FOREIGN TRADE UNIVERSITYFALCULTY OF INTERNATIONAL ECONOMICS---o0o---ECONOMETRICS REPORTTHE DETERMINANTS OF VIETNAMESE’S INTENTION TO INVEST IN DIGITAL CURRENCY IN 2023Ha Noi, December 20
Reason for choosing topic
Digital currency, a groundbreaking technological advancement, has garnered significant attention from researchers, investors, financial institutions, and regulators alike It represents a form of commerce existing solely in electronic realms, devoid of any physical or material underpinning.
The evolution of virtual landscapes in the digital era has accentuated the value of digital currency, recognizing money as an indispensable element in facilitating transactions and trustworthy agreements among global residents The imperative for digital currencies, supporting seamless and real-time financial transactions, has surged in tandem with high-tech advancements and the modernization of financial systems(Brezo & Bringas, 2012) Furthermore, the adoption preferences of citizens for digital currencies play a pivotal role in determining the viability of digital systems,encompassing smart cities, smart governments, and economic frameworks Digital currencies, straddling both economic and cybernetic domains, are crafted through a peer-to-peer approach to meet the evolving financial needs of the digital era (Brière,Oosterlinck, & Szafarz, 2015) Government involvement becomes paramount, as the absence of support raises skepticism about the acceptance of this new technology by the old financial system and the populace (Hern, 2013), sparking uncertainties about whether digital currency could replace traditional money.
Objectives and Questions of Inquiry
This research aims to identify the key factors significantly influencing people's inclination to use digital currency The research team endeavors to analyze and predict the relative importance of each factor in shaping the decision to embrace digital currency Such forecasts hold potential not only for Vietnam but also for other nations seeking to make informed economic predictions, capitalize on advantages, mitigate risks, and avert economic disasters The study revolves around three primary questions: What criteria determine the variables influencing the intention ofVietnamese individuals to use digital currencies? How do these elements impact the decision to adopt digital currencies, and is the impact positive or negative? If the impact is positive, how can it be promoted, and vice versa? What strategies can be employed to overcome adverse effects?
Methodology
The research methodology is grounded in theoretical frameworks and prior experimental studies The research team constructs a linear regression model to explore determinants affecting the intention of Vietnamese individuals to use digital currency Analytical software such as STATA, Microsoft Excel, and Microsoft Word is employed to build and validate models for conclusive and accurate findings Data is exclusively collected from an actual survey to ensure the reliability and accuracy of the information.
Model testing employs the Ordinary Least Squares (OLS) method to minimize the sum of squares of vertical distances between collected data and the regression line,accompanied by other econometric tools Following a thorough review, testing, and validation of the data, the research team draws conclusions regarding the factors influencing the investment intention in digital currency among the Vietnamese populace.
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, also referred to as digital money, denotes a form of payment that exists solely in electronic form without a physical representation like coins or paper bills Its transactions and accounting rely on online systems for operations. It's crucial to differentiate between digital currencies and electronic cash, such as funds held within an online bank account at a commercial bank An online bank account typically reflects the amount of a specific currency, like Australian dollars, associated with that account This demonstrates a link between electronic cash and physical currency In contrast, a digital currency is a virtual medium of exchange existing exclusively in digital form and has no direct correlation to any tangible money Bitcoin stands as the most recognizable instance of a digital currency, but there are various other digital currencies in use today, each at different stages of development.
Understanding the concept of "cash" is essential to grasp the nature of digital currency In traditional terms, "cash" refers to non-transferable paper declared legal tender without any tangible backing, often termed fiat money Unlike money tied to physical assets like gold or silver historically, fiat money's value stems from supply and demand dynamics, reliant on trust in the economy and credit rather than intrinsic material worth.
Similar to fiat money, digital currency lacks physical representation and isn't backed by tangible assets While digital currencies are traded against currencies like the US dollar, they aren't directly linked to physical currencies; their value originates from supply and demand dynamics, akin to fiat money.
By imposing a limit of 21 million bitcoins, for instance, digital currencies introduced scarcity However, unlike fiat money, digital currencies lack support from governments or centralized entities This support is crucial because confidence loss in fiat money, not anchored to tangible goods, can render it valueless as a store of purchasing power, directly impacting a nation's economy and its governmen
Top 10 Cryptocurrencies as of June 26, 2023
Figure 1 Top 10 Cryptocurrency in June 2023 by CoinGecko
Literature Review
Overview and Research Gap
Numerous studies have explored behavioral intent in digital currency investment within developed nations, while in Vietnam, where the use of such currencies has surged recently, limited research has focused on consumers' intentions regarding virtual assets.
Globally, extensive research has delved into factors influencing digital currency investment Researchers like Gomber et al (2018) examined the concept of fungibility in cryptocurrencies, while others, including Fung et al (2015), Athey et al (2016),Yermack (2015), Ciaian et al (2016), Foltice and Wyman (2015), and Kroll et al.
(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 (1991), and Rogers (1995) contributed significantly by exploring models like the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Diffusion of Innovations theory, highlighting aspects like perceived usefulness, ease of use, communication channels, and social systems Vietnamese studies by Le, Nguyen, Dinh (2020), Nguyen, Trinh (2019) emphasized trust in technology, regulatory awareness, and financial literacy
In Vietnam, existing studies mainly employ quantitative methods, using models like TAM, Theory of Reasoned Action (TRA), and Unified Theory of Acceptance andUse of Technology (UTAUT) model to examine behavioral intent However, prior studies employing UTAUT often only partially utilize its constructs, missing factors like perceived risks, crucial for Vietnamese consumers' cautious attitude toward cashless payment methods and digital wallet services adoption The impact of these factors on the behavioral intent of using digital currency in Vietnam remains unexplored.
Theoretical framework
IV Construction of the Empirical Model
I The Theoretical Foundation of Digital Currency
Digital currency, also referred to as digital money, denotes a form of payment that exists solely in electronic form without a physical representation like coins or paper bills Its transactions and accounting rely on online systems for operations. It's crucial to differentiate between digital currencies and electronic cash, such as funds held within an online bank account at a commercial bank An online bank account typically reflects the amount of a specific currency, like Australian dollars, associated with that account This demonstrates a link between electronic cash and physical currency In contrast, a digital currency is a virtual medium of exchange existing exclusively in digital form and has no direct correlation to any tangible money Bitcoin stands as the most recognizable instance of a digital currency, but there are various other digital currencies in use today, each at different stages of development.
Understanding the concept of "cash" is essential to grasp the nature of digital currency In traditional terms, "cash" refers to non-transferable paper declared legal tender without any tangible backing, often termed fiat money Unlike money tied to physical assets like gold or silver historically, fiat money's value stems from supply and demand dynamics, reliant on trust in the economy and credit rather than intrinsic material worth.
Similar to fiat money, digital currency lacks physical representation and isn't backed by tangible assets While digital currencies are traded against currencies like the US dollar, they aren't directly linked to physical currencies; their value originates from supply and demand dynamics, akin to fiat money.
By imposing a limit of 21 million bitcoins, for instance, digital currencies introduced scarcity However, unlike fiat money, digital currencies lack support from governments or centralized entities This support is crucial because confidence loss in fiat money, not anchored to tangible goods, can render it valueless as a store of purchasing power, directly impacting a nation's economy and its governmen
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 offers a streamlined transactional process distinct from physical money Its technological foundations facilitate faster cross-border transactions compared to traditional money This type of currency also simplifies the implementation of monetary policies by central banks Some digital currencies utilize cryptography, making their transactions resistant to censorship and tampering, beyond the control of governments or private bodies.
For instance, envision a US worker whose paycheck might soon be deposited into a digital wallet This could enable her to send money more efficiently and cost- effectively to family members in various countries like Australia or India Lowering remittance costs, as projected by the World Bank, could augment annual remittances to low-income nations by $16 billion, potentially reducing transaction costs, which can currently absorb up to 7% of the transaction value.
Given these advantages, governments worldwide are increasingly considering the adoption of digital currency Sweden's central bank, moving towards a cashless society, has published exploratory papers since 2017, examining the implications of digital currency in its economy China's digital version of its currency, DC/EP (e- Renminbi), is undergoing pilot tests and might encourage other nations to follow suit, fostering digitization, innovation, and financial inclusion in one of the world's largest economies.
The Bahamas introduced the "sand dollar," the world's first central bank digital currency, in October 2020, marking a significant step in the direction of digital currency adoption Mobile money innovation, like Kenya's M-pesa service, has profoundly impacted emerging nations, providing financial access to individuals without bank accounts but with basic mobile phones According to an IMF survey from February 2021, about 111 out of 159 nations in the International Monetary Fund (IMF) membership are exploring or planning to integrate digital currency into their systems.
Numerous studies have explored behavioral intent in digital currency investment within developed nations, while in Vietnam, where the use of such currencies has surged recently, limited research has focused on consumers' intentions regarding virtual assets.
Globally, extensive research has delved into factors influencing digital currency investment Researchers like Gomber et al (2018) examined the concept of fungibility in cryptocurrencies, while others, including Fung et al (2015), Athey et al (2016),Yermack (2015), Ciaian et al (2016), Foltice and Wyman (2015), and Kroll et al.
(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 (1991), and Rogers (1995) contributed significantly by exploring models like the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Diffusion of Innovations theory, highlighting aspects like perceived usefulness, ease of use, communication channels, and social systems Vietnamese studies by Le, Nguyen, Dinh (2020), Nguyen, Trinh (2019) emphasized trust in technology, regulatory awareness, and financial literacy
In Vietnam, existing studies mainly employ quantitative methods, using models like TAM, Theory of Reasoned Action (TRA), and Unified Theory of Acceptance and Use of Technology (UTAUT) model to examine behavioral intent However, prior studies employing UTAUT often only partially utilize its constructs, missing factors like perceived risks, crucial for Vietnamese consumers' cautious attitude toward cashless payment methods and digital wallet services adoption The impact of these factors on the behavioral intent of using digital currency in Vietnam remains unexplored.
2 Determinants Affecting Vietnamese People's Intention to Use Digital Currency:
This study incorporates the Theory of Planned Behavior (TPB) and Social Cognitive Theory (SCT) to identify factors impacting Vietnamese consumers' investment intention in cryptocurrency Factors include education level, income status, internet growth, information interference, herding behavior, positive attitude, and perceived risks The combination of TPB and SCT suggests that user behavior results from a complex interplay of environmental, cognitive, individual, personal attitude, subjective norms, and income factors.
Education Level: Higher education significantly impacts financial literacy, enhancing comprehension of financial aspects like budgeting, savings, loans, and investments Financially literate individuals make informed investment decisions,potentially affecting Vietnamese investment choices.
Income Status: Income determines the portion available for savings, investments, and equity investment Higher income correlates with better access to financial advice, reducing biases and increasing interest in financial instruments like cryptocurrencies.
Empirical model
Research methodology
In consideration of variables identified in the research model, the questionnaire is designed based on the measuring scales Each statement represents an observed variable The questionnaire consists of 2 parts The first part serves the purpose of gathering information of the respondents including age, sex, level of income, and lastly, how many percentages of income they are willing to invest in cryptocurrencies. The second part of the questionnaire consists of 4 questions that are designed based on the research model’s constructs This study employs the 2 - point scale to design the questionnaire including 1 – Agree, 2 – Disagree.
Turning qualitative variables into dummy variables:
Variable EXI = 1 if the respondent agrees with the statement, otherwise = 0 Variable MPI = 1 if the respondent agrees with the statement, otherwise = 0 Variable HRB = 1 if the respondent agrees with the statement, otherwise = 0 Variable POA = 1 if the respondent agrees with the statement, otherwise = 0
Behavioral intention of using digital currency Education Level (EDU) – H1
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 primarily relies on primary data gathered through a questionnaire created and distributed using Google Forms The research targets Vietnamese individuals across various demographics, including age, gender, and occupation,intending to address the general public rather than a specific demographic subset To reach these respondents, the questionnaire link was shared on social media platforms such as Facebook and Instagram.
Given the aforementioned analysis and hypotheses, we have chosen to employ quantitative methodologies and utilize the Ordinary Least Squares (OLS) estimation method to analyze the econometric model concerning the determinants influencing the inclination of Vietnamese individuals to invest in digital currency in 2023.
Definition of Variables
Drawing upon prior research, we have identified independent variables by examining influencing factors found in official electric databases These include education level, income status, internet growth, information interference, herding behavior, positive attitude, and perceived risks The following outlines the meanings of these variables:
Digital Currency Using (variable name DCU): Encompasses any form of payment existing solely in electronic format Digital money lacks physical presence, akin to a traditional dollar bill or coin, and is managed and transferred through online systems.
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 (variable name INS): A vital socioeconomic indicator evaluating an individual's or household's financial well-being and standard of living It significantly influences access to basic necessities, quality of life, and the capacity to save or invest for the future.
Expansion of the Internet (variable name EXI): Involves connecting billions of devices to the internet, potentially leading to new applications for digital currencies, such as micropayments and peer-to-peer transactions.
Manipulation of Information (variable name MPI): Typically refers to actions or processes that disrupt, manipulate, distort, or impede the flow of information across various contexts This interference can manifest in communication, technology, and information warfare.
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 (variable name PER): Considered a characteristic factor with a substantial impact on the value of digital currencies Research suggests that perceived risk negatively affects the value of digital currencies If users are aware of associated risks, there is a higher likelihood of reluctance to accept or use digital currencies, resulting in a decline in their value.
Expected signs of variables and definitions
Figure 4 Expected signs of variables
• β1 (regression coefficient of Education level) is anticipated to be positive:Individuals with a higher education level typically possess the capacity to research and comprehend emerging technologies Their understanding of the technical and business aspects of digital currencies aids in making well-informed investment decisions.Moreover, a well-educated populace often demonstrates proficient computational and data analysis skills, facilitating the assessment of risks and investment potential in the digital currency market.
• β2 (regression coefficient of Income status) is expected to be positive: Individuals with higher incomes often seek to diversify their investment portfolios for risk mitigation Digital currencies present a new investment avenue alongside traditional assets like stocks, bonds, and real estate The digital currency market's substantial value growth over recent years may be viewed by high-income individuals as an opportunity for greater profits compared to traditional investments, with potential losses during the investment process having a relatively minor impact on their lives.
• β3 (regression coefficient of The Expansion of the Internet) is anticipated to be positive: The global reach of the internet enables digital currencies to be used for transactions worldwide, transcending national borders and currencies This makes them particularly suitable for cross-border transactions and for individuals wishing to send money internationally Additionally, the internet facilitates easy access and investment in digital currencies, as they can be bought, sold, and traded online, enhancing accessibility compared to traditional currencies—especially beneficial for those in economically unstable regions or lacking access to traditional banking services.
• β4 (regression coefficient of Manipulation of Information) is expected to be negative: Information noise induces investors to rely on false information from social networks, resulting in the loss of investment funds This erodes confidence in the virtual currency market, leading to reduced investment or even market exit.
• β5 (regression coefficient of Herding behavior) is expected to be negative: Herding behavior contributes to increased volatility as investors, acting collectively, may be prone to sell their digital currency holdings during perceived price declines, irrespective of underlying value This behavior can trigger sharp declines in currency value Additionally, herd behavior can fuel price bubbles, where rapid, disproportionate increases in digital currency prices occur When these bubbles burst, investors may incur substantial losses.
• β6 (regression coefficient of Positive attitude) is expected to be positive:
Investor optimism about the future of digital currency is likely to drive investment,boosting digital currency prices and making them more appealing to other investors.Similarly, businesses with a positive outlook on digital currency are more inclined to accept it as a payment method, potentially increasing demand and utility for everyday transactions.
• β7 (regression coefficient of Perceived risk) is expected to be negative:
Research indicates that consumer concerns primarily revolve around the potential loss of funds or privacy breaches with digital payment methods In the context ofVietnamese consumers, perceived insecurity of digital wallets in safeguarding personal information may deter their adoption of digital currency systems Furthermore, the inherent risks associated with mobile transactions, such as data transmission across networks and the possibility of losing mobile phones, pose threats to 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 data set consists of 168 observations, mostly residents in Hanoi, Vietnam – a developing country with the rising of digital economics The differences between the education level, income status (from 1 of 3 options: below 1 million Dong, from 1-5 million Dong and Higher than 5 million Dong) and the influence on behavior will create a considerable variation in the data set, clearly showing the impact direction of the independent variables on the sub-variable.
Theoretical model
The questionnaire form is in the format of Google form and was uploaded and distributed via social media platforms on the 20th of November 2023 After 2 weeks, the form received 168 responses, which we have the below demographic profile regarding gender and age of the respondents (Table …)
Females account for 63.2% of the total respondents, which leaves 36.7% the respondents are male Over 86.2% of the people surveyed belong to the 18 – 30 age group, while only 2.9% and 10.7% of the respondents are over 30 and under 18, respectively.
Before analyzing the data, we will bring in the general description about the model and the parameters by using the command sum in Stata This command will reveal the Observations (Obs), Mean, Standard Deviation (Std Dev.), Minimum (Min) and Maximum (Max) values of the variables.
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.
Observing the data set, we realized there might be a linear relationship between the dependent variable SSE and seven independent variables Therefore, we decided to build the regression model in the form of a linear model.
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
The multiple linear regression analysis is to predict the value of a dependent variable outcome, which is Digital currency using (DCU) based on the value of 7 independent variables, and to measure the cause-and-effect relationship between independent and dependent variables.
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%
- 34.94%) of the variation of Vietnamese people’s intention of investing in crypto dependent is explained by other variables that are not included in the model By theory, they are included in 𝒖𝒊𝒖𝒊𝒖𝒊𝒖𝒊𝒖𝒊 (error term/ residual).
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: βj≠ 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%
From the data presented in the table above, it can be inferred that 1/7 variables (HRB) is statistically significant at = 1%, which means it has impacts on Vietnamese people’s intention of investing in cryptocurrency While EDU, INS, EXI, MPI, POA, PER variable are not statistically significant, which indicates that the Education level Income status, Expansion of the Internet, Manipulation of Information, Positive Attitude, Perceived Risk have little impact on Vietnamese people’s intention of adopting digital currency as investment. b = 5%
Looking at the table, the results of the data analysis shows that 2/7 variables (HRB, POA) are statistically significant at = 5%, which means they have impacts on Vietnamese people’s intention of investing in cryptocurrency While EDU, INS, EXI, MPI, PER variables are not statistically significant, which indicates that the Education level, Income status, Expansion of the Internet, Manipulation of Information, Perceived Risk have little impact on Vietnamese people’s intention of adopting digital currency as investment. c = 10%
It can be seen from the table that 2/7 variables (HRB, POA) are statistically significant at = 5%, which means they have impacts on Vietnamese people’s intention of investing in cryptocurrency While EDU, INS, EXI, MPI, PER variables are not statistically significant, which indicates that the Education level, Income status, Expansion of the Internet, Manipulation of Information, Perceived Risk have little impact on Vietnamese people’s intention of adopting digital currency as investment.
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
Using the Variance Inflation Factor (VIF) method to detect whether the model has multicollinearity or not If there exists at least one value of VIF greater than 10, the model contracts this defect.
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 used the Ramsey RESET test to find out whether the model has omitted variables or not Using the command quiet reg followed by “ovtest” in STATA, this is the result we have collected:
Conclusion: At α = 5%, model has omitted variables.
Remedies for violations of classical linear regression model assumptions
In the previous part, using the Variance Inflation Factor, the Breusch–Pagan Test,the White Test, the Ramsey RESET Test, we have detected that the model violated three classical linear regression model assumptions The three violations are multicollinearity, heteroscedasticity and omitted variables Thus in this part we will be providing the remedies for the aforementioned 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.
=> By removing the variables, we observe that the VIF value of the remaining variables are all lower than 10 This way, multicollinearity is resolved in our model. However, there were still only 2 variables statistically significant enough in 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 had stringent regulations concerning cryptocurrencies The State Bank of Vietnam had enforced a comprehensive ban on their use for payments and had directed financial institutions to refrain from providing any services related to cryptocurrencies since January 1, 2018 The ban aimed to address various risks associated with cryptocurrencies, such as money laundering, tax evasion, and fraud. Despite these restrictions, some cryptocurrency trading persisted in the country, mainly occurring through peer-to-peer (P2P) platforms that facilitated trading cryptocurrencies as assets rather than for transactions.
While Vietnam took a strict stance on cryptocurrencies, it displayed interest in exploring the potential of blockchain technology beyond just digital currencies In October 2021, Vietnam ranked highly in the Crypto Adoption Index, surpassing many countries in peer-to-peer transaction value and individual payments Industries like supply chain management, logistics, and record-keeping were actively exploring blockchain applications to enhance efficiency and transparency in their operations.
The State Bank of Vietnam demonstrated interest in developing a central bank digital currency (CBDC) to modernize the financial system, improve payment efficiency, and reduce cash usage However, the implementation of CBDC posed challenges, as seen in the limited number of countries adopting this form of currency Although theCentral Bank of Vietnam announced plans to launch CBDC soon, it was concurrently conducting research to evaluate the practicality of implementing CBDC effectively.
Outlook
The digital currency landscape in Vietnam could take various directions, leading to several potential scenarios:
Vietnam may persist in its stringent approach against cryptocurrencies, maintaining the ban on their use in transactions and closely regulating activities related to cryptocurrencies This strategy aims to safeguard financial stability and prevent unlawful financial practices.
There's a possibility that the government might reconsider its position and introduce more refined regulations allowing legal cryptocurrency activities under specific conditions These regulations could establish a framework for exchanges to operate under government oversight.
Vietnam's current cryptocurrency regulations may not align with the rapidly evolving global landscape It's crucial for Vietnam to update its legal framework and take the lead in technological advancement by introducing a central bank digital currency (CBDC) Developing a CBDC could offer a government-backed digital currency for everyday transactions, potentially serving as an alternative to cryptocurrencies while allowing the government to maintain control over the monetary system However, before implementing CBDC as an official currency, Vietnamese authorities should carefully weigh its pros and cons within the country's unique context.
The adoption of blockchain technology is expected to persist across various sectors beyond just cryptocurrencies There might be a growing emphasis on utilizing blockchain to enhance transparency and efficiency Government encouragement for blockchain applications in logistics, supply chain management, and record-keeping might expand further.
Policy Implications
As information technology and communication advancements spread globally, the emergence and widespread adoption of digital currencies follow suit, and Vietnam is not exempt from this trend in developing a national digital currency Presently, amidst the continuous growth and rising popularity of digital money, countries worldwide are grappling with devising effective management strategies for this currency type.Managing cryptocurrencies and digital assets presents numerous challenges due to associated risks, such as confidentiality, anonymity, management complexity, and volatility.
Regulation Requirements for Transactions and Exploitation:
Regulations are necessary for governing the operational parameters of exchanges or electronic markets involved in buying and selling digital currency within Vietnam. These regulations aim to mitigate risks for both buyers and sellers For digital currencies in securities, initial coin offerings (ICOs), trading, and transactions involving these currencies, applying provisions from securities laws or formulating new legal regulations based on existing securities laws is essential.
Specific Measures against Fraudulent Behavior:
There's a crucial need for precise regulations to address fraudulent activities in this domain, allowing state oversight and outlining specific sanctions for such fraudulent acts However, due to the evolving nature of technology platforms and the occurrence of trading and mining activities online, it's impossible to anticipate and control all risks in advance Therefore, legal frameworks should focus on cautioning users about potential risks.
Governments and central banks can collaborate to devise policies aimed at mitigating the risks posed by digital currencies to financial stability These policies may involve measures to restrict leveraging digital currencies or prevent their misuse in money laundering or financing terrorism.
Governments and other entities can implement educational programs to raise public awareness about the risks and advantages associated with digital currencies These initiatives can educate individuals on the safe and responsible use of digital currencies.
Conclusion
Our group has conducted research and utilized the STATA software to explore the factors influencing Vietnamese individuals' inclination to invest in digital currency in 2023 The observations underwent meticulous analysis to enhance our understanding of variables such as EDU, INS, EXI, MPI, HRB, POA, and PER.
Employing data collected from surveys, we employed Ordinary Least Squares (OLS) to delve into this subject The findings revealed a negative correlation between Vietnamese digital currency investment and perceived risks, while it displayed a positive association with education level, income status, and Internet expansion. Consequently, drawing insights from the analysis, the research team formulated recommendations and proposed policies to promote the secure utilization of Vietnamese digital currency in the future We trust that this essay provides an impartial, comprehensive perspective, fostering a better understanding of the contemporary growth of digital currency.
Acknowledging potential limitations stemming from a lack of practical experience and incomplete data, our report might contain flaws, and the model's accuracy may be limited This underscores the necessity for team members to continue studying econometrics-related articles and documents to augment our expertise Given the significant relevance of this subject in reports and future endeavors, we recognize its paramount importance As this was our team's inaugural attempt at independently conducting an econometric model, our practical experience is limited Hence, we eagerly anticipate your constructive feedback to enhance our report We extend our sincere gratitude to PhD Vu Thi Phuong Mai for her invaluable assistance in completing our report.
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