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 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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".