Emotional and Rational Motivations for Customer Loyalty in. Business-to-Business Professional Services

Transcription

1 Emotional and Rational Motivations for Customer Loyalty in Business-to-Business Professional Services BARBARA ČATER *, Ph.D. TOMAŽ ČATER **, Ph.D. * University of Ljubljana, Faculty of Economics, Kardeljeva ploščad 17, SI-1000 Ljubljana, Slovenia; Phone: ; Fax: ; barbara.cater@ef.uni-lj.si. ** University of Ljubljana, Faculty of Economics, Kardeljeva ploščad 17, SI-1000 Ljubljana, Slovenia; Phone: ; Fax: ; tomaz.cater@ef.uni-lj.si.

2 Emotional and Rational Motivations for Customer Loyalty in Business-to-Business Professional Services The purpose of our study is to add to the body of knowledge on customer loyalty in professional services in business-to-business markets. We build on the IMP group constructs and relate them to affective commitment, relational benefits and, through these two, to customer loyalty. The results show that trust and social bonds positively influence affective commitment, while adaptation and knowledge transfers positively influence relational benefits. Although both affective commitment as a more emotional construct and relational benefits as a more rational construct positively influence customer loyalty, emotional motivation seems to be much stronger than rational motivation. Theoretical and managerial implications are discussed on the basis of our empirical findings. Key words: professional services, business-to-business market, business relationship, relationship management, actor bonds, activity links, resource ties, commitment, benefits, customer loyalty; INTRODUCTION There is a growing awareness that, especially in business-to-business markets, firm performance can be improved by focusing on present customers instead of concentrating on attracting new ones [Holmlund and Kock, 1996]. It has been argued that building 1

3 relationships with customers is the essence of business-to-business marketing [Goldsmith, 1999; Hutt and Speh, 2004] and that relationships are the fundamental asset of a firm that determines its future more than anything else [Gordon, 2000]. Researchers also point out that collaborative relationships in business-to-business markets offer opportunities for firms to create competitive advantages and achieve superior results [Hewett et al., 2002; Jap, 1999; Panayides, 2002; Sharma and Sheth, 1997; Ulaga, 2003; Zuzel and Zabkar, 2006]. More recently, the discussion of relationships has been enriched by focusing on the concept of customer value as a key building block of relationship marketing [Holmlund and Kock, 1996; Ulaga and Eggert, 2005] and a relationship-based strategy in building a firm s competitive advantage [Morgan and Hunt, 1999]. Since one of the main marketing goals of firms is to establish customer loyalty [Berry and Parasuraman, 1991] a key question practitioners and researchers face is what is it that makes customers loyal. When discussing the reasons for customer loyalty, researchers primarily rely on different components of commitment. The majority of researchers have defined commitment similarly to Moorman et al. [1992: 316], i.e. as an enduring desire to maintain a valued relationship, and measured it as a global construct. However, in the last decade several researchers have conceptualised commitment with two i components affective and calculative. Affective commitment is reflected in Bagozzi s [1975: 316] social man. It is an emotional, social sentiment [Gilliland and Bello, 2002: 25] and pertains to attachment that is due to liking and identification [Kumar et al., 1994; Geyskens et al., 1996]. On the other hand, calculative commitment is reflected in Bagozzi s [1975: 316] economic man. It relates to a rational, economic calculation [Gilliland and Bello, 2002: 25] and is also addressed as an attachment for instrumental reasons [Kumar et al., 1994; Geyskens et al., 1996]. Both affective and calculative commitments pertain to psychological states, yet they originate from 2

4 different motivations for maintaining a relationship [Geyskens et al., 1996]. While affective commitment represents a positive motivation, calculative commitment mostly represents a negative motivation for continuing the relationship [De Ruyter et al., 2001; Geyskens et al., 1996; Gounaris, 2005; Kumar et al., 1994]. Recently, however, Sharma et al. [2006: 69] have proposed that calculative (cognitive) commitment can be negative ( locked-in commitment ) and positive ( value-based commitment ). Locked-in commitment refers to staying in the relationship due to a perceived lack of choice or perceived switching costs, while value-based commitment involves the rational calculation of benefits from continuing the relationship [Sharma et al., 2006]. Since our aim is to put forward constructs measuring positive motivation for customer loyalty our paper builds on affective commitment and relational benefits as two key positive motivations for customer loyalty. The purpose of our study is to add to the body of knowledge on customer loyalty in the professional service sector ii in business-to-business markets. The study builds on three relationship components, i.e. actor bonds, activity links and resource ties, proposed by the IMP (Industrial Marketing and Purchasing) group of researchers, and relates them to affective commitment, relational benefits and, through these two constructs, to customer loyalty. We propose that actor bonds (as a more emotional relationship component) influence affective commitment, while activity links and resource ties (as more rational relationship components) influence relational benefits. Both affective commitment (as an emotional construct) and relational benefits (as a rational construct) are hypothesised to influence customer loyalty. Based on our empirical findings theoretical and managerial implications are discussed. In particular, we propose what professional service providers in business-to-business markets can do to improve their customers loyalty by improving the relationships with their customers. By concentrating on these implications we seek to contribute to bridging the gap 3

5 between the services-based and relationship-based literature on one side and its strategic/managerial implications on the other [Harrington and Akehurst, 2000; Morgan and Hunt, 1999]. CONCEPTUAL FRAMEWORK Affective Commitment, Relational Benefits and Customer Loyalty As an emotional reason for customer loyalty we hypothesise that affective commitment is the key construct. According to Sharma et al. [2006: 65, 69], affective commitment includes a desire to develop and strengthen a relationship with another person or group because of familiarity, friendship, and personal confidence built through interpersonal interaction over time. It originates from identification, common values, attachment, involvement and similarity [Bansal et al., 2004; Fullerton, 2003; Geyskens et al., 1996; Gruen et al., 2000]. In addition, affectively committed customers continue the relationship because they like their providers and enjoy working with them [Fullerton, 2005a; Fullerton, 2005b; Geyskens et al., 1996]. Affective commitment is therefore based on a general positive feeling towards the relationship partner [Konovsky and Cropanzano, 1991]. Customers with strong affective commitment will stay in the relationship because they want to, based on their positive affect toward the provider [Kumar et al., 1994: 4]. On the other hand, we propose relational benefits as a rational motivation for customer loyalty. Sweeney and Webb [2002: 78] suggest that relational benefits exist when value is added to what is received beyond the basic product, resulting in perceived consequences for the recipient. Similarly, Gao et al. [2005: 25] define relational benefits as utilities beyond 4

6 the core performance and supplementary services that the customer can obtain from the formation, development, and maintenance of a long-term buyer-seller relationship. Relational benefits are often conceptualised within relationship value definitions as what you get opposed to the sacrifices that refer to what you give in a market exchange [Zeithaml, 1988: 14]. The reason we include relational benefits (and not relationship value) in our model is that the operationalisation of relationship value is very complex [Ulaga and Eggert, 2005, 2006] and as such difficult to include in our already complex model. Different authors [Lapierre, 2000; Sweeney and Webb, 2002; Ulaga, 2003; Ulaga and Eggert, 2005, 2006] point out different groups of relational benefits. In our study we focus on the relationship outcome benefits which explain how firms can better compete in the market due to having a long-term relationship with their partner [Morgan and Hunt, 1994]. Both affective commitment and relational benefits are hypothesised to influence customer loyalty. Loyalty has been defined as a construct that measures the probability that the customer will return and is ready to perform partnering activities such as referrals [Bowen and Shoemaker, 2003]. Some authors [Bolton et al., 2003; Lapierre et al., 1999; Woo and Ennew, 2004; Zeithaml et al., 1996] refer to a similar concept as behavioural intentions that include renewing the contract, making recommendations and increasing patronage [Bolton et al., 2003; Zeithaml et al., 1996]. Researchers are not unanimous on the difference between commitment and loyalty. Still, the majority of them believe these two constructs are related but different and that commitment is an antecedent to loyalty [Bansal et al., 2004; De Ruyter et al., 2001; Fullerton, 2005b; Harrison-Walker, 2001; Hennig-Thurau et al., 2002; Pritchard et al., 1999; Wetzels et al., 1998; Wetzels et al., 2000]. This difference is also reflected in our study where commitment is understood as predominantly a motivation and an attitude to 5

7 continue a relationship, while loyalty is a mixture of attitude and behaviour that is most often defined as repeat patronage and referral behaviour. Actor Bonds, Activity Links and Resource Ties The model we use in our study largely builds on the IMP group s ideas, proposing an interaction approach to relationships which recommends that customers and providers should move away from competitive relationships and recognise the benefits of collaborative relationships instead [Durkin and Lawlor, 2001]. The IMP researchers identify three layers of relationship [Håkansson and Snehota, 1995], i.e. actors, activities and resources. A relationship between two firms therefore has a profile in terms of actor bonds, activity links and resource ties. (1) The construct of actor bonds as part of interaction and network theory was introduced to illustrate where the stability of relationships between customers and providers arises from [Holmlund and Kock, 1996]. Actor bonds link actors (firms and individuals) and affect how actors perceive each other and develop their identities. (2) Activity links refer to the technical, administrative, marketing and other activities of a firm that we can connect to the activities of the counterpart during development of the relationship. (3) Resource ties link the different elements of resources (technology, material, knowledge and other intangible resources) of examined firms. These ties are a result of relationship development and represent a firm s resource [Håkansson and Snehota, 1995]. On the basis of a literature review on marketing relationships in services [Halinen, 1997; Woo and Ennew, 2004] as well as on the basis of our in-depth interviews with clients of market research agencies that was carried out in the initial phase of our study, we use trust and social bonds as concepts representing actor bonds, adaptation and co-operation as concepts measuring activity links and knowledge transfers as a concept representing resource ties. 6

8 (1a) Trust is an essential relationship model building block [Anderson and Narus, 1998] and has often been defined as a belief that one relationship partner will act in the best interests of the other partner [Wilson, 1995]. Based on a meta-analysis of studies about trust, Geyskens et al. [1998: 225] pointed out that most of these studies build on interpersonal research and define trust as the extent to which a firm believes that its exchange partner is honest and/or benevolent or some variant thereof. Moorman et al. s [1992] definition, similarly as Doney and Cannon s [1997], reflects two components of trust: credibility and benevolence. Credibility reflects the customer s belief that the provider has sufficient expertise to perform the job effectively and reliably, while benevolence reflects the extent of the customer s belief that the provider s intentions and motives are beneficial to the customer even when new conditions arise about which a commitment was not made [Ganesan, 1994]. (1b) Social bonds are defined as the degree of mutual personal friendship and liking shared by the buyer and seller [Wilson, 1995: 339]. In the professional service context social bonds refer to the human side of the service, including personal contacts, liking and trust [Thunman, 1992]. Social bonds include familiarity, friendship and personal confidence that are built through the exchange process [Rodriguez and Wilson, 2002]. They are created through social interactions where individuals develop strong personal relationships that can strengthen the relationship between both firms [Wilson, 1995]. (2a) Adaptation refers to behavioural or structural modifications at the individual, group or firm level, carried out by one firm, which are initially designed to meet specific needs of another firm [Brennan and Turnbull, 1998: 31]. Adaptation occurs when one party in the relationship adapts its processes or the product to another party. This conceptualisation is broad enough to include one-off investments that are needed to finish a certain transaction as 7

9 well as gradual adaptations that occur through time [Håkansson, 1982]. For professional services in business-to-business markets, Halinen [1997] stresses the importance of passive adaptation that is related to the task content, results and terms of payment, the customer s marketing strategy and task execution. It can also relate to personal relationships, knowledge and roles as well as positions in the relationship. (2b) Co-operation is a result of exchange episodes that take place between the customer and the provider. It refers to the extent that the work of buyer and seller is co-ordinated [Metcalf et al., 1992: 29]. Kalafatis [2002] points out that the conceptualisation of co-operation by the IMP group that sees co-operation as a result of exchange episodes is consistent with the conceptualisation proposed by Anderson and Narus [1990: 45] who defined co-operation as similar or complementary coordinated actions taken by firms in interdependent relationships to achieve mutual outcomes or singular outcomes with expected reciprocation over time. Similarly, Young and Wilkinson [1997: 55] understand co-operation as all activities undertaken jointly or in collaboration with others which is directed towards common interests or achieving rewards. (3a) Probably the most important resource in the professional service context is knowledge. It can be understood in several ways: as the ability of an actor to carry out the tasks which are the subject of a contract, as the knowledge that arises between actors about how to do business with each other, and as the ability of an actor to draw on the knowledge base of those within the actor s relationships [McLoughlin and Horan, 2000]. Relationships present an important tool for connecting the knowledge of different actors [Håkansson and Snehota, 1995]. In a relationship firms can co-operate and learn from each other without actually having to do all the investments themselves [De Ruyter and Semeijn, 2002]. Knowledge transfers, taking place in the customer-provider interface [Gadrey and Gallouj, 1998], as part of resource ties therefore encompass knowledge that is formed between the actors as to how 8

10 to do business with each other and as the capability of actors to utilise the knowledge of the other actors in the relationship. DEVELOPMENT OF THE HYPOTHESES Figure 1 presents a model of customer loyalty in professional service relationships in business-to-business markets. Hypotheses are proposed about the relationships between the layers of substance in the relationships (as developed by the IMP researchers) and affective commitment, relational benefits and customer loyalty. Figure 1 about here Several empirical studies have discovered a positive influence of trust on affective commitment [De Ruyter and Semeijn, 2002; De Ruyter and Wetzels, 1999; De Ruyter et al., 2001; Geyskens et al., 1996; Gounaris, 2005; Wetzels et al., 1998; Wetzels et al., 2000]. In addition, several empirical studies have discovered a positive influence of trust on commitment as a global construct, although a careful examination reveals that in fact the commitment in these studies has been implicitly measured as affective commitment [Friman et al., 2002; Goodman and Dion, 2001; Lohtia et al., 2005; Moorman et al., 1992; Morgan and Hunt, 1994; Perry et al., 2002; Rodriguez and Wilson, 2002; Tellefsen and Thomas, 2005; Walter and Ritter, 2003]. Research also shows that customers and providers who are bound by strong personal relationships are more committed to maintaining relationships than those without such relationships [Wilson, 1995; Wilson and Jantrania, 1994]. Therefore, social bonds have also been found to be one of the key antecedents of commitment in interfirm relationships [Mavondo and Rodrigo, 2001]. Based on these past empirical findings the 9

11 following hypotheses were proposed for relationships between trust, social bonds and affective commitment: Hypothesis 1: Trust positively influences affective commitment. Hypothesis 2: Social bonds positively influence affective commitment. The existing literature on business-to-business relationships clearly lacks empirical support for the relational benefits. Therefore, in developing hypotheses related to relational benefits we build predominantly on the relationship value literature. In so doing we adopt the original terminology (i.e. value instead of benefits ) used by the researchers and at the same time point out how their findings refer to relational benefits. When partners adapt processes and products or specialise their investments this creates value for the other firm [Wilson, 1995]. Adaptations can create value for one or both sides to the extent that they lower costs, increase revenues or create dependence [Cannon and Perreault, 1999]. The more the provider adapts to the customer, the higher are the benefits for the customer. Researchers also mention cooperation as a source of value [Wilson, 1995]. In relationships where providers and customers co-operate to improve their business processes, customers perceive they get more benefits than in relationships where co-operation does not take place. Firms that enter a long-term relationship also expect that, due to the mutual contribution of knowledge, technology and other resources, value will be created that none of the partners could create on its own [Wilson and Jantrania, 1994]. Knowledge that a partner receives in a relationship can be the most valuable outcome of a relationship, however it may also be the most difficult outcome to measure [Wilson, 1995]. Due to the acquired knowledge customers feel less of a risk in making decisions and therefore see knowledge transfers as a relationship component that increases their relational benefits. Based on the arguments presented above the following hypotheses were developed: 10

12 Hypothesis 3: Adaptation positively influences relational benefits. Hypothesis 4: Co-operation positively influences relational benefits. Hypothesis 5: Knowledge transfers positively influence relational benefits. Empirical studies on relationship marketing show that affective commitment is the key driver of customer loyalty [Fullerton, 2005a; Harrison-Walker, 2001; Kumar et al., 1994]. Affective commitment creates positive intentions to maintain and strengthen the relationship [Gounaris, 2005]. In line with De Ruyter et al. [2001], Gounaris [2005], Kumar et al. [1994] and Wetzels et al. [1998], who discovered that affective commitment positively influences customer loyalty, we developed the following hypothesis: Hypothesis 6: Affective commitment positively influences customer loyalty. If customers perceive value from the provider s offering or from the relationship with the provider they are likely to continue to be loyal to that provider [Plank and Newell, 2007]. Several empirical studies in the service literature found a positive influence of relationship value on customer loyalty [Cronin et al., 2000; Harris and Goode, 2004; Lin and Wang, 2006; Sirdeshmukh et al., 2002]. Based on these empirical studies we hypothesise that in relationships where customers perceive greater relational benefits they will be more loyal to the provider. Therefore: Hypothesis 7: Relational benefits positively influence customer loyalty. RESEARCH METHODOLOGY Measure Development 11

13 Variables for our model were operationalised on the basis of operationalisations in past research with some modifications and developments based on nine in-depth interviews with clients of market research agencies conducted in the exploratory phase of our study. With regard to the exogenous constructs, trust was measured on a combined scale developed from the scales of Doney and Cannon [1997] and Moorman et al. [1992], which we adapted on the basis of the in-depth interviews with clients. For measuring social bonds the scale of Mavondo and Rodrigo [2001] was used and adapted to the context of our study. For measuring adaptation the scale of Cannon and Perreault [1999] was used and adapted based on the findings of Brennan and Turnbull [1999], Halinen [1997] and the in-depth interviews with clients. We measured co-operation on a combined scale from Anderson and Narus s [1990] scale for co-operation and Cannon and Perreault s [1999] scale for information exchange, which we adapted to the context of our study. Since there was no explicit scale in the business relationship literature to measure knowledge transfers and since this is a typical industry-specific concept, the operationalisation of this concept was achieved on the basis of a review of conceptual definitions [McLoughlin and Horan, 2000; Håkansson and Snehota, 1995; De Ruyter and Semeijn, 2002] and adapted on the basis of the in-depth interviews with clients. As for the endogenous constructs, Kumar et al. s [1994] scale was used for affective commitment, while for relational benefits the scale was developed based on the findings of Lyons et al. [1990], Morgan and Hunt [1994] and Nowak et al. [1997] as well as on the indepth interviews with clients. Finally, customer loyalty was measured on a scale developed by Zeithaml et al. [1996]. For all scales the respondents were asked to express their agreement with a given statement using a seven-point Likert-type scale (from 1 = not at all true, to 7 = completely true). All variables were measured in a positive direction. In addition, we included questions about the 12

14 industry that the firm operates in, the firm s size, the duration of the relationship and the value share of the projects carried out with the market research agency that the questions referred to. After a scale refinement in line with five experts opinions the questionnaire was tested on ten members of the population. Data Gathering Data was gathered from managers responsible for market research in client firms. The context of market research was chosen because it provides a good representation of a specialised professional service industry [Boughton et al., 1996] and includes a wide continuum of relationships from transactions to partnerships, therefore providing the desired variability of relationships [Tellefsen and Thomas, 2005]. In addition, there has always been a solid agreement that the quality of services is inconsistent across market research firms [Dawson et al., 1994], which was a good reason to expect a wide range of opinions from the respondents. In order to ensure variability in the relationships included in the study the respondents evaluated their relationship with the agency that carried out their most recent market research, like in the Moorman et al. [1992] research. They were instructed to answer questions about the specific relationship and to keep in mind not only the last research but also the entire relationship they had experienced with that agency. The sample framework included 230 firms that were clients of market research agencies. Data collection started in March By the end of July 2005, 150 telephone interviews had been completed which means a response rate was 65.2 per cent. Due to the telephone interviewing we had greater control over which firms were included in the sample (firms that were indeed clients of market research agencies) and who our respondents were (employees who were the most knowledgeable about the 13

15 topic). Both the high response rate and control over the selection of respondents increases our confidence that we included the most relevant units. Sample Characteristics The majority of firms responding to the questionnaire were providers of business services (24.7 per cent), followed by manufacturing (23.3 per cent) and trading firms (22.0 per cent). The rest were providers of services for consumers (12.7 per cent), providers of services for both firms and consumers (11.3 per cent) and public organisations (6.0 per cent) per cent of the firms had up to 50 employees, 13.3 per cent had between 51 and 100 employees, 24.7 per cent had between 101 and 500 employees, while 21.3 per cent of the firms had 501 or more employees. Among the studied relationships (with the agency that performed the last market research) most relationships were two to four (42.0 per cent) and five or more (42.0 per cent) years old. Only 16.0 per cent of the relationships had lasted for less than two years. The average duration of a relationship was 4.4 years. We also examined what was the value share of projects done by the studied agency among all research projects carried out with outside market research providers. This share was 76.1 per cent, which means the majority of respondents had described their relationship with their most important market research provider. Data Analysis Prior to the LISREL analysis a set of items for each construct was examined in the pre-test using exploratory factor analysis to identify those items not belonging to the specified domain. We used principle axis factoring with an oblimin rotation. Items with a loading of 14

16 less than 0.50 and/or cross-loadings greater than 0.35 were discarded. The properties of the proposed research constructs were tested with structural equation modelling (SEM). The maximum likelihood (ML) method of estimation was adopted. The SEM procedure was appropriate to test the proposed theoretical model because it enabled us to evaluate how well a proposed conceptual model that contains observed variables and unobserved constructs explained or fitted the collected data [Bollen, 1989; Hoyle, 1995]. EMPIRICAL ANALYSIS AND RESULTS Measurement Model First we performed confirmatory factor analysis (CFA) to test the measurement model. We used the covariance matrix as an input to LISREL We trimmed the model by discarding items for each construct where necessary in order to ensure the best fitting model. In the final model there were three measurement variables for trust (measuring reliability and benevolence of service provider), two measurement variables for adaptation (measuring adaptation in different aspects of service delivery), three measurement variables for cooperation (measuring working together toward common goals), three measurement variables for knowledge transfers (measuring knowledge transfers in both directions), four measurement variables for affective commitment (measuring motivation to continue relationship due to liking and identification), two measurement variables for relational benefits (measuring what the customer gets out of the relationship) and three measurement variables for customer loyalty (measuring patronage behaviour and referrals). The measurement model has a statistically significant value of the chi-square test (χ 2 = , df = 202, p < 0.01). However, the proportion between the chi-square value and degrees of 15

17 freedom is within an acceptable range (χ 2 /df = 1.36). RMSEA (0.049) shows a good fit, while standardised RMR (0.056) reveals a reasonable fit. All other relevant measures (GFI = 0.862; AGFI = 0.811; NFI = 0.921; NNFI = 0.970; CFI = 0.976) are also in an acceptable range, which enables us to conclude that the fit of the measurement model is acceptable [Bollen, 1989; Hoyle, 1995]. We then tested the item and construct reliability (Table 1). All items are reliable and all values for composite reliability are above the critical limit (0.60). According to a complementary measure for construct reliability the average variance extracted (AVE) all constructs except co-operation (AVE = 0.42) and social bonds (AVE = 0.44) have good reliability. We also tested the model for convergent and discriminant validity. Convergent validity was assessed by examining the t-test values of indicator loadings in the measurement model [Anderson and Gerbing, 1988]. All t-values of the loadings of measurement variables on respective latent variables were statistically significant. Thus, convergent validity was supported. Discriminant validity was assessed with a chi-square test for pairs of latent variables with a constraining correlation coefficient between two latent variables (φ ij ) to 1 [Anderson and Gerbing, 1988]. All unconstrained models had a significantly lower value of chi-square than the constrained models, which enables us to conclude that the latent variables were not perfectly correlated and that discriminant validity exists [Bagozzi and Phillips, 1982]. Table 1 about here Data was also tested for common method bias [Podsakoff and Organ, 1986]. We tested the presence of common method bias using Harman s single factor test. We ran a confirmatory factor analysis loading all items on one factor and compared the model fit. The resulting one- 16

18 factor measurement model had much worse fit indices than the proposed measurement model. Common method bias is therefore not present. Structural Model The final structural equation model (Figure 2) includes exogenous latent variables trust, social bonds, adaptation, co-operation and knowledge transfers and endogenous latent variables affective commitment, relational benefits and customer loyalty. Affective commitment is explained by trust and social bonds (56.7 per cent of variance is explained), relational benefits are explained by co-operation, adaptation and knowledge transfers (altogether 30.7 per cent of variance is explained), and customer loyalty is explained by affective commitment and relational benefits (51.6 per cent of variance is explained). The dependent variables are therefore relatively well explained by the independent variables. The fit indices for the overall model are acceptable as well. Like with the measurement model, the structural model also has a statistically significant value of the chi-square test (χ 2 = , df = 213, p < 0.001), but the proportion between the chi-square value and degrees of freedom is within an acceptable range (χ 2 /df = 1.36). All other relevant fit indices are also within an acceptable range (RMSEA = 0.049; standardised RMR = 0.062; GFI = 0.855; AGFI = 0.812; NFI = 0.915; NNFI = 0.967; CFI = 0.973). Figure 2 about here The parameter estimates were statistically significant and consistent with the proposed direction in the hypotheses, except for co-operation. Therefore, all hypotheses about relationships among constructs that were tested in the final model, except the one regarding co-operation and relational benefits (H4), are supported. Trust and social bonds have a 17

19 moderately strong positive influence on affective commitment (standardised coefficient 0.53 for trust and 0.38 for social bonds), while the effect of knowledge transfers and adaptation on relational benefits is a little weaker (standardised coefficient 0.29 for knowledge transfers and 0.28 for adaptation) but still statistically significant. Affective commitment has a strong positive influence on customer loyalty (standardised coefficient 0.63), while relational benefits have a somewhat weaker (standardised coefficient 0.20) but still statistically significant influence on customer loyalty. Table 2 about here In the last step we applied Anderson and Gerbing s [1988] approach to testing nested models. As a comparison with the proposed (theoretical) model (M T ), we designed a constrained model (M C ) with an eliminated link between the two exogenous constructs (adaptation and co-operation) with the lowest correlation among the exogenous variables. On the other hand, we designed an unconstrained model (M U ) by adding a direct link between trust and customer loyalty that had been found by some researchers in business-to-business and business-toconsumer studies [Chow and Holden, 1997; Harris and Goode, 2004; Lin and Wang, 2006; Sirdeshmukh et al., 2002] so that affective commitment was not a fully mediating construct for trust. The fit of the more restricted model was significantly worse and the fit of the alternative less restricted model was not significantly better. Therefore, we can accept the proposed (theoretical) model (M T ) as being the most parsimonious structural model for the given data [Anderson and Gerbing, 1988]. 18

20 DISCUSSION AND IMPLICATIONS The aim of our study was to add to the body of knowledge on customer loyalty in the professional service sector in business-to-business markets. Our goal was to examine how three relationship components, i.e. actor bonds, activity links and resource ties, influence affective commitment and relational benefits (both posed as fully mediating constructs) as well as how these two constructs affect customer loyalty. The results are in line with our expectations for the effect of trust and social bonds on affective commitment, adaptation and knowledge transfers on relational benefits, and affective commitment and relational benefits on customer loyalty. On the other hand, the results are not in line with our expectations for the effect of co-operation on relational benefits. Our finding that customers affective commitment is positively dependent on both trust and social bonds confirms the findings of several other authors who have studied the relationships between affective commitment and trust (see, e.g., De Ruyter and Semeijn [2002], De Ruyter et al. [2001], Geyskens et al. [1996], Gounaris [2005] and Wetzels et al. [1998, 2000]) and commitment and social bonds (see, e.g., Mavondo and Rodrigo [2001]). Further, this finding brings us to the conclusion that emotional motivation for customer loyalty in business-tobusiness context is related to both firm-level (the provider s trustworthiness) and personallevel (social bonds between individuals from both firms) factors. The high path coefficient for trust confirms that on the firm level customers value relationships where detailed monitoring of the provider is not a must, where customers trust the provider s expertise and where they believe in the information provided. On the other hand, the emotional motivation for customer loyalty could be even further improved if trust at the firm level is upgraded with the personal- 19

21 level connectedness and friendship between individual employees from both firms who are directly involved in the relationship. As for the rational motivation for customer loyalty, the conclusion that adaptation positively influences relational benefits clearly indicates that customers appreciate if their provider is flexible in all phases of service delivery. Similarly, the positive influence of knowledge transfers on relational benefits indicates that customers appreciate it when their provider gives them directions for the future, acts as their consultant and shares the knowledge and experience with them. These findings are in line with the propositions of Cannon and Perreault [1999], Sharma and Sheth [1997], Wilson [1995] and Wilson and Jantrania [1994]. Although we hypothesised that the rational motivation for relationship continuance would also be influenced by the co-operation between the provider and the customer, the results did not confirm this. The reason for this may be that the co-operation itself, i.e. merely being included in joint meetings and developing new models without any short-term tangible positive results in terms of new knowledge and better performance, does not represent a rational motivation for the customer to be loyal to the provider. Customers therefore demand short-term tangible positive outcomes in order to stay in the relationship. Although both emotional and rational motivations for relationship continuance positively influence customer loyalty, our results clearly show that emotional motivation seems to be much stronger than rational motivation. This is in line with Sharma et al. s [2006] conclusion made on the basis of a literature review that affective commitment is stronger and plays a more important role than other types of motives in developing and maintaining long-term relationships. Affective commitment was also found to be the strongest motivator for customer loyalty in studies by De Ruyter et al. [2001], Gounaris [2005], Kumar et al. [1994] 20

22 and Wetzels et al. [1998]. Customers therefore seem to value the emotional we like more than the rational we benefit. This could mean two things: either that customers are not as rational as they could be or, much more likely, that reason-related distinctions among providers (the willingness to adapt and co-operate and the potential to offer new knowledge to customers) are minor, which forces customers to base their decision to stay or not to stay in the relationship primarily on emotional factors. Managerial Implications By proposing some interesting conclusions about customer loyalty factors we believe our study can serve as some kind of learning material for the professional service providers and hopefully lead to an improved quality of their services. Above all, the learning material can be found in our structural model that can serve as an additional tool for providers when assessing current relationships as well as when developing new potential relationships with their customers. More specifically, the managers of professional service providers can use our findings along four fronts. First, since customer loyalty depends more on emotional motivation in the form of affective commitment than on rational motivation in the form of relational benefits, managers should not forget about the emotional side of the relationship. In fact, emotions are what distinguish real relationships from transactions. As soon as emotions are included in the relationship between two firms the use of relationship marketing is required in order to chain customers or prevent them from switching providers. Second, the strong influence of trust on affective commitment (and through that on customer loyalty) suggests that managers should do everything in their power to make them trustworthy in the eyes of their customers. If in a dilemma as to whether to focus on the relationship or on 21

23 the provided service itself, the high level of importance of trust suggests that managers should focus primarily on relationship building. For example, should a failure as regards quality happen, managers should not focus on the costs of correcting the failure but on the total revenues and total costs generated from maintaining the high quality relationship. Therefore, instead of focusing on short-term gains professional service providers should focus on developing long-term advantages. If customers correctly perceive such providers relationship-oriented behaviour they will be more trustful and, as a result, more affectively committed and more loyal. Third, the positive influence of social bonds on affective commitment (and through that on customer loyalty) indicates that although relationships in the business-to-business context are between two firms, managers should not forget that there are individual employees that perform specific activities in the relationship. It is not so unusual that a relationship on the verge of break-up is saved by the personal friendship between the individuals from both parties. If a relationship is to succeed, people involved must be comfortable to work with each other. Therefore, a careful selection of individuals with proper social and other skills must be done before appointing them to manage relationships with customers. Furthermore, turnover among employees responsible for managing relationships with customers must be kept to a minimum. Any unnecessary turnover, in addition to all other problems, means that a part of knowledge about the customers will disappear and that establishing relationships will have to start practically from scratch. Finally, managers should not forget that relational benefits as the rational motivator for customer loyalty are important as well. In this sense our study can serve to increase management awareness about what contributes most to the relational benefits perceived by 22

24 the customers in business-to-business context. Obviously, the professional service providers should focus on being highly professional and flexible. While the former enables customers to learn from the provider the later increases the probability that customers specific needs are properly met in all phases of the collaboration. Without both customers will eventually loose the rational motivation to stay in the relationship. In sum, we believe that based on our findings a pattern of desirable strategic behaviour of professional service providers in business-to-business context can be recognized. Since customers seem to value the emotional we like more than the rational we benefit, professional service providers are advised to differentiate themselves by building unique strategic partnerships with their customers. A similar suggestion to professional service providers was also given by Boughton et al. [1996]. Differentiation strategy by building partnerships not only eliminates the problem of Selling only on price where s the fun in that?, as expressed by Anderson and Narus [1998: 65], but also allows providers to demonstrate that they have something different to offer when there is high market pressure on price. Following our empirical results, we propose these partnerships to be build around two key points. First, they should be highly personalised (people-oriented), and second, they should be perceived by customers as a flexible access to providers expertise. Building around both key points requires from providers to commit the best resources they possess to building and maintaining relationships with their customers. In most cases this requires directing most of the time and money as well as the most socially skilled employees into improving services for existing loyal customers rather that constantly searching for new customers for existing services. 23

25 Limitations and Opportunities for Future Research We are aware that using a construct of value instead of benefits in our study would give a more complete picture of the relationship. However, since value is a complex construct [Gao et al., 2005; Ulaga and Eggert, 2005, 2006] we found it difficult to include in our already complex model. If in the future researchers develop a similar but less complex (i.e. using less constructs) model in which the value construct is used instead of the benefits construct, it will be interesting to compare it with our model. Another limitation of our study is that we gathered data only from the customer s side because the study focused on the customer s loyalty. To get a more complete picture an opportunity for future research is to broaden the research scope by including views from both sides of the dyad. We are also aware that the ceteris paribus way of studying relationships [Anderson et al., 1994] gives only a partial image of the relationship, but the decision is a consequence of the lack of constructs that would measure the impact of other relationships on the focal relationship. Another reason for our approach is that during exploratory research interviewed managers of client firms reported that the focal relationship is only affected by the relationships with other market research agencies in such a way that they adjust their expectation level about the focal relationship based on the experience from other relationships. In addition, managers also reported that relationships with other firms, such as advertising agencies, competitors, suppliers etc., do not significantly affect the focal relationship. Nevertheless, in line with propositions of the IMP researchers future research could also examine how a focal relationship is embedded in a network of relationships. Finally, since our study is based in a relatively narrow context of market research industry another opportunity for future research is to test our model in other professional service industries. In this way we could be more confident to generalize our findings to professional service sector in general. 24

26 NOTES i Only a few researchers (e.g., De Ruyter and Semeijn [2002] and Kumar et al. [1994]) also discuss the third component of commitment, i.e. the normative (attachment due to felt obligations) component. Besides being less frequently discussed in the literature, another reason for not including the normative commitment in our model is that it has similar but much weaker effects as affective commitment [Kumar et al., 1994]. ii More specifically, the context of the study is the market research industry in a country of Central and Eastern European region. When in the paper a discussion is related to this specific context the terms client and (market research) agency are used. When on the other hand a discussion is related to the professional service sector as a whole or to even broader contexts the terms customer and (professional service) provider are used. 25

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