Skip to main content
SpringerLink
Log in

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

  • Published:
Computer Supported Cooperative Work (CSCW) Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

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

References

  • Abubakar, Abubakar Mohammed; and Belal Hamed Taher Shneikat (2017). eLancing motivations. Online Information Review, vol. 41, no. 1, pp. 53–69.

    Google Scholar 

  • Acquier, Aurélien; Thibault Daudigeos; and Jonatan Pinkse (2017). Promises and paradoxes of the sharing economy: An organizing framework. Technological Forecasting and Social Change, vol. 125, pp. 1–10.

    Google Scholar 

  • Agrawal, Ajay; Joshua Gans; and Avi Goldfarb (2017). How AI will change the way we make decisions. Harvard Business Review. https://hbr.org/2017/07/how-ai-will-change-the-way-we-make-decisions. Accessed 15 June 2019.

  • Alkhatib, Ali; Justin Cranshaw; and Andrés Monroy-Hernandez (2015). Laying the Groundwork for a Worker-Centric Peer Economy (No. MSR-TR-2016-50). Microsoft Research. https://www.microsoft.com/en-us/research/publication/laying-groundwork-worker-centric-peer-economy. Accessed 15 June 2019.

  • Alkhatib, Ali; Michael S. Bernstein; and Margaret Levi (2017). Examining Crowd Work and Gig Work Through the Historical Lens of Piecework. In CHI ’17. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, Colorado,  6 – 11 May 2017. New York, NY: ACM, pp. 4599–4616.

  • Aloni, Erez (2016). Pluralizing the Sharing Economy. Washington Law Review, vol. 91, 2016, pp. 1397.

    Google Scholar 

  • AMT (2019). Amazon Mechanical Turk. https://www.mturk.com. Accessed 20 June 2019.

  • Aneesh, A (2009). Global Labor: Algocratic Modes of Organization. Sociological Theory, vol. 27, no. 4, pp. 347–370.

    Google Scholar 

  • Banning, Marlia E. (2016). Shared entanglements – Web 2.0, info-liberalism & digital sharing. Information, Communication and Society, vol. 19, no. 4, pp. 489–503.

    Google Scholar 

  • Bar, François (2001). The construction of marketplace architecture. In The BRIE-IGCC Economy Project Task Force on the Internet (Ed.), Tracking a Transformation: E-commerce and the Terms of Competition in Industries. Brookings Inst Press, Washington, DC.

  • Barrett, Michael; Elizabeth Davidson; Jaideep Prabhu; and Stephen L. Vargo (2015). Service innovation in the digital age: key contributions and future directions. MIS Quarterly, vol. 39, no. 1, pp. 135–154.

    Google Scholar 

  • Boltanski, Luc; and Eve Chiapello (2005). The New Spirit of Capitalism. International Journal of Politics, Culture, and Society, vol. 18, no. 3, pp. 161–188.

    Google Scholar 

  • Brinkley, Ian (2016). In search of the Gig Economy. Lancaster: The Work Foundation, 2016. https://www.theworkfoundation.com/wp-content/uploads/2016/11/407_In-search-of-the-gig-economy_June2016.pdf. Accessed 20 June 2019.

  • Brynjolfsson, Erik; and Andrew McAfee (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York, NY: W. W. Norton & Company.

    Google Scholar 

  • Bucher, Eliane; and Christian Fieseler (2017). The flow of digital labor. New Media & Society, vol. 19, no. 11, pp. 1868–1886.

    Google Scholar 

  • Campbell, John L.; Charles Quincy; Jordan Osserman; and Ove K. Pedersen (2013). Coding In-depth Semistructured Interviews: Problems of Unitization and Intercoder Reliability and Agreement. Sociological Methods & Research, vol. 42, no. 3, pp. 294–320.

    MathSciNet  Google Scholar 

  • Chan, Ngai Keung (2019). “Becoming an expert in driving for Uber”: Uber driver/bloggers’ performance of expertise and self-presentation on YouTube. New Media & Society, First published online: April 1, 2019.

  • Chapman, Cameron (2018, May 9). 25 Top Sites for Finding the Freelance Jobs You Want. SkillCrush. https://skillcrush.com/2015/11/19/25-top-sites-for-finding-freelance-jobs. Accessed 5 July 2019.

  • Claussen, Jörg; Pooyan Khashabi; Tobias Kretschmer; and Mareike Seifried (2018). Knowledge Work in the Sharing Economy: What Drives Project Success in Online Labor Markets? https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3102865. Accessed 15 June 2019.

  • Corbin, Juliet; and Anselm Strauss (2008). Basics of qualitative research: Techniques and procedures for developing grounded theory. Thousand Oaks, CA: Sage.

  • Corporaal, Gretta (2018). Organizing with on-demand freelancers in the platform economy. Oxford Internet Institute. http://ilabour.oii.ox.ac.uk/organizing-with-on-demand-freelancers-in-the-platform-economy-part-one. Accessed 15 June 2019.

  • Cuñat, Alejandro; and Marc J. Melitz (2012). Volatility, Labor Market Flexibility, and the Pattern of Comparative Advantage. Journal of the European Economic Association, vol. 10, no. 2, pp. 225–254.

    Google Scholar 

  • Premilla D’Cruz; and Ernesto Noronha (2016). Positives outweighing negatives: the experiences of Indian crowdsourced workers. Work Organisation, Labour & Globalisation, vol. 10, no. 1, pp. 44–63.

    Google Scholar 

  • Davis, Gerald F (2015). What might replace the modern corporation: Uberization and the web page enterprise. Seattle University Law Review, vol. 39, pp. 501.

    Google Scholar 

  • De Stefano, Valerio (2015). The Rise of the Just-in-Time Workforce: On-Demand Work, Crowdwork, and Labor Protection in the Gig-Economy. Comp. Lab. L. & Pol’y J., vol. 37, pp. 471.

  • Deleuze, Gilles (1995). Postscript on control societies. Negotiations: 1972--1990, 1995, pp. 177–182.

  • Donovan, Sarah A.; David H. Bradley; and Jon O. Shimabukuru (2016). What Does the Gig Economy Mean for Workers?, CRS Report R4436. Washington, DC: Congressional Research Service, 2016. http://digitalcommons.ilr.cornell.edu/key_workplace/1501/. Accessed 18 April 2018.

  • Dourish, Paul (2016). Algorithms and their others: Algorithmic culture in context. Big Data & Society, vol. 3, no. 2, pp. 1–11.

    Google Scholar 

  • Dunn, Michael (2017). Digital Work: New Opportunities or Lost Wages. American Journal of Management, vol. 17, no. 4, pp. 10–27.

    Google Scholar 

  • Eaton, Ben; Silvia Elaluf-Calderwood; Carsten Sorensen; and Youngjin Yoo (2015). Distributed tuning of boundary resources: the case of Apple’s iOS service system. MIS Quarterly, vol. 39, no. 1, pp. 217–243.

    Google Scholar 

  • Farshchian, Babak A.; and Hanne Ekran Thomassen (2019). Co-Creating Platform Governance Models Using Boundary Resources: a Case Study from Dementia Care Services. Computer Supported Cooperative Work (CSCW), vol. 28, no. 3-4, pp. 549–589.

  • Fleming, Peter (2017). The Human Capital Hoax: Work, Debt and Insecurity in the Era of Uberization. Organization Studies, vol. 38, no. 5, pp. 691–709.

    Google Scholar 

  • Friedman, Gerald (2014). Workers without employers: shadow corporations and the rise of the gig economy. Review of Keynesian Economics, vol. 2, no. 2, pp. 171–188.

    Google Scholar 

  • Gershon, Ilana; and Melissa Cefkin (2017). The Problem with Jobs (De-Provincializing Development). http://www.americananthropologist.org/2017/11/07/the-problem-with-jobs-by-ilana-gershon-and-melissa-cefkin-de-provincializing-development-series. Accessed 10 July 2019.

  • Ghazawneh, Ahmad; and Ola Henfridsson (2010). Governing third-party development through platform boundary resources. ICIS 10. Proceedings of the International Conference on Information Systems. St. Louis, MO, Dec 15-18, Atlanta, GA: AIS, pp. 1–18.

  • Ghazawneh, Ahmad; and Ola Henfridsson (2013). Balancing platform control and external contribution in third-party development: the boundary resources model. Information Systems Journal, vol. 23, no. 2, pp. 173–192.

    Google Scholar 

  • Ghazawneh, Ahmad; and Ola Henfridsson (2015). A paradigmatic analysis of digital application marketplaces. Journal of Information Technology, vol. 30, no. 3, pp. 198–208.

    Google Scholar 

  • Gillespie, Tarleton (2010). The politics of “platforms”. New Media & Society, vol. 12, no. 3, pp. 347–364.

    Google Scholar 

  • Gillespie, Tarleton (2014). The relevance of algorithms. In T. Gillespie, P. J. Boczkowski, and K. A. Foot (Eds.), Media Technologies: Essays on Communication, Materiality, and Society. Cambridge, MA: MIT Press.

    Google Scholar 

  • Green, Daryl D.; Craig Walker; Abdulrahman Alabulththim; Daniel Smith; and Michele Phillips (2018). Fueling the Gig Economy: A Case Study Evaluation of Upwork.com. Management and Economics Research Journal, vol. 4, pp. 104–112.

    Google Scholar 

  • Hannák, Anikó; Claudia Wagner; David Garcia; Alan Mislove; Markus Strohmaier; and Christo Wilson (2017). Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr. In CSCW ’17. Proceedings of the ACM Conference on Computer-Supported Cooperative Work & Social Computing, Portland, OR, 25 February – 1 March 2017. New York, NY: ACM, pp. 1914–1933.

  • Heeks, Richard (2017). Digital Economy and Digital Labour Terminology: Making Sense of the “Gig Economy”, “Online Labour”, “Crowd Work”, “Microwork”, “Platform Labour”, Etc. GDI Development Informatics Working Papers, no. 70. The University of Manchester, Manchester.

  • Howcroft, Debra; and Birgitta Bergvall-Kåreborn (2018). A Typology of Crowdwork Platforms. Work Employment and Society, vol. 33, no. 1, pp. 21–38.

    Google Scholar 

  • Irani, Lilly (2015). The cultural work of microwork. New Media & Society, vol. 17, no. 5, pp. 720–739.

    Google Scholar 

  • Irani, Lilly; and M. Six Silberman (2013). Turkopticon: Interrupting Worker Invisibility in Amazon Mechanical Turk. In CHI ’13. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Paris, 27 April – 2 May 2013. New York, NY: ACM, pp. 611–620.

  • Jarrahi, Mohammad Hossein; and Will Sutherland (2019). Algorithmic Management and Algorithmic Competencies: Understanding and Appropriating Algorithms in Gig work. In Proceedings of iConference 2019, Washington D.C., 31 March – 3 April 2019. Cham, Switzerland: Springer.

  • Kalleberg, Arne L.; and Michael Dunn (2016). Good Jobs, Bad Jobs in the Gig Economy. LERA for Libraries, vol. 20, no. 1-2, pp. 10–13.

    Google Scholar 

  • Kalleberg, Arne L.; and Steven P. Vallas (2018). Precarious Work: Causes, Characteristics, and Consequences. Bingley, UK: Emerald.

    Google Scholar 

  • Karhu, Kimmo; Robin Gustafsson; and Kalle Lyytinen (2018). Exploiting and Defending Open Digital Platforms with Boundary Resources: Android’s Five Platform Forks. Information Systems Research, vol. 29, no. 2, pp. 479–497.

    Google Scholar 

  • Kingsley, Sara Constance; Mary L. Gray; and Siddharth Suri (2015). Accounting for market frictions and power asymmetries in online labor markets. Policy & Internet, vol. 7, no. 4, pp. 383–400.

    Google Scholar 

  • Knuth, Donald Ervin (1997). The Art of Computer Programming. London, UK: Pearson Education.

    MATH  Google Scholar 

  • Kuhn, Kristine M.; and Amir Maleki. (2017). Micro-entrepreneurs, Dependent Contractors, and Instaserfs: Understanding Online Labor Platform Workforces. The Academy of Management Perspectives, vol. 31, no. 3, pp. 183–200.

    Google Scholar 

  • Lee, Min Kyung; Daniel Kusbit; Evan Metsky; and Laura Dabbish (2015). Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers. In CHI ’15. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Seoul, South Korea, April 18 – 23, New York, NY: ACM, pp. 1603–1612.

  • Lehdonvirta, Vili (2018). Flexibility in the gig economy: managing time on three online piecework platforms. New Technology, Work and Employment, vol. 33, no. 1, pp. 13–29.

    Google Scholar 

  • Light, Ben; Jean Burgess; and Stefanie Duguay (2016). The walkthrough method: An approach to the study of apps. New Media & Society, vol. 20, no. 3, pp. 881–900.

    Google Scholar 

  • Lustig, Caitlin; Katie Pine; Bonnie Nardi; Lilly Irani; Min Kyung Lee; Dawn Nafus; and Christian Sandvig (2016). Algorithmic Authority: The Ethics, Politics, and Economics of Algorithms That Interpret, Decide, and Manage. In CHI EA ’16. Proceedings of the CHI Conference on Human Factors in Computing Systems (Extended Abstracts), San Jose, CA, 7–12 May  2016. New York, NY: ACM, pp. 1057–1062.

  • Ma, Ning F.; Chien Wen Yuan; Moojan Ghafurian; and Benjamin V. Hanrahan (2018). Using Stakeholder Theory to Examine Drivers’ Stake in Uber. In CHI ’18. Proceedings of the CHI Conference on Human Factors in Computing Systems, 21-26 April, Montréal, Canada. New York, NY: ACM, pp. 83:1–83:12.

  • Maestas, Nicole; Kathleen Mullen; David Powell; Till von Wachter; and Jeffrey Wenger (2017). The American Working Conditions Survey Finds That More Than Half of Retirees Would Return to Work. RAND Corporation. https://www.rand.org/pubs/research_briefs/RB9973.html. Accessed 10 July 2019.

  • Malone, Tom; Robert Laubscher; and Tammy Johns (2011). The age of hyper specialization. Harvard Business Review, vol. 89, no. 7-8, pp. 56.

    Google Scholar 

  • Mazmanian, Melissa; Wanda J. Orlikowski; and Joanne Yates (2013). The autonomy paradox: The implications of mobile email devices for knowledge professionals. Organization Science, vol. 24, pp. 1337–1357.

    Google Scholar 

  • Mikhalkina, Tatiana; and Laure Cabantous (2015). Business Model Innovation: How Iconic Business Models Emerge. In C. Baden-Fuller and V. Mangematin (Eds.), Advances in Strategic Management. Bingley, UK: Emerald.

    Google Scholar 

  • Miller, Alex P (2018). Want less-biased decisions? Use algorithms. Harvard Business Review. https://hbr.org/2018/07/want-less-biased-decisions-use-algorithms. Accessed 10 June 2019.

  • Mintzberg, Henry (1989). Mintzberg on Management: Inside Our Strange World of Organizations. New York, NY: Simon and Schuster.

    Google Scholar 

  • Möhlmann, Mareike (2015). Building Trust in Collaborative Consumption Services Facilitated through an Online Platform. Academy of Management Proceedings, no. 1, January 2015.

  • Möhlmann, Mareike; and Lior Zalmanson (2017). Hands on the wheel: Navigating algorithmic management and Uber drivers’ autonomy. In ICIS ’17. Proceedings of the International Conference on Information Systems, Seoul, South Korea, 10-13 December 2017. Atlanta, GA: AIS.

  • Molla, Rani (2017). The gig economy workforce will double in four years. Recode. https://www.recode.net/2017/5/25/15690106/gig-on-demand-economy-workers-doubling-uber. Accessed 15 April 2018.

  • Newlands, Gemma; Christoph Lutz; and Christian Fieseler (2018). Collective action and provider classification in the sharing economy. New Technology, Work and Employment, vol. 33, no. 3, pp. 250–267.

    Google Scholar 

  • Plantin, Jean-Christophe; Carl Lagoze; Paul N. Edwards; and Christian Sandvig (2016). Infrastructure studies meet platform studies in the age of Google and Facebook. New Media & Society, vol. 20, no. 1, pp. 293–310.

    Google Scholar 

  • Prassl, Jeremias (2018). Humans as a Service: The Promise and Perils of Work in the Gig Economy. Oxford: Oxford University Press.

    Google Scholar 

  • Raval, Noopur; and Paul Dourish (2016). Standing out from the crowd: Emotional labor, body labor, and temporal labor in ridesharing. In CSCW ‘16. Proceedings of the ACM Conference on Computer-Supported Cooperative Work & Social Computing, San Francisco, CA, 27 February –2 March 2016. New York, NY: ACM, pp. 97–107.

  • Rieder, Kerstin; and G. Günter (2010). The working customer--an emerging new type of consumer. Psychology of Everyday Activity, vol. 3, no. 2, pp. 2–10.

    Google Scholar 

  • Rosenblat, Alex (2016). The Truth About How Uber’s App Manages Drivers. Harvard Business Review. https://hbr.org/2016/04/the-truth-about-how-ubers-app-manages-drivers. Accessed 20 June 2018.

  • Rosenblat, Alex; and Luke Stark (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, vol. 10, pp. 3758–3784.

    Google Scholar 

  • Schildt, Henri (2017). Big data and organizational design--the brave new world of algorithmic management and computer augmented transparency. Innovations, vol. 19, no. 1, pp. 23–30.

    Google Scholar 

  • Schreieck, Maximilian; Manuel Wiesche; and Helmut Krcmar (2016). Design and Governance of Platform Ecosystems-Key Concepts and Issues for Future Research. In ECIS ’16. Proceedings of the European Conference on Information Systems, İstanbul, Turkey, 12-15 June 2016. Atlanta, GA: AIS, Research Paper 76.

  • Seaver, Nick (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, vol. 4, no. 2, pp. 1–12.

    Google Scholar 

  • Shapiro, Aaron (2018). Between Autonomy and Control: Strategies of Arbitrage in the “On-demand” Economy. New Media & Society, vol. 20, no. 8, pp. 2954–2971.

    Google Scholar 

  • Simonite, Tom (2015). When Your Boss Is an Uber Algorithm. MIT Technology Review December, vol. 1, pp. 2015.

  • Spreitzer, Gretchen M.; Lindsey Cameron; and Lyndon Garrett (2017). Alternative Work Arrangements: Two Images of the New World of Work. Annual Review of Organizational Psychology and Organizational Behavior, vol. 4, no. 1, pp. 473–499.

    Google Scholar 

  • Sutherland, Will; and Mohammad Hossein Jarrahi (2017). The Gig Economy and Information Infrastructure: The Case of the Digital Nomad Community. Proceedings of the ACM on Human-Computer Interaction, vol. 1, no. 1, October 2017, Article No. 97.

  • Sutherland, Will; and Mohammad Hossein Jarrahi (2018). The Sharing Economy and Digital Platforms: A Review and Research Agenda. International Journal of Information Management, vol. 43, pp. 328–341.

    Google Scholar 

  • Ticona, Julia; Alexandra Mateescu; and Alex Rosenblat (2018). Beyond Disruption How Tech Shapes Labor Across Domestic Work & Ridehailing. Data & Society. https://datasociety.net/output/beyond-disruption/. Accessed 1 July 2019.

  • Torpey, Elka; and Andrew Hogan (2016). Working in a gig economy. U.S. Bureau of Labor Statistics. https://www.bls.gov/careeroutlook/2016/article/what-is-the-gig-economy.htm. Accessed 15 July 2019.

  • Upwork Community (2016). Addressing accounts that don’t show work activity. https://community.upwork.com/t5/Announcements/Addressing-accounts-that-don-t-show-work-activity/td-p/274042. Accessed 10 April 2018.

  • Upwork Help Center (2018). Identity Verification. https://support.upwork.com/hc/en-us/articles/211067788-Identity-Verification?flash_digest=85c87cfdd992431da0b1d8f378a10da4a1669b74. Accessed 10 March 2018.

  • van Doorn, Niels (2017). Platform labor: on the gendered and racialized exploitation of low-income service work in the “on-demand” economy. Information, Communication and Society, vol. 20, no. 6, pp. 898–914.

    Google Scholar 

  • Veen, Alex; Tom Barratt; and Caleb Goods (2019). Platform-Capital’s “App-etite” for Control: A Labour Process Analysis of Food-Delivery Work in Australia. Work, Employment and Society, First Published Online March 25, 10.1177/0950017019836911.

  • Wagenknecht, Susann; Min Lee; Caitlin Lustig; Jacki O’Neill; and Himanshu Zade (2016). Algorithms at Work: Empirical Diversity, Analytic Vocabularies, Design Implications. In CSCW ’16 Companion. Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, San Francisco, CA, 27 February – 2 March 2016. New York, NY: ACM, pp. 536–543.

  • Wood, Alex J.; Mark Graham; Vili Lehdonvirta; and Isis Hjorth (2018a). Good gig, bad gig: autonomy and algorithmic control in the global gig economy. Work, Employment and Society, vol. 33, no. 1, pp. 56–75.

    Google Scholar 

  • Wood, Alex J.; Vili Lehdonvirta; and Mark Graham (2018b). Workers of the Internet unite? Online freelancer organisation among remote gig economy workers in six Asian and African countries. New Technology, Work and Employment, vol. 33, no. 2, pp. 95–112.

    Google Scholar 

  • World Economic Forum (2016). Hosting the world’s largest on demand freelance talent marketplace for companies to source talent. http://reports.weforum.org/digital-transformation/upwork/. Accessed 12 April 2018.

  • Yoganarasimhan, Hema (2013). The Value of Reputation in an Online Freelance Marketplace. Marketing Science, vol. 32, no. 6, pp. 860–891.

    Google Scholar 

  • Zammuto, Raymond F.; Terri L. Griffith; Ann Majchrzak; Deborah J. Dougherty; and Samer Faraj (2007). Information Technology and the Changing Fabric of Organization. Organization Science, vol. 18, no. 5, pp. 749–762.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Mohammad Hossein Jarrahi

  2. University of Washington, Seattle, WA, USA

    Will Sutherland

  3. University of Wisconsin-Whitewater, Whitewater, WI, USA

    Sarah Beth Nelson

  4. Syracuse University, Syracuse, NY, USA

    Steve Sawyer

Authors
  1. Mohammad Hossein Jarrahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Will Sutherland
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sarah Beth Nelson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Steve Sawyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Hossein Jarrahi.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Full size image

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10606-019-09368-7

Keywords

Search

Navigation