To Bundle or Not to Bundle: Determinants of the Profitability of Multi-Item Auctions
Bibliographic record
Abstract
This article introduces and empirically tests a conceptual model of the key determinants of the profitability of bundling in auction markets. The model encapsulates hypotheses about how seller revenue from the combined (i.e., bundle) auction of component products relative to that from separate auctions of the components is influenced by the heterogeneity in bidders’ product valuations, the degree of complementarity between component products, the particular multi-item selling strategy, and the outside availability of the products. The results of three field experiments show that though bundle auctions tend to be less profitable for noncomplementary and substitute products, they are on average 50% more profitable than separate auctions when there is (even only moderate) complementarity between the component products. The latter effect is greater when the bundle and the separate components are offered at different times, and it is more pronounced for services than for tangible goods. The findings also identify conditions under which each of the essential multi-item selling strategies for fixed-price settings (pure components, pure bundling, and mixed bundling) tends to maximize seller revenue in 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 | 0.003 | 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) | 0.003 | 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".