To Bundle or Not to Bundle: Determinants of the Profitability of Multi-Item Auctions
Bibliographic record
Abstract
Bundling, a strategy of selling multiple component products as a package for a single price, is widely practiced in today’s marketplace. This article focuses on bundling in auctions, and it investigates under what conditions it is more profitable to auction two items as a bundle vs. separately. We introduce a conceptual model that identifies determinants of the revenue of a single bundle auction relative to separate component auctions – the degree of complementarity between items, the amount of heterogeneity in bidders’ valuations, whether the components are auctioned both as a bundle and separately, and the outside availability of the individual items. The hypotheses encapsulated in this model are tested in three field experiments. The empirical evidence shows that, in the absence of complementarity, a bundle auction is less profitable than separate component auctions. However, even for relatively low levels of complementarity, bundling results in higher revenue than separate auctions, and this revenue-enhancing effect of bundling in auctions is greater for services than for tangible products. This even occurs when the outside availability of the component items is high. Finally, our results help identify the specific conditions under which mixed bundling, pure bundling, or pure component strategies maximize seller revenue from auctions.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".