Chapter 5 Brave New Digital Work? New Forms of Performance Control in Crowdwork

The impact of using algorithmic management to set goals on the reduction of workers' autonomy has been outlined by many scholars, namely in gig work where autonomy in task selection is often viewed by externals as a major advantage of platform-based work.

“The impact of the use of AM to set goals on the reduction of workers' autonomy has been outlined by many scholars, namely in gig-work where autonomy in task selection is often viewed by externals as a major advantage of platform-based work (Curchod, Patriotta, Cohen, & Neysen, 2019;Gerber & Krzywdzinski, 2019;Griesbach et al., 2019;Rosenblat, 2018;Shapiro, 2018).”

In gig work, the algorithmic task allocation process is highly contingent on demand or customer satisfaction scores.

“In gig work, the algorithmic task allocation process is highly contingent on demand or customer satisfaction scores (Gerber & Krzywdzinski, 2019;Gregory, 2020;Griesbach et al., 2019).”

Algorithmic systems facilitate high contingency of performance scores on customer satisfaction ratings in gig work and traditional work settings.

“Moreover, algorithmic systems facilitate high contingency of performance scores on customer satisfaction ratings, both in gig work (Curchod et al., 2019;Gerber & Krzywdzinski, 2019;Griesbach et al., 2019) and in traditional work settings such as restaurants (O'Donovan, 2018), retail (Evans & Kitchin, 2018;Levy & Barocas, 2018), or telecommunications (Bakewell et al., 2018).”

In the gig economy, the termination procedure starts with the platform notifying a worker of unsatisfying ratings and probability of account deactivation if no improvement, followed by official account deactivation.

“Mainly found in the gig economy, this procedure starts with the platform notifying a worker of unsatisfying ratings and probability of account deactivation if no improvement, followed by official account deactivation (Gerber & Krzywdzinski, 2019;Griesbach et al., 2019;Rosenblat & Stark, 2016).”

For gig workers like ridesharing drivers or delivery workers, having the power to decline tasks without implicit or explicit penalties or prejudice could prevent workers from the accumulation of frustrating, low-paid or alienating tasks.

“Also, for gig workers like ridesharing drivers or delivery workers, having the power to decline tasks without implicit or explicit penalties or prejudice could prevent workers from the accumulation of frustrating, low-paid or alienating tasks (Gerber & Krzywdzinski, 2019).”

Algorithmic task allocation is largely found in the gig economy and platform work, and in traditional work sectors like telecommunication, electronics, public transports, and parcel delivery.

“Algorithmic task allocation is largely found in the gig economy and platform work (Gerber & Krzywdzinski, 2019;Lee et al., 2015;Rosenblat, 2018), and in traditional work sectors like telecommunication (Leclercq-Vandelannoitte, 2017), electronics (Gershgorn, 2015;Hitachi, 2015), public transports (Hodson, 2014), and parcel delivery (Stevens, 2015).”

Algorithmic metrics can play a central role in the performance management process of gig workers.

“Some studies have described how these metrics can play a central role (if not the entire role) in the performance management process of both traditional workers (Bakewell et al., 2018;Dhir & Chhabra, 2019;Evans & Kitchin, 2018;Kirven, 2018;Levy & Barocas, 2018) and gig workers (Gerber & Krzywdzinski, 2019).”

Customer satisfaction ratings are questioned for their volatility, reliability, and validity.

“The strong impact of these ratings, largely questioned about their volatility, the reliability and validity (Gerber & Krzywdzinski, 2019;Greenwood, Adjerid, & Angst, 2019;Luca & Zervas, 2016;Orlikowski & Scott, 2013;Rosenblat, Levy, Barocas, & Hwang, 2017), has been associated with workers' perception of job insecurity (Curchod et al., 2019;Veen et al., 2019).”