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How credibility affects eWOM reading: The influences of expertise, trustworthiness, and similarity on utilitarian and social functions
Abstract
In online information settings, a few people tend to contribute, while the majority of people consume. For this latter group of readers, electronic word of mouth (eWOM) provides information about products or service experiences that rarely are available from manufacturer-controlled sources, which makes this source of information especially helpful. In turn, eWOM influences readers' attitudes, intentions, and behavior. Manufacturers also hope to monitor and positively influence eWOM content, such as by supporting and building brand communities. But eWOM readers might doubt the credibility of information if it is mostly positive, and the usefulness of eWOM information depends on its credibility. This study offers an empirical and theoretical validation of how specific utilitarian and social functions of eWOM affect attitudes toward and intentions to read eWOM. In particular, trustworthiness is the most important credibility dimension; it affects both functions. Perceived expertise also enhances the utilitarian while similarity improves the social function of eWOM.
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Factors affecting YouTube influencer marketing credibility: a heuristic-systematic model
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Metadata
- Authors
- Reichelt, J., Sievert, J., & Jacob, F.
- Venue
- Journal of Marketing Communications
- Publisher
- Informa UK Limited
- Published
- 2013-06-09
- Volume
- 20
- Issue
- 1-2
- Pages
- 65-81
- ISSN
- 1352-7266 (ISSN-L)
- 1466-4445
- OpenAlex
- W2051605196
- Cited by
- 262
- References
- 87
- FWCI
- 37.09
- Type
- journal article
- Language
- en
- Open access
- closed
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- Reichelt, J., Sievert, J., & Jacob, F. (2014). How credibility affects eWOM reading: The influences of expertise, trustworthiness, and similarity on utilitarian and social functions. Journal of Marketing Communications. https://doi.org/10.1080/13527266.2013.797758
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
