Understanding the insight of factors affecting mHealth adoption: A systematic review
Md. Abdul Kaium,
Mohammad Zahedul Alam,
Najmul Hasan and
Md. Rakibul Hoque
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Md. Abdul Kaium: Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan, 430074, P.R. China
Yukun Bao: Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan, 430074, P.R. China
Mohammad Zahedul Alam: School of Management, Wuhan University of Technology, China
Najmul Hasan: Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan, 430074, P.R. China
Md. Rakibul Hoque: Management Information Systems, University of Dhaka, Bangladesh
International Journal of Research in Business and Social Science (2147-4478), 2019, vol. 8, issue 6, 181-200
Numerous studies have addressed the different context of mHealth services among diverse user groups. But due to a lack of understanding the insight of factors affecting the mHealth adoption, itâ€™s crucial need to conduct a systematic review on this issue. The objective of this study was to synthesize the present understanding of the influential factors of mHealth adoption. We performed a systematic literature search on eight electronically reputed scientific databases from 2010 to March 2019, such as Science Direct, Springer, IEEE Xplore, JMIR, Taylor & Francis, Emerald, Mary Ann Liebert and Google Scholar. This was accomplished by gathering data including authors, countries, years, target population, sample size, models/theories, and key influential factors. Primarily, a total of 2969 potentially relatable papers were found, of which 50 met the inclusion criteria. It was found that cross-sectional approach, survey methods and structural equation modeling (SEM) were the most explored research methodologies whereas PLS-SEM was found to be the largest used analysis tools. From the analysis, a total of ninety-four influential factors were clearly recognized and the findings represent that the following 15 factors appeared most recurrently and significantly; perceived usefulness, perceived ease of use, social-influence, subjective norms, self-efficacy, trust, facilitating conditions, technology anxiety, performance expectancy, effort expectancy, cost, attitude, resistance to change, perceived privacy and security, and perceived behavioral control. The research results have significant theoretical and practical implications for mHealth services providers, researchers and policy makers with regards to the Sustainable Development Goals (SDGs) allied to healthcare. Key Words:mHealth; Adoption; Factors; Self-Care; a systematic review.
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Persistent link: https://EconPapers.repec.org/RePEc:rbs:ijbrss:v:8:y:2019:i:6:p:181-200
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