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associated with their level of trust in that technology. In other words, expectancy
factors exert a positive influence on trust in AI. Based on this rationale, the following
hypotheses are proposed:
H6: Performance expectancy has a positive effect on residents’ trust in AI.
H7: Effort expectancy has a positive effect on residents’ trust in AI.
2.3.6. Trust in AI towards switching intentions
In the context of technology adoption in healthcare, trust in AI is defined as the
degree to which patients or healthcare professionals have confidence in the capability,
reliability, and benevolence of AI systems in supporting medical diagnosis and treatment
(Sagona et al., 2025). This construct is considered a key determinant influencing switching
intention (Ajzen, 1991). Empirical evidence across various domains indicates that trust has
a direct effect on switching intention (Li et al., 2007). Moreover, within the healthcare
sector, trust in AI has been shown to significantly influence the adoption of e-health
services; as users develop greater trust in AI-enabled healthcare services, they are more
likely to increase their intention to switch from traditional healthcare to AI-assisted
healthcare solutions (Gadabu et al., 2019). Based on this theoretical foundation, the
following hypothesis is proposed:
H8: Trust in AI has a positive effect on the switching intention to AI-assisted
healthcare
The research model is proposed in figure 1.

Figure 1. Research model
Source: Proposed model by authors, 2025
2.4. Research methodology
2.4.1. Official full survey
The official survey was conducted from October 22 to November 30, 2025, with 450
Hanoi residents; 386 valid responses were retained for analysis. Data were analyzed using
SmartPLS 4.0 following a two-stage PLS-SEM procedure (Hair et al., 2019): measurement
model assessment (CR, AVE, HTMT) and structural model evaluation via bootstrapping
(5,000 resamples), with explanatory power examined through R² and Q².

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