The Effect of Persuasive Design on the Adoption of Exposure Notification Apps: A Case Study of COVID Alert (Preprint)
Notice bibliographique
Résumé
<sec> <title>BACKGROUND</title> 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. </sec> <sec> <title>OBJECTIVE</title> 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. </sec> <sec> <title>METHODS</title> 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. </sec> <sec> <title>RESULTS</title> 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). </sec> <sec> <title>CONCLUSIONS</title> 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. </sec>
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».