• Journal of Information Technology Applications and Management
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• 제31권1호
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• pp.11-25
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• 2024

The purpose of this study is to investigate the improvement of prediction performance and changes in variable importance in SEM-ANN two-stage analysis. 366 cosmetics repurchase-related survey data were analyzed and the results were presented. The results of this study are summarized as follows. First, in SEM-ANN two-stage analysis, SEM and ANN models were trained with train data and predicted with test data, respectively, and the R² was showed. As a result, the prediction performance was doubled from SEM 0.3364 to ANN 0.6836. Looking at this degree of R² improvement as the effect size f² of Cohen (1988), it corresponds to a very large effect at 110%. Second, as a result of comparing changes in normalized variable importance through SEM-ANN two-stage analysis, variables with high importance in SEM were also found to have high importance in ANN, but variables with little or no importance in SEM became important in ANN. This study is meaningful in that it increased the validity of the comparison by using the same learning and evaluation method in the SEM-ANN two-stage analysis. This study is meaningful in that it compared the degree of improvement in prediction performance and the change in variable importance through SEM-ANN two-stage analysis.

• Structural Engineering and Mechanics
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• 제51권6호
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• pp.989-1003
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• 2014

In the preliminary design stage of an RC 3D-frame, repeated sequential analyses to determine optimal members' sizes and the investigation of the parameters required to minimize the differential column shortening are computational effort consuming, especially when considering various types of loads such as dead load, temperature action, time dependent effects, construction and live loads. Because the desired accuracy at this stage does not justify such luxury, two backpropagation feedforward artificial neural networks have been proposed in order to approximate this information. Instead of using a commercial software package, many references providing advanced principles have been considered to code a program and generate these neural networks. The first one predicts the typical amount of time between two phases, needed to achieve the minimum maximorum differential column shortening. The other network aims to prognosticate sequential analysis results from those of the simultaneous analysis. After the training stages, testing procedures have been carried out in order to ensure the generalization ability of these respective systems. Numerical cases are studied in order to find out how good these ANN match with the sequential finite element analysis. Comparison reveals an acceptable fit, enabling these systems to be safely used in the preliminary design stage.

• The Journal of Asian Finance, Economics and Business
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• 제8권7호
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• pp.79-90
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• 2021

The purpose of this research is to identify factors affecting the acceptance and use of e-tax services among medium taxpayers in Phnom Penh, Cambodia. The researcher conducted the study based on a quantitative approach by using multi-stage sampling method, which selects a sample size by two or more stages. The first stage sampling was the stratified random sampling and the subsequent stage was purposive sampling. In this study, the stratified random sampling was first used, followed by purposive sampling. The data were collected from 450 medium taxpayers who experienced using e-tax services located in three tax branches in Phnom Penh. This study adapted the confirmatory factor analysis (CFA) and structural equation model (SEM) to analyze the model accuracy, reliability and influence of various variables. The primary result showed that behavioral intention has a significant effect on user behavior of e-tax services among medium taxpayers in Phnom Penh, Cambodia. Moreover, the results revealed that performance expectancy, effort expectancy, social influence, and anxiety have significant impact on behavioral intention. In addition, social influence has the strongest impact on behavioral intention, followed by anxiety, performance expectancy and effort expectancy. Conversely, facilitating conditions, trust in government, and trust in internet do not influence behavioral intention.