# Developing a Conceptual Model for the Relationship Between Social Media Behavior, Negative Consumer Emotions and Brand Disloyalty

Harri Jalonen, Jari Jussila

► To cite this version:

Harri Jalonen, Jari Jussila. Developing a Conceptual Model for the Relationship Between Social Media Behavior, Negative Consumer Emotions and Brand Disloyalty. 15th Conference on e-Business, e-Services and e-Society (I3E), Sep 2016, Swansea, United Kingdom. pp.134-145, 10.1007/978-3-319-45234-0_13 . hal-01702174

HAL Id: hal-01702174

https://inria.hal.science/hal-01702174

Submitted on 6 Feb 2018

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Developing a conceptual model for the relationship between social media behavior, negative consumer emotions and brand disloyalty

Harri Jalonen*¹ and Jari Jussila²

¹Turku University of Applied Sciences, Turku, Finland
harri.jalonen@turkuamk.fi

²Tampere University of Technology, Tampere, Finland

**Abstract.** Companies have been facing the dark side of social media. Particularly, the odds of customer complaints and brand insults have increased tremendously. Social media has given a voice to disappointed consumers. They use the voice when they feel negative emotions, for example, due to product failures, service problems or unethical behavior. It seems reasonable to expect that the more ubiquitous social media becomes, the more it persuades people to share also their negative experiences. However, although social media raises new challenges for companies, it also gives them new opportunities. Social media enables companies to trace disappointed customers, evaluate their impressiveness and communicate with them. The conceptual paper aims to develop a model for the relationship between social media behavior, negative consumer emotions and brand disloyalty. The argument of this paper is that although social media gives consumers more power which is manifested in sharing negative emotions related to the company, the effect this has on brand disloyalty depends on the company's behavior.

**Keywords:** social media; negative emotions; brand disloyalty

## 1 Introduction

A great deal of social media content is emotionally loaded. People express the highs and lows of their everyday life, establish new friendships and break up old ones, share holiday and party pictures, praise and complain about brands, idolize the achievements of their descendants and pets through different social media sites – behavior which is strongly affected by emotion. Emotions can be expressed through words, pictures, emoticons and videos.

Social media has transformed the ways companies and customers interact. Metaphorically, social media has punctured holes into companies' walls and made them transparent in an unforeseen way. Social media has intensified the development in which the competition is based more on brands' ability to inspire emotional experiences, than on technical details of products. Consequently, companies are nowadays obliged to encounter their customers and other stakeholders more openly. Many companies have witnessed that social media has given customers a powerful medium to voice their negative emotions related, for example, to product failures, service problems or unethical behavior.

For some companies, social media provides new opportunities, whereas many others just face problems. Presumably, the distinction lies in whether or not the company is able to trace disappointed customers, evaluate their impressiveness and communicate with
them. Be it fair or not, social media forces companies to deal with emotionally rationalized
criticism and complaints.

Many studies have touched upon negative consumer emotions shared in social media – particularly studies that have been based on electronic word-of-mouth (eWOM) approach. These studies have, for example, identified various motivations for sharing negative information in social media [1]. Studies have also found out the stronger impact of negative eWOM (NWOM) compared to positive eWOM [2]. Despite of existing research, at least four research gaps can be identified. Firstly, previous studies have not explored the social media behavior of people as an antecedent to sharing negative emotions raised by negative experiences. Secondly, the relationship between disclosing negative emotions in social media behavior and brand disloyalty have received scant attention in the literature to date. Due to the lack of research it is not known whether disclosing negative emotions can contaminate brands and make customers disloyal. Thirdly, there is lack of research related to the mobile use of social media. Of particular interest should be whether mobile social media will increase the odds that negative experiences are expressed and shared. Fourthly, although companies cannot manage the ways their brands are discussed in social media, they are not unarmed. Social media has enabled companies to interact directly with their customers. However, there is lack of research with a focus on companies’ customer retention tactics and their consequence on brand loyalty in the case of negative eWOM. The paper presumes that the methods of creating loyal customers in the age of social media may have been oversimplified. In order to increase customer loyalty, it is suggested that it is useful to look at the hidden side – i.e. the relationship between negative consumer emotions and disloyalty.

This conceptual paper aims to introduce a theoretically sound model for the relation-
ship between social media behavior, negative consumer emotions and brand disloyalty.
The argument of this paper is that although social media gives consumers more power,
which is manifested in sharing negative emotions related to the company, the effect this
has on brand disloyalty depends on the company’s behavior.

The rest of the paper is organized as follows. Section 2 shortly reviews the key liter-
ature and presents the theoretical foundation for the paper. Section 3 introduces the con-
ceptual model. In Section 4 the paper concludes with short managerial implications, in-
cluding limitations and venues for future research.

2 Literature review and theoretical foundation

Emotion refers to an emotional state involving thoughts, physiological changes, and an outward expression or behavior. Emotions are expressed in facial reactions, gestures or postures and they are intuitively or intentionally directed toward a certain target. [3] Psychological literature typically classifies emotions into two axes that describe their valence and arousal. Valence indicates whether the affect related to an emotion is positive or negative, and arousal indicates the personal activity induced by that emotion [4].

The relationship between negative emotions and social media has been addressed
from various perspectives. The following will give a short overview of studies which have
focused or at least touched upon the question of how negative emotions manifest them-
selves in social media. Firstly, psychologically oriented studies have found out that nega-
tive emotions can be so popular in social media because people who suffer psychosocial
problems appreciate the ability to stay connected with others without face-to-face com-
munication. According to these studies disorganised, anxious and lonely people use social
media sites as they provide a context for holding relationships at a psychological arm's
distance and modulating negative emotions associated with these problems [5]. Secondly,
consumer behaviour studies have identified several motivations for negative online word-
of-mouth (WOM). These include sharing dissatisfaction in order to get a solution, disclos-
ing unfavorable experiences to prevent others from enduring similar bad experiences, and
ventilating feelings on a bad experience to give the company a chance to improve its prac-
tices [1]. Thirdly, sociologically inspired studies have focused on cultural and demo-
graphic differences in social media behaviour. These studies indicate that age and gender
affect emotional behaviour in social media [6, 7]. Studies have also shown cultural differ-
ences in emotional behaviour in social media [8]. Fourthly, some studies have addressed
social media sites which are dedicated to allowing people to vent. Rant-sites, as they are
called, provide people a forum to rant, for example, about firms and their products and
services. Rant-sites particularly attract people who feel anger [9].

As this paper focuses on emotions which have negative valence and positive arousal, the psychological approach falls out of the paper's scope. Recognizing the existence of socio-demographically oriented studies, this paper is not aiming to study age, gender or cultural factors that may influence on NWOM. Ranting sites are left out, in turn, because they represent, albeit interesting, extremely negative emotions and marginalised behaviour. The majority of social media users do not commit cyber trolling or bullying.

By concentrating on moderate ways of expressing disagreements in social media, this paper leans on consumer behavior studies [10]. These studies have shown that instead of rational decisions based on utilitarian product attributes and benefits, consumers' decisions are "biased" by emotions. Negative consumer emotions can result from various sources. A dysfunctional product, impolite customer service or insulting ads, to name a few, typically cause frustration, discontentment and other negative emotions. Negative consumer emotions pose a threat to companies for two main reasons. Firstly, negative emotions elicited from bad experiences may decrease customer loyalty, and secondly negative emotions can be spread through eWOM to a large audience. Negative consumer emotions do no good for the brand.

The importance of brand has been known for several decades. Studies have found
out that consumers are willing to pay more for a brand because they perceive some unique
value in the brand compared to a generic product [11]. Companies invest in building loyal
customer relationships because of numerous benefits such as premium price [12], long
lasting customer retention [13], lower price sensitivity by customers [14], higher profita-
bility [15] and greater market share [16]. Oliver [17, p. 34] defines brand loyalty as "a
deeply held commitment to rebuy or repatronize a preferred product/service consistently
in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite
situational influences and marketing efforts having the potential to cause switching behav-
ior". Oliver's definition includes two aspects of brand loyalty – behavioral and attitudinal
[18]. Behavioral loyalty consists of repeated purchases of the brand, whereas attitudinal
loyalty refers to emotional ties with the brand.

Intuitively thinking, it seems that brand disloyalty is just opposite to brand loyalty.
However, it is worthwhile to notice that as satisfied customers are not necessarily loyal,
dissatisfied customers are not always disloyal [19]. Although a company can effectively
handle unpleasant issues in a way which reduces dissatisfaction, the result is not neces-
sarily satisfaction. On the other hand, while the company can behave badly and cause
negative affect, the customer may keep purchasing the brand. A bit paradoxically, satisfied customers can defect, while dissatisfied customers remain faithful. Apparently due to this paradox, Söderlund [20] has suggested that satisfaction and dissatisfaction may, in fact, be two orthogonal axes rather than a bipolar measurement.

The paper defines brand disloyalty as a deeply held negative attitude and emotionally motivated rejection to buy a certain brand in the future, despite customer retention efforts by the company responsible for the brand. Brand disloyalty can take various forms. Adapting Dick & Basu [21], Rowley & Dawes [19] have identified four different manifestations of brand disloyalty: disengaged, disturbed, disenchanted and disruptive. Disengaged customers have typically no intention to purchase, nor direct experience of the brand. Disturbed customers have purchased the brand, but whether they buy in the future is uncertain because of recent dissatisfied experience. Disenchanted customers have purchased previously but are not likely to buy in the future because of many negative experiences. Disruptive customers have so many negative experiences that they have no intention to purchase in the future. In addition, disruptors actively discourage their peers to consider the brand

## 3 A conceptual model – a nexus between social media behavior, negative consumer emotions and brand disloyalty

Social media refers herein to a constellation of Internet-based applications that derive their value from the participation of users through directly creating original content, modifying existing material, contributing to a community dialogue and integrating various media together to create something unique [22]. Consumer-generated content (CGC) is obviously a double-edged sword that can cause both positive and negative outcomes. Sometimes CGC can help companies, for example, with identifying hidden customer needs and cultivating brand communities [23, 24], while in other occasions CGC insults companies and damages brands [24, 25]. Electronic WOM is a particular form of CGC. By definition, it means any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet [2]. Social media has notably lowered the threshold of eWOM. Nowadays anyone can post her or his opinions about brands.

Studies indicate that negative eWOM may have very strong effects on companies' performance. Wangeheim [26], Chevalier & Mayzlin [27] and Park & Lee [28], among others, have identified that negative evaluations of products and services have a stronger effect than positive ones. Negative eWOM affects brand image negatively [29], consumers' preferences [30] and purchase decisions [31]. Consumers share negative experiences mainly for three reasons [1]. Firstly, sharing negative experiences can serve to lessen the frustration and reduce the anxiety associated with the event. Secondly, negative experiences are shared for warning and preventing others from enduring similar events. Thirdly, consumers can share their negative experiences in order to help companies improve their practices. All in all, eWOM is more often negative than positive [32]. Social media has empowered consumers to voice negative experiences and opinions about brands with reduced physical and psychological costs [33]. Therefore, the paper presumes that negative disclosures are particular forms of CGC. Furthermore, it is argued that how these negative social posts influence brand disloyalty depends on companies' own behavior. Fig. 1 illustrates the factors which are discussed in more detail in the following sections.
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** A conceptual model of the relationship between social media behavior, negative consumer emotions and brand disloyalty</figcaption></figure>

**3.1 Social media usage activity and negative eWOM**

The social media era has significant implications for the spread of negative eWOM. Negative opinions about brands are formed and spread by thousands or millions of people within hours via social media [34]. In recent years the adoption of social media has surged. As an indicator of this development, 73% of Fortune 500 companies had a Twitter account and 66% a corporate Facebook page in the year 2012 [35]. In the year 2015, 78% of Fortune 500 companies have a Twitter account and 74% a corporate Facebook Page, and only 2% companies do not use any social media [36]. The amount of time consumers spend online and on social networking has also kept increasing. Time spent online via PCs, laptops, mobiles and tablets has increased from 5.55 hours in 2012 to 6.15 hours in 2014, and the time spent on social networks has climbed from a daily average of 1.61 to 1.72 hours in the same time period [37].

As more and more companies are adopting social media, and consumers are spending more time online and on social networks, the more rapid can also be the spread of negative eWOM. Thus, the paper formulates the following proposition.

P1: Social media usage activity positively influences negative eWOM.

**3.2 Mobile use of social media and negative eWOM**

Mobile phones and devices have become increasingly popular. For instance, 90% of Americans own a mobile phone, and most people rely so heavily on their mobile phones that they wouldn't dare to leave home without them [38]. Nearly two-thirds (64%) of mobile phones owned by Americans are smartphones [39] that make it possible to have all the social media applications (e.g. Facebook, Twitter, etc.) at the disposal of consumers
all the time. Mobile social media has also introduced new characteristics for social media use, such as time-sensitivity and location-sensitivity [38]. The transfer of traditional social media applications to mobile devices has increased the immediacy of feedback [38], [40] and thus made the use of social media applications more time-sensitive. For example, traditionally you would have to log in to Twitter with your computer in order to determine whether you have received any new messages or if there are any discussions that mention you. Whereas, using a mobile device, you get an immediate notification whenever someone posts a message or mentions you in a post (e.g. on Twitter, Facebook, LinkedIn and other applications). Exchange of messages with relevance for one specific location at one specific point of time [38], with mobile applications such as Foursquare and Facebook Places, has introduced a new dimension of interacting with consumers and businesses. For instance, consumers receive discounts to shops or restaurants based on their location information. Location-based services revenues are forecasted to increase from €10.3 billion in 2014 at an annual growth rate of 22.5% to €34.8 billion in 2020 according to a recent market report [41]. With such growth rates in location-based services it is likely that the mobile use of social media will impact the spread of negative eWOM as well. It has been noted that a mobile device is a ‘telephone’, the primary objective of which is message transmission, while a PC is a ‘processor’, with the primary objective of data transmission [42]. Presumably the differences between the objectives affect the behavior. Thus, the paper formulates the following proposition.

P2: Mobile use of social media positively influences negative eWOM.

## 3.3 Perceived usefulness of negative information and negative eWOM

Social media has enabled consumers an easy way to post their experiences of products and services. These experiences are based not only on facts (what has actually happened) but also on consumers’ subjective opinions. Unsurprisingly, brands are forced to face fair and unfair negative eWOM. Social networking sites and online review sites, among others, have considerably increased the probability that peer information is taken into consideration when consumers make judgements about brands. The importance of negative peer information and eWOM can be addressed from two complementary perspectives.

Firstly, information seeking theories suggest that people perceive negative information, in general, to be more persuasive than positive information [43, 44]. Based on the accessibility–diagnosticity model [45], Anderson and Salisbury [32] and Yang & Mai [46] have found out that negative information is more diagnostic and persuasive than positive. For example, information about a product that does not work as it should is more diagnostic than information about a product that does work as it should. In case of product failure, negative information is given more weight because it differs from the expectations. It can be said that negative framing is more effective than positive framing [47]. Ahluwalia [48] has described this “negativity affect” arguing that negative product attributes are believed to be more characteristic of a poor quality product, than positive attributes are for a high quality product [33].

Secondly, eWOM is considered a relatively credible and trustworthy source of information and therefore is more influential than advertising and other marketing information provided by the companies [49]. The credibility of negative eWOM is dependent on the perceived competence of the source providing the information and on the emotional relationship between the information provider and its receiver. If the information source is
ranked as an expert (i.e. she/he possesses greater awareness and knowledge about a market and products within it or by virtue of his/her occupation, social training or experience), the knowledge he/she provides is more useful and persuasive than information provided by a non-expert [49, 50, 51, 52]. In addition to competence, the emotional relationship between the information provider and its receiver influences the credibility of messages. Pan & Chiou [53], for example, have shown that negative online messages were perceived credible when the messages were posted by those perceived to have close social relationships.

The more diagnostic and credible the given information is, the more probable it is that information will be retrieved as an input to judgement about brand. Thus, the paper formulates the following proposition.

P3: *Perceived usefulness of negative information positively influences negative eWOM.*

## 3.4 Company's behavior and negative eWOM

Negative eWOM can damage a company's brand. However, it can be expected that the amount of damage depends on the company's behavior. Recent experiences clearly show that no response is not an option [e.g. 34, 54]. Instead, companies are encouraged to put an effort on handing negative eWOM. There is no shortage of studies which point out that competent complaint management is an effective means of reducing the impact of negative WOM (traditional and online) on brand and purchase intention [24], [55, 56, 57, 58, 59].

Two aspects seem of particular importance in attenuating negative eWOM. Firstly, because individual negative experiences easily escalate into online firestorms [34] and digital groundswells [60], it is important to act timely. A timely response to online complaints offer two potential benefits as it not only resolves the issue with the complainant and prevents follow-up attacks from the consumers who exposed themselves to the original complaint, but can also decrease consumer disloyalty [24], [33], [61]. Secondly, the tone of response should be considered carefully. In the worst case, the company's response can engender a spiral of negative effect undermining its intended goals [24], [61]. In order to avoid the backfire, Kelleher [62] and van Noort & Willemsen [33], among others, have emphasized the conversational human voice approach. Kelleher [62] defines the conversational human voice as "an engaging and natural style of organizational communication as perceived by an organization's publics based on interactions between individuals in the organization and individuals in publics". Contrary to corporate voice, which is profit-driven and persuasive [63], human voice invites individuals to communicate in a non-persuasive manner [33]. The more quickly and the more emphatically the company responds to online complaints the more probable it is that the damage of eWOM can be limited. Thus, the paper formulates the following proposition.

P4: *Company's response to negative eWOM negatively influences negative eWOM.*

## 3.5 Negative eWOM and brand disloyalty

As described earlier, brand disloyalty is not just opposite to brand loyalty. This means that customers can be loyal to certain brands even if they are dissatisfied, whereas satisfied customers can defect. Instead of disloyalty, the latter behavior represents no loyalty [21]. Disloyalty differs from no loyalty in that it includes a negative attitude toward the brand
[19]. Disloyalty is an emotionally motivated rejection to buy a certain brand in the future, despite customer retention efforts by the company responsible for the brand.

Brand disloyalty, as distinct from brand loyalty, in the social media context has re-
ceived scant attention in previous research. However, based on the studies which exam-
ined the relationship between eWOM and brand loyalty, there are reasons to suspect that
negative eWOM impacts brand disloyalty. Several studies have identified that negative
eWOM has a significant power that affects brand loyalty and purchase decisions [64, 65,
66, 67, 68].

In spite of lack of studies on eWOM and disloyalty, one important remark can how-
ever be introduced. Because of the diagnosticity of negative information [45], it can be
argued that a negative review on a brand is a valuable information source for consumers.
Consumers can use negative eWOM for avoiding frustration, dissatisfaction and other
negative emotions elicited by buying certain goods or services [68].

In other words, negative eWOM may increase emotional rejection toward a brand.
Thus, the paper formulates the following proposition.

P5: *Negative eWOM positively influence brand disloyalty.*

4 Conclusions

Advancing the current understanding on the relationship between social media behavior and NWOM on brand disloyalty the conceptual model claims that social media usage and mobile use of social media are antecedents that increase the odds that negative events, such as bad customer experiences, are disclosed in social media. Similarly, the model argues that negative information about something, for example about product usability, is perceived as more useful than positive or neutral information about the same something. The value of negative information is based on its diagnosticity. It is worth noting that companies can influence the audibility of negative eWOM. Through quick and empathetic responses it may be possible to prevent the worst from happening. If, however, eWOM takes place, the result is brand disloyalty – a negative attitude and emotionally motivated rejection to buy a certain brand.

Obviously the conceptual model has its limitations. As antecedents of sharing nega-
tive emotions in social media and the relationship between sharing negative emotions in
social media and brand disloyalty have received little attention in the literature, this paper
has induced the need for empirical research. The authors will conduct a study to validate
the proposed model. It will be carried out as follows: i) a socio-demographically repre-
sentative consumer population will be recruited, ii) the data will be gathered through ques-
tionnaire survey with Likert-scale components, iii) the structural equation modeling using
partial least squares (SEM-PLS) estimation will be employed to test hypothesized rela-
tionships among constructs. This approach will be favored over single regression analyses
because it allows testing the conceptual model as a whole [69]. It is expected that empirical
research will contribute to the development of understanding of the antecedents and con-
sequences of negative consumer emotions expressed in social media
References

1. Verhagen, T., Nauta, A. and Feldberg, F. 2013. Negative online word-of-mouth: Behavioral indicator or emotional release? *Computers in Human Behavior, 29*(4), 1430–1440.

2. Hennig-Thurau, T., Gwinner, K. P., Walsh, G. and Gremler, D. D. 2004. Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? *Journal of Interactive Marketing, 18*(1), 38–52.

3. Cacioppo, J. T. and Gardner, W. L. 1999. Emotion. Annual Review of Psychology, 50, 191–214.

4. Russell, J. A. 1980. A circumplex model of affect. *Journal of Personality and Social Psychology, 39*(6), 1161–1178.

5. Nitzburg, G. C. and Farber, B. A. 2013. Putting up emotional (Facebook) walls? Attachment status and emerging adults' experiences of social networking sites. *Journal of Clinical Psychology, 69*(11), 1183–1190.

6. Shen, A.X.L., Lee, M. K. O., Cheung, C. M. G. & Chen, H. 2010. Gender differences in intentional social action: we-intention to engage in social network-facilitated team collaboration. *Journal of Information Technology, 25*(2), 152–169.

7. Przybylski, A. K., Murayama, K., DeHaan, C. R. and Gladwell, V. 2013. Motivational, emotional, and behavioral correlates of fear of missing out. *Computers in Human Behavior, 29*(4), 1841–1848.

8. Koh, N. S., Hu, N. and Clemons, E. K. 2010. Do online reviews reflect a product's true perceived quality? An investigation of online movie reviews across cultures. *Electronic Commerce Research and Applications, 9*, 374–385.

9. Martin, R. C., Coyier, K. R., van Sistine, L. M. and Schroeder, K. L. 2013. Anger on the Internet: The perceived value of rant-sites. *CyberPsychology, Behavior & Social Networking, 16*(2), 119–122.

10. Laros, F. J. M. and Steenkamp, J.-E. E. M. 2005. Emotions in consumer behavior: a hierarchical approach. *Journal of Business Research, 58*(10), 1437–1445.

11. Keller, K. 1993. Conceptualizing, Measuring and Managing Customer-Based Brand Equity. *Journal of Marketing, 57*(1), 1–22.

12. Doyle, P. 1990. Building successful brands: the strategic options. *Journal of Consumer Marketing, 7*(2), 5–20.

13. Reichheld, F. and Sasser, W. 1990. Zero defects: quality comes to services. *Harvard Business Review, 68*(5), 105–111.

14. Krishamurthi, L. and Raj, S. P. 1991. An empirical analysis of the relationship between brand loyalty and consumer price elasticity. *Marketing Science, 10*(2), 172–183.

15. Hallowell, R. 1996. The relationship of customer satisfaction, customer loyalty and profitability: an empirical study. *International Journal of Service Industries Management, 7*(4), 27–42.

16. Assael, H. 1998. *Consumer Behavior and Marketing Action*. South-Western, Cincinnati, OH.

17. Oliver, R. L. 1999. Whence consumer loyalty? *Journal of Marketing, 63*, 33–44.
18. 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*, 65(2), 81–93.

19. Rowley, J. and Dawes, J. 2000. Disloyalty: a closer look at non-loyals. *Journal of Consumer Marketing*, 17(6), 538–549.

20. Söderlund, M. 1998. Customer satisfaction and its consequence on customer behaviour revisited. *International Journal of Services Industries Management*, 5(8), 21–38.

21. Dick, A. and Basu, K. 1994. Customer loyalty: toward an integrated conceptual framework. *Journal of Marketing Science*, 22(2), 99–113.

22. Kaplan, A. M. and Haenlein, M. 2010. Users of the world, unite! The challenges and opportunities of social media. *Business Horizons*, 53(1), 59–68.

23. Muñiz, A. M. and Schau, H. J. 2011. How to inspire value-laden collaborative consumer-generated content. *Business Horizons*, 54(3), 209–217.

24. Noble, C. H., Noble, S. M. and Adjei, M. T. 2012. Let them talk! Managing primary and extended online brand communities for success. *Business Horizons*, 55(5), 475–483.

25. Kucuk, S. U. 2008. Negative Double Jeopardy: The role of anti-brand sites on the internet. *Brand Management*, 15(3), 209–222.

26. Wangenheim, F. V. 2005. Postswitching negative word of mouth. *Journal of Service Research*, 8(1), 67–78.

27. Chevalier, J. and Mayzlin, D. 2006. The effect of word of mouth on sales: Online book reviews. *Journal of Marketing Research*, 63(3), 345–354.

28. Park, C. and Lee, T. M. 2009. Information direction, website reputation and eWOM effect: A moderating role of product type. *Journal of Business Research*, 62(1), 61–67.

29. Jansen, B. J., Zhang, M., Sobel, K. and Chowdury, A. 2009. Twitter power: Tweets as electronic word of mouth. *Journal of the American Society for Information Science and Technology*, 60(11), 2169–2188.

30. Khare, A., Labrecque, I. I. and Asare, A. K. 2011. The assimilative and contrastive effects of Word-of-mouth volume: An experimental examination of online consumer ratings. *Journal of Retailing*, 87(1), 111–126

31. Fagerstrom, A. and Ghinea, B. 2011. On the motivating impact of price and online recommendations at the point of online purchase. *International Journal of Information Management*, 31(2), 103–110.

32. Anderson, E. W. and Salisbury, L. C. 2003. The formation of market-level expectations and its covariates. *Journal of Consumer Research*, 30(1), 115–124.

33. Van Noort, G. and Willemsen, L. M. 2011. Online damage control: the effects of proactive versus reactive webcare interventions in consumer-generated and brand-generated platforms. *Journal of Interactive Marketing*, 26(3), 131–140.

34. Pfeffer, J., Zorbach, T. and Carley, K. M. 2014. Understanding online firestorms: negative word-of-mouth dynamics in social media networks. *Journal of Marketing Communications*, 20(1-2), 117–128.

35. Barnes, N., Lescault, A. and Andonian, J. 2012. *Social media surge by the 2012 Fortune 500: Increase use of blogs, Facebook, Twitter and more*. Charlton College of Business Center for Marketing Research.

36. Barnes, N., Lescault, A. and Holmes, G. 2015. *The 2015 Fortune 500 and Social Media: Instagram Gains, Blogs Lose*. UMass Dartmouth.
37. Mander, J. 2015. Daily time spent on social networks rises to 1.72 hours. GlobalWebIndex.

38. Kaplan, A. M. 2012. If you love something, let it go mobile: Mobile marketing and mobile social media 4x4. *Business Horizons*, 55(2), 129–139.

39. Smith, A. 2015. *U.S. Smartphone Use in 2015*. PewResearchCenter.

40. Jussila, J. 2015. *Social Media in Business-to-Business Companies' Innovation*. Tampere University of Technology. Publication; 1333.

41. Research and Markets. 2015. *Mobile Location-Based Services*. 9th Ed. http://www.researchandmarkets.com/reports/3396579/

42. Okazaki, S. 2009. Social influence model and electronic word of mouth – PC versus mobile internet. *International Journal of Advertising*, 28(3), 439–472.

43. Fiske, S. T. 1980. Attention and weight in person perception: the impact of negative and extreme behaviour. *Journal of Personality and Social Psychology*, 38(6), 889–906.

44. Crowley, A. E. and Hoyer, W. D. 1994. An integrative framework for understanding two-sided persuasion. *Journal of Consumer Research*, 20(4), 561–574.

45. Herr, P. M., Kardes, F. R. and Kim, J. 1991. Effects of word-of-mouth and product attribute information on persuasion: an accessibility diagnosticity perspective. *Journal of Consumer Research*, 17(4), 454–462.

46. Yang, J. and Mai, E. 2010. Experiental goods with network externalities: effects: An empirical study of online rating system, *Journal of Business Research*, 63(9-10), 1050–1057.

47. Maheswaran, D. & Meyers-Levy, J. 1990. The influence of message framing and issue involvement. *Journal of Marketing Research*, 27(3), 361–367.

48. Ahluwalia, R. 2002. How prevalent is the negativity affect in consumer environments? *Journal of Consumer Research*, 29(2), 270–279.

49. Brown, J., Broderick, A. J. and Lee, N. 2007. Word of mouth communication within online communities: conceptualizing the online social network. *Journal of Interactive Marketing*, 21(3), 1–17.

50. Gotlieb, J. B. and Sarel, D. 1991. Comparative advertising effectiveness: the role of involvement and source credibility. *Journal of Advertising*, 20(1), 38–45.

51. Schiffman, I. G. and Kanuk, L. L. 1955. *Consumer Behavior*, 9th Ed. Prentice Hall, Upper Saddle River, New Jersey, NJ.

52. Mitchell, A. A. and Dacin. P. A. 1996. The assessment of alternative measures of consumer expertise. *Journal Consumer Research*, 23(3), 219–239.

53. Pan, L-Y. and Chiou, J-S. 2011. How much can you trust online information? Cues from perceived trustworthiness of consumer-generated online information? *Journal of Interactive Marketing*, 25(2), 67–74.

54. Hemsley, J. and Mason, R. M. 2013. Knowledge and knowledge management in the social media age. *Journal of Organizational Computing and Electronic Commerce*, 23(1-2), 138–167.

55. Fornell, C. and Wernefelt, B. 1987. Defensive marketing strategy by customer complaint management: a theoretical analysis. *Journal of Marketing Research*, 24(4), 337–346.

56. Tax, S. S., Brown, S. W. and Chandrasherakan, M. 1998. Customer evaluations of service complaint experiences: implication for relationship marketing. *Journal of Marketing*, 62(2), 60–76.
57. Blodgett, J. G. and Anderson, J. 2000. A Bayesian network model of the consumer complaint process. *Journal of Service Research*, 4(2), 321–338.

58. Von der Heyde, F. D. and dos Santos, P. 2008. The antecedents of the consumer complaining behavior (CCB). *Advances in Consumer Research*, 35, 584–592.

59. Sandes, F. S. and Urdan, A. T. 2013. Electronic word-of-mouth impacts on consumer behaviour: exploratory and experimental studies. *Journal of International Consumer Marketing*, 25(3), 181–197.

60. Li, C. and Bernoff, J. 2009. *Groundswell – Winning in a World Transformed by Social Technologies*. Harvard Business Review Publishing, Boston, Massachusetts, MA.

61. Lee, Y. L. and Song, S. 2010. An empirical investigation of electronic word-of-mouth: informational motive and corporate response strategy. *Computers in Human Behavior*, 26(5), 1073–1080.

62. Kelleher, T. 2009. Conversational voice, communicated commitment, and public relations outcomes in interactive online communication. *Journal of Communication*, 59(1), 172–188.

63. Locke, C., Weinberger, D. and Searls, D. 2004. *The Cluetrain Manifesto: The End of Business as Usual*. Perseus Publishing, New York, NY.

64. Chiou, J-S. and Cheng, C. 2003. Should a company have message boards on its web sites. *Journal of Interactive Marketing*, 17(3), 50–61.

65. Gruen, T. W., Osmonbekov, T. and Czaplewski, A. J. 2006. eWOM: the impact of customer-to-customer online know-how exchange on customer value and loyalty. *Journal of Business Research*, 59(4), 449–456.

66. Jones, S. A., Aiken, D. K. and Boush, D. M. 2009. Integrating experience, advertising, and electronic word of mouth. *Journal of Internet Commerce*, 8(3-4), 246–267.

67. Khammans, M. and Griffiths, G. H. 2011. ‘Arrivederci CIAO.com, Buongiorno Bing.com’ – Electronic word-of-mouth (eWOM), antecedents and consequences. *International Journal of Information Management*, 31(1), 82–87.

68. Bambaeur-Sachse, S. and Mangold, S. 2011. Brand equity dilution through negative online word-of-mouth communication. *Journal of Retailing and Consumer Services*, 18(1), 38–45.

69. Henseler, J. 2010. On the convergence of the partial least squares path modeling algorithm. *Computational Statistics*, 25(1), 107–120.