Facebook/Meta usage in higher education: A deep learning‑based dual‑stage SEM‑ANN analysis

Abstract

The paper’s main aim is to investigate and predict major factors in students’ behavioral intentions toward academic use of Facebook/Meta as a virtual classroom, taking into account its adoption level, purpose, and education usage. In contrast to earlier social network research, this one utilized a novel technique that comprised a two-phase analysis and an upcoming the Artifcial Neural Network (ANN) analysis approach known as deep learning was engaged to sort out relatively signifcant predictors acquired from Structural Equation Modeling (SEM). This study has confrmed that perceived task-technology ft is the most afrmative and meaningful efect on Facebook/Meta usage in higher education. Moreover, facilitating conditions, collaboration, subjective norms, and perceived ease of use has strong infuence on Facebook usage in higher education. The study’s fndings can be utilized to improve the usage of social media tools for teaching and learning, such as Facebook/ Meta. There is a discussion of both theoretical and practical implications.