Objective: Based on the technical acceptance model, expectation confirmation model, and perceived risk theory, this study aims to analyze the factors and their effects on shared nurse users’ continuance intention in the process of e-health consumption.
Methods: This research established a measurement tool that fits the topic of this study and a model of shared nurse continuance. From January to May 2020, 373 valid samples from elderly individuals living in urban areas of Jiangxi Province, China, were collected by convenience sampling in order to analyze through empirical research their continuance intentions of selecting shared nurses. The theoretical models and research hypotheses were verified by structural equation modeling with AMOS 25 software.
Results: The measurement model indicated that the theoretical constructs have adequate reliability and validity, while the structured equation model is illustrated as having a high model fit for empirical data. The hypothesis test results showed that expectation confirmation positively affects perceived ease of use; both of them have positive effects on perceived usefulness and satisfaction. Perceived usefulness and satisfaction play an intermediary role in expectation confirmation and continuance intention. Perceived ease of use and perceived usefulness positively affect continuance intention. Perceived risk negatively affects perceived usefulness and continuance intention.
Conclusion: This study expanded the application of the technology acceptance model, expectation confirmation model, and perceived risk model in e-health by investigating the factors that influence elderly users’ continued intention to use shared nurses. Based on these empirical findings, we derived implications for the design and operation of the shared nurse platform, and suggestions on relevant management departments and incentive structures for using e-health. The results of this study provide important implications for further research and practice of mobile health care.
Keywords: e-health, home care, technology acceptance model, expectation confirmation model, perceived risk