Breaking the mold: the pursuit of decentralized trade and supply chain finance
Bibliographic record
Abstract
Purpose Blockchain technology (BT) presents a decentralized approach that has promising potentials to alleviate many of the long-lasting risks and inefficiencies in trade finance (TF) and supply chain finance (SCF) operations, providing international traders greater access to working capital. Despite this, the actual adoption of the technology and related issues in this space has remained under-researched. This paper examines the state of the practice to identify the main drivers and inhibitors faced by TF/SCF parties in their BT adoption efforts. Design/methodology/approach This exploratory study applies a multi-stakeholder perspective and a mixed-methods approach using semi-structured interviews with practitioners in various stages of BT implementation in TF/SCF initiatives across North America, Europe and Asia. The study then determines the priority of the identified factors using the Bayesian best-worst method (BWM). Findings The findings show that while the discussion has focused on the technological drivers of BT adoption for TF/SCF, practitioners rely more on non-technological factors such as peer adoption and fostering innovation. The findings also reveal how practitioners address common BT issues, including scalability and interoperability. Originality/value The study offers insights into important requirements for realizing the full benefits of BT in support of TF and SCF from an extended technology-organization-environment (TOE) perspective. On a more general level, it highlights what is required to transform this industry toward digitization.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".