Beyond confirmatory approaches: integrating quasi-experimental design, clustering and machine learning for metaverse adoption profiling
DOI:
https://doi.org/10.1108/RAUSP-02-2026-0038Palabras clave:
Metaverse adoption, Quasi-experimental design, Clustering analysis, Machine learning, Random forest, Support vector machineResumen
PurposeThis study aims to propose a hybrid methodological framework that integrates quasi-experimental design, unsupervised clustering and supervised machine learning algorithms to identify distinct adoption profiles in metaverse contexts, advancing beyond traditional confirmatory statistical approaches.
Design/methodology/approachA quasi-experimental study involving 310 undergraduates who were exposed to three metaverse applications on Meta Quest devices was conducted. K-means clustering, based on seven constructs, identified segments that were validated using random forest and support vector machine, supported by factor analysis and comparative tests.
FindingsTwo adoption profiles emerged: Skeptics/pragmatists (38.1%) and enthusiasts (61.9%). Perceived value, perceived usefulness and trust were the strongest predictors, with Social Influence and Enjoyment as supporting factors. Both models showed excellent predictive performance (AUC-ROC = 1.000; 0.994) and large effect sizes, confirming the validity of the segments.
Research limitations/implicationsThis study is limited to a single-university sample, thereby restricting generalizability across diverse organizational contexts. Future research should replicate this methodology across various cultural and organizational settings to validate its generalizability.
Practical implicationsThe results offer actionable insights for leaders and educators on tailoring immersive experiences by emphasizing perceived usefulness, trust and value creation to enhance engagement and participation in Metaverse environments.
Social implicationsThis study highlights the opportunities and risks of immersive technologies, emphasizing gamification and attention management mechanisms to reduce cognitive distraction and enhance focus in educational and organizational contexts.
Originality/valueThis study combines quasi-experimental design, unsupervised clustering and supervised machine learning as an alternative to confirmatory-only adoption models. This study advances metaverse adoption research by linking segment-based evidence to behavioral interpretation grounded in usefulness, value, trust, presence and immersive engagement.
Descargas
Referencias
Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694, https://doi.org/10.2307/3250951.
Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, INFORMS, 9(2), 204-215, https://doi.org/10.1287/isre.9.2.204.
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.
Ali, R. A., Soliman, M., Weahama, M. R., Assalihee, M., & Mahmud, I. (2025). Investigating continuous intention to use metaverse in higher education institutions: A dual-staged structural equation modeling-artificial neural network approach. Smart Learning Environments, 12(1), 3, https://doi.org/10.1186/s40561-024-00357-y.
Barnes, S. J., Mattsson, J., & Hartley, N. (2015). Assessing the value of real-life brands in virtual worlds. Technological Forecasting and Social Change, 92, 12-24, https://doi.org/10.1016/j.techfore.2014.10.017.
Bazi, S., & Hajawi, D. (2025). Customer inspiration in the metaverse: Antecedents, mechanisms, and outcomes. Journal of Retailing and Consumer Services, 87, 104439, https://doi.org/10.1016/j.jretconser.2025.104439.
Cha, S.-S., Kim, C. Y., & Tang, Y. (2024). Gamification in the metaverse: Affordance, perceived value, flow state, and engagement. International Journal of Tourism Research, 26(2), e2635.
Chen, W.-C., Chen, C.-W., & Chen, W.-K. (2019). Drivers of mobile payment acceptance in China: An empirical investigation. Information, Multidisciplinary Digital Publishing Institute, 10(12), 384, https://doi.org/10.3390/info10120384.
Coleman, R. (2019). Designing experiments for the social sciences: How to plan, create, and execute research using experiments. Teller Road, Thousand Oaks CA, SAGE Publications, 91320. https://doi.org/10.4135/9781071878958.
Contreras Pinochet, L. H., Barbosa, T. N., & Pardim, V. I. (2024). Developing a Meta-Analysis on personal innovation and intention to participate in productivity in the metaverse: A step-by-step guide. Procedia Computer Science, 242, 904-911, https://doi.org/10.1016/j.procs.2024.08.270.
Dang, T.-Q., Nguyen, T.-M., Tran, P.-T., Phan, T.-T. C., Huynh, T.-B., & Nguyen, L.-T. (2025). From reality to virtuality: Unveiling gen Z’s purchasing behavior through virtual influencers in the metaverse. Digital Business, 5(2), 100141, https://doi.org/10.1016/j.digbus.2025.100141.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, INFORMS, 35(8), 982-1003, https://doi.org/10.1287/mnsc.35.8.982.
Dhiman, N., Arora, N., Dogra, N., & Gupta, A. (2019). Consumer adoption of smartphone fitness apps: An extended UTAUT2 perspective. Journal of Indian Business Research, 12(3), 363-388, https://doi.org/10.1108/JIBR-05-2018-0158.
Duan, H., Li, J., Fan, S., Lin, Z., Wu, X., & Cai, W. (2021). Metaverse for social good: a university campus prototype. Proceedings of the 29th ACM International Conference on Multimedia, Association for Computing Machinery, New York, NY, 153–161, https://doi.org/10.1145/3474085.3479238.
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., Janssen, M., Kim, Y.-G., Kim, J., Koos, S., Kreps, D., Kshetri, N., Kumar, V., Ooi, K.-B., Papagiannidis, S., Pappas, I. O., Polyviou, A., Park, S.-M., Pandey, N., Queiroz, M. M., Raman, R., Rauschnabel, P. A., Shirish, A., Sigala, M., Spanaki, K., Tan, G. W.-H., Tiwari, M. K., Viglia, G., & 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, Article 102542, https://doi.org/10.1016/j.ijinfomgt.2022.102542.
Dwivedi, Y. K., Hughes, L., Wang, Y., Alalwan, A. A., Ahn, S. J., Balakrishnan, J., & Barta, S. (2023). Metaverse marketing: How the metaverse will shape the future of consumer research and practice. Psychology & Marketing, 40(4), 750-776, https://doi.org/10.1002/mar.21767.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Rana, N. P., Baabdullah, A. M., Kar, A. K., & Koohang, A. (2023). Exploring the darkverse: A Multi-Perspective analysis of the negative societal impacts of the metaverse. Information Systems Frontiers, 25(5), 2071-2114, https://doi.org/10.1007/s10796-023-10400-x.
Gandhi, M., Gaur, A., Kar, A. K., & Dwivedi, Y. K. (2024). Crafting user experiences in the metaverse: A design science study. Technological Forecasting and Social Change, 209, 123759, https://doi.org/10.1016/j.techfore.2024.123759.
Goyal, S., Chauhan, S., & Gupta, P. (2021). Users’ response toward online doctor consultation platforms: SOR approach. Management Decision, 60(7), 1990-2018, https://doi.org/10.1108/MD-02-2021-0268.
Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does gamification work? – A literature review of empirical studies on gamification. 2014 47th Hawaii International Conference on System Sciences, presented at the 2014 47th Hawaii International Conference on System Sciences, HI, 3025-3034, https://doi.org/10.1109/HICSS.2014.377.
Herwindiati, D., Hendryli, J., Sulaiman, A., & Nagaputra, H. (2025). The performance of data cleaning on machine learning model for mapping land cover. International Journal of Advances in Soft Computing and Its Applications, 17(3), https://doi.org/10.15849/IJASCA.251130.21.
Hidayat-Ur-Rehman, I., Ahmad, A., Akhter, F., & Ziaur Rehman, M. (2022). Examining consumers’ adoption of smart wearable payments. Sage Open, 12(3), 21582440221117796, https://doi.org/10.1177/21582440221117796.
Jiang, X., Wang, J., & Deng, N. (2025). Creating existential authenticity experience by combining technology capability with gamification in the metaverse: An affordance perspective. Technology in Society, 83, 103019, https://doi.org/10.1016/j.techsoc.2025.103019.
Kankanhalli, A., Tan, B. C. Y., & Wei, K.-K. (2005). Contributing knowledge to electronic knowledge repositories: An empirical Investigation1. MIS Quarterly, 29(1), 113-144.
Kim, H.-W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111-126, https://doi.org/10.1016/j.dss.2005.05.009.
Laiz-Ibanez, H., Mendana-Cuervo, C., & Carus-Candas, J. L. (2024). The metaverse: Privacy and information security risks. SSRN, https://doi.org/10.2139/ssrn.4803584.
Leong, L.-Y., Hew, T.-S., Ooi, K.-B., Tan, G. W.-H., & Koohang, A. (2025). An SEM-ANN Approach – Guidelines in information systems research. Journal of Computer Information Systems, Taylor & Francis, 65(6), 706-737, https://doi.org/10.1080/08874417.2024.2329128.
Leong, L.-Y., Hew, T.-S., Tan, G. W.-H., & Ooi, K.-B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604-5620, https://doi.org/10.1016/j.eswa.2013.04.018.
Liu, F., Zhao, X., Chau, P. Y. K., & Tang, Q. (2015). Roles of perceived value and individual differences in the acceptance of mobile coupon applications. Internet Research, 25(3), 471-495, https://doi.org/10.1108/IntR-02-2014-0053.
Loia, F., Capolupo, N., & Adinolfi, P. (2025). Training me softly. Metaverse-based immersive and gamified learning through a knowledge management perspective. Journal of Knowledge Management, 29(5), 1465-1489, https://doi.org/10.1108/JKM-08-2024-0929.
Lombard, M., & Ditton, T. (1997). At the heart of it all: The concept of presence. Journal of Computer-Mediated Communication, 3(2), JCMC321, https://doi.org/10.1111/j.1083-6101.1997.tb00072.x.
Mustafa, S., Luthia, M., & Darwashi, R. (2025). Fostering the Metaverse to Drive Digital Leadership, Productivity, and an Inclusive Work Environment, 309-338, https://doi.org/10.4018/979-8-3373-0071-9.ch011.
Mvondo, G. F. N. (2025). Why immersive experiences fall short: Exploring public resistance to metaverse adoption. Technology in Society, 83, 102961, https://doi.org/10.1016/j.techsoc.2025.102961.
Parhi, S., Joshi, K., Wuest, T., & Akarte, M. (2022). Factors affecting industry 4.0 adoption – A hybrid SEM-ANN approach. Computers & Industrial Engineering, 168, 108062, https://doi.org/10.1016/j.cie.2022.108062.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). Experimental and Quasi-Experimental designs for generalized causal inference, Belmont, CA, Cengage Learning.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental designs for generalized causal inference. Boston, MA, Houghton, Mifflin and Company, p. 623.
Stephenson, N. (2001). Snow Crash, Demco Media, Published 1 July 2001, English edition, ISBN-10: 0606216367, ISBN-13: 978-0606216364.
Talukder, M. S., Chiong, R., Bao, Y., & Hayat Malik, B. (2018). Acceptance and use predictors of fitness wearable technology and intention to recommend: An empirical study. Industrial Management & Data Systems, 119(1), 170-188, https://doi.org/10.1108/IMDS-01-2018-0009.
Tussyadiah, I. P. (2016). The influence of innovativeness on on-site smartphone use among American travelers: Implications for Context-Based push marketing. Journal of Travel & Tourism Marketing, Routledge, 33(6), 806-823, https://doi.org/10.1080/10548408.2015.1068263.
Venkatesh, V., Thong, J. Y. L., & 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.
Wong, C.-H., Tan, G. W.-H., Tan, B.-I., & Ooi, K.-B. (2015). Mobile advertising: The changing landscape of the advertising industry. Telematics and Informatics, 32(4), 720-734, https://doi.org/10.1016/j.tele.2015.03.003.
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2025 Vanessa Itacaramby Pardim, Luis Hernan Contreras Pinochet, Yogesh K. Dwivedi
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Management Department of the School of Economics, Management and Accounting of the University of São Paulo.
The publication of article segments is allowed, subject to prior authorization and source identification.
Copyright is regulated under Licença Creative Commons Attribution
