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Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model
BACKGROUND: The expansion of the coronavirus pandemic and the extraordinary confinement measures imposed by governments have caused an unprecedented intense and rapid contraction of the global economy. In order to revive the economy, people must be able to move safely, which means that governments m...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924669/ https://www.ncbi.nlm.nih.gov/pubmed/33816983 http://dx.doi.org/10.7717/peerj-cs.316 |
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author | Velicia-Martin, Felix Cabrera-Sanchez, Juan-Pedro Gil-Cordero, Eloy Palos-Sanchez, Pedro R. |
author_facet | Velicia-Martin, Felix Cabrera-Sanchez, Juan-Pedro Gil-Cordero, Eloy Palos-Sanchez, Pedro R. |
author_sort | Velicia-Martin, Felix |
collection | PubMed |
description | BACKGROUND: The expansion of the coronavirus pandemic and the extraordinary confinement measures imposed by governments have caused an unprecedented intense and rapid contraction of the global economy. In order to revive the economy, people must be able to move safely, which means that governments must be able to quickly detect positive cases and track their potential contacts. Different alternatives have been suggested for carrying out this tracking process, one of which uses a mobile APP which has already been shown to be an effective method in some countries. OBJECTIVE: Use an extended Technology Acceptance Model (TAM) model to investigate whether citizens would be willing to accept and adopt a mobile application that indicates if they have been in contact with people infected with COVID-19. Research Methodology: A survey method was used and the information from 482 of these questionnaires was analyzed using Partial Least Squares-Structural Equation Modelling. RESULTS: The results show that the Intention to Use this app would be determined by the Perceived Utility of the app and that any user apprehension about possible loss of privacy would not be a significant handicap. When having to choose between health and privacy, users choose health. CONCLUSIONS: This study shows that the extended TAM model which was used has a high explanatory power. Users believe that the APP is useful (especially users who studied in higher education), that it is easy to use, and that it is not a cause of concern for privacy. The highest acceptance of the app is found in over 35 years old’s, which is the group that is most aware of the possibility of being affected by COVID-19. The information is unbelievably valuable for developers and governments as users would be willing to use the APP. |
format | Online Article Text |
id | pubmed-7924669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79246692021-04-02 Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model Velicia-Martin, Felix Cabrera-Sanchez, Juan-Pedro Gil-Cordero, Eloy Palos-Sanchez, Pedro R. PeerJ Comput Sci Mobile and Ubiquitous Computing BACKGROUND: The expansion of the coronavirus pandemic and the extraordinary confinement measures imposed by governments have caused an unprecedented intense and rapid contraction of the global economy. In order to revive the economy, people must be able to move safely, which means that governments must be able to quickly detect positive cases and track their potential contacts. Different alternatives have been suggested for carrying out this tracking process, one of which uses a mobile APP which has already been shown to be an effective method in some countries. OBJECTIVE: Use an extended Technology Acceptance Model (TAM) model to investigate whether citizens would be willing to accept and adopt a mobile application that indicates if they have been in contact with people infected with COVID-19. Research Methodology: A survey method was used and the information from 482 of these questionnaires was analyzed using Partial Least Squares-Structural Equation Modelling. RESULTS: The results show that the Intention to Use this app would be determined by the Perceived Utility of the app and that any user apprehension about possible loss of privacy would not be a significant handicap. When having to choose between health and privacy, users choose health. CONCLUSIONS: This study shows that the extended TAM model which was used has a high explanatory power. Users believe that the APP is useful (especially users who studied in higher education), that it is easy to use, and that it is not a cause of concern for privacy. The highest acceptance of the app is found in over 35 years old’s, which is the group that is most aware of the possibility of being affected by COVID-19. The information is unbelievably valuable for developers and governments as users would be willing to use the APP. PeerJ Inc. 2021-01-04 /pmc/articles/PMC7924669/ /pubmed/33816983 http://dx.doi.org/10.7717/peerj-cs.316 Text en © 2021 Velicia-Martin et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Mobile and Ubiquitous Computing Velicia-Martin, Felix Cabrera-Sanchez, Juan-Pedro Gil-Cordero, Eloy Palos-Sanchez, Pedro R. Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model |
title | Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model |
title_full | Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model |
title_fullStr | Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model |
title_full_unstemmed | Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model |
title_short | Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model |
title_sort | researching covid-19 tracing app acceptance: incorporating theory from the technological acceptance model |
topic | Mobile and Ubiquitous Computing |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924669/ https://www.ncbi.nlm.nih.gov/pubmed/33816983 http://dx.doi.org/10.7717/peerj-cs.316 |
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