Examining the Impact of COVID-19 on the Banking Industry: A Bibliometric Analysis
Bibliographic record
Abstract
The impact of COVID-19 on the banking industry has had a significant effect. This study tries to map the development of research published in this field. The research was conducted using VOSViewer software. The data analyzed in the form of scientific research related to the impact of COVID-19 on the banking industry totaling 28 articles indexed by the Scopus database. The results show that the number of research publications on the impact of COVID-19 on the banking industry is quite large. The network visualization shows that the map of developing research on the impact of COVID-19 on the banking industry is divided into several clusters with the most popular keywords, namely COVID-19, banking and viral disease. The top authors are Strongbayefa A, Akhmetov Y, Mohan T and Baskaran A, the top agencies are the Technical Expert Group on Sustainable Finance, Brussels and the Department of Accounting Finance, Rennes School of Business. The most popular countries are India, Canada and Saudi Arabia. Besides, it was found that research on the impact of COVID-19 on the banking industry was felt recently due to credit and non-performing loans which then had implications for restructuring policies. In the end, conventional banking is more affected by COVID-19 than Islamic banking in terms of its problematic financing ratio. Keywords: Banking Industry, Covid-19, Bibliometrics JEL Classification: G21, O16.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.192 | 0.218 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".