Working out how users feel about devices and activities
For a recent project we were tasked with understanding how users felt about using different mobile devices when doing different tasks related to job searching. We wanted to go beyond just knowing if people were willing to use a mobile device for a task; we wanted to understand how they felt towards the different tasks and how they compared.
This information could then be used in two ways:
- Discover the tasks that users like doing on a device (e.g. smartphone) and optimise the current designs to take advantage of this.
- Use the knowledge of what and why users don’t like doing a particular task on a device and come up with design solutions that could make it more appealing or useful to change user behaviour.
In order to get the information we wanted we needed two key pieces of data. Firstly, how do they feel in general about each action when using a tablet or smartphone. Secondly, how do these feelings relate and compare to other tasks.
A survey can give an answer to the first question but it is hard to be sure that the second is being collected as people completing a survey often answer each question in isolation. They may say they like using a phone for task X but two questions later when they say they like using a tablet for task Y; is the amount they ‘like’ the tasks the same?
Our solution to this problem was to come up with a system whereby the participant could see their answers to each question clearly and in relation to all the others. And also allow them to mark their answers so that they could easily change their mind without causing confusion (e.g. having lots of crossings out on a page).
We used Lego bricks to allow people to mark their feelings towards each task on a scale of unhappy to do through to happy to do. The bricks’ colour was coded to a different device. This meant that participants could lay out how they felt towards each task and device easily, see how they related, and then change their mind (move a brick) if they upon reflection felt that one device was liked more or less than another.
Here is an example of what a user created:
We could then amalgamate the charts created by each user into a final single chart that showed how users felt towards each action when using different devices.