Abstract: Evaluating the levels of cyber-security risks within an enterprise is most important in protecting its information system, services and all its digital assets against security incidents (e.g. accidents, malicious acts, massive cyber-attacks). The existing risk assessment methodologies (e.g. eBIOS, OCTAVE, CRAMM, NIST-800) adopt a technical approach considering as attack factors only the capability, intention and target of the attacker, and not paying attention to the attacker’s psychological profile and personality traits. In this paper, a socio-technical approach is proposed in cyber risk assessment, in order to achieve more realistic risk estimates by considering the personality traits of the attackers. In particular, based upon principles from investigative psychology and behavioural science, a multi-dimensional, extended, quantifiable model for an attacker’s profile is developed, which becomes an additional factor in the cyber risk level calculation.
Abstract: Today, there is a large number of political transcripts
available on the Web to be mined and used for statistical analysis,
and product recommendations. As the online political resources are
used for various purposes, automatically determining the political
orientation on these transcripts becomes crucial. The methodologies
used by machine learning algorithms to do an automatic classification
are based on different features that are classified under categories
such as Linguistic, Personality etc. Considering the ideological
differences between Liberals and Conservatives, in this paper, the
effect of Personality traits on political orientation classification is
studied. The experiments in this study were based on the correlation
between LIWC features and the BIG Five Personality traits. Several
experiments were conducted using Convote U.S. Congressional-
Speech dataset with seven benchmark classification algorithms. The
different methodologies were applied on several LIWC feature sets
that constituted by 8 to 64 varying number of features that are
correlated to five personality traits. As results of experiments,
Neuroticism trait was obtained to be the most differentiating
personality trait for classification of political orientation. At the same
time, it was observed that the personality trait based classification
methodology gives better and comparable results with the related
work.
Abstract: Authenticity in advertising is the cornerstone of
modern marketing. Despite research advances related to the role of
authenticity in marketing, it remains unclear why customers respond to
authentic brand stories. The results show that different personality
traits will moderate the influence of different authenticity on the levels
of emotion. Whether indexically authentic or iconically authentic
advertisements were shown to extroverts, open people and agreeable
people, they will evoke more positive emotions. When neurotic people
and conscientious people see the iconically authentic advertising
rather than the indexically authentic advertising, they will produce
more negative emotions. In addition, the emotion evoked by
advertising had significant positive impact on brand attitude evoked by
advertising had significant negative impact on brand attitude. These
findings provide some managerial implications and directions for
further research.
Abstract: This study explored the relationship between
psychological traits, demographics and financial behavioral biases for
individual investors in Taiwan stock market. By using questionnaire
survey method conducted in 2010, there are 554 valid convenient
samples collected to examine the determinants of three types of
behavioral biases. Based on literature review, two hypothesized
models are constructed and further used to evaluate the effects of big
five personality traits and demographic variables on investment biases
through Structural Equation Model (SEM) analysis. The results
showed that investment biases of individual investors are significantly
related to four personality traits as well as some demographics.
Abstract: Emerging adulthood, between the ages of 18 and 25, as a distinct developmental stage extending from adolescence to young adulthood. The proportions composing the five-factor model are neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness. In the literature, there is any study which includes the relationship between emerging adults loneliness and personality traits. Therefore, the relationship between emerging adults loneliness and personality traits have to be investigated. This study examines the association between the Big Five personality traits, and loneliness among Turkish emerging adults. A total of 220 emerging adults completed the NEO Five Factor Inventory (NEO-FFI), and the The UCLA Loneliness Scale (UCLALS). Correlation analysis showed that three Big Five personality dimensions which are Neuroticism (positively), and Extraversion and Aggreableness (negatively) are moderately correlated with emerging adults loneliness. Regression analysis shows that Extraversion, Aggreableness and Neuroticism are the most important predictors of emerging adults loneliness. Results can be discussed in the context of emerging adulthood theory.