Systematic Literature Review of Switching Behavior in Service Industry

Although many studies have focused on consumer behavior, a summary of constructs specialized in switching behavior is unexplored. The research aimed to enlarge an extensive and updated overview of customer switching behavior in the service industry. The Systematic Literature Review (SLR) technique evaluated 35 scientific papers released from 2011 to 2021 to analyze drivers, mediating factors, moderating factors, and outcomes related to customer switching behavior. The results improve the understanding and outcome of the drivers to retain consumers. First, the drivers (independent variables) consist of social factors, firm factors, customer behavior, and cost. Second, mediating factors include switching cost, experiential psychological states, inertia, emotion, and consumer perception. Third, moderating constructs have mooring factors, satisfaction, and inertia. Surprisingly, inertia appears in both mediating and moderating variables. The difference depends on service context. Last, outcomes consist of customers’ responses, low satisfaction, and low loyalty. The research contributes to theoretical and managerial implications for sustainable planning by making an overview of several service models. In addition, it includes the drivers of switching behavior in the service industry. Furthermore, the framework offers possibilities and issues for future research and moves the focus from the conventional service domain to social networking that refers to the online platform.


INTRODUCTION
It has been mentioned that service products have intangible characteristics which are more complex than goods (Hirata, 2019). Service-Dominant Logic (SDL) defines service as the application to the advantage of another entity or the entity itself of competencies (knowledge) (Merz, He, & Vargo, 2009). It is related to the consumers' perspective related to all aspects of the provider, including the appearance of the sales representative, quality of customer service, and many more.
Generally, competition in the service industry is fierce, particularly during COVID-19 outbreaks.
In Indonesia, for instance, the aviation industry has decreased domestic departure by 44% in the first quartal of 2020. Indonesian International Civil Aviation Organization states that COVID-19 is a new challenge for the air transport sector (Hakim, 2021). Moreover, the tourism sector in Bali has also experienced a dark period throughout history, which impacts people in business, such as hotel management and the community (Putri, 2021). It impacts firm strategy to defense by minimizing running costs and engaging with loyal consumers.
In contrast, the mobile application industry has positive growth. Gojek, as the main player in Indonesia, reports that the transaction rises to 10% compared to before the pandemic (Stephanie, 2020). The high demand for online mobile applications makes it attractive for new players to compete in the market. There are Anterin, Maxim, Bonceng, Klik Go, BeU Jek, and many more (Habib, 2020). Besides, the demand for online shopping impacts delivery and logistics business, rising to 70% during social distancing (Jawa Pos, 2021). So, many new players utilize this opportunity, such as Wehelpyou, Lalamove, Paxel, Ninja Express, and others. Then, the players' enhancement encourages many alternatives for customers. Hence, consumers have the opportunity to switch easily.
Unfortunately, no literature provides an overview of this topic. Hence, the research outcome presents a systematic insight that may help marketers to decide the informed strategy that has been established and implemented. The research aims to synthesize and build a framework for the consumer behavior literature. Therefore, researchers who are interested in the area will get an overview. Moreover, the findings will help managers to identify the drivers that influence switching behavior in the service sector. In other words, the understanding will increase customer loyalty and retention.
Switching behavior is essential to retain consumers. Previous studies mention that retention strategy is closely related to understanding customers' switching behavior (Hsieh, Hsieh, Chiu, & Feng, 2012;Kuo, 2020;Tang & Chen, 2020). Consumer behavior is defined as the study of understanding how the behavior is shown by searching, buying, using, evaluating, and disposing of products and services expected to meet their various needs (Mou & Benyoucef, 2021). Because every person is unique and has many driver factors that impact their behavior, many theories are frequently used in prior research while discussing consumer behavior. Furthermore, many theories can be implemented to understand customer behavior regarding the aims of behavior they want to understand. The examples are Technology Acceptance Model (TAM) to understand acceptance behavior (Venkatesh & Davis, 2000), Theory Planned Behavior (TPB) to see IT innovation adoption, or Unified Theory of Acceptance and Use of technology (UTAUT) that emphasizes six factors that capture both internal and external factors in influencing consumer behavior (Venkatesh, Morris, Davis, & Davis, 2003). In general, switching behavior is the act of changing the goods or services that have been previously used, and it is subordinated to behavioral intention (Jung, Han, & Oh, 2017). Switching is also defined as replacing or exchanging the current service provider with another service provider (Singh & Rosengren, 2020). According to Park and Jang (2014), consumers' switching behaviors are the opposite of consumer loyalty.
Another study also finds various individuallevel factors within the Push-Pull-Mooring (PPM) framework to explain switching behavior (Sun, 2014). It is also mentioned that consumer behavior receives considerable attention from academics and practitioners because of its effects on financial performance and survival in technology firms (Kamolsook, Badir, & Frank, 2019). It becomes the reason for many studies to take the issue of the consumer's switching motivation and behavior.
Previous studies from the literature review have found the importance of controlling consumers' switching behaviors because it reduces marketing costs significantly (Park & Jang, 2014). About 13 literature works discuss the PPM framework concerning consumer behavior. The research results have a variety of outcomes that impact switching behavior. A study from switching personal cloud storage services in China using a field survey suggests that all push (risk), pull (trust and critical mass), and mooring (switching cost and social norm) factors have direct impacts on switching intention (Sun et al., 2017).
Consumer motivation is an internal state that drives customers to purchase products or services that fulfill conscious and unconscious needs or desires. It is explained that consumers' first motivation is likely to be higher when they regard something personally relevant (Prasad, 2016). On the other hand, other items are considered relevant. They connect to consumers' demands, values, and goals because they represent a significant risk or contradict previous beliefs. Hence, because motivation has an essential role in customerdecision making, it is very common if motivation becomes an issue in customer behavior. For example, it happens in customer engagement (Bazi, Filieri, & Gorton, 2020;Gvili & Levy, 2021) to switching intention (Kamolsook et al., 2019).
Switching behavior is generally defined as an action in which a consumer chooses an alternative to replace previously adopted service providers (Sun et al., 2017). As a behavior that cannot be denied due to the various goods and services offered, many theories and frameworks tried to capture this behavior from different perspectives. According to TPB, a switching behavior-driven behavior can be assessed from an individual's attitudes and beliefs, along with subjective norms and control factors that can lead to this behavior (Pookulangara, Hawley, & Xiao, 2011). However, customers engage in switching behavior from the PPM framework if an alternative offers greater benefit than that is used at present (Jung et al., 2017).
The PPM framework is originally designed for human migration studies (Tang & Chen, 2020). It is a prevalent paradigm in migration studies that explains why individuals move from one place to another over time. In the research, it can be assumed that customers move from one brand to another brand, as said by customer switching. The unfavorable factors at the source that pushes an individual away are called push effects. Meanwhile, the pull effects relate to the favorable aspects of a destination that entice the potential customer to visit. Then, satisfaction or unhappiness is also a significant push element that leads people away from their initial location in human migration study (Sun et al., 2017). The PPM model has been widely used in many disciplines, such as consumer behaviors, marketing, information systems, and human resources domains (Tang & Chen, 2020). That is why the researchers conclude that this framework has a strong relationship with customer switching behaviors.
Customer switching behavior causes factors like service quality, satisfaction, attractiveness of alternatives, lack of alternatives, price, switching costs, and personal characteristics (Jung et al., 2017). Social influence experiments have shown that people often change their opinion to match others' responses (Zhu & Huberman, 2014). A recent study has realized that recommendations are not always the most useful to the customer as many factors influence them.
The behavior tends to make customers prefer a variety of channel options when they undertake the process of purchasing goods and services. When customers have a high intention or attitude toward switching, they may switch purchase channels or service providers (Chou, Shen, Chiu, & Chou, 2016). Research on switching behavior has identified trust to be a critical antecedent (a "push" factor) that causes guests to switch service providers (Gu, Wang, & Lu, 2020).
Customer retention is the driver that can keep customers loyal to the service provider. It is essential to keep the customers and avoid switching intentions to another provider. The previous study focuses on the drivers of determinations, such as perceived risk from customers and the company. It elucidates the roles of those players in predicting the future retention of the same service provider after online searching (Chou et al., 2016).
One of the drivers to keep customer retention is switching barriers. It represents the factor that hinders consumers' decision to change providers. Variables from the service and brand switching literature that fit this conceptualization of mooring effects include switching costs, attitudes toward switching, subjective norms (social influences), past behaviors, and varietyseeking tendencies (Chou et al., 2016). Competition in the service industry may show itself through switching by consumers or through attractive offers to become a strategy in retaining their customers.
Changing behavior can happen in the goods or services industry. However, the research only focuses on the service industry. According to Qiu, Ye, Bai, and Wang (2015), it needs a high level of interaction between service providers and customers to achieve a competitive advantage in the lodging sector. The service industry has a dimension to measure its quality and prevent customer switching. There are five dimensions to measure the quality of service: tangibility, reliability, responsiveness, assurance, and empathy (Wu, Ai, & Cheng, 2019).
Moreover, quality is important in the service industry. According to Wu, Vassileva, and Zhao (2017), experiential quality is defined as the specific received psychological benefit. Meanwhile, satisfaction is the sum of specific advantages or the evaluation of overall experiences. Consumers and organizations are more closely linked when it comes to experiential quality than product and service quality.

METHODS
A comprehensive scientific literature review is an important research approach (Górska-Warsewicz & Kulykovets, 2020). The rest of the article will be arranged accordingly. First, the researchers search and find 35 related articles. Second, the publishers of literature, the development from year to year, current issues shifting, and the methodology used are analyzed. In addition, the research will provide ideas for prospects for study. It develops an overview conceptual model regarding the drivers, moderating factors, and outcomes, which have never been unexplored before.
The researchers also employ several approaches to collect academic and peer-reviewed papers, as presented in Figure 1. First, the researchers choose Science Direct as an academic database and use terms like switching behavior, service, competition, and customer retention to search for these databases. Around 4.707 articles are collected in the beginning. Second, the researchers screen and find 1.013 related articles. The articles with out-of-the-scope subjects are not selected. Moreover, the researchers select articles published in the recent ten years (2011-2021) and research article type only. In total, 3.694 articles are eliminated in this stage. Third, the researchers include open access articles and acquire 87 articles. It means 926 articles are not possible to access. Finally, 35 articles are appropriate to be analyzed in the research.
Based on the number of studies on this issue, the discussion is still limited in previous years. However, at this time, there is a significant increase in this topic compared to previous years. The examination process is carried out by dividing the paper into several areas: the general and design of the study (authorship, published year, domain or context, method, and key findings in Table 1 (see Appendix)), the publishers in Table 2, publishing year in Figure 2, research methodology in Figure 3, and list of the theories in Table 3.
Furthermore, the research presents two kinds of visualization to understand this issue more thoroughly, namely bibliography (see Figure 4) and thematic map (see Figure 5). By visualizing the bibliographic fields using VOSviewer, the construct networks of scientific publications, scientific journals, researchers, research organizations, countries, keywords, or terms about this issue can show a deeper understanding. The researchers have been examining this visualization from the database file Scopus publication since 2017. For instance, the topics that are closely related are price, cost, sales, service quality, satisfaction, loyalty, and many more. It is helpful to find the relationship with other constructs and fill the gap.   Moreover, from the visualization and prior researches, the switching behavior can be understood from different perspectives. Table 3 shows 24 theories in 35 studies that the researchers have chosen. Several theories are frequently used, such as consumer behavior theory, consumer motivation theory, switching behavior theory, and PPM theory.

RESULTS AND DISCUSSIONS
The researchers find several independent variables that represent the driving factors. However, the driving factors are not directly associated with the dependent variable as an outcome. There are mediating and moderating variables that transmit those variables. Figure 6 presents the conceptual framework.
Generally, the driving factors of switching behavior are divided into four general categories. Based on the prior research, these categories positively or negatively impact the final behavior. First, social factors refer to two main types in the conceptual framework: informational and normative social influence. According to Myers (2010), informational social influence defines a situation that people make decisions based on facts or information provided by others. Individuals accept that the information presented is reliable and, as a result, adjust their thinking, beliefs, or behavior to reflect it. In the context of switching behavior, informational social influence takes a role in different forms. Accepting the information from others in the form of WOM becomes the first aspect that significantly influences people to switch. It can be only become a driver into realizing competitor attractiveness (Gu et al., 2020) or even an intention to switch (Singh & Rosengren, 2020).
On the other side, normative social influence differs from informational social influence in how it works. Individuals change their ideas or habits in this circumstance not to be correct but to be liked, accepted, or just fit in (Myers, 2010). In the conceptual framework, normative social influence appears in many forms that will have a different impact. It is found that peer pressure becomes the main influencer that impacts people's behavior, especially in the service context related to the social network (Wu et al., 2014). In this kind of service industry, peer pressure becomes the main essential role in making people tend to switch to be liked or respected. Peer pressure may be an important issue that should be considered, especially in social-related services. People tend to follow a subjective norm as a form of an individual's perception that they think whether or they should engage in a particular behavior or not (Sun et al., 2017). Hence, this driving factor significantly impacts switching intention. This act often happens indirectly because people want to comply with the group norm.

Figure 6 Conceptual Framework
It is explained that even when it is not directly, personally, or publicly chosen as the target of others' disapproval, individuals may choose to conform to others and reverse their opinions to restore their sense of belonging and self-esteem (Cialdini & Goldstein, 2004). Thus, as it is related to switching behavior, this kind of influence frequently happens in many service contexts. Even though many different levels of social influence, especially those that are categorized as normative social influence, often happen in the service industry. It is tightly related to social networking. Then, it is undeniable that the issue of social influence becomes a driver that indirectly impacts customer decision-making, leading them to switch the service provider they use.
Second, based on prior research of firm factors, an intention to switch emerges due to the service providers themselves. This issue comes from the service provider used by the customers and the competitor in the conceptual framework. Frequently, a logical mindset suggests that the intention to switch is related to service quality, which is not surprisingly confirmed by prior research. Due to the range of the service industry being quite broad, the context of quality can be different. In high-contact services, such as airlines, it is found that low service quality measured from the staff to cleanliness of aircraft significantly impacts the final behavior (Jung et al., 2017). On the other side, for low-contact service, the quality is related to experience while using the service (Wu et al., 2019). For example, the staff that gives the service, website quality (Maier, 2016), and issue with the delivery process (Singh & Rosengren, 2020) become the various quality that influences customers' future behavior.
It should be noted that the service quality, whatever it forms, frequently does not directly influence the intention to switch. It often impacts other responses, such as satisfaction (Calvo-Porral et al., 2017;Wu et al., 2014), impacting switching intention. Besides the quality of service, indirect factor, such as corporate image (Calvo-Porral et al., 2017), indirectly impacts satisfaction with the service provider. However, due to the tight competition in the market, competitors' attractiveness and alternative product may encourage the customers to leave and switch, with or without any problem with the prior service provider they use. Prior researchers have found that the forms of the competitor or alternative product are commonly directly significant in switching intention. Alternative attraction and attractiveness commonly become a pull effect that comes from a competitor. It happens when customers measure the other offering, which is perceived to have better performance than the current one (Al-Mashraie et al., 2020;Kuo, 2020;Singh & Rosengren, 2020;Tang & Chen, 2020).
Surprisingly, in the context of Mobile Instant Messenger (MIM), alternative attractiveness fails to influence switching behavior (Sun et al., 2017). This situation may have occurred because the users of this service mostly use MIM to communicate and maintain relationships with their friends. Although another service appeals to the users, they are likely to use the same service to stay in touch with their friends. Network externalities may also be important in service providers, especially in social networking like mobile instant messaging or social commerce. Thus, this factor has an impact on this behavior, both directly and indirectly.
Moreover, alternative network size is typically used to measure direct network externalities since it represents the perceived number of users. The scale of an alternative network is frequently cited as a factor in switching behavior, particularly in services incorporating social networking, that, in the end, make users in this service captivate with others. Meanwhile, indirect network externalities are based on the availability of complementary services rather than the number of network users, as evaluated by the idea of perceived complementarity (Huang, Markovitch, & Ying, 2017;Zhou & Lu, 2011). These two conditions become major reasons for switching behavior in social network services, such as mobile phone services (Kuo, 2020).
Third, besides the external factors, the consumers' internal factors also take an important role in their actual behavior. The internal factors, such as emotion, customer perception, and customer response toward the service they receive, may influence their following behavior that directly or indirectly impacts their decision to switch or not. It is explained that consumer behavior cannot be categorized by one type because their needs shape it (Pizzi, Scarpi, Pichierri, & Vannucci, 2019). Often, a goal that wants to be achieved becomes the main evaluation of whether the customer wants to stay with the service or not. According to Cai et al. (2018), two primary appraisals can approach the switching behavior. Goal congruence is defined as assessing whether a goal is congruent with one's values or beliefs. It may influence the emotional valence and goal relevance referring to how relevant an event or outcome is to an individual's current goals. As a result, it is processed relatively by selecting the most relevant input from the mass of competing stimuli at the time (Kreibig, Gendolla, & Scherer, 2012). The research finds that congruence predicts perceived identity threat, negative emotions, and switching behavior to other providers. At the same time, relevance influences negative emotion significantly, confront misbehaving and customer perceptions, such as perceived powerlessness, betrayal, and identity threat. Based on the research finding, it is undeniable that the psychological factor of perception strongly influences behavior. Generally, perception is a process that involves organizing, identifying, and interpreting sensory inputs to represent and understand the environment (Zhang, Luximon, & Song, 2019). The customers have aims that they attain by utilizing the services provided. Hence, perceived benefits become the driving factors that appear in many service sectors in general.
According to Chang et al. (2017) and Park and Jang (2014), perceived quality has become the main factor that significantly impacts perceived value or satiation. The quality may differ based on the industry context, such as perceived product lifetime (Bölen, 2020). Perceived quality becomes customers' perception of the overall product advantages. Hence, it performs the product's ability to satisfy consumers compared to the alternatives. Moreover, the quality of the product is also related to the users' perception of its usefulness . It mediates partially toward switching intention behavior. On the other side, based on Koo et al. (2020), perceived value is also the first antecedent that significantly impacts customer satisfaction. Perceived value can be referred to as a customers' overall assessment of service utility based on perceptions of what is received and what is given. In particular, the form of values and goals to be achieved may be different for every customer in each sector. For example, security becomes the first antecedent in personal service that needs to be fulfilled. So, the perceived risk is a security risk or privacy risk (Chou et al., 2016;Wu et al., 2017). Meanwhile, in the service related to lifestyle, perceived simplicity, perceived enjoyment, and perceived technical and lifestyle compatibility impact perceived usefulness to the service that will fulfill their satisfaction toward service usage .
Many prior studies involve satisfaction as a driving factor of switching behavior, whether it is in the form of perceived satisfaction or satisfaction behavior itself. Perceived satisfaction while adopting a service positively impacts perceived attractiveness but does not directly support switching behavior (Gu et al., 2020). In a certain way, satisfied customers tend to be loyal to the service they use (Qiu et al., 2015) and have a negative effect on switching behavior (Hsu, 2014;Jung et al., 2017;Steinberg et al., 2021). However, satisfaction can impact other switching intention driving factors, such as satiation (Park & Jang, 2014). Based on this finding, it does not rule out the possibility that satisfied consumers will move. The satisfaction becomes an affective response followed by a coping response (Bagozzi, Gopinath, & Nyer, 1999). This coping reaction refers to creating favorable behavioral intentions toward the services or moving to another service to preserve or raise the level of satisfaction. Moreover, other affective responses, such as trust (Jung et al., 2017;Wu et al., 2017), fatigue with the incumbent (Sun et al., 2017), and convenience (Maier, 2016), have a significant impact on switching behavior. Convenience while using the service often impacts perceived ease of use in technology adoption, which plays an important role in customer response.
Fourth, in previous studies that use PPM theory to explain switching behavior, the switching cost is often used to portray the mooring factor as a personal factor to facilitate consumers from switching from one service to another Jung et al., 2017). Even so, switching cost as the first driver significantly impacts inertia (Sun et al., 2017) or switching barrier (Wu et al., 2014). However, surprisingly, it cannot impact loyalty (Qiu et al., 2015) or switching behavior if the condition is direct without mediation or mediating factors Hsieh et al., 2012;Hsu, 2014). Prior studies support these findings that switching cost generates an effect when other supporting behaviors, such as satisfaction, are low (Jones, Mothersbaugh, & Beatty, 2000). As a result, even when faced with high switching costs, dissatisfied people have a stronger belief that discontinuing is a good or useful move (Hsu, 2014). On the other side, based on Kuo (2020), the cost is described more broadly by involving benefit loss cost, uncertainty cost, sunk cost, and transition cost to define inertia. These studies find that transition cost becomes the only factor that does not support inertia conditions. In the end, it significantly impacts switching intention. Transition cost indicates the amount of time and effort required for a user to adjust to a new service provider that may or may not be a major motivator for people to stick with their current provider (Kuo, 2020).
Next, mediating factor refers to the transmission of the effect of an independent variable on a dependent variable through one or more other variables. First, switching costs are all costs that an individual has to bear when switching from one service, provider, or brand of product to another one (Bölen, 2020). Switching costs also can be defined as the shoppers' estimation of the personal loss or sacrifice in time, effort, and money associated with the customer changing to another service provider (Singh & Rosengren, 2020). It can be concluded that switching costs are described as all the expenses incurred by an individual when switching from one service, supplier, or product brand to another one that can influence a mooring effect on the intention to switch due to push or pull effects caused by retailer driven factors. The negative characteristics like the impression of switching costs can deter people from trying a new product or service. Two additional constructs, such as procedural cost and financial costs, are integrated into the framework to validate the effect of different switching cost dimensions on switching intention. A previous study finds that consumers personally take into account the switching costs they may face when stating switching intent (Blut et al., 2016). During the last decade, the role of switching costs in switching intention has gained considerable attention from researchers in various contexts. Switching cost as mediating variable becomes a broad concept because it can vary in accordance with the research context and product characteristics (Chang et al., 2017;Qiu et al., 2015).
Second, experiential psychological states are described as a consistent feature of human mental activity throughout a set length of time (Gryaznov et al., 2016). It defines confidence as the respondents' belief in their ability to assess the attributes accurately. Experiential psychological states consist of experiential confidence, experiential desire, and experiential motivation. According to Wu et al. (2019), confidence in one's ability generally has a positive impact on motivation and makes it a significant tool for consumers with imperfect willpower. Motivation is defined as the internal and external interaction of incentives or the lack thereof that influences an individual's behavior. Those psychological states, including motivation, desire, and confidence, influence factors of switching behavior.
Third, prior studies have indicated a positive relationship between transition costs and inertia (Kuo, 2020). Inertia refers to consumers' deliberate continuation of the status quo rather than moving to a possibly superior option. It may be a useful theoretical explanation for understanding the fundamental process of mooring effects (Sun et al., 2017). The rule of inertia as a key mediating variable bridges various mooring variables and switching behavior. It is also found that inertia plays a key role in the mooring effects (Al-Mashraie et al., 2020;Sun et al., 2017;Wang et al., 2019). The sources of inertia include personal, institutional, and relational factors (Hsieh et al., 2012). The previous study mentions benefit loss cost, transition cost, uncertainty cost, and sunk cost as the antecedent of inertia and gives an effect to switching behavior (Kuo, 2020).
Fourth, interpersonal emotions are linked to social comparisons. As opposed to fundamental emotions, interpersonal emotions arise in response to specific social-cultural demands and socialization processes and are the outcome of highly sophisticated cognitive processes (Lim & Yang, 2015). Based on cognitive emotion theory, which is a recent topic of active discussion, social comparison causes users to become stressed and stimulating.
Fifth, customer perception is the customer's overall assessment of the utility of a product (or service) based on perceptions of what is received and what is given (Koo et al., 2020). It can be revered as perceived value, which is measured primarily in multiple-item scales with intrinsic and extrinsic, hedonic, and utilitarian values. Perceived risk becomes one example of customers' perception in mediating variables. A high degree of perceived product quality may help to minimize the risk that comes with not knowing whether a product will fulfill its intended purpose successfully. Superior service quality can build good and close relationships with customers. Thus, it leads to lowered psychological and social risk perceptions.
Then, the moderating construct in the paradigm suggests personal and social factors that can hold potential migrants to their place of origin or facilitate migration to the new destination behavior. First, it is found that push, pull, and mooring variables have significant and certain moderating effects on switching intention. The researchers include switching cost and social factors in the framework in moderating variables because they have indirect effects between drivers and outcomes. Mooring effects also have a varying degree of moderating effect on the information search behavior, perceived value, and switching intention (Chang et al., 2017).
Second, as an additional emotional component in moderating variable, satisfaction is predicted to diminish switching intention and attenuate the influence of perceived switching benefit and cost on perceived value. Nevertheless, this section focuses on the function of satisfaction with both its direct effect on switching intention and the moderating effects of the connections from benefit and cost to value.
Third, inertia in moderating variable investigates the key factors in influencing the switching behaviors through the perspective of the PPM framework. In the previous study, inertia acts as a moderating variable that weakens the relationship between the monetary rewards of alternatives and switching behavior. Inertia is also identified as the PPM factor and found to attenuate the relationship between alternative rewards and switching behavior. So, inertia can be treated as the mooring factor. Then, inertia exerts a negative moderating effect on the paths between the monetary rewards of alternatives and switching behavior (Wang et al., 2019). It means inertia weakens the positive relationship between the monetary rewards of alternatives and switching behavior. Meanwhile, inertia also positively predicts switching behavior and negatively moderates the positive relationship between alternative monetary rewards and switching behavior (Chang et al., 2017).
Dependent variables in the research represent the outcome from independent variables of the framework. It consists of customers' responses, satisfaction, and low loyalty. First, WOM and intention to switch are two frequently researched response variables (Sun, 2014). According to Belanche, Flavián, and Pérez-Rueda (2020), the intent to use the service is driven by people's belief that the received value by the consumption of the product or service exceeds the value of the product or service that is not consumed. Consumers who perceive this higher value have an incentive to utilize it and suggest the service to strengthen their decision by this positive behavior of the WOM. Focal customers may show a willingness to stay or quit, fix the problem by interacting with the disruptive customer, challenge service staff, and propagate the negative WOM in contrast (Cai et al., 2018). If consumers are passive, they are likely to enter into a bad WOM or go to others instead of looking for a positive answer on-site (McColl-Kennedy, Patterson, Smith, & Brady, 2009). According to McColl-Kennedy et al. (2009), passive coping is generally an organizational approach that customers communicate dissatisfaction regarding the service provider's poor expertise (vs. the disruptive customer). Besides, another way to express is a direct complaint to make service providers easier (Khalilzadeh, Ghahramani, & Tabari, 2017). The antecedents of customers' responses discussed in the previous section are related to an unwillingness to repurchase. It means switching intention is the opposite of customer loyalty (Jung et al., 2017). Loyalty is described as views of a behavioral notion which means the repeated buy-out of products or services as the series or share of purchases, references, the extent of the connection, or all of the combined (Wu et al., 2019). For example, there is a significant link between satisfaction and loyalty, but only high-traffic hotel visitors impact changing costs (Qiu et al., 2015).
In general, customer satisfaction focuses on the entire assessment of consumer experience following consumption (Wu et al., 2019). Consumers compare experiences with previous after-consumption expectations and generate cognitive dissonance (Gu et al., 2020). The satisfaction, particularly for mobile application users, may also have a saturation effect that lowers their will to change. Users' happiness saturates their demand and decreases their incentive to switch to a competitive application with a similar function and quality. Prior studies also explain satisfaction as an antecedent of switching intention (Al-Mashraie et al., 2020;Qiu et al., 2015;Tang & Chen, 2020). Meanwhile, dissatisfaction significantly affects negative emotions, such as regret (Kuo, 2020). In particular, when consumers have inadequate or insufficient knowledge of the quality of the information, system, and services offered by their mobile payment platform, they may feel more dismayed about their decision in the context of the mobile payment service. Besides, satisfaction is also a successful antecedent of loyalty (Koo et al., 2020). It means low satisfaction will lead to intention to switch (Al-Mashraie et al., 2020;Calvo-Porral et al., 2017;Line et al., 2016) and reduce loyalty simultaneously.

CONCLUSIONS
Some drivers influence customer switching behavior in the service industry. First, social factors, firm factors, consumer behavior, and cost are identified as independent variables. Second, mediating variables include inertia, switching cost, experiential and psychological states, consumer perception, and emotion. Third, moderating variables consist of a catalyst, mooring factors, satisfaction, and inertia. Those constructs are related to customers' response (switching intention, complain, and WOM), low satisfaction, and low loyalty as dependent variables.
The implications of the research are divided into theoretical and managerial. The research gives an overview of related literature on switching behavior and the drivers for theoretical implications. Therefore, the other researchers will know the next area to explore for future research. For managerial implication, the research also informs that several service models have different driving factors. Hence, the research findings can help managers to make a sustainable business plan.
Switching behavior in the market becomes a behavior that happens due to variety-seeking purchases as the representative of individuals' tendency to seek diversity in their choices of services and goods. There is still a lack of discussion about this topic, even though the number of researchers has recently increased. Thus, the research tries to understand switching behavior by focusing on the service industry and mapping different sources, industry scopes, theories, and drivers of switching behavior.
Even though the emergence of the research fills the gap about switching behavior that has not been explained further yet, generally, the research is still lacking in several parts. First, the research only focuses on explaining switching behavior in the service industry. It makes the readers understand switching behavior drivers generally. It should be confirmed that the switching behavior in service and goods is different. Second, the research does not discuss switching behavior in goods, which can be explored further in future research. Third, the research uses broad service industry categories, from mobile applications to retail and B2B to Business to Customer (B2C) services. The variety of categories can give an insight that makes the understanding about this behavior general. On the other side, it shows that the understanding of each industry sector is not in-depth discussion. It should be understood that each sector has different customer characteristics, such as in the aircraft industry compared to the users in personal cloud storage service based on their goals while using the service. Because of that, the driving factors of every industry sector may be different. For example, while discussing the impact of social factors in SNS, the level of social influence is derived from social pressure. On the other side, in the online grocery industry, the level of social influence only happens in the stage of WOM. This finding confirmed that each industry has a different impact form only by seeing one factor. Thus, future research that uses specific industries, such as mobile applications or online industry, is needed. Hence, the knowledge about the switching behavior in a particular industry can be studied in-depth. The logistic regression is predictively accurate. The drop percentage in push factor is one of the most important elements impacting the customer turnover as churners are more sensitive to the quality of service than not. Qiu et al. (2015) Hotel Service Quantitative Study (SEM)

APPENDIX
The results show a considerable beneficial impact on consumer loyalty. However, only high-tariff hotels discover the beneficial influence and moderating part of negative change barriers (i.e., switching costs). The moderating role is not seen in low-tariff hotels.
Wang, Luo, Yang, and Qiao (2019) Mobile Application Quantitative Study (PLS) The link between alternative incentives and behavioral change is shown to be attenuated by inertia. The consumers undergo a systematic assessment process of primary (e.g., congruence and relevance) and secondary (e.g., cognitive and emotional) primary assessment, which leads to coping behavior developments (e.g., active and passive coping). Generally, three factors that make B2B customer stay are procedural factors (i.e., the expenditure of time and effort in locating, adapting, and using a new brand), financial factors (i.e., loss of financially quantifiable resources), and relational switching cost (the loss of identification and emotional bonds with both the brand/provider and employees whom the customer interacts with).

Cai, Lu, and Gursoy
Li and Ku (2018) Social Network Services (SNS) Quantitative Study (PLS) Low transaction effectivity and social factors influence switching intention in SNS.
Koo, Yu, and Han (2020) Hospitality Service Quantitative Study (SEM) Satisfaction with a loyalty program, affective commitment, and switching barriers have a mediating role between the perceived value of a loyalty program and customer brand loyalty.
Hsu ( The research provides overview insight for managers in the service industry and develops knowledge for scholars. In particular, mapping sources, scopes, theories, and drivers of switching behavior is highlighted.