COVID-19’s Impact on Fintech Adoption: Behavioral Intention to Use the Financial Portal
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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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".