Effects of motivation to use social networking sites on students’ media literacy and critical thinking

Chich-Jen Shieh 1, Jaitip Nasongkhla 1 *
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1 Disruptive Innovation Technology in Education Research Unit, Faculty of Education, Chulalongkorn University, Bangkok, THAILAND
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 14, Issue 1, Article No: e202404. https://doi.org/10.30935/ojcmt/14060
OPEN ACCESS   1090 Views   763 Downloads   Published online: 24 Dec 2023
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ABSTRACT

Participation in social networking sites offers many potential benefits for university students. Online interaction on these sites provides various opportunities for them to learn and improve self-control, tolerate and respect the viewpoints of others, express emotions in healthy and orderly ways, and think and make decisions critically. These sites also provide them with a virtual space to execute time, form close connections with friends without being spatially restricted and provide space for young people’s self-development. However, the number of studies examining university students’ social networking sites, media literacy, and critical thinking is very limited in the literature. Therefore, this research examined the effects of motivation to use social networking sites on students’ media literacy and critical thinking. The research also examined the relationships between students’ motivation for using social networks, media literacy, and critical thinking. The data were collected using three data collection instruments. The participants were 211 university students enrolled at two universities in Bangkok, Thailand. The results showed significant positive correlations between motivation to use social networking sites, and critical thinking, that university students with better performance in information and learning show better performance in critical thinking and reflection skills. The results also showed remarkable positive correlations between motivation for using social networking sites and media literacy, indicating that university students with better performance in information and learning show better performance in multimedia messages and multimedia organization and analysis. In addition, the results also revealed positive correlations between critical thinking and media literacy. The implications are made based on the results obtained from this research.
Keywords: social networking sites, motivation, media literacy, critical thinking, information & learning

CITATION

Shieh, C.-J., & Nasongkhla, J. (2024). Effects of motivation to use social networking sites on students’ media literacy and critical thinking. Online Journal of Communication and Media Technologies, 14(1), e202404. https://doi.org/10.30935/ojcmt/14060

REFERENCES

  • Abdalla, R., & Qashou, A. (2020). The influence of social network use on students’ academic performance. Palestine Technical University Research Journal, 8(2), 112-130. https://doi.org/10.53671/pturj.v8i2.120
  • Adenubi, O. S., Olalekan, Y. S., Afolabi, A. A., & Ayelaagbe, S. O. (2013). Online social networking and the academic achievement of university students–The experience of selected Nigerian Universities. Information and Knowledge Management, 3(5), 109-116.
  • Adnan, K., Akbar, R., Khor, S. W., & Ali, A. B. A. (2020). Role and challenges of unstructured big data in healthcare. Data Management, Analytics and Innovation, 1042, 301-323. https://doi.org/10.1007/978-981-32-9949-8_22
  • Ali, F. H., & Qazi, A. A. (2018). Are social networking sites suitable for formal learning among business research students? A mixed method experimental approach. Bulletin of Education and Research, 40(1), 267-284. https://doi.org/10.4324/9781315563671-15
  • Bonnevie, E., Rosenberg, S. D., Kummeth, C., Goldbarg, J., Wartella, E., & Smyser, J. (2020). Using social media influencers to increase knowledge and positive attitudes toward the flu vaccine. PLoS ONE, 15(10), e0240828. https://doi.org/10.1371/journal.pone.0240828
  • Brailovskaia, J., Ströse, F., Schillack, H., & Margraf, J. (2020). Less Facebook use–More well-being and a healthier lifestyle? An experimental intervention study. Computers in Human Behavior, 108, 106332. https://doi.org/10.1016/j.chb.2020.106332
  • Chen, C. M., Chiu, M. C., & Yen, C. C. (2021). Using PZB to explore the impact of leisure farm service quality on the willingness to revisit: Using satisfaction as an intermediary variable. International Journal of Organizational Innovation, 13(3), 12-15. https://doi.org/10.1016/j.tele.2020.101346
  • Cheng, X., Cao, Q., & Liao, S. S. (2022). An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation. Journal of Information Science, 48(3), 304-320. https://doi.org/10.1177/0165551520954674
  • Cheo, R., Ge, G., Godager, G., Liu, R., Wang, J., & Wang, Q. (2020). The effect of a mystery shopper scheme on prescribing behavior in primary care: Results from a field experiment. Health Economics Review, 10(1), 33. https://doi.org/10.1186/s13561-020-00290-z
  • Cho, V. (2015). The impacts of participating social networking sites: A study of university students in Hong Kong and Wuhan in China. Online Journal of Communication and Media Technologies, 5(2), 181-212. https://doi.org/10.29333/ojcmt/2512
  • Choi, Y., Choi, M., Oh, M., & Kim, S. (2020). Service robots in hotels: Understanding the service quality perceptions of human-robot interaction. Journal of Hospitality Marketing & Management, 29(6), 613-635. https://doi.org/10.1080/19368623.2020.1703871
  • Chou, P.-F., & Wang, Y.-C. (2022). Study on customer satisfaction, store image and re-service quality. EPRA International Journal of Economics, Business and Management Studies, 9(8), 72-78.
  • Chowdhury, N. F. H., Debnath, P., & Bhowmik, D. (2020). How does internet usage influence the academic performance of university students? A case of MBSTU. Journal of Research & Method in Education, 10(3), 15-24.
  • Dash, G., & Paul, J. (2021). CB-SEM vs. PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/10.1016/j.techfore.2021.121092
  • Ge, Y., Yuan, Q., Wang, Y., & Park, K. (2021). The structural relationship among perceived service quality, perceived value, and customer satisfaction-focused on Starbucks reserve coffee shops in Shanghai, China. Sustainability, 13(15), 8633. https://doi.org/10.3390/su13158633
  • Grohskopf, L. A., Alyanak, E., Broder, K. R., Blanton, L. H., Fry, A. M., Jernigan, D. B., & Atmar, R. L. (2020). Prevention and control of seasonal influenza with vaccines: Recommendations of the advisory committee on immunization practices–United States, 2020-21 influenza season. MMWR, 69(8). https://doi.org/10.15585/MMWR.RR6908A1
  • Gulnar, B., Balci, S., & Cakir, V. (2010). Motivations of Facebook YouTube and similar web sites users. Bilig, 54, 161-184.
  • Hayes, T. (2021). R-squared change in structural equation models with latent variables and missing data. Behavior Research Methods, 53(5), 2127-2157. https://doi.org/10.3758/s13428-020-01532-y
  • Hernández-García, I., & Giménez-Júlvez, T. (2021). YouTube as a source of influenza vaccine information in Spanish. International Journal of Environmental Research and Public Health, 18(2), 727. https://doi.org/10.3390/ijerph18020727
  • Iqbal, J., Qureshi, N., Ashraf, M. A., Rasool, S. F., & Asghar, M. Z. (2021) The effect of emotional intelligence and academic social networking sites on academic performance during the COVID-19 pandemic. Psychology Research, and Behavior Management, 14, 905-920. https://doi.org/10.2147/PRBM.S316664
  • Issa, T., Alqahtani, S. G. B., Al-Oqily, I., Goktalay, S. B., Kose, U., Issa, T., Salih, B. A., & Almufaraj, W. K. (2021). Use of social networking in the Middle East: Student perspectives in higher education. Heliyon, 7(4), e06676. https://doi.org/10.1016/j.heliyon.2021.e06676
  • Jong, S. T., & Drummond, M. J. N. (2016). Hurry up and “like” me: Immediate feedback on social networking sites and the impact on adolescent girls. Asia-Pacific Journal of Health, Sport and Physical Education, 7(3), 251-267. https://doi.org/10.1080/18377122.2016.1222647
  • Kalra, R. K., & Manani, P. (2013). Effect of social networking sites on academic achievement among introverts and extroverts. Asian Journal of Social Sciences & Humanities, 2(3), 401-406.
  • Khan, A. G., Lima, R. P., & Mahmud, M. S. (2021). Understanding the service quality and customer satisfaction of mobile banking in Bangladesh: Using a structural equation model. Global Business Review, 22(1), 85-100. https://doi.org/10.1177/0972150918795551
  • Lavertu, A., Hamamsy, T., & Altman, R. B. (2021). Quantifying the severity of adverse drug reactions using social media: Network analysis. Journal of Medical Internet Research, 23(10), e27714. https://doi.org/10.2196/27714
  • Lyu, J. C., & Luli, G. K. (2021). Understanding the public discussion about the centers for disease control and prevention during the COVID-19 pandemic using twitter data: Text mining analysis study. Journal of Medical Internet Research, 23(2), e25108. https://doi.org/10.2196/25108
  • Marker, C., Gnambs, T. & Appel, M. (2018). Active on Facebook and failing at school? Meta-analytic findings on the relationship between online social networking activities and academic achievement. Educational Psychology Review, 30, 651-677. https://doi.org/10.1007/s10648-017-9430-6
  • Mitchell, K. J., & Ybarra, M. (2009). Social networking sites: Finding a balance between their risks and benefits. Archives of Pediatrics & Adolescent Medicine, 163(1), 87-89. https://doi.org/10.1001/archpediatrics.2008.534
  • Mohd Dzin, N. H., & Lay, Y. F. (2021). Validity and reliability of adapted self-efficacy scales in Malaysian context using PLS-SEM approach. Education Sciences, 11(11), 676. https://doi.org/10.3390/educsci11110676
  • Murakami, R., & Chakraborty, B. (2022). Investigating the efficient use of word embedding with neural-topic models for interpretable topics from short texts. Sensors, 22(3), 852. https://doi.org/10.3390/s22030852
  • Nanath, K., Kaitheri, S., Malik, S., & Mustafa, S. (2022). Examination of fake news from a viral perspective: An interplay of emotions, resonance, and sentiments. Journal of Systems and Information Technology, 24(2), 131-155. https://doi.org/10.1108/JSIT-11-2020-0257
  • Nguyen, T. D., Lyall, G., Tran, A., Shin, M., Carroll, N. G., Klein, C., & Xie, L. (2022). Mapping topics in 100,000 real-life moral dilemmas. In Proceedings of the International AAAI. Conference on Web and Social Media (pp. 699-710). https://doi.org/10.1609/icwsm.v16i1.19327
  • Park, S. (2012). Dimensions of digital media literacy and the relationship with social exclusion. Media International Australia, 142(1), 87-100. https://doi.org/10.1177/1329878x1214200111
  • Samad, S., Nilashi, M., & Ibrahim, O. (2019). The impact of social networking sites on students’ social wellbeing and academic performance. Education and Information Technologies, 24, 2081-2094. https://doi.org/10.1007/s10639-019-09867-6
  • Shah, A. M., Yan, X., Tariq, S., & Khan, S. (2021). Listening to the patient voice: Using a sentic computing model to evaluate physicians’ healthcare service quality for strategic planning in hospitals. Quality and Quantity, 55, 173-201. https://doi.org/10.1007/s11135-020-00999-3
  • Share, J. (2022). Interrogating power and transforming education with critical media literacy. In B. S. De Abreu (Ed.), Media literacy, equity, and justice (pp. 130-137). Routledge. https://doi.org/10.4324/9781003175599-20
  • Sharma, S. (2015). Use of social networking sites by undergraduates in relation to their academic achievement. Scholarly Research Journal for Interdisciplinary Studies, 21(3), 1229-1234.
  • Shepherd, R. (1993). Why teach media literacy. Center for Media Introduction. http://www.mediaawareness.ca/english/resources/educational/teaching_backgrounders/media_literacywhy_teach_shepherd.cfm
  • Skoric, M. M., Liu, J., & Jaidka, K. (2020). Electoral and public opinion forecasts with social media data: A meta-analysis. Information, 11(4), 187. https://doi.org/10.3390/info11040187
  • Tafesse, W. (2022). Social networking sites use and college students’ academic performance: Testing for an inverted U-shaped relationship using automated mobile app usage data. International Journal of Educational Technology in Higher Education, 19, 16. https://doi.org/10.1186/s41239-022-00322-0
  • Tien, N. H., Diem, P. T., Van On, P., Anh, V. T., Van Dat, N., Hung, N. T., & Tam, B. Q. (2021). The formation and development of CRM system at Thien Hoa electronics supermarket in Vietnam. International Journal of Research and Growth Evaluation, 2, 4.
  • Wang, L., Wang, X.-K., Peng, J.-J., & Wang, J.-Q. (2020). The differences in hotel selection among various types of travelers: A comparative analysis with a useful bounded rationality behavioral decision support model. Tourism Management, 76, 103961. https://doi.org/10.1016/j.tourman.2019.103961
  • Wang, Y.-C. (2020). Word segmentation for classical Chinese Buddhist literature. Journal of the Japanese Association for Digital Humanities, 5(2), 154-172. https://doi.org/10.17928/jjadh.5.2_154
  • Watson, G., & Glasser, E. M. (1980). Watson-Glaser critical thinking appraisal, from Z-Manua. Brace & World, Inc.
  • Yang, C.-L., Huang, C.-Y., & Hsiao, Y.-H. (2021). Using social media mining and PLS-SEM to examine the causal relationship between public environmental concerns and adaptation strategies. International Journal of Environmental Research and Public Health, 18(10), 5270. https://doi.org/10.3390/ijerph18105270
  • Yang, Y., & Wang, F. (2021). Author topic model for co-occurring normal documents and short texts to explore individual user preferences. Information Sciences, 570, 185-199. https://doi.org/10.1016/j.ins.2021.04.060
  • Yuan, K.-H., & Deng, L. (2021). Equivalence of partial-least-squares SEM and the methods of factor-score regression. Structural Equation Modeling: A Multidisciplinary Journal, 28(4), 557-571. https://doi.org/10.1080/10705511.2021.1894940