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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
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    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).
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    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.

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