Research on Educational Sport

The effect of extended technology acceptance model on the use of online education during the corona (a case study of physical education students)

Document Type : Research Paper

Authors

Bahareh yousefjan 1
Shirin zardoshtian 2

1 Master in sport management, Department of sport management, Faculty of physical education and sport sciences, Razi University, Kermanshah, Iranh

2 Assistant professor in sport management, Department of sport management, Faculty of physical education and sport sciences, Razi university, Kermanshah, Iran

https://doi.org/10.22089/res.2022.11997.2215
Abstract
The purpose of this study was to investigate the effect of the extended technology acceptance model on the use of online education during the corona in physical education students. The present study was applied and correlation. The statistical population of this study were all undergraduate students in the field of physical education and sports sciences of Kermanshah, Hamedan, Kurdistan, Lorestan and Ilam universities, which 324 people were selected as sample using random cluster sampling method. To collect data, standard questionnaires of facilitator conditions, perceived usefulness, perceived ease of use, attitude, behavioral intention and use of online education were used. Data analysis was performed using variance-based structural equation model. The results showed that facilitator conditions have a significant effect on perceived usefulness and perceived ease of use that the effect of facilitator conditions on perceived ease was greater. Also, perceived ease of use has a significant effect on perceived usefulness and attitude that the effect of perceived ease of use on attitude was greater. Perceived usefulness has a significant effect on attitudes and behavioral intentions that the perceived usefulness had a greater effect on behavioral intentions. Finally, attitudes have a significant effect on behavioral intentions and behavioral intentions on the use of online education during the coronation period in physical education students. Based on the results, it is suggested that universities with physical education in online education increase facilitator condition, perceived usefulness and perceived ease of use in order to promote students' attitudes, behavioral intention and use of online education.

Keywords

Facilitator Conditions
Perceived Usefulness
Perceived Ease of Use
Attitude to Use
Behavioral Intentions
  1. Akmaliyah, A., Karman, K., Rosyid Ridho, M., & Khomisah, K. (2020). Online-based teaching of Arabic translation in the era of Covid 19 pandemic restrictions. IOSR Journal of Humanities and Social Science (IOSR-JHSS), 25(5), 13-22.
  2. Baooj Lahouti, R., Ghasemi, A. (2016). Explaining the behavior of IT consumer based on the technology acceptance model. Rahyaft, 26(61), 63-74. (Persian)
  3. Buabeng-Andoh, C., Yaokumah, W., & Tarhini, A. (2019). Investigating students’ intentions to use ICT: A comparison of theoretical models. Education and Information Technologies, 24(1), 643-660.
  4. Casey, A., & Jones, B. (2011). Using digital technology to enhance student engagement in physical education. Asia-Pacific Journal of Health, Sport and Physical Education, 2(2), 51-66.
  5. Clark, R. C., & Mayer, R. E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. Hoboken, New Jersey: John Wiley & Sons.
  6. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation). Massachusetts Institute of Technology.
  7. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
  8. Guo, J., Liao, L., Wang, B., Li, X., Guo, L., Tong, Z., et al. (2020). Psychological effects of COVID-19 on hospital staff: A national cross-sectional survey of China Mainland. SSRN Electronic Journal, 6, 4-20.
  9. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). London: Sage Publications.
  10. Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26 (2), 106-121.
  11. Henseler, J, Ringlet, C. M. & Ninkovich, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Bingley: Group Publishing Limited.
  12. Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause Review, 27, 1-12.
  13. Isaac, O., Aldholay, A., Abdullah, Z., & Ramayah, T. (2019). Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the IS success model. Computers & Education, 136, 113-129.
  14. Kim, E. J., Kim, J. J., & Han, S. H. (2021). Understanding student acceptance of online learning systems in higher education: Application of social psychology theories with consideration of user innovativeness. Sustainability, 13(2), 896.
  15. Kimmons, R., & Veletsianos, G. (2018). Public internet data mining methods in instructional design, educational technology, and online learning research. TechTrends, 62(5), 492-500.
  16. Latif Rasool, B. (2021). Attitudes of Iraqi Kurdistan region professors towards Virtual education in physical education courses: An extended model of technology acceptance (Unpublished master's thesis). University of Kurdistan. (Persian)
  17. Legrain, P., Gillet, N., Gernigon, C., & Lafreniere, M. A. (2015). Integration of information and communication technology and pupils’ motivation in a physical education setting. Journal of Teaching in Physical Education, 34(3), 384-401.
  18. Mailizar, A., Abdulsalam, M., & Suci, B. (2020). Secondary school mathematics teachers' views on e-learning implementation barriers during the COVID-19 pandemic: The case of Indonesia. Eurasia Journal of Mathematics, Science & Technology Education, 16(7), 1-9.
  19. Melton, B., Harris, H. B. B., Kelly, D., & Chandler, K. (2015). Evaluating a physical activity app in the classroom: a mixed methodological approach among. The Physical Educator, 72, 601-620.
  20. Mian, A., & Khan, S. (2020). Medical education during pandemics: a UK perspective. BMC Medicine, 18(1), 1-2.
  21. Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374.
  22. Mohammadian, S., Ghasemzadeh Alishahi, A., & Rafiei, M. (2020). Causal model of acceptance and use of information and communication technology by students of Tabriz University of Medical Sciences in Educational and research purposes based on the UTAUT model. Iranian Journal of Information processing and Management, 36(2), 391-418.
  23. Mollahosseini, A., & Foroozanfar, M. (2019). Development and localization of technology acceptance model (TAM) in small and medium-sized enterprises (SMEs). Quarterly Journal of Industrial Technology Development, 16(34), 39-48. (Persian)
  24. Mueller, R. O. (1999). Basic principles of structural equation modeling: An introduction to LISREL and EQS. Cham: Springer Science & Business Media.
  25. Muhaimin, H., Mukminin, A., Pratama, R., & Asrial, H. (2019). Predicting factors affecting intention to use Web 2.0 in learning: Evidence from science education. Journal of Baltic Science Education, 18(4), 595.
  26. Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73.
  27. O’Loughlin, J., Chróinín, D. N., & O’Grady, D. (2013). Digital video: The impact on children’s learning experiences in primary physical education. European Physical Education Review, 19(2), 165-182.
  28. Pookulangara, S., Hawley, J., & Xiao, G. (2011). Explaining consumers’ channel-switching behavior using the theory of planned behavior. Journal of Retailing and Consumer Services, 18(4), 311-321.
  29. Rahimi, E., van den Berg, J., & Veen, W. (2015). Facilitating student-driven constructing of learning environments using Web 2.0 personal learning environments. Computers & Education, 81, 235-246.
  30. Ramírez-Correa, P. E., Arenas-Gaitán, J., & Rondán-Cataluña, F. J. (2015). Gender and acceptance of e-learning: A multi-group analysis based on a structural equation model among college students in Chile and Spain. PloS One, 10(10), e0140460.
  31. Rezaei, A. (2020). Student learning evaluation during the Corona: Challenges and Strategies. Educational Psychology, 16(55), 179-214. (Persian)
  32. Sajed, A. N., & Amgain, K. (2020). Corona virus disease (COVID-19) outbreak and the strategy for prevention. Europasian Journal of Medical Sciences, 2(1), 1-3.
  33. Salloum, S. A. S. (2018). Investigating students' acceptance of E-learning system in higher educational environments in the UAE: Applying the extended technology acceptance model (TAM) (Doctoral dissertation). The British University, Dubai.
  34. Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11), e05410.
  35. Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2019). Investigating pre-service teachers’ acceptance of Web 2.0 technologies in their future teaching: A Chinese perspective. Interactive Learning Environments, 27(4), 530-546.
  36. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.
  37. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
  38. Viner, R. M., Russell, S. J., Croker, H., Packer, J., Ward, J., Stansfield, C., ... & Booy, R. (2020). School closure and management practices during coronavirus outbreaks including COVID-19: a rapid systematic review. The Lancet Child & Adolescent Health, 4(5), 397-404.
  39. Wang, C. J., Ng, C. Y., & Brook, R. H. (2020). Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. JAMA, 323(14), 1341-1342.
  40. Wixom, B., & Todd, P (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102.
Research on Educational Sport
Volume 10, Issue 29
Winter 2023
Pages 127-154

  • Receive Date 28 December 2021
  • Revise Date 20 April 2022
  • Accept Date 30 April 2022

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