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Integrating TTF and IDT to evaluate user intention of big data analytics in mobile cloud healthcare system

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  • Shu Lin Wang
  • Hsin I Lin

Abstract

With the rapid development of mobile technology and cloud computing, observers have recognised the vast potential for mobile cloud healthcare systems in individualised preventive healthcare. Using a mobile cloud healthcare system and big data analysis, this study aids young users in preventive healthcare against diabetes. It also integrates the Task-Technology Fit (TTF) and Innovation Diffusion Theory (IDT) models to evaluate user intentions to use the system, and tests this model using data collected from 423 young people. Results show that task-technology fit is significantly affected by task characteristics and technology characteristics, and also user intention of using the mobile cloud healthcare system is affected by task-technology fit, complexity, and relative benefits. However, observability has no significant effect on user intentions of using the mobile cloud healthcare system. These findings provide some interesting theoretical insights into the usage of the mobile cloud healthcare system. The direct effects of TTF and IDT on young users′ intention of using the mobile cloud healthcare system are shown. This study thus makes an important contribution by highlighting the role that TTF and IDT may have in affecting use of the mobile cloud healthcare system.

Suggested Citation

  • Shu Lin Wang & Hsin I Lin, 2019. "Integrating TTF and IDT to evaluate user intention of big data analytics in mobile cloud healthcare system," Behaviour and Information Technology, Taylor & Francis Journals, vol. 38(9), pages 974-985, September.
    • Handle: RePEc:taf:tbitxx:v:38:y:2019:i:9:p:974-985
    DOI: 10.1080/0144929X.2019.1626486
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