Factors Influencing the Willingness to Download COVID-19 Contact Tracing Apps: The Moderating Effect of Persuasive Design and Smartphone Usage Experience
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Bibliographic record
Abstract
The COVID-19 pandemic culminated in the global roll-out of contact tracing apps, also known as exposure notification apps (ENAs), to contain the spread of the virus. ENAs notify users about a possible infection and advises them to take certain health measures such as self-isolation and COVID-19 test. While some work has been done on the factors that influence ENA adoption, little attention has been paid to the moderating effect of persuasive design and smartphone usage experience. This paper sets out to bridge this gap using the Government of Canada’s official app “COVID Alert” and Canadian non-adopter population (n = 133) as a case study. Of the seven factors we investigated, perceived trust (β = 0.29, p < 0.01) and perceived enjoyment (β = 0.21, p < 0.05) turned out to be the only significant predictors of non-adopters’ willingness to download the app in the overall model. Both persuasive design and smartphone usage experience moderated some of the relationships in the model. For example, while perceived enjoyment is significant in the persuasive design submodel (β = 0.34, p < 0.01) and low-experience submodel (β = 0.36, p < 0.001), it is not in the control design and high-experience submodels. Moreover, perceived enjoyment (β = 0.36, p < 0.001) and privacy concern (β = -0.46, p < 0.05) turned out to be the strongest predictors in the low-experience and high-experience submodels, respectively. These results indicate that, to improve the download of ENAs, sponsors should implement and highlight app features that foster trust in marketing campaigns and app-store descriptions. Particularly, they should highlight their privacy-protection feature and hedonic attribute among the high-experience and low-experience groups, respectively.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it