Moral Hazard in Ride Hailing Services: Provide Disincentives from Ratings System Failures
Bibliographic record
Abstract
This study investigates the inadequacies of rating systems in ride-hailing platforms such as Uber and Lyft and their contribution to moral hazards that affect driver motivation. Utilizing Herzberg’s Two-Factor Theory, it identifies and analyzes key hygiene factors—like platform security, fair compensation, and driver support—that establish a foundation for driver contentment. Additionally, it explores motivators such as performance recognition and growth opportunities, essential for fostering a motivated and efficient workforce. The research highlights the necessity of overhauling rating systems to more accurately reflect actual driver performance, advocating for a shift from customer-centric to driver-centric evaluation metrics. This method addresses the problem of biased evaluations and improves the quality of service in general. Through focusing on the optimization of driver incentives, practical recommendations are made for ride hailing platforms in this study, which increases their effort towards viable operational strategies in the platform economy.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".