Determining the Factors that Affect the Use of Virtual Classrooms: A Modification of the UTAUT Model

Abstract

Aim/Purpose: The aim of this study was to examine the factors that may influence the use of virtual classrooms. Background: The coronavirus (COVID-19) pandemic has affected and interrupted several aspects of our lives, including education. Most educational institutes and universities have changed their teaching mode from being face-to-face or fixable learning to ‘emergency remote education’. As a result, virtual classrooms were utilised in most of these universities to keep the continuity of teaching and delivering education to their students Methodology: Through an online survey, data was collected from 235 of the teaching staff at the University of Ha’il, Saudi Arabia. Structural equation modelling (SEM) using AMOS was applied to analyse the data and testing the hypothesis of the proposed model. Contribution: The study findings have a theoretical, methodological, and practical contribution in the area of virtual classrooms. In terms of its theoretical contribution, this research has enriched the literature on virtual classrooms. Most of previous studies used the basic UTAUT model or other models for different technology, such as LMS, Mobile Cloud Learning and social websites. Few studies have focused on virtual classrooms, and, based on the best knowledge of the researcher, this study is the first study that has extended and modified the UTAUT model by adding an external factor (mobility) with virtual classrooms technology. Thus, it contributes theoretically to IS acceptance literature in general, and to UTAUT models specifically. In terms of methodological contribution, this study assessed all latent constructs in the model in a different context (Saudi Arabia), and tested them through an advanced analysis technique, which some call a second-generation multivariate, by applying a two-step rule in AMOS (CFA and SEM). The CFA has shown solid results that can measure all constructs in the model after deleting a few items that show a low factor loading to the construct. According to Awang (2015), examining the construct, convergent and discriminant validities is essential during the CFA before moving on a step and analysing data using SEM. Thus, this study has validated all constructs and its items using CFA which can be further used in a different culture, specifically in an Arab culture, such as Saudi Arabia. Findings: The results showed that effort expectancy and performance expectancy are significant predictors of behaviour intention toward the use of virtual classrooms. However, in contrast of our expectation, social influence, facilitating condition and mobility were insignificant factors and had no influence on teaching staff’s behaviour intention toward the use of virtual classrooms. Recommendations for Practitioners: This research has made a practical contribution. The findings of this study showed which factors can determine the use of virtual classrooms. Decision makers and university organizations and administration can improve their virtual classrooms services based on the factors that determine their use. Taking care of that will lead to an increased use of virtual classrooms and lead to a successful use of virtual classrooms, which will result in huge benefits in applying virtual classrooms in the teaching process. Recommendation for Researchers: Researchers may develop different models and examine other factors to assess their influence on the usage of different technologies. Understanding the factors that may influence the usage of a technology leads to an increase in its usage and adoption. Impact on Society: This study may increase the usage of virtual classrooms among teaching staff by understanding and examining the factors that might determine the use of virtual classrooms. Future Research: Future studies could examine the moderation variables to establish their impact. This study relied on only a purely quantitative design study. Future research might employ a mixed method using both quantitative and qualitative designs to apply an interview after the quantitative study for a better insight and deeper knowledge as well as confirming the quantitative results.