The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Disentangling the Effects of Social Network Density on Electronic Word-of-Mouth (eWOM) Intention
Open accessAbstract
A widely accepted notion in diffusion literature is that individuals’ word-of-mouth behavior is constrained by the properties of social structures (e.g., tie strength, positions in a network) they belong to. Although many studies have indeed confirmed the existence of such social influence, little is known about how the social structural effects are produced and work at a psychological level. This study attempts to present how one's electronic word-of-mouth (eWOM) intention is shaped by 2 key factors—the valence of product-related information and the social coherence of the communication network in which s/he belongs. To understand the process through which the network structures moderate the impact of information valence on eWOM intention, 2 different moderation processes—a) mediated moderation and b) moderated mediation—were tested in a 2 × 2 factorial experiment. The experimental results supported the moderated mediation model, and the implications of the results were discussed.
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“Social network analysis has been researched in relation to electronic word-of-mouth (Sohn, 2009), identifying influencers (Zhang & Li, 2014) and investigating how individuals influence others, the relationship between influence and tie strength and the flow of information through social media (Gandomi & Haider, 2015).”
From Setting the future of digital and social media marketing research: Perspectives and research propositions (2021) · cites this paper as Sohn, 2009
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Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102168 · Cited by 4,608
“Social network analysis has been researched in relation to electronic word-of-mouth (Sohn, 2009), identifying influencers (Zhang & Li, 2014) and investigating how individuals influence others, the relationship between influence and tie strength and the flow of information through social media…”
BackgroundHayes, J. L., Brinson, N. H., Bott, G. J., & Moeller, C. M. (2021) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2021.01.001 · Cited by 180
Metadata
- Authors
- Sohn, D.
- Affiliations
- The Ohio State University (US)
- Corresponding author
- Sohn, Dongyoung
- ORCID
- Sohn, Dongyoung
- Venue
- Journal of Computer-Mediated Communication
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-01
- Volume
- 14
- Issue
- 2
- Pages
- 352-367
- ISSN
- 1083-6101 (ISSN-L)
- OpenAlex
- W2089946080
- Cited by
- 98
- References
- 39
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 6 · 2023: 7 · 2022: 4 · 2021: 2 · 2020: 10 · 2019: 7 · 2018: 6 · 2017: 9 · 2016: 10 · 2015: 3 · 2014: 8 · 2013: 2 · 2012: 3
- FWCI
- 16.24
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Computer-Mediated Communication (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Decision Sciences › Information Systems and Management › Knowledge Management and Sharing
- Keywords
- electronic word of mouth, social network density, social influence, information valence, social coherence, moderated mediation, diffusion of innovations, social structure
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