A cross-cultural study of the role of religion in consumers' ethical positions

Purpose - Previous studies have looked at how socio-economic and political factors play a role in consumers' ethical positions, but few have considered the role of religion which is a major driver of ethics. This paper seeks to address this. Design/methodology/approach - From a survey of over 700 consumers this paper explores the similarities and differences between consumers' ethical positions in three different religions namely; Christian (from three countries), Islam, and Buddhism. Findings - It was found that a reduced item scale measuring the two factors of Forsyth's idealism and relativism was applicable in all five religions, but variations were seen because of religious teachings. In particular, Austrian Christians were significantly less idealistic and relativistic than all other religions, even other Christians from the United States and Britain. Research limitations/implications - The results have implications for measuring ethical positions internationally and for developing ethically based marketing messages and products. Originality/value - The paper shows for the first time how ethical positions are affected by religions and should be of interest to marketers involved in ethics research and ethical marketing.

GONOME: measuring correlations between GO terms and genomic positions

Background: Current methods to find significantly under- and over-represented gene ontology (GO) terms in a set of genes consider the genes as equally probable balls in a bag, as may be appropriate for transcripts in micro-array data. However, due to the varying length of genes and intergenic regions, that approach is inappropriate for deciding if any GO terms are correlated with a set of genomic positions. Results: We present an algorithm - GONOME - that can determine which GO terms are significantly associated with a set of genomic positions given a genome annotated with (at least) the starts and ends of genes. We show that certain GO terms may appear to be significantly associated with a set of randomly chosen positions in the human genome if gene lengths are not considered, and that these same terms have been reported as significantly over-represented in a number of recent papers. This apparent over-representation disappears when gene lengths are considered, as GONOME does. For example, we show that, when gene length is taken into account, the term development is not significantly enriched in genes associated with human CpG islands, in contradiction to a previous report. We further demonstrate the efficacy of GONOME by showing that occurrences of the proteosome-associated control element (PACE) upstream activating sequence in the S. cerevisiae genome associate significantly to appropriate GO terms. An extension of this approach yields a whole-genome motif discovery algorithm that allows identification of many other promoter sequences linked to different types of genes, including a large group of previously unknown motifs significantly associated with the terms 'translation' and 'translational elongation'. Conclusion: GONOME is an algorithm that correctly extracts over-represented GO terms from a set of genomic positions. By explicitly considering gene size, GONOME avoids a systematic bias toward GO terms linked to large genes. Inappropriate use of existing algorithms that do not take gene size into account has led to erroneous or suspect conclusions. Reciprocally GONOME may be used to identify new features in genomes that are significantly associated with particular categories of genes.