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Influence of cognitive heuristics and herding behavior on cryptocurrency investment decisions: Evidence from Moroccan investors

Year: 2026
Type: article
Abstract: Type of the article: Research ArticleAbstractCryptocurrency markets combine extreme volatility, a lack of established fundamentals, and intense digital connectivity among investors, which favor cognitive biases and social imitation in financial decision-making. Yet evidence on the relative weight of individual heuristics versus herding remains fragmented, particularly for North African markets. This study examines the simultaneous influence of representativeness, availability, and anchoring heuristics, together with herding behavior, on the cryptocurrency investment decisions of Moroccan investors. Data were collected between April and September 2024 through an online questionnaire, yielding a final analytical sample of 265 valid responses after excluding minor respondents. The measurement model was refined through exploratory factor analysis, removing two items with weak or cross-loadings; the hypothesized relationships were then tested via partial least squares structural equation modeling with demographic controls. Representativeness emerges as the most influential and stable predictor of the investment decision, operationalized here through rule adherence and risk avoidance (β = 0.306, p = 0.004), while herding shows only marginal significance (β = 0.234, p = 0.050); availability and anchoring show no significant effect. The model explains 43.5% of the variance in investment decisions, with predictive relevance confirmed by an out-of-sample PLSpredict analysis. These findings suggest that individual heuristic processing outweighs social imitation in shaping cryptocurrency investment decisions in this emerging market. These results carry practical value for investor-education initiatives and platform design, which should prioritize interventions targeting pattern-extrapolation reasoning over efforts aimed solely at curbing herd-like behavior.
Language: English
Open Access status: gold
Estimated APC paid: $738
OpenAlex ID: W7225794918
FWCI:
Cites: 51
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