Understanding the performance impact of the epidemic prevention cloud: an integrative model of the task-technology fit and status quo bias. Issue 8 (2nd August 2020)

• Record Type: Journal Article
• Authors: Hsieh, Pi-Jung; Lin, Weir-Sen
• Is Part Of: Behaviour & information technology. Volume 39:Issue 8(2020)
• Page Start: 899
• Page End: 916
• Publication Date: 2020-08-02
• Subjects: Post-adoption resistance -- task-technology fit -- status quo bias -- communicable disease surveillance report -- system utilisation -- performance impact; Electronic data processing -- Periodicals; Human engineering -- Periodicals; Information technology -- Periodicals; 303.4833
• DOI: 10.1080/0144929X.2019.1624826
• Languages: English
• ISSNs: 0144-929X

Abstract

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 < .01) and technology-related characteristics ( p < .001) influence task-technology fit. Task-technology fit has a positive effect on both utilisation ( p < .001) and performance ( p < .001), while it appears to have a negative effect on resistance to use ( p < .001). Our results showed that resistance to use was caused by uncertainty costs ( p < .01) and perceived value ( p < .01). The results indicate the significant effect of utilisation on performance ( p < .01). Further, the results indicate a significant negative effect of resistance to use on utilisation ( p < .05). This study illustrates the importance of incorporating post-adoption resistance in technology adoption studies