A Narrative Review and Gap Analysis of Blockchain for Transparency, Traceability, and Trust in Data-Driven Supply Chains
Bibliographic record
Abstract
The increasing complexity and digitization of modern supply chains have created an urgent demand for transparent, traceable, and trustworthy systems of data management. Blockchain, with its core features of immutability, decentralization, and smart contracts, has emerged as a promising solution for strengthening data-driven supply chain operations. This paper presents a narrative review synthesizing insights from academic research, industry reports, and regulatory documents to examine blockchain’s role in enhancing transparency, traceability, and trust. References were identified through targeted searches of major databases and gray literature sources, with emphasis on diverse sectors and global perspectives, rather than exhaustive coverage. The review maps how blockchain’s technical capabilities—such as data integrity preservation, access control, automated validation, and provenance tracking—support these outcomes, and assesses the empirical indicators used to evaluate them. A sectoral applicability analysis distinguishes contexts in which blockchain adoption offers clear advantages from those where benefits are limited. The review also identifies critical research gaps, including inconsistent definitions of core concepts, insufficient interoperability standards, overreliance on subjective performance measures, and lack of longitudinal cost–benefit evidence. Finally, it proposes directions for future research, including the development of sector-specific adoption frameworks, integration with complementary technologies, and cross-border regulatory harmonization.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".