Exploring the Role of Blockchain Technology in Improving Sustainable Supply Chain Performance: A System-Analysis-Based Approach
Bibliographic record
Abstract
Due to the complexity of SSC practices, BT can be adopted as an innovative tool for addressing socioenvironmental issues. However, BT adoption has not been receiving growing attention because of organizational challenges (e.g., financial constraints). This study develops a system-analysis-based approach to investigate the impact of BT adoption on the improvement of SSC performance. This approach is proposed based on the FCM to model CRs between SSC-performance-related targets and enablers of BT adoption. These enablers include inherent features of BT identified in the context of sustainability (e.g., social responsibility and environmental sustainability). After developing an FCM model for the BT adoption problem, the impact of enablers on target concepts is investigated by implementing a hybrid FCM learning algorithm according to the extracted CRs. Based on the outputs of the FCM learning algorithm, this study also identifies the most effective enablers for improving SSC performance using the combined compromise solution method. The proposed system-analysis-based approach demonstrates that BT can significantly affect various dimensions of SSC networks’ performance. The results imply that the appropriate BT adoption supports SSC performance by improving environmental sustainability, creating smart contracts, and increasing traceability.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".