Coordination of Supply Chain under Blockchain System-Based Product Lifecycle Information Sharing Effort
Bibliographic record
Abstract
The study proposes a supply chain contractual coordination model based on the product lifecycle information sharing effort and consumers' price sensitivity to a product with the Blockchain system. This paper examined the following five scenarios: (1) centralized supply chain with Blockchain system-based product lifecycle information sharing investment; (2) Stackelberg leader retailer processed and invested Blockchain system scenario; (3) retailer processed the Blockchain system cost-sharing scenario; (4) retailer processed Blockchain system investment through bargaining the revenue-sharing model; (5) Blockchain system investment under the cost and revenue-sharing contract. The study used the game theory reverse induction method to compare the Nash equilibrium solutions under different decision-making scenarios and discussed the chain member’s constraint condition of Blockchain system investment. We simulated and analysed the products’ lifecycle information sharing effort cost factor, the influence of price sensitivity coefficient, and expected profits of the supplier and retailer. The study results show that the product lifecycle information sharing effort under the Blockchain system increases the profit of the whole chain and decreases with the increase of customer’s price sensitivity coefficient.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".