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

Document Type : Original Article

Authors

1 Department of Educational Sciences and Psychology, Payam Noor University, Tehran, Iran

2 Department of Educational Sciences and Psychology, Azad University, Tehran, Iran

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


Lopez-Fernandez O, Rodriguez-Illera JL. Investigating university students’ adaptation to a digital learner course portfolio. Computers & Education. 2009;52(3):608-616.‏ doi:10.1016/j.compedu.2008.11.003.
Barzegar R, Dehghanzadeh H, Moghadamzadeh A. From e-Learning to Mobile Learning: Theoretical Foundations. Faculty of Nursing, Shiraz University of Medical Sciences. 2012;3(2):41-36.(in persion).
Sung Y, Kim Y, Kwon O, Moon J. An explorative study of Korean consumer participation in virtual brand communities in social network sites. Journal of Global Marketing. 2010;23(5):430-445.
Loiacono ET, McCoy S. Factors Affecting Continued Use of Social Media. In: Nah F.FH. (eds) HCI in Business. HCIB 2014. Lecture Notes in Computer Science. Springer, Cham. 2014;3(8527):206-213. DOI:10.1007/978-3-319-07293-7_20
Ray SK, Saeed M. Mobile learning using social media platforms: an empirical analysis of users’ behaviours, Int. J. Mobile Learning and Organization. 2015;8(3):1–15. DOI: 10.1504/IJMLO.2015.074212
Xiang Z, Gretzel U. Role of social media in online travel information search. Tourism management. 2010;31(2):179-188. DOI: 10.1016/j.tourman.2009.02.016
Greenhow C, Askari E. Learning and teaching with social network sites: A decade of research in K-12 related education. Education and Information Technologies. 2015. DOI: 10.1007/s10639-015-9446-9.
Kim Y, Sohn D, Choi SM. Cultural difference in motivations for using social network sites: A comparative study of American and Korean college students. Computers in Human Behavior. 2011;27(1):365-372. DOI: 10.1016/j.chb.2010.08.015.
Ratneswary R, Rasiah V. Transformative higher education teaching and learning: using social media in a team-based learning environment. soc.Behav. 2014;369-378. DOI: 10.1016/j.sbspro.2014.01.1435.
Lee DY, Lehto MR. User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education. 2013;61(1):193-208. DOI: 10.1016/j.compedu.2012.10.001.
Scott-Hayward S, Natarajan S, Sezer S. A Survey of Security in Software Defined Networks. IEEE Communications Surveys and Tutorials. 2016;18(1):623-654. DOI: 10.1109/COMST.2015.2453114
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly. 1989:319-40. DOI: 10.2307/249008.
Nebic Z. Blended E-Learning in Higher Education: Research on Students’ Perspective.  Strategic Human Resource Management at Tertiary Level. 2013 Nov 5:1.
Saenphon Th. An Analysis of the Technology Acceptance Model in Understanding University Student’s Awareness to Using Internet of Things. Association for Computing Machinery. 2017;7(3): 61–64 doi = 10.1145/3108421.3108432.
Ajzen I, Fishbein M. Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice- Hall. 1980.
Fishbein M, Ajzen I. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley. 1975.
Lule I, Omwansa TK, Waema TM. Application of technology acceptance model (TAM) in m-banking adoption in Kenya. International Journal of Computing and ICT Research. 2012;6(1):31-43
Tselios N, Daskalakis S, Papadopoulou M. Assessing the Acceptance of a Blended Learning University Course. Journal of Educational Technology & Society. 2011;14(2): 224-235. Retrieved August 1, 2020, from www.jstor.org/stable/jeductechsoci.14.2.224.
Kissi J, Dai B, Dogbe CS, Banahene J, Ernest O. Predictive factors of physicians' satisfaction with telemedicine services acceptance. Health informatics journal. 2020; 26(3): 1866–1880. DOI: 10.1177/1460458219892162
Rabayah K, The Impact of Culture on Acceptance of E-Learning: A Palestinian Case Study Using Structural Equation Model, 2019 IEEE Global Engineering Education Conference (EDUCON), Dubai, United Arab Emirates, 2019: 467-472, doi: 10.1109/EDUCON.2019.8725110.
Salloum SA, Qasim Mohammad Alhamad A, Al-Emran M. Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model, in IEEE Access. 2019;7(1):128445-128462. doi: 10.1109/ACCESS.2019.2939467.
Hair JF, Ringle CM, Sarstedt M. Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning. 2013;46:1-12. DOI: 10.1016/j.lrp.2013.01.001
Mnkandla E, Minnaar A. The Use of Social Media in E-Learning: A Metasynthesis. IRRODL [Internet]. 2017Aug.15 [cited 2020Jun.10];18(5). Available from: http://www.irrodl.org/index.php/irrodl/article/view/3014. DOI: 10.19173/irrodl.v18i5.3014
Heidari H, Alborzi Mahmoud, Musa Khani M. Effective factors on persuading students to use social networks as a virtual education network. Human interaction and information. 2017;3(2):69-56.
Hosseini kh, Nouri SHA, Zabihi MR. Admission of E-learning in Higher Education: Application of Stream Theory, Technology Acceptance Model and Quality of Services, Quarterly Journal of Research and Planning in Higher Education. 2014;67:136-111.
Kim H, Ku B, Kim JY, Park YJ, Park YB. Confirmatory and Exploratory Factor Analysis for Validating the Phlegm Pattern Questionnaire for Healthy Subjects. Evidence-based complementary and alternative medicine : eCAM. 2016; 2696019.  DOI: 10.1155/2016/2696019
Ammar A. PSYCHOMETRIC THEORY THIRD EDITION Jum C. Nunnally Late Professor of Psychology Vanderbilt University Ira H. Bernstein Professor of Psychology. 2017.
Zahoor H, Chan AP, Utama WP, Gao R, Zafar I. Modeling the Relationship between Safety Climate and Safety Performance in a Developing Construction Industry: A Cross-Cultural Validation Study. International Journal of Environmental Research and Public Health. 2017 Mar;14(4). DOI: 10.3390/ijerph14040351.
Fornell C, Larcker DF. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research. 1981;8(1):39-50.
Davari A, Rezazadeh A. Structural Equation Modeling with PLS Software. Tehran: Publishing House Jihad University. 2015.
Henseler J, Sarstedt M. Goodness-of-fit indices for partial least squares path modeling. Computational Statistics. 2013;28(2):565-580.
Van Riel ACR, Henseler J, Kemény I, Sasovova Z. Estimating hierarchical constructs using consistent partial least squares: The case of second-order composites of common factors. Industrial Management & Data Systems. 2017; 117(3): 459-477. DOI: 10.1108/IMDS-07-2016-0286
Lin KY, Lu HP. Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior. 2011;27(3):1152-1161.     DOI: 10.1016/j.chb.2010.12.009
Sun Y, Liu L, Peng X, Dong Y, Barnes SJ. Understanding Chinese users continuance intention toward online social networks: An integrative theoretical model. Electronic Markets. 2014;24(1):57–66.
Chien SH, Chen YH, Hsu CY. Exploring the impact of trust and relational embeddedness in e-marketplaces: an empirical study in Taiwan. Industrial Marketing Management. 2012;41(3):460-468. DOI:       10.1016/j.indmarman.2011.05.001.
Seif, M. The Relations Causal Model of Cognitive Absorption Components and Perceived Learning: The Mediating Role Cognitive Engagement and Perceived Ease of Use And Perceived Usefulness. Social Cognition, 2018; 7(2): 107-122. doi: 10.30473/sc.2018.32083.2001
Lim WM, Ting DH. E-shopping: an Analysis of the Technology Acceptance Model. Modern Applied Science, 2012; 6(4): 49.
Teo T, Zhou M. Explaining the intention to use technology among university students: a structural equation modeling approach. J Comput High Educ. 2014;26: 124–142. DOI:10.1007/s12528-014-9080-3
Wilson K, Yinping M, Mireku KK. Investigating The Effect of Behavioral Intention on E-learning Systems Usage: Empirical Study on Tertiary Education Institutions in Ghana. Mediterranean Journal of Social Sciences. 2018; 9(3): 201-216. Doi: 10.2478/mjss-2018-0062
Schaik P, Teo T. Understanding Technology Acceptance in Pre-Service Teachers: A Structural-Equation Modeling Approach. 2013.
Al-Adwan A, Al-Adwan A, Smedley J. Exploring students acceptance of e-learning using Technology Acceptance Model in Jordanian universities. Int J Educ Dev Inf Commun Technol. 2013;9(2):4.
Rauniar R, Rawski G, Yang J, Johnson B. Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 2014;27(1):6-30.
Kwon SJ, Park E, Kim KJ. What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter. The Social Science Journal, 2014;51(4):534-544.
Briz-Ponce L, Pereira A, Carvalho L, Antonio J. Learning with mobile technologies e Students’ behavior. Computers in Human Behavior. 2017:612-620. DOI: 10.1016/j.chb.2016.05.027
Tarhini A, Hone K, Liu X. Factors Affecting Students’ Acceptance of e-Learning Environments in Developing Countries:A Structural Equation Modeling Approach. Intl J Inf Educ Technol. 2013:54–9. doi:107763ijiet.2013.v3.233
Sanayei A, Salimian H. Analysis of effective factors in the acceptance of virtual education with an emphasis on internal factors. Journal of Technology Education, 2014;7(4):270-261.