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times, and costs may propel residents to AI-assisted options.
Based on the Meyer and Allen (1991) three-component commitment model, low
commitment reflects diminished affective commitment, marked by reduced emotional
attachment, trust, and relational desire. In services, it weakens loyalty (Mbango, 2018;
George & Sahadevan, 2023). Within the PPM framework, low commitment functions as a
push factor that loosens ties to the current provider and boosts switching. In the
healthcare domain, it may drive residents from traditional healthcare to alternative
options. Thus, the following hypotheses are proposed:
H1: Push factors have a positive effect on the switching intention to AI-assisted
healthcare.
H1a: Low satisfaction has a positive effect on the switching intention to AI-assisted
healthcare.
H1b: Low commitment has a positive effect on the switching intention to AI-assisted
healthcare.
2.3.2. Pull factors
According to the UTAUT model, performance expectancy is a primary determinant
of behavioral intention, defined as the extent to which individuals believe a new system
improves their performance or efficacy (Venkatesh et al., 2003). In healthcare, residents'
expectations of superior benefits from AI-assisted services like precise diagnoses and
effective treatments heighten adoption and switching intentions (Esmaeilzadeh et al.,
2021; Liu et al., 2025). Within the PPM framework, these perceptions serve as pull factors
that draw users toward AI-assisted healthcare.
According to Huang et al.,2024, effort expectancy refers to the degree to which
users perceive the use of a technology as easy and free of effort. Numerous studies have
identified effort expectancy as a strong driving factor influencing users’ switching
intentions (Chi et al., 2021). In other words, the adoption of an innovation is closely
associated with its level of complexity, such that higher perceived complexity leads to a
lower willingness to adopt and use new technologies (Brown & Venkatesh, 2005). In the
healthcare context, the study by Hsu et al. (2016) reported similar findings.
Personal innovativeness denotes an individual's willingness to early adopt and
experiment with new ideas, products, or technologies (Rogers, 1995). In technology
contexts, highly innovative people show positive attitudes, reduced risk aversion, and
greater openness to novel solutions (Agarwal & Prasad, 1998). In the PPM framework,
this trait acts as a pull factor attracting users to advanced options. In healthcare, it is
anticipated to boost switching toward AI-assisted services. Therefore, the following
hypotheses are proposed:
H5: Pull factors have a positive effect on the switching intention to AI-assisted
healthcare.
H5a: Performance expectancy has a positive effect on the switching intention to AI-
assisted healthcare.
H5b: Effort expectancy has a positive effect on the switching intention to AI-assisted
healthcare.
H5c: Personal innovativeness has a positive effect on the switching intention to AI-
assisted healthcare.
2.3.3. Mooring factors
Based on the UTAUT model, social influence is regarded as the pressure from
significant others that shapes an individual’s decision to adopt new technologies

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