Stay or Leave: Investigating Factors Impact Crowd-Based Workers’ Platform-Based Justice Perceptions and Turnover Intentions
Bibliographic record
Abstract
Crowdsourcing has emerged as a transformative business model, harnessing collective intelligence to tackle complex tasks efficiently. However, the impact of crowd-based platforms on workers’ justice perceptions is still understudied. This research delves into organizational justice perceptions among crowd-based workers, focusing on platform features that influence these perceptions as well as workers’ subsequent turnover intentions. Drawing on data collected from 364 workers across multiple platforms, findings indicate that equitable compensation policies, participative evaluation, interactive and considerate communication, and rule-based evaluation can enhance procedural, distributive, and interactional justice perceptions, which in turn, significantly reduce turnover intentions. Moreover, media richness moderates part of these relationships, strengthening the mitigating effects of justice perceptions on turnover intentions. The study contributes to understanding the dynamics of organizational justice in crowdsourcing contexts and provides insights for platform management strategies to enhance worker retention.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".