The concept of understanding the people who focus on constructing their investment decision-making processes has become timely in the contemporary, dynamic financial world, specifically, within the circles of the younger population, like Millennials and Generation Z, as they are exposed to a wide range of investment opportunities and sources of financial information (Asri et. al., 2024). Financial literacy refers to the knowledge, attitudes, and behaviours that help individuals to make sound financial decisions even in investments (Darwish, 2025).

It has been found that financial literacy has a positive impact on the level of confidence and risk assessment in people when making investment decisions (Baveja and Verma, 2024; Bustani, 2024). The role of risk perception and experience of investment experience is also to be listed as a determinant of young investor decision-making because these variables influence the degree of comfort of the new entrants to the market and their attitude towards risk (Pamungkas et. al., 2024; Asri et. al., 2024).

Due to the fast evolution of financial technology, data analytics, the functions of robo-advisors and human financial advice, algorithmic decision making (Patil, S.S., et. al.,2024) are changing, which has consequences in the financial literacy and decision-making process (Aristei, 2025; Nourallah et. al., 2025). Furthermore, sustainable investment practice and Financial Education (FE) have also been identified as essential factors in determining investor behavior among Generation Z in various countries (Gomez Sanchez & Tobon, 2025; Judijanto et. al., 2024).

Financial autonomy (Rubin, J. D., et. al., 2024) and investment choices of Generation Z are also new potential studies in India since the group is facing distinct economic challenges and opportunities (Dugar and Madhavan, 2023). The results of the studies on the joint impact of financial literacy, risk attitudes, experience, and education on investment behaviour can be useful to policymakers and practitioners who are willing to increase financial well-being (Tubastuvi et. al., 2024; Yusup and Gunawan, 2024a).

This research would add to this body of knowledge as it empirically examines these factors in the Indian context and offers evidence-based recommendations that would facilitate financial literacy (Jiang, Y., & Shimizu, S. 2024;Rahman, M., et. al., 2021) and informed investment among Generation Z investors.

It is important today to understand how the financial decisions (Togan A., et. al., 2025) of young investors are made, particularly Generation Zs (Spohn. D., et. al., 2024). It is a consensus among many scholars that financial literacy has a significant influence on investment behaviour. Individuals familiar with finance are more likely to make smarter decisions and feel more confident in making an investment (Asri et. al., 2024; Baveja and Verma, 2024). However, the knowledge is not all, and the ability of investors to perceive and accept risks is equally important, and sometimes even more important (Pamungkas et. al., 2024; Tubastuvi et. al., 2024).

It is also experience that adjusts the mode of investment by people. Experienced investors have traditionally been in a position to control risks more effectively and make more equilibrium-based choices (Asri et. al., 2024; Bustani, 2024). This is in alignment with what is witnessed on the ground - people learn through experience, and investment literacy may encourage people to learn and acquire the experience (Senduk et. al., 2024).

Technology has come into the picture and is creating another dimension. Digital technologies and robo-advisors simplify the investment process and, at the same time, affect the way individuals make financial decisions and choose financial advice (Aristei, 2025; Nourallah et. al., 2025). Today, young investors want quick and tech-based solutions, and yet they require proper education to avoid traps (Dugar & Madhavan, 2023).

Generation Z is increasingly interested in sustainable investing, which is an ideology and a practical approach at the same time (Gomez Sanchez & Tobon, 2025; Judijanto et. al., 2024). This implies that new concepts, beyond traditional money management, need to be brought into financial education (F. W., et. al., 2024) as well.

Generation Z is becoming financially independent in the Indian context, which influences investment patterns in this context in a different way (Dugar and Madhavan, 2023; Tubastuvi et. al., 2024). This combination of literacy, risk, experience, and education should be understood by policymakers, educators, and financial planners in order to advise the young investors (Yusup and Gunawan, 2024a).

On the whole, the literature indicates that investment decisions are dependent on a combination of knowledge, attitudes (Shashidhar, S., et.al., 2025; Pasiusiene, et. al., 2023), technology, and life experience, which need to be taken into account in order to help emerging investors.

Hypotheses for the Study

• H1: Financial Education positively affects Financial Literacy among Generation Z investors
• H2: Financial Literacy positively influences Investment Decision-making among Generation Z investors
• H3: Risk Tolerance has a positive impact on Investment Decision-making among Generation Z investors
• H4: Return Expectation positively affects Investment Decision-making among Generation Z investors
• H5: Investment Experience has a positive effect on Investment Decision-making among Generation Z investors
• H6: Financial Education indirectly influences Investment Decision-making through Financial Literacy among Generation Z investors

4.1 Research Design

This study utilizes a quantitative research design to examine how financial literacy, risk perception, investment experience, and financial education determine the investment decisions of Generation Z.

4.2 Sample and Sampling Techniques

The target population for this study contains Generation Z, mostly students and young working adults between 18–27. The google form link was shared between students in a university group and working professionals. The survey collected over 300 responses, which aids in a strong foundation for SEM analysis.

4.3 Data Collection

Data was collected through a structured questionnaire where the survey questions were carefully framed from previous research. All the questions in the survey used a five-point Likert scale. The questionnaire was designed to calculate financial literacy, risk tolerance, investment experience, financial education, return expectations, and investment decisions. Respondents were informed their survey would be anonymous and used only for study purposes.

4.4 Conceptual Model

The conceptual model of the current research synthesizes the major factors that affect investment decisions within Generation Z. It describes the role of financial literacy and education in the development of knowledge and awareness of financial concepts in individuals. Risk tolerance and return expectations are also included in the model to represent financial risk and possible reward attitudes. Also, experience in investment is considered to have a practical exposure that can influence decision-making. All these combine to create a model of how young investors make their financial decisions, giving them a clear picture of how to analyse their investing decisions.

4.5 Data Analysis

Descriptive statistics was employed to check average and spread for each financial literacy. Following that Cronbach's alpha was calculated to check the reliability and validity. Exploratory Factor Analysis(EFA) and Confirmatory Factor Analysis(CFA) ensured the validity of clusters. SmartPLS software was used to develop Structural Equation Modeling(SEM), to analyze how independent variables influenced dependent variable i.e. investment diversification. Model fit was checked using SRMR and NFI values, and R-squared value was used to examine the variance percentage explained by the model.

5.1 Reliability Test

The alpha of Cronbach will determine the degree of internal consistency of the survey items under each construct. A greater alpha means that the items measure related variables.

Table No.5.1 Construct reliability and validity

Table 5.1 indicates that the constructs are highly reliable, with the Cronbach alpha values being larger than the generally accepted value of 0.7. It means that questions that are tested to evaluate financial education, financial literacy, decision to invest, investment experience, expectation of a return, and risk tolerance are closely tied to each other and are never used to explain the same concept.

This reliability shows that the respondents responded to these constructs in a manner that will make them reliable in future studies. Good convergent validity is shown by the high values of the Average Variance Extracted (AVE), which are over 0.6 in all the constructs. This indicates that the questions associated with each construct have high levels of overlap in information and that most of the variance in items is reflected by the construct i.e. the constructs are that they are intended to measure, which is a significant factor which contributes to the strength in the findings of this study.

Overall, the reliability and validity results give confidence that the measurement model is good and the survey tools used are effective in capturing financial behaviour and perceptions of Generation Z investors. This solid foundation justifies the use of these constructs in testing the hypothetical relationships in the structural model.

Exploratory Factor Analysis – Eigenvalue and Top Loadings

EFA finds common variables in the data by grouping them. Eigenvalues demonstrate the extent to which each factor explains the variance, whereas top loadings demonstrate the strongest relationships among factors and items.

Table No. 5.2:  Exploratory Factor Analysis

The table no. 5.2 presents the results of the exploratory factor analysis, which helped identify six main themes or factors in the survey data. The biggest factor is financial literacy, which covers the largest part of what explains the differences in answers among respondents. Other factors like return expectation, risk tolerance, investment experience, financial education, and investment decision also add meaningful explanation to the data. The eigenvalues depicts the weight of each factor, and the variance percentage exhibits how much each factor contributes overall. The high top loadings indicate that the questions strongly relate to their specific factors. Overall, this means the survey items group well into clear areas of financial knowledge and behavior, which supports the design of the questionnaire.

5.3 Discriminant Validity (Heterotrait-Monotrait Ratio, HTMT)

HTMT determines the level of distinctness of constructs in the model. A value that is less than the threshold means that every construct is a different concept, in which there is no overlap.

Table No. 5.3: Discriminant Validity

The discriminant validity is a measure of ensuring that the constructs used in this study are measuring different concepts, and this is a critical element in the reliability of the entire model. Heterotrait- Monotrait (HTMT) ratio was employed to measure discriminant validity because it is a robust and popular technique in recent studies.

From the Table 5.3, the values of all HTMT between construct pairs in our research fall below the desirable value of 0.85. To illustrate by example, the HTMT value of 0.575 between Financial Education (FEE) and Financial Literacy (FL) shows that, though perhaps the two constructs are related, they are distinctly different, and they represent different aspects of financial knowledge and financial education.

In the same manner, the Investment Diversification factor values are seen to lie between 0.662 and 0.736, with variables like Investment Experience (IE) and Financial Education (FEE) in favoring the uniqueness of such concepts under the framework of investment decision-making.

These findings are good indicators to depict that every construct in the model has its own identity without major overlaps with the other constructs. This justifies the measure model, which is accurate in the interpretation of the association between financial literacy, risk tolerance, expectations of returns, experience, education, and investment choices.

Thus, the good discriminant validity of the model is approved by the HTMT analysis, which justified the inclusion of these constructs in further structural equation modeling and hypothesis testing.

5.4 Descriptive Statistics

The average responses and how much respondent’s answers vary for the survey questions. It gives a quick idea of what most people think and how different their views are, given in the table.

Table No.5.4: Descriptive Statistics

This table no. 5.4 indicates that the means of approximately 2.99 indicate that the survey respondents are either more or less agreeing or giving a moderate rating to the survey questions on financial literacy, risk tolerance, returns expectation, investment experience, financial education, and investment decision. This means that Generation Z investors are neither negative nor positive with these constructs, and moderate in their knowledge and engagement in financial matters. Standard deviations of values are reported in the range of 1.113 to 1.191, which indicates moderate variation of responses around standard deviation values. It means that though a considerable proportion of the respondents lies above the average level, there are those with quite a few responses whose responses can be diffused far and wide; this implies that there is a variety in the number of views and experiences that the sample population represents. The range of response actions is standard in social research, as it is similar to the mixed and varied nature of investor action and financial proficiency throughout Generation Z.

A combination of these statistics indicates a group of young investors with an average financial knowledge and behavior with individual variation that gives significant variation to identify underlying trends and correlations in subsequent studies.

5.5 SEM Coefficients

Table 5.5 Structural Equation Modeling coefficients are the measure and the direction of the relationships between variables, i.e. how one construct will affect another in the research model.

Table 5.5 : SEM Path Coefficients

The findings indicate that the most positive impact on the investment decision was on Return Expectation (RE) with a coefficient of 0.254. This indicates that the expected returns have the greatest impact on influencing investment decisions. There are also high positive influences of Financial Education (FEE) and Investment Experience (IE), which have coefficients of 0.227 and 0.209, respectively, and reflect that prior knowledge and prior experience will improve decision-making skills.

Financial Literacy (FL) and Risk Tolerance (RT) have significant, but weak effects, with the coefficients of 0.171 and 0.162, respectively. Though they have a role to play in investment decisions, they have a relatively low contribution when compared to return expectations, education, and experience.

These results, in general, indicate that there are multiple factors that play a significant role in determining the investment decisions of Generation Z, but the return expectations are of primary importance.

5.6 Correlation Matrix

Table 5.6, the correlation matrix indicates the extent to which pairs of constructs are associated with each other, and this gives an idea of how the theoretical framework may be associated.

Table 5.6: Correlation Matrix

The correlation in the variables, as indicated by the matrix, is positive, implying that all these variables are connected and are likely to rise together. There is a positive correlation between Financial Literacy (FL) and Financial Education (FEE); the correlation is 0.50, which confirms the concept that financial knowledge can be enhanced with the help of education.

Investment Decision (ID) has a very strong correlation with Return Expectation (RE) at 0.52, which indicates the significance of expected returns in making investment decisions. There are also significant positive relationships between Investment Decision and Investment Experience (IE) and Financial Literacy, which means that experience and investment knowledge lead to better investment behaviour.

Moderate correlations between Risk Tolerance (RT) and other variables; however, it is associated positively with Investment Decision, indicating that risk-taking is important to investment decisions.

Overall, these correlations support the theoretical framework by confirming connections between financial education, experience, attitudes, and investment behaviour among Generation Z investors.

Table 5.7: Description of the Variables and the measurement units

5.7 Model Fit

The table 5.7 indicates the description of the variables and the measurement units. Table 5.8 indicates the model fit indices are a measure of the goodness of a proposed model to the observed data, so that the theoretical framework provides a sufficient explanation of the relationship between the variables.

Table 5.8: Model fit

The value of the Standardized Root Mean Squared Error is 0.054, and this value means a good fit between the model and the observed values. In general, the value  less than 0.08 indicates that the residuals of the model are low, that is, between the predicted and actual data, the difference is not very high.

However, the Normed Fit Index (NFI) value of 0.089 is below the ideal threshold of 0.90, which suggests that the model's improvement over a null model is limited. While SRMR shows an acceptable fit, the low NFI indicates room for model refinement.

Overall, the model fit results suggest that the model reasonably fits the data but could be improved further for stronger fit according to all criteria.

5.8 SEM Path Diagram

SEM path diagram graphically represents latent variables, measured variables and the postulated relationships between constructs to a visual representation of the model structure.

5.9 PLS-SEM Results

Figure 1: PLS-SEM Structural Model

The findings of the Structural Equation Modeling give useful clues on the variables that affect investment choices of Generation Z investors. This model demonstrates that financial education, financial literacy, return expectation, risk tolerance and investment experience are all significant in the determination of investment behavior.

Financial Literacy is knowledge and understanding of financial concepts by investors, which is a background to the development of investment behavior. The FL to ID relationship has a positive coefficient, which means the higher the literacy, the better their investment decisions.

Financial Education, which is education on finances, has a close relationship with Investment Decisions. This implies that education provides young investors with skills and confidence, which allows them to diversify and handle risks.

Risk Tolerance represents the level of comfort a person has regarding uncertainty and the possibility of losing money. Its direct impact on investment decisions is less when compared to other factors, but it is a significant one as it influences the preference for risk or more conservative investments.

The most powerful driver in the model is Return Expectation, which highlights the incentive of Generation Z to seek greater returns. This expectation influences the way young investors choose and balance their investment portfolios.

Investment Experience reflects practical experience acquired through previous investing, which has a positive influence on investment decisions. Experience in the real world seems to add value to the quality of decision-making compared to theoretical knowledge. The combination of these factors accounts for a large share of variance in investment decisions, explaining that the combination of these factors provides a solid explanation of how Generation Z makes investment decisions. The standardized path coefficients in the diagram measure the relative significance of each factor, with return expectations, education, and experience being the most important factors, and literacy and risk tolerance being supporting and moderating factors. This research provides helpful insights for educators, policymakers, financial advisors, and young investors. It shows which factors most influence Gen Z's investment choices. Educators and policymakers can use these findings to design better financial literacy(Frees. et. al., 2024) programs focused on what really matters to young investors. Financial advisors can modify their advice to suit this group’s unique attitudes and needs (Putri Susanto, K., et. al ., 2024). Most importantly, the research encourages young investors to gain knowledge and hands-on experience, helping them make smarter, more confident financial decisions. In this way, the study supports building stronger, more practical financial education and investment strategies that can truly make a difference for the next generation (Olajide, O., et. al., 2024). 

This study has explained the importance of financial literacy and risk-return perception in shaping investment decisions. The research indicates that, more Gen Z know about finance and investment experience, the better their chance of diversifying their investments. The higher the financial literacy gives rise to better the understanding of risk and the better the investment diversification. These results mean digital apps, schools, and financial firms should give practical, activity-based education from the beginning. The data confirms that targeted education and experience, not just theory, can close India’s investment knowledge gap. The combined perspective marks the importance of financial education and investment opportunities to help young investors make decisions with confidence (Vanishree, K., et. al.,2024).

Limitations and Future Work

This study has limitations, even though it has contributed on the factors that influence the investment decisions. The data is gathered within a certain geographic region, and this might limit the applicability of the results to other regions or countries, which have a different financial culture. A cross-sectional design also limits the possibility of tracking how financial behavior varies with time. Also, self-reported survey data can be subjected to other biases, like social desirability or poor recall.

Future studies may deal with these limitations by diversifying the sample to represent more varied populations and performing longitudinal studies to have a better idea of how investment behavior changes. Understanding of other psychological or socio-economic variables that might affect investment decisions, including financial stress or personality characteristics, might be explored as well. The use of qualitative research, such as interviews, could offer more information about the financial decision-making of Gen Z. Finally, with the rise of digital finance, work in the future may explore the role of technology and social media influence in the investment behavior of young investors.

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