Research Interests

  • Innovation, Entrepreneurship, Technology Management, and Economics of AI
    • My research interests are innovation and entrepreneurship driven by disruptive technologies such as AI and Quantum Computing.

Peer-Reviewed Publications

  • How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program (with H. Kang, N. Kim, and J. Kim)
    Strategic Management Journal, Conditionally Accepted, 2024. Paper Link: Paper
    Abstract
    Firms increasingly utilize AI to assist or replace human tasks. However, AI can also train humans and make them better. We study how the AI’s instructional role improves human decision-making in the professional Go games where an AI-powered Go program (APG) unexpectedly surpassed the best human player, surpassing the best human knowledge and skill accumulated over thousands of years. To isolate the learning-from-AI effect, we compare the quality of human moves to that of AI’s superior solutions, before and after the initial public release of an APG. Our analysis of 750,990 moves in 25,033 games suggests that APG’s training significantly improved the players’ move quality—reducing the number of errors and the magnitude of the most critical mistake. The improvement is most prominent in the early stage of a game when uncertainty is higher. Further, younger players benefit more than older players, suggesting generational inequality in learning from AI.
  • Practical Quantum Computing is about More Than Just Hardware (with N. Thompson, P. Shukla, and C. Dukatz)
    California Management Review, 2024. Paper Link: Paper
  • When Does AI Payoff?: AI-Adoption Intensity, Complementary Investments, and R&D Strategy (with T. Kim, W. Kim, and Y. Lee (Equally contributed))
    Technovation, 2022. Paper Link: Paper

  • International Alliance Formation: The Effect of Technology Competition Networks (with N. Kim and W. Kim)
    Journal of Business Research, 2022. Paper Link: Paper

  • Are Social Entrepreneurs More Risk-Averse? (with N. Kim and W. Kim)
    Applied Economics Letters, 2019. Paper Link: Paper

Working Papers

  • Standing on the Shoulders of AI? Knowledge Creation by Learning from AI (with H. Kang, N. Kim, and J. Kim)
    Abstract
    Knowledge is a crucial source of competitive advantage, innovation, and economic growth, but creating new knowledge can be difficult. We study whether and how the interaction between human professionals and artificial intelligence (AI) pushes the knowledge frontier. Studying this question is challenging because of the difficulty in measuring new knowledge and quantifying AI’s impact. We circumvent these issues by studying professional Go matches from 2003 through 2021. In 2017, the AI-powered Go program (APG) far surpassed the best human player, and professional players began learning from AI. Such human-AI interaction paved a new way to reassess historical Go knowledge and create new knowledge. We analyze every move in 69,974 games and find that, after APG, professional players significantly changed how they play (1) the first move and (2) the first invasion move in each quadrant. In addition, they adopted different sets of “standard patterns” (defined as a sequence of the first eight alternating moves) that set up the game in the early stage. However, new knowledge catalyzed by AI comes at the expense of higher concentration and reduced diversity of moves. Further, AI’s impact on knowledge creation is greater for highly skilled players; since AI does not explain, learning from AI requires the absorptive capacity of professionals. AI helps humans push the knowledge frontier, but its consequences for knowledge concentration and for differential learning by skill levels provide important implications for how best to seize the opportunities opened up by AI.
    • Finalist for the Best Paper of the 2023 Conference on Information Systems and Technology (CIST)
      • (Top 5 out of 208 papers accepted from 337 submissions)
  • It Ain’t Over ‘Til It’s Over: Post-IPO VC Ownership Effect on Innovation-Enhancing Investment of Newly Public Firms (with T. Kim and H. Woo)
    Revise and Resubmit, 2022.
    Abstract
    Although the existing literature has discussed the effects of VC firms on investee ventures before and at the time of an IPO, less is known about how they influence the strategic decisions of newly public firms after the IPO. Conventional wisdom is that VC investors exit from a venture investment through an IPO. However, we find that VC investors hold a significant amount of shares for years after an IPO. This study examines how VC investors affect a firm after an IPO. Building on the literature on governance through ownership, we argue that post-IPO VC shareholders encourage innovation-enhancing investments of newly public firms and that post-IPO VC ownership positively affects the market value of newly public firms. Our underlying logic is that outcomes created by innovation-enhancing investments of newly public firms can be beneficial to not only themselves but also VC shareholders for VC reputation, network externality, and knowledge acquisition. Consistent with our arguments, our empirical study shows that post-IPO VC ownership is positively related to R&D intensity, CAPEX investment, and Tobin’s Q of newly public firms and that these relationships are amplified when a lead VC is located close to the firm, when a VC investor sits on the board, and when investees are in technology-intensive industries. This study expands the scope of the VC effect on investee ventures beyond an IPO.
  • The Quantum Tortoise and the Classical Hare: A simple framework for understanding which problems quantum computing will accelerate (and which it won’t) (with W. Moses and N. Thompson)
    Revise and Resubmit, 2024. Paper Link: Paper
    Abstract
    Quantum computing promises transformational gains for solving some problems, but little to none for others. For anyone hoping to use quantum computers now or in the future, it is important to know which problems will benefit. In this paper, we answer this question by analyzing the relative strengths of classical and quantum computers. While classical computers operate faster, quantum computers can sometimes run more efficient algorithms. Whether the speed advantage or the algorithmic advantage dominates determines whether a problem will benefit from quantum computing or not. Our analysis reveals that many problems, particularly those of small to moderate size that can be important for typical businesses, will not benefit from quantum computing. Conversely, problems with exponential algorithmic gains, or polynomial gains and large problem sizes, will benefit from present quantum computing. Since exponential gains are rare in practice and theorized to be rare even in principle, our analysis suggests that the benefits from quantum computing will flow either to users of these rare cases, or practitioners processing very large data.
  • Stay the Course? The Effects of Government Matching R&D Funding on Tech Startups (with Y. Lee, T. Kim, and W. Kim)
    Abstract
    This study examines the effectiveness of a government-matched R&D subsidy program for startups, which leverages the selection capabilities of private venture capital firms in awarding funds. Using a regression discontinuity design and a combination of confidential and hand-collected data, we assess the program's impact on follow-up investments, R&D investment, and patenting activities in funded startups. Our findings reveal that startups benefiting from the matching R&D subsidy program secure more subsequent investments (3.3 times), R&D investment (14.1%), and patenting activities (13.7%) compared to non-funded peers, highlighting the potential of government-matched R&D funding programs that engage private sector expertise in the selection process to drive growth and innovation. Through a survey of awardees and non-awardees, we identify underlying mechanisms, including that funded startups were more likely to adhere to their business models and strategic planning, adopt new technologies, and implement performance-based human resource management practices. These insights can inform policymaking on strategies to foster innovation in the startup ecosystem.

Work-in-progress

  • How Does Competition Affect AI Investment in Firms? Evidence from a Quasi-Natural Experiment in the United States (with T. Kim and G. Park) Writing a Draft
    • Winner of the Best Paper Award in the Korean Society for Innovation Management and Economics (KOSIME) Summer Conference 2024
  • How Does AI Improve Human Collaboration and Performance? Evidence from AI-powered X-ray Triage in University Hospitals (with H. Kang and N. Kim)
    Data Analysis After Passing IRB Review
    • Grant: Co-principal investigator, USC Marshall Institute for Outlier Research in Business Grant Program, “How Does AI Improve Human Decision-Making and Performance? Evidence from AI-powered X-ray Triage in University Hospitals” (with H. Kang and N. Kim) (2022-2023) ($19,400)
  • Algorithm Patents Collected and Unveiled (with A. Choudhury and N. Thompson)
    Data Collection