Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5, 3134-3148 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 26 March 2025; Revised: 12 May 2025; Accepted: 15 May 2025; Published: 29 May 2025 * Correspondence: Fauzan.azima@binus.ac.id An analysis of factors influencing the use of online loan applications among university students in Jakarta Fauzan Zulfa Zian Azima1*, Yohannes Kurniawan2 1Information Systems Management Department, BINUS Graduate Program, Master of Information Systems Management, Bina Nusantara University, Jakarta, Indonesia; Fauzan.azima@binus.ac.id (F.Z.Z.A.). 2Information Systems Department, School of Information Systems Management, Bina Nusantara University, Jakarta, Indonesia; YKurniawan@binus.edu (Y.K.). Abstract: Financial structures have changed as a result of the fintech industry's explosive rise in online lending, which provides quick and easy loans that are especially attractive to younger generations. However, there are disadvantages associated with this ease, such as increased debt and concerns about data security. With a focus on digital financial literacy and perceived security threats among DKI Jakarta students, this study investigates the factors influencing the use of online lending services. It incorporates these ideas into a modified UTAUT2 model, which is unique. Partial Least Squares Structural Equation Modeling (PLS-SEM), a quantitative method, was used to examine data from an online survey of 414 Indonesian online lenders. The findings show that behavioral intention is significantly and positively impacted by Hedonic Motivation, Price Value, Digital Financial Literacy, Habit, Effort Expectancy, Social Influence, Performance Expectancy, and Facilitating Conditions. Perceived security risk did not have a substantial impact, which was unexpected. According to these results, in order to increase user adoption and usage, online lenders should prioritize factors such as price competitiveness, social media influence, habit development, user-friendliness, benefits, and easily accessible digital financial literacy materials. Keywords: Digital financial literacy, Financial technology, Online loan, Security risk, UTAUT2. 1. Introduction Technology has changed the way we do things, especially when it comes to money. Fintech, like online loan apps, has made it easier for people to borrow money quickly and easily, anytime, anywhere [1]. These days, online loans are becoming really popular. It’s a great option for people who need money fast, especially if they're having a hard time getting a loan from a traditional bank. Online loans are easier to get because they have more flexible requirements [2]. With the proliferation of Fintech- based online lending startups in Indonesia, there is a wide variety of applications offering convenient loan services to consumers. The increasing number of online lending applications has driven companies to continuously improve their quality and services to meet customer demand [3]. Derived from Populix's "Most Used Lending Apps" survey, reveals the evolution of finance in Indonesia as of October 2023. Among the 1,071 respondents, 420 indicated using online lending applications for various reasons such as housing, business, employment, education, lifestyle, healthcare, and others. Akulaku emerged as the most preferred app, followed by Populix [4]. Sourced from Indonesiabaik.id and OJK's data in June 2023, highlights that the majority of online loan app users in Indonesia fall within the 19-34 age group, with a total loan value of Rp 26.87 trillion. The second largest user group is the 35-54 age bracket, with a loan value of Rp 17.98 trillion [5]. Populix's survey further delves into the user profile. It reveals that Gen Z, aged 17-25, primarily students, constitute the https://orcid.org/0000-0001-8876-3472 3135 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate largest user base of online lending fintech services. Common motivations for these loans include education expenses, lifestyle, healthcare, and other needs [4]. Given the quick and easy nature of online lending applications, borrowers often utilize these platforms for various needs such as bill payments, installment plans, and educational expenses [6]. This research is primarily motivated by the ease with which individuals can obtain online loans through mobile apps, which can lead to impulsive, risky decision-making, especially during times of urgency. Lack of digital financial literacy renders individuals susceptible to making risky financial decisions without considering long-term consequences, such as mounting debt and potential misuse of personal data. Under such circumstances, individuals may be inclined to quickly resolve their problems without considering important factors, opting instead for online loans [7]. Furthermore, Indonesian users of online lending applications express concerns regarding illegal lending practices, high interest rates, unethical debt collection, misuse of personal data, and fraud. The government has enacted regulations to mitigate these issues, as outlined in relevant legislation [8]. There have been cases where individuals have experienced fear due to the misuse of personal data on online lending applications. These users did not recall taking out online loans but found themselves registered as borrowers and were threatened with the sale and dissemination of their personal information by online loan collectors [9]. Furthermore, the Financial Services Authority (OJK) discovered 537 illegal online lending applications in 2024, posing potential harm to the public and violating data privacy regulations [10]. In Indonesia, online lending is regulated by the Financial Services Authority (OJK) Regulation No. 77/POJK.01/2016 Regarding lending and borrowing services based on information technology. In Indonesia, online lenders must apply for a license from the OJK, use fair lending procedures and practices, safeguard the privacy of borrowers, and only make loans to borrowers who can repay them [11]. Digital Financial Literacy refers to an individual's ability to understand, use, and manage financial services effectively in the digital age. Prior to use online lending applications, prospective users should have a solid awareness of financial technology, financial security and protection, financial planning, and the services themselves. Numerous issues, such as a poor understanding of how online lending programs function, an inability to manage the financial risks associated with borrowing, and increased susceptibility to fraud, can arise from a lack of digital financial literacy [12]. Before using online lending apps, consumers should think about the hazards involved with giving these platforms personal information like their name and ID card. Concerns with user data security, including whether users' worries about the security of the data they submit affect their choice to use online lending services, as well as issues with the security of the applications themselves, draw attention to security threats [13]. University students in DKI Jakarta who have urgent financial demands and might not have access to traditional banks because of their age or bad credit history are among the target consumers of these online lending programs. Students with college fees are frequently the borrowers from these online loan programs [14]. The purpose of this study is to examine the variables that affect the choice to take out online loans made by university students living in DKI Jakarta who are either present or potential users of online lending apps. Because it can explain the elements impacting acceptability and consideration of adopting online loan app technology, the UTAUT 2 model serves as the basis for this study model. To make this study special, the researchers added a novelty variable to the UTAUT 2 model. The UTAUT 2 theory is applied in this work since it is suitable for the research. UTAUT's goal is to predict and explain the adoption and use of technology. The core concepts of UTAUT—performance expectancy, effort expectancy, social influence, enabling conditions, pricing value, habit, and behavioral intention—are linked to the factors that impact users' decisions to take out online loans. Additionally, UTAUT has been extensively tested and validated in a range of research environments. The UTAUT theory is also linked to the novelty factors of Digital Financial Literacy and Security Risk to investigate the elements that users of online lending apps assess. 3136 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate Both current and potential users may utilize these factors to support their decisions. Digital financial literacy includes knowledge of consumer rights, risk reduction strategies, dispute resolution procedures, and digital financial hazards [15]. Security risks include events or circumstances such as data loss, misuse, denial of service, disclosure, alteration, error, and falsification[16]. To find out how people perceive the issues with using online lending services, behavioral intention is being used. Behavioral Intention, in this context, refers to an individual's intent to use an online lending application. Users need a basic understanding of what online lending applications are, how they work, and their implications before using the service [17]. Therefore, this study is limited to measuring initial user intent and does not consider external factors that may influence actual behavior. Several previous studies have been conducted on online lending applications, and this research builds upon these studies. Some of these studies include "What are the leading factors for using Spanish peer-to-peer mobile payment platform Bizum? The applied analysis of the UTAUT2 model" [18] "Analysis of financial technology acceptance of peer to peer lending (P2P lending) using extended technology acceptance model (TAM)" [17] "Factor Influencing Continuation Intention of Using Fintech from the Users’ Perspectives: Testing of Unified Theory of Acceptance and Use of Technology (UTAUT2)" [12], "In quest of perceived risk determinants affecting intention to use fintech: Moderating effects of effects of situational factors" [19] and "Factors influencing Fintech adoption for women in the post- Covid-19 pandemic" [13]. Previous studies have extensively discussed the factors influencing users in adopting online lending applications using specific research models, such as UTAUT 1, UTAUT 2, and TAM. However, based on previous journals, it is not common to discuss online lending applications specifically with the variables of Digital Financial Literacy and Security Risk. Although there are some previous studies that have discussed the factors that users consider when taking out online loans, this is because the fintech industry is constantly evolving with the implementation of new technologies and user behavior is constantly changing. The factors of Digital Financial Literacy and Security Risk play a role for users when they are in a pressing situation to take out an online loan, and this research will provide new insights into their impact, as understanding these factors can prevent impulsive decisions, protect consumers, and promote financial inclusion. Based on the background above, the researcher is interested in conducting research titled "Analysis of Factors Affecting the Use of Online Lending Applications Among University Students in DKI Jakarta". This title is investigated to answer the research question: what are the factors that can influence users in their desire to use online loan services? It is hoped that this research can provide input to service providers to improve their services to both current and potential users of online lending applications. 2. Literature Review 2.1. Financial Technology Fintech is a term used to describe software-based technology aimed at providing various financial services with easy and quick access in various forms, such as online banking, mobile payment applications, or even cryptocurrencies [20]. In other words, Fintech is an innovative, technology-based financial service. Fintech is also used to describe various innovations for business efforts in the process, delivery, and use of financial services [21]. The use of Fintech is driven by the unmet needs of users for financial services. It is hoped that by adopting Fintech, financial inclusion can be increased. Additionally, the adoption of Fintech is also influenced by high financial costs, supportive regulations, and other factors, especially macroeconomic conditions [22]. 2.2. Online Loans Online loans are direct lending processes agreed upon by lenders and borrowers through online lending platforms, which are usually app-based. Online loans represent a new breakthrough that signifies innovation in the financial sector, namely the combination of internet technology and private lending [23-27]. Basically, app-based online loans are the same type of loan as conventional loans from 3137 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate traditional banks. The main difference is that online lending services use information technology provided by the service providers, commonly known as financial technology (Fintech) [28]. 2.3. UTAUT Theory The Unified Theory of Acceptance and Use of Technology (UTAUT) is a theory constructed to explain behaviors related to the use of information technology. UTAUT is a combination of 8 previous models, unified from various perspectives with the aim of understanding how individuals can accept and use the latest technology [29]. UTAUT has a rationale for its ongoing, vigorous use of contemporary technology. The factors influencing the adoption of new technology can be explained by this theory, which focuses on user attitudes and behavior. Additionally, this theory is flexible enough to be modified to benefit from the most recent developments in technology, including online loan applications [30]. The UTAUT model has already integrated several research paradigms, including the Theory of Planned Behavior (TPB), Social Cognitive Theory (SCT), Technology Acceptance Model (TAM), Model of PC Utilization (MPCU), Motivational Model (MM), and Model Combined with TPB [29]. 2.4. Research Variables Figure 1 below depicts the UTAUT 2 research model, which the researcher has selected as the starting framework for this investigation. Several variables from the UTAUT 2 model have been selected to facilitate analysis and produce study outcomes. To make this study more unique and creative, the researcher has introduced a new variable, "novelty," to the original UTAUT 2 model. Figure 1. Research Model. 2.4.1. Does Facilitating Conditions Influence Behavioral Intention? A Facilitating Condition (FC) is a user's strong belief that using a fintech technology will improve their performance [29]. Additionally, people might be persuaded to accept innovation as a quick solution to their problems [31]. According to research, the concept of fintech services is still relatively new in a number of developing countries [32] and businesses struggle to make sure users can utilize 3138 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate them successfully, effectively, and on time. These studies have shown that behavioral intention is influenced by the Facilitating Conditions (FC) variable. Consequently, the following hypothesis is proposed: H1: Facilitating Conditions has a positive impact on Behavioral Intention. 2.4.2. Does Habit Influence Behavioral Intention? Examining the Habit (HB) variable is one method of determining a user's inclination to embrace a technology. The more frequently a device is used, the more embedded the habit becomes [18, 33]. This is because these learned actions eventually become habits as consumers become familiar with fintech products [34]. Previous research indicates that the more frequently a person uses or adopts a financial technology, the stronger their habit becomes. The following theory is proposed in view of the previously mentioned: H2: Habit has a positive impact on Behavioral Intention. 2.4.3. Does Effort Expectancy Influence Behavioral Intention? A user's perception of a new technology's ease of use is known as Effort Expectancy (EE). Users are more likely to adopt a technology with a substantial intention when they believe it to be user-friendly [35, 36]. Since Effort Expectancy measures how easily people perceive new technology, particularly fintech, it follows that the more user-friendly a technology is judged to be, the more likely users are to adopt it. Because of the technology's ease of use, which promotes adoption, it may be assumed that Effort Expectancy and Behavioral Intention have a positive relationship. In light of the aforementioned, the following theory is put forth: H3: Effort Expectancy has a positive impact on Behavioral Intention. 2.4.4. Does Social Influence Can Influence Digital Financial Literacy? Social influence (SI) is the process by which a person asks friends, family, and other members of their social circle for their thoughts on the value of a new system or product [37]. Through this process, users learn about fintech technology, which can lead them to want to try and use the application. Previous research conducted [38] suggests that social interaction can facilitate an individual's learning of how to use a technology. Moreover, social support from friends or family can enhance one's self-confidence in using new technology, thereby encouraging them to improve their digital literacy. Therefore, the following hypothesis is proposed: H4: Social Influence has a positive impact on Digital Financial Literacy. 2.4.5. Does Security Risk Influence Digital Financial Literacy? Security Risk (SR) can be defined as the security risk associated with a technology, including the potential for loss of control over personal information, such as the unauthorized use of personal information [39]. Security risk is closely related to fintech technology because it requires strong trust from users for them to use fintech applications [40]. In the context of fintech, particularly online lending, security risks not only threaten user privacy but can also lead to financial losses. If users have knowledge and trust regarding the security risks of an online lending technology, their trust will grow, increasing the likelihood of them using the application. Based on the above, the following hypothesis is proposed: H5: Security Risk has a positive impact on Digital Financial Literacy. 2.4.6. Does Performance Expectancy Influence Digital Financial Literacy? Users' perception that implementing a specific technology or system will improve their job performance is known as Performance Expectancy (PE) [29]. People are driven to learn more when they have strong hopes that improving their digital financial literacy will help them manage their 3139 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate money more effectively, reach their financial objectives, or even enhance their quality of life, according to [41]. The following theory is proposed in view of the previously mentioned: H6: Performance Expectancy has a positive impact on Digital Financial Literacy. 2.4.7. Does Performance Expectancy Influence Hedonic Motivation? Performance expectancy and hedonic motivation are significantly correlated, according to earlier study[42]. The results of the study showed that users are more motivated to use a technology because they love it more when they believe it will be more beneficial. The following theory is proposed in view of the previously mentioned: H7: Performance Expectancy has a positive impact on Hedonic Motivation. 2.4.8. Does Price Value Influence Digital Financial Literacy? Price Value (PV) refers to a user's perception that the benefits of adopting a technology exceed the costs [18, 33]. Previous studies on financial technology, or fintech, have shown that price value influences people's incentive to utilize a tool [43]. In summary, "price value" describes the balance between the benefits a customer receives and the costs incurred when acquiring that technology, such an online loan. The following theory is proposed in view of the previously mentioned: H8: Price Value has a positive impact on Digital Financial Literacy. 2.4.9. Does Price Value Influence Hedonic Motivation? Research has shown that the relationship between price value and hedonic motivation yielded significant results [44]. The study found a complex relationship between hedonic motivation and price value. Customers with higher levels of hedonic motivation appear to be more price-tolerant. However, if the price value is too low, consumers' hedonic motivation might be reduced. The following theory is proposed in view of the previously mentioned: H9: Price Value has a positive impact on Hedonic Motivation. 2.4.10. Does Digital Financial Literacy Influence Hedonic Motivation? Digital financial literacy is the capacity to understand financial ideas and practices in a digital environment [12]. Aside from knowledge, skills, and comprehension, another component of digital financial literacy is the user's capacity to protect themselves against fraud and cybersecurity threats. Numerous studies have demonstrated a substantial correlation between behavioral intention, or the adoption of online lending fintech, and digital financial literacy [45]. Based on earlier research, more may be done to examine the relationship between digital financial literacy and behavioral intention and technology adoption in Indonesia. Consequently, the following hypothesis is proposed. H10: Digital Financial Literacy has a positive impact on Hedonic Motivation. 2.4.11. Does Hedonic Motivation Influence Behavioral Intention? The ability to comprehend financial concepts and procedures in a digital setting is known as digital financial literacy [12]. The ability to defend oneself against fraud and cybersecurity dangers is another aspect of digital financial literacy, in addition to knowledge, skills, and comprehension. Behavioral intention or the adoption of online lending fintech has been shown to be significantly correlated with digital financial literacy in a number of studies [45]. To fully understand the connection between digital financial literacy and Indonesian technology adoption and behavioral intention, more study is needed, building on earlier findings. Therefore, the following theory is put forth. H11: Digital Hedonic Motivation has a positive impact on Behavioral Intention. 3140 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate 3. Methods 3.1. Data Collection The research method used in this study is quantitative, Once the questionnaires pass the validity and reliability tests; the questionnaires will be distributed according to the calculated target number of respondents who are interested in or are currently using online loan applications and distributing questionnaires to 414 respondents [40]. Data was collected using a questionnaire distributed through Google Forms. Respondents answered questions using a Likert scale. The questionnaire was disseminated to students who are interested in or currently using online loan applications. The data from this research can be accessed through our open data portal [data portal]. By making this research data publicly available, we aim to support further study and encourage collaboration across various fields. 3.2. Measurement Items Development The population refers to the target group or individuals whose characteristics a researcher aims to study [46]. Based on the background and variables of this research, the researcher has determined that the respondents for this study are students residing in DKI Jakarta who, at the time of completing the questionnaire, are either interested in or currently using online loan applications, such as AkuLaku, Kredivo, EasyCash, Adakami, SPinjam, and so on. According to the Fintech Lending Statistics report from the official website of the Financial Services Authority (OJK), the total number of loan recipients in DKI Jakarta is 1,635,799 users, with a total loan amount of Rp. 4,261.64 billion. This total will be used by the researcher as the total population for this study. 3.3. Respondent Demographic Profile The research object in this study is students who are interested in or are currently using online loan applications in the DKI Jakarta area. There is no age limit for these individuals, but the requirement is that they are legally considered as students so they can register to use online loan applications, considering that the registration process for online loan applications requires an Identity Card (KTP). This Table 1 and 2 below shows about respondent feature profile and respondent characteristics base on this research, Table 3 below shows the result of validity test based on this research and data processing using Smart PLS 4.0. Table 1. Surveys: feature profile. Variable Respondent Profile Profile Frequency Percentage Gender Male 213 51.4% Female 201 48.6% domicile West Jakarta 125 30.2% East Jakarta 62 15% South Jakarta 101 24.2% North Jakarta 63 15.2% Central Jakarta 63 15.2% age <20 years 93 22.5% 20-24 years 289 69.8% 25-29 years 10 2.4% 30-34 years 13 3.1% 35-40 years 7 1.7% >40 years 2 0.5% University Bina Nusantara University 150 36.2% Trisakti University 19 4.6% Tarumanagara University 19 4.6% 3141 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate Atma Jaya University 35 8.5% Mercu Buana University 18 4.3% Jakarta Veteran University 24 5.8% Muhammadiyah University 17 4.1% Budi Luhur University 18 4.3% Jakarta State University 24 5.8% Krida Wacana University 7 1.7% Esa Unggul University 27 6.5% Borobudur University 34 8.2% Terbuka University 13 3.1% Current education Diploma 1 10 2.4% Diploma 2 2 0.5% Diploma 3 21 5.1% Diploma 4 50 12.1% Bachelor's/Strata 1 303 73.2% Master's/Strata 2 25 6% Doctorate/Strata 3 3 0.7% Table 2. Surveys: characteristics of respondents. Variable Respondent Characteristics Profile Frequency Percentage applications used Aku laku 186 44.9% Kredivo 140 33.8% Easycash 120 29% AdaKami 27 6.5% SPinjam 35 8.5% IndoDana 26 6.3% how many times use each month 1-2 times 251 60.6% 3-4 times 98 23.7% 5-6 times 51 12.3% more than 6 times 14 3.4% Reason Urgent needs 133 32.1% ease of access and easy disbursement process 258 62.3% Not yet have access to conventional banks 45 10.9% Low interest rates 252 60.9% Attractive promotions from applications 180 43.5% Easy requirements 1 0.2% How long have you been using the Online Loan Application Less than 1 month 56 13.5% 1-3 months 54 13% 4-6 months 284 68.6% 7-12 months 13 3.1% More than 1 year 7 1.7% How much money have you borrowed through online loan applications Less than Rp1,000,000 57 13.8% Rp1,000,000 - Rp5,000,000 196 47.3% Rp5,000,000 - Rp10,000,000 152 36.7% More than Rp10,000,000 9 2.2% Where do you know about the online loan application Friends or Family 48 11.6% Social Media 135 32.6% Online Ads 131 31.6% Other Apps 71 17.1% News or Articles 60 14.5% SMS Blast 6 1.4% 3142 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate Table 3. Result of Validity Test. H. Validity Test Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) BI 0.808 0.809 0.886 0.722 DFL 0.873 0.873 0.913 0.724 EE 0.81 0.817 0.887 0.724 FC 0.825 0.83 0.896 0.741 HB 0.849 0.849 0.909 0.768 HM 0.79 0.794 0.877 0.704 PE 0.903 0.904 0.928 0.721 PV 0.81 0.812 0.888 0.725 SR 0.8 0.801 0.882 0.714 SI 0.843 0.844 0.905 0.761 3.4. Structural Equation Modeling Technique According to Hair Joseph and Hult [47] Structural Equation Modeling (SEM) is an advanced statistical technique that overcomes limitations of traditional multivariate analysis methods. SEM enables researchers to simultaneously model and estimate complex relationships among numerous dependent and independent variables. By accounting for measurement errors in observed variables, SEM provides more accurate results grounded in theoretical concepts. In this study, we employed SmartPLS 4 to analyze our data using SEM. SEM consists of two models: the outer model, which assesses the relationship between latent variables and their indicators, and the inner model, which examines the relationships between latent variables. The outer model was evaluated using convergent validity, discriminant validity, and composite reliability, while the inner model was assessed using R- squared, f-squared, q-squared, and path coefficients. Hypothesis testing was conducted using bootstrapping to determine the significance of path coefficients. A PLS-SEM analysis was conducted in this study using SmartPLS 4, There are two models within PLS-SEM: the external model and the internal model. The following presents the path model estimates based on the Outer model, Inner Model, and Hypothesis Testing. 4. Result and Discussion 4.1. Descriptive Analysis Based on Table 1, the majority of online loan application service users are male, accounting for 51,4% of users, with most being aged 20-24 years old. The majority of users reside in West Jakarta, representing 125% of the total, and are students at Bina Nusantara University, pursuing a Bachelor's degree. Table 2, which presents the respondent characteristics, indicates that the most commonly used application for online loans is AkuLaku, with a usage frequency of 1-2 times per month. The primary reason for using this service is the low interest rates of the online loan application. Most respondents have been using online loan applications for 4-6 months, with loan amounts ranging from Rp 1,000,000 to Rp 5,000,000. Additionally, the majority of users learned about and started using online loan applications based on information obtained from social media. 4.2. Data Validation Data validation employs a latent variable approach within the framework by iteratively testing constructs that require disaggregation, contingent on Cronbach's alpha and average variance extracted (AVE). A framework is deemed acceptable when Cronbach's alpha falls within these ranges: below 0.6 indicates low reliability, 0.6-0.7 indicates moderate reliability, 0.7-0.8 indicates good reliability, 0.8-0.9 indicates very good reliability, and above 0.9 indicates excellent reliability. For the average variance extracted (AVE), a value exceeding 0.5 is required for each construct, signifying that the latent 3143 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate construct explains a substantial proportion of the variance of its indicators. Furthermore, the factor loading of each indicator must surpass 0.7, indicating a robust relationship between the indicator and its latent construct. 4.3. (Outer Model) Measurement Analysis The outer model was evaluated to assess the validity and reliability of the latent constructs. Several tests were conducted, including: Convergent validity: The Average Variance Extracted (AVE) value for each construct must exceed 0.5, indicating that the latent construct explains a substantial portion of the variance of its indicators. Additionally, the factor loading of each indicator should be greater than 0.7, indicating a strong relationship between the indicator and its latent construct. Discriminant validity: To ensure that each latent construct measures a distinct concept, the Square Root of AVE (SQRT-AVE) of each construct must be greater than the correlation between that construct and other constructs. Composite reliability: The composite reliability value for each construct must be greater than 0.7, indicating a high internal consistency among the indicators in measuring the latent construct. This Table 4 below shows about outer-loading values have a minimum of 0.821 and a maximum of 0.883; both of these values are considered safe for outer-loading. The AVE values in Table 4 have a minimum of 0.704, which is above the minimum requirement of 0.5 for AVE, indicating that the AVE values are acceptable. Additionally, the composite reliability has a minimum value of 0.794, which exceeds the minimum requirement of 0.7, thus indicating that the composite reliability is also acceptable. Table 4. Result of Outer Loading. H. Outer Loading BI DFL EE FC HB HM PE PV SR SI BI1 0.841 BI2 0.854 BI3 0.855 DFL1 0.842 DFL2 0.85 DFL3 0.865 DFL4 0.846 EE1 0.839 EE2 0.835 EE3 0.877 FC1 0.883 FC2 0.87 FC3 0.828 HB1 0.883 HB2 0.87 HB3 0.877 HM1 0.821 HM2 0.835 HM3 0.861 PE1 0.849 PE2 0.854 PE3 0.834 PE4 0.853 PE5 0.856 PV1 0.851 PV2 0.866 PV3 0.837 SI1 0.88 SI2 0.869 SI3 0.868 3144 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate SR1 0.865 SR2 0.844 SR3 0.825 4.4. (Inner Model) Structural Model Analysis Evaluation of the inner model was conducted to measure the strength and significance of the causal relationship between latent constructs. Several tests were performed, including: R-square: The R-square value obtained for each endogenous construct indicates the proportion of variance in the endogenous construct that can be explained by the exogenous construct. Generally, an R-square value of 0.67 is considered strong, 0.33 is moderate, and 0.19 is weak. F-square: The f-square value indicates the effect of the exogenous construct on the endogenous construct. Commonly used f-square values are 0.02 (small), 0.15 (medium), and 0.35 (large). Table 5. Result of R-square. H. R-square Behavioral Intention 0.484 Digital Financial Literacy 0.574 Hedonic Motivation 0.49 Based on the R-squared value in Table 5, it can be concluded that the R-squared value in this study is moderate. The F-squared values in Table 6 have one value of 0.009, 0.013, 0.014, which is considered low; however, the remaining F-squared values are above the medium and high thresholds. Table 6. Result of F-square. H. F-Square BI DFL HM DFL 0.013 EE 0.046 FC 0.032 HB 0.033 HM 0.014 PE 0.107 0.213 PV 0.046 0.073 SR 0.009 Based on the R-squared value in Table 5, it can be concluded that the R-squared value in this study is moderate. The F-squared values in Table 6 have one value of 0.009, 0.013, 0.014, which is considered low; however, the remaining F-squared values are above the medium and high thresholds. 4.5. Hypothesis Test If the bootstrapping procedure yields a significant value for the T-statistic is greater than 1.96, then the weight of that indicator can be considered significant. Conversely, it is considered insignificant if the value is below 1.96. A p-value will be considered significant if the value is below 0.5, and will be rejected if the p-value is above 0.5. 3145 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate Table 7. Result of direct effect coefficient. H. Direct Effect Coefficient Original Sample (O) Sample Mean (M) Standard Deviation (STDEV) T Statistic P Value Criteria DFL > BI 0.154 0.153 0.053 2.891 0.004 Significant EE > BI 0.242 0.241 0.053 4.595 0.00 Significant FC > BI 0.217 0.217 0.057 3.792 0.00 Significant HB > BI 0.205 0.206 0.055 3.742 0.00 Significant HM > BI 0.118 0.118 0.047 2.529 0.011 Significant PE > DFL 0.352 0.349 0.066 5.354 0.00 Significant PE > HM 0.473 0.471 0.062 7.696 0.00 Significant PV > DFL 0.216 0.214 0.061 3.562 0.00 Significant PV > HM 0.277 0.28 0.062 4.468 0.00 Significant SR > DFL 0.082 0.084 0.049 1.669 0.095 Not Significant SI > DFL 0.226 0.229 0.069 3.283 0.001 Significant This Table 7 above shows about direct effect coefficient, this study highlights a number of important factors that influence users' levels of digital financial literacy and their behavioral intention toward using online loan services. A greater grasp of digital finance fuels the desire to use online loan services, as seen by the positive and substantial relationship between digital financial literacy and behavioral intention (T-Statistic: 2.891, P-Value: 0.004). In addition, behavioral intention (T-Statistic: 4.595, P-Value: 0.00) is significantly influenced by perceived ease of use (Effort Expectancy), support availability (Facilitating Conditions) (T-Statistic: 3.792, P-Value: 0.00), enjoyment (Hedonic Motivation) (T-Statistic: 2.529, P-Value: 0.011), and habit (T-Statistic: 3.742, P-Value: 0.00). This implies that the adoption of online lending services is greatly aided by a favorable user experience, sufficient resources, entertainment features, and established usage habits. Moreover, this research highlights the antecedents of digital financial literacy and user habits. The perceived benefits of the application (Performance Expectancy) significantly enhance both digital financial literacy (T-Statistic: 5.354, P-Value: 0.00) and the formation of usage habits (T-Statistic: 7.696, P-Value: 0.00). Similarly, the perception of worthwhile value (Perceived Voluntariness) also positively contributes to the improvement of digital financial literacy (T-Statistic: 3.562, P-Value: 0.00) and habit formation (T-Statistic: 4.468, P-Value: 0.00). Interestingly, the social influence from the surrounding environment (Social Influence) proves significant in increasing users' digital financial literacy (T- Statistic: 3.283, P-Value: 0.001). However, the perception of the application's social relevance (Social Relevance) does not show a significant influence on digital financial literacy in this context (T-Statistic: 1.669, P-Value: 0.095). 5. Conclusion This study delved into the factors shaping university students' intentions to apply for online loans within the DKI Jakarta region, garnering valuable insights from 414 respondents. Descriptive analysis revealed a significant user base among male students aged 20 to 24 in West Jakarta, suggesting a unique demographic profile warranting further investigation into underlying factors such as risk perception and financial resource accessibility. Notably, AkuLaku emerged as the preferred online lending application, with ease of use and convenience cited as primary drivers. This underscores the importance of user-friendly design and efficient processes in attracting and retaining student users. The results of the study, which were analyzed using SmartPLS 4.0, demonstrated how important habit development, enabling circumstances, perceived advantages, performance expectations, security and privacy issues, and digital financial literacy are in influencing students' choices about taking out online loans. It's interesting to note that security risk had a less substantial effect on digital financial 3146 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3134-3148, 2025 DOI: 10.55214/25768484.v9i5.7654 © 2025 by the authors; licensee Learning Gate literacy, suggesting room for improvement in terms of fostering user confidence in online lending applications' security features. In summary, this study offers vital information to scholars, decision-makers, and online lending companies that want to encourage university students to use online loan services responsibly and sustainably. The results highlight how several elements, such as user experience and security trust, interact to influence students' intents and actions in this changing financial environment. Acknowledgment: The authors express their sincere gratitude to Bina Nusantara University for its financial assistance, which made it possible for this study to be published. Students living at DKI Jakarta served as the human subjects for this study, which was carried out strictly in accordance with ethical guidelines. Conceptualization, Y.K. (Yohannes Kurniawan); methodology, Y.K. and F.Z.Z.A. (Fauzan Zulfa Zian Azima); software, F.Z.Z.A.; validation, F.Z.Z.A.; formal analysis, F.Z.Z.A.; investigation, Y.K. and F.Z.Z.A.; resources, F.Z.Z.A. (Fauzan Zulfa Zian Azima); data curation, Y.K.; writing—original draft preparation, F.Z.Z.A.; writing—review and editing, Y.K.; visualization, F.Z.Z.A.; supervision, Y.K.; project administration, Y.K.; funding acquisition, F.Z.Z.A.. 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