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ANALYSIS OF FACTORS INFLUENCING USER INTENTION IN USING OVO
APPLICATION
By:
Yugo Falafrima Putra
135020307121027
Supervised by:
Dr. Zaki Baridwan., Ak., CA., CPA., CLI, CTA.
ABSTRACT
In this era, technology development has provided many innovations in transaction.
Nowadays, there is a popular trading system called mobile payment. The use of mobile payment
transaction slowly changes the culture in Indonesia. This study examines the factors that
influencing user interest in using OVO applications. In order to do so, this study use perspective
of Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology
2 (UTAUT2). The author uses quantitative research method for this study. The data was collected
using questionnaires. The respondents were 322 undergraduate students of accounting department
in Universitas Brawijaya Malang, who have experience in using OVO application services. The
data and hypothesis were analyzed using Structural Equation Model (SEM) based on Partial Least
Square (PLS). These research findings indicate that intention to use OVO application is determined
by Ease of Use, Perceived Usefulness, Perceived Risk, Price Value, and Social Influence towards
Behavioral Intention to do a transaction using OVO application services. The results show that all
variables are significant influence towards the behavioral intention to use OVO application
services. Moreover, all of the variables have positive influence, except perceived risk variable.
Keywords: Mobile Payment, Behavioral Intention, Ease of Use, Perceived Usefulness, Perceived
Risk, Price Value, and Social Influence.
I. Research Background
In this era of globalization, the
information technology has grown rapidly.
The Internet has become a necessity for the
community in this era. The online shopping
business also participates making the internet
increasingly in need of many people.
According to Asosiasi Penyelenggara Jasa
Internet Indonesia (APJII) in 2018, there are
171.17 out of 262 million internet users in
Indonesia, which surpasses the half of
population in Indonesia. In 1990, electronic
commerce (e-commerce) had introduced a
unique way in business world. Nowadays,
many people are using this unique way to do
a transaction. It enables users to buy and sell
in online transaction. Using e-commerce for
business payments have taken the form of
exchanging money electronically, as we
called electronic payment (e-payment). In
Indonesia, there are many famous mobile
payment systems such as, Go-Pay, Ovo, and
Dana.
In general, e-payments in the context
of e-commerce refers to online transactions
which are conducted via internet, although
there are many other forms of electronic
payment (Armesh et al., 2010). E-payments
can also be defined as the payment process
made without the use of paper instruments
(Tella, 2012). The e-payment systems
consists of online credit card transaction,
electronic wallet (e-wallet), electronic cash
(e-cash), online stored value systems, digital
accumulating balance systems, digital
checking payment systems and wireless
payment systems (Laudon, 2011).
Transaction in e-commerce site using
Electronic Wallets (e-wallets) is common
nowadays. Along with smartphone
production, plenty of services have been
created to utilize the possible functions of
smartphone. Not only smartphone is used as
a communication devices, but it is also to be
used as tool for socialization, entertainment,
internet access, and even payment. Mobile
users nowadays, can use their smartphone to
make money transaction or payment by using
applications installed on their phones.
M-wallet on the one hand, is a
technology that needs to be installed in the
smartphone and allows customers to store
money and do online transactions directly
from the wallet whereas QR code works
through a few banking apps, store apps to
integrate debit/credit card details (Madan &
Yadav, 2016; Singh et al., 2017).
This study is focusing on behavioral
intention in using OVO application, which in
this case is the student of Accounting in
Universitas Brawijaya, Malang. According to
PT Visionet Internasional – the developer of
OVO application – OVO is the platform of
electronic payment in Indonesia was
established in November 2017. Until the end
of November 2018, the OVO user base grew
more than 400 percent with the application
installed on 115 million devices
(http://www.kompas.com.html).
Technology Acceptance Model is a
model arranged to predict and explain how
technology users can accept and use the
technology in their work (Davis, 1989).
According to the theory of Technology
Acceptance Model (Davis, 1989) stated that
there are two factors that influence individual
interest in using a system technology (OVO
application) that are perception of usefulness
and perceived ease of use.
The User Acceptance and Use of
Technology 2 model (UTAUT 2) is a
development model of User Acceptance and
Use of Technology 1 (UTAUT 1). This
model proposed by Venkatesh (2012) stated
that there are several factors that influence
interest in using a technology system. These
factors are performance expectations,
business expectations, social factors,
facilitating conditions, hedonic motivation,
price values and habits.
This study is a replication of the
previous research by Singh et al. (2019). The
research gap between this study with the
previous research done by Singh et al (2019)
is the previous research not using the variable
of price value that found by Venkastesh et al.
(2012) in UTAUT2. Based on the description
above, the author wrote his minor thesis
entitled “ANALYSIS OF FACTORS
INFLUENCING USER INTEREST IN
USING OVO APPLICATION.”
II. Literature Review and Hypothesis
Development
Mobile Payment
According to Au and Kauffman (2008),
mobile payment can be defined as any
payment where a mobile device is used to
initiate, authorise and confirm an exchange of
financial value in return for goods and
services.
Type of Mobile Payment
According to Luna et al (2018) there are
several types of mobile payment services,
such as:
1. SMS (Short Message Service)
The use of SMS for mobile payment
requires a communication protocol
enabling the exchange of short text
messages between two mobile devices
(Valcourt et al., 2005).
2. NFC (Near Field Communication)
NFC is economically attractive because it
is based on open standards and users are
not obliged to pay for licensing fees.
3. QR (Quick Response)
QR codes are storage systems which use
a dot matrix or two dimensional bar code
developed by Denso Wave that can be
printed or shown on a screen and are
interpreted by a special reader to provide
more extensive information than that
found in a traditional bar code (Denso
Wave, 2000).
OVO Application
OVO is a smart financial apps launched
under the auspices of LippoX as a digital
payment company that owned by the Lippo
Group and PT Visionet Internasional. This
application tries to accommodate various
needs related to cashless and mobile
payment. OVO wants to reach its services as
a simple payment and smart financial
services. In general, OVO Cash can be used
for various types of payments which have
been cooperating with OVO can be more
faster (www.cermati.com). Whereas OVO
Point is a loyalty rewards for those who make
transactions using OVO Cash at OVO partner
merchants. For OVO Points, you can
exchange them for various attractive offers to
be exchanged for transactions at OVO
partner merchants.
By using OVO, the users can make
various payment transactions while
collecting OVO Points. The types of
transactions, such as:
a) Perform online or offline transactions
at merchants who work with OVO.
b) Parking payments in places that
cooperate with OVO.
c) Purchase telephone credit.
d) Payment in the GRAB application
services.
e) Insurance payment.
f) Electricity payment.
OVO PayLater
OVO Paylater established at the
beginning of January 2019. OVO PayLater
can be defined as a digital financial
application (fintech/financial technology)
that serves the users to purchase goods in
advance and pay later (www.kompas.com).
The credit limit that provided by OVO comes
from Taralite, a startup company of fintech
lending. OVO PayLater can only be activate
if the user in the certain city. Such as Jakarta,
Depok, Bogor, Bekasi, Tangerang, Bandung,
and Surabaya. If the user is not in the city that
already mention, the user cannot activate the
OVO PayLater. OVO PayLater has validity
for six months. After six month of activation,
the user must have to re-register in OVO
application or in OVO that provided by
Tokopedia.
Technology Acceptance Model (TAM)
Technology Acceptance Model (TAM)
was introduced by Davis in 1986. It is an
adaptation of the Theory of Reasoned Action
(TRA), which is adapted to model the user
acceptance of the system information. The
purpose of Technology Acceptance Model
(TAM) is to provide an explanation of mobile
reception in general, to explain the behavior
of the user includes a broad range and
population. The main purpose of Technology
Acceptance Model (TAM) in this study is to
provide a basis for detecting the impact of
external factors on internal beliefs, attitudes,
and intention.
Figure 1. Technology Acceptance Model
(Davis, 1998)
Unified Theory of Acceptance Use of
Technology 2 (UTAUT2)
Unified Theory of Acceptance Use of
Technology (UTAUT) was introduced by
Venkatesh, Morris, Davis and Davis in 2003
and later to be extended by Venkatesh, Thong
and Xu in 2012. The model is developed by
TRA, TAM, TPB, TAM and TPB,
Motivational Model, Model of PC
Utilization, Innovation Diffusion Theory and
Social Cognitive Theory Unified Theory of
Acceptance Use of Technology (UTAUT)
emerged for reviewing and discussing the
literature of adoption of new information
technology from the main existing models,
compare them empirically, formulating a
unified model and validating it empirically.
The model state several factors that
influence the intention and acceptance in
using technology system. UTAUT 2
explained performance expectancy, effort
expectancy, and social influence affect the
behavioral intention to use a technology, and
the behavioral intention and facilitating
conditions influence the actual use of
technology.
Figure 2. Unified Theory of Acceptance Use
of Technology 2 (Venkatesh, 2012)
Hypothesis
Figure 3. Research Framework
H1: Ease of Use has positive influence on
the user’s intention to use mobile
payment services.
H2: Perceived Usefulness has positive
influence on the user’s intention to use
mobile payment services.
H3: Perceived Risk has negative influence
on user’s intention to use mobile
payment services.
H4: Price Value has positive influence on the
user’s intention to use mobile payment
services.
H5: Social Influence has Positive influence
on user’s intention to use mobile
payment services.
III. Research Method
This study used quantitative research
approach with survey method as the data
collection method. The population in this
study is undergraduate accounting students in
Faculty of Economics and Business,
Universitas Brawijaya, in neither regular
program nor international program. In this
study, the researcher used non-probability
sampling in convenience sampling method.
Convenience sampling refers to the
collection of information from members of
the population who are conveniently
available (Sekaran and Bougie, 2013).
The method used in this study is
Slovin method to calculate the amount of
sample by using the error rate of 5% of the
population. The sample size in this research
is 299 students. The researcher used
realibility testing and validity testing to
determine the rate of correctness of the
questionnaire. After that, the researcher
performs data processing by classifying data
according to the demographics of the
respondents. Furthermore, the data is
analyzed using Partial Least Square (PLS)
after that, the conclusion are formulated.
Definition, Indicator and Variable
Measurement
Variables can be defined as any
aspect of a theory that can vary or change as
part of the interaction within the theory.
Therefore, variables can influence and
change the results of the study. There are five
construct in this study, namely Perceived
Usefulness (PU), Ease of Use (EOU),
Perceived Risk (PR), Social Influence (SI),
and Price Value (PR).
1. Perceived Usefulness
Perceived Usefulness is defined as the
individual’s perception that using the new
technology will enhance or improve his or
her performance (Davis, 1989). In this
study, perceived usefulness composes four
items (Singh et al., 2019). The items stated
that mobile wallet is useful, helpful,
efficien, and makes work become easier.
2. Perceived Ease of Use
Perceived Ease of Use is defined as the
individual’s perception that using the new
technology will be free of effort (Davis,
1989). In this study, Ease of Use composes
four items (Singh et al., 2019). The items
stated that mobile wallet is easy to use, cler
and understandable, time and energy
saving, and easy to interact with.
3. Perceived Risk
Perceived Risk is defined as a combination
of uncertainty plus seriousness of outcome
involved (Bauer, 1967), and the
expectation of losses associated with
purchase and acts as an inhibitor to
purchase behavior (Peter and Ryan, 1976).
In this study, Ease of Use composes four
items (Singh et al., 2019). The items stated
that mobile wallet is not completely
secure, makes users feel not safe with their
personal data and financial information,
the high risk of misusing mobile wallet.
4. Social Influence
Social influence is consumers perceive
that important others (e.g. family and
friends) believe that they should use a
particular technology (Venkatesh et al.,
2012). In this study, social influence
variable composes three items (Venkatesh
et al., 2012). The items stated that people
who important for the respondent suggest
that the respondent should use the system,
people who influence the respondent
behaviour suggest that the respondent
should use the system, people whose
opinions that the respondent value prefer
the respondent to use the system.
5. Price Value
Price value is defined as the perceived
benefits of using a technology given its
costs (Venkatesh et al., 2012). In this
study, price value variable composes
three items (Venkatesh et al., 2012). The
items stated that the system is reasonably
priced, good for value for money,
provides good value at current price.
6. Behavioral Intention
Behavioral intention is the perceived of
individual's willingness to conduct a
behavior in using a technology system. In
this study, the variable is used to measure
the customers’ intention to do a
transaction in OVO applicatio. The
Behavioral intention composes three
items (Singh et al, 2019). The items
stated that the respondent intend to use
mobile wallet, when the opportunities
arise, tha respondent likely to use mobile
wallet in near future, the respondent plan
to use mobile wallet frequently in their
daily life.
IV. Finding and Discussion
In this study, the respondents of the
researcher are the undergraduate students of
Accounting Department, Faculty Economic
and Business, Universitas Brawijaya,
Malang. The number of questionnaires
distributed in this study are 350. As the
number of questionnaires received are 322,
the rest 18 were not returned. After checking,
10 questionnaires cannot be used for research
data.
a. Evaluation Model
The evaluation model was done using
Partial Least Squares (PLS) in order to
estimate the parameters and predict the
relationship casually. Evaluation of the
model is done with three stages, namely the
testing of convergent validity, testing of
discriminant validity, and the testing of
reliability.
Table 1. Algorithm Table
BI: Behavioural Intention, EOU: Ease of
Use, POU: Perceived of Usefulness, PR:
Perceived Risk, PV: Price Value, SI: Social
Influence
Convergent Validity: The assessment in
convergent validity testing is based on the
AVE, communality and factor loading. The
rule of thumb for both AVE and communality
is more than 0.50 (> 0.50) while the factor
loading is > 0.70 (Chin, 1995 in Abdillah and
Hartono, 2015). The rule of thumb is
typically used to make the initial examination
of the matrix factor, where ± 0.30 is
considered as having met as minimum level,
for loading ± 0.40 is considered better and for
loading > 0.50 is considered significantly
practical (Hair et al., 2006 in Abdillah and
Hartono, 2015).
Table 2. Outer Loading
BI EOU POU PR PV SI
BI1 0.867
BI2 0.913
BI3 0.903
EOU1 0.856
EOU2 0.862
EOU3 0.840
EOU4 0.819
EOU5 0.743
POU1 0.820
POU2 0.847
POU3 0.855
POU4 0.808
PR1 0.865
PR2 0.875
PR3 0.860
PR4 0.829
PV1 0.795
BI EOU POU PR PV SI
PV2 0.900
PV3 0.832
SI1 0.858
SI2 0.880
SI3 0.862
SI4 0.864
BI: Behavioural Intention, EOU: Ease of
Use, POU: Perceived Usefulness, PR:
Perceived Risk, PV: Price Value, SI: Social
Influence
Based on the table above, it can be
seen the value AVE and Communality in
each construct is more than 0.5. Similarly, the
outer loading test results in the table Outer
Loading 4.13 above, all indicators value is
above 0.7.
Reliability testing: After a test of
construct validity is done and valid data are
obtained, reliability testing takes place.
Reliability test can be done in two methods:
Cronbach's Alpha value, whose value must
be more than 0.6, and Composite Reliability
value, that should be more than 0.7.
According to algorithm table 4.8 above, all
variables have the value of Cronbach's Alpha
is more than 0.6 and Composite Reliability
value of more than 0.7. Hence, it can be
concluded that the data and the results of
measurements are considered reliable.
b. Hypothesis Testing
In hypothesis testing, if the
coefficient path shown by the T-statistic is
more than 1.96, then the alternative
hypothesis can be stated as supported. If the
statistical value of T-statistic is less than 1.96,
then the alternative hypothesis is not
supported
Table 3. Variable Effect
Original
Sample (O)
Standard
Error
(STERR)
T Statistics
(|O/STERR|)
EOU
-> BI 0.1498 0.0681 2.199
POU -
> BI 0.1289 0.0621 2.0767
PR ->
BI -0.1872 0.0607 3.0825
PV -
> BI 0.2775 0.0618 4.4876
SI ->
BI 0.2126 0.0472 4.5066
BI: Behavioural Intention, EOU: Ease of
use, POU: Perceived of Usefulness, PR:
Perceived Risk, PV: Price Value, SI: Social
Influence
Hypothesis 1
The results of testing the hypothesis 1
states that the relationship of the Perceived
Ease of Use with Behavioral Intention shows
the path coefficient value of 0.1498 with a t
value of 2.199. The direction of a positive
relationship indicates if Perceived Ease of
Use has an effect, it will be followed by an
increase in the Behavioral Intention variable.
The value of t-count is greater than the t-table
(1.96). The value of ogirinal sample estimate
positive, which indicates that variable of
Perceived Ease of Use has significant
influence to the Behavioral Intention in using
OVO application. Based on the result, in can
be concluded that Hypothesis 1 is
supported.
Hypothesis 2
The results of testing the hypothesis 2
states that the relationship of the Perceived
Usefulness with Behavioral Intention shows
the path coefficient value of 0.1289 with a t
value of 2.0767. The direction of a positive
relationship indicates if Perceived Usefulness
has an effect, it will be followed by an
increase in the Behavioral Intention variable.
The value of t-count is greater than the t-table
(1.96). The value of ogirinal sample estimate
positive, which indicates that variable of
Perceived Usefulness has significant
influence to the Behavioral Intention in using
OVO application. Based on the result, in can
be concluded that Hypothesis 2 is supported
Hypothesis 3
The results of testing the hypothesis 3
states that the relationship of the Perceived
Risk with Behavioral Intention shows the
path coefficient value of -0.1872 with a t
value of 3.0825. The direction of a ngeative
relationship indicates if Perceived Ease of
Use has an effect, it will be followed by an
increase in the Behavioral Intention variable.
The value of t-count is greater than the t-table
(1.96). The value of ogirinal sample estimate
negative, which indicates that variable of
Perceived Risk has significant influence to
the Behavioral Intention in using OVO
application. Based on the result, in can be
concluded that Hypothesis 3 is supported
Hypothesis 4
The results of testing the hypothesis 1
states that the relationship of the Price Value
with Behavioral Intention shows the path
coefficient value of 0.2775 with a t value of
4.4876. The direction of a positive
relationship indicates if Price value has an
effect, it will be followed by an increase in
the Behavioral Intention variable. The value
of t-count is greater than the t-table (1.96).
The value of ogirinal sample estimate
positive, which indicates that variable of
Price value has significant influence to the
Behavioral Intention in using OVO
application. Based on the result, in can be
concluded that Hypothesis 4 is supported.
Hypothesis 5
The results of testing the hypothesis 1
states that the relationship of the Social
Influence with Behavioral Intention shows
the path coefficient value of 0.2775 with a t
value of 4.4876. The direction of a positive
relationship indicates if Social Influence has
an effect, it will be followed by an increase in
the Behavioral Intention variable. The value
of t-count is greater than the t-table (1.96).
The value of ogirinal sample estimate
positive, which indicates that variable of
Social Influence has significant influence to
the Behavioral Intention in using OVO
application. Based on the result, in can be
concluded that Hypothesis 5 is supported.
V. Conclusion
Conclusion
In this study, there are several
variable, as follows Perceived Ease of Use,
Perceived Usefulness, Perceived Risk, Price
Value, and Social Influence. The result of the
research is the four variables has significant
influence towards behavioural intention to
use OVO application services. The research
model was tested in students in Accounting
Department of Universitas Brawijaya who
have an experience in doing a transaction in
OVO application services. In conclusion, the
results of this study shows clearly that there
is a significant effect of ease of use, perceived
usefulness, perceived risk, price value, and
social influence towards user’s intention use
OVO application services.
Implication of the Research Result
The results of this study is expected to
provide input for the management of
companies, especially Mobile Payment
provider to give more attention to consumer’s
ease of use, perceived usefulness, perceived
risk, price value, social influence and
intention in doing transaction in OVO
application services. And also this study is
expected to be contribute to improve the
system of OVO application to be easier to the
users to use the OVO application services.
After that, it is recommended for OVO to
make a “recommendation” features so that
customers’ can make a recommendation to
their families, friends, teachers and students
to use Traveloka, therefore people who have
no experience on using OVO can have an
intention to.
Limitation
There are some obstacles in this
study. First is researcher used convinience
sampling method, this technique was used
because the researcher only processing data
of students who have used the OVO
application feature. This method however
was perceived as necessary for the authors
due to the population of the study being
unknown, and a limited time frame that the
primary data collection had to be gathered.
This method enabled the authors to conduct
the study on OVO application features and
collect the necessary sample size.
The collection of the questionnaire
was extremely challenging because author
had to ask the undergraduate students of
Accounting Department, Faculty of
Economics and Business, Universitas
Brawijaya who active in period 2018/2019
through online messaging application.
Furthermore, the results of the study are
limited due to the differences in the
demographic variables (eg. gender, semester,
etc.) of the respondents.
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