International Journal of Cyber and IT Service Management (IJCITSM) Vol.
No.
October 2025, pp.
171Oe185 E-ISSN: 2808-554X | P-ISSN: 2797-1325.
DOI:10.
ye Understanding the Key Drivers Behind User Selection of Digital Banks Erwin Sugiharto1* .
Viany Utami Tjhin2 1,2 Master of Information System Management.
Bina Nusantara University.
Indonesia 1 erwin.
sugiharto@binus.
id, 5 viany.
tjhin@binus.
*Corresponding Author
Article Info
ABSTRACT
Article history:
This study aims to understand the key drivers underlying the selection of digital banks by users in Indonesia.
The research model uses a modified UTAUT (Unified Theory of Acceptance and Use of Technolog.
framework with additional variables of Perceived Trust.
Fear Of Financial Lost.
Perceived Online Identity Theft, and Security & Privacy Concern.
A total of 434 respondents who use digital bank applications were analyzed using the PLS-SEM technique through SmartPLS 4 software.
The results showed that the factors of Performance Expectancy.
Social Influence, and Perceived Trust are the key drivers towards behavioral intentions.
Meanwhile, the variables of Expected Effort.
Fear Of Financial Lost, and Perceived Online Identity Theft did not show significant The findings provide strategic insights to increase the adoption of digital banking services in Indonesia.
Submission July 3, 2025 Revised August 10, 2025 Accepted August 28, 2025 Published September 10, 2025 Keywords:
Digital Banking Performance Expectancy Perceived Trust Social Influence UTAUT This is an open access article under the CC BY 4.
0 license.
DOI: https://doi.
org/10.
34306/ijcitsm.
This is an open-access article under the CC-BY license .
ttps://creativecommons.
org/licenses/by/4.
AAuthors retain all copyrights INTRODUCTION In the current era of globalization, internet users are increasing from year to year.
This is evidenced by the increasing internet penetration in Indonesia year by year.
In a survey conducted by the Association of Indonesian Internet Service Providers (APJII) in 2023, it was found that the internet penetration rate, which is the ratio of internet users to the population, increases every year.
In 2023, it reached 78.
19%, an increase of 17% from 2022, which was 77.
In terms of numbers, in 2023, it amounted to 215 million, compared to 210 million in 2022 .
Significant changes have also occurred in the banking industry in Indonesia due to the emergence of digital banks, such as Bank BHI, which provide and conduct business activities primarily through electronic channels without physical branches other than Customer Points (KP) or with limited physical branches .
During the Board of Governors Meeting (RDG) of Bank Indonesia on January 16-17, 2024, it was mentioned that in 2023, the value of digital banking transactions reached Rp 58,478.
24 trillion, an increase of 13.
, and in 2024, it is estimated to increase by 9.
11% .
to reach Rp 63,803.
77 trillion .
With the increasing value of digital banking transactions from year to year and predicted to continue rising as previously Journal homepage: https://iiast.
iaic-publisher.
org/ijcitsm/index.
php/IJCITSM/index ye
E-ISSN: 2808-554X | P-ISSN: 2797-1325
explained, it turns out that digital banks face challenges or issues both from the user side and the digital bank side itself.
The proliferation of digital banks in Indonesia is driven by changes in customer behavior during the pandemic, with more people preferring digital banking services for their efficiency and convenience.
Competition between digital banks and conventional banks pushes companies to focus on personalized innovations to retain customers.
These innovations include tailoring products and services to individual needs as well as effective marketing strategies.
Some digital banks offer high interest rates to attract customers, but long-term strategies need to focus on services that understand and respond to customersAo needs personally to enhance loyalty .
This study fills a critical gap in the prior literature by integrating trust-related variables such as Perceived Trust.
Fear Of Financial Lost, and Perceived Online Identity Theft into the UTAUT2 framework.
Previous research on digital banking adoption has largely focused on standard UTAUT2 variables like Performance Expectancy and Social Influence, neglecting the important role of user trust and security concerns.
By incorporating these factors, this study offers a more comprehensive understanding of the key drivers behind the behavioral intentions of digital banking users in Indonesia, addressing a significant gap in trust-related banking In 2022, digital banks faced various challenges, both externally and internally.
Seabank confronted fierce competition in the digital banking space, prompting innovations like product diversification and attractive BCA Digital, as a newcomer, focused on gaining customer trust by offering secure, convenient, and innovative services.
NeoCommerce Bank worked on ensuring the security of customer information and transactions while staying up-to-date with technology to remain competitive.
Bank JagoAos 2022 Customer Satisfaction Survey revealed that 87% of respondents were satisfied with its services, especially its user-friendly features and free fees.
Meanwhile.
Bank BTPNAos Jenius product focused on expanding features like investment, foreign currency transactions, and Flexi Cash loans, all while prioritizing data and transaction security.
According to a survey conducted by Populix in 2022 titled AyConsumer Preference Towards Banking and E-Wallet AppsAy with 1000 respondents, the top 5 digital banks mentioned were Jago .
%).
BNC (NeoCommerce Ban.
%).
Jenius .
%).
SeaBank .
%), and blu (BCA Digita.
%) .
Table 1.
Bank App Reviews Bank Rating Reviewer BNC (Bank Neo Commerc.
Jenius blu (BCAdigita.
Jago Seabank In Table 1 above, user reviews of digital banking apps from Google Playstore (January 25, 2.
show that BNC (Bank Neo Commerc.
and Jenius received the lowest ratings among the top 5 banks in the Populix survey.
From the Google Review data (December 1, 2023 - January 11, 2.
, users complained about slow app performance, issues with money not being credited, and other problems.
The rise in digital banking transactions highlights the need for digital banks to continuously develop technology and add new features to enhance customer experiences.
Research is essential to understand factors that influence customers choices, such as fast and efficient transactions, new features, and trust and security.
This research aims to provide insights to digital bank operators, which can be adapted to similar emerging markets such as Southeast Asia and Latin America, where digital banking adoption is increasing rapidly.
These insights can help operators create strategies that offer secure, user-friendly, and personalized banking services to boost adoption rates.
Furthermore, the adoption of digital banking aligns with SDG 9, promoting innovation and the development of digital infrastructure, and SDG 16, contributing to the creation of transparent, accessible, and efficient institutions .
The growth of digital banking in Indonesia serves as a catalyst for fostering inclusive economic growth and strengthening institutions that support sustainable development .
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LITERATURE REVIEW
Digital Bank As per Financial Services Authority Regulation No.
12/POJK.
03/2021, a Digital Bank is a Commercial Bank primarily operating through electronic channels with limited or no physical branches .
Digital Banks differ from traditional banks offering digital services by their absence of physical branch offices, conducting all operations virtually .
Digital banks offer online platforms for account opening and financial management, emphasizing efficiency, cost reduction, and innovation compared to traditional banking methods .
They facilitate transactions through electronic media, including digital payment methods, mobile wallets.
P2P banking, and cryptocurrencies, reflecting a shift towards digital transactions .
Unified Theory of Acceptance and Use of Technology 2 The Unified Theory of Acceptance and Use of Technology 2 (UTAUT.
is an advanced model designed to comprehensively understand the adoption and usage of new technology by individuals, integrating both quantitative and qualitative methods.
This model discards the Voluntariness of Use construct from its predecessor and emphasizes factors such as Performance Expectancy.
Effort Expectancy.
Social Influence.
Facilitating Conditions.
Hedonic Motivation.
Price Value.
Habits.
Behavioral Intention.
Use Behavior, as well as individual differences like Gender.
Age, and Experience as significant moderators .
, .
Performance Expectancy assesses perceived benefits.
Effort Expectancy gauges perceived ease of use.
Social Influence captures the influence of important others.
Facilitating Conditions considers organizational and technical support.
Hedonic Motivation highlights satisfaction derived from technology use.
Price Value underscores the influence of cost.
Habits measure routine actions.
Behavioral Intention gauges desire to use.
Use Behavior quantifies frequency of usage, while Gender.
Age, and Experience account for individual disparities.
Overall.
UTAUT2 offers a holistic framework to comprehend the multifaceted dynamics influencing technology acceptance and usage .
Perceived Trust Perceived Trust can be defined as a positive belief in the reliability of a service, particularly in the context of mobile banking services .
-bankin.
This trust is conceptualized as consumersAo belief in the reliability and integrity of a retailer, which significantly influences customer intentions and behavior.
This definition aligns with previous research that emphasizes trust as an important factor affecting customer satisfaction and commitment to a service .
Security & Privacy The research conducted by .
suggests that efforts to increase customersAo intention to use mobile banking in Jakarta can be made by enhancing security measures, such as improving privacy, authentication, integrity, and non-repudiation.
The study also found that as a personAos intention to use mobile banking increases, their use behavior also increases.
Similarly, .
mentioned that security will influence the level of desire to continue using technology, and security is closely related to privacy in technology usage .
In the development of software for banking digitization, security and privacy should be of special concern because they greatly influence customers intention to use banking applications, as highlighted by .
METHODOLOGY
The study utilizes the UTAUT research model developed by .
Figure 1 depicts the research model using part of the UTAUT model and also includes other variables such as Perceived Trust.
Fear Of Financial Lost.
Perceived Online Identity Theft, and Security & Privacy.
This extended framework aims to give a clearer understanding of user behavioral intentions in adopting digital banking by combining UTAUT constructs with risk-related factors.
According to .
Perceived Trust plays a crucial role as an important predictor influencing behavioral This is because users believe that technology can optimize their work and provide maximum benefits from its use.
Conversely, when it comes to using digital banks, there may also be resistance due to concerns about security & privacy.
Therefore, the level of security and privacy in using digital banks will also affect the desire to use them.
As mentioned by .
security & privacy are also influenced by Fear Of Financial Lost.
Perceived Online Identity Theft, and Security & Privacy.
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In the context of this research, situational adjustments and environmental conditions result in the researchers not including the variables Price Value.
Hedonic Motivation, and Habits in the research model.
This is because bank applications can be downloaded for free through the Play Store platform, so the Price Value factor does not have a significant influence on the use of digital banks.
Additionally, the Habits variable is not relevant in this study because digital banks are a new technology in the banking industry and do not have historical comparisons that can be used to analyze usage habits.
Figure 1.
Research Model Based on Figure 1, this research model outlines the key variables that affect the intention and behavior of using digital banking.
The primary focus of this study is to analyze the impact of performance expectancy, effort expectancy, social influence, and other related factors on users intention and behavior in adopting digital banking services.
Variables such as price value, hedonic motivation, and user habits are excluded because they are considered irrelevant in the context of digital banking, which is a new technology.
This is due to the fact that digital banking services can be downloaded for free, and there are no historical comparisons available to analyze usage habits .
, .
Hypothesis In this section, the research hypotheses related to the utilization of digital banking services are presented.
These hypotheses aim to explore various factors that influence users behavioral intentions and actual usage behaviors in the context of digital banking.
The following Table 2 outlines the proposed hypotheses, which are tested to analyze the relationships between these variables and the behavior of users.
H1a H2a H3a H4a Table 2.
Caption Hypothesis Performance Expectancy in utilizing digital banking services has a positive impact on Behavioral Intention.
Performance Expectancy has a positive impact on Use Behavior in the utilization of digital Effort Expectancy in utilizing digital banking services has a positive impact on Behavioral Intention.
Effort Expectancy has a positive impact on Use Behavior in the utilization of digital banking.
Social Influence in utilizing digital banking services has a positive impact on Behavioral Intention.
Social influence has a positive impact on Use Behavior in digital banking usage.
Perceived Trust in utilizing digital banking services has a positive impact on Behavioral Intention.
Perceived Trust has a positive impact on Use Behavior in digital banking usage.
International Journal of Cyber and IT Service Management (IJCITSM).
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October 2025, pp.
171Ae185 International Journal of Cyber and IT Service Management (IJCITSM) H5a H6a H7a ye Hypothesis Fear Of Financial Lost in utilizing digital banking services has a positive impact on Security and Privacy.
Fear Of Financial Lost has a positive impact on Use Behavior in digital banking usage.
Perceived Online Identity Theft in utilizing digital banking services has a positive impact on Security and Privacy.
Perceived Online Identity Theft has a positive impact on Use Behavior in digital banking Security and Privacy Concern in utilizing digital banking services have a positive impact on Behavioral Intention.
Security and Privacy Concern have a positive impact on Use Behavior in digital banking Behavioral Intention has a positive impact on Use Behavior in digital banking usage.
Measurement Variable This study employs the following independent variables are Performance Expectancy.
Effort Expectancy.
Social Influence.
Perceived Trust.
Fear Of Financial Lost, and Perceived Identity Theft.
It also utilizes moderating variables such as Behavioral Intention and Security & Privacy.
The dependent variable employed is Use Behavior.
Table 3 provides a detailed explanation of the indicators for each variable .
, .
These variables capture both acceptance factors and user risk concerns in digital banking adoption.
Variable Performance Expectancy Effort Expectancy Social Influence Perceived Trust Fear of Financial Lost Table 3.
Variable and Research Indicators Indicator PE1: I use mobile banking services in my daily life.
PE2: Using mobile banking services increases my chances of completing important tasks.
PE3: Mobile banking services allow me to complete tasks more quickly.
PE4: I become more productive when using mobile banking services.
EE1: I find it easy to learn how to use mobile banking services.
EE2: My interaction with Mobile Banking Services is simple and easy to EE3: Internet Mobile Banking is easy for me to use.
EE4: I found it easy to learn how to use mobile banking.
SI1: Key people in my life believe that I should use mobile banking services.
SI2: People who influence my behavior believe that I should use mobile SI3: People whose opinions I respect prefer that I use mobile banking PT1: I believe that using mobile banking services to transfer money is always safe.
PT2: I am convinced that mobile banking is a safe way to transfer money.
PT3: My bank gives me immediate notice if there is a problem with any of my transactions.
PT4: Based on my experience.
I believe that using mobile banking is safe.
FOL1: IAom worried that someone could steal my money when I transfer personal data online.
FOL2: I was afraid that a criminal could use my credit card account number to shop online in my name.
FOL3: I was afraid that someone could do online shopping at my expense.
FOL4: I am concerned that an unauthorized person may make an online purchase using my personal data.
Source .
ye Variable E-ISSN: 2808-554X | P-ISSN: 2797-1325 Indicator POT1: I am worried that when I have to give my credit card number to shop online, it could be misused.
Perceived Online POT2: I am afraid that when I have to give my bank account number to Identity Theft shop online, it could be misused.
POT3: I was afraid that my bank account could be hacked by someone SP1: Electronic banking platforms have mechanisms to ensure the transmission of their users information is secure.
SP2: The electronic banking platform has sufficient technical capacity to ensure data security.
SP3: Electronic financial transactions will not pose a financial risk.
SP4: Secure for personal data confidentiality SP5: Electronic banking platforms comply with personal data protecSecurity and tion laws to ensure data privacy.
Privacy Concern SP6: The electronic banking platform only collects the personal data of users that are necessary for its activities.
SP7: The electronic banking platform does not disclose my personal information to others without their consent.
I feel secure when sending personal information through the electronic banking platform.
FOL4: I am concerned that an unauthorized person may make an online purchase using my personal data.
BI1: I intend to use mobile banking system if I have access to it.
Behavioral BI2: For my banking needs.
I will use mobile banking services.
Intention BI3: If I have access to the mobile banking system.
I would like to make the most of it.
UB1: What is the actual frequency of your usage of the SADAD Internet banking service.
Use Behavior UB2: The frequency of each transaction.
UB3: The frequency of using other features.
Source .
Sample The study will sample 400 users from five digital banks (Jago.
BNC.
Jenius.
Seabank, and bl.
based on Google Play Store reviews, assuming the number of reviews represents the user count.
A 5% margin of error was used to calculate the sample size via the Slovin formula.
Participants will be selected using probability sampling, ensuring equal selection chances for all individuals.
Data will be collected through a Google Form distributed via social media platforms like Telegram.
Line, and WhatsApp.
The questionnaire, based on indicator variables, will use a 5-point Likert scale for responses.
Additionally, the data collection process will aim to reach a diverse range of users, ensuring the sample reflects the characteristics of digital bank users in Indonesia.
The analysis of responses will help uncover trends and insights into user behavior and preferences regarding digital banking services .
, .
Analysis Method The data analysis method for this research involves Partial Least Square Structural Equation Modeling (PLS-SEM) using SmartPLS 4 software.
PLS-SEM encompasses both the measurement model .
uter mode.
and the structural model .
nner mode.
For the measurement model, validity and reliability tests are Validity is assessed through convergent validity .
sing loading factor and average variance extrac.
and discriminant validity .
sing cross loadin.
, .
Reliability is evaluated using CronbachAos Alpha and Composite Reliability.
In the structural model, path coefficient and determination coefficient (R-Squar.
tests are performed to analyze the relationships between variables and the accuracy of predictions.
Hypothesis testing includes T-Statistics and p-value tests to determine the significance of the relationships between latent variables.
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RESULT AND DISCUSSION
Profile of Respondents From the results of the questionnaire, 434 respondents were obtained, who can be categorized by gender, age, place of residence, and the digital banking application they use.
Gender Man Woman Total Table 4.
Gender Amount Percentage Based on the results of the questionnaire in Table 4, the number of male respondents is higher than that of female respondents.
Out of 434 respondents, 46.
8% are male, and 53.
2% are female.
Age 56 and above Total Table 5.
Age Amount Percentage Based on the questionnaire results shown in Table 5, the largest group of respondents falls within the 26-35 age range, accounting for 41.
Out of 434 respondents, 29.
3% are aged 17-25, 41.
7% are aged 26-35, 20% are aged 36-45, 8.
1% are aged 46-55, and 0.
9% are aged 56 and above.
Table 6.
Place of Residence Place Amount Percentage Jabodetabek Outside Jabodetabek Total Based on the questionnaire results in Table 6, the majority of respondents reside in the Greater Jakarta area (Jabodetabe.
Of the 434 respondents, 71.
9% live in Jabodetabek, while the remaining 28.
1% live outside Jabodetabek.
Table 7.
The Digital Banking Application Application Amount Percentage Seabank blu (BCA digita.
Jago Jenius BNC .
ank neo commerc.
Allobank
TMRW by UOB
Bank Raya
DIGIBANK by DBS
Permata ME
BCA mobile LINE Bank by Hana
Bank K
M bca
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ye Application Octo Mobile Bsi mobile.
Lampung online Dana Gopay Total Amount Percentage The data in Table 7 shows Seabank leading the digital banking market with 26.
7% of users, followed by Blu (BCA digita.
Jago and Jenius hold 12.
7% and 11.
5%, respectively.
BNC (Bank Neo Commerc.
and Allobank have 8.
1% and 5.
5%, while apps like TMRW by UOB.
Bank Raya, and DIGIBANK by DBS each hold 2.
1% to 3.
Other apps, including BCA mobile.
LINE Bank, and others, capture less than 2%, reflecting a competitive yet concentrated market.
Analysis of Research Data The research conducted PLS-SEM and bootstrapping tests to obtain results from the measurement model .
uter mode.
, the structural model .
nner mode.
, and hypothesis testing.
Convergent Validity Testing The convergent validity test can be conducted by examining the loading factor .
uter loadin.
and the Average Variance Extracted (AVE) values.
Outer Loading Table 8.
Outer Loading Indicator Loading Factor Performance Expectancy PE1 Ia PE PE2 Ia PE PE3 Ia PE PE4 Ia PE Effort Expectancy EE1 Ia EE EE2 Ia EE EE3 Ia EE EE4 Ia EE Social Influence SI1 Ia SI SI2 Ia SI SI3 Ia SI Perceived Trust PT1 Ia PT PT2 Ia PT PT3 Ia PT PT4 Ia PT Fear Of Financial Lost FOL1 Ia FOL FOL2 Ia FOL FOL3 Ia FOL Perceived Online Identity Theft POT1 Ia POT POT2 Ia POT POT3 Ia POT Result Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid International Journal of Cyber and IT Service Management (IJCITSM).
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171Ae185 ye International Journal of Cyber and IT Service Management (IJCITSM) Indicator Loading Factor Security and Privacy SP1 Ia SP SP2 Ia SP SP3 Ia SP SP4 Ia SP Behavioral Intention BI1 Ia BI BI2 Ia BI BI3 Ia BI Use Behavior UB1 Ia UB UB2 Ia UB UB3 Ia UB Result Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid The results of the loading factor testing for all indicators in Table 8 for each variable are considered valid because they have a value of Ou 0.
Average Variance Extracted (AVE) Table 9.
Average Variance Extracted (AVE) Variable AVE Result Performance Expectancy Valid Effort Expectancy Valid Social Influence Valid Perceived Trust Valid Fear Of Financial Lost Valid Perceived Online Identity Theft 0.
Valid Security and Privacy Valid Behavioral Intention Valid Use Behavior Valid In the AVE testing conducted based on data from the digital bank user questionnaire, all variables are considered valid because they have a value of Ou 0.
50, as shown in Table 9.
Discriminant Validity To assess discriminant validity, cross-loading values are examined to determine how each variable correlates with its associated construct compared to other constructs .
, .
The analysis reveals that all variables exhibit a stronger correlation with their intended constructs than with others, affirming the constructs This outcome ensures that each variable measures its unique dimension without significant overlap with other variables, which is essential for reliable data interpretation.
The details of this analysis can be reviewed in Table 10, where the cross-loading values illustrate the clear distinction between variables.
By confirming that each variable aligns more closely with its designated construct, the study substantiates the measurement modelAos discriminant validity.
This validation process
strengthens confidence in the findings and confirms that each variable captures a unique aspect of the overall BI1
BI2
BI3
EE1
FOL
Table 10.
Cross Loading POT
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EE2
EE3
EE4
FOL1
FOL2
FOL3
PE1
PE2
PE3
PE4
POT1
POT2
POT3
PT1
PT2
PT3
PT4
SI1
SI2
SI3
SP1
SP2
SP3
SP4
UB1
UB2
UB3
FOL
POT
Reliability Testing Table 11.
CronbachAos Alpha Variable CronbachAos alpha Performance Expectancy Effort Expectancy Social Influence Perceived Trust Fear Of Financial Lost Perceived Online Identity Theft Security and Privacy Behavioral Intention Use Behavior Result Reliable Reliable Reliable Reliable Reliable Reliable Reliable Reliable Reliable In the CronbachAos alpha test, the results show that all variables are considered reliable because they have a value > 0.
, as shown in Table 11.
Variable Performance Expectancy Effort Expectancy Social Influence Table 12.
Composite Reliability Composite Reliability .
Composite Reliability .
Result Reliable Reliable Reliable International Journal of Cyber and IT Service Management (IJCITSM).
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171Ae185 ye Composite Reliability .
Result Reliable Reliable Reliable Reliable Reliable Reliable International Journal of Cyber and IT Service Management (IJCITSM) Variable Perceived Trust Fear Of Financial Lost Perceived Online Identity Theft Security and Privacy Behavioral Intention Use Behavior Composite Reliability .
In the composite reliability test, as shown in Table 12, the results indicate that all variables are considered reliable because they have a value > 0.
Ae.
Discussion of Hypotheses Table 13.
Hypotheses Hypothesis Performance Expectancy Ie Behavioral Intention Effort Expectancy Ie Behavioral Intention Social Influence Ie Behavioral Intention Perceived Trust Ie Behavioral Intention Fear Of Financial Lost Ie Security and Privacy Perceived Online Identity Theft Ie Security and Privacy Security and Privacy Ie Behavioral Intention Behavioral Intention Ie Use Behavior T statistics P values Result Accepted Rejected Accepted Accepted Accepted Rejected Rejected Accepted A H1: In the test results for Hypothesis H1 in Table 13, which examines the relationship between Performance Expectancy and Behavioral Intention, a P-value of less than 0.
and a T-statistic value of 4.
reater than or equal to 1.
were obtained.
This leads to the conclusion that the Performance Expectancy of the digital banking application has a positive impact on Behavioral Intention.
A H2: In the test results for Hypothesis H2 in Table 13, which examines the relationship between Effort Expectancy and Behavioral Intention, a P-value greater than or equal to 0.
and a T-statistic value of 1.
ess than 1.
were obtained.
This leads to the conclusion that the Effort Expectancy of the digital banking application does not have a positive impact on Behavioral Intention.
A H3: In the test results for Hypothesis H3 in Table 13, which examines the relationship between Social Influence and Behavioral Intention, a P-value of less than 0.
and a T-statistic value 759 .
reater than or equal to 1.
were obtained.
This leads to the conclusion that the Social Influence of the digital banking application has a positive impact on Behavioral Intention.
A H4: In the test results for Hypothesis H4 in Table 13, which examines the relationship between Perceived Trust and Behavioral Intention, a P-value of less than 0.
and a T-statistic value of 770 .
reater than or equal to 1.
were obtained.
This leads to the conclusion that Perceived Trust in the digital banking application has a positive impact on Behavioral Intention.
A H5: In the test results for Hypothesis H5 in Table 13, which examines the relationship between Fear Of Financial Lost and Security and Privacy, a P-value of less than 0.
and a T-statistic value of 2.
reater than or equal to 1.
were obtained.
This leads to the conclusion that Fear Of Financial Lost in the digital banking application has a positive impact on Security and Privacy.
A H6: In the test results for Hypothesis H6 in Table 13, which explores the connection between Perceived Online Identity Theft and Security and Privacy, a P-value of 0.
reater than or equal to 0.
and a T-statistic of 1.
ess than 1.
were observed.
This indicates that Perceived Online Identity Theft in the digital banking application does not positively influence Security and Privacy.
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ye A H7: In the test results for Hypothesis H7 in Table 13, which investigates the link between Security and Privacy and Behavioral Intention, a P-value of 0.
reater than or equal to 0.
and a T-statistic of 573 .
were observed.
This suggests that Security and Privacy within the digital banking application do not positively influence Behavioral Intention.
A H8: In the test results for Hypothesis H8 in Table 13, which explores the relationship between Behavioral Intention and Use Behavior, a P-value of 0.
ess than 0.
and a T-statistic of 13.
reater than or equal to 1.
were found.
This indicates that Behavioral Intention in the digital banking application positively affects Use Behavior.
Discussion of Diret Effect
H1A
H2A
H3A
H4A
H5A
H6A
H7A
Table 14.
Direct Effect Hypothesis T statistics Performance Expectancy Ie Use Behavior Effort Expectancy Ie Use Behavior Social Influence Ie Use Behavior Perceived Trust Ie Use Behavior Fear Of Financial Lost Ie Use Behavior Perceived Online Identity Theft Ie Use Behavior Security and Privacy Ie Use Behavior P values Result Accepted Rejected Accepted Accepted Rejected Rejected Rejected In Table 14, the discussion focuses on the direct effect of independent variables without moderation It can be seen that the P-values are less than 0.
05 and the T-statistics are greater than or equal to 966 for the independent variables Performance Expectancy.
Social Influence, and Perceived Trust, indicating that they have a significant impact on Use Behavior.
This suggests that the performance of the digital banking application.
Social Influence, and Perceived Trust in digital banking influence users to utilize digital banking.
It was also found that Effort Expectancy.
Fear Of Financial Lost.
Perceived Online Identity Theft, and Security and Privacy do not influence users to use digital banking.
MANAGERIAL IMPLICATIONS
The author provides several recommendations to enhance the appeal and usage of digital banks in Indonesia.
First, improving the performance of applications is essential to make them easier and more convenient to use, which can attract more users.
Second, promotions should be evenly distributed across the Jabodetabek area and beyond to reach users in various regions .
, .
Third, the reliability and security of applications must be maintained to increase users sense of safety and trust in digital banking services.
Lastly, managerial implications include enhancing digital banks marketing strategies by focusing on personalized services, improving the security measures to build trust, and continuously innovating features that meet customer needs .
, .
Digital banks should prioritize user-friendly interfaces, transparent privacy policies, and secure transaction protocols to increase adoption rates and customer loyalty.
Industry practitioners should consider implementing more robust data privacy measures and tailored customer engagement strategies to increase adoption.
By implementing these suggestions, it is hoped that user satisfaction and trust will increase, thereby encouraging the growth of digital banking users in Indonesia .
, .
CONCLUSION
This study finds that Performance Expectancy.
Social Influence, and Perceived Trust have a positive impact on Behavioral Intention and Use Behavior.
These findings align with previous research, which indicates that Performance Expectancy.
Effort Expectancy.
Facilitating Conditions.
Social Influence, and Security positively influence Behavioral Intention.
However, not all of these factors were found to be significantly related to Use Behavior.
The factor of Behavioral Intention was found to have a positive effect on actual usage.
Additionally.
Performance Expectancy.
Effort Expectancy.
Social Influence.
Perceived Risk.
Perceived Trust, and Service Quality also have a significant positive impact on the intention to use an application.
Furthermore.
International Journal of Cyber and IT Service Management (IJCITSM).
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October 2025, pp.
171Ae185 International Journal of Cyber and IT Service Management (IJCITSM) ye Effort Expectancy does not have a positive effect on Behavioral Intention, in line with studies stating that Effort Expectancy.
Price, and Habit are not significant factors influencing Behavioral Intention.
This study also shows differences from previous research regarding security and privacy, where Fear Of Financial Lost and Perceived Online Identity Theft did not impact Security & Privacy Concerns or Behavioral Intention, contrary to previous studies that showed these factors had an effect.
In this context, it illustrates that users opt for digital banking due to convenience and their trust that digital banks properly protect customer data and will not misuse it.
It can be concluded that three main factors influence the userAos Behavioral Intention in using a digital banking app.
First.
Performance Expectancy has a significant impact on Behavioral Intention and Use Behavior, suggesting that improving the appAos performance in terms of usability and convenience can increase user productivity.
Second.
Social Influence also affects Behavioral Intention, where effective promotion can boost users interest in using a digital banking app.
Additionally.
Perceived Trust plays a crucial role in shaping Behavioral Intention, which ultimately affects Use Behavior.
Users trust can be strengthened by ensuring the reliability and security guarantees of the digital banking app, so that users feel safe and confident in using the service.
Overall, these three factors Performance Expectancy.
Social Influence, and Perceived Trust are important in increasing the adoption of digital banking apps among users.
DECLARATIONS
About Authors Erwin Sugiharto (ES) Viany Utami Tjhin (VU) https://orcid.
org/0000-0002-7320-9927 Author Contributions Conceptualization: ES.
Methodology: VU.
Software: ES.
Validation: VU and ES.
Formal Analysis:
ES.
Investigation: VU.
Resources: ES.
Data Curation: VU.
Writing Original Draft Preparation: ES.
Writing Review and Editing: VU.
Visualization: ES.
All authors.
ES, and VU, have read and agreed to the published version of the manuscript.
Data Availability Statement The data presented in this study are available on request from the corresponding author.
Funding The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interest The authors declare that they have no conflicts of interest, known competing financial interests, or personal relationships that could have influenced the work reported in this paper.
REFERENCES