Learning from a Service Guarantee Quasi Experiment
Bibliographic record
Abstract
The authors analyze data from a service guarantee program implemented by a midpriced hotel chain. Using a multisite regression discontinuity quasi-experimental design developed over 16 months, they control for unobserved heterogeneity among guests and treatments across hotels and develop Bayesian posterior estimates of the varying program effect for each hotel. The results contribute to theory and practice. First, they provide new insights into how service guarantee programs operate in the field. Specifically, the guarantee was more effective at hotels with a better prior service history and an easier-to-serve guest population, which is consistent with signaling arguments but does not comport with the incentive argument that guarantees actually improve service quality. Second, the results offer managers better decision rules. Specifically, the authors devise program continuation rules that are sensitive to both observed and unobserved differences across sites. In addition, they devise policies to reward hotels that exceed site-specific expectations. By controlling for observed and unobserved differences across sites, the authors show that these policies potentially reward even hotels with negative net program effects, which is useful in reducing the organizational stigma of failure. Finally, the authors identify sites that should be targeted for future program rollout by computing the odds of succeeding.
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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 | 0.012 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".