Algorithmic Embeddedness and the ‘Gig’ Characteristics Model: Examining the Interplay between Technology and Work Design in Crowdwork

Francesca Bellesia, Elisa Mattarelli, Fabiola Bertolotti, Maurizio Sobrero

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

This article systematically reviews the literature on online task crowdwork to investigate the complex relationship between technology and work design on crowdwork platforms. We highlight the diverse interpretations and uses of technology, specifically platform features and algorithms, in relation to work design. Our review reveals that platform features serve as antecedents to work design characteristics, while algorithms are so intertwined with job execution that a new work characteristic is needed to model this interplay. We introduce this new work characteristic as algorithmic embeddedness and show that it varies in degree. When high, algorithmic embeddedness can be perceived as either an affordance or a constraint; when low, it has a limited impact on crowdworkers' jobs. Our ‘gig characteristics model’ expands previous work design theories and offers a framework for understanding the design of contemporary jobs that rely highly on algorithms. To refine our model and better understand crowdwork dynamics, we provide an agenda for future research directions.

Original languageEnglish
JournalJournal of Management Studies
DOIs
Publication statusAccepted/In press - 2024
Externally publishedYes

Keywords

  • algorithmic embeddedness
  • Algorithms
  • crowdwork
  • digital platforms
  • future of work
  • gig characteristics model
  • gig work
  • work design

ASJC Scopus subject areas

  • Business and International Management
  • Strategy and Management
  • Management of Technology and Innovation

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