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Understanding the performance impact of the epidemic prevention cloud: an integrative model of the task-technology fit and status quo bias

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  • Pi-Jung Hsieh
  • Weir-Sen Lin

Abstract

The epidemic prevention cloud allows infection control professionals to streamline many of their reporting procedures, thereby improving patient safety in a cost-effective manner. Based on task-technology fit and status quo bias perspectives, this study develops an integrated model to explain individuals’ health information technology usage behaviour. We conducted a field survey in 30 Taiwan hospitals to collect data from infection control professionals with using experience of the epidemic prevention cloud. A total of 167 questionnaires were sent out, and 116 were returned from 18 hospitals. To test the proposed research hypothesis, we employed a structural equation model by the partial least squares method. The results found that both task – (p

Suggested Citation

  • Pi-Jung Hsieh & Weir-Sen Lin, 2020. "Understanding the performance impact of the epidemic prevention cloud: an integrative model of the task-technology fit and status quo bias," Behaviour and Information Technology, Taylor & Francis Journals, vol. 39(8), pages 899-916, August.
    • Handle: RePEc:taf:tbitxx:v:39:y:2020:i:8:p:899-916
    DOI: 10.1080/0144929X.2019.1624826
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