Exploring the switching intention of patients to e-health consultations platforms: blending inertia with push–pull–mooring framework

Abstract

Purpose Technology has revolutionized the delivery of health-care services, with e-consultations becoming popular mode of service delivery, especially during the pandemic. Extant research has examined the adoption of e-health consultation services, with little attention paid to examine the switching behavior. This study aims to identify factors affecting patients’ intentions to switch from conventional mode i.e. visiting hospitals/clinics to e-health consultations. Design/methodology/approach To understand this we use the push–pull–mooring (PPM) framework and integrate variables from status quo bias framework to the model. A cross-section research design was used, which rendered 413 valid responses which were obtained from the patients visiting a traditional hospital setup. The data was analyzed using partial least square – structural equation modeling using SmartPLS 3.0. Findings Findings suggest that push effects (inconvenience and perceived risk), pull effects (opportunity for alternatives and ubiquitous care), mooring effects (trust) and inertia significantly influence patients’ switching intentions from visiting hospitals/clinics to e-health consultations. Further, habit and switching cost positively influence inertia. Practical implications This study shall enable online health-care service providers and practitioners to understand patients’ intentions to switch to online health platforms and accordingly develop related marketing strategies, services and policies to encourage them to switch to the new offerings. Originality/value The current study enriches the previous research on e-health services by applying and extending PPM framework as the base model and showing its efficiency in predicting individuals switching intentions in the context of emerging economies. This study bridges the gap by focusing on switching behavior in context of health services.

Switching to peer-to-peer accommodation (P2PA): an extended push-pull-mooring model perspective from emerging economy

“…Previous literature on the PPM model has examined a handful of prominent push factors like low satisfaction, inconvenience, fatigue, perceived risk (Bansal, 2005; Dogra et al , 2022; Sun et al , 2017); pull factors like alternative attractiveness, enjoyment, autonomy, product quality (Dar and Xiongying, 2021; Guo et al , 2021; Ojiaku et al , 2018); and mooring factors like switching cost, consumer innovativeness, inertia, setup costs (Fu et al , 2021; Liao et al , 2019; Wang et al , 2019). Based on thorough review of literature, the current study identifies context-specific factors where satiation and perceived health risk are examined as push factors; economic benefit, home benefits and authentic experience are assessed as pull factors and trust, habit and subjective norms are examined as mooring factors as presented in Figure 1.…”

Impact of IT identity on consumer negatively-valenced engagement in mobile medical consultation: from consumer experience perspective

“…Telepresence is an essential feature of the web-based platform and describes a subjective immersive servicescape experience in a MMC platform, which can shape consumers' cognitive performance (Chen and Yao, 2022). The transition of the medical channel from physical to virtual is a challenge for patients (Dogra et al, 2022), but it is unknown how the virtual institutional environment based on a certain technology value proposition (Yang et al, 2021) can be relied on by consumers. In addition, Pool et al (2022) indicated that medical data security was connected with the degree of telemonitoring and surveillance.…”

“…Besides, grounded in trust theory, SOR theory or signal theory, a majority of current studies on MMC primarily explore how physician-related (Li et al, 2020a), technology-related (Dogra et al, 2022), self-related (Chen et al, 2022b;Wu et al, 2022a), third-party patient-related (Li et al, 2021), disease-related (Shah et al, 2021) factors influence consumer positively valenced engagement in MMC from patient-physician interaction perspective or patienttechnology interaction perspective. As the MMC platform is a technological bridge for doctorpatient interaction, uncovering the factors that influence consumer negative engagement in MMC from a patient-platform interaction lens is needed.…”

“…Besides, grounded in trust theory, SOR theory or signal theory, a majority of current studies on MMC primarily explore how physician-related (Li et al. , 2020a), technology-related (Dogra et al. , 2022), self-related (Chen et al.…”

Impact of Perceived Quality of E-Health Services on Patient Behavioral Intention to Use E-Health Services: A Moderating Role of Knowledge of E-Health Management

“…In the healthcare sector, technology application concerning the various stakeholders is considered much important (Dogra, Bakshi, & Gupta, 2022). The Healthcare industry is considered a large and complicated industry that sometimes contains interactions among stakeholders which results in contradictory purposes and data issues (Dahleez, Bader, & Aboramadan, 2020).…”