A decision support framework for best-fitting blockchain platform selection in sustainable supply chains under uncertainty
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
• Proposing a decision support framework to leverage sustainable blockchain practices. • Exploring evaluation criteria using technology-organization-environment theory. • Analyzing a trade-off between the blockchain features and adoption requirements. • Exploring relationships among criteria using the advanced cognitive map modeling. • Developing the Z-number inference system for more reliable decision-making. Despite blockchain’s potential to enhance visibility and traceability in sustainable supply chains (SCs), its adoption is complex due to the various criteria (e.g., interoperability and cost) required for the best-fitting platform selection. This study aims to investigate conflicting criteria in the blockchain technology (BT) platform selection process for decision-making under uncertainty. We propose a three-phase decision support framework to study BT adoption considering technological, organizational, and environmental contexts. In the first phase, after exploring the evaluation criteria from multiple contexts, the developed framework incorporates uncertainty and reliability to deal with the BT platform evaluation problem. Then, fuzzy cognitive map modeling, advanced by a Z-number-based inference system, is introduced to model the causal relationships between criteria. This is followed by implementing a hybrid learning algorithm to assess the impact of each criterion on adoption decisions. Finally, the fuzzy combined compromise solution embedded in the framework prioritizes BT platforms to identify the most suitable ones for sustainable SC. The findings imply that performance efficiency, implementation costs, maintainability and operability can significantly affect the BT platform selection decisions. The outcomes offer more stable, reliable, and distinguishable solutions for the proposed problem compared to the traditional approaches. The results introduce Hyperledger and R3 Corda as the best-fitting platforms for adoption based on the identified criteria.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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