Adoption of Central Bank Digital Currencies: A Data-Driven Exploration of Key Influential Factors
Bibliographic record
Abstract
Central Bank Digital Currency (CBDC) is a digital form of fiat currency issued and regulated by central banks. Understanding the drivers and barriers for CBDC adoption is crucial for helping the key stakeholders determine the important areas and concerns that need to be addressed to ensure a successful integration of CBDCs in the financial system. This paper employs a data-driven approach to understand major themes regarding CBDC adoption in news articles using topic modeling, followed by aspect-based sentiment analysis (ABSA) to prioritize the factors influencing CBDC adoption. The findings highlight digital infrastructure, digital economy, and financial innovation as top adoption drivers, and security, user trust, and policy and regulations as the major barriers. The data-driven approach of using ABSA to rank a list of drivers and barriers is the first of its kind and can be generalized to rank factors in other domains.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".