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      The Inclusion of Gig Employees and their Career Satisfaction: Do Individual and Collaborative Job Crafting Play a Role?

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      Public Organization Review

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          Crafting a Job: Revisioning Employees as Active Crafters of Their Work

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            Is Open Access

            Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy

            This article evaluates the job quality of work in the remote gig economy. Such work consists of the remote provision of a wide variety of digital services mediated by online labour platforms. Focusing on workers in Southeast Asia and Sub-Saharan Africa, the article draws on semi-structured interviews in six countries (N = 107) and a cross-regional survey (N = 679) to detail the manner in which remote gig work is shaped by platform-based algorithmic control. Despite varying country contexts and types of work, we show that algorithmic control is central to the operation of online labour platforms. Algorithmic management techniques tend to offer workers high levels of flexibility, autonomy, task variety and complexity. However, these mechanisms of control can also result in low pay, social isolation, working unsocial and irregular hours, overwork, sleep deprivation and exhaustion.
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              Perceiving and responding to challenges in job crafting at different ranks: When proactivity requires adaptivity

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Public Organization Review
                Public Organiz Rev
                1566-7170
                1573-7098
                September 2023
                January 05 2022
                September 2023
                : 23
                : 3
                : 1055-1068
                Article
                10.1007/s11115-021-00596-4
                a4b34c71-7997-46e2-8706-3ce8f099e905
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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