Electron Markets (2016) 26:157–171
DOI 10.1007/s12525-016-0218-1
RESEARCH PAPER
Constructing online switching barriers: examining the effects
of switching costs and alternative attractiveness on e-store loyalty
in online pure-play retailers
Ezlika Ghazali 1 & Bang Nguyen 2 & Dilip S. Mutum 3 & Amrul Asraf Mohd-Any 1
Received: 15 June 2015 / Accepted: 26 February 2016 / Published online: 11 March 2016
# Institute of Applied Informatics at University of Leipzig 2016
Abstract Developing switching barriers to retain customers
has become a critical marketing strategy for online retailers.
However, research on the role of switching barriers in eretailing is still limited. Recent trends show that when competitors are just one click away, it is questionable if customer loyalty can be achieved at all in online environments. This leads to
the research question on whether switching barriers have any
impact on e-loyalty in pure-play retailers. The paper examines
the influence of switching barriers on customer retention (i.e., estore loyalty) and further investigates the moderating effects of
switching costs and alternative attractiveness. Data were gathered via a survey of 590 shoppers of online pure-play retailers in
the UK. Findings show that customer satisfaction and the two
dimensions of switching barriers (perceived switching costs and
perceived attractiveness of alternatives) significantly influence
customer loyalty. Contrary to findings in earlier studies, it was
found that switching costs did not moderate the relationships
between satisfaction and loyalty nor between perceived attractiveness of alternatives and loyalty. The paper makes imperative
recommendations to develop switching barriers and to foster
loyalty along with suggestions for future research.
Responsible Editor: Christopher P. Holland
* Bang Nguyen
[email protected]
1
Department of Marketing, Faculty of Business & Accountancy,
University of Malaya, 50603 Kuala Lumpur, Malaysia
2
School of Business, East China University of Science and
Technology, 130 Meilong Road, Xuhui District 200237, Shanghai,
People’s Republic of China
3
Nottingham University Business School Malaysia, The University of
Nottingham Malaysia Campus, Jalan Broga,
Semenyih 43500, Selangor, Malaysia
Keywords Switching barriers . Switching costs . Alternative
attractiveness . Customer satisfaction . E-store loyalty . Online
pure-play retailers
JEL Classification M3 Marketing . Advertising
Introduction
The increased competition in the online marketplace and high
costs for getting access to new customers have required eretailers to continually emphasize on customer retention and
loyalty strategies (Otim and Grover 2010; Polo et al. 2011).
Customer retention and loyalty is especially acute in the online environment since the “competing offer is just a few
clicks away” (Shankar et al. 2003, p. 154). For example,
Harris and Goode (2004, p. 139) note that “generating loyal
customers online is both more difficult and important than in
offline retailing.” Balabanis et al. (2006) show that some satisfied customers might not consider themselves loyal to the estores, indicating that acquiring and retaining customers online is not particularly straightforward. However, customer
retention has become “an economic necessity” (Balabanis
et al. 2006, p. 214), as the cost of acquiring new customers
can be up to five times as costly as maintaining existing ones
(Bauer et al. 2002). Consequently, for retailers operating in the
online environment, developing switching barriers to induce
loyalty has become a critical marketing strategy (Alt and
Österle 2013).
Despite the critical role of switching barriers in marketing, research in this area have focused predominantly
on the offline context (e.g., Jones et al. 2002; Burnham
et al. 2003). More research is needed to understand the
effectiveness of switching barriers in online retailing
(e.g. Goode and Harris 2007; Mutum et al. 2014),
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especially as the competition intensifies and consumers
have numerous alternatives to which they can switch
very easily (e.g., Bakos 1997; Friedman 1999;
Holloway 2003). Hence, it is particularly important to understand the role of attractiveness of alternatives in developing
switching barriers. In addition, while some studies have
employed switching costs as their main construct, there has
been very little effort to develop robust measures of online
switching costs. The multifaceted typologies and dimensions
of switching costs denote the complex nature of the construct.
Yet, empirical research in this area has treated switching costs
as unidimensional (e.g. Fuentes-Blasco et al. 2010; Goode and
Harris 2007; Kim and Son 2009), measuring such costs as a
global construct. This approach ignores the conceptual richness of the switching costs construct, which is too complex to
be operationalized as unidimensional (Burnham et al. 2003).
Aydin et al. (2005) specifically highlight the need for further
research to investigate the sub-dimensions of switching costs.
In addition, Carlson and O’Cass (2011) highlight the importance of distinguishing between online pure-play retailers
(online retailers with no physical retail presence) and bricksand-clicks companies (retailers with both online and offline
presence). Holloway (2003) showed that with bricks-andclicks retailers, stronger consumer relationships can be developed compared to online only retailers. Thus, failure to differentiate between the two types of online retailers (pure-play vs.
bricks-and-clicks) may further distort the results or lead to
over-estimation of relationships. A review of extant research
in switching behavior reveals that no studies have looked at
customer retention of online pure-play retailers.
To fill the above gaps, this research represents the first attempt at studying switching barriers explicitly with respect to
online pure-play retailers. The aims of this study are threefold.
(1) First, the study develops an online customer loyalty framework of customer retention/switching by explicating switching
barriers. By capturing the key factors likely to trigger e-loyalty,
the study is unique in studying the role of alternative attractiveness and switching costs conjointly in the context of loyalty
and satisfaction towards an online retailer (Chen and Hitt
2002). (2) Second, the study incorporates and evaluates the
moderating effects of switching costs in order to provide further insights into the nature of e-loyalty, previously suggested
as an area for future research (Mutum et al. 2014). (3) Finally,
the study advances the literature on online marketing with the
validation of a multi-dimensional measurement scale of
switching barriers (Pappas et al. 2014). Thus, the study fills
important gaps in the online marketing knowledge that links
switching costs, alternative attractiveness, and e-store loyalty.
With an increase in popularity of online pure-play retailers,
this study is also important from a managerial perspective. A
recent report (Retailpro 2015) revealed that 44 % of British
shoppers do more than half of their online shopping on pureplay retailers. Thus, for online retailers, implications exist in
E. Ghazali et al.
constructing switching barriers that increase customer satisfaction and loyalty. By articulating customer-focused strategies that foster loyalty, the study is expected to help managers
with the validation of a multi-dimensional measurement scale
of switching barriers, providing a useful tool to operationalize
switching barriers. The rest of the paper is organized as follows: first, the study reviews the literature on online switching
costs, identifying the existing gaps and developing the
hypotheses. Subsequently, it discusses in detail the research
methodology employed in this study. This is followed by the
results of the analysis. Finally, the study findings are
presented, discussed, and concluded.
Literature review
In the section that follows, the paper presents the key literature
pertaining to online switching barriers and customer loyalty.
This provides an overview of extant literature and reveals the
gaps which the study aims to fill.
Building customer retention and loyalty
Ranaweera and Prabhu (2003) noted two principal strategies
to building customer retention. The first is to improve customer satisfaction, so that the customer ‘wants’ to stay with the
firm. The second is to increase the perception of ‘switching
barriers’, which impede customer switching. These strategies
could also be applied to the online retail setting to induce
customer loyalty. Oliver (1999) describes loyalty as the overall attachment and deep commitment to product, brand, organization, or retailer. Customer loyalty is crucial for a firm’s
success because loyal customers enhance its profitability
(Reichheld 1996), market share (Chaudhuri and Holbrook
2001), and shareholder value (Sindell 2000). The importance
of customer loyalty cannot be emphasized enough in the context of online marketplaces, especially for pure-players, where
it is generally held that customers are able to terminate a relationship with a mere ‘click of the mouse’ (Anderson and
Srinivasan 2003; Reichheld and Schefter 2000). Therefore,
without core loyal customers, pure-players may find it difficult to remain afloat because developing loyalty is not as clear
cut as some studies have suggested (e.g., Coelho and Henseler
2012; Emanuelsson and Uhlén 2007). For example, despite
being assumed as a prerequisite to customer loyalty, customer
satisfaction does not automatically predict loyalty. Previous
studies have established imperfect correlations between the
two constructs, and the strengths of their relationships remain
highly questionable (e.g., Balabanis et al. 2006; Dagger and
David 2012; Emanuelsson and Uhlén 2007). Hence, when
investigating the loyalty inducing influences (Coelho and
Henseler 2012), satisfaction along with other factors that drive
customer retention should be considered, including switching
Constructing online switching barriers
costs, competitors’ offerings (Burnham et al. 2003; Jones et al.
2002), and alternative firms (Roberts 1989).
Understanding switching barriers
When the perception of switching barriers is high and the
options to exit a relationship are limited, the tendency for
loyalty increases (Hirschman 1970). A useful definition of
switching barriers is provided by Jones et al. (2000, p. 261),
who conceptualized it as “…any factor, which makes it more
difficult or costly for consumers to change providers.”
However, there is some confusion between the terms
‘switching barriers and ‘switching costs’ (Balabanis et al.
2006; Colgate et al. 2007), with many authors using them
interchangeably (e.g. Bansal and Taylor 1999; Mathwick
2002). Although Goode and Harris (2007) noted “subtle differences between switching barriers and costs” (p. 517), they
fail to describe any clear differences.
Ranaweera and Prabhu (2003) assert that the switching
barriers construct encompasses ‘self-efficacy’ as well as
‘facilitating condition’ issues. Other authors, namely
Burnham et al. (2003) and Jones et al. (2000, 2002), have
conceptualized switching barriers (offline service context)
as a multidimensional construct encompassing several categories and dimensions. According to Jones et al. (2000,
2002), switching barriers is made up of three main dimensions, namely, perceived switching cost, attractiveness of
available alternatives, and interpersonal relationships,
while switching barriers as conceptualized by Burnham
et al. (2003) does not include attractiveness of alternatives. In this research, we adapt and extend the framework
conceptualized by Jones et al. (2000), considering
switching barriers as a multidimensional construct.
However, since the focus of this research is on the customers of pure play online retailers, the interpersonal relationship construct does not apply because compared to a
physical market environment, there is a considerable lack
of face-to-face interaction (Szymanski and Henard 2001)
between customers and retailer employees in the online
market, which precipitates the importance of the interpersonal relationship component. As such only perceived
switching costs and attractiveness of alternatives are examined in this research. These key concepts and their
antecedents are explained in the subsequent sections.
Barrier 1: Perceived switching costs
In the B2C context, Patterson and Smith (2003) define
switching costs as “the perception of the magnitude of the
additional costs required to terminate a relationship and secure
an alternative one” (p. 108). Some researchers have
questioned the credibility of a single global measure to represent perceived switching costs, although most studies have
159
conceptualized it as unidimensional. According to Fornell
(1992), perceived switching costs are complex and as such
require a higher level of abstraction. Fornell also suggests that
due to its complex nature, perceived switching costs are difficult to measure. Although researchers have questioned the
credibility of its single global measure, there have been only
few attempts to empirically measure it as a multifaceted construct (e.g. Burnham et al. 2003; Jones et al. 2002). Most of
these studies are done in the offline context. Due to these
inconsistencies, there is a need to unify the current theoretical
discussion and to develop a set of switching costs pertinent to
other contexts, such as an online retail setting.
Based on a comprehensive literature review (Balabanis
et al. 2006; Burnham et al. 2003; Colgate et al. 2007;
Fornell 1992; Jones et al. 2002; Thatcher and George 2004;
Mutum et al. 2014), we identified five dimensions of perceived switching costs (see Table 1). As perceived switching
costs are highly salient in the online environment where customers are often co-producers of the services they receive,
three categories of online switching costs are identified.
They are: the procedural costs components (learning costs,
search and evaluation costs, and uncertainty costs), the economic or monetary costs component (artificial costs), and the
relationship-based or psychological costs component (brand
relationship loss costs).
Barrier 2: Attractiveness of alternatives
Another construct underlying customer online switching
barriers is that of perceived alternative attractiveness. This
refers to customers’ perceptions of the extent to which
viable competing alternatives are available in the marketplace (Jones et al. 2000). Individuals’ commitment to a
relationship should increase when they are satisfied with
the relationship and/or when there are no good alternatives available. Further, not only does a large perceived
difference among alternatives lead to customer retention,
but the lack of perceived differences also influences customers to stay in existing relationships. According to
Patterson and Smith (2003), when a customer perceives
that alternatives are no different from their existing provider or does not perceive them as ‘any more attractive’
than their existing relationship, they tend to remain loyal
to their current provider. In this situation, the customer’s
perception is that switching is not worthwhile (Colgate
and Lang 2001) because the net benefit from alternative
relationships is not superior to the current relationship
(Hennig-Thurau et al. 2000). Overall, the offline marketing literature suggests the presence of at least three factors
affecting customers’ perceptions of the attractiveness of
alternatives: existence of alternatives, heterogeneity (severity of difference) among alternatives, and high
switching costs between alternatives. Based on these
160
factors, we label the three dimensions of alternative
attractiveness (Table 2) for the online pure-play market
as retailer indifference, alternative awareness, and alternative preference.
On the whole, our review of the literature, looking
into factors influencing customer retention in the online
environment, revealed that research into switching barriers is, unquestionably, lacking (Mutum et al. 2014).
Therefore, extant theories and normative insights vis-àvis customer retention/customer switching in the context
of the online milieu need to be re-examined. In addition,
previous research on switching behaviors of customers,
have predominantly focused on the offline purchasing
contexts. The limited number of online studies on
switching behaviors of customers has used hybrid samples, that is, they have not differentiated between customers of brick-and-click and pure-player online retailers. This study addresses these above gaps by scrutinizing online customer loyalty/switching behaviors only
in the context of pure-player entities.
Hypothesis development
Building on the literature review, this section develops the
study’s framework and corresponding hypotheses.
Relationships between the key constructs are theorized and
hypotheses are put forward for subsequent testing, here in
the context of online pure-play retailers.
Customer satisfaction and loyalty
Customer satisfaction is one of the most critical constructs and a core concept in marketing (Garbarino and
Johnson 1999; Holloway 2003; Mittal and Kamakura
2001). This study focuses on the overall satisfaction, that
is, the cumulative judgment overtime of customers with
regards to a retailer’s performance (Anderson et al. 1994;
Burnham 1998). A few authors have argued that satisfaction is not a prerequisite to loyalty and/or customer retention. In other words, increased satisfaction may not
necessarily lead to an increase in loyalty to firms, and
evidence shows that dissatisfied customers often remain
with a retailer (Bolton and Drew 1991; Burnham et al.
2003) and that satisfied customers still buy elsewhere
(Keaveney 1995) or do not necessarily buy more
(Seiders et al. 2005). Thus, the role of satisfaction in
influencing loyalty is more complicated than initially
thought (Dagger and David 2012; Mittal and Kamakura
2001; Oliver 1999).
However, there is substantial empirical evidence in the literature linking global cumulative satisfaction to loyalty (Oliver
1997; Szymanski and Henard 2001). In essence, consumers are
E. Ghazali et al.
likely to develop positive intention towards behavior (i.e., repeat purchase or online transaction) (Albert et al. 2014), if they
have a positive attitude (i.e., feeling of satisfaction based on
past performance) towards the behavior. Thus, in this paper,
customer satisfaction is hypothesized to be associated with loyalty, which is conceptualized as the ‘mindful’ mode of customer repeat purchase. Hence, we postulate that:
H1: Satisfaction positively affects loyalty, in online pureplay retailers.
Attractiveness of alternatives and loyalty
Customer consideration of alternatives is a key element in
making choices about whether to stay or defect (Patterson
and Smith 2003; Rust and Kannan 2003). Past studies have
found that when switching costs are perceived as high, as
experienced by customers, such high switching cost perceptions will have a negative influence on the attractiveness of
alternatives. In other words, customers consider that switching
to other companies is less desirable due to higher switching
costs and consequently, the customer eventually loses interest
in the company’s competitors (Kim and Son 2009). On the
whole, there is a tendency for switching cost perceptions to
reduce (a) the level of customer consideration of other alternatives (Heide and Weiss 1995), (b) the customer’s effort in
searching for alternatives (Weiss and Heide 1993) as well as
(c) their propensity to search for alternatives (Zauberman
2003).
As the perceived attractiveness of alternatives increases,
customers are more likely to be involved in solving problems
and less likely to remain loyal (Hirschman 1970; Ping 1993;
Rusbult et al. 1982) and the probability of switching increases
(Bendapudi and Berry 1997; Jones et al. 2000; Sharma and
Patterson 2000). Rusbult et al. (1982) observed that the perception of high quality alternatives positively influences exit
and negatively influences loyalty. Similarly, Jones et al.
(2002) and Yim et al. (2007) showed that attractiveness of
alternatives had negative effects on commitment and repurchase intention. Thus, we hypothesize that:
H2: Attractiveness of alternatives inversely affects loyalty, in online pure-play retailers.
Switching costs and loyalty
According to Jones et al. (2002, p. 441), switching costs are
barriers that “hold customers in service relationships.”
Switching costs in this study refers to ‘perceived’ switching
costs (Morgan and Hunt 1994) as it is not any objective cost
Constructing online switching barriers
Table 1
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Components of perceived switching costs
Dimension
Description
Learning costs
Expenditure of time and effort to learn, understand or use the
new service effectively. This includes familiarizing with
conducting transactions on an unfamiliar website.
Actions initiated by a firm to retain customers and to make it
more expensive for them to switch suppliers. For example,
frequent flyer program.
Customer’s perception of future costs or losses associated
with possible negative consequences incurred by switching
to an unfamiliar or untested retailer. This is closely linked to
the perception of risk such as performance risk, financial
risk, convenience risk and privacy and security risks.
It incurs when searching for a suitable alternative retailer to
switch to. Two types of search costs as potential reasons
for customers remaining with a retailer (lock-in); ‘physical
search cost’ and ‘cognitive search cost’.
The feeling of loss in leaving a brand. Strong brand image
and positive brand attitude reinforce the relationship
between customers and retailers, making the switching
process more costly.
Artificial costs
Uncertainty costs
Search and evaluation costs
Brand relationship loss costs
that will be measured, but rather the switching costs as perceived by customers (Burnham et al. 2003).
Fornell (1992) suggests that while satisfaction makes it
harder for competitors to take away a firm’s customers,
switching costs make it costly for customers to defect to competitors. Indeed, switching costs can be more critical an antecedent to customer retention than satisfaction because customers tend to attribute greater weight to them when making
decisions (Dick and Basu 1994). Many scholars agree that one
potential but crucial antecedent to loyalty is switching cost
(e.g., de Ruyter et al. 1998; Rust and Kannan 2003). It has
also been argued that switching costs positively influence loyalty and retention (Fornell 1992). For example, Patterson and
Smith (2003) and Bell et al. (2005) found significant direct
effects of switching costs on customers’ propensity to remain
with a firm. This is also evident in the online B2C relationships (e.g., Anderson and Srinivasan 2003; Chen and Hitt
2002). The process of changing to alternatives will involve
Table 2
Source
Burnham (1998, p. 107)
Jones et al. (2002)
Klemperer (1987)
To (1996, p. 31)
Colgate and Lang (2001)
Mitchell (1999)
Burnham et al. (2003)
Forsythe and Shi (2003)
Chen and Hitt (2002)
Bakos (1997)
Johnson et al. (2003)
Burnham et al. (2003)
Polo and Sesé (2009)
extra investment in searching, evaluating, and filtering information. Therefore, we postulate that:
H3: Perceived switching costs positively affects loyalty,
in online pure-play retailers.
Moderating effects of switching costs
The above discussion sets the foundation for the study’s final
hypotheses, which posit that the effects of switching costs are
not only evident in its direct effect on e-loyalty, but is also
shown indirectly, as a moderation variable, through its more
complex influence on satisfaction and alternative attraction, in
the online pure-play market. Most offline retailing studies
have regarded switching costs as a moderator in satisfaction
and loyalty relationships (Yang and Peterson 2004). For instance, Burnham et al. (2003) found that switching costs impose a moderating effect on repurchase intention through
Dimensions of alternative attractiveness
Dimension
Description
Source
Retailer Indifference
The overall perception that because most other
retailers are similar (i.e., retailer indifference),
it may not be worthwhile to search for alternatives.
The customers’ high awareness of alternatives
(i.e., competitors) in the market, possibly due to
the customers’ experience in online shopping.
The customers’ preference for alternatives’
(competitors’) service and offerings to their current
retailer’s.
Similar to Balabanis et al.’s (2006)
‘parity barriers’
Alternative Awareness
Alternative Preference
Comparable to Balabanis et al.’s (2006)
‘unawareness barriers’
Concurs with Li et al. (2006) and
Rusbult et al.’s (1998) ‘quality of
alternative’ dimension
162
E. Ghazali et al.
satisfaction in two service industries (viz., credit card and
phone call service). Similar results were observed in mobile
phone service (Lee et al. 2001). Hauser et al. (1994) reported
that a strong level of perceived switching costs reduces the
sensitivity of a customer to perceived satisfaction. Likewise,
Anderson and Sullivan (1993) discovered a negative relationship between switching costs and customer satisfaction sensitivity in the banking industry.
However, opinion with regards to the moderating role of
switching costs in the e-retailing environment is quite mixed.
Some scholars argue that this role may not always be significant and will depend on other variables such as the types of
business, customers or products (Nielson 1996). For example,
Balabanis et al. (2006) found that switching costs will only
moderate the e-satisfaction and loyalty links, when esatisfaction is higher than average. However, Holloway
(2003) while looking at online service failure recovery, found
no moderating effect of switching costs on the relationship
between satisfaction and repurchase intention. Similarly,
Chen and Hitt (2002) did not find any moderation as well.
Due to the mixed findings in prior research, the moderating
role of switching costs warrants further investigation.
Therefore:
H4: Perceived switching costs will moderate the relationship between satisfaction and loyalty.
Attraction towards alternatives in the market will strongly
and negatively influence the development of loyal customers.
However, the prevalence of switching costs can serve as
Fig. 1 Conceptual framework of
switching barriers. RI: Retailer
Indifference; AP: Alternative
Preference; AA Alternative
Awareness are derived and
labelled after running CFA. LC
Learning Costs, AC Artificial
Costs, UC Uncertainty Costs, SE
Search and Evaluation Costs, BR
Brand Relationship
a buffer against the negative impact of high attractiveness of alternatives on loyalty. For example, a customer
may feel that there is price unfairness if a competitor
offers a lower price compared to their current e-retailer.
The logical action of the customer is to end the current
relationship and establish a new one with the competitor.
This action, however, is not without any cost (Xia et al.
2004). If the customer decides to leave the relationship,
they may incur switching costs that include time, effort,
and money. Thus, the costs of action will most likely
moderate the relationship between attractiveness of alternatives and loyalty. The empirical evidence with regards
to this effect has been negligible (Holloway 2003) in the
literature. Hence, we postulate that:
H5: Perceived switching costs will moderate the relationship between attractiveness of alternatives and loyalty.
Figure 1 shows our conceptual framework.
Research method
To achieve the aims of the research, a quantitative research methodology was conducted. This section presents
the research that was implemented to test the framework,
including the data collection and succeeding rigorous process of data analysis.
Overall
Satisfaction
H1+
RI
H4+
H2-
Alternative
Attractiveness
AP
E-Loyalty
H5+
AA
H3+
Perceived
Switching Costs
Direct effect
Moderating effect
LC
AC
UC
SEC
BR
Note: RI: Retailer Indifference; AP: Alternative Preference; AA: Alternative Awareness are derived and labelled after
running CFA
LC: Learning Costs; AC: Artificial Costs; UC: Uncertainty Costs; SE: Search and Evaluation Costs; BR: Brand
Relationship
Constructing online switching barriers
Sample
We conducted a questionnaire-based survey to examine the
impact of online customer switching barriers on loyalty of a
range of pure ‘dot com’ retailers popular with consumers in
the UK which offer various types of products and services.
The respondents were required to evaluate one e-retailer from
which they most frequently purchased. This method of
soliciting respondents to report their experience with providers has been widely accepted in prior research (e.g.
Balabanis et al. 2006; Li et al. 2006). As a guideline, the
sample must be composed of individuals in the UK who are
Internet shoppers of pure online retailers. According to the
ONS (2014), two in every five adults (40 %) aged 65 and over
bought goods or services online in 2014. This is more than
double the 2008 estimate (16 %). A mailing list comprising a
random sample of 4000 consumers was purchased for single
use from Experian UK’s pre-existing database of online shoppers. The sample was restricted to online shoppers over the
age of 16, who are categorized as adults by the Office for
National Statistics UK, and who had transacted at least once
in the last 6 months.
Data collection
Data were collected via a self-administered questionnaire
using a hybrid survey approach (i.e., web with mail design)
(Dillman et al. 2009) in order to reduce non-response error (de
Leeuw et al. 2008). A packet containing a paper questionnaire,
a personalized cover letter, and postage paid returned envelope were sent out to all the potential respondents. They were
given the choice to complete via either paper or online questionnaire hosted on Surveymonkey.com. Out of the 4000
questionnaire packets mailed, 163 were returned as ‘undelivered’, leaving 3837 potential respondents. After a month, a
total of 799 responses were received of which 578 came
through the post, while the rest (221) were collected online.
Since our study focuses on pure-play firms, 209 responses
were removed - either because they were unusable or because
they chose websites of companies with strong offline presence
as well. A total of 590 responses were used for the final analysis, which corresponds to approximately 15.4 % response
rate. Table 3 presents the profiles of the respondents. We compared estimates of demographic characteristics to the general
population1 to assess the quality of the sample in terms of both
non-coverage and non-response and can conclude that our
sample is representative of adult Internet shoppers in the UK.
Table 4 presents the online retailers selected by the respondents in descending order of frequency. The findings show
that the three most dominant pure-players for consumers in
1
Population reports were derived from Nielson-Online (2009), MintelOxygen (2007) and British Population Survey (January 2008).
163
the UK are Amazon UK, Play.com, and Amazon.com (US).
Amazon UK is quite distinct from the US site with respect to
several features and some products are available exclusively
on each of the sites.
The respondents are experienced shoppers on their favorite
e-retailer in general. More than half of the respondents have
been purchasing from the e-retailer for more than 3 years.
Most respondents also indicated that they had visited the
website at least once every 2 months, with one-third of the
respondents doing so at least twice a month. With respect to
the frequency of purchase from their retailer’s website, more
than half of the respondents purchased at least once every
3 months with a number of purchases being made at least three
to five times a year. In terms of money spent on their retailer’s
website, 66 % of the respondents spent between £10 and £40
per transaction. The products purchased most frequently were
books and CDs/DVDs. Other popular products included
clothing, electrical items, and computers.
Measures
Respondents were asked to indicate their level of agreement
on 7-point Likert-type scales derived from previous studies,
anchored by 1 = strongly disagree and 7 = strongly agree. To
operationalize overall satisfaction, we adapted three modified
items from Voss et al. (1998), Seiders et al. (2005), and
Bourdeau (2005). We adapted the loyalty scale using five
items from Bourdeau (2005). The five second-order dimensions of switching cost were measured as follows: 6-item
learning cost, 7-item artificial cost, 6-item uncertainty cost,
5-item search and evaluation cost, and 3-item brand relationship loss (Bourdeau 2005; Burnham et al. 2003; Holloway
2003; Jones et al. 2007; Korgaonkar and Wolin 1999;
Mathwick 2002). Attractiveness of alternatives was measured
using nine items (Burnham 1998; Holloway 2003; Li et al.
2006; Ping 1993). Table 5 provides a list of the measures and
their psychometric properties after purification. Our questionnaire was pre-tested by two academics who are experts in the
area of e-service and consumer behavior followed by another
15 individuals who are familiar with online shopping. The
hypotheses are tested following the assessment of psychometric properties of the measurement scale. Structural equation
modeling is used for this purpose using AMOS 18 software
package.
Reliability and validity of measures
Confirmatory factor analysis was used to assess the validity
and reliability of our multi-item scales (Gerbing and Anderson
1988). As shown in Table 5, all the average variance extracted
and composite reliabilities exceed the standard thresholds in
the literature (Bagozzi and Yi 1988). Though the loadings of
UC1 (0.58) and BR2 (0.51) are lower than the recommended
164
Table 3 Comparative
demographic profile of
respondents (N = 590)
E. Ghazali et al.
Demographic profile
Category
Frequency
Percent
Personal income per annum:
Less than £15,000
£15,000–£19,999
90
52
15.3
8.8
£20,000–£24,999
53
9
£25,000–£29,999
£30,000–£49,999
53
146
9
24.7
£50,000–£75,000
172
29.2
Total
Not Disclosed/Refused
566
4
96
Missing Value
20
16–24
25–34
52
174
8.8
29.5
35–44
175
29.6
45–54
55–64
99
60
16.8
10.2
65 and over
Total
30
590
5.1
100
Sex:
Male
Female
Total
Missing Value
317
270
587
3
53.7
45.8
99.5
Race:
White
Black
Mixed
436
21
11
73.9
3.6
1.9
Asian
Middle Eastern
Other
Total
Not Disclosed/Refused
102
8
3
581
3
17.3
1.4
0.5
98.5
Missing Value
6
Age:
value of 0.6 (Bagozzi and Yi 1988; Chin 1998), we retained
them to support content validity (Hair et al. 2010, p. 715).
Consistent with the literature, our analysis confirmed that the
construct Alternative Attractiveness must be viewed at a
higher level of abstraction with three second-order sub-dimensions. However, from the nine proposed items to measure this
construct, three ‘problematic’ items were removed, leaving six
items reflecting the sub-dimensions. Similar to Balabanis
Table 4 Respondents’ selection
of pure-play online retailers
et al.’s (2006) ‘parity barriers’ and ‘unawareness barriers’,
we named the first two dimensions ‘Retailer Indifference’
and ‘Alternative Awareness’, respectively. Retailer
Indifference refers to the overall perception that because most
other retailers are similar, it may not be worthwhile searching
for alternatives. Alternative Awareness refers to the customers’
high awareness of competitors in the market, possibly due to
the customers’ experience in online shopping. The third factor
Pure-play online retailers
Product description
Frequency (%)
Amazon UK
Play.com
Amazon US
ASOS
Ebuyer
Ryanair
Expedia UK
Books, CDs, consumer electronics etc.
Computers, consumer electronics, apparel etc.
Books, CDs, consumer electronics etc.
Apparel
Computers, consumer electronics
Cheap flights
Holiday/travel products
385 (65)
42 (7.1)
40 (6.8)
15 (2.5)
6 (0.8)
5 (0.8)
3 (0.5)
Constructing online switching barriers
Table 5 Measures and CFA
results
165
Scale/Item
CR
AVE
E-loyalty
.75
.50
1. When I have a need for this type of product, I will
use only this online retailer.
2. I would not even consider another online retailer for
this product.
3. I am unlikely to switch to another online retailer in
the near future.
Overall satisfaction
.84
2. Overall, I am completely satisfied with my shopping
experience.
3. When I think about my shopping experience here, I
am generally pleased.
Switching costs (second-order)
1. I receive special rewards and discounts from doing
business with this online retailer.
2. I will lose the benefits of being a long-term customer
if I leave my online retailer.
3. Staying loyal gives me discounts and special deals.
4. Staying loyal saves me money.
Uncertainty Cost (UC)
1. I am concerned about the security of my personal
information when registering on a new website.
2. I worry that switching my shopping activities to
another online retailer would result in some
unexpected problems.
3. If i were to change online retailer, i fear that the
service I would receive might worsened.
4. Switching to another online retailer would be risky,
since I wouldn’t know the quality of its products/
services.
Search and Evaluation cost (SE)
1. I don’t like spending time searching for a new online
retailer.
2. I cannot afford the time/effort to evaluate alternative
online retailers fully.
3. Comparing the competitors in order to work out
which best suits my needs is a time-consuming task.
4. I don’t think that the process of evaluating a new
online retailer prior to switching would be a hassle.
(r)
Brand Relationship Loss (BR)
1. The brand of this retailer plays a major role in my
decision to stay.
2. I do not care about the brand/company name of the
online retailer that I use to buy this product. (r)
.85
.90
.87
.81
.73
tValues
.82
13.08
.68
10.99
.60
9.72
.79
14.84
.81
15.47
.79
14.82
.66
11.99
.84
16.80
.66
11.89
.73
13.83
.71
13.18
.84
17.31
.85
17.60
.89
.73
18.74
14.02
.58
10.21
.85
16.63
.76
14.44
.76
14.27
.74
13.43
.78
14.44
.65
11.51
.69
12.40
.63
1. I am pleased with the overall service.
Learning Cost (LC)
1. Getting used to new website after I switch would be
very easy. (r)
3. Switching my shopping activities to another online
retailer would require too much learning.
4. I feel that the competitors’ websites are difficult to
use.
5. I am reluctant to change online retailer because I am
familiar with ‘how the system works’ on this
website.
6. It takes time/effort to understand how to use other
online retailers’ websites.
Artificial Cost (AC)
Factor
loading
.53
.69
.62
.52
.49
.76
13.28
.51
8.23
Mean
SE
3.63
.052
5.74
.035
3.00
.043
2.68
.055
4.25
.050
4.16
.056
3.60
.055
166
E. Ghazali et al.
Table 5 (continued)
Scale/Item
CR
AVE
Factor
loading
tValues
.86
.76
.76
12.95
1. I could be buying from a competing website and not
notice much difference.
2. I would probably be just as happy with the service
of another online retailer.
Alternative Preference (AP)
1. I feel that an alternative online retailer is better than
this one. (r)
2. To my mind, another online retailer is closer to my
ideal. (r)
Alternative Awareness (AA)
.92
15.18
.82
13.73
.86
12.03
.75
10.97
1. If i had to change online retailer, I know of another
which is just as good.
2. Compared to this online retailer, there are not many
competitors with whom I could be satisfied.
.68
9.54
.68
9.58
3. I stay because I like the public image of the retailer.
Alternative Attractiveness (second-order)
Retailer Indifference (RI)
Mean
SE
4.48
.052
2.99
.045
3.89
.050
r reversely worded
concurs with Li et al. (2006) and Rusbult et al.’s (1998) ‘quality of alternative’ dimension and is labeled Alternative
Preference, reflecting the customers’ preference for a competitor’s service and offerings to their current retailer’s.
With regard to the two second-order switching barriers
constructs (switching costs and alternative attractiveness),
we conducted additional tests to assess convergent validity
by linking the first-order dimensions to their second-order
global construct and the path coefficient estimates must be
significant (Benson and Bandalos 1992). As a result, convergent validity was established for switching costs and attractiveness of alternatives. To assess discriminant validity between all constructs, we examined the Chi-square (χ2) difference between the standard model and the ‘non-discriminant’
model at unity (Anderson and Gerbing 1988; Bagozzi et al.
1991) and found that the constructs and sub-constructs in our
model are distinct from each other; hence discriminant validity was confirmed as well.
Results of the hypotheses tests
Results of structural equation modeling (Table 6) show the
overall good fit measures are all within the thresholds indicating acceptable fit (re = 1743; (=1df) = 1.972;
NNFI = 0.99 > .95 = good fit; CFI = 0.929 > .90;
RMSEA = 0.040 < .05 = good fit; SRMR = 0.057 < .08). The
NFI, GFI, and AGFI measures are not reported here because
they are no longer recommended (Hu and Bentler, 1999;
Kenny 2014). H1 is supported (1 = 0.10; p < 0.05), which indicates that overall satisfaction is positively related to loyalty.
As reflected by the strong and highly significant negative relationship between alternative attractiveness and loyalty,
alternative attractiveness acts as an important driver in reducing loyalty. H2 is therefore supported (β = −0.58; p < 0.01).
Similarly, as perceived switching costs is posited to have an
effect on loyalty, H3 is also supported (β = 0.18; p < 0.01).
H4 and H5 argue that switching costs moderate the relationship between overall satisfaction and loyalty, and alternative attractiveness and loyalty, respectively. To test this, we
applied the orthogonalization or residual centering approach
(Little et al. 2006), whereby the 4-step approach in creating
orthogonalized latent interaction variables was strictly followed. This procedure, to a large extent, eliminates
multicollinearity issues that have, in the past, plagued many
efforts to model interactions via SEM. Besides, this method
also uses all possible information of the manifest variables to
test the effect, in contrast to previous methods. Compared to
the standard mean-centered approach (e.g., Aiken & West,
1991), this approach ensures complete orthogonality between
the independent and the interaction variable and hence, leads
to identical inferences and better fitting results (Little et al.
2006; Marsh et al. 2007). However, the latent interactions
SAT*PSC and ATA*PSC on loyalty were not significant.
Therefore, both H4 and H5 were not supported.
Discussion
Our results are in line with the findings of some earlier studies,
which found a positive relationship between satisfaction and
loyalty (e.g., Corstjens and Lal 2000; Evanschitzky and
Wunderlich 2006) and in the online context (e.g., Anderson
and Srinivasan 2003; Balabanis et al. 2006; Harris and Goode
2004; Methlie and Nysveen 1999). However, our results
Constructing online switching barriers
167
Table 6 Hypotheses testing
Hypothesized parameter
Std. β
SE
t-value
Sig.
SAT → ELOYALTY
ATA → ELOYALTY
PSC → ELOYALTY
SAT*PSC → ELOYALTY
ATA*PSC → ELOYALTY
.10**
−.58***
.18***
.04
.01
.062
.196
.109
.174
.195
1.899
−5.486
2.338
0.599
0.119
.029
.000
.001
.589
.905
R2
Hyp.
Result
H1
H2
H3
H4
H5
Supported
Supported
Supported
Not Supported
Not Supported
.53
Model fit: df = 884; t2 = 1743.1; e2 /df = 1.972; NNFI = 0.99; CFI = 0.929; RMSEA = 0.040
SAT Satisfaction, PSC Perceived switching costs, ATA Perceived alternative attractiveness
***p < 0.01; **p < 0.05; *p < 0.1
indicate that the influence of satisfaction on loyalty in this
study is not very substantial, that is, satisfaction explains only
10 % of the variance in loyalty. This raises the question whether customer defection can be controlled successfully by simply managing customer satisfaction. The results of previous
studies have also found that satisfaction explains very low
variance in repurchasing behavior (Balabanis et al. 2006;
Bolton 1998; Mazursky et al. 1987). According to Reichheld
(1996), more than 50 % of customers generally defect, despite
being happy, or even delighted with a company. Even though
we found that satisfaction explains only 10 % of variance in
loyalty, dissatisfaction may not necessarily result in defection.
Therefore, we can conclude that our findings support Chebat
et al. (2010), which found that managing satisfaction and/or
service quality are not the only way to foster customer loyalty.
However, most companies seem to be bound by this narrow
and uncreative belief – the so called ‘satisfaction trap’
(Reichheld and Schefter 2000). Another possible explanation
is that the linkage between satisfaction and loyalty is nonlinear. In other words, loyalty increases only after satisfaction
passes a certain critical threshold (supporting Chebat et al.
2010; Dick and Basu 1994; Mittal and Kamakura 2001).
This implies that online customers must be highly satisfied
before loyalty develops. Thus, retaining customers on the basis of satisfaction metrics alone may not be a very sensible
strategy.
The findings also reveal that between the two components
of switching barriers conceptualised in the model, alternative
attractiveness plays a greater role as a driver of customer loyalty. This is evidenced by the huge negative impact perception
of alternative attractiveness has in influencing loyalty (standardized coefficient = −0.58 of 58 %). In other words, the
lower the perception of good alternatives available in the market, the more loyal customers will be. It would appear that an
e-retailer is not as well protected by new entrants in the online
environment as their offline counterparts, as this result contradicts studies in the offline environment. In the offline environment, switching costs play a greater role in determining loyalty as compared to alternative attractiveness, where competitive insulation appears to be more substantial. This has been
found especially among studies on continuous and/or contractual service (e.g., financial, credit card and phone services,
etc.).
Our results also provide convincing evidence to conclude
that online switching costs have positive and direct effects on
loyalty. This is in line with a few studies, which looked at
retailing in the offline context (Burnham et al. 2003; Tsai
and Huang 2007; Tsai et al. 2006). Also, it is worth mentioning here that two previous studies failed to find a significant
direct effect of switching costs on loyalty (viz., Jones et al.
2000; Yang and Peterson 2004). Interestingly, our results reveal that switching costs (18 %) explain loyalty more powerfully as compared to satisfaction (10 %). Previously, Burnham
et al. (2003) found that the influence of switching costs on
customer intention to purchase to be stronger than satisfaction.
The findings show that switching costs are, indeed, a very
important factor for loyalty and of course customer retention,
contrary to past assertions about the negligibility of switching
costs in the online context (see for e.g., Holloway 2003;
Bakos, 1997).
Our hypothesized moderating effects in the satisfactionloyalty and attractiveness-loyalty relationships were not significant. This suggests that switching costs have only direct
positive effect on loyalty and not a moderating effect. This
contradicts the findings of other past studies in other contexts.
For example, Jones et al. (2000), Ranaweera and Prabhu
(2003), and Aydin et al. (2005) found negative moderating
effects of switching costs on service satisfaction and
repurchase intention or loyalty. On the other hand, Lee et al.
(2001) found positive moderating effects of switching costs
on satisfaction and loyalty. It should be noted that these studies involved either ‘high-touch’ offline services (viz., hair salon: Jones et al. 2000) or contractual services contexts (viz.,
financial services: Jones et al. 2000; telecommunications
services: Aydin et al. 2005; Ranaweera and Prabhu 2003;
Lee et al. 2001). The study highlights that customer retention of online pure-play retailers is quite unique and
we cannot assume that relationships between satisfaction,
switching barriers and loyalty are same as in bricks-and
clicks or offline retailers.
168
Conclusion
This paper takes a fresh look on the influence of online
switching barriers and customer satisfaction on customer retention, and examines the moderating effects of switching costs in
the context of online pure-play retailers (online retailers who
have no physical retail presence). The study advances current
understanding of online customer retention/switching by explicating switching barriers from a more nuanced perspective and
linking these to an online customer loyalty framework. Data
were gathered via a survey of 590 shoppers of online pure-play
retailers in the UK. Findings show that customer satisfaction
and the two dimensions of switching barriers (perceived
switching costs and perceived attractiveness of alternatives)
significantly influence customer loyalty. Contrary to findings
in earlier studies, it was found that switching costs did not
moderate the relationships between satisfaction and loyalty
nor between perceived attractiveness of alternatives and loyalty. As explained next, the paper has imperative managerial
implications to foster loyalty online.
Managerial implications
This study has several significant implications for marketing
managers in the online retailing sector. First, managers must
manage their switching barriers more systematically. As indicated by the study, switching barriers ensures that customers
have more incentives to remain loyal to the firm. However,
managers must remember that managing customer satisfaction
is simply not enough to secure loyalty from their customers.
They need to be cognizant of the importance of perceived
switching barriers while preparing marketing plans/initiatives,
which seek to maintain customer loyalty (in the online marketplace). In particular, the research findings highlight the
strategic importance of perceptions of barriers to switching
in a more comprehensive manner.
The findings also suggest that there is a need for marketing
managers to deliberate on the notion of attractiveness of alternatives, which is closely related to the perception of firm heterogeneity or differentiation in strategy research. Managers
need to understand that the perceived lack of good alternatives, forms formidable barriers to exit and hence is a vital
factor in customer retention. On the other hand, this is also
relevant to new start-ups seeking to challenge the more
established retailers. Even though the effect of service differentiation on customer retention was not evaluated in this
study, we suggest offering one-stop shopping; encouraging a
wider usage of the service through product reviews, creation
of wish lists, etc. Online retailers can also offer bundled services and features such as related item suggestions whenever a
customer buys a product and offers of cheaper or even free
postage for multiple purchases. Securing enough differential
advantage might help in enhancing loyalty. On the other hand,
E. Ghazali et al.
as our findings indicate, availability of attractive alternatives is
extremely important and is relevant to new start-ups seeking
to challenge the more established retailers. Finally, in spite of
the importance of constructing switching barriers, managers
must be careful not to ‘lock-in’ their customers (Frow et al.
2011), as this may be seen as unfair conditions of a sale,
leading to perceptions of unfairness, which may damage the
brand (Nguyen et al. 2015). Hence, moderation to the developing of switching barriers as well as consideration of fairness
from the consumers’ perspective is preferred to utilize
switching barriers successfully.
Limitations and future research
While this study has focused on customer perceptions towards
five unique switching cost dimensions in the context of pureplay e-retailers, future research could focus on the role of each
of the five dimensions vis-à-vis loyalty, specifically, focusing
on the importance placed by customers on each of the individual dimensions. Of particular interest would be to find out
whether the influence of learning costs’ on loyalty would differ from that of artificial costs.
One of the unique contributions of this research is that it
focuses specifically on pure-play online retailers. However,
most retailers have both an online and offline presence. As
shown by Shankar et al. (2003), online loyalty does ‘transfer’
from the loyalty of traditional (offline) settings. Therefore,
future research could compare between pure-players and
bricks-and-click companies. Insights from these studies would
be useful in shedding light on the role of perceived switching
barriers as a retention tool. In addition, as our study was based
solely in the UK where e-commerce is quite mature, further
studies could examine the phenomenon in other countries/
groups of countries, which have different levels of Internet
usage and/or are at different stages of economic development.
Further studies could also explore the moderating influence of
various demographic and psychographic characteristics such
as age groups, income, family size, and social class.
It should be noted that our study focuses on loyalty and we
differentiate this from habit or inertia, which refers to behavior
or activities performed repetitively, often unintentionally (Ji
and Wood, 2007). Future studies should examine habit in
terms of its measurement and effects on online customer retention. It would also be interesting to carry out longitudinal
studies on how customer behavior on pure-play retailers,
change over time as these retailers grow. Finally, given the
importance of customer self-participation in value cocreation on the Internet (Mohd-Any et al. 2015), future research could investigate the associations between customers’
perceived value-in-use and their perceived switching costs.
Investigations of the value-in-use concept from a servicedominant logic perspective could deliver significant insights
and could result in an extension of our framework.
Constructing online switching barriers
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