A mechanism for aggregating association network data: An application to brand concept maps

Social tags are user-defined keywords associated with online content that reflect consumers’ perceptions of various objects, including products and brands. This research presents a new approach for harvesting rich, qualitative information on brands from user-generated social tags. The authors first compare their proposed approach with conventional techniques such as brand concept maps and text mining. They highlight the added value of their approach that results from the unconstrained, open-ended, and synoptic nature of consumer-generated content contained within social tags. The authors then apply existing text-mining and data-reduction methods to analyze disaggregate-level social tagging data for marketing research and demonstrate how marketers can utilize the information in social tags by extracting key representative topics, monitoring common dynamic trends, and understanding heterogeneous perceptions of a brand.

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Advanced brand concept maps: A new approach for evaluating the favorability of brand association networks

International Journal of Research in Marketing ◽

10.1016/j.ijresmar.2012.04.002 ◽

2012 ◽

Vol 29 (3) ◽

pp. 265-274 ◽

Cited By ~ 43

Author(s):

Oliver Schnittka ◽

Henrik Sattler ◽

Sebastian Zenker

Keyword(s):

Concept Maps ◽

New Approach ◽

Brand Association ◽

Brand Concept ◽

Brand Concept Maps

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Investigating feedback effects in the field of brand extension using brand concept maps

Baltic Journal of Management ◽

10.1108/bjm-02-2017-0031 ◽

2018 ◽

Vol 13 (1) ◽

pp. 41-64 ◽

Cited By ~ 1

Author(s):

Pascal Kottemann ◽

Anja Plumeyer ◽

Reinhold Decker

Keyword(s):

Cognitive Processing ◽

Concept Maps ◽

Empirical Studies ◽

Brand Extension ◽

Feedback Effects ◽

Content Type ◽

Individual Level ◽

Associative Network ◽

Brand Concept ◽

Brand Concept Maps

Purpose The purpose of this paper is to apply the (advanced) brand concept maps (BCM) approach to reinvestigate previous findings on feedback effects resulting from brand extension information (BEI) and to explore whether this information affects the structure of a brand’s associative network. Design/methodology/approach This research builds on the associative network memory model, as well as Keller’s conceptualization of customer-based brand equity, and uses a series of empirical studies with a total of 839 respondents in two different countries. Findings The findings reveal that BEI has no significant impact on the structure of the parent brand’s associative network at the individual level. Furthermore, key brand image dimensions (i.e. favorability, strength, and uniqueness of brand associations) are not affected. Research limitations/implications By applying the (advanced) BCM approach, this paper is able to address shortcomings that are incorporated with the use of Likert scales for measuring a brand’s image and for investigating feedback effects in the field of brand extension. As the results indicate that the identification of feedback effects might be influenced by the approach used to measure a brand’s image, this paper calls for further investigations of feedback effects on a brand’s image. Originality/value Data from three empirical studies provide insights into the cognitive processing of BEI and their impact on a brand’s associative network.

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Developing and testing a typology of brand benefit differentiation

Journal of Product & Brand Management ◽

10.1108/jpbm-06-2019-2412 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Alexander Farestvedt Hem ◽

Magne Supphellen

Keyword(s):

Concept Maps ◽

Future Research ◽

Negative Effects ◽

Brand Attachment ◽

Content Type ◽

Positive Effects ◽

Benefit Evaluation ◽

Brand Concept ◽

Brand Concept Maps ◽

First Time

Purpose The purpose of this study is to expand the notion of differentiation by developing and testing a typology of brand benefit differentiation. Design/methodology/approach Brand concept maps were used to identify three types of differentiation. The effects of the types of differentiation on benefit evaluation and brand attachment were tested in two follow-up studies using path analysis. Findings A comparison of the association maps of four international brands showed instances of all three types of benefit differentiation – categorical, graded and structural benefit differentiation. The tests of effects revealed that categorical benefit differentiation had negative effects, whereas structural and graded differentiation had positive effects on benefit evaluation and brand attachment, respectively. Research limitations/implications The findings suggest that other types of benefit differentiation are more important than uniqueness. Future research should test the relevance and usefulness of the typology for other brands and consumer segments. Practical implications The new typology opens new opportunities for the differentiation of brands. Brand managers should avoid a myopic focus on uniqueness. Rather, they should analyze networks of benefit associations in detail for all three types of differentiation identified in this research and strengthen the level of structural and/or graded differentiation. Originality/value This research demonstrates, for the first time, the importance of two types of differentiation other than uniqueness. It also supports previous studies showing the negative effects of uniqueness on variables related to brand equity.

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Consumer values of corporate and celebrity brand associations

Qualitative Market Research An International Journal ◽

10.1108/qmr-06-2013-0037 ◽

2015 ◽

Vol 18 (2) ◽

pp. 164-187 ◽

Cited By ~ 12

Author(s):

Jasmina Ilicic ◽

Cynthia M. Webster

Keyword(s):

Concept Maps ◽

Methodological Approach ◽

Personal Meaning ◽

Brand Associations ◽

Corporate Brand ◽

Content Type ◽

Brand Authenticity ◽

Brand Concept ◽

Brand Concept Maps ◽

Corporate Brands

Purpose – This study aims to explore consumer brand associations and values derived from a corporate brand and a celebrity brand endorser prior to their endorsement. Design/methodology/approach – This study uses both hierarchical value mapping and brand concept mapping (BCM) to identify brand attributes that translate to personal meaning for consumers and then to identify whether these attributes are encompassed by a specific brand. Findings – Results from brand concept maps and hierarchical value maps show consumers value accessibility and customer service in financial corporate brands. Consumers value expertise in celebrity brands and respect success in both corporate and celebrity brands. A central finding is the importance of brand authenticity. Corporate brand authenticity establishes a sense of security and assists in the development of brand relationships. Celebrity brand authenticity creates consumer attention and enhances celebrity trustworthiness aiding in the development of a consumer – celebrity brand relationship. Research limitations/implications – The findings have implications for corporate brands utilizing celebrity endorsers. In terms of strategic positioning, corporate brands need to center their marketing communications on desired brand associations at the core of both the corporate and celebrity brand that translate to personal meaning for consumers. Originality/value – This study uses a combined theoretical and methodological approach, drawing on associative network theory and means-end chain theory, and BCM and hierarchical value mapping methods, respectively, to understand and uncover personal meaning or value derived from brand associations.

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Concept maps as network data: Analysis of a concept map using the methods of social network analysis

Evaluation and Program Planning ◽

10.1016/j.evalprogplan.2012.05.001 ◽

2013 ◽

Vol 36 (1) ◽

pp. 40-48 ◽

Cited By ~ 18

Author(s):

Daniel McLinden

Keyword(s):

Social Network ◽

Data Analysis ◽

Social Network Analysis ◽

Network Analysis ◽

Concept Maps ◽

Concept Map ◽

Network Data

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Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

Endocrine Practice ◽

10.1016/s1530-891x(20)42480-2 ◽

2015 ◽

Vol 21 ◽

pp. 301

Author(s):

Armand Krikorian ◽

Lily Peng ◽

Zubair Ilyas ◽

Joumana Chaiban

Keyword(s):

Social Network ◽

Network Data ◽

Social Network Data

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The Multilevel p2 Model

Methodology ◽

10.1027/1614-2241.2.1.42 ◽

2006 ◽

Vol 2 (1) ◽

pp. 42-47 ◽

Cited By ~ 47

Author(s):

Bonne J. H. Zijlstra ◽

Marijtje A. J. van Duijn ◽

Tom A. B. Snijders

Keyword(s):

Social Network ◽

Network Data ◽

Mcmc Algorithm ◽

Independent Observation ◽

Single Observation ◽

Multiple Networks ◽

Social Network Data ◽

Multiple Observations ◽

Practical Support ◽

The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

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