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Cited by 355

The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.

Enhancing Brand Credibility Via Celebrity Endorsement: Trustworthiness Trumps Attractiveness and Expertise

Wang, Stephen W., Scheinbaum, Angeline Close·2018·Journal of Advertising Research

Abstract

ABSTRACT Building on source-credibility theory, the authors tested a structural model for advertisers and scholars to explain brand outcomes of celebrity endorsement. The empirical context is the global airline industry, with a fitting sample surveyed at an international airport (N = 637). Results of structural equation analyses show that consumers9 perception of a celebrity endorser9s attractiveness and trustworthiness brought a lift in brand attitude, brand credibility, and purchase intention toward endorsed brands. The contribution to source-credibility theory is the finding that endorser trustworthiness was the only component of source credibility that was important to low-involvement consumers. The takeaway for advertisers in this industry is to use attractive celebrity endorsers with a global appeal who are trustworthy to enhance brand credibility.

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Metadata

Authors
Wang, S. W., & Scheinbaum, A. C.
Venue
Journal of Advertising Research
Published
2017-11-13
Volume
58
Issue
1
Pages
16-32
ISSN
  • 0021-8499 (ISSN-L)
  • 1740-1909
OpenAlex
W2770599068
Cited by
355
References
85
FWCI
18.32
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Enhancing Brand Credibility Via Celebrity Endorsement: Trustworthiness Trumps Attractiveness and Expertise. https://app.cochat.ai/papers/10.2501/jar-2017-042
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Wang, S. W., & Scheinbaum, A. C. (2018). Enhancing Brand Credibility Via Celebrity Endorsement: Trustworthiness Trumps Attractiveness and Expertise. Journal of Advertising Research. https://doi.org/10.2501/jar-2017-042

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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.