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Collaborative Innovation Networks

Latest Insights from Social Innovation, Education, and Emerging Technologies Research

  • Yang Song
  • Francesca Grippa
  • Peter A. Gloor
  • João Leitão

Table of contents

  1. Front Matter
    Pages i-x
  2. Innovation Methods

  3. Innovation Applications

    1. Front Matter
      Pages 73-73
    2. Katharina Weitz, Florian Johren, Lukas Seifert, Sha Li, Jiexin Zhou, Oliver Posegga et al.
      Pages 75-90
    3. Peter A. Gloor, Andrea Fronzetti Colladon, Joao Marcos de Oliveira, Paola Rovelli, Manuel Galbier, Manfred Vogel
      Pages 91-111
  4. Healthcare Applications

  5. Creativity

    1. Front Matter
      Pages 163-163
    2. Katharina Stolz, Teresa Heyder, Peter A. Gloor, Oliver Posegga
      Pages 165-182
    3. Marius Stein, Peter A. Gloor, Daniel Oster
      Pages 183-200

About this book

Introduction

Collaborative innovation networks are cyberteams of motivated individuals, and are self-organizing emergent social systems with the potential to promote health, happiness and individual growth in real-world work settings.

This book describes how to identify and nurture collaborative innovation networks in order to shape the future working environment and pave the way for health and happiness, and how to develop future technologies to promote economic development, social innovation and entrepreneurship. The expert contributions and case studies presented also offer insights into how large corporations can creatively generate solutions to real-world problems by means of self-organizing mechanisms, while simultaneously promoting the well-being of individual workers. The book also discusses how such networks can benefit startups, offering new self-organizing forms of leadership in which all stakeholders are encouraged to collaborate in the development of new products.


Keywords

Swarm creativity and collaborative innovation Innovation networks and economics of happiness Health management and innovation networks Social network analysis in innovation research Artificial intelligence and deep learning Data mining for innovation

Editors and affiliations

  • Yang Song
    • 1
  • Francesca Grippa
    • 2
  • Peter A. Gloor
    • 3
  • João Leitão
    • 4
  1. 1.Jilin UniversityChangchunChina
  2. 2.College of Professional StudiesNortheastern UniversityBostonUSA
  3. 3.MIT Center for Collective IntelligenceMassachusetts Institute of TechnologyCambridgeUSA
  4. 4.Department of Management and EconomicsUniversity of Beira InteriorCovilhãPortugal

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-17238-1
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Business and Management
  • Print ISBN 978-3-030-17237-4
  • Online ISBN 978-3-030-17238-1
  • Series Print ISSN 2511-2023
  • Series Online ISSN 2511-2031
  • Buy this book on publisher's site