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The ability to manage knowledge is relevant for millions of small and medium sized enterprises (SMEs) that operate in high-tech environments. They strongly depend on external knowledge about customers, technologies, and competitors because, as opposed to large companies, they have limited internal knowledge resources and little power to control their business environments. Present KM literature, however, mainly focuses on large companies and therefore does not explain, how SMEs, for example, can successfully apply groupware, data mining, semantic networks, and knowledge maps. This book addresses this problem by introducing the concept of knowledge integration (KI) that places emphasis on the identification, acquisition and use of external knowledge. Drawing from this theoretical basis, the book presents concepts and instruments specifically designed for SMEs, as well as examples of their implementation and use in practice.
Technology-based firms continue to compete primarily on innovation, and one continuously required to present new solutions to an exacting market. As technological complexity and specialization intensifies, firms increasingly need to integrate and co-ordinate knowledge by means of project groups, diversified organizations, inter-organizational partnerships, and strategic alliances. Innovation processes have progressively become interdisciplinary, collaborative, inter-organizational, and international, and a firm's ability to synthesize knowledge across disciplines, organizations, and geographical locations has a major influence on its viability and success. This book demonstrates how knowledge integration is crucial in facilitating innovation within modern firms. This book provides original, detailed empirical studies of prerequisites, mechanisms, and outcomes of knowledge integration processes on several organizational levels, from key individuals, projects, and internal organizations, to collaboration between firms. It stresses the need to understand knowledge integration as a multi-level phenomenon, which requires a broad repertoire of organizational and technical means. It further clarifies the need for strong internal capabilities for exploiting external knowledge, reveals how costs of knowledge integration affect outcomes and strategic decisions, and discusses the managerial implications of fostering knowledge integration, providing practical guidance and support for managers of knowledge integration in high technology enterprises.
There are many reasons to be curious about the way people learn, and the past several decades have seen an explosion of research that has important implications for individual learning, schooling, workforce training, and policy. In 2000, How People Learn: Brain, Mind, Experience, and School: Expanded Edition was published and its influence has been wide and deep. The report summarized insights on the nature of learning in school-aged children; described principles for the design of effective learning environments; and provided examples of how that could be implemented in the classroom. Since then, researchers have continued to investigate the nature of learning and have generated new findings related to the neurological processes involved in learning, individual and cultural variability related to learning, and educational technologies. In addition to expanding scientific understanding of the mechanisms of learning and how the brain adapts throughout the lifespan, there have been important discoveries about influences on learning, particularly sociocultural factors and the structure of learning environments. How People Learn II: Learners, Contexts, and Cultures provides a much-needed update incorporating insights gained from this research over the past decade. The book expands on the foundation laid out in the 2000 report and takes an in-depth look at the constellation of influences that affect individual learning. How People Learn II will become an indispensable resource to understand learning throughout the lifespan for educators of students and adults.
Knowledge acquisition has become a major area of artificial intelligence and cognitive science research. The papers in this book show that the area of knowledge acquisition for knowledge-based systems is still a diverse field in which a large number of research topics are being addressed. However, several main themes run through the papers. First, the issues of integrating knowledge from different sources and K.A. tools is a salient topic in many papers. A second major topic in the papers is that of knowledge modelling. Research in knowledge-based systems emphasises the use of generic models of reasoning and its underlying knowledge. An important trend in the area of knowledge modelling aims at the formalisation of knowledge models. Where the field of knowledge acquisition was without tools and techniques years ago, now there is a rapidly growing body of techniques and tools. Apart from the integrated workbenches already mentioned above, several papers in this book present new tools. Although knowledge acquisition and machine learning have been considered as separate subfields of AI, there is a tendency for the two fields to come together. This publication combines machine learning techniques with more conventional knowledge elicitation techniques. A framework is presented in which reasoning, problem solving and learning together form a knowledge intensive system that can acquire knowledge from its own experience.
The focus of this book is on how experts adapt to complexity, synthesize and interpret information in context, and transform or "fuse" disparate items of information into coherent knowledge. The chapters examine these processes across experts (e.g. global leaders, individuals in extreme environments, managers, police officers, pilots, commanders, doctors, inventors), across contexts (e.g. space and space analogs, corporate organizations, command and control, crisis and crowd management, air traffic control, the operating room, product development), and for both individual and team performance. Successful information integration is a key factor in the success of diverse endeavors, including team attempts to climb Mt. Everest, crowd control in the Middle East, and remote drilling operations. This volume is divided into four sections, each with a specific focus on an area of expert performance, resulting in a text that covers a wide range of useful information. These sections present well-researched discussions, such as: the management of complex situations in various fields and decision contexts; technological and training approaches to facilitate knowledge management by individual experts and expert teams; new or neglected perspectives in expert decision making; and the importance of ‘modeling’ expert performance through techniques and frameworks such as Cognitive Task Analysis, computational architectures based on the notion of causal belief mapping such as ‘Convince Me,’ or the data/frame model of sensemaking. The volume provides essential reading for researchers and practitioners of Naturalistic Decision Making and those who study Expertise; Organizational and Cognitive Psychologists; and researchers and students in Business and Engineering.