An Analysis of Non-users of Mobile Healthcare Applications: Based on Diffusion of Innovations Theory

Yi, Yong Jeong; Bae, Beom Jun

doi:10.3743/kosim.2017.34.1.135

Cited by 7 publications

(4 citation statements)

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“…College students have high smartphone self-efficacy because the majority of early adopters tend to be good at handling smartphones (S. Y. Lee, 2014;Yi & Bae, 2017). This smartphone self-efficacy or expectation for mobile learning affects the use of smartphones in academic activities, thus increasing the likelihood that using smartphones for academic tasks will result in higher learning outcomes.…”

Section: Technologies Efficacy Behavioural Intention and Academicmentioning

confidence: 99%

How does the smartphone usage of college students affect academic performance?

Han

2018

Computer Assisted Learning

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The present study aims to investigate the effects of smartphone use by college students on their perceived academic performance. Using five hypotheses derived from the literature related to smartphone use, the initial model was set up for path analysis to reveal the relationships among variables regarding college students' smartphone use in the academic setting. Moreover, multiple group analyses were additionally conducted to verify whether students exhibited different relationships in the hypothesized model depending on their majors. The results from path analyses showed that all path coefficients were positive and statistically significant, which indicated that all five hypothesized paths were supported. Above all, the results from multiple group analysis showed that one path (from Behavioural Intention to Use Smartphone to Academic Performance) differed significantly across groups. The findings from the current study provide educational policymakers and educators with information on how smartphone utilization in learning activities influences students' academic performance.

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Section: Technologies Efficacy Behavioural Intention and Academicmentioning

confidence: 99%

How does the smartphone usage of college students affect academic performance?

Han

2018

Computer Assisted Learning

Self Cite

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“…It can effectively provide basic concepts and methodological guidance for the dissemination of innovative technologies, methods, and things. Therefore, it has been widely applied in various fields, such as marketing, public health, information systems, and product and media lifecycle research (Ahmad et al, 2023; Min et al, 2019; Oyelana et al, 2021; Talebian & Mishra, 2018; Vrain & Wilson, 2021; Yi & Bae, 2017).…”

Section: Theoretical Framework Research Hypotheses and Model Establis...mentioning

confidence: 99%

Compatibility as a Prerequisite: Research on the Factors Influencing the Continuous Use Intention of In-vehicle Games Based on Diffusion of Innovations Theory

Deng,

Li,

Chen

2023

SAGE Open

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As an emerging game category, in-vehicle games have great development potential, but the factors influencing users’ acceptance and continuance intention of in-car games were still not determined. This study used the three perceived attributes of Diffusion of Innovations Theory, compatibility, complexity, and relative advantage, as basis and introduced perceived habits, fit, interaction quality, experience, play value, and continuous use intention to establish the users’ continuance intention model of in-vehicle games. The results of 305 valid questionnaires indicate that compatibility and play value have significant positive influence on continuance intention, of which fit shows stronger effect; perceived habits have significant influence on fit and interaction quality; both fit and quality have significant influence on experience; experience have significant influence on play value. The results of this study can provide reference for promotion design of in-vehicle games and important guidance for future development for in-vehicle game industry.

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“…Chiu et al [ 27 ] constructed the Technology–Organization–Environment (TOE) framework to analyze the critical factors of enterprises’ adoption of broadband mobile applications. Yi et al [ 28 ] found that observability, complexity, and relative advantages were not the critical factors affecting health-related mobile applications’ adoption. The above research is the qualitative or empirical analysis of the factors that affect mobile applications’ adoption.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing the Coevolution of Mobile Application Diffusion and Social Network: A Multi-Agent Model

Zhang

Zhou

et al. 2021

Entropy

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The successful diffusion of mobile applications in user groups can establish a good image for enterprises, gain a good reputation, fight for market share, and create commercial profits. Thus, it is of great significance for the successful diffusion of mobile applications to study mobile application diffusion and social network coevolution. Firstly, combined with a social network’s dynamic change characteristics in real life, a mobile application users’ social network evolution mechanism was designed. Then, a multi-agent model of the coevolution of a social network and mobile application innovation diffusion was constructed. Finally, the impact of mobile applications’ value perception revenue, use cost, marketing promotion investment, and the number of seed users on the coevolution of social network and mobile application diffusion were analyzed. The results show that factors such as the network structure, the perceived value income, the cost of use, the marketing promotion investment, and the number of seed users have an important impact on mobile application diffusion.

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An Analysis of Non-users of Mobile Healthcare Applications: Based on Diffusion of Innovations Theory

Cited by 7 publications

References 0 publications

How does the smartphone usage of college students affect academic performance?

How does the smartphone usage of college students affect academic performance?

Compatibility as a Prerequisite: Research on the Factors Influencing the Continuous Use Intention of In-vehicle Games Based on Diffusion of Innovations Theory

Analyzing the Coevolution of Mobile Application Diffusion and Social Network: A Multi-Agent Model

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