Research

Essence

As in any process of inquiry, research stands at the outset of systems analysis and serves to generate raw data points. In the context of systemic investing, research is about harvesting a wide collection of information to gain a high-level and holistic view of the system of interest.

It should enable you to grasp the most important dynamics and identify the main stakeholders and actor groups that shape the system’s behavior.

This means that you will need to start out broad and iteratively increase the level of depth and detail over time.

Also, the work of systemic investing is fundamentally about solving complex societal problems. This means that the process of data gathering should be grounded on a plurality of perspectives and follow a participatory approach to ensure that different voices and viewpoints are represented in the analysis.

Guidance

1. The Basics

Scan the relevant literature, both published and peer-reviewed scientific literature as well as a variety of grey literature which may include everything from white papers and reports to keynotes and podcasts. Increasingly, AI tools like ChatGPT or Google’s Gemini Deep Research will help you identify and summarize relevant sources.

Deepen your insights—and corroborate those found from your literature review—through interviews with system stakeholders. Curate a diverse group of interviewees, including both subject matter experts from domains that are relevant to the system as well as stakeholders who both shape the system and are impacted by its dynamics. Interviews also serve a secondary purpose: to build trustful relationships with people you may want to attract into your system transformation and investment coalition.

Consider running multi-stakeholder workshops to generate novel and context-specific data and insights that require tapping the collective intelligence of a group, and to validate what your other research activities have surfaced.

When interviewing stakeholders with the intention to unearth information about the dynamics shaping a specific system, focus on identifying enablers and blockers of system transformation. This is best done by using semi-structured interviews with open-ended questions. For example, in our prototype on regenerative agriculture in the American Midwest, we sought to identify the factors that both support and hinder the adoption of conservation agriculture practices. We used open-ended questions (e.g., “What enabling factors support farmers to adopt conservation practices?”) and also tested specific hypotheses about drivers of system behavior (e.g., “Do you believe policy, financial or market incentives would scale adoption of conservation practices?”). You can find a sample questionnaire that can be easily adapted to your specific system/challenge constellation in this Miro board.

2. Analytical Frameworks

As part of your research, you should consider using a range of different frameworks to shape the kind of questions you’re exploring and making sense of the answers you will get. The more angles of inquiry you use, the richer the data you’ll collect. What follows is a list of frameworks from disciplines of systems thinking and systems innovation. Each has been designed with a different purpose, and as such will give you different lenses through which to study the system/challenge constellation you’re addressing.

Multi-Level Perspective: to understand how systemic change unfolds across different levels of society (niches, regimes, and landscapes)
Iceberg Model: to distinguish between visible events, underlying patterns, systemic structures, and mental models, enabling you to differentiate between symptoms and root causes
Donella Meadows’ “Places to Intervene” Framework: to differentiate different categories of drivers of a system’s behavior
Three Horizons: to understand trends in a system as a function of time, that is, as short-term incremental improvements over the status quo (Horizon 1), emerging innovations (Horizon 2), and the desirable future state (Horizon 3)
Horizon Scanning: to identify emerging trends, weak signals, and potential disruptions that could shape future systemic transitions, and to anticipate risks and opportunities before they become mainstream

3. Information Management

Where to put all the information you gather during the research phase? The answer depends as much on best practice as it does on personal preference. Some people like working with lists and spreadsheets, others use online documents and presentations, still others prefer working in online whiteboards such as Miro or Mural.

At the TCI, we work extensively with online whiteboards for collective sensemaking and analysis of research data. The reason we like online whiteboards is that they offer a lot of creative freedom for how to record and make sense of information. You can add sticky notes, draw arrows and lines between them, and arrange them in a logical way. You can also upload files or paste screenshots from reports. Most importantly, you can invite others in your coalition to add or browse the content, and use the whiteboard for co-creative workshops. Below is an example of what a research and sensemaking canvas could look like, taken from the online whiteboard we use in our prototype on Swiss food system transformation.

Example

The work of Regen Melbourne provides a great example for systemically-minded and participatory research designs. In one of their foundational pieces of work, they embarked on a 9-month community research process featuring workshops, interviews, and roundtable discussions to produce a Melbourne Doughnut based on the Doughnut Economics framework. Their approach and methodology is detailed in the report “Towards a Regenerative Melbourne”.

In terms of the specific use of different analytical frameworks, below is an example of how we made use of Three Horizons to understand the innovation landscape in the Swiss mobility system (as part of the research we conducted in the context of our Net-Zero Mobility Switzerland prototype). You can find the original version of this on this online whiteboard.

And here is an example of how we used the Horizon Scanning framework to generate insights for the same project (the original version can be found here).

Tips

Systems research is a bottomless pit—don’t fall victim to analysis paralysis. Also, be aware that systems constantly change, and what you might discover in your research today might be outdated tomorrow. So don’t dwell in research mode for too long; better to think of research as a journey rather than a destination, something you build into your long-term investment implementation strategy and revisit on an iterative basis rather than treating as a one-off activity.
A complex system is, by definition, characterized by uncertainty. There’s only so much you can actually know about it. So take all your research results with a grain of salt.
AI tools like ChatGPT or Gemini Deep Research are great for brainstorming and ideation, while interviews and workshops are great for validation and sensemaking.
Don’t underestimate the power of collective human intelligence. The most surprising and insightful moments often happen through dialogue and co-creation.