Emerging data technologies are one of several forces that are changing the world. This textbook shows how technologies such as the Internet of Things, Artificial Intelligence and data analytics are altering business operations and strategy. Following a unique, three-part structure, the book offers: • a macro view of the environmental drivers which are changing organisations • a meso view of how organisations and business functions are responding • a micro view of the skills needed to take advantage of the new opportunities that these technologies bring A wide range of examples featuring well-known companies aid understanding, while practical activities help students to develop the skills they need in business. A downloadable teaching guide and PowerPoints are available for those using the book in their teaching. Managing Emerging Data Technologies: Concepts and Use is essential reading for upper-undergraduate and postgraduate students of courses related to new digital data technologies in business, as well as anyone looking to use these technologies in their organisation. Duncan R. Shaw teaches business strategy and data technology strategy at business schools around the world, including Alliance Manchester Business School in Hong Kong, and Nottingham University Business School in the UK and Malaysia.

"This book covers industrial databases and applications and offers generic database modeling techniques"--Provided by publisher.

The development of emerging technologies demands a rapidly expanding knowledge base and intensive collaboration across organizational, institutional and cultural borders. This book is the first of its kind to focus on the management of key emerging tec

This book provides organizational and managerial views on adopting emerging technologies for organizational transformation. The variety of issues and technologies covered in this book includes Radio Frequency Identification Technology (RFID); Enterprise Systems and Inter-Organizational Systems (IOS); Knowledge Management (KM); E-Business and E-Government initiatives. Eleven in-depth case studies documenting experiences and lessons learned in organizations and government agencies from the Asia Pacific region such as China, India, Malaysia, and Singapore are presented.

Big Data is now highly regarded and accepted as a useful tool to help organizations manage their data and information effectively and efficiently. This new volume, The Emerging Technology of Big Data: Its Impact as a Tool for ICT Development, looks at the new technology that has emerged to meet the growing need and demand and studies the impact of Big Data in several areas of today’s society, including social media, business process re-engineering, science, e-learning, higher education, business intelligence, and green computing. In today’s modern society, information system (IS) through Big Data contributes to the success of organizations because it provides a solid foundation for increasing both efficiency and productivity. Many business organizations and educational institutions realize that compliance with Big Data will affect their prospects for success. Everyday, the amount of data collected from digital tools grows tremendously. As the amount of data increases, the use of IS becomes more and more essential. The book looks at how large datasets and analytics have slowly crept into the world of education and discusses methods of teaching and learning and the collection of student-learning data. The final chapter of the book considers the environmental impacts of ICT and emphasizes green ICT awareness as a corporate strategy through information systems. The global ICT industry accounts for approximately 2 percent of global carbon dioxide (CO2) emissions, and the manufacture, shipping, and disposal of ICT equipment also contributes environmentally. This chapter addresses these issues. The information provided here will be valuable information for education professionals, businesses, faculty, scientists and researchers, and others.

This book covers the fundamentals and concepts of product lifecycle management and terminology. It considers the different roles of information processing systems within the company from the biewpoint of product information management. It survey the deployment and completion of implementation projects for PLM systems; case examples concretize the use of systems in compaines making different kinds of products. It envisages PLM concept from a markedly wider perspective thinking in terms of the development of the business and also considers the significance of cooperation or collaboration between companies and the role of PLM in this.

This volume contains the proceedings of the 6th International Conference of the BCS Specialist Group on Information Systems Methodologies. The conference brought together papers on methodology issues related to the development and management of emerging technology based information systems. As usual there was a good range of papers addressing the 'soft' and 'hard' aspects of IS development and management. Methodologies for Developing and Managing Emerging Technology-based Information Systems will be of interest to practitioners who are engaged in systems development and modifying or aligning existing methodologies to practice.

This book tackles the recent research directions in using the newly emerged technologies during the era of COVID-19 pandemic. It mainly focuses on using emerging technologies and their impact on health care, education, and society. It also provides insights into the current challenges and constraints in using technologies during the era of COVID-19 pandemic and exposes new opportunities for future research in the domain.

"Digital Transformation in Management: Emerging Trends and Best Practices" is a comprehensive guide that explores the impact of digital transformation on modern management practices. It delves into the evolving digital landscape and provides insights into the emerging trends and best practices for organizations navigating the digital era. The book covers a wide range of topics, including digital strategy formulation, digital leadership, organizational change management, emerging technologies, and data-driven decision-making. It offers practical guidance and real-world case studies to help managers and leaders adapt to the digital transformation journey and leverage technology to drive innovation, efficiency, and competitive advantage. With a focus on emerging trends and best practices, this book serves as a valuable resource for professionals, executives, and students seeking to understand and navigate the challenges and opportunities presented by digital transformation in management.

A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers—A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization’s Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context–Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence.