This study delves into twofundamental areas: first, the identification of factors that shape the inclination ofVietnamese individuals to embrace digital currency within select nations; a
Reason for choosing topic
Digital currency is a revolutionary technology that has captured the interest of researchers, investors, financial institutions, and regulators It embodies a type of commerce that operates entirely in digital form, lacking any physical or tangible basis.
The rise of virtual landscapes in the digital age has highlighted the significance of digital currency as a crucial element for facilitating transactions and fostering trust among global users As technological advancements and modern financial systems evolve, the demand for digital currencies has increased, enabling seamless and real-time financial transactions The preferences of citizens regarding digital currencies are essential for the success of digital systems, including smart cities and economic frameworks These currencies, developed through a peer-to-peer model, aim to address the changing financial demands of the digital era Government support is vital, as skepticism about digital currencies can hinder their acceptance, raising questions about their potential to replace traditional money.
Objectives and Questions of Inquiry
This research investigates the key factors that influence individuals' willingness to adopt digital currency, focusing on their significance in decision-making The study aims to analyze and predict the importance of these factors, offering insights not only for Vietnam but also for other countries looking to enhance economic predictions, leverage opportunities, reduce risks, and prevent economic crises Central to the research are three questions: What criteria determine the variables affecting Vietnamese people's intention to use digital currencies? How do these factors influence adoption decisions, and are their effects positive or negative? If the effects are positive, what strategies can be implemented to promote them, and if negative, how can these adverse impacts be mitigated?
Methodology
The research methodology is based on established theoretical frameworks and previous experimental studies The team utilizes a linear regression model to analyze factors influencing Vietnamese individuals' intentions to adopt digital currency To ensure accurate and conclusive results, analytical tools like STATA, Microsoft Excel, and Microsoft Word are used for model construction and validation Data is gathered solely from a real survey, guaranteeing the reliability and precision of the findings.
Model testing utilizes the Ordinary Least Squares (OLS) method to minimize the vertical distance between observed data and the regression line, alongside various econometric tools After a comprehensive analysis, testing, and validation of the data, the research team concludes the key factors affecting investment intentions in digital currency among the Vietnamese population.
Structure of the Study
To fulfill the research objectives, the team will leverage knowledge from Econometrics and related fields pertinent to the economic domain to explore the relationship between variables across various chapters:
I The Theoretical Foundations of Digital Currency
IV Construction of the Empirical Model
The Theoretical Foundation of Digital Currency
Definition of Digital Currency
Digital currency, or digital money, is a form of payment that exists only in electronic form, without physical representation like coins or bills, and relies on online systems for transactions It is important to distinguish digital currencies from electronic cash, such as funds in an online bank account, which are linked to a specific physical currency Unlike electronic cash, digital currencies are virtual mediums of exchange with no direct connection to tangible money Bitcoin is the most well-known digital currency, but many other digital currencies are currently in use and at various stages of development.
Understanding "cash" is crucial for comprehending digital currency, as it traditionally refers to non-transferable paper money recognized as legal tender, known as fiat money Unlike historical currencies backed by physical assets like gold or silver, fiat money derives its value from supply and demand, relying on trust in the economy and credit instead of intrinsic material worth.
Digital currency, like fiat money, does not have a physical form and is not supported by tangible assets Although it is exchanged against traditional currencies such as the US dollar, it is not directly tied to them Its value is primarily determined by supply and demand factors, similar to that of fiat money.
Digital currencies, such as Bitcoin, create scarcity by capping the total supply at 21 million coins Unlike fiat money, these digital assets do not have backing from governments or centralized institutions, which is essential for maintaining trust When confidence in fiat currency wanes, particularly because it is not tied to physical assets, it can lose its value as a reliable store of purchasing power, adversely affecting a nation's economy and governance.
Top 10 Cryptocurrencies as of June 26, 2023
Figure 1 Top 10 Cryptocurrency in June 2023 by CoinGecko
Literature Review
Overview and Research Gap
Recent studies have extensively examined behavioral intent in digital currency investment in developed countries However, in Vietnam, where the adoption of virtual currencies has rapidly increased, there is a notable lack of research addressing consumer intentions toward these digital assets.
Extensive global research has explored the factors affecting digital currency investment, highlighting the significance of fungibility in cryptocurrencies, as examined by Gomber et al (2018) Notable studies by Fung et al (2015), Athey et al (2016), Yermack (2015), Ciaian et al (2016), Foltice and Wyman (2015), and Kroll et al further contribute to understanding the dynamics of cryptocurrency markets.
(2013), investigated various aspects such as financial innovations, pricing, legitimacy, economics, regulations, and mining economics of Bitcoin.
Simultaneously, research from diverse perspectives has aimed to comprehend determinants driving digital currency adoption Scholars like Davis (1989), Ajzen
In their research, Rogers (1995) and other scholars have significantly advanced our understanding of technology adoption through models such as the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Diffusion of Innovations theory These frameworks emphasize key factors including perceived usefulness, ease of use, communication channels, and social systems Additionally, recent Vietnamese studies by Le, Nguyen, and Dinh (2020) and Nguyen and Trinh (2019) have highlighted the importance of trust in technology, regulatory awareness, and financial literacy in influencing technology acceptance.
In Vietnam, research predominantly relies on quantitative methods, particularly models such as the Technology Acceptance Model (TAM), Theory of Reasoned Action (TRA), and the Unified Theory of Acceptance and Use of Technology (UTAUT) to analyze behavioral intentions However, many studies utilizing the UTAUT framework fail to fully incorporate its constructs, overlooking significant factors like perceived risks that influence Vietnamese consumers' cautious approach to adopting cashless payment methods and digital wallet services Consequently, the effects of these overlooked factors on the intention to use digital currency in Vietnam have yet to be thoroughly investigated.
Theoretical framework
IV Construction of the Empirical Model
I The Theoretical Foundation of Digital Currency
Digital currency, or digital money, is a form of payment that exists only in electronic form, lacking any physical representation like coins or bills Unlike electronic cash, which is tied to a specific currency in online bank accounts, digital currency operates independently as a virtual medium of exchange without direct ties to tangible money Bitcoin is the most well-known example of digital currency, but many other digital currencies are also in circulation, each at varying stages of development.
Understanding "cash" is crucial for comprehending digital currency Traditionally, cash refers to non-transferable paper that serves as legal tender, known as fiat money, which lacks tangible backing Unlike historical currencies linked to physical assets like gold or silver, the value of fiat money is determined by supply and demand, relying on trust in the economy and credit rather than inherent material value.
Digital currency, like fiat money, does not have a physical form and is not supported by tangible assets Although it is traded against currencies such as the US dollar, it is not directly tied to physical currencies; its value is determined by the dynamics of supply and demand, similar to that of fiat money.
Digital currencies, with a capped supply of 21 million bitcoins, create a sense of scarcity Unlike fiat money, they do not have the backing of governments or centralized institutions This lack of support is significant, as a decline in confidence in fiat currencies, which are not tied to tangible assets, can lead to a loss of their value as a reliable store of purchasing power, ultimately affecting a nation's economy and governance.
Top 10 Cryptocurrencies as of June 26, 2023
Figure 1 Top 10 Cryptocurrency in June 2023 by CoinGecko
2 The Global Context of Digital Currency:
Digital currency revolutionizes transactions by offering a more efficient alternative to physical money, enabling quicker cross-border transfers than traditional currencies Its technological framework aids central banks in executing monetary policies more effectively Additionally, many digital currencies employ cryptography, ensuring that transactions are secure, resistant to censorship, and operate independently of government or private control.
A US worker may soon receive her paycheck in a digital wallet, allowing her to send money more efficiently and affordably to family in countries like Australia or India The World Bank projects that reducing remittance costs could increase annual remittances to low-income nations by $16 billion, significantly lowering transaction fees that currently consume up to 7% of the transaction value.
Governments around the globe are increasingly exploring the adoption of digital currencies due to their numerous advantages Sweden's central bank has been examining the implications of digital currency since 2017 as it moves towards a cashless society Meanwhile, China's digital currency, the DC/EP (e-Renminbi), is currently in pilot testing, potentially inspiring other nations to embrace similar initiatives and promoting digitization, innovation, and financial inclusion in one of the world's largest economies.
In October 2020, the Bahamas launched the "sand dollar," the first central bank digital currency, representing a pivotal advancement in digital currency adoption Similar to Kenya's transformative M-pesa mobile money service, this innovation significantly enhances financial access for unbanked individuals using basic mobile phones in emerging nations A February 2021 IMF survey revealed that approximately 111 of the 159 IMF member countries are actively exploring or planning to implement digital currencies within their financial systems.
Recent studies have primarily examined behavioral intent in digital currency investments in developed countries In contrast, Vietnam has seen a significant increase in the use of virtual currencies, yet there is a lack of comprehensive research on consumer intentions towards these digital assets.
Extensive global research has explored the factors influencing digital currency investment, highlighting key concepts such as fungibility in cryptocurrencies Notable studies by Gomber et al (2018) and others, including Fung et al (2015), Athey et al (2016), Yermack (2015), Ciaian et al (2016), Foltice and Wyman (2015), and Kroll et al., contribute to a deeper understanding of the digital currency landscape.
(2013), investigated various aspects such as financial innovations, pricing, legitimacy, economics, regulations, and mining economics of Bitcoin.
Simultaneously, research from diverse perspectives has aimed to comprehend determinants driving digital currency adoption Scholars like Davis (1989), Ajzen
In their research, Rogers (1995) and earlier studies from 1991 highlighted key models such as the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Diffusion of Innovations theory, focusing on critical factors like perceived usefulness, ease of use, communication channels, and social systems Recent Vietnamese studies by Le, Nguyen, and Dinh (2020) and Nguyen and Trinh (2019) further emphasized the importance of trust in technology, regulatory awareness, and financial literacy in the adoption of new technologies.
In Vietnam, research predominantly relies on quantitative methods, utilizing models such as the Technology Acceptance Model (TAM), Theory of Reasoned Action (TRA), and the Unified Theory of Acceptance and Use of Technology (UTAUT) to assess behavioral intent However, many studies using the UTAUT model fail to fully incorporate its constructs, overlooking critical elements like perceived risks that influence Vietnamese consumers' cautious approach to adopting cashless payment methods and digital wallet services Consequently, the effect of these overlooked factors on the behavioral intent to use digital currency in Vietnam has yet to be investigated.
2 Determinants Affecting Vietnamese People's Intention to Use Digital Currency:
This study examines the factors influencing Vietnamese consumers' investment intentions in cryptocurrency through the lens of the Theory of Planned Behavior (TPB) and Social Cognitive Theory (SCT) Key factors identified include education level, income status, internet growth, information interference, herding behavior, positive attitudes, and perceived risks The integration of TPB and SCT highlights that user behavior is shaped by a complex interaction of environmental influences, cognitive processes, individual characteristics, personal attitudes, subjective norms, and income levels.
Higher education plays a crucial role in improving financial literacy, as it enhances individuals' understanding of essential financial concepts such as budgeting, savings, loans, and investments Those who are financially literate are more likely to make informed investment decisions, which can significantly influence investment behaviors in Vietnam.
Income plays a crucial role in determining the amount available for savings, investments, and equity opportunities A higher income level is associated with improved access to financial advice, which helps mitigate biases and enhances interest in various financial instruments, including cryptocurrencies.
Empirical model
Research methodology
The questionnaire is structured to align with the research model, featuring two parts that capture essential respondent information and assess constructs The first part collects demographic data such as age, sex, income level, and the percentage of income respondents are willing to invest in cryptocurrencies The second part includes four questions based on the research model's constructs, utilizing a 2-point scale for responses: 1 for Agree and 2 for Disagree.
Turning qualitative variables into dummy variables:
The study utilizes four key variables to gauge respondent agreement: EXI, which equals 1 if the respondent agrees with the statement and 0 otherwise; MPI, similarly coded as 1 for agreement and 0 for disagreement; HRB, also marked as 1 for agreement and 0 for disagreement; and POA, following the same coding pattern.
Behavioral intention of using digital currency
Variable PER = 1 if the respondent agrees with the statement, otherwise = 0
The fast pace of Internet growth has facilitated access to information about digital currencies, making it easier for consumers to research and invest in cryptocurrencies.
The distortion or manipulation of information, often through misinformation or rumors can impact individuals' investing decision in both negative and positive way.
Investors or traders have a tendency to follow the actions of their peers, rather than making independent decisions based on their own analysis and information.
A positive attitude towards digital currencies can empower consumers by giving you greater control over your financial assets so consumers are more confident to invest in digital currency.
The likelihood that something wrong that would happen when I buy digital currency is high
Sukumaran, Shangeetha, Thai Siew Bee, and Shaista Wasiuzzaman (2022)
Figure 3 Statements used in survey
This study utilizes primary data collected via a questionnaire designed and distributed through Google Forms, targeting a diverse range of Vietnamese individuals across different demographics, including age, gender, and occupation The research aims to engage the general public rather than focusing on a specific demographic group, with the questionnaire link shared on social media platforms like Facebook and Instagram to reach respondents effectively.
In 2023, we employed quantitative methodologies, specifically the Ordinary Least Squares (OLS) estimation method, to analyze an econometric model that identifies the key determinants influencing Vietnamese individuals' willingness to invest in digital currency.
Definition of Variables
Based on previous research, we have identified several independent variables that influence electric database usage, including education level, income status, internet growth, information interference, herding behavior, positive attitude, and perceived risks.
Digital Currency (DCU) refers to any payment method that exists exclusively in electronic form, without a physical counterpart like traditional cash This form of money is managed and transferred through online systems, highlighting its intangible nature.
Education Level (variable name EDU): Denotes the stage or degree of education an individual has attained, ranging from basic education to advanced degrees.
Income Status (INS) is a crucial socioeconomic metric that assesses an individual’s or household’s financial health and living standards It plays a significant role in determining access to essential resources, overall quality of life, and the ability to save or invest for future needs.
The expansion of the Internet, referred to as EXI, connects billions of devices globally, paving the way for innovative applications of digital currencies, including micropayments and peer-to-peer transactions.
Manipulation of Information (MPI) encompasses actions that disrupt or distort the flow of information in various contexts, including communication and technology This interference can significantly impact information warfare, affecting how data is conveyed and perceived.
Herding Behavior (variable name HRB): Describes a phenomenon where a significant number of individuals in society adopt or believe in something, leading others to follow suit.
Positive Attitude (variable name POA): Involves maintaining optimism about situations, interactions, and oneself It represents a mental state that focuses on the positive aspects of life and anticipates favorable outcomes.
Perceived risk (PER) significantly influences the value of digital currencies, as research indicates that awareness of associated risks leads to decreased acceptance and usage Consequently, this reluctance among users can result in a decline in the overall value of digital currencies.
Expected signs of variables and definitions
Figure 4 Expected signs of variables
A positive regression coefficient for education level (β1) suggests that individuals with higher education are better equipped to understand and evaluate emerging technologies, including digital currencies Their knowledge of both technical and business dimensions enables them to make informed investment choices Additionally, a well-educated population typically exhibits strong computational and data analysis skills, which are crucial for assessing risks and investment opportunities in the digital currency market.
The regression coefficient of income status (β2) is anticipated to be positive, indicating that higher-income individuals are likely to diversify their investment portfolios to mitigate risk They view digital currencies as a promising investment option alongside traditional assets such as stocks, bonds, and real estate Given the significant growth in the digital currency market in recent years, affluent investors see this as an opportunity for potentially higher profits, with any associated losses being less impactful on their financial well-being.
The positive regression coefficient of the internet's expansion indicates that its global reach significantly enhances the use of digital currencies for international transactions, allowing users to bypass national borders and currencies This accessibility makes digital currencies ideal for cross-border payments and for individuals seeking to send money abroad Furthermore, the internet simplifies the buying, selling, and trading of digital currencies, providing greater accessibility compared to traditional currencies, which is especially advantageous for those in economically unstable areas or without access to conventional banking services.
The regression coefficient of Manipulation of Information (β4) is anticipated to be negative, indicating that information noise causes investors to depend on misleading information from social networks This reliance can lead to significant financial losses, undermining confidence in the virtual currency market and resulting in decreased investment or potential market withdrawal.
Herding behavior is anticipated to have a negative regression coefficient (β5), as it leads to heightened volatility in digital currency markets When investors collectively react to perceived price drops, they may sell off their holdings regardless of the asset's intrinsic value, resulting in significant declines in currency prices Furthermore, this behavior can create price bubbles characterized by rapid and excessive increases in digital currency values, which, when they burst, can lead to considerable financial losses for investors.
• β6 (regression coefficient of Positive attitude) is expected to be positive:
Investor optimism is set to enhance digital currency investments, leading to rising prices and attracting more investors Concurrently, businesses that view digital currency favorably are more likely to adopt it for payments, which could elevate its demand and practical use in everyday transactions.
• β7 (regression coefficient of Perceived risk) is expected to be negative:
Research shows that Vietnamese consumers are primarily concerned about the potential loss of funds and privacy breaches when using digital payment methods The perceived insecurity of digital wallets in protecting personal information may hinder the adoption of digital currencies Additionally, the risks linked to mobile transactions, including data transmission vulnerabilities and the possibility of losing mobile devices, threaten users' personal and financial information.
Data source
Data descriptive
Variable Full variable name Unit Source
DCU Digital Currency using % income level Survey
EDU Education level Years Survey
INS Income status Likert Scale Survey
EXI Expansion of the Internet Likert Scale Survey
HRB Herding Behavior Likert Scale Survey
POA Positive Attitude Likert Scale Survey
PER Perceived Risk Likert Scale Survey
The data set used is cross-section data.:
To analyze the determinants of Digital Currency Usage in Vietnam, we utilized the cross-section data collected from respondents in our survey, conducted inNovember, 2023
The study analyzes a data set of 168 observations primarily from residents in Hanoi, Vietnam, a developing nation experiencing growth in digital economics Variations in education levels and income status—categorized as below 1 million Dong, between 1-5 million Dong, and above 5 million Dong—significantly influence behavior, highlighting the impact of independent variables on sub-variables within the data set.
Theoretical model
On November 20, 2023, a Google Form questionnaire was distributed through social media platforms, resulting in 168 responses over a two-week period The collected data provides a demographic profile of the respondents, including insights into their gender and age distribution (see Table …).
In a recent survey, females represented 63.2% of the total respondents, while males accounted for 36.7% Notably, over 86.2% of participants were aged between 18 and 30, with only 2.9% of respondents being over 30 and 10.7% under 18.
Before delving into data analysis, we will provide a general overview of the model and its parameters using the Stata command 'sum.' This command will display key statistics, including the number of observations (Obs), mean, standard deviation (Std Dev.), and the minimum (Min) and maximum (Max) values for the variables involved.
EDU: The mean value of Education Level among 168 Vietnamese people from the survey is 13.64881, the standard deviation is 2.638136, min value is 10 and max value is 18 (Years).
INS: The mean value of Income Status among 168 Vietnamese people from the survey is 1.75, the standard deviation is 0.6812282, min value is 1 and max value is 3.
EXI: The mean value of Expansion of the Internet among 168 Vietnamese people from the survey is 4.041667, the standard deviation is 7205634, min value is 1 and max value is 5.
MPI: The mean value of Manipulation of Information among 168 Vietnamese people from the survey is 3.285714, the standard deviation is 1.033237, min value is 1 and max value is 5.
HRB: The mean value of Herding Behavior among 168 Vietnamese people from the survey is 3.238095, the standard deviation is 0.9741454, min value is
POA: The mean value of Positive Attitide among 168 Vietnamese people from the survey is 4.190476, the standard deviation is 0.7963418, min value is 1 and max value is 5
PER: The mean value of Perceived Risk among 168 Vietnamese people from the survey is 4.047619, the standard deviation is 0.7243376, min value is 1 and max value is 5
Correlation of variables: To identify the correlation among the variables of the model, we used command corr á the results are illustrated as following:
Figure 8 Results of correlation test
EDU has low correlation coefficient of (-0.0827), and the minus sign indicates a negative impact it has on DCU.
INS has low correlation coefficient of (-0.0290), and the minus sign indicates a negative impact it has on DCU.
EXI has low correlation coefficient of (0.0396), and the plus sign indicates a positive impact it has on DCU.
MPI has relatively low correlation coefficient of (0.1968), and the plus sign indicates a positive impact it has on DCU.
HRB has high correlation coefficient of (0.5618), and the plus sign indicates a positive impact it has on DCU.
POA has relatively low correlation coefficient of (0.1923), and the plus sign indicates a positive impact it has on DCU.
PER has low correlation coefficient of (0.0347), and the minus sign indicates a positive impact it has on DCU.
Upon analyzing the data set, we identified a potential linear relationship between the dependent variable, SSE, and seven independent variables Consequently, we opted to develop a linear regression model to explore this relationship further.
Thus the model has the form of:
DCU = 𝜷𝟎 + 𝜷𝟏 EDU + 𝜷𝟐 INS + 𝜷𝟑 EXI + 𝜷𝟒 MPI + 𝜷𝟓 HRB + 𝜷𝟔 POA + 𝜷𝟕 PER +
^DCU =β 0+ β 1 ^EDU +β 2^INS+β 3^EXI +β 4^MPI+ β 5 ^HRB+β 6^POA
EXI Expansion of the Internet
Estimated free coefficient When independent variables of DCU, EDU, INS, EXI, MI, HRB, POA,
PER equal 0, the average value of dependent variable
Estimated slope coefficient When independent variables of DCU, EDU, INS, EXI, MI, HRB, POA,
PER change by 1 unit (other factors remain unchanged), the average value of the dependent variable will change respectively by ^β1^β2^β3^β4^β5^β6^β7
Regression results
Regression model
Multiple linear regression analysis aims to predict the outcome value of digital currency (DCU) by examining the influence of seven independent variables, while also assessing the cause-and-effect relationships between these independent variables and the dependent variable.
Figure 10 Results of regression model
According to the results of running regression using OLS method on STATA software, we have a sample regression function (SRF) as follows:
DCU = 0.7720303 + 0.0129576 EDU - 0.0669346 INS + 0.5328354 EXI + 0.0261369 MPI + 0.5189635 HRB + 0.1735368 POA - 0.4830042 PER + 𝒖𝒊𝒖𝒊𝒖𝒊𝒖𝒊𝒖𝒊
𝜷𝟎 = 0.7720303: With sample data, Vietnamese people’s intention to invest in cryptocurrency equals 0.7720303 when all independent variables equal 0.
𝜷𝟏 = 0.0129576: Vietnamese people’s intention to invest in cryptocurrency increases by 0.0129576 unit when Education level increases by 1 unit, ceteris paribus.
𝜷𝟐 = - 0.0669346: Vietnamese people’s intention to invest in cryptocurrency decreases by 0.0662359 unit when Income Status increases by 1 unit, ceteris paribus.
𝜷𝟑 = 0.5328354: Vietnamese people’s intention to invest in cryptocurrency increases by 0.5328354 unit when the Expansion of the Internet increases by 1 unit, ceteris paribus.
𝜷𝟒 = 0.0261369: Vietnamese people’s intention to invest in cryptocurrency increases by 0.0261369 unit when the Manipulation of Information increases by
𝜷𝟓 = 0.5189635: Vietnamese people’s intention to invest in cryptocurrency increases by 0.5189635 unit when the Herding Behavior increases by 1 unit, ceteris paribus.
𝜷𝟔 = 0.1735368: Vietnamese people’s intention to invest in cryptocurrency increases by 0.1735368 unit when Positive Attitude increases by 1 unit, ceteris paribus.
𝜷𝟕 = - 0.4830042: Vietnamese people’s intention to invest in cryptocurrency decreases by 0.4830042 unit when Perceived Risk increases by 1 unit, ceteris paribus.
R – squared = 0.3494 signifies that the sample regression function is relatively inappropriate The independent variables in the model can explain 34.94 % of what affects the Vietnamese’s intention of digital currency investment So, 65.06% (0%
Approximately 34.94% of the variation in the investment intentions of Vietnamese individuals in cryptocurrency can be attributed to factors not accounted for in the model, which are represented by the error term or residuals.
Testing the appropriateness of the regression model
Using the P-value method with P-value acquired in the Figure 10 and = 1% H0: J = 0 (The regression model inappropriate)
H1: J 0 (The regression model is appropriate)
According to the regression result in Table 10:
Conclusion: The model is statistically significant at = 1%
Testing the statistical significance of independent variables
H0: β j =0 (The coefficient is statistically significant)
H1: β j≠ 0 (The coefficient is statistically significant)
Using the P-value method with P-value acquired in the Figure 10:
Figure 11 Results of p-value method a = 1%
The analysis of the data indicates that only 1 out of 7 variables, specifically HRB, is statistically significant at a 1% level, demonstrating its influence on the intention of Vietnamese individuals to invest in cryptocurrency In contrast, the variables EDU, INS, EXI, MPI, POA, and PER do not show statistical significance, suggesting that factors such as education level, income status, internet expansion, information manipulation, positive attitude, and perceived risk have minimal effects on the adoption of digital currency as an investment among the Vietnamese population.
The data analysis reveals that 2 out of 7 variables, HRB and POA, are statistically significant at the 5% level, indicating their influence on the intention of Vietnamese individuals to invest in cryptocurrency Conversely, the variables EDU, INS, EXI, MPI, and PER do not show statistical significance, suggesting that education level, income status, internet expansion, information manipulation, and perceived risk have minimal impact on the adoption of digital currency as an investment among Vietnamese people.
The analysis reveals that 2 out of 7 variables, specifically HRB and POA, are statistically significant at the 5% level, indicating their influence on the intention of Vietnamese individuals to invest in cryptocurrency Conversely, the variables EDU, INS, EXI, MPI, and PER show no statistical significance, suggesting that factors such as education level, income status, internet expansion, information manipulation, and perceived risk have minimal impact on the adoption of digital currency as an investment among the Vietnamese population.
Using critical value method method with t value acquired in the Figure 10 Check for the critical value at significant level = 5%; n = 168; k = 7 We use two-tail testing: tα
Figure 12 Results of critical value method
Using critical value method with the 95% confidence interval acquired in the Figure 10:
Figure 13 Results of confidence interval method
Testing for violations of classical linear regression model assumptions
The Variance Inflation Factor (VIF) method is essential for identifying multicollinearity in a model A VIF value exceeding 10 indicates the presence of multicollinearity, signaling a need for model adjustment.
With the “vif” command in STATA, we have the result as follow:
Figure 14 Results for detection of multicollinearity using command [VIF]
All VIF values of independent variables are less than 10 except for PER’s and EXI's, which are 89.41 and 89.37 respectively
Conclusion: The model does have multicollinearity
H0: The variance of disturbance is constant (Homoscedasticity)
H1: The variance of disturbance is not constant (Heteroskedasticity)
Using the “estat hettest” command (Breusch–Pagan test) in STATA, we can detect whether the model incurs heteroskedasticity or not The result we got is shown as the following:
Model has Prob > chi2 = 0.3437 > 0.05 => do not reject H0
Conclusion: At α = 5%, the model is not heteroskedastic
H0: The variance of disturbance is constant (Homoscedasticity)
H1: The variance of disturbance is not constant (Heteroskedasticity)
Using the “estat imtest, white” command (White test) in STATA, we can detect whether the model incurs heteroskedasticity or not The result we got is shown as the following:
Model has Prob > chi2 = 0.000 < 0.05 => Reject H0
Conclusion: At α = 5%, the model is heteroskedastic
H0: There is no omitted variable in the model
H1: Existing at least one omitted variable in the model
We conducted the Ramsey RESET test to determine the presence of omitted variables in our model Utilizing the command "quiet reg" followed by "ovtest" in STATA, we obtained the following results.
Conclusion: At α = 5%, model has omitted variables.
Remedies for violations of classical linear regression model assumptions
In the previous section, we identified violations of three classical linear regression model assumptions—multicollinearity, heteroscedasticity, and omitted variables—through the use of the Variance Inflation Factor, Breusch–Pagan Test, White Test, and Ramsey RESET Test This section will focus on presenting effective remedies for addressing these violations.
Regarding the research’s limitation, we only performed the removal of one or more variables that show high correlation In this case, we removed the variable PER anđ EXI accordingly.
After eliminating certain variables, we found that the Variance Inflation Factor (VIF) values for the remaining variables fell below 10, effectively resolving multicollinearity in our model Nonetheless, only two variables remained statistically significant within the model.
5.2 Remedy for Omitted Variable violation
Step 1: Model transformation to lin-log form by generating new logarithmic variable.
Using the ([gen lvarname=ln(varname)]) command in STATA, we generated now logarithmic variables.
Step 2: Run the newly transformed regression model
Using command [reg DCU lEDU lINS lEXI lMPI lHRB lPOA lPER] we obtained the result below:
Using command [ovtest], we obtained the following results:
H0: The model has no omitted variables
H1: The model has omitted variables
Model P-value = 0.0000 < 0.05 => The model still has omitted value bias
Only variables POA (Positive Attitude) and PER (Perceived Risk) are statistically significant
The Current situation
Overview
In 2023, Vietnam maintained strict regulations on cryptocurrencies, with the State Bank of Vietnam enforcing a comprehensive ban on their use for payments since January 1, 2018 This ban was implemented to mitigate risks related to money laundering, tax evasion, and fraud Despite these limitations, cryptocurrency trading continued in the country, primarily through peer-to-peer (P2P) platforms that allowed trading cryptocurrencies as assets rather than for transactional purposes.
Vietnam has adopted a cautious approach to cryptocurrencies while showing a keen interest in the broader applications of blockchain technology As of October 2021, the country ranked prominently in the Crypto Adoption Index, excelling in peer-to-peer transaction values and individual payments Various sectors, including supply chain management, logistics, and record-keeping, are actively investigating blockchain solutions to improve operational efficiency and transparency.
The State Bank of Vietnam is focused on developing a central bank digital currency (CBDC) to enhance the financial system, boost payment efficiency, and decrease reliance on cash Despite its intentions, the rollout of CBDC faces challenges, highlighted by the few countries that have successfully adopted this currency type While the Central Bank of Vietnam has announced plans for a CBDC launch, it is also conducting research to assess the feasibility of effective implementation.
Outlook
The digital currency landscape in Vietnam could take various directions, leading to several potential scenarios:
Vietnam is likely to continue its strict stance on cryptocurrencies, upholding the prohibition of their use in transactions while enforcing tight regulations on related activities This approach is designed to protect financial stability and deter illegal financial practices.
The government may reevaluate its stance on cryptocurrency, potentially implementing refined regulations that permit legal activities under certain conditions These regulations could create a structured framework for exchanges to function with government oversight.
Vietnam's cryptocurrency regulations are lagging behind the global landscape, highlighting the need for an updated legal framework Introducing a central bank digital currency (CBDC) could position Vietnam as a leader in technological advancement, providing a government-backed digital currency for everyday transactions This move could offer an alternative to cryptocurrencies while allowing the government to retain control over the monetary system However, Vietnamese authorities must thoroughly assess the advantages and disadvantages of implementing a CBDC within the country's specific context before making it an official currency.
The ongoing adoption of blockchain technology is anticipated to extend beyond cryptocurrencies, with a significant focus on improving transparency and efficiency across various sectors Additionally, government support for blockchain applications in logistics, supply chain management, and record-keeping is likely to increase, driving further innovation and implementation in these areas.
Policy Implications
As global advancements in information technology and communication drive the rise of digital currencies, Vietnam is actively developing its own national digital currency Amidst the growing popularity of digital money, countries worldwide face challenges in effectively managing these assets, including risks related to confidentiality, anonymity, management complexity, and volatility.
Regulation Requirements for Transactions and Exploitation:
Regulations are essential for managing the operational standards of digital currency exchanges in Vietnam, ensuring a safer environment for buyers and sellers These rules are designed to reduce risks associated with digital currencies, including initial coin offerings (ICOs) and trading activities It is crucial to either apply existing securities laws or develop new regulations tailored to these digital assets to enhance market security and compliance.
Specific Measures against Fraudulent Behavior:
There is a pressing need for clear regulations to combat fraud in this sector, enabling state oversight and defining specific penalties for fraudulent actions Given the rapidly changing technology landscape and the rise of online trading and mining, it is challenging to predict and mitigate all risks beforehand Consequently, legal frameworks should prioritize informing users about potential risks.
Governments and central banks should work together to create policies that address the potential risks of digital currencies to financial stability Such policies could include restrictions on the use of digital currencies to limit leverage and prevent their involvement in money laundering or terrorist financing.
Governments and organizations can launch educational initiatives to enhance public understanding of the benefits and risks linked to digital currencies Such programs aim to inform individuals about the safe and responsible usage of these digital assets.
Conclusion
In 2023, our research group utilized STATA software to analyze the factors influencing Vietnamese individuals' propensity to invest in digital currency Through careful examination, we gained insights into key variables, including education (EDU), investment knowledge (INS), experience (EXI), market perception index (MPI), habit (HRB), personal opinion (POA), and perception (PER).
Using survey data, we applied Ordinary Least Squares (OLS) analysis to explore the relationship between Vietnamese digital currency investment and various factors Our results indicated a negative correlation with perceived risks, while showing a positive relationship with education level, income status, and Internet expansion Based on these insights, the research team developed recommendations and proposed policies aimed at promoting the secure use of Vietnamese digital currency in the future We believe this article offers an objective and thorough perspective, enhancing the understanding of the current trends in digital currency growth.
Our report acknowledges potential limitations due to a lack of practical experience and incomplete data, which may affect the model's accuracy This highlights the importance of ongoing education in econometrics for our team Recognizing the relevance of this subject in our reports and future projects, we emphasize its significance As this is our team's first attempt at an independent econometric model, we welcome constructive feedback to improve our work We also express our gratitude to PhD Vu Thi Phuong Mai for her invaluable support in completing this report.
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