Search for Articles:

MECS Press Menu

Quang Hung Do

Work place: Fintech Lab, Posts and Telecommunications Institute of Technology, Hanoi, 100000, Vietnam

E-mail: dqhung@ptit.edu.vn

Website:

Research Interests:

Biography

Quang Hung Do is currently an Associate Professor and holds a Ph.D., affiliated with the Posts and Telecommunications Institute of Technology (PTIT), Vietnam, where he also serves as the Head of the Fintech Lab. He previously served as an Associate Professor and Vice Dean of the Department of Information Technology at the University of Transport Technology (UTT), Vietnam. He was also a teaching and research assistant at Feng Chia University (FCU), Taiwan. Dr. Do has published more than 100 papers in academic journals, conferences, and edited books. He has served on program committees and as a chair for various international conferences, including the ISSAT International Conference on Data Science in Business, Finance and Industry (DSBFI), the 4th International Conference on Natural Language Processing and Artificial Intelligence (NLPAI), and the 2nd International Conference on Machine Learning Techniques and NLP (MLNLP). He has also acted as a reviewer for several SCI-indexed journals, such as Applied Soft Computing, Journal of the Operational Research Society, Iranian Journal of Fuzzy Systems, and Concurrent Engineering: Research and Applications. His research interests include artificial intelligence techniques (e.g., neural networks and neuro-fuzzy systems), machine learning, fuzzy methods, information systems, and AI applications in business, management, and finance.

Author Articles
AI-driven Psychographic and Behavioral Segmentation of Prospective University Students in Vietnam

By Nguyen Tat Trung Quang Hung Do Duc Trong Pham Doan Thi Thanh Hang

DOI: https://doi.org/10.5815/ijieeb.2026.03.01, Pub. Date: 8 Jun. 2026

The digital transformation of higher education marketing demands more sophisticated approaches to understanding prospective students beyond traditional demographic segmentation. This study develops a machine learning-based psychographic and behavioral segmentation framework for prospective university students in Vietnam, integrating constructs from consumer choice theory and technology adoption literature. We employ established unsupervised and supervised machine learning techniques (k-means clustering, Gaussian Mixture Models, and XGBoost classification) rather than claiming novel artificial intelligence architectures. Analyzing survey data from 1,486 Grade-12 students, our hybrid methodological approach identified three distinct segments: Intrinsically-Motivated Digital Explorers (27.7%), Prestige-Driven Traditionalists (38.9%), and Undecided Ambivalents (33.4%). Supervised learning (XGBoost) achieved 87.2% accuracy in predicting segment membership, with feature importance analysis revealing intrinsic motivation, technology readiness, and risk aversion as the primary discriminators. The findings extend higher education consumer choice theory by integrating technology readiness as an independent discriminative factor and demonstrate the methodological value of combining unsupervised and supervised machine learning for market segmentation.

[...] Read more.
Other Articles

Subscribe to receive issue release notifications and newsletters from MECS Press journals