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Understanding People's Switching Intentions of Health Apps from Exterior and Interior Drivers

counterparts, the developers no longer have opportunities to extract profits from users and
receive no payback.
To deeply understand why people continuously switch between technologies, past
information system (IS) studies have discussed the issue of switching intentions for IS

and Information Technology (IT). For example, based on Unified Theory of Acceptance
and Use of Technology (UTAUT), Bhattacherjee, Limayem, and Cheung (2012)
develop and test a model of user IT switching behavior. The major determinants for IT

switching behavior, according to their proposed model, are relative advantage, personal
innovativeness, satisfaction with prior IT and habits. Fan and Suh (2014) conduct an
empirical study based on Expectation-Disconfirmation Theory (EDT) to address why
users switch to a disruptive technology. Their model considers not only the EDT but also
two switching costs (financial and procedural) as the major factors to study the users’

intention. Wong, Chang, and Yeh (2019) also propose a conceptual model which is based
on the consumption value theory and the cognition affect behavior model for investigating
smartphone brand switching behavior.

From the literature above, we know that the factors influencing switching intention/
behavior of users may be fairly complicated. Users give up their current IS/IT (incumbent
IS/IT) and turn to alternatives due to a multitude of considerations. Although past studies
have explained users’ switching IS/IT from the variables of expectation, dissatisfaction,
disconfirmation (Bhattacherjee et al., 2012; Fan and Suh, 2014; Hou and Shiau, 2020),

benefit/value (Wong et al., 2019) and task-technology fit theory (Chen and Koufaris,
2020), few studies use a push-pull concept (such as exterior-interior drives) to point out
that users' IS/IT switching stems from a particular dilemma (Hou and Shiau, 2020). In

addition, although past researches are also focused on analyzing the IS/IT switching
problem in the use of hardware or software, such as web browser (Bhattacherjee et al.,
2012), mobile devices (Fan and Suh, 2014), or social networking sites (Sun, Liu, Chen,
Wu, Shen, and Zhang, 2017; Hou and Shiau, 2020); however, few studies have focused
on switching issues of mobile apps with a large number of users. Particularly, different

from traditional IS/IT, there are many candidates for users to replace their current health
apps (Chong, Blut, and Zheng, 2022). Therefore, we believe there is a research need for
understanding what drive users to switch health apps.

In order to fill the research gap, we conduct empirical research and propose an

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