The Making of the “Good Bad” Job: How Algorithmic Management Manufactures Consent Through Constant and Confined Choices

The Rise of On-Demand Work and Its Implications

The number of workers in the on-demand economy is growing, with more than two million jobs added in the U.S. alone in 2020 (Zgola, 2021; Garin et al., 2023), and online on-demand work accounts for up to 12 percent of the global labor force (Datta et al., 2023). ² Digital platforms such as Uber, Instacart, and Amazon Flex have radically transformed the way work is organized, managed, and experienced, challenging how scholars think about work. ³ For legal scholars, on-demand work blurs the legal classifications of employee and independent contractor, thereby complicating the issue of who is entitled to labor protections (Dubal, 2017, 2020). For economists and strategy scholars, digital platforms create frictionless marketplaces in which rating systems and incentives facilitate trust between strangers (Sundararajan, 2016; Tadelis, 2016; Jacobides, Cennamo, and Gawer, 2018). For researchers of communications and information sciences, on-demand work offers an exemplary setting for examining long-standing issues regarding transparency, fairness, and ethics (Christin, 2017; Möhlmann et al., 2021; Cameron et al., 2023). For labor scholars, on-demand work, especially on closed labor platforms like Lyft and DoorDash, is the quintessential precarious, low-paid job of poor quality (Ravenelle, 2019; Wood et al., 2019) whose decentralized nature makes it challenging for individuals to organize for better working conditions (Wood, Lehdonvirta, and Graham, 2018; Mayberry, Cameron, and Rahman, 2024). For organizational psychologists, the atomized nature of this work complicates their understanding of how social processes such as identification and socialization occur (Petriglieri, Ashford, and Wrzesniewski, 2019; Anicich, 2022; Cropanzano et al., 2023). And for organizational scholars, on-demand organizations represent a significant departure from traditional organizational forms because of the former’s reliance on independent contractors and the replacement of managers by algorithms (Aneesh, 2009; Vallas and Schor, 2020; Davis, 2022). Given the widespread implications of this new form of labor, the need for a granular understanding of on-demand work has never been greater.

Algorithmic Management and Control

In the on-demand workplace, algorithms are de facto managers embedded in the work process and have responsibilities that include hiring, allocating tasks, evaluating performance, and firing. First defined by Lee et al. (2015: 1603), algorithmic management means that software algorithms “assume managerial function and surrounding institutional devices that support algorithms in practice.” Drawing on elements of scientific management (Taylor, 1911), scholars have called algorithmic management a digital version of Taylorism because of the high degree of task standardization and decomposition, digital surveillance, information asymmetry, fine-grained measurement of labor, and piece-rate wages (Cherry, 2016; Faraj, Pachidi, and Sayegh, 2018; Curchod et al., 2020; Jarrahi et al., 2020; Kellogg, Valentine, and Christin, 2020; Rahman, 2021; Noponen et al., 2023). ⁴ As a result, much of the extant research has focused on how algorithmic management controls workers.

Matching algorithms either assign workers to tasks in closed labor market platforms (e.g., Instacart) or suggest potential workers to customers via open labor market platforms (e.g., Upwork). A worker’s refusal to accept a task can incur a penalty, such as receiving a status downgrade (Rosenblat and Stark, 2016), removal of privileged status (Cameron, Thomason, and Conzon, 2021), or the loss of access to future assignments (Rahman and Valentine, 2021). Algorithmically mediated telemetrics and customer ratings track, rank, and evaluate multiple aspects of workers’ performance (e.g., acceleration, response times, customer ratings; Wood et al., 2019; Cameron, 2022). The management system’s interactive nature enables companies to update the algorithms that manage workers by analyzing data from those same workers, thereby allowing the companies to experiment on workers at will (Möhlmann and Zalmanson, 2017; Veen, Barratt, and Goods, 2020; Rahman, Weiss, and Karunakaran, 2023). This process creates an environment rich in information asymmetry, in which “workers are given information in a piecemeal fashion, while remote company dispatchers maintain omniscient views” (Shapiro, 2018: 2959). This line of reasoning suggests that algorithmic management’s control would reach unprecedented levels, actualizing the notion of the panopticon.

The concept of consent allows scholars to think of workers as using their agency to participate in rather than refuse their work, even in exploitative conditions (Burawoy, 1979, 1985). ⁵ A few studies have examined how schedule flexibility, a common feature of on-demand work, affords agency (Occhiuto, 2017; Milkman et al., 2021). However, scholars argue that this flexibility is largely mythical because it severely diminishes as one’s economic dependence increases (Schor et al., 2020; Muralidhar, Bossen, and O’Neill, 2022), and most of the work on these platforms is done by people who need the work to survive (Gray and Suri, 2019). Thus, the question of how consent is generated within algorithmic management systems remains unresolved and is crucial for gaining insights into the growing appeal of this work.

Generating Consent in Manufacturing and Service Work Through Social Relations

Emerging from earlier studies on the informal organization and social relations, (Roy, 1952; Roethlisberger and Dickson, 2003; Mayo, 2004), a line of research has examined how consent, or workers’ active participation to align their efforts with managerial objectives, is manufactured through social relations in various white- and blue-collar jobs (e.g., IT professionals, machinists; Hodson, 1999; Barley and Kunda, 2004). Given that on-demand work is an amalgamation of features associated with manufacturing (e.g., piece-rate wages, technologically mediated pacing) and service work (e.g., customer interactions and ratings), the most useful literature in which to situate this study is that which examines how consent is produced in those workplaces. Labor process theorists have found that social relations between workers and other organizational members are key to securing consent. By offering leniency, praise, and friendship, managers can soften the harshness of work conditions while keeping their managerial authority intact. By looking the other way regarding rule-breaking (e.g., allowing smoking at a mine, tolerating craftsmen who use company time and materials to make souvenirs; Gouldner, 1954; Anteby, 2008), managers build relationships with workers on the managers’ own terms while also reinforcing managerial authority. Managers have urged workers to see their jobs as an opportunity to fully be themselves in order to distract their attention away from brutal conditions, such as in heavily surveilled call center work (Fleming and Sturdy, 2009). When supervisors praised aspects of workers’ identity that were stigmatized by broader society, workers were willing to tolerate physically and emotionally demanding conditions. For example, Sallaz (2019) documented how call centers, notorious for their strange hours and abusive customers, were one of the few places where gay men and trans women in the Philippines were praised for their subcultural identity markers (e.g., English fluency, urbaneness). And in VIP lounges, brokers built such strong strategic intimacies by flirting with and offering gifts to attractive young women that these women went out night after night, unpaid and teetering on stilettos, to brokers’ events (Mears, 2015).

Relationships among coworkers are also key in generating consent. Unlike gamification, which relies on rules designed by management to improve workers’ affective experience and to boost productivity (Deterding et al., 2011; Mollick and Rothbard, 2014), workplace games result from organic interactions between workers. In the social game of “making out,” machinists synchronized their efforts with one another to beat the piece rate by an agreed-upon amount (Burawoy, 1979). Similarly, in the “tipping game,” concierges and casino dealers categorized customers, agreeing on how much emotional labor to expend to optimize the tip-to-effort ratio (Sherman, 2007; Sallaz, 2009). These games are socially constructed: workers make the rules, receive feedback from each other, and compete against one another for rewards (Sallaz, 2013). Shared norms can induce workers to work harder than they would under a manager’s gaze. Describing the change from direct managerial oversight to being on a self-managing team, one technical worker reported having dozens of eyes upon him to make sure that he arrived on the line early, ready to work: “Now the whole team is around me and the whole team is observing what I’m doing” (Barker, 1993: 408). On assembly lines, workers shielded their efforts from managers’ eyes so they could ignore safety procedures and speed up production at their coworkers’ behest (Bernstein, 2012).

When consent is manufactured, workers have a sense of psychological and social achievement in that they have done their work well (Sallaz, 2013). Accomplishing the work, either through winning a game or developing group solidarity, elicits pride and mastery. Another byproduct of consent is that it mollifies the inherent conflict between workers and management (Sallaz, 2013) by either attenuating it through the development of friendly relationships with managers (e.g., strategic intimacies; Mears, 2015) or transferring the conflict laterally to coworkers (e.g., peer monitoring; Barker, 1993).

Manufacturing Consent in the On-Demand Economy

This rich literature on the manufacturing of consent is less applicable to on-demand work, which is largely atomized and asocial. Individuals work independently with little to no direct contact with managers and coworkers, and interactions with customers are fleeting and transactional. Thus, the social mechanisms foregrounded in the workplace consent literature are non-existent. And without a cohesive organizational culture and mission statement, normative mechanisms of control, such as shared goals or purposes (e.g., Kunda, 1992), are largely unavailable. Indeed, by being classified as independent contractors, workers are explicitly told by platform organizations that they are not organizational members, as these organizations instead call workers entrepreneurs, co-creators, service providers, consumers, or partners (Ravenelle, 2019).

Moreover, consent is manufactured at the point of production, and on-demand work has a different point of production compared to manufacturing and service work (i.e., worker–machine and customer–worker interactions, respectively; Burawoy, 1979; Leidner, 1993). In on-demand work, the labor process is reconfigured such that the point of production involves an app and its algorithms facilitating connections among multiple actors (e.g., the restaurant, customer, worker, and app in food delivery services; see Figure 1). Hence, irrespective of where exactly the work is done (i.e., digitally by an MTurker or on the road by a Deliveroo courier), the site of the worker–app–customer interaction constitutes a clearly defined system of production at which consent is manufactured, often in the absence of managers and coworkers. Indeed, managerial intent is obscured in this arrangement in which the app—not a supervisor—appears to organize the work process (Chai and Scully, 2019; Vallas and Schor, 2020; Schor, Tirrell, and Vallas, 2023). See Table 1 for an overview of management systems and consent production.

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Figure 1.

Algorithmic Labor Triangle and On-Demand Work*

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Table 1.

Workplace Consent in Manufacturing, Service, and On-Demand Work

	Manufacturing Work	Service Work	On-Demand Work
Geometry of control arrangement	Dyadic	Triadic	Triadic or quadratic
System of production	Labor relations: manager–worker	Customer service triangle: manager–worker–customer	Algorithmic labor triangle: app–worker–customer—(merchant)
Organizational goal	Efficiency	Speed, service, security	Adherence to nudges, workers staying online
Product	Physical item	Interactive customer service experience	Completion of micro-task that is associated with an action on the app
Mechanism of consent	Social relations with coworkers and managers (e.g., peer monitoring, industrial games, selective leniency)	Social relations with coworkers, customers, and managers (e.g., industrial games, identity incentives, strategic intimacies)	Narrow and frequent choice when interacting with the algorithmic output on the app
Outcomes of consent	Skill development Sense of mastery and achievement in executing the work Flow state; absorption Conflict between workers and managers transferred to coworkers	Skill development Sense of mastery and achievement in executing the work Conflict between workers and managers transferred to coworkers	Skill development Sense of mastery and achievement in executing the work, either by following or “getting over” the algorithmic management system Flow state; absorption Conflict between workers and managers eradicated or amplified
Exemplary papers	Gouldner, 1954; Burawoy, 1979; Barker, 1993; Anteby, 2008; Bernstein, 2012	Leidner, 1993; Sherman, 2007; Sallaz, 2009, 2019; Mears, 2015	—

Research has not yet examined how algorithmically mediated changes to the labor process may affect consent. Like the internal combustion engine, algorithms are infrastructural technologies that lie at society’s core, as they have the power to “underwrite almost every aspect of daily life” (Barley, 2020: 8). Already, algorithms are transforming how jobs are filled (Schechner, 2017; Feffer, 2023), structured (MacKenzie, 2021; Lebovitz, Lifshitz-Assaf, and Levina, 2022), monitored (Levy and Barocas, 2018; Allison, 2023), and experienced (Bellesia, Mattarelli, and Bertolotti, 2023; Möhlmann, de Lima Salge, and Marabelli, 2023). Understanding how algorithmic management reconfigures work, especially at the human–algorithm interface, is crucial for understanding workers’ experiences and the broader socio-material work ecosystem (Orlikowski and Iacono, 2001; Barley, 2020). But this kind of granular analysis is missing from the literature. Scholars have hinted at how individuals’ ability to make choices and develop workarounds can keep them working under precarious conditions (e.g., Lee et al., 2015, Shapiro, 2018), but the literature lacks a more comprehensive understanding of the relationship between algorithmic management and manufacturing consent. One step toward answering these questions involves closer examination of workers’ moment-by-moment interactions with the technology, to help us understand the choices workers do have and make vis-a-vis the algorithmic management system.

The Ride-Hailing Industry

First launched in 2011, ride-hailing services such as Uber, Lyft, and Juno have disrupted the taxicab industry. The core innovations enabling these services are digital maps and the algorithms that coordinate the work process by matching independent, distributed drivers (working either in their own or rented cars) with customers within seconds and giving block-by-block directions. Fares dynamically adjust based on consumer demand, and performance is evaluated through customers’ ratings and drivers’ acceptance and cancellation rates. Drivers have little direct contact with company employees and few ways to voice concerns; even firing (which is euphemistically called deactivation) is done online. Work requirements vary, with most companies requiring clean driving records, no moving violations in the previous three years, state vehicle inspections, and increasingly, despite industry protests in some cities, criminal background checks. Once hired, which can take from three days to three weeks, workers can go online and begin working. Classified as independent contractors, drivers pay their own employment taxes and are not eligible for health insurance or other social benefits (e.g., workers’ compensation, unemployment insurance). Pay is based on miles driven and bonuses, demand-based incentives, and tips (if any), and it is delivered weekly, although drivers can daily request “instant pay” for a small fee ($1.49 as of December 2023). Some drivers are active on multiple platforms, and algorithmic features are similar across platforms. (See Online Appendix, Image 7 for an example of a worker using two platforms at the same time.) In this article, I use the term “ride-hail(ing)” when discussing the act of driving, I refer to “RideHail” when discussing one or more of the platform companies examined in this study, and I name a specific company (e.g., Uber, Lyft, Juno) only when doing so is necessary to contextualize a comment.

On-demand platforms explicitly promise to provide immediate access to labor for customers. Thus, ride-hailing platforms have two overlapping objectives: to have workers follow algorithmic nudges and to keep workers online and available to work on demand. Nudges encourage workers to make decisions in alignment with fluctuating customer demand, such as offering incentives to work in busy areas. Given that workers are classified as independent contractors, drivers are not contractually obligated to follow nudges, such that even if drivers do not follow a nudge but stay active online, this meets the ride-hailing platforms’ distal second objective of having drivers available to work and offer rides to passengers.

The Work Structure

The work task

The work process of a ride-hailing driver is based on a three-way interaction between the driver, the rider, and the app. Drivers begin a shift by choosing a location to open their app and swiping right to go online. A complete ride consists of (1) the app matching the driver and rider and the driver accepting; (2) the driver getting to the rider’s location and waiting for the rider to enter the vehicle; (3) the driver swiping “start ride” on the app; (4) the driver and rider interacting; (5) the driver dropping the rider off; and (6) the driver swiping “end ride” and rating the rider (see Figure 2). Work cycles may end prematurely, such as when a rider fails to show up or the app malfunctions. After the end of a work cycle, employees may stay online and wait to be matched again or go offline and stop working. In smaller cities, where trips tend to be shorter, drivers can complete as many as six or seven rides in an hour, while in larger locales they might complete only two or three. Fares are set locally and are based on a pickup fee, distance, time, and demand pricing (if any). Drivers may also be offered bonuses for completing a certain number of rides within a designated period, but this is not offered in all markets. Reports of gross hourly wages range from $12 to $30. ⁷

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Figure 2.

Ride-Hailing Work Cycle

Data Collection and Analysis

Given the emerging nature of on-demand work and my interest in theory development, I designed a multiple-source qualitative study, having spent seven years in the field (from 2016 to 2023). I used four overlapping data sources, which I triangulated to bolster validity (Eisenhardt, 1989): participant observation (160 total hours of driving-related activity), conversational interviews (n = 112), semi-structured interviews in 23 North American cities (n = 136), and social and print media. ⁸

Data Analysis

I analyzed data by using a grounded theory approach (Locke, 2001; Charmaz, 2006; Glaser and Strauss, 2017) with field observations, interviews, and web forum posts as primary data sources.

Algorithmic Management in the Ride-Hailing Industry

Algorithms scaffold the work process

To understand workers’ granular experiences of work, it is first necessary to understand how the work was structured. Unlike manufacturing and service work, in which human management helps to scaffold work, the algorithmic management system set up the work process. In the ride-hailing industry, algorithms scaffolded the work process by (1) matching workers to riders; (2) instructing workers how to proceed through the work tasks (e.g., giving directions); (3) setting and adjusting fares dynamically (demand-based pricing); (4) offering bonuses (e.g., “Do 50 rides in the next 5 days for an extra $50”); and (5) evaluating workers’ performance via customer ratings. See Table 2 for descriptions of all five components. (Table 2A in the Online Appendix shares the same information and includes accompanying images as examples of each algorithm.) Algorithmic management systems thus used rewards, sanctions, and specific directions to manage drivers. Ultimately, the algorithmic management system was the defining feature of the work; for example, in a typical shift a driver may have completed only a dozen rides but would have more than 100 unique interactions with the algorithms.

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Table 2.

Components of the Algorithmic Management System That Scaffold the Work Process

Type of Algorithm	Purpose	How It Communicates to Drivers	How It Structures	Example
Matching	Assigns task to workers	Pop-up notices to accept/decline ride after match has been made	Sanction	“First day. I’m sitting on a shady street two blocks from my house, nervously checking my phone every 20 seconds so I don’t miss a ping. My phone is on my dashboard when it suddenly starts buzzing. Yay—a ride! I see a flashing circle with a timer, counting down. My hands are sweaty, the phone is vibrating, and while trying to swipe I drop the phone under the passenger seat. Darn! After a few seconds the phone goes quiet. I’ve lost my first ride.” (Field notes, July 29, 2016)
Instructions (navigation, timer)	Gives task instructions, such as how long to wait for customers and directions	Directions to/from location; timer when waiting at pick-up spots	Direction	“Always follow the app’s instructions. Keep a close eye on your app for shared rides. The route is built on efficiency, so the order of who is picked up and dropped off first varies from ride to ride.” (RideHailing website FAQ document)
Pricing—Demand-based incentives	Sets task pay above the base rate, based on customer demands	Nudges through in-app notifications and text messages. Heat map of high-demand areas appears after every trip; darker color indicates higher demand.	Reward	“Demand is higher than usual in Center City. Take advantage of higher than normal fares!” (Text message sent to phone) “You turn on your app, and then you see that very orange, bright color around downtown area, that means there is a surge there. There is a high demand . . . so you rush into that area.” (Leah, Chicago)
Pricing—Bonuses	Determines extra payment for a “task bundle”	Nudges through in-app notifications, text messages, and emails	Reward	“For the weekend [of March 16, 2018], my incentive was, an extra $90 for completing 24 trips.” Field notes, March 15, 2018
Ratings	Evaluations/quality control for tasks, with actual evaluation at the customer level	Driver’s rating appears on home screen; drivers must complete rating score at the end of every ride before being matched to a new customer	Sanction	“The nice thing I like about it is if you’re ever paired with somebody who is a bad rider, if you give them a one, two, or three star, you’re not matched with them anymore. If somebody is a bad passenger, then I just give them a one star and I don’t have to worry about ever seeing them again. Ratings are good for drivers and the passengers. I’m for the rating because you can see the passenger, the passenger can see us. So it’s good.” (Ryan, Detroit)

Manufacturing Consent: Engagement and Deviance Tactics Within Algorithmic Management Systems

Although most research emphasizes the all-encompassing control of algorithmic management systems, these systems do not necessarily take away workers’ ability to exercise choice—indeed, because algorithms segmented the work into discrete tasks, there were frequent opportunities for workers to exercise choice, albeit over narrow segments of work. Regardless of whether workers used engagement or deviance tactics, at hundreds of points in every work day, workers made choices within a highly constraining system—choices that elicited their consent to a platform’s algorithmic management system, even when they skirted some of the platform’s formal guidelines. While the tactics were practical opposites in terms of how workers interacted with the management system, they both elicited consent and were associated with workers’ finding meaning and seeing themselves as skillful agents; however, the tactics differed in terms of their potential for worker–management conflict.

Engagement Tactics: Playing Within the Boundaries of the Algorithmic Management System

When deploying engagement tactics, drivers worked in conjunction with the algorithmic management system, using information from the apps to make decisions about how to navigate within the boundaries of the system. In simple engagement tactics, workers followed the algorithmic nudges, such as driving toward high-demand areas. In complex engagement tactics, workers did not follow the algorithmic nudges; instead, they used information provided by the nudges to inform their navigation of the work. Below, I describe workers’ engagement with the pricing and matching algorithms.

Outcomes of Consent Produced by Engagement Tactics: Skill Development and the Eradication of Managerial Conflict

When deploying engagement tactics, drivers found meaning in the work by seeing themselves as skillful agents, deftly navigating the algorithmic management system and having a sense of mastery of their work. Drivers boasted that they had “special skills” (Jorge, D.C.), “knew how to make the money” (George, Boston), and could make things “work out” (Amber, Missoula) “to their advantage” (Arthur, LA). They worked smart (Zach, NYC), only in the times and places “worth” (Boxton, Detroit) their time. Emphasizing the skills and meaning she found in driving, Elizabeth (Detroit) said, “Oddly, it is fulfilling to me. I mean I’ve heard people describe it as a job that you can do if you have little skills or something like that . . . but I don’t think that’s the case for most, I really don’t.”

Another outcome associated with engagement tactics was the absence of conflict with the algorithmic manager. One reason workers did not see themselves having any conflict with the algorithmic management system is that their behaviors were aligned with the nudges, which, in turn, were often associated with various incentives. Often, drivers repeated that the best thing to do was to follow the system, which often worked in their favor. Thrilled at the money he earned by following the nudges, Marshall (Detroit) said, “I made $35 off some perks [incentives] and I did it by doing what I’m supposed to so . . . everything else will work itself out.” Drivers reported no real conflict with the algorithmic management system. Sebastian (Detroit) said, “Sometimes you do a back and forth with them about a cancellation fee or a fare adjustment, but that’s [about it].” Overall, drivers described feeling satisfied, noting that the work was “reasonable” (Betrand, D.C.), and said things such as, “I like it. I can’t complain at all. And honestly, I don’t think anyone can” (Maxwell, D.C.). Confident in their skills navigating the algorithmic management systems, workers said they could “drive forever” (Franklin, Detroit).

Each interaction with the pricing algorithms and the work-matching algorithms thus presented these drivers with an opportunity to exercise choice, such as by analyzing a heat map before deciding to drive toward (simple engagement) or away from (complex engagement) a high-demand area. Workers’ behaviors in simple engagement tactics were in full alignment with the algorithmic nudges and exemplified how nudges were used to encourage individuals to make a specific choice. Drivers exercised greater latitude of choice in complex engagement tactics because they did not explicitly follow the nudges but, instead, used the information presented by the system to inform their behaviors. While behaviors such as drivers manipulating their input to influence the algorithmic management system or using two phones to optimize their location were not prohibited, they were nonetheless not in accordance with RideHail’s provided norms and suggested practices.

Deviance Tactics: Pushing Against the Boundaries of the Algorithmic Management System

When deploying deviance tactics, drivers tried to get around the algorithmic management system’s directives by manipulating their input into the work-matching, pricing, and ratings algorithms and, if penalized, to counter any sanctions. In simple deviance tactics, drivers circumvented the blind matching algorithms by selecting and screening rides. ²⁰ In complex deviance tactics, drivers tried to influence their position within the algorithmic management system either by inflating surge prices or by increasing their acceptance rates or customers’ ratings. If the system detected either of these tactics, it sanctioned drivers; however, because the penalties were applied in a consistent manner, drivers could counter them. In this way, deviance tactics elicited consent because workers were able to make choices, and these choices and their counters to sanctions stayed within the boundaries of the algorithmic management system, so ultimately, drivers remained online, which was one of RideHail’s objectives.

Outcomes of Consent Produced by Deviance Tactics: Skill Development and the Amplification of Managerial Conflict

As in engagement tactics, when deploying deviance tactics, drivers found meaning by seeing themselves as skillful agents, deftly navigating the algorithmic management system. However, unlike engagement tactics, the skill expressed in deviance tactics was not in working with algorithms but, instead, in being able to counter the algorithms, such as by pre-selecting passengers (Marsha, D.C.) and declining shared rides (Jackson, Philadelphia). Drivers described their practices for interacting with the system as “complicated” (Orion, D.C.), “tricks” (Borris, Boston), or “methodolog[ies]” (Arthur, L.A.) and said that they “analyze, prepare, wait, and execute” (Smith, D.C.). Safeguarding their skills, drivers would tell me about them in hushed voices only after I promised not to use them myself, or they asked me not to include their tactics in my papers. ²³ Confident in their abilities, drivers scoffed at penalties—John (Missoula) laughed when he was blocked, noting “that’s what I want it [the algorithm] to do,” and Orion (D.C.) ultimately secured a fare four times higher than the regular fare. Even with changes in the algorithmic management system, drivers who extensively deployed manipulation tactics envisioned themselves as smarter than other drivers. They spoke condescendingly about those who did not curate their matches (David, Detroit) or laughed when they saw a driver get matched with a customer they themselves had just rejected (Smith, D.C.). Describing drivers who simply followed the algorithms, Orion (D.C.) said, “They aren’t smart . . . they don’t understand the implications . . . they make awful, awful decisions and . . . I swear they [are] so proud of themselves.”

The second outcome associated with deviance tactics was the amplification of conflict with the algorithmic manager. When deploying manipulating tactics, drivers were aware of the conflict with the system. For example, Arthur (L.A.) was combative when matched to a low-paying ride, telling himself, “That’s not how this game is going to work for me,” before rejecting the ride. Smith (D.C.), who did not want me to reveal all his workarounds, said he tries to “drive smart [but he] know[s] this is not what [RideHail] wants [him] to do.” He went on to explain the inherent antagonism between him and the algorithmic management system: “We really can’t be friends with our bosses. If you don’t establish your own objective, you’re going to get caught, swept up in what they [RideHail] want you to do.” Comparing driving to his previous work in restaurants, Alphonse (D.C.) said, “In other places, you might get a complaint, but you can solve the problem right away, you don’t need to write. You can say, ‘sorry,’‘help me.’ But here, is nothing. It’s working with a machine.” While deploying deviance tactics set up drivers to be in opposition to the algorithmic management system, amplifying the potential for conflict, for the most part drivers were still able to obtain their desired rides.

When deploying deviance tactics, workers exercised greater latitude of choice about how to interact with the algorithmic management system and to manage these tradeoffs, compared to their use of engagement tactics. In simple deviance tactics, workers tried to circumvent the blind matching policy by declining certain rides. In complex deviance tactics, workers exercised the greatest latitude for choice by manipulating their input into the algorithmic management system to change their status within it. Pushing against the edges of what the management system would allow, drivers could successfully counter any penalties, occasionally using multiple pieces of technology to do so (e.g., requesting themselves through another account). Despite paying a cost in terms of time and money to deploy this tactic (e.g., paying cancellation fees after requesting oneself), drivers saw themselves as skillful agents because they obtained desired rides.

Yet, even though workers manipulated their input into the algorithmic management system when deploying deviance tactics, they were still consenting to the work because RideHail’s second objective was for the driver to remain online. Even when drivers deviated, they were still compliant with the larger management system (though not fully aligned with it), in part because all of their counter-measures were allowable within the system (i.e., programmed leniency). ²⁴ Last, it must be emphasized that while these choices were confined, they were also constant, enacted hundreds of times within a shift. In a single ride, a driver could check the heat map to find high-demand areas (simple engagement tactic), use a passenger app to position oneself (complex engagement tactic), and reject incoming shared rides (complex engagement tactic) before accepting a private ride. Taken together, these findings show how in each tactic, workers could make frequent, albeit narrow, choices that all lay within the bounds of the algorithmic management system.

Changes in algorithmic management

Over the seven years of this study, the algorithmic management system frequently changed, and for the most part, workers were able to adjust, continuing to exercise choice. Often, changes in the pricing algorithms prompted changes in drivers’ behavior, as drivers increasingly believed that it was more profitable to deploy engagement rather than deviance tactics. As described above, many drivers shifted from rejecting to accepting all shared rides as new incentives were introduced. Drivers thought twice about multi-homing when a bonus based on the amount of uninterrupted time spent on an app was introduced. Loyalty programs such as UberPro (introduced in 2018) and Lyft Tier (revamped in 2019) provided drivers with more information and advantages (e.g., being able to see a rider’s destination before accepting a ride, priority airport queueing) only when drivers more closely aligned their behaviors with RideHail’s goals, e.g., such as by keeping their acceptance (cancellation) rates high (low). When comparing data between my first and last data collections, in 2016 and 2023, respectively, I identified fewer mentions in the later data of certain behaviors such as pre-selecting riders, presumably because the matching algorithm no longer used driver–rider proximity as its only input. Instead, workers often discussed the new choices they were offered, such as rider selection (e.g., Uber drivers could select a preference for women riders), ride type preferences (Trip Radar introduced by Uber), up front pricing (Uber, Lyft, Juno, Gett), and instant payment (Uber, Lyft). My informal interactions with two data scientists at one RideHail company suggested that programming of the algorithmic management system was geared toward efficiency optimization, which could include pricing incentives. ²⁵ Taken together, these changes point to algorithmic management’s ability to program out deviance tactics while still emphasizing workers’ ability to exercise choice. Ultimately, these choices, which aligned with the organizations’ twofold goals of having workers follow algorithmic nudges and stay online ready to work, elicited consent. Table 3 provides a summary of all tactics.

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Table 3.

Summary of Engagement and Deviance Tactics in Response to Algorithmic Management

Type of Algorithm	Tactic
	Simple Engagement	Complex Engagement	Simple Deviance	Complex Deviance
Work Matching Work assignment	Accepting all rides	Turning app on/off early in certain areas; comparing multiple platforms before starting driving; using another phone/app to assess customer demand	Declining rides that are too far away, from a certain neighborhood/place, etc.	Rejecting rides of a certain type
Work Instructions Timer, navigation	Waiting predetermined wait time; following in-app GPS directions	Muting or disabling in-app navigation	Positioning car in non-approved spaces to avoid the waiting timer	Not applicable
Pricing–Demand-Based Incentives Linking pay to demand	Chasing demand-based incentives (e.g., surges, primetime); scheduling driving hours around high demand periods	Ignoring or avoiding demand-based incentives	Not applicable	Declining multiple rides in a high-demand area to drive up fares
Pricing–Bonuses Linking pay to output	Scheduling hours around time-based incentives	Assessing the customer app to position oneself in densely populated areas to get more shorter rides to get ride-based incentive	Accepting a ride to hit a ride-based incentive, but then not completing the ride; giving nowhere rides to friends to meet a ride-based incentive	Not applicable
Ratings Customer evaluations, acceptance/cancellation ratings, telemetrics	Giving customers five-star ratings to save time	Keeping phone in special position to maintain a high telemetrics rating	Canceling rides that may receive low customer ratings; giving low ratings to riders for no reason; accepting rides driver does not plan to complete to boost acceptance ratings	Not applicable

Withdrawal: Leaving the Platform or Damaging the Integrity of the Algorithmic Management System

In contrast to the tactics that elicited consent to the algorithmic management system, workers could also withdraw consent from that system. Two ways in which workers withdrew consent were by logging off an app with no intention of returning or by compromising the integrity of the algorithmic management system, which could result in permanent deactivation if detected.

Contributions to our Understanding of Workplace Consent

While Burawoy (1979) emphasized the role of social relations in manufacturing consent, over 40 years ago he also hinted at the dynamic relationship between technology, choice, and consent. He compared what he found at the engine factory he studied, in which machinists played industrious games, with the same factory that his predecessor Roy (1952) had studied, in which machinists had loafed about: “The differences between the organization of work at Geer and Allied suggest even greater ‘quantitative’ choice within even narrower limits. Harry Braverman missed the equally important parallel tendency toward the expansion of choice within those ever-narrower limits” (Burawoy, 1979: 96). In the more than 20 years between these scholars’ studies of work at the same factory, technology had advanced such that the machine’s pace had sped up work, and the technical complexity had given workers more choices about how to accomplish this work. In my research context of on-demand work, which is largely asocial, I build on this idea and seek to “bring the technology back in” to examine how algorithmic management shapes the manufacturing of consent.

In my study, as workers described exercising choices, they emphasized their skill in interacting with the algorithms on their own terms and maximizing their interests. This is consistent with theory on workplace consent: workers cannot dissent from the things they do not like about the work without dissenting from the structure of the work that makes these things possible (Sallaz, 2013). Games helped manufacturing workers beat monotony, schedule flexibility supported taxi drivers’ aspirations to be family men, and call center agents were energized by a fast-paced learning environment. Correspondingly, one reason that ride-hailing drivers enjoyed work under algorithmic management is because it enabled them to make minute choices while executing the work.

Yet, as important as it is to take workers’ experiences seriously, it is also crucial to remember the larger conditions in which the work is situated. The relationship between labor and capital in platform work is obscured because of the invisibility of platform owners and managers, who hide behind an opaque management system (Chai and Scully, 2019; Vallas and Schor, 2020). Work arrangements amplify this information asymmetry. While classified as independent contractors, workers cannot organize (Dubal, 2023a), may not negotiate personally with managers (Maffie, 2022), lack access to official voice channels (Bucher, Schou, and Waldkirch, 2023), have minimal recourse against customer malfeasance (Laemmli, 2023), and have limited knowledge of the actual code that underpins the algorithmic management system (Christin, 2020). Indeed, the algorithmic management systems set the terms of interaction. In engagement tactics, the terms are obfuscated because workers’ choices align with or do not directly counter the algorithmic management system. In deviance tactics, workers’ choices may seem more in alignment with their own interests, especially when individuals counter sanctions; however, this countering works only because leniency is programmed into the system. Thus, workers’ interaction with the algorithmic management system is on its terms, not workers’. As noted, when drivers interact with the algorithmic management system on their own terms, such as by geo-spoofing, the result can be sudden and permanent deactivation. This research thus underscores how important choice can be for workers to secure continued and active participation in the work.

An algorithmic management system in which individuals have circumscribed choices while working is crucial to keeping the platform system running smoothly, because workers can invest in their work and develop a sense of mastery. Elegantly debunking economists’“agency problem,”White (2008: 301) noted that workers’ choice and the agency that comes with it is a solution, a “neat kind of social plumbing” in that organizations can use it to attain and maintain organizational control. In the on-demand economy, this agency, actualized as choice, is extreme in that there is a multiplication of choices in an employment model that is contractual and in work that is algorithmically segmented such that workers can make constant (if confined) choices. Yet, the terms of the labor conditions are obfuscated, and workers do not know the actual conditions of the exchange, keeping workers in a quasi-suspended state in which the choice allowed is encompassed within a socio-technical system of concentrated platform power (Wood and Lehdonvirta, 2021; Davis, 2022). The allure of having a sense of choice alongside structurally precarious work conditions results in what I call the “good bad” job.

The deployment of algorithmic management is increasing in workplaces, and how choice is designed into the system will play an important role in how workers’ consent can be elicited. In this setting, the algorithmic management system functioned similarly to an opt-in system in that individuals made small choices that further embedded them into the work process. Uber’s Trip Radar, for example, presented drivers with several different rides to choose from. Alternatively, drivers could choose to have the system automatically accept all rides on their behalf. These choices at each stage of the work process contributed to the work being compelling, pulling workers in. However, one would expect that an algorithmic management system functioning like an opt-out system would have different implications because it presumes, rather than elicits, consent. Even though an opt-out system might encourage participation in the work, it may not elicit the same positive feelings in workers (e.g., skill development). This suggests that consent generated in an opt-out algorithmic management system may be more tenuous in that it may increase conflict and pushback and weaken long-term commitment. Rahman and colleagues (2023) found that in an online open labor market, the way that choice was designed into the system increased workers’ frustration and disenchantment. In other design architectures, scholars have questioned the ethics and longevity of opt-out systems, noting that they erode participants’ trust (Johnson and Goldstein, 2003; Thaler, Sunstein, and Balz, 2012). Future research should pay close attention to how the design of algorithmic management systems may affect how consent is produced and workers’ experiences.

How choice will appear (e.g., type, frequency) depends on where algorithms are inserted to manage the work process. In piece-rate work, in which the entire work process is algorithmically managed (e.g., grocery delivery), individuals are more likely to have choices throughout the entire process. In retail work, algorithmic management is often embedded in scheduling or evaluation. Thus, workers are more likely to have choice when determining when to work or during the point of sale, in which workers may be able to influence customers’ purchases (Van Oort, 2023; cf. Wu, 2020, for an example in care work and ratings). These examples emphasize the importance of choice-based consent in algorithmically mediated work, suggesting that the location and latitude of workers’ choice depend on how algorithms are embedded within the work. Taken together, these examples underscore the importance of not examining a single feature of technology in isolation and not generalizing an entire technology wholesale.

Algorithmic Management and Workarounds

This research adds nuance to the issue of how to categorize workers’ responses, beyond compliance and resistance, to managerial regimes, adding to the literature on workarounds (e.g., Gasser, 1986; Beane, 2023). Prior research has, for the most part, recognized the agency of on-demand workers only when they resist (e.g., Shapiro, 2018) or engage in off-app activities (e.g., disintermediation, Maffie, 2023). Here, I found many examples of workers expressing agency and developing workarounds. Both engagement and deviance tactics could be simple or complex depending on how closely they aligned with the algorithmic management system. In complex engagement tactics, for instance, workers’ behaviors were off script, benign violations of norms, such as using the customer app to scope out demand and pre-position themselves. This distinction is important because it shows that consent is not merely compliance, and workers are not dupes. Individuals whose behaviors are largely within the algorithmic management system are not perfectly compliant, and authoring these tactics is part of what gives these individuals a sense of mastery. Moreover, in complex tactics, drivers displayed a command of their environment by using multiple pieces of technology as raw inputs for creative workarounds (e.g., requesting themselves on rider accounts). These varied tactics exemplify the “culture worlds” (Seaver, 2017: 7) and “algorithmic imaginaries” (Bucher, 2016: 30) that workers create to navigate their work. Future research should explore other ways that individuals express agency within such systems, such as via voice or creativity.

In addition, this research provides insight into the difference between behaviors that are deviant yet within the boundaries of the system and behaviors that constitute a withdrawal of consent. Most research equates the withdrawal of consent to quitting (e.g., Sallaz, 2013). This study describes another way in which workers may withdraw consent: compromising the integrity of the management system. Compared to deviance tactics, the withdrawal of consent can be even more deleterious for on-demand organizations since they rely on an always available workforce. As organizations increasingly use technology to manage and track workers, research should continue to explore the relationship between consent and technology, including when and how consent is withdrawn and its organizational implications.

Implications of Algorithmically Mediated Choice-Based Consent: The Rise of the “Good Bad” Job?

While robots are not taking over most jobs, algorithms are increasingly taking over managerial functions previously held by humans, such as hiring, firing, directing, and evaluating workers. A large class of jobs—ranging from truck driving to warehouse work to retail work to home health—are being managed by algorithms and done by those classified as independent contractors. Surprisingly, many individuals appear to like or even prefer non-standard or algorithmically managed on-demand work compared to standard employment, even when they are aware of its exploitative aspects (Fielden, Tench, and Fawkes, 2003; Ertel et al., 2005; Farrell and Morris, 2017; Östergaard, 2017; Adler, 2021). This study argues that workers’ ability to make frequent choices in algorithmic management systems is one reason they enjoy this line of work. This observation then raises the question of the broader implications of algorithmically meditated choice-based consent.

One consequence of algorithmically managed jobs is that work that is structurally considered “bad”—with variable (and often low) wages, minimal labor protections, and limited advancement opportunities—may be psychologically experienced as not so bad. Two seminal books—Kalleberg’s (2011)Good Jobs, Bad Jobs and Standing’s (2011)The Precariat—document the growing trend of precarious work, which many scholars argue only becomes more precarious when algorithmic managers are introduced (e.g., Griesbach et al., 2019). At the same time, algorithmic management allows drivers to have varied choices at multiple levels (within rides, between rides, on work itself) and in a greater type and amount than in jobs typically available for the similarly skilled part of the labor force. On-demand consolidator apps such as Gridwise and JackApp afford more choice, allowing workers to coordinate their activities to secure the highest-paying gig at any moment. These constant choices entice individuals to work longer, invest more effort, and, in essence, commit more (cf. Staw, 1981 for how small choices engender long-term commitment). And workers’ enjoyment is not faked. The risk of bad bosses is eliminated. Hours are flexible. Workers take pride in their skills. As noted, some drivers leave what Kalleberg would call a good job for on-demand work (e.g., the unionized carpenter) or remain committed to non-standard work despite other options (Adler, 2021). Similar to Ravenelle (2020), I found that people in my study made a career of on-demand work, switching between offering different app-based services—including rides (Uber), package delivery (Amazon Flex), meal delivery (DoorDash), and grocery shopping (Instacart)—instead of finding traditional off-platform work. Cultural narratives exalt these moves, lionizing hustle culture (Hill, 2020), heroizing workers during the height of the COVID-19 pandemic (Cameron, Chan, and Anteby, 2022), and exalting the “romance of entrepreneurism” (Ravenelle, 2019: 281). ²⁸ These constrained choices, scaffolded by algorithmic management, and cultural “deep stories” (Hochschild, 2016: 128) attract adherents, providing insights into why individuals behave in ways that do not always seem aligned with their own interests.

The Janus-like tension between the structural elements and the psychological experience of doing on-demand work might explain its growing magnetism and appeal. Tensions can be generative (Smith and Lewis, 2011), and technology can amplify the generativity (Murray, Rhymer, and Sirmon, 2021). Mobile email devices reinforce the autonomy paradox, in which individuals work longer hours while claiming to have more schedule flexibility (Mazmanian, Orlikowski, and Yates, 2013), and sophisticated user interfaces keep individuals on machines longer (e.g., Schüll, 2012). Yet, technologies reflect and embody broader sociocultural values and can never be seen as socially or politically neutral. Noting the thrill of a Facebook “like” or being matched on Tinder, McMillan Cottom (2020: 446) wrote, “Knowing the extractive terms of their labor does not diminish their [workers’] enjoyment of the job. Platform capitalism owes much of its dominance to how good it feels to be captured by the platform.” Ultimately, this generativity can keep individuals participating in work that provides a psychological sense of fulfillment yet requires them to take on more economic and physical risk compared to similarly skilled, more-traditional work. Despite reassurances that Uber will not take over the world (Fleming, Rhodes, and Yu, 2019) and strong arguments from academics about why more firms should provide more good jobs (Ton, 2014, 2023), these insights suggest that these “good bad” jobs may be here to stay: as a long-term career, as interim work as people move between jobs and/or labor markets, or as a career stage for those who then enter standard employment. And these findings suggest that workers may be satisfied with these jobs.

This article also has implications for the current legal and policy debates regarding worker classification, by moving beyond the binary classification of on-demand workers as either employees or contractors (Dubal, 2017, 2020). As a new organizational form (Vallas and Schor, 2020), platforms manufacture consent and garner control in ways that are not easily defined, leaving checks like the ABC test, which is used in many states to determine whether a person is an employee or an independent contractor, largely ineffective. Moreover, given platforms’ ever-changing, multi-sided nature, there are broader challenges about how to hold them accountable through regulations (Khan, 2016; Pignot, 2023; Rahman, Karunakaran, and Cameron, 2024). Therefore, labor relations on platforms need to be understood not only through a contractual perspective. This research shows how power and control are deeply involved in the production of consent such that managerial control is achieved in a way that makes precarious work appear desirable through constant choices. Legal scholars and policy officials must pay attention to these new configurations to hold these platforms accountable, for the benefit of workers and customers alike.

Boundary Conditions, Limitations, and Future Directions

There are limitations to these claims and opportunities for future research. First, this study cannot answer how individuals came to learn a tactic. A reasonable hypothesis is that workers learned the tactics from one another; however, less than 15 percent of the drivers I interviewed were active on online forums, and when directly asked, most drivers said they learned from trial and error. My analysis suggests that workers did experiment, since sanctions for deviance tactics occurred only after multiple ride refusals, and as solitary workers, individuals had more time on their hands to experiment. While it is well documented that on-demand organizations experiment with workers as part of their operational strategy (Luca and Bazerman, 2021; Rahman, Weiss, and Karunakaran, 2023), this research suggests that workers themselves might engage in similar experimentation (cf. Beane, 2019). Other research designs and sampling strategies (e.g., longitudinal panels, diaries) could provide insights into how workers learn these tactics.

Second, this study’s claims about how consent is reconfigured are relevant to other settings that use an algorithmic management system. Although this study was undertaken among workers classified as being in an independent contractor work arrangement, this model would likely generalize to workers in standard work arrangements with a similar work process and an algorithmic management system, i.e., if RideHail drivers were suddenly to become employees (as they already have in many countries in Western Europe). Verdin and colleagues (2023) found that in Spain and Germany, independent contractors and employees at a food delivery platform were under near-identical algorithmic management systems, suggesting a similar process in manufacturing consent. Similarly, when a U.S. package delivery company converted its drivers to employees, the labor process and, hence, the process of manufacturing consent remained basically unchanged (Johnston et al., 2023). This suggests that these findings are generalizable to workplaces with workers in different classification systems.

This case study presents a set of claims that could be expanded in other settings. This research is based primarily in the U.S., and the status and experience of platform workers vary across countries according to legal and social norms. Indeed, emerging research based on global data suggests that platform workers’ experiences vary based on configuration of the algorithmic management system (Cameron and Thomason, 2023), social status (Thomason and Cameron, 2023), and the organizing and labor history of a given locale (Tassinari and Maccarrone, 2020; Woodcock, 2021). Comparative research should examine how algorithmic management changes due to variations in organizational form (e.g., open vs. closed labor markets), geographic regions (e.g., Global North vs. Global South vs. Global East), and governance structures (e.g., corporations vs. cooperatives) and how this change affects the production of consent. Driving is largely independent, simple work (Hubbard, 2000; Viscelli, 2016), and as algorithmic management is integrated into more interdependent, complex work, how consent is manufactured may also change (e.g., warehouse work supervised by human and algorithmic managers, content creation by temporary teams; Valentine et al., 2017; Vallas, Johnston and Mommadova, 2022; Mears, 2023). ²⁹

Conclusion

As emerging technologies and shifting organizational paradigms continue to change the workplace, it is imperative to re-examine and update mainstream organizational theories (Barley, Bechky, and Milliken, 2017; Bailey et al., 2019; Barley and Orlikowski, 2023). This study takes an important step in this direction, examining how algorithmic management reconfigures long-standing theories on workplace consent. Choice-based consent illuminates the importance of constant, even if confined, choice as a mechanism that keep workers engaged, especially in jobs considered to be of poor quality. As the relationship between technology and work continues to evolve, the relationship between control and consent will continue to develop in ways that require innovative data collection and theory building.