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Asian Business Research Journal 
Vol. 10, No. 6, 21-27, 2025 
ISSN: 2576-6759 
DOI: 10.55220/25766759.471 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
The Impact of Digital Financial Literacy on Fintech Adoption among Students in 
Hanoi 

 
Dinh The Hung1

 
Tran Nguyen Nhat Nam2 
Tran Vu Hoang Nam3 
 

 
 
 

1National Economics University, Vietnam. 
2,3Nhan Chinh High School, Vietnam. 
Email: hungdt@neu.edu.vn  
Email: nhatnam031108@gmail.com  
Email: tranvuhoangnam031108@gmail.com   
( Corresponding Author) 

 
Abstract 

This paper aims to investigate the impact of digital financial literacy on the adoption of financial 
technology (Fintech) among secondary and tertiary students in Hanoi, Vietnam. Integrating 
theoretical frameworks such as Unified Theory of Acceptance and Use of Technology (UTAUT2) 
and the Theory of Planned Behavior (TPB), this study explores the influence of various factors - 
including performance expectancy, risk perception, subjective norms, perceived behavioral 
control, convenience, security, personal motivation, financial attitude, and financial behavior - on 
the intention and actual use of Fintech. Based on empirical findings, the authors propose 
recommendations to enhance digital financial literacy among students, highlighting its pivotal 
role in expanding access to and adoption of modern financial services. 

 
Keywords: Digital financial literacy, Financial literacy, Fintech, Perceived behavioral control, Subjective norm. 

 
1. Introduction 

In the context of an accelerating digital transformation - particularly within the finance and banking sectors - 
it is essential to understand young people’s awareness and competencies in digital financial literacy to foster a 
sustainable and inclusive financial ecosystem. Fintech services such as e-wallets, digital banking, online investing, 
and peer-to-peer lending are increasingly prevalent in Vietnam, especially among urban youth like students.  

However, the safe and effective usage of such services requires a solid foundation in financial knowledge - 
especially digital financial literacy, which encompasses personal financial management, information security, risk 
assessment, and safe technological practices. Although students are tech-savvy and adaptable, they typically lack 
experience and comprehensive financial understanding, making them prone to mistakes when using digital 
financial products.  

This study aims to clarify the role of digital financial literacy in shaping and influencing Fintech usage 
behavior among students in Hanoi. It assesses students’ understanding of digital financial concepts, tools, and 
applications, and examines the relationship between their level of digital financial literacy and their usage behavior 
of various Fintech services: e-wallets, digital banking, online investing platforms, P2P lending, and personal 
expense management apps. The study then demonstrates the impact of this literacy on Fintech adoption decisions, 
considering moderating variables like age, field of study, and prior experience with technology. Finally, based on 
its findings, the authors offer recommendations for strengthening digital financial literacy among students in 
Hanoi and guide educational policies appropriate for the digital age. 
 

2. Research Overview 
Amid rapid digital transformation, Fintech plays an increasingly crucial role in socio-economic life. For youth, 

who readily adopt technology, Fintech offers numerous conveniences in personal finance management, payments, 
saving, and investment. However, Fintech usage is not only shaped by technology and consumption habits but is 
also significantly influenced by users’ digital financial literacy. 

Nguyen Nam Hai (2021) applied the UTAUT2 model using data from 250 customers in Hochiminh city to 
identify drivers of Fintech adoption: effort expectancy, performance expectancy, social influence, facilitating 
conditions, hedonic motivation, and price value. 

Dao My Hang et al. (2018) focused on determinants of Fintech usage in the payment sector in Vietnam, noting 
positive effects of behavioral perception and convenience on Fintech intentions. 

Tran Thi Thanh Huyen (2021) utilized UTAUT to analyze Fintech adoption, highlighting behavioral 
perceptions, convenience, and social influence. 

To Minh Thu (2022) emphasized that enhancing digital financial knowledge is key to achieving inclusive 
finance amid digital advancements. 

mailto:hungdt@neu.edu.vn
mailto:nhatnam031108@gmail.com
mailto:tranvuhoangnam031108@gmail.com
https://doi.org/10.55220/25766759.471


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Phung Thai Minh Trang (2023) surveyed 1.180 Vietnamese university students and found that digital financial 
literacy directly and indirectly influences Fintech adoption intentions, mediated by Fintech attitudes and perceived 
behavioral control. 

Trinh Thi Phan Lan & Pham Thi Hue (2023) asserted that risk and benefit perceptions are critical 
determinants of Fintech usage among Hanoi youth. 
 

3. Theoretical Foundations 
The Organization for Economic Cooperation and Development (OECD), based on the definition of "financial 

literacy" has incorporated questions about behavior, attitudes, and understanding to measure financial knowledge. 
Although there is no universally accepted term worldwide, the OECD has piloted this approach together with the 
term “global financial knowledge”. The OECD believes that digital financial literacy is understood as a combination of 
basic financial understanding and the ability to use digital tools to make effective financial decisions. 

According to Hogarthe (2002), financial literacy refers to the ways people manage their finances in terms of 
personal budgeting, saving, investment, and financial planning; financial knowledge or financial understanding is 
determined by personal experience, professional knowledge, and individual needs, and it positively influences 
individual participation in the financial services market. 

Schngen (1996) defined financial literacy as “the ability to make informed judgments and effective decisions 
regarding the use and management of money.” 

Meanwhile, Roy Morgan Research (1993) explained the term as follows: “having knowledge and confidence in 
saving, spending, financial planning, and the measurement of financial literacy must reflect the financial situation of 
the individual. Financial literacy should only be considered when examined in relation to each person’s specific 
needs and financial situation rather than in regard to all financial tools or services, as among them, there are ones 
that some individuals may not need and do not demand”. 

Remund (2010) stated that “Financial literacy is a measure of the degree to which one understands fundamental 
financial concepts and has the ability and confidence to manage personal finances through informed short-term 
decisions, long-term financial planning, while also living responsibly and being concerned about life and changes in 
economic conditions”. 

For the purpose of assessment, “Financial knowledge” is defined as “a combination of awareness, 
understanding, skills, attitude, and behavior necessary to make sound financial decisions and ultimately achieve 
personal financial well-being” (Mahdzan and Tabiani, 2013). This definition asserts that financial literacy is not 
only knowledge per se but also includes attitudes, behaviors, and other related skills. It emphasizes the importance 
of decision-making - the application of knowledge and skills in practical processes - and shows that the desired 
impact should be financial improvement at the national level. 

Digital financial literacy is defined as the understanding and ability to apply digital tools, platforms, and 
financial services into real-life practice. 
Financial literacy is reflected through: 

• Financial knowledge: understanding basic financial concepts, recognizing financial products & services, basic 
financial skills (making payments, opening accounts). 

• Financial Behavior: daily money management, financial planning, seeking financial advisory services... 

• Financial Skills: literacy, numeracy. 

• Attitudes influencing financial decisions: saving, lending, confidence in retirement planning. 
In Vietnam, although Fintech is rapidly developing, general financial literacy - especially digital finance - 

remains limited. A report by Standard & Poor’s showed that Vietnam only scored 24 in financial literacy, ranking 
118 out of 144 countries. This reality shows that most people, including students, are not adequately equipped with 
the knowledge to use financial technology services effectively and safely. 

In reality, students are the group that frequently uses platforms such as e-wallets, digital banking or personal 
finance management tools. However, this usage is mostly experiential or trend-based, rather than based on a clear 
understanding of the operation, benefits, and risks of such services. This entails many potential dangers in making 
poor financial decisions, losing spending control, or falling victim to digital fraud. 

Based on the above situation, a survey was conducted among students in Hanoi to assess the relationship 
between digital financial literacy and the use of Fintech. The survey results are expected to provide a more 
comprehensive view of the role of financial education in the digital age, while also suggesting appropriate 
directions for training and raising digital financial literacy in schools and society. 

 

4. Model and Methods Research  
4.1. Model and Hypotheses 

This study is based on the Theory of Planned Behavior (TPB) by Ajzen (1991) and the extended Unified 
Theory of Acceptance and Use of Technology (UTAUT2) developed by Venkatesh et al (2022). These models have 
been widely applied in consumer behavior research related to new technologies, in which product understanding – 
specifically digital financial literacy – plays a crucial role in shaping intention and behavior in using financial 
technologies. 

Digital financial literacy is the understanding of digital financial products and services and the perception of 
risks associated with their use (Morgan & Trinh, 2019). This knowledge originates from Goal-Framing Theory 
(Lindenberg & Steg, 2007). According to this theory, goal framing refers to the way individuals process 
information and act based on their goals. In relation to the decision to use fintech services, digital financial literacy 
helps individuals identify their goals of achieving benefits and act rationally and efficiently (Kumar et al., 2023). 

H1: Digital financial literacy influences students' intention to use Fintech. 
Financial attitude refers to individuals’ emotions and perspectives regarding financial matters, which directly 

affect their behavior and subsequent decision-making (Rai et al., 2019). According to Khuc The Anh (2020), a 
person who values short-term financial benefits more than long-term accumulation tends to rarely consider 



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investing, setting aside funds for emergencies, or making long-term financial plans. In a study on spending habits 
among Asian students, Shahryar and Tan (2014) concluded that the influence of attitude on financial literacy is 
very clear. 

H2: Financial attitude has an impact on students' intention to use Fintech. 
In addition to the factor of financial literacy, recent studies have expanded models for evaluating fintech usage 

behavior by incorporating factors from the TPB (Ajzen, 1991). Among these, subjective norms refer to individuals’ 
perceptions of social expectations, including influence from friends, family, or the community, which have been 
shown to play an important role in shaping intentions and behaviors related to fintech use. According to research 
by Lee (2009) and Alam et al. (2019), if users perceive that using fintech is positively viewed by society, they will be 
more inclined to accept and use these services regardless of their current level of financial knowledge. 

H3: Subjective norms affect students' intention to use Fintech. 
H4: Perceived behavioral control affects students' intention to use Fintech. 

 

 
Figure 1. 
Research Model. 

 
4.2. Measurement of Variables 

The research model consists of four variables (see Figure 1), including one independent variable and three 
control variables. 
• Independent variable: Digital financial literacy (DFL) is measured through four questions adapted from 

Prasad et al. (2018), Morgan & Trinh (2019), and Setiawan et al. (2022). 

• Control variables: Financial Attitude (FAT), Subjective Norms (NOR), and Perceived Behavioral Control 
(PBC) are derived from Liñán & Chen (2009) and Phung (2023), using a 5-point Likert scale ranging from 1 
(strongly disagree) to 5 (strongly agree). 

• Dependent variable: Intention to use Fintech services (IUF) is derived from Liñán & Chen (2009) and Phung 
(2023), using a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). 

 
4.3. Research Method and Data 

The research model is tested through primary data collected from high school and university students in Hanoi 
from March to May 2025. The formulation of hypotheses and model not only contributes to clarifying the role of 
digital financial literacy in the financial behavior of young people but also highlights the factors that promote 
access to financial technology in the context of digital transformation in Vietnam. 

Data was collected through an online survey using the “Google Forms” platform. The target respondents were 
high school and university students in Hanoi, Vietnam. The survey was conducted from March 2025 to May 2025. 
The questionnaire was distributed to students via Facebook, Zalo, and Email. The number of valid observations 
used in this study is 180. 

Three methods are used to test the hypotheses and other regression relationships, including Structural 
Equation Modeling (SEM), Binary Logit, and Ordinary Least Squares (OLS). The software used includes SPSS and 
AMOS. In addition, tests conducted include reliability testing of the scale (Cronbach’s Alpha), Exploratory Factor 
Analysis (EFA), and Confirmatory Factor Analysis (CFA). 
 

5. Research Results 
5.1. Descriptive Statistics 

Descriptive statistics of the survey participants (N = 180) are presented in Table 1. Among them, males 
accounted for 34% and females 66%. The age group from 18 to 20 accounted for 37%, followed by the age group 
from 21 to 22 (28%), 22 to 23 (23%), and from 24 and above (12%). Most of the respondents were university 
students. Overall, students had a relatively good average level of Digital Financial Literacy (DFL) (Mean = 3.42). 
Students also demonstrated a positive Financial Attitude (FAT) (Mean = 4.45), good Perceived Behavioral Control 
(PBC) (Mean = 4.42), and a relatively high level of Subjective Norms (NOR) (Mean = 3.51). The Intention to use 
Fintech services (IUF) was also quite high (Mean = 3.78). 
  



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Table 1. Descriptive Statistics. 

Scale Minimum Maximum Mean Median Standard Deviation 

DFL 1 5 3,42 3,25 0,89 
PBC 1 5 4,42 4,67 0,63 
FAT 1 5 4,45 4,75 0,61 
NOR 1 5 3,51 3,67 0,94 
IUF 1 5 3,78 4,00 0,78 

 

5.2. Reliability Testing of Measurement Scale 
Table 2 presents the results of reliability testing for five measurement scales, including: Intention to Use 

Fintech Services (IUF), Digital Financial Literacy (DFL), Subjective Norms (NOR), Perceived Behavioral Control 
(PBC), and Financial Attitude (FAT). The results indicate that all scales have a Cronbach’s Alpha coefficient 
greater than 0.7. Specifically, IUF has a coefficient of 0.867; PBC (0.749); FAT (0.854); NOR (0.825); and DFL 
(0.874). According to Hair et al. (2014), all five scales were eligible for testing and exploratory factor analysis. 
 

Table 2. Reliability Testing of Measurement Scales. 

Observed 
Variable 

Scale mean if 
Item Deleted 

Scale variance if Item 
Deleted 

Corrected Item-Total 
Correlation 

Cronbach's Alpha if 
Item Deleted 

1. Intention to Use Fintech Services (IUF): Cronbach’s Alpha = 0.867 

IUF 1 7.764 2.857 0.801 0.864 

IUF 2 7.737 2.384 0.716 0.838 

IUF 3 7.563 2.046 0.777 0.850 

IUF 4 7.413 2.541 0.728 0.811 

2. Digital Financial Literacy (DFL): Cronbach’s Alpha= 0.874  

DFL 1 11.157 7.069 0.601 0.789 

DFL 2 11.205 7.701 0.628 0.777 

DFL 3 11.484 7.461 0.669 0.858 

DFL 4 11.452 7.336 0.665 0.860 

3. Subjective Norms (NOR): Cronbach’s Alpha= 0.825  

NOR 1 7.276 5.820 0.794 0.740 

NOR 2 7.322 5.037 0.772 0.741 

NOR 3 7.300 5.086 0.778 0.814 

NOR 4 7.454 5.970 0.762 0.711 

4. Perceived Behavioral Control (PBC): Cronbach’s Alpha= 0.749 

PBC 1 8.413 1.265 0.686 0.744 

PBC 2 8.471 1.492 0.654 0.745 

PBC 3 8.487 1.519 0.779 0.741 

PBC 4 8.483 1.733 0.734 0.742 

5. Financial Attitude (FAT): Cronbach’s Alpha= 0.854  

FAT 1 13.815 3.941 0.752 0.831 

FAT 2 13.665 3.593 0.748 0.847 

FAT 3 13.753 3.619 0.783 0.830 

FAT 4 13.807 3.204 0.752 0.843 

 
Table 3. Exploratory Factor Analysis (EFA). 

Variable 
Component Communalities 

Item Deleted 1 2 3 4 5 
DFL2 0,834     0,789 
DFL1 0,809     0,778 
DFL3 0,800     0,741 
DFL4 0,729     0,706 
FAT 3  0,858    0,780 
FAT 2  0,848    0,714 
FAT 4  0,800    0,694 
FAT 1  0,797    0,604 
NOR 2   0,858   0,791 
NOR 4   0,852   0,803 
NOR 3   0,852   0,663 
NOR 1   0,782   0,737 
PBC1    0,821  0,734 
PBC3    0,805  0,658 
PBC2    0,782  0,762 
PBC4    0,765  0,646 
IUF 2     0,813 0,695 
IUF 1     0,765 0,737 
IUF 3     0,745 0,734 
IUF 4     0,744 0,658 
First Eigenvalues 5,660 2,798 1,574 1,202 1,061  
Total variance % 33,296 16,459 9,260 7,068 6,239 72,321 

Note: KMO and Bartlett's Test: Kaiser-Meyer-Olkin Measure: 0, 875; Chi-Square: 8144; df = 136, p: 0,000; Extraction method: Principal Component Analysis. 
Rotation method: Varimax with Kaiser Normalization. 

 
 
 



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5.3. Exploratory Factor Analysis (EFA) 
The paper conducted an exploratory factor analysis (EFA) on five scales with 16 original variables (see Table 

3). The results show these variables grouped into five factors: FAT (4 items), DFL (4 items), NOR (4 items), PBT 
(4 items), and IUF (4 items). All factor loadings exceed 0.5, and all original items are retained. Communalities for 
all items are above 0.5. The total variance explained by the first five eigenvalues is 72.321%. 
 

5.3.1. Confirmatory Factor Analysis (CFA) 
The study continued to test the confirmatory factors Analysis (CFA) through testing the correlation 

coefficient, convergence and discrimination presented in Table 4. The scales all met the CFA testing standards 
such as the composite reliability coefficient (CR) was greater than 0.7. Specifically, the intention to use fintech 
(IUF) had a CR coefficient of 0.798; digital financial literacy (DFL) (0.894); Financial attitude (FAT) (0.889); 
Subjective norm (NOR) (0.843) and Perceived behavioral control (PBC) (0.796). In addition, related to the 
convergence test, the results showed that all 5 scales had an average variance extracted (AVE) greater than 0.5. 
Testing discrimination, the results also presented that all 5 scales had the maximum individual variance (MSV) 
smaller than AVE.  

In summary, based on the model fit criteria (Hair et al., 2014), all five scales met the specified criteria; thus, 
they were eligible to conduct a structural equation modeling (SEM) test. 
 

Table 4. CFA Results. 

Scale 
 

CR AVE MSV 
Correlation coefficient 

1 2 3 4 5 

1. IUF 0,798 0,587 0,371 0,764     
2. DFL 0,894 0,556 0,371 0,609 0,776    
3. FAT 0,889 0,629 0,371 0,409 0,230 0,817   
4. NOR 0,843 0,668 0,255 0,505 0,411 0,281 0,739  
5. PBC 0,796 0,571 0,371 0,324 0,189 0,609 0,228 0,765 

Note: CR: composite correlation; AVE: average variance extracted; MSV: maximum individual variance 

 

5.3.2. Analysis of Factors Affecting the Intention to use Fintech Services (IUF) 
The study examined the factors affecting the intention to use fintech services. The results are presented in 

Table 5. Four models and three methods were used: structural linear model, binary logit and multivariate 
regression. Models 1-3 only tested 4 factors including digital financial literacy and 3 motivational factors in the 
theory of planned behavior. Model 4, in addition to the 4 factors as in model 3, tested demographic factors. Model 
(2) used the binary logit method. In which, knowledge was coded with 0 being below average knowledge and 1 
being above average knowledge.  

The results showed that knowledge had a positive influence on intention with all 4 models and 3 methods. 
Specifically, Model (1), the coefficient of 0.484*** between Digital Financial Literacy (DFL) and Intention to use 
fintech (IUF) means that when knowledge increases by 1 unit, intention increases by 48.4%. Similarly, Models (3) 
and (4) also show that Digital Financial Literacy (DFL) has a positive impact on Intention to use fintech (IUF) at 
0.312*** and 0.235*** respectively. Model (2) with the Binary Logit method shows a coefficient of 1.046***; 
meaning that students with higher than average Digital Financial Literacy have higher Intention to use fintech 
(IUF) than students with below average knowledge. 

Regarding the three control factors, the results show that Financial Attitude (FAT) has a direct impact on 
Intention to use fintech (IUF) (see the coefficient of 0.024*** with a significance level of 99%). However, Subjective 
Norm (NOR) has a direct impact on Intention to use fintech (IUF) with a coefficient of 0.018***. In contrast, 
Perceived behavioral control (PBC) has no direct effect on Intention to use fintech (IUF). 
 

Table 5. Factors Affecting Fintech Usage Intention (IUF). 

 
SEM 
(1) 

Binary Logit 
(2) 

OLS 
(3) 

OLS 
(4) 

DFL (Digital financial literacy) 
0,484*** 
(11,143) 

 0,312*** 
(13,593) 

0,235*** 
(10,324) 

DFL = 1 
 1,046*** 

(53,314) 
  

FAT (Financial Attitude) 
0,24*** 
(4,620) 

 0,213*** 
(6,118) 

0,219*** 
(5,731) 

FAT = 1 
 0,823*** 

(29,451) 
  

NOR (Subjective Norms) 

0,18*** 
(6,843) 

 0,182*** 
(8,582) 

0,193*** 
(8,991) 

NOR = 1 
 0,865*** (37,625)   

PBC (Perceived Behavioral Control) 
0,067 
(1,42) 

 0,087** 
(2,311) 

0,053 
(1,451) 

PBC = 1 
 0,185 

(1,423) 
  

Gender (Male = 1) 
   -0,045 

(-1,03) 

Block number  -1.541*** 0,684*** 0,581*** 

R2/R2 adjustable 0,516 0,241 0,372 0,420 

Chi-square coefficient/ -2 Log likelihood /Changed F coefficient 314 1183,54 145,681 68,078*** 

Degrees of freedom 122 1 4 10 

Note: ***: p<1%; **: p<5%; *: p<10%. Dependent variable: intention to use fintech services (YDINH). Model fit SEM: Chi-square = 312.144; Df = 122; 
p =0.000; GFI = 0.965; TLI = 0.972; RMSEA = 0.039. 

 



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The study found the influence of digital financial literacy on the intention to use fintech services. Therefore, 
three hypotheses H1, H2, H3 were accepted and hypothesis H4 was rejected. 

The results of the study are consistent with the goal-setting theory (Lindenberg & Steg, 2007) and the TPB 
(Ajzen, 1991; Fishbein & Ajzen, 1975). The results imply that digital financial literacy helps individuals to clearly 
define goals (that is, the intention to use fintech services), and the higher the digital financial literacy, the higher 
the intention to use fintech services. 

Some studies in the world such as have found a relationship between financial knowledge and financial 
decisions in India (Kumar et al., 2023). Household knowledge in India (Prasad et al., 2018) and Indonesia (Setiawan 
et al., 2022). Therefore, the results of the study in Vietnam contribute to the diverse theoretical repertoire of 
knowledge across countries. 

 

6. Policy Implications 
From the research results, the research team proposes of recommendations to improve digital financial literacy 

and promote safe and effective financial technology use among students. 
First, the State and relevant agencies need to clearly identify digital financial knowledge as an indispensable 

part of comprehensive digital capacity of citizens in the 4.0 era. Improving digital financial literacy for young 
people not only serves the goal of financial inclusion, but also helps young people make reasonable financial 
decisions, limit financial risks, increase the ability to save and invest responsibly. Therefore, relevant ministries 
such as the Ministry of Education and Training, the Ministry of Finance, the State Bank, etc. need to integrate 
digital financial education content into high school and university curricula in the form of formal subjects or 
extracurricular activities. Teaching should not only stop at basic financial theory, but also expand to practical skills 
such as using e-wallets, assessing security when making online transactions, and identifying financial fraud risks, 
thereby helping students improve their ability to make financial decisions in the digital environment. 

Second, schools and educational institutions need to proactively coordinate with Fintech businesses and banks 
to organize talk shows, seminars, competitions or practical experience workshop series. These activities not only 
arouse interest but also increase interaction and apply financial knowledge into practice, thereby creating 
motivation to learn and forming positive financial habits among young people. The content should aim to foster 
positive financial behaviors such as budget planning, smart consumption, personal credit control, and protection of 
personal financial information. This is especially necessary because many students today access financial 
technology through social networks and word of mouth, which can easily lead to emotional or unsafe decisions. 

Third, local authorities and youth organizations can launch mass media campaigns on social networks, flyers, 
learning apps, etc. to spread the right awareness of digital finance and the habit of using financial technology safely. 
In particular, it is necessary to emphasize practical topics such as "how to effectively manage pocket money 
through digital applications", "distinguishing between investment and financial gambling", or "how to avoid online 
credit traps and black app loans". Communication campaigns need to disseminate knowledge about the risks of 
technology fraud, personal account security, and clearly explain the rights and obligations of fintech users, thereby 
helping young people be more proactive in evaluating and choosing reputable financial platforms. 

Finally, students themselves need to be proactive in raising awareness and self-studying about personal finance 
through reputable online documents and courses. Setting simple financial goals such as creating an emergency 
fund, saving to buy books, or tracking daily expenses through an application will be the first step in building a solid 
foundation for sustainable financial behavior in the future. Young people should also know how to create a personal 
budget, record expenses, evaluate their level of fintech usage, and thereby adjust their financial behavior 
accordingly. Self-study through online documents, open courses, and financial simulation applications is also 
essential to master modern financial tools. At the same time, young people need to learn how to self-assess the 
safety of financial applications, not follow virtual investment trends, and be aware of the risks when sharing 
personal financial information online. 

 

7. Conclusion 
However, this study still has some limitations in terms of scope and survey subjects. Due to time and practical 

conditions, the survey was mainly conducted in Hanoi, with the number of samples ensuring analysis but not 
enough to generalize to all more than 2 million students nationwide. In addition, the study has not analyzed in 
depth the differences in fintech usage behavior according to demographic variables such as gender, region, family 
income, type of school or major. These are factors that can significantly affect the level of access and application of 
financial technology, and therefore should be included in subsequent studies.In the future, it is necessary to expand 
the scale of the survey, increase the application of more advanced analytical models such as SEM or PLS-SEM to 
assess the relationship between factors affecting fintech usage behavior more comprehensively. From there, policy 
and educational recommendations can be more tailored to specific groups of young people in the context of 
increasingly deep digitalization in Vietnam 
 

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