Between autonomy and control: Strategies of arbitrage in the “on-demand” economy

Abstract

The “on-demand” economy is built upon company strategies of arbitrage between worker autonomy and worker control. Using ethnographic and interview data, I show how these strategies undermine the economic theory that justifies the on-demand business model. The increased freedom and flexibility on offer to workers is countered by “softer” and less visible forms of workforce surveillance and control. These take hold through information asymmetries, which narrow workers’ decision-making capacities and thus undercut arguments for workers’ designation as independent contractors. However, I also show that these forms of control ultimately hinge on workers’ willingness to conform to the calculative rationalities that companies project onto them, and that workers’ motivations diverge from these company assumptions. I conclude by countering the notion of calculability with that of “qualculation,” an affective style of reasoning that workers employ in response to algorithmic workforce management and which highlights how on-the-job decisions are made within a shifting moral economy of work.

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

“…Insofar as algorithmic management systems necessitate the minimization of human intervention to benefit from algorithmic-efficiencies, algorithmic management marks a radical departure from earlier managerial logic, which relied heavily on human supervisors to direct work [5,6,15]. In algorithmically managed contexts, most managerial and decisionmaking processes are reduced into a set of opaque algorithmic processes that are both complex and inaccessible to the typical worker [1,6,8]. Thus, given the scarcity of human intervention and considering that workers' subjective interpretations of procedural justice play a key role in forming perceptions of organizational support, both antecedents of supervisor support and procedural justice will be disrupted by algorithmic management [8].…”

“…While many job conditions critical to the formation of POS are hindered in algorithmically-managed contexts we propose that job autonomy will remain a key determinant of POS for platform workers. Specifically, the need for autonomy has been cited extensively by independent gig-workers as a leading driver for participation in the gig-economy [1,3,5,6,8,17,13].…”

“…In the context of the gig-economy, algorithms typically assume control and responsibility for matching of workers with customers, assigning work, and evaluating workers' performance [4,6,15]. In this frame, a gig-worker's autonomy is defined in terms of their ability to self-schedule when they work, their right to reject or accept gigs and, depending on the platform, their ability to choose the methods and processes they use to conduct their work [1,3,6,8]. Importantly, recent research has proposed that the operational choices embedded within a platform's architecture will implicitly shape a platform workers' autonomy such that platforms can be conceptualized and defined as either autonomy supportive or non-supportive [3,8].…”

“…The gig-economy is an emerging labor market in which organizations engage independent workers to complete short-term contracts known as "gigs", by connecting workers to customers via a platform-enabled digital marketplace. Lauded as the future of work [1], industry experts predict that by 2023 more than half of the U.S. workforce will participate in the gig-economy, at least occasionally [2]. While gig-organizations derive many operational benefits such as agility and reduced costs from their business model, given the size and distributed nature of their workforces, they cannot rely on the traditional means of supervision to coordinate and control work [3].…”

The Taylor Review: understanding the gig economy, dependency and the complexities of control

“…Therefore, it is instructive to consider control in this sector. Algorithmic control has been identified as a central function of gig economy apps and platforms (Lee et al ., ; Rosenblat and Stark, ; Shapiro, ; Gandini, ; Wood et al ., ). Algorithmic control is based upon platform rating and reputation systems.…”