All External Reference Prices Are Not the Same: How Magnitude, Source, and Fairness Shape Payment for Digital Goods
Bibliographic record
Abstract
Music, movies, e-books, news: all industries that have been impacted by free distribution of their products. For many individuals, this wide availability of free substitutes drives users’ willingness-to-pay down. In this environment, how can platforms motivate consumers to pay for goods that they may be able to get for free? We demonstrate providing flexibility in payment through allowing users to “pay what you want,” along with providing external reference prices (ERPs) set by different sources, that is, other similar consumers or the platform itself, can influence payment. Importantly, a site-set ERP has more influence increasing payment than a socially-set ERP. An interesting nuance to this is that when the ERP is perceived to be high, the marginal effect of an increase in ERP on payment is smaller than when it is perceived to be fair; in other words, providing a fair ERP is more effective in increasing payment than providing an ERP that is too high. Altogether, platforms can leverage these findings in designing interfaces to provide information that can motivate consumers to pay for digital goods.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".