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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, it’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.

Sub-Saharan Africa lags other regions in the adoption of both Patient-Centered (PC) and Facility Centered (FC) mobile health (m-health) applications. Transformational Leadership Style (TLS) of Hospital Management Boards (HMBs) is catalytic for the adoption of disruptive technologies such as m-health by hospitals. There is limited evidence on the effect of TLS of HMBs in the adoption of innovations in Low- and Middle-Income Countries (LMICS). This study investigated the moderating role of TLS of HMBs on the adoption of PC and FC m-health by hospitals in Kenya. It used the Logit Regression Model to test null hypotheses that the four constructs of TLS (Idealized Influence (II), Individualized Consideration (IC), Inspirational Motivation (IM), Intellectual Stimulation (IS)) individually or combined had no significant moderating effect on adoption of PC and FC m-health applications.  Primary data was collected from a representative sample size of 211 Top Executives (TEs) of level 4 to 6 hospitals who evaluated the TLS of their HMBs on m-health adoption. At a 5% level of significance, the study found that only the combined application of the four constructs (II, IC, IM, IS) significantly moderated the adoption of PC m-health (p=0.046) but did not moderate FC m-health (p=0.345). Each incremental application of TLS would increase the odds of adopting PC m-health by 1.002 but not for FC. Therefore, this study recommends the capacity strengthening of HMBs in TLS to scale up PC m-health adoption in Kenya and other LMICs. It also recommends a differentiated approach to policies, practices, and theories of adoption of innovations using the PC-FC models.

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, it’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.

Sub-Saharan Africa lags other regions in the adoption of both Patient-Centered (PC) and Facility Centered (FC) mobile health (m-health) applications. Transformational Leadership Style (TLS) of Hospital Management Boards (HMBs) is catalytic for the adoption of disruptive technologies such as m-health by hospitals. There is limited evidence on the effect of TLS of HMBs in the adoption of innovations in Low- and Middle-Income Countries (LMICS). This study investigated the moderating role of TLS of HMBs on the adoption of PC and FC m-health by hospitals in Kenya. It used the Logit Regression Model to test null hypotheses that the four constructs of TLS (Idealized Influence (II), Individualized Consideration (IC), Inspirational Motivation (IM), Intellectual Stimulation (IS)) individually or combined had no significant moderating effect on adoption of PC and FC m-health applications.  Primary data was collected from a representative sample size of 211 Top Executives (TEs) of level 4 to 6 hospitals who evaluated the TLS of their HMBs on m-health adoption. At a 5% level of significance, the study found that only the combined application of the four constructs (II, IC, IM, IS) significantly moderated the adoption of PC m-health (p=0.046) but did not moderate FC m-health (p=0.345). Each incremental application of TLS would increase the odds of adopting PC m-health by 1.002 but not for FC. Therefore, this study recommends the capacity strengthening of HMBs in TLS to scale up PC m-health adoption in Kenya and other LMICs. It also recommends a differentiated approach to policies, practices, and theories of adoption of innovations using the PC-FC models.