Résumés
Abstract
YouTube is one of the most prevalent social media sites across the globe. However, there is a lack of research on factors influencing educational use of YouTube. This study examines high school students’ educational use of YouTube with unified theory of acceptance and use of technology (UTAUT). Using structural equation modeling, the proposed model is tested. Results demonstrate that performance expectancy and social influence are the significant predictors of behavioral intention to use YouTube. Furthermore, behavioral intention is the significant predictor of actual usage. The results suggest that students intend to use YouTube for improving their academic performance. Social influence also contributes to their intention. Based on previous literature, the results are discussed.
Keywords:
- YouTube,
- high school students,
- unified theory of acceptance and use of technology (UTAUT),
- structural equation modeling
Parties annexes
Bibliography
- Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. The International Review of Research in Open and Distance Learning, 14(5), 82-107. doi: 10.19173/irrodl.v14i5.1631
- Adhikari, J., Sharma, P., Arjyal, L., & Uprety, D. (2016). YouTube as a source of information on cervical cancer. North American Journal of Medical Sciences, 16(8), 183-186. doi: 10.4103/1947-2714.179940
- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. doi: 10.1016/0749-5978(91)90020-T
- Ali, R. A., & Arshad, M. R. M. (2018). Empirical analysis on factors impacting on intention to use m-learning in basic education in Egypt. International Review of Research in Open and Distributed Learning, 19(2), 253-270. doi: 10.19173/irrodl.v19i2.3510.
- Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. doi: 10.1037/0033-2909.107.2.238
- Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness-of-fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606. doi: 10.1037/0033-2909.88.3.588
- Bonk, C. J. (2008, March). YouTube anchors and enders: The use of shared online video content as a macrocontext for learning. Paper presented at the American Educational Research Association (AERA) 2008 Annual Meeting, New York, NY.
- Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd edition). MA: Routledge.
- Chaka, J. G., & Govender, I. (2017). Students’ perceptions and readiness towards mobile learning in colleges of education: A Nigerian perspective. South African Journal of Education, 37(1), 1-12. doi: 10.15700/saje.v37n1a1282
- Çokluk, Ö., Şekercioğlu, G., & ve Büyüköztürk, Ş. (2016). Sosyal bilimler için çok değişkenli istatistik SPSS ve LISREL Uygulamaları [Multivariate statistics for social sciences: SPSS and Lisrel applications]. Ankara: Pegem Akademi Yayıncılık.
- Doleck, T., Bazelais, P., & Lemay, D. J. (2017). Examining CEGEP students’ acceptance of computer-based learning environments: A test of two models. Education and Information Technologies, 22, 2523-2543. doi: 10.1007/s10639-016-9559-9
- El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the unified theory of acceptance and use of technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743-763. doi: 10.1007/s11423-016-9508-8
- Erstad, O. (2012). The learning lives of digital youth - beyond the formal and informal. Oxford Review of Education, 38(1), 25-43. doi: 10.1080/03054985.2011.577940
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. doi: 10.2307/3151312
- Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. New Jersey: Pearson Education Inc.
- Hao, S., Dennen, V. P., & Mei, L. (2017). Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65(1), 101-123. doi: 10.1007/s11423-016-9465-2
- Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53-60.
- Hoyle, R. H. (1995). Structural equation modeling: concepts, issues, and application. London: Sage.
- Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information & Management, 48(1), 1-8. doi: 10.1016/j.im.2010.09.001
- Isaias, P., Reis, F., Coutinho, C., & Lencastre, J. A. (2017). Empathic technologies for distance/mobile learning: An empirical research based on the unified theory of acceptance and use of technology (UTAUT). Interactive Technology and Smart Education, 14(2), 159-180. doi: 10.1108/ITSE-02-2017-0014
- Jöreskog, K. G., & Sörbom D. (1988). LISREL 7: A guide to the program and applications. Chicago, IL: SPSS Inc.
- Jung, I., & Lee, Y. (2015). YouTube acceptance by university educators and students: A cross-cultural perspective. Innovations in Education and Teaching International, 52(3), 243-253. doi: 10.1080/14703297.2013.805986
- Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. doi: 10.1007/bf02291575
- Khechine, H., & Lakhal, S. (2018). Technology as a double-edged sword: From behavior prediction with UTAUT to students’ outcomes considering personal characteristics. Journal of Information Technology Education, 17, 63-102. doi: 10.28945/4022
- Kline, R.B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York, NY: Guilford Press.
- Lin, C. C., & Polaniecki, S. (2009). From media consumption to media production: Applications of YouTube™ in an eighth-grade video documentary project. Journal of Visual Literacy, 28(1), 92-107. doi: 10.1080/23796529.2009.11674661
- Lin, S., Zimmer, J. C., & Lee, V. (2013). Podcasting acceptance on campus: The differing perspectives of teachers and students. Computers & Education, 68, 416-428. doi: 10.1016/j.compedu.2013.06.003
- Liu, L. -W., Chang, C. -M, Huang, H. -C., & Chang, Y. -L. A. (2016). Verification of social network site use behavior of the university physical education students. Eurasia Journal of Mathematics, Science & Technology Education, 12(4), 793-805. doi: 10.12973/eurasia.2016.1232a
- MacCallum, R. C., Widaman, K. F., Preacher, K., & Hong, S. (2001). Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36, 611-637. doi: 10.1207/S15327906MBR3604_06
- Madden, M. (2009, July 29). The audience for online video-sharing sites shoots up (Blog post). Retrieved from http://www.pewinternet.org/2009/07/29/the-audience-for-online-video-sharing-sites-shoots-up/
- Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391-410. doi: 10.1037/0033-2909.103.3.391
- Mualik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lin, S., & Stilwel, C. D. (1989). Evaluation of goodness of fit Indices for structural equation models. Psychological Bulletin, 105(3), 430-445. doi: 10.1037/0033-2909.105.3.430
- Ngampornchai, A., & Adams, J. (2016). Students’ acceptance and readiness for E-learning in Northeastern Thailand. International Journal of Educational Technology in Higher Education, 13(34), 1-13. doi: 10.1186/s41239-016-0034-x
- Nicholas-Omoregbe, O. S., Azeta, A. A., Chiazor, I. A., & Omoregbe, N. (2017). Predicting the adoption of e-learning management system: A case of selected private universities in Nigeria. Turkish Online Journal of Distance Education, 18(2), 106-121. doi: 10.17718/tojde.306563
- Nistor, N., Göğüş, A., & Lerche, T. (2013). Educational technology acceptance across national and professional cultures: A European study. Educational Technology Research and Development, 61(4), 733-749. doi: 10.1007/s11423-013-9292-7
- O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673-690. doi: 10.1007/s11135-006-9018-6
- Padhi, N. (2018). Acceptance and usability of OER in Indian higher education: An investigation using UTAUT model. Open Praxis, 10(1), 55-65. doi: 10.5944/openpraxis.10.1.623
- 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), 879-903. doi: 10.1037/0021-9010.88.5.879
- Prasad, P.W.C., Maag, A., Redestowicz, M., & Hoe, L. S. (2018). Unfamiliar technology: Reaction of international students to blended learning. Computers & Education, 122, 92-103. doi: 10.1016/j.compedu.2018.03.016
- Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323-337. doi: 10.3200/JOER.99.6.323-338
- Suki, N. M., & Suki, N. M. (2017). Determining students’ behavioral intention to use animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. The International Journal of Management Education, 15, 528-538. doi: 10.1016/j.ijme.2017.10.002
- Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre-and post-adopters. Computers in Human Behavior, 64, 602-620. doi: 10.1016/j.chb.2016.07.037
- Teo, T., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the unified theory of acceptance and use of technology. Interactive Learning Environments, 22(1), 51-66. doi: 10.1080/10494820.2011.641674
- Terlemez, M. S. (2016). YouTube channels as support materials in distance learning. Journal of Educational and Instructional Studies, 6(4), 77-88.
- Ünlüsoy, A., de Haan, M., Leander, K., & Volker, B. (2013). Learning potential in youth’s online networks: A multilevel approach. Computers and Education, 68, 522-533. doi: 10.1016/j.compedu.2013.06.007
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. doi: 10.2307/30036540
- Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs. China. Journal of Global Information Technology Management, 13(1), 5-27. doi: 1097198X.2010.10856507
- 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. doi: 10.2307/41410412
- Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376. doi: 10.17705/1jais.00428
- Vidaman, K. F., & Thompson, J. S. (2003). On specifying the null model for incremental fit indices in structural equation modeling. Psychological Methods, 8(1), 16-37. doi: 10.1037/1082-989X.8.1.16
- Wong, G. K. W. (2016). The behavioral intentions of Hong Kong primary teachers in adopting educational technology. Educational Technology Research and Development, 64(2), 313-338. doi: 10.1007/s11423-016-9426-9
- Wong, K. T., Teo, T., & Goh, P. S. C. (2015). Understanding the intention to use interactive whiteboards: Model development and testing. Interactive Learning Environments, 23(6), 731-747. doi: 10.1080/10494820.2013.806932
- Yueh, H. -P., Huang, J. -Y., & Chang, C. (2015). Exploring factors affecting students continued Wiki use for individual and collaborative learning: An extended UTAUT perspective. Australasian Journal of Educational Technology, 31(1), 16-31. doi: 10.14742/ajet.170