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Feb. 2014. Vol. 3, No.6 ISSN 2307-227X
International Journal of Research In Social Sciences © 2013-2014 IJRSS & K.A.J. All rights reserved www.ijsk.org/ijrss
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CREATING CUSTOMER LOYALTY IN BUSINESS-TO-BUSINESS
MARKETS
Nasrin gholamizadeh1, Fakhraddin Maroofi
2, Kumars Ahmadi
3
1, 3 Sanandaj branch, Islamic azad university sanandaj ,Iran; corresponding author
Email:[email protected] 2 Department of management university of kurdistan
Abstract
The purpose of this research is to suggest and empirically test a customer loyalty model in a business-to-
business (B2B) market. This research investigates the development and role of dependence and trust in
relationship management from the customer's view and their impact on loyalty. Regarding social exchange
theory, we argue that relationship maintenance should not only include economic restriction- regarding
relationships, which are extracted from dependence, but also loyalty - regarding relationships, which are
extracted from trust. As such, this research suggests that dependence and trust are mediators by which to
understand the confusing relationship between buyers and sellers. According to the research findings, we can
conclude that customers develop restriction - regarding and loyalty - regarding relationships with sellers. The
influence of customer variables (CRSI, social sticking, and RCCs and customer expertise) on CC and AC is
mediated by restriction and loyalty motivations (dependence and trust). Interaction satisfaction plays an
important role in moderating the effects of dependence and trust on loyalty. A survey study conducted with 522
firms in the timber distribution industry showed that customer relationship specific investment, social sticking,
relationship conclusion costs and customer expertise have effects on calculative and affective loyalties via
dependence and trust. It also shows that interaction satisfaction significant the positive effect of dependence on
calculative loyalty and the positive effect of trust on affective loyalty. This research contributes to the
relationship marketing literature by creating a customer loyalty model in B2B markets and also offers
managerial implications.
Keywords: Dependence, Trust, Loyalty, Relationship marketing, B2B marketing
1. Introduction
If firms want to developed business-to-business
(B2B) markets and increase their market share and
profits, relationship maintenance is very important.
Firms have to satisfy customers in each interaction,
thereby create a long-term relationships and keeping
customers from changing. However, previous
relationship marketing research has focused on the
value of relationship marketing from the seller's view
(Barnes, 1997). Barnes (1997) shown that no
relationship exists between a buyer and seller unless
the buyer feels that a relationship exists. Therefore,
our research directs, why buyers want to maintain
relationships with sellers and as such, the findings
will help marketers to create customer loyalty.
Previous studies have focused on developing
restriction - regarding relationships (Anderson &
Narus, 1990). However, previous research neglected
to consider the active role of the customer in the
relationship development, which results in loyalty -
regarding relationships (Bendapudi & Berry, 1997).
According to the social exchange theory, we
emphasize that loyalty -regarding relationships are
extracted from trust. Customers are sacrificing short
term benefits in order to maintain long-term
relationships and such relationships are actively
begun by the customers (Bendapudi & Berry, 1997).
Customer dependence and trust in the seller are
important in relationship marketing and can improve
transaction relationships (Palmatier, Dant, Grewal, &
Evans, 2006). This research suggests that dependence
and trust are mediators that underlie the confusing
relationships between customers and suppliers.
Loyalty refers to the necessity for long-term
relationship maintenance (Geyskens, Steenkamp,
Scheer, & Kumar, 1996) and can be studied an result
of the relationship maintenance (Moorman, Zaltman,
& Deshpande, 1992; To & Li, 2005). This research
includes calculative and affective loyalty's (CC and
Feb. 2014. Vol. 3, No.6 ISSN 2307-227X
International Journal of Research In Social Sciences © 2013-2014 IJRSS & K.A.J. All rights reserved www.ijsk.org/ijrss
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AC, respectively) as result variables in the
relationship maintenance model. Previous studies
have studyd the effects of dependence and trust on
loyalty in B2B marketing (Wetzels, Ruyter, &
Birgelen, 1998), the impact of customer variables on
loyalty have not been empirically tested (Bendapudi
& Berry, 1997). Thus, customer variables, customer
relationship specific investment (CRSI), social
sticking, relationship conclusion costs (RCCs), and
customer expertise, are tested to dependence and trust
in the model. Maintaining relationship is a continuous
process, so interaction factors should play an
important role in this process. The Industrial
Marketing and Purchasing (IMP) Group shows that in
industrial markets the buyer–seller relationship is
affected by the interaction process (Giannakis,
Croom, & Slack, 2004). Since interactions are
important in creating relationships, the impact of
interaction factors, such as interaction satisfaction on
loyalty still needs more investigating (Bendapudi &
Berry, 1997). Previous studies have shown that
interaction satisfaction from the interaction process
leads to loyalty (Turnbull, Ford, & Cunningham,
1996) and that routines of interaction improve
expectations of the cooperation (Anderson &
Tuusjärvi, 2000).
The purpose of this research is to create a customer
loyalty model and study the mediating role of
dependence and trust in relationship maintenance. We
also try to investigate the moderating effects of
interaction satisfaction on the development of loyalty.
This research will contribute to the relationship
marketing literature in several aspects. First, this
research presents the effect of creating loyalties on
customer variables. Second, it empirically tested the
theoretical customer loyalty model according to two
pathways: restriction - based and loyalty - based
relationships. Third, it described the mediating role of
dependence and trust in the effects of customer
variables on loyalties. Finally, it showed that
interaction satisfaction improves positive effects of
dependence and trust on loyalties.
2. Theoretical background and research
framework
2.1. Restriction - based versus loyalty -
based relationship maintenance
Bendapudi and Berry (1997) suggested two customer
motivations: 1. after analyzing gains and losses,
customers are motivated to maintain relationships
because they want to reduce risks of exchanging
firms. This is called restriction- regarding relationship
maintenance. 2. Customers desire to continue the
relationship because Customers are motivated to
maintain relationships. This is called
loyalty regarding relationship maintenance (Gilliland
& Bello, 2002). In relationship marketing,
dependence is studied to be a restriction motivation
(Anderson & Weitz, 1992) and trust is studied to be a
loyalty motivation (Doney & Cannon, 1997). From
an economic view according to restriction - regarding
relationship, firms rely on suppliers to offer resources
and, thus, become relationship cooperators.
Dependence rises from the motivation for which
firms exchange with other firms that control essential
and limited resources (To & Li, 2005). Dependence is
determined separately from interdependence.
Interdependence is determined as a dyadic
relationship between relationship cooperators,
covering each firm' dependence on a cooperator
(Kumar, Scheer, & Steenkamp, 1995). Dependence is
determined a firm's to maintain a relationship with a
cooperator to achieve its goals (Kumar et al., 1995).
That is, interdependence focuses two-way directions
in regard to the cooperators' dependence upon each
other while dependence studies a one-way direction
in the relationship (e.g., a buyer's dependence upon a
supplier). Our research focuses on the latter construct.
This research studies the timber industry in Iran, for
which suppliers have comprehensive purchase in
price negotiations and resource control as the
resources are scare. In this industry between buyers
and suppliers asymmetrical interdependence exists,
that creates a buyer's dependence on the supplier
(Heide & John, 1988). Thus, this study focuses on
customer's view and studies customer's dependence
on the supplier, instead of the interdependence
between the customers and suppliers. On the other
hand, this research studies loyalty - regarding
relationship maintenance from the social exchange
view. Luo, (2002) state that the social exchange
theory has been applied to marketing studies. The
social exchange theory suggests cooperation and
mutual reciprocity when trying to understand
relationships with customers (Metcalf, Frear, &
Krishnan, 1992). Social exchange refers to the
interpersonal relationships that exist between buyers
and sellers (Metcalf et al., 1992) and emphasizes the
relationship extracted from close social interactions,
such investment, and result in long-term loyalties.
Relationship loyalty can be studied to be a
relationship output (Morgan & Hunt, 1994), which is
the core component of the social exchange theory
(Thibaut & Kelly, 1959). Barnes (1997) shown that
the buyer–seller relationship does not exist unless
buyers feel the relationship to exist. Such an
offer suggests the importance of examining the
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relationship maintenance from a buyer's view. Thus,
regarding on the economic and social exchange
views, we studied how customer variables influence
loyalty's via restriction and loyalty motivations. The
customer variables include two relationship-
regarding variables (CRSI, and social sticking) and
two non -relationship- regarding variables (RCCs and
customer expertise). CRSI, social sticking and
RCCs are key variables that influence relationship
maintenance (, while customer expertise is an
important variable that impacts consumer decision
Morgan & Hunt, 1994)-making (Alba & Hutchinson,
1987). The consequences of the motivations for
relationship maintenance are separated into two
loyalties: AC and CC.
2.2. Customer variables
CRSI refers to a customer's feeling of an investment
made by a supplier. The investment includes time,
resources and affect in personal relationships that are
made by the supplier to maintain relationships and
make a customer not easily exit the relationship
(Jones, Mothersbaugh, & Beatty, 2000). CRSI is
different from social sticking as the former refers to
customers' feeling of relationship investment while
social sticking emphasizes customers' loyalty and
friendship toward suppliers. In the relationship
maintaining process, customers have direct or indirect
interactions with suppliers. Therefore, suppliers show
relational selling behaviors (Crosby, Evans, &
Cowles, 1990). The latter occurs when suppliers
create ties with customer families and friends
(Bendapudi & Berry, 1997). Either form of
interaction develops social links that improve
interaction qualities (Crosby et al., 1990) with
suppliers. RCCs refer to exchanging costs (e.g., time
and money) that relationship cooperators have to pay
if they end relationships with their current
cooperators and create relationships with new
cooperators (Jones, Mothersbaugh, & Beatty, 2002).
RCCs include economic, psychological and physical
costs (Jackson, 1985). High RCCs delay customers
from exchanging to new cooperators and increase
customer dependence on relationship cooperators
(Morgan & Hunt, 1994). We consider RCCs to be a
customer factor instead of an interaction factor
(Bendapudi and Berry, 1997). RCCs occur when
customers end a relationship. Such costs do not exist
if they continue transactions with suppliers. The
higher the ability of the customers to search or adjust
to a new supplier, the lower their RCCs are,
regardless of their transaction cooperators (Fornell,
1992). Customer expertise refers to customer
knowledge about products offered by firms (Mitchell
& Dacin, 1996). For example, the customer can recall
what products are offered by the supplier and the
features of the products. Loyalty refers to the
customers which are willing to maintain long-term
relationships with suppliers (Morgan & Hunt, 1994).
CC refers to the level of loyalty that customers are
willing to make investments, relationship benefits,
and conclusion costs (Anderson & Weitz, 1992). That
is, customers analyze the benefits and maintaining
relationships and the costs and risks of creating new
relationships to determine (Li, Browne, & Chau,
2006). As such, both cooperators in the relationship
know their own weaknesses and rely on the
cooperators resources to improve performance. They
also try to create a balance or empower themselves in
the dyadic relationship when they have opportunities
(Matopoulos, Vlachopoulou, Manthou, & Manos,
2007). Thus, the motive for maintaining the
relationship is negative (Geyskens et al., 1996).
Buchanan (1974) determined AC as an affective
attachment to an organization' goals and values. AC
refers to the customers that are willing to maintain a
positive long-term relationship with suppliers
regarding on personal connections (Konovsky &
Cropanzano, 1991). Suppliers can create customer
AC through attachment, which arises from frequent
positive interactions and satisfied customer
expectations. This attachment creates customer trust
and a strong desire to maintain a relationship (To &
Li, 2005). Thus, customers with higher AC are more
willing to maintain positive relationships with
suppliers.
3. Hypotheses
3.1. Effects of customer variables on
loyalty: the mediating role of dependence
Dependence refers to a customer's dependence on a
supplier and is determined as the customer needs to
maintain a relationship with the supplier to achieve
desired goals (Ganesan, 1994). Due to a need for
economic resources, then the customer forms a
restriction - regarding relationship with the supplier
that a customer maintains a relationship with a
supplier (Andaleeb, 1996). Research has shown the
importance of dependence in the relationship
maintaining process (Gilliland & Bello, 2002). CRSI
in B2B transactions is not only increases cooperators'
exchanging costs, but also creates close relationships
and creates synergy for both parties (Schilling, 2000).
Suppliers need various investments, including
personal relationship investments, to maintain
relationships with customers. Investments in
customer relationships result in positive relationships.
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Through such investments, customers can obtain
information and make good decisions. Thus, when
customers feel that suppliers are making an effort to
CRSI, their dependence on the supplier will increase
(To & Li, 2005). Therefore, it is expected that CRSI
is positively related to customer dependence on the
supplier. Regarding on the economic view, customers
rely on a supplier to obtain valuable outputs
sometimes, these outputs are not satisfactory
(Levinger, 1979) as customers feel that the social ties
are better than alternative relationships (Anderson &
Narus, 1990). Thus, positive social interaction results
lead to higher dependence on the cooperator. When
customers feel higher social sticking to the supplier,
then they are develop dependence on the supplier
(Bendapudi & Berry, 1997). White and
Yanamandram (2007) further showed that social
sticking impacts customer dependence and
subsequently lead to CC. Thus, it is expected that
social sticking is positively related to dependence on
the supplier. Suppliers offer quality products,
competitive prices and other benefits to create up
RCCs. Customers often face economic loss and much
inconvenience by closing relationship with current
cooperators and finding new cooperators (White &
Yanamandram, 2007). To and Li (2005) also show
that RCCs are positively related with customer
dependence on the supplier. Wirtz and Mattila (2003)
showed that customers with more expertise prefer a
larger consideration set. Their knowledge decreases
their loyalty and reduces their dependence on
cooperators' advice. In contrast, to evaluate service
quality and product performance, it is difficult for
customers with less expertise. Thus, they have a
higher feeling of risks in decision making (Heilman
Bowman, & Gordon, 2000) and rely on their
cooperator's advice (Sharma & Patterson, 2000).
Thus, it is expected that customer expertise is
negatively related to customer dependence on the
supplier. Dependence in the relationship maintenance
process is determined by both parties' resources and
power. The customer with less power or less
resources has greater dependence on the supplier and
usually pays higher RCCs. Thus, these customers
would consider the restrictions of maintaining
relationships and calculate gains and losses
accordingly. Such situations force the customer to
calculate the benefits sacrificed and losses acquire
related with leaving (Geyskens et al., 1996). Thus, it
is expected that customer dependence on the supplier
is positively related to CC (Gilliland & Bello, 2002).
Research has shown that dependence does not lead to
AC (Breugel, Olffen, & Olie, 2005) and that
dependence reduces the motivation to
create relationships on affective grounds (Hibbard,
Kumar, & Stern, 2001). Suppliers with a higher
power do not need customer cooperation, trust or
loyalty and increase the opportunistic behavior
(Anderson & Weitz, 1992). On the other hand,
customers with a higher dependence on the supplier
do not trust in and make any loyalty to the supplier
(Kumar et al., 1995). Wetzels et al. (1998) show that
if buyers feel a dependence on sellers, they place less
emphasis on affective involvement with sellers and
more on costs and benefit considerations. Thus,
customer dependence on the supplier is expected to
be negatively related with AC. Therefore, the
following hypotheses are formulated:
H1. Dependence is affected positively by (a) CRSI
(b) social sticking and (c) RCCs, but (d) negatively
affected by customer expertise.
H2. Dependence (a) positively affects CC, but (b)
negatively affects AC.
3.2. Effects of customer variables on
loyalty: the mediating role of trust
Morgan and Hunt (1994) suggested trust as a key
mediating variable as it directly affects
relationship loyalty (Rodríguez & Wilson, 2002).
Customer trust in the supplier is extracted from
customer feelings toward the supplier in several
aspects, such as CRSI, social sticking, RCCs and
customer expertise. From the social exchange view,
relationships are developed via interactions. Such
interactions are creating long-term relationships.
Social exchange facilitates problem solving and
overcome communication barriers. It also reflects the
degree of trust in the relationship
cooperator (Hakansson & Ostberg, 1975).
Interpersonal relationships improve trust between
cooperators (Metcalf et al., 1992). Thus, when a
customer feels that an increased amount of CRSI is
being made by the supplier, then the customer is trust
the supplier. Thus, CRSI is positively related to
customer trust in the supplier. Further, social link
increase customer trust in suppliers (Gounaris, 2005;
Liang & Wang, 2006). Social links refer to
interpersonal links between a customer and a
supplier. These ties influence customer feelings and
behavior toward a supplier (Bendapudi & Berry,
1997). Bendapudi and Berry (1997) show that social
sticking can reduce or eliminate the opportunistic
behavior on the part of the relationship
cooperator and as a result, improves trust in the
cooperator. When a customer creates a close social
connection with a supplier, he is more likely to
develop trust in the supplier. As such, it is expected
that social sticking with the supplier is positively
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related to customer trust in the supplier. Low
RCCs show low economic, psychological and
physical costs in regard to ending a relationship.
Under low RTC conditions, customers are flexible in
regard to exchanging alternatives available in the
market and they show less affective involvement in
the relationship with the existing supplier (Smith,
Ross, & Smith, 1997). Customers will be less likely
to initiate a long-term relationship with and form trust
in a supplier. In contrast, when RCCs are high,
customers face a constrained situation and remain in
the relationship with their cooperator (Friman,
Gärling, Millett, Mattsson, & Johnston, 2002). To
adapt such a situation, customers tend to solve
problems and are disinclined to exhibit responses that
are destructive to the relationship when problems
occur (Ping, 1993). High RCCs facilitate long-term
relationship orientations and improve cooperation
intentions (Doney & Cannon, 1997). Positive social
exchanges make customers show more affective
involvement in the relationship (Gounaris, 2005;
Kanagal, 2009). Under such situations, customers are
willing to create a long-term relationship (Dwyer et
al., 1987) and develop trust in a supplier. Thus, it is
expected that RCCs are positively related to customer
trust in a supplier. Researchers show that expertise
affects perception processes (Bettman, 1979) and
product evaluations (Rao & Monroe, 1988). If a
customer possesses high product knowledge and is
willing to engage in transactions with a supplier, then
the customer recognizes the existence of a valuable
transaction relationship. Such a relationship facilitates
the creation of trust in the long run, consistent with
the social exchange theory (Dwyer et al., 1987). It is
expected that customer expertise is positively related
to trust in the supplier. Ganesan (1994) shows that
trust reduces opportunistic behavior in transaction
processes. As such, CC becomes unnecessary as
customers believe that suppliers will not do anything
unexpected to hurt the relationship. Trust improves
relationships and makes relationship cooperators less
likely to calculate benefits and costs and more willing
to sacrifice (Bendapudi & Berry, 1997). On the other
hand, buyer trust in the sellers positively affects in the
long-term relationship maintenance (Doney &
Cannon, 1997). Customer affective involvement will
increase as trust in the supplier increases (Geyskens et
al., 1996). Trust is studied loyalty motivation and
positively impacts AC (Ramsey & Sohi, 1997). Thus,
trust is expected to be negatively related with CC and
positively related with AC (Bloemer & Odekerken-
Schröder, 2006). Taken together, the previous
arguments create the following hypotheses:
H3. Trust is affected positively by (a) CRSI (b) social
sticking (c) RCCs and (d) customer expertise.
H4. Trust (a) negatively affects CC, but (b) positively
affects AC.
3.3. The moderating effect of interaction
satisfaction
Firms form positions after comparing expectations
and feelings from interactions with relationship
cooperators (Szymanski & Hise, 2000). When
interaction satisfaction increases, as they are aware of
the costs and risks inherent in ending the relationship
because cooperators are less likely to exit. As it
assumes that relationship development is a gradual
and continuous process because, interaction
satisfaction should reinforce the positive effect of
dependence on CC. 2) shown that the interaction
satisfaction improve the positive effect of dependence
on C Metcalf et al. (199C and the interaction
satisfaction produces a normative coordination of
interdependence and the norm of reciprocity leads to
loyalty because transaction cooperators are satisfied
with the cooperation. Thus, we suggest that
interaction satisfaction moderates the positive effect
of dependence on CC. That is, the effect of
dependence on CC will be significant when
interaction satisfaction increases. H5a is formulated
as follows:
H5a. Interaction satisfaction will have significantly
positive effect of dependence on CC.
According to the view of relational exchanges,
suppliers create, develop, and maintain relational
exchanges with buyers (Morgan & Hunt, 1994).
Interaction satisfaction would affects the motivation
and the relationship exchanges, and may have an
impact on the relationship between trust and AC.
When relationship cooperators gain economic
benefits and noneconomic satisfactions through
cooperation, they are engage in social exchanges with
their cooperator (Dwyer et al., 1987). Social
exchanges should help the development of trust in
and AC to the supplier. As a result, when interaction
satisfaction increases, customer trust in the supplier
increases (Swann & Gill, 1997) and AC to the
supplier improves. Thus, satisfactory interaction will
significant the relationship between trust and AC.
H5b is develop as follows:
H5b. Interaction satisfaction will have significantly
the positive effect of trust on AC.
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Fig. 1 show conceptual framework, which suggest
that dependence and trust mediate the effects of
customer variables on loyalties.
4. Methodology
4.1. Sample and data collection
The timber distribution market in Iran has been
grown over past few years. Timber suppliers must
maintain good relationships with customers to gain
larger market shares and profits. Thus, customers in
this market are selected for this research purpose.
Survey packets, including a cover letter, a survey
catalog, were mailed to potential respondents. Our
sample frame consisted of 2600 customers of timber
suppliers in Iran and those customers maintain good
relationships with their suppliers. 70% of the
customers in each district (northern, middle, southern,
and eastern) of Iran were randomly selected. In total,
1800 surveys were mailed. The response rate was
67%. However, 156 questionnaires were incomplete,
resulting in 1044 usable questionnaires. Comparing
the first 10% respondents and the last 10% of the
respondents estimated nonresponse bias respondents.
The results shown that this was not a problem in the
current study. Respondents are business owners or
senior managers, of which 85.6% are male and 14.4%
female. The industry category was divided as follows:
52.2% builders, 17.8% contractors, and 30% other
timber related manufacturers. The firms represented
in the sample varied in size, as measured by
employees (b5, 26.6%; 6–20, 40.3%; 21–50, 22.2%;
51–100, 9.1%, >100, 1.7%). With respect to the
number of major suppliers, over 63% of the firms
shown that they have more than 3 major suppliers (1,
8.1%; 2, 10%, 3, 18.9%; >3, 61.2%).
4.2. Measures
All of the items in the suggested model were
measured using multi-items scales drawn from
previous studies. Adapted from Jones et al. (2000),
CRSI was measured by a four-item scale, the four
items of social sticking measure was adapted from
Smith (1998), three item RCCs construct, adapted
from Jones et al. (2002), four-items was adapted from
Mitchell and Dacin (1996), Dependence construct
was using a five-item scales presented by Ganesan
(1994). Following Morgan and Hunt (1994), trust was
measured by a three-item, CC was measured with
three items presented by Gustafsson, Johnson, and
Roos (2005), AC was measured with three items
presented by Gustafsson et al. (2005). The three items
were designed to estimate the customer pleasure in
being a customer of the supplier. Following Ganesan
(1994), interaction satisfaction was measured by a
four-item.
5. Analysis and results
Following procedures suggested by Bagozzi, Yi, and
Phillips (1991), this research conducted two analysis
phases. First, the measurement model is estimated
with confirmatory factor analysis (CFA) to test
reliabilities and validities of the research constructs.
Then, the structural model is used to test the strength
and direction of the suggested relationships among
research constructs.
5.1. Measurement model
We measured the psychometric properties of using a
CFA that combined each factor measured by
reflective scales (Bagozzi et al., 1991). The CFA
results show 3 items for CRSI, 4 items for customer
expertise, 3 items for social sticking, and 2 items for
RTC, 3 items for interaction satisfaction, 4 items for
dependence, 2 items each for trust, CC and AC.
Complex reliability presents the shared variance
among a set of observed variables that measure an
underlying construct (Fornell & Larcker, 1981). All
complex reliabilities for the constructs were above
0.778, which shows acceptable levels of reliability for
each construct (see Table 1). In addition, each of the
Cronbach alpha values passed the threshold value of
0.8 suggested by Nunnally (1978), which suggests
that for each of the constructs, there is a reasonable
degree of internal consistence between the
corresponding indicators. Measures of fit assess how
well a CFA model reproduces the covariance matrix
of the observed variables. The measurement model
showed significant levels of fit: χ2=820.136, d.f.=460,
goodness-of-fit index (GFI)=0.917, adjusted
GFI=0.894, non-normed fit index (NFI)=0.934,
comparative fit index (CFI)=0.971, incremental fit
index (IFI)=0.971, Tucker–Lewis index (TLI)=0.966,
and standardized root mean square residual
(SRMR)=0.035, root mean square error of
approximation (RMSEA)=0.040 (Table 1). Results
also support for the convergent and discriminant
validity. As evidence of convergent validity, each
item loaded significantly on its respective construct
(Anderson & Gerbing, 1988). Evidence of
discriminant validity exists when the square root of
the average of variance extracted in each construct
passes the coefficients showing its correlation with
other constructs (Fornell & Larcker, 1981) (see
Table 2).
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5.2. Structural model
In Structural model, two alternative models were
tested and compared. Model 1, customer variables,
dependence and trust of loyalties, supposing that the
mediating role of dependence and trust are not
required. Model 2 adds two more paths to the
hypothesized model as previous research has shown
that social sticking has a direct effect on AC (Cater &
Zabkar, 2009;) and that RCCs have a direct effect on
CC (Jones, Reynolds, Mothersbaugh, & Beatty, 2007;
White & Yanamandram, 2007).
5.2.1. Model fit
In this research we consider both model and select
one of the best-fitting model. Therefore, to fit
statistics, we use four parsimony measures (Chang, et
al; 2012) (i.e., Akaike information criterion [AIC],
consistent Akaike information criterion [CAIC],
expected cross-validation index [ECVI] and
parsimonious normed fit index [PNFI]). For the
measures of AIC, CAIC and ECVI, lower value
shows a better fit and a more economical model. For
the measure of PNFI, greater value shows a better fit
and a more economical model. Table 3 furnishes the
fit statistics for the hypothesized and two alternative
models. The hypothesized model shows a better fit in
terms of the fit statistics and the measures of AIC,
CAIC, ECVI and PNFI. Specifically, for the
parsimony measures, the hypothesized model shows
lower values of AIC, CAIC, ECVI and a greater value
of PNFI than those of the two alternative models
(Chang, et al; 2012). The results of comparing the
hypothesized model with Model 1 suggest that
dependence and trust mediate the effects of customer
factors on loyalties. In addition, the results of
comparing the hypothesized model with Model 2
suggest that the direct effects between social sticking
and AC and between RCCs and CC are insignificant.
The fit of the data to the suggested model was quite
good (χ2=995.492, d.f.=367, p 0.001; χ
2/d.f.– 2.725,
GFI=0.886, TLI=0.932, CFI=0.938 and
RMSEA=0.054).
5.2.2. Hypothesis testing
Table 4 shows the results of the hypotheses tests and
Fig. 2 furnishes a graphic estimated in the path
diagram. All the hypotheses research are supported.
Our findings suggest that CRSI (γ11=0.375, t=8.542,
p˂0.001), social sticking (γ12=0.376, t=8.515,
p˂0.001), RCCs positively influence dependence
(γ13=0.413, t=9.475, p˂0.001) while customer
expertise has negatively affects dependence
(γ14=−0.285, t=−7.148, p˂0.001), supporting H1a,
H1b, H1c and H1d. In addition, dependence has a
positive and significant impact on CC (β31=0.236,
t=4.125, p˂0.001), but a negative and significant
impact on AC (β41=−0.415, t=−7.405, p˂0.001),
supporting H2a and H2b. On the other hand, CRSI
(γ21=0.170, t=3.769, p˂0.05), social sticking
(γ22=0.629, t=10.548, p˂0.001), RCCs (γ23=0.109,
t=2.456, p˂0.05) and customer expertise (γ24=0.091,
t=2.098, p˂0.05) positively influence trust, supporting
H3a, H3b, H3c and H3d. Moreover, trust has a
negative and significant impact on CC (β32=−0.229,
t= −3.842, p˂0.001), but a positive and significant
impact on AC (β42=0.376, t=6.439, pb0.001),
supporting H4a and H4b.
5.2.3. The moderating effect of interaction
satisfaction
We divided the sample into a high interaction
satisfaction group and a low interaction satisfaction
group to study the moderating effects of interaction
satisfaction on the relationships between dependence,
trust, and loyalties. The results of the path coefficient
growth and equality restriction tests showed that the β
coefficients describing the relationships between
dependence and CC were significantly different
between the two groups (coefficient growth=0.162,
p˂0.001; χ2 difference=52.479, p˂0.001) (Table 5).
Interaction satisfaction significant the positive impact
of dependence on CC, supporting H5a. Further, the
tests also showed that the moderating effect of
interaction satisfaction on the relationship between
trust and AC (coefficient growth = 0.129, p˂0.001; χ2
difference=48.348, p˂0.001) (Table 5). The effect of
trust on AC is significant by interaction satisfaction,
supporting H5b.
6. Discussion
The purpose of this research investigates the
mediating role of dependence and trust in the effects
of customer variables on loyalties. The findings
supported suggested model. Research findings shows
that, customer variables (CRSI, social sticking, and
RCCs and customer expertise) affects CC and AC via
both dependence and trust. First, CRSI is positively
related with customer dependence and trust in
suppliers. With suppliers' effort and time
distinguishing, suppliers know customers' needs and
preferences in developing relationships with
customers. Therefore, those investments help improve
customers' performance and handle market
uncertainty. Thus, the higher CRSI, the more
dependence they have on their suppliers to maintain
the relationships (Jones et al., 2002). But CRSI
improves interpersonal relationships, which defeat
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8
communication barriers (Metcalf et al., 1992) and,
thus, create customer trust in the supplier. Second,
social sticking is positively related with dependence
and trust (Bendapudi & Berry, 1997). Suppliers
should create relationships through indirect
interactions with their customers' families and friends
or social interactions with their customers. These
interactions significant their social links with
customers. Further, RCCs are positively related with
dependence and trust. By the conclusion of
relationships, customers rely on existing relationships
to avoid the costs and risks (To & Li, 2005). To
increase conclusion costs, suppliers should develop
the product lines and services which will result in
higher barriers for customers seeking to exit the
relationship (Stremersch & Tellis, 2002). In addition,
our findings show that higher RCCs help the long-
term relationship development and increase the
degree of trust. Customer expertise is negatively
related with customer dependence on suppliers.
Therefore, for customers with higher expertise to
compare products among various competing
suppliers, reduce their dependence on suppliers.
Customers with less expertise rely more on their
suppliers' to reduce the feeling risks of purchasing
(Locander & Hermann, 1979). If a customer is
willing to perform transactions with the supplier, then
the customer values the relationship, facilitates the
development of trust. Our findings show that
dependence is positively related with CC and
negatively related with AC. The results suggest that
customer dependence on suppliers is determined by
resources (Kumar et al., 1995). When customers feel
their dependence on suppliers, they maintain
relationships with suppliers regarding restriction
motivation and consider gains and losses, which
results in CC (Ganesan, 1994). Therefore,
dependence allows customers to enter into continuing
transactions on expected gains and losses than
regarding on affective considerations (Wetzels et al.,
1998). Hence, AC will be delayed (Breugel et al.,
2005). Trust is negatively related with CC and
positively related with AC. Trust reduces
opportunistic behavior and, thus, CC becomes
unnecessary (Ganesan, 1994). When a customer's
affective involvement in the relationship increases
due to loyalty motivation, CC decreases (Bloemer &
Odekerken-Schröder, 2006). In addition, this research
shows that trust positively affects AC, which is
consistent with previous research findings (Cater &
Zabkar, 2009). Moreover, our findings suggest that
interaction satisfaction significantly and positively
affect the dependence on CC and trust on AC.
Interaction satisfaction reflects customers feeling
about suppliers regarding on their past interactions.
Higher interaction satisfaction reduces customer costs
in information searches, comparison and monitoring.
Positive interactions significant cooperation and
adaptation between cooperators (Metcalf et al., 1992).
Suppliers should ensure that a customer is proud with
each interaction and transaction as interaction
satisfaction creates not only short-term profits, but
also long-term relationships, which facilitate the
pursuit of profit maximization. However, due to
sufficient information available, attractive alternatives
to customers increase. In addition, response time is
important to customers due to low cost and high
quality products and services. Suppliers consider how
to share information and respond in a real time to
meet customer needs. Information sharing between
relationship cooperators can improve production and
distribution efficiency (Straub, Rai, & Klein, 2004).
Through supply chain management and information
sharing, firms can increase interaction satisfaction.
7. Strategic implications
In this study, among the customer variables
investigated, RCCs and social sticking had influence
on dependence and trust, respectively. Thus, firms
should put more efforts on these two variables to
create dependence, trust and, loyalty. Suppliers
should develop the core competencies that customers
rely to ensure that they are capable in the market and
not easily replaced. On the other hand, social sticking
is most important customer variable in creating trust.
Firms should have frequent contact with customers
and look out for the customers' interests in order to
strengthen social sticking to the customers. Firms
may also consider utilizing technology to increase
their social sticking to the customers. By creating
electronic network with customers, suppliers can
receive orders in time, reduce much excessive work,
and increase customer RCCs. In addition, marketers
should be aware of how interaction factors influence
the created relationships between dependence, trust
and loyalties.
8. Limitations and future research
directions
The limitations of this research are as follow: First,
this research focuses on the buyer's feeling of the
relationship maintenance and creating loyalty. Buyers
and suppliers may have different feelings of
interaction satisfaction, leading to different result of
expectations. Thus, future research should investigate
the impact of such differences between the buyers'
and suppliers' feelings on the effects observed in this
research. Second, this research uses timber
distributors as samples. Customer relationship
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9
maintenance factors and methods may vary in
different industries. Future research can study the
suggested model in other industries. In addition,
although the results support our hypothesized
relationships between social sticking, dependence and
loyalty, future research may be to investigate a
mediating effect of loyalty on the relationship
between social sticking and dependence.
9. Conclusion
According to the research findings, we can conclude
that customers develop restriction-based and
loyalty - regarding relationships with sellers. The
influence of customer variables (CRSI, social
sticking, and RCCs and customer expertise) on CC
and AC is restriction and loyalty motivations
(dependence and trust). Interaction satisfaction plays
an important role in moderating the effects of
dependence and trust on loyalty.
References
1. Alba, J. W., & Hutchinson, J. W.
(1987). Dimension of consumer expertise.
Journal of Consumer Research, 13(4), 410–
411.
2. Andaleeb, S. S. (1996). An experimental
investigation of satisfaction and commitment
in marketing channels: The role of trust and
dependence. Journal of Marketing, 72(1),
77–93.
3. Anderson, J. C., & Gerbing, D. W. (1988).
Structural equation modeling in practice: A
review and recommended two-step
approach. Psychological Bulletin, 103(3),
411–423.
4. Anderson, J. C., & Narus, J. A. (1990). A
model of distributor firm and manufacturer
firm working partnerships. Journal of
Marketing, 54(1), 42–58.
5. Anderson, P., & Tuusjärvi, E. (2000).
Structuring of interactions — towards the
change. The 16th IMP-conference. Bath,
UK: University of Bath.
6. Anderson, E., & Weitz, B. (1992). The use
of pledges to build and sustain commitment
in distribution channels. Journal of
Marketing Research, 29(1), 18–34.
7. Bagozzi, R. P., Yi, Y., & Phillips, L. W.
(1991). Assessing construct validity in
organizational research. Administrative
Science Quarterly, 36(3), 421–458.
8. Barnes, J. G. (1997). Closeness, strength,
and satisfaction: Examining the nature of
relationships between providers of financial
services and their retail customers.
Psychology and Marketing, 14(8), 765–790.
9. Bendapudi, N., & Berry, L. L. (1997).
Customer motivation for maintaining
relationships with service provider. Journal
of Marketing Research, 73(1), 15–37.
10. Bettman, J. R. (1979). An information
processing theory of consumer choice.
Reading, MA: Addison-Wesley.
11. Bloemer, J., & Odekerken-Schröder, G.
(2006). The role of employee relationship
proneness in creating employee loyalty. The
International Journal of Bank Marketing,
24(4), 252–264.
12. Breugel, G. V., Olffen, W. V., & Olie, R.
(2005). Temporary liaisons: The
commitment of “temps” towards their
agencies. Journal of Management Studies,
42(3), 539–566.
13. Buchanan, B. (1974). Building
organizational commitment: The
socialization of managers in work
organizations. Administrative Science
Quarterly, 19(4), 533–546.
14. Cater, B., & Zabkar, V. (2009). Antecedents
and consequences of commitment in
marketing research services: The client's
perspective. Industrial Marketing
Management, 38(7), 785–797.
15. Chang, et al.(2012). Building customer
commitment in business-to-business
markets. Industrial Marketing Management
41 (2012) 940–950
16. Crosby, L. A., Evans, K. R., & Cowles, D.
(1990). Relationship quality in services
selling: An interpersonal influence
perspective. Journal of Marketing, 54(3),
68–81.
17. Doney, P. M., & Cannon, J. P. (1997). An
examination of the nature of trust in buyer–
seller relationships. Journal of Marketing,
61(2), 45–51.
18. Dwyer, F. R., Schurr, P. H., & Oh, S. (1987).
Developing buyer–seller relationships.
Journal of Marketing, 51(2), 11–27.
19. Fornell, C. (1992). A national customer
satisfaction barometer: The Swedish
experience. Journal of Marketing, 56(1), 6–
21.
20. Fornell, C., & Larcker, D. F. (1981).
Evaluating structural equation models with
unobservable variables and measurement
error. Journal of Marketing Research, 18(1),
39–50.
Feb. 2014. Vol. 3, No.6 ISSN 2307-227X
International Journal of Research In Social Sciences © 2013-2014 IJRSS & K.A.J. All rights reserved www.ijsk.org/ijrss
10
21. Frazier, G. L. (1983). Interorganizational
exchange behavior in marketing channels: A
broadened perspective. Journal of
Marketing, 47(4), 68–78.
22. Friman, M., Gärling, T., Millett, B.,
Mattsson, J., & Johnston, R. (2002). An
analysis of international business-to-business
relationships based on the commitment–trust
theory. Industrial Marketing Management,
31(5), 403–409.
23. Ganesan, S. (1994). Determinants of long-
term orientation in buyer–seller
relationships. Journal of Marketing, 59(2),
1–19.
24. Geyskens, I., Steenkamp, J. B., Scheer, L.
K., & Kumar, N. (1996). The effects of trust
and interdependence on relationship
commitment: A trans-Atlantic study.
International Journal of Research in
Marketing, 13(4), 303–317.
25. Giannakis, M., Croom, S., & Slack, N.
(2004). Supply chain paradigms. In S. New,
& R. Westbrook (Eds.), Understanding
supply chains: Concepts, critiques and
futures. New York, NY: Oxford.
26. Gilliland, D. I., & Bello, D. C. (2002). Two
sides to attitudinal commitment: The effect
of calculative and loyalty commitment on
enforcement mechanisms in distribution
channels. Journal of the Academy of
Marketing Science, 30(1), 24–43.
27. Gounaris, S. P. (2005). Trust and
commitment influences on customer
retention: Insights from business-to-business
services. Journal of Business Research,
58(2), 126–140.
28. Gustafsson, A., Johnson, M. D., & Roos, I.
(2005). The effects of customer satisfaction,
relationship commitment dimensions, and
triggers on customer retention. Journal of
Marketing, 69(4), 210–218.
29. Hakansson, H., & Ostberg, C. (1975).
Industrial marketing: An organizational
problem? Industrial Marketing
Management, 4(2), 113–123.
30. Heide, J. B., & John, G. (1988). The role of
dependence balancing in safeguarding
transaction-specific assets in conventional
channels. Journal of Marketing, 52(1), 20–
35.
31. Heilman, C., Bowman, D., & Gordon, P.
(2000). The evolution of brand preferences
and choice behaviors to consumer new to a
market. Journal of Marketing Research,
37(2), 139–155.
32. Hibbard, J. D., Kumar, N., & Stern, L. W.
(2001). Examining the impact of destructive
acts in marketing channel relationship.
Journal of Marketing Research, 38(1), 45–
61.
33. Jones, M. A., Mothersbaugh, D. L., &
Beatty, S. E. (2000). Switching barriers and
repurchase intention in services. Journal of
Retailing, 76(2), 259–274.
34. Jones, M. A., Mothersbaugh, D. L., &
Beatty, S. E. (2002). Why customers stay:
Measuring the underlying dimensions of
services switching costs and managing their
differential strategic outcomes. Journal of
Business Research, 55(6), 441–450.
35. Jones, M. A., Reynolds, K. E.,
Mothersbaugh, D. L., & Beatty, S. E. (2007).
The positive and negative effects of
switching costs on relational outcomes.
Journal of Service Research, 9(4), 335–355.
36. Kanagal, N. (2009). Role of relationship
marketing in competitive marketing strategy.
Journal of Management and Marketing
Research, 2(1), 1–17.
37. Konovsky, M. A., & Cropanzano, R. (1991).
Perceived fairness of employee drug testing
as a predictor of employee attitudes and job
performance. Journal of Applied
Psychology, 76(5), 689–707.
38. Kumar, N., Scheer, L. K., & Steenkamp, J.
B. (1995). The effects of perceived
interdependence on dealer attitudes. Journal
of Marketing Research, 32(3), 348–356.
39. Levinger, G. (1979). Marital cohesiveness at
the brink: The fate of applications for
divorce. In G. Levinger, & O. C. Moles
(Eds.), Divorce and separation. New York,
NY: Basic Books.
40. Li, D., Browne, G. J., & Chau, P. Y. K.
(2006). An empirical investigation of web
site use a commitment-based model.
Decision Sciences, 37(3), 427–444.
41. Liang, C. J., & Wang, W. H. (2006).
Evaluating the interrelation of a retailer's
relationship efforts and consumers' attitudes
and behaviour. Journal of Targeting,
Measurement and Analysis for Marketing,
14(2), 156–173.
42. Locander, W. B., & Hermann, P. W. (1979).
The effect of self-confidence and anxiety on
information seeking in consumer risk
reduction. Journal of Marketing Research,
16(2), 268–274.
43. Luo, X. (2002). Trust production and
privacy concerns on the internet a
Feb. 2014. Vol. 3, No.6 ISSN 2307-227X
International Journal of Research In Social Sciences © 2013-2014 IJRSS & K.A.J. All rights reserved www.ijsk.org/ijrss
11
framework based on relationship marketing
and social exchange theory. Industrial
Marketing Management, 31(2), 111–118.
44. Matopoulos, A., Vlachopoulou, M.,
Manthou, V., & Manos, B. (2007). A
conceptual framework for supply chain
collaboration: Empirical evidence from the
agri-food industry. Supply Chain
Management, 12(3), 177–194.
45. Metcalf, L. E., Frear, C. R., & Krishnan, R.
(1992). Buyer–seller relationship: An
application of the IMP interaction model.
European Journal of Marketing, 26(2), 27–
46.
46. Mitchell, A. A., & Dacin, P. A. (1996). The
assessment of alternative measure of
consumer expertise. Journal of Consumer
Research, 23(3), 219–239.
47. Moorman, C., Zaltman, G., & Deshpande, R.
(1992). Relationships between providers and
users of marketing research: The dynamic of
trust within and between organizations.
Journal of Marketing Research, 29(3), 314–
328.
48. Morgan, R. M., & Hunt, S. D. (1994). The
commitment–trust theory of relationship
marketing. Journal of Marketing, 58(3), 20–
38.
49. Nunnally, J. C. (1978). Psychometric theory.
New York, NY: McGraw-Hill.
50. Palmatier, R. W., Dant, R. P., Grewal, D., &
Evans, K. R. (2006). Factors influencing the
effectiveness of relationship marketing: A
meta-analysis. Journal of Marketing, 70(4),
136–153.
51. Ping, R. A., Jr. (1993). The effects of
satisfaction and structural constraints on
retailer exiting, voice, loyalty, opportunism,
and neglect. Journal of Retailing, 69(3),
320–352.
52. Ramsey, R. P., & Sohi, R. S. (1997).
Listening to your customer: The impact of
perceived salesperson listening behavior on
relationship outcome. Journal of the
Academy of Marketing Service, 25(2), 127–
137.
53. Rao, A. R., & Monroe, K. B. (1988). The
moderating effect of prior knowledge on cue
utilization in product evaluations. Journal of
Consumer Research, 15(2), 253–264.
54. Rodríguez, C. M., & Wilson, D. T. (2002).
Relationship bonding and trust as a
foundation for commitment in U.S.–Mexican
strategic alliances: A structural equation
modeling approach. Journal of International
Marketing, 10(4), 53–76.
55. Schilling, M. A. (2000). Toward a general
modular systems theory and its application
to inter-firm product modularity. The
Academy of Management Review, 25(2),
312–334.
56. Schurr, P. H., & Ozanne, J. L. (1985).
Influences on exchange processes: Buyer's
preconceptions of a seller's trustworthiness
and bargaining toughness. Journal of
Consumer Research, 11(4), 939–953.
57. Sharma, N., & Patterson, P. G. (2000).
Switching cost, alternative attractiveness and
experience as moderators of relationship
commitment in professional consumer
service. International Journal of Service
Industry Management, 11(5), 470–490.
58. Smith, B. (1998). Buyer–seller relationships:
Bonds, relationship management and sex-
type. Canadian Journal of Administrative
Sciences, 15(1), 76–92.
59. Smith, P. M., Ross, E. S., & Smith, T.
(1997). A case study of distributor–supplier
business relationships. Journal of Business
Research, 39(1), 39–44.
60. Straub, D., Rai, A., & Klein, R. (2004).
Measuring firm performance at the network
level: A nomology of the business impact of
digital supply network. Journal of
Management Information Systems, 21(1),
83–114.
61. Stremersch, S., & Tellis, G. J. (2002).
Strategic bundling of products and prices: A
new synthesis for marketing. Journal of
Marketing, 66(1), 55–72.
62. Swann, W. B. J., & Gill, M. J. (1997).
Confidence and accuracy in person
perception: Do we know what we think
about our relationship partners? Journal of
Personality and Social Psychology, 73(4),
747–757.
63. Szymanski, D. M., & Hise, R. T. (2000). E-
satisfaction: An initial examination. Journal
of Retailing, 76(3), 309–322.
64. Thibaut, J. W., & Kelly, H. (1959). The
social psychology groups. New York, NY:
John Wiley & Sons.
65. To, P. L., & Li, T. Y. (2005). An integrated
model for customer relationship maintenance
on the internet. Management Review, 24(2),
31–51.
66. Turnbull, P., Ford, D., & Cunningham, M.
(1996). Interaction, relationships and
networks in business markets: An evolving
Feb. 2014. Vol. 3, No.6 ISSN 2307-227X
International Journal of Research In Social Sciences © 2013-2014 IJRSS & K.A.J. All rights reserved www.ijsk.org/ijrss
12
perspective. The Journal of Business and
Industrial Marketing, 11(3/4), 44–62.
67. Wetzels, M., Ruyter, K. D., & Birgelen, M.
V. (1998). Marketing service relationships:
The role of commitment. The Journal of
Business and Industrial Marketing, 13(4),
406–423.
68. White, L., & Yanamandram, V. (2007). A
model of customer retention of dissatisfied
business service customers. Managing
Service Quality, 17(3), 298–314.
69. Wirtz, J., & Mattila, A. S. (2003). The
effects of consumer expertise on evoked set
size and service loyalty. Journal of Services
Marketing, 17(6), 649–665.
Relationship
H1a(+) H5b(+) H5a(+)
H3a(+)
H1b(+) H2a(+)
H1c(+)
H3b(+ H2b(-)
Non-relationship H1d(-)
H4a(-)
H3c(+)
H4b(+)
H3d(+)
Fig. 1. Proposed model (Chang, 2012
Customer
Relationship
Specific
Investment
Relationship
End
Costs
Interaction
Satisfaction
Social
Bonding
Trust
Dependence
Affective
Loyalty
Calculative
Loyalty
Customer
Expertise
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γ 11=0.373***
γ 21=0.168*
γ 12=0.374*** β31=0.234***
γ 22=0.626*** β41= 0.413***
γ 13=0.411*** β32= 0.227***
γ 23=0.107*
γ 14= 0.282*** β42=0.373***
γ 24=0.089*
χ2 (368) = 995.492, p<.001; χ
2/d.f.=2.725, GFI =0.886, TLI =0.932, CFI=0.938,
RMSEA=0.054
Fig. 2. Hypothesized model
Table 1
Analysis of measurement model.
Constructs MLE estimates Complex
reliability
AVE Cronbach's α
Factor loading
(λx/λy)
Measurement
error (δ/ε)
0.867 0.609 0.908
CRSI1 0.780*** 0.644
CRSI2 0.891*** 0.297
CRSI 0.853*** 0.470
SS 0.855 0.585 0.886
SS1 0.796*** 0.488
SS2 0.798*** 0.532
SS3 0.811*** 0.512
ξ1 Customer
Relationship
Investment
η1 Dependence
η3 Calculative
Loyalty ξ2 Social
sticking
ξ3 Relationship
end
Costs
η4 Affective
Loyalty η2 Trust
ξ4 Customer
Expertise
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14
RECs 0.871 0.684 0.899
TC1 0.899*** 0.256
TC2 0.839*** 0.452
CE 0.899 0.685 0.952
CE1 0.912*** 0.426
CE2 0.943*** 0.288
CE3 0.874*** 0.562
D 0.858 0.536 0.899
D1 0.769*** 0.570
D2 0.819*** 0.558
D3 0.759*** 0.814
D4 0.812* ** 0.529
T 0.835 0.619 0.799
T1 0.688*** 0.409
T2 0.789*** 0.339
CL 0.788 0.548 0.812
CL1 0.749*** 0.557
CL2 0.758*** 0.539
AL 0.829 0.618 0.863
AL1 0.817*** 0.459
AL2 0.798*** 0.478
IS 0.885 0.647 0.921
IS1 0.842*** 0.451
IS2 0.849*** 0.447
IS3 0.859*** 0.386
Note: CRSI = customer relationship specific investment; SS = social sticking; RECs = relationship end costs;
CE = customer expertise; D = dependence; T = trust; CL=calculative loyalty; AL=affective loyalty;
IS=interaction satisfaction.
*** p˂0.001
Table 2
Correlation matrix for measurement scales.
Constructs AVE Alpha CRSI SB RECs CE D T CL AL IS
CRSI 0.609 0.899 0.777
SS 0.578 0.878 0.588 0.759
RECs 0.677 0.897 0.394 0.413 0.822
CE 0.676 0.947 −0.139 −0.133 −0.096 0.821
D 0.525 0.893 0.597 0.594 0.552 −0.338 0.728
T 0.615 0.795 0.438 0.588 0.322 −0.004 0.443 0.784
CL 0.539 0.804 0.104 0.026 0.094 0.058 0.127 −0.096 0.736
AL 0.608 0.856 −0.085 −0.049 −0.079 0.052 −0.288 0.133 −0.134 0.782
IS 0.638 0.914 0.486 0.447 0.318 −0.203 0.579 0.474 0.039 −0.098 0.798
Note: CRSI = customer relationship specific investment; SS = social stick; RECs= relationship end costs; CE
=customer expertise; D = dependence; T= trust; CC = calculative loyalty; AC = affective loyalty; IS =
interaction satisfaction; Diagonal elements are the square root of the average variance extracted of each
construct; Pearson correlations are shown below the diagonal.
Table 3
Fit statistics for hypothesized and alternative models.
Model χ2 d.f. Δχ2 Δd.
f.
GFI TLI CFI RMS
EA
AIC CAIC ECVI PNFI
independence
model
10,235.8
43
406 - 0.254 0.000 0.000 0.216 10,293.8
43
10,446.31
5
19.75
8
0.000
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Saturated
model
0.000 0 - - - - 1.000 870.000 3157.085 1.670 0.000
Hypothesized
model
993.495 365 9242.34
8
41 - 0.884 0.929 0.057 1133.495 1501.532 2.176 0.812
Alternative models
direct paths
from CRSI,
SB, RTCs, CE,
D and T to CC
and AC
1653.885 365 660.390 0 0.796 0.845 0.869 0.082 1793.885 2161.921 3.443 0.754
Hypothesized
model + direct
paths
from SB to AC
and RTCs to
CC
992.837 363 0.658 2 0.884 0.928 0.936 0.058 1136.837 1515.389 2.182 0.807
Notes: We compare alternative models with the hypothesized model. d.f. = degree of freedom, GFI = goodness-
of-fit index; TLI = Tucker-Lewis index; CFT = comparative fit index; RMSEA = root mean square error of
approximation; AIC = Akaike information criterion; CAIC = consistent ACI; ECVI = expected cross-validation
index; ENFI = economical normed fit index. For AIC, CAIC and ECVI, lower value indicates a better fit and a
more economical model; for ENFI, greater value indicates a better fit and a more economical mode
Table 4
Results of proposed model.
Paths Path
coefficients
Hypotheses Test
results
γ11 Customer
relationship
investment
→ Dependence 0.373*** H1a Supported
γ21 Customer
relationship
investment
→ Trust 0.168* H3a Supported
γ12 Social
sticking → Dependence 0.374*** H1b Supported
γ22 Social
sticking → Trust 0.626*** H3b Supported
γ13 Relationshi
p
end costs
→ Dependence 0.411*** H1c Supported
γ23 Relationship
end costs → Trust 0.107* H3c Supported
γ14 Customer
expertise → Dependence −0.282*** H1d Supported
γ24 Customer
expertise → Trust 0.089* H3d Supported
β31 Dependence → Calculative
loyalty
0.234*** H2a Supported
β41 Dependence → Affective
loyalty
−0.413*** H2b Supported
β32 Trust → Calculative
loyalty
−0.227*** H4a Supported
β42 Trust → Affective
loyalty
0.373*** H4b Supported
Feb. 2014. Vol. 3, No.6 ISSN 2307-227X
International Journal of Research In Social Sciences © 2013-2014 IJRSS & K.A.J. All rights reserved www.ijsk.org/ijrss
16
*** p˂0.001.
* p˂0.05.
Table 5
Moderating effects of interaction satisfaction
Main effects
Interaction satisfaction level Test of path
coefficient
growth
Difference
in χ2 value
H Test
results
Low (n=287) High
(n=235)
β coefficient β coefficient
Dependence→Calculative loyalty
0.088 0.246 +0.158 52c.475*** H5a Supported
Trust→Affective
loyalty
0.239 0.363 +0.124 49.119*** H5b Supported
***p˂ 0.001