Impact of customer exit drivers on social word-of-mouth: Results extracted from restaurants’ followers

Article history: Received: June 20, 2020 Received in revised format: August 3


Introduction
In 1987, Fornell and Wernerfelt indicated that the success of marketing strategy is a function of customers' flows and identified four types of those flows: change in purchase frequency, shift in brand, customer-market entry and customer exit. The current study is concerned with one of these flows, which is customer exit. The term has been defined by the majority of researchers as the ending of the relationship between the customer and the firm (Stewart, 1998). In fact, customer exit related actions can be divided into two types based on Karakaya (2000) who described customer exit as a switch from using current brand to other brands or the reduction of consumption of particular products or services. Sometimes the firm itself terminates the relationship with the customer for one reason or another such as the non-profit of the customer (Santouridis & Tsachtani, 2015). This aspect is not of the interest of the current study, as its focus is on the termination of the relationship with the firm by the customer. Companies began to be interested in using social media tools after they noticed a growing proliferation of these tools among customers. In addition, companies noted that social media enables customers to share information and opinions, including information on products and services provided by companies. Due to the inability of companies to control the content of social media, they must be present on these means, and must be integrated social media with their tools of marketing communication (Mangold & Faulds, 2009). Regardless of the effects of the customer's exit, researchers have shown that this behavior is influenced by many variables such as service failure, service failure (Karakaya, 2000;Mandagi & Tumiwa , 2018), and many other factors that will be identified when reviewing theoretical literature. Despite the limited number of studies that have been interested in identifying these drivers, the studies that dealt with the impact of the word published by customers through social media are few in the environment of local restaurants. In some cases, however, some studies suggested that customer exit drivers lead to negative word-of-mouth through social media, i.e., social word-of-mouth (Maxham III, 2001;Ryu & Han, 2010;Hwang & Wen, 2009;Lymperopoulos et al., 2013;Nyarko, 2015;Kitapci et al., 2014;Shin et al., 2017). In the seminal model suggested by Hirschman (1970), two negative responses were identified as consequences of service failure: voice and exit responses. The aim of this study is to investigate the effect of drivers of exit response (service failure, service recovery failure, price problems, and customer dissatisfaction) on negative voice response (word-of-mouth via social media). A key contribution of the current study to the literature is that it fills a gap in literature related to the drivers of the customer's exit and their relationship to the word-of-mouth, especially the negative word-of-mouth through social media. On the other hand, the study showed that customer exit drivers can be divided into two types: controllable and uncontrollable drivers. Controllable drivers such as service failure are the responsibility of the firm while uncontrollable drivers like change in customers locations are out of firms' responsibility. In this sense, companies have to deal with controllable drivers. In the current study, four of customer exit drivers under the control of the company have been studied.

Customer exit definition
Customer exit can be categorized within relationships intention vein. Helm et al. (2006) pinpointed customer exit in terms of relationship dissolution. Dimyati and Subagio (2018) specified three major predictors of relationship intentions: economic factors, resource factors (e.g., firm's reputation) and social factors (e.g., social interactions). Customer exit was itemized as a result of product, service, firm or service provider evaluation. According to Bowden (2009), two major responses made by customers after the assessment of a product or service experience, which are customer commitment and customer exit. Stewart (1998) indicated that customer exit refers to the ending of the relationship between the customer and the firm. According to Hirschman's (1970) model (Exit-Voice-Loyalty) as cited in Yanamandram and White (2006) and Preko and Kwami (2015), customer exit was captured as a customer discontinue of the relationship with a prescribed product, service, firm or service provider. Bowden (2009) marked out Michalski (2004) identified six types of customer exit: forced ending, sudden ending, creeping ending, optional ending, involuntary ending and planned ending. Customer exit can be described in terms of two indicators: customer switch and consumption reduction (Karakaya, 2000). Van Doorn et al. (2010) expressed this concept in a similar way based on dividing it into two sub-concepts: consumption reduction and contract ending. According to Chebat and Slusarczyk (2005), customer exit in retail banking refers to customer close of his or her banking account. Eshghi et al. (2007) defined customer exit as a decision made by a customer to end business transactions with a particular company. Customer exit can be described as a results of company failure to dealt with customer complaints (Tahir et al., 2018), while for Villi and Koc (2018) it is a state in which a customer chose to end his relationship with the company due to service failure and dissatisfaction. On the other hand, customer exit can be described as a result of the attractiveness of offers provided from competitors (Amegbe & Osakwe, 2018) or as a result of the customer's lack of loyalty. Based on Dimyati and Subagio (2018) categorization of relationship intention, customer exit can be defined as an intention to stop or switch a particular brand in response to failure of three factors referred to economic benefits, source content and social relationships. The same intention or state for Lubis and Palibutan (2018) can be attributed to causes such as price, packaging, quality, promotion, choices, switch of products in addition to customer curiosity. Consequently, customer exit can be defined as a response from a customer appears in the form of non-completion of the relationship with a company from which the products or services are obtained.

Customer exit drivers
In a study by Colgate and Hedge (2001), three drivers of customer exit were identified: service failure, pricing problems and denied services. For them, the most important driver of customer exit is pricing problems. In a study conducted by Colgate and Norris (2001) on business banking customers, service recovery was the main driver of customer exit after an experience of service failure. Moutinho and Smith (2000) found an impact of attitudes customers in retail banking towards ease of services on the exit behavior. Buttle and Burton (2002) suggested two drivers of customer exit: customer satisfaction and customer satiation. Gerrard and Cunningham (2004), mentioned four customer exit drivers: prices (high prices, price increase or unfair prices), inconvenience of location or opening hour, core service failure, customer attraction by competitors, ethical problems such as unhealthy practices or dishonest behaviors and involuntary switching due to customer changed location. Other drivers of customer exit found in the literature include switching cost (Haj-Salem and Chebat, 2014), such as costs incurred by customers in the case of restricted contracts assigned with wireless telecommunication providers, ease of customer switch (Ascarza et al., 2018), brand reputation and customer satisfaction (Saran and Swamy, 2018), customer satisfaction, customer loyalty and switching cost (Willys, 2018), customer complaints (Tahir et al., 2018), attractiveness of offerings provided by competitors (Amegbe and Osakwe, 2018), loyalty (Olga et al., 2018), case of switch, increased number of choices, packaging quality, price, curiosity, product promotion and product quality (Lubis & Palibutan, 2018) and firm's reputation (Mandagi & Tumiwa, 2018). Santos and Boote (2003) identified four types of post-purchase states: customer delight, customer satisfaction, customer acceptance and customer dissatisfaction. One of these states can be regarded as a key driver of customer exit, which is customer dissatisfaction. Similarly, Clemes et al. (2007) detected the following drivers of firm switch: customer satisfaction, customer commitment, firm's reputation, service quality, young age and low educational level. Solvang (2008) argued that customer loyalty is associated with customer exit, since a high level of customer loyalty can be considered one factor to avoid customer exit. For this reason, lack of customer loyalty can be seen as one of customer exit drivers. Prim-Allaz and Perrien (2000) found significant effects of four relational norms on customer exit or termination decision. Those norms were role integrity, solidarity, reciprocity and firm flexibility. In his study on fast food restaurants, Hanaysha (2016) considered three variables: food quality, price fairness, and physical environment. Two variables; food quality and price fairness were adopted as drivers of customer exit for the current study. Physical environment was excluded sine the focus of this study is customers' perspectives via social media. All drivers found after the review of the literature were formulated negatively as shown in Table  1 because these factors were assumed to have negative effects and result in customer exit. For example customer satisfaction was termed customer dissatisfaction. Out of drivers identified in the current study, five were used to study their effect on social word-of-mouth.

Table 1
Drivers of customer exit in the literature Customer exit drivers Authors 1.
Service recovery failure 5.
Inconvenience location of the firm 9.
Customer attraction by competitors 10.
Negative firm's or brand reputation 12.
Low switching cost 13.
Failure to handle customer complaints 15.
Availability of alternative providers

Social word-of-mouth
Litvin et al. (2008) cited several definitions of word-of-mouth from which one can conclude that word-of-mouth refers to a communication process established by customers to share their opinions on products or services they consume. Hajli et al. (2014) defined social word-of-mouth as a developed version of electronic word-of-mouth, which is a positive or negative statement initiated by a customer and spread over the Internet (Amblee & Bui, 2008). Social media was considered an advantageous environment for word-of-mouth (Pfeffer et al., 2014) and that is why word-of-mouth regarded as a main component of online customer interactions (Bowden, 2009). According to Chu and Kim (2011), a core component of word-of-mouth is the exchange of marketing information. Based on that, social word-of-mouth in this study was defined as a product or servicerelated information and opinions shared between customers using social media applications. These words might express positive or negative perceptions. Maxham III (2001) defined positive word-of-mouth as a customer's willingness to recommend the service (or product) to others. It is understood that a negative word-of-mouth means either not recommending, i.e., customer silence, or recommending others not to use that service. In this sense, a recommendation of a social media user to others not to use the product or service can be a measure of negative word-of-mouth. In a study on service and food quality, customer satisfaction and retention by Al-Tit (2015), customer retention was measured by three dimensions: re-visit intention, willingness to use the same restaurant over the coming year and positive recommendations via traditional word-of-mouth. According to the study, customer retention is used by firms to prevent customers from exit. Since customer retention and customer exist are opposite terms, the current study used negative recommendations by users on social media as an indicator of social wordof-mouth. Three items were used by Maxham III (2001) to measure word-of-mouth: spread of positive word-of-mouth, recommendation of a service to friends and give friends who search for a product or a service an advice.  evaluated word-of-mouth by voluntary and involuntary recommendations, which reflect two types of word-of-mouth; receiver and sender initiated word-of-mouth. According to Chen et al. (2011), the most common attributes of word-of-mouth in the literature are nature of word-of-mouth, that is, positive or negative word-of-mouth and information volume of word-ofmouth. Examples of items used by Casidy and Shin (2015) to measure negative word-of-mouth contain bad-mouth and warning others not to use the firm. Perhaps the most important word of mouth is that they are more confident from the point of view of customers compared to paid ads (Sivadas & Jindal, 2017).

Determinants of customer exit and social word-of-mouth 2.4.1 Service failure
Service failure is a problem-related service which take place during service or product experience by a customer (Maxham III, 2001). In terms of the effect of service failure on word-of-mouth, results of prior studies showed that service failure had a significant impact on word-of-mouth, i.e., a high level of service failure will result in negative word-of-mouth (Maxham III, 2001). Weun et al. (2004) found a significant impact of service failure severity on negative word-of-mouth. Swanson and Hsu (2011) indicated that the higher the severity of service failure, the higher the willingness to discuss that by customers on social media and the more the likelihood to warn others not to use a particular firm, therefore, there was a significant effect of service failure on negative word-of-mouth. Casidy and Shin (2015) found that customer intentions such negative word-of-mouth is positively related to service failure. According to them, direct influences of service failure on the customer is greater than indirect effects on other customers. The results of these results indicate that the failure of the restaurant to provide the service as the customer expects is a cause of negative response from the customer. Therefore, the following hypothesis was suggested: H1: Service failure results in negative social word-of-mouth.

Pricing problems
Pricing problems can be described as high prices compared to similar institutions or prices in the market, price increases, or unfair prices compared to the product or service value obtained by the customer (Gerrard and Cunningham, 2004). In a study on quick-casual restaurants, Ryu and Han (2010) found a positive effect of price on customer satisfaction which in turn play a significant role in customer behavioral intention. Shoemaker et al. (2005( , cited in Yim et al., 2014 emphasized the importance of effective pricing in customer attraction. Kimes andWirtz (2002, cited in Hwang &Wen, 2009) noted that customers view the increase in prices as unfair if the reason behind it is not changes in the market. While the problem of service failure is addressed by service recovery at the time of service experience, price problems are not often addressed as quickly and therefore have a long-term impact. Since pricing problems are one of the drivers of customer exit, the customer often gets involved in negative word-of-mouth response. Consequently, the following hypothesis was postulated: H2: Pricing problems results in negative social word-of-mouth.

Service recovery failure
Casidy and Shin (2015) identified four strategies of service recovery; none, apology, compensation and hybrid recovery. For the current study, service recovery failure means no recovery. The same authors found that no recovery strategy resulted in high negative word-of-mouth. Lymperopoulos et al. (2013) found a significant effect of denied services on customers switch and word-of-mouth. Nyarko (2015) indicated that a firm inability to quickly respond to service failure is one significant predictor of customer switch behavior. Satisfactory service failure recovery had resulted in customer engagement in positive word-of-mouth (Swanson & Kelley, 2001). Accordingly, the following hypothesis was suggested: H3: Service recovery failure results in negative social word-of-mouth.

Customer dissatisfaction
Customer satisfaction is regarded as a critical driver of word-of-mouth . Kim et al. (2009) conducted a study on restaurants to identify the effect of factors such as food quality, service quality, atmosphere, price and value as well as convenience on customer satisfaction, and to explore the effect of customer satisfaction on return intention and word-of-mouth. Their results indicated that improved customer satisfaction had resulted in increased return intention and word-of-mouth. Other prior studies found a significant influence of customer satisfaction on word-of-mouth (Williams et al., 2012;Kitapci et al., 2014;Shin et al., 2017). These studies indicate that customer dissatisfaction leads to negative words and social communication. Hence, the following hypothesis was presumed: H4: Customer dissatisfaction results in negative social word-of-mouth.

Study sample and data collection
A sample consisted of restaurants' followers on social media, e.g., Facebook was received an online questionnaire in order to collect data on drivers of their exit from the relationship with restaurants they used to visit. A total of 874 users of social media were identified within a month. Participants inclusion criterion in the sample was that a user has expressed his opinion about a particular restaurant with respect to the variables used in the study. Hence, 468 questionnaires have been sent to users who meet the requirement of participation in the study. The number of valid completed questionnaires returned was 355 questionnaires with a response rate of 75.85%.

Study model and measures
The conceptual model in Figure 1 involved four independent predictors (service failure, pricing problems, service recovery failure and customer dissatisfaction) assumed to have significant negative effects on a dependent variable (social word-ofmouth). Each driver was measured by four items adapted from the literature as depicted in Table 1. Social word-of-mouth was evaluated based on dimensions of previous studies with the addition of the social dimension through social media. Dimensions used to measure word-of-mouth were negative recommendations (Al-Tit & Nakhleh, 2014), information volume of word-of-mouth (Chen et al., 2011), warning others not to use the firm (Casidy & Shin, 2015) and advice on product or service given to friend via social media (Maxham III, 2001). All factors were assessed using a five-point Likert scale ranged from 1 (strongly disagree) to 5 (strongly agree).

Reliability and Validity
Validity was examined by convergent validity, which evaluated based on the average variance extracted (AVE). Two tests were used to evaluate reliability; Cronbach's alpha coefficient (α) and composite reliability (CR). Calculations of AVE and composite reliability were computed using factor loadings. The results in Table 2 confirmed reliability and validity since AVEs were higher than 0.5, composite reliability values Cronbach's alpha coefficients were higher than 0.7. On the other hand, correlation coefficients between variables indicated that dependent variables investigated in this study were moderately correlated, i.e., free of multicollinearity, and positively correlated to social word-of-mouth (Fornell & Larcker, 1981;Al-Tit, 2015;Liu et al., 2011).

Goodness-of-fit indices and hypotheses testing
Results of goodness-of-fit in Table 2 confirmed the overall fit of the structural model (GFI = 0.940, CFI = 0.924, AGFI = 0.931 and RMSEA = 0.061). GFI, CFI and AGFI values should be greater than 0.90 and RMSEA values should be less than 0.08 (Hooper et al., 2008;Al-Tit and Nakhleh, 2014). In relation to hypotheses testing the results shown in Fig. 2 revealed that service failure significantly predicted negative social word-of-mouth (ß = 0.54, P < 0.05), which means that hypothesis 1 was accepted. Pricing problems was also significantly predicted negative social word-of-mouth (ß = 0.61, P < 0.05), therefore, hypothesis was supported. Service recovery failure had a significant impact on negative social word-of-mouth (ß = 0.35, P < 0.05), hence, hypothesis 3 was confirmed. Finally, customer dissatisfaction had found to significantly affect negative word-of-mouth (ß = 0.46, P < 0.05), that is, hypothesis 4 was accepted. Based on these results, it was clarified that the four drivers of customer exit explored in this study; service failure, pricing problems, service recovery failure and customer dissatisfaction had resulted in negative word-of-mouth posted via social media applications by customers of restaurants.

Discussion and conclusion
This study aimed at identifying drivers of customer exit through a review of literature. Then, to explore the impact of these drivers on word-of-mouth which negatively spread on social media by customers. Four drivers of customer exit were used as independent variables in the present study; service failure, pricing problems, service recovery failure and customer dissatisfaction. The findings of the study pointed out that all these drivers had significant and positive effects on negative social wordof-mouth. In a word, drivers of customer exit in this study resulted in negative word-of-mouth publicized on social media.
These results are logical in line with the findings of several studies. customer exit is a result of customers evaluation of products or service provided by firms (Dimyati and Subagio, 2018), therefore, this term was deemed as a behavioral response of a customer after product or service experience (Bowden, 2009). Two of the most important forms of customer exit: ending the relationship with the company or reduce the amount of consumption (Van Doorn et al., 2010). Research on customer commitment and exit identified numerous drivers of customer exit, such as firm inability to cope with customer complaints (Tahir et al., 2018), attractiveness of competitors' offerings (Amegbe and Osakwe, 2018), pricing, product or service quality, switching cost and customer curiosity (Lubis and Palibutan, 2018). In the current study four drivers of customer exit were identified; service failure (Colgate and Hedge, 2001), pricing problems (Gerrard and Cunningham, 2004), service recovery failure (Casidy and Shin, 2015) and customer dissatisfaction (Buttle and Burton, 2002). In agreement with Maxham III (2001), Weun et al. (2004) and Casidy and Shin (2015) this study found a significant impact of service failure on social word-of-mouth. Customers who watch service failures in restaurants express their displeasure through social media. Furthermore, similar results in terms pricing problems were pointed out by Gerrard and Cunningham (2004), Ryu and Han (2010), which indicated that price problems that can be described by high prices or unfair prices play an important role in customer behavior. On the other hand, after a restaurant fails to recover service failure, it is normal for the customer to express his opinion by the means he deems appropriate such as social media. There is a consensus between the results of the current study and the results of previous studies in that the failure of the restaurant to recover service failure leads to a negative word-of-mouth Kelley, 2001 andLymperopoulos et al., 2013). Finally, the results showed that customer dissatisfaction leads to an increase in the negative word-of-word. Previous studies have supported this finding Kim et al., 2009;Williams et al., 2012;Kitapci et al., 2014;Shin et al., 2017). Based on the findings of the study, it was concluded that the customer's response remains positive as long as the services and products obtained are within his expectations, but if the firm fails to provide the service or cannot process the order even after the customer's complaint, or the firm faces price problems to the extent that the customer is not satisfied with the products and services provided by the firm, it leads to customer engagement in negative word-of-mouth, which may be the advice of friends not to deal with the firm, or at the least, the customer may continue to deal with the company but reduce its consumption volume.

Limitations and recommendations
The results of this study were extracted from social media users who experience a specific restaurant and subjected to one or more of exit drivers and spread their opinions via social media. Therefore, the results can only be generalized to the population from which the sample is drawn and cannot distributed to all restaurants. The nature of this study is cross-sectional, to gain more understanding of customer behaviors a longitudinal study should be used.

Academic and practical implications
The current study presented new possibilities for researchers to study a new topic related to customer exit drivers and their relation to the negative word-of-mouth through social media. For firms or restaurants in particular, there are many drivers that invite customers to exit and end their relationship with the firm. Firms must pay more attention to these drivers and know that their impact is not limited to the customer or friends, but rather through the dissemination of negative views through social media. Firms must be aware that social media are of great importance and that they lack the ability to control the content of social media.