COVID-19’s Impact on Fintech Adoption: Behavioral Intention to Use the Financial Portal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As Fintech has grown exponentially in recent years, several researchers have examined how information technology is applied in the financial services sector, with a focus on the extended practice of its application. However, fewer studies have investigated the factors influencing the acceptance of Fintech services. In order to examine how consumers adopt Fintech services, this research presents an enhanced technology acceptance model (TAM) that integrates perceived usefulness, perceived ease of use, user innovativeness, and trust as factors of attitude towards using Fintech platforms and behavioral intention to use Fintech platforms. The questionnaires were sent to 867 of Portal MyAzZahra’s customers, and 273 complete questionnaires were received. The data were then analyzed to comprehend whether the proposed hypotheses were accepted or rejected. The findings depict that consumers’ trust, perceived ease of use, and customer innovation in Fintech services substantially impact the attitude towards adoption and behavioral intention to use the Fintech online platform. However, perceived usefulness does not significantly influence the attitude towards adoption and the behavioral intention to use the online loan aggregator. By integrating these factors into Fintech services with TAM, this study adds to the literature on adopting Fintech services by offering a more holistic perspective of the factors affecting consumers’ attitudes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it