In 2008, a massive earthquake reduced the financial world to
rubble. Standing in the smoke and ash, Alan Greenspan, the former
chairman of the U.S. Federal Reserve once hailed as “the greatest banker
who ever lived,” confessed to Congress that he was “shocked” that the
markets did not operate according to his lifelong expectations. He had
“made a mistake in presuming that the self-interest of organizations,
specifically banks and others, was such that they were best capable of
protecting their own shareholders.”
We are now paying a terrible price for our unblinking faith in the
power of the invisible hand. We’re painfully blinking awake to the
falsity of standard economic theory—that human beings are capable of
always making rational decisions and that markets and institutions, in
the aggregate, are healthily self-regulating. If assumptions about the
way things are supposed to work have failed us in the hyperrational
world of Wall Street, what damage have they done in other institutions
and organizations that are also made up of fallible, less-than-logical
people? And where do corporate managers, schooled in rational
assumptions but who run messy, often unpredictable businesses, go from
here?
We are finally beginning to understand that irrationality is the real
invisible hand that drives human decision making. It’s been a painful
lesson, but the silver lining may be that companies now see how
important it is to safeguard against bad assumptions. Armed with the
knowledge that human beings are motivated by
cognitive biases
of which they are largely unaware (a true invisible hand if there ever
was one), businesses can start to better defend against foolishness and
waste.
The emerging field of behavioral economics offers a radically
different view of how people and organizations operate. In this article I
will examine a small set of long-held business assumptions through a
behavioral economics lens. In doing so I hope to show not only that
companies can do a better job of making their products and services more
effective, their customers happier, and their employees more productive
but that they can also avoid catastrophic mistakes.
Behavioral Economics 101
Drawing on aspects of both psychology and economics, the operating
assumption of behavioral economics is that cognitive biases often
prevent people from making rational decisions, despite their best
efforts. (If humans were comic book characters, we’d be more closely
related to Homer Simpson than to Superman.) Behavioral economics eschews
the broad tenets of standard economics, long taught as guiding
principles in business schools, and examines the real decisions people
make—how much to spend on a cup of coffee, whether or not to save for
retirement, deciding whether to cheat and by how much, whether to make
healthy choices in diet or sex, and so on. For example, in one study
where people were offered a choice of a fancy Lindt truffle for 15 cents
and a Hershey’s kiss for a penny, a large majority (73%) chose the
truffle. But when we offered the same chocolates for one penny less
each—the truffle for 14 cents and the kiss for nothing—only 31% of
participants selected it. The word “free,” we discovered, is an
immensely strong lure, one that can even turn us away from a better deal
and toward the “free” one.
For the past few decades, behavioral economics has been largely
considered a fringe discipline—a somewhat estranged little cousin of
standard economics. Though practitioners of traditional economics
reluctantly admitted that people may behave irrationally from time to
time, they have tended to stick to their theoretical guns. They have
argued that experiments conducted by behavioral economists and
psychologists, albeit interesting, do not undercut rational models
because they are carried out under controlled conditions and without the
most important regulator of rational behavior: the large, competitive
environment of the market. Then, in October 2008, Greenspan made
his confession.
Belief in the ultimate rationality of humans, organizations, and
markets crumbled, and the attendant dangers to business and public
policy were fully exposed.
Unlike the FDA, for example, which forces medical practitioners and
pharmaceutical companies to test their assumptions before sending
treatments into the marketplace, no entity requires business (and also
the public sector) to get at the truth of things. Accordingly, it’s up
to firms to begin investigating basic beliefs about customers,
employees, operations, and policies. When organizations acknowledge and
anticipate irrational behavior, they can learn to offset it and avoid
damaging results. Let’s take a closer look at a few examples.
The Dark Side of Teamwork
A few years ago, my colleagues and I found that most individuals,
operating on their own and given the opportunity, will cheat—but just a
little bit, all the while indulging in rationalization that allows them
to live with themselves. (See “
How Honest People Cheat,”
HBR, February 2008.) We also found that the simple act of asking people
to think of their ethical foundations—say, the Ten Commandments—or
their own moral code before they had the opportunity to cheat eliminated
the dishonesty.
Most individuals, operating on their own and given the opportunity, will cheat—but just a little bit.
But what happens when people collaborate? Do autonomous teams make
better, more ethical decisions? We decided to find out. In a series of
three experiments, we gave participants 20 math problems to solve in
five minutes and paid them 50 cents for each correct answer. In our
first treatment (the control condition), individual participants were
asked to write the number of problems they answered correctly on
collection slips and give them to an experimenter, who checked the
totals against the problem sheets. In a second treatment, participants
shredded their answer sheets without verification and simply submitted
their collection slips to the experimenter. Perhaps not surprisingly, we
found these participants lied, saying that they’d correctly answered
two more questions, on average, than those in the control treatment.
Things got more interesting in the third treatment, where
participants worked in pairs and shared the spoils. The results showed
that when a person realizes that his or her fudging would benefit other
team members by increasing the payout, dishonesty further increased by
25%.
In another setup, we tried to discover whether monitoring and
supervision would counteract team cheating. In fact, it did not. Though
cheating decreased somewhat, it didn’t disappear. More disturbingly, as
the members of our experimental group became better acquainted, the
tendency to cheat for the sake of the team increased even more. Other
experiments revealed that if one person is clearly seen to be cheating,
team members—particularly those who feel connected to the cheater—are
likely to depart from their own moral compasses and increase their
cheating. It seems that cheating is infectious.
These findings have serious implications for unsupervised
collaborative work in organizations. Although work groups can have many
social and functional advantages, they may also be more vulnerable to
unethical conduct.
The Revenge Motive: When Customers Are Unhappy
Now let’s look at customer behavior, an area that is particularly
fraught with irrationality. It’s a rare company that consistently makes
its customers happy, though many nobly try. And well they should; too
many firms fail to understand the price of customer unhappiness. Indeed,
given the right circumstances, most of us are more than happy to seek
revenge.
Ayalet Gneezy of the University of San Diego and I set out to
discover if even low levels of annoyance would cause people to seek
retribution. If so, we could assume that vengeful behavior in the real
world of dropped calls, flight cancellations, and credit card penalties
would be even greater.
Daniel, an actor who was our hired “agent,” gave participants in a
coffee shop several sheets of paper covered with letters and asked them
to find matching pairs. Participants were promised $5 each for
completing the task. On doing so, each signed a receipt and received a
stack of $1 bills. Daniel “mistakenly” overpaid some of them by two,
three, or four dollars.
In the “no annoyance” condition, Daniel explained the task and set
the participants to it. In the “annoyance” condition, he pretended to
answer his cell phone in the midst of giving instructions, talked for 15
seconds with a friend about pizza, hung up the phone, and then
continued with the instructions without acknowledging or apologizing for
taking the call. We wanted to discover whether the “annoyed”
participants would exact revenge by keeping the extra money he gave
them.
A mere 14% of those subjected to Daniel’s rude treatment returned the
additional money, compared with 45% of those in the other group. The
fact that only 45% returned the extra cash was depressing enough, but it
was striking that a 15-second phone call vastly decreased the
likelihood that the participants would return the cash.
In another version of the experiment, we wanted to find out more
about the impulse to punish. Would it make a difference whether Daniel
claimed that he was working for someone else? Would participants punish
the principal (the researchers behind the study) for the agent’s
misbehavior? Our results suggested that if people feel the need to take
revenge, they don’t differentiate between the two.
This is bad news for employers. If someone who works for you upsets a
customer—even in ways unrelated to the job—you will very likely pay the
price. Even the smallest transgression on the part of an employee can
ignite the instinct for strong revenge against the employer, regardless
of who is at fault.
What can company representatives or individuals do to soothe the
instinct for revenge in business or personal exchanges? Apologies work,
at least temporarily. In yet another version of our experiment, Daniel
apologized for the phone call interruption. We were surprised to find
that the show of regret was a perfect remedy. The percentage of people
who returned the extra cash was the same in the “apology condition” as
in the “no annoyance” condition. As it turns out the word “sorry”
completely counteracted the annoyance. (Of course, the effectiveness of
an apology is likely to diminish with the frequency of the annoyance.)
Revenge and cheating are only two of the irrational behaviors that underlie employees’ and customers’ decisions.
Revenge and cheating are only two of the irrational behaviors that
companies will find underlying their employees’ and customers’ decisions
and actions. Recognizing that, what is the way forward?
Experimenting with Behavior
Behavioral economics experiments that get to the bottom of people’s
decisions and actions are very different from the kinds of tests
companies traditionally use to try out new product ideas and marketing
concepts or to discover opportunities. The difference is not in the
research methodology itself but in the process of selecting ideas to be
tested.
The standard business approach to experiments is similar to an
engineering project. It makes strong assumptions about the laws that
govern the behavior of the different actors; the only question is how to
combine them in a way that makes sense for a particular application.
(Companies that gather large amounts of transaction data are well ahead
in this area. The casino giant Harrahs, for example, is famous for
running experiments using customer data to develop a set of tailored
services and offers.) A behavioral economics approach, in contrast, is
more like a science project: We search simultaneously for the governing
principles and how to implement them. Consider, for example:
Pricing.
I don’t know whether Apple’s executives were conducting a behavioral
economics experiment when they introduced the iPhone at a price of $600
and then quickly discounted it to $400, but that move revealed something
important about human behavior. By imprinting the price of $600 in
people’s minds, Apple was able to make consumers think that $400 was a
real bargain. In a standard approach to price setting, the people
running Apple’s pricing group might have asked focus groups about
various price points for the phone, and based on participants’ feedback,
picked the price they thought would maximize profits ($400). But if
Apple had set the initial price at $400, consumers would have had no
basis for comparison, since they’d never seen such a product before.
Adopting a behavioral economics perspective, Apple might have started
by questioning the assumption that people would know how to value the
pathbreaking product and so set up various pricing experiments. In this
type of test, the goal is not simply to find out the optimal price but
also to discover how people arrive at a decision to buy at that price.
Companies also need to consider how the introductory price could
influence the perception of value for a long time.
Product launches.
Behavioral economists might also look at the roles of habit and trust
in consumer choice. Take a manufacturer who is planning to sell a
triple-concentrated detergent, on the theory that environmentally
conscious consumers would prefer to eliminate waste. Given shoppers’
almost automatic impulse to reach for the same detergent, should the
manufacturer package the concentrate in the standard-size bottle and
charge more for it? Or should the manufacturer try to break consumers’
force of habit and package the concentrate in a bottle one-third the
size of the original? And what about trust? If consumers don’t trust the
manufacturer to deliver a more concentrated product, given that the
product smells and looks the same as before, will they be willing to pay
for it? How can the manufacturer overcome this hurdle?
Customers.
A variety of companies now use a behavioral economics approach to
more closely examine customer and employee behavior. For example, one
automobile insurer discovered that most people, when filling out forms
that ask how many miles they’ve driven in a year, claimed that they
drove less than they actually had. Building on the discovery that people
are less inclined to cheat after being reminded of their own ethical
standards, the company moved the signature line to the top of the form.
Applicants who signed the form at the top reported driving an average of
2,700 more miles a year than those who signed at the end.
In another example, the cable giant Comcast began addressing the
customer-revenge problem by using Twitter to respond to problems
proactively. The director of digital care, Frank Eliason, discovered
that by searching for the word “Comcast” (or, sometimes, “Comcrap”), he
could locate unhappy customers who were simply venting to themselves and
to their friends, and respond to their problems before they became
formal complaints. (Other companies, including JetBlue, General Motors,
Kodak, Dell, and Whole Foods Market are now also tracking customers’
comments on Twitter.)
Building a Behavioral Economics Capability
Behavioral economics can be seen as depressing; after all, many of
our experiments show human beings as incapable of making good decisions.
Multiple findings demonstrate that we are emotional, myopic, and easily
confused and distracted. Nevertheless, companies that make an
investment in behavioral experimentation can radically improve decision
making and lessen risk.
Firms interested in experimenting with behavior should understand
that the process is time-consuming and delicate. All too often,
companies set out to learn something about their customers’ habits only
to find that the way they devised their research was invalid and the
conclusions incorrect. Smart organizations will develop a behavioral
economics capability by hiring qualified experimenters and conducting
small trials that build on each other.
Once the understanding of irrationality is embedded in the fabric of
the organization, a behavioral economics approach can be applied to
virtually every area of the business, from governance and employee
relations to marketing and customer service. It is probably most useful
in the areas that we know the least about—such as the relationships
between compensation and performance, risk and reward, loyalty and
consumer habits, and pricing and purchasing behavior. As companies
become more willing to question their assumptions, discover something
about their stakeholders’ predilections, and share the results of their
learning, they will no doubt become a good deal wiser.
A version of this article appeared in the
July–August 2009 issue of
Harvard Business Review.
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