Building Global Textile and Apparel Brand Image Strategies: A Cross-National Model

This research compared linear regression and artificial neural network models to identify the most accurate methods of predicting the impact of product cues on consumers’ apparel product evaluations and purchase intentions. The research process and findings are discussed below. Also, we examined the potential effectiveness of the Internet as a strategic tool to (a) build global brand images for U.S. products/brands, (b) collect customer information to guide product and image strategies, and (c) sell U.S. textile and apparel products in global markets. Findings are summarized below. RELEVANCE TO NTC MISSION: This research is intended to provide a framework that may be used to create more powerful global brands, thereby facilitating cross-national acceptance of U.S. textile and apparel brands/products. The resulting models may be useful to forecast the potential impact of brand image strategies on consumer purchase intentions in targeted markets worldwide, enabling U.S. apparel marketers to choose the most effective brand image strategy for each market. The potential of the Internet as a tool to deliver a brand strategy to targeted consumers in a cost-effective manner is extraordinary. More effective brand image strategies, efficiently delivered to global markets via the Internet, could result in the creation of powerful brands, providing a sustainable non-price advantage to U.S. firms. OBJECTIVES: 1. To empirically compare the ability of artificial neural network and traditional statistical models to predict consumer product evaluations and intentions to buy U.S. apparel products in selected international markets. 2. To examine the potential effectiveness of the Internet as a strategic tool to (a) build global brand images for U.S. products/brands, (b) collect customer information to guide product and image strategies, and (c) sell U.S. textile and apparel products in global markets. 3. To disseminate findings to U.S. apparel marketers to support their efforts to develop more effective brand image strategies and enhance their competitiveness in targeted international markets. INTRODUCTION: National Textile Center Annual Report: November 1999 I98-A06 2 2 Cross-national research has shown that many apparel consumers rely on brand name when making purchase decisions and that brand name contributes to the value of the product via intangible, subjective product characteristics (i.e., positive image). An effective brand image translates into consumer loyalty and willingness to pay a premium price for the brand, providing marketers with a powerful competitive advantage. To maximize our ability to examine the nature of the relationship between product perceptions and purchase intentions and to predict the impact of brand image and other product cues on purchase intentions, we compared the effectiveness of a variety of artificial neural networks (ANN) and traditional regression-based models. ANN are parallel information processing systems that have the ability to learn and subsequently generalize complex patterns of information. Previous research suggests that ANN models may be more accurate than traditional models in forecasting consumer purchase intentions (necessary to select the most effective brand image strategy for a given market). However, a regressionbased model may be more useful in explaining the impact of the brand image on product evaluations and purchase intentions. This is important as an understanding of the relationship between these variables is necessary to provide guidance for development of new image strategies. This research empirically compares a variety of neural net approaches with regression-based models using survey data from consumer markets that have different cultural and economic environments. SUMMARY OF RESEARCH TO DEVELOP AND COMPARE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK (ANN) MODELS. Our goal was to maximize our ability to predict the impact of product attributes on purchase intentions. A model of cross-national product acceptance, developed and tested in the previous study (I95, A23), shows how consumers manage product information (including brand name) during their purchase decision. An experimental study was used to obtain consumer evaluations of apparel products where the price and brand name were manipulated. Consumers were assigned to evaluate identical apparel products at high or low price point with or without the brand name label intact. The relationships between selected product cues (appearance, brand name and price), evaluative variables (perceived quality and value), and the cutomers's intentions to buy the U.S. products/brands were tested. Statistical ananysis were employed to identify the impact of product cues on consumers' quality and value evaluations and on purchase intentions for consumers in each market. The model of cross-national product acceptance (Figure 1) shows how consumers manage information cues during the purchase decision. MET HOD B ra n d N a m e P r i c e P h y s i c a l A p p e a r a n c e P e r c e p t i o n o f B r a n d N a m e P h y s i c a l a p p e a r a n c e P e r c e p t i o n o f P r i c e P e r c e i v e d Q u a l i t y V a l u e W i l l i n g n e s s t o B u y I n t e r v e n i n g C o n s u m e r V a r i a b l e s F i g u r e 1 : C r o s s n a t i o n a l P r o d u c t A c c e p t a n c e M o d e l C o n s u m e r I n v o l v e m e n t F a s h i o n I n n o v a t i o n B e n e f i t s S o u g h t C o n s u m e r D e m o g r a p h i c s ( S e l e c t e d )