Exploring financial resilience and well-being in college students: a mixed-method analysis using orthogonal and oblique rotation techniques Open Access

Financial Resilience refers to an individual’s ability to cope with and adapt to financial setbacks, such as economic crises or loss of income sources, such as a job. Liu et al. (2025) define it as “a financial coping capability that encompasses both static performances and dynamic processes within human agency and environmental structure, enabling people to respond to and recover from adverse situations” (2025, p. 1). Resilience is crucial in maintaining financial well-being during uncertain times as it enables individuals to manage their resources effectively in the face of unexpected challenges. Financial Well-being is defined as an individual’s perception that, through proper management of income and expenses, they can build a savings buffer that allows them to grow their wealth, whether through savings or small investments. This study explored these two constructs by analyzing how individuals manage their finances and navigate economic challenges. A multivariate statistical approach was used to examine these variables, incorporating both exploratory and confirmatory analysis. The objective was to identify which proposed models offered the most accurate explanation of the phenomena under study. Ultimately, the goal is to develop an integrative model combining both aspects using orthogonal rotation methods, such as Varimax and Quartimax, and oblique rotation methods. This will help identify the most significant correlations between financial resilience and financial well-being, offering a clearer understanding of how these concepts are interrelated.

Recent literature has placed the concepts of financial resilience (FR) and financial well-being (FWB) at the center of the debate, especially in the wake of the pandemic, which has altered many economic and behavioral dynamics. (Tahir et al., 2021, 2022) directly linked financial education to economic well-being, while (Bialowolski et al., 2022) highlighted financial education as a key factor in its development. Kulshreshtha et al. (2023) emphasized that resilience is essential for coping with unexpected expenses. Other elements that strengthen financial resilience include the increasing use of digital financial services and financial literacy (Kass-Hanna et al., 2022; Yadav and Shaikh, 2023).

Financial resilience responds to social and cultural differences. Recent studies have emerged from different regions around the world; yet research into financial resilience in Latin America has tended to focus on businesses or even countries. Studies into individual financial resilience are lacking; this article will contribute to closing that gap.

Research into financial resilience in individuals is relatively recent, mostly as a result of the COVID-19 pandemic (Liu et al., 2025). Despite a thorough review of the literature, no studies have been found that combined exploratory and confirmatory factor analysis with the explicit use of orthogonal (Varimax and Quartimax) and oblique (Oblimin and Promax) rotation methods, validated through structural equation modeling (SEM). This approach would allow for identifying behavioral patterns and analyzing how they relate based on the structures obtained in the factor analyses. Therefore, this study seeks to fill this theoretical gap by providing empirical evidence for both constructs. The following research questions are posed:

The objectives of this study were as follows: Exploratory: To identify the underlying dimensions of FR and FWB using exploratory factor analysis (EFA) with principal component extraction and Varimax, Quartimax, Oblimin, and Promax rotations. Confirmatory: The obtained structure was validated using confirmatory factor analysis (CFA) and SEM to assess the model’s fit to the data.

Financial resilience—the ability to withstand economic shocks and maintain stability—is fundamental to achieving financial well-being (Kulshreshtha et al., 2023). It supports financial inclusion, a critical global goal given that one in four people remain excluded from formal financial systems (Salignac et al., 2019). Resilient individuals are better equipped to navigate crises without incurring debt, directly enhancing overall life satisfaction (Kulshreshtha et al., 2023; Liu and Chen, 2024), as the recent COVID pandemic highlighted.

Financial literacy is a key determinant of resilience, reducing vulnerability and enabling better financial decisions (Kass-Hanna et al., 2022). Both financial and digital education strengthen resilience by improving individuals' capability to manage crises and access financial services (Bialowolski et al., 2022). Planning and innovation, at both household and organizational levels, further enhance adaptive capacity (Glew et al., 2023; Nkundabanyanga et al., 2019; Tahir et al., 2021; Zahedi et al., 2022).

Macroeconomic, legal, and political contexts shape resilience, while labor market disruptions, such as COVID-19-related job losses, increase vulnerability (Yao and Zhang, 2023), triggering the “financial vulnerability trap” (Hoffmann et al., 2021). Gender disparities are evident, with men generally being more resilient; however, employed and educated women show improved outcomes (Chipunza and Fanta, 2022; Zeka and Alhassan, 2024). Rural contexts illustrate that financial literacy enhances well-being primarily when mediated by financial self-efficacy (Mitra and De, 2024).

Financial well-being is understood as the perception that one has enough income and assets to enjoy an acceptable quality of life (Rahman et al., 2021). It comprises capabilities and behaviors, as well as internal and external factors, and is an important driver of overall psychological and physical well-being (Hernandez-Perez and Cruz Rambaud, 2025).

Like resilience, financial well-being is strongly linked to individual capabilities and behaviors, including self-efficacy and self-control (Hernandez-Perez and Cruz Rambaud, 2025). Several studies have explored the connection between financial capability and well-being, although few have used long-term national data. Xiao et al. (2024) address this lack of information using five measures from the National Financial Capability Study (NFCS) between 2009 and 2021. Their findings indicated that financial capability indices positively relate to financial well-being, underscoring the importance of subjective knowledge and good financial habits.

Socialization processes further shape financial well-being and often reflect cultural and gender expectations (Sollis et al., 2024). For example, daughters in some contexts receive more financial education than sons (Abdul Ghafoor and Akhtar, 2024). Research also shows that childhood economic hardship and family financial stress have long-term consequences for adult mental health and financial security (Morrissey et al., 2023).

Broader structural and policy-related factors also play a crucial role. For example, homeownership, especially when paired with high financial literacy, substantially improves financial well-being (Gignac et al., 2024). Similarly, access to pensions and healthcare systems is vital for sustaining financial security in older age (Karthika et al., 2023). Perceptions of financial well-being also influence migration decisions and reveal persistent social inequalities, particularly during periods of economic crisis (El Anshasy et al., 2023; Kelley et al., 2023; Nasr et al., 2024).

The relationship between financial well-being and health outcomes has also been widely documented. Poor financial well-being is associated with adverse physical and mental health outcomes, whereas financial security is linked to improved health indicators (Birkenmaier et al., 2023). Hasan et al. (2024) find that health is the most important factor influencing financial well-being, followed by other economic variables.

Within organizational settings, financial stress has been found to reduce employee productivity and well-being, while strong financial well-being improves job performance and satisfaction. The COVID-19 pandemic amplified these dynamics (Nykiforuk et al., 2023), increasing financial anxiety—such as that associated with student loans (Xiao et al., 2024)—and straining family relationships (Kelley et al., 2023). These studies underscore the importance of financial resilience in times of uncertainty. At the same time, crises can lead to the development of coping and adaptation strategies that create resilience (Liu et al., 2025).

Technological innovation introduces both opportunities and risks. Fintech applications have been shown to enhance financial well-being, especially among marginalized groups, including individuals with disabilities (Gafoor and Amilan, 2024). These findings highlight the need for accessible services. However, without adequate financial education, overreliance on digital platforms may lead to adverse financial outcomes, underscoring the need for balanced technology adoption strategies. Zhang and Fan (2024) warn that excessive use of digital platforms can harm financial well-being without adequate education.

Given these findings, financial education emerges as a critical tool for improving financial well-being. Studies demonstrate that parental financial education (Algarni et al., 2024), formal financial literacy initiatives, and workplace financial wellness programs are consistently effective in strengthening financial outcomes and reducing stress across diverse demographic groups (Bashir et al., 2024; Dhiraj et al., 2023).

Collectively, these studies demonstrate that financial resilience and financial well-being are closely intertwined. While resilience emphasizes the ability to withstand short-term financial shocks, well-being addresses long-term stability and life satisfaction. Both outcomes are shaped by financial literacy, digital capability, social equality, and supportive structural systems such as healthcare and pensions. As such, improving financial outcomes requires a holistic approach that integrates education, policy reform, and technological innovation. The present study empirically examines these dynamics within a specific population of university students.

This study used a non-experimental, cross-sectional design. The target population consisted of university students from diverse areas, including finance, economics, administration, accounting, and international business, in the state of Veracruz, Mexico. This population is especially relevant for studying financial resilience and well-being, as university students are in a transition stage toward economic independence, facing significant financial decisions with limited resources (Shim et al., 2010). The sample was obtained through non-probability self-selection sampling, in which participants freely decided whether to participate. Although this technique limits the statistical generalizability of the results, it is methodologically appropriate for exploratory studies and the initial validation of psychometric scales (Calderón-Garrido et al., 2019). In this initial phase, 950 valid questionnaires were collected, allowing efficient access to a large and diverse sample of students, useful for identifying latent structures of financial behavior in local contexts.

Exploratory Factor Analysis (EFA) was applied to examine the underlying structure of financial resilience and well-being constructs. The basic assumptions were previously verified: in simple terms, it was found that the variables were correlated with each other (which is necessary for EFA) and that they did not present extremely skewed distributions (García-Santillán, 2017). To this end, Bartlett’s test of sphericity and the KMO index were used to assess the adequacy of the analysis, along with skewness and kurtosis coefficients within acceptable ranges (skewness <2, kurtosis <7). In addition, polychoric correlation matrices, appropriate for processing ordinal scales (Holgado–Tello et al., 2010), were used. Orthogonal (Varimax, Quartimax) and oblique (Oblimin, Promax) rotations were employed to facilitate factor interpretation. Oblique rotations were anticipated to be more appropriate due to the expected correlation between resilience and well-being dimensions, which are conceptually not independent of each other. Orthogonal rotations, such as Varimax and Quartimax, assume independence between factors; in contrast, oblique rotations, such as Oblimin and Promax, allow for correlation between them, which is more realistic for interrelated psychosocial variables (Fabrigar et al., 1999).

The selection of these four methods responds to complementary criteria of comparison and robustness: Varimax maximizes the variance explained by each factor. Quartimax reduces the number of factors needed to explain each item. Oblimin is useful when a certain correlation between factors is expected. Promax, for its part, combines computational efficiency with interpretability, starting from Varimax and allowing correlations. Multiple criteria were applied to factor retention: eigenvalues greater than 1 (Kaiser criterion), inspection of the scree plot, and parallel analysis, to ensure a robust and replicable factor solution. The following table shows a comparison describing the characteristics of each rotation method: (see Table 1).

Subsequently, the Structural Equation Model (SEM) was applied to confirm the factorial structure, evaluate causal relationships between latent variables, and verify the fit of the model using absolute, structural, and parsimony goodness of fit indices (Bentler and Bonett, 1980; Hair et al., 2014; Jöreskog and Sörbom, 2001). Reliability was assessed using Cronbach’s alpha and McDonald’s omega coefficients, prioritizing the latter due to its stability and lower sensitivity to the number of items (Timmerman and Lorenzo-Seva, 2011).

All participants provided informed consent following the principles of the Declaration of Helsinki. The Ethics Committee of Cristóbal Colón University approved this study under project code UCC.2b.2025.

Model fit assessment is fundamental in structural equation modeling (SEM). Various indices are used to evaluate how well the proposed model fits the observed data. The chi-square statistic (χ²), which follows a chi-squared distribution, assesses model adequacy. This test is more reliable with samples larger than 200 participants, in complex or saturated models, and when the assumptions of multivariate normality are met (Bentler and Bonett, 1980). Additionally, incremental fit indices are calculated, such as the Normed Fit Index (NFI >0.90), which compares model improvement versus a null model; and the Tucker-Lewis Index (TLI/NNFI >0.90), which adjusts the model for complexity. It improves if the reduction in χ² is proportional to the increase in degrees of freedom. The Comparative Fit Index (CFI >0.90) compares the observed and predicted covariance matrices. For model comparisons, the Akaike information criterion (AIC) and Akaike consistency criterion (CAIC) were used, which penalize overparameterization; the model with the lowest values is preferred (Akaike, 1987; Bozdogan, 1987).

The root mean square error of approximation (RMSEA) evaluates the mean error per degree of freedom, and values less than 0.05 indicate a good fit. Hu and Bentler (1999) recommend that the RMSEA confidence interval not exceed 0.08. The standardized root mean square residual (SRMR) standardizes the RMSEA by dividing it by its standard deviation, and values less than 0.05 suggest a good fit. However, Hu and Bentler (1999) caution that indices such as SRMR, RMSEA, NNFI, and CFI may reject valid models in small samples, so the use of multiple indicators is recommended for a more robust assessment. Regarding scale reliability, McDonald’s omega coefficient was prioritized over Cronbach’s alpha because omega offers a more stable estimate and is less dependent on the number of items and the homogeneity of factor loadings (Timmerman and Lorenzo-Seva, 2011). Since this study combines exploratory and confirmatory analysis, in the event of a suboptimal model fit, respecification steps are considered, such as the review of problematic items, the correlation of theoretically justifiable errors, and the evaluation of modifications indicated by the fit indices, always respecting theoretical coherence.

This study validated the factor structure through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), using orthogonal (Varimax and Quartimax) and oblique (Oblimin and Promax) rotations according to Hair et al. (2014), Jennrich and Bentler (2011) and Kaiser (1985). The analyses were performed using SPSS v. 29 and IBM SPSS AMOS v. 29. The scale showed high reliability, with a Cronbach’s alpha of 0.909 and a McDonald’s omega of 0.906. The SEM criteria are described in Table 2.

The sample consisted of 950 university students of diverse ages, genders, and employment situations, allowing for a variety of conditions that influence financial resilience and subjective well-being. Complete demographic characteristics are presented in Table 3.

Overall, female participants predominated (52.9%), followed by male students (41.5%), and a smaller proportion of those who identified as LGBTQ+ (5.6%). The majority of respondents were between 18 and 25 years old, consistent with the typical profile of university students, although some people over 30 were also represented. Regarding marital status, more than 60% were single, while almost a third were in formal relationships (married or in a common-law relationship). Regarding employment status, 56.2% were working and 39.3% were exclusively studying, while the rest had completed their studies but were not employed. These data are relevant because they allow us to analyze how working conditions (or their lack) can affect financial resilience.

Finally, more than 45% of employed participants had between 1 and 10 years of work experience, reflecting active career paths from early stages. These factors, such as age, employment status, and experience, are factors that, as will be discussed in the results, can significantly influence levels of well-being and financial resilience as perceived by students. This profile suggests that a considerable proportion of participants combine study and employment, which may affect their levels of financial resilience and well-being. For example, working students may face greater financial and time burdens, but also have an income, which affects both their perception and management of their well-being. Similarly, age and marital status may reflect different life stages that modify financial priorities and strategies.

Bartlett’s test of sphericity showed a statistically significant result (χ² = 8943.353, gl = 276, p < 0.001), and the Kaiser-Meyer-Olkin (KMO) index of sampling adequacy was 0.921, indicating excellent factorability. Furthermore, all Sampling Adequacy (SA) values exceeded the recommended threshold of 0.50, and the correlation matrix presented a determinant close to zero. These indicators support the robustness and suitability of the data for exploratory factor analysis (EFA), reinforcing internal consistency and shared variance among items.

The EFA revealed a five-factor solution that explained 57.815% of the total variance, consistent with previous studies on financial resilience and psychological well-being in student populations, where explained variance typically ranges between 50% and 60% (e.g. Joo, 2008; Prawitz et al., 2006; Xiao et al., 2024). The factor structure was obtained using orthogonal and oblique rotation techniques; however, oblique rotations (Promax and Oblimin) showed more significant and theoretically interpretable factor loadings, confirming the presence of correlated latent constructs. Factor loadings greater than 0.50 were considered significant and are presented in Table 4, along with the contribution of each item to the identified dimensions. Figures 1 and 2 visually display the scree plot and factor loadings. The subsequent tables and figures (Tables 5 through 11, Figures 3–8) detail the factor structure by dimension, relating each extracted component to specific items and constructs such as adaptive financial behavior, emotional resilience, and subjective well-being. All tables and figures were clearly labeled and integrated into the logical sequence of the analysis, facilitating the traceability of each factor and item.

The following section describes the main results of the final confirmatory model, based on the rotation method used in each case.

Qualitative interpretation of the factors extracted using orthogonal and oblique rotations revealed consistent patterns and specific nuances in the perception of financial well-being. The evaluated models are detailed below, incorporating examples of items with significant loadings, consistent academic language, and observations on their applicability.

This model reflects a structure in which individuals value the fundamentals of financial stability. For example, the item “I maintain a healthy credit history” showed a high factor loading, indicating its relevance to the dimension. Responsible spending strategies, immediate expense planning, and cash savings are also highlighted. The approach is structured, useful for educational contexts where the goal is to teach basic principles of financial education.

In this model, the emphasis is on present and future financial security. Items such as “I have liquid savings available for emergencies” had high loadings, indicating their role in well-being perceptions. The importance of planning short- and long-term expenses and properly assessing insurance coverage is emphasized. This model can be especially useful in public policy interventions seeking to encourage precautionary savings.

By allowing for correlations between factors, this model shows how individuals understand the interdependence of their financial decisions. The item “I have developed strategies to avoid debt” loads highly, illustrating its centrality in the dimension. It emphasizes the coordinated management of income, savings, debt, and insurance. This model is suitable for research that assumes a comprehensive and dynamic conception of financial resilience.

Similar to the previous model in terms of correlations, this model delves deeper into the reflective assessment of interrelated aspects. It highlights items such as “I have implemented habits to spend less than I earn,” which reflects a complex understanding of financial self-control. Its application is relevant in advanced educational contexts or longitudinal assessments.

All models identify common elements, such as the importance of maintaining a healthy credit history, planning expenses, sustainably managing debt, having adequate insurance, and having liquid savings and long-term assets. This convergence suggests a shared foundation in the conceptualization of financial well-being.

For clarity, these key differences are presented in the following comparison table: (see Table 12).

The results obtained from the analyzed models are largely consistent with the literature on financial resilience (FR) and financial well-being (FWB), although they also reveal important distinctions.

All models align with prior research by identifying core components such as credit history, savings (both liquid and long-term), and financial planning. These findings resonate with Bialowolski et al. (2022), who emphasize financial education as a pillar of resilience. The models also reinforce the connection between FR and FWB by highlighting individuals’ capacity to navigate unexpected financial challenges, as discussed by Kulshreshtha et al. (2023) and Tahir et al. (2021). Insurance as a protective mechanism is also consistently present, in line with Bialowolski et al. (2022).

Each rotation technique reveals unique structural and practical emphases. Varimax prioritizes independent dimensions, making it particularly useful for contexts where targeted interventions are needed (e.g. specific savings behavior or budgeting practices). Quartimax highlights debt sustainability more prominently, offering insights for public policy in over-indebted populations. Oblimin and Promax allow for correlations between factors, reflecting a more holistic and systemic view of financial resilience—as noted by Yadav and Shaikh (2023). These models may guide interventions aimed at strengthening interconnected financial capacities, such as linking planning, debt management, and savings behavior in integrated programs. Despite these strengths, all models fail to explicitly capture the roles of financial education and digital financial literacy, despite their relevance as highlighted by Kass-Hanna et al. (2022) and Bialowolski et al. (2022). This omission represents a conceptual limitation. Future iterations of the scale may benefit from the incorporation of these dimensions, especially given the increasing digitization of financial services and its impact on inclusion and resilience.

Moreover, although the models include both liquid and long-term savings, the distinction between them is not always conceptually or empirically clear. As Tahir et al. (2022) suggest, this differentiation is essential in understanding the temporal dynamics of resilience strategies.

Taken together, the Varimax, Quartimax, Oblimin, and Promax models capture multiple facets of financial resilience and well-being, such as saving, planning, and managing credit. However, they differ in terms of factor structure, interactions among dimensions, and practical scope. Models with oblique rotations (Oblimin, Promax) more accurately reflect the interconnectedness of financial capacities, aligning with a systems-theory approach to resilience. Nonetheless, some models showed signs of cross-loadings and overlapping factor content, which could affect discriminant validity. This highlights a methodological limitation, especially for models based on orthogonal rotation, and should be addressed in future refinements of the scale. Improved item wording or additional constructs may help reduce this overlap.

The findings reinforce the multidimensional and interrelated nature of financial resilience (FR) and financial well-being (FW). In particular, oblique rotation models (Oblimin and Promax) reveal the correlation between dimensions, challenging previous approaches that have treated these factors as independent. This evidence supports a more integrated and systemic conceptual framework, consistent with contemporary perspectives on resilience as a dynamic and adaptive phenomenon. Furthermore, the results can be interpreted through behavioral economics frameworks, which explain certain observed patterns. For example, the use of “mental accounts,” the presence of present bias, or bounded rationality (Kahneman, 2011; Thaler and Sunstein, 2008) can influence decisions such as prioritizing immediate savings over long-term savings, or the underutilization of insurance despite knowing its importance. These approaches complement traditional economic understanding and open new avenues for exploring resilience from a psychological and behavioral perspective.

Based on these findings, a modified theoretical model is suggested that integrates four pillars: savings, planning, credit management, and insurance, all linked and modulated by contextual variables such as financial education and digital literacy. This model can be visualized as a system of interdependent gears, where the alteration of one component can affect the stability of the others (see Figure 9). Finally, it is necessary to recognize that this framework can vary depending on the cultural or socioeconomic context. In informal economies or low-income communities, for example, debt sustainability may depend more on family networks or informal credit than on formal financial mechanisms. Cross-cultural validation of the proposed scale will be a fundamental step to ensure its applicability beyond the Mexican university context.

The results obtained suggest the urgent need to rethink approaches to financial education, especially at the university level. Theoretical modules or informative talks are not enough; experiential strategies such as budget simulations, savings games with immediate feedback, and case studies on over-indebtedness must be incorporated. Programs such as Junior Achievement, Edufinet (Spain), or platforms such as Kahoot Finance can serve as references for a more effective curriculum redesign.

Additionally, mobile banking and fintech can play a key role in operationalizing financial resilience. Apps that allow people to set savings goals, receive personalized payment reminders, or access low-cost digital insurance represent tools with high potential for transforming knowledge into habits. These solutions are especially valuable in young populations with high technological penetration but limited financial support. The reference to “vulnerable contexts” is made specific here to economically disadvantaged students with limited access to support networks and low digital familiarity. For example, students who work part-time to support their studies and their households face daily decisions between immediate spending and future planning. For them, effective intervention requires a combination of education, subsidies, and tailored digital tools.

Finally, the findings of this study should be strategically shared with financial institutions, educational authorities, and decision-makers. This can be achieved through the development of policy documents, presentations in inter-institutional forums, and collaboration with central banks or financial inclusion agencies, to integrate resilience as a key dimension into education programs and financial products.

The findings indicate that financial resilience goes beyond coping with unforeseen events and encompasses interrelated factors, such as savings, spending planning, debt management, and credit history, all of which are essential for financial well-being. Planning strategies should integrate these dimensions in an articulated manner. The literature and models analyzed also highlight the central role of financial education, which must be comprehensive, adapted to each individual’s reality, and oriented toward savings and understanding credit and long-term planning. Oblique rotation models (Oblimin and Promax) reinforce this view by highlighting the interconnectedness of the factors that influence resilience and the challenging approaches that analyze them in isolation. In the context of increasing digitalization, financial inclusion policies must facilitate access to and the use of technological tools, especially for vulnerable populations. Technology is key to improving financial resilience by expanding management possibilities, savings, and access to appropriate products. Therefore, public policies should focus on a more personalized approach that combines financial education, access to affordable products, adequate insurance, and responsible credit to promote a comprehensive vision of economic stability.