Promotor: Prof.dr. B.R. Katzy
|Links||Thesis in Leiden Repository|
Decision making on innovation is difficult because innovation involves large numbers of and constantly changing interactions between actors and their activities. Decision makers lack information about these complex interactions. This makes it hard for them to predict the relationships between decisions and the outcomes. But now, with the easy availability of large amounts of data via internet, it is possible to get down to the details underlying innovation processes and to investigate patterns among these interactions to provide decision support. Under this background, this research explores the following Problem Statement (PS): To what extent can the new available big amounts of data be used to improve decision making on innovations? In order to answer the PS, Chapter 2 provides a new data-driven modelling method to analyse the innovation process data; Chapter 3 develops a more advanced innovation process model that provides decision makers with a good understanding of the overall structure of innovation processes; Chapter 4 investigates the underlying mechanism of emergence which provides decision makers with valuable insights into the interaction patterns on the micro level of innovation processes; Chapter 5 simulates the emergence to support decision making. This research contributes to data science, innovation management, and their cooperation.