The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Understanding online firestorms: Negative word-of-mouth dynamics in social media networks
Abstract
Social media are, on the one hand, a highly beneficial environment for word-of-mouth (WOM) propagation of new ideas and products, and this has increasingly made them a focus of marketing communications. On the other hand, companies and their brands as well as politicians, governmental institutions, and celebrities have increasingly been facing the impact of negative online WOM and complaint behavior. In reaction to any questionable statement or activity, social media users can create huge waves of outrage within just a few hours. These so-called online firestorms pose new challenges for marketing communications. In this article, we group observations from recent online firestorms, identify related social and economic science theories, and derive generalized factors that form the basis for the proliferation of these dynamics. Furthermore, we discuss the consequences of online firestorms for marketing communications, and offer courses of action for marketers to navigate through crises of negative online WOM.
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Cites
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557 · cited 1×
“In order to analyze the structure and the dynamics of social media more in detail, we recommend the use of methods and applications of Social Network Analysis (Wasserman and Faust 1995; Newman, Barabási, and Watts 2006).”
MethodFILTER BUBBLE: Wie wir im Internet entmündigt werden
Pariser, E. (2012) · doi:10.3139/9783446431164 · Cited by 1,849
Cited by
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
“These researchers defined the term online firestorm as the “phenomenon involving waves of negative in-dignation on social media platforms,” which has affected brands, organizations, politicians and celebrities following the release of less than desirable information (Pfeffer et al., 2014, p. 125).”
BackgroundEngagement with Social Media and Social Media Advertising: The Differentiating Role of Platform Type
Voorveld, H. A. M., van Noort, G., Muntinga, D. G., & Bronner, F. (2018) · Journal of Advertising · doi:10.1080/00913367.2017.1405754 · Cited by 819
Social media and the formation of organizational reputation
Etter, M., Ravasi, D., & Colleoni, E. (2019) · Academy of Management Review · doi:10.5465/amr.2014.0280 · Cited by 428
Brady, W. J., Crockett, M. J., & Van Bavel, J. J. (2020) · Perspectives on Psychological Science · doi:10.1177/1745691620917336 · Cited by 417
Hewett, K., Rand, W., Rust, R. T., & van Heerde, H. J. (2016) · Journal of Marketing · doi:10.1509/jm.15.0033 · Cited by 366
The impact of social media on risk perceptions during the MERS outbreak in South Korea
Choi, D.-H., Yoo, W., Noh, G.-Y., & Park, K. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.03.004 · Cited by 246
Schultz, C. D. (2017) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2017.09.005 · Cited by 191
Digital social norm enforcement: Online firestorms in social media
Rost, K., Stahel, L., & Frey, B. S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0155923 · Cited by 190
Grubbs, J. B., Warmke, B., Tosi, J., James, A. S., & Campbell, W. K. (2019) · PLOS ONE · doi:10.1371/journal.pone.0223749 · Cited by 118
Exploring Corporate Reputation and Crisis Communication
Nuortimo, K., Harkonen, J., & Breznik, K. (2026) · Journal of Marketing Analytics · doi:10.1057/s41270-024-00353-8 · Cited by 39
Lappeman, J., Patel, M., & Appalraju, R. (2018) · Communicatio · doi:10.1080/02500167.2018.1478866 · Cited by 25
Metadata
- Authors
- Pfeffer, J., Zorbach, T., & Carley, K. M.
- Venue
- Journal of Marketing Communications
- Publisher
- Informa UK Limited
- Published
- 2013-06-10
- Volume
- 20
- Issue
- 1-2
- Pages
- 117-128
- ISSN
- 1352-7266 (ISSN-L)
- 1466-4445
- OpenAlex
- W2148895878
- Cited by
- 554
- References
- 2
- FWCI
- 62.85
- Type
- journal article
- Language
- en
- Open access
- closed
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