INTERNATIONAL SCHOOL STUDENT RESEARCH REPORT Do trust in financial institutions and perceptive variables influence the investment intention among Vietnamese university students?. INTRO
INTRODUCTION
BACKGROUND OF THE STUDY
Since the early 1990s (20th century), the Party and Government of Vietnam have focused on creating and expanding the stock market as a means of creating a new avenue for capital mobilization for development investments In cooperation with the government, numerous State agencies and research institutes have proposed the research and development of a stock market project The Vietnamese stock market was formally established on July 11, 1998, with the signing of Decree No 48/CP of the Government, two years after the State Securities Commission of Vietnam was established on November 28, 1996, in accordance with Decree No 75/CP of the Government (SSC,
Vietnam's first trading centre was named Ho Chi Minh City Securities Trading Center and opened its doors in 1998 On July 28, 2000, the country's first trading session featured two listed companies and six securities companies The trading centre had a name change and evolved into Ho Chi Minh Stock Exchange (HOSE) by 2006 In order to ensure that securities activities on the stock market are conducted in an open, equitable, safe, and efficient manner, the Ho Chi Minh City Stock Exchange organizes the securities trading market in compliance with securities law It also serves to protect the legitimate rights and interests of investors who participate in securities transactions (HOSE, 2022)
The State established a second securities trading facility, which is Hanoi Securities Trading Center Established on March 8, 2005, the exchange centre formally began operations on June 24, 2009, following the structure of a sole proprietorship company Its members are state-owned, with the Ministry of Finance serving as its representative After four years of establishment, the exchange centre was changed its name to Hanoi Stock Exchange (HNX) HNX has been running trading markets, conducting share auctions, and holding government bond auctions (HNX, 2020)
Vietnam's stock market has experienced significant expansion after more than twenty years of existence and development Since its establishment, the stock market has served as an agent for businesses with medium- and long-term capital needs It has also expedited the process of economic restructuring and made a beneficial contribution to financial and economic liberalization (MOF, 2013) In the last two decades, the stock market has repeatedly demonstrated a strong role in providing capital to the economy from a variety of quantitative angles, including capitalization scale, investor base, product volume, daily transaction value, and market players' expertise (MOF, 2019)
Since its inception, Vietnam's stock market has seen a growth in the number of listed companies, with over 2,290 listed on the three exchanges (HoSE, HNX, and UPCoM) There were 862 listed companies on UPCoM alone, which translates to more than 679 billion outstanding shares (SSC, 2024a, 2024b) Data from the Vietnam Securities Depository (VSD) shows that, from just 3,000 accounts at the end of 2000, there are currently 7.3 million total securities accounts, or around 7.3% of Vietnam's population (VSD, 2022) Accompanying this is the expansion of the VN-Index, an index that encapsulates the oscillations of every stock listed on HOSE The noteworthy thing to note is that in the 21 years that the market has been operating, Vietnam's stock market has broken through and reached previously unheard-of record heights On November
25, the Vn-Index surpassed 1,500 points, or 1,500.81 points, setting a new record This was a gain of about 36% from the end of 2020 This is the second-best rise over the last
11 years, after only 2017 (an increase of about 48%) (MOF, 2022)
Figure 1.1: Number of stock investment accounts opened annually and VN-Index performance (Linh, 2022)
The significant increase in capitalization scale is one of the Vietnamese stock market's most notable features By June 30, 2022, the market will have grown from its starting capitalization of 270 billion VND to 7.8 million billion VND (stocks and bonds included), or 93% of the GDP (as determined by the annual GDP in 2021) (Linh, 2022)
Figure 1.2: Stock capitalization value in the period 2000-2022 (Linh, 2022)
Experts believe that Vietnam's stock market, which has been steadily growing over the previous 20 years, is showing promise as more and more younger investors become involved It is noteworthy that a growing number of "younger" investors are joining the VNDIRECT market If investors between the ages of 18 and 34 made up 43% of all VNDIRECT accounts between 2006 and 2016, then this percentage rose to 76% between 2017 and 2020 Eighty-one percent of all new VNDIRECT accounts opened in
2020 belonged to investors aged 18 to 34; of them, the 18 to 24 year old group made up
In 2023, Vietnam witnessed a surge in retail investing with 7.2 million securities accounts registered, indicating over 8% of the population actively investing This growth is driven by the influx of young investors, with 37% of accounts belonging to individuals under 25 and 44% between 25-34 years of age (Hoang, 2020) Securities firms like Pinetree cater to this young clientele by creating user-friendly platforms and educational resources, fostering a supportive environment for aspiring investors (Quy, 2024).
Experts claim that as the economy grows, there will inevitably be a rise in the number of young people investing in stocks Information technology is developing at the same time as securities firms are releasing a lot of new trading tools For young people, who have been exposed to a lot of technology and are accustomed to utilizing the internet in the digital age, this is a very handy and rapid investment Their love of risk is another factor contributing to the rise of young investors Younger people seek for investment opportunities with a variety of risks, but elderly people tend to favor low-risk and safe investment avenues like gold, real estate, bank savings, etc with plenty of swings, similar to stocks This makes sense because youth frequently have more time and possibilities to try it again in the future, so they do not mind falling and colliding (Thao,
2022) Additionally, since there is no capital limit and young people possess the ability to update information sensitively and consistently, stocks are a good investment channel for them
Social media platforms have emerged as vibrant hubs for stock investing communities, where investors connect to share knowledge, insights, and market commentary Notably, Gen Z has displayed a growing presence within these groups, indicating a shift towards introducing investing concepts to younger generations By engaging in such online communities, individuals gain invaluable perspectives and potentially enhance their financial literacy.
However, a lot of young investors simply follow the herd, purchasing and selling this code, because they lack experience, patience, and a defined approach That is the crowd mentality known as FOMO, or the fear of missing out, which is common among novice stock market investors As a result, stocks have been popular among young people in recent years who, despite lacking sufficient understanding, want to invest with a serious mindset (Dat, 2021; Thao, 2022).
RESEARCH OBJECTIVES AND RESEARCH QUESTIONS
The research aims to provide insights into the factors that influence young investors’ intentions in the context of investment, especially students
To identify the key factors influencing investment intention: The primary objective of this study is to identify and analyze the key factors that influence the investment intentions of students This includes examining factors such as attitude towards behavior, subjective norms, perceived behavioral control, trust in financial institutions, financial literacy, and intention to invest based on the Theory of Planned Behavior (Farida et al., 2023)
• To examine the influence of attitude towards behavior on intention to invest in stocks
• To assess the role of subjective norms in shaping the intention to invest in stocks
• To investigate the impact of perceived behavioral control on intention to invest in stocks
• To explore the relationship between trust in financial institutions and the intention to invest in stocks
• To evaluate the effect of financial literacy on the intention to invest in stocks
• To understand the combined influence of attitude, subjective norms, perceived behavioral control, financial literacy, and trust in financial institutions on the intention to invest in stocks
• How does an individual's attitude towards investing influence their intention to invest in stocks?
• What role do subjective norms play in determining an individual's intention to invest in stocks?
• How does perceived behavioral control affect an individual's intention to invest in stocks?
• What is the relationship between trust in financial institutions and the intention to invest in stocks?
• To what extent does financial literacy impact an individual's intention to invest in stocks?
• How do the factors of attitude towards behavior, subjective norms, perceived behavioral control, trust in financial institutions, and financial literacy collectively influence the intention to invest in stocks?
RESEARCH GAP
Studies have explored Generation Z's investment intentions in Vietnam, including Tran et al.'s (2023) examination of young adults aged 18 to 35 and Dung's (2020) investigation into intentions in the derivatives market However, limited research focuses solely on university students, particularly as stock investing becomes increasingly prevalent among them While Tran et al.'s study includes university students, its sample also encompasses working adults, potentially influencing the results to reflect a broader range of characteristics rather than solely those of university students.
Prior studies conducted by Tran et al (2023), Dung (2020), and Nguyen et al (2022) utilized the TPB perceptive variables, namely ATB, SN, and PBC However, the studies lack the adoption of financial literacy and trust in financial institutions, whereas in foreign contexts, Adil et al (2022), Adil et al (2023), and Yang et al (2021) adopted financial literacy and trust in financial institutions in the research model, along with the perceptive variables to determine respondents’ investment intention; Sobaih and Elshaer
(2023) utilized perceptive variables as moderators to examine the indirect relationship between financial literacy and investment intention Additionally, due to the supporting policies of the Party and Government of Vietnam (SSC, 2022), the intrinsic development of Vietnamese stock market (MOF, 2019), and the global virtualization of financial markets all over the world (Zwick & Schroeder, 2013), including Vietnamese stock market (Anh, 2021) As a results, the authors expected that the variables will have positive influences on the investment intention among Vietnamese university students, by applying the Theory of Planned Behavior as a conceptual framework with two additional constructs, namely financial literacy and trust in financial institutions.
RESEARCH METHODS
The study used a quantitative approach to examine the investment intentions of Vietnamese university students In particular, the authors conducted a structured online questionnaire and distributed it to the students via social media platforms, namely Facebook, Zalo, Instagram, etc To analyze the data collected, the authors utilized a partial least squares structural equation modeling (PLS-SEM) approach PLS-SEM was commonly used in the behavioral finance research field For instance, Nugraha and Rahadi (2021) used PLS-SEM to analyze the intention to invest in stocks of Malaysian young generations Additionally, Mahardhika and Zakiyah (2020) used PLS-SEM for their study on the millennial’s investment intention in the Indonesian market; in a study by Yang et al (2021), PLS-SEM was used to determine the behavior and intention to participate in the stock market for Indonesian working adults The application used for data analysis procedures is SmartPLS 3.0, which is a strong application for PLS-SEM analysis The analysis consists of confirmatory factor analysis (CFA), path coefficient analysis, and multi-group analysis (MGA).
STRUCTURE OF THE STUDY
This study is organized into five sections Section 1 provides an overview of this study Section 2 provides the literature review and hypothesis development Section 3 discusses data collection methods Section 4 presents the data analysis and discussion Section 5 of the study presents several limitations of the research, and the final section presents the conclusion, along with the theoretical and managerial contributions of the study.
LITERATURE REVIEW & HYPOTHESIS DEVELOPMENT
THEORETICAL FRAMEWORK
In the study field of behavioral intention, several famous theories were developed For instance, Eccles (1983), Eccles and Wigfield (2002), and Anderman et al (2001) developed the expectancy-value theory, in which the authors emphasized people’s choices that are related to achievement are affected by their expectations for success and subjective task value (e.g., a student might have the intention to invest his/her time more into a subject if he/she expects to perform well on that subject and he/she values the subject) Expectancy-value theory has been used primarily in education research fields (Beymer et al., 2022; Tsang et al., 2024; Zucker et al., 2021) Additionally, the theory has also been used to examine consumer behavior (Lee et al., 2023) Furthermore, the principle of aggregation is also used for human behavior studies (Ossenkopp & Mazmanian, 1985; Rushton et al., 1983), which states that “the sum of a set of multiple measurements is a more stable and representative estimator than any single measurement," indicating that consistent results can be achieved when considering a broader set of behaviors or data points in order to identify underlying patterns and trends
Behavioral finance studies the impact of psychological factors on investment decision-making Cao et al (2021) examined the relationship between behavioral finance factors (Heuristic, Prospect, Herding, Market) and investment performance on the Vietnam Stock Exchange, finding positive direct effects on both investment decision-making and performance Additionally, the Technology Acceptance Model (TAM) is commonly used in this field Chong et al (2021) used TAM and the Theory of Planned Behavior to investigate the acceptability of mobile stock trading among young Malaysian investors, identifying strong positive relationships between various factors and intention towards mobile stock trading Sharif and Naghavi (2021) integrated TAM, the Theory of Planned Behavior, and the Theory of Flow to examine online stock trading intention, finding strong influences from Theory of Planned Behavior variables on online trading intention.
Despite several widely used behavioral intention theories, the authors decided to use the Theory of Planned Behavior in the current study According to Ajzen (1991), “the principle of aggregation, however, does not explain behavioral variability across situations, nor does it permit prediction of a specific behavior in a given situation." Expectancy-value theory and the principle of aggregation are suitable when investigating sets of behaviors Nevertheless, the two theories do not directly address context-specific factors Furthermore, the scope of the current study is to focus on the intention to participate in stock investing among university students in Vietnam, regardless of their experience in the stock market As a result, applying the theoretical framework, including IDM and IP, would be inappropriate since the scope of the framework is to examine the decision-making of experienced retail investors Finally, the authors decided not to apply the TAM due to the digitalization of investing activities that makes electronic trading platforms dominant in society nowadays (Zwick & Schroeder, 2013) Overall, the ability to examine the behavior in a specific context and to address behavioral control makes TPB an optimal theoretical framework for the study
TPB is a famous theory used to explain and predict human behavior given a specific situation Initially, TPB was developed from the theory of reasoned action (TRA), in which it emphasizes the relationship between the intention of a person to perform a specific action and his/her behavior According to Ajzen (1991), the intention of a person to perform the behavior is considered to be an observational parameter of how much effort that person is willing to exert (i.e., how hard he/she is going to try) in order to perform a given behavior As a result, it is no surprise that the authors considered a person’s intention to be the most important variable affecting his/her behavior tendency in TRA (Nugraha & Rahadi, 2021) Nonetheless, intention is stated to be affected by behavioral beliefs (i.e., the outcomes’ evaluation and the beliefs that the behavior leads to certain outcomes) and normative beliefs (i.e., the beliefs of important referents and the motivation to comply with them), which are the precedents for the two original constructs, namely attitudes toward behavior and subjective norms (Ajzen, 1980; Fishbein & Ajzen, 1977)
The Theory of Planned Behavior (TPB) suggests that behavioral intentions alone are insufficient to predict actual behavior Ajzen (1991) found that only behaviors under volitional control, where individuals have the ability to choose whether or not to act, can be influenced by intention To account for this, the TPB introduced the construct of perceived behavioral control, which encompasses the available resources and opportunities that facilitate or hinder specific behaviors in given situations.
To date, many authors have applied the TPB in the behavioral finance field Nugraha and Rahadi (2021) used TPB as a model for examining the intention to participate in the stock market of millennials (i.e., generation Y) and generation Z; hence, they found that the intention to invest in stocks is only affected by the attitude towards behavior Akhtar and Das (2019) extended the TPB by adding two additional constructs, namely personality traits and financial knowledge, to predict investment intention in Indian stock markets; hence, they found that attitude shows a positive mediating relationship between financial knowledge and investment intention; financial self-efficacy both shows positive mediating and moderating relationships between personality traits and investment intention; and investment intention is not heavily affected by subjective norms Adil et al (2023) studied the relationships between trust in financial institutions, financial literacy, TPB elements, and investment intention and showed a strong positive impact between attitude, subjective norms, financial literacy, and investment intention, while perceived behavioral control showed a weak positive impact on investment intention Surprisingly, trust in financial institutions showed the strongest impact on investment intention; this may be related to the context of COVID-19, when uncertainty arises and trust is the most important factor that enhances investors’ intention to participate in the stock market.
ATTITUDE TOWARDS BEHAVIOR (ATB)
Based on the TPB theory, attitude is a significant factor that influences investment intention Attitudes can be defined as the degree to which a person has a favourable or unfavourable evaluation of specific objects (Ajzen, 1991) However, the conduction has recently been described as an individual's positive or negative judgment of a certain activity A person's attitude toward an action is impacted by their assumptions about the repercussions of that activity (Ajzen & Fishbein, 2005) Positive attitudes may stem from beliefs that investing offers financial security, growth opportunities, or aligns with long-term goals Conversely, negative attitudes may arise from perceptions of investment as risky, complex, or incompatible with immediate financial needs or preferences (Hietanen, 2017) Therefore, if people have a good attitude toward a particular activity, they will likely perform it (O’Connor & Paunonen, 2007) It can be concluded that, in the context of this study, students with positive attitudes are more likely to express intentions to invest, whereas those with negative attitudes may hesitate or avoid investing altogether ATB positively impacts an individual's investment intention (Ashidiqi & Arundina, 2017; Nugraha & Rahadi, 2021) Prior studies expressed a significant influence on the investment intention of ATB (Adil et al., 2023; Akhtar & Das, 2019; Gamel et al., 2022; Nugraha & Rahadi, 2021; Rathee & Aggarwal, 2022; Raut & Das, 2017; Sabiran et al., 2023; Sobaih & Elshaer, 2023) Based on the previous findings, it can be hypothesized that:
H1: Attitude towards behavior has a positive influence on stock investment intention.
SUBJECTIVE NORMS (SN)
According to the TPB model, subjective norm (SN) is the second main factor that influences investment intention after attitude SN defines the perceived social pressure to perform or not to perform the behavior (Ajzen, 1991) In current research, SN refers to an individual's social pressure from influential people that can lead him to engage or not engage in certain behaviors (Adil et al., 2022) In TPB, subjective norms are shown as a major contributing variable that influences behavioral intention (Adil et al., 2022;
Adil et al., 2023; Akhtar & Das, 2019; Nugraha & Rahadi, 2021; Raut, 2020; Sabiran et al., 2023; Sobaih & Elshaer, 2023) Hence, it can be reasonably concluded that, in the context of investing, students may evaluate the opinions and actions of peers, family members, or financial advisors Positive subjective norms, such as friends or family members recommending investment as a good financial option, can encourage favorable attitudes and intentions toward investing Inversely, negative subjective standards, such as peers expressing doubt or discouragement about investing, might undercut positive attitudes and intentions Then, the SN is a vital component in assessing investors' investment intentions The following hypothesis is developed:
H2: Subjective norms have a positive influence on stock investment intention.
PERCEIVED BEHAVIORAL CONTROL (PBC)
The third variable under TPB is perceived behavioral control (PBC) PBC is described as the perception of the individual about ease or difficulty in carrying out a certain action (Ajzen, 1991; Ajzen & Fishbein, 2005) PBC also refers to the ability to perform specific behaviors, which is associated with an individual's confidence in his or her ability to complete the behaviors (Ajzen, 1991) There are many study results that express the relationship between PBC and investment intention The researchers show that PBC was positively influenced the investment intention regarding the stock market (Adil et al., 2023; Hietanen, 2017; Nugraha & Rahadi, 2021; Phan & Zhou, 2014; Raut, 2020; Sabiran et al., 2023) It can be said that the students can evaluate their financial knowledge, resources, and self-efficacy in making investment decisions Higher degrees of perceived control correlate with higher confidence in one's ability to negotiate the difficulties of investment Financial literacy, resource availability, and prior investment experience can all have an impact on perceived behavioral control Students with more financial knowledge and resources may regard investment as more reasonable and reachable, increasing their willingness to invest Therefore, in the context of students’ analysis of stock investment intentions in Vietnam, PBC has positively impacted the investment intention The hypothesis is described as follows:
H3: Perceived behavioral control has a positive influence on stock investment intention.
TRUST IN FINANCIAL INSTITUTIONS (TFI)
Corsetti et al (2010) define trust as the belief that an opponent in a relationship performs in accordance with what he promised and does not take advantage of the person with whom he is dealing Hence, trust is a person's expectation that the opponent will not
‘‘cheat’’ on him Consumers' assumption that financial organizations are typically reliable and can be depended on to deliver on their commitments is referred to as trust in the financial sector (Sirdeshmukh et al., 2002)
Trust in financial institutions plays a pivotal role in the stability and efficiency of financial systems This trust reflects the confidence consumers and businesses have in institutions' ability to safeguard deposits, provide fair services, and contribute to economic growth and financial development The level of trust impacts individuals' and firms' willingness to invest and save, while also influencing the financial system's resilience during economic downturns.
H4: Trust in financial institutions has a positive influence on investment intention.
FINANCIAL LITERACY (FL)
Financial literacy has been identified as an essential qualification for 21st-century consumers (Lord, 2001) Mandell (2008) defined financial literacy as the capacity to appraise complex financial products and make informed decisions about their potential long-term advantages Abdullah and Anderson (2015) emphasize the need to achieve financial literacy while balancing rewards and risks to make sound financial decisions that promote wellbeing Financial literacy refers to the ability to generate knowledge that can guide current and future financial decisions (Baihaqqy et al., 2020) Furthermore, financial literacy also includes personal financial management, budgeting, and investing It was also described by Dewi and Pourbawangsa (2018) and Baihaqqy et al (2020) as helping individuals make solid financial decisions and prevent difficulties caused by bad management Financial literacy enables individuals to make informed financial decisions (Pratiwi et al., 2020) It plays a vital role in allowing individuals to make informed and effective decisions with all their financial resources
Higher levels of financial literacy can lead to better financial outcomes for individuals and can influence economic stability and growth more broadly Financial literacy, as described by Dewi and Pourbawangsa (2018) and Baihaqqy et al (2020), helps individuals make solid financial decisions and prevent difficulties caused by bad management Financial literacy enables individuals to make informed financial decisions (Pratiwi et al., 2020) The following hypothesis is developed:
H5: Financial literacy has a positive influence on investment intention.
INTENTION TO INVEST (INT)
According to East (1993), investment behavior can be predicted by investment intention Intentions are "assumed to capture motivational factors that influence a behavior" (Ajzen, 1991) The intention to invest in any short- or long-term asset is referred to as investing intent Before making such an investment decision, investors often consider both risk and reward An investor's intention to engage in short-term investments is referred to as short-term investment intention, while the desire to invest in long-term investments is referred to as long-term investment intention Recent literature demonstrated that behavior, subjective norm, perceived behavior control, and attitude influenced investment intention (Luky, 2016; Phan & Zhou, 2014) Studying people's investment intentions can improve the national economy and attract more participants to the capital market and securities companies, potentially boosting investor growth
Individuals' intention to invest in stocks is influenced by their attitudes towards investing, social norms, perceived control over their investment decisions, trust in financial institutions, and financial knowledge Studies have shown that positive attitudes towards investing (ATB), strong subjective norms (SN), high perceived behavioral control (PBC), adequate financial literacy (FL), and trust in financial institutions (TFI) lead to increased investment intentions (INT).
The concurrent correlation among these factors and the intention to invest is interdependent Ha (2012) showed that factors including attitude, subjective norms, and perceived behavioral control all have the same influence on intention In particular, attitude towards behavior has the strongest influences on the intention to invest in stocks Moreover, according to Gopi and Ramayah (2007) attitude, subjective norms, and perceived behavioral control have direct positive relationships toward behavioral intention to use internet stock trading The study of Adil et al (2023) showed a positive impact of trust in financial institutions and financial knowledge on investment intention Besides, Kersting et al (2015) stated that financial literate investors have high trust in financial institutions, which enhances their participation in the stock market A comprehensive approach that enhances financial literacy, builds trust in financial systems, and supports positive attitudes and subjective norms regarding investing can effectively foster a stronger intention to invest among individuals, including students.
DATA & METHODOLOGY
DATA COLLECTION
Data collection for this study was done through a standardized survey design to collect responses from university students using a convenience sampling technique such as online Google Forms We gathered a large number of responses from diverse places in Vietnam by sending surveys online through many reliable social platforms, which we then divided into rural and non-rural categories In collecting data, a survey using a questionnaire method was prepared to collect respondents’ answers from December
2023 to February 2024 This survey method was considered due to its benefits in solving several constraints like time, place, and condition for each respondent
The questionnaires ask about respondents’ socio-demographic details, including gender, university, major, education level, occupation, and income level The data for this study comes from a self-administered questionnaire that was initially filled out by 310 Vietnamese university students Out of 310 responses, 23 were found to be insufficient or irrelevant information and they were rejected Hence, the sample size is made up of
287 valid questionnaires The sample size was determined by considering the population and was calculated by a sample calculator at a 95% confidence level.
VARIABLES MEASUREMENT
For this investigation, an explanatory study utilizing primary data was deemed appropriate to evaluate the TPB model after it had been modified with external variables and to investigate the relationship between an individual's investing intention and its predictor variables Respondents were asked to evaluate their agreement or disagreement with ongoing stock market planning and investment intentions using TPB cognitive proxies and added variables such as Trust in Financial Institution The rate used a five-point (1-5) Likert scale, with one representing "strongly disagree" and five indicating "strongly agree." The questions in this study's questionnaire are shown in Appendix
Constructs Items Measurement variables Source
I think that investing in the stock market can enhance the financial knowledge of individuals
Adil et al (2023), Akhtar and Das (2019), Nugraha and Rahadi (2021)
ATB2 I think that stock investment is meaningful
ATB3 I think that it is wise for me to engage in stock investment
ATB4 I think that engaging with stock investment is interesting
ATB5 I think that stock investment is a good idea
I will participate in stock investment if my spouse think it is useful
I will participate in stock investment if my family approves it
SN3 I will participate in stock investment if my colleagues do
I will participate in stock investment if I have proven friend success on it
I will participate in stock investment if the government encourages it
I will participate in stock investment if a famous public figure encourages it
PBC1 I have enough money for stock investment
PBC2 I have enough energy for stock investment
PBC3 I have enough information for stock investment
PBC4 I have enough time for stock investment
I have enough knowledge to overcome obstacles or problems while engaging in stock investment
When somebody buys a share of a company, he owns a part of the company
Adil et al (2023), Akhtar and Das (2019), Moin et al (2017)
The equity shares displays fluctuations (often increase and decrease in price) over time
Compound interest: If I have 100 Rupees in my savings account, and the bank provides an interest of 10 per cent per annum, I will have more than 150 Rupees in my account after 5 years, provided that I do not withdraw any
Risk diversification: When I diversify my money into various investments (such as stocks, bonds, fund certificates, ), my risk level decreases
Simple interest (numerical): If I borrow 100 Rupees It is lower to pay 105 Rupees than to pay 100 Rupees plus 3 per cent
Inflation: If the prices of products that I buy today doubles over the next ten years and my income also doubles, I will be able to buy the same product in the same quantity
I am confident that existing policies and regulations protect customers of financial institutions
Adil et al (2023), Moin et al
TFI2 I have faith and confidence in the financial system
I generally trust financial institutions to act honestly and ethically
TFI4 I trust financial institutions to stick to rules and regulations
I trust all financial institutions to ensure that their employees are well trained and professional
INT1 I intend to engage in stock investment in the near future
Adil et al (2023), Akhtar and Das (2019), Nugraha and Rahadi (2021)
I expect that I will engage in stock investment activities in the near future
INT3 I can stand the inconvenience caused by stock investment
INT4 I will recommend others to invest in the stock market
How much time do you need to spend before engaging in stock investment? (Scale: 1 = No time at all to 6 = Very much time) Table 3.1: Variable measurement of the study
RESEARCH METHODOLOGY
Participants in this study met specific criteria: they were Vietnamese university students eligible to invest in the stock market, with and without prior investment experience Demographic data was collected (gender, university, major, education level, occupation, income) Surveys were voluntarily completed, including demographic questions and detailed inquiries on Theory of Planned Behavior (TPB), financial literacy, and trust in financial institutions in relation to stock investment intentions These elements, including TPB elaborations and variables related to financial literacy and trust, provide insights into individual investors' perceptions of stock investment.
Figure 3.1: Proposed research model (Adil et al., 2023; Nugraha & Rahadi, 2021; Phan
& Zhou, 2014) Finally, after analyzing data, one variable was rejected which is Financial Literacy due to lack of reliability Therefore, the authors adjusted the research model as follows:
Figure 3.2: Adjusted research model (Adil et al., 2023; Nugraha & Rahadi, 2021; Phan
The preliminary study investigated TPB proxies and an additional variable, namely Trust in Financial Institution, with six demographic features acting as moderators: 1) GDER, 2) UNI, 3) MAJ, 4) EDU, 5) OCUP, 6) INCL Investment intention was proposed as the dependent variable for this study The data was analyzed using the partial least squares structural equation model (PLS-SEM) and SmartPLS 3.0 software PLS-SEM is often utilized in management and information systems, but it also performs well in banking and finance, particularly behavioral finance (Avkiran & Ringle, 2018)
In short, PLS-SEM was deemed appropriate for examining the author's proposed model framework Therefore, the data analysis process was carried out in two steps: (1) Evaluate the model by examining item reliability, outlier value, internal consistency, and so on; (2) conduct a structural model analysis to validate the model and test the hypothesis.
RESULTS & DISCUSSIONS
SAMPLE DESCRIPTION
Based on the data collected from 287 respondents (derived from a total of 310, as mentioned in section 3.1 above), the authors conducted the following descriptive statistics:
Do not have a part-time job 124 43.21
Below 6 million VND per month 243 84.67
More than 10 million VND per month 16 5.57 Table 4.1: Descriptive statistics (authors’ calculation)
The study's sample size predominantly consisted of female respondents (64.81%), with the majority attending International School - Vietnam National University (55.75%) Most respondents (87.46%) pursued economics-related majors, indicating the suitability of the sample for investigating university students' investment intentions Additionally, junior respondents comprised the largest year of study group (49.83%), and a significant proportion of respondents held part-time jobs.
(56.79%) Finally, about the income level, 84.67% of respondents had a monthly income less than 6 million VND, 7.32% had a monthly income ranging from 6 to 8 million VND, 2.44% had a monthly income ranging from 8 to 10 million VND, and 5.57% had a monthly income more than 10 million VND It is notable that the respondents for the study are mostly university students, so the income source may not just come from occupation (i.e., part-time jobs, full-time jobs, freelance jobs, etc.) but also from family support and other sources
Figure 4.1: Respondents’ gender description (authors’ calculation)
Figure 4.2: Respondents’ university description (authors’ calculation)
Figure 4.3: Respondents’ major description (authors’ calculation)
Figure 4.4: Respondents’ year of enrollment description (authors’ calculation)
Figure 4.5: Respondents’ occupation enrollment (authors’ calculation)
Figure 4.6: Respondents’ income description (authors’ calculation)
FINANCIAL LITERACY SCORE
Based on the study’s variable measurements in table 3.1, the authors conducted a part that examined the financial literacy level of respondents by constructing six questions related to various topics, namely stocks, volatility, compound interest, simple interest, portfolio diversification, and inflation The results of the questions will be ranked using a 5-point Likert scale
The score for each respondent will be marked as follows: If the respondents choose
“Agree” or “Strongly Agree” for questions 1, 2, 4, and 6, they will have the correct answer On the other hand, if the respondent chooses “Strongly Disagree”, “Disagree”, or “Neutral”, the respondent will have an incorrect answer For questions 3 and 5, the respondent will have a correct answer if he or she chooses “Strongly Disagree” or
“Disagree” due to the reverse-coded items The results are presented below:
Frequency Percentage Number of correct answers
Weighted average number of correct answers
Table 4.2: Respondents’ financial literacy score (authors’ calculation)
Based on the descriptive statistics above, 87.2% respondents’ are currently pursuing economics-related majors Nonetheless, the respondents who have more than 4 correct answers accounts for only 15.34% of total respondents, which is an unfavorable figure despite the dominance of respondents’ major in the sample.
ORIGINAL RESEARCH MODEL
Figure 4.7: Proposed research model (Adil et al., 2023; Nugraha & Rahadi, 2021; Phan
Based on figure 4.7, the authors conducted the following measurement model reliability test:
Alpha rho_A Composite reliability (CR)
Table 4.3: Construct reliability and validity of original model (authors’ calculation)
Table 4.4: Results of the original research model (authors’ calculation)
According to table 4.3, the Cronbach’s Alpha, composite reliability, and average extracted variance of the FL construct are 0.586, 0.741, and 0.327, respectively In order to ensure construct reliability and internal consistency, Cronbach’s Alpha and composite reliability for each variable need to be greater than 0.7 (DeVellis & Thorpe, 2021; Hair et al., 2021), and the average extracted variance needs to be greater than 0.5 (Hair et al.,
The 2021 findings indicate that the Financial Literacy (FL) construct lacks reliability and internal consistency Therefore, the authors removed this construct from their research model This decision stems from the low average number of correct answers (2.12) in the financial literacy assessment This suggests that respondents may have had difficulty understanding the survey or possessed limited financial knowledge, which compromised the reliability of the FL construct.
Based on table 4.4 the FL and TFI constructs do not have significant influences on the dependent variable INT Hence, the authors conducted the adjusted research model, as presented in section 4.4 below.
ADJUSTED RESEARCH MODEL
The proposed model shows that TFI has no direct effect on INT, which is not in line with the study of Adil et al (2023) This may come from the difference in research contexts, whereas Adil et al (2023) conducted the research during the peak of the Covid-
During the COVID-19 pandemic, social distancing measures hindered communication between individual investors and companies Economic uncertainty led to volatile stock prices and increased reliance on financial institutions for investment guidance However, as the pandemic situation improved, investors regained confidence and reduced their reliance on these institutions Nevertheless, trust in financial institutions remains a key factor in investment decisions, as it provides a foundation for individuals to make informed choices.
In the current study, the authors adjusted the research model by considering attitude as a mediator for the indirect relationship between trust in financial institutions and investment intention According to Saparudin et al (2020), the trust construct has an indirect relationship with the behavioral intention through mediators, namely performance expectancy, effort expectancy, and social influence, in examining the intention to use mobile banking applications Moreover, Chawla and Joshi (2023) also examined the intention to use mobile wallets, using attitude as a mediator to examine the relationship between trust and behavioral intention; Alhabash et al (2015) used system trust as a mediator to examine the relationship between institutional trust and the intention to use online banking Hence, the authors adjusted the research model as follows:
Figure 4.8: Adjusted research model (Adil et al., 2023; Nugraha & Rahadi, 2021; Phan
*Note: The hypothesis are adjusted as follows:
H1: Trust in financial institutions has a positive influence on attitude towards behavior H2: Attitude towards behavior has a positive influence on investment intention
H3: Perceived behavioral control has a positive influence on investment intention H4: Subjective norms has a positive influence on investment intention
H5: Trust in financial institutions has a positive influence on investment intention
CONFIRMATORY FACTOR ANALYSIS (CFA)
4.5.1 OUTER LOADINGS, RELIABILITY, VALIDITY AND INTERNAL CONSISTENCY TEST
After the research model was adjusted, the authors examined the measurement model of the study by using the confirmatory factor analysis technique, including analysis of indicator reliability, internal consistency, and discriminant validity Table 4.5 shows the outer loadings, which are correlations between indicators and latent variables
Latent variables Indicators Outer loadings
Trust in financial institutions (TFI)
0.678 Note: The authors have already removed the item INT5 due to insufficient outer loading value
Table 4.5: Item loadings (authors’ calculation)
Alpha rho_A Composite reliability (CR)
Table 4.6: Construct reliability and validity (authors’ calculation)
According to Hair et al (2021), the outer loading for each item needs to be higher than 0.7 to be accepted, and ones that are lower than 0.7 should be considered for elimination However, Hair et al (2019) also mentioned that if the constructs satisfy the reliability and validity criteria and the outer loading values of the indicators are both greater than 0.4 and less than 0.7, the items can be accepted if they are meaningful to the study Based on table 4.6, variables namely ATB, SN, PBC, TFI, and INT have Cronbach’s Alpha, composite reliability, and average extracted variance that meet the criteria above (i.e., at least 0.7 for Cronbach’s Alpha and composite reliability and 0.5 for average extracted variance) As a result, based on table 4.5, the authors decided to keep INT4, PBC1, and SN5, which have outer loading values of 0.678, 0.690, and 0.692, respectively On the other hand, INT5 was removed since the item loading was only 0.309 Tables 4.7, 4.8, and 4.9 below show information about the discriminant validity of the constructs
ATB INT PBC SN TFI
Table 4.7: Cross loading table of indicators (authors’ calculation)
In contrast with convergent validity, discriminant validity analysis is used to determine whether indicators are simultaneously strongly correlated with their latent variables and weakly correlated with other variables (Gefen & Straub, 2005) In other words, an indicator should only reflect the characteristic of its latent variable, not any other variables By using the cross loading table of indicators, the authors compare the outer loading of an indicator (i.e., the intersection between an indicator and its latent variable) with cross loadings of that indicator with other variables (i.e., intersections between an indicator and other latent variables) For instance, to examine the discriminant validity of indicator AT1, the authors will compare the outer loading value of AT1 (0.802) with cross loading values between AT1 and INT, PBC, SN, TFI, which are 0.420, 0.207, 0.306, 0.255, respectively The criteria for using a cross loading table for discriminant validity analysis is that the outer loading of an indicator should be greater than any cross loading value between that indicator and another construct (i.e., the outer loading of an item should be the greatest value in a row) According to table 8, the measurement model has satisfied the criteria
ATB INT PBC SN TFI
Table 4.8: Fornell-Larcker Criterion (authors’ calculation)
ATB INT PBC SN TFI
Table 4.9: Heterotrait-Monotrait Ratio (HTMT) (authors’ calculation)
Discriminant validity can be assessed using the Fornell-Larcker Criterion and Heterotrait-Monotrait Ratio (HTMT) The Fornell-Larcker Criterion states that the square root of the Average Variance Extracted (AVE) for a latent variable should exceed any correlation between that variable and other constructs Conversely, the HTMT value between two constructs should be below 0.9, as per Henseler et al (2015) Meeting these criteria indicates satisfactory discriminant validity, ensuring that the latent variables capture distinct constructs.
STRUCTURAL MODEL ANALYSIS
By using the bootstrapping technique with a number of subsamples of 1000, the authors generated the following table relating to the path coefficients of the structural model
Table 4.10: Bootstrapping results (authors’ calculation) Note: Significant level: *** p < 0.001; ** p < 0.01; * p < 0.05
Table 4.11: Special indirect effect (authors’ calculation) Note: Significant level: *** p < 0.001; ** p < 0.01; * p < 0.05
Based on table 4.10, the results show that ATB, SN, and PBC have significant impacts on INT, which means hypotheses H1, H2, H3, and H4 are supported However, hypothesis H5 is not supported In particular, ATB has the most significant impact on INT, suggesting that the attitude of a person has a great impact on the intention to invest This finding is in line with the studies of Lai (2019), Phan and Zhou (2014), Fishbein and Ajzen (1977) Nonetheless, the finding is not in line with the study of Nugraha and Rahadi (2021) and Adil et al (2023) Also, TFI has a significant effect on ATB, in which ATB acts as a mediator for the relationship between TFI and INT in the research model Table 4.11 shows that TFI has a significant indirect effect on INT with ATB as a mediator
According to Baron and Kenny (1986), the criteria to examine if there is a partial mediation include: (1) the independent variable has a significant effect on the mediator;
(2) the mediator has a significant effect on the dependent variable; and (3) the independent variable has a significant effect on the dependent variable If criterion (3) is not satisfied, the relationship is complete mediation Based on table 4.10, TFI (the independent variable) has a significant influence on ATB (the mediating variable, with a p-value less than 0.001), and ATB has a significant influence on INT (the dependent variable, with a p-value less than 0.001) Finally, TFI has an insignificant influence on INT, indicating that the relationship between TFI and INT cannot exist without ATB as a mediator As a result, the authors concluded that the mediating effect of ATB between TFI and INT is full mediation, which is in line with the study of Alhabash et al (2015) and not in line with the studies of Saparudin et al (2020) and Chawla and Joshi (2023) The full mediation relationship between TFI and INT indicates that a person’s trust in financial institutions will not immediately influence the intention to invest in stocks but rather enhance the confidence of that person through his/her attitude towards behavior, then influence the intention to invest over time
Figure 4.9: Structural model of the study
Table 4.12: R-Square and R-Square adjusted (authors’ calculation)
The R-Square and R-Square adjusted are used to assess the influence of the independent variables on the dependent variables Based on table 4.12, the R-square adjusted values for ATB and INT are 0.129 and 0.391, respectively, which indicates that TFI explains approximately 12.9% of the variance in the variable ATB, and AT, SN, PBC, and TFI explain approximately 39.1% of the variance in the variable INT Additionally, f Square is used to assess the importance of the independent variables on the dependent variables Cohen (1988) suggested the following interpretations for f Square:
Intervals Interpretations f Square value < 0.02 No influence
>= 0.35 Strong influence Table 4.13: Interpretations for f Square (authors’ calculation)
ATB INT PBC SN TFI
TFI 0.152 0.012 Table 4.14: f Square (authors’ calculation)
Based on tables 4.13 and 4.14, the influence of ATB on INT and TFI on ATB is categorized as medium However, the influence of PBC and SN on INT is considered low Notably, there is no influence of TFI on INT due to the presence of full mediation, as discussed in section 4.6.1.
In this section, the authors will assess the collinearity of the independent and dependent variables of the research model in order to determine whether there exists multicollinearity between independent variables or not According to Hair et al (2019), multicollinearity exists when independent variables “depend” on each other (i.e., the relationship between independent constructs can be demonstrated by functions), which means the p-value and path coefficients reflect incorrect characteristics of the research model As a result, Hair et al (2019) proposed the following interpretations of the variance inflation factor (VIF) to test for multicollinearity:
[3,5) Probable chance of existence (needs to consider additional information)