Abstract
Research on negative brand relationships has gained traction over the years. However, most of this research has focussed on the cases involving product or service failure by a firm, leaving conflicts arising as a result of values and ethics espoused by a firm relatively unexplored. In this research, we focus on one such case and explore the factors that influence virality of social media content in the context of Consumer Brand Sabotage (CBS). Based on a week of Twitter data pertaining to specific hashtags associated with a brand’s sabotage, we explore how tweet related attributes affect the potential for amplification of the tweets related to the event. We categorize the factors as informational, interactional, and creator specific, and build machine learning (ML) models to predict the retweet likelihood of CBS tweets. We find that while informational factors associated with the tweets (such as, hashtags, URLs and emotions) are important to predict the diffusion of CBS-related tweets, this was not the case for interactional factors (such as, reply, like, quote, etc.). For creator factors, we found that considering the number of followers of the creator in the ML models reduced the predictability of diffusion of CBS-related tweets, and found verified accounts to be of little importance as well. We discuss the implications of these findings for practice and research, and present scope for future research.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The traditional word of mouth literature has long noted the negative effects of negative word of mouth such as its influence on consumer judgements (Herr et al. 1991).
References
Alba JW, Lutz RJ (2013) Broadening (and narrowing) the scope of brand relationships. J Consum Psychol 23:265–268. https://doi.org/10.1016/j.jcps.2013.01.005
Algharabat R, Rana NP, Alalwan AA et al (2020) Investigating the antecedents of customer brand engagement and consumer-based brand equity in social media. J Retail Consum Serv 53:101767. https://doi.org/10.1016/j.jretconser.2019.01.016
Atwal G, Bryson D, Kaiser M (2020) The chopsticks debacle: how brand hate flattened Dolce & Gabbana in China. J Bus Strateg 43:37–43. https://doi.org/10.1108/JBS-07-2020-0160
Barrett M, Oborn E, Orlikowski W (2016) Creating value in online communities: the sociomaterial configuring of strategy, platform, and stakeholder engagement. Inf Syst Res. https://doi.org/10.1287/isre.2016.0648
Berger J, Milkman KL (2012) What makes online content viral? J Mark Res XLIX:192–205
Bliss CA, Kloumann IM, Harris KD et al (2012) Twitter reciprocal reply networks exhibit assortativity with respect to happiness. J Comput Sci 3:388–397. https://doi.org/10.1016/j.jocs.2012.05.001
Bollen J, Mao H, Pepe A (2011) Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: Proceedings of the fifth international AAAI conference on weblogs and social media, pp 450–453
Borah A, Tellis GJ (2016) Halo (Spillover) effects in social media: do product recalls of one brand hurt or help rival brands? J Mark Res 53:143–160. https://doi.org/10.1509/jmr.13.0009
Borges-Tiago MT, Tiago F, Cosme C (2019) Exploring users’ motivations to participate in viral communication on social media. J Bus Res 101:574–582. https://doi.org/10.1016/j.jbusres.2018.11.011
Boyd DM (2010) Social network sites as networked publics: affordances, dynamics, and implications. In: Papacharissi Z (ed) Networked self: identity, community, and culture on social network sites. pp 39–58
Boyd D, Golder S, Lotan G (2010) Tweet, Tweet, Retweet: conversational aspects of Retweeting on Twitter. In: 2010 43rd Hawaii international conference on system sciences. IEEE, Honolulu, HI, pp 1–10
Bradley A (2015) Battling brand sabotage: The Angry Critic. In: www.entrepreneur.com/. https://www.entrepreneur.com/article/249824
Brodie RJ, Ilic A, Juric B, Hollebeek L (2013) Consumer engagement in a virtual brand community: an exploratory analysis. J Bus Res 66:105–114. https://doi.org/10.1016/j.jbusres.2011.07.029
Brooks S (2017) How to spot and stop brand sabotage. Big Ideas. https://medium.com/texas-mccombs/how-to-spot-and-stop-brand-sabotage-71e06e53229. Accessed 16 Mar 2023
Bruns A (2012) How long is a Tweet? Dynamic conversation networks on Twitter using Gawk and Gephi. Inf Commun Soc 15:1323–1351. https://doi.org/10.1080/1369118X.2011.635214
Bruns A, Burgess J (2011) The use of twitter hashtags in the formation of ad hoc publics. In: Proceedings of the 6th European consortium for political research general conference
Bryson D, Atwal G (2018) Brand hate: the case of Starbucks in France. Br Food J 121:172–182. https://doi.org/10.1108/BFJ-03-2018-0194
Cha M, Haddadi H, Benevenuto, F, Gummadi KP (2010) Measuring user influence in Twitter: the million follower fallacy. In: Proceedings of the fourth international AAAI conference on weblogs and social media. p 8
Chen Y, Ganesan S, Liu Y (2009) Does a firm’s product-recall strategy affect its financial value? An examination of strategic alternatives during product-harm crises. J Mark 73:214–226. https://doi.org/10.1509/jmkg.73.6.214
Clark R (2016) “Hope in a hashtag”: the discursive activism of #WhyIStayed. Fem Media Stud 16:788–804. https://doi.org/10.1080/14680777.2016.1138235
Curina I, Francioni B, Hegner SM, Cioppi M (2020) Brand hate and non-repurchase intention: a service context perspective in a cross-channel setting. J Retail Consum Serv 54:102031. https://doi.org/10.1016/j.jretconser.2019.102031
D’Arco M, Marino V, Resciniti R (2019) How to (not) survive a social media firestorm: The Dolce & Gabbana’s Ad Debacle in China. In: Martínez-López FJ, Gázquez-Abad JC, Roggeveen A (eds) Advances in national brand and private label marketing. Springer, Cham, pp 181–189
DeMasi O, Mason D, Ma J (2016) Understanding communities via hashtag engagement: a clustering based approach. In: Tenth international AAAI conference on web and social media
Dolan R, Conduit J, Fahy J, Goodman S (2016) Social media engagement behaviour: a uses and gratifications perspective. J Strateg Mark 24:261–277. https://doi.org/10.1080/0965254X.2015.1095222
Erz A, Marder B, Osadchaya E (2018) Hashtags: motivational drivers, their use, and differences between influencers and followers. Comput Hum Behav 89:48–60. https://doi.org/10.1016/j.chb.2018.07.030
Fetscherin M (2019) The five types of brand hate: how they affect consumer behavior. J Bus Res 101:116–127. https://doi.org/10.1016/j.jbusres.2019.04.017
Garimella K, Weber I, De Choudhury M (2016) Quote RTs on Twitter: usage of the new feature for political discourse. In: Proceedings of the 8th ACM conference on web science. ACM, Hannover Germany, pp 200–204
Gelbrich K (2010) Anger, frustration, and helplessness after service failure: coping strategies and effective informational support. J Acad Mark Sci 38:567–585. https://doi.org/10.1007/s11747-009-0169-6
Goh KY, Heng CS, Lin Z (2013) Social media brand community and consumer behavior: quantifying the relative impact of user- and marketer-generated content. Inf Syst Res 24:88–107. https://doi.org/10.1287/isre.1120.0469
Gómez M, Lopez C, Molina A (2019) An integrated model of social media brand engagement. Comput Hum Behav 96:196–206. https://doi.org/10.1016/j.chb.2019.01.026
Gonçalves RGF (2019) Brand sabotage: managing social media and reputational crises in utility companies. PhD thesis, ISCTE-Instituto Universitario de Lisboa (Portugal)
Gray A (2019) Brands take a stand for good: the effect of brand activism on social media engagement. Honors Theses and Capstones
Grégoire Y, Salle A, Tripp TM (2015) Managing social media crises with your customers: the good, the bad, and the ugly. Bus Horiz 58:173–182. https://doi.org/10.1016/j.bushor.2014.11.001
Gruzd A, Doiron S, Mai P (2011) Is happiness contagious online? A case of Twitter and the 2010 Winter Olympics. In: 2011 44th Hawaii international conference on system sciences. IEEE, Kauai, HI, pp 1–9
Gruzd A, Mai P (2020) Going viral: how a single tweet spawned a COVID-19 conspiracy theory on Twitter. Big Data Soc. https://doi.org/10.1177/2053951720938405
Guerra PC, Souza RCSNP, Assuncao RM, Meira Jr. W (2017) Antagonism also flows through retweets: the impact of out-of-context quotes in opinion polarization analysis. In: Proceedings of the eleventh international AAAI conference on web and social media (ICWSM 2017). p 4
Gupta C (2009) Hindu Women, Muslim Men: Love Jihad and conversions. Econ Pol Wkly 44:13–15
Han Y, Lappas T, Sabnis G (2020) The importance of interactions between content characteristics and creator characteristics for studying virality in social media. Inf Syst Res 31:576–588. https://doi.org/10.1287/ISRE.2019.0903
Hansen N, Kupfer A-K, Hennig-Thurau T (2018) Brand crises in the digital age: the short- and long-term effects of social media firestorms on consumers and brands. Int J Res Mark 35:557–574. https://doi.org/10.1016/j.ijresmar.2018.08.001
Hegner SM, Fetscherin M, van Delzen M (2017) Determinants and outcomes of brand hate. J Prod Brand Manag 26:13–25. https://doi.org/10.1108/JPBM-01-2016-1070
Helme-Guizon A, Magnoni F (2019) Consumer brand engagement and its social side on brand-hosted social media: how do they contribute to brand loyalty? J Mark Manag 35:716–741. https://doi.org/10.1080/0267257X.2019.1599990
Herr PM, Kardes FR, Kim J (1991) Effects of word-of-mouth and product-attribute information on persuasion: an accessibility-diagnosticity perspective. J Consum Res 17:454–462
Hoang T-A, Lim E-P (2012) Virality and susceptibility in information diffusions. In: Proceedings of the sixth international AAAI conference on weblogs and social media. pp 146–153
Hogg MK, Banister EN, Stephenson CA (2009) Mapping symbolic (anti-)consumption. J Bus Res 62:148–159. https://doi.org/10.1016/j.jbusres.2008.01.022
Hollebeek LD, Glynn MS, Brodie RJ (2014) Consumer brand engagement in social media: conceptualization, scale development and validation. J Interact Mark 28:149–165. https://doi.org/10.1016/j.intmar.2013.12.002
Hsu L, Lawrence B (2016) The role of social media and brand equity during a product recall crisis: a shareholder value perspective. Int J Res Mark 33:59–77. https://doi.org/10.1016/j.ijresmar.2015.04.004
Huang M, Bhattacherjee A, Wong C (2018) Information & management gatekeepers ’ innovative use of IT: an absorptive capacity model at the unit level. Inf Manag 55:235–244. https://doi.org/10.1016/j.im.2017.06.001
Hutto CJ, Gilbert E (2014) VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the eighth international AAAI conference on weblogs and social media. p 10
Jain K, Sharma I (2019) Negative outcomes of positive brand relationships. J Consum Mark 36:986–1002. https://doi.org/10.1108/JCM-07-2018-2764
Jang Y, Park C-H, Seo Y-S (2019) Fake news analysis modeling using quote Retweet. Electronics 8:1377. https://doi.org/10.3390/electronics8121377
Jenders M, Kasneci G, Naumann F (2013) Analyzing and predicting viral tweets. In: Proceedings of the 22nd international conference on world wide web. ACM, Rio de Janeiro Brazil, pp 657–664
Johnson AR, Matear M, Thomson M (2011) A coal in the heart: self-relevance as a post-exit predictor of consumer anti-brand actions. J Consum Res 38:108–125. https://doi.org/10.1086/657924
Kähr A, Nyffenegger B, Krohmer H, Hoyer WD (2016) When hostile consumers wreak havoc on your brand: the phenomenon of consumer brand sabotage. J Mark 80:25–41. https://doi.org/10.1509/jm.15.0006
Kähr A, Nyffenegger B, Krohmer H, Hoyer WD (2018) When consumer brand sabotage harms other consumers relationship with the brand. In: ACR North American Advances
Kähr A, Nyffenegger B, Khamitov M et al (2019) How consumer brand sabotage affects observing consumers: buffering versus amplifying effects of consumers’ prior self-brand connections. In: ACR North American advances
Kaplan AM, Haenlein M (2011) Two hearts in three-quarter time: how to waltz the social media/viral marketing dance. Bus Horiz 54:253–263. https://doi.org/10.1016/j.bushor.2011.01.006
Kuo Y-F, Feng L-H (2013) Relationships among community interaction characteristics, perceived benefits, community commitment, and oppositional brand loyalty in online brand communities. Int J Inf Manag 33:948–962. https://doi.org/10.1016/j.ijinfomgt.2013.08.005
Kwon S, Ha S (2022) Examining identity- and bond-based hashtag community identification: the moderating role of self-brand connections. JRIM. https://doi.org/10.1108/JRIM-07-2021-0183
Lahuerta-Otero E, Cordero-Gutiérrez R, De la Prieta-Pintado F (2018) Retweet or like? That is the question. OIR 42:562–578. https://doi.org/10.1108/OIR-04-2017-0135
Lee J, Agrawal M, Rao HR (2015) Message diffusion through social network service: the case of rumor and non-rumor related tweets during Boston bombing 2013. Inf Syst Front 17:997–1005. https://doi.org/10.1007/s10796-015-9568-z
Liao S, Chou CY, Lin T-H (2015) Adverse behavioral and relational consequences of service innovation failure. J Bus Res 68:834–839. https://doi.org/10.1016/j.jbusres.2014.11.037
Liou D-K, Chih W-H, Hsu L-C, Huang C-Y (2016) Investigating information sharing behavior: the mediating roles of the desire to share information in virtual communities. Inf Syst E-Bus Manag 14:187–216. https://doi.org/10.1007/s10257-015-0279-2
Liu Z, Liu L, Li H (2012) Determinants of information retweeting in microblogging. Internet Res 22:443–466. https://doi.org/10.1108/10662241211250980
Majchrzak A, Faraj S, Kane GC, Azad B (2013) The contradictory influence of social media affordances on online communal knowledge sharing. J Comput Mediat Commun 19:38–55. https://doi.org/10.1111/jcc4.12030
Malhotra A, Malhotra CK, See A (2012) How to Get Your Messages Retweeted. MIT Sloan Manag Rev Winter 53:61–66
Mccollough MA, Berry LL, Yadav MS (2000) An empirical investigation of customer satisfaction after service failure and recovery. J Serv Res 3:121–137. https://doi.org/10.1177/109467050032002
Meier F, Elsweiler D (2019) Studying politicians’ information sharing on social media. In: Proceedings of the 2019 conference on human information interaction and retrieval. ACM, Glasgow Scotland UK, pp 237–241
Meng J, Peng W, Tan P-N et al (2018) Diffusion size and structural virality: the effects of message and network features on spreading health information on twitter. Comput Hum Behav 89:111–120. https://doi.org/10.1016/j.chb.2018.07.039
Morris MR, Counts S, Roseway A et al (2012) Tweeting is believing? Understanding microblog credibility perceptions. In: Proceedings of the ACM 2012 conference on computer supported cooperative work—CSCW’12. ACM Press, Seattle, Washington, USA, p 441
Muñoz-Expósito M, Oviedo-García MÁ, Castellanos-Verdugo M (2017) How to measure engagement in Twitter: advancing a metric. Internet Res 27:1122–1148. https://doi.org/10.1108/IntR-06-2016-0170
Nam H, Joshi YV, Kannan PK (2017) Harvesting brand information from social tags. J Mark 81:88–108. https://doi.org/10.1509/jm.16.0044
Naveed N, Gottron T, Kunegis J, Alhadi AC (2011) Bad news travel fast: a content-based analysis of interestingness on Twitter. In: Proceedings of the 3rd international web science conference on—WebSci’11. ACM Press, Koblenz, pp 1–7
Nesi P, Pantaleo G, Paoli I, Zaza I (2018) Assessing the reTweet proneness of tweets: predictive models for retweeting. Multimed Tools Appl 77:26371–26396. https://doi.org/10.1007/s11042-018-5865-0
Nyffenegger B, Kähr A, Krohmer H, Hoyer WD (2018) How should retailers deal with consumer sabotage of a manufacturer brand? J Assoc Consum Res 3:379–395. https://doi.org/10.1086/698875
O’Dea B, Achilles MR, Larsen ME et al (2018) The rate of reply and nature of responses to suicide-related posts on Twitter. Internet Interv 13:105–107. https://doi.org/10.1016/j.invent.2018.07.004
Pang N, Ng J (2016) Twittering the Little India Riot: audience responses, information behavior and the use of emotive cues. Comput Hum Behav 54:607–619. https://doi.org/10.1016/j.chb.2015.08.047
Paul I, Khattar A, Kumaraguru P et al (2019) Elites tweet? Characterizing the Twitter verified user network. In: 2019 IEEE 35th international conference on data engineering workshops (ICDEW). IEEE, Macao, Macao, pp 278–285
Pfeffer J, Zorbach T, Carley KM (2014) Understanding online firestorms: negative word-of-mouth dynamics in social media networks. J Mark Commun 20:117–128. https://doi.org/10.1080/13527266.2013.797778
Pullig C (2006) Attitude basis, certainty, and challenge alignment: a case of negative brand publicity. J Acad Mark Sci 34:528–542. https://doi.org/10.1177/0092070306287128
Ramírez O, Veloutsou C, Morgan-Thomas A (2019) I hate what you love: brand polarization and negativity towards brands as an opportunity for brand management. J Prod Brand Manag 28:614–632. https://doi.org/10.1108/JPBM-03-2018-1811
Rodrigues C, Brandão A, Rodrigues P (2021) I can’t stop hating you: an anti-brand-community perspective on apple brand hate. J Prod Brand Manag 30:1115–1133
Roma P, Aloini D (2019) How does brand-related user-generated content differ across social media? Evidence reloaded. J Bus Res 96:322–339. https://doi.org/10.1016/j.jbusres.2018.11.055
Romero DM, Meeder B, Kleinberg J (2011) Diifferences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter. In: WWW 2011. Association for computing machinery, pp 695–704
Sarkar A, Sarkar JG, Sreejesh S (2021) Managing customers’ undesirable responses towards hospitality service brands during service failure: the moderating role of other customer perception. Int J Hosp Manag 94:102873. https://doi.org/10.1016/j.ijhm.2021.102873
Scholz J, Smith AN (2019) Branding in the age of social media firestorms: how to create brand value by fighting back online. J Mark Manag 35:1100–1134. https://doi.org/10.1080/0267257X.2019.1620839
Schwaiger J, Hammerl T, Florian J, Leist S (2021) UR: SMART—a tool for analyzing social media content. Inf Syst E-Bus Manag 19:1275–1320. https://doi.org/10.1007/s10257-021-00541-4
Siano A, Confetto MG, Vollero A, Covucci C (2022) Redefining brand hijacking from a non-collaborative brand co-creation perspective. Brand Manag 31:17
Skaza J, Blais B (2017) Modeling the infectiousness of Twitter hashtags. Phys A 465:289–296. https://doi.org/10.1016/j.physa.2016.08.038
Smith AN, Fischer E, Yongjian C (2012) How does brand-related user-generated content differ across YouTube, Facebook, and Twitter? J Interact Mark 26:102–113. https://doi.org/10.1016/j.intmar.2012.01.002
Soboleva A, Burton S, Mallik G, Khan A (2017) ‘Retweet for a Chance to…’: an analysis of what triggers consumers to engage in seeded eWOM on Twitter. J Mark Manag 33:1120–1148. https://doi.org/10.1080/0267257X.2017.1369142
Stieglitz S, Dang-Xuan L (2013) Emotions and information diffusion in social media—sentiment of microblogs and sharing behavior. J Manag Inf Syst 29:217–248. https://doi.org/10.2753/MIS0742-1222290408
Suh B, Hong L, Pirolli P, Chi EH (2010) Want to be Retweeted? Large scale analytics on factors impacting retweet in Twitter Network. In: 2010 IEEE second international conference on social computing. IEEE, Minneapolis, MN, USA, pp 177–184
Sundar SS (2008) The MAIN model: a heuristic approach to understanding technology effects on credibility. In: Metzger MJ, Flanagin AJ (eds) Digital media, youth, and credibility. The MIT Press, Cambridge, pp 73–100
Sussan F, Hall R, Meamber LA (2012) Introspecting the spiritual nature of a brand divorce. J Bus Res 65:520–526. https://doi.org/10.1016/j.jbusres.2011.02.031
Treem JW, Leonardi PM (2012) Social media use in organizations: exploring the affordances of visibility, editability, persistence, and association. Commun Yearb 36:143–189
Truong H-B, Jesudoss SP, Molesworth M (2022) Consumer mischief as playful resistance to marketing in Twitter hashtag hijacking. J Consum Behav 21:828–841
Tsugawa S, Ohsaki H (2017) On the relation between message sentiment and its virality on social media. Soc Netw Anal Min 7:19. https://doi.org/10.1007/s13278-017-0439-0
Tsur O, Rappoport A (2012) What’s in a hashtag? Content based prediction of the spread of ideas in microblogging communities. In: WSDM’12. pp 643–652
Vaidya T, Votipka D, Mazurek ML, Sherr M (2019) Does being verified make you more credible? Account verification’s effect on tweet credibility. In: Proceedings of the 2019 CHI conference on human factors in computing systems. ACM, Glasgow Scotland UK, pp 1–13
van Zoonen W, van der Meer T (2015) The importance of source and credibility perception in times of crisis: crisis communication in a socially mediated era. J Public Relat Res 27:371–388. https://doi.org/10.1080/1062726X.2015.1062382
Varol O, Ferrara E, Davis C et al (2017) Online human-bot interactions: Detection, estimation, and characterization. In: Proceedings of the international AAAI conference on web and social media. pp 280–289
Verhagen T, Nauta A, Feldberg F (2013) Negative online word-of-mouth: behavioral indicator or emotional release? Comput Hum Behav 29:1430–1440. https://doi.org/10.1016/j.chb.2013.01.043
von Hoffen M, Hagge M, Betzing JH, Chasin F (2018) Leveraging social media to gain insights into service delivery: a study on Airbnb. Inf Syst E-Bus Manag 16:247–269. https://doi.org/10.1007/s10257-017-0358-7
Vosoughi S, Mohsenvand M, Roy D (2017) Rumor gauge: predicting the veracity of Rumors on Twitter. ACM Trans Knowl Discov Data 11:1–36. https://doi.org/10.1145/3070644
Wang R, Liu W, Gao S (2016) Hashtags and information virality in networked social movement: examining hashtag co-occurrence patterns. Online Inf Rev 40:850–866. https://doi.org/10.1108/OIR-12-2015-0378
Wang Y, Liu X, Luo J (2017) Tactics and tallies: a study of the 2016 U.S. Presidential Campaign Using Twitter `Likes’. In: The workshops of the eleventh international AAAI conference on web and social media. p 8 Zhang
Wang X, Chen L, Shi J, Peng T-Q (2019) What makes cancer information viral on social media? Comput Hum Behav 93:149–156. https://doi.org/10.1016/j.chb.2018.12.024
Wang K, Tai JCF, Chang H-L (2021) Influences of place attachment and social media affordances on online brand community continuance. Inf Syst E-Bus Manag 19:459–493. https://doi.org/10.1007/s10257-019-00418-7
Watts DJ, Dodds PS (2007) Influentials, networks, and public opinion formation. J Consum Res 34:441–458. https://doi.org/10.1086/518527
Westerman D, Spence PR, Van Der Heide B (2012) A social network as information: the effect of system generated reports of connectedness on credibility on Twitter. Comput Hum Behav 28:199–206. https://doi.org/10.1016/j.chb.2011.09.001
Xu Z, Yang Q (2012) Analyzing user retweet behavior on Twitter. In: 2012 IEEE/ACM international conference on advances in social networks analysis and mining. IEEE, Istanbul, pp 46–50
Yadav A, Chakrabarti S (2022) Brand hate: A systematic literature review and future research agenda. Int J Consumer Stud. https://doi.org/10.1111/ijcs.12772
Yardi S, Romero DM, Schoenebeck G, Boyd D (2010) Detecting spam in a twitter network. First Monday. https://doi.org/10.5210/fm.v15i1.2793
Yoo E, Rand W, Eftekhar M, Rabinovich E (2016) Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises. J Oper Manag 45:123–133. https://doi.org/10.1016/j.jom.2016.05.007
Zarantonello L, Romani S, Grappi S, Bagozzi RP (2016) Brand hate. J Prod Brand Manag 25:11–25. https://doi.org/10.1108/JPBM-01-2015-0799
Zhang C, Laroche M (2020) Brand hate: a multidimensional construct. J Prod Brand Manag 30:392–414. https://doi.org/10.1108/JPBM-11-2018-2103
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Bhatia, R., Gupta, A., Vimalkumar, M. et al. Factors affecting Consumer Brand Sabotage virality: a study of an Indian brand #boycott. Inf Syst E-Bus Manage (2023). https://doi.org/10.1007/s10257-023-00628-0
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10257-023-00628-0