Beyond the hype: AI advice and investor dissonance in crypto trading
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F25%3A63589579" target="_blank" >RIV/70883521:28120/25:63589579 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s12144-025-07430-w" target="_blank" >https://link.springer.com/article/10.1007/s12144-025-07430-w</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s12144-025-07430-w" target="_blank" >10.1007/s12144-025-07430-w</a>
Alternative languages
Result language
angličtina
Original language name
Beyond the hype: AI advice and investor dissonance in crypto trading
Original language description
This study examines the impact of cognitive dissonance on the relationship between investors' intentions to use AI advice and their investment behaviour in the cryptocurrency market. The study recruited 348 individuals through a non-random snow-ball sampling technique. Utilising ChatGPT for investment recommendations, the research involves a trading experiment accompanied by a two-stage survey to evaluate investor attitudes towards AI before and their cognitive dissonance levels after the experiment. Structural Equation Modelling (SEM) identifies the connection between the intent to use AI and the influence of cognitive dissonance on investment decisions. Results indicate that investors following AI advice outperformed those who did not, attributable not to AI's predictive power but to reduced cognitive dissonance. This reduction allowed investors using AI to cut losses more effectively, in contrast to those who eschewed AI advice and tended to hold onto losing positions longer, leading to worse performance. Although focused on the cryptocurrency market, the findings suggest a potential for broader applicability in conventional financial markets. The study's key contribution is demonstrating that AI recommendations can mitigate the disposition effect, implying that AI's broader implementation could enhance market efficiency.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50103 - Cognitive sciences
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Current Psychology
ISSN
1046-1310
e-ISSN
1936-4733
Volume of the periodical
44
Issue of the periodical within the volume
5
Country of publishing house
GB - UNITED KINGDOM
Number of pages
12
Pages from-to
3313-3325
UT code for WoS article
001404832400001
EID of the result in the Scopus database
2-s2.0-105003748049