Gender as a moderating variable in online misinformation acceptance during COVID-19.

Misinformation remained a critical consideration during the COVID-19 that further cultivated fears leading to strong unrest among the public globally. This study clarifies certain misconceptions related to the pandemic by investigating whether factors such as altruism, entertainment, information-sharing, information-seeking, comprehensibility have impact on COVID-19 misinformation acceptance and sharing it in the UAE culture, with gender as a moderating factor. An online survey (google.com) was used, with a sample of 200 university students, and analyzed using PLS-SEM software to determine the effects of constructed factors. The findings indicated that entertainment, information-sharing, and information-seeking factors have impact on sharing COVID-19 misinformation, while comprehensibility has impact on acceptance of misinformation. Interestingly, gender was found to have no impact on all the constructed factors, suggesting that other moderating factors (e.g., age) need to be considered in future research. Generally, online users need to learn how to verify online information that they receive/share on other social media, especially regarding health concern.

Factors affecting social TV acceptance among Generation Z in Jordan.

The integration of television screens to become a new media interaction is one of the most significant shifts that technology has brought us. Today, Social TV is a developed source of communication, education, and entertainment through interactive platforms. This study examines the factors influencing Social TV acceptance among Generation Z in Jordan. We employed a cross-sectional design and obtained data from n = 304 respondents. Findings attained from Structural Equation Modelling revealed that different factors (e.g., personal beliefs, attitude towards behaviour, and intention towards behaviour) predicted respondents' attitudes and behaviour regarding Social TV behavioural acceptance. However, attitude towards behaviour was not linked to subjective norms, with the latter not correlated with intention towards behaviour. The proposed mediation role of perceived ease-of-use and usefulness had significant influence, indicating how Social TV acceptance is deeply associated with respondents' new behavioural patterns and habits. Thus, we conclude that in terms of explaining human behaviour, the role of motivational factors in accelerating one's intention, and their impacts, is a complex phenomenon.

Determinants predicting the electronic medical record adoption in healthcare: A SEM-Artificial Neural Network approach.

An Electronic Medical Record (EMR) has the capability of promoting knowledge and awareness regarding healthcare in both healthcare providers and patients to enhance interconnectivity within various government bodies, and quality healthcare services. This study aims at investigating aspects that predict and explain an EMR system adoption in the healthcare system in the UAE through an integrated approach of the Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Acceptance Model (TAM) using various external factors. The collection of data was through a cross-section design and survey questionnaires as the tool for data collection among 259 participants from 15 healthcare facilities in Dubai. The study further utilised the Artificial Neural Networks (ANN) algorithm and the Partial Least Squares Structural Equation Modeling (PLS-SEM) in the analysis of the data collected. The study's data proved that the intention of using an EMR system was the most influential and predictor of the actual use of the system. It was also found that TAM construct was directly influenced by anxiety, innovativeness, self-efficacy, and trust. The behavioural intention of an individual regarding EMR was also proved to positively influence the use of an EMR system. This study proves to be useful practically by providing healthcare decision-makers with a guide on factors to consider and what to avoid when implementing strategies and policies.

SEM-ANN-based approach to understanding students' academic-performance adoption of YouTube for learning during Covid.

A hybrid analysis of Structural Equation Modeling (SEM) and Artificial Neural Network (ANN), through SmartPLS and SPSS software, as well as the importance-performance map analysis (IPMA) were used to examine the impact of YouTube videos content on Jordanian university students' behavioral intention regarding eLearning acceptance, in Jordan. According to the evaluation of both ANN and IPMA, performance expectancy was the most important and, theoretically, several explanations were provided by the suggested model regarding the impact of intention to adopt eLearning from Internet service determinants at a personal level. The findings coincide greatly with prior research indicating that users' behavioral intention to adopt eLearning is significantly affected by their performance expectancy and effort expectancy. The paper contributed to technology adoption e.g., YouTube in academia, especially in Jordan. Respondents showed a willingness to employ and adopt the new technology in their education. Finally, the findings were presented and discussed through the UTAUT and TAM frameworks.

Using Machine Learning Algorithms to Predict People's Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach.

BACKGROUND: Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-face contact teaching practices, thereby requiring many students to actively use mobile technologies for learning. Mobile learning technologies offer viable web-based teaching and learning platforms that are accessible to teachers and learners worldwide. OBJECTIVE: This study investigated the use of mobile learning platforms for instruction purposes in United Arab Emirates higher education institutions. METHODS: An extended technology acceptance model and theory of planned behavior model were proposed to analyze university students' adoption of mobile learning platforms for accessing course materials, searching the web for information related to their disciplines, sharing knowledge, and submitting assignments during the COVID-19 pandemic. We collected a total of 1880 questionnaires from different universities in the United Arab Emirates. Partial least squares-structural equation modeling and machine learning algorithms were used to assess the research model, which was based on the data gathered from a student survey. RESULTS: Based on our results, each hypothesized relationship within the research model was supported by our data analysis results. It should also be noted that the J48 classifier (89.37% accuracy) typically performed better than the other classifiers when it came to the prediction of the dependent variable. CONCLUSIONS: Our study revealed that teaching and learning could considerably benefit from adopting remote learning systems as educational tools during the COVID-19 pandemic. However, the value of such systems could be lessened because of the emotions that students experience, including a fear of poor grades, stress resulting from family circumstances, and sadness resulting from a loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during the pandemic.