Data-driven Management Engineering Lab

데이터 기반 경영공학 연구실

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Data-driven Management Engineering Lab develops and applies big data analytic algorithms and causal inference techniques to explore causal links between various factors in the innovation ecosystem. Specifically, we provide implications for firm R&D managers and policymakers by empirically analyzing how firms, individuals, and government policies influence technological innovation.

Major research field

Economics of Innovation, Technology Management, Data Science, Causal Inference

Desired field of research

Artificial Intelligence/Machine Learning Application for Data Analytics

Research Keywords and Topics

Knowledge Diffusion and Utilization
Commercialization of Scientific Knowledge
Computational Social Science
Causal Inference Statistical Modeling
Shift-Share Instrumental Variable

Research Publications

Shin, S.R., Lee, J., Jung, Y.R., & Hwang, J. (2022). The diffusion of scientific discoveries in government laboratories: The role of patents filed by government scientists. Research Policy, 51(5), 104496
Park, G., Shin, S.R., & Choy, M. (2020). Early mover (dis)advantages and knowledge spillover effects on blockchain startups' funding and innovation performance, Journal of Business Research, 109, 64-75
Shin, S.R., Han, J.S., Marhold, K., & Kang, J. (2017). Reconfiguring the firm’s core technological portfolio through open innovation: Focusing on technological M&A. Journal of Knowledge Management, 21(3), 571 – 591


  • SC. 경제/경영
  • SC07. 경영전략/윤리
  • SC0701. 경영전략/혁신