Selling Your Product Through Competitors’ Outlets: Channel Strategy When Consumers Comparison Shop
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper develops a new rationale for decentralization in distribution channels: providing a one-stop comparison shopping experience for consumers. In our duopoly model, when consumers are knowledgeable about their brand preferences, each manufacturer would distribute through its own vertically integrated retail outlets only. When some consumers are unsure about their brand preferences, however, it may be optimal for one of the manufacturers to also distribute through its competitor’s outlets. The resulting equilibrium has several interesting properties. First, only one of the manufacturers chooses to add competitor-outlet distribution, not both—even when the manufacturers are symmetric. Second, the manufacturer distributing through its competitor’s outlets also distributes through its own outlets, i.e., its distribution strategy is a hybrid strategy, combining vertical integration and decentralization. Third, when the manufacturers’ brands are asymmetric, it is the weaker brand that has a stronger incentive to pursue hybrid distribution. Fourth, the competitor’s outlets in question welcome the new brand, even when no consumer would actually buy the new brand—a case of pure showrooming. These results highlight the linkages between distribution strategy, shopping efficiency, and retail formats. Shopping costs and consumers’ uncertainty about their own brand preferences create a demand for multibrand retailing, and in pursuing this demand, manufacturers may eschew the efficiency advantages of vertical integration in favor of hybrid distribution. However, the fact that only one of the manufacturers chooses to do so suggests that this strategy also has weaknesses, which we discuss in the paper.
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.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it