Enablers of Organisational Learning, Knowledge Management, and Innovation: Principles, Process, and Practice of Qualitative Data - Couverture rigide
Synopsis
Chapter 1. Introduction.- Chapter 2. Literature Review.- Chapter 3. Theory and Methodology.- Chapter 4. Methods and Introduction to Findings.- Chapter 5. Findings: Social Innovation, Architectural Innovation, and Cultural Innovation.- Chapter 6. Organizational Learning, Knowledge Management, and Innovation Integrated.- Chapter 7. Conclusion.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Preethi Kesavan has extensive teaching and learning experience, educational leadership, and executive oversight of strengthening a high-quality student experience, enriching student global mobility, and enhancing academic quality and professional staff effectiveness. She has extensive experience in the Singapore Private Education systems and local public universities, online/mobile/blended learning and professional learning initiatives, quality assurance of curriculum, assessment, and people development and organizational policy development. She is a contemporary architect of flexi-learning, blended learning, e-learning, and education management. She has also designed and implemented IT solutions for the hospitality industry and private schools. She enjoys teaching various programming languages, database and enterprise solutions, and a strong interest in teaching and research activities in spreadsheet modelling and analytics.
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Enablers of Organisational Learning, Knowledge Management, and Innovation (eng)
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Enablers of Organisational Learning, Knowledge Management, and Innovation
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Springer Nature Singapore Nov 2020, 2020
ISBN 10 : 9811597928
ISBN 13 : 9789811597923
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 328 pp. Englisch. N° de réf. du vendeur 9789811597923
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Enablers of Organisational Learning, Knowledge Management, and Innovation
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Springer Singapore, 2020
ISBN 10 : 9811597928
ISBN 13 : 9789811597923
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides insights on the application of text mining, text analytics process, and adopting qualitative methods in investigating the factors that influence learning, knowledge management, and innovation in dispersed teams within organizations. N° de réf. du vendeur 405560987
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Enablers of Organisational Learning, Knowledge Management, and Innovation : Principles, Process, and Practice of Qualitative Data
Edité par
Springer Nature Singapore, 2020
ISBN 10 : 9811597928
ISBN 13 : 9789811597923
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Etat : Hervorragend. Zustand: Hervorragend | Seiten: 328 | Sprache: Englisch | Produktart: Bücher | 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. N° de réf. du vendeur 36879789/1
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Enablers Of Organisational Learning Know
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Enablers of Organisational Learning, Knowledge Management, and Innovation
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Springer Nature Singapore, Springer Nature Singapore Nov 2020, 2020
ISBN 10 : 9811597928
ISBN 13 : 9789811597923
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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch. N° de réf. du vendeur 9789811597923
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Enablers of Organisational Learning, Knowledge Management, and Innovation : Principles, Process, and Practice of Qualitative Data
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Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10 : 9811597928
ISBN 13 : 9789811597923
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9789811597923
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