the emotional dimension of counterfeit consumption: an ... · the emotional dimension of...

Running Head: Emotions in counterfeit consumption - 1

The emotional dimension of counterfeit consumption: An empirical

taxonomy

NOVEMBER 2012

Leonidas A. Zampetakis*, PhD

Technical University of Crete, Department of Production Engineering and Management,

Management Systems Laboratory,

University Campus, Chania, Crete 73100, Greece.

Tel: +3028210 37323, Fax: +302821 69410

E-mail: [email protected]

*Corresponding author


The emotional dimension of counterfeit consumption: An empirical

taxonomy

Abstract

In the present paper the author investigate consumers’ emotions during non-deceptive

counterfeit consumption situations, and relates them to attitudes and indentation to buy

counterfeit products. Our results, based on a sample of 312 consumers suggest that during

non-deceptive counterfeit consumption situations, consumers experience both positive and

negative emotions (i.e. mixed emotions). Furthermore, four different subgroups of consumers

experienced relative specific but different emotional reactions. Considering that emotions can

be a strong driving force that motivates peoples’ behavioral reactions, this research suggests

that understanding consumers emotional experiences in counterfeit consumption situations

may help public policy makers, marketers and anti-counterfeiting service providers to devise

strategies to inhibit the problem of counterfeit consumption.

Keywords: Counterfeits, emotions, attitudes, appraisal theories, young consumers


INTRODUCTION

Several recent reports illustrate that counterfeit consumption has become a massive

economic issue that is extensive and global. According to the European Commission, customs

seized almost 118 million counterfeit or pirated articles at the EU’s external border in 2009

(European Commission, 2010), while according to the International Anti-Counterfeit

Coalition (IACC, 2010) the global market for counterfeits exceeds $600 billion a year,

accounting for approximately 5-7% of world trade each year. Increasingly, consumer demand

for counterfeit products is considered as one of the major causes of the existence and growth

of the counterfeit phenomenon (Ang, Cheng, Lim, & Tambyah, 2001; Bian & Moutinho,

2009). Existing evidence suggests that many consumers (almost 46% of the total population

worldwide) may knowingly purchase counterfeit products (i.e., non-deceptive counterfeiting -

Phau, Sequeira, & Dix, 2009) on various occasions.

The traditional assumption in consumer research is that consumers purchase counterfeits

predominantly because of their low prices; the benefits of counterfeits outweigh the costs

(Phau, Prendergast, & Chuen, 2001; Tom et al., 1998). However, emotions can be a strong

driving force that influences human judgment and motivates peoples’ behavioral reactions

(for reviews, see Forgas, 1995; Pham, 2007). A growing body of research acknowledges the

role of emotion and emotional responses in different consumption situations (Bagozzi,

Gopinath, & Nyer, 1999; Cohen & Areni, 1998; Lerner, Han, & Keltner, 2007; Weinberg &

Gottwald, 1982; Yeung & Wyer, 2004). Yet, research on consumers’ emotions during non-

deceptive counterfeit consumption situations is limited (e.g., Kim, Cho, & Johnson, 2009).

The purpose of the present research is to investigate consumers’ emotion assessments

during counterfeit consumption situations. Existing evidence suggest that although most

emotional experiences are either positive or negative, there appear to be circumstances that


give rise to mixed emotional experiences with positive and negative emotions occurring

closely together (e.g., Larsen, McGraw, & Cacioppo, 2001; Larsen & McGraw, 2011;

Miyamoto, Uchida, & Ellsworth, 2010; Williams & Aaker, 2002). As we will explain in

more detail later on in the text, the context of counterfeit consumption situations both buying

and not buying should generate both positive and negative affect due to the tradeoff of

multiple goals; consumers likely experience mixed rather single basic emotions. Furthermore,

it also plausible that during counterfeit consumption situations different individuals may have

the same perceived goals leading to the same emotional responses. Thus, it is critical to

explore positive and negative emotions simultaneously in order to examine the potential

effects of positive and negative emotional outcomes on attitudes and intentions to buy

counterfeit products. In our study we will focus on this link as we believe it may provide

important insights to marketers and anti-counterfeiting service providers in establishing more

effective marketing strategies.

LITERATURE REVIEW

Consumers’ emotions during non-deceptive counterfeit consumption situations

In the context of counterfeit products, consumers are likely to be involved in a trade-off

of multiple goals (e.g., the positive feelings brought by the low cost of counterfeit products vs

the negative feelings evoked by ethical concerns). Tom and colleagues (1998), for instance,

demonstrated that price is one of the most important antecedents to purchasing counterfeits;

yet Cordell et al., (1996) found that the more positive consumers’ attitude toward lawfulness,

the less willing they were to purchase a counterfeit and Ang et al. (2001) showed that

consumers with lower ethical standards were expected to feel less guilty when buying

counterfeits.

It is plausible that, when consumers face a counterfeit consumption situation both

buying and not buying should generate positive affect from one source (e.g., buying the


product and feel happy or not buying and feel happy in line with the prescriptive assessments

of what is “right” or “wrong”) but also negative affect from the other source (e.g., not buying

and a lost opportunity for a counterfeit GUCCI or buying and an unpleasant feeling of guilty

or shame because “I am unethical”). This conflict that is induced by the pleasure evoked from

satisfying ones desires versus guilt or shame from such satisfaction creates “mixed emotion”

contexts (i.e., emotional states defined by both positive and negative emotions) (Scherer &

Ceschi, 1997; Mukhopadhyay & Johar, 2007).

Contemporary research suggests that certain circumstances may give rise to mixed

emotional experiences with positive and negative emotions occurring closely together (e.g.,

Larsen & McGraw, 2011; Miyamoto, et al., 2010). Thus, one can simultaneously experience

conflicting emotions and such joint experience may be natural and frequently occurring. In

the context of impulsive purchasing for instance, Gardner and Rook (1988) found that the

positive emotion of “happiness” was frequently reported after an impulse purchase; however,

this positive affect was equally tinged with guilt (a negative emotion). Additionally,

Mukhopadhyay and Johar (2007) demonstrated that mixed emotions can arise from decisions

to either buy or not buy at an unintended purchase opportunity. Specifically, buying seems to

give rise to happiness tempered with guilt and a little regret, while not buying causes pride.

Based on this theorizing and existing evidence we propose the following hypothesis:

Hypothesis 1. Counterfeit consumption situations give rise to mixed emotional experiences

with positive and negative emotions occurring closely together.

Emotional responses and attitudes towards counterfeits

Theoretically, we expect a relationship between counterfeit consumption and

emotional reactions. Appraisal theories of emotions (ATE) (e.g., Lazarus, 1966; 1991), for

example, indicate that people are constantly scanning their environments to detect and

evaluate changes. Emotional reactions can occur when individuals perceive experiences that


are relevant to their goals, affect their well-being or coping potential, and/or impact their self-

concepts or norm systems. Furthermore, the nature of an emotional reaction is based on the

individual’s subjective appraisal or evaluation of an antecedent situation or event. Stated

differently, the major claim made by ATE is that objectively similar situations or events can

elicit, in different individuals, highly dissimilar emotional reactions depending on

idiosyncratic subjective appraisal (Lazarus, 1966, 1991; Siemer, et al., 2007). In the context

of counterfeit consumption situations, mixed emotions may emanate from the simultaneous

experience of positive and negative emotions (ambivalence) or from the coexistence of

emotions with different associated cognitive appraisal patterns.

One value of ATE is that it is possible to specify the conditions leading to emotional

responses. For instance, according to the commonly referenced appraisal framework provided

by Smith and Ellsworth (1985), anger is associated with a heightened sense of certainty and

control, pride involves appraising a situation as consistent with one’s motives and caused by

one’s own person (Roseman, 2001), while interest is associated with appraisals of increased

pleasantness, the desire to attend, the sense that situational factors are producing events, a

perceived need to expend effort, moderate certainty about future outcomes, and little sense of

obstacles or the illegitimacy of events.

Siemer and colleagues (2007) have provided empirical evidence for the status of the

emotion - component relation in terms of sufficiency and necessity. Sufficiency refers to the

prediction that, different appraisals of the same situation will be sufficient to result in

different emotional responses. Stated differently, different appraisals are all that is needed to

evoke different emotions, even if all other circumstances are the same. At present, this

position seems to be shared by most appraisal theorists (Roseman & Smith, 2001). Necessity

holds that different appraisal profiles are necessary conditions to evoke different emotional

reactions toward the same situation, that is, the same situation cannot evoke different


emotions unless it is appraised differently. This implies that only appraisal profiles determine

different emotional reactions toward the same situation. For non-deceptive counterfeit

consumption situations, if a consumer responds with a different emotion than some other

person, this response has been caused by (and requires) a specific appraisal pattern. Following

this reasoning one could expect that during counterfeit consumption situations different

individuals may have the same perceived goals leading to the same emotional responses.

Taken together, we propose the following hypothesis:

Hypothesis 2. During counterfeit consumption situations several groups of consumers

will have the same emotional responses.

These emotional responses in turn, may relate to (a) consumers’ degree of like or dislike

counterfeit (i.e., her attitudes towards counterfeits) and (b) to social functions served by

attitudes in line with the social functional framework of emotion (e.g., Keltner & Haidt,

1999). Emotions are adaptations or solutions to specific problems related to the formation and

maintenance of social relationships and they (1) inform the individual about social events and

conditions that require attention or action and (2) prepare the individual for such social action,

be it through physiological change or an increased sensitivity to emotion-relevant stimuli

(Keltner & Haidt, 1999).

According to broaden-and-build perspective of emotions positive emotions are more

influential than negative emotions in predicting approach-related outcomes, whereas negative

emotions should be more influential than positive emotions in predicting avoidance-related

outcomes (e.g., Fredrickson, 2001; Lazarus, 1991). Consider for example a consumer that has

experienced high levels of positive emotions during non-deceptive counterfeit situations. She

is more likely to have positive attitudes towards counterfeits and higher level of intention to

buy counterfeits compared to a consumer that has experienced high levels of negative

responses.


Wilcox, Kim and Sen, (2009) argued that social motivations guide people’s propensity

to consume counterfeit brands. The authors demonstrate that consumers’ likelihood of

purchasing counterfeit brands varies predictably with the social-adjustive and value-

expressive functions served by their brand attitudes. Functional theories of attitudes state that

attitudes are formed in order to satisfy certain needs and address the motivational bases of

people’s attitudes (Gregory, Munch, & Peterson, 2002; Wilcox et al., 2009). Attitudes serving

a social-adjustive function help consumers to gain approval in social settings and maintain

relationships. Products fulfilling the social-adjustive function help consumers to achieve

desired social goals by providing them with the characteristics they believe they lack (Grewal,

et al., 2004). The function of value-expressive attitudes is to convey values, attitudes and

beliefs that are important to consumers and which reflect and reinforce consumers’ self-

concept (Grewal, et al., 2004).

Taken together, the preceding discussion lays the foundation for our theorizing.

Specifically, we propose that:

Hypothesis 3. Groups of consumers with the same emotional responses during counterfeit

consumption situations will differ in their attitudes towards counterfeits and in the functions

served by their attitudes.

METHOD

Participants and Procedures

Data were collected from 312 randomly selected consumers (undergraduate students

and staff) from a medium university. Surveys were administrated individually to consumers,

through personal contact by the study authors. Participants were randomly located during

leisure activities and working office hours and asked to voluntarily participate in a research


project regarding factors influencing the purchase of counterfeit products. There were no

monetary incentives or extra course credits.

Participants were given the following explanation for the purposes of the study: “This

is an effort to combine research into factors affecting the consumption of counterfeit products.

A counterfeit product is illegal, low-priced, and often lower-quality replica of a product that

typically possesses high brand value. Counterfeit products are also known under several other

names such as replicas, imitation, bogus, fakes, copy, or knock-off. Your participation in this

research is not obligatory; you will answer a short questionnaire without filling in anything

that will identify you, or your department. The results will be used to better understand the

factors that influence counterfeit consumption”.

The sample consisted of 155 male participants (49.7%), between ages of 17-64 years.

The mean sample age was 30.7 years (SD = 10.65). There were 122 students in the sample.

Almost 33% of the participants had a university degree and 18.9 % had a M.Sc. or equivalent

degree. Two hundred and fifty six participants (82.1%) had bought counterfeit products in the

past.

Measurement of constructs

The questionnaire used for data collection, consisted of two parts. The first part,

contained instructions asking participants to think carefully about the last time they had the

chance to purchase a counterfeit product, during the previous six months. Then they were

presented with an alphabetized 17-item affect scale, based largely on Watson, Clark and

Tellegen (1988), Diener, Smith, and Fujita (1995), and Laros and Steenkamp, (2005).

Respondents indicated the extent to which each item reflected how they felt during the

consumption situation. All responses were on a 5-point scale (1-Very slightly or not at all, 2-

A little, 3-Moderately, 4-Quite a bit, 5-Extremely).


The following emotion words representing eight basic emotions were used: “proud,”

“guilty,” “ashamed”, fear (“scared”, “jittery”, “afraid”; Cronbach’s reliability coefficient =

0.72), angry (“hostile”, “irritable”, “angry”; Cronbach’s reliability coefficient=0.81),

distressed (“distressed”, “upset”; Cronbach’s reliability coefficient=0.71) interest (“interest”,

“alert”, “attentive”; Cronbach’s reliability coefficient= 0.67), excitement/happy (“excited”,

“enthusiastic”, “active”; Cronbach’s reliability coefficient = 0.79) (see Laros and Steenkamp,

2005: 1440). These emotions were selected based partly, on previous studies with mixed

emotions (e.g., Gardner & Rook, 1988; Mukhopadhyay & Johar, 2007) and partly on authors’

intuition.

The second part of the survey questionnaire contained items that were used for the

measurement of, general attitudes towards counterfeits, intention to buy counterfeit products,

attitude functions toward luxury brands, along with demographic variables.

Attitudes towards counterfeits were assessed using five items of the scale developed by

Huang et al., (2004). Ratings were made on a five-point scale ranging from (1) -“do not agree

at all’ to (5) -“completely agree”. Items used are: ATT1-“Considering price, I prefer

counterfeit products”, ATT2-“ I like shopping for counterfeit products”, ATT3-“ Buying

counterfeit products generally benefits the consumer”, ATT4-“ There’s nothing wrong with

purchasing counterfeits”, ATT5-“ Generally speaking, buying counterfeit products is a better

choice”. Cronbach’s reliability coefficient (0.76) for all five items was deemed acceptable.

Scores for all five items were averaged, to derive an overall score.

Intention to buy counterfeits was assessed using four items of the scale used in De

Matos et al., (2007) study. Respondents were asked to indicate on a five-point scale ranging

from (1) -“very unlikely” to (5) -“very likely” the chances that they: Int1-“Think about a

counterfeited product as a choice when buying something”, Int2-“Buy a counterfeited

product”, Int3-“Recommend to friends and relatives that they buy a counterfeited product”,


Int4-“Say favorable things about counterfeited products”. Scores for all four items were

averaged, to derive an overall score (Cronbach’s a = 0.89).

We assessed participants’ attitude functions toward luxury brands on seven-point Likert

scales, with the measures developed by Grewal, et al. (2004). Specifically, we used four items

to assess the value-expressive function: VEF1-“Luxury brands reflect the kind of person I see

myself to be”, VEF2-“Luxury brands help me communicate my self-identity”, VEF3-“Luxury

brands help me express myself”, VEF4-“Luxury brands help me define myself” (Cronbach’s

a = 0.82). Additionally we used four items to assess the social-adjustive function: SAF1-

“Luxury brands are a symbol of social status”, SAF2-“Luxury brands help me fit into

important social situations”, SAF3-“I like to be seen wearing luxury brands”, SAF4-“I enjoy

it when people know I am wearing a luxury brand” (Cronbach’s a = 0.84).

As a control variable in our analyses, we used affective disposition (trait affectivity),

since it is a stable tendency to experience specific emotions (either positive or negative) over

time, situations and context. We controlled for trait affectivity for two reasons. First, research

has demonstrated that retrospective emotion assessments may be biased by personality-related

information, such as negative affectivity (see Bolger & Zuckerman, 1995). Second,

Podsakoff, MacKenzie, Lee, and Podsakoff (2003) suggest that controlling for affectivity can

reduce common method bias concerns.

Accordingly, we controlled for trait affectivity with 25 items from the Neutral Objects

Satisfaction Questionnaire (NOSQ), a scale originally developed by Weitz (1952) and

modified by Judge and Bretz (1993). Respondents were asked to indicate their degree of

satisfaction to 25 items of facially neutral objects, on a 3-point scale: (1)-dissatisfied, (2)-

neutral, (3)-satisfied. Conceptually, the scale measures disposition by reflecting affective bias

towards items endemic to everyday life (i.e., “the city in which you live”, “the quality of the

food you buy”, “local newspapers”). Individuals highly satisfied with the objects as a whole


may have a tendency to see everything in a favorable light. The obverse is also true. Results

by Judge and Bretz (1993), suggest that the NOSQ possesses favorable psychometric

properties. In the present study, Cronbach’s reliability coefficient was 0.71. Scores for all

items were averaged, to derive an overall score of affective disposition. Higher scores are

suggestive of positive trait affectivity, while lower scores are suggestive of negative trait

affectivity.

Analytical Strategy

Data analysis was conducted in three steps: (1) descriptive analyses, (2) Analysis of

Variance (ANOVA) with the eight emotions as the within-subjects factor and (3) hierarchical

cluster analysis of the experienced emotions. All the analyses have been conducted with the

SPPS (v.15), statistical package. Before testing our hypotheses, we conducted an exploratory

factor analysis to ensure that our distinction between our measures was appropriate. Results

provided support for this distinction.

RESULTS

Descriptive analyses

Means, standard deviations, and correlations for all variables are shown in Table 1.

Table 2, provides information about the consumers’ self-reported feeling states for the eight

emotions, during counterfeit consumption situations. The percentage of those consumers

reporting not to have felt the emotion at all (scale value 1), as well the mean and the standard

deviation of the ratings of those reporting to have felt the emotion to varying degrees are

shown. The data show that all emotion states are reported by a sizeable number of

participants.

-------------------------------------------

Insert Table 1 here

--------------------------------------------


-------------------------------------------

Insert Table 2 here

--------------------------------------------

Figure 3, provides the bivariate distribution along with number of consumers

experiencing the various combinations of the “happy” and “distressed” emotions during non-

deceptive counterfeit consumption situations. As can be seen several consumers experienced

mixed feelings of happiness and distress at the same time. Our results support Hypothesis 1,

suggesting counterfeit consumption situations give rise to mixed emotional experiences with

positive and negative emotions occurring closely together.

-------------------------------------------

Insert Figure 1 here

--------------------------------------------

Analyses of variance

Figure 2, provides information about the results of one-way within subjects ANOVA,

with the eight emotions as the within-subjects factor. The main effect of affect felt during

counterfeit consumption situations, was significant [F (7, 2177) = 42.37, p < 0.001],

suggesting that respondents felt all emotions to varying degrees.

-------------------------------------------


--------------------------------------------

The average of positive (i.e., happy, interest, proud) and negative (i.e., guilty, ashamed,

fear, angry, distressed) emotions was used as measure of positive (PA) and negative (NA)

affect respectively. Results of one-way within subjects ANOVA, suggested that consumers’


remembered that overall they experienced more PA (M=1.75) compared to NA (M=1.59) and

this difference was statistically significant at the 0.05 level (adjusting for multiple

comparisons with the Bonferroni procedure).

Table 3, provides information about the probabilities for post-hoc tests (multiple

comparisons with the Bonferroni procedure), concerning the different intensities of felt

emotions. For instance, on average consumers felt guilty (M=1.92) and ashamed (M=1.79)

with the same intensity; the difference between these emotion was not statistically significant

at the 0.05 level, p = 0.69). However, consumers felt more happy (M=1.85) than proud

(M=1.58) (this difference was statistically significant, at the 0.001 level).

-------------------------------------------

Insert Table 3 here

--------------------------------------------

In order to investigate the effect of trait affectivity on the emotions felt during non-

deceptive counterfeit situations, we considered consumers with scores on the NOSQ above

the mean (=2.17) as having positive trait affectivity (N=151), while those below the mean as

having negative trait affectivity (N=161).

A mixed ANOVA was run, with trait affectivity (trait NA vs. trait PA) as the between-

subjects factor and the eight emotions as the within-subjects factor. The between-subjects

factor was not significant [F (1, 310) = 0.221, p = 0.64]. This result suggests that trait

affectivity has no effect on the mean intensity of the emotions experienced during non-

deceptive counterfeit consumption situations The main effect of experienced emotions, was

significant [F (7, 2170) = 42.934, p = 0.001] and this was qualified by a significant interaction

between trait affectivity and emotions experienced [F (7, 2170) = 5.2637, p = 0.001],

suggesting for instance, that consumers with positive trait affectivity experienced more

intensely positive emotions than negative emotions. Finally, the main effects of gender,


educational background and previous experience with counterfeits were investigated in a

series of mixed ANOVA analyses. All main effects (between-subjects factor) were found to

be statistically non-significant.

Cluster Analysis

Hierarchical Cluster analysis was performed with the emotion intensity responses that

divided participants into separate groups with similar emotional response profiles.

Specifically, a two clustering approach was used: In the first stage, hierarchical clustering was

used to determine the number of clusters and in the second stage, discriminant analysis was

used to facilitate interpretation.

For hierarchical clustering, Ward’s minimum variance method was used with the

squared Euclidian distance of the emotion intensity responses as metric to obtain a

preliminary solution. This method is designed to minimize the variance within the clusters as

opposed to the variance between the clusters. The agglomeration schedule suggested either a

four, or a five-cluster solution. In Figure 3, the dendrogram is presented, showing the

clustering of the 312 consumers with Ward’s hierarchical grouping method.

-------------------------------------------


--------------------------------------------

In order to facilitate the interpretation of Cluster Analysis results, we used Discriminant

Analysis. The initial classification (membership) of the Cluster Analysis was used as the

dependent variable; the variables used to cluster the cases (that is, the eight emotions), were

taken as the independent variables. Discriminant analysis computes information which

variables separate the clusters best. Results of the discriminant function predicted successfully

the classification of the 89.8% of the cases in the 5-cluster solution and 96.2% of the cases in

the 4-cluster solution. Therefore, four clusters were selected to segregate the data.


Figure 4 shows the emotion-intensity means of the resulting clusters. Before proceeding

to the description of the four clusters, we follow the literature on attitudinal ambivalence

(Priester & Petty, 1996) and we distinguish between dominant emotions and conflicting

emotions for each consumer. Dominant emotions are those positive or negative emotions that

are experienced in greater numbers, while conflicting emotions are those that are experienced

in lesser numbers. For example, when consumers experience five positive and two negative

emotions, the five positive emotions are dominant and the two negative emotions are

conflicting.

The first cluster (N=28, 9% of consumers) consists of consumers who experienced all

five negative emotions with greater intensity than any of the other clusters, whereas

experiencing low levels of positive emotions (negative emotions cluster). For this group of

consumers the dominant emotions are negative, while the positive emotions are conflicting.

The second cluster (N=89, 28.5%) consists of consumers who did not experience any

negative or positive emotions (no emotions cluster). For this group all emotions are

conflicting, in the sense that are experienced in lesser intensity.

Consumers in the third cluster (N=81, 26% of consumers) did not experience any

negative emotion in great intense but, unlike cluster one, they experienced positive emotions

(positive emotions cluster). For this group of consumers the dominant emotions are positive,

while the negative emotions are conflicting.

Finally, the fourth cluster (N=114, 36.5% of consumers) consists of consumers who

primarily experienced guilt and shame (guilt-shame cluster).

-------------------------------------------


--------------------------------------------


On the basis of the Cluster Analysis findings, we examined the four subgroups in terms

of internal validity; that is whether the clusters are really distinct in terms of the eight

emotions. In Table 4, we present the class means and the results of the ANOVA tests.

Overall, the individual ANOVA tests (with Benferoni post hoc comparisons) indicated that

there are differences between the classes in all eight emotions experienced during non-

deceptive counterfeit consumption situations. Thus, our empirical results confirm hypothesis

2, suggesting that during counterfeit consumption situations several groups of consumers will

have the same emotional responses.

-------------------------------------------

Insert Table 4 here

--------------------------------------------

On the basis of the Cluster Analysis findings, we examined the four subgroups in terms

of external validity; that is we tested the homogeneity and the distinctiveness of the clusters in

terms of consumers’ gender, age, education, previous experience with counterfeits, trait

affectivity, attitudes towards counterfeits, intention to buy counterfeit products, and functional

role of attitudes. Results indicate significant differences between and within the clusters

(Table 5).

-------------------------------------------

Insert Table 5 here

--------------------------------------------

More specifically, the between-subjects analysis shown in Table 5 illustrates that the

four clusters vary in terms of consumers’ previous experience with counterfeits, consumers’

age, attitudes towards counterfeits, intention to buy counterfeits and the value expressive

function of their attitudes. Additionally, the within-subjects analysis (with Benferoni post hoc

comparisons), showed, that in the “no emotions” subgroup almost 91% of the consumers had


previous experience with counterfeit products while in the guilt-shame cluster the

corresponding percentage was 67.5. In addition the “positive emotions” subgroup had the

youngest consumers. Almost 70% of the consumers of this group were under 26 years old.

Moreover, consumers in the positive emotions group had more favourable attitudes towards

counterfeits and greater intention to buy counterfeits compared to the rest groups. Finally the

negative emotions group scored higher in the value expressive function of attitudes. Thus,

our analyses confirm hypothesis 3 suggesting that groups of consumers with the same

emotional responses during counterfeit consumption situations will differ in their attitudes

towards counterfeits, intention to buy counterfeits and in the functions served by their

attitudes.

DISCUSSION AND IMPLICATIONS

The purpose of the present research was to investigate consumers’ emotions during non-

deceptive counterfeit consumption situations and to examine whether significant variability

exists in their emotional responses. We provide evidence that during non-deceptive

counterfeit consumption situations, consumers experience mixed emotions. This is the first

study (to our knowledge) suggesting that consumers during counterfeit consumption

situations experience mixed emotions and advances our theoretical understanding of

consumers’ emotional responses during counterfeit consumption situations. Furthermore, in

line with appraisal theories of emotion we have found that during counterfeit consumption

situations, assessments and judgments generate emotional reactions, which in turn impact

consumers’ subsequent attitudes and behaviors towards counterfeits (e.g., Lazarus, 1991).

Emotions experienced may focus or guide consumers toward behaviors that are designed to

deal with the target of the emotion.

Specifically, results from cluster analysis of experienced emotions suggested that four

different subgroups of consumers experienced relative specific but different emotional


reactions. The largest subgroup (36.5% of the sample) of consumers experienced shame and

guilt. Some consumers responded with a kind of emotionless and did not report experiencing

any emotions at all (28.5 % of the sample). Finally, there were two groups, one where

consumers maintained relatively high levels of positive emotions whereas having lower levels

of negative emotional responses (26% of the sample) and one where consumers (9% of the

sample) experienced a mixture of intense negative emotions, reporting high levels of fear,

angry, distress, guilt and shame.

This study contains four important novelties with regard to previous research projects on

the counterfeit phenomenon.

First, while the importance of emotions in marketing research has been recognized

(Bagozzi, et al., 1999; Lerner, et al., 2007; Weinberg & Gottwald, 1982), very few empirical

studies have focused on the role of affective influences in counterfeit consumption (Kim, et

al., 2009). In their recent study, Kim and her colleagues (2009) provided arguments about the

role of moral affect such as shame and guilt on moral judgment. Results of the present study

suggest that guilt and shame where on average, the most intensely experienced emotions

during non-deceptive counterfeit consumption situations (see Figure 2).

Second, from a broader perspective, results support existing evidence that under certain

circumstances positive and negative emotions do occur closely together (e.g., Larsen et al.,

2001). For example, Wiliams and Aaker, (2002) found that participants felt both happy and

sad after viewing certain advertisements, while Larsen et al., (2001) demonstrated that

participants felt both happy and sad on move-out day, and on graduation day. The current

study adds to a growing interest in consumer research on experiences of duality, by providing

evidence that under non-deceptive counterfeit consumption situations consumers respond

with complex emotional reactions.


Third, although the traditional assumption in consumer research is that consumers

purchase non-deceptive counterfeits predominantly because of their low prices (e.g., Phau, et

al., 2001; Tom et al., 1998), our study provides evidence about the notion that emotions

experienced may be sources of information providing informational benefits to consumers.

For instance, according to broaden-and-build theories of emotion, positive emotions are

generally associated with the tendency to engage in approach behaviors that connect

individuals with their environments, whereas negative emotions are generally related to the

tendency to withdraw and avoid, or retaliate (Lazarus, 1991). Our results suggest that the

“positive emotions” cluster where consumers maintain relatively high levels of positive

emotions has stronger attitudes towards counterfeits and intention to buy counterfeits

compared with the “negative emotions” cluster. As such, the present study may be considered

as extending the context of commonly accepted notions of non-deceptive counterfeiting.

Furthermore, results implicitly encourage the use of consumers’ emotional intelligence (e.g.,

Kidwell, Hardesty, & Childers, 2008), that is the ability to attend to, processes, and act upon

information of an emotional nature, for the study of the counterfeit phenomenon.

In addition to our theoretical contributions, the current study offer insights for those

interested in the phenomenon of counterfeit consumption, especially public policy makers,

marketers and anti-counterfeiting service providers. Our results may imply actions to those

interested in curbing consumers’ tendencies towards counterfeits. The emotional differences

identified for the four groups are related to the frequency and/or experience with purchasing

counterfeit goods (see Table 5). Consumers who have more experience with counterfeit goods

reported more positive emotions. The people who have negative emotions, no emotions, guilt-

shame have significant less experience with counterfeits. If “experience with counterfeit

goods” measures the frequency and/or quantity of purchase of counterfeit goods, then the

results of this study suggest that the group who has positive emotions is the one to target (i.e.


cluster 3). This is the group with the demand for counterfeit goods. The other three groups

presumably do not buy counterfeit goods or have less/little demand for counterfeit goods.

Cluster 3, has the youngest consumers (m=26.37 years), while five out of eight emotions

are conflicting. A central argument in the mixed emotion literature is that the conflicting

emotions have a profound effect on judgments rather than dominant ones (e.g., Priester &

Petty, 1996). If we consider that emotions are used by consumers as if it was information

(e.g., Forgas, 1995) about the counterfeit products, then as the level of conflicting emotions

regarding counterfeits increases, consumers will experience the odd mixture of emotions more

strongly and therefore recognize it more easily. As a result, consumers will more likely

experience counterfeits as unusual and feel uncomfortable about it (Williams & Aaker, 2002).

Feeling uncomfortable will lead to a cautious, conservative judgment, about counterfeits and

thus may decrease intention to buy. According to Williams and Aaker (2002), persuasion

appeals with an emphasis on conflicting emotions lead to less favorable attitudes for

individuals with a lower propensity to accept mixed emotional states (e.g., younger adults)

relative to those with a higher propensity (e.g., older adults). Thus, our results suggest that

the use of mixed emotional appeals may be an effective advertising strategy when targeting

younger consumers.

LIMITATIONS AND FUTURE RESEARCH

Although this study provides interesting evidence for mixed emotional responses

during counterfeit consumption situations the study nonetheless should be read with the

following limitations in mind. One limitation is that the data are cross sectional and self-

report measures were used. Although self-reported emotions is considered the most common

and effective way to measure emotional experiences future research should use alternative

methodologies (e.g., experience sampling method, daily diaries) which would allow emotions

to be examined as they occur.


A second limitation is that we measured emotions over the last six months. We used

retrospective self-reports to assess consumers’ reactions. Within the emotions literature, there

has been debate about the use of retrospective ratings and how the time frame used can impact

these ratings (e.g., Robinson & Clore, 2002), with some authors suggesting that asking

respondents to reflect on their emotions over a long time span (e.g., more than one week) is

likely to tap into semantic rather than episodic knowledge (Robinson & Clore, 2002). In

particular, individuals can draw on personality-related information as a source of knowledge

about how they believe they may have felt or behaved. To help minimize this possibility, we

controlled for trait affectivity in our analyses. Our results point to a significant interaction

between trait affectivity and emotions experienced suggesting for instance, that consumers

with positive trait affectivity experienced more intensely positive emotions than negative

emotions. Nevertheless, we have found that trait affectivity has no effect on the mean

intensity of the emotions experienced during non-deceptive counterfeit consumption

situations.

Furthermore, although it is possible that the emotion measure in our study can be

biased by dispositional affect when measured over a long period of time, our emotion

measure was not designed to assess dispositional affect but rather state affect. Specifically,

our measure reflects state affect for three reasons. First, the emotion measure asks participants

to reflect on the emotions that they experienced during non-deceptive counterfeit

consumption situations over the last six months. Thus, the targeted nature of the question stem

directs participants to report state rather than trait affect. Second, there was a relatively weak

correlation between trait affectivity and our emotion measures (see Table 1). Third, prior

research has demonstrated that memories that involve affect or emotional arousal are typically

remembered better and reported more accurately than those that are affectively neutral

(Kihlstrom, Eich, Sandbrand, & Tobias, 2000). Thus, consumers are likely to be able to report


their state emotions over the last six months because they are typically able to retrieve these

types of memories by virtue of their affective nature.

A third limitation is that all participants came from only one country. Previous research

suggests that there are both cultural universals and differences in the experience, expression,

and interpretation of emotions and those kinds of situations that consumers feel mixed

emotions depends on culture (Miyamoto, et al., 2010; Williams & Aaker, 2002). Caution

should therefore be exercised in generalizing these findings to non-comparable populations.

Consequently, future studies might want to consider the implications of the present study for

different populations.

A fourth limitation is that the current study has not tested a particular appraisal theory of

emotions, with specific appraisal dimensions and included a limited number of emotions and

external variables. Future studies should include a broader range of emotions (e.g., Tangney,

Stuewig, & Mashek, 2007) along with appraisal dimensions and emotions. Towards this vein,

the Appraisal-Tendency Framework (Lerner & Keltner, 2000; Han, Lerner & Keltner, 2007)

is very promising theory about emotion-specific influences on consumer judgments and

choices during counterfeit consumption situations.

Future research is clearly needed to examine the generalizability of the results of the

present study. Yet, the limitations mentioned represent, in any case, opportunities to advance

in our efforts to better understand the role of consumers’ affective responses in the context of

non-deceptive counterfeiting.


REFERENCES

Ang, S. H., Cheng, P. S., Lim, E. A., & Tambyah, S. K. (2001). Spot the difference:

Consumer responses towards counterfeits. Journal of Consumer Marketing, 18, 219-

235.

Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). The Role of Emotions in Marketing.

Journal of the Academy of Marketing Science, 27(2), 184-206.

Bian, X., & Moutinho, L. (2009). An investigation of determinants of counterfeit purchase

consideration. Journal of Business Research, 62, 368-378.

Bolger, N., & Zuckerman, A. (1995). A framework for studying personality in the stress

process. Journal of Personality and Social Psychology, 69, 890-902.

Cohen, J. B., & Areni, C. S. (1998). Affect and Consumer Behavior. In T. S. Robertson & H.

H. Kassarjian (Eds.), Handbook of Consumer Behavior (pp. 188-240). Englewood

Cliffs, NJ: Prentice-Hall.

Cordell, V. V., Wongtada, N., & Kieschnick, R. L., Jr. (1996). Counterfeit purchase

intentions: Role of lawfulness attitudes and product traits as determinants. Journal of

Business Research,, 35(1), 41-53.

De Matos, C. A., Ituassu, C. T., & Rossi, C. A. V. (2007). Consumer attitudes toward

counterfeits: A review and extension. Journal of Consumer Marketing, 24(1), 36-47.

Diener, E., Smith, H., & Fujita, F. (1995). The personality structure of affect. Journal of

Personality and Social Psychology, 69, 130-141.

European, C. (2010). Report on EU customs enforcement of intellectual property rights.

Brussels-Taxation and Customs Union.

Fong, C. T. (2006). The effects of emotional ambivalence on creativity. Academy of

Management Journal, 49(5), 1016-1030.

Forgas, J. P. (1995). Mood and judgment: The affect infusion model (AIM). Psychological

Bulletin,, 117(1), 39-66.

Forgas, J. P., & Moylan, S. (1987). After the movies: Transient mood and social judgments.

Personality and Social Psychology Bulletin, 13(4), 467-477.

Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-

and-build theory of positive emotions. American Psychologist, 56, 218-226.

Gardner, M. P., & Rook, D. W. (1988). Effects of impulse purchases on consumers’ affective

states. Advances in Consumer Research, 15, 127-130.


Gregory, G. D., Munch, J. M., & Peterson, M. (2002). Attitude functions in consumer

research: Comparing value–attitude relations in individualist and collectivist cultures.

journal of Business Research, 55, 933-942.

Grewal, R., Mehta, R., & Kardes, F. R. (2004). The timing of repeat purchases of consumer

durable goods: The role of functional bases of consumer attitudes. Journal of

Marketing Research, 41(February), 101-115.

Han, S., Lerner, J. S., & Keltner, D. (2007). Feelings and consumer decision making: The

Appraisal-Tendency Framework. Journal of Consumer Psychology, 17(3), 158-168.

Huang, J. H., Lee, B. C. Y., & Ho, S. H. (2004). Consumer attitude toward gray market

goods. International Marketing Review, 21(6), 598-614.

International Anticounterfeiting Coalition. (2010). The trouth about countrefeiting. available

at: http://www.iacc.org/about-counterfeiting/the-truth-about-counterfeiting.php,

(accessed September 15, 2010).

Judge, T. A., & Bretz, R. D. (1993). Report on an alternative measure of affective disposition.

Educational and Psychological Measurement, 53, 1095-1104.

Keltner, D., & Haidt, J. (1999). Social functions of emotions at four levels of analysis.

Cognition & Emotion, 13, 505-521.

Kidwell, B., Hardesty, D. M., & Childers, T. L. (2008). Consumer Emotional Intelligence:

Conceptualization, measurement, and the prediction of consumer decision making.

Journal of Consumer Research, 35(June), 154-166.

Kihlstrom, J. F., Eich, E., Sandbrand, D., & Tobias, B. A. (2000). Emotion and memory:

Implications for self-report. . In A. A. Stone, J. S. Turkkan, C. A. Bachrach, J. B. Jobe,

H. S. Kurtzman & V. S. Cain (Eds.), The science of self-report: Implications for

research and practice (pp. 81-100). Mahwah, NJ: Lawrence Erlbaum

Kim, J. E., Cho, H. J., & Johnson, K. K. P. (2009). Influence of moral affect, judgment, and

intensity on decision making concerning counterfeit, gray-market, and imitation

products. Clothing & Textiles Research Journal, 27(3), 211-226.

Laros, F. J. M., & Steenkamp, E. M. (2005). Emotions in consumer behavior: a hierarchical

approach. Journal of Business Research, 58, 1437-1445.

Larsen, J. T., McGraw, A. P., & Cacioppo, J. T. (2001). Can people feel happy and sad at the

same time? . Journal of Personality and Social Psychology, 81, 684-696.

Larsen, J. T., & McGraw, P. A. (2011). Further Evidence for Mixed Emotions. Journal of

Personality and Social Psychology, 100(6), 1095-1110.


Lazarus, R. S. (1966). Psychologrcal stress and the coprng process. New York: McGraw

Hill.

Lazarus, R. S. (1991). Emotion and adaptation. New York: Oxford University Press.

Lerner, J. S., Han, S., & Keltner, D. (2007). Feelings and consumer decision making:

extending the appraisal-tendency framework. Journal of Consumer Psychology, 17,

184-187.

Lerner, J. S., & Keltner, D. (2000). Beyond valence: Toward a model of emotion-specific

influences on judgment and choice. Cognition and Emotion, 14(473-493).

Miyamoto, Y., Uchida, Y., & Ellsworth, P. C. (2010). Culture and mixed emotions: Co-

occurrence of positive and negative emotions in Japan and the United States. Emotion,

10(3), 404-415.

Mukhopadhyay, A., & Johar, G. V. (2007). Tempted or not? The effect of recent purchase

history on responses to affective advertising. Journal of Consumer Research 33, 445-

453.

Pham, M. T. (1998). Representativeness, relevance, and the use of feelings in decision

making. Journal of Consumer Research, 25(2), 144-159.

Pham, M. T. (2007). Emotion and rationality: A critical review and interpretation of empirical

evidence. Review of General Psychology, 11(2), 155-178.

Phau, I., Prendergast, G., & Chuen, L. (2001). Profiling brand-piracy-prone consumers: An

exploratory study in Hong Kong’s clothing industry. Journal of Fashion Marketing

and Management, 5(1), 45-55.

Phau, I., Sequeira, M., & Dix, S. (2009). Consumers’ willingness to knowingly purchase

counterfeit products. Direct Marketing: An International Journal 3(4), 262-281.

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method

biases in behavioral research: A critical review of the literature and recommended

remedies. Journal of Applied Psychology, 88, 879-903.

Priester, J. R., & Petty, R. E. (1996). The gradual threshold model of ambivalence: Relating

the positive and the negative bases of attitude to subjective ambivalence. Journal of


Robinson, M. D. C., G. L. . (2002). Episodic and semantic knowledge in emotional self-

report: Evidence for two judgment processes. Journal of Personality and Social

Psychology, 83, 198-215.

Roseman, I. J. (1991). Appraisal determinants of discrete emotions. Cognition and Emotion,

5, 161-200.


Roseman, I. J., & Smith, C. A. (2001). Appraisal theory: Overview, assumptions, varieties,

controversies. In Scherer, K.R., A. Schorr & T. Johnstone (Eds.), Appraisal processes

in emotion (pp. 3–19). Oxford: Oxford University Press.

Scherer, K. R., & Ceschi, G. (1997). Lost luggage: A field study of emotion-antecedent

appraisal. Motivation and Emotion, 21(3), 211-235.

Siemer, M., Mauss, I., & Gross, J. (2007). Same situation-deifferent emotions: How

appraisals share our emotions. Emotions, 7(3), 592-600.

Smith, C. A., & Ellsworth, P. C. (1985). Patterns of cognitive appraisal in emotion. Journal of


Tangney, J. P., Stuewig, J., & Mashek, D. J. (2007). Moral emotions and moral behavior.

Annual Review of Psychology, 58, 345-372.

Tom, G., Garibaldi, B., Zeng, Y., & Pilcher, J. (1998). Consumer demand for counterfeit

goods. Psychology & Marketing, 15(4), 405-421.

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief

measures of positive and negative affect: the PANAS scales. Journal of Personality

and Social Psychology, 54(6), 1063-1070.

Weinberg, P., & Gottwald, W. (1982). Impulsive consumer buying as a result of emotions.

Journal of Business Research, 10, 43-57.

Weitz, J. (1952). A neglected concept in the study of job satisfaction. Personnel Psychology,

5(201-205).

Wilcox, K., Kim, M. H., & Sen, S. (2009). Why do consumers buy counterfeit luxury brands?

Journal of Marketing Research, XLVI(April), 247-259.

Williams, P., & Aaker, J. L. (2002). Can mixed emotions peacefully coexist? Journal of

Consumer Research, 28, 636-649.

Yeung, C. W. M., & Wyer, R. S. (2004). Affect appraisal and consumer judgment. Journal of

Consumer Research, 31(2), 412-424.


Figures

Figure 1. Bivariate distribution of happiness and distress during non-deceptive counterfeit

consumption situations.


Figure 2.

Intensity of felt emotions during non-deceptive counterfeit consumption. Vertical bars denote

95 percent confidence intervals (N=312).


Figure 3.

Results of the hierarchical Cluster analysis


Figure 4.

Number of consumers and means of the emotion-ratings in four clusters obtained by a ward

hierarchical cluster analysis of the squared Euclidean distance matrix of the emotion-ratings.

Fear Angry Distressed Guilty Ashamed Interest Happy Proud

Experienced emotion

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Inte

nsity

Cluster 1 (N = 28, 9.0%) Cluster 2 (N = 89, 28.5%) Cluster 3 (N = 81, 26.0%) Cluster 4 (N=114, 36.5%)


Tables Table 1. Descriptive statistics and intercorrelations of the study variables

Note: N=312. a Gender is coded: 1= male 2 = female; b Education is coded: 1= student, 2=High School, 3=Senior High, 4=University,

5=M.Sc./Ph.D.; c Bought counterfeits in the past is coded: 1=Yes 2=No; * p<0.05; ** p<0.01

M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1. Gender a 1.50 0.50 -

2. Education b 2.59 1.57 -0.01 -

3. Bought counterfeits in the past c

1.18 0.38 0.047 -0.04 -

4. Age 30.70 10.65 -0.04 0.47** -0.04 -

5. Trait affectivity 2.17 0.19 0.15** -0.01 0.09 -0.14* -

6. Fear 1.21 0.47 -0.02 0.009 -0.05 0.12* -0.07 -

7.Angry 1.38 0.67 -0.04 0.08 0.02 0.14* -0.14* 0.57** -

8. Distressed 1.69 0.85 0.06 0.12* 0.23** 0.13* -0.06 0.40** 0.58** -

9. Guilty 1.92 0.93 .014 -0.01 0.006 -0.03 -0.19** 0.30** 0.28** 0.37** -

10. Ashamed 1.79 0.91 -0.04 -0.02 0.17** -0.02 -0.11 0.44** 0.44** 0.39** 0.02*

* -

11.Interest 1.82 0.69 -0.11 -0.07 -0.18** -0.24** -0.009 0.17** 0.16** 0.07 0.11* 0.13* -

12. Happy 1.85 0.77 -0.03 -0.13* -0.15** -0.32** 0.12* 0.18** 0.11 0.05 0.10 0.12* 0.59** -

13. Proud 1.58 0.83 -0.06 0.008 -0.10 -0.17** 0.20** 0.15** 0.11 0.03 -0.02 -0.004 0.46** 0.61** -

14. Attitudes towards counterfeits

2.10 0.71 -0.01 -0.05 -0.27** 0.04 0.02 -0.07 -0.05 -0.07 -0.07 -0.12* 0.31** 0.39** 0.27** -

15. Intention to buy counterfeits

1.99 0.91 -0.68 -0.03 -0.38** -0.04 0.05 -0.04 -0.01 -0.16** -0.02 -0.08 0.48** 0.50** 0.35** 0.55** -

16. Value expressive function 1.97 0.75 0.07 0.05 0.02 0.12* 0.09 0.02 0.03 0.02 -0.05 -0.07 -0.12* -0.12* -0.13* -0.02 -0.03 -

17. Social adjustive function 2.62 1.13 0.004 -0.05 0.27** 0.03 0.08 -0.01 0.06 0.08 -0.04 0.03 -0.17** -0.13* -0.14* -0.13* -0.16** 0.66** -


Table 2. Presence and intensity of emotional experiences during counterfeit consumption

situations

Note: % Not felt = percentage of consumers reporting a scale value of 1 (“Very slightly or

not at all” for the emotion; Mean and standard deviation (SD) computed for consumers with

scale values greater than 1, i.e. those reporting to have felt the emotion to varying degrees

% not felt Mean SD

1. Fear 72.2 1.76 0.63

2. Angry 61.9 1.98 0.78

3. Distressed 43.3 2.21 0.79

4. Guilty 35.9 2.43 0.79

5. Ashamed 42.3 2.48 0.67

6. Interest 13.8 1.95 0.65

7. Happy 25.7 2.14 0.68

8. Proud 59.6 2.44 0.69


Table 3. Bonferroni test; Probabilities for post hoc tests between felt emotions during counterfeit consumption situations

Emotion (1).

1.21

(2).

1.38

(3).

1.69

(4).

1.92

(5).

1.79

(6).

1.82

(7).

1.85

(8).

1.58

1.Fear - 0.04 0.00 0.00 0.00 0.00 0.00 0.00

2.Angry 0.04 - 0.00 0.00 0.00 0.00 0.00 0.00

3.Distressed 0.00 0.00 - 0.00 1.00 0.39 0.07 1.00

4.Guilty 0.00 0.00 0.00 - 0.69 1.00 1.00 0.00

5.Ashamed 0.00 0.00 1.00 0.69 - 1.00 1.00 0.00

6.Interest 0.00 0.00 0.39 1.00 1.00 - 1.00 0.00

7.Happy 0.00 0.00 0.07 1.00 1.00 1.00 - 0.00

8.Proud 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -


Table 4. Emotions experienced per consumer cluster

NOTE: Numbers in bold indicate the highest group average for that measure.

*Welch statistic (asymptotically F distributed)

b Cluster numbers from which the cluster was significantly different at the 0.05 level of significance indicated by the Benferoni pairwise comparison test.

Emotion

Cluster 1

Negative emotions

(N=28)

Cluster 2

No emotions

(N=89)

Cluster 3

Positive emotions

(N=81)

Cluster 4

Guilt-shame

(N=114)

One way ANOVA

M SD M SD M SD M SD F * p

1. Fear 1.97 1.01 (2,3,4)b 1.02 0.10 (1,4) 1.17 0.34 (1) 1.19 0.32 (1,2) 40.336 0.000

2. Angry 2.65 1.07 (2,3,4) 1.04 0.17 (2,3,4) 1.39 0.61 (1,2) 1.31 0.44 (1,2) 66.700 0.000

3. Distressed 3.12 1.21 (2,3,4) 1.15 0.37 (1,3,4) 1.58 0.64 (1,2) 1.82 0.68 (1,2) 63.735 0.000

4. Guilty 3.25 1.43 (2,3,4) 1.18 0.38 (1,3,4) 1.77 0.79 (1,2,4) 2.27 0.58 (1,2,3) 74.543 0.000

5. Ashamed 3.79 1.03 (2,3,4) 1.07 0.25 (1,3,4) 1.63 0.66 (1,2,4) 1.99 0.45 (1,2,3) 183.397 0.000

6. Interest 2.02 0.94 (2,3,4) 1.45 0.41 (1,3) 2.48 0.68 (1,2,4) 1.59 0.40 (1,3) 59.333 0.000

7. Happy 1.87 0.87 (2,3) 1.35 0.49 (1,3,4) 2.69 0.58 (1,2,4) 1.64 0.54 (2,3) 85.397 0.000

8. Proud 1.46 0.84 (2,3) 1.07 0.25 (1,3,4) 2.56 0.85 (1,2,4) 1.32 0.46 (2,3) 104.213 0.000


Table 5. Group differences on external validation variables

NOTE: Numbers in bold indicate the highest group average for that measure; *Welch statistic (asymptotically F distributed); a Gender is coded: 1= male , 2 = female;

b Education is coded: 1= student, 2=High School, 3=Senior High, 4=University, 5=M.Sc./Ph.D.; c Bought counterfeits in the past is coded: 1=Yes 2=No; d Cluster numbers

from which the cluster was significantly different at the 0.05 level of significance indicated by the Benferoni pairwise comparison test.

External variables

Cluster 1 Negative emotions (N=28)

Cluster 2 No emotions (N=89)

Cluster 3 Positive emotions (N=81)

Cluster 4 Guilt-shame (N=114)

One way ANOVA

M SD M SD M SD M SD F * p

1. Gender a 1.57 0.50 1.55 0.50 1.42 0.49 1.51 0.50 1.195 0.318

2. Education b 2.71 1.58 2.75 1.53 2.20 1.52 2.71 1.58 2.232 0.075

3. Previous experience with counterfeits c

1.25 0.44 1.09 0.28 (4) d 1.05 0.22 (4) 1.32 0.47 (2,3) 11.488 0.000

4. Age 31.82 9.98 32.13 10.47 (3) 26.37 8.91 (2,4) 32.16 11.38 (3) 6.088 0.000

5. Trait affectivity 2.13 0.20 2.20 0.20 2.19 0.16 2.14 0.18 2.654 0.061

6. Attitudes towards counterfeits

1.93 0.86 (3) 2.06 0.68 (3) 2.47 0.63 (1,2,4) 1.90 0.65 (3) 11.989 0.000

7. Intention to buy counterfeits

1.72 0.81 (3) 1.82 0.70 (3) 2.72 0.96 (1,2,4) 1.64 0.72 (3) 34.781 0.000

8. Value expressive function

1.93 0.68 2.13 0.70 (3) 1.82 0.80 (2) 1.96 0.74 2.740 0.047

9. Social adjustive function

2.74 1.41 2.72 1.10 2.41 1.13 2.66 1.07 1.302 0.274

the emotional dimension of counterfeit consumption: an ... · the emotional dimension of...

Documents