Bibliographic record
Abstract
Behavior-based pricing (BBP) refers to the practice in which firms collect consumers’ purchase history data, recognize repeat and new consumers from the data, and offer them different prices. This is a prevalent practice for firms and a worldwide concern for consumers. Extant research has examined BBP under the assumption that consumers observe firms’ practice of BBP. However, consumers do not know that specific firms are doing this and are often unaware of how firms collect and use their data. In this article, the authors examine (1) how firms make BBP decisions when consumers do not observe whether firms perform BBP and (2) how the transparency of firms’ BBP practice affects firms and consumers. They find that when consumers do not observe firms’ practice of BBP and the cost of implementing BBP is low, a firm indeed practices BBP, even though BBP is a dominated strategy when consumers observe it. When the cost is moderate, the firm does not use BBP; however, it must distort its first-period price downward to signal and convince consumers of its choice. A high cost of implementing BBP serves as a commitment device that the firm will forfeit BBP, thereby improving firm profit. By comparing regimes in which consumers do and do not observe a firm’s practice of BBP, the authors find that transparency of BBP increases firm profit but decreases consumer surplus and social welfare. Therefore, requiring firms to disclose collection and usage of consumer data could hurt consumers and lead to unintended consequences.
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.
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 | 0.014 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".