User acceptance of virtual reality technology for practicing digital twin-based crisis management

ABSTRACT

Digital Twin can provide emergency response staff with large quantities of useful information about the emergency. However, the capability of the staff to understand and use the information correctly is equally as important. Hence, regular crisis management training is required in sophisticated manufacturing systems. Still, the time and location constraints limit the frequency of conducting such practices. As such, virtual reality-based crisis management training methods were proposed in the literature. However, little research was conducted to investigate the underlying mechanisms that drive both the perception and the adoption of virtual reality for crisis management training among users. To address this research gap, this study proposed a research model to examine the effects of perceived usefulness, perceived ease of use, perceived behavioral control, application-specific self-efficacy, and attitude on users’ acceptance of the system. A partial least squares structural equation modeling approach was used for model testing based on 103 valid observations. The results demonstrated that the constructs proposed in our study determined user acceptance of the system, and the research model was able to explain 60% of the variance in behavioral intention. The likelihood of acceptance of related technologies may be increased by applying the findings of this study.

KEYWORDS:

• digital twin
• virtual reality
• crisis management
• structural equation modeling
• technology acceptance

Acknowledgments

The authors would like to express sincere thanks to Haoyuan Liu, Fan Yang, Naibiao Jin, Yusheng Zhuang, Yalei Feng, and Kang Wen for their dedicated work in the data collection.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71904062]; Fundamental Research Funds for the Central Universities of China [21619320 and 21618317]; The SEED Funding Translation and Applied Research [201711160007].