sustainability
Article
A Study on the Acceptance of Mobile-Banking Applications in
India—Unified Theory of Acceptance and Sustainable Use of
Technology Model (UTAUT)
Vishal Samartha 1 , Samarth Shenoy Basthikar 1 , Iqbal Thonse Hawaldar 2 , Cristi Spulbar 3, *, Ramona Birau 4
and Robert Dorin Filip 4
1
2
3
4
*
Citation: Samartha, V.; Shenoy
Basthikar, S.; Hawaldar, I.T.; Spulbar,
C.; Birau, R.; Filip, R.D. A Study on
the Acceptance of Mobile-Banking
Applications in India—Unified
Theory of Acceptance and
Sustainable Use of Technology Model
(UTAUT). Sustainability 2022, 14,
14506. https://doi.org/10.3390/
su142114506
Academic Editor: Ioannis Nikolaou
Received: 14 September 2022
Department of Business Administration, Sahyadri College of Engineering and Management,
Mangalore 575007, Karnataka, India
Department of Accounting & Finance, College of Business Administration, Kingdom University,
Riffa P.O. Box 40434, Bahrain
Department of Finance, Banking and Economic Analysis, Faculty of Economics and Business Administration,
University of Craiova, 200585 Craiova, Romania
Doctoral School of Economic Sciences, University of Craiova, 200585 Craiova, Romania
Correspondence: cristi_spulbar@yahoo.com
Abstract: This research makes an attempt to understand various factors that influence the adoption
of mobile applications. Within the context of the “Unified theory of acceptance and use of technology”
(UTAUT) modified model, considering the upcoming demand and increase in demand for mobilebanking applications, the researcher tried to explore the theoretical concept between random people
of various states in India. The primary data was collected by preparing a questionnaire and circulating
it using Google Forms. The collected data was further coded into Smart PLS 4 to understand the
model and structural equation with reference to mobile-banking technological adoption and factors
that had a significant impact. The conclusions derived from the study is that social influence,
“effort expectancy”, and “trust” factors had a very strong influence on the “purchase intention”,
whereas “effort” and “risk” factors had a negligible impact on purchase intent. It was also found
that the UTAUT model is appropriate for evaluating the technological adoption of mobile-banking
applications. With the advent of many players in the market and their unique banking management
applications on mobile platforms, consumers are moving towards different third-party app than their
origin bank in which they hold account. This has forced banking institutions to up the pace in the
competition, introducing a lot of new features. It is also important to understand that, as a customer,
there are a lot of attributes that he would be looking into for adoption. This paper is an attempt
to understand the advancements in various variables that consumers would look at in the area of
mobile-banking applications.
Accepted: 31 October 2022
Published: 4 November 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
Keywords: mobile app; UTAUT; acceptance model; technology adoption; m-banking; PLS; SEM;
mobile-banking technological adoption; digital transactions; sustainability; technology development;
sustainable development
published maps and institutional affiliations.
1. Introduction
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Ref. [1] Based on a study conducted with 268 respondents in Kerala, it was found that
the usage of mobile banking has been increasing very rapidly and the pandemic has also
forced people towards online transactions. Ref. [2] Service benefits derived from mobile
banking can be seen from the point of view of customers as well as service providers. It
must be noted that real benefits can be seen only when technologies are adopted widely.
Ref. [3] Even though usage has been increasing, it has not reached its fullest extent in the
country due to several challenges, such as privacy and safety issues. This study reveals
that it is necessary that financial institutions must understand needs to improve services. It
Sustainability 2022, 14, 14506. https://doi.org/10.3390/su142114506
https://www.mdpi.com/journal/sustainability
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must be noted that governments have also been actively encouraging digital transactions.
Thus, mobile banking is a very important part of the digital campaign and enables financial
inclusion. Ref. [4] “Mobile Banking in India: A Review” highlights that India’s banking
system is evolving towards digitalization, and the country’s banking industry has seen a
number of changes. Even though lot of studies are conducted in a similar area [5], adoption
rates are still an increasing phenomenon in many parts, especially rural parts, of India.
There are several factors that influence the acceptability of mobile-banking application,
some are positive and some are negative. The paper reveals that many people have adopted
banking applications as they are very accurate and cost-effective and also points out that,
yet, there are many factors such as illiteracy, social ethics, security and the non-availability
of networks acting as great barriers to the same. Ref. [6] It must be noted that the advent
of banking applications has also created scope for many challenges such as an increase
in complaints. On the other hand, customers frequently express their frustration with
issues or poor service-related apps. As for service providers, they must be aware of
issues relating to the same. Ref. [7] UTAUT, when compared to many tools such as TAM
(technology acceptance model), has better prediction abilities for adoption. Ref. [8] In many
industrialized nations, online banking has captured the interest of the general public, while
mobile apps have not grown to their maximum potential. Ref. [9] Mobile banking and
payment technology are spreading all over the world but it must also be noted that it is not
growing equally everywhere. It has been a huge concern to all policy makers in developing
countries, as it can reap significant benefits such as enabling financial inclusion.
Ref. [10] Based on the literature, it was revealed that several studies conducted on
predictors of mobile-banking adoption using the unified theory of acceptance and use
of technology (UTAUT)showed differing results in various UTAUT relationships. Even
though a lot of studies are found on the traditional UTAUT model, this work, adopting an
extended model, will help to understand the problems from new angles and it will help to
explore more areas, compared to the traditional model, in an Indian context. As a growing
economy, it is very important to identify various factors that form the basis of the adoption
of mobile banking in India.
2. Research Problems
High potentiality in the Indian market has forced many financial and banking companies to introduce many banking services. This fast-growing and dynamic market has
made the best services obligatory. With reference to the same, the study established these
research objectives:
•
•
Explore the various factors that influence a consumer’s decision and also try to understand how these influence the adoption of technology with reference to mobile-banking
services.
Does this modified UTAUT model hold good for predicting the intentions for
mobile banking?
3. Research Model and Hypotheses
In this paper, the authors developed models mainly from past studies. Ref. [9] For
clarity, the following terms are defined by the researchers. “Performance expectancy” is
the perceived benefit of the mobile-banking app being used, whereas “effort expectancy”
refers to the effort required to achieve the best results. The effect of social circles and
peer groups on the decision that motivates a consumer to use a service is referred to as
“social influence.” The concept of “risk” refers to the risk of using the service and “trust” is
availability and consistency.
Ref. [11] Even though the original UTAUT model used various moderators such as
age, gender and voluntariness, in the proposed model, these have been avoided to reduce
complexity and to keep the model simple. Refs. [7,12–14] Based on the past research, two
separate constructs have been considered, and they are trust and risk. This model has also
shown good authenticity in various past studies. Authors have kept the model simple,
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considering various factors such as time and scope and using a few corporate-governance
factors as predictors.
4. Literature Review
Various scholars have investigated various factors that influence technology acceptance. The author attempted to understand the theoretical background for the UTAUT
model by reading various previous works in similar areas, and the results have assisted the
author in gaining more concrete knowledge in the subject [15]. There are four attributes that
decide behavioural intentions towards technological acceptance. Various studies have used
various moderations such as “age”, “gender” and “experience” to establish models, but
the core determinants that influence behavioural intention are “performance expectancy”,
“effort expectancy”, “social influence” and “facilitating conditions”. Ref. [16]. To know
more about the acceptance or adoption of any new technology, various models are used,
among which UTAUT is considered a prominent or very commonly accepted model.
There have been several models developed over a period of time. TAM (technological
acceptance model) is one such model which was widely used earlier, but was noted to
have various disadvantages over a period of time. Ref. [17] The researcher notes several
drawbacks of the TAM model in this paper, including the fact that the model does not
provide satisfactory information about individuals with regard to novel systems. It is also
noted that TAM also neglects various indicators when investigating external factors directly.
4.1. Mobile Banking
Ref. [18] Technological progress in mobile banking has resulted in great advancements
and modifications in banking applications and has made tremendous contributions in the
financial sector. Ref. [19] This advanced technology has resulted in reducing expenses
such as operating fees when compared to traditional banking systems. The study has
also shown that mobile banking has helped to gain better control over finance. Ref. [20]
Modern banking has seen quite a changes from traditional paper-based to new advanced
mobile-banking applications. The usage of such applications on mobile phones when
compared to computers has gained more attention in recent years with increasing numbers
on the market; it must be noted that, as there is improvement in such technologies, there has
been a rise in barriers to acceptance. Ref. [21] Through the unfolding of Wi-Fi connectivity,
mobile applications have scope for increasing industrial businesses in a straightforward
manner and features make available an extensive variety of business options. With little
effort, these improvements make it simpler to operate commercial enterprises, trade, and
purchase merchandise and offerings. It may also help a country improve its financial
offerings. The improvement in mobile banking, however, relies upon customer knowledge
of the capabilities of mobile-banking offerings and the way banks can set up excellent
relationships with customers through mobile banking.
4.2. UTAUT Model
Ref. [7] The UTAUT model serves as the theoretical cornerstone for our investigation.
Its wide application and capacity to describe technological adoption and acceptance were
key factors in its selection. This model has been extensively developed in past studies.
Ref. [11] TAM (technological acceptance model) was developed in the 1990s, and it was
later developed in 2003 with four main constructs keeping social relevance of acceptance
and usage in mind. It had four constraints for moderation variables. The model would
assess these variables to understand the impact on behavioural intentions. Ref. [22] The
UTAUT model was created and has served as the standard model ever since. It helps
to explore numerous technologies in various settings. Although powerful, it is argued
that the “UTAUT” is essentially a general model of the adoption of technology. Ref. [23]
Various studies used the “UTAUT” model to analyze various determinants that may have
various types of effects on behavior related to technology acceptance. Ref. [24] The model,
which concentrates on a single technology class, is more effective and descriptive than a
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generic construct model that tries to cover numerous technologies. The paper supported
the significant influence of the user’s intention to accept internet banking. With this, it
has been seen that many academicians continue to improve or broaden the UTAUT by
incorporating new constructs to shed light on various variables.
Based on the results of various past studies, the four primary UTAUT factors were
kept in this study, since it is believed that they have a considerable impact on adoption
behaviours as cited under Figure 1.
Figure 1. Modified UTAUT model.
4.3. “Performance Expectancy” (PE)
The expectation of users concerning the performance of technology has a significant
impact on their willingness to adopt the technology Ref. [11]. There is a lot of historical
research evidence that reveals the impact of “perceived performance” on “behavioural
intentions”. Ref. [25] The researcher revealed, in his literature review, that PE could be
taken as the most important predictor in the future; however, it requires additional research
to confirm relational tendencies. PE refers to the argument that if someone thinks about
adopting a certain technology, their performance at work would considerably increase.
Ref. [26] This construct was used in many studies, even in the TAM model, as a perceived
utility component. PE is regarded as the element directly influencing behavioural intention
in many studies. Refs. [18,27] The positive significance of performance expectancy has been
seen in many countries such as Saudi Arabia and in Jordan. This study tries to understand
how much PE would also impact in the Indian context.
Hypothesis 1 (H1). Performance expectancy has a significant influence over purchase intentions
of mobile-banking applications.
4.4. Effort Expectancy
Ref. [11] Similar to “performance expectancy”, “effort expectancy” is recognized as
having a significant influence on technology usage intentions. Ref. [28] The term “effort expectation,” in the context of mobile-banking applications, is the extent to which consumers
think that using mobile-banking applications is simple and also requires little effort. The
author argues that, since using mobile-banking applications requires some knowledge and
ability, users’ expectations of effort might be a key indicator of whether they would accept
and utilize these apps. Ref. [29] One of the UTAUT model’s original variables is effort
effectiveness. It is described as a person’s subjective perception of the challenges involved
in utilizing new technology. Ref. [30] Research conducted on mobile-banking adoption in
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Ghana showed no effects on intentions Ref. [12], whereas research in Indian rural areas
showed great significance. Thus, there is more scope to learn about the same.
Hypothesis 2 (H2). Effort expectancy has a significant influence over purchase intentions of
mobile-banking applications.
4.5. Social Influence
Ref. [11] “Social influence” is described as the extent to which an individual feels
significant individuals, such as family and friends, believe that he/she should utilise a
certain technology. Ref. [31] Research conducted in Maldives using the UTAUT2 model
found that social influence had a significant influence on behavioural intentions. Ref. [32]
A study was conducted to learn more about the key variables that affect E-Wallet usage
intention and to determine whether there are any variations in the relationships between
the moderators. Social influence showed the greatest association with the desire to use
among the factors examined. Ref. [33] The UTAUT model has shown informative findings
when used for mobile-application-based shopping. These outcomes also demonstrated
the importance of social influence. This shows people frequently respond to peer pressure.
Results show that users are impacted by the judgments, advice and recommendations of
key people. Ref. [13] A study was conducted on the variables influencing Indian customers’
use of payments banks. The findings show that every element, such as credibility and
social influence, directly influences behavioural intention.
Hypothesis 3 (H3). Social influence has significant influence over purchase intentions of mobilebanking applications.
4.6. Trust Factor
Ref. [34] In the proposed paper, trust is included as one of the factors in UTAUT to
understand the effect of trust on the fundamental components. Ref. [35] Based on a study
undertaken to understand the causes of behavioural intention to use (BIU) mobile payments
in Indonesia, several conclusions were drawn. The results supported the significant impact
of trust on behavioural intention to use the technology. Ref. [36] Based on a study on
adoption in Thailand too, various results were confirmed. The study showed that trust
had a big impact on BI. The researcher in this paper concluded that only when risk factor
is controlled is consumer’s behaviours for accepting the technology possible. Ref. [13] A
study conducted in Delhi with 660 respondents found that trust and security are major
reasons for not completely adopting mobile banking. Traditional research was conducted
on shopkeepers and drivers and from many unbanked participants. This extends the scope
to also understanding the impact of trust on general targets.
Hypothesis 4 (H4). Trust has a significant influence over purchase intentions of mobilebanking applications.
4.7. Risk Factor
Ref. [37] A study conducted in rural India reveals a strong presence of a risk factor
that cannot be unseen in the mobile-baking sector. Ref. [38] A study on mobile payments
with a total of 25 compiled studies and 377 studies that were collected via questionnaire
found that the most important determinants of M-payment adoption were perceived
risk and perceived trust. Ref. [39] A study conducted on ShopeePay mobile payments
was constructed using the UTAUT method. The factors that affect Shopeepay’s adoption
of mobile payments include “perceived risk”, “perceived trust”, “perceived cost”, “selfefficacy”, “effort expectation”, “performance expectation”, and “social influence”. Ref. [34]
Based on the conducted research, the researcher tried to study how trust and perceived
risk affect “citizen adoption” of e-government services. The study revealed a number of
interesting results, one of which is that perceived risk (PR) is negatively correlated with
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people’s e-government adoption. With dynamic growth in the current market, there is
scope to learn about the impact of risk on intention.
Hypothesis 5 (H5).
banking applications.
Risk has a significant influence on purchase intentions of mobile-
5. Research Methodology
This study was conducted in India and majority of respondents were from Karnataka.
With this, mobile-banking adoption can also be seen as having more potential opportunities.
Ref. [40] The research paper reveals the types of samples to be collected in SEM models.
The researcher argues that the threshold of the three categories, which are small, medium
and large, be 100, 200 and above 200, respectively.
The adopted questionnaire was initiated via pilot testing with a small group of
11 members consisting of academicians, industry expert and PG students. Further, using a
convenience sampling method, 273 respondents were chosen as sample for the study, which
was spread over 5 months, February 2022 to June 2022. The questionnaire was framed
and disseminated through Google Forms, and data was gathered. The questionnaire used
for the same can be cited under Appendix A. To reach active users of mobile banking, the
survey link was disseminated using emails and social-media platforms such as WhatsApp
also. As data was collected electronically, the authors were able to overcome the limitation
of missing, unengaged and outlier responses.
The literature was used to develop all of the measuring items. Construct and questionnaire for “performance expectancy” measurement and construction of “effort expectancy”
were derived from Ref. [11]. The items measuring “social influence”, “risk”, and “trust” in
the questionnaire [2] were adopted from Javed (2018), To make the questionnaire’s contents
easier for the respondents to grasp, the items were translated into the local language,
Kannada, and English. To make sure that each statement still had the same meaning as the
original, they were retranslated. Likert scale was used to evaluate every item, with five
being strongly disagreed to strongly agreed.
Ref. [41] Since PLS helps to calculate complex models with multiple constructs, indicator variables, and structured pathways, neglecting distributional assumptions on the
data, the PLS-SEM approach is of significant interest to many scholars. PLS-SEM tool is
predictive cause view of SEM which places an emphasis on prediction while calculating
various statistical models, whose structure is intended to give causal justification. Based
on the NED Kock’s recommendation, we also tested common method variance through
full collinearity method [42], we checked VIF for every latent variable and also rest of the
variables to ensure the values are below suggested threshold of 3.3. Based on the results, it
was confirmed that data were free from common methods variance as all generated values
never showed values greater than 3.3.
6. Ethics
Research ethics is one of the most important components of any research because it
forms the basis of every stage of research. One of the major requirements was obtaining
consent acknowledgement from the people who answered the questionnaire and keeping
all the information relating to them safely, which was performed with the utmost ethical
consideration. Apart from primary data, a lot of information was studied and adopted
from many research journals, articles, and web sources, which are given the appropriate
credit and are cited for their work.
7. Demographical Information
Based on the data collected, it was found that males and females constituted about
52 and 48 percent of respondents, respectively.
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Based on the age distribution, the majority of the respondents, that is 71 percent,
were aged between 18–25, 11 percent belong to 46–55 and the rest were distributed in the
remaining categories.
Based on the occupational information, 44 percent were not employed, 21 percent of
the respondents were self-employed, 26 percent were salaried (Private Organization) and
the rest fell under government sector, which was 9 percent. The same has been shown in
Table 1 below (Demographical information).
Table 1. Demographical information.
Gender
Male
Female
143
130
Age Distribution
18–25
196
26–35
19
36–45
10
46–55
29
55 and above
18
Occupation
Self employed
58
Salaried (pvt)
71
Salaried (govt)
23
Unemployed
121
8. Statistical Results
(a)
Outer Model Evaluation
Ref. [41] In order to have evidence of reliability and validity, the calculation of the outer
model is necessary. Calculating the measurement of the inner model may also be necessary.
It is clear that an outer-model evaluation is required to perform an inner-model evaluation.
The researcher also feels the same. The researcher also stated that when standardized
indicator loadings are ≥0.70, the reliability indicators are acceptable.
(b)
Indicator Reliability
As shown in Table 2, all the indicators are more than 0.70, thus proving the great
reliability of the model. With the values generated, the reliability measurement falls in the
acceptance region.
(c)
Construct Reliability and Validity
Ref. [41] Hair suggested that internal consistency, reliability, not be measured using
“Cronbach’s alpha”. The internal consistency and reliability should be measured by composite reliability. Composite reliability must be ≥0.70, and the same in this study is acceptable.
Based on the above table, we can observe that all the composite reliability values fall into
the acceptable region, making the model more reliable. Ref. [43] The average variance
extracted (AVE) value requirement for each construct should exceed 0.5. The AVE values
generated in the above table are also above 0.5, thus making the model convergent valid,
or it is concluded that the convergent validity with reference to all variables is acceptable.
The same has been shown below in Table 3.
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Table 2. Reliability results.
Effort
Expectancy
EE1
0.849
EE2
0.899
EE3
0.866
Performance
Expectancy
PE1
0.725
PE2
0.744
PE3
0.716
PE4
0.828
Purchase
Intention
PI1
0.856
PI2
0.860
PI3
0.829
PI4
0.738
Risk
R1
0.864
R2
0.891
R3
0.890
Social
Influence
SI1
0.834
SI2
0.874
SI3
0.784
Trust
Factor
T1
0.716
T2
0.813
T3
0.750
Table 3. Cronbach’s Alpha, Composite Reliability and AVE.
Cronbach’s Alpha
Composite Reliability
Average Variance Extracted (AVE)
Effort Expectancy
0.842
0.905
0.760
Performance expectancy
0.748
0.840
0.569
Purchase Intention
0.840
0.893
0.676
Risk
0.858
0.913
0.778
Social Influence
0.717
0.842
0.642
Trust Factor
0.751
0.804
0.579
(d)
Discriminant Validity (DV)
Ref. [43] The Fornell–Larcker criteria is an analysis that contrasts the AVE square
root value with the construct correlation value that has the greatest value in any column
or row relative to the highest correlation value of any other construct. This technique
is predicted on the idea that latent variables should account for the item variation more
accurately than they should for other latent variables. The table below shows the higher
AVE squared values compared to the correlation values for each other construct below the
parent construct.
(e)
Findings of Fornell–Larcker Criteria Analysis
Based on the Table 4, we can state that the model has good discriminant validity. The
table shows overall roots in each construct have a greater value than its correlating latent
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values; this is because the square root of AVE in every construct produced a higher value
that its correlating value. Refs. [43,44] The same is supported in various research.
Table 4. Discriminant validly value.
EE
(f)
PE
PI
R
SI
EE
0.872
PE
0.542
0.754
PI
0.647
0.459
0.822
R
0.320
0.322
0.392
0.882
SI
0.453
0.402
0.505
0.369
0.802
T
0.360
0.332
0.483
0.455
0.432
T
0.761
Heterotrait–Monotrait Ratio (HTMT)
The value of the heterotrait–monotrait ratio is higher in number when compared to
the previous table. Ref. [40] Based on the study, the threshold for gauging discriminant
validity is suggested to be a value under 0.9. This study also employs the HTMT test and
all the values fall within this acceptability region. The same has been shown in the Table 5.
Table 5. Values showing HTMT ratio.
EE
PE
PI
R
SI
T
EE
PE
0.681
PI
0.745
0.556
R
0.372
0.394
0.453
SI
0.577
0.535
0.645
0.466
T
0.456
0.465
0.621
0.594
0.618
9. Structural Model Assessment
Ref. [43] The first step is to measure the model, which is followed by measuring the
co-efficient of determination R2 to determine the path coefficients using 5000 resamples
with the bootstrapping technique. It must be noted that in the table given below, the
structural model gives the value with reference to the value for hypothesis testing.
(a)
Path Coefficients Values
We performed boot strapping with 5000 numbers to see how much our results support
the hypotheses. Based on the below in the Table 6. the first hypothesis is significant
because the “T-value” is 6.219 > 1.96 and p-value is 0.000 < 0.05; however, the second
hypothesis is insignificant because the “T-value” 0.974 < 1.96 and p-value is 0.330 > 0.05;
the third hypothesis is also insignificant because the “T-value” 1.393 < 1.96 and “p-value”
is 0.164 > 0.05; the fourth hypothesis is significant because the “T-value” is 2.707 > 1.96
and “p-value” is 0.007 < 0.05; and the last fifth hypothesis is also significant because the
“T-value” is 3.097 > 1.96 and “p-value” is 0.002 < 0.05.
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Table 6. Information showing significance.
Path Coefficients
T Statistics
(|O/STDEV|)
p Values
Status
Effort Expectancy→Purchase Intention
0.442
6.219
0.000
Accepted
Performance Expectancy→Purchase Intention
0.062
0.974
0.330
Rejected
Risk→Purchase Intention
0.081
1.393
0.164
Rejected
Social Influence→Purchase Intention
0.166
2.707
0.007
Accepted
Trust Factor→Purchase Intention
0.195
3.097
0.002
Accepted
(b)
Test for Goodness of Fit
It is very important to understand the goodness of fit of the model for validation. This
was studied by employing previous literature as a reference. As per [43], the standardized
threshold limit for “standard root mean square residual” (SRMR) is 0.14; the current model
falls into the acceptable region with a score of 0.077, which is below the threshold. The
same can seen in the Table 7.
Table 7. Information showing SRMR Value with reference to Model fitness.
SRMR
(c)
0.075
R-Square
Based on the results of calculations using SmartPLS, the values of R Square in each
construct are as follows (Table 8):
Table 8. Information relating to model fitness with reference to R value.
PI
R-Square
R-Square Adjusted
0.524
0.515
R-square is measured to understand the consistence of the model or to understand how
well the model fits. There are many studies to support the same and also provide thresholds
to measure the fitness of the model. Ref. [45] Based on the research conducted, the researcher
suggested that every construct, to prove its internal validity, should follow the rule of thumb
with reference to r-square. Researchers mention three targets for assessment; that is, R2
values of 0.25 is weak, 0.50 would be moderate, and 0.75 would be strong, respectively. As
per the data generated, the R-square value is 0.524, making the construct a moderate fit.
10. Discussion
The UTAUT model helps us to learn more about “performance expectancy”, “effort
expectancy”, “social influence”, “risk” and “trust” and their influence on “purchase intention” in the adoption of mobile-banking applications. The discussion related to the same
are further noted below.
10.1. “Performance Expectancy” Has a Significant Influence on “Purchase Intention”
Based on the study conducted, we were not able to obtain more evidence on the impact
of “performance expectancy” on “purchase intention”, so there may be many other factors
that really influence “purchase intention.” Ref. [46] is a study on mobile banking which
draws comparison between bankers and non-bankers. It also reveals that non-bankers
have very low awareness of the benefits that they could enjoy by adopting services with
reference to mobile-banking applications. However, this contradicts some preliminary
research [47]. Research conducted in Oman with 436 respondents also showed the great
significance on behavioural intention. Refs. [14,46,47] are some of many past studies that
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discuss this significant impact. However, based on the data we collected, we were not able
to establish any significant relation.
10.2. The Significance of “Effort Expectancy” on “Purchase Intention”
Based on the study conducted, we found that “effort expectancy” has a significant
influence on “purchase intention”. This can be supported by previous research as well.
Refs. [48,49], based on the research conducted in other geographical location such as
Vietnam and Pakistan, also supports the same conclusion. Based on the data collected,
we can understand that effort expectation would be a very important point that service
providers cannot neglect and concentrating on this same may result in positive behaviour.
10.3. “Social Influence” Has a Significant Impact on “Purchase Intention”
Ref [50] social influence has impact on PI however some time it shows indirect impact
Ref. [51], based on the research conducted on 125 participants, “social Influence” has little
impact on the acceptance of mobile-banking applications. Ref. [49] The literature conducted
recently reveals a significant and positive observation. The paper also supports the point
that social influence is a very important factor and cannot be neglected when conducting
research on the adoption of mobile banking. Data collected for the proposed study also
supports the same, showing a significant impact on purchase intention.
10.4. Significance Influence of “Risk” over “Purchase Intention”
Ref. [52] Research conducted on millennials for e-banking services in India revealed
a negative intention to use the services. Based on the data and analysis, we were not
able to find a good amount of evidence proving that the “risk” factor has a great deal
of influence on the “purchase intention”. However, this contradicts with a few previous
studies [53] conducted in India which revealed that perceived “risk” had a major influence
over “purchase intention” but authors also suggested the influence of demonetization on
the same. Refs. [7,22,54] It must be noted that research conducted in other geographical
regions showed some significant impact.
10.5. Significance Influence of “Trust” over “Purchase Intention”
Ref. [55] extended the literature performed from an Indian perspective and found that
trust was the strongest factor for adopting mobile payments. Ref. [56], based on the research
conducted in India with 465 respondents, found that trust had a very significant impact on
the adoption of mobile baking. Ref. [57], based on the research conducted on the attitude
of consumers on mobile banking in India, showed that trust is highly influential. Ref. [58]
However, research conducted on 720 mobile-banking users did not show significance in
other geographical area such as Morocco. Based on the data collected, we discovered
that trust has a significant impact on purchase intent. This can be acknowledged with
past studies also. Ref. [59] It must be noted there is no assurance that vendors will act
opportunistically toward internet customers. Following adoption of wireless banking,
consumers must rely on trust in order to navigate various circumstances, thus making it
very evident that organizations cannot neglect the trust factor in the area of mobile banking.
Table 9 shows results of testing of hypothesis.
Table 9. Results of the testing of hypothesis.
Hypothesis
Status
H1: Performance expectancy has a significant influence over purchase intentions of mobile-banking applications.
Rejected
H2: Effort expectancy has a significant influence over purchase intentions of mobile-banking applications.
Accepted
H3: Social influence has a significant influence over purchase intentions of mobile-banking applications.
Accepted
H4: Risk has a significant influence over purchase intentions of mobile-banking applications
Rejected
H5: Trust has a significant influence over purchase intentions of mobile-banking applications”
Accepted
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Data’s are boot strapped to see the strength of the hypothesis. The generated figure
can be referred below (Figure 2).
Figure 2. PLS algorithm results.
11. Practical Implications
Based on the proposed model, we can expect a few practical implications. Firstly, this
study will contribute directly to future researchers and academicians who would and who
perceive their interest to be in a similar vein. This study will support all banking and financial
institutions in building new strategies to retain and increase their customer base. These
institutions can use these studies to differentiate between a few significant and non-significant
factors that would influence Indian consumers with reference to purchasing intention.
It must be noted that every feature newly added to the existing banking technology
may be or may not be appreciated by consumers but, based on the proposed study, chances
of adoption are higher when they work on areas such as efforts that consumer may have to
put in, which showed very significant influence in the study. The adoption rate may also
increase when industries adopt a socialistic approach, where people are forced to be part of
technology which is being used in and around them. Based on a few past studies and the
proposed study, it is suggested that ‘trust’ is one of the important factors that should not be
neglected by these financial mobile application developers.
Thus, the above study will definitely serve the purpose of increasing adoption for
applications and, in return, countries may benefit from an increase in financial inclusion.
12. Conclusions and Future Directions
The overall objective of the research was to find various factors influencing a consumer’s decision to use mobile-banking services and also to know if the UTAUT model
holds good for predicting intentions for mobile banking. This study is an implementation
of the UTAUT model originally proposed by Venkatesh (2002) and modified further [7].
Based on the results, it was found that various variables used in the model (UTAUT)
have a great influence on the acceptance of mobile-banking applications in India. It must
be noted that “effort expectancy” had the most significant impact on the purchase or
usage of the mobile-banking applications. We can see that customers are very comfortable
with the similar app, and they like to self-study and feel that they are able to operate it
Sustainability 2022, 14, 14506
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without any formal training. This study also reveals that “social Influence” has a great
impact on “purchase intention”. According to the study, customers are influenced by their
peer group or people around them to use mobile-banking applications. The paper also
provided evidence that the “trust” factor had a significant impact on adoption. Based on
the questionnaire given to customers, it was found that they gain trust when they see some
other people use an application. They also trust availability and reliability. The study found
no evidence of a significant impact of performance and risk on purchase intent.
The researchers conducted various reliability and construct validity tests to understand
whether the UTAUT is fit to study technology adoptions. Data collected and analysis
proved that the UTAUT model is the right model to understand technological adoption
or acceptance.
13. Limitations
The research did not take into account potential variation brought about by respondent
socioeconomic class, gender, or age. It has previously been mentioned that the research
concentrates on individuals from a certain geographic area. Output cannot be generalized
for people from another place or another country. This limitation is due to limited time and
the respondent’s hesitation to answer a few demographical questions. A limitation could
also be that it was not possible to see that every respondent was very honest and focused
while answering the questionnaire.
14. Scope for Future Studies
Technology development and infrastructural improvements are creating opportunities
to assist and streamline daily financial transactions and corporate operations. Ref. [60]
Research performed for the clients in Malaysia also reported financial inclusion as one the
factors for mobile banking. However, we did not find any direct discussion on the same,
which could be a good area to explore in upcoming research [61]. Many authors have also
suggested that finance could also be taken as an independent variable. This gives scope to
extend the model in the future.
The adoption of mobile banking gives customers greater freedom to be active as
well as the ability to pay and purchase on the go. With the right assistance, this portable
transaction method may be incredibly user-friendly, allowing consumers to take part
without experiencing much difficulty. However, as there will be more advancement in
technologies, there will be more scope for studies, meaning the same model can be applied
to many other domains of technology segments. The researcher has used few variables to
identify significant effects; in the future, there is lot of scope to use many other variables
to see how they are responsible for intention or adoption. There is also scope to check the
results by adding various components via mediation and moderation effects. The outcomes
could have better business implications for the better implementation of technologies in
the market.
Author Contributions: All authors contributed equally to this research work. All authors discussed
the results and contributed to the final manuscript. All authors have read and agreed to the published
version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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Appendix A. Data Imputation
After conducting confirmatory factor analysis, the data imputation technique was
applied to calculate the factors from the associated items.
Performance expectancy was derived from the following questionnaire items:
In conducting banking affairs, [5]
(PE1) I would perform better using mobile banking
(PE2) I would save time using mobile banking
(PE3) I would use mobile banking everywhere
(PE4) I would find mobile banking beneficial
The user’s effort expectancy to use mobile banking was calculated from the following
questionnaire items: [2,5]
(EE1) I have no trouble learning how to utilise mobile banking
(EE2) I have no trouble learning how to use mobile banking
(EE3) I have no trouble using mobile banking.
The social influence exerted by the peer group and surroundings was captured by the
following questions: [2,5]
(SI1) People who matter to me believe I should use mobile banking
(SI2) People have the power to affect my behaviour
(SI3) Most of the people in my close surroundings use mobile banking
User’s perception of risks associated with mobile banking was captured by the following questionnaire items: [2,5]
When using mobile banking,
(R1) My belief is that my information is kept private
(R2) My belief is that my transactions are safe
(R3) My belief is that my privacy would not be compromised
The influence of trust factor was illustrated using the following three questionnaire
items: [2]
I could use mobile banking.
(T1) if I had the built-in help guidance for assistance
(T2) I trust the system’s reliability and availability
(T3) if I had seen someone else using it
Participant’s intention to use the mobile banking service was demonstrated by the
following questions: [2]
When dealing with banking affairs
(PI1) I like using mobile banking
(PI2) I plan to use it
(PI3) I would use it
(PI4) I would rely on using mobile banking for a lot of transactions
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