How harmful are online firestorms for brands? An approach to the phenomenon from the participant level

Elena Delgado-Ballester (Department of Marketing, Faculty of Business, Campus de Espinardo, Murcia, Spain)
Inés López-López (Department of Marketing, Faculty of Business, Campus de Espinardo, Murcia, Spain)
Alicia Bernal-Palazón (Department of Marketing, Faculty of Business, Campus de Espinardo, Murcia, Spain)

Spanish Journal of Marketing - ESIC

ISSN: 2444-9709

Article publication date: 3 December 2019

Issue publication date: 15 April 2020




Adopting an exploratory approach, this paper aims to focus on the potential negative consequences that online firestorms (OFs) might have on consumer–brand relationships. Specifically, the authors focus on the individual level through taking a close look at the content that users generate during these attacks.


The authors conducted content analysis to study four recent brand-related OFs that occurred on Twitter.


The results show that brands are at the core of the users’ conversations, although other actors, such as competing firms, can also be affected. Negative comments greatly exceed positive ones. Actions against the brand, both passive (avoidance) and active (vengeance), emerged during the OFs.

Research limitations/implications

The exploratory nature of the study could cast doubt on the generalizability of the results. Moreover, the number of OF analyzed is limited, although they represent an interesting variety of brand misconducts.

Practical implications

Nowadays, brands are publicly scrutinized through social networks, as the networks enable users to speak out about brands’ perceived mistakes and wrongdoings. This paper confirms that managers should monitor, understand and try to respond to OFs to minimize their impact.


Online firestorms are a recent phenomenon that has gained attention finally, as they can reach hundreds of users in real-time and can involve a huge amount of comments posted online against a brand. These attacks could severely damage the brand, even when there is no strong evidence of the posted content being true. This paper adds to the scarce literature on the topic and analyzes the negative effects for brands.


Este trabajo exploratorio identifica posibles consecuencias negativas que los ataques colaborativos online contra las marcas podrían tener en las relaciones marca-consumidor. Para ello se adopta la perspectiva del participante en el ataque colaborativo mediante un análisis pormenorizado del contenido que los usuarios generan durante el tiempo que dura el ataque contra la marca.


Se ha llevado a cabo un análisis del contenido generado durante cuatro ataques colaborativos recientes que protagonizaron distintas marcas en Twitter.


Del análisis de contenido se desprende que las marcas son las protagonistas de las conversaciones de los usuarios aunque otros actores tales como marcas competidoras también han sido mencionadas. Los comentarios negativos predominan sobre los positivos. También se identifican acciones contra la marca, tanto pasivas (evitar comprarla) como activas (venganza).


El carácter exploratorio del estudio impide la generalización de sus resultados. El número de ataques colaborativos analizados son limitados aunque representan una interesante variedad de errores y malas conductas por parte de las marcas.

Implicaciones prácticas

Actualmente las marcas son objeto de escrutinio público en las redes sociales en tanto que facilitan la interacción entre usuarios y el comportamiento informativo de cualquier tipo. Este trabajo confirma que los responsables de marca deben controlar, comprender y tratar de responder a estos ataques colaborativos contra las marcas para minimizar su impacto.


Los ataques colaborativos contra las marcas son un fenómeno que está recibiendo una enorme atención últimamente en la medida en la que miles de usuarios participan en ellos en tiempo real generando una enorme cantidad de comentarios online contra una marca. Estos ataques pueden dañarla seriamente, incluso ante la ausencia de evidencias claras de que el contenido que se comparte sea cierto. Este trabajo contribuye a la escasa literatura existente sobre los ataques colaborativos online contra las marcas y analiza sus posibles efectos negativos.

Tipo de artículo

Artículo de investigación



Delgado-Ballester, E., López-López, I. and Bernal-Palazón, A. (2020), "How harmful are online firestorms for brands? An approach to the phenomenon from the participant level", Spanish Journal of Marketing - ESIC, Vol. 24 No. 1, pp. 133-151.



Emerald Publishing Limited

Copyright © 2019, Elena Delgado-Ballester, Inés López-López and Alicia Bernal-Palazón.


Published in Spanish Journal of Marketing – ESIC. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at

1. Introduction

Since Fournier (1998) introduced the term “consumer–brand relationships,” a research stream has emerged to understand customers’ interactions with brands because it is a powerful mechanism to build customer brand loyalty (Khamitov et al., 2019). As a result, a plethora of useful constructs have been used to describe these interactions, such as trust and commitment (Chaudhuri and Holbrook, 2001; Delgado-Ballester and Munuera-Alemán, 2001); self–brand connection (Escalas and Bettman, 2003); brand attachment (Park et al., 2010) and brand love (Batra et al., 2012).

All these constructs have in common their focus on positive consumer–brand interactions. However, they do not represent the whole story of these interactions. Fournier and Alvarez (2013) demonstrated that negative brand relationships are more common than positive ones (55 per cent versus 45 per cent). Tenzer and Chalmers (2017) pointed out that 42 per cent of customers claimed to distrust brands and 37 per cent trusted brands less than they used to. Finally, the latest global Meaningful Brands® survey, published in February 2019, found that a staggering 77 per cent of brands could disappear and no one would care. This percentage was the highest since the Meaningful Brands® research began in 2008 (

The need to further understand negative brand interactions is evident, and several authors have called for research into negative brand relationships (Fournier and Alvarez, 2013; Romani et al., 2012) because a bias toward positive ones characterizes existing branding models. Furthermore, the existence of asymmetric effects of positive and negative interactions (Duhachek et al., 2007; Floh et al., 2013) makes it worth focusing on understanding and managing negative consumer–brand interactions, as negative information is more memorable, diagnostic and shared than positive information.

In online environments, these negative interactions are alarming because the interactive and engaging nature of social media platforms has empowered consumers to create brand meaning that is outside of the control of companies and can reach a large number of users (Gensler et al., 2013). They can share the initial message with others and change its intensity and meaning in different ways by making their own parodies. This chaotic and interactive situation resembles what Hennig-Thurau et al. (2013) refer to as “playing pinball” with active and networked consumers who share information that becomes multidirectional, interconnected and difficult to predict and control. Because of that the study of negative consumer–brand interactions in such environments requires setting a new research agenda to shed initial insights into the interactions’ consequences on brands (Hennig-Thurau et al., 2013). In this context, the research on collaborative brand attacks on social media is of special interest because they are a relatively new phenomenon that posits a challenge for brand management (Rauschnabel et al., 2016).

These collaborative brand attacks are known as “online firestorms (OFs)” (Pfeffer et al., 2014) or “Twitterstorms” when they happen on Twitter (Lamba et al., 2015). Regardless of the term used, these huge waves of outrage within a short period of time may put the brand in a difficult position. One single mistake, as perceived by consumers, can overshadow the effort and growing expenditure allocated to social media[1], as well as impair the brand image (Pfeffer et al., 2014). Furthermore, the buzz generated about the brand could be the result of sharing fake news created to deceive readers by disguising the shared information as authentic news (Baccarella et al., 2018).

The novelty and complexity of this phenomenon have opened a new line of research that is in its early infancy because no study has focused on exploring the implications that OFs might have for consumer–brand relationships. Will the customer who initiated the OF stop buying the brand’s products? Will he/she promote a boycott? Will other customers taking part in the OF follow and switch providers too? Will negative consumer emotions against the brand be elicited during the episode? Will the effect extend beyond the focal brand and affect competing firms? The purpose of the current study is to fill these gaps by analyzing the negative consequences that OFs have on consumer–brand relationships through taking a close look at the content that users generate during OFs. As participants’ comments may be the most informative content to address the former questions, the current study presents the results derived from content analysis of four recent OFs. Before going into the content analysis, we briefly review the literature to provide an overview of the current state of knowledge regarding OFs.

2. The dark side of social media

The academic literature has focused its efforts on the bright side of social networks. The interactive and engaging characteristics of social media platforms make them a very attractive tool for building brands and maintaining desirable consumer–brand relationships through social media interactions (Hudson et al., 2016).

However, this interactive nature also has a dark side. Recent studies highlighted that social networks have a negative dimension, as they put people, brands or society in general in undesirable situations (Grégoire et al., 2015). These situations can occur in different ways. One way is the tone that people use to communicate on social media because sometimes very high levels of aggressiveness and hostility are reached (Cipriani, 2012). The situations can also cause social relationship problems, as there is a high risk of users being deceived by people that impersonate others (Kwan and Skoric, 2013). Furthermore, in relation to the posted content, the freedom of expression and lack of control within social media give anyone the opportunity to publish any information, no matter whether it is true, which sometimes causes reputational problems for others if false information is published (Baccarella et al., 2018).

Brands are not oblivious to this (Scholz and Smith, 2019). On the contrary, much user-generated content (UGC) is brand related and has the potential to change consumers’ brand perceptions (Smith et al., 2012). For example, social networks provide consumers with greater power to voice their opinions, create and generate the brand content that are consumed by others, and even manipulate this content (Hennig-Thurau and Walsh, 2004). They also make it possible for consumers to directly attack, offend or speak badly about a brand without the brand having any control over what is said (Pfeffer et al., 2014). This generated and shared content can provoke a brand crisis because it can result in a significant deviation from the intended brand image, which might, in turn, result in the loss of consumers and brand value (Rauschnabel et al., 2016; Herhausen et al., 2019).

Twitter has attracted the attention of many researchers because of the virality with which specific brand posts suddenly spread. These spontaneous waves of buzzes are better known as “OFs.” Because the complex dynamics of OFs are unclear, it is assumed that brands that go through an OF might suffer from unforeseen and uncontrollable consequences for their images as a result of a decrease in short- and long-term brand perceptions (Hansen et al., 2018).

2.1 Online firestorms: definition and prior research

The most commonly accepted definition of an OF is “the sudden discharge of large quantities of messages containing negative word-of-mouth (WoM) and complaint behavior against a person, company or group in social media networks” (Pfeffer et al., 2014, p. 118). Grounded in past literature on negative WoM and its detrimental effects on brand evaluation, much of the literature agrees on accepting that OFs provoke similar and uncontrollable harmful consequences. Assuming the existence of these detrimental effects, much of the current OFs literature (Table I) adopts a macro-level perspective to approach this phenomenon. They have mainly aimed to detect the occurrence of OFs more efficiently (Drasch et al., 2015; Stich et al., 2014) and to provide some guidance on how to restrain their effects and counteract them through the use of appropriate management styles (Hauser et al., 2017), the use of both reactive and proactive marketing strategies (Rauschnabel et al., 2016; Stich et al., 2014) and the identification and effective use of brand supporters (Mochalova and Nanopoulos, 2014). To a lesser extent, another research line has paid more attention to the interaction of social media communication with traditional brand communications and the role of news media in covering OFs (Einwiller et al., 2017; Hauser et al., 2017; Hewett et al., 2016).

Another set of studies has embraced a micro-level perspective to identify individuals’ motivations to join OFs (Johnen et al., 2018) and the factors that determine their participation, such as social ties (Lamba et al., 2015), online anonymity (Rost et al., 2016) and the specific characteristics of the online context (Chan et al., 2018; Johnen et al., 2018).

This summary of the state of knowledge illustrates that the vast majority of studies have adopted a macro perspective to analyze OFs. However, no study has focused on identifying their implications on brands because it has been largely assumed that their detrimental effects have already been demonstrated by past empirical evidence on the effects of negative WoM. However, OFs differ from electronic WoM (Johnen et al., 2018), as they resemble more the characteristics of moral panic proposed by Goode and Ben-Yehuda (1994), such as concern, hostility, disproportionality and consensus. Although the findings of the negative WoM literature may be informative, their extrapolation to the context of OFs might not be appropriate, and new empirical evidence is required to explore and identify the specific effects they have on brands. In other words, understanding this UGC is critical for brands if they aim to dampen their potential negative effects not only in terms of brand knowledge but also regarding consumers’ reactions toward keeping or ending their relationships with the brand. Following this line of reasoning, the study of Grégoire et al. (2010) could be informative concerning some of the negative effects that could be expected. Although it does not focus on OFs, they suggest that in the event of a service failure, consumers can react by avoiding and rejecting the brand (e.g. a flight strategy). In other words, they might claim that they will avoid or stop consuming the brand. Another type of action against the brand is more aggressive because it implies a brand attack (Fournier and Alvarez, 2013). Attacking implies a stronger enemy relationship with the brand compared to avoidance/rejection because individuals engage in effortful actions against it (e.g. a fight strategy).

3. Method

This paper analyzes four recent OFs from a micro-level perspective: it focuses on the comments posted by individual users once a collaborative attack has started because users play a pivotal role in building brand stories, either positive or negative. Given the exploratory nature of the research questions proposed, content analysis is conducted.

3.1 Online firestorm identification

The study focused on OFs that occurred on Twitter. It is one of the most popular microblogging providers and because of its characteristics, it is generally agreed that it stands out in the propagation of OFs (Pfeffer et al., 2014). Furthermore, the openness and availability of the messages posted on it perfectly fit with the definition of OFs, as details about a breaking event or even post-purchase quality impressions about brands can spread quickly (Lamba et al., 2015) in a form of microblogging WoM named “the Twitter effect.”

To deal with a manageable number of OFs, some criteria were settled on before the data collection as follows:

  • the period under consideration was the past five years, from 2013 onwards;

  • brands/companies were the target of the OFs; and

  • the OF reached at least 1,000 retweets within a week. The initial tweet was the one with the highest number of retweets.

A Web search with keywords such as “firestorm,” “Twitter crisis” and “brand crisis on Twitter” was conducted. A total of four cases that met the previous criteria were identified. They exhibited diversity in aspects such as the initiator (a customer or the brand) and the industries to which the brands belonged. The initial tweet and the corresponding retweets, which were publicly available at the time of gathering the data, were downloaded. Basic information was collected for each firestorm (Table II).

3.2 Sample profile

Data on the sociodemographic profile of the sample is very limited because Twitter users choose, which data to provide on their profiles: photo, name and biography (e.g. hobbies and profession), and specific information about age or nationality is not available. For practical reasons, the researchers collected the sociodemographic information that appeared in the profiles of only those users who were highly involved in the episodes and participated extensively (e.g. those who posted three or more comments).

The number of the most participatory users differed depending on the case analyzed: Air Europa (6), Gil Stauffer (48), ElPozo (2) and Ballantine’s (8). On average, 38 per cent of the most participatory profiles corresponded to men, while 19 per cent were women. A significant percentage of these heavy participants had anonymous profiles (e.g. they provided neither a photo nor their name).

3.3 Coding procedure and categories

To know what is said about the brand during an OF, a scheme to be used for coding was drawn both from prior literature on social media networks and from an inductive analysis of brand-related UGC conducted by the researchers. Five main categories emerged to be analyzed in the UGC as follows:

  1. to whom was the UGC directed. It identifies the addressee with whom the user intended to initiate interpersonal communication. The use of @mentions indicates an underlying attempt to strike up a conversation with one or more specific Twitter users;

  2. Brand centrality: was the brand central or peripheral to the UGC? This variable referred to the role of the brand in the UGC. A post was considered brand related if the content was about the brand (its actions or behaviors, its products or services), regardless of whether the post was directed toward the brand or other users. Although it is assumed that the brand is at the core of the conversation when an OF occurs, no research has demonstrated the extent so far;

  3. Brand valence: it is quite a popular measure when describing social media interactions and it categorizes UGC in terms of valence (positive, negative or neutral) and nature (cognitive vs emotional). The UGC was coded as positive when it concerned favorable comments, while negative UGC was seen as consisting of unpleasant or unfavorable aspects. The UGC was coded as emotional when they expressed feelings, sensations or emotions, whereas it was categorized as cognitive when they contained reasons, motives or objective statements;

  4. Based on the researchers’ own criteria, the style of the comments was also coded (Table III) because it has not been previously coded. A thorough reading of the comments let us identify four recurring styles: surprise/disbelief, outrage, insult/offense and irony. To back up these categories with literature, we dug into relevant streams of research. Tsarenko and Strizhakova (2013) stated that some customers might experience outrage when coping with a service failure, as could be the case with an OF. Similarly, Krishnamurthy and Kucuk (2009) claimed that consumers commonly use insults with the aim of affecting brand value on anti-brand websites. Brunk and Blümelhuber (2011) stated that consumers tend to distrust a company and its communications after ethics-related problems. Finally, Makarem and Jae (2016) demonstrated that sarcasm (i.e. irony) also appears in the comments posted by consumers with non-instrumental motives during a boycott on Twitter; and

  5. Finally, the actions toward the brand suggested by the users were also quantified to find out what the most immediate consequences for the brand could be.

NVivo 10 software was used to code all the comments. As in other previous studies (Delgado-Ballester and Fernández-Sabiote, 2016; Parson, 2013), one of the researchers has acted as a coder because no hypotheses were to be tested and the only goal related to understanding this new phenomenon from the information it produced. After having coded all the comments, all members of the team reached an agreement about the codification made.

4. Analysis and results

To examine the consequences of OFs for consumer–brand relationships, descriptive analysis was conducted to understand the following:

  • brand-directed interactions and brand centrality in UGC;

  • how harmful consumers’ brand comments are; and

  • the types of actions promoted against the brand.

4.1 Brand-directed communication

The content analysis revealed that in all four cases, the brand was present in the users’ interpersonal interactions on Twitter (Table III) because explicit use of @mentions with the brand name was observed (e.g. @Gil_Stauffer). This indicates the existence of an underlying intention to initiate an interaction with the brand, but the intensity of this intention varied across the four OFs.

In the Ballantine’s case, more than half of the total posts were directed toward the brand (59.46 per cent). In the other three cases, the intentions to start interactions with other specific Twitter users through the use of @mentions or the inclusion of hashtags in the posts were higher. For example, for ElPozo, Air Europa and Gill Stauffer 88.18 per cent, 75.67 per cent and 96.92 per cent of comments were directed toward users, respectively. These high percentages of posts in which other Twitter users were mentioned might be explained by the seriousness of the issues in which these three brands were involved. In these three cases, their consumer orientation was questioned because their wrongdoings were associated with the quality of the products and services they sold. Consequently, making other Twitter users aware of the issue at hand required the users to put the effort into their follower networks, instead of interactions with the brand.

4.2 Brand centrality in user-generated content

The brand centrality in the UGC was quite evident in three cases because, in more than half of the comments, the focal brands were at the core of the conversations (Table III).

Interestingly, other brands were mentioned in the OFs. In the case of Air Europa, 6.7 per cent of the posts focused on stating that other companies of the same industry did not treat consumers the way that Air Europa did. Referring to Ballantine’s, 6.3 per cent of the comments indicated individuals’ intentions of abandoning it and buying a different brand. The case of ElPozo was a little bit different because Twitter users mentioned other brands to indicate that they also misbehaved the way that ElPozo had.

Using a word frequency query in NVivo with the aim of getting a feel for what people were saying, a word cloud was created for each case. The brand name was at the center of each cloud, meaning that it was at the core of the conversation and imbued the discourse of the participants (Figure 1)[2].

To complete the picture shown by the word clouds, we calculated the frequency of appearance of all the terms in the figures and classified them into different categories. The brand name represented more than 40 per cent of the word count for Ballantine’s and Gil Stauffer, 22 per cent for Air Europa and around 9 per cent for ElPozo. If we combine the two categories, brand and user, which are the main agents in an OF, the accumulated percentage was around 50 per cent. Then, the discussion about the focal topic of the OF accounted for a non-negligible percentage in all cases, varying from 21 per cent for Air Europa to 65 per cent for ElPozo. Another important cluster included words allowing users to express their opinions (mostly negative) about the OF, varying from 10 per cent for ElPozo to 23 per cent for Air Europa. We conclude then that the words the participants mainly used focused on the brand, the user, the OF itself and their evaluation of the OF. The ElPozo case deviated slightly from the others, as the most frequent words related to the OF in general. It is likely that this difference was rooted in the cause of the OF. The ElPozo firestorm was initiated by a television program that subtly suggested that the company could be blamed for animal mistreatment. This external origin stimulated a wider debate between people in favor and against companies in the meat industry, lending less attention to the brand.

4.3 Valence and styles of comments about the brand

From those posts referring to the brand, the vast majority were negative and fell within the 50-65 per cent interval in all the cases (Table V), with the potential harmful effects that might derive for it. The other comments are mainly neutral, which means that despite the assumed destructive orientation of OFs, many opinions were not really valenced. Finally, positive comments were quite scarce. In other words, negative comments greatly exceeded positive ones. Additionally, the distinction between cognitive and emotional nature shows a relative balance between the two categories, with the number of cognitively laden comments being higher than their emotional counterparts in all cases, with the exception of Gil Stauffer, where more emotional comments were posted. Therefore, although individuals used their comments to show their anger or frustration, they also took the opportunity to share more-informative content to support their stance. In relation to the linguistic style (Table IV), a non-insignificant percentage of comments portrayed outrage, in line with the overwhelming bias toward negative opinions. In this sense, the percentage of posts containing clear indications of anger and offense varied from 15 to 25 per cent.

4.4 Actions toward the brand

Contrary to what could be expected, a significant percentage of comments showed that OFs do not lead to negative behavioral responses that might harm the brands. Lots of comments did not propose any negative actions, and the percentage varied from 37.7 to 74.9 per cent (Table V).

The fact that some posts directly encouraged actions against the brand, even if they were not the majority (from 18.4 to 50 per cent), is highly relevant. A close examination of the actions identified revealed some interesting facts. They varied from passive responses (i.e. brand avoidance) to more-active ones (e.g. vengeance), with the former being less harmful to the brand than the latter.

Passive responses were expressed by either switching to competing brands and boycotting the brand or merely not buying it. Active responses are expressed in a type of brand vengeance when individuals actively and directly take actions to bring down the brands in some fashion (Grégoire et al., 2010). Grégoire and Fisher (2008) refer to these actions as retaliation. In all the cases, retaliatory behaviors took the form of both spreading negative WoM (from 10.4 to 31 per cent) and vindictive complaining in the form of verbal and insulting reactions (from 2.3 per cent to 16.2 per cent). It was observed that the brand action was shared not only with other Twitter users within the follower–followee network of each participant but also was publicized to a vast audience with the use of specific hashtags (Table II) or explicit mentions to media and consumer agencies that protect consumers’ interests (e.g. @radiocable, @ElHuffPost, @Union_Europea). This is known as “third-party complaining for publicity” (Grégoire and Fisher, 2008).

5. Conclusions and discussion

Many researchers agree on viewing OFs as an important threat to firms’ reputations because their effects parallel the detrimental effects of negative WoM. This judgment is not undisputed because other opinions suggest that this negative effect has been exaggerated (McKinsey, 2012) and that positive effects can be reached through increased awareness levels (Brady and Crockett, 2019).

Rooted in this debate, this study explored, more deeply, the potential negative consequences that they might have on consumer–brand relationships. It analyzed the UGC during the occurrence of different OFs. Because not all firestorm tweets might be equally important to brand equity, they were categorized and coded.

At the core of the conversations, different targets were identified. This suggests that an OF is not a story with a unique main character (e.g. the brand), as the majority of definitions of OFs might suggest. In terms of brand centrality the brand was mentioned. Actually, a significant percentage of tweets were not relevant for brand equity as far as the brand was not the central issue in the conversation. Additionally, the critical incident was also highly discussed in the four cases, although the percentage of these comments varied. Interestingly, competing brands were not kept out of the OFs. They were frequently mentioned during the outbursts.

The data set also revealed that the four brands involved in the OFs suffered from a higher percentage of negative UGC, while positive posts were nearly absent. Despite the prevalence of negatively valenced comments, a significant percentage of the tweets were neutral.

With respect to the posts’ emotional or cognitive nature, our findings revealed that the two dimensions were highly balanced. Despite the assumed emotional content associated with OFs, cognitive information is equally posted. Thus, users tend to offer opinions, reasons and more-instrumental motives when participating in the attack. This could be due to an intention to back their attitudes with objective information instead of mere gut reactions, which could impair their credibility.

In terms of the actions promoted toward the focal brands, individuals’ intentions of developing negative relations with the brand did predominate. They manifested intentions of adopting different strategies against it than those described in the theoretical framework: a flight strategy and a fight strategy. The rate of occurrence of the flight strategy, which assimilates the avoidance and brand rejection strategies, was significant lower compared to the manifested intentions of developing a fight strategy. This strategy is a type of action against the brand that is thought to emerge more frequently, and it takes the form of an attack through both public online complaining and vindictive complaining (e.g. insulting verbal reactions). The existence of this brand relationship is confirmed by the fact that outrage and insulting vocabulary are the linguistic styles identified when individuals talk about the brand, and previous research has suggested that outrage is associated with attacks (Romani et al., 2012).

However, these aggressive actions against the brand cannot be viewed as brand sabotage in the way that Kähr et al. (2016, p. 26) defined it (“A deliberate form of hostile, aggressive behavior on the part of a consumer, designed to harm a brand”). Brand sabotage has to do with conscious and planned behavior to cause harm to the brand, while the aggressive actions observed here had a more instrumental nature. They were instinctive and automatic responses to the critical brand incident because individuals were angry or unhappy with the brand, so their underlying motive was not harming it per se but venting negative emotions caused by the brand behavior. This instrumental nature of the brand attack was congruent with the existence of significant percentages of other users’ comments containing recommendations, pieces of advice or suggestions for the brand as a way to provide users with a satisfactory response, and, in turn, gain them back and restore brand equity. In other words, this instrumental nature of the aggression indicates that in the customer–brand relationships “the bridges are not burned” (Kähr et al., 2016), and consequently, the consumers wished to (re)-establish the brand relationships. This is in line with literature claiming that consumers confronted with negative situations might feel the urge to alleviate their distress by sharing their emotions with others. Then, in turn, they feel relieved and more predisposed to repair and continue their relationships with the brand (López-López et al., 2014).

Although the goal is not to harm the brand, the truth is that these actions in conjunction with the negative-valenced comments identified are damaging to brands. Whether brand power and true value lie in the associations made and held by consumers, altering these associations in a negative way represents a means of causing harm to brand equity.

5.1 Managerial implications

The focal brand is clearly at the core of an OF, so the potential negative impact seems to be conspicuous. Therefore, managers should pay attention to the elicitation of OFs and monitor their evolution. According to our findings, lots of comments are posted in a short period of time, and the dimension of the phenomenon escalates easily. This should serve as a clue for managers to be on the alert. Additionally, they should come up with intervention responses to restrain the attack. In this regard, companies should avoid fueling the fire through inappropriate participation: aggressive responses, non-credible excuses, denial, blaming others and opposing users in a vehement way are likely to make the situation worse. On the contrary, offering explanations, admitting responsibility, promising redress and any other conciliatory behavior could be helpful and prevent retaliatory actions. Mobilizing supporters might also counterbalance the negative spiral usually present in OFs (Rauschnabel et al., 2016), especially if they are able to pose well-grounded arguments in favor of the brand. However, if they introduce themselves as plain supporters led by the company, it may backfire and propel anger and indignation.

5.2 Limitations and future research directions

This research had two main limitations. The first related to its qualitative nature because the use of content analysis might bring into question the generalizability of both its results and its conclusions. Second, the number of real-life OFs analyzed was quite limited. To overcome these limitations, future studies might undertake more quantitative methodologies to confirm the results obtained.

Nevertheless, it was not in the authors’ interest to identify a specific type of harm to the brand but to dig deeper into the phenomenon through the analysis of what users really say during the time that an online outburst lasts. To the best of our knowledge, this is the first study to conduct this type of analysis. As a result, several new research insights have been identified that might inspire future work on this issue.

Future studies might focus on confirming the instrumental nature of the OFs identified. This would better distinguish this phenomenon from brand sabotage. Further research efforts are also needed to confirm that the two negative consumer–brand relationships resulting from OFs are brand avoidance and brand rejection. Their public nature means that many users might be exposed to the brand attack. Even if they adopt a passive attitude, reading what others say could influence their brand perceptions and future intentions. Thus, future research could assess the extent to which OFs result in brand rejection or avoidance among third parties exposed to the outburst.

Having identified that OFs result in a variety of negative comments and reactions toward the brand, the key issue at hand is to analyze how UGC affects the core brand associations. For this purpose, a very useful methodology could be the elaboration of brand concept maps (Roedder John et al., 2006). They provide a visual representation of how consumers think about brands, what their core associations are and how they are interconnected. The comparison of brand concept maps before and after an OF occurred might help to check whether the core brand associations were damaged.

Few details about the users posting the comments were collected. An interesting avenue for future research relates to depicting a profile of the individuals who participate in OFs. Are there many different users posting one single comment or on the contrary, are there just a few active users posting comments repeatedly? Additionally, do the participants have defining features, such as being socially concerned, engaged in a wide variety of OFs whose topics are diverse, prone to controversy […]?

With regard to their emotional dimension, no distinction among emotions was made. However, focusing on valence might not be enough to fully understand consumer reactions (Rowe et al., 2019). The analysis of a specific emotions framework could be useful to shed more light on the topic and help to devise different intervention strategies depending on the prevailing emotions.

As discussed previously, OFs can reach not only the focal brand but also other companies. That is, to say, the negativity of the information spreads to affect firms pertaining to the same or a different industry than the brand at hand. This could impair brand equity through a brand dilution effect, where the brands involved lose value due to something one single company is thought to have done. The scope of such dilution deserves more attention.


Word cloud for @AirEuropa

Figure 1.

Word cloud for @AirEuropa

Main studies on OF

Studies Purpose Theoretical perspective Level of analysis Implications/main conclusion
Pfeffer et al. (2014) To identify and characterize the structural factors that explain the dynamics of OFs Social and economic science theories Macro How OFs can be managed better through marketing communications
Stich et al. (2014) To detect the conditions under which the spread of negative WOM might result on OFs Existing diffusion models of information through a social network Macro Managing effective responses related to when and how to react
Mochalova and Nanopoulos (2014) To identify ways to restrict the negative effect of OFs through the use of effective selection methods with which brand supporters (seeds) can be identified N/A Macro How OFs can be counteracted through the analysis of the social network structure and the identification of seeds that might initiate the spread of positive WoM
Drasch et al. (2015) To design an automatic approach to detect OFs in real-time Research on information diffusion on social media networks Macro How to prevent the domino effect of OFs by the use of an automated real time detection procedure of initial negative online WoM
Lamba et al. (2015) To analyze the relationship between social ties and firestorm participation Literature on biographical consequences of activism Macro Online firestorms do not have long-term impact as far as they do not result in a significant change on the social network structure among individuals
Rauschnabel et al. (2016) To develop a theoretical framework that distinguishes collaborative brand attacks from traditional communication crises Crisis communication theory Macro How to manage collaborative brand attacks through a different set of strategies that differ from the traditional crises management practices
Hewett et al. (2016) To demonstrate the existence of complex feedback loops between corporate communications, news media and UGC on social media N/A Macro Managers have to rethink brand communication strategies and give online communications a central place
Rost et al. (2016) To understand whether aggressive behavior in a social-political online setting depends on individuals’ anonymity Social norm theory Micro The abolition of online anonymity does not necessarily prevent individuals from participating in online aggression in social media
Salek (2016) To describe how Mia and Ronan Farrow’s online accusation of Woody Allen of being a child molester results into a macro controversy against Allen on Twitter N/A Micro How online coverage on Twitter of a personal accusation transforms into a an OF attacking Woody Allen
Hauser et al. (2017) To study how organizations can manage OFs better through different active conflict management styles Social conflict, information diffusion and opinion adaption theories Macro A collaborating conflict management style characterized by high levels of assertiveness and cooperativeness is key in avoiding the harmful effects of conflict in online social settings. The effectiveness of this management style depends on several other factors
Einwiller et al. (2017) To explore how journalists cover OFs and elevate their negative effects N/A Micro Journalistic coverage of OFs focuses more on reporting image-repair responses than on the vilification of the organization involved
Lim (2017) To analyze the role played by perceptions of social norms and visual mockery in eliciting negative affect that potentially lead to boycotts Cognitive appraisal theory Micro Social proofs and visual mockery contribute to the negative climate of opinion and perception of a crisis
Chan et al. (2018) To understand individuals’ perceptions and reactions toward social information regarding the OF Social impact theory and dual-process model of social influences Micro Individuals’ responses to social information depend on the informational and normative influences that social information characteristics exert on them
Johnen et al. (2018) To identify the specific characteristics of the OFs and the perceived social context that make individuals engage in OFs Moral panics literature Micro Managing the appropriate responses to mitigate the negative effects of OFs depending on the group dynamics and opinion diversity
Hansen et al. (2018) To identify the consequences of digital brand crises and their contingency factors Elaboration likelihood model Micro Trigger and firestorm characteristics result in negative consequences for brand perceptions

Overview of the OFs cases

Company (year) Firestorm hashtag/mention Brief description Initiator and target Start date Duration (days) Data Users Media coverage
Air Europa (2013) #discapacidad #discriminación
Air Europa denies the right to fly to a customer who goes in a wheelchair. The customer shares her experience on Twitter A customer; targeted at Air Europa May 30 2013 4 2415 retweets, 92 likes and 74 comments 43 Digital newspaper, social networks and blogs
Gil Stauffer (2014) #Vergonzoso
After a customer’s claim, the company publishes a post threatening to denounce her if she does not erase the post Gil Stauffer; targeted at a customer January 30 2014 6 1100 retweets, 195 likes and 391 comments 350 Digital newspaper, social networks and blogs
Ballantines (2015) #boicotballantines #ManoloCM #JeSuisManolo #ManoloCMReadmision #FREEDOMaNOLO #Justiciaparamanolo #MANOLOFOREVER #Manolocrack #SiempreManolo #boicotballantinesxmanolo #TodosSomosManolo #ManoloPutoAmo #oSinManoloCM The company posts a statement on Twitter announcing the dismissal of its CM for supporting a football team on Twitter and facing the followers of the opposing team Ballantines; targeted at community manager May 04 2015 4 1900 retweets, 776 likes and 222 comments 193 Digital newspaper, social networks and blogs
El Pozo (2018) #Salvadosgranjas #Elpozomiente The company posts a statement on Twitter after a TV program induces to believe the malpractice occurs in the company El Pozo; targeted at Salvados (TV program) February 04 `2018 13 2340 retweets, 2390 likes and 220 comments 195 Digital newspaper, social networks, blogs, radio, TV and newspaper

TV = Television

Brand directed interactions and brand centrality

Air Europa: #74 comments Ballantines: #222 comments El Pozo: #220 comments Gill Stauffer: #391 comments
Categories n (%) n (%) n (%) n (%)
Who do the UGC address to?
Only to the brand 4 5.41 132 59.46 1 0.45 7 1.80
To the brand and other users 47 63.51 72 32.44 24 10.90 350 89.51
Only to users 9 12.16 18 8.10 194 88.18 29 7.41
Only to the key user 14 18.91 0 0.00 1 0.45 5 1.29
About whom users talk about
The focal brand 42 56.76 101 45.50 180 81.82 249 63.68
Key user and other users 8 10.81 98 44.14 17 7.73 29 8.03
Other brands or companies 5 6.76 14 6.31 10 4.55 1 0.28
No target identified 22 29.73 34 15.32 20 9.09 112 31.02

Valence and linguistic styles of comments about the brand

Air Europa: #42 comments Ballantines: #101 comments El Pozo: #180 comments Gill Stauffer: #249 comments
Categories n (%) n (%) n (%) n (%)
Valence of comments about the brand
Negative 26 61.90 66 65.35 115 63.89 126 50.60
Negative-cognitive 15 35.71 34 33.66 71 39.44 54 21.69
Negative-emotional 11 26.19 33 32.67 51 28.33 73 29.32
Positive 0 0.00 8 7.92 12 6.67 0 0.00
Positive-cognitive 0 0.00 5 4.95 9 5.00 0 0.00
Positive-emotional 0 0.00 3 2.97 3 1.67 0 0.00
Neutral 16 38.10 27 26.73 53 29.44 123 49.40
Linguistic styles of comments about the brand*
No 28 66.67 65 64.36 122 67.78 81 32.53
Yes 14 33.33 36 35.64 58 32.22 168 67.47
Surprise/disbelieve 3 7.14 0 0.00 4 2.22 29 11.65
Outrage 9 21.43 25 24.75 28 15.56 39 15.66
Containing insults 1 2.38 10 9.90 8 4.44 19 7.63
Irony/jokes 1 2.38 2 1.98 11 6.11 87 34.94

*These figures do not add up, as some comments fall in more than one category

Actions toward the brand*

Air Europa: #74 comments Ballantines: #222 comments El Pozo: #220 comments Gill Stauffer: #391 comments
Categories n (%) n (%) n (%) n (%)
None 34 46 126 56.75 83 37.72 293 74.93
Recommendations, advices or claims to the brand 7 9.45 29 13.06 54 24.54 27 6.90
In support to the brand 0 0.00 5 2.25 15 6.81 0 0.00
Against the brand 37 50.00 88 39.63 72 32.72 72 18.41
Boycott/stop using 2 2.70 34 15.31 30 13.63 10 2.55
Spread the word 23 31.08 31 13.96 23 10.45 53 13.55
Vindictive complaining 12 16.21 23 10.36 35 15.90 9 2.30

*These figures do not add up, as some comments fall in more than one category



The results from the February 2018 CMO Survey suggest that companies spend 12 per cent of their marketing budgets on social media, while in 2009 this spending only represented 3.5 per cent. It is expected that it will reach 20.5 per cent in the next five years.


For space restrictions, word of clouds for the other cases are available upon request.


Baccarella, V., Wagner, T.F., Kietzmann, J.H. and McCarthy, I.P. (2018), “Social media? It’s serious! Understanding the dark side of social media”, European Management Journal, Vol. 36 No. 4, pp. 431-438.

Batra, R., Ahuvia, A. and Bagozzi, R.P. (2012), “Brand love”, Journal of Marketing, Vol. 76 No. 2, pp. 1-16.

Brady, W.J. and Crockett, M.J. (2019), “How effective is online outrage?”, Trends in Cognitive Sciences, Vol. 23 No. 2, pp. 79-80.

Brunk, K.H. and Blümelhuber, C. (2011), “One strike and you’re out: qualitative insights into the formation of consumers' ethical company or brand perceptions”, Journal of Business Research, Vol. 64 No. 2, pp. 134-141.

Chan, T.K.H., Skoumpopoulou, D. and Yu, Q. (2018), “Firestorms on social media: effects of social information characteristics on customer responses”, PACIS Proceedings, Vol. 69.

Chaudhuri, A. and Holbrook, M.B. (2001), “The chain of effects from brand trust and brand affect to brand performance: the role of brand loyalty”, Journal of Marketing, Vol. 65 No. 2, pp. 81-93.

Cipriani, J. (2012), “How to prevent Facebook messenger from sharing your location”, available at:

CMO Survey (2018), available at:

Delgado-Ballester, E. and Fernández-Sabiote, E. (2016), “‘Once upon a brand’: storytelling practices by Spanish brands”, Spanish Journal of Marketing - Esic, Vol. 20 No. 2, pp. 115-131.

Delgado-Ballester, E. and Munuera-Alemán, J.L. (2001), “Brand trust in the context of consumer loyalty”, European Journal of Marketing, Vol. 35 Nos 11/12, pp. 1238-1258.

Drasch, B., Huber, J., Panz, S. and Probst, F. (2015), “Detecting online firestorms in social media”, Thirty Sixth International Conference on Information Systems, Fort Worth.

Duhachek, A., Zhang, S. and Krishnan, S. (2007), “Anticipated group interaction: coping with valence asymmetries in attitude shift”, Journal of Consumer Research, Vol. 34 No. 3, pp. 395-405.

Einwiller, S., Viererbl, B. and Himmelreich, S. (2017), “Journalists’ coverage of online firestorms in German-language news media”, Journalism Practice, Vol. 11 No. 9, pp. 1197-1778.

Escalas, J.E. and Bettman, J.R. (2003), “You are what they eat: the influence of reference groups on consumer connections to brands”, Journal of Consumer Psychology, Vol. 13 No. 3, pp. 339-348.

Floh, A., Koller, M. and Zauner, A. (2013), “Taking a deeper look at online reviews: the asymmetric effect of valence intensity on shopping behavior”, Journal of Marketing Management, Vol. 29 Nos 5/6, pp. 646-670.

Fournier, S. (1998), “Consumer and their brands: developing relationship theory in consumer research”, Journal of Consumer Research, Vol. 24 No. 4, pp. 343-373.

Fournier, S. and Alvarez, C. (2013), “Research dialogue: relating badly to brands”, Journal of Consumer Psychology, Vol. 23 No. 2, pp. 253-264.

Gensler, S., Völckner, F., Liu-Thompkins, Y. and Wiertz, C. (2013), “Managing brands in the social media environment”, Journal of Interactive Marketing, Vol. 27 No. 4, pp. 242-256.

Goode, E. and Ben-Yehuda, N. (1994), “Moral panics: culture, politics, and social construction”, Annual Review of Sociology, Vol. 20 No. 1, pp. 149-171.

Grégoire, Y. and Fisher, R.J. (2008), “Customer betrayal and retaliation: when your best customers become your worst enemies”, Journal of the Academy of Marketing Science, Vol. 36 No. 2, pp. 247-261.

Grégoire, Y., Laufer, D. and Tripp, T. (2010), “A comprehensive model of customer direct and indirect revenge: understanding the effects of perceived greed and customer power”, Journal of the Academy of Marketing Science, Vol. 38 No. 6, pp. 738-758.

Grégoire, Y., Salle, A. and Tripp, T.M. (2015), “Managing social media crises with your customers: the good, the bad and the ugly”, Business Horizons, Vol. 58 No. 2, pp. 173-182.

Hansen, N., Kupfer, A. and Hennig-Thurau, T. (2018), “Brand crises in the digital age: the short- and long-term effects of social media firestorms on consumers and brands”, International Journal of Research in Marketing, Vol. 25, pp. 557-574.

Hauser, F., Hautz, J., Hutter, K. and Füller, J. (2017), “Firestorms: modeling conflict diffusion and management strategies in online communities”, The Journal of Strategic Information Systems, Vol. 26 No. 4, pp. 285-321.

Hennig-Thurau, T. and Walsh, G. (2004), “Electronic word-of-mouth: consequences of and motives for reading customer articulations on the internet”, International Journal of Electronic Commerce, Vol. 8 No. 2, pp. 51-74.

Hennig-Thurau, T., Hofacker, C.F. and Bloching, B. (2013), “Marketing the pinball way: understanding how social media change the generation of value for consumers and companies”, Journal of Interactive Marketing, Vol. 27 No. 4, pp. 237-241.

Herhausen, D., Ludwig, S., Grewal, D., Wulf, J. and Schoegel, M. (2019), “Detecting, preventing, and mitigating online firestorms in brand communities”, Journal of Marketing, Vol. 83 No. 3, pp. 1-25.

Hewett, K., Rand, W., Rust, R.Y. and van Heerde, H. (2016), “Brand buzz in the echoverse”, Journal of Marketing, Vol. 80 No. 3, pp. 1-24.

Hudson, S., Huang, L., Roth, M.S. and Madden, T.J. (2016), “The influence of social media interactions on consumer–brand relationships: a three country-study of brand perceptions and marketing behaviors”, International Journal of Research in Marketing, Vol. 33 No. 1, pp. 27-41.

Johnen, M., Jungblut, M. and Ziegele, M. (2018), “The digital outcry: what incites participation behavior in an online firestorm?”, New Media and Society, Vol. 20 No. 9, pp. 3140-3160.

Kähr, A., Nyffenegger, B., Krohmer, H. and Hoyer, W.D. (2016), “When hostile consumers wreak havoc on your brand: the phenomenon of consumer brand sabotage”, Journal of Marketing, Vol. 80 No. 3, pp. 25-41.

Khamitov, M., Wang, X.S. and Thomson, M. (2019), “How well do consumer-brand relationships drive customer brand loyalty? Generalizations from a meta-analysis of brand relationship elasticities”, Journal of Consumer Research, Vol. 46 No. 3, pp. 435-459.

Krishnamurthy, S. and Kucuk, S.U. (2009), “Anti-branding on the internet”, Journal of Business Research, Vol. 62 No. 11, pp. 1119-1126.

Kwan, G.C. and Skoric, M.M. (2013), “Facebook bullying: an extension of battles in school”, Computers in Human Behavior, Vol. 29 No. 1, pp. 16-25.

Lamba, H., Malik, M.M. and Pfeffer, J. (2015), “Tempest in a teacup? Analyzing firestorms on twitter”, Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 17-24.

Lim, J.S. (2017), “How a paracrisis situation is instigated by an online firestorm and visual mockery: testing a paracrisis development model”, Computers in Human Behaviour, Vol. 67 (February), pp. 252-263.

López-López, I., Ruiz de Maya, S. and Warlop, L. (2014), “When sharing consumption emotions with strangers is more satisfying than sharing them with friends”, Journal of Service Research, Vol. 17 No. 4, pp. 475-488.

McKinsey (2012), “Turning buzz into gold”, available at:

Makarem, S.C. and Jae, H. (2016), “Consumer boycott behavior: an exploratory analysis of Twitter feeds”, Journal of Consumer Affairs, Vol. 50 No. 1, pp. 193-223.

Mochalova, A. and Nanopoulos, A. (2014), “Restricting the spread of firestorms in social networks”, Twenty Second European Conference on Information Systems, Tel Aviv.

Park, C.W., MacInnis, D.J., Priester, J., Eisingerich, A.B. and Iacobucci, D. (2010), “Brand attachment and brand attitude strength: conceptual and empirical differentiation of two critical brand equity drivers”, Journal of Marketing, Vol. 74 No. 6, pp. 1-17.

Parson, A. (2013), “Using social media to reach consumers: a content analysis of official Facebook pages”, Academy of Marketing Studies Journal, Vol. 17 No. 2, pp. 27-36.

Pfeffer, J., Zorbach, T.N. and Carley, K.M. (2014), “Understanding online firestorms: negative word-of-mouth dynamics in social media networks”, Journal of Marketing Communications, Vol. 20 Nos 1/2, pp. 117-128.

Rauschnabel, P.A., Kammerlander, N. and Ivens, B.S. (2016), “Collaborative brand attacks in social media: exploring the antecedents, characteristics, and consequences of a new form of brand crises”, Journal of Marketing Theory and Practice, Vol. 24 No. 4, pp. 381-410.

Roedder John, D., Loken, B., Kyeongheui, K. and Alokparna, S.M. (2006), “Brand concept maps: a methodology for identifying brand association networks”, Journal of Marketing Research, Vol. 43 No. 4, pp. 549-563.

Romani, S., Grappi, S. and Dalli, D. (2012), “Emotions that drive consumers away from brands: measuring negative emotions toward brands and their behavioral effects”, International Journal of Research in Marketing, Vol. 29 No. 1, pp. 55-67.

Rost, K., Stahel, L. and Frey, B.S. (2016), “Digital social norm enforcement: online firestorms in social media”, Plos One, Vol. 11 No. 6, pp. 1-26.

Rowe, Z.O., Wilson, H.N., Dimitriu, R., Charnley, F.J. and Lastrucci, G. (2019), “Pride in my past: influencing sustainable choices through behavioral recall”, Psychology and Marketing, Vol. 36 No. 4, pp. 276-286.

Salek, T.A. (2016), “Controversy trending: the rhetorical form of Mia and Ronan Farrow’s 2014 online firestorms against #Woodyallen”, Communication, Culture & Critique, Vol. 9 No. 3, pp. 477-494.

Scholz, J. and Smith, A.N. (2019), “Branding in the age of social media firestorms: how to create brand value by fighting back online”, Journal of Marketing Management, Vol. 35 Nos 11/12, pp. 1100-1134.

Smith, A.N., Fischer, E. and Yongjian, C. (2012), “How does brand-related user-generated content differ across YouTube, Facebook and Twitter?”, Journal of Interactive Marketing, Vol. 26 No. 2, pp. 102-113.

Stich, L., Golla, G. and Nanopoulos, A. (2014), “Modelling the spread of negative word-of-mouth in online social networks”, Journal of Decision Systems, Vol. 23 No. 2, pp. 203-221.

Tenzer, A. and Chalmers, H. (2017), “When trust falls down: how brands got here and what they need to do about it”, available at:

Tsarenko, Y. and Strizhakova, Y. (2013), “Coping with service failures”, European Journal of Marketing, Vol. 47 Nos 1/2, pp. 71-92.

Further reading

Brenner, L., Rottenstreich, Y., Sood, S. and Bilgin, B. (2007), “On the psychology of loss aversion: possession, valence, and reversals of the endowment effect”, Journal of Consumer Research, Vol. 34 No. 3, pp. 369-376.

Chan, T., Lee, Z.W.Y. and Skoumpopoulou, D. (2019), “The effects of rhetorical appeals and moral judgement on intention to participate in social media firestorms”, Research in Progress Papers, 33, available at:

Hennig-Thurau, T., Malthouse, E.C., Friege, C., Gensler, S., Lobschat, L., Rangaswamy, A. and Skiera, B. (2010), “The impact of new media on customer relationships”, Journal of Service Research, Vol. 13 No. 3, pp. 311-330.

Labrecque, L.I., Esche, J.V., Mathwick, C., Novak, T.P. and Hofacker, C.F. (2013), “Consumer power: evolution in the digital age”, Journal of Interactive Marketing, Vol. 27 No. 4, pp. 257-269.

Nitins, T. and Burgess, J. (2014), “Twitter, brands, and user engagement”, in Weller, K., Axel, B., Burgess, J., Mahrt, M. and Puschmann, C. (Eds), Twitter and Society, Peter Lang, New York, NY, pp. 293-304.

Pace, S., Balboni, B. and Gistri, G. (2014), “The effects of social media on brand attitude and WoM during a brand crisis: evidences from the Barilla case”, Journal of Marketing Communications, Vol. 23 No. 2, pp. 135-148.

Park, C.W., Eisingerich, A.D. and Park, J.W. (2013), “Research dialogue: attachment–aversion (AA) model of customer–brand relationships”, Journal of Consumer Psychology, Vol. 23 No. 2, pp. 229-248.

Wang, Y., Norcie, G., Komanduri, S., Acquisti, A., Leon, P.G. and Cranor, L. (2011), “I regretted the minute I pressed share: a qualitative study of regrets on Facebook”, Proceedings of the Seventh Symposium on Usable Privacy and Security, Vol. 10, pp. 20-22.

Wilson, D. and Quinton, S. (2012), “Let’s talk about wine: does Twitter have value?”, International Journal of Wine Business Research, Vol. 24 No. 4, pp. 271-286.


Funding acknowledgment: This research was supported by the Fundación Ramón Areces under the XVI national contest for the adjudication of aids to research in Social Sciences.

Corresponding author

Elena Delgado-Ballester can be contacted at:

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