Journal
BMC HEALTH SERVICES RESEARCH
Volume 21, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s12913-021-06244-3
Keywords
Health technology; Behavioral intention; Mobile applications; Nursing
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This study explored Chinese nurses' behavioral intention to use a mobile nursing application, finding that factors such as performance expectancy, social influence, facilitating conditions, self-efficacy, and perceived incentives all significantly influence their intention. Further investigation is suggested to examine actual usage behavior for potential adopters.
BackgroundAlthough a mobile nursing application has began to adopt in nursing, few studies have focused on nurses' behavioral intention of it. The objective of this study is to gain insight into the behavioral intention of nurses, i.e. chinese nurses of the future, to use a mobile nursing application. This study adopted an extension of the Unified Theory of Acceptance and Use of Technology to examine Chinese nurses' acceptance of a mobile nursing application.MethodsA total of 1207 nurses participated in the cross-sectional survey. The majority of nurses were female (96.2%). The mean age of the participants was 34.18 (SD 7.39). The hypothesized relationships were tested using AMOS structural equation model.ResultsAll constructs exhibited an acceptable level of reliability and validity with C alpha and CR>0.7 and AVE>0.5. An extension of the Unified Theory of Acceptance and Use of Technology Model had good explanatory power for nurses' behavioral intention of a mobile nursing application. Although effort expectancy and perceived risks had a surprisingly insignificant effect on nurses' behavioral intention to use a mobile nursing application, performance expectancy, social influence, facilitating conditions, self-efficacy, and perceived incentives demonstrated significant influence with beta=.259, p <.001, = .296, p<.001, = .063, p = .037, beta=.344, p<.001, = .091, p = .001, respectively.ConclusionWith 70.2% of the variance in behavioral intention to use a mobile nursing app explained by this model, it could be helpful for potential adopters, and further investigation should test the actual usage behavior for a mobile nursing app and investigate the related factors.
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