Online Low-Price Guarantees—A Real Options Analysis
Bibliographic record
Abstract
A common practice among large retailers is the low-price guarantee, rebating consumers if they find an identical product cheaper elsewhere. This provides consumers with some level of comfort in their purchase decision. A similar low-price guarantee is provided by numerous service industries that allow reservation of capacity, yet do not penalize the consumer for failure to keep that reservation—examples include hotels and car rental. Given that a consumer is not required to keep the reservation, they may make another reservation, either at a competing firm or the same firm, if future prices decline. The increasing availability of pricing information on the Internet affords consumers the opportunity to be more strategic in their purchasing behavior. As consumers, we are able to quickly and easily check prices from numerous service or goods providers. The ease of price information potentially makes these guarantees very costly to the service or good provider. We analyze the implied costs associated with these guarantees by making analogies to financial options. Motivation for this research comes from a large car rental firm, Dollar Thrifty Automotive Group Inc., that considered offering a low-price guarantee to all consumers that book a reservation though their website.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 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".