Optimal pricing strategies with remanufacturing technological innovation under different power structures
Bibliographic record
Abstract
With the development and promotion of remanufacturing, remanufactured products have gradually become popular. And the production of remanufactured products cannot be separated from the support of remanufacturing technology. However, it is not clear whether the implementation of remanufacturing technological innovation can benefit enterprises. Moreover, the evidence from industry practice shows that the power structure has a substantial influence on the decision-making behavior of enterprises. Therefore, we combined remanufacturing technological innovation and three power structures and constructed six models to analyze the effects of remanufacturing technological innovation and power structures on supply chain decisions in the competitive environment of new and remanufactured products. The results show that under a certain power structure, remanufacturing technological innovation can make supply chain members obtain higher profits. In the case of remanufacturing technological innovation, there is less investment in remanufacturing technological innovation under the manufacturer-led structure. At this time, when consumers are not sufficiently sensitive to remanufacturing technological innovation, the manufacturer will try to dominate as much as possible in pursuit of their interests. However, the supply chain system profit and social welfare under the manufacturer-led structure are not dominant. The Vertical Nash structure is more beneficial to the system profit and social welfare under this condition. When consumers are sufficiently sensitive to remanufacturing technological innovation, member profits, system profit, and social welfare can benefit from the retailer-led structure. In addition, changes in consumers’ sensitivity to remanufacturing technological innovation will not affect the retailer’s pursuit of more profits. The retailer-led is always the best choice for the retailer. This research result fills in the blank of the impact of remanufacturing technological innovation on supply chain decision-making in the competitive environment of new products and remanufactured products and can provide decision-making references for manufacturers, remanufacturers, retailers, and the government.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".