Detecting, Preventing, and Mitigating Online Firestorms in Brand Communities

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.

[1]  M. Hoffman,et al.  Sex differences in empathy and related behaviors. , 1977, Psychological bulletin.

[2]  Peter H. Reingen,et al.  Social Ties and Word-of-Mouth Referral Behavior , 1987 .

[3]  R. Burt Social Contagion and Innovation: Cohesion versus Structural Equivalence , 1987, American Journal of Sociology.

[4]  Mary Jo Bitner,et al.  Evaluating Service Encounters: The Effects of Physical Surroundings and Employee Responses , 1990 .

[5]  Mary Jo Bitner,et al.  The Service Encounter: Diagnosing Favorable and Unfavorable Incidents: , 1990 .

[6]  Mary Jo Bitner,et al.  The Service Encounter: Diagnosing Favorable and Unfavorable Incidents , 1990 .

[7]  Richard S. Lazarus,et al.  Cognition and motivation in emotion. , 1991 .

[8]  Jonathan K. Frenzen,et al.  Structure, Cooperation, and the Flow of Market Information , 1993 .

[9]  A. Satorra,et al.  Complex Sample Data in Structural Equation Modeling , 1995 .

[10]  J. Forgas Mood and judgment: the affect infusion model (AIM). , 1995, Psychological bulletin.

[11]  J. Russell,et al.  Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. , 1999, Journal of personality and social psychology.

[12]  C. Heath,et al.  Emotional Selection in Memes : The Case of Urban Legends Chip Heath , 2004 .

[13]  J. Gross Emotion regulation: affective, cognitive, and social consequences. , 2002, Psychophysiology.

[14]  U. Dholakia,et al.  Auction or agent (or both)? A study of moderators of the herding bias in digital auctions , 2002 .

[15]  Sigal G. Barsade The Ripple Effect: Emotional Contagion and its Influence on Group Behavior , 2002 .

[16]  Shankar Ganesan,et al.  Service failure and recovery: The impact of relationship factors on customer satisfaction , 2003 .

[17]  David Obstfeld Social Networks, the Tertius Iungens Orientation, and Involvement in Innovation , 2005 .

[18]  Christian Homburg,et al.  Responsiveness to Customers and Competitors:The Role of Affective and Cognitive Organizational Systems , 2007 .

[19]  Ross A. Thompson,et al.  Emotion regulation: Conceptual foundations , 2007 .

[20]  D. Seibold,et al.  Group Argument , 2007 .

[21]  V. Mittal,et al.  Customer Complaining: The Role of Tie Strength and Information Control , 2008 .

[22]  Carl F. Mela,et al.  Customer Channel Migration , 2008 .

[23]  Joel B. Cohen,et al.  The Nature and Role of Affect in Consumer Behavior , 2008 .

[24]  Dhruv Grewal,et al.  The Effect of Compensation on Repurchase Intentions in Service Recovery , 2008 .

[25]  Richard G. McFarland,et al.  Supply Chain Contagion , 2008 .

[26]  Arun Sundararajan,et al.  Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks , 2009, Proceedings of the National Academy of Sciences.

[27]  J. Goldenberg,et al.  The Role of Hubs in the Adoption Process , 2009 .

[28]  P. Allison Fixed Effects Regression Models , 2009 .

[29]  Gerardine DeSanctis,et al.  Enacting language games: the development of a sense of ‘we‐ness’ in online forums , 2009, Inf. Syst. J..

[30]  J. Pennebaker,et al.  Language style matching in writing: synchrony in essays, correspondence, and poetry. , 2010, Journal of personality and social psychology.

[31]  David R. Seibold,et al.  Extending the Conversational Argument Coding Scheme in Studies of Argument Quality in Group Deliberations , 2010 .

[32]  M. Gelfand,et al.  The Road to Forgiveness: a Meta-analytic Synthesis of Its Situational and Dispositional Correlates This Article Has Been Corrected. See Last Page , 2022 .

[33]  J. Gross,et al.  Emotion-Regulation Choice , 2011, Psychological science.

[34]  M. Sarvary,et al.  Network Effects and Personal Influences: The Diffusion of an Online Social Network , 2011 .

[35]  Eric M. Schwartz,et al.  What Drives Immediate and Ongoing Word of Mouth? , 2011 .

[36]  Joost Broekens,et al.  Modeling emotional contagion based on experimental evidence for moderating factors , 2012 .

[37]  P. Leeflang,et al.  Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing , 2012 .

[38]  Katherine L. Milkman,et al.  What Makes Online Content Viral? , 2012 .

[39]  Martin Wetzels,et al.  More than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates , 2013 .

[40]  Dhruv Grewal,et al.  Understanding social media effects across seller, retailer, and consumer interactions , 2013, Journal of the Academy of Marketing Science.

[41]  David A. Schweidel,et al.  Listening in on Social Media: A Joint Model of Sentiment and Venue Format Choice , 2014 .

[42]  Kathleen M. Carley,et al.  Understanding online firestorms: Negative word-of-mouth dynamics in social media networks , 2014 .

[43]  Stephan Ludwig,et al.  Take Their Word for It: The Symbolic Role of Linguistic Style Matches in User Communities , 2014, MIS Q..

[44]  Jonah A. Berger Word of mouth and interpersonal communication: A review and directions for future research , 2014 .

[45]  E. Hatfield,et al.  New Perspectives on Emotional Contagion: A Review of Classic and Recent Research on Facial Mimicry and Contagion , 2014 .

[46]  Camilla Vásquez,et al.  The Discourse of Online Consumer Reviews , 2014 .

[47]  Tammo H. A. Bijmolt,et al.  Dynamic Effects of Social Influence and Direct Marketing on the Adoption of High-Technology Products , 2014 .

[48]  Sunder Kekre,et al.  The Squeaky Wheel Gets the Grease - An Empirical Analysis of Customer Voice and Firm Intervention on Twitter , 2015, Mark. Sci..

[49]  Amit M. Joshi,et al.  A Meta-Analysis of Electronic Word-of-Mouth Elasticity , 2015 .

[50]  Ryan L. Boyd,et al.  The Development and Psychometric Properties of LIWC2015 , 2015 .

[51]  Michael R. Sciandra,et al.  Is it What You Say or How You Say It? How Content Characteristics Affect Consumer Engagement with Brands on Facebook , 2015 .

[52]  The Impact of Service Recovery Strategies on Consumer Responses: a Conceptual Model and Meta-Analysis , 2015 .

[53]  Bernhard Rieder,et al.  Data critique and analytical opportunities for very large Facebook Pages: Lessons learned from exploring “We are all Khaled Said” , 2015, Big Data Soc..

[54]  Christian Homburg,et al.  Measuring and Managing Consumer Sentiment in an Online Community Environment , 2015 .

[55]  V. Kumar,et al.  Investigating how Word-of-Mouth Conversations about Brands Influence Purchase and Retransmission Intentions , 2016 .

[56]  Kevin Lane Keller,et al.  Integrating Marketing Communications: New Findings, New Lessons, and New Ideas , 2016 .

[57]  P. K. Kannan,et al.  From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior , 2016 .

[58]  William Rand,et al.  Brand Buzz in the Echoverse , 2016 .

[59]  H. Sattler,et al.  Each can help or hurt: Negative and positive word of mouth in social network brand communities , 2016 .

[60]  Jonah Berger,et al.  Word of mouth and interpersonal communication , 2016 .

[61]  Darren W. Dahl,et al.  The Benefit of Becoming Friends: Complaining after Service Failures Leads Customers with Strong Ties to Increase Loyalty , 2017 .

[62]  Gary Hsieh,et al.  Send Me a Different Message: Utilizing Cognitive Space to Create Engaging Message Triggers , 2017, CSCW.

[63]  Katja Hutter,et al.  Firestorms: Modeling conflict diffusion and management strategies in online communities , 2017, J. Strateg. Inf. Syst..

[64]  M. Dekimpe,et al.  Marketing research on product-harm crises: a review, managerial implications, and an agenda for future research , 2017 .

[65]  Timothy Gubler,et al.  Doing Well by Making Well: The Impact of Corporate Wellness Programs on Employee Productivity , 2017 .

[66]  P. Boyer,et al.  Origins of sinister rumors: A preference for threat-related material in the supply and demand of information , 2018 .

[67]  Derek D. Rucker,et al.  The Evaluative Lexicon 2.0: The measurement of emotionality, extremity, and valence in language , 2017, Behavior Research Methods.

[68]  Jing Peng,et al.  Network Overlap and Content Sharing on Social Media Platforms , 2018, Journal of Marketing Research.

[69]  Vamsi K. Kanuri,et al.  Scheduling Content on Social Media: Theory, Evidence, and Application , 2018, Journal of Marketing.

[70]  A. Chaudhry,et al.  When and how Managers' Responses to Online Reviews Affect Subsequent Reviews , 2018 .

[71]  Dokyun Lee,et al.  Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook , 2017, Manag. Sci..

[72]  How can firms stop customer revenge? The effects of direct and indirect revenge on post-complaint responses , 2018, Journal of the Academy of Marketing Science.

[73]  Rebecca Jen-Hui Wang,et al.  Automated Text Analysis for Consumer Research , 2018 .

[74]  Brett Hollenbeck Online Reputation Mechanisms and the Decreasing Value of Chain Affiliation , 2018, Journal of Marketing Research.

[75]  Judith A. Chevalier,et al.  Channels of Impact: User Reviews When Quality is Dynamic and Managers Respond , 2017, Mark. Sci..

[76]  D. Mahr,et al.  Cutting through Content Clutter: How Speech and Image Acts Drive Consumer Sharing of Social Media Brand Messages , 2019 .