Digital Transformation and the New Normal in China: How Can Enterprises Use Digital Technologies to Respond to COVID-19?
Bibliographic record
Abstract
The COVID-19 pandemic, a worldwide health and humanitarian crisis, has created unique challenges for citizens, governments, and organizations alike. Business leaders ask ‘what is the new normal, post-pandemic?’ while industries become more complex and uncertain. Premises sit empty, employees work remotely, and customers possess less disposable income. However bleak, opportunities do exist. Organizations will, however, need to transform. Technology will need to be used to respond to the pandemic, and organizations must re-structure to better function. This paper examines the role that digital technologies can play in responding to pandemics and outlines four classifications of digital technologies for pandemic response. An as-is/to-be pathway analysis is presented using case studies from Chinese enterprises to provide trajectory guidance for moving forward to the new normal. After analysis from an incorporated technology-centric and business model-based framework perspective, six fundamental enterprise strategies are presented that are derived from literature and empirical observations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".