Pathway analysis of clinical nurse educator's intention to use virtual reality technology based on the UTAUT model.

PURPOSE

This study aims to investigate the willingness of clinical nurse educator to adopt virtual reality technology, while also examining the underlying mechanisms that influence this willingness through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT).

METHODS

A convenience sampling method was employed to select 225 clinical nurse educator, all of whom possess a professional qualification certificate as nurse practitioners, from a tertiary hospital in Wuhan City, Hubei Province. The study utilized an adapted UTAUT model theory-based design to develop several questionnaires: the performance expectancy questionnaire (11 items), the effort expectancy questionnaire (4 items), the social influence questionnaire (6 items), the facilitating conditions questionnaire (7 items), and the behavioral intention questionnaire (4 items). These instruments were designed to assess the clinical nurse educators' willingness to adopt VR technology. Furthermore, a regression model was established to analyze the factors influencing this willingness, utilizing SPSS 26.0 for statistical analysis and validating the model through path analysis with AMOS 24.0, where a -value of less than 0.05 was considered statistically significant.

RESULTS

The questionnaire demonstrated strong reliability and validity, yielding a total of 222 valid samples, comprising 209 females (94.14%) and 13 males (5.86%). Among the clinical nurse educators, 163 (73.42%) reported a willingness to use virtual reality technology, with scores of 4 or higher. Pearson correlation analysis revealed positive correlations between performance expectancy, effort expectancy, social influence, and facilitating conditions with behavioral intention ( < 0.05). Furthermore, regression analysis indicated that performance expectancy, effort expectancy, social influence, and facilitating conditions had a positive impact on behavioral intention ( < 0.05). The path model exhibited a good fit, and the results were consistent with the regression analysis, showing that the effects of performance expectancy, effort expectancy, and social influence on the behavioral intention to use virtual reality technology were 0.231, 0.150, 0.236, and 0.247, respectively.

CONCLUSION

Clinical nurse educators exhibit a robust willingness to engage with VR technology. Moreover, improving factors such as performance expectancy, effort expectancy, social influence, and facilitating conditions can substantially enhance their readiness to adopt this technology.