An Alternative to Coping with COVID-19—Knowledge Management Applied to the Banking Industry in Taiwan
Bibliographic record
Abstract
This study seeks to find an alternative strategy to cope with the impact of COVID-19. Though various measures have been adopted to respond to the threat of the pandemic, the problem remains unchanged. Undoubtedly, COVID-19 is also a crisis of knowledge, so this study explores whether the banking industry in Taiwan can apply knowledge management (KM) and fight the catastrophe of the century successfully and effectively. This study adopts an actual case to analyze the relationship between KM implementation and the banking industry; applies consistent fuzzy preference relations (CFPRs) to evaluate influential criteria including computational simplicity and guarantee the consistency of decision matrices; illustrates a decision-making model with seven criteria; and conducts pairwise comparisons, which are utilized to determine the priority weights of influential criteria amongst the outcome rankings and to formulate accurate KM strategies. The results show that predictions of success probabilities are higher than those of failure probabilities among the seven influential criteria and, in particular, the headquarters system and human resources are the most important priority indicators for implementing KM successfully during the pandemic or post-pandemic. The conclusion suggests significant policy implications for policymakers within other industries or countries in coping with COVID-19.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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 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".