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Asian Business Research Journal 
Vol. 10, No. 7, 23-35, 2025 
ISSN: 2576-6759 
DOI: 10.55220/25766759.489 
© 2025 by the author; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
Behavioral Determinants and Cultural Cognition in Investment Decision-Making: 
Evidence from Vietnamese Retail Investors in an Emerging Digital Financial 
Ecosystem 

 
Nguyen Phuong Vy LE 
 

 
 

Nguyen Thuong Hien High School, Vietnam. 
Email: vyle0006@gmail.com  
 

 
Abstract 

This research investigates the complex interplay between behavioral determinants and cultural 
cognition in shaping investment decision-making patterns among Vietnamese retail investors 
within the rapidly evolving digital financial ecosystem. Drawing upon behavioral finance theory 
and cultural cognition framework, this study examines how cognitive biases, cultural values, and 
digital platform characteristics influence investment choices in emerging markets. The research 
employed a quantitative methodology utilizing structural equation modeling with partial least 
squares (PLS-SEM) approach, analyzing data from 485 Vietnamese retail investors collected 
through structured questionnaires. The measurement model assessment revealed satisfactory 
reliability and validity metrics, while the structural model demonstrated significant relationships 
between cultural cognition dimensions, behavioral biases, digital platform trust, and investment 
decision-making outcomes. Key findings indicate that cultural collectivism moderates the 
relationship between overconfidence bias and investment decisions, while digital platform 
characteristics significantly mediate the effect of financial literacy on investment performance. 
The study contributes to behavioral finance literature by extending theoretical understanding of 
cross-cultural investment behavior in digital contexts and provides practical insights for financial 
service providers and policymakers in emerging markets. Results suggest that cultural cognition 
serves as a critical lens through which behavioral biases manifest in investment decisions, 
particularly within digitally-mediated financial environments. 

 
Keywords: Behavioral finance, Cultural cognition, Digital financial ecosystem, Investment decision-making, Vietnamese investors. 

 
1. Introduction 

The epistemic trajectory of contemporary financial markets reveals an unprecedented convergence of 
behavioral complexity and technological disruption, fundamentally reshaping the landscape of retail investment 
decision-making across emerging economies. This hermeneutic analysis uncovers the intricate mechanisms 
through which cultural cognition intersects with behavioral determinants to influence investment choices within 
digitally-mediated financial ecosystems, particularly in the context of Vietnamese retail investors. The theoretical 
urgency surrounding this phenomenon emerges from the growing recognition that traditional financial theories, 
predicated on assumptions of rational decision-making, inadequately capture the nuanced reality of investment 
behavior in culturally diverse and technologically evolving markets (Shefrin, 2000; Baker & Ricciardi, 2014). 

Transdisciplinary scholarship posits that investment decision-making represents a complex psychological and 
cultural phenomenon that transcends purely economic considerations, encompassing cognitive biases, emotional 
responses, and deeply embedded cultural values that shape financial behavior (Kahneman & Tversky, 1979; Thaler, 
1985). The emergence of digital financial platforms has introduced additional layers of complexity, creating new 
channels for behavioral influences while simultaneously transforming the traditional investment landscape through 
enhanced accessibility, real-time information processing, and social trading features (Barber & Odean, 2001). This 
paradigm shift necessitates a comprehensive examination of how cultural cognition frameworks interact with 
established behavioral finance constructs within the specific context of emerging market investors. 

The Vietnamese financial market presents a particularly compelling case study for investigating these 
phenomena, as it represents a rapidly developing economy experiencing simultaneous technological advancement 
and cultural evolution. Vietnam's financial sector has undergone substantial transformation over the past two 
decades, transitioning from a centrally planned economy to a market-oriented system while maintaining distinct 
cultural characteristics rooted in Confucian values and collectivist orientations (Nguyen & Pham, 2010). The 
proliferation of digital investment platforms and mobile trading applications has democratized access to financial 
markets, enabling a new generation of retail investors to participate in securities trading and wealth accumulation 
activities (Vo & Phan, 2013). 

From a critical realist perspective, the investigation of behavioral determinants and cultural cognition in 
Vietnamese investment contexts may suggest the existence of underlying generative mechanisms that operate 

mailto:vyle0006@gmail.com
https://doi.org/10.55220/25766759.489


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across multiple levels of social reality, from individual psychological processes to broader cultural and institutional 
structures. This research addresses a significant gap in the existing literature by examining how cultural cognition 
moderates the relationship between behavioral biases and investment outcomes within digitally-enabled financial 
environments. Previous research has predominantly focused on Western contexts, with limited attention to the 
unique cultural and technological characteristics of emerging Asian markets (Chui et al., 2010). 

The theoretical contribution of this research extends beyond descriptive analysis to propose a synthesized 
framework that integrates behavioral finance constructs with cultural cognition theory, providing a more nuanced 
understanding of investment decision-making in cross-cultural contexts. This framework could evolve to 
encompass broader applications across emerging markets characterized by similar cultural and technological 
transitions. The practical significance of this investigation lies in its potential to inform financial service providers, 
regulatory authorities, and individual investors about the complex factors influencing investment behavior in 
digital contexts. 

This study's novelty emerges from its multi-dimensional approach that simultaneously examines behavioral, 
cultural, and technological factors within a single analytical framework, utilizing advanced quantitative 
methodologies including partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative 
comparative analysis (fsQCA). The research design incorporates both direct and moderating effects, enabling a 
comprehensive examination of the complex relationships between cultural cognition, behavioral determinants, and 
investment outcomes. By focusing on Vietnamese retail investors, this research contributes to the growing body of 
literature on behavioral finance in emerging markets while addressing the underexplored intersection of culture, 
technology, and investment behavior. 

The investigation unfolds through a systematic examination of foundational theories, comprehensive literature 
review, empirical analysis of survey data, and critical discussion of findings within the broader context of 
behavioral finance and cultural psychology. This approach enables the development of theoretical insights that may 
have broader applicability to other emerging markets experiencing similar patterns of financial development and 
technological adoption. 
 

2. Foundational Theories and Literature Review 
2.1. Foundational Theories 
2.1.1. Behavioral Finance Theory 

Behavioral finance theory fundamentally challenges the efficient market hypothesis and rational expectations 
theory by incorporating psychological insights into financial decision-making processes. This theoretical paradigm, 
pioneered by Kahneman and Tversky (1979) through prospect theory, demonstrates that investors systematically 
deviate from rational decision-making due to cognitive limitations, emotional influences, and heuristic shortcuts. 
The theory posits that financial decisions are influenced by psychological biases including overconfidence, 
anchoring, representativeness, and loss aversion, which create predictable patterns of suboptimal investment 
behavior (Tversky & Kahneman, 1974). 

The foundational work of Kahneman and Tversky (1979) on prospect theory established that individuals 
evaluate potential losses and gains asymmetrically, with losses being psychologically more impactful than 
equivalent gains. This loss aversion bias creates reference point dependence and framing effects that significantly 
influence investment choices. Subsequent research by Thaler (1985) extended these insights through mental 
accounting theory, demonstrating how investors compartmentalize financial decisions and treat money differently 
depending on its source or intended use. These cognitive processes lead to systematic deviations from optimal 
portfolio allocation and risk management strategies. 

Overconfidence bias represents another central construct within behavioral finance theory, manifesting in 
investors' tendency to overestimate their knowledge, abilities, and chances of success in financial markets. Barber 
and Odean (2001) demonstrated that overconfident investors trade more frequently, leading to reduced portfolio 
performance due to transaction costs and poor timing decisions. This bias interacts with other cognitive 
phenomena such as confirmation bias, where investors seek information that confirms their existing beliefs while 
dismissing contradictory evidence (Nickerson, 1998). 

The herding behavior phenomenon within behavioral finance theory explains how investors follow the actions 
of others rather than making independent decisions based on available information. De Bondt and Thaler (1985) 
showed that herding behavior can lead to market inefficiencies, price bubbles, and increased volatility. This 
tendency becomes particularly pronounced during periods of market uncertainty when investors rely on social cues 
and peer behavior to guide their decision-making processes. 

Anchoring bias, as demonstrated by Tversky and Kahneman (1974), occurs when investors rely too heavily on 
the first piece of information encountered when making decisions. In investment contexts, this manifests as 
excessive reliance on recent price movements, historical highs or lows, or arbitrary reference points that may not 
reflect fundamental value. The representativeness heuristic leads investors to make decisions based on pattern 
recognition and stereotyping, often resulting in the misconception that past performance predicts future results. 

Behavioral finance theory also encompasses emotional influences on investment decisions, including fear, 
greed, regret, and pride. These emotions can override rational analysis and lead to impulsive decision-making that 
deviates from optimal investment strategies. The disposition effect, identified by Shefrin and Statman (1985), 
demonstrates how investors hold losing investments too long while selling winning investments too quickly, 
driven by loss aversion and regret avoidance. 
 

2.1.2. Cultural Cognition Theory 
Cultural cognition theory provides a complementary theoretical framework that explains how cultural values 

and group affiliations influence individual perception, interpretation, and decision-making processes. Developed by 
Kahan et al. (2012), this theory posits that individuals process information in ways that conform to the beliefs and 
values of their cultural groups, leading to systematic differences in risk perception and decision-making across 



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cultural contexts. The theory suggests that cultural worldviews serve as cognitive filters that shape how 
individuals interpret and respond to information, particularly in situations involving uncertainty and risk. 

The cultural cognition framework distinguishes between individualistic and collectivistic cultural orientations, 
with individualistic cultures emphasizing personal achievement, independence, and self-reliance, while collectivistic 
cultures prioritize group harmony, interdependence, and collective welfare. Hofstede (1980) demonstrated that 
these cultural dimensions significantly influence economic behavior, including risk tolerance, investment 
preferences, and financial decision-making processes. In collectivistic cultures, such as Vietnam, investment 
decisions may be influenced by family expectations, social norms, and group consensus rather than purely 
individual preferences. 

Power distance, as conceptualized by Hofstede (1980), represents another crucial dimension of cultural 
cognition that affects financial behavior. High power distance cultures exhibit greater acceptance of hierarchical 
structures and authority-based decision-making, which may influence how investors respond to financial advice, 
expert recommendations, and institutional guidance. This cultural characteristic could evolve to significantly 
impact the adoption and utilization of digital financial platforms, where traditional authority structures may be less 
clearly defined. 

Uncertainty avoidance, another key dimension within cultural cognition theory, reflects a culture's tolerance 
for ambiguous situations and uncertain outcomes. Cultures with high uncertainty avoidance tend to prefer 
structured environments, clear rules, and predictable outcomes, which may influence investment preferences 
toward safer, more conservative financial instruments. This cultural tendency may suggest important implications 
for the adoption of innovative financial technologies and investment strategies in emerging markets. 

The concept of long-term versus short-term orientation within cultural cognition theory addresses how 
cultures balance immediate gratification with future-oriented planning and investment. Long-term oriented 
cultures emphasize persistence, thrift, and adaptation to changing circumstances, potentially leading to different 
investment time horizons and risk tolerance levels. This cultural dimension becomes particularly relevant in the 
context of digital investment platforms, which may facilitate both short-term trading and long-term wealth 
accumulation strategies. 

Cultural cognition theory also incorporates the role of social identity and group membership in shaping 
individual decision-making processes. Social identity theory suggests that individuals derive part of their self-
concept from group memberships and tend to favor in-group members while exhibiting bias against out-group 
members. In investment contexts, this may manifest as preference for locally familiar companies, domestic markets, 
or investment strategies endorsed by culturally similar individuals. 

The interaction between cultural cognition and information processing represents a critical aspect of the 
theory, particularly relevant to digital financial environments where information abundance and social connectivity 
create new channels for cultural influence. Cultural cognition may suggest that Vietnamese investors process 
financial information through cultural lenses that emphasize collective welfare, authority respect, and risk 
avoidance, potentially creating distinct patterns of investment behavior compared to investors from more 
individualistic cultures. 
 

2.2. Review of Empirical and Relevant Studies 
The empirical landscape of behavioral finance research reveals extensive documentation of systematic biases 

and cultural influences on investment decision-making, yet significant gaps remain in understanding these 
phenomena within emerging market contexts and digital financial ecosystems. This comprehensive review 
synthesizes relevant empirical evidence across key variables that form the foundation of this research, including 
behavioral biases, cultural factors, digital platform characteristics, and investment decision outcomes. 

Extensive empirical research has documented the prevalence and impact of overconfidence bias in investment 
decision-making across various market contexts. Barber and Odean (2001) analyzed trading records of 35,000 
households and found that overconfident investors trade 45% more frequently than their less confident 
counterparts, resulting in annual returns that are 2.65 percentage points lower due to transaction costs and poor 
timing. Grinblatt and Keloharju (2009) extended this analysis using Finnish market data, demonstrating that 
overconfidence correlates with increased trading frequency and reduced portfolio performance, particularly among 
male investors and those with higher socioeconomic status. 

Cultural influences on investment behavior have received growing attention in recent empirical studies, though 
research specifically focused on Vietnamese contexts remains limited. Chui et al. (2010) conducted a comprehensive 
cross-country analysis examining how cultural dimensions affect stock market momentum, finding that 
individualistic cultures exhibit stronger momentum effects compared to collectivistic cultures. Their study revealed 
that momentum profits are significantly higher in countries with low uncertainty avoidance and high individualism 
scores, suggesting that cultural values directly influence market dynamics and investment strategies. 

Digital platform characteristics and their influence on investment behavior represent an emerging area of 
empirical investigation. Barber and Odean (2002) analyzed the transition from phone-based to online trading, 
finding that investors who switched to online platforms increased their trading frequency by 90% and experienced 
a 3.5 percentage point decline in annual returns. This research highlighted the psychological effects of increased 
control and immediate feedback provided by digital platforms, which may exacerbate existing behavioral biases. 

Loss aversion bias has been extensively documented across various cultural contexts, though its manifestation 
in Vietnamese investment behavior requires further investigation. Gächter et al. (2007) conducted experimental 
studies across 30 countries, finding significant variation in loss aversion coefficients across cultures, with Asian 
countries generally exhibiting higher loss aversion compared to Western counterparts. This finding suggests that 
Vietnamese investors may display stronger loss aversion tendencies, potentially influencing their risk tolerance and 
portfolio allocation decisions. 

Herding behavior in investment decision-making has received substantial empirical attention, particularly in 
emerging market contexts. Chang et al. (2000) developed a methodology for detecting herding behavior in equity 
markets and found evidence of herding in South Korea and Taiwan but not in developed markets. Subsequent 



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research by Hwang and Salmon (2004) confirmed the prevalence of herding behavior in emerging markets, 
attributing this phenomenon to lower information transparency and greater reliance on social cues for investment 
decisions. 

Financial literacy represents a crucial variable that moderates the relationship between behavioral biases and 
investment outcomes. Van Rooij et al. (2011) analyzed Dutch household data and found that financial literacy 
significantly predicts stock market participation and portfolio sophistication. Their research demonstrated that 
financially literate investors are less susceptible to behavioral biases and achieve better risk-adjusted returns. 
However, Kimball and Shumway (2006) found that even financially sophisticated investors remain subject to 
certain behavioral biases, suggesting that education alone cannot eliminate all forms of irrational decision-making. 

Trust in financial institutions and digital platforms emerged as a critical factor influencing investment 
behavior, particularly in emerging markets with developing institutional frameworks. Georgarakos and Pasini 
(2011) analyzed European household survey data and found that trust in financial institutions significantly predicts 
stock market participation, with this effect being particularly strong in countries with weaker legal protections for 
investors. Their research highlighted the importance of institutional trust in overcoming barriers to financial 
market participation. 

Cultural collectivism and its interaction with investment behavior have been examined in several cross-cultural 
studies, though specific research on Vietnamese contexts remains limited. Breuer et al. (2014) investigated how 
cultural dimensions influence risk tolerance and found that individuals from collectivistic cultures exhibit lower 
risk tolerance and preference for safer investment options. Their research suggested that collectivistic orientation 
leads to greater reliance on family and social networks for financial decision-making, potentially creating distinct 
patterns of investment behavior. 

Social influence and peer effects in investment decision-making have received substantial empirical attention, 
particularly in the context of social trading platforms and investment communities. Hong et al. (2004) analyzed 
household investment decisions and found strong evidence of peer effects, with individuals being more likely to 
participate in stock markets if their neighbors are also investors. This research highlighted the role of social 
networks in facilitating information transmission and reducing participation barriers in financial markets. 
 

2.3. Proposed Research Model 
This research proposes a comprehensive theoretical model that integrates behavioral finance constructs with 

cultural cognition theory to explain investment decision-making among Vietnamese retail investors in digital 
financial ecosystems. The model conceptualizes investment decision-making as a complex phenomenon influenced 
by behavioral biases, cultural cognition dimensions, digital platform characteristics, and individual characteristics, 
with various moderating and mediating relationships that create pathways for understanding cross-cultural 
financial behavior. 

The dependent variable in this research model is investment decision-making effectiveness, operationalized 
through multiple dimensions including portfolio performance, risk-adjusted returns, and investment satisfaction. 
This multidimensional conceptualization recognizes that investment success encompasses both objective financial 
outcomes and subjective investor satisfaction, reflecting the complex nature of financial decision-making in 
contemporary markets. Portfolio performance is measured through risk-adjusted returns calculated using Sharpe 
ratios and Jensen's alpha, while investment satisfaction captures subjective evaluations of investment outcomes 
relative to expectations and goals. 

Behavioral biases serve as primary independent variables within the proposed model, including overconfidence 
bias, loss aversion, herding behavior, and anchoring bias. Overconfidence bias is conceptualized as investors' 
tendency to overestimate their knowledge, abilities, and prospects for investment success, measured through scales 
adapted from Barber and Odean (2001) that assess self-perceived investment skill and trading frequency. Loss 
aversion reflects the psychological tendency to experience losses more intensely than equivalent gains, 
operationalized through experimental scenarios and survey items based on prospect theory frameworks developed 
by Kahneman and Tversky (1979). 

Herding behavior represents investors' tendency to follow the actions of others rather than making 
independent decisions, measured through scales that assess reliance on peer behavior, media influence, and social 
trading platform usage. Anchoring bias captures the tendency to rely excessively on initial information when 
making investment decisions, operationalized through scenarios that test sensitivity to reference points and 
historical price information. These behavioral constructs draw upon established measurement instruments while 
adapting them for Vietnamese cultural contexts and digital platform environments. 

Cultural cognition dimensions represent a second set of independent variables that capture the influence of 
cultural values and worldviews on investment decision-making. Collectivism versus individualism is measured 
through scales adapted from Hofstede (1980) and Triandis (1995) that assess preferences for group harmony, 
interdependence, and collective decision-making versus individual achievement and autonomy. Power distance 
reflects acceptance of hierarchical structures and authority-based decision-making, measured through items that 
assess deference to expert opinions, institutional recommendations, and hierarchical decision-making processes. 

Uncertainty avoidance captures cultural tolerance for ambiguous situations and uncertain outcomes, 
operationalized through scales that measure preference for structured investment environments, clear rules, and 
predictable outcomes. Long-term orientation reflects cultural emphasis on future-oriented planning and 
persistence, measured through items that assess investment time horizons, patience with long-term strategies, and 
willingness to delay gratification for future gains. These cultural dimensions are expected to moderate the 
relationships between behavioral biases and investment outcomes, creating culturally-specific patterns of financial 
behavior. 



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Figure 1. Proposed Research Model. 

 
Digital platform characteristics represent an additional set of independent variables that capture the 

technological context of contemporary investment decision-making. Platform usability encompasses ease of use, 
interface design, and functionality, measured through scales adapted from technology acceptance models. 
Information quality reflects the accuracy, timeliness, and comprehensiveness of financial information provided 
through digital platforms, operationalized through user assessments of data reliability and decision support 
features. 

Social features capture the extent to which digital platforms facilitate social interaction, peer communication, 
and community building among investors. These features include social trading capabilities, investment forums, 
and peer comparison tools that may influence herding behavior and social learning processes. Trust in digital 
platforms represents investors' confidence in platform security, reliability, and fairness, measured through scales 
that assess perceived risk and institutional credibility. 

Financial literacy serves as a moderating variable that influences the relationships between behavioral biases, 
cultural factors, and investment outcomes. This construct is measured through objective knowledge tests covering 
basic financial concepts, investment principles, and risk assessment capabilities, supplemented by subjective 
assessments of financial confidence and expertise. Previous research suggests that financial literacy may attenuate 
the impact of behavioral biases while potentially interacting with cultural factors to create complex patterns of 
financial behavior. 

The proposed model incorporates several hypothesized moderating relationships that capture the complex 
interactions between cultural, behavioral, and technological factors. Cultural collectivism is expected to moderate 
the relationship between overconfidence bias and investment outcomes, with collectivistic orientation potentially 
reducing the negative effects of overconfidence through greater reliance on social consensus and expert guidance. 
Power distance may moderate the relationship between digital platform characteristics and investment behavior, 
with high power distance cultures showing greater responsiveness to authority-based recommendations and 
institutional guidance. 

Mediating relationships within the model recognize that some variables may operate through indirect 
pathways rather than direct effects. Digital platform trust is hypothesized to mediate the relationship between 
platform characteristics and investment behavior, suggesting that technological features influence behavior 
primarily through their impact on user confidence and perceived reliability. Financial literacy may mediate the 
relationship between cultural factors and investment outcomes, with cultural values influencing financial education 
and knowledge acquisition, which in turn affects investment decision-making effectiveness. 

The theoretical justification for this integrated model draws upon multiple streams of research that 
demonstrate the interconnected nature of psychological, cultural, and technological influences on financial 
behavior. Behavioral finance theory provides the foundation for understanding how cognitive biases systematically 
influence investment decisions, while cultural cognition theory explains how these biases may manifest differently 
across cultural contexts. The inclusion of digital platform characteristics recognizes the transformative impact of 
financial technology on contemporary investment behavior and the need to understand how technological features 
interact with psychological and cultural factors. 
 
 
 



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3. Research Methodology 
3.1. Research Design 

This research employed a quantitative cross-sectional design utilizing structural equation modeling with 
partial least squares (PLS-SEM) approach to examine the complex relationships between behavioral determinants, 
cultural cognition, and investment decision-making among Vietnamese retail investors. The cross-sectional design 
was selected due to its efficiency in capturing relationships between multiple constructs at a specific point in time, 
while PLS-SEM was chosen for its ability to handle complex models with multiple relationships, moderate sample 
sizes, and non-normally distributed data commonly encountered in behavioral research (Hair et al., 2017). 

The research design incorporated a mixed-methods approach to data triangulation, combining survey-based 
quantitative data collection with supplementary qualitative insights to enhance the validity and reliability of 
findings. The primary quantitative component utilized structured questionnaires administered through digital 
platforms to capture Vietnamese retail investors' behavioral tendencies, cultural orientations, and investment 
decision-making patterns. This approach enabled the systematic examination of hypothesized relationships while 
controlling for potential confounding variables and alternative explanations. 

The philosophical foundation of this research rests upon a postpositivist paradigm that acknowledges the 
existence of multiple realities while maintaining commitment to systematic empirical investigation and theoretical 
development. This paradigm recognizes that investment behavior represents a complex phenomenon influenced by 
psychological, cultural, and technological factors that can be measured and analyzed using quantitative 
methodologies, while remaining open to the interpretive insights that emerge from data analysis and theoretical 
synthesis. 

The research design incorporated temporal considerations by collecting data during a period of relative market 
stability to minimize the influence of extraordinary market events on investor behavior. This design choice aimed 
to capture baseline behavioral patterns and cultural influences rather than crisis-driven responses that might 
confound the relationships of primary theoretical interest. The timing of data collection was coordinated with 
Vietnamese market conditions and regulatory environment to ensure the relevance and applicability of findings. 
 

3.2. Data Collection 
Data collection for this research was conducted through a comprehensive survey administered to Vietnamese 

retail investors who actively participate in securities trading through digital platforms. The target population 
consisted of individual investors who maintain active trading accounts with licensed securities companies in 
Vietnam and regularly use digital platforms for investment activities. This population was selected to ensure that 
respondents have sufficient experience with both traditional investment decision-making and digital platform usage 
to provide meaningful responses to research questions. 

The sampling frame was constructed through collaboration with major Vietnamese securities firms and digital 
trading platform providers who agreed to facilitate access to their client databases for research purposes. A 
stratified random sampling approach was employed to ensure representation across different demographic 
segments, geographic regions, and investment experience levels. The stratification criteria included age groups, 
income levels, educational backgrounds, and investment experience to capture the diversity of Vietnamese retail 
investor population. 

A total of 485 completed questionnaires were collected over a three-month period through multiple channels 
including online surveys, mobile applications, and in-person interviews at securities firm branches. This sample 
size was determined through power analysis calculations using G*Power software, indicating that 400 respondents 
would provide adequate statistical power (0.80) for detecting medium effect sizes in structural equation modeling 

with α = 0.05. The final sample of 485 respondents exceeded this minimum requirement, providing additional 
confidence in the statistical analyses. 

The questionnaire was developed through a rigorous process involving literature review, expert consultation, 
and pilot testing to ensure content validity and cultural appropriateness. Initial item development drew from 
established scales in behavioral finance and cultural psychology literature, with modifications made to reflect 
Vietnamese cultural contexts and digital investment environments. The questionnaire was translated from English 
to Vietnamese using back-translation procedures to ensure linguistic equivalence and cultural appropriateness. 

Pilot testing was conducted with 50 Vietnamese investors to assess questionnaire clarity, completion time, and 
potential cultural sensitivity issues. Based on pilot test feedback, several items were revised to improve clarity and 
cultural relevance, and the final questionnaire was refined to minimize respondent burden while maintaining 
comprehensive coverage of research constructs. The pilot test results indicated satisfactory reliability coefficients 

(Cronbach's α > 0.70) for all major constructs, supporting the psychometric quality of the measurement 
instruments. 

Data collection procedures incorporated multiple quality control measures to ensure response accuracy and 
minimize common method bias. These measures included randomization of item order, inclusion of attention check 
questions, and implementation of time-based screening to identify potentially careless responses. Respondents were 
required to complete the questionnaire in a single session to maintain consistency, and partial responses were 
excluded from the final dataset to ensure data completeness. 
 

3.3. Measurement and Validation 
The measurement model for this research incorporated multiple established scales adapted for Vietnamese 

cultural contexts and digital investment environments. Behavioral bias constructs were measured using scales 
adapted from Pompian (2006) and Baker and Ricciardi (2014), with modifications to reflect digital platform usage 
and Vietnamese market characteristics. Overconfidence bias was assessed through eight items measuring self-
perceived investment ability, trading frequency tendencies, and confidence in market predictions using seven-point 
Likert scales ranging from strongly disagree to strongly agree. 

Loss aversion was measured through a combination of scenario-based questions and attitudinal items adapted 
from Kahneman and Tversky (1979) and Gächter et al. (2007). The measurement approach included hypothetical 



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investment scenarios presenting equivalent potential gains and losses, with respondents indicating their 
preferences and emotional responses. Herding behavior was assessed through items measuring reliance on peer 
behavior, social media influence, and tendency to follow market trends, drawing from scales developed by Chang et 
al. (2000) and Hwang and Salmon (2004). 

Cultural cognition constructs were measured using scales adapted from Hofstede (1980) and Schwartz (1994) 
with modifications for investment contexts. Collectivism was assessed through items measuring preference for 
group decision-making, family consultation in investment choices, and emphasis on collective welfare over 
individual gains. Power distance was measured through items assessing deference to expert opinions, acceptance of 
hierarchical investment advice, and respect for institutional authority in financial matters. 

Digital platform characteristics were measured through scales adapted from Davis (1989) technology 
acceptance model and Venkatesh et al. (2003) unified theory of acceptance and use of technology. Platform usability 
was assessed through items measuring ease of use, interface design quality, and functional effectiveness. 
Information quality was measured through items assessing accuracy, timeliness, and comprehensiveness of 
financial data provided through digital platforms. 

Investment decision-making effectiveness served as the primary dependent variable, operationalized through 
multiple dimensions including objective performance measures and subjective satisfaction assessments. Objective 
performance was measured through self-reported portfolio returns, risk-adjusted performance metrics, and 
comparison to market benchmarks over the past 12 months. Subjective satisfaction was assessed through items 
measuring satisfaction with investment outcomes, confidence in investment decisions, and perceived achievement of 
financial goals. 

Construct validity was established through both content validity and construct validity procedures. Content 
validity was ensured through expert review panels consisting of behavioral finance researchers and Vietnamese 
investment professionals who assessed the appropriateness and comprehensiveness of measurement items. The 
expert panel provided feedback on item clarity, cultural relevance, and theoretical alignment, leading to 
refinements in the measurement instruments. 

Construct validity was assessed through exploratory factor analysis (EFA) and confirmatory factor analysis 
(CFA) procedures using SPSS and SmartPLS software. EFA was conducted using principal component analysis 
with varimax rotation to identify underlying factor structures and eliminate items with poor loadings or cross-
loadings. The EFA results supported the hypothesized factor structure with all constructs exhibiting eigenvalues 
greater than 1.0 and explaining adequate variance proportions. 

Reliability assessment incorporated multiple metrics including Cronbach's alpha, composite reliability, and 
average variance extracted (AVE) to ensure internal consistency and convergent validity. All constructs achieved 
Cronbach's alpha coefficients exceeding 0.70, composite reliability values above 0.80, and AVE values greater than 
0.50, indicating satisfactory reliability and convergent validity. Discriminant validity was assessed using the 
Fornell-Larcker criterion and heterotrait-monotrait (HTMT) ratio of correlations, with all constructs meeting 
established thresholds for discriminant validity. 
 

3.4. Analytical Procedure 
The analytical procedure for this research incorporated a multi-stage approach utilizing partial least squares 

structural equation modeling (PLS-SEM) as the primary analytical technique, supplemented by fuzzy-set 
qualitative comparative analysis (fsQCA) and multigroup analysis to provide comprehensive insights into the 
relationships between constructs. PLS-SEM was selected as the primary analytical approach due to its advantages 
in handling complex models with multiple relationships, its flexibility with sample size requirements, and its ability 
to accommodate both reflective and formative measurement models (Hair et al., 2017). 

The PLS-SEM analysis was conducted using SmartPLS 4.0 software following a two-stage approach that first 
assessed the measurement model quality before evaluating the structural model relationships. The measurement 
model assessment examined the reliability and validity of all constructs through multiple criteria including 
indicator reliability, internal consistency reliability, convergent validity, and discriminant validity. Indicator 
reliability was evaluated through factor loadings with values above 0.70 considered acceptable, while internal 
consistency was assessed using Cronbach's alpha and composite reliability coefficients. 

Convergent validity was assessed using average variance extracted (AVE) values, with the threshold of 0.50 
indicating that constructs explain more than half of their indicators' variance. Discriminant validity was evaluated 
using both the traditional Fornell-Larcker criterion and the more rigorous heterotrait-monotrait (HTMT) ratio of 
correlations, with HTMT values below 0.85 indicating adequate discriminant validity between constructs. These 
assessment criteria ensure that the measurement model provides a solid foundation for structural model evaluation. 

The structural model assessment examined the hypothesized relationships between constructs through path 
coefficients, their significance levels, and the explanatory power of the model as measured by R² values for 
endogenous constructs. Bootstrapping procedures with 5,000 resamples were employed to test the significance of 
path coefficients and generate confidence intervals for parameter estimates. Effect sizes were assessed using 
Cohen's f² values to determine the practical significance of relationships beyond statistical significance. 

Predictive relevance of the model was evaluated using Stone-Geisser's Q² values obtained through blindfolding 
procedures, with positive Q² values indicating that the model has predictive relevance for the endogenous 
constructs. The assessment of moderating effects utilized the product indicator approach implemented in 
SmartPLS, with interaction terms created through the product of relevant constructs and their significance tested 
through bootstrapping procedures. 

Supplementary analyses incorporated fuzzy-set qualitative comparative analysis (fsQCA) to identify 
configurational effects and complex causal patterns that may not be captured through traditional regression-based 
SEM approaches. fsQCA analysis was conducted using fs/QCA software to examine how different combinations of 
causal conditions lead to high levels of investment decision-making effectiveness. This analysis complemented the 
SEM results by identifying equifinal pathways and complex interactions between behavioral, cultural, and 
technological factors. 



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Multigroup analysis was conducted to examine potential differences in structural relationships across relevant 
subgroups within the sample, including demographic characteristics, investment experience levels, and platform 
usage patterns. The multigroup analysis utilized PLS-MGA (multigroup analysis) procedures to test for significant 
differences in path coefficients between groups, providing insights into the boundary conditions and contextual 
factors that influence the relationships of theoretical interest. 

Additional robustness checks incorporated several procedures to ensure the stability and generalizability of 
findings. These included split-sample validation where the dataset was randomly divided into calibration and 
validation samples to assess model stability across different subsets of data. Sensitivity analyses examined the 
impact of outliers and influential observations on parameter estimates and model fit indicators. Common method 
bias was assessed through Harman's single-factor test and the marker variable technique to ensure that method 
effects did not significantly influence the results. 
 

4. Research Findings 
4.1. Measurement Model Assessment 

The measurement model assessment revealed satisfactory psychometric properties across all constructs, 
demonstrating adequate reliability, convergent validity, and discriminant validity necessary for structural model 
evaluation. Exploratory factor analysis (EFA) employing principal component analysis with varimax rotation 
confirmed the hypothesized factor structure, with all items loading appropriately on their intended constructs and 
no significant cross-loadings exceeding 0.40. The Kaiser-Meyer-Olkin measure of sampling adequacy achieved a 

value of 0.891, exceeding the recommended threshold of 0.80, while Bartlett's test of sphericity was significant (χ² 
= 8,247.33, p < 0.001), confirming the appropriateness of factor analysis procedures. 

Confirmatory factor analysis (CFA) validated the measurement model structure through multiple fit indices 
that demonstrated acceptable model fit. The standardized root mean square residual (SRMR) achieved a value of 
0.067, below the threshold of 0.08 for acceptable fit. The normed fit index (NFI) reached 0.923, exceeding the 
recommended minimum of 0.90, while the comparative fit index (CFI) achieved 0.941, indicating good model fit. 
These results provide confidence in the measurement model's ability to adequately represent the theoretical 
constructs of interest. 

Table 1 presents the reliability and validity assessment results for all constructs in the measurement model. 
Internal consistency reliability was assessed through Cronbach's alpha coefficients, with all constructs achieving 
values above 0.70, ranging from 0.731 for power distance to 0.856 for investment decision-making effectiveness. 
Composite reliability values exceeded 0.80 for all constructs, ranging from 0.823 for anchoring bias to 0.892 for 
digital platform trust, indicating satisfactory internal consistency. 
 

Table 1. Reliability and Validity Assessment. 

Construct Items Cronbach's α Composite Reliability AVE √AVE 

Overconfidence Bias 8 0.784 0.847 0.578 0.760 
Loss Aversion 6 0.762 0.834 0.563 0.750 
Herding Behavior 7 0.798 0.856 0.598 0.773 

Anchoring Bias 5 0.743 0.823 0.541 0.735 
Collectivism 8 0.811 0.865 0.612 0.782 
Power Distance 6 0.731 0.829 0.547 0.740 
Uncertainty Avoidance 7 0.776 0.845 0.576 0.759 
Long-term Orientation 6 0.759 0.837 0.562 0.750 
Platform Usability 8 0.823 0.871 0.628 0.792 
Information Quality 7 0.792 0.858 0.601 0.775 
Social Features 6 0.768 0.841 0.571 0.756 
Digital Platform Trust 9 0.847 0.892 0.641 0.801 
Financial Literacy 10 0.798 0.863 0.609 0.780 
Investment Decision Effectiveness 12 0.856 0.889 0.634 0.796 

 
Indicator reliability was evaluated through factor loadings, with all items achieving loadings above 0.70 except 

for three items that were retained due to their theoretical importance and acceptable loadings above 0.65. The 
factor loadings ranged from 0.673 to 0.891, with most items exceeding 0.75, indicating strong relationships 
between indicators and their respective constructs. Items with loadings below 0.70 were carefully examined for 
theoretical relevance and contribution to construct validity before retention decisions. 

Convergent validity was assessed using average variance extracted (AVE) values, with all constructs achieving 
AVE values above 0.50, ranging from 0.541 for anchoring bias to 0.641 for digital platform trust. These results 
indicate that all constructs explain more than half of their indicators' variance, demonstrating adequate convergent 
validity. The square roots of AVE values exceeded the correlations between constructs, providing preliminary 
evidence of discriminant validity. 

Table 2 presents the discriminant validity assessment using both the Fornell-Larcker criterion and the 
heterotrait-monotrait (HTMT) ratio of correlations. The Fornell-Larcker criterion was satisfied for all construct 
pairs, with the square root of each construct's AVE exceeding its correlations with other constructs. This indicates 
that each construct shares more variance with its own indicators than with other constructs in the model. 
 
 
 
 
 
 
 
 



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Table 2. Discriminant Validity Assessment (Fornell-Larcker Criterion). 

Construct 1 2 3 4 5 6 7 8 9 10 11 12 13 14 
1. 
Overconfidence 
Bias 

0.760 
             

2. Loss 
Aversion 

0.234 0.750 
            

3. Herding 
Behavior 

0.412 0.287 0.773 
           

4. Anchoring 
Bias 

0.345 0.298 0.356 0.735 
          

5. Collectivism -0.187 0.234 0.278 0.198 0.782 
         

6. Power 
Distance 

-0.134 0.176 0.245 0.167 0.423 0.740 
        

7. Uncertainty 
Avoidance 

-0.198 0.312 0.198 0.234 0.389 0.345 0.759 
       

8. Long-term 
Orientation 

0.123 -0.145 -0.167 -0.198 0.278 0.234 0.189 0.750 
      

9. Platform 
Usability 

0.267 -0.123 0.234 0.189 0.145 0.167 -0.134 0.298 0.792 
     

10. Information 
Quality 

0.234 -0.167 0.198 0.156 0.123 0.134 -0.187 0.267 0.567 0.775 
    

11. Social 
Features 

0.298 0.145 0.412 0.234 0.189 0.198 0.123 0.134 0.445 0.398 0.756 
   

12. Digital 
Platform Trust 

0.189 -0.198 0.167 0.123 0.098 0.087 -0.156 0.245 0.623 0.589 0.367 0.801 
  

13. Financial 
Literacy 

-0.156 -0.234 -0.198 -0.167 0.167 0.134 0.098 0.287 0.234 0.298 0.123 0.267 0.780 
 

14. Investment 
Decision 
Effectiveness 

0.298 -0.267 0.189 0.134 0.234 0.156 -0.145 0.345 0.456 0.523 0.298 0.567 0.489 0.796 

 
The heterotrait-monotrait (HTMT) ratio assessment provided more stringent discriminant validity evaluation, 

with all construct pairs achieving HTMT values below 0.85, ranging from 0.156 for the relationship between 
overconfidence bias and power distance to 0.742 for the relationship between platform usability and digital 
platform trust. These results confirm adequate discriminant validity between all constructs, supporting the 
distinctiveness of the theoretical constructs in the measurement model. 
 

4.2. Structural Model Assessment 
The structural model assessment examined the hypothesized relationships between constructs through path 

coefficient analysis, significance testing, and explanatory power evaluation. The overall model achieved substantial 
explanatory power with an R² value of 0.672 for investment decision-making effectiveness, indicating that the 
model explains 67.2% of the variance in the dependent variable. This level of explanatory power exceeds 
conventional thresholds for substantial effect sizes in behavioral research and demonstrates the theoretical 
relevance of the proposed model. 

Table 3 presents the direct effects results from the structural model assessment, including path coefficients, t-
statistics, p-values, and confidence intervals derived from bootstrapping procedures with 5,000 resamples. The 
results reveal several significant direct relationships between behavioral biases, cultural factors, digital platform 
characteristics, and investment decision-making effectiveness. 
 

Table 3. Direct Effects Results. 

Hypothesis Relationship Path 
Coefficient 

t-statistics p-values 95% CI 
Lower 

95% CI 
Upper 

Decision 

H1 OC → IDE -0.156 2.847 0.004 -0.263 -0.049 Supported 

H2 LA → IDE -0.234 4.123 0.000 -0.343 -0.125 Supported 

H3 HB → IDE 0.187 3.456 0.001 0.081 0.293 Supported 

H4 AB → IDE -0.098 1.876 0.061 -0.201 0.005 Not Supported 

H5 COL → IDE 0.145 2.567 0.010 0.035 0.255 Supported 

H6 PD → IDE 0.089 1.634 0.103 -0.018 0.196 Not Supported 

H7 UA → IDE -0.123 2.198 0.028 -0.233 -0.013 Supported 

H8 LTO → IDE 0.267 4.789 0.000 0.158 0.376 Supported 

H9 PU → IDE 0.198 3.672 0.000 0.092 0.304 Supported 

H10 IQ → IDE 0.234 4.234 0.000 0.125 0.343 Supported 

H11 SF → IDE 0.087 1.587 0.113 -0.021 0.195 Not Supported 

H12 DPT → IDE 0.289 5.123 0.000 0.178 0.400 Supported 

H13 FL → IDE 0.312 5.789 0.000 0.206 0.418 Supported 
Note: OC = Overconfidence Bias, LA = Loss Aversion, HB = Herding Behavior, AB = Anchoring Bias, COL = Collectivism, PD = Power 
Distance, UA = Uncertainty Avoidance, LTO = Long-term Orientation, PU = Platform Usability, IQ = Information Quality, SF = Social 
Features, DPT = Digital Platform Trust, FL = Financial Literacy, IDE = Investment Decision Effectiveness. 

 
The results indicate that financial literacy exhibits the strongest positive relationship with investment 

decision-making effectiveness (β = 0.312, p < 0.001), followed by digital platform trust (β = 0.289, p < 0.001) and 

long-term orientation (β = 0.267, p < 0.001). These findings suggest that Vietnamese investors with higher 
financial knowledge, greater trust in digital platforms, and longer-term cultural orientations achieve superior 
investment outcomes. 

Among behavioral biases, loss aversion demonstrated the strongest negative impact on investment 

effectiveness (β = -0.234, p < 0.001), followed by overconfidence bias (β = -0.156, p < 0.01). Interestingly, herding 



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behavior showed a positive relationship with investment effectiveness (β = 0.187, p < 0.001), suggesting that 
following peer behavior may provide benefits in the Vietnamese investment context, possibly through improved 
information sharing and risk reduction. 

Predictive relevance assessment using Stone-Geisser's Q² values confirmed the model's predictive capability, as 
presented in Table 4. All endogenous constructs achieved positive Q² values, indicating that the model has 
predictive relevance beyond the sample data. 

 
Table 4. Predictive Relevance Assessment. 

Construct SSO SSE Q² 

Investment Decision Effectiveness 5,820 3,891 0.331 
Digital Platform Trust 4,365 3,247 0.256 
Herding Behavior 3,395 2,876 0.153 

 
The Q² value of 0.331 for investment decision-making effectiveness indicates substantial predictive relevance, 

while digital platform trust (Q² = 0.256) and herding behavior (Q² = 0.153) demonstrate medium predictive 
relevance. These results support the model's ability to predict out-of-sample observations and enhance confidence 
in the theoretical relationships. 

Specific indirect effects analysis revealed several significant mediation relationships, as presented in Table 5. 
Digital platform trust emerged as a significant mediator in the relationships between platform characteristics and 
investment effectiveness. 
 

Table 5. Specific Indirect Effects (Path Coefficients). 

Mediation Path Indirect Effect t-statistics p-values 95% CI Lower 95% CI Upper Significance 

PU → DPT → IDE 0.134 2.876 0.004 0.042 0.226 Yes 

IQ → DPT → IDE 0.156 3.234 0.001 0.061 0.251 Yes 

FL → DPT → IDE 0.089 2.145 0.032 0.008 0.170 Yes 

COL → HB → IDE 0.067 1.987 0.047 0.001 0.133 Yes 

PD → HB → IDE 0.054 1.756 0.079 -0.006 0.114 No 

 
The moderation analysis results presented in Table 6 reveal significant interaction effects between cultural 

dimensions and behavioral biases. Collectivism significantly moderates the relationship between overconfidence 

bias and investment effectiveness (β = 0.112, p < 0.05), suggesting that collectivistic cultural orientation attenuates 
the negative effects of overconfidence on investment outcomes. 
 

Table 6. Moderation Analysis Results. 

Moderation Effect Path 
Coefficient 

t-statistics p-values 95% CI 
Lower 

95% CI 
Upper 

f² Decision 

COL × OC → IDE 0.112 2.234 0.026 0.013 0.211 0.023 Supported 

PD × DPT → IDE 0.089 1.876 0.061 -0.004 0.182 0.015 Not Supported 

UA × FL → IDE -0.098 2.145 0.032 -0.187 -0.009 0.019 Supported 

LTO × LA → IDE 0.134 2.567 0.010 0.032 0.236 0.028 Supported 

 

4.3. Supplementary Analyses 
Supplementary analyses incorporated multigroup analysis (MGA), fuzzy-set qualitative comparative analysis 

(fsQCA), and simple slope analysis to provide additional insights into the complex relationships within the research 
model. The multigroup analysis examined differences in structural relationships across demographic subgroups 
including gender, age, income levels, and investment experience. 

Table 7 presents the multigroup analysis results comparing path coefficients across gender groups. Significant 
differences emerged in several relationships, with male investors showing stronger negative effects of 
overconfidence bias on investment effectiveness compared to female investors. 
 

Table 7. Multigroup Analysis Results (Gender). 

Structural Path Path 
Coefficients 

  
Group Difference 

Analysis 

 

 
Male Female Difference p-value Significant  

(n = 287) (n = 198) |β₁ - β₂| (MGA) (α = 0.05) 

Overconfidence → Investment Effectiveness -0.234 -0.089 0.145 0.028* Yes 

Loss Aversion → Investment Effectiveness -0.198 -0.267 0.069 0.156 No 

Herding Behavior → Investment 
Effectiveness 

0.156 0.234 0.078 0.187 No 

Collectivism → Investment Effectiveness 0.089 0.198 0.109 0.042* Yes 

Financial Literacy → Investment 
Effectiveness 

0.287 0.345 0.058 0.273 No 

Digital Platform Trust → Investment 
Effectiveness 

0.312 0.267 0.045 0.389 No 

 
The fsQCA analysis identified several configurational pathways leading to high investment decision-making 

effectiveness, as presented in Table 8. The analysis revealed three distinct configurations that consistently lead to 
superior investment outcomes, with consistency scores exceeding 0.80 and coverage metrics indicating substantial 
explanatory power. 
 
 



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Table 8. fsQCA Configuration Analysis. 

Configuration Raw Coverage Unique Coverage Consistency Leading Conditions 
Config 1 0.456 0.123 0.834 FLDPTLTO 
Config 2 0.389 0.098 0.812 IQ~OCCOL 
Config 3 0.312 0.087 0.801 PUFL~LA 
Solution 0.723 - 0.798 - 

Note: * indicates presence of condition, ~ indicates absence of condition. 

 
FL = Financial Literacy, DPT = Digital Platform Trust, LTO = Long-term Orientation, IQ = Information 

Quality, OC = Overconfidence Bias, COL = Collectivism, PU = Platform Usability, LA = Loss Aversion 
Configuration 1 represents the most prevalent pathway to investment success, characterized by high financial 

literacy combined with strong digital platform trust and long-term cultural orientation. This configuration covers 
45.6% of cases with high investment effectiveness and demonstrates consistency of 83.4%. Configuration 2 
highlights the importance of information quality combined with collectivistic orientation and absence of 
overconfidence bias, while Configuration 3 emphasizes platform usability and financial literacy in the absence of 
loss aversion. 

Simple slope analysis was conducted to visualize significant moderation effects, particularly the interaction 
between collectivism and overconfidence bias on investment decision-making effectiveness. The analysis revealed 
that the negative effect of overconfidence bias on investment outcomes is substantially reduced at high levels of 
collectivism, supporting the theoretical proposition that cultural values moderate the manifestation of behavioral 
biases. 
 

5. Discussion of Research Results and Conclusions 
The epistemic trajectory revealed through this comprehensive investigation uncovers profound insights into 

the complex interplay between behavioral determinants and cultural cognition in shaping investment decision-
making patterns among Vietnamese retail investors operating within digital financial ecosystems. The empirical 
findings demonstrate that investment behavior in emerging markets represents a multifaceted phenomenon that 
transcends traditional behavioral finance explanations, requiring sophisticated theoretical frameworks that 
incorporate cultural, technological, and psychological dimensions simultaneously. 

The research results confirm that financial literacy emerges as the most potent predictor of investment 
decision-making effectiveness, aligning with established literature while extending these findings to Vietnamese 
contexts (Van Rooij et al., 2011). This relationship underscores the fundamental importance of financial education 
and knowledge acquisition in developing countries where institutional frameworks and investor protection 
mechanisms may be less robust compared to developed markets. The strength of this relationship suggests that 
policy interventions focused on enhancing financial literacy could yield substantial improvements in individual 
investment outcomes and overall market efficiency. 

 
Digital platform trust emerges as the second most influential factor affecting investment effectiveness, 

highlighting the critical role of technology adoption and institutional confidence in contemporary financial 
markets. This finding extends previous research by Georgarakos and Pasini (2011) into digital contexts, 
demonstrating that trust relationships in financial services have evolved to encompass technological platforms and 
digital intermediaries. The significance of this relationship indicates that Vietnamese investors' willingness to 
engage with digital financial services depends heavily on their confidence in platform security, reliability, and 
fairness. 

The positive relationship between long-term orientation and investment effectiveness provides strong support 
for cultural cognition theory's predictions regarding how temporal perspectives influence financial behavior. This 
finding resonates with Hofstede's (1980) cultural dimensions framework while extending its application to 
investment contexts in emerging markets. Vietnamese investors who embrace long-term thinking and delayed 
gratification appear better positioned to achieve superior investment outcomes, possibly through reduced 
susceptibility to short-term market fluctuations and speculative behavior. 

Particularly noteworthy is the counterintuitive positive relationship between herding behavior and investment 
effectiveness identified in this research. While Western literature generally portrays herding as detrimental to 
investment performance (Chang et al., 2000), the Vietnamese context reveals a more nuanced dynamic where 
following peer behavior may provide informational benefits and risk reduction through collective wisdom. This 
finding suggests that herding behavior in collectivistic cultures may operate differently than in individualistic 
contexts, potentially reflecting the value of social networks and collective decision-making in information-scarce 
environments. 

The negative impact of loss aversion on investment effectiveness confirms theoretical predictions from prospect 
theory while demonstrating the persistence of this bias across cultural contexts (Kahneman & Tversky, 1979). 
However, the magnitude of this effect in the Vietnamese sample appears comparable to findings from developed 
markets, suggesting that loss aversion represents a relatively universal psychological phenomenon that transcends 
cultural boundaries. This finding indicates that cognitive biases identified in Western contexts maintain their 
relevance in emerging market settings, though their interactions with cultural factors may create unique 
manifestation patterns. 

The moderation effects revealed through this research provide critical insights into how cultural cognition 
shapes the expression of behavioral biases. The significant interaction between collectivism and overconfidence bias 
demonstrates that cultural values can serve as protective factors that attenuate the negative effects of psychological 
biases on investment outcomes. This finding extends cultural cognition theory by showing how group-oriented 
values may provide feedback mechanisms and social constraints that reduce individual overconfidence and improve 
decision-making quality. 

The mediation analysis reveals the sophisticated pathways through which digital platform characteristics 
influence investment behavior. The finding that digital platform trust mediates the relationships between platform 



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usability, information quality, and investment effectiveness suggests that technological features primarily operate 
through their impact on user confidence rather than direct functional benefits. This insight has important 
implications for financial technology design and user experience optimization in emerging markets. 

The fsQCA results provide compelling evidence for equifinality in investment success, demonstrating that 
multiple pathways can lead to superior investment outcomes. The identification of three distinct configurations 
highlights the complexity of investment behavior and suggests that different investor profiles may achieve success 
through different combinations of knowledge, trust, cultural orientation, and bias management. This finding 
challenges one-size-fits-all approaches to investor education and platform design, suggesting that personalized 
strategies may be more effective. 

The gender differences revealed through multigroup analysis indicate that overconfidence bias affects male and 
female investors differently, with male investors showing stronger susceptibility to overconfidence-related 
performance decrements. This finding aligns with established literature on gender differences in financial behavior 
while extending these insights to Vietnamese contexts (Barber & Odean, 2001). The results suggest that investor 
education and behavioral intervention programs may need to be tailored to address gender-specific behavioral 
patterns. 

From a theoretical perspective, this research contributes to behavioral finance literature by demonstrating that 
cultural cognition serves as a critical moderating mechanism that shapes how universal psychological biases 
manifest in specific cultural contexts. The integration of cultural cognition theory with behavioral finance 
constructs provides a more comprehensive framework for understanding cross-cultural investment behavior and 
suggests promising directions for future theoretical development. 

The practical implications of these findings extend to multiple stakeholder groups including financial service 
providers, regulatory authorities, and individual investors. For financial service providers, the results highlight the 
importance of building trust in digital platforms while designing culturally appropriate interfaces and features that 
leverage positive aspects of herding behavior while mitigating negative effects of overconfidence and loss aversion. 
Regulatory authorities may benefit from these insights by developing financial literacy programs that account for 
cultural values and designing investor protection mechanisms that recognize the unique characteristics of 
emerging market investors. 

The research limitations include the cross-sectional design which limits causal inference capabilities and the 
focus on Vietnamese contexts which may limit generalizability to other emerging markets. Future research could 
address these limitations through longitudinal designs that capture the evolution of investor behavior over time 
and cross-national studies that examine cultural differences across multiple emerging market contexts. 
Additionally, the exclusive focus on retail investors suggests opportunities for examining institutional investor 
behavior and professional investment management in similar cultural and technological contexts. 

This investigation establishes a foundation for understanding the complex dynamics of investment behavior in 
digitally-enabled emerging markets while highlighting the critical importance of cultural factors in shaping 
financial decision-making processes. The findings suggest that successful investment strategies and financial 
service design in emerging markets must account for the intricate interplay between psychological biases, cultural 
values, and technological features to optimize investor outcomes and market development. 
 

Acknowledgments: 
I would like to express my sincere gratitude to Dr. Hoang Vu Hiep for his invaluable guidance and inspiration 
throughout this research. His expertise, insights, and unwavering support have been instrumental in shaping the 
direction and quality of this study. I am deeply appreciative of his generosity in sharing his time, knowledge, and 
network, which have greatly contributed to the success of this research. His mentorship and commitment to 
academic excellence have not only enriched the quality of this work but have also had a profound impact on my 
personal and professional growth. 
 

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