From Literacy to Capability: A Behavioral Finance Perspective on Investment Decision Making

Studies Management and Finance Economics, of Journal 0504-2644 (online): ISSN 0490,-2644 (print): ISSN 5202 December 12 Issue 80 Volume 8.317 Factor: Impact ,10-i12-10.47191/jefms/v8 DOI: Article 8276-7671 No: Page JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7671 From Literacy to Capability: A Behavioral Finance Perspective on Investment Decision Making Debby Arisandi1, Ridwan Nurazi2, Fitri Santi3, Dewi Rahmayanti4 1,2,3,4Management Science Doctoral Program, Faculty of Economics and Business, Bengkulu University, Indonesia ABSTRACT: This study examines the determinants of Investment Decision Making (IDM) by analyzing the roles of Financial Literacy (FL), Financial Capability (FC), and Herding Behavior (HB) among individual investors in the Indonesian capital market. Using an explanatory quantitative approach, data were collected from 340 valid respondents through an online survey conducted between May and July 2025. Multiple linear regression analysis was employed to assess both partial and simultaneous effects of the independent variables on IDM. The findings reveal that Financial Capability and Herding Behavior significantly influence investment decision-making, whereas Financial Literacy shows a positive but statistically insignificant effect. Financial Capability emerges as the most dominant predictor, indicating that practical financial skills and confidence play a more substantial role than theoretical knowledge in shaping investment behavior. Moreover, the significant influence of herding behavior highlights the strong presence of social and psychological factors in investors’ decision processes. These results contribute to the development of Behavioral Finance theory by demonstrating that investment decisions are shaped by a combination of cognitive, behavioral, and social elements. Practically, the study suggests that financial education programs should emphasize skill-building and behavioral awareness to enhance investment quality. Future research is recommended to incorporate additional behavioral constructs and adopt longitudinal or mixed-method approaches to deepen understanding of investor behavior. KEYWORDS: Financial Literacy, Financial Capability, Herding Behavior, Investment Decision Making I. INTRODUCTION The concept of financial literacy is rooted in Behavioral Finance theory, which emphasizes that financial decisionmaking is influenced by an individual's level of understanding and perception of financial information. According to Lusardi and Mitchell (2014), financial literacy encompasses the ability to understand fundamental financial concepts such as compound interest, inflation, diversification, and investment risk. Individuals with higher levels of financial literacy tend to possess stronger analytical abilities in assessing risk and returns, leading them to make more rational and profitable investment decisions (Van Rooij et al., 2011). In this context, financial literacy acts as the cognitive foundation for investment decision making because individuals who grasp financial markets are better equipped to avoid misperceptions and behavioral biases commonly found among novice investors. Meanwhile, financial capability includes not only literacy but also the skills and actual behaviors involved in managing personal finances. Amartya Sen’s (1993) Capability Approach explains that an individual’s capability is shaped not merely by knowledge but also by the opportunities and resources required to apply that knowledge in real-life situations. Accordingly, financial capability stresses practical competencies such as budgeting, saving, investing, and managing risk. Xiao and O’Neill (2018) show that strong financial capability enhances the efficiency of financial decision-making—including investment decisions—because individuals not only know *what ought to be done* but also have the ability to *put it into practice*. Thus, financial capability strengthens the link between financial knowledge and actual investment behavior, serving as a mediator that transforms literacy into effective financial actions. In contrast to these rational variables, herding behavior stems from Behavioral Finance insights that acknowledge the existence of irrational biases in investment decisions. Herding behavior refers to an individual’s tendency to imitate the actions of the majority of investors regardless of rational analysis of market information (Bikhchandani & Sharma, 2001). Based on Social Proof Theory (Cialdini, 2007), individuals tend to follow others—especially in uncertain environments—because they perceive From Literacy to Capability: A Behavioral Finance Perspective on Investment Decision Making JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7672 majority behavior as a reference for correctness. In capital markets, herding can influence investment decisions through psychological pressure: when investors observe a rising trend in the purchase of a particular stock, they may join in without sufficient fundamental analysis. Nofsinger and Sias (1999) demonstrate that herding behavior contributes to market volatility and leads to inefficient decision-making, particularly among investors with low financial literacy. Conceptually, financial literacy and financial capability function as internal and rational factors that enhance investment decision quality, whereas herding behavior represents an external psychological factor that may distort rationality. The Theory of Planned Behavior (Ajzen, 1991) also helps explain this relationship: literacy and capability shape attitudes and perceived behavioral control that influence investment intentions and actions, while herding behavior reflects subjective norms driven by social pressure and group influence. Empirical evidence supports this integrated view; for example, Nguyen et al. (2019) and Hassan et al. (2021) find that financial literacy and capability positively affect investment decisions, but their impact may weaken when herding behavior intensifies. Therefore, balancing financial rationality and resistance to social biases is crucial for investors seeking optimal investment outcomes. From a theoretical standpoint, the interplay among these four variables underscores the importance of synergy between cognitive aspects (literacy), behavioral aspects (capability), and psychological aspects (herding) in explaining investment decision making. Practically, the synthesis of theories and empirical findings indicates that enhancing financial literacy and strengthening financial capability must be accompanied by behavioral education aimed at reducing herding tendencies. Consequently, national financial literacy initiatives—particularly in the capital market sector—should not only focus on knowledge dissemination but also on building critical and independent financial behavior. Within Behavioral Finance theory, individual investment decisions are shaped not only by rational factors such as financial knowledge or information but also by the ability to manage financial resources and by social or psychological influences (Shefrin, 2000; Shiller, 2003). This study simultaneously examines three key determinants of Investment Decision Making (IDM): Financial Literacy (FL), Financial Capability (FC), and Herding Behavior (HB). Most prior studies highlight the role of Financial Literacy as a foundation for rational financial decision-making. Knowledge of financial products, risks, and diversification is believed to improve investment decision quality (Van Rooij, Lusardi, & Alessie, 2011; Al-Tamimi & Bin Kalli, 2009). However, empirical findings remain inconsistent. Some studies report that financial literacy has no significant effect on actual investment behavior (Fernandes, Lynch, & Netemeyer, 2014; Potrich et al., 2016). These mixed results suggest the existence of other factors that may bridge the gap between knowledge and real financial actions. One such factor is Financial Capability, defined as an individual’s ability to apply financial knowledge in real-life contexts (Sen, 1999; Kempson, Finney, & Poppe, 2017). The Capability Approach posits that economic well-being and financial behavior depend on a person’s ability to convert knowledge and resources into effective action. Several studies (Xiao & O’Neill, 2016; Pinasti & Achiria, 2024) find that FC has a stronger influence on financial behavior—including investment decisions—than literacy itself. Nonetheless, research comparing the relative strength of FL and FC in the context of investment decisions remains limited, particularly in emerging markets and retail investor segments. In addition, Herding Behavior plays a significant role in shaping investment decisions. According to Bikhchandani, Hirshleifer, and Welch (1992), herding reflects an informational cascade in which individuals base their decisions on others' actions rather than their own private information. Empirical studies (Dewan & Dharni, 2019; Kresnawati, Sofia, & Utami, 2024) indicate that herding significantly influences investment decisions, especially in markets with high uncertainty or among investors with low literacy. However, studies examining the simultaneous role of rational factors (FL, FC) and social–psychological factors (HB) in a single integrative model are still scarce, particularly in the context of emerging economies. Although numerous studies have investigated the relationship between financial literacy and investment decisionmaking, the results remain mixed and inconclusive. Theoretically, previous studies tend to focus primarily on financial literacy— emphasizing knowledge and information—while giving less attention to the extent to which such knowledge is applied in daily financial activities. The ability to convert knowledge into effective financial actions, known as financial capability, is crucial for distinguishing theoretical understanding from practical competence in investment decisions. This indicates a theoretical gap in understanding the relative role of practical capability compared with financial knowledge alone. There is also an empirical gap reflected in the varying results of prior studies. Some research finds a positive influence of financial literacy on investment decisions (Van Rooij, Lusardi, & Alessie, 2011; Al-Tamimi & Bin Kalli, 2009), while others report weak or insignificant effects (Fernandes, Lynch, & Netemeyer, 2014; Potrich et al., 2016). These inconsistencies may stem from cultural differences, market maturity levels, and respondent characteristics. Therefore, further studies are needed to reexamine these relationships in different contexts, including Indonesia. From Literacy to Capability: A Behavioral Finance Perspective on Investment Decision Making JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7673 A contextual gap also exists because prior studies often overlook social and psychological factors such as herding behavior in models that simultaneously include rational factors. Yet, from a Behavioral Finance perspective, investment decisions are shaped not only by rational considerations but also by social influences. This phenomenon is particularly relevant in Indonesia’s Islamic financial market, where investment decisions are shaped not only by profit motives but also by social values, religious norms, and community trust. Addressing the theoretical, empirical, and contextual gaps described above, this study aims to simultaneously examine the influence of Financial Literacy (FL), Financial Capability (FC), and Herding Behavior (HB) on Investment Decision Making (IDM). This integrative approach is expected to offer a more comprehensive understanding of the determinants of investment decision quality and to identify which factor plays the most dominant role, particularly within Indonesian society and its financial system. These gaps highlight the need for a more holistic and contextually grounded approach to understanding investment decision-making. In modern financial systems—especially within Islamic financial frameworks—investment decisions cannot be explained solely by rational factors such as financial literacy. They also require consideration of an individual’s capability to implement their knowledge and the behavioral dynamics that influence financial choices. Therefore, the urgency of this study lies in bridging the gap between financial knowledge (financial literacy) and actual financial behavior (investment decision making) through the roles of financial capability as a practical element and herding behavior as a social behavioral factor. From a practical standpoint, this study is also strategically important for supporting national initiatives to improve financial literacy and capability, as emphasized in Indonesia’s National Strategy for Financial Literacy (SNLKI) by the Financial Services Authority (OJK). The findings are expected to provide insights for financial institutions, regulators, and policymakers in designing educational programs that emphasize not only knowledge enhancement but also the strengthening of adaptive financial behavior. By advancing financial capability and awareness, investment decisions are expected to become more rational, productive, and aligned with sound financial principles. This study therefore contributes both theoretically—by developing a more comprehensive behavioral financial model—and practically—by supporting policy development in financial literacy and inclusion in Indonesia. II. LITERATURE REVIEW A. Financial Literacy Theoretically, financial literacy enhances an individual’s ability to understand investment risks and returns and to perform effective portfolio diversification. Based on Expected Utility Theory (von Neumann & Morgenstern, 1944), rational decisions rely on an individual’s capacity to evaluate risks. Therefore, higher levels of financial literacy are expected to improve the quality of investment decisions. Several studies support this positive relationship (Al-Tamimi & Bin Kalli, 2009; Van Rooij et al., 2011), although other empirical findings—such as those by Fernandes et al. (2014) and Potrich et al. (2016)—report weak or insignificant effects. These inconsistencies justify the need to reassess the influence of FL on IDM in the context of this study. B. Financial Capability Financial Capability reflects the extent to which individuals are able to apply financial knowledge, skills, and confidence to manage their finances effectively (Kempson et al., 2017). According to the Capability Approach (Sen, 1999), the actual ability to act is more important than merely possessing resources or knowledge. Empirical findings demonstrate that individuals with higher financial capability are more likely to make sound investment decisions, including selecting appropriate instruments, managing risk, and adopting long-term investment strategies (Xiao & O’Neill, 2016; Pinasti & Achiria, 2024; Hamurcu et al., 2025). Thus, financial capability is expected to significantly influence investment decision making. C. Herding Behavior Within Behavioral Finance, herding behavior describes the tendency of individuals to follow group decisions without independent analysis. Bikhchandani and Sharma (2001) and Shiller (2003) explain that herding emerges due to informational cascades and social conformity, particularly in inefficient market environments. Empirical studies across various countries, including Indonesia, show that herding behavior affects individual investment decisions in terms of investment timing and instrument selection (Dewan & Dharni, 2019; Kresnawati et al., 2024). Accordingly, this study hypothesizes that herding behavior significantly influences investment decision making. From Literacy to Capability: A Behavioral Finance Perspective on Investment Decision Making JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7674 D. Investment Decision Making (IDM) Investment Decision Making (IDM) refers to the process through which individuals or investors evaluate, select, and allocate financial resources into various investment instruments. It is a central theme in financial behavior research because investment decisions directly determine financial outcomes, risk exposure, and long-term wealth accumulation. The literature conceptualizes IDM as a multidimensional construct influenced by cognitive, psychological, and contextual factors, shaped by both classical finance and Behavioral Finance perspectives. Traditional finance views IDM through the lens of rationality. According to Expected Utility Theory (von Neumann & Morgenstern, 1944), investors make decisions by maximizing expected utility after assessing risk and return. This perspective assumes that investors possess stable preferences, process information accurately, and select investment alternatives that yield the highest utility. However, empirical evidence increasingly shows that actual investment behavior deviates from full rationality. Behavioral Finance challenges the classical view by arguing that investors are boundedly rational and subject to cognitive limitations, emotional influences, and social pressures (Shefrin, 2000; Shiller, 2003). As a result, IDM is better understood as an interplay between analytical reasoning, psychological tendencies, and environmental cues rather than purely rational optimization. The literature identifies several key dimensions used to evaluate the quality of investment decisions: a. Risk and Return Assessment Effective decision-making requires the ability to assess risk–return trade-offs, perceive uncertainty accurately, and align investments with personal risk tolerance. This assessment forms the foundation of rational investment behavior. b. Information Processing and Analysis IDM involves the use of relevant information—fundamental, technical, or macroeconomic—in evaluating investment options. Investors exhibiting strong analytical tendencies tend to rely on data-driven analysis rather than intuition or speculation. c. Instrument Selection According to Al-Tamimi and Bin Kalli (2009), IDM is reflected in the investor’s ability to choose investment instruments that match financial goals, investment horizons, and risk profiles. d. Portfolio Diversification Diversification is widely recognized as a primary indicator of sound IDM. Investors capable of allocating assets across different instruments minimize unsystematic risk and enhance portfolio stability (Van Rooij et al., 2011). e. Long-Term Orientation and Consistency High-quality IDM involves consistency in decision-making, avoidance of impulsive reactions, and adherence to long-term financial plans, even during periods of market volatility. Together, these dimensions illustrate that IDM is not merely a single action but a comprehensive evaluative process involving analytical and behavioral components. Behavioral Finance provides a richer understanding of IDM by acknowledging that investors are influenced by bounded rationality and susceptible to systematic behavioral biases. Investors often rely on heuristics—mental shortcuts that simplify decisions but may lead to systematic errors. Studies show that investor behavior frequently deviates from analytical norms due to emotional reactions, perceived norms, and misinterpretation of market signals (Nofsinger & Sias, 1999). Furthermore, Behavioral Finance reveals that social dynamics, such as herding and collective sentiment, play a significant role in shaping investment decisions. This indicates that IDM is not purely an individual cognitive process but is embedded within a broader psychological and social context. From Literacy to Capability: A Behavioral Finance Perspective on Investment Decision Making JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7675 Figure 1: Conceptual Framework Based on the research model described above, the researcher proposes the following hypotheses: H₁ : Financial Literacy has a positive effect on Investment Decision Making. H₂ : Financial Capability has a positive effect on Investment Decision Making. H₃ : Herding Behavior has a positive effect on Investment Decision Making. H4 : Simultaneously, there is a significant influence of Financial Literacy, Financial Capability, and Herding Behavior on Investment Decision Making. III. METHOD, DATA, AND ANALYSIS This study employs a quantitative approach with an explanatory design to examine the effects of Financial Literacy (FL), Financial Capability (FC), and Herding Behavior (HB) on Investment Decision Making (IDM). Data were collected through an online survey using the tsurvey platform during the period of May–July 2025. A total of 340 valid responses from individual investors in the Indonesian capital market were included in the analysis. The study population comprises all investors registered with the Indonesia Stock Exchange (IDX) and the Financial Services Authority (OJK). According to the latest IDX data (2025), the number of Single Investor Identification (SID) holders has reached approximately 18–19 million, reflecting significant growth in recent years. A purposive sampling technique was used with the following criteria: (1) respondents are active investors in the Indonesian capital market; (2) have at least one year of investment experience; and (3) are willing to complete the survey in full. The unit of analysis is the individual investor, and the data are cross-sectional. All constructs were measured using a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). Variable Description Items Reference Financial Literacy (FL) Respondents’ knowledge and understanding of basic financial concepts such as risk, return, inflation, and investment diversification. 8 items Van Rooij, Lusardi, & Alessie (2011) Financial Capability (FC) Ability to apply financial knowledge in practice, including budgeting, saving, and financial decision confidence. 7 items Kempson, Finney, & Poppe (2017) Herding Behavior (HB) Tendency to follow others’ investment decisions or market trends without independent analysis. 2 items Bikhchandani & Sharma (2001) Herding Behavior Financial Capability Financial Literacy Investment Decision Making From Literacy to Capability: A Behavioral Finance Perspective on Investment Decision Making JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7676 Variable Description Items Reference Investment Decision Making (IDM) Quality of individual investment decisions, including rationality, information assessment, and diversification. 7 items Al-Tamimi & Bin Kalli (2009) A pilot test involving 30 respondents was conducted to assess item clarity. All constructs achieved Cronbach’s Alpha values above 0.70, indicating good reliability. The relationships between variables were analyzed using multiple linear regression with the JASP 0.19 application. The use of JASP ensures transparency, ease of replication, and verification in line with open science principles. The research instruments were adopted from previous studies. Turnover intention was measured using the instrument developed by Michaels & Spector (1982). This instrument assesses employees' intention to leave their organization or workplace voluntarily within the past six months and consists of three indicators or statement items. Toxic leadership was measured using the Short Version Toxic Leadership Scale developed by Schmidt & Hanges (2014). This instrument includes five dimensions (abusive supervision, authoritarian leadership, narcissism, self-promotion, and unpredictability) and 15 indicators. Job stress was measured using Parker’s Job Stress Scale developed by Parker & DeCotiis (1983), which includes two dimensions (time stress and job anxiety) and 13 indicators. Perceived alternative job opportunities were measured using the Perceived Job Alternatives Scale developed by Treuren (2013), consisting of six indicators. In addition to collecting respondents' responses to the research variables, this study also gathered individual data such as age, gender, highest education level, and length of service. The data analysis employed multiple linear regression using IBM SPSS Statistics 25. Multiple linear regression analysis was used to test the effects of toxic leadership, job stress, and perceived alternative job opportunities on turnover intention, both partially and simultaneously. IV. RESULT AND DISCUSSION A. RESULT 1. Respondent Characteristics The characteristics of the respondents in this study include age, gender, highest level of education, and length of service. Table 1: Respondent Characteristics Characteristic Description Total Percentage (%) Age 20 – 30 Years 107 31,47 31 – 40 Years 182 53,53 41 – 50 Years 46 13,53 > 50 Years 5 1,47 Gender Male 231 97,94 Female 109 32,06 Education High School 162 47,65 Bachelor’s Degree 152 44,71 Master Degree Doctoral Degree 15 11 4,41 3,24 Long investment period < 1 Years 114 33,53 1-3 Years 4-6 Years 7-10 Years >10 Years 133 59 21 13 39,12 17,35 6,18 3,82 From Literacy to Capability: A Behavioral Finance Perspective on Investment Decision Making JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7677 2. The Validity And Reliability Tests Table 2. Recapitulation of Validity and Reliability Test Results of Research Instruments No Research Variable Number of Statement Items Range of Pearson’s r Values Significance Value (p) Average r Validity Description Cronbach’s Alpha Estimate Reliability Description 1 Financial Literacy (FL) 8 (FL1–FL8) 0.538 – 0.764 < 0.001 0.683 All items valid 0.85 High (reliable) 2 Financial Confidence (FC) 7 (FC1–FC7) 0.793 – 0.869 < 0.001 0.831 All items valid 0.93 Very high (very reliable) 3 Herding Behavior (HB) 2 (HB1–HB2) 0.880 – 0.885 < 0.001 0.883 All items valid 0.72 Fairly high (reliable) 4 Investment Decision Making (IDM) 7 (IDM1–IDM7) 0.721 – 0.843 < 0.001 0.801 All items valid 0.91 Very high (very reliable) Source: Processed research data, 2025 Based on the results of the validity and reliability tests as presented in Table 4.1, all statement items in the variables Financial Literacy, Financial Confidence, Herding Behavior, and Investment Decision Making show Pearson correlation values greater than 0.30 with a significance level of p < 0.05. Thus, all items are declared valid. Moreover, the reliability estimation using Cronbach’s Alpha shows values above 0.70 for each variable, indicating that the research instruments are reliable. With these results, the instruments can be used in subsequent stages of analysis, as they meet the criteria for good validity and reliability. 3. Multiple Linear Regression Analysis Regression analysis was conducted to determine the effect of Financial Literacy (FL), Financial Confidence (FC), and Herding Behavior (HB) on Investment Decision Making (IDM). The p-value < 0.001 in the F-test (ANOVA) also indicates that the regression model as a whole is significant and suitable for further analysis. The results are presented in Table 5 below: Coefficients Model Unstandardized Standard Error Standardized t p M₀ (Intercept) 3.789 0.038 100.487 < .001 M₁ (Intercept) 0.526 0.147 3.573 < .001 FL 0.081 0.044 0.077 1.855 .064 FC 0.631 0.041 0.657 15.423 < .001 HB 0.155 0.032 0.178 4.871 < .001 a. Dependent Variable: Investment Decision Making Multiple Linear Regression Model Investment Decision Making = 0.526 + 0.081 X1 (FL) + 0.631 X2 (FC) + 0.155 X3 (HB) Interpretation: 1. Constant (0.526) The constant value of 0.526 indicates that when Financial Literacy (FL), Financial Confidence (FC), and Herding Behavior (HB) are assumed to be zero, the baseline level of Investment Decision Making (IDM) is 0.526. This represents the intercept or the minimum expected value of IDM without the influence of the independent variables. 2. Financial Literacy (FL) Coefficient = 0.081 The coefficient of 0.081 shows that for every one-unit increase in Financial Literacy, while keeping other variables constant, Investment Decision Making increases by 0.081 units. This suggests that financial literacy has a From Literacy to Capability: A Behavioral Finance Perspective on Investment Decision Making JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7678 positive but relatively small effect on investment decisions. The better an individual's understanding of financial concepts, the slightly better their investment decision-making ability. 3. Financial Confidence (FC) Coefficient = 0.631 The coefficient of 0.631 implies that for every one-unit increase in Financial Confidence, holding other variables constant, Investment Decision Making increases by 0.631 units. This indicates that financial confidence is the most influential factor in this model. A higher level of confidence in managing personal finances strongly enhances one’s ability to make sound investment decisions. 4. Herding Behavior (HB) Coefficient = 0.155 The coefficient of 0.155 means that for every one-unit increase in Herding Behavior, while keeping other factors constant, Investment Decision Making increases by 0.155 units. This shows a positive but moderate effect, suggesting that following the behavior or decisions of others can influence one’s own investment decisions — possibly due to social proof or market trend considerations. 4. Coefficient of Determination Analysis The coefficient of determination analysis aims to determine the percentage of the independent variables influence on the dependent variable. The results are presented in Table 6: Table 6: Coefficient of Determination Results Model Summary - IDM Model R R² Adjusted R² RMSE M₀ 0.000 0.000 0.000 0.695 M₁ 0.796 0.633 0.630 0.423 The test results show that the regression model (M₁) has an R value of 0.796, R² = 0.633, and Adjusted R² = 0.630, which means that the three independent (financial literacy, financial capability, and herding behavior) variables collectively explain 63.3% of the variation in investment decision-making, while the remaining 36.7% is explained by other variables outside this research model. 5. Hypothesis Testing Partial Testing Partial testing evaluates the effect of an independent variable on the dependent variable. The results are shown in Table 7: Variabel Koefisien (B) Beta t Sig. Interpretasi (Konstanta) 0.526 — 3.573 <.001 Significant Financial Literacy (FL) 0.081 0.077 1.855 0.064 Not Significant Financial Capability (FC) 0.631 0.657 15.423 <.001 Significant Herding Behavior (HB) 0.155 0.178 4.871 <.001 Significant Based on Table 7, the results of the partial testing above can be presented for each research variable as follows: 1. (Constant) B = 0.526, t = 3.573, p < .001. This means that when all independent variables are equal to 0, the value of the dependent variable (Y) will be 0.526. The constant is significant, indicating that the model has a statistically meaningful intercept. 2. Financial Literacy (FL) B = 0.081, β = 0.077, t = 1.855, p = 0.064. Positive coefficient → unidirectional relationship, meaning that higher financial literacy leads to higher outcomes. However, p = 0.064 > 0.05, making it not statistically significant at the 95% confidence level. Financial knowledge alone is not strong enough to directly influence the dependent variable. The effect may be stronger if mediated by other factors (e.g., financial behavior or self-efficacy) From Literacy to Capability: A Behavioral Finance Perspective on Investment Decision Making JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7679 3. Financial Capability (FC) B = 0.631, β = 0.657, t = 15.423, p < .001. The largest and most significant coefficient value. Financial capability has a positive and dominant influence on the dependent variable. Every 1-unit increase in FC increases the dependent variable score by 0.631, with a very high level of significance. Beta (0.657) indicates that among all variables, FC is the most influential predictor. Financial Capability (the ability to manage and apply financial knowledge) is the most important factor in explaining good financial behavior or investment decisions. 4. Herding Behavior (HB) B = 0.155, β = 0.178, t = 4.871, p < .001. Significant and positive. It means that the higher the herding behavior, the greater the likelihood of improved outcomes. Although its effect is smaller than FC, this variable still has a significant effect on outcomes. Herding Behavior also has a real influence, showing that psychological and social factors (following the behavior of others) influence individual decisions. Simultaneous Testing Simultaneous testing evaluates whether independent variables collectively influence the dependent variable. Results are shown in Table 8: Table 8: Simultaneous Testing Results ANOVA Model Sum of Squares df Mean Square F p M₁ Regression 103.78 3 34.592 193.5 < .001 Residual 60.05 336 0.179 Total 163.83 339 Note. M₁ includes FL, FC, HB Note. The intercept model is omitted, as no meaningful information can be shown. Based on Table 8, the results of the simultaneous test above show that F-value = 193.5. This figure shows the ratio between the explained variation (regression) and the unexplained variation (residual/error). The higher the F-value, the better the model explains the total variance in the data. Table 8 shows p-value (Sig.) < .001. This means that the probability of incorrectly rejecting H₀ (the model is not significant) is less than 0.1%. In other words, the model is highly statistically significant at the 99.9% confidence level. Thus, it can be concluded that H0 is rejected and Ha is accepted.The regression model, which simultaneously includes FL, FC, and HB, has a significant effect on the dependent variable (Investment decision making). B. DISCUSSION a. The Effect of Financial Literacy on Investment Decision Making The results of this study indicate that financial literacy (FL) has a positive but statistically insignificant effect on Investment Decision Making (IDM) (B = 0.081; p = 0.064). This means that an increase in a person’s level of financial literacy does not necessarily lead to an improvement in the quality of investment decisions made. This finding suggests that financial knowledge alone is not sufficient to drive individuals to make optimal investment decisions. Although higher financial literacy enables individuals to understand basic financial concepts such as risk, return, and diversification, such understanding alone may not be enough to produce optimal investment decisions. Remund (2010) defines financial literacy as the knowledge and skills that enable individuals to manage their finances effectively but also emphasizes that literacy alone does not guarantee rational financial behavior. The findings of this study are consistent with the meta-analysis conducted by Fernandes, Lynch, and Netemeyer (2014), which concluded that financial literacy education has only a small effect on actual financial behavior. However, the study by AlTamimi and Bin Kalli (2009) found that financial literacy had a significant effect on investment decisions in the United Arab Emirates, suggesting that social and economic contexts may moderate this relationship. In the context of this study, the insignificant result may be due to a lack of confidence or practical skills in applying the financial knowledge possessed by respondents. Therefore, educational interventions are needed not only to increase knowledge but also to build practical abilities that transform literacy into effective investment decision-making.