The 33rd Asia-Pacific Conference on Finance, Economics, Accounting, and Management (APCFEAM)

The 33rd Asia-Pacific Conference on Finance, Economics, Accounting, and Management (APCFEAM) was successfully held at the College of Business, Chung Yuan Christian University (CYCU), on June 13–14, 2025. The conference featured renowned speakers from prestigious U.S. universities and attracted scholars from around the globe.

Faculty and students from our Ph.D. Program in Business actively participated in the event. In addition to current students, alumni from Indonesia and Pakistan also returned to join the conference. Attendees had the valuable opportunity to hear from international experts and engage in academic exchange with scholars from various countries.


Out of the 70 papers selected for presentation at the conference, six were from students and alumni of our program—an impressive showing that demonstrates the program’s strong research capacity and global competitiveness.


Selected Paper Presentations from our Ph.D. Program in Business:

No.StatusNameNationalityPaper TitleAdvisor
12nd-Year Ph.D. StudentMing-Yung ChenTaiwanApplication of AI-empowered Intrusion Prevention Systems in Cybersecurity for Multinational EnterprisesCheng-Wen Lee
21st-Year Ph.D. StudentMuhammad Tharmizi JunaidIndonesiaExploring the Impact of Ownership Structure, Corporate Governance, Capital Structure, and Profitability on Dividend Policy: A Comprehensive AnalysisCheng-Wen Lee
31st-Year Ph.D. StudentHong Huan WuVietnamFactors Impacting the Shift from Gold to Digital Assets: An Empirical Analysis in Viet NamCheng-Wen Lee
41st-Year Ph.D. StudentDodi ApriadiIndonesiaConsumer Behavior of Students in the Digital Era: Mobile Banking, Social Media, and the Moderating Effect of Financial LiteracyCheng-Wen Lee
5AlumnusBoedi Hartadi KuslinaIndonesiaThe Interplay between Political Connections, ESG, and Tax Avoidance: Insights from Indonesia
6AlumnusHussain SabborPakistanThe Impact of Macroeconomic Indicators on Utility ETF: A Grey Relational Analysis–Machine Learning ApproachJo-Hui Chen
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