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Americans’ perceptions of privacy and surveillance in the COVID-19 pandemic

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  • Zhang, Baobao
  • Kreps, Sarah
  • McMurry, Nina
  • McCain, R. Miles

Abstract

Objective To study the U.S. public’s attitudes toward surveillance measures aimed at curbing the spread of COVID-19, particularly smartphone applications (apps) that supplement traditional contact tracing. Method We deployed a survey of approximately 2,000 American adults to measure support for nine COVID-19 surveillance measures. We assessed attitudes toward contact tracing apps by manipulating six different attributes of a hypothetical app through a conjoint analysis experiment. Results A smaller percentage of respondents support the government encouraging everyone to download and use contact tracing apps (42%) compared with other surveillance measures such as enforcing temperature checks (62%), expanding traditional contact tracing (57%), carrying out centralized quarantine (49%), deploying electronic device monitoring (44%), or implementing immunity passes (44%). Despite partisan differences on a range of surveillance measures, support for the government encouraging digital contact tracing is indistinguishable between Democrats (47%) and Republicans (46%), although more Republicans oppose the policy (39%) compared to Democrats (27%). Of the app features we tested in our conjoint analysis experiment, only one had statistically significant effects on the self-reported likelihood of downloading the app: decentralized data architecture increased the likelihood by 5.4 percentage points. Conclusion Support for public health surveillance policies to curb the spread of COVID-19 is relatively low in the U.S. Contact tracing apps that use decentralized data storage, compared with those that use centralized data storage, are more accepted by the public. While respondents’ support for expanding traditional contact tracing is greater than their support for the government encouraging the public to download and use contact tracing apps, there are smaller partisan differences in support for the latter policy.

Suggested Citation

  • Zhang, Baobao & Kreps, Sarah & McMurry, Nina & McCain, R. Miles, 2020. "Americans’ perceptions of privacy and surveillance in the COVID-19 pandemic," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(12), pages 1-1.
    • Handle: RePEc:zbw:espost:228526
    DOI: 10.1371/journal.pone.0242652
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    References listed on IDEAS

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    1. Fischbacher, Urs & Gachter, Simon & Fehr, Ernst, 2001. "Are people conditionally cooperative? Evidence from a public goods experiment," Economics Letters, Elsevier, vol. 71(3), pages 397-404, June.
    2. Hainmueller, Jens & Hopkins, Daniel J. & Yamamoto, Teppei, 2014. "Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments," Political Analysis, Cambridge University Press, vol. 22(1), pages 1-30, January.
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    1. Bamel, Umesh & Talwar, Shalini & Pereira, Vijay & Corazza, Laura & Dhir, Amandeep, 2023. "Disruptive digital innovations in healthcare: Knowing the past and anticipating the future," Technovation, Elsevier, vol. 125(C).
    2. Ioannou, Athina & Tussyadiah, Iis, 2021. "Privacy and surveillance attitudes during health crises: Acceptance of surveillance and privacy protection behaviours," Technology in Society, Elsevier, vol. 67(C).
    3. van der Waal, Nadine Elisa & de Wit, Jan & Bol, Nadine & Ebbers, Wolfgang & Hooft, Lotty & Metting, Esther & van der Laan, Laura Nynke, 2022. "Predictors of contact tracing app adoption: Integrating the UTAUT, HBM and contextual factors," Technology in Society, Elsevier, vol. 71(C).
    4. Leah Ruppanner & Xiao Tan & Andrea Carson & Shaun Ratcliff, 2021. "Emotional and financial health during COVID‐19: The role of housework, employment and childcare in Australia and the United States," Gender, Work and Organization, Wiley Blackwell, vol. 28(5), pages 1937-1955, September.
    5. Dana Naous & Manus Bonner & Mathias Humbert & Christine Legner, 2022. "Learning From the Past to Improve the Future," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(5), pages 597-614, October.
    6. Chen, Tiantian & Fu, Xiaowen & Hensher, David A. & Li, Zhi-Chun & Sze, N.N., 2024. "Effects of proactive and reactive health control measures on public transport preferences of passengers – A stated preference study during the COVID-19 pandemic," Transport Policy, Elsevier, vol. 146(C), pages 175-192.

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