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CBRN Mission Statement

The China Business Research Network (CBRN) is jointly organized by researchers at the Chinese University of Hong Kong (Shenzhen), National University of Singapore, Peking University, the University of Southern California, and the University of Toronto. The primary objective of CBRN is to promote a deep understanding of China’s business environment through the lens of high-quality academic research. Through monthly seminars and other activities, we hope to provide a neutral platform for academic researchers around the world to discuss cutting-edge China-related topics across various business fields, including but not limited to accounting, finance, information systems, and management. Over time, we hope that CBRN can become a central intellectual hub that provides researchers in business and other related disciplines with the opportunity to network with colleagues with similar research interests on a regular basis and better understand China and its business environment.

CBRN Seminar Format

The seminars are hosted on a monthly basis over Zoom on Wednesdays 9:00-10:00am CST/SGT; Tuesdays 5:00-6:00pm PST; Tuesdays 8:00-9:00pm EST. Each seminar consists of about 10-minute overview of broad related literature and a 50-minute presentation of the research paper. There is also an optional 15-minute Q&A session after the presentation.

We expect the participants of the seminars to be active researchers and PhD students all over the world. We also expect the audience to be diverse as we hope to promote interdisciplinary research across various business fields and other disciplines. We hope that the 10-minute overview of research field can help familiarize the audience with different business fields.

As CBRN is about China, the papers to be presented will be directly related to China or have significant implications for China (e.g., papers using a new research methodology that will be of significant interest to China-focused researchers). We especially welcome papers that cover new research areas, use new data sources (e.g., big data), or use new research methods (e.g., machine learning).