Duggan, James, Sherman, Ultan, Carbery, Ronan and McDonnell, Anthony (2022) Boundaryless careers and algorithmic constraints in the gig economy. The International Journal of Human Resource Management, 33 (22). pp. 4468-4498. ISSN 0958-5192
Preview
JamesDugganBoundary2021.pdf
Download (2MB) | Preview
Abstract
With low barriers to entry and ease of access to work, the
gig economy offers the prospect of boundaryless opportunities for flexible working arrangements characterised by
increased autonomy. This form of work, however, may leave
individuals without development opportunities and could
stymie career progression. Drawing on boundaryless career
theory, this study examines the potential of gig workers to
develop the transferable career competencies required to
effectively pursue opportunities beyond these precarious
roles. Through insights from 56 gig worker interviews, we
analyse the lived experiences of workers in attempting to
develop ‘knowing-why’, ‘knowing-how’, and ‘knowing-whom’
competencies. In so doing, we find that the potentially
unmovable boundaries posed by algorithmic management
practices within platform organisations constrains workers’
abilities to navigate their roles and develop transferable
competencies. The study lends empirical support to the
bounded effect of gig work on individuals’ careers in a
domain characterised by precarity where organisations dismiss the existence of an employment relationship, where
individuals may simultaneously work for multiple platforms,
and where secretive algorithms heavily influence the experience of work.
Item Type: | Article |
---|---|
Academic Unit: | Faculty of Social Sciences > School of Business |
Item ID: | 18696 |
Identification Number: | 10.1080/09585192.2021.1953565 |
Depositing User: | James Duggan |
Date Deposited: | 27 Jun 2024 09:59 |
Journal or Publication Title: | The International Journal of Human Resource Management |
Publisher: | Routledge |
Refereed: | Yes |
Related URLs: | |
URI: | https://mural.maynoothuniversity.ie/id/eprint/18696 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
Downloads
Downloads per month over past year