Financial well-being – A Generation Z perspective using a Structural Equation Modeling approach

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The current pandemic situation in the global economy has urged the need to revolutionize the financial services industry with a keen eye on consumers’ financial needs for sound financial decisions, which is necessary for financial well-being. The purpose of the study is to assess the financial well-being of Indian Gen Z students in relation to financial literacy, financial fragility, financial behavior, and financial technology. In addition, the study also tries to determine how Gen Z students’ financial well-being is influenced by other factors such as gender, age, parental education, employment status, and monthly income in India. The study uses the scientific data analysis approach, Partial Least Squares-SEM model to estimate, predict, and assess the hypotheses. A sample of 271 University students from India was surveyed using a self-administered structured questionnaire. Questions were incorporated to understand the effect of financial literacy, technology, fragility, behavior, demographic and parental characteristics on financial well-being. The results indicate that financial behavior is positively related to financial well-being, while financial fragility is negatively associated. However, financial literacy and financial technology do not significantly affect financial well-being. The results also show that financial well-being is significantly influenced by gender, parental education, employment status, and monthly income change. Understanding Indian Gen Z student financial well-being will expand the students’ understanding of the importance of financial literacy for well-planned financial behavior and informed decisions, hence high levels of financial well-being. Government and financial institutions can more effectively identify gaps and deficiencies in student financial well-being.

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    • Figure 1. Conceptual model
    • Figure 2. Path coefficients and outer loadings
    • Figure 3. PLS-SEM with t-statistics, bootstrapping with 5,000 samples
    • Figure 4. Distribution of financial well-being scores by age and gender
    • Figure 5. Distribution of financial well-being scores by place of residence and work experience
    • Figure 6. Distribution of financial well-being scores for students who felt a reduction of standard of living and kept expenses record versus those who did not
    • Table 1. Demographic and financial profile of 271 respondents
    • Table 2. Chi-square tests for financial well-being indicators against demographic and financial indicators
    • Table 3. Validity and reliability measures of constructs
    • Table 4. Cross loadings of the indicators
    • Table 5. Latent variable correlations and square root of AVE values (diagonal)
    • Table 6. Total effects of the factors
    • Table 7. Path coefficients, t-statistics and p-values of the structural model
    • Table 8. Specific indirect effects for mediation analysis
    • Table 9. Independent sample t-tests for financial well-being and its factors by gender
    • Table 10. Independent sample t-tests for financial well-being and its factors by age
    • Table 11. Independent samples t-test on factors versus place of residence
    • Table 12. One-way ANOVA for each factor across work experience
    • Table 13. One-way ANOVA for each factor across father’s education
    • Table 14. One-way ANOVA for each factor across mother’s education
    • Table 15. Independent samples t-tests for the factors by reduction in standard of living
    • Table 16. One-way ANOVA across students who kept expense records never/rarely/sometimes/regularly
    • Table 17. Hypothesis testing
    • Conceptualization
      Nisha Shankar
    • Data curation
      Nisha Shankar, Smitha Vinod
    • Formal Analysis
      Nisha Shankar, Rajashree Kamath
    • Methodology
      Nisha Shankar, Rajashree Kamath
    • Project administration
      Nisha Shankar, Smitha Vinod
    • Resources
      Nisha Shankar, Smitha Vinod
    • Supervision
      Nisha Shankar
    • Writing – original draft
      Nisha Shankar
    • Writing – review & editing
      Nisha Shankar, Smitha Vinod, Rajashree Kamath
    • Investigation
      Smitha Vinod
    • Software
      Smitha Vinod, Rajashree Kamath
    • Validation
      Smitha Vinod, Rajashree Kamath
    • Visualization
      Rajashree Kamath