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Platformic Management, Boundary Resources for Gig Work, and Worker Autonomy

Abstract

We advance the concept of platformic management, and the ways in which platforms help to structure project-based or “gig” work. We do so knowing that the popular press and a substantial number of the scholarly publications characterize the “rise of the gig economy” as advancing worker autonomy and flexibility, focusing attention to online digital labor platforms such as Uber and Amazon’s Mechanical Turk. Scholars have conceptualized the procedures of control exercised by these platforms as exerting “algorithmic management,” reflecting the use of extensive data collection to feed algorithms that structure work. In this paper, we broaden the attention to algorithmic management and gig-working control in two ways. First, we characterize the managerial functions of Upwork, an online platform that facilitates knowledge-intensive freelance labor - to advance discourse beyond ride-sharing and room-renting labor. Second, we advance the concept of platformic management as a means to convey a broader and sociotechnical premise of these platforms’ functions in structuring work. We draw on data collected from Upwork forum discussions, interviews with gig workers who use Upwork, and a walkthrough analysis of the Upwork platform to develop our analysis. Our findings lead us to articulate platformic management -- extending beyond algorithms -- and to present the platform as a “boundary resource” to illustrate the paradoxical affordances of Upwork and similar labor platforms. That is, the platform (1) enables the autonomy desired by gig workers, while (2) also serving as a means of control that helps maintain the viability of transactions and protects the platform from disintermediation.

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Notes

  1. http://www.upwork.com

  2. /r/upwork

  3. https://www.upwork.com/legal

  4. https://support.upwork.com/hc/en-us/articles/211067628

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Appendix: Upwork Job Example

Appendix: Upwork Job Example

Here we present an example of an Upwork job, contrasting this with the more commonly used examples of Amazon Mechanical Turk (AMT) and Uber. For example, AMT tasks include “the moderation of web and social media content, categorization of products or images, and the collection of data from websites or other resources” (AMT 2019). These tasks include specific instructions on how they ought to be completed and are often tightly time-bound. For example, in AMT, a worker may be asked to categorize one image per task and only be able to categorize that image in a handful of ways. In contrast, most jobs advertised on Upwork involve larger projects with fewer instructions from clients on how exactly the jobs should be completed. These can be open-ended projects that require high skills or specialization in certain areas (reflecting what Malone et al. (2011) calls hyperspecialization of work). Figure 1 provides an example of such a project posted on Upwork.

Figure 1.
figure 1

Example of a knowledge-intensive job on Upwork

Projects on Upwork, such as what is presented in Fig. 1, tend to have a longer scope and less specification than task-based, ride-sharing or delivery gigs. This means the freelancer must make a plan of action and update this in the face of changes to the scope, needs and deliverables that arise. This also requires ongoing communication between the worker and the client, and perhaps others, as part of the work.

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Jarrahi, M.H., Sutherland, W., Nelson, S.B. et al. Platformic Management, Boundary Resources for Gig Work, and Worker Autonomy. Comput Supported Coop Work 29, 153–189 (2020). https://doi.org/10.1007/s10606-019-09368-7

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Keywords

  • Gig work
  • Knowledge work
  • Upwork
  • Platformic management
  • Algorithmic management
  • Autonomy paradox
  • Boundary resources
  • Sociotechnical systems