Effect of counterfeits and fake reviews in markets for credence goods
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
• A competition between authentic seller and deceptive counterfeiter, with savvy and novice customers. • Sellers decide if acquire fake reviews to influence product endorsement and mislead customers. • In equilibrium, authentic seller does not acquire fake reviews, while counterfeiter may do so. • Amount of fake reviews is decreasing in the proportion of savvy consumers. • Option to acquire fake reviews may benefit both sellers but always hurts consumers. Counterfeits are a persistent problem in online marketplaces, in particular regarding credence goods (e.g., nutritional supplements), as their qualities are difficult or impossible to evaluate even after consumption. Concerned about product quality, customers frequently rely on external signals, such as product badges based on ratings. However, even product ratings are not foolproof as unethical sellers may acquire fake positive reviews to exploit product ratings and badge systems. To analyze the impact fake reviews have on credence goods, we consider a two-stage competition between an authentic seller and a deceptive counterfeiter. The market consists of two types of consumers: savvy customers, who understand that endorsement badges are product-dependent and not seller-dependent, and novice customers, who mistakenly believe product badges testify to a seller's authenticity. In the first stage, both sellers simultaneously decide on whether to acquire fake reviews, which partially influences if the product receives an endorsement badge. In the second stage, both sellers simultaneously set their prices and customers make purchasing decisions. Our results indicate that, in equilibrium, the authentic seller does not acquire fake reviews, while the counterfeiter may do so to mislead customers. Moreover, the amount of fake reviews is decreasing in the fraction of savvy consumers, suggesting that online platforms can combat fake reviews by, for instance, clearly highlighting that badges are product-dependent. We also find that having the option to acquire fake reviews may benefit both sellers but always hurts consumers, emphasizing the need for regulation to protect consumers.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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