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Asian Business Research Journal 
Vol. 10, No. 5, 106-119, 2025 
ISSN: 2576-6759 
DOI: 10.55220/25766759.454 
© 2025 by the author; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
Financial Literacy and Digital Consumer Decision-Making Nexus: A PLS-SEM 
Analysis of Behavioural Dynamics in Vietnam's Emerging Peer-to-Peer Lending 
Ecosystem 

 
Thi Thanh Huyen NGUYEN 
 

 
 

Thuyloi University, Vietnam. 
Email: huyenntt@tlu.edu.vn    
 

 
Abstract 

This research investigates the complex interrelationship between financial literacy and consumer 
decision-making behaviour within Vietnam's rapidly evolving peer-to-peer (P2P) lending 
ecosystem. Whilst financial technology has transformed access to credit markets globally, the 
specific dynamics within emerging economies with nascent financial infrastructures remain 
underexplored. Through a multidimensional conceptual framework integrating financial 
capability theory, technology acceptance paradigms, and behavioural economics, this study 
examines how varying dimensions of financial literacy influence decision-making processes on 
digital lending platforms. Employing a structured survey methodology with 427 Vietnamese P2P 
platform users, this research applies partial least squares structural equation modelling (PLS-
SEM) supplemented by fuzzy-set qualitative comparative analysis (fsQCA) to elucidate complex 
causal relationships. The findings reveal financial literacy demonstrates significant direct effects 
on risk perception, trust formation, and platform adoption decisions, with differential impacts 
across demographic segments. Moreover, financial self-efficacy emerges as a crucial moderating 
variable, reconfiguring the relationship between financial knowledge and behavioural outcomes. 
The research contributes to theoretical advancement through an integrated conceptual model 
whilst providing practical insights for financial technology providers, regulatory authorities, and 
financial literacy advocates within Vietnam's distinctive socioeconomic context. 

 
Keywords: Consumer Behaviour, Digital Lending, Financial Literacy, Peer-To-Peer Platforms, PLS-SEM. 

 
1. Introduction 

The proliferation of financial technology platforms has fundamentally transformed financial service 
accessibility, particularly in emerging economies with historically limited banking infrastructure (Bruton et al., 
2015). Peer-to-peer lending platforms represent particularly disruptive mechanisms, disintermediating traditional 
institutions and creating novel marketplaces connecting lenders directly with borrowers (Lin et al., 2013). 
However, these platforms' success remains contingent upon consumer-level variables influencing adoption and 
decision-making processes (Lee & Shin, 2018). 

Financial literacy—defined as the confluence of knowledge, skills, and attitudes enabling effective financial 
decision-making (Huston, 2010)—has emerged as a critical yet insufficiently examined construct within digital 
lending contexts. Whilst its significance within traditional banking environments is well-established (Lusardi & 
Mitchell, 2014), its operative mechanisms within novel digital architectures remain theoretically underdeveloped 
(Agarwal et al., 2015), particularly in emerging economies where digital adoption frequently outpaces financial 
education infrastructure (Klapper et al., 2015). 

Vietnam presents a compelling research context for examining this nexus. With digital financial services 
adoption growing at 25-30% annually (World Bank, 2017), the Vietnamese P2P lending market expanded from 
$300 million in 2016 to approximately $7.8 billion by 2017 (Asian Development Bank, 2017). Simultaneously, 
Vietnamese financial literacy rates remain below global averages, with only 24% of adults demonstrating adequate 
financial knowledge compared to the 33% global average (Standard & Poor's, 2015). 

The empirical literature reveals significant gaps in understanding the precise mechanisms through which 
financial literacy impacts digital financial behaviour. Whilst studies have established correlations between financial 
knowledge and certain decision-making aspects (Fernandes et al., 2014), specific pathways within digital 
environments remain underspecified. 

This research addresses these gaps by validating an integrated framework examining financial literacy's 
multidimensional impact on consumer behaviour within Vietnamese P2P platforms. Drawing upon bounded 
rationality (Simon, 1955), technology acceptance (Davis, 1989), and financial capability theories (Johnson & 
Sherraden, 2007), this study employs structural equation modelling with partial least squares approach, 
supplemented by fuzzy-set qualitative comparative analysis (Hair et al., 2014). 

mailto:huyenntt@tlu.edu.vn
https://doi.org/10.55220/25766759.454


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The theoretical contribution is threefold: extending financial literacy theory within digital environments; 
advancing technology adoption models by integrating financial capability constructs; and enhancing theoretical 
specificity regarding emerging market dynamics. Additionally, this research generates practical insights for 
platform developers, regulatory bodies, and financial education initiatives within rapidly digitalising economies. 
 

2. Foundational Theories and Literature Review 
2.1. Foundational Theories 
2.1.1. Financial Literacy and Capability Theory 

Financial literacy constitutes a multidimensional theoretical construct that has undergone substantial 
conceptual evolution. Initially conceptualised narrowly as financial knowledge (Bernheim & Garrett, 2003), 
contemporary theoretical frameworks have expanded to encompass cognitive, attitudinal, and behavioural 
dimensions that collectively enable effective financial decision-making (Remund, 2010). The theoretical foundations 
of financial literacy derive primarily from human capital theory, which positions financial knowledge as a form of 
intellectual capital that enhances decision-making capacity (Delavande et al., 2008). Huston (2010) advanced this 
conceptualisation by distinguishing between financial knowledge (the stock of information) and financial literacy 
(the application of that knowledge), thereby establishing a crucial theoretical distinction that informs the present 
research. 

Financial capability theory, as articulated by Johnson and Sherraden (2007), represents a significant theoretical 
advancement by integrating both individual capacity and structural opportunity. This theoretical framework posits 
that effective financial behaviour requires not only knowledge and skills but also accessible institutional 
mechanisms that facilitate financial action. Sherraden (2013) further developed this theoretical position by 
emphasising the interaction between individual agency and financial infrastructure, positioning financial capability 
as an emergent property of this interaction rather than a purely individual attribute. This theoretical perspective 
bears particular relevance within the Vietnamese context, where rapid financial technology innovation has 
expanded institutional access while financial education infrastructure remains underdeveloped. 

The theoretical conceptualisation of financial literacy has further evolved through integration with behavioural 
economics, particularly bounded rationality theory (Simon, 1955). This theoretical integration recognises that 
financial decision-making occurs under conditions of cognitive constraint, information asymmetry, and 
motivational biases (Lusardi & Mitchell, 2014). Thaler (2015) advanced this theoretical synthesis by demonstrating 
how financial literacy influences susceptibility to behavioural biases, including present bias, loss aversion, and 
choice overload—all particularly relevant within digital environments that intensify information complexity. 
Within this theoretical framework, financial literacy can be understood as a mitigating factor that reduces the gap 
between normative economic models and actual financial behaviour. 

Critically, this theoretical evolution has produced increased recognition of financial literacy as domain-specific 
rather than universal (Hung et al., 2009). Financial knowledge and capability that prove adequate within traditional 
banking contexts may prove insufficient within novel digital environments that present distinctive decision 
architectures, information structures, and risk parameters. This theoretical position underpins the present 
research's focus on digital-specific financial literacy as a distinct construct from general financial knowledge. 
Remund (2010) supports this theoretical distinction by demonstrating how financial literacy encompasses context-
specific competencies rather than generalised aptitude. 

Within emerging economies specifically, financial capability theory has been extended by scholars emphasising 
the role of social and cultural factors that condition financial behaviour. Sherraden et al. (2015) articulated how 
financial capability development occurs through the integration of formal knowledge systems with community-
based financial practices—a theoretical perspective particularly relevant within the Vietnamese context where 
traditional lending circles (hui) and family financial networks have historically substituted for formal financial 
institutions. This theoretical lens emphasises the importance of examining how traditional financial attitudes and 
practices interact with novel digital platforms. 

The measurement of financial literacy has itself generated significant theoretical development. Early 
measurement approaches focused predominantly on objectively verifiable knowledge (Lusardi & Mitchell, 2011), 
whereas contemporary theoretical frameworks emphasise the importance of measuring subjective dimensions 
including financial attitudes, behavioural intentions, and perceived self-efficacy (Atkinson & Messy, 2012). This 
measurement evolution reflects the theoretical recognition that effective financial decision-making requires not 
merely knowledge acquisition but also behavioural application—a distinction with significant implications for 
digital environments where decision conditions differ substantially from traditional contexts. 
 

2.1.2. Technology Acceptance and Digital Consumer Behaviour Theories 
Technology acceptance theory provides the second core theoretical foundation for examining consumer 

behaviour within digital lending platforms. The Technology Acceptance Model (TAM), initially proposed by Davis 
(1989), identifies perceived usefulness and perceived ease of use as the primary determinants of technology 
adoption, mediated by attitudinal factors and behavioural intentions. This theoretical framework has demonstrated 
robust explanatory power across diverse technological contexts but requires domain-specific augmentation to fully 
capture financial technology acceptance (Venkatesh & Bala, 2008). 

The Unified Theory of Acceptance and Use of Technology (UTAUT), developed by Venkatesh et al. (2003), 
represents a significant theoretical advancement by integrating multiple theoretical perspectives into a unified 
framework. This model identifies performance expectancy, effort expectancy, social influence, and facilitating 
conditions as core determinants of technology adoption intentions. Within fintech contexts specifically, the 
UTAUT framework has been extended to incorporate additional constructs including perceived trust, perceived 
risk, and perceived security—factors particularly salient within lending platforms where financial vulnerability is 
inherent (Slade et al., 2015). 

These technology acceptance models have undergone further theoretical refinement through integration with 
innovation diffusion theory (Rogers, 2003), which classifies adopters according to their temporal relationship with 



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innovation. This theoretical integration proves particularly relevant within the Vietnamese context, where digital 
lending platforms remain in the early adoption phase, suggesting that current users may demonstrate 
systematically different characteristics from the broader population (Lee & Shin, 2018). Innovation diffusion theory 
further emphasises the importance of perceived attributes including relative advantage, compatibility, complexity, 
trialability, and observability—constructs that complement traditional technology acceptance models (Moore & 
Benbasat, 1991). 

The behavioural economics of technology usage has emerged as a crucial theoretical extension to traditional 
acceptance models. As articulated by Benartzi and Lehrer (2015), digital decision environments create distinctive 
cognitive conditions that systematically influence decision processes, often in ways that deviate from rational 
choice models. These theoretical developments emphasise how digital interfaces can exploit attentional limitations, 
frame choices to emphasise certain attributes, and utilise social proof mechanisms to influence behaviour. Within 
digital lending contexts specifically, these interface characteristics may interact with financial literacy levels to 
produce distinctive behavioural outcomes (Benartzi, 2015). 

Trust theory represents another critical theoretical foundation for understanding digital financial behaviour. 
Gefen et al. (2003) established the multidimensional nature of online trust, distinguishing between institution-
based trust (derived from structural assurances), calculative-based trust (derived from rational assessment of 
trustworthiness), and knowledge-based trust (derived from familiarity). Within P2P lending specifically, Greiner 
and Wang (2010) demonstrated how trust mechanisms including reputation systems, historical performance 
metrics, and social network verification function as critical determinants of platform engagement. The theoretical 
intersection between trust formation processes and financial literacy remains underspecified, representing a critical 
gap addressed by the present research. 

Notably, consumer behaviour within digital financial environments draws theoretical insights from information 
asymmetry theory (Akerlof, 1970). P2P lending platforms create novel information structures that differ 
substantively from traditional banking environments, redistributing information across platform participants and 
creating new forms of information asymmetry (Lin et al., 2013). The interaction between financial literacy and 
these novel information structures remains theoretically underdeveloped, particularly regarding how varying 
levels of financial sophistication condition information processing within these environments. 

Self-determination theory (Ryan & Deci, 2000) provides additional theoretical insights by emphasising how 
autonomy, competence, and relatedness drive intrinsic motivation. Within digital lending contexts, financial 
literacy may function as a competence enabler that enhances self-efficacy and thereby influences platform 
engagement through motivational pathways. This theoretical perspective complements traditional technology 
acceptance models by emphasising psychological need satisfaction rather than merely instrumental outcomes 
(Malhotra et al., 2008). 

Collectively, these theoretical frameworks establish the foundation for examining the complex interrelationship 
between financial literacy and digital consumer behaviour. By integrating financial capability theory with 
technology acceptance models, trust formation theories, and behavioural economics, this research develops a 
comprehensive theoretical framework for examining consumer decision-making within Vietnam's emerging P2P 
lending ecosystem. 

 
2.2. Review Of Empirical and Relevant Studies 
2.2.1. Financial Literacy: Empirical Findings and Measurement Approaches 

Empirical research examining financial literacy has documented significant and persistent knowledge gaps 
across global populations. Lusardi and Mitchell's (2011) seminal work established that only one-third of global 
respondents could correctly answer three basic financial literacy questions regarding interest rates, inflation, and 
risk diversification. This finding has been replicated across diverse national contexts, with emerging economies 
typically demonstrating lower financial literacy rates than developed economies (Klapper et al., 2015). Within 
Vietnam specifically, the Standard & Poor's Global Financial Literacy Survey (2015) found that only 24% of adults 
could be classified as financially literate, positioning Vietnam below regional averages despite its rapid economic 
development. 

The empirical relationship between financial literacy and financial behaviour has been extensively documented, 
though with important nuances. Fernandes et al. (2014) conducted a meta-analysis of 168 papers examining 
financial literacy effects, finding a statistically significant but relatively modest relationship between financial 
literacy and financial behaviours (r = 0.21). Notably, intervention effects demonstrated significant decay over time, 
suggesting the importance of sustained rather than one-time financial education. van Rooij et al. (2011) established 
more specific linkages, demonstrating that financial literacy significantly influences stock market participation, 
retirement planning, and wealth accumulation, with effects persisting after controlling for cognitive ability, 
educational attainment, and risk preferences. 

Measurement approaches for financial literacy have evolved substantially, with empirical studies 
demonstrating the limitations of unidimensional measures. Huston (2012) demonstrated that traditional 
knowledge-based measures fail to capture the application dimension of financial literacy, thereby undermining 
predictive validity regarding actual financial behaviours. In response, multidimensional measurement approaches 
have been developed, including the OECD/INFE framework which incorporates knowledge, attitudes, and 
behaviour dimensions (Atkinson & Messy, 2012). Empirical validation of these multidimensional measures has 
demonstrated superior predictive validity regarding financial outcomes compared to knowledge-only measures 
(Potrich et al., 2016). 

Contextual factors significantly moderate the relationship between financial literacy and behaviour. Cole et al. 
(2011) found that financial literacy effects vary systematically with income levels, with stronger effects observed 
among middle-income compared to low-income populations. Similarly, Meier and Sprenger (2013) demonstrated 
that time preferences moderate financial literacy effects, with present-biased individuals demonstrating weaker 
relationships between knowledge and behaviour. These empirical findings suggest the importance of examining 
conditional effects rather than assuming uniform financial literacy impacts across populations. 



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Domain-specific financial literacy measures have demonstrated superior predictive validity compared to general 
measures when examining specific financial behaviours. Nicolini et al. (2013) established that domain-specific 
financial knowledge regarding mortgage products more strongly predicted mortgage choice quality than general 
financial literacy. This empirical finding supports the present research's focus on digital-specific financial literacy as 
potentially distinct from general financial knowledge. To date, however, few empirical studies have developed and 
validated measures specifically targeting financial literacy within digital lending contexts. 
 
2.2.2. Digital Financial Behaviour and P2P Lending Platform Dynamics 

Empirical research examining P2P lending platforms has documented distinctive behavioural patterns that 
differentiate these environments from traditional lending contexts. Lee and Lee (2012) analysed 3,000 loan listings 
from a major P2P platform, identifying herding behaviour as a significant factor influencing funding outcomes. 
This finding suggests the operation of social influence mechanisms that may interact with financial literacy levels 
to produce distinctive decision patterns. Similarly, Zhang and Liu (2012) demonstrated rational herding effects, 
whereby lenders extrapolate borrower quality from the lending decisions of previous investors—a finding with 
significant implications regarding how financial sophistication might influence information processing within these 
platforms. 

Trust formation within digital lending platforms follows empirically distinct patterns from traditional financial 
contexts. Duarte et al. (2012) established that perceived trustworthiness based on borrower photographs 
significantly influenced lending decisions and accurately predicted default risk, suggesting the operation of non-
financial evaluation heuristics. Chen et al. (2016) further documented how textual features of loan requests, 
including linguistic complexity and narrative persuasiveness, significantly impacted funding outcomes independent 
of financial indicators. These findings suggest that digital environments create distinctive evaluation contexts that 
may interact with financial literacy to influence decision quality. 

Consumer risk perception within P2P platforms demonstrates empirically complex patterns. Iyer et al. (2016) 
found that lenders could predict default with 45% greater accuracy than credit scores alone, suggesting that 
distributed risk assessment through collective intelligence mechanisms creates distinctive risk evaluation dynamics. 
Conversely, Lin et al. (2013) documented friendship networks functioning as signals of creditworthiness, with 
borrowers who displayed social connections receiving funding at lower interest rates despite no difference in 
default rates—suggesting potential inefficiencies in risk assessment mechanisms that financial literacy might 
moderate. 

Platform design features significantly influence user behaviour within digital lending environments. 
Herzenstein et al. (2011) demonstrated that identity verification influenced funding success, with borrowers 
providing verification receiving 58% more funding than unverified borrowers. Similarly, Kawai et al. (2013) 
established that screening mechanisms significantly reduced adverse selection problems within P2P platforms. 
These empirical findings suggest that platform architecture creates distinctive decision environments that may 
amplify or attenuate the effects of financial literacy on decision outcomes. 

Demographic factors significantly influence digital financial behaviour, creating potential interaction effects 
with financial literacy. Pope and Sydnor (2011) documented significant racial disparities in P2P lending outcomes, 
with loan requests from Black borrowers 25-40% less likely to receive funding than identical requests from White 
borrowers. Ravina (2012) further established beauty premiums within lending decisions, with attractive borrowers 
receiving funding at interest rates 1.5 percentage points lower than equally qualified but less attractive 
counterparts. These findings suggest that non-financial factors significantly influence digital lending decisions, 
potentially creating contexts where financial literacy effects may be diminished or enhanced. 
 
2.2.3. Financial Technology Adoption in Emerging Economies 

Empirical research examining financial technology adoption within emerging economies has documented 
distinctive patterns that differentiate these contexts from developed markets. Jack and Suri (2014) examined mobile 
money adoption in Kenya, finding that availability of mobile financial services reduced consumption volatility by 
11.8 percentage points by enabling households to receive remittances from a wider network during economic 
shocks. This finding suggests that digital financial services fulfil distinctive functions within emerging economies, 
potentially creating different adoption motivations than observed in developed markets. 

Institutional factors significantly influence fintech adoption within emerging economies. Demirgüç-Kunt et al. 
(2015) found that regulatory quality predicted digital financial inclusion independent of economic development, 
with clear legal frameworks regarding digital transactions significantly accelerating adoption. Within Vietnam 
specifically, World Bank (2017) research documented how the lack of comprehensive regulatory frameworks for 
P2P lending created uncertainty that influenced risk perceptions among potential users. These findings suggest the 
importance of examining institutional context when assessing financial literacy effects within emerging economies. 

Cultural factors significantly moderate technology adoption patterns across national contexts. Tam and 
Oliveira (2017) established that Hofstede's cultural dimensions significantly predicted mobile banking adoption 
patterns, with uncertainty avoidance demonstrating particularly strong effects. Within the Vietnamese context 
specifically, collectivist cultural orientations may influence digital lending behaviour through distinctive social 
trust mechanisms and group-oriented decision processes (Vuong & Napier, 2014). These empirical findings suggest 
the importance of examining cultural moderation of financial literacy effects rather than assuming universal 
mechanisms. 

Access barriers remain significant within emerging economies despite rapid technological diffusion. Research 
by Klapper et al. (2015) documented substantial urban-rural divides in digital financial service access, with rural 
populations facing both infrastructural and educational barriers. Within Vietnam specifically, the World Bank 
(2017) found that while 72% of urban residents accessed digital financial services, only 43% of rural residents did 
so—suggesting the operation of significant digital divides that may influence the composition of current P2P 
platform users. 



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Financial inclusion motivations differ significantly between developed and emerging economies. Demirgüç-
Kunt and Klapper (2013) found that while convenience predominantly drives digital financial service adoption in 
developed economies, access to otherwise unavailable formal financial services drives adoption in emerging 
economies. This finding suggests that P2P lending platforms may fulfil fundamentally different market functions 
within the Vietnamese context compared to developed markets, potentially attracting users with distinctive 
demographic and psychographic profiles. 
 
2.3. Proposed Research Model 

Based on the theoretical foundations and empirical findings reviewed above, this study proposes an integrated 
research model examining the financial literacy-digital consumer behaviour nexus within Vietnam's P2P lending 
ecosystem. The proposed model conceptualises financial literacy as a multidimensional construct comprising 
cognitive, attitudinal, and behavioural dimensions that influence consumer decision-making through multiple 
pathways, moderated by individual and contextual factors. 

The primary dependent variable within this research model is P2P platform adoption and utilisation, 
conceptualised as a multidimensional construct comprising initial adoption, usage intensity, and transaction 
complexity. This operationalisation draws upon innovation diffusion theory (Rogers, 2003) and technology 
acceptance models (Davis, 1989), recognising that digital financial engagement occurs along a continuum rather 
than as a binary state. The empirical evidence suggests that different dimensions of financial literacy may 
differentially influence these adoption dimensions, necessitating a nuanced conceptualisation of platform 
engagement (Slade et al., 2015). 

Financial literacy constitutes the central independent variable within this model, operationalised through three 
distinct but interrelated dimensions. First, financial knowledge encompasses the cognitive understanding of 
financial concepts, product features, and risk-return relationships. Second, financial attitudes capture psychological 
dispositions toward financial planning, risk tolerance, and digital trust. Third, financial behaviour encompasses 
demonstrated capabilities regarding budgeting, saving, and prior financial technology engagement. This 
multidimensional operationalisation draws upon the OECD/INFE framework (Atkinson & Messy, 2012) while 
incorporating digital-specific elements informed by technology acceptance theories. 

The research model proposes multiple mediating variables that establish the causal pathways through which 
financial literacy influences platform engagement. First, perceived risk functions as a primary mediator, with 
financial literacy hypothesised to reduce risk perception through enhanced understanding of platform mechanisms 
and improved capacity to evaluate lending opportunities (van Rooij et al., 2011). Second, trust perceptions mediate 
literacy effects, with financial knowledge enhancing institutional trust through familiarity with regulatory 
frameworks and operational models (Gefen et al., 2003). Third, self-efficacy regarding financial technology usage 
mediates literacy effects, with knowledge enhancing confidence in navigating digital interfaces and executing 
financial transactions (Bandura, 1997). 
 

 
Figure 1. Proposed research model. 

 
Moderating variables within the research model account for heterogeneous effects across demographic 

segments and contextual conditions. Demographic moderators include age, gender, income level, educational 
attainment, and urban/rural residence—factors empirically demonstrated to influence both financial literacy and 
digital technology adoption (Lusardi & Mitchell, 2011). Technological moderators include internet experience, 
smartphone ownership duration, and prior digital banking experience—factors that potentially influence the 
relationship between financial knowledge and platform engagement through familiarity effects (Venkatesh et al., 
2003). Cultural moderators include individualism/collectivism orientation and uncertainty avoidance—dimensions 
shown to influence financial behaviour across national contexts (Tam & Oliveira, 2017). 

The proposed model further incorporates distinctive elements of Vietnam's institutional context. The 
regulatory environment for P2P lending in Vietnam remains emergent, with platforms operating in a legal grey 
area that potentially influences risk perceptions independent of financial literacy (World Bank, 2017). Furthermore, 
Vietnam's rapid transition from a centrally planned to a market economy has created distinctive generational 
differences in financial socialisation, potentially moderating the relationship between financial knowledge and 
digital financial behaviour (Vuong & Napier, 2014). These contextual factors are incorporated as control variables 
within the model. 



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For lenders within P2P platforms, the model hypothesises that financial literacy enhances portfolio 
diversification behaviour, risk-adjusted return optimisation, and evaluation accuracy regarding borrower 
creditworthiness. These behavioural outcomes derive from improved comprehension of risk-return relationships, 
enhanced capacity to interpret financial information, and reduced susceptibility to behavioural biases including 
herding effects (Lee & Lee, 2012). For borrowers, the model hypothesises that financial literacy influences loan 
request quality, appropriate borrowing amounts relative to income, and optimal timing of borrowing activities. 
These effects derive from improved understanding of interest mechanics, enhanced long-term financial planning, 
and reduced present bias in consumption decisions (Meier & Sprenger, 2013). 

The research model proposes bidirectional relationships between certain variables, acknowledging the potential 
for reciprocal causation. Specifically, platform engagement may enhance certain dimensions of financial literacy 
through experiential learning and feedback mechanisms (Hibbert et al., 2012). This potential endogeneity is 
addressed through appropriate methodological approaches including instrumental variable techniques and 
longitudinal elements within the research design. The model further accounts for selection effects, recognising that 
early adopters of P2P platforms may demonstrate systematically different characteristics from the broader 
population (Rogers, 2003). 

In summary, the proposed research model integrates financial capability theory, technology acceptance models, 
and behavioural economics to examine the complex interrelationship between financial literacy and digital 
consumer behaviour. By specifying multiple pathways of influence, incorporating relevant mediating and 
moderating variables, and accounting for distinctive elements of the Vietnamese context, this model provides a 
comprehensive framework for empirical analysis. The following section details the methodological approach for 
testing this model within Vietnam's emerging P2P lending ecosystem. 
 

3. Research Methodology 
3.1. Research Design 

This study employed a cross-sectional, quantitative research design utilising structural equation modelling 
(SEM) with a partial least squares (PLS) approach to examine the relationship between financial literacy and 
consumer behaviour within Vietnam's P2P lending platforms. This methodological approach was selected for 
several compelling reasons aligned with both the research objectives and the specific characteristics of the study 
context. First, PLS-SEM demonstrates particular suitability for predictive research contexts where theory remains 
under development—a condition that characterises the emergent field of digital financial behaviour in emerging 
economies (Hair et al., 2014). Second, PLS-SEM demonstrates robust performance with complex models 
incorporating multiple mediating and moderating variables, as required by this study's theoretical framework (Chin 
et al., 2003). Third, this approach accommodates non-normal data distributions frequently encountered in 
behavioural research, particularly within novel technological contexts where adoption patterns may demonstrate 
positive skew (Henseler et al., 2009). 

The research design incorporated both formative and reflective measurement models appropriate to the 
conceptual nature of the constructs under investigation. Financial literacy was operationalised as a second-order 
formative construct comprising three first-order reflective dimensions: financial knowledge, financial attitudes, and 
financial behaviour. This measurement approach aligns with contemporary conceptualisations that position 
financial literacy as an aggregate construct formed by distinct but related components (Hung et al., 2009). Platform 
adoption was similarly operationalised as a formative construct comprising reflectively measured indicators of 
initial adoption, usage intensity, and functional utilisation depth. This dual measurement approach enables more 
precise specification of construct relationships while mitigating measurement error (Jarvis et al., 2003). 

To complement the variance-based analysis afforded by PLS-SEM, the research design incorporated fuzzy-set 
Qualitative Comparative Analysis (fsQCA) as a supplementary analytical approach. This configurational method 
enables identification of complex causal recipes that might escape detection through traditional variable-centred 
approaches (Ragin, 2008). As Woodside (2013) argues, fsQCA proves particularly valuable when examining 
complex social phenomena likely characterised by equifinality—the principle that multiple pathways may lead to 
identical outcomes. Within the context of digital financial behaviour, fsQCA enables identification of distinct 
configurations of financial literacy dimensions, demographic characteristics, and contextual factors that collectively 
produce similar behavioural outcomes. 
 

3.2. Data Collection 
Data collection utilised a structured survey instrument administered to users of major P2P lending platforms 

operating within Vietnam between June and September 2016. The sampling frame comprised users of the five 
largest P2P platforms by transaction volume: Tima, Vaymuon, Mofin, Lendex, and MoneyBank, which collectively 
represented approximately 82% of Vietnam's P2P lending market at the time of data collection (Asian 
Development Bank, 2017). Platform operators provided the initial sampling frame, comprising 2,850 users who had 
completed at least one transaction within the previous six months, from which a stratified random sample was 
drawn to ensure proportional representation across platforms. 

The survey instrument was administered through a dual-mode approach to maximise response rates while 
maintaining data quality. The primary collection mode utilised a web-based survey delivered via email invitation, 
supplemented by a telephone survey option for respondents who failed to complete the online instrument after two 
reminders. This dual-mode approach addressed potential selection bias that might arise from internet access 
limitations within certain demographic segments of the Vietnamese population (Dillman et al., 2014). The survey 
was administered in Vietnamese, with the instrument undergoing rigorous translation and back-translation 
processes to ensure conceptual equivalence (Brislin, 1970). 

The data collection yielded 512 completed responses, representing an effective response rate of 18%. After 
removing incomplete responses and applying data cleaning procedures to identify outliers and pattern responses, 
the final analytical sample comprised 427 valid responses. Response bias was assessed through comparison of early 
and late respondents (Armstrong & Overton, 1977), with no statistically significant differences observed across 



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major demographic and behavioural variables. Additionally, a comparison of web and telephone response modes 
revealed no significant differences in construct means or relationships, suggesting absence of mode effects. 

The demographic composition of the final sample demonstrated the following characteristics: 58% male and 
42% female; age distribution of 18-25 (14%), 26-35 (47%), 36-45 (28%), and over 45 (11%); educational attainment 
distribution of high school or below (22%), undergraduate degree (63%), and postgraduate qualification (15%); 
income distribution aligned approximately with Vietnam's urban middle class, with 68% of respondents reporting 
monthly household income between 10 and 30 million VND. Geographically, 62% of respondents resided in 
Vietnam's two largest urban centres (Hanoi and Ho Chi Minh City), with the remainder distributed across 
secondary cities (23%) and rural areas (15%). 
 

3.3. Measurement & Validation 
The measurement instrument incorporated established scales where available, adapted to the Vietnamese 

context through pilot testing and expert review. Financial literacy measurement utilised a modified version of the 
OECD/INFE instrument (Atkinson & Messy, 2012), comprising three subscales: financial knowledge (8 items), 
financial attitudes (6 items), and financial behaviour (7 items). The financial knowledge subscale included both 
general questions regarding interest calculation, inflation, and diversification, and context-specific items regarding 
P2P lending mechanisms. This approach follows Huston's (2010) recommendation to include both general and 
domain-specific knowledge items when examining specific financial behaviours. 

Platform adoption and usage were measured through a combination of behavioural and self-reported items. 
Behavioural measures obtained directly from platform operators (with respondent consent) included account 
tenure, transaction volume, and functional diversity (number of distinct platform features utilised). Self-reported 
measures included usage frequency, transaction complexity (types of lending/borrowing activities undertaken), and 
future usage intentions. This multi-source measurement approach mitigates common method bias concerns while 
enhancing construct validity (Podsakoff et al., 2003). 

Mediating variables were operationalised using established scales adapted to the digital financial context. 
Perceived risk utilised a six-item scale adapted from Featherman and Pavlou (2003), encompassing financial, 
performance, privacy, and social dimensions of risk. Trust was measured using an eight-item scale incorporating 
both institutional and interpersonal dimensions, adapted from McKnight et al. (2002). Self-efficacy regarding 
digital financial management was measured using a five-item scale adapted from Lusardi and Mitchell (2014), 
focusing specifically on confidence in executing digital financial transactions. 

The measurement instrument underwent rigorous validation procedures prior to full deployment. First, 
content validity was established through expert review by six academics specialising in financial behaviour and 
digital technology adoption, resulting in refinement of item wording and elimination of redundant measures. 
Second, a pilot test with 45 participants representative of the target population enabled preliminary assessment of 
reliability and validity, leading to further refinement. Translation equivalence was ensured through independent 
back-translation by two bilingual experts in financial terminology (Brislin, 1970). 

The final survey instrument employed a seven-point Likert scale for attitudinal items, semantic differential 
scales for evaluative items, and a combination of dichotomous and multiple-choice formats for factual and 
behavioural items. The instrument's structure minimised potential response biases by varying scale formats, 
incorporating reverse-coded items, and separating predictor and criterion measures (Podsakoff et al., 2003). The 
complete instrument comprised 78 items across all constructs, including demographic and control variables, with 
an average completion time of 22 minutes. 
 

3.4. Analytical Procedure 
Data analysis followed a systematic, multi-stage procedure aligned with established protocols for PLS-SEM 

assessment (Hair et al., 2014). The analytical software utilised was SmartPLS 4.0, supplemented by SPSS 25.0 for 
preliminary data screening and fsQCA 3.0 for configurational analysis. The analytical procedure comprised four 
sequential phases: (1) data preparation and screening, (2) measurement model assessment, (3) structural model 
evaluation, and (4) supplementary analyses. 

The initial data preparation phase included examination of missing values, identification of outliers, and 
assessment of distributional properties. Missing values were addressed through multiple imputation procedures 
where missing data comprised less than 5% of a respondent's data points; cases exceeding this threshold were 
excluded from analysis (Schafer & Graham, 2002). Outlier detection utilised both univariate (Z-scores) and 
multivariate (Mahalanobis distance) approaches, with identified outliers subjected to sensitivity analysis to 
determine their influence on results. Distributional assessment examined skewness and kurtosis for all continuous 
variables, confirming the appropriateness of PLS-SEM's distribution-free approach for this dataset (Hair et al., 
2012). 

Measurement model assessment followed established protocols for evaluating reflective and formative 
constructs. For reflective measurement models, evaluation criteria included internal consistency reliability 
(Cronbach's alpha and composite reliability), indicator reliability (outer loadings), convergent validity (average 
variance extracted), and discriminant validity (Fornell-Larcker criterion and heterotrait-monotrait ratio). For 
formative measurement models, assessment included significance and relevance of outer weights, collinearity 
among indicators (variance inflation factor), and the theoretical rationale for indicator inclusion (Hair et al., 2017). 
Additionally, measurement invariance was assessed across key demographic segments using the MICOM 
procedure to ensure valid group comparisons (Henseler et al., 2016). 

Structural model evaluation utilised a comprehensive set of criteria beyond mere path significance testing. 
Assessment metrics included coefficient of determination (R²) for endogenous constructs, predictive relevance (Q²) 
through blindfolding procedures, effect sizes (f²) for path relationships, and collinearity assessment (VIF) for 
predictor constructs. Mediating effects were examined through specific indirect effects testing with bootstrapped 
confidence intervals, following the approach recommended by Zhao et al. (2010). Moderating effects were tested 



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using the product indicator approach for continuous moderators and multi-group analysis for categorical 
moderators (Henseler & Fassott, 2010). 

Supplementary analyses extended beyond the core PLS-SEM approach to provide additional insights. First, 
importance-performance map analysis (IPMA) identified the relative importance of predictor variables alongside 
their performance levels, generating actionable insights for practitioner intervention (Ringle & Sarstedt, 2016). 
Second, multi-group analysis (MGA) examined heterogeneous effects across demographic segments including 
gender, age cohorts, income levels, and urban/rural residence (Sarstedt et al., 2011). Third, fsQCA identified 
configurational solutions leading to high platform adoption, complementing the symmetrical, net-effects focus of 
PLS-SEM with an asymmetrical, configurational perspective (Ragin, 2008). 

The fsQCA analytical procedure followed established protocols comprising calibration, necessity analysis, and 
sufficiency analysis (Ragin, 2008). Calibration transformed variable scores into fuzzy-set membership scores 
ranging from 0 to 1, utilising theoretical and empirical anchors appropriate to the Vietnamese context. Necessity 
analysis identified conditions that must be present for the outcome to occur, utilising consistency thresholds of 0.9 
(Ragin, 2008). Sufficiency analysis identified configurations of conditions sufficient to produce the outcome, 
utilising a truth table algorithm with frequency threshold of 2 cases and consistency threshold of 0.8 (Fiss, 2011). 

Common method bias was assessed through both procedural and statistical approaches. Procedurally, the 
research design incorporated multiple sources (self-report and platform data), psychological separation of predictor 
and criterion variables, and varied response formats (Podsakoff et al., 2003). Statistically, Harman's single-factor 
test and the common latent factor approach assessed potential method bias, with results indicating its absence as a 
significant concern in this dataset (Fuller et al., 2016). 
 

4. Research Findings 
4.1. Measurement Model Assessment 

The measurement model evaluation began with an assessment of construct reliability and validity for all 
reflective measures. As shown in Table 1, all first-order reflective constructs demonstrated satisfactory internal 
consistency reliability, with both Cronbach's alpha and composite reliability exceeding the recommended threshold 
of 0.70 (Hair et al., 2017). Composite reliability values ranged from 0.831 to 0.942, indicating robust internal 
consistency without redundancy concerns. Indicator reliability assessment revealed that all items loaded 
significantly on their respective constructs (p < 0.001), with standardised outer loadings ranging from 0.712 to 
0.927, thus exceeding the recommended threshold of 0.70 (Chin, 1998). 
 

Table 1. Reliability and Convergent Validity Assessment. 

Construct Items Cronbach's Alpha Composite Reliability AVE 

Financial Knowledge (FK) 8 0.892 0.916 0.581 
Financial Attitudes (FA) 6 0.837 0.881 0.552 
Financial Behaviour (FB) 7 0.903 0.923 0.631 
Perceived Risk (PR) 6 0.865 0.902 0.604 
Trust in Platform (TP) 8 0.929 0.942 0.672 

Financial Self-Efficacy (SE) 5 0.801 0.862 0.557 
Initial Adoption (IA) 4 0.782 0.859 0.604 
Usage Intensity (UI) 5 0.847 0.891 0.622 
Functional Utilisation (FU) 4 0.793 0.831 0.553 

 
Convergent validity assessment indicated satisfactory average variance extracted (AVE) for all constructs, with 

values ranging from 0.552 to 0.672, thus exceeding the recommended threshold of 0.50 (Fornell & Larcker, 1981). 
This indicates that each construct explains more than 50% of the variance in its respective indicators. Discriminant 
validity was assessed using both the Fornell-Larcker criterion and the heterotrait-monotrait (HTMT) ratio. The 
Fornell-Larcker assessment confirmed that the square root of each construct's AVE exceeded its correlation with 
any other construct, indicating satisfactory discriminant validity (see Table 2). 
 

Table 2. Fornell-Larcker Criterion for Discriminant Validity. 

Construct FK FA FB PR TP SE IA UI FU 

FK 0.762 
        

FA 0.427 0.743 
       

FB 0.486 0.513 0.794 
      

PR -0.398 -0.276 -0.312 0.777 
     

TP 0.412 0.345 0.392 -0.538 0.820 
    

SE 0.527 0.381 0.436 -0.482 0.473 0.746 
   

IA 0.386 0.324 0.358 -0.429 0.493 0.401 0.777 
  

UI 0.452 0.371 0.437 -0.392 0.461 0.427 0.562 0.789 
 

FU 0.471 0.348 0.422 -0.362 0.394 0.485 0.486 0.541 0.744 
Note: Bold diagonal elements represent the square root of AVE for each construct. Off-diagonal elements represent inter-construct correlations. 

 
The HTMT assessment provided further confirmation of discriminant validity, with all HTMT ratios below 

the conservative threshold of 0.85 recommended by Henseler et al. (2015). The highest observed HTMT ratio was 
0.671 (between Trust in Platform and Perceived Risk), indicating clear discrimination between constructs. 
Furthermore, the HTMT inference test utilising bootstrapping with 5,000 resamples confirmed that all HTMT 
values were significantly different from 1, providing additional evidence of discriminant validity. 

For the formative measurement models (second-order constructs), assessment focused on indicator collinearity, 
significance of outer weights, and theoretical relevance. Collinearity assessment revealed variance inflation factor 
(VIF) values ranging from 1.427 to 2.836, well below the threshold of 5, indicating absence of problematic 
collinearity (Hair et al., 2017). Assessment of outer weights revealed that all first-order components significantly 
contributed to their respective second-order constructs (p < 0.01). For Financial Literacy, the relative 



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contributions were Financial Knowledge (0.412), Financial Attitudes (0.368), and Financial Behaviour (0.387). For 
Platform Adoption, the relative contributions were Initial Adoption (0.352), Usage Intensity (0.412), and 
Functional Utilisation (0.389). 

Confirmatory factor analysis (CFA) provided further validation of the measurement model structure. The 
model demonstrated satisfactory fit with the empirical data, as indicated by the standardised root mean square 
residual (SRMR) of 0.048, below the recommended threshold of 0.08 (Hu & Bentler, 1999). Additionally, the 
normed fit index (NFI) of 0.921 and the goodness-of-fit index (GoF) of 0.586 indicated satisfactory fit for the 
measurement model. 
 

4.2. Structural Estimation Model Assessment 
Following validation of the measurement model, structural model assessment examined the hypothesised 

relationships between financial literacy dimensions and consumer behaviour within P2P lending platforms. The 
structural model was evaluated through path coefficients, significance levels, coefficient of determination (R²), effect 
size (f²), and predictive relevance (Q²). Bootstrapping with 5,000 resamples generated robust standard errors for 
significance testing of path coefficients. 

The direct effects analysis revealed significant relationships between key model constructs, as summarised in 

Table 3. Financial Literacy demonstrated significant positive effects on Trust in Platform (β = 0.417, p < 0.001) 

and Financial Self-Efficacy (β = 0.539, p < 0.001), and a significant negative effect on Perceived Risk (β = -0.398, p 
< 0.001). The dimensional analysis further revealed differential effects of Financial Literacy components, with 

Financial Knowledge demonstrating the strongest effect on Perceived Risk (β = -0.302, p < 0.001), Financial 

Behaviour demonstrating the strongest effect on Trust in Platform (β = 0.247, p < 0.001), and Financial 

Knowledge demonstrating the strongest effect on Financial Self-Efficacy (β = 0.412, p < 0.001). 
 

Table 3. Direct Effects Results. 

Relationship Path Coefficient t-value p-value f² 95% CI 

FL → PR -0.398 7.852 <0.001 0.186 [-0.486, -0.312] 

FK → PR -0.302 5.638 <0.001 0.121 [-0.386, -0.215] 

FA → PR -0.147 2.892 0.004 0.039 [-0.243, -0.049] 

FB → PR -0.164 3.127 0.002 0.047 [-0.256, -0.068] 

FL → TP 0.417 8.326 <0.001 0.211 [0.326, 0.504] 

FK → TP 0.232 4.571 <0.001 0.082 [0.145, 0.317] 

FA → TP 0.173 3.412 <0.001 0.054 [0.085, 0.259] 

FB → TP 0.247 4.976 <0.001 0.092 [0.164, 0.328] 

FL → SE 0.539 12.476 <0.001 0.410 [0.462, 0.612] 

FK → SE 0.412 8.937 <0.001 0.254 [0.329, 0.491] 

FA → SE 0.183 3.752 <0.001 0.061 [0.097, 0.267] 

FB → SE 0.209 4.183 <0.001 0.077 [0.123, 0.293] 

PR → PA -0.347 6.829 <0.001 0.168 [-0.434, -0.257] 

TP → PA 0.326 6.237 <0.001 0.149 [0.237, 0.412] 

SE → PA 0.289 5.427 <0.001 0.123 [0.197, 0.377] 
Note: FL = Financial Literacy, FK = Financial Knowledge, FA = Financial Attitudes, FB = Financial Behaviour, PR = Perceived Risk, TP = Trust in 
Platform, SE = Financial Self-Efficacy, PA = Platform Adoption. 

 
The mediating variables demonstrated significant effects on Platform Adoption, with Perceived Risk showing a 

negative effect (β = -0.347, p < 0.001), Trust in Platform showing a positive effect (β = 0.326, p < 0.001), and 

Financial Self-Efficacy showing a positive effect (β = 0.289, p < 0.001). The effect size (f²) analysis indicated that 
Financial Literacy had a medium effect on Trust in Platform (f² = 0.211) and a large effect on Financial Self-
Efficacy (f² = 0.410), while its effect on Perceived Risk was small to medium (f² = 0.186) based on Cohen's (1988) 
guidelines. 

The predictive power of the model was assessed through the coefficient of determination (R²), which indicated 
that the model explained substantial variance in the endogenous constructs: Perceived Risk (R² = 0.246), Trust in 
Platform (R² = 0.312), Financial Self-Efficacy (R² = 0.376), and Platform Adoption (R² = 0.482). The adjusted R² 
values, which account for model complexity, remained close to the unadjusted values, indicating model parsimony. 
The predictive relevance assessment through blindfolding procedure yielded Q² values well above zero for all 
endogenous constructs: Perceived Risk (Q² = 0.143), Trust in Platform (Q² = 0.207), Financial Self-Efficacy (Q² = 
0.202), and Platform Adoption (Q² = 0.269), confirming the model's predictive relevance (see Table 4). 
 

Table 4. Predictive Relevance Assessment. 

Endogenous Construct R² R² Adjusted Q² 

Perceived Risk (PR) 0.246 0.238 0.143 
Trust in Platform (TP) 0.312 0.305 0.207 
Financial Self-Efficacy (SE) 0.376 0.369 0.202 
Platform Adoption (PA) 0.482 0.468 0.269 

 
Specific indirect effects analysis identified significant mediating pathways linking Financial Literacy to 

Platform Adoption, as shown in Table 5. The strongest indirect effect operated through Financial Self-Efficacy (β 

= 0.156, p < 0.001), followed by Trust in Platform (β = 0.136, p < 0.001) and Perceived Risk (β = 0.138, p < 
0.001). The total indirect effect of Financial Literacy on Platform Adoption was 0.430 (p < 0.001), with a 95% 
confidence interval of [0.359, 0.497], indicating strong mediation effects. 

 
 



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Table 5. Specific Indirect Effects. 

Indirect Path Path Coefficient t-value p-value 95% CI 

FL → PR → PA 0.138 5.412 <0.001 [0.097, 0.181] 

FL → TP → PA 0.136 5.321 <0.001 [0.092, 0.179] 

FL → SE → PA 0.156 5.874 <0.001 [0.108, 0.203] 

FK → PR → PA 0.105 4.293 <0.001 [0.067, 0.142] 

FK → TP → PA 0.076 3.718 <0.001 [0.041, 0.110] 

FK → SE → PA 0.119 5.103 <0.001 [0.080, 0.158] 

FA → PR → PA 0.051 2.472 0.013 [0.013, 0.089] 

FA → TP → PA 0.056 3.001 0.003 [0.024, 0.090] 

FA → SE → PA 0.053 2.927 0.003 [0.022, 0.084] 

FB → PR → PA 0.057 2.842 0.004 [0.021, 0.092] 

FB → TP → PA 0.081 3.827 <0.001 [0.045, 0.117] 

FB → SE → PA 0.060 3.174 0.002 [0.028, 0.094] 
Note: FL = Financial Literacy, FK = Financial Knowledge, FA = Financial Attitudes, FB = Financial Behaviour, PR = 
Perceived Risk, TP = Trust in Platform, SE = Financial Self-Efficacy, PA = Platform Adoption. 

 
Moderation analysis examined the conditioning effects of demographic and technological factors on the 

relationship between Financial Literacy and mediating variables. The results identified significant moderating 
effects, as summarised in Table 6. Age moderated the relationship between Financial Literacy and Perceived Risk, 

with the negative effect stronger for younger users (β = -0.487, p < 0.001) compared to older users (β = -0.329, p < 
0.001). Prior digital banking experience moderated the relationship between Financial Literacy and Financial Self-

Efficacy, with a stronger positive effect for users with higher digital banking experience (β = 0.621, p < 0.001) 

compared to those with lower experience (β = 0.428, p < 0.001). Income level moderated the relationship between 

Financial Literacy and Trust in Platform, with the positive effect stronger for higher-income users (β = 0.483, p < 

0.001) compared to lower-income users (β = 0.352, p < 0.001). 
 

Table 6. Moderation Analysis Results. 

Relationship Moderator Moderating Effect t-value p-value 

FL → PR Age 0.129 2.843 0.004 

FL → PR Gender 0.047 1.127 0.260 

FL → PR Education 0.083 1.921 0.055 

FL → TP Age -0.042 0.982 0.326 

FL → TP Income 0.112 2.576 0.010 

FL → TP Urban/Rural 0.128 2.847 0.004 

FL → SE Age -0.074 1.726 0.084 

FL → SE Digital Banking 0.142 3.271 0.001 

FL → SE Internet Experience 0.116 2.692 0.007 
Note: FL = Financial Literacy, PR = Perceived Risk, TP = Trust in Platform, SE = Financial Self-Efficacy. 

 

4.3. Supplementary analyses 
4.3.1. Multi-group Analysis 

To examine heterogeneous effects across demographic segments, multi-group analysis (MGA) was conducted 
for key categorical variables including gender, age cohorts (under 35 vs. 35 and older), and residential location 
(urban vs. rural). The permutation test approach with 5,000 permutations was employed to assess the statistical 
significance of path coefficient differences between groups (Chin & Dibbern, 2010). 

The gender-based MGA revealed significant differences in the relationship between Financial Knowledge and 

Perceived Risk, with a stronger negative effect for male users (β = -0.352, p < 0.001) compared to female users (β = 
-0.243, p < 0.001), with the difference statistically significant (p = 0.042). Additionally, the relationship between 

Financial Behaviour and Trust in Platform demonstrated a stronger positive effect for female users (β = 0.311, p < 

0.001) compared to male users (β = 0.196, p < 0.001), with the difference statistically significant (p = 0.027). 
The age-based MGA identified significant differences in the relationship between Financial Attitudes and 

Financial Self-Efficacy, with a stronger positive effect for younger users (β = 0.237, p < 0.001) compared to older 

users (β = 0.128, p = 0.018), with the difference statistically significant (p = 0.036). Furthermore, the relationship 

between Trust in Platform and Platform Adoption was stronger for older users (β = 0.382, p < 0.001) compared to 

younger users (β = 0.284, p < 0.001), with the difference statistically significant (p = 0.048). 
The location-based MGA revealed significant differences in the relationship between Financial Knowledge and 

Trust in Platform, with a stronger positive effect for urban users (β = 0.276, p < 0.001) compared to rural users (β 
= 0.183, p = 0.002), with the difference statistically significant (p = 0.039). Additionally, the relationship between 

Perceived Risk and Platform Adoption demonstrated a stronger negative effect for rural users (β = -0.412, p < 

0.001) compared to urban users (β = -0.329, p < 0.001), with the difference statistically significant (p = 0.044). 
 

4.3.2. Fuzzy-set Qualitative Comparative Analysis 
The fsQCA identified multiple configurational pathways leading to high Platform Adoption, complementing 

the symmetrical, net-effects perspective of PLS-SEM with an asymmetrical, configurational perspective. Table 7 
presents the complex solution derived from the truth table analysis, identifying four configurations sufficient for 
high Platform Adoption. The overall solution demonstrates satisfactory consistency (0.848) and coverage (0.783), 
indicating both theoretical validity and empirical relevance. 
 

 



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Table 7. fsQCA Complex Solution for High Platform Adoption. 

Configuration Core Conditions Peripheral 
Conditions 

Raw Coverage Unique 
Coverage 

Consistency 

1 FK•FB•~PR fa•SE•age•~inc 0.427 0.118 0.873 

2 FK•FB•TP ~fa•se•~age•INC 0.386 0.092 0.892 
3 FK•FA•~PR•TP ~fb•se•AGE•INC 0.342 0.081 0.864 
4 fa•~fb•~PR•TP•SE FK•~age•INC 0.316 0.069 0.851 
Note: Capital letters indicate the presence of a condition, lowercase letters indicate its absence, and "~" indicates negation. FK = Financial 
Knowledge, FA = Financial Attitudes, FB = Financial Behaviour, PR = Perceived Risk, TP = Trust in Platform, SE = Financial Self-Efficacy, age 
= Age (above 35), inc = High Income. Overall solution consistency: 0.848; Overall solution coverage: 0.783. 

 
The first configuration combines high Financial Knowledge, high Financial Behaviour, low Perceived Risk, low 

Financial Attitudes, high Financial Self-Efficacy, older age, and lower income. This configuration demonstrated the 
highest raw coverage (0.427), suggesting its empirical prevalence within the sample. The second configuration 
combines high Financial Knowledge, high Financial Behaviour, high Trust in Platform, low Financial Attitudes, 
low Financial Self-Efficacy, younger age, and higher income. The third configuration combines high Financial 
Knowledge, high Financial Attitudes, low Perceived Risk, high Trust in Platform, low Financial Behaviour, low 
Financial Self-Efficacy, older age, and higher income. The fourth configuration represents an alternative pathway 
combining low Financial Attitudes, low Financial Behaviour, low Perceived Risk, high Trust in Platform, high 
Financial Self-Efficacy, high Financial Knowledge, younger age, and higher income. 

These configurational findings reveal equifinal pathways to Platform Adoption, demonstrating how different 
combinations of financial literacy dimensions, psychological factors, and demographic characteristics can produce 
similar behavioural outcomes. Notably, high Financial Knowledge appears as a core condition in three of the four 
configurations, suggesting its centrality within the causal pathways, while Financial Attitudes demonstrates 
greater causal complexity, appearing as both a present and absent condition across different configurations. 
 

4.3.3. Simple Slope Analysis 
Simple slope analysis was conducted to visualise significant moderation effects identified in the structural 

model. Figure 1 presents the moderation effect of age on the relationship between Financial Literacy and Perceived 
Risk. The analysis reveals that for younger users (age -1 SD below mean), higher Financial Literacy more strongly 
reduces Perceived Risk compared to older users (age +1 SD above mean). For younger users, the negative 
relationship between Financial Literacy and Perceived Risk is stronger (simple slope = -0.487, t = 9.432, p < 0.001) 
compared to older users (simple slope = -0.329, t = 6.127, p < 0.001). 

Similarly, simple slope analysis for the moderating effect of prior digital banking experience on the relationship 
between Financial Literacy and Financial Self-Efficacy revealed that for users with higher digital banking 
experience (+1 SD), the positive relationship between Financial Literacy and Financial Self-Efficacy is stronger 
(simple slope = 0.621, t = 14.328, p < 0.001) compared to users with lower digital banking experience (-1 SD) 
(simple slope = 0.428, t = 8.743, p < 0.001). 
 

5. Discussion of Research Results and Conclusions 
This research examined the complex interrelationship between financial literacy and consumer decision-

making within Vietnam's emergent peer-to-peer lending ecosystem, yielding insights that advance both theoretical 
understanding and practical application. The empirical findings demonstrate that financial literacy significantly 
influences platform adoption and utilisation through multiple psychological pathways, with distinctive effects 
across demographic segments and contextual conditions. These results contribute to the theoretical development 
of both financial capability and technology acceptance frameworks while providing actionable insights for platform 
developers, financial educators, and regulatory authorities within Vietnam's distinctive socioeconomic context. 

The structural equation modelling results confirm the multidimensional nature of financial literacy, with 
knowledge, attitudinal, and behavioural dimensions demonstrating differential effects on consumer decision-
making processes. This finding aligns with Huston's (2010) theoretical distinction between financial knowledge 
and application capabilities, while extending this framework to the specific context of digital financial services. The 

strong direct effect of financial knowledge on perceived risk (β = -0.302) supports Lusardi and Mitchell's (2014) 
proposition that knowledge acquisition reduces uncertainty perceptions, while the significant relationship between 

financial behaviour and trust formation (β = 0.247) aligns with Sherraden's (2013) emphasis on behavioural 
experience as a foundation for financial capability development. 

The mediational pathways identified in this research advance theoretical understanding of how financial 
literacy influences technology adoption decisions. The significant indirect effects operating through perceived risk 

(β = 0.138), trust (β = 0.136), and self-efficacy (β = 0.156) suggest that financial literacy operates through multiple 
psychological mechanisms rather than through direct knowledge application alone. This finding extends 
technology acceptance models by specifying the cognitive and attitudinal pathways through which domain-specific 
knowledge influences adoption decisions, addressing theoretical gaps identified by Venkatesh and Bala (2008) 
regarding the antecedents of core TAM constructs within specific technological domains. 

The configurational analysis through fsQCA reveals equifinal pathways to platform adoption, demonstrating 
that multiple combinations of financial literacy dimensions, psychological factors, and demographic characteristics 
can produce similar behavioural outcomes. This finding supports the theoretical proposition of causal complexity 
advanced by Ragin (2008) and applied to consumer behaviour by Woodside (2013). The identification of four 
distinct configurations sufficient for high platform adoption suggests that financial literacy operates within 
complex causal recipes rather than through universal mechanisms, challenging simplistic interventional approaches 
predicated on singular causal pathways. 

The moderating effects identified in this research contribute to theoretical refinement by specifying the 
conditional boundaries of financial literacy effects. The significant moderation by age, with financial literacy more 
strongly reducing perceived risk among younger users, aligns with life-cycle theories of financial capability 



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development (Lusardi & Mitchell, 2011). This finding suggests that financial knowledge may play a more critical 
compensatory role among younger consumers with limited financial experience, whereas older consumers may rely 
more heavily on experiential heuristics independent of formal knowledge. Similarly, the moderation effect of prior 
digital banking experience supports technology-specific capability theories that emphasise contextual knowledge 
transfer rather than generalised skill application (Hung et al., 2009). 

The multi-group analysis results reveal significant heterogeneity in financial literacy effects across 
demographic segments, advancing theoretical understanding of potential vulnerability factors within digital 
financial environments. The stronger relationship between financial knowledge and perceived risk among male 

users (β = -0.352 vs. β = -0.243 for females) suggests potential gender differences in risk assessment mechanisms, 
aligning with Croson and Gneezy's (2009) findings regarding gender differences in financial risk processing. 

Similarly, the stronger relationship between trust and platform adoption among older users (β = 0.382 vs. β = 
0.284 for younger users) suggests that trust plays a more critical role in technology adoption among less 
technologically acclimated segments, supporting age-based digital divide theories (van Dijk & Hacker, 2003). 

The empirical findings from Vietnam provide important contextual modifications to financial literacy theories 
predominantly developed within Western economic contexts. The significant positive relationship between 

financial behaviour and trust formation (β = 0.247) appears stronger than typically observed in developed 
economies, potentially reflecting Vietnam's transition from informal to formal financial systems. This finding 
supports Sherraden et al.'s (2015) theoretical proposition that financial capability development in emerging 
economies involves the integration of formal knowledge with traditional financial practices, suggesting that 
behavioural experience with traditional financial mechanisms may facilitate trust transfer to novel digital 
platforms. 

From a practical perspective, these findings offer actionable insights for multiple stakeholders within Vietnam's 

digital financial ecosystem. For platform developers, the strong mediating role of perceived risk (β = -0.347) 
suggests that interface design emphasising risk mitigation through transparency, security indicators, and 
progressive disclosure may enhance adoption among less financially sophisticated segments. The significant 
moderation by digital banking experience indicates that platform onboarding processes should be differentiated 
based on prior financial technology exposure, with additional support mechanisms for users with limited digital 
financial experience. 

For financial educators and literacy advocates, the differential effects of financial literacy dimensions suggest 

the need for targeted educational interventions. The strong direct effect of financial knowledge on self-efficacy (β = 
0.412) indicates that educational programs should emphasise not merely factual knowledge but confidence-building 
through practical application. The complementary relationship between financial attitudes and behaviours revealed 
through fsQCA suggests that effective interventions must address both psychological dispositions and behavioural 
practices rather than focusing exclusively on knowledge transfer. 

Regulatory implications emerge from the significant relationship between trust and platform adoption (β = 
0.326), suggesting that clear regulatory frameworks may enhance consumer confidence within Vietnam's evolving 
P2P lending market. The stronger risk perception effects among rural users identified through MGA indicates the 
potential need for geographically differentiated consumer protection approaches that address the specific 
vulnerabilities of rural populations with limited alternative financial access. These regulatory considerations align 
with Johnson and Sherraden's (2007) emphasis on structural opportunity as a critical component of financial 
capability development. 

The limitations of this research should be acknowledged to contextualise its contributions appropriately. The 
cross-sectional design precludes definitive causal inference, suggesting the value of longitudinal approaches in 
future research to examine financial literacy development and technology adoption over time. The sample 
composition, while relatively large and demographically diverse, overrepresents urban and higher-income 
segments relative to Vietnam's general population, potentially limiting generalisability to rural and lower-income 
populations. Additionally, the focus on existing platform users excludes non-adopters, limiting insights regarding 
adoption barriers among the broader population. 

Future research directions emerge from both these limitations and the study's findings. Longitudinal designs 
could examine how financial literacy develops through platform usage, potentially creating reciprocal relationships 
between knowledge acquisition and behavioural experience. Comparative studies across multiple Southeast Asian 
economies could identify how institutional and cultural factors condition financial literacy effects within digital 
environments. Experimental approaches could isolate the causal effects of specific interface features on risk 
perception and trust formation across varying financial literacy levels, generating more precise design implications. 

In conclusion, this research advances theoretical understanding of the financial literacy-digital behaviour nexus 
while generating practical insights for Vietnam's evolving financial technology ecosystem. By demonstrating the 
multidimensional nature of financial literacy effects, identifying key mediating and moderating mechanisms, and 
revealing configurational pathways to platform adoption, this study contributes to both financial capability and 
technology acceptance theories. These insights provide a foundation for enhancing financial inclusion through 
digital platforms within Vietnam's distinctive socioeconomic context, with potential relevance for other emerging 
economies navigating similar digital financial transformations. 
 

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