Gamified Feedback and Customer Stickiness: A Case Study of Meituan and DoorDash
Bibliographic record
Abstract
The explosive growth of food-delivery platforms has shifted the focus of competition from user acquisition to user retention. However, most platforms still rely on a one-time five-star rating system, whose weak incentive mechanism and linear dispute process cannot build long-term loyalty or obtain high-quality feedback. This paper compares the "gamification + crowdsourced review" model (Meituan) and the traditional star rating model (DoorDash). Drawing on the literature, Self-Determination Theory (SDT) and procedural justice, this paper constructs a three-stage framework-design layer → psychological/operational layer → outcome layer-to explain how hierarchical badges, points, and user arbitration can simultaneously meet the needs of competence, autonomy, and relatedness, and resolve disputes in parallel. The findings indicate that gamified crowdsourced review feedback is not just an embellishment of the user experience but a sustainable strategy that integrates user engagement, data assets, and governance efficiency. This paper finally proposes actionable design guidelines and an experimental agenda for cross-cultural replication and longitudinal causal testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".