Blockchain adoption and coordination strategies for green supply chains considering consumer privacy concern
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Consumers’ uncertainty about the value of green products will reduce their willingness to pay, thereby obstructing green product promotion. Blockchain can eliminate this uncertainty but bring privacy concerns. We develop a game theoretical model to study a green supply chain composed of one manufacturer and one retailer, aiming to explore the implications of partial or full blockchain adoption on green product manufacturing. Subsequently, we consider the use of revenue-sharing and cost-sharing contracts as mechanisms to coordinate the supply chain that adopts blockchain technologies. We show that adopting blockchain for some products benefits the manufacturer and the retailer, and consumers’ privacy concerns make it impossible for blockchain to be adopted for all products. Interestingly, partial or full blockchain adoption does not affect the green investment level. Furthermore, we find that revenue-sharing and cost-sharing contracts are always beneficial for the manufacturer. However, it can be beneficial for the retailer only when the revenue-sharing or cost-sharing ratio is small. Surprisingly, the effectiveness of the coordinating contract is not affected by consumers’ privacy concerns. Finally, when comparing the wholesale price contract with two coordination mechanisms, we find that the manufacturer and the retailer can agree on adopting a cost-sharing contract when both revenue- and cost-sharing ratios are low. When the revenue-sharing ratio is moderate and the cost-sharing ratio is low, a revenue-sharing contract is adopted. In all other cases, trading is conducted according to the wholesale price contract. These insights can contribute to optimize the application of blockchain in green supply chains.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it