The implications and future directions were discussed. Although HenselerвЂ™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.

However, the relationship of SI to BI was not supported for respondents with prior IVR experience and for respondents coming from Western region. The results show that the behavioural intention (BI) towards adoption of IVR was influenced by PE, EE, and HM for all abovementioned subpopulation. Partial least squares structural equation modelling and multi-group analysis based on SmartPLSв„ў version 3 were used to carry out the path and multi-group analyses. The quantitative research data were gathered using an online questionnaire from 438 chemical operators and/or employees who either speak German, French, or English. 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 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.

Smartpls 3 bootstraping settings