An empirical investigation of three-city Chinese fast-food brand selection
TL;DRAbstract
The empirical study examines the Brisoux-Laroche Brand Categorization Model in the context of the brand choice in China fast-food market in Changsha, Chengdu, and Wuhan three cities and explore the nature of the consideration, hold, reject and foggy sets , in relation to making the brand selection process. It also provides some insights with respect to Chinese consumers' fast-food choice patterns, perceptions of their favourite restaurants, and the potential segments in the fast-food market. The results generally support the Brisoux-Laroche Brand Categorization Model by confirming the existence of four sets of brands, namely the consideration, reject, foggy, and hold sets , and the hypothesized profiles of the four sets in terms of four parameters (attitude toward brands, intention to purchase, amount of information used to evaluate brands, and the confidence of judging) on both aggregate and city level. In addition, by using factor analysis and cluster analysis, attributes influencing
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The empirical study examines the Brisoux-Laroche Brand Categorization Model in the context of the brand choice in China fast-food market in Changsha, Chengdu, and Wuhan three cities and explore the nature of the consideration, hold, reject and foggy sets , in relation to making the brand selection process. It also provides some insights with respect to Chinese consumers' fast-food choice patterns, perceptions of their favourite restaurants, and the potential segments in the fast-food market. The results generally support the Brisoux-Laroche Brand Categorization Model by confirming the existence of four sets of brands, namely the consideration, reject, foggy, and hold sets , and the hypothesized profiles of the four sets in terms of four parameters (attitude toward brands, intention to purchase, amount of information used to evaluate brands, and the confidence of judging) on both aggregate and city level. In addition, by using factor analysis and cluster analysis, attributes influencing
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