Category: application

Uber: Translating Behavioural Science to the Work Floor with Gamification and Experimentation

Yesterday, I read the most interesting article on how Uber uses academic research from the field of behavioral psychology to persuade their drivers to display desired behaviors. The tone of the article is quite negative and I most definitely agree there are several ethical issues at hand here. However, as a data scientist, I was fascinated by the way in which Uber has translated academic insights and statistical methodology into applications within their own organization that actually seem to pay off. Well, at least in the short term, as this does not seem a viable long-term strategy.

The full article is quite a long read (~20 min), and although I definitely recommend you read it yourself, here are my summary notes, for convenience quoted from the original article:

“Employing hundreds of social scientists and data scientists, Uber has experimented with video game techniques, graphics and noncash rewards of little value that can prod drivers into working longer and harder — and sometimes at hours and locations that are less lucrative for them.”
“To keep drivers on the road, the company has exploited some people’s tendency to set earnings goals — alerting them that they are ever so close to hitting a precious target when they try to log off.”
“Uber exists in a kind of legal and ethical purgatory […] because its drivers are independent contractors, they lack most of the protections associated with employment.”
“[…] much of Uber’s communication with drivers over the years has aimed at combating shortages by advising drivers to move to areas where they exist, or where they might arise. Uber encouraged its local managers to experiment with ways of achieving this.[…] Some local managers who were men went so far as to adopt a female persona for texting drivers, having found that the uptake was higher when they did.”
“[…] Uber was increasingly concerned that many new drivers were leaving the platform before completing the 25 rides that would earn them a signing bonus. To stem that tide, Uber officials in some cities began experimenting with simple encouragement: You’re almost halfway there, congratulations! While the experiment seemed warm and innocuous, it had in fact been exquisitely calibrated. The company’s data scientists had previously discovered that once drivers reached the 25-ride threshold, their rate of attrition fell sharply.”
“For months, when drivers tried to log out, the app would frequently tell them they were only a certain amount away from making a seemingly arbitrary sum for the day, or from matching their earnings from that point one week earlier.The messages were intended to exploit another relatively widespread behavioral tic — people’s preoccupation with goals — to nudge them into driving longer. […] Are you sure you want to go offline?” Below were two prompts: “Go offline” and “Keep driving.” The latter was already highlighted.”
“Sometimes the so-called gamification is quite literal. Like players on video game platforms such as Xbox, PlayStation and Pogo, Uber drivers can earn badges for achievements like Above and Beyond (denoted on the app by a cartoon of a rocket blasting off), Excellent Service (marked by a picture of a sparkling diamond) and Entertaining Drive (a pair of Groucho Marx glasses with nose and eyebrows).”
“More important, some of the psychological levers that Uber pulls to increase the supply of drivers have quite powerful effects. Consider an algorithm called forward dispatch […] that dispatches a new ride to a driver before the current one ends. Forward dispatch shortens waiting times for passengers, who may no longer have to wait for a driver 10 minutes away when a second driver is dropping off a passenger two minutes away. Perhaps no less important, forward dispatch causes drivers to stay on the road substantially longer during busy periods […]
[But] there is another way to think of the logic of forward dispatch: It overrides self-control. Perhaps the most prominent example is that such automatic queuing appears to have fostered the rise of binge-watching on Netflix. “When one program is nearing the end of its running time, Netflix will automatically cue up the next episode in that series for you,” wrote the scholars Matthew Pittman and Kim Sheehan in a 2015 study of the phenomenon. “It requires very little effort to binge on Netflix; in fact, it takes more effort to stop than to keep going.””
“Kevin Werbach, a business professor who has written extensively on the subject, said that while gamification could be a force for good in the gig economy — for example, by creating bonds among workers who do not share a physical space — there was a danger of abuse.”
“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. 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.”

Click the below for the full article.

Robert Coombs and his application robot

Robert Coombs wanted to see whether he could land a new job. He was aware that, these days, organizations often employ applicant tracking systems to progress/fail incoming applications. Hence, Robert concluded that he had two challenges in his search for a new job:

He was up against leaders in their field, so his resume wouldn’t simply jump to the top of the pile.
Robots would read his application, along with those of his competition.

Being a tech enthusiast and having some programming skills, he decided to build his own application robot, capable of sending a customized CV and resume to the thousands of jobs posted online every day, in a matter of seconds. I strongly recommend you read his full story here, but these were his conclusions:

It’s not how you apply, it’s who you know. And if you don’t know someone, don’t bother.
Companies are trying to fill a position with minimal risk, not discover someone who breaks the mold.
The number of jobs you apply to has no correlation to whether you’ll be considered, and you won’t be considered for jobs you don’t get the chance to apply to.

What I found most amusing is that he A/B tested one normal-looking cover letter and a letter in which he that admits right in the second sentence that it was being sent by a robot. “Now, one of those letters should have performed either a lot better or a lot worse than the other. For my purposes, I didn’t care which” he states. But as far as he could tell from the results of this experiment, it seems that nobody even reads cover letters anymore – not even the robots supposedly used in application tracking systems.

Expanding the methodological toolbox of HRM researchers

Update 26-10-2017: the paper has been published open access and is freely available here: http://onlinelibrary.wiley.com/doi/10.1002/hrm.21847/abstract.

The HR technology landscape is evolving rapidly and with it, the HR function is becoming more and more data-driven (though not fast enough, some argue). HRM research, however, is still characterized by a strong reliance on general linear models like linear regression and ANOVA. In our forthcoming article in the special issue on Workforce Analytics of Human Resource Management, my co-authors and I argue that HRM research would benefit from an outside-in perspective, drawing on techniques that are commonly used in fields other than HRM.

Our article first outlines how the current developments in the measurement of HRM implementation and employee behaviors and cognitions may cause the more traditional statistical techniques to fall short. Using the relationship between work engagement and performance as a worked example, we then provide two illustrations of alternative methodologies that may benefit HRM research:

Using latent variables, bathtub models are put forward as the solution to examine multi-level mechanisms with outcomes at the team or organizational level without decreasing the sample size or neglecting the variation inherent in employees’ responses to HRM activities (see figure 1). Optimal matching analysis is proposed as particularly useful to examine the longitudinal patterns that occur in repeated observations over a prolonged timeframe. We describe both methods in a fair amount of detail, touching on elements such as the data requirements all the way up to the actual modeling steps and limitations.

figure-bathtub-model — An illustration of the two parts of a latent bathtub model.

I want to thank my co-authors and Shell colleagues Zsuzsa Bakk, Vasileios Giagkoulas, Linda van Leeuwen, and Esther Bongenaar for writing this, in my own biased opinion, wonderful article with me and I hope you will enjoy reading it as much as we did writing it.

Link to pre-publication