Behavioral Intention and Use Behavior of University Students in Chengdu in Using Virtual Reality Technology for Learning
DOI:
https://doi.org/10.14456/shserj.2023.10Keywords:
Virtual Reality, Perceived Ease of Use, Perceived Enjoyment, Perceived Behavioral Control, Subjective NormAbstract
The purpose of this study is to investigate the factors that influence the usage of virtual reality (VR) technology in learning among university students in Chengdu, China. Scholars created a virtual reality teaching game based on Unreal Engine 4 software that was utilized to instruct a videography course at the Design College of Sichuan University of Media and Communications in Chengdu, China, with 1160 university students participating in a two-year pedagogical reform project. The researchers employed a quantitative research approach with a sample size of 50 participants, as well as a face-to-face questionnaire survey of the target respondents. The data was gathered via stratified random sampling. The Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were applied to analyze the data (SEM). The findings indicate that all factors have a substantial influence on students' utilization of virtual reality (VR) technology in learning, with behavioral intention having the biggest impact on actual usage, and that satisfaction has a considerable impact on actual usage. As a result, academic institutions that promote virtual reality (VR) technology as a teaching tool may be able to examine the factors that influence students' usage of VR technology in their learning, thereby boosting students' enthusiasm for learning and performance.
References
Abbad, M. M., Morris, D., Al-Ayyoub, A., & Abbad, J. M. (2009). Students’ decisions to use an elearning system: A structural equation modelling analysis. International Journal of Emerging Technologies in Learning, 4(4), 4-13.
Adams, D. A., Nelson, R., Todd, P. A., & Nelson, R. R. (1992). Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication Increasing Systems Usage Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication. MIS Quarterly, 16(2), 227-247.
Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261-277.
Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22(5), 453-474.
Alam, M. Z., Hu, W., Hoque, M. R., & Kaium, M. A. (2019). Adoption intention and usage behavior of mHealth services in Bangladesh and China: A cross-country analysis. International Journal of Pharmaceutical and Healthcare Marketing, 14(1), 37–60.
Al-Debei, M. M., Al-Lozi, E., & Papazafeiropoulou, A. (2013). Why people keep coming back to Facebook: Explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decision Support Systems, 55(1), 43-54.
Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27-50.
Altawallbeh, M., Soon, F., Thiam, W., & Alshourah, S. (2015). Mediating role of attitude, subjective norm and perceived behavioural control in the relationships between their respective salient beliefs and behavioural intention to adopt e-learning among instructors in Jordanian universities. Journal of Education and Practice, 6(11), 152-160.
Boateng, R., Mbrokoh, A. S., Boateng, L., Senyo, P. K., & Ansong, E. (2016). Determinants of e-learning adoption among students of developing countries. International Journal of Information and Learning Technology, 33(4), 248-262.
Chen Ying, L., Chih-Hsuan, T., & Wan-Chuan, C. (2015). The relationship between attitude toward using and customer satisfaction with mobile application services: An empirical study from the life insurance industry. Journal of Enterprise Information Management, 53(4), 194-200.
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers and Education, 63, 160-175.
Chiu, C. M., & Wang, E. T. G. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information and Management, 45(3), 194-201.
Chu, T.-H., & Chen, Y.-Y. (2016). With Good We Become Good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92, 37-52.
Cipresso, P., Giglioli, I. A. C., Raya, M. A., & Riva, G. (2018). The past, present, and future of virtual and augmented reality research: A network and cluster analysis of the literature. Frontiers in Psychology, 9(11), 1-20.
Davis, F. D., & Venkatesh, V. (2004). Toward preprototype user acceptance testing of new information systems: Implications for software project management. IEEE Transactions on Engineering Management, 51(1), 31-46.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003.
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.
Elie-Dit-Cosaque, C., Pallud, J., & Kalika, M. (2011). The influence of individual, contextual, and social factors on perceived behavioral control of information technology: A field theory approach. Journal of Management Information Systems, 28(3), 201-234.
El-Wajeh, Y. A. M., Hatton, P. V., & Lee, N. J. (2022). Unreal Engine 5 and immersive surgical training: translating advances in gaming technology into extended-reality surgical simulation training programmes. British Journal of Surgery, 109(5), 470-471.
Farahat, T. (2012). Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities. Procedia - Social and Behavioral Sciences, 64, 95-104.
Fussell, S. G., & Truong, D. (2021). Using virtual reality for dynamic learning: an extended technology acceptance model. Virtual Reality ,26, 249-267.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2009). Multivariate Data Analysis: A Global Perspective (7th ed.). Prentice Hall.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariant Data Analysis (6th ed.). Pearson International Edition.
Hartshorne, R., & Ajjan, H. (2009). Examining student decisions to adopt Web 2.0 technologies: Theory and empirical tests. Journal of Computing in Higher Education, 21(3), 183-198.
Holdack, E., Lurie-Stoyanov, K., & Fromme, H. F. (2022). The role of perceived enjoyment and perceived informativeness in assessing the acceptance of AR wearables. Journal of Retailing and Consumer Services, 65(7), 1-11.
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.
Hu, L.-T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling, 6(1), 1-55.
Huang, H. M., Rauch, U., & Liaw, S. S. (2010). Investigating learners’ attitudes toward virtual reality learning environments: Based on a constructivist approach. Computers and Education, 55(3), 1171-1182.
Hujran, O., Abu-Shanab, E., & Aljaafreh, A. (2020). Predictors for the adoption of e-democracy: an empirical evaluation based on a citizen-centric approach. Transforming Government: People, Process and Policy, 14(3), 523-544.
Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A Motivational Model of Microcomputer Usage. Journal of Management Information Systems, 13(1), 127-143.
Jaruwanakul, T. (2021). Key Influencers of Innovative Work Behavior in Leading Thai Property Developers. AU-GSB e-Journal, 14(1), 61-70.
Jie, H. (2019). Talking about the Advantages and Disadvantages of the Application of Virtual Reality Technology in Psychological Research. Advances in Psychology, 9(3), 552-557.
Jimenez, I. A. C., García, L. C. C., Violante, M. G., Marcolin, F., & Vezzetti, E. (2021). Commonly used external tam variables in e-learning, agriculture and virtual reality applications. Future Internet, 13(1), 1-21.
Kline, R. (2015). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
Lee, Y. (2006). An empirical investigation into factors influencing the adoption of an e- learning system. Online Information Review, 30(5), 517-541.
Litwin, M. S. (1995). How To Measure Survey Reliability and Validity? (7th ed.). Sage Publications.
Mahmodi, M. (2017). The Analysis of the Factors Affecting the Acceptance of E-learning in Higher Education. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 8(1), 1-9.
Manis, K. T., & Danny, C. (2019). The virtual reality hardware acceptance model (VR-HAM): Extending and individuating the technology acceptance model (TAM) for virtual reality hardware. Journal of Business Research, 100(10), 503-513.
Manstead, A. S. R., & Eekelen, S. A. M. (1998). Distinguishing Between Perceived Behavioral Control and Self-Efficacy in the Domain of Academic Achievement Intentions and Behaviors. Journal of Applied Social Psychology, 28(15), 1375-1392.
Marcinkiewicz, H. R., & Regstad, N. G. (1996). Using Subjective Norms to Predict Teachers’ Computer Use. Journal of Computing in Teacher Education, 13(1), 27-33.
Mathieson, K. (1991). Comparing The Technology Acceptance Model with The Theory of Planned Behaviour. Information Systems Research, 3(3), 173-191.
McDonough, E. F., Kahn, K. B., & Barczak, G. (2001). An investigation of the use of global, virtual, and colocated new product development teams. Journal of Product Innovation Management, 18(2), 110-120.
Merchant, Z., Goetz, E. T., Cifuentes, L., Keeney-Kennicutt, W., & Davis, T. J. (2014). Effectiveness of virtual reality-based instruction on students’ learning outcomes in K-12 and higher education: A meta-analysis. Computers and Education, 70, 29-40.
Morris, M. G., & Dillon, A. (1997). The Influence of User Perceptions on Software Utilization: Application and Evaluation of a Theoretical Model of Technology Acceptance. IEEE Software, 14(4), 58-60.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. (3rd ed.). McGraw-Hill.
Pantelidis, V. S. (2010). Reasons to Use Virtual Reality in Education and Training Courses and a Model to Determine When to Use Virtual Reality. Themes in Science and Technology Education ,2(2), 9-70.
Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592-605.
Raja, M., & G, L. P. (2022). Factors Affecting the Intention to Use Virtual Reality in Education. Psychology and Education, 57(9), 2014-2022.
Rotchanakitumnuai, S., & Speece, M. (2009). Modeling electronic service acceptance of an e-securities trading system. Industrial Management and Data Systems, 109(8), 1069-1084.
Sarmento, R., & Costa, V. (2016). Comparative Approaches to Using R and Python for Statistical Data Analysis Porto (1st ed.). IGI Global Press.
Sattar, M. U., Palaniappan, S., Lokman, A., Shah, N.,Khalid, U., & Hasan, R. (2020). Motivating Medical Students Using Virtual Reality Based Education. International Journal of Emerging Technologies in Learning (IJET), 15(02), 160-174.
Schermelleh-engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research Online, 8(2), 23-74.
Sivo, S. A., Ku, C. H., & Acharya, P. (2018). Understanding how university student perceptions of resources affect technology acceptance in online learning courses. Australasian Journal of Educational Technology, 34(4), 72-91. https://doi.org/10.14742/ajet.2806
Suki, N. M., Ramayah, T., & Ly, K. K. (2012). Empirical investigation on factors influencing the behavioral intention to use Facebook. Universal Access in the Information Society, 11(2), 223-231.
Syed-Abdul, S., Malwade, S., Nursetyo, A. A., Sood, M., Bhatia, M., Barsasella, D., Liu, M. F., Chang, C. C., Srinivasan, K., Raja, M., & Li, Y. C. J. (2019). Virtual reality among the elderly: A usefulness and acceptance study from Taiwan. BMC Geriatrics, 19(1), 1-10.
Tarmuji, N. H., & Ahmad, S. (2019). Proceedings of the Regional Conference on Science, Technology and Social Sciences (RCSTSS 2016): Social Sciences (1st ed.). Springer Singapore.
Taylor, S., & Todd, P. (1995a). Assessing IT usage: The role of prior experience. MIS Quarterly: Management Information Systems, 19(4), 561-568.
Taylor, S., & Todd, P. (1995b). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155.
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 and Education, 57(2), 1645-1653.
Van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers and Education, 50(3), 838-852.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.
Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system USE: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly: Management Information Systems, 32(3), 483-502.
Vidanagama, D. U. (2016). Acceptance of E-Learning among Undergraduates of Computing Degrees in Sri Lanka. International Journal of Modern Education and Computer Science, 8(4), 25-32.
Viswanath Venkatesh, & Fred D. Davis. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.
Wojciechowski, R., & Cellary, W. (2013). Evaluation of learners’ attitude toward learning in ARIES augmented reality environments. Computers and Education, 68, 570-585.
Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human Computer Studies, 59(4), 431-449.
Zhao, R., & Shen, Y. (2022). Learning and Teaching Cameras Using Virtual Reality Games ‐‐ A Case Study of the Combination of Virtual Reality and Virtual Lab in Universities. Scientific Journal of Humanities and Social Sciences, 4(4), 295-303.