An Analysis of Fashion Brand Extensions by Artificial Neural Networks

Abstract

The fashion industry is one of the fastest-moving industries. It is highly competitive due to its features such as short product life cycle, low predictability of demand (Bruce and Daly 2011; Ni and Fan 2011; Yu et al. 2011a), and high impulse purchase rate (Christopher and Peck 1990). In order to capture market share and increase brand equity, brand extension is a well-established strategy in the fashion industry (Hergeth 2004; Liao et al. 2008; Sullivan 1992). There are two kinds of brand extensions, namely category extension and line extension. Here, category extension refers to the case when the mother brand launches new product categories as extensions; line extension occurs when the mother brand introduces new product lines (within the same category) as extensions with different attributes such as color. Both types of brand extensions are in principle beneficial to the consumers because they will enjoy the availability of a larger variety of products under the respective brand. While fashion brand extensions in different domains are common strategies for business growth, there are very few prior scientific studies which explore the number of category and line extensions in different regions over different periods of time. An exception appears in the recent literature in which a cross-region cross-cluster analysis is conducted on fashion brand extensions by employing a statistical approach (Choi et al. 2011b). However, in Choi et al. (2011b), many important hypotheses are not found to be statistically significant by standard methods such as ANOVA, and many more sophisticated statistical analysis tools, such as Poisson regression, failed to help (because of the special features of the dataset). Motivated by the importance of the topic as well as the failure of the standard statistical tools in conducting analysis, this chapter scientifically studies how different fashion retail brands adopt category and line extensions in different regions over different periods of time by using artificial neural network (ANN). (PsycINFO Database Record (c) 2016 APA, all rights reserved)