By S. Kavitha Devi & M. Priyanka
Abstract:
The purpose of this research is to investigate the Impact of financial literacy on retirement planning of women employees in public Electricity companies in Telangana. The current research study is an investigative and exploratory research. It uses primary data. The study examined partial least square-structural equation modelling (PLS-SEM) obtained by sampling data from 406 women employees of Public Electricity Companies in Telangana. The findings of this study have important inferences for both researchers and practitioners in the field of personal finance. They highlight the significance of FL in influencing individuals' Retirement Planning. Moreover, the role of psychological factors emphasizes the need to consider these factors when examining the relationship between FL and Retirement Planning. These findings suggest that interventions aimed at improving FL should also focus on enhancing individuals' Psychological Factors and cultivating positive Retirement Planning Behavior.
Keywords: financial literacy; financial risk tolerance; retirement planning; herding behavior.
Introduction
Financial education or financial literacy has gained relevance in recent years as a result of the rising complexity of the financial products and services available, as well as information asymmetry between financial service providers and consumers. Financial education is the process of obtaining the information and abilities needed to handle and use money in an educated and efficient manner. It is a lifelong process that assists people and households in becoming more knowledgeable about the financial goods and services offered in the market in order to make wise decisions regarding their use. Financial education is broadly defined as the understanding of financial market products, particularly rewards and risk, in order to make educated decisions. Organisation for Economic Co-operation and Development (OECD, 2013) has defined financial education as “the process by which financial consumers/ investors improve their understanding of financial products, concepts and risks through information, instruction and/or objective advice, develop the skills and confidence to become more aware of financial risks and opportunities, to make informed choices, to know where to go for help, and to take other effective actions to improve their financial well-being”. According to Standard & Poor’s Ratings Services Global Financial Literacy Survey, 2014 “76% of Indian adults do not understand key financial concepts like inflation, compounded interest rate, and risk diversification adequately. This finding says that financial literacy is lower than the worldwide average”. Authors Lusardi and Mitchell, 2011, Bucher-Koenen and Lusardi, 2011, Grohmann et al. have revealed in their papers that there is a positive impact of financial literacy on retirement planning.
The development and expansion of any country is heavily influenced by its economic condition. Proper capital formation is necessary to stimulate the process of economic growth. The financial market is crucial in accelerating capital development by encouraging savings and using investment alternatives, which contributes to speeding up the process of wealth creation.
Being a developing country, India needs rapid capital generation. This could only be accomplished by encouraging smart planning and guiding people's financial habits. The Indian economy has expanded at a quicker rate from the previous decade, however in order to achieve the goal, economic growth alone is not enough must improve citizen living standards. According to Singh (2008) “development cannot be measured only in terms of growth, instead the objective must be to achieve the improvement in the standard living of people.”
According to Ahuwalia (2008) “Indians are poor investors but smart savers. They do not prepare for the long term and do not invest in long-term investment products. Furthermore, it was stated that Indians like to save their money into their houses instead of saving in banks or other investments. This will be a major issue in India, where social security is non-existent”.
Indian Population Context:
(Source: IMPORTANCE OF SAVINGS FOR RETIREMENT AND EARLY DECISION
MAKING IN HUMAN LIFE, N Sheikh & S Karnati - 2021)
India is young demographically with 90% of population under the age of 60 years but ageing gradually, it is estimated that persons above the age of 60 would increase from ~8.9% of the population now to ~15% by 2050. Those above 80 are likely to increase from ~0.9% to ~2.8%. According to United Nations World Population Prospects, India’s 60-plus population is expected to reach 323 million by 2050 – a number greater than US Population of 2012.
Figure above shows historical data and future forecasts on the Indian population's dependency from 1980 to 2050. It can be seen that the percentage of dependent people gradually increased between1980 to 2015. However, the share of the dependent population is predicted to rise faster between 2015 and 2050. In 2050, 15% of India's elderly population would be dependent on the working population.
Despite the fact that the transition from a young to an older age structure indicates a successful and satisfying outcome of health improvement, the rate of old and the size of the Older population with diverse requirements and resources creates various obstacles for health care providers and Government officials. The percentage of old age people has increased and is expected to increase further, while the percentage of the young age group is decreasing, resulting in a slow but continuous shift to an older population structure in the country. Furthermore, the transition from a young age structure is not uniform across the country. A rising old population requires increased quantity and quality of elder services, income security, and overall improved quality of life. The necessity for social pension payments and the resulting financial outlays to meet expanding old-age dependency and a decreasing support base is more demanding for policy consideration now and in the future.
Research Gap
According to the review of the literature, even though women's literacy rates have improved significantly in recent years, there are still significant gender gaps in financial education in
India. More research is needed on the factors that contribute to these gaps and an apparent gap is observed in understanding the retirement financial planning of women in India. Previous research on financial literacy usually focuses on its potential effects on financial decision-making; however, little research is done on its effects on retirement planning. Therefore, the present study having spotlight on Financial Literacy and Retirement planning aimed and focused on women employees in electricity companies in Telangana. Majorly it considers respondents awareness levels towards financial literacy and retirement planning decisions of respondents using three components to calculate the financial literacy (financial knowledge, financial attitude and financial behaviour) of women employees to assess the holistic impact on retirement planning decisions. We examine the potential effects of financial literacy on retirement planning of women employees in Public electricity companies in Telangana. This study will fill in this research gap.
Objectives of Research
1) To find the relationship between financial literacy levels and retirement financial planning.
2) To study the impact of psychological constructs variables on the retirement planning of women employees in public electricity companies of Telangana and analyses the financial literacy levels.
Hypotheses
Hypotheses are considered to be the most significant tool in a research study. It makes a difference in representing new tests and their views. Hypotheses are based on fundamental assumptions in every research study. Following a thorough analysis of the relevant literature, an attempt was made to create the conditional assumption in constructing the test and its reasonable consequences. The following hypotheses have been developed for the aim of the research.
H01: There is no significant relationship between financial literacy levels and retirement financial planning.
H02: There is no significant influence of psychological constructs on retirement financial planning.
H02a: There is no significant influence of Future time prospective on retirement financial planning.
H02b: There is no significant influence of Attitude towards Retirement on retirement financial planning.
H02c: There is no significant influence of Risk tolerance on retirement financial planning.
H02d: There is no significant influence of Retirement Goal Clarity on retirement financial planning.
Methodology
Primary Data
Primary Data collected through a Survey Questionnaire from the respondents of women employees in Public Electricity Companies in Telangana
For current study both convenience and snowball sampling methods (non-probability) sampling techniques were used to recruit potential samples for the achievement of the research objectives. Convenience sampling refers to the collection of data from immediately available representative respondents of the population of the study. Convenience sampling would help a researcher when he could not have access to the entire population of the study and/or when a researcher had difficulty identifying the representative sample of the study.
Snowball sampling refers to the researcher initially recruiting participants, and these initial participants help to recruit future respondents for the study. This technique helps the researcher when he is facing challenges or difficulties to collect data from the target potential population of the study. The researcher may be face difficulty due to unknown to the respondents and hesitate to give important personal information to strangers.
This study involved the collection of personal and financial information of the respondents. Some respondents may be unwilling to provide their personal and financial information.
Therefore, convenience and snowball sampling techniques were employed in this study to gather the data to evaluate the research hypothesis. The blend of convenience and snowball sampling methods helps to achieve reliable results for the research investigation.
Secondary Data:
Secondary data collected from various Publications, Journals, Articles, Newspapers and official websites Viz. RBI, SEBI, IRDAI, PFRDA, NCFE, etc.,
Period of the study is between July 2022 and November 2022.
Calculation of Sample Size
The present research study is an investigative in nature, the study is done based on four public electricity companies in Telangana selected on the basis of population as criteria. In order to study the perception of women employee’s financial retirement planning from each company, sample variables are selected proportionately. Hence the total sample size is 406.
Sl. No. | Name of the company | Population (women employees) |
1 | TSSPDCL | 1320 |
2 | TSNPDCL | 1182 |
3 | TSGENCO | 2429 |
4 | TSTRANSCO | 2125 |
TOTAL | 7056 | |
(Source: collected from respective HR Department by Researcher)
The total women employees of Public Electricity Companies in Telangana is 7056, out of that population the sample is detrained and drawn according to Krejcie Morgan table, at Confidence Level of 95%, Confidence Interval is 4%, Proportion is 5% and if Population is below 8000,
Sample size determined is 367 respondents. In present study 430 respondents sample size was taken, among them 406 were found to be relevant for study.
Proportionately the sample is determined from each company as follows:
Sl.
No. | Name of the company | Population (womenemployees) | Proportionatesample |
1 | TSSPDCL | 1320 | 80 |
2 | TSNPDCL | 1182 | 72 |
3 | TSGENCO | 2429 | 131 |
4 | TSTRANSCO | 2125 | 123 |
TOTAL | 7056 | 406 | |
Measurement of Reliability
Cronbach’s Alpha | No of Items |
0.867 | 45 |
The degree of consistency between multiple measurements of variables was measured by the reliability test. Reliability calculates the accuracy and precision of a measurement procedure. Cronbach’s Alpha is widely used to measure thereliability of data. The coefficient of Cronbach’s Alpha value for financial literacy and retirement planning of womenemployees in public electricity companies of Telangana for 45 variables was 0.867 as presented in the above table.
Analytical Tools and Software
The current research study is an investigative and exploratory research. It uses primary data. Thus data would be analyzed through descriptive statistics, structural equation modeling, factor analysis and frequency tables etc, The software package like SmartPLS is used to analyze the data.
Data Analysis and Results:
Correlation Between Latent Constructs
Correlation refers to the extent to which two variables move together in a systematic way. It quantifies the strength and direction of the relationship between variables. Correlation coefficients, often represented as path coefficients in SEM, indicate the extent to which the latent constructs are related.
Correlation between latent constructs
Constructs | Financial Literacy | FUTURE TIMEPERSPECTIVE | ATTITUDETOWARDSRETIREMENT | RISKTOLERANCE | RETIREMENTGOALCLARITY | SOCIALGROUPSUPPORT | PLANNINGACTIVITY | SAVINGS |
Financial Literacy | 1.000 | 0.320 | 0.303 | 0.417 | 0.272 | 0.449 | 0.443 | 0.268 |
FUTURE TIMEPERSPECTIVE | 0.320 | 1.000 | 0.326 | 0.299 | 0.293 | 0.322 | 0.318 | 0.288 |
ATTITUDETOWARDSRETIREMENT | 0.303 | 0.326 | 1.000 | 0.284 | 0.277 | 0.305 | 0.301 | 0.274 |
RISKTOLERANCE | 0.417 | 0.299 | 0.284 | 1.000 | 0.255 | 0.420 | 0.414 | 0.251 |
RETIREMENTGOALCLARITY | 0.272 | 0.293 | 0.277 | 0.255 | 1.000 | 0.274 | 0.270 | 0.245 |
SOCIALGROUPSUPPORT | 0.449 | 0.322 | 0.305 | 0.420 | 0.274 | 1.000 | 0.445 | 0.270 |
PLANNINGACTIVITY | 0.443 | 0.318 | 0.301 | 0.414 | 0.270 | 0.445 | 1.000 | 0.266 |
SAVINGS | 0.268 | 0.288 | 0.274 | 0.251 | 0.245 | 0.270 | 0.266 | 1.000 |
These correlations provide insights into the relationships between the latent constructs. For example, Retirement Planning is positively associated with Financial Literacy. As well as, FTP, ATR, RT, RGC, SGS, PA and Savings shows positive associations with Financial Literacy. However, it's important to note that correlation does not imply causation, and further analysis is needed to understand the underlying factors influencing these relationships.
Common Method Bais (CMB)
The Common method bias can be caused by different groups responding differently to the same questions or scales, leading to inaccurate results
Inner Model VIF Values using Random Variable method
Constructs | Random Variable |
Financial Literacy | 1.720 |
Future Time perspective | 1.303 |
Attitude Towards Retirement | 1.507 |
Risk Tolerance | 1.635 |
Retirement Goal Clarity | 1.121 |
Social Group Support | 1.565 |
Planning Activity | 1.626 |
Savings | 1.747 |
To mitigate the CMB, used different anchors of constructs while collecting the data from respondents, different scales were also adopted, research instrument was pre-tested with two academicians in the field and six respondents. and report a full collinearity measure by reporting that all inner and Outer VIF values are less than 3.3(Kock & Lynn, 2012) (Kock, 2015).
Hence the model is free from CMB.
Factor Loading and AVE ( From author collected data)
These results indicate that the indicators generally have strong to moderate relationships with their respective constructs, and the constructs explain a substantial amount of variance in their indicators.
The Model Assessment Procedure introduced by Hair et al. in 2017a is a methodology used to evaluate the performance and validity of a statistical model. This procedure involves several steps to ensure the accuracy and reliability of the model's results. The Model Assessment Procedure by Hair et al. provides a systematic framework for developing and evaluating statistical models, ensuring that they are robust, reliable, and appropriate for the research objectives at hand.
1. Evaluation of the Measurement Model:
1.1.Internal Consistency & Reliability: Internal consistency and reliability are important concepts in the field of measurement and psychometrics. They refer to the extent to which a measurement instrument, such as a questionnaire or a test, consistently and reliably measures a particular construct or attribute.
Reliability Thresholds
Constructs | Cronbach's alpha | Composite reliability (rho_a) | Composite reliability (rho_c) |
Future Time Prospective | 0.702 | 0.783 | 0.812 |
Attitude Towards Retirement | 0.700 | 0.711 | 0.752 |
Risk Tolerance | 0.720 | 0.743 | 0.753 |
Retirement Goal Clarity | 0.909 | 0.923 | 0.931 |
Social Group Support | 0.702 | 0.719 | 0.749 |
Planning Activity | 0.726 | 0.730 | 0.731 |
Savings | 0.715 | 0.721 | 0.765 |
Cronbach’s alpha values greater than 0.60 for the early stages of the research, values of at least 0.70 required, values higher than 0.95 are not desirable(Nunnally,1978)
Cronbach’s alpha can be considered the lower bound and composite reliability(rho_c) the upper bound of the exact internal consistency and reliability.
1.2.Discriminant validity: Discriminant validity is a concept in measurement and psychometrics that assesses the extent to which different measures or indicators of distinct constructs are distinct or discriminate from each other. It examines whether measures designed to capture different constructs are truly measuring separate concepts and not converging or overlapping.
Heterotrait-Monotrait Ratio (HTMT)
Constructs | Attitude Towards Retirement | F L | F T P | P A | R G C | R P | R T | Savings |
Financial Literacy | 0.61 | | | | | | | |
Future Time Prospective | 0.60 | 0.84 | | | | | | |
Planning Activity | 0.57 | 0.83 | 0.86 | | | | | |
Retirement Goal Clarity | 0.52 | 0.76 | 0.41 | 0.80 | | | | |
Retirement Planning | 0.51 | 0.65 | 0.54 | 0.72 | 0.74 | | | |
Risk Tolerance | 0.49 | 0.97 | 0.69 | 0.53 | 0.63 | 0.66 | | |
Savings | 0.45 | 0.66 | 0.57 | 0.85 | 0.55 | 0.59 | 0.68 | |
Social Group Support | 0.44 | 0.71 | 0.60 | 0.65 | 0.54 | 0.62 | 0.61 | 0.78 |
Based on the HTMT values and their confidence intervals, it can be concluded that all the constructs (Financial Literacy, Future Time Prospective, Planning Activity, Retirement Goal Clarity, Retirement Planning, Risk Tolerance, Savings, Social Group support) exhibit discriminant validity. This suggests that these constructs are distinct from each other and do not overlap significantly in measurement.
2. Evaluation of the Structural model:
Evaluation of the Structural Model involves assessing collinearity among constructs, significance and relevance of path coefficients, predictive accuracy (R-squared, F-squared, Q-squared, PLS predict), predictive model selection, and goodness-of-fit.
2.1. Collinearity among constructs:
The Variance Inflation Factor (VIF) is a measure of the degree of multicollinearity between predictor variables in a linear regression model. A VIF of 1 indicates no correlation between the predictor variable and other predictor variables in the model, while a VIF more significant than 1 indicates some degree of multicollinearity. Typically, a VIF value of 5 or greater indicates high multicollinearity and may require corrective action. The VIF values were, listed in Table 5.6, below 5 confirm there was non-existence of multi-collinearity between constructs in this study. . For this, we report a full collinearity measure by reporting that all inner VIF values are less than 3.3
Inner Model VIF Values
Constructs | Attitude Towards Retirement | F L | FTP | PA | RGC | RP | RT | Savings | SGS |
Financial Literacy | | | | | | 1.458 | | | |
Retirement Planning | 1.659 | | 1.885 | 1.215 | 1.632 | | 1.145 | 1.745 | 1.656 |
Source: Calculated by Author
In summary, based on the VIF values provided, there is no substantial collinearity issue among the constructs in the model. The VIF values are all relatively low, indicating that the variables are not highly correlated, and the model is not affected by multicollinearity.
2.2. Hypotheses Testing:
After confirmation of the reliability and validity of the outer model, the significance of research model (hypothesized) relationships was examined with standardized path coefficient (b) and critical value (T-Value) at the significant level of 5 % (P-Values) by using the PLS bootstrapping.
The first hypothesis (H1) is supported by (β=0.626, P<0.05) Financial Literacy positively effects Retirement Planning.The second hypothesis (H2) is supported by (β=0.932, P<0.05) Retirement Planning positively effects Future Time Prospective.The third hypothesis(H3) is supported by (β=0.905, P<0.05) Retirement Planning positively effects Savings. The fourth hypothesis(H4) is also supported (β=0.817, P<0.05) as Retirement Planning has a positive significant effect on ATR. The fifth hypothesis (H5) is also supported (β=0.874, P<0.05) as Retirement Planning has a positive significant effect on Planning Activity.
The sixth hypothesis (H6) is also supported (β=0.839, P<0.05) as Retirement Planning has a positive significant effect on Risk Tolerance.
The seventh hypothesis (H7) is supported by (β=0.921, P<0.05), as Retirement Planning has a positive significant effect on Retirement Goal Clarity.
The eighth hypothesis(H8) is supported by (β=0.892, P<0.05), as Retirement Planning has a positive significant effect on Social Group Support.
Hypothesis | Relationship | Path Coefficients (b) | Standard Deviation (STDEV) | T Value (|b/STDEV|) | P Values | Decision |
H1 | Financial Literacy - Retirement Planning | 0.626 | 0.057 | 10.982 | 0.000 | supported |
H2 | Retirement Planning Future Time Prospective | 0.932 | 0.043 | 21.674 | 0.000 | supported |
H3 | Retirement Planning -Savings | 0.905 | 0.039 | 23.205 | 0.000 | Supported |
H4 | Retirement Planning-> Attitude Towards Retirement | 0.817 | 0.046 | 17.760 | 0.001 | supported |
H5 | Retirement Planning-> Planning Activity | 0.874 | 0.048 | 18.208 | 0.000 | supported |
H6 | Retirement Planning- Risk Tolerance | 0.839 | 0.071 | 11.816 | 0.012 | supported |
H7 | Retirement planning-Retirement Goal Clarity | 0.921 | 0.083 | 11.096 | 0.000 | supported |
H8 | Retirement planning- Social Group Support | 0.892 | 0.049 | 18.204 | 0.000 | supported |
2.3.Goodness-of-fit: For PLS-SEM SRMR will give a goodness-of-fit index.
Standardized root mean square residual (SRMR): squared discrepancy between the observed correlations and the model implied indicator correlations.
SRMR assessing the quality of the whole model results (i.e., jointly evaluating the outer and inner model results). It Should be less than 0.08 (Hair et al.,2019).
As per PLS algorithm results, the research model’s SRMR is 0.075, which is less than the threshold limit (0.08). Hence it is concluded as our model has a good fit.
Discussion:
The frequency statistics of age represent that most of the women working in Public Electricity companies in Telangana were aged between 31 to 40 years representing almost 32.5 %; aged between 41 to 50 years represented 29.3 %, 21.2 % of respondents were from the age group of 51-60 years and 7 % of respondents were above the age 60 who were near to retirement and 10.0% of individuals falls under the age group 20 to 30 years. All the respondents were below their retirement age. The Profession of the respondents were either financial or non-financial. Maximum respondents i.e., 61.33% respondents were from non-financial background. The rest 38.66% respondents were from financial background. Findings of the study reveal that most of the respondents were from non-financial background.
The findings of this study have important inferences for both researchers and practitioners in the field of personal finance. They highlight the significance of FL in influencing individuals' Retirement Planning. Moreover, the role of psychological factors emphasizes the need to consider these factors when examining the relationship between FL and Retirement Planning. From a practical standpoint, these findings suggest that interventions aimed at improving FL should also focus on enhancing individuals' Psychological Factors and cultivating positive Retirement Planning Behavior. This could be achieved through targeted educational programs, financial counselling, and promoting a financial environment that fosters positive financial behaviors.
Conclusion:
Result shows that those who practice constructive financial habits tend to achieve good Retirement Planning. The well Retirement Planning can be enhanced through sound FL, FTP, ATR, SGS, RGC, Planning Activity, Savings. Among the predictors of Retirement Planning, Psychological factors has a higher impact followed by financial literacy of women employees. It is very important to understand the concepts like the impact of simple and compound interest rates, understands inflation, risk diversification, and the time value of money, have a positive perception of money, budget money in a planned manner, and explore financial products/services like a savings account, debit card, credit card, and insurance, to achieve the Retirement Planning of women employees. The research model has explained 39.2% of the variance in financial wellbeing. So, it can be concluded as Retirement Panning is a long-term goal to achieve by admitting financial literacy, psychological factors. By prioritizing financial literacy, psychological factors individuals can achieve Retirement Planning and improve their overall quality of life.
References:
[1.] Hogarth, R. M., & Karelaia, N. (2006). “Take-the-best” and other simple strategies: why and when they work “well” with binary cues. Theory and Decision, 61, 205-249.
[2.] Lusardi, A., & Tufano, P. (2015). Debt literacy, financial experiences, and overindebtedness. Journal of Pension Economics & Finance, 14(4), 332-368.
[3.] Lusardi, A. (2008). Financial literacy: an essential tool for informed consumer choice? (No. w14084). National Bureau of Economic Research.
[4.] Lusardi, A. (2008). Household saving behavior: The role of financial literacy, information, and financial education programs (No. w13824). National Bureau of Economic Research.
[5.] Thomson, S., De Bortoli, L., Underwood, C., & Schmid, M. (2020). PISA 2018: Financial Literacy in Australia.
[6.] Awais, M., Laber, M. F., Rasheed, N., & Khursheed, A. (2016). Impact of financial literacy and investment experience on risk tolerance and investment decisions: Empirical evidence from Pakistan. International Journal of Economics and Financial Issues, 6(1).
[7.] Garman, E. T., MacDicken, B., Hunt, H., Shatwell, P., Haynes, G., Hanson, K. C., ... & Woehler, M. B. (2007). Progress in measuring changes in financial distress and financial well-being as a result of financial literacy programs. Consumer interests annual, 53, 199-211.
[8.] Beal, D., & Delpachitra, S. (2003). Financial literacy among Australian university students. Economic Papers: A journal of applied economics and policy, 22(1), 65-78.
[9.] Bianchi, M. (2018). Financial literacy and portfolio dynamics. The Journal of Finance, 73(2), 831-859.
[10.] Bialowolski, P., Cwynar, A., Xiao, J. J., & Weziak‐Bialowolska, D. (2020). Consumer financial literacy and the efficiency of mortgage‐related decisions: New evidence from the Panel Study of Income dynamics. International Journal of Consumer Studies.
[11.] Lusardi, A., & Tufano, P. (2015). Debt literacy, financial experiences, and overindebtedness. Journal of Pension Economics & Finance, 14(4), 332-368.
[12.] Taft, M. K., Hosein, Z. Z., Mehrizi, S. M. T., & Roshan, A. (2013). The relation between financial literacy, financial wellbeing and financial concerns. International journal of business and management, 8(11), 63.
[13.] Chu, Z., Wang, Z., Xiao, J. J., & Zhang, W. (2017). Financial literacy, portfolio choice and financial well-being. Social indicators research, 132(2), 799-820.
[14.] Frijns, B., Gilbert, A., & Tourani-Rad, A. (2014). Learning by doing: The role of financial experience in financial literacy. Journal of Public Policy, 34(1), 123-154.
[15.] Ameliawati, M., & Setiyani, R. (2018). The influence of financial attitude, financial socialization, and financial experience to financial management behavior with financial literacy as the mediation variable. KnE Social Sciences, 811-832.
[16.] Khan, F., & Surisetti, S. (2020). Financial well-being of working women: mediating effect of cashless financial experience and digital financial self-socialization. Khan, F and Surisetti, s. Financial well-being of working women: mediating effect of cashless financial experience and digital financial self-socialization. MDIM Business Review, 1(2), 51-68.
[17.] Nikolaos D. Philippas & Christos Avdoulas (2020) Financial literacy and financial well-being among generation-Z university students: Evidence from Greece, The European Journal of Finance, 26:4-5, 360-381, DOI: 10.1080/1351847X.2019.1701512
[18.] Yakoboski, P. J., Lusardi, A., & Hasler, A. Financial literacy and well-being in a five generation America.
[19.] Pandey, A., Ashta, A., Spiegelman, E., & Sutan, A. (2020). Catch them young: Impact of financial socialization, financial literacy and attitude towards money on financial well‐being of young adults. International Journal of Consumer Studies, 44(6), 531-541.
[20.] Chhatwani, M. (2022). Mortgage delinquency during COVID-19: do financial literacy and personality traits matter?. International Journal of Bank Marketing.
[21.] Thongrak, N., Chancharat, S., & Kijkasiwat, P. (2021). Financial Literacy: Does It Improve Well-being? A Case Study of Farmers in Khon Kaen, Thailand. In Environmental, Social, and Governance Perspectives on Economic Development in Asia. Emerald Publishing Limited
[22.] Nunnally, J. C. (1978). Psychometric Theory 2nd ed. Mcgraw hill book company.
[23.] Gutiérrez‐Nieto, B., Serrano‐Cinca, C., & de la CuestaߚGonzález, M. (2017). A multivariate study of over‐indebtedness' causes and consequences. International Journal of Consumer Studies, 41(2), 188-198.
[24.] Huston, S. J. (2010). Measuring financial literacy. Journal of consumer affairs, 44(2), 296-316.
[25.] Fornero, E., Monticone, C., & Trucchi, S. (2011). The effect of financial literacy on mortgage choices.
[26.] Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of economic literature, 52(1), 5-44.
[27.] Bhalla, G. S., & Chadha, G. K. (1982). Green Revolution and the Small Peasant: A Study of Income Distribution in Punjab Agriculture: II. Economic and Political Weekly, 870-877.
[28.] Madi, A., & Yusof, R. M. (2018). Financial Literacy and Behavioral Finance: Conceptual Foundations and Research Issues. Journal of Economics and Sustainable Development, 9(10), 81-89.
[29.] Wolfe-Hayes, M. A. (2010). Financial literacy and education: An environmental scan. The International Information & Library Review, 42(2), 105-110.
[30.] Amisi, S. A. R. A. H. (2012). The effect of financial literacy on investment decision making by pension fund managers in Kenya (Doctoral dissertation).
[31.] Musundi, K. M. (2014). The effects of financial literacy on personal investment decisions in real estate in Nairobi count (Doctoral dissertation, University of Nairobi).
[32.] Financial Express Bureau, November 24,2020
[33.] Atkinson, A., & Messy, F. A. (2012). Measuring financial literacy: Results of the OECD/International Network on Financial Education (INFE) pilot study.
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