This paper investigates adopting metaverse in academic institutions based on a conceptual model. The proposed conceptual model was created to empower future perspectives of the metaverse system. Even though the metaverses system was just recently launched, few attempts have been made to evaluate its impact on the educational sector. This paper attempted to determine the impact of metaverse system by providing a conceptual model that encompasses innovativeness, context awareness, perceived enjoyment, ubiquity, complexity, and value. The information was gathered using an online questionnaire completed by 953 people. The findings indicate that an innovative academic environment can influence teachers’ and students’ attitudes toward new technology. Understanding how the metaverse system functions as an innovative educational tool can influence students’ views regarding the use of new technology, assisting higher education institutions in developing regulations that enhance the learning process. As a result, the study discovered that the moderating impact of’ innovativeness is critical since it contributes to the pervasiveness of users’ perceptions of adopting new technology. The findings show that inventiveness is important in determining the metaverse system’s effectiveness. However, apparent ubiquity appears to have a less effective function in promoting the use of the metaverse system. The factors of context-awareness, perceived complexity and perceived enjoyment possess a substantial impact on the metaverse system’s adoption. Researchers have gained some insight into how university leaders can use current findings to spread awareness about the metaverse system in the educational environment by organizing seminars and workshops, offering incentives for teachers to use it, and allowing experts to assist teachers in better understanding the system’s benefits.

This study examines the incorporation of artificial intelligence (AI) into decision support systems at higher education institutions in the United Arab Emirates (UAE), using the theoretical framework of the Diffusion of Innovations Theory. This study employs a cross-sectional survey design to examine the significant correlations among system complexity, data quality, organizational readiness, user engagement, technological infrastructure, and the efficacy of decision-making systems utilizing AI in higher education institutions in the UAE. UAE higher education institutions are targeted. A representative sample of institutions is selected via purposive sampling based on size, geography, and academic reputation. To ensure position and department representation, stratified random sampling is used to pick participants within each institution. Structural Equation Modeling (SEM) was used to examine the connections among the investigated variables. Data quality, organizational readiness, user engagement, and technology infrastructure were key factors influencing effective decision-making processes with β = 0.503; 0.281; 0.193; 0.244 at p-value less than .05, respectively, although system complexity did not reveal a significant association (β = -0.016, p-value = 0.65). Recommendations suggest focusing on investments in data quality assurance, preparing the organization, increasing user involvement, and improving technology infrastructure. Although limited by geographical focus and a cross-sectional design, this research provides valuable insights for policymakers, educators, and practitioners interested in using AI to enhance educational results and promote innovation in higher education.

This study explores the complex environment of smart applications technology in the UAE's public and private sectors, looking into the complex interactions between technological adaptability and accessibility as well as the effects of Total Quality Management (TQM) studies presented by policies and regulations. Data was collected by a multistage sampling strategy, mixing probability and non-probability methods, using a cross-sectional research design. To ensure impartial item creation and adherence to best practices, a pretested questionnaire underwent extensive examination. Through the revelation of unexpected results, the research tests and expands upon the Technology Acceptance Model (TAM). There is a request for an enlarged TAM framework because there is no substantial correlation found between accessibility and the performance of smart applications. A negative link is resulted, unexpectedly, between technology adaptation and TQM adoption, underscoring the necessity of a cautious integration strategy. The report also emphasizes how important TQM policies and regulations are to the functioning of smart applications, supporting the need for a thorough TAM framework that takes in regulatory dynamics into consideration. Practical implications for stakeholders, quality policymakers, and institutions are presented by the findings, which highlight several adoption aspects beyond accessibility. This research adds a contribution on smart application performance and develops theoretical models for integrating technology in organizational settings. The rigorous design of the methodology highlights the integrity of the study and guarantees the validity of the insights obtained from the investigation.