Essay

Influence of Moral Affect, Judgment, and Intensity on Decision Making Concerning Counterfeit, Gray-Market, and Imitation Products Jae-Eun Kim University of Minnesota

Hyeon Jeong Cho Oklahoma State University

Kim K. P. Johnson University of Minnesota

We examined the direct effects of moral judgment, moral intensity, and moral affect, specifi- cally shame and guilt, on undergraduates’ purchase intent concerning counterfeits, gray-market products, and imitations. The indirect effects of moral intensity, shame, and guilt on moral judg- ment were also investigated. A between subjects experiment was designed and participants (n = 313) responded to a scenario. For both the counterfeit and imitation products, moral judgment had a significant negative effect on purchase intent. Moral intensity had no significant effect on purchase intent for all product types but it had significant positive influence on moral judgment for all product types. Guilt had a significant negative influence on purchase intent for gray- market products and a positive influence on moral judgment for all product types. In addition, moral judgment mediated the impact of guilt on intent to purchase gray-market products.

Keywords: counterfeit; purchase intent; moral intensity; shame; guilt; moral affect; imitation; gray-market; moral judgment

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The global market for illicit products, 1

such as counterfeits, is large and contin- ues to grow. A counterfeit product is defined as an identical copy of an authentic product (Lai & Zaichkowsky, 1999). In 2006, the U.S. Customs and Border Protection (2007) and Immigration and Customs Enforcement made more than 14,000 seizures of counter- feit goods worth more than $155 million. This figure represents a 67% increase from the previous year. In the 1980s, most firms affected by the production of fake products were in the luxury sector (Guttierez, Verheugen, Mandelson, & Schwab, 2006). Since then, the fake product market

has expanded to include the medical, agricul- tural, and apparel industries (Bloch, Bush, & Campbell, 1993), along with computers, hardware, electrical appliances, car parts, air- plane parts, and toys (Guttierez et al., 2006).

Several researchers have proposed theo- retical models and constructs to explain indi- viduals’ moral2 decision-making processes (Hunt & Vitell, 1986; Jones, 1991; Rest, 1986). These models and constructs have been used in attempts to explain consumers’ decision making regarding the purchase of illicit products (Cho, Yoo, & Johnson, 2005; Ha & Lennon, 2006; Moores & Chang, 2006). In developing these models, researchers have

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focused on the cognitive components of moral decision making (Hunt & Vitell, 1986; Jones, 1991; Kohlberg, 1984; Rest, 1986). Affective components have received little attention, even though research findings sug- gest affect is important in decision making (Tangney, Stuewig, & Mashek, 2007). Thus, our purpose was to examine both cognitive and affective influences on moral judgment and behavioral intentions concerning counter- feits, gray-market products, and imitations.

Background

Gentry, Putrevu, Schultz, and Commuri (2001) proposed that consumers view coun- terfeits versus genuine products on a contin- uum. This continuum ranges from genuine on one end to seconds, overruns, legitimate copy- cats, high-quality counterfeits, and ending with low-quality counterfeits at the other end. The levels represent perceived differences in quality. The products we examined represent a portion of the counterfeit–genuine contin- uum: counterfeits, gray-market products, and imitations (Gentry et al., 2001).

Counterfeits

The most common types of illicit prod- ucts are counterfeits and pirated goods. A counterfeit, by definition, is intended to deceive consumers by having them believe that the product is genuine (D’Astous & Gargouri, 2001; Lai & Zaichkowsky, 1999; McDonald & Roberts, 1994). A counterfeit may be offered for sale at a fraction of the selling price of a genuine product or may be offered at the same price. The term counter- feit is frequently used to describe products such as apparel, computers, electrical appli- ances, and automobile parts.

On the other hand, a pirated good, by definition, is a product that is a copy of a genuine item, but consumers are aware that

the item is not genuine (D’Astous & Gargouri, 2001; Lai & Zaichkowsky, 1999). The term pirated good is often used to refer to illegal copies of software programs, mov- ies, and music CDs. Researchers have dif- ferentiated a counterfeit from a pirated good on the basis of whether a consumer was aware that the product was a fake or was genuine at the moment of purchase (McDonald & Roberts, 1994; Wee, Tan, & Cheok, 1995). Consumers do not seem to distinguish between the terms pirated and counterfeit product. Indeed, they often use these terms inter- changeably. We are interested in nondecep- tive counterfeits (Grossman & Shapiro, 1988) and use the term counterfeit to refer to a prod- uct that appears genuine, is offered for sale at a fraction of the cost of a genuine item, and consumers recognize as fake.

Most existing research about illicit product consumption has focused on counterfeits. Several researchers have examined factors fueling demand for counterfeit products (Albers- Miller, 1999; Ang, Cheng, Lim, & Tambyah, 2001; Cordell, Wongtada, & Kieschnick, 1996; Tom, Garibaldi, Zeng, & Pilcher, 1998; Wee et al., 1995). There is ample evidence that price is the one of the most important antecedents to purchasing counterfeits (Albers-Miller, 1999; Tom et al., 1998). Others have investigated the influence of nonprice antecedents, including personality, attitudes, and demographics, on purchase intent for counterfeits. Ang et al. (2001) found that the less normatively suscep- tible, the more value conscious,3 and the less honest individuals were, the more likely they were to favor piracy. Wee et al. (1995) found that individuals who held a favorable attitude toward counterfeiting and had low household incomes had higher purchase intent for coun- terfeit products as compared to individuals with negative attitudes and high household incomes. Cordell et al. (1996) found that the more positive people’s attitude toward lawful- ness, the less willing they were to purchase a counterfeit.

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Gray-Market Products

A category of goods that represent genu- ine products but are illegally distributed through unauthorized channels are gray- market products4 (Antia, Bergen, & Dutta, 2004; Bucklin, 1993). These products are often the result of merchandise overpro- duced in factories that were contracted by a branded product’s owners to produce genu- ine products (Wada, 1996). Unlike counter- feits, gray-market products are produced under the original brands’ specifications and materials but consumers do not receive the warranties that accompany those of genuine goods distributed through authorized chan- nels (Carney, 2006). Consumers may or may not be aware of this fact at the time of pur- chase. Moreover, gray-market products are not consistently considered illegal products because of the differing nature of the various intellectual property rights associated with different countries (Rothnie, 1993).

Information on the market share and sales of gray-market products is hard to obtain (Antia et al., 2004; Eagle, Kitchen, Rose, & Moyle, 2003). Nevertheless, the sale of gray- market products is prevalent in many product categories, including watches, health and beauty aids, high-end fashion apparel, and prescription drugs (Antia et al., 2004). Since the 1980s, the sales of gray-market products have increased (Barlass, 1988; Mitchell, 1998), with some sectors tripling their business in as little as three years (Myers, 1999). For exam- ple, Antia et al. (2004) noted that health and beauty aid gray-market sales were estimated at 20% to 50% of total authorized sales.

Researchers interested in gray-market products have focused on the costs and ben- efits associated with the existence of gray markets (Antia et al., 2004; Eagle et al., 2003) and the demand perspectives of gray- market products (Huang, Lee, & Ho, 2004). Antia et al. (2004) summarized the costs of gray markets as the dilution of the exclusive rights of the authorized sellers, the free riding

of unauthorized distributors, and negative influences on the distributors’ relationships. Eagle et al. (2003) also revealed that gray markets damage an original brand’s equity, value, image, and investor attractiveness. On the other hand, the benefits of gray markets included creation of incremental sales in untapped markets. Concerning the demand perspectives of gray-market products, Huang et al. (2004) found that consumers who main- tained strong price-quality inferences (i.e., high price equals high quality) and those who were risk averse expressed unfavorable atti- tudes toward gray-market products.

Imitations

An imitation is not identical to a genuine product but is similar in substance, name, form, meaning, or intent to an acknowledged and widely known product or service (Lai & Zaichkowsky, 1999). d’Astous and Gargouri (2001) defined imitation5 as the re-creation of existing products with small modifications. At some level, the development of imitation prod- ucts represents executing another’s original idea or design and is viewed by some as the equivalent of stealing or cheating (Bagley, 1963). Producing imitations is a legitimate marketing strategy and provides a way for later entrants into the market to follow market leaders (Schnaars, 1994). Thus, multitudes of manufacturers and retailers imitate market leaders to promote “look-a-likes” and make consumers “think of” genuine brands (D’Astous & Gargouri, 2001).

Most research concerning imitations focused on consumers’ confusion between imitations and genuine brands and their responses toward imitations (Foxman, Muehling, & Berger, 1990; Lai & Zaichkowsky, 1999; Loken, Ross, & Hinkle, 1986). Foxman et al. (1990) investi- gated the relationship between individual characteristics and brand confusion. They found that brand confusion occurred when consumers’ product familiarity, product expe- rience, and involvement with the product

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category were low. Lai and Zaichkowsky (1999) showed that individuals with more knowledge about brand leaders in the market were less likely to accept imitation products than individuals with less knowledge about brand leaders. Loken et al. (1986) found that similarity in appearance, including similar package designs, across brands and products significantly influenced judgments about the brand’s origin. Partici pants who perceived orig- inal brands and imitations as similar were likely to think that the same company made both.

Hypotheses Development

To provide a context for investigating deci- sion processes regarding purchase intent for counterfeit, gray-market, and imitation products, we reviewed existing moral deci- sion models. One frequently referenced model is the four-component model (Rest, 1979, 1986). According to Rest (1979, 1986), individuals make decisions regarding moral issues starting from an awareness of a moral problem. Next, the individual makes a judg- ment, establishes an intent, and finally implements behavior. In existing models, moral judgment has been a commonly used construct in investigations of the cognitive aspects of ethical decision making (Hunt & Vitell, 1986; Jones, 1991; Rest, 1986).

Moral Judgment

Moral judgments are defined as “prescrip- tive assessments of what is right or wrong” (Trevino, 1986, p. 604). Moral judgment has been identified as an important element in explaining moral or immoral behaviors and behavioral intentions in various contexts. For example, DeConinck and Lewis (1997) found that moral judgments influenced sales man- agers’ intent to reward or punish sales force behaviors. Researchers have also found that

students’ moral judgments have a negative influence on purchase intent for pirated soft- ware (Moores & Chang, 2006; Tan, 2002; Wagner & Sanders, 2001) as well as nonde- ceptive fashion counterfeit products (Ha & Lennon, 2006). That is, individuals who judge the act of pirating software or purchasing non- deceptive counterfeits as wrong are unlikely to indicate that they intend to pirate or pur- chase such items. These findings led to the development of the following hypotheses:

Hypothesis 1: The higher the participants’ moral judgment, the lower their level of intent to purchase products known to be (a) counter- feit, (b) gray market, or (c) imitation.

Relationships Between Moral Intensity, Moral Judgment, and Purchase Intent

Jones (1991) proposed moral intensity as a variable that may influence the judgment- intention-behavior progression. Moral inten- sity captures “the extent of issue-related moral imperative in a situation” (Jones, 1991, p. 372) and includes the magnitude of consequences (i.e., the degree of harms and benefits of the moral problem), the proba- bility of effect (i.e., the joint probability of happening and causing harms or benefits), the temporal immediacy (i.e., the temporal distance from the present to the consequence of the moral problem), and the social con- sensus (i.e., the degree of social agreement regarding the moral problem).

Tan (2002) proposed an issue-risk-judg- ment model of decision making for pirated products relating moral judgment, moral intensity, and perceived risk. He showed that moral intensity directly influences intent to purchase pirated software. When pur- chasing pirated software was deemed immoral by members of a group, the mem- bers were unlikely to intend to purchase.

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There is also evidence that suggests moral judgment may be influenced by moral intensity (May & Pauli, 2002; Morris & McDonald, 1995; Singer, 1996; Singer & Singer, 1997). For example, May and Pauli (2002) asked participants how they would behave in situations reflecting moral deci- sions (e.g., pollution, car safety) and found that moral intensity influenced moral judg- ments. In research assessing the perceived importance of an ethical issue (i.e., moral intensity), Robin, Reidenbach, and Forrest (1996) found that the importance of an ethi- cal issue influenced both judgments and behavioral intent. Therefore, we formulated the following hypotheses:

Hypothesis 2: The higher the participants’ moral intensity, the lower their level of intent to purchase products known to be (a) counter- feit, (b) gray market, or (c) imitation.

Hypothesis 3: The higher the participants’ moral intensity, the higher their level of moral judg- ment concerning a (a) counterfeit, (b) gray- market, or (c) imitation product.

Relationships Between Moral Affect, Moral Judgment, and Purchase Intent

In addition to moral judgment and moral intensity, moral affect may be important in “understanding people’s behavioral adherence to their moral standards” (Tangney et al., 2007, p. 347) because moral affect provides the moti- vational force to do well and to avoid doing things considered wrong. Moral affect includes feelings of shame, guilt, pride, and embarrass- ment (Tangney et al., 2007). Our focus was on two of these emotions: shame and guilt.

Shame and guilt develop from some of our earliest social interactions with others. They are both “self-conscious and moral emotions: self-conscious in that they involve the self evaluating the self and moral in that

they presumably play a key role in fostering moral behavior” (Tangney et al., 2007, p. 2). Shame and guilt are distinct concepts, although they share similar features. Both concepts involve internal attributions, and both are experienced in interpersonal set- tings. These concepts are distinct from each other in that with respect to shame, the focus of attention is on the self. Shame is considered to be more painful than guilt because one’s core self is at stake (Tangney et al., 2007). Consider the intent to purchase a counterfeit product, something that is clearly illegal to produce. Those who are prone to feel shame may not intend to pur- chase a counterfeit because doing so would require them to see themselves as contribu- tors to an illegal business. Thus, you are bad because of your intent.

In contrast, with the experience of guilt, “the self is negatively evaluated in connec- tion with something but is not the focus of the experience” (Tangney & Dearing, 2002, p. 18). Experiences of guilt may result in regret or remorse over something that hap- pened but are less painful than shame because the focus is on the behavior, not the self. Applied to a situation involving pur- chase intent for counterfeits, people who are prone to experience guilt may be less likely to purchase because they will likely inter- pret such behavior as wrong and they want to avoid engaging in wrongful behaviors. It is the intended behavior that is bad and should be avoided. Based on this logic, we formulated the following hypotheses:

Hypothesis 4: The greater the participants’ prone- ness to shame, the lower their level of intent to purchase products known to be (a) counterfeit, (b) gray market, or (c) imitation.

Hypothesis 5: The greater the participants’ prone- ness to guilt, the lower their level of intent to purchase products known to be (a) counter- feit, (b) gray market, or (c) imitation.

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Researchers have theorized that moral affect may precede moral judgment (Monin, Pizarro, & Beer, 2007), and there is some empirical support for this view. Haidt, Koller, and Dias (1993) tested the idea that moral affect preceded moral judgment with stories that were intended to reflect victim- less but offensive situations. Situations included cleaning one’s bathroom with a flag or promising to visit a parent’s grave but not honoring the promise. After listen- ing to the stories, participants were asked to make a judgment concerning the rightness or wrongness of the behavior described. Whether the situation described was mor- ally right or wrong, results demonstrated that moral affect preceded and influenced moral judgment. Based on their finding, the researchers suggested adding moral affect as the primary antecedent of moral judg- ment. Applying this perspective to a situa- tion involving purchase intent for illicit products, people would judge the rightness or wrongness of their behavior based on feelings of guilt or shame. Their affect would influence their moral judgment, which would subsequently influence their purchase intent. Therefore, we formulated the following hypotheses:

Hypothesis 6: The greater the participants’ proneness to shame, the higher their level of moral judgment concerning a (a) counterfeit, (b) gray-market, or (c) imitation product.

Hypothesis 7: The greater the participants’ proneness to guilt, the higher their level of moral judgment concerning a (a) counterfeit, (b) gray-market, or (c) imitation product.

Hypothesis 8: Moral judgment mediates the influence of proneness to shame on intent to purchase products known to be (a) counter- feit, (b) gray market, or (c) imitation.

Hypothesis 9: Moral judgment mediates the influence of proneness to guilt on intent to purchase products known to be (a) counter- feit, (b) gray market, or (c) imitation.

Method

Participants

After receiving approval for the use of humans in research (Grant 0702E03223), data were collected from 313 women enrolled at either a Midwestern university or a Southeastern university. A college-aged student sample was selected as appropriate because students tend to be heavy users of illicit products (Cordell et al., 1996).

Data Collection

Participants were recruited from under- graduate courses. One of the researchers was introduced to the class by the instructor, who explained that students could partici- pate in a research project. The researcher explained the study and distributed ques- tionnaires to those who volunteered. The researcher instructed the volunteers to read the consent form and complete the question- naire. Instructions verified that participation was voluntary, that it was unrelated to course performance or evaluation, and that students were free to discontinue participation at any time. Students who elected not to participate were politely asked not to interrupt others and to sit quietly. The procedure took approximately 20 minutes. Participants were then debriefed.

Research Design

A one-way (product type: counterfeit, gray market, imitation) between-subjects experiment was designed. Scenarios were developed for each experimental condition. Scenarios were used because ethically prob- lematic situations presented in scenarios are believed to trigger the ethical decision- making process (Moores & Chang, 2006; Tan, 2002). Participants were asked to imagine

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themselves in a place where both genuine and illicit products were accessible. The scenarios are presented with the gray- market and imitation versions in brackets, respectively:

You are participating in a student study tour in New York City. The second day of the tour, you have some free time in the afternoon to visit interesting places in New York and do some shopping. You decide to go to Manhattan’s Fifth Avenue to see the designer boutiques. You visit a well-known designer boutique and find a handbag that you really like. The price is $375. While you are shopping, a stranger notices you like the handbag and shares quietly with you that you can find a coun- terfeit [gray-market good/imitation] of the same handbag if you go to a store located on Canal Street [nearby/around the corner and about a block down the street]. The stranger gives you some easy directions and indicates that the counterfeit [gray market/imitation] will likely cost a frac- tion of the genuine handbag. She indicates the counterfeit will look identical to the genuine handbag you like. [She indicates the gray-market good is a genuine product that is produced in factories that have been contracted by the brand manufactur- ers to produce the originals. These facto- ries produce more quantity than required and sell it to unauthorized distributors. She indicates the imitation will look iden- tical to the original handbag except it will not have the designer name and will be made from less expensive materials.] You decide to see if the stranger is correct. You find a counterfeit [gray-market good/imi- tation] that does look exactly like the genuine and the price is $75 [$260/$120].

A handbag was selected for the item because handbags are fashion products that are frequently copied or imitated in the mar- ketplace, and we assumed that most partici- pants owned and had shopping experience

for handbags. The prices of the handbags were determined through market research. The counterfeit handbag was priced at 20% of the cost of the genuine product; the gray- market handbag was priced at 70%, and the imitation handbag was priced at 32%. Each participant was randomly assigned to one of the experimental conditions. After reading the scenario, participants completed the questionnaire.

Questionnaire Development

Participants were asked to indicate their relative purchase intent for a genuine prod- uct and a counterfeit/gray-market product/ imitation. Three statements were used to measure purchase intent: “I am more likely to buy,” “I would buy,” and “I am more inter- ested in buying.” The statements were pre- ceded by, “In this situation, which of the two products are you more likely to buy?” Participants responded using 9-point scales anchored at one end with the term the genuine product (1) and at the other end with the term the counterfeit/gray-market product/imitation (9) (Nowlis, Khan, & Dhar, 2002). Responses to all items were averaged.

Proneness to shame and guilt was meas- ured using the Test of Self-Conscious Affect (Tangney, Wagner, & Gramzow, 1992), which contained 16 items using 5-point scales (1 = not likely, 5 = very likely). For this measure, participants read a set of sce- narios involving ethically problematical situations and reported the likelihood that they would respond in one of two ways, each linked with either shame or guilt. A sample scenario item was “While out with a group of friends, you make fun of a friend who’s not there.” The associated responses were “You would feel small . . . like a rat” (shame) or “You would apologize and talk about that person’s good points” (guilt). Reported reliabilities of the shame measure

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ranged from .73 to .82; reported reliabili- ties of the guilt measure ranged from .60 to .73 (Tangney, 1990; Tangney et al., 1992).

Moral intensity and moral judgment were measured using 7-point Likert-type scales (1 = strongly disagree, 7 = strongly agree). Reported reliability for moral intensity was α = .86 and for moral judgment it was α = .85 (Tan, 2002). The moral intensity measure contained eight items. A sample item was “The genuine brand seller would lose revenue immediately if you decided to buy a counter- feit/gray-market product/imitation.” The prod- uct was changed based on con dition of the experiment. The moral judgment measure contained four items. A sample item was “In your opinion, the act of buying counterfeit/ gray-market/imitation fashion products rather than genuine products is wrong.” The product was again changed based on condition of the experiment. To obtain a score on each construct, the responses to all items were averaged.

Finally, items were designed to gather background information. Included were items about previous experience buying counterfeit/gray-market/imitation products, income, and average amount paid for a handbag. These items were used as control variables in the analysis.

Results

Preliminary Data Analyses

We conducted t-tests to examine whether responses differed across institutions. No significant differences were found as a func- tion of institution on the variables of interest within each condition (counterfeit: t = .89, p = .36; gray market: t = .69, p = .49; imita- tion: t = –.15, p = .88), and therefore, data were pooled. We also assessed reliabilities of all measures. Cronbach’s alpha coefficient

was .90 for purchase intent, .68 for shame, .62 for guilt, .84 for moral intensity, and .88 for moral judgment.

To use Multiple Linear Regression for the main analyses, we verified assumptions concerning normality and the constant vari- ance of the residuals (homoscedasticity). The scatter plot of the studentized deleted residuals versus standardized predicted val- ues showed that most residuals fell between –2 and 2, satisfying the assumption of the constant variance of residuals. Normality of residuals was checked by constructing a normal probability plot. Results confirmed the assumption that residuals were normally distributed for each condition. No outliers were found based on the casewise plot of the studentized residuals. Finally, to assess multicollinearity between the independent variables, bivariate correlations were calcu- lated to assess associations between all vari- ables. Table 1 displays the correlations of the measures. Because some correlations indicated possible problems of multicolline- arity, the variance inflation factors (VIFs) of the independent variables were examined (Stevens, 1996). Generally, high variance inflation (VIF) values indicate greater mul- ticollinearity, and values greater than 10 indicate the presence of multicollinearity (Hair, Anderson, Tatham, & Black, 1995). Results showed very low variance inflation (VIF < 1.73), indicating that each independ- ent variable had weak associations with other independent variables.

Participants

Responses from 313 (counterfeit: n = 102; gray market: n = 100; imitation: n = 111) participants were included in the data analysis. The majority of participants (n = 247) were Caucasian (78.9%). The next largest group was Hispanic (9.3%). Ages

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ranged from 18 to 40 years (M = 21.3). Two thirds (67.7%) reported incomes of less than $10,000, whereas 22.4% reported incomes of $10,000 to $20,000. Spending for a hand- bag in the previous year ranged from less than $100 (29.7%) to more than $400 (20.8%). One third had purchased counter- feits (34.5%), half had purchased imitations (56.9%), and nearly a quarter (23.0%) had purchased gray-market products.

Main Data Analyses

Two multiple regression analyses for each product were performed to assess the effect of moral judgment, moral intensity, shame, and guilt on purchase intent and the effect of moral intensity, shame, and guilt on moral judgment. The control variables were previous purchase experience, handbag expenditures, and income level. The regres- sion models were significant. The R2 values were .30, .29, and .33 when the dependent variable was purchase intent and .33, .42,

and .32 when the dependent variable was moral judgment concerning a counterfeit, gray-market product, and imitation, respec- tively (see Table 2).

Multiple regression with moral judgment, moral intensity, shame, and guilt as inde- pendent variables and purchase intent as the dependent variable revealed that moral judg- ment had a significant negative effect on purchase intent for counterfeit and imita- tion products (counterfeit: β = –.34, p < .01; imitation: β = –.34, p < .01), supporting Hypotheses 1a and 1c. However, moral judgment had no effect on purchase intent for a gray-market product. Hypotheses 2a, 2b, and 2c postulated the negative influence of moral intensity on purchase intent for all product types; however, the effects were not significant. Multiple regression with moral intensity, shame, and guilt as independent variables and moral judgment as the depend- ent variable revealed that moral intensity had a significant positive effect on moral judg- ments concerning counterfeit, gray-market,

Table 1 Descriptive Statistics and Correlations for Counterfeit,

Gray-Market, and Imitation Products

Construct Products M SD 1 2 3 4 5

1. Purchase intention Counterfeit 4.20 2.40 1.00 –0.49** –0.34** 0.02 –0.09 Gray market 3.83 2.31 1.00 –0.37** –0.16 –0.08 –0.37** Imitation 3.90 2.60 1.00 –0.49** –0.26** –0.02 –0.14 2. Moral judgment Counterfeit 4.10 1.64 1.00 0.43** 0.02 0.29** Gray market 4.22 1.54 1.00 0.41** 0.01 0.51** Imitation 3.89 1.62 1.00 0.47** 0.20* 0.22* 3. Moral intensity Counterfeit 3.70 1.28 1.00 0.15 0.04 Gray market 3.81 1.10 1.00 –0.01 0.10 Imitation 3.66 1.20 1.00 0.17 –0.05 4. Shame Counterfeit 3.19 0.49 1.00 0.09 Gray market 3.25 0.55 1.00 0.29** Imitation 3.29 0.49 1.00 0.28** 5. Guilt Counterfeit 3.83 0.36 1.00 Gray market 3.77 0.45 1.00 Imitation 3.82 0.39 1.00

*p < .05. **p < .01.

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and imitation products (counterfeit: β = .39, p < .01; gray market: β = .35 , p < .01; imita- tion: β = .41, p < .01), supporting Hypothe- ses 3a, 3b, and 3c. Participants who scored high on moral intensity were more likely to judge the purchase of the products as mor- ally wrong.

Hypotheses 4a, 4b, and 4c hypothesized a negative influence of proneness to shame on purchase intent for all product types; how- ever, the effects were not significant. In con- trast, proneness to guilt had a significant

negative influence on purchase intent con- cerning a gray-market product (β = –.31, p < .01), therefore supporting Hypothesis 5b. Participants who were prone to feel guilt were less likely to purchase a gray-market prod- uct compared to participants who were less prone to feel guilt. However, proneness to guilt had no effect on purchase intent for either a counterfeit or an imitation product. Thus, Hypotheses 5a and 5c were not supported.

Hypotheses 6a, 6b, and 6c hypothesized a positive effect of proneness to shame on

Table 2 Results of Multiple Regression Analyses on Purchase Intent and Moral Judgment

Concerning Counterfeit, Gray-Market, and Imitation Products

Standardized Regression Coefficients Dependent Independent Hypothesized Variable Variables Effect Counterfeit Gray Market Imitation

1. Purchase Moral judgment Hypothesis −0.34** −0.14 −0.34** intention 1a, 1b, 1c ( − ) Moral intensity Hypothesis −0.17 0.00 −0.05 2a, 2b, 2c ( − ) Shame Hypothesis 0.07 −0.04 0.11 4a, 4b, 4c ( − ) Guilt Hypothesis −0.01 −0.31** −0.16 5a, 5b, 5c ( − ) Control variables Purchase experience −0.21* −0.10 −0.27** Income 0.02 0.06 0.00 Handbag expenditure −0.30** −0.42** −0.27**

2. Moral Moral intensity Hypothesis 0.39** 0.35** 0.41** judgment 3a, 3b, 3c ( + ) Shame Hypothesis −0.09 −0.12 0.02 6a, 6b, 6c ( + ) Guilt Hypothesis 0.28** 0.49** 0.27** 7a, 7b, 7c ( + ) Control variables Purchase experience 0.24** 0.08 0.14 Income 0.06 −0.04 0.08 Handbag expenditure 0.15 0.11 0.19*

Note: R2 = .30, F(7, 94) = 7.29, for a counterfeit product (p < .01); R2 =.29, F(7, 100) = 6.70 for a gray-market product (p < .01); R2 =.33, F(7, 90) = 8.37, for an imitation product (p < .01) when the dependent variable was purchase intent. R2 = .33, F(6, 95) = 7.80, for a counterfeit product (p < .01); R2 =.42, F(6, 91) = 11.01 for a gray-market product (p < .01); R2 =.32, F(6, 101) = 7.98, for an imitation product (p < .01) when the dependent variable was moral judgment. *p < .05. **p < .01.

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moral judgment for all product types; how- ever, effects were not significant. On the other hand, proneness to guilt had a signifi- cant positive influence on moral judgment for all three types of products, supporting Hypotheses 7a, 7b, and 7c (counterfeit: β = .28, p < .01; gray market: β = .49, p < .01; imitation: β = .27, p < .01). Participants who were prone to feel guilt were more likely to judge the purchase of counterfeit, gray- market, and imitation products as morally wrong, compared to participants who were less prone to feel guilt.

In terms of the effect of the control vari- ables, previous purchase experience with illicit products significantly influenced pur- chase intent concerning a counterfeit prod- uct and an imitation product (counterfeit: β = –.21, p < .05; imitation: β = –.27, p < .01). This supports previous researchers who indicated that consumers with past purchase experience involving illicit products are more likely to intend to buy illicit products (Tan, 2002; Tom et al., 1998). Moreover, the amount of money typically spent for a handbag

had a significant negative influence on pur- chase intent for all products (counterfeit: β = –.30, p < .01; gray-market: β = –.42, p < .01; imitation: β = –.27, p < .01). The higher the amount of money a participant generally spent on a handbag, the less likely the par- ticipant was to intend to purchase a counter- feit, gray-market, or imitation product (see Figure 1 for significant hypothesized relationships).

Mediation Analyses

We tested whether moral judgment medi- ated the impact of moral affect on intent to purchase illicit products by following the three-step procedure outlined by Baron and Kenny (1986). First, we regressed moral judgment on moral affect and regressed pur- chase intent on moral judgment. Second, we regressed purchase intent on moral affect. Finally, we regressed purchase intent on moral affect and moral judgment together. We followed this procedure for each product condition and both aspects of moral affect

H1a∗/b/c∗ H3a∗/b∗/c∗

H6a/b/c

H4a/b/c

H2a/b/c

H7a∗/b∗/c∗

Purchase Intent

Moral Affect (Shame)

Moral Affect (Guilt)

Moral Intensity

Moral Judgment

H5a/b∗/c

Figure 1 Diagram of the Hypothesized Relationships Between Moral Affect,

Moral Judgment, Moral Intensity, and Purchase Intent

Note: * indicates a significant relationship. H = Hypothesis.

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(i.e., shame, guilt). There were no significant effects for shame. There were also no sig- nificant effects for guilt on purchase intent (i.e., second step) for either the counterfeit or imitation products. Thus, Hypotheses 8a, 8b, 8c, 9a, and 9c were not supported.

We conducted mediation analysis for the gray-market product. Our results showed, first, that guilt significantly influenced moral judgment (gray market: β = .51, t = 5.79, p = .000) and moral judgment significantly influenced intent to purchase (gray market: β = –.37, t = –3.9, p = .000). Second, guilt also significantly influenced intent to pur- chase (gray market: β = –.37, t = –3.88, p = .000). Finally, when we regressed intent to purchase on both guilt and moral judgment, the effect of guilt on intent to purchase was significantly reduced (gray market: β = –.24, t = –2.26, p = .026) after controlling for moral judgment (Baron & Kenny, 1986). This reduction in the significance level of the independent variable when the mediator was included in the model was confirmed (gray market: z = –2.14, p < .05) by apply- ing Sobel’s (1982) test. Therefore, the results support Hypothesis 9b that moral judgment mediates the effect of guilt on intent to pur- chase gray-market products.

Discussion and Conclusion

Moral judgment had a significant nega- tive effect on purchase intent concerning both counterfeit and imitation products. These findings are consistent with those of others who studied college students and found that individuals who believed the pur- chase of counterfeits was morally wrong were less likely to intend to buy counterfeit products (Ha & Lennon, 2006; Moores & Chang, 2006; Wagner & Sanders, 2001). These findings are also consistent with researchers who surveyed individuals from the general population (Tan, 2002).

Moral intensity had no significant effect on purchase intent for any of the products. In other words, purchase intent was unre- lated to views concerning whether purchas- ing any of the types of illicit products was harmful financially to the designers or to the manufacturers of genuine brands. The lack of a direct influence of moral intensity on purchase intent was inconsistent with previous findings (Kini, Ramakrishna, & Vijayaraman, 2004; Tan, 2002). The incon- sistency may be due to type of product investigated (Wee et al., 1995). We investi- gated behavioral intent concerning hand- bags, whereas other researchers investigated software products. Software is expensive to develop and represents a great deal of intel- lectual property. As compared to counter- feiting a handbag, perhaps pirating software was viewed as more financially damaging, with more immediate impact and more direct consequences to the designers and manufacturers.

For all product types, proneness to feel shame had no effect on purchase intent. The lack of significance may be related to the measure of shame. Despite the fact that we used measures with acceptable reliabilities with college student samples, the reliability of shame (α = .68) was relatively low, sug- gesting issues with the ability of the meas- ure to assess shame consistently. We also may have gotten nonsignificant findings concerning both moral intensity and shame on intent to purchase these types of products because the purchase is not illegal in the United States. Rather, it is both the produc- tion of these items (e.g., counterfeit) and their distribution (e.g., gray market) that are illegal. Participants may have simply held the view that if the intended behaviors rela- tive to these products were not illegal, then these behaviors were not bad.

Guilt had a significant negative influence on purchase intent for gray-market products only. Guilt may not have influenced purchase

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intent for counterfeits or imitation products because participants perceived differences in the type of product. It is possible that these different perceptions included differ- ences in the degree of illegitimacy of each product. This, in turn, may have influenced the relative importance of the moral-related variables. For example, imitations abound in the marketplace, and because of their abundance, they may appear as normal and acceptable purchase alternatives to genuine products.

Moral intensity had a significant positive influence on moral judgments concerning all product types. This finding is consistent with results from others (May & Pauli, 2002; Morris & McDonald, 1995; Singer, 1996; Singer & Singer, 1997) who found that people took into account the magnitude of consequences, the probability of effect, the temporal immediacy, and the degree of social consensus as they formed judgments concerning a moral problem.

For all product types, proneness to feel shame had no effect on moral judgment. In contrast, proneness to feel guilt had a posi- tive influence on moral judgment for all types of products. Furthermore, moral judg- ment mediated the impact of guilt on intent to purchase gray-market products. This find- ing is consistent with those of previous researchers who noted that moral affect pre- cedes moral judgment (Haidt et al., 1993; Monin et al., 2007). This finding suggests that researchers interested in understanding consumer behavior relative to illicit products consider the influence of affect in addition to cognitive influences on moral judgments.

Implications and Limitations

Our findings provide support for the inclusion of moral affect, specifically guilt, as an influence on decision making con- cerning different types of illicit products.

Moral affect includes other emotions in addition to shame and guilt. Haidt (2003) identified types of moral emotions that may influence moral decision making, including anger, embarrassment, elevation, and empa- thy. Therefore, we suggest that researchers interested in moral decision making relative to illicit products build on our research to explore these emotions along with moral intensity and moral judgment for their influ- ence in the consumption process. If we understand further the role of moral affect in decision making concerning moral situa- tions, we can better understand both motiva- tions to purchase and, as a result, potential methods to curtail consumption.

There are also practical implications for designing persuasive messages to reduce the consumption of certain types of illicit products stemming from our findings. Although our findings do not provide evi- dence that feeling guilty in the context of making judgments about the purchase of illicit products influences behavioral intent, it may be prudent for manufacturers to develop advertising directed at consumers to highlight the harms to individuals as well as society as a whole associated with the consumption of illicit products. Perhaps it is knowledge of the harms that might make individuals experience guilt feelings in a consumption situation. For example, one advertising campaign directed at educating consumers is the Harper’s Bazaar Fakes Are Never in Fashion promo- tion dedicated to exposing criminal activities connected to the sale of counterfeit goods. A study could be designed to investigate the effectiveness of these types of ads on students and others in influencing feelings of guilt and subsequently shaping changes concerning behavioral intent toward counterfeits and other illicit products.

Although as educators we may never alter the practice of producing imitations (knocking- off), as this appears to be an accepted procedure

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224 Clothing and Textiles Research Journal

in the apparel industry, there is a concern connected with the disposal of imitations, especially those produced as a result of fast fashion. Disposal practices that may be viewed as immoral may trigger guilt, which may subsequently influence consumption. The role of moral affect and moral intensity on judgments concerning disposal of imita- tions could be an area of further research.

Our study has several limitations that could be considered in carrying out future studies. First, scenarios were used that described hypothetical situations. Even though a realistic situation was created, par- ticipants had to respond to the questions by assuming that they were in a shopping situa- tion. Their actual behavior might have been different. Perhaps this limitation could be addressed by designing a field study wherein actual purchasing behavior could be observed. Second, this study was limited to participation by women, and results cannot be generalized to general consumers’ pur- chase intent concerning illicit products. The inclusion of males as well as individuals representing other ethnicities may reveal different patterns of effects across the cognitive–affective components of morality and decision making concerning illicit prod- ucts. This investigation was limited to a fashion product. Further research could be directed at determining whether people hold similar levels of purchase intent with less publicly visible products (e.g., auto parts, shampoo) and how the social recognition of a brand and visibility fuel the demand for counterfeits and other illicit products. Finally, we did not assess participants’ feelings dur- ing the experiment. We assessed proneness to experience guilt or shame. It would be valuable to design a study in which some level of guilt or shame was evoked and see if the experience of either emotion influenced behavioral intent concerning illicit products. It might also be possible to test the influence

of moral affect on behavioral intent concern- ing illicit products by having participants share what emotions they are experiencing as they respond to different consumption scenarios concerning illicit products.

Notes

1. We use the term illicit product to refer to goods that represent stealing other people’s design ideas and goods that are either illegally produced or illegally distributed.

2. The terms moral and ethical are often used interchangeably. We use the term moral to mean the distinction between right and wrong behavior. Consumer ethics is defined as the rules, principles, and standards that guide the behavior of an individual (or group) in the selection, purchase, use, or selling of a good or service (Muncy & Vitell, 1992).

3. Value conscious was defined as “a concern for paying lower prices subject to some quality con- straint” (Ang et al., 2001, p. 223).

4. When gray-market products are distributed between countries, the term parallel imports has been used (Eagle et al., 2003).

5. In the apparel industry, the term knocking off is used to describe this process.

References

Albers-Miller, N. D. (1999). Consumer misbehavior: Why people buy illicit goods. Journal of Consumer Marketing, 16(3), 273-287.

Ang, S. H., Cheng, P. S., Lim, E. A. C., & Tambyah, S. K. (2001). Spot the difference: Consumer responses towards counterfeits. Journal of Consumer Marketing, 18(3), 219-235.

Antia, K. D., Bergen, M., & Dutta, S. (2004). Competing with gray markets. MIT Sloan Management Review, 46(1), 63-69.

Bagley, A. (1963). The idea of imitation. Art Education, 16(4), 8-10.

Barlass, S. (1988). Gray marketing war escalates. Marketing News, 26, 8-9.

Baron, R. M., & Kenny, D. A. (1986). The moderator– mediator variable distinction in social psychological research: Conceptual, strategic, and statistical con- siderations. Journal of Personality and Social Psychology, 51(6), 1173-1182.

Bloch, P. H., Bush, R. F., & Campbell, L. (1993). Consumer “accomplices” in product counterfeiting:

at CALIFORNIA ST UNIV NORTHRIDGE on November 7, 2015ctr.sagepub.comDownloaded from

Kim et al. / Moral Affect 225

A demand-side investigation. Journal of Consumer Marketing, 10(4), 27-36.

Bucklin, L. P. (1993). Modeling the international gray market for public policy decisions. International Journal of Research in Marketing, 10(4), 387-405.

Carney, S. (2006, August 23). iPod gray market booms in India. Wired. Retrieved May 23, 2007, from http://www.wired.com/gadgets/mac/news/2006/ 08/71639

Cho, H., Yoo, J., & Johnson, K. K. P. (2005). Ethical ideologies: Do they affect shopping behaviors and perceptions of morality? Journal of Family and Consumer Sciences, 97(3), 48-55.

Cordell, V. V., Wongtada, N., & Kieschnick, R. L., Jr. (1996). Counterfeit purchase intentions: Role of lawfulness attitudes and product traits as determi- nants. Journal of Business Research, 35(1), 41-53.

d’Astous, A., & Gargouri, E. (2001). Consumer evalu- ations of brand imitations. European Journal of Marketing, 35(1/2), 153-167.

DeConinck, J. B., & Lewis, W. F. (1997). The influ- ence of deontological and teleological considera- tions and ethical climate on sales managers’ intentions to reward or punish sales force behavior. Journal of Business Ethics, 16(5), 497-506.

Eagle, L., Kitchen, P. J., Rose, L., & Moyle, B. (2003). Brand equity and brand vulnerability: The impact of gray marketing and parallel importing on brand equity and brand values. European Journal of Marketing, 37(10), 1332-1349.

Foxman, E. R., Muehling, D. D., & Berger, P. W. (1990). An investigation of factors contributing to consumer brand confusion. Journal of Consumer Affairs, 24(1), 170-189.

Gentry, J. W., Putrevu, S., Schultz, C., & Commuri, S. (2001). How now Ralph Lauren? The separation of brand and product in a counterfeit culture. Advances in Consumer Research, 28(1), 258-265.

Grossman, G. M., & Shapiro, C. (1988). Foreign counterfeiting of status goods. The Quarterly Journal of Economics, 103(412), 79-100.

Guttierez, C., Verheugen, G., Mandelson, P., & Schwab, S. (2006, June 20). Countering counter- feits. Wall Street Journal. Retrieved June 2, 2007, from http://online.wsj.com/public/search/search_ center.html

Ha, S., & Lennon, S. (2006). Purchase intent for fash- ion counterfeit products: Ethical ideologies, ethi- cal judgments, and perceived risks. Clothing and Textiles Research Journal, 24(4), 297-315.

Haidt, J. (2003). The moral emotions. In R. J. Davidson, K. R. Scherer, & H. H. Goldsmith

(Eds.), Handbook of affective sciences (pp. 852- 870). Oxford, UK: Oxford University Press.

Haidt, J., Koller, S. H., & Dias, M. G. (1993). Affect, culture and morality, or is it wrong to eat your dog? Journal of Personality and Social Psychology, 65(4), 613-628.

Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis: With readings (4th ed.). Englewood Cliffs, NJ: Prentice Hall.

Huang, J., Lee, B. C. Y., & Ho, S. H. (2004). Consumer attitude toward gray market goods. International Marketing Review, 21(6), 598-614.

Hunt, S. D., & Vitell, S. (1986). A general theory of marketing ethics. Journal of Macromarketing, 8(1), 5-16.

Jones, T. M. (1991). Ethical decision making by indi- viduals in organizations: An issue-contingent model. Academy of Management Review, 16(2), 366-395.

Kini, R. B., Ramakrishna, H. V., & Vijayaraman, B. S. (2004). Shaping of moral intensity regarding soft- ware piracy: A comparison between Thailand and U.S. students. Journal of Business Ethics, 49(1), 91-104.

Kohlberg, L. (1984). The philosophy of moral devel- opment: Essays on moral development. San Francisco: Harper & Row.

Lai, K. K.- Y., & Zaichkowsky, J. L. (1999). Brand imitation: Do the Chinese have different views? Asia Pacific Journal of Management, 16(2), 179-192.

Loken, B., Ross, I., & Hinkle, R. L. (1986). Consumer confusion of origin and brand similarity percep- tions. Journal of Public Policy and Marketing, 5(1), 195-211.

May, D. R., & Pauli, K. P. (2002). The role of moral intensity in ethical decision making. Business & Society, 41(1), 84-117.

McDonald, G., & Roberts, C. (1994). Product piracy: The problem that will not go away. Journal of Product & Brand Management, 3(4), 55-65.

Mitchell, A. (1998). Customer rights a gray area in distribution ban. Marketing Week, 21(21), 30-31.

Monin, B., Pizarro, D. A., & Beer, J. (2007). Deciding versus reacting: Conceptions of moral judgment and the reason–affect debate. Review of General Psychology, 11(2), 99-111.

Moores, T. T., & Chang, J. C. (2006). Ethical decision making in software piracy: Initial development and test of a four-component model. Management Information Systems Quarterly, 30(1), 167-180.

Morris, S. A., & McDonald, R. A. (1995). The role of moral intensity in moral judgments: An empirical

at CALIFORNIA ST UNIV NORTHRIDGE on November 7, 2015ctr.sagepub.comDownloaded from

investigation. Journal of Business Ethics, 14(9), 715-726.

Muncy, J. A., & Vitell, S. J. (1992). Consumer ethics: An investigation of the ethical beliefs of the final consumer. Journal of Business Research, 24(4), 297-311.

Myers, M. (1999). Incidents of gray market activity among U.S. exporters: Occurrences, characteris- tics and consequences. Journal of International Business Studies, 30(1), 105-126.

Nowlis, S. M., Khan, B. E., & Dhar, R. (2002). Coping with ambivalence: The effect of removing a neutral option on consumer attitude and prefer- ence judgments. Journal of Consumer Research, 29(3), 319-334.

Rest, J. R. (1979). Development in judging moral issues. Minneapolis: University of Minnesota Press.

Rest, J. R. (1986). Moral development: Advances in research and theory. New York: Praeger.

Robin, D. P., Reidenbach, R. E., & Forrest, P. J. (1996). The perceived importance of an ethical issue as an influence on the ethical decision-making of ad managers. Journal of Business Research, 35(1), 17-28.

Rothnie, W. A. (1993). Parallel imports. London: Sweet & Maxwell.

Schnaars, S. P. (1994). Managing imitation strategies: How later entrants seize market share from pio- neers. New York: Free Press.

Singer, M. S. (1996). The role of moral intensity and fairness perception in judgments of ethicality: A comparison of managerial professionals and the general public. Journal of Business Ethics, 15(4), 469-474.

Singer, M. S., & Singer, A. E. (1997). Observer judg- ments about moral agent’s ethical decisions: The role of scope of justice and moral intensity. Journal of Business Ethics, 16(5), 473-484.

Sobel, M. E. (1982). Asymptotic intervals for indirect effects in structural equations models. In S. Leinhart (Ed.), Sociological methodology 1982 (pp. 290-312). San Francisco: Jossey-Bass.

Stevens, J. (1996). Applied multivariate statistics for the social sciences. Mahwah, NJ: Lawrence Erlbaum.

Tan, B. (2002). Understanding consumer ethical deci- sion making with respect to purchase of pirated software. Journal of Consumer Marketing, 19(2), 96-111.

Tangney, J. P. (1990). Assessing individual differences in proneness to shame and guilt: Development of the self-conscious affect and attribution inventory.

Journal of Personality and Social Psychology, 59(1), 102-111.

Tangney, J. P., & Dearing, R. L. (2002). Shame and guilt. New York: Guilford.

Tangney, J. P., Stuewig, J., & Mashek, D. J. (2007). Moral emotions and moral behavior. Annual Review of Psychology, 58, 345-372.

Tangney, J. P., Wagner, P., & Gramzow, R. (1992). Proneness to shame, proneness to guilt, and psy- chopathology. Journal of Abnormal Psychology, 101(3), 469-478.

Tom, G., Garibaldi, B., Zeng, Y., & Pilcher, J. (1998). Consumer demand for counterfeit goods. Psychology & Marketing, 15(5), 405-421.

Trevino, L. K. (1986). Ethical decision making in organ- izations: A person–situation interactionist model. Academy of Management Review, 11(3), 601-617.

United States Customs and Border Protection. (2007, January 11). Counterfeit goods seizures up 83% in FY 2006. Retrieved January 12, 2007, from http:// w w w. c b p . g o v / x p / c g o v / n e w s r o o m / n e w s _ releases/01112007_1.xml

Wada, T. (1996). Brand name demand brings more fakes. The Nikkei Weekly, 32(1), 742.

Wagner, S. C., & Sanders, G. L. (2001). Considerations in ethical decision-making and software piracy. Journal of Business Ethics, 29(1/2), 161-167.

Wee, C., Tan, S., & Cheok, K. (1995). Non-price deter- minants of intention to purchase counterfeit goods. International Marketing Review, 12(6), 19-46.

Jae-Eun Kim is a doctoral candidate in the Design, Housing and Apparel Graduate Program at the University of Minnesota. Her research interests include consumer behavior, retail environments, and cross-cultural studies.

Hyeon Jeong Cho is a doctoral student in the Retail Merchandising Program in the College of Human Environmental Sciences at Oklahoma State University. Her research interests include interna- tional retailing, cross-cultural consumer behavior, and retail innovation.

Kim K. P. Johnson, PhD, is a professor in the Retail Merchandising Program in the College of Design at the University of Minnesota. Her research interests include social psychological aspects of appearance and consumer behavior.

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