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Extending the technology acceptance model (TAM) to predict university students’ intentions to use metaverse-based learning platforms
Open accessAbstract
Metaverse, which combines a number of information technologies, is the Internet of the future. A media for immersive learning, metaverse could set future educational trends and lead to significant reform in education. Although the metaverse has the potential to improve the effectiveness of online learning experiences, metaverse-based educational implementations are still in their infancy. Additionally, what factors impact higher education students' adoption of the educational metaverse remains unclear. Consequently, the aim of this study is to explore the main factors that affect higher education students' behavioral intentions to adopt metaverse technology for education. This study has proposed an extended Technology Acceptance Model (TAM) to achieve this aim. The novelty of this study resides in its conceptual model, which incorporates both technological, personal, and inhibiting/enabling factors. The empirical data were collected via online questionnaires from 574 students in both private and public universities in Jordan. Based on the PLS-SEM analysis, the study identifies perceived usefulness, personal innovativeness in IT, and perceived enjoyment as key enablers of students' behavioral intentions to adopt the metaverse. Additionally, perceived cyber risk is found as the main inhibitor of students' metaverse adoption intentions. Surprisingly, the effect of perceived ease of use on metaverse adoption intentions is found to be insignificant. Furthermore, it is found that self-efficacy, personal innovativeness, and perceived cyber risk are the main determinants of perceived usefulness and perceived ease of use. While the findings of this study contribute to the extension of the TAM model, the practical value of these findings is significant since they will help educational authorities understand each factor's role and enable them to plan their future strategies.
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“They also aligned with evidence from mobile learning research showing that positive attitudes can strengthen students’ intention to use new technologies [91].”
From Advancing Undergraduate Student Mental Healthcare of Social Anxiety Disorder: Evaluating the Acceptance of AR-Assisted Cognitive Behavioral Therapy Through TAM-Based Constructs. (2026) · cites this paper as [91]
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“Therefore, the extended TAM framework has been used in many studies to predict student acceptance of AI technologies and ChatGPT (Al-Adwan et al. 2023; Baig and Yadegaridehkordi, 2024; Saihi et al. 2024).”
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“%) attributable to contextual moderators (Al-Adwan et al., 2023; Salloum, 2018; Tawafak et al., 2023).”
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“…TAM has undergone extensive empirical testing as a valid model for explaining individuals’ technology acceptance within educational settings, spanning a diverse array of technologies and demographic groups (Al-Adwan et al., 2023; Huang et al., 2021; Marangunić & Granić, 2015; Teo et al., 2019).”
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“The study utilized the key variables from TAM — perceived usefulness, perceived ease of use, attitude, and behavioral intention (Davis, 1989; Gado et al. 2022; Ullah et al. 2021; Al-Hattami, 2023; Al-Hattami and Almaqtari, 2023; Al-Adwan et al. 2023).”
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“Besides that, most TAM studies have confirmed that PEOU can significantly impact users’ PU of AI products, including AI robots [37] and metaverse-based learning platforms [38].”
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Shen, L., Qiu, N., & Wang, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-13789-4 · Cited by 33
“TAM developed by Davis (1989), is one of the most widely used frameworks for understanding user acceptance of new technologies 12 .”
Maričić, M., Anđić, B., Soeharto, S., Mumcu, F., Cvjetičanin, S., & Lavicza, Z. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13141-9 · Cited by 31
“In addition to the mentioned variables, in recent research, numerous authors have added new external variables (e.g. see Al-Adwan et al., 2023; Binyamin et al., 2019; Cheng, 2017; Mutambara & Bayaga, 2021; Zhang et al., 2022; Zhu et al., 2019), and new exogenous variables with a direct effect on…”
Bamasoud, D. M., Nassr, R. M., & Bilal, S. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9100264 · Cited by 43
“EOU, positively affecting intention, is likewise in line with prior findings that usability can directly encourage technology acceptance [35,36].”
Fan, C. (2023) · Cogent Social Sciences · doi:10.1080/23311886.2023.2260566 · Cited by 34
“Al-Adwan et al. (2023) discovered that PEOU significantly and positively affects PU of university students’ acceptance of Metaverse-based learning platforms.”
BackgroundXing, J., & Jiang, Q. (2024) · Kybernetes · doi:10.1108/k-10-2023-2237 · Cited by 28
Truc, L. T. (2024) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2024.100322 · Cited by 39
Ahankoob, A., Abbasnejad, B., & Aranda‐Mena, G. (2025) · Buildings · doi:10.3390/buildings15111804 · Cited by 29
Lu, H., Chang, Y.-C., & Chen, C.-S. (2024) · Virtual Reality · doi:10.1007/s10055-024-00983-4 · Cited by 20
“Another widely used theory in the field is the TAM (Al-Adwan et al. 2023), which focuses on individual users’ acceptance of new technology.”
Kavitha, K., & Joshith, V. P. (2024) · Bulletin of Science Technology & Society · doi:10.1177/02704676241283362 · Cited by 33
“…realm of information technology, individuals with an elevated level of personal innovativeness are more likely to appraise technological advancements (Al-Adwan et al., 2023) favorably and possess the capacity to overcome the challenges associated with adopting new technology (Ciftci et al., 2021).”
Critical factors for why ChatGPT enhances learning engagement and outcomes
Wu, C., & Ho, V. T. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13346-6 · Cited by 25
“TAM has been widely applied in educational technology research, emphasizing that perceived usefulness and ease of use contribute to improving users’ attitudes (Al-Adwan et al., 2023; Rafique et al., 2020; Yang & Wang, 2019).”
Liu, J., Wang, X., & Zhang, J. (2025) · Behavioral Sciences · doi:10.3390/bs15050697 · Cited by 19
Patre, S., Chakraborty, D., Kar, A. K., Singu, H. B., & Tiwari, D. A. (2023) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2023.3328261 · Cited by 19
“…of upskilling content for diverse employees from different countries, thereby promoting easier collaboration; the remote training and real-time monitoring for evaluation are facilitated by 5G and IoT; and VR and AR help to expose users to real-life scenarios in simulated environments [19].”
BackgroundTrust shaping the social relationship of diverse learners in the online education environment
Kanaris, M., & Mujtaba, B. G. (2023) · Environment and Social Psychology · doi:10.54517/esp.v9i2.2197 · Cited by 19
“Recent researchers on online learning using the technology acceptance model (TAM) and the impact of various platforms, such as social media, indicate that there is still much to be learned before its anticipated potential can be reached [16] .”
BackgroundAl‐Emran, M. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2024.2324326 · Cited by 22
“It is described as the next iteration of the Internet (Al-Adwan et al., 2023; Ooi, Wei-Han Tan, Al-Emran, Al-Shara fi , Arpaci, et al., 2023).”
Yang, J., & Lou, K. (2024) · Learning and Motivation · doi:10.1016/j.lmot.2024.101986 · Cited by 25
AI in medical education: the moderating role of the chilling effect and STARA awareness
Wu, M., Huang, X., Jiang, B., Li, Z., Zhang, Y., & Gao, B. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05627-4 · Cited by 26
“They both have strong theoretical foundations that have been tested with different samples and in different situations and proved to be valid and reliable [4–6], and therefore, the pres-ent study uses them as the modeling foundation for this study of them.”
BackgroundEveryday Metaverse: The Metaverse as an Integral Part of Everyday Life
Wang, G., Zhang, Z., Nandhakumar, J., & Manoharan, N. (2025) · Journal of Management Information Systems · doi:10.1080/07421222.2025.2455772 · Cited by 18
Sabiteka, M., Yu, X., & Sun, C. (2025) · Sustainability · doi:10.3390/su17083592 · Cited by 18
Aldreabi, H., Halalsheh, N. Z., Alrawashdeh, M. N., Alnajdawi, A. M., Alsawalqa, R. O., & Al-Shboul, M. A. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1226718 · Cited by 18
Khlaif, Z. N., Salama, N., Hamamra, B. T., & Mousa, A. (2025) · JMIR Medical Education · doi:10.2196/65042 · Cited by 20
Rashid, S. (2025) · Education Sciences · doi:10.3390/educsci15060756 · Cited by 26
Shopping in the Metaverse: Decoding Consumer Intentions
Abdeljaber, O., Al‐Adwan, A. S., Yaseen, H., Falahat, M., Abdullah, A., & Fauzi, M. A. (2025) · The International Information & Library Review · doi:10.1080/10572317.2025.2594293 · Cited by 20
“Many studies conducted in the field of VR and metaverse have revealed that the behavioral intention to use such technologies is influenced by both PU (Huang & Liaw, 2018; Israel et al., 2019; Al-Adwan et al., 2023), and PEU (Akour et al., 2022; Alkarney & Almakki, 2022).”
InfluentialThe effect of Arabic language type on banking chatbots adoption
Hmoud, H., Shishan, F., Qasem, Z., & Bazi, S. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20686 · Cited by 23
“The Literature of technology acceptance presents several theories and models that where utilized to explain consumer adoption of new technology (e.g., Technology acceptance model ([41,49 – 51]), IS success model ([52,53]), and the UTAUT ([54]).”
BackgroundFaith in metaverse: understanding adoption intentions of metaverse amongst the Muslim students
Azhar, M., Ali, R., Naz, A., & Sujood, S. (2024) · Journal of Islamic marketing · doi:10.1108/jima-01-2024-0051 · Cited by 21
Understanding Metaverse Consumers Through Escapism Motives: Focusing on Roblox and Zepeto
Hur, H. J., & Baek, E. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2384830 · Cited by 18
“These results align with existing research indicating that metaverse users find new technologies enjoyable and useful when the platform is user-friendly (e.g., Al-Adwan et al., 2023).”
Xia, T., Pan, X., Cao, M., & Guo, J. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13491-y · Cited by 21
“These results stand in contrast to the findings recently reported by Zogheib and Zogheib (2024) and Al-Adwan et al. (2023) regarding students’ adoption of ChatGPT; namely, those authors concluded that PU positively affects BI, whereas PEOU has a nonsignificant effect on BI.”
Alkhawaja, L. A., Idris, M., Al-Sayyed, S. W., & Jaber, A. M. A. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1691148 · Cited by 16
Metadata
- Authors
- Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A.
- Affiliations
- Xi’an Jiaotong-Liverpool University (CN)
- Al-Ahliyya Amman University (JO)
- King Fahd University of Petroleum and Minerals (SA)
- University of Malaya (MY)
- Universitas Jambi (ID)
- Corresponding author
- Al-Adwan, Amer
- ORCID
- Venue
- Education and Information Technologies
- Published
- 2023-04-28
- Volume
- 28
- Issue
- 11
- Pages
- 15381-15413
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- PMC
- PMC10140721
- OpenAlex
- W4367316498
- Cited by
- 637
- References
- 116
- Citations by year
- 2026: 169 · 2025: 244 · 2024: 191 · 2023: 33
- FWCI
- 732.78
- Type
- book chapter
- Language
- en
- Update notices
- correction (2023-06-02)
- Open access
- hybrid
- doi.org
- Locations
- Education and Information Technologies (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Hamad bin Khalifa University
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Education and Learning Interventions
- Social Sciences › Education › Online and Blended Learning
- Keywords
- metaverse, technology acceptance model, technology adoption, behavioral intention, university students, perceived usefulness, perceived ease of use, perceived enjoyment, personal innovativeness, self-efficacy, immersive learning, online learning
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