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Woo, Hangyun (우한균)

Department
School of Business Administration / Graduate School of Technology and Innovation Management(기술경영전문대학원)
Website
http://hwoo.blogspot.com/
Lab
Business Analytics & Technology Management (데이터 기반 기술경영)
Research Keywords
기술경영, 비즈니스 애널리틱스, 경영정보시스템, 데이터마이닝, Technology Management, Management Information Systems, Data Mining, Decision Making, Business Analytics
Research Interests
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 coevolution of business and machines: how do AI technologies affect humans in their workplace, organizations, and industries? how users and firms adopt AI technologies?
This table browses all dspace content
Issue DateTitleAuthor(s)TypeViewAltmetrics
2020-08A study of battery operational optimization with data-driven clusteringShin, Minsu; Jeon, Cheol-Hwan; Nam, Seungwan, et alARTICLE0 A study of battery operational optimization with data-driven clustering
2019-05Screening early stage ideas in technology development processes: a text mining and k-nearest neighbours approach using patent informationWoo, Han-Gyun; Yeom, Jaesun; Lee, ChangyongARTICLE726 Screening early stage ideas in technology development processes: a text mining and k-nearest neighbours approach using patent information
2017-11The transformation of ownership structure and changes in principal-principal conflicts: evidence from corporate governance reforms in South KoreaGang, KwangWook; Lee, Changyong; Woo, HangyunARTICLE596 The transformation of ownership structure and changes in principal-principal conflicts: evidence from corporate governance reforms in South Korea
2017-05Hawkes process-based technology impact analysisJang, Hyunjin; Woo, Hangyun; Lee, ChangyongARTICLE1065 Hawkes process-based technology impact analysis
2017-04Pro-innovation culture, ambidexterity and new product development performance: Polynomial regression and response surface analysisLee, Kyootai; Woo, Han-Gyun; Joshi , KailashARTICLE743 Pro-innovation culture, ambidexterity and new product development performance: Polynomial regression and response surface analysis
2017-01Patterns of technology life cycles: stochastic analysis based on patent citationsLee, Changyong; Kim, Juram; Noh, Meansun, et alARTICLE1392 Patterns of technology life cycles: stochastic analysis based on patent citations
2016-05Stochastic technology life cycle analysis using multiple patent indicatorsLee, Changyong; Kim, Juram; Kwon, Ohjin, et alARTICLE1003 Stochastic technology life cycle analysis using multiple patent indicators
2012-12The role of absorptive capacity in partnership retentionLee, Kyootai; Woo, Hangyun; Joshi, KailashARTICLE1086 The role of absorptive capacity in partnership retention
2004-09Finding reusable UML sequence diagrams automaticallyRobinson, WN; Woo, HangyunARTICLE881 Finding reusable UML sequence diagrams automatically

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