Blockchain technologies as enablers of supply chain mapping for sustainable supply chains
Bibliographic record
Abstract
Abstract The advent of blockchain technologies is transmuting the way conventional supply chains are being managed. Due to the complexity of dealing with many actors involved in the supply chain networks, contemporary supply chains have limited visibility, transparency, and accountability. Likewise, supply chains are increasingly facing the challenge of integration and sustainability. In this vein, blockchain technologies can play a groundbreaking role in improving the traceability, accountability, and sustainability of complex supply chain networks. The present study examines the instrumentality of blockchain technologies in enabling supply chain mapping and supply chain integration. The study also tests the direct impact of blockchain technologies on supply chain sustainability. Data are collected from 132 Malaysian Electrical and Electronics firms using a close‐ended questionnaire. The study employs Partial Least Squares‐Structural Equation Modelling (PLS‐SEM) and Partial Least Squares‐Multi Group Analysis (PLS‐MGA) for analyzing the hypothesized relationships. The results show that blockchain technologies do not have a direct impact on supply chain sustainability. Nevertheless, this finding reveals a robust indirect effect of BT, through SC integration and SC mapping, on the SC sustainability. The study's findings imply that the notion of the sustainable supply chain can be significantly attained by mapping upstream, midstream, and downstream supply chains. The well‐mapped supply chain can further improve supply chain sustainability. The findings of the study also suggest the adoption of blockchain technologies as a broad‐based strategy to attain multi‐tier goals, for example, supply chain mapping, sustainability, and integration.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".