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

摘要

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