The empirical study of e-learning post-acceptance after the spread of COVID-19: A multi-analytical approach based hybrid SEM-ANN

There are several reasons why the fear of vaccination has caused population-rejection. Questions have been raised by students regarding the effectiveness of vaccines, which in turn has led to vaccination hesitancy. Students’ perceptions are influenced by vaccination hesitancy, which affects the acceptance of e-learning platforms. Hence, this research aimed to examine the postacceptance of e-learning platforms on the basis of a conceptual model that employs different variables. Distinct contribution is made by every variable to the post-acceptance of e-learning platforms. A hybrid model was used in the current study in which technology acceptance model (TAM) determinants were employed along with other external factors such as “fear of vaccination, perceived routine use, perceived enjoyment, perceived critical mass, and self-efficiency” which are directly linked to “post-acceptance of e-learning platforms”. The focus of earlier studies on this topic has been on the significance of e-learning acceptance in various environments and countries. However, in this study, the newly-spread use of e-learning platforms in the gulf area was examined using a hybrid conceptual model. The empirical studies carried out in the past mainly used structural equation modelling (SEM) analysis; however, this study used an evolving hybrid analysis approach, in which SEM and the artificial neural network (ANN) that are based on deep learning were employed. The importance-performance map analysis (IPMA) was also used in this study to determine the significance and performance of each factor. The proposed model is backed by the findings of data analysis. It is shown in the findings that “fear of vaccination, perceived ease of use, perceived usefulness, perceived routine use, perceived enjoyment, perceived critical mass, and self-efficiency” significantly affect students’ behavioral intention to utilise elearning platforms for educational objectives. It is also shown in the analysis of ANN as well as IPMA that perceived ease of use is the most significant predictor of post-acceptance of e-learning platforms. Theoretically, sufficient explanations have been offered by the suggested model regarding the factors that influence the post-acceptance of e-learning platforms in terms of the internet service factors at the individual level. In the practical sense, these findings would help decision-makers and practitioners in higher educational institutions identify those factors that should be given more significance compared to others and plan their policies appropriately. Methodologically, the ability of the deep ANN architecture to identify the non-linear relationships between the factors involved in the theoretical model has been determined in this research. The implication offers extensive information about taking effective steps to decrease the fear of vaccination among people and increase vaccination confidence among teachers, educators, and students, which will consequently have an impact on the entire society.