IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i6p3323-d769097.html
   My bibliography  Save this article

High Acceptance of COVID-19 Tracing Technologies in Taiwan: A Nationally Representative Survey Analysis

Author

Listed:
  • Paul M. Garrett

    (School of Psychological Sciences, University of Melbourne, Melbourne 3010, Australia)

  • Yu-Wen Wang

    (Department of Psychology, National Cheng Kung University, Tainan 701, Taiwan)

  • Joshua P. White

    (School of Psychological Sciences, University of Melbourne, Melbourne 3010, Australia)

  • Yoshihsa Kashima

    (School of Psychological Sciences, University of Melbourne, Melbourne 3010, Australia)

  • Simon Dennis

    (School of Psychological Sciences, University of Melbourne, Melbourne 3010, Australia
    Unforgettable Research Services, Melbourne 3010, Australia)

  • Cheng-Ta Yang

    (Department of Psychology, National Cheng Kung University, Tainan 701, Taiwan
    Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei 110, Taiwan)

Abstract

Taiwan has been a world leader in controlling the spread of SARS-CoV-2 during the COVID-19 pandemic. Recently, the Taiwan Government launched its COVID-19 tracing app, ‘Taiwan Social Distancing App’; however, the effectiveness of this tracing app depends on its acceptance and uptake among the general population. We measured the acceptance of three hypothetical tracing technologies (telecommunication network tracing, a government app, and the Apple and Google Bluetooth exposure notification system) in four nationally representative Taiwanese samples. Using Bayesian methods, we found a high acceptance of all three tracking technologies, with acceptance increasing with the inclusion of additional privacy measures. Modeling revealed that acceptance increased with the perceived technology benefits, trust in the providers’ intent, data security and privacy measures, the level of ongoing control, and one’s level of education. Acceptance decreased with data sensitivity perceptions and a perceived low policy compliance by others among the general public. We consider the policy implications of these results for Taiwan during the COVID-19 pandemic and in the future.

Suggested Citation

  • Paul M. Garrett & Yu-Wen Wang & Joshua P. White & Yoshihsa Kashima & Simon Dennis & Cheng-Ta Yang, 2022. "High Acceptance of COVID-19 Tracing Technologies in Taiwan: A Nationally Representative Survey Analysis," IJERPH, MDPI, vol. 19(6), pages 1-15, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3323-:d:769097
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/6/3323/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/6/3323/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Paul M. Garrett & YuWen Wang & Joshua P. White & Shulan Hsieh & Carol Strong & Yi-Chan Lee & Stephan Lewandowsky & Simon Dennis & Cheng-Ta Yang, 2021. "Young Adults View Smartphone Tracking Technologies for COVID-19 as Acceptable: The Case of Taiwan," IJERPH, MDPI, vol. 18(3), pages 1-18, February.
    2. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Nattakit Yuduang & Reny Nadlifatin & Satria Fadil Persada & Kirstien Paola E. Robas & Thanatorn Chuenyindee & Thapanat Buaphiban, 2022. "Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand," IJERPH, MDPI, vol. 19(13), pages 1-28, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hengtao Zhang & Guosheng Yin, 2021. "Response‐adaptive rerandomization," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1281-1298, November.
    2. Andrew Gelman & Daniel Lee & Jiqiang Guo, 2015. "Stan," Journal of Educational and Behavioral Statistics, , vol. 40(5), pages 530-543, October.
    3. Zuoxian Gan & Tao Feng & Min Yang, 2018. "Exploring the Effects of Car Ownership and Commuting on Subjective Well-Being: A Nationwide Questionnaire Study," Sustainability, MDPI, vol. 11(1), pages 1-20, December.
    4. Antonio Parisi & B. Liseo, 2018. "Objective Bayesian analysis for the multivariate skew-t model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 277-295, June.
    5. Bakar, Khandoker Shuvo & Sahu, Sujit K., 2015. "spTimer: Spatio-Temporal Bayesian Modeling Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i15).
    6. Baştürk, Nalan & Grassi, Stefano & Hoogerheide, Lennart & Opschoor, Anne & van Dijk, Herman K., 2017. "The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i01).
    7. White, Robin R. & Brady, Michael, 2014. "Can consumers’ willingness to pay incentivize adoption of environmental impact reducing technologies in meat animal production?," Food Policy, Elsevier, vol. 49(P1), pages 41-49.
    8. Xin Fang & Bo Fang & Chunfang Wang & Tian Xia & Matteo Bottai & Fang Fang & Yang Cao, 2019. "Comparison of Frequentist and Bayesian Generalized Additive Models for Assessing the Association between Daily Exposure to Fine Particles and Respiratory Mortality: A Simulation Study," IJERPH, MDPI, vol. 16(5), pages 1-20, March.
    9. Ji, Yonggang & Lin, Nan & Zhang, Baoxue, 2012. "Model selection in binary and tobit quantile regression using the Gibbs sampler," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 827-839.
    10. Schaarschmidt, Frank & Gerhard, Daniel & Vogel, Charlotte, 2017. "Simultaneous confidence intervals for comparisons of several multinomial samples," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 65-76.
    11. Eijffinger, Sylvester & Mahieu, Ronald & Raes, Louis, 2018. "Inferring hawks and doves from voting records," European Journal of Political Economy, Elsevier, vol. 51(C), pages 107-120.
    12. Martin Hernani Merino & Enver Gerald Tarazona Vargas & Antonieta Hamann Pastorino & José Afonso Mazzon, 2014. "Validation of Sustainable Development Practices Scale Using the Bayesian Approach to Item Response Theory," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 26(2), pages 147-162.
    13. repec:jss:jstsof:39:i12 is not listed on IDEAS
    14. Emmanuel Mensaklo & Chukiat Chaiboonsri & Kanchana Chokethaworn & Songsak Sriboonchitta, 2023. "Comparing Classical and Bayesian Panel Kink Regression Frameworks in Estimating the Impact of Economic Freedom on Economic Growth," Economies, MDPI, vol. 11(10), pages 1-24, October.
    15. Daniel W. Hill Jr., 2016. "Avoiding Obligation," Journal of Conflict Resolution, Peace Science Society (International), vol. 60(6), pages 1129-1158, September.
    16. Marisol Valencia Cárdenas & Juan Gabriel Vanegas López & Juan Carlos Correa Morales & Jorge Aníbal Restrepo Morales, 2017. "Comparing forecasts for tourism dynamics in Medellín, Colombia," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 86, pages 199-230, Enero - J.
    17. Mason Dyana P., 2017. "Measuring Latent Constructs in Nonprofit Surveys with Item Response Theory: The Example of Political Ideology," Nonprofit Policy Forum, De Gruyter, vol. 8(1), pages 91-110, January.
    18. Brett V. Benson & Joshua D. Clinton, 2016. "Assessing the Variation of Formal Military Alliances," Journal of Conflict Resolution, Peace Science Society (International), vol. 60(5), pages 866-898, August.
    19. Valencia Cárdenas, Marisol & Vanegas López, Juan Gabriel & Correa Morales, Juan Carlos & Restrepo Morales, Jorge Aníbal, 2016. "Comparación de pronósticos para la dinámica del turismo en Medellín, Colombia," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 86, pages 199-230, December.
    20. Mark David Nieman, 2016. "Moments in time: Temporal patterns in the effect of democracy and trade on conflict," Conflict Management and Peace Science, Peace Science Society (International), vol. 33(3), pages 273-293, July.
    21. José Antonio López-López & Juan Botella & Julio Sánchez-Meca & Fulgencio Marín-Martínez, 2013. "Alternatives for Mixed-Effects Meta-Regression Models in the Reliability Generalization Approach," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 443-469, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3323-:d:769097. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.