Demand and Revenue Impacts of an Opaque Channel: Evidence from the Airline Industry
Bibliographic record
Abstract
Over time, opaque intermediaries, such as Hotwire and Priceline.com, have become an established distribution channel for the travel industry. We use a market response model and a dataset of economy class reservations from a major international airline to empirically examine the demand and cannibalization effects of the opaque channel. We find that: (1) the impact of the opaque channel on total demand is positive and significant in markets with high levels of competition; and (2) overall, the opaque channel cannibalizes the online transparent channel, but not the offline channel nor the full‐fare segment. However, we find that cannibalization of the offline channel moderately increases as markets become more concentrated. These results together suggest that airlines can benefit from opaque offerings mainly in markets with high levels of competition. Further, we develop a methodology to assess the revenue impacts of the opaque channel and show how it can be used by managers to develop and implement pricing tactics to increase demand and decrease cannibalization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".