Examining the Impact of Algorithmic Control on Uber Drivers’ Technostress
Bibliographic record
Abstract
This study examines how the use of algorithmic control within gig economy platforms relates to the well-being and behavior of workers. Specifically, we explore how two different forms of algorithmic control—gatekeeping and guiding—correspond with (positive) challenge technostressors and (negative) threat technostressors experienced by Uber drivers. We also examine the moderating impact of algorithmic control transparency on these relationships, as well as the outcomes of technostressors in terms of continuance intentions and workaround use. Based on a survey of 621 U.S.-based Uber drivers, we find that gatekeeping and guiding algorithmic control positively relate to both challenge and threat technostressors. The study bridges the literature on control and technostress by conceptualizing algorithmic control as a condition that puts workers under stress. This stress is found to contribute to important behavioral consequences pertaining to both continuance intentions and workaround use. Findings from our work suggest that gig economy organizations can use algorithmic control to enhance challenge technostressors for their workers, thereby contributing to the cultivation of a more committed workforce. Furthermore, we find evidence disputing the assumption that algorithmic control transparency can mitigate the negative effects of threat technostressors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".