Document Type

Article

Publication Date

8-13-2021

Publication Title

Computers in Human Behavior Reports

Volume

4

Publisher Name

Elsevier

Abstract

The use of strong passwords is viewed as a recommended cybersecurity practice, as the hacking of weak passwords led to major cybersecurity breaches. The present research investigated whether nudging with messages based on participants’ self-schemas could lead them to create stronger passwords. We modeled our study on prior health-related research demonstrating positive results using messages based on self-schema categories (i.e., True Colors categories -compassionate, loyal, intellectual, and adventurous). We carried out an online study, one with 256 (185 women, 66 men, 5 other) undergraduates and one with 424 (240 men, 179 women, 5 other) Amazon Mechanical Turk (MTurk) workers, in which we randomly assigned participants to receive messages that matched or mismatched their self-schema. We also investigated whether differences across the Big Five personality traits, secure password knowledge, attitudes and behavior, need for cognition, and general risk-taking predicted the strength of passwords that participants created during the study. Multiple individual difference variables predicted password strength (i.e., conscientiousness, emotional stability, need for cognition, self-reported secure password knowledge, attitude, and behavior, and general risk-taking). MTurk workers had higher levels of cybersecurity knowledge and created stronger passwords than college students. The nudging messages did not lead to stronger passwords. Implications for strategies to increase the use of secure passwords are discussed.

Identifier

2451-9588

Comments

Author Posting (c) Elsevier, 2021. This article is posted here by permission of Elsevier for personal use. This article was published open access in Computers in Human Behavior Reports, VOL.2, (August 13, 2021), http://dx.doi.org/10.1016/j.chbr.2021.100132

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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