Design Thinking for Innovation

Reflection Exercise – Mind Mapping

Challenge  Every year in New Zealand, there are incidents of deaths and/or serious injuries to road workers  from motorists speeding through roadwork sites. The Health and Safety team of the New Zealand  Transport Agency (NZTA) challenged us (the NZTA Innovation Lab team) to “find ways to reduce the  harm to road workers from speeding motorists”, and provided a budget for eight people to spend a  week to develop interventions that could subsequently be deployed and tested. We assembled a  multidisciplinary and diverse team of people with knowledge of road works, including academics,  consultants, designers, developers, marketers, and engineers with a spread of ages, genders, and  cultural backgrounds. There was already a lot of data available to us, including raw data from crash  reports, a summary report from a road worker safety advisory group, and driver insights work  conducted by a local university, but we decided to collect additional information through empathy  interviews with road workers and drivers, as well as expert insights from groups such as police and  Worksafe (occupational health and safety organisation).

Selection  We needed a way of making sense of this mountain of data to arrive at a clear set of design  principles for our proposed interventions, so we decided on Mind Mapping as the preferred tool. As  described in the video lecture by Brittany Ward at the end of Week 3 of this course, Mind  Mapping promises a way to combine the different perspectives of our team to surface patterns in  large amounts of data and build up a map of the minds of our user groups (in this case, drivers and  road workers) relating to our challenge, leading to insights that can be translated into design  principles – criteria that our interventions would need to fulfil, without already specifying the  potential solutions.

Application  We followed the steps for “holding a yard sale” described in the video lecture fairly closely:

1. We invited shoppers (our multidisciplinary team of 8)

2. We offered tours (going through the different sources of secondary data through readings

and presentations, and conducting site visits and empathy interviews with drivers and road  workers)

Presentation of driver insights research (left) and observations from discussions with members of the police (right)

3. We asked our shoppers to pick out the good stuff (the team took notes throughout this

process, recording observations individually on post-its, with a focus on things that appeared  surprising and/or important to them)

4. We clustered the good stuff in teams (team members took turns to bring up an observation

they thought was particularly important, placing the corresponding post-it on a large sheet  of butchers-paper on the wall. Anyone with related observations could then add these to  form a cluster, before moving on to the next discrete observation.)

5. We identified insights related to each cluster and looked for connections between and

among the clusters (looking at the clusters that had formed, we sought to summarise the  point of each cluster on a separate post-it of a different colour. Distinct groupings quickly  emerged, such as clusters that related to driver behaviour, as opposed to clusters related to  the tools and practices of road workers)

Clustering observations (left) and emerging insights (right)

6. And finally, we translated these insights into design criteria (in our case, we phrased these

design criteria as “How Might We” statements – e.g. the equivalent design criterion for  “How Might We Reduce Driver Frustration” would be “should reduce driver frustration”.  Since we could come up with multiple potential interventions, not all of our interventions  would need to satisfy all design criteria.

Design criteria for our interventions, cast as “how might we” statements

This approach quickly yielded surprising and valuable insights from our data.

Insight  Our most valuable insight from this work was the discovery of a significant, but invisible source of  harm to road workers in New Zealand – that of mental harm from verbal and sometimes physical  abuse by motorists. We experienced this several times first-hand during our empathy interviews and  found it hard to understand why motorists would behave this way - but the mind mapping technique  revealed the underlying drivers that might lead to (though not excusing) the abusive behaviour  towards road workers: motorists were often frustrated, confused, and confident that poor  behaviour would not have any consequences for them. These same driving factors also contributed  to excessive speeds through road-works (our original challenge), and so our design criteria would  address both sources of harm.

This experience demonstrated to us how understanding the underlying drivers of user groups  through a person-centered approach and genuine empathy is one of the most powerful features of  design thinking,

Approach  The approach to mind mapping used in this instance consisted purely of clustering, summarising and  re-casting of observations, without any visual “mapping” of insights that might reveal further  structure and relationships. Doing so might have revealed richer relationships between some of the  concepts we discovered – e.g. the relationships between enforcement, driver confusion, and driver  frustration – but we felt that the relationships were explored sufficiently in discussions, and  subsequently captured in our design criteria.

In other design projects, we have combined this approach to mind-mapping with more visual  techniques, such as systems thinking diagrams, 2x2 frameworks, journey maps, Venn diagrams, etc –  and often, the best structure to use only reveals itself in the course of the mind mapping process  itself.


Design Thinking for Innovation Reflection Exercise – Mind Mapping Challenge Every year in New Zealand , there are incidents of deaths and/or serious injuries to road workers from motorists speeding through roadwork sites . The Health and Safety team of the New Zealand Transport Agency ( NZTA ) challenged us ( the NZTA Innovation Lab team ) to “ find ways to reduce the harm to road workers from speeding motorists ” , and provided a budget for eight people to spend a week to develop interventions that could subsequently be deployed and tested . We assembled a multidisciplinary and diverse team of people with knowledge of road works , including academics , consultants , designers , developers , marketers , and engineers with a spread of ages , genders , and cultural backgrounds . There was already a lot of data available to us , including raw data from crash reports , a summary report from a road worker safety advisory group , and driver insights work conducted by a local university , but we decided to collect additional information through empathy interviews with road workers and drivers , as well as expert insights from groups such as police and Worksafe ( occupational health and safety organisation ) . Selection We needed a way of making sense of this mountain of data to arrive at a clear set of design principles for our proposed interventions , so we decided on Mind Mapping as the preferred tool . As described in the video lecture by Brittany Ward at the end of Week 3 of this course , Mind Mapping promises a way to combine the different perspectives of our team to surface patterns in large amounts of data and build up a map of the minds of our user groups ( in this case , drivers and road workers ) relating to our challenge , leading to insights that can be translated into design principles – criteria that our interventions would need to fulfil , without already specifying the potential solutions . Application We followed the steps for “ holding a yard sale ” described in the video lecture fairly closely : 1 . We invited shoppers ( our multidisciplinary team of 8) 2 . We offered tours ( going through the different sources of secondary data through readings and presentations , and conducting site visits and empathy interviews with drivers and road workers ) Presentation of driver insights research ( left ) and observations from discussions with members of the police ( right ) 3 . We asked our shoppers to pick out the good stuff ( the team took notes throughout this process , recording observations individually on post - its , with a focus on things that appeared surprising and/or important to them ) 4 . We clustered the good stuff in teams ( team members took turns to bring up an observation they thought was particularly important , placing the corresponding post - it on a large sheet of butchers - paper on the wall . Anyone with related observations could then add these to form a cluster , before moving on to the next discrete observation . ) 5 . We identified insights related to each cluster and looked for connections between and among the clusters ( looking at the clusters that had formed , we sought to summarise the point of each cluster on a separate post - it of a different colour . Distinct groupings quickly emerged , such as clusters that related to driver behaviour , as opposed to clusters related to the tools and practices of road workers ) Clustering observations ( left ) and emerging insights ( right ) 6 . And finally , we translated these insights into design criteria ( in our case , we phrased these design criteria as “ How Might We ” statements – e.g. the equivalent design criterion for “ How Might We Reduce Driver Frustration ” would be “ should reduce driver frustration ” . Since we could come up with multiple potential interventions , not all of our interventions would need to satisfy all design criteria . Design criteria for our interventions , cast as “ how might we ” statements This approach quickly yielded surprising and valuable insights from our data . Insight Our most valuable insight from this work was the discovery of a significant , but invisible source of harm to road workers in New Zealand – that of mental harm from verbal and sometimes physical abuse by motorists . We experienced this several times first - hand during our empathy interviews and found it hard to understand why motorists would behave this way - but the mind mapping technique revealed the underlying drivers that might lead to ( though not excusing ) the abusive behaviour towards road workers : motorists were often frustrated , confused , and confident that poor behaviour would not have any consequences for them . These same driving factors also contributed to excessive speeds through road - works ( our original challenge ) , and so our design criteria would address both sources of harm . This experience demonstrated to us how understanding the underlying drivers of user groups through a person - centered approach and genuine empathy is one of the most powerful features of design thinking , Approach The approach to mind mapping used in this instance consisted purely of clustering , summarising and re - casting of observations , without any visual “ mapping ” of insights that might reveal further structure and relationships . Doing so might have revealed richer relationships between some of the concepts we discovered – e.g. the relationships between enforcement , driver confusion , and driver frustration – but we felt that the relationships were explored sufficiently in discussions , and subsequently captured in our design criteria . In other design projects , we have combined this approach to mind - mapping with more visual techniques , such as systems thinking diagrams , 2x2 frameworks , journey maps , Venn diagrams , etc – and often , the best structure to use only reveals itself in the course of the mind mapping process itself .