Recent studies on e-feedback have answered many questions concerning the effectiveness of e-feedback in educational and non-educational sectors. They stated clearly that e-feedback is efficient and practical. From both teachers’ and students’ perspectives, e-feedback has influenced their learning and teaching environment effectively. It is a good technique to personalize the learning strategies. Based on the previous assumption, this study aims at exploring the effectiveness of e-feedback in an educational environment taking into consideration the TAM model and the external factors of trustworthiness and enjoyment. The data is collected by an online questionnaire that was distributed among a group of students. Facilitating communication among teachers and students. It helps in replacing the traditional feedback and assess the learning environment during the pandemic periods. The two constructs of perceived ease of use and perceived usefulness affect positively the intention to use the e-feedback and initiates this type of feedback as a prominent procedure to be used frequently in the learning environment. In addition, the perceived enjoyment and perceived trustworthiness increase the chance of using e-feedback. Recently, e-feedback is highly dominant among online platform users.

This analysis integrates the “technology acceptance model (TAM)” with the “use of gratifications theory (U&G)” to develop an embedded model that predicts the use and satisfaction of emotional icons called stickers through WhatsApp. The explanation for combining these two theories is that U&G offers accurate information and a thorough knowledge of use, while TAM theory has been firmly established in several technical implementations. A newly developed hybrid analysis procedure has been applied within this research. Using an artificial neural network (ANN), and the structural equation model (SEM) have been combined. The research also uses the importance-performance map analysis (IPMA) to present each factor’s performance as well as importance. The ANN and IPMA research have both indicated that for sticker use intention, a highly essential predictor is Socialization. An online questionnaire survey was developed to assess the recommended model. The intention to use stickers was significantly affected by “Socialization, Self Presentation, Enjoyment, Novelty, Unique Function, Perceived Ease of Use, and Perceived Usefulness”. The research's main achievement is the convergence of two separate theories into a single conceptualization to accurately calculate the TAM components when it comes to the usage of stickers in WhatsApp. Theoretically, the recommended model provides enough insight for aspects which affect the intention to use stickers with relevance to the socialization’s factors considering interpersonal aspects. Practically, the higher education decision-makers along with professionals would extract variables that are important as compared to others and policies would be developed accordingly. The deep ANN model competence has been analyzed within the research to decide upon the non-linear associations between variables of the theoretical model, methodologically.

There are various reasons why vaccine fear has resulted in public rejection. Students have raised concerns about vaccine effectiveness, leading to hesitation when it comes to vaccination. Vaccination apprehension impacts students' perceptions, which has an impact on the acceptability of an e-learning platform. As a result, the goal of this study is to look at the post-acceptance of an e-learning platform using a conceptual model with several factors. Every variable makes a unique contribution to the e-learning platform's post-acceptance. In the current study, TAM variables were combined with additional external factors such as fear of vaccination, perceived routine use, perceived enjoyment, perceived critical mass, and self- efficacy, all of which are directly associated with post-acceptance of an e-learning platform. Here, a hybrid conceptual model was used to evaluate the newly widespread use of e-learning platforms in this area in this study in the UAE. In the past, empirical investigations primarily used Structural Equation Modeling (SEM) analysis; however, this study used a developing hybrid analysis approach that combines SEM with deep learning–based Artificial Neural Networks (ANN). This study also employed the Importance–Performance Map Analysis (IPMA) to determine the significance and performance of each element. Through the findings, it was found that fear of vaccination, perceived ease of use, perceived usefulness, perceived routine use, perceived enjoyment, perceived critical mass, and self-efficiency all had a significant impact on students' behavioral intention to use the e-learning platform for educational purposes. It was also shown in the analysis of ANN as well as IPMA that the perceived ease of use of the e-learning platform is the most important indicator of post-acceptance. The proposed model, in theory, provides appropriate explanations for the elements that influence post-acceptance of the e-learning platform in terms of internet service factors at the individual level. In the practical sense, these findings will help decision-makers and practitioners in higher education institutions identify the factors that should be given extra care and plan their policies accordingly. 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 and educators and students, consequently impacting society.