High score, low pay : why the gig economy loves gamification | Business | The Guardian
Using ratings, competitions and bonuses to incentivise workers isn’t new – but as I found when I became a Lyft driver, the gig economy is taking it to another level.
Every week, it sends its drivers a personalised “Weekly Feedback Summary”. This includes passenger comments from the previous week’s rides and a freshly calculated driver rating. It also contains a bar graph showing how a driver’s current rating “stacks up” against previous weeks, and tells them whether they have been “flagged” for cleanliness, friendliness, navigation or safety.
At first, I looked forward to my summaries; for the most part, they were a welcome boost to my self-esteem. My rating consistently fluctuated between 4.89 stars and 4.96 stars, and the comments said things like: “Good driver, positive attitude” and “Thanks for getting me to the airport on time!!” There was the occasional critique, such as “She weird”, or just “Attitude”, but overall, the comments served as a kind of positive reinforcement mechanism. I felt good knowing that I was helping people and that people liked me.
But one week, after completing what felt like a million rides, I opened my feedback summary to discover that my rating had plummeted from a 4.91 (“Awesome”) to a 4.79 (“OK”), without comment. Stunned, I combed through my ride history trying to recall any unusual interactions or disgruntled passengers. Nothing. What happened? What did I do? I felt sick to my stomach.
Because driver ratings are calculated using your last 100 passenger reviews, one logical solution is to crowd out the old, bad ratings with new, presumably better ratings as fast as humanly possible. And that is exactly what I did.
In a certain sense, Kalanick is right. Unlike employees in a spatially fixed worksite (the factory, the office, the distribution centre), rideshare drivers are technically free to choose when they work, where they work and for how long. They are liberated from the constraining rhythms of conventional employment or shift work. But that apparent freedom poses a unique challenge to the platforms’ need to provide reliable, “on demand” service to their riders – and so a driver’s freedom has to be aggressively, if subtly, managed. One of the main ways these companies have sought to do this is through the use of gamification.
Simply defined, gamification is the use of game elements – point-scoring, levels, competition with others, measurable evidence of accomplishment, ratings and rules of play – in non-game contexts. Games deliver an instantaneous, visceral experience of success and reward, and they are increasingly used in the workplace to promote emotional engagement with the work process, to increase workers’ psychological investment in completing otherwise uninspiring tasks, and to influence, or “nudge”, workers’ behaviour. This is what my weekly feedback summary, my starred ratings and other gamified features of the Lyft app did.
There is a growing body of evidence to suggest that gamifying business operations has real, quantifiable effects. Target, the US-based retail giant, reports that gamifying its in-store checkout process has resulted in lower customer wait times and shorter lines. During checkout, a cashier’s screen flashes green if items are scanned at an “optimum rate”. If the cashier goes too slowly, the screen flashes red. Scores are logged and cashiers are expected to maintain an 88% green rating. In online communities for Target employees, cashiers compare scores, share techniques, and bemoan the game’s most challenging obstacles.
But colour-coding checkout screens is a pretty rudimental kind of gamification. In the world of ride-hailing work, where almost the entirety of one’s activity is prompted and guided by screen – and where everything can be measured, logged and analysed – there are few limitations on what can be gamified.
Every Sunday morning, I receive an algorithmically generated “challenge” from Lyft that goes something like this: “Complete 34 rides between the hours of 5am on Monday and 5am on Sunday to receive a $63 bonus.” I scroll down, concerned about the declining value of my bonuses, which once hovered around $100-$220 per week, but have now dropped to less than half that.
“Click here to accept this challenge.” I tap the screen to accept. Now, whenever I log into driver mode, a stat meter will appear showing my progress: only 21 more rides before I hit my first bonus.
In addition to enticing drivers to show up when and where demand hits, one of the main goals of this gamification is worker retention. According to Uber, 50% of drivers stop using the application within their first two months, and a recent report from the Institute of Transportation Studies at the University of California in Davis suggests that just 4% of ride-hail drivers make it past their first year.
Before Lyft rolled out weekly ride challenges, there was the “Power Driver Bonus”, a weekly challenge that required drivers to complete a set number of regular rides. I sometimes worked more than 50 hours per week trying to secure my PDB, which often meant driving in unsafe conditions, at irregular hours and accepting nearly every ride request, including those that felt potentially dangerous (I am thinking specifically of an extremely drunk and visibly agitated late-night passenger).
Of course, this was largely motivated by a real need for a boost in my weekly earnings. But, in addition to a hope that I would somehow transcend Lyft’s crappy economics, the intensity with which I pursued my PDBs was also the result of what Burawoy observed four decades ago: a bizarre desire to beat the game.
Former Google “design ethicist” Tristan Harris has also described how the “pull-to-refresh” mechanism used in most social media feeds mimics the clever architecture of a slot machine: users never know when they are going to experience gratification – a dozen new likes or retweets – but they know that gratification will eventually come. This unpredictability is addictive: behavioural psychologists have long understood that gambling uses variable reinforcement schedules – unpredictable intervals of uncertainty, anticipation and feedback – to condition players into playing just one more round.
It is not uncommon to hear ride-hailing drivers compare even the mundane act of operating their vehicles to the immersive and addictive experience of playing a video game or a slot machine. In an article published by the Financial Times, long-time driver Herb Croakley put it perfectly: “It gets to a point where the app sort of takes over your motor functions in a way. It becomes almost like a hypnotic experience. You can talk to drivers and you’ll hear them say things like, I just drove a bunch of Uber pools for two hours, I probably picked up 30–40 people and I have no idea where I went. In that state, they are literally just listening to the sounds [of the driver’s apps]. Stopping when they said stop, pick up when they say pick up, turn when they say turn. You get into a rhythm of that, and you begin to feel almost like an android.”
In their foundational text Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers, Alex Rosenblat and Luke Stark write: “Uber’s self-proclaimed role as a connective intermediary belies the important employment structures and hierarchies that emerge through its software and interface design.” “Algorithmic management” is the term Rosenblat and Stark use to describe the mechanisms through which Uber and Lyft drivers are directed. To be clear, there is no singular algorithm. Rather, there are a number of algorithms operating and interacting with one another at any given moment. Taken together, they produce a seamless system of automatic decision-making that requires very little human intervention.
For many on-demand platforms, algorithmic management has completely replaced the decision-making roles previously occupied by shift supervisors, foremen and middle- to upper- level management. Uber actually refers to its algorithms as “decision engines”. These “decision engines” track, log and crunch millions of metrics every day, from ride frequency to the harshness with which individual drivers brake. It then uses these analytics to deliver gamified prompts perfectly matched to drivers’ data profiles.
To increase the prospect of surge pricing, drivers in online forums regularly propose deliberate, coordinated, mass “log-offs” with the expectation that a sudden drop in available drivers will “trick” the algorithm into generating higher surges. I have never seen one work, but the authors of a recently published paper say that mass log-offs are occasionally successful.
Viewed from another angle, though, mass log-offs can be understood as good, old-fashioned work stoppages. The temporary and purposeful cessation of work as a form of protest is the core of strike action, and remains the sharpest weapon workers have to fight exploitation. But the ability to log-off en masse has not assumed a particularly emancipatory function.
After weeks of driving like a maniac in order to restore my higher-than-average driver rating, I managed to raise it back up to a 4.93. Although it felt great, it is almost shameful and astonishing to admit that one’s rating, so long as it stays above 4.6, has no actual bearing on anything other than your sense of self-worth. You do not receive a weekly bonus for being a highly rated driver. Your rate of pay does not increase for being a highly rated driver. In fact, I was losing money trying to flatter customers with candy and keep my car scrupulously clean. And yet, I wanted to be a highly rated driver.
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And this is the thing that is so brilliant and awful about the gamification of Lyft and Uber: it preys on our desire to be of service, to be liked, to be good. On weeks that I am rated highly, I am more motivated to drive. On weeks that I am rated poorly, I am more motivated to drive. It works on me, even though I know better. To date, I have completed more than 2,200 rides.