So Young Sohn
Yonsei University · Business, Management and Accounting
소영순 교수의 연구실은 기업의 혁신 전략과 정책 효과를 중심으로 한 응용 연구를 수행합니다. 특히 오픈 이노베이션, 정부 지원 정책의 효과 분석, 교통사고 심각도 예측 등 실증적 데이터 기반의 구조적 모델링(예: 구조방정식모형, 분류 알고리즘)을 활용한 정책 및 기업 성과 분석에 집중하고 있습니다. 산업별 혁신 능력 비교와 국가 차원의 혁신 지수 분석을 통해 실질적인 정책 제언을 도출하는 데 목적이 있습니다.
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The literature has shown that open innovation (OI) can be a winning strategy in improving firm performance. However, in order to adopt and implement it, managers need to resolve practical problems, such as understanding the role played by OI capacities and openness on firm performance. In response to these needs, this study aims to investigate the hierarchical relationships between openness, OI capacities and performance using a structural equation model approach. This paper also attempts to com
Various classification algorithms became available due to a surge of interdisciplinary research interests in the areas of data mining and knowledge discovery. We develop a statistical meta-model which compares the classification performances of several algorithms in terms of data characteristics. This empirical model is expected to aid decision making processes of finding the best classification tool in the sense of providing the minimum classification error among alternatives.
An increasing number of road traffic accidents (RTA) in Korea has emerged as being harmful both for the economy and for safety. An accurately estimated classification model for several severity types of RTA as a function of related factors provides crucial information for the prevention of potential accidents. Here, three data-mining techniques (neural network, logistic regression, decision tree) are used to select a set of influential factors and to build up classification models for accident s
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