Adoption of social media as a learning technology in maritime education
DOI:
https://doi.org/10.33175/mtr.2026.281935Keywords:
Social media; Intention to use; Technology acceptance model; Maritime students; Higher educationAbstract
This study aims to investigate the factors influencing the intention to use social media for learning among maritime students. It examines and applies the Technology Acceptance Model (TAM) to examine key constructs such as perceived usefulness, ease of use, and enjoyment. The goal is to offer information that can guide the incorporation of social media into educational strategies in maritime and related fields. The study applies the TAM to explore students' intentions to use social media for learning. A questionnaire based on TAM constructs was administered to 104 maritime students at University Malaysia Terengganu. The data were analyzed to test hypotheses about the relationships among key factors, including perceived usefulness, perceived ease of use, and perceived enjoyment. The findings revealed that the most preferred social media platforms for learning are WhatsApp, YouTube, and Facebook. The model demonstrated a good fit, with seven out of nine hypotheses supported. However, perceived enjoyment did not significantly influence the intention to use or the attitude toward using social media for learning. The study is limited to maritime students at a single Malaysian university, which may affect the generalizability of the findings. Future research could explore diverse student populations and additional factors influencing social media adoption for learning. This study contributes to understanding how social media can be effectively integrated into educational practices. These insights provide practical guidance for designing learning models that leverage social media technologies to enhance educational outcomes.
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Cite this article:
Mohammad, A. M., Sohaimi, N. A. F. B., Menhat, M. N. S., & Ariffin, E. H. (2026). Adoption of social media as a learning technology in maritime education. Maritime Technology and Research, 8(1), 281935. https://doi.org/10.33175/mtr.2026.281935
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Highlights
This study presented the most preferred social media platform for learning, according to maritime management students. WhatsApp is the most preferred social media platform for learning, followed by Facebook and YouTube. The findings explained the factors that influence ITU social media for learning among maritime students according to the extended TAM, which are PU, PEOU, JR, and ITU, whereas the other three factors consist of external variables, which are PE, EXP, and attitudes toward use. Additionally, attitudes toward use (AOU) are actually influenced by PU, PEOU, and PE factors, where students who are eager and responsible for their learning want to use social media tools to be easy to use and access, social media to be mostly user friendly, and sufficient information on certain subjects to be provided and be useful for group study or socializing with other friends.
References
Acarli, D. S., & Sağlam, Y. (2015). Investigation of pre-service teachers’ intentions to use of social media in teaching activities within the framework of technology acceptance model. Procedia - Social and Behavioral Sciences, 176, 709-713. https://doi.org/10.1016/j.sbspro.2015.01.530
Alkhwaldi, A. F. (2024). Understanding learners’ intention toward Metaverse in higher education institutions from a developing country perspective: UTAUT and ISS integrated model. Kybernetes, 53(12), 6008-6035. https://doi.org/10.1108/K-03-2023-0459
Alshaye, R. A., Wannas, A. S., & Bakr, M. S. (2024). Learning English for specific purposes (ESPs) through social media platforms (SMPs): A systematic review. Journal of Innovative Digital Transformation, 1(1), 2-13. https://doi.org/10.1108/JIDT-10-2023-0036
Alshurideh, M., Abuanzeh, A., Kurdi, B., Akour, I., & AlHamad, A. (2023). The effect of teaching methods on university students’ intention to use online learning: Technology Acceptance Model (TAM) validation and testing. International Journal of Data and Network Science, 7(1), 235-250.
Aston, K. J. (2023). ‘Why is this hard, to have critical thinking?’ Exploring the factors affecting critical thinking with international higher education students. Active Learning in Higher Education, 25(3), 537-550. https://doi.org/10.1177/14697874231168341
Ateş, H., & Yilmaz, R. M. (2024). A comprehensive model explaining teachers’ intentions to use mobile-based assessment. Interactive Learning Environments, 32(8), 4063-4087. https://doi.org/10.1080/10494820.2023.2194928
Badr, A. M. M., Al-Abdi, B. S., Rfeqallah, M., Kasim, R., & Ali, F. A. M. (2024). Information quality and students’ academic performance: The mediating roles of perceived usefulness, entertainment and social media usage. Smart Learning Environments, 11(1), 45. https://doi.org/10.1186/s40561-024-00329-2
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.
Eraslan Yalcin, M., & Kutlu, B. (2019). Examination of students’ acceptance of and intention to use learning management systems using extended TAM. British Journal of Educational Technology, 50(5), 2414-2432. https://doi.org/10.1111/bjet.12798
Harrigan, M., Feddema, K., Wang, S., Harrigan, P., & Diot, E. (2021). How trust leads to online purchase intention founded in perceived usefulness and peer communication. Journal of Consumer Behavioour, 20(5), 1297-1312. https://doi.org/10.1002/cb.1936
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Humida, T., Al Mamun, M. H., & Keikhosrokiani, P. (2022). Predicting behavioral intention to use e-learning system: A case-study in Begum Rokeya University, Rangpur, Bangladesh. Education and Information Technologies, 27(2), 2241-2265. https://doi.org/10.1007/s10639-021-10707-9
Jan, J., Alshare, K. A., & Lane, P. L. (2024). Hofstede’s cultural dimensions in technology acceptance models: A meta-analysis. Universal Access in the Information Society, 23(2), 717-741. https://doi.org/10.1007/s10209-022-00930-7
Jiang, M. Y., Jong, M. S., Lau, W. W., Meng, Y., Chai, C., & Chen, M. (2021). Validating the general extended technology acceptance model for e-learning: Evidence from an online English as a foreign language course amid COVID-19. Frontiers in Psychology, 12, 671615. https://doi.org/10.3389/fpsyg.2021.671615
Jusubaidi, Mujahidin, A., Abdullah, I., & Choirul Rofiq, A. (2025). Students’ critical awareness of the internet and social media use as resources for Islamic learning in Indonesian public senior high schools. British Journal of Religious Education, 47(2), 140-155. https://doi.org/10.1080/01416200.2024.2368888
Kurfalı, M., Arifoğlu, A., Tokdemir, G., & Paçin, Y. (2017). Adoption of e-government services in Turkey. Computers in Human Behavior, 66, 168-178. https://doi.org/10.1016/j.chb.2016.09.041
Lantolf, J. P., & Poehner, M. E. (2023). Sociocultural theory and classroom second language learning in the East Asian context: Introduction to the special issue. The Modern Language Journal, 107(S1), 3-23. https://doi.org/10.1111/modl.12816
Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., & Hemphill, L. (2024). ChatGPT in education: A discourse analysis of worries and concerns on social media. Education and Information Technologies, 29(9), 10729-10762. https://doi.org/10.1007/s10639-023-12256-9
Lin, & Kim, T. (2016). Predicting user response to sponsored advertising on social media via the technology acceptance model. Computers in Human Behavior, 64, 710-718. https://doi.org/10.1016/j.chb.2016.07.027
Lin, M. S.-M., & Sarza, N. A. (2024). Identifying critical challenges and government’s responses for Filipino seafarers during the COVID-19 pandemic. Maritime Business Review, 9(1), 57-73. https://doi.org/10.1108/MABR-02-2023-0019
Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 54(2), 600-610. https://doi.org/10.1016/j.compedu.2009.09.009
Liu, Y. (2010). Social media tools as a learning resource. Journal of Educational Technology Development and Exchange, 3(1), 8. https://doi.org/10.18785/jetde.0301.08
Ma, J., Wang, P., Li, B., Wang, T., Pang, X. S., & Wang, D. (2025). Exploring user adoption of ChatGPT: A technology acceptance model perspective. International Journal of Human-Computer Interaction, 41(2), 1431-1445. https://doi.org/10.1080/10447318.2024.2314358
Mastour, H., Yousefi, R., & Niroumand, S. (2025). Exploring the acceptance of e-learning in health professions education in Iran based on the technology acceptance model (TAM). Scientific Reports, 15(1), 8178. https://doi.org/10.1038/s41598-025-90742-5
Muda, M., & Hamzah, M. I. (2021). Should I suggest this YouTube clip? The impact of UGC source credibility on eWOM and purchase intention. Journal of Research in Interactive Marketing, 15(3), 441-459. https://doi.org/10.1108/JRIM-04-2020-0072
Murad, M., Wang, M., Shah, S. H. A., & Islam, M. U. (2024). Transitioning from entrepreneurial education to entrepreneurial behavior: The role of opportunity recognition, entrepreneurial social networks, and risk-taking propensity. The International Journal of Management Education, 22(3), 101053. https://doi.org/10.1016/j.ijme.2024.101053
Ngoc Hoi, V. (2023). Augmenting student engagement through the use of social media: the role of knowledge sharing behaviour and knowledge sharing self-efficacy. Interactive Learning Environments, 31(7), 4021-4033. https://doi.org/10.1080/10494820.2021.1948871
Nikolaidou, A., Kopsacheilis, A., Gavanas, N., & Politis, I. (2024). Assessing the EU climate and energy policy priorities for transport and mobility through the analysis of user-generated social media content based on text-mining techniques. Sustainability, 16(10), 3932. https://doi.org/10.3390/su16103932
Nilashi, M., & Abumalloh, R. A. (2025). i-TAM: A model for immersive technology acceptance. Education and Information Technologies, 30(6), 7689-7717. https://doi.org/10.1007/s10639-024-13080-5
Oyetola, S. O., Aderibigbe, N. A., & Oladokun, B. D. (2023). Implications of social media technologies (SMTs) to library services. Library Hi Tech News. https://doi.org/10.1108/LHTN-03-2023-0037
Özdemir, P. (2024). An assessment on maritime students’ awareness, perceptions and needs in career planning in tertiary education. Transactions on Maritime Science, 13(2), 20. https://doi.org/10.7225/toms.v13.n02.020
Park, Y. E., Tak, Y. W., Kim, I., Lee, H. J., Lee, J. B., Lee, J. W., & Lee, Y. (2024). User experience and extended technology acceptance model in commercial health care app usage among patients with cancer: Mixed methods study. Journal of Medical Internet Research, 26, e55176. https://doi.org/10.2196/55176
Pitono, A., & Fauzi, F. Z. (2025). Public opinions on social media: How to become a trustworthy leader in times of crisis. International Journal of Public Leadership, 21(1), 54-71. https://doi.org/10.1108/IJPL-07-2024-0076
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. https://doi.org/10.1037/0021-9010.88.5.879
Qassrawi, R. M., & Al Karasneh, S. M. (2023). Benefits of Facebook usage (as a Web 2.0 application) in foreign language instruction in higher education: A meta-analysis study. Cogent Arts & Humanities, 10(1), 2185447. https://doi.org/10.1080/23311983.2023.2185447
Shahzalal, M., Adnan, H. M., & Abdullah, F. (2023). University students’ perceived self-control and prosocial norms for beneficial social media use. Studies in Media and Communication, 11(6), 171-180. https://doi.org/10.11114/smc.v11i6.6103
Sidik, D., & Syafar, F. (2020). Exploring the factors influencing student’s intention to use mobile learning in Indonesia higher education. Education and Information Technologies, 25(6), 4781-4796. https://doi.org/10.1007/s10639-019-10018-0
Singh, H., Singh, V. V., Gupta, A. K., & Kapur, P. K. (2024). Assessing e-learning platforms in higher education with reference to student satisfaction: A PLS-SEM approach. International Journal of System Assurance Engineering and Management, 15(10), 4885-4896. https://doi.org/10.1007/s13198-024-02497-3
Stephen, A. M., & Ritzhaupt, A. (2023). Nursing students’ acceptance of an online computer-based simulation system utilizing the technology acceptance model. Clinical Simulation in Nursing, 81, 101418. https://doi.org/10.1016/j.ecns.2023.04.004
Tan, A. Y. N., Loh, H. S., Hsieh, C. H., & Lopez, M. C. R. (2025). Adoption of digital technologies in the maritime industry: Insights from Singapore. Maritime Technology and Research, 7(3), 275821. https://doi.org/10.33175/mtr.2025.275821
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers and Education, 57(4), 2432-2440. https://doi.org/10.1016/j.compedu.2011.06.008
Teo, T., Moses, P., Cheah, P. K., Huang, F., & Tey, T. C. Y. (2024). Influence of achievement goal on technology use among undergraduates in Malaysia. Interactive Learning Environments, 32(8), 4314-4331. https://doi.org/10.1080/10494820.2023.2197957
Travaglini, A., Brand, E., Meier, P., & Christ, O. (2023). Job relevance or perceived usefulness? What features of immersive virtual reality software predict intention to use in a future project-based-learning scenario: a mixed method approach. Frontiers in Virtual Reality, 4, 1286877. https://doi.org/10.3389/frvir.2023.1286877
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
Wohlfart, O., & Wagner, I. (2025). Longitudinal perspectives on technology acceptance: Teachers’ integration of digital tools through the COVID-19 transition. Education and Information Technologies, 30(5), 6091-6115. https://doi.org/10.1007/s10639-024-12954-y
Yan, S., Eng, L. G., & Seong, L. C. (2024). Influencing factors of continuous intention to use e-learning system of undergraduates in Guangxi, China: The mediating role of perceived ease of use and perceived usefulness. SAGE Open, 14(4), 21582440241305230. https://doi.org/10.1177/21582440241305231
Yılmaz, F. G. K., & Yılmaz, R. (2023). Exploring the role of sociability, sense of community and course satisfaction on students’ engagement in flipped classroom supported by facebook groups. Journal of Computers in Education, 10(1), 135-162. https://doi.org/10.1007/s40692-022-00226-y
Yu, A. Y., Tian, S. W., Vogel, D., & Chi-Wai Kwok, R. (2010). Can learning be virtually boosted? An investigation of online social networking impacts. Computers & Education, 55(4), 1494-1503. https://doi.org/10.1016/j.compedu.2010.06.015
Zhang, X., Abbas, J., Shahzad, M. F., Shankar, A., Ercisli, S., & Dobhal, D. C. (2024). Association between social media use and students’ academic performance through family bonding and collective learning: The moderating role of mental well-being. Education and Information Technologies, 29(11), 14059-14089. https://doi.org/10.1007/s10639-023-12407-y
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