Bibliographic record
Abstract
Recent advances in information technology, advanced manufacturing (robotics, 3D printing, etc.), and logistics have allowed firms to customize their products to the specifications of individual consumers, who, in turn, prefer these products to standard ones. In the unlikely event that customized products do not match expectations, however, consumers often feel entitled to a return. Should firms offer returns on customized products? We examine this question via a Stackelberg game model, in which the firm (leader) decides the prices and returns policies for its customized and standard products; consumers (followers) decide which product to buy, given the initial noisy valuations and, upon experiencing the product, whether to return it. Both parties act strategically: Forward-looking consumers incorporate the real option value of possible returns into their initial purchasing decisions, and the firm incorporates consumers’ best purchase and return response into its pricing and returns policy decisions. Our model produces three key insights. First, firms can use customized products to induce some consumers who otherwise would buy and return a standard product to switch to lower-return-rate customized products. Second, it may be optimal to offer returns on customized products, despite their lower salvage value. Third, firms can increase profits and reduce (total) returns by offering returnable customized products. This paper was accepted by Duncan Simester, marketing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".