Imagine a sprinter running an Olympic race. He’s competing in the 1600 meter run.
The first two laps he runs at a steady but hard pace, trying to keep himself consistently near the head, or at least the middle, of the pack, hoping not to fall too far behind while also conserving energy for the whole race.
About 800 meters in, he feels himself start to fatigue and slow. At 1000 meters, he feels himself consciously expending less energy. At 1200, he’s convinced that he didn’t train enough.
Now watch him approach the last 100 meters, the “mad dash” for the finish. He’s been running what would be an all-out sprint to us mortals for 1500 meters, and yet what happens now, as he feels himself neck and neck with his competitors, the finish line in sight?
He speeds up. That energy drag is done. The goal is right there, and all he needs is one last push. So he pushes.
This is called the Goal Gradient Effect, or more precisely, the Goal Gradient Hypothesis. Its effect on biological creatures is not just a feeling, but a real and measurable thing.
The Math of Human Behavior
The first person to try explaining the goal gradient hypothesis was an early behavioral psychologist named Clark L. Hull.
As with other animals, when it came to humans, Hull was a pretty hardcore behaviorist, thinking that human behavior could eventually be reduced to mathematical prediction based on rewards and conditioning. As insane as this sounds now, he had a neat mathematical formula for human behavior:
Some of his ideas eventually came to be seen as extremely limiting Procrustean Bed type models of human behavior, but the Goal Gradient Hypothesis was replicated many times over the years.
Hull himself wrote papers with titles like The Goal-Gradient Hypothesis and Maze Learning to explore the effect of the idea in rats. As Hull put it, “...animals in traversing a maze will move at a progressively more rapid pace as the goal is approached.” Just like the runner above.
Most of the work Hull focused on were animals rather than humans, showing somewhat unequivocally that in the context of approaching a reward, the animals did seem to speed up as the goal approached, enticed by the end of the maze. The idea was, however, resurrected in the human realm in 2006 with a paper entitled The Goal-Gradient Hypothesis Resurrected: Purchase Acceleration, Illusionary Goal Progress, and Customer Retention. (link)
The paper examined consumer behavior in the “goal gradient” sense and found, alas, it wasn’t just rats that felt the tug of the “end of the race” — we do too. Examining a few different measurable areas of human behavior, the researchers found that consumers would work harder to earn incentives as the goal came within sight and that after the reward was earned, they’d slow down their efforts:
We found that members of a café RP accelerated their coffee purchases as they progressed toward earning a free coffee. The goal-gradient effect also generalized to a very different incentive system, in which shorter goal distance led members to visit a song-rating Web site more frequently, rate more songs during each visit, and persist longer in the rating effort. Importantly, in both incentive systems, we observed the phenomenon of post-reward resetting, whereby customers who accelerated toward their first reward exhibited a slowdown in their efforts when they began work (and subsequently accelerated) toward their second reward. To the best of our knowledge, this article is the first to demonstrate unequivocal, systematic behavioural goal gradients in the context of the human psychology of rewards.
Fascinating.
Putting The Goal Gradient Hypothesis to Work
If we’re to take the idea seriously, the Goal Gradient Hypothesis has some interesting implications for leaders and decision-makers.
The first and most important is probably that incentive structures should take the idea into account. This is a fairly intuitive (but often unrecognized) idea: Far-away rewards are much less motivating than near term ones. Given a chance to earn $1,000 at the end of this month, and each after that, or $12,000 at the end of the year, which would you be more likely to work hard for?
What if I pushed it back even more but gave you some “interest” to compensate: Would you work harder for the potential to earn $90,000 five years from now or to earn $1,000 this month, followed by $1,000 the following month, and so on, every single month during five year period?
Companies like Nucor take the idea seriously: They pay bonuses to lower-level employees based on monthly production, not letting it wait until the end of the year. Essentially, the end of the maze happens every 30 days rather than once per year. The time between doing the work and the reward is shortened.
The other takeaway comes to consumer behavior, as referenced in the marketing paper. If you’re offering rewards for a specific action from your customer, do you reward them sooner, or later?
The answer is almost always going to be “sooner.” In fact, the effect may be strong enough that you can get away with less total rewards by increasing their velocity.
Lastly, we might be able to harness the Hypothesis in our personal lives.
Let’s say we want to start reading more. Do we set a goal to read 52 books this year and hold ourselves accountable, or to read 1 book a week? What about 25 pages per day?
Not only does moving the goalposts forward tend to increase our motivation, but we repeatedly prove to ourselves that we’re capable of accomplishing them. This is classic behavioral psychology: Instant rewards rather than delayed. (Even if they’re psychological.) Not only that, but it forces us to avoid procrastination — leaving 35 books to be read in the last two months of the year, for example.
Those three seem like useful lessons, but here’s a challenge: Try synthesizing a new rule or idea of your own, combining the Goal Gradient Effect with at least one other psychological principle from The Psychology of Human Misjudgment, and start testing it out in your personal life or in your organization. Don’t let useful nuggets sit around; instead, start eating the broccoli.
