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This study attempts to assess individuals' Bitcoin, Ethereum and Litecoin literacy, and then identifies different profiles based on the literacy levels of these cryptocurrencies, and further investigates whether financial literacy explains Bitcoin, Ethereum and Litecoin literacy. Using a sample from higher learning institutions in Malaysia, the results indicate that individuals exhibit higher knowledge on Bitcoin compared to Ethereum and Litecoin. Cluster analysis revealed two clusters, where the high-cryptocurrency-literacy cluster (high in Bitcoin, Ethereum, and Litecoin literacy) has a distinct demographic profile and higher financial literacy than the low-cryptocurrency-literacy cluster (low in Bitcoin, Ethereum, and Litecoin literacy). Financial literacy is shown to explain Bitcoin and Ethereum literacy, but not Litecoin literacy. [ABSTRACT FROM AUTHOR]

Investors' perception of past portfolio returns predicts their investment behavior, but does this relationship mediate by overconfidence? Taking into account different aspects of overconfidence, this paper examines whether overconfidence manifested as illusion of control, miscalibration and better-than-average mediates the association between perception of past portfolio returns and investment behavior. In a survey study with individual and institutional investors from Malaysia, the results indicate that perception of higher past portfolio returns increases investors' trading, percentage of risky share investment and the number of financial asset holding, through the mediating channel of better-than-average effect. While individual investors are influenced by this overconfidence mechanism, institutional investors are not sensitive. This finding has theoretical implication for overconfidence model, house money effect and naïve reinforcement learning. Practically, the results imply that individual investors should be careful about underlying overconfidence biases as it can lead to inefficient decisions. [ABSTRACT FROM AUTHOR]

AbstractDigital Game-Based Learning (DGBL) combines gamification with educational content to create engaging learning experiences tailored to today’s learners. Non-Playable Characters (NPCs), integral to many video games, are computer-generated characters controlled by game algorithms rather than players. Despite their prominence in gaming, research on NPCs’ impact on learners’ perceptions, experiences, and acceptance of DGBL environments remains scarce. This study presents a framework that investigates the impact of non-player characters’ (NPCs) attributes, including social presence, telepresence, social support, and storytelling, on factors related to technology acceptance, such as perceived usefulness and perceived ease of use, as well as on the flow state and the intention to continue using a digital game-based learning (DGBL) environment. An experiment was conducted where learners were administered a survey after interacting with NPCs, simulating peers, professors, or industry experts teaching Fintech topics within a DGBL environment. Analysis of 200 valid responses using Partial Least Squares Structural Equation Modeling revealed that NPCs’ attributes significantly enhance perceived usefulness, perceived ease of use, and flow state in the DGBL environment. Furthermore, the findings indicate that perceived ease of use and the flow state positively affect learners’ intentions to continue using the DGBL environment. Thematic analysis per participant feedback highlighted the value of DGBL as an innovative educational approach that makes learning about Fintech accessible and engaging. These results are interpreted through the Technology Acceptance Model, flow theory, presence theory, and gamified learning model, offering insights into how NPCs can be effectively designed to enrich the DGBL learning experience and foster technology adoption. [ABSTRACT FROM AUTHOR]