Our research group focuses on the impacts of AI and IT in business and society. Specifically, we are examining how firms develop AI and robot strategies for innovation, how advanced AI technologies (e.g., generative AI, deep learning, computer vision) affect tech platforms, and how we can mitigate the unintended consequences of AI and IT. For some papers, we share interactive visualization and datasets. Working papers are available upon request.
Economic and Societal Impacts of AI and IT
Lee, Myunghwan, Victor Cui, Gene Moo Lee (2023) Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation.
[AOM 2023, CIST 2023]
Lee, Myunghwan, Gene Moo Lee (2022) Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance.
[DS 2022]
Zhang, Xiaoke, Mi Zhou, Gene Moo Lee (2023) Generative AI and Creator Economy: Investigating the Effects of AI-Generated Voice on Online Video Creation.
[DS 2022, WITS 2022, KrAIS 2023, CSWIM 2023, ISMS MKSC 2023, KrAIS 2023, CIST 2023] [API by Ensemble Data]
Park, Jaecheol, Myunghwan Lee, Gene Moo Lee (2023) Mobile Resilience: The Effect of Mobile Device Management on Firm Performance during the COVID-19 Pandemic.
[UBC 2023, MSISR 2023, KrAIS 2023, WeB 2023]
Lee, Myunghwan, Gene Moo Lee, Donghyuk Shin, Sang-Pil Han (2022) Robots Serve Humans? Understanding the Economic and Societal Impacts of AI Robots in the Service Industry.
[WITS 2020, KrAIS 2020, DS 2022, BIGS 2022]
Schulte-Althoff, Matthais, Daniel Fürstenau, Gene Moo Lee, Hannes Rothes, Robert Kauffman (2022) What Fuels Growth? A Comparative Analysis of the Scaling Intesity of AI Start-ups.
[HICSS 2021, WITS 2022]
Data Analytics Methods and Frameworks
Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David Seidel (2023) Strategic Competitive Positioning: Unsupervised Operationalization of a Structural Hole-based Firm-specific Construct.
[CIST 2019, DS 2021, KrAIS 2021, INFORMS 2022] [Interactive viz]
Kwon, Soonjae, Sunghyuk Park, Gene Moo Lee, Dongwon Lee (2022) Learning Faces to Predict Matching Probability in an Online Dating Market, In Proceedings of International Conference on Information Systems (ICIS) 2022.
[DS 2021, WITS 2021, ICIS 2022]
Cao, Rui, Gene Moo Lee, Hasan Cavusoglu (2021) Corporate Social Network Analysis: A Deep Learning Approach.
[WITS 2020, DS 2021] [Research demo site]
Empirical Analysis of Data-intensive Platforms
Park, Sungho, Gene Moo Lee, Donghyuk Shin, Sang-Pil Han (2022) When Does Congruence Matter for Pre-roll Video Ads? The Effect of Multimodal, Ad-Content Congruence on the Ad Completion.
[INFORMS 2020, AIMLBA 2020, WITS 2020]
Park, Jaecheol, Arslan Aziz, Gene Moo Lee (2022) Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.com.
[WISE 2021, PACIS 2022, SCECR 2022, BU 2022, CIST 2022, BIGS 2022]
Yuan, Lin, Gene Moo Lee, Hao Xia, Qiang Ye (2023) From Spacing to Scheduling: The Effect of Seller Posting Time Strategies for Short Video Advertising.
[CIST 2023]
Schulte-Althoff, Matthias, Kai Schewina, Gene Moo Lee, Daniel Fürstenau (2021) On the Heterogeneity of Startup Tech Stacks.
[HICSS 2020]
Koh, Yumi, Gea M. Lee, Gene Moo Lee (2023) Price Competition and Active or Inactive Consumer Search.
[APIOC 2019, EARIE 2023]
IT Security and Risk
Song, Victor, Hasan Cavusoglu, Li Zhi Ma, Gene Moo Lee (2022) IT Risk and Stock Price Crashes, Major Revision, Information Systems Research.
[HICSS 2020]
Lee, Gene Moo, James Naughton, Xin Zheng, Dexin Zhou (2020) Predicting Litigation Risk via Machine Learning.
[CFMA 2019] [Litigation risk score data 1996-2015]
Disclaimer: Some of the images on the page are generated by DALL-E 2.