Assessing the Human Operating System: A Quantitative Study of Demographics and General Decision-Making Styles (GDMS) on Digital Native Student's Security Behavior Intentions (SeBIS)

Autor/in	Boyce, Stephen
Titel	Assessing the Human Operating System: A Quantitative Study of Demographics and General Decision-Making Styles (GDMS) on Digital Native Student's Security Behavior Intentions (SeBIS)
Quelle	(2021), (127 Seiten) PDF als Volltext Verfügbarkeit D.Sc. Dissertation, Marymount University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	979-8-4268-2976-3
Schlagwörter	Hochschulschrift; Dissertation; Demography; Decision Making; Computer Security; Undergraduate Students; Intention; Predictor Variables; Computer Selection + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Demografie; Decision-making; Entscheidungsfindung; Computervirus; Computersicherheit; Prädiktor
Abstract	The world continues to be plagued by cyber incidents that impact businesses, schools, hospitals, and government institutions. Some organizations have given cybersecurity a line item on the annual budget, while others have created dedicated departments to improve their technical defenses. Executive boards and C-level executives have shifted from an "if" to a "when" mindset regarding hacking. At the same time, adversaries are successfully evading the latest security control like multifactor authentication and zero trust solutions while the cost of a cyber incident increases. Despite mandatory security education training and awareness programs, cyber investigations into some of the most notable intrusions have proved human error to be the initial entry point. Previous scholars, however, have not looked at the upcoming generations of digital natives to enter the workforce in the coming months and years to determine whether they are more security-aware about their online security behaviors and decision-making styles. This researcher surveyed undergraduate students within a heavy workforce-bound department at a small private university in the United States using the Security Behavior Intentions Scale (SeBIS) and the General Decision-Making Style (GDMS) scale. Data were collected from a significant number of participants using the snowball sampling technique with chain control and custody. Descriptive statistics, correlation analysis, and two multiple dependent variable regression analyses were performed to test the hypotheses. The results revealed that the digital native students entering the workforce in the coming years are conscious of their online security behavior intentions. Demographics such as education were a significant predictor of good password generation, whereas rationality and spontaneity significantly predicted participants' decisions in device securement and proactive awareness. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01