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Journal of International Consumer Marketing
ISSN: 0896-1530 (Print) 1528-7068 (Online) Journal homepage: http://www.tandfonline.com/loi/wicm20
Voluntary Purchase of Counterfeit Products:Empirical Evidence From Four Countries
Elfriede Penz , Bodo B. Schlegelmilch & Barbara Stttinger
To cite this article: Elfriede Penz , Bodo B. Schlegelmilch & Barbara Stttinger (2008) VoluntaryPurchase of Counterfeit Products: Empirical Evidence From Four Countries, Journal ofInternational Consumer Marketing, 21:1, 67-84
To link to this article: http://dx.doi.org/10.1080/08961530802125456
Published online: 02 Jan 2009.
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Journal of International Consumer Marketing, 21:6784, 2009Copyright c Taylor & Francis Group, LLCISSN: 0896-1530 print / 1528-7068 onlineDOI: 10.1080/08961530802125456
Voluntary Purchase of Counterfeit Products:Empirical Evidence From Four Countries
Elfriede PenzBodo B. Schlegelmilch
Barbara Stottinger
ABSTRACT. Counterfeiting, i.e., producing and selling copies of branded products that stronglyresemble the original, has emerged as a major problem for global marketers, rising dramatically overthe past several years. Surprisingly, factors driving the demand for fake products have attracted far lessattention than supply-side remedies. This paper attempts to redress this imbalance by investigating thedemand for counterfeits. Specifically, we consolidate existing findings and develop a comprehensiveyet parsimonious model of the antecedents and drivers of voluntary counterfeit purchases. Followingthe frequent call for more cross-national comparisons, we used a sample of over 900 consumers fromSlovenia, the Czech Republic, Austria, and Mexico to test our model. This large-scale approach callsexisting findings into question and raises provocative thoughts guiding future research in the area.
KEYWORDS. Counterfeits, demand, cross-national comparison, international marketing research,remedies against counterfeits
Counterfeiting has emerged as a majorproblem for global marketers. It has risendramatically over the past several years andnow represents some five to eight percent oftotal world trade (Balfour, 2005; Freedman,1999; Hunt, 2002; IACC, 2002). Expertsdescribe counterfeiting nowadays as moreprofitable and less risky than drug trafficking(European Commission, 2000; Huband, 2004).While counterfeiters benefit from almostno investments in brand-name recognitionand research and development (Harvey andRonkainen, 1985), the original manufacturersuffers from tremendous damage to its brandreputation and profits through the sale of fakeproducts (Green and Smith, 2002; Kay, 1990;Nash, 1989; Wee, Tan, and Cheok, 1995).
Elfriede Penz, Bodo B. Schlegelmilch and Barbara Stottinger are all affiliated with the Institute forInternational Marketing & Management, Wirtschaftsuniverstat Wien, Vienna, Austria.
Address correspondence to Elfriede Penz, Institute for International Marketing & Management,Wirtschaftsuniversitat Wien, Augasse 2-6, 1090 Vienna, Austria. E-mail: [email protected]
Most efforts to mitigate counterfeits aredirected toward limiting the supply. To detercounterfeiters, companies resort to technologicaldevices such as special inks and dyes orultraviolet and electronic signatures. However,each new measure only appears to be a pawnin the battle against time (Sasser, 1990; Saywelland McManus, 2002; Shultz and Saporito,1996). Legal actions against counterfeitersare also costly. Moreover, the outcomes areuncertain and enforcement is difficult (Blakeney,1995; Chaudry and Walsh, 1996; Harvey, 1987;Keats and Joyner, 1985; Nill and Shultz, 1996;Sandler, 1994; Saywell and McManus, 2002).As long as the demand is thriving, counterfeiterswill always find new ways to serve customers(Albers-Miller, 1999; Ang et al., 2001). It
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appears necessary, therefore, to focus more onthe demand side for counterfeits. In contrastto supply-side deterrents, demand-side drivershave attracted little attention. To redress this,we focus on three issues.
First, we attempt to consolidate contributionsfrom different fields. Past research has lookedat perceived price benefits (e.g., Albers-Miller,1999; Bloch, Bush, and Campbell, 1993), psy-chographic characteristics (e.g., Cordell, Wong-tada, and Kieschnick, 1996; Swinyard, Rinne,and Keng Kau, 1990; Wee, Tan, and Cheok,1995), product characteristics (e.g., Cordell,Wongtada, and Kieschnick, 1996; Wee, Tan,and Cheok, 1995), demographic variables (e.g.,Solomon & OBrien, 1991) and social influ-ences (e.g., Ang et al., 2001). Notwithstandingtheir merits, a comprehensive understanding ofdemand-side drivers has not yet been achieved,as much of the existing work relies on studentsamples and is scattered across different dis-ciplines. Consequently, we attempt to reassessthe validity of the existing findings in terms ofpredicting the demand for counterfeits in a morerealistic empirical setting.
Second, we revisit the role of prices incounterfeit purchases. Although it is widelyacknowledged that the low price of counterfeitsis the key driver of demand (Ang et al., 2001;Bloch, Bush, and Campbell, 1993; Rangoni-Machiavelli, 1999; Tom et al., 1998; Wee, Tan,and Cheok, 1995), the issue of price sensitivitymerits further investigation. This is evidenced bythe recent emergence of so called super copies,i.e., replicas that are of equivalent or even betterquality than the original, say a $500 LouisVuitton handbag. Such super copies are reportedto sell like hotcakes despite offering only asmall discount versus the original (EuropeanCommission, 2000; Kattoulas, 2002).
Finally, since counterfeiting is a global phe-nomenon, it calls for global solutions, for exam-ple, global communication strategies. These areonly feasible if consumers worldwide are drivenby similar factors when buying counterfeits.To date, research on fakes has mostly reliedon single-country settings, which limits thescope for generalization (Fullerton and Punj,1997; Husted, 2000). Therefore, we re-assessthe applicability of existing research findings inmore than one country.
Taken collectively, we attempt to take afresh look at the demand side of counterfeiting.Specifically, we aim to consolidate existing find-ings and assess their usefulness by developingand testing a comprehensive yet parsimoniousmodel of the antecedents and drivers of vol-untary counterfeit purchases. Following the fre-quent call for more cross-national comparisons(Brislin, Lonner, and Thorndike, 1973; Kline,1988; Verma and Mallick, 1988), we use asample of four countries to test our model.
BACKGROUND
The literature distinguishes two types ofcounterfeiting, namely deceptive and nonde-ceptive counterfeiting (Cordell, Wongtada, andKieschnick, 1996; Grossman and Shapiro, 1988;Nia and Zaichkowsky, 2000). In the case ofdeceptive counterfeiting, consumers do not re-alize that they are buying a counterfeit product.Thus, such counterfeits can only be deterredthrough supply-side measures. However, con-sumers often willfully buy a counterfeit good:a so-called nondeceptive counterfeit (Grossmanand Shapiro, 1988; Nia and Zaichkowsky, 2000;Phau and Prendergast, 1998). While supply-sidedeterrents may still be helpful in this context,the active role of the consumer as accomplice(Bloch, Bush, and Campbell, 1993) calls fordemand-side counteractions.
To gain a better understanding of what drivesconsumers to buy fake products, we refer totwo streams of literature to explain purchasesof nondeceptive counterfeits: First, we evokethe branding literature. If branded productsdid not attract consumers, counterfeits wouldnot be an issue (Bloch, Bush, and Camp-bell, 1993; Cordell, Wongtada, and Kieschnick,1996). Buying fake products means getting theprestige of branded products without paying forit (Cordell, Wongtada, and Kieschnick, 1996;Grossman and Shapiro, 1988). Second, we drawon consumer misbehavior research, which usesthe purchase of counterfeits as one of its classicexamples (Albers-Miller, 1999; Fullerton andPunj, 2004; Green and Smith, 2002). From thisresearch, we conclude that consumer awarenessof the negative consequences of counterfeits mayimpact the demand for fake products. Below, weoutline the conceptual framework used in this
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paper and develop specific hypotheses regardingthe determinants of counterfeit purchases.
CONCEPTUAL FRAMEWORK ANDHYPOTHESES
We rely on the theory of reasoned action(TRA) (Fishbein and Ajzen, 1980) for guidancein systematizing existing findings and addingvariables. TRA states that behavior, such asthe purchase of counterfeits, is determined bythe intention to engage in such behavior. Theintention, on the other hand, is influenced by theattitude toward the behavior and the subjectivenorm (Eagly and Chaiken, 1993; Fishbein andAjzen, 1980). We measure attitudes towardbehavior rather than attitude toward objects (e.g.,attitude toward a counterfeit item), as the formerare said to be better predictors of behavior(Fishbein, 1967; Fishbein and Ajzen, 1975). Fi-nally, the subjective norm is deemed to representperceptions of preferences held by significantothers about whether a person should engage ina behavior (i.e., purchasing counterfeits) (Eaglyand Chaiken, 1993; Fishbein and Ajzen, 1980).In the following, the theoretical constructs andhypothesized relationships between the mea-sures are outlined in more detail.
Determinants of Voluntary CounterfeitPurchasesAnti-Big Business Attitude and Efficiencyof Counterfeiters
To reduce cognitive dissonance, i.e., themental discomforts caused by e.g., misbehavior(Clow, Kurtz, and Ozment, 1998; Festinger,1957), consumers frequently use pseudorationalexcuses and deflect the blame to another party(Fullerton and Punj, 2004; Sykes and Matza,1957). As such, consumers tend to deny theirresponsibility and play down the damages tothe victims, even blame them for their be-havior or appeal to higher loyalties. As pastresearch shows, consumers justify the purchaseof counterfeits through feelings of sympathyfor small rather than large businesses basedon the impression of large companies beingimpersonal, overwhelming, and socially distant(Fullerton and Punj, 1993; Moore, 1984; Tom
et al., 1998). A similar argument claims thatcounterfeiters deserve support, as they are moreefficient in terms of how they conduct businessand are more customer-oriented in charginglower margins than the original manufacturers(Ang et al., 2001; Tom et al., 1998; Wee, Tan, andCheok, 1995). Both arguments serve as rational-izations for consumer misbehavior and reducecognitive dissonance by intensifying positiveinformation about counterfeiters (Andersson,2004). Consequently, it is proposed that:
H1: The more negative consumers at-titudes toward big business are, thestronger their intention to purchase coun-terfeits.H2: The more efficient counterfeiters areperceived to be, the stronger consumersintention to purchase counterfeits.
Negative Effects on R&DOne issue that influences the level of accep-
tance of consumer misbehavior is the impact onthe victim (Muncy and Vitell, 1992). In this con-text, Dodge, Edge, and Fullerton (1996) pointedout that specific adverse economic effects on theproducer would lead to stronger condemnationof misbehavior than more general ones (Fuller-ton and Punj, 1997). As the literature stronglypoints out the chilling effect of counterfeitson technology development and firms researchand development (R&D) expenditure (Jacobs,Samli, and Jedlik, 2001; Nill and Shultz, 1996;Wilke and Zaichkowsky, 1999), we suggest that:
H3: The more consumers are aware of thenegative impact of counterfeiting on R&D,the weaker their intention to purchasecounterfeits.
Price/Value Relationship
Previous research stresses the favorableprice/value relationship as the major incentivefor the purchase of counterfeits. Bloch, Bush,and Campbell (1993, p. 31) states that peo-ple buy counterfeits, because they are gettingprestige without paying for it. This price/valuerelationship works in two ways: Consumersmay think of themselves as smart shoppers,
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as counterfeits are comparable in quality andperformance to the original, but superior in price.Alternatively, consumers may view counterfeitsas inferior to the original in terms of qualityand performance, but think the price advan-tage more than compensates for any shortfalls(Albers-Miller, 1999; Ang et al., 2001; Cordell,Wongtada, and Kieschnick, 1996; Tom et al.,1998). Thus, we hypothesize:
H4: The more positive the price/valuerelationship of counterfeits is perceived,the stronger the intention to purchasecounterfeits.
Embarrassment Potential
Branded products are used to improve theself-concept through the transfer of attributedmeanings and thus the enrichment of self-value. This requires interaction with others, whodeliver meaning to brands (Aaker, 1999; Hogg,Cox, and Keeling, 2000; Keller, 1993). Peoplewho buy branded products may be describedas self-conscious, especially concerned aboutthe impression they make and more sensitiveto interpersonal rejection (Bushman, 1993; Niaand Zaichkowsky, 2000). When making a goodimpression and looking good are importantvalues, consumers who buy counterfeits run aninherent risk. Flaws in the physical appearanceof the counterfeit products may lead to beingdetected as the user of a copy rather than theoriginal. Their friends or peers might make funof them or look down on them for not acquiringthe original product (e.g., Ang et al., 2001).Therefore, it is anticipated that:
H5: The stronger the perceived embarrass-ment potential of counterfeits, the weakerthe intention to purchase counterfeits.
AntecedentsReadiness to Take Risk
The perceived risk of purchase decisions isa central construct in marketing, as consumerscannot always be certain that their goals willbe satisfied through a purchase. The risksincurred are manifold, ranging from financial,
performance, and physical, to psychological andsocial risks (Cordell, Wongtada, and Kieschnick,1996; Cox, 1967; Tan, 2002). However, whilerisk may have a positive connotation, i.e., whenconsumers want to be stimulated and entertaineddue to a sensation-seeking personality (Fullertonand Punj, 1998; Hoyer and McInnis, 2004), weexpect a negative connotation of risk in thecase of counterfeits. Buying counterfeits maybe considered risky in the light of the amountof money lost through malfunction, other qual-ity deficiencies, or due to embarrassment andadverse reactions from the social environment(Ang et al., 2001; Wee, Tan, and Cheok, 1995).Therefore, readiness to take risks was used as anantecedent to the perceived price/value relation-ship. Accordingly, the following hypotheses areformulated:
H6a: Readiness to take risks has a posi-tive impact on the perceived price-/valuerelationship of counterfeits.H6b: Readiness to take risks has a negativeimpact on the perceived embarrassmentpotential of counterfeit goods.
Understanding attitude formation towardcounterfeits would be incomplete withoutconsideration of demographic characteristics(Fullerton, Kerch, and Dodge, 1996). For ex-ample, Solomon and OBrien (1991) as wellas Tan (2002) found that demographics suchas age or educational background are correlatedwith consumers attitude toward counterfeit soft-ware. Other studies, however, show more mixedfindings ranging from generally strong impactto context dependent impact on purchaseintentions (e.g., Tom et al., 1998; Wee, Tan,and Cheok, 1995), or no effect at all (e.g.,Ang et al., 2001). To this end, no clear pictureof how demographic characteristics make animpact emerges. To give demographics adequateconsideration, we decided to phrase the follow-ing hypotheses nondirectionally, supposing bothindirect (on attitudes toward counterfeiting) anddirect effects on purchase intentions:
H7a: Age has an effect on attitudes to-ward counterfeiting (on purchase inten-tions, H7a).
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H7b: Education has an effect on attitudestoward counterfeiting (on purchase inten-tions, H7b).H7c: Income has an effect on attitudestoward counterfeiting (on purchase inten-tions, H7c).
Moreover, knowledge about counterfeiting isdeemed to have an influence on attitudes towardfake products and counterfeit purchase (Gentry,Putrevu, and Shultz, 2006). Attitudes are formedand modified as information is received, inter-preted, and integrated into prior attitudes on acertain object. The literature on information pro-cessing indicates that information with a positivecontent enforces positive attitudes toward anobject, while negative information strengthensnegative attitudes toward an object (Eagly andChaiken, 1993). Therefore, we predict that:
H7d: More knowledge about the negativeeffects of counterfeiting leads to strongernegative attitudes toward counterfeiting(lower purchase intention, H7d).
Moderating VariablesCountry Background
Counterfeits find buyers around the globe.Consequently, one might assume that the factorsleading to the purchase of counterfeits will beuniversal. However, the literature suggests thecontrary and points to country-specific influ-ences rooted in different cultural values, legalnorms, ethical codes, personal experiences, andeconomic welfare (e.g., Fullerton and Punj,1997; Husted, 2000). Husted (2000) investigatedwhich country-specific factors would impingeon the demand for counterfeit products (piratedsoftware in his case). Empirically, he establishedthree factors as important drivers: GNP (grossnational product) per capita, distribution ofincome among the population, and the positionof a country on Hofstedes (1998) individualismscale. In testing our theoretical model, weassume that the overall model structure will notdeviate between countries, but expect the rela-tionships between constructs to be moderated bythe specific country background.
Price Discount toward the Original Product
Undisputedly, the favorable price/value rela-tionship is a key determinant in the demand forfake products (e.g., Ang et al., 2001; Bloch,Bush, and Campbell, 1993; Tom et al., 1998).The price discount that counterfeits offer com-pared to the original product represents a sub-stantial benefit. Still, consumers are quite diversein how much attention they pay to price and howand when they react to price discounts (Dicksonand Sawyer, 1990). For instance, the same fakeLouis Vuitton bag is available at different pricelevels ranging from very inexpensive copies tothose with a price close to the original. Tocomprehensively gauge the effect of pricing onthe demand for counterfeits, it is not enough toevaluate the overall relevance of the favorableprice/value relationship of counterfeits (cf. H4),but also the issue of price elasticity. How demandreacts to changes in the price level helps toestablish whether supply-side activities aimedat increasing production costs for counterfeiters,and consequently prices for consumers, actuallyhave an effect on the demand. To investigate theimpact of price elasticity, we look at how fivedifferent price levels moderate the intention topurchase fake products.
Figure 1 shows the conceptual model andsummarizes the hypotheses.
METHODOLOGY
Sample and Data CollectionWhile traditionally countries in the Far East,
such as China, Pakistan, or Indonesia, wereheld accountable for the largest number ofcounterfeits, producing fake products has nowbecome a global business. Many companies haveto fight counterfeiters in their backyard. Out ofthe counterfeit products seized in Germany in1999, a large part came from Eastern Europe,particularly the Czech Republic and Turkey(European Commission, 2000). In addition, thetrade with counterfeit products is exacerbatedthrough the emergence of the Internet. Out of anestimated 20,000 Web sites selling luxury goods,10 to 20 percent are promoting counterfeits.
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FIGURE 1. Conceptual Model
Anti-Big Business
Efficiency
Intention toPurchase CF
Negative Effects on R&D
Embarrassment Potential
Risk Readiness
Age
Education
Income
Knowledge
Price/Value Relationship
Attitudes towards Counterfeiting
Subjective Norm
Intention
H1
H2
H3
H5
H4
H7b
H7c
H7d
H6a
H6b
H7a
External Factors:Sociodemographic and Personal
Variables
Price Level of CF (20%, 40%, 60%, 80% and 90% below Price of Original Product)Product Type (Polo/Lacoste versus Cartier/Rolex)
H7b'
H7c'
H7a'
H7d'
Unsurprisingly, curbing Internet-based counter-feiting is even more tedious than fighting coun-terfeits in conventional trade channels (Euro-pean Commission, 2000; Freedman, 1999).
The same holds true for the demandfor counterfeits: Counterfeits from across theglobe find interested consumers globally. Interms of actual country selection, we usedthe criteria outlined by Husted (2000) andtried to reach a certain geographic spread ofcountries.
Specifically, we conducted a survey in fourdifferent countries, namely Austria (AT), Mex-ico (MEX), Slovenia (SLO), and the Czech Re-public (CZ). These countries share similaritiesin terms of availability and access to counterfeitproducts, the desire for branded products, etc.,but display differences in terms of cultural andeconomic background. With a gross domesticproduct (GDP) per capita of $30,000 (adjustedfor purchasing power parity, or PPP) and afairly equal distribution of family income (Gini
index: 31), Austria is the wealthiest among thecountries selected. In terms of GDP per capita,both former Eastern Bloc countries, Sloveniaand the Czech Republic, score comparativelylower but display similar economic welfarestructures (GDP per capita: $19,000 [SLO]versus $15,700 [CZ]; Gini-Index: 28.4 [SLO]versus 25.4 [CZ]). Mexico, in turn, differssubstantially in economic terms from the otherthree countries. GDP per capita amounts to$9,000, and household income is distributedunequally among the population (Gini index:53.1) (CIA, 2004).
Data were gathered by means of a writtenquestionnaire survey with the help of specifi-cally trained research assistants in the differentcountries. All respondents were assured fullanonymity. The questionnaire was based onthe pertinent literature and expert interviews inthe countries under investigation. After pretest-ing and checking for content validity of themeasures, the final questionnaire was made
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available in German, Czech, Slovenian, andSpanish. Linguistic equivalence between thedifferent versions was established through back-translation (Brislin, 1970). Given that to date, amodel of this complexity has not been developedand tested in such a demanding environment,convenience sampling appeared acceptable ineach country except for Austria, where a quotasample could be used (based on age, gender,and education). In the other countries, question-naires were randomly distributed to visitors ofshopping malls in major cities and collected im-mediately on site after completion. For Mexico,this sampling procedure appeared less useful.In line with Brislin and Baumgardner (1971),we endeavored to maintain cross-cultural com-parability among respondents regarding thesampling characteristics of age, gender, andeducation. To secure comparability particularlywith respect to education, we chose a differentsampling procedure in Mexico, namely within amultinational corporation (MNC). Specific carewas taken that this MNC displayed a reason-able distribution of economic welfare amongrespondents.
In total, 940 questionnaires were returned andused for further analysis. The Austrian question-naire was completed by 385 respondents, whichrepresents 38.4 percent of the total sample.This is followed by the Czech sample, whichconsists of 242 respondents (25.5 percent), andthe Slovenian sample, which comprises 195respondents (20.5 percent). Finally, 118 Mex-ican respondents completed the questionnaire(12.4 percent). In terms of demographics (genderand age), the samples show a high degree ofsimilarity. With regard to education, the majorityof Austrian and Slovenian respondents havecompleted a vocational or secondary school,while the Czech and the Mexican sample showa higher level of education.
Measurement
Consumers attitudes toward counterfeitingare investigated through the use of five-point,multi-item Likert scales. The measurement ap-proach for each theoretical construct will nowbe described in more detail.
Intention
We operationalized this construct by usingtwo product categories of luxury brands, specifi-cally Rolex/Cartier watches and Polo/Lacoste T-shirts, which are particularly sought-after fakesand appear in a variety of different qualitylevels. To take this variation into account yetnot confuse respondents, they were advised tothink of counterfeits as products that look exactlythe same as the original branded product. Inaddition, we examined the intention to purchasecounterfeits as a function of price (Bloch, Bush,and Campbell, 1993). Taking that into account,the reaction to five different price levels wasmeasured (20, 40, 60, 80, and 90 percent belowthe price of the original items). Thus, with 2brand choices and 5 price reductions versus theoriginal, a total of 10 items were developed tomeasure the intention to purchase counterfeits.Attitudes
Fishbein (1967) as well as Fishbein and Ajzen(1975) state that attitudes toward behavior arebetter predictors of behavior than are attitudetoward objects (e.g., attitude toward a counterfeititem). In accordance with their view and basedon the literature, 11 items were developed tomeasures consumers attitudes toward counter-feiting and purchasing counterfeit luxury brands.Embarrassment Potential
Consumers beliefs about whether significantothers think they should engage in the behavior(e.g., wearing counterfeit clothes) have beenfound important (Ang et al., 2001; Bushman,1993; Nia and Zaichkowsky, 2000). Fishbein andAjzen (1975) proposed including the subjectivenorm in attitude models in order to provide anadditional determinant of the intention. In ourstudy, we operationalized the subjective normconstruct by asking respondents to assess thereactions of others to their own (hypothetical)behavior, namely wearing and purchasing coun-terfeits. Two statements were included in thequestionnaire.Knowledge
Consumers knowledge about products andtheir consequences influence their perceptions
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and preferences (see also Alba and Hutchin-son, 1987; Brucks, 1985; Moreau, Lehmann,and Markman, 2001; Park, Mothersbaugh, andFeick, 1994). Consequently, the questionnaireincluded four newly developed items that testwhether consumers are aware of the economicand societal problems associated with counter-feits.
Readiness to Take Risks
To measure readiness to take risks, a slightlymodified Risk Taker (Purchase) scale wasapplied. This scale consists of six items andmeasures a persons reported willingness to takea risk by, for example, trying unfamiliar productsor brands (Raju, 1980).Product Category
Past research assumed that consumers pur-chasing fake products differ by product type(Bloch, Bush, and Campbell, 1993; Tomet al., 1998) and has used different productcategories to investigate the demand for counter-feits (e.g., Cordell, Wongtada, and Kieschnick,1996; Tom et al., 1998; Wee, Tan, and Cheok,1995). Along these lines, we selected twoproduct categories with a high fashion appeal:textiles (Polo/Lacoste) and luxury watches(Cartier/Rolex). In both cases, appearanceand visibility are salient product features. Inaddition, watches convey a certain risk of mal-function, which is less important with textiles(Nia and Zaichkowsky, 2000).
ANALYSIS AND RESULTS
Multi-item scales were used in our struc-tural equation modeling. Employing Amos 5(Arbuckle and Wothke, 2003) for analysis,the objective was to assess the cross-nationalapplicability of the developed scales and, subse-quently, to test our hypotheses by estimating thestructural models.
Assessing Measurement Invariance
In assessing measurement invariance, wefollow Steenkamp and Baumgartner (1998),who propose the multigroup confirmatory factor
analysis (CFA) model as the most powerfuland versatile approach. Table 1 summarizes andcompares the models derived in the course of theinvariance measurement process.
First, a test of equality of variance matricesand mean vectors was employed, showing thatcovariances and means are not invariant acrosscountries (multigroup CFA). Thus, we couldestablish that a separate country analysis isrequired. Configural invariance was examinednext. Overall, the five-factor structure of theproposed model seems to be invariant across thefour countries, and the factor structure is similaracross countries.
Next, a test for full metric invariance wasapplied to check further whether cross-nationalconsumer responses to various scale items canbe meaningfully compared. Contrasting the fitstatistics of the full metric invariance modelwith the baseline model (configural invariancemodel), it can be seen that there is a significantdecrease in 2. Based on this analysis, validcomparisons of latent means across countries areallowed. In addition, factor covariance and factorvariance were assessed. In this way, we addressthe issues of discriminant validity (covariances)and homogeneity of factor scores in the pop-ulation (variances). Relative to the configuralinvariance model, 2 decreased significantlyand therefore we assume factor covariance andfactor variance in our data. Finally, error varianceinvariance was established (see Table 1). Afterthese methodological tests, we can concludethat the overall model structure holds in allcountries and can therefore be used for furthercross-country comparisons.
Intention to Purchase Counterfeitsat Various Price Levels
In a subsequent step, a two-factorial anal-ysis of variance with repeated measures wasapplied in order to assess the statistical sig-nificance of differences between countries andprice levels. The analysis used the intention topurchase counterfeit products (Polo/Lacoste andCartier/Rolex) as dependent variables, countries(Austria, Slovenia, Czech Republic, and Mex-ico) as independent variables, and different pricelevels (20, 40, 60, 80, and 90 percent) as repeated
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TABLE 1. Multi-Group Analysis of the Attitude Toward Counterfeits Measure(Five-Factor Model)
2 df RMSEA CFI TLI
Equality of Covariance and Means 1,007.13 292 0.05 0.71 0.64Equality of Covariance 761.81 277 0.04 0.80 0.74Equality of Means 835.00 273 0.05 0.77 0.70Configural Invariance 940.85 237 0.06 0.72 0.56Metric Invariance 723.93 245 0.05 0.75 0.69Scalar Invariance 724.39 248 0.05 0.72 0.81Factor Covariance Invariance 765.94 253 0.05 0.79 0.70Factor Variance Invariance 742.56 242 0.05 0.80 0.70Error Variance Invariance 904.78 260 0.05 0.74 0.64
measures. Mauchlys test of sphericity indicatesthat the multivariate analysis of variance isappropriate for the data analysis, based on theassumption that the dependent variables arecorrelated (Polo/Lacoste: 2 = 1772.13 with 9df; p < 0.001 and Cartier/Rolex: 2 = 1988.65with 9 df; p < 0.001). A significant overallmain effect was found for the four countries, theprice levels, and the product types (p < 0.001).In addition, there is a significant interactionbetween countries and price levels with bothproduct types, indicating that the differencesin intention to purchase counterfeits at variousprice levels are country-specific.
Structural ModelsHaving satisfied the various measurement
issues, the structural models were estimated.Based on the results discussed above, differ-ent models were considered in the followingmultigroup analyses (SLO, AT, MEX, CZ), eachdealing with a product type (Polo/Lacoste andCartier/Rolex) at a specific price level (20, 40,60, 80 and 90 percent). For space reasons, Tables2 and 3 only display the results of the models thatconsider the intention to purchase counterfeitsat the lowest and the highest percentage beloworiginal price. Based on the goodness-of-fitstatistics, the models are found acceptable.
FindingsPast research has stressed that price is a key
driver of the demand for fake products. Indeed,
the perceived, favorable price/value relationshipof counterfeits proved to have a strong impact onthe intention to purchase, except for Mexicans.This effect increases, as the price gap betweenoriginal and fake product widens. The higher theprice discount and thus the more favorable theperceived price/value relationship, the more con-sumers are lured into buying fakes. These find-ings are not unexpected. However, completelysurprising is that all other attitudes towardcounterfeiting, which were reported influentialin previous studies, did not affect the intentionsto buy fakes. Anti-Big Business sentiments,the perceived efficiency of counterfeiters, andthe embarrassment potential of fake productsshowed only sporadic impact. The attitudestoward the negative effects of counterfeiting onR&D did not affect the intention to purchasefake products at all. In addition, opinions didnot change in substance or magnitude with pricevariations. These findings clearly contradictresults from previous studies.
In past research, demographic characteristicshave also been linked to intentions to purchasecounterfeits. As past findings were mixed, wetested for a direct link as well as for an indirectlink through the attitudes toward counterfeitingon intention. Supporters of counterfeiting oftenmaintain that original manufacturers exaggeratetheir losses in sales through fake products,as purchasers of the copy would not be ableto afford the original anyway. Following thisargument, it would be the less wealthy whorevert to counterfeits. Again, our results do notsupport this contention. As it turns out, income
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5
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TABL
E2.
Resu
ltsofP
ath
Anal
ysis
Polo
/Lac
oste
1
Pric
e20
%be
low
Orig
inal
Pric
ePr
ice
90%
belo
wO
rigin
alPr
ice
Hyp
othe
size
dpa
thAu
stria
Slov
en
iaM
exic
oCz
ech
Rep
.Au
stria
Slov
en
iaM
exic
oCz
ech
Rep
.
Endo
geno
usva
riabl
eson
Inte
ntio
n
C.R.
C.
R.
C.R.
C.
R.
C.R.
C.
R.
C.R.
C.
R.
Pric
e/Va
lue
Rel
atio
nshi
p2>
Inte
ntio
n0
.65
6.3
40
.36
3.2
80
.27
1.4
50
.35
4.0
61
.09
8.8
71
.13
8.4
80
.43
1.9
01
.34
12.
75
Neg
ative
Effe
cts
on
R&D
>
Inte
ntio
n0
0.0
20.
060.
470.
141.
160.
020.
290.
171.
840.
151.
060
.08
0.5
40
.04
0.4
5
Anti-
Big
Busi
ness
>In
tent
ion
0.26
1.37
0.37
1.68
0.53
2.65
0.0
30
.14
0.03
0.15
0.00
0.01
0.42
1.69
0.52
2.22
Effic
ienc
y>
Inte
ntio
n0.
312.
940.
472.
30
.03
0.2
10.
345.
480.
241.
970.
331.
430.
160.
950
.09
1.3
3Em
barra
ssm
en
t>In
tent
ion
0.0
30
.45
0.2
32
.54
0.0
80
.67
0.0
80
.92
0.03
0.37
0.25
2.45
0.00
0.00
0.00
0.0
4
Exog
enou
sva
riabl
eson
Inte
ntio
n
C.R.
C.
R.
C.R.
C.
R.
C.R.
C.
R.
C.R.
C.
R.
Inco
me
>In
tent
ion
0.07
1.02
0.13
1.69
0.1
42
.24
0.11
2.22
0.05
0.55
0.0
90
.98
0.1
31
.69
0.04
0.63
Educ
atio
n>
Inte
ntio
n0
.06
0.8
30
.19
1.8
20.
152.
030
.01
0.1
50.
070.
820.
060.
470.
111.
230
.12
1.8
3Ag
e>
Inte
ntio
n0
.12
.02
0.1
21
.55
0.02
0.16
0.0
91
.34
0.1
32
.25
0.0
70
.75
0.3
22
.24
0.1
82
.15
Know
ledg
e>
Inte
ntio
n0.
692.
190
.03
0.0
70
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1.2
50.
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621.
770.
200.
390
.62
1.1
51.
433.
00
Endo
geno
us/E
xoge
nous
varia
bles
onAt
titud
es
C.R.
C.
R.
C.R.
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R.
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R.
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R.
Age
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ti-Bi
gBu
sine
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590
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10.
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072.
610.
081.
540
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10.
154.
21Ed
ucat
ion
>An
ti-Bi
gBu
sine
ss0
.11
2.7
90
.17
2.4
20
.13
2.2
10
.03
0.8
90
.11
2.8
50
.17
2.5
30
.14
2.3
20
.03
0.9
1In
com
e>
Anti-
Big
Busi
ness
0.0
10
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0.03
0.47
0.07
1.41
0.03
1.04
0.0
20
.38
0.02
0.44
0.07
1.39
0.03
1.03
Know
ledg
e>
Anti-
Big
Busi
ness
0.4
33
.44
0.6
2.9
20
.14
0.9
40
.93
7.5
30
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3.1
90
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2.6
30
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0.8
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6Ag
e>
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ienc
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143.
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260.
162.
34Ed
ucat
ion
>Ef
ficie
ncy
0.0
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0.2
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0.0
71
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91
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0.0
91
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0.2
02
.52
0.0
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me
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0.57
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080
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5Kn
owle
dge
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0.7
24
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52
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0.8
24
.76
0.8
44
.19
0.6
73
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0.6
62
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0.7
94
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0.8
44
.29
Age
>N
egat
iveEf
fect
son
R&D
0.07
1.87
0.09
1.33
0.02
0.14
0.1
11
.89
0.07
1.89
0.09
1.33
0.02
0.19
0.1
11
.88
76
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nloa
ded
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iti M
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ia Ke
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embe
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5
-
.Educ
atio
n>
Neg
ative
Effe
cts
on
R&D
0.0
91
.62
0.0
70
.84
00.
020
.05
0.7
90
.09
1.6
20
.08
0.9
50
.01
0.0
70
.05
0.8
0
Inco
me
>N
egat
iveEf
fect
son
R&D
0.01
0.19
0.0
50
.79
0.0
10
.11
0.02
0.46
0.01
0.15
0.0
60
.85
0.0
10
.08
0.02
0.45
Know
ledg
e>
Neg
ative
Effe
cts
on
R&D
0.03
0.15
0.35
1.4
0.26
1.15
0.57
3.22
0.06
0.34
0.43
1.67
0.29
1.24
0.60
3.34
Age
>Pr
ice/
Valu
eR
elat
ions
hip
0.01
0.26
0.13
1.95
0.19
1.83
0.0
91
.54
0.02
0.42
0.14
2.03
0.19
1.82
0.0
81
.43
Educ
atio
n>
Pric
e/Va
lue
Rel
atio
nshi
p0.
091.
50
.03
0.3
10
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0.3
30
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0.9
40.
091.
500
.02
0.1
90
.02
0.3
40
.05
0.9
0
Inco
me
>Pr
ice/
Valu
eR
elat
ions
hip
0.05
0.78
0.09
1.24
0.01
0.24
0.09
1.93
0.05
0.77
0.07
1.07
0.02
0.30
0.09
1.81
Know
ledg
e>
Pric
e/Va
lue
Rel
atio
nshi
p0.
94.
220.
421.
310.
792.
240.
722.
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854.
150.
431.
290.
812.
300.
792.
29
Ris
kTa
ker>
Pric
e/Va
lue
Rel
atio
nshi
p0
.21
1.8
30
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0.4
30
.29
0.9
60
.20
.89
0.1
61
.38
0.0
20
.17
0.2
60
.86
0.2
20
.96
Exog
enou
sva
riabl
eon
Emba
rrass
men
tPot
entia
l(S
ubjec
tive
Norm
,SN
)
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R.
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kTa
ker>
Emba
rrass
me
nt
0.2
52
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0.0
80
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0.3
2.2
30
.25
1.8
30
.25
2.0
00
.08
0.6
30
.30
2.2
20
.25
1.8
3
Goo
dnes
sofF
it
2(11
89)=
3126
.72.
p
Inte
ntio
n0.
081.
150
.05
0.3
80.
030.
270.
111.
590.
101.
100.
040.
280
.05
0.3
30
.01
0.1
4
Anti-
Big
Busi
ness
>In
tent
ion
0.05
0.27
0.07
0.33
0.09
0.45
0.0
50
.25
0.53
2.33
0.49
1.66
0.53
1.91
1.02
3.93
Effic
ienc
y>
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ntio
n0.
323.
420.
472.
220.
131.
000.
132.
270.
423.
360.
632.
200.
301.
620.
040.
47Em
barra
ssm
en
t>In
tent
ion
0.0
91
.69
0.3
13
.21
0.03
0.22
0.1
21
.42
0.01
0.12
0.1
31
.11
0.0
80
.51
0.0
10
.08
Exog
enou
sva
riabl
eson
Inte
ntio
n
C.R.
C.
R.
C.R.
C.
R.
C.R.
C.
R.
C.R.
C.
R.
Inco
me
>In
tent
ion
0.02
0.35
0.0
10
.14
0.0
10
.22
0.07
1.56
0.07
0.78
0.1
61
.52
0.1
61
.88
0.00
0.0
3Ed
ucat
ion
>In
tent
ion
0.0
10
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0.0
90
.85
0.00
0.0
30.
000.
070.
050.
540.
251.
810.
151.
400
.12
1.6
1Ag
e>
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ntio
n0
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0.5
00
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0.5
90.
010.
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890
.12
2.1
10
.23
2.0
90.
160.
980
.22
2.3
6Kn
owle
dge
>In
tent
ion
0.33
1.24
0.33
0.71
0.10
0.22
0.1
20
.33
1.02
2.68
1.06
1.73
0.78
1.24
1.59
3.05
Endo
geno
us/E
xoge
nous
varia
bles
onAt
titud
es
C.R.
C.
R.
C.R.
C.
R.
C.R.
C.
R.
C.R.
C.
R.
Age
>An
ti-Bi
gBu
sine
ss0.
072.
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081.
540
.10
1.4
00.
154.
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072.
610.
081.
590
.10
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00.
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08Ed
ucat
ion
>An
ti-Bi
gBu
sine
ss0
.11
2.8
30
.17
2.5
00
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2.3
70
.03
0.9
10
.11
2.8
30
.17
2.4
20
.14
2.3
90
.04
1.0
2In
com
e>
Anti-
Big
Busi
ness
0.0
10
.36
0.02
0.43
0.07
1.44
0.03
1.03
0.0
10
.35
0.03
0.48
0.08
1.51
0.03
1.04
Know
ledg
e>
Anti-
Big
Busi
ness
0.4
03
.24
0.5
52
.69
0.1
30
.85
0.9
17
.44
0.4
13
.26
0.5
82
.78
0.1
40
.91
0.9
07
.37
Age
>Ef
ficie
ncy
0.14
3.85
0.15
2.46
0.02
0.21
0.15
2.30
0.14
3.80
0.15
2.47
0.02
0.22
0.15
2.28
Educ
atio
n>
Effic
ienc
y0
.09
1.6
90
.20
2.5
50
.07
1.0
90
.09
1.3
60
.09
1.7
10
.20
2.4
60
.07
1.0
60
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1.4
0In
com
e>
Effic
ienc
y0
.02
0.2
90.
040.
580.
010.
100
.01
0.1
60
.02
0.2
80.
040.
670.
010.
100
.01
0.1
3Kn
owle
dge
>Ef
ficie
ncy
0.6
83
.92
0.6
72
.66
0.8
04
.66
0.8
34
.22
0.7
04
.02
0.7
32
.83
0.8
24
.80
0.8
54
.34
Age
>N
egat
iveEf
fect
son
R&D
0.07
1.88
0.09
1.31
0.02
0.18
0.1
11
.88
0.07
1.88
0.09
1.36
0.02
0.18
0.1
11
.90
78
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nloa
ded
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iti M
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ia Ke
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embe
r 201
5
-
.Educ
atio
n>
Neg
ative
Effe
cts
on
R&D
0.0
91
.63
0.0
80
.88
0.0
10
.09
0.0
50
.80
0.0
91
.62
0.0
80
.92
0.00
0.0
40
.05
0.8
2
Inco
me
>N
egat
iveEf
fect
son
R&D
0.01
0.15
0.0
60
.85
0.00
0.0
50.
020.
450.
010.
150
.06
0.8
40
.01
0.0
90.
020.
46
Know
ledg
e>
Neg
ative
Effe
cts
on
R&D
0.05
0.33
0.41
1.61
0.28
1.22
0.59
3.33
0.04
0.26
0.40
1.54
0.27
1.17
0.59
3.33
Age
>Pr
ice/
Valu
eR
elat
ions
hip
0.01
0.27
0.14
1.99
0.20
1.87
0.0
91
.53
0.02
0.40
0.14
2.08
0.21
2.02
0.0
81
.46
Educ
atio
n>
Pric
e/Va
lue
Rel
atio
nshi
p0.
091.
530
.02
0.1
90
.02
0.2
40
.05
0.9
30.
091.
580
.03
0.3
10
.01
0.1
00
.05
0.8
7
Inco
me
>Pr
ice/
Valu
eR
elat
ions
hip
0.05
0.77
0.08
1.11
0.02
0.29
0.09
1.92
0.04
0.71
0.07
1.04
0.01
0.19
0.09
1.87
Know
ledg
e>
Pric
e/Va
lue
Rel
atio
nshi
p0.
884.
210.
421.
290.
832.
370.
712.
090.
874.
210.
441.
330.
912.
540.
732.
23
Ris
kTa
ker>
Pric
e/Va
lue
Rel
atio
nshi
p0
.19
1.6
60
.06
0.4
00
.32
1.0
70
.19
0.8
60
.14
1.2
10.
010.
060
.34
1.1
00
.15
0.6
8
Exog
enou
sva
riabl
eon
Emba
rrass
men
tPot
entia
l(S
ubjec
tive
Norm
,SN
)
C.R.
C.
R.
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R.
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C.
R.
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R.
Ris
kTa
ker>
Emba
rrass
me
nt
Pote
ntia
l(SN)
0.2
52
.02
0.0
80
.63
0.3
02
.23
0.2
51
.84
0.2
31
.90
0.0
70
.52
0.2
92
.12
0.2
31
.72
Goo
dnes
sofF
it
2(11
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3116
.58.
p