Abstract
The present study uses the modified Unified Theory of Acceptance and Use of Technology 2 to examine the effect of factors such as performance expectancy (PE), effort expectancy (EE), social influence (SI), and hedonic motivation (HM) that may motivate operators and employees to adopt IVR-based technology into their training. Results of a multi-group analysis based on nationality, prior IVR experience, and/or length of work experience, to analyse the potential similarities and/or differences in perception and acceptance towards using IVR-based technology are also presented. The quantitative research data were gathered using an online questionnaire from 438 chemical operators and/or employees who either speak German, French, or English. Partial least squares structural equation modelling and multi-group analysis based on SmartPLS™ version 3 were used to carry out the path and multi-group analyses. The results show that the behavioural intention (BI) towards adoption of IVR was influenced by PE, EE, and HM for all abovementioned subpopulation. However, the relationship of SI to BI was not supported for respondents with prior IVR experience and for respondents coming from Western region. Although Henseler’s-based multi-group PLS analysis reveals that there was no significant difference between the group comparisons, it is still important to take into account these socio-demographic factors as there are definite group differences in terms of the ranking order of each construct for the IVR adoption intentions among each subpopulation. The implications and future directions were discussed.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10055-021-00586-3.
Highlights
Since several companies around the world are adapting and embracing the concept of industry 4.0, technologies such as virtual reality (VR) technology gained a significant level of attention and created a paradigm shift in several areas of training in the fields of chemical (Colombo et al 2014), medical (Bissonnette et al 2019), and aviation industries (Clifford et al 2019)
Given that the aim of this study is to examine the differences in hypothetical relationships between groups, a multi-group analysis approach (MGA) in partial least square (PLS)-Structural Equation Modelling (SEM) was
Out of the 438 participants, those coming from Eastern countries (i.e. Asia) account for 32.9% of the group compared to the participants coming from Western countries (i.e. Europe) representing 67.1% of the group
Summary
Since several companies around the world are adapting and embracing the concept of industry 4.0, technologies such as virtual reality (VR) technology gained a significant level of attention and created a paradigm shift in several areas of training in the fields of chemical (Colombo et al 2014), medical (Bissonnette et al 2019), and aviation industries (Clifford et al 2019). As pointed out by many researchers, training materials such as PowerPoint presentations or pre-recorded lectures only provide and explain instructions and rules without realistic feeling for the given scenarios (Arkorful and Abaidoo 2015) Such approaches are not effective, especially in the abovementioned fields (Dholakiya et al 2019). As VR technology, immersive virtual technology (IVR), can provide users with a safe 3D training environment space, promoting knowledge acquisition through active involvement, it is possible to create a representation of real-life scenario for training under normal or abnormal situations within a safe setting while retaining stress drivers (Bissonnette et al 2019; Dholakiya et al 2019)
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