Adoption of Mobile Health Applications for Diabetes Management From a Push–Pull–Mooring Perspective

Adoption of Mobile Health Applications for Diabetes Management From a Push–Pull–Mooring Perspective

Jingrong Zhu, Meng Gu, Jinlin Li, Yi Cui
Copyright: © 2024 |Pages: 21
DOI: 10.4018/JGIM.347514
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Abstract

Diabetes management applications enable diabetes self-management in a more convenient and cost-effective manner. This study develops a push–pull–mooring model in order to understand patient-switching intentions to the conventional offline and the novel mobile diabetes management. Data collected from 412 adult patients with diabetes in China are analyzed to test the proposed hypotheses. The results show that push effects and pull effects have significantly positive effects on switching intention. Mooring effects negatively affect switching behavior. Meanwhile, the moderating effects of all three mooring factors (switching cost, offline habit, and private risk) on the relationship between both push-switching intentions and pull-switching intentions are also detected. These findings contribute to a deeper understanding of patient switching intentions towards diabetes management applications and, accordingly, can help marketers, healthcare providers, and health policymakers develop and appropriate their future marketing and administrative strategies.
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Literature Review

Adoption of Mobile Health Services

Previous studies regarding the adoption or use intentions of digital technologies have applied theories such as the innovation diffusion theory, the technology acceptance model (TAM), and the unified theory of acceptance and use of technology (UTAUT; Chakraborty et al., 2023; Díaz de León Castañeda & Martínez Domínguez, 2021; Liu et al., 2023; Zhou et al., 2019; Zhou, 2022). These theories assume that user behavioral intentions largely derive from their comprehension and acceptance of the new technology (Zhou et al., 2019). Mobile health technologies extend traditional offline health services and address unmet medical needs that offline healthcare fails to fulfill (Liu et al., 2022; Zhang et al., 2019). Accordingly, individual perceptions of mobile health applications are not only based on factors related to the new technology but also closely associated with their prior experiences with offline healthcare (Zhang et al., 2017). However, patient health service seeking behavior has thus far only been studied in the same context, i.e., online (or offline) related factors influence online (or offline) service acceptance or use intention.

The Push–Pull–Mooring Model

Numerous studies have demonstrated that the PPM theory is a robust framework for explaining and predicting user switching behavior across various contexts (Chen & Keng, 2019; Fu et al., 2021; Haridasan et al., 2021). Traditionally, the PPM has been employed to understand user’s switching behavior between offline service providers (Bansal et al., 2005). In line with the prevalence of online business activities, the PPM has been extensively used to explain user’s switching behavior between online service providers (Hsieh et al., 2012; Tang et al., 2016). The most recent studies have applied the PPM theory to explain user’s switching behavior from a physical service to a virtual service in the offline to online context (Chang et al., 2017; Hsieh, 2021). Therefore, in the context of multichannel diabetes care, patient switching intention is defined as the willingness of patients to substitute the traditional physical healthcare provider with a mobile application for their daily diabetes management. According to the PPM framework, this paper explained patient switching behavior from the perspective of push, pull, and mooring factors, shedding light on the factors that have an impact on patients’ switching behaviors from multiple perspectives.

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