Earlier this month, I was in the Netherlands visiting some of our key partners. As always, there were some interesting conversations and I gained intriguing insights into new product developments and concepts. Amongst the many topics we explored, there was one particular comment that triggered a fascinating discussion and I have been thinking about its possible significance.
The conversation was about developments relating to the BMA Smart Chair, which I have blogged about. The technology provides data about sitting behaviour, chair use and user habits. Sensors in the chair monitor how the user is sitting and, as well as buzzing to prompt the user to change poor postures, they also record posture data.
The latest development, Smart Cloud, polls user data from each chair at 15 minute intervals giving almost-real-time statistics about how the chair is being used.
A chance comment from one of the project team triggered my lateral thinking – and this blog! He said that they were starting to anticipate sitting behaviour according to the character of the individual. All users are trained to setup and use the chair before recording begins but “people in accounts”, for example, tend to follow the training and sit (and move) well, whilst “sales people” adopt all sorts of postures. For the purposes of this article, I have simplified the comments but let us assume for a moment that character types really do point us to sitting behaviour. We can then see where that hypothesis might take us.
For nearly ten years, we have worked with The Colour Works to develop our recruitment, teamwork and customer relationships by understanding how different individuals think, behave and interact. As a result, I have a lot of experience, albeit on an unqualified level, of some of Carl Jung’s psychology concepts and behavioural dynamics. I know, for example, that different character types respond differently to particular types of communication.
Not one of our customers!
Following this thinking, we could use a psychometric assessment to identify the character types of chair users (and my guess is that this would not need to be too in-depth or sophisticated). With the data created, we could then tailor the posture training to suit not only their learning style and attitude but also their likely sitting behaviour. It is still just a hunch at the moment, but I believe this could be really significant.
I am now planning some practical case-study research into how this thinking could be optimised and used. In the meantime, I would love to hear from anyone who knows of any previous work in this area or who agrees (or disagrees!) strongly with my thinking so far.