The research examines the relationships between knowledge and information KI and fundamental analysis FA, market trends MT and risk appetite RA, market sentiment MS and risk appetite RA,
INTRODUCTION
Research Motivation
The motivation behind conducting research on factors influencing VNU - IS students' decision to invest in cryptocurrency stems from the growing interest and participation of young individuals in the cryptocurrency market Cryptocurrencies have gained significant attention worldwide, and Hanoi, as the capital city of Vietnam, is not an exception
Understanding the factors that drive VNU - IS students' decision-making processes when it comes to investing in cryptocurrencies is of great importance This research aims to explore and analyze the various factors that influence their investment decisions, considering their unique characteristics, perspectives, and behaviors
By investigating factors such as market trends and performance, fundamental analysis, regulatory environment, risk appetite, we can gain insights into the specific determinants of cryptocurrency investment decisions among VNU - IS students This knowledge will not only contribute to the understanding of their investment behavior but also provide valuable information for educators, parents, and policymakers to design appropriate educational programs and regulatory measures
Moreover, the research seeks to address the existing research gap in understanding the investment decisions of VNU - IS students specifically While there is a growing body of literature on factors influencing cryptocurrency investment decisions, limited research has focused on the unique context of Hanoi and its student population By examining the factors that are particularly relevant to VNU - IS students, we can provide a more nuanced understanding of their investment decision-making processes and tailor strategies to address their specific needs and concerns
Additionally, this research holds practical implications for VNU - IS students themselves By identifying the key factors that influence their decisions to invest in cryptocurrencies, we can help them make more informed and responsible investment
11 choices This can contribute to their financial well-being, promote financial literacy, and empower them to navigate the complexities of the cryptocurrency market effectively
Overall, the research on factors influencing VNU - IS students' decision to invest in cryptocurrency aims to contribute to the broader academic field of behavioral finance, investment decision-making, and the unique characteristics of the cryptocurrency market By shedding light on the determinants of cryptocurrency investment decisions among VNU - IS students, this research can inform policy discussions, educational initiatives, and regulatory frameworks, ultimately fostering a more informed and sustainable investment culture among young individuals in Hanoi.
Research purposes and tasks
The study aims to identify and analyze the specific factors that play a significant role in shaping the investment decisions of VNU - IS students By understanding these factors, we can gain insights into the motivations, preferences, and decision-making processes of VNU - IS students when it comes to investing in cryptocurrencies
● Equipping VNU - IS students with knowledge about cryptocurrency investment factors
● Improve your financial knowledge in the context of cryptocurrency investing
● Providing detailed information for educators and universities in Hanoi
● Guides policymakers in developing effective regulations for cryptocurrency investments
● Contribute to academic knowledge in the field of cryptocurrency investment behavior.
Research question
How does the regulatory environment surrounding cryptocurrencies impact VNU - IS students' investment decisions?
How does the knowledge and information about cryptocurrencies impact VNU - IS students' investment decisions?
How does the risk appetite impact VNU - IS students' investment decisions?
Research subjects
The study is expected to collect data from 150-200 students studying and working at the International universities, Hanoi National University, aged 18-25 years old
- Space: Learning facilities of the International universities, VNU, including Hacinco, TVB, and Hoa Lac campuses
Research methods
Once the research objectives were established, the research team proceeded to conduct a survey of secondary information sources This involved delving into relevant theories, doctrines, research models, and previous scientific studies pertaining to the factors influencing students' learning attitudes at VNU-IS (Vietnam National University - International School) The primary purpose of this survey was to identify the essential scales required for constructing the research model
Furthermore, the team reviewed and incorporated research findings from similar topics conducted both domestically and abroad This enabled them to gain insights from various contexts and expand the knowledge base Additionally, the team sought out opinions and suggestions from experts in the field to further enrich their understanding
The final outcome of this phase was the establishment of a scale comprising factors that affect VNU-IS 's intention to use cryptocurrency These identified elements formed the basis for the subsequent qualitative research process Moreover, they were utilized to modify and supplement the set of interview questions during the quantitative research phase
To ensure the authenticity and specificity of the observed sample, the research team engaged in discussions with experts in the field Although a convenient sampling method was employed, care was taken to ensure that the opinions and perspectives of the experts were representative of the target population These qualitative interviews with experts provided valuable insights that informed adjustments to the research model and facilitated the development of the scale by incorporating expert opinions and knowledge
The research team employed a survey method to collect data, utilizing questionnaires designed based on a predefined scale The target participants were students between the ages of 18 and 25, enrolled in various majors at the International School, VNU (Vietnam National University) The questionnaires were distributed electronically using Google Forms, a form management tool, to facilitate the data collection process
Convenience sampling was employed to gather responses from participants, making the survey more accessible and convenient Once a sufficient number of responses were collected, the research team proceeded to analyze the data using SmartPLS software Descriptive statistics were performed to gain a comprehensive understanding of the collected data
The software also tested the reliability and validity of the data, ensuring the quality and accuracy of the collected information This step is crucial in establishing the credibility and trustworthiness of the research findings
Moving forward, the research team plans to test and adjust the proposed research model using the collected data The SmartPLS software will be utilized to examine the influence of each variable within the model on the learning attitudes of students at the International School, VNU This analysis will provide insights into the relationships and effects of the variables on the study's focal points
Subsequently, the research team will evaluate the validity of the hypotheses proposed by the software based on the analysis results This evaluation will determine whether
14 the hypotheses are supported by the data or require further refinement Finally, the research team will derive a formal research model from the obtained results, incorporating the relationships and effects identified through the analysis
THEORETICAL BACKGROUND
Hypothesis and Theoretical research model
Therefore, this study uses a quantitative method applied in the form of a questionnaire survey with several questions related to our research topic This study provides an important opportunity about the Factors influencing IS students' decision to invest in cryptocurrency So we decided to make some assumptions as follows:
H1a: The relationship between Knowledge and information (KI) and Fundamental Analysis (FA)
H2a: The relationship between Market Trends (MT) and Risk Appetite (RA)
H2a: The relationship between Market Sentiment (MS) and Risk Appetite (SA)
H3a: The relationship between Fundamental Analysis (FA) and Cryptocurrency Investment Decision (CID)
H3b: The relationship between Risk Appetite (SA) andCryptocurrency Investment Decision (CID)
H3c: The relationship between Regulatory Environment (RE) and Cryptocurrency Investment Decision (CID)
Definition of crypto currency
Cryptocurrency is a digital or virtual form of currency that operates on the principles of cryptography and decentralization It enables secure financial transactions, controls the creation of new units, and verifies the transfer of assets without the need for intermediaries like banks or financial institutions
In the seminal whitepaper titled "Bitcoin: A peer-to-peer electronic cash system," Satoshi Nakamoto introduced Bitcoin, the first decentralized cryptocurrency Nakamoto described cryptocurrency as a "purely peer-to-peer version of electronic cash that allows online payments to be sent directly from one party to another without going through a financial institution." This definition emphasized the direct and decentralized nature of cryptocurrency transactions
Further exploration of the concept is found in Michael Swan's book "Blockchain: Blueprint for a new economy." Swan defines cryptocurrency as "a digital or virtual form of money that relies on cryptography for securing transactions, controlling the creation of additional units, and verifying the transfer of assets." This definition emphasizes the role of cryptography in ensuring the security and integrity of cryptocurrency transactions
A similar perspective is presented by Tapscott and Tapscott in their book "Blockchain Revolution: How the technology behind bitcoin is changing money, business, and the world." They describe cryptocurrency as "a form of digital currency that uses cryptography for security and operates independently of a central bank." They highlight that cryptocurrencies are based on decentralized ledger technology called blockchain, which ensures transparency and immutability of transactions
Andreas Antonopoulos, in his book "Mastering Bitcoin: Unlocking digital cryptocurrencies," provides a comprehensive guide to cryptocurrencies He defines them as "a digital representation of value that relies on cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets." Antonopoulos emphasizes the decentralized and transparent nature of cryptocurrencies, which operate on a peer-to-peer network
In the context of corporate governance and blockchain technology, Yermack defines cryptocurrency as "a type of digital or virtual currency that uses cryptography for secure financial transactions and operates on a decentralized network." Yermack highlights that cryptocurrencies facilitate direct peer-to-peer transfers, eliminating the need for intermediaries Additionally, cryptocurrencies offer transparency, security, and immutability of transactions, thanks to their reliance on blockchain technology
These various definitions of cryptocurrency converge on key characteristics, including its digital nature, reliance on cryptography for security, decentralized operation, and utilization of blockchain technology for transparent and secure transactions Cryptocurrencies enable direct peer-to-peer transactions, independent of traditional financial intermediaries They offer features such as transparency, security, and immutability, which contribute to their growing appeal in the financial and technological landscape.
Research hypothesis development
2.3.1 The relationship between Knowledge and information (KI) and Fundamental Analysis (FA)
The relationship between knowledge and information (KI) and fundamental analysis (FA) has been extensively studied in the financial literature, with compelling evidence suggesting a positive impact of knowledge and information on fundamental analysis Several studies have explored various aspects of this relationship, shedding light on how the availability and utilization of knowledge and information can significantly enhance the effectiveness of fundamental analysis and contribute to better investment decision- making
One study by Chan, Jegadeesh, and Lakonishok (1996) delves into momentum strategies in the stock market They find that investors who possess superior knowledge and information about a company's fundamental factors can effectively incorporate that information into their investment strategies, resulting in improved performance This implies that knowledge and information plays a crucial role in identifying investment opportunities and making informed decisions based on a deeper understanding of a
18 company's fundamentals By leveraging their knowledge and information, investors can identify stocks with positive momentum and capitalize on potential price movements
Another research piece by Loughran and McDonald (2011) focuses on the significance of textual analysis in financial reports They highlight the importance of textual information, such as management's discussion and analysis (MD&A) sections, in influencing stock returns Through the use of textual analysis techniques, investors can extract valuable knowledge from financial reports, allowing them to gain insights into a company's performance, risks, and future prospects This knowledge, when incorporated into fundamental analysis, enhances the accuracy and depth of the analysis, leading to improved investment decisions
The investor sentiment, influenced by knowledge and information, affects asset pricing and fundamental analysis outcomes (Barberis, Shleifer, & Vishny, 1998) When investors have positive sentiment driven by favorable knowledge and information, they tend to overvalue assets, potentially leading to mispricing This emphasizes the interconnectedness between knowledge, investor sentiment, and the efficacy of fundamental analysis Investors with superior knowledge can assess market sentiment and incorporate it into their analysis for more accurate predictions.
Merton (1987) examines the impact of incomplete information on asset pricing He argues that investors with superior knowledge can exploit mispricing opportunities, contributing to the positive impact of knowledge and information on fundamental analysis When investors possess better information than the market, they can identify undervalued or overvalued securities and adjust their investment strategies accordingly This highlights the importance of knowledge and information in identifying market inefficiencies and conducting successful fundamental analysis
Bhojraj and Lee (2002) investigate the role of knowledge and information in selecting comparable firms for valuation purposes They emphasize the importance of accurate
19 information and knowledge and information in conducting fundamental analysis and determining appropriate valuation metrics Investors rely on knowledge and information to identify comparable firms in terms of industry, size, growth prospects, and financial performance This enables them to make meaningful comparisons and derive accurate valuations The availability of reliable information and accurate knowledge and information is crucial in ensuring the validity and accuracy of the fundamental analysis process
Furthermore, Chen, Matsumoto, and Rajgopal (2011) explore the impact of information disclosure on market participants' valuation and analyst coverage They find that accurate information and knowledge and information positively influence fundamental analysis and subsequent market outcomes When companies provide transparent and reliable information, investors can make more informed decisions based on comprehensive knowledge and information This, in turn, enhances the effectiveness of fundamental analysis and leads to more accurate valuations and analyst coverage
2.3.2 The relationship between Market Trends (MT) and Risk Appetite (RA)
One prominent study by Baker and Wurgler (2007) examines the impact of market trends on investor sentiment and risk-taking behavior They find that during periods of positive market trends, investors tend to exhibit higher risk appetite This phenomenon is attributed to the "herding effect," where investors are influenced by the actions and behaviors of others in the market Positive market trends create a sense of optimism and confidence among investors, leading to a greater willingness to take on risk
Another study by Barberis, Huang, and Santos (2001) explores the role of market trends in shaping investor preferences and risk appetite They introduce the concept of
"extrapolative beliefs," which suggests that investors tend to project past trends into the future, leading to a positive correlation between market trends and risk appetite When investors witness positive market trends, they develop a belief that these trends will continue, leading to an increase in risk-taking behavior
The influence of market trends on risk appetite is further examined by Daniel, Hirshleifer, and Subrahmanyam (1998) They propose the concept of "feedback trading," which suggests that investors tend to buy (sell) more stocks when they observe
20 positive (negative) market trends Positive market trends create a positive feedback loop, as investors become more willing to take on risk, leading to further price increases This positive feedback reinforces investors' risk appetite and encourages them to engage in more aggressive investment strategies
In addition to these theoretical studies, empirical research by Dorn and Huberman
(2005) provides evidence of the impact of market trends on risk appetite They find that investors' risk appetite is significantly influenced by short-term market trends, with positive trends leading to increased risk-taking behavior This empirical evidence supports the notion that market trends have a positive impact on risk appetite, as investors are more likely to engage in riskier investment decisions during periods of positive market momentum
2.3.3 The relationship between Market Sentiment (MS) and Risk Appetite (SA)
Research has extensively examined the correlation between market sentiment and risk-taking behavior These studies have shed light on the factors influencing investors' inclination to assume risk in varying market conditions, offering valuable insights into the dynamics of financial decision-making.
Baker and Wurgler (2006) highlight the impact of investor sentiment on risk appetite They argue that market sentiment, encompassing investors' emotional and psychological outlook on the market, has a significant positive influence on risk appetite Positive market sentiment generates a sense of optimism and confidence among investors, leading to a greater inclination to take on risk Conversely, negative market sentiment can induce risk aversion and a decrease in risk appetite This finding suggests that investors' perceptions and sentiments about the market play a crucial role in shaping their risk preferences and willingness to engage in risk-taking activities
Hong and Stein (1999) introduce the concept of "strategic complementarity" to explain the relationship between market sentiment and risk appetite They propose that investors' risk-taking decisions are influenced by the behaviors and actions of others in the market When market sentiment is positive, investors observe others taking on more risk, which creates a social norm and increases their own risk appetite Conversely, negative market sentiment leads to a decrease in risk appetite as investors observe others becoming more
21 risk-averse This concept highlights the importance of market sentiment as a social influence that shapes investors' risk preferences
METHODOLOGY
Survey design
According to VanHeck and Smith (2009), survey design involves the careful planning and implementation of data collection methods from a selected sample of individuals It encompasses various aspects such as sample selection, question development, and survey administration
To assess investment intentions towards cryptocurrency, researchers utilized quantitative methods in a survey study The survey comprised two sections: general demographic questions (name, age, gender, education) to establish participant profiles; and specific questions to gauge their cryptocurrency investment intentions, which formed the core objective of the study.
The second part of the survey comprised multiple-choice questions related to the main variables of the research model These variables included Knowledge and Information (KI), Fundamental Analysis (FA), Market Trends (MT), Market Sentiment (MS), Risk Appetite (SA), Regulatory Environment (RE), and Cryptocurrency Investment Decision (CID) By collecting comprehensive and accurate data on these variables, the researchers aimed to facilitate later analysis
An online survey was conducted using Google Forms to gather data from over 200 Vietnamese respondents The survey, available in Vietnamese, was distributed from February 30, 2024, to March 20, 2024, targeting individuals of various demographics, including gender, age, education, and income level.
Sample and data collection
A sample is a group of people, objects, or items taken from a large population for measurement The sample must be representative of the population to ensure that we
26 can generalize the findings from the study sample to the entire population (Jones, 1955; Salant & Dillman, 1994) Sampling is the act, process or technique of selecting an appropriate sample or a representative portion of a population to determine the parameters or characteristics of the population as a whole
Data collection entails gathering and measuring information on specific topics Data is acquired from surveys, focus groups, interviews, questionnaires, observations, and databases The collected data is organized in tables or charts for analysis, aiming to uncover patterns and enhance processes (Joe Eckel, 2023)
In our study, we employed both primary and secondary data collection methods, with a predominant focus on primary data Primary data refers to the data collected directly from the survey process Our questionnaire consisted of three main types of questions for the respondents to answer
Firstly, we included survey questions that aimed to gather general information about the respondents, such as their gender, age, and major of study at universities
Secondly, we incorporated general questions that assessed student satisfaction with service quality and system translation quality These questions provided a broader understanding of the respondents' overall satisfaction levels
To assess students' perceptions of service and system quality, a detailed questionnaire was utilized Participants evaluated specific aspects of these dimensions using a 5-point Likert scale, where "1" indicated complete disagreement and "5" signified full agreement This methodology ensured comprehensive insights into students' perspectives on the service level and system effectiveness provided by universities.
In addition to primary data, we also relied on secondary data, which served as the theoretical foundation for our research We extensively reviewed and referenced previous research reports and scientific journals, using them as a theoretical basis to inform our further research and development
By combining primary data collected through surveys and secondary data from existing research, we aimed to gather comprehensive and reliable information for our study.
Data Analysis Techniques
Descriptive analysis is a statistical method used to describe and summarize the characteristics of a data set This analysis helps uncover underlying patterns and relationships in the data It involves testing the reliability of measurement items and running the data through software like SmartPLS 4 to obtain results.
3.3.2 Reliability and Content Validity Analysis
In this research, we will be utilizing Reliability Analysis, a method commonly employed to evaluate the reliability of measurement results in a study Reliability analysis involves various measures, such as Cronbach's alpha coefficient, test-retest reliability, correlation analysis between variables, and the use of diagnostic models to estimate the likelihood of re-measurement The primary objective of reliability analysis is to assess the stability, consistency, and repeatability of measurement results when conducting similar measurements again (Streiner, D L., 2003)
Cronbach's alpha coefficient is a widely used measure of reliability and can be expressed as a function of the number of test items and their average inter-correlation The formula for Cronbach's alpha is as follows:
Here, N represents the number of items, c denotes the average inter-item covariance among the items, and v represents the average variance The average inter-item covariance (c) is a crucial component of Cronbach's alpha as it reflects the degree to which the items in the questionnaire measure the same underlying construct Generally, a higher average inter-item covariance indicates greater internal consistency reliability and results in a higher Cronbach's alpha coefficient However, the specific value of the average inter-item covariance may vary depending on factors such as the number of
28 items in the questionnaire and the nature of the construct being measured (Cronbach, L J., 1951)
Average variance gauges the homogeneity of questionnaire items, indicating their alignment in measuring a single underlying construct A high average variance implies greater homogeneity, while a lower variance suggests potential discrepancies in measuring the same construct (Kline, 2016) Like the average inter-item covariance, the average variance in Cronbach's alpha lacks a fixed value, fluctuating based on variables like the number of items and the nature of the measured construct (Cronbach, 1951).
It is important to note that Cronbach's alpha coefficient ranges between 0 and 1 A value of 0 implies no correlation between the observed variables, while a value of 1 suggests perfect correlation However, it is rare to encounter either of these extreme values in data analysis In some cases, the Cronbach's alpha coefficient can even be negative, indicating that the scale lacks reliability and consistency Negative values suggest that the observed variables in the scale oppose each other and lack directionality
Nunnally (1978) suggested that a good scale should have a Cronbach's alpha reliability coefficient of 0.7 or higher Similarly, Hair et al (2009) affirmed that a reliable and unidimensional scale should achieve a Cronbach's alpha threshold of 0.7 or higher For exploratory studies, a threshold of 0.6 may be deemed acceptable A higher Cronbach's alpha coefficient indicates a higher level of scale reliability
Two commonly used statistical tests for comparing means are the Student's t-test and the analysis of variance (ANOVA) test
The Student's t-test is a hypothesis test that assesses whether there are significant differences between the means of two groups In our study, we employed the t-test to analyze and provide a more detailed understanding of potential significant differences
29 between demographic characteristics (such as age and survey products) and customer feedback The t-test allowed us to examine if these variables had a statistically significant impact on customer feedback (Field, 2013)
On the other hand, ANOVA is a statistical test used to compare means among three or more groups It measures the relative size of variance between group means compared to the mean within groups (H.-Y Kim, 2014) In our research, ANOVA was deemed appropriate instead of the t-test because we had three or more groups to compare Specifically, we used a one-way ANOVA when there was only one independent variable (e.g., different treatments or groups) and one dependent variable Our aim was to analyze and gain a more comprehensive understanding of potential significant differences between demographic characteristics (e.g., experience) and customer feedback
To determine if there was a significant difference in the mean values of the variables, we conducted a one-way ANOVA test at a 95% significance level The results indicated that there is a significant difference in the mean values of the variables, with an F-value of 33.36 and a p-value less than 0.05 This suggests that the demographic characteristics (experience) have a statistically significant impact on customer feedback
Structural equation modeling (SEM) is a statistical technique utilized to analyze the relationships between observed and latent variables It has found widespread application in the social and behavioral sciences for testing theoretical models and exploring complex relationships between variables
The selection of formative or reflective indicators hinges on the causal relationship between indicators and latent variables Reflective indicators reflect a causal flow from the latent variable to the observed variable, allowing latent variables to explain variance Conversely, formative indicators do not imply causality from the latent variable to the indicator but rather represent a composite of the latent variable and the shared variance of its indicators This distinction guides the appropriate choice of indicators based on the underlying causal relationships within the measurement model.
A measurement model is a statistical model employed to measure latent variables or structures that are not directly observable Henseler, Ringle, and Sinkovics (2009)
Reflective measurement models should be assessed for reliability and validity The composite reliability index, which measures model reliability, can be used to evaluate indicator reliability Values over 0.95 indicate invalid measurement as they suggest all indicators measure the same phenomenon Values below 0.60 suggest a lack of internal consistency, while values between 0.7 and 0.95 indicate acceptable reliability.
Convergent validity and discriminant validity are two types of validity typically examined Discriminant validity is assessed by examining the correlation between measures of different latent variables, while convergent validity is determined by the degree of correlation between measures of the same latent variable Henseler et al
For assessing convergent validity, Fornell and Larcker (2009) recommend using the average variance extracted (AVE) Measures of the same latent variable should exhibit correlations of 0.5 or higher, whereas correlations between measures of different latent variables should remain below 0.85 to ensure discriminant validity.
RESULTS AND DISCUSSION
Descriptive Analysis
The data indicates that out of the total 200 respondents, 51.5% are male, while 48.5% are female This distribution suggests a relatively balanced representation of genders within the sample
The sample consists of students from different academic years, including first-year, second-year, third-year, and fourth-year students The frequency analysis shows that the largest group is second-year students, comprising 26.5% of the total respondents The distribution of students across the academic years appears relatively balanced, indicating a diverse representation from each year This balanced representation is beneficial for ensuring a comprehensive understanding of the overall trends and patterns observed in subsequent analyses
The data provides information on the specialization of the students within the sample The frequency analysis reveals the number of students in each specialization Notably, the ICE specialization has the highest representation, accounting for 11% of the total respondents Other specializations with relatively high representation include TROY
(10.5%), BEL (10%), AAI (9.5%), and UEL (9.5%) On the other hand, FDB (3%) has the lowest representation among the specializations The distribution of students across different specializations indicates a diverse sample with students pursuing various areas of study This diversity is valuable as it allows for a more comprehensive analysis, taking into account the perspectives and characteristics of students from different fields However, it is important to consider potential variations in responses and behaviors among students from different specializations when interpreting the findings
The descriptive analysis of the demographic variables provides a detailed overview of the sample composition The representation of genders is relatively balanced, ensuring a fair analysis from both male and female perspectives The distribution across academic years is diverse, with a slightly higher representation of second-year students The presence of students from various specialOizations contributes to a comprehensive understanding of the data, although it is crucial to acknowledge potential variations in responses based on the chosen field of study These insights lay the foundation for further analysis and interpretation of the data, enabling researchers to explore trends, patterns, and relationships among variables in subsequent analyses.
Reliability Analysis
Cronbach's alpha Composite reliability(rho_a)
Table 2: Reliabilities among the variables
Cronbach's alpha is a widely used measure of internal consistency reliability It assesses the extent to which the items within a construct are interrelated The reported values of Cronbach's alpha range from 0.983 to 0.997, indicating high levels of internal consistency for all constructs These values suggest that the items within each construct are strongly interrelated and measure the same underlying construct reliably Higher values of Cronbach's alpha indicate that the items within each construct are highly consistent and contribute strongly to measuring the intended construct In this analysis, all constructs exhibit excellent internal consistency, providing confidence in the reliability of the measurement
Composite reliability (rho_a and rho_c) is another measure of reliability that assesses the internal consistency of a construct Similar to Cronbach's alpha, the reported values of composite reliability range from 0.983 to 0.997, indicating high levels of reliability for all constructs These values reinforce the notion that the items within each construct are highly consistent and reliable in measuring the intended constructs The high values of composite reliability suggest that the constructs have strong internal consistency and that the items within each construct work well together This indicates that the constructs are reliable and can be relied upon for accurate measurement in subsequent analyses
Average variance extracted (AVE) is a measure of convergent validity It indicates the proportion of variance explained by the items within a construct The reported AVE values range from 0.951 to 0.990, indicating that a substantial amount of variance is captured by the items within each construct These values suggest that the items within each construct converge well and are consistent in measuring the underlying constructs Higher values of AVE indicate a higher level of convergent validity, indicating that the items within each construct are strongly related and effectively measure the intended construct The high AVE values observed in this analysis indicate that the constructs have good convergent validity, lending support to their measurement accuracy
R-squared, also known as the coefficient of determination, represents the proportion of variance of the dependent variable that is explained by the independent variables in the regression model The reported R-squared values for the constructs were CID = 0.979,
FA = 0.959, and RA = 0.978 These values indicate that the independent variables included in the regression model account for a significant amount of variance in the corresponding dependent variables Thus, in the case of Cryptocurrency Investment Decision (CID), about 97.9% of the variance of the dependent variable can be explained by the independent variables in the model Similarly, for Fundamental Analysis (FA) and Risk Tolerance (RA), the independent variables explain about 95.9% and 97.8% of the variance, respectively These high R-squared values indicate that the regression models fit well and that the independent variables have a strong relationship with the dependent variables
Adjusted R-squared accounts for the number of independent variables and sample size, correcting for overfitting that occurs when additional variables are introduced This metric shares the same reporting format as R-squared, with values for CID (0.979), FA (0.959), and RA (0.978) These values indicate that the inclusion of independent variables does not substantially diminish the model's explanatory power when adjustments are made for the number of variables and sample size.
The high values of both R squared and adjusted R squared for the constructs indicate that the independent variables included in the regression model (CID, FA, and RA) explain a significant amount of variance in the constructs respective dependent variable These findings indicate that the selected independent variables have a strong influence on the dependent variables and provide valuable insights into the relationships between the constructs.
Measurement model
Construct Item Standardized Factor loading
First, let's look at the Cryptocurrency Investment Decision (CID) construct The standardized factor loadings for the items in the CID are all very high, with values of 0.991, 0.993, 0.997, and 0.997, respectively This shows that all four items (CID 1, CID
2, CID 3 and CID 4) have a strong relationship with the Cryptocurrency Investment Decision construct These items can be considered reliable and effective indicators to measure cryptocurrency investment decisions
The standardized factor loadings for the Fundamental Analysis (FA) construct were also quite high, indicating a strong relationship between the items and the construct Values of 0.982, 0.971, 0.975, and 0.972 indicate that these items measure reliably and contribute significantly to the measurement of the Fundamental Analysis construct
Construct Knowledge and Information (KI) exhibits high standardized factor loadings, indicating a robust correlation between individual items and the overall construct Scores of 0.980, 0.975, 0.968, and 0.989 demonstrate the exceptional reliability of these items as metrics for assessing knowledge and information.
Standardized factor loadings for the Market Sentiment (MS) construct are the same, indicating that each item has a strong relationship with the construct The value of 0.986 shows that these items are highly correlated with the Market Sentiment construct
Standardized factor loadings for the Market Trends (MT) construct vary slightly, but all four items have a strong relationship with the construct The values 0.973, 0.987, 0.973 and 0.986 indicate that these entries are reliable measurements of the Market Trends construct
The standardized factor loadings for Risk Appetite (RA) are high, with values of 0.984, 0.994, 0.994, and 0.984, respectively The items in RA (RA 1, RA 2, RA 3 and RA 4) have a strong relationship with this construct This shows that these items are highly reliable indicators and contribute significantly to the measurement of the Risk Appetite construct
Standardized factor loadings for the Regulatory Environment (RE) construct are also high, indicating a strong relationship between items and constructs The values 0.992,
0.998, 0.995 and 0.995 show that these items are reliable measurements of the Regulatory Environment construct
In summary, standardized factor loadings provide information about the degree of correlation between items and the corresponding construct High standardized factor loadings values indicate that the items are reliable and valid measures of the respective constructs Researchers can be confident in the reliability and validity of these measurement models when studying the constructs Cryptocurrency Investment Decision (CID), Fundamental Analysis (FA), Knowledge and Information (KI), Market Sentiment ( MS), Market Trends (MT), Risk Appetite (RA) and Regulatory Environment (RE).
Structural equation model
Path Coefficients t- value p-value Hypothesis Hypothesis support
Table 5: Results of Structural equation model
To begin, we examined the connection between Knowledge and Information (KI) and Fundamental Analysis (FA) The findings indicated a path coefficient of 0.133, with a
The statistical analysis revealed a positive and significant relationship between Key Indicator (KI) and Financial Accessibility (FA) with a t-value of 2.036 and a p-value of 0.042 The p-value below 0.05 indicates a statistical significance level, supporting the hypothesis that KI influences FA.
Moving on, we investigated the relationship between Market Sentiment (MS) and Risk Appetite (RA) The analysis yielded a coefficient of 0.979, a t-value of 111.285, and a p-value of 0.000 These findings reveal an exceptionally strong and statistically significant positive relationship between MS and RA The exceedingly low p-value (p
= 0.000) indicates an extremely high level of statistical significance Consequently, hypothesis H2a, suggesting that MS affects RA, is supported
Subsequently, we explored the association between Market Trends (MT) and Risk Appetite (RA) The results revealed a path coefficient of 0.481, a t-value of 3.087, and a p-value of 0.002 These findings indicate a positive and statistically significant relationship between MT and RA With a p-value of 0.002, this relationship is considered statistically significant at a significance level of 0.05 Consequently, hypothesis H2b, proposing that MT influences RA, is supported
Next, we examined the connection between Fundamental Analysis (FA) and Cryptocurrency Investment Decision (CID) The analysis showed a path coefficient of 0.517, a t-value of 3.496, and a p-value of 0.000 These results demonstrate a very strong and statistically significant positive relationship between FA and CID The extremely low p-value (p = 0.000) indicates an exceptionally high level of statistical significance Consequently, hypothesis H3a, stating that FA influences CID, is supported
Analysis revealed a positive and statistically significant relationship between Risk Appetite (RA) and Cryptocurrency Investment Decision (CID), with a path coefficient of 0.312, t-value of 1.860, and p-value of 0.043 This relationship is significant because the p-value of 0.043 is less than the significance level of 0.05, supporting hypothesis H3b, which suggests that RA positively influences CID.
Finally, we examined the relationship between Regulatory Environment (RE) and Cryptocurrency Investment Decision (CID) The analysis yielded a coefficient of 0.810, a t-value of 5.117, and a p-value of 0.000 These findings reveal a very strong and statistically significant positive relationship between RE and CID The extremely low p-value (p = 0.000) indicates an exceptionally high level of statistical significance Consequently, hypothesis H3c, which suggests that RE affects CID, is supported
Coefficient Confidence interval 97,5% t value p-value
Table 6: Role of mediating variables
First, we consider the relationship KI → FA → CID The results showed a coefficient of 0.130, indicating a positive effect of KI on FA The resulting 97.5% confidence interval is [0.261, 0.704], indicating that this effect is statistically significant Additionally, the t-value is 2.041 and the p-value is 0.041, indicating the statistical significance of this relationship This result demonstrates that KI has a positive and statistically significant impact on FA, and FA also has a positive and statistically significant impact on CID
Next, we consider the relationship MS → RA → CID The results showed a coefficient of 0.137, indicating a positive effect of MS on RA The resulting 97.5% confidence interval is [0.129, 0.312], indicating that this effect is statistically significant The t- value is 1.592 and the p-value is 0.011, indicating the statistical significance of this relationship This result demonstrates that MS has a positive and statistically significant effect on RA, and that RA also has a positive and statistically significant effect on CID
Finally, we consider the MT → RA → CID relationship The results showed a coefficient of 0.175, indicating a positive effect of MT on RA The resulting 97.5% confidence interval is [0.056, 0.383], indicating that this effect is statistically significant The t-value is 1.691 and the p-value is 0.008, indicating the statistical significance of this relationship This result demonstrates that MT has a positive and statistically significant impact on RA, and RA also has a positive and statistically significant impact on CID
LIMITATIONS, RECOMMENDATIONS AND CONCLUSION
Limitations of the study
One of the main limitations of this study is its limited generalizability The study focused on VNU - IS students, which limits the generalizability of the results to a broader set of the population The results may not apply to students from other universities or to non- student populations The study also limited the data collection period to February to March 2024, which may limit its ability to capture changes or developments in students' investment decisions over a longer period of time
External factors can also be excluded from the study The study only focused on factors related to student decisions without considering broader external factors The cryptocurrency market is influenced by many economic, political and technological factors that can influence students' investment decisions
Transparency and accurate interpretation of research findings necessitate acknowledging study limitations Identifying these limitations in future investigations can reinforce knowledge on the factors influencing students' cryptocurrency investment decisions.
Recommendation to the universities
Universities play a crucial role in preparing students for the challenges and opportunities they will encounter in the ever-evolving world of finance and investments Based on the findings of the study regarding the factors influencing VNU - IS students' decision to invest in cryptocurrency, there are several recommendations that universities can consider to support and guide their students in making informed investment decisions
First and foremost, universities should consider incorporating cryptocurrency education into their curriculum By introducing courses or modules specifically focused on cryptocurrencies, blockchain technology, and investment strategies related to digital assets, universities can provide students with a solid foundation of knowledge and understanding This will enable them to make informed decisions when considering cryptocurrency investments By staying up-to-date with the latest developments and emerging trends in the cryptocurrency market, universities can ensure that their
45 curriculum remains relevant and provides students with the necessary skills and knowledge to navigate this rapidly changing landscape
In addition to formal education, universities should foster partnerships with industry experts Collaborating with professionals and experts in the cryptocurrency field can provide students with valuable insights and real-world perspectives Universities can invite guest speakers from the industry, organize workshops, or establish partnerships with industry organizations These initiatives will facilitate knowledge sharing and networking opportunities for students interested in cryptocurrency investments By connecting students with professionals who have practical experience in the field, universities can enhance the learning experience and provide students with a deeper understanding of the industry
Furthermore, universities can encourage research and analysis in the field of cryptocurrencies By promoting research initiatives and projects related to cryptocurrencies and investment analysis, universities can provide students with opportunities to explore and contribute to the body of knowledge in this field This can involve providing resources, mentorship, and guidance to students who wish to delve into topics such as fundamental analysis, market trends, and regulatory frameworks Engaging in research activities will not only enhance students' analytical skills but also deepen their understanding of the cryptocurrency market
To promote practical learning and application of knowledge, universities can consider establishing investment clubs or societies focused on cryptocurrencies These student- led organizations can provide a platform for students to exchange ideas, share experiences, and collectively analyze investment opportunities in the cryptocurrency market By organizing guest lectures, investment simulations, and discussions, these clubs can enhance students' practical knowledge and decision-making abilities in the cryptocurrency investment space Such initiatives can also foster a sense of community and collaboration among like-minded students
It is essential for universities to provide guidance on risk management when it comes to cryptocurrency investments The volatile nature of the cryptocurrency market calls for a comprehensive understanding of risk management strategies Universities can educate
46 students about diversification, setting investment goals, and understanding risk-reward trade-offs By promoting responsible investment practices, universities can help students navigate the challenges and uncertainties associated with cryptocurrency investments.
Recommendations
Firstly, the study confirmed a positive relationship between Knowledge and Information (KI) and Fundamental Analysis (FA) This means that students who possess a higher level of knowledge and information about cryptocurrencies and are skilled in conducting fundamental analysis are more likely to make informed investment decisions This highlights the importance of understanding the fundamental principles of cryptocurrencies and utilizing this knowledge to evaluate investment opportunities effectively
Furthermore, the study supported the hypotheses regarding the relationship between Market Trends (MT) and Risk Appetite (RA) as well as between Market Sentiment (MS) and Risk Appetite (RA) The results indicated that when VNU - IS students perceive favorable market trends or positive market sentiment in the cryptocurrency market, they tend to exhibit a higher risk appetite Positive market trends and sentiment create a sense of optimism and confidence, which influences students' willingness to take on risks associated with cryptocurrency investments
The study also found a positive relationship between Fundamental Analysis (FA) and the Cryptocurrency Investment Decision (CID) This suggests that students who engage in fundamental analysis techniques to evaluate cryptocurrencies are more likely to make investment decisions in this asset class Fundamental analysis enables students to assess the intrinsic value of cryptocurrencies by considering factors such as technology, team, and market demand By using this analysis, students can make more informed investment choices
Additionally, the study supported the hypotheses regarding the relationships between Risk Appetite (RA) and the Cryptocurrency Investment Decision (CID) as well as between the Regulatory Environment (RE) and the Cryptocurrency Investment Decision (CID) The findings indicated that students with a higher risk appetite are more inclined
47 to make cryptocurrency investment decisions These students are comfortable with taking risks and have a higher tolerance for potential losses Moreover, a favorable regulatory environment, characterized by clear guidelines, legal frameworks, and government support, positively influences students' decision to invest in cryptocurrencies A supportive regulatory environment enhances students' confidence and trust in the cryptocurrency market, making them more willing to participate in such investments
The study uncovers the key factors influencing the cryptocurrency investment decisions of VNU-IS students These factors encompass knowledge about cryptocurrencies, the ability to analyze fundamentals, market trends, market sentiment, risk tolerance, and regulatory considerations Understanding these factors is imperative for universities, policymakers, and investors to promote informed decision-making and responsible investments in the cryptocurrency market By leveraging these insights, stakeholders can devise strategies and initiatives to assist students in making well-informed decisions amid the dynamic and evolving cryptocurrency landscape.
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