From my distant perspective into academia, it seems that theory is something of a holy grail in published papers. Presenting novel information is interesting. Being able to explain why that information looks the way it does is better.
It’s also more difficult, and not exclusive to academics. I saw instances of this all the time as a data scientist and now as a product manager, I still continue to see them.
First things first: What exactly is theory? My mental model is that theory is the why. When done right, it’s an explanation of the processes and systematic reasons for something occurring or not occurring. Another definition:
A theory is a well-substantiated explanation of an aspect of the natural world that can incorporate laws, hypotheses and facts.
You might be thinking this sounds like a close cousin of causality and I think that’s right. In my view, causality is a subset of theory. I’m open to being wrong on that.
Practically, theory comes to my mind in the context of presenting information to others. Whether that’s via a published paper in academia or a slide deck in the business world.
When you present information without theory, you are leaving the story in the hands of the reader or listener. Chances are high that they are going to go with the first story in their mind that fits with the findings that you provided. You are giving them an abstract piece of art and trusting them to decide what it is.
So what’s a better approach? Take things a step further and connect the dots. Don’t just present your findings. Present your hypotheses for why the findings are what they are. You’ve built up a mental model digging through data and putting together these findings, put that mental model on the page for readers.
State your findings. Propose a set of theories and point to the most probable. Simple, but powerful when done right.