Algorithmic Insecurity, Schedule Nonstandardness, and Gig Worker Wellbeing

23 Pages Posted: 18 Feb 2021

Date Written: January 25, 2021

Abstract

While gig companies promise gig workers autonomy and financial stability, increasing research on working conditions in the gig economy points to “algorithmic management” or control over when, where, and how gig workers do their jobs. First, I introduce a new concept algorithmic insecurity as a way to understand how algorithms have created new dimensions of work that give rise to stress and undermine workers’ wellbeing. Next, combining data from an original nationwide survey of gig workers with 45 in-depth interviews with gig workers, this article explores whether and how schedule nonstandardness (schedule variation and nonstandard work hours) and algorithmic insecurity (worry about customer ratings and demand) are associated with gig worker wellbeing. Quantitative results indicate that schedule variation, but not nonstandard hours are significant predictors of poor gig worker health. Worry about rating and demand are also significant predictors of poor gig worker health. Gig work-life conflict mediates the relationships, suggesting the conditions of gig work are not as simple as companies advertise. Qualitative results further reveal how worrying about ratings and demand can lead to stress and poor health. Together, this research demonstrates the importance of examining algorithmic insecurity as a new and significant dimension of precarity that affects health.

Keywords: algorithmic management, gig economy, health, schedules, ratings, demand

Suggested Citation

Hill, Katherine, Algorithmic Insecurity, Schedule Nonstandardness, and Gig Worker Wellbeing (January 25, 2021). Available at SSRN: https://ssrn.com/abstract=3773164 or http://dx.doi.org/10.2139/ssrn.3773164

Katherine Hill (Contact Author)

University of Texas at Austin ( email )

United States

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