Deloitte Insights and our research centers deliver proprietary research designed to help organizations turn their aspirations into action.

DELOITTE INSIGHTS

  • Home
  • Spotlight
    • Weekly Global Economic Outlook
    • Top 10 Reading Guide
    • Future of Sports
    • Technology Management
    • Growth & Competitive Advantage
  • Topics
    • Economics
    • Environmental, Social, & Governance
    • Operations
    • Strategy
    • Technology
    • Workforce
    • Industries
  • More
    • About
    • Deloitte Insights Magazine
    • Press Room Podcasts

DELOITTE RESEARCH CENTERS

  • Cross-Industry
    • Home
    • Workforce Trends
    • Enterprise Growth & Innovation
    • Technology & Transformation
    • Environmental & Social Issues
  • Economics
    • Home
    • Consumer Spending
    • Housing
    • Business Investment
    • Globalization & International Trade
    • Fiscal & Monetary Policy
    • Sustainability, Equity & Climate
    • Labor Markets
    • Prices & Inflation
  • Consumer
    • Home
    • Automotive
    • Consumer Products
    • Food
    • Retail, Wholesale & Distribution
    • Hospitality
    • Airlines & Transportation
  • Energy & Industrials
    • Home
    • Aerospace & Defense
    • Chemicals & Specialty Materials
    • Engineering & Construction
    • Mining & Metals
    • Oil & Gas
    • Power & Utilities
    • Renewable Energy
  • Financial Services
    • Home
    • Banking & Capital Markets
    • Commercial Real Estate
    • Insurance
    • Investment Management
    • Cross Financial Services
  • Government & Public Services
    • Home
    • Defense, Security & Justice
    • Government Health
    • State & Local Government
    • Whole of Government
    • Transportation & Infrastructure
    • Human Services
    • Higher Education
  • Life Sciences & Health Care
    • Home
    • Hospitals, Health Systems & Providers​
    • Pharmaceutical Manufacturers​
    • Health Plans & Payers​
    • Medtech & Health Tech Organizations
  • Tech, Media & Telecom
    • Home
    • Technology
    • Media & Entertainment
    • Telecommunications
    • Semiconductor
    • Sports
Deloitte.com
Deloitte Insights logo
  • SPOTLIGHT
    • Weekly Global Economic Outlook
    • Top 10 Reading Guide
    • Future of Sports
    • Technology Management
    • Growth & Competitive Advantage
  • TOPICS
    • Economics
    • Environmental, Social, & Governance
    • Operations
    • Strategy
    • Technology
    • Workforce
    • Industries
  • MORE
    • About
    • Deloitte Insights Magazine
    • Press Room Podcasts
    • Research Centers
  • Welcome!

    For personalized content and settings, go to your My Deloitte Dashboard

    Latest Insights

    Creating opportunity at the intersection of climate disruption and regulatory change

    Article
     • 
    7-min read

    Better questions about generative AI

    Article
     • 
    2-min read

    Recommendations

    Tech Trends 2025

    Article

    TMT Predictions 2025

    Article

    About Deloitte Insights

    About Deloitte Insights

    Deloitte Insights Magazine, issue 33

    Magazine

    Topics for you

    • Business Strategy & Growth
    • Leadership
    • Operations
    • Marketing & Sales
    • Diversity, Equity, & Inclusion
    • Emerging Technologies
    • Economy

    Watch & Listen

    Dbriefs

    Stay informed on the issues impacting your business with Deloitte's live webcast series. Gain valuable insights and practical knowledge from our specialists while earning CPE credits.

    Deloitte Insights Podcasts

    Join host Tanya Ott as she interviews influential voices discussing the business trends and challenges that matter most to your business today. 

    Subscribe

    Deloitte Insights Newsletters

    Looking to stay on top of the latest news and trends? With MyDeloitte you'll never miss out on the information you need to lead. Simply link your email or social profile and select the newsletters and alerts that matter most to you.

Welcome back

To join via SSO please click on the key button below
Still not a member? Join My Deloitte

Framing the future of mobility

by Derek Pankratz, Philipp Willigmann, Sarah Kovar, Jordan Sanders
  • Save for later
  • Download
  • Share
    • Share on Facebook
    • Share on Twitter
    • Share on Linkedin
    • Share by email
23 January 2017

Framing the future of mobility Using behavioral economics to accelerate consumer adoption

23 January 2017
  • Derek Pankratz United States
  • Philipp Willigmann United States
  • Sarah Kovar United States
  • Jordan Sanders United States
  • Jordan Sanders United States
  • Save for later
  • Download
  • Share
    • Share on Facebook
    • Share on Twitter
    • Share on Linkedin
    • Share by email
  • The user adoption hurdle
  • The promise of the future of mobility
  • Pumping the brakes
  • Culture and the car
  • The perils of personalized mobility
  • Stepping on the gas

When it comes to shared mobility and autonomous vehicles, winning consumers over could be as challenging as developing the technology. How can organizations at the leading edge of the changing mobility ecosystem encourage adoption of these new modes of transport?

The user adoption hurdle

Explore

Visit the Future of Mobility collection

View the Behavioral Economics and Management collection

Watch a video of Ben's journey

Subscribe to receive updates on Future of Mobility

Read Deloitte Review, issue 20

The extended automotive industry is in the early stages of a potentially transformative evolution, one in which today’s personally owned, driver-driven vehicles will likely travel alongside shared and self-driving cars. Incumbent players and new entrants are working hard to make that shift a reality, investing billions in developing autonomous driving systems and shared mobility platforms.1 Governments at all levels are mulling over how to regulate a highly transformed transportation landscape, while also encouraging innovation. For many advocates of the future of mobility, the expected societal benefits of these changes are self-evident: less congestion, lower emissions, greater efficiency, lower costs, and—most compelling—saved lives.2

But even if we accept the advantages, the speed with which this future vision arrives likely hinges not only on technological and regulatory advances, but also on how quickly consumer expectations and behavior shift. How we get from point A to B is ultimately an individual, rather than a collective, choice that is influenced by a multitude of factors, from the obvious (cost, convenience) to the obscure (perceived prestige, peer pressure). Just because a new technology offers benefits “on paper” does not mean customers will ultimately embrace it. This is especially true with something as deeply ingrained in our individual and collective consciousness as the automobile. For many, owning and driving a car is a rite of passage and a symbol of freedom and prestige—reinforced by decades of advertising—and, in the United States at least, consumers may rebel against this perceived erosion of the American dream.

In this article, we explore how limitations in human cognition can lead us to delay or forego adopting a new technology (in this case, shared and autonomous vehicles), even if that technology provides demonstrable individual and collective benefits. While research in behavioral economics and social psychology has revealed deep and consistent biases that can lead to suboptimal choices, it has also uncovered ways to potentially overcome these mental limitations. By constructing choices and framing new mobility options in ways that encourage adoption, companies, governments, nonprofits, and others can help ensure that the future of mobility arrives sooner rather than later.

Just because a new technology offers benefits “on paper” does not mean customers will ultimately embrace it. This is especially true with something as deeply ingrained in our individual and collective consciousness as the automobile.

The promise of the future of mobility

A series of converging trends, both technological and social, seem poised to dramatically reshape the ways that people and goods move about. In particular, the confluence of shared, on-demand transport via ridesharing and carsharing and the development of fully autonomous vehicles could transform the nature of mobility. The implications of this shift are potentially profound, affecting not only the automotive industry but also insurers, lenders, technology companies, telecom providers, energy suppliers, and governments at all levels.3

There could be a myriad of profound societal benefits as well. Traffic congestion could ease as autonomous vehicles safely follow one another, inches apart, and fluidly navigate intersections.4 Electric powertrains coupled with lighter, more efficient cars promise to improve air quality and reduce energy consumption.5 Autonomous vehicles could eliminate many of the roughly 90 percent of vehicle accidents caused by human error, which, in the United States, contributes to approximately 30,000 traffic deaths each year.6 And shared self-driving cars could bring mobility to swaths of the population that are, today, effectively stranded, such as many elderly people.7

At the individual level, the future of mobility could mean many fewer white-knuckle commutes, which researchers have consistently found to be a “particularly unpleasant” part of our days based on measures of subjective happiness.8 It means putting the time currently spent in transit (46 minutes per day, on average)9 to better use. It means getting the vehicle you need when you need it, not having to choose between a pickup that has an empty bed most of the time or forcing a chest of drawers into a hatchback during your annual trip to the flea market. It means more affordable mobility; the cost per mile of traveling might decline by as much as two-thirds, based on Deloitte’s analysis, as shared autonomous vehicles free drivers’ time and reduce insurance and financing costs.10 And it means no longer worrying about putting your teenage son or aging mother behind the wheel.

But even if the benefits of a world of shared self-driving cars seem self-evident, companies should not assume that consumers will reach a similar conclusion. In fact, a series of cognitive biases suggest that many people may be reluctant to relinquish their personally owned and driver-driven vehicles.

Pumping the brakes: The cognitive biases that could slow the future of mobility

For decades, psychologists and economists have documented the ways in which human decision making departs from classic assumptions of rational, cost-benefit calculation.11 In countless studies, in the lab and in real-life situations, we have been shown to exhibit a reliable set of biases that shape the choices we make—including choices about how we move from point A to point B.12 Here, we explore just a handful of salient biases that could lead customers to balk at adopting the future of mobility’s technological and service innovations (see figure 1 for a summary).

Summary of select biases and their effects

Gains and losses: Loss aversion, endowment effects, and the status quo bias. The fear of losses typically looms larger than the anticipation of perceived gains, causing us to overweight what we might give up relative to the potential improvements created by some new choice.13 This creates a gap between our “willingness to pay” and “willingness to accept” and can, for example, lead sellers to demand higher prices when faced with a nominal loss. For example, during a down market when real estate prices fell below what many had paid for their homes, home sellers in Boston asked 35 percent more than the expected market value; that gap shrank to just 12 percent as the market recovered.14 In short, to induce a switch, the expected gains from new goods or services must overwhelm the (overvalued) anticipated losses from relinquishing what we already have. Perhaps unsurprisingly, having an emotional attachment to the goods in question exacerbates loss aversion (see sidebar, “Culture and the car”).15

In related findings, researchers demonstrated that we have a strong preference for what we already own—even if it came to us entirely by chance.16 In a canonical experiment, students were “endowed” with either a coffee mug or a chocolate bar, and then given the opportunity to trade for the item they were not assigned. In the end, 89 percent of those given a mug kept it, while just 10 percent of those with chocolate bars opted to trade for a mug; in a control group endowed with neither item, 56 percent chose the mug.17

Finally, because we tend to overvalue current benefits and undervalue potential gains, we also strongly favor the option already in place relative to alternatives. When asked to select from an array of choices, study participants more frequently opt for the one framed as the current state;18 outside the lab, this phenomenon has been linked to the reluctance of employees to adopt new information technology systems,19 individuals’ tolerance of electrical service outages,20 patients’ preferences for existing cancer screening options,21 and more.

Taken together, these cognitive biases can be a powerful force for inertia. While many consumers are likely to take advantage of multiple types of transportation, the most ambitious realization of the future of mobility envisions at least some foregoing personally owned, driver-driven cars in favor of on-demand autonomous vehicles. That means surrendering all of the real or perceived advantages that we derive from the car sitting in the garage—our car—in favor of some lesser-known alternative. And while all new products and services are likely to face the headwinds posed by loss aversion, endowment effects, and status quo bias, the future of mobility’s shared autonomous vehicles could prove particularly susceptible. Unlike many consumer choices, moving from owned to shared mobility is not a simple like-for-like trade of one tangible product for another, comparable one. Instead, we are replacing a durable product with an intangible service, which implies that the substitution of carsharing and ridesharing for car ownership may be more muted or take longer to materialize than early studies have suggested.22

Because we tend to overvalue current benefits and undervalue potential gains, we also strongly favor the option already in place relative to alternatives.

Culture and the car

For many, cars and other modes of transport are not only conveyances. They represent something more fundamental about individual and collective identities. We purchase hybrids not only for fuel economy, but to signal to others that we are environmentally conscious. We buy a luxury sedan not just for the leather interior, but as a sign that years of hard work have paid off. Because vehicles are often so much more than just a collection of steel and rubber, how the car is embedded in the broader society could prove to be just as powerful a hindrance to the future of the mobility as any individual cognitive bias.

The hurdle may be highest in the United States. The automobile is deeply rooted in American culture: It is a symbol of individual freedom, personal expression, and aspiration.23 With over 250 million cars in operation (more than one per licensed driver), 88 percent of households own at least one car, nearly the highest among any country polled (Italy leads the list at 89 percent).24 Underscoring the car’s resilience in the American psyche, as the United States underwent an economic recession between 2006 and 2009, the proportion of Americans who viewed cars as necessities declined only slightly (from 91 percent to 86 percent), while responses for items such as air conditioning (70 percent to 55 percent) and clothes dryers (83 percent to 59 percent) dipped precipitously.25 The car’s symbolism has been reinforced and extended through decades of marketing that has helped cement the automobile in American culture. Three of the ten largest advertisers in the United States by spend in 2015 were automakers.26 In the United States, the means (the personal automobile) and the end (personal mobility) have been deeply intertwined.

But the car’s place in the collective consciousness varies widely across the globe. For example, household vehicle ownership in sub-Saharan Africa ranges from 31 percent in South Africa to just 3 percent in Uganda.27 In India, where only 6 percent of households own a vehicle, consumers prefer cheaper vehicles and are less willing to pay for upgrades or customization—an indicator not only of economic conditions but also social perceptions that a vehicle is a form of transportation, nothing more.28 Of course, ownership rates are only one indicator of the car’s role in a particular culture. Taxes and other policies have made owning a vehicle extremely expensive in Singapore, contributing to just 15 percent of the country owning a vehicle. However, making cars unaffordable has actually made them more desirable to consumers and has elevated the car as a status symbol. As a result, even though Singapore is greatly expanding its subway system, public transportation has struggled to be seen as an attractive alternative to a car.29

While difficult to quantify, these cultural differences could have profound implications for the adoption of new types of mobility. All else equal, we might expect quicker uptake in countries with less established car cultures. Of course, all else is rarely equal, and the places where the car is mostly deeply entrenched are also likely to be those with the resources to move most quickly toward the future of mobility. Thankfully for those advocating for shared and autonomous vehicles, cultural attitudes are far from immutable, and early signs suggest the car’s hold on the collective imagination—at least in the United States—may be easing. Fewer teens are obtaining driver’s licenses,30 and even car enthusiasts recognize that, “instead of customizing their ride, [young adults] customize their phones with covers and apps . . . You express yourself through your phone, whereas lately, cars have become more like appliances, with 100,000-mile warranties.”31

The dreaded unknown: Evaluating risk. We are generally quite poor at accurately assessing risk, at least as it has been traditionally defined by economics. Rather than gauging the potential losses some incident might cause, and discounting them by the likelihood of that event actually occurring (forming an expected value), most non-experts’ perceptions of risk are impacted by other characteristics. Researchers have mapped these characteristics onto two dimensions of a possible event: the degree to which the risk is unknown, and the degree to which it instills dread.32 New categories of events whose impacts are delayed, unobserved, or unknown to those affected create a heightened sense of risk; new and poorly understood technologies often rate highly on this dimension.33 Even more influential to the perceived riskiness of an event is the degree to which it is perceived as beyond control, lethal, indiscriminate, involuntary, irreducible, and likely to impact future generations.

Figure 2 identifies a number of traits contributing to perceived riskiness, and it highlights those that seem likely to be particularly applicable to self-driving cars.34 When first introduced, the risk posed by autonomous vehicles may be relatively unknown to the buying public, and 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. As important, the 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. And for many, the consequences of a mishap—bluntly, a crash—are likely to be perceived as quite serious, even fatal, exacerbating the sense of “dread” associated with stepping into a self-driving vehicle.

Characteristics affecting perceived risk

Even as consumers may perceive self-driving cars as much riskier than they objectively are, they are also likely to downplay the risks inherent in their own driving. In studies about driving, respondents persistently demonstrated optimism bias, the tendency to overestimate their own abilities or underestimate the probability that something bad could happen to them. In fact, most drivers routinely believe they are safer and at lower risk of being involved in an accident than the average driver, meaning they are also less likely to be compelled to adopt an autonomous vehicle for safety reasons, regardless of the statistics.35

These examples underscore the fact that the perception of risk is often socially and psychologically constructed.36 Despite the statistics on the safety and reliability of shared and autonomous mobility, the bar for consumers’ acceptance could be higher than many advocates appreciate, at least until they gain greater exposure and experience over time.

The perception of risk is often socially and psychologically constructed.

The availability heuristic. Exacerbating these risk-based biases is our tendency to overemphasize the likelihood of certain events. When handicapping the chances of a difficult-to-estimate or complicated outcome, a person may employ a mental shortcut (or heuristic) to simplify the problem. One such shortcut, the availability heuristic, comes into play whenever someone “estimates frequency or probability by the ease with which instances or associations could be brought to mind.”37 That means that particularly salient and easy-to-recall examples can have an outsized impact on how we judge the likelihood of future events.38 For example, in a classic demonstration, researchers exposed participants to short fictional accounts of a single death from a specific cause. Compared to a control group, those who had read these stories provided higher estimates of the number of annual fatalities the hazard caused, and also reported being significantly more worried about their personal risk.39 After witnessing or reading about a car accident, we think getting in an accident ourselves is more likely. After watching a movie about nuclear war, the chances of a real nuclear war seem greater.40 Events that are easier to visualize—like a car crash—also tend to more readily come to mind when we estimate risk.41

For consumers contemplating transportation options, the availability heuristic could lead them to balk at new mobility choices since the most familiar and salient examples are likely to be those reflecting negative experiences. For instance, when choosing between using ridesharing to get to work or driving a personal vehicle, a commuter might 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. He may then overestimate the odds of being put out today, a prompt to go with the “safe choice” of driving his own car. More dramatically in the case of autonomous cars, consumers could focus on the rare instances of cyberattacks or system failures leading to a crash, underemphasizing the much greater odds of being involved in an accident in a human-controlled car. Media coverage of such events, especially in the early stages of autonomous vehicle rollout, seems likely to only exacerbate this bias.42

The perils of personalized mobility

Today’s automotive industry has come a long way since Henry Ford famously said, “Any customer can have a car painted any colour [sic] that he wants so long as it is black.”43 Consumers now enjoy an impressive array of choices, with a plethora of models, powertrains, colors, and interior and exterior options to select from. And as vehicles increasingly become on-demand technology platforms, the opportunities for customization will likely only increase. Customers may be able to “order” the exact type of vehicle they need for a specific trip, loaded with a tailored selection of entertainment and informational options.

While it may seem obvious that being offered greater flexibility and personalization would be valuable to consumers, it’s been shown that offering too many choices can backfire. Psychologists have gathered evidence that suggests that increasing levels of choice can contribute to anxiety, confusion, and an inability to choose.44 For example, researchers presented shoppers entering a grocery store with an assortment of jams and provided a coupon toward purchase. Some were shown 24 varieties, others just 6. Nearly one in three who were shown the smaller number ended up purchasing one of the jams, while just 3 percent of those who saw the larger display did so.45 In other experiments, participants reported being less satisfied with their ultimate choices when confronted with a large number of options.46

While additional research has softened some of these findings and added important mitigating factors,47 mobility players should still be wary of an approach that assumes more is always better.48

The choice of personal mobility is a complex one, and this discussion only begins the exploration of the myriad factors at play; indeed, we have not yet exhausted the potential set of cognitive biases that could factor in. And it is certainly possible that some psychological factors could work in favor of adopting shared autonomous mobility. For example, many people are likely to have directly experienced a car accident or know someone who has, prompting an availability heuristic that leads them to overestimate the risk of conventional human-driven vehicles. Despite those mitigating factors, as a new and poorly understood technology that challenges the status quo, it seems likely that a future of ridesharing, carsharing, and self-driving cars could face significant (some might say irrational) resistance from consumers. While unlikely to stop the powerful converging forces propelling the future of mobility, such psychological factors could slow the pace of adoption. Fortunately, consumer attitudes remain inchoate,49 providing future of mobility evangelists a window to shape perceptions and spur adoption. The next section explores how companies, governments, and others might craft messages to overcome these cognitive biases.

Stepping on the gas: Overcoming psychological barriers to the future of mobility

The significant investments being made by companies and the public sector in the future of mobility could be undermined without a careful and thorough consideration of how consumers might perceive and adopt these new technologies and services. This goes beyond marketing, surveys, and focus groups, and requires crafting a strategy informed by the deep-seated patterns of human cognition. Here we have highlighted a handful of the lessons from behavioral economics that can be used to “nudge” consumers and help overcome cognitive barriers to adoption.50 Depending on an actor’s aspirations and location within the mobility ecosystem, much more will likely be needed to maximize the chances of success. Deloitte’s taxonomy and managerial framework offer one way to think through the relevant biases and possible responses (see figure 3).51

Summary of popular behavioral economics concepts

Most of the interventions highlighted here fall under the category of manipulating someone’s choice architecture; that is, the way a choice is presented or framed.52 It asks, “Can we influence behavior by how we organize choice?,” and captures the variety of concepts that demonstrate our ability to influence decisions with the layout of alternatives.53 Here we offer a few steps that leaders can take to enhance the probability of accelerated adoption.

  • Recast losses as foregone gains and gains as foregone losses.54 Because losses are typically overvalued relative to gains, advocates might stress what a consumer would miss by, for example, not choosing an autonomous vehicle, such as free time during their commute. Similarly, negative framing can also be effective.55 So instead of promoting that “Buying a driverless car saves lives,” advocates might consider a variation of “Not buying a driverless car costs lives.”
  • Aggregate the costs and risks. To overcome potentially skewed perceptions of loss and risk, consider expanding the relevant timeframe or pooling the costs. For example, study participants who were presented with the overall probability of being involved in an accident over a 50-year time period were more likely to support additional safety regulations versus those provided the relatively small probability of having an accident on a single trip.56 Similarly, proponents of shared and autonomous mobility could emphasize the average time lost in an entire year to commuting (31 days for so-called “mega-commuters”), rather than the few minutes that accrue every day.57 Companies are already employing these techniques in other fields. Users of Nest connected thermostats receive a monthly “home report” that begins not with individual results but with the number of kilowatt-hours all users in the United States and Canada have collectively saved.58
  • Create social proofs. We often look to the behavior of others for clues as to the correct course of action. Such “social proofs” can serve as powerful motivators, and messaging that invokes peers is often effective in changing behavior. This is particularly true when consumers are confronting a product they are ambivalent or uncertain about.59 To that end, companies might stress how many people have used ridesharing or ridden safely in a semi-autonomous vehicle. Such messages could be particularly effective when localized, focusing on people “like you” or in your neighborhood. Well-publicized pilot efforts in various cities, like those being undertaken in Pittsburgh, Boston, Nevada, and elsewhere, could also provide important examples.60 Returning to Nest users, the company also sends individuals “kudos” or “leafs” when their energy consumption is lower relative to others using the thermostats.61
  • Set default options. Creating pre-selected options can have a powerful effect on what we ultimately choose.62 Users of Uber’s popular app often find the UberPool (ridesharing) option pre-selected, effectively “nudging” them toward that option.63 In the future, it may be possible to access a range of transportation options through a single application on a mobile device. To encourage uptake, service providers can make shared or autonomous mobility the default option. Likewise, automakers and dealers might make fully autonomous vehicles a “standard” feature, only reverting to limited autonomy at the customer’s request.64
  • Make autonomy a peripheral, rather than a core, feature. In a similar vein, 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. In a study of car “autopilot” technology, survey respondents were presented with one of three scenarios: an integrated self-driving system that is the only way to control the vehicle; an integrated system that also had the option of human control; and a vehicle with a plug-in accessory that offered self-driving capabilities. Those presented with the latter condition (add-on option) were two to three times more likely to sign up for a test drive.65 For automakers, that could mean creating “plug-and-play” autonomous capabilities, putting fully autonomous capabilities in vehicles that look and feel like today’s cars (for example, with steering wheels and pedals that may never be used), or making autonomous control “standard” with human control an “option.”

Shared mobility and autonomous vehicles offer many potential benefits, and while important developments emerge near-daily, the future of mobility still lies ahead of us; it is foreshadowed, not foregone. How and how quickly that future emerges is likely to depend not only on the merits of the technological solutions that emerge, but also on how well key players understand and address consumers’ cognitive biases—and failure to do so could put those future benefits in jeopardy. This article illustrated just a handful of the potential risks and opportunities, and hopes to begin a broader discussion about how the future of mobility can impact individuals and the broader society.

The future of mobility still lies ahead of us; it is foreshadowed, not foregone.

Technology adoption is typically characterized by an S-shaped distribution: Relatively few “early adopters” initially use the new technology, followed by a rapid proliferation until a point of saturation is approached and adoption rates taper off. The speed of adoption can vary dramatically. It took 25 years for the telephone to reach 10 percent of American households, and just five years for the tablet to do the same.66 By understanding and attending to consumers’ cognitive biases and the special role the automobile plays in American and other cultures, companies, governments, and others at the leading edge of changes in the mobility ecosystem can help ensure that the adoption of shared mobility and autonomous vehicles more closely resembles a “skinny S.”

Credits

Written By: Derek Pankratz, Philipp Willigmann, Sarah Kovar, Jordan Sanders

Cover image by: Traci Daberko

Acknowledgements

The authors would like to thank Mark Cotteleer, Karen Edelman, Susan Hogan, Joe Mariani, Kelly Monahan, Tim Murphy, and Negina Rood of Deloitte Services LP, along with Scott Corwin of Deloitte Consulting LLP, for their invaluable contributions to this article.

Endnotes
    1. See Scott Corwin, Nick Jameson, Derek M. Pankratz, and Philipp Willigmann, The future of mobility: What’s next?, Deloitte University Press, September 14, 2016, http://www2.deloitte.com/insights/us/en/focus/future-of-mobility/roadmap-for-future-of-urban-mobility.html; Scott Corwin, Joe Vitale, Eamonn Kelly, and Elizabeth Cathles, The future of mobility: How transportation technology and social trends are creating a new business ecosystem, Deloitte University Press, September 24, 2015, http://dupress.com/articles/future-of-mobility-transportation-technology/. View in article

    2. For one example of such an advocate’s vision, see Elon Musk’s Tesla “Master plan, part deux,” July 20, 2016, https://www.tesla.com/blog/master-plan-part-deux. View in article

    3. Scott Corwin et al., The future of mobility: What's next?. View in article

    4. Remi Tachet, Paolo Santi, Stanislav Sobolevsky, Luis Ignacio Reyes-Castro, Emilio Frazzoli, Dirk Helbing, and Carlo Ratti, “Revisiting street intersections using slot-based systems,” PloS one 11, no. 3 (2016): e0149607. View in article

    5. Troy R. Hawkins, Ola Moa Gausen, and Anders Hammer Strømman, “Environmental impacts of hybrid and electric vehicles—a review,”  International Journal of Life Cycle Assessment 17, no. 8 (2012): pp. 997–1014; US Department of Energy, “Vehicle technologies office: Lightweight materials for cars and trucks,” http://energy.gov/eere/vehicles/vehicle-technologies-office-lightweight-materials-cars-and-trucks. View in article

    6. National Highway Traffic Safety Administration (NHTSA), “Critical reasons for crashes investigated in the national motor vehicle crash causation survey,” Traffic Safety Facts, February 2015, https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812115. There were 32,675 vehicle-related fatalities in the United States in 2014. NHTSA, Fatality Analysis Reporting System (FARS) encyclopedia, http://www-fars.nhtsa.dot.gov/Main/index.aspx; NHTSA, The economic and societal impact of motor vehicle crashes, 2010 (revised), May 2015, https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812013. View in article

    7. Dana Hull and Carol Hymowitz, “Google thinks self-driving cars will be great for stranded seniors,” Bloomberg BusinessWeek, March 2, 2016, http://www.bloomberg.com/news/articles/2016-03-02/google-thinks-self-driving-cars-will-be-great-for-stranded-seniors. View in article

    8. Daniel Kahneman and Alan B. Krueger, “Developments in the measurement of subjective well-being,” Journal of Economic Perspectives 20, no. 1 (2006): p. 12. View in article

    9. AAA, American driving survey: Methodology and year one results, May 2013–May 2014, April 2015, http://newsroom.aaa.com/wp-content/uploads/2015/04/REPORT_American_Driving_Survey_Methodology_and_year_1_results_May_2013_to_May_2014.pdf. View in article

    10. Scott Corwin et al., The future of mobility: What's next? . View in article

    11. For more about how cognitive biases affect business decisions, see Deloitte’s full corpus of work on behavioral economics and management at http://dupress.com/collection/behavioral-insights/. View in article

    12. For overviews see, for example, Timothy Murphy and Mark J. Cotteleer, Behavioral strategy to combat choice overload, Deloitte University Press, December 10, 2015, http://dupress.com/articles/behavioral-strategy-choice-overload-framework/?coll=11936; Sendhil Mullainathan and Richard H. Thaler, Behavioral economics, no. w7948, National Bureau of Economic Research, 2000; and Daniel Kahneman, “Maps of bounded rationality: Psychology for behavioral economics,” American Economic Review 93, no. 5 (2003): pp. 1,449–1,475. View in article

    13. Daniel Kahneman, Jack L. Knetsch, and Richard H. Thaler, “Anomalies: The endowment effect, loss aversion, and status quo bias,” Journal of Economic Perspectives 5, no. 1 (1991): pp. 193–206. View in article

    14. David Genesove and Christopher Mayer, Loss aversion and seller behavior: Evidence from the housing market, no. w8143, National Bureau of Economic Research, 2001. View in article

    15. Dan Ariely, Joel Huber, and Klaus Wertenbroch, “When do losses loom larger than gains?,” Journal of Marketing Research 42, no. 2 (2005): pp. 134–138. View in article

    16. Kahneman, Knetsch, and Thaler, “Anomalities.” Also, Jack L. Knetsch, “The endowment effect and evidence of nonreversible indifference curves,”  American Economic Review 79, no. 5 (1989): pp. 1,277–1,284; and Carey K. Morewedge, Lisa L. Shu, Daniel T. Gilbert, and Timothy D. Wilson, “Bad riddance or good rubbish? Ownership and not loss aversion causes the endowment effect,” Journal of Experimental Social Psychology 45, no. 4 (2009): pp. 947–951. View in article

    17. Daniel Kahneman, Jack L. Knetsch, and Richard H. Thaler, “Experimental tests of the endowment effect and the Coase theorem,” Journal of Political Economy (1990): pp. 1,325–1,348. View in article

    18. William Samuelson and Richard Zeckhauser, “Status quo bias in decision making,” Journal of Risk and Uncertainty 1, no. 1 (1988): pp. 7–59. View in article

    19. Hee-Woong Kim and Atreyi Kankanhalli, “Investigating user resistance to information systems implementation: A status quo bias perspective,” MIS quarterly (2009): pp. 567–582. View in article

    20. Raymond S. Hartman, Michael J. Doane, and Chi-Keung Woo, “Consumer rationality and the status quo,” Quarterly Journal of Economics (1991): pp. 141–162.View in article

    21. Glenn Salkeld, Mandy Ryan, and L. Short, “The veil of experience: Do consumers prefer what they know best?” Health Economics 9, no. 3 (2000): pp. 267–270. View in article

    22. Elliot Martin and Susan Shaheen, Impacts of car2go on vehicle ownership, modal shift, vehicle miles traveled, and greenhouse gas emissions: An analysis of five North American cities, Transportation Sustainability Research Center, University of California-Berkeley, July 2016, http://innovativemobility.org/wp-content/uploads/2016/07/Impactsofcar2go_FiveCities_2016.pdf. View in article

    23. David Lanier Lewis and Laurence Goldstein, eds., The Automobile and American Culture (Ann Arbor: University of Michigan Press, 1983). View in article

    24. Jerry Hirsch, “253 million cars and trucks on U.S. roads; average age is 11.4 years,” Los Angeles Times, June 9, 2014, http://www.latimes.com/business/autos/la-fi-hy-ihs-automotive-average-age-car-20140609-story.html; Jacob Poushter, Car, bike or motorcycle? Depends on where you live, Pew Research fact tank, April 16, 2015, http://www.pewresearch.org/fact-tank/2015/04/16/car-bike-or-motorcycle-depends-on-where-you-live/. View in article

    25. Pew Research Daily Number, A car is a necessity,, September 13, 2010, http://www.pewresearch.org/daily-number/a-car-is-a-necessity/. View in article

    26. Bradley Johnson, “How nation's top 200 marketers are honing digital strategies,” Advertising Age, June 27, 2016, http://adage.com/article/advertising/top-200-u-s-advertisers-spend-smarter/304625/. View in article

    27. Poushter, Car, bike or motorcycle? Depends on where you live.View in article

    28. Megha Bahree, “An auto maker’s new tack in India: Starting from scratch,” New Yorker, December 31, 2013, http://www.newyorker.com/business/currency/an-auto-makers-new-tack-in-india-starting-from-scratch. View in article

    29. Mimi Kirk, “In Singapore, making cars unaffordable has only made them more desirable,” CityLab, June 18, 2013, http://www.citylab.com/commute/2013/06/singapore-making-cars-unaffordable-has-only-made-them-more-desirable/5931/. View in article

    30. Michael Sivak and Brandon Schoettle, Percent decreases in the proportion of persons with a driver’s license across all age groups, University of Michigan Transportation Research Institute, January 2016, http://www.umich.edu/~umtriswt/PDF/UMTRI-2016-4.pdf. View in article

    31. Mark Lizewskie, executive director of the Antique Automobile Club of America Museum in Hershey, Pa., quoted in Marc Fisher, “Cruising toward oblivion,” Washington Post, September 2, 2015, http://www.washingtonpost.com/sf/style/2015/09/02/americas-fading-car-culture/. View in article

    32. Paul Slovic, “Perception of risk,” Science 236, no. 4799 (1987): pp. 267–270. View in article

    33. Baruch Fischhoff, Paul Slovic, Sarah Lichtenstein, Stephen Read, and Barbara Combs, “How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits,” Policy Sciences 9, no. 2 (1978): pp. 127–152. View in article

    34. For a popular take, see Adam Waytz, see “The terrifying technological unknown,” Slate, June 24, 2016, http://www.slate.com/articles/technology/future_tense/2016/06/research_shows_why_people_are_bad_at_assessing_the_risks_of_self_driving.html. View in article

    35. David M. DeJoy, “The optimism bias and traffic accident risk perception,” Accident Analysis & Prevention 21, no. 4 (1989): pp. 333–340; Melanie J. White, Lauren C. Cunningham, and Kirsteen Titchener, “Young drivers’ optimism bias for accident risk and driving skill: Accountability and insight experience manipulations,” Accident Analysis & Prevention 43, no. 4 (2011): pp. 1,309–1,315. View in article

    36. Paul Slovic and Elke U. Weber, “Perception of risk posed by extreme events,” in Regulation of Toxic Substances and Hazardous Wastes, 2nd edition, ed. John Applegate, Jan Laitos, Jeffrey Gaba, and Noah Sachs (Foundation Press, 2002). View in article

    37. Amos Tversky and Daniel Kahneman, Judgment Under Uncertainty: Heuristics and Biases (Cambridge: Cambridge University Press, 1973), p. 164. View in article

    38. Pablo Briñol, Richard E. Petty, and Zakary L. Tormala, “The malleable meaning of subjective ease,” Psychological Science 17, no. 3 (2006): pp. 200–206. View in article

    39. Eric J. Johnson and Amos Tversky, “Affect, generalization, and the perception of risk,” Journal of Personality and Social Psychology 45, no. 1 (1983): p. 20. Interestingly, the increased estimates of lethality extended to a more general set of hazards, not just the specific cause of death respondents were exposed to. View in article

    40. Amos Tversky and Daniel Kahneman, “Availability: A heuristic for judging frequency and probability,” Cognitive Psychology, 5, no. 1 (1973): pp. 207–233. View in article

    41. Steven J. Sherman et al., “Imagining can heighten or lower the perceived likelihood of contracting a disease: The mediating effect of ease of imagery,” Personality and Social Psychology Bulletin 11, no. 1 (1985): pp. 118–127. View in article

    42. Karyn Riddle, “Always on my mind: Exploring how frequent, recent, and vivid television portrayals are used in the formation of social reality judgments,” Media Psychology 13, no. 2 (2010): pp. 155–179. View in article

    43. Henry Ford and Samuel Crowther, My Life and Work (Garden City, NY: Doubleday, Page & Co., 1922). View in article

    44. For an overview, see Benjamin Scheibehenne, Rainer Greifeneder, and Peter M. Todd, “Can there ever be too many options? A meta-analytic review of choice overload,” Journal of Consumer Research 37, no. 3 (2010): pp. 409–425. View in article

    45. Sheena S. Iyengar and Mark R. Lepper. “When choice is demotivating: Can one desire too much of a good thing?” Journal of Personality and Social Psychology 79, no. 6 (2000): pp. 995–1,006. View in article

    46. Ibid. View in article

    47. If the choices are familiar or the individual is an expert on the topic, an increased number of choices does not appear to have a deleterious effect, for example. View in article

    48. Alexandr Chernev, “When more is less and less is more: The role of ideal point availability and assortment in consumer choice,” Journal of Consumer Research 30, no. 2 (2003): pp. 170–183; Cassie Mogilner, Tamar Rudnick, and Sheena S. Iyengar, “The mere categorization effect: How the presence of categories increases choosers’ perceptions of assortment variety and outcome satisfaction,” Journal of Consumer Research 35, no. 2 (2008): pp. 202–215; Scheibehenne, Greifeneder, and Todd, “Can there ever be too many options? A meta-analytic review of choice overload.” View in article

    49. A 2016 survey from the Pew Research Center found that only 15 percent of adult Americans have used ridesharing technologies: http://blogs.wsj.com/economics/2016/05/19/most-americans-dont-know-about-ride-sharing-and-the-gig-economy/. Another survey conducted by the American Automobile Association released in March 2016 found that three out of four respondents were “afraid” to ride in a self-driving car: http://www.reuters.com/article/us-autos-tech-selfdriving-idUSKCN0YE1TE. Deloitte’s analysis suggests just 36 percent of respondents would find full automation desirable: Deloitte Global Automotive Study (2014). View in article

    50. Richard H. Thaler and Cass R. Sunstein, Nudge (London: Penguin Books, 2009). View in article

    51. Timothy Murphy and Mark Cotteleer, Behavioral strategy to combat choice overload, Deloitte University Press, December 10, 2015, http://www2.deloitte.com/insights/us/en/focus/behavioral-economics/strategy-choice-overload-framework.html. View in article

    52. Ibid.. View in article

    53. Ibid. View in article

    54. Timothy L. McDaniels, “Reference points, loss aversion, and contingent values for auto safety,” Journal of Risk and Uncertainty 5, no. 2 (1992): pp. 187–200. View in article

    55. This might be especially true for in-depth and involved decisions (such as car buying). Durairaj Maheswaran and Joan Meyers-Levy, “The influence of message framing and issue involvement,” Journal of Marketing Research (1990): pp. 361–367. View in article

    56. Paul Slovic, Baruch Fischhoff, and Sarah Lichtenstein, “Accident probabilities and seat belt usage: A psychological perspective,” Accident Analysis & Prevention 10, no. 4 (1978): pp. 281–285. View in article

    57. Christopher Ingraham, “The astonishing human potential wasted on commutes,” Washington Post, February 25, 2016, https://www.washingtonpost.com/news/wonk/wp/2016/02/25/how-much-of-your-life-youre-wasting-on-your-commute/. “Mega commuters” are those who commute 90 or more minutes each way. View in article

    58. Nest website, “About the Nest home report,” https://nest.com/support/article/About-the-Nest-Home-Report, accessed October 7, 2016. View in article

    59. David B. Wooten and Americus Reed, “Informational influence and the ambiguity of product experience: Order effects on the weighting of evidence,” Journal of Consumer Psychology 7, no. 1 (1998): pp. 79–99. View in article

    60. Nevada Governor’s Office of Economic Development, “Establishing Las Vegas as demonstrator for advanced mobility,” March 16, 2016, http://diversifynevada.com/news/press-releases/nevada-working-to-establish-las-vegas-as-demonstrator-city-for-advanced-mob. View in article

    61. Nest website, “About the Nest home report.” View in article

    62. Thaler and Sunstein, Nudge, pp. 85–89. View in article

    63. Mary Beth Quirk, “Uber nudging users toward carpooling with tests of ‘upfront pricing’ feature,” Consumerist, May 27, 2016, https://consumerist.com/2016/05/27/uber-testing-feature-that-lets-riders-compare-uberpool-uberx-prices/. View in article

    64. The setting of default options is well-understood and widely employed in some industries, such as financial services. See, for example, Jaykumar Shah and Michelle Canaan, Covering all the bases: Overcoming behavioral biases to help individuals achieve retirement security, Deloitte University Press, May 11, 2016, http://www2.deloitte.com/insights/us/en/focus/behavioral-economics/overcoming-behavioral-bias-in-retirement-security-planning.html. View in article

    65. Zhenfeng Ma, Tripat Gill, and Ying Jiang, “Core vs. peripheral innovations: The effect of innovation locus on consumer adoption of new products,” Journal of Marketing Research 52 (June 2015): pp.309–324. View in article

    66. Rita McGrath, “The pace of technology adoption is speeding up,” Harvard Business Review, November 25, 2013, https://hbr.org/2013/11/the-pace-of-technology-adoption-is-speeding-up. View in article

Show moreShow less

Topics in this article

Future of Mobility , Automotive , Consumer Industry Center

Learn about Deloitte's future of mobility services

View
Download Subscribe

Related

img Trending

Interactive 3 days ago

Derek Pankratz

Derek Pankratz

Senior Research Leader, Climate

Derek is a senior manager with the Center for Integrated Research in Deloitte Services LP. His research focuses on the confluence of emerging technological and social trends across industries.

  • dpankratz@deloitte.com
  • +1 920 242 1141
Philipp Willigmann

Philipp Willigmann

Senior Manager

Philipp is a Monitor Deloitte strategy senior manager. He has more than 10 years of experience acting as trusted advisor to senior executives and boards driving strategic growth transformations for multi-national clients facing unprecedented market uncertainty by capturing value from innovative business models to enable growth. His projects have focused on strategy development, scenario planning, change management, governance, and large-scale organizational and digital transformation. Philipp co-leads Deloitte Consulting Market Sensing & Scenario Planning practice. Prior to this role, Philipp assumed several leadership positions across Deloitte US, Germany, and Deloitte Global, including deputy leader of Deloitte’s Global Future of Mobility practice, head of US/German Center of Practice, and Office of the Global CEO. Before joining Deloitte, he was a manager at Europcar International and Accor Hotels. Philipp holds a Master from Corporate University Mannheim (D), Ashcraft Business School, University of Cambridge (UK) as well as executive leadership degree’s from IMD International (CH), University Bamberg (DE), and European Business School “ebs” (DE).

  • phwilligmann@deloitte.com
  • +1 347 549 2804
Sarah Kovar

Sarah Kovar

Consultant | Deloitte Consulting LLP

Sarah is a consultant in Deloitte Consulting LLP’s Strategy & Operations practice, focusing on innovation, behavioral insights, strategy, and business transformation. She is the execution lead for Deloitte’s Behavioral Insights Community of Practice (CoP), which combines behavioral sciences and design thinking to achieve clients’ missions. Sarah is passionate about how behavioral insights can identify simple changes to affect massive impact.

  • skovar@deloitte.com
  • +1 571 814 7472
Jordan Sanders

Jordan Sanders

Analyst | Deloitte Consulting LLP

Jordan is an analyst in Deloitte Consulting LLP’s Strategy and Operations practice specializing in the future of mobility and smart cities. He is passionate about technology that improves lives by creating connected communities. Jordan collaborates with clients across the public sector—regulators, researchers and developers, and potential users—to explore the impact of emerging transportation technologies and new mobility business models.

  • josanders@deloitte.com
  • +1 571 882 7550

Share article highlights

See something interesting? Simply select text and choose how to share it:

Email a customized link that shows your highlighted text.
Copy a customized link that shows your highlighted text.
Copy your highlighted text.

Framing the future of mobility has been saved

Framing the future of mobility has been removed

An Article Titled Framing the future of mobility already exists in Saved items

Invalid special characters found 
Forgot password

To stay logged in, change your functional cookie settings.

OR

Social login not available on Microsoft Edge browser at this time.

Connect Accounts

Connect your social accounts

This is the first time you have logged in with a social network.

You have previously logged in with a different account. To link your accounts, please re-authenticate.

Log in with an existing social network:

To connect with your existing account, please enter your password:

OR

Log in with an existing site account:

To connect with your existing account, please enter your password:

Forgot password

Subscribe

to receive more business insights, analysis, and perspectives from Deloitte Insights
✓ Link copied to clipboard

Deloitte Insights and our research centers deliver proprietary research designed to help organizations turn their aspirations into action.

Deloitte Insights

  • Home
  • Topics
  • Industries
  • About Deloitte Insights

DELOITTE RESEARCH CENTERS

  • Cross-Industry
  • Economics
  • Consumer
  • Energy & Industrials
  • Financial Services
  • Government & Public Services
  • Life Sciences & Health Care
  • Tech, Media & Telecom
Deloitte logo

Learn about Deloitte’s offerings, people, and culture as a global provider of audit, assurance, consulting, financial advisory, risk advisory, tax, and related services.

© 2025. See Terms of Use for more information.

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee ("DTTL"), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as "Deloitte Global") does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the "Deloitte" name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see www.deloitte.com/about to learn more about our global network of member firms.

  • About Deloitte
  • Terms of Use
  • Privacy
  • Data Privacy Framework
  • Cookies
  • Cookie Settings
  • Legal Information for Job Seekers
  • Labor Condition Applications
  • Do Not Sell My Personal Information