Pricing in C2C Sharing Platforms
Bibliographic record
Abstract
Sharing platforms such as zilok.com enable sharing of durable goods among consumers, and seek to maximize profits by charging transaction-based platform fees. We develop a model in which consumers who have heterogeneous needs concerning the use of a durable good decide whether to purchase and share (i.e., be a lender) or borrow (i.e., be a borrower), and a monopoly sharing platform determines the platform fees. We find, first, that consumers with greater need to use a durable good purchase and share, and that consumers with lesser need borrow. Second, sharing platforms maximize profits only if the supply of a durable good matches demand—that is, the market must clear in order for platform fees to be profit maximizing. Third, the market-clearing condition requires lender and borrower fees are classic strategic complements. Fourth, to maintain the market-clearing condition, sharing platforms have to increase their lender fee or decrease their borrower fee in response to increases in the sharing price, increases in usage capacity, and decreases in the purchase price of a durable good, and vice versa. These findings indicate that commonly applied one-sided pricing models in sharing platforms can be improved.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".