Dr Helga Marija Kauzonė’s research paper, “A Multidimensional Quadrant-Based Methodological Framework for System Assessment: The Case of National R&D Systems”, was published in March in the Q1 journal Quality & Quantity (Springer Nature). Today, we speak with the researcher about how this new methodology helps us understand complex systems.
– What is the main idea of your work?
The main idea is very simple – in order to change a system, one must first understand its structure.
Only then is it possible to develop solutions that are not temporary, but fundamentally alter the logic of how the system operates.
– We are used to measuring success by a position in one ranking or another. Why, in your view, is this approach no longer sufficient?
Rankings simplify reality. They combine many different indicators into a single figure and allow systems to be arranged from “best” to “weakest”.
However, this approach comes at a cost – it loses information about how a system actually functions.
A system is not just a number – it is a structure.
When everything is reduced to a single index, we see the level, but not the relationships between elements. Yet it is precisely these relationships that determine how the system works.
– How did you realise that a single aggregate indicator is not enough to understand complex systems?
While working with international data on research and experimental development and analysing datasets, I began to see innovation systems differently.
When indicators are examined separately, each tells a fairly familiar “story”: about levels of funding, scientific quality, the role of business, or inclusiveness. However, when viewed as a whole, much deeper patterns begin to emerge.
– What is the key difference between your proposed approach and the traditional logic of indices?
A traditional index asks: who ranks higher?
My proposed method asks: what type of system is this?
I developed a methodology and an accompanying algorithm that allows for the synthesis of interrelated parameters, which are usually analysed separately, and for the examination of their configuration.
This makes it possible to move from one-dimensional evaluation to structural analysis.
Importantly, this is not merely a theoretical model – the method is algorithmically implementable and can be applied systematically and automatically.
– You have developed a specific method and model to define this approach. What is the “Kauzon Quadrant Model”?
The “Kauzon Quadrant Model” is a methodology that defines a new approach to system analysis, together with an algorithmic method that enables its practical application.
Instead of a single scale, it creates a structural analytical space – a kind of map in which each system acquires a specific position and is assigned to a particular structural regime.
This space makes it possible to see not only how strong a system is, but also how it is constructed.
– How did you apply the methodology?
First, I applied it to the analysis of national innovation systems.
Typically, these are assessed based on investments, patents, or other indicators, which are then combined into a single index.
However, my analysis raises different questions: within what structure does this innovative capacity operate? Why is the system the way it is? What opportunities does this create?
My research showed that a country’s innovative capacity is closely linked to the structure of labour market demand.
In other words, it is not only important how much knowledge a system produces – it is equally important whether the economy can continuously generate demand for the application of that knowledge.
– What are the main dimensions underlying your model?
The model is based on two main dimensions, which form a space divided into four quadrants, allowing different system regimes to be identified.
The first describes the overall capacity of the system – a composite indicator that brings together several structural parameters.
The second reflects the structure of human resources in science – how researchers’ participation is distributed within the system.
By combining these dimensions, each system acquires a specific position in the analytical space.
– What does combining these two dimensions into a single analytical space reveal?
When these two dimensions are combined into a single analytical space, each country takes on a specific position. And this is where one of the most interesting insights emerges: countries are not distributed randomly. They cluster into recurring regions. This suggests that innovation systems exhibit certain structural equilibria.
– Your methodology identifies four regimes of innovation systems. What are they?
This analysis makes it possible to distinguish four recurring types of innovation systems.
The first type is mature, balanced systems, where both a strong technological market and a sufficiently broad institutional base exist.
The second type is high-capacity but selective systems, where the engine of innovation is typically the business sector, but professional opportunities are more concentrated.
The third type is fragmented systems, which lack technological scale, meaning that investments do not generate self-sustaining innovation growth.
The fourth type is more inclusive but lower-density systems, where participation in innovation is broader, but the concentration of technological activities remains limited.
These types show that innovation systems differ not only in terms of intensity, but also in their structural logic.
– What does this change in practice?
One of the most important insights is that the same level of funding does not necessarily imply the same system. Two countries may invest similarly, yet their innovation systems may function very differently.
For example, some countries with similar R&D investment intensity fall into different quadrants. In some, innovation capacity is driven by strong business demand and a concentration of technological activities in industry. In others, the research system is more closely linked to academic institutions and the public sector, while business demand for innovation is weaker. Although the scale of investment may be similar, the structure and operational logic of such systems differ fundamentally.
This makes it possible to see a crucial point: innovation systems differ not only in the amount of resources, but also in how the economy is able to absorb those resources and convert them into sustained technological demand.
– What message does this analysis send to policymakers?
This is where an important message for policymakers lies. If innovation policy focuses solely on increasing funding, it often overlooks structural constraints. Growth in investment may temporarily boost innovation activity, but if the structure of the labour market and industry does not create stable demand for highly skilled knowledge, the system tends to revert to its previous equilibrium over time.
Therefore, my model makes it possible to see what rankings do not show: innovation capacity is not merely a question of funding. It depends on whether the economy generates sufficiently strong and continuously renewing demand for knowledge.
– How does this perspective change the logic of policy decisions?
This perspective allows us to reconsider policy decisions. If a system is understood only through an aggregate index or a single indicator, decisions are usually limited to increasing resources – more funding, more programmes, more initiatives. However, configurational analysis shows that simply increasing resources rarely changes the underlying logic of how a system operates.
Therefore, the first step is to understand the type of system in which we operate: what its structural configuration is and which equilibria sustain it. Only then can we consider what changes might genuinely alter the system’s trajectory.
– Can this methodology be relevant beyond innovation systems?
My methodology can be applied to a variety of complex systems.
It allows us to move from the question “how many resources do we have?” to the more important one – “how is the system organised and what equilibria does it sustain?”.
Only by understanding this structure can we plan changes that are not short-term, but sustainable.
