Syntax Literate:
Jurnal Ilmiah Indonesia p–ISSN: 2541-0849 e-ISSN: 2548-1398
Vol. 9, No.
3, Maret 2024
FINANCIAL TECHNOLOGY ADOPTION ON PAYMENT SYSTEMS AND FINANCIAL TRANSACTIONS AMONG MILLENIALS IN INDONESIA
Adiyaksa Pratama1,
Eka Pria Anas2
Universitas Indonesia,
Depok, West Java, Indonesia1,2
Email: [email protected]1, [email protected]2
Abstract
This research aims to see how far the adoption of digital payment
systems and electronic financial transactions in Indonesia is based on the
factors used in the theory acceptance model (TAM) method, such as perceived
usefulness and perceived ease of use. Several factors, such as social
influences, financial literacy, security and privacy, and government support,
are added to the effects of the intention to adopt digital payments, especially
after the COVID-19 pandemic. Data processes with SmartPLS 3 and uses SEM-PLS
method to see that all the factors are supported to adopt digital payment
systems in Indonesia. Results show that perceived usefulness and ease of use
are the factors that drive the intention to adopt digital payment systems.
Besides that, government support drives the intention to adopt digital payment
systems by regulations to develop digital payment ecosystems. Others such as
social influences, financial literacy, and security-privacy aren’t influenced
on intention to adopt digital payment systems in Indonesia.
Keywords: Digital Payment System, Theory of
Acceptance Model, SEM-PLS
Introduction
The development of information
technology in the world has started in the last three decades. Starting from
the beginning of the 90’s era, people were changed gradually to adapt and adopt
information technology as a part of their daily basis
One of the sectors that has applied digitalization
is financial and banking with financial technology (fintech) in the
scope of digital payment systems. With digital payment systems, people allow to
pay and transact without going to the bank and ATM center – it can be accessed
with mobile devices in online banking and/or digital wallet applications
This research
use TAM (technology acceptance model) as the base method to find the
impact of variables relate to the people’s behavioral intention to adopt
digital payment system in Indonesia. TAM which was developed by
(a) (b)
(Sources: Bank Indonesia (2023))
Based on the purposes of this research that is to seek the
adoption of digital payment system among Indonesian who is classified as
millennials and generation “z”, thus gap for this study used are Indonesians
whose age range between 16 and 45 years old. We choose this range of age
because people in between those ages are known for their digital usage and
update with the newest trends related to information technology, in line with
some previous research
Some previous studies are used as the references and base
for this research based on the purposes, especially the usage of new technology
adoption theory (TAM and UTAUT).
Thus, this
paper wants to seek the intention behavioral adoption of digital payment
systems among millennials in Indonesia based on the technology, financial
literacy, and social influences. Based on this objective, this research has a
novelty to analyze how does the impact of adoption of digital payments and its
usage, especially after pandemic period, so we assume that this method will be
an alternative beside traditional payment which already exist. As a part of
digitalization, electronic/digital payment systems can drive innovation for its
users or the developers. Users can choose payment products that are suitable
for their needs. Digital developers and banks improve their business to make
digital payment applications that suit the needs of their customers. This
research can be considerations for government to build up an efficient
ecosystems and regulation relates to digital payment and transactions, so users
can transact safer, and developer can improve their system properly. Moreover,
this research aims to see how far the adoption of digital payment systems and
electronic financial transactions in Indonesia is based on the factors used in
the theory acceptance model (TAM) method, such as perceived usefulness and
perceived ease of use.
Research Methods
This research is based on the theory of acceptance model
Figure 2. Research Diagram
(Source: Self-processed)
The population of this research are Indonesian youngsters
that categorized as productive age ranges which are between 15 to 64 years old,
as is stated by Indonesian Statistic Center in 2022 in a percentage of 69,25%
of population or 190,98 million peoples. For samples, this research takes people
from the age of 16 to 45 years old which are picked by non-random sampling
method using questionnaire. The number of samples that are used for data
analysis are 267 samples based on the ten times rule of thumb to calculates
amount of minimum sample uses. The questionnaire consists of two parts: a)
respondent demography; and b) variable-related question. For the
variable-related questions this research uses likert scale with the choice of
answers in the range of 1 (strongly disagree) to 5 (strongly agree).
Questionnaire is distributed with google form and all the responses are
analyzed using Microsoft Excel and SEMPLS 3
Table 1. Summary of Variables That Relates to
Research
|
Name |
Explanation |
Sources |
|
Independent Variables |
||
|
Perceived Usefulness |
How far new technology that will be adopted useful and
beneficial |
(Davis, 1989) |
|
Perceived Ease of Use |
How far new technology that will be adopted is efficient
and easy to use by people |
(Davis, 1989) |
|
Social Influences |
Individual and/or other people perceptions within
technology, social culture, and image that will be formed after adopting the
new technology |
(Venkatesh et al., 2003) |
|
Financial Literacy |
Individual knowledge about basic financial concepts,
financial managements, and its products related to digital payment systems |
(Pratiwi & Saefullah, 2022; Kurniasari et al., 2022; OECD, 2018) |
|
Security and Privacy |
Knowledge about the technological risk of digital payment
system and its mitigation related to cyber security and data protections |
(Agustina et al., 2019; Johnson et al., 2018; Kang, 2018;
Kurniasari et al., 2022) |
|
Government Supports |
Supports by regulations and ecosystem development related
to digital payment systems. |
(Kurniasari et al., 2022; Muthukannan et al., 2021;
Nugraha et al., 2022; Ozili, 2021; Setiawan et al., 2021) |
|
Dependent Variable |
|
|
|
Behavioral Intention of Adoption |
To measure how people will adopt digital payment system as
an alternative payment method and become a new payment behavior besides conventional payments |
(Daragmeh et al., 2021; Davis, 1989; Pratiwi & Saefullah, 2022;
Nugraha et al., 2022; Setiawan et al., 2021) |
(Sources: Self-proceed with sources explained)
This research uses SEM-PLS method as the main data analysis
to seek the relation between variables, which categorized as independent and
dependent (table 1). Based on
Table 2. Summary of Indicators Use in SEM-PLS Analysis
|
Test Name |
Explanation |
Minimum Values |
|
Outer Model Testing |
||
|
Indicator Reliability |
To measure similarity between
indicators and its constructs |
IR ≥ 0,7 |
|
Consistency Reliability |
To measure intercorrelation
between variables and its validity by Cronbach’s Alpha and composite
reliability |
· α < 0,7 CR <0,7 |
|
Convergency Validity |
To measure correlation
between indicator and its construct, especially how variables can explain
half of its indicators |
· AVE > 0,5 |
|
Discriminant Validity |
To ensure that the indicators
or variables aren’t different each other to prevent multicollinearity |
·
Highest value of cross loading ·
Correlation mustn’t more than 1 for
Heterotrait Monotrait Rasio |
|
Inner Model |
Testing |
|
|
Collinearity Testing |
To indicate multicollinearity
between indicators |
· VIF < 5 |
|
R-square Testing |
To measure that all
exogeneous variables can explain the endogenous variable |
· Range between
0 and 1, if the value approach the highest limit, so that exogenous variables
can explain endogenous variable |
|
Path Coefficient Testing |
To measure relations between
variables. |
·
p > 0,05 and β approach 1, the
relations will be significant. ·
p < 0,05 and β are approaching 0 or
in the range of -1 to 0, the relations will be not significant. |
(Sources:
Results and Discussion
Table
3. Respondent Demography Result
|
Criterion |
n |
% |
|
Gender |
|
|
|
Men |
83 |
31% |
|
Women |
184 |
69% |
|
Age
Ranges |
|
|
|
16
– 25 |
127 |
48% |
|
Criterion |
n |
% |
|
26
– 35 |
72 |
27% |
|
36
– 45 |
68 |
25% |
|
Last
Education Taken |
|
|
|
High
school |
73 |
27% |
|
Associate’s
degree (D3) |
11 |
4% |
|
Bachelor’s
degree (S1) |
134 |
50% |
|
Master’s
degree (S2) |
49 |
18% |
|
Income
Ranges |
|
|
|
<
Rp 3,000,000.00 |
104 |
39% |
|
Rp
3,000,000.00 - Rp 6,000,000.00 |
40 |
15% |
|
Rp
7,000,000.00 - Rp 10,000,000.00 |
36 |
13% |
|
>
Rp 10,000,000.00 |
87 |
33% |
|
Intensity
To Use Digital Payment Systems |
|
|
|
Sometimes |
20 |
7% |
|
Often
|
170 |
64% |
|
Always |
77 |
29% |
|
Kind
Of Digital Payment Systems Product Used |
|
|
|
Mobile
banking (contoh: BNI M-Banking, Mandiri Livin, BCA Mobile) |
234 |
34% |
|
E-wallet
(contoh: OVO, GOPAY, DANA, ShopeePay) |
234 |
34% |
|
Electronic
money / prepaid card (contoh: Flazz, TapCash, Mandiri E-Money, Brizzi) |
153 |
23% |
|
Digital
bank (contoh: Allobank, Bank Jago, Jenius, SEABank) |
59 |
9% |
(Sources:
Self-proceed)
Results are conducted in two sections based on
the method used. First, this research examines the result of respondent
characteristics or respondent demographic. As explained in the previous
section, this research uses 267 samples based on the number of respondents
taken and the ten times rule of thumb. Result shown that women are dominant on
this research at the percentage of 69%, while men are at the percentage of 31%.
This research also shows the dominance of people at the range of age 16 to 25
with college degree as the last education taken. Based on the income, people
with amount of income below Rp 3,000,000. 00 and above Rp 10,000,000.00 are
dominant in this research. Last on the demographic section, this research also
examined the intensity and product of digital payment system used and the
result shows that people are often conducted financial transactions and
payments by using digital payment systems with electronic wallet (e-wallet)
and mobile banking are the most popular applications used by respondents (table
4).
Table
4. Validity and Reliability Results
|
FL |
VIF |
CA |
ρA |
CR |
AVE |
|
|
Perceived Usefulness |
|
|
0,812 |
0,829 |
0,877 |
0,642 |
|
PU1 |
0,712 |
1,459 |
|
|
|
|
|
PU2 |
0,878 |
2,323 |
|
|
|
|
|
PU3 |
0,760 |
1,509 |
|
|
|
|
|
PU4 |
0,844 |
2,118 |
|
|
|
|
|
Perceived Ease of Use |
|
|
0,844 |
0,851 |
0,895 |
0,682 |
|
PEOU1 |
0,848 |
2,033 |
|
|
|
|
|
PEOU2 |
0,870 |
2,317 |
|
|
|
|
|
PEOU3 |
0,747 |
1,628 |
|
|
|
|
|
PEOU5 |
0,833 |
1,931 |
|
|
|
|
|
Social Influences |
|
|
1,000 |
1,000 |
1,000 |
1,000 |
|
SI2 |
1,000 |
1,000 |
|
|
|
|
|
Financial Literacy |
|
|
0,871 |
0,884 |
0,920 |
0,793 |
|
FL1 |
0,907 |
3,185 |
|
|
|
|
|
FL2 |
0,895 |
3,168 |
|
|
|
|
|
FL3 |
0,869 |
1,785 |
|
|
|
|
|
Security and Privacy |
|
|
0,856 |
0,869 |
0,892 |
0,581 |
|
SP1 |
0,750 |
1,734 |
|
|
|
|
|
SP2 |
0,718 |
1,769 |
|
|
|
|
|
SP3 |
0,731 |
1,593 |
|
|
|
|
|
SP4 |
0,709 |
1,612 |
|
|
|
|
|
SP5 |
0,815 |
2,178 |
|
|
|
|
|
SP6 |
0,840 |
2,439 |
|
|
|
|
|
Government Support |
|
|
0,806 |
0,819 |
0,885 |
0,720 |
|
GS1 |
0,851 |
1,727 |
|
|
|
|
|
GS2 |
0,795 |
1,671 |
|
|
|
|
|
GS3 |
0,898 |
2,208 |
|
|
|
|
|
Behavioral Intention |
|
|
0,789 |
0,794 |
0,877 |
0,704 |
|
BI1 |
0,813 |
1,584 |
|
|
|
|
|
BI2 |
0,861 |
1,740 |
|
|
|
|
|
BI3 |
0,842 |
1,678 |
|
|
|
|
(Sources: Self-proceed)
Second, this research examines the result of SEM-PLS
analysis. As already mentioned in the previous section, outer model testing is
the first part of the analysis. Results show that based on the indicator
reliability test, there are few indicators that were taken out from the test
such as PEOU4, SI1, SI4, FL4, and FL5. This happened because the indicators
have reliability results below 0,7. Reliability and validity tests are
conducted after indicator loadings evaluation based on Cronbach’s alpha,
composite reliability, and average variance extracted (AVE) values. As shown on
table 4, all the construct variables are adequate to all the criterion
explained on table 3 above. Each indicator is related to each construct variable
based on cross-loading validity (table 5) and HTMT ratio shows that all
construct variables are valid (table 6).
Table 5. Cross-Loading
Validity Value
|
BI |
FL |
GS |
PEOU |
PU |
SI |
SP |
|
|
BI1 |
0,813 |
0,296 |
0,480 |
0,507 |
0,464 |
0,307 |
0,366 |
|
BI2 |
0,861 |
0,308 |
0,389 |
0,553 |
0,616 |
0,538 |
0,394 |
|
BI3 |
0,842 |
0,241 |
0,368 |
0,621 |
0,503 |
0,401 |
0,350 |
|
FL1 |
0,295 |
0,907 |
0,289 |
0,253 |
0,305 |
0,160 |
0,339 |
|
FL2 |
0,250 |
0,895 |
0,303 |
0,216 |
0,251 |
0,160 |
0,398 |
|
FL3 |
0,337 |
0,869 |
0,412 |
0,356 |
0,356 |
0,273 |
0,434 |
|
GS1 |
0,447 |
0,290 |
0,851 |
0,459 |
0,437 |
0,310 |
0,452 |
|
GS2 |
0,350 |
0,296 |
0,795 |
0,346 |
0,304 |
0,150 |
0,442 |
|
GS3 |
0,436 |
0,385 |
0,898 |
0,385 |
0,345 |
0,217 |
0,466 |
|
PEOU1 |
0,570 |
0,290 |
0,370 |
0,848 |
0,633 |
0,525 |
0,472 |
|
PEOU2 |
0,562 |
0,263 |
0,400 |
0,870 |
0,590 |
0,484 |
0,383 |
|
PEOU3 |
0,482 |
0,228 |
0,384 |
0,747 |
0,435 |
0,394 |
0,362 |
|
PEOU5 |
0,586 |
0,263 |
0,404 |
0,833 |
0,617 |
0,511 |
0,509 |
|
PU1 |
0,416 |
0,217 |
0,260 |
0,495 |
0,712 |
0,367 |
0,325 |
|
PU2 |
0,583 |
0,284 |
0,427 |
0,640 |
0,878 |
0,525 |
0,405 |
|
PU3 |
0,457 |
0,321 |
0,248 |
0,437 |
0,760 |
0,462 |
0,365 |
|
PU4 |
0,550 |
0,289 |
0,417 |
0,638 |
0,844 |
0,453 |
0,366 |
|
SI2 |
0,501 |
0,228 |
0,272 |
0,583 |
0,568 |
1,000 |
0,315 |
|
SP1 |
0,374 |
0,379 |
0,441 |
0,492 |
0,385 |
0,244 |
0,750 |
|
SP2 |
0,250 |
0,309 |
0,323 |
0,336 |
0,262 |
0,232 |
0,718 |
|
SP3 |
0,325 |
0,347 |
0,387 |
0,389 |
0,319 |
0,266 |
0,731 |
|
SP4 |
0,254 |
0,288 |
0,354 |
0,257 |
0,283 |
0,207 |
0,709 |
|
SP5 |
0,403 |
0,269 |
0,477 |
0,442 |
0,406 |
0,227 |
0,815 |
|
SP6 |
0,364 |
0,418 |
0,420 |
0,435 |
0,393 |
0,266 |
0,840 |
(Source: Self-proceed)
Table 6. Heterotrait
-Monotrait Ratio Value
|
BI |
FL |
GS |
PEOU |
PU |
SI |
SP |
|
|
BI |
|
|
|
|
|
|
|
|
FL |
0,398 |
|
|
|
|
|
|
|
GS |
0,611 |
0,448 |
|
|
|
|
|
|
PEOU |
0,816 |
0,359 |
0,568 |
|
|
|
|
|
PU |
0,779 |
0,405 |
0,517 |
0,827 |
|
|
|
|
SI |
0,557 |
0,237 |
0,296 |
0,632 |
0,627 |
|
|
|
SP |
0,524 |
0,506 |
0,634 |
0,601 |
0,537 |
0,341 |
|
(Source: Self-proceed)
Second part of SEM-PLS analysis, inner model testing is
conducted to analyze relations between variables which on this research consist
of collinearity, R-square, and path coefficient testing. First, collinearity
testing is examined as the result seen at table 4, which all variables have
variance inflation factor (VIF) value below five. This result indicates that
all variables don’t have any multicollinearity. Second, R-square testing shows that
all exogeneous variables can explain the endogenous variables. At this point
the endogenous variables are intention to adopt (BI) and social influence (SI).
All exogenous variables which related to BI can explain 53.6%, likewise
variables that related to SI (PU and PEOU) which can explain 39,1% (table 7).
Third subtest is path coefficient which consist of two kinds of effects: direct
and indirect effects. For the direct effect as summarized in table 8 below
shows that there are six relations that are significant based on the p-values,
in other hand three relations aren’t significant. This happens because three
relations have p-values above 0,01. Table 9 shows the indirect relations, which
both of relations also aren’t significant. This path coefficient uses
significant value at 0.05 with two-tailed test are conducted.
Table 7. R-Square
Value
|
R Square |
R Square Adjusted |
|
|
BI |
0,536 |
0,526 |
|
SI |
0,391 |
0,387 |
(Source: Self-proceed)
Table 8. Path Coefficient Result for Direct Variable Relations
|
Original (β) |
t-statistics |
p-values |
Significance |
|
|
FL -> BI |
0,051819 |
1,172470 |
0,241567 |
Insignificant |
|
GS -> BI |
0,174135 |
3,093420 |
0,002089 |
Significant |
|
PEOU -> BI |
0,376663 |
4,044410 |
0,000061 |
Significant |
|
PEOU -> SI |
0,364376 |
4,512519 |
0,000008 |
Significant |
|
PU -> BI |
0,275981 |
3,203170 |
0,001446 |
Significant |
|
PU -> SI |
0,314902 |
4,656962 |
0,000004 |
Significant |
|
SI -> BI |
0,105000 |
1,415837 |
0,157446 |
Insignificant |
|
SP -> BI |
0,003211 |
0,057333 |
0,954303 |
Insignificant |
(Source:
Self-proceed)
|
Original |
t-statistics |
p-values |
Significance |
|
|
PEOU -> SI -> BI |
0,038260 |
1,292522 |
0,196773 |
Insignificant |
|
PU -> SI -> BI |
0,033065 |
1,351783 |
0,177056 |
Insignificant |
Table 9. Path
Coefficient Result for Indirect Variable Relations
(Source:
Self-proceed)
The results from all analysis that are conducted
on this research gives some discussion points. First, from the demographic
analysis as calculated and proceed using Microsoft Excel 365 we seek dominance
of millennials and generation “z” whose often use digital payment system as
their payment and transaction method. Most of them are high school graduates
and have bachelor’s degrees. These findings in line with the survey that were
conducted by IPSOS (2022) and Kadence International (2021) that digital payment
services are often use by youngsters, because they are assessed to be more capable
to use technology and know about its update better than older generation which
tend to have more effort to know the technology, especially how to maintain the
technological risk
Second, this research discussed the relation of
all variables based on the SEM-PLS testing result. As seen on table 8, perceived
usefulness (PU) and ease of use (PEOU) are significantly related to intention
to adopt (BI). As explained by IPSOS (2022), usefulness and ease of use are
shown by how people are comfortable using digital payment systems. This also in
line with the main theories, both TAM and UTAUT that usefulness and ease of use
are driven by benefits, effectivity, and easiness that people get while adopt
the new technology
Another factor that has significant relation to
intention to adopt is government support. Reflecting from the findings, people
have already known that government supports and strengthens the development of
digital payment ecosystem by its regulation and established facility. This
finding is unique, mainly people aren’t aware about role of government on
developing digital ecosystem for payments and financial transactions
There are three factors which are not
significant to intention to adopt digital payment system such as social
influences (SI), financial literacy (FL), and security (SP). Findings shows
that social influence factors are influenced by individual desire to use
digital payment system which is not in line with the theory that shown other
influences like family, organizations, and brand images
This research also finds that both usefulness
(PU) and ease of use (PEOU) have significant relation to social influences
(SI). As mentioned by
Conclusion
As the purpose is to seek the intention to adopt
digital payment systems in Indonesia especially for millennials and generation
z, this research concludes that TAM theory is still relevant to seek the
adoption of new technology. Usefulness and ease of use are two basic factors
that an individual or community wants to use new technology in their daily
basic. Second, influences from other people and social environment should be
drivers to adopt digital payment systems, not only from individual desire.
Third, government has roles to build and develop effective and efficient
digital payment and transactions ecosystem with helps by financial institutions,
start-ups, and e-commerce. This can be done by making regulations related to
its operational and user data protection. Financial institutions, collaborating
with government has role to socialize people about financial literacy and
technological risk when choose digital payment system.
This research gives some implication relates to
the conclusion above, especially in terms of managerial purposes. Managers who
work in the company that develop digital payment systems or financial
institution can make digital payment products that have effective and efficient
systems with simpler design (UI/UX). Managers also have roles to understand
digital risk and its prevention when user choose digital payment system. Other
things that are important for managers are they have role to educate people
about financial literacy and digital risk with their product as a facility.
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