Abstract
The decisions on public health policies have great impact on our society and citizens. These decisions made by policy makers are typically driven by various types of continuously changing and interlinked determinants, such as economic, social, political, and technological factors. In this dynamic setting, it is not possible for one person, or even for a team, to understand the whole system, and all cause–effect relations. Therefore, there is a need for datadriven decision support tools.
Here, attention is turned to the potential of system dynamics modeling and innovation network orchestration for developing such tools. In the book chapter, it is shown how orchestration of a network and the use of system dynamics modeling (that makes visible the causes and effects of systemic challenges) come together in relation to developing an innovative data-driven decision support system for policy makers.
Here, attention is turned to the potential of system dynamics modeling and innovation network orchestration for developing such tools. In the book chapter, it is shown how orchestration of a network and the use of system dynamics modeling (that makes visible the causes and effects of systemic challenges) come together in relation to developing an innovative data-driven decision support system for policy makers.
| Original language | English |
|---|---|
| Title of host publication | Managing Digital Open Innovation |
| Editors | Pierre-Jean Barlatier, Anne-Laure Mention |
| Publisher | World Scientific Publishing |
| Chapter | 7 |
| Pages | 177-198 |
| ISBN (Electronic) | 978-981-121-924-5 |
| ISBN (Print) | 978-981-121-922-1 |
| DOIs | |
| Publication status | Published - Jun 2020 |
| MoE publication type | A3 Part of a book or another research book |
Publication series
| Series | Open Innovation: Bridging Theory and Practice |
|---|---|
| Volume | 5 |
| ISSN | 2424-8231 |