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Abstract

Objective

To examine how domain-switching and user characteristics may predict broad susceptibility to digital deception.

Background

Despite successful automated filtering techniques, humans remain vulnerable to fraud, losing billions of dollars annually. Many scams are delivered by digitally mediated methods, such as phishing emails or fake social media accounts. However, research typically explores susceptibility to these deceptions independently, making it difficult to draw broad conclusions regarding susceptibility to digital deception.

Method

We recruited a representative sample to investigate how susceptibility to deception may vary across digital domains, particularly when switching between domains (i.e., domain-switching). Participants classified stimuli from five different digital domains (i.e., emails, text messages, news headlines, social media accounts, and voicemails), either randomly (i.e., domain-switching) or in separate blocks, and completed measures of cognitive reflection and digital literacy.

Results

The results suggest that when users struggle to discriminate between deceptive and legitimate stimuli in one digital deception domain, they are likely to struggle in others. Additionally, the results suggest that while cognitive reflection and digital literacy may help insulate users from deception, domain-switching may generally hinder user performance (i.e., slower responses).

Conclusion

Overall, individuals appear to be consistently vulnerable to deception across digital domains and this vulnerability can be exacerbated by certain task factors (e.g., domain-switching) and user characteristics (e.g., cognitive reflection and digital literacy).

Application

To develop more efficacious interventions that enhance user resiliency, research should consider broad training that incorporates correlates of susceptibility (e.g., cognitive reflection and digital literacy), and more realistic task settings (e.g., domain-switching).

Keywords

cybersecurity decision making signal detection theory task switching experience

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References

Allcott

Gentzkow

(2017). Social media and fake news in the 2016 election. The Journal of Economic Perspectives, 31(2), 211–236. https://doi.org/10.1257/jep.31.2.211

Allen

Howland

Mobius

Rothschild

Watts

D. J.

(2020). Evaluating the fake news problem at the scale of the information ecosystem. Science Advances, 6(14), eaay3539. https://doi.org/10.1126/sciadv.aay3539

Allport

D. A.

Styles

E. A.

Hsieh

(1994). Shifting intentional set: Exploring the dynamic control of tasks. In Umilta`

Moscovitch

(Eds.), Attention and Performance XV: Conscious and nonconscious information processing (pp. 421–452). MIT Press.

Amazeen

M. A.

Bucy

E. P.

(2019). Conferring resistance to digital disinformation: The inoculating influence of procedural news knowledge. Journal of Broadcasting & Electronic Media, 63(3), 415–432. https://doi.org/10.1080/08838151.2019.1653101

Armstrong

M. E.

Jones

K. S.

Namin

A. S.

(2023). How perceptions of caller honesty vary during vishing attacks that include highly sensitive or seemingly innocuous requests. Human Factors, 65(2), 275–287. https://doi.org/10.1177/00187208211012818

Badman

(2023). Spear phishing vs. phishing: what’s the difference? IBM Blog. https://admin01.prod.blogs.cis.ibm.net/blog/spear-phishing-vs-standard-phishing-attacks/

Batailler

Brannon

S. M.

Teas

P. E.

Gawronski

(2022). A signal detection approach to understanding the identification of fake news. Perspectives on Psychological Science, 17(1), 78–98. https://doi.org/10.1177/1745691620986135

Brashier

N. M.

Schacter

D. L.

(2020). Aging in an Era of Fake News. Current Directions in Psychological Science, 29(3), 316–323. https://doi.org/10.1177/0963721420915872

Broadbent

D. E.

(1958). Perception and communication. Pergamon Press. https://doi.org/10.1037/10037-000

10.

Bryanov

Vziatysheva

(2021). Determinants of individuals’ belief in fake news: A scoping review determinants of belief in fake news. PLoS One, 16(6), Article e0253717. https://doi.org/10.1371/journal.pone.0253717

11.

Buchanan

(2020). Why do people spread false information online? The effects of message and viewer characteristics on self-reported likelihood of sharing social media disinformation. PLoS One, 15(10), Article e0239666. https://doi.org/10.1371/journal.pone.0239666

12.

Butavicius

Parsons

Pattinson

McCormac

(2016). Breaching the Human Firewall: Social engineering in Phishing and Spear-Phishing Emails (No. arXiv:1606.00887). arXiv. https://doi.org/10.48550/arXiv.1606.00887

13.

Cain

A. A.

Edwards

M. E.

Still

J. D.

(2018). An exploratory study of cyber hygiene behaviors and knowledge. Journal of Information Security and Applications, 42(1), 36–45. https://doi.org/10.1016/j.jisa.2018.08.002

14.

Canfield

C. I.

Fischhoff

Davis

(2016). Quantifying phishing susceptibility for detection and behavior decisions. Human Factors, 58(8), 1158–1172. https://doi.org/10.1177/0018720816665025

15.

Clasen

Williams

(2021). Friend or foe: An investigation into recipient identification of SMS-based phishing. In Furnell

Clarke

(Eds.), Human aspects of information security and assurance (pp. 148–163). Springer International Publishing. https://doi.org/10.1007/978-3-030-81111-2_13

16.

DeNicola

(2023). The latest scams you need to be aware of in 2024. Experian. https://www.experian.com/blogs/ask-experian/the-latest-scams-you-need-to-aware-of/

17.

Downs

J. S.

Holbrook

M. B.

Cranor

L. F.

(2006). Decision strategies and susceptibility to phishing. In Proceedings of the second symposium on usable privacy and security (pp. 79–90). ACM. https://doi.org/10.1145/1143120.1143131

18.

Embretson

S. E.

Reise

S. P.

(2013). Item response theory. Psychology Press.

19.

Faul

Erdfelder

Lang

A.-G.

Buchner

(2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146

20.

Federal Trade Commission . (2025). Consumer sentinel network data book 2024. https://www.ftc.gov/system/files/ftc_gov/pdf/csn-annual-data-book-2024.pdf

21.

Frederick

(2005). Cognitive reflection and decision making. The Journal of Economic Perspectives, 19(4), 25–42. https://doi.org/10.1257/089533005775196732

22.

Cui

Chen

(2021). How personal characteristics impact phishing susceptibility: The mediating role of mail processing. Applied Ergonomics, 97(1), Article 103526. https://doi.org/10.1016/j.apergo.2021.103526. https://www.sciencedirect.com/science/article/pii/S0003687021001733

23.

Green

D. M.

Swets

J. A.

(1966). Signal detection theory and psychophysics (Vol. 1, pp. 1969–2012). Wiley.

24.

Hadlington

(2017). Human factors in cybersecurity; examining the link between Internet addiction, impulsivity, attitudes towards cybersecurity, and risky cybersecurity behaviours. Heliyon, 3(7), Article e00346. https://doi.org/10.1016/j.heliyon.2017.e00346

25.

Jones

H. S.

Towse

J. N.

Race

Harrison

(2019). Email fraud: The search for psychological predictors of susceptibility. PLoS One, 14(1), Article e0209684. https://doi.org/10.1371/journal.pone.0209684

26.

Jones-Jang

S. M.

Mortensen

Liu

(2021). Does media literacy help identification of fake news? Information literacy helps, but other literacies don’t. American Behavioral Scientist, 65(2), 371–388. https://doi.org/10.1177/0002764219869406

27.

June

(2021). Could Gen Z free the world from email? The New York Times. https://www.nytimes.com/2021/07/10/business/gen-z-email.html

28.

Kahneman

(2011). Thinking, fast and slow. Macmillan.

29.

Kiesel

Steinhauser

Wendt

Falkenstein

Jost

Philipp

A. M.

Koch

(2010). Control and interference in task switching—a review. Psychological Bulletin, 136(5), 849–874. https://doi.org/10.1037/a0019842

30.

Kircanski

Notthoff

DeLiema

Samanez-Larkin

G. R.

Shadel

Mottola

Carstensen

L. L.

Gotlib

I. H.

(2018). Emotional arousal may increase susceptibility to fraud in older and younger adults. Psychology and Aging, 33(2), 325–337. https://doi.org/10.1037/pag0000228

31.

Koch

Poljac

Müller

Kiesel

(2018). Cognitive structure, flexibility, and plasticity in human multitasking—an integrative review of dual-task and task-switching research. Psychological Bulletin, 144(6), 557–583. https://doi.org/10.1037/bul0000144

32.

Lawson

Pearson

C. J.

Crowson

Mayhorn

C. B.

(2020). Email phishing and signal detection: How persuasion principles and personality influence response patterns and accuracy. Applied Ergonomics, 86(1), Article 103084. https://doi.org/10.1016/j.apergo.2020.103084

33.

Lees

Banas

J. A.

Linvill

Meirick

P. C.

Warren

(2023). The Spot the Troll Quiz game increases accuracy in discerning between real and inauthentic social media accounts. PNAS nexus, 2(4), Article pgad094. https://doi.org/10.1093/pnasnexus/pgad094

34.

Levine

T. R.

(2014). Truth-default theory (TDT) a theory of human deception and deception detection. Journal of Language and Social Psychology, 33(4), 378–392. https://doi.org/10.1177/0261927X14535916

35.

Lindgren

(2024). FBI elder fraud report highlights frauds and scams targeting older Americans. FBI. Retrieved from. https://www.fbi.gov/contact-us/field-offices/cincinnati/news/fbi-elder-fraud-report-highlights-frauds-and-scams-targeting-older-americans

36.

Martin

Dubé

Coovert

M. D.

(2018). Signal detection theory (SDT) is effective for modeling user behavior toward phishing and spear-phishing attacks. Human Factors, 60(8), 1179–1191. https://doi.org/10.1177/0018720818789818

37.

Masson

M. E. J.

(2011). A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behavior Research Methods, 43(3), 679–690. https://doi.org/10.3758/s13428-010-0049-5

38.

Meiran

(1996). Reconfiguration of processing mode prior to task performance. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(6), 1423–1442. https://doi.org/10.1037/0278-7393.22.6.1423

39.

Meta . (2025). Community standards enforcement report: Fake accounts on Facebook. Meta Transparency Center. https://transparency.meta.com/reports/community-standards-enforcement/fake-accounts/facebook/

40.

Musuva

P. M. W.

Getao

K. W.

Chepken

C. K.

(2019). A new approach to modelling the effects of cognitive processing and threat detection on phishing susceptibility. Computers in Human Behavior, 94(1), 154–175. https://doi.org/10.1016/j.chb.2018.12.036

41.

Nathanson

Yamunan

(2025). How to defend against malware and phishing attacks. Google Workspace Blog. https://workspace.google.com/blog/identity-and-security/how-guide-defending-against-malware-and-phishing-attacks

42.

Nicas

(2020). Why can’t the social networks stop fake accounts? The New York Times. https://www.nytimes.com/2020/12/08/technology/why-cant-the-social-networks-stop-fake-accounts.html

43.

Parsons

McCormac

Pattinson

Butavicius

Jerram

(2013). Phishing for the truth: A scenario-based experiment of users’ behavioural response to emails. In Janczewski

L. J.

Wolfe

H. B.

Shenoi

(Eds.), Security and privacy protection in information processing systems (pp. 366–378). Springer. https://doi.org/10.1007/978-3-642-39218-4_27

44.

Pashler

(1994). Dual-task interference in simple tasks: Data and theory. Psychological Bulletin, 116(2), 220–244. https://doi.org/10.1037/0033-2909.116.2.220

45.

Pennycook

Rand

D. G.

(2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188(1), 39–50. https://doi.org/10.1016/j.cognition.2018.06.011

46.

Petty

R. E.

Cacioppo

J. T.

(1986). The elaboration likelihood model of persuasion. In Petty

R. E.

Cacioppo

J. T.

(Eds.), Communication and persuasion: Central and peripheral routes to attitude change (pp. 1–24). Springer. https://doi.org/10.1007/978-1-4612-4964-1_1

47.

Rauwolf

(2022). Interpersonal factors and mental well‐being are associated with accuracy in judging the veracity of political news. Applied Cognitive Psychology, 36(3), 581–601.

48.

Rocha Flores

Holm

Svensson

Ericsson

(2014). Using phishing experiments and scenario-based surveys to understand security behaviours in practice. Information Management & Computer Security, 22(4), 393–406.

49.

Rodríguez-de-Dios

van Oosten

J. M. F.

Igartua

J.-J.

(2018). A study of the relationship between parental mediation and adolescents’ digital skills, online risks and online opportunities. Computers in Human Behavior, 82(1), 186–198. https://doi.org/10.1016/j.chb.2018.01.012

50.

Rogers

R. D.

Monsell

(1995). Costs of a predictable switch between simple cognitive tasks. Journal of Experimental Psychology: General, 124(2), 207–231. https://doi.org/10.1037/0096-3445.124.2.207

51.

Sarno

D. M.

Black

(2024). Who gets caught in the web of lies? Understanding susceptibility to phishing emails, fake news headlines, and scam text messages. Human Factors, 66(6), 1742–1753. https://doi.org/10.1177/00187208231173263

52.

Sarno

D. M.

Lewis

J. E.

Bohil

C. J.

Neider

M. B.

(2020). Which phish is on the hook? Phishing vulnerability for older versus younger adults. Human Factors, 62(5), 704–717. https://doi.org/10.1177/0018720819855570

53.

Sarno

D. M.

McPherson

Neider

M. B.

(2022). Is the key to phishing training persistence? Developing a novel persistent intervention. Journal of Experimental Psychology: Applied, 28(1), 85–99. https://doi.org/10.1037/xap0000410

54.

Sarno

D. M.

Neider

M. B.

(2022). So many phish, so little time: Exploring email task factors and phishing susceptibility. Human Factors, 64(8), 1379–1403. https://doi.org/10.1177/0018720821999174

55.

Sawyer

B. D.

Hancock

P. A.

(2018). Hacking the human: The prevalence paradox in cybersecurity. Human Factors, 60(5), 597–609. https://doi.org/10.1177/0018720818780472

56.

Schaewitz

Kluck

J. P.

Klösters

Krämer

N. C.

(2020). When is disinformation (in)credible? Experimental findings on message characteristics and individual differences. Mass Communication & Society, 23(4), 484–509. https://doi.org/10.1080/15205436.2020.1716983

57.

Sheng

Holbrook

Kumaraguru

Cranor

L. F.

Downs

(2010). Who falls for phish? A demographic analysis of phishing susceptibility and effectiveness of interventions. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 373–382). ACM. https://doi.org/10.1145/1753326.1753383

58.

Slifkin

E. J. D.

Neider

M. B.

(2023). Phishing interrupted: The impact of task interruptions on phishing email classification. International Journal of Human-Computer Studies, 174(1), Article 103017. https://doi.org/10.1016/j.ijhcs.2023.103017

59.

Stanislaw

Todorov

(1999). Calculation of signal detection theory measures. Behavior Research Methods, Instruments, & Computers, 31(1), 137–149. https://doi.org/10.3758/bf03207704

60.

Tabassum

Faklaris

Lipford

H. R.

(2024). What drives {SMiShing} susceptibility? A {US}. Interview study of how and why mobile phone users judge text messages to be real or fake. In Twentieth symposium on usable privacy and security (SOUPS 2024) (pp. 393–411). ACM.

61.

Thomson

K. S.

Oppenheimer

D. M.

(2016). Investigating an alternate form of the cognitive reflection test. Judgment and Decision Making, 11(1), 99–113. https://doi.org/10.1017/S1930297500007622

62.

Vijaykumar

Jin

Rogerson

Sharma

Maughan

Fadel

de Oliveira Costa

M. S.

Pagliari

Morris

(2021). How shades of truth and age affect responses to COVID-19 (Mis)information: Randomized survey experiment among WhatsApp users in UK and Brazil. Humanities and Social Sciences Communications, 8(1), 1–12. https://doi.org/10.1057/s41599-021-00752-7

63.

Vishwanath

Harrison

Y. J.

(2018). Suspicion, cognition, and automaticity model of phishing susceptibility. Communication Research, 45(8), 1146–1166. https://doi.org/10.1177/0093650215627483

64.

Welk

A. K.

Hong

K. W.

Zielinska

O. A.

Tembe

Murphy-Hill

Mayhorn

C. B.

(2015). Will the “phisher-men” reel you in? Assessing individual differences in a phishing detection task. International Journal of Cyber Behavior, Psychology and Learning, 5(4), 1–17. https://doi.org/10.4018/IJCBPL.2015100101

65.

Zhuo

Biddle

Koh

Y. S.

Lottridge

Russello

(2023). SoK: Human-centered phishing susceptibility. ACM Transactions on Privacy and Security, 26(3), 1–24. https://doi.org/10.1145/3575797

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Untangling the Web of Deceit: Examining Shared User Susceptibility Across Five Types of Digital Deceptions

Abstract

Objective

Background

Method

Results

Conclusion

Application

Keywords

Get full access to this article

References

Supplementary Material