THE IMPACT OF COVID‐19 ON THE TECHNOLOGY SECTOR: THE CASE OF THE TURKISH CONSULTANCY COMPANY
Bibliographic record
Abstract
The COVID-19 pandemic has caused unprecedented changes in the global economy and society, with many studies attempting to understand the impact of the virus on different countries and industries. This study focuses on the effects of COVID-19 on a consulting company that specializes in technology services. By analyzing the company's sales data for the five-year period before the pandemic, and using machine learning techniques via the KNIME platform, the study aims to predict the sales data for the COVID-19 period. Three different regression models - linear, gradient boosting, and random forest - were used to make these predictions, and the models were compared based on their coefficient of determination (R2) to determine which model performed best. The chosen model was then used to interpret the impact of COVID-19 on the company. The findings of the study provide insights into how COVID-19 has affected the consulting company. The chosen model showed that the pandemic had a significant negative impact on the company's sales, with a sharp decline in the second quarter of 2020. However, the company was able to recover some of its losses by the fourth quarter of the year. The study also highlights the importance of using machine learning techniques to predict future sales data during unpredictable events such as the COVID-19 pandemic. Overall, this study sheds light on the impact of COVID-19 on a technology consulting company and demonstrates the importance of using data analysis and machine learning techniques to make predictions and interpret the effects of significant events on business operations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".