In this blog series, created for Experimentation Works, we will focus on the first two phases of our (BC BIG)’s RIDE Model for Behaviour Shift. This post, the first in the series, will cover the ‘scoping’ phase. While scoping is certainly nothing new to public servants, there are a few aspects of scoping a behavioural insights (BI) project that make it unique.
By Christine Kormos and Mikayla Ford, BC BIG
Part 1: Scoping
Behavioural insights (BI) helps us to understand how people make decisions in everyday life. These insights can be used to design better services and programs. Although the terms BI, “nudging” and “behavioural science” are often used interchangeably, some distinctions exist among these concepts; for this reason, we will focus on “BI” in these posts.
In this post, we’ll answer a few key questions:
How do we identify a behavioural challenge in the first place (i.e., what makes it different from other policy challenges?).
How do we translate a complex policy challenge into a behavioural one using behavioural mapping to identify a target group/behaviour?
What criteria can we use to select behavioural challenges that are suitable for a BI approach?
How can we use available information to begin building an “objective statement”?
As public servants, we tackle all kinds of tricky challenges. Some challenges are structural, financial, or logistical. However, others are behavioural in nature, meaning that their core issue is related to cognitive biases, tendencies, heuristics, habits, etc. It can sometimes be difficult to establish whether the challenge a client ministry is facing is a behavioural one that can be addressed using BI. It is for this reason that we devote a fair amount of time to exploring this question during the ‘scoping’ phase of our projects.
Identifying a behavioural challenge
It can be difficult to discern whether a challenge is truly a behavioural one. The three questions below are a good place to start. If the answer is “no” to all three, then you just might have yourself a behavioural policy challenge:
Is the challenge solely an awareness or educational issue?
If the issue is solely that people don’t know what the optimal behaviour is (e.g., to turn their thermostat to 19–20°C in order to conserve electricity), an awareness or educational approach in the form of a marketing campaign may be required. Although BI can help to inform these efforts, typically this is not the best use of BI tools and methods. Instead, BI works best when people are already aware of the appropriate behaviour, intend to act accordingly, but fail to do so. Research shows this happens all the time¹, which is perhaps unsurprising. We experience it regularly in our day-to-day lives. For example, we may intend to conserve electricity but don’t make the necessary changes to our thermostat regularly. We may intend to exercise but don’t make it to the gym as often as we’d like. And, we may intend to save money but get distracted by purchases in the moment. Rather than improving awareness or knowledge, BI is best suited to situations where awareness is already high but there is evidence of an intention-action gap. BI is a powerful tool for closing these gaps.
2. Is the challenge solely about changing attitudes or beliefs?
Just like research has failed to demonstrate a reliable link between intentions and behaviour, evidence suggests that beliefs and attitudes are also poor predictors of behaviour. For example, having a belief that water conservation is important may not be sufficient to drive actual behaviour change for many people. Someone may believe in the importance of water conservation but that belief doesn’t necessarily impact the amount of time they spend in the shower or how frequently they water their lawn. It is for this reason that BI focuses on changing behaviours directly, rather than on changing attitudes or beliefs.
3. Does the challenge require a “hard” approach, like a change to regulation/legislation?
While a BI lens can help to inform upstream development of legislation and regulation, BI methods and tools are most often leveraged further downstream where the impact of the policy actually reaches individuals. You can think of BI as being one tool in a toolbelt where other policy levers and methods also exist to support governments. BI can help to strengthen compliance with legislation or regulation but cannot be expected to replace it when consequences loom large. For example, BI methods can be used to encourage seatbelt use but laws governing their use are still needed to ensure the safety of citizens.
Creating a behavioural map of a policy challenge
Often, “real-world questions” — even if we can deduce that they’re behavioural at their core — are still too broad or too complex to work with. In these situations, our first goal is to distil broad policy challenges down into tangible and workable behavioural ones.
For example, let’s say our goal is to improve the health of children. Of course, there are many behaviours that underlie children’s health, such as vaccination, exercise, healthy eating, etc. We can see how a broad challenge can be broken down into myriad smaller behaviours that could each be the focus of a BI project. In cases such as this, it can be helpful to develop a behavioural map.
Generally speaking, a behavioural map has three key elements, plotted at different levels:
The goal: What is the overarching objective?
The actors: Who are the key people or groups whose behaviour we would hope to change to reach this policy goal?
The behaviours: What do we need the actors to do to reach the goal?
If the goal is to improve school children’s health, a behavioural map including parents, children in schools, and schools as the actors, could look like the following:
And, of course, your actors will change depending on the nature of your goal; for instance, a behavioural map based on the overarching objective to reduce antibiotic resistance may involve completely different actors, including policymakers, clinical professionals, and the public.
Identifying a target behaviour using the MIST Framework
In a world of unlimited time, money, and executive support, we could try to change all of the behaviours that populate a behavioural map for a policy challenge. But, more often than not, there is only capacity to address one target behaviour at a time. So how, you may ask, do we choose among the many contenders? Fortunately, there are criteria we can use to help identify those behaviours that lend themselves well (and less well) to a BI project. Sometimes, we may start with a whole host of potential target behaviours and find that — after applying the following set of “MIST” criteria (first put forward by the Ontario Behavioural Insights Unit) — there is really only one potential target behaviour that fits the bill to use in a BI project.
So, what does MIST stand for?
Measurable: Is the behaviour already recorded as administrative data or could it be recorded easily?
Impactful: Why would changing this behaviour be important for individuals and for government? How much room is there to improve? Would even a small change matter?
Sizable: Is there sufficient sample size to ensure that we can set up a rigorous evaluation (i.e. typically 100s or 1000s, ensuring any differences we’re observing aren’t just happening by chance?)
Touchpoint: Is there an existing or low-cost opportunity to deliver an intervention to the focal population?
For instance, if we layer the MIST criteria onto the behavioural map we built in the previous section, we might conclude that encouraging high school students to choose healthy school lunches may be our best target behaviour here. This behaviour is measurable (inventory at the school cafeteria is tracked via sales records), impactful (lunches make up a significant portion of the food students consume in a day), would have a sizable sample (a study could include all of the students attending schools with cafeterias), and could have several available touchpoints (the cafeteria itself or signage within the school).
Ideally, by the end of the Scoping phase we’ve outlined here, you should be able to answer the following four key questions:
What behaviour are we trying to change? (behaviour)
Who is the population of interest? (actors)
How will we reach the population? (touchpoint)
How will we measure the behaviour? (data)
Stay tuned for the next post in this two-part blog series, which will focus on how to identify possible barriers in the way of your goal behaviour — the “R” (or “Research”) phase of BC BIG’s RIDE model.
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Post by Mikayla Ford (Methods Specialist) and Christine Kormos (Senior Behavioural Scientist), both with the BC Behavioural Insights Group (BC BIG). Housed within British Columbia’s Public Service Agency, BC BIG employs a small team of behavioural scientists, innovation methods specialists, and other professionals who, under a consultancy co-design model, collaborate with ministries and academic partners to generate and test simple solutions to policy problems. The team applies a behavioural insights approach to evidence-based policymaking that draws on evidence from the behavioural sciences (psychology, economics, and neuroscience) about how conscious deliberation interacts with nonconscious processes to influence behaviour, and uses that knowledge to design more effective policies, programs and services for citizens and client ministries.
This article has been translated by Experimentation Works for our French-speaking readers:
Article également disponible en français ici: Jeter les bases d’un projet d’intervention comportementale — Partie 1 | by L’expérimentation à l’œuvre| Mai, 2021 | Medium
See the original article, posted on Experimentation Works, here.
References:
¹ Webb, T. L., & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin, 132(2), 249.