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.

In higher education learning, e-learning systems have become renowned tools worldwide. The evident importance of e-learning in higher education has resulted in a prenominal increase in the number of e-learning systems delivering various forms of services, especially when traditional education (face-to-face) was suddenly forced to move online due to the COVID-19 outbreak. Accordingly, assessing e-learning systems is pivotal in the interest of effective use and successful implementation. By relying on the related literature review, an extensive model is developed by integrating the information system success model (ISSM) and the technology acceptance model (TAM) to illustrate key factors that influence the success of e-learning systems. Based on the proposed model, theory-based hypotheses are tested through structural equation modeling employing empirical data gathered through a survey questionnaire of 537 students from three private universities in Jordan. The findings demonstrate that quality factors, including instructor, technical system, support service, educational systems, and course content quality, have a direct positive influence on students’ satisfaction, perceived usefulness, and system use. Moreover, self-regulated learning negatively affects students’ satisfaction, perceived usefulness, and system use. Students’ satisfaction, perceived usefulness, and system use are key predictors of their academic performance. These findings provide e-learning stakeholders with important implications that guarantee the effective, successful use of e-learning that positively affects students’ learning.

Introduction: The Internet of Vehicles (IoV) was enabled through innovative developments featuring advanced automotive networking and communication to fulfill the need for real-time applications that are latency-sensitive, such as autonomous driving and emergency management. Given that the servers were much farther away from the actual site of operation, traditional cloud computing faced huge delays in processing. Mobile Edge Computing (MEC) resolved this challenge by enabling localized data processing, reducing latency and enhancing resource utilization.Methods: This study proposed an Efficient Mobile Edge Computing-based Internet of Vehicles Task Offloading Framework (EMEC-IoVTOF). The framework integrated deep reinforcement learning (DRL) to optimize task offloading decisions, focusing on minimizing latency and energy consumption while accounting for bandwidth and computational constraints. Offloading costs were calculated using mathematical modeling and further optimized through Particle Swarm Optimization (PSO). An adaptive inertia weight mechanism was implemented to avoid local optimization and enhance task allocation decisions.Result: The proposed framework was thus proved effective for any latency reduction and energy consumption optimization in efficiently improving the overall system performance. DRL and MEC together facilitate scalability in task distribution by ensuring robust performance in dynamic vehicular environments. Integration with PSO further enhances the decision-making process and makes the system highly adaptable to dynamic task demands and network conditions.Discussion:The findings highlighted the potential of EMEC-IoVTOF to address key challenges in IoV systems, including latency, energy efficiency, and bandwidth utilization. Future research could explore real-world deployment and adaptability to complex vehicular scenarios, further validating its scalability and reliability.

Purpose: The primary objective of this study is to explore consumers' non-adoption intentions towards meta-commerce (or metaverse retailing). Utilizing the Innovation Resistance Theory (IRT) as the theoretical foundation, this study investigates the impact of diverse barriers on non-adoption intentions within the meta-commerce context.Design/methodology/approach: A total of 356 responses were gathered to test the proposed hypotheses. Structural Equation Modelling (SEM) with SmartPLS 4 software was used to examine these hypotheses.Findings: The findings of this study show that perceived cyber risk, perceived regulatory uncertainty, perceived switching cost and perceived technical uncertainty are significantly linked to non-adoption intention towards meta-commerce. Furthermore, the study suggests that the moderating influence of technostress on these connections is more pronounced for consumers with high technostress compared to those with low technostress.Originality/value: This study makes a significant contribution to the current body of literature by providing valuable insights into the fundamental barriers that consumers encounter when contemplating the adoption of meta-commerce. This contribution is particularly noteworthy as it fills a gap in the existing literature, as no prior study has comprehensively examined the primary obstacles that shape consumer intentions towards meta-commerce adoption. This novel perspective offers scholars, businesses and policymakers a foundation for developing strategies to address these barriers effectively.

The COVID-19 pandemic significantly shifted education from traditional to an online version, which was an emergent state for teachers and students. The substantive situation thus raises the importance of technology integration in education, and teachers are required to update their competencies, respectively. In this regard, the study assessed online teaching competencies of faculty members following, technological pedagogical content knowledge (TPACK) model. Closed-ended surveys were employed for quantitative analysis of randomly selected 256 faculty members from public universities in Karachi, Pakistan. Results indicated that teachers possessed adequate levels of knowledge across all the domains of TPACK. The highest competency was obtained by content knowledge (CK), while technological knowledge (TK) was reported at the lowest level. Furthermore, a significant difference was noted in terms of gender and teaching experience. Correspondingly, the study proposes that the TPACK model should be employed in the professional development programs to develop teachers’ TPACK for integrating information communication and technology in the pedagogical practices. The findings of the study present a constructive overview of teachers’ digital competencies and technology use in teaching and learning in the time of the COVID-19 and also play a significant role in the integration of technology in the post-pandemic time in higher education. The study also suggests relevant educational authorities and policymakers for assessing and enhancing the technological competencies of teachers for quality online education.