Four archetypes of system transformation

There are many ambitious partnerships and leaders who recognise that to transcend our complex social and environmental challenges – in relation to health, food, or energy systems, for instance – we need to take systemic or transformational approaches to change. Yet, when approaching change, many also assume that transformation generally follows a similar path of a gradual decline of the current pattern and a steady emergence of something new. 

Transformation, however, can unfold in many ways. Understanding how different patterns of transformational change play out provides important insights for those seeking to steward transformation.

Four archetypes of transformation

There are four easily recognisable archetypal patterns of how transformation can play out: 

Smooth Transformation. This pattern occurs when the dominant system declines gradually as a new pattern grows to replace it. An example is Norway's co-ordinated approach to electric vehicle adoption, which included incentives, disincentives and charging infrastructure together. Smooth transformations are, however, very rare. They require governance, regulation and infrastructure explicitly designed for transformational kinds of change, including active management of the old pattern's decline while simultaneously supporting the emergence of the new.

Capture and Extension. This pattern occurs when well-intentioned innovations that have potential to disrupt dominant patterns are instead captured to reinforce a status quo, extending its life rather than disrupting it and allowing more radical innovations and systems to emerge. The continued disruption through innovation and pull-back to the old pattern results in an oscillation without systemic shift. This tends to occur in the public sector where there is not a real mandate for major change. In education, for example, radical innovations that cannot be fitted to existing examination requirements or employer expectations are routinely co-opted to improve the existing system rather than shift it. When this pattern takes hold, it can often be because visionary practitioners who have developed valuable innovations are invited to replicate their work at larger scales and within mainstream institutions – but this is where what gets replicated is only what fits the wider system. The result is a dampening of the effect of the innovation and no real shift in the wider system. Holding onto the original idea, and working more gradually to build it rather than rushing to scale it, is often the more effective path.

Collapse and Renewal. This pattern arises when significant resources are invested in reinforcing a failing system, driven by ideological commitment rather than sound management. The dominant pattern is propped up until collapse becomes unavoidable. Renewal is possible in the aftermath, but is not guaranteed. After the 2007-2008 financial crash, the old pattern reasserted itself: high executive salaries and shareholder primacy returned rather than something fundamentally different. It is, however, possible to identify warning signs of this archetypal pattern before collapse. They include suppression of dissent, loss of curiosity, and denial of inconvenient evidence. If collapse looks likely, preparing for this can be done by building resources, relationships and alternative arrangements ready to fill the space after collapse occurs.

The Investment Bubble. This pattern emerges when a surge of resources – financial, political, attentional – converges on specific innovations that fail to deliver at scale. The dotcom bubble and the recurring enthusiasm for hydrogen energy are both examples. The bubble bursts, investment disperses, and transformation is deferred. Unlike Collapse and Renewal, resources are at least directed at innovation rather than the status quo. The herd dynamic where everyone rushes in to create a bubble, however, rarely produces the sustained change transformation requires. The practical challenge is then to learn to engage on your own terms rather than being swept along. The energy a bubble generates can serve longer-term transformation but only if the focus stays on building something durable, not chasing the moment.

Implications for practice

All of the archetypes may be occurring for different parts of a system during transformation. Understanding these different patterns then highlights that transformation is not a single process to be managed in a single way. Each archetype requires different approaches, and responding well requires being able to read which pattern is operating and acting accordingly.

There are three wider messages that come from these patterns when you are seeking to manage transformations:

1. Diagnose before intervening. Each archetype has a distinct dynamic. Identifying which pattern is operating – and when it shifts – could enhance effective responses.

2. Expect multiple archetypes simultaneously. Different parts of a system can exhibit different patterns at the same time. Effective stewardship means holding and flexibly navigating that complexity rather than reducing the change to a single story.

3. Plan for non-smooth transformation. Absorption, collapse and deflation after hype are common dynamics. Realism about how change unfolds and the complexity involved, as well as retaining a direct focus on transformational rather than other qualities of change, is an important starting point if the stewarding of smooth transitions is to be achieved.

The four archetypes presented here, and examination of their underlying dynamics, show what it means to work seriously with change and enable us to develop new capacities for stewarding transformation to new futures. 

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Prof Ioan Fazey | Department of Environment and Geography, University of York

Want to explore this further? The concepts introduced here are drawn from the open-access paper 'Archetypes of system transition and transformation: Six lessons for stewarding change' by Ioan Fazey and Graham Leicester.

AI use statement: this article was developed from the source report by the authors. AI tools were used to support editing and to suggest structure. The analysis, framing and content are drawn directly from the report and reflect the authors' own judgement throughout.