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This book establishes constructivist, interpretivist, and linguistic approaches based on conventions about the nature of qualitative and text data, the author’s influence on text interpretation, and the validity checks used to justify text interpretations. Vast quantities of text and qualitative data in organizations often go unexplored. Text analytics outlined in this book allow readers to understand the process of converting unstructured text data into meaningful data for analysis in order to measure employee opinions, feedback, and reviews through sentiment analysis to support fact-based decision making. The methods involve using NVivo and RapidMiner software to perform lexical analysis, categorization, clustering, pattern recognition, tagging, annotation, memo creation, information extraction, association analysis, and visualization. The methodological approach in the book uses innovation theory as a sensitizing concept to lay the foundation for the analysis of research data, suggesting approaches for empirical exploration of organizational learning, knowledge management, and innovation practices amongst geographically dispersed individuals and team members. Based on data obtained from a private educational organization that has offices dispersed across Asia through focus group discussions and interviews on these topics, the author highlights the need for integrating organizational learning, knowledge management, and innovation to improve organizational performance, exploring perspectives on collective relationships and networks, organizational characteristics and structures, and tacit and overt values which influence such innovation initiatives. In the process, the author puts forward a new theory which is built on three themes: relationship and networks, knowledge sharing mechanisms, and the role of social cognitive schema that facilitate emergent learning, knowledge management, and innovation.
This is an overview of how the concept of organisational learning emerged, how it has been used and debated, and where it may be going.
We are living in interesting times characterized by increasing digitalization of business enterprises in a global interconnected knowledge economy. With waning euphoria about the first wave of digital e-business enterprises and a sobering dot-com stock market, business model innovation is being recognized as the key enabler that can unleash value creation for new digital enterprises. In contrast to traditional factors of production, knowledge assets and intellectual capital are expected to play a dominant role in determining both valuation and value-creation capabilities of most new age enterprises. Not surprisingly, Knowledge Management for Business Model Innovation is anticipated to be the mantra for survival, competence and success of Net enterprises as well as traditional brick-and-mortar enterprises faced with the challenge of transforming their business models into and beyond click-and-mortar companies.
Knowledge management (KM) is a set of relatively-new organizational activities that are aimed at improving knowledge, knowledge-related practices, organizational behaviors and decisions and organizational performance. KM focuses on knowledge processes—knowledge creation, acquisition, refinement, storage, transfer, sharing and utilization. These processes support organizational processes involving innovation, individual learning, collective learning and collaborative decision-making. The “intermediate outcomes” of KM are improved organizational behaviors, decisions, products, services, processes and relationships that enable the organization to improve its overall performance. Knowledge Management and Organizational Learning presents some 20 papers organized into five sections covering basic concepts of knowledge management; knowledge management issues; knowledge management applications; measurement and evaluation of knowledge management and organizational learning; and organizational learning.
As the most comprehensive reference work dealing with knowledge management (KM), this work, consisting of 2 volumes, is essential for the library of every KM practitioner, researcher, and educator. Written by an international array of KM luminaries, its approx. 60 chapters approach knowledge management from a wide variety of perspectives ranging from classic foundations to cutting-edge thought, informative to provocative, theoretical to practical, historical to futuristic, human to technological, and operational to strategic. Novices and experts alike will refer to the authoritative and stimulating content again and again for years to come.
Knowledge management has been growing in importance and popularity as a research topic and business initiative. This book documents the key issues of knowledge management and serves as an useful resource for academicians, practitioners, researchers, and students.
This book is about turning data into smart decisions, knowledge into wisdom and business into business intelligence and insight. It explores diverse paradigms, methodologies, models, tools and techniques of the emerging knowledge domain of digitalized business analytics applications. The book covers almost every crucial aspect of applied artificial intelligence in business, smart mobile and digital services in business administration, marketing, accounting, logistics, finance and IT management. This book aids researchers, practitioners and decisions makers to gain enough knowledge and insight on how to effectively leverage data into competitive intelligence.
This book establishes constructivist, interpretivist, and linguistic approaches based on conventions about the nature of qualitative and text data, the author's influence on text interpretation, and the validity checks used to justify text interpretations. Vast quantities of text and qualitative data in organizations often go unexplored. Text analytics outlined in this book allow readers to understand the process of converting unstructured text data into meaningful data for analysis in order to measure employee opinions, feedback, and reviews through sentiment analysis to support fact-based decision making. The methods involve using NVivo and RapidMiner software to perform lexical analysis, categorization, clustering, pattern recognition, tagging, annotation, memo creation, information extraction, association analysis, and visualization. The methodological approach in the book uses innovation theory as a sensitizing concept to lay the foundation for the analysis of research data, suggesting approaches for empirical exploration of organizational learning, knowledge management, and innovation practices amongst geographically dispersed individuals and team members. Based on data obtained from a private educational organization that has offices dispersed across Asia through focus group discussions and interviews on these topics, the author highlights the need for integrating organizational learning, knowledge management, and innovation to improve organizational performance, exploring perspectives on collective relationships and networks, organizational characteristics and structures, and tacit and overt values which influence such innovation initiatives. In the process, the author puts forward a new theory which is built on three themes: relationship and networks, knowledge sharing mechanisms, and the role of social cognitive schema that facilitate emergent learning, knowledge management, and innovation. .
The fully revised and updated version of this successful Handbook is welcomed by management scholars world-wide. By bringing together the latest approaches from the leading experts in organizational learning & knowledge management the volume provides a unique and valuable overview of current thinking about how organizations accumulate 'knowledge' and learn from experience. Key areas of update in the new edition are: Resource based view of the firm Capability management Global management Organizational culture Mergers & acquisitions Strategic management Leadership