Authors:
Adam Szekeres
;
Pankaj Shivdayal Wasnik
and
Einar Arthur Snekkenes
Affiliation:
Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Gjøvik and Norway
Keyword(s):
Information Security Risk Management, Stakeholder Motivation, Psychological Perspective, Motivational Profiles.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Computer-Supported Education
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Human Factors
;
Human-Computer Interaction
;
Information Systems Analysis and Specification
;
Information Technologies Supporting Learning
;
Knowledge Discovery and Information Retrieval
;
Knowledge Management
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Physiological Computing Systems
;
Security
;
Security and Privacy
;
Society, e-Business and e-Government
;
Symbolic Systems
;
User Profiling and Recommender Systems
;
Web Information Systems and Technologies
Abstract:
Human behavior plays a significant role within the domain of information security. The Conflicting Incentives Risk Analysis (CIRA) method focuses on stakeholder motivation to analyze risks resulting from the actions of key decision makers. In order to enhance the real-world applicability of the method, it is necessary to characterize relevant stakeholders by their motivational profile, without relying on direct psychological assessment methods. Thus, the main objective of this study was to assess the utility of demographic features-that are observable in any context-for deriving stakeholder motivational profiles. To this end, this study utilized the European Social Survey, which is a high-quality international database, and is comprised of representative samples from 23 European countries. The predictive performances of a pattern-matching algorithm and a machine-learning method are compared to establish the findings. Our results show that demographic features are marginally useful fo
r predicting stakeholder motivational profiles. These findings can be utilized in settings where interaction between a stakeholder and an analyst is limited, and the results provide a solid benchmark baseline for other methods, which focus on different classes of observable features for predicting stakeholder motivational profiles.
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