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RISS 인기검색어
- 검색키워드
- 저자 : Teichmann, Daniel (검색결과 3 건)
- 무료
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GOOD FASHION TASTE: HOW DO TASTE APPEALS INFLUENCE THE PREFERENCE FOR LUXURY FASHION BRANDS?
Karin Teichmann,Nicola E. Stokburger-Sauer 글로벌지식마케팅경영학회(GFMC) 2015 Global Fashion Management Conference Vol.2015 No.06
Marketing research on experiential consumption (Hirschman & Holbrook, 1982) and aesthetic consumption (e.g., Luchs, Brower, & Chitturi, 2012; Venkatesh, Joy, Sherry, & Deschenes, 2010) has strongly focused on what consumers like (i.e., their actual preferences) based on sensory stimulation and product characteristics (e.g., Bloch, 1995). Only little research has centered on what consumers should like. To date, no final answer has been given to the question if consumers’ product evaluation is influenced by knowing what should be preferred (i.e., appeals of “good fashion taste” as prescribed by fashion experts; Holbrook, 2005) versus what is actually preferred (i.e., appeals of “popular fashion taste” as demonstrated by the mass market; Holbrook, 2005). This research therefore seeks to, first, advance our knowledge of fashion taste appeals by considering the difference between what fashion experts understand as good fashion taste and what consumers actually prefer (i.e., popular fashion taste). Second, this research looks into how fashion taste appeals influence consumers’ evaluation of luxury fashion brands by examining their willingness to pay (as a more cognitive product evaluation) and their preference for a luxury fashion brand (as a more affective product evaluation). Third, this research assists in answering the question if fashion experts differ from consumers in their evaluations of a luxury fashion brand based on taste appeals. A 2 (fashion taste appeal; good vs. popular) x 2 (fashion expertise; expert vs. consumer) between-subjects design is applied to test the hypotheses using a scenario-based technique in the context of luxury fashion products. Data was collected from 122 respondents (60 fashion experts, 62 consumers, 73.8% females). The results show that good fashion taste appeals result in higher levels of willingness to pay than popular fashion taste appeals. This effect is moderated by fashion expertise. Fashion experts show a higher willingness to pay for good fashion taste appeals than “ordinary” consumers. Concerning affective product evaluation, the results reveal that “ordinary” consumers show higher levels of “liking” the product than fashion experts. Fashion expertise moderates the influence of fashion taste appeals on product liking. Popular fashion taste appeals result in higher levels of luxury fashion brand preference for consumers than for fashion experts. Marketers are well advised to take the results of what the mass market prefers (i.e., popular fashion taste) into consideration; and, additionally communicate a luxury fashion brand’s popularity with other consumers in marketing campaigns to increase the brand’s success.
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Jurmeister, Philipp,Bockmayr, Michael,Seegerer, Philipp,Bockmayr, Teresa,Treue, Denise,Montavon, Gré,goire,Vollbrecht, Claudia,Arnold, Alexander,Teichmann, Daniel,Bressem, Keno,Schu¨ller, Ulrich American Association for the Advancement of Scienc 2019 Science translational medicine Vol.11 No.509
<P>Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.</P>
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