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Top1. Introduction
Mobile health (mHealth) technology is inevitably vital in transforming healthcare delivery nowadays vis-à-vis the co-creation of treatment experience and empowerment of patient self-management in the continuum of care (Free et al., 2013). In fact, mHealth has been touted as an essential facilitator to the achievement of United Nations’ (UN) sustainable development goal (SDG) #3: “Ensure healthy lives and promote well-being for all at all ages” (ITU, 2017; UN, 2015).
Since 2016, companies and health organizations have provided 259,000 health apps in app stores in which 65% has targeted chronically-ill patients (Research2Guidance, 2016). By 2026, the global mHealth consumer market is forecasted to reach $206.1 billion, representing a growth of 31.6% CAGR from 2020 (ReportLinker, 2020). While the consumer mHealth market records significant growth in apps provision, public health providers also seek to accelerate mHealth adoption among hospital patients to reduce healthcare cost while enhancing the quality of patient care (Jacob, Sanchez-Vazquez, & Ivory, 2020). Amidst the recent COVID-19, governments and hospitals across the globe have advocated the role and importance of mHealth in the monitoring and management of the pandemic (Ming et al., 2020; Singh, Couch, & Yap, 2020). Additionally, for patients with chronic illnesses who are in quarantine with social-physical distancing restrictions, mHealth provides a good platform for healthcare delivery (Torous & Keshavan, 2020). Yet, the low mHealth adoption rate has posed a grand challenge to policymakers, healthcare technologists and clinicians (Jacob et al., 2020; Ye et al., 2019).
To date, mHealth studies have focused primarily on the mHealth adoption from information communication technologies (ICTs) or health informatics perspectives whereas social influence on its adoption has largely been neglected (Dwivedi, Shareef, Simintiras, Lal, & Weerakkody, 2016). In prior mHealth studies, personal and social motivations have simply been examined as one of the antecedents of mHealth adoption along with technology-related factors (Davis, Bagozzi, & Warshaw, 1989; Jacob et al., 2020; Sun, Wang, Guo, & Peng, 2013; Venkatesh, Morris, Davis, & Davis, 2003). Nonetheless, since people react in different ways under social influence from others around them (Griffin, Grace, & O'Cass, 2014), it is crucial to understand the interplay between patients’ characteristics and social motivation for policymakers to enhance the promotional effectiveness of mHealth adoption (Lupton, 2014). Accordingly, this study intends to bridge this noteworthy research gap by exploring the impact of social influence on mHealth adoption among hospital patients with the two primary research objectives posited herein:
- 1.
To explore the impact of patients’ usage frequency of hospital services on personal and social motivations to mHealth adoption;
- 2.
To collect quantitative data to assess (i) the impact of patients’ hospital usage behavior on their motivation to adopt mHealth and (ii) the relative importance of social influencers, namely clinicians, caretakers, and other patients, on mHealth adoption.
Apparently, while demographics of patients has been shown to moderate motivation-adoption relationship (Francis, 2019; Zhao, Ni, & Zhou, 2018), hospital policymakers will typically find it difficult, if not impossible, to promote mHealth interventions to patients segmented by demographical characteristics. Conversely, it is more feasible to implement advertising and promotion plan vis-à-vis the patients’ behavioral characteristics (Davis, Jacklin, Sevdalis, & Vincent, 2007). Hence, audience segmentation based on patients’ behavioral characteristics such as usage frequency of hospital services is an emerging field of interest with limited evidence as advanced by objective (1) of this study.