Examining the Impact of Psychological, Social, and Quality Factors on the Continuous Intention to Use Virtual Meeting Platforms During and beyond COVID-19 Pandemic: A Hybrid SEM-ANN Approach

Factors affecting metaverse adoption in education: A systematic review, adoption framework, and future research agenda

The Metaverse, underpinned by its technical infrastructure, heavily relies on user engagement and behavior for successful integration into educational settings. Understanding its driving factors is essential for such a platform to transition from theory to practice, especially in educational settings. However, these factors remain elusive due to inconsistencies in infrastructure and environments. Therefore, this systematic review aims to fill this void by presenting an integrative view on Metaverse adoption in education. This is achieved via three primary dimensions: establishing a taxonomy of the factors influencing Metaverse adoption in education, proposing a framework for Metaverse adoption, and suggesting future research trajectories in this domain. The review systematically classifies the influential factors into four distinct categories: psychological and motivational factors, quality factors, social factors, and inhibiting factors. The proposed framework provides a structured approach for future studies investigating the Metaverse adoption in educational settings. The proposed framework also emphasizes that educational institutions should not only consider the technical prerequisites but also the social, psychological, and motivational aspects of the Metaverse. The study also pinpoints several critical research agendas to enhance our understanding of Metaverse adoption in education. The insights from this review are invaluable for educational institutions, policymakers, developers, and researchers, significantly enriching the emerging field of Metaverse adoption.

Student intentions to continue with distance learning post-COVID: An empirical analysis

The aftermath of COVID changed how students learn, mainly moving to a distance learning model. The research reported in this paper investigated the organizational and individual factors that influence the preference for continuing with distance / online learning post-COVID. Partial least squares structural equation modeling was applied to a model developed for this research, based on data from 452 students from residential universities in South Africa. The key results reveal an overall reluctance to continue with distance learning. This is despite the technological and faculty support offered to university students and how distance learning fits their learning styles. This is likely due to student living conditions and their perception of low institutional concern. On the other hand, faculty support has a more substantial impact on continuance, compared with a generally negative perception of support from the universities. The research underscores the importance of addressing student reluctance to continue with distance learning by improving institutional support and tailoring learning styles. The research enhances our understanding of crucial factors influencing students' preference for distance/online learning post-COVID. It also underscores the pedagogical shifts brought about by the pandemic, particularly highlighting the changing roles of faculty support and the impact on students' living conditions.

Ranking the Critical Success Factors for Hospital Information Systems Using a Fuzzy Analytical Hierarchy Process

The design, development, and maintenance of hospital information systems face major challenges, which ultimately cause failures of these information systems. This study aimed to identify and rank critical success factors for hospital information systems by applying a fuzzy analytical hierarchy process. Potential critical success factors that could contribute to the success of hospital information systems were identified and extracted through a systematic review of the relevant studies. A questionnaire containing the critical success factors was designed and distributed to 250 hospital information system professionals. The hierarchical structure of the critical success factors was defined by using an exploratory factor analysis, and pairwise comparison matrices of the fuzzy analytical hierarchy process model were designed based on the identified factor structure. As a result, 50 potential critical success factors were extracted from 21 articles, and their content validity and face validity were assessed by the experts. Based on the exploratory factor analysis results, 36 critical success factors were classified into seven dimensions: organizational fitness, user-friendliness, maintainability, portability, productivity, reliability, and organizational and external support. The fuzzy analytical hierarchy process results indicated that reliability, user-friendliness, and organizational fitness (with 20.3, 19.9, and 18 points, respectively) had the greatest impact on the success of hospital information systems. The findings revealed that managers and policymakers should consider these critical success factors in designing and developing hospital information systems.

Affordance, usefulness, enjoyment, and aesthetics in sustaining virtual reality engagement

As virtual reality (VR) continues to develop, it's attracting an increasing number of consumers who are seeking more diverse functions and experiences. This study presents a theoretical model designed to identify predictors of VR users' continuance intentions. Data was collected from VR users who had firsthand experiences with the technology, and partial least squares structural equation modeling was employed to analyze this data. The results showed a significant correlation between functional affordance and perceived usefulness. Cognitive affordance was found to have a significant association with perceived usefulness, but it also influenced perceived enjoyment. Moreover, physical affordance significantly related to both perceived usefulness and enjoyment. Perceived usefulness was found to directly affect both attitude and continuance intention, while empirical results validated the impact of perceived enjoyment on attitude. The element of shape showed a significant correlation with attitude. Finally, attitude was found to have a significant association with continuance intention. The findings from this study will provide valuable insights for VR companies, developers, and consumers.

Air pollution in marmara region before and during the COVID-19 outbreak

The lockdowns and curfews during the COVID-19 pandemic have halted economic and transportation activities across the world. This study aims to investigate air pollution levels in the Marmara region, particularly in Istanbul, before and during the COVID-19 pandemic. The study used real data provided by the General Directorate of Meteorology and applied three machine learning algorithms (ANN, RBFreg, and SMOreg) to analyze air pollution data. In addition, a one-sample t-test was performed to compare air pollution levels before and during the COVID-19 pandemic in the Marmara region and Istanbul. The results of the study showed a significant reduction in the particulate matter (PM) value, which indicates the degree of air pollution, in both the Marmara region and Istanbul during the COVID-19 pandemic. The one-sample t-test results showed that the reduction in air pollution levels was statistically significant in both areas (t = 11.45, p < .001 for the Marmara region, and t = 3.188, p < .001 for Istanbul). These findings have important practical implications for decision-makers planning for a more sustainable environment. Overall, the study provides valuable insights into the impact of the COVID-19 pandemic on air pollution levels in the Marmara region, particularly in Istanbul. The application of machine learning algorithms and statistical analysis provides a rigorous approach to the investigation of this important issue by comparing before and during the COVID-19 outbreak.

An integrated model of UTAUT2 to understand consumers' 5G technology acceptance using SEM-ANN approach

It has been a decade since the first extensive study on the internet's adoption and use was conducted. Circumstances have changed in the last decade internet has become an essential need for every human being. Socio-psychological, economic, and personal factors play a significant role in shaping human behaviour. But their role in shaping consumer behaviour toward 5G is still unexplored. In order to determine the impact of socio-psychological elements on 5G technology adoption intention, the study integrated curiosity, perceived value, functional value, and environmental awareness into UTAUT2 and analyzed how they interact. Instead of relying on linear models, this study employed a dual-stage SEM-ANN approach because customers' decision-making process to adopt new technology is complex. Valid responses from 840 respondents were collected, investigated, and ranked using the deep learning ANN approach. All predictors were found statistically significant except social influence. ANN sensitivity analysis revealed that newly integrated predictors (environmental awareness, curiosity) are surprisingly the most important predictors, followed by facilitating conditions and perceived satisfaction. SEM-ANN hybrid two-step deep learning approach explained 83.6% variance higher than the baseline model (UTAUT2). The study improved UTAUT2 by adding new variables and expanding its canvas to predict user technology adoption. This will show how consumers react to 5G services and help telecoms grow into new markets.

Validation and Translation of the Relational Aspect of Care Questionnaire into the Malay Language (RAC-QM) to Evaluate the Compassionate Care Level of Healthcare Workers from the Patient's Perspective

BACKGROUND

Compassionate care has been increasingly highlighted in the past few decades worldwide, including in Malaysia. Despite acknowledging its importance, Malaysia still lacks a validated tool that can be used to assess the level of compassionate care from the patient's perspective. Therefore, this study aims to validate and translate the Relational Aspect of Care Questionnaire (RAC-Q) into the Malay language.

METHODS

Permission to use and translate the original RAC-Q into the Malay language was obtained. The RAC-Q was then translated into the Malay language following the 10 steps proposed for the translation of a patient-reported outcome questionnaire. A pretest was conducted based on 30 inpatients to assess the appropriateness and clarity of the finalized translated questionnaire. A cross-sectional study was performed based on 138 inpatients from six adult wards of a teaching hospital so as to validate the translated questionnaire. The data were analyzed using R software version 4.1.3 (R Core Team, Vienna, Austria, 2020). The results were presented descriptively as numbers and percentages or means and standard deviations. A confirmatory factor analysis was performed using robust estimators.

RESULTS

The analysis showed that the measurement model of the RAC-Q Malay version (RAC-QM) fits well based on several fit indices: a standardized factor loading range from 0.40 to 0.73, comparative fit index (CFI) of 0.917, Tucker-Lewis fit index (TLI) of 0.904, root mean square error of approximation (RMSEA) of 0.06, and a standardized root mean square residual (SRMR) of 0.073. It has good reliability, with a Cronbach's alpha of 0.857 and a composite ratio of 0.857.

CONCLUSION

The RAC-QM demonstrated good psychometric properties and is valid and reliable based on the confirmatory analysis, and it can thus be used as a tool for evaluating the level of compassionate care in Malaysia.