Casually cynical or trapped? Exploring gig workers’ reactions to psychological contract violation
Bibliographic record
Abstract
Purpose Drawing on psychological contract (PC) theory and platform labor research, the purpose of our study was to explore gig workers’ reactions to perceived PC violation. Our study was set against the backdrop of the COVID-19 pandemic, which brought workplace health and safety issues into much sharper focus, even in nonstandard employment arrangements like gig work. Design/methodology/approach This study employed a mixed-methods design. In Study 1, we tested a conceptual model of US-based ride-hail drivers’ (n = 202) affective and cognitive reactions to Uber’s (lack of) commitment to safe working conditions. In Study 2, we conducted interviews with 32 platform workers to further explore an unexpected finding from Study 1. Findings In Study 1, we found that drivers’ perceptions of PC violation were related to decreased trust in Uber and higher intentions to leave this line of work; however, cynicism toward Uber only predicted withdrawal intentions for those drivers who did not believe that they had job alternatives available outside of gig work. We explored this further in Study 2, where we found that workers with low economic dependence on gig work could afford to be casually cynical toward the platform, while high-dependence workers felt “trapped” in this line of work. Originality/value We contribute to the social/relational theoretical approach to gig work more broadly and to the literature on PC in platform work more specifically. We also add to the emerging literature on how economic dependence shapes workers’ experience of platform work. Our findings around low-dependence gig workers experiencing a more indifferent form of cynicism – which we have termed casual cynicism – highlight the importance of treating the context of gig work as unique, not merely an extension of traditional management research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".