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Electronic Commerce Research and Applications 13 (2014) 295–304
Contents lists available at ScienceDirect
Electronic Commerce Research and Applications
journal homepage: www.elsevier.com/locate/ecra
How does customer self-construal moderate CRM value creation chain?
Jia-Yin Qi a, Qi-Xing Qu a,⇑, Yong-Pin Zhou b
a
b
School of Economics and Management, Beijing University of Posts and Telecommunications, PR China
Michael G. Foster School of Business, University of Washington, Seattle, United States
a r t i c l e
i n f o
Article history:
Received 1 February 2013
Received in revised form 28 May 2014
Accepted 1 June 2014
Available online 4 July 2014
Keywords:
CRM value creation chain
Customer value
Organization value
Self-construal
a b s t r a c t
Most of the existing literature on CRM value chain creation has focused on the effect of customer satisfaction and customer loyalty on customer profitability. In contrast, little has been studied about the CRM
value creation chain at individual customer level and the role of self-construal (i.e., independent self-construal and interdependent self-construal) in such a chain. This research aims to construct the chain from
customer value to organization value (i.e., customer satisfaction ? customer loyalty ? patronage behavior) and investigate the moderating effect of self-construal. To test the hypotheses suggested by our conceptual framework, we collected 846 data points from China in the context of mobile data services. The
results show that customer’s self-construal can moderate the relationship chain from customer satisfaction to customer loyalty to relationship maintenance and development. This implies firms should tailor
their customer strategies based on different self-construal features.
Ó 2014 Elsevier B.V. All rights reserved.
1. Introduction
Over the past decades, many firms have implemented customer
relationship management (CRM) to facilitate the development of
firm-customer relationships (Rogers 2005). According to a 2013
Gartner report, the worldwide CRM market experienced 12%
growth in 2012, three times the average of all enterprise software
categories1. However, according to the same survey, 70% of CRM
projects resulted in either loss or no bottom line improvement. Many
executives reported that their CRM initiatives not only failed to deliver profitable growth, but also had damaged long-standing customer
relationships (Richards and Jones 2008). Thus, it is essential to critically examine whether and how CRM creates value and provide
guidance for practitioners to improve the CRM value creation chain.
According to Payne and Frow (2005), CRM creates two kinds of
value: value that customer receives from the organization and
value that the organization receives from its customers. The former
is called customer value and is the base of CRM value creation; the
latter is called organization value and becomes the outcome of providing and delivering superior product/service. Thus, the CRM
value creation chain spans from customer value to organization
value. A firm’s CRM implementation will achieve better performance if it can effectively manage its CRM value creation chain.
⇑ Corresponding author. Address: P.O. BOX 295, Beijing University of Posts and
Telecommunications, Beijing 100876, PR China.
E-mail address: [email protected] (Q.-X. Qu).
1
http://www.forbes.com/sites/louiscolumbus/2013/04/26/2013-crm-marketshare-update-40-of-crm-systems-sold-are-saas-based/
http://dx.doi.org/10.1016/j.elerap.2014.06.003
1567-4223/Ó 2014 Elsevier B.V. All rights reserved.
The research area most related to the CRM value creation chain
is one that explores the relationship between CRM dimensions
(value drivers) and CRM outcomes. Yim et al. (2004) identified four
dimensions of CRM implementations and investigated their effect
on customer satisfaction, customer retention, and sales growth.
Richards and Jones (2008) outlined ten propositions to describe
the relationship among seven CRM drivers and three kinds of customer equity. Krasnikov et al. (2009) examined the positive impact
of CRM implementation on firm’s operational cost efficiency and
firm’s profit efficiency. Liu et al. (2012) linked CRM applications
to shareholder value, and showed that competition features can
moderate the CRM value. Shang and Lu (2012) examined four
CRM dimensions’ impacts on firm performance in the context of
freight forwarder services. Hakkak et al. (2012) studied the role
of CRM in improving organizational effectiveness. It is evident that
much of the CRM research reviewed above has focused on organization value; empirical exploration of the link between customer
value and organization value is still in its infancy (Graf and Maas
2008). Moreover, almost all the research is at the aggregate level;
very little has been done at the individual customer level.
Another related active research area is service profit chain
(SPC). The SPC model, introduced by Schlesinger and Heskett
(1991) and later expanded by Heskett et al. (1997), illustrates the
relationship between employee satisfaction, customer satisfaction,
customer loyalty, and firm’s financial performance. SPC integrates
operation management (OM) and human resources management
(HRM) for organizational improvement in the context of a service
organization (Yee et al. 2009). The SPC proposition has inspired
much investigation into the relationship between customer
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J.-Y. Qi et al. / Electronic Commerce Research and Applications 13 (2014) 295–304
satisfaction, loyalty, and firm performance (Gelade and Young
2005, Maritz and Nieman’ 2008, Chi and Gursoy 2009, Kim et al.
2014), which is very similar to our CRM value creation chain.
Nevertheless, work in SPC is still done at the aggregate level.
Our work explores the mechanism between customer value and
organization value at the individual customer level.
In this paper we aim to investigate the following research questions (RQ):
RQ1. Is there a general CRM value creation chain model at the
individual customer level which to provide the link from customer
value to organization value? Which factors are involved in the CRM
value creation chain, what are the relationships between these factors, and which is the most crucial factor?
To our knowledge, our paper is the first to construct an entire
CRM value creation chain at the individual customer level from
customer value to organization value.
RQ2. Does customer self-construal moderate CRM value creation chain? At the national level, cultural factors have attracted
some research attention. Huber et al. (2007), and Cunningham
et al. (2006) introduced cross-culture into customer value analysis,
and found that customers in different countries have different
value drivers of customer value. Graf and Maas (2008) suggested
that future customer value research pay more attention to the
characteristics of cultural differences. Qi et al. (2012) investigated
the impact of cross-cultural factors on customer lifetime value, but
they did not consider the difference between customers from the
same cultural background. Markus and Kitayama (1991) argued
that although independent and interdependent self-construal are
predominant in individualistic and collectivistic cultures respectively, individuals within a given society can be less or more independent or interdependent. Moreover, Hackman et al. (1999)
demonstrated that independent and interdependent self-construal
orientations are two separate constructs and suggested that the
predictive capacities of these two variables are investigated separately. In this research, we investigate the roles of independent
and interdependent self-construal in the CRM value creation chain.
By answering RQ1 and RQ2, we make two major contributions.
First, our CRM value creation chain from customer value to organization value at the individual customer level provides new insights
for both theoretical CRM development and practical CRM implementation. For theoretical CRM development, this value chain
model suggests the mediation effect of customer loyalty on the
relationship between customer satisfaction and customer patronage behavior. For CRM practice, this value chain model suggests
managers pay more attention to the entire value chain, rather than
just the beginning of the chain (i.e., customer satisfaction).
Second, we are the first to investigate the moderating effect of
customer’s self-construal on CRM value creation chain. We hope
to stimulate more research on CRM value creation process. We also
believe that our findings can help executives to formulate better
customer strategies to make their CRM value creation chain
effective.
The data for our analysis were collected from mobile data service in the telecommunication industry of China, for three major
reasons. First, China is the world’s largest mobile telecom market,
with the number of subscribers increasing rapidly from 145 million
in 2001 to 986 million in 2011. Second, based on China Mobile’s
annual reports, in the past five years, the proportion of voice services in the total revenue decreased 4.8%, 9.9% and 16.7% for China
Mobile, China Telecom, and China Unicom, the three main telecommunication service providers in China. In its place, various
value-added mobile data services – including short message
service (SMS), games, electronic transaction, and web browsing –
are becoming greater revenue sources for telecommunication
service providers. Third, while it is clear that the adoption and
consumption of mobile data services differ by customers based
on their characteristics (Kim et al. 2004, Choi et al. 2005, Qi et al.
2009, Kakousis et al. 2010), very little is known about whether
and how self-construal could impact on this difference.
The rest of the paper is organized as follows: We begin with the
theoretical background and hypotheses development. We then
present our research methodology and the results. Next, we offer
possible explanations of the results, the theoretical and managerial
implications of the findings. Finally, we present limitation and
future research direction.
2. Theoretical background
2.1. Customer value
There are various ways to define, understand and apply customer value (Parasuraman et al. 1985, Eggert and Ulaga 2002,
Walker et al. 2006, Flint et al. 2002, Rosendahl 2009). In this paper,
our definition of customer value follows ‘‘customer value from a
customer’s perspective’’ in Graf and Maas (2008), which is the
value generated by a firm’s product or service as perceived by
the customer, or the fulfilment of customer goals and desires.
Graf and Maas (2008) also reviewed the relationships between
the customer value construct and other central marketing constructs. Among these relationships, they generalized the relationship between customer value and customer satisfaction, and
concluded that customer satisfaction is a post-consumption
assessment by customer about the purchased product or service.
Customer value is the antecedent of customer satisfaction, and customer satisfaction is the consequent of customer value (Fishbein
and Ajzen 1975, Lin and Wang 2006, Turel and Serenko 2006,
Kuo et al. 2009). Thus, customer satisfaction can be regarded as
an indicator of customer value.
2.2. Organization value
There are also several close concepts related to organization
value (Richards and jones 2008, Songailiene et al. 2011,
Srivastava et al. 1998, Rust et al. 2004, Blattberg and Deighton
1996). In this paper, organization value means the economics of
customer acquisition and customer retention for cross-selling,
up-selling and getting customer loyalty. This is similar to customer
value from a company perspective in Srivastava et al. (1998), in
which the ultimate goal of organization value is to maximize a
firm’s profitability from customer.
There are two kinds of indicators for organization value: customer loyalty and customer patronage behavior. Customer loyalty
can be seen as the emotional organization value, and customer
patronage behavior is the behavioral organization value.
The most direct way to monitor the change of organization
value is to observe the change of customer patronage behavior.
Aurier and N’Goala (2010) classified customer patronage behavior
into relationship maintenance and relationship development, both
of which contribute to long-term profitability. Relationship maintenance describes how long a firm can keep relationship with a
customer, and relationship development reflects how much a firm
can get profitability from a customer.
Customer retention is a central concern for CRM (Reinartz and
Kumar 2000, Gustafsson et al. 2005). Based on earlier research,
we use customer relationship duration (‘‘duration’’ for brevity) to
represent relationship maintenance. Duration refers to the length
of a customer’s business relationship with a specific firm.
Retaining customers alone is not enough to develop revenues,
margins and profits (Reinartz and Kumar 2000). Firms’ profitability
also depends on their ability to get current customers to use their
services/products more intensively (usage of service) and buy
J.-Y. Qi et al. / Electronic Commerce Research and Applications 13 (2014) 295–304
additional services/products (cross-buying). In line with Bolton
et al. (2004), and Aurier and N’Goala (2010), we take service usage
and cross-buying as fundamental elements of customer development. Service usage reflects the level of usage of the consumed services, and represents the customer transaction intensity and value
which reveal regularities in the purchase behavior of customer.
Cross-buying refers to the number of associated or closely related
products/services that a customer uses within a firm over time
(Aurier and N’Goala 2010). Therefore, in addition to duration
(length of relationship), usage of service (depth of relationship)
and cross-buying (breath of relationship) are the other two value
resource for multiservice providers (see Verhoefa et al. 2001).
2.3. CRM value creation chain model
The CRM value creation chain refers to the relationship chain
from customer value to organization value. As discussed before,
customer satisfaction is an indicator of customer value; and
customer loyalty and customer patronage behavior are two indicators of organization value. Therefore, the CRM value creation
chain at the individual customer level is the chain between customer satisfaction, customer loyalty and customer patronage
behavior. Fig. 1 shows the conceptual model of the CRM value creation chain.
Although there is an extensive literature on the positive relationship between customer satisfaction and customer loyalty
(Lages et al. 2005, Romano and Fjermestad 2003, and the references therein) and the positive relationship between customer loyalty and customer patronage behavior (Siddiqi 2011), the existence
of the CRM value creation chain cannot be taken for granted. It is
necessary to test the mediating effect of customer loyalty on the
link from customer satisfaction to customer patronage behavior.
3. Research model and development of hypotheses
3.1. The mediating role of customer loyalty between customer
satisfaction and patronage behavior
Customer value is the core issue of marketing research. Thus,
customer satisfaction becomes the most common construct. At
the aggregate level, customer satisfaction is commonly considered
a very important factor for predicting a firm’s financial performance by many executives (Nysveen et al. 2005, Lages et al.
2005, Romano and Fjermestad 2003). Moreover, customer satisfaction is one of the most frequently used marketing metrics and is
often part of a Balanced Scorecard in practice (Nysveen et al.
2005). At the individual customer level, however, questions have
been raised on the assumption that satisfied customers are more
profitable (Jones and Sasser 1995, Seiders et al. 2005). In fact,
satisfied customers can also change into churned customers, and
Fig. 1. CRM Value Creation Chain.
297
satisfied customers are not always profitable customers. Many
organizations have fallen into a satisfaction trap, with management focusing on satisfaction scores at the expense of enhancing
customer retention and lifetime purchases. In light of this, some
researchers attribute customer patronage behavior more to customer loyalty than to customer satisfaction (Oliver 1997).
Recent research findings demonstrate that the link between
customer satisfaction and the various outcomes are not as simple
and direct as they appear. Mediating mechanisms between customer satisfaction and firm’s performance (at the aggregate level),
between customer satisfaction and customer patronage behavior
(at the individual level) are discovered. For example, Homburg
et al. (2005) revealed that willingness to pay is one mediator variable for the link between customer satisfaction and firm performance. Aurier and N’Goala (2010) showed that trust and
commitment mediate the impact of customer satisfaction, which
appears a necessary but not sufficient condition for relationship
maintenance and development. Reichheld (2003), Pritchard et al.
(1992), and Liddy (2000) see willingness to pay as one aspect of
customer loyalty, and customer trust and customer commitment
are empirically tested extensively as the antecedents of customer
loyalty (Keiningham et al. 2007). As an important factor of customer relationship quality, customer loyalty is regarded as the
highest level of customer relationship bonding (Kim et al. 2006).
Although there may be such factors that mediate the relationship
between customer satisfaction and the various outcomes, the
existing models have mostly focused on those factors which are
related to customer loyalty.
Based on these findings we hypothesize that customer loyalty is
the mediator between customer satisfaction and customer patronage behavior.
H1a. The impact of customer satisfaction on relationship maintenance is indirect, positive, and mediated through customer
loyalty.
H1b. The impact of customer satisfaction on relationship development is indirect, positive and mediated through customer loyalty.
The conceptual model on the mediating role of customer loyalty
is shown in Fig. 2.
3.2. Self-construal and its moderating effect on the CRM value creation
chain
Self-construal is concerned with how individuals perceive
themselves in a relationship with others and how individuals form
a set of thoughts, feelings, and actions with a concern for their connection to or separation from others (Markus and Kitayama 1991,
Singelis 1994). There are two types of self-construal: independent
Fig. 2. Research model of the mediator.
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J.-Y. Qi et al. / Electronic Commerce Research and Applications 13 (2014) 295–304
and interdependent. Singelis (1994) defined independent selfconstrual as a ‘‘bounded, unitary, stable’’ self that is separate from
social context. It emphasizes (a) internal abilities, thoughts, and
feelings, (b) being unique and expressing the self, (c) realizing
internal attributes and promoting one’s own goals, and (d) being
direct in communication. An interdependent self-construal is a
‘‘flexible, variable’’ self that emphasizes (a) external, public features such as status, roles, and relationships, (b) belonging and fitting in, (c) occupying one’s proper place and engaging in
appropriate action, and (d) being indirect in communication and
‘‘reading others’ minds.’’ Zhang and Shrum (2009) found that while
an individual show either an independent or interdependent selfconstrual, both types of self-construal can resonate with an individual simultaneously. We study both types of self-construal in
this paper.
The existing research in self-construal alluded to the potential
influence of self-construal on customer behavior (Fan 2002, Dong
2010, Wei et al. 2012). To explore the effect of self-construal on
CRM value creation chain, the objective of our present study is to
examine the effect of self-construal (independent vs. interdependent) on the relationships (1) from customer satisfaction to
customer loyalty and (2) from customer loyalty to customer
patronage behavior.
3.2.1. The moderating role of self-construal between customer
satisfaction and customer loyalty
People with interdependent self-construal prefer a tightly-knit
social framework in which individuals can expect their relatives,
clan, or others in the group to look after them in exchange for loyalty. Wheeler et al. (1989) showed that people with interdependent self-construal tend to engage in long-term in-group
relationships and have more intimate relationships among ingroup members. In other words, interdependent self-construal is
likely to reject out-group members (Turner et al. 1987). To have
loyal interdependent self-construal customers requires more effort
from a firm, because the firm must first make itself considered as
in-group member by the interdependent self-construal customers.
In general, the threshold for changing from an out-group member
to an in-group member of an interdependent self-construal
customer is relatively high. Therefore, it is more difficult for customers with interdependent self-construal to have feelings of
interdependence with the firm even if the firm provides good customer care.
In contrast, people with independent self-construal prefer a
loosely-knit social framework in society wherein individuals are
supposed to take care of themselves (Triandis 1989). People with
high independent self-construct places particular importance on
independence and the expression of one’s own attributes. They
do not classify members into in-group and out-group. Hence, the
psychological threshold for independent self-construal customers
to be loyal to a firm is comparatively low. If the firm can provide
unexpected excellent consumption experiences, it will have high
probability to win the heart of independent self-construal
customers.
In the research on corporate social responsibility (CSR), Dong
(2010) found that, compared with interdependent self-construal
individuals, independent self-construal individuals seem more
sensitive to CSR information. Specifically, when the CSR information is positive, the independent self-construal individuals show
higher level of brand loyalty than interdependent individuals. In
contrast, when the CSR information is negative, all customers show
the same low level of loyalty. Although Dong’s research is about
the moderating effect of self-construal on the relationship between
CSR and customer loyalty, it provides indirect support for our
hypothesis below:
H2. Independent self-construal will positively moderate the
positive relationship between customer satisfaction and customer
loyalty, such that higher customer satisfaction will bring even
higher customer loyalty when independent self-construal is
stronger.
3.2.2. The moderating role of self-construal between customer loyalty
and patronage behavior
For customers with interdependent self-construal, they are
likely to value connectedness and group harmony. When they
develop loyalty with the firm, they have already considered the
firm as an in-group member, and are more likely to show stronger
loyalty to the firm, especially brand loyalty (Fan 2002). Thus they
are more likely to adopt other products from the same firm and
to increase volume and frequency with the firm (Sharma et al.
1999).
Customers with independent self-construal view themselves as
independent individual entities, distinct from the group. Consequently, those with independent self-construal are likely to pursue
their own goals of expressing individuality regardless of the social
context (Trafimow et al. 1991, Ybarra and Trafimow 1998). They
can easily be tempted by competitive products from other firms,
and may not patronize the original products/services provider anymore, even if they are satisfied. Therefore, while it may be relatively easy for a firm to get the loyalty from independent
customers, if the firm wants to keep them longer, it has to do more.
Thus, we hypothesize that:
H3a. Interdependent self-construal will moderate the positive
relationship between the customer loyalty and duration, such that
customers with higher levels of loyalty will stay even longer when
interdependent self-construal is stronger.
H3b. Interdependent self-construal will moderate the positive
relationship between customer loyalty and usage of service, such
that customers with higher levels of loyalty will bring more revenue when interdependent self-construal is stronger.
H3c. Interdependent self-construal will moderate the positive
relationship between customer loyalty and cross-buying, such that
customers with higher levels of loyalty will purchase more other
products/services when interdependent self-construal is stronger.
The conceptual model is summarized in Fig. 3.
4. Research methodology
4.1. Sampling procedure and sample characteristics
The data for this study were collected through survey questionnaire. The 328 potential student respondents were surveyed by
e-mail, while the other respondents were surveyed through field
survey. In order to match quota criteria, the field survey was carried
out in business districts, restaurants, office buildings, and residential communities in Beijing. The field survey was conducted both
during the work week and on the weekend to get more qualified
samples. Ten college students were trained as questionnaire investigators and organized into five pairs. They were dispatched to different locations to conduct the survey. During the field survey in
each place, the pair of investigators explained to each participant
the nature of the study and that their participation was completely
voluntary and non-related to the services of the providers where
they were contacted. The questionnaire survey took place from
November 1, 2009 to December 10, 2009. Discarding incomplete
J.-Y. Qi et al. / Electronic Commerce Research and Applications 13 (2014) 295–304
299
monthly charge, we asked the respondents to provide their average
monthly spending on mobile data services during the last year.
Cross-buying refers to the variety or the number of different services acquired by a customer from the telecom provider. To measure that, we asked customers in the survey to self-report the
number of mobile data services – among Short Message Service
(SMS), Instant Message (IM), mobile e-mail, and location-based
services – that they currently use. Customers provided binary
answers to these options (1 if used, 0 otherwise). The sum of these
four indicators was used as a global indicator to represent the level
of cross-buying.
4.2.2. Independent variables – customer satisfaction
Our measurement of customer satisfaction is based on Lin and
Wang (2006) for M-commerce and we modified their work to fit
the mobile data services for four-item measure in this paper (for
details see Table 2).
Fig. 3. Research model of the moderator.
and obviously untruthful responses, the surveys yielded a final
sample size of 846 respondents, for a response rate of 97.2 percent.
Table 1 shows the detailed characteristics of the sample.
4.2. Measurements
4.2.1. Dependent variables—customer patronage behavior
Relationship maintenance captures telecommunication provider’s ability to keep or retain current customers. We define relationship duration to be the existing length of the relationship from
the time of registration to the survey date, and measure it by asking each customer ‘‘how long you had been with the current telecom provider’’. This approach is in line with that of Seo et al. (2008)
in the US mobile telecommunications service market, in which
they calculated the length of stay to be the number of months since
the beginning of service.
Relationship development is measured by two indicators: usage
of services and cross-buying. Usage of services refers to the level of
the services usage (Aurier and N’Goala 2010). By following Ahn
et al. (2006), who measured the level of service usage by the
Table 1
Sample characteristics.
Demographic variable
Age
15–19 years
20–29 years
30–39 years
40–49 years
50–59 years
60 years and over
Gender
Males
Females
Occupation
Students
Government staffs
Company employees
Animal husbandry and fishery workers
Rural migrant workers
Service staffs
Self-employed and freelancers
Farmers and others
Family pattern
One-generation family
Two-generation family
Three generation family and over
Service
SMS
IM
Mobile email
Location based services
%
n
27.5
44
17.5
4.5
5.9
0.5
241
385
153
39
52
4
60.1
39.9
527
348
37.49
9.14
23.66
0.23
2.74
3.31
17.71
5.71
328
80
207
2
24
29
155
50
17.9
60
22.1
157
525
193
99.7
40
18.7
12.5
872
350
164
109
4.2.3. Mediator – customer loyalty
Based on Xue and Liang (2005), and Lin and Wang (2006), we
measured customer loyalty using four factors: repeated purchase,
positive attitude, expected purchase, and recommendation. We
further tailored these four measurements to better fit mobile data
services (for details see Table 2).
4.2.4. Moderator – self-construal
The measures for self-construal are based on the work of
Singelis (1994). We conducted a separate survey of 200 people to
test Singelis’s measurement in China. According to reliability and
validity, we then selected four items from Singelis (1994) to measure interdependent self-construal and three items for measuring
independent self-construal (for details see Table 2).
4.2.5. Measurements
In order to ensure the clarity of the questionnaire, we first conducted in-depth interviews with 15 customers similar to those in
our sample pool. The respondents showed no difficulty in understanding and answering the questions. We then slightly revised
the questionnaire based on feedback from the interviewees. All
questions employed a seven-point Likert scale. We carefully followed the standard procedure to make sure the measures are good
(Patton 1980). Table 2 contains a complete list of all items used.
4.3. Construct reliability and validity
4.3.1. Construct reliability
To test the reliability of these variables, we used confirmatory
factor analysis (CFA) to assess the scale properties of the measurement model. CFA results indicated that the four measures about
customer satisfaction can be merged into a single one, with the
Cronbach’s Alphas coefficient (0.78, see Table 3) being sufficiently
high to allow further analysis. Similarly, the measures of customer
loyalty, interdependent self-construal, and independent self-construal converge can all be merged into a single factor with Cronbach’s Alpha values of 0.7, 0.92, and 0.84, respectively (see
Table 3). The variance inflation factors (VIF) values in Table 3 are
also below the recommended threshold of 10 (Segars 1997). In
addition, average variance extracted (AVE) values for the constructs also exceed the cut-off point of 0.5 (Fornell and Larcker
1981), indicating that the constructs have captured a sufficiently
high level of variance.
4.3.2. Convergent and discriminant validity
We assess convergent validity by examining the factor loadings
through the exploratory factor analysis. The criteria for an acceptable level of convergent validity are: (1) individual item loadings
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J.-Y. Qi et al. / Electronic Commerce Research and Applications 13 (2014) 295–304
Table 2
Measurement items.
Constructs
Items
Item wordinga
Customer satisfaction (CS)
CS1
CS2
CS3
CS4
CL1
CL2
CL3
CL4
InterS1
Overall, I am satisfied with the mobile data services I am using
Using mobile data services has met with my expectations
I am pleased with the experience of using mobile data services
My decision to use mobile data services was a wise one
My preference for using mobile data services would not change
It would be difficult to change my beliefs about mobile data services
I will continue using mobile data services in the future
Even if friends recommended that I give up using mobile data services, my preference for mobile data services would
not change
I will sacrifice my self-interest for the benefit of the group I am in
InterS2
InterS3
InterS4
InS1
InS2
InS3
I often have the feeling that my relationships with others are more important than my own accomplishments
I will stay in a group if they need me, even when I am not happy with the group.
If my brother or sister fails, I feel responsible.
Being able to take care of myself is a primary concern for me
I prefer to be direct and forthright when dealing with people I’ve just met
I enjoy being unique and different from others in many respects
Customer loyalty (CL)
Interdependent self-construal
(InterS)
Independent self-construal (InS)
a
All items measured on a seven-scale ranging from 1 = ‘‘strongly disagree’’ to 7 = ‘‘strongly agree’’.
Table 3
Reliability, means and standard deviations of research variables.
4.4. Common method bias
Variable
Mean
SD
CA
AVE
VIF
Customer satisfaction
Customer loyalty
Independent self-construal
Interdependent self-construal
0.32
0.29
0.24
0.04
0.1
0.09
0.88
0.86
0.78
0.75
0.84
0.92
0.67
0.97
0.88
0.85
1.58
1.57
1.14
1.40
Note: CA = Cronbach’s alpha; AVE = average variance extracted; VIF = variance
inflation factors.
greater than 0.5, and (2) cumulative variance contribution greater
than 40%. The results of all item loadings are reported in Table 4,
and they support the dimensionality of the constructs.
One additional guideline for discriminant validity is that the
square root of AVE for each construct should be greater than the
correlation values of the construct with other constructs (Fornell
and Larcker 1981). As reported in Table 5, all constructs across
the samples met the guideline. Therefore, the discriminant validity
criterion is also met.
In summary, the measures of the proposed constructs
achieve high reliability as well as convergent and discriminant
validity.
Table 4
Factor loading analysis for the variables.
Self-reported data collection and logical constructs of items lead
naturally to variance in the measurement method (Podsakoff et al.
2003). Whereas this variance is commonly acknowledged, very few
papers actually address it, despite the popularity of self-report survey method (Woszczynski and Whitman 2004).
Given that we measure customer satisfaction, customer loyalty,
and self-construal with information gathered from the same
respondents, we must address the common method variance issue.
We conducted a Harmon single-factor test (Lindell and Whitney
2001), and it showed common method variance unlikely to be a
concern. Table 5 shows the results of the adjustments, with zeroorder correlations below the diagonal and adjusted correlations
above it. This test indicates that our results do not show a substantial bias due to common method variance. All relevant correlations
remain significant after the correction.
5. Results
We aim to explore the mediating role of customer loyalty and
moderating effect of customer self-construal on the CRM value creation chain. To achieve this objective, we employed the following
basic model as a linear specification to test our hypotheses:
patronagej;i ¼ a0 þ a1 genderi þ a2 agei þ a3 familyi þ a4 CSi þ a5 CLi
Variable
Items
Factor
loading
Overall explanation
degree
þ a6 Int selfi þ a7 Ind selfi þ a8 CSi Int selfi þ a9 CSi
Customer satisfaction
SAT1
SAT2
SAT3
SAT4
CL1
CL2
CL3
CL4
InterS1
.811
.817
.785
.671
.763
.718
.744
.776
0.675
59.82%
Ind selfi þ ei
40.86%
InterS2
InterS3
InterS4
InS1
0.613
0.672
0.698
0.679
45.07%
InS2
InS3
0.717
0.751
Customer loyalty
Interdependent selfconstrual
Independent selfconstrual
56.32%
where subscript i indicates the ith customer, j = 1, 2, 3 represents
duration, usage of service, and cross-buying, respectively, and all
the aks are the parameters to be estimated. patronagej,i refers to
the jth element of relationship maintenance and development of
the ith customer. Int_selfi and Ind_selfi indicate interdependent
self-construal and independent self-construal of the ith customer,
respectively. The control variables included gender, age, and family
pattern.
In order to identify the mediating effect of customer loyalty and
the moderating effect of self-construal from the conceptual framework, we applied Ordinary Least Squares (OLS) regressions by
using PROCESS (Hayes 2012) in SPSS version 17.0.
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Table 5
Results of discriminant validity and construct correlations.
1
Customer satisfaction
Customer loyalty
Independent self-construal
Interdependent self-construal
Duration
Usage of service
Cross-buying
0.82
0.57*
0.17*
0.41*
0.45*
0.09*
0.06*
2
3
*
4
*
0.57
0.99
0.17*
0.41*
0.68*
0.14
0.05
5
*
0.17
0.20*
0.94
0.35*
0.16
0.15*
0.07
0.38
0.40*
0.29*
0.92
0.31*
0.10
-0.02
6
*
7
*
0.42
0.63*
0.16
0.35*
n.a.
0.10*
-0.03
0.12
0.15
0.09*
0.13
0.10*
n.a.
-0.03
0.04
0.06
0.12
-0.01
0.02
0.01
n.a.
Note: Square roots of AVEs are presented on the diagonal. Construct correlations are below the diagonal. Construct correlations corrected for common method bias are above
the diagonal.
*
Marks the significance levels.
Table 6
Result of predictive power of customer loyalty including standardized coefficients (b) and significance (sig).
Sample size = 846
Total effect
Direct effect
Indirect effect
Antecedent
Mediator
Consequences
b
Sig
b
Sig
b
Sig
Satisfaction
Satisfaction
Satisfaction
Loyalty
Loyalty
Loyalty
Duration
Usage of service
Cross-buying
2.19
1.73
0.04
p < 0.01
p < 0.01
p < 0.1
0.47
0.27
0.03
p < 0.01
p > 0.1
p > 0.1
0.7139
1.4577
0.007
p < 0.01
p < 0.02
p > 0.1
Entries represent standardized regression coefficients.
Table 7
The moderating role of independent self-construal in the relationship from customer satisfaction to loyalty.
Sample size = 846
Antecedent
Satisfaction
Loyalty
Loyalty
Loyalty
Interaction
Moderator
Independent
Interdependent
Interdependent
Interdependent
Consequence
Loyalty
Duration
Usage of service
Cross-buying
Conditional effect
b
10th
b
25th
b
50th
B
75th
B
90th
b
0.064***
1.312***
0.576
0.062
0.48***
1.797***
1.634
0.04
0.52***
2.616***
1.993
0.04
0.56***
3.227***
2.262**
0.04
0.59***
3.904***
2.559**
0.04
0.63***
4.618***
2.872**
0.04
Regression with robust standard errors. Entries represent standardized regression coefficients. The 10th, 25th, 50th, 75th and 90th percentiles denote the values of
independent self-construal.
***
P < 0.01.
**
P < 0.05.
5.1. Testing the mediator: customer loyalty
As a first step, we tested the mediating role of customer loyalty
in the relationship between customer satisfaction and patronage
behavior. The results can be found in Table 6. As shown, the total
effect of customer satisfaction on patronage behavior is statistically
different from zero (2.19, p < 0.01 on duration; 1.73, p < 0.01 on
usage of service; 0.04, p < 0.1 on cross-buying). This total effect consists of the direct effect of customer satisfaction (0.47, p < 0.01 on
duration; 0.27, p > 0.1 on usage of service; 0.03, p > 0.1 on crossbuying) and the indirect effect through customer loyalty
(0.5738 * 2.987 = 0.7139, p < 0.01 on duration; 0.5738 * 2.5404 =
1.4577, p < 0.05 on usage of service; 0.5738 * 0.0122 = 0.007,
p > 0.1 on cross-buying). Thus, the direct effect of customer satisfaction is statistically different from zero on duration, but not on usage
of service or cross-buying. The indirect effect of customer satisfaction is significant on duration and usage of service but not on
cross-buying. Thus this result is consistent with Hypothesis H1a,
and partially supports Hypothesis H1b.
5.2. Testing the moderators: independent and interdependent selfconstrual
Muller et al. (2005) proposed three more models to specifically
test the simultaneous existence of mediated moderation effects.
Given evidence of interaction between independent variable and
moderator, as established by a statistically significant interaction
coefficient (Hayes 2012), we should probe the interaction by
estimating the conditional effect of the independent variable at
various value of the moderator, deriving its standard error
and testing whether it is statistically different from zero by
either a null hypothesis test or the construction of a confidence
interval.
Table 7 presents the results of the moderating effects of independent self-construal on the relationship between customer satisfaction and customer loyalty. As shown, the coefficient for the
interaction between customer satisfaction and independent selfconstrual was 0.064 and statistically different from zero
(p < 0.01). In addition, PROCESS displays the conditional effects of
customer satisfaction, as well as their significance, at levels of
10th, 25th, 50th, 75th and 90th percentiles of independent selfconstrual. These results can be found in Table 7 too. We conclude,
based on Hayes (2012), that independent self-construal has a moderating effect on the impact of customer satisfaction on customer
loyalty. This gives full support to Hypothesis 2.
Table 7 also shows the results of a similar test of the moderating
effect of interdependent self-construal. The moderating effect of
interdependent self-construal on the magnitude of the effect of
customer loyalty on duration is statistically significant (1.312,
p < 0.01). This gives full support to Hypothesis 3a.
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Although the moderating effect on the relationship between
customer loyalty and usage of service is statistically insignificant
(0.576, p > 0.1), we think it should not be ignored, based on the
work of Zhao et al. (2010). As can be seen in Table 7 among low values of the perceived prevalence of interdependent self-construal
(at 10th and 25th percentiles), customer loyalty had no effect on
usage of service, with conditional effects of the experimental
manipulation of 1.634 and 1.993, respectively (both p > 0.1). However, among those moderate (50th percentile), high (75th percentile), and very high (90th) values of the perceived prevalence of
independent self-construal, customers with high loyalty will spend
more money on services than those with low loyalty (with conditional effects of 2.262, 2.559 and 2.872, respectively, all p < 0.05).
Therefore, based on Hayes (2012), we also found support for the
moderating effect of customer interdependent self-construal on
the relationship between customer loyalty and usage of service,
which means Hypothesis 3b is supported.
Finally, Table 7 shows the interaction effect between customer
loyalty and interdependent self-construal to be insignificant
(p > 0.1). Furthermore, the conditional effects of customer loyalty
at various values of interdependent self-construal are also not significant (p > 0.1). Therefore, Hypothesis 3c is not supported.
6. General discussion and conclusion
6.1. Theoretical implications
The overall objective of this study is to illuminate the CRM
value creation mechanism, which links customer value to organization value, at the individual customer level. Our theoretical contribution is the construction of an entire CRM value creation chain
at the individual customer level, in which customer satisfaction
affects customer loyalty which in turn affects customer patronage
behavior including relationship maintenance and development. In
other words, customer satisfaction indirectly affects relationship
maintenance and development, while customer loyalty directly
influences them. Thus, customer loyalty plays a larger role than
customer satisfaction when the objective of the firm is to increase
revenue and margins and to leverage customer lifetime value
(CLV). To test this, we conducted an empirical study based on survey results of mobile data services consumption in China. First, we
found a surprising result that although customer loyalty enhances
the length (duration) and depth (usage of services), it has no
impact on the number of mobile data services purchased by customers (cross-buying). As additional services are intangible and
difficult for customers to evaluate beforehand, customers are more
hesitant to broaden the current relationship even if they are loyal
to the firm. This finding is in line with Verhoefa et al. (2001) in
which they empirically showed the impact of customer loyalty
on cross-buying to be weak in financial services. We also found
that customer satisfaction is an indirect antecedent of relationship
maintenance and development—through customer loyalty—and
thus contributes to the long-term perspective of CLV management.
Thus, our study complements those of Seiders et al. (2005),
Keiningham et al. (2007), Carpenter and Fairhurst (2005), Jones
and Reyndds (2006), Aurier and N’Goala (2010), but contradicts
those of Lages et al. (2005), Romano and Fjermestad (2003).
Second, we demonstrated that the relationship between customer satisfaction and customer loyalty can be moderated by customer’s self-construal. This finding is consistent with that in
Trafimow et al. (1991) where individuals with independent selfconstrual are defined as different from others and motivated to
seek autonomy, achievements and success relative to others. They
are more likely to accept new innovations. Once customers
with high independent self-construal are highly satisfied, these
customers can lessen the intensity of blame and anger in damaged
relationship and will show their loyalty and maintain the relationship longer. Thus, we add another important factor – self-construal
– to the considerable literature that counts customer income, product category, market characteristics, convenience motivation, and
purchase size (Oliver 1999, Anderson and Srinivasan 2003) as
moderators of the relationship between customer satisfaction
and customer loyalty. Furthermore, ever since Jones and Sasser
(1995) posed the question of why satisfied customer defect, many
studies have been conducted and different answers have been
given. In this paper, we found a new possible answer, that customers’ independent self-construal could play a contributing role to
this phenomenon.
Third, we found that self-construal is a significant moderator of
the relationship between customer loyalty and duration. For customers with higher interdependent self-construal, the positive
relationship between customer loyalty and duration will be stronger. Since most service firms use customer loyalty as a metric to
design marketing strategies aimed at decreasing customer churn
and increasing customer consumption, our finding provides a significant moderator, namely self-construal, on the relationship
between loyalty and duration. Customers with higher level of loyalty will maintain duration longer when the interdependent selfconstrual is higher. Conversely, independent self-construal may
be an important factor in understanding the question of why loyal
customers do not always have long duration (Jones and Sasser
1995 and Reichheld 2003).
6.2. Managerial implications
In our construction of the CRM value creation chain, customer
loyalty plays a crucial mediating role between customer satisfaction and patronage behavior. We were able to verify this empirically with survey responses about mobile data services. This
suggests that customer loyalty should be regarded as a more crucial metric than customer satisfaction in the design of CRM value
creation strategies. To achieve that, firms should enhance their
interactions with their customers. The more actively and timely a
firm communicates with its customers, the tighter and the more
intimate the bond it can establish with its customers, thus achieving higher customer loyalty (Baird and Parasnis 2011). Nowadays,
firms can leverage social media such as Facebook and Twitter as
convenient service channels to interact with customers.
Since the CRM value creation chain may vary with customers’
self-construal, it becomes necessary for firms to tailor their CRM
value creation strategies to individual customers with different
self-construal features. Although it’s a challenging task to identify
each customer’s self-construal directly (Kag˘itçibasßi 1997,
Oyserman et al. 2002), if firms can correlate customers’ self-construal with their demographic characteristics, then they may be
able to make useful predictions on each customer’s self-construal,
and then target specific marketing programs based on self-construal features to customers in the corresponding demographic segments. Some research on customer segmentation has provided
good clues to complete this indirect customer’s self-construal identification (Hahn and Rita 2009 and Lalwani and Shavitt 2013).
For customers with interdependent self-construal, firms should
be patient while waiting to be accepted as in-group members. During this period of time, firms should show more sincerity and
actions to influence this kind of customers gradually. Moreover,
firms can benefit significantly by adjusting products/services to
meet the sense of identity from an in-group members. For examples, for telecommunication operators, they can provide mobile
data services that can strengthen group connection. Meanwhile,
it is also necessary for firms to discover the most active and loyal
customers with interdependent self-construal, and use them to
J.-Y. Qi et al. / Electronic Commerce Research and Applications 13 (2014) 295–304
bring more customers from their social groups. Therefore, for interdependent self-construal customers, firms should focus more on
keeping their loyalty. In fact, there are plenty of loyal brand cheerleaders that are active social media users, and they are all too willing to share positive experiences and comments, even without
being asked (McAlexander et al. 2002 and Kaplan and Haenlein
2010). Firms should target such loyal customers (Williams and
Cothrell 2000), and identify these loyal customers on social media
though data mining techniques (Li et al. 2010 and Shaw et al.
2001).
For independent customers it’s more important to keep them
satisfied. Although self-construal is largely beyond the control of
firms’ management, customer satisfaction can be somewhat controlled by it. Once customers with high independent self-construal
come to a high level of customer satisfaction, they will show their
high loyalty. Therefore, in the long run, for independent self-construal customers, firms should focus more on winning their satisfaction, and spend more effort on customer service, in order to
obtain customer loyalty. Firms may actively involve their customers with independent self-construal in new product development
which includes being the source of innovative ideas, providing
input for new product designs and enhancements and participating
in product testing (Wayne et al. 2010). Such approaches can lead to
novel, personalized, fashionable, and useful services/products to
enhance these customer experience quality, and then increase customer satisfaction.
6.3. Limitations and future research directions
The theoretical and managerial contributions should be considered in light of the limitations of this research.
First, our data about customer patronage behavior are selfreported. To calculate duration and usage of service more precisely,
one must obtain users’ actual usage data directly from services
providers. This is a promising yet challenging direction for future
research.
Second, besides self-construal, other factors such as service
types and service quality, could play a similar moderating role in
the CRM value creation chain. This could be another fruitful direction for future research.
Third, our results are based on a limited set of data on mobile
data services consumption. Compared with other industries such
as retailing, telecommunication has relatively low competition
intensity, which may decrease the effect of customer’s self-construal in the value chain. Future studies may compare other industry sectors to verify the results from this study.
Acknowledgments
This paper was supported by the National Natural Science
Foundation of China (Project No.:71171023), the National Science
Foundation (Project No. CMMI-0645075), Research Fund for the
Doctoral Program of Higher Education of China (Program No.:
20120005110015), and Co-Research Fund of the ministry of education of P.R. China& China Mobile (ProgramNo.MCM20123021), Program for New Century Excellent Talents in University (Program
No.: NCET-10-0241), Major State Basic Research Development Program of China (973 Program) (No. 2012CB315805), and BUPT
Excellent Ph.D. Students Foundation (No. CX201335).
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