The Science Behind Uber’s Nudges

By: Brian Hengesbaugh

Behavioral science has long been used by media and advertisers to influence the decision-making of consumers (e.g., pricing items at 99 cents instead of the full dollar). The growing “gig economy,” in which temporary jobs are completed by independent contractors instead of full-time employees, has led employers to look towards behavioral science concepts in an effort to increase their influence over gig workers.

The New York Times article, ‘How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons,’ explains that the Uber drivers’ status as independent business owners leads to significant cost savings for the company, however, a key trade-off is that Uber cannot control the times or locations that the drivers choose to work. This lack of control over drivers’ schedules can lead to the company’s inability to meet customer demand at peak times (e.g., rush hour) and peak locations (e.g., concert venues). In these situations, Uber currently uses a price increase called surge-pricing to reduce customer demand and entice more drivers to get behind the wheel. The challenge is that surge-pricing scenarios are bad for Uber, but good for drivers. When prices increase, Uber is losing potential customers and current customers face longer wait-times between rides – not good for Uber. For the drivers, the price increase leads to greater compensation per ride and shorter wait time between rides, which increases their effective hourly rate – good for drivers. The misalignment of incentives between the company and the drivers, coupled with the lack of control over drivers’ schedules, has lead Uber to explore methods other than surge pricing to influence the decision-making of drivers. Below is an examination of three interventions, and the associated behavioral science concepts, that Uber has used in an effort to influence their drivers.

Intervention: Almost There!

Goal: Extend shift length

Description: When drivers attempt to log-off, the app sends them a message stating that they are only a specific number of dollars short of reaching an arbitrarily set income goal for the day.

Behavioral Science Concept: Loss Framing – Prospect theory states that people are motivated more by the threat of loss than by the potential of equivalent gain (Kahneman & Tversky, 1979). As a result, people are more likely to take risks to avoid losses than to secure gains. The message from the app frames the daily income as a loss relative to the arbitrary income goal and the motivation of loss aversion leads the driver to continue their shift.


Intervention: Forward Dispatch (or Auto-Queuing)

Goal: Extend shift length

Description: Uber pre-loads the driver’s next ride before the current ride has ended.

Behavioral Science Concept: Regret Avoidance – People feel greater regret for bad outcomes that are produced by new actions than for bad outcomes that result from inaction, and therefore exhibit preferences for inaction (Kahneman & Tversky, 1982). By pre-loading the next ride, Uber has created a scenario in which the driver’s inaction results in continuing the shift.


Intervention: New Driver Signing Bonus

Goal: Reduce attrition rate of new drivers.

Description: New drivers are given a financial bonus when they reach 25 rides.

Behavioral Science Concept: Sunk Cost Fallacy – The investment of time, money, or effort produces a greater tendency to continue an endeavor (Arkes & Blumer, 1985). In order to reach 25 rides, the driver will have invested a significant amount of time and effort in the process and will be more likely to continue working as a driver. As the “gig economy” continues to grow employers will continue to explore new ways to use behavioral science to increase control over independent contractors. Clear ethical guidelines must be developed to help companies navigate scenarios in which there is a misalignment of incentives, or an asymmetry of information, between the company and workers.


Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263-291.

Kahneman, D., & Tversky, A. (1982). The psychology of preference. Scientific American, 246, 160-173.

Arkes, H. R., & Blumer, C. (1985), The psychology of sunk costs. Organizational Behavior and Human Decision Processes, 35, 124-140

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