Financial Knowledge, Confidence, Credit Use, and

Financial Satisfaction
Stephen A. Atlas,a Jialing Lu,b P. Dorin Micu,c and Nilton Portod

This article investigates associations between confidence about financial knowledge and two outcome variables,
financial behaviors and financial satisfaction. On one hand, subjective financial knowledge (confidence) is
necessary to make proactive decisions, yet overconfidence has been associated with a range of negative financial
behaviors and outcomes. Both types of objective and subjective knowledge may be related to critical financial
behaviors and choices such as credit card usage which in turn may be associated with financial satisfaction, an
important component of consumer well-being. This article analyzes data from the 2015 National Financial
Capability Study to examine how financial knowledge confidence relates to credit card behaviors and financial
satisfaction. We use mediation and floodlight analyses to uncover relevant relationships between variables of
interest. We find evidence that confidence is associated with healthy credit card use that contributes to financial
satisfaction. We also observe strong interactions with knowledge to find that confidence is more strongly
associated with credit card use and overall financial satisfaction as knowledge increases. Findings from this
study can help financial educators and advisors to deliver the right mix of financial knowledge to better financial
choices and behaviors.

Keywords: confidence, consumer credit use, financial behaviors, financial satisfaction, subjective knowledge

T
he credit card industry in the United States is near- Costly credit usage habits such as revolving high interest
ing the $1 trillion mark in outstanding balances, balances and missing payments may lead to misuse of other
which represents over $15,000 in credit card debt types of consumer debt and lower credit scores.
per indebted household (Federal Reserve of New York,
2016). The industry as a whole is still growing and evolving A hallmark of household finance research finds that people
new products and contract features. As a result, consumers with higher financial knowledge tend to make better finan-
are faced with a multitude of complex choices beginning cial decisions (Gutter & Copur, 2011; Lusardi, 2008; Robb,
with choosing the right credit card issuer and the credit card Babiarz, Woodyard, & Seay, 2015; Shim, Xiao, Barber,
features that better fit their needs. While this first step is & Lyons, 2009). However, as credit card accounts involve
crucial in choosing the right card, consumers are then faced decisions made by individuals, perhaps individuals’ confi-
with choices on how to better utilize their cards including dence in their financial knowledge—their subjective knowl-
how much to pay from the outstanding balance and when to edge (Xiao, Tang, Serido, & Shim, 2011)—explains some
use potentially costly features such as cash advances. These credit usage behaviors, with consequence to their financial
choices involve tradeoffs between availability of funds for satisfaction.
immediate purchases and long-term debt, credit rating, and
financial satisfaction. For many consumers, credit cards rep- Although subjective financial knowledge and financial sat-
resent their first experience in obtaining consumer credit isfaction have been explored in the past (Xiao, Chen, &
and also an entry way in developing their credit report. Chen, 2014; Xiao & Porto, 2017), previous research has

a
Associate Professor, Department of Marketing, College of Business, University of Rhode Island, 7 Lippitt Rd., Kingston, RI 02881. E-mail: satlas@uri.edu
b
MS Analytics Student, Harrisburg University of Science and Technology, 326 Market St, Harrisburg, PA 17101. E-mail: jlu4@my.harrisburgu.edu
c
PhD Student, Department of Marketing, College of Business, University of Rhode Island, 7 Lippitt Rd., Kingston, RI 02881. E-mail: dmicu@uri.edu
d
Assistant Professor, Department of Human Development and Family Studies, University of Rhode Island, 2 Lower College Road, Transition Center #212,
Kingston RI 02810. E-mail: nilton_porto@uri.edu
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Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019, 175-190 175
© 2019 Association for Financial Counseling and Planning Education®
http://dx.doi.org/10.1891/1052-3073.30.2.175
not yet reviewed how subjective financial knowledge (aka future wealth and utility. While this analysis is limited to
confidence) might be associated with credit usage. Allgood credit card usage, findings from this relationship between
and Walstad (2013) examined credit behaviors related to confidence, knowledge, credit card behaviors, and finan-
both objective and subjective financial knowledge by age cial satisfaction can help inform other research in areas of
group. The authors found that when both types of financial consumer debt, such as auto and home loans. Confidence
knowledge are present, credit usage is less costly. In a later in financial knowledge and actual financial knowledge are
article, Allgood and Walstad (2016) employ similar strate- considered to be building blocks of broader concepts such
gies to review the relationship between objective/subjective as financial capability or literacy (see Sherraden, 2013 for
financial knowledge and a series of financial behaviors. The more details). This study also contributes to the research lit-
present research builds on Allgood and Walstad by bringing erature on how financial capability might foster more opti-
financial knowledge and credit card behaviors to the fore- mal financial behaviors.
front of the analysis, while also taking it further by including
a financial satisfaction component, a different approach to This article reports results from analysis of data from
financial knowledge questions, and a comprehensive scale the 2015 National Financial Capability Study (NFCS) to
of credit card behaviors. Additionally, we employ a num- explore how confidence and knowledge together relate with
ber of statistical methods to examine the relevant relation- credit card use, and how these predict the overall finan-
ships between the variables of interest. The present study cial satisfaction. We report floodlight analysis and media-
also answers a call from Allgood and Walstad to examine tion results providing evidence that confidence in financial
the objective and subjective components of financial liter- knowledge is linked with healthy credit card use and that it
acy jointly in reference to financial behaviors. By employ- interacts with objective financial knowledge to predict over-
ing mediation and floodlight techniques, this study was able all financial satisfaction. The similar relationship between
to further explore the relationships between those important confidence and both credit use and financial satisfaction
factors of financial literacy and their association with credit helps support the proposition that confidence predicts credit
card usage. use, rather than the other way around.

Previous research found associations between subjective We close by discussing these results in the context of the
financial knowledge and a range of healthy financial behav- limitations on how confidence is measured in the NFCS
iors (Robb & Woodyard, 2011) or the interaction of subjec- study and call for more research understanding the interplay
tive and objective knowledge on financial behaviors (Porto between confidence and knowledge in consumer financial
& Xiao, 2016), but to date no research has specifically choice.
explored the interaction of subjective and objective knowl-
edge with credit card behaviors and financial satisfaction. Background and Hypotheses
This article tries to fill this gap by addressing not only In this study, we examine the effect of financial knowl-
the interactions between objective (i.e., actual) and subjec- edge on credit behavior. Two distinct components of knowl-
tive knowledge (i.e., financial confidence) with a number edge are recognized in the literature: subjective knowledge,
of credit card choices, but also the relationship of these which refers to a person’s perception of the amount of infor-
constructs to financial satisfaction by using data from a mation about a product or topic stored in his or her memory
large and nationally representative survey. While credit card (Brucks, 1985), and objective knowledge, which pertains to
behavior such as paying a late fee is considered to be the actual amount of accurate information stored in his or
detrimental, this choice can be better understood by also her memory (Brucks, 1985). In this article, we referred to
considering financial satisfaction and investigating how subjective knowledge as financial confidence, and to objec-
confidence and knowledge contribute to subjective financial tive knowledge as financial knowledge. Objective financial
well-being. knowledge has also been referred in the literature as finan-
cial literacy.
Credit card choices are a useful domain to study finan-
cial choices more broadly; they are representative financial Financial literacy, however, is a concept still ill-defined
choices involving complex tradeoffs between current and
Pdf_Folio:176 in most of the literature, often used interchangeably

176 Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019
with financial knowledge (Van Rooij, Lusardi, & Alessie, (Henager & Cude, 2016) and soldiers (Carlson, Britt, &
2011) or financial capability, but not less frequently con- Goff, 2015).
sidered to be a component of financial capability—or even
an umbrella to include financial knowledge and behaviors Interestingly, financial confidence has also been found to
(Fox, Bartholomae, & Lee, 2005). Huston (2010) provided result in poor financial choices, particularly when it exceeds
an excellent review of the issue and its potential implica- actual knowledge. Overconfidence has been linked with a
tions in the field, while providing an outline for a knowledge variety of negative financial behaviors. Those with high sub-
and an application dimension to this construct. To allevi- jective financial knowledge are less likely to seek finan-
ate the potential confusion, the present study treats financial cial advice (Kramer, 2016), trade the most, but perform the
literacy as a synonym of objective financial knowledge, worst (Barber & Odean, 2000), and are more likely to start
measured via a quiz containing five financial questions. a business that will fail (Camerer & Lovallo, 1999). Due
Similarly, the literature cited here mostly discusses financial to overconfidence, they fail to ensure against risks, save
literacy or objective financial knowledge as substitutes. for the future, and rarely seek appropriate financial advice
and education (Barber & Odean, 2000; Camerer & Lovallo,
Financial literacy, or its lack of, has been linked to a number 1999; Kramer, 2016; Menkhoff, Schmeling, & Schmidt,
of financial behaviors (Hilgert, Hogarth, & Beverly, 2003; 2013). Consumers who are overconfident about their finan-
Lusardi & Mitchell, 2014). With regard to credit card use, cial knowledge are also more likely to need debt counseling,
previous research shows that consumer misunderstanding of an indication of financial stress (Porto & Xiao, 2016). Prior
statement features results in their misjudging monthly pay- studies showed that when objective and subjective financial
ments (Soll, Keeney, & Larrick, 2013). Additionally, Agar- knowledge were compared, over half of participants who
wal, Chomsisengphet, Liu, and Souleles (2015) show that considered themselves financially knowledgeable were, in
consumers with strong numerical skills are more likely to fact, lacking the basic financial knowledge (Courchane,
overcome this bias. Ludlum et al. (2012) found that many 2005). Indeed, in the context of genetically modified foods,
cardholding students lack knowledge of important features Klerck and Sweeney (2007) report a large gap between
of their cards such as the interest rate and late payment fees. objective and subjective knowledge, showing that objec-
Lack of financial knowledge has been found to be related tive knowledge moderates the relationship between subjec-
to more credit card debt (Norvilitis et al., 2006) and use tive knowledge and perceived risk of genetically modified
of cash advances (Yao & Meng, 2018), while higher finan- foods. In the context of the present study, these findings sug-
cial knowledge is related to more responsible credit card use gest a negative relationship between confidence (subjective
of college students (Robb, 2011; Xiao et al., 2011). Thus, knowledge) and healthy financial behaviors when knowl-
objective financial knowledge is believed to act as a possi- edge is low, but a positive one when objective knowledge is
ble remedy to improve credit card usage. high. Thus, we posit that

Financial confidence has also been associated with healthy H1: The relationship between confidence in financial
financial behaviors. Allgood and Walstad (2013) found knowledge and healthy credit use will strengthen as
that perceived financial knowledge (i.e., confidence) was financial knowledge increases.
a stronger predictor of more positive credit card practices
than actual financial knowledge, showing that credit card Another variable of interest in this study is financial satis-
behaviors were healthiest among those with both types of faction. There has been a growing interest in the research
knowledge. In the same vein, Xiao, Ahn, Serido, and Shim of financial satisfaction either as its own construct or as a
(2014) found that both financial knowledge types decreased component of overall subjective well-being or life satisfac-
risky credit and borrowing behaviors, suggesting that they tion as a whole (Vera-Toscano, Ateca-Amestoy, & Serrano-
work in different paths to reduce risk. Kim, Kwon, and Del-Rosal, 2006). An earlier exploratory study of Joo and
Anderson (2005) observed that workplace financial edu- Grable (2004) revealed a number of factors related to finan-
cation leads to higher confidence on retirement prepared- cial satisfaction such as financial behaviors and financial
ness. Subjective financial knowledge (confidence) has also knowledge. Xiao, Chen, et al. (2014) uncovered a positive
been linked to better financial behaviors of young adults
Pdf_Folio:177
relationship between financial satisfaction and perceived

Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019 177
financial capability, a form of financial self-efficacy. Using Figure 1. Conceptual model.
an earlier wave of the NFCS, Woodyard and Robb (2016)
found that subjective knowledge is more strongly correlated
to financial satisfaction than objective knowledge. Other
factors that have been found to have an association with
financial satisfaction include gender differences in financial
perceptions (Hira & Mugenda, 2000), income on financial
status (Parrotta & Johnson, 1998), risk tolerance (Aboagye
& Jung, 2018), debt and financial anxiety (Archuleta, Dale,
use, whose relationship with financial confidence is moder-
& Spann, 2013), and perceived income adequacy (Grable,
ated by financial knowledge.
Cupples, Fernatt, & Anderson, 2013).

On a more comprehensive scale and using cross-national Methods

data, Ng and Diener (2014) suggested that financial satisfac- Data
tion is associated with overall financial well-being and life This study analyzes the data from 27,564 respondents com-
evaluation. A review of these and other studies shows that ing from the state-level 2015 NFCS included in the FINRA
financial knowledge, measured using an objective or subjec- IEF 2015 report (Lin et al., 2016). We selected the 2015
tive (confidence) approach, appears to be related to overall NFCS because it contains information about objective and
financial satisfaction. However, previous studies have not subjective financial knowledge, credit card use, financial
directly examined the relationships among financial knowl- satisfaction, and a number of personal and economic con-
edge, credit card use behavior, and financial satisfaction. trols. In subsequent analyses, we report replications of all
Thus, building on this previous research, we expect the analyses on the 2009 NFCS data and include them in the
strong relationship between confidence and healthy credit discussion.
behavior in the context of high knowledge (hypothesized
in H1) to extend to financial satisfaction. Additionally, we Although the 2015 NFCS data set contains weights at the
expect this relationship to mirror people’s credit use suffi- national, divisional, and state levels, we have decided to use
ciently for a moderated mediation relationship. Accordingly unweighted data in our analysis, following the findings of
Dew and Xiao (2011) which showed that weighting the data
H2: The relationship between confidence in finan- did not significantly impact the results. Further, we do not
cial knowledge and financial satisfaction attributable to know how applying the weight is going to accurately sample
credit use will strengthen with financial knowledge. among confident and unconfident people, and since we are
primarily concerned with extracting the multistage relation-
Additionally, Hypothesis 2 helps address an important ship between confidence, credit card use, and satisfaction,
potential counterargument. Perhaps credit behaviors are pre- do not want to risk distorting the data with weights.
dicting the increase in confidence, rather than confidence
predicting the credit card behaviors. Hypothesis 2 helps Variables
reduce the likelihood of this alternate model, as testing for a We study credit card decisions because credit use is the most
consistent relationship between confidence with credit card common financial decision-making and a typical gateway
use and financial satisfaction helps increase the likelihood to other forms of consumer credit. Fortunately, the 2015
that confidence is not predicted by credit card use alone. NFCS contained six questions in a survey otherwise limited
While this still leaves open the possibility that some third in financial behaviors. These items include paying credit
factor is associated with both confidence and credit use, cards in full, being charged interest for carrying a balance,
the testing of this hypothesis provides stronger evidence making a minimum-payment, being charged a late fee, being
that confidence has a different relationship with credit use charged an overdraft fee, and withdrawing cash advances.
depending on knowledge. Our model presented in Figure 1 After reverse-coding all items except paying credit cards
proposes that the relationship between financial confidence in full, we average these measures to form a scale measur-
and financial satisfaction is mediated by healthy credit card
Pdf_Folio:178 ing healthy credit card use (Cronbach’s 𝛼 = .75). These six

178 Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019
binary items and their coding, along with all main measures, graduation of a single measure. Financial satisfaction was
appear in the FINRA IEF 2015 report (Lin et al., 2016). measured on a 0 (not at all satisfied) to 10 (extremely
Detailed information about the NFCS data can be obtained satisfied) scale using the survey question “Overall, thinking
by visiting the appropriate links presented in Tables 1–5. of your assets, debts and savings, how satisfied are you with
your current personal financial condition?”
We next examine reliability and consistency of related items
in the credit behaviors scale. In particular, paying in full is Finally, we also considered a variety of demographic and
correlated with making minimum payments (r = 0.77), yet economic controls, based on prior research finding extrane-
there is sufficient variability between the items that having ous relationships between these factors and financial behav-
both items in the scale can improve the overall accuracy iors (Robb & Woodyard, 2011). These include age, gender,
of the scale. A Principal Component Analysis shows load- race, number of dependent children, income, and employ-
ings of 0.69 and 0.75 for these two items, supporting the use ment status. Models considering these controls provide an
of both. Based on these results, and on the fact that scales important robustness check as the relationship between con-
including both “paying in full” and “making minimum pay- fidence, financial knowledge, credit card use, and financial
ments” items are well established in the field (Lusardi & satisfaction is being evaluated.
Tufano, 2015; Robb, Moody, & Abdel-Ghany, 2011), we
have decided to retain all six items in our healthy credit Sample Characteristics
card use index. Although the correlation is high, there are Table 1 describes a sample of 21,327 respondents out of
no concerns for collinearity because we are not evaluat- the 27,564 individuals provided by the 2015 NFCS. Indi-
ing them as separate predictors of common dependent mea- viduals who did not have credit cards (23%) were excluded
sures. We conclude that the treatment of credit behaviors as from analysis. The respondents report a high level of con-
a scale adequately represents respondents’ aggregate credit fidence in their financial knowledge (5.4 out of 7), with
card behaviors. 83% of participants rating their knowledge as a 5 or higher
on the 7-point confidence scale. However, they had mod-
We are also interested in self-reported financial confidence, erate actual financial knowledge. A financial knowledge
financial knowledge, and financial satisfaction. Knowledge score was computed by assigning “1” if the question was
was measured by five questions about financial knowledge, answered correctly and “0” otherwise, and summing the
including financial calculations and understanding finan- answers for the five questions; hence resulting in a scale
cial concepts such as diversification and bonds. These five midpoint of 2.5. 66% of participants correctly answered 2 to
questions, out of which three were proposed by Lusardi 4 knowledge questions out of 5, with the average being 3.14.
and Mitchell (2008), and two were introduced in the NFCS Finally, financial satisfaction was measured on a scale of 1
in 2009, have become a foundational standard in several (Not at all satisfied) to 10 (Very satisfied), with the average
financial literacy surveys in the United States and have satisfaction being 6.15; thus slightly higher than the scale
now been translated to other countries as well (Lusardi midpoint.
& Mitchell, 2011). When computing the financial knowl-
edge index, we considered “don’t know” and “prefer not to Respondents’ credit card use reveals a variety of behav-
answer” responses as being incorrect, in accordance with iors. Roughly half (54%) pay their credit card bills in full.
the common practice in literature (Lusardi & Tufano, 2015). 47% were charged interest for carrying a balance, and 31%
The Cronbach’s 𝛼 for the financial knowledge scale is .70. paid the minimum balance. 13% paid a late fee, while 7%
The confidence variable was taken from the survey item exceeded their credit line and 10% requested cash advances.
“On a scale from 1 to 7, where 1 means very low and 7 After reverse-coding all items except “paid in full,” we com-
means very high, how would you assess your overall finan- bined the six credit use behaviors to form a healthy credit
cial knowledge?” This measure is central for exploring the use score (𝛼 = .77).
role of financial confidence (subjective knowledge) with
other financial behaviors, but note that the ability to draw Otherwise, the 2015 NFCS is a representative sample of
conclusions around overconfidence is limited by the limited the U.S. population (Table 1). Participants ranged in age
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Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019 179
TABLE 1. Summary Statistics
Mean Standard Deviation Min Max Observations
Confidence 5.26 (1.20) 1 7 21,327
Knowledge 3.18 (1.39) 0 5 21,327
Credit card health index 4.39 (1.65) 0 6 21,327
Financial satisfaction 6.15 (2.62) 1 10 21,327
Frequencies Observations
Gender (female) 53% 21,327
Race (White) 74% 21,327
Young (<25) 8% 21,327
Old (≥65) 21% 21,327
Single 25% 21,327
Have dependent child 38% 21,327
Household size of 2 39% 21,327
Household size of 3 14% 21,327
Household size of 4 13% 21,327
Household size of 5 5% 21,327
Household size ≥6 2% 21,327
Low income (<$25k) 14% 21,327
High income (≥$100k) 22% 21,327
Disabled 3% 21,327
Temporary unemployed 7% 21,327
Have a job 68% 21,327

TABLE 2. Correlations Between Credit Card Health Index, Confidence,

Knowledge, and Satisfaction
CCH Index Confidence Knowledge
CCH Index 1
Confidence .145a 1
Knowledge .253a .179a 1
Satisfaction .345a .408a .077a
a
Correlation is significant at the 0.01 level (2-tailed).

from 18 to over 65. A majority of respondents were white which are used in this article. Whereas spotlight analysis
(74%), and the gender mix was roughly even (47% male; helps interpreting the simple effects of a categorical vari-
53% female). One quarter of respondents were single, 38% able at specific levels of a continuous variable in a regres-
had at least one dependent child, and 68% were work- sion model (Spiller, Fitzsimons, Lynch, & McClelland,
ing. Roughly half of the sample (47%) had annual income 2013), floodlight analysis is used when examining only at
between $25,000 and $75,000, with 25% being retired or a few specific points of the continuous variable may not
unable to work, and 7% laid off or students. be enough (Johnson & Neyman, 1936). Specifically, flood-
light analysis looks at the entire range of the continuous
Data Analyses variable and identifies the point(s) where the simple effect
Prior to presenting the results, we would like to give a turns from significant to not significant, known as Johnson–
brief explanation of the spotlight and floodlight analyses Neyman point (Spiller et al., 2013). Floodlight analyses can
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180 Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019
TABLE 3. Multivariate Regressions Relating Credit Card Health Index With Knowledge and Confidence
(1) (2) (3) (4)
Variables Basic Demographic Economic Comprehensive
***
Confidence 0.17 (0.011) 0.16*** (0.011) 0.13*** (0.011) 0.13*** (0.011)
Knowledge 0.27*** (0.008) 0.21*** (0.009) 0.20*** (0.009) 0.18*** (0.009)
Confidence* Knowledge 0.04*** (0.007) 0.03*** (0.007) 0.02*** (0.007) 0.02** (0.007)
Low income (<$25k) −0.26*** (0.036) −0.23*** (0.037)
High income (>$100k) 0.46*** (0.025) 0.44*** (0.025)
Disabled −0.83*** (0.070) −0.65*** (0.072)
Household size of 2 −0.05+ (0.028) −0.04 (0.032)
Household size of 3 −0.44*** (0.037) 0.02 (0.077)
Household size of 4 −0.50*** (0.040) −0.04 (0.079)
Household size of 5 −0.65*** (0.055) −0.19* (0.087)
Household size ≥ 6 −0.90*** (0.080) −0.43*** (0.105)
Temporary unemployed −0.52*** (0.052) −0.28*** (0.058)
Have a job −0.52*** (0.025) −0.32*** (0.032)
Young (<25) 0.06 (0.048) 0.09+ (0.050)
Old (≥65) 0.41*** (0.026) 0.24*** (0.032)
Female −0.07** (0.022) −0.03 (0.022)
White 0.23*** (0.027) 0.22*** (0.026)
Have dependent child −0.50*** (0.025) −0.48*** (0.062)
Single −0.16*** (0.030) −0.08* (0.034)
Constant 4.36*** (0.011) 4.37*** (0.032) 4.92*** (0.029) 4.59*** (0.048)
Observations 21,327 21,327 21,327 21,327
R-squared 0.08 0.12 0.13 0.14
Adj. R-squared 0.08 0.12 0.13 0.14
Note. Multivariate OLS regression results; Robust standard errors in parentheses.
***
p < .001. ** p < .01. * p < .05. + p < .10.

be conducted with the help of complex macros available credit card behaviors with financial confidence and knowl-
for existing statistical software, or by running a series of edge. We then combine the six items to form a healthy credit
spotlight analyses for several values of the continuous vari- card behaviors scale. Next, we relate this scale with finan-
able. In the latter case, one would observe the values for cial confidence and knowledge, and add demographic and
which the spotlight test fluctuates around the predetermined financial controls. We also consider the overall relation-
p-value and repeat the spotlight analyses until pinpoint- ships between financial satisfaction, financial confidence
ing the Johnson–Neyman point with the desired accuracy and knowledge, and the credit card index. Finally, we eval-
(Spiller et al., 2013). uate whether the credit card index mediates the relationship
between confidence, knowledge, and financial satisfaction.
Results We used case wise deletion for missing values. Less than
We organize the results as follows. First, because both 1,000 observations or 5% of the original survey included
knowledge and confidence variable have a mean close to missing values to the variables of interest and we do not see
the scale mid-point, we mean-center these variables to bet- evidence of nonrandom missing data. To make sure there
ter capture the main effects of confidence at a representa- are no issues of multicollinearity, we looked at the cor-
tive level of knowledge. Then we relate the six measures of relation between financial confidence (subjective financial
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Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019 181
TABLE 4. Financial Satisfaction by Confidence, Knowledge, and Credit Card Health Index
(1) (2) (3) (4) (5)
Variables Basic CC Index Demographic Economics Comprehensive
***
Confidence 0.96 (0.016) 0.88*** (0.016) 0.83*** (0.016) 0.79*** (0.016) 0.76*** (0.016)
Knowledge 0.01 (0.012) −0.12*** (0.012) −0.17*** (0.012) −0.23*** (0.012) −0.24*** (0.012)
Confidence* Knowledge −0.01 (0.010) −0.03** (0.010) −0.04*** (0.010) −0.04*** (0.010) −0.04*** (0.010)
ccind 0.49*** (0.011) 0.48*** (0.011) 0.43*** (0.011) 0.43*** (0.011)
Low income (<$25k) −1.00*** (0.056) −1.01*** (0.056)
High income >$100k) 0.73*** (0.035) 0.71*** (0.035)
Disabled −1.18*** (0.107) −0.99*** (0.111)
Household size of 2 0.09* (0.041) 0.19*** (0.047)
Household size of 3 0.08 (0.052) 0.37** (0.115)
Household size of 4 0.13* (0.055) 0.44*** (0.117)
Household size of 5 −0.04 (0.078) 0.28* (0.129)
Household size ≥ 6 −0.11 (0.111) 0.22 (0.151)
Temporary unemployed −1.00*** (0.077) −1.04*** (0.085)
Have a job −0.53*** (0.039) −0.40*** (0.049)
Young (<25) 0.21** (0.064) 0.53*** (0.066)
Old (≥65) 0.49*** (0.042) 0.32*** (0.049)
Female −0.46*** (0.031) −0.38*** (0.031)
White −0.09* (0.037) −0.12*** (0.036)
Have dependent child 0.18*** (0.036) −0.17+ (0.094)
Single −0.33*** (0.043) 0.07 (0.049)
*** ***
Constant 6.02 (0.017) 3.89 (0.051) 4.16*** (0.067) 4.53 ***
(0.070) 4.55*** (0.087)

Observations 21,327 21,327 21,327 21,327 21,327

R-squared 0.17 0.25 0.27 0.31 0.31
Adj. R-squared 0.17 0.25 0.27 0.31 0.31
Note. Multivariate OLS Regression results; Robust standard errors in parentheses.
***
p < .001. ** p < .01. * p < .05. + p < .10.

knowledge) and objective financial knowledge and found a Table 3 shows OLS regression analysis relating the healthy
value of 0.179. Table 2 presents these results. credit card use scale with confidence, knowledge, and
a series of demographic and economic factors. The first
Confidence, Knowledge, and Credit Card Use model finds that confidence, knowledge, and their interac-
Given the generally consistent results across the credit card tion each have a positive relationship with healthy credit
behaviors, we combined the six items of the healthy credit card use (p < .001), in support of H1. These results are
card use scale (always paying in full, carrying over a bal- largely unchanged in magnitude and significance when
ance, making minimum payments, being charged a late fee, considering predictive demographic factors (Model 2)
being charged an over the limit fee, and getting a cash including age (young/old), gender, race, having at least
advance) into a single scale measuring healthy credit card one dependent child, and marital status. These results
use to draw more general conclusions relating confidence, largely persist when considering economic factors
knowledge, and credit card use. The healthy credit card use (Model 3) including income (low/high), employment
scale reverse-coded all items except paying credit card in (employed/unemployed), and household size, as well
full, and is internally consistent (𝛼 = .75).
Pdf_Folio:182

as both economic and demographic factors (Model 4).

182 Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019
ID:t1810ID:t1855ID:t1900ID:t1945ID:t1990ID:t2040ID:t2090ID:t2095ID:t2140ID:t2190ID:t2240ID:t2290ID:t2340ID:t2390ID:t2440ID:t2490ID:t2540ID:t2590ID:t2640ID:t2690ID:t2740ID:t2790ID:t2840ID:t2890ID:t2940ID:t2990ID:t3045ID:t3095ID:t3140ID:t3190ID:t3240ID:t3245ID:t3290ID:t3340ID:t3390ID:t3440ID:t3490
TABLE 5. Summary of the Mediation Analyses
Model 1 Model 2
Bootstrapped St. Err. 95% CI Bootstrapped St. Err. 95% CI
Beta Beta
Low High Low High
Confidence 0.056 0.005 0.047 0.065 0.061 0.005 0.051 0.071
Knowledge 0.078 0.004 0.070 0.086 0.079 0.004 0.071 0.087
Confidence* Knowledge 0.009 0.003 0.003 0.015 0.011 0.003 0.005 0.017
Confidence to Knowledge 0.009 0.001 0.007 0.010
Fin Satisfaction Coef St. Err. z p(z) Coef St. Err. z p(z)
Confidence 0.765 0.015 52.65 <0.001 0.765 0.015 52.65 <0.001
Knowledge −0.236 0.012 −19.77 <0.001 −0.236 0.012 −19.77 <0.001
Confidence* Knowledge −0.043 0.009 −4.63 <0.001 −0.043 0.009 −4.63 <0.001
ccind 0.432 0.010 44.52 <0.001 0.432 0.010 44.52 <0.001
Young (<25) 0.529 0.062 8.50 <0.001 0.529 0.062 8.50 <0.001
Old (≥65) 0.319 0.051 6.23 <0.001 0.319 0.051 6.23 <0.001
Female −0.376 0.031 −12.25 <0.001 −0.376 0.031 −12.25 <0.001
White −0.121 0.035 −3.43 <0.001 −0.121 0.035 −3.43 <0.001
Have dependent child −0.167 0.085 −1.96 0.05 −0.167 0.085 −1.96 0.05
Single 0.074 0.047 1.58 0.114 0.074 0.047 1.58 0.114
Low income (<$25k) −1.012 0.048 −21.07 <0.001 −1.012 0.048 −21.07 <0.001
High income (>$100k) 0.707 0.039 18.35 <0.001 0.707 0.039 18.35 <0.001
Disabled −0.992 0.102 −9.76 <0.001 −0.992 0.102 −9.76 <0.001
Household size of 2 0.192 0.046 4.17 <0.001 0.192 0.046 4.17 <0.001
Household size of 3 0.366 0.106 3.45 <0.001 0.366 0.106 3.45 <0.001
Household size of 4 0.444 0.108 4.11 <0.001 0.444 0.108 4.11 <0.001
Household size of 5 0.284 0.120 2.37 0.018 0.284 0.120 2.37 0.018
Household size ≥ 6 0.225 0.140 1.61 0.108 0.225 0.140 1.61 0.108
Temporary unemployed −1.038 0.078 −13.34 <0.001 −1.038 0.078 −13.34 <0.001
Have job −0.404 0.050 −8.03 <0.001 −0.404 0.050 −8.03 <0.001
Constant 4.548 0.083 55.13 <0.001 4.548 0.083 55.13 <0.001
R-squared 0.315 0.315
N 21327 21327
CCH index Coef St. Err. z p(z) Coef St. Err. z p(z)
Confidence 0.130 0.010 12.73 <0.001 0.130 0.010 12.73 <0.001
Knowledge 0.181 0.008 21.80 <0.001 0.181 0.008 21.80 <0.001
Confidence* Knowledge 0.021 0.006 3.29 <0.001 0.021 0.006 3.29 <0.001
Young (<25) 0.091 0.044 2.07 0.039 0.091 0.044 2.07 0.039
Old (≥65) 0.239 0.036 6.62 <0.001 0.239 0.036 6.62 <0.001
Female −0.027 0.022 −1.26 0.208 −0.027 0.022 −1.26 0.208
White 0.223 0.025 8.98 <0.001 0.223 0.025 8.98 <0.001
Have dependent child −0.477 0.060 −7.96 <0.001 −0.477 0.060 −7.96 <0.001
(Continued)
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Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019 183
ID:p0240
ID:t3540ID:t3590ID:t3640ID:t3690ID:t3740ID:t3790ID:t3840ID:t3890ID:t3940ID:t3990ID:t4040ID:t4090ID:t4145ID:t4195ID:t4340
ID:p0275
ID:p0245
TABLE 5. Summary of the Mediation Analyses (Continued)
Model 1 Model 2
Bootstrapped St. Err. 95% CI Bootstrapped St. Err. 95% CI
Beta Beta
Low High Low High
Single −0.081 0.033 −2.46 0.014 −0.081 0.033 −2.46 0.014
Low income (<$25k) −0.230 0.034 −6.81 <0.001 −0.230 0.034 −6.81 <0.001
High income (>$100k) 0.437 0.027 16.18 <0.001 0.437 0.027 16.18 <0.001
Disabled −0.646 0.072 −9.03 <0.001 −0.646 0.072 −9.03 <0.001
Household size of 2 −0.042 0.032 −1.30 0.194 −0.042 0.032 −1.30 0.194
Household size of 3 0.022 0.075 0.30 0.767 0.022 0.075 0.30 0.767
Household size of 4 −0.045 0.076 −0.59 0.556 −0.045 0.076 −0.59 0.556
Household size of 5 −0.188 0.085 −2.22 0.026 −0.188 0.085 −2.22 0.026
Household size ≥ 6 −0.434 0.099 −4.40 <0.001 −0.434 0.099 −4.40 <0.001
Temporary unemployed −0.284 0.055 −5.19 <0.001 −0.284 0.055 −5.19 <0.001
Have job −0.315 0.035 −8.91 <0.001 −0.315 0.035 −8.91 <0.001
Constant 4.591 0.049 93.81 <0.001 4.591 0.049 93.81 <0.001
R-squared 0.143 0.143
N 21327 21327
Confidence Coef St. Err. z p(z) Coef St. Err. z p(z)
Knowledge 0.141 0.005 26.5 <0.001
Note. Model 1: Financial Satisfaction (Confidence, Knowledge, Conf. × Knowl., Healthy CC Use, Controls) + Healthy CC
use (Conf., Knowl., Conf. × Knowl., Controls).
Model 2: Financial Satisfaction (Confidence, Knowledge, Conf. × Knowl., Healthy CC Use, Controls) + Healthy CC use
(Conf., Knowl., Conf. × Knowl., Controls) + Confidence (Knowledge).

These results suggest that healthy credit card use increases Figure 2 shows the relationship between confidence and
with knowledge and confidence, and also that knowl- healthy credit behavior at different levels of knowledge
edge strengthens the relationship between confidence and through a floodlight analysis. We followed the procedure
healthy credit use. detailed by Spiller et al. (2013), running 21 new regres-
sions to generate point estimates for credit behavior given
In further support of H1, a spotlight analysis reveals that at each level of knowledge and spotlight confidence levels.
confidence relates to credit use more when knowledge is This analysis similarly supports that healthy credit behav-
one standard deviation above the mean knowledge (B = ior increases with confidence more strongly when objective
0.16, s.e. = 0.02, t = 10.47, p < .001, 95% CI [0.13, 0.19]) knowledge is high, supporting Hypothesis 1.
than when knowledge is one standard deviation below mean
knowledge (B = 0.10, s.e. = 0.01, t = 8.50, p < .001, 95% As a robustness check, we examined whether the results
CI [0.08, 0.12]). Viewed alternately, knowledge relates to are explained by non-normal distribution of the credit card
credit card use more when confidence is high (B = 0.21, health use variable, due to an excessive number of the
s.e. = 0.01, t = 19.22, p < .001, 95% CI [0.19, 0.23]) endpoints of the scale (0s and 6s). To test whether this
than when confidence is low (B = 0.17, s.e. = 0.01, t = explains the focal results, as a robustness check we
14.59, p < .001, 95% CI [0.14, 0.18]). It seems that healthy ran a Tobit regression censoring 0 and 6, and the new
credit card usage increases with confidence and knowl- results have a similar direction and statistical significance
edge more strongly when confidence and knowledge appear as the prior analysis, providing additional support for
together. Hypothesis 1.
Pdf_Folio:184

184 Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019
Figure 2. Floodlight analysis of credit behaviors by confidence, at high and low knowledge.

Note. NFCS reports can be accessed by the following links:

https://www.usfinancialcapability.org/downloads/NFCS_2015_Report_Natl_Findings.pdf
https://www.usfinancialcapability.org/downloads/NFCS_2012_Report_Natl_Findings.pdf
https://www.usfinancialcapability.org/downloads/NFCS_2009_Natl_Full_Report.pdf

Knowledge, Confidence, and Financial Satisfaction We find that indirect paths of confidence, knowledge, and
Table 4 summarizes OLS models evaluating the relation- the interaction between knowledge and confidence are sig-
ship between confidence and knowledge with financial sat- nificant (p < .001), and each 95% confidence interval
isfaction. The first model finds a direct relationship between excludes zero (Zhao, Lynch, & Chen, 2010). These results
confidence and financial satisfaction (p < .001), in sup- indicate that there is a relationship between confidence
port of Hypothesis 2. After considering healthy credit use and financial satisfaction which is explained by credit use.
(Model 2), knowledge becomes a significant factor (p < The results also indicate moderated mediation, in other
.001). The relationship between credit card use and financial words, knowledge moderates the relationship between con-
satisfaction remains strong and roughly stable after account- fidence and financial satisfaction due to credit use, fur-
ing for demographic controls (Model 3), economic factors ther supporting Hypothesis 2. Following Zhao et al. (2010),
(Model 4) and both (Model 5). Credit card use is highly pre- these mediation models reveal indirect mediation, which
dictive of financial satisfaction (p < .001). has lower susceptibility to omitted variable bias than direct
mediation.
We then test whether credit card use mediates the
relationship between confidence, knowledge, and financial Finally, we consider how confidence and knowledge jointly
satisfaction. To test for mediation, we simultaneously esti- relate with credit card use and financial satisfaction. In
mated the models of credit card use and financial satisfac- particular, we examine how the relationships with confi-
tion with all controls (Model 4 of Table 3 and Model 5 of dence depend on knowledge, and how the relationships with
Table 4). For an estimate of the size and statistical signif- knowledge depend on confidence. To do this we adapted
icance of the indirect paths between confidence and finan- the mediation model at one standard deviation above and
cial satisfaction through credit card use without further below the mean values. This is essentially applying a spot-
parametric assumptions, we bootstrap with 5,000 replica- light analysis to the mediation model, to understand the con-
tions (Preacher & Hayes, 2008). In particular, we calcu- fidence => credit use => financial satisfaction path for high
lated the indirect paths between confidence, knowledge, and low knowledge. We find that confidence has a stronger

use when knowledge is high (knowhigh = 4.5576: 𝛽 indirect = .07,

and their interaction with financial satisfaction, mediated relationship with financial satisfaction through credit card
through credit card use.
Pdf_Folio:185

Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019 185
s.e. = 0.01, z = 10.45, p < .001, 95% CI [0.06, 0.08]) than controls, and we also find a similar pattern for financial
when knowledge is low (knowlow = 1.7408: 𝛽 indirect = .04, s.e. satisfaction. The results support Hypothesis 2, that credit
= 0.01, z = 7.14, p < .001, 95% CI [0.03, 0.06]). We also use mediates the predictive effect of knowledge in the rela-
find that knowledge has a stronger relationship with finan- tionship between confidence and financial satisfaction. We
cial satisfaction through credit card use when confidence is interpret these results to support the idea that knowledge
high (confhigh = 6.5033: 𝛽 indirect = .09, s.e. = 0.01, z = 17.02, predicts how confidence impacts credit card choice and this
p < .001, 95% CI [0.08, 0.10]) than when confidence is contributes to financial satisfaction.
low (conflow = 4.2920: 𝛽 indirect = .07, s.e. = 0.01, z = 13.29,
p < .001, 95% CI [0.06, 0.08]). Past research found that an increase in confidence and
knowledge from −1 SD below the mean to +1 SD above
As a robustness check, we investigated if the results the mean results in decreasing the probability of making
are explained by the relationship between knowledge minimum payments with 7% and 6%, respectively (Allgood
and confidence. Perhaps objective knowledge increases and Walstad, 2016). Our results maintain this directionality,
with confidence, which is associated with healthy credit showing that 1-point increase in confidence and knowledge
card use, which predicts financial satisfaction. We esti- is associated with a decrease of 16% and 18%, respectively,
mated a multiple-mediation model considering both the in the probability of making minimum payments.
alternate explanation and the hypothesized mediation
that confidence predicts credit card use and financial Extant literature recognizes financial education as an impor-
satisfaction, controlling for knowledge. As shown in tant and rather singular way of boosting users’ financial con-
Table 5, there is evidence that both paths exist, suggest- fidence, thus leading to healthy financial behaviors (Brown,
ing that objective knowledge is associated with subjective Grigsby, van der Klaauw, Wen, & Zafar, 2016; Xiao &
knowledge and that aspects of subjective knowledge are not O’Neill 2016; Xiao & Porto 2017). However, Xiao et al.
well explained by objective knowledge, and that both of (2011) found that confidence has a bigger impact in reduc-
these relate to credit card use and resulting financial satis- ing poor financial behavior than financial knowledge. As
faction. In other words, objective knowledge alone does not Bandura (1997) showed in his work, self-efficacy (or con-
predict the impact of confidence in healthy credit card use. fidence) is a personal belief that a subject can successfully
complete a certain task. Our findings suggest that boost-
These results support two conclusions: first, we find that ing people’s self-efficacy may help their ability to engage
confidence has a positive relationship with healthy credit in healthy behaviors by helping them gain and preserve the
card use and financial satisfaction even when knowledge capability to be in charge of their personal finances. Sim-
is low, and a similar result for the return on knowledge ilar results have been found in recent research that linked
when confidence is low. Additionally, as the return on financial self-efficacy with preretirement savings (Asebedo
confidence increases with knowledge and the return on & Seay, 2018). Nonetheless, financial knowledge and
knowledge increases with confidence, we conclude that financial confidence must be balanced in order to avoid
knowledge and confidence are complements to increased overconfidence or under confidence, which both lead to
healthy credit card use and financial satisfaction. These nonoptimal financial behaviors.
results support Hypothesis 2 and help increase our confi-
dence that the earlier relationship between financial confi- As one robustness check in an alternate economic climate,
dence and healthy credit card use is not predicted by the we replicated the analysis on equivalent measures in the
effect of credit use on confidence. 2009 NFCS dataset. In that replication, financial satisfaction
was lower, and unhealthy credit behaviors were more preva-
Discussions, Limitations, and Implications lent, potentially reflecting the effects of the recent financial
Discussions crisis experienced by the respondents. We replicate nearly
Across the analyses, we find support for Hypothesis 1, all results with roughly similar relationships. Some notable
that knowledge has a predictive effect on the relationship departures in credit use are that, in 2009, confidence was
between confidence and healthy credit card use. This rela- related with a lower likelihood of late fees and exceeding
tionship persists in models including several significant
Pdf_Folio:186
limits, and knowledge had no relationship with paying in

186 Journal of Financial Counseling and Planning, Volume 30, Number 2, 2019
full. When combined to become the credit use scale, the financial knowledge. As such, financial education interven-
relationships match in direction and significance. Turning tions aimed at improving financial behaviors should include
to financial satisfaction, in 2009 confidence and knowledge components to foster participants’ self-efficacy and confi-
jointly relate with financial satisfaction (b = 0.03, p < .001), dence on their knowledge. Similarly, financial counselors
a relationship that is accounted for by credit card use, and, could cultivate their clients’ best financial practices by help-
as the main mediation and spotlight results persist, it indi- ing those clients trust their own knowledge and ability to
cates direct mediation in the 2009 data set, and indirect-only navigate the complexities of the financial arena.
mediation in the 2015 data set (results are not shown here
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s11205-013-0414-8 Disclosure. The authors have no relevant financial interest
Xiao, J. J., & O’Neill, B. (2016). Consumer financial or affiliations with any commercial interests related to the
education and financial capability. International subjects discussed within this article.
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doi:10.1111/ijcs.12285 Author Contributions. Stephen Atlas, supervising author,
Xiao, J. J., & Porto, N. (2017). Financial education contributed to writing, literature review, data analysis and
and financial satisfaction: Financial literacy, behavior, revision. Jialing Lu ran the main analyses for the article,
and capability as mediators. International Journal of contributed to writing the first draft, and designed the anal-
Bank Marketing, 35(5), 805–817. doi:10.1108/IJBM- yses requested by reviewers. P. Dorin Micu participated in
01-2016-0009 writing and data analysis during the revision process. Nilton
Xiao, J. J., Tang, C., Serido, J., & Shim, S. (2011). Porto contributed to the initial formulation of the research
Antecedents and consequences of risky credit behav- question and choice of statistical model, reviewing previous
ior among college students: Application and exten- literature, and writing initial draft.
sion of the theory of planned behavior. Journal

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