Multi-dimensional analysis of perceived switching costs

Industrial Marketing Management 41 (2012) 531–543

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Industrial Marketing Management

Multi-dimensional analysis of perceived switching costs Carmen Barroso ⁎, Araceli Picón 1 University of Seville, Department of Business Management and Marketing, Av. Ramón y Cajal 1, 41018 Seville, Spain

a r t i c l e

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Article history: Received 16 March 2009 Received in revised form 27 January 2011 Accepted 3 June 2011 Available online 20 July 2011 Keywords: Switching costs Multi-dimensional analysis Nomological network Higher-order construct Customer loyalty

a b s t r a c t The creation of switching costs for customers is an important aspect of strategic planning in today's competitive environment. These costs enable firms to address variations in customer preferences and competitor influence in attempting to gain their customers' loyalty. Although the recognition of the importance of such switching costs has long existed in a variety of contexts, the conceptualization and measurement of the construct is lacking in clarity and consistency. This study proposes that perceived switching costs (PSC) constitute a higher-order construct made up of six dimensions that reflect the customers' perception of the time, effort, and money involved in the switching process. The study also proposes that each of the six dimensions has a distinctive set of antecedents and outcomes. The test of the model is an empirical study in the Spanish insurance sector. The results confirm the validity of the higherorder formative construct of PSC and provide insights for specific strategies to address the perceptions of various customers with regard to switching costs. © 2011 Elsevier Inc. All rights reserved.

1. Introduction For most business organizations, one priority is to gain customer loyalty and develop long-term relationships with them. The rates of supplier abandonment and switching have increased by an average of 15% in recent years, particularly in the banking and insurance services (Customer Think, February 2008). A McKinsey study carried out in the European insurance sector estimated that a churn rate of 10%, centered on the most valuable clients, could reduce profits by nearly 40% (The McKinsey Quarterly, December 2004). Faced with economic recession and the intensification of competition, customers choose to change provider in the hope of reducing costs in the short term (even though this involves the loss of important benefits), while companies choose to retain customers by offering discounts and benefits to loyal customers (The Wall Street Journal, January 2009). Perceived switching costs (PSC) are an important strategic tool in the pursuit of this objective (Aydin, Özer, & Arasil, 2005; Beerli, Martín, & Quintana, 2004; Bell, Auh, & Smalley, 2005; Caruana, 2004; Lam, Shankar, Erramilli, & Murthy, 2004). However, despite the acknowledged importance of PSC, there is a lack of clarity and consensus regarding the uni- or multi-dimensional character of this construct. Most studies adopt a unidimensional approach in which the operationalization of PSC is directly as a single item (Antón, Camarero, & Carrero, 2007; Apaolaza, Hartmann, & Zorilla, 2006; Hellier, Geursen, Carr, & Rickard, 2003). However, several authors argue that this approach ignores the complexity of the construct (Lam et al., 2004; ⁎ Corresponding author. Tel.: + 34 954557521; fax: + 34 954556989. E-mail addresses: [email protected] (C. Barroso), [email protected] (A. Picón). 1 Tel.: + 34 954554426; fax: + 34 954556989. 0019-8501/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.indmarman.2011.06.020

Ruiz, Gremler, Washburn, & Cepeda, 2008), and others contend that a multi-dimensional perspective of the construct provides significant management advantages by identifying different types of switching costs—for which various descriptions exist such as ‘tangible’, ‘intangible’, ‘artificial’, ‘positive’, and ‘negative’ switching costs (De Ruyter, Wetzels, & Bloemer, 1998; Guiltinan, 1989; Jones, Reynolds, Mothersbaugh, & Beatty, 2007; Klemperer, 1987, 1995; Maicas, Polo, & Sesé, 2007; Ping, 1993; Weiss & Anderson, 1992). Despite this increasing acknowledgment of the multi-dimensional character of switching costs, only a few studies attempt to analyze the antecedents and outcomes of each dimension in any depth (Chen & Hitt, 2002; Jones, Mothersbaugh, & Beatty, 2002; Sheer & Smith, 1996; Whitten & Wakefield, 2006; Zhang & Gosain, 2003), and even fewer have tested them empirically (Burnham, Frels, & Mahajan, 2003; Jones et al., 2007). To address this gap in the literature, this study proposes and tests a nomological network that allows the different antecedents and consequences of the dimensions that make up the PSC to be studied. By doing so, the study contributes to the extant literature by: (i) putting forward a comprehensive conceptualization of the PSC construct; and (ii) assessing the nomological validity of the multi-dimensional character of the PSC by analyzing the degree to which this construct (measured by a set of indicators) predicts other constructs that previous theoretical and empirical work suggests that it should. In summary, the present study proposes that PSC is a higher-order formative construct made up of six first-order dimensions that refer to a customer's perception of the effort, time and money (both negative and positive) involved in switching service suppliers. The model considers supplier switching as a process (Bansal, Irving, & Taylor, 2004; Bansal, Taylor, & James, 2005; Roos, 1999, 2002), taking into account the costs and benefits of putting an end to an existing

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relationship and taking on a new supplier. The model thus posits switching to be a dynamic process in which the associated costs include all those that a customer faces from the setting off of the supplier switching decision until it is finally carried out (Roos, 2002). The structure of the remainder of this paper is as follows. The next section reviews the theory behind the concept, the aim of which is to establish that PSC is a multi-dimensional formative construct. An examination of the nomological network of antecedents and outcomes for each dimension then takes place. The paper afterwards presents an empirical study of the proposed model in the setting of the Spanish insurance industry. Finally, the paper concludes by summarizing the theoretical and practical implications of the findings. 2. Literature review A link has traditionally existed between perceived switching costs (PSC) and customer retention and switching behavior (Dick & Basú, 1994; Ganesh, Arnold, & Reynolds, 2000; Jones & Sasser, 1995). However, the nature of these costs can differ among different markets, and the general acknowledgement is that switching costs are greater with more complex products and services (Fornell, 1992; Gremler & Brown, 1996; Jackson, 1985). The subdivision of PSC can be into: (i) monetary costs; (ii) psychological costs; and (iii) relational costs (Bitner, 1995). The categorization of the first of these, the monetary costs, can be into two types: (i) the loss of benefits associated with giving up the current relationship—such as foregone commissions and/or the loss of benefits from loyalty schemes (Guiltinan, 1989; Kim, Park, & Jeong, 2004; Klemperer, 1987; Patterson & Smith, 2003); and (ii) financial losses incurred in the short term when beginning a new relationship—such as deposits and other initial costs. The second of these, the psychological costs, refer to the feelings and/or attitudes associated with a switch of supplier—such as frustration, dissatisfaction, risk, and uncertainty. These psychological costs also include the inconvenience and effort of learning about a new supplier and the anxiety caused by the inability of customers to foresee the consequences of their choice. These costs are also referred to as ‘procedural costs’ (Aydin & Özer, 2005; Chen & Hitt, 2002; Wathne, Biong, & Heide, 2001), ‘information costs’ (Burnham et al., 2003). These psychological costs include ‘economic risk costs’ (Jones et al., 2002; Samuelson & Zeckhauser, 1988); ‘search and evaluation costs’ (Lee, Lee, & Feick, 2001), ‘learning costs’ (Aydin et al., 2005), ‘adaptation costs’ (Kim et al., 2004; Whitten & Wakefield, 2006), and ‘set-up costs’ (Guiltinan, 1989; Weiss & Anderson, 1992). Close links also exist between the costs in the third category, relational costs, and psychological switching costs. Relational costs include those costs resulting from breaking bonds of affection with the supplier's staff (referred to as ‘personal relationship loss costs’ by Patterson & Smith, 2003) and/or with the brand (referred to as ‘brand relationship loss costs’ by Burnham et al., 2003). The need to regard PSC as a multi-dimensional construct is apparent from this review of the literature. However, apart from some recent studies (Burnham et al., 2003; Hu & Hwang, 2006; Jones et al., 2002; Patterson, 2004; Whitten & Wakefield, 2006; Zhang & Gosain, 2003), the measuring of these dimensions has received little attention in a consistent fashion. Indeed, most research has merely opted for an overall measure (Antón et al., 2007; Apaolaza et al., 2006; Hellier et al., 2003; Lam et al., 2004;) with a greater emphasis on procedural switching costs, or the measurement of only one dimension (Lee et al., 2001). Examining only one dimension of a multi-dimensional concept is unlikely to provide an adequate assessment of the concept itself or its relationships with other concepts (Hu & Hwang, 2006; Whitten & Wakefield, 2006). In summary, this literature review reveals that PSC is a higherorder multi-dimensional construct in which each dimension represents an important aspect of the overall construct (Bollen & Lenox, 1991; Wong, Law, & Huang, 2008).

3. Conceptual model and hypothesis development 3.1. Dimensions of PSC Previous studies posit each of the dimensions of the PSC construct as individually strong manifestations of the overall construct; that is, they assume that a change in one dimension produces changes in the others (as in a common latent construct model). However, this underlying assumption is invalid. For example, some customers might perceive the switching costs associated with abandoning a current relationship (such as the loss of benefits derived from loyalty programs) without perceiving the costs associated with beginning a new relationship with an alternative supplier. Such a failure to perceive new costs might occur because customers do not know that different suppliers operate in different ways (that is, they would not perceive set-up or learning costs) or because they already have perfect information about the competition (that is, they would do not incur any search or evaluation costs). It is thus apparent that a modification in one dimension does not necessarily imply a modification in another. In other words, they do not necessarily covary; rather, each dimension can vary independently of the others (Chin & Gopal, 1995). For this reason, unlike previous research, the present study proposes that PSC is a latent aggregate construct that is expressed as an algebraic composition of its different dimensions (Podsakoff, Shen, & Podsakoff, 2006). Drawing on previous research (Burnham et al., 2003; Caruana, 2004; Hu & Hwang, 2006), the model proposed here posits PSC as a higherorder construct with six first-order formative dimensions: (i) benefits loss costs; (ii) personal relationships loss costs; (iii) economic risk costs; (iv) cost of searching and evaluation; (v) set-up costs; and (vi) monetary loss costs. Fig. 1 shows how the paper posits each of these dimensions as having certain antecedents and outcomes. The following are the possible observations about the dimensions presented in this conceptual model. 1) Each dimension represents certain specific characteristics that make a unique contribution to the explanation of the higher-order construct. A change in any one of the six dimensions alters the meaning of the global PSC construct (which depends on the extent to which customers perceive each of the dimensions). 2) Each dimension covers an independent part of the whole concept; if any of them were to be removed, this would significantly reduce the comprehensiveness of the model (Guiltinan, 1989). 3) In accordance with previous studies (Colgate, Thuy-Uyen, & Farley, 2007; Julander & Söderlund, 2003; Vázquez-Carrasco & Foxall, 2006), the model does not posit all dimensions as contributing to PSC in the same direction. It is possible to make a distinction between positive switching costs and negative switching costs (Jones et al., 2007), with the former being those that retain customers in the company “by dedication” whereas the latter are barriers that retain customers “by obligation” (Bendapudi & Berry, 1997). 4) The dimensions do not all have the same antecedents and outcomes. Because certain dimensions are more related to the existence of interpersonal bonds and service quality than others, the effects of various dimensions on behavior and attitudes will differ (Jones et al., 2002). The model thus proposes a nomological network for each of the first-order dimensions of the higher-order formative construct (Diamantopoulos & Siguaw, 2006; Mackenzie, Podsakoff, & Jarvis, 2005; Podsakoff et al., 2006; Seiders, Voss, Godfrey, & Grewal, 2007). It should be stressed that, when switching costs are studied, it is possible to analyze both the internal construct (the buyer–seller dyad) and the external construct (industry scene). From the internal perspective, the supplier can make the switching process difficult, without it having to be a monopoly (Fornell, 1992). To achieve this aim, companies use different strategies to create switching costs, based on four main factors: (i) the knowledge and learning about a

C. Barroso, A. Picón / Industrial Marketing Management 41 (2012) 531–543

ANTECEDENTS

PERCEIVED SWITCHING COSTS

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OUTCOMES

BENEFIT LOSS COSTS LENGTH PERSONAL RELATIONSHIP LOSS COSTS

BREADTH

AFFECTIVE LOYALTY

Relationship Characteristics ECONOMIC RISK COSTS

EVALUATION COSTS

COGNITIVE LOYALTY

INVOLVEMENT SET-UP COSTS PROPENSITY FOR SWITCHING Customer Characteristics

MONETARY LOSS COSTS

BEHAVIORAL INTENTION LOYALTY

Note: Between antecedents and consequences of switching costs dimensions, solid lines indicate relationships expected to be positive; dotted lines indicate relationships expected to be negative. Fig. 1. Nomological network of PSC dimensions, antecedents, and outcomes.

product/service that a customer has acquired, which involves his/her investment in time and effort; (ii) tangible incentives for customers, either in rewards, discounts or points awarded through loyalty programs; (iii) contractual agreements with the customer; and (iv) the development of stable and continuous relationships with the customer, which are mutually beneficial for both parties. From an external perspective, the literature points to the existence of other environmental factors that are external to the dyad, such as the degree of attractiveness of the alternatives on offer in the sector (Capraro, Broniarczyk, & Srivastava, 2003), the existing level of competition (Klemperer, 1995) or the type of service (Jones et al., 2002), all of which condition the influence of the PSC on loyalty. However, this paper adopts a dyadic perspective and we focus on the ways in which firms can build beneficial relationships with individual customers (Mitręga & Katrichis, 2009). 3.2. Antecedents of dimensions Based on previous research, the antecedent variables proposed in this model reflect: (i) the characteristics of the customer–supplier relationship; and (ii) the characteristics of the customer. As Fig. 1 shows, the proposed antecedents on the relationship level are: (i) the length (duration) of the relationship; and (ii) the breadth of the relationship. At the customer level, the proposed antecedents are: (i) the level of customer involvement; and (ii) the propensity for switching. The need exists of pointing out that in this research an exhaustive identification of the antecedents and outcomes of the PSC do not take place. Rather, in line with preceding research, the idea is to determine that the first-order dimensions proposed present antecedents or outcomes that are different or of different intensity. 3.2.1. Duration of relationship The first proposed antecedent refers to how long the relationship between supplier and customer has lasted (Bolton, Lemon, & Verhoef, 2004; Chiao, Chiu, & Guan, 2008). As the relationship develops,

customers go through different phases in which their expectations change (Liu, Su, Li, & Liu, 2010). The early stages are characterized by a degree of uncertainty and inexperience, which leads to inaccurate evaluations by the supplier (Gounaris & Venetis, 2002; Maicas, Polo, & Sesé, 2006; Verhoef, Franses, & Hoekstra, 2002). Over time, strengthened bonds between the parties are established as a result of specific investments in the relationship (economic, psychological, time, and effort) (Bell et al., 2005; Burnham et al., 2003; Jones et al., 2002). In general, the longer the duration of the relationship, the greater are the social and financial bonds between the parties (Chiu, Hsieh, Li, & Lee, 2005; Reinartz & Kumar, 2003) and the lower the likelihood of the abandoning of the relationship — because that would represent the effective loss of the advantages associated with the investments. It is, therefore, reasonable to postulate (as Fig. 1 shows) that the duration of the relationship is positively associated with the costs derived from: (i) a loss of benefits; (ii) a loss of personal relationship; and (iii) economic risk costs related to uncertainty. However, a link does not necessarily exist between duration and the perception of the costs of evaluating other offers, setting-up costs, and/or the monetary costs associated with initiating a relationship with a new supplier— because the shaping of these dimensions by the competitive structure of the industry and customer characteristics is more likely. The paper therefore proposes the following hypothesis with respect to the first antecedent of PSC (‘duration of the relationship’). H1. A positive association exists between the duration of the relationship and: (a) benefits loss costs; (b) personal relationship loss costs; and (c) economic risk costs. 3.3. Breadth of relationship The second antecedent refers to the number of additional (or complementary) products/services acquired with the same company that provides the main service (Burnham et al., 2003; Maicas et al., 2006; Ngobo, 2004).

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Customers are less likely to abandon a supplier from whom they receive a range of products/services for at least three reasons. First, they will have accumulated a significant level of experience and learning (Keaveney & Parathasarathy, 2001; Sharma, 2003). Secondly, more complication will exist with the switching process for multiple products (especially if those products are interdependent) (Burnham et al., 2003). Thirdly, the breadth of relationship implies more interaction with the firm's staff and a greater likelihood of trusting bonds between the parties (Chen & Forman, 2006). A reasonable postulation is, therefore, (as Fig. 1 shows) that the breadth of the relationship is positively related to the costs associated with: (i) loss of benefits; (ii) loss of personal relationship; (iii) economic risk costs; and (iv) the cost of evaluating alternative offers. However, a relationship is not likely to exist between breadth of relationship and the set-up costs and monetary loss costs associated with the new relationship—because these issues are more closely related to the switching method and the procedures employed by different competitors. The paper therefore proposes the following hypothesis with respect to the second antecedent of PSC (‘breadth of relationship’). H2. A positive association exists between the breadth of the relationship and: (a) benefits loss costs; (b) personal relationships loss costs; (c) economic risk costs; and (d) evaluation costs. 3.3.1. Customer involvement The third antecedent refers to the personal relevance (both cognitive and affective) that individuals attach to a decision on the basis of their fundamental values, objectives, and personality (Bienstock & Stafford, 2006; Zaichkowsky, 1985). Because a greater degree of involvement and commitment with a decision is associated with greater resistance to a change of beliefs (Keaveney & Parathasarathy, 2001), the customer's perception of switching costs is likely to increase as the degree of involvement (and hence resistance to change) increases (Young & Denize, 1995). An association is also likely between a high degree of involvement and stronger bonds between the parties (Jones et al., 2002), thus increasing customers' perceptions of relational benefits and their desire to avoid change (Kinard & Capella, 2006; Varki & Wong, 2003). Moreover, deeply committed customers are more likely to attribute significant uncertainty and risk to a potential switch (Bienstock & Stafford, 2006; Keaveney & Parathasarathy, 2001). A reasonable postulation is, therefore, (as Fig. 1 shows) that the level of customer involvement relates positively to the costs associated with: (i) loss of benefits; (ii) loss of personal relationship with the usual supplier; and (iii) the economic risk costs associated with uncertainty due to a lack of desire to proceed with the switching process. In contrast, given the affective nature of personal involvement, the likelihood that this antecedent is related to the perceived costs of evaluation, initiation, and monetary loss associated with the switching process is less. The paper therefore proposes the following hypothesis with respect to the third antecedent of PSC (‘customer involvement’). H3. A positive association exists between the degree of customer involvement and: (a) benefits loss costs; (b) personal relationships loss costs; and (c) economic risk costs. 3.3.2. Propensity for switching Customers with a marked propensity for switching are “keen to change” (Ganesh et al., 2000) and might therefore initiate the change process without any apparent motivation. These customers actually enjoy looking for information and alternative suppliers (Raju, 1980), as they seek stimulation by searching for new experiences and the pleasure of trying out new suppliers/brands (Steenkamp & Baumgartner, 1995; Vázquez-Carrasco & Foxall, 2006).

These change-prone customers and risk-takers, with their experience of the switching process and the product/service category, have a greater capacity for quickly and reliably evaluating the various options and their associated information, the switching costs involved, and the rewards they stand to gain by switching provider. Moreover, if he/she is familiar with switching, the process will be easier (Burnham et al., 2003) and even pleasurable (Antón & Rodríguez, 2004; Keaveney & Parathasarathy, 2001). When the relationship between propensity for switching and the PSC is analyzed, the interaction between three factors must be borne in mind: 1) the purchase decision environment, which depends on the degree of complexity and novelty of the purchase situation; 2) consumer personality, in terms of the extent of the “risk preference” or “risk affinity” which the individual displays; and 3) the person's experience or familiarity with the process of switching providers (Burnham et al., 2003). Therefore, an individual's propensity to switch lies along a continuum, depending on the meeting point of these three factors. Thus at one end of the continuum are those individuals with a lower propensity to switch provider, characterized as being “risk-averse”, with a low tendency to take risky actions. Furthermore, if they have little experience of changing provider, they will perceive high switching costs when they are faced with a highly uncertain choice of making a purchase or changing provider. However, this does not mean that customers will persist with the same supplier or product/service forever, as this will depend on the product/service category and on other situational and personal characteristics (Bolton & Bhattacharya, 2000). At the opposite end of the continuum are those individuals with a propensity to switch, who have experience of the process of changing provider and who are, furthermore, “risk-takers” and therefore tend to take more risky actions regardless of their consequences. In addition to this, if the decision environment of the purchase/change of provider carries a low level of uncertainty, the switching costs will be perceived as being low. Given the characteristics of insurance services, the consumer's experience is important, since it reduces the uncertainty surrounding the consequences of the decision to change, because it involves little novelty or complexity (Van der Valk, Wynstra, & Axelsson, 2008). The risk-taking consumer will voluntarily assume the economic risk that accompanies the decision to switch, even if this means achieving the same reward as those who are risk-averse. The postulation (as the broken line in Fig. 1 shows) of the association of a greater propensity to initiate a switching process with a diminished perception of the economic risk connected with the switch is, therefore, reasonable. In contrast the likelihood exists of a check (and perception) by such a customer of the monetary loss and set-up costs in the new relationship. With respect to the other three dimensions of PSC, given the lack of loyalty shown by these customers, a propensity for switching is not likely in relation to the dimensions of loss of benefits or loss of personal relationship. Similarly, these customers' apparent enjoyment of evaluating alternative suppliers makes it unlikely that a propensity for switching is related to the cost of evaluating different offers. The paper therefore proposed the following hypothesis with respect to the fourth antecedent of PSC (‘propensity for switching’). H4. A negative association exists between the propensity for switching and (a) economic risk costs, but a positive association exists between it and: (b) monetary loss costs; and (c) set-up costs. 3.4. Outcomes of dimensions Although switching costs have negative connotations because they represent barriers that impede a switch in suppliers and thus ‘trap’ customers in an existing (perhaps unsatisfactory) relationship (Jones & Sasser, 1995), they can also have positive connotations as an

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important mechanism for the creation of benefits and value for customers (Jones et al., 2007). PSC can thus influence the loyalty of customers—defined as the psychological state that customers have as a result of a company's retention strategy (Caruana, 2004). Therefore, in accordance with Gremler and Brown (1996), the present study posits three loyalty-related outcomes of PSC (as Fig. 1 shows): (i) affective loyalty (a positive and willing attitude); (ii) cognitive loyalty (awareness of, or preference for, the products/ services of a particular supplier); and (iii) future behavioral intention (intention to repeat purchase behavior).

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they will tend to remain in their current relationship (even if they feel, to some extent, that they are locked into the current relationship and believe that it is not the best alternative for them to obtain the services they seek). The paper therefore proposes the following hypothesis with respect to the second outcome of PSC (‘cognitive loyalty’). H6. A positive association exists between cognitive loyalty and (a) benefits loss costs; (b) personal relationship loss costs; (c) economic risk costs; and (d) monetary loss costs; but a negative association exists with: (e) evaluation costs; and (f) set-up costs.

3.4.1. Affective loyalty Affective loyalty implies a desire to maintain a relationship on the basis of a generally positive feeling towards established ties and purchasing experience (Oliver, 1999). Although the traditional supposition is for switching costs to exert a negative effect on affective loyalty (Dick & Basú, 1994), the current study contends that the contrary is true (Methlie & Nysveen, 1999). As Caruana (2004) notes, customers who perceive that they are saving money and time by avoiding a switch prefer to be guided by the positive aspects of loyalty (added value, relationship, and so on). However, if the customers perceive that they are locked into a relationship by the high costs of evaluating and initiating a new relationship with an alternative supplier, a diminishing of their affective loyalty will take place (Methlie & Nysveen, 1999). With regard to monetary loss, to find any study in the literature that establishes a definite link between this dimension and affective loyalty is difficult (despite this being the dimension most closely related to the cognitive and behavioral aspects of loyalty). Therefore, as Fig. 1 shows, the paper proposes the following hypothesis with respect to the first outcome of PSC (‘affective loyalty’).

3.4.3. Future behavioral intention loyalty The second outcome of PSC refers to the extent to which the customers intend to repeat their purchasing behavior pattern in the future (Gremler, Brown, Bitner, & Parasuraman, 2001; Methlie & Nysveen, 1999; Zeithaml, Berry, & Parasuraman, 1996) and have no intention of switching suppliers (Bansal & Taylor, 1999; Dabholkar & Walls, 1999; Hu & Hwang, 2006). Based on the literature, the present study postulates (as Fig. 1 shows) that a positive relationship exists between relational and economic switching costs and future behavioral intentions loyalty. Moreover, as the complexity of the switching process increases, customers will tend to be loyal only in behavior, not in attitude (that is, to show so-called ‘spurious loyalty’) (Dick & Basú, 1994). Although a positive association exits between all switching cost dimensions and future behavioral intentions loyalty, their association is not likely to the same extent. Rather, what is likely is for relational and procedural costs to have a closer link to behavioral intentions loyalty than for financial costs. The paper therefore proposed the following hypothesis with respect to the third outcome of PSC (behavioral intentions loyalty).

H5. A positive association exists between affective loyalty and: (a) benefits loss costs; (b) personal relationship loss costs; and (c) economic risk costs; but a negative association exists between it and: (d) evaluation costs; and (e) set-up costs.

H7. A positive relationship exists between future behavioral intentions loyalty and (a) benefits loss costs; (b) personal relationship loss costs; (c) economic risk costs; (d) monetary loss costs; (e) evaluation costs; and (f) set-up costs.

3.4.2. Cognitive loyalty The second outcome of PSC reflects customers' awareness of (or preference for) the products/services of a particular supplier, which they consider to be ‘exclusive’ in comparison with those of the competition (Bloemer, de Ruyter, & Wetzels, 1999; De Ruyter et al., 1998; Jones & Taylor, 2007) and therefore the only alternative that exists for future transactions (Gremler & Brown, 1996). According to Jones and Taylor (2007), cognitive loyalty is closely linked to affective loyalty (and is, perhaps, even the same dimension). Oliver (1999) contends that, before developing affective loyalty, consumers are first cognitively loyal; according to this view, affective loyalty can develop only with repeated purchasing. Therefore the argument that cognitive loyalty constitutes the first of the states of loyalty, and one in which customers are more likely to make a switch is possible. The present study postulates (as Fig. 1 shows) that contractual or financial costs are positively linked to cognitive loyalty— because customers are aware of the economic losses that they might suffer if they switch suppliers (and, therefore, decide to remain loyal). Similarly, the postulation is that a positive association exists between cognitive loyalty and economic risk costs connected with uncertainty concerning other suppliers' performance and with the potential loss of personal relationships—because the affective bonds already established generate a greater commitment and recognition from the customer towards the relationship with the current supplier (Butcher, Sparkes, & O'Callaghan, 2001). In contrast (as the broken lines in Fig. 1 show), the positing of the costs of evaluating and initiating a new relationship is as being negatively related to cognitive loyalty—because the greater the difficulty the customers face in initiating a switching process, the more

Fig. 2 shows the empirical model of PSC as a higher-order construct, including the hypothesized relationships between the dimensions and the antecedents/outcomes. 4. Research method 4.1. Sample and data collection This empirical study was among firms in the Spanish insurance sector, which included insurance companies, mutual funds, and banks offering various types of insurance policies nationwide. Several authors (Burnham et al., 2003; Hellier et al., 2003; Ngobo, 2004; Verhoef et al., 2002) note that this sector is suitable for analysis of switching costs because: (i) of its strong competitiveness; (ii) its relationships with customers are significant; (iii) its customers usually make an annual decision to renew their policies or change to an alternative policy or supplier; and (iv) customers are usually aware of different types of switching costs when they make their annual decisions. Information comes from 785 customers of 74 companies (accounting for 83.94% of the total volume of premiums in the sector in Spain). All of these companies: (i) offer different types of insurance policies (car, life, home, funeral, and health insurance); (ii) do not have any specific geographical location within Spain; and (iii) do not have a link with any specific demographic or professional group. Data comes by: (i) personal interviews; and (ii) electronic survey (via a website). The interviews have taken place in the branch offices of the companies by a team of interviewers, all of whom were

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Relationship Length

.11**

Personal Relationship loss costs

Benefit loss costs .23

Relationship Breadth

.74

.08**

Economic risk costs .37

.47*** .57***

PSC

Cognitive loyalty

.24*** Involvement

.05ns

Affective loyalty

.26*** -.10 Evaluation costs

-.13 Set-up costs

-.06 Monetary loss costs

Behavioral intention loyalty

Propensity for switching

Structural model Measurement model ***p<0.001, **p<0.01,*p<0.05, ns: not significant (based on t(499),one-tailed test) Fig. 2. Empirical model of PSC as a higher-order construct.

properly identified and trained in field research. To reach customers who did not usually attend company offices, an electronic website survey allows customers to answer the questionnaire directly. An e-mail invitation, containing the embedded URL link to the website hosting the survey has been sent to each of the potential respondents (colleagues and acquaintances) by employing a snowball sampling technique. Nevertheless, the possibility of a nonresponse bias was a concern. The test recommended by Armstrong and Overton (1977) was therefore conducted. This compares the answers from the first incoming surveys with the latest received surveys. The results showed no significant difference between the two (p = 0.05) on any item. Table 1 summarizes the information about the sample and data collection. 4.2. Measures The study establishes the positing of six dimensions in the conceptual model of PSC: (i) benefit loss costs (BL); (ii) personal relationship loss costs (RL); (iii) economic risk costs (ER); (iv) evaluation costs (EV); (v) set-up costs (SET); and (vi) monetary loss costs (ML). As Table 2 shows, the six dimensions measured by items (responses to statements) adapted from Burnham et al. (2003). The recording of the responses to these items take place on 7-point Likert-type scales

Table 1 Sample and data collection. Universe

20 customers of each one of the 74 insurance companies, distributed evenly between six age ranges (i.e., 18–30, 31–40, 41–50, 51–60, 61–70, and over 70 years of age) and gender. Types of policies Car, life, home, funeral and health insurance Information gathering Questionnaires were distributed in branch offices of the methods companies, locations of high concentration of the public and by website (http://www.sav.us.es/encuestas/6806) Final sample 785 customers (137 via web and 648 by personal interview)a Response rate 53% Respondent profile The majority (60%) of respondents were male. The largest age group was 18–30 years of age. A majority (56.49%) had a relationship with their usual insurance company of at least 5 years. Most (74.30%) had subscribed only one type of insurance with their usual company. a

An ANOVA shows that no significant differences existed in the replies (p b 0.05).

(1= ‘strongly disagree’; 7 = ‘strongly agree’). With the aim of choosing the more representative switching cost dimensions, drawing on the literature, we have made a complete list with all the possible losses faced by customer when switching supplier. The distribution of this list is between 15 managers of different services companies and a focus group of 20 customers. Each of them weighed the degree of importance of each one of the types of switching cost. This classification allows us to choose the six most significant dimensions. The model also posits four antecedents and three outcomes. The antecedents were (i) length of relationship (LR); (ii) breadth of relationship (BR); (iii) customer involvement (CI); and (iv) propensity for switching (PS). The outcomes are: affective loyalty (AL); (ii) cognitive loyalty (CL); and (iii) future behavioral intentions loyalty (FL). Apart from the two relationship antecedents (LR and BR), response to items on 7-point Likert-type scales (1= strongly disagree·; 7 = strongly agree) measure all of these antecedents and outcomes. Table 3 shows these items, derived and adapted from the literature (Antón & Rodríguez, 2004; Gremler et al., 2001; Varki &Wong, 2003; Zaichkowsky, 1994).The number of years that a respondent had been a customer of the same company (Ranganathan, Seo, & Babad, 2006) measures the LR antecedent and the number of different products/ services that a respondent had with the same company measures the BR antecedent. Based on the conceptual model, the designing of the perceived switching costs (PSC) variable is as a higher-order factor. This implies that its measurement is by six first-order latent variables (LV) or dimensions, that is to say, standard LVs with measured indicators. Therefore, a direct connection does not exist between this higherorder factor and any indicators (Chin, 1998a). 5. Data analysis and results The testing of the research model is by using Partial Least Squares (PLS), a multivariate analysis technique for testing structural models (Wold, 1985). As a structural equation modeling tool, PLS simultaneously allows the assessing of the reliability and validity of the measures of theoretical constructs and the estimating of the relationships among these constructs (Barclay, Higgins, & Thompson, 1995). Wold (1979) states that PLS is primarily intended for causal-predictive analysis, where the problems explored are

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Table 2 Items for measuring dimensions of PSC. Dimension/item descriptions (BL) Benefit loss costs Switching to a new company would mean losing or replacing points, credits, length of services, etc., that I have accumulated with my insurance company. I would lose a lot of credits, accumulated points, services that I have already paid for, if I switch to a new insurance company. I will lose benefits of being a long-term customer if I leave my insurance company. (RL) Personal relationship loss costs I would miss working with the people at my insurance company if I switched suppliers. I am more comfortable interacting with the people working for my insurance company than I would be if I switched suppliers. I like talking to the people where I get my service. (ER) Economic risk costs Switching to a new insurance company will probably involve hidden costs/ charges. I am likely to end up with a bad deal financially if I switch to a new insurance company. Switching to a new insurance company will probably result in some unexpected hassle. (EV) Evaluation costs I cannot afford the time to get the information to fully evaluate other insurance companies. I consider that it takes a lot time/effort to get the information needed to feel comfortable evaluating new insurance companies. (SET) Set-up costs Switching insurance companies involves an unpleasant sales process. There are a lot of formalities involved in switching to a new insurance company. (ML) Monetary loss costs Switching to a new insurance company would involve some up-front costs (set-up fees, membership fees, deposits, etc.) In my opinion, it takes a lot of money to pay for all of the costs associated with switching insurance companies.

complex and prior theoretical knowledge is scarce. Therefore, PLS is an appropriate technique to use in a theory development situation, such as in this research. This technique allows for the use of both formative and reflective measures, something not generally achievable with covariance-based structural equation modeling techniques such as LISREL or AMOS (Chin, 1998a). Particularly, the current design of the research model impedes the use of a covariance-based structural equation model because of an irresolvable problem of indeterminacy with the formative dimensions of the higher-order construct (PSC). This study uses PLS-Graph software version 3.00 (Chin & Frye, 2003).

5.1. Measurement model With regards to the measurement model, this section begins by assessing the individual item reliability, construct reliability, average variance extracted (AVE), and discriminant validity. Table 2 shows the results in the measurement model for each of the first-order dimensions. Individual item reliability is adequate when an item has a factor loading greater than 0.7 on its respective construct, which implies more shared variance between the construct and its measures than error variance (Carmines & Zeller, 1979). As Table 2 shows, all items have loadings close to or above 0.7. Notwithstanding, in the case of composite latent constructs with formative dimensions, the loadings are misleading. The basing of the interpretation of this kind of composite construct should be on the weights (Chin, 1998b). These provide information about how each formative dimension contributes to the respective composite construct. We have used the SPSS program to test the potential multicollinearity among the first-order factors. As Table 3 shows, the results show that the variance inflation factor (VIF) of first-order dimensions ranges from 1.27 to 1.98, which is well below the

Lambda loading

Composite reliability

Average variance extracted

0.89

0.74

0.93

0.83

0.91

0.79

0.91

0.84

0.87

0.77

0.86

0.76

0.87 0.89 0.82

0.93 0.93 0.86

0.90 0.92 0.83

0.89 0.94

0.89 0.85

0.89 0.85

commonly accepted cut-off threshold of 5 to 10 (Kleinbaum, Kupper, & Muller, 1988). No evidence exists, therefore, of multicollinearity among the variables that measured the composite construct (BL, RL, ER, EV, SET, and ML) (Table 3). The assessment of the construct reliability uses a measure of internal consistency: composite reliability (Werts, Linn, & Jöreskog, 1974). As Tables 2 and 3 show, all constructs have a composite reliability greater than Nunnally's (1978) suggested benchmark of 0.7 for modest reliability. AVE assesses the amount of variance that a construct captures from its variables relative to the amount due to measurement error (Chin, 1998b). Table 2 shows that all AVE measures for common latent variables are greater than the benchmark of 0.50. To confirm discriminant validity, AVE should be greater than the variance shared between the construct and other constructs in the model (that is, the squared correlation between two constructs). This condition is correct for the first-order dimensions (Table 4) and for the common latent constructs CI, PS, AL, CL, and FL (Table 5). It is not possible to analyze the composite construct (PSC) because no AVE value is available. Table 4 also shows that the correlations among the different dimensions of the PSC are moderate and do not follow a specific pattern, which indicates that they do not have a direct effect on the stability of the coefficients of the indicators (Collier & Bienstock, 2006; Mackenzie et al., 2005; Podsakoff et al., 2006).

5.2. Structural model In order to test the nomological network for the different dimensions of the PSC, the study uses bootstrapping (500 resamples) to generate standard errors and t-statistics (Chin, 1998b). Bootstrapping represents a non-parametric approach for estimating the

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Table 3 Items for measuring antecedents and outcomes of PSC dimensions. Construct/dimension/item descriptions

Lambda loading

(CI) Customer involvement (adapted from Varki and Wong, 2003; Zaichkowsky, 1994) For me, the company's service is important/unimportant. For me, the company's service is significant/insignificant. For me, the company's service is valuable/worthless. For me, the company's service is interesting/boring.

0.81 0.88 0.88 0.57

(PS) Propensity towards switching (Antón & Rodríguez, 2004) I like switching insurance companies to compare. I like switching insurance companies seeking variety. I am tired of always having the same insurance company.

0.83 0.94 0.89

(AL) Affective loyalty (Gremler et al., 2001) I really like doing business with this company. To me, this company is clearly the best one with which to do business. I believe this is a good company.

0.88 0.87 0.83

(CL) Cognitive loyalty (Gremler et al., 2001) I try to use this company every time I need insurance services. I consider this company to be my primary insurance company. I consider this company to be my first choice when I need insurance services.

0.73 0.91 0.87

(FL) Future behavioral intentions loyalty (Gremler et al., 2001) I intend to continue doing business with this company over the next few years. As long as the present service continues, I doubt that I would switch insurance companies.

Weight

Composite reliability

Average variance extracted

0.87

0.63

0.92

0.79

0.89

0.74

0.88

0.71

0.76

0.61

n.a.

n.a.

Variance inflation factor (VIF)

0.69 0.86

(PSC) Perceived switching costs (Aggregate 2nd order factor) (BL) Benefit loss costs (RL) Personal relationship loss costs (ER) Economic risk costs (EV) Evaluation costs (SET) Set-up costs (ML) Monetary loss costs

0.23 0.74 0.37 − 0.10 − 0.13 − 0.06

1.52 1.32 1.95 1.27 1.98 1.90

n.a.: non-applicable.

precision of the PLS estimates. This allows us to assess the statistical significance of the path coefficients. Table 6a and 6b, show the analysis of all the possible relationships between the first-order switching cost dimensions and their antecedents/outcomes in spite of the absence of a hypothesis with a certain dimension. This is due to formulating the hypothesis to check the existence or absence of relationships (Seiders et al., 2007). 5.2.1. Hypothesized antecedents As Table 6a shows, the findings support 10 of the 13 hypotheses regarding antecedents since these surpass the minimum level indicated by a Student's t-distribution with one tail and n-1 (n= number of resamples) degrees of freedom. The study confirms Hypothesis H1 in full. A positive association exists between the length of the relationship (LR) and: (i) benefits loss costs (Hypothesis H1a), (ii) personal relationships loss costs (H1b);

and (iii) economic risk costs associated with switching suppliers (H1c). As expected, this variable (LR) is not significant for the other dimensions. With respect to the breadth of the relationship (BR), this variable relates positively to benefits loss costs (Hypothesis H2a) and the costs of losing the personal relationship (H2b). In contrast, no significant relationship exists between BR and economic risk costs (H2c) or between BR and evaluation costs (H2d). In passing, a relevant point to note is that a positive relationship, not initially hypothesized, exists between BR and set-up costs. It would seem that an association exists between greater procedural costs and a greater number of products/ services acquired with the same company. A positive link exists between the level of customer involvement (CI) and benefits loss costs (Hypothesis H3a), personal relationships

Table 5 Discriminant validity coefficients. Table 4 Discriminant validity coefficients for PSC.

BL RL ER EV SET ML

BL

RL

0.86 0.41 0.52 0.17 0.29 0.33

0.91 0.34 0.12 0.33 0.18

ER

0.88 0.37 0.51 0.55

EV

0.91 0.42 0.33

SET

0.87 0.62

ML

Mean

SD

0.87

4.2 3.5 4.1 4.5 3.7 3.8

1.73 1.60 1.45 1.44 1.43 1.81

Note: Diagonal elements (bold) are the square root of the variance shared between the construct and their measures (AVE). Off-diagonal elements are the correlation among constructs. For discriminant validity, diagonal elements should be larger than offdiagonal elements. SD: Standard deviation.

LR BR PS CI AL CL FL PSC

LR

BR

n.a. 0.22 0.20 0.08 0.20 0.17 0.21 0.16

n.a. 0.08 0.04 0.06 0.20 0.05 0.12

PS

0.89 0.16 0.19 0.14 0.18 0.12

CI

0.79 0.46 0.31 0.32 0.26

AL

0.86 0.55 0.49 0.47

CL

0.84 0.39 0.57

FL

0.78 0.26

PSC

Mean

SD

n.a.

6.5 1.3 2.1 5.2 4.6 4.2 5.1 n.a.

6.58 0.71 1.22 1.33 1.00 1.59 1.76 n.a.

Note: Diagonal elements (bold) are the square root of the variance shared between the construct and their measures (AVE). Off-diagonal elements are the correlation among constructs. For discriminant validity, diagonal elements should be larger than offdiagonal elements. n.a.: non-applicable. SD: Standard deviation.

C. Barroso, A. Picón / Industrial Marketing Management 41 (2012) 531–543 Table 6 a. Structural model results for antecedents. Antecedents

Hypothesis suggested effect

Path coefficients (ß)

t-Value (bootstrap)

Support

H1a: LR → BL H1b: LR → RL H1c: LR → ER LR → EV LR → SET LR → ML H2a: BR → BL H2b: BR → RL H2c: BR → ER H2d: BR → EV BR → SET BR → ML H3a: CI → BL H3b: CI → RL H3c: CI → ER CI → EV CI → SET CI → ML H4a: PS → ER H4b: PS → ML H4c: PS → SET PS → EV PS → BL PS → RL

+ + + Not Not Not + + + + Not Not + + + Not Not Not _ + + Not Not Not

0.06* 0.13*** 0.06* 0.01ns 0.02ns 0.01ns 0.05* 0.12*** 0.01ns 0.03ns 0.07* 0.01ns 0.13*** 0.23*** 0.13*** 0.04ns − 0.01ns 0.01ns − 0.02ns 0.13** 0.08* 0.03ns 0.01ns 0.08ns

1.66 3.26 1.72 0.38 0.61 0.02 1.72 4.25 0.02 0.97 2.01 0.11 3.75 6.54 3.80 1.16 − 0.22 0.26 − 0.32 2.47 1.74 0.91 0.33 1.63

Yes Yes Yes – – – Yes Yes No No – – Yes Yes Yes – – – No Yes Yes – – –

suggested suggested suggested

suggested suggested

suggested suggested suggested

suggested suggested suggested

Note: ***pb 0.001, **p b 0.01, *pb 0.05, ns: not significant (based on t(499), one-tailed test). t(0.05, 499) = 1.64791345, t(0.01,499)= 2.333843952, t(0.001,499)= 3.106644601.

loss costs (H3b), and economic risk costs (H3c), which fully confirms Hypothesis H3. A significant relationship does not exist between this variable (CI) and the other switching cost dimensions. With regard to the propensity for switching (PS), no empirical support exists for the relationship between PS and economic risk costs (Hypothesis H4a). However, a positive association exists between PS and monetary loss costs (H4b) and set-up costs (H4c). A significant relationship between this variable (PS) and the other switching cost dimensions does not exist.

5.2.2. Hypothesized outcomes As Table 6b shows, the findings support 11 of 17 hypotheses regarding outcomes. Table 6 b. Structural model results for outcomes. Consequences

Hypothesis suggested effect

Path coefficients (ß)

t-Value (bootstrap)

Support

H5a: BL → AL H5b: RL → AL H5c: ER → AL H5d: EV → AL H5e: SET → AL ML → AL H6a: BL → CL H6b: RL → CL H6c: ER → CL H6d: ML → CL H6e: EV → CL H6f: SET → CL H7a: BL → FL H7b: RL → FL H7c: ER → FL H7d: ML → FL H7e: EV → FL H7f: SET → FL

+ + + _ _ Not suggested + + + + _ _ + + + + + +

0.11** 0.35*** 0.18*** − 0.08* − 0.07ns − 0.04ns 0.13*** 0.45*** 0.11** − 0.04ns − 0.08** − 0.07ns 0.12** 0.07* 0.20*** 0.02ns 0.03ns − 0.06ns

2.50 8.67 4.00 − 2.13 − 1.56 − 0.89 3.35 13.84 2.34 − 0.34 − 2.39 − 0.10 2.55 1.73 3.53 0.58 0.80 − 1.17

Yes Yes Yes Yes No – Yes Yes Yes No Yes No Yes Yes Yes No No No

Note: ***p b 0.001, **p b 0.01, *p b 0.05, ns: not significant (based on t (499), one-tailed test). t(0.05,499) = 1.64791345, t(0.01,499) = 2.333843952, t(0.001,499) = 3.106644601.

539

Hypothesis H5 proposes that a link exists between all switching cost dimensions and affective loyalty (AL), with the exception of monetary loss costs. As Table 6b shows, benefits loss costs (Hypothesis H5a), relationships loss costs (H5b), and economic risk costs (H5c) all positively relate to affective loyalty, and evaluation costs negatively relate to this variable (thus supporting H5d). However, AL does not negatively associate with set-up costs (thus negating H5e). With regard to Hypothesis H6, the results confirm that benefits loss costs (H6a), personal relationships loss costs (H6b), and economic risk costs (H6c) all have a direct and positive effect on cognitive loyalty (CL). However, contrary to Hypothesis H6d, no finding of a significant relationship exists between CL and monetary loss costs. For the remaining two dimensions, the results confirms Hypothesis H6e (that a significant negative relationship exists between evaluation costs and CL), but do not support Hypothesis H6f (that a significant negative relationship exists between set-up costs and CL). With regard to H7, which has proposed that a positive relationship exists between all switching cost dimensions and future behavioral intentions loyalty (FL), the establishing of a significant relationship takes place only for the dimensions of benefits loss costs (H7a), relationship loss costs (H7b), and economic risk costs (H7c).

5.2.3. Higher-order construct Concerning the higher-order factor (PSC), the study follows a twostep approach (Bruhn, Georgi, & Hadwich, 2008; Calvo-Mora, Leal, & Roldán, 2005). This method creates latent variable scores by optimally weighting and combining items for each dimension using the PLS algorithm. The resulting score reflects the underlying construct more accurately than any of the individual items by accounting for the unique factors and error measurements that may also affect each item (Chin & Gopal, 1995). As a result, the dimensions or first-order factors become the observed indicators of higher-order factor. Next, in order to establish statistical identification of the formative measurement model to enable their estimation, the study analyses PSC within the network of antecedents and consequences (Diamantopoulos, Riefler, & Roth, 2008; Howell, Breivek, & Wilcox, 2007). As Table 7 shows, three of the postulated antecedents (LR, BR, and CI) reveal a significant influence on PSC; however, PS fails to show such a positive overall influence. The results in Table 7 also confirm that PSC has a direct positive influence on the three postulated outcomes (AL, CL, and FL). In particular, PSC has a marked influence on cognitive loyalty (CL), in which it explains 32.6% of the variance (compared with 22.2% and 0.07% for AL and FL respectively). Finally, taking into account the weights the six dimensions show (Table 3), three stand out as positive (BL, 0.23; RL, 0.74; and ER, 0.37), whereas the other three are negative (EV, −0.10; SET −0.13; and ML −0.06). Boot-strap t statistics indicate that only three dimensions contribute significantly to the formation of PSC: BL (p b 0.01); RL (p b 0.001); and ER (p b 0.001). At a lower level of significance, SET (p b 0.1) also has some influence. These results suggest that the Table 7 Structural model results for higher-order construct. Hypothesis

Suggested effect

Path coefficients (ß)

t-Value (bootstrap)

Support

LR → PSC BR → PSC CI → PSC PS → PSC PSC → AL PSC → CL PSC → FL

+ + + + + + +

0.11** 0.08** 0.24*** 0.05ns 0.47*** 0.57*** 0.26***

2.88 3.01 7.03 0.63 15.52 21.00 5.18

Yes Yes Yes No Yes Yes Yes

Note: ***p b 0.001, **p b 0.01, *p b 0.05, ns: not significant (based on t(499), one-tailed test). t(0.05,499) = 1.64791345, t(0.01,499) = 2.333843952, t(0.001,499) = 3.106644601.

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dimension of relationship loss costs (RL) is the most important factor in forming PSC, followed by economic risk costs (ER), benefit loss costs (BL) and set-up costs (SET). The remaining dimensions seem not to be critical factors in forming PSC. 6. Discussion The findings confirm that: (i) the understanding of perceived switching costs (PSC) should be as a higher-order construct made up of independent first-order dimensions that refer to the effort, time and money invested by customers in putting an end to their previous relationship and initiating a new one; and (ii) different sets or different intensities of antecedents and outcomes exist for each firstorder dimension. Given that different sets of antecedents and outcomes exist for each of the six first-order dimensions and that no multicollinearity exists among them, the findings confirm the multi-dimensional and formative conceptualization of the PSC construct. In addition, the results show that three of the first-order dimensions make a positive contribution to the formation of PSC and that the other three make a negative contribution. Thus, it is possible to consider benefit loss costs, personal relationship loss costs, and economic risk costs as positive switching costs because they represent valued factors that increase customers' satisfaction and well-being in a relationship and encourage them to remain with their current supplier. In contrast, it is possible to consider evaluation costs, set-up costs, and monetary loss costs as negative switching costs because they are barriers that obstruct exit from a relationship that is unsatisfactory for customers. The results confirm that all proposed relationships between PSC with theoretically-related constructs are statistically significant and consistent with their hypothesized directions—with the exception of those relating to ‘propensity for switching’. This variable was a significant antecedent only for the dimensions of set-up costs and monetary loss costs, and these dimensions made only a modest contribution to the formation of the PSC construct. Finally, a formative measurement model implies that a latent variable completely mediates the effects of its indicators (dimensions) on other outcome variables. Because the present study has established that the dimensions do not all have the same outcomes (nor always in the same direction), model fit (R 2) cannot be expected (for example, with respect to future behavioral intentions loyalty). This is one of the main criticisms of the use of formative constructs (Wilcox, Howell, & Breivik, 2008). However, the value of R 2 in the other two outcome variables, and the fact that the relationships between the PSC construct and the three outcome variables stand out as significant, leads to the conclusion that the PSC construct appears to behave as a unitary entity. Its external validity is thus confirmed. 7. Conclusions, implications, and limitations The findings of this study have both theoretical and practical implications. In terms of theory, the higher-order formative conceptualisation of the PSC construct reveals a hierarchy of importance among the dimensions in forming the higher-order construct; in particular, the study establishes that benefit loss costs, relationship loss costs, and economic risk costs contribute most to the formation of the PSC construct. In addition, the formative nature of the construct reflects the heterogeneous nature of the PSC concept and the fact that causality can be either positive or negative. Models whose specification is not like this can bias structural parameter estimates (Mackenzie et al., 2005) and distort understanding of the relationships of PSC with its antecedent and outcome variables. In terms of practice, the analysis provides insights into the relationship between customers' perceptions of switching costs and their degree of loyalty. In particular, the study shows that the possible loss of benefits and personal relationships in a current

relationship, together with the economic risk costs associated with switching suppliers, are important in determining loyalty towards a service supplier. These ‘positive’ switching costs favor the three loyalty-related outcomes. A customer's cognitive loyalty (to a greater degree) and affective loyalty (to a lesser degree) are adversely affected by the costs of searching for and evaluating alternative offers. The greater a customer's difficulty in evaluating and comparing various alternatives, the more they will feel ‘locked into’ the current relationship (even if the customer believes that the present supplier might not be the best possible choice). Moreover, perceptions of costs associated with losing benefits, losing personal relationships, and switch-associated risk are greater if customers are more involved with the service supplier in a relationship that is long and broad. In view of these findings, firms that wish to retain their customer base should adopt strategies that create specific switching costs for different segments of customers. They should also utilize incentives and loyalty programs to provide financial rewards for the loyalty of longstanding and closely-involved customers or those who have a large number of products under contract. However, with this segment of customers, the question is not simply one of establishing financial bonds; rather, the importance also exists of offering social benefits (such as special treatment or enhanced personalization of the service offer). Nevertheless, alternative suppliers wish to capture their competitors' customers and, therefore, to incentive change, reducing the socalled replacement switching costs (Sheer & Smith, 1996) or the costs associated with the switching process itself (evaluation costs, set-up costs and monetary loss costs). By making the switching process easier and by offering economic incentives, even the most risk-averse consumers decide to switch, if the expected reward exceeds the perceived level of risk. This occurs above all in the electronics arena, where online customers will not be affected by some of the tangible switching costs (set-up costs, evaluation or learning costs) of online shopping, although there may be some economic and psychological costs (Yen, 2010). Customers with a greater propensity to change service suppliers perceive switching costs in terms of the monetary losses and the setup costs incurred by starting up a new relationship. Although a positive relationship is not initially hypothesized between a broader relationship and set-up costs, such a relationship is identified in the analysis—presumably because the switching process becomes more complex as the number of implied products increases. Companies should therefore try to strengthen those links with specific strategies that supply these customers with sufficiently strong relational benefits to prevent switching. In summary, the perception of benefits loss costs, personal relationships loss costs, and economic risk costs represent the positive reasons that influence the future behavioral intentions of customers. Companies should therefore dedicate more resources to developing policies and strategies to generate economic, relational, and information switching costs (Burnham et al., 2003) because they are the main reasons for staying (Colgate et al., 2007). In contrast, the perception of monetary loss costs and set-up costs associated with starting up a new relationship have no effect on the affective, cognitive, or behavioral bonds established with the supplier. This raises the question of whether these costs are really not especially relevant for determining customers' loyalty or whether these costs produce other outcomes that this study does not examine. In this regard, these findings (with regard to loyalty), together with the fact that the present study do not find any significant antecedent of the costs of searching for and evaluating different alternatives, does suggest that it is necessary to consider variables that are sectorspecific. Examples of this might be the degree of competitive intensity or the degree of attractiveness that the different offers available in the market have for the customers (Antón et al., 2007; Capraro et al., 2003).

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In summary, measuring and identifying the different types of PSC will enable firms to achieve greater efficiency in their strategic customer management. 8. Implications for industrial marketers Switching costs have, at an organizational level, a key influence on the switching behavior (Han & Sung, 2008). So, instead of switching, the buyers can use a competitive offer to renegotiate with their usual supplier, which is why the supplier must never underestimate the real switching barriers that the customer faces. However, the presence of these costs places the buyer at a considerable disadvantage at the moment of negotiating (Wathne et al., 2001).The relational exchanges between a supplier and a customer are typified by high switching costs when the relationship between the parties is lasting and broad (Cater & Zabkar, 2009) and the more involved the customer is with the product/service offered by the supplier. Therefore, the industrial marketers must train their salespeople appropriately and motivate flexibility and adaptation to the characteristics of each customer, so that the customer develops a previous compromise with a particular seller (Jackson, 1985; Heide & Weiss, 1995). As a consequence, the industrial marketers, in order to create switching costs for their buyers, must consider the different nature and composition of each type of switching costs. Thus, to raise the switching costs of a psychological nature, that is, all those relative to inconveniences, effort and uncertainty associated with the switching procedure (i.e., economic risk costs, set-up costs, and evaluation costs), they must assure themselves that the consumer knows perfectly well the characteristics and functionalities of the product/service offered and its performance, so that he/she is familiarized with the way of working of the supplier (Low & Johnston, 2006). Thus, in time, the relationships already established will develop a series of routines and procedures relative to the current supplier, that need to be modified if a new relationship is established (Heide & John, 1992). It is the responsibility of the industrial marketer to develop a specific knowledge about the customer that favors his/her dependence (Young & Denize, 1995) and to motivate the mutual learning and adaptation to narrow and strengthen the bonds (Heide & Weiss, 1995). With respect to the switching costs of a monetary nature (i.e., benefit loss costs and monetary loss costs),the supplier firm can, on the one hand, incentivate its customers via “push” type strategies, such as, for example, offering special discounts or sales for the promotions that the client company carries out in its establishment, or, on the other hand, develop “pull” type strategies via fidelity programs with the end users and the development of advertising efforts together (Sengupta, Krapfer & Pusateri, 1997; Zeng, Yang, Li, & Fam, 2011). However, the industrial marketers must take into account that not all the clients wish to maintain a relationship (Low & Johnston, 2006) and that not all the customers perceive the same switching costs and with the same intensity. Therefore, the most appropriate option is to segment the market. This way, for each segment it is necessary to design specific strategies to increase the switching costs, taking into account that fear of dependence can discourage some customers from establishing a narrow relationship and that specific strategies can generate a fear in the customer that the supplier may develop opportunist behaviors, for example, in the form of price increases (Wong, Chan, Ngai, & Oswald, 2009). As a consequence, the identification and analysis of the antecedents and consequences of the different switching costs perceived by the customer allows the marketing manager to be able to opt for managing each one of them independently, as well as valuing the joint effect on other variables indicative of the success of the relationship such as affective and cognitive loyalty and the future intention of loyalty. Despite the theoretical and practical importance of the findings, certain limitations are acknowledged. First, the particular antecedents and outcomes chosen for this model constitute a limitation to

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the generalization of the results to other models (Wilcox et al., 2008). In particular, the present study assesses the influence of PSC only on loyalty (affective, cognitive and future behavioral intentions). Future research could analyze the effect of switching costs on other outcomes—such as the degree of commitment and trust developed within the relationship (Jones et al., 2007; Sharma & Patterson, 2000) or the intention of switching suppliers (Hu & Hwang, 2006). Secondly, the study is limited to one country (Spain) and one industry (insurance). Caution is therefore advisable when generalizing the results to other settings. Similar studies in other sectors and contexts would be useful to verify the validity of the higher-order formative conceptualization of PSC. Thirdly, the cross-sectional design of the study (rather than a longitudinal design) might not have adequately captured variables that shape a process that develops over time and whose effects are appreciated in the long term. An appropriate idea would be for future studies to adopt a longitudinal approach in analyzing these matters. Finally, future research could consider positing a formative construct of PSC as a moderating variable in a structural model of the relationship between satisfaction and loyalty (Aydin et al., 2005; Sharma, 2003). More research using formative constructs in studying such interactions is necessary (Diamantopoulos et al., 2008; Wilcox et al., 2008). References Antón, C., Camarero, C., & Carrero, M. (2007). Analysing firms' failures as determinants of consumer switching intentions: The effect of moderating factors. European Journal of Marketing, 41, 135–158. Antón, C., & Rodríguez, A. I. (2004). Formas de lealtad a la marca: identificación empírica y determinación de sus principales características. Cuadernos de Economía y Dirección de Empresas, 18, 122–145. Apaolaza, V., Hartmann, P., & Zorilla, P. (2006). Antecedents of customer loyalty in residential energy markets: Service quality, satisfaction, trust and switching costs. Service Industries Journal, 26(6), 633–650. Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14, 396–402. Aydin, S., & Özer, G. (2005). The analysis of antecedents of customer loyalty in the Turkish mobile telecommunication market. European Journal of Marketing, 39, 910–925. Aydin, S., Özer, G., & Arasil, Ö. (2005). Customer loyalty and the effect of switching costs as a moderator variable. Marketing Intelligence & Planning, 23, 89–103. Bansal, H., Irving, G., & Taylor, S. (2004). A three-component model of customer commitment to service providers. Journal of the Academy of Marketing Science, 32, 234–250. Bansal, H., & Taylor, S. (1999). The service provider switching model (SPSM): A model of consumer switching behavior in the services industry. Journal of Service Research, 2(November), 200–218. Bansal, H., Taylor, S., & James, Y. (2005). Migrating to new service providers: Toward a unifying framework of consumers' switching behaviors. Journal of the Academy of Marketing Science, 33, 96–115. Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares approach to causal modelling: Personal computer adoption and use as an illustration. Technology Studies, 2, 285–309 Special issue on research methodology. Beerli, A., Martín, J., & Quintana, A. (2004). A model of customer loyalty in the retail banking market. European Journal of Marketing, 38, 253–275. Bell, S., Auh, S., & Smalley, K. (2005). Customer relationship dynamics: Service quality and customer loyalty in the context of varying levels of customer expertise and switching costs. Journal of the Academy of Marketing Science, 33, 169–183. Bendapudi, N., & Berry, L. (1997). Customer motivations for maintaining relationships with service providers. Journal of Retailing, 73, 15–37. Bienstock, C., & Stafford, M. (2006). Measuring involvement with the service: A further investigation of scale validity and dimensionality. Journal of Marketing Theory and Practice, 14, 209–221. Bitner, M. J. (1995). Building service relationships: It's all about promises. Journal of the Academy of Marketing Science, 23, 246–251. Bloemer, J., de Ruyter, K., & Wetzels, M. (1999). Linking perceived service quality and service loyalty: A multi-dimensional perspective. European Journal of Marketing, 33, 1082–1106. Bollen, K., & Lenox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychologycal Bulletin, 110, 305–314. Bolton, R., & Bhattacharya, C. B. (2000). In Jagdish N. Sheth, & Parvatiyar Atul (Eds.), Relationship marketing in mass markets. Handbook of relationship marketing (pp. 327–354). Thousand Oaks, CA: Sage Publications. Bolton, R., Lemon, K., & Verhoef, P. (2004). The theoretical underpinnings of customer asset management: A framework and propositions for future research. Journal of the Academy of Marketing Science, 32, 271–292.

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