Analytics and AI Experts

Analytics and AI are not new at the Warrington College of Business. The College is home to more than 40 faculty members with research or teachings interest directly related to analytics and AI, and that number is only growing. Their research and teaching focuses on the analysis of large volumes of corporate, social media, stock market and electronic commerce data using cutting-edge methods. They also contribute to a growing body of research on social implications of analytics and AI, such as the governance of AI for corporate digital responsibility, AI’s role in structural bias in decision making in several domains.

PhD Dissertations

Every year, Warrington College grants about 15 PhDs. A majority of the dissertations written by the students focus on the analysis of corporate data using analytics and AI methods.

Faculty publications

In the last three years, faculty in the Warrington College have published over 130 research papers using methods related to Analytics and AI.

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Recent Faculty Research

• Young Kwark, Gene Moo Lee, Paul Pavlou, and Liangfei Qiu (all authors contribute equally), “On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data,” Forthcoming in Information Systems Research (2021).
• Jingchuan Pu, Yuan Chen, Liangfei Qiu, and Hsing Kenneth Cheng, “Does Identity Disclosure Help or Hurt User Content Generation? Social Presence, Inhibition, and Displacement Effects,” Information Systems Research (2020), 31(2), 297-322, Lead Article.
• Naveen Kumar, Deepak Venugopal, Liangfei Qiu, and Subodha Kumar, “Detecting Anomalous Online Reviewers: An Unsupervised Approach Using Mixture Models,” Journal of Management Information Systems (2019), 36(4), 1313-1346.
• Lee GK & Chen J. “Managing resource redeployment with data science: Discovering uncertain sunkenness” Symposium on Resource Allocation and Resource Redeployment, 2020 Academy of Management Annual Conference.
• Kolkman D, Lee GK & van Witteloostuijn A. “Data Science and Automation in the Process of Theorizing: Machine Learning’s Power of Abduction”
• Gillette, J., & Pundrich, G. (2020). “Grammatical Violations and Financial Reporting Quality.” Available at SSRN 3496434.
• Zhang, N., Wang, M., & Xu, H. (in press). “Disentangling effect size heterogeneity in meta-analysis: A latent mixture approach.” Psychological Methods.
• Zhou, L., Wang, M., & Zhang, Z. (in press). “Intensive longitudinal data analyses with dynamic structural equation modeling.” Organizational Research Methods.
• Prosperi, M., Guo, Y., Sperrin, M., Koopman, J. S., Min, J. S., He, X., Rich, S., Wang, M., Buchan, I. E., & Bian, J. (2020). “Causal inference and counterfactual prediction in machine learning for actionable healthcare.” Nature Machine Intelligence, 2, 369-375.
• Bian, J., Min, J., Prosperi, M., & Wang, M. (2020). “A Call for Deep-learning Healthcare.” Epidemiology, 31, e22.

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