How COVID-19 has Impacted Digital Transformation – From the Perspective of C-Suite Professionals
Bibliographic record
Abstract
The present study is aimed at understanding how COVID-19 has impacted the digital transformation of businesses globally.The study considered numerous sub-aspects related to the digital transformation of businesses, including drivers to digital transformation, impact on people and society, and the impact of COVID-19.The research was based on the interpretivist paradigm using a qualitative research approach and interview method where data was collected from the C-suite category of selected businesses from Canada, India, Germany, Austria, and Switzerland.The study's results have identified COVID-19 as the most significant challenging factor for transforming businesses where lack of coherence among employees and inability of people to adapt to the technological interventions might demotivate them and cause disrupted operational activities of businesses.The study's results also suggested for businesses to focus on training and development of employees to capacitate them to adapt to the new normal working environment.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".