Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy

By A. Wood, Mark Graham, Vili Lehdonvirta, Isis Amelie Hjorth. Work, Employment and Society, 2018

1,658 citations recorded by the source corpus.

This article evaluates the job quality of work in the remote gig economy. Such work consists of the remote provision of a wide variety of digital services mediated by online labour platforms. Focusing on workers in Southeast Asia and Sub-Saharan Africa, the article draws on semi-structured interviews in six countries (N = 107) and a cross-regional survey (N = 679) to detail the manner in which remote gig work is shaped by platform-based algorithmic control. Despite varying country contexts and types of work, we show that algorithmic control is central to the operation of online labour platforms. Algorithmic management techniques tend to offer workers high levels of flexibility, autonomy, task variety and complexity. However, these mechanisms of control can also result in low pay, social isolation, working unsocial and irregular hours, overwork, sleep deprivation and exhaustion.

Subfields: General Health Professions, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/de21b652cd622c93576ff95bd93be8009b575ebf

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Citations in the corpus

Conceptualizing the Gig Economy and Its Regulatory Problems (2020, background): “Shapiro, 2018; Wood et al., 2019).”
Über-Alienated: Powerless and Alone in the Gig Economy (2021, background): “Several researchers have documented, for example, the efforts of rideshare drivers and online crowdworkers to game algorithmic control systems or avoid monitoring entirely (Newlands, 2021; Wood et al., 2019).”
What Do Platforms Do? Understanding the Gig Economy (2020, background): “Studying crowdworkers in sub-Saharan Africa and Southeast Asia, Wood et al. (2018, p. 9) found that “monitoring mechanisms were easily circumvented,” especially as workers grew more experienced.”
Algorithms at Work: The New Contested Terrain of Control (2020, background): “In such work-oriented online communities, workers have been shown to help each other learn new systems and practices, anticipate or avoid disciplinary processes, regain access when locked out of platforms, identify desirable clients or jobs, or learn how to smooth their earnings (Martin et al., 2014”
Digital platforms for development: Foundations and research agenda (2021, background): “The negative consequences of these platforms and gig working on individuals encompass issues such as low pay, discrimination, unreasonable working hours, precarity, unfair dismissal and unsafe working conditions (Page-Tickell, 2020; Rosenblat & Stark, 2016; Wood et al., 2019).”
Hidden transcripts of the gig economy: labour agency and the new art of resistance among African gig workers (2019, background): “In the age of platform capitalism, digital communication channels are fast becoming central to collective organisation among workers (Wood et al., 2018), and as a way to reduce overhead costs, perform work and develop social bonds and support to make work manageable (Gray and Suri, 2019).”
Working on my own: Measuring the challenges of gig work (2021, background): “, 2018) with ideas from recent scholarship on job quality in the gig economy (e.g. Goods et al., 2018; Wood et al., 2019) and classic psychological theory on individuals’ universal needs (Alderfer, 1972).”
Platform-Capital’s ‘App-etite’ for Control: A Labour Process Analysis of Food-Delivery Work in Australia (2019, methodology): “The platform-economy can be viewed as a technologically driven transformation of work (Gandini, 2018; Wood et al., 2018), whereby platforms’ digital eco-systems play a critical role in harnessing and galvanising both physical and creative labour effort (Howcroft and Bergvall-Kåreborn, 2019).”
Algorithmic management in a work context (2021, background): “In what Huws (2016) describes as a “new paradigm of work”, algorithmic systems in the gig economy track worker performance, perform job matching, generate employee rankings, and can even resolve disputes between workers (Duggan et al., 2019; Wood et al., 2018).”