International Journal of Social Science (IJSS) Vol.
5 Issue.
3 Oktober 2025, pp: 283-296 ISSN: 2798-3463 (Printe.
| 2798-4079 (Onlin.
DOI: https://doi.
org/10.
53625/ijss.
DIGITAL FINANCIAL ADOPTION STRATEGY MODERATED
BY FINANCIAL LITERACY AT BPR SYARIAH IN EAST JAVA
Putri Septi Naulina Hasibuan1.
Aminullah Assagaf2.
Sri Handini3
1,2,3
Department of Management Doctoral Program.
Universitas Dr.
Soetomo.
Surabaya Email: 1putriseptinaulina@gmail.
com , 2aminullah@unitomo.
id , 3srihandini@unitomo.
Article Info Article history:
Received Aug 01, 2025 Revised Aug 23, 2025 Accepted Sept 02, 2025 Keywords:
Digital Finance Financial Technology (FinTec.
Financial Literacy Technology Adoption Risk Perception Ease of Use Usefulness ABSTRACT The use of technology in the banking sector through Financial Technology .
igital financ.
is expected to provide convenience in services for the public.
However, its implementation still faces challenges due to the low level of public understanding regarding digital finance and investment.
This study aims to analyse the influence of risk, ease of use, usefulness, and benefits on attitudes toward the use and adoption of digital finance, with financial literacy as a moderating variable.
This quantitative research involved 180 respondents who are customers of BPR Syariah, selected using purposive sampling and the Slovin formula.
Data were collected through questionnaires and analysed using SEM-PLS.
The results indicate that risk has a negative effect, while ease of use, usefulness, and benefits have positive effects on attitudes toward the use and adoption of digital finance.
Nevertheless, attitudes toward the use of digital finance do not significantly affect adoption, and financial literacy is not proven to moderate the relationship.
These findings highlight that ease of use, usefulness, and benefits are more dominant factors in encouraging the use of digital finance compared to financial literacy itself.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Putri Septi Naulina Hasibuan.
Department of Management Doctoral Program.
Universitas Dr.
Soetomo Semolowaru Road.
No.
Sukolilo Distric.
Surabaya City, 60118.
Indonesia.
Email: putriseptinaulinahasibuan@gmail.
INTRODUCTION
The rapid development of technology has significantly transformed the banking industry, shifting from conventional face-to-face services to digital-based services accessible anytime and anywhere (Fajria, 2.
One major innovation is Financial Technology (Fintec.
or digital finance, defined by Bank Indonesia (PBI No.
19/12/PBI/2.
as the use of technology in financial systems to generate new products, services, or business models that influence monetary stability, the financial system, and payment efficiency.
Fintech not only broadens financial access but also reduces operational costs, enhances efficiency, and accelerates service transformation (Rahmawati et al.
, 2.
In the context of Islamic banking, collaboration between BPR Syariah and fintech firms has been expanding, aligned with OJKAos initiatives to strengthen financial inclusion in the Islamic finance sector (Octaviano & Mahadi.
Fintech also reaches underserved segments of the market, such as through crowdfunding and peer-to-peer lending services (Cupian & Akbar, 2.
In Indonesia, the value of Islamic digital financial services has reached IDR 7 trillion, placing Indonesia fifth globally (Global Islamic Fintech Report, 2.
Nevertheless, the utilisation of fintech has not been fully accompanied by adequate financial literacy.
The 2022 National Survey on Financial Literacy and Inclusion (SNLIK) recorded a financial literacy index of only 49.
far below the financial inclusion index of 85.
This shows that although financial access is increasing, many people still lack sufficient understanding of risks, benefits, and governance of digital finance (Pratiwi & Saefullah, 2.
This gap presents challenges for BPR Syariah, particularly in East Java, which has 25 BPRS (Purmadani, 2.
and plays a crucial role in MSME financing.
Islamic financial education, and digital service development.
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Previous studies have shown mixed results regarding factors influencing digital finance adoption.
Risk has been found to significantly affect adoption (Wijaya & Susilawati, 2021.
Meyliana et al.
, 2.
, while some studies reported negative correlations (Ming et al.
, 2.
Perceived benefits have been shown to positively influence adoption (Effendy, 2020.
Gupta et al.
, 2.
, while user attitudes are influenced by ease of use and usefulness (Winarto, 2022.
Baraba & Mahmudi, 2.
Moreover, financial literacy may strengthen the relationship between attitudes and digital finance adoption (Martini et al.
, 2.
Based on these considerations, this research is entitled AuDigital Finance Adoption Strategy Moderated by Financial Literacy in BPR Syariah of East JavaAy, with the following objectives: .
to analyze the effects of risk, ease of use, usefulness, and benefits on attitudes toward digital finance.
to examine the effects of risk, ease of use, usefulness, benefits, and attitudes on digital finance adoption.
to test the moderating role of financial literacy in the relationship between user attitudes and digital finance adoption in BPR Syariah of East Java.
Strategic management is the process of integrating strategy formulation, implementation, and evaluation to achieve organisational goals (Fadhli, 2.
Functional strategy emphasises coordination across organisational functions .
arketing, finance.
HR.
IT) to support business strategy (Pearce & Robinson, 2.
Risk is a subjective belief about the likelihood of incurring losses in using a product or service (Hasan et al.
Indicators: high risk level, high uncertainty, and lower benefits compared to traditional services (Jain & Raman.
Ease of use refers to the perception that technology is easy to learn, use, and access (Widiyanti, 2.
Indicators: easy to learn, simplifies tasks, increases user intention, and is easy to operate (Arta & Azizah, 2.
Perceived usefulness is the belief that technology provides real benefits (Chairunnisah et al.
, 2.
Indicators:
speeds up work, improves performance, increases productivity, effectiveness, makes tasks easier, and is useful (Putra & Husna, 2.
Benefits are the continuous advantages perceived from technology usage (Ardiansah, 2019.
Hossain & Zhou.
Indicators: many advantages, fast usage, usefulness, and higher quality outcomes compared to traditional services (Jain & Raman, 2.
Attitude consists of cognitive, affective, and conative aspects that influence behaviour (Azwar, 2.
Indicators: favourable, enjoyable, good, useful, and likeable (Ajzen, 2.
Financial literacy is the ability to manage financial information and make decisions (Nafisah & Susanto.
Indicators: understanding digital finance usage, time periods, and transaction risks (Raut, 2.
Digital finance adoption refers to the acceptance of technology-based services to improve service quality (Alkhwaldi et al.
, 2.
Indicators: positive consideration, intention to continue, and future usage (Jain & Raman.
A conceptual framework is the relationship between one concept and another in the problem being researched (Setiadi, 2.
The conceptual framework for this study is as follows:
Figure 1: Conceptual Framework a.
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53625/ijss.
Risk is perceived as the potential loss of using technology.
Several studies found a significant negative effect on attitudes and adoption of digital finance (Balcazar & Angel, 2021.
Baraba & Mahmudi, 2.
, although different results were also reported (Pahlevi et al.
, 2.
H1: Risk affects Attitude Toward Digital Finance Usage.
H5: Risk affects Digital Finance Adoption.
Ease of use reflects the perception that technology is easy to operate (Widiyanti, 2.
Research shows a significant positive influence on attitudes and adoption (Winarto, 2022.
Natsir et al.
, 2.
H2: Ease of Use affects Attitude Toward Digital Finance Usage.
H6: Ease of Use affects Digital Finance Adoption.
Usefulness relates to the belief that technology improves performance (Putra & Husna, 2.
Previous studies found a significant positive effect on attitudes and adoption (Arta & Azizah, 2020.
Shaikh et al.
, 2.
H3: Usefulness affects Attitude Toward Digital Finance Usage.
H7: Usefulness affects Digital Finance Adoption.
Benefits are understood as the advantages gained from using technology.
Prior studies confirmed a significant positive impact on attitudes and adoption (Leong et al.
, 2020.
Gupta et al.
, 2.
H4: Benefits affect Attitude Toward Digital Finance Usage.
H8: Benefits affect Digital Finance Adoption.
A positive attitude toward technology is believed to encourage adoption, although findings vary (Missiafi & Jaka, 2021.
Ezenwafor et al.
, 2.
H9: Attitude Toward Digital Finance Usage affects Digital Finance Adoption.
Financial literacy provides a better understanding of decision-making (Shen et al.
, 2.
and can strengthen the relationship between attitude and adoption (Martini et al.
, 2.
H10: Financial Literacy moderates the effect of Attitude on Digital Finance Adoption.
RESEARCH METHOD
This study employs a quantitative approach using a Likert scale questionnaire .
Ae.
The population consists of 2,606,282 Islamic Rural Bank (BPR Syaria.
customers in East Java (OJK, 2.
, with a sample of 180 respondents determined using SlovinAos formula and purposive sampling.
The variables examined include risk, ease of use, usefulness, benefits, attitude toward usage, financial literacy, and digital finance adoption.
Data analysis was conducted using Structural Equation Modelling-Partial Least Squares (SEM-PLS) with SmartPLS 3.
28, including validity, reliability.
RA.
QA, and hypothesis testing through bootstrapping.
RESULTS AND ANALYSIS
Convergent Validity Table 1.
Convergent Validity Variables Risk (X.
Convenience (X.
Usefulness (X.
sample P-Values X1.
X1.
0,000
X1.
X2.
X2.
0,000
X2.
X2.
X3.
0,000
X3.
Item Information Valid Valid Valid a.
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Variables Item X3.
X3.
X3.
X3.
X4.
X4.
X4.
X4.
Benefits (X.
Attitudes towards Digital Finance Usage (Z) Financial Literacy (M) Digital Finance Adoption (Y) Attitude Use Digital Finance (Z) * Literacy Finance (M) sample P-Values 0,000 Z*M 1,580 Information Valid
0,000
Valid
0,000
Valid
0,000
Valid
0,000
Valid Convergent validity is assessed using outer loadings .
oading factor.
An indicator is considered valid if the original sample value > 0.
Table 1 presents the outer loading values for each research variable indicator.
Discriminant Validity Table 2.
Discriminant Validity Variables Risk (X.
Convenience (X.
Usefulness (X.
Benefits (X.
Attitudes towards Digital Finance Usage (Z) Financial Literacy (M) Digital Finance Adoption (Y) Z*M Average Variance Extracted (AVE) 1,000 The results of the AVE value for the indicator block that measures the construct can be stated to have a good discriminant validity value because the AVE value > 0.
To test discriminant validity, use the mark cross-loading.
An indicator is said to fulfil discriminant validity if the cross-value loading the indicator on one variable is the largest compared to the other.
Table 3.
Cross Loading Z*M X1.
X1.
X1.
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53625/ijss.
X2.
X2.
X2.
Z*M
X2.
X3.
X3.
X3.
X3.
X3.
X3.
X4.
X4.
X4.
X4.
(Z) * (M)
1,000
The cross-loading value in the Table above shows that each indicator in the research variable has a crossloading value the biggest on variables that form, compared to with mark cross-loading on other variables.
Based on the results obtained, it can be stated that the indicators used in the study have good discriminant validity in compiling their respective variables Reliability Test Composite Reliability is the part used to test the reliability value of indicators on a variable.
A variable can be declared to meet composite reliability if it has a composite value reliability > 0.
The following are the composite reliability values for each variable.
Test reliability with composite reliability in one can be strengthened by using the Cronbach's alpha value.
variable can be expressed reliable or fulfil Cronbach's alpha if its own Cronbach's alpha > 0.
Following this is Composite Reliability and Cronbach's alpha for each variable:
Table 4.
Reliability Test Composite Reliability Cronbach Alpha Variables Risk (X.
Convenience (X.
Usefulness (X.
Benefits (X.
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Variables Composite Reliability Cronbach Alpha Attitudes towards Digital Finance Usage (Z) Financial Literacy (M) Digital Finance Adoption (Y) Z*M 1,000 1,000 Based on the data presented in Table 4 above, it can be seen that the mark composite reliability of all variables studied is> 0.
Results.
This shows that each variable has fulfilled composite reliability, so it can be concluded that all variables are adequate in measuring variables latent/ construct that can be used in analysis.
Based on the test results in the table above, the Cronbach alpha value of each research variable is > 0.
Thus, these results show that each variable has met the requirements for the Cronbach alpha value, so it can be concluded that the variables have their own high level of reliability overall.
Normality Test Normality test uses skewness and kurtosis as a method to determine whether the data is normally distributed or not.
To determine data normality, skewness and kurtosis values must be is at in the range -2.
58 to 2.
If skewness and kurtosis values are not is at in the range said, then the data is not normally distributed ( Ghozali, 2.
The following are the results of the normality test that have been done:
Table 5.
Normality Test Variables Indicator Items Excess Kurtosis Skewness Risk (X.
Convenience (X.
Usefulness (X.
Benefits (X.
Attitude Use Finance Digital (Z) Literacy Finance (M) Adoption Digital Finance (Y) X1.
X1.
X1.
X2.
X2.
X2.
X2.
-1,099
X3.
-1,027
X3.
X3.
-1,019
X3.
X3.
X3.
X4.
X4.
-1,169
X4.
X4.
-1,399
-1,231
-1,342
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Based on the results of the normality test, it can be known that the overall variables' mark skewness and kurtosis are in the range -2.
58 to 2.
58, so that the overall variables are normally distributed.
Multicollinearity Test Multicollinearity test with the Variance Inflation Factor (VIF) is performed to know whether there is high correlation or perfect correlation between variables in the regression model.
VIF is a measure of the amount of multicollinearity in regression analysis.
The criteria for making a decision related to multicollinearity testing are that if the VIF value O 10, then stated that no multicollinearity happens.
If the VIF value Ou 10.
1, then multicollinearity is The following results testing Multicollinearity that is :
Table 6.
Multicolinearity Test
VIF
X1.
2,337
X1.
2,731
X1.
3,010
X2.
2,652
X2.
3,952
X2.
3,435
X2.
3,784
X3.
2,900
X3.
3,455
X3.
3,935
X3.
4,012
X3.
3,569
X3.
4,357
X4.
2,639
X4.
3,660
X4.
3,464
X4.
3,913
2,611
7,142
5,716
5,524
6,995
1,976
3,612
3,673
2,319
4,280
Z * M 1,000
Based on the results, testing multicollinearity on the known overall indicator, own mark VIF is below 10, so there is no problem with multicollinearity.
Heteroscedasticity Test Heteroscedasticity testing can also be used to examine the relationship between research variables.
The following are the results of the Inner VIF test :
Table 7.
Inner VIF Test Adoption Digital Finance (Y) Attitude Use Digital Finance (Z) Risk (X.
2,486 2,410 a.
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Convenience (X.
2,479 1,828 Usefulness (X.
3,466 3,136 Benefits (X.
3,038 2,808 Attitude Use Digital Finance (Z) 3,896 Literacy Finance (M) 4,031 Adoption Digital Finance (Y) Z*M 1,375 Based on the results of the Inner VIF test in Table 8, it can be seen that all independent variables, namely Risk (X.
Ease (X.
Usefulness (X.
, and Benefits (X.
on the dependent variable Digital Finance Adoption (Y) and the mediating variable Digital Finance Usage Attitude (Z) have VIF values below the multicollinearity tolerance threshold, which is <5.
This indicates that there is no high correlation between the independent variables in the model, so there is no multicollinearity problem that can interfere with the validity of the regression coefficient estimate.
Some rows in the VIF column for the Digital Finance Adoption Attitude (Z) variable appear empty, as variables such as Financial Literacy (M), the dependent variable (Y), and the moderating interaction (Z*M) are not used as direct predictors of Z.
Therefore, their VIFs are not calculated in that context.
Instead, these variables only play a role in predicting the Digital Finance Adoption (Y) variable, so their VIF values are only listed in that column.
Thus, it can be concluded that this research model does not contain multicollinearity issues overall.
Interpretation of the relationships between variables can be conducted without bias caused by correlations between predictors, strengthening the reliability of the structural model in explaining the influence between variables.
Intervening Variable Test Intervening variables, also known as intermediary variables or mediators, are variables that are between independent variables ( free ) and dependent variables ( bound ) in A research.
Based on results, testing the hypothesis of known influence intervening variables on connection variables independent of variables dependent, that is :
Table 8.
Intervening Test Results Variable Relationship Original t Statistics P Value Results Sample (|O/STDEV|) (O) Risk (X.
-> Attitude Use Not Digital Finance (Z) -> Significant Adoption Digital Finance (Y) Ease (X.
-> Attitude Use Not Digital Finance (Z) -> Significant Adoption Digital Finance (Y) Usefulness (X.
-> Attitude Not Use Digital Finance (Z) -> Significant Adoption Digital Finance (Y) Benefits (X.
-> Attitude Not Use Digital Finance (Z) -> Significant Adoption Digital Finance (Y) Inner Model Test In this study, the test hypothesis was tested using Partial Least Squares (PLS) analysis with the program Smart PLS.
The following is the model image PLS that was submitted.
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Figure 2: PLS Research Measurement The inner weight values in Figure 2 above show that the variable Attitude towards Using Digital Finance (Z) is influenced by the variables Risk (X1 ).
Ease ( X.
Usefulness (X.
, and Benefits (X.
Meanwhile.
Adoption of Digital Finance (Y) is influenced by the variables Risk ( X1 ).
Ease (X.
Usefulness (X.
Benefits (X.
Attitude towards Using Digital Finance (Z).
Financial Literacy (M) and Moderation of Financial Literacy towards Attitude towards Using Digital Finance (Z*M).
The following equality shows the structure of the relationship:
Z = - 0.
139 X 1 0.
165 X2 0.
329 X3 0.
Y = - 0.
179 X 1 0.
157 X2 0.
273 X3 0.
R-Square Change mark R- S square can be used to assess the influence of certain independent latent variables on the dependent latent variable, whether it has a substantive influence.
For deep endogenous latent variables structural model that has an R 2 result of 0.
75 indicates that the model is AustrongAy.
R 2 of 0.
50 indicates that the model is AumoderateAy.
R 2 of 0.
25 indicates that the model is "weak" (Ghozali, 2.
As for output PLS, as explained, following:
Table 9.
R-Square R-Square Digital Finance Adoption (Y) Attitudes towards Digital Finance Usage (Z) Based on results testing mark R- S square on so can interpreted that Risk variables (X1 ).
Ease ( X.
Usefulness (X.
, and Benefits (X.
, which influence the Digital Finance Adoption variable (Z), have an R 2 value of 648, which indicates that the model is Au ModerateAy.
Then the Risk Variable (X1 ).
Benefits (X.
Attitude towards Using Digital Finance (Z) and Financial Literacy moderation towards Attitude towards Using Digital Finance (Z*M) have an R 2 value of 0.
611, which indicates that the model is Au Moderate Ay.
Q-Square Q-square is a value used to determine how well both predicted models.
Q-square can be used for known strength connections for all variables.
The Q-square value that is classified as small is 0.
02 to O 0.
15, classified as currently is 0.
15 to O 0.
35, and is classified as big is Ou 0.
The compliance model structural can be seen from Q 2, as a.
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= 1 Ae [.
Ae R.
Ae R.
] = 1 Ae [.
Ae 0.
Ae 0.
= 1 Ae [.
*( 0.
= 1 - .
= 0.
The results of the Q2 calculation show that the Q2 value is 0.
864, which indicates that it is in the AustrongAy According to Ghozali .
, the Q2 value can measure how well the model generates the observed values and parameter estimates.
So, the Q2 value of the predictions made by the model assessed has its own predictive Hypothesis Testing To answer the research hypothesis can be seen in the following Bootstrapping Model Image:
Figure 3 Bootstrapping PLS Research In testing a hypothesis, there are two conditions: if the t-statistic> 1.
96, then there is a significant influence.
While if if the t-statistic O 1.
95, which means No, there is a significant influence.
Then, for see direction connection variables can be seen from the mark Original Sample if the mark Original Sample is positive.
Hence, the connection between the variables is positive or in the same direction.
If the Original Sample is worth negative, so connection between variables is negative or opposite direction.
Here hypothesis testing results:
Table 10.
Hypothesis Testing Results Variable Relationship Risk (X.
-> Attitude Use Digital Finance (Z) Ease (X.
-> Attitude Use Digital Finance (Z) Usefulness (X.
-> Attitude Use Digital Finance (Z) Benefits (X.
-> Attitude Use Digital Finance (Z) Risk (X.
-> Adoption Digital Finance (Y) Ease (X.
-> Adoption Digital Finance (Y) Usability (X.
-> Adoption Digital Finance (Y) Original t Statistics Sample (O) (|O/STDEV|) P Value Results 1,521 Not Significant 2,437 Significant 3,288 Significant 3,183 Significant 2,027 Significant 1,997 Significant 2,760 Significant a.
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Variable Relationship Benefits (X.
-> Adoption Digital Finance (Y) Attitude Use Digital Finance (Z) -> Adoption Digital Finance (Y) Literacy Finance (M) -> Adoption Digital Finance (Y) Z*M -> Adoption Digital Finance (Y) Original t Statistics Sample (O) (|O/STDEV|) P Value Results 3,015 Significant Not Significant 2,848 Significant Not Significant Discussion Risk and Attitude toward Digital Finance Risk has a negative, non-significant effect on attitudes toward digital finance .
= 1.
521 < 1.
= -0.
This indicates that BPRS customers in East Java do not strongly consider risk in using digital finance, consistent with Pahlevi et al.
Ease of Use and Attitude Ease of use positively and significantly affects attitudes .
= 2.
437 > 1.
= 0.
, indicating that simpler, more accessible digital finance encourages usage, aligning with Winarto .
and Wulandari et al.
Usefulness and Attitude Usefulness positively and significantly influences attitudes .
= 3.
288 > 1.
= 0.
According to Winarto .
and Baraba & Mahmudi .
, customers are more inclined to adopt digital finance if it provides practical .
Benefits and Attitude Benefits have a positive, significant effect on attitude .
= 3.
183 > 1.
= 0.
, showing that perceived advantages, such as efficiency and accessibility, encourage digital finance use, supported by Leonardo .
and Bangkit et al.
Risk and Adoption Risk negatively and significantly affects digital finance adoption .
= 2.
027 > 1.
= -0.
, confirming that higher perceived risk reduces willingness to adopt, consistent with Meyliana et al.
Ease of Use and Adoption Ease of use positively and significantly influences adoption .
= 1.
997 > 1.
= 0.
A user-friendly system promotes adoption, aligning with Natsir et al.
and Sukandar & Hermawan .
Usefulness and Adoption Usefulness positively and significantly affects adoption .
= 2.
760 > 1.
= 0.
Perceived usefulness increases interest in adopting digital finance, supported by Shaikh et al.
and Nurfadilah & Samidi .
Benefits and Adoption Benefits have a positive, significant effect on adoption .
= 3.
015 > 1.
= 0.
, indicating that practical advantages drive adoption, consistent with Amelia & Wibowo .
and Gupta et al.
Attitude and Adoption Attitude toward digital finance has a positive but non-significant effect on adoption .
= 0.
019 < 1.
= 0.
Traditional customer characteristics, limited digital access, and infrastructure constraints reduce the impact of attitude on actual adoption, aligning with Ezenwafor et al.
and Wulan .
Attitude.
Financial Literacy, and Adoption Attitude, moderated by financial literacy, positively but non-significantly affects adoption .
= 0.
200 < 1.
Knowledge alone is insufficient to change behaviour.
practical experience, system accessibility, and trustbuilding are required, partially contrasting with Martini et al.
CONCLUSION
The study shows that Risk negatively affects both the attitude toward and adoption of digital finance, while Ease of Use.
Usefulness, and Benefits positively and significantly influence both.
The attitude toward digital finance does not significantly affect adoption, either directly or when moderated by financial literacy.
Theoretical: Confirms the importance of Risk.
Ease of Use.
Usefulness, and Benefits in shaping attitudes and adoption of digital finance, a.
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while financial literacy alone is insufficient to drive adoption.
Practical: BPRS should enhance the security, ease of use, usefulness, and benefits of digital services and educate customers on financial literacy.
Strategic: Optimising interface design, accelerating transactions, integrating services, and implementing digital acceleration (AI.
IoT.
Cloud Computin.
can improve both attitude and adoption of digital finance.
The study is limited to BPRS in East Java and relies on questionnaires, so results may not be fully generalizable and could be biased by respondentsAo perceptions.
The recommendations of this study are as follows: for BPRS, it is important to improve transaction systems while enhancing ease of use, usefulness, benefits, and customer literacy.
for OJK, the focus should be on providing supportive regulations, incentives, digital sandbox environments, digital skills training for staff, and public digital literacy programs.
for customers, efforts are needed to increase digital literacy, remain open to innovations, protect personal data, and provide constructive feedback to the bank.
and for future research, it is suggested to expand the geographic scope and explore additional moderating or mediating variables such as technology trust, regulatory support, or risk perception.
ACKNOWLEDGEMENTS
Thank you to all parties who helped complete this research until it was finished.
REFERENCES