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Record W2809594880 · doi:10.1093/jcr/ucy056

Bidding Frenzy: Speed of Competitor Reaction and Willingness to Pay in Auctions

2018· article· en· W2809594880 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Consumer Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiddingCommon value auctionWillingness to payMicroeconomicsProduct (mathematics)EconomicsPerceptionVickrey–Clarke–Groves auctionAuction theoryPsychologyMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.175
GPT teacher head0.496
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it