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Cited by 1,142

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Human Brands: Investigating Antecedents to Consumers’ Strong Attachments to Celebrities

Thomson, Matthew·2006·Journal of Marketing

Abstract

This article explores recent advances in self-determination research to address why consumers develop strong attachments to “human brands,” a term that refers to any well-known persona who is the subject of marketing communications efforts. Study 1 uses a survey that is analyzed with structural equation modeling. Study 2 is qualitative and offers corroborating evidence for the proposed theoretical model. Study 3 extends the model with a more naturalistic sample and tests several alternative hypotheses using hierarchical regression. The results suggest that when a human brand enhances a person's feelings of autonomy and relatedness and does not suppress feelings of competence, the person is likely to become more strongly attached to it. This article documents that strong attachments are predictive of satisfied, trusting, and committed relationships and proposes that attachment strength may be a parsimonious proxy for consumer–brand relationship strength. The results imply that benefits would accrue to organizations such as entertainment firms and political parties that establish direct and routine interaction between human brands and consumers, that human brands to which consumers are attached offer significant potential as endorsers, and that organizations should address how to make the human brands they manage more authentic.

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Metadata

Authors
Thomson, M.
Venue
Journal of Marketing
Published
2006-07
Volume
70
Issue
3
Pages
104-119
ISSN
  • 0022-2429 (ISSN-L)
  • 1547-7185
OpenAlex
W2128068758
Cited by
1,142
References
9
FWCI
36.37
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.