Coping Information Affects Older and Young Adults’ Security Update Decisions

Abstract

Software updating is a critical cybersecurity measure that often depends on decision making by end users on whether to apply, delay, or ignore updates when available. Little is known about how aging may interact with cognitive factors and cybersecurity attitudes to affect software update decisions in older versus younger adults. The present study used an online experiment (N = 120) to compare older and younger adults’ responses to hypothetical software update scenarios that varied in cognitive load, threat information, and coping information, along with measurements of cybersecurity-related attitudes. Across both age groups, coping-related information was more effective than threat information at yielding adaptive responses to software update decisions. Additionally, among older adults, the OSBBQ was associated with more adaptive responses, suggesting that it has practical utility in assessing older adults’ cybersecurity behaviors. The results have implications for future research and promotion of safe cybersecurity practices among younger and older adults.

Keywords

cybersecurity software updates cognitive aging protection motivation theory

Get full access to this article

View all access options for this article.

References

Aharonov‐Majar

Rajivan

Gonzalez

Erev

(2020). The impact of variability and prechoice experience on taking safety measures: The case of security updates. Journal of Behavioral Decision Making, 33(1), 3–14. https://doi.org/10.1002/bdm.2131

Anwar

Ash

Yuan

(2017). Gender difference and employees’ cybersecurity behaviors. Computers in Human Behavior, 69, 437–443. https://doi.org/10.1016/j.chb.2016.12.040

Beaudoin

Desrichard

(2011). Are memory self-efficacy and memory performance related? A meta-analysis. Psychological Bulletin, 137(2), 211–241. https://doi.org/10.1037/a0022106

Branley-Bell

Coventry

Dixon

Joinson

Briggs

(2022). Exploring age and gender differences in ICT cybersecurity behaviour. Human Behavior and Emerging Technologies, 2022 , Article 2693080. https://doi.org/10.1155/2022/2693080

Brod

(1984). Technostress: The human cost of the computer revolution. Addison Wesley.

Chen

Gully

S. M.

Eden

(2001). Validation of a new general self-efficacy scale. Organizational Research Methods, 4(1), 62–83. https://doi.org/10.1177/109442810141004

Czaja

S. J.

Sharit

(1993). Age differences in the performance of computer-based work. Psychology and Aging, 8(1), 59–67. https://doi.org/10.1037/0882-7974.8.1.59

Debb

S. M.

Schaffer

D. R.

Colson

D. G.

(2020). A reverse digital divide: Comparing information security behaviors of generation Y and generation Z adults. International Journal of Cybersecurity Intelligence and Cybercrime, 3(1), 42–55. https://doi.org/10.52306/03010420GXUV5876

Fagan

Khan

M. M. H.

Buck

(2015). A study of users’ experiences and beliefs about software update messages. Computers in Human Behavior, 51, 504–519. https://doi.org/10.1016/j.chb.2015.04.075

10.

Floyd

D. L.

Prentice‐Dunn

Rogers

R. W.

(2000). A meta‐analysis of research on protection motivation theory. Journal of Applied Social Psychology, 30(2), 407–429. https://doi.org/10.1111/j.1559-1816.2000.tb02323.x

11.

Ganye

Smith

(2022). Examining cognitive load and information systems security compliance. ICIS 2022 TREOs, 10. https://aisel.aisnet.org/treos_icis2022/10

12.

Gillam

A. R.

Foster

W. T.

(2020). Factors affecting risky cybersecurity behaviors by US workers: An exploratory study. Computers in Human Behavior, 108, Article 106319. https://doi.org/10.1016/j.chb.2020.106319

13.

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

14.

Hastings

E. C.

West

R. L.

(2009). The relative success of a self-help and a group-based memory training program for older adults. Psychology and Aging, 24(3), 586–594. https://doi.org/10.1037/a0016951

15.

Jiang

Tsai

H. S.

Cotton

S. R.

Rifon

N. J.

LaRose

Alhabash

(2016). Generational differences in online safety perceptions, knowledge, and practices. Educational Gerontology, 42(9), 621–634. https://doi.org/10.1080/03601277.2016.1205408

16.

Lachman

M. E.

Andreoletti

Pearman

(2006). Memory control beliefs: How are they related to age, strategy use and memory improvement? Social Cognition, 24(3), 359–385. https://doi.org/10.1521/soco.2006.24.3.359

17.

Ash

Anwar

Yuan

(2019). Investigating the impact of cybersecurity policy awareness on employees’ cybersecurity behavior. International Journal of Information Management, 45, 13–24. https://doi.org/10.1016/j.ijinfomgt.2018.10.017

18.

Liang

Xue

(2009). Avoidance of information technology threats: A theoretical perspective. MIS Quarterly, 33(1), 71–90. https://doi.org/10.2307/20650279

19.

Lighthall

N. R.

(2020). Neural mechanisms of decision‐making in aging. WIREs Cognitive Science, 11(1), Article e1519. https://doi.org/10.1002/wcs.1519

20.

Mayer

Kunz

Volkamer

(2017). Reliable behavioural factors in the information security context. In: Proceedings of the 12th International Conference on Availability, Reliability and Security, article 9. (pp. 1–10). 29 August 2017, Reggio Calabria, Italy. https://doi.org/10.1145/3098954.3098986

21.

Morrison

Coventry

Briggs

(2021). How do older adults feel about engaging with cyber-security? Human Behavior and Emerging Technologies, 3(5), 1033–1049. https://doi.org/10.1002/hbe2.291

22.

Morrison

B. A.

Coventry

Briggs

(2020). Technological change in the retirement transition and the implications for cybersecurity vulnerability in older adults. Frontiers in Psychology, 11, 623. https://doi.org/10.3389/fpsyg.2020.00623

23.

Nimrod

(2018). Technostress: Measuring a new threat to well-being in later life. Aging & Mental Health, 22(8), 1086–1093. https://doi.org/10.1080/13607863.2017.1334037

24.

Rajivan

Aharonov-Majar

Gonzalez

(2020). Update now or later? Effects of experience, cost, and risk preference on update decisions. Journal of Cybersecurity, 6(1), tyaa002. https://doi.org/10.1093/cybsec/tyaa002

25.

Rogers

R. W.

(1975). A protection motivation theory of fear appeals and attitude change. Journal of Psychology, 91(1), 93–114. https://doi.org/10.1080/00223980.1975.9915803

26.

Samanez-Larkin

G. R.

Knutson

(2015). Decision making in the ageing brain: Changes in affective and motivational circuits. Nature Reviews Neuroscience, 16(5), 278–289. https://doi.org/10.1038/nrn3917

27.

Schaffer

D. R.

Debb

S. M.

(2019). Validation of the online security behaviors and beliefs questionnaire with college students in the United States. Cyberpsychology, Behavior, and Social Networking, 22(12), 766–770. https://doi.org/10.1089/cyber.2019.0248

28.

Schuetz

S. W.

Lowry

P. B.

Pienta

D. A.

Thatcher

J. B.

(2020). The effectiveness of abstract versus concrete fear appeals in information security. Journal of Management Information Systems, 37(3), 723–757. https://doi.org/10.1080/07421222.2020.1790187

29.

Sweller

(2010). Cognitive load theory: Recent theoretical advances. In Plass

J. L.

Moreno

Brünken

(Eds.), Cognitive load theory (pp. 29–47). Cambridge University Press. https://doi.org/10.1017/CBO9780511844744.004

30.

Van Bavel

Rodríguez-Priego

Vila

Briggs

(2019). Using protection motivation theory in the design of nudges to improve online security behavior. International Journal of Human-Computer Studies, 123, 29–39. https://doi.org/10.1016/j.ijhcs.2018.11.003

31.

West

R. L.

Bagwell

D. K.

Dark-Freudeman

(2005). Memory and goal setting: The response of older and younger adults to positive and objective feedback. Psychology and Aging, 20(2), 195–201. https://doi.org/10.1037/0882-7974.20.2.195

32.

Witte

(1992). Putting the fear back into fear appeals: The extended parallel process model. Communication Monographs, 59(4), 329–349. https://doi.org/10.1080/03637759209376276