Determinants of elapsed time to switch between auctions
Bibliographic record
Abstract
Purpose Online auctions, which have become an important aspect of online sales, are generally regarded as stand‐alone events. However, in contrast to offline auctions, online auctions can be subject to the presence of simultaneous competing auctions. The purpose of this study is to model and estimate determinants of elapsed time to switch across concurrent auctions, with special attention to unobserved heterogeneity among bidders. Design/methodology/approach Since auctions are dynamic and since the current winning bid progresses over time, the authors study time dependency over the course of an auction with hazard function models. To account for unobserved heterogeneity, the paper uses a latent class approach, which identifies bidder segments based on both observed and unobserved factors. Findings The findings show significant heterogeneity across bidders, revealed by their varying degrees of propensity to switch across auctions. The three segments of bidders are The Inerts – about 30 percent, The Switchers – less than 10 percent, and The In‐Betweens. According to the findings, bidders can induce other bidders to switch to a concurrent auction by responding quickly to the current high bid. Moreover, the paper finds a surprisingly high degree of inertia and reluctance to switch towards the end of the auction when bidding is most critical. Originality/value To the authors' knowledge, this study is the first to model elapsed time to switch from one auction to a simultaneous auction for an identical product, and to investigate determinants of the time required to switch, with special attention to unobserved heterogeneity across 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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".