Bibliographic record
Abstract
Oftentimes, close competitors carry partially overlapping assortments in seeming contradiction to the principle of maximum differentiation. One of the justifications of such practice is that an overlapping assortment with competitive prices on the common products may prevent further consumer search and therefore could be useful even when profits from the overlapping products do not justify the costs of carrying them. In this paper, we examine the validity of this intuition and show that such strategy may indeed be optimal when consumers are uncertain about prices they might find elsewhere and face shopping costs for discovery of all prices. Specifically, we show that the (larger) assortment with product overlap may signal a “competitive” price of the relatively unique product and prevent further consumer search for a lower price on it. An implication of this finding is that a consumer may rationally behave as if she likes a larger assortment even if the assortment is enlarged by adding products the consumer has no interest in. Furthermore, we show that the optimal pricing strategy may include pricing of common products or products with known costs at a loss, which provides a novel explanation of loss-leader pricing. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2384 . This paper was accepted by Pradeep Chintagunta, marketing.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".