Article Preview
TopIntroduction
With the rapid development and popularization of mobile communication technology, a wide variety of M-Health apps has emerged, and the global estimated size of the M-Health app market is forecast to exceed 11 billion dollars by 20251. These M-Health apps not only offer online diagnosis support from a physician but also provide users with mobile, convenient, flexible, and personalized mobile health information (MHI) or knowledge, which is expected to help enhance users’ involvement in health management and health behavioral changes (Xie et al., 2018; Chao et al., 2019). However, exposure or access to health information does not guarantee the adoption of health-related suggestions and subsequent behavioral changes (Liobikienė & Bernatonienė, 2018). The question of how to bridge the gap between simply providing MHI and persuading users to accept the MHI and change their health behaviors, therefore, remains a preeminent issue for both researchers and practitioners.
To further understand this question, our study applies the elaboration likelihood model (ELM) to examine how the MHI might be made to persuade users to change their attitudes and health behaviors. The ELM is often used to explain how different processing conditions influence the persuasion routes (central cues vs. peripheral cues) by which individuals come to change their attitudes and behaviors (Petty & Cacioppo, 1986). The central cues are mainly about the quality or relevance of arguments around an issue or a target, and the peripheral cues are derived from the identification with the sources (Bhattacherjee & Sanford, 2006). Given the strength of dual modes of persuasion, ELM has been widely used in the adoption and usage of M-Health technology or services (Guo et al., 2020; Cao et al., 2020), however, how different mechanisms can be used to make MHI more persuasive has received minimal attention.
Previous studies in the M-Health context tend to focus on the central route of persuasion, as health-related decisions are usually considered high involvement (Zhang et al., 2018; Meng et al., 2019). Most prior studies focus on service quality, information quality, or argument quality as central-route persuasion cues (Chen et al., 2018; Handayani et al., 2020). However, for recipients with diverse health conditions, effective dissemination of health information requires adequate personalization of information (Swan, 2012). Recent research also implies that matching M-Health services and personalizing health knowledge are crucial factors in persuasion (Wang et al., 2018; Zhang, 2013), especially through the central persuasion route in ELM (Tam & Ho, 2005). Nonetheless, how information matching as a central persuasion cue persuades users to change attitudes and health behaviors in the M-Health context is seen to have been understudied.
In addition to the content characteristics of MHI, the source characteristics of MHI also matter to users (Huo et al., 2018). Previous studies have demonstrated that a source’s credibility encourages use intention among M-Health service users (Meng et al., 2019; Chen et al., 2018). Another critical consideration for M-Health apps is the use of information source authentication mechanisms, which help assure users that the health information is being supplied by individuals or groups with the necessary domain expertise (Huo et al., 2018). For instance, some apps identify a professional physician as the source (such as haodf.com in China or the WebMD in the U.S.), while others only provide a content endorsement by the platform. It is not known whether the use of platform credibility as source credibility can change patients' attitudes towards MHI and then change their health behaviors, and this question is worth further exploration. Therefore, we still use platform reliability as the peripheral cue. Consequently, we propose our first research question: “What are the persuasion effects of information matching and M-Health platform credibility on users’ attitudes and behavior changes?”