Work Experience on Algorithm-Based Platforms: The Bright and Dark Sides of Turking

Mehmet A. Orhan (), Insaf Khelladi, S. Castellano () and Sanjay Singh
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Mehmet A. Orhan: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
Insaf Khelladi: PULV - Pôle Universitaire Léonard de Vinci
S. Castellano: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
Sanjay Singh: Maynooth University - National University of Ireland Maynooth

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Abstract: The prevalent use of digital labor platforms has transformed the nature of work globally. Such algorithm-based platforms have triggered many technological, legal, ethical, and human resource management challenges. Despite some benefits (i.e., flexibility), the precarious conditions and commodification of jobs are major concerns in these platform-based employment conditions. The remote-work paradigm shift during the COVID-19 pandemic has made the interplay between technology, digitalization, and precarious workers' well-being a critical issue to address. This paper focuses on microtask platforms by examining overall well-being associated with turking as a work experience. Using a sample of 401 Amazon Mechanical Turk workers during the early stage of the COVID-19 pandemic, data were collected on individual conditions affecting the overall quality of workers' lives. The results from two structural equation models demonstrated the direct and mediating effects of task characteristics, excessive working, and financial pressure, mirroring the bright and dark sides of turking. Greater turking task significance and meaningfulness increase personal growth opportunities, ultimately improving workers' perceived quality of life. However, excessive work and greater financial pressure decrease self-acceptance and overall quality of life. This study examines the complicated nature of work experience on algorithm-based platforms by unpacking individual factors that affect workers' well-being. \textcopyright 2022 Elsevier Inc.

Keywords: Algorithm-based platforms; Crowdworking; Heavy Work Investment; Microtasking; MTurk; Well-being (search for similar items in EconPapers)
Date: 2022
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Published in Technological Forecasting and Social Change, 2022, 183, ⟨10.1016/j.techfore.2022.121907⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04300355

DOI: 10.1016/j.techfore.2022.121907

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