Bridging behavioural models and explainable AI in cryptocurrency adoption: a study of emerging markets with evidence from Vietnam

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F25%3A63592859" target="_blank" >RIV/70883521:28120/25:63592859 - isvavai.cz</a>

Result on the web

<a href="https://www.tandfonline.com/doi/full/10.1080/12460125.2025.2593248" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/12460125.2025.2593248</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/12460125.2025.2593248" target="_blank" >10.1080/12460125.2025.2593248</a>

Result language

angličtina

Original language name

Bridging behavioural models and explainable AI in cryptocurrency adoption: a study of emerging markets with evidence from Vietnam

Original language description

Cryptocurrencies have become mainstream financial instruments, yet adoption remains uneven in emerging markets such as Vietnam, where rapid digitalization and regulatory uncertainty shape user behavior. Existing studies either use behavioral models that offer theoretical clarity but assume linear effects, or machine learning models that capture complexity but lack interpretability. This study combines behavioral theory with explainable artificial intelligence to examine cryptocurrency adoption in Vietnam using survey data from 1,039 respondents. Ten supervised learning algorithms were tested through repeated cross validation, and Random Forest delivered the highest accuracy. SHapley Additive exPlanations were used to interpret model outputs. Results show that trust, perceived usefulness, behavioral control, and financial literacy are key predictors, while perceived risk follows a curvilinear pattern. Interaction analysis reveals that usefulness rises with stronger behavioral control, and trust reduces risk only to a certain point. The study offers a theory informed and interpretable machine learning framework.Cryptocurrencies have become mainstream financial instruments, yet adoption remains uneven in emerging markets such as Vietnam, where rapid digitalization and regulatory uncertainty shape user behavior. Existing studies either use behavioral models that offer theoretical clarity but assume linear effects, or machine learning models that capture complexity but lack interpretability. This study combines behavioral theory with explainable artificial intelligence to examine cryptocurrency adoption in Vietnam using survey data from 1,039 respondents. Ten supervised learning algorithms were tested through repeated cross validation, and Random Forest delivered the highest accuracy. SHapley Additive exPlanations were used to interpret model outputs. Results show that trust, perceived usefulness, behavioral control, and financial literacy are key predictors, while perceived risk follows a curvilinear pattern. Interaction analysis reveals that usefulness rises with stronger behavioral control, and trust reduces risk only to a certain point. The study offers a theory informed and interpretable machine learning framework.

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

—

OECD FORD branch

50203 - Industrial relations

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

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

Name of the periodical

Journal of Decision Systems

ISSN

1246-0125

e-ISSN

2116-7052

Volume of the periodical

34

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

35

Pages from-to

1-35

UT code for WoS article

001631271400001

EID of the result in the Scopus database

2-s2.0-105024341186