1388 American Journal of Economic and Management Business e-ISSN: 2835-5199 Vol. 4 No. 8 August 2025 The Influence of Financial Literacy, Financial Technology Literacy, Risk Perception, and Self-Efficacy on Investment Decisions of Capital Market Investors in Maluku Through Multi-Group Analysis Rahma Artika Salampessy, Astrie Krisnawati Telkom University, Indonesia Email: rahmaartika29@gmail.com, astriekrisnawati@telkomuniversity.ac.id Abstract This study aims to analyze the influence of financial literacy, financial technology literacy, risk perception, and self- efficacy on investment decisions among capital market investors in Maluku. Additionally, it examines gender differences in these relationships through a multi-group analysis approach. In regions with relatively low financial literacy levels like Maluku, understanding factors that influence investment behavior is crucial for strengthening financial inclusion strategies. This research employed a descriptive-verificative approach using survey methodology through questionnaires. The sample was determined using Cochran's formula at a 5% significance level and adjusted based on gender distribution of capital market investors in Maluku, resulting in 228 male and 172 female respondents. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), supplemented by Partial Least Squares Multi-Group Analysis (PLS-MGA) to evaluate differences across gender groups. Results indicate that simultaneously, financial literacy, financial technology literacy, risk perception, and self-efficacy significantly influence investment decisions in both gender groups. Partially, all four variables significantly influence investment decisions among male investors. However, among female investors, self-efficacy does not show significant effect. Furthermore, PLS-MGA results reveal significant differences between male and female investors only in risk perception influence, while other variables do not exhibit statistically significant differences. These findings contribute to deeper understanding of gender roles in investment behavior and provide practical implications for regulators and financial institutions in designing more inclusive financial literacy strategies. Keywords: financial literacy, financial technology literacy, risk perception, self-efficacy, investment decision INTRODUCTION Investment represents a fundamental pillar of economic development and personal financial well-being globally. As defined by Suhardi et al. (2022), investment is a commitment to delay current consumption by allocating funds with the objective of obtaining future returns. This concept has become increasingly significant as economies worldwide recognize the critical role of capital markets in facilitating economic growth and providing individuals with opportunities to build wealth (Mensah et al., 2024). The global investment landscape has experienced American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1389 unprecedented transformation, particularly with the integration of digital technologies that have democratized access to financial markets and investment opportunities (Sari & Budiarto, 2024). The significance of investment decisions extends beyond individual financial planning to encompass broader economic implications. According to recent global financial inclusion data from the World Bank (2022), approximately 1.4 billion adults worldwide remain unbanked, with significant disparities in financial access and literacy across regions. Developed economies such as the United States and European Union demonstrate financial literacy rates exceeding 60%, while emerging markets in Southeast Asia, Africa, and Latin America struggle with rates below 40%. These disparities highlight the critical importance of understanding investment behavior patterns in different economic and cultural contexts, particularly in regions where financial inclusion initiatives are rapidly expanding but financial education may lag behind access to financial services (Kass-Hanna et al., 2022). According to Apriliansyah and Suyatno (2024), investment plays a crucial role in driving economic growth, creating employment opportunities, and fostering innovation across various sectors. However, the complexity of modern financial markets, coupled with the proliferation of investment products and digital platforms, has created new challenges for investors in making informed decisions (Fernández-López et al., 2025). This complexity is particularly pronounced in emerging markets where financial literacy levels remain relatively low, and traditional investment education may not adequately address the nuances of contemporary financial instruments and technologies (Hidayat & Sari, 2022). Globally, research has consistently demonstrated that investment decisions are influenced by a multitude of factors beyond traditional economic considerations. The behavioral finance literature, pioneered by Kahneman and Tversky (1979) through their Prospect Theory, reveals that investors often exhibit systematic biases and heuristics that deviate from purely rational decision- making models. These behavioral aspects become even more complex when considering demographic factors such as gender, age, education, and cultural background. International studies have shown significant variations in investment behavior across different populations, highlighting the need for context-specific research that can inform targeted policy interventions and educational programs (Grohmann, 2018). In the Indonesian context, the capital market has experienced remarkable growth in recent years, with the number of investors increasing from 7.49 million in 2021 to 14.87 million in 2024, representing an impressive growth rate of 98.53% (KSEI, 2024). This dramatic increase reflects the government's successful efforts to promote financial inclusion and the growing awareness among Indonesians about the importance of investment for long-term financial security. However, this growth has not been evenly distributed across the archipelago, revealing significant geographical disparities that warrant closer examination. The geographical distribution of investors in Indonesia presents a stark picture of inequality, with Java Island dominating the investment landscape at 69.09% of total domestic investors, followed by Sumatra (15.43%), Kalimantan (4.85%), Sulawesi (5.40%), and Bali-NTT- NTB (3.66%). Most concerning is the minimal representation of eastern Indonesian regions, with American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1390 Maluku and Papua accounting for only 1.27% of total domestic investors despite representing a significant portion of Indonesia's geographical area and population (KSEI, 2024). This disparity reflects deeper structural challenges including limited access to financial services, lower levels of financial literacy, and inadequate infrastructure for supporting capital market activities in these regions. Focusing specifically on Maluku Province, the investment landscape presents both challenges and opportunities that make it an ideal case study for understanding investment behavior in underdeveloped financial markets. Despite the overall low representation in national statistics, Maluku has experienced substantial growth in investor participation, with numbers increasing from 16,333 in 2021 to 52,000 in 2024, representing an extraordinary growth rate of 218%. This growth trajectory, while impressive, must be contextualized within the broader framework of financial inclusion and literacy indicators for the region. The financial inclusion rate in Maluku, at 78.70%, appears encouraging and suggests that a significant portion of the population has access to formal financial services. However, this high inclusion rate masks a critical gap in financial understanding, as evidenced by the financial literacy rate of only 40.78% (Damayanti & Listiawati, 2024). This disparity between access and understanding creates a potentially problematic scenario where individuals may have the means to participate in financial markets but lack the knowledge necessary to make informed investment decisions. The situation becomes even more complex when considering capital market-specific literacy, which nationally stands at only 4.11% with inclusion at 5.19%, indicating that the vast majority of Indonesians, including those in Maluku, have limited understanding of capital market instruments and mechanisms. Gender-based disparities in investment participation represent another critical dimension of the investment landscape that requires systematic investigation. National data from KSEI (2024) reveals that male investors dominate the capital market with 62.35% participation, while female investors account for only 37.65%. This gender gap is mirrored in Maluku, where male investors constitute 57% of the investor population compared to 43% for female investors. These disparities are not merely statistical curiosities but reflect deeper societal, cultural, and psychological factors that influence how different demographic groups approach financial decision-making and risk- taking. The urgency of investigating investment decision-making factors in regions like Maluku stems from several converging factors. First, the rapid growth in investor numbers without corresponding improvements in financial literacy creates risks for individual investors and systemic stability. Second, the significant gender disparities in investment participation suggest that current approaches to financial education and market development may not be adequately serving all segments of the population. Third, the unique geographical, cultural, and economic characteristics of eastern Indonesian regions require localized understanding rather than assumptions based on research conducted in more developed areas like Java and Bali. From a theoretical perspective, investment decision-making has been extensively studied through various lenses, with behavioral finance theory providing particularly valuable insights. American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1391 The Theory of Planned Behavior, developed by Ajzen (1991), suggests that investment decisions are influenced by attitudes, subjective norms, and perceived behavioral control. This framework has been complemented by behavioral finance theories, particularly Kahneman and Tversky's (1979) Prospect Theory, which demonstrates that individuals exhibit loss aversion and make decisions based on perceived gains and losses rather than absolute outcomes. These theoretical foundations provide the basis for understanding how factors such as financial literacy, risk perception, and self-efficacy influence investment behavior. Financial literacy, as conceptualized by Ramadhania and Krisnawati (2024), represents the combination of knowledge, skills, and confidence that individuals possess in shaping wise financial attitudes and behaviors. Research by Shafira et al. (2024) and Chandra et al. (2023) has consistently shown that financial literacy significantly influences investment decision-making, with higher levels of financial understanding leading to more rational and effective investment choices. However, studies by Makkulau et al. (2023) have produced conflicting results, suggesting that the relationship between financial literacy and investment decisions may be more complex than initially understood, particularly in different cultural and economic contexts. The emergence of financial technology has introduced a new dimension to investment decision-making that requires separate consideration. Financial technology literacy, as defined by Maryam et al. (2023), encompasses awareness of financial technology, better utilization of financial product knowledge, and risk control arising from financial technology. Research by Sunarko and Sutrisno (2025) demonstrates that investment decisions are positively and significantly influenced by individuals' financial technology literacy, particularly among millennials. The Technology Acceptance Model (TAM), by Davis (1989), provides theoretical grounding for understanding how perceived usefulness and ease of use influence technology adoption in financial contexts. Risk perception represents another crucial factor in investment decision-making, with substantial research demonstrating its influence on investor behavior. According to Afrida and Sari (2021), risk perception refers to how individuals assess potential negative outcomes that may occur in investment activities. Studies by Bhattacharjee et al. (2021) and Deb and Singh (2018) have shown that risk perception significantly determines the extent to which individuals are willing to make investment decisions under uncertain conditions. However, research results have been mixed, with some studies like Sunarko and Sutrisno (2025) finding no significant relationship between risk perception and investment decisions, while others like Makkulau et al. (2023) demonstrate positive and significant relationships. Self-efficacy, defined as an individual's belief in their ability to accomplish specific tasks, has emerged as an important psychological factor in financial decision-making. According to Akbar and Armansyah (2023), the higher an individual's level of self-efficacy, the greater their confidence in achieving success. Research by Angraini et al. (2024) shows that individuals with high levels of financial self-efficacy tend to be more confident and optimistic in determining investment steps and more active in managing their portfolios. However, contrasting findings by Sunarko and Sutrisno (2025) suggest that financial self-efficacy may have negative effects on American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1392 investment decisions, potentially leading to overconfidence bias as described by Yundari and Artati (2021). Gender differences in investment behavior have been extensively documented in international literature, with studies consistently showing that men and women approach financial decision-making differently. Research by Aren and Zengin (2016) identifies gaps in financial literacy levels between men and women, with men showing better understanding, particularly in advanced financial literacy aspects. Similarly, studies by Cupak et al. (2022) and Qibthiyah et al. (2024) demonstrate that gender influences investment behavior, with men generally showing higher levels of financial understanding and greater willingness to take risks compared to women. The novelty of this research lies in several key aspects that distinguish it from previous studies. First, while most prior research has been conducted in regions with high financial inclusion such as Java and Bali, this study specifically focuses on Maluku, a region characterized by low financial literacy levels and unique geographical and cultural characteristics. Second, the research employs multi-group analysis using Partial Least Squares Structural Equation Modeling (PLS- SEM) to systematically examine gender differences in investment decision-making factors, providing more nuanced insights than studies that treat gender as a simple control variable. Third, the study simultaneously examines four key factors—financial literacy, financial technology literacy, risk perception, and self-efficacy—in their influence on investment decisions, providing a more comprehensive understanding of the decision-making process. The primary objective of this research is to analyze the influence of financial literacy, financial technology literacy, risk perception, and self-efficacy on investment decisions among capital market investors in Maluku, with particular attention to gender differences through multi- group analysis. Specifically, the study aims to determine the partial and simultaneous effects of these variables on investment decisions for the overall sample and for male and female subgroups separately, as well as to identify significant differences in these relationships between gender groups. The potential benefits and implications of this research extend across multiple stakeholder groups. For academic researchers, the study contributes to the growing body of literature on behavioral finance and investment decision-making in emerging market contexts, particularly regarding gender differences and regional variations. The methodological approach using multi- group analysis provides a template for future research examining demographic differences in financial behavior. For policymakers and regulators, particularly the Financial Services Authority (Otoritas Jasa Keuangan - OJK), the findings offer evidence-based insights for designing more effective and inclusive financial literacy programs that account for gender differences and regional characteristics. For financial institutions and capital market intermediaries, the research provides guidance for developing targeted products and services that meet the specific needs and preferences of different demographic groups. Finally, for individual investors, the study offers insights into the factors that influence investment decision-making, potentially helping them make more informed and rational investment choices. American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1393 METHOD This research employed a descriptive-verificative approach with a quantitative methodology using a survey strategy through questionnaires. The population studied was capital market investors domiciled in Maluku Province, totaling 52,000 people as of 2024. Sample selection was conducted using the purposive sampling method with specific criteria: active capital market investors domiciled in Maluku who have been investing in capital markets for at least 6 months to 1 year. The sample size was calculated using Cochran's formula with a 95% confidence level and 5% margin of error, resulting in 400 respondents. The distribution of male and female respondents was adjusted to the proportion of capital market investors recorded in Maluku, consisting of 228 male respondents (57%) and 172 female respondents (43%). Data collection was conducted through online questionnaires using a 5-point Likert scale to measure five main variables: financial literacy (6 indicators), financial technology literacy (5 indicators), risk perception (5 indicators), self-efficacy (5 indicators), and investment decisions (6 indicators). Before the main analysis, data were tested for validity and reliability to ensure measurement accuracy. Data analysis utilized Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 software to test relationships between latent constructs. Additionally, Partial Least Squares Multi-Group Analysis (PLS-MGA) was used to evaluate differences in variable influences based on gender. The analysis included outer model evaluation (convergent validity, discriminant validity, composite reliability), inner model evaluation (R², predictive relevance, effect size), and hypothesis testing through t-statistics and F-statistics. RESULT AND DISCUSSION Respondent Characteristics The research respondents consisted of 400 capital market investors in Maluku, strategically distributed to reflect the actual gender composition of investors in the region. The sample comprised 228 male investors (57%) and 172 female investors (43%), mirroring the gender distribution reported by KSEI for Maluku Province. This demographic alignment ensures that the research findings accurately represent the investment behavior patterns in the region. Table 1. Respondent Demographics Characteristic Category Frequency Percentage Gender Male 228 57.0% Female 172 43.0% Age < 30 years 237 59.3% 31-40 years 122 30.5% 41-50 years 31 7.8% 51-60 years 9 2.3% > 60 years 1 0.3% Occupation Student 149 37.3% American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1394 Characteristic Category Frequency Percentage Civil Servant 109 27.3% Private Employee 90 22.5% Entrepreneur 35 8.8% Full Trader/Investor 3 0.8% Others 14 3.5% Education High School/Vocational 135 33.8% Diploma 28 7.0% Bachelor's Degree 191 47.8% Master's Degree 43 10.8% Doctoral Degree 3 0.8% The age distribution reveals a predominantly young investor population, with 59.3% under 30 years old, reflecting the broader trend of millennial and Generation Z participation in capital markets facilitated by digital platforms. The educational profile shows a highly educated sample, with 58.6% holding tertiary education qualifications, suggesting that current capital market participants in Maluku represent a relatively educated segment of the population. Descriptive Analysis of Research Variables Descriptive analysis employed interval categorization using Sugiyono's (2021) scale interpretation to assess respondent perceptions across all variables. The interval calculation formula used was: i = (Highest value - Lowest value) / Number of categories = (5-1)/5 = 0.80 This resulted in the following interpretation categories: 1.00-1.80 (Very Poor), 1.81-2.60 (Poor), 2.61-3.40 (Fair), 3.41-4.20 (Good), and 4.21-5.00 (Very Good). Table 2. Descriptive Statistics of Research Variables Variable Mean Min Max Category Financial Literacy 4.331 2 5 Very Good Financial Technology Literacy 4.173 2 5 Good Risk Perception 4.250 2 5 Very Good Self-Efficacy 4.086 2 5 Good Investment Decision 4.270 1 5 Very Good The descriptive analysis reveals encouraging results regarding respondent capabilities and attitudes. Financial literacy achieved the highest mean score of 4.331, indicating that Maluku investors possess very good understanding of basic financial concepts. This finding contrasts with the national financial literacy statistics, suggesting that active capital market participants represent a more financially literate segment of the population. Risk perception also scored highly at 4.250, demonstrating that investors are well aware of investment risks and their implications. American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1395 Financial technology literacy scored 4.173, placing it in the good category, which reflects the growing adoption of digital financial services in the region. Self-efficacy, with a mean of 4.086, indicates that investors generally have good confidence in their financial management abilities. Investment decision quality scored 4.270, suggesting that respondents make well-considered investment choices. Common Method Bias Testing Before proceeding with structural analysis, common method bias (CMB) was assessed using the Full Collinearity Variance Inflation Factor (FCVIF) approach. All FCVIF values were below the threshold of 3.3, with the highest value being 2.408 for risk perception, indicating that common method bias does not threaten the validity of this research. Measurement Model Evaluation (Outer Model) The measurement model evaluation encompasses three critical assessments: convergent validity, discriminant validity, and reliability testing. These evaluations ensure that the research instruments accurately measure the intended constructs. Convergent Validity Convergent validity was assessed through factor loadings and Average Variance Extracted (AVE) values. All indicators demonstrated factor loadings above the 0.70 threshold, with values ranging from 0.725 to 0.842, indicating strong relationships between indicators and their respective constructs. Table 3. Factor Loadings and Average Variance Extracted Variable Indicators Factor Loading AVE Financial Literacy LK1-LK6 0.742-0.799 0.596 Financial Technology Literacy LTK1-LTK5 0.725-0.794 0.595 Risk Perception PR1-PR5 0.768-0.816 0.627 Self-Efficacy ED1-ED5 0.750-0.842 0.638 Investment Decision KI1-KI6 0.732-0.801 0.595 All AVE values exceeded 0.50, confirming that each construct explains more than half of the variance in its indicators, thus establishing convergent validity. Discriminant Validity Discriminant validity was evaluated using the Fornell-Larcker criterion and Heterotrait- Monotrait Ratio (HTMT). The Fornell-Larcker criterion requires that the square root of each construct's AVE be greater than its correlations with other constructs. Table 4. Fornell-Larcker Criterion Matrix Variable FL FTL RP SE ID Financial Literacy (FL) 0.772 American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1396 Variable FL FTL RP SE ID Financial Technology Literacy (FTL) 0.679 0.771 Risk Perception (RP) 0.698 0.678 0.792 Self-Efficacy (SE) 0.553 0.576 0.576 0.799 Investment Decision (ID) 0.731 0.720 0.733 0.605 0.771 The HTMT assessment further confirmed discriminant validity, with all values below the 0.90 threshold, ranging from 0.641 to 0.852. Reliability Testing Reliability was assessed through Cronbach's Alpha and Composite Reliability measures. All constructs demonstrated excellent reliability with Cronbach's Alpha values ranging from 0.830 to 0.864 and Composite Reliability values from 0.880 to 0.898, all exceeding the 0.70 threshold. Structural Model Evaluation (Inner Model) The structural model evaluation assessed the relationships between constructs and the model's predictive capability through several key metrics. Coefficient of Determination (R²) The R² value for the investment decision construct was 0.681, with an adjusted R² of 0.678, indicating that the four predictor variables collectively explain 67.8% of the variance in investment decisions. According to Hair et al. (2019) classification, this represents moderate to substantial explanatory power. Predictive Relevance (Q²) The Q² value was calculated using the formula: Q² = 1 - (SSE/SSO) Where SSE = Sum of Squared Errors and SSO = Sum of Squared Observations. The Q² value of 0.398 indicates that the model has good predictive relevance, as it exceeds zero and approaches the moderate threshold of 0.25. Effect Size (f²) Effect size values revealed the relative importance of each predictor: Table 5. Effect Size Analysis Variable f² Value Effect Size Category Financial Literacy 0.109 Small to Medium Financial Technology Literacy 0.087 Small Risk Perception 0.104 Small to Medium Self-Efficacy 0.033 Small American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1397 Hypothesis Testing Results Partial Effects Analysis (t-statistics) The partial effects analysis examined individual variable influences on investment decisions for the complete sample and gender subgroups. Table 6. Partial Effects Results Variable Complete Sample Male Group Female Group t-stat (p-value) t-stat (p-value) t-stat (p-value) Financial Literacy → Investment Decision 4.971 (0.000)** 3.605 (0.000)** 3.662 (0.000)** Financial Technology Literacy → Investment Decision 4.900 (0.000)** 3.992 (0.000)** 2.528 (0.012)** Risk Perception → Investment Decision 5.332 (0.000)** 2.115 (0.033)** 6.003 (0.000)** Self-Efficacy → Investment Decision 3.580 (0.000)** 4.337 (0.000)** 1.069 (0.285) Note: ** indicates significance at p < 0.05 Path Coefficients Analysis The path coefficients reveal the strength and direction of relationships: Table 7. Path Coefficients by Gender Variable Complete Sample Male Group Female Group Financial Literacy 0.286 0.306 0.299 Financial Technology Literacy 0.255 0.290 0.190 Risk Perception 0.284 0.174 0.413 Self-Efficacy 0.135 0.196 0.060 Simultaneous Effects Analysis (F-statistics) F-test results demonstrate the collective influence of all predictor variables: Table 8. F-Statistics Results Group F-Statistic p-value Decision Complete Sample 238.472 0.000 Significant Male Group 163.856 0.000 Significant Female Group 78.708 0.000 Significant Multi-Group Analysis (PLS-MGA) The PLS-MGA tested for significant differences between gender groups using the following approach: American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1398 Table 9. Multi-Group Analysis Results Variable Path Difference (Male-Female) p-value (Original) p-value (New) Significance Financial Literacy 0.003 0.492 0.983 Not Significant Financial Technology Literacy 0.105 0.155 0.309 Not Significant Risk Perception -0.243 0.980 0.020 Significant Self-Efficacy 0.139 0.028 0.057 Marginally Significant Discussion of Findings Financial Literacy and Investment Decisions The significant positive influence of financial literacy on investment decisions (β=0.286, t=4.971, p<0.001) confirms H1 and aligns with established literature. This finding supports Lusardi and Mitchell (2023) and Chandra et al. (2023), who demonstrated that individuals with higher financial literacy make more rational and effective investment decisions. The consistency of this relationship across both gender groups (males: β=0.306, females: β=0.299) suggests that financial knowledge serves as a universal foundation for investment decision-making, regardless of gender. Interestingly, the absence of significant gender differences in financial literacy's influence (p=0.983) contradicts some previous research, such as Aren and Zengin (2016), who found gender- based differences in financial literacy effects. This finding may reflect the specific characteristics of Maluku's investor population, many of whom have participated in financial education programs offered by Indonesia Stock Exchange's Galeri Investasi, potentially equalizing financial knowledge across genders. Financial Technology Literacy and Investment Decisions Financial technology literacy significantly influences investment decisions (β=0.255, t=4.900, p<0.001), supporting H5. This finding validates the Technology Acceptance Model's application to investment contexts, where perceived usefulness and ease of use drive technology adoption in financial decision-making. The influence remains significant across both genders, though stronger for males (β=0.290) than females (β=0.190). The lack of significant gender differences (p=0.309) in financial technology literacy's influence suggests that digital financial services adoption has become more gender-neutral in recent years. This finding challenges traditional assumptions about gender-based technology adoption differences and may reflect the increasing digitalization of financial services in Indonesia (Erlando et al., 2020). Risk Perception and Investment Decisions Risk perception demonstrates the strongest overall influence on investment decisions (β=0.284, t=5.332, p<0.001), confirming H9. More importantly, this study reveals significant American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1399 gender differences in risk perception's influence (p=0.020), supporting H12. Female investors show substantially stronger sensitivity to risk perception (β=0.413) compared to males (β=0.174). This finding strongly supports Cupak et al. (2022) and Qibthiyah et al. (2024) research indicating that women are more risk-averse and place greater emphasis on risk considerations in investment decisions. The behavioral explanation lies in Prospect Theory's loss aversion principle, where women typically exhibit stronger loss aversion than men, making risk assessment more central to their decision-making process. Self-Efficacy and Investment Decisions Self-efficacy shows significant influence on investment decisions for the complete sample (β=0.135, t=3.580, p<0.001) and male subgroup (β=0.196, t=4.337, p<0.001), but not for females (β=0.060, t=1.069, p=0.285). This gender disparity represents one of the study's most intriguing findings. The lack of self-efficacy's influence on female investment decisions contradicts some previous research, such as Farrell et al. (2016), who found that women with high financial self- efficacy are more likely to engage in financial planning activities. However, it aligns with studies suggesting that women may rely more heavily on external information and expert advice rather than personal confidence when making investment decisions. Theoretical and Practical Implications From a theoretical perspective, these findings advance behavioral finance theory by demonstrating that classical models of investment decision-making require gender-specific adjustments. The Theory of Planned Behavior's applicability varies across demographic groups, with risk perception playing a more prominent role for women and self-efficacy being more relevant for men. Practically, these findings have significant implications for financial education and product development. Risk management education should be prioritized for female investors, while confidence-building programs may be more beneficial for male investors. Financial institutions should consider developing gender-sensitive marketing and educational strategies that acknowledge these behavioral differences. Limitations and Future Research Directions While this study provides valuable insights, several limitations should be acknowledged. The sample, while representative of Maluku's investor population, may not generalize to other Indonesian regions with different cultural and economic characteristics. Additionally, the cross- sectional design limits causal inference, suggesting that longitudinal studies could provide stronger evidence for the observed relationships. Future research should explore these relationships in different geographical and cultural contexts, examine the role of other demographic factors such as age and education, and investigate the mediating mechanisms through which these variables influence investment decisions. American Journal of Economic and Management Business Vol. 4 No. 8 Agustus 2025 1400 CONCLUSION This study reveals that financial literacy, financial technology literacy, risk perception, and self-efficacy significantly influence investment decisions among capital market investors in Maluku, both partially and simultaneously. However, important gender differences exist, particularly in the influences of risk perception and self-efficacy. Risk perception shows a significantly stronger influence on female investors compared to males, reflecting women's tendency to be more cautious in investment decisions. Conversely, self-efficacy significantly influences male investors but not female investors, suggesting that different psychological factors drive investment decisions across genders. These findings provide important implications for regulators and financial institutions to design more inclusive and gender-sensitive financial literacy and investment education strategies. For female investors, emphasis should be placed on risk understanding and management, while for male investors, building appropriate self-confidence becomes important. Future research should explore other demographic factors such as education level, age, and investment experience to provide a more comprehensive understanding of investment decision-making behavior in regions with specific economic and demographic characteristics like Maluku. 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