Sticky price for declining risk? Business strategies with “behavioral” customers in the hotel industry
Bibliographic record
Abstract
Abstract Using data from about 25 million hotel room postings in four countries, we document that rather than decreasing to zero as the likelihood of cancellation declines, the difference between the prices for refundable and nonrefundable reservations remains positive at roughly 10%–15% of the full price. A model where travelers have different willingness to pay and some of them overestimate the probability to cancel their trip explains these price‐setting patterns more consistently than alternative interpretations. We denote these business strategies as naiveté‐based price discrimination. Our data and theory, therefore, show that this form of apparent inertial behavior of companies regarding a major strategic variable can be an intentional managerial choice. We demonstrate, finally, that this profit‐enhancing commitment to limited flexibility may also benefit customers in some cases, by expanding the reach of the market. Thus, strategies that rely on cognitive biases on the demand side may not necessarily exploit consumers.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".