Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/6749
Title: | User acceptance of virtual reality technology for practicing digital twin-based crisis management |
Authors: | Dr. KWOK Pak Ki, Alex Yan, Mian Qu, Ting Lau, Y. K. Henry |
Issue Date: | 2020 |
Source: | International Journal of Computer Integrated Manufacturing, 2020, vol. 34(7-8), pp. 874-887. |
Journal: | International Journal of Computer Integrated Manufacturing |
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. |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/6749 |
ISSN: | 0951-192X 1362-3052 |
DOI: | 10.1080/0951192X.2020.1803502 |
Appears in Collections: | Applied Data Science - Publication |
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