Dual Perspectives on Financial Performance: Analyzing the Impact of Digital Transformation and COVID-19 on European Listed Companies
Bibliographic record
Abstract
This paper conducts an analysis of the impact of COVID-19 and digital transformation (DT) on the financial performance of European listed companies. Using a panel data regression model from 2015 to 2021, the study analyzed the financial performance of 2179 companies. The sample of companies was chosen based on the availability of financial statements and aimed to examine the effects of COVID-19 and DT on financial performance, as measured by return on assets (ROA). The study used a fixed-effect model and checked for robustness by introducing return on equity (ROE) as a dependent variable. The results indicated that COVID-19 had a negative significant impact on financial performance, while DT had a positive significant impact, consistent with previous research. This study provides valuable insights into the impacts of the COVID-19 pandemic and DT on the financial performance of listed companies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".