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.
Park, Jaecheol, Arslan Aziz, Gene Moo Lee (2024) Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.com. R&R (3rd round), Information Systems Research.
[WISE ’21, PACIS ’22, SCECR ’22, BU Platform ’22, CIST ’22, BIGS ’22] #onlinereviews #incentives #platform #amazon
Zhang, Xiaoke, Mi Zhou, Gene Moo Lee (2024) AI Voice in Online Video Platforms: A Multimodal Perspective on Content Creation and Consumption. R&R (2nd round), MIS Quarterly.
[DS ’22, WITS ’22, KrAIS ’23, CSWIM ’23, KrAIS ’23, CIST ’23] [API Sponsored by Ensemble Data] #AI #video #creativity #TTS #tiktok
Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David Seidel (2024) Strategic Competitive Positioning: A Structural Hole-based Firm-level Opportunity Construct for Information Systems Research. R&R (3rd round), Journal of Management Information Systems.
[Visualization] [CIST ’19, DS ’21, KrAIS ’21] #strategy #competition
Zhang, Xiaoke, Myunghwan Lee, Mi Zhou, Gene Moo Lee (2024) News Quality vs. Promptness: Investigating Large Language Models’ Impact on the Institutional Press. R&R (2nd round), MIS Quarterly.
[DS ’24, CIST ’24, BIGS ’24] #ai #journalism #llm
Lee, Myunghwan, Timo Sturm, Gene Moo Lee (2024) Exploring the Influence of Machine Learning on Organizational Learning: An Empirical Analysis of Publicly Listed Organizations. Working Paper.
[JUSWIS ’24, KrAIS ’24] #ai #org-learning #exploration #performance
Park, Jiyong, Myunghwan Lee, Yoonseock Son, Gene Moo Lee (2024) The New Industrial Revolution: AI, Labor Unions, and the Future of Work. Working Paper.
[CIST ’24, BIGS’24, WISE ’24] #ai #labor #unionization #value
Song, Victor, Hasan Cavusoglu, Li Zhi Ma, Gene Moo Lee (2024) IT Risk and Stock Price Crashes. Under Review.
[HICSS ’20]
Park, Jaecheol, Myunghwan Lee, Gene Moo Lee (2024) The Effect of Mobile Device Management on Work-from-home Productivity: Insights from U.S. Public Firms. Preparing for Journal Submission.
[MSISR ’23, KrAIS ’23, WeB ’23, BIGS ’23, AOM ’24] #mobile #resilience #productivity
Park, Jaecheol, Myunghwan Lee, J. Frank Li, Gene Moo Lee (2024) Unpacking the AI Transformation: The Impact of AI Strategies on Firm Performance from the Dynamic Capabilities Perspective. Preparing for Journal Submission.
[CIST ’24, INFORMS ’24, KrAIS ’24, BIGS ’24] #ai #strategy #product #process #value
Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sunghyuk Park (2024) Seeing the Unseen: The Effects of Implicit Representation in an Online Dating Platform.
[DS ’21, WITS ’21, ICIS ’22, WITS ’24] #genAI #matching #onlinedating
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 ’20, KrAIS ’20, DS ’22, BIGS ’22] #AI #servicerobots #restaurants
Inactive working papers
Lee, Myunghwan, Victor Cui, Gene Moo Lee (2023) Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation. [AOM ’23, CIST ’23]
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 ’22]
Schulte-Althoff, Matthais, Daniel Fürstenau, Gene Moo Lee, Hannes Rothes, Robert Kauffman (2022) What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups [HICSS ’21, WITS ’22]
Cao, Rui, Gene Moo Lee, Hasan Cavusoglu (2021) Corporate Social Network Analysis: A Deep Learning Approach. [WITS ’20, DS ’21] [Research demo site]
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 ’20, AIMLBA ’20, WITS ’20]
Schulte-Althoff, Matthias, Kai Schewina, Gene Moo Lee, Daniel Fürstenau (2021) On the Heterogeneity of Startup Tech Stacks. [HICSS ’21]
Koh, Yumi, Gea M. Lee, Gene Moo Lee (2023) Price Competition and Active or Inactive Consumer Search. [APIOC ’19, EARIE ’23]
Bera, Debalina, Gene Moo Lee, Dan J. Kim (2024) Anatomy of Phishing Tactics and Susceptibility: An Investigation of the Dynamics of Phishing Tactics and Contextual Traits in Susceptibility.
Lee, Gene Moo, James Naughton, Xin Zheng, Dexin Zhou (2020) Predicting Litigation Risk via Machine Learning. [CFMA ’19] [Litigation risk score data 1996-2015]
Disclaimer: Some of the images on the page are generated by DALL-E 2.