Journal of Consumer MarketingConsumer misbehavior: why people buy illicit goodsNancy D. Albers-Miller

Article information:To cite this document:Nancy D. Albers-Miller, (1999),"Consumer misbehavior: why people buy illicit goods ", Journal of Consumer Marketing, Vol. 16Iss 3 pp. 273 - 287Permanent link to this document:http://dx.doi.org/10.1108/07363769910271504

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Users who downloaded this article also downloaded:Arghavan Nia, Judith Lynne Zaichkowsky, (2000),"Do counterfeits devalue the ownership of luxury brands?", Journal of Product& Brand Management, Vol. 9 Iss 7 pp. 485-497 http://dx.doi.org/10.1108/10610420010351402Chow-Hou Wee, Soo-Jiuan Ta, Kim-Hong Cheok, (1995),"Non-price determinants of intention to purchase counterfeit goods: anexploratory study", International Marketing Review, Vol. 12 Iss 6 pp. 19-46 http://dx.doi.org/10.1108/02651339510102949Celso Augusto de Matos, Cristiana Trindade Ituassu, Carlos Alberto Vargas Rossi, (2007),"Consumer attitudestoward counterfeits: a review and extension", Journal of Consumer Marketing, Vol. 24 Iss 1 pp. 36-47 http://dx.doi.org/10.1108/07363760710720975

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Consumer misbehavior: whypeople buy illicit goodsNancy D. Albers-MillerAssistant Professor of Marketing, College of Business Administration,University of North Texas, Denton, Texas, USA

Keywords Consumer behaviour, Consumer marketing, Counterfeiting, Crime, Ethics,Legal matters

Abstract Trade in contraband amounts to billions of dollars each year, and yet thebuyers of these products are still a mystery. The purpose of this study was to model thedecision to purchase illicit goods, using four predictor measures: product type, buyingsituation, perceived criminal risk, and price. Part-worth conjoint analysis was used toobtain individual weights of main effects and selected interaction effects on thewillingness to purchase. Individual respondents evaluated the purchase of illicit goodsdifferently. Cluster analysis was used to segment the respondents. Discriminant analysiswas used to assess variable importance. The overall model was shown to be significant.Although the results varied by cluster, the main effects of product type, buying situationand price were all significant predictors of willingness to buy. The interactions of riskwith product type and price with product type were also significant predictors for someclusters.

Traffic in illegal goods is big business in the USA. Counterfeiting,

shoplifting, illegal drug trade and other illicit consuming behavior costs

Americans billions of dollars annually (Budden and Griffin, 1996; Bush

et al., 1989; Kallis et al., 1986). Counterfeit production reportedly amounts

to 4 percent to 8 percent of US GNP (Slater, 1985) and is increasing (Harvey,

1987).

Although this problem might appear to be a topic more appropriately studied

by criminologists and legal scholars, a closer look reveals that work in this

area has been largely one-sided. The supply side has garnered most of the

research. Clearly, legal scholars and law enforcement officials have

addressed the issues of controlling the source and flow of contraband.

Researchers of criminal psychiatry and sociology of deviant behavior have

examined personal and social aspects leading to general criminal and deviant

behavior. The behavior of interest to these scholars has also been supply-side

oriented. For example, they have tried to develop an understanding of why

people steal, not why people buy stolen goods. Even marketing researchers

have focused on issues relating to control of supply (Bush et al., 1989;

Globerman, 1988; Harvey, 1987, 1988; Harvey and Ronkainen, 1985; Olsen

and Granzin, 1992). Yet, economic theory would suggest that if there is little

or no demand for a product, supply will decrease as well.

The demand side of this problem is clearly an issue of consumer behavior, or

perhaps more appropriately termed consumer misbehavior. Despite the fact

that such behavior is potentially harmful to businesses (Globerman, 1988;

Harvey and Ronkainen, 1985; Olsen and Granzin, 1992), the consumer

(Dillon, 1989; Harvey, 1988; Pinkerton, 1990) and society as a whole

(Stotland, 1977), little marketing academic interest has been generated until

recently. In 1991, Hirschman called for further research into the `̀ dark side

of consumer behavior''. Perhaps in response to this call, during the past few

years, marketing scholars have begun to explore in greater depth issues of

consumer misdeeds. Unfortunately, few pieces of research have been

published in this entire topic area. Of that, only a small portion of this

research has addressed determinants of illicit consumer behavior.

Controlling source ofcontraband

JOURNAL OF CONSUMER MARKETING, VOL. 16 NO. 3 1999, pp. 273-287 # MCB UNIVERSITY PRESS, 0736-3761 273

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For the purpose of this study, illicit goods are illegal goods, freely chosen by

the consumer. An illicit purchase would be one where the product sold and

purchased was offered illegally ± being either illegally produced

(counterfeit) or illegally obtained (stolen). Additionally, these are purchases

where the buyer can freely accept or reject the product offered; there is no

physical or psychological need for the product. Specifically excluded from

this study are products that create dependency, products passed

unsuspectingly to an uninformed consumer or products that might offer a

`̀ last chance'' to a terminally ill patient.

Literature reviewGeneral, inappropriate consumer behavior has been explored by marketing

scholars. Budden and Griffin (1996) prepared a special issue of Psychology

and Marketing dedicated to the study of aberrant and dysfunctional

consumer behavior. In this area, the most commonly studied issues have

concentrated on compulsive buying behavior, addictive consuming behavior,

consumer fraud and shoplifting.

Much of the research has focused on compulsive consumption. Faber and

O'Guinn (1988) were some of the earliest researchers to study compulsive

consumers. They reported that compulsive consumers are systematically

different from other consumers in some ways, such as being more

materialistic, but did not differ in others, such as possessiveness. Valence

et al. (1996) developed a scale to measure compulsive buying behavior.

Faber and Christenson (1996) described the mood state of compulsive

consumers. Hassey and Smith (1996) defined the scope of compulsive

consumption. Rindfleisch et al. (1997) examined the influence of family

structure on compulsive consumption. Roberts and Martinez (1997) reported

on compulsive buying in Mexico. Elliot et al. (1996) discussed issues related

to consumers addicted to consumption.

Another area of aberrant behavior studied by consumer behavioralists has

been consumption of addictive substances. Hirschman (1992) provided a

review of consumer behavior, medicine, sociology, psychiatry and

psychology to develop a framework of addiction. Bearden et al. (1994)

studied underage consumption of alcohol and illegal drug use among high

school and college students. Powers and Anglin (1996) examined couples

engaged in addictive drug use.

Consumer fraud has also been examined in the consumer behavior literature.

Cole (1989) used deterrence theory to examine fraudulent consumer

behavior. Strutton et al. (1994) reported on the methods that consumers use

to rationalize fraudulent behavior.

Shoplifting is another illegal consuming behavior which has been examined

(Cox et al., 1990). Kallis et al. (1986) researched the image of the shoplifter.

Babin and Babin (1996) reported on emotional influences on shoplifting.

Recently, some scholars have examined the problem of counterfeiting from

the consumer's perspective. Bloch et al. (1993) reported on the consumer's

role in the growth of trademark piracy. Wee et al. (1995) studied variables,

other than price, such as age, income and product attributes, that influence

the purchase of counterfeits. Both Cordell et al. (1996) and Wee et al. (1995)

have researched the attitudes of the consumer.

Several recurring concepts have been discussed both in this literature and in

the research of general criminal behavior. The aberrant and/or criminal

behavior, both consuming and other, is often motivated or abetted by certain

Illicit goods are illegalgoods

Consumer behaviorliterature

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characteristics or situational factors. Price, penalty and situation-specific

elements all appear to be related to the decision to willingly participate in

criminal behavior.

Not surprisingly, price pressures have been associated with illicit behavior

(Dillon, 1989). In all purchases, consumers balance monetary outlays against

perceived benefits (O'Shaughnessy, 1987). Dodge et al. (1996) reported that

direct economic consequences influence the tolerance of questionable

behavior by consumers. Wee et al. (1995) suggested that price is the main

motive for the purchase of counterfeit goods. Bloch et al. (1993) indicated

that a consumer will select a counterfeit good over a genuine product

offering if there is a price advantage.

Stolen and counterfeit goods represent a cost/benefit advantage to the

consumer. Stolen goods represent quality products and extreme cost savings.

Counterfeits represent a prestigious (Grossman and Shapiro, 1988), albeit

inferior product (Ehrlich, 1986), at a good price.

While price advantages serve to motivate the consumer to engage in aberrant

behavior, fear of criminal penalty should deter such behavior. Grasmick and

Bryjak (1981) found an inverse relationship between perceived severity of

punishment and criminal conduct. Hollinger and Clark (1983) discussed a

negative association between the perceived chance of being caught and

criminal behavior. Conversely, when there is a lack of fear of punishment,

people do engage in inappropriate behavior. The lower the risk of detection,

the more likely a person is to deviate (Feldman, 1977; Hayner, 1929).

Some consumer researchers have supported this notion. O'Shaughnessy

(1987) reported that a consumer's choice might be constrained by fear of

sanctions. Cole (1989) found a negative relationship between the perceived

probability of getting caught and consumer fraud. Pitts et al. (1991)

discovered a relationship between unethical behavior and personal

consequences.

But fear of criminal punishment does not always serve as a deterrent to

crime. There are people who get satisfaction out of the performance of

deviant acts and who want to rebel against the system (Walker, 1977).

Cordell et al. (1996) studied the relationship between lawfulness attitudes

and counterfeit purchase intentions.

Finally, situational elements may affect illicit behavior. Specifically,

situational influences affect the decision to engage in unethical consuming

behavior (Rindfleisch et al., 1997; Whalen et al., 1991). Social pressure can

lead people to follow rules as well as to break rules (Walker, 1977). There

are two potential situations: the behavior occurs while the person is alone

and is free from direct social pressure or the person is not alone and is

subjected to direct social pressure. The direct social pressure to conform

being either to join others who are engaging in the illicit behavior or to avoid

the illicit behavior because the others present are not participating. Previous

research has examined these possibilities.

First, consider the situation where the person is alone. Hayner (1929) found

that when a person is alone, and released from the restraints offered by

intimate circles, that person is more likely to act in accordance with impulses

than by ideals and standards of a group. Gellerman (1986) reported that

people are more likely to engage in misconduct when they think the act will

not be found out and publicized.

Price the main motive

Satisfaction from deviantacts

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In addition to the situation providing an opportunity to avoid detection, the

situation may encourage criminal behavior. A person with friends exhibiting

deviant behavior is more likely to exhibit deviant behavior (Conger, 1980).

Barker (1977) discovered that peer group support could create a social

situation in which certain corrupt acts are tolerated and accepted. Peer

pressure to conform has been reported as a factor leading to inappropriate

consuming behavior (Bearden et al., 1994; Kallis et al., 1986; Powers and

Anglin, 1996).

While peer support of the behavior encourages participation, peer rejection

of the behavior serves as a deterrent. Social controls may be an even better

deterrent to crime than physical controls (Hollinger and Clark, 1983).

Individuals will attempt to avoid exposure if they engage in behavior that is

not supported by their peers (Downes and Rock, 1982).

Finally, the decision to participate is complicated by the ability of the

participant to rationalize the behavior (Strutton et al., 1997; Strutton et al.,

1994). Feldman (1977) suggested that with the right combination of rewards

and cost, transgressions might be committed by virtually anyone. This

concept is echoed by Cox et al. (1990) who reported that basically honest

people resort to dishonesty when faced with temptation, a perceived low risk

of apprehension and punishment, and the ability to rationalize the behavior.

One way that people rationalize their behavior is by concluding that it is not

`̀ really'' illegal or immoral (Gellerman, 1986). Society reinforces these

rationalizations because members of society are surprised by some law

breaking and not by others (Walker, 1977).

HypothesesDrawing on the previous research, this study attempts to develop a model of

illicit consumption behavior. This study proposes that the decision to

purchase an illicit product, instead of a legitimately offered product, can be

explained by a combination of variables drawn from the study of criminal

behavior and buyer behavior. The behavior is predictable based on three

variables:

(1) the selling price;

(2) the situation under which the purchase takes place; and

(3) the risk associated with the purchase.

The model is further complicated by the interaction of the product offered. A

significant F-statistics, for the models estimated on the individual level, will

indicate that, overall, the variables predict a willingness to buy illicit goods.

Given a significant model, several hypotheses can be examined. The first of

these is the obvious economic hypothesis regarding price.

H1: Willingness to buy is negatively associated with selling price.

Significance of the price coefficient will support this hypothesis. It is

expected, however, that the importance of price will not be the same for

counterfeit and stolen goods. Because counterfeit goods are typically of

lower quality than goods produced by the brand name manufacturer, price

should be a more important variable in the consideration to purchase a

counterfeit good.

H1a: Price will provide a higher degree of influence in the decision to

purchase counterfeit goods, compared to stolen goods.

Resort to dishonesty whenfaced with temptation

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This hypothesis will be supported if the mean interaction weight for price

and counterfeit is significant and greater than the mean interaction weight for

price and stolen.

The social pressures exerted on the actual buying situation are also expected

to affect willingness to buy. The perceived support or lack of support by

others will affect the decision to participate in a criminal activity. Several

hypotheses follow from this train of thought. The literature suggests that

people are likely to give in to social pressure when others are participating

also; hence:

H2: A buyer is most willing to buy an illicit good when others present are

buying illicit goods.

Significant negative weights for the variables representing the two

alternative buying situations will provide support for this hypothesis.

Considering this a base level, two additional hypotheses, based on two other

buying settings, are suggested. When a buyer is alone, they are no longer

under the social pressure to participate in the activity; hence:

H2a: A buyer is less willing to buy an illicit good when they are alone, than

when others are present and buying the illicit product.

A significant negative coefficient for the dummy variable representing the

`̀ alone'' buying situation will provide support for this hypothesis. A buyer,

however, will not want people who might not support a decision to buy illicit

goods to know about the purchase; therefore, the situation where the buyer is

alone, should be preferred to a setting where people are present, but not

buying. This logic suggests that a buyer will want to avoid a situation where

their behavior is outside of the group norm; hence:

H2b: A buyer is least willing to buy an illicit good when others are present

and not buying the illicit product.

A significant negative coefficient for the dummy variable representing the

`̀ friend present, but not making a purchase'' buying situation will provide

support for this hypothesis.

The literature suggests conflicting evidence regarding perceived criminal

risk. Although evidence suggests that criminal risk is a deterrent to crime, the

literature also suggests that people will participate in deviant acts if they can

rationalize that the act really is not bad. While it is expected that the greater

the level of perceived criminal risk, the less likely a person is to engage in

illicit behavior, the ability to rationalize the behavior will moderate the

effect.

H3: Willingness to buy illicit goods is negatively associated with the level of

perceived criminal risk.

Significance of the perceived criminal risk coefficient will support this

hypothesis. However, the importance of perceived criminal risk is not

expected to be the same for both counterfeit and stolen goods. Because

counterfeit goods are likely to be explained away as not `̀ really illegal''

more easily than stolen goods, perceived criminal risk should be a more

important variable in the consideration to purchase a stolen good.

H3a: Perceived criminal risk will provide a higher degree of influence in the

decision to purchase stolen goods, compared to counterfeit goods.

Social pressures

Conflicting evidence

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This hypothesis will be supported if a significant mean interaction weight for

perceived criminal risk and stolen is greater than the mean interaction weight

for perceived criminal risk and counterfeit.

MethodSample and procedure

The respondents for this study were night graduate students at a large

southwestern university taking core MBA classes. The majority of these

students were currently employed in jobs they considered to be their careers.

They were invited to participate in the study during class and 153 students

agreed to participate by signing a consent form. Participants were given a

SASE and a copy of the survey instrument to be completed at their

convenience at another time outside of class. A total of 92 surveys were

returned for a response rate of 60.1 percent. One response was unusable

because a large portion of the profiles were not evaluated. This survey was

not used for data analysis.

This method of data collection was selected to assure the anonymity of the

respondent. Krohn et al. (1974) reported that self-report data may be affected

by the method of data collection and that a questionnaire may be preferable

if anonymity can be assured.

Survey instrument

The data were collected and analyzed using part-worth conjoint analysis. A

survey was structured in a policy-capturing format. Policy capturing

techniques have been designed to provide a method for revealing basic

structures and characteristics of individuals' judgmental structures with an

indirect method of attitude measurement (Madden, 1981). Rather than

require the respondents to rank order the profiles, a Likert-type scale, which

has been found to be more powerful than rank ordering (Madden, 1975), was

used for analyzing judgments. The data were collected with a survey

instrument that provided a judgmental situation, in this case a willingness to

buy, influenced by a number of informational cues.

Self-report data have been frequently used by criminologists and sociologists

to collect data on criminal and delinquent behavior. Use of self-report data is

one of the most popular methods of measuring delinquent behavior

(Farrington, 1973). The seriousness of the acts themselves has not altered the

use of self-report data. Criminology concerns on theft (Ruggiero et al.,

1982), shoplifting (Klemke, 1978), aggression and violence (Steadman and

Felson, 1985), cheating (LaBeff et al., 1990), drug use (Keane et al., 1989)

and rape (Tieger, 1981) have all been examined using self-report data. Akers

et al. (1983) found self-report questionnaire data collection to be valid for

examining deviant behavior. Self-report data, used under these conditions,

have been shown to have predictive validity, internal consistency and

concurrent validity (Farrington, 1973).

The judgment situation for this study was developed after several pilot

studies. The respondent was asked to evaluate several combinations of

differing levels of cues. The product category of color televisions was

selected because of the range in television prices, the perceived range of

product quality among televisions and the highly visible nature of the

product. Pilot study respondents reported a perceived difference in quality

among televisions legitimately offered for sale over the chosen price range of

$150 to $600. Sony was selected as a recognized brand name that has a

perceived level of high quality. Generic goods, unbranded, were included for

Policy-capturing format

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a basis of comparison. Pilot study respondents agreed that it was reasonable

to assume that stolen, counterfeit and unbranded televisions were available

for sale.

The questionnaire was created using decision analysis system (DAS)

software. The design was a fractional replication of a full factorial design.

The design was developed to test all main effects, and selected first-order

interaction effects. This software has been designed specifically to develop

and analyze personal subjective judgmental choice in probabilistic

environments. Individuals responded to 27 actual profiles, six practice

profiles and ten repeated profiles, for a total of 43.

Variable measures

The dependent variable was a measurement of the respondent's willingness

to buy a described product under a given profile. The variable was measured

with a Likert-type, seven-point scale, ranging from not at all willing (1) to

extremely willing (7).

The independent variables included measures of product type, buying

situation, perceived criminal risk and product price. Three product types

were used in the study, generic, counterfeit and stolen. Three buying settings

were considered: `̀ You are alone while making the purchase'', `̀ A friend is

with you, but is not making a purchase'', and `̀ A friend is with you who is

purchasing a (product type) TV''. The product type described in the profile

was entered in this statement. Three levels of perceived criminal risk were

alternated in the conjoint profiles. The lowest level of risk was described as

`̀ The DA is not prosecuting illegal purchases''. The mid-level risk was

described as `̀ The DA is occasionally prosecuting illegal purchases''. The

highest level of risk was described as `̀ The DA is heavily and consistently

prosecuting illegal purchases''. These statements were assumed to be levels

of a single variable. Three price levels were alternated in the profiles. The

prices given were $150, $300, and $450. The Sony TV, described in the

scenario as the desired branded good, was priced at $600. The actual price

values were used in the analysis. The interactions between the two illicit

product types with price and perceived criminal risk were also evaluated as

hypothesized.

Data analysis

The part-worth conjoint profiles were analyzed using regression analysis. A

regression model, without an intercept, was estimated for each of the 91

respondents. The individual regression models provided the part-worth

utilities for each individual respondent.

A measurement of judgment consistency was based on the similarity of

judgments across ten repeated profiles. Consistency was computed by

correlating judgments from repeated profiles with the judgments on original

profiles. This correlation was multiplied by ten to provide a consistency

index ranging from 1 to 10. The individual consistency indices ranged from

a low of 5.1 to the maximum of 10.0. The average consistency index was

8.965 with a standard deviation of 1.34, suggesting the respondents carefully

analyzed the profiles and did not randomly mark answers.

ResultsThe 91 individual regression models were used to establish the significance

of the overall model. Ten respondents had no variation on their responses.

These individuals indicated that they would be unwilling to buy under any of

Replication of full factorialdesign

Regression analysis used

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the profiles described. These ten responses were considered a separate

cluster. Additionally, four respondents based their willingness to buy only on

the basis of product type. These respondents were also considered a separate

cluster. These 14 responses were not subjected to additional data analysis.

Of the remaining models, all but one was significant at the 0.0001 alpha

level. The F-statistics support the overall model, indicating that the decision

to purchase an illicit good, instead of a legitimately offered product, can be

explained by the model's variables.

Given a significant model, the independent variables were examined more

closely. Preference ratings, pooled across all respondents, were very similar,

with generic, counterfeit and stolen receiving 5.71, 4.16 and 4.04,

respectively. This suggested the possibility of `̀ majority fallacy''. Majority

fallacy occurs when there is heterogeneity of preferences (Moore, 1980).

Segmented models were used to avoid this problem. The segmented models

were developed using Ward's cluster analysis. Using an eigenvalue of 1.00

as a cut-off, the results strongly suggested the presence of three clusters.

There was a drastic change in eigenvalues between the three (2.878) and four

(0.544) cluster solution. The clusters have memberships of 33, 24 and 21,

reinforcing the validity of the clusters since Ward's is biased to equal sized

clusters. The three cluster solution was validated using non-hierarchical

k-means clustering. The solution was limited to a maximum of three clusters.

The k-means cluster membership was identical to Ward's membership for

88.3 percent of the cases, again validating Ward's cluster solution.

The three clusters of respondents were used to test the remaining hypotheses.

The three clusters appeared to yield significantly different decision criteria.

The aggregated levels of regression weights for each of the three clusters

appears in Table I. Respondents in Cluster 1 were more willing to purchase

generic, than counterfeit, and counterfeit was preferred to stolen. The

respondents from Cluster 2, however, weighted stolen more favorably than

either generic or counterfeit. Finally, members of Cluster 3 considered

generic and counterfeit essentially equally preferable to stolen.

In addition to product type, buying setting appears to be disproportionately

important among the clusters. Members of Cluster 3 put greater weight on

`̀ A friend is with you, but is not making a purchase'' and `̀ You are alone

while making the purchase'' than either Cluster 1 or Cluster 2 respondents.

Mean weights for perceived levels of criminal risk were different across the

clusters as well, as were the weights for price. These differences do not,

however, mean that these variables are significant predictors.

Cluster

Variable 1 2 3

Generic good 6.1828 3.0030 8.0706

Counterfeit good 3.0405 2.7438 7.4991

Stolen good 1.4433 6.3364 5.3986

Friend present but not buying ±0.0035 ±0.0972 ±0.3968

Alone when buying ±0.0243 ±0.0232 ±0.2434

Level of criminal risk ±0.1875 ±0.1181 ±0.3571

Product price ±0.0069 ±0.0020 ±0.0096

Level of criminal risk6counterfeit 0.0365 ±0.0556 ±0.3571

Level of criminal risk6stolen 0.1302 ±0.8264 ±0.3016

Product price6counterfeit 0.0044 0.0005 0.0023

Product price6stolen 0.0064 ±0.0023 0.0056

Table I. Regression weights by cluster

Majority fallacy

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A discriminant analysis was performed to determine the significance of the

variables. The discriminant model was significant at the 0.0001 level. The

results were validated by reclassification. Reclassification resulted in only

two misclassified respondents, or a 2.63 percent error rate. The discriminant

results indicated that most of the variables are significant predictors. The

results appear in Table II. The three product types, generic, counterfeit and

stolen, were all significant at the 0.001 level. The two types of buying

situations were also significant, `̀ A friend is with you, but is not making a

purchase'', at the 0.001 level and `̀ You are alone while making the

purchase'', at 0.10. The main effect of perceived level of criminal risk was

not significant, however, both the interactions between counterfeit and risk

and stolen and risk were significant at 0.01. The main effect of price, as well

as both interactions between counterfeit and price and stolen and price, were

significant at the 0.01 level.

H1, that willingness to buy is negatively associated with selling price of the

good offered for sale, was supported by the significant discriminant

coefficient for price. The significant interactions of price with counterfeit

and price with stolen partially supported H1a, that price will provide a higher

degree of influence in the decision to purchase counterfeit goods, compared

to stolen goods. For this to be fully supported, the two mean interaction

weights would need to be significantly different. A t-statistic was used to test

this hypothesis on a cluster-by-cluster basis.

The results were mixed. The resulting t-statistics for clusters one, two and

three were ±1.49 (31 df), 2.39 (23 df) and ±2.36 (20 df), respectively. These

results were not significant for Cluster 1. They were significant at the 0.05

level for Clusters 2 and 3, however, the results were counter to the

hypothesized direction for Cluster 2.

The mixed results are not all that surprising, considering the decision

criterion differences between groups. The lack of significance for Cluster 1

appears to result from the strong preference for generic goods over either

counterfeit or stolen. The reversed sign for Cluster 2 appears to be a result of

the preference for stolen goods over counterfeit and generic. Finally, the

supported hypothesis by Cluster 3 makes sense in light of the preference for

counterfeit or generic goods over stolen.

H2, that a buyer is most willing to buy an illicit good when others are buyingalso, was supported by the significant discriminant coefficient and negative

weights for the two dummy variables representing the two alternative buyingsituations, `̀ A friend is with you, but is not making a purchase'', and `̀ You

Variable R2 F

Generic good 0.60 54.55 *

Counterfeit good 0.58 50.01 *

Stolen good 0.50 37.37 *

Friend present but not buying 0.26 13.18 *

Alone when buying 0.07 2.61 **

Level of criminal risk 0.05 1.79 N/S

Product price 0.34 18.66 *

Level of criminal risk6counterfeit 0.12 4.96 *

Level of criminal risk6stolen 0.28 14.42 *

Product price 6 counterfeit 0.11 4.63 *

Product price 6 stolen 0.43 28.12 *

Note: *significant at 0.01; **significant at 0.10

Table II. F-statistics for discriminant analysis

Results validated byreclassification

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are alone while making the purchase''. H2a, that a buyer is less willing to

buy illicit goods when they are alone, and therefore are less likely to face

social pressure to deviate was supported by the negative weight and

significant discriminant coefficient for the variable `̀ You are alone while

making the purchase''. H2b, that a buyer is least willing to buy an illicit good

when others are present and not buying, would be supported by a significant

difference between the `̀ You are alone while making the purchase'' variable

and `̀ A friend is with you, but is not making a purchase'' variable mean

weight. Again, these must be computed on a cluster-by-cluster basis.

The resulting t-statistics for Clusters 1, 2 and 3 were 0.42 (31 df), 0.51 (23

df) and ±1.86 (20 df), respectively. These results were not significant for any

of the clusters. These results suggest that while both alternative buying

situations were significantly different from the base level, of a friend buying

also, they were not significantly different from each other. Again, this result

is not surprising. Criminology theory suggests that deviance is strongly

influenced by peer pressure, which these results support.

H3, that willingness to buy illicit goods is negatively associated with the

level of perceived criminal risk, was not supported. This also is not

completely surprising in light of the significant interactions. The significant

interactions of risk with counterfeit and risk with stolen partially support

H3a, that perceived criminal risk will provide a higher degree of influence in

the decision to purchase stolen goods, compared to counterfeit goods. For

this to be fully supported, the two mean interaction weights would need to be

significantly different. A t-statistic can be used to test this hypothesis on a

cluster-by-cluster basis.

These results were mixed also. The resulting t statistics for Clusters 1, 2 and

3 were 0.88 (31 df), 4.15 (23 df) and ±0.26 (20 df), respectively. These

results were not significant for Clusters 1 and 3. They were significant at the

0.001 level for Cluster 2.

Again, the mixed results are not all that surprising, considering the decision

criterion differences between groups. The lack of significance for Clusters 1

and 3 appears to result from the strong preference for generic or counterfeit

instead of stolen. The supported hypothesis by Cluster 2 makes sense in light

of the preference for stolen over either counterfeit or generic goods. The

significant discriminant coefficient suggests that the interactions are

significant, but not always significantly different from one another.

Table III provides a summary of the supported hypotheses.

ConclusionsThe objective of this study was to evaluate the variables which are most

important in the decision to purchase illicit goods. To model this

phenomenon, four predictor measures were used. These measures included:

(1) product type;

(2) buying situation;

(3) perceived criminal risk; and

(4) price.

Part-worth conjoint analysis was used to obtain individual weights of these

variables on the willingness to purchase. These weights were aggregated into

three clusters using Ward's method. The importance of these variables was

established using discriminant analysis.

Results not significant

Mixed results

282 JOURNAL OF CONSUMER MARKETING, VOL. 16 NO. 3 1999

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The overall model was shown to be significant. The main effects of product

type, buying situation and price were all significant predictors of willingness

to buy. The interactions of risk with product type and price with product type

were also significant predictors.

All the respondents were most likely to engage in illicit behavior if there

was peer pressure to do so. They were less likely to purchase an illicit good

if they were alone or with someone who was not engaging in the illegal

behavior. In regard to the other variables, the data suggest that the

members of the three clusters evaluated the purchase of illicit goods

differently.

The respondents in Cluster 1, representing 42.3 percent of the respondents,

indicated that they would prefer a generic good, but would consider a

counterfeit. Members of this group did not consider stolen goods to be a

viable alternative. These respondents were discouraged from participating in

illicit trade by the degree of perceived criminal risk. They were more

concerned about the risk associated with stolen goods, but were also

concerned about the risk associated with counterfeit goods. These

respondents considered price to be an equally important issue in the

consideration of all three product types.

Cluster

1 2 3

Model Significant Significant Significant

H1 Willingness to buy is

negatively associated with

selling price

Supported Supported Supported

H1a Price will provide a higher

degree of influence in the

decision to purchase

counterfeit goods, compared

with stolen goods

Not

supported

Not *

supported

Supported

H2 A buyer is most willing to

buy illicit goods when

others are present and buying

illlicit goods

Supported Supported Supported

H2a A buyer is less willing to buy

illicit goods when they are

alone, than when others are

present and buying the illicit

product

Supported Supported Supported

H2b A buyer is least willing to buy

illicit goods when others are

present and not buying the

illlicit product

Not

supported

Not

supported

Not

supported

H3 Willingness to buy illicit goods

is negatively associated with

the level of perceived criminal

risk

Not

supported

Not

supported

Not

supported

H3a Perceived criminal risk will

provide a higher degree of

influence in the decision to

purchase stolen goods,

compared with counterfeit goods

Not

supported

Supported Not

supported

Note: *H1a was significant in the direction opposite hypothesis

Table III. Supported hypotheses

Peer pressure

JOURNAL OF CONSUMER MARKETING, VOL. 16 NO. 3 1999 283

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Members of Cluster 2, 30.8 percent of the respondents, preferred the stolen

products more than two-to-one over either counterfeit or generic.

Statistically, they treated counterfeit and generic goods similarly. These

respondents were inclined to reject both generic and counterfeit goods

regardless of risk. They were, however, strongly discouraged from an illicit

stolen purchase, when there was a perceived risk associated with that

purchase. These respondents were more likely to reject the stolen good as

price increased. Lowering the price on the counterfeit good did not make it

more appealing to this group.

Finally, members of Cluster 3, 26.9 percent, would consider purchasing all

three products. While they would consider stolen goods, they favored generic

or counterfeit goods. This group reacted the most strongly to level of

perceived risk. The risk associated with both counterfeit and stolen goods

was equally concerning to this group. Lowering the price on both the stolen

good and the counterfeit good made the product more appealing, but this

reaction was strongest for the counterfeit offering.

The results leave several questions unanswered, most obviously what drives

the differences between the clusters. Future research should address that

issues as well as addressing some of this study's limitations. Although the

literature suggests that social class is not a significant predictor of deviancy,

and therefore the study should not have been adversely affected by use of a

MBA student population (Krohn et al., 1980; Tittle and Villemez, 1977),

future research should include more varied populations.

Managerial implications and applicationsThe results suggest some interesting conclusions for public policy and

businesses. Even a relatively homogeneous group of respondents, employed

MBA students, view the decision scenarios quite differently. Different

individuals will respond to different stimuli from business and policy

makers.

Some respondents appeared to be able to rationalize the illicit purchase

decision. Some of the respondents treated counterfeit and generic goods

indiscriminately. Others were strongly willing to purchase stolen products.

People not inclined to engage in illicit behavior were discouraged by the

level of perceived risk. People inclined to engage in illicit behavior were less

inclined as fear of criminal reprisals increased for the particular type of illicit

behavior they considered. Because illicit trade is harmful to legitimate

business, managers should consider lobbying for the strict enforcement of

criminal sanctions against consumers, as well as merchants of illicit goods.

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&

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