Microphones, not megaphones: Functional crowdworker voice regimes on digital work platforms

Abstract

Digital work platforms are often said to view crowdworkers as replaceable cogs in the machine, favouring exit rather than voice as a means of resolving concerns. Based on a qualitative study of six German medium-sized platforms offering a range of standardized and creative tasks, we show that platforms provide voice mechanisms, albeit in varying degrees and levels. We find that all platforms in our sample enabled crowdworkers to communicate task-related issues to ensure crowdworker availability and quality output. Five platforms proactively consulted crowdworkers on task-related issues, and two on platform-wide organisation. Differences in the ways in which voice was implemented were driven by considerations about costs, control and a crowd’s social structure, as well as by platforms’ varying interest in fair work standards. We conclude that the platforms in our sample equip crowdworkers with ‘microphones’ by letting them have a say on workflow improvements in a highly controlled and easily mutable setting, but do not provide ‘megaphones’ for co-determining or even controlling platform decisions. By connecting the literature on employee voice with platform research, our study provides a nuanced picture of how voice is technologically and organisationally enabled and constrained in non-standard, digital work contexts.

Pacifying the algorithm – Anticipatory compliance in the face of algorithmic management in the gig economy

“…First, future research needs to explore whether algorithmic management differs across digital platforms. As recent evidence outlines, there are stark differences in how platforms treat their workers, with some platform providers striving to alleviate precarious and exploitative conditions and instead facilitate voice and fair work standards (Gegenhuber et al, 2020). Second, future research should explore algorithmic management in traditional organizations, which increasingly transition toward algorithmic over human management (Kellogg et al, 2020).…”

Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM

“…This is clearly different from organizations in a digitalization process towards digital HRM that seem to use software algorithms mostly to augment HRM decision making (Marler & Boudreau, 2017;Marler & Fisher, 2013). Besides differing in the material agency of HRM algorithms, human agency and social processes in OLPs are likely to differ due to their business model and how they interact with (platform) workers (Gegenhuber et al, 2020;Kuhn & Maleki, 2017;Meijerink & Keegan, 2019;Wood et al, 2019). As such, we believe comparing OLPs -alongside other types of digital natives -to organizations transitioning to greater use of algorithmic HRM offers fertile ground for future research into algorithm-enabled HRM decision making.…”

“…Although the workforce that performs tasks through OLPs generally consists of freelance workers, OLPs also have employees, for example software engineers and data specialists, who are responsible for designing the algorithmic HRM activities that automate HRM decisions regarding platform workers (Kuhn, Meijerink & Keegan, 2021). OLPs also employ community managers and marketing specialists to manage communication within and outside of the platform (Gegenhuber et al, 2020;Meijerink & Keegan, 2019). In contrast with 'traditional' contexts, platform workers are not supervised by human (line) managers tasked with implementing HRM activities (Bos-Nehles et al, 2013) or HR specialists on HR analytics teams (Ellmer & Reichl, this issue; Wiblen & Marler, this issue).…”

Co-creation in Macrotask Knowledge Work on Online Labor Platforms

“…Platform work is typically divided into two kinds of tasks by their skills requirements. 'Microtasks' (Gegenhuber et al 2020;Howcroft & Bergvall-Kåreborn 2019;Rolandsson et al 2020;Sutherland et al 2020), 'microwork' (Panteli et al 2020;Wood et al 2019), and 'low-skilled work' (Jesnes 2019) comprise tasks that require low discretion and skill. These are highly defined and determined by the client or platform and often fragmented.…”