The Role UX Design Attributes Play in the Perceived Persuasiveness of Contact Tracing Apps
Bibliographic record
Abstract
Contact tracing apps (CTAs) were deployed worldwide in 2020 to combat COVID-19. Due to their low uptake, a growing amount of empirical research is being conducted to understand the factors that drive their adoption. For CTAs to be adopted, users must, first and foremost, find them persuasive. However, there is little research to understand the role user experience (UX) plays in their perceived persuasiveness. Consequently, we conducted an online study on Amazon Mechanical Turk among Canadian and American residents (n = 446) to investigate the most important UX design attributes associated with the perceived persuasiveness of CTAs. The study was based on two app designs (control and persuasive), each of which comprises three use cases: no exposure, exposure, and diagnosis report interfaces. One interface (screenshot) was randomly presented to a participant to view and provide their responses on the perceived UX design attributes and perceived persuasiveness of the interface. In the overall model, we found that perceived usefulness is the most important and consistent UX design attribute that influences perceived persuasiveness (β = 0.29, p < 0.001), followed by perceived trustworthiness (β = 0.24, p < 0.001) and perceived privacy protection (β = 0.16, p < 0.05). Respectively, the three predictors were consistently significant in two-thirds, half, and one-third of the 12 submodels based on app design, adoption status, and country of residence. The relationships regarding the persuasive designs are more likely to be significant, with the variance of the target construct explained by the predictors ranging from 71% to 89% compared with 54% to 69% for the control designs. The three significant attributes will help designers know which UX design attributes to focus on when designing CTAs for future epidemics. More importantly, in predictive modeling, if their ratings are known, they hold potential in predicting new users’ responsiveness to multiple persuasive strategies/messages featured in behavior-change support systems.
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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 | 0.007 | 0.050 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".