The evolution of knowledge management theory and the special emphasis on human and social capital sets new challenges for knowledge-driven and technology-enabled innovation. Emerging technologies including big data and analytics have significant implications for sustainability, policy making, and competitiveness. This edited volume promotes scientific research into the potential contributions knowledge management can make to the new era of innovation and social inclusive economic growth. We are grateful to all the contributors of this edition for their intellectual work. The organization of the relevant debate is aligned around three pillars: SECTION A. DATA, KNOWLEDGE, HUMAN AND SOCIAL CAPITAL FOR INNOVATION We elaborate on the new era of knowledge types and the emerging forms of social capital and their impact on technology-driven innovation. Topics include: · Social Networks · Smart Education · Social Capital · Corporate Innovation · Disruptive Innovation · Knowledge integration · Enhanced Decision-Making. SECTION B. KNOWLEDGE MANAGEMENT & BIG DATA ENABLED INNOVATION In this section, knowledge management and big data applications and systems are presented. Selective topic include: · Crowdsourcing Analysis · Natural Language Processing · Data Governance · Knowledge Extraction · Ontology Design Semantic Modeling SECTION C. SUSTAINABLE DEVELOPMENT In the section, the debate on the impact of knowledge management and big data research to sustainability is promoted with integrative discussion of complementary social and technological factors including: · Big Social Networks on Sustainable Economic Development · Business Intelligence

The evolution of knowledge management theory and the special emphasis on human and social capital sets new challenges for knowledge-driven and technology-enabled innovation. Emerging technologies including big data and analytics have significant implications for sustainability, policy making, and competitiveness. This edited volume promotes scientific research into the potential contributions knowledge management can make to the new era of innovation and social inclusive economic growth. We are grateful to all the contributors of this edition for their intellectual work. The organization of the relevant debate is aligned around three pillars: SECTION A. DATA, KNOWLEDGE, HUMAN AND SOCIAL CAPITAL FOR INNOVATION: We elaborate on the new era of knowledge types and the emerging forms of social capital and their impact on technology-driven innovation. Topics include: Social Networks; Smart Education; Social Capital; Corporate Innovation; Disruptive Innovation; Knowledge integration; Enhanced Decision-Making. SECTION B. KNOWLEDGE MANAGEMENT & BIG DATA ENABLED INNOVATION: In this section, knowledge management and big data applications and systems are presented. Selective topic include: Crowdsourcing Analysis; Natural Language Processing; Data Governance; Knowledge Extraction; Ontology Design Semantic Modeling SECTION C. SUSTAINABLE DEVELOPMENT: In the section, the debate on the impact of knowledge management and big data research to sustainability is promoted with integrative discussion of complementary social and technological factors including: Big Social Networks on Sustainable Economic Development; Business Intelligence.

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.

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that.

Although there are numerous publications in the field of knowledge management (KM), there are still gaps in the literature regarding the aspects of KM that reflect new technology adoption and a deeper analysis discussing the interlinked process between KM and data analytics in business process improvement. It is essential for business leaders to understand the role and responsibilities of leaders for the adoption and consolidation of a KM system that is effective and profitable. Understanding, Implementing, and Evaluating Knowledge Management in Business Settings provides a comprehensive approach to KM concepts and practices in corporations and business organizations. Covering topics such as information overload, knowledge sharing adoption, and collective wisdom, this premier reference source is a comprehensive and essential resource for business executives, managers, IT specialists and consultants, libraries, students, entrepreneurs, researchers, and academicians.

Knowledge when properly leveraged and harnessed contributes to effective organizational performance. How much an organization benefits from knowledge would depend on how well knowledge has been managed. There have been challenges to implementing knowledge management in today’s dramatically different world from before. This comprehensive reference work is a timely guide to understanding knowledge management. The book covers key themes of knowledge management which includes the basic framework of knowledge management and helps readers to understand the state of art of knowledge management both from the aspects of theory and practice, from the perspectives of strategy, organization, resources, as well as institution and organizational culture. This reference work reflects the increasingly important role of both philosophy and digital technologies in knowledge management research and practice. This handbook will be an essential resource for knowledge management scholars, researchers and graduate students.

The present publication focusses the attention on new avenues in regional information and knowledge management, while we will zoom in particularly on the potential promises and hurdles of digital technology. This digital challenge has already generated a wealth of implications in the area of smart or intelligent cities, but as yet far less has been achieved in the field of regional planning and regional science. There is clearly a need for a more systematic and wide-ranging assessment and presentation of emerging approaches and concepts in this field, for instance, in regard to principles (e.g. geographic rule modeling), methodologies (e.g. blockchain systems), data analytics (e.g. machine learning) and data governance (e.g. data sovereignty) of regional information and knowledge. Especially in our ‘big data’ era, a systematic, comprehensible and reliable acquisition, storage, sharing and handling of data (e.g. on the basis of systematic decomposition and filtering principles) is more needed than ever before. The present study seeks to present a selection of state-of-the-art contributions on advanced – often digitally-oriented – regional information and knowledge management foundations, principles and practices written by several experts in the field of spatial informatics. These contributions were collected with a view to the design of a comprehensive knowledge and research agenda, which was discussed during a brainstorm workshop in Lyon, France (October 2021). This book covers various fields of interest, such as GeoAI, knowledge modelling, IoT and scalability, space syntax, rule extraction, data governance and data self-sovereignty. It is concluded with a knowledge and research agenda outlining future endeavors in the field of the spatial information sciences (or spatial informatics).