Lately, there was some attention for the Variance Based SEM (VB-SEM) against that of Covariance Based SEM (CB-SEM) from social science researches regarding the fitness indexes, sample size requirement, and normality assumption. Not many of them aware that VB-SEM is developed based on the non-parametric approach compared to the parametric approach of CB-SEM. In fact the fitness of a model should not be taken lightly since it reflects the behavior of data in relation to the proposed model for the study. Furthermore, the adequacy of sample size and the normality of data is among the main assumptions of parametric test itself. This study intended to clarify the ambiguities among the social science community by employing the data-set which do not meet the fitness requirements and normality assumptions to execute both CB-SEM and VB-SEM. The findings reveal that the result of CB-SEM with bootstrapping is almost similar to that of VB-SEM (bootstrapping as usual). Therefore, the failure to meet the fitness and normality requirements should not be the reason for employing Non-Parametric SEM.

Medical tourism has become one of the most important sectors generating foreign exchange for Malaysia. The government has intensified its effort to promote the country as a popular destination among foreign travellers seeking medical care outside their country. Thus, rigorous research is expected in this particular field. In line with that, this study intends to validate the measurement model for medical tourism research. The mixed method study begins with qualitative approach and ends-up with the quantitative analysis. The medical tourism constructs involved are human capital, physical infrastructure, and patient satisfaction. In the qualitative phase, personal and focus group interviews were conducted to generate items for the respective constructs. The confirmatory factor analysis (CFA) has validated the measurement model of human capital, physical infrastructure, and patient satisfaction for unidimensionality, validity and reliability. Therefore, these measurement models can be assessed for the future research improvements.

Reliable estimation of solar irradiance is required for many solar energy applications such as photovoltaics, water heating, cooking, solar microgrids, etc. Deep Learning techniques have shown outstanding behaviour for analysing complex datasets efficiently with high accuracy. Multi-Layer Perceptron, Long-Short Term Memory, and Gated Recurrent Unit techniques are found to be the most competitive techniques in the literature for solar irradiance forecasting. Therefore, in this study, a comparative analysis of those models is carried out using eleven years of NASA satellite data for training and testing. The grid search technique is used to optimize the networks architectures to ensure the best performance of the models for forecasting daily global solar irradiance. The results show that all models have almost the same accuracy with a mean square error close to 0.017 kWh/m2/day. However, the speed of training and the complexity differ for each model. The MLP is found to be the most efficient model due to using a low number of parameters. The study is of importance for reliable solar irradiance forecasting for any location worldwide.