Loading...
Please wait, while we are loading the content...
Similar Documents
Building Global Textile and Apparel Brand Image Strategies: A Cross-National Model
| Content Provider | Semantic Scholar |
|---|---|
| Author | Batey, Deborah Li, Xing Liu, Chen Ahmed, Shaila |
| Copyright Year | 1999 |
| Abstract | 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://infohouse.p2ric.org/ref/08/07194.pdf |
| Alternate Webpage(s) | http://www.auburn.edu/~forsysa/public/i98-a06_1999.pdf |
| Alternate Webpage(s) | http://www.humsci.auburn.edu/cahs/00_building_global.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
