Can VUCA events catalyze digital public sector innovations? Evidence from three digital innovation trends in Asia
Bibliographic record
Abstract
VUCA (volatile, uncertain, complex and ambiguous) events like COVID-19 can create unpredictable scenarios for public sector to tackle, forcing them to adopt digital applications to respond to challenges more quickly. COVID-19 saw governments pursuing digital public sector innovation more proactively, especially in Asia. We use the following three case studies to understand how COVID-19 accelerated digital public sector innovation across Asian countries: 1) digital contact tracing applications, 2) digital health certificates, and 3) AI chatbots. Using the OECD Framework on Facets of Innovation, we analyze the evolution of these innovations across Asian countries between 2020 and 2024. Some of the key findings from the analysis are 1) innovations developed and/or adopted during the crisis evolve from uncertainty to certainty in terms of the intended goals of the innovation, 2) innovations that begin as a top-down reform tend to evolve and take bottom-up approaches for greater citizen participation over time, 3) advanced technologies adopted during the crisis tend to continue its evolution even after the crisis has ended, giving rise to potential new applications. The findings suggest a clear shaping effect of crisis events on digital public sector innovations in Asia.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".