Summary
In recent years, we have been witnessing a move to algorithmic management, in which supervisory tasks—instructing workers; appraising their performance; and determining their compensation—are being delegated to software applications. Current understanding of such effects is limited, however, as most research on algorithmic management focuses on gig-economy platforms in which traditional supervisory relationships are absent. The proposed research thus aims to explore: How does the introduction of algorithmic management in traditional organizations—in which algorithms operate alongside rather than instead of a human boss—shape human supervisory relationships and practices? To achieve this objective, I will undertake four subprojects, grounded in an empirical, in-depth qualitative approach. Subproject 1 will develop a theory to describe how the introduction of algorithmic management changes supervisory dynamics in traditional organizations. I will collaborate with five organizations spanning different industries and observe workers and supervisors to uncover their experiences of supervisory relationships. Subproject 2 will provide an articulated account of how algorithmic management changes managerial roles and practices, through interviews with a large sample of managers that implement algorithmic management. Subproject 3 will reveal how workers respond to conflicting directions from humans and algorithms. In this study, workers will participate in simulations of actual workplace dilemmas, created using immersive VR technology. Subproject 4 will address the mitigation of detrimental outcomes of algorithmic management through ethnographic action research. The proposed research will provide ground-breaking insights regarding the emerging yet unexplored phenomenon of algorithmic management in traditional organizations, and it will enable practitioners to effectively and ethically update managerial practices for the age of AI oversight.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101116585 |
| Start date: | 01-10-2024 |
| End date: | 30-09-2029 |
| Total budget - Public funding: | 1 467 455,00 Euro - 1 467 455,00 Euro |
Cordis data
Original description
In recent years, we have been witnessing a move to algorithmic management, in which supervisory tasks—instructing workers; appraising their performance; and determining their compensation—are being delegated to software applications. Current understanding of such effects is limited, however, as most research on algorithmic management focuses on gig-economy platforms in which traditional supervisory relationships are absent. The proposed research thus aims to explore: How does the introduction of algorithmic management in traditional organizations—in which algorithms operate alongside rather than instead of a human boss—shape human supervisory relationships and practices? To achieve this objective, I will undertake four subprojects, grounded in an empirical, in-depth qualitative approach. Subproject 1 will develop a theory to describe how the introduction of algorithmic management changes supervisory dynamics in traditional organizations. I will collaborate with five organizations spanning different industries and observe workers and supervisors to uncover their experiences of supervisory relationships. Subproject 2 will provide an articulated account of how algorithmic management changes managerial roles and practices, through interviews with a large sample of managers that implement algorithmic management. Subproject 3 will reveal how workers respond to conflicting directions from humans and algorithms. In this study, workers will participate in simulations of actual workplace dilemmas, created using immersive VR technology. Subproject 4 will address the mitigation of detrimental outcomes of algorithmic management through ethnographic action research. The proposed research will provide ground-breaking insights regarding the emerging yet unexplored phenomenon of algorithmic management in traditional organizations, and it will enable practitioners to effectively and ethically update managerial practices for the age of AI oversight.Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
24-11-2024
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