Dynamic Learning Networks: Models and Cases In Action represents an attempt to provide a network perspective of organizational learning to drive dynamic competition through extended firm learning processes. This edited volume contributed by worldwide experts in this field, provide academics and company managers with an extended view of organizational learning networks from real cases and different perspectives.

Dynamic Learning Networks: Models and Cases In Action is based on the workshop, Managing Uncertainty and Competition through Dynamic Learning Networks. It was organized by the E-Business Management Section of Scuola Superiore ISUFI University of Salento (Italy) and held in Ostuni (Italy) 3rd - 5th July 2008.

Dynamic Learning Networks: Models and Cases In Action is designed for a professional audience, composed of researchers and practitioners working in corporate learning. This volume is also suitable for advanced-level students in computer science.

This book proposes a comprehensive analysis of the existing schools of thought on technology-driven entrepreneurship to point out the process-based nature of this phenomenon. It explores whether entrepreneurship can be learned and examines the main processes that help influence entrepreneurial mind-sets. In the current economic landscape, technology-driven entrepreneurship is the driving force behind national economies and entrepreneurial societies. It is the engine of innovation, job creation, productivity and economic growth, bringing benefits both at the level of the individual and of the society and promoting sustainable smart growth and development. This book provides a comprehensive view of "how" entrepreneurs and future entrepreneurs learn and develop their business ventures in a wider environment. Moreover, it discusses issues concerning setting up the suitable entrepreneurial environments, processes, values and policies to encourage and foster individual entrepreneurial aptitudes. It also explores practices for developing technology-driven entrepreneurship in a European context as well as in emerging regions.

This book proposes a comprehensive analysis of the existing schools of thought on technology-driven entrepreneurship to point out the process-based nature of this phenomenon. It explores whether entrepreneurship can be learned and examines the main processes that help influence entrepreneurial mind-sets. In the current economic landscape, technology-driven entrepreneurship is the driving force behind national economies and entrepreneurial societies. It is the engine of innovation, job creation, productivity and economic growth, bringing benefits both at the level of the individual and of the society and promoting sustainable smart growth and development. This book provides a comprehensive view of "how" entrepreneurs and future entrepreneurs learn and develop their business ventures in a wider environment. Moreover, it discusses issues concerning setting up the suitable entrepreneurial environments, processes, values and policies to encourage and foster individual entrepreneurial aptitudes. It also explores practices for developing technology-driven entrepreneurship in a European context as well as in emerging regions.

Dynamic Learning Networks: Models and Cases In Action represents an attempt to provide a network perspective of organizational learning to drive dynamic competition through extended firm learning processes. This edited volume contributed by worldwide experts in this field, provide academics and company managers with an extended view of organizational learning networks from real cases and different perspectives.

Dynamic Learning Networks: Models and Cases In Action is based on the workshop, Managing Uncertainty and Competition through Dynamic Learning Networks. It was organized by the E-Business Management Section of Scuola Superiore ISUFI University of Salento (Italy) and held in Ostuni (Italy) 3rd - 5th July 2008.

Dynamic Learning Networks: Models and Cases In Action is designed for a professional audience, composed of researchers and practitioners working in corporate learning. This volume is also suitable for advanced-level students in computer science.