Predicting the intention to use social media among medical students in the United Arab Emirates: A machine learning approach

Abstract

Aim: The volume of research being conducted on the acceptance of social media platforms is rising. But the factors influencing the acceptance for academic reasons are still not properly identified. This study's goal is two-fold. Initially, by including Technology Acceptance Model (TAM) and external variables, analyze the students' intention to use social media networks. Secondly, to employ Machine Learning (ML) algorithms and Partial Least Squares-Structural Equation Modeling (PLS-SEM) to verify the proposed theoretical model.

Methods: The focus of this research is to create a conceptual model by supplementing TAM with a subjective norm to assess students' adoption of social media in the classroom. Students currently at one private university in the United Arab Emirates (UAE) provided a sum of 627 acceptable questionnaire surveys out of 700 distributed corresponding to 89.6%. The collected data were evaluated using ML and PLS-SEM.

Results: According to the research findings, students' intention to utilize social media networks for learning is significantly predicted by “subjective norms, perceived usefulness, and perceived ease of use”. These findings illustrated how crucial it is for students to feel capable and secure using social networks in their academic work. For validation using machine learning classifiers, the results showed that J48 (a decision tree) typically outperformed other classifiers.

Conclusion: According to the empirical findings, "subjective norm," "perceived usefulness and ease of use" all significantly increase students' intention to use social networks for learning. These results were in line with earlier research on social network acceptability. Lawmakers and managers of social media platforms in education must therefore concentrate on those factors that are crucial to promoting education and enhancing students' capacity for developing and implementing successful social media applications.

Conflicts of interest: None declared.