2,262 research outputs found

    Impacts of Perceived Behavior Control and Emotional Labor on Gig Workers

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    Gig economy workers enjoy flexibility in choosing certain aspects of their work. Nonetheless, platform companies still need to control workers’ behaviors to scale their business and ensure customers quality service. Mechanisms of control have been widely studied in traditional organizations; however, work in the gig economy differs from traditional organizations in that the role of a human supervisor is replaced with digital systems. Thus, there is reason to suspect that our traditional theories of control may not hold for new forms of work in the gig economy. To address these concerns, this study examines how gig economy workers, specifically Uber drivers, perceive behavior control and its effect on their job satisfaction. Our results suggest that emotional labor mediates the relationship between perceived behavior control and job satisfaction.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145617/1/Marquis_cscwp072_Abstract.pd

    Workers’ Affective Commitment in The Gig Economy: The Role of IS Quality, Organizational Support, and Fairness

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    Background: The rapidly growing gig economy brings lots of opportunities and challenges, and one of them is workers’ affective commitment. Because of the gig economy’s nature, gig workers depend on the technology-enabled platform to finish their tasks. We investigate how gig workers’ perception of the platform’s quality, or IS quality, will affect how they perceive organizational support and fairness, which further affects their affective commitment. Method: We surveyed 239 Uber drivers in Indonesia to test our model via snow-balling technique. We used PLS with a second-order formative construct model to validate our hypotheses. Results: The results showed that the two dimensions of IS quality, information quality and system quality, were positively associated with organizational support. Only information quality was positively associated with fairness. Both organizational support and fairness were positively associated with affective commitment. Conclusion: For uber drivers, information quality and system quality of the Uber App serve as drivers of perceived organization support. Information quality also contributes to perceived fairness. Drivers who perceive high organization support and fairness are more likely to be affectively committed to the organization

    The Gig Economy: Workers, Work and Platform Perspective

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    In recent years, the gig economy has changed the way many people work. This research phenomenon has attracted scientists from many different fields to an emerging field of research. Given the actuality of the topic and diversity of perspectives, there is a great need to collect and connect what has been researched which can serve as a basis for future discussions. Starting with a collection of 139 publications on the gig economy, gig work and related terms, we identify some trends in the literature and the underlying research interests. In particular, we organize the literature around the concept of the gig economy in terms of gig workers, gig work, and digital platforms, and draw several interesting insights from the literature. Finally, we identify important gaps in the existing literature on working in the gig economy and provide guidance for future research

    Uber Effort: The Production of Worker Consent in Online Ride Sharing Platforms

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    The rise of the online gig economy alters ways of working. Mediated by algorithmically programmed mobile apps, platforms such as Uber and Lyft allow workers to work by driving and completing rides at any time or in any place that the drivers choose. This hybrid form of labor in an online gig economy which combines independent contract work with computer-mediated work differs from traditional manufacturing jobs in both its production activity and production relations. Through nine interviews with Lyft/Uber drivers, I found that workers’ consent, which was first articulated by Michael Burawoy in the context of the manufacturing economy, is still present in the work of the online gig economy in post-industrial capitalism. Workers willingly engage in the on-demand work not only to earn money but also to play a learning game motivated by the ambiguity of the management system, in which process they earn a sense of self-satisfaction and an illusion of autonomous control. This research points to the important role of technology in shaping contemporary labor process and suggests the potential mechanism which produces workers’ consent in technology-driven workplaces

    How well did I do? The effect of feedback on affective commitment in the context of microwork

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    Crowdwork is a relatively new form of platform-mediated and paid online work that creates different types of relationships between all parties involved. This paper focuses on the crowdworker-requester relationship and investigates how the option of receiving feedback impacts the affective commitment of microworkers. An online vignette experiment (N= 145) on a German crowdworking platform was conducted. We found that the integration of feedback options within the task description influences the affective commitment positively toward the requester as well as the perceived requester attractiveness

    Shifting to Gig Labor: Perceptions of Sustainability

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    Digitalization is a global megatrend. Digital labour platforms allow companies to outsource work through an open call to a crowd of people and are the forefront of the “gig economy”, characterized by one-off tasks, without further commitments for the involved partners. Sustainability is another megatrend and controversial from the gig economy perspective. Non-standard gig arrangements bring higher time flexibility for the workers, allowing more individuals to integrate with the labour market. However, these digital employment relationships are associated with relatively weak labour market institutions and regulations, resulting in precarious jobs.Using data collected by semi-structured interviews, this paper explores the experience of Swiss workers who switched from a “standard-contract” employment position to occasional gig employment. This study finds evidence that the voluntary change towards a gig job may be associated with an improvement in perceived social sustainability, but a degradation of economic and environmental sustainability.The conclusion may be specific to the high development context where the study took place, Switzerland. However, if that is the case, a stronger policy message emerges - even in newer forms of employment, protective legal frameworks ensuring a basic safety net for individuals continue to be key for more sustainable labour arrangements

    Algorithmic Exploitation: Understanding Labor Process and Control among RideHailing Platform Workers

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    This study analyzes the labor process of ride-hailing workers and the platformcompany’s control over them. This research employs Labour Process Theory (LPT)to examine the labor process and control critically. Based on in-depth interviewswith motorcycle drivers working for Go-Jek and Grab between June 2020 and June2021 in Yogyakarta, Kediri, and Jakarta, this research found that labor or workersare transformed into a commodity where job supply and demand are confounded andmediated by digital platforms. Algorithmic management, utilized in this industry, playsa significant role in this digital industry, including its use as a point of production and asa mechanism of control and monitoring in the workplace. The use of feedback systems,ratings, and platform ratings as a consequence of emotional work is used by managersto help monitor their employees. Furthermore, this study reveals that the form of controlconducted by the platform represents an unequal power relation, which producesdissatisfaction and conflicts. We argue that the labor process in this job resembles whatwe coined “algorithmic exploitation,” which means that the platform companies aredeliberately using technology and obscuring the control process over the operations ofthe workers to instill the high standards of service, while the workers are trapped in avery weak employment status as “partners.”Studi ini menganalisis proses kerja para pekerja angkutan umum daring (onjek online) dan kontrol oleh perusahaan platform terhadap mereka. Riset ini menggunakan Labour Process Theory (LPT) untuk menyelidiki proses dan control kerja secara kritis. Berdasarkan wawancara mendalam dengan pengendara ojek online yang bekerja di bawah Go-Jek dan Grab, antara Juni 2020 dan Juni 2021 di Yogyakarta, Kediri, dan Jakarta, riset ini menemukan bahwa para pekerja ditransformasikan menajdi komoditas dimana pasokan dan kebutuhan pekerjaan dimediasi oleh platform digital. Manajemen algoritsmis yang digunakan dalam industri ini memainkan peran penting dalam indusri ini, termasuk penggunaanya  sebagai point of production, dan mekanisme control dan pengawasan di tempat kerja. Penggunaan sistem feedback, rating, dan rating oleh platform sebagai konsekuensi dari pekerjaan emosional telah digunakan manajer untuk mengawasi para pekerja mereka. Lebih jauh, studi ini menunjukkan bahwa bentuk-bentuk pengawasan dengan platform merepresentasikan relasi kuasa yang tidak setara, yang melahirkan ketidakpuasan dan konflik. Kami berargumen bahwa perusahaan platform secara sengaja menggunakan teknologi dan memaksakan proses pengawasan atas kerja para pekerja untuk mencapai standar layanan yang tinggi, sementara para pekerja terjebak di dalam status ketenagakerjaan yang sangat lemah sebagai “mitra”. &nbsp

    Algorithmic Exploitation: Understanding Labor Process and Control among RideHailing Platform Workers

    Get PDF
    This study analyzes the labor process of ride-hailing workers and the platformcompany’s control over them. This research employs Labour Process Theory (LPT)to examine the labor process and control critically. Based on in-depth interviewswith motorcycle drivers working for Go-Jek and Grab between June 2020 and June2021 in Yogyakarta, Kediri, and Jakarta, this research found that labor or workersare transformed into a commodity where job supply and demand are confounded andmediated by digital platforms. Algorithmic management, utilized in this industry, playsa significant role in this digital industry, including its use as a point of production and asa mechanism of control and monitoring in the workplace. The use of feedback systems,ratings, and platform ratings as a consequence of emotional work is used by managersto help monitor their employees. Furthermore, this study reveals that the form of controlconducted by the platform represents an unequal power relation, which producesdissatisfaction and conflicts. We argue that the labor process in this job resembles whatwe coined “algorithmic exploitation,” which means that the platform companies aredeliberately using technology and obscuring the control process over the operations ofthe workers to instill the high standards of service, while the workers are trapped in avery weak employment status as “partners.”Studi ini menganalisis proses kerja para pekerja angkutan umum daring (onjek online) dan kontrol oleh perusahaan platform terhadap mereka. Riset ini menggunakan Labour Process Theory (LPT) untuk menyelidiki proses dan control kerja secara kritis. Berdasarkan wawancara mendalam dengan pengendara ojek online yang bekerja di bawah Go-Jek dan Grab, antara Juni 2020 dan Juni 2021 di Yogyakarta, Kediri, dan Jakarta, riset ini menemukan bahwa para pekerja ditransformasikan menajdi komoditas dimana pasokan dan kebutuhan pekerjaan dimediasi oleh platform digital. Manajemen algoritsmis yang digunakan dalam industri ini memainkan peran penting dalam indusri ini, termasuk penggunaanya  sebagai point of production, dan mekanisme control dan pengawasan di tempat kerja. Penggunaan sistem feedback, rating, dan rating oleh platform sebagai konsekuensi dari pekerjaan emosional telah digunakan manajer untuk mengawasi para pekerja mereka. Lebih jauh, studi ini menunjukkan bahwa bentuk-bentuk pengawasan dengan platform merepresentasikan relasi kuasa yang tidak setara, yang melahirkan ketidakpuasan dan konflik. Kami berargumen bahwa perusahaan platform secara sengaja menggunakan teknologi dan memaksakan proses pengawasan atas kerja para pekerja untuk mencapai standar layanan yang tinggi, sementara para pekerja terjebak di dalam status ketenagakerjaan yang sangat lemah sebagai “mitra”. &nbsp

    Algorithms as a Manager: A Critical Literature Review of Algorithm Management

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    We review the literature on algorithmic management to help future researchers acquire a comprehensive recap of past research with detailed discussions on the main findings and develop a taxonomy as a tool of summarization that assists researchers in reflecting critically on their systems and identifying potential gaps. We determine five critical areas of algorithmic management: the mechanisms of algorithmic management, effects of algorithmic management, second party\u27s response to algorithmic management, concerns around algorithmic management, design of algorithmic management, and policy implications. These topics are analyzed and discussed

    Workers\u27 Perceived Algorithmic Exploitation on Online Labor Platforms

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    Online labor platforms (OLPs) like Uber have become increasingly prevalent, attracting numerous workers with the appeal of flexible work arrangements. OLPs present themselves as an innovative alternative to traditional employment structures, but there remains a sense of exploitation among their workers. This perception is impelled by the platforms’ heavy reliance on algorithmic management (AM), which often exerts a tighter form of management than traditional human-led oversight. This study examines how AM induces workers’ exploitation perceptions (i.e., perceived algorithmic exploitation) by conducting a grounded theory methodology on 22 interviews with Uber drivers. We identified several forms of perceived algorithmic exploitation (i.e., manipulation, falsification, disempowerment, and dependency), which include AM practices that workers perceive as disadvantaging them to the potential benefit of the OLP. Overall, this study contributes to the “dark side” of AM and offers platform providers and policymakers crucial insights to create more sustainable working environments for platform workers
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