An Investigation into the Adoption Behavior of mHealth Users: From the Perspective of the Push-Pull-Mooring Framework

Yizhi Liu, Zihan Liang, Chengjiang Li, Jiezhou Guo and Gang Zhao ()
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Yizhi Liu: School of Management, Guizhou University, Guiyang 550025, China
Zihan Liang: School of Management, Guizhou University, Guiyang 550025, China
Chengjiang Li: School of Management, Guizhou University, Guiyang 550025, China
Jiezhou Guo: Academic Bridge Human Resources Co., Ltd., Shanghai 200040, China
Gang Zhao: School of Engineering, University of Tasmania, Hobart, TAS 7005, Australia

Sustainability, 2022, vol. 14, issue 21, 1-17

Abstract: As an important branch of the modern electronic health care services, mobile health applications (mHealth APP) have been widely accepted as a novel health care-providing platform. Based on mobile communications, mHealth is operated on smart terminals such as smart phones, tablet computers, wireless devices or wearable devices, providing multi-channel, multi-terminal and multi-network services. Because mHealth is not restricted by time and space, it serves as a more effective disease management tool for communications between patients and medical workers. In the background of “Internet+”, this study aims to explore the internal adoption behavior of mHealth users to improve the efficiency of medical services, reduce medical costs, and enrich the “Internet + medical health” research. Guided by the push-pull-mooring framework (PPM), this study proposes a conceptual model of mHealth users’ adoption behavior. A specially designed survey was used to collect data on users’ adoption behavior ( n = 183). SPSS 25.0 (Guiyang, China) and AMOS 21.0 are used for data analysis. The results show that users’ adoption attitude partially mediates the relationship between the adoption intentions and three key factors (inconvenience, APP attractiveness, and high risk). The adoption intention also partially mediates the relationship between adoption attitude and adoption behavior. Peer influence does not have a direct effect on adoption intention, but it shows a statistically significant indirect effect on adoption intention and adoption behavior through adoption attitude. The negative effect of high switching cost is not significant for both adoption attitude and adoption intention. This study elucidates the internal mechanisms underlying mHealth users’ adoption behavior. The findings can help mHealth providers to arouse more users’ adoption behavior, improve the quality of medical services, and reduce medical costs.

Keywords: mHealth; PPM model; adoption intention; adoption behavior (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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