Platform work as an extremely precarious form of work, in which independent workers find fragmented tasks via digital platforms, becomes increasingly pervasive. More than one-third of U.S. workers engage in economic activities through gig work arrangements, such as independent contract and platform work (Gallup 2020). With the growth of this industry, these precarious positions have been mostly filled by marginalized groups of workers, many of whom are women, people of color, and immigrants. Unlike those who are shackled by managers and bureaucratic rules in organizations, platform workers as “micro-entrepreneurs” manage their own small business by exercising their autonomy in the market. However, their footlooseness is guided by algorithmically mediated tools in an insidious way, which results in manipulated behaviors that are aligned with platform companies’ interests. These features of platform work are bases of platform workers’ lived experience, which is an important source of consent to work. While being involved in the labor process and interpreting their experience, platform workers generate consent to work that takes the form of naturalizing the status quo and ooperating with or acquiescing to the labor process (Burawoy 1982; Purcell and Brook 2022). To understand how platform workers provide their consent to platform work, this essay focuses on three features of platform work that distinguish it from work in traditional organizations: market, technology, and social status.
Being in the market means that platform workers have to navigate cycles of famine or feast in the market. This is accompanied with ecstasy and agony (Petriglieri, Ashford and Wrzesniewski 2019). Fantasy or hope is an essential component of ecstasy in the market (Miyazaki 2006). Similar with what Adrian Wright (2015) described in his study on independent game developers, the feast cycle imbues these workers with beliefs that their strong commitment to work will be compensated by future economic prosperity. Furthermore, this commitment is reinforced by the characteristic of independent work that being productive is essential for platform workers to maintain positive self-image as well as to enhance their economic interests (Petriglieri, Ashford and Wrzesniewski 2019). In contrast, the famine cycle endangers a sense of anxiety or fear of losing contracts and incomes. As Evans, Kunda and Barly (2004) pointed out, this anxiety is a key motivational drive for endlessly calculating opportunity costs of not working and leading them to be obsessed with work. In addition, concerns about losing contracts often poses existential concerns that lead high-skilled gig workers to cultivate holding environments for improving their productivity because work is not only a source of money, but also a medium of self-expression (Cameron 2022; Petriglieri, Ashford and Wrzesniewski 2019).
This volatility in the market is more heightened when algorithms come into play to nudge or manipulate platform workers’ choice. To match customer demands with platform workers on a real-time basis without using direct management tools, platform firms transform the shop floor into choice architectures operated by algorithms, wherein workers make decisions under the influence of incentives and constraints based on performance metrics, such as task acceptance rating, and customer ratings (Rosenblatt and Stark 2016; Shapiro 2018). In these choice architectures, platform workers have to deal with unexplained decisions made by algorithms to pursue their interests. This opaqueness of algorithms often leads the workers helplessly to comply with work rules designed by platforms (Curchod et al. 2020). In this vein, Rahman (2021) showed that online platform workers show paranoid about how algorithms work due to the difficulty of learning and the lack of resources for understanding the black box of algorithms. However, while some workers acquiesce to work rules due to the opaqueness of algorithms, others actively pursue their own interests by playing game with algorithms. For example, Uber or Lyft drivers selectively reject rides recommended by algorithms to complete rides quickly and efficiently (Cameron 2022). Also, these workers individually or collectively experiment with algorithms to open the black box and leverage loopholes to increase their incomes (Curchod et al. 2020; Rahman 2021). The important point is that gaming with algorithms and finding their weaknesses results in more commitment to work, rather than overturing the rule of game.
Ironically, platform workers’ consent to platform work also comes from their subjective precarity marked by their lack of social recognition and integration (Alberti et al. 2018). As the platform industry has grown on marginalized and underemployed groups of workers, many of them entry into this sector with their socially devalued social identities. Furthermore, the replaceable and trivial nature of platform work makes these workers find meaningfulness in their work (Anicich 2022; Cameron 2022). Given that social devaluation or stigmatization generates strong social desires to restore their damaged identities (Ashforth and Kreiner 1999; Goffman 2009), social recognition can induce consent to platform work from these workers. This point was well demonstrated by public moralization on platform work during the COVID-19 pandemic. For example, Cameron, Chan and Anteby (2022) showed that platform workers wrestling with accepting the moralization devote more energy in helping others to live up to the expectations from the public and earn moral credentials. In this way, social recognition unintentionally generates platform workers’ consent by imbuing them with a strong attachment to their work.
One distinguishing characteristic of platform work is the absence of direction control over the labor process. Unlike those in the traditional workplace, platform workers have more autonomy in determining when, how, and where to work. In this work setting, the aforementioned dimensions of platform work drive them to consent and channel their intensive efforts into work. In place of coercion in the traditional workplace, consent becomes a key mechanism by which platforms extract profits from workers. Platform workers work hard to chase fantasy and alleviate anxiety, to acquiesce or tweak with the black box of algorithms, or to get social recognition. These dynamics around platform work indicate that platform workers are more influenced by discourse existing beyond the workplace (Purcell and Brook, 2022). Neoliberal ideas on entrepreneurship, blind faith in technological efficacy, and social stigma on marginalized workers come into play when workers give their consent to platform work, coupled with digitalized work practices designed by platforms. This point suggests that the analysis on the political dynamics surround platform work should focus on how digitalized work practices and societal discourse are combined to induce consent from platform workers.
Alberti, Gabriella, Ioulia Bessa, Kate Hardy, Vera Trappmann, and Charles Umney. 2018. “In, Against and Beyond Precarity: Work in Insecure Times.” Work, Employment and Society 32 (3): 447-457. https://doi.org/10.1177/0950017018762088.
Anicich, Eric M. 2022. "Flexing and Floundering in the On-demand Economy: Narrative Identity Construction under Algorithmic Management." Organizational Behavior and Human Decision Processes 169: 104138. https://doi.org/10.1016/j.obhdp.2022.104138.
Burawoy, Michael. 1979. Manufacturing Consent. Chicago: University of Chicago Press.
Cameron, Lindsey D. 2022. ““Making Out” While Driving: Relational and Efficiency Games in the Gig Economy.” Organization Science 33 (1): 231-252. https://doi.org/10.1287/orsc.2021.1547.
Cameron, Lindsey D., Curtis K. Chan, and Michel Anteby. 2022. “Heroes from Above but not (always) from Within? Gig Workers’ Reactions to the Sudden Public Moralization of their Work.” Organizational Behavior and Human Decision Processes 172: 104179. https://doi.org/10.1016/j.obhdp.2022.104179.
Curchod, Corentin, Gerardo Patriotta, Laurie Cohen, and Nicolas Neysen. 2020. “Working for an Algorithm: Power Asymmetries and Agency in Online Work Settings.” Administrative Science Quarterly 65 (3): 644-676. https://doi.org/10.1177/0001839219867024.
Evans, James A., Gideon Kunda, and Stephen R. Barley. 2004. “Beach time, Bridge time, and Billable Hours: The Temporal Structure of Technical Contracting.” Administrative Science Quarterly 49 (1): 1-38. https://www.jstor.org/stable/4131454.
Gallup. 2018. Gallup’s Perspective on The Gig Economy and Alternative Work Arrangements. Washington, DC: Gallup.
Goffman, Erving. 2009. Stigma: Notes on the Management of Spoiled Identity. New York, NY: Simon & Schuster.
Miyazaki, Hirokazu. 2006. “Economy of Dreams: Hope in Global Capitalism and its Critiques.” Cultural Anthropology 21 (2): 147-172. https://doi.org/10.1525/can.2006.21.2.147.
Petriglieri, Gianpiero, Susan J. Ashford, and Amy Wrzesniewski. 2019. “Agony and Ecstasy in the Gig Economy: Cultivating Holding Environments for Precarious and Personalized Work Identities.” Administrative Science Quarterly 64 (1): 124-170. https://www.jstor.org/stable/48589024.
Rahman, Hatim A. 2021. “The Invisible Cage: Workers’ Reactivity to Opaque Algorithmic Evaluations.” Administrative Science Quarterly 66 (4): 945-988. https://doi.org/10.1177/00018392211010118.
Rosenblat, Alex, and Luke Stark. 2016. “Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers.” International Journal of Communication 10: 3758–84.
Shapiro, Aaron. 2018. “Between Autonomy and Control: Strategies of Arbitrage in the “On-Demand” Economy.” New Media & Society 20 (8): 2954-2971. https://doi.org/10.1177/1461444817738236.
Jeonghun Kim is a Ph.D. student at the Cornell ILR school. He is interested in how labor movements respond to the changing nature of work. His main research interests are about how workers come to be controlled by digital platforms but also how they resist against them. Currently, he is working on a study on comparing the different narrative strategies of two food-delivery unions in South Korea.
Creative Commons Attribution 4.0 International License (CC-BY 4.0)