Bibliographic record
Abstract
Problem definition: This paper provides a theoretical investigation into the value and design of a traceability-driven blockchain under different supply chain structures. Methodology/results: We use game theory to study the quality contracting equilibrium between one buyer and two suppliers and identify two fundamental functionalities of a traceability-driven blockchain. In serial supply chains, the ability to trace the sequential production process creates value by mitigating double moral hazard. In this case, traceability always improves product quality and all firms’ profits and naturally creates a win-win. In parallel supply chains, the ability to trace the product origin enables flexible product recall, which can reduce product quality. In this case, traceability can benefit the buyer while hurting the suppliers, creating an incentive conflict. Managerial implications: Firms operating in different kinds of supply chains could face unique challenges when they adopt and design a traceability-driven blockchain. First, in serial supply chains, any firm can be the initiator of the blockchain, whereas in parallel supply chains, it may be critical for the buyer to take the lead in initiating the blockchain and properly compensate the suppliers. Second, in serial supply chains, a restricted data permission policy where each supplier shares their own traceability data with the buyer but not with each other can improve the supply chain profit, whereas in parallel supply chains, it is never optimal to restrict a firm’s access to the traceability data. Third, the suppliers’ incentive to enhance the governance of data quality is more aligned with the supply chain optimum in serial supply chains compared with parallel supply chains. Funding: M. Hu was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295]. J. Liu was supported by the National Natural Science Foundation of China [Grant 72101110] and The MOE (Ministry of Education in China) Project of Humanities and Social Sciences [Grant 20YJC630084]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1161 .
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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 | 0.006 | 0.025 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".