The ‘pathway to impact’ is the concept of a causal chain from research inputs (e.g. funding, expertise), to research outputs (e.g. publications, patents), to uptake and adoption of those outputs, and finally to the realisation of impacts (benefits). 

Engagement with users and others can take place all along the impact pathway.

Impact is a continuum

One quandary the pathway model may create is whether there is a threshold for impact – a point on the pathway after which the consequences of your research suddenly resolve or solidify into ‘impact’? In other words, when on this pathway does impact ‘begin’? And are some effects too inconsequential to be considered bona fide impact? Will it take years until impact emerges from your research? 

Rather than adopting a ‘threshold’ approach to impact, it’s more useful to think of impact occurring along a continuum that encompasses adoption as well as the resulting downstream benefits.

The ARC defines this pathway as an ‘analysis or plan which identifies causal links by which research achieves or will achieve its impact’. Understanding it as an analysis or plan is helpful:

• as an analysis, you can use the pathway to understand and communicate how past impacts resulted from your research
  
• as a plan, you can use it to identify ways to produce future impacts. 

The canonical model for impact is called a ‘logic model’ because of its stepwise structure. The linearity of this pathway model is simplistic, of course. The route from research to impact is often more complex, and some impacts may even be tangential to the primary intent of the research. For example, research that is co-designed and co-conducted with end-users is likely to deliver impacts during the research process itself, including the upskilling of those end-users.

There are other models of impact that recognise this non-linearity and that also place engagement with users more squarely at the centre of the process.

These examples are from University of Sydney researchers in a range of disciplines and at different points along the impact continuum. All of these examples are of real world impact beyond academia. 

You’ll notice there are many more examples of adoption than there are of downstream benefits that flow from adoption. That’s because benefit is a lot more difficult and slow to measure and prove!