Bibliographic record
Abstract
This study focused on the interactions between manufacturer encroachment and extended warranty (EW) provision by examining the manufacturer’s optimal encroachment decision with or without the extended warranty and the optimal EW provision decision under encroachment or no encroachment. Based on the combination of the two strategies, this study discussed four different models in which the manufacturer and retailer act as the Stackelberg leader and follower, respectively. It was shown that the manufacturer always finds it optimal to offer EW without encroachment. However, under encroachment, the manufacturer’s motivation to offer EW weakens. Furthermore, when EW is not offered, the manufacturer can benefit from encroachment if the selling cost of the product is not sufficiently high. When the selling cost of EW is low, the manufacturer’s motivation for encroachment strengthens. As the selling cost becomes moderate to high, offering EW weakens the manufacturer’s motivation for encroachment. Our analysis reveals that for different values of the co-payment rate and the manufacturer’s selling costs of products and EW, encroachment and EW provision may reinforce or impair each other. Therefore, manufacturer has to pay attention to the influence of one decison on another decision.
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 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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".