Measuring Individual Brand Image Perceptions and Brand Reputation using Associative Networks: Theory and Implications for Consumer Goods
This study specifically addresses the question of how associative networks contribute to brand image perception measurement to segment the market more effectively, as well as to demonstrate brand image impairment. Carried out using surveys and experimental designs, this dissertation is positioned as part of the research stream on the use of associative networks in marketing, and particularly consumer mapping. Firstly, this study provides clear arguments for using brand concept maps (BCM) as highly suitable method to capture the complete brand image and segment the market based on brand perception. Secondly, thanks to BCM, the present research provide empirical evidences on the likelihood of brand image confusion, namely the dilution of attributes and the creation of unattractive associations, as a result of the introduction of a new brand. Thirdly, BCM captures the effect of exposure to negative user-generated content on the likelihood of doppelganger brand image, namely the appearance, or reinforcement, of negative associations at the brand reputation level. This work reveals that BCM is a superior approach to measure brand image and brand reputation, compared to dyadic methods. In addition, it broadens the applications of associative networks in marketing. Moreover this research shows how consumers and competitors may influence the brand image. Besides this scientific output, this research aims in providing brand mangers with an effective method to monitor and capture brand image and brand reputation.
Business & economic sciences, Marketing, Sciences économiques & de gestion
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