Analysis of the demand for counterfeit goods pdf

Analysis of the demand for
counterfeit goods
Pamela S. Norum
Department of Textile and Apparel Management, University of Missouri,
Columbia, Missouri, USA, and
Angela Cuno
Northcentral University, Prescott Valley, Arizona, USA
Abstract
Purpose – The production, distribution and consumption of counterfeit goods have been increasing
at an alarming rate. Current legislation addresses the supply side of the problem, but not the demand
side of the problem. The purpose of this paper is to examine, empirically, factors affecting consumer
demand for counterfeit goods were analyzed.
Design/methodology/approach – The economic theory of consumer demand provided the
theoretical framework. Data were collected from students enrolled at a major mid-western university,
and logistic regression was used to estimate demand functions for counterfeit goods.
Findings – The results indicated that student sensitivity to the counterfeit problem did not
significantly deter the purchase of counterfeit goods.
Research limitations/implications – Educators in textiles and apparel should have a vested interest
in providing education about counterfeiting, resulting in students with greater sensitivity to the issue.
Originality/value – The production, distribution and consumption of counterfeit goods have been
increasing at an alarming rate. Current legislation addresses the supply side of the problem, but not
the demand side of the problem. Consumer education may be a feasible approach for addressing the
demand side of the problem.
Keywords United States of America, Consumer behaviour, Counterfeiting, Fashion, Demand model
Paper type Case study
Introduction
A global trend that has been increasing at an alarming rate is the production,
distribution and consumption of counterfeit goods. In spite of legislation intended to
reduce the sale of counterfeit merchandise, industry leaders and designers all over the
world have identified this as a growing problem, and are working with groups such as
the International Anti-Counterfeiting Coalition (IACC) to protect their designs from being

copied. The IACC (2008) estimates that 5-7 percent of world trade is in illegitimate goods.
Trade in counterfeit goods has reached $600 billion annually on a worldwide basis. This
problem has grown over 10,000 percent in the past 20 years, partly due to an increase in
consumer demand. In the USA, counterfeiting costs businesses up to $250 billion each
year. The highest profile counterfeit investigations and prosecutions have focused
luxury goods. Often times these products are sold by street merchants and vendors at
mall kiosks. Counterfeit handbags are the most widely copied product. Kate Spade
executives believe that the sales ratio of real bags to counterfeits is one-to-one.
Counterfeit luxury items have become a multi-million dollar business for traffickers
because of the commonplace acceptance of counterfeit purses in our society and the
sophisticated strategies for evading state or federal agents (Amendolara, 2005).
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1361-2026.htm
Counterfeit
goods
27
Received March 2009
Revised October 2009
Accepted February 2010
Journal of Fashion Marketing and
Management
Vol. 15 No. 1, 2011
pp. 27-40
q Emerald Group Publishing Limited
1361-2026
DOI 10.1108/13612021111112322
To deter counterfeit goods, Congress passed the Trademark Counterfeiting Act (TCA)
of 1984. Up to this point in time, penalties for counterfeiting were minimal and did not
prevent counterfeiters from trafficking goods into the USA. Under the Trademark
Counterfeiting Act, any corporation or individual who is found guilty of intentionally

trafficking counterfeit goods risks a maximum penalty of one million dollars and/or five
years imprisonment (Amendolara, 2005). The Trademark Counterfeiting Act of 1984 was
a giant leap towards the protection of trademarks. However, without bilateral action from
countries that value trade relationships with the USA, it cannot be completely successful.
In addition, this legislation does not address the demand side of the problem. The demand
side of the counterfeit problem is clearly an issue of consumer behaviour, or perhaps more
appropriately termed, “consumer misbehaviour” (Albers-Miller, 1999).
With legislation, such as the TCA, the focus is on deterring the suppliers and sellers
of counterfeit goods, but not the consumers, or ultimate purchasers of the goods. There
are limited legal ramifications for consumers of counterfeit goods. However, in recent
years, with the growth in trafficking of counterfeit goods, greater interest in
understanding consumer behaviour with regard to purchasing counterfeit goods has
developed. This shift has occurred because without the demand, there would be no
need for the supply. Legal consequences and consumer education are both options for
addressing the demand side of the counterfeit issue.
Educators in textiles and apparel, in particular, have a responsibility to educate
their students about the counterfeit industry, and its consequences. Singhapakdi (2004)
indicated that sensitivity to an issue would reduce the likelihood of engaging in a
negative or unethical behaviour. If this is true, then one would expect that textile and
apparel students would be, or should be, sensitive to the counterfeit issue and less
likely to engage in it.
Prior research has shown that consumer’s ethical attitudes can affect the likelihood
of purchasing counterfeit goods (Muncy and Vitell, 1992). Economic benefits can also
drive the demand for counterfeit goods (Bloch et al., 1993; Dodge et al., 1996). In
addition, socio-economic and demographic characteristics also influence purchasing
behaviour. The purpose of this study is to gain a better understanding of selected
factors affecting consumer demand for counterfeit goods. Given the role that textile
and apparel educators in higher education could play in addressing this issue, college
students are the focus of the study. The primary objectives are to:
.

determine whether consumer attitudes towards counterfeiting differ between
purchasers and non-purchasers of counterfeit goods;
.
determine if consumer attitudes regarding the legal/ethical aspects of
counterfeiting influences the purchase of counterfeit goods;
.
identify whether sensitivity to the counterfeit problem affects purchase
behaviour; and
.
examine the effect of selected socio-economic and demographic characteristics on
the demand for counterfeit goods.
Background
Attitudes
By almost any standard, US consumers are viewed as being very materialistic. The
desire to own possessions can lead to a consumer acting unethically in order to obtain
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the possessions they desire. To study the ethical beliefs of consumers, Muncy and
Vitell (1992) developed and administered a consumer ethics scale which measured
consumer practices that have ethical implications. Consumer ethics can be defined as
the moral rules, principles and standards guiding the behaviour of an individual (or
group) in the selection, purchase, use or selling of a good or service. Based on their
sample of 1,900 heads of households within the USA, Muncy and Vitell’s respondents
tended to believe that it was more ethical to passively benefit in some way than to
actively benefit from an illegal activity. According to the study, the “no harm no foul”
activities were considered to not be unethical. Many of these activities included
intellectual property rights such as the copying of software, tapes, or movies.
Tom et al. (1998) investigated consumer attitudes toward counterfeiting on several
different dimensions, measuring attitudes about the economic, legal, anti-business, and

quality aspects of counterfeiting. In this study, approximately, 40 percent of the 129
respondents had knowingly purchased counterfeit goods. Purchasers had more lax
attitudes about the lawfulness of counterfeiting, were less likely to believe that
counterfeiting hurt the US economy, held greater anti-big business sentiments, and
perceived the quality of counterfeit goods to be as good legitimate goods. Ang et al.
(2001) surveyed a total 3,621 respondents, aged 15 and above, who had purchased CDs
in the past. The results indicated that one’s attitude towards piracy was a significant
predictor of one’s purchase intentions. Consumers who have bought pirated CDs before
had more favourable views about counterfeit goods than those who have never bought
counterfeit products. Buyers and non-buyers alike did not consider anything wrong
with purchasing counterfeit goods.
Hunt and Vitell (1986) argued that the perception of an ethical issue or problem is an
important prerequisite for the ethical decision-making process. A person who perceives
an ethical problem more readily tends to behave more ethically than an individual who
does not. A study of students in marketing classes from two major universities found
that perceived ethical problems and perceived importance of ethics have a positive
impact on the ethical intentions of students (Singhapakdi, 2004). The results of this
study concluded that a marketing student’s perceived importance of ethics is a
significant predictor of one’s ethical intentions.
Gender differences
Early studies found gender to be unrelated to ethical behaviour, ethical problems,
and reasonable alternatives to resolving ethical problems (Hegarty and Sims, 1978;
Singhapakdi and Vitell, 1990) whereas more recent studies indicated gender
differences. Recent studies have concluded that female students tend to be more
ethical in their intentions than male students (Singhapakdi, 2004) and more
accepting of questionable ethical responses (Cole and Smith, 1996). Kwong et al.
(2003) found gender and age were significantly related to the intention to buy
pirated CDs, with male respondents more likely to purchase counterfeit CDs than
were female respondents. A study conducted by Chen and Tang (2006) examined
business and psychology students’ attitudes toward unethical behaviour and the

likelihood of them engaging in unethical behaviour. The study found that male
students tend to believe that theft, corruption and deception were more ethical than
their female counterparts.
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Economic influences
Past research has shown that direct economic consequences such as paying a lower
price, influence the tolerance of questionable behaviour by consumers (Dodge et al.,
1996). A study conducted by Bloch et al. (1993) found that consumers would select a
counterfeit item over a genuine product when there is a price advantage. For their
first study, 100 adult consumers were surveyed, using a mall intercept, to determine
a consumer’s willingness to knowingly buy counterfeit apparel. Respondents were
shown a set of three knit shirts (a designer label with a logo, a counterfeit of the
designer label with a logo, and a store brand without a logo). Participants were
made aware that of the products to choose from, one was counterfeit, and that
counterfeit products are illegal. Prices were also associated with each shirt ($45.00
for the designer shirt and $18.00 for the other two shirts). The respondents were
then asked to select the shirt they were most likely to buy. Of the 100 respondents,
29 percent selected the designer label, 37.5 percent selected the counterfeit shirt, and
33.5 percent selected the non-brand name shirt. When the study was repeated at a
flea market, identical choice patterns were observed between the flea market and the
local mall. Their results indicate that even though counterfeit products compromise
the quality, consumers are willing to over look this due to the cost saving prices.
The authors concluded that government and businesses must push to eliminate the
demand-side of counterfeit. Without more research to determine how to target these
consumers, and which appeal to use, dollars spent on reducing the demand-side of
counterfeit will likely be wasted.
A study conducted by Albers-Miller (1999) was designed to assess consumer’s
misbehaviour, and what causes a consumer to buy illicit goods, using the following

three variables to predict consumer behaviour:
(1) the selling price;
(2) the situation under which the purchase takes place; and
(3) the risk associated with the purchase.
The study showed that all respondents were more likely to engage in illicit behaviour if
there was peer pressure to do so. While it has been shown that peer support of an illegal
behaviour encourages deviant behaviour, peer rejection may also serve as a deterrent.
Albers-Miller concluded that legitimate business managers should consider lobbying
for the strict enforcement of criminal sanctions against consumers as well as
merchants of illicit goods.
Theoretical model
Consumer demand theory
Consumer demand theory provides the theoretical framework for this study. From this
perspective, consumers maximize their utility subject to their budget constraint from
which demand functions for various goods and services are derived (Varian, 1999).
A consumer’s utility function is defined as:
U ¼ uðX
1
; X
AOG
Þ
where X
1
¼ good 1 and X
AOG
¼ all other goods.
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A consumer maximizes utility subject to its budget constraint:

I ¼ P
1
X
1
þ P
AOG
X
AOG
where I ¼ total income, P
1
¼ price of clothing, and P
AOG
¼ price of all other goods.
Maximization of the utility function subject to the income constraint yields the
demand function for good one:
Q
1
¼ f ðP
1
; P
AOG
; I ; TÞ
where Q
1
¼ quantity demanded of good one and T ¼ tastes & preferences.
The quantity demanded of a good is function of income, prices, and tastes and
preferences (Varian, 1999). When using cross-sectional data, as in this study, prices are
assumed to be constant across consumers over the time period of the analysis. Therefore,
prices can be suppressed in the empirical equation. The empirical equation will include
measures for income, and factors to control for tastes and preferences. Selected variables

found to influence the demand for counterfeit goods (e.g. ethical attitudes, sensitivity,
and gender) in previous studies will be included to measure tastes and preferences.
Method
Empirical model and analysis
For this study, both simple t-tests and logistic regression are used for the analyses. To
achieve the first objective, t-tests are used to determine whether differences exist
between purchasers and non-purchasers of counterfeit goods with respect to their
attitudes about counterfeiting. For the remaining objectives, a multivariate framework
is used in order to analyze the effect of one variable while controlling for other factors
that could influence demand. Information is not available on the exact quantity of, or
expenditure on a counterfeit good. It is known, however, whether the consumer
purchased a counterfeit good in the past year. Consequently, the dependent variable is
a dichotomous variable, and logistic regression analysis is an appropriate statistical
technique (Tacq, 1997). The general form of the equation estimated in this study is:
CF ¼ a þ b
1
I þ b
2
A þ b
3
S þ b
4
S þ b
5
Y þ e
where:
CF ¼ Counterfeit good purchase
I ¼ Income
A ¼ Attitudes
S ¼ Sensitivity

G ¼ Gender
Y ¼ Year in school
a ¼ the intercept
b
i
¼ regression coefficient
e ¼ error term
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The coefficients produced by the logistic procedure cannot be interpreted in the same
way as the regression coefficients from ordinary least squares regression. However, the
odds ratio provides coefficients that represent the effect of changes in the independent
variables on the dependent variable. In this context, the coefficient on a dummy
variable can be interpreted as a percent difference relative to the comparison variable.
For each set of dummy variables, there is an omitted category that serves as the
comparison category. It is assigned a value of 1.00, or it can also be multiplied by 100 to
get 100 percent). The value on the odds ratio is compared to the 1.00 (or 100 percent) by
taking the difference between the values. For example, in Table I, the omitted category
for parental income, shown in parentheses, is for parental income less than $25,000.
Thus, all other income categories will be compared to the ,$25,000 category. If an
odds ratio is greater than 1.0 (or 100 percent), it indicates that students with that level
of parental income are more likely to buy counterfeit goods. For parental income of
$100,000 or more, the odds ratio is 1.36 (or 136 percent). It indicates that students with
parental income in this bracket are 36 percent more likely to buy counterfeit goods than
students with parental income under $25,000 (1:36 2 1:00 ¼ 0:36 £ 100 ¼ 36 percent
or 136 percent 2 100 percent ¼ 36 percent). On the other hand, a coefficient of 0.92 for
parental income of $50,000-74,999 indicates these students are 8 percent less likely to
buy counterfeit goods compared to students with parental income under $25,000
(0:92 2 1:00 ¼ 20:08 £ 100 ¼ 28 percent or 92 percent 2 100 percent ¼ 28 percent).

The choice of omitted category for each dummy variable is discussed below under the
variable definitions, and shown in parentheses in the tables.
Variable Parameter estimate Standard error Odds ratio estimate
Intercept 1.78
***
0.56
Income
, $25,000
$25,000-49,999 2 0.25 0.51 0.78
$50,000-74,999 2 0.08 0.39 0.92
$75,000-99,000 0.26 0.36 1.30
$100,000 or more 0.31 0.29 1.36
Counterfeit buyers 2 0.31
**
0.14 0.73
Counterfeit sellers 2 0.28
**
0.13 0.76
Sensitivity
(Didn’t discuss in class)
Discussed in class 2 0.14 0.10 0.87
Gender
Female
Male 2 0.22 0.24 0.81
Education
Freshmen
Sophomore 2 0.51
*
0.28 0.60
Junior 2 0.90

***
0.34 0.41
Senior/graduate student 2 0.88
**
0.35 0.42
Notes:
*
p , 0.10;
**
p , 0.05;
***
p , 0.01; n ¼ 437
Table I.
Logistic regression
indicating counterfeit
purchasing including
class discussion
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Sample and data collection
To collect data for this study, students enrolled in a major university located in a
medium-sized Midwestern town were asked to complete a questionnaire. The
questionnaire was administered to seven classes. Convenience sampling was used with
the intent of trying to have a wide representation of students from across campus. Four
classes from a textile and apparel department were chosen based on the assumption
that these students were more likely to have been exposed to the negative aspects of
purchasing counterfeit goods, compared to other students. They were also selected
since the behaviour of these students would be of most direct relevance to textile and
apparel academicians. However, in order to ensure a diverse sample of students, the

surveys were also administered to two large classes, Principles of Microeconomics and
Introduction to Sociology and one small class, Introduction to Astronomy. These
classes were selected based on their class sizes, different educational levels, and/or
representation of students from many different departments throughout campus. A
total of 517 students responded to the questionnaire. However, to be included in the
analyses, the students had to provide complete information on the variables of interest
which resulted in 437 respondents for the logistic regressions.
Dependent variable
Counterfeit good purchase. The introduction to the survey contained the following
statement:
Counterfeit or fake goods are items that imitate other products with the intent to deceive.
Examples of these items include fake designer handbags, Rolex watches, Callaway golf clubs
and pirated CDs.
Respondents were asked to respond to the statement “I have bought counterfeit goods in
the past year.” “Yes”, “no”, and “didn’t know” were the response options provided. This
variable was coded as a one if they did make a counterfeit purchase, and a zero if they
did not. If they “didn’t know” then they were excluded from the analysis. This variable
was the dependent variable in the logistic regression equations. Those respondents who
had made a counterfeit purchase were also asked to indicate the type(s) of goods or
services they had bought, and these results are shown in the descriptive statistics.
Independent variables
Parent’s income. Economics factors, and in particular income, greatly influence
purchasing behaviour. To capture this effect, parental income was included in the
empirical equation since students are frequently dependent on their parents for their
support. Dummy variables were created with the following categories:
.
$25,000-49,999;
.
$50,000-74,999;
.

$75,000-99,999; and
.
$100,000 or more.
The omitted category was $25,000 and under. Students who said they “didn’t know”
their parents income were excluded from the analysis. Based on economic theory, as
income increases, the demand for normal goods increases. However, if counterfeit
goods are viewed as inferior goods, then as income goes up, the demand for inferior
Counterfeit
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33
goods decreases. There is no prior research on the relationship between income and the
demand for counterfeit goods that provides insight regarding the direction of this
effect. Counterfeit goods are generally considered to be of inferior quality to the actual
good being copied, and therefore, one would expect a negative effect. However, prior
research indicates that some consumers perceive the quality of counterfeit goods to be
as good as legitimate goods (Tom et al., 1998), and from that perspective, a positive
effect would be hypothesized. In addition, if the trademark is the primary motivation
for the purchase, and it is viewed in the consumer’s eye to be similar to the original (and
not inferior), then the income effect could be positive.
Consumer attitudes towards counterfeiting. A total of 12 items, drawn from Tom et al.
(1998), were used to measure consumer attitudes towards counterfeiting. Two of the
items were designed specifically to measure consumer attitudes about the
legality/illegality of selling and buying counterfeit goods. These two items are used in
the logistic regression equation as measures of consumer attitudes regarding the ethics
of purchasing counterfeit goods (since if something is viewed as illegal, then it would be
more unethical to participate in it). The first of these items asked consumers the extent to
which they agreed or disagreed with the statement “People who buy counterfeit products
are committing a crime.” The second statement was “People who sell counterfeit
products are committing a crime.” However, for the logistic regression, these items were
reverse coded for ease of interpretation. These items were originally coded on a five-point

scale with one being strongly agree and five being strongly disagree. Respondents who
agree with these statements are expected to be less likely to buy counterfeit goods.
Five items were used to assess the extent to which buying counterfeit items was a
way to express anti-big business attitudes. An additional two items were designed to
measure consumer attitudes regarding the impact of counterfeiting on the US economy
and the manufacturers of legitimate goods. Finally, three items measured consumer
attitudes regarding the quality of counterfeit merchandise. Each item was measured on
a five-point scale with one equal to strongly agree and five equal to strongly disagree.
All 12 of these items were included in the t-tests. For the t-tests, the original scale was
used for all calculations.
Sensitivity. Sensitivity was measured in two ways. Since students enrolled in textile
and apparel courses were of specific interest in this study, one measure was whether a
student was a textile and apparel major, or not. The variable was coded as a one if they
were a textile and apparel major, and a zero otherwise. Textile and apparel students
were hypothesized to be more sensitized to the issue of counterfeiting, and therefore,
less likely to purchase counterfeit goods. This sensitivity may have come through
classroom instruction, or their own market interactions since fashion items, such as
handbags, are a common counterfeit product.
The second measure was by asking students the extent to which they agreed or
disagreed with the following statement “Counterfeit products have been discussed in
my classes.” A scale of one to five was used with one equal to strongly agree and five
equal to strongly disagree. Separate regression equations were estimated using the two
measures for this variable.
Gender. A dummy variable for gender was created with females equal to a one, and
males equal to a zero. Although there were some mixed results in the prior literature
(Singhapakdi and Vitell, 1990; Hegarty and Sims, 1978), males appeared more likely to
engage in, or approve of, unethical behaviour than females (Chen and Tang, 2006;
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Singhapakdi, 2004). Thus, it is hypothesized that males will be more likely to purchase
counterfeit goods relative to females.
Education level . Education level was measured based on the students’ year in school.
Three dummy variables were created. A dummy variable was assigned a value of one
for each of the following years in school: sophomore, junior, and senior/graduate
students. Freshmen were used as the comparison category and assigned a value equal
to zero. These variables were included to control for possible exposure to the
counterfeit issue throughout the college experience. For example, freshmen students
who are a textile and apparel major would not have had the same level of exposure as a
senior in that major.
Descriptive statistics
The mean age of the respondents was 19.69 years. Almost 70 percent (69.9 percent) of
the respondents were female and 30 percent male (see Table I). The freshman class
made up the largest group in the sample (47.52 percent), followed by sophomores (21.39
percent), seniors (15.45 percent), juniors (14.26 percent) and graduate students (1.39
percent). Over 40 percent (42.17 percent) of the sample came from households with
parental income of $100,000 or more. Textile and apparel majors accounted for 26.08
percent of the sample. Almost one-third (30.35 percent) of the sample had purchased
counterfeit goods while a little over one-half (53.89 percent) had not. Nine categories of
counterfeit goods were identified as having been previously purchased by respondents
(see Table II). The most commonly purchased counterfeit items were handbags (61.84
percent), music (31.58 percent), clothing (22.37 percent) and jewellery (18.42 percent).
Results
T-tests for attitudinal variables
A total of 12 attitudinal questions regarding counterfeit goods were taken from the study
conducted by Tom et al. (1998). Table III presents the t-tests showing whether or not a
significant difference exists between buyers and non-buyers of counterfeit goods with
respect to these 12 attitudes. The results indicate significant differences between buyers
of counterfeit goods and non-buyers on 11 of the 12 attitudinal questions. Only one
question was not statistically significant, “I like buying counterfeit products because it’s

like playing a practical joke on the manufacturer of the non-counterfeit product.”
Purchasers were more likely to express anti-big business sentiments, and were less likely
to believe that counterfeiting hurt the US economy. Purchasers were more likely to
believe that counterfeit goods are just as good as designer goods, and less to view
counterfeiting as unlawful. These results support those of Tom et al. (1998). However,
both purchasers and non-purchasers believe that sellers of counterfeit goods are
committing a crime relative to the buyers of counterfeit goods.
Logistic regression results
In general, income was not a significant factor affecting the purchases of counterfeit
goods for this sample. However, in the first regression equation, (see Table IV) one
income category, parental income over $100,000 was significant. The odds ratio
indicates that students with parents in this income bracket were 99 percent more likely
to buy a counterfeit product compared to students with parental income of less than
$25,000. This positive effect does suggest that counterfeit goods are a normal good.
Counterfeit
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35
Attitudes about counterfeit buyers and sellers (being criminals) were significant (see
Tables I and IV). If the respondent believed that the buyers and/or sellers were
committing a crime, they were less likely to buy counterfeit goods themselves.
Two variables were used to measure sensitivity, which accounts for the two
different logistic regression equations. Major was used in one equation (see Table IV),
and class discussion was used in the second equation (see Table I). Neither variable
was statistical significant. This suggests that textile and apparel majors are no more or
less likely to buy counterfeit goods relative to other majors. It also suggests that
exposure to the issue through class discussion has little impact on whether or not
students buy counterfeit goods.
Variable
Frequency
n ¼ 517 Percentages

Gender
Male 152 30.04
Female 354 69.96
Education
Freshman 240 47.52
Sophomore 108 21.39
Junior 72 14.26
Senior 78 15.45
Graduate 7 1.39
Income
25,000 or less 9 1.81
25,000-49,999 36 7.23
50,000-74.999 74 14.86
75,000-99,999 78 15.66
100,000-149,999 101 20.28
150,000-plus 109 21.89
Not sure 91 18.27
Major verses non-major
Major 121 26.08
Non-major 343 73.92
Purchased counterfeit items
Yes 157 30.35
No 279 53.89
Don’t know 81 15.76
Counterfeit items purchased
Handbags 94 61.84
Music 48 31.58
Clothing 34 22.37
Jewellery 28 18.42
Sporting goods 10 6.58

Cards 5 3.29
Toiletries 2 1.32
Medicine 1 0.66
Car parts 1 0.66
Table II.
Demographic
characteristics and
purchase of counterfeit
goods
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Gender was not significant in either regression equation. It was hypothesized that
males would be more likely to purchase counterfeit goods, based on previous studies,
but this did not bear out. However, there are several previous studies that found no
difference due to gender (Hegarty and Sims, 1978; Singhapakdi and Vitell, 1990).
The results for year in school indicate a negative relationship with the purchase of
counterfeit goods. Holding other factors constant, older students were less likely to buy
counterfeit goods compared to freshmen students.
Discussion and implications
This study was designed to investigate factors affecting the demand for counterfeit
goods among college students, and to identify differences in attitudes about
counterfeiting between purchasers and non-purchasers. Textile and apparel majors
and non-majors were surveyed to determine if textile and apparel majors, who were
expected to be more sensitive to the issue of counterfeiting, would be less likely to
purchase counterfeit goods than non-majors.
Many purchasers of counterfeit goods do not feel they are doing any harm by
purchasing these goods. However, prior research has shown differences in attitudes
between the purchasers and non-purchasers of counterfeit goods (Tom et al., 1998). In
this study, t-test results indicated significant differences between buyers of counterfeit

Group means
Attitude statements t Buyers Non-buyers
Legal/ethical
People who buy counterfeit products are committing a crime 5.03
**
3.74 3.20
People who sell counterfeit products are committing a crime 5.00
**
3.24 2.68
Economic
Counterfeit products do not hurt the US economy 2 4.28
**
3.04 3.45
Counterfeit products hurt the companies that manufacture the
legitimate product 2.08
*
2.44 2.23
Anti-big business
I like counterfeit goods because they demonstrate initiative and
ingenuity on the part of the counterfeiters 2 5.23
**
3.59 4.06
Buying counterfeit products is a way to get back at uncaring and
unfair “big business” 2 4.00
**
3.25 3.70
I buy counterfeit products because counterfeiters are “little guys”
who fight big business 23.33
**
3.77 4.07

I like buying counterfeit products because it’s like playing a
practical joke on the manufacturer of the non-counterfeit product 2 1.68 4.04 4.18
I buy counterfeit products because the prices of designers’ products
are unfair and gouge 2 8.85
**
2.36 3.33
Quality
I would buy counterfeit products even if I could easily afford to buy
genuine designer products 25.70
**
3.53 4.11
Buying counterfeit products demonstrates that I am a wise shopper 2 7.61
**
3.00 3.73
Counterfeit products are just as good as designer products 26.10
**
3.31 3.90
Notes:
*
p , 0.05;
**
p , 0.01
Table III.
T-tests comparing buyers
and non-buyers of
counterfeit goods and
attitudes towards
counterfeiting
Counterfeit
goods

37
goods and non-buyers on 11 of the 12 attitudinal questions asked. Consumers who had
previously purchased counterfeit goods held attitudes more supportive of
counterfeiting than consumers who had not. In general, prior purchasers felt that
purchasing a counterfeit product is just as good as purchasing the real brand, and by
doing so, they were not hurting the US economy. These results support the first
objective of this study and suggest that changes in attitudes may be necessary to deter
the demand for counterfeit goods. One way to influence attitudes could be through
consumer education. Sometimes consumers do not feel they are committing a crime by
purchasing counterfeit goods, yet they do believe the sellers of these goods should be
punished. According to the Messick and Brewer (1983) typology, strategies to
discourage counterfeit purchases could be either structural or behavioural. The
structural model could be used to reduce counterfeit purchases by instilling fear of
punishment. In return this would then be linked to a change in behaviour towards the
purchase of counterfeit goods. The researchers suggest that consumers must be
convinced that benefits will accrue to a group with which the consumer identifies with
most. As most counterfeits in the USA are imported, anti-counterfeiting appeals need
to focus on the negative impact that buying fakes has on American jobs, tax revenues,
and balance of payments, as well as crime (Cordell et al., 1996). Americans may not be
aware that their contribution to purchasing counterfeit may be used to fund organized
crime and illicit activities such as prostitution (Tom et al., 1998).
To achieve the remaining objective, two logistic regression equations were
estimated. It was expected that textile and apparel majors would be more sensitive to
Variable Parameter estimate Standard error Odds ratio estimate
Intercept 0.96
*
0.55
Income
, $25,000
$25,000-49,999 2 0.064 0.56 0.939

$50,000-74,999 0.32 0.42 1.38
$75,000-99,000 0.57 0.40 1.77
$100,000 or more 0.69
*
0.33 1.99
Counterfeit buyers 2 0.31
*
0.15 0.74
Counterfeit sellers 2 0.34
*
0.14 0.71
Major
Non-T&A major
T&A major 0.41 0.32 1.51
Gender
Female
Male 2 0.15 0.27 0.87
Education
Freshmen
Sophomore 2 0.46 0.30 0.63
Junior 2 0.74
*
0.37 0.48
Senior/graduate student 2 0.82
*
0.39 0.44
Notes:
*
p , 0.05; n ¼ 437
Table IV.

Logistic regression
indicating counterfeit
purchasing including
major
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the issues surrounding counterfeit goods, and therefore, less likely to purchases such
goods. Of the textile and apparel majors, 66.05 percent agreed or strongly agreed that
counterfeit goods had been discussed in class(es), while only 11.62 percent of the
non-majors had been exposed to such discussions. Of the non-majors, 83 percent
disagreed or strongly disagreed that counterfeit goods had been discussed in class(es).
However, there was no significant difference between the majors and non-majors with
respect to the purchase of counterfeit goods. It was expected that textile and apparel
majors, who were assumed to have more sensitivity to the issue, would be less likely to
buy such goods. However, this hypothesis was not supported. Furthermore, the second
variable used to measure sensitivity, whether counterfeit goods had been discussed in
class, was also not found to be significant.
Although some students are exposed to the counterfeit issue in their classes, this
exposure does not seem to have a significant effect on their purchasing behaviour. What
is not known, is the exact content or the frequency of the messages delivered in class.
With respect to content, there are many different aspects of the counterfeit issue (legal,
economic, ethical, political, and social, etc.). An area of research that could be insightful is
to analyze the content that textile and apparel students are actually being exposed to
with respect to counterfeit goods. The development of curriculum that addresses this
issue is something that academicians in the field may want to give some consideration. It
could be that a class discussion covers the impact of counterfeit goods on the designers of
legitimate goods, but ignores other facets. Thus one consideration that is particularly
relevant for faculty in textiles and apparel, is to what extent are they educating their
students about counterfeit goods? If deterring consumer demand is one way to help solve

the problem, then greater attention may need to be paid to educational content.
Accrediting organizations, such as the American Apparel and Footwear
Manufacturers (AAFA) may want to consider updating their guidelines to reflect
the need for education on counterfeit goods.
In general, the findings of this study support previous research that shows
consumers’ attitudes are an important factor affecting the purchase (on choice not to
purchase) counterfeit goods. One implication of this finding is that a change in
attitudes may be of greater importance in deterring the demand for counterfeit goods
than an increase in consumer awareness or education about the problem. Future
research could examine the impact of changing attitudes versus greater education on
the demand for counterfeit goods. In addition, consumer education, or changing
consumer attitudes towards purchasing may not be sufficient to abate the growth of
counterfeit goods. Legal action against consumers may be necessary, but this is an area
for further study.
One limitation of the study was the use of convenience sampling. Although
attempts were made to select a sample that would contain a cross-section of students,
random sampling is suggested for future studies, to the extent possible.
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Corresponding author
Pamela S. Norum can be contacted at:
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