ÌÇÐÄ´«Ã½

Yuanyuan (April) SongÌÇÐÄ´«Ã½

O'Brien Hall, 448J

MilwaukeeWI53201United States of America(414) 288-2428yuanyuan.song@marquette.edu

Assistant Professor of Information Systems and Analytics

WIPFLI Fellow in Artificial Intelligence

I am the inaugural WIPFLI Fellow in Artificial Intelligence and an Assistant Professor of
Information Systems at ÌÇÐÄ´«Ã½

I earned my Ph.D. from the University of Georgia. My dissertation developed a novel method,
Causal Knowledge Analytics, to enhance scholarly productivity. My dissertation serves as the
foundation for the Theory Research Exchange (T-Rex) project, funded by the Alfred P. Sloan
Foundation. By establishing a standard for digitizing knowledge and developing analytics, this
method assists scholars in processing the literature. My research employs various methods,
such as graph theory, network science, and natural language processing. My Ph.D. dissertation
is forthcoming as a book with Edward Elgar Publishing.

My work is published in leading IS journals (e.g., Journal of the Association for Information
Systems) and major conferences (e.g., International Conference on Information Systems). I have
also actively presented my research at conferences such as the Academy of Management
(AOM), Americas Conference on Information Systems (AMCIS), Pacific Asia Conference on
Information Systems (PACIS), Hawaii International Conference on System Sciences (HICSS),
Australasian Conference on Information Systems (ACIS), and Middle East & North Africa
Conference on Information Systems (MENACIS). Notably, my research was awarded Best Paper
in Track at ICIS 2021.

Education

• Ph.D. 2024, University of Georgia, Advisors: Drs. Rick Watson & Xia Zhao Dissertation: Causal Knowledge Analytics Using Graph Theory, Network Science, and Natural Language Processing Dissertation Proposal Defended: Dec 2021
• M.S. 2019, Harbin Institute of Technology, China
• B.S. 2017, Ocean University of China

Publications

Watson, R., Song, Y., Zhao, X., & Webster, J. (2024). Extending the Foresight of Phillip
Ein-Dor: Causal Knowledge Analytics. Journal of the Association for Information Systems, 25(1), 
145-157.

Song, Y., Zhao, X., & Watson, R. (2024). Digitised knowledge-based literature reviewing: a tutorial  on coding causal and process models as graphs. Journal of Decision Systems, 1-12.

Song, Y., Watson, R., Zhao, X. (2021) "Literature Reviewing: Addressing the Jingle and Jangle 
Fallacies and Jungle Conundrum Using Graph Theory and NLP" ICIS 2021 Proceedings (Best Paper in Track Award)

Song, Y., Watson, R., Zhao, X; and Kelley, N. (2020) "Theory Research Exchange: A
Causal Model Approach to Literature Reviewing". ICIS 2020 TREOs.

Song, Y., & Karahanna, E. (2020). "Giving What a User Needs: Constructing Reference
Groups in Fitness Technologies". AMCIS 2020 Proceedings.

Li, Y., Song, Y., Zhao, W., Guo, X., Ju, X., & Vogel, D. (2019). Exploring the role of online
health community information in patients’ decisions to switch from online to offline
medical services. International Journal of Medical Informatics, 130, 103951.

Presentations

Coding causal and process models as graphs for enhanced literature reviewing
• Americas Conference on Information Systems (AMCIS), Aug 10, 2021
• Harbin Institute of Technology and Xi’an Jiaotong University, Oct 20, 2021
• King Fahd University of Petroleum and Minerals, Nov 3, 2021
• Middle East & North Africa Conference for Information System (MENACIS), Nov 13, 2021
• Australasian Conference on Information Systems (ACIS), Dec 7, 2021

Coding causal and process models as graphs for knowledge analytics
• Pacific Asia Conference on Information Systems (PACIS), July 6, 2022
• Academy of Management (AOM), August 7, 2022
• Workshop proposal accepted at Hawaii International Conference on System Sciences (HICSS), January 2023