Research

My research is to advance Business Analytics using Artificial Intelligence approaches (e.g., NLP, computer vision, machine learning, deep learning) thereby creating value from big data in the data-intensive platforms (e.g., mobile, social media) and examining the impacts of AI in business and society.

Refereed Journal Publications (N=9) [Google Scholar] [SSRN] [DBLP] [ORCiD]

Kwark, Young, Gene Moo Lee, Paul A. Pavlou, Liangfei Qiu (2021) On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream DataInformation Systems Research, 32(3): 895-913.

[Data awarded by Wharton Customer Analytics Initiative]


Shin, Donghyuk, Shu He, Gene Moo Lee, Andrew B. Whinston, Suleyman Cetintas, Kuang-Chih Lee (2020) Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning ApproachMIS Quarterly 44(4): 1459-1492

[Literature on visual data analytics] [MISQ curation]


Zhuang, Yunhui, Yunsik Choi, Shu He, Alvin C. M. Leung, Gene Moo Lee, Andrew B. Whinston (2020) Understanding Security Vulnerability Awareness, Firm Incentives, and ICT Development in Pan-AsiaJournal of Management Information Systems37(3): 668-693.

[Literature on cybersecurity] [NSF Award]


Lee, Gene Moo, Shu He, Joowon Lee, Andrew B. Whinston (2020) Matching Mobile Applications for Cross-Promotion, Information Systems Research 31(3): 865-891.

[Data awarded by IGAWorks]


Song, Reo, Ho Kim, Gene Moo Lee, Sungha Jang (2019) Does Deceptive Marketing Pay? The Evolution of Consumer Sentiment Surrounding a Pseudo-Product-Harm Crisis, Journal of Business Ethics 158(3): 743-761.

[Featured in The Globe and Mail]


Park, Sunyeong, Gene Moo Lee, You-Il Kim, Jinny Seo (2018) Development of Topic Trend Analysis Model for Industrial Intelligence using Public Data, Journal of Technology Innovation 26(4): 199-232.

[Research demo site


Lee, Gene Moo, Liangfei Qiu, Andrew B. Whinston (2016) A Friend Like Me: Modeling Network Formation in a Location-Based Social Network, Journal of Management Information Systems33(4): 1008-1033.

[Best Paper Nomination at HICSS 2016]


He, Shu, Gene Moo Lee, Sukjin Han, Andrew B. Whinston (2016) How Would Information Disclosure Influence Organizations’ Outbound Spam Volume? Evidence from a Field Experiment, Journal of Cybersecurity 2(1): 99-118.

[NSF Award]


Shi, Zhan, Gene Moo Lee, Andrew B. Whinston (2016) Toward a Better Measure of Business Proximity: Topic Modeling for Industry Intelligence, MIS Quarterly 40(4): 1035-1056.

[Research demo site] [MISQ curation]


  • I regularly present papers at IS conferences such as CIST, ICIS, WITS, DS, KrAIS, and HICSS. Before switching to the IS field, I published papers at Computer Science conferences.
  • Project reports, unpublished manuscripts, lecture notes
  • My work was covered in media outlets (e.g., The Globe and Mail, Huffington Post).

Working Papers (N=8) (* equal contribution)
  1. Lee M, Lee GM, Cavusoglu H, Seidel MDL (2022) Strategic Competitive Positioning: An Unstructured Structural Hole-based Firm-specific Measure, Working Paper. [Last update: July 30, 2022]
  2. Schulte-Althoff, M., Lee, G. M., Rothes, H., Kauffman, R., Fürstenau, D. (2022) “What Fuels Growth? A Comparative Analysis of the Scaling Intesity of AI Start-ups”, Working Paper. [Last update: Aug 3, 2022]
  3. Han K, Choi JH, Choi Y, Lee GM, Whinston AB (2022) Security Defense against Long-term and Stealthy Cyberattacks, Revise and Resubmit at Decision Support Systems. [Last update: Sept 10, 2022]
  4. Park S, Lee GM, Shin D, Han SP (2022) When Does Congruence Matter for Pre-roll Video Ads? The Effect of Multimodal, Ad-Content Congruence on the Ad Completion, Under review. [Submitted: June 27, 2022]
  5. Song V, Cavusoglu H, Ma MLZ, Lee GM, (2022) IT Risk and Stock Price Crashes, Under Review. [Submitted: Aug 21, 2022]
  6. Schulte-Althoff M, Schewina K., Lee GM, Fürstenau D. (2021) On the Heterogeneity of Startup Tech Stacks, Working Paper [Latest version: May 20, 2021]
  7. Koh Y, Lee GM, Lee GM (2021) Price Competition and Inactive Search, Working Paper. [Latest version: June 16, 2021]
  8. Lee GM*, Naughton J*, Zheng X*, Zhou D* (2020) Predicting Litigation Risk via Machine Learning, Working Paper. [Latest version: Dec 1, 2020]

Invited Presentations [Full Information]
  • 2022: Arizona, George Mason, KAIST, Hanyang, Vancouver KDD, Kyung Hee, McGill
  • 2021 (11): HKUST, Saarland, Maryland, Korea Univ, American Univ, Kyung Hee, Chung-Ang, NUS, Tennesse Chattanooga, Rochester, KAIST, Yonsei Univ
  • 2020 (5): Rutgers, UBC Commerce Scholars Program, UBC Sauder, Univ of Washington, AKCSE Young Generation Forum
  • 2019 (2): UBC AI Research Centre (CAIDA), Simon Fraser
  • 2018 (4): UBC Information School, Rutgers, Arizona State, Korea Univ, Korea Insurance Research Institute (KIRI)
  • 2017 (4): Univ of North Texas, UT Arlington, Consulate General of Republic of Korea, Korea Institute of Science and Technology Information (KISTI)
  • 2016 (10): UT Arlington, SKKU, Korea Univ, Hanyang, Kyung Hee, Chung-Ang, Yonsei, Seoul National, Kyungpook National, UBC Sauder
  • 2015 (1): KAIST
  • 2014 (7): Notre Dame, Temple, UC Irvine, Indiana, UT Dallas, Minnesota, UT Arlington

Resources