Fare Prediction Websites and Transaction Prices: Empirical Evidence from the Airline Industry
Bibliographic record
Abstract
The marketing and operations disciplines have increasingly accounted for the presence of strategic consumer behavior. Theory suggests that such behavior exists when consumers are able to consider future distribution of prices, and that this behavior exposes firms to intertemporal competition that results with a downward pressure on prices. However, deriving future distribution of prices is not a trivial task. Online decision support tools that provide consumers with information about future distributions of prices can facilitate strategic consumer behavior. This paper studies whether the availability of such information affects transacted prices by conducting an empirical analysis in the context of the airline industry. Studying the effect at the route level, we find significant price reduction effects as such information becomes available for a route, both in fixed-effects and difference-in-differences estimation models. This effect is consistent across the different fare percentiles and amounts to a reduction of approximately 4%–6% in transactions’ prices. Our results lend ample support to the notion that price prediction decision tools make a statistically significant economic impact. Presumably, consumers are able to exploit the information available online and exhibit strategic behavior. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0965 .
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".