Customer Switching Resistance (CSR): The Effects of Perceived Equity, Trust and Relationship Commitment

Transcription

1 Customer Switching Resistance (CSR): The Effects of Perceived Equity, Trust and Relationship Commitment Dr Gilles N Goala, Associate Professor, University of Montpellier 2, Place Eugène Bataillon Montpellier Cedex 5 Gilles.Ngoala@free.fr Tel:

2 Abstract: This research is an attempt to understand why - or why not customers resist switching service providers when a critical incident occurs. We examine how service relationship perceptions, such as perceived equity, trust (perceived reliability and benevolence) and relationship commitment (affective and calculative), enhance relationship maintenance and customer switching resistance in many critical situations (Keaveney, 1995). We conducted a survey was conducted in the financial service industry on a sample of 1,999 consumers (retail banking). Customer Switching Resistance has been conceptualized and measured in several critical situations. We then demonstrate that perceived equity, perceived reliability, perceived benevolence, affective commitment and calculative commitment do not influence customer switching resistance in the same way. Customer switching resistance mainly depends on the type of critical incident which occurs. For instance, calculative commitment, which is an evaluation of the costs associated with leaving the service provider, enhances customer switching resistance in three critical situations (service encounter failures, employee responses to service failures, pricing problems), whereas it leads to relationship disengagement in two other critical situations (inconvenience, changes in the consumer or service provider situation). Thus, this research highlights the need to better take into account the different types of critical incidents discussed in the relationship marketing literature and to better consider the complementary roles of perceived equity, trust and relationship commitment in the service switching literature. This research implies that service companies have to anticipate the critical incidents and develop specific shock absorbers to continue doing business with their current customers. Structured Abstract: Purpose: This research attempts to understand why - or why not customers resist switching service providers when a critical incident occurs. We examine how service relationship perceptions, such as perceived equity, trust (perceived reliability and benevolence) and relationship commitment (affective and calculative), enhance relationship maintenance and customer switching resistance in many critical situations (Keaveney, 1995). Methodology/Approach: We conducted a survey in the financial service industry on a sample of 1,999 consumers (retail banking) and then conceptualized and measured Customer Switching Resistance in several critical situations. Findings: We then demonstrate that perceived equity, perceived reliability, perceived benevolence, affective commitment and calculative commitment do not influence customer switching resistance the same way. Customer switching resistance mainly depends on the type of critical incident which occurs. For instance, calculative commitment, which is an evaluation of the costs associated with leaving the service provider, enhances customer switching resistance in three critical situations (service encounter failures, employee responses to service failures, pricing problems), whereas it leads to relationship disengagement in two other critical situations (inconvenience, changes in the consumer or service provider situation). Research implications: Thus, this research highlights the need to better take into account the different types of critical incident discussed in the relationship marketing literature and to better consider the complementary roles of perceived equity, trust and relationship commitment in the service switching literature. Practical implications: This research implies that service companies have to anticipate the critical incidents and to develop specific shock absorbers to continue doing business with their current customers. 2

3 Keywords: relationship marketing, critical incidents, perceived equity, trust, relationship commitment, customer loyalty. 3

4 INTRODUCTION Customer loyalty has become a top priority in service industries, since it has been proven to strongly affect profitability (Reichheld and Sasser, 1990; Rust, Zahorik and Keiningham, 1995; Rust, Zeithaml and Lemon, 2000; Verhoef, 2003). However, preventing current customers from switching to other service providers is a very difficult task. During their lifetime, customers have many opportunities to switch service providers (competitor offers, sales promotions, etc.), and many events within the established relationship are likely to cause service relationship deterioration and dissolution (Bitner, Booms, and Tetreault, 1990; Gustafsson, Johnson and Roos, 2005). For Keaveney (1995), service switching may be due to critical incidents, such as attraction by competitors, inappropriate employee responses to service failures, pricing problems, core service failures, service encounter failures, lack of convenience, ethical problems or changes in the consumer s or service provider s situation (involuntary switching). Switching does not necessarily refer to immediate business relationship dissolution. According to Zeithaml, Parasuraman and Berry (1996, pp38), switching means doing less business with the current service provider in the next few years. In many service situations, switching is a progressive process by which customers disengage from the established relationship and allocate more and more of their expenses to competitors (banking, insurance, telecommunications, utilities, etc.). Since long life customers are not necessarily profitable customers (Reinartz and Kumar, 2000, pp 17), companies also aim to develop customer share, which is the ratio of a customer s purchases of a particular category of products or services from supplier X to the customer s total purchases of that category of products or services from all suppliers (Verhoef, 2003, pp30). How can customer retention and customer share be maintained? How can customers be prevented from switching service providers when they are attracted by competitors, dissatisfied with contact persons, or disappointed by service pricing? What should service companies do to maintain the business relationship with their customers despite the several critical incidents which may occur during a service relationship? How will their customers react when they are attracted by competitors, when they experience service failures (core service, service encounters, and ethical problems), when the service prices are unfair or too high, or when service recovery by employees is inappropriate? How can service companies make customers absorb these critical incidents and assure that these customers keep doing business with them? To deal with these problems, the relationship marketing literature often suggests that service providers improve the quality of the service relationship and/or develop switching costs (Dwyer, Schurr and Oh, 1987; Morgan and Hunt, 1994; Bendapudi and Berry, 1997; Garbarino and Johnson, 1999). Indeed, business relationship maintenance depends on the way the customer perceives the service relationship. In particular, it should be strongly linked to key relational processes, such as: - Perceived equity, or the fair distribution of inputs and outcomes between the customer and the service provider (Johnson, Barksdale, and Boles, 2001; Feinberg, Krishna, and Zhang, 2002; Musa, Pallister, and Robson, 2005); - Trust, or. the service provider s perceived reliability and benevolence (Ganesan, 1994; Ganesan and Hess, 1997); 4

5 - Affective commitment, or the customer s relative intensity of identification and affiliation with the service provider and the involvement in the service relationship (Garbarino and Johnson, 1999); - Calculative commitment or the awareness of the costs associated with leaving the service provider (Geykens et al, 1996; Verhoef, Franses and Hoekstra, 2002). Thus, prior service switching and relationship marketing literature has provided us with very useful contributions concerning customer loyalty (why consumers decide to stay with their current service provider) and switching behaviors (why they decide to switch service providers). However, different questions remain unresolved: Why do customers switch when they have reasons to stay with the same service provider (trust, perceived equity, affective and calculative commitment)? Another is why do customers stay when they have reasons to switch to another service provider (core service failures, pricing problems, lack of convenience, etc.)? Indeed, a gap in the literature needs to be filled. Empirical studies which examine the respective roles of perceived equity, trust, affective and calculative commitment in preventing customers from switching service providers in various critical situations (service encounter failures, lack of convenience, ethical problems, etc.) are still lacking. On the one hand, service switching literature focuses on critical incidents, i.e. on the event, combination of events, or series of events between the customer and one or more service firms that causes the customer to switch service providers (Keaveney, 1995). It also strongly emphasizes the effects of customer dissatisfaction on service switching (Ganesh, Arnold, and Reynolds, 2000; Gremler, 2004). However, empirical studies which examine the respective roles of relational constructs, such as perceived equity, trust, affective commitment and calculative commitment, in each and every critical situation need to be carried out. How to explain that, for the same critical incident, some customers leave while others stay? Does a service provider s perceived reliability and benevolence lead customers to maintain the relationship despite these problems? Would a customer be more reluctant to leave a service provider if the exchange with the service provider has always been fair? Do customer identification and affiliation with the service provider enhance relationship maintenance in each and every critical situation? Should service companies implement switching costs and develop calculative commitment to prevent customers from switching even though they have reasons to switch? And do perceived equity, trust, affective commitment, and calculative commitment have the same role in each and every critical situation? On the other hand, relationship marketing literature demonstrates that perceived equity, trust, affective commitment, and calculative commitment positively influence the customers general intentions to maintain the business relationship, to repurchase the provider s products and services and/or to recommend its products (Morgan and Hunt, 1994; Garbarino and Johnson, 1999). As a result, most studies do not take into account the various critical incidents which are likely to cause customers to switch service providers. Instead of measuring general intentions or favorable attitudes, we suggest measuring Customer Switching Resistance, which reflects customers reluctance to switch service providers, even though they face a critical incident in the service relationship. Indeed, we suggest measuring customer loyalty when consumers have a reason to switch service providers and not when there is no reason or opportunity for them to do so. This approach is consistent with composite approaches of loyalty which were developed in packaged goods settings (Pessemier, 1959; Cunningham, 1967; Jacoby and Kyner, 1973; Jacoby and Chesnut, 1978). It is also consistent with Oliver s definition which considers loyalty as a deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or 5

6 same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior (Oliver, 1997, p 392). The more customers resist switching service providers, the more loyal they should be. To our knowledge, relatively few studies have developed this approach in service settings. Most of them have simply focused on consumers competitive resistance or price tolerance (Parasuraman et al, 1991; Fornell et al, 1996; Reynolds and Arnold, 2000). As a result, most problems which occur in the service relationship (core service failures, ethical problems, etc.) tend to be underestimated, although they are supposed to be the main drivers of switching behaviors in service industries (Bitner, Booms, and Tetreault, 1990; Keaveney, 1995; Gremler, 1994). We propose examining Customer Switching Resistance in more varied situations. Using the critical incident technique, Keaveney (1995) identified and classified most of the critical incidents which occur in service settings: offers by competitors, inappropriate employee responses to service failures, pricing problems, core service failures, service encounter failures, lack of convenience, ethical problems or changes in the consumer or service provider situation (involuntary switching). Even though her study is not necessarily exhaustive, we will examine Customer Switching Resistance in the eight main critical situations which were identified by Keaveney (1995) in service industries. Therefore, this article aims at shedding light on how the service relationship perceptions (perceived equity, trust, affective commitment and calculative commitment) affect Customer Switching Resistance in various critical situations. In the first part, we present a conceptual framework that connects perceived equity, trust and relationship commitment with Customer Switching Resistance. In the second part, we test the model in the financial services industry on a sample of 1,999 bank customers. CONCEPTUAL FRAMEWORK Relationship maintenance and development should depend on relational constructs such as perceived equity, trust, affective commitment and calculative commitment. Below we develop a composite approach of customer loyalty and, as a consequence, reexamine the effects of service relationship perceptions on Customer Switching Resistance. Customer Switching Resistance (CSR) In consumer goods settings, consumer loyalty has often been measured as a consumer s switching resistance when faced with competitors counter persuasion, promotions, price decreases and/or stock shortage problems (Pessemier, 1959; Cunningham, 1967; Day, 1969; Jacoby and Kyner, 1973; Jacoby and Chesnut, 1978; Oliver, 1997). We suggest applying a similar composite approach to the service industry. The composite measurements of customer loyalty should have better predictive validity than the attitudinal measurements and better trait validity than the behavioral measurements (Jacoby and Kyner, 1973; Jacoby and Chesnut, 1978; Dick and Basu, 1994). Our approach differs from attitudinal measurements which have often assessed customers general intentions to maintain the service relationship, to repurchase the provider s services, and/or to recommend its products (Zeithaml, Parasuraman, and Berry, 1996; Garbarino and Johnson, 1999; De Wulf, Odekerken-Schröder and Iacobucci, 2001; Hennig-Thurau, Gwinner and Gremler, 2002). Our approach also differs from behavioral measurements which have recently emerged in the literature to capture the customers repeat purchase behaviors: service usage, frequency of purchase, proportion of purchase and/or service relationship duration (Verhoef, 2003; Gustafsson, Johnson and Roos, 2005). In this research, according to Oliver (1997, p 6

7 392), we examine how customers respond to situational influences and marketing efforts which have the potential to cause switching behavior. Our purpose is to assess a customer s likelihood to stay or to leave a service provider if a critical incident occurs. In a consumer goods setting, Fournier (1998) has considered different kinds of stressors that can lead to the relationship dissolution: situational factors (geographical situation), intrusion of alternatives, change in the consumer s situation, managerial decisions that change the exchange relationship, failure to keep a promise or perception of neglect. She suggests that relationship maintenance is then based on psychological processes, such as accommodation, tolerance/forgiveness, biased partner perceptions, devaluation of alternatives and attribution biases. In service settings, we also have to consider a wide variety of critical incidents. Most studies in service research apply the critical incident technique, which was previously developed by Flanagan (1954) (for a literature review, see Gremler, 2004). Bitner, Booms, and Tetreault (1990) classified three major groups of employee behavior that account for all unsatisfactory incidents: employee response to service delivery system failures, employee response to customer needs and requests, and unprompted and unsolicited employee actions. In 1995, Keaveney completed this classification and considered eight main categories of critical incidents: attraction by competitors, employee responses to service failures, pricing, core service failures, service encounter failures, involuntary switching, inconvenience, and ethical problems. This approach is consistent with the research of Gustafsson, Johnson and Roos (2005) who have recently distinguished situational triggers (changes in consumers lives) from reactional triggers (deterioration in perceived performance of the service provider). Building on Keaveney s results, we will consider eight main critical situations which are likely to occur in service settings. Then, we will assess customer reluctance to switch service providers, even though they face a critical incident in the service relationship. The more customers resist switching in various critical situations, the more they should be truly loyal to the focal service provider. This research represents an extension of the service switching literature. We do not use the critical incident methodology at all, since we do attempt to identify the events which cause service relationship dissolution. It has been done many times (Gremler, 2004). We will hereafter measure customer resistance to service switching. To achieve this objective, we will create what if hypothetical scenarios based on Keaveney s (1995) service switching categories to assess to what extent customers resist switching service providers in various critical situations. The effects of relationship commitment on Customer Switching Resistance In the relationship marketing literature, the concept of relationship commitment has been often viewed as one-dimensional and refers to customers general intentions to maintain the business relationship. Morgan and Hunt (1994, pp 23) define commitment to the relationship as "an exchange partner believing that an ongoing relationship with another is so important as to warrant maximum efforts at maintaining it; that is, the committed party believes the relationship is worth working on to ensure that it endures indefinitely". However, a multidimensional view of relationship commitment has also been developed to highlight the customer motivations (Meyer and Allen, 1991; Gundlach, Achrol and Mentzer, 1995). Customers maintain a business relationship because they want (affective commitment), they need (calculative or continuance commitment) or they ought (normative commitment) to do so. 7

8 Affective commitment is defined as the relative intensity of identification and affiliation with the service provider and the involvement in the service relationship (Crosby, Evans and Cowles, 1990; Garbarino and Johnson, 1999; De Wulf, Odekerken-Schröder and Iacobucci, 2001). As a result, affective commitment is not similar to a positive attitude towards the service provider. It refers to an identification process (congruence of values, affiliation, and belongingness) rather than to an evaluation process. Calculative commitment refers to an awareness of the costs associated with leaving the service provider (Geykens et al, 1996; Verhoef, Franses and Hoekstra, 2002). Calculative commitment also called continuance commitment - results from an accumulation of side bets which would be lost if the relationship were discontinued (Meyer and Allen, 1991). The perceived costs can be either monetary or non-monetary (time, effort, risk taking, etc.). As Bendapudi and Berry (1997) note, a customer that closes bank accounts due to poor service typically would open new accounts with another financial institution; the time, effort, and money required to identify an alternative supplier and establish new accounts illustrate relationship termination costs. Calculative commitment represents a global calculus of the switching costs. Normative commitment has received much less attention in marketing, except for the examination of membership behaviors in professional associations (Gruen, Summers and Acito, 2000). In most service settings, consumers do not feel a moral obligation to continue the business relationship. In this study, following Gustafsson, Johnson, and Roos (2005), we will focus only on affective and calculative commitment. As a result, the concept of relationship commitment (calculative / affective) has to be distinguished from the notion of brand commitment (Customer Switching Resistance). Calculative and affective commitment refer to customer motivations to maintain a relationship (awareness of termination costs versus identification and affiliation), whereas Customer Switching Resistance refers to behavioral intentions. Therefore, it becomes critical to estimate the effects of each relationship commitment facet (calculative / affective) on consumers intentions to resist switching to another service provider. Both affective commitment and calculative commitment should affect customer loyalty and lead customers to accept efforts and sacrifices in the short term (Pritchard, Havitz and Howard, 1999; Johnson et al, 2001; De Wulf, Odekerken-Schröder and Iacobucci, 2001; Verhoef, Franses and Hoekstra, 2002; Verhoef, 2003; Fullerton, 2005; Gustafsson, Johnson and Roos, 2005). However, the effects of calculative and affective commitment on Customer Switching Resistance might not be the same, depending on the critical incident which is likely to occur. Therefore, we will consider Customer Switching Resistance (CSR) in the eight critical situations which have been identified by Keaveney (1995). We can then hypothesize (see Figure 1): H1: Calculative commitment has a direct and positive effect on Customer Switching Resistance in critical situations, such as a) attraction by competitors, b) inappropriate employee responses to service failures, c) pricing problems, d) core service failures, e) service encounter failures, f) involuntary switching, g) inconvenience, and h) ethical problems. H2: Affective commitment has a direct and positive effect on Customer Switching Resistance in critical situations, such as a) attraction by competitors, b) inappropriate employee responses to service failures, c) pricing problems, d) core service failures, e) service encounter failures, f) involuntary switching, g) inconvenience, and h) ethical problems. TAKE IN FIGURE 1 The effects of trust on Customer Switching Resistance 8

9 Customers would probably be reluctant to commit to a service relationship unless they have confidence in the service provider s ability to constantly meet their expectations in the future (reliability) and in its willingness to avoid any behavior that could be detrimental to them (benevolence) (Morgan and Hunt, 1994; Garbarino and Johnson, 1999). For Ganesan (1994) and Ganesan and Hess (1997), trust has two main dimensions: - Reliability concerns the service provider s perceived ability to perform a service that conforms to the consumer's expectations over time. It is related to the service provider s competence, expertise, know-how and general reputation. - Benevolence designates perceived willingness to consistently meet consumer expectations and to avoid doing anything that might be detrimental to customers. It is inversely related to the partner s opportunistic behavior. Prior work suggests that trust reinforces affective commitment (Morgan and Hunt, 1994; Garbarino and Johnson, 1999; Hennig-Thurau, Gwinner and Gremler, 2002). Bitner, Gwinner, and Gremler (1998) show that trust affects consumer perception of congruence in values with the provider (identification / affiliation). We can hypothesize (see figure 1): H3: The service provider s perceived reliability has a direct and positive effect on affective commitment H4: The service provider s perceived benevolence has a direct and positive effect on affective commitment Trust should also have a direct effect on consumer resistance to switch to another service provider when a critical incident occurs (Garbarino and Johnson, 1999; Singh and Sirdesmukh, 2000; Sirdesmukh, Singh and Sabol, 2002; Harris and Goode, 2004). Trust implies uncertainty and vulnerability, and as such, is critical when services are intangible, difficult to evaluate, complex and technical (as with financial and insurance products, for instance). Therefore, once a critical incident occurs, a consumer s responses should depend on the level of confidence the customer has in the service provider. If a customer perceives deterioration in the service provider s performance, that customer would believe that the service provider has the ability and the willingness to solve the problem in the customer s interests (Ganesan, 1994; Morgan and Hunt, 1994). The customer would expect compensation or a recovery from the service provider over time and would then be reluctant to switch to another service provider when a critical incident occurs. In contrast, if one does not trust a service provider, (s)he would be more sensitive to the critical incidents and would strive to decrease vulnerability to the service provider. Moreover, consumers expect confidence benefits from a long lasting relationship: maintaining a business relationship permits consumers to decrease the perceived risk associated with each specific transaction (Bitner, Gwinner and Gremler, 1998). When a critical incident occurs, trust enables the consumer to make confident predictions about the provider's future behavior (Morgan and Hunt 1994; Sirdesmukh, Singh and Sabol, 2002). H5: Perceived reliability has a direct and positive effect on Customer Switching Resistance in critical situations, such as a) attraction by competitors, b) inappropriate employee responses to service failures, c) pricing problems, d) core service failures, e) service encounter failures, f) involuntary switching, g) inconvenience, and h) ethical problems. H6: Perceived benevolence has a direct and positive effect on Customer Switching Resistance in critical situations, such as a) attraction by competitors, b) inappropriate employee responses to service failures, c) pricing problems, d) core service failures, e) service encounter failures, f) involuntary switching, g) inconvenience, and h) ethical problems. 9

10 The effects of perceived equity on Customer Switching Resistance Perceived equity concerns the extent to which the consumer determines that there is fair distribution of inputs and outcomes between the service provider and himself (or herself) (Oliver and Swan, 1989). Therefore, perceived equity refers to fair treatment received by customers and many researchers view equity as being similar to distributive fairness (Johnson, Barksdale, and Boles, 2001; Feinberg, Krishna, and Zhang, 2002; Musa, Pallister, and Robson, 2005). Perceived equity is said to have a positive effect on affective commitment (Dwyer, Schurr and Oh, 1987; Anderson and Weitz, 1989; Tax, Brown and Chandrashekaran, 1998; Johnson, Barksdale, and Boles, 2001): consumers should be more involved in the service relationship if the relational exchange is perceived to be fair (Gundlach and Murphy, 1993; Reynolds and Arnold, 2000; Ganesh, Arnold, and Reynolds, 2000). Thus, we can hypothesize: H7: Perceived equity has a direct and positive effect on affective commitment Moreover, relational exchanges involve reciprocity and there are reciprocal commitments between the partners, which may be explicit or tacit. Dwyer, Schurr and Oh (1987) and Gundlach and Murphy (1993) have underlined the role of this norm of reciprocity in relationship maintenance and development. If a service provider has always been fair with its customers, as a tradeoff the customers will tend to be fair with it and thus be reluctant to leave for a competitor s offer of better prices or products. On the other hand, if the service exchanges are not perceived as fair, if they have mainly benefited the company, the customers will switch to other service providers as soon as they have the opportunity to do so. As Johnson, Barksdale, and Boles (2001, p 124) note, Relationships viewed by a buyer as inequitable are at greater risk of customer defection than those perceived as equitable. Thus, we can hypothesize: H8: Perceived equity has a direct and positive effect on Customer Switching Resistance in critical situations, such as a) attraction by competitors, b) inappropriate employee responses to service failures, c) pricing problems, d) core service failures, e) service encounter failures, f) involuntary switching, g) inconvenience, and h) ethical problems. AN EMPIRICAL STUDY IN THE FINANCIAL SERVICE INDUSTRY The financial service industry is an interesting field for studying the service providerconsumer relationship (Berry, 1995; Verhoef, 2003; Ryals, 2005). Today, leading European banks strive to strengthen customer switching resistance since they still stand to lose business when their competitors launch new, better performing products, when they increase their service prices or when they do not completely succeed in a zero defect strategy (Datamonitor report, 2003). They need to prevent switching behavior and enhance short term tolerance, given the fact that all critical incidents cannot always be stopped before they happen. Determining the drivers of Customer Switching Resistance is thus a key issue in this industry. Methodology In the summer of 2003, 30,000 questionnaires were sent by mail in France to a convenience sample comprised of respondents in the age group of 18 to 75. The sample was randomly drawn from a mailing list of people. The questionnaires were mailed by the university (Edhec Business School). A total of 1,999 completed questionnaires were returned. They were asked to reply about their main bank in terms of turnover. More than 12 European banks were considered by the respondents. The respondents were often committed to long-lasting banking relationships (22 years on average). 10

11 Respondents first indicated their level of trust and relationship commitment towards their main bank. The rating scales used were borrowed or adapted from the literature on reliability and benevolence (Ganesan and Hess, 1997) and perceived equity (Tax, Brown and Chandrashekaran, 1998; Johnson, Barksdale, and Boles, 2001). For affective commitment, we used the scale developed by Garbarino and Johnson (1999). For calculative commitment, we used the scale of Gruen, Summers and Acito (2000). All scales had been adapted and validated previously in a French retail banking context. We used five-point Likert scales (strongly disagree to strongly agree). Then, later in the questionnaire, the respondents were then offered a list of 40 critical situations elaborated from the previous work of Keaveney (1995) in service industries (see Appendix 1, for examples). For each of the 40 critical incidents ( high prices in comparison with those of competitors, the incompetence of banking personnel, bad management service of the account, etc.), the respondents had to indicate their propensity or likelihood to do more business with competitors on a five-point Likert scale, which goes from very unlikely (1) to very likely (5). As a consequence, we used the reverse score to estimate the Customer Switching Resistance. To sum up, the customer switching resistance (CSR) scale creates what if hypothetical scenarios based on Keaveney s (1995) service switching categories. In our study, switching does not refer to customer churn (Gustafsson, Johnson and Roos, 2005) or to service relationship dissolution (Keaveney, 1995). It is more consistent with the approach of Zeithaml, Parasuraman and Berry (1996, pp 38) who consider an exit dimension in their research and estimate the customers propensity to do less business with XYZ in the next few years. In the financial service industry, most customers tend to avoid breaking the service relationship (retention rate = 95% on average in France). They prefer to patronize different suppliers and allocate their resources to other service providers. This leads to a drop in the customer share for the bank concerned (Verhoef, 2003). Exploratory and Confirmatory Factor Analysis First, we performed a factor analysis from the items which was used to estimate the Customer Switching Resistance (CSR) in 40 critical situations. The purpose was to reduce the number of variables (CSR in 40 critical situations), by combining two or more variables into a single factor, and to identify groups of inter-related variables which are strongly related to each other since they refer to the same category of critical incident. As a result, we were able to check if the respondents classified the 40 critical incidents we had formulated into the previous eight categories which were identified by Keaveney (1995). The exploratory factor analysis gave most of the results we expected. However, we found out that ethical problems and inappropriate employee responses to service failures fall into the same category same factor - in the exploratory factor analysis. Those items concern Customer Switching Resistance (CSR) when employees appear not to be benevolent and fair with their customers, especially when they complain. Finally, thanks to this result, we decided to keep only seven factors or components (and not eight) and to consider CSR in seven critical situations: 1) CSR - Attraction by competitors, 2) CSR - Employee responses to service failures (also including the items which refer to ethical problems), 3) CSR - Pricing, 4) CSR - Core service failures, 5) CSR - Service encounter failures, 6) CSR - Involuntary switching, 7) CSR - Inconvenience. Seventeen variables (out of 40) were not strongly correlated with any of the seven factors which were extracted (loadings < 0.50). We decided to remove them from our measurement scales to improve their psychometric qualities (reliability and validity). After measurement scale purification, Cronbach's alpha coefficients ranged from.76 to.92, which can be considered satisfactory (Table I). 11

12 We then tested in a confirmatory factor analysis the one-dimensionality (in the sense defined by Anderson, Gerbing, and Hunter, 1987) of each of the seven constructs separately, then two by two, three by three, etc., and finally for all the seven constructs taken together (CSR in seven critical situations). The confirmatory factor analysis was performed with Amos 4 software. The estimation was performed using the maximum likelihood method because of its robustness for large sample sizes (N=1999). The final 23 items we used are presented in Appendix 1. According to our conceptual framework, trust was divided into two constructs (reliability and benevolence). We tested a one-dimensional (trust) model of trust that did not fit the data. The two-dimensional (reliability + benevolence) model of trust exhibited an acceptable fit (RMSEA = 0.05). We did the same for relationship commitment dimensions (calculative and affective commitment). The one-dimensional model resulted in a bad fit (RMSEA = 0.266), whereas the two-dimensional model exhibited a satisfactory fit (RMSEA = 0.044). According to the exploratory factor analysis results, Customer Switching Resistance is estimated in seven categories of critical situations. The seven component model exhibits a satisfactory fit (RMSEA = 0.05). We noted an unacceptable fit when we consider together some of the most tightly correlated constructs. TAKE IN TABLE I Taken together, the constructs demonstrated a satisfying degree of reliability and convergent validity (Table I). The reliability coefficients are between 0.76 and 0.92 ( coefficients). The average variances extracted are between 0.52 and 0.85, which can be considered satisfactory. Moreover, latent variable discriminant validity was checked using the Fornell and Larcker (1981) criterion. As shown in Table II and Table III, the square root of the average variance extracted (AVE) exceeds the correlations between every pair of latent variables. This indicates a satisfactory level of discriminant validity. The respondents were able to clearly dissociate their intentions to switch to another service provider according to the critical incidents that are likely to occur in the near future. TAKE IN TABLE II AND TABLE III Results of the model Structural equation modeling was used to test the model (see figure 1). We examined successively the effects of trust and perceived equity (direct and indirect) and relationship commitment (direct) on Customer Switching Resistance in seven critical situations: a) CSR attraction by competitors, b) CSR inappropriate employee responses to service failures, c) CSR pricing problems, d) CSR core service failures, e) CSR service encounter failures, f) CSR involuntary switching, and g) CSR inconvenience. Customer Switching Resistance has been successively examined in these seven critical situations. All the seven models fit the data well, since we obtained a RMSEA ranking between and and fit indexes (NFI, RFI, CFI) were all higher than 0.99 (Bollen and Scott, 1993). The path coefficients are indicated below (Table IV). TAKE IN TABLE IV Perceived equity, trust and relationship commitment do not have exactly the same role in each and every critical situation. Affective commitment has a positive, direct and significant effect on Customer Switching Resistance. According to our H1 (a, b, c, d, f, g) hypotheses, the more the consumer is affectively committed, the more he (or she) will resist switching when a reason to change occurs. However, contrary to our H1e hypothesis, affective commitment does not lead the 12

13 consumer to resist switching when he (or she) faces a service encounter failure. Contrary to calculative commitment, affective commitment is insufficient to limit the negative effects of bank employee impoliteness, incompetence or lack of attention. Calculative commitment has a limited influence. According to our H2 (b, c, e) hypotheses, calculative commitment leads consumers to resist switching when there are inappropriate employee responses to service failures (0.12), a pricing problem (.075) or a service encounter failure (.14). But it has no significant effect on their switching resistance when competitors offer better performing products or when the core service fails. When the deterioration in perceived performance concerns the core service or product (technical quality), calculative commitment has no more influence on Customer Switching Resistance. But it slows down the switching process when employee attitudes and behaviors are seen as sources of failure or when pricing policy does not meet their expectations. Furthermore, a surprising result appears: calculative commitment tends to lower Customer Switching Resistance when an event apart from the service relationship occurs, such as involuntary switching and inconvenience. A change in the bank (closing of a branch) or customer (moving to a new locale) situation or the opening of new competitor branches closer to customers homes or workplaces are seen as opportunities to switch to another service provider and to recover more freedom and independence, as previously demonstrated in the resource dependence theory (Pfeffer and Salancik, 1978). As a consequence, the hypotheses H2 (a, d, f, g) have to be rejected. As expected, trust has a direct and positive effect on affective commitment. Perceived reliability and perceived benevolence have a slight, but significant positive influence (.09). Thus, our H3 and H4 hypotheses are confirmed. According to previous relationship marketing literature, affective commitment is a key mediating construct between trust and behavioral intentions (Morgan and Hunt, 1994; Garbarino and Johnson, 1999). Most effects of trust on Customer Switching Resistance are mediated by affective commitment. Reliability has only an indirect and positive effect on Customer Switching Resistance when customers face inappropriate employee responses to service failures, pricing problems, core service failures, service encounter failures and inconvenience problems. As a result, our H5 (b, c, d, e, g) hypotheses are rejected. But, according to our H5 (a, f) hypotheses, reliability has a direct and positive effect on Customer Switching Resistance when customers are exposed to competitors offers. Banking products are quite complex, difficult to evaluate and risky. Thus, consumers tend to resist switching if their focal service provider is credible and competent enough to deliver similar products and services (.10). Perceptions of service provider reliability also lead the consumer to resist switching when there is a change in the customer or bank situation (.10). Consumers will strive to maintain the service relationship if they are dealing with a competent financial advisor. Benevolence has no direct effect on Customer Switching Resistance. All its effects are mediated by affective commitment. Our H6 (a, b, c, d, e, f, g) hypotheses have to be rejected. This result is surprising because benevolence has always been described as a key concept when a critical incident occurs (Ganesan, 1994; Ganesan and Hess, 1997). Perceived equity has a stronger effect on Customer Switching Resistance. According to hypothesis H7, perceived equity has a strong impact on affective commitment (.55). As a result, it has an indirect influence on consumer resistance to switch to another service provider in several critical situations (6 out of 7 critical situations). According to our H 8 (a, b, c, g), it also has a direct influence on Customer Switching Resistance in critical situations, such as attraction by competitors (.13), employee responses to service failures (.14), pricing problems (0.12) and inconvenience (0.11). However, the H8 hypotheses (d, e, f) have to be 13

14 rejected since perceived equity does not directly influence Customer Switching Resistance in situations, such as core service failures, service encounter failures and involuntary switching. Nevertheless, perceived equity remains a key process that strongly affects consumer responses to critical incidents. We note that consumer responses to competitors products, prices and points of sale (opening of another branch) depend strongly on perceived equity. If the customer has always been treated fairly by the service provider, (s)he will be more reluctant to leave it as soon as (s)he has a better opportunity offered by competitors. On the other hand, when there is an unfair distribution of inputs and outcomes between the service provider and the customer, there is a higher risk of customer defection. Discussion To sum up, affective commitment has the strongest impact on Customer Switching Resistance. Thus, financial service companies should enhance identification and affiliation processes, communication on their corporate and brand values, as well as their identity and personality in order to develop customer feelings of belonging to the service organization. Customer clubs or brand communities represent solutions for strengthening relationships with customers (Roos, Gustafsson and Edvardsson, 2005). There is only one exception: when a service encounter with contact persons fails, affective commitment does not allow for the maintenance of the service relationship over time. On the other hand, calculative commitment, which refers to an evaluation of the termination costs, has three positive influences on Customer Switching Resistance (service encounter failures, employee responses to service failures, pricing problems) and two negative effects (inconvenience, involuntary switching). As a consequence, in the long run, companies cannot count on calculative commitment and termination costs to keep their customers. Once consumers have an opportunity to switch to another service provider, they will tend to do so (Bendapudi and Berry, 1997). In the financial service industry for instance, customers will often tend to do more business with competitors: on the short term, there will be no effect on customer retention rate but in the long run, it will result in a drop in customer share and in company profits. Trust (reliability and benevolence) mainly has an indirect effect on Customer Switching Resistance via affective commitment. Benevolence and reliability are key components of trust in the marketing literature (Ganesan and Hess, 1997; Sirdesmukh, Singh and Sabol, 2002). However, their respective influence both direct and indirect - on Customer Switching Resistance is rather low. Perceived equity is also a key factor of affective commitment (.55) and of Customer Switching Resistance in four situations: attraction by competitors (.13), employee responses to service failures (.14), pricing problems (.12), and inconvenience (.11). Companies should measure and manage the perceived equity of the relationships from the customers point of view. An unfair exchange relationship will lead customers to switch to another service provider once a critical incident occurs. As different authors have already postulated (Dwyer, Schurr and Oh, 1987; Gundlach and Murphy, 1993; Sirdesmukh, Singh and Sabol, 2002), reciprocity is important in developing enduring relationships. If companies want their customers to be truly loyal and their Customer Relationship Management to be effective, they should get back to basics: consumers will resist switching to another service provider if they obtain a fair return on their sacrifices and efforts (Mac Neil, 1978; Dwyer, Schurr and Oh, 1987; Bitner, Gwinner and Gremler, 1998; Johnson, Barksdale, and Boles, 2001; Feinberg, Krishna, and Zhang, 2002; Musa, Pallister, and Robson, 2005). 14

15 MANAGERIAL IMPLICATIONS While between 35% and 75% of Customer Relationship Management projects are considered to be failures, it is important to better understand why customers are doing even more business with competitors (Zablah, Bellenger and Johnston, 2004). The critical incidents we considered in this research can be seen as precipitating events which have to be effectively managed. Of course, managers can prevent service performance deterioration from occurring (service quality programs). They can also develop procedures to solve the problems (service recovery management). However, several critical incidents cannot be predicted nor avoided. Therefore, managers have to anticipate several critical incidents that will come from the company (pricing policy, complaint management, service quality management, core service production and delivery), from its environment (competitors products and new branches) or from its customers (change in his (her) personal situation). Some events are more critical than others, i.e. they are more likely to cause the customer to switch service providers. Below we rank Customer Switching Resistance according to the mean scores presented in Appendix 1 (from the lowest to the strongest customer switching resistance = from 0 very likely to do more business with competitors to 4 very unlikely to do more business with competitors ). Moreover, according to our findings, we examine for each critical situation what the most effective shock absorbers are: affective commitment, calculative commitment, reliability, benevolence and/or perceived equity. 1 CSR - Service encounter failures (mean score =0.89) This represents the most important critical incident in the financial service industry (i.e. lowest Customer Switching Resistance). If contact persons (financial advisors, branch employees, etc.) are incompetent or impolite or do not pay sufficient attention to customers, only calculative commitment can help to maintain the service relationship. As a result, if a service organization is unable to train and control its employees so that they become customeroriented, they will be obliged to implement high switching costs. However, as Bendapudi and Berry (1997, p 28) note, when customers stay in relationships because of the constraints against leaving, the relationship tends to last only as long as the constraints do; when the constraints no longer apply, the customer feels no compelling reason to continue in the relationship. 2 CSR - Employee responses to service failures (mean score = 1.09) If a service provider does not succeed in managing customer complaints, it should pay attention to perceived equity as Tax, Brown and Chandrashekaran (2002) have already demonstrated. Affective commitment also has a positive influence (0.10): affectively committed customers would probably attribute their dissatisfaction to the employees personality or incompetence instead of switching to another service provider. If they perceive high switching costs, they would also tolerate the situation (0.12). 3 CSR Pricing (mean score = 1.25) If the provider increases service prices, the customers who have affective commitment are less likely than other customers to switch to a competitor. They would probably reduce the value of the alternatives considering that lower prices are signs of poor quality. Or they would reevaluate the service quality delivered by a favorite supplier (biased partner perception). If customers perceive that the exchange relationship is fair, they would also be more tolerant and accept to pay more. In the reverse situation, they would have a higher propensity to switch to another service provider. Calculative commitment also has a slight but significant effect in this situation (0.07). Switching costs lead customers to accept to pay a price premium. 4 CSR - Core service failures (mean score = 1.51) 15

16 If a service provider faces some problems in delivering core services, affective commitment will be the only shock absorber from the customer s point of view. Some customers identify with the service provider (identification and affiliation) and will tend to tolerate a core service failure on the short term. As Dwyer, Schurr and Oh (1987) or Morgan and Hunt (1994) demonstrated in other business settings, affective commitment leads customers to accept sacrifices on the short run. 5 CSR - Attraction by competitors (mean score = 1.53) Whereas most studies focus on consumers competitive resistance (Zeithaml, Parasuraman and Berry 1996; Fornell et al, 1996; Reynolds and Arnold, 2000), we demonstrate that attraction by competitors is not the most critical factor of switching in the financial service industry (rank: 5). Nevertheless, if a competitor launches a new offer, the service provider can prevent customer defection by improving the competence of its personnel (recruitment, training, incentives, etc.). Financial services are often intangible and difficult to evaluate. Thus, if the customers rely on their financial advisors, they will try to avoid the risks associated with dealing with another exchange partner. Perceived equity also has a positive effect (0.13): the customers will accept opportunity costs ( miss out on a good deal ) because they believe that their partners will find a fair solution in the long run (reciprocity in the exchange). Affective commitment will also lead customers to accept sacrifices (opportunity costs) in the short run because they expect more long term benefits from their suppliers (Bitner, Gwinner and Gremler, 1998). 6 CSR - Involuntary switching (mean score = 1.66) If there is a change in the consumer (change of address) or the bank (closure) situation, the competence of bank personnel will directly slow down the switching process (0.09). Nevertheless, affective commitment is once again the main factor in enhancing Customer Switching Resistance (0.14). Calculative commitment has the opposite effect (-0.09). When consumers have to move to another country or when a bank closes its branches, this represents a good opportunity for consumers to recover more freedom (Pfeffer and Salancik, 1978). 7 CSR Inconvenience (mean score = 2.46) If consumers face convenience problems (distance from home or workplace), calculative commitment also has the same negative effect: consumers will be likely to switch to another service provider to recover their independence. Affective commitment has a more positive effect on Customer Switching Resistance. The feeling of belonging to a specific bank community leads consumers to make efforts (time, distance, money) in order to maintain the relationship. Perceived equity also has a positive influence (0.11). Reciprocity within the exchange relationship will lead individuals to make efforts in return for what they obtain from the service provider. Ranking the critical incidents according to their adverse impacts, we note as in previous literature (Bitner, Booms and Treteault, 1990) that the main drivers of switching intentions are closely related to employee behaviors. As a result, human resource management -i.e. employee satisfaction and involvement, and contact person recruitment and training, etc.- remains extremely important in maintaining long lasting-relationships with customers. Furthermore, this research allows managers to determine what the drivers of Customer Switching Resistance are. As such, it enables managers to maintain customer retention and customer share, even though they face difficulties in applying a zero defect strategy or if new entrants arrive on the market with new better performing products and low prices. It is quite impossible to prevent a critical incident from occurring throughout the business relationship. 16

17 But our results demonstrate that managers can slow down the switching process by managing the service provider s perceived reliability, benevolence and perceived equity and by developing affective and/or calculative commitment. It depends in great part on the type of critical incident which is likely to occur. To sum up, managers should: 1. Anticipate the critical incidents that are likely to occur (core service failures, inconvenience problems, competitor offers, price increases, etc.); 2. Identify the customers who will be the most likely to switch to another service provider in such a situation (age, occupation, lifetime value); 3. Develop specific shock absorbers : reliability, benevolence, perceived equity, calculative commitment and/or affective commitment. CONTRIBUTIONS, LIMITS AND FUTURE AREAS OF RESEARCH This research has two main contributions: First, we measure customer loyalty when consumers experience a reason to switch service providers and not when there is no reason or opportunity to do so. As Jacoby and Kyner (1973) and Jacoby and Chesnut (1978) note, the composite measurements of loyalty should have better trait validity than behavioral indicators and better predictive validity than attitudinal indicators. Most studies tend to estimate customer attitudes and intentions (Zeithaml, Parasuraman, and Berry, 1996) or to capture behavioral loyalty (Verhoef, 2003; Gustafsson, Johnson and Roos, 2005). We propose a composite approach that aims at considering true customer loyalty. The more customers resist doing more business with competitors, the more they should be loyal to their service provider. Moreover, we consider Customer Switching Resistance according to the main types of critical incident customers are likely to experience in their service relationship (Bitner, Booms, and Tetreault, 1990; Keaveney, 1995; Gremler, 2004). To our knowledge, no research in the service industry has examined Customer Switching Resistance in seven critical situations. Most studies focus on consumers competitive resistance or price tolerance (Parasuraman et al, 1991; Fornell et al, 1996; Reynolds and Arnold, 2000), although they do not represent the main drivers of switching behaviors - especially in the financial service industry. The critical incidents that occur within the established service relationship (service encounter failure, employee responses to service failures, etc.) are more likely to cause the customer to switch service providers (Keaveney, 1995; Bitner, Booms, and Tetreault, 1990; Gremler, 2004). Second, we examine the roles of perceived equity, trust and relationship commitment and their direct and indirect effects on Customer Switching Resistance. We explain why customers switch when they have some reasons to stay with the same service provider. We demonstrate that perceived equity, trust, affective and calculative commitment which are key constructs in the relationship marketing literature do not always lead to the service relationship maintenance. In various critical situations (service encounter failure for instance), the service relationship quality do not permit to maintain the business relationship. Furthermore, some constructs, such as calculative commitment, may have a positive, a negative or a non significant effect on relationship maintenance: it depends on the situation. Thus, this research complements the relationship marketing literature and underlines the need to better take into account critical incidents. We also explain why customers stay when they have some reasons to switch to another service provider (core service failures, pricing problems, lack of convenience, etc.). This research highlights the need to consider the service 17

18 relationship evaluations to better understand service switching behaviors. We show that customers will often be reluctant to switch service providers if they feel a sort of identification and affiliation with the service provider and if they believe that there is fair distribution of inputs and outcomes between the customer and the service provider. Thus, this research extends the service switching literature, which is often focused on customer satisfaction / dissatisfaction (Ganesh, Arnold, and Reynolds, 2000) and often applies the critical incident technique to identify the most frequent critical incidents (Bitner, Booms, and Tetreault, 1990; Keaveney, 1995; Gremler, 2004). In our empirical study, we tested a model on a sample of 1,999 bank customers and measured the effects of key relational constructs (perceived equity, trust, relationship commitment) on service switching intentions. However, this research has limitations, which can be overcome in future research: Firstly, this research concerns a contractual service setting: the long term relationship between a bank and its customers. It will be necessary to explore other industries which are usually non-contractual, such as transportation, restaurant or health services. Moreover, the financial service industry delivers an essentially utilitarian service and it will be interesting to test the model for more hedonic consumption experiences, such as sports, leisure activities, or artistic pursuits. Finally, we note a low response rate (1,999/30,000 respondents = 6.66%) which is due to the data collection process: mail survey and long questionnaire. Further research has to be done to strengthen the external validity of our results. Secondly, the effects of perceived equity, trust and relationship commitment might be moderated by the relationship phase (awareness, exploration, expansion, commitment and dissolution), (Dwyer, Schurr and Oh, 1987). For example, as shown by Verhoef, Franses and Hoekstra (2002), the role of trust could be stronger in the relationship s first stage, when clients do not have enough experience or expertise. Future research should also take into consideration the characteristics of the service relationship (length, breadth and depth) and of the individuals (age, occupation, lifetime value, etc.). Thirdly, we only measure behavioral intentions and not actual behaviors. As a consequence, we cannot check the predictive validity of the Customer Switching Resistance s indicators. Gustafsson, Johnson and Roos (2005) have examined the role of a few reactional and situational triggers on customer churn during nine months in the telecommunication industry, and they did not find any impact on customer churn. However, Gustafsson, Johnson and Roos (2005) suggest doing more research on the switching path (length, immediate or progressive dissolution of the relationship) and to consider several critical incidents that occur within a relationship (change in the consumer situation, competitor offers, core service failures, etc). Finally, in consumer goods settings, Fournier (1998) suggest that Customer Switching Resistance depends on psychological processes, such as accommodation, tolerance/forgiveness, biased partner perceptions, devaluation of alternatives and attribution biases. In the service industry, we need also to develop a deeper analysis of how consumers resist switching to another service provider. How do they really cope with the critical incidents that occur? What happens in the minds of consumers? 18

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