By adopting the theory of planned behaviour, our study develops research model to analyse the effect of structural support on the entrepreneurial cognitive process among university students in Vietnam. Authors employ a meta-analytic path analysis with a sample of 2218 students at 14 universities in Vietnam in order to show that although structural support has positive effect on attitude towards entrepreneurship and perceived behavioural control, it also has negative influence on subjective norms and entrepreneurial intention. Interestingly, different with some previous studies, authors investigate the significant and direct relationship between subjective norms and entrepreneurial in-tention in the economic transitional context of Vietnam. Moreover, analogously to many researches adapting the planed behavioural control, attitude toward entrepreneurship is still seen as the strongest influencing factor on entrepreneurial intention, followed by perceived behavioural control and subjective norms.

This study develops a mediated model to investigate the relationship between entrepreneurial self-efficacy and entrepreneurial intention among Vietnamese students. This model also focuses on examining the role of subjective norms on shaping entrepreneurial intention. By using a meta-analytic path analysis with a sample of 2218 students at 14 universities in Vietnam and based on the theory of planned behaviour, authors find that subjective norms did not directly influence on entrepreneurial intention but it had strong indirect effect on entrepreneurial intention through entrepreneurial self-efficacy, attitude toward entrepreneurship and perceived behavioural control. Moreover, the relationship between self-efficacy and entrepreneurial intention was also mediated by attitude towards entrepreneurship and perceived behavioural control. In terms of direct effect, the result illustrates that attitudes towards entrepreneurship had the strongest effect on entrepreneurial intention among Vietnamese students, followed by entrepreneurial self-efficacy. These findings also show that the theory of planned behaviour could be effectively applied in the context of transitional economics in Vietnam.

The main goal of this study is to investigate the classification capability of several machine learning (ML) techniques, including decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for predicting purchase decisions. The application case is related to consumer purchase decisions of domestic goods in the context of Vietnam. Firstly, factors influencing Vietnamese consumers’ purchase decision of domestic products were identified. Then, data from 240 consumers in Vietnam were collected. Different classifying models based on ML techniques were developed to analyse the sampling data after the performances of the models were evaluated and compared using confusion matrix, accuracy rate and several error indexes. The results indicate that the DT(J48) obtained the highest performance with the corrected prediction percentage of 91.6667%. The findings also show that machine-learning techniques can be used to explicitly in forecasting Vietnamese consumers’ purchase behaviour.

Forecasting stock index has been received great interest because an accurate prediction of stock index may yield benefits and profits for investors, economists and practitioners. The objective of this study is to develop two efficient forecasting models and compare their performances in one day-ahead forecasting the daily Vietnamese stock index. The model development used the data across 9 years of the trading days. The developed models are based on two artificial intelligence techniques, including adaptive network based fuzzy inference system (ANFIS) and long short-term memory (LSTM). The performance indexes including RMSE, MAPE, MAE and R were used to make comparison of the models. The experimental results reveal that both models successfully forecasted the daily Vietnamese stock index with a high accuracy rate. The comparative results of the two models were then discussed and analyzed. It was found that the LSTM model outperformed the hierarchical ANFIS model in forecasting stock index of the Vietnamese stock market.