Between a Rock and a Hard Place: Negotiating Dependencies and Precarity in the On-Demand Economy

Muralidhar, Srihari Hulikal; Bossen, Claus; O’Neill, Jacki

doi:10.1007/s10606-022-09434-7

Cited by 4 publications

(5 citation statements)

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“…We identified the following key debates among the topic: First, algorithmic management practices simultaneously restrain and enable workers' autonomy (e.g., Lee et al 2015;Möhlmann and Zalmanson 2017) yet the need to make ends meet seems to force workers to long days and specific work practices (e.g., Wood et al 2019;Schor et al 2020). Second, algorithmic management systems create a digital version of Taylorism, with both new and old characteristics (e.g., Rosenblat and Stark 2016;Schildt 2017;Altenried 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Perhaps the most crucial determinant of perceived autonomy in algorithmically managed platform work is the worker’s income dependency on the job. Muralidhar, Bossen and O’Neill’s ( 2022 ) case study of the Indian ride-hailing platform Ola encapsulates the phenomenon. The authors describe how the platform and its design (1) heighten Ola drivers’ precarity while providing little benefits and (2) shift more customers from street-hailing to app-based hailing, which intensifies the drivers’ dependence of the platforms (Muralidhar et al 2022 ).…”

Section: Methods: Systematic Literature Reviewmentioning

confidence: 99%

“…Muralidhar, Bossen and O’Neill’s ( 2022 ) case study of the Indian ride-hailing platform Ola encapsulates the phenomenon. The authors describe how the platform and its design (1) heighten Ola drivers’ precarity while providing little benefits and (2) shift more customers from street-hailing to app-based hailing, which intensifies the drivers’ dependence of the platforms (Muralidhar et al 2022 ). Similarly, Ma and Hanrahan ( 2019 ) found that dependency on platform income heavily influenced Uber drivers’ attitude toward the platform and difficulties faced on the job.…”

Section: Methods: Systematic Literature Reviewmentioning

confidence: 99%

“…Although they acknowledge the platform workers' freedom to choose their own schedule and their clients, Bucher et al ( 2021) stress that the platform severely constrains the client-worker relationship, since workers must please both the client and the algorithm to make sure they are able to continue participating on the platform. Möhlmann and Zalmanson (2017) call the tension between workers' sense of autonomy and the algorithmic systems' need for control a paradox of autonomy. Similarly, Meijerink and Bondarouk (2021) build on previous studies to emphasize how human resource management algorithms simultaneously restrain and enable worker autonomy and value.…”

Section: The Paradox Of Autonomy and How Income Dependency Impacts Th...mentioning

confidence: 99%

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Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management

et al. 2023

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The use of algorithmic management systems is rapidly changing organizational models and practices, as millions of workers in multiple sectors worldwide are managed by computer software. Despite receiving increasing academic interest, little summarizing literature exist on the ways algorithmic systems are used in management. This article aims to fill this gap by systematically reviewing and qualitatively analyzing 172 articles on the topic. Our research contributes to the existent algorithmic management literature in three ways. First, we provide a descriptive overview of algorithmic management as a field of research. Second, we identify and synthesize the discussion on the key concepts of the topic, namely how algorithmic management: (1) simultaneously restrains and enables workers’ autonomy—yet income dependency and other factors force inflexible work practices; (2) creates a complex, digital version of Taylorism; and (3) creates new and changes existing organizational power structures. Third, as our main theoretical contribution, we create the framework of Algorithmic Management Grid that demonstrates the ways in which organizations use algorithmic systems in management. The Algorithmic Management Grid illustrates how, in both novel and traditional organizational models, algorithmic management may be used with emphasis either on controlling or enabling workers. Based on the reviewed literature, we claim that so far, companies have mostly utilized algorithmic systems in a controlling manner, neglecting the technology’s enabling potential in organizing.

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Section: Discussionmentioning

confidence: 99%

Section: Methods: Systematic Literature Reviewmentioning

confidence: 99%

Section: Methods: Systematic Literature Reviewmentioning

confidence: 99%

Section: The Paradox Of Autonomy and How Income Dependency Impacts Th...mentioning

confidence: 99%

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Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management

et al. 2023

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“…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.…”

Section: On-demand Work Algorithmic Management and Workplace Consentmentioning

confidence: 99%

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

Cameron

2024

Administrative Science Quarterly

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This research explores how a new relation of production—the shift from human managers to algorithmic managers on digital platforms—manufactures workplace consent. While most research has argued that the task standardization and surveillance that accompany algorithmic management will give rise to the quintessential “bad job” (Kalleberg, Reskin, and Hudson, 2000; Kalleberg, 2011), I find that, surprisingly, many workers report liking and finding choice while working under algorithmic management. Drawing on a seven-year qualitative study of the largest sector in the gig economy, the ride-hailing industry, I describe how workers navigate being managed by an algorithm. I begin by showing how algorithms segment the work at multiple sites of human–algorithm interactions and how this configuration of the work process allows for more-frequent and narrow choice. I find that workers use two sets of tactics. In engagement tactics, individuals generally follow the algorithmic nudges and do not try to get around the system; in deviance tactics, individuals manipulate their input into the algorithmic management system. While the behaviors associated with these tactics are practical opposites, they both elicit consent, or active, enthusiastic participation by workers to align their efforts with managerial interests, and both contribute to workers seeing themselves as skillful agents. However, this choice-based consent can mask the more-structurally problematic elements of the work, contributing to the growing popularity of what I call the “good bad” job.

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Fleeting Alliances and Frugal Collaboration in Piecework: A Video-Analysis of Food Delivery Work in India

Shaikh,

McGregor,

Brown

et al. 2024

Comput Supported Coop Work

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Food delivery platforms are designed to match on-demand workers with jobs and then manage, monitor, and assess their performance. These platforms provide workers with a digital representation of delivery work. Once a worker accepts a delivery job they need to deal with the complexities of an unsettled urban landscape with varied infrastructures, traffic, and regulations. In particular, the Global South presents a demanding context for this type of work, given less clearly mapped addresses alongside other socio-cultural intricacies. In order to understand how food delivery workers bridge gaps and mismatches between the demands of the app and the realities encountered in situ, for this paper we shadowed six delivery workers over the course of their working day delivering food in Pune, India. The six workers included a complete novice and more experienced riders. We used helmet mounted cameras to record the delivery work, and how our participants managed the extra demands of food delivery work during the COVID-19 pandemic. Our moment-by-moment analysis of the video data is informed by the methodological traditions of ethnomethodology and conversation analysis. While the food delivery platform imposes a detailed workflow expected to be performed alone by the worker, our detailed video analysis reveals the collaborative nature of delivery work. We highlight how workers draw upon their ability to participate in ‘fleeting alliances’ and produce ‘frugal collaboration’ with co-located others, such as other delivery workers or security guards. This allows them to resolve everyday troubles, often learning or imparting ‘the tricks of the trade’ in the process. While gig platforms have commonly been presented as disruptive technologies for coordinating, regulating, and assessing gig workers individually and independently, our findings highlight collaboration as a critically important aspect of food delivery work.

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Between a Rock and a Hard Place: Negotiating Dependencies and Precarity in the On-Demand Economy

Cited by 4 publications

References 46 publications

Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management

Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management

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

Fleeting Alliances and Frugal Collaboration in Piecework: A Video-Analysis of Food Delivery Work in India

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