Factors that influence users' adoption intention of mobile health: a structural equation modeling approach. Issue 19 (2nd October 2017)

• Record Type: Journal Article
• Authors: Miao, Rui; Wu, Qi; Wang, Zheng; Zhang, Xilin; Song, Yuqin; Zhang, Hui; Sun, Qingfang; Jiang, Zhibin
• Is Part Of: International journal of production research. Volume 55:Issue 19(2017)
• Page Start: 5801
• Page End: 5815
• Publication Date: 2017-10-02
• Subjects: mobile health -- adoption intention -- patients with chronic disease -- structural equation modeling -- house of quality -- TRIZ method; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2017.1336681
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : Mobile health represents the future trend of health care due to its great potential in improving health care efficiency, accessibility and quality. It is particularly beneficial for chronic disease patients who require long-term and regular services. To improve the products, mobile health developers need to understand patient needs, values and preferences, and assess the key factors that influence their mobile health adoption intentions. This study focuses on identifying the influential factors of patients' adoption intention of m-Health. A structural equation model is constructed, and the confirmatory factor analysis and standard path coefficient are used to explore the key factors in chronic disease patients' adoption process of m-Health. The results show that perceived usefulness and perceived ease of use have the strongest positive effect on patients' adoption intention. Meanwhile, subjective norm, existing degree of satisfaction, network effect, and cost factor also influence adoption intention. Finally, the House of Quality method is used to examine the relative importance of various properties of m-Health. The Teoriya Resheniya Izobreatatelskikh Zadatch method is applied to resolve the contradictions between these properties. Our study offers important insights for mobile health developers on how to optimally design a product, thereby increasing users' adoption intention and overall satisfaction.