Quality and Pricing Decisions in a Market with Consumer Information Sharing
Bibliographic record
Abstract
We provide a dynamic, game-theoretic model to examine a firm’s quality and pricing decisions for its new experience goods. Early consumers do not observe product quality prior to purchase but can learn it after purchase and share that product-quality information with later consumers—for example, through online reviews. Both the firm’s quality decision and its cost efficiency are the firm’s private information and not directly observed by the consumer. The early consumers can make a rational inference from the firm’s price about its cost and quality taking into account the firm’s profit incentive from the later informed consumers. We find that in equilibrium a more cost-efficient firm chooses higher quality than does an inefficient firm. One might intuit that a firm will offer higher quality if its high efficiency is known to consumers than if its efficiency is not known, because it will no longer need to convince consumers that it is not the inefficient firm. Our analysis shows that, surprisingly, the opposite may be true—when a firm’s high efficiency is publicly known, the firm may reduce its product quality rather than increase it. Furthermore, consumers’ knowledge about the firm’s cost efficiency can reduce the consumer surplus. We also show that an improvement in the average cost efficiency in the market can lower the consumer surplus. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2930 . This paper was accepted by J. Miguel Villas-Boas, 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".