Working Papers

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, Information Systems Research.

[WISE 2021, PACIS 2022, SCECR 2022, BU 2022, CIST 2022, BIGS 2022] #onlinereviews #incentives #platform #amazon


Zhang, Xiaoke, Mi Zhou, Gene Moo Lee (2024) How Does AI-Generated Voice Affect Online Video Creation? Evidence from TikTok. Under Review.

[DS 2022, WITS 2022, KrAIS 2023, CSWIM 2023, KrAIS 2023, CIST 2023] [API Sponsored by Ensemble Data] #AI #video #creativity #TTS #tiktok


Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David Seidel (2024) Strategic Competitive Positioning: Unsupervised Operationalization of a Structural Hole-based Firm-specific Construct. R&R, Journal of Management Information Systems

[Visualization] [CIST 2019, DS 2021, KrAIS 2021]  #strategy #competition


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.

[MSISR 2023, KrAIS 2023, WeB 2023, BIGS 2023, AOM 2024] #mobile #resilience #productivity


Park, Jaecheol, Myunghwan Lee, J. Frank Li, Gene Moo Lee (2024) Unpacking AI Transformation: The Impact of AI Strategies on Firm Performance with a Dual Lens on Product and Process Orientation.

[INFORMS 2024] #ai #strategy #product #process #value


Park, Jiyong, Myunghwan Lee, Yoonseock Son, Gene Moo Lee (2024) The New Industrial Revolution: AI, Labor Unions, and the Future of Work.

#ai #labor #unionization

 


Lee, Myunghwan, Victor Cui, Gene Moo Lee (2023) Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation.

[AOM 2023, CIST 2023] #ai #exploration #deeplearning

 


Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sunghyuk Park (2024) Digital Cupid: Empowering Generative AI for Fair and Efficient Matchmaking.

[DS 2021, WITS 2021, ICIS 2022] #generativeAI #AI #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 2020, KrAIS 2020, DS 2022, BIGS 2022] #AI #servicerobots #restaurants


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]


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]


Cao, Rui, Gene Moo Lee, Hasan Cavusoglu (2021) Corporate Social Network Analysis: A Deep Learning Approach.

[WITS 2020, DS 2021] [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 2020, AIMLBA 2020, WITS 2020]


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]

 


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.

 


Song, Victor, Hasan Cavusoglu, Li Zhi Ma, Gene Moo Lee (2023) IT Risk and Stock Price Crashes.

[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.