Introduction
Derivatives are essential financial instruments that derive their value from the price of underlying assets, functioning as contracts for future transactions They play a crucial role in risk management by helping to mitigate the impact of asset value fluctuations Additionally, derivatives serve as hedging tools against commodity price volatility The derivatives market is divided into two primary segments: financial derivatives and commodity derivatives This study specifically focuses on the financial derivatives market within the context of the Vietnam stock market.
The Vietnam stock market, established over a decade ago, has experienced significant growth, highlighted by the creation of two major exchanges: the Ho Chi Minh City Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX) With nearly 89 active securities companies and over 700 listed firms, the market capitalization at HOSE surpassed $32 billion by 2013, representing 25% of the country's GDP Investor participation has surged, with trading accounts exceeding 1.3 million, including around 16,000 foreign accounts, marking a 3.5-fold increase since 2007 Daily trading values across both exchanges have reached over 5,000 billion VND, reflecting a robust demand for securities among investors.
Despite over 13 years of development, Vietnam's stock market lacks a derivatives market for hedging against price fluctuations, leading to diminished trust among small investors due to macroeconomic uncertainties and financial risks Currently, investors have access only to basic tools such as stocks, bonds, and fund certificates However, the derivatives market is set to launch soon with the introduction of the first traded futures contract While this trading form remains unfamiliar to many investors, its establishment is expected to enhance market vibrancy and diversity, ultimately improving investor knowledge and skills for future growth in the derivatives market.
Numerous studies have explored customer behavioral intentions, including notable works by Jeong & Lambert (2001), Burton, Sheather & Roberts (2003), Liu et al (2004), Amoako & Gyampah (2007), Gu et al (2009), and Han & Kim (2010) In the financial market context, research has also been extensive, with significant contributions from Berry et al (1996), Athanassopoulos (2000), Auh et al (2007), Keh & Xie (2009), and Bolton et al (2010).
This study explores the behavioral intentions of investors in Vietnam's derivatives markets by identifying key factors through various approaches, particularly the theory of planned behavior (TPB) The TPB has proven effective in predicting human behaviors and is widely referenced in studies on behavioral intention By adopting a behavioral approach, this research aims to analyze individual investor behavior and their perception of different derivatives markets, highlighting the significance of understanding these factors in the financial context.
Derivatives play a crucial role for large corporations in managing exchange rate risks, loans, and financial expenses Studies indicate that these financial instruments are essential for effective risk management, utilizing tools such as call and put options, as well as forward and futures contracts to mitigate market risks As indispensable components of a diverse and expansive financial market, derivatives have experienced rapid global growth and increasingly significant influence within the financial and monetary systems While they offer substantial benefits in risk prevention and cater to the needs of various market participants, their complexity necessitates careful management to avoid potential economic instability.
In Vietnam, the use of derivative products related to currencies and commodities has a long-standing history, highlighted by the establishment of the Buon Me Thuot coffee trading center in 2006, which facilitates spot and forward coffee trading Currently, currency derivatives are widely utilized by various domestic and foreign commercial banks, offering instruments such as swaps, options, and futures contracts The financial derivatives market is poised for significant growth, with plans to introduce basic futures contracts on stock indices (VN30 and HNX30) and government bonds in its initial phase, paving the way for future derivative contracts on additional asset types.
In the derivatives market, there are four main contributing factors to the derivative market: infrastructure, legal framework, products and people (Hull,
In recent years, the government has established a legal framework and technical infrastructure for the derivatives market; however, it struggles to enhance human factors in the same way Human behavior varies significantly across different circumstances, particularly in financial environments where individual investor actions can greatly influence market effectiveness and growth Factors such as education, experience, gender, and culture, alongside psychological influences, play crucial roles in shaping investor behavior Despite individual investors becoming more professional, studies indicate that the VN-Index's performance is not random, highlighting the impact of psychological factors on investment decisions, even when based on logical analysis.
This study is essential in the context of Vietnam to assess investors' attitudes towards derivative financial instruments as the market becomes operational It aims to identify the key factors influencing investors' intentions to utilize these financial tools in Vietnam.
This study aims to investigate the factors that affect Vietnamese investors' decisions to engage in the financial derivative market, which officially commenced on August 10, 1977 The Vietnamese State Securities Commission has granted trading eligibility certificates to five key securities firms: Saigon Securities Inc (SSI), Vietnam Prosperity Securities Company (VPBS), Vietnam Securities Corporation (BSC), MB Securities (MBS), and VNDIRECT Securities (VND) Consequently, the research specifically targets investors associated with these five companies.
This study aims to enhance understanding of individual investor behavior in Vietnam's derivatives market by assessing the impact of various factors on their decision-making The findings are crucial for brokers and the State Security Commission of Vietnam, as they provide insights into investor behavior, which can foster greater adoption of derivative instruments for effective risk management Ultimately, this knowledge can contribute to increased market liquidity and a growing number of investors in Vietnam's stock market.
Theoretical background and hypotheses
Foundational Theory
The Theory of Planned Behavior (TPB), formulated by Ajzen and Fishbein in 1980, is a foundational concept in psychosocial research, extensively utilized to understand human behavior Key studies by Ajzen (1985, 1991, 2002) have empirically validated the connection between intention and behavior across various domains (Ajzen, 1988; Ajzen & Fishbein, 1980; Canary & Seibold, 1984; Sheppard, Hartwick, and Warshaw, 1988) TPB evolved from the earlier Theory of Reasoned Action (TRA), highlighting its significance in behavioral research.
The Theory of Reasoned Action (TRA) examines the motivational aspects of personal behavior, emphasizing two key components: attitude towards behavior (AT) and subjective norms (SN) Despite its widespread acceptance in academic literature, TRA has limitations, particularly regarding individuals' inability to act due to a lack of opportunities or resources such as time, capital, or skills To address these shortcomings, Ajzen (2002) introduced the concept of perceived behavioral control (PBC), which expanded the original TRA framework into what is now known as the Theory of Planned Behavior (TPB).
Perceived behavioral control reflects the ease or difficulty of performing the behavior and whether the behavior is controlled or restricted (Ajzen, 1991) The TPB model is shown in figure 1.
Figure 1 The theory of planned behavior – (Ajzen, 1991)
The theory of planned behavior (TPB) posits that perceived behavioral control (PBC) influences actions in two significant ways: it can shape behavioral intentions and directly affect actual behavior This dynamic is particularly relevant to investor decision-making, where both internal factors—such as emotions, personal knowledge, experiences, and skills—and external factors, including financial resources, time, and partnerships, play crucial roles TPB identifies three core components: behavioral attitudes, subjective norms, and perceived behavioral control, all of which have been substantiated through extensive research.
The Theory of Planned Behavior (TPB) has been a reliable framework for predicting behavioral intentions based on attitudes, subjective norms, and perceived behavioral control, as demonstrated through numerous empirical studies Widely applied in various fields, TPB has effectively forecasted human behavior in business contexts, the study of bad habits, and tobacco control efforts Additionally, it serves to predict community-benefiting behaviors, such as resource sharing within organizations and decision-making in human resource management TPB is also instrumental in analyzing intentions related to new technologies, including online shopping, household technology adoption, and credit card usage.
The Theory of Planned Behavior (TPB) is extensively applied in financial and securities markets, as demonstrated by Gopi and Ramayah (2007), who explored online home-based business intentions and the use of internet banking for securities trading This suggests that TPB is an effective model for predicting behavior East (1993) further validated TPB by successfully forecasting short-term behaviors of securities investors Ajzen (2005) notes that TPB indicates individuals are likely to take action when they view it positively, feel social pressure to do so, and believe they have the necessary resources and opportunities This framework effectively elucidates the primary factors influencing individual investment behavior.
The Vietnamese stock market has experienced significant development over the years, yet limited research has utilized the Theory of Planned Behavior (TPB) to analyze stock investment behavior Previous studies predominantly concentrated on behavioral finance theory, financial literacy, and demographic factors influencing investment decisions Recently, the introduction of derivatives as an effective risk management tool in securities trading has garnered attention in Vietnam This prompted the author to adopt TPB as a theoretical framework for developing a research model aimed at examining the intention to use derivatives in securities investment within the country.
Research model and hypotheses
The Theory of Planned Behavior (TPB) has numerous applications in understanding human behavior and has been validated through extensive global research This article proposes a research model aimed at examining the factors that influence the intention to use derivatives in securities investments, focusing on identifying key determinants and their interrelationships Additionally, it highlights psychological factors that indirectly affect the intention to use derivatives, as noted by Phan & Zhou (2014) The subsequent sections will detail the research model and its associated hypotheses.
According to the Theory of Planned Behavior (TPB), behavioral intentions refer to the motivation to perform a specific action, such as usage intentions in this study This positions behavior as a dependent variable in numerous experimental research utilizing TPB Extensive empirical studies have validated the significance of behavioral intentions, with Ajzen (1991) highlighting that motivation factors within the TPB model play a crucial role in shaping these intentions.
The 17 intentions suggest that individuals are inclined to take action or pursue specific goals Consequently, the intention to utilize derivatives signifies that investors are likely to engage in derivatives trading within the securities market.
Attitude refers to how positive or negative emotions influence specific behaviors, as highlighted by Fishbein and Ajzen (1980) An individual's attitude is shaped by their beliefs and appreciation of that behavior, making it a valuable predictor of future actions Furthermore, attitudes have evolved to encompass how individuals react to various objects, according to Ajzen and Fishbein (2000).
Attitudes significantly influence behavioral intentions, with individuals exhibiting positive attitudes more likely to engage in specific behaviors, while those with negative attitudes tend to avoid or criticize them Numerous studies, including research by Gibler & Nelson (1998), confirm the strong correlation between attitude and behavioral intention, highlighting that a favorable attitude drives action, whereas a negative attitude can lead to obstruction or criticism Consequently, attitude serves as a crucial determinant of personal behavior.
Ajzen and Fishbein (1980) define attitude towards behavior as the overall feelings of favorableness or unfavorableness regarding a concept This attitude is influenced by several underlying factors, with Phan and Zhou (2004) identifying four key psychological influences: overconfidence, excessive optimism, herd behavior, and risk aversion Consequently, attitude towards behavior is viewed as a dependent variable shaped by these four psychological factors.
Overconfidence refers to an inflated sense of self-assurance regarding one's knowledge or decision-making abilities, particularly evident in the stock market (Barberis & Thaler, 2003) Many investors exhibit overconfidence in their trading skills, often believing they possess superior knowledge, despite their actual performance falling short of expectations This disconnect leads them to overlook the reality of their trading results, as they are convinced they can select the best stocks and time their sales for maximum profit.
Excessive confidence significantly impacts decision-making, leading investors to overlook valuable data essential for informed investment choices This tendency results in misguided investment decisions, as highlighted by various studies (Odean, 1998; Wang, 2000; Gervais & Heaton, 2002; Grinblatt & Keloharju, 2009; Montier, 2009) Furthermore, overconfidence plays a crucial role in the utilization of derivatives in securities transactions.
Many investors exhibit overconfidence in their trading abilities, often neglecting essential risk control measures, especially in derivatives trading This overconfidence leads to high-frequency trading, which not only amplifies market volume and volatility but also diminishes expected returns Consequently, an individual's confidence significantly shapes their investment behavior, resulting in an increase in transaction frequency.
Overconfident investors often overestimate their investment knowledge and abilities, leading them to disregard market realities and the true performance of their stocks This excessive optimism can be particularly detrimental during market downturns, as these investors tend to believe that negative trends are temporary and will have minimal impact on their portfolios.
Many investors hold a strong belief in the resilience of their portfolios, anticipating a quick rebound without the need for selling (Wang, 2001; Gervais & Heaton, 2002; Johnson & Lindblom, 2002) This excessive optimism often leads them to expand their portfolios, driven by the expectation of market improvement and the potential for high short-term returns (Johnsson & Lindblom, 2002).
The use of derivatives in trading is heavily influenced by the investor's sentiment When investors are optimistic, they often forgo derivatives; however, as their optimism wanes, they tend to utilize derivatives as a protective hedge against potential market downturns.
Herd behavior in stock investment refers to investors mimicking the actions of others, often driven by the perceived success of those investments This phenomenon occurs when individuals quickly follow the trading decisions of others, leading to a collective movement in the market While this behavior may not significantly impact the market when it occurs on a small scale, it highlights the influence of social dynamics on investment decisions (Banerjee, 1992; Bikhchandani & Sharma, 2000; Hwang & Salmon, 2004).
When a significant number of investors follow the actions of reputable figures, it can distort the market, potentially causing stock prices to become overvalued and heightening investment risks This behavior characterizes what are known as unreasonable investors Excessive reliance on specific individuals or organizations can grant them considerable influence over the market, further increasing the associated investment risks (Barber & Odean, 2009).
Herd behavior significantly influences investor actions, with those exhibiting strong herd tendencies often neglecting derivatives for risk management In contrast, investors with a lower propensity for herd behavior are more likely to utilize derivatives as an effective strategy to mitigate risk.
R e s ea r c h m e t h o d o l og y
Research approach
Quantitative and qualitative research methods are fundamental approaches in scientific research (Spencer, Ritchie, and O'Connor, 2003) Quantitative research focuses on experimental surveys of phenomena, utilizing statistics in various forms, including mathematics and computer engineering This method is essential for the research, development, and application of theories, hypotheses, and models related to the subject of study By employing quantitative methods, researchers can verify quantitative relationships, with measurement data typically expressed as percentages, means, and standard deviations.
Qualitative research remains a valuable investigative method across various fields, despite its long history It enables researchers to synthesize and derive insights from authenticated information and previous studies, offering an objective approach to understanding subjects and individuals Key questions addressed in qualitative research include "what, where, when, and how?" Typically, the sample size in qualitative studies is small, allowing for in-depth exploration of complex phenomena (Sogunro, 2001).
Quantitative methods are structured approaches used to statistically measure problems, attitudes, behaviors, and other determinants, allowing for inferences to be made about larger populations These methods enable the construction of factors and models of exposure based on measurement data Compared to qualitative methods, quantitative data collection is more organized, employing diverse techniques such as online surveys, offline methods, direct telephone interviews, and systematic monitoring activities, as highlighted by Neuman (2006).
To understand the level and test the relationship for establishing cause and effect in the studied object, this research will utilize quantitative methods The research process consists of nine key steps, which are outlined below.
Figure 3 Main steps of research process
Questionnaire design
The questionnaire is distributed through online surveys and hard copies to achieve a sufficient sample size, given the limited knowledge of the subject regarding derivatives It has been translated into Vietnamese, ensuring that all questions are concise, easy to understand, and free from ambiguity.
There are two forms of measurement scales in this questionnaire design:
• Nominal scale: present data into categories (Crossman, 2009).
• 5-point Likert scale: level of agreement or disagreement with each of a series of statement (Naresh, 2009) The range from 1 to 5 corresponds to strongly disagree and strongly agree.
The questionnaire included two parts The first part was respondents’ demographics included age, gender and education displayed in categories questions.
The last part was main survey displayed in 5-point Likert scale questions.
Variable Code Measurement Statements Adapted from
OVC1 I am confident in my ability to trade securities
OVC2 I am confident in the holding stock will rise OVC3 I am confident in market information OVC4 There is no need to use derivative to reduce risk
EO1 I do not sell stocks when the market is plummeting EO2 I trust the stock will rise
EO3 I believe that the market will stabilize after several sessions of declines
EO4 There is no need to use derivative when the market shows signs of deterioration
HERD BEHAVIOR (HB) HB1 I invest by following the specialist ‘s portfolio
HB2 I invest by following friend’s portfolio HB3 I invest in stocks according to the crowd.
HB4 I sold out when I saw a large number of sellers HB5 I bought into stock being bought a lot
RA1 I have low risk tolerance
Klos & Weber (2005) RA2 I like safe investing
RA3 I like to invest in “hot” stock
RA4 I sell stock when prices falling.
RA5 I like to use derivative for hedging.
ATTITUDE TOWARDS BEHAVIOR (ATB) ATB1 Derivative helps me better control risk when trading stocks.
ATB2 Derivative is more beneficial than the cost that I have to spend ATB3 I feel derivative brings a lot of benefits
ATB4 I am more confident when using derivative in stock trading
SUBJECTIVE NORM (SN) SN1 Friends, colleagues advised me to use derivative
SN2 Relatives advised me to use derivative in stock trading
SN3 The broker recommends me to use the derivation in stock trading
SN4 The information available is advisable to use derivative in stock trading.
PBC1 I can use derivative as soon as I need it PBC2 I can manually use derivative
PBC3 I have no problem using derivative PBC4 I can easily use derivative with the help of broker
BEHAVIORAL INTENTION TO USE(BI)
BI1 I intend to use derivative in stock trading
BI2 I intend to introduce my friends to use derivative in stock trading BI3 I will introduce family members to use derivative in stock trading
Data collection
Prior to executing a large sample survey, a pilot test was performed to evaluate the effectiveness of the questionnaire (Iarossi, 2006) According to Aaker, Kumar, and Day (2006), a sample size of 15 to 25 is typically sufficient for pilot testing Consequently, the author opted to conduct a pilot test with 30 respondents for this research.
According to Gorsuch (1983) and Hair et al (2010), a minimum ratio of 5:1 between subjects and variables is essential, indicating that there should be five respondents for each variable However, the preferred sample size ratio is 10:1, which suggests ten samples for every variable Consequently, the minimum sample size required is 165, with a recommended sample size being higher for optimal results.
330 On the other hand, according to Comfrey & Lee (1992), the number of samples ranked from very poor to very good as follows:
Table 2 Sample size Criteria (Comfrey & Lee, 1992)
Hence, the sample size is 300 seems to meet all requirements.
The convenient method is the most widely used approach for data collection, prioritizing ease of access and convenience This strategy facilitates the efficient gathering of necessary data, enabling researchers to assess the significance of their research problems while effectively saving time and costs.
In addition, the number of people with derivative knowledge is limited so this is the most feasible method Questionnaire will be delivered both online and offline.
Surveys will be conducted with individual investors from five key securities firms in the derivative stock market, including Saigon Securities Inc (SSI), Vietnam Prosperity Securities Company (VPBS), Vietnam Securities Corporation (VSC), MB Securities (MBS), and VNDIRECT Securities (VND) Both online and offline methods will be utilized to gather valuable insights from these investors.
Research Method
Conducting a pilot test is a crucial step in the survey process, as it allows researchers to assess the clarity and consistency of the questionnaire among a small group of subjects This ensures that all participants interpret the questions in the same way, leading to accurate and reliable data collection By identifying and addressing potential issues during the pilot test, researchers can enhance the validity of their findings and avoid skewed statistical results Additionally, the pilot test aids in detecting and correcting possible data errors before launching a larger survey, ultimately improving the overall quality of the research.
A pilot test involving 50 individual investors from five specified securities companies was conducted to gather data This data underwent analysis using Cronbach’s alpha for reliability and exploratory factor analysis (EFA) to enhance the measurement scale Subsequently, the main survey was distributed to these investors via an online survey created on the Google Docs platform.
SPSS (Statistical Package for Social Sciences) is a statistical software utilized for data analysis in this research The analysis process includes descriptive statistics, reliability assessment, and exploratory factor analysis Additionally, confirmatory factor analysis (CFA) and structural equation modeling (SEM) will be performed using AMOS, an add-on for SPSS, following the methodology established by Anderson and Gerbing (1988).
This research employs Cronbach's alpha to assess reliability, while exploratory factor analysis (EFA) will be utilized to evaluate data validity Confirmatory factor analysis (CFA) will subsequently validate the measurement scales Finally, structural equation modeling (SEM) will be applied to test the research model Additionally, demographic and descriptive statistics will be analyzed alongside the measurement scales.
A reliability test is conducted to assess the internal consistency of a construct, aiming to identify and eliminate any failures prior to performing factor analysis The most commonly used method for measuring internal consistency is Cronbach’s alpha, as noted by George and Mallery.
According to Cronbach (2003), a Cronbach’s alpha value of 0.6 is considered acceptable for measuring reliability Additionally, if the Cronbach’s alpha for an item when deleted exceeds the overall alpha and the corrected item-total correlation is below 0.4, that item should be removed from the variable list The table below presents the outcomes of the reliability test conducted.
Exploratory factor analysis was performed to assess the reliability of independent and dependent variables after confirming the reliability of each item in one dimension The primary objective of this analysis is to group observed variables with similar characteristics, thereby enhancing the validity of the scale.
Based on Pallant (2005), the KMO (Kaiser-Meyer-Olkin) must be from 0.6 and above to have a good factor analysis.
In the Bartlett’s Test of Sphericity, the Sig would be less than 0.05 for factor analysis to be considered appropriate (Tabachnick & Fidell, 2007).
Number of factors extracted must have Eigenvalue greater than 1.0
The Total Variance Explained value of each item needs to be higher than
Confirmatory Factor Analysis (CFA) is a statistical technique utilized to validate the representation of constructs by measured variables This method offers researchers critical insights into the relationships among measured variables and their underlying factor structure, enhancing the understanding of the data's dimensionality.
The table below shows the criteria to evaluate the measurement model fit.
Table 3 Criteria for Measurement Model
Chi-square/DF (CMIN/DF) < 3 good; < 5 sometimes permissible p-value for the model > 0.05
CFI (Comparative Fit Index) > 0.95 great; > 0.9 traditional; > permissible
GFI (Goodness-of-Fit Index) > 0.95 great; > 0.9 traditional; > permissible
RMSEA (Root Mean Squared Error of Approximation)
< 0.06: good fit 0.06 – 0.08: acceptable fit 0.08 – 0.1: mediocre fit
CR (Composite Reliability) > 0.7 and > AVE
Source: Joreskog (1969), Bagozzi (1981), Brown and Cudeck (1993), Hair et al (2010)
According to Anderson, Black, Babin, and Hair (2010) in "Multivariate Data Analysis," assessing the Composite Reliability (CR), Average Variance Extracted (AVE), Maximum Shared Variance (MSV), and Average Shared Variance (ASV) is essential for evaluating the reliability, convergent validity, and discriminant validity of a construct Specifically, the criteria for reliability require a CR greater than 0.7, for convergent validity an AVE greater than 0.5, and for discriminant validity, the conditions are that MSV must be less than AVE and ASV.
< AVE and square root of AVE greater than inter-construct correlations.
Convergent validity issues arise when variables fail to correlate effectively within their parent factor, indicating a lack of cohesion Conversely, discriminant validity problems occur when variables exhibit stronger correlations with those outside their parent factor, rather than with their own group.
Data analysis and results
Descriptive statistics
Descriptive statistics serve as the essential first step in understanding data, providing key insights through calculations of minimum, maximum, mean, and standard deviation The mean indicates the central tendency of the data distribution, while the standard deviation measures the dispersion within that distribution A low standard deviation signifies that the majority of data points are closely clustered around the mean, highlighting the data's consistency.
N Minimum Maximum Mean Std Deviation
Table 4 reveals significant differences in average means across various items, particularly highlighting that the means for overconfidence and excessive optimism range from 2.03 to 2.19 This low average indicates that respondents generally disagreed with these statements, suggesting they possess low levels of overconfidence and excessive optimism In contrast, the means for other items varied from 3.42 to 3.96 Additionally, the standard deviation for all items was below 1, indicating a consensus among respondents, with their opinions closely aligned to the mean.
Reliability Analysis
Dimensions Items Corrected Item-Total
Table 5 reveals that items RA3 and SN2 were excluded due to their "Corrected Item-Total Correlation" values falling below 0.4 Following this elimination, a reliability test showed that the Cronbach’s Alpha for the remaining items exceeded 0.8, with all item-to-total correlations surpassing the standard threshold of 0.4, indicating high internal consistency reliability across the scales Specifically, the initial Cronbach’s Alpha values for overconfidence, excessive optimism, herd behavior, risk aversion, attitude towards behavior, subjective norm, perceived behavioral control, and behavioral intention to use were recorded at 0.867, 0.890, 0.865, 0.810, 0.858, 0.868, 0.904, and 0.844, respectively.
Exploratory Factor Analysis (EFA)
Table 6 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Approx Chi-Square 5623.077 Bartlett's Test of df 465 Sphericity
The results from Table 6 indicate a KMO value of 0.907, which exceeds the threshold of 0.5, and a significant Bartlett's test (p < 0.05) These findings confirm the applicability of Exploratory Factor Analysis (EFA).
Factor Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
Table 7 indicates that all eight components exhibited eigenvalues exceeding 1, with a total variance explained of 63.433% This suggests that these eight key factors account for 63.433% of the total data variance in the variable.
Extraction Method: Principal Axis Factoring.
Rotation Method: Promax with Kaiser Normalization. a Rotation converged in 6 iterations.
Table 8 clearly demonstrates that all items are allocated to their respective groups, with no overlap among multiple groups Each item's factor loading value exceeds 0.5, confirming the validity of the measurement scale.
Confirmatory Factor Analysis (CFA)
A crucial step in Confirmatory Factor Analysis (CFA) is assessing both the model's fitness and the quality of its data According to Pire (2007), model fit refers to the degree to which the proposed model accurately represents the observed data.
The model fit is considered good when the discrepancy between the implied model and the sample data is minimal Among various criteria for evaluating model fit, key fit indices include Chi-square/df = 1.189, GFI = 0.912, TLI = 0.984, CFI = 0.986, RMR = 0.024, and RMSEA = 0.024 These results indicate that the model's data fit is appropriate and aligns well with the implied model.
In section 4.2, the reliability statistics reveal that all variables exhibit Cronbach’s alpha values exceeding 0.6, indicating strong reliability This section will emphasize the composite reliability for each variable, providing a deeper understanding of their consistency.
The same methodology was utilized for other variables, with results presented in the table below Notably, all composite reliability values exceed 0.7, indicating strong reliability across the measures.
Table 9 Value of Composite Reliability
Components Value of composite reliability
4.4.2 Convergent Validity of all variables
The Average Variance Extracted (AVE) measures the average variance in indicated variables that a construct can explain Table 23 presents the calculated AVE for each factor.
Table 10 Value of Average Variance Extracted
The average variance extracted for all constructs, including overconfidence, excessive optimism, herd behavior, risk aversion, attitude towards behavior, subjective norm, perceived behavioral control, and behavioral intention to use, exceeds 0.5, confirming the convergent validity of the measurement.
4.4.3 Discriminant Validity of all variables
Table 12 Square root of AVE results
SN PBC HB EO OVC ATB RA BI
Table 11 illustrates that the MSV and ASV values for each scale are lower than the AVE values, indicating a strong discriminant validity among constructs Additionally, as shown in table 14, all AVE values exceed the r² values; for instance, the AVE for Subjective Norm and Perceived Behavioral Control are 0.690 and 0.704, respectively, while their r² value is 0.830 This indicates that the square root of the AVE is greater than the inter-construct correlations, further confirming the discriminant validity.
The study confirms that all composite reliability values exceed 0.7, and the average variance extracted is above 0.5, ensuring the convergent validity of measurements related to overconfidence, excessive optimism, herd behavior, risk aversion, attitude towards behavior, subjective norm, perceived behavioral control, and behavioral intention to use.
MSV and ASV values of each scale are smaller than the AVE values.
Moreover, it is obvious that all the AVE values are higher than the r 2 in table 12. For example, the AVE of subjective norm and perceived behavioral control are
When the correlation coefficient (r) between two variables is 0.690 and 0.704, resulting in an r² value of 0.830, it indicates that the square root of the Average Variance Extracted (AVE) is greater than the inter-construct correlations This finding confirms the presence of discriminant validity among the constructs.
Figure 4 First Measurement Standardized Modelling
Structural Equation Modeling (SEM)
Structural Equation Modeling (SEM) is a comprehensive multivariate analysis technique that encompasses various specialized methods, including path analysis, confirmatory factor analysis, second-order factor analysis, regression models, covariance structure models, and correlation structure models SEM is widely applied across different fields, providing a robust framework for analyzing complex relationships among variables The outcomes of Structural Equation Modeling are presented in a detailed format that illustrates these relationships effectively.
The SEM results indicate a strong model fit, with a Chi-square/df ratio of 1.296, GFI of 0.903, TLI of 0.974, CFI of 0.977, and RMSEA of 0.031 All hypotheses were supported, as evidenced by the highest p-value of 0.034, which is below the 0.05 threshold The standardized regression weights demonstrate the significant impact of factors such as overconfidence, excessive optimism, herd behavior, risk aversion, and subjective norms on attitudes towards behavior Additionally, attitudes towards behavior, subjective norms, and perceived behavioral control were found to influence the behavioral intention to use derivatives.
Table 13 Regression Weights of Model
ATTITUDE_TOWARD_BEHAVIOR < - SUBJECTIVE_NORM ***
ATTITUDE_TOWARD_BEHAVIOR < - EXCESSIVE_OPTIMISM 002
ATTITUDE_TOWARD_BEHAVIOR < - RISK_AVERSION 029
ATTITUDE_TOWARD_BEHAVIOR < - HERD_BEHAVIOR ***
BEHAVIORAL_INTENTION_TO_USE < - ATTITUDE_TOWARD_BEHAVIOR *** BEHAVIORAL_INTENTION_TO_USE < - PERCEIVED_BEHAVIORAL_CONTROL ***
BEHAVIORAL_INTENTION_TO_USE < - SUBJECTIVE_NORM 003
Indirect Effects of Behavior intention to use
Path analysis, an extension of Structural Equation Modeling (SEM), focuses on identifying the indirect effects of variables on the behavioral intention to use derivatives A significant value below 0.05 indicates that a variable contributes uniquely and significantly to predicting the dependent variable, while a value above 0.05 suggests no significant contribution (Pallant, 2005).
The data indicates that factors such as overconfidence, excessive optimism, herd behavior, risk aversion, and subjective norms significantly influence attitudes towards behavior Furthermore, attitudes towards behavior are also statistically significant in directly affecting the intention to use.
Table 14 Indirect effects on Behavior intention to use
Variable Calculation Indirect effect on
Total effect on Behavior intention to use
Independent Sample T-test and Oneway Anova
This study employs ANOVA and independent sample t-tests to analyze the relationship between demographic qualitative variables—such as gender, age, and education—and behavioral intentions to use derivatives.
Levene's Test for Equality of Variances t-test for Equality of Means
95% Confidence Interval of the Difference Lower Upper
The results of Levene's test indicated a p-value greater than 0.05, leading to the acceptance of the hypothesis of equal variances Additionally, the t-test also produced a p-value exceeding 0.05, suggesting that there is no significant difference in behavioral intentions to use derivatives between males and females.
In the Levene test, a significance level (sig.) of 0.05 or less indicates that the variance among the groups is significantly different, while a sig greater than 0.05 suggests no significant variance difference Subsequently, the ANOVA table should be examined; a sig value exceeding 0.05 denotes no significant difference between the variables, whereas a sig of 0.05 or lower indicates a significant difference among the variables.
Table 16 Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
The results from the "test of homogeneity of variances" in Table 16 indicate that there is no significant difference in variance across the age variable options Consequently, the ANOVA table will be examined next.
Sum of Squares df Mean
The ANOVA analysis reveals a p-value of less than 0.05, indicating a significant difference in behavioral intentions to use derivatives across different age groups The accompanying descriptive table clearly illustrates the variations in behavioral intentions based on age.
Maxi mum Lower Bound Upper Bound
Respondents with primary or secondary education tend to use fewer derivatives in the stock market, averaging around 2.5 In contrast, those with a high school education demonstrate a higher usage of derivatives, with an average of 3.7.
Individuals with a university degree or higher demonstrated the strongest intention to use derivatives in trading, with an average score of 4.3 This indicates that as education levels increase, the likelihood of engaging in derivative trading also rises.
Table 19 Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig
The result from the table 19 shows that there is no difference in variance between options in age variable Therefore, the anova table will be checked.
Sum of Squares df Mean Square F Sig
The findings from Table 9 indicate a significant difference in behavioral intentions to use derivatives based on age, as evidenced by a p-value of less than 0.05 The descriptive table further illustrates the varying levels of behavioral intention to use derivatives across different age groups.
Maxi mum Lower Bound Upper Bound
Respondents aged 18 to 25 utilize derivatives in the stock market less frequently, averaging 2.98, while those aged 26 to 35 show increased usage with an average of 3.7 Individuals over 35 exhibit the highest average derivative use at 4.6 This data indicates a positive correlation between age and the likelihood of using derivatives in stock trading.
Hypothesis testing results
The SEM results and statistical analyses, including T-test and ANOVA, indicated that eight out of nine hypotheses were supported by the data The supported hypotheses include H1, H1a, H1b, H1c, H1d, H2, H3, H4, H5b, and H5c, while H5a, which posited a difference between male and female investors' behavioral intentions in using financial derivative instruments, was not supported.
A detailed analysis of the structural paths reveals that, in line with hypothesis H1, investors' attitudes towards behavior significantly influence their intention to use financial derivative instruments (γ = 0.409, p < 0.05) Additionally, hypothesis H1a, which posits a negative relationship between overconfidence and individual investors' attitudes, is supported by the data (γ = -0.104, p < 0.05) Similarly, hypothesis H1b, suggesting a negative relationship between excessive optimism and individual investors' attitudes, is also validated (γ = -0.121, p < 0.05).
The study found that herd behavior significantly influences individual investors' attitudes, with a coefficient of γ = 0.272 (p < 0.05) Additionally, risk aversion also positively affects investor attitudes, indicated by γ = 0.153 (p < 0.05) Furthermore, a significant relationship exists between subjective norms and investors' behavioral intentions to use financial derivative instruments (γ = 0.170, p < 0.05), as well as between subjective norms and attitudes towards behavior (γ = 0.197, p < 0.05).
The study found a significant relationship between perceived behavioral control and investors' intentions to use financial derivative instruments (γ 0.347, p ATTITUDE_TOWARD_BEHAVIO
HERD_BEHAVIOR < > BEHAVIORAL_INTENTION_TO_
EXCESSIVE_OPTIMISM < > ATTITUDE_TOWARD_BEHAVIO
EXCESSIVE_OPTIMISM < > BEHAVIORAL_INTENTION_TO_
ATTITUDE_TOWARD_BEHAVIOR < > RISK_AVERSION 561
ATTITUDE_TOWARD_BEHAVIOR < > SUBJECTIVE_NORM 622
ATTITUDE_TOWARD_BEHAVIOR < > BEHAVIORAL_INTENTION_TO_
RISK_AVERSION < > BEHAVIORAL_INTENTION_TO_
SUBJECTIVE_NORM < > BEHAVIORAL_INTENTION_TO_
Variances: (Group number 1 - Default model)
Estimate S.E C.R P Label PERCEIVED_BEHAVIORAL_CONTROL 637 072 8.895 *** par_52
BEHAVIORAL_INTENTION_TO_USE 471 054 8.723 *** par_59 e1 275 029 9.556 *** par_60 e2 248 028 8.945 *** par_61 e3 250 028 8.965 *** par_62 e4 284 028 10.208 *** par_63 e5 330 032 10.219 *** par_64 e6 355 035 10.176 *** par_65
Estimate S.E C.R P Label e7 324 033 9.690 *** par_66 e8 433 040 10.719 *** par_67 e9 343 034 10.048 *** par_68 e10 224 027 8.438 *** par_69 e11 255 026 9.691 *** par_70 e12 277 030 9.372 *** par_71 e13 263 026 10.040 *** par_72 e14 304 031 9.692 *** par_73 e15 261 030 8.724 *** par_74 e16 202 024 8.296 *** par_75 e17 380 034 11.040 *** par_76 e18 193 020 9.610 *** par_77 e19 178 020 8.915 *** par_78 e20 260 025 10.522 *** par_79 e21 235 023 10.289 *** par_80 e22 234 026 9.106 *** par_81 e23 273 030 9.142 *** par_82 e24 308 029 10.591 *** par_83 e25 334 033 10.151 *** par_84 e26 250 033 7.554 *** par_85 e27 298 035 8.529 *** par_86 e28 321 033 9.620 *** par_87 e29 190 025 7.711 *** par_88 e30 242 026 9.216 *** par_89 e31 277 029 9.621 *** par_90
Model NPAR CMIN DF P CMIN/DF
Model RMR GFI AGFI PGFI
TLI rho2 CFI Default model 917 905 986 984 986
Independence model 000 000 000 000 000 Parsimony-Adjusted Measures
Model RMSEA LO 90 HI 90 PCLOSE
Model AIC BCC BIC CAIC
Default model 662.670 682.952 1000.971 1090.971Saturated model 992.000 1103.775 2856.415 3352.415Independence model 5891.062 5898.048 6007.588 6038.588
Model ECVI LO 90 HI 90 MECVI
Structural Equation Modeling
Regression Weights: (Group number 1 - Default model)
TION_TO_USE ARD_BEHAVIOR 409 086 4.759 *** par_24
TION_TO_USE AVIORAL_CONT 347 048 7.229 *** par_25
ROL BEHAVIORAL_INTEN SUBJECTIVE_NORM 151
Variances: (Group number 1 - Default model)
Estimate S.E C.R P Label PERCEIVED_BEHAVIORAL_CONTROL 640 072 8.918 *** par_43
SUBJECTIVE_NORM 687 075 9.223 *** par_48 e32 133 020 6.717 *** par_49 e33 217 030 7.210 *** par_50 e1 272 029 9.462 *** par_51 e2 250 028 8.939 *** par_52 e3 249 028 8.893 *** par_53 e4 285 028 10.202 *** par_54 e5 330 032 10.216 *** par_55 e6 354 035 10.163 *** par_56 e7 323 033 9.674 *** par_57 e8 434 040 10.721 *** par_58 e9 345 034 10.074 *** par_59 e10 223 027 8.372 *** par_60 e11 252 026 9.603 *** par_61 e12 278 030 9.349 *** par_62
Estimate S.E C.R P Label e13 266 026 10.071 *** par_63 e14 302 031 9.644 *** par_64 e15 262 030 8.742 *** par_65 e16 201 024 8.250 *** par_66 e17 382 035 11.063 *** par_67 e18 195 020 9.696 *** par_68 e19 180 020 9.045 *** par_69 e20 261 025 10.574 *** par_70 e21 234 023 10.320 *** par_71 e22 231 026 8.995 *** par_72 e23 277 030 9.193 *** par_73 e24 309 029 10.585 *** par_74 e25 334 033 10.127 *** par_75 e26 249 033 7.529 *** par_76 e27 298 035 8.518 *** par_77 e28 322 033 9.645 *** par_78 e29 191 025 7.640 *** par_79 e30 247 027 9.290 *** par_80 e31 270 029 9.412 *** par_81
Squared Multiple Correlations: (Group number 1 - Default model)
Model NPAR CMIN DF P CMIN/DF
Model RMR GFI AGFI PGFI
TLI rho2 CFI Default model 908 897 977 974 977
Independence model 000 000 000 000 000 Parsimony-Adjusted Measures