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View corpus contextOlder consumers' uptake of AI financial tools depends more on their skills and local information/social context than on brand prestige; self‑efficacy and AI literacy boost acceptance across China and Vietnam, with information quality and social influence operating differently between the two countries.
Determinants of intention to use AI for personal financial management among middle-aged and older adults: evidence from Vietnam and China
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correlational
low evidence
7/10 relevance
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In surveys of 713 middle-aged and older adults in China and Vietnam, AI self-efficacy and AI literacy are consistently associated with favorable evaluations and intentions to use AI for personal financial management, while information diagnosticity influences perceived usefulness only in Vietnam and social influence predicts intention only in China; brand reputation and personal innovativeness do not moderate intention.
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Purpose This study examines factors associated with older adults' intention to use artificial intelligence (AI) for personal financial management in China and Vietnam by integrating the technology acceptance model (TAM) and the knowledge–behavior gap (KBG) model. Design/methodology/approach Using cross-sectional survey data from 713 respondents, including 407 respondents from Vietnam and 306 from China, the proposed model was examined using partial least squares structural equation modeling (PLS-SEM). Findings Information diagnosticity was positively associated with assessment perceived utility in Vietnam, whereas the corresponding association was not statistically significant in China. Social influence was positively associated with intention to use AI for personal financial management in China but showed no statistically significant association with intention in Vietnam. AI self-efficacy and AI literacy showed significant positive associations with several technology-evaluation and acceptance constructs in both countries. Neither personal innovativeness nor brand reputation significantly moderated the association between acceptance and intention in either national sample. Originality/value Research on AI adoption has predominantly focused on technologically experienced or younger populations. This study extends the literature by examining AI-supported personal financial management among middle-aged and older adults and by comparing the structural associations observed in two Asian countries with different technological and institutional environments.
Summary
Main Finding
Older adults' intention to use AI for personal financial management depends more on their AI-related skills and the local social/information context than on brand reputation or personal innovativeness. Specifically, AI self-efficacy and AI literacy reliably increase favorable evaluations and acceptance across China and Vietnam; information diagnosticity boosts perceived usefulness only in Vietnam; and social influence increases intention only in China.
Key Points
- Sample: 713 middle-aged and older adults (407 Vietnam; 306 China).
- The study integrates the Technology Acceptance Model (TAM) with the Knowledge–Behavior Gap (KBG) model and tests relationships using PLS-SEM.
- AI self-efficacy and AI literacy show significant, positive associations with multiple technology-evaluation and acceptance constructs in both countries.
- Information diagnosticity → perceived usefulness: positive and significant in Vietnam; not significant in China.
- Social influence → intention to use AI: positive and significant in China; not significant in Vietnam.
- Personal innovativeness and brand reputation do not significantly moderate the acceptance→intention link in either country.
- Focus extends prior AI-adoption literature by targeting middle-aged and older adults and comparing two Asian countries with different technological/institutional settings.
Data & Methods
- Data: Cross-sectional survey of 713 respondents (age profile centered on middle-aged and older adults) from China (n=306) and Vietnam (n=407).
- Analytical method: Partial least squares structural equation modeling (PLS-SEM) to estimate the integrated TAM–KBG model and test direct and moderating effects.
- Key constructs: information diagnosticity, perceived utility/usefulness, social influence, intention to use AI for personal financial management, AI self-efficacy, AI literacy, personal innovativeness, brand reputation (moderators).
- Tested cross-national differences by estimating structural associations separately for each country.
Implications for AI Economics
- Demand and diffusion
- AI adoption among older adults hinges primarily on human capital (AI literacy, self-efficacy) rather than brand signals—policies and firms that raise user skills can materially expand market demand.
- Country context matters: information quality matters more for perceived usefulness in Vietnam, while social networks and social influence drive intention more in China. Market-entry and marketing strategies should be localized.
- Product design and pricing
- Investment in user-friendly design and tools that build self-efficacy (tutorials, guided onboarding, explainability) likely yields higher adoption than focusing on brand prestige.
- Subsidies or tiered pricing that reduce learning costs for older users could increase uptake and consumer surplus.
- Financial markets and intermediaries
- Wider uptake of AI financial-management tools by older adults can change demand for traditional financial advisors (substitution) and expand participation in digital financial services (inclusion), with distributional implications across age and socio-economic groups.
- Regulators should monitor impacts on market structure (concentration, platform dominance) and on consumer welfare.
- Policy and inequality
- Targeted literacy programs and digital inclusion policies reduce knowledge–behavior gaps and mitigate age-related digital divides.
- Because moderators like brand reputation are weak, public information campaigns and community-based interventions may be more cost-effective than relying on incumbent brands to drive uptake.
- Research and modeling
- Economic models of AI adoption should incorporate heterogeneity by age, skill (AI literacy), and social-context channels; cross-country institutional differences can change key pathway strengths.
- Future work should link stated intentions to realized behavior and assess macroeconomic impacts (e.g., changes in savings/investment behavior, advisory demand) from broader adoption among older cohorts.
Assessment
Paper Typecorrelational
Evidence Strengthlow — Findings are based on cross-sectional, self-reported survey data and PLS-SEM associations, which support correlational inference but do not establish causality; potential common-method bias, reverse causality, omitted variables, and unclear sampling limit causal claims and external validity despite reasonable sample size and cross-country replication.
Methods Rigormedium — The study uses appropriate latent-variable modeling (PLS-SEM) and explicitly tests moderators and cross-national differences, which is standard for TAM/KBG theory testing; however, key rigor gaps include cross-sectional design (no temporal ordering), likely reliance on convenience/self-selected samples, potential measurement invariance issues across countries not addressed here, and limited strategies for endogeneity or common-method bias.
SampleCross-sectional survey of 713 middle-aged and older adults: 306 respondents in China and 407 in Vietnam; age profile centered on middle-aged and older cohorts; sampling method not specified in the summary (likely convenience or non-probability survey).
Themesadoption skills_training
IdentificationCross-sectional survey with Partial Least Squares Structural Equation Modeling (PLS-SEM) to estimate associations and moderation effects; comparisons made by estimating models separately for China and Vietnam (no experimental or instrumental-variable identification).
GeneralizabilitySample limited to middle-aged and older adults — not generalizable to younger cohorts., Only two countries (China and Vietnam); cultural, institutional, and market differences limit extrapolation to other regions., Self-reported intentions measured, not actual uptake or usage behavior., Cross-sectional design prevents causal inference and temporal generalization., Sampling method and representativeness unclear — potential selection bias (e.g., more digitally engaged older adults).
Claims (7)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| AI self-efficacy is positively associated with favorable evaluations and acceptance of AI for personal financial management among middle-aged and older adults in both China and Vietnam. Adoption Rate | positive | Favorable technology evaluations and acceptance/intention to use AI for personal financial management |
Reading fidelity
high
Study strength
medium
|
n=713
|
| AI literacy is positively associated with favorable evaluations and acceptance of AI for personal financial management among middle-aged and older adults in both China and Vietnam. Adoption Rate | positive | Technology evaluations and acceptance/intention to use AI for personal financial management |
Reading fidelity
high
Study strength
medium
|
n=713
|
| Information diagnosticity has a positive and significant association with perceived usefulness of AI for personal financial management in Vietnam, but not in China. Adoption Rate | mixed | Perceived usefulness of AI for personal financial management |
Reading fidelity
high
Study strength
medium
|
n=713
|
| Social influence has a positive and significant association with intention to use AI for personal financial management in China, but not in Vietnam. Adoption Rate | mixed | Intention to use AI for personal financial management |
Reading fidelity
high
Study strength
medium
|
n=713
|
| Personal innovativeness does not significantly moderate the relationship between AI acceptance and intention to use AI for personal financial management in either China or Vietnam. Adoption Rate | null_result | Intention to use AI conditional on acceptance and personal innovativeness |
Reading fidelity
high
Study strength
medium
|
n=713
|
| Brand reputation does not significantly moderate the relationship between AI acceptance and intention to use AI for personal financial management in either China or Vietnam. Adoption Rate | null_result | Intention to use AI conditional on acceptance and brand reputation |
Reading fidelity
high
Study strength
medium
|
n=713
|
| AI-related skills and local social and information context are more important predictors of intention to use AI for personal financial management than brand reputation or personal innovativeness. Adoption Rate | positive | Intention and acceptance of AI for personal financial management |
Reading fidelity
high
Study strength
medium
|
n=713
|