Quality and Pricing Decisions in a Market with Consumer Information Sharing
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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