JBR-08029; No of Pages 9
Journal of Business Research xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Journal of Business Research

Credit card behavior, financial styles, and heuristics☆
Hersh Shefrin a,⁎, Christina M. Nicols b
a
b

Santa Clara University, Department of Finance, Lucas Hall, 500 El Camino Real, Santa Clara, CA 95053, United States
Ketchum Public Relations, 2000 L Street NW, Washington, DC, United States

a r t i c l e

i n f o

Article history:
Received 1 September 2013
Received in revised form 1 December 2013
Accepted 1 January 2014
Available online xxxx
Keywords:
Credit cards
Spending
Borrowing
Financial literacy

a b s t r a c t

The paper makes four contributions. First, the paper provides new data and findings about credit card usage segmentation in respect to spending and borrowing behavior. Second, it sets the new findings against the backdrop
of the newly emerging literature on financial literacy. A great variability occurs in financial literacy across
American consumers. Third, the paper describes fast and frugal heuristics aimed to help consumers make effective, and in some cases better, budgeting decisions when they use credit cards. Fourth, the paper describes the
introduction of a new set of online financial tools, offered by a large credit card company, which consumers
are now using to make decisions about their spending and borrowing, and links these tools to the heuristics
under discussion. Fast and frugal heuristics are likely to be especially valuable to consumers with low confidence
in their online skills. Notably, 25% of credit cardholders report that they have low confidence using online technology to manage their finances, with the corresponding figure being 44% for those most at risk.
© 2014 Published by Elsevier Inc.

Introduction
By its nature, household decision making is a heuristic enterprise, as
most household decision tasks are far too complex to be fully specified,
let alone solved through optimization. In this paper, the authors discuss
how households can use fast and frugal heuristics when using credit
cards to engage in spending and borrowing.
The paper makes four contributions. First, it provides new data and
findings about credit card usage segmentation in respect to spending
and borrowing behavior. Second, it sets the new findings against the
backdrop of the newly emerging literature on financial literacy. Third,
it describes fast and frugal heuristics aimed to help consumers make
effective, and in some cases better, budgeting decisions when they use
credit cards. Fourth, it describes the introduction of a new set of online
financial tools, offered by a large credit card company, which consumers
are now using to make decisions about their spending and borrowing. In
this regard, it links these tools to the heuristics under discussion.
As Bendor (2010) points out, Herbert Simon's bounded rationality
approach has given rise to several research streams involving heuristics.

☆ The authors thank Jay Weiner, Ipsos, for his work in analyzing the data, Gail Hurdis
and Paul Hartwick, JPMorgan Chase, for comments, and Edward Schultz, U.S. Bank, for

sharing his insights with us about financial literacy. The authors thank Nathan Berg, our
referee, and Shabnam Mousavi, the journal guest editor for this special issue, for their helpful comments on the paper.
⁎ Corresponding author.
E-mail addresses: (H. Shefrin),
(C.M. Nicols).

Examples of the different streams include the fast and frugal heuristic
approach described in Gigerenzer, Todd, and the ABC Research Group
(1999), the heuristics and biases approach described in Kahneman,
Slovic, and Tversky (1982), and self-control heuristics described in
Shefrin and Thaler (1988).
Heuristics for household consumer decisions are ubiquitous. A large
literature in marketing documents how households make use of heuristics that feature, for example, private labels and national brands (Putsis
& Dhar, 2001), satisficing in respect to the ordering of cues (Levav,
Heitmann, Herrmann, & Iyengar, 2010), and self-control through the
choice of which cash denominations to carry (Raghubir & Srivastava,
2009).
In respect to self-control, the role of heuristics has also been a subject
of study in the literature in economics (Shefrin & Thaler, 1988), and
more generally in retirement saving behavior (Benartzi & Thaler,
2007) and borrowing behavior (Karlan & Zinman, 2012). In this regard,
the economics literature documents the degree to which many U.S.
households lack financial literacy (Bernheim, 1995, 1998). A study by
Hilgert, Hogarth, and Beverly (2003) finds that most Americans do not
understand the basic financial concepts of stocks, bonds, and mutual
funds. Moore (2003) finds that people often fail to understand terms
and conditions of consumer loans and mortgages. Ameriks, Caplin, and
Leahy (2003) report that households with superior financial planning
and budgeting skills accumulate wealth at higher rates than other
households.

The economics literature is also developing tools for measuring
the degree to which consumers express preferences consistent with a
neoclassical preference ordering. For example, see Choi, Kariv, Müller,
and Silverman (2010). These tools are interesting, but focus more on

/>0148-2963/© 2014 Published by Elsevier Inc.

Please cite this article as: Shefrin, H., & Nicols, C.M., Credit card behavior, financial styles, and heuristics, Journal of Business Research (2014), http://
dx.doi.org/10.1016/j.jbusres.2014.02.014


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H. Shefrin, C.M. Nicols / Journal of Business Research xxx (2014) xxx–xxx

consistency of choice than on the quality of the decisions. The main
focus of the present paper is on steps consumers can take to improve
the quality of their financial decisions. In this regard, see the normative
discussion in Agnew (2010).
Lusardi (2010) summarizes findings from the National Financial Capability Study (NFCS), which was commissioned by the FINRA Investor
Executive Foundation, and in which she played a lead role. The NFCS
comprises three linked studies, one focused on national traits, one
focused on comparisons state-by-state, and one focused on military personnel. Lusardi (2010) describes the results from the national survey,
which pertains to (1) making ends meet; (2) planning ahead; (3) managing financial products; and (4) financial knowledge and decision
making.
In terms of making ends meet, the first study finds that approximately half of the Americans surveyed report difficulty in keeping up
with monthly expenses. Roughly a quarter report overdrawing their
checking accounts, and of these, about three quarters admit to being
challenged in making ends meet.
As for planning ahead, 51% of Americans fail to accumulate precautionary savings. Only 42% have attempted to assess their retirement

savings needs. Only 41% of those with financially dependent children
have set aside funds for college education.
Credit cards are among the financial products addressed within the
NFCS. Interestingly, 68% of those surveyed reported that they possess
credit cards. Of these, 51% indicated that in some months they carried
a balance and paid interest, 29% indicated that in some months they
paid the minimum amount due, and 23% indicated that in some months
they incurred a fee for late payment. In contrast, 54% report that they
always pay their credit card balances in full. Notably, 28% of those
possessing credit cards appear to be challenged in making ends meet,
which contrasts with 49% for the full population.
When it comes to financial knowledge, Americans delude themselves. Although 37% rate their overall knowledge of finance at the
high end, with the corresponding percentage for mathematics being
46%, the survey found that people overrate their abilities on both
dimensions. For those who rated themselves at the highest end in
both finance and mathematics, only half could correctly perform two
calculations pertaining to interest rates and inflation respectively.
The financial service landscape is changing in notable ways. To begin
with, studies such as the NFCS are documenting the spectrum of financial literacy across households. In addition, the credit card act passed in
2009, with the acronym CARD (the Credit Card Accountability, Responsibility, and Disclosure Act), introduced a series of major changes, most
of which became effective in February 2010.
Even before the passage of CARD, financial institutions, such as
banks, credit card companies, and mutual funds had begun plans to
offer financial tools online. This is especially important in the case of
banks and credit card companies, as these tools can access household
transaction data. In this regard, Mui (2010) discusses innovations at
credit card firms Chase, Citi, Discover, and American Express. These
innovations involve options for lowering interest payments. Mui notes
that innovations at Chase have attracted the most attention because
they feature important budgeting tools to help credit cardholders

manage their balances over time.
As part of a consulting team, the authors worked with Chase to develop a system to help credit card users engage in some self-diagnosis,
in order to ascertain how best to use the budgeting tools offered by
Chase. In this paper, the authors describe some of the key findings
of the research underlying this effort, and use the data to analyze important heuristic features. In light of the NFCS findings, the central
questions addressed in the paper ask whether households can
make use of fast and frugal heuristics (Gigerenzer and Gaissmaier,
2011; Gigerenzer et al., 1999; Todd and Gigerenzer, 2000), to achieve
greater self-awareness about their credit card financial styles, and in
consequence to employ financial tools to make better decisions
about their spending and borrowing.

The remainder of the paper is organized as follows: The first section
describes a fast and frugal heuristic for identifying credit card financial
styles. The second section provides an overview of different types of
financial styles in practice. The third section describes the data and associated key characteristics. The fourth section explains how the authors
applied cluster analysis to identify financial styles. The fifth section
contrasts the results from cluster analysis with those from the application of fast and frugal heuristics. The sixth section describes the Chase
Blueprint program, for which the authors developed their style analysis.
Notably, this section discusses research findings about consumer reaction to the relationship between style analysis and the program. A
final section provides conclusions.
Developing fast and frugal heuristics
Consider the objective of helping credit cardholders make better financial decisions. As a first step, suppose one seeks to categorize credit
cardholders based upon repayment characteristics, general attitude to
budgeting, and reliance on budgeting heuristics. How might one develop a fast and frugal style heuristic for the categorization task? What
might be a reasonable set of cues, alternatives, stopping rules, and decision rules in respect to credit card behavior? To fix ideas, consider a specific example, involving cues for repayment characteristics, general
attitude to budgeting, and reliance on budgeting heuristics.
Cues
According to the NFCS findings, the most important characteristic of
repayment behavior is the frequency with which a holder pays only the

minimum due, pays the balance in full, pays something in between, or is
occasionally in default, thereby incurring late fees. One cue for repayment behavior is the answer to the following question: When it
comes to paying your credit card(s), do you tend to pay the entire
balance on the card each month, or just the minimum due? Possible
answers might be restricted to 1) minimum due; 2) entire balance; or
3) something in between.
An indicator of attitude toward household budgeting is the degree to
which a holder believes that it is important to be in control of his or her
finances. One cue for importance of control is the answer to the following question: When it comes to managing finances, how important is it
to you that managing your finances is completely within your control,
even if it involves considerable effort on your part? Possible answers
might be 1) unimportant; 2) important; 3) something in between.
As for budgeting heuristics, Shefrin and Thaler (1988) propose that
households base their spending on how they mentally categorize their
wealth into particular mental accounts. Examples of categories are
current income, liquid assets, home equity, and future income. In the
Shefrin–Thaler framework, households establish a pecking order for
funding, with current income at the top of the pecking order and future
income at the bottom. Specifically, households spend first from current
income, and only after this account is fully depleted, reluctantly “break
into” the liquid asset account. The idea of reluctance is measured as a
mental setup cost, so that the household only breaks into the account
if the benefit from doing so is large enough to compensate for the
setup cost of doing so.
In a similar vein, Raghubir and Srivastava (2009) provide evidence
that people can control impulse purchases by only carrying large
denominations of cash, effectively placing large denominations into
mental accounts that they are reluctant to invade. Raghubir and
Srivastava call this tendency the “denomination effect”.
Notably, credit cards rather than cash, checks, or debit cards are the

most preferred mode of payment by households. When it comes to selfcontrol heuristics, credit card usage declined from 87% in 2007 to 56% in
2009, with a corresponding rise in the use of debit cards. This is consistent with the use of a heuristic to increase self-control in respect to taking on debt. At the same time, some households achieve self-control by

Please cite this article as: Shefrin, H., & Nicols, C.M., Credit card behavior, financial styles, and heuristics, Journal of Business Research (2014), http://
dx.doi.org/10.1016/j.jbusres.2014.02.014


H. Shefrin, C.M. Nicols / Journal of Business Research xxx (2014) xxx–xxx

using multiple credit cards to help themselves budget, and dedicate particular cards for specific purchases. Through expenditure limits on these
cards, or by not carrying some cards all the time, households can institute mental accounting principles to control their spending. One cue
for mental accounting-based credit card spending is the answer to the
following question: Do you tend to use a single credit card for all of
your expenses, or do you use different cards for different reasons, such
as one card for everyday expenses and another for emergencies only?
Possible answers might be (1) single credit card or (2) multiple credit
cards.
Alternatives
Consider the following four style alternatives for a fast and frugal
categorization heuristic, in which a non-compensatory classification
tree is used to assign credit cardholders to one of four styles.
1.
2.
3.
4.

Low control minimum payers
High control minimum payers
Full balance paying multiple cardholders
Full balance paying single cardholders


Below is an intuitive description of these four categories. Low control
minimum payers are likely to be most at risk when it comes to financial
behavior that is lacking in discipline. These credit cardholders report
that financial control is not important to them, and usually or always
make the minimum payment on their monthly credit card balances. A
lack of financial literacy for credit cardholders in this category makes
them highly vulnerable to falling into debt for extended periods of
time, and paying high fees and interest.
High control minimum payers also make the minimum payment on
their monthly credit card balances, usually or always. However, this
group reports that being in control of their finances is important. Credit
cardholders falling into this group are likely to be more purposeful and
disciplined in using credit card debt to make ends meet.
Full balance paying multiple cardholders routinely pay most if not all
of their monthly balances, and in addition use multiple credit cards.
Credit cardholders in this category are likely to be more sophisticated
than the first two groups, and to rely on mental accounting heuristics
to achieve financial discipline.
Full balance paying single card users almost always pay their balance
in full, and plausibly do not need the complexity of heuristics based on
multiple credit cards. Credit cardholders in this category might carry a
credit card balance only if they use their card for particularly large
purchases, especially those that are unexpected. This specific heuristic
relies on three cues. Later in the paper, the authors discuss other cues.
Decision rule
This particular categorization task involves four options and three
cues, a situation that lends itself to an elimination heuristic such as
QuickEst, or some variation. Consider the following possibility.
Suppose a credit cardholder answers the first question by saying that

he or she makes only the minimum payment each month. This answer
eliminates categories 3 and 4, as these categories feature credit cardholders routinely paying most, if not all, of their monthly balances.
The second question, relating to the importance of control, is then
used to decide whether to assign the holder to either category 1 or
category 2. If the holder answers the second question with the answer
“important,” then the heuristic assigns him (or her, henceforth him)
to category 2, “high control minimum payers.” Otherwise, the heuristic
assigns him to category 1, “low control minimum payers.”
On the other hand, if the credit cardholder answers the first question
by saying that he pays more than the minimum payment, then the
heuristic uses the answer to the third question to assign him to either
category 3 or category 4. If the credit cardholder reports that he uses
multiple credit cards, then the heuristic assigns him to category 3, “full

3

balance paying multiple credit cards.” If the credit cardholder reports
that he uses a single credit card, then the heuristic assigns him to “full
balance paying single credit card.” This non-compensatory classification
tree for typing consumers into one of four styles is similar to the
Breiman-classification procedure for possible heart attack patients
which Todd and Gigerenzer (2000) use at the outset of their article
about how simple heuristics can make people smart.
General advice heuristic
The style outcome produced by the classification tree is intended as
a cue for improving household budgeting, pointing the credit cardholder to the identification of a planning tool that is likely to prove most useful. Below is an example of advice, depending on the category into
which credit cardholders fall.
Low control minimum payers: use a planning tool to avoid paying
interest on everyday purchases, such as groceries and fuel. High control
minimum payers: use a planning tool to manage the timeframe over

which credit card balances will be repaid. Full balance paying multiple
cardholders: simplify budgeting by using a planning tool to track spending across consumption categories, along with a more efficient use
of the number of cards. Full balance paying single cardholders: use a
planning tool to isolate large credit card purchases, especially those
associated with unplanned expenditures.
The prescriptions that are imbedded in the above heuristic are
generic in nature. Later in the paper, the authors discuss prescriptions
that are more specific.
Cues and styles
There is a rich psychology literature on personality traits: See John,
Naumann, and Soto (2008). This literature has inspired the development of instruments designed to identify financial personalities. In
this section, the authors describe some of these instruments, with the
intent to provide some background for the analysis in this paper and
to highlight some of the major cues used in these instruments.
The financial personality quizzes available to credit cardholders are
quite varied. Examples include VISA UK's “Better Money Skills,” FNBO
Direct, First Bank of Omaha's “What's Your Savings Style?” Jordan
Goodman's “Master Your Money Type,” and Louisiana State University's
“What Color Is Your Money?”
A typical test is “Better Money Skills” offered by VISA UK. For illustrative purposes, the authors discuss some of its key features. Its stated
purpose is to allow credit cardholders to test themselves in order to
identify their financial personalities. They do so by taking an online
test in which they choose answers to eleven online questions that closely
reflect their attitudes and experiences with money. Three of the test
questions are:
1. When I get my credit card statement, I normally repay:
A. The minimum — and sometimes that comes from another credit
card.
B. As much of the outstanding amount as possible.
C. The entire balance.

2. My monthly budget is:
A. An ideal rather than a reality — I never have enough money to go
round.
B. Usually OK but I sometimes overspend.
C. Very occasionally a struggle but I generally stick to it.
3. Savings are:
A. What savings? I spend every penny that comes in.
B. There to pay for important things, such as a deposit for a home of
our own or a new kitchen.
C. Part of my monthly budget plan.
Notice that all three questions offer three choices of answers, labeled
A, B, and C. Indeed, answers to all eleven questions in “Better Money

Please cite this article as: Shefrin, H., & Nicols, C.M., Credit card behavior, financial styles, and heuristics, Journal of Business Research (2014), http://
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H. Shefrin, C.M. Nicols / Journal of Business Research xxx (2014) xxx–xxx

Skills” are structured in this manner, and the preponderance of A, B, or C
responses is used to assign the test taker to one of three financial
personality profiles, designated A, B, or C. The personality profiles are
as follows:
A. Money is for enjoying, as far as you're concerned. You may consider
luxuries such as new computers, cars or designer clothes as modern
essentials, which can make it hard to cut back. Many of you will have
a great time, though — until your overspending catches up with you.
You need to remember that credit isn't free cash and that all your

bills ultimately have to be paid. You might want to avoid temptation,
by saving a small but regular amount to pay off your debts — and
learning to say no.
B. Money is for sharing with others and for living a comfortable life.
You're quite well organized financially but are likely to overspend
when you're stressed or unhappy — and you can excuse your extravagance if it's designed to make other people happy. You might want
to start saving little and often so you can afford to indulge those you
love, drawing up a budget that prioritizes essentials over treats —
and learn to say no more often.
C. Money is for security, which is fine when you have enough of it to
feel safe. When you're short, though, you can feel panic-stricken.
You're more than capable of sticking to a sensible budget, making repayments on time and saving for the future. You might want to start
paying off debts before you add to savings, asking for advice and help
if you're in trouble — and learn to manage your level of debt sensibly.
These three personality profiles are informative, and tied to general
advice. However, they are also vague in so far as serving as cues for specific actions associated with spending and borrowing.
“Better money skills” also offers a test taker the opportunity to assess
his or her degree of their financial literacy. After receiving a profile, the
test taker is then given a profile and additional information to help
educate him or her about finances.
For present purposes, the key issue concerns the nature of the other
nine questions, in so far as cues are concerned. These questions relate to
issues such as: being in control of household finances, what the test
taker would do with a sizeable windfall, how the test taker would handle an unexpected bill, how carefully the test taker chooses new debt,
and reasons for discomfort when managing spending and borrowing.
The other tests mentioned above offer similar types of questions,
with similarly structured financial profiles as outputs. There are also
tests that focus on other financial dimensions, and therefore provide
other types of questions/cues. MoneySense offers two such quizzes.
The first is the MoneySense Self-Analysis Quiz which addresses issues

related both to saving and investing, but not to spending per se. The
questions in this test pertain to having rainy day accounts, adequate
life insurance, diversified investments, a current will, etc. The quiz
consists of ten questions, with answers on a 0-to-5 point scale. The
total number of points is used to assign test takers to one of 4 categories:
Poor, Fair, Good, and Excellent. The second is what MoneySense calls its
Personality Quiz, and it focuses exclusively on investment. This quiz was
developed by Statman and Wood (2004). The quiz consists of sixteen
questions, with four choices for each question, which then map into
the following four personality profiles: artisans (good instincts will
prevail), idealists (money just isn't the top priority), guardians (discipline
is the key to security), and rationals (cool reason conquers all).
Data: attitudes, behavior, efficacy, and credit cards
The data for this study come from two surveys respectively conducted in May and June of 2009. Both surveys focus on eliciting characteristics and behavior patterns of credit cardholders that are associated
with specificity of financial goals, spontaneity of purchasing behavior,
tendency to procrastinate, perceived control, importance of control,
confidence in managing household finances, confidence in using online

technology, monthly payment behavior, use of cards for large expenses,
number of credit cards employed, and general decision style.
The May 2009 survey, conducted by Opinion Research Corporation,
provides information about consumers' general propensities along
with associated demographics. The sample for this survey consists of
1047 U.S. adults, comprising 501 men and 546 women 18 years of age
and older. Opinion Research Corporation conducts the online omnibus
study twice a week among a demographically representative U.S.
sample of 1000 adults, aged 18 years and above.
The June 2009 survey was conducted among a sample of 4026 U.S.
adults, comprising 2087 men and 1939 women 18 years of age and
older. Ipsos, a market research firm, conducted the online survey

among a demographically representative U.S. sample of adults on June
11–15, 2009. With a sample of 4026, one can say with 95% certainty
that the overall results are within ± 1.5% of what they would have
been had the entire population of adults in the country been surveyed.
The margin of error for specific demographic segments will be lower.
Also, Ipsos collects comparable information on demographics compared
to Opinion Research Corporation, but they display the information in
their banners differently – for example, Ipsos offers fewer breaks in
age groups – 18–34, 35–54, and 55 and over instead of the more narrow
age segments done by ORC. For this reason, the results reported pertain
to the May survey, unless otherwise indicated.
The present section describes univariate analysis involving the demographics based on the May survey, organized into four subsections.
In most respects, the results from the May survey and the June survey
are mutually consistent. Rather than aggregate the two datasets, the
authors report any differences in results as ranges, with the first entry
referring to the May survey and the second to the June survey.

Attitudes to budgets and planning
Self-control is a determining factor in consumers' planning skills.
Self-control was assessed by posing the following question: When you
have to do an unpleasant task, do you tend to do it right away or are
you more likely to put it off? About half (49–45%) answered that they
would do the task right away, 30–37% responded that they would put
it off, and the other responses were neutral. Seniors are the most likely
to do the task right away, and younger consumers are more likely than
others to put off the task. In this regard, 60% of those over age 65 indicate that they would do the task right away, in contrast to 42% between
the ages of 18 and 24. (To simplify exposition, the authors provide the
range, but omit values for intermediate values for age, education, etc.)
For those putting off the task, the corresponding responses were 23%
for those over age 65 and 38% for those between the ages of 18 and 24.

When asked whether they tend to set specific financial goals or more
general financial goals, 40–50% of consumers report that they set general goals, whereas 31–24% report setting specific goals. Middle-income
and affluent consumers are more likely than lower, lower-middle and
upper-middle income consumers to set specific financial goals. Interestingly, younger households and college graduates are more likely to set
specific financial goals than others. Caucasian consumers are more likely
than other groups to set general financial goals.
When asked how important it is that managing their finances be
completely within their control, even if it involves considerable effort
on their part, 81–74% respond that it is important. The older is a consumer, the more likely he or she is to say that it is very important to
have complete control. Hispanic consumers are more likely than others
to say that it is very important to have complete control. In this regard,
89% of those aged 65 and over so respond, whereas 75% of those between the ages of 18 and 24 respond in this way. As for ethnicity, 87%
of Hispanic consumers respond in this fashion, compared to 82% of
Caucasians and 73% of African Americans. In the June survey, those
with higher incomes, college graduates, and women are more prone
to respond by saying that control is very important.

Please cite this article as: Shefrin, H., & Nicols, C.M., Credit card behavior, financial styles, and heuristics, Journal of Business Research (2014), http://
dx.doi.org/10.1016/j.jbusres.2014.02.014


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When asked about whether they rely on instinct and intuition, facts
and logic, or concern for others in making financial decisions, the
respective percentages from the June data are 18, 72, and 10.
General budgeting behavior
When asked whether they tend to stay within a set monthly budget
or to have no set budget, 55% say that they stay within a set budget, 24%
say that they have no set budget, and the remaining responses are neutral. In terms of demographics, 67% of Hispanic consumers report

staying within a set budget, compared to 54% of Caucasian consumers
and 56% of African Americans.
When asked about being more of a deliberate spender, usually planning their purchases, or a spontaneous spender, usually spending if they
happen to see something they like, 51–49% respond that they are deliberate spenders. Those responding that they are spontaneous spenders
comprise 24–30% of consumers. In this regard, seniors, who are typically
on fixed incomes, are the most deliberate spenders. Similarly, college
graduates are more likely than others to be deliberate spenders. Younger
consumers are more likely than others to be spontaneous spenders, as
are Hispanic consumers. In this regard, 62% of those 65 and older are deliberate spenders, in contrast to 47% of those between the ages of 18 and
24. In respect to level of education, 57% of college graduates report being
deliberate spenders, compared to 47% for high school incompletes, 43%
for of high school graduates, and 52% for college incompletes. In respect
to ethnicity, 35% of Hispanic consumers report being spontaneous
spenders, compared to 24% of Caucasian consumers and 14% of African
American consumers.
Perceptions about budgeting efficacy
When asked how confident they are that they manage their money
well, 66–64% respond that they have high confidence and 15–19% indicate that they have low confidence. Seniors and affluent consumers are
the most likely to indicate that they have high confidence. (The June
survey also identifies college graduates as judging themselves to be in
control of their finances.) In this regard, 82% of those aged 65 and over
have high confidence, compared to 60% of those between the ages of
18 and 24.
When asked how confident are consumers in using online technology to manage their finances, 56–52% express high confidence, whereas
19–25% express low confidence. Not surprisingly, younger, higher income, college graduates are more confident in using online technology.
In this regard, 69% of those between the ages of 18 and 24 express high
confidence, whereas only 46% of those aged 65 and over do. The results
for income and education are similar.
When asked whether they are surprised by the balance on their
monthly credit card statements, or the balance is as expected, 70% respond that the balance is as expected, and 8% respond that it is more

of a surprise. The older a consumer is, the more likely he or she is to
say that the statement is as expected. College graduates are more likely
than others to say that the statement is as expected. Of those aged 65
and older, 87% respond by saying that the balance is as expected, whereas for those aged 18 to 24, the corresponding response rate is 56%. For
education, 76% of college graduates respond by saying that the balance
is as expected, compared to 50% of high school incompletes.
Credit card issues
When asked about making monthly payments on their credit card,
in so far as paying the entire balance each month, just the minimum
due, or something in between, 52–53% report that they pay the entire
balance, 25% report that they pay just the minimum, and the rest answer in between. Seniors are the most likely to pay the entire balance
each month: 65% of those aged 65 and over pay the full balance, compared to 50% of those aged 18 to 24. Middle-income and affluent

5

consumers are more likely than lower, lower-middle and uppermiddle income consumers to pay the entire balance each month. College
graduates are more likely than others to pay the entire balance each
month.
The proportion of our sample that routinely or always pays the full
balance is 56%, which conforms to larger databases. The proportion of
our sample that reports always paying the minimum balance is 12.4%,
which is significantly higher than the corresponding figures in larger
databases, which are less than 5%. This difference might be due to the
timing of our sample, which coincides with the recession beginning in
December 2007 and the associated high rate of unemployment.
When asked whether they tend to use their credit card(s) only for
emergencies and/or “big ticket” purchases, or for everyday purchases
as well, 39–37% report that they use their card(s) only for emergencies
or big ticket purchases, and 32–39% report that they use their card(s) for
everyday purchases as well as emergencies and big ticket purchases.

The higher a consumer's income, the more likely he or she is to use
his or her card(s) for everyday purchases as well as emergencies and
big ticket purchases. The range is 42% for groups whose annual incomes
exceed $75,000 and 24% for those whose incomes lie below $25,000.
Interestingly, college graduates are more likely than others to use
their card(s) for everyday purchases as well as emergencies and big
ticket purchases. The range is 43% for college graduates to 9% for high
school incompletes.
In the June survey, the authors also find that older consumers are
more likely than younger consumers to use their card(s) for everyday
purchases as well as emergencies and big ticket purchases. Specifically,
47% of those aged 55 and over report using their cards for everyday
purchases, compared to 36% of those aged 35–54 and 34% of those
aged 18–34.
When asked whether they tend to use a single credit card for all of
their expenses or different cards for different reasons, 45–49% replied
single credit card, and 20–30% responded that they use different cards
for different reasons. The higher the income a consumer has, the more
likely he or she is to use different cards for different reasons. In this regard, 33% of consumers whose incomes exceed $75,000 use multiple
cards, in contrast to 22% of consumers with incomes below $25,000.
College graduates are more likely than others to use different cards for
different reasons. In this regard, 40% of college graduates use different
credit cards, compared to 17% of high school incompletes.
In the June survey, the authors also find that older consumers are
more likely than younger consumers to use different cards for different
reasons. Specifically, 36% of those 55 years old and over report using
multiple credit cards, compared to 32% of those between 35 and 54
and 22% of those between 18 and 34.

Cluster analysis

Cluster analysis is a statistical technique for classifying objects
into mutually exclusive groups, based on combinations of interval
variables. Each object corresponds to an observation with a set of
values X1, X2, … Xn. Cluster analysis is used to identify a system of organizing objects, so that objects in the same group share properties
in common. This section discusses the application of cluster analysis
to identifying financial styles associated with credit card behaviors
for spending and borrowing.
Since the number of groups is known a priori, “k-means cluster analysis” can be used. With this method, objects, in this case credit cardholders, are assigned to a given group at the first step, based on some
initial criterion. Then means for each group are calculated, where
means are based on the values X1, X2, …, Xn. At the next step, the objects
are reassigned (into groups), so that objects are assigned to groups
based on the similarity of the object to the current mean of that group.
At the end of this step, the means of the groups are recalculated. This
process continues recursively until no objects change groups.

Please cite this article as: Shefrin, H., & Nicols, C.M., Credit card behavior, financial styles, and heuristics, Journal of Business Research (2014), http://
dx.doi.org/10.1016/j.jbusres.2014.02.014


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H. Shefrin, C.M. Nicols / Journal of Business Research xxx (2014) xxx–xxx

The June sample features properties that are similar to the general
population of credit cardholders, in terms of income, age, education,
and ethnicity. The sample size was 4026. The values X1, X2, … Xn are
responses on an interval scale of 1 to 5 to the ten questions described
in the previous section.
Cluster analysis was used in conjunction with the June data to identify four specific groups, roughly along the lines of the categories
described in the section Developing fast and frugal heuristics. The

main findings were roughly as follows. For the sake of continuity, the
authors preserve the same labels as in the section Developing fast and
frugal heuristics.
1. Low control minimum payers: This group comprises 27% of the
sample. Only 17% of this group pays the full balance each month.
The remainder is evenly divided among those who pay only the
minimum and those who pay more than the minimum but less
than the full balance. Only 40% report attaching high importance to
being in control of their finances. Correspondingly, just over half of
the sample, 52%, report having low confidence in their ability to
manage their finances. Notably, 44% of this group report low confidence in being able to manage their finances using online technology.
Only 9% of this group report setting specific goals, and 53% regard
themselves as spontaneous spenders. Three quarters of this group
are below 55 in age.
2. High control minimum payers: This group comprises 20% of the
sample. Two thirds (67%) always pay only the minimum amount
due, and only 1% pay the full balance each month. Interestingly,
91% indicate that they attach high importance to being in control of
their finances, 31% report setting specific financial goals, and 72%
have high confidence that they manage their money well. Two thirds
of the group has high confidence in using online technology to manage their finances. Interestingly, 45% of this group reports using their
credit cards only for emergencies or large purchases. Approximately
80% of the group is below 55 in age.
3. Full balance paying multiple cardholders: This group comprises
20% of the sample. Notably, 85% of this group reports using
more than one credit card. Almost none pay only the minimum
amount due, and 92% pay the full balance each month. Interestingly, 85% indicate that they attach high importance to being in
control of their finances, 23% report setting specific financial
goals, and 88% have high confidence that they manage their
money well. A little under two thirds of the group (63%) has

high confidence in using online technology to manage their finances. Interestingly, 59% of this group reports using their credit
cards for everyday purchases. Approximately 55% of the group is
below 55 in age.
4. Full balance paying single cardholders: This group comprises 33% of
the sample. Notably, 99% of this group reports using a single credit
card. Almost none pay only the minimum amount due, and 90%
pay the full balance each month. Interestingly, 86% indicate that
they attach high importance to being in control of their finances,
32% report setting specific financial goals, and 86% have high confidence that they manage their money well. A significant 83% has
high confidence in using online technology to manage their finances.
Interestingly, 41% of this group reports using their credit cards for
everyday purchases. Approximately 65% of the group is below 55 in
age.
Consider the choice of four groups for the cluster analysis. The data
naturally decompose into at least four groups. In performing cluster
analysis with five groups, the authors find there to be no clear difference
in primary characteristics between two of the five groups, which leads
us to conclude that the choice of four is sufficient for the analysis. In addition, having more than four groups requires additional cues, leading to
less frugality in the heuristic approach.

Comparing fast and frugal to cluster analysis
Clearly, the addition of questions beyond those described in the
section Developing fast and frugal heuristics, along with cluster analysis,
provides a much richer description of how credit cardholders vary in
terms of financial style. Not surprisingly, the features of the four groupings are similar in terms of repayment behavior, control, and attitude, as
these are the bases for using the fast and frugal classification heuristic. In
this respect, cluster analysis provides a natural selection of cues for the
four categories identified.
In this section the authors compare the results of the two classification methods. To do so, both cluster analysis and the classification heuristic are used to categorize the sample, and compute the incidence in
which the two methods produce the same result. For the sample of

4026, the heuristic produces the same result as cluster analysis in
66.9% of cases. If one were to treat the cluster analysis as providing
the right categorization, the heuristic would produce the correct answer
for two out of three cardholders.
This finding is robust. The June data also included an additional 2526
observations for consumers located in the ten largest metropolitan
areas. These observations were not used in the analysis described in
previous sections, and therefore provide an out-of-sample test. For
this sample, the heuristic produces the same result as cluster analysis
in 68.4% of cases. For the out-of-sample comparison, the cluster groupings are determined by applying the coefficients obtained from the insample analysis, which is described below. In the remainder of this
section, the authors pool the two samples, thereby providing a sample
size of 6552.
Of interest is the question of why the two procedures differ in one of
three cases. Answering this question requires further explanation of the
cluster analysis methodology. The cluster technique used in this paper
employs a linear model that is used to identify objects. Let Xi be an
11-dimensional column vector of object i's responses to the ten questions, with a 1 as the eleventh component. Let β be a 4 × 11 matrix
of coefficients. Cluster analysis focuses on the product βXi, which is a
4-element column vector with the four elements corresponding to the
four styles respectively. The assignment algorithm assigns an object to
the group associated with the maximum of the four elements.
Because the cluster analysis algorithm is linear, it treats the underlying environment as compensatory. In this respect, a credit cardholder
being a deliberate spender can compensate for him or her only making
the minimum required payment each month. With this in mind, consider
the possible reasons for misclassification.
It turns out that 38% of the misclassifications occur when cluster
analysis places a cardholder into the first category, low control minimum payers, while the heuristic places them into the fourth category,
full balance paying single cardholders. Notably, these two categories
are diametrically opposite in terms of their repayment behavior.
Most of the classification differences arise for credit cardholders who

fall between making only the minimum payment and always paying the
full balance. Minimum payers constitute 25% of the sample. However,
cluster analysis, because it factors in other variables within a compensatory framework, assigns 47% of the sample to the two groups designated
as “minimum paying”. This 22% difference will account for a major
component of the alignment discrepancy.
If one were to modify the heuristic, by reclassifying a cardholder
who was originally identified as being a full balance paying single cardholder, but who does not clearly state that he or she almost always pays
their balance in full, then the rate of agreement between heuristic and
cluster analysis would rise from 67.5% to 73.5%: a little less fast, just as
frugal, and a little more accurate.
The modified heuristic provides the same classification as cluster
analysis in roughly three of four cases. As to the fourth case, 43% of the
differences involve identifying someone who is a low control minimum
payer with someone who is a high control minimum payer. These differences largely occur for responses that are away from the extremes.

Please cite this article as: Shefrin, H., & Nicols, C.M., Credit card behavior, financial styles, and heuristics, Journal of Business Research (2014), http://
dx.doi.org/10.1016/j.jbusres.2014.02.014


H. Shefrin, C.M. Nicols / Journal of Business Research xxx (2014) xxx–xxx

Consider the case when cluster analysis identifies a cardholder as a
low control minimum payer. The most important difference between
the cases when the modified heuristic produces the same classification
as cluster analysis and when the modified heuristic classifies the cardholder as a high control minimum payer pertains to the importance
attached to control. The cases featuring the difference attach much
higher value to control than the cases where the two procedures
agree. Similarly, consider the case when cluster analysis identifies a
cardholder as a high control minimum payer, but the modified heuristic
classifies him or her as a low control minimum payer. The most important difference between the two cases is that the cases featuring the

difference attach are much less inclined to pay their balance in full
than the cases where the two procedures agree. For these two types
of differences, an argument can be made that the modified heuristic
actually produces a more reasonable classification.
The economic significance of the misclassification discussed in
the previous two paragraphs is relatively minor, in so far as advice
is concerned. Minimum payers, be they low control or high control,
should plan to avoid paying interest on everyday expenses and
develop a schedule for paying down their credit card balances with
a view to keeping interest payments over time in check. As far as
advice is concerned, the only issue is the order in which these are
prioritized.
The most frequent single difference between cluster analysis and the
heuristic classification occurs when cluster analysis identifies a cardholder as a full balance paying multiple cardholder, but the modified
heuristic identifies him or her as a full balance paying single cardholder.
This occurs in 24% of all cases where the two methods produce different
classifications. For the out-of-sample test, the corresponding figure is
virtually identical at 73%.
The single most important difference between cases where the two
agree and those where they do not is importance of control. Control is
less important for the cases where the two methods differ. The second
most important difference is number of credit cards used. Cases where
the two methods disagree are associated with fewer cards.
In concluding this section, the authors wish to mention a second outof-sample test which provides a nice illustration of the principle “less is
more.” The test involved 34 participants, 32 of whom were high school
economics teachers and two of which were university economics professors. Given their expertise, my initial hypothesis was that the cluster
analysis technique would classify all 34 as either full balance paying single cardholders or full balance paying multiple cardholders. However,
surprisingly the analysis identified six of the 34 as low control minimum
payers, which is a rather high 17.6%. The reason is not that these particular teachers do not think that control is important, or that they only
make the minimum payment required each month. The reason is that

these particular cardholders are not confident about using online technology to manage their finances, and in the cluster analysis compensatory framework, that factor loomed large. In this regard, 20% of the
cluster group “full paying single credit cardholders” report that they
have low confidence in managing their finances online. Interestingly,
the one difference produced by the modified heuristic and cluster analysis is that the latter identified a cardholder as a low control minimum
payer and the modified heuristic identified the cardholder as a full
balance paying multiple cardholder.

Chase blueprint program
As indicated in the section Developing fast and frugal heuristics, the
main purpose in developing a financial style heuristic is to help households better manage their spending and borrowing. In this respect, a
financial style should serve as a cue for taking some particular action.
The financial styles discussed in the section Cluster analysis were developed with this task in mind, and the associated actions relate to a set of
features offered by JPMorgan Chase.

7

Blueprint features
In September 2009, Chase introduced a set of online credit card features, called Blueprint, which are designed to help credit cardholders
manage their spending and borrowing. Blueprint is available to over
20 million Chase card customers. Below, the authors briefly describe
Blueprint's features, and then indicate how they relate to the financial
styles described in the section Cluster analysis.
Blueprint offers four main features, respectively called Full Pay,
Finish It, Track It, and Split.
1. Full Pay provides cardholders with an opportunity to set up a plan
whereby they avoid paying interest on everyday expenses such as
groceries and gasoline. In return for being diligent in making payments that conform to the plan, credit cardholders save on interest.
2. Finish It enables credit cardholders to set up a plan for repaying their
balances over time, so that they avoid maintaining unnecessary debt.
3. Track It enables credit cardholders to set up a system which monitors

their monthly expenses by designated categories such as food, clothing, and travel.
4. Split enables credit cardholders to set up a system which breaks
out large expenses in order to enhance their salience in respect to
payment of associated interest and principal.
Blueprint website: financial styles employed and normative issues
A key normative issue associated with Blueprint features is whether
consumers who use them can make better decisions about their spending and borrowing. A related question is whether consumers can make
better use of Blueprint features by first identifying their financial styles.
Preliminary evidence about consumers using Blueprint is encouraging. Chase reports that nearly 3 million plans have been created since
2009. Whereas approximately 40% of U.S. cardholders pay more than
the minimum payment each month, 91% of Blueprint do so. Moreover,
90% of Blueprint users stay committed to the plans they establish.
Chase offers one specific credit card called “Slate with Blueprint.”
Slate is distinctive in that unlike other credit cards, it does not offer
traditional rewards: instead it offers access to Blueprint. Therefore,
consumers with an interest in better financial tools to manage their
spending and borrowing have a clear incentive to choose a Slate credit
card. Notably, Chase reports that credit cardholders who use Slate
with Blueprint pay down their balances twice as fast as other credit
cardholders.
The authors developed the cluster analysis described above to provide credit cardholders with guidance about which Blueprint features
would be most important to them. Cluster analysis produces an algorithm, so that consumers can ascertain to which cluster they belong
by responding to a series of questions. Although 11 questions were
used in the cluster analysis, only six are needed to assign people to the
four groups above with 95% confidence, where the significance level
means that there is a 5% chance of misclassifying a person into the
wrong group.
In Blueprint, the four groups bear different names than those used in
the section Cluster analysis. Low control minimum payers are called
“Make It Easy.” High control minimum payers are called “Control Seeking.” Full balance paying multiple cardholders are called “Financially

Savvy.” Full balance paying single cardholders are called “Confident
and in Control.”
In the remainder of this section, the authors describe the research
that led to these particular names for financial styles, along with comments by consumers about normative issues involving which specific
Blueprint features would be useful to people with particular financial
styles.
The primary objective of the research in question was to gauge
consumer response to the use of financial styles in respect to spending
and borrowing behavior using credit cards. Related objectives included

Please cite this article as: Shefrin, H., & Nicols, C.M., Credit card behavior, financial styles, and heuristics, Journal of Business Research (2014), http://
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H. Shefrin, C.M. Nicols / Journal of Business Research xxx (2014) xxx–xxx

exploring the acceptability of the financial style quiz, identifying appropriate names for the different styles, and understanding normative
issues associated with whether the quiz would increase the degree to
which Blueprint features would be used, and if so how.
The research was conducted in a series of focus groups consisting of
six two-hour sessions in two locations (Philadelphia and Chicago) held
on July 6–7, 2009. Each group contained seven or eight participants. All
group participants were between the ages of 25 and 60, partly or solely
responsible for financial decision making, had and regularly used at
least two general purpose credit cards. In addition, half of each group
used a Chase credit card as their primary card, and most were college
graduates. For four of the six groups, household incomes lay in the
range of $50,000 to $125,000. For two of the groups, either household

income exceeded $150,000 with at least one household member's income being at least $125,000, or the household had at least $250,000
in investable assets.
Notably, most focus group participants easily identified with the
descriptions of the four different personality types provided in the
section Cluster analysis. In Table 1 the authors list some of the comments made by focus group participants in respect to financial styles,
as these provide additional insights into the nature of these styles.
The authors have no direct evidence about the normative question
linking consumer financial styles to choice of Blueprint features. In
this regard, the Chase website does not keep track of cardholders' financial styles, and then connect these to the features they use. However, the
focus group research does provide some indirect evidence on this issue.
Focus group participants were presented with the following recommendations in the form of a prioritized feature list for how consumers
with different financial styles might use Blueprint features.
1. Consumers who belong to the group “Make It Easy” should consider
using Finish It, Full Pay, and Track It to reduce interest payments and
increase control of their budgets.
2. Consumers who belong to the group “Control Seeking” should
consider using “Finish It,” “Full Pay,” and “Split” to reduce interest.
3. Consumers who belong to the group “Financially Savvy” should
consider using “Track It” and “Split” instead of relying on ad hoc
budgeting heuristics based on the use of multiple credit cards.
4. Consumers who belong to the group “Confident and in Control”
should consider using “Track It” and “Split” for ease of monitoring
Table 1
Focus group participant comments about the nature of the four financial styles.
1. Make It Easy
Buy what you need right now, don't worry about it, don't plan
I will worry about it later and pay it off eventually
This was me twenty years ago I was the minimum queen
Don't have any strategies
No big picture thinking

2. Control Seeking
Credit is scary for this person
They only use credit when they can't afford cash
I see this as someone on a strict budget
More of a cash, check, debit person
3. Financially Savvy
They are calculating what the best card is to use
This person must have a lot of willpower
There is a method to their madness
They are going to stick to their regimen. The tools may not be interesting to them
I want to be this person
4. Confident and in Control
This seems like someone who doesn't worry about money. In control, confident
It states my goals
I wish this was me… It's an enviable position to be in
They are living within their means
Very type A… organized
They probably use a credit card for the convenience or the rewards

and for those occasions when they use their credit cards to make
large purchases, especially when those purchases are unexpected.
After being presented with the recommendations, focus group
participants were asked for their reactions to the recommendations.
Overall, many of the recommendations made sense to focus group participants; however, participants did feel that there were better options
for some of the specific types. Below the authors summarize participants' major comments in regard to each of the four financial styles.
1. Make It Easy: Participants were not sure if people with this style
would actually do any of the recommendations, unless doing so
was very easy. Most thought Finish It to be most important feature
for this financial style, but several participants wondered if the feature would require too much discipline. There was consensus that
Track It is very important and useful for the consumers with this

financial style, because they need some help with budgeting. Most
thought that Full Pay was feasible for consumers with this style,
and would help them gain control of their everyday spending.
2. Control Seeking: Although Finish It was not included in the recommendation list for this style, participants were surprised by its
absence and felt that it should be the feature assigned top priority.
One participant stated: “They would want to bring the balance
down as quickly as possible so that it was ready to use again for an
emergency.” Split was regarded as the second most important
feature for this style. Participants commented that Track It (the top
recommendation) was not at all relevant because this person does
not use the card except for emergencies or large purchases, and
therefore there would be nothing on the card to track. Participants
were not certain if Full Pay would be appropriate, since consumers
with this style do not appear to be charging everyday expenses on
their cards. However, some participants did feel that consumers
with this style might be open to doing so if they could separate out
the charges.
3. Financially Savvy: Participants noted that Track It seemed like a
suitable recommendation, as consumers with this style could more
easily track all of their expenses, which would otherwise be spread
across different cards. At the same time, other participants thought
that consumers with this style would already know where they
were spending their money. Participants also suggested that Split
could benefit consumers with this financial style, if they were willing
to put large purchases on their cards and pay them off over time.
However, as a general matter, participants had a difficult time generating recommendations for this style, because people with this style
do not appear to need help with their finances. One participant
stated: “I don't know if you need any recommendations for this
person.” Some pointed out that Full Pay provides little assistance
for those who almost always pay their balance in full.

4. Confident and in Control: Participants agreed that Track It would be
very useful for this financial type, as all of their spending tends to
be on one card. They also indicated that Split should be a top priority
because consumers with this style might be open to financing a
larger purchase. On the other hand, Full Pay seemed redundant for
consumers with this style, as they are already paying off their monthly
balances.
From a normative perspective, the authors note that consumers
using Blueprint typically identify their financial styles online and access
Blueprint features online. For the 25% of consumers having low confidence in their online skills for managing finances, this will prove to be
a challenge. For them, a fast and frugal heuristic can be an appropriate
substitute. For low control minimum payers especially, the issue is
important. Chase customers who wish to use Blueprint features to set
up an appropriate plan, but lack confidence in their online skills, can
use the telephone to seek help from a Chase call center representative.

Please cite this article as: Shefrin, H., & Nicols, C.M., Credit card behavior, financial styles, and heuristics, Journal of Business Research (2014), http://
dx.doi.org/10.1016/j.jbusres.2014.02.014


H. Shefrin, C.M. Nicols / Journal of Business Research xxx (2014) xxx–xxx

Conclusion
In respect to financial literacy, a great variability occurs across
American consumers. This paper investigates the use of fast and frugal
heuristics to help consumers identify their financial styles for using
credit cards to engage in spending and borrowing. Fast and frugal heuristics are likely to be especially valuable to consumers with low confidence in their online skills. Notably, 25% of credit cardholders report
that they have low confidence using online technology to manage
their finances, with the corresponding figure being 44% for those most
at risk. This feature suggests online skills as a possible fifth group for

a heuristic-based classification. However, because of the diversity in
respect to other variables of those with low confidence in their online
skills, cluster analysis does not produce a fifth group whose major
feature emphasizes this attribute.
In addition to the analysis of fast and frugal heuristics for identifying
credit card styles, the paper makes three other contributions. First, it
provides new data and findings about credit card usage segmentation
in respect to spending and borrowing behavior. Second, it sets the
new findings against the backdrop of the newly emerging literature
on financial literacy. Third, it describes the introduction of a new set of
online financial tools, offered by a large credit card company, which
consumers are now using to make decisions about their spending and
borrowing. In this regard, the paper links these tools to the heuristics
under discussion.

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Please cite this article as: Shefrin, H., & Nicols, C.M., Credit card behavior, financial styles, and heuristics, Journal of Business Research (2014), http://
dx.doi.org/10.1016/j.jbusres.2014.02.014