The Effect of Persuasive Design on the Adoption of Exposure Notification Apps: A Case Study of COVID Alert (Preprint)
Bibliographic record
Abstract
BACKGROUND The COVID-19 pandemic, which began in the first quarter of 2020, necessitated the imposition of public health restrictions and the shutting down of the global economy. To slow down the spread of the coronavirus, governments worldwide rolled out nationwide contact tracing apps (CTAs) to notify people that may have been exposed to COVID-19. The emergence of new variants of COVID-19, which can cause breakthrough infections, necessitate the continued use of CTAs. However, the uptake of these apps has been low and slow worldwide. Some experts have argued that the low adoption rate of CTAs can be attributed to their minimalist design and lack of motivational features, trust- and privacy-related issues aside. However, there is little to no research to show that the incorporation of persuasive principles in the design of CTAs has the potential of increasing their effectiveness and adoption. OBJECTIVE The objective of this article is to uncover how the persuasive design of CTAs influences their effectiveness by focusing on three key user interfaces: no-exposure status, exposure status, and diagnosis report. METHODS We conducted an empirical study on Amazon Mechanical Turk to investigate the effect of persuasive design in CTAs using the Government of Canada’s exposure notification app (“COVID Alert”) as a case study. Our study is based on 204 participants (comprising adopters and non-adopters) resident in Canada and two app designs: persuasive and control. RESULTS Regarding the willingness to download the COVID Alert app, our three-way analysis of variance (ANOVA) shows there is an interaction between adoption status and app design. Among adopters, there is no significant difference between the persuasive and the control design. However, among non-adopters, there is an effect of app design (p < 0·001), with participants being more likely to download the app using the persuasive design (M = 5·37) than the control design (M = 4·57). Similarly, regarding the intention to report COVID-19 diagnosis, there is an interaction between adoption status and app design. Among non-adopters, there is no significant difference between the persuasive design and the control design. However, among adopters, there is an effect of app design (p < 0·01), with participants being more likely to report their diagnosis using the persuasive design (M = 6·00) than the control design (M = 5·03). CONCLUSIONS The results show that non-adopters are more likely to download the persuasive version of a CTA (equipped with self-monitoring) than the control version. Moreover, adopters are more likely to report their COVID-19 diagnosis using the persuasive version of a CTA (equipped with social-learning) than the control version. Overall, the percentage of non-adopters willing to download the COVID-Alert app from the app stores increased by over 10% due to the incorporation of persuasive features in its interface design. In a nutshell, the study shows that CTAs are more likely to be effective and adopted if equipped with persuasive features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".