COVID-19, national culture, and privacy calculus: factors predicting the cross-cultural acceptance and uptake of contact-tracing technologies
Bibliographic record
Abstract
The use of information technologies for the public interest, such as COVID-19 tracking apps that aim to reduce the spread of COVID-19 during the pandemic, involve a dilemma between public interest benefits and privacy concerns. Critical in resolving this conflict of interest are citizens’ trust in the government and the risks posed by COVID-19. How much can the government be trusted to access private information? Furthermore, to what extend do the health benefits posed by the technology outweigh the personal risks to one's privacy? We hypothesize that citizens’ acceptance of the technology can be conceptualized as a calculus of privacy concerns, government trust, and the public benefit of adopting a potentially privacy-encroaching technology. The importance that citizens place on their privacy and the extent to which they trust their governments vary though out the world. The present study examined the public’s privacy calculus across nine countries (Australia, Germany, Italy, Japan, Spain, Switzerland, Taiwan, the United Kingdom, and the United States) focusing on social acceptance of contact-tracing technologies during the COVID-19 pandemic. We found that across countries, privacy concerns were negatively associated with citizens’ acceptance of the technology, while government trust, perceived effectiveness of the technology, and the health threats of COVID-19 were positively associated. National cultural orientations moderate the effects of the basic factors of privacy calculus. In particular, individualism (value of the individual) amplified the effect of privacy concerns, whereas general trust (trust in the wider public) amplified the effect of government trust. National culture therefore requires careful attention in resolving public policy dilemmas of privacy, trust, and public interest.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".