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Strategy Errors Made by Even the Smartest CEOs: How to Avoid Them in Credit Unions
Strategy Errors Made by
Even the Smartest CEOs:
How to Avoid Them
in Credit Unions
A Colloquium at Pepperdine University
Research For a Better Tomorrow
1752-108 (07/05)
ISBN 1-880572-89-3
ISBN 1-880572-89-3
P.O. Box 2998
Madison, WI 53701-2998
Phone: (608) 231-8550
www.filene.org
Strategy Errors Made by
Even the Smartest CEOs:
How to Avoid Them
in Credit Unions
A Colloquium at Pepperdine University
This colloquium was sponsored by
the Center for Credit Union Research at the
University of Wisconsin-Madison and the Filene Research
Institute; and hosted by Pepperdine University
Copyright © 2005 by Filene Research Institute.
ISBN 1-880572-89-3
All rights reserved.
Printed in U.S.A.
Filene
Research
Institute
The Filene Research Institute is a non-profit organization dedicated to
scientific and thoughtful analysis about issues affecting the future of
consumer finance and credit unions. It supports research efforts that
will ultimately enhance the well-being of consumers and will assist
credit unions in adapting to rapidly changing economic, legal, and
social environments.
Deeply imbedded in the credit union tradition is an ongoing search
for better ways to understand and serve credit union members and the
general public. Credit unions, like other democratic institutions, make
great progress when they welcome and carefully consider high-quality
research, new perspectives, and innovative, sometimes controversial,
proposals. Open inquiry, the free flow of ideas, and debate are essential
parts of the true democratic process. In this spirit, the Filene Research
Institute grants researchers considerable latitude in their studies of
high-priority consumer finance issues and encourages them to candidly
communicate their findings and recommendations.
The name of the institute honors Edward A. Filene, the “father of the
U.S. credit union movement.” He was an innovative leader who relied
on insightful research and analysis when encouraging credit union
development.
Progress is the constant replacing of the best there is with something still
better!
— Edward A. Filene
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Acknowledgements
The Filene Research Institute would like to thank Professor David
Smith and the Graziadio Executive Center of Pepperdine University
for hosting this important colloquium. Thanks also go to David Brock,
Community Educators’ Credit Union; Mary Cunningham, USA
Federal Credit Union; Gordon Dames, Mountain America Credit
Union; Hubert Hoosman, Vantage Credit Union; Rick Rice, Teachers
Credit Union; and Patrician Smith, Unitus Community Credit Union,
for facilitating small group discussions during the colloquium. And
thanks to John Tippets, President/CEO, American Airlines Credit
Union, for sharing notes taken during and after the colloquium.
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Table of
Contents
Executive Summar y . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
CHAPTER 1:
Introduction . . . . . . . . . . . . . . . . . . . . . 5
CHAPTER 2:
Strategy Errors Made by
Even the Smartest CEOs . . . . . . . . . . . . . . 7
Background . . . . . . . . . . . . . . . . . . . . . . 7
Overconfidence . . . . . . . . . . . . . . . . . . . 8
The Winner’s Curse . . . . . . . . . . . . . . . . . 12
Selection Bias/Adverse Selection . . . . . . . 14
A Guessing Game . . . . . . . . . . . . . . . . . . 14
Mental Accounting . . . . . . . . . . . . . . . . . 17
Status Quo Bias/Ownership Effects . . . . 20
Ownership Issues . . . . . . . . . . . . . . . . . . 21
Loss Aversion . . . . . . . . . . . . . . . . . . . . 22
Anchoring . . . . . . . . . . . . . . . . . . . . . . 23
Sunk Cost Effect . . . . . . . . . . . . . . . . . . 24
Herding Behavior . . . . . . . . . . . . . . . . . 25
Information Cascades . . . . . . . . . . . . . 26
Guessing Again . . . . . . . . . . . . . . . . . . 27
Confirmation Bias . . . . . . . . . . . . . . . . 27
An Experiment in Information Cascading 28
Economic Bubbles . . . . . . . . . . . . . . . . 30
Misestimating Future Pleasure Levels . . . 32
CHAPTER 3:
Reports of CEO Discussion Groups . . . . . 35
CHAPTER 4:
Closing Obser vations . . . . . . . . . . . . . . 47
About the Presenters . . . . . . . . . . . . . . . . . . . . . . . . 49
Filene Research Institute Administrative Board/
Research Council . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Filene Research Institute Publications . . . . . . . . . . . . 55
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Executive
Summar y
The purpose of this colloquium was to examine emerging theories
of behavioral economics, and to apply those theories to credit union
practices. The 2003 Nobel Prize in Economics was awarded to a pioneer
researcher in the field of behavioral economics, an indication of the
great value of this research.
Even the best executives are subject to making predictable errors
that affect their organizations and their careers negatively. Many of
these errors relate to how CEOs think about decisions in the face of
substantial uncertainty.
The colloquium presentations were led by researchers Charles
Holt, A. Willis Robertson Professor of Political Economy at the
University of Virginia; and Gary Charness, Assistant Professor of
Economics, University of California, Santa Barbara. Holt and Charness
challenged participants to understand the biases that come into play in
making strategic decisions, and apply this knowledge to their work in
credit unions.
The field of behavioral economics – a coalescence of economics and
psychology – examines the biases that affect the way we make economic
decisions. When these biases come into play in making strategic business
decisions they can affect the outcome of a given initiative. The first step
toward managing our biases is to be aware of them.
The presenters took the group through a number of experiments to
demonstrate the power of 10 psychological components in shaping
the decision process. These 10 components are psychological shortcuts
that may have proved useful in our past experience, but are based upon
factors other than rational thought. Environmental, cultural and other
factors can easily skew decisions that ideally should be based on purely
rational grounds. The pitfalls examined at the colloquium include:
Overconfidence: Business executives are naturally confident people.
They do not rise to the top of their profession without the talent to
make insightful decisions and confidence to rely on their own judgment.
Taken to extreme, however, confidence can impair the thinking of the
CEO, with negative results for both the individual and the organization.
A credit union CEO may have been successful in previous branch
location sitings, for example, but become overconfident in assessing the
profitability of new locations.
The Winner’s Curse: In a situation involving competitive bidding,
the contract is usually awarded to the low bidder. However, in cases
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involving several bidders, the low bid may come at the price of profit
margin and may bring risk without adequate compensation. The
winner’s curse refers to situations in which we win a negotiation,
but may wish we hadn’t. In a credit union setting, a CEO may see
apparent opportunities in initiating an indirect or member business
lending program, only to find later that the practice requires specialized
knowledge beyond the credit union’s resources.
Selection Bias: Selection bias is related to overconfidence, and refers to
situations in which we are matched with others who do not constitute a
random sample from the general population. In a self-selected sample,
we can find ourselves in a high-risk competitive group whose knowledge
of a particular situation exceeds our own. Also, we can find ourselves
with a portfolio of inappropriately risky business relationships.
Mental Accounting: Researchers have found that we tend to
compartmentalize money into various accounts, rather than look at it
as a commodity. Money in each of these accounts is treated differently
depending upon its source. Personal funds are placed in mental
accounts such as “entertainment money,” “college money,” “household
expenses,” and other categories. Businesses create similar accounts.
Moving resources among categories often leads to turf wars as we take
money from one department or purpose and allocate it to another. But
the organization as a whole might be better off exercising flexibility.
In credit union management, all money has the same value, but CEOs
may politicize it or place certain labels on it. Credit Unions can return
value to members in 1) lower loan rates, 2) higher dividends, 3) lower
fees and 4) educational services, but managers are subject to a number
of mental account biases.
Status Quo Bias/Ownership Effects: In experiments involving the
allocation of investment funds, researchers find that participants more
often choose to keep a current investment than replace it with another
investment. This result occurs regardless of which investment is the
status quo. In a related bias, ownership of an object tends to increase
the value we attach to it. We value an object more highly if we actually
own it than if we are merely looking at it or considering purchasing
it. In consumer finance, individuals often show a status quo bias when
they resist transferring their accounts to another financial institution
even though its rates and terms may be more favorable. Credit unions
may cling to the status quo by failing to close money-losing branches,
prune outdated products, or deploy innovative strategies to reach new
markets.
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Loss Aversion: Individuals care more about losses than they do about
gains. While the perception of loss and gain depends upon where our
reference point is, in general we tend to feel worse about a loss than
we feel good about a win. The disutility of a loss is stronger than the
utility of a win.
Anchoring: During negotiation, people tend to anchor on the first
number proposed. The tendency is to regard that first number as a
baseline from which negotiations can proceed. The first number creates
a mental model for subsequent negotiation. In some situations, we can
drive the bargaining process by putting the first number on the table.
In other situations, we may be better off to play a waiting game to
determine first what the other party is willing to offer.
Sunk Cost Effect: Sunk cost effect is related to loss aversion, because
it induces a resistance to abandoning a large investment that is not
turning out as planned. If the project is abandoned, someone will take
the blame for a failure. In most situations, when it becomes apparent
that the project is not viable, rational thought would tell us we need to
cut our losses. Yet we often throw good money after bad in an attempt
to rescue a failing project.
Herd Behavior: When we see others moving in a particular direction,
it is natural to want to follow their lead. The desire to conform to the
opinions and behavior of others is a fundamental human characteristic.
As a result, we are inclined to follow the group rather than base our
decisions on empirical evidence and thorough analysis. CEOs dislike
the prospect of being the only one to make a catastrophic mistake or be
slow to participate in a trend, so they rely on the assumed competence
of others. In the credit union world, executives may develop a herd
mentality as they define their position within a “movement” that is
“cooperative” and “small and must stick together.” Even these noble
concepts can lead to seductive conformity.
Confirmation Bias: Confirmation bias deals with our tendency to
overestimate the extent to which others share our views. Dominant
individuals often create, demand or hear a false consensus among
their colleagues, which inhibits rational decision-making. The role of
independent advisors is to balance this tendency toward false consensus.
A credit union CEO should encourage independent thinking on the
part of subordinates, to challenge conventions in finance, operations,
marketing and product development.
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Misestimating Future Pleasure Levels: This bias suggests that we do
not always realize what our mental state or pleasure level will be after an
event takes place. We think something will make us happy or unhappy,
but our estimates of the magnitude of that feeling are often mistaken.
A long rational view of the eventual outcome and its consequences is
an asset in making strategic decisions.
To apply the principles presented by Holt and Charness to real-life
credit union policies and practices, credit union CEOs were asked
to discuss examples of decision-making bias in their own experience.
In small groups, they addressed issues such as indirect lending,
CEO/board relations, technology mandates and strategic planning
considerations. Reports on these discussions follow the presentation
material detailed here.
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CHAPTER 1
Introduction
Three years ago at the time the Nobel Prizes were awarded, economists
throughout this country and around the world waited expectantly for
the name of the winner to be announced. And in that particular year,
nobody on the short list of economists who were expected to be in the
running received the auspicious telephone call. Instead, a professor of
psychology at Princeton University, Daniel Kahneman, received the
call. Kahneman had never taken an economics course. He received the
Nobel Prize because he had developed a theory that postulates that
people – even the very brightest people – make decisions based on
factors other than rational thought.
Kahneman was awarded the prize “for having integrated insights from
psychological research into economic science, especially concerning
human judgment and decision-making under uncertainty,” the
Royal Swedish Academy of Science said. Kahneman explained his
prizewinning work as “an attempt to provide a more realistic set of
ideas for what the economic agent is really like.”
In one of Kahneman’s studies, he gave half of the participants a mug
and the other half no mug. The results showed that people with a mug
wanted to trade the mug for an average of approximately $7 cash, while
the people without a mug valued the same type of mug at $3 cash. The
two acts are logically the same – trading a mug for money – but those
with mugs did not want to give up something they already had. This
“very myopic” aspect of human decision-making was, when Kahneman
first started this line of research in 1969, not a widely accepted part of
economic theory.
As a result of Kahneman’s research, economists and psychologists have
been working together to determine the factors that drive decisionmaking. His landmark paper, “Prospect Theory: An Analysis of
Decision under Risk,” was published in Econometrica. Our job at Filene
is to keep credit unions abreast of this kind of cutting edge research,
and help them apply that research to environmental challenges facing
our industry. In that way, we hope to help executives do their job better,
and serve their members better.
When we refer to strategy errors, we are not talking about a failure
to think things through, or an inattention to detail. Here we are
addressing the mental processes that intercede upon rational thinking
in the human mind, and the consequences of those processes.
We want to learn what kind of strategic errors executives are likely to
make because of irrational thinking and how we can avoid them. Our
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path to that goal will be first to hear about the types of well-established
thinking errors that are likely to intrude upon our decision-making
process. Our presenters will develop a list of key thinking errors upon
which we can focus in our deliberations. The afternoon will be devoted
to small group discussions during which we will apply the key thinking
errors we learn about this morning to the business of running a credit
union. Each group will be asked to cite examples in its experience of the
type of thinking errors that our presenters have examined during the
morning session. The groups will also be asked to explore ways in which
these thinking errors committed by credit union people might have been
avoided. Each group will then present a report to the colloquium as a
whole, summarizing the findings of its discussion.
Our purpose is to understand the kinds of biases each of us is subject to
in making strategic decisions, and thereby place ourselves in a position to
deal with them. Awareness is the path to optimal decision-making skills.
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CHAPTER 2:
Strategy Errors
Made by Even
the Smartest
CEOs
CHARLES HOLT AND GARY CHARNESS
This session was led by researchers Charles Holt, A. Willis Robertson
Professor of Political Economy at the University of Virginia; and Gary
Charness, Assistant Professor of Economics, University of California,
Santa Barbara. Holt and Charness challenged participants to understand
the biases that come into play in making strategic decisions, and apply this
knowledge to their work in their credit unions. The presenters took the group
through a number of experiments in behavioral economics to demonstrate
the power of psychological components upon the decision process.
In our work, we frequently perform psychologically-oriented experiments
in an economic framework. It is significant that Daniel Kahneman won
a Nobel Prize for his work on issues of psychology and economics. It
is a useful and practical undertaking, as we hope to demonstrate in our
work today.
We all have built-in biases that affect the way we make decisions, in
business as well as in personal transactions. The first step toward
managing these biases is to be aware of them. If we are aware of our
predilections, we can keep them in mind as we make strategic decisions.
Even very bright people are prone to biases. Overconfidence, for
example, is a particular pitfall for intelligent people because they are
correct in their assumptions more often than not.
The discipline that examines these psychological phenomena is
called behavioral economics. Behavioral economics has gone through
cycles over a period of several decades. As recently as 30 to 40 years
ago, the discipline was considered antiquated, as scholars moved
toward a strictly rational explanation of economics. Today, however,
the behavioral component of economic decision-making enjoys a
newfound popularity.
BACKGROUND
Business strategy literature is generally founded on classical
microeconomic principles. The usual assumptions in economics are
based on rationality. Rationality in this context is defined to mean that
our economic goal is to maximize our own funds.
One of the classic experiments in this field is the ultimatum game,
in which we imagine that we are paired with another, anonymous
competitor. The experimenter gives one participant the opportunity
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to propose a division of ten dollars between the two individuals. The
first participant can propose any division, and the second participant
can either accept or reject the proposal. If the second person accepts
the proposal, the deal is implemented. If the second person rejects the
proposal, both participants get nothing.
Standard economic thought says that the first person will reason that
if the second person is concerned only with maximizing their economic
return, that person will take any amount proposed. Even if person
number one proposes to keep $9.99 and give person number two one
cent, person number two will take it, rather than receive nothing by
rejecting the proposal.
In actual fact, person number two is not likely to accept one penny, even
though that would be in his or her economic interest. The experiment
demonstrates an obvious insight into the psychological dimensions of
negotiation. Perfect rationality is not consistent with human behavior.
Aside from considerations of fairness, some negotiations are so
complex that we have great difficulty figuring them out.
In the arena of decision-making, it has become popular to incorporate
how people think into economic models. One such approach is neuroeconomics, which looks at brain scans to identify what happens in our
brains when they are presented with particular types of decisions. The
trend is toward openness and non-standard approaches in economic
decision strategy.
Today we will examine 10 pitfalls faced by executives in making
decisions, flaws that inhibit sound business judgment and increase
the risk of failure. These biases provide shortcuts that have proved
useful in our past experience, but are based upon factors other than
rational thought. Because CEOs are human, they are influenced
by environmental, cultural and other factors that can easily bias
their strategic decisions. Good executives need to be aware of and
understand these biases in order to serve their organizations and
publics most effectively.
OVERCONFIDENCE
To illustrate the kind of processes we are referring to, we would like to
conduct an experiment with you credit union CEOs. In this experiment,
each of you will make a decision, and then we will choose three of you
to play a game. The situation for our game is summarized in Figure 1.
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“…it has
become popular
to incorporate
how people
think into
economic
models.”
Figure 1 – The Takeover Game
• Company is going to undertake a risky exploration that will
result in a share value between $0 and $99, with each number
[0, 99] equally likely.
• You are a better manager, so if you acquire it, you will earn 1.5
times the value to me, but you must make a takeover bid before
the exploration outcome is known.
• You will make a single take-it-or leave-it offer, which I’ll take
if it exceeds my value (after the exploration results are known).
Your payoff:
0
for a rejected offer,
1.5 x (my value) – your bid for an accepted offer
Your company is about to undertake a risky project. You will find that
the value of this undertaking is somewhere between zero and $99.
Each value is equally likely, for a uniform distribution. You have an
opportunity to acquire this project. Because you are a better manager,
the project will be worth 50% more under your management than it
is to present management. But you must make a bid before the value
outcome is known to you. At the same time, the present owner of the
project knows its value before making a decision on whether to sell.
You will make a single offer. The owner will look at your offer, and take
your offer if it is at least as large as the actual value. (We will ascertain
the actual value randomly.) If you make a bid and the bid is at least as
large as the actual value, you will receive that value multiplied by 1.5,
and you must pay what you bid. If your bid is not as high as the actual
value, the bid will be rejected and nothing will happen.
Now, please make your bid – any value between zero and $99. We
will then throw two ten-sided dice to determine the actual value. For
example, if we throw an eight and a four, for eighty four, we would add
forty-two for a total value to you of $126.
Each participant made a bid, and the presenters chose one bid from the
group. This bid was for $60. The presenters rolled the dice and determined
an actual value for the project of $19. Thus, the value of the project to
player number one would be $28.50. Therefore, player one would lose
$31.50 - $60 minus $28.50.
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Player number two bid $45 for the project. The presenters rolled the dice
and determined an actual value for the project of $44. This established
a value of the project to player number two of $90, or a profit to player
number two of 43.
Player number three bid $75 for the project. The presenters rolled the dice
and determined an actual value of $35. Thus, the value of the project to
player number three would be $52.50. Player three would lose $22.50.
This kind of game experiment is often conducted with MBA students.
The typical bid in these situations is $50-$60. When asked what their
reasoning is in making a decision, students often cite an average value
of $50, which means the project will be worth an average of $75 to the
bidder (1.5 times actual value). This leads them to bid somewhere in
excess of the average value of $50. But there is a problem with that kind
of decision-making process.
In performing this experiment with a class at the University of Virginia,
the average bid was $63, not too far from the $55 average among the
three individuals we worked with today. Two-thirds of the class lost
money, just as our three subjects here did.
The question is why a bid of $60 would be a loser. If we bid $60 and our
bid is accepted, what does that tell us about the value of the project?
If we bid $60, the average value to us is $75 ($50 X 1.5). That seems a
reasonable aspiration.
But if we bid $60 and our bid is accepted, we know that the value to the
owner is below $60. That means that on average, the value to the owner
would be $30, or halfway between zero and $60. On average, then, the
project is worth $45 to us.
Next, let’s assume that our bid is $30. That bid might be rejected, but if
it is accepted it means the average value to the owner is $15, and $22.50
to us. We bid $30, so again we would lose money on our bid of $30.
In fact, the experiment is a trick problem because any positive bid above
one is likely to lose money. One third of the time our bid is high enough
and we will win, but two-thirds of the time we will lose. Theoretically,
the best bid would be zero, or a decision to stay out of the bidding.
Typically, people do not think through the problem in order to perceive
that result. For example, we do not take into account the cognition of
the seller. We may be reasonably confident about our bids, but we often
do not take all factors into account in making our decisions.
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A real life example of what we are talking about is illustrated in two
studies done by researchers in the mid-1980s that examined why raiders
were paying more than market value for target firms, considering
that stockholders of acquired firms typically make significant profits
while the buyers gain little on average. The research found that raiders
performed a valuation on the firms they considered buying. If their
valuation was below market value they would not bid, but if their
valuation was above market value they would try to take over the
company. The situation may have been influenced by the fact that the
raiders were flush with cash at the time.
But estimates of value can be very tenuous, based on conjecture about
future sales, product development and other factors that are difficult
to quantify. Therefore, some valuations will be overestimates and some
will be underestimates. In addition, the buyer may be overconfident
about the real value of the company.
“…estimates
of value can be
tenuous, based
on conjecture
about future
sales, product
development
and other
factors difficult
to quantify…”
In another example, a Stanford University professor was asked
for advice on bidding for an oil lease estimated to be worth $200
million. The discussion centered on bids around $70 to $90 million. The
professor asked why bids were not in the $150 million range, to increase
the bidders’ chances of securing the lease. With a bid of $150 million,
the successful bidder would still reap a profit of $50 million.
In response to the professor’s question, the bidders replied that people
who bid at that level were no longer in the oil lease business. Bidders
had learned that it was a mistake to bid too high on oil leases. To guard
against possible overconfidence, we need to be careful of new situations
where tested rules of thumb no longer apply.
We tend to be overconfident in our ability to make accurate estimates. If
we asked this group how many believe that your products and services
are “above average” among those in the financial services industry, the
response would likely be overwhelmingly positive.
To overcome the tendency toward overconfidence and over-optimism,
says writer Charles Roxburgh1, we should test our strategies under
a wide range of scenarios; assume a more extreme downside to our
most pessimistic scenarios; and build more flexibility and options into
our strategies.
1
Hidden flaws in strategy, The McKinsey Quarterly, 2003 Number 2
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THE WINNER’S CURSE
Suppose a team assembled to bid on an oil lease had been expecting to
compete with two other bidders for the project. Now, our team learns
that there are in fact 20 other bidders. Immediately, there is vastly
greater competition. The question facing our team is whether to raise
our bid in anticipation of greater competition. The danger of raising
the bid, however, is that in so doing we may overbid.
But how is the number of bidders related to the degree of overbid
risk? One possibility is that a herding effect will develop, in which all
bidders raise their bids in anticipation of greater competition. There
is also the consideration that if our team wins in a competition with
two other bidders, it has valued the lease higher than just two other
teams of engineers; but if our team wins in competition with 20 other
bidders, it has valued the lease higher than 19 other bidders. It is
possible, therefore, that we had the highest bid because we had the most
egregious overestimate of value.
The sad fact is that in bidding situations with a greater number of
bidders, the winner tends to make less money – or in fact, loses money.
Winning is an informative event. In university classes, experiments of
five rounds of two-person auctions produced 18 of 25 winners who
earned a profit. Conversely, in five rounds of 12 person auctions, only
one in five winners earned a profit. In terms of strategy flaws, we call
this the winner’s curse.
In actual field practice, one researcher found that several major
companies took a careful look at their record on bidding for oil leases
in the Gulf of Mexico where sealed competitive bidding was the
method for acquiring leases. The researcher found that if he ignored
the era before 1950 when land was a good deal less expensive, Gulf oil
leases paid off at something less than one might obtain in dividends
from the local credit union.
The same kind of phenomenon holds true in personal business deals.
A homeowner solicits bids to replace his kitchen floor. When he
selects the low bid winner from among three contractors, the winner
is immediately worried about what his profit margin will be. It may be
a relatively complicated job involving a pantry and lots of irregular
spaces. The winner agrees to do the job, but he is concerned about the
validity of his strategic decision.
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We might also examine how construction contractors might perform
when placed in theoretical situations like those faced by university
students. At Texas A&M University, a professor did just that.
Contractors were given the same kinds of situations that students
faced, and their performance level was essentially the same as student
performance levels. This experiment suggests that the winner’s curse
applies across the board, whether the subject is a business school student,
a remodeling contractor, or an oil lease company. At a cognitive level,
we do not understand the situation well enough to make an informed
decision, even if we depend on that practice for our livelihood. We are
simply not familiar enough with the ranges of values involved.
In still another experiment, suppose we solicit capital investment
project proposals from a number of different departments within the
credit union, each of which makes a proposal. Each department is also
responsible for making an estimate of the profitability of its project. It
is not surprising that the winning proposal is likely to be the one with
the most over-optimistic projections. That doesn’t mean the project
should not be funded, but it does underscore the institutional tendency
toward overconfidence in the outcome.
We need to remember that things may not be as rosy as the estimates
indicate, and adjust for that likelihood. Even though we as individuals
are not overconfident, the institutional process may generate an aura of
overconfidence that creates an unsupportable bias.
When we refer to the winner’s curse, we are talking about situations in
which we win, but may wish we hadn’t. This is a pervasive bias, and
something we need to be aware of as we make business decisions. It is
easy to be optimistic, but many times people who have won negotiations
in the past have simply been lucky. That doesn’t mean they will continue
to be lucky.
In the credit union world, we may see the phenomenon of the winner’s
curse in connection with commercial lending: that is, credit unions
may be selected as lenders because they lack experience in making
commercial loans, and the ability to value those loans effectively.
Some credit unions that have found success in business lending,
particularly in commercial loans involving larger, multiple owners
instead of smaller, single member business loans, may owe their success
to the fact that banks have been charging high rates and fees for such
loans. Banks may not be interested in serving a particular segment,
a particular borrower, a class of collateral, or a geographic area.
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However, it is also possible that credit unions are finding success in the
commercial lending arena because they do not understand fully the
costs, risks, and environment of the market.
SELEC TION BIAS/ADVERSE SELEC TION
Another experiment that reveals elements of overconfidence was
performed at CalTech and the Wharton School of the University of
Pennsylvania. Participants were given the option to enter a market or
not. If they did not enter the market they earned $5. If they did enter
the market, they were placed in a pool in which $50 was divided among
all entrants in proportion to their scores on a sports trivia test.
We know that individual knowledge of sports varies widely. Some
people know very little about sports, and others know quite a lot.
In this experiment, there was considerable over-entry – that is, a
substantial majority of participants chose to take the test. They were
optimistic about their chances of winning. As a result, entrants in the
market earned less than $5 on average. Those who took the test faced
stiff competition from participants who were indeed experts in sports
trivia. Economists call this selection bias. Individuals who choose
to take the test do not constitute a random sample from the general
population. They are self-selected, which makes it more risky to be in
this group.
A Nobel Prize winning researcher at the University of California at
Berkeley, in investigating the used car market, theorized that cars sold in
the used car market are typically those with mechanical problems. The
fact is that the seller knows more about the product than the buyer does,
and hence the age-old adage applies: caveat emptor – buyer beware.
A similar situation might develop in making loans. If an individual
is willing to pay very high interest rates, it says something about the
individual as a borrower. When the lender grants loans to people with
impaired credit, he should do everything he can to obtain security to
protect himself. The issue of adverse selection is another of the biases
that we confront in our daily decision-making process.
A GUESSING GAME
Now let’s play another game. Choose an integer between zero and 100.
We will collect the guesses, and the person whose choice is closest to
one-half the average will win $20. All others will win zero.
14
“Individuals
may be subject
to selection
bias, in which
participants do
not constitute a
random sample
from the general
population.”
The average choice among the colloquium group was 19.75926. Half
of that is 9.87963. The closest bid to that number among these
participants was 8.
The group was asked to choose a number between zero and 100, yet
the average choice here was about 20. One might expect participants
to average a number close to 50, or halfway between the two extremes.
To win the game given those circumstances, a participant would be
expected to choose a number around 25, or half the average. But if
participants expect others to think in the same way they themselves are
thinking, they are likely to choose a smaller number. This experiment
illustrates the tendency of individuals, when given no feedback, to bid
lower. The bids go down. It is a matter of trying to predict how other
people are going to think.
In a University of Virginia class over a number of rounds of the game,
guesses started high and gradually came down to zero as we see in
Figure 2. Then the researcher changed the formula. Instead of using 10
plus half the average, he made the formula 20 plus half the average. The
closest to 20 plus half the average would win. He expected the average
to go to 20.
Figure 2 – Guessing Game: effects of adding 20 to the target
In the first round under the new formula, the average guess was about
28. But the next round, the average went up to 34! In succeeding rounds,
the average leveled off at over 40. The reason? Half of 40 is 20. Add 20
to that and we’re back to 40. If everybody were guessing 40, then the
target would be 40, and that’s where we want to be. If everybody guesses
15
below 40, the average would be rising. If everybody guesses above 40, the
target would be below the average and the target will be falling.
This game is intended to generate a discussion of what is optimal in
many strategic business situations. The participants’ actions depend
upon what they think the others in the group are going to do. And if
a participant is thinking strategically, she will be thinking about what
is in the minds of the other participants. How are they making their
decisions? What structure are they going through? And in particular,
what are they thinking she is going to do?
In Edgar Allen Poe’s story The Purloined Letter, the inspector thinks
about where the thief might have hidden the article in question, while
the thief thinks about where the inspector might look to find that article.
The story contains layers of inter-related reasoning. Each participant is
attempting to “read the mind” of the other.
We observed earlier that even though the equilibrium in our first
experiment was zero, if we were to guess zero we would almost always
lose or make no money. The game-theoretic choice would not win in
this situation because not everybody else thinks through all possible
iterations. We need to take into account the fact that some randomness
among people enters into the equation.
There is an experiment called “the dollar auction,” in which participants
bid for a dollar. The highest bid wins the dollar. However, the second
highest bid also must pay the bid price but wins nothing. What typically
happens in this experiment is that the bid rises to 98 cents, then 99 cents,
and ultimately to one dollar. At this point the person who bid 99 cents
realizes that he is going to lose 99 cents. So he bids $1.01. As a result,
the bidding continues to increase ad infinitum.
A participant might decide to bid one penny, because she might
encounter someone who would choose not to bid at all. In that case,
she would win the dollar for a penny. It is irrational to bid a penny, but
it might be a good strategy. Then, if competition arose, the participant
could stop bidding. To be successful at this game, we need to think
about how other people are likely to behave.
One application of selection bias and adverse selection in the financial
services community might relate to bank motives in their continuing
attacks on credit unions. For many bankers, those attacks may be the
result of a sincere competitive antagonism, but for others they may
be part of a far more rational gaming theory. For example, in losing
16
the battle over credit union taxation, banks may see an opportunity
to place themselves in an advantageous position with respect to more
important agenda items.
Another possibility is that by focusing the legislative debate on the credit
union/bank fight, bankers hope to reduce the time and attention given to
their off-shore tax avoidance schemes by legislators, or distract attention
from manipulative credit card and other anti-consumer practices.
Another game theory possibility is that by arguing that credit union
pricing advantages are due to tax advantages, bankers will diminish the
effect of real credit union branding, and obscure the fact that the credit
union pricing advantage is due to the consumer-owned, not-for-profit,
cooperative business model of credit unions.
Finally, bankers may believe that a tax war with credit unions gives
smoke screen cover and trade unity to the big banks as they overwhelm
both credit unions and small banks.
Assuming one or more of these possibilities has a basis in fact, credit
unions may want to give some thought to their own position in the
game. What are credit unions doing right, and what might they consider
doing differently?
“…we tend to
compartmentalize
our money into
various accounts,
rather than
look at it as a
commodity.”
MENTAL ACCOUNTING
Professor Richard H. Thaler at the Graduate School of Business of
the University of Chicago is one of the leading experts in the field of
behavioral science and economics. Thaler examines behavioral finance,
and one of his ideas is related to mental accounts. The theory is that
we tend to compartmentalize our money into various accounts, rather
than look at it as a commodity. Money in each of these accounts is
treated differently depending upon its source. Funds are placed in
mental accounts such as “gambling wins,” “college money,” “household
expenses,” and other categories.
We treat “house money” won at a casino, for example, differently than
we treat other classes of money because it is “found money.” College
money, on the other hand, might be funds set aside for education
expenses, and therefore regarded as untouchable. Our college money
might be used to solve a current problem, but we do not want to use it
for other purposes because it is sacrosanct.
17
Mental accounting opens us to the potential to damage ourselves
financially. A study by Charles Holt along with Cyert and Degroot
demonstrates the point. The researchers examined aggregate business
investment in the 1970s and concluded that individuals often make
decisions on business investments not on the basis of how good
the investment is but rather on where the funds come from. A firm that
has retained earnings feels obligated to invest those funds. Investment
decisions are made on the basis of availability of funds rather than on
the basis of the opportunity cost of money.
It is difficult to overcome the urge to do mental accounting. We should
do so if possible, but at a minimum we should be aware that this is a bias
that colors our decision-making process. In an organization, moving
money among categories often leads to internecine squabbling as we
take money from one department or function and allocate it to another
department or function. But to the extent it is feasible, the organization
might be better off exercising as much flexibility as possible.
In brain studies, researchers watch the blood flow to different parts of
the brain during a transaction. They have found that a real, tangible
reward causes blood to flow to different parts of the brain than it does
when the subject is thinking in more abstract terms, such as what might
occur a month hence. The stimulus and response varies, depending on
the situation.
We see the same kind of process at work in credit union management.
Although all money is the same, we are likely to politicize it or
place certain labels on it. Credit Unions can return value to
members in 1) lower loan rates, 2) higher dividends, 3) lower fees and
4) educational services, but we are subject to a number of mental
account biases, including:
Budget boxes: A budget ought to be a flexible tool for monitoring
credit union expenses. When a budget is set in stone, it cannot
respond to changes in the environment or internal needs of the
organization.
Household vs. product profitability: The credit union may run a deficit
on its youth accounts at the same time it is attracting profitable
business in several other product lines from parents.
Windfall incomes that go to the bottom line: A drop in uncollectibles,
for example, may produce unexpected undivided earnings unrelated
to the CEO’s management abilities. If losses are less than anticipated,
we often think of the remaining funds as a windfall that amounts to
mad money.
18
Still another example of mental accounting in credit unions is when we
set aside a specific amount for anticipated loan losses. If those losses
are less than anticipated, we often think of the remaining funds as a
windfall that amounts to mad money.
In order to avoid the pitfalls associated with mental accounting, a CEO
can work to create a culture of challenge within the organization, a
culture in which nothing is sacred and key players understand that the
overall good of the credit union comes before turf considerations.
The process of mental accounting creates a bias toward spending more
freely when we receive an unexpected bonus. What if, for example, we
win a $1,000 bet on the outcome of the Superbowl? We would be more
likely to spend that money on luxury items than if it came from hardearned wages.
Likewise, a 1966 study of Israeli recipients of German reparations
examined the spending patterns of 267 families. Those families
who received large payments – in a magnitude of two-thirds of their
annual income – spent 23% of the money in the first year. But among
families who received smaller payments equal to seven percent of their
annual income, first year expenditures averaged 14% of their annual
income! These families spent twice the amount of the windfall during
the first year.
And in a credit union example, members of one credit union who are
lottery winners receive as much as $100,000 annually after taxes. But
it is these very members who borrow even more to support a newly
realized affluent lifestyle, even though prior to winning the lottery they
lived very modestly.
There is also evidence that anticipated income has little effect on
present consumption. Studies of increases in Social Security that
are announced six to eight months in advance reveal that recipients
maintain their current spending levels until the increase goes into effect,
after which their spending increases.
There is research to suggest that when we think about future receipts,
different parts of our brains are activated and we undervalue
those receipts. Anticipation of future payments is associated with
frontal lobe activity.
Let’s go back to our example of winning a bet on the Superbowl: if we
win our bet and receive the proceeds immediately, we are likely to spend
a significant portion of our winnings immediately. But what if we win
19
$1,000 to be paid at the time of next year’s Superbowl? Odds are that
the win will affect our consumption very little, because the money is
categorized in a mental account as future income.
To avoid the mental accounting bias, we can remind ourselves that every
dollar is worth precisely the same as every other dollar, whatever the
category. In this way, we can be sure that all investment are judged on
consistent criteria and be wary of spending that has been reclassified.
STATUS QUO BIAS AND OWNERSHIP EFFEC TS
In exploring the role status quo bias plays in our decision-making, we
might imagine ourselves as serious readers of the financial pages, but
until recently have had few funds to invest. Now we inherit a portfolio
of cash and securities from our great uncle. A significant portion of
this portfolio is invested in moderate-risk Company A. We deliberate
about whether to leave the portfolio intact, or to change it by investing
in other securities. Our choices are shown in Figure 3.
Figure 3 – Investment Choices for Inherited Portfolio
YOUR CHOICES ARE:
• Retain investment in the moderate risk Company A. Over a
year’s time, the stock has a .5 chance of increasing 30% in value,
a .2 chance of being unchanged, and a .3 chance of declining
20% in value.
• Invest in high-risk Company B. Over a year’s time, the stock
has a .4 chance of doubling in value, a .3 chance of being
unchanged, and a .3 chance of declining 30% in value.
• Invest in Treasury Bills. Over a year’s time, they will yield a
nearly certain return of 9%.
• Invest in Municipal Bonds. Over a year’s time, these will yield a
tax-free rate of return of 6%.
This particular decision is framed by having the portfolio currently
invested largely in Company A. For another participant in the
experiment, researchers might assign the money invested to Company
B, thereby changing the status quo. Researchers find that participants
more often choose to keep the investment in the company that
20
constituted the status quo than change the asset allocation. This result
obtains regardless of which investment is the status quo.
Furthermore, the status quo advantage increases as the number of
alternatives increases. The assumption is that participants’ confidence
in other options decreases as the number of options increased,
or they choose to avoid the responsibility of moving their funds to a
new vehicle.
This kind of bias is often evident in the behavior of participants in
retirement programs such as 401(k) programs. We make our initial
decision, and after that are loath to make changes. Changes are actions
of commission, while the status quo is an action of omission, and we are
more likely to opt for an act of omission than for one of commission.
If we want to change the outcome of a decision question, we might
change the way the question is framed, and thereby change the
status quo.
OWNERSHIP ISSUES
“Psychologists…
suggest that
our preferences
are fluid and
changing,
depending on
factors such as
context.”
Another component in the psychology of economics literature is the
idea that the ownership of an object might increase the value we attach
to it. Economists think in terms of the utility function of objects we
acquire or consume, and suggest that these functions are relatively
stable. Psychologists, on the other hand, suggest that our preferences are
fluid and changing, depending on factors such as context. Therefore, we
may value an object higher if we actually own it than if we are merely
looking at it.
In his research, Richard Thaler gave coffee mugs to a group of students,
and did not give them to others. Students with the mugs would not
part with them for less than $5.25 on average, while students without
the mugs would not pay more than $2.75 to acquire one. The gap in
valuation suggests an incremental value of $2.50 for owning a mug, or
a ratio of almost two-to-one. One explanation for the difference is that
when we put on our seller hat we are motivated to obtain a high price,
while as buyers we want to acquire the object at a low price.
In follow up experiments, researchers gave half the subjects a mug and
the other half money, and then gave each person the opportunity to
switch to the opposite position. Of the people who received the mug, a
vast majority decided to keep the mug, and of the people who received
the money, a vast majority decided to keep it.
21
In the financial services marketplace, offers of free products or
services – and similar promotions – may be relatively ineffective,
because they threaten to upset the status quo. A status quo bias may
exist in appealing to bank customers to join the credit union, or
move their checking accounts. Today’s consumers also have a wide
variety of alternatives, rendering marketing programs ineffective.
Some possible answers include:
• Focus on safety of credit union saving programs to allay
consumer loss aversion
• Offer coop ATM networks to match the convenience of
big bank numbers
• Engage in shared branching for convenience
• Segment advertising, marketing and pricing efforts
LOSS AVERSION
Researchers in the field have found that we care more about losses than
we do about gains. This is rather tricky, because it depends upon where
our reference point is relative to loss or gain. In general, we tend to feel
worse about a loss than we feel good about a win. The disutility of a
loss is stronger than the utility of a win.
Consider the participants in a poker game. Those who are losing are
inclined to want to keep playing. They are inclined to keep playing until
they are back to even. But not everybody is likely to get back to even.
As a result, the game goes on and on. Those who are ahead would like
to say “let’s quit now,” but losing players won’t let them go. One answer
to this problem is to set absolute time limits in advance.
In his research, Richard Thaler found that when clients in a mutual
fund check the value of their assets more often, they feel the effects of
loss more deeply. As a result, these clients may be inclined to make bad
investment choices based on short-term results. Those who only check
quarterly or annual statements, on the other hand, are less likely to see
a loss and therefore suffer less. These participants feel more comfortable
about keeping their assets in equities.
Thaler postulates that loss aversion – possible loss relative to the status
quo – is more focal than the loss associated with keeping the status quo
and considering what one might have done. And, as we noted earlier,
when we look at the areas of the brain that are activated by different
types of decision-making, we find that current objects stimulate brain
activity in base areas, while objects with less immediacy produce more
stimulation in the pre-frontal lobe.
22
What, then, can we do to avoid the perception by people we are dealing
with that they have suffered a loss? A business that charges different
prices depending upon whether the customer pays with cash or credit
tends to charge the posted price for credit purchases, while offering a
discount for cash. This practice has a framing effect, establishing the
credit price as the base and the discounted price as a reward for cash
payments. The price is framed so that no customers are placed in a
position of having “lost” in the transaction.
The objective in negotiating economic agreements is to set the status
quo so that loss aversion will cause people to support our position or
the take actions we prefer. The key is to influence the reference level
from which alternatives are evaluated.
ANCHORING
Research in negotiation reveals that people tend to anchor on the first
number proposed during the negotiation. The tendency is to regard that
first number as a baseline from which negotiations can proceed. The
first number creates a mental model for subsequent negotiation.
One school of thought says that we ought to make the first offer in
order to anchor the negotiation in a place where we are comfortable. In
many situations, we can drive the bargaining process by putting the first
number on the table. In other situations, we may be better off to play a
waiting game to determine first what the other party is willing to offer.
An old story concerns two siblings who are asked to divide a pie. The
first sibling is responsible for cutting the pie, and the second sibling has
the choice of which piece to claim. The first sibling cuts the pie, and the
second selects the larger piece.
“Wait a minute,” the first sibling says, “that’s not fair. You took
the larger piece.”
“And what would you have done if our roles were reversed?” the
second sibling asks.
“I would have taken the smaller piece,” the first sibling says.
“And that’s exactly what you have,” the second sibling says.
“What’s the problem?”
By establishing the anchor, we may frame the negotiation – or we may
put ourselves at a disadvantage. To deal effectively with our tendency
23
to accept an anchoring bias in negotiations, we should take a long
historical perspective and remember that “past performance is no
guarantee of future returns.”
SUNK COST EFFEC T
Sunk cost effect is related to loss aversion, because loss aversion induces
a resistance to abandoning a large investment that is not turning out
as planned. If the project is abandoned, someone will have to take the
blame for a failure.
At the same time, it would be a mistake to throw good money after bad.
In most situations, when it becomes apparent that the project is not
viable, rational thought tells us we need to cut our losses. We should not
become mired in a situation simply because we have already devoted
considerable resources into it.
One key in recognizing and avoiding the sunk cost trap is to examine
returns at the margin, not averages. In principle, we should look at
the continuation value rather than at what we have at the moment.
In a poker game, for example, it is often difficult to fold the hand and
accept the loss, rather than stay in and perhaps increase our losses. We
become emotionally involved in these decisions, and decisions based on
emotion are likely to be flawed.
On the other hand, if our initial investment is a sunk cost that is
irretrievable, it may be reasonable to stick with the project. The initial
cost may be very high, but we may anticipate only maintenance cost in
the future. Imagine that the firm has already invested $100 million in
startup funds, and that money can never be recouped. Now imagine
that future annual revenue from the project will be $2 million on an
annual cost of $1 million. After two years we would return $4 million
on an investment of $102, a rate far too low to justify continuing.
Looking at the investment that way, the rational decision would be to
abandon the project. However, the sunk cost is irretrievable, and future
returns will be excellent, suggesting that we should decide to continue
the project.
In his writing, Charles Roxburgh advises executives to avoid the
sunk cost bias by analyzing incremental investments in detail; halting
strategic plans immediately upon discovering losses; and using “gated
funding” for strategic investments, releasing new funding only upon
meeting interim targets.
24
“…when
it becomes
apparent that
the project
is not viable,
rational thought
tells us we
need to cut our
losses.”
HERDING BEHAVIOR
The desire to conform to the opinions and behavior of others is a
fundamental human characteristic. The tendency toward herd behavior
is evident not only in our personal lives, but in our business transactions
as well. Executives in the financial services industry are not immune
to herd behavior bias. Consider, for example, the rush to offer indirect
lending among credit unions in the not-too-distant past. Or the wave
of commercial lending initiatives in the credit union community.
Or the industry-wide move to community charters and subsequent
organizational name changes. The simple truth of the matter is that
CEOs hate the prospect of being the only one in the industry to make a
catastrophic mistake, so they rely on the assumed competence of others
to direct their decision-making.
Herd behavior may be so compelling because it does contain some
basis in fact. In his book The Wisdom of Crowds: Why the Many Are
Smarter Than the Few and How Collective Wisdom Shapes Business,
Economies, Societies and Nations, author and New Yorker business
columnist James Surowiecki suggests that while our culture generally
trusts experts and distrusts the wisdom of the masses, “under the
right circumstances, groups are remarkably intelligent, and are often
smarter than the smartest people in them.” To support this almost
counterintuitive proposition, Surowiecki explores problems involving
cognition (we’re all trying to identify a correct answer), coordination
(we need to synchronize our individual activities with others) and
cooperation (we have to act together despite our self-interest). His
argument covers a range of problems, including driving in traffic,
competing on TV game shows, maximizing stock market performance,
voting for political candidates, navigating busy sidewalks, tracking
SARS and designing Internet search engines like Google. “If four basic
conditions are met, a crowd’s “collective intelligence” will produce
better outcomes than a small group of experts,” Surowiecki says, “even
if members of the crowd don’t know all the facts or choose, individually,
to act irrationally.” “Wise crowds” need (1) diversity of opinion; (2)
independence of members from one another; (3) decentralization;
and (4) a good method for aggregating opinions. Diversity brings in
different information; independence keeps people from being swayed
by a single opinion leader; people’s errors balance each other out; and
including all opinions guarantees that the results are “smarter” than if
a single expert had been in charge.
25
Shortly before the 2004 presidential election, business correspondent
Paul Solomon of WGBH-Boston conducted a segment on PBS’ News
Hour with Jim Lehrer demonstrating the wisdom of crowds (www.
pbs.org/newshour/bb/politics/july-dec04/winner). The segment was
prompted by the Surowiecki book, which argues that markets are better
than polls at calling elections.
In an interview, Surowiecki reports that in 1988 a group of people at
the business school at the University of Iowa decided to set up the Iowa
Electronic Markets (IEM). The idea was that you would set up a market
where people could essentially wager on the outcome of the presidential
election. From 1988 to 2000, the IEM basically has outperformed polls
three-quarters of the time. Historically, the election eve forecast in this
market has only been off by 1.4 percent, which is better than any poll.
In a spontaneous experiment to demonstrate the wisdom of crowds,
Surowiecki and Solomon asked pedestrians on a New York street corner
to estimate how many jelly beans were in a jar. The first two subjects
guessed 800 and 500 respectively, for an average of 650. The average
after eight responses rose to 2,109. After 20 subjects, the average had
moved back down to 1,419.
The actual number of jelly beans in the jar was 1,350. Not one person
in the group interviewed did better than the group did collectively.
INFORMATION CASCADES
Another dimension of herd instinct is what is referred to as information
cascades. Suppose an academic writes a paper only to have it rejected by
a prestigious journal. The academic then sends it out to another journal,
and it is rejected again. The reviewers in this case may be motivated by
the quality of the research, but other elements may also play a role,
including the personal tastes of the reviewer and the reviewer’s attitudes
toward the researcher.
At this point the researcher might complain to friends and colleagues
that the reviewers are not treating him fairly. Word gets around, and
when the researcher sends the paper to a third publication, the editor
receives a favorable report but reasons that because the paper was
rejected by two previous publications, her publication should reject
it as well because the other two publications obviously have more
information at their disposal than she does.
26
GUESSING AGAIN
Colloquium participants were divided into two groups in an experiment
to replicate the jelly-bean guessing game. The first group was asked to
provide a simultaneous written estimate the number of jelly beans in a
container, and advised that they would win $10 if their guess came within
20 beans of the actual number. Participants were further advised that
they could alternatively take a reward of $2 instead of the $10 for a close
guess. Finally, this group was asked to guess the average of all guesses
in the group.
The second group was now asked to estimate the number of jelly beans
in the container on a sequential basis, announcing their estimates in turn.
They were also given the option of taking $2 sure instead of $10 for a
guess within 20 beans of actual.
The average guess for the first group was 367, and for the second group
365, an extremely similar result. However, the range of guesses was much
wider among the group that made a simultaneous guess (72 to 750) than
among the group that made a sequential guess (178 to 540), demonstrating
a tendency for those in a sequential group to “herd” in their estimates.
In this experiment, nobody came within 20 of guessing the correct
number of jelly beans in the container, and approximately one-third of
participants chose to receive the $2 reward rather than going for the $10
reward for a close estimate. The tendency for participants to go for the
$10 prize suggests a bias toward overconfidence, since nobody took away
that prize; and the more clustered pattern of the sequential group suggests
a tendency toward herding behavior.
Charles Roxburgh points out that unique sources of strategic advantage
are what leadership is all about. ATMs and home banking are not
breakthrough products – they have become necessary tools to allow
the financial institution to compete. A perceptive CEO does not simply
copy what others in the industry are doing and then call it strategic
decision-making, says Roxburgh. Strategic decision-making is found on
the fringes, not in the mainstream.
CONFIRMATION BIAS
Confirmation bias deals with our tendency to overestimate the extent
to which others share our views. Dominant individuals often have the
ability to single-handedly create a false consensus among their colleagues,
27
which interrupts rational decision-making. The role of independent
advisors is to balance this tendency toward false consensus.
CEOs may be particularly susceptible to the trap of confirmation
bias. The best CEOs make sure that their staff is not expected to
automatically agree with them. But even in the largest and most
successful companies, the boss is often viewed as infallible. In an
examination of the management difficulties caused within the Walt
Disney Company after Michael Eisner hired Michael Ovitz as his
heir apparent, James B. Stewart wrote in The New Yorker that when
“Ovitz attended, for the first time, the weekly staff lunch that Eisner
held, and which, Eisner had often proudly told him, was a forum for
freewheeling, spontaneous exchange of ideas,” Ovitz found that “most
of the lunch was taken up by a stream-of-consciousness monologue by
Eisner. No one disagreed with anything he said. As the weeks went by,
Ovitz came to think of the lunches as a waste of time, and was often
late or excused himself…”
There are several possible reasons why we conform. People may
seek supporting opinions and tend to ignore other evidence, thereby
exhibiting confirmation bias. This may be more pervasive when we are
exposed to a wide range of information on the subject at hand. We need
to make sense of what is being presented, so we focus on those elements
that support our preconceptions. Conversely, evidence that rebuts our
preconceptions may be discounted or ignored.
We may also exhibit selective recall, and interpret the evidence in a
biased manner. And we may falsely infer too much inside information
from the conforming decisions of others.
If we are aware of our tendency to follow others, we are in a better
position to structure the way we obtain information to minimize the
dangers of bias. For example, if we ask others to forecast independently
rather than in sequence, the results will be more reliable. We can also
work to balance personalities within the organization and create a
culture of challenge. As John Dryden warns: “Nor is the people’s
judgment always true; the most may err as grossly as the few.”
AN EXPERIMENT IN INFORMATION CASCADING
Presenter Charles Holt conducted an experiment among his students
to illustrate the pitfalls inherent in confirmation bias. The experiment
was done with two cups. One cup held two red marbles and one blue
marble; the other had two blue marbles and one red marble. The cups
28
“…to make
sense of what is
being presented,
we focus on
elements that
support our
preconceptions.”
were selected randomly and presented to each person in the experiment.
The researcher shook the cup before the subject and withdrew one
marble for the subject to look at. The subject then was asked to write
down which cup was presented – the one with two red marbles, or
the one with two blue marbles. Subjects who guessed right received a
small reward. Each successive subject knew what previous subjects had
guessed, but not whether those guesses were correct. The results of the
first round of this experiment are shown in Figure 4.
Figure 4 – Red and Blue Marbles Experiment
EXPERIMENT SETUP:
6 people make predictions in sequence,
See r or b signal, predict Red or Blue outcome
Red Cup: r r b
Blue Cup: r b b
Each person sees their own signal and previous
predictions, not previous signals.
5
1b
3r
B
R
$0.00 $1.00
5r
R
$1.00
6r
R
$1.00
2b
4b
R
R
cup R
$1.00 $1.00
The first subject saw a blue marble, and guessed that the cup presented
contained two blue marbles. The second subject did not know the first
person saw a blue marble, but did know the first person said blue. The
second subject said red. The third and forth subjects also saw red and
guessed red, and the fifth and sixth subjects saw blue but followed the
example of the subjects before them and chose red. Subjects five and
six received a reward by going against the evidence of their own eyes
and following the crowd, and in fact the cup was the one with two red
marbles. The later subjects did well by ignoring their own signal and
conforming to the information cascade.
The experiment demonstrates our tendency toward rational
conformity. John Maynard Keynes once noted: “Worldly wisdom
teaches that it is better for reputation to fail conventionally than to
succeed unconventionally.”
29
ECONOMIC BUBBLES
An economic bubble occurs when speculation in a commodity causes
the price to increase, thus producing more speculation. The price of
the commodity then reaches absurd levels and the bubble is usually
followed by a sudden drop in prices, known as a crash.
Economic bubbles are generally considered to be bad because they
cause misallocation of resources into non-productive uses. In addition,
the crash which follows an economic bubble can destroy a large
amount of wealth and cause continuing economic malaise as was the
case of the Great Depression of the 1930s and the Japanese economy
of the 1990s.
An important aspect of economic bubbles is their impact on spending
habits. Participants in a market with goods that are overvalued spend
more because they “feel” richer.
History is replete with examples of irrational behavior predicated upon
the belief that the crowd knows more than the individual. Perhaps the
most famous is the case of the sixteenth century tulip market in Holland.
The price of tulip bulbs continued to rise to extraordinary levels until,
ultimately, a collapse occurred in the market. Most recently, we are all
aware of the dotcom bubble. People bought Internet stocks recklessly,
and thereby drove prices far beyond their fundamental value.
Some people make money trading on a bubbled asset, but one must be
extremely careful when riding this tiger. Because this is herd behavior, it
is easy to become overconfident about getting off in time.
In one experiment, the first subject who buys a unit of an asset must
pay one dollar. For the next subject, the buy price is two dollars, one
dollar more than the price asked of the first subject. Now the sell price
becomes one dollar, or one dollar below the buy price. Each succeeding
unit is worth one additional dollar, and existing shares can be sold for
one dollar less than the buy price. As prices go up, the buyers of the
earlier shares can realize substantial profit percentages. Eventually,
however, we learn that the firm is worthless, and prices come down
suddenly as people bail out. Some people make money in such a
situation, but more than half lose money.
In another experiment by Professor Holt, subjects were told they were
endowed with an asset that pays dividends over time. Traders were
given cash and shares. Cash paid a safe interest rate of 10 percent per
30
period, and shares paid a random dividend which had a ½ chance of 40
cents and a ½ chance of $1.00 (on average, 70 cents per share).
The buying and selling of shares continued for 40 periods. At the end
of 40 periods, all shares were redeemed at $7.00. Traders who wanted
to buy shares were asked to name the number of shares they wanted to
buy and the price at which they wanted to buy. Traders who wanted to
sell shares were asked to make offers of the price at which they would
sell. Offers were ranked from low to high on a supply curve, and where
the demand and supply curves crossed was considered the trading price.
Everything traded at that uniform price.
The market cleared about every two minutes, and trading began again.
Traders saw how many shares they had, how much cash they had, how
much interest they earned on the cash, and how their random dividends
on shares stood. At this point, the total asset value was rising. Figure
5 shows data gathered in this experiment in which participants were
University of Virginia undergraduates who were separated from each
other in order to make independent investment decisions.
Figure 5 – History of a Limited Order Asset Market
$ 300.00
$ 270.00
$ 240.00
$ 210.00
$ 180.00
$ 150.00
$ 120.00
$ 90.00
$ 60.00
$ 30.00
$
0.00
5
10
15
20
25
30
35
40
Limit Order Asset Market
prices bids asks present value
Figure 5 graphs the price curve for investments in this asset, and
illustrates a bubble effect. The trading price started at about eight
dollars, and moved up slowly until around round 15, when it started
to rise more rapidly. By period 17, the price rose above $60.00. As
transactions diminished, the price continued to escalate more rapidly.
At its peak, the bid price approached $300.00 But the present value
remained constant throughout this market turbulence, and the price
finally dropped precipitously, to around $7.00 per share.
31
Each time this experiment was performed, it created a price
bubble. The fundamental value of the shares was, indeed, $7.00, as
shown in Figure 6.
Figure 6 – Fundamental Value of Shares
• Expected dividend is $0.70
• Interest rate is 1/10
• PV for infinite horizon = 0.70/0.10 = $7.00
• Shares are redeemed for the PV in round 40
• This induces a flat present value of $7.00 in all rounds.
• In class last semester, some calculated the PV on calculators, and
then audibly gasped and interjected comments when the price
rose each period above $7
• “Noise traders”
People who buy low can make money in a scenario such as this if they sell
on the up market. However, when the market crashes there is typically
low trading volume and not many transactions. It becomes difficult to
find buyers. The experiment demonstrates how confirmation bias can
lead to irrational thinking in performing financial transactions.
Charles Roxburgh advises CEOs to avoid confirmation bias and false
consensus by:
1.Creating a culture of challenge within the organization. The CEO
should view constructive criticism as helpful, not destructive.
2.Creating checks and balances to control dominant role models.
3.Encouraging opposing points of view. Just as attorneys are
instructed by judges not to “lead the witness,” CEOs should hold
defer their judgment until all opinions have been aired.
MISESTIMATING FUTURE PLEASURE LEVELS
This bias suggests that we do not always realize what our mental state
will be after an event takes place. Studies of marriage, for example,
show that people are happiest two years before and two years after
the marriage.
32
A noted research experiment asked academics how they think they
will feel if they are denied tenure. Their responses are generally very
extreme, supposing that failing to achieve tenure would be tantamount
to an end to their career. When it happens, however, the results are
much less extreme as they accept their fate.
In another research project, quadriplegic amputees and lottery winners
were interviewed 18 months after the event, and the two groups were
found to be equally happy. We think something will make us happy
or unhappy, but our estimates of the magnitude of that feeling are
often mistaken.
In business, awareness of this potential bias is the first step toward
avoiding its pitfalls. Takeovers, for example, are taken as the corporate
equivalent of death. But sometimes takeovers are warranted. Mergers
may result in the greatest good for the greatest number of people. The
combined organization may be blessed with better management, a
stronger financial base and an enhanced reputation.
To avoid misestimating outcomes, Roxburgh advises business leaders
to adopt a dispassionate view of the situation and keep things in
perspective. A long view of the eventual outcome is an asset in making
strategic decisions.
In summary, awareness of the biases inherent in our thinking processes
can help CEOs recognize them when they occur, and adjust their
decision-making processes to account for them. Behavioral economics
has given us important tools with which to approach negotiations in the
business arena. These tools cannot guarantee positive results in every
conceivable negotiation, but they can help good executives become even
better in managing the operations and people in their organization.
33
34
CHAPTER 3:
Reports of
CEO Discussion
Groups
In this session, colloquium participants were divided into six small groups
and asked to discuss the application in their own credit union experience
of the 10 behavioral flaws presented during the morning session. Following
a one-hour discussion period, each group presented its findings.
GROUP 1: FACILITATOR
Mary Cunningham, USA Federal Credit Union
Our group identified three or four pitfalls we have observed in our
credit union work. In the category of opting for the status quo, we
examined the tendency of credit union executives to exhibit an inability
to “prune the tree.” Often, CEOs who find themselves with unprofitable
branches or products, or an overabundance of staff, do not prune the
tree as effectively as they ought to do.
In the area of staffing, we can overcome this status quo bias by
instituting a policy of waiting for a period of time – two weeks to
several months – before filling each vacancy on the credit union staff.
During that time, it may become apparent that the vacated position
can be absorbed within the organization, or that it can be replaced by
a part-time position.
“We formulate
strategic
planning around
an activity that
enjoys current
fashion…”
Another solution to this problem is to develop a “drop list.” When
managers present the strategies they propose to accomplish for the
coming year, they might also be asked to present activities or events
they are prepared to drop during the year. This would help managers
become more efficient by streamlining their operations.
Credit unions also demonstrate the herd behavior flaw. We find that
as a new practice, product or service is introduced by one credit
union, others are often quick to follow. Indirect lending, conversion
to community charters, offering member business services, selling a
credit card portfolio, and establishing a CUSO are all examples of the
tendency of credit unions to follow a vogue with the belief that they are
required to get on the band wagon. We formulate our strategic planning
around an activity that enjoys current fashion, and talk ourselves into
the idea that we need to follow suit without looking objectively at how
that activity fits into our particular operation.
Possible solutions to this flaw are to create a clear strategic visioning
process and to undergo an objective analysis of the facts. We need to be
sure that credit unions undertaking new programs are similar enough to
our own organization to suggest a rational comparison. For example,
35
a $50 million credit union may not be justified in following the lead
of a $4 billion organization without the resources to support a new
program. When the smaller credit union follows that route, it may find
that it cannot sustain the effort.
Confirmation bias is another error we have observed in credit union
operations. We labeled this flaw a dysfunctional culture. One example
involves what was once a military credit union that employed a
strict, top-down leadership style in which managers and staff stood
at attention waiting for orders from the top. The result was a lack
of leadership depth within the organization. When a new CEO
took over the credit union, she found managers that never doubted
or questioned her conclusions or opinions. She found this to be an
uncomfortable position, for when one individual calls all the shots,
mistakes in judgment are likely and the best solutions may be overlooked.
Without healthy, lively debate the organization can be stifled in its
strategic planning.
The new CEO worked to develop a more participative style, which
involved instituting leadership training and changing managers over a
period of three years. Currently, this CEO has a team whose members
are willing to challenge her on specific issues and offer constructive
criticism on strategic initiatives. In staff forums, she is inclined to
remain at the back of the room and take notes rather than dominating
the discussion. In this way, the CEO is able to get the positions and
opinions of various managers without directing the discussion. By the
time the CEO speaks, she has a range of objective information in her
grasp with which to assess the situation.
Credit union people also are inclined to accept certain results based
solely upon the acceptance of peers. If, for example, the credit union
experiences a disappointing ROA, higher operating expenses, or higher
loan losses for a given year, executives of that credit union might visit
with peer credit unions and find that they have had similar results. This
information makes poor performance more palatable.
The solution to this problem might be to create new or uncomfortable
diversity, or new peer groups whose performance we hope to match.
Finding new models serves us better than relying on clones of ourselves
with whom to compare ourselves.
Finally, we were challenged to identify flaws that might not have
been discussed earlier, and we focused on what we called “the credit
union philosophy think.” We believe that credit unions often allow
36
philosophy to override solid business decisions, and we do this
deliberately. Our group did not have an answer for this flaw, but we
know that many credit unions have unprofitable branches, or ATMs in
places where members have only limited access and perform only 300
transactions a week, or unprofitable product lines. As business people
we know this is inappropriate but we seek refuge in the idea of credit
union philosophy.
GROUP 2: FACILITATOR
Gordon Dames, Mountain America Federal Credit Union
“Management
did not consider
elements of
risk it faced
in entering
businesses
outside its core
competence.”
The first flaw our group dealt with is overconfidence. In one example of
this pitfall, a credit union purchased a data processing system that had
capacity greater than the organization needed in house, so the credit
union decided to share the system with others. The system hadn’t been
in place for long when the credit union discovered that training others
was its responsibility, and when problems occurred the sponsoring
credit union was the natural place to lodge complaints. In the end, the
credit union was obliged to pay its partner credit unions to give up
their place on the system. The overconfidence displayed in adopting
this arrangement became quickly apparent when the credit union found
itself in a business in which it was not an expert.
A similar situation occurred in a credit union that undertook to share
its item processing capability with others. Before corporate credit
unions took on the function of item processing, the credit union used
banks for this task. One credit union purchased a high capacity system
that could be shared with others, and signed up 15 other credit unions
to use the system. Participating credit unions expected their checks to
be processed daily, but when system problems occurred those problems
caused delays. As a result, the originating credit union was forced
to purchase a backup system, rendering the overall enterprise less
profitable. The backup system also meant devoting more space to item
processing. This is another case of overconfidence by a credit union had
little experience in the technological area it provided to others.
In both these cases, credit union management was overly optimistic
in estimating the potential value of the systems they purchased, and
overly confident of their ability to provide service efficiently to others.
Management did not consider all the elements of risk it faced in getting
into businesses outside its core competence.
37
Our group also examined the herd effect observable in the rush to
provide indirect lending programs. Some credit unions may even
perceive this channel as being imposed upon them, reasoning that if
they do not offer indirect lending, they may be out of the auto lending
business entirely. At the same time, many credit union managers do not
have the necessary background and skills to understand how indirect
lending works, and what risks it presents. The credit union must be
careful not to let dealers make lending decisions for them. When that
happens, the credit union is liable to be offering A borrower rates to D
borrowers. The result can be huge losses.
To avoid the herd mentality, CEOs need to do their homework up
front, and talk to credit unions that have succeeded in the indirect
lending business before launching their program. Equally important is
discussing the risks involved with credit unions whose indirect lending
programs have failed.
Mergers can involve a number of strategic decision-making risks,
including overconfidence, the winner’s curse, and misestimating how
we will feel after the deal is done. Regardless of the size of our
partners, mergers bring with them certain common problems. One
key consideration is the political environment, the integration of
two cultures. People in the merged credit union are likely to feel a
loss of identity. Both partners need to understand at the outset that
an integration process must be in place in order to avoid significant
problems. The alternative is to change management, which also brings
with it significant challenges.
Significant costs are associated with the merger process. Most merged
credit unions ask for guarantees for their employees as part of the
agreement. The board of directors of the merged credit union must also
be either integrated into the new organization or disbanded. Branches
and their managers must be integrated or replaced.
At first glance, a potential merger may promise additional revenue
to the bottom line, economies of scale, better service for members
and a host of other benefits. But the reality is that each individual in
the combined organization has a similar psychological profile, and
attempts at rational decision-making may face prodigious barriers to
effective implementation.
To avoid the pitfalls of overconfidence and misestimating results in
merger situations, CEOs need to understand in advance what they
are undertaking. They need to recognize that the integration period
38
will be a challenge for all involved, and that their patience and skills
will be tested during this period. To minimize conflict, the credit
union may begin the integration process before the merger takes
effect, by developing agreements on specific post-merger arrangements
in advance.
In another example, a credit union CEO may fall into the traps of
status quo and sunk cost effect while making a substantial investment
in technology. If an investment in technology does not work out, many
executives are inclined to deal with the failure by pouring more money
into it. The executive has a psychological investment in the technology
because he has convinced his board of directors that the system will
deliver better member service, presumably at lower cost. It is difficult
to face up to the fact that this expensive system has not performed as
expected, and cut the organization’s losses. The CEO may go to his
board and rail against the vendor that provided the system, to absolve
himself of responsibility. In this way, he positions himself as the
problem solver for a problem he himself created.
The rational solution to this problem is to recognize the loss, take
whatever criticism ensues, and move on to fix the problem.
On another subject, when credit unions create branches they may do
so halfheartedly, without committing adequate resources. This is often
an example of anchoring. The logic might be that we don’t know if
the expansion will be successful, so we’ll start very small and if that
works we’ll go to the next level. This kind of thinking can result in what
amounts to a commitment to failure from the start. Successful projects
require adequate resources to make them work.
Psychologically, we may be prey to the mindset that if we fail at a small
thing it is not as bad as if we fail at a big thing. Better by far, however,
to succeed at a big thing.
GROUP 3: FACILITATOR
Rick Rice, Teachers Credit Union
Our group considered examples of confirmation bias and herding effect,
particularly as they apply to indirect lending and commercial lending.
In offering an indirect lending program, CEOs need to understand that
the objective is to manage risk, not to eliminate risk. The solution is to
look at all the data, and confirm what factors are in play.
39
We also looked at branch locations and electronic banking. In both
these areas, we identified cases in which the CEO was overconfident
that the program would work. The driving force was the perception
that members wanted these particular services. In the case of branching
operations, one credit union built seven branches at the same time. The
executive team involved believed these branches were sited properly,
in the best possible locations. In this case, false consensus played a
part in placing the branches in locations that turned out to be less
than optimal.
In the past, the credit union’s new branches had performed at high levels
from the very start, which may have created a bias in decision-making
when the siting of seven additional branches was proposed. The team
did not take into account the fact that at the time of the expansion,
more competitors were in the field as a result of deregulation. The idea
that “if we build it, they will come” was not a realistic expectation in
this case. Not all the branches encountered problems, but the difficulties
of two branches caused the credit union CEO to rethink branch
strategy in the future.
Herd behavior is apparently an example involving electronic banking
services. A credit union saw others moving rapidly into the home
banking field, and did not want to be left at the back of the pack.
In the end, however, the credit union’s members greeted electronic
banking with little enthusiasm, and the credit union was saddled with a
substantial investment that paid poor dividends.
The mantra that “we are responding to our members demands” can be
a decision trap unique to the credit union industry. Credit unions tend
to justify every new service, every new product in terms of response
to member demand. But credit unions sometimes do not do enough
research to determine whether these products and services are really in
demand by the membership, or whether they are a response to industry
fads. To avoid these assumptions, tools such as member surveys
should be employed to confirm the actual level of interest on the
part of members.
Strategic planning is a necessary requisite in choosing appropriate
options with respect to product development and member service. The
board of directors and the management team need to work in concert
to agree on strategic direction, and then take steps to implement the
plan. The success of the CEO should be measured against the credit
union’s strategic goals by metrics that are concrete and observable.
40
“The mantra
that ‘we are
responding to
our members
demands’ can
be a decision
trap unique to
the credit union
industry.”
Credit unions strive to give members the services they want and need,
but we often do not know whether the implementation of those services
is cost effective. In one example, a credit union embarked upon a
check imaging program that received rave reviews from members. But
members were not willing to pay for this service, and ultimately the
program was halted due to cost factors.
The herding effect can also be seen in credit union operation in
connection with Customer Relationship Management (CRM) systems
and in investments in technology. CEOs may look at their peers in the
industry and assume that because others have invested in these systems,
they must be appropriate for all credit unions. When executives depend
upon the practices of others to rationalize their own decisions, they
may be falling into the trap of herd behavior.
CRM programs are an example of a trap one might describe as the
desire for prestige. Many organizations purchased very expensive
hardware and software before understanding the basic principles and
applications of CRM. One credit union spent millions on this concept
before beginning a search for staff that understood what the hardware
would do and how to apply it.
GROUP 4: FACILITATOR
Hubert Hoosman, Vantage Credit Union
One of the examples our group examined was an auto leasing and
brokerage CUSO that was created in 1992. The CUSO was producing
about 45 units per month with an initial investment of $150,000. The
objective in establishing the corporation was to meet member needs for
auto loans and leases. At the direction of the board of directors, the
CEO initiated a plan to expand the services of the CUSO, approaching
several other credit unions to offer the service to their members.
However, the expansion faced several barriers. Perhaps most important,
the offices of the CUSO were located at the site of the original credit
union, making it difficult for the members of other credit unions to
access them. In addition, the board of the original credit union was not
willing to sell shares in the CUSO to other participating credit unions.
The arrangements made with other credit unions were not contractual,
but were word of mouth agreements among CEOs. To complicate
matters, the auto market in the Midwest became soft during this period.
Because the CUSO needed the capacity to serve several credit unions,
41
its expenses rose as the market softened. In this environment, even the
originating credit union had difficulty maintaining its sales volume.
In the leasing area, manufacturers were subsidizing the residual
balance on high value cars. This created a situation in which the credit
union CUSO’s competitive position was further eroded. The ultimate
result was that the CUSO could not maintain viability, and was
eventually closed.
This example demonstrates an attitude of overconfidence with respect
to being able to expand the CUSO’s reach. There was also a tendency
to herd behavior in a market that turned out to be more volatile than
expected. To avoid the pitfall, the credit union might have asked for
written contracts from the credit unions with which it partnered.
Adequate research might also have revealed the soft market for leased
and brokered vehicles, resulting in a more appropriate time frame
for expansion.
The group also looked at rather extreme decision-making pitfalls in the
case of an indirect lending program at a credit union with $68 million
in assets. One year into its program, the credit union held an $18
million portfolio. Also during that time, the portfolio had charge-offs
of 2.45 percent and delinquencies of 4.65 percent, and the ROA of the
program was -1.20. This is a regrettable example of a credit union that
followed the herd to its own serious detriment. Net capital dropped to
5.7 percent from 8.57 percent one year earlier. The credit union could
sell this portfolio, but at a very steep discount of approximately 41 cents
on the dollar. The credit union is currently under Prompt Corrective
Action by the NCUA.
This example illustrates any number of decision pitfalls, including
overconfidence, herd mentality, the winner’s curse, confirmation bias
and others. If this credit union can survive, it will have to abandon its
indirect lending program. It may be forced to consider a merger with
another, healthier credit union. In any event, the credit union will be
forced to stabilize and reduce its operating expenses, and seek other
sources of revenue. This is an extreme example of individual and
collective strategic decision-making errors by both management and
the board of directors.
Our final example looks into organizational roles of the board and
the CEO. The board of this credit union perceived the CEO as
no more than a rubber stamp for its policies, and was inclined to
micro-manage the credit union’s operations. The result was a loss
42
of understanding and communication between management and
the board. In this confrontational relationship, the board raised
standards and expectations of the CEO, and challenged his operational
leadership. Friction between the board and the CEO also hindered
staff development.
The situation was resolved when the board received education in
the duties and responsibilities of directors. Once the board began
communicating with directors from other credit unions, the relationship
between the CEO and the board improved measurably. Now, the
CEO and the board work in concert to set the strategic goals of the
organization. New directors are required to meet basic standards.
Senior management and directors also see each other socially, to get to
know each other.
GROUP 5: FACILITATOR
Patricia Smith, Unitus Community Credit Union
Our first example deals with a credit union undergoing conversion in
its core data processing system. The core conversion decision involved
vendor selection and the responsibility of the credit union to do
everything possible to assure success before making a final decision.
This case illustrates the flaw of sunk costs, including leadership time
in addressing the problem. A six-month delay in making a decision to
abandon the project exacerbated the problem.
The CEO in this case had extensive previous experience in performing
data processing conversions, and in each prior case the conversions
were successful and on time. By delaying a decision in this case, those
involved demonstrated loss aversion, overconfidence and herd mentality.
Other credit unions had converted to the new system successfully, the
vendor assured the credit union that it could manage the conversion,
and as a result the expectation was for a successful project.
To avoid this kind of situation in the future, the credit union might
perform due diligence on the claims of vendors in advance, and make
sure that all promises are backed up by performance guarantees. The
credit union should also do a detailed analysis of the proposed system
before committing to it.
Our second example involves product service. In this case, the credit
union selected self-service delivery systems for all branches including
those that were community focused. The original sponsorship of this
43
credit union was a technology company that employed computer-savvy
individuals. When the decision was made to go self-service, the branch
locations serving employees did well, but those in the community
suffered due to lack of technological sophistication by members outside
the core sponsor group.
The credit union saw others in the industry adopting electronic systems
to reduce costs, and went along with this trend as part of the herd.
The decision also demonstrated overconfidence and a selection bias
on the part of management. To avoid this kind of pitfall, the credit
union needed to segment and then take into account the diverse
needs of its members in fulfilling their financial needs. The particular
technology employed here was not appropriate for the credit union’s
community branches.
In another example, when a credit union adopts a community charter it
needs to look at its business model to determine if that business model
can support a community charter. A credit union with one branch
that adopts a community charter may have a difficult time serving its
expanded membership, or face serious expense challenges as it places
new branches in the appropriate locations. Overconfidence in the
business model can create problems.
GROUP 6: FACILITATOR
David Brock, Community Educators’ Credit Union
Our group began by looking at the business of credit unions from a
long view, discussing the things we need to do to remain relevant to
our members. That discussion produced focus on one particular area
through which to examine the kinds of errors committed in the course
of business.
We’ve heard of indirect lending referred to as the crack cocaine of the
auto lending business. In one case, a credit union brought in a noted
expert to examine its indirect lending portfolio. After looking at the
portfolio, the expert advised the credit union to buy a tow truck. The
comment was a sure indication that the credit union’s indirect portfolio
was not performing well.
This credit union had heard stories about the opportunities inherent
in indirect lending, and had taken these stories as confirmation of its
desire to enter this field. Indirect lending was billed as a way the credit
union could build its loan volume. It provided additional convenience to
44
“…the credit
union needed
to segment and
then take into
account the
diverse needs of
its members…”
members. It established a point-of-sale lending channel. It promised to
help membership growth. All these assumptions created an atmosphere
in which the downside risk was overlooked, and biases crept into the
strategic decision-making process.
In considering an indirect lending program, the CEO can be subject to
overconfidence. Indirect lending cannot be perceived as just another
channel. Current underwriting policies are not likely to be adequate to
cover indirect lending risk. Collections procedures may also need to be
revised and augmented. The assumption that indirect borrowers can
be converted into full service members may also be flawed. In short,
management may overestimate its ability to adapt to an entirely new
line of business and do it successfully.
Overconfidence can also occur in the credit union’s reporting systems.
When the credit union’s indirect loan volume is growing, loss and
delinquency ratios may actually fall, but only temporarily. But as
the numerator of these ratios becomes larger, potential losses and
delinquencies will increase. This requires more sophisticated tracking
systems to obtain a true picture of the portfolio.
“Hope is not a
strategy.”
We also see examples of herd mentality in the rush by credit unions to
adopt an indirect lending program. When CEOs see peers moving in
the direction of indirect lending, when they see the channel promoted
by trade publications, they are likely to consider responding without
adequate research and analysis of their particular situation. They want
to get in on the action.
And once an indirect lending program has been adopted, the CEO may
be afflicted by a sunk cost bias. Dealers may remind the credit union
that they have referred several very good risks, and on that basis suggest
that the credit union now accept a marginal loan. The dealer’s interest
is in selling the car, and does not need to be concerned with the future
performance of this loan. The credit union, however, should examine
each loan on its own merit, not simply because it wants to maintain a
good relationship with the dealer.
Indirect lending can also produce examples of the winner’s curse.
Typically, a credit union wins more business from a dealer by putting
more money in the dealer’s pocket. That means the winner is the bidder
who is working on the lowest margin. A credit union at the top of its
area’s indirect lender list may want to take another look at its bidding
practices, for it could be taking on undue risk simply to buy the
dealer’s business.
45
To avoid the pitfalls inherent in indirect lending, the credit union should
do a detailed analysis of its own situation, including scenario planning
that assumes a worst-case situation. Internally, the CEO should be
aware of how the program fits with the credit union’s overall business
plan, and determine how committed she is to the business. Hope is not
a strategy. When problems arise, the credit union needs to assess the
future viability of the program and conduct a process of disciplined
decision-making.
46
Closing
Obser vations
At the beginning of the day we talked about a merger of economics
and psychology, and about how psychological principles have begun
being applied to economics to help people make strategic decisions
with an eye to the pitfalls and biases they encounter along the way.
It is the job of Filene to take the latest and best research in the fields
relating to consumer finance and credit unions, and relate it to credit
union operations so that you can apply it in your important work. The
examples you have given us will be helpful to other credit union CEOs
in making strategic decisions.
Recommended additional reading includes the article by Charles
Roxburgh in the 2003 Number 2 issue of the McKinsey Quarterly,
titled “Hidden Flaws in Strategy.” This essay is an excellent summary
of the principles of behavioral economics discussed today. The pitfalls
Roxburgh discusses give additional depth to the components of our
examination. These principles apply to the activities of all human
beings, not just to CEOs, and a study of them is worthwhile for
credit union managers at any level. We might, for example, be on the
lookout for these pitfalls in board discussions; in the deliberations of
the senior management team; and even in the decisions made by first
line supervisors.
Even the behavior of regulators can be scrutinized for the kinds
of errors we have discussed. Demands of a regulator may seem
unreasonable, and regulators are subject to the same flaws in decisionmaking judgment as credit union executives.
On the other hand, in our dealings with our regulators we ourselves
may be subject to certain selective biases when we point to regulations
as the problem. In some cases regulators are correct in their criticism
and comments on credit union operations, and we need to be rational
and open-minded in accepting those comments.
The credit union community may need to do more to develop a formal
business decisioning process to guide us in long term planning. We
would all benefit from a process that would define our objectives,
analyze the pros and cons, consider how we will define success,
examine the costs involved, and detail the assumptions we are making.
Many of us have been exposed to the principles presented in business
school, but too few of us apply these principles in a formal way within
our organizations.
Part of the solution to overcoming the pitfalls Charlie Holt and Gary
Charness introduced us to today is to be aware that we all have these
47
biases, and left unchecked they are liable to intrude on our strategic
decision-making. To be a CEO, for example, an individual must
possess confidence. But to excess, confidence becomes overconfidence.
We expect our staffs to support the strategic direction of the credit
union, but if they always agree without question, we may be putting
the organization at risk.
Today’s meeting should be more than a discussion of how not to make
mistakes. Credit unions are in the risk management business, not the
risk avoidance business. Credit unions that fear making errors are likely
to be stifled in their efforts to innovate and serve. Certainty is not a
common element of human endeavor. The objective is to understand
and try to avoid some of the biases that are a natural part of the
human condition.
It might be said that the credit union industry is too risk averse in
general. There are good reasons for this risk aversion. Credit union
boards of directors are volunteers who receive only modest rewards for
great successes and substantial penalties for perceived errors. Another
underlying factor of risk aversion in credit unions is the fact that
the industry is regulated. Still a third reason for risk aversion is the
member-owned, cooperative nature of the credit union: stewards of
member funds are loathe to put those funds at risk.
Bias in strategic decision-making is an issue worth examining. Our
psychological biases are an important component of the process
involved in making strategic decisions, and need to be taken into
account as we serve member interests.
48
About the
Presenters
CHARLES A . HOLT
Charles A. Holt is the A. Willis Robertson Professor of Political Economy
at the University of Virginia. He has also taught or visited at a number
of other universities (Amsterdam, New York University, Stanford,
Caltech, Autonomous University of Barcelona, and the University
of Minnesota). Holt has written widely on experimental economics
and game theory, and is a founding co-editor of the academic journal
Experimental Economics. His current research pertains to models of
strategic behavior that incorporates “noise” and bounded rationality.
He has consulted for the Federal Trade Commission, the World Bank,
and most recently for the Federal Communications Commission on
the use of experiments to select and refine design features of upcoming
wireless bandwidth auctions. Holt also recently helped design and
execute an auction for irrigation reduction in Georgia.
GARY CHARNESS
Gary Charness was awarded a Ph.D. in economics from the University
of California at Berkeley, and is currently a member of the economics
department at the University of California at Santa Barbara. Charness’s
primary area of research is behavioral economics, with a primary
research strategy involving experimental methods. He has published
more than 20 articles, most in journals such as The Quarterly Journal
of Economics, The Economic Journal, Games and Economic Behavior,
The Journal of Labor Economics, and Management Science. Prior to
his academic career, Charness was a market maker in stock options on
the floor of the Pacific Stock Exchange, a financial planner, a real estate
broker and lender, and a senior panelist with the American Arbitration
Association. Those experiences have been useful in studying human
behavior in economics and finance.
49
50
Filene
Research
Institute
Administrative
Board
CHAIRMAN
Thomas R. Dorety, President/CEO
Suncoast Schools Federal Credit Union
VICE CHAIRMAN
Lawrence D. Knoll, President/CEO
Midwest Financial Credit Union
VICE PRESIDENT/TREASURER
Daniel A. Mica, President/CEO
CUNA & Affiliates
SECRETARY
Paul Mercer
Chairman, American Association of Credit Union Leagues
President, Ohio Credit Union League
DIRECTOR
Jeff H. Post, President/CEO
CUNA Mutual Group
DIRECTOR
Patsy Van Ouwerkerk, President/CEO
Travis Credit Union
PRESIDENT EMERITUS
Richard M. Heins, Director Emeritus
CUNA Mutual Group
Research
Council
Martin M. Breland, President/CEO
Tower Federal Credit Union
David Brock, President/CEO
Community Educators’ Credit Union
Bruce Brumfield, President/CEO
Founders Federal Credit Union
Michael J. Connery, President/CEO
United Nations Federal Credit Union
51
Sharon Custer, President
BMI Federal Credit Union
Charles F. Emmer, President/CEO
Ent Federal Credit Union
W. Craig Esrael, President/CEO
First South Credit Union
Kathy Garner, President/CEO
Northwest Corporate Credit Union
Charles Grossklaus, President/CEO
Royal Credit Union
Robert H. Harvey, President/CEO
Seattle Metropolitan Credit Union
Hubert H. Hoosman, President/CEO
Vantage Credit Union
Andrew Hunter, President/CEO
Patelco Credit Union
Gary W. Irvin, President/CEO
Forum Credit Union
Olan O. Jones, President/CEO
Eastman Credit Union
Kirk Kordeleski, President/CEO
Bethpage Federal Credit Union
Mike L’Ecuyer, President/CEO
Telephone Credit Union of New Hampshire
Harriet B. May, President/CEO
Government Employees Credit Union of El Paso
David Mooney, President/CEO
Alliant Credit Union
Marcus B. Schaefer, President/CEO
Truliant Federal Credit Union
Robb Scott, President/CEO
Deer Valley Credit Union
52
Jack Sheets, President/CEO
Elkhart County Farm Bureau Credit Union
Bob Siravo, President/CEO
WesCorp
Patricia E. Smith, President/CEO
Oregon Telco Community Credit Union
A. Lee Williams, President/CEO
Aviation Associates Credit Union
Ex-Officio:
Fred B. Johnson, President
Credit Union Executives Society
FILENE RESEARCH INSTITUTE
Robert F. Hoel, Ph.D.
Executive Director
George A. Hofheimer
Director of Research
53
54
Filene
Research
Institute
Publications
Aldag, Ramon J. and Antonioni, David, University of WisconsinMadison. Mission Values and Leadership Styles In Credit Unions,
2000.
Amburgey, Terry L., University of Kentucky and Dacin, M. Tina,
Texas A&M University. Evolutionary Development of Credit
Unions, 1993.
Barrick, Murray R., University of Iowa. Human Resource Testing:
What Credit Unions Should Know, 2002.
Barrick, Murray R., University of Iowa. Predicting Employee
Turnover and Performance: Pre-Employment Tests and
Questions that Work, 2003.
Barron, David N. and West, Elizabeth, University of Oxford; and
Hannan, Michael T., Stanford University. Competition, Deregulation,
and the Fortunes of Credit Unions, 1995.
Burger, Albert E. and Dacin, Tina, University of Wisconsin-Madison.
Field of Membership: An Evolving Concept, 1991.
Burger, Albert E., University of Wisconsin-Madison; Fried, Harold
O., Union College; Lovell, C. A. Knox, University of Georgia.
Technology Strategies of Best Practice Credit Unions: Today,
the Near Future, and the Far Future, 1997.
Burger, Albert E., University of Wisconsin-Madison and Kelly, Jr.,
William A., CUNA Research & Development. Building High Loan/
Share Ratios: Challenges and Strategies, 1993.
Burger, Albert E., University of Wisconsin-Madison and Lypny,
Gregory M., Concordia University, Montreal, Canada. Taxation of
Credit Unions, 1991.
Burger, Albert E. and Zellmer, Mary, University of WisconsinMadison. Strategic Opportunities in Serving Low to Moderate
Income Individuals, 1995.
Burger, Albert E., Zellmer, Mary and Robinson, David, University of
Wisconsin-Madison. The Digital Revolution: Delivering Financial
Services in the Future, 1997.
Caskey, John P., Swarthmore College. The Economics of Payday
Lending, 2002.
55
Caskey, John P., Swarthmore College. Lower Income Americans,
Higher Cost Financial Services, 1997.
Caskey, John P., Swarthmore College; Humphrey, David B., Florida
State University; Kem, Reade, research assistant. Credit Unions and
Asset Accumulation by Lower-Income Households, 1999.
Caskey, John P., Swarthmore College and Brayman, Susan J., assistant.
Check Cashing and Savings Programs for Low-Income
Households: An Action Plan for Credit Unions, 2001.
Colloquium at Stanford University. Consolidation of the Financial
Services Industry: Implications for Credit Unions, 1999.
Colloquium at the University of California-Berkeley. Financial
Incentives to Motivate Credit Union Managers and Staff, 2001.
Colloquium at the University of California–Berkeley. Three Innovative
Searches for Better Incentive Programs, 2001.
Colloquium at the University of California–San Diego. Serving New
Americans: A Strategic Opportunity for Credit Unions, 2003.
Colloquium at the University of Virginia. Attracting and Retaining
High-Quality Employees: New Strategies for Credit Unions, 2001.
Colloquium at the University of Virginia. Fresh Approaches to
Bankruptcy and Financial Distress – Volume I: Why Don’t More
People Declare Bankruptcy?, 2000.
Colloquium at the University of Virginia. Fresh Approaches to
Bankruptcy and Financial Distress – Volume II: Working With
Members in Financial Distress, 2000.
Colloquium at the University of Virginia. Managing Credit Union
Capital: Subordinated Debt, Uninsured Deposits, and Other Secondary
Sources, 2004.
Colloquium at the University of Wisconsin–Madison. Financial Stress
and Workplace Performance: Developing Employer-Credit Union
Partnerships, 2002.
Colloquium sponsored by the Filene Research Institute and the Center
for Credit Union Research – Madison, Wisconsin. Outsourcing and
Sharing Credit Union Management , 2003.
56
Colloquium sponsored by the Filene Research Institute and the Center
for Credit Union Research – Madison, Wisconsin. Strategy Errors
Made by Even the Smartest CEOs: How to Avoid Them in Credit
Unions, 2005.
Compeau, Larry D., Clarkson University. Successful Turnarounds
from Bad Credit to Good: What We Can Learn from the
Borrower’s Experience, 2001.
Dacin, Peter A., Texas A&M University. Marketing Credit Union
Services: The Role of Perceived Value, 1995.
Donkersgoed, William L. and Hautaluoma, Jacob E., Colorado State
University; and Pipal, Janet E. Consensus Building Strategies for
Productive CEO-Board Relationships, 1998.
Doyle, Joanne M., James Madison University; Kelly, William A. Jr.,
University of Wisconsin-Madison. Predicting and Managing a
Credit Union’s Expense Ratio, 2004.
Feinberg, Robert M., American University. The Effects of Credit Unions
on Bank Rates in Local Consumer Lending Markets, 2001.
Feinberg, Robert M., American University. The Effect of Credit
Unions on Market Rates for Unsecured Consumer Loans, 1999.
Feinberg, Robert M., American University and Kelly, Jr., William A.,
University Wisconsin–Madison. Less-Restricted Fields of Membership for Credit Unions: Public Policy Implications, 2003.
Feinberg, Robert M., American University; and Rahman, Ataur,
American University. Key Influences on Loan Pricing at Credit
Unions and Banks, 2004.
Filene Research Institute and The Center for Credit Union Innovation,
LLC, in cooperation with the National Credit Union Foundation. 15
Steps to an Effective SEG Program, 2003.
Filene Research Institute. Attracting and Retaining Young Adult
Members, 2003
Fried, Harold O., Union College; Hoel, Robert F., Filene Research
Institute; Kelly, Jr., William A., University of Wisconsin-Madison.
Member Satisfaction Levels: National Norms for Comparing
Local Survey Results, 1998.
57
Fried, Harold O., Union College; Hoel, Robert F., Filene Research
Institute; Kelly, Jr., William A., University of Wisconsin-Madison.
Member Satisfaction Levels: National Norms for Comparing
Local Survey Results Second Edition, 2002.
Fried, Harold O., Union College and Lovell, C. A. Knox, University of
Georgia. Credit Union Service-Oriented Peer Groups, 1994.
Fried, Harold O., Union College and Lovell, C. A. Knox, University
of North Carolina. Evaluating the Performance of Credit Unions,
1992.
Fried, Harold O., Union College; Lovell, C. A. Knox, University of
Georgia and University of New South Wales; Yaisawarng, Suthathip,
Union College. How Credit Union Mergers Affect Service to
Members, 1999.
Fried, Harold O., Union College and Overstreet, Jr., George A.,
University of Virginia, editors; Frank Berrish, Thomas Sargent,
and James Ware, contributors. Information Technology and
Management Structure: A Case Study of First Technology
Credit Union, 1998.
Fried, Harold O., Union College and Overstreet, Jr., George A.,
University of Virginia, editors; Richard Grenci, Peter Keen, R. Ryan
Nelson, and Nancy Pierce, contributors. Information Technology
and Management Structure II: Insights for Credit Unions,
1999.
Grube, Jean A. and Aldag, Ramon J., University of WisconsinMadison. How Organizational Values Affect Credit Union
Performance, 1996.
Hannan, Michael T., Stanford University; and West, Elizabeth and
Barron, David N., McGill University. Dynamics of Populations of
Credit Unions, 1994.
Hautaluoma, Jacob E., Donkersgoed, William J. and Morgan,
Kimberly J., Colorado State University. Board-CEO Relationships:
Successes, Failures, and Remedies, 1996.
Hautaluoma, Jacob E., Jobe, Lloyd, Donkersgoed, Bill, Suri, Taaj
and Cropanzano, Russell, Colorado State University. Credit Union
Boards and Credit Union Effectiveness, 1993.
58
Hoel, Robert F., Filene Research Institute and Kelly, Jr., William
A., University of Wisconsin-Madison. Why Many Small Credit
Unions Are Thriving, 1999.
Humphrey, David B., Florida State University. Prospective Changes
in Payment Systems: Implications for Credit Unions, 1997.
Jackson, III, William E., University of North Carolina–Chapel Hill.
The Future of Credit Unions: Public Policy Issues, 2003.
Jackson, III, William E., University of North Carolina-Chapel Hill.
Pricing Movements and For-Profit Behavior: A Comparison of Banks
and Credit Unions, 2005
Johnson, Ramon E., University of Utah. Field of Membership and
Performance: Evidence from the State of Utah, 1995.
Joseph, Matt L. Changes in the Automotive Distribution System:
Challenges and Opportunities for Credit Unions, 2001.
Kane, Edward J., Boston College. Deposit Insurance Reform: A
Plan for the Credit Union Movement, 1992.
Kane, Edward J., Boston College; and Hickman, James C. and
Burger, Albert E., University of Wisconsin-Madison. Implementing
a Private-Federal Deposit Insurance Partnership, 1993.
Karofsky, Judith F., Center for Credit Union Research, University of
Wisconsin-Madison School of Business. Shopping Strategies for
Financial Consumers: a Study of Three Markets, 2000.
Kelly, Jr., William A., University of Wisconsin-Madison. Financial
Strength: A Comparison of State and Federal Credit Unions,
1998.
Kelly, Jr., William A. and Karofsky, Judith F., University of WisconsinMadison. Federal Credit Unions Without Federal Share
Insurance: Implications for the Future, 1999.
Kelly, Jr., William A. and Karofsky, Judith F., University of WisconsinMadison; HARK Management, Inc.; Krueckeberg, Harry F., Colorado
State University (retired). Monetary Incentives for Credit Union
Staffs, 1998.
Lambrinos, James, Union College and Kelly, Jr., William A., University
of Wisconsin-Madison. The Effects of Member Income Levels on
Credit Union Financial Performance, 1996.
59
Lee, Jinkook, Ohio State University and Kelly, Jr., William A.,
University of Wisconsin-Madison. Building Savings-Per-Member at
Credit Unions, 2004.
Lee, Jinkook, University of Georgia and Kelly, Jr., William A.,
University of Wisconsin-Madison. Financial Product Use over
Household Life Cycles: A Guide for Credit Unions, 2002.
Lee, Jinkook, Ohio State University and Kelly, Jr., William A.,
University of Wisconsin-Madison. Gifts That Connect the Generations:
A Role for Credit Unions, 2004.
Lee, Jinkook, University of Georgia and Kelly, Jr., William A., University
of Wisconsin-Madison. The Human Touch in the Information Age:
What Do Members Want?, 2001.
Lee, Jinkook, Ohio State University and Kelly, Jr., William A.,
University of Wisconsin-Madison. Inheritances: Who Expects to
Leave Money to Heirs?, 2004.
Lee, Jinkook, University of Georgia and Kelly, Jr., William A.,
University of Wisconsin-Madison. Life Cycle Marketing for Credit
Unions: Mid Age Households, 2002.
Lee, Jinkook, University of Georgia and Kelly, Jr., William A.,
University of Wisconsin-Madison. Life Cycle Marketing for Credit
Unions: Senior Households, 2002.
Lee, Jinkook, University of Georgia and Kelly, Jr., William A.,
University of Wisconsin-Madison. Life Cycle Marketing for Credit
Unions: Young Households, 2001.
Lee, Jinkook, Ohio State University and Kelly, Jr., William A.,
University of Wisconsin-Madison. Marketing Checking Accounts to
Members: A Guide for Credit Unions, 2003.
Lee, Jinkook, Ohio State University and Kelly, Jr., William A.,
University of Wisconsin-Madison. Professional Financial Advice for
Consumers: Implications for Credit Unions, 2003.
Lee, Jinkook, Ohio State University and Kelly, Jr., William A., University
of Wisconsin-Madison. Uninsured Accounts: An Assessment of
Member Interest, 2003.
60
Lee, Jinkook, University of Georgia and Kelly, Jr., William A.,
University of Wisconsin-Madison. Where Are Households’ Financial
Assets?, 2001.
Lee, Jinkook, University of Georgia and Kelly, Jr., William A.,
University of Wisconsin-Madison. Who Uses Credit Unions?
Second Edition, 2001.
Lee, Jinkook, Ohio State University and Kelly, Jr., William A.,
University of Wisconsin-Madison. Who Uses Credit Unions? Third
Edition, 2004.
Lee, Jinkook, University of Tennessee and Kelly, Jr., William A.,
University of Wisconsin-Madison. Who Uses Credit Unions?,
1999.
Lemmon, Nicolette, LEMMON-AID Marketing Services; Gourley,
David, Arizona State University; Ward, James, Arizona State University.
Member Acceptance of Electronic Access Systems: Innovators
versus Laggards, 1999.
Lepisto, Lawrence R., Central Michigan University. Consumer
Relationships with Financial Institutions, 1993.
Lepisto, Lawrence R., Central Michigan University. Psychological
and Demographic Factors Affecting Relationships with Financial
Institutions, 1994.
Matsumura, Ella Mae and Dickson, Peter, University of WisconsinMadison; and Kelly, Jr., William A., University of Wisconsin-Madison,
Member Segmentation and Profitability: Current Practice and
Future Possibilities, 1999.
Meyer, Mark C. The Implementation of Check-Cashing Services: A
Growth Opportunity for Credit Unions, 2004.
Overstreet, Jr., George A., University of Virginia and Rubin, Geoffrey
M., Princeton University. The Applicability of Credit Scoring in
Credit Unions, 1996.
61
Overstreet, Jr., George A. and Rubin, Geoffrey M., University of
Virginia. Blurred Vision: Challenges in Credit Union Research
and Modeling, 1991.
Proceedings from the Second Annual Credit Union Colloquium
co-sponsored by Filene Research Institute, Center for Credit Union
Research, and the Center for Financial Services Studies. Discrimination
in Lending: What Are the Issues?, 1995.
Reynolds, Bruce L., University of Virginia. Household Credit in
China: Recent Experience and Lessons from Other Countries,
2005.
Sayles, William W., The Center for Credit Union Innovation, LLC.
Serving Members Around the Globe, 2001.
Sayles, William W., The Center for Credit Union Innovation, LLC.
Small Business: The New Frontier, 2002.
Sayles, William W., The Center for Credit Union Innovation, LLC.
Small Credit Union Data Processors: Survey Results, 2002.
Siciliano, Julie, Florida Institute of Technology. Enhancing Board
Satisfaction at Credit Unions, 2004.
Smith, David M., Pepperdine University and Woodbury, Stephen A.,
Michigan State University. Differences in Bank and Credit Union
Capital Needs, 2001.
Sollenberger, Harold M., Michigan State University and Schneckenburger,
Kurt, Olson Research Associates, Inc. Applying Risk-Based Capital
Ratios to Credit Unions, 1994.
Sullivan, A. Charlene, Purdue University and Worden, D. Drecnik,
Olivet Nazarene University. Personal Bankruptcy: Causes and
Consequences, 1992.
Udell, Jon G., University of Wisconsin-Madison and Kelly, Jr., William
A., University of Wisconsin-Madison. Asset Growth at Credit Unions:
Growth in Membership vs. Assets per Member, 2004.
Udell, Jon G., University of Wisconsin-Madison. Management Practices
and Growth at Mid-sized Credit Unions (Assets of $50-200 Million),
2003.
62
Warfield, Terry D., University of Wisconsin-Madison and Henning,
Steven L., University of Colorado-Boulder. Financial Reporting by
Credit Unions in the United States, 1994.
Whitener, Ellen M., University of Virginia. The Effects of Human
Resource Practices on Credit Union Employees and Performance,
1998.
Whitener, Ellen M., University of Virginia and Brodt, Susan E., Duke
University. Forging Employee Morale, Trust and Performance,
2000.
Wilcox, James A., Haas School of Business, University of California,
Berkeley. Capital Instruments for Credit Unions: Precedents, Issuance
and Implementation, 2003.
Wilcox, James A., Haas School of Business, University of California,
Berkeley. Subordinated Debt for Credit Unions, 2002.
Woodbury, Stephen A. and Smith, David M., Michigan State University;
and Kelly, Jr., William A., University of Wisconsin-Madison. An
Analysis of Public Policy on Credit Union Select Employee
Groups, 1997.
Woodbury, Stephen A., Michigan State University; Smith, David M.,
Pepperdine University; Kelly, Jr., William A., University of WisconsinMadison. A State and Regional Analysis: Effects of Public
Policy on Credit Union Select Employee Groups, 1997.
63