Pay-What-You-Want Pricing in the Digital Product Marketplace: A Feasible Alternative to Piracy Prevention?
Bibliographic record
Abstract
In pay-what-you-want (PWYW) pricing, buyers are allowed to pay any amount they want, often including a price of zero. Standard theory predicts that buyers are driven solely by their own interest and will always choose to pay nothing, making PWYW pricing impractical to use. Nonetheless, PWYW pricing has been consistently occurring in the marketplace. We build and analyze a theoretical model to explain the presence of PWYW pricing in the marketplace and identify the situations under which businesses are better off adopting it over the traditional posted pricing. Because the digital product domain is a particularly good fit for PWYW pricing because of its constant exposure to piracy threats, we focus on digital product firms and examine PWYW pricing as an alternative to their piracy prevention efforts. We show that PWYW pricing becomes a superior pricing strategy when the pirate version is quite similar to the authentic product and it is costly for the firm to improve its product quality. Moreover, if network externalities are present, PWYW pricing can outperform posted pricing only when the network externalities are weak. The results explain why PWYW pricing is rare in the established digital product marketplace, which exhibits strong network externalities.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".