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Explaining citizens’ resistance to use digital contact tracing apps: A mixed-methods study

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  • Prakash, Ashish Viswanath
  • Das, Saini

Abstract

Governments worldwide are using digital contact tracing (DCT) apps as a critical element in their COVID-19 pandemic lockdown exit strategy. Despite substantial investment in research and development, the public’s acceptance of DCT apps has been phenomenally low, signaling resistance among potential users. Little is known about why people would resist using the DCT app, a useful innovation that can potentially save millions of human lives. This study explores the determinants and consequences of citizens' resistance to use DCT apps using a sequential two-stage mixed-methods approach. The preliminary qualitative study analyzed interviews of 24 Indian smartphone users who chose not to use or discontinued the DCT app after an initial trial. In the quantitative stage, an integrated model based on innovation resistance theory and distrust theory was tested using the survey data collected from 194 non-adopters of the DCT app from India. The findings revealed that the factors, distrust, value barrier, information privacy concerns, and usage barrier predicted the resistance to the DCT app, and resistance, in turn, predicted intention to use. Additionally, distrust was found to be a key mediator between innovation barriers and resistance. The insights from this study could help the developers and policymakers formulate strategies for implementing DCT interventions during future disease outbreaks.

Suggested Citation

  • Prakash, Ashish Viswanath & Das, Saini, 2022. "Explaining citizens’ resistance to use digital contact tracing apps: A mixed-methods study," International Journal of Information Management, Elsevier, vol. 63(C).
    • Handle: RePEc:eee:ininma:v:63:y:2022:i:c:s0268401221001614
    DOI: 10.1016/j.ijinfomgt.2021.102468
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    Cited by:

    1. Fu, Shihui & Xue, Kunkun & Yang, Mengya & Wang, Xiaona, 2023. "An exploratory study on users' resistance to mobile app updates: Using netnography and fsQCA," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    2. Chakraborty, Debarun & Singu, Hari Babu & Patre, Smruti, 2022. "Fitness Apps's purchase behaviour: Amalgamation of Stimulus-Organism-Behaviour-Consequence framework (S–O–B–C) and the innovation resistance theory (IRT)," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).

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