Methodological Perspectives on Behavioral Biases in Individual Investors' Decision-Making: A Systematic Literature Review

This paper systematically reviews the literature on behavioral biases affecting individual ‎investors' decision-making, with a particular focus on methodological approaches, including ‎sampling techniques and data analysis methods. In accordance with the Preferred Reporting ‎Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study analyzes 63 ‎peer-reviewed studies published between 2015 and 2024, identifying key trends in research ‎methodology, discussing their limitations, and exploring strategies to enhance methodological ‎rigor. Findings indicate that most studies focus on emerging markets and predominantly employ ‎survey-based research using non-probability sampling techniques, particularly convenience and ‎snowball sampling. Additionally, there has been a notable shift from traditional regression ‎analysis to Structural Equation Modeling (SEM) in recent years. However, survey-based ‎research, such as a questionnaire with a cross-sectional design, raises concerns, for example, ‎the inability to prove temporal relationships and self-report bias. Therefore, the paper highlights ‎the need for future studies to address methodological shortcomings through advanced ‎techniques such as AI, deep learning, and big data analytics. This paper will be useful in ‎guiding potential researchers in selecting suitable methodologies for their studies on behavioral ‎biases that affect individual investors' decision-making‎.