Innovation landscape & adaptive cycles – Part 1

Over the course of this blogging journey, I explored several conceptual ideas that have started to frame my own understanding of innovation, what it is and how it works.

The first of those major concepts is the innovation landscape (see initial introduction here, overview of previous posts here) that spans between the demand for innovation (the problems that need to be solved) and the supply of innovation (novel ideas for potential solutions). More recently, I’ve taken a deep dive into adaptive cycles (see introduction here, overview here), which offer a perspective of complex adaptive systems and how they evolve over time. Today I’ll try a cross-walk to find out how those two concepts relate: how do demand for and supply of innovation influence a complex adaptive system on its path through the adaptive cycle? To that end, let’s see their different strengths and purposes first.

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The innovation landscape provides a general map of innovation as it is shaped between a supply-side push and a demand-side pull. This map allows for a simple categorisation of types of innovation, so that it serves as a static orientation aid. But it is neither specific nor dynamic.

The adaptive cycle on the other hand describes the dynamic behaviour of a specific complex adaptive system between the system’s potential for change and its internal connectedness. This is a powerful tool to look into the dynamic ups and downs of human systems, which include, e.g., technologies, business models, corporations, or even societies. Addressing the overall system behaviour within its holistic context, the adaptive cycle can incorporate innovation as one of many elements.

Both concepts have complementary strengths and benefits; they also have several characteristics in common: both are described along two main axes, both fall into four quadrants / four phases. So maybe there’s a direct relation between the two? Well – yes, there is; but it’s not as simple as a straightforward one-on-one match. To investigate this relation in detail, let’s use the four quadrants of the innovation landscape as orientation, addressing them one after the other, beginning with business as usual.

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The business as usual quadrant is shaped by known problems and known ideas for solutions; the novelty in this quadrant comes from finding new matches of ideas and problems to generate innovative solutions. The focus is on efficient implementation of low-risk solutions, often tied to reducing delivery cost for the same product or service; that’s what Clayton Christensen called efficiency innovation.

In the adaptive cycle, such innovation is employed along the front–loop, from the end of the growth phase and throughout the entire conservation phase. During this section of the front–loop, the system’s potential for change (i.e., accumulated resources) continuously increases, at least initially. This increase occurs in perfect harmony with the objectives of efficiency innovation. However, together with the increasing potential, the system’s internal connectedness grows as well. After a while, the increase in potential comes to a halt and is reversed, while the internal dependencies kept growing further to the point that the system becomes susceptible to external shock. This is a strong indication that efficiency innovation alone is not sufficient to sustain a complex adaptive system, be it a business model, a corporation, or a political system.

Landscape and cycles.004

The research quadrant is shaped by known problems and novel ideas for solutions; it’s focused on the genuine search for innovative ideas that didn’t exist before. Such innovation strives to replace existing services and products with new ones that have advanced or even additional functionalities; that’s what Clayton Christensen called sustaining innovation.

In the adaptive cycle, such innovation occurs at the beginning of the front–loop, through the growth phase and over to conservation phase. It creates the initial conditions for the continuous increase of potential the shapes the centre of the front–loop. However, research itself is costly, hence cannot be as cost-efficient as business as usual. No wonder that for a given complex adaptive system the innovation focus shifts from sustaining innovation in the beginning of the front–loop to efficiency innovation towards the end of the front–loop. That transition will occur somewhere along the steady increase of potential in the middle of the front–loop, with exact position depending on the specific system under investigation: for example, the higher the research investments are, the later business as usual can take over.

Taken together, those two quadrants cover half of the innovation landscape, more precisely: the part that is shaped by known problems. The innovation that is driven by such known demand shapes the front–loop of the adaptive cycle, a period of slow, incremental growth and accumulation. This innovation works within a given system and within the established rules. Under these circumstances, where the problem is usually well understood, only the identification and implementation of the most appropriate solution is in doubt. This might seem difficult enough, but in fact it’s still a relatively simple task, if you compare with the disruptive or the wicked quadrant.

In those other quadrants, the situation is fundamentally more challenging: rules are not yet established when the problem to be solved is unknown or not yet understood. And problems from the outside can easily challenge the established rules and even the existence of a system, very much in tune with the back–loop of the adaptive cycle and its objective of invention and renewal.

That’s more than enough food for the upcoming post


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