Commitment in marketing research services: two alternative models/Isipareigojimai atliekant rinkodaros tyrimus: du alternatyves modeliai.
Cater, Barbara ; Zabkar, Vesna ; Cater, Tomaz 等
1. Introduction
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).
doi: <DO>10.3846/16111699.2011.599410</DO>
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doi:10.2307/1251929
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) barbara.cater@ef.uni-lj.si (corresponding author); (2)
vesna.zabkar@ef.uni-lj.si; 3tomaz.cater@ef.uni-lj.si
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
