Jun-Hyung Bae
Sungkyunkwan University · 経営学
研究室紹介
Professor Jun-Hyung Bae's research lab specializes in innovation management, corporate venturing, and the intersection of technology, governance, and organizational behavior. The lab explores how technological capabilities, leadership transitions, and geographic clustering shape corporate investment decisions, alliance formation, and innovation performance. It also investigates the role of leadership—particularly founder versus professional CEOs—in driving innovation, as well as the design of educational tools to enhance machine learning literacy among non-technical students. The lab integrates empirical analysis with innovative data sources, including firm-level datasets, survey data on artists, and synthetic personas for cultural analytics.
Research Overview
Research Output Trend
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Selected Papers
15Drawing on the literature on entrepreneurship and competitive dynamics, we investigate how technological overlap between spinouts and parent firms influences corporate investors’ decisions regarding investments in spinouts. We suggest that a high level of technological overlap between a spinout and its parent firm deters potential corporate investors from making an investment in the spinout because of competitive tension arising from anticipated hostile actions by the parent firm. We further sug
Using a unique dataset on U.S. publicly listed firms that experienced sudden deaths of CEOs during the 1979–2002 period, this paper investigates the relationship between founder CEOs and innovation in public firms. Our main results show that the exogenous change from a founder CEO to a professional CEO is associated with a 41 percent decrease in a firm’s citation-weighted patent count, even when controlling for R&D expenditures, suggesting that founder CEOs are better managers of innovations tha
Abstract Research Summary We investigate why a corporate investor makes more corporate venture capital (CVC) investments in certain areas than in others. Focusing on firms' different technological capabilities across distinct technology domains, we argue that a corporate investor's technological capabilities in a given domain affect its likelihood of investing in (1) ventures within the domain in an inverted U‐shaped manner and (2) ventures operating in other technologically complementary domain
We investigate how the size of the geographic cluster in which a firm is located influences its governance choice between equity and non‐equity alliances and subsequent innovation performance. We argue that firms located in larger clusters tend to form non‐equity alliances rather than equity alliances because the communication and control benefits of cluster membership, which increase with cluster size, reduce in‐cluster firms' need to form equity alliances. We also claim that the effect of this
As machine learning (ML) became more relevant to our lives, ML education for college students without technical background arose important. However, not many educational games designed to suit challenges they experience exist. We introduce an educational game Classy Trash Monster (CTM), designed to better educate ML and data dependency to non-major students who learn ML for the first time. The player can easily learn to train a classification model and solve tasks by engaging in simple game acti
The automated piano enables note densities, polyphony, and register changes far beyond human physical limits, yet the three dominant traditions for composing such textures--Nancarrow's tempo canons, Xenakis's stochastic distributions, and L-system grammars--have developed in isolation. This paper presents Amanous, a hardware-aware composition system for Yamaha Disklavier that unifies these methodologies through distribution-switching: L-system symbols select distinct distributional regimes rathe
Piano fingering shapes how a passage can be played, yet it is difficult to label after a performance. An annotator must decide which finger produced each note while reconciling the score, timing, video, and hand motion. We present PiAnnotate, a web-based pipeline for adding expert fingering annotations to the FurElise performance dataset. The tool brings together a piano-roll view, performance video, and a 3D MANO hand mesh so that reviewers can inspect each assignment in musical and physical co
This study advances previous research on the competitive tension associated with knowledge diffusion through employee entrepreneurship that has mainly focused on the dyadic relationship between spinouts and parent firms. Drawing on the literature on spinouts and employee mobility, I argue that technological knowledge of spinouts inherited from parent firms creates critical tension between competitive risks and benefits for other industry incumbents when they make corporate venture capital (CVC)
Synthesizing realistic piano hand motions requires both precision and naturalness. Physics-based methods achieve precision but produce stiff motions; data-driven models learn natural dynamics but struggle with positional accuracy. Piano motion exhibits a natural hierarchy: fingertip positions are nearly deterministic given piano geometry and fingering, while wrist and intermediate joints offer stylistic freedom. We present [OURS], a four-stage framework exploiting this hierarchy: (1) statistics-
Piano fingering shapes how a passage can be played, yet it is difficult to label after a performance. An annotator must decide which finger produced each note while reconciling the score, timing, video, and hand motion. We present PiAnnotate, a web-based pipeline for adding expert fingering annotations to the FurElise performance dataset. The tool brings together a piano-roll view, performance video, and a 3D MANO hand mesh so that reviewers can inspect each assignment in musical and physical co
Prior studies on R&D alliances have assumed that technological complementarity is a subset of or a moderate level of technological similarity. This study suggests that technological similarity and complementarity are two distinct concepts and affect firms’ decision-making in R&D alliance formation differently. While firms conduct local searches to identify alliance partners that possess similar areas of technological knowledge, they also conduct relatively distant searches to identify al
Prior studies on R&D alliances have assumed that technological complementarity is a subset or a moderate of technological similarity. In this article, we argue that they are distinct concepts and affect firms’ decisions in R&D alliance formation with different mechanisms. We distinguish technological similarity and complementarity both theoretically and empirically and investigate their effects on the likelihood of R&D alliance formation between firms in the biopharmaceutical industry. We furthe