Based on the Stimulus Organism Response theory, this study develops and tests an integrative model that examines, for the first time in the consumer revenge literature, how online acts of revenge influence other consumers present on social media to do the same. This study examines the mediating role of message trustworthiness to better explain the adoption of an online avenger message by other consumers. The study also investigates the moderating effect of avenger expertise on this mediated relationship. Moreover, the moderating role of online activation in triggering other consumers’ desire for revenge is also examined. Using SmartPLS on 211 Jordanian consumers, support for the mediating role of online message trustworthiness in post adoption is found. In addition, this indirect effect of message trustworthiness on the likelihood of other consumers adopting an online revenge post left by a dissatisfied customer is also stronger when avenger expertise is perceived to be high. In addition, it was found that online activation moderated the transition from a desire for revenge to the act of committing online revenge.
Recently, a young Jordanian consumer, Akram, posted an online vindictive complaint on the Facebook page of Umniah, one of the largest telecommunications companies in Jordan, to highlight how the company treated him and how awful their Internet service was. Due to his use of simple language and a comedic tone, his post became a social media sensation for over a week with 47000 likes, 20000 comments, and 1700 shares. To make matters worse for Umniah, many consumers used this post as a means to spam the company's Facebook page asking, “Why did you do this to Akram?” and the Hashtag “we are all Akram”. Moreover, Umniah telecommunications rivals, such as Zain and Orange, joined the media frenzy with promotional materials inviting Akram to switch to their services (Nabbout, 2017).
Online consumer revenge acts, such as the one described above, refer to the use of the Internet and its platforms to get revenge on a service provider for a perceived wrongdoing in both legal and illegal ways (Gregoire et al., 2018a; Obeidat, 2014). Such acts have led many scholars (e.g. Funches et al., 2009) to call for an examination of this phenomenon and its causes. Using the theory of justice as well as cognitive appraisal, the consumer revenge literature in both contexts (i.e. online and offline) has largely examined the causes of revenge behaviour by consumers (e.g. Alalwan et al., 2016; Surachartkumtonkun et al., 2013; Joireman et al., 2013; Patterson et al., 2016), its forms (e.g. Huefner & Hunt, 2000; Obeidat et al., 2018), and methods of stopping such behaviour (e.g. Tripp & Gregoire, 2011; Gregoire et al., 2018a). Moreover, the current literature supports the examination of the situational service-based triggers of consumer revenge such as fairness violations, type of service failure, and double deviations (i.e. failure in service process and recovery) (Joireman et al., 2013; Obeidat et al., 2020), negative emotions (e.g. anger and betrayal) (Gregoire and Fisher, 2008), and mediating and moderating factors that influences consumers’ desire for revenge (i.e. Relationship quality, reach, control, risk, social presence) (Mdakane et al., 2010; Obeidat et al., 2018). These factors were identified as the key predictors of consumers’ revenge and online revenge intentions. Nevertheless, from a theoretical standpoint, all previous studies on consumer revenge have focused on examining the forms and antecedents of this behaviour from the avenging consumer perspective (e.g. Gregoire et al., 2018b; Obeidat et al., 2017). Thus, limited research was done on how these acts are perceived and how they influence other consumers’ (i.e. the consumers who view the online revenge post) desires and intentions towards the allegedly misbehaving service providers (Obeidat et al., 2020). Owing to this focus of the literature, several important factors (e.g. poster expertise and post trustworthiness) that are highly influential in driving consumers’ adoption and behavioural intentions in the consumer online review literature have been overlooked (Aswani et al., 2019; Xie et al., 2017). Consequently, it is vital to examine how factors in relation to the avenger, the revenge message, and social media-related components influence the perceptions and behavioural intentions of consumers witnessing acts of online revenge (Gregoire et al., 2018a). Additionally, most of the previous literature has focused on simple direct relationships between cognitive, emotional, and behavioural revenge factors which could mask true relationships that could play a role in the transition from one stage to the other (Obeidat et al., 2017). While a number of mediating and moderating variables were examined, as mentioned earlier, they were also based on the avenging consumer point of view and not on the perspective of other consumers present on social media (e.g. Ali et al., 2022; Aladwani & Dwivedi, 2018; Obeidat et al., 2017; Joireman et al., 2013; Gregoire et al., 2010).
Generally, the majority of consumers now share their service experiences online (Kar et al., 2021) as social media platforms enable consumers to voice their failed service experiences very easily (Kumar et al., 2021; Dwivedi et al., 2018). Moreover, these platforms provide consumers wishing to get back at misbehaving companies with a high-reach, interactive, and effortless platform to do so (Velichety & Shrivastava, 2022; Azemi et al., 2020; Baines, 2017). Additionally, research shows that 85% of young consumers use social media to obtain revenge and complain of negative publicity after a service failure (Grant 2013). Another study showed that the majority of consumers in America (60%) publicly share their dissatisfaction online after a service failure (Gutbezhahl, 2014). Therefore, in terms of practical importance, understanding consumers’ experiences in online communities is vital for service providers who wish to sustain viable businesses (Liu et al., 2021). Additionally, negative consumer acts, such as revenge, can hurt other customers’ experiences due to the interactivity of online and virtual environments (Sweiss et al., 2021; Huang and Wang 2014). It is essential to identify how customer-to-customer exchanges and reactions to customers’ revenge posts within online communities could affect other customers’ experiences and reactions (Sigurdsson et al., 2021; Sweiss et al., 2021) as online revenge actions could cause substantial financial and reputational losses to service providers if not handled properly (Gregoire et al., 2018b; Gregoire et al., 2010). More specifically, examining how and why acts of online revenge influence other consumers is vital because consumer reviews and service encounter posts are important predictors of other customers using and adopting products and services (Kalia et al., 2022; Xie et al., 2020; Alalwan et al., 2017).
As a result, this study develops and tests an empirical model that investigates how vengeful online posts by disgruntled customers (avengers) encourages witnessing customers (i.e. other consumers) to do the same. Specifically, this study examines the mediating role of message trustworthiness in driving witnessing customers’ adoption of online revenge posts and the moderating role of avenger expertise in this relationship. In addition, the moderating role of other consumers’ likes, comments, and shares on Facebook (i.e. online activation) on the path between consumers’ desire for revenge and online revenge is also examined. Furthermore, these factors were selected because of the weight they carry in this digital age in influencing consumers (Alalwan et al., 2017). Generally, these factors and functions can help brands build awareness and engage consumers (Šola et al., 2022; Coulter & Roggeveen, 2012). Moreover, these variables (i.e. expertise, post trustworthiness, likes, comments, and shares) were generally found to have a significant impact in influencing consumers cognitive, emotional, and behavioral responses (Xie et al., 2017; Zhu, Yin, and He, 2014). In addition, it provides consumers with the ability to respond and interact with firms regarding service failures and product reviews (Smith & Gallicano, 2015). Therefore, examining their influence on motivating negative consumer reactions, such as revenge, is essential.
Consequently, this study extends the customer revenge literature in three main ways. First, by focusing on the perspective of other consumers who witness the online revenge act, this study provides a holistic view of online consumer revenge behaviour. It identifies such revenge behaviour's consequences on other consumers and the reasons behind its acceptance and widespread use within social media platforms. Second, we introduce a number of new variables to the consumer revenge literature to shift focus from the avenger centric variables to the examination of both avenger-centric and other-customer-centric variables (i.e. online revenge by disgruntled customers, expertise, activation, and online post/message adoption). Generally, in addition to examining the influence of general social media traits (i.e. interactivity, community, recklessness, reach, control, and social presence), no other study has examined the influence of specific Facebook tools (i.e. other consumers’ likes, comments, and shares) on consumer revenge intentions (Obeidat et al., 2020). Third, using the stimulus, organism, and response framework, we conducted a moderated mediation analysis for the first time in consumer revenge literature to highlight the interrelationships between the variables influencing consumers’ adoption of online revenge posts. Therefore, this study aimed to answer the following questions.
- 1)
What is the influence of disgruntled customers’ (avengers’) online revenge posts on other consumers’ adoption of revenge posts and their desire for revenge?
- 2)
What is the mediating influence of the revenge message trustworthiness on other consumers’ adoption of the vengeful online post?
- 3)
What is the moderating influence of avenger expertise on the mediating role of message trustworthiness?
- 4)
What is the moderating influence of likes, comments, and shares on the relationship between other consumers’ desire for revenge and online revenge intention?
We first review the literature on consumer revenge, followed by the theoretical framework and our developed model. The methodology of the study is then presented, followed by the results. Finally, this study discusses the findings and the implications derived from these findings.
Literature reviewAs seen in Table 1, two main streams of research have dominated the consumer revenge literature by examining the motivations and types of consumer revenge behaviour from the avenging consumer point of view in both contexts (i.e. offline/online). Regarding the precursors of revenge following service failures, previous studies have highlighted the lack of procedural, interactional, and distributive fairness, and the perceived severity of service failure as the main triggers for acts of revenge (Obeidat, 2014; Gregoire & Fisher, 2008). Generally, the literature asserts that when consumers sense a violation in terms of these fairness dimensions, negative emotions are triggered and subsequently lead to a desire for revenge (Gregoire et al., 2010). Moreover, to incorporate the important role of recovery actions after a service failure, Tripp & Gregoire (2011), and later, Joireman et al., (2013) identified the concept of double deviations as a key trigger for acts of revenge. This concept relates to the failure of primary service performance and its recovery. Some attempts have also identified the type of service failure (i.e. process/outcome) as an influential driver in triggering different cognitive, emotional, and coping revenge actions among consumers (Obeidat et al., 2017). With regards to personal perceptions, the previous literature has identified factors such helplessness after a service failure (Obeidat et al., 2017), perception of the service provider's greed (Gregoire et al., 2010), the company's motive (Joireman et al., 2013), blame attributions (Bechwati & Morrin, 2003, 2007), and dissatisfaction (e.g., Bougie et al., 2003). In terms of personal traits, narcissism and social presence have also been identified (Obeidat et al., 2020).
Previous literature on consumer revenge
| Study | Focus | Context | Key purpose and findings |
|---|---|---|---|
| Huefner and Hunt (2000) | The avenger experience | Offline | Provided a typology of six consumer revenge acts: verbal and physical attacks, negative WoM, creating loss, vandalism, shoplifting, and trashing. |
| Bechwati and Morrin, (2003, 2007) | The avenger experience | Offline | Revenge can motivate consumer to choose a less optimal choice |
| Gregoire and Fisher, (2005) | The avenger experience | Offline | Prior relationship with firms can reduce the desire for revenge |
| Gregoire and Fisher (2008) | The avenger experience | Online-offline | Fairness violations and betrayal motivate acts of revenge |
| Zourrig et al. (2009) | The avenger experience | Offline | Ideocentric consumers are more likely to get revenge compared to Allocentric ones |
| Funches et al. (2009) | The avenger experience | Offline | Identified a typology of consumer revenge acts: consumption prevention and boycotting, switching and creating loss. |
| Gregoire et al. (2010) | The avenger experience | Online | Fairness violations, perceived firm greed and customer power all influence revenge |
| Trip and Gregoire (2011) | Companies handling of online revenge acts | Online | Avoiding double service failures and time reduces desires for revenge |
| Mdakane et al. (2012) | The avenger experience | Offline | Perceived customer power was a key driver in influencing consumers desire for revenge |
| Joriman et al.,(2013) | The avenger experience | Online-offline | Perceived firm motive was found to influence the desire to reconcile and revenge |
| Obeidat et al. (2017) | The avenger experience | Online | The type of service failure as well as control, reach, and risklessness were found to influence online revenge intentions |
| Gregoire et al. (2018) | The avenger experience | Online | Identified two schemas leading to post online complaints; vigilant and reparation |
| Gregoire et al. (2018) | The avenger experience | Online-offline | post-revenge reactions are influenced by justice restoration, and public exposure perceptions. |
| Obeidat et al. (2018) | The avenger experience | Online | Identified a typology of online avengers; rebellious, aggressive, materialistic, and ego-defending |
| Obeidat et al. (2020) | The avenger experience | Online | Identified interactivity, community, and social presence as key drivers of online revenge behaviors |
| Our study | Other customers responses to the avenger | Online | Identified the avenger experience, online activation, and message trustworthiness as key drivers of other consumers adoption of the avenger message and their desires for revenge |
Concerning the emotional triggers of consumer revenge, almost all previous studies agree that two key emotional drivers encourage revenge: anger and betrayal (Gregoire & Fisher, 2008). In terms of mediating and moderating factors, several studies have focused on examining the role of relationship quality in minimising or increasing consumers’ desire for revenge after experiencing service failure (e.g. Mdakane et al., 2012; Gregoire & Fisher, 2005; Santos et al., 2022). Furthermore, these studies asserted a ‘love is blind’ influence, where if consumers have a good relationship with the service provider, they are less likely to seek retaliation or revenge following a service failure. However, when the service failure is perceived to be controllable by the firm and the firm is to blame, the ‘love is blind’ influence does not act strongly on the consumers’ desire to retaliate. Moreover, the role of consumer-perceived power has also been examined (e.g. Mdakane et al., 2012; Gregoire et al., 2010). These studies asserted that power plays a direct moderating role in the relationship between the desire for revenge and direct revenge behaviours. Later, Joireman et al., (2013) found a mediating role for the negative or positive firm motive between double deviation perceptions and consumers’ negative emotions and revenge intentions. With regards to revenge behaviours in online platforms, Obeidat et al., (2017) identified the perception of Internet reach, recklessness, and perceived behavioural control as key mediators between consumers desire for revenge and their online revenge intentions. More recently, the community and interactivity of social media has also been found to facilitate online revenge (Obeidat et al., 2020).
Another minor trend focused on the types of revenge consumers use, rather than on its antecedents. Huefner & Hunt (2000) reported that consumers employ six main methods to get revenge and retaliate against firms: vandalism, verbal or physical attacks, negative word-of-mouth, shoplifting, and theft, in addition to trashing and creating a loss for the service provider. Funches et al., (2009) identified another form of consumer revenge, which they labelled ‘consumption prevention’, which refers to acts by angry consumers that involve talking damagingly about the service provider and encouraging other consumers to stop purchasing from it. Gregoire et al., (2010) broadly categorised consumer revenge behaviour as either occurring directly (i.e. marketplace aggression) or indirectly (i.e. a third party complaining of seeking publicity). Gregoire et al., (2018b) recently identified two main schemas, vigilante and reparation, that influence consumers’ online revenge and complaining behaviour. Obeidat et al., (2018) proposed four types of online avengers—ego-defending, rebellious, materialistic and aggressive—based on the type of service failure suffered, their motivations for selecting the online channels for revenge, and their affective status after revenge.
While this stream of studies expanded our understanding of consumer revenge from a number of perspectives, research in this area is relatively underdeveloped, mainly because it neglects examining the influence of vengeful behaviour on other consumers’ adoption and desire for revenge. Consequently, the influence of avenger-related factors such as expertise, the misbehaviour post itself, and perceived trustworthiness of the vengeful posts in the eyes of other consumers was largely overlooked (Obeidat et al., 2020). Moreover, no study has examined the impact of social media-related factors on other consumers’ desire for revenge (i.e. consumers’ likes, comments, and shares). As a result, in the following section, a proposed conceptual model for the influence of online revenge acts on other consumers’ online revenge intentions is proposed.
Conceptual Model and Hypotheses DevelopmentMuch of the customer vengeance literature has used two main theories to explain consumer revenge behaviour: the theory of justice and fairness (e.g. Gregoire & Fisher, 2008; Gregoire et al., 2010; Joireman et al., 2013) and cognitive appraisal theory (e.g. Zourrig et al., 2009,2014; Obeidat et al., 2017; Obeidat et al., 2020). However, focusing only on these frameworks as a basis for explaining the motivations and processes underlying consumer revenge could hinder the understanding of other situational factors that could trigger revenge acts among other consumers on online media. Especially, it could hinder the analysis of the mediating role of factors such as message trust and online activation in triggering other consumers’ desire for revenge. Furthermore, such features (namely, avenger expertise, online activation, and post adoption) encourage consumers to engage in various ways with online posts on these platforms (Obeidat et al., 2020). Generally, justice theory was used to explain that violations of fairness would trigger negative customer emotions and, subsequently, acts of revenge. Cognitive appraisal is often used to explain how primary and secondary appraisals after stressful failures in services transform into negative emotions and, consequently, into coping and revenge. Consequently, because this study focuses on other consumers’ reactions to online revenge actions rather than on the cognitive emotional and behavioural processes of the avenging consumers themselves, we drew from the stimulus-organism and response model of Mehrabian & Russell (1974). This theory can be used to describe a holistic view of customer behaviour and explain how an external stimulus can elicit a response from other consumers (Jacoby, 2002). Therefore, this study proposes, as seen in Figure 1 that one main stimulus leads to an evaluation of the online avenger post, which in turn leads to a response from other consumers. Generally, Eroglu et al., (2001) state that a stimulus denotes an effect that motivates a person. The organism relates to an emotive and perceptive intermediate condition that arbitrates the connection between stimulus and response. Response is the final outcome in terms of approach or customer avoidance. As a result, we propose that an act of online customer revenge (i.e. stimulus) will positively impact post-adoption, mediated by the perception of message trustworthiness (i.e. organism). Post-adoption, in turn, results in the desire for revenge for other consumers and, consequently, online revenge intentions (i.e. response).
Other-customer online misbehaviour/ revenge
Generally, the concept of consumer misbehaviour refers to behavioural acts that violate the acceptable norms of consumption (Baek et al., 2015; Fullerton and Punj, 2004; Huang et al., 2010). With increased research attention given to the literature on consumer misbehaviour, Huang (2008) later introduced the concept of other customer misbehaviour/failure. This concept refers to misbehaving acts committed by other customers in the service setting which have a harmful effect on one's service experience or on other consumers who are present during the misbehavior incident (Huang, 2010). This concept encompasses a number of commonly known revenge behaviours, such as negative and vindictive word of mouth, verbal and physical abuse to employees and other customers, cutting queues, and trashing and vandalism (Huang and Wang, 2014). However, in online media, misbehaviour and revenge can range from writing malicious reviews, creating social media pages and websites, spamming, and even hacking (Obeidat et al., 2018). Consequently, in this study, online customer revenge refer to behavioural online revenge actions that negatively affect the firm and are witnessed by other consumers on social media. This conceptualisation is based on Gregoire et al.,’s (2010) retaliatory behaviours which include negative eWOM, vindictive complaining, and negative publicity complaining using social media and the Internet. Moreover, this study proposed that other online customer revenge will positively influence other consumers present on social media and their adoption of the online revenge post, especially when the message is perceived to be trustworthy.
Normally, the SOR theory has been examined in the consumer behaviour and service literature, where the stimulus element is considered to be an online environmental cue that includes web features (Mollen & Wilson, 2010), online brand community characteristics (Islam & Rahman, 2017), and service-scape perceptions (Sahoo & Pillai, 2017). Consequently, we identify online customer revenge as an environmental cue (Stimuli) that drives other consumers to form or enter an evaluation state. Therefore, this study proposes that online customer misbehaviours will influence the adoption of online revenge posts by other consumers. Normally, acts of customer misbehaviour and revenge impact other consumers’ overall evaluations towards service providers (Sweiss et al., 2021), making them more likely to influence their behavioural intentions and behaviours, especially when they believe the service provider is to blame (Huang, 2008). This perception of blame could be enhanced if the avenger is perceived to be an expert with a high degree of trustworthiness as customers are unlikely to adopt an online revenge post if the online revenge post or message is not perceived to be trustworthy (Vermeulen & Seegers, 2009). Prior research in the digital marketing literature has found that negative online reviews are more likely to be believed if the review is believed to be trustworthy (Rahim et al., 2016; Vermeulen & Seegers, 2009). Furthermore, several associations have been found between customer interactions, evaluations, and consumer responses in both attitudinal and behavioural terms (Grove & Fisk, 1997; McColl-Kennedy et al., 2008). For example, a few attempts in the offline context have indeed examined the influence of customer misbehavior acts on other customers present during the incidents and found positive significant results (e.g. Rummelhagen & Benkenstein, 2017; Tseng, 2015; Huang and Wang, 2014; Huang et al., 2010; Huang 2010; Harris & Dennis, 2011). Furthermore, it has been established that acts of customer misbehaviour influence other consumers’ service satisfaction and evaluations of the service provider (Wu, 2007; Huang, 2008; Wu et al., 2014). In addition, they were also found to influence customers’ behavioural intention toward the service provider (Brocato et al., 2012) and create a contagious domino effect (Harris & Reynolds, 2003). Thus, we propose the following hypotheses:
H1: An online act of revenge by a customer positively affects other customers’ adoption of the vengeful online post
Message trustworthiness refers to the perception of trust in messages and posts regarding products and service experiences (Vermeulen & Seegers, 2009). This study proposes that the trustworthiness of the vengeful online post will positively influence other consumers adoption of the post and consequently, their desires for revenge. Generally, trust is a key factor in affecting online consumer activities and cognitive, emotional, and behavioural engagement (Nam et al., 2020; Fan & Miao, 2012) such as the acceptance of others opinion and activation (Obeidat, 2019; Alalwan et al., 2017). Moreover, when online community members perceive the content of a post/review of an online entrepreneur to be trustworthy (Kitsios et al., 2022), they are more likely to adopt the information and the post itself, and this adoption influences their subsequent intentions and behaviours (Doh & Hwang, 2009). Previous research shows that when consumers perceive a post/review to be credible, they have more assurance in adopting the post, and this affects their eWOM behaviours (Kitsios et al., 2022; Rahim et al., 2016; Vermeulen & Seegers, 2009). Thus, we propose the following hypothesis.
H2: Message trustworthiness will positively influence other consumers’ post adoption.
This study also considers message trustworthiness to be an organism element that arbitrates the relationship between online customer revenge and the post-adoption response. Therefore, it was proposed that message trustworthiness mediates the relationship between online customer revenge and the post-adoption of the avenger message. Normally, when consumers perceive the avenging customer's online revenge message to be trustworthy, they are more likely to adopt the views expressed in it (Filieri, 2016; Filieri et al., 2019). This is because customers’ trust in a particular brands or content is not something to be taken for granted; rather, it could be articulated as a changeable attitude that leads to further effects on the customer's perception and behaviors (Alalwan et al., 2017; Palmatier et al., 2006). Theoretically, several studies have supported trust or other similar constructs as mediating factors. For example, Alalwan et al., (2019) successfully demonstrated how social trust mediates the relationship between social commerce activities (i.e. online communities, online ratings, recommendations) with functional value, hedonic value, and social value. Vohra & Bhardwaj (2019) provide strong statistical evidence to confirm the mediating role of trust in the relationship between activation and online community engagement. Another study byDeWitt et al., (2008) empirically approved trust as a factor fully mediating the relationship between justice and two aspects of customer loyalty: attitudinal and behavioural. Moreover, within the consumer review literature, several findings highlight the association between review trustworthiness and consumers adopting the views presented in the review. For example, Su et al., (2021) found a correlational influence of review trustworthiness on the relationship between review emotional intensity and valence and travel intentions. Another study by Fillieri et al., (2019) also found a mediating role for trust in reviews and consumers’ behavioural intentions. Strong statistical evidence has also been found between review trustworthiness and review adoption (Lee & Hong, 2019). Thus, we propose the following hypothesis.
H3: Message trustworthiness will mediate the effect of the vengeful online post on other customers’ adoption of the vengeful online post
Expertise is the perception of the degree to which an individual possesses information, experience, and skills to provide correct information to others (Rahim et al., 2016). In a consumer behavior context, reviewer/poster expertise relates to skills and credentials of providing quality posts or reviews on social media platforms and consumer platforms along with valuable information for the benefit of other consumers (Zhu, Yin, & He, 2014). In this study, online avenger expertise refers to the extent of knowledge, experience, and skills possessed by the avenging customer who posts an online revenge post to be viewed by other consumers. It also refers to the familiarity of an online avenger with a product, service or firm. By definition, avenger expertise relates to how an online avenger is perceived by other customers on social media.
Moreover, this study proposes that the online avenger's expertise will positively moderate how online message trustworthiness mediates the relationship between online customer revenge and post-adoption. The higher the perceived expertise of the online avenger, the higher the level of trust that his/her vengeful post will receive. The linkage between expertise and perceived trustworthiness is well established in consumer reviews, misinformation, and eWOM literature (Aswani et al., 2019; Xie et al., 2017; Rahim et al., 2016; Zhu et al., 2014). Furthermore, this relationship often leads to adoption and subsequent purchase intentions (Tandon et al., 2021; Rahim et al., 2016), in addition to leading consumers to generate electronic word of mouth and forming positive/negative consideration of service providers (Vermeulen & Seegers, 2009). Additionally, this factor in relation to online reviews (i.e. reviewer expertise) can also influence firm reputation and financial performance (Tandon et al., 2021; Xie et al., 2017). Generally, the poster or reviewer expertise in social media platforms is often based on their track records (Willemsen et al., 2011), badges such as elite badges on Yelp (Xie et al., 2017), or the number of likes, comments, and shares they receive (Aswani et al., 2019; Zhu et al., 2014). These serve as good reasons for other consumers’ trust in posts, online revenge posts, and reviews left by strangers (Willemsen et al., 2012). Normally, consumers are more open to reviews and posts with many likes, comments, shares, and badges (Baek et al., 2013) and are more likely to trust and adopt products and services based on them (Archak, Ghose & Ipeirotis 2011). The expertise of online reviewers was also found to directly influence the adoption of reviews (Shen, Zhang, & Zhao, 2016). Additionally, a moderating influence on reviewer expertise was found to influence the relationship between negative reviews and review helpfulness. The more expert the reviewer is perceived to be, the more helpful the review is perceived to be (Filieri et al., 2018). Thus, we propose the following:
H4: The mediating effect of online other customer revenge on post adoption through message/post trustworthiness is moderated by avenger expertise such that the higher the avenger experience, the higher the message trustworthiness.
Post adoption refers to other consumers’ adoption of the online avenger revenge eWOM and post and how they make use of that information (Cheung et al., 2014). In the context of consumer behaviour and service marketing, adopting a product, service, or even an online review normally affects the consumer subsequent actions (Obeidat, 2014). Moreover, the desire for revenge (DR) refers to the intention to cause harm to firms or companies due to perceived injustice or misbehaviour on their part (Gregoire et al., 2010; Dwivedi et al., 2017). Consequently, we argue that other consumers’ adoption of the online avenger's post will lead to the formation of online revenge intentions and a desire for revenge of their own. Generally, negative experiences shared online often encourage herd behaviour, wherein other members in the online community often move into action to show social support (Zhou et al., 2019). Social support is often translated into behavioural intentions and actual behaviours (Xiao & Li, 2019; Shen et al., 2016). Consequently, this study proposes that consumers’ adoption of other customer online revenge posts will lead them to form a desire for revenge. The link between the adoption of eWOM and online reviews and behavioural intentions and actions is well established in the literature (Zhou et al., 2019; Shen et al., 2016). Hence, we propose the following.
H5: Post adoption will positively influence other consumers’ desire for revenge.
The customer's desire for revenge was presented in previous studies on consumer revenge to highlight the role of mediating or moderating factors that could describe the shift from general intention to actual behaviour (Gregoire et al., 2010). Generally, the link between the desire for revenge and online revenge intentions which refers to the intention to use the Internet and social media to get back at a misbehaving firm, is already well-established and documented (Obeidat et al., 2020; Obeidat et al., 2017; Gregoire et al., 2010). Nevertheless, previous findings have also highlighted that the path between these two variables is often mediated by factors such risk, reach, and behavioural control (Obeidat et al., 2017), in addition to a moderating influence for social presence (Obeidat et al., 2020). Consequently, this study proposes that consumers’ likes, comments, and shares on other customer online revenge posts (i.e. online activation) will affect the relationship between the desire for revenge and online revenge intentions. The more numerous the comments, likes, and shares on an online avenger revenge post, the higher is the online revenge intention.
The Facebook ‘like’ feature refers to the click function that allows consumers and users to show approval or preference for the brand posts, consumers reviews, or even complaints and revenge messages (Harris & Dennis, 2011). Facebook also allows consumers to leave brief comments about any post (i.e. commenting) where this information is shown to the consumer network via their newsfeed. (Debatin et al., 2009). This feature allows consumers to share their opinions and experiences with others with relative ease (Hennig-Thurau et al., 2004). In terms of sharing, Facebook also allows users to share posts that appear relevant to them (Branckaute, 2010). Recent research suggests herd behaviour and mentality among consumers on online platforms and communities (Jiang et al., 2020; Zhou et al., 2019; Shen et al., 2016). Moreover, evidence suggests that with the transition to online shopping and shopping through social media, recommendations made by friends and other customers through Facebook likes, comments, and shares tends to carry more weight and highly influences their online intentions (Harris & Dennis, 2011; Marriott et al., 2017). These functions of social media in general and Facebook in particular have gained massive popularity due to the ability of these functions in helping firms build awareness and engagement (Smith & Gallicano, 2015). In addition, these functions have enabled consumers to respond to posts about brands, reviews, and complaints very easily (Obeidat et al., 2020; Coulter et al., 2012). Thus, it has become a key measure of online consumer engagement. Similar to liking a post for a product or brand, these functions can also help consumers casually express their empathy for an online revenge post and share it with their own friends and family on Facebook (Obeidat et al., 2017), In addition to lending their support to the online avenger by merely liking, commenting, or sharing the post (Tandon et al., 2021; Aswani et al., 2019; Naylor et al., 2012). Consequently, in this study, we propose that these functions of Facebook will increase consumers’ desire for revenge and motivation to commit online revenge. Since consumers are more open to reviews and posts with many likes, comments, shares, and badges (Aswani et al., 2019; Baek et al., 2013) and are more likely to trust and adopt products and services based on them due to cognitive and emotional influences (Archak et al., 2011), we propose that online activation of the avenger revenge message/post will motivate other consumers to get revenge online. Generally, research findings suggest that commenting and writing a review on a page or on an vengeful online post can influence other consumers’ trust and choices, and create negative publicity for the misbehaving firm (Kumar et al., 2021; Obeidat et al., 2020; Obeidat et al., 2018). Evidence also suggests that comments on social media play a key role in influencing consumers’ choices because of their ability to provide information and past experiences to other consumers (Algharabat & Rana, 2021; Hennig-Thurau et al., 2004). Indeed, customer activation on social media reflects the extent to which a customer is really interested and involved with a particular post or content published by others (Hepola et al., 2017; Hollebeek et al., 2014). In this regard, Dwivedi (2015), Leckie et al., (2016), and, more recently, Algharabat & Rana (2021) validated the positive impact of activation on customer brand loyalty. Hollebeek et al., (2014) also reported a strong association between the level of activation and adoption behaviour. It could also be argued that as long as customers are involved and actively participate (Hepola et al., 2017), they are more likely to form positive attitudes, perceptions, and behaviours toward the targeted posts or comments (Hollebeek et al., 2014). Accordingly, we propose that customers who are behaviourally engaged with the avengers’ posts are more likely to trust such posts and adopt them. This proposition is in line with the one proposed and empirically validated by Algharabat & Rana (2021), who noticed a significant relationship between activation and customer-perceived quality. Kang et al. (2014) also observed that a customer's active participation has a positive role in contributing to the level of customers’ trustworthiness in the content published by fans on Facebook. Therefore, we could assume that when other customers perceive active participation (liking, commenting, sharing) in an online revenge post, they are more likely to form online revenge intentions. Furthermore, comments, shares, and likes from the consumer network on social media greatly influence the decision made on a product (Harris & Dennis, 2011) in addition to the adoption of online posts and reviews (Alalwan et al., 2017). In addition, if a post is shared several times, the chances of it appearing in the recommendation list and newsfeed increase which increases its visibility to other consumers, thus increasing the negative publicity of an avenger's message. Hence, we propose the following.
H6: The desire for revenge will positively influence online revenge intentions.
H7: Online activation will moderate the relationship between the desire for revenge and online revenge intentions.
A quantitative approach was employed to examine the causality between the variables in our model. Consequently, a questionnaire was developed using a purposive sample of Jordanian consumers who witnessed online customer revenge/misbehavior. The focus of this purposive sample was based on consumers who have previously witnessed an act of customer misbehaviour and revenge on social media to ensure a knowledgeable representation from this purposive sample (Malhorta, 2010; Sekaran, 2003). Normally, these types of consumers are in the best position to provide the desired information needed for the purposes of this research because of their previous knowledge and experience of the examined topic (Saunders et al., 2009). Thus, they are more likely to provide answers based on real-life experiences (Gregoire et al., 2018a). Consequently, to gauge their familiarity with the topic, a screening question was asked first to identify if the participant had witnessed other-customer revenge on social media. Furthermore, the questionnaire was composed of two parts. The first covered the participants general information and demographics. The second part contained the main measures related to the study variables. A pilot test was administered to 30 students who were asked to read related questions after providing their demographic information. No problems were raised during the pretesting; thus, data collection started formally. The online survey was distributed on the Jordanian customer service pages on Facebook. This was done because consumers on these pages were more likely to have witnessed acts of other customer misbehaviour and revenge before. Consequently, 252 responses were collected, and 41 were removed due to incomplete answers. As a result, a sample of (N=211) was finally collected.
MeasuresWith respect to the scales employed, we drew from consumer revenge and engagement literature. Other online customer revenge was measured using the 5-item scale by Gregoire et al., (2010) and contained items such as “Other customers complain to third-party customer service review pages (e.g. Jordan customer service page) to make public the behaviour and practices of the company.” Avenger expertise and message trustworthiness were measured using the 4-Item scale by and 7-item scales developed by Flanagin & Metzger (2000) and Su et al., (2021) and contained items such as “I think online avengers who leave an online misbehavior/revenge posts on social media are accurate” and “I believe each other-customer negative message about firms is trustworthy”. Desire for revenge was measured using the 5-item scale developed by Gregoire & Fisher (2008) and contained items such as “I would want to get even with the service firm”. Online revenge intentions were measured using the 5-item scale of Obeidat (2014) and contained items such as “When I see such posts, I would want to get revenge by vindictively complaining to the firm page-group”. Online post adoption was measured using the Cheung (2014) scale with items such as “When I buy a product/service, the posts/reviews presented on the SNS and websites are helpful for my decision making”. Finally, online activation was measured using the scale developed by Richard & Guppy (2014), which contained items such as “I pay attention to the number of likes a post has”. Generally, all scales were revised and measured using a five-point Likert scale.
Sample demographicsOverall, 56.7% of the sample were women, 43.4% were men, 80.5% were between the ages of 24-30, 80.5% held a bachelor's degree, and the rest were currently completing postgraduate programs. In terms of computer use levels, 89% had been using the Internet and computers for more than four years. Regarding the sample familiarity with consumer revenge behaviour, respondents were also asked whether they had committed acts of revenge before, and if they had witnessed such acts before. Furthermore, 40% of the sample revealed that they had committed acts of online revenge before, whereas 60% had no history of performing acts of revenge. Nevertheless, all participants had witnessed acts of online revenge before participating in the study. As previously mentioned, this reflects a knowledgeable representation of the topic from this purposive sample (Saunders et al., 2009).
ResultsDescriptive Statistics of Measurement ItemsAs seen in Table 2, the average mean values were noticed to be ranging from 2.784 (online revenge intention) to 3.7825 (online post adoption). The highest average mean value (3.7825) was accounted for by the online post-adoption scale items with Std. deviation value of 1.016. The second-largest mean value (3.75; Std. deviation value:1.153) was accounted for by online activation items, followed by other online customer revenge items which have an average mean value of 3.554 and Std. deviation value of 1.1266. The message trustworthiness scale items had an average mean value of 3.271, and Std. deviation value of 1.0337. All scale items of avenger expertise had an average mean value of 3.165, with an average Std. deviation value of 1.032. The vast majority of participants showed that they had a low desire for revenge, as the average mean of their responses on the desire for revenge items was about 2.914, with an average Std. deviation value of 1.2472. Finally, the lowest average mean value (2.784) was accounted for by the scale items of online revenge intention (See Table 2).
Mean and Std. deviation of the Scale Items
A common method bias test was used to examine if bias existed in the respondent's data due to the use of a single method and self-reported measures (Podsakoff et al., 2003). Harman's single-factor test (1976) was employed using unrotated factor analysis. In this case, while limiting the number of factors to one, the single factor will describe more than 50% of the variance if there is an issue of common method bias. Nevertheless, the influence was very low at 24.565% (Podsakoff et al., 2003).
Measurement modelSmartPLS structural equation modeling was used as it allowed for building and testing a complete reflective theoretical model that links the theoretical and empirical concepts and their hypothesis altogether into latent variables and indicators (Carrion, Henseler, Ringle, & Roldan, 2016; Henseler, Hubona, & Ray, 2016; Sarstedt, Hair, Ringle, Thiele, & Gudergan, 2016). Furthermore, SmartPLS is more suitable when the research model has dependent variables and when the sample size is less than 250 (Rigdon, 2016; Reinartz, Haenlein, & Henseler 2009). SmartPLS with bootstrapping resampling using 5000 bias-corrected and accelerated bootstrap were also used, as recommended by Chin (1998), as it avoids the constraints of the maximum likelihood methods of other analytical software (Podsakoff & Organ 1986).
Moreover, we examined the measurement model before evaluating the structural model through bivariate associations between the indicators and constructs (Ringle, Wende, & Becker, 2015). In the first stage and the assessment of the measurement model, the validity and reliability of the constructs were examined (i.e. reliability, composite reliability, average variance extracted, discriminant validity, and convergent validity). As shown in Table 3 all composite reliability values for the variables (i.e. CR) were higher than .70, as recommended (Straub, 1989; Hair et al., 2010). The desire for revenge scored the highest CR (0.92), followed by online activation (.918), message trustworthiness (0.915), avenger expertise (.913), online revenge intentions (.90), online customer revenge (.89), and post adoption (.885). The average variance extracted (AVE) was within the suggested range (Hair et al., 2010). Avenger expertise had the highest AVE (0.723), followed by desire for revenge (.712), adoption (0.658), and online revenge intentions (.654), with online activation scoring the lowest AVE (.584). Furthermore, the VIF (i.e. variance inflation factor) for the latent variables was below 1.5. Consequently, there were no collinearity issues between the predictor variables.
Factors loadings, Cronbach alpha, CR, & AVE
Regarding convergent validity, all the unremoved items were also within range and higher than .70 and sufficiently loaded on their constructs (see Table 3). Moreover, discriminant validity was also reached, as the items loaded largely on their constructs but differently on others, and all values were lower than 0.85, indicating discriminant validity (Fornell & Larcker, 1981). The final requirement was to test the discriminant validity, as shown in Table 4 the correlation values between constructs were less than the square root of AVE.
Discriminant validity
A path coefficient analysis was then conducted to test H2, H5, and H6 (see Table 5). Consequently, all the hypotheses were significantly supported. The desire for revenge had the highest influence on online revenge intentions with a regression weight of (.696) followed by message trustworthiness post-adoption (.595) and post-adoption on the desire for revenge (.172).
Furthermore, to examine H1 and H3 and the mediation effect for post/message trustworthiness, there was a need to validate the indirect influence: the impact of independent constructs (online other customer misbehaviour) on the dependent factor (post adoption) via the mediating role of message trustworthiness) using the consistent bootstrapping technique on SmartPLS as suggested by Shrout & Bolger (2002), Hair et al., (2017), and Chin et al., (2010). As seen in Table 6, bootstrapping analyses strongly support that message trustworthiness significantly mediates the relationship between other customer online misbehaviour and post-adoption (β=0.89, p<0.000). The direct path between other customer misbehaviour and adoption was also significant (β=0.177, p<0.000), suggesting partial mediation. It was also important to test the goodness of fit of the model after including mediating influence, as recommended by Schumacker & Lomax (2010). The results in this regard largely support model fitness after considering the mediating influence of message trustworthiness, as all fit indices were again found to be within their recommended level. Accordingly, H1 and H3 are supported.
Moreover, to test the moderation effect regarding H4 and H7, the product indicator method with bootstrapping was used, as it employs all the indicators of the latent predictor and moderator and all the pair combinations which serve the interaction term in the structural model (Hair et al., 2017; Chin et al., 2010). As seen in Table 7 a significant influence for the avenger expertise on the mediating relationship message trustworthiness plays between online other customer revenge and adoption as the findings show (β=.132, p<0.001). Nevertheless, we also conducted a two-stage approach which employed the scores of the latent predictor and moderator, without the interaction term. These are saved and used to calculate the product indicator for the second stage, which includes the interaction term and moderator and predictor variables. Similarly, this test showed a significant result, albeit with less weight (β=.112, p<0.001), as seen below. Moreover, regarding H6, there is also a significant influence of online activation on the path between the desire for revenge and online revenge intentions. The results show that the significance level p-value was 0.000 (< 0.05) for both tests. Thus, the moderation hypothesis test is statistically accepted (supported). The results are shown in Figure 2.
Moderation analysis
Based on a sample of (N=211) Jordanian consumers who previously witnessed an act of online consumer revenge and misbehavior and by adopting the S-O-R theory, this study investigated the impact of online customer revenge on other consumers adoption through the mediating role of message trustworthiness and the moderating role of the avenger expertise. Other consumers’ adoption of the online revenge message and, subsequently, their own desire for revenge and online revenge intentions was also examined through the moderating role of online activation. Consequently, all the proposed hypotheses were supported.
With regard to H1 and the influence of other forms of online customer revenge on consumers’ post-adoption, the findings of this study highlight a positive effect. Generally, previous findings in the literature support this result, as it has been recognised that acts of customer misbehaviour impact other customers’ satisfaction, evaluations, and intentions towards the service provider (e.g. Huang, 2008; Brocato et al., 2012; Wu et al., 2014; Sweiss et al., 2021). For example, Harris & Reynolds (2003) established that acts of customer misbehaviour have a domino effect and often ruin the consumption experience of other consumers. Huang (2008) also found that acts of other customer misbehavior/failure significantly influenced consumer satisfaction with the service provider, especially if it was perceived to be at blame. Huang et al., (2010) also found a significant influence of acts of other customer misbehaviour on consumers’ evaluations of the firm. More recently, Sweiss et al., (2021) found a direct influence of acts of online customer misbehaviours on both consumers’ attitudes towards the firm and their online engagement with it. This finding demonstrates, for the first time in the literature, the impact of acts of online customer revenge on other consumers who are present on social media. With a general focus on service failure types, double deviations, and fairness violations (e.g. Joireman et al., 2013; Gregoire et al., 2010; Gregoire & Fisher, 2008), limited research attention has been paid to how the acts of misbehaviour itself influence other consumers’ adoption of revenge posts. Consequently, this study shows for the first time how the act of other customers’ online revenge is perceived and adopted by consumers and how it influences online acts of revenge among other consumers.
Additionally, regarding the role of message trustworthiness in influencing the adoption of online revenge messages by other consumers, a significant positive link was found. Generally, positive links between trust in brands, reviews, adoption, and consumers’ behavioural intentions and behaviours have been found (Alalwan et al., 2017; Rahim et al., 2016; Fan & Miao, 2012; Doh & Hwang, 2009; Vermeulen & Seegers, 2009). With regard to the mediating role of message trustworthiness in the relationship between online customer revenge and other consumers’ post adoption, the results of the bootstrapping analysis also revealed that the direct and indirect effects were significant. Consequently, these findings mean that the more consumers believe the online message or post, the more likely they are to adopt and subsequently form revenge intentions of their own towards the firm. The role of message trustworthiness as a mediator is generally supported by previous findings. For example, Su et al., (2021) found a significant mediating impact on online reviews’ trustworthiness on the relationship between review emotional intensity and valence and travel intentions. Fillieri et al., (2019) and Lee & Hong (2019) also established a facilitating role of review trustworthiness on the tendency of adopting a review. This study also introduced and validated perceived message/post trustworthiness as a key mediating factor in explaining the transition of other online customer revenge posts into adoption among other consumers. As a result, this result shows that if online acts of revenge are perceived to be honest and trustworthy on social media, they are likely to be adopted by other consumers even if they have not experienced these acts themselves. Previously, altruism was found to be one of the motivators of revenge behaviour (Funches et al., 2009). Previous findings show that in addition to playing the role of an avenger or victim, some consumers sometimes embody the role of an altruist when committing acts of revenge in order to defend or protect other consumers from suffering the same experiences they did (Obeidat et al., 2018). Consequently, this study highlights that it also could also influence other consumers to commit online revenge in solidarity with the online avenger to protect other consumers out of a sense of altruism. Therefore, when witnessing the act of online revenge, other consumers present on social media witnessing the post could be encouraged to share it and form intentions of revenge of their own out of a sense of altruism. Generally, the role of trust has never been examined in the consumer revenge literature, despite its importance in the consumer behaviour and service marketing literature (Aldweeri et al., 2019) and its role in facilitating the transition for other consumers from simply engaging with a revenge post to forming a desire for revenge, as seen in the findings of this study. This finding could explain the importance of trust in driving online revenge posts and reviews to go viral (Alalwan et al., 2019; Obeidat et al., 2018; DeWitt et al., 2008).
Furthermore, regarding the role of avenger expertise in moderating the mediating role of message/post trustworthiness between online customer revenge and post adoption, this study confirms this moderating effect. This finding could mean that if the online avenger is perceived to be an expert, other consumers are more likely to adopt the avenger's message and perceive it to be trustworthy. Previous findings in the consumer revenge literature have pointed out that sometimes consumers use social media to disguise their revenge posts behind claims of altruism and to help other consumers (Obeidat et al., 2018; Funches et al., 2009). As a result, if the avenger is not perceived as an expert, his/her revenge post will not be perceived as trustworthy and helpful, and vice versa. Overall, the previous literature supports this finding as a number of studies found significant links for expertise directly with the trustworthiness of the review and as a moderator. For example, Zhu et al., (2014) and Rahim et al., (2016) established a direct link between reviewer expertise and trust in reviews. In terms of moderation effects, Filieri et al., (2018) highlight the moderating impact of expertise on the path between negative reviews and review helpfulness, where the higher the perceived expertise of the reviewer, the more helpful it is perceived. The moderated mediation analysis also showed that this relationship was affected by the perceived expertise of the entrepreneur. This result highlights for the first time in the revenge literature that if the avenger is perceived to have experience and knowledge, the level of trust in his/her online revenge post will increase. With a focus on examining factors such as the perceived firm greed (Gregoire et al., 2010) and perceived firm motive (Joireman et al., 2013), no other study previously examined how an avenger-centric factor such as expertise will influence and motivate other consumers to get online revenge. This study sheds new light by showing that when online avengers are perceived to have experience, it will greatly influence other consumers’ trust, adoption, and desire to get revenge themselves against a firm that they may have never dealt with before.
Additionally, both H5 and H6 were supported, and in the process confirmed the influence of the adoption of the vengeful online post and formation of a desire for revenge, as well as the influence of the desire for revenge on online revenge intentions. Consequently, these findings highlight that negative experiences shared online could influence other consumers or members of the online community to show social support and commit acts of online revenge, especially if the online entrepreneur is perceived to be an expert and trustworthy. This result is also consistent with previous findings that link other consumers’ adoption of online reviews with their online intentions towards product or service providers (e.g. Obeidat et al., 2020; Zhou et al., 2019; Xiao & Li, 2019; Obeidat et al., 2017; Shen et al., 2016). Consequently, these findings could also imply a herd behaviour among online consumers and community members in response to online revenge acts on social media as a way of support to the online avenger actions against the firm (Obeidat et al., 2020; Shen et al., 2016). As a result, consumers could also commit acts of revenge due to a sense of altruism to warn other fellow consumers who are dealing with or could be dealing with a misbehaving service provider (Obeidat et al., 2018).
With regard to H7 and the role of online activation in moderating the relationship between desire for revenge and online revenge intentions, moderation analysis showed significant results. Generally, on social media platforms, posts with many likes, comments, and shares are likely to motivate other consumers to commit online revenge, as they often signal expertise and reputation (Xie et al., 2017; Zhu et al., 2014; Willemsen et al., 2011). In addition, consumers were found to be more likely to trust posts with many likes, comments, shares, and badges (Baek et al., 2013), thus making them more likely to commit revenge online. These findings indicate that likes, comments, and shares on online posts constitute a good reason to believe and adopt the vengeful online post (Willemsen et al., 2012). In addition, this could also mean that other consumers could be more open to online revenge posts that receive strong online engagement from the community (Baek et al., 2013). Previous literature tends to support this finding, as online activation was found to influence online trust and the adoption of reviews from other consumers (Ghose & Ipeirotis 2010). Consequently, this study introduced and found the concept of online activation (i.e. consumer likes, shares, and comments) to be a key moderator in influencing the relationship between the desire for revenge and online revenge intentions. In addition to examining the influence of general social media traits (i.e. interactivity, community, recklessness, reach, control, and social presence), no other study has examined the influence of specific Facebook tools on consumer misbehaviour intentions such as revenge (Obeidat et al., 2020; Obeidat et al., 2018; Funches et al., 2009).
The existing literature on consumer revenge behaviour supports the notion that double service failure deviations, as well as violations of fairness, often trigger negative emotions and, consequently, the desire to get revenge (Gregoire et al., 2010). Generally, these triggers have been identified as key antecedents to consumer revenge (Joireman et al., 2013). However, most previous research, as mentioned before, has mainly focused on simple direct effects between the cognition-emotion-behavior sequence of consumer revenge. Consequently, this study also contributes to the literature on consumer revenge through its approach. It did so through the construction and testing of a moderated mediation model for the first time in consumer revenge literature. In addition to taking a different perspective in examining consumer revenge and the use of a new set of variables, the use of this methodological and analytical approach has helped uncover the interplay of relationships between these newly examined variables. Furthermore, to offer a holistic framework of consumers’ reactions to acts of online revenge that incorporates the many situational factors that can motivate consumers to commit acts of revenge in the online context, such as online activation, perceived expertise, and message trustworthiness, this study employed the S-R-O framework for the first time in the revenge literature. By doing so, we move away from the highly dominant justice and cognitive appraisal theories to incorporate a theory that provides a more suitable framework for examining other consumer reactions to online consumer revenge behaviour.
ImplicationsOverall, this study aimed to examine, for the first time, the influence of revenge acts on social media on other consumers’ desire for revenge, as no examination was given to how these acts influence other consumers in the community and their desire for revenge. Consequently, this approach sheds new light on the devastating nature of such acts on other consumers’ perceptions of and intentions towards service providers. Moreover, it highlights how negative publicity is highlighted and spread among social media consumers, especially if the avenging consumer is perceived to be experienced and trustworthy and the engagement on the revenge post is high. With consumer feedback, posts, and reviews becoming a major source of information for other consumers looking to evaluate service providers and products (Obeidat et al., 2020; Ananda et al., 2016; Wolfe et al., 2021), firms should pay special attention to online revenge posts on social media and ensure that the situation is resolved before the posts gain traction.
Generally, the previous literature on consumer revenge highlights that acts of online consumer revenge are best reduced throughout reducing service failures (Obeidat et al., 2020) in addition to ensuring quick and suitable recovery actions (Yoo, 2020). Consequently, firms and social media administrators should provide quick recovery actions to consumers posting online revenge posts to prevent them from going viral and causing negative publicity to the service provider. When dealing with consumers who are perceived to have a high level of expertise, followers, and engagements, firms should take particular care and use specific public recovery actions to avoid negative publicity, including an apology and some sort of compensation. While some acts of online revenge on Facebook and social media could be unjustified and vindictive, when dealing with online revenge posts, firms should expend appropriate resources on their technical arrangements and complaint-handling systems to ensure that possible online revenge posts are recovered before they go viral.
As seen by the findings of this study, online avenger expertise plays a big role in affecting other consumers trust in the revenge message and subsequently its adoption. While firms must deal with hundreds and sometimes thousands of complaints and posts on their pages, they should place particular importance on posts with high engagement, especially those placed by experts. In addition, the number of likes, shares, and comments also plays a significant role in influencing other consumers’ trust in and adoption of the vengeful online post. Firms, along with social media platforms, could implement measures to hide the number of engagements to be viewed only by the poster or the firm themselves. Similar measures have recently been adopted by Instagram. Furthermore, with the growing popularity of these media platforms, service providers could use their social media platforms as customer service platforms instead of merely using them to promote services. Consequently, offering customised recovery actions would go a long way in making offended avengers feel valued and thus, reducing their online revenge intentions.
Limitations and Future ResearchAlthough this research offers a number of new insights into the literature, there are still some limitations that might hinder the generalisability of the results. First, our sample consisted mostly of youth, and while they are suitable for representing the digital natives who are the heaviest users of the Internet and social media platforms and provide a good representation for empirical studies on social media (Aghakhani et al., 2018; Peterson & Merunka, 2014), it could underrepresent some segments of the population. Consequently, future research should test this model in a larger population with a larger sample size. Second, due to its cross-sectional design, the generalisability of the findings could be limited due to the inability to measure and analyse this behaviour over different periods in time. Therefore, to better establish the causality of the relationships between the variables examined, employing a longitudinal design could provide better proof of the relationships examined here. Moreover, as this research was based on consumers’ recall of witnessing such revenge acts online, further studies using scenarios and experimental designs could provide additional insights into the influence of these acts on other consumers. Third, consumers from collectivist cultures, such as Jordan, often engage in greater levels of social support on social media and sharing of information than consumers from individualistic cultures. Therefore, while the hypothesis testing results here are supported by several different studies across the marketing and consumer behaviour literature which supports the generalisability of the results, testing this model in different cultures and contexts could provide additional insights into the interrelationships between the variables. In addition, comparative studies should examine whether these differences exist, and to what extent. Finally, while this study focuses on other online customer misbehavior/revenge in triggering acts of online revenge, future studies could examine the components of the online revenge post/message and how they may trigger different responses from other consumers. In particular, the roles of humour and gender in triggering other consumer revenge responses could be promising.