Applied Quantitative Methods in Technology Foresight

A Graduate Guide to Advanced Analytics, Machine Learning Applications, and AI-Enhanced Approaches
Erscheinungsjahr 2026
ISBN: 978-3-032-32135-0
Verlag: Springer

Buch, Englisch, 602 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Springer Texts in Business and Economics

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.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Burmaoglu, Serhat

Fachgebiete

• Wirtschaftswissenschaften Wirtschaftswissenschaften Wirtschaftswissenschaften: Allgemeines
• Wirtschaftswissenschaften Betriebswirtschaft Marktforschung
• Wirtschaftswissenschaften Betriebswirtschaft Management Forschung & Entwicklung (F&E), Innovation
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Chapter 1: Introduction to Quantitative Technology Foresight.- Chapter 2: Fundamentals of Data in Technology Foresight.- Chapter 3: Statistical Foundations for Technology Analysis.- Chapter 4: Bibliometric Analysis of Digital Transformation Research: A Science Mapping Approach.- Chapter 5: Patent Analytics and Technology Mining: Recognition of Hidden Innovation Trajectories.- Chapter 6: Multivariate Analysis for Technology Assessment.- Chapter 7: Machine Learning in Technology Forecasting.- Chapter 8: Deep Learning Applications in Foresight.- Chapter 9: Text Mining and Natural Language Processing.- Chapter 10: AI-Augmented Weak Signal Interpretation for Emerging Technology Foresight.- Chapter 11: Visualization and Communication.- Chapter 12: Data-Led Technology Roadmapping.- Chapter 13: Emerging Technology Detection.- Chapter 14: Strategic Technology Planning.- Chapter 15: Signal in the Noise: AI-Enhanced Evaluation of Quantitative Technology Foresight in an Era of Content Inflation.

Über Autor(innen)

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).