Determinants of Financial Literacy and Investment Decisions Among Generation Z in Indonesia

Abstract

This study examines the major determinants associated with financial literacy and investment choices among Generation Z in Indonesia, a generation of digital natives experiencing severe financial management issues. Utilizing a quantitative approach with multiple linear regression, the research analyzes data from 315 respondents across ten Indonesian provinces to explore the relationships between demographic factors, financial attitudes, behaviors, and knowledge. The findings indicate that demographic variables alone are inconsistent predictors of financial literacy, with gender emerging as the only significant partial predictor (t=-2.380). Instead, a combination of cognitive and behavioral factors, specifically financial knowledge (t=3.352) and access to information (t=3.274), are significantly associated with investment activity, while Model 1 demonstrates the strongest overall fit (F=43.291, R=0.749). The study reveals a notable behavioral gap where responsible daily money management does not necessarily translate into increased investment growth, highlighting income as a critical determinant for market participation. While Generation Z benefits from high digital literacy and easy access to diverse financial products via social media, they often struggle with the "YOLO" (You Only Live Once) mentality, which prioritizes immediate spending over long-term saving and retirement planning. The research offers essential insights for financial institutions and regulators seeking to close the literacy-inclusion gap and empower the forthcoming generation of Indonesian investors.

Keywords: financial literacy; financial knowledge; investment decision; generation.

Introduction

In high-income economies, account ownership is almost universal among adults. In contrast, the majority of individuals without accounts dwell in low- and middle-income economies, with over 650 million adults, or 53% of them, living in only eight countries: Bangladesh, China, the Arab Republic of Egypt, India, Mexico, Nigeria, and Pakistan and also Indonesia (World Bank, 2025). According to the data, Indonesia is underperforming compared to other Southeast Asian nations such as Thailand (77.7%), Malaysia (66.7%), the Philippines (26.5%), and Vietnam (21.4%). This suggests that there are strategies to enhance Indonesia's financial sector, hence increasing access to financial institutions for a greater number of individuals. Furthermore, merely 52% of the 19.6% of Indonesians possess access to formal financial services offered by established financial organizations. Low-income households account for 21% of the 52% of households served by financial institutions (Yuneline et al., 2021). Many countries, especially in Southeast Asia (ASEAN), are still experiencing difficulties in financial literacy. According to Findex data, 1.7 billion people were unbanked in 2017, or make up more than 40% of all adults worldwide. Therefore, financial literacy is critical in increasing financial inclusion, according to S&P's global financial literature survey. Financial inclusion can be used as a policy tool to help achieve the Sustainable Development Goals (V. Y. Putri & Nurhayati, 2023). A better understanding of finance is a major issue today. Every year, the world, including Indonesia, makes small steps toward economic progress. One thing that has changed is how people handle their money. Because of this, people need to know how This study examines the major determinants associated with financial literacy and investment choices among Generation Z in Indonesia, a generation of digital natives experiencing severe financial management issues. Utilizing a quantitative approach with multiple linear regression, the research analyzes data from 315 respondents across ten Indonesian provinces to explore the relationships between demographic factors, financial attitudes, behaviors, and knowledge. The findings indicate that demographic variables alone are inconsistent predictors of financial literacy, with gender emerging as the only significant partial predictor (t=-2.380). Instead, a combination of cognitive and behavioral factors, specifically financial knowledge (t=3.352) and access to information (t=3.274), are significantly associated with investment activity, while Model 1 demonstrates the strongest overall fit (F=43.291, R=0.749). The study reveals a notable behavioral gap where responsible daily money management does not necessarily translate into increased investment growth, highlighting income as a critical determinant for market participation. While Generation Z benefits from high digital literacy and easy access to diverse financial products via social media, they often struggle with the "YOLO" (You Only Live Once) mentality, which prioritizes immediate spending over long-term saving and retirement planning. The research offers essential insights for financial institutions and regulators seeking to close the literacy-inclusion gap and empower the forthcoming generation of Indonesian investors. Figure 1.Indonesia's Financial Literacy and Inclusion Index (Otoritas Jasa Keuangan & Badan Pusat Statistik, 2025a) to handle their money. Financial literacy means knowing how to use your money wisely. Individuals frequently struggle with financial management not due to insufficient income, but rather due to a deficiency in understanding on the effective management and allocation of their finances (Anjani & Darto, 2023). According to the 2025 SNLIK statistics, Indonesia's financial inclusion index was 92.74% for the DNKI Coverage Method and 80.51% for the Sustainability Method. This indicates that only 80 individuals for the Sustainability Method and 92 individuals for the DNKI Coverage Method are financially included out of 100 individuals between the ages of 15 and 79. Additionally, Indonesia's sharia financial literacy score was 13.41% for both the Sustainability Method and the DNKI Coverage Method, while its conventional financial inclusion index was 79.71% for the Sustainability Method and 92.61% for the DNKI Coverage Method (Otoritas Jasa Keuangan & Badan Pusat Statistik, 2025b). A consistent influx of new participants serves as a positive metric for the market's evolution and maturity. As the Indonesian capital market continues to strengthen, it creates a more compelling environment for further investment (Fitria et al., 2019; Ningtyas et al., 2024). The financial literacy index for the age categories 26–35, 18–25, and 36–50 is the highest, with composite scores of 74.04%, 73.22%, and 72.05%, respectively. The conventional literacy indices are 74.04%, 73.22%, and 72.05%, whereas the sharia literacy indices are 51.82%, 50.80%, and 49.22%. The 15–17 and 51–79 age groups have the lowest financial literacy indices, with composite literacy scores of 51.68% and 54.55%, respectively. The traditional literacy level is 51.68% and 54.53%, while the sharia literacy index is 30.22% and 29.92% (Otoritas Jasa Keuangan & Badan Pusat Statistik, 2025a). While the scope of Generation Z (Gen Z) is based on the Figure 1., divided into two categories: 15-17 years old and 18-25 years old. In Composite category the percentage for 15- 17 years old is 51.68% and 18-25 years old is 73.22%. In the Conventional Category, the same figure can be seen with the 15–17-year-old group at 51.68% and the 18–25-year-old group at 73.22%. While in the Sharia category, the percentage is lower than the two previous categories, the 15–17-year-old group is 30.22% and the 18–25-year-old group is 50.80%. This is especially important for Generation Z, where the 15-17 age group had the lowest composite literacy score among all categories (51.68%) and the 18-25 age group, despite being within the same generational cohort, scored much higher at 73.22%. This significant intra-generational gap suggests that the financial literacy of Gen Z is not uniformly low, but highly sensitive to life stage and exposure, and thus the need to identify the specific demographic, cognitive or behavioural determinants that associate this divergence and shape their subsequent investment decisions. The financial landscape of Generation Z varies. As a generation of digital natives, these individuals exhibit a high degree of technological fluency, navigating online platforms and fintech ecosystems with innate ease. However, this convenience also exposes them to a wide variety of financial products and services. To make the right investment decisions in this fast-paced environment, Generation Z must have the knowledge to navigate these choices wisely. As Generation Z increasingly seeks to build wealth through investing, financial literacy becomes essential for navigating risks and opportunities effectively, since informed investors are better equipped to make sound investment choices (Candera et al., 2026; Gusti et al., 2024). Generation Z possesses effortless access to information and various opportunities, hence providing them with numerous life choices. Generation Z subscribes to the YOLO (You Only Live Once) philosophy, emphasizing current living with little regard for future consequences. This can be seen from Generation Z's decision to take a vacation instead of saving for retirement because they believe that retirement is still far away. According to Karma's credit research, 39% of Generation Z had debt to keep up with the trend of their community in 2018, and according to IDN, Research Institute's 2019 research, the allocation of savings from income was only 10.17% in Generation Z. Based on Future of Money research conducted by Luno in collaboration with Dalia Research, as many as 69% of Generation Z do not have an investment strategy (Laturette et al., 2021). Improving financial understanding among Generation Z is becoming increasingly essential given the ongoing global economic changes (HS & Indriayu, 2017). Generation Z needs to understand the basic concepts in money management, investment, financial planning, and understand financial risks. This will help them be better prepared for financial challenges as they enter the workforce. In addition, digital literacy is also the key to facing the current digital era (Negara et al., 2022). Investing is risky, and Generation Z is more likely to make rash decisions and lack the knowledge they need to avoid financial disasters if they don't know enough about money. To lower risk and get the most out of your investments, you need 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% Composite Conventional Sharia Financial Literacy Index 15 - 17 years old 18 - 25 years old 26 - 35 years old 36 - 50 years old 51 - 79 years old to understand risk tolerance, diversification, and asset classes. Generation Z can set and reach their financial goals, like saving for a house, paying for college, or getting ready for retirement, because they know how to manage their money. This gives them a plan for how to succeed. Therefore, it is imperative to investigate the financial literacy levels of Generation Z, alongside the underlying determinants that dictate their investment behavior. While prior studies have examined financial literacy and investment determinants separately among Indonesian millennials and the general population (Fitria et al., 2019; Zahwa & Soekarno, 2023), this study contributes to the literature in three ways. First, it focuses on the Generation Z respondents in ten Indonesian provinces, thus capturing intra-generational variation that is usually overlooked in studies that treat Gen Z as a homogenous cohort. Second, it employs a combined demographic and cognitive–behavioral model across five comparative regression specifications, allowing a direct test of whether demographic identity or cognitive–behavioral capacity better explains financial literacy and investment outcomes. Third and most importantly, this study finds and confirms a behavioural gap, i.e., a pattern in which disciplined daily financial behaviour does not translate into investment participation, but financial knowledge and information access do. This distinction has not been explicitly tested in the context of Indonesian Generation Z, and it provides a more nuanced understanding than literacy scores alone. The purpose of this study is to find the determinants of financial literacy index and investment decisions of Generation Z in Indonesia.

Methods

The present study follows a quantitative research design, employing multiple linear regression to test hypotheses related to all proposed determinants (Creswell, 2009). The research is an exploratory descriptive survey. The study investigates the unique financial behaviors of Generation Z, a generation viewed as financially conservative but digitally connected natives who may paradoxically lack crucial emotional and practical financial skills (İNCE, 2020; Octrina et al., 2023). The population of the study is the generation Z in Indonesia. The respondents were selected by purposive sampling technique through online survey distributed across ten provinces in Indonesia. The survey link was opened by 340 people, and 315 of them responded fully and validly. The 25 responses were discarded due to incomplete answers or because the respondents were not within the Generation Z age range (15–26 years old). The final sample of 315 respondents allows for a robust test of the “behavioral gap” where the daily financial responsibility does not always translate into investment growth. Respondents come from different provinces, so the “Net Generation” and its different levels of financial socialization are represented (Firli & Fanesa, 2022). The study was conducted in 10 provinces in Indonesia including major urban and semi-urban areas. This setting enables the full view of the Indonesian financial landscape, considering the gap between financial literacy (knowledge) and financial inclusion (the accessibility of services) in the recent domestic studies (Viana et al., 2021). The main instrument employed in this study was a detailed validated questionnaire, designed to capture the multi-dimensional nature of financial decision making. The instrumentation was split into a number of specialized thematic scales 1. Financial Literacy: Financial literacy encompasses a fundamental understanding of core economic concepts, ranging from the ability to perform interest calculations to the mastery of essential fiscal competencies such as budgetary management and resource allocation (Indrastoto et al., 2023). The scale was developed based on the framework of (Asfira et al., 2020; V. Y. Putri & Nurhayati, 2023) in which literacy is the basic knowledge needed to achieve long-term prosperity. It specifically measures two different dimensions namely the Knowledge Dimension which measures the theoretical collection of financial information and the Application Dimension which measures the individual’s practical ability and confidence in the correct use of financial products (Primatami & Primadhita, 2020; L. Putri et al., 2021). The two measurements allow the instrument to distinguish between information and long-lasting useful actions (Fanta & Mutsonziwa, 2021; Nogueira et al., 2025). 2. Financial Attitude and Behavior Scales The instrument includes a Financial Behavior scale, defined as the use of psychology in trading and financial activities (Fitria et al., 2019), to understand the psychological undercurrents of money management. This reflects how respondents react to information via actions like budgeting, saving, and loan management (Mirati et al., 2024; Nogueira et al., 2025). Moreover, the Financial Attitude scale was used to measure the internal values and beliefs of the participants, which were focused on their ability to exercise self-control, patience, and risk-awareness (Dewi et al., 2020; HC & Gusaptono, 2020). This is in accordance with the notion that positive financial mentality is a precondition for good management behavior (Anjani & Darto, 2023). 3. Investment Decision-Making Framework: This component of the tool evaluated activities or policies implemented by individuals to invest their assets, such stocks, bonds, or mutual funds, with the anticipation of future returns (Nugraha et al., 2022; L. Putri et al., 2021). The decision is based on three completely subjective factors: risk perception, knowledge of new procedures, and predicted expenses. In order to make wise investment selections, investors must be aware of the potential and dangers involved; therefore, judgments should not be taken in a hurry (Pricillia et al., 2025). The instrument was developed based on the criteria of (Adwitiya & Abdurrahman, 2025) and comprised factors such as the recognition of risk-return trade-offs and selection of vehicles that match personal risk tolerance. The scale also considered the mental, emotional, and social aspects that naturally predict these choices (HC & Gusaptono, 2020). 4. Demographic and Informational Metrics: Finally, the instrument gathered essential demographic information like age, sex, education, and income based on the pre- established notion that age and experience are crucial in the maturity of investment (Nga & Ken Yien, 2013; Zahwa & Soekarno, 2023). The tool also took into account the role of Modern Information and how the presence of context- specific knowledge and expert advice can result in a decrease in mistakes in decision-making, even when dealing with people without prior financial expertise. Data was collected quantitatively using an online survey platform. Participants were given a consent form and clear instructions prior to completing the structured questionnaire. The structured questionnaire consisted of six construct groups as illustrated in Table 1. Demographic variables (Q1–Q5) captured socio-economic background of respondents using nominal and ordinal scales. Financial Attitude (Q6–Q9) and Financial Behaviour (Q10–Q16) were measured in terms of respondents’ orientations and habitual money management practices using a five-point Likert scale. Financial Knowledge (Q17–Q24) was measured using a true-or-false, dichotomous format to capture objective knowledge of economic and investment concepts. Three items (Q19, Q20, Q21) were kept as valid measures after iterative validity testing. The information access related to the capital market was captured Table 1. Operational Variables Factor Indicators (Summary) Item No. No. of Items Scale Reference Demographic Age, gender, education level, education background, monthly income/allowance Q1–Q5 5 Nominal/Ordinal (Nga & Ken Yien, 2013; Zahwa & Soekarno, 2023) Financial Attitude Money mindset future- oriented, saving overspending, belief in investing, importance of financial planning Q6–Q9 4 Likert 1–5 (Dewi et al., 2020; HC & Gusaptono, 2020) Financial Behavior Prudent spending, debt payment discipline, personal finance monitoring, long-term planning, awareness of financial constraints, concern for needs, risk tolerance in saving/investing Q10– Q16 7 Likert 1–5 (Fitria et al., 2019; Mirati et al., 2024) Financial Knowledge Knowledge of inflation, interest rates, loan behaviour, value of savings, investment products (mutual funds, stocks), risk-return relationship, diversification Q17– Q24 8 Dichotomous (True/False) (Fanta & Mutsonziwa, 2021; Nogueira et al., 2025) Information (Capital Market) Awareness of capital market concepts Awareness of information sources Familiarity of investment products Investment participation status Investment duration Q25– Q29 5 Nominal/Ordinal (Fitria et al., 2019) Investment Decision Identifying risk-return trade- offs, selecting investment vehicles consistent with risk tolerance, and psychological and social factors predicting investment decisions Q34– Q39 6 Likert 1–5 (Adwitiya & Abdurrahman, 2025; HC & Gusaptono, 2020) Source: Processed data (2026) by nominal and ordinal items reflecting the respondents’ awareness, information sources and investment participation status (Q25-Q29). Finally, Investment Decision (Q34–Q39) was measured using a five-point Likert scale, which focused on risk-return recognition, vehicle selection, and psychological factors that predict investment decisions. The analysis was carried out in two rigorous steps: 1. Instruments purification: Validity and reliability assessments were performed on the data to verify the accuracy of the respondents' responses. 2. Model Testing: The study used a structural model with the use of T-tests, F-tests and multicollinearity tests to investigate the relationship between variables. The approach enables the predictive model to examine the association between financial knowledge and long-term financial behaviours, as proposed by (Fanta & Mutsonziwa, 2021). Hypothesis Development The author's literature review elucidates various factors that substantially associated with the financial literacy index and investment decisions. (Fitria et al., 2019) found that how much education someone has and how much experience they have with investing predict their decisions about buying stocks. Based on the results of the previous research and the research questions, the following hypotheses are presented: 1. Model 1 Financial literacy is associated with certain demographic, socioeconomic and psychological factors such as age, gender, education, income, and parental influence. (Kaiser & Menkhoff, 2017) pointed out that financial literacy programs are most effective when they take into account both an individual’s background and cognitive abilities, therefore, a combined model such as Model 1 is theoretically the strongest predictor. H1: Age, gender, education level, education background, income, financial attitude, financial behavior, financial knowledge, and information have a significant association with the Financial Literacy Index. 2. Model 2 This model tests if “demographic identity” alone is enough to explain literacy. (Kaiser & Menkhoff, 2017) found a link between financial literacy and gender equality, but demographics are not a cause. This model thus extends the argument that background variables are moderating rather than determining variables. H1: age, gender, education level, Education background, and income factor has significant association with financial literacy index 3. Model 3 The Theory of Planned Behaviour (Ajzen, 2012) indicates that attitude, subjective norms and perceived behavioural control constitute behavioural intention including investment intention. This means cognitive factors like financial knowledge associated with perceived behavioural control, and demographics such as income associated with the conversion of control to action. H1: age, gender, education level, Education background, income, financial attitude, financial behavior, financial knowledge and information factor has significant association with investment decision. 4. Model 4 The idea that high-income investors should invest large amounts of money is a widely accepted idea that creates a barrier for low-income individuals to participate in the stock market because they have less capital. Model 4 is a theoretical test of whether ‘financial capability’, such as income, is the main gatekeeper of the capital market, regardless of an individual’s knowledge. H1: age, gender, education level, education background, and income factor has significant association with Investment decision. 5. Model 5 The Theory of Planned Behaviour applied to investment demonstrates that attitudes, subjective norms, perceived control and past behaviour predict investment intentions. We test the extent to which market knowledge and access to information encourage entry into the capital market independently of demographic conditions in Model 5, where we isolate the strength of purely cognitive factors. H1: financial attitude, financial behavior, financial knowledge and information factor has significant association with investment decision

Table 1. Operational Variables

Result and Discussion

The demographic profile of the 315 respondents is presented in Table 2. The respondents were primarily from West Java (25.0%), East Java (23.6%) and DKI Jakarta (20.7%), with smaller proportions from Banten, Lampung, Central Java and four other provinces, showing a sample concentrated in major urban and semi-urban centers of Indonesia. The sample was biased towards the 19–22-year- old range, with the largest single group being 21 year olds (24.4%). This is consistent with the study’s focus on the upper segment of Generation Z, who are more likely to have entered higher education or early employment and therefore have more exposure to financial decision making. This concentration is methodologically relevant, as respondents aged between 17 and 26 are at the tipping point where financial literacy is translated, or not translated, into actual investment behaviour, and therefore are an appropriate population for testing the demographic and cognitive- behavioral determinant models proposed in this study. The Figure 2 below shows how many people answered questions about investment items that the participants understood. A total of 252 respondents understood and were knowledgeable about stock investing products. One hundred eighty-four people who answered the survey know about mutual funds. Ninety-eight people who answered the question know about bonds. Nineteen people who answered the question know what sukuk are. Ten people who answered the survey knew about exchange-traded funds (ETFs), and ten people who answered the survey knew about derivative investing products. According to Figure 3, respondents utilize a diverse array of channels to acquire financial and capital market knowledge. Social media emerges as the primary source of information, utilized by 207 individuals. This is followed by formal educational institutions, including schools, universities, and specialized capital market programs, which serve as a knowledge base for 133 respondents. Additionally, electronic media remains a significant contributor, providing information to a cohort of 114 participants. Information sourced from websites was used by 99 respondents. Meanwhile, 64 respondents relied on relatives as a source of information. Printed media was used by 38 respondents. Finally, a small portion of respondents, totaling 11 people, gained information from other sources. Validity Test The first step in analyzing the data was to run validation tests on the results of a questionnaire with 19 questions that 315 people answered. Using SPSS software, we checked the validity by comparing the r table values to the r count values. Most of the survey items were statistically sound. In particular, the variables related to Financial Attitude (Q6–Q9), Financial Behavior (7 items), and Decision-Making Attributes (Q34–Q39) all passed the critical threshold of 0.111, which means that their constructs are valid for further analysis. On the other hand, the Financial Knowledge variable was developed by an iterative item deletion process across six testing stages, with one item deleted at each stage (from the lowest item-total correlation to the highest) as seen on the Table 3. In the first test, the correlations for six of the eight items (Q17, Q18, Q21, Q22, Q23, Q24) were below the r-table value of 0.111, the lowest correlation being obtained by Q24 at -0.370. When items were removed one by one (Q24, then Q18, Q23, Q17 and finally Q22), the correlation values of the remaining items increased as the item-total correlation is recalculated with respect to the remaining item set. Only Q19, Q20 and Q21 met the validity criterion after 6 rounds. The final r-count values for these were 0.419, 0.395 and 0.349 respectively. In the end, three valid items (Q19, Q20, and Q21) were left, which proved that 19 of the 25 total questions were valid measures of the study's reliability. Although six items were removed from the Financial Knowledge construct, the three remaining items (Q19, Q20, Q21) were retained because they jointly represent the two fundamental dimensions of financial knowledge measurement: theoretical understanding of financial concepts and practical ability to apply financial information (Fanta & Mutsonziwa, 2021; Nogueira et al., 2025). Iterative item deletion was performed using corrected item-total correlation thresholds. Removal of any further item was found to further reduce construct representativeness, without improving statistical reliability. Reliability Test The last stage following the validity assessment is to conduct a reliability evaluation of the questionnaire items employed to evaluate various constructs. Consistency in responses to a questionnaire item indicates reliability. SPSS is employed to evaluate reliability, utilising Cronbach's Alpha values for validation. A value greater than 0.6 signifies reliability. The Financial Attitude variable in this study demonstrated a Cronbach's Alpha of 0.738, showing its reliability. The Financial Behaviour variable achieved a score of 0.685 in the assessment. The Financial Knowledge variable registered a value of 0.578, just below the acceptable threshold. Further research reveals that the elimination of items would diminish this value, indicating that the current set remains optimal, albeit somewhat below the threshold. The Cronbach’s Alpha of Financial Knowledge variable is 0.578, which is slightly below the conventional threshold of 0.6. It is the limitation of this study. Previous research has found that knowledge-based constructs with factual true-or-false item formats have lower internal consistency scores than attitude or behaviour scales because respondents’ correct responses depend on objective knowledge rather than on consistent attitudinal tendencies (Fanta & Mutsonziwa, 2021; Nogueira et al., 2025). The removal of additional items further reduced the alpha value, and the three-item set represents the best available configuration. Findings relating to this construct should be interpreted with appropriate caution. The Decision-Making Attributes variable had the highest reliability, with a Cronbach’s Alpha of 0.863, which is indicative of high internal consistency. It was found that three of the four variables (Financial Attitude, Financial Behaviour and Decision-Making Attributes) were internally consistent with high reliability above the 0.6 Figure 2. Investment Product Preferences Figure 3. Respondent’s Information Source benchmark, apart from Financial Knowledge which was marginally reliable (α = 0.578). Based on the justification provided above, all four constructs were kept for further analysis, with results pertaining to the Financial Knowledge variable interpreted with appropriate caution. Multicollinearity Test Employing multiple linear regression as the primary analytical framework, this study utilized SPSS software to conduct a robust multicollinearity diagnostic. The assessment relied on Tolerance values and the Variance Inflation Factor (VIF) to ensure model integrity. Specifically, the absence of multicollinearity was confirmed by Tolerance values exceeding 0.10 and VIF scores remaining consistently below the threshold of 10. The examination of Models 1, 2, and 3 revealed that all independent variables, including demographic aspects (age, gender, education, income) and financial indicators (attitude, behaviour, knowledge), satisfied the necessary statistical criteria. The Tolerance values were higher than 0.1, and the VIF scores ranged from 1.010 to 1.178. The results showed that the independent variables are not significantly related, which means that the regression coefficients will be stable and reliable. The Pearson Correlation method was also used in the study to double- check that the variables were independent. The correlation coefficients between all pairs of variables stayed below 0.7. This supports the idea that the five regression models don't have any problems with multicollinearity. This suggests that we may depend on the results illustrating the association between financial knowledge and demographic factors on investment decisions. Heteroscedasticity Test The heteroscedasticity test was performed using the SPSS and the Spearman technique for testing the variance of residuals of the five regression models. The 2-tailed level of significance was 0.1. P-values above the level indicate no heteroscedasticity. Models 1, 3, 4 and 5 passed the heteroscedasticity test with all the independent variables having p-values above 0.10 indicating that the residual variance was stable and therefore the reliability of their regression estimates was supported. The inferences of this study are mainly based on these four models. In contrast, Model 2 failed the heteroscedasticity test as the demographic predictors (Age, Gender, Educational Background, and Income) had significance values less than 0.10, indicating that residual variance was not constant. The authors recognise that, in terms of methodology, the preferred corrective approach would be to re-estimate using Weighted Least Squares (WLS) or heteroscedasticity-robust standard errors (HC standard errors). Table 2. Demographic Profile of Respondents (N = 315) Characteristic Category n % Province Jawa Barat 79 25.08 Jawa Timur 74 23.49 DKI Jakarta 65 20.63 Banten 25 7.94 Lampung 23 7.30 Jawa Tengah 22 6.98 Sulawesi Barat 11 3.49 Sulawesi Selatan 7 2.22 DI Yogyakarta 5 1.59 Sumatera Utara 4 1.27 Age 17 years 23 7.30 18 years 15 4.76 19 years 41 13.02 20 years 53 16.83 21 years 77 24.44 22 years 50 15.87 23 years 23 7.30 24 years 10 3.17 25 years 17 5.40 26 years 6 1.90 Total 315 100.0 Table 3. Item-Total Correlation for Financial Knowledge Construct Question r-count (Stage 1) r-count (Final, Stage 6) Explanation Q17 0.045 removed (Stage 2) Invalid Q18 -0.074 removed (Stage 3) Invalid Q19 0.134 0.419 Valid Q20 0.142 0.395 Valid Q21 0.015 0.349 Valid Q22 0.092 removed (Stage 6) Invalid Q23 0.054 removed (Stage 4) Invalid Q24 -0.370 removed (Stage 1) Invalid However, given that Model 2 also does not achieve simultaneous statistical significance in the F-test (F = 1.556, p = 0.172), its substantive conclusions remain unchanged regardless of the estimation method used: demographic variables alone do not constitute a statistically meaningful predictor set for the Financial Literacy Index in this sample. Therefore, we acknowledge the heteroscedasticity in Model 2 as a methodological limitation, but do not ignore the results entirely, instead interpret them with due caution. Multiple Regression Test The relationship between the independent factors and the Financial Literacy or the Investment Decisions was tested for the five regression models using SPSS. A Sig. F value less than 0.10 represents a statistically significant model fit. Model 1 has the highest F-calculated value (F = 43.291, Sig. F = 0.000) and the highest correlation (R = 0.749) which confirms it is the strongest predictor of the Financial Literacy Index. By contrast, Model 2 was not statistically significant (F = 1.556, Sig. F = 0.172) and had a weak correlation (R = 0.157). Models 3 and 5 had moderate to strong correlations (R = 0.355 and R = 0.326 respectively). Model 4 was significant at 10% level (F = 2.193, Sig. F = 0.055) but with a relatively weak correlation (R = 0.185). In sum, the outcomes suggest demographic factors are weak predictors of financial literacy and investment decisions, whereas behavioural and cognitive factors provide a stronger explanatory power. F-test (simulant) To evaluate the collective significance of the relationship between independent and dependent variables within the multivariate linear regression model, the researchers employed the F-test. Following exploratory survey-based research on behavioural constructs (Hair et al., 2010) is a significance level α=0.10 used throughout. Where relevant, results that also meet the more conservative p < 0.05 criterion are explicitly indicated. Such a result indicates a statistically significant association, thereby validating the overall fit of the regression model. The outcomes of the multiple linear regression analysis demonstrate that although the majority of models are statistically significant at the 10% level, the association between predictors fluctuates according on the dependent variable (see Table 4). Models 1, 3 and 5 rejected the null hypothesis at the strict 5% level of significance (p= 0.000, 0.000 and 0.000 respectively). This implies strong simultaneous associations between the sets of predictors and their respective dependent variables. Model 4 failed to reject the null hypothesis at 10% level (F = 2.193, p = 0.055) which indicates a statistically marginal association of demographic factors with Investment Decision and hence should be interpreted with caution. Model 2 did not become significant even at the 10% level of significance (F = 1.556, p = 0.172). This confirms that demographic variables, in and of themselves, do not constitute a meaningful predictor set for the Financial Literacy Index. T-test (Partially) This study employed the t-test to assess the individual significance of the relationships between the independent variables and the dependent variable within the multiple linear regression framework (see Table 5). Statistical significance is confirmed if the p-value is below the 0.1 threshold and the calculated t-statistic exceeds the critical t-value. Such a result demonstrates that the predictor variables exert a statistically significant association within the established regression equation. The statistical analysis indicates that demographic and behavioural factors have limited explanatory power, whereas cognitive factors are much more powerful in explaining financial outcomes. In Model 1, none of the demographic predictors (Age, Gender, Education Level, Education Background, Income) were statistically significant at the partial level (all t-values < 1.6499, p > 0.10). In Model 2, Gender was the only significant demographic predictor (t = -2.380, p < 0.10), indicating a persistent literacy gap between male and female respondents despite educational attainment. In contrast, Income was a significant predictor for Investment Decisions in both Model 3 (t=1.730, p=0.085) and Model 4 (t=2.806, p=0.005) acting as a gatekeeper variable. These results point to a greater association between income level and investment participation than financial literacy itself. Specifically, income was the only demographic variable that was significantly related to Investment Decision in both Model 3 (B = 0.117, t = 1.730, p = 0.085) and Model 4 (B = 0.191, t = 2.806, p = 0.005). This result indicates that financial capacity may be a boundary condition for capital market entry among Generation Z respondents, although a cross-sectional design constrains causal interpretation. Interestingly, although Model 1 was strongly significant at the same time (F = 43.291, p = 0.000, R = 0.749), none of the nine predictors was significant in the partial t-test. This pattern should be interpreted with caution. The Financial Knowledge variable has a large unstandardised coefficient (B = 0.995) which is substantially larger than all other predictors in the model. This means it may be absorbing a disproportionate Table 4. F-Test Results Model Dependent Variable Independent Variables F-calc F-table Sig. Decision 1 Financial Literacy Demographics + Financial Factors 43.291 1.75 0.000 H1 Accepted 2 Financial Literacy Demographic Factors only 1.556 1.91 0.172 H0 Accepted 3 Investment Decision Demographics + Financial Factors 4.884 1.75 0.000 H1 Accepted 4 Investment Decision Demographic Factors only 2.193 1.91 0.055 H1 Accepted 5 Investment Decision Financial Factors only 9.230 1.97 0.000 H1 Accepted Source: Processed data (2026) Table 5. T-Test Results Model Predictor B t p Model 1 DV: Financial Literacy Index (F = 43.291, R = 0.749, p = 0.000) 1 Age 0.000 0.000 > 0.10 Gender 0.008 0.008 > 0.10 Education Level -0.028 -0.028 > 0.10 Education Background 0.000 0.000 > 0.10 Income -0.011 -0.011 > 0.10 Financial Attitude 0.003 0.003 > 0.10 Financial Behavior 0.015 0.015 > 0.10 Financial Knowledge 0.995 0.995 > 0.10 Information 0.015 0.015 > 0.10 Model 2 DV: Financial Literacy Index (F = 1.556, R = 0.157, p = 0.172) 2 Age 0.004 0.380 > 0.10 Gender* -0.107 -2.380 0.018** Education Level 0.028 0.314 > 0.10 Education Background -0.001 -1.216 > 0.10 Income 0.002 0.078 > 0.10 Model 3 DV: Investment Decision (F = 4.884, R = 0.355, p = 0.000) 3 Age 0.014 0.433 0.665 Gender* 0.259 1.843 0.066* Education Level -0.129 -0.462 0.644 Education Background 0.001 0.318 0.751 Income* 0.117 1.730 0.085* Financial Attitude* 0.226 1.989 0.048** Financial Behavior -0.185 -1.400 0.163 Financial Knowledge* 0.817 3.352 0.001** Information* 0.118 3.274 0.001** Model 4 DV: Investment Decision (F = 2.193, R = 0.185, p = 0.055) 4 Age 0.015 0.436 0.663 Gender 0.142 0.990 0.323 Education Level -0.088 -0.303 0.762 Education Background -0.002 -0.593 0.553 Income* 0.191 2.806 0.005** Model 5 DV: Investment Decision (F = 9.230, R = 0.326, p = 0.000) Financial Attitude* 0.266 2.393 0.017** Financial Behavior -0.224 -1.705 0.089 Financial Knowledge* 0.733 3.067 0.002** Information* 0.128 3.655 0.000** Note: B = unstandardized regression coefficient; t = t-statistic; p = two-tailed significance value. Predictors marked with * are statistically significant. * p < 0.10; ** p < 0.05. t-table = 1.6499 (Models 1, 3) and 1.6498 (Models 2, 4, 5), α = 0.10. Standard errors are omitted from this table as t-statistics and exact p-values reported here provide sufficient information for independent verification of hypothesis decisions; standard errors can be derived from B/t where required. Source: Processed data (2026) share of the explained variance. However, classical multicollinearity is not present, as VIF values for all predictors remained below 10 and Pearson correlations between pairs of variables were below 0.7. A more cautious interpretation is thus that the simultaneous significance of Model 1 reflects the collective, non-redundant contribution of all nine predictors rather than a suppression effect per se. The lack of statistically significant partial effects for individual variables may be indicative of shared, non-collinear explanatory processes, where each predictor makes a small contribution to the variance explained when the other variables are held constant, a pattern that is consistent with a complex, multi- determined outcome such as financial literacy. Further research using standardised beta coefficients and dominance analysis could more accurately break down the relative contribution of each predictor. Conversely, Financial Knowledge, Information Access, and Financial Attitude consistently demonstrated superior predictive power regarding investment activity compared to all other variables. Specifically, Models 3 and 5 identified knowledge and access to information as the most critical determinants in forecasting an individual's propensity to invest. A person's propensity to invest is more significantly shaped by their market comprehension and psychological preparedness than by their everyday routines or educational rigor. This is similar with the research conducted by (Mirati et al., 2024) which indicated that financial awareness and financial conduct positively associated with investment decisions. Interestingly, Financial Behavior (such as budgeting) consistently failed to reach statistical significance. This highlights a notable behavioral gap, suggesting that disciplined daily money management does not inherently translate into investment growth. While individuals may demonstrate proficiency in short-term resource allocation, this does not automatically catalyze the transition toward long-term wealth accumulation or market participation. Such a finding underscores the distinction between frugality and active capital appreciation. Instead, it's the combination of certain cognitive empowerment and market data that makes a person go from saving to investing. Model 1: The Synergy of Demographics and Cognitive Factors on Financial Literacy Model 1 looked at how demographic factors and behavioral-cognitive variables predicted the Financial Literacy Index at the same time. The results show a very strong link (F = 43.291, p < 0.1), which means that these factors work well together to help people understand money. This backs up what (Kaiser & Menkhoff, 2017) found: that financial education programs work best when they take into account a person's background and their mental state. The model is strong overall, but the high F-value compared to other models shows that literacy is not just about who you are, but also about what you know and how you feel about money. Model 2: The Limitation of Demographic Identity In Model 2, which focused solely on the five demographic variables (Age, Gender, Education Level, Educational Background, and Income) related to Financial Literacy, the null hypothesis was accepted (F = 1.556, p = 0.172). This finding is significant as it suggests that "demographic identity" is an insufficient predictor of literacy for Generation Z. This aligns with the conclusions of (Yanti & Suryadi, 2024), which demonstrate that traditional demographic indicators often fail to accurately reflect financial proficiency in the digital age. Gender was identified as the only significant partial predictor, aligning with the findings of (Setiana et al., 2023), which indicated that localized socio-cultural norms in Indonesia persist in creating distinct gendered pathways in financial socialization, despite similar educational backgrounds. Model 3: The Multi-Dimensional Drivers of Investment Model 3 indicated that a combination of demographic and cognitive characteristics significantly predicts Investment Decisions (F = 4.884, p < 0.1). The preliminary findings indicated that life-stage variables such as age and overall education were not significant, however Financial Knowledge and Information emerged as substantial determinants. This supports the Theory of Planned Behavior (Ajzen, 2012), which posits that perceived behavioral control, derived from information, is a fundamental predictor of action. The importance of income in this model further substantiates that although literacy may be elevated among various populations, investment participation is more strongly associated with sufficient financial means than with literacy levels alone, a notion endorsed by (Andini et al., 2025). Model 4: Demographic Factors as Investment Gatekeepers Model 4 looked at different demographic factors to see how they predicted Investment Decisions, and it found a significant result at the same time (F = 2.193, p = 0.055). Still, the partial analysis showed that Income was the only important factor for this group (t = 2.806). This shows a practical truth: for Gen Z, how much money they have to spend, not their age or education, determines how much they invest. The CFA Institute says that even tech-savvy kids who are very good with computers often can't get into the capital market because they don't have enough money. This model shows that the main problem this generation has to deal with is the "ability to invest" (Mottola & Munson, 2023). Model 5: The Cognitive Engine of the Capital Market Model 5 focused on behavioral and cognitive variables (Attitude, Behavior, Knowledge, and Information) and was found to be highly significant (F = 9.230, p < 0.1). Financial Knowledge and Information provided the highest coefficients, suggesting that "knowing what to do" and "having the data" are the most effective predictors of investment. Interestingly, Financial Behavior (daily habits like budgeting) was non- significant. This aligns with (Tubastuvi et al., 2024), who observed a disconnect between daily money management and long-term investment activity. It suggests that while Gen Z may be responsible with their daily cash, they require a specific cognitive "leap" and external information to move into the investment sphere. In this study we define the behavioural gap as the difference between disciplined financial behaviour on a daily basis (e.g. budgeting, debt repayment) and actual engagement in wealth-building activities over the long-term, such as investing in the capital markets. Financial Behaviour theoretically is expected to predict financial outcomes (Fitria et al., 2019; Mirati et al., 2024). However, in this study, Financial Behaviour did not predict Investment Decision (Model 3: t = - 1.400, p = 0.163; Model 5: t = -1.705, p = 0.089). Financial Knowledge and Information were still solid predictors in both models. This means that good money management in itself does not result in investment activity. There are two other reasons worth considering. First, the Financial Behaviour construct is a measure of short-term conduct rather than investment-specific actions, a possible measurement limitation rather than a true absence of behavioural association. Second, the narrow age concentration of the sample (19–22 years old) may reflect a life stage with limited disposable income, implying that the gap reflects structural income constraints in part, and not just psychological ones. Future research employing investment-specific behavioural scales or longitudinal designs may help to disentangle these explanations. Conclusion The study found that financial empowerment of Generation Z in Indonesia was associated with cognitive ability, availability of resources, and access to information rather than demographic characteristics alone. High digital literacy did not necessarily equate to sophisticated investment behaviour, as daily financial behaviour was consistently not significant across all models. Participation in the capital market appeared to be largely related to financial capacity (income) and specific financial knowledge. Implications for Theory The findings offer a possible extension of the Theory of Planned Behaviour (Ajzen, 2012) to the investment context among Generation Z. While this study did not employ a validated TPB instrument, the pattern of results is broadly consistent with TPB propositions. Financial Knowledge, which captures objective understanding of financial concepts, may partially reflect the cognitive foundation of perceived behavioural control, while Information Access, which measures breadth of capital market exposure, may function analogously to subjective norms by signalling what is socially and contextually available to act upon. Financial Attitude, meanwhile, aligns conceptually with the attitude-toward- behaviour construct. These mappings are interpretive rather than confirmatory, and the equivalence between the measured variables and TPB constructs cannot be assumed without dedicated scale validation. Future research employing validated TPB measures in the Indonesian Generation Z context would be needed to formally test this theoretical extension.

Table 2. Demographic Profile of Respondents (N = 315)

Table 3. Item-Total Correlation for Financial Knowledge Construct

Table 4. F-Test Results

Table 5. T-Test Results

Note: * p < 0.10; ** p < 0.05. Source: Processed data (2026).

Practical Implications

Based on these findings, several recommendations are made to financial institutions and regulatory bodies. In addition to basic money management, financial literacy programs need to explore asset classes, risk diversification, and the inner workings of capital markets. As the main source of information for this cohort, social media should be taken advantage of by OJK and financial institutions to spread accurate investment information. Gender-responsive financial education is also needed to address the literacy gap between male and female respondents. Furthermore, income gatekeeping effects should be reduced by expanding low-entry investment products, such as micro-investing and fractional shares.

Methodological Limitations

The results show associations rather than causality, owing to the cross-sectional design. The construct of Financial Knowledge retained only three of eight items, with marginal reliability (α = 0.578). Heteroscedasticity was also present in Model 2. Additionally, the purposive sample had a narrow age range (19–22 years), which reduces the generalisability of the findings.

References

Adwitiya, Ri., & Abdurrahman. (2025). Pengaruh Financial Literacy terhadap Investment Decision. AKUA: Jurnal Akuntansi Dan Keuangan, 4(2), 203–211. https://doi.org/10.54259/akua.v4i2.4200

Ajzen, I. (2012). The theory of planned behavior. Handbook of Theories of Social Psychology: Volume 1, 438–459. https://doi.org/10.4135/9781446249215.n22

Andini, A., Astuti, S., & Supendi, M. (2025). The Influence of Income on Generation Z Investment Decisions in South Jakarta. Jurnal Pemberdayaan Masyarakat, 4(3), 724–734. https://doi.org/10.46843/jpm.v4i3.479

Anjani, C., & Darto, D. (2023). Financial Literacy, Income and Self -Control on Financial Management Behavior of Generation Z. BASKARA: Journal of Business and Entrepreneurship, 5(2), 152. https://doi.org/10.54268/baskara.v5i2.16363

Asfira, N., Rokhmawati, A., & Fathoni, A. F. (2020). Pengaruh Financial Literacy dan Investment Experience terhadap Risk Tolerance dan Investment Decision. Jurnal Ekonomi, 27(4), 340–352.

Candera, M., Safitri, E., Kasuwara, C., & Departement, M. (2026). KNOWLEDGE TO ACTION: HOW DIGITAL FINANCIAL LITERACY SHAPES INVESTMENT DECISIONS OF GENERATION Z THROUGH FINTECH Mister. Jurnal Reviu Akuntansi Dan Keuangan, 16(02), 345–369. https://doi.org/10.22219/jrak.v16i2.42776

Creswell, J. W. (2009). Research Design: Qualitative, Quantitative, and Mixed Methods Approached. In Muqarnas (Vol. 8). https://doi.org/10.2307/1523157

Dewi, V. I., Febrian, E., Effendi, N., & Anwar, M. (2020). Financial literacy among the millennial generation: Relationships between knowledge, skills, attitude, and behavior. Australasian Accounting, Business and Finance Journal, 14(4), 24–37. https://doi.org/10.14453/aabfj.v14i4.3

Fanta, A., & Mutsonziwa, K. (2021). Financial Literacy as a Driver of Financial Inclusion in Kenya and Tanzania. Journal of Risk and Financial

Management, 14(11), 0–13. https://doi.org/10.3390/jrfm14110561 Firli, A., & Fanesa, A. (2022). The Influence of Financial Literacy and Income on Generation Z’s Interest in Using Fintech Lending: A Study in a Major City of Indonesia. Review of Integrative Business and Economics

Research, 11(2), 129–143. Fitria, Y., Rahadi, R. A., Afgani, K. F., R. Putranto, N. A., Murtaqi, I., & Faturohman, T. (2019). The Influence of Demographic, Financial Literacy and Information Factors on Investment Decision Among Millenial Generations in Bandung. European Journal of Business and Management Research, 4(6), 1–7. https://doi.org/10.24018/ejbmr.2019.4.6.152

Gusti, G. P., -, G., & -, W. (2024). the Influence of Financial Literacy on Investment Decisions on Investors in Indonesia. Malaysian Business Management Journal, 3(2), 67–72. https://doi.org/10.26480/mbmj.02.2024.67.72

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis 7th Edition. Pearson Prentice Hall.

HC, R. H. K., & Gusaptono, R. H. (2020). The Impact of Financial Literacy on Investment Decisions Between Saving and Credit: Studies on Sharia Bank Customers in the Special Region of Yogyakarta. Journal of Economics and Business, 3(4). https://doi.org/10.31014/aior.1992.03.04.291

HS, S. R., & Indriayu, M. (2017). Kajian Literasi Keuangan Pada Siswa Menengah Atas (SMA): Sebuah Pemikiran. Prosiding Seminar Pendidikan Ekonomi Dan Bisnis, 1–9.

İNCE, F. (2020). Financial Literacy in Generation Z: Healthcare Management Students. Social Mentality and Researcher Thinkers Journal, 6(36), 1647–1658. https://doi.org/10.31576/smryj.616

Indrastoto, M. R., Juwita, R., Studi Manajemen Keuangan, P., & Negeri Jakarta, P. (2023). The influence of financial literacy and psychological factors on money market mutual fund investment decisions. Journal of Applied Sciences in Accounting, Finance and Tax, 6(2), 90–99.

Kaiser, T., & Menkhoff, L. (2017). Does financial education impact financial literacy and financial behavior, and if so, when? World Bank Economic

Review, 31(3), 611–630. https://doi.org/10.1093/wber/lhx018 Laturette, K., Widianingsih, L. P., & Subandi, L. (2021). Literasi Keuangan Pada Generasi Z. Jurnal Pendidikan Akuntansi (JPAK), 9(1), 131–139. https://doi.org/10.26740/jpak.v9n1.p131-139

Mirati, R. E., Purwaningrum, E., & Masjono, A. (2024). The Influence Of Financial Literacy And Financial Behavior On Welma -Mybca Mutual Fund Investment Decisions. Ekonomi & Bisnis, 23(1), 79–89. https://doi.org/10.32722/eb.v23i1.6621

Mottola, G., & Munson, R. (2023). Gen Z and Investing: Social Media, Crypto,

FOMO, and Family. CFA Institute, (May), 1–16. Negara, A. K., Febrianto, H. G., & Fitriana, A. I. (2022). Mengelola keuangan dalam pandangan gen z. Akuntabel, 19(2), 296–304. https://doi.org/10.30872/jakt.v19i2.11176

Nga, J. K. h., & Ken Yien, L. (2013). The influence of personality trait and demographics on financial decision making among Generation Y. Young

Consumers, 14(3), 230–243. https://doi.org/10.1108/YC -11-2012- 00325 Ningtyas, M. N., Prajawati, M. I., & Munir, M. (2024). Financial Literacy and Cognitive Biases: Key Determinants of Gen Z Investment Choices Mega. Journaal of Finance and Islamic Banking, 7(1), 102–123.

Nogueira, M. C., Almeida, L., & Tavares, F. O. (2025). Financial Literacy, Financial

Knowledge, and Financial Behaviors in OECD Countries. Journal of Risk and Financial Management, 18(3), 1–15. https://doi.org/10.3390/jrfm18030167 Nugraha, R. K., Eksanti, A. P., & Haloho, Y. O. (2022). The Influence Of Financial Literacy And Financial Behavior On Investment Decision. Jurnal Ilmiah Manajemen Dan Bisnis, 8(1), 68. https://doi.org/10.22441/jimb.v8i1.13535

Octrina, F., Rizal, N. A., Krisnawati, A., & Hendayani, R. (2023). Sosialisasi literasi keuangan dan investasi bagi gen z. Jurnal Masyarakat Mandiri, 7(5), 4195–4203. Otoritas Jasa Keuangan, & Badan Pusat Statistik. (2025a). Survei Nasional Literasi dan Inklusi Keuangan (SNLIK) Tahun 2025. Otoritas Jasa Keuangan, & Badan Pusat Statistik. (2025b). Survei Nasional Literasi dan Inklusi Keuangan (SNLIK) Tahun 2025.

Pricillia, A., Tamtomo, S., Setiawan, D., Kristia, G., Budiono, S., & Ulfa, M. (2025). Journal of Advanced Studies in Management Generation Z ’ s Behavior In Investing: Unique And Odd Number of SIDs in the Indonesian Stock Market by Age. Journal of Advanced Studies in Management, 1(2), 89–106.

Primatami, A., & Primadhita, Y. (2020). Efisiensi Umkm Makanan Dengan Pendekatan Data Envelopment Analysis. Jurnal Pengembangan

Wiraswasta, 22(01), 1. https://doi.org/10.33370/jpw.v22i01.388 Putri, L., Christiana, I., Kalsum, U., & Justianti, M. (2021). The Influence of Financial Literacy on Investment Decisions during The Pandemic. Journal of International Conference Proceedings (JICP), 4(2), 301–308.

Putri, V. Y., & Nurhayati, I. (2023). Does Financial Inclusion Improve Financial Literacy? An ASEAN Cross -Country Analysis (Number 2018). Atlantis Press SARL. https://doi.org/10.2991/978-2-38476-132-6_77

Setiana, S., Debbianita, D., & Eunike, E. (2023). Financial literacy, gender, and economic behavior: The perspective of undergraduate accounting students. AKURASI, 5(3).

Tubastuvi, N., Azaria, M. J. F., Purwidianti, W., & Aryoko, Y. P. (2024). Gen Z Investment Decision: Role of Financial Literacy, Financial Behaviour, Financial Experience and Risk Tolerance. Airlangga Journal of Innovation Management, 5(4), 721–739. https://doi.org/10.20473/ajim.v5i4.61315

Viana, E. D., Febrianti, F., & Dewi, F. R. (2021). Literasi Keuangan, Inklusi Keuangan dan Minat Investasi Generasi Z di Jabodetabek Financial

Literacy, Financial Inclusion, and Investment Interest Generation Z’s in Jabodetabek. Jurnal Manajemen Dan Organisasi (JMO), 12(3), 252–264. World Bank. (2025). The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy.

Yanti, R. D., & Suryadi, E. (2024). The Influence of Financial Literacy, Financial Self-Efficacy and Fintech Payment on the Financial Behavior of QRIS Users. International Journal of Science and Society, 6(1), 367–377. https://doi.org/10.54783/ijsoc.v6i1.1019

Yuneline, M. H., Suryana, U., & Hilman, I. (2021). Perencanaan Keuangan untuk Menumbuhkan Awareness Literasi Keuangan pada Siswa SMA PMB Bandung. Warta LPM, 24(2), 239–248. https://doi.org/10.23917/warta.v24i2.12288

Zahwa, A. F. N., & Soekarno, S. (2023). The Influence of Financial Literacy, Risk

Tolerance, and Demographic Factors on Investment Decision among Generation Z and Millennial in Greater Jakarta and Greater Bandung. International Journal of Current Science Research and Review, 06(06), 3517–3527. https://doi.org/10.47191/ijcsrr/v6-i6-44