Predicting adoption of mHealth technology in resource constrained environments

Maiga, Gilbert; Namagembe, Flavia

doi:10.1109/istafrica.2014.6880628

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Our results contribute to the existing literature in several ways. First, we addressed the research gap previously discussed, noting that existing studies about technology adoption have focused mainly on patient-related factors rather than external environments [15,16]. On the basis of channel complementarity theory and attribution theory, we conducted an empirical study to analyze the adoption of mHealth from the perspective of resource competition and found that medical resource scarcity and accessibility have significant impacts on the adoption of mHealth.…”

Section: Discussionmentioning

confidence: 99%

“…Although mHealth presents many opportunities for improving health care delivery, its adoption and use have been low [2,7,11,12]. A robust body of work in information systems has studied factors associated with mHealth adoption and use [11,13-15]. Although extant studies have explored and discussed reasons for these low adoption rates [14,16], empirical research examining this question from the perspective of resource competition is still rare.…”

Section: Introductionmentioning

confidence: 99%

“…Although extant studies have explored and discussed reasons for these low adoption rates [14,16], empirical research examining this question from the perspective of resource competition is still rare. Previous studies have revealed that a growing number of patients who ordinarily seek health care services offline will move to online channels when medical resources are limited, which will inevitably affect patients’ adoption and use of mHealth [11,15]. However, extant studies of mHealth adoption have mainly focused on patient-related factors, ignoring the impact of the external environment on technology adoption [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Extant studies mainly focus on the impact of individual characteristics or technological factors on adoption of mHealth [44-46]. To our knowledge, few studies have considered the impact of the external environment, which may also exert a great influence on patients and their behavior [11,15]. Therefore, we believe that it is necessary to address this gap by studying the impact of the external environment on the adoption of mHealth.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

How Resource Scarcity and Accessibility Affect Patients’ Usage of Mobile Health in China: Resource Competition Perspective

Ye¹,

Deng²,

Chen³

et al. 2019

JMIR Mhealth Uhealth

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Background The last decade has witnessed many achievements in China’s health care industry, but the industry still faces major challenges among which the uneven distribution of medical resources and the imbalance between supply and demand are the most pressing problems. Although mobile health (mHealth) services play a significant role in mitigating problems associated with health care delivery, their adoption rates have been low. Objective The objective of this study was to explore the impact of resource scarcity and resource accessibility on the adoption of mHealth from the perspective of resource competition, to examine the concerning factors, and to provide a theoretical basis for promoting mHealth in China. Methods We used 229,516 original registration records of outpatients to conduct an empirical analysis to examine the adoption of mHealth services from the perspective of resource competition. Results The adoption rate of mobile services for outpatients was low, accounting for only 31.5% (N=71,707). The empirical results indicated that resource scarcity (beta=.435, P=.01) and accessibility (beta=−.134, P=.02) have a significant impact on the adoption of mHealth. In addition, gender (beta=.073, P=.01) and age (beta=−.009, P<.001) are significantly related to adoption of mHealth. Experience with mHealth has a moderating role in the relationship between resource scarcity (beta=−.129, P=.02), accessibility (beta=.138, P=.04), and adoption of mHealth. Conclusions In this study we demonstrate that the external environment (resource scarcity and resource accessibility) has a significant impact on the adoption of mHealth. This study also demonstrates that experience with mHealth has a moderating role in the relationship between the elements of the external environment. Finally, we confirm that mHealth is a key factor in the delivery and allocation of medical resources and provide a theoretical basis for government agencies to develop policies on mHealth.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How Resource Scarcity and Accessibility Affect Patients’ Usage of Mobile Health in China: Resource Competition Perspective

Ye¹,

Deng²,

Chen³

et al. 2019

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

Factors influencing the elderly’s adoption of mHealth: an empirical study using extended UTAUT2 model

Palas

Sorwar

Hoque

et al. 2022

BMC Med Inform Decis Mak

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Background Despite the high usage of mobile phones in daily life in developing countries like Bangladesh, the adoption and usage of mHealth services have been significantly low among the elderly population. When searching previous studies, the researchers have found that no studies have empirically investigated whether the quality of life and service quality are significant for mHealth adoption by elderlies in Bangladesh. Hence, this study aimed to extend the Unified Theory of Acceptance and Use of Technology by adding service quality and the quality of life to empirically find the key factors that influence elderlies’ intention to adopt and use mHealth services in Bangladesh. Methods A face-to-face structured questionnaire survey method was used to collect data from 493 elderlies (aged 60 years and above) in Bangladesh. The data were analyzed with the Structural Equations Modelling (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). Results SEM results suggested that Social Influence, Hedonic Motivation, Price Value, Habit, and Service Quality had significant impact (p < 0.05) on the elderlies’ behavioral intention to adopt mHealth services. Service Quality, Quality of Life, and elderlies’ Habit were found significant in explaining the Use Behavior of mHealth services. Quality of Life did not show significant (p > 0.05) effect on Behavioral Intention, which is inconsistent with existing literature. In addition, fsQCA findings suggest how the intensity of the influencers may contribute to high versus low m-health behavioral outcomes. Conclusions The findings have significant implications for theory, practice and future research as explained in the paper. The originality of this study is the integration of quality of life and service quality into UTUAT2 to explain the users’ behavioural intention and use behaviour. Overall, the findings may contribute to shaping appropriate policies for designing and implementing mHealth services effectively for elderlies in developing countries.

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Understanding the insight of factors affecting mHealth adoption

Kaium

Bao²,

Alam³

et al. 2019

IJRBS

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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.

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Predicting adoption of mHealth technology in resource constrained environments

Cited by 6 publications

References 13 publications

How Resource Scarcity and Accessibility Affect Patients’ Usage of Mobile Health in China: Resource Competition Perspective

How Resource Scarcity and Accessibility Affect Patients’ Usage of Mobile Health in China: Resource Competition Perspective

Factors influencing the elderly’s adoption of mHealth: an empirical study using extended UTAUT2 model

Understanding the insight of factors affecting mHealth adoption

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