Determinants affecting the intention to adopt financial technology
Bibliographic record
Abstract
Recently, due to the tremendous development in information and communication technology, the world is moving rapidly towards digitization in all areas of life. In the financial context, Financial Technology (Fintech) has the potential to transform the financial sector by offering innovative digital solutions, but its adoption depends on various individual, organizational, and environmental factors. This research paper aims to identify and analyze the determinants that influence the intention to adopt fintech. A self-administered survey was utilized to collect the necessary data. Data were analyzed using SPSS version 26 for descriptive analysis as well as SmartPLS version 3.0 by implementing the PLS algorithm and Bootstrapping techniques. This study finds that intention to adopt Fintech is affected by perceived usefulness, perceived ease of use, trust, social influence, and facilitating conditions. This study also examined the relationships between these variables. The findings will provide insights for fintech stakeholders, policymakers, and researchers to foster a conducive environment for Fintech adoption and usage.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".