Determinants of Financial Resilience and Its Impact on Economic Well-Being of Young Adults Aged 20–29 in Mexico

1. Introduction

The financial well-being of young adults is determined by a complex interaction of contextual and personal factors that influence their ability to manage their finances effectively. In this regard, She, Waheed, Lim, and E-Vahdati (2022) highlight that, in contextual terms, changes in the macroeconomic environment, market fluctuations, technological advances, and social comparisons about finances play a crucial role. At the personal level, factors such as socio-demographics, personality traits and values, money skills and attitudes, financial practices, financial socialization, lifestyles, and early life experiences are also determinants. Furthermore, the influence of mental health and subjective financial situation cannot be overlooked, as financial stress directly impacts the emotional well-being of young people.

With regard to how younger generations manage these influences, She, Ma, Sharif, and Karim (2024) emphasize that Millennials tend to adopt a perspective that is more focused on the present than on the future, which influences their financial decision-making. This present-oriented mindset can lead to financial decisions that do not consider long-term implications, which could result in irresponsible financial practices and, ultimately, lower economic well-being. This behavior highlights the urgent need to foster a more strategic and future-oriented financial mindset in young adults. García-Santillán, Escalera-Chávez, and Santana (2024) also emphasize the importance of preparing young people to face not only personal adversities but also the economic challenges that may arise throughout their lives. According to these authors, the current economic context, characterized by its volatility, makes financial education essential to enable young people to better manage their resources and make informed decisions.

Regarding people's ability to cope with unforeseen events, Bhutta, Blair, and Dettling (2021) assert that financial knowledge is crucial for families to prepare for unexpected expenses or interruptions in their income. This knowledge not only contributes to economic security but also plays an essential role in financial resilience, allowing for a faster recovery from any financial shock. For their part, Kamble, Mehta, and Rani (2024) highlight that, in response to past financial crises, financial well-being has gained significant attention in both academic research and public policy.

The focus has gone beyond mere economic stability to incorporate financial inclusion as a priority, especially in developing countries. Financial inclusion has the potential to reduce poverty and provide vulnerable populations with greater participation in the formal financial system, which is essential for their long-term economic well-being. In terms of resilience, the Bank of Mexico (2021) defines it as the ability of a system to recover after an economic shock, highlighting the importance of having mechanisms and instruments in place to adjust the system in an orderly manner in the face of adverse situations. Therefore, people should focus on the ability to manage basic needs and prepare for unforeseen events that may affect economic security. Liu and Chen (2024), in their study, redefine financial resilience as a comprehensive concept that encompasses both static and dynamic situations within human and environmental processes. This definition broadens previous perspectives, allowing us to understand how people cope with and adapt to economic adversity, emphasizing their resilience as a continuous process.

2. Problem description

Young people between the ages of 20 and 29 face a complex financial situation, characterized by the transition from economic dependence to independence. At this stage, financial decisions are influenced by limited starting salaries and social pressures to acquire goods, combined with insufficient financial education. According to BBVA's analysis (2020) based on the National Survey of Financial Inclusion (ENIF), more than 65% of respondents do not prepare a budget or formally record their income and expenses, and among those who do, nearly 60% keep such records only in their heads.

Despite growing academic interest in economic well-being and financial health, there is still a theoretical gap in understanding how young people develop and sustain financial resilience in the face of adverse situations, as well as how this resilience interacts with their financial capabilities and economic well-being. Evidence indicates that financial literacy remains insufficient in various contexts, especially among young people and groups with lower levels of education, which limits their ability to plan, cope with economic contingencies, and achieve sustainable financial well-being (Samuelsson, Levinsson, & Ahlström, 2024). Recent studies show that those with greater financial knowledge tend to manage their debt better, plan for retirement, and show greater resilience to economic shocks than individuals with lower financial literacy (Samuelsson et al., 2024).

On the other hand, financial resilience, defined as the ability to withstand, recover from, and adapt to unexpected financial events, is recognized as a critical component of financial health. However, its exploration in the literature has been fragmented and is not always integrated with financial behavior indicators that reflect actual management practices (Xiao, Tang, & Shim, 2025). A systematic review of financial resilience in individuals and households highlights the importance of multiple components and coping strategies, but also highlights the heterogeneity of studies and the need for more robust conceptual and empirical development (Xiao et al., 2025).

Financial health indicators, such as spending less than one earns, paying bills on time, having sufficient liquid savings, owning long-term assets, maintaining a manageable level of debt, and maintaining a positive credit history, provide quantifiable tools for assessing personal economic stability (Lusardi & Mitchell, 2024). However, research linking these indicators to financial resilience, financial capabilities, and economic well-being in young people remains limited, making it difficult to identify which practices and behaviors significantly influence youth financial resilience and, therefore, sustainable financial well-being.

The relevance of this approach is reinforced by studies suggesting that greater financial resilience is associated with better economic outcomes and greater financial satisfaction, even when controlling for demographic and economic variables, implying that financial resilience not only acts as an adaptive response to shocks but can also be a key determinant of medium- and long-term financial well-being (García-Santillán, 2025). .

In this regard, there is a clear need to develop integrative models that examine how financial health indicators relate to financial capabilities and financial resilience in young people, and how these interactions affect their economic well-being. Addressing this gap would not only advance the theory of financial health, but also offer practical inputs for public policies and financial education programs aimed at strengthening the economic stability and resilience of new generations (Samuelsson et al., 2024; Xiao et al., 2025; Lusardi & Mitchell, 2024; García-Santillán, 2025). With these arguments previously presented, the following question now arises: How does financial resilience influence the financial well-being of young people aged 20 to 29? Therefore, the objective is set as follows: To determine the influence of financial resilience on the financial well-being of young people aged 20 to 29.  A priori, the following hypotheses are established:

H₁: Financial resilience has a significant influence on the financial well-being of young people aged 20 to 29.

H₀: Financial resilience does not have a significant influence on the financial well-being of young people aged 20 to 29.

2. Literature Review

Financial health in adolescents is defined as the ability to manage economic resources, make decisions about saving and spending, and deal with unforeseen situations in a responsible manner. Studies on adults and businesses suggest that prior financial preparation is crucial for resilience in the face of external shocks (Tascón, Castro & Valdunciel, 2024), which in adolescents translates into the importance of early saving and planning habits. In the words of Angsten, Davies, Owen, & Williams (2024), financial resilience is widely used to assess people's ability to cope with financial disruptions, such as sudden expenses or loss of income, and is linked to indicators such as financial knowledge, savings levels, and indebtedness. According to these authors, financial resilience consists of three capacities: (1) absorption, i.e., the ability to mitigate or prevent negative impacts; (2) adaptation, the ability to adjust and change in the face of future shocks; and (3) transformation, the ability to create new opportunities or systems in the face of shocks that are too large to cope with.

Complementarily, Hrishikesh (2024) highlights that financial resilience includes money control, expense management, creating a financial cushion, managing deficits and stress, as well as financial planning; while Adam, Panjaitan, Sumarlin, & Adriana (2021) point out that stress is one of the main obstacles to financial difficulties. Carton, Xiong, and McCarthy (2024) identify two methods of measuring financial resilience: objective, using ratios such as savings-to-income, debt-to-income, or financial assets-to-debt, and subjective, through perceptions of the ability to manage money and cope with unexpected financial disruptions, including financial knowledge and skills. Financial self-efficacy also has a significant influence on adolescents: Tahir & Richards (2025) and Hernández-Perez & Cruz-Rambaud (2025) show that those who are confident in their financial abilities tend to budget, save, and invest, promoting economic stability, while low self-efficacy reduces the ability to plan and cope with difficulties. On the other hand, Bialowolski, Cwynar & Weziak-Bialowolska (2022) highlight that financial education protects financial resilience, although its effect is greater in preserving it than in increasing it.

Evidence also indicates that financial resilience contributes directly to economic well-being (Hamid, Loke & Chin, 2023) by enabling people to cope with unexpected shocks, such as illness, job loss, or family emergencies, by using savings, family loans, or financial loans. Prideaux, Vaughn, Chuisano, Thrower, & DeJonckheere (2024) add that resources, personal perspective, and social relationships are essential for building and maintaining resilience, although this is difficult to practice when individuals feel out of control. Applying these concepts to adolescents, strengthening their financial education, savings habits, self-efficacy, and exposure to a supportive environment contributes to strong and resilient financial health, laying the foundation for responsible decisions and future economic stability (Fang, Hao & Reyers, 2022; Zhou, Lu, Liu & Gan, 2024; Bragoli et al., 2026; Brana, Bro de Comères & Vaubourg, 2025; Chen, Yang & Liu, 2025; Zhou, He, Eggleston & Liu, 2022).

2.1. Financial resilience

Financial resilience has established itself as a key concept for understanding how individuals, organizations, and economic systems can respond, adapt, and recover from adverse situations. Several recent studies show that this resilience is multidimensional and dynamic, depending on the context and the interaction between financial resources, behavioral capacities, and social support. At the individual level, financial resilience is related to the ability to save, plan, manage debt, and financial education, factors that not only enable individuals to cope with economic emergencies but also have a direct impact on mental health and life satisfaction (Liu & Chen, 2025; Tahir, Ullah & Umar, 2025; García-Santillán & Santana, 2025). Research in different contexts, from young people in Pakistan to refugees in Malaysia, highlights how financial inclusion and the adoption of active saving behaviors strengthen this resilience, reducing financial anxiety and improving perceptions of well-being (Abd Sukor, Che Hashim & Mahdzan, 2025; Belayeth Hussain et al., 2019; Sjam & Kuang, 2025).

At the organizational level, financial resilience depends both on internal resources and on the ability to adapt to external changes. Digitalization and FinTech innovation have been identified as essential tools for improving operational efficiency, fostering innovation, and sustaining business competitiveness (Al-Okaily & Al-Okaily, 2025). Intellectual capital also contributes to financial performance stability, although its effect varies depending on the level of leverage and the financial health of the company (Okon, Atanda & Ozele, 2026). Studies of human service organizations during the pandemic show that the combination of strategic resource mobilization, stakeholder collaboration, and adaptive leadership enables organizations to maintain or even increase their resilience in crisis contexts (Kang, Lim & Hwang, 2025). The position of companies within supply networks also influences their resilience, revealing that structural gaps can limit operational efficiency and create financial constraints (Wang, Yang & Liu, 2025).

At the macroeconomic level, financial resilience is linked to market structure, banking regulation, and sectoral interconnection. Countries with more concentrated banking systems and strict regulations are better able to withstand shocks such as the COVID-19 pandemic, while markets with complex sectoral networks show how shocks can spread or be absorbed collectively (Danisman, Demir & Zaremba, 2021; Ouyang, Deng & Zhu, 2025). Digitalization and financial inclusion strengthen systemic resilience by improving the quality of accounting information, cooperation in supply chains, and the ability of economic systems to absorb shocks (Shahid, Ahmar, Ali & Islam, 2025).

Evidence indicates that financial resilience is a multilevel phenomenon that integrates individual, organizational, and systemic factors. Savings skills and behaviors protect individuals; innovation, intellectual capital, and adaptive strategies sustain the competitiveness of organizations; and regulation, market structure, and cross-sector connectivity ensure the financial stability of the system (Liu, Chen & Xiao, 2025; Tahir & Richards, 2025; Singh, Moid & Kumar, 2026). In addition, there are mediation and moderation mechanisms: financial education and savings mediate the relationship between access to financial services and resilience; digitization mediates the relationship between innovation and organizational performance; and regulation moderates the relationship between financial inclusion and systemic resilience (Sjam & Kuang, 2025; Al-Okaily & Al-Okaily, 2025; Liu & Chen, 2025).

In summary, the literature suggests that investing in financial resilience is not only crucial for coping with crises, but also for improving well-being, competitiveness, and long-term economic stability. This integrated vision provides a useful framework for designing public policies, organizational strategies, and educational programs that strengthen the ability of individuals, businesses, and financial systems to adapt and thrive in the face of adverse situations, consolidating resilience as a central component of sustainable economic development.

3. Methodology

Using a non-experimental design with a deductive and cross-sectional approach, we seek to evaluate the influence of financial resilience on financial well-being. To this end, young adults aged between 20 and 29 were established as the participating population. This age group was selected because of its transition to economic independence, which represents a key period for the formation of financial habits and the consolidation of financial resilience. Participants of various genders (women, men, and other genders) and different marital statuses were included. Likewise, the category of current employment status was considered: working, studying, working and studying, and not applicable (N/A).

The sample was selected using non-probability convenience sampling, given that participation was voluntary and not random. This type of sampling consists of selecting the subjects who are most accessible or available to the researcher, rather than using a complete sampling frame with known selection probabilities (Creswell, 2014). In exploratory research and studies that seek to understand emerging phenomena in specific populations—such as financial resilience in young adults—convenience sampling is methodologically acceptable, as it allows relevant data to be obtained efficiently in the face of practical limitations of time and resources (Patton, 2002).

Although probabilistic designs allow for broader generalizations, non-probability sampling is useful for generating initial knowledge and identifying patterns that guide further research (Marshall & Rossman, 2016; Teddlie & Tashakkori, 2009). In this study, convenience sampling was used to access participants who met the established criteria, without affecting the quality of the exploratory analysis. The sample consisted of 169 young adults between the ages of 20 and 29, who voluntarily participated in a structured survey on financial resilience and economic well-being. No restrictions were placed on gender, marital status, or socioeconomic status, with the aim of including diverse profiles within the age group. Although this approach does not allow for probabilistic generalizations, it is consistent with the objective of exploring theoretical relationships in a specific cohort and providing evidence on financial management at a key stage of adulthood.

3.1. Instruments

The instrument used in this study is the scale proposed by Flores-Bañuelos et al. (2024), which includes both indicators of the participants' sociodemographic profile and 24 items distributed across three broad dimensions: perceptions of financial health indicators, experiences related to these indicators, and actions taken to cope with adverse economic and financial situations. Each of these three blocks consists of eight items, and responses are collected using a five-point Likert scale, ranging from “strongly disagree” to “strongly agree.”

The first dimension, perception of financial health, consists of eight items that assess how participants rate their economic well-being. Based on BBVA (2020) and the CFSI, these questions address aspects such as savings, debt management, and financial planning, considering both the current situation and future expectations. The second dimension, experiences related to financial health, explores the economic situations that participants have faced, such as crises, difficulties in meeting basic needs, or unforeseen events that affected their stability. The third dimension, also consisting of eight items, assesses the actions taken in response to adverse economic situations, including seeking advice, adjusting expenses, taking on debt, and reorganizing personal finances.

For data collection, the questionnaire was designed using the Google Forms platform, which allowed for efficient digital distribution. The questionnaire was shared through various social networks, including Facebook, LinkedIn, Instagram, and WhatsApp, which facilitated access to a diverse sample of participants. Before completing the questionnaire, all prospects were explained the purpose of the research and assured of the confidentiality of their responses. The responses obtained were compiled into an Excel database, which was subsequently used for data analysis.

3.2. Measurement procedure

The procedure for measuring the data begins by evaluating the reliability of the instrument using Cronbach's alpha (α) and McDonald's omega (ω), with values above 0.70 considered adequate. The normality of the data is verified using the Kolmogorov-Smirnov (KS) and Shapiro-Wilk (SW) tests, in addition to the asymmetry and kurtosis criteria proposed by Kim (2013). If the data do not follow a normal distribution, polychoric correlation matrices are used. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are then performed using SEM, evaluating the model fit indicators. For the mediation hypothesis, Hayes' (2022) PROCESS macro is used with a regression and bootstrap resampling approach, estimating the direct and indirect effects with confidence intervals.

4. Data analysis and interpretation

The internal consistency of the instrument was high, with a Cronbach's alpha of .934 and a McDonald's omega of .932. Both coefficients exceed the criterion of .90, indicating excellent reliability and adequate consistency among the items. The similarity between α and ω confirms the stability of the estimate. The item analysis showed corrected item-total correlations in adequate ranges and showed that the removal of any item does not significantly increase the reliability coefficients. Therefore, all items contribute consistently to the measured construct. The detailed values (mean and variance of the scale if the item is deleted, multiple correlation squared, α and ω if the item is deleted) are presented in Table A1 of the Appendices.

Normality was assessed using the Kolmogorov–Smirnov (with Lilliefors correction) and Shapiro–Wilk tests. In all items (N = 169), both tests were significant (p < .001), indicating deviations from normality. However, the values for skewness (between −0.470 and 1.092) and kurtosis (between −1.085 and 0.312) are within acceptable ranges, suggesting that the deviations are not severe (see Table 2). Overall, although formal tests indicate non-normality, the distribution of the data can be considered reasonably adequate for robust statistical analysis, according to criteria set forth by Kim (2013).

Table 2: Normality test

4.1. Descriptive analysis of sociodemographic variables

Various sociodemographic characteristics are described in the sample of 169 participants (see Table 3). In terms of gender, the majority of participants are men (n = 91, 53.8%), followed by women (n = 76, 45.0%). A small percentage identified as “other” (n = 2, 1.2%). This pattern indicates a slight male predominance in the sample, although female representation remains significant. Regarding age, the distribution shows that the most represented group is 27-29 years old (n = 81, 47.9%), followed by participants aged 24-26 (n = 51, 30.2%). To a lesser extent, there are young people aged 20-23 (n = 37, 21.9%). This suggests that the sample is predominantly composed of young adults, with fewer participants in the younger age groups. In terms of who influenced their knowledge, the mother figure is the most relevant, with 34.3% of participants (n = 58) indicating that she was the main source of influence. She is followed by the father (n = 37, 21.9%) and “other” (n = 34, 20.1%). To a lesser extent, siblings had an influence (n = 9, 5.3%), and 18.3% (n = 31) mentioned that there was no one in particular who influenced their knowledge. This reflects a general tendency to recognize parents as the main agents of influence, with a strong presence of the mother figure.

In terms of marital status, most participants are single (n = 110, 65.1%), followed by those living in a domestic partnership (n = 33, 19.5%) and those who are married (n = 21, 12.4%). Cases of separation (n = 1, 0.6%) and divorce (n = 4, 2.4%) are very few. This pattern suggests that the sample is mostly composed of single people, with a low representation of those in situations of separation or divorce. Finally, with regard to employment status, it can be seen that the majority of participants work exclusively (n = 114, 67.5%), followed by those who combine study and work (n = 36, 21.3%). A smaller percentage study without working (n = 14, 8.3%), and 3.0% (n = 5) selected “none of the above.” This indicates that the majority of the sample is made up of people who are actively employed, with a significant proportion of individuals who also combine study with work. In summary, the sample is mainly composed of men, aged between 24 and 29. Most are single and employed, and mothers are the most influential figures in the participants' knowledge.

Table 3: Sociodemographic Profile

The suitability of the data for factor analysis was confirmed using the Kaiser–Meyer–Olkin measure (KMO = 0.889), indicating adequate sample adequacy. Likewise, Bartlett's sphericity test was statistically significant (χ² = 2533.813; df = 276; p < .001), confirming that the correlation matrix is factorizable. Likewise, the MSA values of the anti-image matrix ranged from .793 to .957, exceeding the minimum recommended criterion (.50), which supports the individual adequacy of the items for factor analysis. Component extraction, with Varimax orthogonal rotation, identified five components that together explain 67% of the total variance (see Table 4), evidence of a solid factorial structure. Subsequently, the solution obtained was subjected to confirmation using Structural Equation Models (SEM) in order to evaluate the fit of the measurement model. Complementarily, Appendix A2 presents the matrix of components rotated under different cut-off points for factor loadings (>.10, >.40, and >.50), with the purpose of showing the process of refining and purifying the model until reaching the final solution reported in Table 5.

Table 4: Total Variance Explained

Table 5: Rotated Component Matrix of the Measurement Model

4.2. Interpretation of the underlying structure

The component matrix yields five components, which are interpreted as follows according to their structure: Component 1, referred to as “Active financial management strategies,” exclusively groups items DU17, DU18, DU19, DU20, DU21, DU22, DU23, and DU24, all associated with the implementation of specific measures aimed at improving personal financial situations. The items reflect actions such as increasing liquid savings, establishing plans to clean up credit history, planning future expenses, reducing spending relative to income, strengthening long-term assets, maintaining sustainable debt, adjusting insurance coverage, and regularizing outstanding payments. Taken together, this component represents the strategic behavioral dimension of financial resilience, that is, the ability to make deliberate decisions to strengthen economic stability. Component 2, called “Financial soundness and asset protection,” which is made up of EX12, PE7, EX11, EX15, PE4, and PE8, brings together indicators related to the availability of savings, long-term assets, insurance coverage, and financial planning. Unlike the first component, here the emphasis is not on corrective action, but on the current or perceived financial condition. Conceptually, it represents the individual's level of stability and economic foresight.

Component 3, “Credit discipline and compliance,” groups together EX14, PE6, EX10, and EX9, all related to credit history and timely fulfillment of financial obligations. It reflects behavior associated with timely debt repayment, maintaining a good credit history, and controlling spending. It represents the dimension of formal financial responsibility. Component 4, called “Normative beliefs about financial prudence,” which is made up of PE3, PE1, and PE2, integrates normative items that express judgments about what “should” be done in financial matters, such as saving, spending less than one earns, or paying bills on time. This dimension reflects the cognitive framework or belief system that guides financial behavior. Finally, component 5, called “Debt sustainability,” is composed of indicators EX13 and PE5, both of which focus on the level of sustainable debt. Unlike timely payment compliance (Component 3), this component evaluates the structural balance between debt and payment capacity. It represents a specific dimension of financial stability related to debt management.

Following this interpretation, and based on the factor structure identified through exploratory factor analysis, the measurement model was validated using the Structural Equation Modeling (SEM) methodology. This confirmatory phase allowed for empirical testing of the five-component solution under a previously defined theoretical specification, assigning each item to the corresponding factor according to its highest factor loading. The model fit was evaluated using different types of indices. First, absolute fit indicators were considered, such as the chi-square statistic (χ²) and its ratio with degrees of freedom (χ²/df), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). Second, incremental or comparative fit indices were examined, including the Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI), which allow for the evaluation of the improvement of the proposed model with respect to a null model. Finally, parsimony and model comparison criteria were incorporated, such as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), in order to evaluate the efficiency of the model in terms of the balance between fit and complexity. In short, these indicators allow us to determine the overall adequacy of the measurement model, as well as its structural stability and empirical consistency within the confirmatory framework.

5. Analysis of the measurement model

In order to empirically test the factor structure derived from the exploratory analysis, two measurement models were specified using the Structural Equation Modeling (SEM) approach: (a) the initial five-factor model and (b) the refined model after indicator purification. Figures 1 and 2 present the standardized trajectories corresponding to each specification. Next, the absolute, incremental, and parsimony fit indices are reported and compared, as well as the evidence of convergent and discriminant validity.

In order to evaluate the structural validity of the instrument originally validated by Flores et al. (2024) in workers in the industrial security industry in Mexico, a confirmatory factor analysis (CFA) was performed using the Maximum Likelihood (ML) method. This estimator is widely recommended in structural equation models due to its consistency, efficiency, and asymptotic normal distribution properties, particularly in moderate to large sample sizes (Hair et al., 2022; Kline, 2023). The initial model was specified with the factor structure derived from exploratory factor analysis with Varimax rotation. However, the fit indices showed insufficient performance on several key indicators, which led to a refinement process based on theoretical and statistical criteria.

5.1 Adjustment of the initial model

The initial model had a χ²/df ratio of 2.993, a value within the acceptable upper limit (< 3), but with incremental indices below the recommended threshold of .90 (CFI = .814; TLI = .786). The RMSEA was .109 (90% CI [.099–.119]), indicating a high approximation error. Likewise, the PCLOSE = .000 confirmed that the model did not achieve an acceptable close fit. These results suggest that the originally validated structure was not adequately replicated in the sample of young adults.

5.2 Model refinement process

Following the recommendations of Hair et al. (2022), indicators with standardized factor loadings below .70 were eliminated, a conservative criterion that ensures adequate explained variance of the construct. This decision was also based on conceptual consistency, avoiding modifications based solely on modification indices so as not to compromise the theoretical validity of the model (Kline, 2023).  It is important to note that correlations between errors were not incorporated, even though some modification indices suggested marginal improvements, because such adjustments require explicit theoretical justification and can artificially inflate the fit (Brown, 2015).

5.3 Adjustment of the final model

After refinement, the final model showed a substantial and consistent improvement in all fit indices (Table 6):

Table 6: Comparison of the fit of the initial model and the final model

The final model achieved values considered excellent in incremental indices (CFI = .964; TLI = .952) and an RMSEA = .069, within the acceptable range (< .08). The PCLOSE (.077) indicated a close fit, strengthening the evidence of structural adequacy. The substantial reduction in AIC, BIC, and ECVI confirms that the refined model is more parsimonious and has better replicability in similar samples.

5.4 Convergent validity

Standardized factor loadings ranged from .728 to .951, mostly exceeding the recommended threshold of .70 (Hair et al., 2022). All were statistically significant (p < .001). Compound reliability (CR) was above .70 in all constructs, and the average extracted variance (AVE) exceeded .50, confirming adequate convergent validity (Fornell & Larcker, 1981).

5.5 Discriminant validity

Discriminant validity was assessed using the Fornell-Larcker criterion and HTMT (Henseler et al., 2015). Interfactor correlations remained below .85, and the square root of AVE was higher than the correlations between constructs. Likewise, HTMT values were below .85, indicating adequate conceptual differentiation between dimensions.

5.6 Contextual discussion and structural stability

The instrument was originally validated in a working population with consolidated experience. The present research applied it to young adults aged 20 to 29, a stage characterized by career transition and professional identity consolidation. The insufficient initial fit suggests that some items exhibit contextual sensitivity. However, the underlying factor structure remained after refinement, demonstrating the conceptual stability of the construct within the same cultural context (Mexico), but at a different stage of development. This expands the evidence of the instrument's external validity and contributes to its population generalization.

5.7. Structural reconfiguration of the instrument in the young population

The initial exploratory factor analysis identified a five-component solution consistent with the original validation of the instrument by Flores et al. (2024) in workers in the industrial safety industry in Mexico. However, when this structure was subjected to confirmatory factor analysis using structural equation modeling, it was observed that one of the factors—corresponding to normative beliefs about financial prudence—did not achieve factorial stability in the sample of young adults. After refinement based on standardized loadings ≥ .70 and conceptual consistency criteria, the final model was configured in four empirically robust dimensions: (1) Active financial management strategies, (2) Financial soundness and wealth planning, (3) Credit discipline and compliance, and (4) Structural sustainability of indebtedness. Far from being interpreted as a structural loss, this reconfiguration suggests a process of dimensional integration characteristic of the evolutionary stage analyzed.

In the case of Factor 1, which has been termed “Active financial management strategies,” this factor is composed of items DU17, DU19, DU20, DU21, DU22, and DU24, representing the strategic behavioral dimension of financial resilience. The items reflect deliberate actions aimed at strengthening economic stability, such as increasing savings, planning expenses, regularizing obligations, and maintaining sustainable debt levels. In conceptual terms, this dimension captures the capacity for active intervention in the face of adverse financial scenarios, constituting the operational core of the construct.

Factor 2, entitled “Financial soundness and asset forecasting,” comprises items EX12, EX11, and PE4. This component describes the structural condition of economic stability as perceived by the study participants. Unlike the first factor, here the emphasis is not on corrective action but on accumulated financial backing, planning, and the availability of resources. This dimension represents the equity base that allows contingencies to be addressed without the need for immediate adjustments. In the case of Factor 3, “Credit discipline and compliance,” which consists of items EX14 and PE6, this factor reflects the timely fulfillment of financial obligations and the responsibility associated with maintaining a credit history. It represents the formal-normative dimension of financial behavior, linked to the contractual reliability of the individual. Finally, there is Factor 4, called “Structural Sustainability of Indebtedness,” which is made up of items EX13 and PE5. This dimension assesses the balance between debt and repayment capacity. It is not limited to timely payment, but captures the structural proportionality of indebtedness relative to income. It represents a more systemic assessment of personal financial risk.

Theoretical integration of the finding. In the original validation, the factor “Normative beliefs about financial prudence” emerged as an independent dimension, which is consistent with working populations where internalized norms can constitute a cognitive framework that is distinct from observable behavior. However, in the sample of young adults (20–29 years old), these beliefs were not structured as an autonomous construct in the confirmatory model. This can be interpreted from an evolutionary perspective: in the early stages of career consolidation, financial beliefs tend to manifest themselves directly through behaviors, rather than as distinct normative systems. In other words, in this population, prudential beliefs appear to be functionally integrated into behavioral dimensions, particularly in active financial management strategies and credit discipline. This result does not weaken the instrument; on the contrary, it broadens its contextual understanding and provides evidence of structural sensitivity to generational changes.

Psychometric implications. The transition from five to four factors is not due to statistical overfitting or ad hoc modifications, but rather to a refinement process based on: Standardized factor loadings ≥ .70; Consistent improvement in absolute and incremental fit indices; Substantial reduction in AIC and BIC, as well as the maintenance of conceptual coherence. This suggests that the underlying structure of the construct maintains its theoretical core, but with a more parsimonious and empirically stable dimensional configuration in the young population.

Theoretical implications. This finding opens up a relevant line of research: the possible structural variability of financial resilience according to life stage. Future research could formally evaluate factor invariance between established working populations and young people in labor transition, which would allow us to determine whether the observed difference responds to evolutionary changes or contextual dynamics.

6. Discussion

The results obtained show a structural reconfiguration of the instrument when applied to a young population (20–29 years old), compared to the original validation carried out by Flores et al. (2024) on workers in the industrial safety industry. While the original model considered five distinct dimensions—including an independent factor of normative beliefs about financial prudence—in the present sample, this component did not emerge as an autonomous construct within the confirmatory model. This finding should not be interpreted as a psychometric weakness, but rather as evidence of the construct's contextual sensitivity. Recent literature has pointed out that financial resilience is a dynamic phenomenon that depends on the life cycle (Liu & Chen, 2025; Tahir & Richards, 2025).

In the early stages of career consolidation, financial beliefs tend to manifest themselves directly through observable behaviors, rather than as differentiated normative systems. In other words, in young adults, financial “oughtness” seems to be functionally integrated into action. From the perspective of Angsten et al. (2024), financial resilience consists of absorption, adaptation, and transformation capacities. In this sense, the dimensions identified in the final model can be interpreted as operational expressions of these capacities: financial soundness and wealth foresight represent the capacity for absorption; active financial management strategies reflect deliberate adaptation; and the structural sustainability of indebtedness expresses a form of strategic regulation in the face of future constraints.

In contrast, the normative component observed in workers by Flores et al. (2024) could be linked to established career paths, where internalized norms of financial prudence constitute relatively stable cognitive frameworks. In young adults, however, financial behavior is in the process of consolidation, which is consistent with studies showing that financial self-efficacy operates as a direct mediator between knowledge and action (Hernández-Perez & Cruz-Rambaud, 2025; Tahir & Richards, 2025). In this context, beliefs are not structurally separate from behavior, but rather drive it immediately.

6.1. Consistency with the literature on financial resilience

The final structure obtained is consistent with the multidimensional conceptualization of financial resilience proposed in recent studies. Carton, Xiong, and McCarthy (2024) distinguish between objective (financial ratios) and subjective (perception of coping ability) measurements. In the model validated here, both dimensions coexist: financial soundness captures the structural perception of economic support, while active strategies reflect specific coping behaviors. Likewise, Hrishikesh (2024) emphasizes that financial resilience involves money control, expense management, creation of financial buffers, and strategic planning. These elements are directly represented in Factor 1 of the final model, which supports its conceptual validity.

Furthermore, the observed relationship between credit discipline and debt sustainability is consistent with research linking responsible credit management with financial well-being and lower economic anxiety (Abd Sukor et al., 2025; Liu & Chen, 2025). The literature has also pointed out that sustainable indebtedness does not depend solely on timely payment, but on the structural balance between obligations and income-generating capacity (Hamid, Loke & Chin, 2023), an aspect captured in the fourth factor identified. Taken together, the four-dimensional structure found in young adults does not contradict the previous literature, but rather reorganizes its components into a more integrated and behaviorally oriented configuration.

6.2. Differences from the original validation

Flores et al. (2024) found that contextual experiences—particularly those arising from COVID-19 lockdown—significantly determined financial resilience actions. Among established workers, perceptions of financial health indicators did not directly translate into action, whereas lived experiences did.  In contrast, the present research does not incorporate a specific disruptive event as an explanatory variable, but rather analyzes the internal structure of the construct in young adults. The integration of the normative component within behavioral dimensions suggests that, in this population, financial resilience operates more immediately and is less mediated by differentiated cognitive structures. This difference can be interpreted in light of theories of financial development, where the transition to adulthood is characterized by experiential learning and continuous adaptive adjustment (Fang, Hao & Reyers, 2022; Zhou et al., 2024). Consequently, financial resilience in young people may exhibit greater structural plasticity than in established working populations.

6.3. Theoretical implications and future directions

The findings suggest that financial resilience is not an immutable structure, but rather a construct that is sensitive to generational and contextual conditions. This opens up the possibility of formally evaluating factor invariance between young populations and established workers in order to determine whether the observed differences respond to life cycle effects or socioeconomic variations. Likewise, the integration of beliefs into behaviors could be linked to models of self-efficacy and financial learning, where the cognitive component acts as a direct driver of action (Bialowolski, Cwynar & Weziak-Bialowolska, 2022). Evaluating mediations between financial education, self-efficacy, and active management strategies would be a promising line of inquiry. Finally, the results reinforce the multilevel view of financial resilience (Liu, Chen & Xiao, 2025), suggesting that individual configurations may vary according to developmental stage, while remaining consistent with the overall conceptual framework.

7. Conclusion

This study provides empirical evidence on the structural configuration of financial resilience in young Mexican adults, showing that, although the instrument originally validated by Flores et al. (2024) maintains its conceptual core, its dimensional organization undergoes a significant reconfiguration when applied to a population of young adults aged 20 to 29. The final structure of four dimensions—active financial management strategies, financial soundness and wealth planning, credit discipline and compliance, and structural sustainability of indebtedness—showed better statistical fit, greater parsimony, and empirical consistency compared to the initial five-factor model.

From a theoretical point of view, the findings reinforce the concept of financial resilience as a dynamic and contextual construct, sensitive to the stage of the life cycle. The integration of the normative component within behavioral dimensions suggests that, in young adults, prudential beliefs do not operate as independent cognitive structures, but as direct drivers of financial action. This evidence broadens the understanding of the construct by showing that its internal architecture can vary without losing conceptual coherence, which adds an important nuance to the multidimensional models proposed in recent literature.  In practical terms, the results indicate that interventions aimed at strengthening financial resilience in young people should prioritize the development of active strategies for debt management, planning, and sustainability, rather than focusing exclusively on the transmission of abstract norms of financial prudence. The promotion of strategic behaviors, supported by self-efficacy and applied financial education, could generate more consistent effects at this stage of transition toward job consolidation.

Finally, we can point out that the study opens up future lines of research aimed at evaluating the factorial invariance of the instrument among populations of different ages and work histories, as well as exploring longitudinal models that allow for the analysis of the structural evolution of financial resilience throughout the life cycle. Understanding these variations will contribute to consolidating a more flexible and robust theoretical framework capable of integrating individual, contextual, and generational dimensions into the analysis of financial health.

Acknowledgements: We would like to express our sincere gratitude to Tecnològico Nacional de Mèxico, Instituto Tecnològico Superior de Misantla, Veracruz, for all the support to develop this research. As well to reviewer for all suggestions to improve this manuscript. 

Consent for publication: The instrument used to collect participants' data included a statement of informed consent. By completing and submitting the questionnaire, each participant explicitly indicated their agreement to take part in the study, acknowledging the objectives and the intended use of the information provided.

Clinical trial number: Not applicable.

Competing interests: The author declares no competing interests.

Funding: Not applicable. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Ethics approval and consent to participate: The research adhered to the principles established in the Declaration of Helsinki. The study’s objectives and procedures were explained to participants during the administration of the questionnaire, ensuring their full confidentiality and anonymity. Informed consent was obtained from all participants after they had read and understood the provided instructions and statements.

Declaration of Generative AI and AI-assisted Technologies: This study has not used any generative AI tools or technologies in the preparation of this manuscript.