Business analysis in the times of COVID-19: Empirical testing of the contemporary academic findings
Bibliographic record
Abstract
The pandemic of the coronavirus known as 'COVID-19' has spread rapidly across the globe, resulting in a worldwide economic recession. Although global efforts are in place to combat the virus, it continues to spread on a massive scale. This is not just a medical crisis; rather, it is a business crisis as well. Therefore, the aim of this paper is to develop a research scale that could be used to analyze the impact of COVID-19 on business. We adopted a scale variables approach that is generated from topics covered in the papers of leading academic business journals to form the basis of our analysis. We exposed the scale to qualitative and quantitative testing and concluded that it is reliable for research on the negative effects of COVID-19 on enterprises. The scale was used to investigate the impact of the COVID-19 on businesses in two countries, namely Serbia and Kuwait, to represent two different continents. The results of this research indicate that the influence of this coronavirus is equally devastating in both countries, Kuwait with its otherwise good economic conditions and Serbia with relatively poor ones. The findings of the research are beneficial for both academics in producing quality output papers, as well as their support to managers in various business industries in their fight against coronavirus to keep their businesses sustainable.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".