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Saturday, September 27 • 16:11 - 16:30
"A Study of Competing Facebook Brand Communities"

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Background:  Brand communities play a key role in the marketing process by educating, influencing and engaging customers. Brand communities are defined as “a specialized, non-geographically bound community, based on a structured set of social relationships among admirers of a brand (Muniz and O’Guinn 2001). Facebook pages of brands are one type of brand community which marketers use for building brand awareness, increasing conversion and engaging their customers. Much of the work on social media brand communities comes from practitioners, with a few exceptions (e.g., de Vries, Gensler and Leeflang, 2012; Goh, Heng and Lin, 2013; Laroche et al. 2012).

Objective: Previous studies have attempted to understand how influence works in social media or how word-of-mouth spreads in social media brand communities. This paper uses social network analysis to examine and understand the structure of competing brand communities on Facebook.  We wanted to examine if the structure of communities of competing brands, which are positioned in close proximity to each other, tend to be similar or different.  

Methods:  We selected two different product categories with considerable volume and activity in Facebook – Luxury Brands and Retail.  Using social network analysis, 4 brands within each of these categories were examined using social network analysis.  For each brand, Facebook postings for a 12-month period were collected. The data included vertex (node) and edges (ties). Vertex included gender, language type (roughly refers to nations), and comment. Edge included sender, receiver, and post resource.  We focused on the “Facebook fan page network”, which is an egocentric network.  We conducted social network analysis, followed by one-way ANOVA using SPSS to see if graph metrics were statistically different across competing brands.

Results:  We compared brand within geodesic distance, graph density, betweenness centrality and eigenvector centrality. Degree, Eigenvector Centrality and Clustering Coefficient have significant difference between luxury and retail industries. However, Betweenness and closeness Centrality didn’t show significant difference between two industries. Within retail brands, there were significant differences on all key graphic metrics.  Similarly, with luxury brands also we found significant differences in network structure and processes.  Some of the results are shown in Figures 1 and 2.  Figure 3 shows differences network diagrams of two competing retail brands.

Conclusions:  The network differences are indicative of differences in the social media strategies of competing brands and also differences in the type of fans attracted by differences.  In the paper, research and managerial implications of our findings are discussed.

de Vries, L., Gensler, S. & Leeflang, P.H.S (2012). Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing. Journal of Interactive Marketing. Vol. 26 (2), 83-91.

Goh, K, Heng, C. Lin, Z. (2013). Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content. Information Systems Research. Vol. 24 (1), 88-107.

Laroche, M., Habibi, M., Richard, M., and Sankaranarayanan, R. (2012). The effects of social media based brand communities on brand community markers, value creation practices, brand trust and brand loyalty. Computers in Human Behavior. Vol. 28 (5) 1755-1767.

Muniz, Jr A. M. and O'Guinn. T.C. (2001). Brand community. Journal of Consumer Research. Vol. 27 (4), 


Jin Ke

Saint Mary's University

Ramesh Venkat

Saint Mary's University
St. Mary’s University, Canada

Saturday September 27, 2014 16:11 - 16:30
TRS 1-003 Ted Rogers School of Management

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