讲座时间：2018年6月13日 上午10：30-12：00
讲座地点：创管学院218教室
邀请人：杨丽凤
讲座内容简介：
Brand naming challenges compound in logographic languages (e.g., Chinese), compared to phonographic languages (e.g., English), because logographic languages have a loose correspondence between sound and meaning. Using the dimensions of sound and meaning the authors describe a four-way categorization of brand name types for logographic languages: alphanumeric, phonetic, phonosemantic, or semantic—to help multinational corporations in understanding the association of brand name types and consumer demand. The categorization postulates that alphanumeric names are most standardized and semantic names are most adaptable. Using automobile sales data from China and a discrete choice model for differentiated products the authors relate brand name type to demand showing Chinese consumers prefer vehicle models with semantic brand names (7.64% more sales than alphanumeric) but exhibit the least preference for phonosemantic ones (4.92% fewer sales than alphanumeric). Domestic firms in China benefit from semantic brand names, whereas foreign firms gain from foreign sounding brand names. Entry-level products benefit from semantic brand names and high-end products benefit from foreign sounding brand names.
讲座嘉宾简介：
Dr. Fang Wu is the associate professor of Marketing at College of Business at Shanghai University of Finance and Economics. She received her PhD degree in Marketing from University of Alberta, Canada. Her research focuses on modeling of consumer choice, marketing structure analysis, market segmentation and competitive strategy. She uses econometric and statistical methods in her research, which involves entertainment industry such as the motion pictures and automobile industries. Professor Wu's research is published in Journal of Marketing Research, Journal of Regulatory Economics, Health Economics, and Journal of Transport Economics and Policy. Professor Wu has taught Principle of Marketing, Marketing Management, International Marketing, Marketing Models, Multivariate Data Analysis at undergraduate, MBA and Ph.D. levels.
