The Big Five and brand personality: investigating the impact of consumer personality on preferences towards particular brand personality

The aim of this study was to explore the relationship between consumer personality and brand personality as measured by constructs refl ecting The Big Five dimensions in the context of fashion products. The fi ndings of the study show that some dimensions of The Big Five constructs are signifi cantly related to preferences on particular dimensions of brand personality. It was found that consumers who exhibit a Conscientious personality demonstrate preferences towards ‘ Trusted ’ brands. In contrast, those who are Extrovert in nature are motivated by ‘ Sociable ’ brands. Findings related to gender reveal that male and female consumers differ in how they express their personality when it comes to brand personality. Male respondents who are dominant on the Neuroticism dimension prefer ‘ Trusted ’ brand, whereas ‘ Trusted ’ brand is preferred by females who are dominant on the Conscientiousness dimension. The results of this study will inform brand managers about how to tailor specifi c marketing strategies such that brand personalities communicated to consumers are congruent with their personalities.

'Hottie Doctors': academics as celetoids, a case study of Dr Bonnie Blossman and Big, Rich Texas

Cassandra Atherton is a senior lecturer at Deakin University. She has written several articles on academic public intellectuals for international journals and is currently writing a book, Wise Guys, on the changing role of the public intellectual in academe, based on her interviews with Noam Chomsky, Harold Bloom, Camille Paglia, Stephen Greenblatt and others. A book of interviews with these American public intellectuals, entitled In So Many Words, was published by Australian Scholarly Press in 2013. She has also written a critical monograph, a novel and a book of poetry.

Mr Big of Bankstown : the scandalous Fitzpatrick and Browne affair

Review of  Mr Big of Bankstown: The Scandalous Fitzpatrick and Browne Affair.
By Andrew Moore. Perth: University of Western Australia Publishing, 2011. 

Big clumsy feet stomping all over: shame, caution and fear in performance for peace building

This article examines some of the tensions implicit in performing in peace contexts. Drawing on the community-based performance form playback theatre, the article interrogates the (citizen) artist/performer within the demands of improvised performance. The article investigates the demands on the actor in a practice context that features refugee and asylum-seeker audience members/participants: the way in which performative risk, the risk of intimacy, the risk of getting it wrong and the risk of shaming self and other are considered in light of the challenges associated with the specificity of the ethnicity, cultural context/s, values and protocols of these audiences.

The big tease

Review of the Australian Burlesque Festival's Gala Event, The Big Tease.

Predicting psychological and subjective well-being from personality: incremental prediction from 30 facets over the Big 5

This study investigated the relationship between the Big 5, measured at factor and facet levels, and dimensions of both psychological and subjective well-being. Three hundred and thirty-seven participants completed the 30 Facet International Personality Item Pool Scale, Satisfaction with Life Scale, Positive and Negative Affectivity Schedule, and Ryff’s Scales of Psychological Well-Being. Cross-correlation decomposition presented a parsimonious picture of how well-being is related to personality factors. Incremental facet prediction was examined using double-adjusted r2 confidence intervals and semi-partial correlations. Incremental prediction by facets over factors ranged from almost nothing to a third more variance explained, suggesting a more modest incremental prediction than presented in the literature previously. Examination of semi-partial correlations controlling for factors revealed a small number of important facet-well-being correlations. All data and R analysis scripts are made available in an online repository.

Large iterative multitier ensemble classifiers for security of big data

This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for big data. These classifiers are very large, but are quite easy to generate and use. They can be so large that it makes sense to use them only for big data. They are generated automatically as a result of several iterations in applying ensemble meta classifiers. They incorporate diverse ensemble meta classifiers into several tiers simultaneously and combine them into one automatically generated iterative system so that many ensemble meta classifiers function as integral parts of other ensemble meta classifiers at higher tiers. In this paper, we carry out a comprehensive investigation of the performance of LIME classifiers for a problem concerning security of big data. Our experiments compare LIME classifiers with various base classifiers and standard ordinary ensemble meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of classifications. LIME classifiers performed better than the base classifiers and standard ensemble meta classifiers.

Comprehensive analysis of big data variety landscape

Big data presents a remarkable opportunity for organisations to obtain critical intelligence to drive decisions and obtain insights as never before. However, big data generates high network traffic. Moreover, the continuous growth in the variety of network traffic due to big data variety has rendered the network to be one of the key big data challenges. In this article, we present a comprehensive analysis of big data variety and its adverse effects on the network performance. We present taxonomy of big data variety and discuss various dimensions of the big data variety features. We also discuss how the features influence the interconnection network requirements. Finally, we discuss some of the challenges each big data variety dimension presents and possible approach to address them.