Novelty & Enterpise Innovation Planning
What problem it address:
Novelty is recognized with discoveries, creativity and innovation. Novelty is often considered an issue of luck, but this means that novelty should appear random. However, novelty always happens in hubs: cultural hubs, technological hubs, etc. Novelty is something that is emerges via self-organizing feedback mechanisms, but what is the system that ensure this self-organizing happens in hubs?
How it will help others/apply to other problems:
By defining novelty we try to break down a problem, as discoveries, creativity and innovation involve more than just novelty. With a focus on novelty we focus on learning what is emerging. This way we gain insights in feedback mechanisms of self-organization and are able to formalize it. The theory can be support other activities around discoveries, creativity and innovation. For my PhD I'll focus on support for innovation management, but in previous research Artificial Intelligence was the focus.
What has been done and why it is not good enough:
In Artificial Intelligence holistic complexity has been pruned out. This has helped to create specific solutions and useful applications, but it also made us lose understanding of Artificial General Intelligence . Our investigations show that only by the ambiguity we can understand regulation of creativity (novelty).
The innovation management literature is based on experience within the domain of management, which can only result into incremental growth. To have more grasp on innovation, we need a better understanding on how to manage what is yet unknown. In other words theory is needed to create radical new experiments. Getting more theory for innovation experiments will only be reached when the management makes use of the interdisciplinary knowledge on the topic.
What will be done and why it is better:
While a theory based on natural phenomena has undeniable facts, it is not always easy to setup a flexible experiment. On the other hand, in social science facts are often relative, but experiment have a greater degree of freedom. By creating information system support for innovation management, based on a design from natural studies, we use the best of two worlds.
Empirical investigation:
Experiments in both cognition an management have been done independent from each other.
The first experiment was an agent simulations to investigate a creative act. Later I've beep part of the project "Knowledge sharing over Social Software". I've been using my course as a controlled environment to experiment the last five years. There many are other projects planned that did not get executed or are still in the planning phase. All the experience, also the failed once, will get combined in the PhD