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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 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.
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