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International Journal of Bank Marketing
Assessing the intentions to use internet banking: The role of perceived risk and
trust as mediating factors
Kanokkarn Snae Namahoot, Tipparat Laohavichien,
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Kanokkarn Snae Namahoot, Tipparat Laohavichien, (2018) "Assessing the intentions to use internet
banking: The role of perceived risk and trust as mediating factors", International Journal of Bank
Marketing, Vol. 36 Issue: 2, pp.256-276, https://doi.org/10.1108/IJBM-11-2016-0159
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Assessing the intentions to use
internet banking
The role of perceived risk and trust as
mediating factors
Kanokkarn Snae Namahoot
Kasetsart University, Bangkok, Thailand, and
Tipparat Laohavichien
Department of Operations Management, Kasetsart University, Bangkok, Thailand
Abstract
Purpose –The purpose of this paper is to examine the relationships among five dimensions of service
quality towards the overall behavioural intentions to use internet banking in Thailand and explain the
indirect effects between service quality and behavioural intentions to use internet banking using perceived
risk and trust as the mediating variables.
Design/methodology/approach –A multi-stage sampling procedure was performed to select the
505 respondents for this study. The participants were selected based on their experiences using internet
banking in Thailand. The data obtained from the participants was analysed using a structural equation
modelling approach.
Findings –The results show that service quality, perceived risk and trust influence behavioural intentions to
use internet banking. This study primarily aims to find out whether perceived risk and trust worked as a
mediator variable between service quality and behavioural intentions to use internet banking. The study will
be useful for the developers of internet banking, when they are implementing and developing a system that is
in accordance with the needs and lifestyles of the potential users; and CEOs, to create strategies and relevant
policies to achieve competitive advantage.
Originality/value –Literature has focused on understanding service quality dimensions that influence the
behavioural intentions to use internet banking. By expanding on the previous research in internet banking,
this paper empirically examines the overall direct and indirect influences between service quality, perceived
risk, trust and behavioural intentions.
Keywords Services quality, Trust, Structural equation modelling, Perceived risk, Internet banking,
Behavioural intentions to use
Paper type Research paper
Introduction
Banking services are important in everyday life. But many banks nowadays are
experiencing operational difficulties due to changing technologies and the changing
demands of their customers. As a result, banks have to adjust their strategies to meet the
current economic conditions and to efficiently manage risk. Additionally, they have to
respond to customers’demand for convenience by making banking transactions possible
through a one-stop service. Many banks have developed internet-based service models to
ease customer transactions. The use of the internet in banking services has been considered
a financial innovation, since its primary objective is to improve the quality of services and to
benefit customers. It also helps a bank reduce costs, as less face-to-face contact with the
customer is required. In turn, customers can enjoy the ease of use, convenience, and fast
service of an online banking system. The problem of “ATM Skimming”, which makes
customers concerned about the safety of their transactions when using an automated teller
machine, makes online banking a more appealing alternative (Kuisma et al., 2007).
The expansion of internet use in many countries and actual internet banking usage is
rapidly rising (Nasri and Charfeddine, 2012), even though customers feel concerned about its
security, such as the pitfalls of password changes (Kuisma et al., 2007). Thus, banks are
International Journal of Bank
Marketing
Vol. 36 No. 2, 2018
pp. 256-276
© Emerald Publishing Limited
0265-2323
DOI 10.1108/IJBM-11-2016-0159
Received 2 November 2016
Revised 16 February 2017
11 March 2017
Accepted 4 April 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0265-2323.htm
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trying to find innovative ways to secure transactions that will convince customers to trust
and continue to use their services.
A survey of 157 IT executives in 14 countries by PwC Consulting, Thailand found that
consumers are increasingly using digital banking services. The data showed that in 2016,
the number of financial transactions through mobile networks increased by 64 per cent
(Taweelappontong, 2015). Internet banking is a good choice for banks with respect to
developing transaction services via the internet by focusing on reducing the cost of
operations, increasing service quality, creating confidence in security, reducing risk and
improving efficiency in operations (Xue et al., 2011). This is a combination of bank
operations and information technology (IT) that can make internet transactions more
efficient (Montazemi and Qahri-Saremi, 2015). Several factors such as service quality,
perceived risk, trust, website usability, perceived security, ease of use, access, availability
and usefulness have affected behavioural intentions to use internet banking (Luo et al., 2010;
Zhao et al., 2010; Yoon and Steege, 2013), depending on individual characteristics, societies,
economies, politics and technologies (Montazemi and Qahri-Saremi, 2015). Although
individuals’behavioural intentions to use internet banking are complicated and
unpredictable, it is necessary for each bank to continuously develop internet banking
services such as service quality, structure of trust and service reliability in order to
enhance efficiency of operations and encourage customers to employ banking services
(Im et al., 2011; Xue et al., 2011).
Service quality has gained importance in many internet banking services as a useful factor
to increase behavioural intentions to use. SERVQUAL was broadly used to measure the
service quality of internet banking services (Janyawardhena, 2004; Han and Back 2004; Rod
et al., 2009; Zafar et al., 2012). The evidence shows that service quality and behavioural
intentions to use have a positive relationship, which means internet banking services are
necessary for banks and customers require the use of these services (Yang and Fang, 2004;
Im et al., 2011). For this reason, it is necessary to improve the service quality of internet
banking services by including information presented via internet websites, input commands,
responsibility, reliability, assurance and showing empathy for customers’demands, which can
enhance the efficiency of services and the response to customers’demands (Zeithaml et al.,
1996; Takieddine and Sun, 2015).
Perceived risk and trust are pervasive concepts that influence the behavioural intentions
to use internet banking (Martins et al., 2014). The majority of customers believe that internet
banking lacks security, efficiency, ease of use, trust, and service quality (Littler and
Melanthiou, 2006; Zhao et al., 2010) including the perceived risk factors such as the
performance risk, financial risk, time risk, and privacy risk (Martins et al., 2014). Song Hong
Lei (2010) integrated trust perception and perceived risk to predict the behavioural
intentions to use internet banking. The study found that improving the service quality and
system quality of internet banking helps in reducing the perceived risks of the customers
and thereby enhances their intentions to use. Liebana-Cabanillas et al. (2013) found that
trust is a major determinant of customers’intentions to switch to internet banking.
Akhlaq and Ahmed (2013) provided further evidence that supports the fact that trust affects
internet banking adoption in the context of low-income countries.
Thus, banks must find a way to create internet banking strategies to improve service
quality even further. This can reduce the perceived risk that the customers accept and also
increase trust in order to affect behavioural intentions to use internet banking in the future.
This research area is primarily dedicated to three aspects: first, to identify the relationship
between service quality dimensions and behavioural intentions to use internet banking in
Thailand. The establishment of these relations has highlighted the importance of using
service quality tactics as one of several procedures to improve internet banking’s service
quality. Second, to identify the mediators (perceived risk and trust) between service quality
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dimensions and behavioural intentions to use internet banking in Thailand. This leads us to
consider if service quality tactics can be used as one of several methods in risk adjustment,
improving service quality and building trust among the internet banking users. Third,
to suggest appropriate service quality building strategies to encourage increased
behavioural intentions to use internet banking.
Literature review
This study focuses on service quality, perceived risk and trust as explanatory and
predictive variables for attitude and behavioural intentions in adopting the use of internet
banking. The research model is proposed to address this issue (see Figure 1). All variables
hypothesised in this study and the nature of their expected relationships with
behavioural intentions to use (or continuing to use) internet banking are discussed in the
following sections.
Behavioural intentions to use
Behavioural intentions to use is a factor that measures the success of acceptance in the use
of technology. Fishbein and Ajzen (1975) stated that behavioural intentions to use is a
measurement of individuals’interest, which causes behaviour or connects individuals’
attitudes and recognition of service contributions. The relationship between attitude and
behavioural intentions to use results in the intentions of individuals to use a service
(Davis, 1989). Regarding the literature review, there are three theories related to
technology acceptance which are mainly applied to study the behavioural intentions to
use, comprising of theory of reasoned action (TRA), theory of planned behaviour (TPB),
and the technology acceptance model (TAM). These theories measure the success of the
development in use of technology and are differently employed depending on the context
of each study.
First, the TRA was presented by Ajzen and Fishbein (1980). This social psychology
theory is basically utilised to study human behaviour (Venkatesh et al., 2003). TRA explains
the relationship between the behaviour and attitudes of human beings. It is believed that
changes in human behaviour are caused by changes in beliefs. Individuals take action
because they believe that their behaviours are appropriate ways to act. Behavioural
intentions are motivated from two main factors: attitude, which affects behaviours and
compliance with subjective norms (Davis, 1989).
Second, the TPB was presented by Ajzen (1985) and relates to the social psychology
theory. This theory was developed from part of the TRA theory by Ajzen (1991). Ajzen added
the perceived behavioural control factor to reinforce the understanding of individuals’
perceptions in using technology. However, this theory has limitations due to deviations of
attitudes and behaviours. For instance, there might be an inconsistency between an
individual’s intentions to act and his or her actual actions as time passes (Ajzen, 1985).
Third, the TAM was accepted as a model of achievement measurement in technology
development and was applied to study the recognition of IT systems (Davis, 1985).
The TAM is composed of two factors: perceived usefulness and perceived ease of use.
The research of Rouibah and Hamdy (2009) applied the concepts of TAM (using perceived
Service Quality
Perceived Risk
Trust
Behavioural
Intentions to Use
H1 H3
H4H2
Figure 1.
Research model
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usefulness and perceived ease of use) to study factors which impact the behavioural
perception of using internet technology. The results showed that attitudes of consumers
became a major factor that highly affected behaviour of using internet technology.
According to Yousafzai et al. (2010), the integration of TRA, TPB, and TAM was used in
a comparative study of ability to predict behavioural intentions to use internet banking
services. This study found that the TAM has the highest ability to predict behavioural
intentions, and the reliability became the most important factor. However, a study of
Gerrard et al. (2006), which conducted a descriptive comparison between TRA, TAM and
DOI, found that there was no explicit conclusion as to which of these theories is best.
In addition, Gerrard et al. (2006) noted that each theory depends on variables and other
factors such as population, technology access, and the context of countries (e.g. developed
countries, developing countries, or undeveloped countries).
With regard to research of technology acceptance and use, a majority of researchers
employed well-known theories and models such as TRA, TPB and TAM, whereas others
employed DOI, MM, SCI, and C-TAM-TPB. The reasons for using these theories depend on
research settings and contexts. The literature review concluded that behavioural intentions
were the most important factor to drive and determine behavioural conducts and affected
individuals’decisions to accept and intend to use technology (Fishbein and Ajzen, 1975).
Research findings indicated that behavioural intentions was one of the most important and
essential variables which can be used to study and develop a model to explain an
individual’s perceived behaviour when using technology. Behavioural intentions can be
defined as a perception of individuals’opportunities to conduct or not conduct their
behaviours in the future. These behaviours were influenced by attitudes and beliefs.
In addition, Fishbein (2010) stated that behavioural intentions were accepted as an indicator
to measure human’s behaviour, which can be determined and measured from their messages
and expressions.
Service quality
Service quality makesan noticeable difference in the service business. Maintaining a high level
of service is a requirement to meet customers’expectations. Customers expect quality service
based on their past experiences, word of mouth and advertising services. After the service,
customers can compare the services experienced against what they have previously perceived
(Parasuraman et al., 1985). The most popular model used for the evaluation of service
quality is SERVQUAL, which was developed by Parasuraman et al. (1985, 1988). There are ten
attributes to service quality: tangibles, reliability, responsiveness, competency, courtesy,
assurance, credibility, security, access and understanding (Parasuraman et al., 1985).
Parasuraman et al. (1988) later distilled these ten attributes into five overall dimensions (with a
22-item survey instrument) as the following:
(1) Tangibles: this refers to the physical surroundings that appear to come from various
facilities such as personnel, equipment, tools, materials used in the communications
and symbols as well as the environment that makes customers feel cared for and the
concern and willingness of service providers.
(2) Reliability: this refers to the ability to provide services to match the promises made
to the customers. Every service must be accurate and dependable. This consistency
makes the customers feel that the service has been very reliable.
(3) Responsiveness: this is the availability and willingness to serve and assist through
fast and efficient means, which is in accordance with the needs of customers.
(4) Assurance: this is the ability to build confidence with the customers. Service
providers need to show their knowledge, skills, ability to serve and meet the needs of
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the customers in a gentle manner through effective communication and ensure that
customers get the best service.
(5) Empathy: this is the ability to care and provide each customer with a personal service.
Perceived risk
Stem et al. (1977), Assael (1981), Stone and Gronhaug (1993) and Martin et al. (2014) stated
that perceived risk is an important factor in running a business. Different businesses take
different types of risks. Customers should be aware of the security that the bank
company provides before using internet banking in transactions such as transferring
money, paying bills and/or balance checking. According to the studies about the
perceived risks of internet banking, effective risk management is crucial for its success
(Nair et al., 2014). Furthermore, risks can arise from different reasons such as the lack of
knowledge of IT, updated information, individual identity and the tendency of innovation
(Martin et al., 2014).
To avoid loss, customers should take precautions to mitigate the risks of internet
banking (Martin et al., 2014). According to Bauer (1960), Ostlund (1974) and Yaghoubi and
Bahmani (2011) risk can be classified into six dimensions ( financial risk, performance risk,
time risk, social risk, psychological risk and privacy risk). The negative consequences that
may arise from consumers’actions leads to an important well-established concept in
consumer behaviour: perceived risk (Yaghoubi and Bahmani, 2011). Personal code change
can protect information from hackers (Rotchanakitumnuai and Speece, 2009). Even though
the system gives users an SMS with a code (OTP) for accessing the account before
transferring money or making payment, there is no direct contact with a bank officer that
might result in a personal code change error or information stolen by hackers.
Kuisma et al. (2007) investigated customers’resistance to internet banking and their
connections to the values of individuals. They concluded that both functional and
psychological barriers arise from service channel, consumer and communication processes.
ATM services are still preferred by customers because they are wedded to their old routine
and because the internet’s insecurity, inefficiency, and inconvenience still remain issues for
them. Besides the fear of possible misuse of changeable passwords and the lack of proof
provided by an official receipt, customers seem to perceive no performance-to-price value
owing to the high purchasing costs of a computer and an internet connection. In addition,
non-users also complain about the lack of a social dimension, that is, the absence of a face-to-
face encounter (Daneshgadeh and Yıldırım, 2014). The conflict between internet banking
and customers mostly occurs from performance, service channel and communication
processes. A bank should therefore find strategies to reduce risk, particularly on
information security in order to gain customers’confidence with respect to using internet
banking (Yaghoubi and Bahmani, 2011).
This present study reviewed six types of risks and the details of the risks related to
online banking are described as follows:
(1) Social risk refers to the possibility of using the internet banking service which may
result in the dissatisfaction of friends or family or work group. An increase or
decrease of users in the society could be based on how internet banking is viewed,
which can result in a good or bad perception. The adoption of internet banking is
one factor that can determine the direction of the users that can result in both
positive and negative service selection.
(2) Financial risk refers to the loss which may be due to financial transactions.
For example, many users are afraid of losing money while dealing with an online
transaction. Since there is no signing evidence of the transaction, as provided by the
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traditional banks, customers perceive that their stakes are high for losses and
missing accounts. Therefore, users often have difficulty in demanding compensation
arising from errors or transaction problems during the financial operation.
(3) Psychological risk refers to customer’s perceptions about the efficiency of the
system, as failure in successfully doing an online transaction will result in stress and
anxiety. Moreover, the internet banking service may not be suitable for their own
usage (i.e., unsuitable for their age) and the user experience may not meet
expectations, etc. (Lim, 2003).
(4) Performance risk refers to the system ability and quality, accuracy of financial
transactions for customers, including completing a transaction within a reasonable
time and the speed of downloading data (Kuisma et al., 2007). For example, losses
incurred by deficiencies or malfunctions (i.e. slow process) of online banking
websites lead to high performance risks. Customers are anxious that a system
failure or disconnection from the internet will occur during online transactions.
(5) Privacy risk refers to a loss that may be incurred on account of a fraud or a hacker,
which can affect the security of internet banking. The sense that it is not safe to use
internet banking, which can cause losses such as theft, stolen data during transfer,
and personal information being accessed by unauthorised users, poses greater
privacy risks (Demirdogen et al., 2010). Both fraud and hacker intrusion do not only
lead to users’monetary loss, but they also violate users’privacy (Littler and
Melanthiou, 2006).
(6) Time risk refers to loss of time and the inconvenience incurred owing to delays in
receiving payment. This also takes place when the customers experience difficulties
in navigating the appropriate transactions, or have to go through a learning process
to use the internet banking service. Disorganised websites can create confusion, and
cause user dissatisfaction, especially in the delay of accessing internet banking
services (Forsythe and Shi, 2003). If the service takes a lot of time to learn or users
confront difficulties in how to use internet banking at the time of need, these matters
will be taken into consideration when disusing a service.
Trust
Mayer and Davis (1999) stated that there are three components of trust that influence
business service: competence refers to the skill, knowledge or ability of the service provider
that can persuade other people; benevolence is the willingness to do a good thing in return
for their trust; and integrity is the consistency of sincerity and transparency. The more
customers positively perceive these components, the more they will trust the service.
In terms of internet banking service, competence could be observed when the internet
banking service providers successfully serve a customer’s need or the service provider has
the sufficient skill and ability to protect the privacy of customers. Benevolence, on the other
hand, is the trust in the moral principles of internet banking. This can be felt when the
service providers care for the well-being of its customers and primarily consider how to
benefit the customers rather than advantage themselves. Finally, integrity is the trust in the
honesty of banks. This is measured when the internet banking service providers can keep a
promise and not deceive their customers.
Gefen et al. (2003) and Wu and Chen (2005) emphasised that trust is a major factor in the
success of internet banking, while Gill et al. (2006) found that trust plays an essential role in
customers’trust in internet banking. Benamati et al. (2010) and Kim et al. (2010) emphasised
Mayer and Davis (1999) point regarding the three important elements of trust, as they found
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that ability, goodwill and honesty are three crucial components of trust. They are important
in developing trust in a service provider along with the customers’and subjective norms
(Zhao et al., 2010; Raisian et al., 2014; Montazemi and Qahri-Saremi, 2015). These studies
imply that trust leads to the behavioural intentions to use internet banking; that is, the
higher the trust the customers have in the service, the greater their positive attitude towards
and greater the individual intention they give to using internet banking (Mayer et al., 1995).
Development of hypotheses and proposed framework
Service quality has a negative relationship towards perceived risk of internet banking
according to the studies of Chang and Chiu (2008), Pisnik and Snoj (2010) and Chen et al. (2013).
The majority of customers believe that internet banking is unsafe, inefficient and inconvenient
to use. Furthermore, customers are worried about the errors from personal code changes since
it bears no official signature confirmation from the bank officers (Kuisma et al., 2007). Although
good service quality can reduce perceived risk (Chen et al., 2013), little research has studied the
relationship between service quality towards perceived risk of internet banking. This can lead
to a disparity in terms of reducing the perceived risk between theory and practice:
H1. Service quality has a direct effect on perceived risk towards the use of internet
banking.
Many studies have shown that service quality has a positive relationship towards trust of
internet banking (Teo et al., 2008; Montazemi and Qahri-Saremi, 2015). Although customers
have no experience in internet banking, they expect that the service quality will allow users
to communicate with internet banking efficiently. Service quality can be measured on a set
of three dimensions: responsiveness, which refers to the willingness to quickly respond to
customers; assurance, which is the ability of employees to answer customer queries;
and empathy, which is the process of providing what customers need (DeLone and
McLean, 2003). Customers usually develop a sense of confidence in the bank when they
receive a rapid service response (Clow et al., 1998). Therefore, as customers experience quick
problem solving, this has made internet banking easy to use, which maximises benefits for
users. Furthermore, good service quality creates a perception of a high ability of service, and
this creates a positive impact on customer attitude. If internet providers attempt to improve
service to gain customer trust, the use of internet banking will increase continuously
(Kim and Prabhakar 2004):
H2. Service quality has a direct effect on trust towards the use of internet banking.
Zhao et al. (2010) and Martins et al. (2014) found that the lack of confidence towards the
ability of the service provider (Zhao et al., 2010) to handle risks such as risk of performance,
financial risk, time risk and personal risk (Martins et al., 2014) has become an important
issue that affect customers’behavioural intentions to use internet banking. When customers
learn about hackers affecting banking operations or the bank system failing due to a virus
or similar technical problems, customers will then develop less confidence in using the
service. Consequently, banks should provide a security code for safe banking, and with this,
customers will increase their trust and this will spur the growth of internet banking
(Martins et al., 2014).
As banks prepare strategies to reduce risks for users, they should reduce the
psychological risk that affects the behavioural intentions of the customers to use internet
banking. Zhao et al. (2010) explained that the values shared between the customers and the
bank is a good indicator of the customers’intention to use internet banking. When the bank
cares about the well-being of its customers by lessening the perceived risks, customers will
favourably regard its service. That means that when the perceived risk was at or near zero
(Bussakorn and Dieter, 2005), it could increase the behavioural intention of the customers to
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use internet banking. However, Bussakorn and Dieter (2005) hinted that it still depends on
different people accepting the different levels of risks:
H3. Perceived risk has a direct effect on behavioural intentions to use internet banking.
Zhao et al. (2010), Yap et al. (2010) and Montazemi and Qahri-Saremi (2015) studied the
relationship between trust and the behavioural intentions to use internet banking and found that
trust shows a positive relationship towards behavioural intentions by the increase of its number
of users. For example, if banks increase the trust among their customers by providing them with
information about the security and stability of internet banking, it will potentially decrease the
customers’fears about the efficiency of the service (Montazemi and Qahri-Saremi, 2015).
As a result, customers develop greater trust regarding the use of internet banking service; it can
therefore be said that confidence in using internet banking is the main factor that attracts more
users (Kim and Prabhakar, 2004; Montazemi and Qahri-Saremi, 2015):
H4. Trust has a direct effect on behavioural intentions to use internet banking.
However, empirical studies show no evidence of the mediating effects of perceived risk and
trust on service quality and behavioural intentions to use internet banking in Thailand.
Therefore, this study aims to determine the indirect effect of service quality and behavioural
intentions to use internet banking through perceived risk and trust as mediating variables
(H5 and H6):
H5. Service quality has an indirect effect on the behavioural intentions to use internet
banking mediated by perceived risk.
H6. Service quality has an indirect effect on the behavioural intentions to use internet
banking mediated by trust.
After reviewing the theoretical frameworks and empirical evidence gathered, this paper
develops a number of hypotheses to test the factors that influence the behavioural intentions
to use internet banking, and to construct a model (Figure 1). The factors reviewed include
service quality, perceived risk, and trust. Perceived risk and trust work as the mediating
variables. Using a questionnaire as a survey instrument, the researchers collected data in
Thailand in 2016. In all, 500 usable questionnaires were returned and gathered, which were
subjected to analysis using structure equation modelling (SEM). It is expected that with the
use of the internet banking system, the image of the bank will improve as they will know the
effects of perceived risk and trust towards behavioural intentions to use internet banking,
and can safeguard against security risks and problems in the future.
Research methodology
Population, sampling and data collection
Since the population is large and the researchers practically do not have the data about the
total number of the population, the sample size used in this study was calculated using the
formula developed by WG Cochran (Cochran, 1953). Thus, the minimum sample size for this
study was 385.
Multi-stage sampling was used to select the respondents in the study. First, information
was gathered from the 14 banks of Thailand (all of whom have internetbanking), and then the
banks divided into three groups based on size –large (four banks), medium ( four banks) and
small (six banks) –using the cluster sampling technique.
Second, the researchers employed a simple random sampling technique using the lottery
method (Koul, 1984) to select a 50 per cent sample in each group mentioned above. Therefore
the remaining samples are: large banks (two banks), medium banks (two banks) and small
banks (three banks).
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Third, the sample groups from the second stage applied a quota sampling method.
The researcher required minimum sample as 400 calculated with proportional
allocation. The sample groups were divided into 50, 35 and 15 per cent (Battaglia,
2008) in large, medium and small sample groups, respectively. Thus, the respondents
were composed of 200 respondents from large banks, 140 from medium banks and
60 from small banks.
The data were collected by using online questionnaires. There are three fundamental
requirements for the particular research respondents: the respondents must be Thai people
who have saving bank accounts in Thai commercial banks; the respondents must register
for internet banking service from websites of the banks where they have opened the bank
accounts; and the respondents must have experience in dealing with internet banking
transactions. A snowball sampling technique was applied in which respondents were
continuously selected through friends or acquaintances. Online questionnaires were sent to
the respondents via e-mail and social networks (Couto et al., 2013). The first respondent was
chosen from a simple random sampling technique. These respondents recommended other
respondents or distributed online questionnaires to people who have experiences in
the internet banking services. To this end, the online questionnaires were provided to
respondents categorised by gender, age, education, occupations and incomes in order
to cover all the internet banking services of each bank. Finally, the sample size of this study
was sufficient to meet the aims of the study.
Variable measurement
Given the research problems and research hypotheses, a survey is the most appropriate
method for this study. The instrument used for collecting the research data was a
questionnaire. The questionnaire was adapted from the instruments used in previous
research, as follows:
•The measurement of service quality was adapted from Han and Baek (2004),
Rod et al. (2009) and Nimako et al. (2013), which consists of five variables and 22 items.
•The scale for perceived risk, which includes six variables and 20 items, was adapted
from Featherman and Pavlou (2003), Lei et al. (2008) and Martins et al. (2014).
•The scale of trust, which comprises of three variables and nine items, was adapted
from Morgan and Hunt (1994) and McKnight et al. (2002).
•Items of behavioural intentions to use, which consists of three items, were adapted
from Lai and Li (2005), Lallmahamood (2007), Al-Somali et al. (2009) and Nasri and
Charfeddine (2012).
All of the scales included in questionnaire were measured using a five-point Likert scale,
with 1 for “strongly disagree”; 2 for “disagree”; 3 for “neither agree nor disagree”; 4 for
“agree”; and 5 for “strongly agree”. The respondents were asked to respond to each of the
statements by marking these scales.
Validation of measures
Before conducting the main survey, a pre-test was performed to validate the instrument.
The pre-test involved 30 respondents who have experience in using internet banking in
Thailand. Respondents were asked to comment on the instrument such as the sentence
structure, wordings, format, length, language used and the wording of the scales.
As a result, the content validity of the instrument was confirmed. Using the IBM SPSS
version 20, the responses of these 30 users were analysed to assess the reliability of the
measurements. The reliability test conducted on 54 items present in the questionnaire
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yielded a Cronbach’sαof 0.903, which is essentially higher than the recommended
minimum value of 0.7. Thus, it can be concluded that there is a high level of instrument
reliability (Hair et al., 2010).
Data analysis and results
The statistical analyses carried out were frequency and descriptive analyses focusing on
average, percentage, variance and testing normality of distribution for assessing
unidimensionality; the confirmatory factor analysis (CFA), the convergent validity or
average variance extracted (AVE), discriminant validity and structural equation modelling
(SEM) were used to test the established hypotheses. SEM is a confirmatory method
providing a comprehensive means for assessing and modifying theoretical models
(Anderson and Gerbing, 1988).
Respondent characteristics
The demographic profile of the 505 respondents was analysed using frequency and it
showed that the majority of the respondents were females (57 per cent), aged between 31 and
35 years (37.00 per cent), hold an undergraduate degree (52.30 per cent), worked as
government servants (28.50 per cent) and earn monthly incomes higher than 20,000 Baht
(48.7 per cent). About 36.60 per cent of the respondents have one to three years’experience
in using internet banking.
Assessment of univariate normality
The three-stage approach of measurement model can be described as follows. In first
stage, all items of the model have the values of skewness less than 2 (between ‒2and+2)
and a value of kurtosis less than 7 (between ‒7and+7), which met the assumption for
normality (Curran et al., 1996). After that, the internal consistency of the scales and the
reliability of the constructs were measured using Cronbach’sα.Allthevaluesof
Cronbach’sαshould be above 0.70, which is the common threshold value recommended
(Hair et al., 2010). As a result of these refinements and the reliability test conducted on
51 items present in the questionnaire, the Cronbach’sαs were 0.926 (SQ), 0.902 (PCR),
0.849 (TRUST) and 0.880 (BI). However, the observed variable of social risk Cronbach’s
αwas less than 0.7, thus, items PS1 and PS2 were dropped from the analysis model.
The overall results are displayed in Table I.
The reliability was examined by analysing the standardized regression weights of
individual items. The standardized regression weights values in a measurement model were
initially investigated and the item with standardized factor loadings less than 0.6 would be
removed from the measurement model (Hair et al., 2010). Thus, from our analysis,
the loadings values of 11 items were below 0.60 and were dropped at this stage: PF4, SP13,
SP10, SR5, SR7, ST2, PP13, PT18, SE20, SE21 and SE22. Furthermore, the composite
reliability (CR) and the AVE were calculated using completely standardized solutions in the
CFA results (Hult et al., 2004).
Convergent validity
To measure the convergent validity in this study, the following values and conditions were
employed: factor loadings, CR and AVE (Fornell and Larcker, 1981). The results of the
analysis from our study is illustrated in Table II:
(1) Standardized factor loadings are indicative of the degree of association between
scale items and a single latent variable. The level of factor loadings should be above
the value of 0.6.
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(2) CR measures the degree to which items are free from random error and therefore yield
consistent results. The CR values should be above the value of 0.7 (Hair et al., 2010).
(3) AVE measures the variation explained by the latent variable to the random
measurement error. The acceptable level of AVE should be more than 0.5 (Fornell
and Larcker, 1981).
The results of standardized loadings and validity (Table II) show that all the values ( factor
loadings, CR and AVE) met conditions mentioned above. This means that the convergent
validity is good.
Discriminant validity
The discriminant validity of the constructs was calculated by using the square root of
AVE and comparing it with the correlation between the variables and all other variables
(Fornell and Larcker, 1981).
The results of standardized loadings and validity (Table II) show that the square root of the
AVE values is well above the correlation values, thus suggesting good discriminant validity.
Assessment of the measurement model
In the second stage, the CFA was assessed by using a total of 39 items. The goodness-of-fit
of the SEM is indicated by how well it reproduces the observed covariance matrix among
the indicator items. The model fit was assessed by reviewing a set of indices, and it can be
Construct Observed variable Cronbach’sα
Service quality 0.926
Tangibles (ST1, ST2, ST3, ST4) 0.768
Reliability (SR5, SR6, SR7, SR8, SR9) 0.795
Responsiveness (SP10, SP11, SP12, SP13) 0.716
Assurance (SA14, SA15, SA16, SA17) 0.798
Empathy (SE18, SE19, SE20, SE21, SE22) 0.842
Perceived risk 0.902
Social risk (PS1, PS2, PS3) 0.345
Financial risk (PF4, PF5, PF6) 0.831
Psychological risk (PY7, PY8, PY9) 0.712
Performance risk (PP10, PP11, PP12, PP13) 0.782
Privacy risk (PR14, PR15, PR16) 0.806
Time risk (PT17, PT18, PT19, PT20) 0.804
Trust 0.849
Competence (TA1, TA2, TA3) 0.868
Benevolence (TB4, TB5, TB6) 0.852
Integrity (TI7, TI8, TI9) 0.205
Behavioural Intentions to use BI1, BI2, and BI3 0.880
Table I.
Results of construct
reliability
Discriminant validity
Model and construct
Range of
standardized loadings
Composite
reliability CR AVE 1234
Service quality 0.75-0.89 0.926 0.932 0.734 0.856
Trust 0.80-0.98 0.849 0.933 0.824 0.719 0.908
Perceived risk 0.71-1.04 0.902 0.927 0.723 −0.550 0.640 0.850
Behavioural intentions to use 0.76-0.93 0.880 0.886 0.723 0.561 0.645 −0.548 0.851
Table II.
Results of
standardized loadings
and validity
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divided into the following four categories (Hair et al., 2010) : χ
2
measures include χ
2
, degree of
freedom (df ) and probability; measures of absolute fit includes goodness-of-fit index (GFI),
root mean square error of approximation (RMSEA) and root mean square residual (RMR);
incremental fit measures include the normed fit index (NFI) and the comparative fit index
(CFI); and parsimony fit measures include the adjusted goodness-of-fit index (AGFI) and the
parsimony normed fit index (PNFI). Iacobucci (2010) stated that among the SEM fit indices,
the χ
2
is the only inferential statistic, while all the others are descriptive. In this case,
χ
2
provides significance for hypothesis testing while for the others, they only suggest
“rules-of-thumb”to assess goodness-of-fit. The measurement model fit the data
satisfactorily ( χ²¼1783.33, df ¼680, p-valueW0.05, RMSEA ¼0.057, RMR ¼0.044,
CFI ¼0.901, IFI ¼0.900, TLI ¼0.896, AGFI ¼0.820 and PNFI ¼0.735), which are
presented in Table III and Figure 2.
Structural model and hypotheses testing
In the final stage, the squared multiple correlation (R
2
) value for the relationship between the
three variables and behavioural intentions to use internet banking was 0.448, thereby
suggesting a 44.8 per cent variance in behavioural intentions to use by the combination of
all hypotheses: H1 (β¼−0.599, p⩽0.00), H2 (β¼0.748, p⩽0.00), H3 (β¼−0.228, p⩽0.00),
H4 (β¼0.403, p⩽0.00). The overall results as summarised in Table IV and Figure 3
indicate that all hypotheses were fully supported.
Regarding the indirect effect, H5 and H6 were associated with equally strong unique
latent variables and a bootstrapped sampling distribution of the difference in ωs estimates
(Δωs) was estimated via 2,000 parametric bootstrapped replications. In AMOS 21 (Arbuckle,
2012), such a test can be performed by creating a user-defined estimate. The results
supported H5 and H6 in which PCR and TRUST worked as a mediator of SQ and BI.
We found that the indirect effect of SQ through TRUST and BI was greater ( β¼0.654,
p⩽0.000) than the indirect effect of SQ through PCR and BI ( β¼−0.278, p⩽0.00).
Discussion and implications
The empirical analysis demonstrated several major findings. Interpretations based on these
findings and implications are discussed as follows.
First, the findings of this study strongly support using service quality to predict
behavioural intentions to use internet banking in Thailand. The results supported the first
and second hypotheses of this study that service quality has a significant negative effect on
perceived risk of internet banking ( β¼−0.599, p⩽0.00); and service quality has a
Fit indices Fit criteria Measurement model
χ
2
1783.33
df 680
p-value (probability) ⩾0.5 0.000
CMIN ( χ
2
)/df o3.00 2.697
Root mean square error of approximation (RMSEA) ⩽0.05 0.058
Root mean square residual (RMR) ⩽0.05 0.044
Comparative fit index (CFI) ⩾0.9 0.901
TLI ⩾0.8 0.896
IFI ⩾0.9 0.900
Adjusted goodness-of-fit index (AGFI) ⩾0.8 0.820
Parsimonious normed fit index (PNFI) ⩾0.5 0.735
Source: Hair et al. (2010)
Table III.
Fit indices for the
measurement mode
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0.40
e1
e23
e24
ST1
ST3 ST
SP11
SP12
SA14
SA15
SA16
SA17
SE18
SE19
ST4
SR6
SR8 SR
SP
SA
SE
SR9
e3
e4
e6
e8
e9
e11
e12
e14
e25
e26
e27
e50 e51 e52
e58 e59 e60
e53 e54 e55 e56 e57
e15
e16
e17
e18
e19
0.30
0.55
0.48
0.65
0.57
0.60
0.56
0.58
0.43
0.50
0.45
0.54
0.71
0.48
PF5
PF
e29
e38 e39 e40
e30 e31
0.37
0.77
0.88 0.95
0.80
0.74
0.69
0.81
0.88
0.49
0.70 1.04
0.36
0.60
1.08
0.87
0.87
0.75
0.75
0.75
0.65
0.67
0.74 0.75
0.58
0.76 0.68
0.64
0.64 0.77
0.80
0.95
0.77
0.73
0.87
0.80
0.87
0.87
0.82
0.93
0.75
0.57
r2
r3
r1
0.97
0.54 0.59
0.21
0.32 0.84
0.910.56
0.84
0.69
0.75
0.70
0.87
0.75
0.76
0.78
0.64
0.54 0.58 0.71 0.64 0.63 0.76
0.55 0.83
0.82
0.65
0.69
0.68
0.42
0.36
0.98 0.74
0.76
0.52
0.72 0.87
0.84
0.27
0.44 0.85
0.93
0.72
0.87
0.57
0.75
0.50
0.90 0.30 0.34 0.50 0.41 0.40 0.58
e32 e33 e34 e35 e36
e41
e44
e46
e47
e61
e62
e63
e49
PT
0.71
0.75
0.58
e42
e43
e48
PF6 PY7
PY
PY8 PY9 PP10 PP11
PP PR
PR14
PR15
PR16
PT17
PT19
PT20
BI
TRUST
TA
TA1 TA2 TA3 TB4 TB5 TB6 TI8 TI9
TB TI
PCR
SQ
BI1
BI2
BI3
PP12
Figure 2.
Results of analysis of
structural model
Hypothesis Result Standardized estimate
H1: service quality has a direct effect on perceived risk of
behavioural intentions to use internet banking
Supported −0.599***
H2: service quality has a direct effect on trust of behavioural
intentions to use internet banking
Supported 0.748***
H3: perceived risk has a direct effect on behavioural intentions to use
internet banking
Supported −0.228***
H4: trust has a direct effect on behavioural intentions to use internet
banking
Supported 0.403***
H5: service quality has an indirect effect on the behavioural intentions
to use internet banking by mediating perceived risk
Supported −0.278***
H6: service quality has an indirect effect on the behavioural intentions
to use internet banking by mediating trust
Supported 0.654***
Effects Direct effect Indirect effect Total effect
SQ →PCR −0.603 –−0.599
SQ →TRUST 0.754 –0.748
PCR →BI −0.228 –−0.228
TRUST →BI 0.403 –0.403
SQ →PCR →BI –−0.278 −0.278
SQ →TRUST →BI –0.654 0.654
Note: ***po0.001
Table IV.
Summary of the
effects and research
hypotheses testing
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significant positive effect on trust of internet banking ( β¼0.748, p⩽0.00). This means
banks should improve service quality, which can reduce the perceived risk and increase
trust, thus affecting behavioural intentions to use internet banking. Service quality, in this
study, is a major driver of behavioural intentions to use internet banking and should
be developed as an important part of bank strategy (Luo et al., 2010; Montazemi and
Qahri-Saremi, 2015).
The results of this study point to the possibility that higher-order service quality
dimensions can reduce perceived risk and build trust in internet banking; these can be
ranked in the following order: tangible, reliability, assurance, response and empathy.
The “tangible”dimension is the most important for internet banking in Thailand and has
the most significant 0.88 factor loading (as shown in Figure 3). This means the internet
banking system should have a variety of new service functions beyond money transfers,
account balance checking and payment services, such as checking past transaction history,
providing up-to-date and useful financial information or promotions, an instalment service,
a service to check the nearest location of banks, transaction reminders and so on.
The reliability, assurance and response are the second most important dimensions which
gave the same significant 0.87 factor loading. For reliability, the internet banking system
should have an accurate and complete transaction processing and should also be consistent
(reliability). For assurance and response, the system should have secured transactions and
prompt service, according to customer requirements. However, these results are different from
the most important dimensions ranked by Yap et al. (2010), who reported customers expected
service quality dimensions in the following order: reliability, tangibles and assurance
(highest expectations), responsiveness and empathy (not applicable in building trust).
Second, perceived risk has a significant negative effect on behavioural intentions to use
internet banking. This means that if customers feel the risk is declining, then they will be
more likely to use internet banking. This study is similar to the findings of Luo et al. (2010),
Zhao et al. (2010) and Martins et al. (2014). This study found that there are five salient types
of perceived risk which affected the behavioural intentions to use the internet banking in
Thailand, in the following order: psychological, performance, privacy, time and financial
risks. For example, customers were concerned about failure, errors or problems created from
internet banking system (psychological and performance risks), losing control of privacy
and financial and account information because of data theft (privacy risk), time loss of using
internet banking (time risk) and financial loss during transaction ( financial risk). Therefore,
Service Quality –0.599 –0.228
R2=0.448
R2=0.560
R2=0.356
0.748 0.403
Tangibles
Financial Risk
Psychological Risk
Performance Risk
Privacy Risk
Time Risk
Trust
Competence
Benevolence
Integrity
Reliability
Responsiveness
Assurance
Empathy
Perceived Risk
Behavioural Intentions
to Use
Figure 3.
Final analysis of
structural model
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effective systems should be developed to remove the perceived concerns associated with all
five risks above, and these decreased concerns will result in more potential customers.
This study found that the first factor the internet banking service should be concerned
with is reducing the psychological risk among users. Banks should encourage users to be
aware of the ease of use of the internet banking system. This can help to reduce customers’
anxiety. With easy to access features and easy to understand transaction systems, users will
have more confidence that financial transactions occur in real time. Consequently, users
will be more likely to use the internet banking system continuously in the future.
When users have favourable experiences with internet banking, they tend to recommend it
to others as well.
However, the findings are different in terms of the order and number of perceived risk
types from Martin et al. (2014), who identified only three of the six perceived risk types that
were salient in affecting the behavioural intentions to use the internet banking in Portugal in
the following order: performance, financial, time and privacy risks.
Third, trust has a significant positive effect on behavioural intentions to use internet
banking. If customers are usually confident and trusting, then they tend to use the services
more readily. From the empirical evidence in Thailand, we found three significant
components of trust comprising benevolence, competence, and integrity (orderly).
For example, Thai users rely on trust of accountability in online financial transactions,
accuracy, and prompts to help users of the internet banking system. Also, Thai users want
to build trust in the reputation of banks, the competence of the internet banking system
when managing financial entities, bank integrity, completeness of financial information and
a bank’s ability to keep user information confidential.
Nevertheless, the results of this study were different from the research of Zhao et al. (2010),
who studied and used three different components (calculative, predictive and identification) of
trust-building processes to predict trust in regards to behavioural intentions to use internet
banking in China. The research contexts seemed to be similar to our study. For example,
the calculative factor was used to measure possible cheating and engaging in opportunistic
behaviour, which is a similar context to integrity. The predictive factor was used to measure
the ability of internet banking processes to observe and interact with bank staff to assess
whether they have a working knowledge and experience with the system and whether they
can effectively manage an online offer required by customers. This factor is a similar context
to competence. The identification referred to positive treatment and perceptions of fairness in
interaction with banks such as politeness, respect for rights and dignified treatment, which
relates to benevolence in our study.
The role of perceived risk and trust as mediating variables
The significant finding of this study is that the mediating effect of trust ( β¼0.748, p⩽0.00)
is more than the mediating effect of perceived risk ( β¼−0.278, p⩽0.00) on behavioural
intentions to use internet banking. Considering only the factors that indirectly influence the
service quality and behavioural intentions, trust as a mediating variable gives the highest
value in this model. It can be said that if more users develop trust and feel there are fewer
risks in performing internet banking, they will become more willing to utilise internet
banking services.
Banks can strategize by developing a system that focuses on the quality of service, which
can build trust and reduce risk to the consumers. They should consider the three aspects of
trust: benevolence, competence, and integrity. For example, the bank should have a
notification system linked to account access, so that users can immediately know when their
accounts are accessed or when they are, or are not, using their own service. The user can
check their past transaction services at any time, and the system should be able to assist and
support users at any time and meet their needs with a sense of warmth and security.
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Even though the variables of perceived risks have minimal mediating influence
compared to trust, it is still has potential to increase internet banking use. Users ascribe
importance to all five aspects of risks such as the psychological, performance, financial,
privacy and time risks. Therefore, the bank should have a stable system that results in no
failure during transactions. It should have robust service capabilities and should facilitate
the users’needs as a one-stop service. It should eliminate the concern of the users regarding
financial losses and information security.
Implications for theory
Prior research that has studied the behavioural intentions to use internet banking, such as
the research of Lee and Chung (2011) and Boateng et al. (2016), has barely mentioned
perceived risk and trust as mediating variables. However, our results suggest that perceived
risk and trust can be mediating variables. These two factors can increase the predictive
power of the model in explaining behavioural intentions to use internet banking.
Furthermore, the integration of service quality, perceived risk and trust models can predict
44.8 per cent of behavioural intentions to use internet banking in Thailand. Compared with
other investigations of behavioural intentions to use internet banking, our study presents a
stronger predictive power than Lee and Chung (2011), who applied the TAM and added
self-efficacy as one of the antecedent variables such as risk, internet experience and
facilitating conditions in South Korea’s users. Their intention was 32.3 per cent through
internet experience, perceived usefulness and perceived ease of use; Boateng et al. (2016)
adoption of internet banking in Ghana using the TAM explained 28.9 per cent of intention,
with social feature, trust and ease of use.
Implications for practice
The study found that the influence of good service quality can affect the trust and reduce
the perceived risk among the users, which can affect behavioural intentions to use internet
banking services. This suggests that banks should develop their internet banking system in
accordance to the customers’needs and should consider the following points:
(1) Banks should build a fast and convenient internet banking service to fulfil the
lifestyles and needs of their users. In addition, banks should have a policy to
promote internet banking among potential users, such as media publicity in order to
familiarise them with the service. This can establish a good image for internet
banking services through special promotions or campaigns (e.g. to reduce the fee for
service). The campaign should offer money transfers or bill payments for free or
within a one month free trial, or use other promotional tools to encourage customers
to feel the need to use their services. For example, if users register and use the
internet banking service on the same day, the users will receive gifts, or when users
transfer money or pay for goods with a minimum payment, they will be refunded
5 per cent of the amount of the transaction. When users have experience using
internet banking services, they will feel familiar and comfortable with the system
and will enjoy not having to travel to banks to deal with transactions by themselves.
This can save them time and money. Once they use internet banking, the customer
will see the service is easily accessible, comfortable and convenient. As a result,
users will have more confidence and trust in the use of the services.
(2) Banks should also provide certain features that allow customers to adjust their
usage as needed. For example, customers should be able to change colours or create
themes to customise their on-screen appearance. Banks should also develop a
function that can support voice transaction instead of typing through text.
The system will also be able to assist and advise users who are not familiar with the
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internet banking system to learn the process by providing simple instructions.
In this manner, users will be able to freely use the system and will not get confused
by the menu of services.
(3) Banks should also create a strategy that increases security standards, especially by
requesting a new password when the user forgets it or when there is an apparent
need to change their password. To prevent data theft via internet banking, users will
need to show identity documents, such as an ID card and a real book bank account
before they are able to reset the password manually. Banks must be strict with these
matters to prevent unauthorised use of a copy document (identification card and a
copy of the book bank account), which could lead to financial theft. The bank should
also have a policy on advertising, which informs users on how to prevent identity
theft. They should also have a financial support base for anti-corruption that allows
users to feel their data are safe. Currently, laws on IT have been used in conjunction
with the development of information security services of the bank, such as the
electronic transactions laws, electronic signature laws, protection of privacy laws,
computer crime laws, etc. These can reduce the perceived risks of the users, who will
then not feel anxious about using internet banking for their financial transactions.
Conclusion
The purpose of this research is to study the relationship among service quality, perceived
risk, trust and behavioural intentions to use internet banking in Thailand. The results from
the analysis indicated that service quality has a direct influence on perceived risk and trust.
Moreover, service quality has an indirect influence towards behavioural intentions to use
internet banking through perceived risk and trust as mediators. In addition, perceived risk
and trust has a direct influence on behavioural intentions to use internet banking at 0.001
level of significance
The outcomes stemmed from this research have a profound contributions to the field of
service quality. It clearly shows useful direction and the significance of internet banking
service quality and to understand the phenomenon of influence between perceived risk and
trust upon relationship of service quality and behavioural intentions to use internet
banking. Furthermore, this research allows academic to further understand important
factors of service quality which have an influence on behavioural intentions to use internet
banking. All this will be a useful tool for executing in the banking industry in planning
policy and strategy for internet banking improvement and development.
Future studies should consider using the model from this study to analyse whether
results can be generalised to non-users or to dormant users of internet banking. In addition,
further work could be carried out to identify factors that can affect non-users of internet
banking and to make a comparison between users from different income groups.
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Further reading
Suh, B. and Han, I. (2002), “Effect of trust on customer acceptance of internet banking”,Electronic
Commerce Research and Applications, Vol. 1 No. 3, pp. 247-263.
Corresponding author
Kanokkarn Snae Namahoot can be contacted at: Kanokkarnn@nu.ac.th
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