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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

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