Using Network Analysis to Understand Brands

In this paper, we model brand associations as associative networks. Although the idea of brand associative networks is weU accepted, rarely are these networks elicited and modeled explicitly. Here, we do so in the context of a branding experiment by using a combination of qualitative (repertory grid) and quantitative (network analysis) techniques. This elicitation and analysis technique was used prior to and after subjects saw an advertisement for one of three types of brand extensions for a popular core brand. Results confirmed the usefulness of network analysis for identifying brand association stmcture and assessing changes in brand associations as a result of brand extension activity. Recently, a new approach to understanding brand equity has been introduced to the literature (Henderson, Iacobucci, and Calder 1998). In their paper, the authors present a conceptual framework to uncover ten branding effects based on brand associations (Keller 1993). In this paper, we put the Henderson, et al. (1998) framework to an empirical test. We then map these 10 branding effects into three branding constmcts: positioning, complementarity, and substitutabiUty. Below, we present a brief review of the consumer associative network approach. We then pursue these conjectures with an empirical test: Consumers were exposed to information about a purported forthcoming brand extension. Consumers made judgments of associative Unks and their perceptions are represented as associative networks. Post-manipulation perceptions are compared to those held prior to the intervention, and we examine the resultant stmctures for brand constmcts. CONSUMER ASSOCIATIVE NETWORKS Representation It is commonly held that consumers store information in memory in the form of associative networks (Anderson and Bower 1973; Ellis and Hunt 1992). In general, researchers contend that knowledge is represented as links of associations among concept nodes (cf. Sirsi, Ward, and Reingen 1996). The nodes are units of information such as brands, attributes, advertisements, etc., and the links contain the relational tie between the concepts (e.g., a brand "possesses" much of an attribute, a brand image is "like" the spokesperson). The links make various associations by connecting nodes together to form a network of ideas, or a knowledge stmcture.' 1 Associations in network representations of mental models can also possess strengths, e.g., for an association based on many experiences or exposures to communications. In a graph, strength is indicated by the thickness of the Une, the number of links between two nodes, or by a numerical indicator near the link. Asymmetrical relations may also be represented if one node evokes another but the reverse is not true. We are presenting symmetric, binary ties for the purpose of simplicity, though we note that aU that we present is easily extended to ties with strength and direction using standard network methods (e.g., Knoke and Kuklinski 1982). Researchers in marketing have recently discussed brand associations, primarily as they relate to issues of brand equity, brand image, and brand knowledge (e.g., Keller 1993; Sirsi, et al. 1996). Many cognitive theories of consumer behavior posit associative network stmctures (e.g., Bettman 1971; Calder and Gmder 1989), and yet rarely are they elicited or modeled empirically. It is desirable to represent brand associations as networks in order to allow these stmctural data to be modeled in a matuier most consistent with existing theoretical views of consumer memory structure.