dc.creator | Velicia Martín, Félix Antonio | es |
dc.creator | Cabrera-Sánchez, Juan-Pedro | es |
dc.creator | Gil Cordero, Eloy | es |
dc.creator | Palos Sánchez, Pedro Ramiro | es |
dc.date.accessioned | 2021-05-24T11:10:00Z | |
dc.date.available | 2021-05-24T11:10:00Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Velicia Martín, F.A., Cabrera-Sánchez, J., Gil Cordero, E. y Palos Sánchez, P.R. (2021). Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model. PeerJ Computer Science, 7, e316. | |
dc.identifier.issn | 2376-5992 | es |
dc.identifier.uri | https://hdl.handle.net/11441/109256 | |
dc.description.abstract | 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. | es |
dc.format | application/pdf | es |
dc.format.extent | 20 p. | es |
dc.language.iso | eng | es |
dc.relation.ispartof | PeerJ Computer Science, 7, e316. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Covid-19 | es |
dc.subject | TAM | es |
dc.subject | Privacy | es |
dc.subject | APP | es |
dc.subject | Technology adoption | es |
dc.title | Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Administración de Empresas y Comercialización e Investigación de Mercados (Marketing) | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Economía Financiera y Dirección de Operaciones | es |
dc.relation.publisherversion | http://dx.doi.org/10.7717/peerj-cs.316 | es |
dc.identifier.doi | 10.7717/peerj-cs.316 | es |
dc.journaltitle | PeerJ Computer Science | es |
dc.publication.volumen | 7 | es |
dc.publication.initialPage | e316 | es |
dc.description.awardwinning | Premio Trimestral Publicación Científica Destacada de la US. Facultad de Ciencias Económicas y Empresariales | |