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Defaults Are Not the Same — By Default

by Jon Jachimowicz, Shannon Duncan, Elke U. Weber and Eric J. Johnson

When introducing defaults into real-world environments, choice architects need to be mindful that defaults can have varied responses.

Defaults 

WHEN YOU RESEARCH DECISION-MAKING for a living, it’s hard not to observe choice architecture everywhere you look — even on vacation. Disney World is the land of magic and fairytales, but even there you cannot escape science. When ordering something to eat, one of us (Jon) noticed that the default choices for kids’ meals were all geared towards healthier options. The menu swapped soda for juice and french fries for fruits and vegetables. Indeed, a recent study shows that this change in Disney World’s policy led to the consumption of 21 per cent less calories, 44 per cent less fat and 43 per cent less sodium. These defaults are helping ‘the happiest place on Earth’ become a healthier one.

Defaults are one of applied behavioural science’s biggest success stories. There are two reasons underlying their widespread adoption. First, defaults can be very simple — even consisting of just the one-word difference between ‘If you want to be an organ donor, please check here’ (opt-in) and ‘If you don’t want to be an organ donor, please check here’ (opt-out). Second, defaults are surprisingly effective in a wide variety of contexts, from retirement planning decisions to health decisions to consumer decisions.

Despite — or perhaps because of — the widespread use and success of defaults, a couple of important questions have remained in the background until now: How are defaults being implemented? And does it matter how they are implemented? Knowing when and why defaults work highlights the importance of actively rather than passively considering and applying choice architecture tools.

There are many ways researchers, policymakers and other practitioners have attempted to use defaults. As alluded to above, defaults can be implemented in a variety of domains, such as in consumer settings (CFL versus incandescent light bulbs) or health domains (organ donations). Defaults can also vary in how easy it is for the user to opt out, ranging from a simple click on a website to requesting several forms under Austria’s organ donation law. 


In decisions where there are two possible options, the option that is pre-selected is chosen 27 per cent more often.Tweet this


The second question revolves around how to see the effectiveness and widespread adoption of defaults in context. Defaults are only one of many tools available in a choice architect’s toolbox. For example, while citizens could be defaulted into health insurance plans, they could also be asked to select their health insurance plan from a smaller, curated set. Similarly, employees could be defaulted into retirement savings plans when joining a company, or alternatively, they could be given a limited time window in which to sign up. Policymakers thus have an array of options to choose from, beyond defaults, when determining how to use choice architecture to attain desired outcomes.

What matters, then, is understanding how effective defaults are as a choice architecture tool, as well as how different kinds of implementations alter a default’s effectiveness. This was the aim of our recent meta-analysis of all prior default studies, which was published in Behavioural Public Policy.

In total, we analyzed 58 default studies with a sample size of 73,675 participants. The studies came from a wide variety of contexts, topics, fields and countries. One thing quickly became apparent: on average, defaults are a strong choice architecture tool, shifting decisions by 0.63 to 0.68 standard deviations. What this means is that in decisions where there are two possible options, the option that is pre-selected is on average chosen 27 per cent more often than the option that is not preselected. That means that the average default study was about two times more effective in changing behaviours as other strong behavioural interventions that shift decisions by 0.2 to 0.3 standard deviations — one of them being, for example, Opower’s social norm intervention on energy savings, another widely popular choice architecture tool. So, on the one hand, defaults work!

         
 

Designing Defaults: The 3E’s

 
 

Factor

 

Question

 
  ENDORSEMENT

  Does the decision-maker believe that choice architect has their best interest in mind?
How much autonomy do decision-makers believe they have?

 
  EASE

  How (physically and psychologically) easy is it for the decision-maker to change away from the default?
How important is the decision for the decision-maker?

 
  ENDOWMENT

  How much prior experience do decision-makers have within the decision domain?
How much do decision-makers believe they are endowed with the default?
 
  Intensity and Distribution
of Preferences
  What are the decision-makers’ preferences in the defaulted decision?
What is the variability in individuals’ preferences?
 
         

On the other hand, there were also substantial differences in the effectiveness of defaults. In some studies, a default was far more effective than in other studies; and in others yet, defaults did not alter participants’ decisions. This is an important caveat, which highlights that choice architects should not blindly apply defaults to all situations, but instead be more careful in when and how they implement them.

We wondered what factors make defaults particularly more likely to be effective. To do so, we drew on a theoretical framework which highlights that defaults operate through three channels:

1. Defaults work because they reflect an implicit endorsement from the choice architect — your company’s HR department, your city’s policy office, your credit card company or your child’s school.

2. Defaults work because staying with the defaulted choice is easier than switching away from it.

3. Defaults work because they endow decision-makers with an option, meaning they’re less likely to want to give it up, now that it’s theirs.

As a result, we hypothesized that default designs that trigger more of these ‘three Es’ (endorsement, ease and endowment) would be more effective. In our analysis, we found partial support for this idea. That is, we found that studies that were designed to trigger endorsement (defaults that are seen as conveying what the choice architect thinks the decision-maker should do) or endowment (defaults that are seen as reflecting the status quo) were more likely to be effective.

In addition, we found that defaults in consumer domains tend to be more effective, and that defaults in pro-environmental domains (such as green energy defaults) tend to be less effective. What this highlights is that the intensity and the distribution of decision-makers’ underlying preferences — what it is that they care about and want — plays an important role in how effective defaults are. When decision-makers care less about a particular choice, a default may be more persuasive in swaying their decision. Likewise, when preferences within a population are more varied — such that some people may have preferences that align with the default, but many people may not — then a default may be less effective.

One domain that people tend to care about deeply — and which tends to be divisive — is their environmental attitudes. As a result, someone who holds more pro-environmental attitudes may be more likely to stick with a default that offsets the carbon emissions arising from their flight, while someone who holds anti-environmentalism attitudes may be more likely to switch away from the default. In addition, environmental attitudes tend to vary broadly throughout the population, as research on the acknowledgement of human-caused climate change, or lack thereof, shows. As a result, both the strong intensity with which people hold environmental attitudes and their broad distribution in the population make it less likely that defaults will be effective.

In contrast, a domain that people tend to care less deeply about — and which tends to be less divisive — is which search engine they use. While there are many search engines available, including lesser-known ones like DuckDuckGo or Qwant, more than 75 per cent of searchers currently go through Google. This metric is accounted for in part because Google is the default search engine on a number of browsers, including the company-owned Chrome, but also Firefox and Safari — a default setting that prompted Google to pay Mozilla and Apple billions of dollars last year. Because people don’t care very deeply about which search engine they use, a default setting is likely to be more effective.

To help understand how to best design defaults, using the three Es and taking into account intensity and distribution of preferences, we put together a checklist of questions that policymakers and other practitioners could ask themselves during the next choice-architecture design meeting (see Figure One). We note that these questions are not exhaustive but highlight specific aspects to pay attention to when designing defaults.

In closing

Our research exploring when and why defaults work highlights the importance of actively, rather than passively, considering and applying choice architecture tools. It also shows the benefits of understanding how they work. Such ideas may help us predict how well a default could operate in a given setting and figure out how to design defaults that work better.

In addition, defaults may not always be the most effective solution. They represent just one of many tools in the choice architect’s toolbox. To better explore when defaults should be used
over other tools, choice architects should also evaluate the effectiveness of defaults versus other possible interventions.  


Jon Jachimowicz is an Assistant Professor of Organizational Behaviour at Harvard Business School. Shannon Duncan is Associate Director for the Center for Decision Sciences at Columbia Business School and is a PhD candidate in Marketing at the Wharton School. Elke U. Weber is the Gerhard R. Andlinger Professor in Energy and the Environment, Professor of Psychology and Public Affairs at Princeton University. Eric J. Johnson is a faculty member at Columbia Business School where he is the inaugural holder of the Norman Eig Chair of Business and Director of the Center for Decision Sciences. This article appeared in The Behavioural Scientist, a non-profit digital magazine. For more: www.behavioralscientist.org.

This article appeared in the Spring 2020 issue. Published by the University of Toronto’s Rotman School of Management, Rotman Management explores themes of interest to leaders, innovators and entrepreneurs.

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