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Understanding the gap between perceived threats to and preparedness for cybersecurity

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  • Nam, Taewoo

Abstract

This study aims to investigate the influences on perceived threat to and preparedness for cybersecurity, as well as what causes the gap between these two perceptions regarding public infrastructure and business systems in the United States. To that end, the study considers Cybersecurity Survey data from the Pew Research Center. Using ordered logistic regression analysis, the significant determinants of perceived threat and preparedness are determined. Personal experience in and awareness of cybersecurity breaches increased the level of perceived cyber threats, but reduced that of perceived preparedness. In contrast, confidence in organizational cybersecurity capacity, social trust, and liberalism exhibited opposite trends regarding these two outcomes. By employing multinomial logistic regression analysis, the study investigates the unexplored relationships of various theoretical determinants with the gap between perceived threat and preparedness. The analysis results show that the effects of these determinants differ with the type of gap. The determinants for perceived overperformance (good preparedness relative to low threat) and perceived underperformance (poor preparedness relative to high threat) are significantly different from those for perceived fair performance (matching levels of threat and preparedness).

Suggested Citation

  • Nam, Taewoo, 2019. "Understanding the gap between perceived threats to and preparedness for cybersecurity," Technology in Society, Elsevier, vol. 58(C).
    • Handle: RePEc:eee:teinso:v:58:y:2019:i:c:s0160791x18301179
    DOI: 10.1016/j.techsoc.2019.03.005
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    Cited by:

    1. Straub, Jeremy, 2021. "Defining, evaluating, preparing for and responding to a cyber Pearl Harbor," Technology in Society, Elsevier, vol. 65(C).
    2. Meghisan-Toma Georgeta-Madalina & Nicula Vasile Cosmin, 2020. "ICT Security Measures for the Companies within European Union Member States – Perspectives in COVID-19 Context," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 14(1), pages 362-370, July.
    3. Hooks, D. & Davis, Z. & Agrawal, V. & Li, Z., 2022. "Exploring factors influencing technology adoption rate at the macro level: A predictive model," Technology in Society, Elsevier, vol. 68(C).

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