Embracing intelligent insights: Unveiling investor adoption of AI advice and risk appetite

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F25%3A63595939" target="_blank" >RIV/70883521:28120/25:63595939 - isvavai.cz</a>

Result on the web

<a href="https://editorial.upce.cz/1804-8048/33/1/2136" target="_blank" >https://editorial.upce.cz/1804-8048/33/1/2136</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.46585/sp33012136" target="_blank" >10.46585/sp33012136</a>

Result language

angličtina

Original language name

Embracing intelligent insights: Unveiling investor adoption of AI advice and risk appetite

Original language description

This paper aims to reveal the factors influencing investors&apos; intention to accept AI advice in financial decision-making. By integrating the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM), it proposes a comprehensive model that elucidates the intricate relationships between social norms, attitude, perceived behavioral control, and the intention to accept AI advice, with a particular focus on examining risk tolerance as a moderating factor. A questionnaire survey was conducted with 569 Vietnamese investors to collect data in three different times. Partial least squares structural equation modeling (PLS-SEM) was utilized to analyze the measurement model and test the hypotheses. Results indicate that perceived usefulness, perceived ease of use, attitude, subjective norms, and perceived behavioral control positively influence the intention to accept AI advice. Furthermore, risk tolerance significantly moderates the link between attitude, subjective norms, perceived behavioral control, and intention to accept AI advice. This pioneering study introduces a comprehensive model unveiling the dynamics of AI advice acceptance in finance. It explores the novel concept of risk tolerance as a moderator, marking an important step in understanding human-AI interaction for financial decisions. Findings provide valuable insights into evolving AI adoption, especially in high-risk contexts.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50204 - Business and management

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Papers of the University of Pardubice. Series D. Faculty of Economics and Administration

ISSN

1211-555X

e-ISSN

1804-8048

Volume of the periodical

33

Issue of the periodical within the volume

1

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

15

Pages from-to

1-15

UT code for WoS article

001464937000001

EID of the result in the Scopus database

2-s2.0-105004449300