How Uber Manages Drivers Without Technically Managing Drivers

The popular car service is using apps to tell the people behind the wheel what to do. Welcome to the era of algorithmic management.

How Uber Manages Drivers Without Technically Managing Drivers
[Photo: Flickr user Max Klingensmith; App Photo: Columbia Pictures ("Taxi Driver", 1976)]

If Uber exerts too much control over its drivers, it risks providing evidence to the many lawyers who accuse the company of treating its independent contractors as employees. But like any company, Uber also wants to provide great service to its customers, which usually involves telling workers what to do. A new case study details how the company has figured out how to walk a fine line by using its app and notifications as management tools.


The study, which was recently published in the International Journal of Communication, was coauthored by Alex Rosenblat of the Data & Society Research Institute and Luke Stark of New York University. It looked at archival and real-time posts in five online forums for Uber drivers to investigate how ride-sharing apps and other “gig economy” companies nudge workers toward specific behaviors. This is known as “algorithmic management.”

The research shows how Uber (which did not respond to a request for comment on this article) manages driver behavior via:

1. Blind Passenger Acceptance And Low Minimum Fares

The message: “You can agree to or reject this job, but you only have 15 seconds and half of the information you need to decide.”

Before an Uber driver accepts a job, he or she can see the pick-up point, but not the destination. This may help prevent destination-based discrimination, but it means drivers sometimes accept rides that aren’t profitable. “You’re driving around blind,” said one North Carolina driver interviewed for the study. “When it does ping, you might drive 15 minutes to drive someone half a mile.”

Two other aspects of Uber’s app make this dilemma worse. The first is that Uber’s minimum fares can be low. Though they vary by city and sometimes change, the example cited in the study is a $5 minimum fare for UberX in Savannah, Georgia, which, after Uber’s commission and other fees, leaves the driver with about $3.20 before accounting for various expenses.


Uber has also previously penalized drivers with “deactivation” if they turned down or canceled too many rides, which discouraged drivers from opting out of rides that they knew would be unprofitable. Better to drive a long distance to pick up a passenger, even if that passenger might only be paying for a short ride, rather than risk getting kicked out of Uber. As part of a lawsuit settlement in April, Uber agreed to stop deactivating drivers who frequently turn down rides.

2. Incentive-Based Pay

The message: “We’re not scheduling you in shifts, but you’ll earn more if you work this many hours, in this area, without taking jobs from other services.”

Uber sometimes sends drivers offers for “guaranteed fares” that promise a certain hourly rate ($22 per hour, for instance) if they drive during certain times. It doesn’t make the offer to all drivers, and it doesn’t post the criteria for receiving the offer, which, says Rosenblat, oddly creates “a tiered wage system in which some drivers have the ability to earn more than others driving in the same place at the same time.”

To earn the guaranteed rate, drivers who opt in typically need to accept 90% of ride requests, complete one trip per hour, be online for at least 50 minutes of every hour, and maintain a specified high rating during those trips. Others have pointed out that under these conditions, accepting trips from another ride-hailing service, like Lyft, during the same time would be difficult. The case study argues that this is effectively a way for Uber to schedule shifts. “[The] language of opt-in or RSVP buffers the narrative of freedom and choice that Uber promotes to its drivers, while simultaneously masking a hierarchy in which select drivers are invited to earn more based on opaque criteria,” it says. “Drivers have the freedom to drive at ‘flexible’ hours at lower rates, but their flexibility is tailored to and dependent on demand as well as on the viability of base rates.”

3. The Surge

The message: “We would never tell you when to drive, but we’re pinging you to let you know that demand is super high right now and also probably will be this weekend.”


Uber describes its practice of raising rates in areas that have significant demand as a reflection of the market that helps drivers make decisions about where to work. The case study argues that Uber uses the surge to nudge drivers to continue or start working. For instance, instead of a message from Uber that says, “We’d like you to keep working!” drivers receive messages that, for example, say, “Are you sure you want to go offline? Demand is very high in your area. Make more money. Don’t stop now!” Likewise, instead of a message that says “Start working!” Uber might send a message that says, “[UBER ALERT] Happy hour demand is extremely high right now! Log into your app and take advantage of extra earnings. #UberOn.”

Sometimes Uber sends drivers notifications about where and when it predicts there will be high demand, such as, “We also want to remind you that we predict New Year’s Eve will be the busiest night of the year. With such high demand, it will be a great night to go out and drive!”

These predictive notifications are written in the same language as Uber uses to describe its real-time messages about demand.

4. Ratings

The message: “We would never tell you what to do, but drivers who get good ratings do these specific things.”

Uber deactivates drivers with low ratings from riders. On the one hand, this type of rating system is a way to establish trust and safety among people in a marketplace, such as Uber’s marketplace for labor. The case study argues that Uber also uses the ratings system to give drivers suggestions that are easily interpreted as requirements.


For instance, one “tips for how to improve” list from Uber notes, “Riders give the best ratings to drivers who: Never ask for a 5-star review, but instead focus on providing an excellent experience; stay calm, patient, and polite with riders on the road; and go above and beyond to make the experience special, such as opening doors for riders when possible.”

What does it all mean?

This research comes with a few caveats, including that people who participate in forums may be full-time drivers who are more invested in working for Uber, or they could be drivers who are less happy with their experience than the typical driver. Both could slant the results. Another study found that drivers were “generally satisfied with their level of control over assignment algorithms.”

Regardless of how Uber drivers feel about it, however, the study illustrates how Uber has some control over its drivers’ work hours and decision making. It’s far from the first academic work to do so.

Some argue this control warrants a new category of worker. Whereas today, employers choose between independent contractors and staff designations, a new category would give companies like Uber a third choice, such as “independent workers,” that could provide workers some protections without turning them into full-fledged employees.


Others see Uber’s various means of control as evidence that all of the company’s workers should be classified as employees, who, unlike independent contractors, fall under laws mandating contributions to social security programs and have a federally protected right to organize. The AFL-CIO has strongly backed this view, arguing that “the reasons why businesses want to shed their responsibilities as employers are not new or limited to the on-demand economy.”

Rosenblat says that she’ll leave the classification question to legal scholars, but suggests that there may be another recourse that isn’t as frequently discussed: Uber has positioned its drivers as customers who pay a commission to use its software. That could make the ways that Uber nudges them toward certain practices interesting to customer-protection agencies.

About the author

Sarah Kessler is a senior writer at Fast Company, where she writes about the on-demand/gig/sharing "economies" and the future of work.