Insight #5: Learning from M&E for Greater Research Impact

Posted 4 days ago

Does learning from M&E need a different approach?

Research and development (R&D) projects are often assessed through traditional monitoring and evaluation (M&E) lenses: milestones delivered, outputs produced, and progress reported against agreed plans. While necessary, these lenses rarely reflect the real value proposition of R&D.

At its core, R&D is not just about generating knowledge. It is about testing assumptions, about problems, solutions, adoption pathways, and the conditions under which research might eventually lead to impact.

Learning is the mechanism through which assumptions are surfaced, examined, and refined. Without deliberately designed learning, M&E risks becoming an administrative exercise rather than a driver of better research, smarter funding decisions, and stronger impact pathways. Just putting an “L” on the end of M&E so it becomes MEL is not the answer.

This brief insight shares what we’re hearing from the research community and what some standout organisations are doing differently. It outlines where the constraints lie and what funders, grant managers, and research providers can do to remove them. It’s offered in the spirit of collaboration, with the aim of helping everyone who works in and around research to lift the overall measurability and appreciation of R&D impact.

Learning as impact‑focused inquiry

In the context of R&D, when learning is treated as structured inquiry into impact pathways, not just technical performance, these pathways typically include assumptions about:

The problem (why it matters and for whom)
The research itself (what is being tested and why)
Potential solutions (what could realistically emerge from the research)
Adoption and translation (who might carry solutions forward)
Future investment (what types of funders or investors need to be engaged next)

Many of these assumptions sit outside the technical scope of research design, yet they strongly influence whether research can ever translate into real‑world impact. Learning is how these assumptions are explored – often gradually and indirectly – across the life of a project.

We’ve observed that the programs and projects benefitting most from M&E learning usually feature the following impact‑focused inquiry characteristics.

Learning across the R&D lifecycle

1. Early stage: learning through explicit assumptions

At the design and inception stage, learning focuses less on “what will be delivered” and more on what is assumed to be true. This includes assumptions about:

How the research might plausibly contribute to change
What a viable solution could look like (even in broad terms)
Who might take carriage of that solution beyond the research phase

For research teams, learning at this stage:

Clarifies the intent of the research beyond technical outputs
Helps distinguish between what is being proven versus what is being explored

For funders, the value of learning is often indirect:

Building confidence that the research is positioned within a credible impact pathway
Understanding where uncertainty is acceptable (and where it is not)

Practical learning approaches at this stage include:

Explicit articulation of impact pathway assumptions
Early identification of “next investors” or delivery actors, even if engagement is premature
M&E frameworks that prioritise learning questions over indicators

2. Implementation stage: learning through sense‑making, not just monitoring

During implementation, learning shifts from articulation to interrogation. This is where M&E can support research teams to ask:

Which assumptions are holding, and which are weakening?
Are emerging findings suggesting different solution pathways?
What signals are appearing about feasibility, relevance, or adoption?

For research teams, learning during implementation:

Supports methodological adaptation
Helps avoid over‑investment in unpromising directions
Surfaces unexpected opportunities or constraints

For funders, learning at this stage may remain largely passive:

Informing portfolio‑level understanding rather than project‑level intervention
Building intelligence about where future investment might be viable

Practical learning approaches at this stage include:

Short, structured reflection cycles linked to research milestones
Qualitative learning alongside quantitative monitoring
Periodic revisiting of impact pathway assumptions, not just technical progress

Crucially, learning in the implementation stage is not always about changing course immediately; it is often about building understanding that will matter later.

3. Implementation stage: learning through sense‑making, not just monitoring

As projects mature, learning becomes less about adaptation and more about positioning. Key learning questions at this stage include:

What has this research revealed about viable solution pathways?
Under what conditions might impact be pursued realistically?
Who is best placed to take this work forward, and what would they need?

For research teams, learning at this stage helps:

Frame findings in ways that are meaningful beyond academic contexts
Distinguish between technical success and translational readiness

For funders, learning becomes more strategic:

Informing future funding models
Identifying where additional capital or partners are required
Understanding why some pathways stalled while others progressed

Best practice learning at this stage includes:

Clear articulation of what the research enables (and what it does not)
Explicit consideration of next investors, adopters, or delivery partners
Synthesis of learning across projects rather than isolated reporting

Designing M&E to support R&D learning

Across all stages, learning is strongest when M&E systems are designed to:

Make assumptions visible rather than implicit
Accept that learning value may be delayed
Serve multiple audiences with different decision horizons

This requires separating learning from narrow performance judgement and recognising that, in R&D contexts, learning itself is often the primary outcome.

A final reflection

For R&D projects, a more useful M&E question than “Did this work?” is “What do we now understand better about how impact might occur and who might be involved next?”

When learning is treated as a structured exploration of impact pathways, M&E becomes a tool not just for accountability, but for building investable, transferable, and ultimately impactful research.

Have you noticed similar results in your experience? What’s working well in your field or industry that we should be documenting? Let us know what you think the R&D landscape needs for impact to be recognised and prioritised: community.mgr@impactinnovation.com

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