Digital Goods Reselling: Implications on Cannibalization and Price Discrimination
Bibliographic record
Abstract
The resale of used products presents the challenge of cannibalization, particularly pronounced in digital goods markets where perfect substitutes are easily replicable. In this article, we assert that, rather than a threat, resale can serve as an effective pricing tool for managing heterogeneous demand. We consider a seller of digital goods/services who offers a contract to a heterogeneous group of customers at a fixed price for a specified amount of usage allowance. Rather than imposing restrictive sharing barriers, the seller allows subscribers to share their allowances with others in a secondary market. Our analysis reveals that the seller’s optimal strategy involves facilitating resale by eliminating transaction costs. The sharing contract effectively achieves the same outcome as a two-part tariff, wherein subscribers pay an entry fee along with a marginal usage rate. Both approaches generate equivalent revenue and market coverage, and result in idential demand and individual surplus for customers of the same type. Consequently, the sharing contract acts as a mechanism for price discrimination. Our finding provides a new perspective on peer-to-peer resales and also challenges the conventional belief that successful price discrimination hinges on preventing resale.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".