Bibliographic record
Abstract
Internet auctions are common in nearly all consumer categories. Hence, it is not surprising that a great deal of research has emerged on the topic in recent years. New design and format considerations and a wealth of available data from various platforms provide new questions and promising research opportunities for marketing researchers. This monograph begins with the introduction of the basic settings, concepts, and processes that are the building blocks of auction research. It then focuses on the transition from pre-Internet auction research to more recent topics. Special attention is given to research opportunities as well as to experimental methods that can provide both laboratory and field data to answer important questions. The survey reviews recent empirical and theoretical works on Internet auctions with a focus on Internet auction design, formats, and features that are currently debated in the marketing literature. Some of these issues are extensions of general auction topics, but the findings can be quite different in Internet environments. We touch on new design features that are particularly relevant to Internet auctions such as feedback ratings, buy-it-now options, and different closing rules. We also look at strategic and behavioral models that are shaping marketing research on Internet auctions. Particular emphasis is given to behaviors that are relevant in offline environments but take on new meanings and forms in Internet auction environments.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".