June 10, 2020
BEAM: Behavioural thinking seems to have been an unstoppable force in its influence of governments and businesses over recent years. How do you characterize the development of the discipline, and why do you think it is attracting such attention?
Behavioural Science concentrates on trying to understand why we do what we do, with a real focus on the often overlooked issue of human biases and heuristics, and the role of the environment in our decision-making; basically all of the mechanisms that make us a little bit less rational than we would like to be but are probably not aware of.
It provides an alternative framework to the models of classical economists who try and interpret consumer behaviour through rational trade-offs or arbitrations, and this alternative approach has been boosted in recent years by Nobel prizewinners such as Daniel Kahneman, and famous academics such as Dan Ariely or Cass Sunstein (co-author of the book "Nudge" with Richard Thaler).
Its adoption by governments was accelerated through various public policy units created by early adopters like President Obama in 2009, and UK Prime Minister David Cameron who opened the first “nudge unit” within Government, formerly called the Behavioural Insight Team. In addition, the increasing availability of “big data” has been a driver, because when you can measure behaviours, you feel more able to manage them, which in turn encourages experimentation.
Perhaps more importantly, the era of austerity that followed the financial crisis increased pressure on fiscal revenues, and made the prospect of being able to enact low cost policy nudges with very big potential impacts even more attractive.
And last but not least, there’s an ever growing body of published proof showing that it works!
BEAM: So with their extensive social and economic challenges and opportunities, cities are an obvious canvas for behavioural design. In your experience, how are local governments using the methodologies, and to what degree of success?
The cities of APAC are now leaning hard into behavioural science. Singapore is one of the most advanced advocates in the region, government agencies having used the approach almost since its inception.
Over recent years, most countries have created units or agencies that are dedicated to the use of the methodologies to improve public policies. The OECD believes there are more than 200 such units worldwide now, and APAC is well represented in that list.
The more countries and cities that use behavioural science, the greater the momentum behind the discipline because public policy units tend to publish their results to a degree that private-sector businesses would be reluctant to. That, in turn, increases the bedrock of shared knowledge and learning that everyone can benefit from, which helps to validate the approach. A good example is an interesting site in the US called “What Works”, where all the cities in the US share their results, and are very open about what's working and what's not.
That kind of openness also encourages iteration. We've been working with some transportation companies, such as RATP (Metro) in Paris, to try and discourage littering within the stations in the tube network, and similar learnings were then applied on high speed trains run by SNCF. We also worked for SouthEastern Railways in the UK, using nudge techniques to mitigate passenger-caused delays. There is a really contagious effect in the application of these learnings.
The approach is particularly well suited to areas of public life where you can quickly measure the actual impact of any “nudges”, because you have a clear “before”and “after”, or a “with” or “without” that let you see very clearly whether your interventions have been efficient. For example, we’ve been working on one of the first “nudge-designed” buildings in a major city, which was specifically constructed to help people adopt eco-friendly behaviours, and in a case like that it is very easy to measure the impact of the design on consumption of energy. It’s more challenging to measure more intangible issues such as community wellbeing, but it’s still possible if the studies are long term.
As long as there is data and things are connected, cities have a great opportunity to improve the impacts of their policies; whether it’s energy conservation and transport, safety, voting, littering, or health. There are many, many areas where local authorities can either encourage the positive behaviours, or discourage or mitigate the ones that are damaging the public good.
In terms of methodologies, there are many to choose from, but one we’ve found most useful with cities is what we call “TRACER”, which is an acronym we have created to summarise a number of different ways to make behaviours “sticky”.
T is for Triggered, because no behaviour starts in a vacuum; there is always either an internal or an external trigger. It can be willpower (but as we all know from personal experience that is the most difficult to muster) or it can be a notification or another external force that makes you feel like there's a routine to start.
It then has to be R for Rewarding, because no behaviour can be adopted in the long-term if it does not give you some sense of satisfaction. That does not just mean rewarding in the long-term, as human beings are not great at deferring gratification, but rather, it has to have some sense of immediate benefit.
Then the behaviour has to, at some stage, be A for Automated; either intuitively, or in a way that can easily be learned and automated, just as we've all learned to use Zoom in recent months and became so familiar that it became a routine. This is where the environment becomes really important in Channelling that behaviour; and that's our C. How do you create the right environment for the behaviour? So in the case of transportation, that role is heavily played by infrastructure that (literally) channels the behaviours of citizens to ensure that at the right time, in the right moment, at the right place, they are encouraged to behave in the way you desire.
Knocking down any barriers in the environment that might prevent the ideal behaviour is equally important, so after T-R-A-C, the E is for Easy - "Make it easy” as Richard Thaler would say – or more importantly, Easier. The relativity is important because it has to be easier than the current way of doing things, or the alternative way of doing it, and we do not always understand or appreciate the variety of choices people have. The use cases for transportation are a case in point at the moment, when the choice between different modes to commute has been disrupted by the sudden addition of a choice not to go to the office at all, and work from home.
And the last one is about the social norms; R for Recognized. Any behaviour that is recognized, i.e. valued by others, and becomes socially accepted. Which is arguably the ultimate reinforcer of behaviour.
What others are doing is a massive influence on our behaviours, but only if we realize it. If we don't know what others are doing or thinking, we are not going to change our behaviour. So there is a lot of work to be done to make a norm salient.
And depending on where you are in the adoption curve, there are different tactics for doing that.
So this model can be used both as a tool for diagnosis of barriers and issues, but also as a stimulus to create the nudges or interventions that can help solve those issues. It also helps align stakeholders around a common understanding of the interdependence of factors, which is particularly important for cities who have so many moving parts and parties. For behaviour change to work there needs to be a complete convergence of message, touchpoints, and channels to help herd people into adopting new behaviours. You have to address these things as a systemic challenge.
BEAM: So when it comes to transportation, how can behavioural design drive better outcomes in terms of quality of life, safety, and sustainability?
Transportation is a fascinating canvas for behavioural thinking, because it looks very functional on the surface, but that hides deep complexity in the trade-offs and choice architecture below. There is a basic assessment of the “job to be done” in choosing a travel mode, but travel decisions are also massively influenced by issues outside of that. For example, if you want to go to a restaurant tonight, you have the choice of travel modes to get there and that might seem straightforward, but if there's this particular restaurant you really love that’s further way, the choice architecture dramatically expands and suddenly subsumes emotions, and estimates of likely satisfaction, appetite, joy, status even.
Inevitably, safety is one of the heuristics that still flows through transport decision making.
One of the most consistent observations in the car sector is the “overconfidence” bias of car drivers, where surveys consistently show that most drivers – upwards of 80 or 90% in some cases – believe they are “better than average” drivers; which by definition is impossible.
And I gather you’ve found similar insights in your own category already which is fascinating. So at least people are consistent in their overconfidence, regardless of the vehicle they’re controlling!
So tackling that bias is really important, and the very existence of a program such as your Beam Safe Academy starts to do that. And then the question becomes how to help riders to help themselves? And that's where I think overcoming some of the biases that people have. If you list all of the barriers about wearing a helmet for example, you would see that some of them are social barriers (do i Iook silly? Less macho? Am I the only one who wears one?) and some of them are situational barriers (there isn’t a helmet available, the one provided looks dirty etc), and of course, operators must do everything they can to tackle these, but there are many that are purely behavioural and that you can affect relatively easily.
BEAM: So as micro mobility develops as a fundamental part of a city's soft infrastructure, how do you think behavioural design can amplify those benefits whilst limiting any externalities?
One thing that has really stuck me about micromobility is the lack of constraints driven by infrastructure or rules and regulations, relative to other forms of transport. This is a form of mobility that is highly individual, very free, pick up-put down, very accessible in terms of price, and with no prerequisite qualification required. And you can pretty much ride in most places. Partly due to its novelty, social norms have yet to evolve, and regulation is relatively light. And that’s not to mention the incredible shifting sands of social norms around road usage right now, which are changing so dramatically due to post COVID pressures.
So because the level of user independence and the margin of liberty is so great, then I think you have an almost unparalleled opportunity to influence those riders through behavioural design. For people like me, it’s a fantastic sandpit.
But the really good news is that all of these individual behaviours are not completely uncoordinated in the way that personally owned e-scooters are. Ridesharing operators such as yourselves are constantly collecting data, observing patterns, and because you talk to your customers every ride through their most precious asset (the smartphone) you are able to create a sense of a community. So I think there's a lot of social norm and behavioural drivers to activate in that area, and initiatives such as your “Ride Kind” campaign for more courteous riding show the direction in which you should continue to move.
In a way, operators are the regulators of their own community, and the better they do that job, the less of a need there will be for more formal rules and regulations from the authorities.
You can even adopt a classic behavioural economics approach of designing incentives and informational systems that make people aware of the risks and give them trust and leeway to handle those risks appropriately, but with the ability to limit some of that liberty at some stage if individuals prove unable to make the right decisions – just as the driving licence points system does for car drivers. But in the tradition of balancing carrots and sticks, that liberty is also something that riders can regain if they are compliant for a long period of time. So you effectively create a living, learning, self-correcting system where data is informing behaviour, and behaviour is informing the central decisions you make as a business.
Lastly, as a category, micromobility fits into the bigger picture of urban transportation by closing gaps in different areas. Cities need those gaps closed and so do users, and micromobility is probably the least capital intensive way that could be invented to do that. It doesn't require a lot of changes in a city itself, and the users are not personally investing into any device. So I think there's a lot of flexibility and huge opportunity to create interventions at low cost that have a huge impact.
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