ISTANBUL UNIVERSITY –
JOURNAL OF ELECTRICAL & ELECTRONICS ENGINEERING
YEAR
VOLUME
NUMBER
: 2009
:9
:2
(1015-1028)
THE MEASUREMENT OF SWITCHING COSTS AS A
PERCEPTION OF CUSTOMERS IN THE TURKISH CREDIT
CARD MARKET
Serkan AYDIN Gökhan ÖZER
Halim Kazan M. Cüneyd Doğruer
Gebze Yüksek Teknploji Enstitüsü. Çayırova –Gebze/KOCAELİ
e-mail:[email protected], [email protected], [email protected],
[email protected]
ABSTRACT
Nowadays, protecting market share against rival firms is more important than ever due to the decline in
market growth rate and the increase in the competition. The main requirement for protecting market share is to
create loyal customers. Switching costs are one of the strategies for reaching this goal.
Switching costs are important factors in terms of both banks and customers. Switching costs occur, when a
customer prefers a different brand in his new purchasing decision process and they contain financial, procedural
and psychological dimensions.
In this study, the aim is to develop a third-order measurement model with all the dimensions. Such
dimension as multi-dimensional switching costs is used to determine switching costs value of subscribers in Turkish
credit card market. We also examined the existing studies in literature on switching costs with regard to credit
cards. It may have been previously studied but in the literature we didn’t come across any metrics for measuring the
switching costs and perception costs of the customers in considering three dimensions of this structure or model. We
developed a third-order measuring model and tested the third-order measurement model’s validity and reliability
and it was verified using exploratory and confirmatory factor analysis. We used a survey of switching costs and
perception costs associated with the banks and credit card users. The survey was sent to 1020 credit card users. 450
users responded to the survey. A return rate of the survey was 45%. Psychological switching costs were found to be
the most important factor.
Keywords: switching costs, credit card market, third-order measurement model, confirmatory factor
analysis.
Received Date: 02.07.2009
Accepted Date: 05.11.2009
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The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
1. INTRODUCTION
Porter [42] defines customer switching costs
as one-time costs associated with the buyer switching
from one supplier’s product to another’s. In other
words, customer switching costs make it costly for
customers to change service providers. In this
context, customer switching costs give firms market
power over their customer base.
Indeed, many empirical studies show that
customer switching costs play a crucial role in
protecting market share, and achieving and sustaining
competitive advantage [16]; [29]; [32] etc.).
Customer switching costs are the main decision area
for marketing activities especially for credit card
markets. This is because credit card markets have to
deal with a challenge of increased competitiveness
and reduced market growth rate; therefore protecting
market share is more important. Accordingly, it is
seen that banks focus on activities, which can affect
customer switching costs.
Also we suggest that the credit card market is
a natural setting for analyzing customer switching
costs. The importance of switching costs in the credit
card sector had been firstly suggested by Ausubel
(1991). A while back an empirical work was done by
Calem and Mester (1995). They had found that in the
credit card market there were observable consumer
characteristics that were correlated with switching
costs. They show that customers who do not search
and have high debts have much more difficulty
switching cards than those who do not have debts.
Stango (2001) suggests that there is a natural
environment between the pricing of the credit card
sector and customer switching costs.
Since the switching costs have different
aspects, display the difference among the sectors and,
have to do with customer perception, switching costs
cannot be measured directly. Hence, a valid and
reliable measurement model should be developed to
measure customer switching costs.
Measurement models are important tools for
measuring abstract phenomena such as, an attitude, a
behavior and motivation especially in behavioral
science. Items used for these variables (latent) include
error terms. The measurement models enable
researchers to determine the error terms and the
relationship between latent variable and items. In
order to determine the relationship, explanatory and
confirmatory factor analysis can be used.
In this paper, customer switching costs
measurement model was developed consistent with
literature for measuring customer switching costs in
the Turkish credit card market. Firstly, we did a
literature review of switching costs. Secondly, we
developed a measurement model of switching costs
based on the literature. Following these steps, validity
and reliability of the measurement model were tested
using exploratory and confirmatory factor analysis.
Moreover, we wanted to determine switching costs
value of the customers for each banks in the market,
and to make comparisons among the banks. In the
conclusion section, limitations and further research
directions have been discussed.
2.
DEFINITION
OF
CUSTOMER
SWITCHING COSTS
According to Jackson (1985) switching costs
are the sum of economic, psychological and physical
costs. Thus, switching cost can be seen as a cost that
deters customers from demanding rival firm’s brand.
These costs include not only the cost that can
be measured as monetary value but also the
psychological effect of becoming a customer of a new
firm, and effort and time required to buy a new brand
[32]; [28]. Hence, switching costs are associated with
customer perception, related to individual criterion
along with monetary value that can be objectively
measured. Thus, switching costs are partly consumerspecific (Shy, 2002).
Switching cost provides firms with some
advantages, which directly affect customer loyalty
level: (1) These costs reduce customers’ sensitivity to
the price and satisfaction level [17] (2) Customers
perceive functionally homogeneous brands as
differentiated heterogeneous brands [29] etc. In other
words, in a market with switching costs when a
customer has functionally identical brand alternatives,
the customer will prefer the same brand by exhibiting
brand loyalty [29].
On the other hand, since switching costs
confer market power on firms, in markets with
switching costs current market share is a crucial
antecedent of future profit [29]; [32]; [45]; [7].
Switching costs cause customers to buy the same
brand even if rival brands are much cheaper. This
behavior is called customer loyalty and the main
consequence of customer loyalty is the ability of
firms to charge prices above marginal costs (Shy,
2002). The charging prices above marginal costs
enables firms to achieve more profit than its rivals
(Türkay, 1993). In addition, research shows that with
each additional year of a relationship between a
company and a consumer, the customer becomes less
costly to serve because of learning effects and
decreased servicing costs (Ganesh et al., 2000, p. 65).
At a micro level, switching costs directly
influence customers’ sensitivity to the price level
[29]; [29] and thereby influencing customer loyalty
(e.g. Eber, 1999; [25]; [9]; [11]; Feick et al., 2001).
At a macro level, switching costs are the factors,
S. AYDIN, G. ÖZER, H KAZAN., M.C. DOĞRUER
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The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
which affect domestic demand for imported or
domestic brands and thereby affecting foreign policy
(Greaney, 1996 and 2000; Benito et al., 2002).
In general, since switching costs deter
potential rivals from entering the sector, they are seen
as strategic factors that determine the density of
competition [42]. Therefore, switching costs are taken
into account for strategic decisions [42]. Especially,
switching costs become more strategic in networked
competitive environment [23].
It may be due to the lack of comprehensive
studies in the literature, the concept of customer
switching costs, which is a multi-dimensional factor,
is evaluated as a unidimensional factor. This may be
because the concept of customer switching costs is
relatively a new issue, consumer specific (Shy, 2002),
and a multi-dimensional construct [32]. Additionally,
it varies from sector to sector (Fornell, 1992).
Therefore, there has not been a generally accepted
measurement model for switching costs yet.
3. MEASUREMENT OF CUSTOMER
SWITCHING COSTS
As reported above, the customer switching
cost is multidimensional and partly consumer specific
concept that contains different dimensions such as
financial, time and psychological costs. Therefore,
the components of customer switching costs must be
determined and the reliable and valid measurement
model must be developed in order for customers to be
protected against rival firms.
According to [29], switching costs include
learning costs, transaction costs and artificial or
contractual costs. Transaction costs occur when a
customer gives up a service provider and find a new
service provider. For example, two banks may offer
identical checking accounts, but there may be high
transaction costs associated with closing an account
with one bank and opening another with the
competitor [29]. Another switching cost is learning
cost such as the costs of switching to a new brand of
computer or cake mix after learning to use another
brand [29]. The final switching cost is artificial or
contractual cost such as repeat-purchase coupons and
“frequent-flyer” programs that reward customers for
repeated travel on the same airline, and penalize
brand switchers [29].
Guiltinan (1989) [20] decomposed switching
costs into four main groups; (1) contractual costs, (2)
set-up costs, (3) risk or continuity costs and (4)
psychological costs. In fact, Jones and Sasser (1995)
determine that switching costs in medical institutions
are higher than other sectors, since customer
switching costs are based on risk perception,
consistent with classification of Guiltinan (1989).
Burnham et al. (2003) [11] develop a
switching cost typology that identifies three types of
switching costs, each of which contained multiple
facets: (1) procedural switching costs (consisting of
economic risk costs, evaluation costs, learning costs,
and set-up costs), (2) financial switching costs
(consisting of benefit loss costs and monetary loss
costs), and (3) relational costs (consisting of personal
relationship loss costs and brand relationship loss
costs). Burnham et al. (2003) [11] found support for
eight switching cost facets. This eight-facet typology
is based on a study of two continuous service
industries. These industries consist of consumers that
make a conscious effort to switch away from an
ongoing relationship. The comprehensive scope of
the typology and the strong theoretical basis for the
facets included suggest that the typology provides a
sound framework for understanding the structure of
switching costs across consumer contexts. Thus, we
can improve a new method and a new categorization
for analyzing switching costs, as seen in Table 1.
Table 1: The types & facets of switching costs and the affecting antecedents
Types
Facets
Financial
Switching
Costs
Monetary Loss Costs
Benefit Loss Costs
Evaluation costs
Procedural
Switching
Costs
Set-up Costs
Learning Costs
Antecedents
The prices of the products
The time needed for improving products
Firm applications
Geographical distribution
The number of alternatives
The hardship of accessing to the information
The degree of not being physical
The degree of originality to the customer
Geographical distribution
The technical specifications of the product
The degree of originality to the customer
The technical specifications of the product
The degree of standardization
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The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
Psychological
Switching
Costs
Brand and/or institutional image
The trust in firm and/or employees
Relationship Loss Costs
The time of relationship
The degree of attendance for service production
The quality of the product
The degree of diversify
Uncertainty (Fuzzy) Costs The degree of not being physical
The behaviour of risk avoidance
Past experience related with alternative products
(Reference: Hess and Ricart (2001); Burnham et al. (2003); Porter (1998); Jones et al. (2002); Patterson and
Smith (2001))
Economic risk costs in procedural costs in
Burnham et al. (2003) [11] mean the perceived
psychological cost based on the risk of alternatives.
Alternatives are highly risky for a customer, because
unused brands may not meet the customer
expectations and they have uncertainty. In the
same way, both personal relationship loss
costs and brand relationship loss costs are perceived
as psychological costs. In this context, a new
measurement and classification model of customer
switching costs can be developed for measuring
switching costs. Hence, switching costs consist of
three sub-factors: financial costs, procedural costs
and psychological costs. The new model can be seen
in
Figure
1.
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The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
SC FM
SC F
SC FB
SC P L
SC
SC P
SC PS
SC P E
SC Ps R
SC Ps
SC Ps U
Figure 1: Third-Order Measurement Model of Customer Switching Costs
3.1. Financial Costs (SCF)
3.1.1. Monetary Loss Cost (SCFM)
It is the cost that is associated with the break
of the current relationship and incremental cost to
start a new relationship when a customer switches his
brand [46]; [47] . Switching the brand involves onetime expenditures for the customer.
3.1.2. Benefit Loss Cost (SCFB)
3.2. Procedural Costs (SCP)
3.2.1. Evaluation Cost (SCPE)
Evaluation or search cost is the time and
effort required to identify a new brand [46]. This cost
stems from customer’s buying decision-making
process. Buying decision-making process includes the
following steps: need recognition, information search,
evaluation of alternatives, purchase decision and post
purchase behavior (Kotler, 1997; [9].
The process deters customers from switching
current brand, because it requires effort and time, thus
causing switching cost. For example, if a mobile
phone user decides to switch his operator, he must
collect information about the other operators in the
market and their different alternatives, and compare
them with regard to billing, coverage area, valueadded services etc.
Indeed, Gonul and Srinivasan (1997) find
that since income level is a measure of the value of
Benefit loss cost is related to the contracts
that create additional benefits for staying with an
incumbent firm[9]. The additional economic benefits
are based on loyalty programs of firms. Firms supply
additional economic benefits for creating customer
loyalty and preserving its customer base against rival
firms.
time, households with higher income levels are more
likely to be brand loyal than the households with
middle to lower income levels in disposable diaper
market in USA. Therefore, time is a crucial factor for
customers and causes switching costs.
On the other hand, according to “cognitive
dissonance” customers collect information in the
period of pre and post purchase for reducing
dissonance [9], when it is difficult to evaluate
alternatives or a customer has insufficient information
about alternatives, the customer tends to purchase
current brand for reducing dissonance.
3.2.2. Set-up Cost (SCPS)
Set-up cost is the time and effort cost related
to the process of initiating a relationship with a new
service provider or brand [20]; [32]. Also, especially
in sectors with high customization, service provider
(firm or staff), must learn customer’s expectations for
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The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
facilitating customer satisfaction [25]. This learning
process creates uncertainty for the customer to meet
his expectations. Explaining the desired hair style
while changing a hairdresser can be given as an
example.
3.2.3. Learning Cost (SCPL)
Learning cost is the time and effort cost of
acquiring new skills or know-how in order to use a
new product or service effectively [11], and it is one
time cost (Nilssen, 1992). Even though all brands are
functionally identical, if one brand’s use is different
from others and learning things cannot be transferred
into others, then learning cost occurs. In this
situation, it is required to spend time and effort for
effectively using new brand [29]. “A number of
computer manufacturers may make machines that are
functionally identical but, if a consumer has learned
to use one firm’s product line and has invested in the
appropriate software, he has a strong incentive to
continue to buy machines from the same firm, and to
buy software compatible with them” [32].
3.3. Psychological Costs (SCPs)
Switching costs also include psychological
and emotional costs. Social bonds and trust are built
up over a period of time between a customer, staff
and a firm [41]. Also, the customer faces uncertainty,
because he cannot completely evaluate a brand’s
performance before purchasing it. The customer
perceives all as psychological costs.
In fact, when switching costs stem from long
term relationship and repeated contacts between the
customer and the firm, and switching costs bolster
this relation, switching costs may be more
pronounced [28]. In this case, switching the service
provider will cause not only financial costs that
resulted from relation over a period time, but also
psychological costs.
3.3.1. Relational Cost (SCPsR)
Relational cost is related to relationship
between customers and firms. Relational cost has two
sub dimensions: (1) personal relationship loss cost
and (2) brand relationship loss cost [11]. [25] call
relational cost as sunk cost and, define it as customer
perceptions of the non-recoupable time, money and
effort invested in establishing and maintaining a
relationship,
The relationship may be between customersstaff and/or customers-firm, according to the
characteristics of services. For example; losing trust
of the customer on staff (e.g. Sabol et al., 2002;
Kennedy et al., 2001; Doney and Canon, 1997; Swan
et al., 1999 etc.) and firm or, brand [38]; Chaudhuri
and Holbrook, 2001; Lau and Lee, 1999; Garbarino
and Johnson, 1999 etc.) brings about the perception
of relationship loss cost for customers. According to
Ganesan (1994, p. 3), “a retailer’s trust in a vendor
affects the long term orientation of a retailer in three
ways: (1) it reduces the perception of risk associated
with opportunistic behaviors by the vendor, (2) it
increases the confidence of the retailer that short-term
inequities will be resolved over a long period, and (3)
it reduces the transaction costs in an exchange
relationship”.
In markets where customers play an integral
role in service process, interpersonal relationships
between customers and staff is seen as an element of
switching cost [25]; [11].
Actually, both [26] and [25] found that
switching costs for hairstylist in which
interrelationships were stronger, were higher than for
banks. In the same way, [41] showed that the social
bonds in hairdressing and automobile service
significantly related with relationship commitment,
but there was no significant relation between two
variables in travel and banking market.
Also, customers have to reveal a lot about
their personal information in some markets. In such
markets, insurance, banking, financial planning
services etc., the revealing personal details causes
relational switching cost [46]). On the other hand,
customer will perceive losing the name or image, or
trusting on the brand, when switching brand or
service provider (Porter, 1988b; Burnham et al.,
2003).
3.3.2. Uncertainty Cost (SCPsU)
A customer faces a risk when he switches
the brand which he can evaluate and estimate its
performance. This is because the customer cannot
evaluate the brand’s performance or quality before
using it. In this case, the cost associated with using
new brand is called as uncertainty [25], stress [46],
failure risk [42], quality uncertainty [32], perceived
risk [14] or economic risk [11].
Individuals avoid uncertainty in their
decisions. Therefore, customers try to minimize the
risk of new brand by collecting information in prepurchase phase. When there is not enough
information or quality is difficult to judge or varies
considerably across alternatives, tendency of
customers to switch will decrease due to increasing
risk and uncertainty [25]. Especially in high
technology markets, the level of uncertainty is higher
than other markets because of a lack of relevant
experience with the product and particular market
conditions that impose demands on a buyers’
information processing capacity (Heide and Weiss,
1995).
Additionally, customers to a new market
prefer topdog brands (dominant brands) to underdog
brands (lesser-known brands), in order to minimize
perceived risk and uncertainty associated with
S. AYDIN, G. ÖZER, H KAZAN., M.C. DOĞRUER
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The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
inexperience (Wright et al., 2000). As the information
and experiences of customers increase over time, the
preference possibility of underdog brands increases.
“This is to say that the greater the number of productrelated experiences accumulated by consumers, the
more likely they are to recognize and try an underdog
brand” (Wright et al., 2000, p. 140). Briefly,
information and experience of customers about
brands influence the perceived uncertainty or risk.
One of the main information sources of a
customer for evaluating alternatives is his and other
customers’ experience. Referring to cognitive
dissonance [9], the customer collects information and
use all the past purchasing experience, in order to
decrease his anxiety that whether the purchasing
decision is true or not. In this process, if the customer
switches the brand, he would compare the brands.
Therefore, the more the switched brand’s
performance is, the higher the uncertainty of
alternative brands is. Hence, customers who want to
decrease cognitive dissonance prefer the brand, which
they have used before [32].
Moreover, customer satisfaction, which is a
function of pre-purchasing expectations, post
purchasing performance perception, (Kotler, 1997).
Grewal et al. (1998) indicates that post purchasing
performance perception positively affects customer
satisfaction. For this reason, it can be claimed that as
the customer satisfaction increases, perceived
uncertainty of alternatives increases too, thereby
uncertainty switching cost extends.
4. ANALYSIS OF THE MEASUREMENT
MODEL
As highlighted above, switching costs is the
variable that cannot directly be measured due to its
latent facets. Hence switching costs can be only
measured indirectly. Therefore, a model must be
developed to contain all the dimensions and the
purpose of the measurement of switching costs.
As seen in Fig.1 switching cost is
conceptualized as third-order factor model that
consists of financial (SCF), procedural (SCPr) and
psychological cost (SCPs) perceptions; and consistent
with these a testing model has been developed.
Financial cost (SCF) perception is adapted
from the study of [11] and [5]. Monetary loss cost
perception (SCFM) is tested by four and benefit loss
cost perception is tested by two questions.
Procedural switching cost perception (SCP) is
adapted from [11] and the study of [20]. As a result;
the facets of the procedural switching cost is tested by
different number of questions: learning cost
perception (SCPL) tested by three, set-up cost
perception (SCPrS) tested by four, and evaluation
cost perception (SCPE) tested by five questions.
Uncertainty costs perception (SCPsU) and
relationship loss cost perception (SCPsR) that forms
the psychological switching cost perception (SCPs) is
tested by adapting the studies done by [11], [4] and
[25], [26], and three questions are dedicated to each.
4.1. Data Gathering
In this study, the main population is credit
card users in Turkey. In Turkey, there are
approximately 27 million credit card users. Because
of the size of the main population, we limited
sampling area with Istanbul, since Istanbul is the
biggest city in Turkey. In order to represent the main
population optimally, credit card users sampling was
applied. A survey was sent to 1020 credit card users.
450 forms the questions formed by 5-Likert survey
were directly applied to 940 credit card users in
Istanbul. 450 users responded to the survey. A return
rate of the survey was 45%. 25 surveys were
discarded because they weren’t correctly filled out.
For this reason, final data set contains 375 credit
card users. Final data is composed of a population
with various demographical and economical
properties. A total of 47 percent of the sample is
female; the mean age of the sample is 31 years; and
respondents’ average monthly revenue is 604.5 US$.
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The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
Table 2: Sample Characteristics
Duration
İncome Groups
Monthly Expenditures
0-2 year
2-4
4-8
8->
0-250 US$
251-500 US$
501-1001 US$
1001-2000 US$
2001- > US$
0-100 US$
101-250 US$
251-500 US$
501 -> US$
4.2. Exploratory Factor Analysis
Confirmatory factor analysis can be used to
determine the validity and the reliability of thirdorder factor testing model that was developed to test
switching cost. But, before analyzing the model with
confirmatory factor analysis, it must be determined
whether the observed variables which reflect a
specific latent variable really measure that variable
with exploratory factor analysis [19].
All the questions were tested by principal
component analysis with varimax factor rotation
using SPSS. Factor score coefficients showed that
two questions for testing the evaluation costs (SCPE)
and one question for testing the set-up cost perception
(SCPS) did not capture the intended factor. Thus,
these questions were removed from the analysis and
the factor analysis was rerun.
According to the results of repeated factor
analysis, eight factors were determined and they
explained about 63 percent of the variance observed.
The factors and item factor score coefficients are
listed in Appendix A. Both Kaiser-Meyer-Olkin
measure of sampling adequacy (0.741) and Bartlett’s
test of sphericity (Chi-square test statistic of 2222.27
with 210 degrees of freedom) showed the
significance of exploratory factor analysis [37].
4.3. Confirmatory Factor Analysis:
In confirmatory factor analysis, there is
limited information concerning the measurement
model and one factor structure is developed using the
information. Moreover, the factor score coefficients
of the variables and variance values can be
determined respectively, and their significances can
be examined [47]. According to the second factor
analysis, 21 items were used as an indicator of the
first-order factors. These first-order factors were used
in determining of the second- order factors (SCF,
(%)
33
37
22
8
15
25
44
10
6
20
56
21
3
Mean: ≈ 3.5 years
Mean: 604.5 $US
Mean: 201.75 $US
SCP and SCPs) and finally these factors were also
used in the calculation of switching cost, which is a
third-order factor. Confirmatory factor analysis was
conducted using Lisrel-Simplis and covariance matrix
was used as input data.
Chi-square value of (622; df: 179) is
significant at 1 percent level, but it is known that the
statistic of X2 is sensitive to the sample size. Fit
indices (GFI: 0.88, AGFI: 0.85, RMSEA: 0.073, CFI:
0.87, NFI: 0.82) indicated that the model was nearly
sufficient (Baumgartner and Homburg, 1996). GFI
shows that 88 percent of the variance is explained by
the measurement model (85 percent including degrees
of freedom).
Content Validity: Content validity measures if
the scale reflects the concept being measured.
Because the questions in the switching cost testing
model are adapted from the related literature, content
validity is assumed to be validated [35].
Convergent Validity:
Convergent
validity indicates the degree of agreement between
the measurements of the same trait [21]. In Appendix
A; the parameters of the model, standardized factor
loads, and the t – values of factor loads can be seen.
All the t-values (for the first-order factors (λij), for the
second-order factors (βij) and for the third-order
factors (γij)) were significant at 1 percent level. This
finding supported the model’s convergent validity
[3].
Discriminant validity: In order to prove the
discriminant validity; firstly confirmatory factor
analysis is done while the correlation between
whichever two factors is set free. After that; an
analysis is done by setting the correlation between
these two factors to 1. Finally, achieved chi-square
values are compared [21] ; [35]. To verify the
discriminant validity; the chi-square value that is
S. AYDIN, G. ÖZER, H KAZAN., M.C. DOĞRUER
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The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
achieved from the first model whose condition
correlation has been set free, must be significantly
smaller than the chi-square value, which is set to 1
[33].
By means of the methodology; to prove the
discriminant validity in switching cost model, all the
correlations between the first-order factors are
examined by considering all the possible binary
combinations and chi-square values of ten different
models were determined. Similarly; in order to prove
discriminant validity between the second-order
factors, the same method was used and chi-square
values that belong to three alternative models were
calculated. Calculated X2dif values for all-possible
alternatives were higher than zero (minimum: 142.05,
df: 1) at 1 percent level, and it proved discriminant
validity of third-order model.
Composite
Reliability:
The
construct
reliability of the model can be determined with “total
coefficient determination (CRC)” value by way of
confirmatory factor analysis. CRC value was
determined by using the formula below for each
factor. All the CRC values can be seen in Appendix
A. These values were above the acceptable limit
(0.60) [50].
p
(∑ λij ) 2
CRC =
i
p
p
(∑ λij ) + ∑V (δi )
2
i
i
(Sharma, 1996,
p.165)
In the testing of composite reliability, the
complementary test is the average variance extracted
(ρv). The ρv values were calculated by using the
standardized factor loading coefficients:
p
∑ (λij )
ρv =
2
i
p
p
i
i
∑ (λij ) 2 + ∑ V (δi )
[50]
The ρv values can be seen in Appendix A.
These values for the all factors were above the
acceptable limit (0.50) [17]. We also determined
−
“mean factor loads ( λ )”).
n
−
λ
∑ (λi )
= i =1
n
(Morgan and Hunt, 1994)
−
According to
λ
values; as seen in Appendix
A, mean factor values were higher than the critical
value of 0.50 for all the factors. Statistically, all the
complementary reliability analyses verify the
reliability of testing model of switching cost.
To assess the reliability of the factors,
cronbah’s alpha coefficient was calculated. As seen in
Appendix A, cronbach’s alpha value for all the
factors was over 0.70, as suggested by Nunnally
(1978). In summary, all the findings from analyses
showed that the 21-question third-order model was
statically valid and reliable.
5. Conclusions
The findings of this empirical research prove
the validity and the reliability of third-order factor
model, which is developed using the existing
theoretical studies to test the switching costs. This
result supports the claim that the switching costs are
multi-facet concept in theoretical frame. Also, the
findings indicate third-order factor model of customer
switching costs. The main finding is novel and
different from the current literature on switching
costs. [11] and [25] have proposed and
operationalized the switching costs as a second-order
factor model. Also, we conceptualized customer
switching costs based on a new typology. Third-order
measurement model reveals that the switching costs
are made up of such sub-factors as the procedural,
financial and psychological switching costs. Once
again according to the measurement model; the
financial switching cost perception is made up of the
monetary and benefit loss costs perception.
When the procedural switching cost is
examined; it is verified that this factor is evaluated
consistently by the consumers throughout the buying
decision process. It was also seen to be made up of
such sub factors as the evaluation, set-up and learning
costs.
It is proposed that psychological cost is
composed of uncertainty and relationship loss cost,
and it is consistent with the existing studies in the
literature, and the model has been set up based on this
conjecture. The results indicate that the psychological
cost concept can be explained using the uncertainty
and relational loss cost concepts. It is assumed that
this situation fits well with the general characteristics
of the sector because in the credit card sector, the
customer must trust his/her bank in terms of interest
rates, points, gifts etc. which are related to his credit
card. When the credit card customer has a problem
about his/her credit card and calls the bank, this
customer is usually directed to a staff. The customer
must be assured that the staff is really trustworthy and
S. AYDIN, G. ÖZER, H KAZAN., M.C. DOĞRUER
1024
The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
does not violate confidentiality of the information
pertaining to each customer. The staff must also
guide the customer correctly about his/her questions
or problems. The customer must be assured of the
staff’s honesty.
5.1. Managerial Implications
In Turkey, the banks make profit by the
lending money to the public. After the crisis and their
effects, nowadays it seems that the banks have to be
more careful about their banking operations so they
may make more profit. Although there are so many
public and private banks, a number of foreign banks
are looking forward to entering the sector. Moreover,
recently, all the banks offer special credit cards with
various advantages. Because of this competitive
environment, gaining new customers and retaining
the existing ones are really quite important for the
banks. Moreover; the penetration ratio of the Turkish
market is low when compared with the developed
countries (see Table 3). Because of this reason,
Turkish credit card market has a high growth
potential in banking and credit card sector.
Table 3: Credit card numbers in
some countries through the year 2003
(Millions)
Country
Credit Cards
Population
United Kingdom
63.9
60.1
Greece
4.3
10.6
Holland
10.9
16.1
Turkey
27.0
72
A way of retaining the existing credit card
users and expanding customers’ loyalty is to create
switching costs and to increase these costs as much as
possible. In Turkey all the credit card customers may
have all the advantages such as gift bonuses, traveling
miles, and spending of earned bonuses at the member
shops. If a customer decides to change his/her credit
card, this customer knows that he/she will lose all
these advantages if they switch their credit card.
Therefore the banks are continuously offering new
advantages and installments chances for their
customers. Thus, switching costs of credit cards
increases as customers do not want to lose advantages
that they had earned before. Banks also impose lower
interest rates and annual card fees to gain new
customers and to retain their existing customers’
loyalty.
On the other hand, by using the standardized
factor loads from confirmatory factor analysis we
determined the values of the first-order (monetary,
benefit loss…set-up) factors via aggregation. By
using the first-order factors, second-order (financial,
procedural, psychological) switching cost factors
were calculated. Similarly, the values of switching
cost were determined by using the second-order
factors. According to the this weighting method, it is
determined that the mean value of financial cost is
2.86+-0.94, the mean value of psychological cost is
3.76+-0.74, the mean value of procedural cost is
3.34+-0.81, and finally according to these values the
mean value of switching cost is 3.30 +-0.61.
The t-test for three sub-dimensions of
switching cost indicated that there were significant
differences at 1% level among the average values of
the variables. Hence, the most important switching
cost element is psychological switching cost. If the
customer changes his credit card, the services that
come with the new credit card may not meet his
expectations. Also consumers’ familiarity with the
employees of an incumbent bank often creates a level
of comfort that is not immediately available with a
new bank. It is understood that, this uncertainty of
quality, economic risk and the relationships with the
bank are very important for credit card customers.
Second switching cost facet is procedural
switching cost. Time and effort are associated with
collecting the information needed to evaluate
potential alternative credit cards. Customers do not
want to spend a lot of time and effort comparing the
credit cards. Also, for credit card customers, time and
effort costs of acquiring new skills or know-how to
use a credit card and its services effectively are very
important too.
Third switching cost element is financial
switching cost construct. When switching to a new
bank, consumers may lose points they have
accumulated and discounts or benefits that are not
afforded to new credit card customers.
On the other hand, we tested the differences in
switching cost and its facets between credit card users
with switching experience and users without
switching experience. A t-test indicated that there was
a significant difference between the two user groups
for psychological switching costs and there was no
difference for others. The finding is consistent with
the result of [11] for long-distance telephone and
credit card services in USA. Also, the finding shows
the importance of psychological switching cost for
credit card market and users with switching
experience may more easily switch their banks than
users without switching experience.
Moreover, we calculated switching costs
values for the biggest four banks and tested them to
S. AYDIN, G. ÖZER, H KAZAN., M.C. DOĞRUER
1025
The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
determine the differences among banks for switching
costs. The findings from ANOVA showed that there
was no difference among banks. In fact, the finding
reveals that the banks in Turkey cannot sufficiently
manipulate switching costs.
5.2. Limitations and Further Research
At first, by repeating with the studies, which
will be actualized in the credit card sectors of
different countries; the generalization of the method
for the credit card sector may ensure. Beyond doubt,
for the future researches; the appliance of this
improved third-order model in different sectors, may
provide
meaningful
contributions
generalization of the model.
to
the
In addition, changing of the credit cards of the
customers will be a cost to banks. To determine better
of these costs must make a study interested with
customer switching costs.
Moreover, although the model used in this
research is tested using a sample with different
demographical and economical characteristics, it
entails significant results. Therefore; it seems to be an
encouraging element for the future research.
S. AYDIN, G. ÖZER, H KAZAN., M.C. DOĞRUER
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The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
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The Measurement of Switching Costs as a Perception of Customers in the Turkish Credit Card Market
EFA
CFA
M/S
.828
.768
.749
.671
.70
.73
.81
.85
2.84/1.54
2.25/1.36
2.32/122
2.33/1.42
.65
3.35/1.52
.98
3.41/1.46
.78
.89
3.38/1.33
3.34/1.28
.62
3.43/1.29
.803
.53
3.26/1.53
.686
.658
.85
.61
3.34/1.35
2.89/1.45
.820
.819
.641
.54
.71
.76
3.03/1.45
3.30/1.41
3.32/1.41
.813
.725
.661
.84
.81
.56
3.25/1.60
3.51/1.46
3.30/1.54
.837
.774
.505
.85
.81
.33
4.46/0.88
4.11/1.02
3.85/1.29
-
.33
.99
2.35/1.07
3.38/1.30
-
.79
.47
3.38/1.05
4.14/0.79
-
.51
.75
.47
3.17/1.09
3.21/1.14
3.35/1.14
-
.41
.86
.79
2.86/0.94
3.76/0.74
3.30/0.61
−
λ
Monetary Loss Cost (SCFM) CRC: 0.86 ρv : 0.62
: 0.78 α: 0.78
Changing my credit card with a new one has a cost
There is a monetary cost of cancellation of my present credit card
The monetary cost of cancellation of my present credit card is high
The monetary cost of getting a new credit card is high
−
λ
Benefit Loss Cost (SCFB) CRC: 0.76 ρv : 0.62
: 0.79 α: 0.59
If I change my credit card, I will lose some chances (bonuses, points, miles, special discounts, presents etc.) that I won
.842
from my present card
The chances (bonuses, points,miles,special discounts, presents etc.) that I won from my present credit card are important
.774
for me
−
λ
Uncertainty Cost (SCPsU) CRC: 0.87 ρv : 0.70
: 0.76 α: 0.69
If I change my present credit card with a new one;
The offered service may not be as well as I expected
.838
I may come upon inadequate service for a short time
.817
I am not sure that my new credit card’s operating costs (annual card payment, interest rates, taking credit interests etc.)
.668
will be more advantageous
−
λ
Relational Cost (SCPsR) CRC: 0.83 ρv : 0.64
: 0.66 α: 0.63
It is important for me that my bank is a trusted corporation (when I bethink of factors like pricing, service quality etc.)
I am thinking positive about my bank
The name (brand) of the bank is important for me
−
λ
Evaluation Cost (SCPE) CRC: 0.84 ρv : 0.64
: 0.67 α: 0.72
To buy a new credit card;
I can not waste my time to get information for comparing credit cards
Although I have enough information, comparing my present card with other cards needs time and effort
It is hard to compare credit cards
−
Learning Cost (SCPL) CRC: 0.86 ρv : 0.68
λ
: 0.73 α: 0.74
If I change my present credit card with a new one;
I may not take advantages of some services (like double bonuses) till learning its usage
A specific time may be needed to learn the usage of some services which are served by new card
The time needed for learning the usage of some services is important for me
−
Set-up Cost (SCPS) CRC: 0.84 ρv : 0.66
λ
: 0.66 α: 0.60
If I get a new credit card;
To cancel my present credit card, I have to waste some time
Cancelling my present credit card requires a process which I do not like
Getting a new credit card is a process which is burdensome, exhausting and including a lot of formalities
Financial Cost (Second-Order) (SCF)
Monetary Loss Cost (SCFM)
Benefit Loss Cost (SCFB)
Psychological Cost (Second-Order) (SCPs)
Uncertainty Cost (SCPsU)
Relational Cost (SCPsR)
Procedural Cost (Second-Order) (SCP)
Evaluation Cost (SCPrE)
Evaluation Cost (SCPrE)
Set-up Cost
Switching Cost (Third-Order)
Financial Cost
Psychological Cost
Procedural Cost
EFA: Factor score coefficients from EFA
CFA: Factor score coefficients from CFA
M: Mean value
S: Standart deviation
S. AYDIN, G. ÖZER, H KAZAN., M.C. DOĞRUER