An Overview of International Fintech Instruments Using Innovation Diffusion Theory Adoption Strategies
Bibliographic record
Abstract
The aim of this chapter is to evaluate and predict the future of international fintech instruments in the domain of innovation diffusion theory (IDT) adoption strategies. Further, the consequences of the new payments system directive (PSD2) in Europe and blockchain applications are discussed. For instance, money transfer and payments have the highest rate of adoption (ROA) while insurance services have the highest speed of growing ROA due to relative advantages, high compatibility and trialability levels, and low level of complexity and uncertainty. Cross country comparisons include descriptive statistics about fintech deal value and volume, innovation rank, B2C commerce market, ROA and internet penetration. Germany is the only country listed in all of the top 10 ranking lists, followed by the U.S., the U.K., and France. Also, China, India, and Canada have distinguished success in terms of fintech indicators while the growth in Japan is expected to be slow. Accordingly, ROA in five emerging markets is much higher than some of the developed countries which can be explained by the Cancian Theory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".