Autonomy and Algorithmic Control in the Global Gig Economy

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* Article: Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy. By Alex J Wood, Mark Graham, Vili Lehdonvirta et al. Work, Employment and Society, August 2018



"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."


From the conclusions:

"Despite conducting a study across diverse national contexts and job types, we find certain key commonalities in the job quality determinants and outcomes of remote gig work. In particular, we find that algorithmic control is central to the operation of online labour platforms. This form of control differs significantly from the Taylorist control often attributed to the extensive use of informational management tools. In contrast to Taylorism, algorithmic management techniques enabled by platform-based rating and ranking systems facilitate high levels of autonomy, task variety and complexity, as well as potential spatial and temporal flexibility. Thus remote gig work is a long way from being an ‘assembly line in the head’ (Bain and Taylor, 2000) or ‘electronic sweatshop’ (Fernie and Metcalf, 1998).

However, while algorithmic control provides remote gig workers with formal control over where they work, workers may have little real choice but to work from home, and this can lead to a lack of social contact and feelings of social isolation. Likewise, despite valuing the potential to control their working hours, most workers had to work intense unsocial and irregular hours in order to meet client demand. The autonomy resulting from algorithmic control can lead to overwork, sleep deprivation and exhaustion as a consequence of the weak structural power of workers vis-a-vis clients. This weak structural power is an outcome of platform-based rating and ranking systems enabling a form of control which is able to overcome the spatial and temporal barriers that non-proximity places on the effectiveness of direct labour process surveillance and supervision. Online labour platforms thus facilitate clients in connecting with a largely unregulated global oversupply of labour.

As suggested by Kalleberg (2011), remote gig workers’ job quality in this open, market-mediated environment is also determined by workers’ marketplace bargaining power in relation to both employers and other workers and thus individual worker resources. In the case of remote gig work, the individual resources that we find to be most important are skills and platform reputation. Workers lacking these individual resources suffered from low incomes and insecurity. The importance of platform reputation is a consequence of the algorithmic control inherent to online labour platforms. The identification of the ‘symbolic power’ (Thompson, 1991) of platform reputations as an emerging form of marketplace bargaining power is an important contribution of this article. Marketplace bargaining power is a concept developed by Silver (2003) and Wright (2000), neither of whom consider the importance of symbolic forms of power. These findings are likely to become increasingly relevant to the wider world of work as gig economy style algorithmic controls are increasingly adopted within standard employment relationships (Keller, 2017; O’Conner, 2016)."