RESUMEN
Since late 2019, coronavirus disease 2019 (COVID-19) has led to a significant increase in the demand for medical resources. To publish data on face mask supplies, the Taiwanese government collaborated with program developers to construct a mask-supply information transitional platform (MITP). To comprehend the adoption of MITP, the study proposes a research model that integrates the health behavior model (HBM) and IS/IT continuance model for examining the factors affecting intention to use an MITP. Survey data collected from 524 respondents indicated that (1) intention to use an MITP is directly influenced by perceived threat of COVID-19 and beliefs toward using the MITP; (2) cues to action directly influence the perceived threat of COVID-19; and (3) perceived ease of use of MITP is a significant determinant of perceived usefulness of MITP. These results provide practical guidelines for health authorities and government to develop health information systems and strategies to control pandemics.
RESUMEN
Numerous mobile apps have been developed for making our lives more convenient and improving our quality of life. Health apps are among them. These types of apps are designed to help users for recording their health-related behaviors and to give advice about improving users' physical conditions. However, users frequently do not continue to use these health apps. As a result, the companies of health apps have paid the development cost but cannot get back the benefit from the apps they launch. To find out the reason, this study conducts an empirical investigation and develops a trade-off dual-factor model to dissect the reason why users discontinue use of health apps. The research model is based on the perspectives of information disclosure and expectation-confirmation theory. Users may worry about the disclosure of individual health privacy; however, on the other hand, they enjoy the functions of health apps, proffering various kinds of health-related assistance. The decision of whether or not to continue using this kind of app turns into a trade-off issue. To delve into the determinants, we conduct an online survey and collect 242 qualified responses as our research samples. Structural equation modeling is employed to analyze the samples in this study. The result reveals that our research model explains 31% of the variance. The findings and implications can serve as references for researchers and practitioners.