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This research assesses the levels of digital financial literacy (DFL) and the patterns of Fintech adoption among university students in South Africa. It expands the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to investigate the impact of demographic factors on DFL as a precursor to Fintech utilisation. A quantitative methodology utilised convenience sampling to distribute 376 questionnaires at two public universities in South Africa. Data analysis employed descriptive statistics, exploratory factor analysis, Kruskal-Wallis tests, and multiple linear regression using IBM SPSS version 29.0, with reliability evaluated through Cronbach's alpha coefficients. Findings reveal moderate to high levels of DFL among university students; however, notable knowledge deficiencies exist concerning consumer rights and redress mechanisms. Demographic analysis indicated that gender had no significant impact on DFL levels, whereas income, age, and educational attainment showed significant correlations. Three components of DFL demonstrated positive and significant correlations with university students' intentions to adopt Fintech products and services; however, awareness of digital financial risks did not significantly influence these adoption intentions. The findings enhance the theoretical framework of technology acceptance models in emerging markets and offer empirical evidence regarding DFL's influence on Fintech adoption. This study provides evidence-based recommendations for policymakers, financial institutions, and educational administrators aiming to improve financial inclusion initiatives and increase Fintech utilisation among university students in South Africa.
ARTICLE INFO
Article history:
Received 19 May 2025
Received in rev. form 20 Sept 2025
Accepted 10 October 2025
Keywords:
Digital Financial Literacy; Fintech Products and Services; University Students; Demographic Variables; South Africa.
JEL Classification:
D10, D14, D18, D19, D31, G41, G53
ABSTRACT
This research assesses the levels of digital financial literacy (DFL) and the patterns of Fintech adoption among university students in South Africa. It expands the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to investigate the impact of demographic factors on DFL as a precursor to Fintech utilisation. A quantitative methodology utilised convenience sampling to distribute 376 questionnaires at two public universities in South Africa. Data analysis employed descriptive statistics, exploratory factor analysis, Kruskal-Wallis tests, and multiple linear regression using IBM SPSS version 29.0, with reliability evaluated through Cronbach's alpha coefficients. Findings reveal moderate to high levels of DFL among university students; however, notable knowledge deficiencies exist concerning consumer rights and redress mechanisms. Demographic analysis indicated that gender had no significant impact on DFL levels, whereas income, age, and educational attainment showed significant correlations. Three components of DFL demonstrated positive and significant correlations with university students' intentions to adopt Fintech products and services; however, awareness of digital financial risks did not significantly influence these adoption intentions. The findings enhance the theoretical framework of technology acceptance models in emerging markets and offer empirical evidence regarding DFL's influence on Fintech adoption. This study provides evidence-based recommendations for policymakers, financial institutions, and educational administrators aiming to improve financial inclusion initiatives and increase Fintech utilisation among university students in South Africa.
Introduction
The incorporation of information technology (IT) into financial systems has significantly altered the global financial landscape, facilitating the rise of financial technology (Fintech). This paradigm shift signifies the integration of financial services with technological innovation, resulting in unique opportunities for financial inclusion and service delivery (Rani & Kumar, 2024). The swift advancement of Fintech has transformed the financial services sector by offering automated, efficient, and accessible solutions via technological platforms (Hettiarachchi & Wijekumara, 2023). The technological transformation has enabled the creation of innovative financial services, such as digital insurance platforms, crowdfunding mechanisms, peer-to-peer lending networks, and contemporary payment systems that utilise quick response (QR) codes and mobile banking applications (Rani & Kumar, 2024).
The rise of digital platforms and mobile technology has greatly improved Fintech accessibility, democratising access to advanced financial services, including online banking, investment management, and digital payment systems (Benny et al., 2024). This technological advancement presents a significant challenge: the swift evolution of Fintech products and services has surpassed the growth of digital financial literacy (DFL) among consumers, especially young adults who are navigating these systems during their formative financial years. DFL denotes the capacity to securely use electronic devices for accessing financial products and services via digital platforms, enabling consumers to attain their financial objectives while safeguarding against financial risks and improving their comprehension of available resources (Li & Fisher, 2022; Respati et al., 2023). Morgan et al. (2019) contend that enhanced accessibility of financial services via Fintech requires elevated levels of Digital Financial Literacy (DFL) for effective utilisation. This serves as a safeguard against potential risks, including misselling frauds, phishing attacks, unauthorised data usage, discriminatory policies, and adverse financial behaviours such as excessive borrowing.
With the growing integration of digital financial systems into daily financial activities, DFL has become a crucial element of education in the digital era. Respati et al. (2023) assert that DFL is a crucial component of modern education, as individuals need improved financial literacy to utilise Fintech products and services effectively while minimising unnecessary costs. Zhu et al. (2022) identified DFL as the primary barrier hindering the full realisation of Fintech's potential benefits and services, despite its crucial role in promoting social inclusivity and rapidly delivering digital financial services. University students constitute a notably important demographic in this context (Kekana, 2014). They are concurrently cultivating financial independence, forming financial behaviours likely to endure into adulthood, and exhibiting elevated rates of technology adoption. Analysing the levels of DFL within this population and the factors affecting their engagement with Fintech services is essential for creating targeted educational interventions, developing user-friendly financial technologies, and encouraging responsible financial behaviour among emerging adults.
This research examines the correlation between DFL components and the utilisation of Fintech products and services among university students in South Africa. The study evaluates the moderating influence of demographic variables on the relationship between DFL and the utilisation of Fintech products and services. This research seeks to enhance the academic understanding of DFL among university students and to provide empirical evidence that informs educational policies, financial institution practices, and regulatory frameworks aimed at improving financial inclusion through technology while safeguarding consumers in the digital financial ecosystem. This section of the paper presents a literature review on Fintech. The literature review establishes the basis for the theoretical and conceptual frameworks that inform hypothesis development. The following section addresses the research methodology employed in this study. The results are subsequently examined, findings are discussed, and the implications and conclusions are presented.
Literature Review
Digital Financial Literacy (DFL)
DFL represents the convergence of two distinct but interconnected domains: financial literacy and digital literacy (Respati et al., 2023). While traditional financial literacy encompasses knowledge, skills, and behaviours related to financial management, DFL extends this concept to include the technological competencies required to navigate digital financial environments. Li and Fisher (2022) define DFL as the ability to securely use electronic devices to access financial products and services through digital platforms, enabling consumers to achieve their financial objectives while protecting themselves from risks and improving their understanding of available resources.
Morgan et al. (2019) propose a comprehensive framework conceptualizing DFL as comprising four essential components. The first component, knowledge of digital financial products and services, encompasses an individual's foundational understanding of digital financial offerings accessible through diverse platforms, including smartphones and other electronic devices (Rahayu et al., 2022). This dimension includes comprehension of mobile wallets, online banking, crowdfunding mechanisms, and digital insurance platforms. Nawang and Shukor (2023) emphasise that understanding both the benefits and limitations of various digital financial services is crucial for informed decision making.
The second component, awareness of digital financial risks, reflects an individual's recognition that digital financial services, despite their convenience, entail unique risks not present in traditional financial systems. Morgan et al. (2019) highlight that these risks include phishing attacks, pharming schemes, and spyware vulnerabilities that can compromise financial security. This awareness is a prerequisite for protective behaviours in digital financial environments.
The third component, knowledge of digital financial risk control, is related to individuals' ability to implement protective measures when using digital financial services (Rahayu et al., 2022). This includes understanding encryption, secure authentication mechanisms, and best practices for protecting personal and financial information in digital spaces.
The fourth component, knowledge of consumer rights and redress procedures, includes understanding the legal protections available to consumers in digital financial transactions and the procedural knowledge required to seek remediation in cases of fraud or other financial harm (Morgan et al., 2019). This component is critical for consumer empowerment in increasingly complex digital financial ecosystems.
The Significance of DFL for University Students
University students represent a critical demographic for DFL research and interventions for several compelling reasons. First, this population is experiencing a transitional phase of financial socialisation, developing financial behaviours and attitudes that can persist throughout adulthood (Zhu et al., 2022). Enhanced DFL during this formative period can establish lifelong patterns of responsible digital financial engagement.
Second, university students typically demonstrate high technological receptivity and early adoption tendencies for digital innovations, making them both ideal candidates for Fintech adoption and potentially vulnerable to associated risks if not adequately prepared through DFL education (Malik & Malik, 2024). Their technological aptitude creates opportunities to introduce sophisticated digital financial tools, while simultaneously necessitating the corresponding development of risk awareness and protective behaviours.
Third, university students often face unique financial challenges, including managing limited budgets, navigating student loans, and beginning independent financial decision making, frequently without substantial prior experience (Muzakir & Supatra, 2024). Fintech platforms can offer valuable tools to address these challenges through budgeting applications, financial tracking features, and educational resources, but these benefits are only fully realised when students possess enough DFL to use these tools effectively.
Fourth, university students represent a diverse demographic that can provide insight into how factors such as educational specialisation, income levels, and age differences within the young adult spectrum influence DFL and technology adoption patterns (Zaimovic et al., 2024). Understanding these moderating effects can inform the development of targeted educational interventions and considerations of the design of the platform.
Finally, university students in South Africa specifically represent an important population for DFL and Fintech research due to the country's unique position at the intersection of technological advancement and persistent socioeconomic inequalities (World Bank, 2022). Enhancing DFL among this population has the potential to promote financial inclusion, reduce digital divides, and contribute to broader economic development objectives through the cultivation of a financially sophisticated future workforce. For these reasons, understanding the current state of DFL among university students, the factors influencing their Fintech adoption decisions, and the relationship between specific components of DFL and Fintech participation is critical to developing evidence-based interventions that can improve financial well-being and technological inclusion among this important demographic.
Fintech Adoption Among University Students
University students represent a particularly important demographic for Fintech adoption studies, as they typically demonstrate high technological receptivity while simultaneously developing financial behaviours that may persist throughout adulthood. Fintech services offer these two benefits to this population: streamlining transaction processes while potentially enhancing financial literacy through educational features embedded in digital platforms (Muzakir & Supatra, 2024). For students managing limited financial resources, Fintech provides access to financial services that are often more cost-effective and efficient than traditional banking offerings (Malik & Malik, 2024).
Research that examines specific FinTech platforms preferred by university students indicates diverse adoption patterns. Dwiki et al. (2022) found extensive awareness of Fintech among university students, with ShopeePay emerging as the leading payment platform. Complementary findings from Lasmini and Zulvia (2020) identified Goopay as the most popular Fintech product among students, primarily used for transportation payments, food purchases, and mobile airtime top-ups. Kadam and Shalini (2023) found that students generally perceive Fintech platforms as providing easy and economical methods for conducting online transactions, suggesting positive attitudinal factors influencing adoption.
Despite these encouraging findings, research also indicates significant gaps in Fintech literacy and adoption among university students. Malik and Malik (2024) highlight that, while Fintech usage is increasing among students, there remains a substantial knowledge gap regarding the compliance and security features of these services. Similarly, Lasmini and Zulvia (2020) note inconsistent awareness of Fintech products across student populations, with many students demonstrating limited understanding of the risks associated with sharing personal and financial information in digital environments. Additionally, the complexity of certain Fintech applications presents challenges to students with limited technological proficiency (Malik & Malik, 2024).
These findings collectively underscore the importance of examining the relationship between DFL and Fintech adoption among university students. While this population demonstrates significant potential for Fintech engagement, existing knowledge gaps and risk awareness deficiencies highlight the need for targeted educational interventions and user-friendly platform designs to promote safe and effective Fintech utilisation.
Theoretical Framework and Hypotheses Development
This research uses the unified theory of acceptance and use of technology (UTAUT) (see figure 1) framework established by Venkatesh et al. (2003) as the main model to examine the relationship between DFL and Fintech adoption among South African university students and to analyse the moderating effects of demographic variables (age, gender, income and field of study) on this relationship. The UTAUT theoretical model indicates that technological usage is influenced by behavioural intention. The perceived probability of embracing the technology relies on the direct impact of four essential factors: performance expectancy, effort expectancy, social influence, and facilitating conditions. The predictors are influenced by age, gender, experience, and the voluntary nature of the usage (Venkatesh et al., 2003).
Performance expectancy refers to how much consumers believe that utilising technology will be beneficial for carrying out a specific task. Effort expectancy describes the level of convenience related to technology used by consumers. Social influence indicates the degree to which consumers think others (family and friends) feel they ought to adopt a particular technology. Facilitating conditions pertain to consumers' views on the resources and support to engage in their behaviour (Venkatesh et al., 2012).
The UTAUT model provides empirical insights into technology acceptance by contrasting major theories, frequently presenting conflicting or incomplete viewpoints. UTAUT shows that the suggested factors explained 70% of the variation in usage intention (Marikyan & Papagiannidis, 2023). Importantly, UTAUT also recognises that demographic variables moderate the relationships between its core constructs and behavioural intentions. Our study extends UTAUT by specifically examining how demographic variables (age, gender, income, and education) influence DFL, which serves as a precursor to Fintech adoption intentions. This application of UTAUT provides a theoretically grounded framework for examining the complex relationships between demographic characteristics, DFL components, and Fintech adoption among university students. Figure 1 shows the UTAUT model.
Hypotheses Development
Building on the UTAUT framework and extant literature on DFL and Fintech adoption, this study proposes a series of hypotheses examining the moderating effects of demographic variables on DFL and the relationship between specific DFL components and Fintech adoption intentions. These hypotheses are structured around two primary domains: (1) the influence of demographic variables on DFL and (2) the relationship between DFL components and Fintech adoption.
Demographic variables and DFL
Gender
The literature that examines the relationship between gender and DFL has produced inconsistent findings, suggesting complex interactions between gender, technological engagement, and financial knowledge. Several studies have identified significant gender disparities in DFL, with men demonstrating higher levels than women in multiple national contexts (Zaimovic et al., 2024; Chhillar et al., 2024). Abdul Azeez et al. (2022) documented this disparity in rural regions of the Aligarh district, where the DFL index for males (0.25) substantially exceeded that for females (0.17), with no female participants achieving the "good" or "outstanding" DFL categories.
These gender disparities can reflect broader patterns of technological involvement, with some research suggesting that men demonstrate greater interest in technological innovations and, correspondingly, higher levels of digital literacy. Furthermore, persistent gender gaps in financial socialisation, education, and economic participation may contribute to the differential acquisition of financial knowledge, which is a critical component of DFL. However, other studies have not identified significant gender differences in DFL (Rajdev et al., 2020; Muthia et al., 2023), suggesting that the gender-DFL relationship may be contingent on contextual factors or evolving in response to changing educational and technological landscapes. These contradictory findings underscore the importance of further investigation into gender's influence on DFL, particularly among university student populations where educational access may attenuate traditional gender disparities. Accordingly, this study proposes:
H01: There is no significant difference in DFL between male and female students.
Age
Age has been theorised to influence DFL through multiple pathways, including cohort effects on technological exposure, developmental differences in financial responsibility, and cumulative learning experiences. Previous research has identified statistically significant age-related variations in DFL scores and their constituent components, and younger adults (below 30 years) typically demonstrating higher DFL levels than older cohorts (Zaimovic et al., 2024). This pattern may reflect the "digital native" status of younger adults who have experienced technological immersion throughout their developmental years, potentially facilitating greater comfort and proficiency with digital financial systems.
Abdul Azeez et al. (2022) corroborated this pattern, finding that younger individuals demonstrated significantly higher DFL compared to middle-aged and older respondents. Similarly, Muthia et al. (2023) documented a negative correlation between age and DFL, suggesting that DFL tends to decrease as individuals age. This inverse relationship may reflect generational differences in technological adaptability, with older individuals potentially experiencing greater challenges in acquiring and maintaining the technical competencies required for effective digital financial engagement.
However, contradictory evidence also exists in the literature. Arshath et al. (2024) found no significant correlation between participant ages and DFL levels, suggesting that the generational digital divide may not directly translate into DFL disparities. Similarly, Fachrudin and Wahyuni (2023) reported no significant age-based differences in financial literacy, indicating that age may not uniformly influence all components of DFL. Given these conflicting findings and the relatively narrow age range typically represented among university student populations, additional research is needed to clarify age's influence on DFL within this demographic. Accordingly, this study proposes:
H02: There are no significant differences in DFL levels between students of different age groups.
Income
Income has been theoretically linked to DFL through multiple mechanisms, including the availability of resources for technological engagement, the exposure to diverse financial products and services, and potential correlations with educational opportunities and financial socialisation experiences. Empirical research has generally supported a positive relationship between income and DFL, with Zaimovic et al. (2024) documenting significant disparities in DFL scores according to income brackets, where higher income individuals demonstrated greater DFL.
Abdul Azeez et al. (2022) similarly found that individuals with higher DFL tended to report higher income levels, with increases in income associated with improved individual DFL. This positive relationship was further corroborated by Chhillar et al. (2024), who identified considerable differences in DFL scores between income groups, with higher income cohorts exhibiting significantly elevated DFL levels.
Several causal mechanisms may explain this relationship. Higher income may facilitate greater access to technological devices and digital financial services, providing more opportunities to develop and apply digital financial knowledge. Additionally, individuals with higher incomes generally engage with a broader range of financial products and services, potentially enhancing their financial literacy through expanded practical experience. The correlation may also reflect the influence of unobserved variables, such as educational attainment or occupational characteristics, which might simultaneously influence both income and DFL.
However, contradicting these findings, Muthia et al. (2023) concluded that income does not significantly affect DFL levels, suggesting that the income-DFL relationship may be context dependent or moderated by additional factors. Given these mixed findings and the economic circumstances of university students, who often have limited personal income despite varying socioeconomic backgrounds, further investigation of this relationship within the university student population is warranted. Thus, this study proposes the following.
H03: There are no significant differences in the levels of DFL between students at different income levels.
Educational level
Educational attainment has been conceptually linked to DFL through several mechanisms, including formal learning opportunities, the development of critical thinking skills applicable to financial decision making, and the potential correlation with technological exposure. Empirical research has generally supported a positive relationship between educational level and DFL, with Abdul Azeez et al. (2022) finding that individuals' DFL levels increased correspondingly with educational attainment. This pattern was further corroborated by Chhillar et al. (2024), who documented those individuals with master's degrees or higher qualifications demonstrated significantly greater DFL compared to those with secondary-level qualifications. Similarly, Muthia et al. (2023) concluded that education substantially influences DFL, suggesting that formal educational experiences provide valuable opportunities to develop both financial knowledge and technological competencies.
The mechanisms underlying this relationship may include explicit financial education embedded in educational curricula, enhanced general cognitive abilities that facilitate financial learning, greater exposure to technological resources in educational settings, and potential correlations with socioeconomic factors that simultaneously influence educational attainment and financial knowledge acquisition. However, some research has yielded contradictory findings regarding the education-DFL relationship. Zaimovic et al. (2024) reported no significant differences in DFL between individuals with postgraduate or advanced degrees and those with only undergraduate qualifications, suggesting a potential plateau effect where additional formal education beyond the undergraduate level may not substantially improve DFL. Similarly, Fachrudin and Wahyuni (2023) found that the level of education did not significantly influence financial literacy, indicating that formal educational credentials may not uniformly predict financial knowledge.
Within university student populations specifically, the range of educational levels typically extends from early undergraduate to advanced postgraduate studies, providing an opportunity to examine whether progression through higher education corresponds to enhanced DFL. Accordingly, this study proposes the following.
H04: There are no significant differences in the levels of DFL between undergraduate and post-graduate students.
DFL and Fintech adoption
Digital financial literacy (DFL)
DFL represents the synthesis of financial and digital competencies necessary for effective engagement with technological financial systems (Respati et al., 2023). Following Morgan et al.'s (2019) conceptualisation, this study examines four distinct components of DFL and their relationship with Fintech adoption: knowledge of digital financial products and services, awareness of digital financial risks, knowledge of digital financial risk control, and knowledge of consumer rights and redress procedures.
The first component, knowledge of digital financial products and services, encompasses an individual's understanding of digital financial offerings accessible through various technological platforms. This includes understanding how mobile wallets, online banking systems, crowdfunding mechanisms, and insurance platforms function in digital environments (Rahayu et al., 2022). This knowledge component aligns conceptually with UTAUT's effort expectancy construct, as greater familiarity with digital financial products likely reduces the perceived difficulty of using these systems. Theoretically, improved knowledge in this domain should facilitate Fintech adoption by reducing uncertainty, clarifying potential benefits, and lowering perceptual barriers to engagement.
Empirical support for this relationship comes from Basar et al. (2022), who found that Fintech adoption among SME entrepreneurs was significantly and positively influenced by knowledge of digital financial products and services. Similarly, Indrawati (2021) demonstrated that DFL levels substantially impacted interest in purchasing financial technology products. These findings suggest that knowledge of digital financial offerings serves as a foundation for Fintech adoption decisions by providing the conceptual framework necessary for evaluating potential benefits and implementation requirements. Based on this theoretical reasoning and empirical evidence, this study proposes the following.
H1a: Knowledge of digital financial products and services has a positive and significant relationship with the use of Fintech products and services among South African university students.
The second component, awareness of digital financial risks, refers to consumers' recognition that digital financial environments entail unique vulnerabilities not present in traditional financial systems. Morgan et al. (2019) highlighted that this awareness includes understanding threats such as phishing, pharming, and spyware that specifically target digital financial transactions. Theoretically, risk awareness may influence Fintech adoption through competing mechanisms. On the one hand, heightened risk perception may discourage adoption by raising concerns about potential negative outcomes. On the contrary, adequate risk awareness, particularly when accompanied by the corresponding knowledge of risk control, can facilitate informed adoption decisions by enabling realistic risk assessment rather than uninformed avoidance.
Previous research has yielded mixed findings regarding the relationship between risk awareness and Fintech adoption. Basar et al. (2022) found that knowledge of digital financial risks adversely affected Fintech adoption among entrepreneurs, suggesting that increased risk awareness can create adoption barriers. However, other studies have suggested that appropriate risk understanding, when integrated into a comprehensive DFL, may support rather than hinder technology adoption by promoting informed decision making (Conner, 2022). Given these conflicting theoretical possibilities and empirical findings, further investigation of this relationship is warranted, leading to the hypothesis.
H1b: Awareness of digital financial risks has a positive and significant relationship with the use of Fintech products and services among South African university students.
The third component, knowledge of digital financial risk control, relates to the understanding of individuals of protective mechanisms and strategies for mitigating risks in digital financial environments (Rahayu et al., 2022). This component aligns with UTAUT's facilitating conditions construct, as knowledge of risk control measures enhances perceptions that adequate support systems exist for safe technology usage. Theoretically, greater knowledge in this domain should positively influence Fintech adoption by increasing security confidence, reducing vulnerability perceptions, and improving self-efficacy in digital financial management.
Empirical support for this relationship comes from Basar et al. (2022), who documented that knowledge of digital financial risk control positively impacted Fintech adoption among entrepreneurs. This finding suggests that understanding protective mechanisms can counterbalance risk awareness, providing individuals with the confidence necessary to engage with digital financial systems despite recognition of potential vulnerabilities. Based on this theoretical reasoning and empirical evidence, this study proposes the following.
H1c: Knowledge of digital financial risk control has a positive and significant relationship with the use of Fintech products and services among South African university students.
The fourth component, knowledge of consumer rights and redress procedures, encompasses understanding legal protections for digital financial consumers and the processes available for addressing grievances or fraudulent activities (Morgan et al., 2019). Like risk control knowledge, this component conceptually aligns with UTAUT's facilitating conditions by establishing that institutional support mechanisms exist for technology users. Theoretically, enhanced knowledge in this domain should promote Fintech adoption by reducing perceived vulnerability, establishing accountability expectations, and providing assurance that remediation options exist if problems occur.
Research on this specific relationship has yielded inconsistent findings. Although some studies suggest that consumer rights knowledge enhances trust in financial systems and thereby facilitates technology adoption (Conner, 2022), other research has failed to establish a significant relationship between awareness of consumer rights and Fintech adoption (Basar et al., 2022). These contradictory findings suggest the need for further investigation of how consumer rights knowledge influences adoption decisions, particularly among university students who may have limited experience with financial consumer protection systems. Accordingly, this study proposes the following.
H1d: Knowledge of consumer rights and consumer redress procedures has a positive and significant relationship with the use of Fintech products and services among South African university students.
Conceptual Framework
Based on the preceding theoretical discussion and hypothesis development, this study proposes a comprehensive conceptual framework integrating DFL components, Fintech adoption intentions, and demographic moderating variables. This framework, illustrated in Figure 2, visually represents the hypothesised relationships between the four components of the DFL components (independent variables) and interest in the use of Fintech products and services (dependent variable), with demographic variables (gender, age, income, and education) moderating the influence of these relationships.
This conceptual model aligns with UTAUT (Figure 1) by incorporating both individual knowledge factors that influence technology adoption decisions and demographic characteristics that moderate these relationships. By examining both the direct effects of the DFL components on Fintech adoption and the moderating influence of demographic variables, this framework provides a comprehensive approach to understanding the complex determinants of digital financial engagement among university students in South Africa.
Research and Methodology
Research Design
This study follows the positivist paradigm and employs a quantitative approach to examine the relationship between DFL and the use of Fintech products and services among university students. The study seeks to evaluate how individuals' profiles affect their DFL. This study utilised a cross-sectional survey approach, using structured questionnaires to collect data from a group of university students. The purpose is to determine the relationship between DFL and the use of Fintech products and services and the influence of demographic variables such as age, gender, income level, and educational level on DFL.
Population and Sampling
The target population includes students between 18 and 29 years of age who are registered and enrolled full-time at the University of the Western Cape (UWC) and the Cape Peninsula University of Technology (CPUT) for the 2023 academic year. This research employed a non-probability sampling method, selecting participants based on convenience. This method is suitable because it is simpler, more affordable, and does not take much time (Welman et al., 2005). The selection of university students from the two institutions of higher learning was made for convenience, allowing faster and more cost-effective data collection. A convenience sampling technique was employed to sample 376 students for this research, using a table created by Krejcie and Morgan (1970) to determine the sample size.
Data Collection
Primary data was collected using a paper questionnaire completed by the researchers. This study adopted the questionnaire developed by Basar et al. (2022) and Indrawati (2021). The questionnaire consists of three sections, designed to be concise and easy to complete. Section A contains background details, including demographics of respondents such as gender, age, study faculty, income, and ethnicity. Section B includes questions related to DFL; drawing from the research conducted by Basar et al. (2022), participants were requested to rate 17 questions that assessed their understanding of digital financial products, awareness of digital financial risk, knowledge of financial risk control, and knowledge of consumer rights and the redress procedure, using a 5-point Likert scale, where responses ranged from (1) strongly disagree to (5) strongly agree. Section C concentrated on assessing the dependent variable: the use of Fintech products and services, which aligns with Indrawati (2021).
Data Analysis
After the data was collected, it was cleaned and arranged for analysis. This process involved verifying incomplete responses, identifying outliers, and ensuring data consistency. The data was subsequently encoded and inputted into IBM's version 29.0 statistical package for social sciences (SPSS). Descriptive statistics are employed to outline the demographic characteristics of the sample profile. This encompasses the frequency distribution for each demographic variable. Descriptive statistics, including mean and standard deviation, were performed to achieve the study's first objective, assessing the level of digital financial literacy among university students. These tests were conducted to display average responses in terms of DFL.
To achieve the second objective of evaluating how demographic variables (age, gender, income and education) influence DFL, hypotheses on demographic variables and DFL were tested using the Kruskal-Wallis test, a non-parametric rank-based method used to evaluate whether there is a statistically significant difference between two or more independent variables that measure numerical and ordinal data (Irsya & Faturohman, 2024).
To achieve the third objective, that is, to examine the relationship between DFL and the use of Fintech products and services, the hypotheses on DFL and Fintech were tested using multiple linear regression. Cronbach's alpha, a non-parametric statistical method, was used to evaluate the reliability of the measurement tool and determine the internal consistency of the questionnaire employed in this research. Normality was evaluated using both the Kolmogorov-Smirnov and Shapiro-Wilk tests, which indicated non-normal distributions for most variables, justifying the use of non-parametric tests.
Multiple linear regression analysis was employed to test the hypotheses on the relationship between DFL components and Fintech adoption. The regression model includes all four DFL components as predictors of Fintech usage interest, controlling for demographic variables. Multicollinearity was assessed using variance inflation factors (VIF), with all values below 3 indicating that there were no significant multicollinearity issues. The assumptions of linearity, homoscedasticity, and independence of the residuals were verified through residual analysis.
Ethical Considerations
Before data collection, comprehensive ethical approval was obtained from the School of Accounting Research Ethics Committee (Ethical Clearance Code: SAREC20231002/03) at the University of Johannesburg, with additional institutional permissions secured from the research offices of UWC and CPUT. Informed consent procedures emphasised the voluntary nature of participation, with explicit notification that non-participation would have no academic or institutional consequences. Participants received detailed information sheets explaining the objectives, procedures, time commitments, and potential benefits and risks before providing their written consent. The right to withdraw from the study at any time without penalty was explicitly communicated and respected throughout the data collection process.
Findings and Results
Descriptive Statistics
This section contains the demographic characteristics of the participants, a brief overview of descriptive statistics, and the reliability of the construct. Table 1 shows that most of the respondents were male (51.1%), while (47.9%) were female, and (1.1%) preferred not to disclose their gender. Most participants were between the ages of 21 and 23 (37%), between 18 and 20 (36%), while participants between the ages of 24 and 26 and 17 and over were (19%) and 8%, respectively. Most of the respondents were postgraduates (83%) earning less than R3000 (83.2%). Most of the respondents to this study were African (92%), with Indians and others the least (0.3%).
Reliability of the Questionnaire
Cronbach's alpha was used to evaluate the internal consistency of the questionnaire. The Cronbach alpha for DFL-related questions (knowledge of digital financial products and services, awareness of digital financial risks, knowledge of digital financial risk control, knowledge of consumer rights and redress procedures) was 0.904, 0.871, 0.874, and 0.866, respectively. The Cronbach's alpha for the use of Fintech questions was 0.947, as presented in Table 2. Cronbach's alpha values exceed the suggested minimum threshold of 0.7 (Pallant, 2016), suggesting that the DFL and interest in the use of the Fintech scale demonstrate strong internal consistency. These results provide strong statistical evidence for the construct validity and reliability of the measurement instrument, thus supporting its use in subsequent analyses.
DFL levels
Table 3 indicates that the four evaluated items received mainly favourable reactions regarding knowledge of digital financial products and services, as reflected by the mean that exceeded 3.5. Consequently, 87.5% of the respondents confirmed their ability to use cashless transactions. The findings also show that 90.4% of the participants can use online banking. An additional 84.0% of the participants agreed that they can use mobile application payments. The average mean was 4.21, which implies that most students agreed with the statement, indicating a significant understanding of digital financial products and services among university students.
As presented in Table 4, all average mean values were above 3.5, signifying a strong agreement with the items evaluating the awareness of the digital financial risk component. Participants expressed their agreement with each item. 89.6% of the participants thought they were aware that hackers could impersonate an institution to acquire their personal information, while 79% of respondents noted that they were aware of the possibility of malicious software that might be installed on mobile devices and sending personal data. The average mean score of 4.26 suggests that most participants acknowledged their awareness of the risks related to digital financial platforms.
Table 5 presents the descriptive statistics of the DFL's third component: knowledge of digital financial risk control. Most of the students agreed with the items that assessed their knowledge of digital financial risk control. Most of the students (70.2%) agreed that they knew how to protect their PIN, achieving the highest mean of 3.93. The item with the second highest number of students who agreed is "I know how to protect my personal information when using digital financial products," with 65.2% of students agreeing with the statement, achieving a mean of 3.77, which implies confidence among students regarding protecting their personal information when using digital financial services. Overall, students agreed with all the items assessing the knowledge of digital financial risk control, achieving an average mean of 3.65.
The descriptive statistics of the fourth component of DFL, namely knowledge of consumer rights and redress procedures, are presented in Table 6. Most students (65.7%) agree that they know their rights regarding personal data. However, all three items assessing knowledge of consumer rights and redress procedures achieved a mean below 3.5, with less than 50% of the students agreeing with these statements, implying that the students are not confident in their knowledge of consumer rights and redress procedures.
Use of Fintech Products and Services
Table 7 reveals that students expressed interest in using Fintech products and services, as evidenced by 66.2% agreeing that they are intrigued by Fintech as an innovation within the financial sector and 65.2% agreeing to consider using Fintech products for payments when making transactions. Additionally, 51.2% of the students believed that it was easy to access financial products through Fintech, which is consistent with the 56.4% of the students who concurred that transactions are easier with Fintech. At the same time, 62% of the students expressed their intention to use Fintech products. This may stem from 58.8% of the students believing that there are advantages to using Fintech products for transactions, resulting in a 3.66 average mean score, suggesting that most of the students agree with the statement. Meanwhile, 61.2% of students stated they prefer to use Fintech products instead of visiting a bank, ATM or payment counter. The mean average was 3.56, suggesting that the students showed interest in using technological and financial products and services.
Hypothesis Testing
Hypotheses on demographic variables and DFL
The Kolmogorov-Smirnov and Shapiro-Wilk tests were conducted to test for normality. The results are presented in Table 8. For gender (male and female), age and study level (undergraduate and postgraduate), the p-value were 0.000< 0.05, signifying deviation from the normality. Thus, the null hypothesis that the data are normally distributed is rejected. For income variable, Kolmogorov- Smirnov test and the Shapiro-Wilk results revealed that only high-income university students (income above R9000) follow a normal distribution in the entire population on two DFL components, the Knowledge of Digital Financial Risk and Control knowledge of consumer rights and redress procedures (p-value 0.153 and 0.200) respectively. Therefore, the non-parametric test of Independent- Samples Kruskal-Wallis Test was considered for hypothesis testing.
H01: There is no significant difference in DFL between male and female students who were tested using the Kruskal-Wallis test. The results did not show differences in the level of DFL between male and female students, with a p-value, 0.787, 0.131,0.781 and 0.258 <0.05, for all four DFL components as presented in table 9. Therefore, the null hypothesis was accepted that there is no significant difference in DFL between male and female students. This is in line with the findings of Rajdev et al. (2020), who also discovered no significant distinction between male and female students' average financial literacy scores regarding DFL. However, Chhillar et al. (2024) mentioned a significant difference in DFL levels between male and female employees in India.
H02: The hypothesis that there is no significant difference in DFL levels among students from different age groups was tested using the Kruskal-Wallis Test, and the results are presented in Table 9. The analysis revealed a significant difference in DFL levels between different age groups, with a p-value of 0.001, 0.018, 0.004, 0.018, <0.05 for the four DFL components at a 5% significance level. Therefore, the null hypothesis was rejected and the alternative hypothesis that there are significant differences in DFL levels between students from different age groups was accepted. These findings support the results of Zaimovic et al. (2024), who found statistically significant age-related differences in DFL scores and all its components, indicating that younger adults (< 30 years) showed higher levels of DFL. Rajdev et al. (2020) also agreed that age significantly influences all dimensions of DFL.
H03: Kruskal-Wallis was employed in Table 9 to assess the hypothesis on the consistency of DFL levels in various income brackets among university students. The results indicate a significant difference in DFL levels across income levels on only three components of DFL, the knowledge of digital financial products and services, knowledge of digital financial risk control, and knowledge of consumer rights and redress procedures, with a p-value of 0.004, 0.015 and 0.021 respectively, < 0.05, at a level of significance of 5%. Thus, the null hypothesis, which states that there is no significant difference in DFL levels among students at different income levels, is rejected. These results are supported by Chhillar et al. (2024), who showed a considerable difference in DFL scores between income brackets, with those in higher-income groups demonstrating significantly elevated DFL levels. However, the results also found no significant differences in DFL levels between income levels in one of the DFL components (awareness of digital financial risks), with a p-value of 0.232 >0.05, leading to partial acceptance of the null hypothesis.
H04: The hypothesis that examines the absence of significance in DFL levels between undergraduate and postgraduate students was evaluated using the Kruskal-Wallis test in table 9. The analysis did not indicate significant differences in DFL levels between undergraduate and postgraduate students in the two components of DFL, the knowledge of digital financial products/services and awareness of digital financial risks, with p-values of 0.101 and 0.193, >0.05 respectively. Therefore, we failed to reject the null hypothesis that there is no significant difference in DFL levels between undergraduate and postgraduate students for these two components. This finding contradicts Rajdev et al. (2020), who found a significant difference between graduate and postgraduate students at DFL levels their knowledge of digital financial products and services and their awareness of digital financial risks.
However, the results also suggest a significant difference in the knowledge of digital financial risk control and the knowledge of consumer rights and redress procedures, with a p-value of 0.050 and 0.031 < 0.05. Consequently, null hypothesis is rejected and alternative hypothesis that there is a significant difference on two DFL components between undergraduate and postgraduate students is accepted
Hypothesis on DFL and use of Fintech products and services
To statistically assess the significance of the results, an ANOVA test was performed. According to Table 10, the analysis findings revealed a significant F value (F = 8.316, p =.001). This indicates that the model was suitable, since the p-value is below 0.05. Consequently, a statistically significant connection exists between DFL and the dependent variable: Interest in using technological financial products and services.
H1a: The hypothesis of a positive and significant relationship between knowledge of digital financial products and services and the use of fintech products and services was tested using multiple linear regression. As shown in Table 11, the regression findings indicate a positive and significant correlation between awareness of digital products and services and interest in the use of technological financial products and services (β = 0.204, p = 0.002, < 0.05). This indicates that students who are aware of digital financial products and services are more inclined to show interest in using technological financial products and services. An increase in the understanding of digital products/services consequently leads to greater interest in using Fintech products and services. Therefore, the alternative hypothesis is accepted that there is a positive and significant relationship between knowledge of digital financial products and services and use of Fintech products and services. This finding aligns with the UTAUT framework, in which knowledge of digital financial products enhances effort expectancy (perceived ease of use), making students more likely to adopt Fintech solutions. Basar et al. (2022) also conducted a study that identified a positive correlation between awareness of digital products/services and adoption of FinTech among Bumiputera SME entrepreneurs.
H1b: The hypothesis of a positive and significant relationship between awareness of digital financial risks and the use of Fintech products and services among South African university students was evaluated through multiple linear regression. The findings in Table 11 suggest that awareness of digital financial risks has a positive, but insignificant relationship with interest in utilising Fintech products and services (β = 0.015, p = 0.828, > 0.05). Thus, awareness of digital financial risk does not significantly influence students' use of Fintech products/services. Therefore, the alternative hypothesis is rejected: Awareness of digital financial risks has a positive and significant relationship with the use of Fintech products and services among South African university students. This finding contradicts the expectation that risk awareness positively influences adoption intentions. However, this may suggest that mere awareness of risks without corresponding knowledge of risk control mechanisms may not be sufficient to drive adoption behaviour. This result diverges from Ryu (2018), who discovered that risk has a markedly negative influence on the intention to adopt Fintech.
H1c: It was hypothesised that the knowledge of digital financial risk control has a positive and significant relationship with the use of Fintech products and services among South African university students. The regression analysis revealed that the knowledge of digital financial risk control had a positive and significant relationship (β = 0.156, p = 0.024, < 0.05), as presented in Table 11. Therefore, the alternative hypothesis that knowledge of digital financial risk control has a positive and significant relationship with the use of Fintech products and services is accepted. This finding is consistent with UTAUT's facilitating conditions construct, indicating that students who know how to protect themselves in digital environments perceive fewer barriers to Fintech adoption. This indicates that greater awareness of digital financial risk control results in a rise in Fintech products and service utilisation. This is supported by Basar et al. (2022), who suggested that Fintech adoption is positively impacted by knowledge of digital financial risk control.
H1d: The regression test was used to test the hypothesis regarding the relationship between knowledge of consumer rights and consumer redress procedures and the use of Fintech products and services among South African university students. The results reveal a positive and significant relationship (β = 0.189, p = 0.002, < 0.05). Therefore, the alternative hypothesis that knowledge of consumer rights and consumer redress procedures has a positive and significant relationship with the use of Fintech products and services among South African university students is accepted. This finding suggests that awareness of consumer rights and redress procedures assists students in embracing Fintech. Understanding consumer protection appears to increase confidence and trust in Fintech platforms, thereby increasing adoption intentions. However, Basar et al. (2022) do not agree with these findings when they put forward their findings, suggesting that awareness of consumer rights and redress procedures does not facilitate the adoption of Fintech by entrepreneurs.
The regression equation can be represented as:
Where:
Y = Interest in using Fintech products and services.
X1 = Knowledge of digital financial products and services.
X2 = Awareness of digital financial risks.
X3 = Knowledge of digital financial risk control.
X4 = Knowledge of consumer rights and redress procedures.
Conclusion
This research analysed the correlation between DFL and the adoption of Fintech among university students in South Africa. The findings indicate that university students exhibit moderate to high levels of digital financial literacy, especially regarding their understanding of digital financial products and their awareness of associated risks. The students demonstrated diminished confidence in their comprehension of consumer rights and redress procedures, indicating a potential area for educational intervention. The results indicated that three of the four DFL components are significant predictors of Fintech adoption. Furthermore, demographic variables demonstrated differing impacts on DFL levels, with age, income, and education significantly affecting specific components, whereas gender did not have a notable influence on DFL.
The findings indicated a significant interest in the utilisation of Fintech products and services among university students. Additionally, hypothesis testing did not indicate significant differences in DFL between male and female students. This aligns with the findings of Rajdev et al. (2020), who similarly observed no significant difference in average financial literacy scores between male and female students concerning DFL. DFL levels exhibited significant variation across age groups for all four DFL components. The results corroborate the findings of Zaimovic et al. (2024), which identified statistically significant age-related differences in DFL scores and all associated components, demonstrating that younger adults (< 30 years) exhibited higher levels of DFL. Rajdev et al. (2020) indicate that age has a significant impact on all dimensions of DFL.
The results indicate a significant difference in DFL levels across income levels in three components: knowledge of digital financial products and services, knowledge of digital financial risk control, and knowledge of consumer rights and redress procedures. The findings are corroborated by Chhillar et al. (2024), who identified a notable disparity in DFL scores across income brackets, revealing that individuals in higher-income groups exhibited significantly higher DFL levels.
Nonetheless, the findings revealed no significant differences in DFL levels across income levels for one of the DFL components (awareness of digital financial risks). The analysis revealed no significant differences in DFL levels between undergraduate and postgraduate students across the two components. Understanding of digital financial products and services, along with recognition of associated digital financial risks. This finding contradicts the results of Rajdev et al. (2020), which indicated a significant difference in DFL levels regarding knowledge of digital financial products and services, as well as awareness of digital financial risks, between graduate and postgraduate students. The analysis revealed a significant disparity in DFL levels concerning knowledge of digital financial risk control and consumer rights and redress procedures.
The results indicated a significant positive correlation among awareness of digital products and services, knowledge of digital financial risk control, understanding of consumer rights and redress procedures, and interest in utilising technological financial products and services. This finding aligns with Basar et al. (2022). Conversely, the awareness of digital financial risk demonstrated an insignificant impact on students' interest in Fintech products and services.
Theoretical Implications
This research provides significant theoretical advancements to the DFL literature. Our findings empirically validate the multidimensional nature of DFL, confirming that it consists of distinct yet interrelated components: knowledge of digital financial products and services, awareness of digital financial risks, knowledge of risk control, and understanding of consumer rights and redress procedures. The factor analysis results yielded robust statistical support for this conceptualisation, with each component exhibiting strong unidimensionality and reliability.
Secondly, this study expands the UTAUT framework by analysing the impact of DFL components on Fintech adoption intentions among university students. The notable positive correlations among the three DFL components and Fintech adoption intentions correspond with UTAUT's focus on facilitating conditions and effort expectancy as key factors influencing technology adoption. Students possessing greater knowledge of digital financial products, risk control mechanisms, and consumer rights identify fewer obstacles to Fintech adoption, aligning with the effort expectancy construct of UTAUT.
Third, our findings regarding the lack of a significant relationship between awareness of digital financial risks and Fintech adoption challenge some prior research (Ryu, 2018) and indicate a possible refinement of the UTAUT model in the context of financial technologies. This finding suggests that awareness of risks alone may not prevent adoption if users have knowledge of risk control mechanisms, indicating a complex interaction between these components that requires further theoretical exploration.
Our findings regarding demographic variables offer detailed insights into the moderating effects of UTAUT. Gender did not significantly affect DFL levels; however, age, income, and education exhibited varying impacts on DFL components. This indicates that the moderating effects of demographics on technology adoption may be specific to individual components rather than universally applicable.
Practical Implications
The findings offer significant insights for financial institutions, educational entities, and policymakers aiming to enhance financial inclusion via technology. Higher education institutions should incorporate DFL into their academic curriculum, focussing on essential topics including budgeting, saving, investing, and the utilisation of digital services like digital wallets and banking applications. Financial institutions should advance DFL by implementing educational initiatives via banking applications and conducting awareness campaigns. These initiatives may emphasise the responsible utilisation of digital tools, the prevention of fraud, and the security of online transactions. Finally, policymakers should regularly assess the potential of Fintech to improve financial inclusion, while also mitigating the related risks to consumers. To promote DFL at the policy level, the objective should be to develop a DFL strategy.
Limitations and Future Research
This study presented multiple limitations. The cross-sectional design restricts causal inferences regarding the relationship between DFL and Fintech adoption. Future research should utilise longitudinal designs to investigate the impact of DFL on Fintech usage behaviour over time. The sample was restricted to students from two South African universities, which may constrain its generalisability. Future research should incorporate students from various geographic and institutional contexts to improve external validity. Future research should encompass a broader range of higher education institutions, including universities of technology and Technical Vocational Education and Training (TVET) colleges across different provinces for comparative analysis. Third, although the study considered demographic variables, it did not include other potential moderating variables, such as personality traits, risk tolerance, and cultural factors. Subsequent research should integrate these variables to create a more thorough model of DFL and Fintech adoption. The study utilised self-reported measures, which may be influenced by social desirability bias. Future research may integrate objective assessments of DFL via performance evaluations or behavioural data regarding actual Fintech utilisation.
References
Abdul Azeez, N. P., Akhtar, S. J., & Nasira Banu, M. (2022). Relationship between demographic factors and digital financial literacy. Indian Development Policy Review, 3(2), 155-166.
Arshath, T., Naamani, T. A., Rubaii, A. R., Hasni, U. I., & Riyami, Y. A. (2024). Determining digital financial literacy level of social generations in Muscat. International Journal for Research Trends and Innovation, 9(6), 2456-3315.
Basar, S. A., Zain, N. N. M., Tamsir, F., Rahman, A. R. A., Ibrahim, N. A., & Poniran, H. (2022). How digital financial literacy affects i-FinTech adoption among Bumiputera SMEs in Selangor, Malaysia. International Journal of Accounting, Finance and Business (IJAFB), 7(43), 587-601.
Benny, B., Thomas, A. T., James, J., Joseph, S., & Kanjirapally, K. (2024). Examining the antecedents of fintech adoption among college students using UTAUT2 model. International Journal of Creative Research Thoughts, 12, 2320-2882.
Chhillar, N., Arora, S., & Chawla, P. (2024). Measuring digital financial literacy: A comparative analysis. International Journal of Banking, Risk & Insurance. Retrieved from Measuring-Digital-Financial-Literacy-A-Comparative-Analysis.pdf
Conner, J. M. (2022). Exploring how financial knowledge and generations impact the adoption rate of financial technology (Doctoral dissertation). University of Alabama Libraries.
Dwiki, A. I. M., Suteja, M. I. M., & Setia, D. K. T. (2022). Percentage analysis of knowledge and use of fintech products (payment, clearing, and settlement) in students (case study in information & business information Darmajaya Bandar Lampung). In Proceeding International Conference on Information Technology and Business (pp. 40-45).
Fachrudin, R. F., & Wahyuni, S. W. (2023). The effect of education and age on financial literature. IJARIIE, 9, 395-4396.
Hettiarachchi, H. S., & Wijekumara, J. M. N. (2023). Factors affecting the knowledge and awareness of the Fintech among the management undergraduates in state universities in Sri Lanka: With the moderating role of demographic factors. Retrieved from https://www.researchgate.net/publication/375826544_factors_affecting_the_knowledge_and_awareness_of_the_fintech_among_the_management_undergraduates_in_state_universities_in_sri_lanka_with_the_moderating_role_of_demographi c_factors
Indrawati, A. (2021). Digital financial literacy and financial technology: Case studies of Faculty of Economics, University 17 August 1945 Samarinda. DiE: Jurnal Ilmu Ekonomi dan Manajemen, 12(1), 1-10.
Irsya, D. K., & Faturohman, T. (2024). The demographical analysis of Indonesian buy-now-pay-later users' financial wellbeing. EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis, 12(4), 3861-3872.
Kekana, M. K. (2014). The saving behaviour of university students in South Africa [Master's dissertation, University of Pretoria (South Africa)]. ProQuest.
Krejcie, R. V., & Morgan, D. W. (1970). Sample size determination table. Educational and Psychological Measurement, 30(3), 607- 610.
Lasmini, R. S., & Zulvia, Y. (2020). Fintech utilization and student investing decision. In The Fifth Padang International Conference on Economics Education, Economics, Business and Management, Accounting and Entrepreneurship (PICEEBA-5 2020) (pp. 578-582). Atlantis Press.
Li, Y., & Fisher, I. (2022, December). Digital financial literacy, risk aversion, and college students' online security behavior. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 4807-4811). IEEE.
Malik, A., & Malik, R. (2024). [Conference presentation]. Proceeding of International Conference on Sharia Economic Law, 1(1), 96-103.
Marikyan, D., & Papagiannidis, S. (2023). Unified theory of acceptance and use of technology: A review. In S. Papagiannidis (Ed.), TheoryHub Book. https://open.ncl.ac.uk
Morgan, P. J., Huang, B., & Trinh, L. Q. (2019). The need to promote digital financial literacy in the digital age. Retrieved from https://www.researchgate.net/publication/343682203_The_Need_to_Promote_Digital_Financial_Literacy_for_the_Digita l_Age
Muthia, F., Novriansa, A., & Andaiyani, S. (2023). Do demographic factors affect digital financial literacy? Sriwijaya International Journal of Dynamic Economics and Business, 7(1), 41-50.
Muzakir, M., & Saputra, A. (2024). The effect of using fintech on the financial behaviour of Teuku Umar University students. Jurnal Bisnis dan Kajian Strategi Manajemen, 8(2).
Nawang, W. R. W., & Shukor, S. A. (2023). Digital financial literacy among young adults in Malaysia. International Business Education Journal, 16(2), 115-126.
Niu, G., Wang, Q., & Zhou, Y. (2020). Education and fintech adoption: Evidence from China. SSRN. https://doi.org/10.2139/ssrn.3765224
Pratiwi, R. E., & Saefullah, K. (2022). The use of payment technology through financial literacy. Journal of Digital Innovation Studies, 1(1). https://doi.org/10.24198/digits.v1i1.38516
Rahayu, R., Ali, S., Aulia, A., & Hidayah, R. (2022). The current digital financial literacy and financial behavior in Indonesian millennial generation. Journal of Accounting and Investment, 23(1), 78-94.
Rajdev, A. A., Modhvadiya, T., & Sudra, P. (2020). An analysis of digital financial literacy among college students. Pacific Business Review International, 12, 32-40.
Rani, V., & Kumar, J. (2024). Gender differences in FinTech adoption: What do we know, and what do we need to know? Journal of Modelling in Management, 19(4), 1215-1236.
Respati, D. K., Widyastuti, U., Nuryati, T., Musyaffi, A. M., Handayani, B. D., & Ali, N. R. (2023). How do students' digital financial literacy and financial confidence influence their financial behavior and financial wellbeing? Nurture, 17(2), 40-50.
Ryu, H. S. (2018). Understanding benefit and risk framework of fintech adoption: Comparison of early adopters and late adopters. In Proceedings of the 51st Hawaii International Conference on System Sciences. Sungkyunkwan University.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
Welman, C., Kruger, F., & Mitchell, B. (2005). Research methodology (3rd ed.). Cape Town: Oxford University Press.
World Bank. (2022). Financial sector assessment program: Competition and efficiency in the financial system. Retrieved from https://www.worldbank.org/fsap
Zaimovic, A., Meskovic, M. N., Dedovic, L., Arnaut-Berilo, A., Zaimovic, T., & Torlakovic, A. (2024). Measuring digital financial literacy. Procedia Computer Science, 236, 574-581.
Zhu, R., Paul, S., & Muck, I. (2022). Digital financial literacy as a business model: The case study of a FinTech start-up. In Proceedings of the International Conference on Electronic Business (ICEB 2022). Retrieved from https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1059&context=iceb2022
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