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Customer Behaviour by Usage of ISP Industry

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Journal of Retailing and Consumer Services 29 (2016) 104–113
Contents lists available at ScienceDirect
Journal of Retailing and Consumer Services
journal homepage: www.elsevier.com/locate/jretconser
Internet service providers' service quality and its effect on customer
loyalty of different usage patterns
Thu Nguyen Quach a, Paramaporn Thaichon b,n, Charles Jebarajakirthy a
a
b
Swinburne University of Technology, Victoria, Australia
S P Jain School of Global Management, 5 Figtree Drive, Sydney Olympic Park, Sydney, New South Wales 2127, Australia
art ic l e i nf o
a b s t r a c t
Article history:
Received 22 May 2015
Received in revised form
18 November 2015
Accepted 21 November 2015
Available online 5 December 2015
This study attempts to investigate the dimensions of an ISP's service quality, and their effects on customer loyalty in high-tech services. Data was obtained from 1231 internet users. The analyses include
segmenting ISPs' customers on the basis of their usage pattern and evaluating their perceptions of Internet service quality dimensions. Through the use of structural equation modelling and bias correct
bootstrapping techniques, the study confirms that service quality dimensions can influence both attitudinal and behavioural loyalty. These effects, however, are different across different groups of ISP
customers. The contribution of the present paper stems from the modelling of mediation effects and the
incorporation of Internet usage that can help better explain the impact of service quality dimensions on
customers' loyalty in high-tech service settings.
& 2015 Elsevier Ltd. All rights reserved.
Keywords:
Internet usage pattern
Attitudinal loyalty
Behavioural intentions
Services quality
Internet service provider (ISP)
High-tech services
1. Introduction
Service quality is an important differentiator in a competitive
business environment, and a driver of service-based businesses
(Zhao and Benedetto, 2013). By enhancing service quality, businesses can influence customers' retention and loyalty (Thaichon
et al., 2012). However, very few studies have assessed how different aspects of Internet service providers' (ISP) service quality
would influence their customers' loyalty (Vlachos and Vrechopoulos, 2008). ISPs may benefit from obtaining accurate information regarding their customers' assessments of their brand's delivered service quality; such information may enable service brand
managers to formulate appropriate marketing strategies in order
to achieve competitive advantage and long term profitability. This
paper attempts to fill this important research gap by investigating
the effects of ISPs' service quality on their customers' loyalty in the
high-tech Internet services. In addition, this paper segmenting
ISPs' customers on the basis of their usage pattern and evaluating
their perceptions of Internet service quality dimensions.
With the rise of technology-enabled services, the attention of
the services literature has shifted to measurement and operationalisation issues in service quality (Carlson and O’Cass, 2011;
Ganguli and Roy, 2010). The earliest service quality model was
n
Corresponding author.
E-mail addresses: [email protected] (T.N. Quach),
[email protected] (P. Thaichon),
[email protected] (C. Jebarajakirthy).
http://dx.doi.org/10.1016/j.jretconser.2015.11.012
0969-6989/& 2015 Elsevier Ltd. All rights reserved.
introduced by Parasuraman et al. (1985), and was referred as
SERVQUAL, including (1) tangibles; (2) reliability; (3) responsiveness; (4) assurance; and (5) empathy. In addition to SERVQUAL,
E-S-QUAL has been developed by Parasuraman et al. (2005) as an
attempt to fully capture service quality in the new information
age. However, telecommunications service quality cannot effectively be measured by SERVQUAL or E-S-QUAL (He and Li, 2010) as
these scales lack the ability of addressing specific issues relevant to
this particular context, especially high-tech ISPs. In particular,
while SERVQUAL applies to general service, E-S-QUAL focuses on
service providers who operate via the internet platform (Vlachos
and Vrechopoulos, 2008) and not those who provide the internet
connection and platform for online business-to-business and
business-to-customer activities.
On the other hand, segmentation can help better leverage a
service provider's resources and capabilities to fully take advantage of existing opportunities (Weinstein, 1987). As the needs
of consumers are not homogenous, it is essential to divide the
market into various segments (Mazzoni et al., 2007). Although the
concept of market segmentation has been discussed extensively in
the literature (Mazzoni et al., 2007; Wedel and Kamakura, 2003),
there is scarce empirical evidence of how ISPs can effectively
segment their target audience. In this study customers are segmented based on their usage pattern, which is one of the most
logical basis of segmentation in similar types of services (Mazzoni
et al., 2007; Wedel and Kamakura, 2003). More specifically perceptions of service quality dimensions and their relationships with
loyalty of light, medium and heavy users will be evaluated. Based
T.N. Quach et al. / Journal of Retailing and Consumer Services 29 (2016) 104–113
on the foregoing discussion, the objectives of this research study
are: firstly, to establish the relationships between specific service
quality dimensions of residential internet services and customers'
behavioural and attitudinal loyalty. Secondly, to investigate the
differences between light, medium and heavy users of the hightech residential internet services. Lastly, to provide managerial
implications to high-tech residential internet service providers.
2. Literature review and development of hypotheses
2.1. ISPs' service quality dimensions
Previous research indicates that judgment of overall service
quality in the telecommunications industry comes from customers' perceptions of a stable and strong network quality (Lai et al.,
2009), ready-to-serve customer support team (Aydin and Özer,
2005), informative website support (Thaichon et al., 2012) and a
high level of security and privacy that is trusted by customers
(Roca et al., 2009). Network quality is one of the core service
drivers in the telecommunications industry (Lai et al., 2009).
Network quality in the internet services industry involves the
quality and strength of the network signals (Wang et al., 2004),
number of errors, downloading and uploading speed (Vlachos and
Vrechopoulos, 2008). Any break in the internet connectivity may
lead to low perceptions of service quality. Moreover, when customers face problems in high-tech internet services, they often
seek help and support from technical and customer service staff.
For this reason, customer service teams are under constant pressure to perform their work reliably, dependably, and according to
set protocols in order to meet their productivity goals and deliver
quality customer service (Rod and Ashill, 2013). A study in the
Turkish telecommunications industry demonstrates that handling
customers' complaint efficiently contributes to overall service
quality (Aydin and Özer, 2005).
Information technology tools are utilised to increase efficiency
and effectiveness of information delivery (Ganguli and Roy, 2010).
Relevant, timely, and reliable information helps customers to obtain information and enable effective decision making (Hsieh,
2013). Moreover, information quality plays an important role in
building customers' overall positive attitude towards the company
(Jaiswal et al., 2010). In fact, a service provider facilitating high
levels of information quality and website support is often perceived to have better service quality. There have been considerable
concerns regarding safety and ethical behaviour in e-commerce
(Limbu et al., 2011). Customers are prone to attribute low risks in
purchasing from service providers who are reputable in relation to
their security practices (Roca et al., 2009). Security refers to the
extent a customer perceives the entire transaction as being safe,
which includes payment methods and transmitting confidential
information (Chang and Chen, 2009; Thaichon et al., 2014). Privacy
is often a concern of customers of high-tech services, and this
dimension relates to the customer perception of the quality of
processes used for personal information transmission and storage
(Özgüven, 2011). Several studies report that security and privacy
are related to service quality (Wolfinbarge and Gilly, 2003).
2.2. Behavioural and attitudinal loyalty
The concept of customer loyalty has received considerable attention in the marketing literature. There are many approaches to
measuring customer loyalty and several studies have attempted to
define the “true nature” of loyalty. Basically, several researchers
explain loyalty purely from the behavioural point of view (Jaiswal
and Niraj, 2011) whilst some argue that an attitudinal perspective
is more reflective of customer loyalty (Flint et al., 2011; Jacoby and
105
Chestnut, 1978). This research embraces an integrated theory,
which suggests that customer loyalty is a combination of both
behavioural and attitudinal loyalty (Dick and Basu, 1994; Oliver,
1999). In this respect, Flint et al. (2011) consider customers' loyalty
as a concept with multiple aspects including repurchase intention
and corresponding preferences and attitudes towards the brand.
While behavioural loyalty is defined as repeat purchase (Zeithaml
et al., 1996), this study considers attitudinal loyalty as customers'
inner thoughts of attachment, word-of-mouth, and recommendations (Zeithaml et al., 1996).
2.3. Relationships between service quality dimensions, behavioural
and attitudinal loyalty
It is commonly acknowledged that service quality drives customer loyalty and company profitability (Prentice, 2014). This
study has endeavoured to study the effects of each service quality
dimension on behavioural and attitudinal loyalty. Network quality
including connectivity quality, clarity of signals, and speed of internet is deemed to be the fundamental quality characteristics in
high-tech services which affect customer retention (Seo et al.,
2008). Other scholars also confirm that network quality is one of
the most important drivers of customer loyalty when dealing with
prepaid cell phone (Miranda-Gumucio et al., 2012). In the context
of the US mobile phone services, Cassab (2009) demonstrated that
network quality has the largest coefficient values in the regression
analysis of experimental data and therefore, has stronger influence
on customers' loyalty intention. Similar results are reported in the
US wireless services by Seo et al. (2008), who state that the connectivity quality of wireless is positively and significant related to
customers' repurchase intention. Based on the foregoing discussion, we propose the following hypotheses:
H1a : Network quality is positively associated with customers'
attitudinal loyalty.
H1b : Network quality is positively associated with customers'
behavioural loyalty.
It has been suggested that customer service has a positive and
significant impact on customer loyalty in technology-based banking services (Ganguli and Roy, 2011). Responsiveness of technical
and customer service staff has significant positive influence on
behavioural loyalty in the Greek mobile telephony service (Santouridis and Trivellas, 2010). Customer care is the major determinant of repeat purchase intention and customer loyalty, while
deficiencies in the quality of customer service are the main reasons for customers switching (Miranda-Gumucio et al., 2012).
Hence, the following has been hypothesised:
H2a : Customer service and technical support influence customers' attitudinal loyalty.
H2b : Customer service and technical support influence customers' behavioural loyalty.
A service provider facilitating high levels of information quality
and website support is able to maintain a long term relationship
with customers (Canhoto and Clark, 2013). Likewise, researchers
report that website design is found to be a determinant of loyalty
of online customers in South Africa and Australia (Caruana and
Ewing, 2010). Other website characteristics such as ease of use and
information are significant influencers of customer loyalty in
e-commerce (Toufaily et al., 2013) and content websites (Jaiswal
et al., 2010). Based on the above discussion, the following are
hypothesised:
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T.N. Quach et al. / Journal of Retailing and Consumer Services 29 (2016) 104–113
H3a : Information quality influence customers' attitudinal loyalty.
H3b : Information
loyalty.
quality
influence
customers'
behavioural
Previous research reports that security and privacy are related
to customer loyalty, especially in the e-commerce contexts (Ratnasingham, 1998). Recently, Limbu et al. (2011) reported a positive
link between customer privacy and website loyalty in the USA. In
online financial services, privacy is shown to have a direct effect on
intention to recommend (Finn et al., 2009). Researchers believe
that customer privacy plays a significant part in determining loyalty (Jaiswal et al., 2010). Jin and Kim (2010) state that the contribution of security and privacy towards customer loyalty is
greater in online multichannel retailers than in offline retail settings in Korea. Hence, the following have been hypothesised:
H4a : Security and privacy influence customers' attitudinal loyalty.
H4b : Security and privacy influence customers' behavioural
loyalty.
There exists a strong relationship between attitudinal loyalty
and customer repurchase intentions as suggested by Bandyopadhyay and Martell (2007). Han and Hyun (2012) demonstrate
that conative loyalty has a positive effect on action loyalty in hotel
services in the United States. In the Korean online marketplaces,
attitudinal loyalty is positively related to purchase intentions
(Hong and Cho, 2011). A research in the Chinese mobile phone
service reported that attitudinal loyalty has a significant positive
effect on behavioural loyalty (Zhang et al., 2010). Therefore, we
propose that an ISP's service quality dimensions have both a direct
and indirect effect on its customers' behavioural loyalty. The indirect effect is channelled via its customers' attitudinal loyalty.
This is articulated in the proposed conceptual model depicted in
Fig. 1, and the following is hypothesised:
Transactions Development Agency (ETDA, 2013) discloses that in
general Internet users in Thailand who are online for 11 h per
week make up 35.7 per cent; those who use Internet from 11 to
20 h weekly constitute 25.8 per cent; 10.7 per cent spend from 21
to 41 h on the Internet per week; and 27.8 per cent spend more
than 41 h every week. Based on the usage segmentation of previous research, this study determines three main groups of Internet users, namely light (i.e. less than 9 hours per week), medium
(i.e. 9–29 h per week), and heavy users (i.e. more than 29 h per
week) (Thaichon et al., 2014). Heavy internet users, who are the
likes of online game players or frequent internet surfers, would
possibly perceive network quality as the pre-dominant dimension
which influences their perception of an ISP, and might consider
network quality as the priority to choose and remain with a service provider. Light users would possibly perceive customer services and technical support as their priority for recommending an
internet service or in actual behaviour of staying with an ISP, since
they are usually unfamiliar with technical issues. Therefore, it is
most likely that each specific service quality dimension distinctively impacts customer loyalty (Prentice, 2014), which refers
to both attitudinal and behavioural perspectives, depending on
differently segmented groups of customers. Hence, the following
have been hypothesised:
H6 : ISPs' service quality dimensions differently influence attitudinal loyalty across the three segments of light, medium and heavy
users.
H7 : ISPs' service quality dimensions differently influence behavioural loyalty across the three segments of light, medium and
heavy users.
3. Method
H5 : Attitudinal loyalty is a mediator in the relationship between
specific ISPs' service quality dimensions and behavioural loyalty.
3.1. The study sample
Apart from identifying the four ISP service quality dimensions
in high-tech residential internet services, it is of interest that,
different customers have distinctive needs and require tailored
approaches (Mazzoni et al., 2007). In this process, customers are
generally segmented as heavy, medium and light users (Thaichon
et al., 2014). Heavy users are those who spend more than 29 h on
the internet every week (Assael, 2005). The average time spent on
Internet of a normal person is from 9 hours to 20 h per week
(ACMA, 2012). Heavy users often spend more than 29 h on the
Internet weekly, while light users only use the Internet for less
than 9 h every week (Assael, 2005). A study by Electronic
To test the hypotheses, an online survey was designed and
conducted in all regions of Thailand. Thailand is endowed with a
wide variety of natural resources, a substantial population and a
relatively strong economy. The enhanced investment on education
has resulted in knowledge improvement and higher educational
qualification of the Thai people. Thailand is fast becoming an information society. This is part of the reason for the considerable
development of the telecommunications industry in Thailand.
Overall contribution to GDP from the Internet in Thailand is expected to be 3.8 per cent p.a. in 2020 (Telenor, 2015). As the Internet is a capital good that enables increased production across all
economic activities, ISP industry plays a very important part in the
Thai economy (Telenor, 2015).
Internet in Thailand has passed its infancy and is currently in
the growth stage, poised to take off (Telenor, 2015). Thailand is
ranked third in South East Asia by way of residential internet
usage with an estimated 17 million internet users in 2009 (CIA,
2013) and over 27 million internet users in 2014 (NECTEC, 2015),
representing a penetration rate of approximately 40% (NECTEC,
2015). The Internet penetration in Thailand has seen a rapid increase since 2014 and the average internet user growth rate is 30%
per year (NECTEC, 2015). Therefore, this provides a suitable scenario to study the behaviour of Internet users. However limited
research on customers' buyer behaviour of home internet services
has been conducted in Thailand. On the other hand, the competition in Thailand among residential internet service providers is
intense (Kim, 2015). Currently there are three majors ISPs and 16
smaller ones across the country (Thaichon and Quach, 2013). In
this highly competitive market, the churn rate of internet users
Fig. 1. Conceptual framework.
T.N. Quach et al. / Journal of Retailing and Consumer Services 29 (2016) 104–113
was approximately 12% in 2009 (Thaichon et al., 2012). This scenario, therefore, poses huge challenges to ISPs especially in the
area of customers' repurchase intention.
107
Table 1
Sample structure.
Demographic profile
Current study
(%)
2013 (%) Chi square
statistics
3.2. Data collection
χ2 (1) ¼2.388,
p ¼ 0.877
Gender
Data was collected from residential internet users in all regions
of Thailand in 2013. Customer databases of well-established ISPs
who account for 95% of the Thai home Internet services (True,
2013) in Thailand were utilised as the sampling frame. The selected comprehensive customer databases incorporates diverse
customer profiles, including those who have switched from other
ISPs or those who wish to change to other service providers.
Hence, the sampling frame is representative of the entire population of Thai home Internet service customers. Simple randomisation was chosen to achieve freedom from human bias and to
avoid classification errors (Black, 1999). This means that each individual in the population had an equal chance of being included
in the sample (Teddlie and Yu, 2007). As such, the participants for
the survey were randomly selected from the customer databases
by computer software. This sampling technique has several advantages as it is simple and easy to apply, especially when a
comprehensive list of ISP customers was obtained (Rossi et al.,
2013). The corporations did not have any control in determining
who participated in the survey, and did not benefit from influencing or creating bias during the data collection process. The survey
responses were stored in the Opinio database. The companies did
not have access to this data. They were only interested in an independent and academically rigorous process, the findings of
which would assist them in developing and designing long-term
customer retention strategies.
It was calculated that the representative sample of Thailand's
population would be a number exceeding 700 (using a confidence
level of 95 per cent and a sampling error of 72.5). Hence we
emailed a total of 4000 surveys in two stages, i.e. 2000 surveys
were distributed in all geographical regions of Thailand and the
other 2000 were similarly emailed to participants a week later.
The online survey was made available via the Opinio platform. The
web link was relayed for the online survey and emailed to ISP
customers who were randomly selected from the databases. Responses to the online survey were automatically returned to the
researchers through the Opinio platform. Opinio software enables
the production and reporting of a survey and assures the anonymity, confidentiality and privacy of the respondents. In order to
achieve accurate results, and in particular, to prevent multiple
attempts by the same respondent, the default in Opinio was set as
follows: ‘not allow multiple submissions', and ‘prevent with
cookies and IP-address check'. This means that the Opinio software
recognised every respondent's IP address (computer ID), and only
one completed survey was accepted from a particular computer.
The Opinio platform was kept live for a period of three months.
3.3. Respondent profiles
The final usable sample size was 1231. In terms of the profile of
the respondents, as illustrated in the Table 1, the sample structure
is very similar to the overall structure of Internet users as specified
in previous research in Thailand (ETDA, 2013). For instance, 45.6
per cent of the total respondents were males, while 55.4 per cent
were females. This is consistent with the results of previous study
of Internet users in Thailand in 2013 with 47.8 per cent males and
52.2 per cent females (ETDA, 2013). Similarly, Generation Y made
up more than 50 per cent of the respondents, consistent with the
findings of the earlier research (ETDA, 2013). In addition, respondents with income from 10,001 to 30,000 baths were the
largest group while Bachelor's degree dominated the categories of
Male
Female
Age
45.6
55.4
o 19
20–29
30–39
40–49
50þ
Monthly household income
7.5
29.1
34.3
17.2
11.9
Under 10,000
10,001–30,000
30,001–50,000
50,001–100,000
Over 100,000
Education
7.2
34.9
20.6
23.9
13.4
Secondary or below
qualification
2 Years college or associate
degree
Bachelor's degree
Postgraduate degree or
higher
Number of Internet users in a
household
1
2
3
4
5 or more
Location
6.1
5.5
8.3
6.6
59.7
25.9
61.1
26.8
Bangkok and surrounding
suburbs
Others
47.8
52.2
χ2 (4) ¼8.405,
p ¼ 0.078
6.6
30.8
32.7
19.3
10.6
χ2 (4) ¼7.344,
p ¼0.119
6.4
36.3
21.5
24.5
11.3
χ2 (3) ¼6.963,
p ¼ 0.073
χ2 (4) ¼5.327,
p ¼ 0.255
13.2
14.7
18.9
25.9
27.3
14.7
15.9
19.2
24.2
26
χ2 (1) ¼2.855,
p ¼0.091
48.3
45.9
51.7
54.1
education level. Most of respondents stayed in a household with
3 or more Internet users. These findings were in line with the 2013
study of ISP customers in Thailand (ETDA, 2013). Almost half of the
respondents in this study came from Bangkok and its surrounding
suburbs. This can be explained by the fact that the penetration rate
of Internet in Thailand is only approximately 40% (NECTEC, 2015)
and Internet is mainly available in cities and urban areas. In order
to further compare the respondents' structure in the sample and
the overall population structure, Chi-square statistics were obtained for each of the socio-demographic variables. The results
presented in Table 1 indicate that there is no significant difference
between the sample and population distributions. In terms of Internet usage, in this study 21.2 per cent of the respondents were
light users, 33.1 per cent were medium users and 45.7 per cent
were heavy users.
In terms of Internet usage groups, a majority of light users were
between 29 and 38 years (i.e. 41.2 per cent) and 39 and 49 years
(i.e. 35.7 per cent). Interestingly 68.9 per cent of pensioners fell in
the category of light users. This could be explained by the fact
elder people are usually not very conversant with technology,
especially in an Asian context. In terms of gender, females were
more likely to be light users than males. In addition, approximately 70 per cent of full time workers were medium users. There
were no noticeably uncommon patterns in the age groups and the
distribution of gender among medium users. Heavy users were
more distinctive when compared to the other two segments.
Young Internet users made up the largest percentage of this group;
108
T.N. Quach et al. / Journal of Retailing and Consumer Services 29 (2016) 104–113
Table 2
Online activitiesn.
Online activity
Heavy
user (%)
Medium user Light
(%)
user (%)
Difference test
Emails
30.4
44.3
67.4
Search for
information
Social media
54.2
15.1
40.2
38.1
33.5
20.1
χ2 (2)¼ 5.823,
p ¼.054
χ2 (2)¼ 10.621,
p ¼.006
χ2 (2)¼ 2.669,
p ¼.259
n
Respondents could give more than one answer.
more than 61.3 per cent were in the 18–28 age bracket and 49.7
per cent were in the 29–38 age bracket. Consequently, more than
half of the students in this study were classified as heavy users. In
relations to online activities, heavy users spent most time on
searching for information (i.e. 54.2 per cent), 38.1 per cent selected
social media and 33.9 per cent picked emails. In contrast, 44.3 per
cent of medium users chose emails as their primary activity followed by social media (i.e. 33.5 per cent) and information search
(i.e. 15.1 per cent). Similarly, emails were the most popular among
light users with 67.4 per cent; information search and social media
were next with 40.2 per cent and 20.1 per cent respectively. Chi
square difference test was conducted and reported in Table 2. The
results indicate a significant difference between these groups of
customers in terms of information search behaviour.
survey are shown in Table 3, which is depicted in the Section 4.
Vlachos and Vrechopoulos (2008) connection quality scale was
used to measure network quality. The scale examines connection
quality for mobile phone services which is very similar to the
nature of an ISP's network quality. The customer service scale was
taken from Wolfinbarge and Gilly (2003) which addresses both
customer service and technical support in the ISP's offerings. Four
different scales relating to information and website support from
Chae et al. (2002), Lin (2007), Kim and Niehm (2009) and Vlachos
and Vrechopoulos (2008) were considered. After a thoughtful
analysis, the Kim and Niehm's (2009) information quality scale
was selected as this scale has stronger factor loadings (.80–.83),
and Cronbach's alpha rate (α ¼.96). Vlachos and Vrehopoulos's
(2008) privacy scale was selected. The scale's measurement items
investigate whether customers feel safe parting with information
during transactions, and also seek their opinions on security features of an ISP. The loyalty scale from Kim and Niehm (2009) was
chosen to measure attitudinal loyalty since it has strong factor
loadings (.71–.95) and Cronbach's alpha (α ¼ .93). Additionally, this
scale covers all the aspects of attitudinal loyalty and is used to
determine if customers consider themselves to be loyal patrons of
a particular ISP. Zeithaml et al. (1996) behavioural loyalty scale and
complaining behaviour scale were selected to measure another
aspect of loyalty in the ISP market. They have reasonable factor
loadings (.74–.94) for behavioural loyalty scale; and (.76–.99) for
complaining behaviour scale. They aim to investigate whether the
customer wishes to stay with a particular ISP in the next few years
or to switch.
3.4. Measures
Respondents were required to rate their perceptions for every
item using a likert scale which was anchored at 1 for strongly
disagree and 5 for strongly agree. These statements originally in
English were translated into Thai language by a professional
translator. Subsequently, the translated Thai version was back
translated into English. Significant misunderstanding or confusion
caused by a cross-cultural transformation was detected through
the back-translation process. Discrepancies in the translation were
carefully inspected and corrected to ensure that the items reflected the original meaning, and did not contain any social
judgments. To confirm the error-free translation, the translated
versions were then crosschecked by three other bilingual researchers to ensure face and content validity. The items of the
4. Analysis and results
4.1. Factor analysis and validity testing
The multi-scale nature of the data comprising ordinal scales
requires the use of polychoric correlation matrices of software
programmes (Hair et al., 1998). Hence, AMOS version 20 (Analysis
of Moment Structures) was employed to analyse the data. The
items used to assess the ISP's service quality were factor analysed
to confirm dimensionalities. Confirmatory factor analysis (CFA)
was performed to examine whether theoretical relationship between items and their hypothesised factors were supported by the
data (Cunningham, 2010). Table 3 depicts 6 constructs in the
Table 3
Instrument items and reliability indices.
NQ
CS
IW
SP
AL
BL
Items
FL
α
CR
AVE
I do not experience any Internet disconnection from this ISP
The Internet downloading and uploading speed meet my expectations
The Internet speed does not reduce regardless peak or off-peak hours
Customer service personnel are knowledgeable
Customer service personnel are willing to respond to my enquiries
My technical problems are solved promptly
This ISP provides sufficient information
This ISP provides up-to-date information
This ISP provides relevant information
I feel that my personal information is protected at this ISP
I feel that my financial information is protected at this ISP
I feel that the transactions with this ISP are secured
I consider myself to be a loyal patron of this ISP
I would say positive things about this ISP to other people
I would recommend this ISP to someone who seeks my advice
I would consider this ISP as my first choice to buy services
I would do more business with this ISP in the next few years
I would do less business with this ISP in the next few years (-)
.714
.845
.809
.872
.886
.798
.832
.822
.818
.734
.765
.911
.822
.898
.924
.897
.800
.532
.828
.833
.625
.887
.889
.727
.876
.864
.679
.855
.847
.651
.917
.913
.779
.797
.796
.576
Notes: FL ¼ factor loadings, α ¼Cronbach's alpha, CR¼ Construct reliability, AVE ¼Average variance extracted, NQ¼ network quality; CS ¼ customer service and technical
support; IW¼ information and website support; PS ¼privacy and security; AL ¼attitudinal loyalty; BL ¼ behavioural loyalty.
T.N. Quach et al. / Journal of Retailing and Consumer Services 29 (2016) 104–113
Table 4
Correlations between variables.
NQ
CS
IW
PS
AL
BL
Table 6
Regression results for the mediation of the effect of attitudinal loyalty on behavioural loyalty.
NQ
CS
IW
PS
AL
BL
.625
.607
.679
.583
.615
.619
.368
.727
.746
.603
.569
.568
.461
.557
.679
.761
.700
.677
.340
.364
.579
.651
.657
.647
.378
.324
.490
.432
.779
.748
.383
.323
.458
.419
.560
.576
Notes: The diagonal elements are the AVEs (italicised and bolded). The lower-left
triangle elements (italicised) are correlations and the upper-right triangle elements
are the squared correlations between constructs. All correlations are significant at
the.01 level (2-tailed). NQ¼ network quality; CS ¼customer service and technical
support; IW¼ information and website support; PS ¼ privacy and security;
AL¼ attitudinal loyalty; BL ¼ behavioural loyalty.
conceptual model, associated indicators, and their standardised
coefficients, reliability indices and average variance extracted.
Factor loadings of all indicators were positive and statistically
significant. Reliability estimates for each of the construct, i.e.
Cronbach's alpha, and composite reliabilities exceeded the
threshold .70 (Nunnally, 1978). Table 4 presents the correlations
and squared correlations between constructs. The correlations
between constructs were significant, ranging from .583 to .761
which were well below the .90 cut-off (Tabachinick and Fidell,
2001). Therefore, there was no redundancy or violation of multicollinearity. In addition, the average variance extracted (AVE) for
each factor was over .50, indicative of adequate convergence
(Fornell and Larcker, 1981). The construct AVE estimates were
larger than the corresponding squared inter-construct correlation
estimates (SIC), thereby supporting discriminant validity.
4.2. Hypotheses testing
Structural equation modelling (SEM) was conducted to examine the research model. SEM is suitable as it allows testing of
structural models, specifically those containing latent constructs
(Anderson and Gerbing, 1988). SEM enables the estimation of
multiple and crossed relationships between dependent and independent variables, and is able to denote constructs unobserved
in these relationships as well as dealing with measurement error
in the estimation process (Beerli et al. (2004). The results are
presented in Table 5. Whereas Chi-square statistic known to be
highly sensitive to sample size was significant (χ2 (120) ¼563.180,
p ¼.000), the other fit indices (CMIN/DF ¼4.693, GFI.970,
Table 5
Results for the relationships between service quality dimensions, attitudinal and
behavioural loyalty, coefficients.
AL
AL
AL
AL
BL
BL
BL
BL
BL
o—
o—
o—
o—
o—
o—
o—
o—
o—
NQ
CS
IW
SP
NQ
CS
IW
SP
AL
Estimate
S.E.
C.R.
.234
.045
.400
.297
.045
.038
-.062
.029
.941
.030
.038
.053
.037
.023
.028
.041
.028
.024
7.885
1.193
7.566
8.048
1.958
1.358
-1.515
1.036
39.805
P
nnn
.233
nnn
nnn
.050
.174
.130
.300
nnn
Notes: 1. NQ ¼network quality; CS ¼customer service and technical support;
IW¼ information and website support; PS ¼ privacy and security; AL ¼attitudinal
loyalty; BL ¼behavioural loyalty; CFI ¼ comparative fit index; GFI goodness of fit
index; AGFI¼ adjusted goodness of fit index; RMSEA ¼root mean square error of
approximation; TLI¼ The Tucker-Lewis coefficient;
2. Model fit indices: χ2 (120) ¼ 563.180, p ¼ .000, χ2/df ¼4.693, GFI¼ .970, AGFI ¼.957,
TLI¼ .978, CFI¼ .983, RMSEA ¼.042; 3. R2 (BL) ¼ .913; R2 (AL) ¼.53.
nnn
109
p Values are statistical significant at .001 levels;
Structural path
Direct model
NQ-BL
CS-BL
IW-BL
PS-BL
R2 BL1 ¼ .524
Mediation model
NQ-AL
CS-AL
IW-AL
PS-AL
AL-BL
NQ-BL
CS-BL
IW-BL
PS-BL
R2BL2 ¼ .913
Standardised coefficients
Standardised indirect effects
.242nnn
.063
.268nnn
.267nnn
–
–
–
–
.222nnn
.036
.326nnn
.258nnn
.938nnn
.043
.030
.050
.025
–
–
–
–
–
.208nnn
.033
.306nnn
.242nnn
Notes: NQ¼ Network quality; CS ¼Customer service and technical support;
IW¼ Information and website support; PS ¼ Privacy and security; AL¼ Attitudinal
loyalty; BL ¼ Behavioural loyalty
R2BL1 is the proportion of variance in BL explained by the direct model
R2 BL2 is the proportion of variance in BL explained by the mediation model
nnn
pr .001.
AGFI¼.957, TLI¼ .978, CFI¼ .983, RMSEA ¼.042) indicate that the
model was a good fit to the data. The ISP's service quality explained 53.0% of variance in attitudinal loyalty (R2 ¼53.0%), and
the whole model explained 91.3% of variance in behavioural loyalty (R2 ¼91.3%). The direct effects of network quality, information
and website support, privacy and security were found to be significant on attitudinal loyalty. However, none of service quality
dimensions directly influenced behavioural loyalty. Attitudinal
loyalty was the only direct determinant of behavioural loyalty.
In order to test the mediation relationships and indirect effects
of service quality dimensions on behavioural loyalty, two competing models, namely direct effect model and mediation model
(i.e. the research model) were examined in line with the approach
of Singh et al. (1994). The mediation relationship was tested on the
basis of standardised residual co-variances and modification index
values. Additionally, the bias corrected bootstrap was used in order
to aid the confirmation of the mediation effect as recommended
by Preacher and Kelley (2011). The results are provided in Table 6.
All service quality dimensions, except for customer service and
technical support, significantly influenced behavioural loyalty in
the direct model. The mediation model test demonstrates that the
explained variance in behavioural loyalty increased from 52.4% to
91.3% by incorporating attitudinal loyalty as a mediator. The
mediation effect of attitudinal loyalty was confirmed in the mediation model which demonstrated the following: (1) higher variances, (2) a significant effect of the ISP's service quality dimensions (except for customer service and website support) on attitudinal loyalty, (3) insignificant relationship between service
quality dimensions and behavioural loyalty, and (4) significantly
influence of attitudinal loyalty on behavioural loyalty. In other
words, attitudinal loyalty fully mediated the relationships between
network quality, information and website support, privacy and
security, and behavioural loyalty. Drawing upon the results of
obtained by the bias-corrected bootstrap with 10,000 resamples,
significant indirect effects on behavioural loyalty were found for
all dimensions, except for customer service and website support.
James and Brett (1984) assert that complete mediation happens
when the effect of the independent variable(s) on the dependent
variable completely disappears once the mediator is added as a
110
T.N. Quach et al. / Journal of Retailing and Consumer Services 29 (2016) 104–113
Table 7
Results for the relationships between service quality dimensions, attitudinal and behavioural loyalty, coefficients among light, medium and heavy users groups.
Path
Light users
Medium users
Direct effects
AL
AL
AL
AL
BL
BL
BL
BL
BL
Indirect effects Total effects Direct effects Indirect effects Total effects Direct effects Indirect effects Total effects
.213nnn
.044
.458nnn
.308nnn
.033
.001
.023
.001
.945nnn
o — NQ
o — CS
o — IW
o — SP
o— NQ
o— CS
o— IW
o— SP
o— AL
Goodness of fit indices
Heavy users
.201nn
.042
.433nn
.291nn
.166n
.034
.398nn
.386nnn
.169nn
.119n
.323nn
.084
.894nnn
.234nn
.04
.41nn
.291nn
.945nnn
χ2(120) ¼ 261.439, CMIN/DF ¼2.179, GFI¼ .953,
AGFI ¼.933, TLI ¼.976, CFI¼ .981, RMSEA ¼ .045,
90% CI ¼ .037:.052
.149n
.031
.356nn
.345nn
.318nn
.149n
.034
.429nn
.894nnn
χ2(120)¼ 23.768, CMIN/DF ¼1.923,
GFI ¼.951, AGFI¼ .930, TLI¼ .976, CFI¼ .982,
RMSEA ¼ .044,
90% CI ¼ .036:.053
.279nnn
.054
.364nnn
.257nnn
.019
.018
.003
.021
.959nnn
.268nn
.052
.349nn
.246nn
.287nn
.069
.352nn
.267nn
.959nnn
χ2(120) ¼362.058, CMIN/DF ¼3.017,
GFI ¼ .960, AGFI ¼.943, TLI ¼.975, CFI¼ .980,
RMSEA ¼ .045,
90% CI ¼.040:.050
Chi Square difference test Δχ2(24)¼39.765, p¼ .023
Notes: NQ¼ network quality; CS¼ customer service and technical support; IW¼ information and website support; PS ¼ privacy and security; AL¼ attitudinal loyalty;
BL ¼behavioural loyalty; CFI ¼comparative fit index; GFI goodness of fit index; AGFI ¼adjusted goodness of fit index; RMSEA ¼root mean square error of approximation;
TLI¼ The Tucker-Lewis coefficient;
n
pr .05
p r.01
nnn
p r.001.
nn
Table 8
Chi square difference tests.
Path
AL
AL
AL
AL
BL
BL
BL
BL
o—
o—
o—
o—
o—
o—
o—
o—
NQ
CS
IW
SP
NQ
CS
IW
SP
Δχ2
df
p
2.189
.047
.635
1.762
5.516
2.724
7.715
2.189
2
2
2
2
2
2
2
2
.335
.977
.728
.414
.063
.256
.021
.579
Notes: CS¼ Customer Service; NQ¼ Network Quality; IW ¼Information and Website
support; SP ¼ Security and Privacy; AL¼ attitudinal loyalty; BL ¼ behavioural loyalty.
predictor of the dependent variable. On this basis, it can be concluded that the relationship between network quality, information
and website support, privacy and security, and behavioural loyalty
was fully mediated by attitudinal loyalty. Hence, H1a, H1b, H3a, H3b,
H4a, H4b, and H5 found support.
In order to test H6 and H7 structural equation modelling with
multigroup invariance testing was conducted. Results are shown in
Table 7. Firstly, the possibility that a fully constrained model was
invariant across groups was tested. This means specification of a
model in which all factor loadings, all factor variances and all
factor covariances were constrained equal across light, medium,
and heavy users (Byrne, 2004). As indicated in Table 7, the difference of overall Chi square test (Δχ2(24) ¼39.765, p ¼.023) was
statistically significant, indicating that some equality constraints
did not hold across the light, medium and heavy users.
To further verify the differences in each structural path between the three groups of users, it is recommended to separately
examine structural models for the three segments. The models of
Internet usage showed reasonable fit to the data (Table 7). A
preliminary examination of the structural models of the three
usage groups illustrates that customer service and technical support's effects on both behavioural and attitudinal loyalty remained
insignificant among the three internet user groups. Network
quality, privacy and security, and information and website support
were significant predictors of attitudinal loyalty among the three
groups. Noticeably, information and website support's total effects
on behavioural loyalty was only found to be significant among
light users and heavy users. In fact, though positively affecting
attitudinal loyalty, information and website support had a negative direct impact on behavioural loyalty of medium users, which
resulted in insignificant total effect. This signifies some potential
differences in the relationships between four service quality dimensions and behavioural loyalty across the light, medium and
heavy groups.
In order to confirm differences in the structural paths of interest among three groups of Internet usage, a test for invariance
of factor loadings was conducted. In this process, the unconstrained model was run, and eight paths (from four quality
dimensions to attitudinal and behavioural loyalty) were fixed to be
invariant in all groups to arrive at a constrained model (Cunningham, 2010). In the eight models, only one model testing the
path from information and website support towards behavioural
loyalty resulted in significant difference in the chi-square test
(Table 8). This result confirms that the effects of information and
website support on behavioural loyalty was not the same for
people from different internet usage groups (Δχ2 (2) ¼7.715,
p¼ .021). Therefore, H6 was rejected and H7 was partially
supported.
5. Discussion
This study advances the literature related to service quality as a
key determinant of customer loyalty among different groups of
customers in high tech services context. The contribution to extant
research can be considered relatively robust as the research model
explained a considerable proportion of the variance in the criterion variables.
5.1. ISPs' service quality dimensions, attitudinal and behavioural
loyalty
The results show that all service quality dimensions except for
T.N. Quach et al. / Journal of Retailing and Consumer Services 29 (2016) 104–113
customer service and technical support were positively related to
attitudinal and behavioural loyalty. Information and website support was the predominant predictor of attitudinal loyalty, and
behavioural loyalty of ISP customers. The role of privacy and security was also confirmed. These results support previous findings
on the relationship between loyalty and information and website
support (Toufaily et al., 2013), and privacy and security (Limbu
et al., 2011). Interestingly, customer service and technical support
was neither a significant predictor of attitudinal nor behavioural
loyalty, contrasting with the finding of Santouridis and Trivellas
(2010), which claimed that this dimension had the most significant impact on loyalty. This can be explained by the fact that
customers in Asian culture typically often seek help from their
friends or family for technical issues and tend to avoid communicating directly with the service providers due to ego concerns
and value orientations (Neuliep, 2012). Another possible explanation could be that this dimension manifests its effect via
other variables not included in this study, for example, satisfaction
(Caruana and Ewing, 2010).
Network quality significantly contributed to attitudinal and
behavioural loyalty mirroring prior research by Miranda-Gumucio
et al. (2012). This finding confirms that the basic need of an ISP
customer is still the core service performance, i.e. internet
downloading/uploading speed, signal stability and consistency.
However, unlike findings of previous research, this dimension was
not the most significant predictor of attitudinal and behavioural
loyalty in this study. This phenomenon can be justified as network
quality has become more stable and reliable among the ISPs,
especially in big cities such as Bangkok, which no longer makes it a
differentiating tool.
5.2. ISPs' service quality dimensions, loyalty among light, medium
and heavy users
In considering the usage profile of respondent, information
quality had different effects on behavioural loyalty across different
groups of internet users. Information and website support manifested its positive and significant impact on behavioural loyalty of
light and heavy users through attitudinal loyalty. The current
study was conducted in high-tech services in which customers are
more likely to communicate with the company through websites.
When customers feel that they have access to correct and adequate information, they are most likely to remain with the
company.
In contrast, information and website support directly had a
significant negative effect on behavioural loyalty among medium
users. Medium users are often more conversant with the internet
than light users, which makes information support less necessary.
In addition, they do not exhibit a need for considerable amount of
information like heavy users or light users. In fact, Table 2 illustrates that medium users spent more time on emails and social
media than on information search, in contrast to the other two
groups of users. Also, whereas many pensioners were light users
and a majority of heavy users were students, most of medium
users were full time employees who are usually “money rich time
poor”. Therefore, too much information might result in information overload for medium users and become dysfunctional (Jacoby
1984). In this case, it lessens the desire of customers to stay with
their current service provider. These reasons together might explain why medium users do not attach much importance to information support in their quest towards behavioural loyalty.
5.3. The mediation role of attitudinal loyalty in the relationship between ISPs' service quality dimensions and behavioural loyalty
Results from the testing of the mediation effects confirmed that
111
the relationship between the ISP's service quality dimensions, i.e.
network quality, information and website support, privacy and
security, and behavioural loyalty were fully mediated by attitudinal loyalty. The model which incorporated attitudinal loyalty as a
mediator explained 92.9% of the variance in behavioural loyalty of
the ISP's customers in this study. This finding reveals a salient and
active role of attitudes of customers as a significant determinant
for their repeat purchase behaviour. Fostering attitudinal loyalty
increases the likelihood of the ISP becoming a supplier of choice,
winning new customers through positive word of mouth and recommendations, and improving customer retention. For this reason, it is essential for service providers to investigate the factors
that influence customer attitudes towards the firm.
6. Implications
This study has both theoretical and practical implications.
Overall service quality is widely considered as one of key factors
determining customer attitudinal and behavioural loyalty. However the results in this study highlight that in fact different ISP's
service quality dimensions generate different effects on the two
perspectives of loyalty. It is recommended that research on service
quality should not just focus primarily on network quality but on
other service quality dimensions, as prominence has been given to
this dimension in extant literature (He and Li, 2010; Kim and Yoon,
2004). This study revealed that service quality dimensions exert
different effects on customer loyalty among the various groups of
customers segmented based on their usage pattern, confirming
the importance of customer segmentation. Moreover, this study is
original in that it is the first of its kind which attempts to investigate the dimensions of an ISP's service quality, and their effects on loyalty in high-tech services.
This study has evaluated ISPs' service quality dimensions which
are identified as (1) network quality, (2) customer service and
technical support, (3) information quality and website information
support, (4) security and privacy. By enhancing service quality,
firms can influence customers' behavioural and attitudinal loyalty,
which are critical for an ISP's success and long term sustainability.
As a result, ISPs will be able to reduce the current issues relating to
customer switch and churn rate in the residential internet services
market.
Managers of ISPs should also be aware that, although the
concept of service quality is multidimensional, not all dimensions
contribute equally to loyalty. Different service quality dimensions
exert different effects on customer loyalty. Information and website support was found to be the predominant predictor of both
types of loyalty instead of the presumed network quality, signifying a potential change of behaviour and perception of ISP
customers. This underscores a need of improving company websites and information support to the service subscribers, such as
website accessibility and user-friendly interface, information accuracy, and timeliness. Although customer service was not found
to be a direct antecedent of loyalty in this study, companies still
need to maintain a satisfactory level of their customer service and
technical support performance and keep up with the industry
standards. In addition ISPs can promote a diversity of support
forms, such as online and offline support. Particularly online
support is convenient for customers experiencing simple issues or
those perceiving direct face-to-face communication as being too
intimate and personal. Furthermore, the finding confirms that
internet customers are heterogeneous. In order to effectively and
efficiently retain customers, an ISP needs to understand its customers' characteristics. It is critical to look into aspects that are
relevant to the attitudinal and behavioural loyalty of specific customer groups in order to leverage its key resources and maximise
112
T.N. Quach et al. / Journal of Retailing and Consumer Services 29 (2016) 104–113
profitability. By highlighting features of customers' interest, service providers can better enhance customer loyalty.
7. Limitations and future research directions
There are several limitations of this study. Firstly it is possible
that customers may have no choice but to stay with the service
provider as the switching costs are high (Shirin and Puth, 2011).
This explains why seemingly loyal customers, who have large
volumes and high frequency of purchases, can quickly switch to
other alternatives as switching costs decrease (Dick ad Basu, 1994;
Shirin and Puth, 2011). Switching costs can be considered as the
costs (including monetary, psychological and emotional costs)
involved in moving from a service provider to another (Porter,
1980). Switching costs help companies to deal with inevitable
variations in service quality (Jones et al., 2000). In other words,
despite a decrease in service quality, customers might stay with
the company because the perceived costs of changing to a new
service provider exceed the potential benefits of switching (Lam
et al., 2004). De Ruyter, Wetzels and Bloemer (1998) confirm the
moderating effects of switching costs on the relationship between
service quality and loyalty. As such, incorporating switching cost
into future research could open up more opportunities to understand the relationship between service quality and loyalty.
In addition, the choice of context (home internet services in
Thailand) where the proposed conceptual model in this study was
empirically tested may restrict the generalisability of the findings.
In the interest of generalisation, replication in other countries and
sectors to further evaluate the model would be a step towards
addressing this limitation. Finally, this study examines the effects
of the cognitive evaluation, i.e. service quality dimensions, on
customer loyalty. An investigation on customers' affective evaluations, for instance trust, satisfaction, and commitment, as
antecedents of loyalty and their interrelationships with service
quality dimensions might be a fruitful area of research and also
valuable in overcoming the limitation of this study.
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Thu Nguyen Quach holds a Master's Degree in Marketing and is currently working
towards a PhD programme in Marketing in the Faculty of Business & EnterpriseSwinburne University of Technology, Melbourne, Australia. Her research interests
are in the area of services marketing, marketing research, consumer behaviour and
relationship marketing. Thu's research has been published in the Journal of Retailing
and Consumer Services, Journal of Business and Industrial Marketing, and Services
Marketing Quarterly, among others.
Paramaporn Thaichon is an Assistant Professor of Marketing at the S P Jain School
of Global Management, Sydney, Australia. His research has been published in
leading marketing journals throughout Europe, North America and Australasia including but not limited to the Journal of Retailing and Consumer Services, Journal of
Business and Industrial Marketing, Asia Pacific Journal of Marketing and Logistics, and
Journal of Relationship Marketing.
Dr Charles Jebarajakirthy is based in the Faculty of Business and Law at the
Swinburne University of Technology, Melbourne, Australia. His research interests
are in the areas of market orientation, consumer behaviour, social marketing and
corporate social responsibility. Charles's research has been published in the Journal
of Retailing and Consumer Services, Asia Pacific Journal of Marketing and Logistics,
International Journal of Consumer Studies and Journal of Young Consumers, among
others.
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