Understanding the Adoption and Usage Behaviors of Popular and Emerging Metaverse Platforms: A Study Based on the Extended Technology Acceptance Model

References

• Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analyzing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036
   Web of Science ®Google Scholar
• Akour, I. A., Al-Maroof, R. S., Alfaisal, R., & Salloum, S. A. (2022). A conceptual framework for determining metaverse adoption in higher institutions of gulf area: An empirical study using hybrid SEM-ANN approach. Computers and Education: Artificial Intelligence, 3, 100052. https://doi.org/10.1016/j.caeai.2022.100052
   Google Scholar
• Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100–110. https://doi.org/10.1016/j.techsoc.2018.06.007
   Web of Science ®Google Scholar
• Anderson, J., & Rainie, L. (2022). The metaverse in 2040. Retrieved from: https://www.pewresearch.org/internet/wp-content/uploads/sites/9/2022/06/PI_2022.06.30_Metaverse-Predictions_FINAL.pdf Retrieved December 20, 2022)
   Google Scholar
• Aral, S., & Walker, D. (2012). Identifying influential and susceptible members of social networks. Science, 337(6092), 337–341. https://doi.org/10.1126/science.1215842
   PubMed Web of Science ®Google Scholar
• Barrett, A. J., Pack, A., & Quaid, E. D. (2021). Understanding learners’ acceptance of high-immersion virtual reality systems: Insights from confirmatory and exploratory PLS-SEM analyses. Computers & Education, 169, 104214. https://doi.org/10.1016/j.compedu.2021.104214
   Web of Science ®Google Scholar
• Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128–143. https://doi.org/10.1016/j.compedu.2017.04.010
   Web of Science ®Google Scholar
• Coltman, T., Devinney, T. M., Midgley, D. F., & Venaik, S. (2008). Formative versus reflective measurement models: Two applications of formative measurement. Journal of Business Research, 61(12), 1250–1262.
   Web of Science ®Google Scholar
• Das, S. (2022). How many people played Roblox in 2022? Player count, regions, and more.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Dearing, J. W., & Cox, J. G. (2018). Diffusion of innovations theory, principles, and practice. Health Affairs, 37(2), 183–190. https://doi.org/10.1377/hlthaff.2017.1104
   PubMed Web of Science ®Google Scholar
• Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.
   Google Scholar
• Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689
   Web of Science ®Google Scholar
• Grand View research. (2022). Metaverse market size, share & trends report, 2030. Retrieved from: https://www.grandviewresearch.com/industry-analysis/metaverse-market-report Retrieved June 20, 2022)
   Google Scholar
• Guay, F., Vallerand, R. J., & Blanchard, C. (2000). On the assessment of situational intrinsic and extrinsic motivation: The Situational Motivation Scale (SIMS). Motivation and Emotion, 24(3), 175–213. https://doi.org/10.1023/A:1005614228250
   Web of Science ®Google Scholar
• Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2022). A primer on partial least squares structural equation modeling (PLS-SEM). (3rd ed.). Sage publications. https://doi.org/10.1007/978-3-030-80519-7
   Google Scholar
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Hornbæk, K., & Hertzum, M. (2017). Technology acceptance and user experience: A review of the experiential component in HCI. ACM Transactions on Computer-Human Interaction (TOCHI), 24(5), 1–30. https://doi.org/10.1145/3127358
   Web of Science ®Google Scholar
• Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision Sciences, 28(2), 357–389. https://doi.org/10.1111/j.1540-5915.1997.tb01315.x
   Web of Science ®Google Scholar
• Karaali, D., Gumussoy, C. A., & Calisir, F. (2011). Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Computers in Human Behavior, 27(1), 343–354. https://doi.org/10.1016/j.chb.2010.08.012
   Web of Science ®Google Scholar
• Kwok, A. O., & Koh, S. G. (2021). COVID-19 and extended reality (XR). Current Issues in Tourism, 24(14), 1935–1940. https://doi.org/10.1080/13683500.2020.1798896
   Web of Science ®Google Scholar
• Lee, L. H., Braud, T., Zhou, P., Wang, L., Xu, D., Lin, Z., & Hui, P. (2021). All one needs to know about metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda. Retrieved from https://arxiv.org/abs/2110.05352 (Retrieved March 15, 2023)
   Google Scholar
• Lee, Y. H., Hsieh, Y. C., & Chen, Y. H. (2013). An investigation of employees’ use of e-learning systems: Applying the technology acceptance model. Behaviour & Information Technology, 32(2), 173–189. https://doi.org/10.1080/0144929X.2011.577190
   Web of Science ®Google Scholar
• Lee, U. K., & Kim, H. (2022). UTAUT in Metaverse: An “Ifland” case. Journal of Theoretical & Applied Electronic Commerce Research, 17(2), 613–635. https://doi.org/10.3390/jtaer17020032
   Web of Science ®Google Scholar
• Lee, J., Kim, J., & Choi, J. Y. (2019). The adoption of virtual reality devices: The technology acceptance model integrating enjoyment, social interaction, and strength of the social ties. Telematics and Informatics, 39, 37–48. https://doi.org/10.1016/j.tele.2018.12.006
   Web of Science ®Google Scholar
• Narin, N. G. (2021). A content analysis of the metaverse articles. Journal of Metaverse, 1(1), 17–24.
   Google Scholar
• Nesbo, E. (2021), The Metaverse vs. Virtual Reality: 6 key differences. Retrieved from: https://www.makeuseof.com/metaverse-vs-virtual-reality/ (Retrieved July 20, 2022).
   Google Scholar
• Ning, H., Wang, H., Lin, Y., Wang, W., Dhelim, S., Farha, F., Ding, J., & Daneshmand, M. (2021). A survey on Metaverse: The state-of-the-art, technologies, applications, and challenges. Retrieved from https://arxiv.org/abs/2111.09673 , April 16)
   Google Scholar
• Nolan, J. M., Schultz, P. W., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2008). Normative social influence is underdetected. Personality & Social Psychology Bulletin, 34(7), 913–923.
   PubMed Web of Science ®Google Scholar
• Pan, S., & Jordan-Marsh, M. (2010). Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Computers in Human Behavior, 26(5), 1111–1119. https://doi.org/10.1016/j.chb.2010.03.015
   Web of Science ®Google Scholar
• Park, S. M., & Kim, Y. G. (2022). A Metaverse: Taxonomy, components, applications, and open challenges. IEEE Access, 10, 4209–4251. https://doi.org/10.1109/ACCESS.2021.3140175
   Web of Science ®Google Scholar
• Ramansyah, W., Aini, N., Fitriansyah, W., & Pratama, M. D. (2021). Virtual reality and educational game to learn madurese history and alphabet for elementary school students. Journal of Physics: Conference Series, 1842(1), 012012. https://doi.org/10.1088/1742-6596/1842/1/012012
   Google Scholar
• Rogers, E. M. (2003). ”Diffusion of innovations” (5th ed.). Free Press.
   Google Scholar
• Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020
   PubMed Web of Science ®Google Scholar
• Sagnier, C., Loup-Escande, E., Lourdeaux, D., Thouvenin, I., & Valléry, G. (2020). User acceptance of virtual reality: An extended technology acceptance model. International Journal of Human–Computer Interaction, 36(11), 993–1007. https://doi.org/10.1080/10447318.2019.1708612
   Web of Science ®Google Scholar
• Sun, H., & Zhang, P. (2006). Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach. Journal of the Association for Information Systems, 7(9), 618–645. https://doi.org/10.17705/1jais.00100
   Web of Science ®Google Scholar
• Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144
   Web of Science ®Google Scholar
• Teng, Z., Cai, Y., Gao, Y., Zhang, X., Li, X., & Shin, J. (2022). Factors affecting learners’ adoption of an educational metaverse platform: An empirical study based on an extended UTAUT model. Mobile Information Systems, 2022, 1–15. https://doi.org/10.1155/2022/5479215
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1080/10494820.2011.641674
   Web of Science ®Google Scholar
• Toraman, Y. (2022). User acceptance of metaverse: Insights from technology acceptance model (TAM) and planned behavior theory (PBT). EMAJ: Emerging Markets Journal, 12(1), 67–75. https://doi.org/10.5195/emaj.2022.258
   Google Scholar
• Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. In M. P. Zanna (Ed.), Advances in experimental social psychology (pp. 271–360). Academic Press. https://doi.org/10.1016/S0065-2601(08)60019-2
   Google Scholar
• Vasconcellos, D., Parker, P. D., Hilland, T., Cinelli, R., Owen, K. B., Kapsal, N., Lee, J., Antczak, D., Ntoumanis, N., Ryan, R. M., & Lonsdale, C. (2020). Self-determination theory applied to physical education: A systematic review and meta-analysis. Journal of Educational Psychology, 112(7), 1444–1469. https://doi.org/10.1037/edu0000420
   Web of Science ®Google Scholar
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
   Web of Science ®Google Scholar
• Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139. https://doi.org/10.2307/3250981
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Wang, Y., Su, Z., Zhang, N., Liu, D., Xing, R., Luan, T. H., & Shen, X. (2023). A survey on metaverse: Fundamentals, security, and privacy. IEEE Communications Surveys & Tutorials, 25(1), 319–352. https://doi.org/10.1109/COMST.2022.3202047
   Web of Science ®Google Scholar
• Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: A meta‐analysis of the TAM: Part 2. Journal of Modelling in Management, 2(3), 281–304. https://doi.org/10.1108/17465660710834462
   Google Scholar