- * 프린트는 Chrome에 최적화 되어있습니다. print
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
국가과학기술표준분류
- SC. 경제/경영
- SC07. 경영전략/윤리
- SC0701. 경영전략/혁신