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Comparing public support for digital surveillance policies in 50 countries.

  • Published In: Social Science Quarterly (Wiley-Blackwell), 2024, v. 105, n. 5. P. 1600 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Jin, Jing; Guo, Yufan; Lu, Jia 3 of 3

Abstract

Objective: This article employs three theoretical approaches (cultural, institutional, and informational) to explain public willingness to support two major forms of digital surveillance policies—video surveillance and internet surveillance—in the countries with varying levels of political trust. Methods: Utilizing the data from World Values Survey (WVS) and the other sources, this study conducts a multi‐level analysis involving 75,721 respondents from 50 countries or regions. Results: The results show that public support for digital surveillance policies varies across digital surveillance types and political trust contexts. The cultural approach demonstrates consistent effects that remain robust irrespective of the type of digital surveillance or political trust. The institutional approach varies by the level of political trust, and the informational approach differs between video and internet surveillance. Conclusion: The effects of three theoretical approaches are compared across digital surveillance types and across political trust contexts. The comparison examines the privacy calculus theory in different scenarios and reveals the intricate trade‐off mechanism inherent in public support for digital surveillance policy. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Social Science Quarterly (Wiley-Blackwell). 2024/09, Vol. 105, Issue 5, p1600
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2024
  • ISSN:0038-4941
  • DOI:10.1111/ssqu.13423
  • Accession Number:180088550
  • Copyright Statement:Copyright of Social Science Quarterly (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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