System innovation occurs in complex, dynamic and often unpredictable environments. This means that traditional measurement methods do not capture the changes that actually matter. Classic KPIs are based on linear assumptions where input leads to results in a predictable chain. In system change, reality works differently. Effects often arise as unexpected consequences of interactions between actors, changing norms, new technologies or shifts in rules and markets. Therefore, measurement methods are required that capture changes over time, even when the causal chains are unclear or distributed.
Why classic KPIs are not enough
KPIs are developed for stable and controllable environments. Three common problems arise in system innovation.
- KPIs guide actors towards local optimizations instead of system benefits.
- They do not capture shifts in relationships, behaviours or norms.
- They lead to misleading conclusions because the effects often only appear much later or in completely different parts of the system.
This means that KPIs can give the illusion of control while missing the real changes. When the system changes, it is relationships, collaboration patterns, power structures, capabilities, norms and incentives that shift first. This is rarely visible in classic measurement tools. Therefore, the system innovation leader needs to work with methods that capture patterns and change instead of linear results.
Outcome harvesting in system innovation
Outcome harvesting is a method that reverses the classic logic. Instead of starting with goals and then measuring results, one examines what changes have actually occurred and then analyzes which activities have contributed. This makes the method suitable in systems where changes often arise through interactions and not through planned project plans.
The method captures changes that occur in actors, in structures, in collaborations and in behaviors. It helps the system innovation leader to detect early signals, for example, that actors begin to share data, that a new type of collaboration arises or that a policy document contains completely new concepts. Outcome harvesting focuses on verifiable changes and analyzes contributions rather than attributes, which is crucial in systems where no one actor controls everything.
Contribution analysis in complex systems
Contribution analysis is used to understand how an intervention may have influenced a change even when the causal chains are unclear. The method is based on creating a plausible causal scenario where multiple actors contributed to the system shift. In systems innovation, this is realism. No organization alone causes a new mobility system, a new data market or a new health system.
The method helps the system innovation leader identify which interventions have strengthened the possibilities for change, which have been irrelevant and which can be scaled. By working with hypotheses about how change occurs in the system, you get a dynamic mapping that can be adjusted when new information arrives. This creates an iterative logic that suits complex environments.
System indicators that capture shifts
System indicators do not measure results at the project level but shifts in the dynamics of the system. They can be about changes in collaboration patterns, trust between actors, investments in new technology, changing norms, policy development or increased innovation capacity. System indicators are often qualitative, since behaviors and relationships are difficult to capture numerically.
Examples of system indicators:
- Degree of data sharing between organizations within an ecosystem.
- The frequency of cross-sectoral meetings and which actors participate.
- If policies are beginning to reflect new understandings of problems.
- How quickly experiments and prototypes can be implemented.
- What narrative change is visible in the media, industry dialogues or political discussions.
System indicators measure movement rather than goal achievement. It is often this movement that signals that transformation is possible.
How the system innovation leader follows system shifts
System shifts reveal themselves in early signals. The leader therefore needs a way to continuously listen to the system. This requires analysis of both formal and informal changes. A policy language that begins to use new concepts is one signal. An unexpected alliance between actors is another. A shift in norms around sharing or digitalization is a third.
The leader needs to be able to observe when these patterns arise and when they change direction. Instead of locking the system into fixed indicators, one works with adaptive measurement logics. It is about continuously asking the question what is changing and why, and how this affects the next step in the effort.
Adapting efforts based on system movements
When the system moves, the efforts need to do the same. A new policy can create windows of opportunity that require acceleration. Resistance from a central actor may require relief or a change of strategy. A changed narrative may require reinforcement.
The system innovation leader uses measurement as a navigational instrument rather than a control mechanism. Data is not used to assign blame or success, but to understand direction and opportunities. Measurement thus becomes part of the innovation work itself.
How to work with these methods in practice
The system innovation leader needs to combine outcome harvesting, contribution analysis and system indicators in a coherent process. This usually means starting by defining what counts as systemic movement. Mechanisms are established to continuously capture changes, both formal and informal. How these changes have arisen and what role one’s own efforts have played are analyzed. The strategy is updated based on the insights.
A concrete working method could look like this.
- The system innovation leader formulates a set of system indicators together with stakeholders that capture the direction.
- Channels are established to listen to the system through interviews, observations, data analysis and signal reconnaissance.
- Regular sessions are held where outcome harvesting is used to identify changes that have actually occurred.
- These are analyzed through contribution analysis to understand whether the effort should be scaled up, adjusted or paused.
- The strategy is adjusted, new efforts are tested and what happens is monitored.
This creates a continuous learning process where measurement and innovation are integrated.
Measurement as part of the change capacity
Measuring system innovation is not about proving effect but about understanding the direction of change. With the right method, the system innovation leader can capture movements that would otherwise have gone unnoticed. By understanding patterns instead of points, the leader can guide the system in a direction that creates greater societal benefit.
Example: A measurement approach for circular transformation
The aim is to help a system innovation leader understand how circular shifts arise, how actors move, how behaviors change and how the strategy should be adapted over time. The measurement approach is based on the fact that circular transformation processes are not linear but distributed, dependent on collaboration and highly dependent on behaviors, governance and market dynamics.
Step 1: Define which system to measure
The system innovation leader starts by defining the system boundary that is appropriate. This could be a regional circular material flow, an industry, a geographical area or a cross-sectoral ecosystem linked to resources, waste or consumption.
It is important that the aim is not to capture everything but to find a level where shifts can be observed. The system should be broad enough to provide relevant signals but narrow enough to be manageable.
Step 2: Formulate desired system movements
The circular transition needs to be described in the form of movements instead of goals. For example, desired movements could be more loops, more circular business models, less resource consumption, more collaboration across the value chain or increased demand for circular products.
The system innovation leader defines the movements together with actors but without tying them to fixed KPIs. It is directions, not quantified targets, that drive systems thinking.
Step 3: Create a first set of system indicators
The indicators should measure dynamics, not project results. They should say something about how the system is moving towards a more circular logic.
Examples of system indicators for circular transition
- Degree of data sharing on material flows between companies and public actors.
- Number of new value chain-based collaborations initiated during the year.
- Trust levels in the ecosystem according to recurring qualitative interviews.
- Whether new policy initiatives express circular narratives or stimulate circular business models.
- Increase in circular product or service offerings.
- Frequency of experiments and prototypes in a critical situation.
- Companies’ perception of economic incentives for participating in circular solutions.
- Media and industry narratives around circularity in the region.
Indicators can be both qualitative and quantitative but must be adaptive and re-evaluated regularly.
Step 4: Establish a system for continuous signal detection
The leader builds a listening structure. This can include monthly conversations with stakeholders, collecting material flow data, policy monitoring, trend analysis and observations from design processes.
The point is not to collect as much data as possible but to actively listen for shifts, changing relationships and shifts in behaviour and narrative.
The system innovation leader appoints those responsible for different types of signals, such as policy, market, behaviour, technology and social norms.
Step 5: Use outcome harvesting to identify actual changes
Every quarter or six months, an outcome harvesting session is carried out. The leader collects information from the ecosystem and maps out what changes have occurred. This could be a municipality introducing new requirements, a company launching a circular service, that two competitors start to cooperate or that a trade association has adopted a new standard.
The important thing is that the changes should be verifiable and described in concrete terms. You then work backwards to understand what contributed to the changes, directly or indirectly.
Step 6: Carry out analysis to understand the effects
The next step is to analyse how different interventions may have affected the shifts that outcome harvesting has identified. This means building up a number of hypotheses about how changes occur in this type of system. These are tested against the data collected.
The system innovation manager guides the process through questions such as
- What interventions have likely created the conditions for this change?
- Have several actors interacted in a way that contributed to the shift?
- Is the shift a consequence of external factors?
- What does this say about the next step?
The important thing is not to calculate the exact cause but to understand reasonable contribution scenarios that help the system learn faster.
Step 7: Adjust the strategy based on the insights
Once the system shifts have become visible and the analysis has been carried out, the strategy is adjusted. This may involve accelerating certain collaborations, providing support where there are thresholds, designing new experiments or removing obstacles in policy or norms.
In circular transition, the biggest obstacles are often coordination, uncertainty and incentives. Therefore, the strategy needs to constantly follow the direction the system is actually moving in and not a static plan.
Step 8: Create a visual system dashboard
The system innovation leader documents indicators, outcome harvesting, contribution scenarios and strategy changes in a live system dashboard. It should be simple, visual and create a shared understanding of where the system stands right now and what patterns are driving change.
Examples of visual elements
- A map of collaborations that change over time.
- A timeline of policy changes and new narratives.
- Curves that show increased data sharing or more circular experiments.
- A heat map pattern that shows where the system is moving fastest.
The dashboard is not a reporting board but a learning instrument.
Step 9: For a lively dialogue about what is being measured and why
Measurement only becomes effective when the actors participate in the interpretation. The system innovation leader therefore gathers the ecosystem at regular intervals for joint sensemaking sessions. There, indicators, changes and patterns are interpreted together. This strengthens both trust and direction for the system.
Practical rules of thumb for the leader
- Measurement should reinforce the system’s learning rather than evaluate organizations.
- Indicators should be adaptive and revised regularly.
- Outcome harvesting should be used to capture unexpected effects.
- Contribution analysis should help the system understand why something works.
- Dialogue and visualization should be as important as data collection.
A practical measurement approach for circular transformation needs to be both system-aware and situation-adapted. By combining signal detection, adaptive indicators and iterative analysis, the system innovation leader is given a navigational instrument that makes it possible to follow shifts, learn from them and carefully steer the system towards a more circular future.
