The purpose of this study is to investigate the importance of factors that influence entrepreneurial intention among tourism undergraduate students in Vietnam. A research framework based on Theory of Planned Behavior (TPB) was proposed to understand the relationship between the TPB factors and entrepreneurial intention. Additionally, the relationship between entrepreneurship education and entrepreneurial intention was also investigated. A questionnaire was administered to a sample of 388 tourism undergraduate students at different universities across Vietnam. The partial least squares structural equation modeling (PLS-SEM) method was utilized to test the hypotheses of this research. The study findings show that subjective norms (SN), attitude toward entrepreneurship (AE), perceived behavioral control (PBC) and entrepreneurship education (EE) had direct effects on entrepreneurial intention (EI); however, perceived behavioral control had the strongest influence on entrepreneurial intention. The findings provide educators with insights into how to promote entrepreneurship activities among tourism undergraduate students in Vietnam through educational programs and training.

The objective of this study is to investigate the factors that affect the implementation of outcome-based education (OBE) in the master program in Economic Management. A research framework was proposed to investigate the relationship between the factors including professional knowledge, the ability to detect and solve problems, team work and communication skills, work attitude with students’ perceived value and these factors with practical application ability. A questionnaire was administered to a sample of 388 graduate students of economic management at different universities in Hanoi, Vietnam. The partial least squares structural equation modelling (PLS-SEM) method was utilized to test the hypotheses of this research. The study findings show that professional knowledge, the ability to detect and solve problems, team work and communication skills and work attitude had direct impacts on students’ perceived value; and students’ perceived value had a direct impact on practical application ability of Economic Management’s program in work. The findings also indicate that professional knowledge did not have any direct impact on practical application ability of Economic Management’s program in work and the ability to detect and solve problems only had indirect impact on practical application ability through the moderator of students’ perceived value. It is stressed the importance role of students’ perceived value; and when students understand that the program benefits and they provide knowledge and skills effectively in practice

Railway transportation (RT) plays a crucial role and it is an inseparable part of one country’s main traffic network. However, due to the advantages of aviation industry and other modes of transportation, the share of RT of total traffic volume gradually decreases and RT enterprises are facing various difficulties. Customer satisfaction is one of the essential factors for the survival of any business organization. In order to accordingly offer products and services, RT companies must understand their customers and find out to what extent the consumer is sat-isfied with their offered services and products. The objective of this study is to evaluate the effect of each factor on the passengers’ satisfaction and freight owners for RT service in Vietnam’s context. The study utilizes 7Ps marketing mix (Product, Price, Place, Promotion, People, Process and Physical evidence) to analyze the customer satisfaction level. The collected data are analyzed through the multiple regression method by the use of SPSS software to understand the relationship of marketing mix elements and consumers’ satisfaction. The study finding helps us guide the RT operators on their marketing strategy formulation. Customers will benefit through enhanced knowledge regarding both core and augmented products associated with RT services. It is also expected that this work can be used as a reference material for RT managers to enhance competitiveness.

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.