Factors Affecting the Instructional Application of Virtual Social Networks in Higher Education

Abstract

Background: Permanent access to virtual social networks enables individuals to use them as a platform for continuous learning. This study aimed to identify and analyze the factors affecting the use of social networks for virtual learning purposes. Methods: This was an applied research using descriptive-analytic design and partial least squares structural equation modeling (PLSSEM) for data analysis. The statistical population consisted of 110 students (including 98 freshmen) studying sociology at Payam Noor University, Tehran Center, Iran. The participants were active users of at least one of the social networks under study. A researcher-made questionnaire was developed using elements from similar research tools such as Mnkandla and Minnaar (2017). To analyze the content validity of the questionnaire, the research variables were reviewed and modified based on existing standard scales and consensus opinions of 5 academic experts (using Delphi technique). Stratified sampling was applied, and the questionnaires were administered to a sample of 90 students. Finally, 72 questionnaires were completed by the participants, and statistical analysis was conducted using Smart PLS software. The reliability of the instrument, as measured by Cronbach’s alpha, was above 0.7 for all variables. Results: The findings showed that perceived complementary features and perceived ease of use indirectly influence students’ intention to use virtual social networks. Also, perceived usefulness (t=1.02, P>0.05) and attitude toward use (t=1.93, P>0.05) have no effect on their intention to use and ‘trustworthiness’ (t=4.13, p <0.01), and ‘flow’ has a direct effect on the intention to use the networks (t=2.05, p <0.05). Conclusion: The results of this research would further support the academics’ push for the use of social networks as a platform for virtual instruction and innovation in teaching-learning process.

Keywords

• social networking
• virtual education
• technology acceptance model
• higher education