The “logic model” is a tool that is widely used in public and social sector initiatives. Like any tool, there are obvious on-target applications (e.g. hammer for inserting nail) as well as more creative applications (e.g. hammer to open a paint can). In all cases, the user is responsible for picking the right tool for the application. To me, there is relevance for the logic model in the private sector because this tool can expose assumptions (logical or not) and bring rigour to the thinking. Here is a quick primer on logic models, followed by some suggestions on if/how/when to use it for your business.
Theory of Change: this is a set of fundamental assumptions that underpin a line of reasoning. This is often referred to in solving large social issues like homelessness or poverty. Relevance to a private sector context could be, for example, an ad agency president believes that to be successful, her team has to know our clients business better than they do. She believes sees her team as “providers of insight” rather than “meeters of needs.”
Logic Model: a framework that allows you to portray the specific linkages of your reasoning from the resources you expend to the final impact that you will have. The model takes into account the linkages between four fundamental components:
- Inputs – These are resources that we control and choose to deploy toward the end objective. This is usually about money and time. Energy fits in here, too.
- Outputs – This is what we create or produce or get from expending the “input” resources. This could be a report, the provision of a service, creation of some capacity, etc.
- Outcomes – What we get helps us out in some way. This is the specific way in which it helps us out. We are better able to do something or something is improved because of the output created from the inputs.
- Impact – This is the higher order calling of the whole endeavour. What did we set out to address in the first place? This is what we were after all along.
The thing about logic is that it can seem both commonsensical and obvious, while also seeming a bit opaque. To alleviate the latter, here is a quick example: Our agency leader (who believes that “provider of insight” is the way to success) might have the following idea.
Let’s get some of our junior staff to work on developing industry reports that capture both analyst information, as well as “chatter” from social networks. They will create an overview document as a summer project, and monitor/update on an ongoing basis. Our senior account people will refer to these before client meetings, and also share insights gained from the direct client interaction.
Breakdown using Logic Model:
- Inputs – Junior staff hours in creating foundational document and ongoing monitoring (hours); Senior account staff time in inputting client insights (hours)
- Outputs – The actual document, once it is created. The document is actually updated.
- Outcomes – Senior account staff go to meetings with broad industry knowledge that they use to: (1) demonstrate knowledge to clients; (2) share value-adding insights; (3) initiate strategic conversations, etc.
- Impact – Clients will use us more
Note: The understood “we hope” as a qualifier gets louder with each step of the model.
USING THE TOOL
Really thinking through these connections demands a good degree of effort and will: what do we want to “impact”? And how we will actually go about getting there? To illustrate the difficulty, recall the success of the ALS Ice Bucket Challenge. (Remember, this space is the sweet spot of the logic model). This was a huge success in gaining awareness (Mel B. did the challenge on America’s Got Talent!), but you may still ask: “So what? Are those afflicted by ALS better off? If so, how?” You can imagine that asking such questions without being labelled as “doubter,” “hater,” “loser,” etc., would be no mean achievement. This is an inherent challenge of such models. People don’t like to have the gaps in their logic exposed.
To use this tool effectively, leadership has to be comfortable explaining their logic (e.g. “provider of insight” beats “meeter of needs) and the followership has to be comfortable trying it out (if they don’t believe it in the first place).
Building the connections between the elements is an important exercise. You end up asking really good questions, for example:
Input to output questions: What are we getting for all these hours that we have put into research?
Output to outcomes: Is our new report, tool, capacity, etc. actually contributing to something that we are using, noticing, applying, etc.?
Outcomes to impact: Is our idea of the “means to the end” actually playing out? What do we really want here? What are we trying to achieve anyway?
This is the kind of thinking that goes into our “performance playbook” process to help ensure that the measures you are choosing hang together with the logic under which you are operating.