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A Cluster Analysis of the Acceptance of a Contact Tracing App—The Identification of Profiles for the Italian Immuni Contact Tracing App
Digital contact tracing apps have been introduced by governments as a strategy to limit the spread of the COVID-19 pandemic. Digital contact tracking is an alternative to traditional contact tracing performed by human tracers who have to reconstruct each contact an infected person had in the recent...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140954/ https://www.ncbi.nlm.nih.gov/pubmed/35628024 http://dx.doi.org/10.3390/healthcare10050888 |
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author | Sarini, Marcello Actis Grosso, Rossana Magrin, Maria Elena Mari, Silvia Olivero, Nadia Paganin, Giulia Simbula, Silvia |
author_facet | Sarini, Marcello Actis Grosso, Rossana Magrin, Maria Elena Mari, Silvia Olivero, Nadia Paganin, Giulia Simbula, Silvia |
author_sort | Sarini, Marcello |
collection | PubMed |
description | Digital contact tracing apps have been introduced by governments as a strategy to limit the spread of the COVID-19 pandemic. Digital contact tracking is an alternative to traditional contact tracing performed by human tracers who have to reconstruct each contact an infected person had in the recent past by means of interviews. The Italian government proposed the Immuni digital contact tracking app as a solution. Immuni uses Bluetooth technology to anonymously register all close contacts a person had: if she tests positive for COVID-19 then all registered contacts are notified. The main aim of the paper is to propose a cluster analysis of some factors concerning the possible acceptance of the Immuni app to build behaviour profiles that explain and predict the possible behaviours of the respondents. The factors considered referred to three different pillars: the technological pillar, investigated by considering factors from the technology acceptance models family; the health pillar, where variables of the health belief model were used; and the sociopolitical pillar, where some values of the respondents were considered as possible barriers to or facilitators of the acceptance of this technology. As a result of the cluster analysis, three behavioural profiles were built: the ProApp profile, the Hesitant profile, and the AntiApp profile. The first is the profile grouping the respondents who intend to use the contact tracing app; the second is more about people who are favourable of the use of the app, but some issues such as privacy reduce the strength of their intention; the last profile is about people who are less favourable to use the app. We are confident that the behaviour profiles found would be useful to build more tailored communication campaigns to help promote the use of the app by managing factors that could either be facilitators or barriers. |
format | Online Article Text |
id | pubmed-9140954 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91409542022-05-28 A Cluster Analysis of the Acceptance of a Contact Tracing App—The Identification of Profiles for the Italian Immuni Contact Tracing App Sarini, Marcello Actis Grosso, Rossana Magrin, Maria Elena Mari, Silvia Olivero, Nadia Paganin, Giulia Simbula, Silvia Healthcare (Basel) Article Digital contact tracing apps have been introduced by governments as a strategy to limit the spread of the COVID-19 pandemic. Digital contact tracking is an alternative to traditional contact tracing performed by human tracers who have to reconstruct each contact an infected person had in the recent past by means of interviews. The Italian government proposed the Immuni digital contact tracking app as a solution. Immuni uses Bluetooth technology to anonymously register all close contacts a person had: if she tests positive for COVID-19 then all registered contacts are notified. The main aim of the paper is to propose a cluster analysis of some factors concerning the possible acceptance of the Immuni app to build behaviour profiles that explain and predict the possible behaviours of the respondents. The factors considered referred to three different pillars: the technological pillar, investigated by considering factors from the technology acceptance models family; the health pillar, where variables of the health belief model were used; and the sociopolitical pillar, where some values of the respondents were considered as possible barriers to or facilitators of the acceptance of this technology. As a result of the cluster analysis, three behavioural profiles were built: the ProApp profile, the Hesitant profile, and the AntiApp profile. The first is the profile grouping the respondents who intend to use the contact tracing app; the second is more about people who are favourable of the use of the app, but some issues such as privacy reduce the strength of their intention; the last profile is about people who are less favourable to use the app. We are confident that the behaviour profiles found would be useful to build more tailored communication campaigns to help promote the use of the app by managing factors that could either be facilitators or barriers. MDPI 2022-05-11 /pmc/articles/PMC9140954/ /pubmed/35628024 http://dx.doi.org/10.3390/healthcare10050888 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sarini, Marcello Actis Grosso, Rossana Magrin, Maria Elena Mari, Silvia Olivero, Nadia Paganin, Giulia Simbula, Silvia A Cluster Analysis of the Acceptance of a Contact Tracing App—The Identification of Profiles for the Italian Immuni Contact Tracing App |
title | A Cluster Analysis of the Acceptance of a Contact Tracing App—The Identification of Profiles for the Italian Immuni Contact Tracing App |
title_full | A Cluster Analysis of the Acceptance of a Contact Tracing App—The Identification of Profiles for the Italian Immuni Contact Tracing App |
title_fullStr | A Cluster Analysis of the Acceptance of a Contact Tracing App—The Identification of Profiles for the Italian Immuni Contact Tracing App |
title_full_unstemmed | A Cluster Analysis of the Acceptance of a Contact Tracing App—The Identification of Profiles for the Italian Immuni Contact Tracing App |
title_short | A Cluster Analysis of the Acceptance of a Contact Tracing App—The Identification of Profiles for the Italian Immuni Contact Tracing App |
title_sort | cluster analysis of the acceptance of a contact tracing app—the identification of profiles for the italian immuni contact tracing app |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140954/ https://www.ncbi.nlm.nih.gov/pubmed/35628024 http://dx.doi.org/10.3390/healthcare10050888 |
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