A quantitative examination of the factors that influence users' perceptions of trust towards using mobile banking services

Abstract

An examination of previous research related to m-banking in developing countries revealed that research conducted on the drivers of trust in mobile banking is somewhat limited. Therefore, this study attempted to quantitatively investigate the factors that influence users' perceptions of trust towards using mobile banking services. The model is empirically tested using an online survey from a convenience sample of 404 respondents, and analysed using SEM. The study found that the six variables (perceived benefits, perceived credibility, perceived behavioural control, social influence, privacy and security risks) have direct impact on users' trust in m-banking. In particular, perceived credibility has the highest positive effect on users' trusts in m-banking, followed by perceived benefits and PBC while social influence has the lowest effect. In contrast, security risk and privacy risk exhibited a moderate negative impact on users' trust in mobile banking.

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nt. J. Internet Marketing and Advertising, Vol. 12, No. 2, 2018 181
Copyright © 2018 Inderscience Enterprises Ltd.
A quantitative examination of the factors that
influence users’ perceptions of trust towards
using mobile banking services
Mohammad Hamdi Al Khasawneh*
Department of Business & E-Marketing and Social Media,
Princess Sumaya University for Technology (PSUT),
Khalil Al Saket St 112, 11941, Amman, Jordan
Email: m.alkhasaawneh@psut.edu.jo
*Corresponding author
Omar Hujran
Management Information System Department,
Princess Sumaya University for Technology (PSUT),
Khalil Al Saket St 112, 11941, Amman, Jordan
Email: o.hujran@psut.edu.jo
Tariq Abdrabbo
Department of Business & E-Marketing and Social Media,
Princess Sumaya University for Technology (PSUT),
Khalil Al Saket St 112, 11941, Amman, Jordan
Email: t.abdrabbo@psut.edu.jo
Abstract: An examination of previous research related to m-banking in
developing countries revealed that research conducted on the drivers of trust in
mobile banking is somewhat limited. Therefore, this study attempted to
quantitatively investigate the factors that influence users’ perceptions of trust
towards using mobile banking services. The model is empirically tested using
an online survey from a convenience sample of 404 respondents, and analysed
using SEM. The study found that the six variables (perceived benefits,
perceived credibility, perceived behavioural control, social influence, privacy
and security risks) have direct impact on users’ trust in m-banking. In
particular, perceived credibility has the highest positive effect on users’ trusts
in m-banking, followed by perceived benefits and PBC while social influence
has the lowest effect. In contrast, security risk and privacy risk exhibited a
moderate negative impact on users’ trust in mobile banking.
Keywords: mobile banking; trust; credibility; perceived benefits; perceived
behavioural control; social influence; risk.
Reference to this paper should be made as follows: Al Khasawneh, M.H.,
Hujran, O. and Abdrabbo, T. (2018) ‘A quantitative examination of
the factors that influence users’ perceptions of trust towards using mobile
banking services’, Int. J. Internet Marketing and Advertising, Vol. 12, No. 2,
pp.181–207.

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.H. Al Khasawneh, O. Hujran and T. Abdrabbo
Biographical notes: Mohammad Hamdi Al Khasawneh is an Assistant
Professor in the Department of Business & E-Marketing and Social Media at
the Princess Sumaya University for Technology Amman, Jordan. He earned his
Bachelor’s degree in Accounting from Yarmouk University, Jordan, followed
by a Master of Business Administration in Marketing from Coventry
University, UK, and a PhD degree in E-Marketing from Griffith University,
Australia. His work has been published in several international journals such as
Journal of Internet Commerce, International Journal of Electronic Marketing
& Retailing, and International Journal of Business Information Systems, and he
has presented papers at several international conferences. His research interests
include internet advertising, search engine advertising, social media marketing,
viral marketing, mobile marketing, mobile banking, consumer behaviour and
corporate social responsibility.
Omar Hujran is an Associate Professor in the Department of Management
Information Systems and Technology at PSUT. He earned his Bachelor’s
degree in Computer Science from Mu’tah University, Jordan, followed by a
Master of Science in Computing from the University of Technology, Sydney,
and a PhD degree in Information Systems from Wollongong University,
Australia. His work has been published in several international journals such as
the Journal of Information Technology for Development, Computers in Human
Behavior, Internet Research, Electronic Journal of E-Government, and he
has presented papers at several international. His current research interests
include e-government, cloud computing, social media and the adoption of
e-government in developing countries.
Tariq Abdrabbo is a Lecturer in the Business Administration and E-Marketing
& Social Media Department at the Princess Sumaya University for
Technology. He earned his Bachelor’s degree in Marketing from Kean
University, USA, followed by a Master of Business Administration (MBA)
from AlBalqa Applied University, Jordan. His Current research interests
include Social Media Marketing, Mobile Banking and Internet Marketing.
1 Introduction
Mobile phones are emerging as a valuable tool for people to use in daily life, which has
given rise to an opportunity to radically change banking services in order to reach
populations who did not have access to banking in the past through mobile banking
(CGAP, 2006). The mobile phone has unquestionably brought on a fundamental shift that
has affected the lives of both consumers and businesses. It has become a device that
billions of people around the world can’t live without (Laukkanen, 2007). It is expected
that more than one billion mobile device users will use of their mobile phones for
banking purposes globally by the end of 2017 (Koksal, 2016). Mobile banking or
M-banking is defined as a number of procedures allowing consumers to access their
mobile phones to manage their bank accounts, make deposits in their accounts, transfer
funds and gain access to credit or insurance products (Malaquias and Hwang, 2016;
Donner and Tellez, 2008). Mobile banking has transformed the methods by which
consumers perform transactions that were in the past performed through conventional
banking channels (Sulaiman et al., 2007). In a highly competitive banking environment,
mobile banking can be utilised by banks to give more value to their customers by
offering them a more convenient way to bank (Mohammadi, 2015; Barati and

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Mohammadi, 2009). Customers who use mobile banking can have access to their bank
accounts from anywhere and at any time by using their mobile phones or other mobile
devices (Crosman, 2011). According to Cruz et al. (2010) and Dasgupta et al. (2011),
mobile banking has the potential to deliver dependable services to individuals living in
distant areas where internet access is limited. However, despite the technological
advancement of m-banking, the number of users is still below experts’ expectation
especially in developing countries (Malaquias and Hwang, 2016; Mohammadi, 2015).
While some users are quickly adapting the new technologies in banking industry, it is
being identified in the literature that many of them are facing adoption and trust problems
(Burucuoglu and Erdogan, 2016). However, after previous research related to m-banking
in developing countries was examined, it became apparent that research conducted on
the drivers of trust in mobile banking is somewhat limited and less focused on by
researchers. Therefore, this research investigates main determinants of consumers’ usage
intention and behaviour of mobile banking in Jordan as a developing country in the
Middle East.
2 Literature review
Moormann et al. (1992) defined trust as the inclination to depend on an exchange partner
in whom one has confidence. Many studies have examined the role of trust on the
adoption of mobile and online banking in both developed and developing countries
(Burucuoglu and Erdogan, 2016; Efremidou et al., 2014; Floh and Treiblmaier, 2006;
Mahad et al., 2015; Mashhour and Saleh, 2015; Masrek et al., 2012; Popoola, 2013).
Other studies related to mobile banking adoption and usage were also reviewed. Many
researchers focused on the factors that impact mobile adoption. For example a study by
Kabir (2013) about the factors influencing the usage of mobile banking concluded that
perceived risk (excluding social risk), trust, convenience, and comparative advantage are
the factors affecting the behavioural intention of mobile users to use mobile banking
services in Bangladesh. Another study by Anus et al. (2011) concentrated on the risks
that impact the initial adoption of mobile banking in Pakistan. The results of this study
showed that risk perception obtained from eight different components is a major
perquisite to innovative technology acceptance.
A study in Bahrain by AlSoufi and Ali (2014) concluded that users of mobile banking
are for the most part motivated by the following two factors: how useful and how easy
this use might be. The study showed that some factors such as possible costs and risk did
not seem to influence their decision to use this type of banking. Yu (2012) investigated
the factors affecting individuals adopting mobile banking. The study, which used the
Unified Theory of Acceptance and Use of Technology (UTAUT) to examine what
impacts people to adopt mobile banking, determined that personal inclination to prefer
mobile banking was greatly impacted by such factors as social considerations, estimated
costs, perceived level of performance, and extent of trustworthiness. Cudjoe et al. (2015)
studied the factors that determine mobile banking adoptions among Ghanaian customers.
Their study focused on a bank called Access Bank. The results of the study indicated that
extent of trustworthiness and estimated costs have a more pronounced influence on
consumer to adopt and use mobile banking services than perceived usefulness and
perceived ease of use.

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Rumanyika (2015) investigated the barriers to adopting mobile banking in Tanzania
concluded that poor network coverage, lack of knowledge of m-banking users, lack of
enough float of mobile money agents and ATM breakdown and theft are main barriers
perceived in the way of m-banking in Tanzania. Iddris (2013) investigated the perceived
obstacles to the adoption of mobile banking in Ghana. The results of the study showed
that there are four reasons that caused customers to reject mobile banking. The reasons
are: (a) m-banking needs knowledge and learning, (b) it may create additional banking
charges, (c) poor telecommunication network in mobile banking, and (d) consumers
prefer traditional means of banking instead of mobile banking.
A study by Govender and Sihlali (2014) examined the factors that affect the adoption
of mobile banking by students. They based their theoretical framework on an extension
of the Technology Acceptance Model (TAM) for mobile services to investigate the
factors that affect IT students’ adoption of m-banking. The constructs of TAM for mobile
services used were Perceived Ease of Use (PEOU), Perceived Value (PV), Trust (T), and
Intention to Use (IU), Perceived Ease of adoption (PEOA) and Usage Behaviour (UB).
The result of the study showed that the independent variables, trust, perceived value,
perceived ease of use and social influence may justify 42% of the explanatory power for
the dependent variable which is the intention to use mobile banking. A study by
Ramdhony and Munien (2013) attempted to explore m-banking adoption and usage in
Mauritius. The study found that convenience, time and effort savings, privacy, easy
access to banking services, compatibility with lifestyle and banking needs were identified
as the major elements motivating m-banking adoption. Furthermore the study concluded
that perceived security risk and reliability were found to be the key barriers to m-banking
usage.
Khraim et al. (2011) attempted to find the elements that impact mobile banking
adoption in Jordan in their study. Their research findings showed that the six factors that
they examined which were: self-efficacy, trailability, compatibility, complexity, risk and
relative advantage were statistically important in affecting mobile banking adoption in
Jordan. Ouyang (2012) proposed internet trust and security anxiety as perquisite factors
of the TAM model, which directly impact the intention to use mobile banking. The
findings of the study showed that internet trust and security anxiety are important factors
that indirectly influence whether consumers will adopt mobile banking. Furthermore, the
study concluded that security anxiety has a major negative impact on trust and has no
direct effect on perceived ease-of-use. The research findings showed that trust greatly
impacts perceived ease-of-use and has no major impact on perceived usefulness.
Our literature review showed that the majority of the previous research on mobile
banking reviewed here have focused on determining the factors that affect whether
consumers will adopt mobile banking or not. This review has shown that factors that may
drive consumers to adopt mobile banking and factors that constitute barriers to mobile
banking usage have been investigated by researchers in several different countries.
Although researchers examined how trust may be a factor in mobile banking adoption,
less emphasis was placed on the drivers of consumer trust of mobile banking and this
research aims to fill this gap.
3 Research model and hypotheses development
In this research, the researcher will study the factors that influence the adoption of mobile
banking in Jordan. Figure 1 presents a proposed framework that is conceptually based on

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quantitative examination of the factors 185
several factors identified in the literature. Trust, perceived credibility, perceived benefits,
security risks, privacy risks, social influence and perceived behavioural control are
proposed to affect mobile banking adoption.
Figure 1 Research model (see online version for colours)
Perceived
Benefits
Perceived
H1
Credibility
H2
H3
Perceived Trust
PBC in Mobile
H4
Social
Influence
H5
Privacy Risk
H6
Security Risk
3.1 Perceived benefits
Perceived benefits are defined as the degree of difference between an innovation and its
predecessor (Kim et al., 2009; Hsu et al., 2011; Kim et al., 2008; Lin, 2011). A number
of researchers examined the impact of perceived benefits on mobile and online banking
customers’ behaviour and their decision to use or not use these types of banking. A study
by Mwithaga and Muturi (2015) dealt with the factors that impact the subscription to
mobile banking by JKUAT University students in Kenya. The results of the research
showed that the key perceived benefits of mobile banking that were important to students
of the university were: time saving, cost saving and convenience. Chitungo and Munongo
(2013) investigated the suitability of the extension of TAM for impacting the intention of
rural unbanked individuals in Zimbabwe to use mobile banking. One of the findings
of the study indicated that the relative advantages of using mobile banking played a
major role in whether consumers adopt this method of banking. The study showed that

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consumers are more likely to use mobile banking if they believe that they will benefit
more from using mobile banking than from using ATM or non-mobile internet banking.
Jahangir and Begum (2008) examined the impact of perceived usefulness and other
factors on customer adoption of e-banking in Bangladesh. Their research showed
that expected benefits and other factors are positively related to customer adoption of
e-banking in Bangladesh. Furthermore, the results of their research suggested that it is
not enough for banks to just introduce e-banking, they also need to show consumers how
e-banking will be useful for them. A study by Sayid and Echchabi (2013) analysed
Somali customers’ feelings about mobile banking. The results of the research indicated
that expected ease of use has major effect on the perceived usefulness of this type of
banking. Furthermore, the study showed that customers attitude and perceived usefulness
have a big impact on customers intention to use mobile banking. Some studies examined
how the relative advantages of mobile banking adoption influenced consumers’ to use
this type of banking. Behl and Pal (2016) examined the barriers that stand in the way of
consumers adopting mobile banking in rural India. Some of the results of the research
indicated that the perception of users and non-users towards the usefulness, ease of use,
and risk aversion impacted mobile banking adoption and usage. Rammile and Nel (2012)
examined consumers’ reluctance to adopt mobile phone banking. Two results of the
research showed that expected ease of use has a strong effect on perceived usefulness and
the intent to use mobile phone banking, with perceived usefulness having a big effect
on behavioural intention. A study by Mohammadi (2015) investigated banking loyalty
among mobile bank customers in Iran. One of the results of the study indicated that
perceived usefulness facilitated the relationship between ease of use and users’ attitudes.
Kim et al. (2009) and Susanto et al. (2013) found that relative advantages (i.e. benefits)
have a significant and positive influence on customer’s initial trust. In another context,
several internet banking studies found that trust relates to perceived benefits (Moga et al.,
2012; Popoola, 2013; Suh and Han, 2003; Yousafzai et al., 2009). However, limited
studies have examined such relationship within the mobile banking context. Therefore,
and based on the previous discussion, the following hypothesis is proposed:
Hypothesis 1: Perceived benefits have a significant positive impact on users’ trust in
m-banking.
3.2 Perceived credibility
Luarn and Lin (2005) defined the perception of credibility as the degree to which a
person believes that using mobile banking will have no security or privacy threats.
Several studies focused on the impact of perceived credibility on consumers’ adoption of
mobile and online banking. A study by Amin et al. (2007) used the technology
acceptance model to study the factors that decide whether an individual adopts or does
not adopt mobile banking in Malaysia. The results of the study showed that perceived
credibility, among other factors, determines consumers’ inclination to adopt mobile
banking. Ramlugun and Issuree (2014) attempted to shed more light on the factors that
affect consumers’ behavioural intention towards adopting m-banking in Mauritius. The
researchers identified five factors that impact consumers’ decision to adopt mobile
banking from the extended TAM. Their results determined that perceived credibility
among other factors such as perceived usefulness, perceived ease of use, perceived self-
efficacy have a positive impact on whether consumers will adopt mobile banking. A

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study by Daud et al. (2011) explored the elements that determine the success of mobile
banking adoption by consumers in Malaysia. They used the extended TAM for their
study. The results of their study indicated that perceived credibility, among other factors,
significantly impacted consumers’ attitudes that influence their intention towards mobile
banking usage. Agha and Saeed (2015) analysed the elements that affected customer
inclination to use online banking in Pakistan. Some of their findings showed that
perceived credibility is positively connected with customer acceptance of online banking.
The study indicated that banks that are perceived as credible, trustworthy and helpful
have a higher chance of being accepted by consumers. Koksal (2016) investigated
the elements that separate the customers who are more likely to adopt mobile banking
from the customers who are not likely. Some of the findings of the study revealed that
perceived credibility is one of the factors that separate customers who have a high
probability of adopting mobile banking from those customers who have a low probability
of adopting this type of banking. In particular, Kazi (2013) found perceived credibility to
have a significant impact on the students’ trustworthiness of internet banking. Brown
et al. (2003), Riquelme and Rios (2010), and Natarajan et al. (2010) indicated that mobile
banking adoption studies have supported that people refuse or unwilling to use mobile
banking mainly because of perceived credibility. However, our literature review
indicated that very little empirical work establishes perceived credibility as an antecedent
of trusting beliefs in the mobile banking context. The increase in perceived credibility
results in greater trust and willingness to transact with a firm online system (Lowry et al.,
2014). In light of the above discussion, the following hypothesis is proposed:
Hypothesis2: Perceived credibility has a significant positive impact on users’ trust in
m-banking.
3.3 Perceived behavioural control
Ajzen (1991) defined Perceived Behavioural Control (PBC) as people’s perception of
how challenging or easy it is to carry out a behaviour of interest. Several studies dealt
with the impact of perceived behavioural control on the adoption of mobile online
banking, and mobile commerce. Abadi et al. (2012) examined customers’ behavioural
intention to use mobile banking. The study used the Theory of Planned Behaviour (TPB),
TAM and perceived risk to build a model to investigate the adoption intentions of mobile
banking users. The findings of the study showed that the behavioural intention to
use mobile banking is positively impacted primarily by perceived behavioural control
and subjective norm. Bhatti (2007) analysed the variables that impact mobile commerce
adoption. One of the results of the study showed that subjective norms and perceived
behavioural control affect perceived ease of use and the intent to adopt mobile
commerce. Al-Ajam and Md Nor (2013) applied the TPB to examine consumers’
intention to adopt internet banking in Yemen. Some of the findings of their research
showed that perceived behavioural control, subjective norms, and attitude all have a
noteworthy impact on consumers’ decision to adopt internet banking in Yemen. A study
by Takele and Sira (2013) examined the factors that affect consumers’ intent to use e-
banking service channels in Bahir Dar city, Ethiopia. One of the findings of their study
revealed that perceived behavioural control is the number one factor that impacts
consumers’ decision to adopt e-banking services. In addition, although few studies
explored the influence of PBC on trust, the relationship between control over information

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and trust appears to be confirmed in the technology setting context (Taddei and Contena,
2013). Thus, the following hypothesis is proposed:
Hypothesis 3: Perceived Behavioural Control has a significant positive impact on users’
trust in m-banking.
3.4 Social influence
Davis (1989) defined social influence as the level to which an individual feels that others
who are important to him or her believe he or she should use the new system. Several
studies examined the effect of social influence on mobile and online banking adoption.
For example, de Silva et al. (2011) examined the effect of social influence on mobile
adoption in Bangladesh, Pakistan, India, Sri Lanka, the Philippines, and Thailand. The
findings of the study showed that social influence affect mobile phone adoptions in two
ways: first, it puts pressure on consumers to adopt mobile phones, and, second, it creates
benefits through social networks that are connected to economic and business networks.
Similarly, Kim and Lee (2015) investigated the factors that influence smartphone
banking adoptions by smartphone Mongolians users. Their research indicated social
influence positively influences consumer decisions to adopt smartphone banking or not.
Kaziia and Mannan (2013) also examined the factors that may influence consumers’
adoption of mobile banking in Pakistan. Their research emphasised the low-income
population in Pakistan. The results of the study showed that social influence is the most
positive factor that may affect consumers’ adoption of mobile banking. Arahita and
Hatammimi (2015) investigated the impact of five factors on customers’ intention
to reuse a mobile banking provider. The study which focused on an Indonesian bank
called Mobile Bank Central Asia (BCA) showed that social influence among other
factors impacts a customer’s decision on whether to reuse a mobile banking provider.
Abdulkadir et al. (2013) analysed the variables that impact university student’s decision
to adopt mobile banking in Malaysia by applying TAM and TPB. Their findings
indicated that social influence had a big effect on students’ decision to adopt mobile
banking. However, although the careful review of the literature indicated that there are
many empirical studies that confirmed the impact of social influence on the adoption
intentions, there is very little empirical research, if any, that studied the influence of
social influence on users’ trust. This paper argues that when important individual are
using the mobile banking services, this might promote trust among their peers or
followers. Thus, the following hypothesis is posited:
Hypothesis 4: Social influence has a significant positive impact on users’ trust in
m-banking.
3.5 Privacy risks
Aldás-Manzano et al. (2009) defined privacy concerns in banking as the level of
consumer worries regarding the possibility that their privacy might be violated, and the
possibility that personal information might be revealed to other companies or to cross sell
other banking products. Several studies examined with the impact of privacy risks on the
adoption of mobile and online banking. Priya and Raj (2015) investigated the effects of
risk factors on using mobile banking in India. The findings of their research showed that
privacy and security risks determine mobile banking usage in India. Jalal et al. (2011)

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examined certain factors that impact consumers’ intent to use internet banking in
Bahrain. One of the results of their studies showed that credibility factors such as privacy
and security are key sources of dissatisfaction for users of internet banking. Ankit (2011)
analysed the factors that impact customers’ satisfaction with online banking in India.
The study concluded that privacy risk concerns, among other factors, strongly influenced
customers’ overall satisfaction with online banking. Abuga and Manyange (2015)
researched the differences in mobile banking service capability of various commercial
banks in Rwanda. Their study showed that most of the commercial banks chosen for the
study performed well with regard to mobile banking services. Furthermore, the study
indicated that the two most effective features of mobile banking are privacy and security
standards. Arif et al. (2016) investigated consumers’ reluctance to adopt mobile banking
in Pakistan. One of the results of their research showed that there is a negative
relationship between privacy and financial risks on the one hand, and consumers’ attitude
towards mobile banking technology on the other. In light of the above discussion, the
following hypothesis is proposed:
Hypothesis 5: Privacy risks have a significant negative impact on users’ trust in
m-banking.
3.6 Security risks
Koenig-Lewis et al. (2010) defined security risk in the m-banking arena as customers’
anxiety that their money might be transferred to a third party without their knowledge.
Akturan and Tezcan (2012) stated that security risk involves the possible loss of control
over transactions and financial information.
Several studies dealt with the impact of security risks on the adoption of mobile and
online banking. A study by Islam (2014) reviewing the security issues of mobile banking
and payments systems found that mobile banking customers’ concern about security
while using their mobile phones to manage banking and financial transactions may not be
as real as they perceive. Achieng and Ingari (2015) investigated the factors that affect
using mobile banking in Kenya’s commercial banks. The study selected a bank in Kenya
called (KCB) Kilindini branch. One of the results of the study showed that perceived risk
has a major impact on the adoption of mobile banking. Consumers did not feel safe
giving their personal information while engaged in a mobile banking transaction.
Consumers were concerned that they will not get their money back if an error occurred
during the transaction. Lee et al. (2013) investigated the mobile banking strategies
employed by financial institutions in the USA. They showed that commercial banks are
in a better position to supply more advanced banking services and better security to their
customers. Waheed et al. (2013) examined the role of satisfaction, security and risk
towards customers switching from traditional to internet banking. The findings showed
that security is strongly related to customers’ intention to switch from traditional to
internet banking. Demirdogen et al. (2010) examined customer risk perceptions regarding
online banking in Turkey. Some of their findings indicated that a clear relationship
between the income levels of online banking customers and risk perceptions. The study
indicated that customers with high incomes have high security risk perceptions. A study
by Jepleting et al. (2013) investigated the extent mobile banking affected customer

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satisfaction at a bank in Kenya. The results of the study showed that most mobile
banking customers are satisfied with the service, while a majority of non-users do not
trust this type of banking due to security risks and reliability of service. Similarly, a study
by Ong and Lin (2015) investigated the impact of security, risk, and trust on adopting
internet banking in Taiwan. One of the findings of their research revealed that perceived
security risk is an important precursor of trust. In light of the above discussion, the
following hypothesis is proposed:
Hypothesis 6: Security risks have a significant negative impact on users’ trust in
m-banking.
4 Research methods
4.1 Data collection and measurement scales
Empirical quantitative data was collected by conducting an online field survey of mobile
banking users. Hence, a self-completion, well-structured questionnaire was developed
based on previous literature and was then distributed to a random sample and
participation was completely voluntary. In this research, the survey was designed and
organised to ensure the clarity and the accuracy of the questions. Survey monkey was
used to develop the web-based survey. The questionnaire design was divided into two
sections. The first section consists of scale questions which are used to measure the
independent and dependent variables related to the research model. The second
section was designed to capture the respondents’ profiles (i.e. demographics data) using
multiple-choice questions. In this section, yes/no questions were also used at the
beginning of the questionnaire for the purpose of filtering the participants (e.g. if they are
using mobile banking or not). Whereas, all items of the constructs were measured on a
seven-point Likert-type scale, ranging from 1 – totally disagree – to 7 – totally agree. To
achieve the clarity of the survey, each type of questions was separated from other types.
Thus, the demographic questions and yes/no questions were placed at the end of the
questionnaire.
As mentioned earlier, the survey utilised the previous literature to develop a series of
measures which were suitable for measuring users’ trust in m-banking. The constructs of
interest in this study were ‘Perceived benefits’, ‘Perceived credibility’, ‘social influence’,
‘security risk’, ‘privacy risk’, ‘perceived behavioural control’ and ‘Trust in M-banking’.
Each construct was measured with multiple items. All items were adapted from extant
literature using validated items drawn from prior research (refer to Table 1) to improve
content validity (Straub et al., 2004). The scales of security and privacy risks were
adopted from Pikkarainen et al. (2004). Perceived benefits items were adopted from Lee
(2009). The measurements of perceived credibility were adopted from Luarn and Lin
(2005) and Foon and Fah (2011). Social influence and perceived behavioural control
items were adopted from Venkatesh et al. (2003, 2012). Perceived trust items were
adopted from Suh and Han (2003). Adjustments were made in some of the phrases in
order to adapt their meaning to fit the mobile banking context. In Table 1, measurement
scales list the survey items.

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Table 1 Measurement scales
Constructs Items Measures Source
Perceived
Benefits
PB01 1) I think that using mobile banking can save my
time in performing banking transactions
Lee
(2009)
PB02 2)
I think that using mobile banking can offer me a
wider range of banking products, services and
investment opportunities
PB03 3)
I think that using mobile banking can save the
transaction handling fees in performing banking
transactions
Perceived
Credibility
When using m-banking
Luarn and Lin
(2005) Foon
and Fah
(2011)
PC01 1) I believe my information is kept confidential
PC02 2) I believe my transactions are secured
PC03 3) I believe my privacy would not be divulged
PC04 4) I believe that banking environment is safe
Perceived
Behavioural
Control
PBC01 1) I would be able to operate mobile banking Taylor and
Todd (1995)
and Shih and
Fang (2004)
PBC02 2) I have the resources to use mobile banking
PBC03 3) I have the knowledge to use mobile banking
PBC04 4) I have the ability to use mobile banking
Social
Influence
SocI01 1) People important to me would think that using
MB would be a wise idea Venkatesh et
al. (2003) and
Venkatesh et
al. (2012)
SocI02 2) People important to me would think that using
MB is a good idea
SocI03 3) Most people important to me would think I should
use MB
Privacy
Risks
PRVR01 1) Using mobile banking is financially secure
Pikkarainen
et al. (2004)
PRVR02 2) I trust in the ability of mobile banking to protect
my privacy
PRVR03 3) I trust in the technology mobile banking is using
Security
Risks
SCR01 1) I trust in mobile banking as a bank
Pikkarainen
et al. (2004)
SCR02 2) I am not worried about the security of mobile
banking
SCR03 3) Matters of security have no influence on using
an mobile banking
Perceived
Trust
PT01 1) Mobile banking is trustworthy
Suh and
Han (2003)
PT02 2) I would trust my bank to offer secure mobile
banking
PT03 3) Mobile banking keeps its promises and
commitments
PT04 4) Mobile banking services keep customers’ best
interests in mind
PT05 5) I trust mobile banking
Source: Developed for the current research

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4.2 Study population and sample
As mentioned earlier, this study employed the survey questionnaire as the main method
for collecting data. Online self-administered questionnaires were developed based on
previous published literature. As this research is studying consumers’ trust in m-banking
services, it is vital that the targeted sample elements are having this technology in place.
In order to empirically test the hypotheses developed in the previous section, data were
collected using a convenience sampling approach via an online self-administered survey.
The first reason for using this sampling technique is because it offers an easy way to
collect the raw data for further analysis. Secondly, it saves time and costs as the
respondents are randomly selected. M-banking was identified to participants as a mobile
commerce application that gives the user the opportunity to make the everyday bank
transactions (such as balance inquiries, check book requests, make money transfers, etc.),
mobile brokerage (trading financial instruments), and financial inquiries (bank balance,
statement requests, ATM locations, foreign exchange rates, etc.) using a mobile phone or
other portable devices. The participation in the study was voluntary. It was indicated to
respondents that the survey should be filled out by respondents who are only familiar
with m-banking concept, thus enhancing content validity. All participants were given the
opportunity to receive the findings of the study in order to encourage participation and
reduce self-reporting bias.
Before conducting the main survey, both a pretest and a pilot test were conducted to
validate the instrument. The pretest involved 15 respondents who were selected experts
and knowledgeable in the mobile banking context including business, marketing and
information systems academics. The respondents were then requested to comment on
listed items that corresponded to the constructs, including the wording of the scales, the
length of the instrument, the format of the questionnaires, and other comments on how
the questionnaire could be improved. Finally, to ensure that the questionnaire adequately
addressed the relevant issues and to reduce possible ambiguity in the questions, a pilot
test was administered among a group of 50 college students who were not included in the
main survey. Consequently, the wording of some questions was modified. Preliminary
evidence showed that the scales were reliable and valid, except one item (PT6) with
loading less than 0.40 as recommended by Henseler et al. (2009); thus, it was eliminated
from the questionnaire.
Following the pretest, the survey was sent to a user base of people with one or more
mobile phones. The survey was mainly promoted and hosted online by survey monkey
website: a provider of web-based survey solutions (Surveymonkey.com). Respondents
were invited to take the questionnaire by sending them the link of the survey webpage on
their e-mail addresses, Facebook pages, and via a popular mobile-device application
called WhatsApp. A total of 835 e-mails were sent using personal hyperlinks that could
be used only once, thus preventing repeated responses. The message outlined the aim of
this study, provided a hyperlink to the survey form, and, as an incentive, offered
respondents an opportunity to participate in a drawing for a prize. A follow-up reminder
was sent to non-respondents after four weeks. A total of 432 responses were collected. 28
responses were discarded due to duplicate submissions or incompletion, a net sample of
404 usable questionnaires remained. The common method bias was examined using
Harman’s one-factor test (Podsakoff et al., 2003). No significant common method bias
was found in the data set.

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Data analysis was conducted using SPSS version 19.0. Part of it was descriptive,
while the inferential part of the statistical analysis examined the factors influencing
consumers’ trust in m-banking. Analysis is shown in the scenario below.
4.3 Sample characteristics
Data were gathered from a convenience sample of 404 respondents via an online survey.
The data relating to respondents’ profiles were tabulated to obtain a better feel of the
data, as recommended by Sekaran (2003). Therefore, the respondents’ demographic
profiles were tabulated for gender, age, education level and marital status (Table 2).
Table 2 Sample characteristics
Variables Category Response information
N = 404 (%)
Gender Male 53
Female 47
Age
Less than 20 27
21–29 47
30–30 13
40–49 10
50–59 2
Above 60 1
Education
High School 5
University Degree 82
Higher Education 13
Marital status
Single 54
Married 44
Divorced 2
As shown in Table 2, male respondents accounted for the majority of the sample (53%)
and the majority of the respondents’ ages were less than 29 (74%). In relation to
educational level, 13% of the respondents reported completing higher education, while
82% reported achieving a university degree. For marital status, single respondents
accounted for the majority of the sample (54%).
5 Data analysis and results
5.1 Results
The Partial Least Squares (PLS) method was used to analyse the study results in order to
consider the influence of all constructs on the framework simultaneously. PLS can be a
powerful method of analysis because of its minimal demands on measurement scales,
sample size and residual distributions (Chin, 1998). In consideration of these points, and
due to its increasing acceptance within marketing domain, PLS was chosen to evaluate
the research model and test the hypotheses. In particular, a two-step method was used as
recommended by Anderson and Gerbing (1988), beginning with the measurement model
to examine the reliability and validity of the instrument and then analysing the structural

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model. PLS estimation requires ten times the largest number of structural paths directed
at a particular construct in the model (Chin, 1998; Gefen et al., 2000). The sample in our
study met the necessary conditions for using PLS.
5.2 Measurement model
Tables 3 and 4 present the measurement model results. Composite reliability (CR) is
above 0.70 indicating that the scales have internal consistency (Table 3). As seen in
Table 3, the instrument presents good indicator reliability as the loadings are above 0.70.
Average Variance Extracted (AVE) was used to test convergent validity. AVE should be
higher than 0.50 so that the latent variables explain more than half of the variance of its
indicators (Fornell and Larcker, 1981; Hair et al., 2012; Henseler et al., 2009). As seen in
Table 3, all constructs meet these criteria. The AVE, CR, and Alpha values are higher
than the recommended thresholds of 0.500, 0.700, and 0.700, respectively (Bagozzi and
Yi, 1988; Gefen et al., 2000; Nunnally, 1978). This demonstrates convergent validity and
validity indicating that the constructs can be used to test the conceptual model.
Table 3 Individual item reliability and construct validity
Construct Factor Loading AVE
Composite
reliability
Cronbach’s
alpha
Perceived Benefits
PB01 0.793
0.605 0.914 0.889 PB02 0.667
PB03 0.709
Perceived Credibility
PC01 0.888
0.83 0.951 0.931
PC02 0.929
PC03 0.927
PC04 0.897
Social Influence
Socl01 0.770
0.650 0.878 0.834
Socl02 0.897
Socl03 0.793
Privacy Risks
PRVR01 0.935
0.835 0.938 0.902
PRVR02 0.951
PRVR03 0.852
Security Risks
SCR01 0.834
0.696 0.873 0.788
SCR02 0.845
SCR03 0.819
Perceived Behavioural
Control
PBC01 0.856
0.703 0.904 0.859
PBC02 0.779
PBC03 0.803
PBC04 0.906
Trust
PT01 0.895
0.731 0.931 0.907
PT02 0.896
PT03 0.827
PT04 0.765
PT05 0.886
Source: Developed for the current research

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Finally, discriminant validity was tested based on the square root of AVE for each
construct should be greater than the correlations with all constructs (Boudreau et al.,
2001; Fornell and Larcker, 1981). In Table 4, we can see that the square root of AVE
(in bold) is higher than the correlation between constructs.
Table 4 Latent variable correlations
PB 0.778
PBC 0.446
0.838
PC 0.617 0.566
0.911
PRVR 0.360 0.509 0.656 0.913
SCR 0.391 0.435 0.650 0.816
0.834
SOCI 0.543 0.533 0.495 0.485 0.439 0.806
PT 0.646 0.582 0.816 0.562 0.524 0.528 0.855
To establish the evidence for the discriminant validity among the constructs, we actually
compared the shared variance among the constructs with AVE from each construct. The
discriminant validity is established between two constructs if the AVE of each one is
higher than the shared variance (Chin, 1998). Comparing the shared variance and AVE
values indicated a support for the discriminant validity among the latent variables in our
model (see Table 4).
5.3 Structural model
The analysis of hypotheses and constructs’ relationships were based on the examination
of standardised paths. The path significance levels were estimated using the bootstrap
resampling method (Henseler et al., 2009), with 500 iterations of resampling (Chin,
1998). The results are summarised in Table 5. The results of the PLS-SEM analysis
show, as in Table 5, the structural model estimation and evaluation of the formulated
hypotheses. Results indicated that all study hypotheses are accepted according to
obtained t-values and p-values (see Table 5).
Table 5 Partial least squares results for the theoretical model
Predicted variable Predictor variable Hypotheses Path R2 Critical ration
Trust in m-banking
PB H1 0.192 2.273
PC H2 0.606 6.524
PBC H3 0.120 2.289
SOC H4 0.061 2.001
PRVR H5 –0.171 3.125
SCR H6 –0.182 3.008
0.718
The PLS results, as shown in Table 5, indicate that perceived benefits of using
m-banking has a significant positive effect on users (consumers) trust in m-banking
adoption (ß = 0.192, t = 2.273, p < 0.01), indicating that users who perceived m-banking
as beneficial tend to trust using m-banking, thereby supporting H1. As proposed in H2,
a significant positive relationship between perceived credibility and consumer trust in

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.H. Al Khasawneh, O. Hujran and T. Abdrabbo
m-banking was found (ß = 0.606, t = 6.524, p < 0.01), suggesting that those consumers
who perceive m-banking as credible are more likely to have trust in m-banking. This
finding supports H2.
Consistent with H3, perceived behavioural control of using m-banking did
significantly affect consumers’ trust in m-banking (ß = 0.120, t = 2.289, p < 0.01),
implying that perceived behavioural control is a concern when using m-banking services,
thereby confirming H3. As proposed in H4, a significant positive relationship between
social influence and trust was found (ß = 0.061, t = 2.001, p < 0.01), providing support
for H4. Further, privacy risks (ß = –0.171, t = 3.125, p < 0.01) and security risks
(ß = –0.182, t = 3.008, p < 0.01) are statistically significant in explaining users’ trust in
m-banking, thus supporting H5 and H6.
As shown in Table 5, 72% of the variance in consumer perceived trust in m-banking
is explained by perceived benefits, perceived credibility, perceived behavioural control,
social influence, privacy and security risks of m-banking.
6 Discussion and conclusions
This study attempted to quantitatively investigate the factors that influence users’
perceptions of trust towards using mobile banking services. The comprehensive, yet
parsimonious model, developed in the current study makes a significant contribution to
the existing literature on online customer trust and internet banking by integrating
variables from the trust literature and applying them to the context of mobile banking.
The empirical results provided strong evidence for the explanatory power of the
current research model. In particular, the study found that the six variables (perceived
benefits, perceived credibility, perceived behavioural control, social influence, privacy
and security risks) have direct impact on users’ trust in m-banking. However, the betas
(standardised coefficient) are different for each individual independent variable.
Perceived credibility has the highest positive effect on users’ trusts in m-banking
(β = 0.606), followed by perceived benefits (β = 0.192), and PBC (β = 0.120), while
social influence has the lowest effect (β = 0.061). In contrast, security risk (β = –0.182)
and privacy risk (β = –0. 171) exhibited a moderate negative impact on users’ trust in
mobile banking.
These findings confirm and disconfirm previous studies in the context of internet and
mobile banking. With regard to the positive impact of perceived benefits and trust, this
study is in line with many internet banking studies that succeed to find a significant
positive relationship between perceived benefits and users trust in technology usage
(Chircu et al., 2000; Gu et al., 2009; Gefen, 1997; Gefen et al., 2000; Pavlou, 2003). In
particular and within the e-service context, trust was found to be closely related to
perceived benefits (Gefen, 1997; Gefen et al., 2000). In another context, several internet
banking studies found that trust relates to perceived benefits (Moga et al., 2012; Popoola,
2013; Suh and Han, 2003; Yousafzai et al., 2009). However, limited studies have
examined such relationship within the mobile banking context, thus adding to the scant
knowledge in such field. The results of the data analysis of the current study demonstrate
that perceived benefits influence customer trust and determine behaviour motivation.
Taking this point further, when users perceive mobile banking to be useful and they are
willing to use it, they will be more likely to develop their trust towards it (Gu et al.,
2009). Therefore, it can be suggested that Banks could effectively enhance users’

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perception of trust through disseminating the benefits of mobile banking services to
users. Consistent demonstration of the advantages of mobile banking through
advertisements and marketing events is recommended. Such marketing activities can
enrich customers’ positive impression and increase trust beliefs and can ultimately
motivate potential customers to use mobile banking.
With regard to perceived credibility, the current study adds a significant contribution
to the body of the relevant existing literature by enhancing our knowledge to the impact
of perceived credibility on users’ trust in the mobile banking context. However, it is
noteworthy to mention that perceived credibility has been widely and empirically
supported and used not only in mobile banking adoption studies (Al Khasawneh, 2015;
Amin et al., 2007; Luarn and Lin, 2005) but also in many internet banking studies as
discussed in Wang et al. (2003), Amin (2009), Kazi (2013), and Yuen et al. (2010). In
particular, Kazi (2013) found perceived credibility to have a significant impact on the
students’ trustworthiness of internet banking. Brown et al. (2003), Riquelme and Rios
(2010), Natarajan et al. (2010), and Amin et al. (2008) indicated that mobile banking
adoption studies have supported that people refuse or unwilling to use mobile banking
mainly because of perceived credibility. Taking this point further, the empirical evidence
of our study extended the existing knowledge by findings that perceived credibility is the
most powerful factor in influencing users’ trust in m-banking. Therefore, management
should focus more on belief formation – users’ credibility – than on directly influencing
behavioural intentions or actual behaviour towards mobile banking.
With regard to PBC and its positive impact on perceptions of Trust in the mobile
banking services, the current study may be considered as the first study to the best of our
knowledge that examines such relationship in the mobile banking context. In particular,
most of the existing studies in the mobile banking settings have focused on examining
the impact of PBC on behavioural intentions and adoption of mobile banking (Abadi
et al., 2012; Al Khasawneh and Irshaidat, 2017; Bhatti, 2007; Hsu et al., 2009; Pedersen,
2005; Quan et al., 2010; Shin et al., 2010), while other studies in various contexts found a
significant relationship between perceived behavioural control and actual behaviour
(Al-Majali and Mat, 2010; Gopi and Ramayah, 2007; Fusilier and Durlabhji, 2005; Chu
and Wu, 2004). Therefore, prior studies have suggested a positive link between PBC and
the intention to adopt technologies, but none of the previous relevant studies examined
the direct impact of PBC on users’ perceptions of trust towards mobile banking services,
thus adding a significant contribution to the existing mobile banking literature. Based on
the previous discussion, the current study enhanced our knowledge by determining a
positive and significant influence of PBC on consumers’ trust in the mobile banking
context in Jordan.
With regard to the social influence, the current study found a weak positive influence
on users’ trust in mobile banking services usage which is consistent with the finding
of Arahita and Hatammimi (2015), Sripalawat et al. (2011), and Suoranta and Mattila
(2004) in various technological contexts, but inconsistent with the results presented by
Laforet and Li (2005). However, it should be noted that Laforet and Li (2005) performed
their study in China while the other study by Suoranta and Mattila (2004) was conducted
in Taiwan and Finland, respectively. Taking the previous assertion into consideration,
the contradicting result may be due to the differences in the consuming culture or
competitive environment related to banks; therefore, it may become possible reasons,
which is worthwhile to be further analysed. In addition, and as discussed in Section 3.4,
the current study adds a significant contribution to the mobile banking literature as there

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is very little empirical research, if any, that studied the influence of social influence on
users’ trust. This paper found that when important individual are using the mobile
banking services, this might promote trust among their peers or followers.
Regarding the influence of security risk on trust, the current research found that there
are closely and negatively related which is consistent with the existing e-commerce and
online banking literature (Benassi, 1999; Dayal et al., 1999; Kasemsan and Hunngam,
2011; Vance et al., 2008; White and Nteli, 2004). In the context of general internet usage,
White and Nteli (2004) found that security concern is a strong factor that relates to the
reluctance of using internet for transaction. Moving into the internet banking context,
previous research found that security risk was considered as a significant factor
associated to the lack of trust in such services (Kasemsan and Hunngam, 2011).
Additional revision of the concepts of trust and security within the online banking
context revealed that security risk has been identified as a factor that has the potential of
affecting trust towards using e-banking services (Ally and Toleman, 2005; Bargh et al.,
2002; Belanger et al., 2002; Chellappa, 2002; Lim, 2003; Suh and Han, 2003; Yousafzai
et al., 2005). For example, Belanger et al. (2002) argue that the first factor that assists
trust development in online users is the assurance of safety and security. Taking this
point further, visible security mechanisms are recognised to be the major antecedent of
users’ perception of trust.
The findings of this study showed that customers’ perception of privacy risk
associated with the usage of mobile banking is found to have significant impact on
their trust in mobile banking. The results of the quantitative study revealed that the
relationship between privacy risk and trust in mobile banking is a direct and negative
relationship. This result is found to be consistent with the results of empirical study
conducted by Dupas et al. (2012), Govender and Sihlali (2014), and Rammile and Nel
(2012) which found privacy risk negatively affects the uptake of mobile banking.
Moreover, the result is consistent with the findings of previous studies that examined the
effect of initial trust on intention to use mobile banking (Kim et al., 2009; Zhou, 2011),
intention to use e-government (Carter and Belanger, 2005; Alsaghier, 2010), and
e-commerce (Gefen et al., 2000; Chen and Barnes, 2007; Kim, 2012). Therefore, it is
suggested that banks have to develop and promote a privacy protection policy and the
underlying technological support for anti-fraud protection to assure potential consumers
about the minimal privacy risks.
7 Managerial implications
This proposed and validated model of the current has comprehensively integrated aspects
from the parent disciplines of internet banking and consumer behaviour, together with
exploratory, empirical, conceptual and anecdotal literature conducted in the immediate
discipline of internet banking. Thus, the proposed model was theoretically based. In
particular, the newly developed model has not been applied in Jordan. Moreover, there is
little prior research that investigates users trust to use mobile banking services (Shih and
Fang, 2004). Thus, the model generated from this research may be a useful for academics
to understand these antecedents in the future. In particular, the study develops a model of
users’ trust in mobile banking that combines variables that have not been examined in a
previous single model. Additionally, this study helps academicians who are interested in
the intention to use topic, since there are only a few similar studies that tackle this
particular issuer (Yousafzai et al., 2009).

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Although mobile banking usage is strongly associated with high levels of users’ trust
related to security and privacy issues, factors affecting this trust has not yet been fully
examined in the existing relevant literature (Yousafzai et al., 2005; Yousafzai et al.,
2009). Extant literature on trust related to mobile banking services is scarce and focused
on more issues related to the role of perceived trust on mobile banking adoption and
usage. Taking this point further, the way in which trust may be enhanced and the set of
constructs that influence users’ trust in mobile banking is not yet fully understood.
Therefore, the present research provides valuable insights regarding future prospects trust
in mobile banking and the most important factors that have impact on such trust.
In particular, the study found that the six variables (perceived benefits, perceived
credibility, perceived behavioural control, social influence, privacy and security risks)
have direct impact on users’ trust in m-banking. In the issue of factors related to
enhancing mobile banking trust, perceived credibility is recognised to play a significant
role. Perceived benefits and perceived behavioural control act also as major influencers
on the customers’ trust towards the usage of mobile banking technology.
8 Practical implications
Understanding the nature of consumer trust and the factors that affect it provides vital
clues for its development. Associated with the mobile banking platform, there are several
features that are used to build trust, including usability, easy to be used and navigated,
credibility cues, security and privacy issue and so on. Security mechanisms are noted as
one of the most important features that influence consumer trust in the e-commerce
context (Suh and Han, 2003). The research findings are useful for drawing the focus of
the banks to creating greater credibility and trustworthiness building mechanisms with
the emphasis on communicating the higher benefits and easiness to use mobile banking
to the target customers. Additionally, security and privacy risks aspects are required to be
treated with caution. In that sense, the banks should keep in mind that information and
guidance enhance the perceived trust by mobile banking and decrease the perceived risks
related to the innovation (Laukkanen and Pasanen, 2008). Yousafzai et al. (2005)
confirmed the previous finding by demonstrating that trust in a physical banking scenario
is highly dependent on security and privacy concerns for the consumer. Thus, in mobile
banking, the role of security and privacy becomes crucial for building trust. Security of
information is often found as being the most important concern for businesses and
consumers in e-commerce and its related activities (Bargh et al., 2002; Belanger et al.,
2002; Hutchinson and Warren, 2003; Lim, 2003). Thus, security and privacy issues can
be guaranteed with adequate encryption, digital signatures, safe data transmission and
firewalls. Simple statements and clear graphical presentation stating that transaction
conducted through mobile banking platforms are guaranteed and risk free may reduce
consumers’ concerns regarding security risk. In addition, a concise and well-presented
privacy policy may assist in reducing privacy concerns to mobile banking users.
Therefore, the current model of factors influencing users’ trust in mobile banking acts
as a starting point in assisting banks to transform a potential customer from a curious
observer to a user who is ready to conduct mobile banking transactions.

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9 Limitations and future research
As with any prior studies, the current study has its limitations. Apart from the examined
factors that impact users’ trust towards mobile banking usage, there may be more factors;
so it is recommended to extend the current study by incorporating other relevant factors
that may have influence on users’ trust. Future research should attempt to determine the
extent to which the findings of the current study may be duplicated to include other
sample, settings and times. For example, the current study has been conducted in Jordan,
thus, it is possible that their Jordanians’ perceptions of trustworthiness are different from
other population. Thus, it is necessary to verify the results through investigations in other
developed and developing countries in order to be able to generalise the findings. In
addition, the current study used cross-sectional survey to examine customers’ adoption of
m-banking where the data were collected at the same point of time. It was indicated that
customers’ perceptions may change over time when consumers have gained more
experience (Mathieson, 1991). Thus, future research is needed to replicate and validate
the findings using a longitudinal research which would allow for further examination of
m-banking adoption at multiple points of time, thus allowing for tracking customer
decision adoption process which may change and fluctuate over time.
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