Durable Products with Multiple Used Goods Markets: Product Upgrade and Retail Pricing Implications
Bibliographic record
Abstract
Used goods markets are currently important transaction channels for durable products. For some durable products, such markets first appeared when retailers started buying back used products from “old” customers and selling them to new ones for a profit (retail used goods market). The growth of electronic peer-to-peer (P2P) markets opened up a second, frictionless used goods channel where new customers can buy used products directly from old customers (P2P used goods market). Both these markets compete with the original primary market where retailers sell unused products procured from the manufacturer. This paper focuses on understanding the role that the sequential emergence of the above two used goods markets plays in shaping the product upgrade strategy of the manufacturer and the pricing strategy of the primary market retailer in the context of a decentralized, dyadic channel dealing with a renewable set of consumers. Our analysis establishes that frequent product upgrades and rising retail prices in durable product sectors of our interest are due to the emergence of the P2P used goods market and how the market interacts with the retail used goods source in altering the relative powers of the channel partners. Moreover, contrary to popular belief, we show that the initial introduction of the retail used goods channel actually discourages introduction of new versions and restrains the rise in retail prices. We also comment on how the two used goods markets affect the profits of the channel partners. We then provide empirical support for our theoretical result regarding product upgrades using data from the college textbook industry, a durable product that fits our model setup.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".