臺灣博碩士論文加值系統

金融投資是現代經濟的基石，促使個人和組織能夠有效分配資本，以產生利潤並支持經濟持續增長。投資決策對個人的財務穩定和福祉至關重要，進而顯著影響整體社會的經濟健康情形。儘管作出明智投資決策的重要性不言而喻，但許多個人仍然缺乏必要的金融知識、風險承受能力和信心，無法有效應對投資領域的複雜性。本研究旨在探討金融素養、風險承受能力和收入因素對個人投資決策的影響成效，旨在找出改善個人投資成果的方法。本研究從多樣化的投資社群中收集數據，並使用PLS-SEM評估模型中中介和調節變量的作用。結果顯示，風險承受能力在金融素養和投資決策之間起中介作用 強調其在決策過程中與金融素養有直接影響並具中介角色。此外，本研究確定收入因素是越南個人投資者市場中的重要決定因素。收入影響影響投資決策的因素，例如金融知識和風險承受能力。這些發現強調了提升金融素養的重要性，以及理解風險承受能力和收入在塑造投資行為中的細微角色。通過解決這些因素，可以開發針對性的干預措施，改進個人投資者的財務決策過程和結果，進而在更大範圍內促進強化的財務穩定和福祉.

Financial investment is the cornerstone of modern economies, enabling individuals and organizations to allocate capital to generate profits and support growth. Investment decisions are crucial for an individual's financial stability and well-being, and by extension, they significantly influence the overall economic health of society. Despite the importance of making informed investment decisions, many individuals still lack the necessary financial knowledge, risk tolerance, and confidence in their financial capabilities to effectively navigate the complexities of the investment landscape. This study aims to explore the impacts of financial literacy, risk tolerance, and income on individual investment decisions, with the goal of identifying ways to improve investment outcomes for individuals. The research collected data from a diverse investment community and employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the role of mediating and moderating variables in the model. The analysis reveals that risk tolerance acts as a mediator between financial literacy and investment decisions. Additionally, the study identifies income as a significant determinant in the individual investor market in Vietnam. Income influences factors that impact investment decisions, such as financial literacy and risk tolerance. These findings underscore the importance of improving risk tolerance and understanding the role of financial literacy and income in shaping investment behavior. By addressing these factors, it is possible to develop targeted interventions that can enhance financial decision-making processes and outcomes for individual investors, thereby contributing to greater financial stability on a broader scale.

TABLE OF CONTENTS
ACKNOWLEDGEMENT i
ABSTRACT ii
摘要 iv
TABLE OF CONTENTS v
LIST OF FIGURES viii
LIST OF TABLES ix
CHAPTER 1: INTRODUCTION 1
1.1. Introduction 1
1.2. Motivations 3
1.3. Research Background 3
1.4. The objectives and research questions 6
1.5. The scope of the research 7
1.6. Research structure 8
Summary of chapter 1 9
CHAPTER 2: REVIEW OF LITERATURE 10
2.1. Literature Review 10
2.1.1. Base theories 10
2.1.2. Investment Decision 15
2.1.2.1. Interrelationship between Financial Literacy and Investment Decision 16
2.1.2.2. Interrelationship between Risk Tolerance and Investment Decision 18
2.1.2.3. The mediating effect of Risk Tolerance 19
2.1.2.4. Interrelationship between Income and other factors 20
2.1.2.5.The moderating effects of Income 22
2.2. The conceptual frameworks among empirical studies 23
2.3. Proposal research hypotheses 27
Summary of chapter 2 30
CHAPTER 3: METHODOLOGY 31
3.1. Research process diagram 31
3.2. Empirical framework and hypothesis development 32
3.3. Research Method 38
3.3.1. Sample and sampling procedure 38
3.3.2. Measures 39
3.3.3. Data analysis methods 40
3.3.3.1. Reliability Test - Cronbach's Alpha 40
3.3.3.2. Exploratory Factor Analysis 41
3.3.3.3. Partial Least Squares Structural Equation Analysis 42
Summary of chapter 3 49
CHAPTER 4: RESULT AND DISCUSSION OF THE KEY FINDINGS 50
4.1. Descriptive statistics 50
4.2. Cronbach’s Alpha for the measured variables 53
4.3. Exploratory Factors Analysis 56
4.3.1. Exploratory Factors Analysis for intermediate and independent variables 56
4.3.2. Exploratory Factors Analysis for dependent variable 58
4.4. Measurement Model Assessment 60
4.4.1. Outer Loading 60
4.4.2. Internal Consistency Reliability and Convergent Validity analysis 62
4.4.3. Discriminant Validity 62
4.4.4. Model Fit 63
4.5. Structural Model Assessment 64
4.5.1. Collinearity Statistics 65
4.5.2. Coefficient of determination R2 65
4.5.3. Effect size coefficient f2 67
4.5.4. Predictive relevance coefficient Q2 68
4.5.5. Path model analysis 68
Summary of chapter 4 72
CHAPTER 5: CONCLUSION AND RECOMMENDATION 73
5.1. Conclusion 73
5.2. Recommendation 75
5.3. Limitation and Future Research Suggestion 79
Summary of chapter 5 81
REFERENCES 82
APPENDIX 96

LIST OF FIGURES
Figure 2.1. The Theory of Planned Behavior model 13
Figure 2.2. The conceptual framework (1) 24
Figure 2.3. The conceptual framework (2) 24
Figure 2.4. The conceptual framework (3) 25
Figure 2.5. The conceptual framework (4) 26
Figure 2.6. The conceptual framework (5) 26
Figure 2.7. The conceptual framework (6) 27
Figure 3.1. Research Process 32
Figure 3.2. Research Model 33
Figure 4.1. Coefficient of outer loading after FL1 eliminated 61
Figure 4.2. The result of model 70

LIST OF TABLES
Table 3.1. Survey questions (measurement scales) for variables in the model 36
Table 4.1. Frequency of individual investor’s gender 50
Table 4.2. Frequency of individual investor’s age group 50
Table 4.3. Frequency of individual investor’s living place 51
Table 4.4. Frequency of individual investor’s education 52
Table 4.5. Frequency of individual investor’s working experience 52
Table 4.6. Frequency of individual investor’s major 53
Table 4.7. Cronbach’s Alpha for the measured variables 54
Table 4.8. KMO and Bartlett's Test 56
Table 4.9. Communalities 56
Table 4.10. Total Variance Explained 57
Table 4.11. Rotated Component Matrix 57
Table 4.12. Analyze KMO, Bartlett's test and factor loadings 59
Table 4.13. Component Matrix 59
Table 4.14. Coefficient of outer loading 60
Table 4.15. Cronbach's Alpha, Composite Reliability, and Average Variance Extracted 62
Table 4.16. Discriminant validity 63
Table 4.17. Model Fit 63
Table 4.18. VIF- inner model 65
Table 4.19. R2 Value 66
Table 4.20. f2 Coefficient 67
Table 4.21. Q2 Coefficient 68
Table 4.22. Path Coefficient 69

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