As part of their series on mobility, Deloitte explored how human behavior can cause delays in the adoption of new technology in the article “Framing the future of mobility: Using behavioral economics to accelerate consumer adoption.”
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Deloitte argues that “the speed with which this future vision arrives likely hinges...on how quickly consumer expectations and behavior shift.” The same research that revealed these change-prohibitive biases shed light on ways to overcome them and encourage consumers to welcome the future of mobility.<2>Implications of the Shift2>
If/when the automotive shift that Deloitte anticipates comes to fruition, it’s not just the auto industry that will be majorly affected, but insurance, financing, technology, and energy industries as well. This isn’t simply a change in how people use transportation, but one affecting government regulations and producing major infrastructure changes.
As ridesharing and self-driving transportation options become more prevalent, consumers of all ages could potentially benefit. The previously “immobile” generations who can not yet drive or are now unable to would no longer find themselves stranded, and families wouldn’t have to worry about transporting them. Other societal benefits could result, like a decrease in traffic congestion and an increase in vehicle efficiency; resulting in reduced emissions and improved air quality.
Most importantly, autonomous vehicles would likely eliminate the element of human error, helping reduce the 30,000 deaths that occur each year during traffic accidents. A safer and more productive commute—an average of 46 minutes per day—could reduce stress and be more affordable; Deloitte analysis shows that the cost of traveling per mile might decrease as much as two-thirds.
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The positive results of an autonomous driving world could likely be abundant, but Deloitte cautions that “just because a new technology offers benefits ‘on paper’ does not mean customers will ultimately embrace it.”<2>Cognitive Biases Towards Losses/Gains2>
Studies in behavioral economics and social psychology have demonstrated that we as humans have a set of biases that affect the choices we make. Figure 1 in the article shows a list of these biases and their impact on how the future of mobility would be adopted—or, more specifically, why these biases could likely hinder the adoption of autonomous vehicles.
A loss aversion bias causes humans to overrate what we would lose compared to what we would gain from something new. This goes along with the endowment effect, where we overvalue things we already possess, and a status quo bias: a reluctance to change because we overvalue the current state.
These three biases together could cause individuals to feel like they are giving up more with their personally owned car than they would gain from a new autonomous driving state. To justify a change, the gain must overwhelm what is being giving up, so these biases make it even harder to achieve when you factor in the emotional attachment to a car. Trading a tangible good for a service also doesn’t feel like a fair trade, so substituting personally owned vehicles for car/ridesharing may take longer than Deloitte’s initial time projections.<2>Cognitive Biases Towards Calculating Risk2>
Three other types of biases related to risk would also predispose humans to resisting the change to a future mobility with autonomous driving. There is a risk miscalculation bias at play, which shows that humans are generally poor at assessing risk and assume the worst when faced with something new or unknown. In the instance of this new technology, there are no known effects as to how driverless vehicles will work, so it is perceived as more risky than it actually is.
The chart in figure 2 shows that the types of risk categorized as new, unknown, uncontrollable, involuntary—all of which would be associated with self-driving cars—are viewed as the most risky. “Regardless of the testing done by regulators or carmakers, the underlying technology of a self-driving car will likely remain mysterious to the average consumer. [T]he very nature of an autonomous vehicle makes it fundamentally uncontrollable (by the passenger, at least), which means customers are likely to see riding in them as particularly risky.”
Likewise, an experience that can be controlled is an “old risk”, or is a known and observable technology that would automatically be viewed as less risky by the human brain. This is reflected in the optimism bias, where drivers overestimate their own ability and underestimate the probability of a bad event happening to them. Most drivers think they are better than the average driver and safer than they really are, which could reduce the likelihood that consumers will adopt self-driving cars due to safety reasons; they surely believe they are safer than trusting an unknown technology.
Another cognitive bias working against a future mobility system is the tendency to overemphasize a familiar or “signature event” that sticks out as the norm even though it may be an outlier. If a specific airline has a crash, people may easily associate that airline with crashing planes even though it may be a statistical anomaly and extremely rare. This tendency, known as the availability heuristic, might make a commuter “focus on the few occasions when he was inconvenienced by ridesharing (by a long wait for his vehicle, for example) or a story of someone being harassed by a driver rather than the majority of instances when shared mobility was fast, convenient, and inexpensive.”<2>How to Overcome Psychological Barriers2>
After stepping into the psyche to see why we are predisposed to thinking in a certain way, Deloitte offers steps leaders can take to facilitate an accelerate adoption of autonomous driving technology. By manipulating the way a choice is presented or framed, we can overcome the aforementioned cognitive barriers.
Negative framing — Using the loss aversion bias, we know losses are seen with more importance than gains. This method would involve making a consumer feel like they are missing out on something instead of gaining something. So a choice framed as costing time/money/lives instead of saving them would be more effective.
Aggregating — When presenting data, expanding timeframes and aggregating costs over the longer period has more impact. Showing the amount of time or money that can be saved in a year seems a lot larger than the minutes or pennies from each day, so by changing the timeframe and forcing the consumer to look at the bigger picture can have a greater influence.
Creating “social proofs” — Deloitte points out that “we often look to the behavior of others for clues as to the correct course of action.” As juvenile as it may sound, the saying “everyone is doing it” really does come into play here. By making it seem like our peers are participating, we are more likely to as well; especially in the case of a product that a consumer doesn’t feel strongly about one way or another.
Using default options — Pre-selected options give the illusion that something is the norm, so by making an option the standard selection, a consumer will be influenced to use it. Making an autonomous vehicle the default option could encourage consumers to use that technology like Uber does with the UberPool feature.
Packaging as an ‘add-on’ — According to Deloitte, “research suggests that any new innovation is more readily accepted by consumers when it is packaged as an add-on to an existing, familiar item, rather than as a change to the central form and function of a product.” By creating a familiar vehicle that has autonomous driving capabilities as an additional feature, it would mitigate the “new-ness” of such a technology and make it seem more acceptable.
How quickly the future of mobility takes hold in our society depends on a large number of factors; chief among them is the way it is marketed. By understanding the cognitive biases behind how consumers will perceive autonomous vehicles, decision-makers can alter their approach to make it more appealing and reduce the fear and hesitance that typically comes along with change.