Drivers and Barriers of Leveraging Blockchain Technology in Supply Chain Finance and Trade Finance: A Mixed Methods Approach to Examine the State of Adoption
Bibliographic record
Abstract
Blockchain technology has the potential to alleviate paper-based administration and risks of double-financing in supply chain finance (SCF) and trade finance (TF), providing buyers and sellers greater access to working capital. This research examines the drivers and barriers of blockchain adoption from a multi-stakeholder approach: banks, technology providers, consultants, buyers and sellers. Adoption factors are also investigated using different stages of implementation: evaluation, proof-of-concept, development, and production. A mixedmethodology of semi-structured interviews with 11 participants and a follow-up study using the Best-Worst Method (BWM), a multi-criteria decision-making (MCDM) methodology, is conducted. The study identifies 16 drivers and 18 barriers where adoption by network peers is the top driver and legal and regulatory uncertainties is the top barrier. Intra-organizational and environmental drivers were found to be more important than technological factors. This research contributes novel managerial and theoretical insights to blockchain adoption literature using the technology-organization-environment (TOE) framework.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".