Functional fixedness bias, and how to overcome it

Functional fixedness bias, and how to overcome it
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When all you have is a hammer, every problem looks like a nail.

One of the primary biases affecting innovation teams is called functional fixedness.

This bias limits a person’s ability to only see an objects only in the way it is traditionally used.

So for example, if a person has always worked with a hammer in order to hammer nails, and then needs a paperweight, some people would not think of using the hammer as the paperweight because “that is not what it is used for“.

In a more innovation specific example, it is the reason why many companies fail to see the ways in which their products and services need to change, or how they could even be used differently (such as the same product with a different business model).

There are other variants of fixedness which also limit innovation thinking, such as structural or relational fixedness.

The term was first coined by Karl Duncker in 1945, after observing that many people could not find solutions to challenges which required objects to be used in unexpected ways. These experiments were then further verified with more participants in 1952.

The classic example proving the bias is called the “candle problem”, where a participant is given a candle a box of thumb tacks, and a box of matches to light the candle, and asked to fix the candle to a nearby cork board so that it can be lit.

Most people fail to find the solution.

The correct solution is not to use the tacks to pin the candle to the wall, but instead to use the box that the matches came in as a shelf, use a tack to fix that to the wall, and simply put the candle in the open box.

But because people don’t see the matchbox as anything other than a box, most fail the test.

However, interestingly, children appear to be immune to the functional fixedness bias at the age of 5, and only start to exhibit functional fixedness around the age of 7.

This could be due to children around the age of 6-7 beginning to learn set thinking patterns and learning “correct answers”.

Overcoming functional fixedness

One potential method to overcome functional fixedness is called the Generic Parts Technique.

As described by Anthony McCaffrey in 2012, the GPT asks a series of two questions to participants when they are being challenges with describing an object:

  1. “Can this be decomposed further?”
  2. “Does this description imply a use?”

If they answer “Yes” to the second question, they should be challenged to further break down the object into further component parts.

This process will result in any object being broken down into fundamental components, which can be drawn out in each step like a tree. The example he gives below is how you would break down a candle:

When participants were given tests for functional fixedness either having learned or not learned the GPT, those who used the GPT solved 67% more of the challenges than a control group (82% compared to 49%).

It appears that thinking about the object as a collection of components made it easier for people to find solutions which would use those components individually, compared to just using the object the “normal” way.

So if your team is ever challenged with innovating with a product, process or service which is well established, maybe use the GPT to break that down, and you will be less limited by the functional fixedness bias.

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