A multi-analytical approach to predict the Facebook usage in higher education. (February 2016)

• Record Type: Journal Article
• Authors: Sharma, Sujeet Kumar; Joshi, Ankita; Sharma, Himanshu
• Is Part Of: Computers in human behavior. Volume 55: Part A(2016)
• Page Start: 340
• Page End: 353
• Publication Date: 2016-02
• Subjects: Facebook -- Social media -- Structural equation modeling -- Neural network -- Higher education -- Oman; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2015.09.020
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: Socio constructivist approach has an important say in cognitive absorption of learning in a student's life. This era of social networking services has given substantial importance to collaborative nature of learning, thus supporting Vygotsky's socio constructivist approach. The aim of this paper is to predict key determinants that affect students' intention towards academic use of Facebook. The usable data were gathered from 215 Omani students, and multi-analytical methods were employed to test the proposed research model. The results obtained from structural equation modeling (SEM) showed that resource sharing is the most influencing determinant in the decision of Facebook usage in higher education, followed by perceived usefulness, perceived enjoyment, collaboration and social influence. Further, the results obtained from SEM were used as input to the neural network model and results showed that collaboration is the most important predictor of Facebook adoption for academic purposes followed by, resource sharing, perceived enjoyment, social influence, and perceived usefulness. The findings of this study can be used to enhance the use of social media tool like Facebook for teaching and learning purposes. This is the first study which analyzed Facebook adoption for academic purposes by using a linear and nonlinear modelling. Theoretical and practical implications are discussed. Highlights: Factors influencing Facebook usage in higher education were examined. Structural Equation Modeling was employed to test the proposed hypotheses. Neural Network Modeling was employed for prediction of Facebook usage. Resource sharing and collaborations are important predictors of Facebook usage in higher education.