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All Journal International Journal of Electrical and Computer Engineering Gadjah Mada International Journal of Business
Bui Thanh Khoa
Industrial University of Ho Chi Minh city
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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Electronic Loyalty In Social Commerce: Scale Development and Validation Bui Thanh Khoa; Ha Minh Nguyen
Gadjah Mada International Journal of Business Vol 22, No 3 (2020): September-December
Publisher : Master in Management, Faculty of Economics and Business, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/gamaijb.50683

Abstract

Loyalty is an important key performance indicator to assess a business's success, especially in an online business environment with fierce competition. The explosion of social networking sites has created a new form of business: social commerce. Simultaneously, the scale of loyalty in online transactions has some limitations; hence, this research aims to develop and validate an electronic loyalty scale in the context of social commerce. The study used a mixed research method with two phases of a sequential exploratory strategy. Qualitative research generated the scale and was used in the initial filtering to develop an e-loyalty scale for social commerce. This study conducted two quantitative studies with 715 social commerce shoppers in five developed areas in Vietnam: Ho Chi Minh City, Ha Noi City, Hai Phong City, Da Nang City, and Binh Duong Province. Based on our research survey and literature review, the research results showed that electronic loyalty in social commerce is expressed in three dimensions: preference, interaction, and personal information’s disclosure. Then, the research proposed several relevant implications for other researchers and administrators of online businesses.
Forecasting stock price movement direction by machine learning algorithm Bui Thanh Khoa; Tran Trong Huynh
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6625-6634

Abstract

Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.
Co-Authors Ha Minh Nguyen Tran Trong Huynh