A mediating and multigroup analysis of customer loyalty

Depósito de Investigación de la Universidad de Sevilla https://idus.us.es/ This is an Accepted Manuscript of an article published by Elsevier in European Management Journal, 2016 Vol. 34, on December, available at: https://doi.org/10.1016/j.emj.2016.07.006 Copyright 2016 Elsevier. En idUS Licencia Creative Commons CC BY-NC-ND 1 A MEDIATING AND MULTIGROUP ANALYSIS OF CUSTOMER LOYALTY ABSTRACT Loyalty is configured here as a key determinant of firm performance. Many studies have proposed models that analyze the relationship between loyalty and its main determinants: Perceived Value (PV), Satisfaction and Perceived Switching Costs (PSC). Thus, the first aim of this work is to validate a model of the direct and indirect relationships between those two variables and loyalty, analyzing the mediating roles of both PSC and satisfaction in the relation between PV and loyalty. The second aim is to analyze the influence of customer psychographic characteristics -tendency towards loyalty (based on customer involvement and propensity towards switching)- on the proposed model in the insurance industry. The results show that (a) perceived value has a direct influence on affective loyalty and an indirect influence through two mediating variables, while only PSC plays a mediating role in the case of behavioral loyalty; (b) the results show significant differences between those clients with a high tendency towards loyalty and those customers with a low tendency towards loyalty, in the relations between satisfaction and affective loyalty and the relation between PSC and both affective and behavioral loyalty; (c) the proposed model presents greater predictive validity for clients with a higher tendency towards loyalty. Keywords: customer perceived value; customer satisfaction; perceived switching costs; loyalty; customer heterogeneity; multigroup analysis; partial least squares. 2 1. Introduction The analysis of customer loyalty continues to be, at present, an area of immense relevance and interest both for marketing scholars and for practitioners (Nyadzayo and Khajezadeh, 2016). In fact, managing to achieve customer loyalty is one the principal objectives for service firms (Polo-Peña et al., 2016); an aim that is due to the consequences that are derived from loyalty. So, the literature points out that loyal customers have a greater probability of completing new purchases, generating higher profits, withstanding the actions of rival firms and they usually imply lower retention costs (e.g. McNaughton et al., 2001; El-Manstrly, 2016). Service firms and, in particular, those from the insurance sector find it increasingly difficult to retain the loyalty of their customers, due to the greater facility with which companies may be compared, the high influence of the digital environment and the high levels of customer expectations (PwC-PricewaterhouseCoopers, 2013; World Insurance Report, 2016). In fact, the data indicates that in this industry, the level of satisfactory experiences with the current supplier has fallen over recent years, both at a Spanish and at a European level (World Insurance Report, 2016). According to this study, increased customer expectations, above all in certain segments, has made it necessary for these firms to identify different behaviors and preferences, in order to generate more effective strategies that will mean they adapt to these factors and generate durable and beneficial relations with their customers. So, the analysis of the literature clearly shows that the following aspects relate to customer loyalty. First, the literature that analyzes the antecedent and moderating factors of loyalty is very extensive (Kadampully et al., 2015), although with inconsistent results in many cases. These variations, which also generate differences in the results, may be due to the nature of the proposed (direct/indirect and mediating/moderating) relation, to the set of variables under consideration, to the type of product or service that is studied, as well as to the characteristics of the customers in those markets. Second, although there are many decisive factors for loyalty reported in the literature, three of them stand out because of their importance: perceived value, satisfaction and switching costs (McNaughton et al , 2001; Yang and Peterson, 2004; Lin et al., 2005; Flint et al., 2011; Floh et al., 2014; Chocarro et al., 2015; El-Manstrly, 2016). Many works have proposed the positive influence of customer perceived value (PV) on loyalty. However, some of them propose this relation in a direct way, while others analyze the role of other mediating variables in the relation such as satisfaction and perceived switching costs (PSC), proposing an indirect effect through these variables on loyalty (Lam et al., 2004; García et al., 2006). The literature highlights that both aspects can act on customer loyalty, hence the numerous works that have attempted to define the most effective options. But there is general agreement that the presence of both variables in business strategies is necessary. Centering uniquely on creating switching costs can cause difficulties for the customer when purchasing the product or service (Fornell, 1992) or can be neutralized by the actions of competitors (Burnham et al., 2003). On the other hand, mere satisfaction itself is not enough (Oliver, 1999), as this link is very sensitive to factors such as the type of customer and industry (Kumar et al., 2013). Third, as pointed out earlier, the reality in this industry highlights the consideration of a key variable: customer heterogeneity. In spite of this fruitful line of research, it is true that the empirical results have not been as conclusive as expected. In fact, recent research (Hair et al., 2016; Fuentes-Blasco et al., 2014) has pointed out that the use of an aggregated market perspective can prompt unrealistic analyses, errors and inconsistencies in the estimated parameters, etc. Therefore, some authors have underlined the importance of studying the 3 differences between customers, due to their heterogeneous behavior patterns (Floh et al., 2014). Hence, differences at demographic, socioeconomic, and psychographic levels between customers can influence their expectations and behaviors (Mittal and Kamakura, 2001; Castro et al., 2007). Fourth, numerous works have centered on mechanisms for the identification of customer profiles with a propensity to remain loyal. This kind of customer knowledge facilitates the development of appropriate strategies for marketing actions (Roos and Gustafsson, 2007). The majority of these works center on customer-related demographic and socioeconomic factors, such as age, sex, income level, educational level… However, there are few works that center on psychographic factors, related with personality, tastes, interests and values. In addition, these works usually analyze a single variable separately (Chocarro et al., 2015). However, these variables can jointly impact on the strength of the relations between different determinants of loyalty, which makes it necessary to study customer heterogeneity. Based on all of the above, we seek to cover various gaps with our work. On the one hand, we set up our model in a different way to the existing literature on loyalty. Although there is a lot of literature on marketing that sustains the relation between value and loyalty, there are few works that analyze the role of the variables that mediate in that relation. In this sense, our first contribution is our proposal of a model for the generation of loyalty based on perceived value, through two mediator variables: customer satisfaction and perceived switching costs. This model, seeks to give a more complete explanation of the relations that exist between perceived value, satisfaction, perceived switching costs, and customer loyalty. Moreover, the generation of strategies that are adapted to customers from the insurance industry require that the existence of heterogeneity be taken into account, determined among other factors, by the psychographic characteristics of the customers, the effect of which has hardly been analyzed in this field. This fact leads us to the second contribution of our work that considers the effect of these characteristics on the relationships that are established in our model, through latent segmentation and multigroup analysis. Of all the customer characteristics included in this category, those that are best adapted to the insurance industry is their tendency towards loyalty, based on the level of customer involvement with the service and the degree of customer propensity towards switching. In other words, we aim to demonstrate the influence that customer heterogeneity has on generating customer loyalty and its determinant variables. In pursuit of these objectives, we will first conduct a theoretical review of the determinants of customer loyalty and the relations between these constructs and the concept of customer heterogeneity, centering on their psychographic characteristics. On the basis of this theoretical review, the research hypotheses will be formulated and the conceptual model developed that describe the aim of the study. Subsequently, we will present the empirical study in the Spanish insurance industry and the methodology that has been developed, with particular emphasis on latent segmentation, mediation analysis and multigroup analysis. Finally, we will present a discussion of the results and the main conclusions of our work. 4 2. Conceptual Framework 2.1. Customer loyalty: determinant factors. The literature has pointed to the relevant role of loyalty as one of the principal priorities of marketing (Fuentes-Blasco et al., 2014). The degree of loyalty instilled in customers is an indicator of the probability of their remaining in a future relationship with a company. If future patterns are to be determined, the triggers of customer behavior have to be established, as the likelihood of customers continuing their long-term relationship with the company will vary according to their origins (Roos and Gustafsson, 2007). Likewise, the various approaches to the analysis of loyalty have to be differentiated. The literature has attached greater relevance to behavioral and attitudinal approaches. Thus, behavioral loyalty refers to the degree to which a customer engages in repeated purchasing behavior towards a service provider (Gremler and Brown, 1996). Affective loyalty reflects a positive feeling towards the products or services of a firm that awakens desire in the customer to maintain a relationship with the firm over time (Oliver, 1999). Among the principal antecedents of loyalty, we can find such variables as: customer satisfaction, perceived switching costs, service quality, commitment, customer perceived value, customer trust, and brand experience (i.e. Kandampully et al., 2015). In line with Lam et al. (2004) and Yang and Peterson (2004), the focus of this study is on customer perceived value, customer satisfaction and perceived switching costs. These factors are critical in helping firms to maintain customer loyalty and to enhance it. The literature on services argues that firms will only achieve true loyalty in their customers through the delivery of greater added value. In this sense, many investigations point to perceived value as one of the determining principles of loyalty, although not the only one (Floh et al., 2014; Chen, 2015). The relationship between customer value and loyalty remains one of the most fruitful lines of research in the area of marketing (Flint et al., 2011). The high level of interest in customer value and loyalty among academics and business managers is due to their joint influence on firm profitability (Olsen and Johnson, 2003) and their importance for the survival of the firm and the maintenance of competitive advantage (Grönroos, 2009). From the marketing perspective, PV refers to the criteria or perceived value of the proposal in the mind of the customer. These criteria are based on the perceptions held by the customer in relation to the benefits obtained from the product, and the service from or relation with the provider firm (in terms of quality, image, etc.), as well as the sacrifices that have to be made for their acquisition (in terms of time, money, and effort). Moreover, the literature points out that this valuation is done in comparison with the other competing offers (Woodall, 2003). Nonetheless, perceived value cannot be considered as an ultimate aim for organizations, but rather those results or consequences that flow directly from it. The role of loyalty is to be highlighted, given its influence on firm results. Thus, some authors indicate that loyalty generates better financial results, as it not only enables an increase in income - increase in sales, in references, etc.- but it also reduces costs – less marketing effort, a lower number of returns, etc. (McNaughton et al., 2001). Equally important are the positive references generated towards the firm that, are very relevant in the services industry, as they allow the customer to reduce risk in the stages leading up to the purchasing process (Fuentes-Blasco et al., 2014). All of the above observations led us to take PV as the starting point of our model. The literature proposes that the relation between these two variables, value and loyalty, can occur directly (i.e., Floh et al., 2014; Polo-Peña et al., 2016). Given that two perspectives towards loyalty exist, the attitudinal and the behavioral, as pointed out earlier, it leads us to propose the first hypotheses of our work: 5 H1: The value that the customer perceives impacts on the degree of affective loyalty of that customer. H2: The value that the customer perceives impacts on the degree of behavioral loyalty of that customer. However, as we proposed in the Introduction, one of our objectives was to propose the existence of variables that measure that relation, helping to explain the link between perceived value and loyalty. Following an exhaustive analysis of these variables, we have chosen to consider the role of satisfaction and switching costs as mediator variables in the relationship between customer value and loyalty, following the lines of other works (Lam et al., 2004; Yang and Peterson, 2004). Thus, two ways are considered: one with positive connotations related to satisfaction and the generation of benefits and relational links; and another that is negative and connected with those factors that hinder the switching process to a new provider. We will now move on to explain each of these relations. Customer satisfaction is another of the determinant variables of loyalty of greater weight in the literature. It is considered as the degree to which the customer evaluations of service provision are positive, as satisfaction fulfils their needs, desires and expectations (Hellier et al., 2003). The relationship between PV and satisfaction has been widely discussed, with a positive and a direct relationship emerging between both variables (Lam et al., 2004; Yang and Peterson, 2004; Lin et al., 2005; Sánchez-Fernández et al., 2013; Xu et al., 2015). On the other hand, customer satisfaction can be examined from a transactional perspective. The evaluation formed after a specific service encounter and the experiences generated throughout the different service encounters or episodes constitute a bundle (Roos, 1999) known as “global satisfaction with the service”. Indeed, most research suggests that this global or accumulated satisfaction is what turns out to be most relevant in determining customer loyalty (Olsen and Johnson, 2003). The literature points to the positive influence of satisfaction on the level of both affective loyalty (Gremler et al., 2001; Beerli et al., 2004) and future behavioral loyalty (Beerli et al., 2004). Yet, loyalty is not the same in all of its dimensions: the relationship between satisfaction and affective loyalty is of greater strength. So much so that Reichheld (1993) considered satisfaction to be a “poor substitute” for loyalty. These aspects lead us to propose the following hypotheses in our work: H3: The relation between perceived value and attitudinal loyalty is measured by satisfaction. H4: The relation between perceived value and behavioral loyalty is measured by satisfaction. PSC represents another variable underlined by the literature as relevant for loyalty. They are defined as “those costs which are associated with moving from one supplier to another” (Porter, 1980). In general, they imply monetary, psychological and time losses for the customer (Bitner, 1995), which are connected with both abandoning the current relationship and starting up a new relationship with an alternative provider. Through the creation of switching costs, firms can discourage customers from attempting to abandon the relationship and increase the difficulties that the switching procedure entails. PSC has been recognized as a multidimensional construct in the recent literature (Barroso and Picón, 2012). Therefore, the nature of these costs (relational, monetary, psychological, or associated with the time the switching process takes up) can be very different, depending on the industry. Most contributions link the concept of switching costs to customer loyalty (affective and behavioral) and to provider switching behavior (Hellier et al., 2003; Picón et al., 2014). All of them point out that switching costs encourage loyalty, hindering the switch to another provider even when the satisfaction level is low. 6 These aspects of switching costs lead us to propose the following hypotheses: H5: The relation between perceived value and behavioral loyalty is measured by the customer’s perceived switching costs. H6: The relation between perceived value and behavioral loyalty is measured by the customer’s perceived switching costs. 2.2. Customer heterogeneity based on psychographic characteristics: the customer tendency towards loyalty. Although the previously mentioned relationships are valid in the majority of contexts, there are differences with regard to their intensity, which arise from the confluence of various factors, among which customer characteristics mainly stand out, although so do types of products/services. In this sense, Castro et al. (2007) underlined that consideration has to be given to market or customer heterogeneity, to be able to predict the behaviors and preferences that lead to customer loyalty. Customer heterogeneity is reflected in the concept of segmentation. Customer segmentation is very important in the field of marketing and, in particular, in the services sector, where it is fundamental that firms adapt their strategies to certain segments of customers that present different needs and preferences, which can, in addition, yield further benefits (Fuentes-Blasco et al., 2014). Segmentation based on the characteristics of customers may be done through socioeconomic, demographic and psychographic factors, making “a priori” segmentation a useful approximation to predict the feelings and the future behavior of customers. In fact, nonconsideration of heterogeneity can lead organizations to erroneous and ambiguous conclusions with regard to the relationship between customer loyalty and firm performance (Castro et al., 2007). We therefore conceptualize customer heterogeneity as the existence of differences in customer characteristics (demographic, socioeconomic, or psychographic) that generate different needs and preferences. However, there is limited empirical evidence that endorses the notion of customer heterogeneity that affects the nature of provider-customer relationships (Castro et al., 2007). The literature has shown that customers with different personal characteristics also show differences in the effects of satisfaction, value and switching costs on future behavioral intentions, varying in accordance with customer characteristics (Ganesh et al., 2000; Keaveney and Parthasarathy, 2001; Mittal and Kamakura, 2001). Although the literature has centered a lot of attention on customer loyalty, no empirical evidence has been shown of the moderating effect that psychographic customer characteristics may exert on the relationship between customer loyalty and its principal antecedents. However, as we have highlighted, these characteristics, linked to customer personality, can influence customer perceptions of the service they receive, affecting their degree of satisfaction and the perception of greater provider switching barriers. These characteristics will be decisive in any decision that the customer makes to switch the provider or, on the contrary, to remain loyal. In addition, even in the case of loyal customers, the reasons upon which they base their loyalty can differ according to the type of customer. From among all possible segmentation variables to identify customer profiles, we opted to analyze two psychographic customer characteristics: the level of customer involvement with the service and the propensity toward switching (Ho et al., 2004; Sun et al., 2004; Ruiz et al., 2007; Barroso and Picón, 2012). Customer involvement refers to (both cognitive and affective) personal relevance that people attribute to a decision on the basis of their fundamental values, aims, and personality (Bienstock and Stafford, 2006). In general, those individuals with a higher level of involvement 7 toward a provider will be more inclined to maintain their relationship with it (Jones et al., 2002), given that their perceptions of the service value received will be greater and they will be capable of making further sacrifices to ensure the continued delivery of that service (Martín-Ruiz et al., 2007). On the one hand, thoroughly committed customers will have a stronger emotional link and will be more inclined to attribute considerable risk and uncertainty to possible changes (Bienstock and Stafford, 2006; Keaveney and Parthasarathy, 2001), generating higher levels of PSC. On the other hand, this group of customers will tend to be more loyal, given that their positive assessment of the service provision will yield higher levels of satisfaction (Varki and Wong, 2003). Customers with a thorough propensity toward switching are “anxious for change” (Ganesh et al., 2000) and could therefore begin a switching process without any apparent motive. These clients conduct a broad search for alternatives, and are motivated to try different products and services with different providers (Raju, 1980; Steenkamp and Baumgartner, 1995; Vázquez and Foxall, 2006). They are inclined to switch and are “risk takers”, gaining additional satisfaction in their own switching process (Antón and Rodríguez, 2004; Keaveney and Parthasarathy, 2001). This behavior will lessen their perceived switching costs and the switching process will be easier (Burnham et al., 2003). However, this type of customer would be represented at the extreme of the continuum, with the risk-adverse customers at that other extreme. These are individual customers with a weak tendency to carry out risky actions. In any case, their behavior will depend on the degree of experience with the switching process and other factors such as the type of product/service, its personal characteristics (Bhattacharya and Bolton, 2000) and on other variables such as perceived value and satisfaction with the service. In this way, the level of these variables will impact on their preferences, meaning that from the outset, their tendency towards loyalty will vary. So, if we consider both characteristics and, we center on the extreme values of both variables (high/low involvement; high/low propensity to change), we can differentiate two customers in accordance with their tendency towards loyalty: customers with a high tendency towards loyalty (highly involved and with little propensity to change supplier) and customers with a low tendency towards loyalty (little involvement and with a high propensity to change supplier). So, the first segment of customers will be very involved with the service, so they will tend to value it in a positive way and, what is more, they may be willing to make greater sacrifices to contract it from their current service provider. This aspect, plus its direct reference to customers unlikely to switch, implies both the perception of stronger barriers to switching and a higher propensity toward loyalty. The products and services are of little importance for the second segment, which means that they show less involvement. This aspect, together with the association of the group with customers who are attracted by risk and, therefore, likely to switch, implies greater difficulty for their retention by the firm. So, if the aim is for them to maintain their loyalty towards the firm, it might be necessary to construct strong barriers against switching, as the satisfaction that they experience with the service they received might not be sufficient to maintain them. Therefore, whether the customers are found in one or another segment (customers with a high tendency towards loyalty and customers with a low tendency towards loyalty) can affect the relationships between the different antecedents and customer loyalty. In our model, we propose that the customer tendency to loyalty (determined by propensity towards switching and customer involvement) will moderate all the relations that constitute the conceptual model. These arguments lead us to advance the following hypotheses: 8 H7: The strength of the relationship between perceived value and affective loyalty differs between customers with either a high or a low tendency towards loyalty. H8: The strength of the relationship between perceived value and behavioral loyalty differs between customers with either a high or a low tendency towards loyalty. H9: The strength of the relationship between perceived value and customer satisfaction differs between customers with either a high or a low tendency towards loyalty. H10: The strength of the relationship between perceived value and perceived switching costs differs between customer with either a high or a low tendency towards loyalty. H11: The strength of the relationship between customer satisfaction and affective loyalty differs between customer with either a high or a low tendency towards loyalty. H12: The strength of the relationship between customer satisfaction and behavioral loyalty differs between customers with either a high or a low tendency towards loyalty. H13: The strength of the relationship between perceived switching costs and affective loyalty differs between customers with either a high or a low tendency towards loyalty. H14: The strength of the relationship between perceived switching costs and behavioral loyalty differs between customers with either a high or a low tendency towards loyalty. INSERT FIGURE 1 3. Research methodology 3.1. Sample and data collection This study analyzes firms in the Spanish insurance industry. The sector covers insurance companies, mutual insurance companies, and banks that offer different types of insurance including firms that offer these services both electronically and online. The information was gathered from 20 customers of each of the 74 companies (83.94% of the total volume of premiums in the Spanish insurance industry). All of these companies operate across Spain and offer different types of insurance policies. The final sample was composed of 786 customers (response rate of 53%), distributed evenly across six age ranges and gender. The majority (60%) of respondents were male and the largest age group was 18–30. The data were taken from personal interviews (648) and an online survey, via a website (138). The interviews were conducted in the branch offices of the companies. An online survey meant that customers could answer the questionnaire directly, increasing survey penetration and reaching customers who may not visit company offices often. A snowball sampling technique was used, whereby potential respondents (colleagues and acquaintances) received email invitations with the embedded URL of the website hosting the survey. Nevertheless, we took account of possible differences in the responses. Using the test recommended by Armstrong and Overton (1977), we compared the answers to the first and the last questionnaires; the results showed no significant differences between each other (p = 0.05) in any of the items. Likewise, no differences were found in relation to the data-gathering process. The diversity of insurance services selected (cars, life, multirisk/home, funeral, health and sickness), with different characteristics between them and the fact that the majority (56.5%) of respondents had at least a five-year commercial relationship with their insurance company, facilitated the collection of heterogeneous perceptions of the variables under analysis. Given that the purchase process and 15 measurement invariance of our proposed model. These results support the universal validity of the constructs: PV, Satisfaction, and PSC, the main pillars on which customer loyalty is based. Nevertheless, the results in Table 8 show significant differences between these two groups for the relationship between satisfaction and affective loyalty (H11).This relation is stronger in those customers with high levels of involvement and with a low propensity to switching providers and, therefore, with a greater tendency towards loyalty. We found that the feeling of loyalty for this type of customer was principally upheld by the positive aspects of the relationship, to a greater extent than in those customers with a low tendency to be loyal. We also found significant differences between these two groups for the relationship between PSC and affective loyalty (H13). In this case, this relationship was not significant for those customers with a high tendency towards loyalty. The personal characteristics of this type of customer mean that they developed their affective loyalty regardless of the barriers to perceived change, as they were very involved with the service. Furthermore, the results in Table 7 show significant differences between these two groups for the relationship between PSC and behavioral loyalty (H14). This relationship was stronger among customers with high tendencies towards loyalty. It may be that these customers are more willing to continue the relationship in the future; first, because their personal characteristics lead them to perceive higher switching barriers, because of the implicit difficulties and drawbacks (lock-in effect), and second, because they find themselves highly involved in the service. 5. Conclusions and managerial implications. As has been pointed out in this work, customer loyalty continues to be one of the central research topics in the area of firm marketing and management. Its importance is due, among other factors, to its relation with profitability. In any case, if we wish to achieve customer loyalty, we should study its principal determinants or antecedents and its relations with a view to constructing effective strategies. Insurance companies must seek customer loyalty, on the basis of the provision and development of a value that the customers perceive as superior. This perception of a superior value that is more than the competition can offer will generate greater customer loyalty in two ways: bringing a feeling of satisfaction to the customer and building switching barriers. Both factors will mean that the customers maintain their relationship with the firm over time. Indeed, these results obtained in the insurance industry are consistent with those of research works in other industries (Yang and Peterson, 2004; Sánchez-Fernández et al., 2013). The results have shown the importance of offering a service to customers that is not perceived as a superior value, as this will not only impact on their degree of affective loyalty, but will also turn into a behavior that tends towards repeated purchases from the same supplier. Likewise, it demonstrates the role of the mediator variables in both relations, such as satisfaction with the service received and the costs of perceived change. In all cases, the mediator role of satisfaction was only seen for affective loyalty. This result reinforces the idea, proposed in the theoretical review, that the relation with this perspective of loyalty is stronger, considering satisfaction as a poor substitute of loyalty. We may therefore conclude that we have achieved the first objective of our investigation. Our second proposal has centered on analyzing non-observable heterogeneity in the Spanish insurance industry, based on the psychographic characteristics of its customers. This approach offers greater insight into the influence of the principal determinant variables of loyalty -perceived value, customer satisfaction and switching costs- . The selected customer characteristics –level of customer involvement and the degree of customer propensity toward 16 switchingdetermined their tendency towards loyalty and has enabled us to segment the sample into two principal groups of different customers: a group of individuals with a high tendency toward loyalty (cluster 1), and a second group with a low tendency toward loyalty (cluster 2). However, if we consider the characteristics of the customers, some important differences are found in relation to the determinants of loyalty. The most significant differences are in the relationships between PSC and both affective and behavioral loyalty. Conversely, there are significant differences in the satisfaction-affective loyalty link. In the case of customers with a high tendency towards loyalty (cluster 1), we have confirmed, in the first place, that the generation of positive feelings that tend to maintain the relation with the current supplier arises from their perception of a value that is higher than the value placed on competition, as well as the generation of satisfaction with that relation. This competitive aspect instills a concern in firms to generate positive experiences in the service that they offer. However, the perception of high barriers to change has no effect on this feeling Moreover, if we are analyzing behavioral loyalty, the provision of a service that is valued by customers is not sufficient in itself, as it should also be based on the accumulation of satisfactory experiences over time and the construction of barriers based on emotional links, economic benefits, etc. In this sense, we can affirm that this model presents a high predictive power of the behavioral loyalty of these customers. Likewise, the importance of the mediator variables is clearly shown in the relation PV-behavioral loyalty for the same group of customers. So, those customers with a low tendency towards loyalty (cluster 2) will generate positive feelings towards remaining in the relationship in the future, not only because of the perceived value of the service and the degree of satisfaction it generates, but also because of the strategies that the companies implement to construct negative switching barriers (hindering a search for other providers and the beginning of a new relationship, etc.) or positive switching barriers (generating social or economic benefits, links, and so on). However, whether or not this feeling among these clients is turned into behavioral loyalty is neither determined by their degree of satisfaction nor by their perception of switching costs. Greater effort has to be invested in this group is customers, in order to achieve their loyalty, which is perhaps explained by the fact that their own personal characteristics (their low level of involvement and their high propensity to risk) lead them to change supplier, given that change in itself brings them satisfaction. In addition, when analyzing the results, we can see that the model presents a low predictive value for this segment, above all with a view to generating behavioral loyalty. This observation points to a new line of investigation that centers on including additional variables in the model to explain the loyalty behavior of the segment. In summary, the principal contribution of this work has centered on two complementary aspects: (a) the proposal of a model for the generation of loyalty based on perceived value, through two mediator variables, customer satisfaction and perceived switching costs. (b) the effect of the psychographic characteristics of the customer on the relations that are established in our model. The need therefore arises for firms to manage customers in a different way, in accordance with their tendency towards loyalty; an aspect that implies an advance in the study of loyalty and in market segmentation, areas of great relevance in the field of marketing. Likewise, relevant implications for business managers have been set out. The first is the importance of customer segmentation on the basis of characteristics that differentiate their perceptions and behaviors in the relation with the insurance firm. In this way, they can direct their investments toward those variables with greater influence on each segment of customers, making their relationship marketing both more effective and more efficient by adapting it to individual customer profiles. 17 Moreover, we should underline that, although the literature has pointed to the relationship between loyalty and customer profitability (Helgesen, 2006), not all customers are profitable or equally profitable for insurance industry firms. Although, in general, loyalty is linked to greater benefits, there may be some markets or segments of customers with which it is not profitable to maintain a relationship, due, for example, to the expectations of value of these customers, to the high rates of rotation, or the high costs involved in maintaining the relationship. In some cases, where profitability is reduced or negative, the insurance companies raise the premiums for those less profitable customers (those with a higher number of parties or accidents), turning them, in effect, into an invitation to abandon the relationship. This research presents a series of limitations, although each one could form the basis for future lines of research that would complement its principal findings. The main limitation is not to have included other variables relating to the characteristics of the customer, such as the degree of attractiveness of alternatives available on the market (Capraro et al, 2003), previous experience with alternative providers (Burnham et al., 2003) and the length of the relationship (Jones et al., 2002). The length of the relationship can be especially relevant in the case of services, where the relations are formally established over a set period of time, as in the insurance industry, and may influence the degree of customer loyalty. 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Journal of Advertising, 23, 59-70. 21 Table 1: BIC Indicator Model Likelihood Nº of parameters BIC 1-Cluster -2490.71 4 5008.095 2-Cluster -1909.31 22 3965.294 3-Cluster -1743.09 40 3752.859 4-Cluster -1683.18 58 3753.04 22 Table 2: Cluster parameters Cluster 1 Cluster 2 Cluster 3 Wald p-value R² Level of involvement 1.3118 -1.0837 -0.2281 1210.5562 1.40E-263 0.6956 Propensity toward switching -0.3847 0.2406 0.1441 38.3163 4.80E-09 0.0604 23 Table 3: Cluster sizes and profiles Cluster 1 Cluster 2 Cluster 3 Size 0.3231 0.3396 0.3371 Mean Level of involvement 3.3955 1 1.8556 Mean Propensity toward switching 4.6555 5.2808 5.1843 24 Table 4: Loadings and Weights for the Measurement Model (Total Sample) CONSTRUCT/Dimension/Indicator VIF Weight Loading (CR) (AVE) PERCEIVED VALUE (reflective construct) 0.955 0.682 PV1: Compared to what I have had to give, the ability of the firm to satisfy my needs is… 0.831 PV2: Considering the time, effort and money spent, my assessment of the value received is… 0.866 PV3: Compared to other providers, the value of the firm’s services is… 0.765 PV4: I think the service of the firm meets my requirements of quality at a reasonable price. 0.844 PV5: The acquisition of this service is worthwhile. 0.746 PV6: Given the characteristics of the service, I consider that I am getting good value for my money. 0.847 PV7: Of all the available alternatives, the services of the company are of value. 0.846 PV8: This company is really convenient. PV9: Compared with the maximum price that I would be willing to pay for a service of this type, the tariffs of this company are of good value. 0.814 0.818 PV10: Considering the quality of services received, and the sacrifices supported for their acquisition, my assessment of the company is… 0.871 SATISFACTION (reflective construct) 0.946 0.747 S1: This company covers my needs. 0.857 S2: This company is as good as or even better than other companies. 0.840 S3: My claims or problems are always dealt with quite well. 0.825 S5: This company gives me the service that I expect. 0.890 S6: This company gives an excellent service. 0.890 S7: In general, my experience with the company is positive. 0.880 PERCEIVED SWITCHING COSTS (aggregate multidimensional construct) n.a. n.a. Evaluation costs (reflective dimension) 1.260 -0.181 0.911 0.836 EC1: I can not afford the time to get the information to evaluate other insurance companies fully. 0.914 EC2: I consider that it takes a lot time/effort to get the information needed to feel comfortable evaluating new insurance companies. 0.916 Monetary loss costs(reflective dimension) 2.046 -0.200 0.846 0.735 MC1: Switching to a new insurance company would involve some up-front costs (set-up fees, membership fees, deposits, etc.) . 0.913 MC2: In my opinion, it takes a lot of money to pay for all of the costs associated with switching insurance companies. 0.797 Benefit loss costs(reflective dimension) 1.643 0.287 0.896 0.741 BC1: Switching to a new company would mean losing or replacing points, credits, length of services, etc., that I have accumulated with my insurance company. 0.861 BC2: I would lose a lot of credits, accumulated points, services that I have already paid for, if I switch to a new insurance company. 0.918 BC3: I will lose the benefits of being a long-term customer, if I leave my insurance company. 0.800 Set-up costs(reflective dimension) 2.050 -0.062 0.866 0.764 SC1: Switching insurance companies involves an unpleasant sales process. 0.805 SC2: There are a lot of formalities involved in switching to a new insurance company. 0.938 31