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Digital Tracing during the COVID-19 Pandemic: User Appraisal, Emotion, and Continuance Intention

Author

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  • Ayoung Suh

    (Business School, Sungkyunkwan University, Seoul 03063, Korea)

  • Mengjun Li

    (School of Creative Media, City University of Hong Kong, Hong Kong, China)

Abstract

This study explores how people appraise the use of contact tracing apps during the novel coronavirus (COVID-19) pandemic in South Korea. Despite increasing attention paid to digital tracing for health disasters, few studies have empirically examined user appraisal, emotion, and their continuance intention to use contact tracing apps for disaster management during an infectious disease outbreak. A mixed-method approach combining qualitative and quantitative inquiries was employed. In the qualitative study, by conducting interviews with 25 people who have used mobile apps for contact tracing, the way users appraise contact tracing apps for COVID-19 was explored. In the quantitative study, using data collected from 506 users of the apps, the interplay among cognitive appraisal (threats and opportunities) and its association with user emotion, and continuance intention was examined. The findings indicate that once users experience loss emotions, such as anger, frustration, and disgust, they are not willing to continue using the apps. App designers should consider providing technological affordances that enable users to have a sense of control over the technology so that they do not experience loss emotions. Public policymakers should also consider developing measures that can balance public health and personal privacy.

Suggested Citation

  • Ayoung Suh & Mengjun Li, 2021. "Digital Tracing during the COVID-19 Pandemic: User Appraisal, Emotion, and Continuance Intention," Sustainability, MDPI, vol. 13(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:608-:d:477955
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    References listed on IDEAS

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    Cited by:

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    2. Hassan Adamu & Syaheerah Lebai Lutfi & Nurul Hashimah Ahamed Hassain Malim & Rohail Hassan & Assunta Di Vaio & Ahmad Sufril Azlan Mohamed, 2021. "Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    3. Anderson Ngelambong & Siti Norsyafiqah Salim & Saiful Bahri Mohd Kamal & Mauren Gita Miranti, 2024. "When Tech Goes Sour: A Directed Qualitative Content Analysis of Negative User Experiences in Food and Beverage Mobile Apps," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(6), pages 1620-1635, June.
    4. Hoffmann, Stefan & Lasarov, Wassili & Reimers, Hanna, 2022. "Carbon footprint tracking apps. What drives consumers' adoption intention?," Technology in Society, Elsevier, vol. 69(C).
    5. Saheb, Tahereh & Sabour, Elham & Qanbary, Fatimah & Saheb, Tayebeh, 2022. "Delineating privacy aspects of COVID tracing applications embedded with proximity measurement technologies & digital technologies," Technology in Society, Elsevier, vol. 69(C).

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