Shin, Seungryul Ryan
신승렬
School of Business Administration(경영과학부)
- Data-driven Management Engineering Lab
- Research Interests
- Knowledge Diffusion and Utilization;Commercialization of Scientific Knowledge;Computational Social Science;Causal Inference Statistical Modeling;Shift-Share Instrumental Variable;지식 확산 및 활용 전략;과학기술 사업화 전략;계산적 사회과학;통계 모델링 기반 인과성 추론;Shift-Share 도구변수
- Lab Description
- 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.
The diffusion of scientific discoveries in government laboratories: The role of patents filed by government scientists
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Article
Issue Date2022-06
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Early mover (dis)advantages and knowledge spillover effects on blockchain startups’ funding and innovation performance
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Article
Issue Date2020-03
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Reconfiguring the firm’s core technological portfolio through open innovation: focusing on technological M&A
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Issue Date2017-05
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