Novelty & Enterpise Innovation Planning
What problem it address:
The research starts with defining novelty as a commonality in both cognitive learning as creativity and in organizational learning as innovation. Novelty is something that is inherently unknown, but nevertheless can be learned. You learn through experience, which makes learning biased. Creativity & innovation however is almost by definition unbiased. The more you learn, through experience, the less inclined you are to be creative. The research works on learning novelty by a discontinuous process.
How it will help others/apply to other problems:
By defining novelty we try to break down a problem, as both creativity as innovation involve more than just novelty. With a focus on novelty we focus on discontinuous learning. It is useful to create cognitive theory and help to understand Artificial Intelligence. The theory is useful for many things, we will apply it to innovation management for the PhD.
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 discontinuous innovation management literature is based on experience within the domain of management. Based on our investigation on novelty it seems a full understanding comes only when learning occurs in two directions. When you can create useful analyzes out of the practice and when you have a useful theory to create constructive change. For discontinuous innovation management we need more theory to create radical new experiments. Getting more theory for 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:
By combining the two research domains (cognition & management) a mutually reinforcement is expected. Some cognitive fact can become tested and for discontinuous innovation management the theory should allow us to create a more formal process for breakthrough innovation management. By using social software, that is software that triggers the social reflexes of the user, the model can be implemented in terms of software systems.
Empirical investigation:
Experiments in both cognition an management have been done independent from each other.
There was the artificial agent simulations to investigate a creative act and the project "Knowledge sharing over Social Software". With the PhD one new project will combine prior experience and construct the Enterprise Innovation Planning (EIP). The EIP-system is the so called "innovation engine". It should overcome the theoretical novelty paradox. Approaching the EIP framework as a socio-technical system in which both social and technical components co-evolve will allow us to support collective discontinuous innovation at a collective level. You may want to check my latest EIP-EIASM outline