To Hinder or to Facilitate: Retailers’ Strategy of Consumer Information Sharing
Bibliographic record
Abstract
Consumer information sharing is considered an effective strategy to attract consumers, yet certain high-end retailers, such as Bergdorf Goodman and Farfetch, tend to hinder consumers from sharing information through online reviews. We study a retailer's strategy for consumer information sharing in a supply chain. We find that a retailer's information sharing strategy can prevent manufacturers from extracting excessive profit when consumers are heterogeneous in their valuations of the selling product. Specifically, a retailer can achieve a higher profit margin by targeting all consumer segments. By strategically choosing the information sharing strategy to influence consumer beliefs, the retailer can induce the manufacturer to conform to the retailer's preferred targeting segment through a low wholesale price. Thus, a high-end retailer, whose consumers have a high ex-ante quality belief, favors hindering information sharing among consumers because it enables the retailer to target all consumer segments. Interestingly, deterring consumers from learning about the product quality may generate more consumer surplus. Our main results are robust under extensions such as consumer search behavior, consumer waiting, and multiple product selling. When selling multiple products, a retailer with a large quality variation is better off hindering consumers from sharing information. Our work shows that strategically choosing a consumer information sharing strategy enables retailers to enhance profit margins in their interactions with upstream manufacturers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".