Behavioral and Financial Determinants of Cryptocurrency Investment Decisions: The Role of Cognitive Biases, Spiritual Beliefs, and Financial Literacy - Ignatius Edward Riantono (Journal of Management and Business Analytics, 2026)
This study aims to analyze the influence of overconfidence bias, representativeness bias, loss aversion bias, mental accounting, spiritual beliefs, and financial literacy on investment decisions among Generation Z cryptocurrency investors. The research is designed as explanatory research employing a quantitative approach to identify causal relationships between behavioral, psychological, and literacy-related factors and investment decision-making. Data were collected from Generation Z investors domiciled in DKI Jakarta, reflecting a rapidly growing demographic within the cryptocurrency market that is characterized by high digital engagement and risk exposure. Given that the exact population size of Generation Z cryptocurrency investors is unknown, this study applies the sampling guidelines proposed by Hair et al., utilizing purposive sampling to ensure respondents meet specific criteria relevant to the research objectives. Data analysis was conducted using SmartPLS 4 software, enabling robust assessment of structural relationships and variable significance within the proposed model. The empirical results reveal that overconfidence bias, representativeness bias, loss aversion bias, mental accounting, and financial literacy significantly influence investment decisions. These findings highlight the dominant role of behavioral biases and financial knowledge in shaping how young investors perceive risk, process information, and allocate resources within cryptocurrency markets. Conversely, spiritual beliefs were found to have no significant effect on investment decisions, suggesting that investment behavior in this context is primarily driven by cognitive and rational considerations rather than personal belief systems. For future research, it is recommended to broaden the literature framework, refine sample selection criteria, apply more diverse Likert scale measurements, and incorporate additional variables that may further explain investment decision behavior.