Cited by 25Information and Cyber SecuritySpam and Phishing DetectionDecision-Making and Behavioral Economics

Impact of digital nudging on information security behavior: an experimental study on framing and priming in cybersecurity

Open access
Sharma, Kavya, Zhan, Xinhui, Nah, Fiona Fui-Hoon, Siau, Keng, Cheng, Maggie X.·2021·Organizational Cybersecurity Journal: Practice, Process and People

Abstract

Purpose: Phishing attacks are the most common cyber threats targeted at users. Digital nudging in the form of framing and priming may reduce user susceptibility to phishing. This research focuses on two types of digital nudging, framing and priming, and examines the impact of framing and priming on users' behavior (i.e. action) in a cybersecurity setting. It draws on prospect theory, instance-based learning theory and dual-process theory to generate the research hypotheses. Design/methodology/approach: A 3 × 2 experimental study was carried out to test the hypotheses. The experiment consisted of three levels for framing (i.e. no framing, negative framing and positive framing) and two levels for priming (i.e. with and without priming). Findings: The findings suggest that priming users to information security risks reduces their risk-taking behavior, whereas positive and negative framing of information security messages regarding potential consequences of the available choices do not change users' behavior. The results also indicate that risk-averse cybersecurity behavior is associated with greater confidence with the action, greater perceived severity of cybersecurity risks, lower perceived susceptibility to cybersecurity risks resulting from the action and lower trust in the download link. Originality/value: This research shows that digital nudging in the form of priming is an effective way to reduce users' exposure to cybersecurity risks.

Integrity signals

Registry

Crossref · 10.1108/ocj-03-2021-0009

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Sharma, K., Zhan, X., Nah, F. F.-H., Siau, K., & Cheng, M. X.
Affiliations
  • University of Oklahoma (US)
  • City University of Hong Kong (HK)
  • Illinois Institute of Technology (US)
Corresponding author
Nah, Fiona Fui-Hoon
Venue
Organizational Cybersecurity Journal: Practice, Process and People
Publisher
Emerald
Published
2021-09-20
Volume
1
Issue
1
Pages
69-91
ISSN
  • 2635-0270 (ISSN-L)
  • 2635-0289
OpenAlex
W3199391345
Cited by
25
References
1
Citations by year
2026: 4 · 2025: 6 · 2024: 6 · 2023: 4 · 2022: 5
FWCI
3.39
Type
journal article
Language
en
Open access
Locations
  • Organizational Cybersecurity Journal Practice Process and People (publishedVersion, cc-by)
  • CityU Scholars (submittedVersion, cc-by)
  • Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
Keywords
digital nudging, framing effects, priming, cybersecurity behavior, phishing susceptibility, risk-averse behavior, perceived severity, perceived susceptibility, prospect theory, dual-process theory, instance-based learning

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review