Applied Quantitative Methods in Technology Foresight
This book provides a comprehensive guide to applying advanced quantitative methods and artificial intelligence in technology foresight, bridging traditional statistical approaches with emerging AI-enabled techniques. It offers graduate students and researchers a structured pathway to understand, implement, and integrate modern analytical tools for analyzing and forecasting technological developments.
The book responds to the growing need for sophisticated methods in an era of rapid technological change. It progresses from fundamental statistical concepts to advanced machine learning applications, ensuring a strong foundation while introducing state-of-the-art techniques.
Key features include coverage of bibliometric analysis, patent analytics, and technology mining; integration of machine learning and deep learning approaches; practical implementation using Python and R; and real-world case studies.
Designed primarily for students in technology management, innovation studies, and business analytics, it also serves as a reference for researchers and practitioners. Basic knowledge of statistics and programming is recommended.
Serhat Burmaoglu is an instructor in the Department of Data Science and Analytics at Izmir Katip Celebi University, Turkey. He was a visiting scholar at Georgia Institute of Technology, USA, and dean of the Faculty of Economics and Administrative Sciences at Kyrgyz-Turkish Manas University, Kyrgyzstan. His research focuses on technology foresight, innovation policy, and machine- and deep-learning predictive analytics. With over 15 years of experience, his work appears in leading major international journals, and he co-edited Covid-19 and Society (Springer, 2022).