Cited by 75Information and Cyber SecurityUser Authentication and Security SystemsPrivacy, Security, and Data Protection

Holistic cyber hygiene education: Accounting for the human factors

Open access
Neigel, Alexis R., Claypoole, Victoria L., Waldfogle, Grace E., Acharya, Subrata, Hancock, Gabriella M.·2020·Computers & Security

Integrity signals

Registry

Crossref · 10.1016/j.cose.2020.101731

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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Authors
Neigel, A. R., Claypoole, V. L., Waldfogle, G. E., Acharya, S., & Hancock, G. M.
Affiliations
  • University of Central Florida (US)
  • Towson University (US)
  • California State University, Long Beach (US)
Corresponding author
Neigel, Alexis R.
Venue
Computers & Security
Publisher
Elsevier BV
Published
2020-05
Volume
92
Pages
101731
ISSN
0167-4048 (ISSN-L)
OpenAlex
W3001071999
Cited by
75
References
2
Citations by year
2026: 6 · 2025: 13 · 2024: 18 · 2023: 15 · 2022: 9 · 2021: 13 · 2020: 1
FWCI
8.94
Type
journal article
Language
en
Open access
Locations
Computers & Security (acceptedVersion)
Funders
Army Research Laboratory (W911NF-17–2–0088)
Topics
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
  • Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
Keywords
cyber hygiene, cybersecurity education, human factors in cybersecurity, individual differences, trust in technology, intrinsic motivation, password management, incident reporting, information handling, sex differences, academic major differences, cybersecurity breaches, computer science curriculum

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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