Enhancing sustainable supply chain readiness to adopt blockchain: A decision support approach for barriers analysis
Bibliographic record
Abstract
Blockchain technology (BT) enhances the capacity to monitor products consistently, fostering supply chain responsiveness to a wide range of societal and environmental issues. Although BT is known as an innovative tool, there exist potential operational and organizational challenges affecting BT adoption. This study proposes a decision support approach to leverage risk management to analyze potential barriers associated with BT adoption in sustainable supply chains (SSCs). This approach is developed to model how the economic, social, and environmental-related barriers (e.g., energy consumption) and their corresponding risk factors are interrelated. To model the causal relationships (CRs) among the barriers identified through the literature review, the fuzzy cognitive map advanced by Z-number theory is embedded in the proposed approach. Then, a hybrid learning algorithm is employed to determine the criticality of the barriers. As the reliability of information affects the accuracy of decision-making, the Z-number theory applies uncertainty and reliability simultaneously in specifying the values of risk factors and the weights of the CRs. Taking advantage of the learning algorithm and Z-number theory, the findings show a reliable and unbiased ranking compared to the failure mode and effect analysis. This helps managers develop more efficient mitigation strategies to deal with critical barriers. The results of the study also imply that adoption costs, extra audits, and regulatory uncertainty are the critical barriers affecting SSC readiness.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".