Springer Texts in Business and Economics- Applied Quantitative Methods Technology Foresight

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; 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.