The COVID_19 Pandemic’s Effects on Fintech in Banking Sector
Bibliographic record
Abstract
As a result of the effects of the COVID_19 pandemic, which has greatly affected the global economy, individuals have resorted to using financial technology and modern applications for financial transactions, which help reduce gatherings, given the centralization of the virus and the emergence of new, advanced pests. This paper aims to determine the impact of the COVID_19 pandemic on financial technology in the Jordanian banking sector. However, the quantitative approach was adopted, through electronic survey questionnaires being distributed to 2450 respondents from the population, which are all customers of Jordanian banks who use electronic banking services in the presence of the COVID_19 pandemic. As a result of analyzing 1930 resolution, it was found that the perception of the COVID_19 pandemic has a significant positive impact on Fintech in the Jordanian banking sector and that the perception of the COVID_19 pandemic has a significant positive impact on the dimensions of Fintech in Jordan which are (ease of use, reliability, responsiveness, assurance, interface design, and privacy). This study contributed to determining the extent to which electronic banking services reduce customer visits to branches according to social distancing. The paper explains how the development of technical services should go hand in hand with the bank's development strategies aimed at acquiring and retaining more customers. This paper recommends the need to improve the application of electronic banking services in proportion to customer satisfaction as much as possible.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".