Abstract
Online firestorms pose severe threats to online brand communities. Any negative electronic word of mouth (eWOM) has the potential to become an online firestorm, yet not every post does, so finding ways to detect and respond to negative eWOM constitutes a critical managerial priority. The authors develop a comprehensive framework that integrates different drivers of negative eWOM and the response approaches that firms use to engage in and disengage from online conversations with complaining customers. A text-mining study of negative eWOM demonstrates distinct impacts of high-and low-arousal emotions, structural tie strength, and linguistic style match (between sender and brand community) on firestorm potential. The firm’s response must be tailored to the intensity of arousal in the negative eWOM to limit the virality of potential online firestorms. The impact of initiated firestorms can be mitigated by distinct firm responses over time, and the effectiveness of different disengagement approaches also varies with their timing. For managers, these insights provide guidance on how to detect and reduce the virality of online firestorms.
| Original language | English |
|---|---|
| Pages (from-to) | 1-21 |
| Number of pages | 21 |
| Journal | Journal of Marketing |
| Volume | 83 |
| Issue number | 3 |
| Early online date | 18 Jan 2019 |
| DOIs | |
| Publication status | Published - 1 May 2019 |
Bibliographical note
Funding Information:The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author received a grant from the Basic Research Fund (GFF) of the University of St. Gallen for this project.
Funding Information:
The authors thank Dinesh Gauri, Yu Ma, and Hannes Datta for methodological advice, and Andrew Stephen, Jonah Berger, Grant Packard, Luigi De Luca, and Oliver Emrich as well as participants of research seminars at Cardiff University, Nova School of Business and Economics, Copenhagen Business School, the University of St. Gallen, and the Interactive Marketing Research Conference 2018 for their helpful comments. The authors are grateful to Stephen Hahngriffiths from the Reputation Institute for sharing their data on brand familiarity and brand reputation. The first author handled the data analysis and the fourth author processed the social media data.. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author received a grant from the Basic Research Fund (GFF) of the University of St. Gallen for this project.
Publisher Copyright:
© American Marketing Association 2019.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author received a grant from the Basic Research Fund (GFF) of the University of St. Gallen for this project. The authors thank Dinesh Gauri, Yu Ma, and Hannes Datta for methodological advice, and Andrew Stephen, Jonah Berger, Grant Packard, Luigi De Luca, and Oliver Emrich as well as participants of research seminars at Cardiff University, Nova School of Business and Economics, Copenhagen Business School, the University of St. Gallen, and the Interactive Marketing Research Conference 2018 for their helpful comments. The authors are grateful to Stephen Hahngriffiths from the Reputation Institute for sharing their data on brand familiarity and brand reputation. The first author handled the data analysis and the fourth author processed the social media data.. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author received a grant from the Basic Research Fund (GFF) of the University of St. Gallen for this project.
Keywords
- Message dynamics
- Online brand community
- Online firestorms
- Text mining
- Word of mouth