How Uber Used Psychology to Manipulate Its Drivers

Available in 600 cities spread across 65 countries with more than 75 million users, Uber has become the default transport choice for many.

The scale and speed of adoption have been incredible, and Uber cites its unique business model and experience as the drivers.

But underlying their massive growth lies a delicate system that has to be maintained for Uber to survive: the supply of riders and drivers must be balanced.

If either one of these has too many or too few participants, then the whole system can break down. That’s why keeping drivers driving for longer is one of the most important parts of a functional Uber ecosystem.


Uber’s Challenge: Keeping Drivers on the Road

Source: Adobe Stock

If you’ve ever tried to get an Uber on New Year’s Eve you know that not enough drivers and too much demand results in eye-wateringly high prices and waits so long you might as well walk.

But there’s no magic button Uber can press to keep people driving, so it uses behavioral science to try and keep things perfectly aligned.

In 2017, the New York Times ran an expose about Uber’s attempts to keep their drivers driving for longer. They interviewed dozens of current and former Uber officials, drivers, and social scientists and came to the conclusion that Uber was using behavioral science to undermine its drivers’ well-being.

According to the article, while giving lip service to treating its drivers with more dignity and fairness, it also:

“Engaged in an extraordinary behind-the-scenes experiment in behavioral science to manipulate them in the service of its corporate growth.”

The first thing that New York Times dug up was Uber’s use of concrete financial goals to motivate its drivers to stay online. Josh Streeter, a former Uber driver in Florida showed the New York Times some of the messages he got on the company’s driver app when he tried to log off. The first message said:

“Make it to $330.”

Another message soon followed:

“You’re $10 away from making $330 in net earnings. Are you sure you want to go offline?”

Below this message, there were two choices:

  • Go offline
  • Keep driving

“Keep driving” was highlighted so people would notice and click it, but “go offline” was not. From a behavioral science perspective, there are a few things going on here.

  1. These messages play on Loss Aversion, a behavioral science principle that says people hate loss much more than they like gain, and they’re motivated to avoid loss as much as possible.
  2. The Goal Gradient Effect, which says that people are much more motivated by how much they have left to go before they hit a goal. In this case, the arbitrary $330 earning goal that Uber selected and suggested for this driver.
  3. Goal Gradient is supercharged by an effect called Income Targeting. It says when workers who can decide how long they want to work each day (and therefore how much they might earn) start their day with a financial goal in mind to motivate themselves.

An internal Uber study even found that many new drivers practice an “extreme form of income targeting.” But as drivers got more experience on the platform, they found that income targeting behavior was inefficient because they’d have to work extremely long shifts on slow days and then get off early when they were busy.

In other words, by creating these arbitrary income goals to keep drivers driving, Uber was asking them to do something that experience would show was not actually in their best interest.

Source: Adobe Stock

Uber’s Gamification of Driving

Because Uber’s drivers tend to function as lone wolves — with no office in which to bump into colleagues and chat — their entire connection to the company comes in the form of the Uber driver app.

In its pursuit of keeping drivers engaged as long as possible, the company used common gamification elements like badging to motivate and reward its drivers.

That sounds fine until you begin to consider the bigger picture.

In most markets, Uber drivers are independent contractors who have to pay for fuel, insurance, car maintenance, and other associated costs.

Drivers can very easily lose money working with Uber.

Scott Weber, an Uber and Lyft driver, told the New York Times that he drove full time and in the year prior to his interview reported less than $20,000 in income before expenses.

He told the paper:

“I was a business that had a loss. I’m using payday loans.”

These types of gig economy workers don’t have unions to advocate on their behalf, and they can go weeks, months, or even forever before comparing their reality to other drivers.

So in this context, keeping people driving — which is not always in their financial interest — and rewarding them with digital badges instead of money, in a closed context where they don’t have many ways to increase or even understand their income, isn’t helping to improve drivers’ welfare and earning potential.


Did Uber’s Use of Behavioral Science Cross the Line?

To some of you, Uber’s nudging might seem like fair play. Sure, Uber was applying behavioral science, and they never forced anyone to drive for them. But because in all markets at the time, Uber’s drivers were considered contractors, none of these behavioral strategies were limited by employment laws.

According to the company, its new CEO — who was brought into the company in 2017 to replace the controversial Travis Kalanick — has helped Uber undergo a radical culture change to become more ethical and accountable for decisions like these.

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The Bottom Line: How to Avoid Uber’s Missteps

Regardless of its current culture or approach to applying behavioral strategies, their mistakes have lessons to teach everyone who wants to apply behavioral science to businesses.

  • It can be easy to manipulate people with these effects if we’re not consciously paying attention to the behaviors that they’re driving. Most people aren’t savvy about behavioral nudges, they don’t even know what a nudge is, so expecting them to fight against a system that’s set up to maximize Uber’s corporate income and not drivers’ income isn’t realistic.
  • Consider the context of these nudges and not just how they drive the target behaviors. In this case, drivers were operating without any bargaining power, leverage, or even a realistic understanding of their financial upside. Ask yourself if your nudges are making life better for these folks or if you’re helping to misdirect from bigger issues — just like the digital badging helped distract from the financial reality of driving for Uber.
  • Spend some time thinking about where your ethical lines are drawn. For example, you might not want work with certain types of companies like alcohol, gambling, or tobacco firms. Think about where your moral lines are drawn, before they’re tested.

As a little inspiration, Richard Thaler, author of Nudge, has an ethical code that he prescribes. And I’m sure he wouldn’t mind if you borrowed it:

  1. All nudging should be transparent and never misleading
  2. It should be easy to opt-out of the nudge
  3. There should be good reason to believe the behavior being encouraged will improve the welfare of the people being nudged.

Liked this article? Why not check out Part One, “How Uber Used Psychology to Perfect Its Experience.”

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