Multicultural, not multinational: emerging branding strategies in culturally diverse societies

This paper proposes that approaches to culture-based brand positioning are diversifying in response to increasing complexities of consumer cultural identities, with uni- bi- and multicultural identities emerging. Acting as visualisations of consumers’ cultural identities, brands represent people’s ideas about their membership of cultural groups. Findings from a critical visual analysis of brand communications reveal that the brands’ positioning concepts include associations with single cultures, or two or more distinct types of culture that go beyond traditional global-local positioning strategies. This suggests that in culturally diverse marketplaces, coherent branding strategies that create ‘multi-cultural’ meanings can be used by marketers as a competitive positioning tool, to appeal to consumers that integrate multiple cultures in their identities

`They don't want us to become them': Brand Local Integration and consumer ethnocentrism

This paper investigates whether positioning strategies of foreign brands that integrate both foreign and 'localised' dimensions of country-of-origin (COO) appeals shape perceptions and attitudes of domestically biased consumers. Ethnocentric consumers hold strong favourable attitudes towards local-perceived brands. At the same time, brand positioning strategies of local brands acquired by multinational corporations and of foreign brands entering the local market often integrate foreign COO appeals with locally relevant manufacturing and/or symbolic appeals. The results indicate that foreign brand identities that integrate 'localised' appeals communicating respect of local traditions (through the use of local images, symbols, and recipes) and contribution to the local society's well-being (through local manufacture, employment, use of local ingredients) lead to more favourable consumer perceptions. In distinguishing between 'purely foreign' and 'locally integrated foreign brands', consumers perceive the latter to be more acceptable for consumption. The paper concludes by considering the implications of the findings and outlining directions for further research.No Full Tex

Identifying transient and variable sources in radio images

With the arrival of a number of wide-field snapshot image-plane radio transient surveys, there will be a huge influx of images in the coming years making it impossible to manually analyse the datasets. Automated pipelines to process the information stored in the images are being developed, such as the LOFAR Transients Pipeline, outputting light curves and various transient parameters. These pipelines have a number of tuneable parameters that require training to meet the survey requirements. This paper utilises both observed and simulated datasets to demonstrate different machine learning strategies that can be used to train these parameters. We use a simple anomaly detection algorithm and a penalised logistic regression algorithm. The datasets used are from LOFAR observations and we process the data using the LOFAR Transients Pipeline; however the strategies developed are applicable to any light curve datasets at different frequencies and can be adapted to different automated pipelines. These machine learning strategies are publicly available as PYTHON tools that can be downloaded and adapted to different datasets (https://github.com/AntoniaR/TraP_ML_tools)