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This study aims to investigate the factors influencing the usage intentions of metaverse technology in digital library services within higher learning institutions, using the unified system information theory. To achieve this, an online survey was conducted among university staff and students, utilizing a link-scale measurement. Various factors affecting the usage intention of metaverse technology in library services were computed through transformation models such as UTAUT, DM, ISS, and TTF. Subsequently, the model parameters were empirically tested using partial least squares structural equation modeling (PLS-SEM) to identify the significant factors influencing the usage intention of metaverse technology. The results of the study reveal that users' intentions to use metaverse technology in digital library systems are influenced by perceptions of system use, perceived interaction, perceived usefulness, and perceived ease of use. Notably, these influences vary depending on the user's intended task. These findings provide valuable insights into the factors that affect the adoption and usage intentions of metaverse technology in the context of digital library services in higher learning institutions. This research contributes to enhancing understanding and guiding future strategies for leveraging metaverse technology effectively in educational environments.
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INTRODUCTION: Using spatial-temporal analyses to understand coverage and trends in elimination of mother-to-child transmission of HIV (e-MTCT) efforts may be helpful in ensuring timely services are delivered to the right place. We present spatial-temporal analysis of seven years of HIV early infant diagnosis (EID) data collected from 12 districts in western Kenya from January 2007 to November 2013, during pre-Option B+ use. METHODS: We included in the analysis infants up to one year old. We performed trend analysis using extended Cochran-Mantel-Haenszel stratified test and logistic regression models to examine trends and associations of infant HIV status at first diagnosis with: early diagnosis (<8 weeks after birth), age at specimen collection, infant ever having breastfed, use of single dose nevirapine, and maternal antiretroviral therapy status. We examined these covariates and fitted spatial and spatial-temporal semiparametric Poisson regression models to explain HIV-infection rates using R-integrated nested Laplace approximation package. We calculated new infections per 100,000 live births and used Quantum GIS to map fitted MTCT estimates for each district in Nyanza region. RESULTS: Median age was two months, interquartile range 1.5-5.8 months. Unadjusted pooled positive rate was 11.8% in the seven-years period and declined from 19.7% in 2007 to 7.0% in 2013, p < 0.01. Uptake of testing ≤8 weeks after birth was under 50% in 2007 and increased to 64.1% by 2013, p < 0.01. By 2013, the overall standardized MTCT rate was 447 infections per 100,000 live births. Based on Bayesian deviance information criterion comparisons, the spatial-temporal model with maternal and infant covariates was best in explaining geographical variation in MTCT. DISCUSSION: Improved EID uptake and reduced MTCT rates are indicators of progress towards e-MTCT. Cojoined analysis of time and covariates in a spatial context provides a robust approach for explaining differences in programmatic impact over time. CONCLUSION: During this pre-Option B+ period, the prevention of mother to child transmission program in this region has not achieved e-MTCT target of ≤50 infections per 100,000 live births. Geographical disparities in program achievements may signify gaps in spatial distribution of e-MTCT efforts and could indicate areas needing further resources and interventions.