Commitment in marketing research services: two alternative models/Isipareigojimai atliekant rinkodaros tyrimus: du alternatyves modeliai.
Customers and customer relationships are perceived as the most important assets of business firms, closely related to long-term succes in the market (Korsakiene 2009). As a result, relationship commitment has been found to be the key component of establishing and maintaining long-term relationships between business partners (Dwyer et al. 1987; Morgan, Hunt 1994; Gundlach et al. 1995; Geyskens et al. 1996). Most researchers have studied commitment as a singular construct that measures the intention to continue the relationship; however, there have been some attempts to transfer findings from organizational psychology and study commitment as consisting of two or more components, namely affective, (positive and negative) calculative, and normative commitment (de Ruyter, Semeijn 2002; Sharma et al. 2006; Rauyruen, Miller 2007; Cater, Zabkar 2009). Such operationalization of commitment should contribute to enhancing the sensitivity of our research instruments and consequently to our understanding of the associations identified between the components of commitment, structural and social bonding mechanisms and outcomes (Kelly 2004). Past studies on commitment have primarily focused on affective and calculative commitment and generally not incorporated normative commitment in their analysis (with some exceptions, e.g., Bansal et al. 2004; Cater, Zabkar 2009; de Ruyter, Semeijn 2002; Kumar et al. 1994). If, on one hand, the literature addresses relatively well the link between the components of commitment, albeit rarely all of them simultaneously, and customer loyalty, limited evidence is available of how these components of commitment depend on other relationship characteristics. Therefore, there is a need for more research on distinguishing the different components of commitment and studying the links between the components of commitment and the variables representing the determinants (and consequences) of these components (Bansal et al. 2004; Sharma et al. 2006). Therefore, a contribution of this paper lies in the development and testing of a model that includes three components of commitment in a professional business services sector.
Since the mid-1970s, a variety of theoretical perspectives has been advanced to provide an understanding of marketing relationships and their components. The focus on relationships emerged from different marketing contexts and was developed within diverse research traditions (O'Malley et al. 2008; Pels et al. 2009). Marketing relationships in professional services have been studied according to two broad approaches: the Relationship Marketing (RM) approach and the Industrial Marketing and Purchasing (IMP) approach. The main differences between these two approaches are explained later in the paper. The purpose of this study is to add to the body of knowledge on client commitment in the professional service sector in business-to-business markets by developing, testing and comparing two alternative models of commitment between marketing research firms and their clients, with the first being based on the RM approach and the second on the IMP approach. We propose that actor bonds (as the focus of RM models) play an important role in explaining commitment but are not enough to paint a complete picture. For that, we also need activity links and resource ties. The contribution of this paper over previous studies of commitment is that it compares the two models in the same data-set, thus enabling a direct comparison of the explanatory power of the two alternative lines of research. We are well aware that comparing RM and IMP approaches may be problematic because they differ from the philosophy of research point of view (Easton 1995). RM researchers rely more on quantitative studies, while IMP researchers mostly use case studies. To ensure comparability of the influence of constructs that are used in both approaches on relationship commitment, this study uses structural equation modeling as the main research approach. Although this approach is more widely used in RM-based studies, it has also been used in IMP-based research (e.g., Hallen et al. 1991; de Ruyter, Semeijn 2002; Kalafatis 2002; Woo, Ennew 2004). The two models are compared on the basis of the overall model fit, explanatory power and significance of paths.
2. Conceptual framework and the development of the hypotheses
2.1. Comparison of the RM and IMP approach to studying marketing relationships
Pels et al. (2009) in their review of research approaches to studying marketing relationships point out that each tradition provides a particular and partial view of its focal phenomena, reliant both on its ontological and epistemological assumptions and the issues researchers have chosen to bring to the foreground. The RM approach originates from services marketing (Berry 1983), marketing channels research (e.g., Anderson, Narus 1984) and customer--supplier interaction (e.g., Dwyer et al. 1987), while the IMP approach has roots in the early study of purchasing in industrial markets (Hakansson 1982). Both approaches also borrow from other disciplines outside of marketing (Mattson 1997; Hakansson, Snehota 2000; Parvatiyar, Sheth 2000). In general, definitions of relationship marketing focus on relationship life cycle management from the point of view of the focal firm. The RM approach has had a normative purpose from the very beginning, while the IMP approach is more explorative and descriptive (Mattsson 1997; Hakansson, Snehota 2000). McLoughlin and Horan (2002) maintain that RM is a response to managerial requirements for a more competitive structure or more effective marketing investment.
A common axiom of RM is that cooperative relationships lead to greater value creation for both parties in the relationship (Parvatiyar, Sheth 2000). In RM the typical research questions address the supplier's interests. Researchers are particularly interested in how the outcomes of relationships are connected with commitment and trust. Researchers are also interested in perceived quality, customer satisfaction, customer retention and how to define and measure the effect of relationship marketing activities (Mattsson 1997). On the other hand, IMP researchers are more interested in conceptual questions: what are the relationships, how can we describe the interaction, what is the position of a firm within a network and how are firms and dyads embedded within a network. IMP researchers reject the variable-based approach to understanding social action that is a characteristic of RM and instead focus on the "space" that contains relationships of whatever kind. They focus on studying the structure and dynamics of the governance structure on the meso and macro level. Therefore, the conceptual, descriptive and measurement aspects of these levels, including embeddedness and time, are very important characteristics of this approach (Mattsson 1997; Hakansson, Snehota 2000; McLoughlin, Horan 2002). In line with this, the RM approach assumes that relationships can be established and discontinued at will, while according to the IMP approach relationships are enacted through the interaction of firms (McLoughlin, Horan 2002).
Based on the literature review, two conceptual models were built that include the typical concepts that researchers in RM and IMP lines of research use when studying marketing relationships. Both models encompass the same endogenous constructs (three components of commitment and two components of loyalty), while they differ in the exogenous constructs. When choosing possible antecedents we build on Maister's (2003) findings that in professional services clients focus more on the quality of services than on the quality of work due to the ambiguity that surrounds technical excellence and the difficulty the client has in evaluating it. Our objective is not to show the superiority of either of the models but to look into the relationships among the antecedents and consequences for the two alternative models. First, we define commitment and loyalty and propose hypotheses about how they relate to each other, followed by a presentation of the antecedents in both models.
2.2. Commitment and loyalty
The central construct of this study, i.e. commitment, is characterized by a disincentive to replace relationship partners (Young, Denize 1995). Different types of commitment have been identified in studies of interfirm relationships in business marketing contexts (Sharma et al. 2006). The three components of commitment that this study focuses on are: affective (attachment due to liking and identification), calculative or continuance (attachment due to instrumental reasons) and normative or moral (attachment due to felt obligations). All these components of commitment pertain to psychological states, yet they originate from different motivations for maintaining a relationship (Geyskens et al. 1996). Affective commitment means that firms want to stay in the relationship because they like their partner, enjoy the partnership and feel a sense of loyalty and belongingness. On the other hand, calculative commitment is the extent to which partners perceive the need to maintain a relationship due to the significant anticipated switching costs or lack of alternatives. Normative commitment means that partners stay in the relationships because they feel they ought to (Kumar et al. 1994; Geyskens et al. 1996; Bansal et al. 2004).
Although there is no agreement about the exact definition or nature of the customer loyalty concept, many of the loyalty definitions concur that there is a relationship of some sort between an actor and another entity and that the actor shows behavioral or psychological allegiance to that entity in the presence of alternative entities (Melnyk et al. 2009). Some authors (e.g., Zeithaml et al. 1996; Bolton et al. 2003; Woo, Ennew 2004) refer to a similar concept of behavioral intentions that include increasing patronage, renewing the contract and making recommendations. The majority of studies on loyalty have measured it through a composite mix of items that form different components of loyalty. However, according to Soderlund (2006) the "cocktail approach" should be avoided, meaning that repurchase intentions and word-of-mouth intentions should be considered as separate constructs. In line with several other authors (e.g., Chaudhuri, Holbrook 2001; Evanschitzky et al. 2006; Rauyruen, Miller 2007), we separately examine behavioral and attitudinal loyalty. While behavioral loyalty can be defined as the customer's willingness to continue a relationship with the supplier and repurchase the product, attitudinal loyalty is the level of the customer's attitudinal advocacy and psychological attachments to the supplier (Chaudhuri, Holbrook 2001; Rauyruen, Miller 2007).
Based on affective commitment, intentions to maintain and strengthen the relationship are developed (Kumar et al. 1994; Wetzels et al. 1998; de Ruyter et al. 2001; Rauyruen, Miller 2007). The emotional attachment of affective commitment translates into strong attitudinal loyalty and in customer patronage of the brand or the firm (Evanschitzky et al. 2006). The results of previous studies regarding the relationship between affective commitment and loyalty are summarized in Table 1. The effects of normative commitment (see Table 1) are consistent with affective commitment but weaker in their magnitude, as shown in a meta-analysis of studies on organizational commitment (Meyer et al. 2002). Because of all the positive experience they have had with the supplier, customers may feel obliged to stay with that firm (Bansal et al. 2004) and may be willing to recommend the brand or the firm to others. With regard to calculative commitment and its influence on attitudinal loyalty, previous studies report mixed results (see Table 1). One possible explanation of such results may be that an individual or firm with high calculative commitment may or may not like the supplier firm (Harrison-Walker 2001). In line with the above explanation, we propose a non-positive influence of calculative commitment on attitudinal loyalty. Finally, since calculatively committed customers continue the relationship because they see no better alternative, we propose a positive effect of calculative commitment on behavioral loyalty:
H1: The degree of affective commitment (a) positively influences the degree of attitudinal loyalty and (b) positively influences the degree of behavioral loyalty.
H2: The degree of normative commitment (a) positively influences the degree of attitudinal loyalty and (b) positively influences the degree of behavioral loyalty.
H3: The degree of calculative commitment (a) non-positively influences the degree of attitudinal loyalty and (b) positively influences the degree of behavioral loyalty.
2.3. Antecedents of commitment in the RM model
Based on a literature review, the antecedents of commitment included in our RM model are trust, social bonds and satisfaction (Fig. 1). These are also the key concepts in relationships in professional services (Maister 2003). Although these variables have often been used in marketing relationship research, except for trust not much is known about their relations with different components of commitment. The chosen antecedents are more related to the quality of service / relationship than to the quality of work which is difficult for the client to evaluate (Maister 2003). In the following paragraphs, the conceptual definitions of trust, satisfaction and social bonds are presented, followed by the hypotheses related to these three antecedents of commitment.
[FIGURE 1 OMITTED]
Trust is "the extent to which a firm believes that its exchange partner is honest and / or benevolent" or some variant thereof (Geyskens et al. 1998: 225). This study adopts the definition of Moorman et al. (1992: 82) who studied trust in relationships between clients and service providers in the marketing research context and define trust as "a willingness to rely on an exchange partner in whom one has confidence". According to their definition, trust is an expectation, belief or feeling about an exchange partner which can be concluded from the partner's expertise and reliability. Moorman et al. (1992) definition, similar to those of Morgan and Hunt (1994) and Doney and Cannon (1997), points to two components of trust: credibility and benevolence. Researchers often also use reliability and credibility / competence (Seppanen et al. 2007).
Several empirical studies have found a positive influence of trust on affective commitment and a negative influence on calculative commitment (e.g., Geyskens et al. 1996; de Ruyter et al. 2001; Gounaris 2005). Trust leads firms to focus on the "positive" motivation to stay in the relationship because of a feeling of connectedness and identification with each other (affective commitment) and less due to calculative reasons to stay with the supplier (calculative commitment) (de Ruyter et al. 2001). De Ruyter and Semeijn (2002) conceptualized commitment with three components and found a positive effect of trust on normative commitment in the context of international business relationships. We therefore propose that when actor bonds are established and trust increases, firms feel a sense of moral obligation to the counterpart they trust.
H4: A higher degree of trust fosters (a) higher affective commitment, (b) higher normative commitment, and (c) lower calculative commitment.
Closely related to trust is the human dimension, i.e. the interpersonal aspect, of the relationship (Maister et al. 2000). Social bonds are described as "the degree of mutual personal friendship and liking shared by the buyer and seller" (Wilson 1995: 339). In the context of business services, social bonds refer to personal contacts, liking and trust or to the human side of the business service (Thunman 1992). This study follows Wilson's (1995) definition and limits the concept of social bonds to friendship and liking between boundary personnel in client and supplier firms, although some researchers (McCall 1970; Wilson, Mummalaneni 1986; Perry et al. 2002) also include attachment, commitment and other concepts as social bonds.
Research shows that customers and suppliers who are bound by strong personal relationships are more committed to maintaining relationships than those without such relationships (Seabright et al. 1992; Wilson 1995; Barnes et al. 2005). We therefore propose that the stronger the social bonds between employees of the provider and the client, the more they are motivated to continue the relationship for affective reasons, such as liking and identification (affective commitment) and the more they feel obliged to continue the relationship (normative commitment). On the other hand, Wilson (1995) mentions that in a more complex buying situation, social bonds have no influence on commitment between customers and suppliers. It is very rare that a firm can justify bad decisions or poor performance on the basis of friendship between boundary personnel. When rational elements enter into the evaluation of the relationship, social bonds have no influence on commitment. This is also in accordance with the conceptual definition of calculative commitment that is associated with the perceived cost of discontinuing a relationship and with the perception that there is a lack of alternatives available (Meyer, Allen 1997; Meyer, Herscovitch 2001). Therefore, we propose there is no relationship between social bonds and calculative commitment.
H5: A higher degree of social bonds fosters (a) higher affective commitment and (b) higher normative commitment.
There is perhaps no more valuable asset that a professional services firm has than the satisfaction of its clients (Maister 2003). Two ways to conceptualize satisfaction exist in the literature: service encounter satisfaction and overall or cumulative satisfaction (Johnson et al. 1995). This study focuses on overall satisfaction that "tends to sum up all the past service exchanges experienced by customers and is therefore seen as a main consequence of product / service attribute valuations" (Aurier, N'Goala 2010: 308-309). Satisfaction includes economic and non-economic components: economic components are related to the economic rewards from the relationship such as sales volume and margins, while non-economic components are related to the non-economic, psychosocial aspects of the relationship (Geyskens et al. 1999).
Several authors (e.g., Halinen 1997; Tellefsen 2002; Abdul-Muhmin 2005) have observed the positive influence of satisfaction on commitment. We propose a positive influence of satisfaction on affective commitment; that is, in relationships with high satisfaction, firms are more motivated to continue the relationship due to liking and identification (Wetzels et al. 1998; Beatson et al. 2006). Based on the findings that affective and normative commitment have similar patterns of connections with antecedents and consequences (Kumar et al. 1994; Meyer et al. 2002), a positive influence of satisfaction on normative commitment is expected. The logic behind this assumption is that satisfied clients should feel a higher moral obligation to continue the relationship with the provider they are satisfied with. In addition, we propose a negative relationship between satisfaction and calculative commitment. In a similar manner as for trust, we propose that when satisfaction increases, firms make a direct comparison of the pros and cons of the relationship less frequently, and a lower level of calculative commitment thereby results, in contrast to Wetzels et al. (1998) who found a positive influence of satisfaction on calculative commitment.
H6: A higher degree of satisfaction fosters (a) higher affective commitment, (b) higher normative commitment, and (c) lower calculative commitment.
2.4. Antecedents of commitment in the IMP model
Our second model largely builds on the IMP group's ideas, proposing an interaction approach to relationships. The IMP researchers identify three layers of relationship (Hakansson, Snehota 1995): actors, activities and resources. A relationship between two firms therefore has a profile in terms of actor bonds, activity links and resource ties. Actor bonds link actors (firms and individuals) and affect how actors perceive each other and develop their identities. 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. 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 (Hakansson, Snehota 1995). On the basis of a literature review on marketing relationships in services (Halinen 1997; Purchase, Olaru 2004; Woo, Ennew 2004), we use trust as a concept representing actor bonds, adaptation as a concept measuring activity links and knowledge transfers as a concept representing resource ties. In any research project, both clients and research firms want their research experience to include accessibility and responsiveness, knowledge and risk reduction (Latta, Schwartz 2004). In addition, a professional firm should demonstrate a willingness to be responsive and adaptable in order to win the confidence of today's client (Maister 2003). Therefore, a conceptual model was built that includes trust, adaptation and knowledge transfers positioned as antecedents of affective, calculative and normative commitment (Fig. 2). In the following paragraphs, conceptual definitions of adaptation and knowledge transfers and the development of the hypotheses for these two constructs are presented.
Adaptation refers to "behavioral 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, Turnbull 1998: 31). Adaptation occurs when one party in the relationship adapts its processes or the product to another party (Hakansson 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 client's marketing strategy and task execution. It can also relate to personal relationships, knowledge and roles as well as positions in the relationship.
[FIGURE 2 OMITTED]
Adaptation positively influences commitment (Hakansson, Snehota 1995; Brennan, Turnbull 1999). To our knowledge, only de Ruyter and Semeijn (2002) empirically examine the influence of adaptation on different components of commitment and find a positive influence of adaptation on affective commitment. Based on this finding and building on Hallen et al. (1991) discussion that adaptation promotes a closer relationship between customer and supplier, this article proposes a positive influence of adaptation on affective commitment. But adaptation can also influence other aspects of commitment. De Ruyter and Semeijn (2002) could not find support for a positive influence of adaptation on normative commitment. The explanation could be that there are mostly minor adaptations, which only create limited value. We therefore propose that in professional services, adaptation should not result in developing a moral obligation to the supplier and hypothesize the non-positive influence of adaptation on normative commitment. As for the calculative commitment, Cannon and Perreault (1999) point out that adaptation reflects a feature of calculative commitment to the relationship, but there is no direct support for this relationship in the literature. On the basis of conceptual definitions of commitment components and consistently with the previously stated hypotheses regarding calculative commitment in the RM model, we propose a negative relationship between adaptation and calculative commitment.
H7: A higher degree of adaptation fosters (a) higher affective commitment and (c) lower calculative commitment. The degree of adaptation is (b) nonpositively related to normative commitment.
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, Horan 2000). Knowledge transfer is defined as the act of moving knowledge from one entity to another in an optimal and reliable manner (Geraghty, Desouza 2005). Relationships present an important tool for connecting the knowledge of different actors (Hakansson, 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, Semeijn 2002).
The limited empirical support for the influence of knowledge transfers on customer commitment includes a study by de Ruyter and Semeijn (2002) who find a positive influence of resource ties on calculative commitment and Bond et al. (2008) who find an indirect effect of knowledge-transfer benefits on affective commitment. Hakansson and Waluszewski (1997) and de Ruyter and Semeijn (2002) maintain that resource ties make manufacturing firms mutually dependent and thus dissolution of the relationship may be very disruptive to them. However, on the basis of conceptual definitions of commitment components and consistent with the previously stated hypotheses in this paper regarding calculative commitment, we propose a negative relationship between knowledge transfers and calculative commitment. Further, we propose that knowledge transfers positively influence affective commitment. Since knowledge transfers include closer (personal) relationships between clients and providers, dissolution of the relationship can be very disruptive to them also in the emotional sense. Finally, in line with de Ruyter and Semeijn (2002) this article also proposes the absence of an influence of knowledge transfers on normative commitment.
H8: The degree of knowledge transfers fosters (a) higher affective commitment, and (b) lower calculative commitment.
3. Research design
3.1. Measurement development
Scales for the concepts were developed on the basis of operationalizations from past research. As for the constructs that were the same in both models, Kumar et al. (1994) scale was used for the components of commitment, while client loyalty was measured on a scale developed by Zeithaml et al. (1996). Trust was measured on a combined scale developed from the scales of Moorman et al. (1992), Doney and Cannon (1997) and Gounaris and Venetis (2002). In the RM model, Mavondo and Rodrigo's (2001) scale for social bonds and Lam et al. (2004) scale for satisfaction were adapted to the context of this research. For measuring adaptation in the IMP model, the scale of Cannon and Perreault (1999) was modified based on the findings of Halinen (1997) and Brennan and Turnbull (1999). Scales were further modified and adapted based on in-depth interviews with nine clients of marketing research providers from diverse industries. Since there was no explicit scale in the marketing relationship literature to measure knowledge transfers, the operationalization of this concept was achieved on the basis of a review of conceptual definitions (Hakansson, Snehota 1995; McLoughlin, Horan 2000; de Ruyter, Semeijn 2002) and the in-depth interviews with clients. All variables except one (a variable for social bonds) were measured in a positive direction. The variable with a negative direction was reverse scored in the consequent analysis. After a scale refinement in line with the opinions of five experts, the questionnaire was further tested on ten clients of marketing research providers.
3.2. Data gathering
The context of marketing research was selected because it provides the desired variability of relationships (Tellefsen, Thomas 2005) and a good representation of a specialized professional service industry (Boughton et al. 1996). Data were gathered from managers responsible for marketing research in client firms in Slovenia. The respondents evaluated their relationship with the research firm that carried out their most recent research project which should ensure variability in the marketing relationships included in the survey. They were instructed to answer questions about the specific relationship with regard not only to the last research but the total relationship they had had with that provider.
The sample frame included referred firms on marketing research firms' websites as well as firms similar to those by size and industry. The precondition for inclusion in the survey was that a firm had ordered at least one research project from a marketing research provider in the two previous years. An e-mail with an invitation was sent to 500 addresses and data were later gathered through telephone interviews. Out of the 500 firms contacted, only 230 fulfilled the conditions for inclusion in the survey (they had ordered research from marketing research firms in the last two years). Telephone interviews with 150 respondents were completed, with a response rate of 65.2%. Telephone interviewing enabled control over the relevancy of respondents and firms included in the sample.
3.3. Sample characteristics
The majority of firms in the final sample were providers of business services (24.7%), manufacturing firms (23.3%) and trading firms (22.0%). According to size, 40.7% of the firms had up to 50 employees, 13.3% had between 51 and 100 employees, 24.7% had between 101 and 500 employees, while 21.3% of the firms had 501 or more employees. On average, they had worked with the examined research firm for 4.4 years; with 84.0% of the firms having worked with this research provider for over two years. Therefore, we can be confident that an insignificant proportion of firms would have based their judgment of the relationship on just one transaction. Based on the value share of projects undertaken by the studied research firms in the relationship, the majority of respondents described their relationship with their most important marketing research provider.
3.4. Data analysis
Before conducting the structural equation modeling (SEM) analysis, a set of items for each construct was examined using exploratory factor analysis to identify those items not belonging to the specified domain. The properties of the proposed research constructs in the proposed models were tested with SEM using the maximum likelihood method of estimation. When testing the structural model, we added error covariances between the components of commitment and between the two components of loyalty as these are relationships without interest to this article but they could exist in the model. We assumed that the dimensions could be related to other common causes not captured in our model (Lam et al. 2004).
4. Empirical analysis and results
4.1. Measurement models
First we performed a confirmatory factor analysis (CFA) to test the measurement models. We used the covariance matrix as an input to LISREL 8.72. Although we had used some previously validated scales, certain items turned out problematic, presumably due to translation or cultural differences. Therefore, we trimmed the model by discarding the problematic items for each construct. Retained measurement variables and the proposed constructs are shown in Table 2 (RM model) and Table 3 (IMP model). The only problematic variable was calculative commitment, where: (1) average values for measuring variables on the seven-point Likert-type scale were very low, indicating its low presence; and (2) exploratory factor analysis revealed two dimensions. We decided to use only one indicator that had the highest average value as a representative measure of this construct.
The goodness-of-fit indices for the CFA for both models were within an acceptable range (Bollen 1989). For the RM model, measures of absolute fit ([chi square] = 254.38, df = 203, p = 0.008; [chi square]/df = 1.25, RMSEA = 0.04, SRMR = 0.06 and GFI = 0.87) indicated a good fit, as well as incremental fit measures (NFI = 0.95, NNFI = 0.98, AGFI = 0.82) and parsimonious fit measures (CFI = 0.99). The same could be said for the IMP model ([chi square] = 218.72 df = 143, p = 0.000; [chi square]/df=1.53, RMSEA = 0.06, SRMR = 0.07, GFI = 0.87, NFI = 0.92, NNFI = 0.96, AGFI = 0.81, CFI = 0.97), although these measures indicate a slightly worse fit of the measurement model.
We then tested the item and construct reliability (Table 2 and 3). All values for composite reliability were above the critical limit (0.60). According to a complementary measure for construct reliability, average variance extracted (AVE), all constructs except social bonds (AVE is 0.45) and behavioral loyalty (AVE is 0.49) demonstrated good reliability. We also tested the model for convergent and discriminant validity. All the t-values of the loadings of measurement variables on respective latent variables were statistically significant and above 0.5 (Anderson, Gerbing 1988). Thus, convergent validity was supported. Discriminant validity was assessed with the approach proposed by Fornell and Larcker (1981). For all pairs of latent variables, except for the pair attitudinal and behavioral loyalty, the values of AVE were greater than the square of the correlation between the latent variables. Nevertheless, we decided to use attitudinal and behavioral loyalty as separate constructs and not as one composite measure of loyalty (as suggested by Soderlund 2006).
4.2. Structural models and hypotheses testing
The two alternative models that apply to antecedents and consequences of commitment in marketing research services are compared for model fit, explanatory power and path coefficients (Hair et al. 1995). Table 4 summarizes the degree of fit and explanatory power for both models. In view of the fit indices ([chi square]/df RMSEA, SRMR, CFI) both models fit the data reasonably well. Also, the fit statistics for both models are comparable.
It seems that the influences between the common antecedent (trust), components of commitment and common consequences (attitudinal and behavioral loyalty) are equivalent in both models. In both models, the influence of affective commitment on attitudinal and behavioral loyalty is significant (H1a and H1b are supported), while the path coefficients for normative and calculative commitment on attitudinal and behavioral loyalty are not significant (H2a, H2b and H3b are not supported, while H3a is supported). Trust positively influences affective and normative commitment (H4a and H4b are supported); while the path coefficients for trust on calculative commitment are not significant (H4c is not supported).
With respect to the hypothesized theoretical structure, not all the parameter estimates are significant. For instance, the RM model has an insignificant path coefficient for social bonds on normative commitment and for satisfaction on normative and calculative commitment (H5b, H6b and H6c are not supported). In the IMP model, a higher degree of adaptation has a significant (negative) influence only on the degree of calculative commitment (lending support to H7b and H7c, but not to H7a). Also, the path coefficient for the effect of knowledge transfers on calculative commitment is not significant (H8b is not supported). On the other hand, all significant relationships in the two models point in the expected direction (H5a and H6a are supported in RM model as well as H8a in IMP model). Standardized path coefficients that are significant, support the hypothesized relationships in both models, although the effect sizes (e.g., for trust on affective and normative commitment) differ between the two models. Thus, according to the IMP perspective, a higher degree of actor bonds and resource ties increase affective commitment, while higher degrees of activity links among partners significantly reduces calculative commitment. Alternatively, according to the RM perspective, a higher degree of trust positively influences normative commitment, whereas higher degrees of satisfaction and social bonds together with trust positively influence affective commitment.
In terms of the explanatory power of the different components of commitment, the relationship marketing (RM) model has greater explanatory power than the IMP model for affective commitment. For calculative commitment, the explanatory power is higher in the IMP model yet low in both models. Normative commitment, on the other hand, has approximately the same explanatory power in both models. The same is true also for attitudinal and behavioral loyalty. This means that both models perform comparably well in predicting loyalty based on elements of commitment.
Next, a series of parsimonious fit measures was used to compare the two models with different degrees of freedom: parsimonious NFI, parsimonious GFI and the Akaike Information Criterion (AIC). According these indices we cannot claim that any of the models has better fit and greater parsimony. The two models are parsimonious and contribute to distinguishing the different components of commitment and better understanding the links between these components, their determinants and consequences. A more detailed discussion of the contribution of the two models to the understanding of client commitment and loyalty follows.
5. Conclusions and implications
5.1. Theoretical contributions
From a theoretical standpoint, this research contributes by developing, testing and comparing two three-component models of customer commitment in professional services, with the first being based on the RM approach and the second on the IMP approach. Previous studies on commitment have focused on one line of research only. The main contribution of this paper over previous studies of commitment is that it is the first study that compares the two models in the same data-set, thus enabling a direct comparison of the explanatory power of the two alternative lines of research. Both models deal with the three components of commitment and set out to explain loyalty in marketing relationships, but differ in the antecedents to commitment. According to the RM model, trust, social bonds and satisfaction are crucial to commitment. The IMP model differs from the RM model in the way that activity links (adaptation) and resource ties (knowledge transfers) are seen as antecedents to commitment in addition to actor bonds (trust). The comparison of the two alternative models should provide a good basis for studies of the antecedents of commitment in the business-to-business context.
Further, the presentation of relationships in integrative models should provide us with a richer insight into how commitment refers to its consequences. In our case, the two alternative models do not differ in their explanatory power of attitudinal and behavioral loyalty as the consequences of commitment. Therefore, both models contribute to our understanding of marketing relationships: (1) by identifying the interactions among actors; and (2) by tracing the sources for relationship development from the two perspectives, with both predicting the amount of variance in attitudinal and behavioral loyalty at a comparable level.
This research also helps elaborate upon existing theory to develop an understanding of the theoretical linkages between the antecedents of commitment and three components of commitment. In most of the previous studies, commitment has been studied either as a singular construct or primarily with two compoments (affective and calculative). In this study we developed and tested additional theoretical linkages between the antecedents of commitment and the normative component of commitment. This study suggests that affective commitment has the dominant mediating role in understanding customer loyalty, while calculative and normative type of motives seem to be too weak to have a significant influence on a client's repurchase intentions and word-of mouth in the examined context.
5.2. Managerial implications
Besides offering theoretical contributions, this study can also serve as some kind of learning material for professional service providers to help them improve the quality of their services and increase commitment of their clients. Firms have to make customer relationship management a top priority in order to gain competitive advantages in today's turbulent environment (Tamosiuniene, Jasilioniene 2007). Relationship commitment is one of the key strategic issues for managers when establishing and maintaining long-term relationships with their clients. To create competitive advantages of their firms, managers must constantly try to increase commitment and thus client retention and profitability within their markets by consciously managing each component of commitment (Kelly 2004). More specifically, the managers of professional service providers can use our findings along four fronts.
First, both models show that marketing research providers can rely mostly on the development of affective commitment to generate loyal clients. This suggests that client loyalty depends more on emotional motivation in the form of affective commitment than on rational motivation in the form of calculative commitment or moral motivation in the form of normative commitment. Managers should therefore strongly consider the emotional side of the relationship. In fact, emotions are what distinguish "real relationships" from "transactions". With the identification of the main variables of affective commitment development a firm can elaborate the experience and thus adopt an effective strategy of building a strong base of loyal clients.
Second, the strong influence of trust on affective commitment (as found in both models) suggests that managers of professional service firms should do everything in their power to make them trustworthy in the eyes of their clients. They need to prove to their clients that they do not have to be monitored on a regular basis, that their clients can let them make important decisions without getting frequently involved and that their clients can trust that the research firm will plan the research with expertise. To be able to do this, managers must focus on the relationship as a whole (which may result in long-term advantages) instead of on the provided service itself (which usually only brings short-term gains). If clients correctly perceive such providers' relationship-oriented behavior they will be more trustful and, as a result, more affectively committed and more loyal.
Third, besides the positive influence of trust on affective commitment the RM model also shows positive effects of satisfaction and social bonds on affective commitment. The high coefficient for satisfaction indicates that client overall satisfaction is the most important determinant of affective commitment. More satisfied clients stay because they like their partner and enjoy the partnership. They generally do not feel any obligations to stay and do not perceive a significant lack of alternatives. Research firms should therefore carefully track client satisfaction levels and also identify the determinants of client satisfaction. As Jasilioniene and Tamosiuniene (2009) point out, consistently great service is needed to generate customer loyalty, but only one bad interaction is enough to create dissatisfaction and customer loss. On the other hand, the positive effect of social bonds indicates that although relationships in the business-to-business context are between two firms, managers should not forget that there are individual employees who perform specific activities in the relationship. The management of interpersonal relationships is therefore important for the development of a marketing relationship (Halinen 1997). If a relationship is to succeed, the people involved must be comfortable working with each other. Therefore, individuals with proper communication and other social skills must be carefully selected before appointing them to manage relationships with clients. Further, turnover among employees responsible for managing relationships with clients must be kept to a minimum. Any unnecessary turnover, in addition to all other problems, means that some knowledge about the clients will disappear and that the establishing of relationships will have to start again practically from scratch. As Maister (2003) points out, the ability to attract, develop, retain and deploy staff is and will remain the major determinant of a professional service firm's competitive success.
Fourth, the IMP model shows that the inclusion of activity links (adaptation) and resource ties (knowledge transfers) in the picture can offer additional insights allowing a better understanding of the antecedents of client commitment and loyalty. Namely, besides the positive influence of trust on affective commitment, the IMP model also indicates positive effects of knowledge transfers on affective commitment and negative effects of adaptation on calculative commitment (indicating that clients are less focused on the lack of alternatives if the provider is more adaptive). Managers of marketing research firms can therefore increase their clients' commitment and loyalty by being ready to adapt to clients' needs as well as by providing relevant information to support (i.e. help remove some of the uncertainty in) clients' decision-making. The latter not only includes an objective presentation of research findings but also recommendations based on reports, more in the direction of strategic support from the research firms, which seems to be even more important in more complex research projects. Marketing research firms can also organize free internal seminars for a client organization where the research provider's staff can not only transfer their knowledge to the client's employees, but also demonstrate their expertise, meet the client's employees and develop social bonds with them. Our findings based on the IMP model are important because they indicate that relationships do not only survive on more emotional concepts such as trust and social bonds. Obviously, professional service providers should also focus on being highly professional and flexible. While the former enables clients to learn from the provider, the latter enhances the probability that clients' specific needs are properly met in all phases of the collaboration. Without both, clients will eventually lose the motivation to stay in the relationship.
5.3. Limitations and recommendations for future research
This research focuses on the client view of the relationship and the data were gathered from the client's side of the relationship. To paint a more complete picture, future research could broaden the research scope by including views from both sides of the dyad. In our case, the very limited number of marketing research providers in the investigated market makes it practically impossible to include both sides of the dyad. The ESOMAR Directory of Research Organizations contains a list of over 1.600 research organizations worldwide and only a few of them are present in Central and Eastern Europe (in most countries there are less than 20). High growth rates and the specific competitive situation make this area interesting for our study. However, firms operating in countries with a larger marketing research supplier base may perceive the selection of marketing research providers and relationships with them differently.
Further, the population size and consequently the sample size limited the number of variables that we could include in the models. Several more constructs (e.g., Hakansson, Snehota 1995; Palmatier et al. 2006) could be included in our models if the sample size were larger. Regarding the constructs and hypotheses testing, nine out of nineteen hypotheses were not supported. These are all hypotheses relating to calculative and normative commitment. Since some of them have not been tested extensively in past studies and not much is known about these relationships, further study is called for to contribute to theory development in this area. Even though our proposed models demonstrate a good fit with the data, we recognize that results could be specific to our particular sample. Therefore, future research should provide a cross-validation with the same instruments and other industry samples to validate our findings and to check if the models fit beyond the marketing research sample used in this study. Future research should also undertake qualitative interviews to further explore the normative and calculative components of commitment, improve their measurement and seek to identify their antecedents for both approaches (RM and IMP).
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Barbara Cater (1), Vesna Zabkar (2), Tomaz Cater (3)
Faculty of Economics, University of Ljubljana, Kardeljeva pl. 17, SI-1000 Ljubljana, Slovenia
E-mails: (1) email@example.com (corresponding author); (2) firstname.lastname@example.org; email@example.com
Received 27 January 2011; accepted 25 May 2011
Barbara CATER is an Assistant Professor of Marketing at the Faculty of Economics, University of Ljubljana, Slovenia. Her research interests lie in marketing relationships, professional services and business-to-business marketing. Her work appeared in several international journals, including Industrial Marketing Management, Journal and Business & Industrial Marketing, Transformations in Business and Economics, and The Service Industries Journal.
Vesna ZABKAR is a Professor of Marketing and Head of the Institute for Marketing at the Faculty of Economics at the University of Ljubljana. She is the author and co-author of several articles published in professional and scientific journals in Slovenia and internationally, among which are Journal of Advertising Research, Journal of Marketing Management, Industrial Marketing Management and International Marketing Review. Her research interests involve marketing relationships, marketing communications and business-to-business marketing.
Tomaz CATER is an Associate Professor of Management, Head of the Department of Management and Organisation, and Director of the Sport Management master program at the Faculty of Economics, University of Ljubljana, Slovenia. He has been involved in a number of research projects related to relationship-based competitive advantage, corporate and business strategies, and environmental strategies. His work has been published in several international journals, including Industrial Marketing Management, Journal of Business and Industrial Marketing, Transformations in Business and Economics, and The Service Industries Journal.
Table 1. Support for examined relationships between components of commitment and loyalty Relationship Support in the literature Affective commitment-- Positive link: Harrison-Walker (2001), attitudinal loyalty Fullerton (2005c), Evanschitzky et al. (2006), Rauyuren, Miller (2007) Affective commitment-- Positive link: Kumar et al. (1994), behavioral loyalty Fullerton (2005b), Evanschitzky et al. (2006), Jones et al. (2007) Calculative commitment-- Positive link: Evanschitzky et al. (2006) attitudinal loyalty Negative link: Fullerton (2005c), Bloemer, Odekerken-Schroder (2007) No link: Harrison-Walker (2001), Rauyruen, Miller (2007) Calculative commitment-- Positive link: Wetzels et al. (1998); de behavioral loyalty Ruyter et al. (2001), Evanschitzky et al. (2006), Jones et al. (2007) Negative link: Gounaris (2005); Bloemer, Odekerken-Schroder (2007) No link: Kumar et al. (1994), Fullerton (2005a), Rauyruen, Miller (2007) Normative commitment-- Positive link: Bloemer, attitudinal loyalty Odekerken-Schroder (2007) Normative commitment-- Positive link: Kumar et al. (1994); behavioral loyalty Bansal et al. (2004), Bloemer, Odekerken- Schroder (2007) Table 2. Overall CFA for the modified measurement model based on the RM approach (n = 150) Completely Construct AVE standardized and and Constructs and indicators loading indicator error (t-value) reliability variance 1 2 3 4 Trust (EX)(a) 0.83 0.62 I can let my researcher make 0.88 (std.) 0.78 0.22 important research decisions without my involvement. I would be willing to trust 0.82 (11.60) 0.67 0.33 my researcher to get the job done right without monitoring. I can trust that the agency 0.63 (8.29) 0.40 0.60 will plan the research with expertise. Social bonds (EX) 0.71 0.45 Our contact person and I are 0.78 (std.) 0.62 0.38 able to talk openly as friends. We talk only about business 0.65 (6.10) 0.42 0.58 matters (R). I know his / her life 0.57 (5.64) 0.33 0.67 outside work. Satisfaction (EX) 0.95 0.79 In general, our firm is very 0.92 (std.) 0.84 0.16 satisfied with the services offered by this agency. Overall, our firm is very 0.90 (17.99) 0.81 0.19 satisfied with its relationship with this agency. Overall, this agency is a 0.93 (19.89) 0.86 0.14 good firm to do business with. Overall, the service of this 0.89 (17.58) 0.79 0.21 agency comes up to our expectations. We think we did the right 0.80 (13.79) 0.65 0.36 thing when we decided to use this agency. Affective commitment (ED)(b) 0.85 0.59 It is pleasant working with 0.76 (std.) 0.57 0.43 the agency, that's why we continue to work with them. Our decision to remain a 0.70 (8.50) 0.49 0.51 client of this firm is based on our attraction to the things the agency stands for as a firm. We want to remain a client 0.87 (10.64) 0.76 0.24 of this agency because we genuinely enjoy our relationship with the agency. Because we like working with 0.74 (8.99) 0.55 0.45 the agency we want to remain their client. Calculative commitment (ED) 1.00 1.00 It is too difficult to 1.00 1.00 0.00 switch to another agency because of the lack of good alternatives; therefore we are staying with the agency; otherwise we'd consider leaving. Normative commitment (ED) 0.77 0.54 Employees who work with the 0.73 (std.) 0.53 0.47 agency would feel guilty if we dropped them as a supplier. We feel a sense of duty to 0.88 (7.14) 0.78 0.22 remain a client to this agency. Even if it were to our 0.55 (6.11) 0.31 0.69 firm's advantage, we feel it would be dishonorable if we were to leave the agency. Attitudinal Loyalty (ED) 0.88 0.78 I say positive things about 0.88 (std.) 0.78 0.22 this agency to my colleagues in other firms. I recommend this agency to 0.89 (13.30) 0.79 0.21 colleagues who seek my advice. Behavioral Loyalty (ED) 0.65 0.49 This agency is our first 0.85 (std.) 0.71 0.29 choice for marketing research services. It is probable that our firm 0.52 (5.38) 0.27 0.73 will increase business with this research agency in the following few years. Notes: (a) EX = exogenous construct. (b) ED = endogenous construct Table 3. Overall CFA for the modified measurement model based on the IMP approach (n = 150) Completely Construct AVE Constructs and indicators standardized and and loading indicator error (t-value) reliability variance 1 2 3 4 Trust (EX)(a) 0.83 0.62 I can let my researcher make 0.93 (std.) 0.87 0.13 important research decisions without my involvement. I would be willing to trust 0.76 (10.50) 0.59 0.42 my researcher to get the job done right without monitoring. I can trust that the agency 0.64 (8.48) 0.41 0.59 will plan the research with expertise. Adaptation (EX) 0.83 0.62 The agency adapts to our 0.83 (std.) 0.69 0.31 needs and requests when planning the research. The agency adapts to our 0.80 (9.80) 0.64 0.36 needs and requests when preparing the form of research report. This agency adapts to us 0.72 (8.93) 0.53 0.47 regarding deadlines for research execution. Knowledge transfers (EX) 0.68 0.51 We learn a lot about 0.72 (std.) 0.52 0.49 research from this agency during the research project. The agency gives us 0.71 (5.79) 0.51 0.49 directions for the future on the basis of conducted research. Affective commitment (ED)(b) 0.85 0.59 It is pleasant working with 0.76 (std.) 0.57 0.43 the agency, that's why we continue to work with them. Our decision to remain a 0.70 (8.37) 0.49 0.51 client of this firm is based on our attraction to the things the agency stands for as a firm. We want to remain a client 0.86 (10.35) 0.74 0.26 of this agency because we genuinely enjoy our relationship with the agency. Because we like working with 0.73 (8.83) 0.54 0.46 the agency we want to remain their client. Calculative commitment (ED) 1.00 1.00 It is too difficult to 1.00 1.00 0.00 switch to another agency because of the lack of good alternatives; therefore we are staying with the agency; otherwise we'd consider leaving. Normative commitment (ED) 0.77 0.54 Employees who work with the 0.72 (std.) 0.52 0.48 agency would feel guilty if we dropped them as a supplier. We feel a sense of duty to 0.89 (7.06) 0.80 0.20 remain a client to this agency. Even if it were to our 0.55 (6.11) 0.30 0.70 firm's advantage, we feel it would be dishonorable if we were to leave the agency. Attitudinal Loyalty (ED) 0.86 0.75 I say positive things about 0.87 (std.) 0.75 0.25 this agency to my colleagues in other firms. I recommend this agency to 0.87 (11.70) 0.75 0.25 colleagues who seek my advice. Behavioral Loyalty (ED) 0.66 0.50 This agency is our first 0.82 (std.) 0.67 0.33 choice for marketing research services. It is probable that our firm 0.57 (5.79) 0.32 0.68 will increase business with this research agency in the following few years. Notes: (a) EX = exogenous construct. (b) ED = endogenous construct. Table 4. Overall fit indices, path coefficients and explanatory power of the models Fit indices RM model [chi square] (P, df) 264.66 (P = 0.01, df = 210) [chi square]/df 1.26 RMSEA 0.04 SRMR 0.06 CFI 0.99 NFI 0.95 NNFI 0.98 GFI 0.87 AGFI 0.82 PNFI 0.79 PGFI 0.66 AIC 396.66 Path coefficients (t-value) RM model Affective commitment--Attitudinal loyalty 0.75 (7.47) * Normative commitment--Attitudinal loyalty -0.15 (-1.80) Calculative commitment--Attitudinal loyalty -0.02 (-0.25) Affective commitment--Behavioral loyalty 0.63 (5.80) * Normative commitment--Behavioral loyalty -0.03 (-0.25) Calculative commitment--Behavioral loyalty 0.01 (0.10) Trust--Affective commitment 0.26 (2.15) * Trust--Normative commitment 0.34 (1.97) * Trust--Calculative commitment 0.13 (0.82) Social bonds--Affective commitment 0.21 (2.35) * Social bonds--Normative commitment 0.05 (0.42) Satisfaction--Affective commitment 0.42 (3.35) * Satisfaction--Normative commitment -0.15 (-0.87) Satisfaction--Calculative commitment -0.28 (-1.91) Adaptation--Affective commitment -- Adaptation--Normative commitment -- Adaptation--Calculative commitment -- Knowledge transfers--Affective commitment -- Knowledge transfers--Calculative commitment -- Explanatory power RM model Affective commitment 0.594 Normative commitment 0.068 Calculative commitment 0.040 Attitudinal loyalty 0.510 Behavioral loyalty 0.380 Fit indices IMP model [chi square] (P, df) 225.78 (P = 0.00, df = 150) [chi square]/df 1.51 RMSEA 0.06 SRMR 0.07 CFI 0.97 NFI 0.92 NNFI 0.96 GFI 0.87 AGFI 0.82 PNFI 0.73 PGFI 0.62 AIC 345.78 Path coefficients (t-value) IMP model Affective commitment--Attitudinal loyalty 0.76 (7.30) * Normative commitment--Attitudinal loyalty -0.15 (-1.71) Calculative commitment--Attitudinal loyalty 0.03 (0.40) Affective commitment--Behavioral loyalty 0.63 (5.70) * Normative commitment--Behavioral loyalty 0.01 (0.11) Calculative commitment--Behavioral loyalty 0.09 (1.01) Trust--Affective commitment 0.39 (3.61) * Trust--Normative commitment 0.28 (2.31) * Trust--Calculative commitment 0.10 (0.86) Social bonds--Affective commitment -- Social bonds--Normative commitment -- Satisfaction--Affective commitment -- Satisfaction--Normative commitment -- Satisfaction--Calculative commitment -- Adaptation--Affective commitment 0.18 (1.80) Adaptation--Normative commitment -0.09 (-0.75) Adaptation--Calculative commitment -0.33 (-2.83) * Knowledge transfers--Affective commitment 0.26 (2.18) * Knowledge transfers--Calculative commitment -0.04 (-0.30) Explanatory power IMP model Affective commitment 0.498 Normative commitment 0.057 Calculative commitment 0.096 Attitudinal loyalty 0.515 Behavioral loyalty 0.401 Note: * Significant at p < 0.05
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|Author:||Cater, Barbara; Zabkar, Vesna; Cater, Tomaz|
|Publication:||Journal of Business Economics and Management|
|Date:||Dec 1, 2011|
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