So-Young Son
Yonsei University · Business, Management and Accounting
About the Lab
Professor So-Young Son's research lab specializes in innovation management, data analytics, and policy evaluation, with a strong focus on applying advanced statistical and machine learning methods to real-world challenges in business, public safety, and national development. The lab investigates the drivers of firm performance through open innovation and organizational capabilities, while also developing predictive models for traffic accident severity and national innovation performance using structural equation modeling and data mining techniques. A key research direction involves evaluating the effectiveness of government policies—particularly those supporting women entrepreneurs—through empirical modeling and causal analysis. The lab bridges quantitative methods with practical policy and managerial applications to enhance decision-making in both public and private sectors.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15The 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
Research Areas
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