Exploring The Affecting Factors of Behavioral Intention to Use Virtual Reality for Dynamic Learning: A Study of Top Three Private Universities in Yangon, Myanmar
Article Sidebar
Main Article Content
Abstract
Purpose: This research aims to examine the factors affecting the students’ behavioral intentions towards using virtual reality technology in replacing online learning/eLearning platforms to better engage in dynamic learning with the studies and improve their learning journey. The key variables are performance expectancy, perceived usefulness, perceived enjoyment, effort expectancy, social influence, perceived ease of use, hedonic motivation, attitude toward technology, and behavioral intention. Research design, data, and methodology: The quantitative questionnaire survey was provided to the target population of 500 students who are currently attending or have been attending the selected top three private universities of Yangon, Myanmar. The sampling techniques involve judgmental, snowball, quota, and convenience. Confirmatory factor analysis (CFA) and structural equation modeling were applied to analyze the data. Results: Perceived enjoyment has a significant effect on perceived usefulness, and attitude toward technology using VR. Effort expectancy Hedonic motivation and social influence significantly affect behavioral intention to use VR. However, there are no significant support between the relationship between performance expectancy, perceived usefulness, attitude toward technology using, perceived ease of use and behavioral intention to use VR. Conclusion: From the studies, the researcher can indicate that using new technology is useful and enjoyable for the users/students.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
References
Abdelsalama, M., Khedrb, A. E., Emamb, O., & Helmy, Y. (2019). A General Approach Students’ Attitude towards to Virtual Reality Technology in Distance Education Environment. Future Computing and Informatics Journal, 4(1), 10-15.
Al-Bashayreh, M., Almajali, D., Altamimi, A., Masa’deh, R., & Al-Okaily, M. (2022). An Empirical Investigation of Reasons Influencing Student Acceptance and Rejection of Mobile Learning Apps Usage. Sustainability, 14(7), 4325. https://doi.org/10.3390/su14074325
Albelbisi, N. A., & Yusop, F. D. (2018). Secondary School Students’ Use of and Attitudes toward Online Mathematics Homework. The Turkish Online Journal of Educational Technology, 17(1), 144-153.
Alowayr, A., & Al-Azawei, A. (2021). Predicting mobile learning acceptance: An integrated model and empirical study based on the perceptions of higher education students. Australasian Journal of Educational Technology, 37(3), 38-55. https://doi.org/10.14742/ajet.6154
Andrew, M., Taylorson, J., Langille, D. J., Grange, A., & Williams, N. (2018). Student attitudes towards technology and their preferences for learning tools/devices at two universities in the UAE. Journal of Information Technology Education: Research, 17, 309-344. https://doi.org/10.28945/4111
Azizi, S. M., Roozbahani, N., & Khatony, A. (2020). Factors affecting the acceptance of blended learning in medical education: application of UTAUT2 model. BMC Medical Education, 20(1), 367-376. https://doi.org/10.1186/s12909-020-02302-2
Bagozzi, R., & Yi, Y. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Sciences, 16, 74-94. http://dx.doi.org/10.1007/BF02723327
Basu, N., & Ahmad, G. (2016). Attitude towards Using New Technology among Higher Secondary School Teachers in District Budgam. Journal of Information Engineering and Applications, 6(2), 16-21.
Basuki, R., Tarigan, Z. J., Siagian, H., Limanta, L. S., Setiawan, D., & Mochtar, J. (2022). The effects of perceived ease of use, usefulness, enjoyment, and intention to use online platforms on behavioral intention in online movie watching during the pandemic era. International Journal of Data and Network Science, 6, 253-262. https://doi.org/10.5267/j.ijdns.2021.9.003
Bentler, P. M. (1990). Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107, 238-246. http://dx.doi.org/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. https://doi.org/10.1037/0033-2909.88.3.588
Bervell, B., Nyagorme, P., & Arkorful, V. (2020). LMS-Enabled Blended Learning Use Intentions among Distance Education Tutors: Examining the Mediation Role of Attitude Based on Technology-Related Stimulus-Response Theoretical Framework. Contemporary Educational Technology, 12(2), ep273. https://doi.org/10.30935/cedtech/8317
Bower, M., DeWitt, D., & Lai, J. W. (2020). Reasons associated with preservice teachers’ intention to use immersive virtual reality in education. British Journal of Educational Technology, 51(6), 2215-2233. https://doi.org/10.1111/bjet.13009
Buabeng-Andoh, C. (2018). Predicting students’ intention to adopt mobile learning: A combination of theory of reasoned action and technology acceptance model. Journal of Research in Innovative Teaching & Learning, 11(2), 2397-7604.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
Domingo, M. G., & Gargante, A. B. (2016). Exploring the use of educational technology in primary education: Teachers' perception of mobile technology learning impacts and applications' use in the classroom. Computers in Human Behavior, 56, 21-28. https://doi.org/10.1016/j.chb.2015.11.023
Escobar-Rodríguez, T., Carvajal-Trujillo, E., & Monge-Lozano, P. (2014). Factors that influence the perceived advantages and relevance of Facebook as a learning tool: An extension of the UTAUT. Australasian Journal of Educational Technology, 30(2), 136-151. https://doi.org/10.14742/ajet.585
Fearnley, M. R., & Amora, J. T. (2020). Learning Management System Adoption in Higher Education Using the Extended Technology Acceptance Model. Journal of Education: Technology in Education, 8(2), 89-106. https://doi.org/10.22492/ije.8.2.05
Fedorko, I., Bačik, R., & Gavurova, B. (2021). Effort expectancy and social influence factors as main determinants of performance expectancy using electronic banking. Banks and Bank Systems, 16(2), 27 -37. https://doi.org/10.21511/bbs.16(2).2021.03
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Fussell, S. G., & Truong, D. (2021). Using virtual reality for dynamic learning: an extended technology acceptance model. Springer, 26, 249-267. https://doi.org/10.1007/s10055-021-00554-x
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson, New York.
Halverson, L. R., & Graham, C. R. (2019). Learner Engagement in Blended Learning Environments: A Conceptual Framework. Online Learning, 23(2), 145-178. https://doi.org/10.24059/olj.v23i2.1481
Hamid, M. A., Sami, W., & Sidek, M. M. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890, 012163. https://doi.org/10.1088/1742-6596/890/1/012163
Herting, D. C., Pros, R. C., & Tarrida, A. C. (2021). Habit and social influence as determinants of PowerPoint use in higher education: A study from a technology acceptance approach. Interactive Learning Environments, 31(1), 1-17.
Holdack, A., Stoyanov, K., & Fromme, H. F. (2020). The role of perceived enjoyment and perceived informativeness in assessing Holdack, E., Lurie the acceptance of AR wearables. Journal of Retailing and Consumer Services, 65(1), 102259. https://doi.org/10.1016/j.jretconser.2020.102259
Huang, C.-H. (2021). Using PLS-SEM Model to Explore the Influencing Factors of Learning Satisfaction in Blended Learning. Education Sciences, 11(5), 249. https://doi.org/10.3390/educsci11050249
Huedo-Martínez, S., Molina-Carmona, R., & Llorens-Largo, F. (2018). Study on the Attitude of Young People Towards Technology. Learning and Collaboration Technologies, 10925, 26-43. https://doi.org/10.1007/978-3-319-91152-6_3
Ikhsan, R. B., Prabowo, H., & Yuniarty, Y. (2021). Drivers of the Mobile-Learning Management System's Actual Usage: Applying the UTAUT Model. ICIC International, 12(11), 1067-1074.
Jaradat, M.-I. R., & Rababaa, M. S. (2013). Assessing Key Factor that Influence on the Acceptance of Mobile Commerce Based on Modified UTAUT. International Journal of Business and Management, 8(23), 102-112.
Jihoon, K., Yong, K. J., & Connaughton, D. P. (2021). Performance Expectancy of Officiating Technology in Spector-Based Sport Events: Scale Development and Validation. Communication & Sport, 11(3), 528-550. https://doi.org/10.1177/21674795211022006
Jyothi, V., & Renuka, D. (2015). Role of Computer Technology in Developing Positive Attitude towards Mathematics. International Journal of Scientific & Engineering Research, 6(9), 77-79.
Keni, K. (2020). How Perceived Usefulness and Perceived Ease of Use Affecting Intent to Repurchase? Jurnal Manajemen, 14(3), 481-496. https://doi.org/10.24912/jm.v24i3.680
Kerschner, C., & Melf-Hinrich, E. (2016). A framework of attitudes towards technology in theory and practice. Ecological Economics, 126, 139-151. https://doi.org/10.1016/j.ecolecon.2016.02.010
Kerzˇič, D., Tomazˇevič, N., Aristovnik, A., & Umek, L. (2019). Exploring critical factors of the perceived usefulness of blended learning for higher education students. PLoS ONE, 14(11), e0223767. https://doi.org/10.1371/journal.pone.0223767
Koch, J., Frommeyer, B., & Schewe, G. (2020). Online Shopping Motives during the COVID-19 Pandemic—Lessons from the Crisis. Sustainability, 12(24), 10247. https://doi.org/10.3390/su12241024710247), 1-20.
Kumar, J. A., & Bervell, B. (2019). Google Classroom for mobile learning in higher education: Modelling the initial perceptions of students. Education and Information Technologies, 1(2), 1793-1817. https://doi.org/10.1007/s10639-018-09858-z
Kustandi, C., Fadhillah, D. N., Situmorang, R., Prawiradilaga, D. S., & Hartati, S. (2020). VR Use in Online Learning for Higher Education in Indonesia. International Journal of Interactive Mobile Technologies, 14(1), 31-46. https://doi.org/10.3991/ijim.v14i01.11337
Lakhal, S., & Khechine, H. (2021). Technological factors of students’ persistence in online courses in higher education: The moderating role of gender, age, and prior online course experience. Education and Information Technologies, 26(3), 1-18.
Liesa Orús, M., Latorre Cosculluela, C., Sierra Sánchez, V., & Vázquez Toledo, S. (2022). Links between ease of use, perceived usefulness and attitudes towards technology in older people in university: A structural equation modelling approach. Education and Information Technologies, 28, 1-18.
Madini, A. A., & Alshaikhi, D. (2017). Female ESP Postgraduates' Acceptance of Virtual Reality Learning: Aye or Nay. International Journal of Humanities and Social Sciences, 9(6), 12-31.
Marikyan, D., & Papagiannidis, S. (2022). TheoryHub Book. Technology Acceptance Model: A review, 1-12 (1st ed.). Retrieved from Technology Acceptance Model.
Marlina, E., Tjahjadi, B., & Ningsih, S. (2021). Factors Affecting Student Performance in E-Learning: A Case Study of Higher Educational Institutions in Indonesia. Journal of Asian Finance, Economics and Business, 8(4), 0993-1001.
Marsh, H. W., Guo, J., Dicke, T., Parker, P. D., & Craven, R. G. (2019). Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (ESEM), and Set-ESEM: Optimal Balance Between Goodness of Fit and Parsimony. Multivariate Behavioral Research, 55(1), 102-119. https://doi.org/10.1080/00273171.2019.1602503
Mpungose, C. B. (2021). Lecturers’ reflections on use of Zoom video conferencing technology for e-learning at a South African university in the context of coronavirus. African Identities, 21(2), 266-282. https://doi.org/10.1080/14725843.2021.1902268
Munoz-Carril, P.-C., Hernandez-Selles, N., Fuentes-Abeledo, E.-J., & Gonzalez-Sanmamed, M. (2021). Factors influencing students’ perceived impact of learning and satisfaction in Computer Supported Collaborative Learning. Computers & Education, 174(104310), 1-13.
Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open, 2, 100041. https://doi.org/10.1016/j.caeo.2021.100041
Novikova, I. A., Bychkova, P. A., & Novikov, A. L. (2022). Attitudes towards Digital Educational Technologies among Russian University Students before and during the COVID-19 Pandemic. Sustainability, 14(10), 6203. https://doi.org/10.3390/su14106203
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Nuraziza, N., Oktaviani, L., & Sari, F. M. (2021). EFL Learners’ Perceptions on ZOOM Application in the Online Classes. Jambura Journal of English Teaching and Literature, 2(1), 41-51. https://doi.org/10.37905/jetl.v2i1.7318
Onaolapo, S., & Oyewole, O. (2018). Performance expectancy, effort expectancy, and facilitating conditions as factors influencing smart phones use for mobile learning by postgraduate students of the University of Ibadan, Nigeria. Interdisciplinary Journal of E-Skills and Lifelong Learning, 14, 95 - 115. https://doi.org/10.28945/4085
Pedroso, C. B., Silva, A. L., & Tate, W. L. (2016). Sales and Operations Planning (S&OP): insights from a multi-case study of Brazilian organizations. International Journal of Production Economics, 182, 213-229. http://dx.doi.org/10.1016/j.ijpe.2016.08.035.
Raja, R., & Nagasubramani, P. C. (2018). Impact of modern technology in education. Journal of Applied and Advanced Research, 3(1), 33-35. https://doi.org/10.21839/jaar.2018.v3is1.165
Rashid, A., Rasheed, R., Amirah, N. A., Yusof, Y., Khan, S., & Agha, A. A. (2021). A Quantitative Perspective of Systematic Research: Easy and Step-by-Step Initial Guidelines. Turkish Online Journal of Qualitative Inquiry, 12(9), 2874-2883.
Ratheeswari, K. (2018). Information Communication Technology in Education. Journal of Applied and Advanced Research, 3(1), 45-47. https://doi.org/10.21839/jaar.2018.v3is1.169
Rizun, M., & Strzelecki, A. (2020). Students’ Acceptance of the COVID-19 Impact on Shifting Higher Education to Distance Learning in Poland. International Journal of Environmental Research and Public Health, 17(18), 6468. https://doi.org/10.3390/ijerph17186468
Sabas, J., & Kiwango, T. A. (2021). Evaluating the Influence of Performance Expectancy on the Adoption of Students’ Information System in Higher Learning Institutions. Accountancy and Business Review, 13(2), 39 - 50. https://doi.org/10.59645/abr.v13i2.23
Sair, S. A., & Danish, R. Q. (2018). Effect of Performance Expectancy and Effort Expectancy on the Mobile Commerce Adoption Intention through Personal Innovativeness among Pakistani Consumers. Pakistan Journal of Commerce and Social Sciences, 12(2), 501-520.
Salloum, S. A., & Al-Emran, M. (2018). Factors affecting the adoption of e-payment systems by university students: extending the TAM with trust. International Journal of Electronic Business, 14(4), 371-390. https://doi.org/10.1504/ijeb.2018.098130
Saraswat, S., Jain, R., & Awasthi, S. (2021). Outlook of Consumers towards Online Pharmacies: Roles of Performance Expectancy, Effort Expectancy and Adoption. Effulgence, 19(1), 16 - 26. https://doi.org/10.33601/effulgence.rdias/v19/i1/2021/16-26
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M. A. Lange (Ed.), Leading-edge psychological tests and testing research (pp. 27–50). Nova Science
Sitar-Taut, D.-A. (2021). Mobile learning acceptance in social distancing during the COVID-19 outbreak: The mediation effect of hedonic motivation. Human Behavior and Emerging Technologies, 3(3), 366-378.
Solano, L., Cabrera, P., Ulehlova, E., & Espinoza, V. (2017). Exploring The Use of Educational Technology in EFL Teaching: A case study of Primary Education in the South Region of Ecuador. Teaching English with Technology, 17(2), 77-86.
Tahar, A., Riyadh, H. A., Sofyani, H., & Purnomo, W. E. (2020). Perceived Ease of Use, Perceived Usefulness, Perceived Security and Intention to Use E-Filing: The Role of Technology Readiness. Journal of Asian Finance, Economics and Business, 7(9), 537-547. https://doi.org/10.13106/jafeb.2020.vol7.no9.537
Tan, P. J. (2013). Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan. SAGE Open, 3(4), 215824401350383. https://doi.org/10.1177/2158244013503837
Teo, T., & Noyes, J. (2011). An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers & Education, 57(2), 1645-1653. https://doi.org/10.1016/j.compedu.2011.03.002
Torrentira, M. C. (2020). Online Data Collection as Adaptation in Conducting Quantitative and Qualitative Research During the Covid-19 Pandemic. European Journal of Education Studies, 7(11), 78-87. https://doi.org/10.46827/ejes.v7i11.3336
Tusyanah, T., Wahyudin, A., & Khafid, M. (2021). Analyzing Factors Affecting the Behavioral Intention to Use e-Wallet with the UTAUT Model with Experience as Moderating Variable. Journal of Economic Education, 10(2), 113-123.
Udang, L. N. (2022). UTAUT Factors Influencing Perception & Behavioral Intention of Learners & Lecturers Towards Adoption of Online Classes During COVID-19 Pandemic. Research Square, 2(3), 1-27. https://doi.org/10.21203/rs.3.rs-2124853/v
Ukut, I. I., & Krairit, D. (2019). Justifying students’ performance, A comparative study of both ICT students’ and instructors’ perspective. Interactive Technology and Smart Education, 16(1), 18-35.
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. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y., & 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
Villena-Taranilla, R., Tirado-Olivares, S., Cozar-Gutierrez, R., & Gonzalez-Calero, J. A. (2022). Effects of virtual reality on learning outcomes in K-6 education: A meta-analysis. Educational Research Review, 35, 100434. https://doi.org/10.1016/j.edurev.2022.100434
Wijaya, T. T., Cao, Y., Weinhand, R., Yusron, E., & Lavicza, Z. (2022). Applying the UTAUT Model to Understand Factors Affecting Micro-Lecture Usage by Mathematics Teachers in China. Mathematics, 10(7), 1008. https://doi.org/10.3390/math10071008
Wilson, N., Keni, K., & Pattyranie Tan, P. H. (2021). The Role of Perceived Usefulness and Perceived Ease-of-Use Toward Satisfaction and Trust which Influence Computer Consumers’ Loyalty in China. Gadjah Mada International Journal of Business, 23(3), 262-294. https://doi.org/10.22146/gamaijb.32106
Winata, S., & Tjokrosaputro, M. (2021). The Roles of Effort Expectancy, Attitude, and Service Quality in Mobile Payment Users Continuance Intention. Advances in Economics, Business and Management Research, 653, 121-126. https://doi.org/10.2991/aebmr.k.220501.020
Yee, M. L., & Abdullah, M. S. (2021). A Review of UTAUT and Extended Model as a Conceptual Framework in Education Research. Jurnal Pendidikan Sains Dan Matematik Malaysia, 11, 1-20.
Zardari, B. A., Hussain, Z., Arain, A. A., Rizvi, W. H., & Vighio, M. S. (2021). Development and Validation of User Experience-Based E-Learning Acceptance Model for Sustainable Higher Education. Sustainability, 13(11), 6201. https://doi.org/10.3390/su13116201