Factors Influencing the Intention to Use Social Media for Learning Among Adolescents in Bangkok
Article Sidebar
Main Article Content
Abstract
The objective of this research was to investigate the level of intention to use online social media for learning among adolescents in Bangkok, and to examine the factors that influence attitudes and intentions to use online social media for learning among adolescents in Bangkok. The study sample consisted of 348 adolescents in Bangkok. The data collection tool was a questionnaire, and data analysis involved frequency distribution, percentages, means, standard deviations, and structural equation modeling. The key findings of the study are as follows. 1) It was found that the intention to use online social media for learning and actual usage among adolescents in Bangkok were both at high levels, namely with (=4.04, S.D.=0.74) and (
=4.05, S.D.=0.75), respectively. 2) The hypothesis testing using structural equation modeling showed that the structural equation model of factors influencing the intention to use online social media for learning among adolescents in Bangkok met the statistical criteria (X2 not significant (p>0.05), X2/df=1.149, RMSEA=0.021, GFI=0.96, AGFI=0.92, RMR=0.02, SRMR=0.03, NFI=0.99, and CFI=1.00.). Additionally, it was found that the completeness of content influenced user satisfaction, and it also influenced perceived usefulness, while perceived ease of use influenced attitudes toward usage, perceived usefulness influenced attitudes toward usage, attitudes toward usage influenced intention to use, perceived enjoyment influenced intention to use, and intention to use influenced actual usage. Furthermore, user satisfaction did not influence attitudes toward usage.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
เนื้อหาและข้อมูลในบทความที่ตีพิมพ์ในวารสารการศึกษาและนวัตกรรมการเรียนรู้ ถือเป็นข้อคิดเห็นและความรับผิดชอบของผู้เขียน ซึ่งกองบรรณาธิการวารสาร ไม่จำเป็นต้องเห็นด้วยหรือร่วมรับผิดชอบใด ๆ และไม่สงวนสิทธิ์การคัดลอกบทความเพื่อใช้ประโยชน์ทางวิชาการ แต่ให้อ้างอิงข้อมูลแสดงที่มาของบทความทุกครั้งที่นำไปใช้ประโยชน์
References
Al-Khasawneh, M., Sharabati, A., Al-Haddad, S., Tbakhi, R., & Abusaimeh, H. (2022). The adoption of TikTok application using TAM model. International Journal of Data and Network Science, 6(4), 1389-1402. doi:10.5267/j.ijdns.2022.5.012
Al-Maroof, R., Ayoubi, K., Alhumaid, K., Aburayya, A., Alshurideh, M., Alfaisal, R., & Salloum, S. (2021). The acceptance of social media video for knowledge acquisition, sharing and application: A comparative study among YouYube users and TikTok users’ for medical purposes. International Journal of Data and Network Science, 5(3), 197-214. doi:10.5267/j.ijdns.2021.6.013
Alshurideh, M., Salloum, S. A., Al Kurdi, B., & Al-Emran, M. (2019, February 19-21). Factors affecting the social networks acceptance: an empirical study using PLS-SEM approach. In Proceedings of the 2019 8th international conference on software and computer applications (414-418). Penang Malaysia. doi:10.1145/3316615.3316720
Breslyn, W., & Green, A. E. (2022). Learning science with YouTube videos and the impacts of Covid-19. Disciplinary and Interdisciplinary Science Education Research, 4(1), 1-20. doi:10.1186/s43031-022-00051-4
Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. doi:10.1016/j.technovation.2021.102312
Dumpit, D. Z., & Fernandez, C. J. (2017). Analysis of the use of social media in Higher Education Institutions (HEIs) using the Technology Acceptance Model. International Journal of Educational Technology in Higher Education, 14(1), 1-16. doi:10.1186/s41239-017-0045-2
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis: Global edition (7th ed.). New York: Pearson.
Hoi, V. N., & Hang, H. L. (2021). Understanding students’ behavioural intention to use facebook as a supplementary learning platform: A mixed methods approach. Education and Information Technologies, 26(5), 5991-6011. doi:10.1007/s10639-021-10565-5
Institute for the Promotion of Teaching Science and Technology (IPST). (2021). IPST Collaborates with TikTok to Stimulate New Era Thai Children's Learning - Digital Age Thai Teachers. Retrieved from https://www.ipst.ac.th/news/7797/tiktok01.html [in Thai]
Kahil, B., & Alobidyeen, B. (2021). Social Media Apps as a Tool for Procedural Learning During COVID-19: Analysis of Tiktok Users. Journal of Digitovation and Information System, 1(2), 25-39. doi:10.54433/JDIIS.2021100007
Kasetsart University. (2020). COVID-19 and Epidemiology. Retrieved from https://learningcovid.ku.ac.th [in Thai]
Lin, C. W., Lin, Y. S., Liao, C. C., & Chen, C. C. (2021). Utilizing technology acceptance model for influences of smartphone addiction on behavioural intention. Mathematical Problems in Engineering, 2021, 1-7. doi:10.1155/2021/5592187
Luthar, S. S., Kumar, N. L., & Zillmer, N. (2020). High-achieving schools connote risks for adolescents: Problems documented, processes implicated, and directions for interventions. American Psychologist, 75(7), 983-995. doi:10.1037/amp0000556
Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077. doi:10.1007/s10639-021-10557-5
Muninger, M. I., Hammedi, W., & Mahr, D. (2019). The value of social media for innovation: A capability perspective. Journal of Business Research, 95, 116-127. doi:10.1016/j.jbusres.2018.10.012
Qashou, A. (2021). Influencing factors in M-learning adoption in higher education. Education and Information Technologies, 26(2), 1755-1785. doi:10.1007/s10639-020-10323-z
Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2021). Social isolation and acceptance of the learning management system (LMS) in the time of COVID-19 pandemic: an expansion of the UTAUT model. Journal of Educational Computing Research, 59(2), 183-208. doi:10.1177/0735633120960421
Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445-128462. doi:10.1109/ACCESS.2019.2939467
Soper, D. S. (2021). A-priori sample size calculator for structural equation models [Software]. Retrieved from https://www.danielsoper.com/statcalc
Sritongsuk, R., & Choeychanya. P. (2017). Factors influencing decision-making behavior to participate in event marketing of Generation Y and Generation Z in the Bangkok metropolitan area. Journal of Communication and Management NIDA, 3(1), 1-19. [in Thai]
Thongbai, N. (2020, December 18). TikTok: The Beloved Entertainment of Generation Z. In Proceedings of the 15th National and International Conference: Research and Innovation for Sustainable Development (1059-1068). Bangkok, Thailand: Sripatum University. [in Thai]
Vate-U-Lan, P. (2020). Psychological impact of e-learning on social network sites: online students’ attitudes and their satisfaction with life. Journal of Computing in Higher Education, 32(1), 27-40. doi:10.1007/s12528-019-09222-1
Wang, K., Zhu, C., & Tondeur, J. (2021). Using micro-lectures in small private online courses: what do we learn from students’ behavioural intentions?. Technology, Pedagogy and Education, 30(3), 427-441. doi:10.1080/1475939X.2020.1832565