Part-Time Ride-Sharing

Ning, F.; Hanrahan, Benjamin V.

doi:10.1145/3361128

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…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. Schor et al ( 2020 ) emphasized this heterogeneity of the workforce in various platform companies as well as the disparate experiences of part-time and full-time gig workers.…”

Section: Methods: Systematic Literature Reviewmentioning

confidence: 97%

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: Methods: Systematic Literature Reviewmentioning

confidence: 97%

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

et al. 2023

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“…Further, regarding H4, where the enhancement generated by the combination of consumer community identification messages and negative framing is only significant for participation and quality approval, but not satisfaction, for simple tasks, it is possible that agents with more vs. less experience identify more strongly with the crowdsourcing platform due to their higher number of interactions over time. Ma and Hanrahan (2019) find some evidence of this, reporting that crowdsourced drivers who are heavily dependent on crowdsourcing income have higher use and interaction with the platform. Following this rationale, we explore the interaction effects of experience, measured by the number of jobs completed, as another independent variable, in lieu of being a control.…”

Section: Post Hoc Testsmentioning

confidence: 99%

Appealing to the Crowd: Motivation Message Framing and Crowdsourcing Performance in Retail Operations

Esper

Tokar

2021

Production and Operations Management

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F or the execution of many supply chain operations tasks, firms are increasingly engaging in crowdsourcing-the act of dynamically delegating work via digital channels to for-hire individuals intermittently available in the marketplace (also called "the crowd"). The success of this practice hinges on the ability to efficiently attract workers who produce quality work from among the crowd. We draw on the foundations of Self-Determination Theory and the heuristic-systematic model to examine the ways that variations in messages presented to crowdsourced agents can serve as a mechanism to enhance participation and associated performance outcomes. Data from a field experiment involving a retail inventory audit task reveal that messages appealing to the crowd's consumer identity, as opposed to crowdsourcing platform identification or firm identification, generally lead to superior performance outcomes, particularly shorter reservation time, higher task quality approval, and post-task satisfaction. However, these effects are contingent on the valence of the message frame and the nature of the task. These findings shed light on elements critical to the successful utilization of this new type of crowdsourced "employment" in supply chain and operations tasks and suggest the careful crafting of crowdsourced task messages as a low-cost way for managers to improve task performance outcomes.

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“…Their advent has dramatically lowered the barriers to entry for participation in these labour markets and thus enabled job opportunities for all, albeit arguably at the expense of undermining the interests of full-timers (Rosenblat, 2018). In their study of 'part-time' ride-hailing drivers in the USA, Ma and Hanrahan (2019), however, argue that 'full-time' or 'part-time' classifications cannot be made merely based on the number of hours drivers spend working on a platform, but on how dependent they are on the earnings from it. Their study found that gig workers work for multiple platforms 'part-time' to make ends meet.…”

Section: Working For Ride-hailing Platformsmentioning

confidence: 99%

“…The peer-to-peer (P2P) exchange of goods, services and labour have received much attention in CSCW and HCI. Prior research has identified certain core issues with digital labour platforms, including the following: a) the modelling of workers as 'independent contractors' or as 'consumers of the platforms' technological services' (Rosenblat, 2018;Gray and Suri, 2019); b) precarious work conditions requiring 'part-time' workers to work on multiple platforms to make ends meet (Ma and Hanrahan, 2019); c) a neglect of workers' personal safety (Almoqbel and Wohn, 2019); and d) problems with evaluation mechanisms, which often create power imbalances (Lee et al, 2015;Rosenblat and Stark, 2016). The information asymmetries around critical aspects of work such as task assignment coupled with a lack of control over wage determination and lack of adequate grievance redressal mechanisms have been shown to disenfranchise workers (Gupta et al, 2014;Ahmed et al, 2016;Hanrahan, Ma, and Yuan, 2017;Rosenblat, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The potential for exploitation because of the availability of a 'reserve army of labour' willing to take on these precarious jobs for low pay has been a recurrent theme across contexts (Martin et al, 2016;Raval and Dourish, 2016;Rosenblat, 2018). Nonetheless, as Ma and Hanrahan (2019) point out from their recent study of ridesharing drivers in the US, millions of workers continue to join or work on one or more of these digital labour platforms. The question is why?…”

Section: Introductionmentioning

confidence: 99%

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

Muralidhar

Bossen

O’Neill

2022

Comput Supported Coop Work

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There is growing evidence of ride-hailing platforms' adverse impact on drivers. Nonetheless, hundreds of thousands of drivers continue to work on these platforms. Why? What considerations propel their continued usage over time? By drawing upon a qualitative study with auto-rickshaw drivers using Ola, a ride-hailing platform similar to Uber in India, we show how Ola over time shapes relations between itself, customers and drivers. The platform adds to the drivers' precarity and provides little benefit, and the platform's customer-centric design often leads to tensions with drivers. We illustrate how this has come about through the impact of ride-hailing platforms on the market: The duopolistic (Uber vs Ola) nature of the urban taxi market in India, paralleled by a shift of more and more customers from street-hailing to app-based hailing over time, has forged new dependencies for drivers on these platforms, which leaves drivers with little choice but to accept them. Thus, drivers continue to work for the platform, not because of any benefits, but because of the 'new dependencies' created by the infrastructuralization of ride-hailing platforms like Ola in the urban transport market. Our findings also reveal some ancillary benefits for drivers from their use of ride-hailing applications as first-time internet/smartphone users. The paper concludes with key implications for regulation as well as platform design that can improve customer-driver interaction as well as make the marketplace fairer, more equitable, and protect drivers' welfare.

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Part-Time Ride-Sharing

Cited by 11 publications

References 23 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

Appealing to the Crowd: Motivation Message Framing and Crowdsourcing Performance in Retail Operations

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

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