Journal of African Business
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/wjab20
Internet Banking Services User Adoption in Ghana:
An Empirical Study
James Agyei, Shaorong Sun, Emmanuel Kofi Penney, Eugene Abrokwah, Eric
Kofi Boadi & Darko Dennis Fiifi
To cite this article: James Agyei, Shaorong Sun, Emmanuel Kofi Penney, Eugene
Abrokwah, Eric Kofi Boadi & Darko Dennis Fiifi (2022) Internet Banking Services User
Adoption in Ghana: An Empirical Study, Journal of African Business, 23:3, 599-616, DOI:
10.1080/15228916.2021.1904756
To link to this article: https://doi.org/10.1080/15228916.2021.1904756
Published online: 01 Apr 2021.
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JOURNAL OF AFRICAN BUSINESS
2022, VOL. 23, NO. 3, 599–616
https://doi.org/10.1080/15228916.2021.1904756
Internet Banking Services User Adoption in Ghana: An
Empirical Study
James Agyei a, Shaorong Suna, Emmanuel Kofi Penneyb, Eugene Abrokwaha,
Eric Kofi Boadic and Darko Dennis Fiifid
a
Business School, University of Shanghai for Science & Technology, Shanghai, China; bDepartment of
Accountancy, University of Professional Studies, Accra, Ghana; c Department of Accountancy, Koforidua
Technical University, Koforidua, Ghana; dSchool of Economics and Management, University of Electronic
Science and Technology of China, Chengdu, China
ABSTRACT
KEYWORDS
Despite the numerous benefits that customers can reap from inter­
net banking, the existing literature indicates that its adoption
remains limited. Therefore, this paper proposes and examines
a conceptual framework that clarifies the salient factors that drive
customers’ intention to adopt internet banking. The proposed
model was built on the unified theory of acceptance and use of
technology. This was extended by incorporating trust, word of
mouth, perceived enjoyment, and users’ internet experience.
A total of 490 valid responses collected via the intercept approach
from bank customers in Ghana were analyzed employing structural
equation modeling. The analysis demonstrates that performance
expectancy, trust, perceived enjoyment, word of mouth, and users’
internet experience significantly influence behavioral intention to
adopt internet banking. However, the study finds no support for
effort expectancy and social influence. The study concludes with
several useful implications for theory and practice.
Internet banking; behavioral
intention; UTAUT; users’
internet experience; Ghana
Introduction
The rapid growth of internet technologies in recent years has tremendously transformed
and influenced the way banks run their businesses and how customers conduct their
banking transactions (Tarhini, El-Masri, Ali, & Serrano, 2016). This development,
termed Internet banking (IB), has changed the banking sector worldwide (Malhotra &
Singh, 2010), and ushered the world into another continuum of banking by permitting
customers to conduct their everyday business and banking associated activities on the go,
irrespective of their physical position (Hanafizadeh, Keating, & Khedmatgozar, 2014).
However, despite its numerous benefits, the acceptance of IB has not been widespread
and, in various situations, has fallen short of anticipations in both developed and growing
economies (Martins, Oliveira, & Popovic, 2014).
This observation suggests that customers have serious worries about utilizing internet
technology for banking and confirms the view that advances in technology and
CONTACT James Agyei
davidkelly206@ymail.com
Business School, University of Shanghai for Science &
Technology, Shanghai, China, Jun gong Rd. 200093, Shanghai, China.
© 2021 Informa UK Limited, trading as Taylor & Francis Group
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J. AGYEI ET AL.
availability of service do not mechanically lead to extensive adoption and use (Mehrad &
Mohammadi, 2017). Indeed, prior studies (e.g. Alalwan, Dwivedi, Rana, & Algharabat,
2018) highlights that the success of IB much depends on consumers’ acceptance of it. For
this reason, understanding the success factors as well as the resistance factors that
motivate IB adoption is exceptionally imperative.
Many scholarly works have applied different information technology (IT) adoption
theories such as the technology acceptance model (TAM), innovation diffusion theory
(IDT), and the unified theory of acceptance and use of technology (UTAUT) to explore
the factors that affect customers’ intention to use IB. However, scholars (e.g. Alzaidi &
Qamar, 2018; Hanafizadeh et al., 2014) have recommended the need for more research
into the dynamics that influence IB adoption, particularly in parts of South America and
Africa. Indeed, in Ghana, where this study is undertaken, a few empirical studies (e.g.
Boateng, Adam, Okoe, & Anning-Dorson, 2016) have been done on the subject, indicat­
ing the need for further and a more comprehensive study to advance our knowledge as
regards the factors that saliently drive users’ adoption behavior.
Given the ongoing discussion, this paper attempts to add to the existing insight by
shedding new light on the drivers that shape customers’ intentions to adopt IB. To this
end, this study draws on the UTAUT model by Venkatesh, Morris, Davis, and Davis
(2003) and extends it with four new constructs. We adopt this model because it is
a well-proven model that has demonstrated to be “superior to other prevailing compet­
ing models” (Venkatesh et al., 2003). Second, this model is regarded as the most
comprehensive theoretical model for intention assessment (Afshan & Sharif, 2016;
Farah, Hasni, & Abbas, 2018). Third, this model has proven to be an effective and
successful model for understanding and explaining technology adoption in various
contexts, including mobile and internet banking (Afshan & Sharif, 2016; Martins et al.,
2014).
Despite its extensive adoption in prior studies, there is a discussion among scholars
(see Venkatesh, Thong, & Xu, 2012) that the constructs of the UTAUT may not be
adequate to expound user acceptance of new technology in a voluntary setting since the
original UTAUT research concentrated on big organizations in the business milieu.
Thus, researchers argue that this constrains the model’s explanatory power.
Furthermore, Qasim and Abu-Shanab (2016) contend that since technology acceptance
theories are technology-specific, it is imperative to consider other variables that are
linked to the technology or the environment being investigated. In this sense, this
study incorporates four new variables into the UTAUT model and examines their
influence on consumers’ behavioral intention, hereafter, BI, to adopt IB in the context
of Ghana.
The UTAUT model argues that four constructs drive BI and use behavior: namely,
performance expectancy, effort expectancy, social influence, and facilitating conditions.
However, since facilitating conditions can only predict use behavior, it is excluded from
the proposed research model as this study focuses on BI rather than use behavior
(Giovanis, Assimakopoulos, & Sarmaniotis, 2018; Qasim & Abu-Shanab, 2016;
Venkatesh et al., 2003). So, this study adopts performance expectancy, effort expectancy,
and social influence from the UTAUT and extends it with trust, word of mouth,
perceived enjoyment, and users’ internet experience. Moreover, this study further inves­
tigates the interrelations between these constructs influencing BI to adopt internet
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601
banking. This research may help derive useful managerial implications as regards how
internet banking could be marketed more effectively and efficiently, thus leading to
higher adoption.
Study context: Ghana
This study focuses on Ghana, a country in the Sub-Saharan Africa region with a total
population of 28, 833, 629, and a GDP (current US$) of 58.997 billion in 2017 (data.
worldbank.org). Internet penetration in Ghana has increased considerably over the past
years. For instance, a report by the National Communications Authority (NCA), the
regulator of the country’s telecommunications industry, indicates that mobile data
subscriptions in Ghana by the close of March 2018 was 23, 892, 754, up from
21,419,477 in March 2017. These figures represent penetration rates of 82.53% and
75.78%, respectively. Also, as per the Africa 2017 population and Internet users’ statistics
from internetworldstats.com, Ghana has 10,110,000 Internet users signifying 34.38% of
the population as of December 2017. With high internet growth and as a competitive
strategy, almost all the commercial banks in the country such as Barclays, Fidelity Bank,
etc. offer internet banking services to their clients, albeit with lower attention from
customers than expected as daily ads continue to invite potential and existing customers
to use IB solutions. Hence, undertaking this research in a middle-income economy like
Ghana that has embraced internet banking technology, just like the rest of the developed
economies, offers the potential to deepen our understanding of such crucial factors that
drive customers’ BI to adopt internet banking. Therefore, with this contextual frame­
work, this paper examines the critical determinants of users’ intentions to take IB and
suggests guidelines that could help boost acceptance of the system.
Theoretical framework
A large body of academic studies (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989; Taylor
& Todd, 1995) has concentrated on identifying the factors that influence technology
adoption and use. The framework that has been utilized extensively for this aim appears
to be the Technology acceptance model (TAM) by Davis (1989) and Davis et al. (1989).
The TAM framework suggests that perceived usefulness and perceived ease of use are the
key drivers of BI to use a specific technology. Several studies have endeavored to extend
TAM by introducing new constructs such as social norms and user performance (Lucas
& Spitler, 1999), self-efficacy (Taylor & Todd, 1995), among others.
Following a review of earlier technology acceptance scholarly works, Venkatesh et al.
(2003) proposed the unified theory of acceptance and use of technology (UTAUT)
model, having assessed empirically eight prominent models of technology acceptance.
The UTAUT explains the most efficient variance of the intention model- i.e. as much as
70%, which is higher than the earlier adopted and well-known theories like TAM and
theory of planned behavior (TPB) (Afshan & Sharif, 2016). The eight theories on which
the UTAUT was developed are the theory of reasoned action (TRA), TAM, the motiva­
tional model, TPB, the PC utilization model, the innovation diffusion theory (IDT), the
social cognitive theory (SCT), and the integrated model of technology acceptance and
planned behavior (Venkatesh et al., 2003). Thus, UTAUT combines both psychological
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J. AGYEI ET AL.
and behavioral theories to absorb the downsides of each (Farah et al., 2018); it combines
constructs from each of the theories mentioned above and hones them to provide an
empirically validated model that allows scholars to explore all the fundamental dynamics
that impact technology adoption intention (Farah et al., 2018). Consequently, UTAUT is
considered as a complete theoretical framework for predicting consumer behavior con­
cerning technology adoption and use (Martins et al., 2014).
The UTAUT maintains that the effect of technology is grounded on four core con­
structs, namely, performance expectancy, effort expectancy, social influence, and facil­
itating conditions. Proof of its strength is the variety of scholars who have employed the
UTAUT model in their studies (e.g. Afshan & Sharif, 2016; Martins et al., 2014; Riffai,
Grant, & Edgar, 2012; Tarhini et al., 2016; Zhou, Lu, & Wang, 2010). Given its academic
pedigree and capacity, this study adopts the UTAUT model as its basic theoretical
framework to study internet banking adoption from Ghanaian customers’ perspectives.
Research model and hypotheses development
Figure 1 shows the proposed research model and a detailed explanation as regards the
study’s hypotheses are discussed in the subsections that follow.
Performance expectancy
Performance expectancy denotes the extent to which an individual thinks that in
performing certain activities, he or she will experience some benefits as a result of
using a particular technology or innovation (Venkatesh et al., 2003). According to
Venkatesh et al. (2003), performance expectancy is parallel to TAM’s perceived
Figure 1. The proposed research model.
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603
usefulness and IDT’s relative advantage. It reflects consumer’s perception of performance
enhancement by utilizing IB, such as fast response, the “convenience of payment, and
service effectiveness” (Zhou et al., 2010). Many researchers consider this construct as one
of the most significant drivers of BI to adopt and use Information systems (IS) or
Information technology (IT) in general (Farah et al., 2018). Studies have shown that if
consumers think that using IB will be helpful or improve their performance, they will
have a higher propensity to adopt it (Abu-Shanab, Pearson, & Setterstrom, 2010; Alalwan
et al., 2018). Therefore, we propose that:
H1. Performance expectancy will have a positive impact on BI to adopt IB.
Effort expectancy
Effort expectancy reflects the degree of ease linked to utilizing a specific technology
(Venkatesh et al., 2003). It is comparable to the complexity of IDT and the perceived
ease-of-use of TAM, according to Venkatesh et al. (2003). Research shows that the more
consumers think using IB is easy, the stronger their BI to adopt it (e.g. Riffai et al., 2012).
Further, studies claim that effort expectancy can have a positive effect on performance
expectancy (Venkatesh et al., 2003). Thus, if users sense that IB is simple to use and does
not involve much struggle, they will have a high expectancy toward getting the antici­
pated performance (Zhou et al., 2010). As such, we hypothesize that:
H2a. Effort expectancy will have a positive impact on performance expectancy.
H2b. Effort expectancy will have a positive impact on BI to adopt IB.
Social influence
Social influence is similar to TRA’s subjective norm. It denotes the significance users
ascribe to the perception of close relations like family and friends to use a specific
innovation (Venkatesh et al., 2003). This construct has established its worth as
a predictor of technology acceptance in many contexts (Qasim & Abu-Shanab, 2016)
and is particularly vital for the initial stages when users first face a new technology and do
not have any prior experience with using it (Farah et al., 2018). The considerable role of
social influence in impacting user’s willingness to use IB has been confirmed by prior
studies (e.g. Tarhini et al., 2016). Given the importance of social exchanges in
a traditional Ghanaian society, it is expected that social influence will enhance customers’
readiness to adopt and use internet banking. Therefore, we posit that:
H3. Social influence will have a positive impact on BI to adopt IB.
Trust
The first extension proposed in this present study relates to trust. Trust is considered
a very vital concept in IB adoption (Abu-Shanab et al., 2010; Boateng et al., 2016). Indeed,
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J. AGYEI ET AL.
a construct analysis of the relevant literature shows that researchers propose the addition
of trust to UTAUT (Alalwan et al., 2018; Qasim & Abu-Shanab, 2016). Bashir and
Madhavaiah (2015) define trust as “the assured confidence a consumer has in the IB
service provider’s ability to provide reliable services through the internet.” Developing
individual trust is very critical as it minimizes customers’ concerns and uncertainties,
thereby lessening the decision complexity and improving adoption intentions (Farah et al.,
2018). Taken further, IB is an exchange situation that does lack not only the physical
presence of the branch but also lack personal contact. Hence, in the context of this study, it
is conceivable that consumers’ trust in the bank and the internet as a secure stage to do
financial transactions will likely impact their decision to adopt and use IB. Hence:
H4. Trust will have a positive impact on BI to adopt IB.
Perceived enjoyment
Perceived enjoyment reflects the activity, interaction, process, or experience that utilizing
innovative technology is fun, enjoyable, or pleasurable in its own right (Davis, Bagozzi, &
Warshaw, 1992). It is considered to be a form of intrinsic motivation to use IS contrary to
perceived usefulness, which is seen as a form of extrinsic motivation (Davis et al., 1992).
Scholars argue that consumers or users do not only adopt new technologies as devices to
improve performance but also as sources of enjoyment (Davis et al., 1992; Koenig-Lewis,
Marquet, Palmer, & Zhao, 2015). Perceived enjoyment has been identified to be a robust
determinant of intention to use new technologies in different contexts, including internet
use (Agarwal, Sambamurthy, & Stair, 2000) and e-banking (Abbad, 2013). Furthermore,
research suggests that perceived enjoyment can act as a precursor of perceived usefulness
(similar to performance expectancy) and perceived ease of use (identical to effort
expectancy), signifying that an enjoyable technology is likewise considered as more useful
and easier to use (Koenig-Lewis et al., 2015). For these reasons, we formulate the
following:
H5a: Perceived enjoyment will have a positive impact on performance expectancy.
H5b: Perceived enjoyment will have a positive impact on effort expectancy.
H5c: Perceived enjoyment will have a positive impact on BI to adopt IB.
Word of mouth
Silverman (2001) defines word of mouth as “communication about products and services
between people who are perceived to be independent of the company providing the
product or service, in a medium perceived to be independent of the company.”
Velázquez, Blasco, and Gil Saura (2015) assert that consumers place much trust in word
of mouth information when dealing with high-risk purchase procedures. Consumers
participate in word of mouth with a varied range of purposes and motivations such as
to assist other consumers or prevent likely mistakes (Mehrad & Mohammadi, 2017;
Velázquez et al., 2015). Research has shown that several individuals dealing with banks
rely on word of mouth connections to lessen the perceived level of risk and uncertainty
related to service purchase decisions (Mohammed & Al-Tarawneh, 2014). Mehrad and
JOURNAL OF AFRICAN BUSINESS
605
Mohammadi (2017) and Mohammed and Al-Tarawneh (2014) found word of mouth to
be a significant predictor of users’ intention to use mobile and electronic banking
respectively. Also, we argue in this study that word of mouth can act as an antecedent
of performance expectancy and effort expectancy (Mehrad & Mohammadi, 2017). Hence:
H6a. Word of mouth will have a positive impact on performance expectancy.
H6b. Word of mouth will have a positive impact on effort expectancy.
H6c. Word of mouth will have a positive impact on BI to adopt IB
Internet experience
Experience has been established to play a vital facilitative role in technology acceptance
decisions (Su, Wang, & Yan, 2018). Lu, Cao, and Yang (2010) define internet experience as
users’ experience in browsing webpages in general. Indeed, users’ experience is a relevant
factor in many studies in technology adoption and has been utilized as either a predictor
or moderator variable (Abbad, 2013; Su et al., 2018). Thus, previous knowledge, such as
internet or computer use experience, has been demonstrated in previous works (e.g.
Agarwal & Prasad, 1999) to strongly impact intention to use a particular system via
perceived usefulness and perceived ease of use. Thus, as individuals get more experience
about systems and study the essential skills, they are more likely to develop favorable
perceptions of their ease of use. Similarly, Su et al. (2018) found users’ internet experience
to be a strong predictor of users’ BI to adopt mobile payment. Therefore, we propose that:
H7a. Internet experience will have a positive impact on performance expectancy.
H7b. Internet experience will have a positive impact on effort expectancy.
H7c. Internet experience will have a positive impact on BI to adopt IB.
Methodology
Measurement instruments
The study employed a questionnaire survey to gather data. Previously validated and
reliable measurement scales were used in this study to enhance content validity. The
items in the scale were modified and adapted to fit the study’s context. All constructs
were gauged with multiple items on a five-point Likert scale ranging from 1 = strongly
disagree to 5 = strongly agree. The measures of performance expectancy were taken from
Venkatesh et al. (2003) and Martins et al. (2014). Next, the items measuring effort
expectancy were adapted from Venkatesh et al. (2003), Zhou et al. (2010), and Alalwan
et al. (2017), and the three items gauging social influence were taken from Venkatesh
et al. (2003) and Alalwan et al. (2017). Trust was based on items used by Boateng et al.
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J. AGYEI ET AL.
(2016). Three items measuring perceived enjoyment were sourced from Koenig-Lewis
et al. (2015), and three items of word of mouth were taken from Mehrad and
Mohammadi (2017). Similarly, internet experience scales were based on items employed
by Lu et al. (2010). Lastly, the measures of behavioral intention were adapted from
Venkatesh et al. (2003).
When the instrument was designed, it was pretested among 20 bank customers who
have had various bank accounts for more than five years, and also had rich internet banking
services use experience. We asked them to freely give their feedback about any comprehen­
sion or wording challenges that could be present in the questionnaire to ensure that any
mistakes were duly corrected before data collection. The wording of some of the items was
carefully modified and then reviewed to make them more precise and understandable based
on the pilot test’s outcome. Table 2 contains a list of all the measurement items.
Sampling and data collection
The population of our study involves all bank customers in Ghana. The respondents were
randomly intercepted as they walked out of six major banks (three domestic-controlled
and three foreign-controlled banks) in Accra, the capital city of Ghana, chosen based on
size and popularity. Further, Accra was selected because it hosts the headquarters of all
the banks as well as most of their branches. The city also plays a vital role in the
commercial and economic affairs of the Ghanaian economy. Moreover, it is considered
a critical economic hub within the West-African sub-region, and economic activities
there include industrial and financial sectors, textiles, fishing, and many more. The
intercept approach employed in this study is consistent with other prior studies
(Boateng et al., 2016; Makanyeza, 2017). The permission to approach the respondents
was obtained from each of the bank’s branch managers before the data collection. The
purpose of the research was clearly explained to the respondents before completing the
questionnaire, and participation was voluntary, with no incentives given. Averagely, it
took not more than fifteen minutes to complete the survey by each participant. The data
collection took place from October 2, 2017, to November 24, 2017, and was conducted by
three trained senior students and two researchers. Among the 520 questionnaires that
were distributed, 30 were dropped due to invalid responses and missing data.
Consequently, 490 questionnaires were used for empirical analysis. Table 1 presents
the descriptive statistics of the respondents surveyed.
Common method bias
Harman’s single-factor test, which establishes if the majority of the variance can be
expounded by a single factor (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003), was
employed to assess the likelihood of Common method bias (CMB). Applying principal
component factor analysis without rotation, and setting the number of factors to 1 using
SPSS 22, the total number of variance explained is established to be below 50%. Thus, the
results reveal that all factors are extracted with the first factor accounting 23.761% (i.e. <
50%) of the total variance, signifying that CMB is not a concern in this data set.
JOURNAL OF AFRICAN BUSINESS
607
Table 1. Profile of respondents.
Variable
Gender
Age (in years)
Education
Internet usage literacy
Relationship with bank (in years)
Category
Male
Female
20–29
30–39
40–49
50 and above
School certificate
Diploma
Bachelor’s degree
Masters’ degree
PhD
Beginner
Intermediate
Advanced
Less than 1
1–5
Above 5
Frequency (N = 490)
269
221
187
233
58
12
42
110
259
65
14
96
159
235
68
121
301
%
54.9
45.1
38.2
47.6
11.8
2.4
8.6
22.4
52.9
13.3
2.8
19.6
32.4
48.0
13.9
24.7
61.4
Table 2. Measurement scale and scale reliability values.
Constructs and items
Performance expectancy (α = 0.827; CR = 0.834; AVE = 0.630)
I find internet banking useful in my daily life
I believe using internet banking enables me to complete tasks more swiftly
I think that using internet banking increases my productivity
Effort expectancy (α = 0.804; CR = 0.816; AVE = 0.601)
Learning how to use internet banking is easy for me
I find internet banking easy to use
It is easy for me to become skillful at using internet banking
Social Influence (α = 0.812; CR = 0.815; AVE = 0.600)
People who are important to me feel that I should use internet banking
People who influence my behavior think that I should use internet banking
People whose opinions I value prefer that I use internet banking
Trust (α = 0.720; CR = 0.721; AVE = 0.563)
I would trust my bank to offer secure internet banking
I think internet banking is reliable
Perceived enjoyment (α = 0.772; CR = 0.778; AVE = 0.539)
Using internet banking is fun
Using internet banking is pleasant
Using internet banking is positive
Word of mouth (α = 0.784; CR = 0.793; AVE = 0.565)
I will talk about the strengths of internet banking with people I know
I will talk about internet banking to be quite positive
If you ask me about internet banking, I will definitely recommend it
Internet experience (α = 0.716; CR = 0.732; AVE = 0.579)
I think that I am familiar with the internet
I frequently use the internet.
Behavioral intention (α = 0.881; CR = 0.881; AVE = 0.711)
I intend to use internet banking in the future
I expect that I will use internet banking in my daily life
I plan to use internet banking frequently
Loadings
.683
.902
.781
.763
.898
.643
.682
.861
.766
.741
.760
.730
.796
.672
.629
.792
.819
.686
.829
.848
.815
.866
Data analysis and results
The two-stage approach recommended by Anderson and Gerbing (1988) was employed
in this study. A confirmatory factor analysis (CFA) was first carried out using AMOS 21
to assess the reliability of the constructs, in addition to their discriminant validity
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J. AGYEI ET AL.
(Campbell & Fiske, 1959). SEM was utilized to test the proposed research model and
evaluate the proposed relations between the constructs in the second stage. This
approach is consistent with earlier studies (e.g. Farah et al., 2018; Tarhini et al., 2016).
Measurement model
The CFA results show that the model provides an acceptable fit to the data as revealed by
a range of goodness-of-fit statistics (x2 = 288.687, df = 181, p ≤ 0.001). The chi-squared
value divided by the degree of freedom ratio (x2/df) was 1.595, and thus within the
suggested value; the goodness of fit index (GFI = 0.949), adjusted goodness of fit
(AGFI = 0.929), comparative fit index (CFI = 0.974), normed fit index (NFI = 0.934),
Tucker-Lewis index (TLI = 0.967) and root mean square error of approximation
(RMSEA = 0.035), thus providing evidence of acceptable model fit (Hair, Black, Babin,
& Anderson, 2010).
Additionally, all constructs were examined to ensure a sufficient level of scale relia­
bility. The factor loadings (see Table 2) for the items were significant (p = 0.000) and
surpassed the 0.50 benchmark (Hair et al., 2010). We evaluated the constructs’ internal
consistency via Cronbach’s alpha (α). In this study, all the Cronbach’s alpha (α) values
were higher than the acceptable value of 0.60, demonstrating support for internal
consistency (Bagozzi & Yi, 1988). The composite reliability coefficients for all the con­
structs were also greater than the recommended value of 0.70 (Hair et al., 2010). Further,
the AVE values (see Table 2) of the latent constructs were all greater than the threshold
value of 0.50 (Fornell & Larcker, 1981), suggesting the measurement model had satisfac­
tory convergent validity.
Finally, discriminant validity was evaluated by relating the squared root of every
construct’s AVE with its parallel correlations. The squared root of every construct’s
AVE provided and highlighted in the diagonal (see Table 3), are higher than its respective
inter-construct correlations. Thus, this establishes the presence of discriminant validity
between our constructs (Hair et al., 2010).
Structural model and hypothesis testing
The fit indices of the structural model were assessed and established to be within their
limits (x2 = 538.247, df = 261, p ≤ 0.001, x2/df = 2.062; GFI = 0.919; AGFI = 0.900;
CFI = 0.933; NFI = 0.912; TLI = 0.923; RMSEA = 0.047), implying that the structural
model shows a good fit to the data (Anderson & Gerbing, 1988; Hair et al., 2010). The R2
for BI was 0.672. Thus, the proposed research model explains 67.2% of the variance in
intention to adopt IB.
Table 4 and Figure 2 show the hypothesized results. The path from performance
expectancy to behavioral intention was significant (β = 0.324, t = 5.554, p = 0.000); thus,
H1 was supported. While effort expectancy had a significant impact on performance
expectancy (β = 0.198, t = 3.117, p = 0.002), it had no influence on behavioral intention
(β = 0.041, t = 0.654, p = 0.513). Thus, H2a was supported, but H2b was not supported.
Furthermore, social influence exhibited a positive but insignificant impact on behavioral
intention. So, H3 was not supported.
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609
Table 3. Mean, standard deviation, correlation and discriminant validity.
Performance expectancy
Effort expectancy
Social influence
Trust
Perceived enjoyment
Word of mouth
Internet experience
Behavioral intention
Mean
4.001
3.915
4.003
4.234
4.018
3.657
3.920
3.859
SD
.749
.752
.819
.739
.728
.841
.793
.794
1
.793
.310
.017
−.048
.384
.313
.283
.449
2
3
4
5
6
7
8
.775
.022
.021
.302
.291
.282
.249
.774
.037
.004
.144
−.010
.046
.750
.044
.029
.056
.140
.734
.344
.560
.404
.751
.410
.334
.761
.336
.843
Table 4. Results of hypothesis testing.
Hypothesis
H1
H2a
H2b
H3
H4
H5a
H5b
H5c
H6a
H6b
H6c
H7a
H7b
H7c
Path
PE → BI
EE → PE
EE → BI
SI → BI
TR → BI
PEJ → PE
PEJ → EE
PEJ→ BI
WOM → PE
WOM → EE
WOM→ BI
IEP→ PE
IEP → EE
IEP → BI
Coefficient
.324
.198
.041
.015
.162
.240
.168
.204
.145
.154
.142
.147
.129
.111
t-value
5.554
3.117
0.654
0.350
2.344
4.604
3.731
3.765
3.204
3.827
3.046
2.622
2.588
1.962
p-value
.000
.002
.513
.726
.019
.000
.000
.000
.001
.000
.002
.009
.010
.050
Result
Supported
Supported
Not supported
Not supported
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Supported
BI = behavioral intention; PE = performance expectancy; EE = effort expectancy; SI = social influence; TR = trust;
PEJ = perceived enjoyment; WOM = word of mouth; IEP = internet experience. Control for gender, age and education.
The results also revealed that trust (β = 0.162, t = 2.344, p = 0.019) is a significant
predictor of behavioral intention, supporting H4. In addition, perceived enjoyment was
uncovered to be a strong driver of performance expectancy (β = 0.240, t = 4.604, p
= 0.000), effort expectancy (β = 0.168, t = 3.731, p = 0.000), and behavioral intention
(β = 0.204, t = 3.765, p = 0.000), indicating that H5a, H5b and H5c were supported.
Similarly, the results also show that word of mouth has a positive significant effect on
performance expectancy (β = 0.145, t = 3.204, p = 0.001), effort expectancy (β = 0.154,
t = 3.827, p = 0.000), and behavioral intention (β = 0.142, t = 3.046, p = 0.002). Thus, H6a,
H6b and H6c were confirmed. Additionally, internet experience showed a significant
positive impact on performance expectancy (β = 0.147, t = 2.622, p = 0.009), effort
expectancy (β = 0.129, t = 2.588, p = 0.010) and behavioral intention (β = 0.111, t = 1.962,
p = 0.050), thus providing support for H7a, H7b and H7c. The control variables, gender,
age and education had no significant impacts on behavioral intention.
Discussion
This study was conducted to analyze and offer further insights into factors that shape
users’ intention to adopt internet banking in Ghana. Largely, the findings confirm the
applicability of the UTAUT model in the study’s context and also support the goodness
of fit and predictive validity of the proposed research model. Thus, the model explains
about 67.2% of the variance in intention to adopt IB. Moreover, most of the suggested
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J. AGYEI ET AL.
Figure 2. Results of proposed research model. Note: ***p < 0.001; **p < 0.01; *p < 0.05; N.S. and the
dashed line = non-significant.
causal paths in our model were established to have salient values. So, our results, apart
from being in line with what was posited in the research model, are also in agreement
with what was argued in the previous literature.
As anticipated, the results show that performance expectancy significantly shapes
consumers’ BI to adopt IB. Thus, customers appear to be more moved to accept IB
outlets if they sense that such a medium is more effective, productive, and a good
innovation in their day-to-day life. This finding seems to agree with earlier studies’
argument that when users find IB to be useful, they are more in the cards to have an
enhanced perception of utilizing it (Tarhini et al., 2016). Furthermore, this could be
explained by the ability of IB as a convenient and useful tool that permits clients to access
a variety of higher quality and quicker financial services such as bill payments, fund
transfers, among others without any place or time constraints (Alalwan et al., 2018). This
result is in agreement with several prior scholarly works (e.g. Martins et al., 2014; Owusu,
Bekoe, Addo-Yobo, & Otieku, 2020; Rootman & Krüger, 2020).
Regardless of its significant effect on performance expectancy, effort expectancy did
not have any substantial impact on BI. This could be ascribed to the fact that the difficulty
in utilizing the internet, mobile phones, and computers has reduced and become less of
a concern for Ghanaian customers since these things have become more user-friendly.
Indeed, the results confirm the suggestion that the impact of ease of use will reduce over
time as users become more proficient with a particular system (Koenig-Lewis et al.,
2015). While our finding contradicts prior works such as Martins et al. (2014) and AbuShanab et al. (2010), it concurs with Tarhini et al. (2016). This could be explained by
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611
differences or similarities in culture since culture manifests itself differently in different
environments or surroundings. Nonetheless, effort expectancy did have a robust impact
on performance expectancy, thereby supporting studies by Makanyeza (2017) and
Alalwan et al. (2018). The result implies that if customers feel that using IB is easy and
does not entail much effort, they will have a high prospect toward acquiring the
anticipated performance (Venkatesh et al., 2003; Zhou et al., 2010).
Although social influence was predicted to play a critical role in shaping BI to adopt
IB, the empirical results indicate otherwise. The results obtained in this current study
denote that the participants’ willingness to use IB is not driven by the opinions of other
people, such as relatives, friends, or essential others in their environments. This suggests
that customers will not plan or intend to utilize IB because their friends, relatives, or
essential others in their lives recommended it. This finding is similar to what has been
found by past scholarly works by Riffai et al. (2012) and Alalwan et al. (2018).
What’s more, trust was found to be a salient driver of BI to adopt IB, thereby
confirming the assertion that trust is a crucial notion in the IB context, and even
more significant from an online business perspective (Bashir & Madhavaiah, 2015;
Boateng et al., 2016). Thus, customers need to trust the technology (i.e. IB) in
addition to the service providers (i.e. banks) to provide secured services and safe­
guard their personal information before performing banking transactions via the
internet. Such results are in line with many other earlier studies (e.g. Boateng et al.,
2016; Giovanis et al., 2018).
The results also revealed that perceived enjoyment positively influences BI to adopt IB,
endorsing suggestions by past studies (Koenig-Lewis et al., 2015) for the need to give
more attention to affective features of the experience of technologies. Our findings are
consistent with the argument that consumers adopt new technologies not just for
performance enhancement but also to gain enjoyment (Abbad, 2013; Davis et al., 1992;
Koenig-Lewis et al., 2015). Thus, such findings highlight the critical role of fun and
pleasure in influencing consumers to adopt internet banking. Our results are similar to
those of Abbad (2013). Further, the study confirmed that perceived enjoyment signifi­
cantly impacts both performance expectancy and effort expectancy. As consumers
recognize higher pleasure in adopting internet banking, their performance and effort
expectancies will equally become higher (Abbad, 2013; Koenig-Lewis et al., 2015).
Additionally, the findings confirmed that word of mouth does not only have
a significant direct influence on BI to adopt IB but also has significant indirect impacts
via performance expectancy and effort expectancy. Thus, the further positive talk con­
sumers hear about internet banking, the more they will be willing to adopt and use it. The
results agree with research suggesting that several individuals dealing with banks depend
on the word of mouth connections to lower the perceived level of risk and doubts linked
to service purchase decisions (Mohammed & Al-Tarawneh, 2014). The study, thus,
uncovers that word of mouth is a salient factor in explaining consumers’ IB to use
internet banking, which is in line with past research in the context of mobile banking
(Mehrad & Mohammadi, 2017) and in e-banking (Mohammed & Al-Tarawneh, 2014).
Finally, the important role of users’ internet experience in influencing consumers’ BI
to adopt IB was also established. The finding supports the argument that users’ experi­
ence of financial activities grounded on computers and the internet significantly impacts
their BI (Su et al., 2018). In additions, the findings indicate that internet experience
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J. AGYEI ET AL.
enhances users’ performance expectancy and effort expectancy of internet banking,
which, in turn, further increase their BI to adopt internet banking, agreeing with several
studies’ findings that prior experience, like internet use experience strongly affects
intention to use a particular system via the perception of usefulness and ease of use
(Agarwal & Prasad, 1999). Our results agree with those of Lu et al. (2010) and Abbad
(2013) and that of Su et al. (2018) in the mobile payment context.
Theoretical and practical implications
From a theoretical standpoint, this study contributes to the literature by enhancing our
understanding of factors that impact users’ BI to adopt internet banking in the context of
Africa, specifically Ghana, thereby filling a fundamental knowledge gap that has been
identified in the IB adoption literature (Alzaidi & Qamar, 2018; Hanafizadeh et al., 2014).
Further, the study adds to the existing literature not only by applying the UTAUT model
in a different context (internet banking) and a developing country context (Ghana), but
more significantly incorporating new constructs and exploring the strong causal links
between the explanatory constructs of behavioral intention, and thus highlighting the
existence of both direct and indirect effects in the model. Accordingly, the proposed
model investigates and validates the coexistence of the direct and indirect precursors to
consumer acceptance decisions, thereby allowing for a richer comprehension of user
technology acceptance intentions. Third, the study also complements the existing litera­
ture by including word of mouth and internet experience in the UTAUT model, for the
first time, as variables to comprehend their relationships with BI. The research highlights
and confirms the significant roles played by these variables in influencing customers’
behavioral intention, thus offering more in-depth insights into future works that intend
to analyze new technology acceptance. In this sense, given the importance of word of
mouth and internet experience on internet banking adoption, we believe that it is best to
have them integrated into future IB adoption studies. Although they are not standard
variables in technology acceptance models (e.g. TAM, IDT, or UTAUT), they ought to be
considered as critical predictors of technology adoption to provide deeper insights.
Moreover, these constructs need to be examined further in different cultures and
countries as well as in different technology contexts (e.g. mobile payments).
Practically, the findings provide some useful implications for banks in Ghana, as
well as other emerging economies. To increase the uptake of IB, service providers must
emphasize delivering IB services that offer great useful value and at the same time, are
simple and easy to use as the results demonstrate that effort expectancy significantly
impacts performance expectancy. Thus, service providers need to undertake clarifica­
tion campaigns, underscore and expound the numerous benefits of using IB, and teach
individuals how to utilize the platform to conduct their banking activities (Farah et al.,
2018; Martins et al., 2014), as consumers would hugely welcome services that are
useful and fulfill their specific wants. Besides, the findings also reveal the vital role of
trust in affecting BI in IB adoption. Therefore, banks must keep in mind that the
readiness of customers to adopt IB is to a great extent, tied to their ability to trust the
technology as much as it is about trusting its service provider (Qasim & Abu-Shanab,
2016). Hence, banks should instill trust and assurance in their clients regarding
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613
safeguarding their financial information, personal particulars, and their monies
(Boateng et al., 2016).
The study further revealed that perceived enjoyment, word of mouth, and internet
experience are noticeable factors that drive consumers’ adoption intentions. As such,
banks should keep in mind the relevance of perceived enjoyment when designing and
promoting internet banking. By offering enjoyable navigation, visually appealing inter­
faces and easy downloadable user instructions could help increase the perceived enjoy­
ment of internet banking, which in turn, could improve uptake. Further, advertisements
and promotional messages relating to internet banking services should importantly
emphasize their enjoyment features. Correspondingly, banks should ensure that IB offers
customers the utmost and experience and satisfaction, as satisfied users will most likely
provide positive talks about IB which have the potential to influence others to use the
service. Additionally, banks should ensure that the design and function of internet
banking services are well aligned with consumers’ experiences and perceptions related
to these services.
Limitations and future lines of research
The study has some limitations that could offer direction for future research. First, the
study was limited to customers of six banks in Accra, the capital city of Ghana. Therefore,
future research can improve on the conclusion reached in this study by involving other
banks’ customers as well as including other regional and district capitals. Second, this
research only focused on internet banking, which restrains the generalization of the
findings to other online banking outlets. So, future studies can examine other channels
such as mobile banking and m-shopping. Moreover, the results and the implications of
our research were obtained via a cross-sectional study. Accordingly, the ability to reflect
the temporal changes in the study constructs is lowered (Giovanis et al., 2018; Mehrad &
Mohammadi, 2017). Hence, future studies should use a longitudinal study to clarify the
effects of temporal variations.
Conclusion
This study examined the salient factors that impact customers’ BI to adopt IB in Ghana.
An extended conceptual model based on the UTAUT factors, trust, word of mouth,
perceived enjoyment, and internet experience was developed. In detail, we modeled the
UTAUT factors and the four newly-introduced constructs: trust, word of mouth, per­
ceived enjoyment, and internet experience as crucial antecedents of BI. Also, causal paths
from perceived enjoyment, word of mouth, and internet experience to performance
expectancy and effort expectancy were proposed in the same model. Our findings reveal
that the proposed model satisfactorily fits the data along with being able to explain 67.2%
of the variance in BI.
Further, the results demonstrate that performance expectancy, trust, word of mouth,
perceived enjoyment, and internet experience are the key factors that shape customers’
BI to use IB. Likewise, our research uncovered certain links that were not present in the
original UTAUT model. These paths identified in this study were due to the addition of
new constructs (trust, word of mouth, perceived enjoyment, and internet experience)
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J. AGYEI ET AL.
not present in the original model. These paths (see Table 4) comprise PEJ → PE, PEJ →
EE, WOM → PE, WOM → EE, IEP→ PE, and IEP → EE, provide new insights as
regards users’ BI to accept IB. Thus, this research does not only add to the applicability
of the UTAUT model to new settings, but it likewise uncovers several additional factors
that need to be looked at in the adoption of IB. It also offers many useful, practical
insights.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
James Agyei
http://orcid.org/0000-0003-4749-6747
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