Financial Capabilities and Financial Well-Being: The Mediating Role of Financial Resilience

Abstract

This study proposes a two-stage structural model integrating financial literacy, education, attitudes, behavior, financial advice, and financial stress as predictors of financial capabilities. It examines the relationship between financial capabilities and financial well-being, highlighting financial resilience as a potential mediator. The main contribution is positioning financial resilience as a central explanatory mechanism, offering a holistic perspective that addresses theoretical gaps and provides empirical evidence in the context of an emerging economy. A non-experimental, quantitative, cross-sectional design was applied with a sample of 365 university students from Veracruz, Mexico. Data were collected via an online questionnaire and analyzed using exploratory and confirmatory factor analysis, structural equation modeling (SEM), and mediation analysis with bootstrap procedures. The results indicate that financial literacy, education, attitudes, financial advice, and behavior positively influence financial capabilities, with financial advice being the strongest predictor. Financial capabilities strongly affect financial well-being, whereas financial resilience did not mediate this relationship. Limitations include the cross-sectional design and non-probability sampling. Future research could examine additional mediators and moderators and evaluate interventions in diverse socio-economic contexts.

1. Introduction

In the contemporary context, where the complexity of economic and financial systems directly influences daily life, proper personal financial management has become an essential skill for individual and collective well-being. This need has fostered the development and evolution of key concepts such as financial literacy and financial education, which, while closely related, have distinct theoretical and practical nuances that must be distinguished. The term “literacy” initially refers to the process of acquiring basic reading and writing skills. However, educators like Paulo Freire broadened this concept, arguing that literacy is also an act of critical consciousness that enables individuals to “read the world,” equipping them to question and transform it. From this perspective, literacy implies empowerment, not merely the teaching of technical skills (Freire, 1968, 1970, 1985).

This pedagogical framework has been useful for understanding the emergence of new types of literacy. One of them is financial literacy, a concept that gained relevance in the late 20th century in response to the growing need for individuals to understand and manage saving, investing, borrowing, and budgeting. Authors such as Noctor et al. (1992) defined financial literacy as the ability to make informed financial decisions based on knowledge acquired in family or community contexts. The OECD (2005) expanded this definition, incorporating not only expertise but also responsible attitudes and behaviors toward finances. Furthermore, it evaluates the effectiveness of financial education programs and presents the OECD Council Recommendation on Principles and Good Practices for Financial Education and Awareness (OECD, 2005).

As educational strategies aimed at strengthening citizens’ financial literacy were formalized, a broader concept emerged: financial education. Unlike literacy, financial education is conceived as a structured learning process, both formal and informal, designed to equip individuals with the skills to understand financial products, make informed decisions, and develop sustainable financial behaviors. Formal financial education provides the necessary foundation for teaching individuals how to manage their money effectively, which, according to Bernheim et al. (2001), has a positive impact on long-term financial management. Lusardi and Mitchell (2007, 2011) have emphasized that this approach involves not only the acquisition of knowledge, but also the evaluation of the processes by which this knowledge is taught and applied. This conceptual development finds support even in historical sources. In a letter from 1787, John Adams warned that many of the economic difficulties of his time were not due to structural flaws, but rather to widespread ignorance about currency, credit, and the circulation of money. This early observation highlights the importance of having basic economic knowledge to participate fully in social and political life (Adams, 1787).

However, access to financial information, while necessary, is not sufficient in itself. It is essential to develop an appropriate attitude towards money, which is known as financial literacy. Studies have shown that women are more likely to discuss financial matters with their parents, which is associated with a more positive attitude towards financial management (Edwards et al., 2007). Furthermore, family involvement moderates the relationship between financial knowledge and attitudes towards debt (Koropp et al., 2013). Similarly, financial education programs for women promote changes in their attitudes, improving their ability to make more informed, sustainable financial decisions that support their families (Prandini & Baconguis, 2020). This financial literacy directly influences everyday financial decisions, planning capabilities, and, in the long term, economic stability. However, even with knowledge, financial decisions are not always rational.

Financial behavior is influenced by psychological and emotional factors, as explained by Kahneman (2011) and Thaler and Sunstein (2008), who demonstrate that people do not always make decisions logically but are influenced by cognitive and emotional biases. This is where financial attitude comes into play, a key component that influences how people approach economic decisions, whether in terms of saving, consumption, or investment.

Furthermore, financial advice plays a crucial role in decision-making, as professional guidance helps individuals better understand and manage complex financial matters, as emphasized by Lusardi and Tufano (2015). In this sense, financial advice appears to be a useful tool, but its effectiveness ultimately depends on the individual’s financial knowledge and behavior. Financial advice improves decision-making by enhancing confidence (Streich, 2021), encouraging participation in retirement plans (Fang et al., 2022), and influencing investment portfolio composition (Lei, 2018). Additionally, it can be beneficial in the early stages of financial planning (Allie et al., 2016) and affects a client’s risk tolerance (Monne et al., 2023).

Often, a lack of education and preparation in these areas leads to financial stress, a growing phenomenon in modern societies. Its consequences are not limited to the economic sphere, but also affect mental health, quality of life, and overall well-being. Financial stress negatively impacts relationships, health, and work performance and is not simply a result of a lack of money; rather, it stems from structural, emotional, and social factors. It can be passed down through generations (Hubler et al., 2015), decrease productivity (Sabri & Aw, 2020), be exacerbated by certain types of debt in old age (Loibl et al., 2020), and affect the sense of purpose in young people (Goktan et al., 2025). While in some cases it can motivate work effort (Wei et al., 2024), in general it requires a comprehensive approach that combines financial education, psychosocial support, and life-cycle-sensitive public policies. Therefore, developing strong financial skills is crucial for promoting sustainable financial well-being and greater emotional stability. Financial literacy goes beyond technical knowledge; it encompasses attitudes, behaviors, self-confidence, and social context. Anders et al. (2023) show that the family environment has a greater influence on its development than formal education. In small businesses, responsible financial practices enhance sustainability (Babajide et al., 2021). Furthermore, financial self-efficacy is key to applying knowledge in practice (Çera et al., 2020; Rothwell et al., 2016).

Finally, training professionals to work effectively in vulnerable contexts also strengthens financial literacy (Sherraden et al., 2016). In this regard, evidence suggests that financial literacy is a multidimensional construct that requires support from family, education, and the workplace. These skills and knowledge, acquired through education and experience, constitute an individual’s financial literacy. According to Sherraden et al. (2016), financial literacy encompasses not only technical knowledge but also the ability to apply that knowledge in everyday situations. Individuals with higher levels of financial literacy manage their resources more effectively, make sound financial decisions, and adapt more easily to economic changes, thereby strengthening their financial resilience. García-Santillán et al. (2024b) state that it not only depends on access to financial services, but also on education, personal confidence, and social support. Carton et al. (2024) add that beliefs, habits, and past experiences influence how people cope with uncertainty. Sakyi-Nyarko et al. (2022) emphasize that having a bank account, savings capacity, and support networks, such as remittances, improves resilience to crises, especially in rural areas.

For the reasons outlined above, this study examines how interrelated concepts, such as financial education, attitudes, and skills, influence financial behavior and quality of life. The objective is to bridge existing theoretical and empirical gaps by proposing an integrative framework that connects financial literacy, financial counseling, financial stress, financial capabilities, well-being, and financial resilience. In this context, the following question arises: How do financial literacy, financial education, financial attitudes and knowledge, financial behavior, financial counseling, and financial stress relate to financial capabilities, and how do these capabilities, in turn, relate to financial well-being, mediated by individuals’ financial resilience? Therefore, this research seeks to address an important theoretical gap based on the following arguments.

Despite growing interest in financial well-being, the academic literature presents a conceptual fragmentation, treating factors such as financial literacy, attitudes, knowledge, and financial behavior, among others, in isolation, as well as key variables such as financial stress, which also broadens the scope of analysis, providing a more complete view of the phenomenon. This holistic approach is crucial for designing educational interventions and public policies to improve financial well-being, addressing both technical and psychosocial factors that influence individuals’ economic stability. Based on the arguments presented in the literature review regarding the relevant variables, the conceptual model described in Figure 1 is shown below:

To validate the model shown in Figure 1, it is divided into two sections: one that examines the relationship between financial literacy (H1), financial education (H2), financial attitude (H3), financial advice (H4), financial knowledge and behavior (H5), and financial stress (H6), and their relationship with financial capabilities. The other section examines the relationships between financial capabilities and financial well-being and financial resilience (H7 and H8), as well as the mediating role of financial resilience in the relationship between financial capabilities and financial well-being (H9).

2. Literature Review

2.1. Theoretical Framework

To situate this study within the theoretical framework that explains the relevant variables, this section presents a detailed analysis of the theories that underpin the key variables of the study. Following this, the latest state-of-the-art research on these variables is reviewed through a thorough examination of contemporary studies that explore and develop them. This approach helps us to contextualize current perspectives, laying the foundation for a deeper understanding of the phenomenon we are investigating. The variables in the conceptual model—financial literacy, financial education, financial attitude, financial advice, financial knowledge and behavior, financial stress, financial skills, financial resilience, and financial well-being—are explained based on the theories described in

Table 1. Variables of the conceptual model.

Variables	Theories	Brief Description
Financial Attitude, Financial Behavior, Financial Education, Financial Advice	Theory of Planned Behavior (Ajzen, 1991)	This theory proposes that our attitudes, perceived norms, and sense of control over finances play a crucial role in how we plan financial behaviors. Financial education and advice strengthen this sense of power, while attitudes and behaviors reflect intentions.
Financial Knowledge, Financial Behavior, Financial Education, Financial Capabilities	Financial Capability Framework (Atkinson et al., 2007)	This framework links knowledge, education, and financial skills to the ability to make informed decisions and engage in responsible financial behavior.
Financial Behavior, Financial Attitude, Financial Stress, Financial Resilience	Behavioral Life-Cycle Hypothesis (Shefrin & Thaler, 1988)	This hypothesis addresses how psychological factors, such as attitude and self-control, influence financial decisions. Stress and financial resilience relate to how individuals manage resources over time.
Financial Knowledge, Financial Education, Financial Attitude, Financial Advice	Consumer Socialization Theory (Ward, 1974)	According to this theory, we learn financial behaviors through social influences like education, advice, and experience, which shape our knowledge and attitudes toward finances.
Financial Stress, Financial Resilience, Financial Well-being	Conservation of Resources Theory (Hobfoll, 1989)	The theory suggests that people tend to conserve and protect their resources. Financial stress arises when these resources are threatened, while resilience and financial well-being develop when resources are preserved or recovered.
Financial Well-being, Financial Stress, Financial Attitude	Subjective Well-being Theory (Diener, 1984)	This theory posits that subjective well-being is linked to perceptions of life. Attitudes and financial stress directly influence the evaluation of personal financial well-being.

Source: based on literature.

.

2.1.1. Financial Education, Knowledge, and Capabilities

Financial education is widely recognized as a foundational element in developing the knowledge and skills required to make informed decisions. According to Atkinson et al. (2007), financial capability extends beyond technical knowledge to include practical skills, motivation, and the confidence to apply learning in real-life situations. This holistic perspective is embodied in the Financial Capability Framework, which emphasizes that educational interventions should not only teach but also empower individuals to manage their finances responsibly. Consumer socialization theory (Ward, 1974) further illuminates how financial attitudes, values, and behaviors are shaped by environmental influences, particularly during childhood. Family, school, and media play a critical role in shaping financial knowledge and perceptions regarding money management.

2.1.2. Financial Attitudes and Behaviors

Financial attitudes and behaviors can be effectively understood through the Theory of Planned Behavior (Ajzen, 1991), which posits that an individual’s intention to perform a specific behavior is determined by attitudes toward the behavior, perceived social norms, and perceived control over it. In financial contexts, this framework helps explain why individuals with financial knowledge may still fail to act prudently, depending on their perspectives on saving, spending, and borrowing, as well as their perceived control over these actions. The Behavioral Life-Cycle Hypothesis (Shefrin & Thaler, 1988) adds that individuals often deviate from purely rational decision-making due to limited self-control and mental categorization of money. Understanding these behavioral tendencies is crucial for explaining why even financially literate individuals may exhibit impulsive or inconsistent financial behaviors.

2.1.3. Financial Advice and Decision-Making

The use and effectiveness of financial advice are closely linked to both financial education and personal attitudes toward money. From a social learning perspective, as suggested by consumer socialization theory, guidance from experts, parents, and institutions can strongly influence financial decision-making. However, the impact of such advice depends on an individual’s confidence and their ability to comprehend and apply the recommendations. Ajzen (1991) further notes that perceptions of social norms, such as beliefs about what constitutes a sound financial decision, can affect whether individuals seek professional advice.

2.1.4. Financial Stress and Economic Resilience

Financial stress is an emotional and behavioral response that arises when individuals perceive they lack sufficient economic resources to meet basic needs or achieve financial goals. According to Hobfoll’s (1989) Conservation of Resources Theory, this type of stress occurs when a person perceives that they are losing, or are at risk of losing, their financial resources. The theory also introduces the concept of financial resilience, defined as the ability to cope with and recover from adverse situations by mobilizing and replenishing resources. From a behavioral and cognitive perspective, financial stress may impair executive functioning, reduce planning capacity, and increase short-term decision bias. Under stress conditions, individuals tend to prioritize immediate relief over long-term financial optimization, which may weaken their effective financial capabilities. Therefore, financial stress is not conceptualized solely as an outcome, but as a constraining psychological factor that can undermine the translation of knowledge and attitudes into effective financial action. Shefrin and Thaler (1988) extend this perspective by highlighting that a lack of self-control can exacerbate financial stress, as impulsive decisions often lead to negative long-term consequences.

2.1.5. Financial Well-Being and Subjectivity

Financial well-being refers to an individual’s perception of stability and security in relation to their personal finances. Subjective Well-Being Theory (Diener, 1984) posits that well-being is not solely determined by objective indicators such as income but also by the individual’s subjective evaluation of their situation. Consequently, two people with similar incomes may experience very different levels of financial well-being, depending on their expectations, goals, social context, and stress levels. Empirical studies have confirmed that financial well-being is closely linked to variables such as financial education, behavior, and resilience, demonstrating that higher financial literacy and better behavioral control are positively associated with perceived economic well-being.

2.2. State of the Art of Conceptual Model Variables

2.2.1. Financial Literacy and Financial Education

Recent studies have explored the relationship between financial education and financial literacy, revealing a positive but nuanced association. H. Zhang and Xiong (2020) found that financial education enhances literacy among rural residents in China, although methodological issues such as self-selection and endogeneity can reduce this effect. Individual factors, including education level, gender, age, and employment type, modulate this relationship. In a different context, Soejono et al. (2025) reported that young couples in Indonesia with higher prior financial education displayed better financial planning practices, highlighting the behavioral utility of literacy. Y. Zhang and Fan (2024) showed that while education improves literacy, higher literacy may be negatively associated with mobile Fintech adoption but positively associated with healthy financial behaviors and well-being.

Ofori (2024) emphasized that formal education, age, and financial attitudes are key determinants of literacy among Ghanaian self-employed workers. Guerini et al. (2024) examined financial education institutionally, noting its status as a credence good, whose effectiveness depends on public certification and political motivations, especially amid rapid financial innovation. Estelami and Estelami (2024) added a psychological perspective, demonstrating that cognitive styles (analytical, intuitive, adaptive) influence the impact of financial education on literacy. Taken together, these studies suggest that financial education effectively enhances financial literacy, though its effects vary across individuals, contexts, and structural factors. Higher literacy derived from education translates into better practical financial skills. Based on this evidence, we propose the following hypotheses:

H1. 

Financial literacy positively influences individuals’ financial capabilities.

H2. 

Financial education positively influences individuals’ financial capabilities.

2.2.2. Financial Attitude

Financial attitudes play a critical role in decision-making and behavior. Mamo et al. (2021) found that higher education correlates with more responsible attitudes. Hassan et al. (2020) highlighted that even limited knowledge combined with a positive learning attitude enhances financial decision-making. Obreja et al. (2023) showed that conservative attitudes toward science can limit the adoption of new financial tools. Kumar et al. (2024) confirmed that financial attitude is a key determinant of behavior in periurban areas, while She et al. (2024) found similar results among Malaysian millennials. Socialization within families significantly shapes young people’s attitudes (Abdul Ghafoor & Akhtar, 2024). Studies in India and Brazil (Nayak et al., 2024) illustrate that positive attitudes improve resilience and long-term financial outcomes. Xu and Jiang (2024) and Ananda et al. (2024) emphasized the importance of risk-taking attitudes for entrepreneurship and savings behavior. Digital competencies further enhance positive attitudes toward financial technology (Marconi et al., 2024). Da Cunha et al. (2019) and Islam et al. (2023) confirmed that responsible attitudes toward resources and emerging technologies influence financial decisions. Therefore, we propose the following hypothesis:

H3. 

Financial attitude positively influences individuals’ financial capabilities.

2.2.3. Financial Advice

Financial advisors play a vital role across contexts, from sustainability to retirement planning. de Jong and Wagensveld (2024) emphasized the role of advisors in promoting social and ecological value in SMEs. Staehelin et al. (2024) demonstrated that hybrid interfaces combining paper and digital tools enhance client service. Schepen and Burger (2022) found that reliance on professional advice improves subjective well-being, particularly for households with rising income or low financial knowledge. Mustafa et al. (2025) highlighted the impact of advisors on retirement planning in Malaysia, moderated by financial literacy. Sun et al. (2024) examined advisors’ influence on risk investment decisions. Consequently, we hypothesize:

H4. 

Financial advice positively influences individuals’ financial capabilities.

2.2.4. Financial Knowledge and Behavior

Studies have linked financial knowledge and behavior to skill development. Shanmugam et al. (2022) found that access to financial information influences risk-taking and sustainable investments in India. Morris et al. (2022) highlighted the role of financial confidence in behavior. FINRA (2019) and Lee and Dustin (2021) emphasized the influence of behavior on financial satisfaction. Peter et al. (2024) showed that financial literacy and practical skills enhance women entrepreneurs’ decisions, while Rabbani et al. (2021) identified profiles of knowledge linked to well-being and risk tolerance. She et al. (2024) and Sabri et al. (2024) further demonstrated the mediating role of behavior between knowledge and financial well-being. Thus, we propose:

H5. 

Financial knowledge and behavior influence individuals’ financial capabilities.

2.2.5. Financial Stress

Financial stress affects emotional, relational, and behavioral outcomes. Joseph and Peetz (2024) observed that financial anxiety predicts permissive attitudes toward financial monitoring in couples. Choi et al. (2025) and Heo et al. (2025) highlighted its impact on affect and self-esteem, with coping strategies moderating this effect. Sorgente et al. (2023) distinguished financial stress from subjective financial well-being, while Amonhaemanon (2024) emphasized literacy and advice as interventions for informal workers. Accordingly, we propose the following hypothesis:

H6. 

Financial stress influences individuals’ financial capabilities.

2.2.6. Financial Capabilities, Financial Resilience, and Financial Well-Being

Financial well-being integrates economic, psychological, and contextual factors, reflecting not only the possession of financial knowledge and skills but also the capacity to apply them effectively under varying circumstances (Saeedi & Nishad, 2024; Weida et al., 2024). Financial capabilities, which encompass practical skills, knowledge, and confidence in financial decision-making, equip individuals with the tools necessary to manage resources effectively. However, possessing capabilities alone does not guarantee stable financial well-being, particularly in the presence of financial stress or adverse economic conditions. Financial resilience is conceptualized as an adaptive capacity that enables individuals to absorb financial shocks, reorganize resources, and maintain functional stability under economic pressure. Unlike financial capability, which reflects knowledge and operational skills, resilience represents a dynamic psychological and behavioral mechanism that facilitates the translation of financial capability into sustained financial well-being.

In this sense, resilience functions as a mediating bridge: individuals with strong financial capabilities may experience higher well-being only if they can leverage resilience to cope with stress and uncertainty. Empirical evidence supports the distinctiveness and importance of resilience. Studies indicate that adaptive coping, self-efficacy, and resource mobilization contribute to the ability to maintain financial functioning when facing adversity (Rai et al., 2025; Wheeler & Brooks, 2024; Sharma et al., 2025). While resilience positively influences well-being, its effect is context-dependent: structural constraints, socio-economic factors, and cultural norms may limit the extent to which resilience can buffer the negative consequences of financial stress, particularly in emerging economies such as Mexico. Based on this reasoning, the following hypotheses are proposed:

H7. 

Financial capabilities positively influence financial well-being.

H8. 

Financial resilience positively influences financial well-being.

H9. 

Financial resilience mediates the relationship between financial capabilities and financial well-being.

This formulation explicitly positions resilience as a partial mediator, acknowledging that its buffering role may not be absolute and may vary according to contextual and individual factors.

3. Methodology

3.1. Design Study

This study employs a non-experimental, quantitative, cross-sectional design aimed at exploring the interconnections among financial literacy, financial education, financial attitude, financial advice, financial knowledge and behavior, and financial stress, and their influence on financial capabilities. Additionally, the study examines the mediating role of financial resilience in the relationship between financial capabilities and financial well-being. This design was selected because it allows for the analysis of associative and predictive relationships among multiple variables at a single point in time, without manipulating participants’ conditions. While cross-sectional studies do not permit causal inference, they are particularly suitable for testing theoretical constructs and mediation models in exploratory research contexts.

By combining correlational analysis, factor analysis, and structural equation modeling, this approach provides a rigorous framework to validate the conceptual model and assess the statistical significance of direct and indirect relationships among variables, preparing the ground for robust interpretation of the findings within the context of Mexican university students.

3.2. Participants and Sample

The study population consisted of individuals aged 18 or older enrolled in higher education institutions in Veracruz, Mexico. A non-probability, self-selection sampling technique was employed, in which participants voluntarily chose to participate by completing an online questionnaire. This approach was considered appropriate for exploratory research aimed at validating theoretical constructs and examining relationships among financial variables, rather than for making statistical generalizations to the broader population (Bryman, 2016; Saunders et al., 2019). Although non-probability sampling limits generalizability, it allows the inclusion of participants who are engaged and familiar with the study topic, enhancing content validity (Etikan et al., 2016). The online distribution of the questionnaire facilitated access to a diverse pool of university students, maximizing response reach while maintaining efficiency in data collection.

A total of 365 valid responses were obtained, providing sufficient data to conduct preliminary correlational, factor, and structural analyses. While the sample reflects the behavior of university students, it may not fully represent other age groups or populations with different cultural or socio-economic backgrounds. Therefore, the results should be interpreted cautiously regarding generalization, particularly for older adults or in cultural contexts different from Veracruz. This pragmatic and exploratory approach aligns the research design with the study’s objectives, as recommended by Creswell and Creswell (2018), ensuring both internal consistency and conceptual robustness, while providing a solid foundation for testing the proposed relationships among financial literacy, capabilities, resilience, and well-being.

3.3. Ethical Code

This study was approved by the Ethics Committee of the Business School at Universidad Cristóbal Colón (Project ID P-07/2025) and adhered to the principles established in the Declaration of Helsinki. All participants were fully informed about the study’s objectives, procedures, and the voluntary nature of participation before completing the questionnaire. Confidentiality and anonymity were ensured throughout the research process. Participants provided informed consent by reading and acknowledging the instructions and statements before participation. They were informed that they could withdraw at any time without any penalty or negative consequence. This ethical framework ensured that the study met international standards for the protection of human subjects, particularly regarding the collection and handling of sensitive financial and personal data.

3.4. Test Used

The instrument employed in this study was previously utilized by García-Santillán et al. (2024a) and is structured as follows: Sociodemographic Information: The first section collects participants’ basic information, including age, gender, educational level, and employment status, providing context for the analysis of financial behaviors. Financial Education, Attitudes, Advice, Knowledge, and Capabilities: The second section comprises dimensions related to financial behavior. Items were adapted from the scale developed by Elrayah and Tufail (2024), who refined earlier instruments, and complemented with indicators from recent empirical studies (Widyastuti et al., 2020; Khan et al., 2022; Çoşkun & Dalziel, 2020). Each item was measured on a five-point Likert-type scale (1 = strongly disagree; 5 = strongly agree). These dimensions capture the knowledge, skills, attitudes, and behaviors necessary for effective financial management.

Financial Well-Being: The third section includes eight indicators of financial well-being based on frameworks proposed by BBVA (2025) and the Center for Financial Services Innovation (CFSI), complemented by sixteen items developed by Flores et al. (2024). These items assess participants’ perceptions, lived experiences, and coping strategies in the face of economic challenges. Early Financial Literacy: Items assessing early financial literacy were included, grounded in theoretical frameworks on financial socialization, observational learning, and the family environment (Bandura, 1986; Gudmunson & Danes, 2011; Mandell, 2008; Lusardi & Mitchell, 2011; OECD/INFE, 2012).

Financial Stress and Financial Resilience: Financial stress was measured using items adapted from internationally recognized scales, including the Financial Stress Scale (Prawitz et al., 2006), the Financial Well-Being Scale (CFPB, 2015), and the Financial Anxiety Scale (Archuleta et al., 2013). Financial resilience was operationalized as the ability to cope with and recover from adverse financial situations by mobilizing resources and strategies. Factor loadings, convergent validity (AVE > 0.50), and discriminant validity were carefully examined to confirm that the construct is distinct from financial capabilities and other variables. Reliability was high (Cronbach’s α = 0.80; McDonald’s ω = 0.81; Composite Reliability = 0.82), providing confidence in its measurement.

Overall, the instrument captures multiple dimensions of financial behavior and well-being, integrating theoretical and empirical foundations while maintaining robust psychometric properties, ensuring validity and reliability for subsequent statistical analyses (Hair et al., 1999; Tavakol & Dennick, 2011).

3.5. Statistical Procedure and Reliability and Validity Analysis

To validate the instrument and the data collected in this study, several statistical indicators were applied to assess the internal consistency and validity of the measurement model. Internal reliability coefficients, Cronbach’s alpha (α) and McDonald’s omega (ω), were calculated, along with Composite Reliability (CR) and Average Variance Extracted (AVE). The α and ω coefficients assess the internal consistency of the items within each scale, with ω considered a more robust indicator when variable factor loadings are present among items (Cronbach, 1951; McDonald, 1999; Tavakol & Dennick, 2011). Composite Reliability (CR) measures the internal consistency of the latent construct and is deemed acceptable when values exceed 0.70 (Fornell & Larcker, 1981). AVE assesses the model’s convergent validity; values above 0.50 indicate that the items adequately explain the variance of their corresponding constructs (Fornell & Larcker, 1981). These indicators ensure that the scales used are statistically consistent and valid for the study.

Subsequently, multivariate normality of the data was evaluated. According to Kim’s (2013) theoretical criterion, a distribution can be considered normal if skewness is approximately ±2 and kurtosis is approximately ±7. Values outside these ranges indicate potential significant deviations, such as skewness or leptokurtosis, which may affect the analysis. George and Mallery (2010) recommend examining both indicators jointly, whereas Field (2013) emphasizes the need to verify normality before performing parametric tests. Fisher’s criteria were also applied to evaluate skewness and kurtosis (Spiegel et al., 2009).

Table 2. Theoretical values for skewness and kurtosis.

Sample	Z	Skewness	Kurtosis	p Value	HO	Distribution
small n < 50	>1.96	Ignore	Ignore	0.05	Reject	Not normally
medium 50 < n < 300	>3.29	Ignore	Ignore	0.05	Reject	Not normally
large > 300	Not ignore	≤2 → Do not reject H0 >2 → Reject H0	≤7 → Do not reject H0 >7 → Reject H0	0.05	According to condition	Normal/Not normal

Note: H₀ (Null Hypothesis): The data follow a normal distribution: For small and medium samples, the Z-transform of skewness is used because absolute values of skewness/kurtosis are less reliable, For large samples, absolute skewness and kurtosis values are used because Z becomes overly sensitive and The “Normal/Not normal” outcome for large samples depends on whether skewness and kurtosis fall within the acceptable range (Kim, 2013).

presents the theoretical values proposed by Kim (2013).

3.5.1. Pearson Correlation

Pearson correlation analyses were initially conducted to test hypotheses H1 through H6, examining the relationships among Financial Literacy, Financial Education, Financial Attitude, Financial Advice, Financial Knowledge–Behavior, Financial Stress, and the dependent variable Financial Capabilities. This step allowed assessment of the statistical significance and magnitude of associations between constructs. Although correlations indicate direction and strength, they do not imply causality. These preliminary analyses helped identify theoretically meaningful relationships to be further tested through SEM and mediation analysis.

3.5.2. Exploratory and Confirmatory Factor Analysis (EFA and CFA)

Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were conducted using Structural Equation Modeling (SEM) in AMOS v23 to confirm the latent structure of the constructs. Prior to these analyses, data reliability, internal consistency, and normality were assessed. Bartlett’s Test of Sphericity and the Kaiser–Meyer–Olkin (KMO) index were calculated to ensure suitability for factor analysis (García-Santillán, 2017). For variables showing significant deviations from normality (skewness or kurtosis), polychoric correlation matrices were employed (Muthén & Kaplan, 1985; Timmerman & Lorenzo-Seva, 2011).

The EFA identified the underlying structure and reduced dimensionality, providing factor loadings that reflect internal consistency (Field, 2013). The CFA subsequently assessed factorial validity using multiple fit indices: Absolute fit: CFI > 0.90, RMSEA < 0.08, NFI ≈ 0.90; Incremental and parsimonious fit: GFI and TLI close to 1.0, AIC and BIC favoring simpler models. Structural fit: χ² statistic, RMSEA, and SRMR < 0.08. Given the relatively large number of variables, model stability was verified through careful examination of factor loadings, multicollinearity diagnostics, and model fit, ensuring the SEM could accommodate the complexity without overfitting or instability. Items for Financial Resilience were specifically scrutinized for factor loadings, convergent validity (AVE > 0.50), and discriminant validity, confirming the construct is distinct from Financial Capabilities and other variables.

3.5.3. Mediation Model (Conceptual Procedure)

To test hypotheses H7 and H8, the PROCESS macro for SPSS (version 5.0) by Andrew F. Hayes was employed to examine the influence of Financial Capabilities on Financial Well-being and to determine whether Financial Resilience mediates this relationship. A bootstrap procedure with 5000 samples generated 95% confidence intervals for the indirect effect. Standardized coefficients were computed to facilitate interpretation. This approach allowed simultaneous assessment of the direct effect of Financial Capabilities on Financial Well-being and the indirect effect through Financial Resilience, providing robust evidence of partial mediation. These results support the theoretical proposition that Financial Resilience buffers and enhances the impact of capabilities on well-being, rather than acting as a complete determinant.

3.5.4. Reliability, Validity, and Normality

Internal consistency was assessed using Cronbach’s α and McDonald’s ω, with composite reliability (CR) and average variance extracted (AVE) calculated to evaluate convergent validity. Values for α and ω exceeded 0.70 for all constructs, CR > 0.70, and AVE > 0.50, indicating strong psychometric properties (Cronbach, 1951; McDonald, 1999; Tavakol & Dennick, 2011; Fornell & Larcker, 1981). Multivariate normality was assessed through skewness (±2) and kurtosis (±7) criteria (Kim, 2013), complemented by Fisher’s criteria (Spiegel et al., 2009). Data falling outside these ranges were treated with polychoric correlations to ensure robust SEM estimation.

4. Data Analysis and Discussion

4.1. Reliability

Internal consistency of the instrument was satisfactory. Cronbach’s alpha reached α = 0.925 and McDonald’s omega ω = 0.913, indicating strong reliability of the scales. Normality was assessed using the Kolmogorov–Smirnov and Shapiro–Wilk tests, along with skewness and kurtosis indicators. Although the normality tests were statistically significant (p < 0.001), skewness and kurtosis values remained within acceptable thresholds (±2 and ±7, respectively), supporting approximate normality of the data (see

Table 3. Descriptive Statistics and Normality Values.

Variable	Kolmogorov–Smirnov	Sig.	Shapiro–Wilk	Sig.	Media	St. Dev.	Skewness	Kurtosis
FinLiteracy	0.081	0.00	0.975	0.00	34.45	8.16	−0.52	0.41
FinCapability	0.067	0.00	0.984	0.00	22.98	7.25	0.25	−0.36
FinAdvise	0.094	0.00	0.977	0.00	18.09	4.36	−0.39	0.66
FinEducation	0.137	0.00	0.915	0.00	8.54	3.97	0.58	−0.37
FinAttitude	0.13	0.00	0.957	0.00	18.14	4.17	−0.63	1.11
FinBehaviour	0.136	0.00	0.937	0.00	19.11	3.98	−0.86	1.92
FinWellbeing	0.085	0.00	0.975	0.00	26.64	6.16	−0.52	0.64
FinStress	0.086	0.00	0.977	0.00	14.77	4.69	−0.06	−0.09
FinResilience	0.136	0.00	0.977	0.00	19.61	6.04	−0.10	−0.18

Note. The Lilliefors significance correction is applied to the Kolmogorov–Smirnov test. The standard errors for skewness (SE = 0.128) and kurtosis (SE = 0.255) are constant across all variables. The degrees of freedom were identical in all cases (df = 365).

).

Overall, the distributional properties of the variables were considered adequate for subsequent parametric analyses and structural equation modeling.

4.2. Hypothesis Test for H1 to H6

To examine the relationships proposed in hypotheses H1–H6, Pearson correlation analyses were conducted between financial capability and its proposed determinants (see

Table 4. Pearson Correlations for Hypotheses H1–H6.

Variable	r	FL	FC	FA	FE	FAt	FS
Financial Literacy (FL)	Pearson correlation	--
Financial Capability (FC)	Pearson correlation	0.364 **	--
	Sig. (two-tailed)	0.000
Financial Advice (FA)	Pearson correlation	0.460 **	0.586 **	--
	Sig. (two-tailed)	0.000	0.000
Financial Education (FE)	Pearson correlation	0.175 **	0.435 **	0.382 **	--
	Sig. (two-tailed)	0.001	0.000	0.000
Financial Attitude (Fat)	Pearson correlation	0.425 **	0.566 **	0.617 **	0.247 **	--
	Sig. (two-tailed)	0.000	0.000	0.000	0.000
Financial Stress (FS)	Pearson correlation	0.221 **	−0.193 **	0.143 **	−0.102	0.058	--
	Sig. (two-tailed)	0.000	0.000	0.006	0.050	0.269
Variable	Correlation Financial Capability		Sig. (Two-Tailed)			N
Financial Literacy	0.364 **		0.00			365
Financial Advise	0.586 **		0.00			365
Financial Education	0.435 **		0.00			365
Financial Attitude	0.566 **		0.00			365
Financial Stress	−0.193 **		0.00			365

** Note. Correlation is significant at the 0.01 level (two-tailed).

).

As shown in Table 4, financial literacy was positively associated with financial capability (r = 0.364, p < 0.01), supporting H1. Financial education (r = 0.435, p < 0.01) and financial attitude (r = 0.566, p < 0.01) also demonstrated significant positive associations with financial capability, supporting H2 and H3, respectively. Financial advisory exhibited the strongest positive correlation with financial capability (r = 0.586, p < 0.01), providing support for H4.

Collectively, the dimensions related to financial knowledge and behavior were positively linked to financial capability, supporting H5. In contrast, financial stress showed a significant negative association with financial capability (r = −0.193, p < 0.01), supporting H6.

Overall, the results indicate that higher levels of financial literacy, education, advisory engagement, and positive financial attitudes are associated with greater financial capability, whereas financial stress is inversely related to capability.

4.2.1. Dimensional Assessment of the First Block

A principal component analysis (PCA) was conducted as a preliminary dimensional assessment of the variables included in the first block of the model. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.786, and Bartlett’s test of sphericity was significant (χ² = 858.743, df = 21, p < 0.001), indicating that the data were suitable for component extraction.

Table 5. Component Matrix and Explained Variance.

Variables	Component		Communalities	Total Variance Explained Extraction Sums of Squared Loading
	1	2	Extraction	Total	% Variance	% Cumulative
FinLiteracy	0.678	0.287	0.542	3.198	45.681	45.681
FinCapability	0.763	−0.422	0.760
FinAdvise	0.835	−0.032	0.698
FinEducation	0.484	−0.498	0.483
FinAtittude	0.806	−0.020	0.650	1.363	19.476	65.157
FinBehaviour	0.741	0.370	0.687
FinStress	0.162	0.846	0.743

presents the communalities and component loadings obtained from the PCA.

Two components were extracted, explaining 65.16% of the total variance. The first component accounted for 45.68% of the variance and grouped financial literacy, financial capability, financial advisory, financial education, financial attitude, and financial behavior. The second component explained 19.48% of the variance and was primarily associated with financial stress, suggesting partial structural independence from the remaining variables. Communality values were generally adequate, indicating that most variables were well represented by the extracted components. Overall, the dimensional structure supports the distinction between a financial capability–knowledge cluster and a separate financial stress dimension.

Structural Equation Modeling (SEM) was conducted to examine the proposed relationships among the study variables. The estimated structural models, including the initial, adjusted, and final specifications, are presented in Figure 2, Figure 3 and Figure 4, respectively.

4.2.2. Structural Model—Initial Specification

The initial structural model (Model 1) presented inadequate overall fit. Although the CMIN/DF ratio (3.073) was within the acceptable upper limit, most absolute and incremental fit indices were below recommended thresholds (GFI = 0.699; AGFI = 0.663; CFI = 0.725; TLI = 0.705; IFI = 0.727; NFI = 0.643). The RMR value (0.137) exceeded acceptable limits (<0.08), indicating substantial residual discrepancies. Additionally, the ECVI value (8.41) suggested limited model replicability. Overall, Model 1 did not provide an adequate representation of the observed data (see Figure 2).

4.2.3. Structural Model—Re-Specified Model

Based on theoretical considerations and modification indices, a first re-specification (Model 2) was conducted. This adjustment resulted in a notable improvement in model fit. The CMIN/DF ratio decreased to 2.583, and incremental fit indices improved substantially (CFI = 0.870; TLI = 0.853; IFI = 0.872). Absolute fit indices also increased (GFI = 0.830; AGFI = 0.794), although they remained slightly below recommended levels. Parsimony indices improved (PGFI = 0.686; PNFI = 0.711), and the ECVI decreased markedly to 3.382, indicating improved potential for replication. Despite these improvements, some indices still fell short of optimal criteria, justifying further refinement. The adjusted structural model is presented in Figure 3.

Model respecifications were conducted cautiously and based on theoretical justification rather than purely statistical criteria. Suggested modifications were evaluated through modification indices, and only those consistent with the conceptual framework were retained. The final model therefore reflects an empirically improved yet theoretically grounded representation of the proposed relationships.

4.2.4. Structural Model—Final Specification

A second re-specification produced the final model (Model 3), which demonstrated the best overall fit among the competing specifications. The CMIN/DF ratio reached 1.89, indicating excellent absolute fit. The GFI surpassed the 0.90 threshold (0.907), and AGFI approached adequacy (0.88). Incremental fit indices exceeded recommended levels (CFI = 0.945; TLI = 0.934; IFI = 0.945), indicating strong structural consistency. Although NFI (0.891) was slightly below 0.90, it showed substantial improvement compared to previous models. The RMR (0.087) approached acceptable levels. Parsimony indices were satisfactory (PGFI = 0.703; PNFI = 0.748), and the ECVI (1.71) was the lowest among the three models, suggesting superior replicability.

The final model (Model 3) demonstrated the best overall performance across absolute, relative, and parsimonious fit indices. Specifically, CMIN/DF = 1.890 indicated an adequate chi-square adjustment; GFI = 0.907 and AGFI = 0.880 reflected acceptable absolute fit; and incremental indices (CFI = 0.945, TLI = 0.934, IFI = 0.945) approached excellent levels. Although NFI (0.891) and RMR (0.087) were slightly below ideal thresholds, their values improved substantially compared to previous models. Parsimony indicators (PGFI = 0.703; PNFI = 0.748; ECVI = 1.710) further supported the stability and replicability of the final specification. A comparative summary of model performance is presented in

Table 6. Summary of Fit Indices versus Reference Criteria.

Indices	Criterion	Model 1	Model 2	Model 3	Interpretation
CMIN/DF	<2–3	3.073	2.583	1.89	Model 3: best absolute fit
RMR	<0.08	0.137	0.112	0.087	Model 3: closest to ideal
GFI	>0.90	0.699	0.83	0.907	Model 3: meets requirements, better than 1 and 2
AGFI	>0.90	0.663	0.794	0.88	Model 3: almost meets requirements, better than 1 and 2
CFI	>0.90 (good), >0.95 (excellent)	0.725	0.87	0.945	Model 3: excellent relative fit
TLI	>0.90	0.705	0.853	0.934	Model 3: best structural fit
IFI	>0.90	0.727	0.872	0.945	Model 3: excellent
NFI	>0.90	0.643	0.807	0.891	Model 3: almost meets requirements, better than 1 and 2
PGFI	>0.50	0.624	0.686	0.703	Model 3: more parsimonious
PNFI	>0.50	0.6	0.711	0.748	Model 3: better parsimony
ECVI	Less is better	8.41	3.382	1.71	Model 3: more replicable

Source: own.

.

The comparative results presented in Table 6 confirm that Model 3 provides the best overall representation of the data. In the final specification, the structural paths linking the financial literacy–behavioral components to financial capabilities were examined. Most relationships were positive and statistically significant, indicating that financial literacy, education, advisory orientation, attitude, and financial behavior contribute meaningfully to the development of financial capabilities. In contrast, financial stress showed a weaker and negative association, suggesting that higher perceived stress may undermine capability development. Overall, the results provide empirical support for hypotheses H1–H6.

Pearson correlation analysis was conducted to examine the associations among financial capabilities, financial resilience, and financial well-being. The results are presented in

Table 7. Pearson correlations between financial variables.

Variable 1	Variable 2	r	Sig. (Two-Tailed)
FinCapabilities	FinWellbeing	0.679 **	<0.001
FinCapabilities	FinResilience	0.134 *	0.011
FinWellbeing	FinResilience	0.151 **	0.004

** The correlation is significant at the 0.01 level (two-tailed). * The correlation is significant at the 0.05 level (two-tailed). N = 365. Source: own.

.

As shown in Table 7, financial capabilities are strongly and positively associated with financial well-being (r = 0.679, p < 0.001), indicating a substantial relationship between individuals’ perceived financial skills and their subjective financial condition. Financial resilience shows a positive but modest association with financial well-being (r = 0.151, p = 0.004). Additionally, financial capabilities are positively related to financial resilience (r = 0.134, p = 0.011). Although the magnitude of the latter associations is small, their statistical significance justifies further examination of the potential mediating role of financial resilience in the relationship between financial capabilities and financial well-being.

4.3. Exploratory Assessment of Shared Variance in the Second Block

To explore the underlying structure among financial capability, financial resilience, and financial well-being, an exploratory principal component analysis (PCA) was conducted (see Figure 5). The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.521, slightly above the minimum acceptable threshold (0.50), indicating modest sampling adequacy. Bartlett’s test of sphericity was significant (χ² (3) = 233.288, p < 0.001), suggesting that the correlation matrix was not an identity matrix and that sufficient intercorrelations existed to examine shared variance. The analysis yielded a single component explaining 57.82% of the total variance. Financial capability (0.896) and financial well-being (0.900) loaded strongly on this component, with high communalities (0.803 and 0.811, respectively), indicating substantial shared variance. In contrast, financial resilience exhibited a comparatively low loading (0.348) and communality (0.121), suggesting that it behaves as a relatively independent dimension within the model. The detailed component loadings and variance explained are presented in

Table 8. Component matrix and variance explained.

Component Matrix a			Total Variance Explained Extraction Sum of Squared Loading
	Component	Communalities
	1	Extraction	Total	% De Variance	% Cumulative
FinCapability	0.896	0.803	1.735	57.823	57.823
FinWellbeing	0.900	0.811
FinResilience	0.348	0.121

Kaiser–Meyer–Olkin Measure of Sampling Adequacy (KMO = 0.521). Bartlett’s Test of Sphericity: χ² (3) = 233.288, p < 0.001 Extraction Method: Principal Component Analysis. ^a One component extracted. Source: own.

.

Overall, the exploratory results indicate a closer structural association between financial capability and financial well-being than with financial resilience.

Structural Equation Modeling of the Second Block

To further examine the structural relationships among financial capability, financial resilience, and financial well-being, Structural Equation Modeling (SEM) was conducted.

The initial and final structural specifications are presented in Figure 6 and Figure 7.

Model fit was evaluated using absolute, incremental, and parsimonious fit indices. The results are summarized in

Table 9. Summary of Indices versus Reference Criteria.

Index	Reference Value	Model 1	Model 2	Interpretation (Model 2)
CMIN/DF	<3 good, 3–5 acceptable	4.371	2.474	Good fit
RMR	<0.05 ideal, <0.08 acceptable	0.118	0.064	Acceptable
GFI	>0.90 good	0.766	0.92	Good
AGFI	>0.90 good	0.719	0.893	Nearly good/acceptable
RMSEA	<0.05 excellent, 0.05–0.08 acceptable, >0.10 poor	0.096	0.064	Acceptable
PCLOSE	>0.05 indicates good fit	0	0.009	Acceptable fit
NFI	>0.90 good	0.746	0.902	Good
RFI	>0.90 good	0.719	0.883	Close to good
IFI	>0.90 good	0.792	0.939	Good
TLI	>0.90 good	0.768	0.927	Good
CFI	>0.90 good	0.791	0.939	Good
PRATIO	>0.50 acceptable	0.902	0.838	Good parsimony
PNFI	>0.50 acceptable	0.673	0.756	Good parsimony
PCFI	>0.50 acceptable	0.713	0.787	Good parsimony

.

As shown in Table 9, Model 2 demonstrates superior fit compared to Model 1. The final specification achieved acceptable absolute fit (CMIN/DF = 2.474; RMSEA = 0.064; RMR = 0.064), good incremental fit (CFI = 0.939; TLI = 0.927; IFI = 0.939; NFI = 0.902), and satisfactory parsimony (PNFI = 0.756; PCFI = 0.787). These results indicate that the refined model provides a robust representation of the structural relationships among the constructs.

4.4. Mediation Analysis (H9)

To evaluate whether financial resilience mediates the relationship between financial capability and financial well-being, a mediation analysis was conducted using regression-based procedures with bootstrap confidence intervals (see

Table 10. Regression Statistics: FinCap → FinRes.

Predictor	β	SE	t	p	LLCI	ULCI
Intercept	17.052	1.044	16.334	0	14.999	19.105
FinCap	0.111	0.043	2.57	0.011	0.026	0.197

Model: R = 0.134, R² = 0.018, F(1,363) = 6.606, p = 0.011. LLCI (Lower-Level Confidence Interval): The lower bound of the confidence interval (typically 95%). It represents the lowest possible value of the estimated effect based on the data. ULCI (Upper-Level Confidence Interval): This is the upper bound of the confidence interval. It represents the highest possible value of the estimated effect. SE: Standard error β: Unstandardized coefficient. Source: own.

,

Table 11. Regression statistics FinCap + FinRes → FinWB.

Predictor	β	SE	t	p	LLCI	ULCI
Intercept	12.325	1.035	11.909	0	10.29	14.36
FinCap	0.57	0.033	17.308	0	0.505	0.634
FinRes	0.063	0.04	1.586	0.114	−0.015	0.14

Source: own.

and

Table 12. Direct and Indirect Effects of FinCap on FinWB.

Effect	Coefficient (β)	SE/BootSE	t	p	IC 95% Bootstrap (LLCI, ULCI)
Direct (FinCap → FinWB)	0.57	0.033	17.308	0	0.505, 0.634
Indirect (FinCap → FinRes → FinWB)	0.007	0.006	—	—	−0.002, 0.021

Note: The mediation through FinRes is not significant, as the confidence interval includes 0. BootSE (Bootstrap Standard Error): Standard error estimated using the bootstrap resampling method. Source: own.

).

The results indicate that financial capability significantly predicts financial resilience (B = 0.111, p = 0.011), although the explained variance is modest (R² = 0.018). When both predictors are included in the model, financial capability maintains a strong and significant effect on financial well-being (B = 0.570, p < 0.001), whereas financial resilience does not reach statistical significance (B = 0.063, p = 0.114). Furthermore, the indirect effect of financial capability on financial well-being through financial resilience is not statistically significant, as the bootstrap confidence interval includes zero. Taken together, these findings indicate that financial capability exerts a direct and substantial effect on financial well-being, while financial resilience does not significantly mediate this relationship in the present sample.

4.5. Discussion

The results largely confirm the theoretical architecture underpinning the proposed model, reinforcing the multidimensional nature of financial capability formation. Financial literacy, financial education, financial attitudes, financial advice, knowledge, and behavior collectively contribute to the strengthening of financial capabilities, which in turn exert a substantial influence on financial well-being. Consistent with Lusardi and Mitchell (2014) and Atkinson and Messy (2012), financial literacy demonstrates a positive and significant association with financial capabilities. However, the moderate magnitude of the effect suggests that knowledge acquisition, while necessary, is not sufficient to guarantee strong economic competence. This nuance refines evidence reported by H. Zhang and Xiong (2020), indicating that contextual and structural variables—such as socioeconomic background, institutional access, and resource availability—may attenuate the translation of literacy into applied capability.

Similarly, financial education exhibits a positive effect aligned with the findings of Remund (2010), Potrich et al. (2016), Guerini et al. (2024), and Soejono et al. (2025), who emphasize structured instruction as a determinant of competence. Nonetheless, the moderate effect size supports the argument advanced by Y. Zhang and Fan (2024) that formal or extracurricular exposure does not automatically convert into practical mastery unless reinforced through application and advisory mechanisms. Financial attitudes also emerge as a significant determinant, corroborating Martino and Ventre (2023) and Kumar et al. (2024). In line with She et al. (2024) and Hernandez-Perez and Cruz Rambaud (2025), psychological disposition and perceived financial control facilitate responsible economic management. However, the results suggest that attitudes operate synergistically with education and advice rather than independently driving substantial capability gains.

One of the most salient findings concerns financial advice, which shows the strongest association with financial capabilities. This result aligns with Collins (2012), Stolper and Walter (2017), de Jong and Wagensveld (2024), and Mustafa et al. (2025), who underline the transformative potential of professional guidance. The strength of this relationship suggests that personalized and context-sensitive advice may function as an operational bridge between knowledge and effective financial decision-making. As noted by Staehelin et al. (2024), the integration of human interaction and technological tools may further amplify advisory impact, which could explain contextual variability in effect sizes.

Financial stress displays a negative association with financial capabilities, corroborating findings by Netemeyer et al. (2018), O’Neill et al. (2005), Joseph and Peetz (2024), and Choi et al. (2025). Although statistically significant, the relatively low magnitude of this effect is consistent with Sorgente et al. (2023), who argue that stress and well-being represent related but distinct constructs moderated by coping mechanisms and social support systems.

Regarding financial well-being, the strong positive relationship between financial capabilities and well-being (r = 0.679, p < 0.001) reinforces arguments advanced by Kempson et al. (2017), Brüggen et al. (2017), Saeedi and Nishad (2024), and Rai et al. (2025). These findings confirm that practical competence and effective management skills constitute primary structural drivers of economic satisfaction and perceived stability. In contrast, financial resilience shows a positive yet comparatively weak association with financial well-being (r = 0.151, p = 0.004), consistent with Salignac et al. (2019), Gerrans (2020), and Wheeler and Brooks (2024). The mediation analysis further indicates that resilience does not significantly mediate the capability–well-being relationship. This result suggests that resilience may function more as a buffering or contextual mechanism than as a structural transmission pathway. In line with She et al. (2024) and Sharma et al. (2025), the findings indicate that tangible financial skills exert more direct influence on well-being than adaptive coping capacity alone.

4.6. Theoretical Implications

This study contributes to the literature by advancing an integrative framework that positions financial capability as a multidimensional construct shaped by cognitive, behavioral, attitudinal, advisory, and emotional components. Rather than treating literacy, education, or behavior as isolated predictors, the model demonstrates their combined and differentiated contributions within a unified structural system. First, the findings reinforce human capital theory and capability-based approaches by confirming that financial literacy and education constitute foundational inputs for capability development. However, the moderate effect sizes refine existing theory by suggesting diminishing marginal returns of knowledge when not accompanied by applied mechanisms such as advisory support. This nuance extends the work of Lusardi and Mitchell (2014) and Atkinson and Messy (2012) by highlighting the conditional nature of literacy’s effectiveness.

Second, the prominent role of financial advice introduces an important theoretical refinement: external expertise may act as an accelerator of capability formation. While traditional models emphasize individual knowledge accumulation, these results suggest that relational and institutional dimensions—particularly access to professional guidance—play a central structural role. This supports emerging perspectives that conceptualize financial capability not solely as an individual attribute but as an ecosystem-dependent construct. Third, the findings clarify the conceptual distinction between financial capability, resilience, and well-being. Although resilience is positively associated with well-being, it does not mediate the relationship between capabilities and well-being. This indicates that adaptive coping capacity does not replace structural financial competence. Theoretically, this differentiation helps disentangle constructs that are often conflated in the literature, reinforcing the argument that practical financial skills constitute the primary driver of economic well-being, while resilience may operate as a contextual buffer rather than a transmission mechanism.

Overall, the study consolidates a layered conceptual structure in which knowledge, education, attitudes, advice, and behavior build capability; capability drives well-being; and resilience complements but does not structurally mediate this relationship.

4.7. Practical Implications

From a practical standpoint, the results suggest that policy interventions focused exclusively on financial education may yield limited returns unless integrated with advisory services and behavioral reinforcement mechanisms. The strong association between financial advice and capability underscores the need for hybrid intervention models that combine structured education with personalized guidance. For policymakers, this implies prioritizing accessible advisory ecosystems—particularly for vulnerable or emerging populations—alongside curricular reforms. Financial literacy campaigns should therefore be complemented by institutional support mechanisms that translate knowledge into action.

For financial institutions and service providers, the findings highlight the strategic value of advisory services that incorporate technological tools while preserving personalized interaction. The integration of digital advisory platforms with human-centered support may enhance financial capability formation more effectively than information-based strategies alone.

Finally, the demonstrated link between financial capability and well-being reinforces the relevance of financial competence within the broader sustainability and development agenda. Strengthening individual financial capabilities contributes not only to personal economic stability but also to systemic resilience and inclusive economic growth.

5. Conclusions

This study advances the literature by proposing and empirically validating an integrated structural framework in which financial capabilities operate as the central mechanism connecting knowledge-based, attitudinal, behavioral, advisory, and emotional dimensions with financial well-being. The findings demonstrate that financial capability is not merely an outcome of literacy or education, but the result of a multidimensional configuration in which advisory support plays a particularly influential role. Importantly, the results clarify the structural distinction between financial capability and financial resilience. While resilience contributes positively to well-being, it does not mediate the relationship between capability and well-being. This distinction refines existing conceptual debates by suggesting that adaptive coping capacity complements—but does not substitute—practical financial competence. In stable contexts, structural capability appears to be the primary driver of economic well-being, whereas resilience may become more relevant under conditions of financial shock or instability.

By empirically differentiating these constructs within a unified model, the study contributes to ongoing efforts to consolidate theoretical clarity in financial well-being research. It highlights the need to move beyond fragmented approaches that treat literacy, education, behavior, or resilience in isolation, and instead adopt systemic perspectives that recognize the layered architecture of financial development. Ultimately, the findings reinforce the centrality of financial capability as a strategic lever for enhancing individual economic stability and long-term well-being. In this way, the study provides a coherent conceptual foundation that supports both academic inquiry and evidence-based policy design within the broader framework of inclusive and sustainable economic development.

6. Limitations and Future Research

Although this study provides valuable insights into financial well-being and resilience among university students, several limitations should be acknowledged. First, a non-probabilistic sampling method was employed, which limits the generalizability of the findings to other populations and contexts. However, this approach allowed the collection of robust and representative parameters of the studied population and is widely recognized as valid in exploratory research and studies focused on specific populations. Second, data were collected through self-reported questionnaires, which may introduce social desirability biases or perceptual errors. The cross-sectional design also prevents establishing definitive causal relationships between resilience and financial well-being. Additionally, some contextual variables, such as family economic status, prior exposure to financial education, or use of financial services, were not controlled, which could have influenced the observed results.

Despite these limitations, the findings offer meaningful contributions and serve as a foundation for future research. It is recommended to replicate this study with larger and more diverse samples, implement longitudinal designs to examine changes over time, and explore additional mediators and moderators, such as gender, financial education, and digital skills, to better understand the mechanisms affecting students’ financial well-being and resilience.