Returns Policies Between Channel Partners for Durable Products
Bibliographic record
Abstract
Many durable products with relatively short selling seasons have been using returns policies between manufacturers and retailers as the contractual protocol for some time. Recently, these sectors have witnessed the growing popularity of peer-to-peer Web-based used goods markets as important transaction channels between buyers and sellers. Given that these two issues are critically linked from both supply and demand perspectives, in this paper we study the role that consumer valuation of used products plays in shaping a manufacturer's incentive to offer a returns policy option to a retailer when used goods might be devalued compared to new ones as a result of physical deterioration (or obsolescence). We do so through a two-period dyadic channel framework where the retailer faces uncertain demand for a durable product from a renewable set of customers who are impatient but forward looking. The manufacturer, on the other hand, needs to decide whether or not to offer a returns contract to the retailer. We first characterize the necessary and sufficient condition under which a returns contract is the equilibrium strategy as well as the corresponding channel decisions. Further analysis of this condition reveals that a higher consumer valuation of used products increases the likelihood of a returns contract being the equilibrium strategy. This result seems to be robust except when the potential demands for the two periods are quite deterministic and uncorrelated. However, it contradicts the burgeoning managerial trend to replace returns contracts with price-only ones in sectors where used goods are valued relatively highly by the consumers. We also discuss how used goods markets affect the equilibrium channel decisions as well as how demand uncertainty and logistics costs associated with returns influence the equilibrium contracting strategy.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".