Digital financial literacy and trust in hybrid AI advisory: Explaining perceived private wealth management capability among digitally engaged retail investors in an emerging market
- 1Ho Chi Minh City University of Economics and Finance, Ho Chi Minh City, Vietnam.
- 2Tomas Bata University in Zlín, Zlín, Czech Republic.
Abstract
This study investigates how digital financial literacy, perceived transparency of artificial intelligence advisory services, investment and wealth knowledge, and cyber-risk self-protection shape Perceived Private Wealth Management Capability (PWMC) among digitally engaged retail investors in an emerging-market context. PWMC is conceptualized as a self-assessed capability outcome rather than an objective indicator of wealth-management performance, portfolio returns, or private-banking status. Drawing on the financial capability framework, trust in automation theory, and protection motivation theory, the study develops a trust-to-capability model in which trust in hybrid artificial intelligence advisory and digital wealth management behavior serves as a key explanatory mechanism. A non-probability sample of 249 digitally engaged retail investors in Vietnam was analyzed using partial least squares structural equation modeling in SmartPLS 4.1, with 5,000 bootstrap subsamples. The results show that digital financial literacy is the strongest antecedent of trust in hybrid artificial intelligence advisory (β = 0.517, p < 0.001). At the same time, perceived advisory transparency also exerts a significant positive effect (β = 0.261, p < 0.001). Investment and wealth knowledge emerged as the strongest predictor of digital wealth management behavior (β = 0.348, p < 0.001). Trust, digital wealth management behavior, and investment and wealth knowledge each positively influence perceived private wealth management capability. Digital wealth management behavior partially mediates the effects of trust, investment, and wealth knowledge on perceived capability. Cyber-risk self-protection limited evidence of a boundary condition by strengthening the relationship between trust and digital wealth management behavior. However, the effect size is small and should be interpreted cautiously (β = 0.098, p = 0.038). The model explains 60.2% of the variance in digital wealth management behavior and 44.3% of the variance in perceived private wealth management capability. By moving beyond conventional explanations of robo-advisory adoption and technology acceptance, this study theorizes hybrid AI advisory as a calibrated trust environment in which retail investors’ perceived capability depends not only on whether they trust AI-supported advice but also on whether that trust translates into protective, enacted digital wealth management behavior. The findings should be interpreted in the context of digitally engaged retail investors. They should not be generalized to private banking clients, high-net-worth investors, objective portfolio performance, or verified wealth outcomes.
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BibTeX
@article{tran2026digital,
title = {{Digital financial literacy and trust in hybrid AI advisory: Explaining perceived private wealth management capability among digitally engaged retail investors in an emerging market}},
author = {Minh Tu Tran and Marie Paseková},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0353922},
url = {https://doi.org/10.1371/journal.pone.0353922},
note = {CC BY},
}
RIS
TY - JOUR
TI - Digital financial literacy and trust in hybrid AI advisory: Explaining perceived private wealth management capability among digitally engaged retail investors in an emerging market
AU - Minh Tu Tran
AU - Marie Paseková
PY - 2026
DA - 2026/10/30
JO - PLOS One
DO - 10.1371/journal.pone.0353922
UR - https://doi.org/10.1371/journal.pone.0353922
AB - This study investigates how digital financial literacy, perceived transparency of artificial intelligence advisory services, investment and wealth knowledge, and cyber-risk self-protection shape Perceived Private Wealth Management Capability (PWMC) among digitally engaged retail investors in an emerging-market context. PWMC is conceptualized as a self-assessed capability outcome rather than an objective indicator of wealth-management performance, portfolio returns, or private-banking status. Drawing on the financial capability framework, trust in automation theory, and protection motivation theory, the study develops a trust-to-capability model in which trust in hybrid artificial intelligence advisory and digital wealth management behavior serves as a key explanatory mechanism. A non-probability sample of 249 digitally engaged retail investors in Vietnam was analyzed using partial least squares structural equation modeling in SmartPLS 4.1, with 5,000 bootstrap subsamples. The results show that digital financial literacy is the strongest antecedent of trust in hybrid artificial intelligence advisory (β = 0.517, p < 0.001). At the same time, perceived advisory transparency also exerts a significant positive effect (β = 0.261, p < 0.001). Investment and wealth knowledge emerged as the strongest predictor of digital wealth management behavior (β = 0.348, p < 0.001). Trust, digital wealth management behavior, and investment and wealth knowledge each positively influence perceived private wealth management capability. Digital wealth management behavior partially mediates the effects of trust, investment, and wealth knowledge on perceived capability. Cyber-risk self-protection limited evidence of a boundary condition by strengthening the relationship between trust and digital wealth management behavior. However, the effect size is small and should be interpreted cautiously (β = 0.098, p = 0.038). The model explains 60.2% of the variance in digital wealth management behavior and 44.3% of the variance in perceived private wealth management capability. By moving beyond conventional explanations of robo-advisory adoption and technology acceptance, this study theorizes hybrid AI advisory as a calibrated trust environment in which retail investors’ perceived capability depends not only on whether they trust AI-supported advice but also on whether that trust translates into protective, enacted digital wealth management behavior. The findings should be interpreted in the context of digitally engaged retail investors. They should not be generalized to private banking clients, high-net-worth investors, objective portfolio performance, or verified wealth outcomes.
SN - 1932-6203
ER -