Business Analytics & Technology Management

데이터 기반 기술경영

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Our main research theme centers on the intersection between business analytics (in other words, machine learning, data science, data mining, artificial intelligence) and decision making in business, especially in technology management. The interactions between the two key elements, analytics and decision making, are bi-directional. From analytics to decision making, we propose and test applications of novel machine learning techniques to improve decision makings in diverse management contexts, such as technology forecasting and technology strategy, and in various industries like IT manufacturing, energy, shipbuilding, oil refinery, etc. In the other direction, from decision making to analytics, we try to fill in the gaps between analytics and decision making by incorporating theoretical findings from management research into advances in machine learning methodologies. Our research interest further extends to the coevolution of business and machines: how AI technologies affect humans in their workplace, organizations, and industries? how users and firms adopt AI technologies?

관심분야

Technology Management
Management Information Systems
Data Mining
Data-driven Decision Making
AI-User co-evolution

희망분야

Technology Management
Data Mining
Data-driven Decision Making
AI-User co-evolution

Research Keywords and Topics

applications of machine learning in technology management
technology forecasting and evaluation
network analysis in R&D collaborations
demand and price forecasting using deep learning techniques
experiments on covolution between users and AI applications

Research Publications
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Jang, H. J., Woo, H., and Lee, C. "Hawkes process-based technology impact analysis," Journal of Informetrics, 11(2), 511-529, 2017.
Lee, K., Woo, H. and Joshi, K. "Pro-innovation Culture, Ambidexterity and New Product Development Performance: Polynomial Regression and Response Surface Analysis, " European Management Journal, 35(2), 249-260, 2017.
Lee, C., Kim, J., Kwon., O. and Woo, H. "Stochastic Technology Life Cycle Analysis using Multiple Patent Indicators," Technological Forecasting and Social Change, 106, 53-64, 2016.