Supply chain sustainability in VUCA: role of BCT-driven SC mapping and ‘Visiceability’
Bibliographic record
Abstract
The study investigates the role of three essential supply chain capabilities: visibility, traceability, and mapping, collectivity termed as 'visiceability', in the relationship between blockchain technology and supply chain sustainability. The study focuses on Malaysia's Electronics Component manufacturing firms, a sub-sector of the electrical and electronics industry. Data were collected from 105 through a close-ended questionnaire. PLS-SEM was employed to examine the modeled relationships. The findings of the study challenge the notion that supply chain (SC) traceability alone is responsible for mediating the impact of blockchain technology (BCT) on SC sustainability. However, findings confirm the significant roles of SC Mapping and Visibility in the association between BCT and SC sustainability. Findings further validate the significant impact of BCT on SC sustainability, highlighting its multifaceted role. The findings suggest that firms can build their intermediary capabilities instead of exclusively focusing on adopting BCT for SC sustainability. These capabilities can further channel the impact of BCT on improving SC Sustainable. Our findings illustrate that BCT can enhance SC visibility by offering a precise and transparent record of the products, inventory, and transactions. Hence, we strongly suggest that managers consider leveraging BCT to improve their SC visibility, thereby uplifting the sustainability of a supply.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| 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".