Creating customer loyalty in business-to-business markets
Nasrin gholamizadeh1, Fakhraddin Maroofi2, Kumars Ahmadi3
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
1
Sanandaj branch, Islamic azad university sanandaj ,Iran; corresponding author
Email:nasrin_gholami2000@yahoo.com
2
Department of management university of kurdistan
Sanandaj branch, Islamic azad university sanandaj ,Iran
3
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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 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
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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 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
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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. 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 &
4
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 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).
5
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
6
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.
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
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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).
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; χ2/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
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(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 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
9
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
effect 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 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.
10
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.
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15
Relationship
Customer
Interaction
Relationship
Satisfaction
Specific
H1a(+)
Investment
H5b(+)
H5a(+)
H3a(+)
Social
Bonding
H1b(+)
H2a(+)
Dependence
Calculative
Loyalty
H1c(+)
H3b(+
H2b(-)
Non-relationship
H1d(-)
H4a(-)
Relationship
End
Affective
Trust
Loyalty
H3c(+)
Costs
H4b(+)
H3d(+)
Customer
Expertise
Fig. 1. Proposed model (Chang, 2012)
16
ξ1 Customer
Relationship
γ 11=0.373***
Investment
γ 21=0.168*
γ 12=0.374***
η1 Dependence
β31=0.234***
Loyalty
ξ2 Social
sticking
γ 22=0.626***
η3 Calculative
β41= 0.413***
γ 13=0.411***
β32= 0.227***
ξ3 Relationship
end
Costs
ξ4 Customer
γ 23=0.107*
η4 Affective
γ 14= 0.282***
η2 Trust
β42=0.373***
γ 24=0.089*
Expertise
χ2 (368) = 995.492, p<.001; χ2/d.f.=2.725, GFI =0.886, TLI =0.932, CFI=0.938,
RMSEA=0.054
Fig. 2. Hypothesized model
17
Loyalty
Table 1
Analysis of measurement model.
Constructs
CRSI1
CRSI2
CRSI
SS
SS1
SS2
SS3
RECs
TC1
TC2
CE
CE1
CE2
CE3
D
D1
D2
D3
D4
T
T1
T2
CL
CL1
CL2
AL
AL1
AL2
IS
IS1
IS2
IS3
MLE estimates
Factor
Measurement
loading
error (δ/ε)
(λx/λy)
0.780***
0.891***
0.853***
AVE
Cronbach's α
0.867
0.609
0.908
0.855
0.585
0.886
0.871
0.684
0.899
0.899
0.685
0.952
0.858
0.536
0.899
0.835
0.619
0.799
0.788
0.548
0.812
0.829
0.618
0.863
0.885
0.647
0.921
0.644
0.297
0.470
0.796***
0.798***
0.811***
0.488
0.532
0.512
0.899***
0.839***
0.256
0.452
0.912***
0.943***
0.874***
0.426
0.288
0.562
0.769***
0.819***
0.759***
0.812* **
0.570
0.558
0.814
0.529
0.688***
0.789***
0.409
0.339
0.749***
0.758***
Complex
reliability
0.557
0.539
0.817***
0.798***
0.459
0.478
0.842***
0.849***
0.859***
0.451
0.447
0.386
18
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.
Construct
s
CRSI
SS
RECs
CE
D
T
CL
AL
IS
AVE
Alpha CRSI
SB
RECs
CE
D
0.609
0.578
0.677
0.676
0.525
0.615
0.539
0.608
0.638
0.899
0.878
0.897
0.947
0.893
0.795
0.804
0.856
0.914
0.759
0.413
−0.133
0.594
0.588
0.026
−0.049
0.447
0.822
−0.096
0.552
0.322
0.094
−0.079
0.318
0.821
−0.338
−0.004
0.058
0.052
−0.203
0.728
0.443
0.127
−0.288
0.579
0.777
0.588
0.394
−0.139
0.597
0.438
0.104
−0.085
0.486
T
CL
AL
IS
0.784
−0.096 0.736
0.133
−0.134 0.782
0.474
0.039
−0.098 0.79
8
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.
19
Table 3
Fit statistics for hypothesized and alternative models.
Model
χ2
independence 10,235.8
model
43
Saturated
0.000
model
Hypothesized 993.495
model
Alternative models
direct paths
1653.88
from CRSI,
5
SB, RTCs,
CE, D and T
to CC and
AC
Hypothesized 992.837
model +
direct paths
from SB to
AC and
RTCs to CC
d.f.
Δχ2
406
-
0
-
Δd.
f.
-
GFI
TLI
CFI
0.254
0.000
0.000
-
-
RMS
EA
0.216
AIC
CAIC
10,293.8 10,446.3
43
15
870.000 3157.085
ECV
I
19.75
8
1.670
PNFI
0.000
365
9242.3
48
41
-
0.884
1.000
0.929
365
660.39
0
0
0.796
0.845
0.869
0.082
1793.88
5
2161.921
3.443
0.754
363
0.658
2
0.884
0.928
0.936
0.058
1136.83
7
1515.389
2.182
0.807
0.057
1133.49
5
1501.532
2.176
0.812
Notes: We compare alternative models with the hypothesized model. d.f. = degree of freedom, GFI = goodnessof-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 model
Table 4
Results of proposed model.
Paths
0.000
Path
coefficients
20
Hypotheses
Test
results
γ11 Customer
relationship
investment
γ21 Customer
relationship
investment
γ12 Social
sticking
γ22 Social
sticking
γ13
Relationshi p
end costs
γ23
Relationship
end costs
γ14 Customer
expertise
γ24 Customer
expertise
β31
Dependence
β41
Dependence
β32 Trust
β42 Trust
→ Dependence
0.373***
H1a
Supported
→ Trust
0.168*
H3a
Supported
→ Dependence
0.374***
H1b
Supported
→ Trust
0.626***
H3b
Supported
→ Dependence
0.411***
H1c
Supported
→ Trust
0.107*
H3c
Supported
→ Dependence
−0.282***
H1d
Supported
→ Trust
0.089*
H3d
Supported
→ Calculative
loyalty
→ Affective
loyalty
→ Calculative
loyalty
→ Affective
loyalty
0.234***
H2a
Supported
−0.413***
H2b
Supported
−0.227***
H4a
Supported
0.373***
H4b
Supported
*** p˂0.001.
* p˂0.05.
Table 5
Moderating effects of interaction satisfaction
Main effects
Dependence→
Calculative
loyalty
Trust→Affective
loyalty
***p˂ 0.001
Interaction satisfaction
level
Test of path
coefficient
growth
Difference
in χ2 value
H
Test
results
Low
(n=287)
β
coefficient
0.088
High
(n=235)
β
coefficient
0.246
+0.158
52c.475***
H5a
Supported
0.239
0.363
49.119***
H5b
Supported
+0.124
21