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Information & Technology Management

Intention for enhancing metaverse-based learning using gamification among university students: a study using Delphi and structural equation modelling approaches

ORCID Icon, ORCID Icon, , , &
Article: 2380016 | Received 05 Mar 2024, Accepted 02 Jul 2024, Published online: 26 Jul 2024

References

  • Abu Rbeian, A. M., Owda, A. Y., & Owda, M. (2022). A technology acceptance model survey of the metaverse prospects. AI, 3(2), 285–302. https://doi.org/10.3390/ai3020018[Mismatch]
  • Al Deir, C., Al Khasawneh, M., Abuhashesh, M., Masa’deh, R., & Ahmad, A. M. (2023). A development of a newly constructed model related to the impact of entrepreneurial motivation on entrepreneurial intention. Studies in Computational Intelligence, 1056, 1559–1584. https://doi.org/10.1007/978-3-031-12382-5_86/COVER
  • Al-Adwan, A. S., & Al-Debei, M. M. (2023). The determinants of Gen Z’s metaverse adoption decisions in higher education: Integrating UTAUT2 with personal innovativeness in IT. Education and Information Technologies, 29(6), 7413–7445. https://doi.org/10.1007/s10639-023-12080-1
  • Al-Adwan, A. S., Li, N., Al., Adwan, G. A., Abbasi, N. A., Albelbis, , & Habibi, A. (2023). Extending the technology acceptance model (TAM) to predict university students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28(11), 1–33. https://doi.org/10.1007/s10639-023-11816-3
  • Ali, Sikandar, Armand, Tagne Poupi Theodore, Athar, Ali, Hussain, Ali, Ali, Maisam, Yaseen, Muhammad, Joo, Moon-Il, Kim, Hee-Cheol, Abdullah, (2023). Metaverse in healthcare integrated with explainable AI and blockchain: Enabling immersiveness, ensuring trust, and providing patient data security. Sensors (Basel, Switzerland) 23(2), 1–17. https://doi.org/10.3390/s23020565
  • An, Y. (2023). The impact of gamification on doctoral students’ perceptions, emotions, and learning in an online environment. TechTrends, 67(4), 706–717. https://doi.org/10.1007/s11528-022-00833-7
  • Andaleeb, A. A., Idrus, R. M., Ismail, I., & Mokaram, A. K. (2010). Technology readiness index (TRI) among USM distance education students according to age. World Academy of Science, Engineering and Technology, 39, 1039–1042.
  • Andembubtob, D. R., Keikhosrokiani, P., & Abdullah, N. L. (2023). A concise review on the concept of metaverse: Types, history, features, and technological perspectives. In Handbook of research on consumer behavioral analytics in metaverse and the adoption of a virtual world (pp. 40–67). https://doi.org/10.4018/978-1-6684-7029-9.CH003
  • Anjum, T., Amoozegar, A., Farrukh, M., & Heidler, P. (2023). Entrepreneurial intentions among business students: the mediating role of attitude and the moderating role of university support. Education + Training, 65(4), 587–606. https://doi.org/10.1108/ET-01-2021-0020
  • Arpaci, I., Karatas, K., Kusci, I., & Al-Emran, M. (2022). Understanding the social sustainability of the Metaverse by integrating UTAUT2 and big five personality traits: A hybrid SEM-ANN approach. Technology in Society, 71, 102120. https://doi.org/10.1016/j.techsoc.2022.102120
  • Badilla Quintana, M. G., & Meza Fernández, S. (2015). A pedagogical model to develop teaching skills. the collaborative learning experience in the Immersive Virtual World TYMMI. Computers in Human Behavior, 51, 594–603. https://doi.org/10.1016/j.chb.2015.03.016
  • Bitrián, P., Buil, I., Catalán, S., & Hatfield, S. (2023). The use of gamification strategies to enhance employees’ attitudes towards e-training systems. The International Journal of Management Education, 21(3), 100892. https://doi.org/10.1016/j.ijme.2023.100892
  • Bornschlegl, M., Townshend, K., & Caltabiano, N. J. (2021). Application of the theory of planned behavior to identify variables related to academic help seeking in higher education. Frontiers in Education, 6, 738790. https://doi.org/10.3389/feduc.2021.738790
  • Bouarir, H., Diani, A., Boubker, O., & Rharzouz, J. (2023). Key determinants of women’s entrepreneurial intention and behavior: The role of business opportunity recognition and need for achievement. Administrative Sciences, 13(2), 33. https://doi.org/10.3390/admsci13020033
  • Camacho-Sánchez, R., Manzano-León, A., Rodríguez-Ferrer, J. M., Serna, J., & Lavega-Burgués, P. (2023). Game-based learning and gamification in physical education: A systematic review. Education Sciences, 13(2), 183. https://doi.org/10.3390/educsci13020183
  • Çera, G., Pagria, I., Khan, K. A., & Muaremi, L. (2020). Mobile banking usage and gamification: the moderating effect of generational cohorts. Journal of Systems and Information Technology, 22(3), 243–263. https://doi.org/10.1108/JSIT-01-2020-0005
  • Chapman, J. R., Kohler, T. B., & Gedeborg, S. (2023). So, why do students perform better in gamified courses? Understanding motivational styles in educational gamification. Journal of Educational Computing Research, 61(5), 927–950. https://doi.org/10.1177/07356331221127635
  • Chen, Z. (2022). Exploring the application scenarios and issues facing Metaverse technology in education. Interactive Learning Environments, 32(7), 1–13. https://doi.org/10.1080/10494820.2022.2133148
  • Cheng, Y. M. (2023). How gamification and social interaction stimulate MOOCs continuance intention via cognitive presence, teaching presence and social presence? Library Hi Tech, 41(6), 1781–1801. https://doi.org/10.1108/LHT-03-2022-0160/FULL/XML
  • Cordano, M., & Frieze, I. H. (2000). Pollution reduction preferences of US environmental managers: Applying Ajzen’s theory of planned behavior. Academy of Management Journal, 43(4), 627–641.
  • Dalkey, N., & Helmer, O. (1963). An experimental application of the DELPHI method to the use of experts. Management Science, 9(3), 458–467. https://doi.org/10.1287/mnsc.9.3.458
  • Davis, F. (1985a). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Unpublished Doctoral Dissertation, MIT Sloan School of Management, Cambridge, MA]. https://doi.org/10.1126/science.146.3652.1648
  • Davis, F. (1985b). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Unpublished Doctoral Dissertation, MIT Sloan School of Management, Cambridge, MA]. https://doi.org/10.1126/science.146.3652.1648
  • Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.5962/bhl.title.33621
  • De Leon, M. V. (2019). Factors influencing behavioural intention to use mobile banking among retail banking clients. Jurnal Studi Komunikasi (Indonesian Journal of Communications Studies), 3(2), 118. https://doi.org/10.25139/jsk.v3i2.1469
  • De Notaris, D., Canazza, S., Mariconda, C., & Paulon, C. (2021). How to play a MOOC: Practices and simulation. Entertainment Computing, 37, 100395. https://doi.org/10.1016/j.entcom.2020.100395
  • DeVellis, R. F., Lewis, M. A., & Sterba, K. R. (2003). Interpersonal emotional processes in adjustment to chronic illness. In Social psychological foundations of health and illness. Wiley Online Library.
  • Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., Conboy, K., Doyle, R., Dubey, R., Dutot, V., Felix, R., Goyal, D. P., Gustafsson, A., Hinsch, C., Jebabli, I., … Wamba, S. F. (2022). Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 66, 102542. https://doi.org/10.1016/j.ijinfomgt.2022.102542
  • Faqih, K. M. S., & Jaradat, M. I. R. M. (2021). Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country. Technology in Society, 67, 101787. https://doi.org/10.1016/j.techsoc.2021.101787
  • Far, S. B., & Rad, A. I. (2022). Applying digital twins in metaverse: User interface, security and privacy challenges. Journal of Metaverse, 2(1), 8–15.
  • Gejandran, P., & Abdullah, N. (2024). Gamification in e-learning: A systematic review of benefits, challenges, and future possibilities. Journal of Logistics, Informatics and Service Science, 11(2), 84–104. https://doi.org/10.33168/JLISS.2024.0206
  • Giannarou, L., & Zervas, E. (2014). Using Delphi technique to build consensus in practice. International Journal of Business Science & Applied Management (IJBSAM), 9(2), 65–82. http://hdl.handle.net/10419/190657https://creativecommons.org/licenses/by/2.0/uk/
  • Groh, F. (2012). Gamification: State of the art definition and utilization. Institute of Media Informatics Ulm University. 31.
  • Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
  • Hamari, J., & Koivisto, J. (2015). “Working out for likes”: An empirical study on social influence in exercise gamification. Computers in Human Behavior, 50, 333–347. https://doi.org/10.1016/j.chb.2015.04.018
  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. New Challenges to International Marketing (Advances in International Marketing), 20, 277–319. https://doi.org/10.1007/978-3-319-53469-5_12
  • Hu, S., Laxman, K., & Lee, K. (2020). Exploring factors affecting academics’ adoption of emerging mobile technologies-an extended UTAUT perspective. Education and Information Technologies, 25(5), 4615–4635. https://doi.org/10.1007/s10639-020-10171-x
  • Hubona, G. S., & Cheney, P. H. (1994). System effectiveness of knowledge-based technology: the relationship of user performance and attitudinal measures. Proceedings of the Hawaii International Conference on System Sciences, 4, 532—541. https://doi.org/10.1109/hicss.1994.323465
  • Humbani, M., & Wiese, M. (2019). An integrated framework for the adoption and continuance intention to use mobile payment apps. International Journal of Bank Marketing, 37(2), 646–664. https://doi.org/10.1108/IJBM-03-2018-0072
  • Hussain, M., Mollik, A. T., Johns, R., & Rahman, M. S. (2019). M-payment adoption for bottom of pyramid segment: An empirical investigation. International Journal of Bank Marketing, 37(1), 362–381. https://doi.org/10.1108/IJBM-01-2018-0013
  • Hwang, G. J., & Chien, S. Y. (2022). Definition, roles, and potential research issues of the metaverse in education: An artificial intelligence perspective. Computers and Education: Artificial Intelligence, 3(April), 100082. https://doi.org/10.1016/j.caeai.2022.100082
  • İbili, E., Ölmez, M., Cihan, A., Bilal, F., İbili, A. B., Okumus, N., & Billinghurst, M. (2023). Investigation of learners’ behavioral intentions to use metaverse learning environment in higher education: a virtual computer laboratory. Interactive Learning Environments, 1–26. https://doi.org/10.1080/10494820.2023.2240860
  • Iivari, N., Sharma, S., & Ventä-Olkkonen, L. (2020). Digital transformation of everyday life – How COVID-19 pandemic transformed the basic education of the young generation and why information management research should care? International Journal of Information Management, 55, 102183. https://doi.org/10.1016/j.ijinfomgt.2020.102183
  • Jaiswal, D., Mohan, A., & Deshmukh, A. K. (2023). Cash rich to cashless market: Segmentation and profiling of fintech-led-mobile payment users. Technological Forecasting and Social Change, 193(June 2022), 122627. https://doi.org/10.1016/j.techfore.2023.122627
  • Jarrar, Y., Awobamise, A. O., & Sellos, P. S. (2020). Technological readiness index (TRI) and the intention to use smartphone apps for tourism: A focus on in Dubai mobile tourism app. International Journal of Data and Network Science, 4(3), 297–304. https://doi.org/10.5267/j.ijdns.2020.6.003
  • Jayashankar, P., Nilakanta, S., Johnston, W. J., Gill, P., & Burres, R. (2018). IoT adoption in agriculture: the role of trust, perceived value and risk. Journal of Business & Industrial Marketing, 33(6), 804–821. https://doi.org/10.1108/JBIM-01-2018-0023/FULL/PDF
  • Jeong, S. H., & Kim, H. K. (2023). Effect of trust in metaverse on usage intention through technology readiness and technology acceptance model. Tehnicki Vjesnik, 30(3), 837–845. https://doi.org/10.17559/TV-20221111061245
  • Jo, H. (2023). Tourism in the digital frontier: A study on user continuance intention in the metaverse. Information Technology & Tourism, 25(3), 307–330. https://doi.org/10.1007/s40558-023-00257-w
  • Kansal, P. (2016). Factors affecting adoption of mobile banking at the bottom of the pyramid in India. International Journal of Marketing & Business Communication, 5(3), 8–19.
  • Kaplan, A. D., Cruit, J., Endsley, M., Beers, S. M., Sawyer, B. D., & Hancock, P. A. (2021). The effects of virtual reality, augmented reality, and mixed reality as training enhancement methods: A meta-analysis. Human Factors, 63(4), 706–726. https://doi.org/10.1177/0018720820904229
  • Kashive, N., & Mohite, S. (2023). Use of gamification to enhance e-learning experience. Interactive Technology and Smart Education, 20(4), 554–575. https://doi.org/10.1108/ITSE-05-2022-0058/FULL/XML
  • Kasirye, F., & Wok, S. (2023). Factors influencing the usage of web-based video conferencing platforms in knowledge acquisition among students. International Social Science Journal, 73(248), 261–277. https://doi.org/10.1111/issj.12392
  • Khurshid, J., & Khan, M. I. (2017). Impact of self-efficacy on women entrepreneurial intention: Mediating role of perceived behavior control and moderating role of openness to experience. Journal of Managerial Sciences, 9(3), 275–292.
  • Kumar, R. (2011). Research Methodology.
  • Kusumawardani, K. A., & Soegihono, L. F. (2024). Does gamification on an e-commerce application lead intention to use the application and spread word of mouth? International Journal of Business and Systems Research, 18(1), 65–84. https://doi.org/10.1504/IJBSR.2024.135779
  • Lai, V. S., & Li, H. (2005). Technology acceptance model for internet banking: An invariance analysis. Information & Management, 42(2), 373–386. https://doi.org/10.1016/j.im.2004.01.007
  • Lai, Y. L., & Lee, J. (2020). Integration of technology readiness index (TRI) into the technology acceptance model (TAM) for explaining behavior in adoption of BIM. Asian Education Studies, 5(2), 10. https://doi.org/10.20849/aes.v5i2.816
  • Lee, N., & Jo, M. (2023). Exploring problem-based learning curricula in the metaverse: The hospitality students’ perspective. Journal of Hospitality, Leisure, Sport and Tourism Education, 32, 100427. https://doi.org/10.1016/j.jhlste.2023.100427
  • Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: past, present, and future. Communications of the Association for Information Systems, 12(1), 50. https://doi.org/10.17705/1CAIS.01250
  • Li, M., & Yu, Z. (2022). A systematic review on the metaverse-based blended English learning. Frontiers in Psychology, 13, 1087508. https://doi.org/10.3389/fpsyg.2022.1087508
  • Lim, J. Y., Kim, G. M., & Kim, E. J. (2021). Predictors of entrepreneurial intention of nursing students based on theory of planned behavior. Journal of Multidisciplinary Healthcare, 14, 533–543. https://doi.org/10.2147/JMDH.S288532
  • Lin, J. S. C., & Hsieh, P. L. (2012). Refinement of the technology readiness index scale: A replication and cross-validation in the self-service technology context. Journal of Service Management, 23(1), 34–53. (https://doi.org/10.1108/09564231211208961
  • Liu, H., & Park, K. S. (2024). Exploring the impact of metaverse tourism experiences on actual visit intentions: An integrated model of presence, the technology acceptance model, and the theory of planned behavior. International Journal of Tourism Research, 26(1), e2616. https://doi.org/10.1002/jtr.2616
  • Liu, Y., Li, H., Kostakos, V., Goncalves, J., Hosio, S., & Hu, F. (2014). An empirical investigation of mobile government adoption in rural China: A case study in Zhejiang province. Government Information Quarterly, 31(3), 432–442. https://doi.org/10.1016/j.giq.2014.02.008
  • Luarn, P., Chen, C. C., & Chiu, Y. P. (2023). Enhancing intrinsic learning motivation through gamification: A self-determination theory perspective. The International Journal of Information and Learning Technology, 40(5), 413–424. https://doi.org/10.1108/IJILT-07-2022-0145/FULL/XML
  • Luo, Z. (2023). Determinants of the perceived usefulness (PU) in the context of using gamification for classroom-based ESL teaching: A scale development study. Education and Information Technologies, 28(4), 4741–4768. https://doi.org/10.1007/s10639-022-11409-6
  • Majuri, J., Koivisto, J., & Hamari, J. (2018). Gamification of education and learning: A review of empirical literature. CEUR Workshop Proceedings, GamiFIN (pp. 11–19).
  • Malaquias, R. F., & Silva, A. F. (2020). Understanding the use of mobile banking in rural areas of Brazil. Technology in Society, 62, 101260. https://doi.org/10.1016/j.techsoc.2020.101260
  • Martín-Gutiérrez, J., Mora, C. E., Añorbe-Díaz, B., & González-Marrero, A. (2017). Virtual technologies trends in education. EURASIA Journal of Mathematics, Science and Technology Education, 13(2), 469–486. https://doi.org/10.12973/eurasia.2017.00626a
  • Masrom, M. (2007). Technology acceptance model and E-learning. Technology, 21(24), 81.
  • Mou, T. Y., Kao, C. P., Lin, K. Y., & Osborne, M. (2023). Exploring the mediator in science service learning: analysis of university students’ behavioural intention to use digital platforms. The Asia-Pacific Education Researcher, 32(6), 841–854. https://doi.org/10.1007/s40299-022-00700-2
  • Mukherjee, S., Baral, M. M., Venkataiah, C., Pal, S. K., & Nagariya, R. (2021). Service robots are an option for contactless services due to the COVID-19 pandemic in the hotels. DECISION, 48(4), 445–460. https://doi.org/10.1007/s40622-021-00300-x
  • Mukherjee, S., & Chittipaka, V. (2022). Analysing the adoption of intelligent agent technology in food supply chain management: An empirical evidence. FIIB Business Review, 11(4), 438–454. https://doi.org/10.1177/23197145211059243
  • Ngowtanasawan, G. (2017). A causal model of BIM adoption in the Thai architectural and engineering design industry. Procedia Engineering, 180, 793–803. https://doi.org/10.1016/j.proeng.2017.04.240
  • Nguyen, A. T., Do, T. H. H., Vu, T. B. T., Dang, K. A., & Nguyen, H. L. (2019). Factors affecting entrepreneurial intentions among youths in Vietnam. Children and Youth Services Review, 99, 186–193. https://doi.org/10.1016/j.childyouth.2019.01.039
  • Nguyen, P. M., Dinh, V. T., Luu, T. M. N., & Choo, Y. (2020). Sociological and theory of planned behaviour approach to understanding entrepreneurship: Comparison of Vietnam and South Korea. Cogent Business & Management, 7(1), 1815288. https://doi.org/10.1080/23311975.2020.1815288
  • Nguyen, P. N. D., & Nguyen, H. H. (2024). Unveiling the link between digital entrepreneurship education and intention among university students in an emerging economy. Technological Forecasting and Social Change, 203, 123330. https://doi.org/10.1016/j.techfore.2024.123330
  • Nkoro, E. C., Nwakanma, C. I., Lee, J. M., & Kim, D. S. (2024). Detecting cyberthreats in Metaverse learning platforms using an explainable DNN. Internet of Things, 25, 101046. https://doi.org/10.1016/j.iot.2023.101046
  • Oliveira, R. P., Santos, I. L., de Souza, C. G., Reis, A. d C., & de Souza, W. M. (2023). A study on the relation between industry 4.0 technologies and gamification in e-learning. Interactive Technology and Smart Education, 20(4), 449–474. https://doi.org/10.1108/ITSE-02-2022-0020/FULL/XML
  • Ooi, K. B., Tan, G. W. H., Aw, E. C. X., Cham, T. H., Dwivedi, Y. K., Dwivedi, R., Hughes, L., Kar, A. K., Loh, X. M., Mogaji, E., Phau, I., & Sharma, A. (2023). Banking in the metaverse: a new frontier for financial institutions. International Journal of Bank Marketing, 41(7), 1829–1846. https://doi.org/10.1108/IJBM-03-2023-0168
  • Ostrom, A. L., Parasuraman, A., Bowen, D. E., Patrício, L., & Voss, C. A. (2015). Service research priorities in a rapidly changing context. Journal of Service Research, 18(2), 127–159. https://doi.org/10.1177/1094670515576315
  • Pal, D., & Patra, S. (2021). University students’ perception of video-based learning in times of COVID-19: A TAM/TTF perspective. International Journal of Human–Computer Interaction, 37(10), 903–921. https://doi.org/10.1080/10447318.2020.1848164
  • Pappas, I. O., & Giannakos, M. N. (2021). Rethinking learning design in IT education during a pandemic. Frontiers in Education, 6, 652856. https://doi.org/10.3389/feduc.2021.652856
  • Parasuraman, A. (2000). Technology readiness Index (TRI) A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
  • Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730
  • 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
  • Park, S., Min, K., & Kim, S. (2021). Differences in learning motivation among Bartle’s player types and measures for the delivery of sustainable gameful experiences. Sustainability, 13(16), 9121. https://doi.org/10.3390/su13169121
  • Parker, D., & Manstead, A. S. R. (1995). Evaluating and extending the theory of planned behaviour. European Review of Social Psychology, 6(1), 69–95. https://doi.org/10.1080/14792779443000012
  • Parvan, K. (2012). Estimating the impact of building information modeling (BIM) utilization on building project performance. University of Maryland, College Park.
  • 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. The Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
  • Purohit, S., & Arora, R. (2021). Adoption of mobile banking at the bottom of the pyramid: an emerging market perspective. International Journal of Emerging Markets, 18(1), 200–222. https://doi.org/10.1108/IJOEM-07-2020-0821
  • Qin, Y. (2022 Investment Potential Analysis on Chinese Stock Market in Metaverse- Take VR Industry as a Sample [Paper presentation]. Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022), 648(ICFIED), 1001–1007. https://doi.org/10.2991/aebmr.k.220307.165
  • Rafdinal, W., & Senalasari, W. (2021). Predicting the adoption of mobile payment applications during the COVID-19 pandemic. International Journal of Bank Marketing, 39(6), 984–1002. https://doi.org/10.1108/IJBM-10-2020-0532
  • Rodrigues, L. F., Oliveira, A., & Costa, C. J. (2016). Playing seriously - How gamification and social cues influence bank customers to use gamified e-business applications. Computers in Human Behavior, 63, 392–407. https://doi.org/10.1016/j.chb.2016.05.063
  • Rohan, R., Pal, D., & Funilkul, S. (2020). Gamifying MOOC’s a step in the right direction?: A systematic literature review [Paper presentation]. Proceedings of the 11th International Conference on Advances in Information Technology, 1–10. https://doi.org/10.1145/3406601.3406607
  • Rohan, R., Pal, D., Funilkul, S., Chutimaskul, W., & Eamsinvattana, W. (2021). How gamification leads to continued usage of MOOCs? A theoretical perspective. IEEE Access, 9, 108144–108161. https://doi.org/10.1109/ACCESS.2021.3102293
  • Rohan, R., Roy, P., Vanijja, V., Funilkul, S., Mukherjee, S., & Pal, D. (2023). What affects the adoption of metaverse in education ? A SEM-based approach [Paper presentation]. 2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE), 448–453. https://doi.org/10.1109/JCSSE58229.2023.10202090
  • Rohan, R., Roy, P., Vanijja, V., Funilkul, S., Mukherjee, S., & Pal, D. (2023). What affects the adoption of metaverse in education? A SEM-based approach [Paper presentation].2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE), 448–453. https://doi.org/10.1109/JCSSE58229.2023.10202090
  • Rospigliosi, P. (2022). Metaverse or Simulacra? Roblox, Minecraft, Meta and the turn to virtual reality for education, socialisation and work. Interactive Learning Environments, 30(1), 1–3. https://doi.org/10.1080/10494820.2022.2022899
  • Roy, R., Babakerkhell, M. D., Mukherjee, S., Pal, D., & Funilkul, S. (2022). Evaluating the intention for the adoption of artificial intelligence-based robots in the university to educate the students. IEEE Access, 10, 125666–125678. https://doi.org/10.1109/ACCESS.2022.3225555
  • Said, G. R. E. (2023). Metaverse-based learning opportunities and challenges: A phenomenological metaverse human–computer interaction study. Electronics, 12(6), 1379. https://doi.org/10.3390/electronics12061379
  • Said, G. R. E. (2023). Metaverse-based learning opportunities and challenges: A phenomenological metaverse human–computer interaction study. Electronics (Switzerland), 12(6), 1379. https://doi.org/10.3390/electronics12061379
  • Salman, H., Almohsen, E., Henari, T., Shatnawi, S., Buzaboon, A., Fardan, M., & Albinali, K. (2023 Using machine learning and SEM to analyze attitudes towards adopting metaverse in higher education [Paper presentation]. 2023 International Conference on Smart Applications, Communications and Networking, SmartNets, 2023, 1–6. https://doi.org/10.1109/SmartNets58706.2023.10215936
  • Sommer, L. (2011). The theory of planned behaviour and the impact of past behaviour. International Business & Economics Research Journal (IBER), 10(1), 91–110. https://doi.org/10.19030/iber.v10i1.930
  • Spector, J. M., & Seung, W. P. (2018). Motivation, learning, and technology: Embodied educational motivation. Routledge.
  • Srisawat, S., & Piriyasurawong, P. (2022). Metaverse virtual learning management based on gamification techniques model to enhance total experience. International Education Studies, 15(5), 153. https://doi.org/10.5539/ies.v15n5p153
  • T R, M., & Gala, B. (2023). Gamification for digital humanities in libraries. DESIDOC Journal of Library & Information Technology, 43(04), 241–248. https://doi.org/10.14429/djlit.43.04.19259
  • Teng, Z., Cai, Y., Gao, Y., Zhang, X., & Li, X. (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
  • Usmani, S. S., Sharath, M., & Mehendale, M. (2022). Future of mental health in the metaverse. General Psychiatry, 35(4), e100825. https://doi.org/10.1136/gpsych-2022-100825
  • Vanduhe, V. Z., Nat, M., & Hasan, H. F. (2020). Continuance intentions to use gamification for training in higher education: Integrating the Technology Acceptance Model (TAM), social motivation, and task technology fit (TTF). IEEE Access, 8, 21473–21484. https://doi.org/10.1109/ACCESS.2020.2966179
  • 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
  • Victorino, L., Karniouchina, E., & Verma, R. (2009). Exploring the use of the abbreviated technology readiness index for hotel customer segmentation. Cornell Hospitality Quarterly, 50(3), 342–359. https://doi.org/10.1177/1938965509336809
  • Wang, Q., Zhong, Y., Zhao, G., Song, R., & Zeng, C. (2023). Relationship among content type of smartphone use, technostress, and sleep difficulty: A study of University students in China. Education and Information Technologies, 28(2), 1697–1714. https://doi.org/10.1007/s10639-022-11222-1
  • Wang, Y., Lee, L. H., Braud, T., & Hui, P. (2022). Re-shaping Post-COVID-19 teaching and learning: a blueprint of virtual-physical blended classrooms in the metaverse era [Paper presentation]. Proceedings – 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops, ICDCSW 2022, 241–247. https://doi.org/10.1109/ICDCSW56584.2022.00053
  • Warden, C. A., Yi-Shun, W., Stanworth, J. O., & Chen, J. F. (2022). Millennials’ technology readiness and self-efficacy in online classes. Innovations in Education and Teaching International, 59(2), 226–236. https://doi.org/10.1080/14703297.2020.1798269
  • Wei, X., Peng, X., & Prybutok, V. (2024). Consumer behavioral intention of adopting emerging healthcare technology. IEEE Transactions on Engineering Management, 71, 888–898. https://doi.org/10.1109/TEM.2022.3140952
  • Wei, Z., Zhang, J., Huang, X., & Qiu, H. (2023). Can gamification improve the virtual reality tourism experience? Analyzing the mediating role of tourism fatigue. Tourism Management, 96(December 2022), 104715. https://doi.org/10.1016/j.tourman.2022.104715
  • Yang, C., Yan, S., Wang, J., & Xue, Y. (2022). Flow experiences and virtual tourism: The role of technological acceptance and technological readiness. Sustainability, 14(9), 5361. https://doi.org/10.3390/su14095361
  • Yang, F., Ren, L., & Gu, C. (2022). A study of college students’ intention to use metaverse technology for basketball learning based on UTAUT2. Heliyon, 8(9), e10562. https://doi.org/10.1016/j.heliyon.2022.e10562
  • Yang, Q., Zhao, Y., Huang, H., Xiong, Z., Kang, J., & Zheng, Z. (2022). Fusing blockchain and AI with metaverse: A survey. IEEE Open Journal of the Computer Society, 3, 122–136. https://doi.org/10.1109/OJCS.2022.3188249
  • Yang, X., Yang, J., Hou, Y., Li, S., & Sun, S. (2023). Gamification of mobile wallet as an unconventional innovation for promoting Fintech: An fsQCA approach. Journal of Business Research, 155, 113406. https://doi.org/10.1016/j.jbusres.2022.113406
  • Yildirim, I. (2017). The effects of gamification-based teaching practices on student achievement and students’ attitudes toward lessons. The Internet and Higher Education, 33, 86–92. https://doi.org/10.1016/j.iheduc.2017.02.002
  • Yoon, H., & Mecca, M. (2022). Fieldwork from desktop: Webdoc for teaching in the time of pandemic. Journal of Geography in Higher Education, 47(3), 349–368. https://doi.org/10.1080/03098265.2022.2045575
  • Younas Mughal, M., Andleeb, N., Farooq Ahmad Khurram, A., Ali, Y., Aslam, S., & Saleem, N. (2022). Perceptions of teaching-learning force about metaverse for education: A qualitative study. Journal of Positive School Psychology, 6(9), 1738–1745.
  • Yue, K. (2022 Breaking down the Barrier between Teachers and Students by Using Metaverse Technology in Education: Based on A Survey and Analysis of Shenzhen City, China [Paper presentation]. ACM International Conference Proceeding Series, 40–44. https://doi.org/10.1145/3514262.3514345
  • Zhang, X., Chen, Y., Hu, L., & Wang, Y. (2022). The metaverse in education: Definition, framework, features, potential applications, challenges, and future research topics. Frontiers in Psychology, 13, 1016300. https://doi.org/10.3389/fpsyg.2022.1016300