CREATING CUSTOMER LOYALTY IN BUSINESS-TO-BUSINESS … · CREATING CUSTOMER LOYALTY IN BUSINESS-TO-BUSINESS MARKETS Nasrin gholamizadeh1, ... value of relationship marketing from the

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

1

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

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Feb. 2014. Vol. 3, No.6 ISSN 2307-227X


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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


Feb. 2014. Vol. 3, No.6 ISSN 2307-227X


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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).


Feb. 2014. Vol. 3, No.6 ISSN 2307-227X


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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


Feb. 2014. Vol. 3, No.6 ISSN 2307-227X


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


Feb. 2014. Vol. 3, No.6 ISSN 2307-227X


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.

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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


Feb. 2014. Vol. 3, No.6 ISSN 2307-227X


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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15

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


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


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