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AI, Society and Digital Transformation
Hardback
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- Book Synopsis
- This book presents recent research and practice advances in the field of Service Science. It contains selected papers from the 2025 INFORMS International Conference on Service Science, held in Oxford, UK from July 1-3, 2025. The book explores how AI, data, and digital technologies are driving sustainable transformation across industries and society at both national and international levels. It presents new theoretical frameworks, empirical research, and practical applications in the interdisciplinary fields of AI, digital transformation, and service science. The content includes empirical studies, modeling, and theoretical research that examine how AI and data are advancing sustainability across service systems. Topics covered include AI-driven smart cities, sustainable systems, the digital economy, AI ethics and governance, public health and AI, human-AI collaboration, machine learning and data-driven decision-making, quantum computing in service science, and the digital transformation of traditional industries.
- About The Author
- Xiaolei Xie is an Associate Professor in the Department of Industrial Engineering, Tsinghua University. His research focuses on the intersection of data science, operations research, simulation, and healthcare management. He has published over 30 international academic papers, with some of his work highlighted by academic organizations as outstanding contributions and selected as best papers of the year. He has received the Tsinghua University Outstanding Performance Award and the Annual Teaching Excellence Award. Kejia Hu is an Associate Professor of management science at Saïd Business School, University of Oxford, UK and a Governing Body Fellow at Exeter College. Her research focuses on the integration of artificial intelligence (AI) with human decision-making in operations, aiming to transform real-world practice. By leveraging AI-driven data analytics, she develops innovative solutions to enhance resilience, operational efficiency, and service delivery in complex systems. Her work is widely published in leading academic journals and recognized for advancing the role of AI in practical, data-informed decision-making. Guiping Hu is a Professor and the Head of School of Industrial Engineering and Management at Oklahoma State University, USA. She is also an IISE Fellow and the Donald & Cathey Humphreys Chair. Her research focuses on operations research and data analytics with applications in supply chain design, manufacturing production, renewable energy systems, and sustainable agriculture. Dr. Hu's research has been supported by NSF, USDA, DOE, and DOD with over $11.9M funding. She has published about 100 journal articles and 50 conference proceedings with 4800+ citations. She is an ELATES fellow and an NSF IAspire Leadership Academy Fellow. Weiwei Chen is a Professor at Rutgers University, USA. His research interests lie in operations and finance interface, as well as supply chain operations planning and scheduling. He also works on simulation and randomized global optimization methodologies. He has extensive experience working with businesses and the public sector, especially in energy and healthcare, to improve strategic decisions and operational efficiency using data analytics. Robin Qiu is a tenured Professor of information science at Pennsylvania State University, USA. He teaches a variety of information and computing science courses, such as predictive analytics, management science, business process management, decision support systems, project management, enterprise integration, enterprise service computing, software engineering, Web-based systems, distributed systems, computer architecture/SOA, computer security, Web security, operations research, and system engineering. His research interests include big data, data/business analytics, smart service systems, service science, service operations and management, information systems, cybersecurity, blockchain, and manufacturing and supply chain management.
- Product Details
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- ISBN
- 9783032131157
- Format
- Hardback
- Publisher
- Springer, (19 February 2026)
- Number of Pages
- 316
- Language
- English
- Dimensions
- 235 x 155 mm
- Series:
- See all books in this series
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