This five-part article on the use of behavioral techniques by Uber to stimulate a higher performance from its drivers, which appeared in The New York Times, reaches its conclusion with some prognostication on what this could lead to in the future for gig economy workers in the tech world.
By NOAM SCHEIBER, April 2, 2017, for The New York Times
There are aspects of the platforms that genuinely do increase drivers’ control over their work lives, as Uber frequently points out. Unlike most workers, an Uber driver can put in a few hours each day between dropping children off at school and picking them up in the afternoon.
Uber is even in the process of developing a feature that allows drivers to tell the app in advance that they need to arrive at a given location at a given time. “If you need to pick up your kids at soccer practice at 6 p.m.,” said Nundu Janakiram, the Uber official in charge of products that improve drivers’ experiences, “it will start to give you trips to take you in the general direction to get to a specific place in time.”
There is also the possibility that as the online gig economy matures, companies like Uber may adopt a set of norms that limit their ability to manipulate workers through cleverly designed apps.
Kelly Peters, chief executive of BEworks, a management consulting firm specializing in behavioral science, argued that the same data that makes it easier for Uber to nudge drivers into working an additional 30 or 60 minutes also makes it hard to escape the obligation to look after them.
For example, the company has access to a variety of metrics, like braking and acceleration speed, that indicate whether someone is driving erratically and may need to rest. “The next step may be individualized targeting and nudging in the moment,” Ms. Peters said. “‘Hey, you just got three passengers in a row who said they felt unsafe. Go home.’” Uber has already rolled out efforts in this vein in numerous cities.
That moment of maturity does not appear to have arrived yet, however. Consider a prompt that Uber rolled out this year, inviting drivers to press a large box if they want the app to navigate them to an area where they have a “higher chance” of finding passengers. The accompanying graphic resembles the one that indicates that an area’s fares are “surging,” except in this case fares are not necessarily higher.
Some drivers believe that the intent is to trick them into driving where Uber wants them to go, rather than where driving would be most profitable, by implying that they will find a surge there. “They’re trying to move people where they want them,” said Mr. Weber, the Tampa-area driver. “But you get there and it’s nothing. It happens all the time.” Mr. Weber noted that the design of the graphic makes the prompt much easier to accept than decline, which requires pressing a small rectangle in the top left corner.
Uber said that the feature was an experiment intended primarily to help new drivers who frequently say they do not know where to find passengers, and that it could be changed if drivers were dissatisfied.
Individual features aside, the broader question of how much Uber seeks to influence drivers through behavioral science may come down to how much its business model requires it.
While the company has made no secret of its investment in self-driving cars, it could be a decade or more before they completely replace human drivers. In the meantime, as long as Uber continues to set growth and passenger volume as critical goals, it will have an incentive to make wringing more hours out of drivers a higher priority than the drivers’ bottom line whenever it faces a close call between the two.
It will also have an incentive to obtain these hours as cheaply as possible. And there is simply no cheaper way than hiring contractors and nudging them to drive when and where they are needed. Industry insiders estimate that relying on independent contractors rather than employees can lower direct costs by roughly 25 percent.
Moreover, the contractor model itself provides a strong impetus for companies like Uber to grow. Many companies in the gig economy simply do not have enough workers, or rich enough data about their workers’ behavior, to navigate busy periods using nudges and the like. To avoid chronic understaffing, they have switched to an employee model that allows them to compel workers to log in when the companies most need them.
Once companies achieve a certain scale, on the other hand, they enter a virtuous cycle: The risk of understaffing drops with a big enough pool of workers, and the cost savings of using contractors begins to outweigh the inefficiencies. This in turn frees up money to enter new markets and acquire new customers, which makes the contractor model still more efficient, and throws off still more savings.
It is, as a result, not too hard to imagine a future in which massive digital platforms like Uber have an appetite for tens of millions of workers — not only for ferrying people, but also for delivering food and retail goods. Nor is it hard to imagine workers’ obliging them, perhaps because their skills do not match the needs of more traditional employers, or because they need to supplement their wages.
In such an economy, experts say, using big data and algorithms to manage workers will not simply be a niche phenomenon. It may become one of the most common ways of managing the American labor force.
“You have all these players entering into this space, and the assumption is they’ll do it through vast armies of underemployed people looking for extra hours, and we can control every nuance about what they do but not have to pay them,” said David Weil, the top wage-and-hour official under President Barack Obama.
When you stop to consider the enormous cost advantages, Mr. Weil said, “it says to me this is an area that will grow fast.”
This concludes the five-part article on Uber and its drivers