitudes, experiences, and outcomes of workers who use these platforms. We explore how labor platform firms' operational choices shape how control is allocated across workers, clients, and the firm, and how they influence workers' autonomy, incentives, and degree of economic dependence on the firm. Implications for theory development, research, and managing worker-firm relations are discussed.

Perceived Organizational Support in the Face of Algorithmic Management: A Conceptual Model

“…In recent years, the societal and managerial implications of algorithms have spawned much public debate and drawn increasing attention from scholars [4,5,7]. With research into the impacts of algorithms on human workers and work practices still in its nascent stages, one area of study that remains underexplored is the impact of algorithmic management on gig-workers' perceptions of organizational support [4,8]. Perceived organizational support (POS) is the degree to which employees believe that their "organization values their contributions and cares about their well-being" [9, p. 698].…”

“…While the notion of POS may, at first thought, seem irrelevant to the gig-work context given that independent work is typically defined in part by its lack of organizational support [8], and where workers are not employees and have no official human supervisor, POS has been found to apply in non-traditional work contexts akin to gig-work like contract work [12]. Such research has not only demonstrated that contingent workers can, and do, experience POS, but that they form perceptions of support from multiple organizational relationships, thereby suggesting the existence of unique antecedents for each relational source of POS [12].…”

“…Like many organizational behavior theories, POS was developed in traditional management contexts where human managers supervise and support full-time employees, often by building close, trust-based relationships [12]. Unsurprisingly, there is a gap in the literature concerning how workers develop POS when managed by a "faceless boss" and a set of organizational policies enacted through codes [4,8].…”

“…As the application of new information technologies to organizational design continues to change the nature of work [3,13], there is a need to conceptualize new POS antecedents for non-traditional work contexts [8,10,12]. Thus, the goal of our paper is to understand how do the impersonal, technology-mediated practices inherent of algorithmic management impact workers' perceptions of organizational support, and to explore whether the lack of a human supervisor can be compensated for by other POS antecedents.…”

“…Thus, the goal of our paper is to understand how do the impersonal, technology-mediated practices inherent of algorithmic management impact workers' perceptions of organizational support, and to explore whether the lack of a human supervisor can be compensated for by other POS antecedents. As the use of algorithmic management reaches beyond the gigeconomy [3,6,8,14,15], closing this research gap stands to offer theoretical and practical contributions to the social and managerial study of algorithmstwo growing and critical areas of research [5,6,16].…”