Biography

Dr. Qingning Cao received his Ph.D. in Management Science from the University of Texas at Dallas (UTD) in 2017 and is currently an Associate Professor. He is also a member of the Anhui Provincial Think Tank for Science and Technology Finance. His research focuses on platform strategy and technology-driven business model innovation, covering frontier areas such as live-streaming retail, the digital economy (e.g., online gaming), blockchain, and generative AI. Using quantitative methods, he studies firm behavior and mechanism design in digital economies. His research has been published in leading international journals in management and operations, and he has led multiple national-level research projects.

Professor Cao has extensive experience teaching in MBA and executive education (EDP) programs. His teaching includes courses such as Marketing Management and Big Data and Business Analytics, where he focuses on how data and emerging technologies shape modern business decision-making.

He has also delivered numerous invited lectures and executive training programs for corporations and government agencies. These engagements often focus on topics such as how big data transforms traditional industries in the era of digital transformation and data-driven decision making for business innovation.

Representative Publications

Ji Luo, Qingning Cao, Shuguang Zhang, Dongxiao Gu. 2025. Generative AI Usage Among Investor Types: The Role of Personality and Perceptions. Finance Research Letters. 82: 107604.

Ji Luo, Qingning Cao, Shuguang Zhang. 2024. How do personality traits affect investors' decision on crypto market including cryptocurrencies and NFTs? Review of Behavioral Finance 16 (4): 600-619.

Yuanzhao Tang, Sandun Perera, Qingning Cao, Xiang Ji. 2023. Supplier versus platform bundling: Optimal strategies under agency selling, Transportation Research Part E: Logistics and Transportation Review. 179: 103325.

Jianqiang Zhang, Qingning Cao, Xiuli He. 2023. Competitor referral by platforms, Annals of Operations Research. 329: 757-780.

Qingning Cao, Yuanzhao Tang, Sandun Perera, Jianqiang Zhang. 2022. Manufacturer- versus retailer-initiated bundling: Implications for the supply chain, Transportation Research Part E: Logistics and Transportation Review. 157: 102552.

Qingning Cao, Xianjun Geng, Jun Zhang. 2022. Impact of channel structure on a manufacturer's bundling decision with an application to digital goods, Production and Operations Management. 31(4): 1679-1697.

Jianqiang Zhang, Qingning Cao, Xiaohang Yue. 2020. Target or not? Endogenous advertising strategy under competition. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 50(11): 4472-4481.

Jianqiang Zhang, Qingning Cao, Xiuli He. 2020. Manufacturer encroachment with advertising. Omega. 91: 102013. 

Qingning Cao, Jianqiang Zhang. Gray market’s product quality in the circular economy era. 2020. International Journal of Production Research.  58(1): 308-331.

Qingning Cao, Xianjun Geng, Kathryn E. Stecke, Jun Zhang. 2019. Operational role of retail bundling and its implications in a supply chain. Production and Operations Management. 28(8): 1903-1920.

Jianqiang Zhang, Qingning Cao, Xiuli He. 2019. Contract and product quality in platform selling. European Journal of Operational Research. 272(3): 928-944.

Qingning Cao, Kathryn E. Stecke, Jun Zhang. 2015. The Impact of Limited Supply on a Firm's Bundling Strategy. Production and Operations Management. 24(12): 1931-1944.

Qingning Cao, Xianjun Geng, Jun Zhang. 2015. Strategic Role of Retailer Bundling in a Distribution Channel. Journal of Retailing. 91(1): 50-67.

Student Opportunities

I welcome applications from both research-oriented graduate students and professional master’s students, including MBA (Marketing and Strategy), Master of Finance (MF), Master of Statistics, and Master of Logistics Engineering.

Who Should Apply

I am looking for students interested in the digital economy, platform strategy, and technology-driven business innovation. Students from diverse academic backgrounds are welcome. Helpful preparation may include interest in data, artificial intelligence, and digital technologies, including the practical use of large language models and data analysis tools. Familiarity with statistics, economics, or data analysis can also be beneficial. More importantly, I value students who are motivated to study business and management problems using structured reasoning and evidence-based approaches. Students may pursue research using theoretical modeling, data-driven analysis, or mixed approaches, depending on their interests and strengths. Applicants with an international perspective, familiarity with English academic literature, and willingness to engage with global research topics will benefit most from the program.

Research Areas

Research in my group focuses on the digital economy and technology-enabled business innovation. Major topics include new retail and social media operations (e-commerce platforms, social commerce, and supply chain operations), FinTech and data intelligence (financial analytics, risk management, and data-driven decision making), and the platform economy and AI-driven innovation (platform strategy, digital business models, and AI-enabled business transformation).

Career Development

Students trained in these areas pursue careers in academia, data science and analytics, FinTech and financial institutions, consulting, and strategy or innovation roles in technology companies and digital platforms.

Training and Mentorship

My research group emphasizes both research excellence and real-world relevance. Students will have opportunities to participate in research projects, data collaborations, and industry-related studies.

For students interested in academic careers, I actively support international collaboration and publication opportunities. The goal is to help students develop broad perspectives, critical thinking, and the ability to connect emerging technologies with management practice.