Understanding Productivity.

The Linear Assumption.

Every time you have a conversation with people about productivity in general, you realize that most of them have a misconception about what it is.

This is especially so when you are discussing about the relationship between productivity and the causes of productivity.

According to most people this relationship is linear. The more you apply the causal agents of productivity to a project, a task, or work in general, the more productivity you get.

To illustrate: If a big project requires the best software engineers to be completed within the shortest time and for us to get the highest quality of work possible, how many of these engineers should we hire?

Assuming we are not on a tight budget, how many of these do we bring on board?

Producing high quality work within the deadline is what would constitute productivity in this scenario.

The causal variable considered here to make these outcomes possible, are highly trained software engineers. We are also assuming that all other causal variables have been well taken care of.

If you hired too few of these engineers than is required, you would not get the project completed in good time, and chances are that you will not get good quality work.

The Non-Linear Reality.

However, if you hired too many of them, it is also likely that the project will be too chaotic to meet the timeline nor meet the required standards.

This is counterintuitive for most knowledge workers. They assume that the more software engineers you keep adding to the project, the more productive they are.

While this may be counterintuitive to the knowledge worker, it is not to the farmer. The farmer understands that with each addition of a cause of productivity, you are fast approaching the optimum level.

There is only so much sunlight that a plant can take, and the same applies to water and fertilizers. Beyond a certain level of these things and productivity falls.

There is a magic number of these software engineers that you’d need for productivity.

If you were to put what we have said in a graph for visualization, it would look like this: The quality of work, and the ability to meet the deadline would increase with each addition of a software engineer.

However, beyond a certain number of engineers, there would be a decline in the quality of work and in their ability to meet the set deadline.

The Inverted-U-Curve Theory.

The Inverted-U-Curve Theory, also called the Yerkes-Dodson Law, is what we are discussing. It is a graph that looks like an inverted U.

It demonstrates the importance of understanding where the optimum level of a variable is.

Little of something may not be good, like was the case of having too few engineers working on our project. But too much of the same thing, like the example of having too many engineers demonstrates, may be bad too.

Working for many hours a week is good for your performance. This is true until you put in many more hours than is good for your health. Or until your important relationships start crumbling because of your unavailability.

With this, we learn that there is no unmitigated good.

Call to Action.

There is a need for us to question our assumptions about the right amount of variables that cause productivity.

The delicacy of this balance requires that we be aware of the optimum levels of the causes of productivity.

Tunneling into the outcomes that we badly want may lead us astray. It may lead to us lacking context awareness to see what works thereby violating the Inverted-U-Curve Theory.