Bidding Frenzy: Speed of Competitor Reaction and Willingness to Pay in Auctions
Bibliographic record
Abstract
This research examines how the intensity of the dynamic competitive interaction with other bidders in ascending auctions influences consumers’ willingness to pay (WTP) for auctioned products. It focuses on one important aspect of this interaction: the speed of competitor reaction. The key hypothesis is that having one’s own bids reciprocated by competing bidders more quickly increases one’s WTP in an auction. Evidence from five experiments demonstrates this effect and pinpoints the essential aspects of the psychological mechanism that underlies it. In particular, the effect of speed of competitor reaction on bidding behavior (1) is serially mediated by the perception that the auction is more intensely competitive and by a greater desire to win, (2) is distinct from the effects of time pressure and of the auction’s duration or overall rate of progression, (3) is not driven by inferences about the auctioned product’s market value, (4) is not qualified by the number of competing bidders nor due to any inferences about the latter, and (5) hinges on direct competitive interaction with other human bidders.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".