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Novelty regulation & Agile-Enterprise Innovation Planning

What problem it address:

The research starts with defining novelty as a common aspect in both individual creativity and organizational innovation. Novelty is something that is unknown nor can it be retrieved from existing knowledge. The regulation and learning of novelty is a paradox. The common belief is that innovation implies luck. With plain luck one would expect random distribution, but what is observed are concentrations or "hubs": cultural hubs, scientific hubs, technological hubs, etc. Concentrations indicate a hidden order. Consequentially a research challenge exists to understand that hidden order.

The novelty research originates as a cognitive study on intelligence by the discipline of software development. In particular the concept of Intelligence Amplification is essential. Ashby (1956) claims that intellectual power, like physical power, can be amplified. In this research I use Intelligence Amplification to pursuit the support and regulation of innovation that surpasses our expectations. The challenge is to build an software framework as foundation of an architecture to make Intelligence Amplification possible.

How it will help others/apply to other problems:

A split between incremental innovation and radical innovation helps to understand innovation that surpasses our expectations. Incremental innovation does not disturb society that much and make gradual transformations. Our current organizational knowledge is capable of regulating incremental innovation. Incremental innovation can be managed by knowledge, but radial innovation requires something else; it requires novelty regulation.

Radical innovations happen when an invisible barrier holds back some development, the pressure builds up until change cannot be stopped and we breakthrough the barrier with chaotic forces. The breaking during radical innovation is often called a revolution, particular with the biggest radical innovations like the agricultural evolution and industrial revolution. Such conceptualization is colorful, but does not help to understand the dynamics of the innovation hubs.

What has been done and why it is not good enough:

The biggest problem with any research related to novelty regulation is the restriction by the discipline. Each discipline shows advantages and disadvantaged. The study of novelty regulation in topics like the origin of life help us to ensure we are dealing with the proper dynamics, but it cannot help to solve real innovation needs. The organizational studies focus on innovation needs and solving real problem but lack good theoretic foundation because of the artificiality of innovative development. Studies like software engineering are more familiar to deal with artificiality, but research on concepts like Artificial Intelligence have been slow and disappointing, in particular the management of software development has changed dramatically in recognition of its complex adaptive nature and its problems of long-term planning. Studies on Science and Technology have examined how innovation happen across long-term planning, but such studies only deal with historical event, they are not in the business of engineering new events. A huge task exist to properly bridge all the disciplines and develop a science of innovation with profound theoretic models based on biological studies, grasping the artificial construction by software studies, create long-term planning with science and technology studies and apply all this to leverage organization studies so they can develop radical innovations.

What I do and why it is better:

By starting form an interdisciplinary angle and the basic studies on complex distributed and self-organizing systems, I create a theory on novelty regulation. The theory is only a small piece to the much more ambitious challenge for a science of innovation. Even a profound research on novelty regulation is already too ambitious for a PhD. In other words most of the disciplines are only touched to create a proof-of-concept for novelty regulation in particular and a science of innovation in general.

Starting form cognitive studies on intelligence to explore the concept of Intelligence Amplification a journey across disciplines is engaged. The whole PhD is split in three parts. The first part is about a theory to frame the novelty regulation to build a foundation. It is then used to be build up an understanding of innovation and how to create what is called an Agile-Enterprise Innovation Planning (ÆIP), which is an organizational design to manage radical innovations. To validate such a design creates a challenge to adapt method and construct experiment that gives us the proof-of-concept. Each part creates a design:

  1. Novelty model: Evolutionary cybernetics investigation of novelty regulation.
  2. ÆIP grid: Several novelty regulations describe the dynamics of radical innovation.
  3. ÆIP architecture: The eventual organizational design that can get engineered.

The validation of ÆIP architecture happens by three methods related to the amount of resources required to validate that part of the architecture. Each time the validation becomes weaker, while reaching more our goal to test the ÆIP architecture. The first validation is by controlled actions, experimenting by action research. The second validation is by coordinated action, participating in a specific innovation hub. The third validation is by hypothetical actions, planning a large scale project using the ÆIP architecture.

First two action research experiment show how knowledge creation is possible by self-organizing and distributed methods using collective intelligence for Intelligence Amplification. The first experiment involves 6 iteration, each taking a year to demonstrate a concept of agile education that can show how research and innovation becomes easier to do for master students by technological and methodical support using project-oriented education. The second experiment is about scalable education showing how project-oriented education can benefit of Intelligence Amplification by collective intelligence.

The research and innovation in the experiments are still very simple in respect to what is going to be needed for radical innovation. To get involved with the real challenges participation research in development ecosystem has been conducted. In particular the development around the project Drupal is examined as it shows a very interesting incubation period between 2005-2010. It is a transition of the historical case studied by organization studies to the actual development happening at the moment. Again the case is too limited to show systemic development of radical innovation, it does show the dynamics of innovation by ecosystems much more clearly, but a final test is requited to build a proof-of-concept.

The final test is conceptual, it is a plan for a next generation university that would have the ÆIP architecture as design of the organization. By developing more detail for such an organization, in combination with the experiments and participation research, a proof-of-concept is reached. If the design can actually be implemented, is a challenges I like to take on for my post-doc activities. It is a challenge that cannot be taken on as isolated as a PhD. It will require many people to get involved.