analysis of factors influencing user intention in …

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

http://www.kompas.com.html/

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

../Journal/OVO%20-%20Cara%20Top%20Up,%20Cara%20Cek%20Saldo,%20dan%20Promo%20-%20Cermati_files

../Journal/OVO%20Rilis%20Fitur%20OVO%20PayLater%20Dalam%20Aplikasi%20OVO_files

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


Use, POU: Perceived Usefulness, PR:


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


use, POU: Perceived of Usefulness, PR:


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



Usefulness with Behavioral Intention shows



relationship indicates if Perceived Usefulness

has an effect, it will be followed by an





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



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




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


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


Price value has significant influence to the

Behavioral Intention in using OVO


concluded that Hypothesis 4 is supported.

Hypothesis 5


states that the relationship of the Social

Influence with Behavioral Intention shows



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


Social Influence has significant influence to

the Behavioral Intention in using OVO


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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