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A Model to Predict Users’ Intentions to Adopt Contact-Tracing Apps for Prevention from COVID-19
Technological advances are increasingly progressing and have brought unprecedented solutions for real-world problems for various domains, particularly, when it comes to a health-related domain. This study aims to examine the predictors of users’ intentions to adopt contact-tracing apps for preventio...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7798071/ http://dx.doi.org/10.1007/978-3-030-65785-7_51 |
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author | Ezzaouia, Imane Bulchand-Gidumal, Jacques |
author_facet | Ezzaouia, Imane Bulchand-Gidumal, Jacques |
author_sort | Ezzaouia, Imane |
collection | PubMed |
description | Technological advances are increasingly progressing and have brought unprecedented solutions for real-world problems for various domains, particularly, when it comes to a health-related domain. This study aims to examine the predictors of users’ intentions to adopt contact-tracing apps for prevention from COVID-19. Based on the extended unified theory of acceptance and use of technology (UTAUT2), our research model incorporates the following eight constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, perceived privacy, perceived value, safety and accuracy. The empirical results were obtained from a sample of 93 questionnaires (currently still in course). We used the partial least squares approach to test our hypotheses. The results reveal that performance expectancy has the strongest impact on the intentions to use contact-tracing apps. The accuracy, effort expectancy and social influence are also important, followed by perceived value, safety and perceived privacy. Facilitating condition is listed as much less important. The theoretical and managerial implications of these results are discussed. |
format | Online Article Text |
id | pubmed-7798071 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-77980712021-01-11 A Model to Predict Users’ Intentions to Adopt Contact-Tracing Apps for Prevention from COVID-19 Ezzaouia, Imane Bulchand-Gidumal, Jacques Information and Communication Technologies in Tourism 2021 Article Technological advances are increasingly progressing and have brought unprecedented solutions for real-world problems for various domains, particularly, when it comes to a health-related domain. This study aims to examine the predictors of users’ intentions to adopt contact-tracing apps for prevention from COVID-19. Based on the extended unified theory of acceptance and use of technology (UTAUT2), our research model incorporates the following eight constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, perceived privacy, perceived value, safety and accuracy. The empirical results were obtained from a sample of 93 questionnaires (currently still in course). We used the partial least squares approach to test our hypotheses. The results reveal that performance expectancy has the strongest impact on the intentions to use contact-tracing apps. The accuracy, effort expectancy and social influence are also important, followed by perceived value, safety and perceived privacy. Facilitating condition is listed as much less important. The theoretical and managerial implications of these results are discussed. 2020-11-28 /pmc/articles/PMC7798071/ http://dx.doi.org/10.1007/978-3-030-65785-7_51 Text en © The Author(s) 2021 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
spellingShingle | Article Ezzaouia, Imane Bulchand-Gidumal, Jacques A Model to Predict Users’ Intentions to Adopt Contact-Tracing Apps for Prevention from COVID-19 |
title | A Model to Predict Users’ Intentions to Adopt Contact-Tracing Apps for Prevention from COVID-19 |
title_full | A Model to Predict Users’ Intentions to Adopt Contact-Tracing Apps for Prevention from COVID-19 |
title_fullStr | A Model to Predict Users’ Intentions to Adopt Contact-Tracing Apps for Prevention from COVID-19 |
title_full_unstemmed | A Model to Predict Users’ Intentions to Adopt Contact-Tracing Apps for Prevention from COVID-19 |
title_short | A Model to Predict Users’ Intentions to Adopt Contact-Tracing Apps for Prevention from COVID-19 |
title_sort | model to predict users’ intentions to adopt contact-tracing apps for prevention from covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7798071/ http://dx.doi.org/10.1007/978-3-030-65785-7_51 |
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