Abdullah Baabdullah

An integrated model for m-banking adoption in Saudi Arabia
Item Type Article
Authors Baabdullah, A.M.; Alalwan, A.A.; Rana, Nripendra P.; Patil, P.;
Dwivedi, Y.K.
Citation Baabdullah AM, Alalwan AA, Rana NP et al (2019) An integrated

model for m-banking adoption in Saudi Arabia. International
Journal of Bank Marketing. 37(2): 452-478.
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with the publisher's self-archiving policy.
Download date 25/05/2021 18:56:22
Link to Item http://hdl.handle.net/10454/17463

An Integrated Model for M-Banking Adoption in Saudi Arabia
Abdullah Baabdullah
Department of Management Information Systems
King Abdulaziz University, Jeddah, Saudi Arabia
baabdullah@kau.edu.sa
Ali Abdallah Alalwan

Amman College of Banking and Financial Sciences
Al-Balqa’ Applied University, Amman, Jordan
alwan.a.a.ali@gmail.com
Nripendra P. Rana
School of Management
Swansea University Bay Campus
Fabian Way, Swansea, SA1 8EN, UK
Tel: +44(0) 1792 295179
nrananp@gmail.com
PushP P. Patil
Swansea University Bay Campus
pushpppatil@gmail.com
Yogesh K. Dwivedi
Swansea University Bay campus
ykdwivedi@gmail.com
Abstract
Purpose – This study aims to identify and examine the most important factors that could
predict the Saudi customer’s continued intention towards adoption of mobile banking.
Design/methodology/approach – The proposed conceptual model was based on the
Technology Acceptance Model (TAM) and Task-Technology Fit (TTF) model. This is also
expanded by considering two additional factors: perceived privacy and perceived security. By
using a self-administered questionnaire, the data was collected from a convenience sample of
Saudi banking customers from different parts of Saudi Arabia.
Findings – The main results based on structural equation modelling analyses supported the
impact of perceived privacy, perceived security, perceived usefulness, and task-technology fit
on the customers’ continued intention to use mobile banking.
Research limitations/implications – The moderation influence of the demographic factors
(i.e. age, gender, income level, educational level) was not tested. The data was also collected
using a self-report questionnaire; however, it would be more accurate to utilise more statistics
from the bank database about the users of m-banking.
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Originality/value – This study represents a worthy attempt to test such novel technology (m-
banking) in the KSA where there is a scarcity of literature. A considerable theoretical
contribution was also made by integrating the TTF model with the TAM in addition to
consider privacy and security in one single model. Moreover, considering both perceived
privacy and security in the current model creates an accurate picture about the adoption of m-
banking especially as there are a limited number of m-banking studies that have considered
privacy and security alongside the TTF model and TAM in the same model.
Keywords: Mobile banking, TTF, TAM, Saudi Arabia, Intention, Adoption
1. Introduction
Mobile banking (m-banking) addresses the essential restriction of e-banking as it decreases

the user’s requirement to merely a mobile phone rather than relying on a PC device with
Internet connections (Adholiya et al., 2012; Alalwan et al., 2016; Al-Otaibi et al., 2018;
Laukkanen and Pasanen, 2008; Mullan et al., 2017; Sharma and Al-Muharrami, 2018; Zhou,
2012; Zhou, 2011). For this reason, mobile devices have increasingly become instruments
that consumers adopt for banking services such as making payments, checking balances and
statements, viewing account details, viewing and cancelling Direct Debits, and easily
identifying user transactions (Alalwan et al., 2017; Coetzee and Eksteen, 2011; Jeong and
Yoon, 2013; Kim et al., 2009; Lee and Chung, 2009; Priya et al., 2018; Santander Bank,
2017; Singh and Srivastava, 2018; Slade et al., 2015a; Slade et al., 2015b).
According to the Board of Governors of the Federal Reserve System (2013), among mobile
phone users, the rate of m-banking adoption increased from 21% to 28% between 2011 and
2012. Furthermore, among owners of smartphones, the rate of using m-banking went up from
42% to 48% between 2011 and 2012. Moreover, according to the British Bankers’
Association (2015), there is a notable growth with m-banking as payment via apps, which
ascended in 2015 by 54% with a value of £347m. Interestingly, China has witnessed a radical
increase in the adoption of m-banking since 2015. As such, the gross transaction volume
which was done through using m-banking soared from 65.5 trillion Yuan in 2015 to reach
179 trillion Yuan in 2017. In addition, the transaction volume is supposed to increase in 2018
to reach 244.9 trillion Yuan (Statista, 2017).
The steadily growing adoption of m-banking cannot merely be interpreted as being due to the
ability of m-banking to provide accessibility to financial services remotely, but also as being
due to the unique characteristics of mobiles used in m-banking which support the issue of
personal security (Coetzee and Eksteen, 2011; Jahangir and Begum, 2008; Jeong and Yoon,
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2013; Shareef et al., 2018). For instance, through using an iPhone, the user can upload a
securely protected photo of his/her debit/credit card and use it in financial transactions and
payments without the need to hold physical cards anymore (Cellan-Jones, 2016; Kaymaz,
2017; Santander Bank, 2017). Furthermore, with a passcode and finger ID check, there is no
possibility of a third party stealing or penetrating the device (Apple Pay, 2017; Deloitte LLP
UK member firm of DTT, 2016; Martin, 2016). These multiple shields of privacy and
security have increased the reliance of users on their mobiles while doing m-banking and,
hence, maximised their continued intention to use an m-banking service (Bannister, 2014;
Barclays, 2017; Pay by Bank app, 2016; Rolfe, 2015).
M-banking technology comprises several kinds of benefits that really accelerate customers’
continued intention to use such technology. For instance, using m-banking helps customers to
access a wide range of banking transactions (i.e. balance enquiries, fund transfers, payment of
bills) without time and place restrictions (Lee et al., 2015; Lin, 2011; Lin, 2013). This, in
turn, reduces the associated cost and time of launching such banking services, and
accordingly, maximises the level of perceived usefulness in using m-banking (Alalwan et al.,
2016). Further, using m-banking could also help banks to reach customers who reside in a
particular area that has a weak Internet infrastructure or difficulties in establishing bank
branches (Cruz et al., 2010; Mishra and Bisht, 2013; Shareef et al., 2012; Shareef et al.,
2014). By the same token, due to the growth of m-banking espousal at an international level,
the literature of the information systems field has investigated the factors that increase the
continued intention to use m-banking (Afshan and Sharif, 2016; Akturan and Tezcan; 2012;
Alalwan et al., 2016; Isac, 2013).
On the other hand, there are a number of barriers that could hinder customers’ intention or
continued intention to use m-banking. For example, customers in developing countries such
as Saudi Arabia display habitual behaviour in accessing banking transactions via traditional
channels, and therefore, there is always difficulty in persuading such bank clients to use m-
banking (Laukkanen et al., 2007; Laukkanen and Cruz, 2009). Customers are also concerned
about security and privacy issues when they are in process of using m-banking. For instance,
problems related to the initial cost of using m-banking or having Internet access are reported
by Yang (2009), Yu (2012), and Hanafizadeh et al. (2014) to be the main barriers hindering
the customer’s willingness to use m-banking. Other m-banking researchers (i.e. Hanafizadeh
et al., 2014; Jeong and Yoon, 2013) have reported the negative impact of perceived risk in
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hindering the adoption of m-banking as well. Furthermore, customers usually do not have
adequate information, observability, and technical support to successfully use m-banking.
This is attributed to the weak role of banks in this regard (Cruz et al., 2010).
The m-banking service, as a ubiquitous phenomenon, has been extended to include users
within the context of Saudi Arabia. According to AVAYA (2017) and SyndiGate (2017),
24% of Saudi mobile users expressed their intentions to contact their financial institutions via
mobile apps. Furthermore, 40% of Saudi users prefer using their mobiles for all services
including m-banking. Also, in case of lack of security and privacy, 52% of Saudi m-banking
users would shift their m-banking use from one bank to another one. In addition, 43% of
them prefer contacting Virtual Financial Advisors (VFAs) through using m-banking for
renewing their card details. Moreover, through Vision 2030, the government of Saudi Arabia
has embraced and asserted the importance of information systems, including m-banking, in
easing the lives of Saudi people. Also, through the NEOM Project, with a budget of nearly
half a trillion US dollars on an area of 23000 km2, the Saudi government aims to create a
smart city on the national borders between Saudi Arabia, Jordan, and Egypt to be an
economic technological hub as enshrined by Crown Prince Mohammad Bin Salman. Thus, it
can be deduced that the current and future political direction of Saudi decision makers is to
increase the reliance of people on using information technology services including m-banking
(CNBC, 2017; Elnakat, 2017; Vision 2030, 2017).
Hence, it is a scientific as well as practical and societal necessity to try to identify factors that
influence the continued use of m-banking among users within the context of Saudi Arabia as
duly mentioned by Al-Husein and Sadi (2015), Al-Jabri and Sohail (2012), Alkhaldi (2016),
Al-Otaibi et al. (2018), Alsheikh and Bojei (2014) and Hidayat-ur-Rehman (2014), who have
examined these factors.
This study, however, is unique as it combines two models (i.e. the Technology Acceptance
Model (TAM) of Davis (1989) and the Task-Technology Fit (TTF) model of Goodhue and
Thompson (1995). In relation to the TAM, this study adopts two variables (i.e. perceived
usefulness (USF) and perceived ease of use (EOU)). With regard to the TTF model, this
study adopts three variables (i.e. task characteristics, technology characteristics, and TTF). In
addition, the study includes two independent factors (i.e. perceived privacy and perceived
security). This combination of the two theories and perceived security and perceived privacy
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variables aims to increase the prediction power of the continued intention to use m-banking
among Saudi users.
2. Literature Review
Identification of the factors which influence continuous intention to use m-banking has been
conducted through reliance on a number of theories in the Information Technology and
Information Systems field (Shaikh and Karjaluoto, 2015). For instance, Abbas et al. (2018),
Al-Husein and Sadi (2015), Chaouali et al. (2017), Mostafa and Eneizan (2018), and Mutahar
et al. (2018) implemented an extended TAM; Chawla and Joshi (2018) extended the TAM
and Innovation Diffusion Theory (IDT); Lee (2009) integrated Theory of Planned Behaviour
(TPB) and the TAM; Al-Jabri and Sohail (2012) and Kapoor et al. (2015) embraced IDT; Yu
(2012) embraced the Unified Theory of Acceptance and Use of Technology (UTAUT); and
Alalwan et al. (2017) adopted UTAUT2 with trust.
With regard to the TAM, there are extensive studies that used this model in order to examine
the factors that influence the continued intention to use m-banking in an international context
(e.g. Lee, 2009; Munoz-Leiva et al., 2018; Nasri and Charfeddine, 2012; Yeow et al., 2008).
In their systemic review study of 55 articles examining the adoption of m-banking, Shaikh
and Karjaluoto (2015) highlighted the fact that the TAM has been the most frequent model
applied as theoretical foundation to predict the customers’ intention to adopt m-banking. In
detail, as stated by Shaikh and Karjaluoto (2015), about 23 studies have employed the TAM
and its basic constructs: perceived usefulness and ease of use. Nevertheless, the TAM does
not cover the most important aspects which could be considered by customers in forming
their intention and decision to use m-banking (Shaikh and Karjaluoto, 2015). And therefore,
there are several different factors (i.e. relative advantage, personal innovativeness, perceived
risk, perceived cost of use, compatibility, awareness, lifestyle, and perceived security) that
have been proposed alongside with TAM constructs (see Shaikh and Karjaluoto, 2015).
In relation to the TTF model, a considerable number of studies have affirmed the power of
explanation the TTF model has on individual performance within the context of m-
technologies in general (e.g. Junglas et al., 2008), mobile information system studies (e.g.
Gebauer and Shaw, 2004; Junglas and Watson 2003; Malaquias et al., 2018; Mehrad and
Mohammadi, 2017; Liang and Wei 2004), and m-banking in particular (Gebauer et al., 2005;
Lee and Chung, 2009; Lin, 2012; Oliveira et al., 2014; Vatanasombut et al., 2008; Zhou et al.,
5
2010; Zhou et al., 2010). However, regarding the continued intention to use m-banking
within the context of Saudi Arabia, up to date, the application of TTF on m-banking in Saudi
Arabia has not been undertaken and, hence, using TTF as a predicting power for examining
continued intention to use m-banking is tackled in this study. Moreover, the issue of
perceived security as a key factor in influencing continued intention to use m-banking has
been thoughtfully examined in the literature (i.e. Haider et al., 2018; Jahangir and Begum,
2008; Kim et al., 2009; Linck et al., 2006; Nie and Hu, 2008; Sharma and Sharma, 2019).
Also, perceived privacy was indicated as an efficient predictive factor over continued
intention to use m-banking (e.g. Casalo et al., 2007; Choudrie et al., 2018; Jahangir and
Begum, 2008; Finn et al., 2013).
Furthermore, various studies tend to combine factors mentioned in two models or more in
order to achieve maximum power of prediction of the user’s continued intention to use m-
banking (e.g. Oliveira et al., 2014; Zhou et al., 2010, Zhou, 2018; Zoghlami et al., 2018). As
such, Zhou et al. (2010) combined UTAUT2 and TTF in order to examine m-banking. They
found that performance expectancy, social influence, and facilitating conditions from
UTAUT and TTF positively impact users’ continued intention to use m-banking.
Furthermore, Oliveira et al. (2014) combined three models (i.e. UTAUT, and TTF, and the
initial trust model (ITM)). After analysing the data of 194 users of m-banking within the
context of Portugal, they indicated that facilitating conditions and behavioural intention
positively influenced the continuous usage of m-banking. Furthermore, Yuan et al. (2016)
adopted a model that combined TTF, the TAM, and perceived risk into the expectance-
confirmation model (ECM) in order to measure the continuous intention to use m-banking
within the domain of China. After collecting and analysing the data from 434 m-banking
users who already have prior experience, the findings suggest that perceived TTF, perceived
usefulness, satisfaction, and perceived risk significantly influence users’ continuous intention
to use. Furthermore, they found that perceived USF is impacted by perceived EOU,
confirmation, and perceived TTF. Nonetheless, they found that perceived EOU does not
significantly influence users’ continuous intention to use m-banking.
A recent study conducted by Munoz-Leiva et al. (2018) has supported the role of both
perceived usefulness and perceived ease of use on customers’ attitudes toward m-banking.
However, Munoz-Leiva et al. (2018) stated that there was no impact of perceived usefulness
on customers’ intention to use m-banking. Another recent study by Chaouali et al. (2017)
6
asserted the positive role of customers’ attitudes in predicting customers’ willingness to adopt
m-banking in Tunis. In their longitudinal study to test the adoption of m-banking in Brazil,
Malaquias et al. (2018) empirically noticed that with the passage of time on the use of m-
banking, there is an improvement in the level of consumer perception toward aspects
pertaining to task characteristics, trust, ease of use, and social influence. More recently, three
factors from the IS success model (information quality, system quality, and service quality)
along with trust were proposed by Sharma and Sharma (2019) to predict the customer’s
intention to use m-banking. Sharma and Sharma (2019) found out that both customers’
intention and satisfaction were largely predicted by the role of service quality, information
quality, and trust. In their meta-analytic study, they supported the moderation role of power
distance on the relationship between social influence and trust with customers’ intention to
use m-banking. According to the same study of Zhang et al. (2018), individualism has a
significant role in moderating the relationship between factors such as performance
expectancy, effort expectancy, and perceived risk with customers’ intention.
Looking carefully at these studies leads the reader to notice the importance of the benefits
perceived in using m-banking as well as the extent to which m-banking is easy to use by
customers. In addition, according to the outcomes of these studies, it is important to consider
the aspects pertaining to characteristics of the financial tasks performed by m-banking as well
as characteristics of m-banking as a technology. Further, m-banking could expose banking
customers to a number of issues related to their own information and money privacy and
security. Such issues have been largely addressed either generally for online banking
channels (i.e. Internet banking) or specifically for m-banking. Therefore, there is always a
need to consider these aspects in one single model to see how the customer’s attitude,
intention, continued intention, or actual adoption could be shaped. More discussion in this
regard will be provided in the conceptual model section.
It is also worth mentioning that even though there are a good number of studies that have
tested the adoption of m-banking over developing countries (i.e. Malaysia, Singapore, Ghana,
Zimbabwe, and India) (see Shaikh and Karjaluoto, 2015), there are few attempts over the
Arab countries (i.e. Jordan and Saudi Arabia) (Alalwan et al., 2017; Baabdullah et al., 2019).
More specifically, within the context of Saudi Arabia, a small number of studies have focused
on the issue of online banking in general (e.g. Alabdan, 2017; Al-Ghaith et al., 2010; Al-
Malkawi et al., 2016; Al-Somali et al., 2009; Awan et al., 2016) and m-banking in particular
7
(i.e. Al-Husein and Sadi, 2015; Al-Jabri and Sohail, 2012, Alkhaldi, 2016; Al-Otaibi et al.,
2018; Alsheikh and Bojei, 2014; Hidayat-ur-Rehman, 2014).
In relation to the TAM, Al-Husein and Sadi (2015) adopted an extended TAM that includes
self-efficacy, quality of Internet connection, resistance to change, and trust. Their findings
show that behavioural intention significantly impacts the continued intention to use m-
banking within Saudi Arabia. In addition, Alkhaldi (2016) asserted the importance of
perceived security and perceived privacy factors as either deterrent factors that prevent
continued intention to use m-banking among Saudi users in cases of perceiving low levels of
security and privacy or attracting factors in cases of perceiving high levels of security and
privacy. In addition, the findings of Al-Husein and Sadi (2015), which derived from the
TAM, indicate that financial centres should emphasise their security features in order to
increase the security perceptions of users. This would result in aggregating the level of
continued intention to use m-banking within Saudi Arabia. Moreover, through relying on a
modified TAM, Alabdan (2017) found that the continued intention to use m-banking among
Saudi females is affected by a number of factors (i.e. trust, perceived security, user-friendly,
comfortable, and availability). Among the aforementioned factors, Alabdan (2017) indicated
that perceived security constitutes the most significant factor over continued intention to use
m-banking in Saudi Arabia.
Thus, within the context of Saudi Arabia, there are a few studies that have adopted the TAM,
perceived security, and perceived privacy; however, no study used TTF in order to predict
continued intention to use m-banking. In the following conceptual model section, the TAM,
TTF, perceived security, and perceived privacy will be discussed.
3. Conceptual Model
This study adopts a conceptual model that combines four distinctive factors (i.e. factors
belonging to the TAM, factors belonging to the TTF model, perceived privacy, and perceived
security) as seen in Figure 1.
<Figure 1 about here>
With regard to TTF, strictly speaking, there are two theories in the literature. The theory of
TTF was suggested by Goodhue and Thompson (1995) and argues that TTF is an essential
idiom in explaining and evaluating information system success. This theory is concerned with
8
a person’s adoption of information systems and established a predominantly positivistic
approach pertinent to information systems in common. Another theory is that of Zigurs and
Buckland (1998). It introduces a systematic profile for the combination of task technology
between Group Tasks and Group Support Systems. This theory concentrates on the adoption
of information systems by groups, rather than individuals, and develops a fit profile
applicable particularly to Group Support Systems. This study will consider the theory of
Goodhue and Thompson (1995) when examining the TTF model.
Indeed, the TAM considers only two main aspects regarding technology acceptance from the
individual perspective: perceived ease of use and perceived usefulness. This, in turn, restricts
the ability of the TAM to provide an accurate and comprehensive view about the main
aspects predicting the individual’s intention and actual adoption of new systems (Agrawal
and Prasad, 1999). Accordingly, it has usually been noticed that the TAM is weakly able to
predict adequate level of variance in both intention and actual adoption (see Legris et al.,
2003; Venkatesh et al., 2003; Venkatesh and Davis, 2000). This could be due to the fact that
the TAM model does not cover the impact of individuals’ characteristics, technological
characteristics, environmental impact, and the role of such factors (privacy and security)
(Alalwan et al., 2016; Venkatesh and Davis, 2000; Venkatesh et al., 2003). The main aspects
missing in the TAM has been covered in the TTF model, such as task characteristics and
technology characteristics, while customers’ belief toward aspects of ease of use and
usefulness is not covered in the TTF model but addressed by the TAM (Dishaw and Strong,
1999). All things considered have motivated the current study to have an expanded and
integrated model considering the TAM, the TTF model, and two other external factors:
perceived privacy and perceived security.
Regarding the justification of the integration of the TTF model and the TAM, it can be
indicated that this elaboration is provided by the arguments of Goodhue and Thompson
(1995) as they made a link between their models and the technology usage model of Bagozzi
(1982) (Dishaw and Strong, 1999). Bagozzi’s model, which resembles the TAM, was
constructed and developed based on attitude and behaviour models to interpret the utilisation
of technology (Dishaw and Strong, 1999).
The common argument for putting the TTF model and the TAM into one model is that each
one of these models captures two distinctive facets of consumers’ selections to utilise
information systems (Dishaw and Strong, 1999; Klopping and McKinney, 2004). As such,
9
the TAM presumes that attitudes and beliefs of the consumers regarding certain information
technology extensively determined whether consumers show the behaviour of utilising the
information technology (Goodhue, 1995; Shaikh et al., 2018). The TTF model, however,
assumes that users tend to utilise information technology to get benefits such as enhancing
their job performance (Lee et al., 2007).
That is to say, users tend repeatedly to adopt information technology when it boosts their
work even if they do not like it or have negative attitudes towards it (Lee et al., 2007). From
this comparison, it can be deduced that the TTF model adopts an approach that is decidedly
rational (Goodhue, 1995). This approach is based on the utilisation of the information system
to satisfy job performance rather than considering the attitudes of users towards the
technological service (Klopping and McKinney, 2004). Accordingly, choices of the users to
utilise an information technology service is impacted by two issues (i.e. their beliefs as duly
mentioned by the TAM and their rationale as assumed by the TTF model).
Based on the above-mentioned argument, combining the TAM and TTF model is likely to
give a robust explanation on the utilisation of information technology, and this explanation is
higher in its accuracy when comparing it with the one that is based on either the TAM or TTF
model (Klopping and McKinney, 2004). In addition, this study added perceived security and
perceived privacy to the conceptual model due to their substantial roles in sensitive
technological services that are related to financial dealings such as m-banking (e.g. Alabdan,
2017; Alkhaldi, 2016; Casalo et al., 2007; Finn et al., 2013; Khasawneh et al., 2018; Linck et
al., 2006; Mashhour and Saleh, 2015).
3.1 Task Characteristics (Task.C) – Task-Technology Fit (TTF)

Task means “actions” fulfilled by persons in order to turn input into output. Task
characteristics include “those [actions] that might move a user to rely more heavily on certain
aspects of information technology” (Goodhue and Thompson, 1995, p. 216). There are two
main constructs for identifying task characteristics (i.e. “task equivocality” and “task
interdependence”) (Goodhue and Thompson, 1995, p. 235). Task equivocality aims at dealing
with ill-defined business difficulties, ad hoc non-routine business difficulties, and answering
new queries in work, while task interdependence focuses on dealing with multi-function
business problems (Goodhue and Thompson, 1995). In the model of Fit Focus, Goodhue and
Thompson (1995) found that task characteristics positively impact TTF in the field of
information technology. With regard to the context of mobile technology, Gebauer et al.
10
(2010) indicated that dependency on task characteristics is determined through three
parameters (i.e. time, location, and identity). As such, in the context of m-banking when tasks
are deeply time-critical, the fit focus of the users would be high when using their mobiles
(Zhou et al., 2010). This means that users create a high level of TTF (Zhou et al., 2010).
Furthermore, when the characteristic of the task requires a high level of mobility, using m-
technologies would be optimal for doing such tasks as it increases focus on the task (Oliveira
et al., 2014). Thus, there is a fit that exists between tasks that is required mobility and using
mobiles. Accordingly, task characteristics do positively impact TTF (Gebauer, 2008). Also,
in m-banking when the identity of the user does matter for fulfilling the task, the importance
of mobile devices would increase as it plays a role in determining the user’s identity (Afshan
and Sharif, 2016; Oliveira et al., 2014). A prominent example is the recent production of
facial recognition technology in iPhone 8 and 8 Plus which might be decisive in future
dealing with transactions that are done via mobiles (Dormehl, 2017). Oliveira et al. (2014)
and Zhou et al. (2010) asserted the positive role of task characteristics on TTF as it enables
the users of an m-banking service to manage their accounts anytime, transfer anytime
elsewhere, and have real time control over their accounts as their financial transactions are
urgent. Hence, this study proposes that:
H1: Task characteristics have a positive influence on TTF when using m-banking among
consumers within the context of Saudi Arabia.
3.2 Technology Characteristics (Techno.C) – Task-Technology Fit (TTF)

Technology is defined as “tools” adopted by persons in carrying out their tasks (Goodhue and
Thompson, 1995, p. 216). While Goodhue and Thompson (1995) indicated that technology
referred to computers as their work is relatively old, Oliveira et al. (2014) and Zhou et al.
(2010) indicated that it referred to mobiles. Technology characteristics refer to the traits of
technological devices or services used by users in order to fulfil their tasks (Afshan and
Sharif, 2016). According to Goodhue and Thompson (1995), technology characteristics
significantly influence TTF in the Utilisation Focus Model, where utilisation is about the way
in which the jobs are designed and not about the usefulness or quality of the adopted
information system. There are a number of studies that investigated the effect of technology
characteristics over TTF within the domain of m-banking (e.g. Oliveira et al., 2014; Tam and
Oliveira, 2016; Yuan et al., 2016; Zhou et al., 2010). Tam and Oliveira (2016) analysed the
data from 256 participants, and they found that technology characteristics affect TTF among
the users of m-banking in Portugal. According to Zhou et al. (2010), Oliveira et al. (2014),
11
and Yuan et al. (2016), technology characteristics in m-banking impact the TTF, which
results in the continued intention to use m-banking technology. Based on the aforementioned
discussion, this study suggests that:
H2: Technology characteristics have a positive influence on TTF when using m-banking
among consumers within the context of Saudi Arabia.
3.3 Technology Characteristics (Techno.C) – Perceived Ease of Use (EOU)

EOU is influenced by the technology characteristics, which include tool experience and tool
functionality (Dishaw and Strong, 1999). In detail, increasing the level of functionality would
result in making the service easier to use (Yuan et al., 2016). Also, as consumers gain greater
experience from their use of the service, the technological service is likely to be easier to use
(Dishaw and Strong, 1999). Furthermore, according to Pagani (2006), technology
characteristics can positively impact EOU of users when using high-speed data services via
mobiles. Within the context of m-banking, Zhou et al. (2010) found that technology
characteristics affect consumer effort expectancy, which resembles EOU. Therefore, this
study assumes that:
H3: Technology characteristics have a positive influence on users’ EOU when using m-
banking within the context of Saudi Arabia.
3.4 Task-Technology Fit (TTF) – Perceived Usefulness (USF)

If a technological service gives a high level of fit with the task, users would perceive that the
adopted service is useful for fulfilling the proposed task (Goodhue and Thompson, 1995).
Thus, the positive effect of TTF on USF is derived directly from the definitions of both
concepts. A number of studies have found that TTF positively influences USF within the
context of mobile technologies (e.g. Chang, 2008; Klopping and McKinney, 2004; Pagani,
2006). According to Yuan et al. (2016), TTF positively impacts perceived USF of Chinese
users. Based on this analysis, this study suggests that:
H4: Task-Technology Fit (TTF) has a positive influence on users’ USF when using m-
banking within the context of Saudi Arabia.
3.5 Task-Technology Fit (TTF) – Continued Intention (CI)

The rationale behind using a technological service is that it helps users in performing their
tasks well, and this in turn would motivate users to continue using it (e.g. Goodhue and
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Thompson, 1995; Klopping and McKinney, 2004). Accordingly, a high level of TTF would
sustain a higher level of continuous intention to use a technological service (e.g. Afshan and
Sharif, 2016; Chang, 2008; Pagani, 2006). Within the context of m-banking, Zhou et al.
(2010) indicated that TTF positively affects users’ continuous intention to use m-banking.
Furthermore, Tam and Oliveira (2016) analysed the data from 256 participants, and they
found that TTF is an essential precedent that impacts on users’ continued intention to use m-
banking, particularly when the path of TTF-continued intention to use m-banking is
moderated by age. Furthermore, Yuan et al. (2016) found that TTF positively and
significantly impacts the continued intention to use m-banking in China. Based on this
discussion, this study presumes that:
H5: Task-Technology Fit (TTF) has a positive influence on users’ continuous intention when
using m-banking within the context of Saudi Arabia.
3.6 Perceived Usefulness (USF) – Continued Intention (CI)

USF is defined as “the degree to which a person believes that using a particular system would
enhance his or her job performance” (Davis, 1989, p. 320). Kim et al. (2009) suggested that a
person often evaluates the consequences of their actions and then establishes a selection
reliant on the favourability of perceived USF. Accordingly, USF would impact an
individual’s intention to use m-banking as a technological service (Dwivedi et al., 2017b;
Gumussoy et al., 2018; Williams et al., 2015). Within the domain of m-banking, persons tend
to adopt it when perceiving the usefulness of the service for their financial transactions as
well as saving their time (Gu et al., 2009; Shaikh and Karjaluoto, 2015; Zhou, 2012).
Moreover, banks tend to use m-banking as it would enable them to reduce their physical
branches and staff workforce, which in turn reduces their costs (Jeong and Yoon, 2013;
Mashhour and Saleh, 2015; Yuan et al., 2016). Within Saudi Arabia, a number of studies
affirmed the positive effect of USF on intention to use m-banking among consumers (e.g. Al-
Ghaith et al., 2010; Al-Jabri and Sohail, 2012). Accordingly, this study supposes that:
H6: Perceived usefulness has a positive influence on users’ continuous intention when using
m-banking within the context of Saudi Arabia.
3.7 Perceived Ease of Use (EOU) – Continued Intention (CI)

EOU is defined as “the degree to which a person believes that using a particular system
would be free of effort” (Davis, 1989, p. 320). A considerable number of studies have
13
indicated that EOU positively impacts the user’s continuous intention to use m-banking (e.g.
Crabbe et al., 2009; Gu et al., 2009; Laforet and Li, 2005; Mashhour and Saleh, 2015; Priya
et al., 2018; Richard and Mandari, 2018). According to Jeong and Yoon (2013), perceiving
an m-banking service as easy to use indicates that users felt free from challenge and free from
great effort when using it. In contrast, users would denounce continuing use of m-banking
when finding that the updates or changes in the service make using it either difficult to learn
and/or not easy to use (Hanafizadeh et al., 2014). Therefore, information about m-banking,
such as its benefits and guidelines as well as service details, are required to be given in order
to make it easier for the users to continue using it (Dwivedi et al., 2016; Dwivedi et al.,
2017c; Kim et al., 2009; Rana et al., 2017; Shareef et al., 2012). Within the context of Saudi
Arabia, a number of studies examined the positive role of EOU on continuous use of m-
banking (e.g. Al-Somali et al., 2008; Sohail and Al-Jabri, 2014). Based on the
aforementioned discussion, this study suggests:
H7: Perceived ease of use has a positive influence on intention to use m-banking among
3.8 Perceived Security (SR) – Continued Intention (CI)

Perceived security is defined as “the extent to which a consumer believes that making
payments online is secure” (Vijayasarathy, 2004, p. 748). According to Casalo et al. (2007),
perceived security is a two-dimensional construct. Firstly, it includes the perceptions of users
about the ways of dealing with and protecting personal data in issues of financial services
such as m-banking. Secondly, it includes the security of the information technology used in
these financial services. With regard to mobile payments, Linck et al. (2006) delineated a
division of the idiom of security into subjective and objective security. While objective
security addresses the issues of authorisation, confidentiality, integrity, authentication, and
non-repudiation, subjective security is perceived security, and it expresses the level to which
a user believes that adopting a certain mobile payment method or using m-banking would be
secure. Distinctive studies have affirmed the importance of the role of perceived security on
continuous intention of customers to use m-banking (e.g. Gu et al., 2009; Laukkanen, 2007;
Mashhour and Saleh, 2015). Moreover, studies by Al-Husein and Sadi (2015) and Alkhaldi
(2016) focused on the path of perceived security and the user’s intention to use in Saudi
Arabia. Hence, this study proposes that:
14
H8: Perceived security has a positive influence on intention to use m-banking among
3.9 Perceived Privacy (PV) – Continued Intention (CI)

Perceived privacy is defined as the “user’s perceptions about the protection of all the data that
is collected (with or without users being aware of it) during users’ interactions with an
Internet banking system” (Wang et al. 2003, p. 503). It explains the view of users about the
facet of an information system that focuses on the capacity an individual possesses to
determine the type of electronic data that can be shared with third parties (Dwivedi et al.,
2017a; Luarn and Lin, 2005). Perceived privacy affects the process of processing, storing,
and distributing the electronic information, and this in turn impacts on the intention of users
to adopt m-banking privacy (Finn et al., 2013). Indeed, when it comes to financial
transactions, privacy becomes of utmost importance in affecting the continuous behaviour of
persons as it affects the privacy of the person, the behaviour and action, communication, data
and image, thoughts and feelings, location and space, and association (Finn et al., 2013). That
is to say, a low level of privacy enables a third party to know the ways in which the m-
banking user spends his/her money, his/her debit and credit. Hence, a low level of privacy
would deter the continuous use of m-banking as duly mentioned by Akturan and Tezcan
(2012). Various studies have highlighted the role of privacy over continuous intention to use
m-banking (e.g. Laukkanen, 2007). Within the context of Saudi Arabia, Alsheikh and Bojei
(2014) examined the significant role of privacy on users’ intention to use m-banking. Based
on the previous discussion, this study suggests that:
H9: Perceived privacy has a positive influence on intention to use m-banking among
4. Research Methodology
A field survey using a self-administered questionnaire as a data collection instrument was

adopted to collect the required data from a convenience sample of Saudi banking customers
from four main cities of the KSA (Riyadh, Jeddah, Mecca, and Medina). In spite of the fact
that using the probability sampling technique could avoid a number of problems in terms of
sampling bias and results generalisability (Bhattacherjee, 2012), using this kind of probability
samples was restricted and difficult in the current study. For instance, it was difficult to
capture an inclusive and recent list of all the users of m-banking technology in the KSA. This
15
is one of the most fundamental elements required in the sample frame to use the probability
sampling technique (Bhattacherjee, 2012; Dwivedi et al., 2006). Banks in Saudi Arabia never
allow information (i.e. contact numbers, addresses, emails) to be given regarding their
customers. All things considered, the convenience sampling technique was selected as a more
appropriate technique for the current study population (Castillo, 2009; Dwivedi et al., 2006).
As for the four cities considered in the current study, Riyadh, Jeddah, Mecca, and Medina are
the largest cities in the KSA respectively. The actual population size of these cities are as
follows: Riyadh: 6,506,700; Jeddah: 3,976,400; Mecca: 1,919,900; and Medina: 1,271,800
(Worldatlas, 2018). Further, these cities geographically represent all parts of the KSA.
Riyadh is the political capital in the south; Jeddah is in the east of the KSA; Mecca is the
capital city of the Hejaz province in the KSA; and Madina is the fourth largest city in the
KSA (Worldatlas, 2018).
This study was conducted over the period from December 2017 till the end of February 2018.
As the study aims to test customers’ continued intention to use m-banking, it targeted the
actual users of m-banking who have used such a system. Importantly, it also considered the
m-banking services delivered using specific kinds of mobile applications provided by banks.
Five hundred banking customers who have used m-banking were allocated questionnaires
using several techniques. A large number of those participants were approached by their
banks while others were accessed at their work places (Dwivedi et al., 2006).
The questionnaire consists of three parts (i.e. cover page (research’s aim and researchers’
information), participant’s demographic profile (age, gender, occupation and income) and
constructs’ items derived from the relevant literature (30 scale items) - See Appendix. For
example, both perceived USF and EOU were measured using items extracted from Davis et
al. (1989). Items of the TTF model constructs were adopted from Zhou et al. (2010) and Lin
and Huang (2008). The main items of privacy and security were derived from Vijayasarathy
(2004) and Casalo et al. (2007). As Arabic is the main language in the KSA, the
questionnaire was translated from English to Arabic using the back-translation method
suggested by Brislin (1976). Then, the Arabic version of the questionnaire was judged by a
panel of experts at the department of Management Information System (MIS) and marketing
in the King Abdulaziz University who are fluent in both Arabic and English (Dwivedi et al.,
2006). After that, a pilot study with 30 master students at the Business School in the King
Abdulaziz University was conducted to see if there was any problem regarding the language
used as well as to test the factors reliability (Dwivedi et al., 2006). Most participants
16
mentioned that the language used was clear and there were no vague sentences. Factors
reliability was tested using Cronbach’s alpha as suggested by Nunnally (1978), and all values
were found to be higher than 0.70 (Nunnally, 1978).
4.1 Treatment of Missing Data

As recommended by Churchill (1995), the amount of missing data was examined in addition
to their distribution pattern. The yielded results showed that the percentage of the missing
values for each construct was within the recommended level (<5%). Furthermore, the p value
(p = .785) of missing completely at random (MCAR) was non-significant (Churchill, 1995).
This ensures the random pattern of the missing data values (non-systematically distributed)
(Little, 1988). Accordingly, all the missing data values were filled by the mean value of each
variable (Hair et al., 2010; Tabachnick and Fidell, 2007).
5. Results
5.1 Response Rate and Participants’ Characteristics

Out of the 500 questionnaires that were allocated, 352 were returned. Out of these, 320
questionnaires were found to be completed and valid, and accordingly these 320 that were
subjected to further analyses. As seen in Table 1, among the 320 participants, 189 of them are
male (59.1%) while 131 are female (40.9%). The largest number of participants (215; 67.2%)
is noted to be within the age group of 21-29 followed by the age group 30-39 (44; 13.8%).
About 178 (55.6%) of the respondents are public sector employees while 60 (18.8%) are
private sector employees. One hundred and forty (43.8%) have monthly income ranging
between 8001 and 14000 Saudi Riyals followed by those who have monthly income between
4001 and 8000 Saudi Riyals.
<Table 1 about here>
5.2 Structural Equation Modelling

Two stages of structural equation modelling (SEM) were applied using AMOS 22. In the first
stage, measurement model, confirmatory factor analyses (CFA) were tested to ensure
adequate level of model fitness as well as to attain construct reliability and validity. Then, the
proposed model and research hypotheses were targeted in the second stage: structural model
analyses.
The CFA was tested firstly, and the initial fit indices of the measurement model (i.e.
CMIN/DF was 3.214, GFI= 0.851, AGFI= 0.751, NFI= 0.894, CFI= 0.905, and RMSEA=
17
0.085) indicated that the model has poor fit (see Table 2). Therefore, the main problematic
items were removed from the revised version of the measurement model (see Table 3). These
problematic items were found to be as follows: one item from SR and two items from PV. All
of these items were found to have a factor loading less than 0.50 and accordingly were
dropped from the revised version of the measurement model. The fit indices of the revised
measurement model were found to be as follows: CMIN/DF= 2.365, GFI= 0.912, AGFI=
0.861, NFI= 0.947, CFI= 0.974, and RMSEA= 0.064. This proved the goodness of fit of the
measurement model at this time (Anderson and Gerbing, 1988; Hair et al., 2010).
As seen in Table 4, all constructs were found to have composite reliability (CR) value higher
than 0.70 as suggested by Hair et al. (2010). The highest value of CR (0.985) was recorded
for Techno.C whereas the lowest one (0.821) was for Task.C. As for the average variance
extracted (AVE), all constructs have also been recorded as having an acceptable value higher
than 0.50 as suggested by Hair et al. (2010). The AVE values range from 0.606 for Task.C to
0.956 for Techno.C. Finally, the discriminant validity was ensured in the current study as all
inter-correlation values between constructs are less than the squared root of AVE value of the
targeted construct (Kline, 2011).
At the second stage, the structural model was tested to see the fitness of the proposed model
to the observed data and to verify the main causal relationships hypothesised. Statistically,
the model seems to adequately fit the observed data as all fit indices were observed to be
within their cut-off values (CMIN/DF= 2.932, GFI= 0.906, AGFI= 0.842, NFI= 0.934, CFI=
0.961, and RMSEA= 0.068). As seen in Figure 2, the R2 values were found as such: .58 for
continued intention, .48 for TTF, .31 for usefulness, and .35 for ease of use. This, in turn,
supports the predictive validity of the current proposed model.
<Figure 2 about here>
The main results of path coefficient analyses are presented in Table 5. Accurately, USF (γ=
0.49, p<0.000), TTF (γ= 0.25, p<0.000), SR (γ= 0.21, p<0.000), and PV (γ= 0.15, p<0.000)
significantly predict intention to continue to use m-banking. TTF was found to be strongly
affected by Task.C (γ= 0.38, p<0.000) and Techno.C (γ= 0.12, p<0.028). TTF has a positive
influence on USF (γ= 0.48, p<0.000). Techno.C also significantly predicts EOU (γ= 0.60,
18
p<0.000). However, EOU (γ= 0.10, p<0.135) was not able to have a significant impact on the
intention to continue use. Accordingly, all research hypotheses, H1, H2, H3, H4, H5, H6, H8,
and H9, are accepted while H7 alone was rejected.
6. Discussion
As presented in the prior section, ‘Results’, this study was able to empirically support what
has been discussed and proposed in the conceptual model. Researchers were keen to clarify
how Saudi bank customers are interested in continuing to use m-banking as well as discover
the main factors that could shape the perception of Saudi bank customers toward such
innovative technology. The statistical results largely supported the predictive validity of the
current study model. For example, about .58, .48, .31, .35 of variance were accounted
continued intention, TTF, USF, and EOU sequentially. Such values were found to be more
than the .30 that was suggested by Arambewela and Hall (2009), Holmes-Smith et al. (2006),
and Kline (2011), and also these values are very similar to other studies that have tested the
adoption of m-banking such as Foon and Fah (2011), who accounted .56 of variance in
behavioural intention to use m-banking and Zhou et al. (2010), whose model predicted about
.57 of variance in the adoption behaviour.
Such results extracted in the current study largely support the selection of both the TTF
model and the TAM to propose the current study model. This also gives clues that such
integration between the TTF model and the TAM could provide an accurate picture regarding
the continued intention to adopt m-banking in the KSA as a developing country. Furthermore,
excluding the path between EOU and CI, all proposed paths discussed in the conceptual
model and literature review have been supported. In the following paragraphs, the current
study results regarding each hypothesis are discussed more in light of the current study data
as well as what has been discussed over the literature review and proposed in the conceptual
model.
According to path coefficient analyses, perceived USF was observed to be the strongest
factor predicting Saudi customers’ continued intention to use m-banking. This means that
Saudi customers largely value m-banking as a more useful and productive technology making
their interaction with their banks much easier and more efficient. One of the main reasons
behind this is the nature of m-banking as movable technology enabling customers to access a
wide range of banking services without any time and place restrictions. To put it differently,
19
as a kind of mobile technology, m-banking is enjoyed with a high level of mobility and
ubiquity in comparison with other online or traditional banking channels. This means that m-
banking enhances customers’ ability to access banking services whenever and wherever they
need. Accordingly, by using m-banking, customers are more able to save their time and effort
in comparison to the traditional way of accessing m-banking via human encounter. In fact,
there are a wide range of financial services (i.e. balance enquiries and downloading bank
statements, funds transfer, and paying bills) that customers can access 24/7 from anywhere.
Further, as mentioned in the introduction section, the nature of m-banking, which supports
the consumer in relying on himself/herself and without any human interaction, also supports
the level of privacy and security. Such results of USF are very similar to those reached by
prior m-banking studies (i.e. Hanafizadeh et al., 2014; Wessels and Drennan, 2010).
TTF was also the second largest significant factor predicting Saudi customers’ continued
intention to use m-banking. This is in addition to the significant impact of TTF on USF. As
long as m-banking can provide the targeted users with an adequate and high-quality level of
functions and tasks that match their requirements, they are more likely to perceive using such
technology as more useful in their daily life and accordingly they will be more interested in
using this technology in future (Dwivedi et al., 2015). Indeed, the statistical results of the
current study indicate that Saudi customers see m-banking as having a higher level of TTF,
which positively reflects on their perception and intention to use m-banking in future.
Different studies have extracted similar results to these attained in the current study (i.e.
Chang, 2008; Klopping and McKinney, 2004; Pagani, 2006; Zhou et al., 2010).
The task characteristics construct was shown to have a significant impact on the TTF as
presented in the results section. The main dimensions of any functions that customers need to
accomplish regarding banking transactions will directly reflect on the TTF of the technology
that helps customers to perform these functions. In fact, with the pressure of time and
responsibilities, people seem to be more engaged with their banking. More specifically,
customers currently need to have an interactive and constant communication with their banks.
And accordingly, they are demanding more facilities to access banking services. This is in
addition to the sensitive necessity for financial transactions that customers need to conduct
continuously. In this respect, and according to Tam and Oliveira (2016) and Zhou et al.
(2010), customers’ requirements to manage, control, and conduct banking services whenever
and wherever they are largely shape their perception of TTF related to using m-banking
technology.
20
Technology characteristics were found to be a very crucial factor in the conceptual model as
both TTF and EOU are largely influenced by this construct. As discussed above, compared
with other bank channels (human encounter, ATM, and Internet banking) (Hanafizadeh et al.,
2014), m-banking has several features that make customers more attracted to such a system
and more positively value it either in terms of EOU or of TTF. For instance, there is the
ubiquitous nature of m-banking that enables customers to access a wide range of banking
services, at the time needed, quickly and safely. This, in turn, positively reflects the quality of
the functions performed using m-banking as well as the usefulness and ease of use perceived
in such technology. Such results are consonant with these found by Oliveira et al. (2014),
Tam and Oliveira (2016), Yuan et al. (2016), and Zhou et al. (2010).
Both privacy and security were observed to have a significant impact on Saudi customers’
continued intention to use m-banking. To put it differently, banking customers in the KSA
seem to be more concerned regarding their own privacy and the level of security when they
are in the process of using m-banking applications. This could be attributed to the sensitive
nature of financial transactions overall and especially those conducted via online applications
such as m-banking (Alalwan et al., 2017; Dwivedi et al., 2017a). Therefore, customers will
pay more attention to tools and mechanisms to securely undertake financial transactions using
mobile technology (Casalo et al., 2007; Vijayasarathy, 2004). Some aspects, such as
authorisation, confidentiality, integrity, authentication, and non-repudiation, are very
important in m-banking applications to let customers perceive that using these systems is
secure and safe enough (Gu et al., 2009; Laukkanen, 2007; Mashhour and Saleh, 2015). By
the same token, in order to use m-banking, the customer is requested to disclose important
and personal information. This, in turn, could raise the customer’s concerns regarding their
own privacy especially due to the fact that some data could be shared with third parties
(Luarn and Lin, 2005). Theoretically, there are a good number of studies that have supported
the role of both security and privacy in customers’ continued intention to use m-banking (i.e.
Akturan and Tezcan, 2012; Finn et al., 2013; Laukkanen, 2007; Luarn and Lin, 2005).
In contrast with what has been proposed regarding the role of ease of use, Saudi customers
seem to be not concerned about the level of easiness or difficulty in using m-banking. This
could simply be due to the fact that m-banking has been increasingly becoming more adopted
and technologies have been becoming better known as well as customers being more
experienced in interacting with m-banking. This is in addition to the fact that the current
study participants are actual users of m-banking and enjoy a certain level of experience in
21
using it. Thus, they are less concerned regarding any difficulties that could be found in using
m-banking. Moreover, even if there are some difficulties in using it, customers are more
interested in using such systems as long as they highly value m-banking in terms of
usefulness and TTF as for participants of the current study sample (Castañeda et al., 2007;
Davis et al., 1989; Venkatesh et al., 2003; Venkatesh et al., 2012; Wang et al., 2006; Wessels
and Drennan, 2010). Over the prior literature, there are a number of studies that have
disproved the role of ease of use or similar factors such as effort expectancy, for example
Brown et al. (2003), Wessels and Drennan (2010), Wu and Wang (2005), Yu (2012), and
Zhou et al. (2010).
6.1 Theoretical and Practical Implications

This study early noted the importance of discovering and testing the main dimensions that
could shape customers’ perception and continued intention to use m-banking in the KSA. As
discussed in the introduction section, the related issues of m-banking have received less
attention by researchers over the developing countries especially in the KSA. Thus, this study
represents a worthy attempt to test such technology (m-banking) in the KSA where there is a
scarcity of literature. In fact, most studies that have tested the related issues of m-banking
adoption simply considered the potential adopters and their intention toward such adoption.
Other studies have considered the current usage behaviour. However, customers’ continued
intention has rarely been tested using a solid theoretical foundation considering models such
as the TTF model and the TAM. Therefore, a considerable theoretical contribution was made
by integrating the TTF model with the TAM in addition to considering privacy and security
in one single model. In detail, careful reviewing of the current literature of m-banking leads
to noticing a need to discover what the key mechanisms that could shape the customer’s
perception toward the functionality and fitness of the technology. This has been covered in
the current study by testing the impact of task characteristics and technology characteristics
on the TTF.
Furthermore, the current literature of m-banking does not cover well the role of TTF in
shaping the customers’ perceived usefulness. This study accordingly comprises another
contribution by proposing and empirically validating such relationship between TTF and
USF. Further, this study proposes a new relationship between technology characteristics and
perceived EOU. This relationship has been less examined in the prior studies, which, in turn,
comprises another contribution made by the current study. Moreover, considering both
perceived privacy and security in the current model creates an accurate picture about the
22
adoption of m-banking especially as there are a limited number of m-banking studies that
have considered privacy and security alongside the TTF model and the TAM in the same
model.
Practically, this study could offer marketers and service providers clues that could help them
to improve the key aspects that are considered by Saudi customers with respect to m-banking.
For instance, the study supports that role of privacy and security, and accordingly, marketers
and m-banking service providers have to focus more on enhancing the customer’s perception
that using m-banking is a safe channel to access banking services. In this regard, different
mechanisms (biometric technology, authentication, and encryption) could be used by banks
to ensure the level of security in any banking transaction conducted using m-banking. As well
as this, banks should ensure the level of privacy related to any information provided by
customers in order to use m-banking. Therefore, banks have to express their concern
regarding the privacy of information provided by customers. In this respect, banks also have
to ensure their moral and legal system is protecting the customer’s personal information.
Indeed, banks’ attitude to the importance of privacy and security in m-banking services will
also contribute to the customer’s perception for the related issues of TTF.
Results of the current study also show the importance of technology characteristics in shaping
the customer’s perception toward ease of use and TTF. Thus, marketers and m-banking
designers have to pay more attention to the main aspects of mobile technology. For example,
the ubiquitous nature of mobile technology should be more accelerated to guarantee that
customers can access m-banking services from wherever they are. This could call banks to
collaborate more with mobile service providers to maintain their networks and geographical
coverage. Banks also should ensure the quality and reliability of m-banking services as well
as fix any problem that could hinder accessibility to such services. A m-banking platform
with a friendly and simple design could also help with quick and easy use of m-banking
services. Task characteristics were also the focus of attention of customers. Marketing
campaigns should accelerate the customer perception that m-banking is a more flexible and
adaptable channel to access banking services in comparison with traditional banking
channels. Further, customers could have more controllability via using m-banking. It could be
important to ensure the fit of m-banking services to the customer’s requirements and needs.
This could be enhanced by providing a wide range of banking services through m-banking as
well as by ensuring its appropriateness to the new customer’s lifestyle and work needs.
23
7. Conclusion
The related issues of m-banking have received little attention by researchers in the KSA.
Therefore, this study was conducted to fill this gap and to provide researchers and
practitioners with clues about the main dimensions that have to be considered to guarantee a
better understanding about factors predicting the customer’s intention and adoption of m-
banking, and accordingly, having a successful implementation for such technology.
Therefore, based on a critical review of the main body of literature either in m-banking or in
the IS area overall, the researchers have successfully proposed an integrated model from the
TAM and TTF model which was also expanded by including privacy and security. This study
examines customers’ continued intention to use m-banking; therefore, the actual users of m-
banking were targeted in the current study sample. Data was collected using a convenience
sample of m-banking users from four main cities of the KSA (Riyadh, Jeddah, Mecca, and
Medina). The data was then tested using SEM, and the main results largely support the
predictive validity of the current study model. Accurately, TTF, USF, SR, and PV were able
to predict about .58 of variance in the customers’ continued intention to use m-banking.
Based on the current results, this paper has discussed the main theoretical and practical
implications as presented in the prior section.
8. Research Limitations and Future Directions

Even though this study represents a valuable attempt to address the adoption of m-banking
over emerging countries, there are still a number of concerns that restrict it. For example, the
moderation influence of the demographic factors (i.e. age, gender, income level, educational
level) is not tested. Thus, it could be more useful to see how the current study model yields
different indicators by considering the moderation influence of the demographic factors
(Haider et al., 2018). The study also does not consider the impact of personal and
psychological factors (i.e. innovativeness, self-efficacy, perceived behavioural control, need
for interaction). Accordingly, future studies could add value by testing these aspects. As a
consumer technology, the role of hedonic motivation and price value is very critical, yet it
was not considered in the current study. It would be very important to consider these hedonic
and monetary aspects in future studies. The data was collected using a self-report
questionnaire; however, it would be more accurate to utilise more statistics from the bank
database about the users of m-banking. Finally, this study just examined the customers’
continued use of m-banking; however, the outcomes of using m-banking on the customers’
24
actual use behaviour and customers’ satisfaction would provide a comprehensive picture
about the successful implementation of m-banking technology in the KSA.
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34
Table 1: Participant Demographics
Participants’ Characteristics Frequency Percent
1000-4000 24 7.5
4001-8000 71 22.2
8001-14000 140 43.8
Monthly income (Saudi Riyals)
14001-20000 60 18.8
More than 20000 25 7.8
Total 320 100.0
Student 41 12.8
Government employee 178 55.6
Occupation Private sector employee 60 18.8
Self-employed 41 12.8
Total 320 100.0
Gender Male 189 59.1
Female 131 40.9
Total 320 100.0
Age >= 18-20 37 11.6
21-29 215 67.2
30-39 44 13.8
40-49 20 6.3
50 and above 4 1.3
Total 320 100.0
Table 2: Results of Measurement Model

Fit indices Cut-off point Initial measurement model Modified measurement model
CMIN/DF ≤3.000 3.214 2.365
GFI ≥ 0.90 0.851 0.912
AGFI ≥ 0.80 0.751 0.861
NFI ≥ 0.90 0.894 0.947
CFI ≥ 0.90 0.905 0.974
RMSEA ≤ 0.08 0.085 0.064
35
Table 3: Standardised Regression Weights
Construct Measurement Items Estimate Construct Measurement Items Estimate
USF USF1 .902 PV PV1 .887
USF2 .835 PV2 .706
USF3 .835 PV3 .848
USF4 .789 Techno.C Techno.C1 .983
SR SR1 .994 Techno.C2 .971
SR2 .853 Techno.C3 .979
SR3 .594 EOU EOU1 .870
Task.C Task.C1 .823 EOU2 .870
Task.C2 .687 EOU3 .885
Task.C3 .817 EOU4 .908
TTF TTF1 .955 CI CI1 .919
TTF2 .755 CI2 .907
TTF3 .661 CI3 .904
TTF4 .939
Table 4: Construct Reliability and Validity

CR AVE SR Task.C TTF CI PV USF Techno.C EOU
SR 0.865 0.689 0.830
Task.C 0.821 0.606 0.125 0.778
TTF 0.901 0.700 0.165 0.280 0.837
CI 0.935 0.828 0.448 0.407 0.559 0.910
PV 0.857 0.668 0.163 0.129 0.164 0.321 0.817
USF 0.906 0.708 0.220 0.264 0.548 0.701 0.207 0.841
Techno.C 0.985 0.956 0.265 0.151 0.193 0.370 0.108 0.214 0.978
EOU 0.934 0.780 0.138 0.242 0.244 0.366 0.094 0.401 0.594 0.883
Note: Diagonal values are squared roots of AVE; oﬀ-diagonal values are the estimates of inter-correlation
between the latent constructs.
36
Table 5: Results of Standardised Estimates
Hypothesised path Standardised estimate P-value Significance
USF→ CI 0.49 *** Sig
TTF→ CI 0.25 *** Sig
TTF→ USF 0.48 *** Sig
EOU→ CI 0.10 0.135 Non-Sig
PV→ CI 0.15 *** Sig
SR→ CI 0.21 *** Sig
Task.C→ TTF 0.38 *** Sig
Techno.C→ TTF 0.12 0.028 Sig
Techno.C → EOU 0.60 *** Sig
37
Construct Items Source
Perceived Usefulness PU1 I find mobile banking useful in my daily life. Davis et al. (1989)
PU2 Using mobile banking increases my chances of achieving tasks that are important to
me.
PU3 Using mobile banking helps me accomplish tasks more quickly.
PU4 Using mobile banking increases my productivity.
Perceived EOU1 Learning how to use mobile banking is easy for me.
Ease of Use EOU2 My interaction with mobile banking is clear and understandable.
EOU3 I find mobile banking easy to use.
EOU4 It is easy for me to become skillful at using mobile banking.
Task Characteristics Task.C1 I need to manage my account anytime anywhere. Zhou et al. (2010)
Task.C2 I need to transfer money anytime anywhere.
Task.C3 I need to acquire account information in real time.
Technology Techno.C1 Mobile banking provides ubiquitous services.
Characteristics Techno.C2 Mobile banking provides real-time services.
Techno.C3 Mobile banking provides secure services.
Technology Task Fit TTF1 The functionalities of mobile banking are very adequate. Lin and Huang (2008)
TTF2 The functionalities of mobile banking are very appropriate.
TTF3 The functionalities of mobile banking were very sufficient.
TTF4 The functions of mobile banking fully meet my banking needs.
Perceived Privacy PV1 I feel safe when I send personal information to mobile banking. Casalo et al. (2007)
PV2 I think mobile banking abides by personal data protection laws.
PV3 I think mobile banking only collects user personal data that are necessary for its
activity.
PV4 I think mobile banking respects the user’s rights when obtaining personal
information.
PV5 I think that mobile banking will not provide my personal information to other
companies without my consent.
Perceived Security SR1 I think mobile banking has mechanisms to ensure the safe transmission of its users’
information.
SR2 I think mobile banking shows great concern for the security of any transactions.
SR3 When I send data to mobile banking, I am sure that they will not be intercepted by
unauthorised third parties.
SR4 In general, making transactions on mobile banking is secure. Vijayasarathy (2004)
38
Continued Intention CI1 I intend to continue using mobile banking in the future. Venkatesh et al. (2012)
CI2 I will always try to use mobile banking in my daily life.
CI3 I plan to continue to use mobile banking frequently.
Figure 1: Conceptual Model – Adopted from Davis et al. (1989), Goodhue and Thompson (1995), Vijayasarathy (2004), and Wang et al. (2003)
[Task.C: Task Characteristics, Techno.C: Technology Characteristics, TTF: Task-Technology Fit, USF: Perceived Usefulness, EOU: Perceived Ease of Use, SR: Perceived
Security, PV: Perceived Privacy, CI: Continued Intention]
39
Figure 2: Validation of the Conceptual Model
[ns: P > 0.05, *: P ≤ 0.05, **: P ≤ 0.01, ***: P ≤ 0.001]
40

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An integrated model for m-banking adoption in Saudi Arabia

Item Type Article

Citation Baabdullah AM, Alalwan AA, Rana NP et al (2019) An integrated

Rights (c) 2019 Emerald Publishing. Full-text reproduced in accordance

Download date 25/05/2021 18:56:22

Link to Item http://hdl.handle.net/10454/17463

Ali Abdallah Alalwan

Keywords: Mobile banking, TTF, TAM, Saudi Arabia, Intention, Adoption

Mobile banking (m-banking) addresses the essential restriction of e-banking as it decreases

<Figure 1 about here>

3.1 Task Characteristics (Task.C) – Task-Technology Fit (TTF)

3.2 Technology Characteristics (Techno.C) – Task-Technology Fit (TTF)

3.3 Technology Characteristics (Techno.C) – Perceived Ease of Use (EOU)

3.4 Task-Technology Fit (TTF) – Perceived Usefulness (USF)

3.5 Task-Technology Fit (TTF) – Continued Intention (CI)

3.6 Perceived Usefulness (USF) – Continued Intention (CI)

3.7 Perceived Ease of Use (EOU) – Continued Intention (CI)

3.8 Perceived Security (SR) – Continued Intention (CI)

3.9 Perceived Privacy (PV) – Continued Intention (CI)

A field survey using a self-administered questionnaire as a data collection instrument was

4.1 Treatment of Missing Data

5.1 Response Rate and Participants’ Characteristics

<Table 1 about here>

5.2 Structural Equation Modelling

<Table 2 about here>

<Table 3 about here>

<Table 4 about here>

<Figure 2 about here>

<Table 5 about here>

6.1 Theoretical and Practical Implications

8. Research Limitations and Future Directions

Table 2: Results of Measurement Model

Table 4: Construct Reliability and Validity

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