From Rationality to Algorithms: A Systematic Review of Behavioural, Intentional and Technological Determinants of Individual Investment Decisions

Investment decision-making increasingly involves interactions between behavioural biases, financial knowledge, cognitive intentions, and AI-enabled financial technologies. Understanding these interactions is important for developing evidence-informed financial decision-support systems. This systematic review synthesises empirical evidence on behavioural, intentional, and technological determinants of individual investment decisions, with particular attention to their implications for economic decision-making and AI-assisted investment platforms. Following PRISMA 2020 guidelines, an initial search across five data-bases identified 742 records, yielding 20 eligible studies. Updated searches conducted in September 2026 identified 485 records, of which 369 underwent screening, resulting in 34 additional studies and a final sample of 54 peer-reviewed empirical studies published between 2021 and 2025. The review employed thematic synthesis, Mixed Methods Appraisal Tool (MMAT) assessments, weight analysis, and quali-ty-based sensitivity analysis. Findings identified seven consistently supported predictors, including be-havioural biases, financial literacy, attitude, perceived behavioural control, trust, and risk perception. However, only behavioural biases and financial literacy retained consistent support in higher-quality stud-ies. Evidence was concentrated in Asian markets, particularly Indonesia and India, and predominantly relied on cross-sectional surveys and structural equation modelling. Financial literacy attenuated but did not eliminate behavioural biases, while intention-based models rarely examined subsequent investment be-haviour. Technology-focused studies emphasised platform trust and adoption without integrating behav-ioural biases. The review proposes an integrated multi-layer decision-support framework, four testable propositions, and a Theory–Context–Characteristics–Methodology (TCCM) research agenda to inform the future design and evaluation of AI-assisted investment decision systems, financial advisory platforms, and investor protection strategies.