Modelling Cryptocurrency Investment Behavior: the Roles of FOMO, Risk Tolerance, and Perceived Benefit

Tan, Hii (2025) Modelling Cryptocurrency Investment Behavior: the Roles of FOMO, Risk Tolerance, and Perceived Benefit. Masters thesis, Tunku Abdul Rahman University of Technology and Management.

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Abstract

This study explains why and how retail investors convert intentions into cryptocurrency trades in a market defined by thin fundamentals and near-frictionless execution. Using cross-sectional survey data (N = 400) and partial least squares structural equation modeling (PLS-SEM) with bootstrapped inferences, we examine social, affective, and evaluative drivers of investment intention and behavior. The measurement model satisfies standard reliability and validity criteria, and an early–late comparison on demographics indicates no material non-response bias. Results show a strong, positive intention→behavior link. Herding, fear of missing out (FOMO), perceived benefits, and risk tolerance positively shape investment intention, whereas classical loss aversion is attenuated. Intention mediates these drivers’ effects on behavior, yet several direct paths remain significant—consistent with a dual-route mechanism in which cue-responsive execution (e.g., trending lists, alerts, copy-trading prompts) coexists with reflective planning. Moderation analyses indicate that higher risk tolerance strengthens the translation of FOMO into intention, while experience is discussed as a contextual factor that may temper sensitivity to social proof under certain market states. The findings extend behavioral finance by formalizing a salience–intention–execution lens for high-velocity digital assets and yield actionable guidance: platforms should rebalance choice architecture toward proportional risk salience and deploy brief, context-sensitive cool-offs; regulators should standardize evidence-based disclosures. Limitations of cross-sectional design and self-report motivate a longitudinal, telemetry-linked research agenda.