Search

Purpose: The purpose of this study is to understand the mediating role of psychological need and immersive experience on graduates' skill gaps on massive open online courses (MOOCs) adoption intention. Design/methodology/approach: The proposed research model is developed by combining two popular theoretical models, namely, the self-determination theory, network externalities theory along technology adoption theory. Data are collected from 318 respondents to test the model. Structural equation modeling (SEM) is applied to analyze the data. Findings: The study reveals that the sense of psychological needs and immersive experience mediates the influence of skill gap and social interaction on MOOC adaption willingness. However, immersive experience alone cannot influence adoption intention. Similarly, psychological needs cannot have a significant impact on adoption intention without the graduate skill gap. Originality/value: This study specifically focuses on investigating the mechanism of how psychological needs satisfaction mediates the relationship between graduates' skill gap and MOOC adoption intention. The findings suggest us to develop a course that will increase learners employability skill. This study also contributes by incorporating the idea of immersive experience to facilitate improved virtual social interaction.

BackgroundUric acid is associated with noncommunicable diseases such as cardiovascular diseases, chronic kidney disease, coronary artery disease, stroke, diabetes, metabolic syndrome, vascular dementia, and hypertension. Therefore, uric acid is considered to be a risk factor for the development of noncommunicable diseases. Most studies on uric acid have been performed in developed countries, and the application of machine-learning approaches in uric acid prediction in developing countries is rare. Different machine-learning algorithms will work differently on different types of data in various diseases; therefore, a different investigation is needed for different types of data to identify the most accurate algorithms. Specifically, no study has yet focused on the urban corporate population in Bangladesh, despite the high risk of developing noncommunicable diseases for this population. ObjectiveThe aim of this study was to develop a model for predicting blood uric acid values based on basic health checkup test results, dietary information, and sociodemographic characteristics using machine-learning algorithms. The prediction of health checkup test measurements can be very helpful to reduce health management costs. MethodsVarious machine-learning approaches were used in this study because clinical input data are not completely independent and exhibit complex interactions. Conventional statistical models have limitations to consider these complex interactions, whereas machine learning can consider all possible interactions among input data. We used boosted decision tree regression, decision forest regression, Bayesian linear regression, and linear regression to predict personalized blood uric acid based on basic health checkup test results, dietary information, and sociodemographic characteristics. We evaluated the performance of these five widely used machine-learning models using data collected from 271 employees in the Grameen Bank complex of Dhaka, Bangladesh. ResultsThe mean uric acid level was 6.63 mg/dL, indicating a borderline result for the majority of the sample (normal range

BackgroundThe public health system in Bangladesh has been struggling to provide coverage and utilization of basic maternal health care services in pursuit of achieving maternal and child mortality-related goals. Interestingly, the rapid popularization of mobile technology in the country is transforming the landscape of health care access and delivery. However, little is known regarding the use of mobile phones from the perspective of maternal health care service utilization. ObjectiveIn this study, we aimed to investigate the prevalence and sociodemographic pattern of mobile phone use for health services among women and relationship between the use of mobile phone use and the uptake of essential maternal health services (MHSs). MethodsCross-sectional data from the Bangladesh Demographic and Health Survey on 4494 mothers aged between 15 and 39 years were used in the analysis. Using mobile phones to get health services or advice was hypothesized to have a positive association with the uptake of basic MHSs (antenatal care, ANC, facility delivery services, postnatal care) and postnatal care for the newborn. Data were analyzed using bivariate and multivariable techniques. ResultsMore than a quarter (1276/4494, 28.4%; 95% CI 26.8-30.3) of the women aged 15-39 years reported using mobile phones to get health services with significant sociodemographic variations in the use of mobile phones. Analysis of the specific purposes revealed that, in most cases, mobile phones were used to contact service providers and consult with the same about what to do, whereas a smaller proportion reported using mobile phone for the purposes of arranging money and transportation. Multivariable analysis showed that compared with respondents who reported not using mobile phones for health care services, those who used them had higher odds of making 3+ ANC visits and delivering at a health facility. The odds were slightly higher for rural residents than for those in the urban areas. ConclusionsThe findings of this study conclude that women who use mobile phones are more likely to use ANC and professional delivery services than those who do not. More in-depth studies are necessary to understand the mechanism through which mobile phone-based services enhance the uptake of maternal health care.

The COVID-19 epidemic has hastened the growth of Virtual Communities and is affecting virtually every part of work in the public and business sectors. Virtual communities, popular forums for communication and entertainment, increasingly affected the users' decisions. Though many technology adoption models/theories are available, a distinctive model for decision-making in a virtual environment is scarce. This research developed the virtual communities' decision model and empirically tested its performance. This study examined 16 well-established theories/models of information technology, social science, marketing, and behavioral finance and extracted nine constructs from 58 identified constructs considering theoretical cohesiveness along with the three-stage method proposed by Moore and Benbasat. A unified model for virtual communities' decisions (VCDM) is developed and validated using the data collected from individual capital market investors in Bangladesh. The structural equation modeling technique is used to analyze the data. The upshot implies that VCDM performs adequately and explains the maximum variances in intention to decision and investment. VCDM also outperforms the majority of the related theoretical models. The acceptance levels of fit indices and all significant relationships among different constructs are also empirically validated. The moderating effect of the virtual group use experience is also confirmed. Future research can use VCDM in marketing, behavioral finance, ecommerce, information systems and social science context. VCDM thus facilitates a beneficial tool for managers, service providers, and other users to assess the likelihood of effectiveness for decisions in a virtual environment. [ABSTRACT FROM AUTHOR]