Exploring the Relationship Between Financial Education, Financial Attitude, Financial Advice, and Financial Knowledge: Insights Through Financial Capabilities and Financial Well-Being

Abstract

This study analyzes the relationship between financial education, financial attitude, financial advice, financial knowledge, and behavior and its influence on financial capabilities, as well as their impact on financial well-being. The population consists of individuals over 18 years of age, who are primarily higher education students. A non-probabilistic self-selection sampling method was used, and data were collected through an electronic form on Google Forms. The design is quantitative, non-experimental, and cross-sectional. The instrument includes sections on sociodemographic profiles, financial education, financial attitudes, financial advice, financial knowledge and behavior, financial capabilities, and financial well-being using a 1 to 5 Likert scale. To ensure validity and reliability, statistical indices such as Cronbach’s alpha and McDonald’s omega were applied. Data normality was assessed, and exploratory and confirmatory factor analyses were conducted using structural equation modeling (SEM). The findings from the results of this study largely align with the existing literature regarding the relationship between financial knowledge and financial capabilities, as well as between financial capabilities and financial well-being. However, a discrepancy is observed in the hypotheses related to financial education, financial attitudes, and financial counseling, suggesting that although these factors are important, their influence may depend on other contextual elements or mediators not considered in this study. This opens the possibility for further investigation into how these factors interact in the development of financial capabilities.

1. Introduction

Financial literacy is crucial for economic well-being, as it enables individuals to make informed decisions about their finances. Lusardi and Mitchell (2014) argue that the lack of financial education leads to poor decisions in areas such as savings, investments, and debt management, ultimately impacting financial security. Access to financial education improves financial management skills and promotes stability (Lusardi & Mitchell, 2011a). Financial decisions require numerical skills, which many individuals lack, particularly women, the elderly, and those with lower levels of education (Lusardi, 2012). The absence of numerical skills affects both individuals and society as financial decisions are inherently linked to numerical literacy. Lusardi (2012) emphasizes that both numeracy and financial education are essential in today’s complex financial environment. Financial education encompasses not only theoretical knowledge but also the ability to apply it (Mandell, 2006). Students with a strong financial knowledge base are better equipped to manage their money, save, and invest (James et al., 2012). Lusardi (2019) underscores the importance of financial knowledge in making sound decisions. Students with higher levels of financial knowledge are more likely to take calculated risks, such as investing or starting businesses (Sobaih & Elshaer, 2023), while those with less knowledge tend to be more risk-averse (Seraj et al., 2022). Financial education is also crucial for long-term planning, such as retirement (Lusardi, 2019).

Financial advice is another important tool for improving decision-making. Agarwal and Chua (2020) demonstrate that individuals who receive financial advice make better decisions regarding savings and investments. However, the effectiveness of financial advice depends on the level of financial literacy. Higher financial knowledge enables individuals to better understand advisors’ recommendations (Lusardi & Mitchell, 2011b). Those with more financial knowledge are more likely to seek external help and achieve better financial outcomes. Strengthening financial education can enhance the benefits of financial advice. Attitudes toward money also influence economic decisions. According to Furnham (1984), beliefs and attitudes toward money affect behaviors such as saving or impulsive spending. These attitudes develop throughout life and are shaped by socio-economic and cultural factors. People with a positive attitude toward saving tend to be more disciplined (Perry & Morris, 2005). Along with financial knowledge, these attitudes are critical in developing healthy financial habits. Gender differences play a role in financial behavior. Research has shown that women tend to be more conservative and have less confidence in their financial knowledge compared to men (Joo & Grable, 2004). This affects their ability to accumulate wealth. Financial education that addresses these gender differences is vital for improving the financial capabilities of both genders (Lusardi et al., 2010).

2. Theoretical Gap

Despite the extensive literature on financial education, financial advice, financial knowledge and behavior, financial attitudes, financial capabilities, and financial well-being, there remains a clear theoretical gap in terms of integrating all these factors within a single analytical framework. Most existing studies address these elements in isolation, without considering how they interact and influence each other in the context of individuals’ financial well-being. This lack of theoretical integration limits a deeper understanding of the mechanisms affecting financial stability and economic well-being. While there is research exploring these topics individually, the literature has yet to offer a holistic view that systematically connects all these factors. The absence of studies examining how financial education, advice, knowledge and behavior, attitudes, and capabilities interrelate and affect overall financial well-being leaves a significant gap in the comprehensive understanding of the processes leading to economic well-being. This gap in the literature is crucial, as it hinders the development of more effective public policies and educational programs that address these aspects in an integrated manner, rather than in isolation.

In this regard, it can be pointed out that, on the one hand, studies on financial education have primarily focused on the relationship between financial literacy and access to financial products (Guerini et al., 2024; Song et al., 2024). However, empirical research has tended to address only one aspect of financial education, whether its impact on financial inclusion or its role in economic decision-making. The literature has failed to integrate financial advice as a key factor in financial education, particularly in terms of how financial advisors can influence individuals’ decisions, considering the socio-economic and cultural differences that impact people’s financial capabilities (de Jong & Wagensveld, 2024; Yadav & Banerji, 2024). Furthermore, although links have been identified between financial education and financial attitudes, there has been little research into how these attitudes correlate with actual behavior in terms of financial management, such as saving and investing, or how they affect decisions during times of crisis or uncertainty (Mamo et al., 2021; She et al., 2024).

On the other hand, studies on financial knowledge and behavior have explored how knowledge influences investment decisions or retirement planning (Saini et al., 2024; Bellofatto et al., 2024), but few have delved into how financial behavior is conditioned by factors such as a positive attitude toward finances or individuals’ prior experiences with financial services. In particular, it has been observed that attitudes toward finances, combined with financial knowledge, can be determining factors for financial capability and financial well-being (Xiao et al., 2024; Gignac et al., 2024), but existing studies rarely integrate all these elements simultaneously. There is a lack of research that comprehensively addresses how these factors interact and jointly contribute to individuals’ economic well-being, especially in contexts where access to financial services, low educational levels, and economic vulnerability are combined.

Regarding financial capabilities and their relationship with financial well-being, while it has been shown that a higher level of financial literacy and better financial skills are key determinants for sustainable financial well-being (Xiao et al., 2024; K. T. Kim & Lee, 2024), studies have focused on the effects of these elements in isolation. There is still no comprehensive model that assesses how the interaction between financial education, financial advice, financial knowledge and behavior, financial attitudes, and financial capabilities affects individuals’ well-being as a whole. For example, the influence of personalized financial advice, combined with a high level of financial education and positive attitudes toward financial management, could have a much greater impact on financial well-being than each of these factors individually. However, this integrative perspective remains insufficiently explored.

Although valuable literature exists on each of these factors in isolation, no theoretical framework has been developed to consider their interaction within a holistic context. This theoretical gap is critical to understanding the mechanisms that lead to sustainable financial well-being. Lusardi and Mitchell (2014) highlight that financial education and financial knowledge are essential for individuals to make informed decisions about savings, investments, and debt management. However, few studies have explored how the combination of financial education with other factors, such as financial advice, attitudes toward money, and financial behavior, can influence individuals’ financial capabilities and financial well-being. Despite research on these topics individually, there is a clear lack of studies examining how these factors interact to improve economic well-being. Most studies focus on one or two variables at a time, leaving a gap in the understanding of how financial advice can optimize individuals’ decisions, especially when combined with knowledge and attitudes toward money.

Finally, although financial advice is crucial in decision-making, the literature on how financial education and financial advice work together to improve financial capabilities and, consequently, financial well-being remains limited. Additionally, attitudes toward money play a crucial role in financial behavior (Furnham, 1984), but few studies address how these attitudes, along with financial knowledge and advice, directly affect financial capabilities. The interaction of these elements, particularly in terms of gender and differences in decision making, also requires more attention in the literature (Joo & Grable, 2004). Therefore, although there is growing concern about improving financial capabilities (Lusardi, 2019), studies are still lacking that connect these capabilities with financial well-being through a combination of education, advice, and attitudes. This gap in research presents an important opportunity to address the relationship between these factors and how their interaction can influence long-term financial well-being.

Therefore, the study aims to (1) assess the relationship between individuals’ level of financial education and their ability to manage personal finances, (2) analyze the influence of attitudes toward money management, saving, and investing on individuals’ financial capabilities, (3) evaluate the impact of financial advice received on individuals’ financial capabilities, (4) determine whether financial knowledge and behavior contribute to enhancing financial capabilities, and (5) analyze the influence of financial capabilities on financial well-being. Based on this, the following hypotheses are proposed:

H1. 

Financial education level has a significant influence on financial capabilities;

H2. 

Financial attitude influences financial capabilities;

H3. 

Financial advice has a significant influence on financial capabilities;

H4. 

Financial knowledge and behavior significantly influence financial capabilities;

H5. 

Financial capabilities significantly influence financial well-being.

3. Review of Literature

Based on the key variables that constitute the core of the research questions, objectives, and hypotheses to be tested in this study (represented in the conceptual model Figure 1) a theoretical analysis of the following concepts is presented: financial education, financial attitudes, financial advice, financial knowledge and behavior, financial capabilities, and financial well-being.

3.1. Financial Education

In the current debate on financial education, various approaches and empirical findings are discussed, highlighting its impact on financial literacy and other economic aspects. It has been found that several countries implement educational policies through the certification of institutions offering financial education courses. While useful, this process is influenced by political and bureaucratic incentives, which may bias its effectiveness. It is suggested that financial education is a “credibility good,” whose impact largely depends on trust in educational providers (Guerini et al., 2024). Levels of financial education foster financial inclusion, which directly impacts employment. This inclusion enhances job opportunities, especially for women and in low- and middle-income countries (Song et al., 2024). It is observed that digital empowerment, combined with financial inclusion policies, can help improve employment opportunities.

The design of pension systems is also analyzed, suggesting that financial education adapted to perceptual biases can optimize economic decisions (Canta & Leroux, 2024). In the workplace context, financial education and a positive attitude toward finances are key to economic well-being (Samuel & Kumar, 2024). In Colombia, the cancellation of the “Ser Pilo Paga” scholarship affected the educational decisions of low-income students (Bernal et al., 2024). In China, the Aflatoun program improved the financial behavior of children (Zhou et al., 2024). In India, the “PM Jan Dhan Yojana” program promoted financial inclusion and financial awareness (Jalota et al., 2024). In the U.S., financial education improves saving habits, especially with continuous exposure (Walstad & Wagner, 2022). In Sweden, the importance of emotions in financial education is emphasized (Pettersson & Wettergren, 2020). Based on these findings, the following hypothesis can be proposed:

H1. 

Financial education level has a significant influence on financial capabilities;

3.2. Financial Attitudes

Attitudes play a decisive role in financial decision-making. Mamo et al. (2021) found that individuals with higher education tend to have more responsible attitudes toward finances. This extends to positive attitudes toward finances, which are essential for making responsible decisions. Hassan et al. (2020) demonstrate that, even without knowledge, positive attitudes can improve financial decision-making. Obreja et al. (2023) suggest that conservative attitudes toward science also affect financial decisions, limiting the use of new tools. Kumar et al. (2024) conclude that a positive attitude toward finances is key to financial behavior in peri-urban areas. She et al. (2024) highlight that a positive attitude and strong financial knowledge improve long-term financial outcomes. Financial socialization in the household is also crucial for shaping young people’s attitudes (Abdul Ghafoor & Akhtar, 2024). In India, Nayak et al. (2024) found that, despite educational efforts, attitudes toward financial behavior remained inadequate in rural areas, hindering financial inclusion. Vieira et al. (2021) demonstrated that a positive attitude helps improve financial resilience during crises, such as the COVID-19 pandemic. Based on these studies, the following hypothesis can be formulated:

H2. 

Financial attitude influences financial capabilities.

3.3. Financial Advisory

In the specialized literature, the importance of financial advisory services and their crucial role in sustainability, as well as in how businesses and individuals navigate new economic challenges, is emphasized. According to de Jong and Wagensveld (2024), financial advisors have the ability to influence individuals’ sustainability goals, and especially those of small and medium-sized enterprises (SMEs), not only from an economic perspective but also from social and ecological viewpoints. Specifically, in the case of businesses, by adopting a more integrated approach, advisors can guide SMEs to create multiple forms of value and align with global sustainability objectives. However, sustainability is not just a business concern; it also affects how advisors manage risks. A study by Megeid (2024) highlights how the disclosure of climate risk can improve financial management, stressing the need for advisors to consider these factors when guiding sustainable investments. It is crucial for financial advisors to be familiar with ESG (Environmental, Social, and Governance) criteria, as these factors will increasingly influence investment decisions.

With the rise of financial technologies (FinTech), advisors must also stay updated on digital advancements. Yadav and Banerji (2024) argue that digital financial literacy is essential for advisors to offer relevant services in a rapidly evolving environment. This also generates regulatory challenges, as noted in the study by Busch (2024), which analyzes how changes in EU regulations can affect advisory services related to international investments, particularly in post-Brexit contexts. On the other hand, Ashraf (2025) highlights how automation in accounting and financial reporting enhances the quality of advisory services by reducing errors and increasing efficiency, although concerns about internal control are also raised. Financial advisors must also learn from other fields. Bonadonna et al. (2021) demonstrate how, by addressing the risks of natural disasters such as volcanic ones, a similar approach to financial advisory can be applied, considering both immediate and long-term risks. This integrated approach can be useful when advisors help clients manage complex situations such as severe illnesses, as analyzed in the study by Watson et al. (2023). Health disruptions can create significant financial burdens, so advisors can play a key role in helping clients plan for health-related contingencies. In line with this, Dare et al. (2023) emphasize how advisors to improve clients’ financial well-being can foster financial self-efficacy, helping reduce stress and increase economic security.

An interesting example is the study by Kirikkaleli (2024), which highlights how financial innovation and investments in renewable energy can benefit both the environment and economic well-being. Financial advisors need to be aware of how these investments can align both the financial and sustainable interests of their clients. Moreover, advisors can also help clients manage financial stress in unforeseen situations, as demonstrated in the study by Lakshmanan et al. (2022) on the financial burden of families with premature babies. Effective advisory can mitigate these issues through proper planning and access to resources like social security. In an increasingly interconnected world, economic well-being is also influenced by energy policies and new technologies, as shown in the study by Subhan et al. (2024) on the relationship between renewable energy and economic well-being in India. In their findings, the authors suggest that financial advisors should consider these factors when offering investment strategies related to the energy transition. On the other hand, L. Zhang et al. (2021) show that clients still prefer experienced human advisors over robo-advisors, highlighting the importance of personal relationships in financial advisory. Although robo-advisors are on the rise, clients still value the expertise and ability of advisors to understand not only financial aspects but also the values and emotions associated with money.

Finally, Scholz (2024) discusses how the popularity of robo-advisors is changing the financial industry. Traditional financial advisors must adapt to this new reality by integrating artificial intelligence and more sustainable investment strategies to remain relevant. Meanwhile, the impact of FinTech technologies on domestic life, reviewed by Agarwal and Chua (2020), has transformed consumption and saving decisions, making the role of financial advisors as guides to navigate these new technologies securely even more relevant. As observed in the study by Lozza et al. (2022), advisors can enhance relationships with clients by understanding the emotional associations they have with money, which will strengthen trust and loyalty in their relationship. According to the specialized literature on the subject, it is undeniable that financial advisory is evolving to adapt to new risks, technologies, and client needs, including individuals, businesses, and any type of organization requiring financial advisory services. Advisors must integrate multiple dimensions into their strategies, ranging from sustainability and digitization to comprehensive well-being planning, all with the aim of offering a relevant and complete service that addresses their clients’ current and future challenges. Based on these findings, the following hypothesis can be proposed:

H3. 

Financial advisory significantly influences financial capabilities.

3.4. Financial Behavior and Knowledge

Financial knowledge has garnered significant attention in contemporary research due to its impact on individuals’ financial decision-making and how these decisions can affect both economic well-being and financial stability. Several factors, such as financial experience, education, investor behavior, and socio-economic disparities (including racial and gender inequities), play a pivotal role in the acquisition and application of financial knowledge. One of the most influential studies in this domain is that of Chen et al. (2024), which explores the relationship between financial literacy and racial disparities in the United States. Through a comprehensive analysis of the 2018 National Financial Capability Study data, the authors find that racial minority groups, such as African Americans and Latinos, exhibit lower levels of financial knowledge compared to their white counterparts. However, they also note that the traditional methodology, which categorizes responses as correct, incorrect, or “Do Not Know” (DK), fails to adequately reflect the true knowledge levels of these groups. Minority groups are more likely to respond with “Do Not Know,” but this does not necessarily indicate a lack of knowledge; rather, it may be a reflection of their limited financial experience, as these groups typically have less access to financial products such as savings accounts, mortgages, or investments, which in turn affects their performance on financial literacy tests.

In the European context, Bellofatto et al. (2024) examine the impact of online informational tools on the behavior of retail investors following the implementation of MiFID regulation. The authors conclude that financial literacy and education are critical factors in acquiring financial knowledge. However, the effects of this knowledge on investor behavior are mixed: while knowledge improves portfolio diversification, it also increases transaction frequency and reduces net returns. This suggests that acquiring more financial knowledge does not necessarily lead to better investment decisions but may instead increase activity without necessarily improving outcomes. In India, Saini et al. (2024) explore the relationship between financial knowledge and investment strategy within the context of pension plans. Their findings indicate that higher financial literacy is positively correlated with better investment strategies and greater investor satisfaction. Furthermore, they emphasize the importance of moderating factors, such as future financial goals and security, which influence how financial knowledge impacts investor satisfaction. This study underscores the role of aligning financial knowledge with future concerns and objectives in financial planning.

On the other hand, Cheng et al. (2024) investigate how financial knowledge influences the financial behavior of university students in China using a moderated mediation model. Their results reveal that financial knowledge positively affects the rationality of students’ financial behavior. However, this impact is partially mediated by self-efficacy, suggesting that not only knowledge but also personal confidence in decision-making is crucial for students to make rational financial decisions. Adopting a different methodological approach, Palazzo et al. (2024) present an innovative analysis using Item Response Theory (IRT) combined with Archetypal Analysis (AA) to identify homogeneous groups based on their financial knowledge levels. In their study, applied to a sample of 625 Italians, the authors find that this approach allows for a more nuanced segmentation of groups according to their financial literacy. They consider these results valuable for the formulation of public policies and the design of personalized educational interventions.

Rostamkalaei et al. (2019) explore differences between self-employed workers and employees regarding financial knowledge and behavior. Although self-employed individuals do not exhibit significant differences in financial knowledge compared to employees, they are more likely to use alternative financial services (AFS), such as payday loans. This tendency is partly attributed to greater financial self-efficacy, meaning that self-employed individuals, with higher confidence in their financial abilities, are more prone to take on riskier financial decisions, such as using expensive financial services. Similarly, Nitani et al. (2019) investigate the use of alternative financial services among self-employed individuals, finding that they tend to use high-cost loans more frequently, potentially jeopardizing the financial stability of their businesses. The authors suggest that, although self-employed individuals’ financial knowledge is not significantly different from that of employees, self-efficacy plays a crucial role in making risky financial decisions.

In the realm of financial anxiety, K. T. Kim et al. (2023) analyze the relationship between the use of alternative financial services (AFS) and financial anxiety. The results reveal that the use of AFS is associated with increased financial anxiety; however, this effect is moderated by financial knowledge. For non-users of AFS, financial knowledge has a negative effect on anxiety, suggesting that those who are better informed about finances experience less anxiety. However, for users of AFS, financial knowledge does not appear to have the same effect on reducing anxiety, posing implications for interventions aimed at alleviating financial anxiety. Wang (2009) adds a gender dimension to the analysis of financial knowledge. The findings reveal significant gender differences, with men generally possessing greater objective and subjective financial knowledge, as well as a higher propensity for risk-taking. Furthermore, subjective financial knowledge seems to mediate the relationship between objective knowledge and risk tolerance, highlighting that financial knowledge is not solely about theoretical understanding but also experience and confidence. Based on these findings, the following hypothesis can be proposed:

H4. 

Financial knowledge and behavior significantly influence financial capabilities.

3.5. Financial Capacities and Financial Well-Being

In the realm of financial well-being, it is crucial to establish a conceptual definition that allows for a clear understanding of its scope and origin. In this regard, Brüggen et al. (2017) propose a new definition of financial well-being, describing it as an individual’s perception of their desired standard of living and future financial freedom. Financial well-being stems from sound finances, which are the result of effective management of an individual’s economic resources, supported by their financial capacity. The relationship between financial capacity and financial well-being has been widely researched; however, few studies have adopted a longitudinal perspective using long-term national data. Xiao et al. (2024) address this gap by utilizing data from five waves of the National Financial Capability Study (NFCS) between 2009 and 2021, finding a positive relationship between financial capacity indices and financial well-being, highlighting the role of subjective financial knowledge and desirable financial behaviors.

Moreover, Abdul Ghafoor and Akhtar (2024) explore the impact of parental financial socialization, observing how parental strategies, especially with daughters, influence the financial well-being of Generation Z. Gignac et al. (2024) investigate the impact of homeownership on the financial well-being of older adults in Australia, concluding that homeownership, coupled with high financial literacy, improves well-being by preventing over-indebtedness. Nykiforuk et al. (2023) propose a public health approach to address the financial strain resulting from the COVID-19 pandemic, which would contribute to reducing inequalities in individuals’ financial health. Globally, El Anshasy et al. (2023) find that negative perceptions of financial well-being drive migration, particularly among those with a pessimistic view of future economic prospects. This finding aligns with the work of Morrissey et al. (2023), which demonstrates how economic difficulties in childhood affect mental well-being in adulthood, particularly during middle age. Similarly, Karthika et al. (2023) discover that, in old age, financial well-being depends on pension systems and access to healthcare services.

In other contexts, financial well-being has also been studied. Nasr et al. (2024) document how the economic crisis in Lebanon severely impacted the financial well-being of university students due to financial stress. Cultural differences in the conceptualization of financial well-being highlight the need to adapt measurement tools, as noted by Sollis et al. (2024). Financial stress was particularly prominent during the pandemic; in this regard, the study by Kelley et al. (2023) shows how fluctuations in financial stress during the pandemic affected family relationships and financial behavior. Y. Zhang and Fan (2024) explore the impact of fintech, highlighting that excessive use of these technologies can harm financial behavior and, consequently, financial well-being. Regarding gender, Hasan et al. (2024) examine how gender and social class affect financial well-being in Bangladesh, concluding that health is the primary determinant, followed by finances. Mundi et al. (2024) find that retirees with fixed pensions and high social capital enjoy greater financial well-being in India. K. T. Kim and Lee (2024) analyze the impact of student loans on financial well-being in the U.S. during the pandemic, finding an increase in financial anxiety and payment delinquency.

Several studies support the idea that a positive perception of one’s financial status can mitigate the negative effects of a low credit score on health, leading to sustainable financial well-being. In this context, the adoption of Industry 4.0 technologies has a positive impact on financial well-being. Wan Ismail et al. (2024) explore how the adoption of these technologies affects both financial and social well-being for employees in Malaysia, concluding that financial transparency and technological adoption are essential. Similarly, Gafoor and Amilan (2024) examine how fintech adoption improves the financial capacity and well-being of people with disabilities, facilitating access to financial services, with financial literacy being a key factor.

In this regard, Rahman et al. (2021) previously investigated financial well-being within the B40 group of Malaysia, concluding that improving financial education and managing stress are key to enhancing financial well-being in this group. In turn, Algarni et al. (2024) emphasize the role of parental financial education in the financial well-being of young Saudis, highlighting financial socialization during childhood. Dhiraj et al. (2023) found that financial stress in India’s tourism sector is negatively correlated with the financial well-being of employees, suggesting that improving stress management could enhance their financial quality of life. Bashir et al. (2024) explore how financial well-being impacts labor productivity, with stronger effects on men than on women. Finally, She et al. (2023) report that greater clarity in financial goals and improved financial knowledge contribute to the financial well-being of Chinese millennials. These studies underline the importance of financial education, stress reduction, and access to new technologies as key tools for improving financial well-being.

Based on this, the following hypothesis is proposed regarding the influence of financial capabilities on financial well-being:

H5. 

Financial capabilities significantly influence financial well-being.

Several studies support the hypothesis that financial capabilities significantly influence financial well-being. A key example is the work of Xiao et al. (2024), who demonstrate that financial capability indices are positively related to financial well-being over time, emphasizing the role of financial knowledge and desirable financial behaviors. Similarly, Gignac et al. (2024) find that financial literacy, combined with homeownership, improves financial well-being, highlighting the importance of financial capabilities in enhancing well-being. Additionally, Y. Zhang and Fan (2024) suggest that financial literacy mediates the relationship between fintech technology use and financial well-being, indicating that strong financial knowledge is crucial for improving well-being. In the same vein, Hasan et al. (2024) point out that financial capabilities are key determinants of well-being, as they influence the effective management of personal finances. Finally, K. T. Kim and Lee (2024) demonstrate that the lack of financial skills, as in the case of student loan borrowers, increases financial anxiety and negatively affects financial well-being, further reinforcing the relationship between financial capabilities and financial well-being.

Based on the arguments presented in the literature review regarding the relevant variables, the conceptual model described in Figure 1 is shown below:

4. Methodology

Design Study. This study employed a non-experimental, quantitative, and cross-sectional design to analyze a database exploring the relationship between financial education, financial attitude, financial advice, financial knowledge and behavior, and financial capabilities, as well as the relationship of these capabilities with financial well-being. Participants and Sample: The population consists of individuals over 18 years of age with higher educational levels, aiming to provide an initial insight into their behavior regarding the topics addressed in the study, which are part of the conceptual model and the instrument used to gather information. A non-probability self-selection sampling method was employed, which, although it may introduce biases, was chosen for its practical feasibility given resource limitations. This method allows for the inclusion of the maximum number of cases within a limited time frame, making it ideal for an initial exploratory study. As a pilot study, this approach provides valuable preliminary data and insights that can inform future research using more robust sampling techniques. The use of a non-probabilistic sample in this context is justified by the goal of obtaining early and exploratory insights into the conceptual model, with the understanding that subsequent studies will aim to address the inherent limitations of this approach. The results obtained from this sample should be interpreted with caution, acknowledging the potential for selection bias. However, the study represents an important first step in understanding the dynamics at play and lays the groundwork for future investigations that may refine and expand upon these initial findings.

Test Used: The instrument used consisted of three sections: The first section gathered sociodemographic profile variables of the participants (García-Santillán et al., 2024). The second section included dimensions on Financial Education, Financial Attitudes, Financial Advice, Financial Knowledge, Financial Capabilities, and the third section contained eight indicators of Financial Health. The dimensions of Financial Education, Financial Attitudes, Financial Advice, Financial Knowledge, and Financial Capabilities were adapted from the scale used by Elrayah and Tufail (2024), who modified the instrument from previous studies. Financial education was assessed through four items (Widyastuti et al., 2020). Financial capabilities were measured with five questions (Khan et al., 2022), and financial advice was evaluated using six questions (Khan et al., 2022). Financial attitude and behavior were each measured using six items (Çoşkun & Dalziel, 2020). The survey employed a Likert scale from 1 to 5, where 5 = “Strongly Agree” and 1 = “Strongly Disagree”. Financial well-being was assessed using eight indicators that measure perception, proposed by BBVA (2020) in collaboration with the BBVA Center for Financial Education and Capabilities, in accordance with the Center for Financial Services Innovation (CFSI). In addition, we use the test designed by Flores et al. (2024) with eight indicators related to lived experiences and eight indicators addressing actions taken to cope with financial crises.

Statistical Procedure: To validate the instrument and the dataset collected in this study, various essential statistical indicators were employed to ensure the reliability and consistency of the data. First, internal reliability was assessed using Cronbach’s alpha (α), followed by hierarchical reliability with McDonald’s omega (ω), the average variance extracted (AVE) for convergent validity, and composite reliability (CR) for construct evaluation. Cronbach’s alpha and McDonald’s omega measure the internal consistency of the items within a scale, with omega being a more robust option for scales with items exhibiting different factor loadings (McDonald, 1999; Tavakol & Dennick, 2011). Simultaneously, composite reliability (CR) evaluates the reliability of a construct or latent factor, with a value greater than 0.7 considered acceptable (Fornell & Larcker, 1981). Furthermore, average variance extracted (AVE) is crucial for assessing how well the items of a construct explain its variance, with a value greater than 0.5 being accepted (Fornell & Larcker, 1981). These indicators are fundamental in ensuring that the scales and data used in the study are consistent and measure what they are intended to measure.

Before conducting more complex parametric analyses, the multivariate normality of the data was checked, a crucial step to ensure that the assumptions of normality were met for subsequent analyses. According to H. Y. Kim (2013), a normal distribution is characterized by skewness of 0 and kurtosis of 3, with normality considered acceptable if skewness falls between −2 and 2, and kurtosis between −7 and 7. Values outside these ranges may indicate the presence of outliers. George and Mallery (2010) suggest evaluating both indicators together, and Field (2013) emphasizes the importance of verifying normality before conducting parametric tests, especially in more complex models such as structural equation modeling, which requires normal data to ensure the validity of estimates.

Therefore, to assess the normality of the data, the criteria for skewness (<2) and kurtosis (<7) are applied (see

Table 1. 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	Ignore	>2	>7	0.05	Reject	Not normally
	Not ignore	<2	<7	0.05	Not reject	normally

Source: H. Y. Kim (2013).

), using Fisher’s skewness coefficient.

$$C{A}_F={\displaystyle \frac{{\displaystyle {\sum }_{i=1}^N{({\chi }_i-\overline{\chi })}^3}}{Ng·S_x^3}}$$

where the higher the sum ${\displaystyle \sum {({\chi }_i-\overline{\chi })}^3}$ is, the greater the skewness will be.

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

After verifying the reliability, consistency, and normality of the data, Bartlett’s test of sphericity and the Kaiser–Meyer–Olkin (KMO) index are calculated (Garcìa-Santillàn, 2017). Unless there is a significant violation of normality, polychoric correlation matrices are used (Muthén & Kaplan, 1985; Timmerman & Lorenzo-Seva, 2011). In other words, if the univariate distribution shows skewness or excessive kurtosis, polychoric correlation matrices are used for analysis. An exploratory factor analysis (EFA) is then conducted to identify the underlying structure of the variables and reduce their dimensionality (Field, 2013). Based on the resulting solution, a confirmatory factor analysis (CFA) is performed using structural equation modeling (SEM) with AMOS v23 software. The final model is evaluated for absolute fit, structural fit, and parsimony to determine the best-fitting model (Hair et al., 1999).

For goodness of fit, the Comparative Fit Index (CFI) is used, where a value greater than 0.90 is considered acceptable, along with the RMSEA, where a value below 0.08 indicates good fit, and the Normed Fit Index (NFI), which should also be close to 0.90. Other indices such as the GFI and TLI, which should approach 1.0, are also considered. Regarding structural fit, the Chi-square statistic is important but is often complemented by the RMSEA or SRMR (which should be below 0.08). To assess parsimony, the AIC and BIC are used, which help compare models with different numbers of parameters, favoring simpler models with a good fit. These indices allow for a comprehensive evaluation of SEM model quality.

4.2. Justification of the Technique Used

Once the reliability and normality of the data were ensured, an exploratory analysis was conducted, which is suitable for pilot studies, such as the present one. This analysis aims to explore how the variables within the proposed conceptual model (financial education, financial attitude, financial advice, financial behavior and knowledge, financial capabilities, and financial well-being) behave. The exploratory analysis is particularly useful at this initial stage as it helps identify emerging patterns, explore relationships between variables, and detect issues such as outliers or multicollinearity. This approach is appropriate in pilot studies, where the goal is to gain a preliminary understanding before conducting more rigorous confirmatory analyses. Subsequently, a confirmatory analysis using structural equation modeling (SEM) was applied to validate the measurement model, assess the relationships between latent variables, and verify whether the proposed model is supported by the data. This combined approach of exploratory and confirmatory analysis is ideal for pilot studies, as it facilitates a preliminary understanding of the relationships and validates the model structure, ensuring reliability and validity for more complex subsequent analyses. Together, these methods provide a comprehensive view of how the variables interact and affect financial well-being, laying the foundation for future research and the development of more effective financial education and advisory policies.

5. Data Analysis and Discussion

The values obtained for the reliability and internal consistency of the database are Cronbach’s alpha and McDonald’s omega, both of which show acceptable values with a Cronbach’s alpha of 0.868 and a McDonald’s omega of 0.821. Regarding the values of skewness and kurtosis (Table 1), the observed values do not align with the theoretical criteria suggested by H. Y. Kim (2013); therefore, polychoric correlation matrices and Bartlett’s test of sphericity with Kaiser’s measure are employed to verify their suitability. The values obtained from Bartlett’s test of sphericity with Kaiser’s measure show acceptable results, with a Chi-square value of 5098.085 with 703 degrees of freedom and a p-value < 0.001. The Kaiser–Meyer–Olkin measure is 0.808, demonstrating that factorization of the dataset is feasible. The correlation matrices presented in

Table 2. Correlation matrix.

	FC1	FC2	FC3	FC4	FC5	FC6	FC7	FC8	FC9
FC1	1.000
FC2	0.402	1.000
FC3	0.359	0.295	1.000
FC4	0.292	0.435	0.235	1.000
FC5	0.531	0.367	0.516	0.290	1.000
FC6	0.173	0.406	0.258	0.351	0.368	1.000
FC7	0.148	0.311	0.034	0.254	0.301	0.590	1.000
FC8	0.275	0.326	0.255	0.278	0.234	0.267	0.205	1.000
FC9	0.374	0.244	0.374	0.284	0.390	0.272	0.211	0.358	1.000
	FA10	FA11	FA12	FA13	FA14	FE15	FE16	FE17	FE18
FA10	1.000
FA11	−0.107	1.000
FA12	0.491	−0.026	1.000
FA13	0.209	−0.016	0.274	1.000
FA14	0.209	0.230	0.293	0.137	1.000
FE15	−0.002	0.225	0.045	−0.026	0.172	1.000
FE16	0.120	0.040	0.144	0.029	0.137	0.468	1.000
FE17	0.087	0.063	0.092	0.060	0.184	0.475	0.632	1.000
FE18	0.102	0.074	0.143	0.087	0.203	0.432	0.575	0.678	1.000
	FAt19	FAt20	FAt21	FAt22	FAt23	FAt24	FB25	FB26	FB27
FAt19	1.000
FAt20	−0.017	1.000
FAt21	0.363	0.038	1.000
FAt22	−0.074	0.122	0.056	1.000
FAt23	−0.124	0.305	−0.023	0.427	1.000
FAt24	−0.010	0.283	0.009	0.130	0.347	1.000
FB25	0.093	0.182	0.043	−0.194	0.115	0.363	1.000
FB26	0.107	0.129	0.189	−0.026	−0.050	0.096	0.389	1.000
FB27	−0.220	0.314	−0.136	0.125	0.368	0.375	0.269	0.155	1.000
	FB28	FB29	FB30	FWB31	FWB32	FWB33	FWB34	FWB35	FWB36	FWB37	FWB38
FB28	1.000
FB29	0.426	1.000
FB30	0.012	0.018	1.000
FWB31	0.179	0.381	0.048	1.000
FWB32	0.323	0.558	−0.078	0.531	1.000
FWB33	0.219	0.335	0.152	0.396	0.457	1.000
FWB34	0.440	0.233	0.136	0.061	0.106	0.329	1.000
FWB35	0.301	0.494	0.071	0.291	0.359	0.290	0.222	1.000
FWB36	0.390	0.492	−0.003	0.252	0.391	0.274	0.292	0.461	1.000
FWB37	0.266	0.272	0.027	0.214	0.190	0.300	0.436	0.227	0.288	1.000
FWB38	0.404	0.542	−0.030	0.252	0.418	0.318	0.340	0.378	0.395	0.400	1.00

show acceptable values, indicating that there is correlation among the items of the instrument, providing evidence that the data matrix is not an identity matrix.

As can be seen in the previously described correlation matrices, they all show various relationships between the variables, indicating that they are not highly collinear. The off-diagonal values, which are not close to 1, suggest that the variables contribute unique information to the model, avoiding multicollinearity. This pattern is desirable as it allows for the evaluation of meaningful relationships and enhances the accuracy of the analysis, in contrast to an identity matrix where the variables would be too interrelated.

Now,

Table 3. Factor loading, squared multiple correlation (SMC), 1–SMC, composite reliability (CR), and average variance extracted (AVE).

Items	Initial	Extraction	Factor Loading	SMC	1-SMC	AVE	CR
FC1	1.000	0.479	0.6923	0.4793	0.5207	0.564	0.941
FC2	1.000	0.584	0.7642	0.5840	0.4160
FC3	1.000	0.658	0.8111	0.6579	0.3421
FC4	1.000	0.386	0.6215	0.3863	0.6137
FC5	1.000	0.635	0.7967	0.6347	0.3653
FC6	1.000	0.623	0.7893	0.6230	0.3770
FC7	1.000	0.604	0.7772	0.6041	0.3959
FC8	1.000	0.607	0.7790	0.6068	0.3932
FC9	1.000	0.491	0.7006	0.4909	0.5091
FA10	1.000	0.569	0.7546	0.5694	0.4306
FA11	1.000	0.606	0.7784	0.6059	0.3941
FA12	1.000	0.611	0.7814	0.6106	0.3894
FA13	1.000	0.454	0.6738	0.4540	0.5460
FA14	1.000	0.447	0.6687	0.4472	0.5528
FE15	1.000	0.558	0.7470	0.5580	0.4420
FE16	1.000	0.718	0.8476	0.7184	0.2816
FE17	1.000	0.763	0.8735	0.7631	0.2369
FE18	1.000	0.700	0.8367	0.7001	0.2999
FAt19	1.000	0.622	0.7884	0.6216	0.3784
FAt20	1.000	0.503	0.7092	0.5030	0.4970
FAt21	1.000	0.631	0.7943	0.6309	0.3691
FAt22	1.000	0.524	0.7242	0.5244	0.4756
FAt23	1.000	0.549	0.7410	0.5491	0.4509
FAt24	1.000	0.674	0.8209	0.6739	0.3261
FB25	1.000	0.625	0.7904	0.6247	0.3753
FB26	1.000	0.569	0.7541	0.5687	0.4313
FB27	1.000	0.588	0.7669	0.5882	0.4118
FB28	1.000	0.635	0.7971	0.6353	0.3647
FB29	1.000	0.652	0.8074	0.6519	0.3481
FB30	1.000	0.445	0.6675	0.4455	0.5545
FWB31	1.000	0.471	0.6863	0.4709	0.5291
FWB32	1.000	0.647	0.8044	0.6471	0.3529
FWB33	1.000	0.485	0.6961	0.4846	0.5154
FWB34	1.000	0.568	0.7534	0.5677	0.4323
FWB35	1.000	0.468	0.6843	0.4683	0.5317
FWB36	1.000	0.456	0.6749	0.4555	0.5445
FWB37	1.000	0.677	0.8231	0.6774	0.3226
FWB38	1.000	0.625	0.7906	0.6250	0.3750

shows the values of the key indicators used to evaluate the quality and adequacy of the factor model, which include factor loading, squared multiple correlation (SMC), 1-SMC, composite reliability (CR), and average variance extracted (AVE). Factor loading describes the magnitude of the relationship between the observed variables and the latent factors, indicating how well each variable is represented by its corresponding factor. The squared multiple correlation (SMC) reflects the proportion of the variance of an observed variable that is explained by the latent factors of the model, while 1-SMC indicates the portion of the variance that remains unexplained, or the residual “noise” outside of the model.

On the other hand, composite reliability (CR) measures the internal consistency of the items representing a latent factor, providing an indication of the reliability of the model. A value greater than 0.7 is considered acceptable. Finally, average variance extracted (AVE) assesses convergent validity, determining how much of the variance in the items is explained by the latent factor, with a value greater than 0.5 indicating adequate validity. These indicators are crucial for verifying that the observed variables are properly associated with the latent factors and that the model demonstrates adequate reliability and validity, ensuring the robustness of the results. The values obtained are acceptable.

As seen in Table 3, the values obtained to assess the quality and adequacy of the factor model, including the factor loadings, composite reliability (CR), and average variance extracted (AVE), are generally strong. The factor loadings indicate the strength of the relationship between the observed variables and their corresponding latent factors, showing how well each variable is represented by its factor. In this case, the factor loadings were appropriate, indicating a solid relationship between the variables and their factors. The composite reliability (CR), which measures the internal consistency of the items representing a latent factor, showed values greater than 0.7, suggesting high reliability of the model. On the other hand, the average variance extracted (AVE), which assesses convergent validity, presented values exceeding 0.5, indicating that the latent factors adequately explain the variance of the observed variables. Together, these results confirm that the observed variables are well associated with their latent factors and that the model demonstrates sufficient reliability and validity, supporting the robustness of the findings.

Now, the structural equation model (SEM) is shown, which aims to analyze the relationships between the study variables. Figure 2 shows the initial measurement model, based on the scale used by Elrayah and Tufail (2024), which integrates the dimensions of Financial Education (Widyastuti et al., 2020), Financial Attitudes (Çoşkun & Dalziel, 2020), Financial Advice (Khan et al., 2022), Financial Knowledge, Financial Capacities (Khan et al., 2022), and Financial Behavior (Çoşkun & Dalziel, 2020). This model defines the connections between the observed and latent variables, allowing for the assessment of their validity and reliability.

The values for the fit index, structural fit, and parsimony presented by the model are PCMIN/DF (3.587), RMR (0.133), GFI (0.688), AGFI (0.645), PGFI (0.605), TLI (0.611), CFI (0.639), PRATIO (0.928), PNFI (0.524), PCFI (0.593), and RMSEA (0.087). These indices do not fully align with the theoretical criteria suggested, indicating that adjustments to the model are necessary.

Based on the preliminary study model (Figure 2) and after adjusting the indices for absolute fit, structural fit, and parsimony in the factors FE, FA, FAt, FB, FWB, and FC, the following results are obtained, which are shown in Figure 3 and Figure 4.

For H1, the level of financial education significantly influences financial capabilities, there is no evidence suggesting a strong relationship (−0.01) with financial capabilities. On the contrary, it shows a −1% inverse relationship. It is important to note that H1 only seeks to demonstrate if there is a relationship, which, although minimal, does exist. For H2, the financial attitude influences financial capabilities, this variable was not significant in the measurement model and was excluded from the final structural model. Therefore, H2 is rejected, as it is not determinant in the proposed conceptual model. For H3, the influence of financial advice on financial capabilities is low (0.09) in relation to financial capabilities, representing a 9%. However, it still has an influence, so H3 is accepted. For H4, the influence of financial knowledge and behavior on financial capabilities is very high (0.84 and 0.98), so H4 cannot be rejected. Finally, for H5, financial capabilities significantly influence financial well-being, the influence is very high (0.98 and 1.06), so H5 cannot be rejected.

5.1. The Statistical Validation of the Final Model

The final model presented in Figure 4 shows the best absolute fit, structural fit, and parsimony according to the theoretical criteria suggested. The following provides a detailed explanation: Measures of Absolute Fit: The obtained Chi-Square (χ²/d.f.) value was 2.476, which falls within the acceptable range (between 2 and 5), indicating a good fit of the model. Regarding the Root Mean Square Error of Approximation (RMSEA), the value was 0.066, which is below the threshold of 0.08, suggesting an adequate fit. For the Goodness-of-Fit (GFI) index, the value was 0.923, surpassing the recommended threshold of 0.90, indicating a good fit in this index. The Adjusted Goodness-of-Fit (AGFI) value was 0.888, exceeding the minimum threshold of 0.80, suggesting a good fit of the model. For the Standardized Root Mean Square Residual (SRMR) index, the value was 0.092, slightly above the acceptable value for this sample size (0.08).

Measures of Incremental Fit: Regarding the Normed Fit Index (NFI), the value was 0.887, close to the minimum threshold of 0.90 but not reaching the optimal value, suggesting that the fit could be improved. The Comparative Fit Index (CFI) value was 0.929, which is above the minimum threshold of 0.90, indicating a good fit of the model. For the Tucker–Lewis Index (TLI), the value was 0.909, greater than 0.90, also suggesting an adequate fit. Measures of Parsimony: The Parsimonious Goodness-of-Fit Index (PGFI) yielded a value of 0.929, above the minimum threshold of 0.50, indicating a reasonable fit in terms of parsimony. Similarly, the Parsimonious Normed Fit Index (PNFI) yielded a value of 0.695, surpassing the minimum threshold of 0.50, suggesting an adequate fit in terms of parsimony. Finally, the Parsimonious Comparative Fit Index (PCFI) presented a value of 0.728, exceeding the minimum threshold, indicating an acceptable and reasonable fit for the model.

Based on the previously obtained data, which confirmed the best model, it can be concluded that financial education does not significantly influence the participants’ financial capacities. Similarly, financial attitude does not present evidence of influencing financial capacities, nor does it show significant factor loadings in the model with standardized estimators, and it was thus excluded from the final model. On the other hand, financial counseling and financial behavior and knowledge showed a slight relationship with financial capacities. However, financial knowledge and behavior exhibited a highly significant influence (0.84) on financial capacities. This might be attributed to the financial education component, as knowledge is acquired through an educational process; however, this was not the case in the final measurement model. Financial capacities have a significant impact on financial well-being (0.98). Moreover, the relationship between the dimensions of financial education, knowledge, and financial behavior shows a relationship of 0.12 and 0.15 between financial education and financial counseling, with a higher relationship between financial counseling and financial knowledge and behavior (0.49).

5.2. Results of the Hypotheses and Theoretical Discussion

The results based on the proposed model (Figure 4) reveal a series of findings that allow for reflection on the validity of the hypotheses based on the existing literature. Hypothesis H1, which posits that financial education level significantly influences financial capabilities, did not find sufficient evidence to support it. The observed relationship was weak (−0.01), with a −1% negative effect, indicating a minimal but still present influence. While literature suggests that financial education improves economic decisions (Guerini et al., 2024; Zhou et al., 2024), in this particular context, other factors might have a more direct impact on participants’ financial capabilities. This finding suggests that while financial education is generally considered crucial, it does not emerge as a significant variable in the conceptual model of this study.

For H2, the hypothesis that financial attitudes influence financial capabilities was not supported. Despite previous studies indicating that a positive attitude toward finance influences economic decision making (Mamo et al., 2021; Kumar et al., 2024), the results of this study showed that financial attitudes had no significant effect on financial capabilities. This suggests that the relationship between attitude and financial capabilities may be more complex than the literature suggests, or that this relationship is mediated by other factors, such as financial knowledge. Regarding hypothesis H3, which proposes that financial advice significantly influences financial capabilities, the results were mixed. Although previous studies (de Jong & Wagensveld, 2024; L. Zhang et al., 2021) support the idea that financial advice improves financial decision-making, in this study, the observed influence was weak (0.09), representing 9% of variation. This finding suggests that while financial advice may be important, its impact on financial capabilities could depend on other variables, such as the individual’s level of knowledge or financial experience.

On the other hand, hypothesis H4, which establishes that financial knowledge and behavior significantly influence financial capabilities, was widely supported. The observed correlation was very strong (0.84 and 0.98), reinforcing the idea that financial knowledge is a crucial factor in developing financial capabilities. These results are consistent with previous studies, such as those by Chen et al. (2024) and Saini et al. (2024), which demonstrated that greater financial knowledge is associated with better decision-making and higher satisfaction with investments. Finally, H5, which posits that financial capabilities significantly influence financial well-being, was confirmed, showing a very strong relationship (0.98 and 1.06). This result is consistent with previous studies by Xiao et al. (2024) and Gignac et al. (2024), who found that higher financial capability is directly related to greater financial well-being. The results suggest that individuals with greater financial capabilities tend to experience superior financial well-being.

5.3. Theoretical Implications

This study provides a valuable contribution to the literature by systematically integrating key factors such as financial education, financial advice, financial knowledge and behavior, financial attitude, and financial capabilities within a single conceptual framework, a topic that had been scarcely addressed before. Through exploring the interaction between these variables and their impact on financial well-being, the study overcomes the limitation of previous research that analyzed these factors in isolation, thus bridging a significant gap in understanding how these elements interrelate to improve economic well-being. One of the main theoretical findings is that financial education (H1) and financial attitude (H2) did not demonstrate a significant influence on participants’ financial capabilities, suggesting that despite being widely discussed in the literature, their impact is neither as direct nor as strong as previously assumed. This result challenges prior research highlighting the importance of these factors (Guerini et al., 2024; Mamo et al., 2021), indicating that other mediators or unexplored contexts may moderate these relationships.

Financial advice (H3), although significant, showed a weaker influence than other factors, suggesting that while advice is valuable, its impact may depend on additional variables such as financial knowledge or personal experience. On the other hand, financial capabilities (H4 and H5) showed a very significant influence on financial well-being, reinforcing the idea that improving individuals’ financial skills and competencies has a direct and powerful impact on their economic well-being. This confirms previous theories suggesting that greater financial knowledge and behavior, along with better financial management, are essential for a healthy financial life (Xiao et al., 2024; Saini et al., 2024). In summary, this study fills an important gap by coherently and practically integrating these factors and offers a new perspective on how the individual elements, traditionally treated separately, should be considered as part of a more complex system to achieve financial well-being.

5.4. Practical Implications

From a practical perspective, the results of this study offer important recommendations for improving educational policies, financial advisory programs, and practices within financial institutions. First, the findings regarding financial education and financial attitude suggest that these areas need to be reevaluated and strengthened in educational programs. However, given their limited influence on financial capabilities, it is recommended that educational interventions go beyond simple knowledge transmission, incorporating practical aspects and personalized guidance that may better connect with participants’ realities. Regarding financial advisors, the fact that the influence of financial advice is significant, albeit weak, implies that advisory programs should include more dynamic strategies that adapt advice to each individual’s prior experiences and knowledge. Furthermore, personalization in financial advice could enhance the effectiveness of these interventions, especially when complemented by high-quality financial education.

A key aspect emerging from this study is the relevance of financial capabilities in economic well-being. Financial institutions should focus on programs that not only promote access to financial services but also strengthen individuals’ capabilities to manage their money effectively. Educational programs that promote informed financial decision-making, combined with strategic advice, can help individuals improve their financial competencies and, consequently, their well-being. From the educational policy realm, it is crucial to review how financial education programs are designed and implemented. This study emphasizes that traditional approaches that focus solely on increasing financial knowledge may not be sufficient to generate sustainable changes in individuals’ financial capabilities. A more holistic approach that integrates advice and considers participants’ financial attitudes and behaviors is needed.

6. Conclusions

In conclusion, the results of this study provide valuable insights into the complex interactions between variables related to financial well-being. While some hypotheses did not receive the expected support, such as the direct influence of financial education and financial attitudes on financial capabilities, the study reinforces the importance of adopting a more holistic approach to improving economic well-being. In particular, financial knowledge and financial capabilities emerge as key factors that significantly affect individuals’ financial well-being. These findings highlight the need for a more integrated model that not only focuses on financial education but also considers the impact of personalized financial advice and how financial attitudes and behaviors collectively contribute to the development of financial capabilities.

It is crucial that future research continues to explore and test the proposed model, systematically integrating these variables and considering their interactions in diverse contexts. Additionally, it is recommended that financial education and advice programs evolve toward more integrated approaches, incorporating solid training in financial capabilities, knowledge, and positive attitudes towards financial management. Furthermore, the results suggest that in order to strengthen the effectiveness of educational programs and financial advice, it would be necessary to adjust and personalize the approach for each individual, considering not only their knowledge and attitudes but also their specific needs and contexts. This study also calls for reflection on the importance of further developing public policies and programs that do not address the factors in isolation but integrate them into a cohesive framework that maximizes the impact on individuals’ financial well-being. The integration of all these elements—financial education, personalized advice, knowledge and financial behavior, attitudes, and financial capabilities—can provide a solid foundation for improving individual financial stability and ultimately fostering a more financially resilient society.

In summary, this work expands the understanding of the mechanisms that drive financial well-being, offering a more comprehensive view and practical suggestions for future research, educational policies, and financial advisory strategies. It is essential to continue developing and integrating these variables into more comprehensive analytical models and to assess how their interactions can more profoundly influence the improvement of individuals’ economic well-being.

7. Future Research

A proposal for future research lies in the systematic integration of variables such as financial education, financial advice, financial knowledge and behavior, attitudes, and financial capabilities into a coherent and dynamic model. It is crucial to explore how these variables interact with each other and jointly affect individuals’ financial well-being, especially in diverse socioeconomic contexts. Additionally, further investigation is needed to examine how additional factors or mediators, such as access to financial services or differences in gender and age, may influence these relationships. Continuing to develop models that address these interactions in an integrated way will enable the creation of more effective strategies to improve financial education and advice, contributing to the design of more inclusive public policies tailored to the real needs of individuals.