1

Associative Networks: A Method of Capturing Doppelgänger

Brand Image

Abstract

In an Internet 2.0 context, negative user-generated content (UGC) that damage the brand

reputation can be easily produced and quickly spread. There is a lack of literature on how to

measure the impact of this value destruction, known as doppelgänger brand image (DBI).

Using brand concept mapping (BCM) on two corporate brands, this study shows the effect of

exposure to negative UGC and measures the impact on DBI. The authors use a two*two

between-subjects design with 280 consumers in order to study the effects of media and source

credibility, brand experience and Internet experience on the DBI.

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Introduction

Contrary to classical consumerism, anti-brand movements emphasize several issues (e.g.,

workplace inequality, economic domination of large corporations, environmentalism). These

movements also proliferate quickly thanks to the Internet (Hollenbeck and Zinkhan, 2006).

Currently, there is a multitude of brand protest sites (for example, antibrand.net) advocating

anticapitalism, environmental protection or social responsibility (Mortimer, 2002).

In generating critical content about it on the Internet, consumers are able to lower a brand’s

value. There is a lack of research on how to measure and quantify this value destruction,

called doppelgänger brand image (DBI). DBI is defined as “a family of disparaging images

and stories about a brand that are circulated in popular culture by a loosely organized network

of consumers, anti-brand activists, bloggers, and opinion leaders in the news and

entertainment media” (Thompson, Rindfleisch and Arsel, 2006, p.50). Furthermore, with the

development of Internet 2.0, the amount of user-generated content (UGC) exchanged is

increasing. Some of the content is sponsored by companies; however, many consumers also

express their complaints about the brand in non-sponsored UGC. For organizations to tackle

the threat of DBI, there is a need for a method to capture the nature and extent of this

phenomenon. Because Internet media are evolving, managers should be informed about the

danger of these different tools, as well as the effect of the source credibility and the type of

consumer most likely to be influenced by UGC. Thompson et al. (2006) define the concept of

DBI and conclude that it can actually benefit a brand by providing early warning signs that an

emotional branding story is beginning to lose its authenticity. Emotional branding is a

“consumer-centric, relational and story-driven approach to forging deep and enduring

affective bonds between consumer and brands”; it is made of “narratives and tactics that

demonstrate an empathetic understanding of customers’ inspirations, aspirations, and life

circumstances and that generate warm feelings of community among brand users” (Thompson

et al., 2006, p50).

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The present study will be guided by the following research question: How does non-

sponsored UGC influence brand image perception? We propose an operational definition of

DBI in order to capture and quantify DBI using brand concept mapping (BCM) and z-tests of

equality of proportions. For our method, we use an experimental design. The article is

structured as follows. In a literature review, the concepts of emotional branding, UGC and

DBI are defined and clarified. Then BCM is described as a good method for capturing DBI.

Hypotheses are formulated and tested in an experimental design. The findings are discussed

extensively, and managerial implications are provided. Finally, limitations of the study and

suggestions for further research are presented.

Literature Review

Emotional Branding

Leading corporations consider emotional branding as a key to market success (Thompson et

al., 2006). This strategy increases brand differentiation, sales improvement, consumer loyalty

(Gapper, 2004; Gobe, 2001) and the feeling of community among brand users (Algesheimer,

Dholakia and Hermann, 2005; McAlexander, Schouten and Koenig, 2002; Muniz and

O’Guinn, 2001; Thompson et al., 2006). Brands such as Apple, Harley Davidson, Mini

Cooper, Nutella and Star Wars (R) generate many loyal customers not only because of their

distinctive products but also thanks to emotional branding. Research has shown that

consumers buy a brand exclusively not only for the functional needs it fulfils but also for its

capacity to mirror their values and lifestyle needs (Czellar and Palazzo, 2004). Palazzo and

Basu (2007) identified a shift in corporate communication from showcasing product features

to explaining corporate values, like ecology, ethics or social justice and the consideration of

the corporate brand as a supplier of a large range of values. Consumers are looking for

authentic corporate values (Lafferty and Goldsmith, 1998). The word “authentic” is key in

story-driven emotional branding strategies (Fournier, 1998; Grayson and Martinec, 2004;

Kozinets, 2001; Thompson and Tambyah, 1999). Champy (2009, p12) describes authentic

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corporations as “companies that remain true to themselves; uphold their values in their

products, services, and actions; and are what they say they are”.

User-Generated Branding and User-Generated Content

Emotional branding strategies are coherent with the vision that brand image is co-created by

the company and by interactions among the users (Thompson et al., 2006). In encouraging co-

creation of meaning and interactions among the users, more and more companies ask for

consumer contributions through blogs, contests or voting, that is, at different stages of the

value chain (Arnhold and Burmann, 2009). Consumers are therefore acknowledged as

creative agents participating in the co-production of brand value (Kozinets, Hemetsberger and

Schau, 2008). UGB is defined as “unpaid advertising and marketing efforts, including one-to-

one, one-to-many, and many-to-many commercially oriented communications, undertaken by

consumers on behalf of the brand” (Arnhold and Burmann, 2009, p2). UGB includes the

expression of complaints, as well as brand dedication shared on the Internet or via mobile

devices like phones or cameras (Morrissey, 2005). As an example, consider the advertising

developed by the Apple Newton brand community with the aim of convincing Steve Jobs to

start producing the personal digital assistant again (Jensen Schau and Muniz, 2006) or the

negative consumer opinion on the iPod battery spread on the Internet (Kahney, 2004). The

products of this trend are folk ads (O'Guinn, December 2003), open source branding

(Garfield, 2005), vigilante marketing (Ives, 2004) and user-generated branding (Burmann and

Arnhold, 2008), e-tribalized branding (Kozinets, 2008), listenomics (Garfield, 2005),

brandhackers (Hecht, 2006), citizen marketers (McConnell and Huba, 2006), or do-it-yourself

advertisers (Ives, 2004). In extreme cases, the brand becomes nonproprietary and users

become the producers; the consumption then becomes a “prosumption” (e.g., Linux or

Wikipedia) (Cherkoff, 2005; Leyland, Watson, Berthon, Wynn and Zinkhan, 2006). The

creators of UGC are prosumers (Leyland et al., 2006; Toffler, 1980), users who spread the

word on brands (Gladwell, 2001; Nyilasy, 2006; Reichheld, 2003), members of brand

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communities (McAlexander et al., 2002; Muniz and O’Guinn, 2001; Muniz and Schau, 2007)

and journalists (Gillmor, 2004). Some authors have studied sponsored UGB, which occurs

when producers ask for consumer contributions as creative agents (Kozinets, 2008).

However, consumers are also able to destroy value. There is a lack of research on how

to deal with negative, non-sponsored UGB. Some authors advise managers to “stand back and

learn” (Donaton, 2006; Thompson et al., 2006), while others recommend supervising and

controlling this content (Mortimer, 2002).

Hypothesis development

From the company’s standpoint, fairness is required throughout the entire supply chain

(Palazzo and Basu, 2007) and corporate brands run the risk of obtaining a negative reputation

when corporate behavior contradicts the values of the brand. The result of this behavior is the

backlash of DBI. DBI is defined as “a family of disparaging images and stories about a brand

that are circulated in popular culture by a loosely organized network of consumers, anti-brand

activists, bloggers, and opinion leaders in the news and entertainment media” (Thompson et

al., 2006, p.50). As an operational description, DBI is the appearance, or reinforcement, of

negative associations coming from these stories at the brand reputation level, namely the

aggregation of all the individual brand images. Winchester and Romaniuk (2003, p.23) define

negative attributes as “those that are generally considered to be undesirable for the brand to be

associated with”. When this DBI is shared, it represents a new ensemble of brand attributes

that influences consumers’ perceptions of the corporate brand. Brand management theories

show that consumers avoid brands with negative associations (Thompson et al., 2006), to

cancel the risk that these negative meanings will damage their public image (Keller, 2003).

Nike, Starbucks and Coca Cola are some examples of corporate brands that have experienced

a DBI. Through negative UGC, the loss of control is so substantial that consumers may

damage the brand’s value (Christodoulides, 2009).

H1a: A negative non-sponsored UGB will provoke or reinforce a DBI.

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H1b: A negative non-sponsored UGB will increase the frequency of mentions of negative

attributes when consumers try to retrieve brand knowledge.

Having diagnosed a DBI, managers become aware of the contradictions undermining the

perceived authenticity of the brand story and can reconfigure it in a way that fits new

customers’ values. We will also study the moderator effect of the consumer’s Internet usage,

brand experience, the source’s credibility and the choice of Internet communication tool.

These hypotheses are integrated into our theoretical model (see Figure 1).

INSERT FIGURE 1

One the one hand, several authors (Hong-Youl and Perks, 2005; Jones, Leonard and

Riemenschneider, 2009; Xiaoni, Prybutok, Ryan and Pavur, 2009) have shown that a Web

experience positively influences Web trust. On the other hand, Riemenschneider, Jones and

Leonard (2009) showed that the Web's perceived individual impact is influenced by trust.

Consumers who frequently use the Internet may be more trustful and therefore more

influenced by Internet content. Therefore, we hypothesize:

H2: A high frequency of Internet usage will increase the frequency of mentions of negative

attributes when consumers try to retrieve brand knowledge, due to the exposure to a negative

UGC.

Several authors have shown that brand experience reduces negative impact on the brand

image, such as brand confusion (Alba and Marmorstein, 1987; Brengman, Geuens and De

Pelsmacker, 2001; Keller and Staelin, 1987) and brand image confusion (Brandt, Pahud de

Mortanges and Bluemelhuber, 2009). Therefore, we hypothesize:

H3: A good experience with the brand will reduce the frequency of mentions of negative

attributes when consumers try to retrieve brand knowledge, due to the exposure to a negative

UGC.

As mentioned before, UGB are produced by different sources. We would like to examine

which sender will have a stronger effect on the brand image. Credibility of varied sorts, such

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as endorsers’ credibility (Grimm, Hyunjung and Yilmaz, 2009) and corporate credibility

(Keller, 2003), has been studied. Considering that credible spokespersons elicit a greater

attitude change than less credible spokespersons (Lafferty, Goldsmith and Newell, 2002;

Sternthal, Dholakia and Leavitt, 1978), we hypothesize that:

H4: A higher level of source credibility will reinforce the frequency of mentions of negative

attributes when consumers try to retrieve brand knowledge, due to the exposure to a negative

UGC.

Several communication tools are available on the Internet to share UGC: social networks like

Facebook, virtual worlds like Second Life, blogs, e-mails, ads, SMS, podcasting and

videocasting like YouTube. We would like to show which communication tool will have the

strongest effect on the brand image. Considering that credible media elicit a greater attitude

change than less credible media, we hypothesize that:

H5: The more credible the medium, the higher the frequency of mentions of negative

attributes when consumers try to retrieve brand knowledge, due to the exposure to a negative

UGC.

Methodology

Research design: To capture the nature of DBI, Thompson et al. (2006) made qualitative,

tape-recorded, open interviews followed by content analysis on one single brand. However, to

validate and generalize the results and to quantify the extent of the DBI as a consequence of

negative UGB, a quantitative method is required. This quantitative method should show the

change in the brand equity (made up of brand image and brand awareness). However, scales

like the Yoo and Donthu scale (2001), brand personality scales (Aaker, 1997), brand ratings

(Kardes, 2002), brand uniqueness (Kapferer and Laurent, 1988) or multidimensional scaling

based on similarity measures would not illustrate the creation of new negative associations

and changes in the links between associations. Because of its emphasis on precisely these two

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aspects, we propose to use BCM to capture DBI. Concept mapping techniques assume that the

structure of the map reveals the inherent relationships represented in a person’s mind (Joiner,

1998) and shows how the brand performs on these attributes, which attributes are directly or

indirectly linked to the brand, the intensity of the links and which associations are

interdependent (John, Loken, Kyeong-Heui and Alokparna Basu, 2005). Several authors have

applied this technique in marketing (Bird, Channon and Ehrenberg, 1970; Boivin, 1986;

Carbonara and Scozzi, 2006; Dobni and Zinkhan, 1990; Elliot, Swain and Wright, 2003;

Green and Devita, 1977; MacKay and Easley, 1996; Pohlman and Mudd, 1973). It allows

managers to understand which associations are parts of the brand’s core identity and how

other associations may influence these core associations. As most of these techniques are

labor-intensive processes, beyond the scope of most company marketing departments, John et

al. (2005) have pioneered a valid and reliable quantitative consumer mapping technique with

less labor-intensive processes. However, most of the emotions that influence our thinking and

actions do occur below the level of awareness (Zaltman and Coulter, 1995), and verbal

questioning has been criticized for not generating emotional attributes (Zambardino and

Goodfellow, 2007). For this reason, we will use the elicitation stage of Zaltman Metaphor

Elicitation Technique (ZMET), a non-verbal method used to get at hidden knowledge and

overcome cognitive bias (Zambardino and Goodfellow, 2007).

First, we measure the brand reputation in the absence of any UGC. Then we capture changes

in the corporate brand perception as a consequence of an exposure to negative UGC. DBI

materializes through an increase in the frequency of mentions of negative associations as part

of the brand image. Therefore, for each corporate brand, we first build a list of attributes using

the elicitation stage of ZMET. Afterwards, a control group is asked to build individual brand

maps for the two senior brands, using the attributes listed as an input. After the aggregation

procedure, we know how the corporate brands are perceived in the absence of any exposure to

UGC. Subsequently, we build a two (high and low source credibility) x two (more and less

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credible media) between-subjects experiment, exposing four subsamples to two UGC’s about

two different brands (see the design in Figure 2). Then we ask these four subgroups to build

new individual brand maps for the two corporate brands. In the following phase, we aggregate

these individual brands for each subgroup and compare these aggregated BCM’s with the

BCM’s of the control group to show the DBI. Frequencies of mentions of negative attributes

are also compared using z-tests for equality of proportions (Dehon, Droesbeke and

Vermandele, 2008) to show if the differences in frequency of mention of the attributes

(frequency of mention and frequency of first-order mention) are statistically significant.

Considering the high importance consumers may give to one attribute in the attitude building

process, a significant change in the mention of one single attribute is sufficient to conclude

that there is a change in the frequency of mention of negative attributes. Finally, we analyse

the moderating effect of experience with the brand and of Internet usage.

INSERT FIGURE 2

Choice of the corporate brands: The selected corporate brands should be market leaders.

Market challengers, or followers, may be less susceptible to being attacked on their

authenticity because such brands may not be considered by Internet anti-brand activists

(Thompson et al., 2006), To provide internal validity to our results and show that changes in

brand image are due to our treatment, we measure the effects on two brands: Nike and Coke,

respectively, one brand with negative associations and one without negative associations.

Choice of the UGC sources: As mentioned earlier, negative UGC is created by anti-brand

activists, consumers who spread the word on brands (Gladwell, 2001; Nyilasy, 2006;

Reichheld, 2003), members of brand communities (McAlexander et al., 2002; Muniz and

O’Guinn, 2001; Muniz and Schau, 2007) and journalists (Gillmor, 2004). Using the

traditional dimensions of source credibility: expertise, trustworthiness and likeability (Keller,

2003; Ohanian, 1990), a sample of 50 respondents rate the credibility of the 4 sources on a 7-

point scale (Cronbach’s alpha between 0.76 and 0.88). In our experiment, we decided to keep

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the sources with the highest and the lowest credibility ratings, respectively, journalists and

anti-brand activists (see the results in Table 1 and the scale in Appendix 3).

INSERT TABLE 1 Choice of the communication tools: UGC is transmitted through social networks like

Facebook, virtual worlds like Second Life, blogs, e-mails, ads, podcasting and videocasting

like YouTube. Following Brakett and Carr’s (2001) measure of media credibility, a sample of

50 respondents rates the credibility of the medium on a 7-point scale (Cronbach’s alpha

between 0.81 and 0.91). In our experiment, we decided to keep the media with the highest and

the lowest credibility ratings, respectively, videocasting (like YouTube) and blogs (see the

results in Table 2 and the scale in Appendix 4).

INSERT TABLE 2 Attributes Elicitation using ZMET: To create a complete list of brand attributes, we follow

the elicitation stage of ZMET, the validity and reliability of which have been demonstrated

(Zaltman and Coulter, 1995). A convenience sample of 30 respondents is recruited because

saturation is supposed to be reached after the 20st respondent (Zaltman and Coulter, 1995),

and the topic is introduced. They are asked to collect 12 pictures of the 2 corporate brands.

One week later, during individual interviews, respondents show their pictures and explain

why they selected them. Based on these justifications, we obtain 37 attributes for the brand

Coke and 27 attributes for the brand Nike. The attributes listed in table 3 are used as an input

for BCM. The following picture description illustrates the attribute construction process: “I

choose this picture of a Christmas tree because I like the Christmas advertisements of Coke”

(Attribute: Christmas).

INSERT TABLE 3 Choice and test of the material: On YouTube (more credible medium), we found two

UGC’s, one for Coke and one for Nike (Appendix 5), and we copied the content of the two

videos to create a blog (http://brands-x.blogspot.com/) that was used as the low credibility

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communication tool (Appendix 6). Then we asked 30 respondents to generate associations

linked to these two UGC’s, using ZMET. These lists of attributes of the two UGC’s were used

as an input in the experiment. We obtained 8 attributes for the Coke UGC and 6 attributes for

the Nike UGC, as described in Table 4.

INSERT TABLE 4 Control Group: One control group was used by the researcher to show how the senior brand

is perceived in the absence of any exposure to the UGC. The participants were 60 randomly

assigned undergraduate students, chosen because of their growing interest in new

technologies, SMS, blogs and forums (Keller_Fay_Group, 2007). The sample is described in

Table 5.

INSERT TABLE 5 We built a BCM for each corporate brand in four steps: First, the 60 respondents had to select

brand associations in the list we had made previously (ZMET). They were provided with 45

cards for the brand Coke and 31 cards for the brand Nike; each card contained one attribute.

For each brand, they had to answer the question: “Considering the brand and the associations

listed on the cards, what comes to mind when you think about this brand?”. Respondents were

allowed to select as many cards as they wished. At the beginning of the second step, the

interviewer showed another example of BCM for another brand (Volkswagen Beetle in this

case; see Appendix 2) to describe the different links appearing on the BCM. Some attributes

were directly linked to the brand (like “German car” or “easy to park”), while other

associations were linked to each other (like “neat colors” and “lime green or silver”, meaning

that the lime green and silver colors of Volkswagen Beetles are neat colors). The BCM also

contains different types of links between the brand and the attributes, as well as between

attributes (single, double or triple links). It indicates how strong the associations are, with a

triple link meaning very strong. The main goal of the third step was to create a brand concept

map using the cards they had previously selected; a blank poster; as well as single, double and

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triple lines to connect the cards. Respondents were asked to stick the cards with the attributes

on the poster and to connect them with single, double and triple links. Keeping the

Volkswagen Beetle BCM next to them as an example, respondents were given all the time

they needed. After these interviews, which lasted 15-30 minutes on average, we obtained 60

individual BCM’s for each corporate brand (see Figure 3 for two examples of individual

BCM).

INSERT FIGURE 3

For each individual BCM, two independent coders coded the presence/absence of each of the

attributes, the types of links between associations and between the brand and the associations

(single, double and triple) and the level at which each attribute was placed on the map (1

means directly linked to the brand, 2 means linked to an attribute that is linked to the brand).

First, the two coders needed to decide, using “the frequency of mentions” and “the number of

interconnections”, which attributes would be “core” in our aggregated BCM. “The frequency

of mentions” was calculated by dividing the number of times an attribute is cited in the

individual BCM by the total amount of individual BCM’s. The second measure was “the

number of interconnections”, which counts the number of times the attribute is linked to all

the other associations. Regarding the frequency of mention, the 50% cut-off was chosen to

keep the attributes that were listed by a majority of respondents (John et al., 2005). The

borderline frequencies (>40%) were included, resulting in a brand map containing 8 core

brand associations for Coke and 12 core brand associations for Nike (John et al., 2005).

Within these core attributes, the two coders decided which of the core associations was linked

directly to the brand on the aggregated BCM by using two measures: “the frequency of first-

order mentions” and “the ratio of first-order mentions”. “The frequency of first-order

mentions” measures the number of times an association is directly linked to the brand. “The

ratio of first-order mentions” is the frequency of first-order mentions divided by the frequency

of mentions. If the frequency of first-order mentions was higher than 50%, the attribute was

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considered a first-order attribute. The two independent coders obtained 5 first-order attributes

for the brand Coke and 8 for the brand Nike. The third step involved an analysis of the

associations’ links to find where to place the remaining core brand attributes. Therefore, the

frequency of links between associations was examined (for example, “Made in China” was

frequently connected to “Low salaries”). Finally, we incorporated certain non-core brand

associations that were frequently linked to core associations to show which non-core brand

associations drive consumer perception of core associations. We linked these non-core

associations to core associations by taking the average link used in the individual brand maps.

We obtained the aggregated BCM (inter-coder agreement: 98%) described in Figure 5 (Coke)

and Figure 4 (Nike).

Experiment: Subsequently, for each brand, we built a two (high source credibility: Journalist;

and low source credibility: anti-brand activist) x two (more credible medium: YouTube; and

less credible medium: blog) between-subjects experiment, exposing four subsamples to two

UGC coming from two different sources (see the schema of the experiment in Figure 2).

Participants were 220 randomly assigned undergraduate students. The sample is described in

Table 5. For each corporate brand, students were provided with a hyperlink to a UGC

emphasizing negative attributes about the brand and were asked to read this content

attentively. After the treatment, respondents built individual BCM’s for the two corporate

brands, using the list of attributes coming from ZMET and the attributes of the UGC.

Afterwards, they completed a questionnaire that measured their experience with the brand

(Cronbach’s alpha 0.85 for Coke and 0.83 for Nike), their experience with the Internet

(Cronbach’s alpha= 0.86), the source credibility and medium credibility on a 7-point scale.

Finally, an open question was added to check whether the respondents had been aware of the

UGC. Following the same aggregation procedure as with the control group, we build one

aggregated BCM for each treatment condition. We obtained the following maps, presented in

Figures 4 and 5.

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INSERT FIGURE 4 INSERT FIGURE 5

Data Analysis

First we will analyse how the two corporate brands were perceived without any negative

UGC. For Nike, at the reputation level, we observe four negative core attributes, including

one first-order attribute and three second-order attributes. In the aggregated BCM of Coke, we

do not see any negative attributes, meaning that Coke does not suffer from a DBI without a

negative UGC.

To capture the effect of the negative UGC on the brand image, we perform two analyses. At

the aggregated level, we compare the aggregated BCM of the four treatment conditions with

one of the control group (see Figures 4 and 5) to capture the DBI. At the individual level, we

use a z-test for equality of proportions (Dehon et al., 2008) to investigate whether the

difference in frequency of mention is significant. In the Coke case, we do not observe any

negative association in the BCM of the control group and in the four treatment conditions,

meaning that the frequency of mentions is not high enough to be considered as part of the

brand reputation. However, the z-tests show significant increase in the frequency of mentions

of these negative attributes and the danger of negative UGC. In the Nike case, unlike with

Coke, the brand already suffers from a DBI circulating among consumers, even in absence of

any negative UGC. Therefore, as in the BCM of the control group, the negative associations

appear in the BCM in the four treatment conditions, with some reinforcements of the links

between them. Our z-tests show significant increases in the frequency of mentions of the

negative attributes after the exposure to the negative UGC. All these findings support H1b.

However, H1a, which states that a negative non-sponsored UGC will provoke a DBI, is only

partially supported. The details are described in Tables 6 and 7.

INSERT TABLE 6

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INSERT TABLE 7

Effect of source credibility and media credibility: As shown in Tables 8 and 9, for the Nike

brand, we found one significant difference between the frequencies of mentions of negative

attributes: YouTube has a stronger negative effect on the brand image than a blog (in support

of H5). For the Coke brand, we found one significant difference between the frequencies of

first-order mentions of negative attributes: a journalist has a larger negative impact on the

brand image than an anti-brand activist (in support of H4).

INSERT TABLE 8 INSERT TABLE 9 Effect of the experience with the brand and experience with the Internet: We define

people with an intense (low) experience with the brand and people with a high (low)

experience with the Internet using a 3.5 cutoff point on our 7-point scale (see scales in

Appendix 1 and 2). As shown in Tables 10 and 11, we observe several significant differences

in frequency of mentions between consumers with high and low brand experience for the

Nike and Coke brands, in support of H3. We also detect three significant differences in

frequency of mentions between consumers with high and low Internet usage for the Nike

brand and the Coke brand, though not in the expected direction. H2 is therefore rejected.

INSERT TABLE 10 INSERT TABLE 11 Conclusions and Implications

The aims of our study were to capture the reputation of two leading corporate brands, to study

how the exposure to a negative UGC could negatively impact the brand reputation and to

detect which situations are the most risky in terms of brand reputation damages. With respect

to the reputation of our two brands, we observed a negative DBI circulating among consumers

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about the Nike brand. Indeed, Nike is perceived as being “Made in China” and as collecting

“Big profits”, thanks to “Low wages” and “Child labor”. On the one hand, this DBI

undermines the authenticity of its emotional-branding story, but on the other hand, it also

allows the company to gain insights into how this brand story could be altered to avoid such

an undesirable outcome. To pursue this goal, Nike is working to take a leadership role in labor

reform through a newly formed business leadership team (Niemi, 2004). Coke is not suffering

from any DBI in absence of a negative UGC. This may be due to the proactive strategy of

Coke that destroys systematically all the brand rumors on its website (Gillard, 2009).

On the one hand, our study shows significant evidence of DBI for the Nike brand but not for

Coke. On the other, our study shows a significant increase in the frequency of mention of

negative attributes in the four treatment conditions for the two brands. These findings have

several implications.

First, our study shows that even if the information has been controlled and verified by a

journalist or is only a rumor; and even if the information is transferred through videos or

blogs; the negative UGC will cause an increase in the frequency of mentions of negative

associations. This situation has been experienced by IKEA, which was the target of negative

UGC, a rumor about a child having allegedly been kidnapped at an IKEA in Belgium (see

hoaxbuster.com). This shows how a fallacious rumor can have a negative impact on

individual brand image.

Second, our study confirms the possibly greater impact on the frequency of mentions of

negative attributes when the negative UGC has been created by a credible source (like a

respected journalist) than when it has been created by a less credible source (like an anti-

brand activist).

Third, credible Internet media like YouTube may also have a greater impact on the negative

attributes than less credible media like blogs.

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Fourth, our study supports the idea that a negative UGC will have a stronger impact on

consumers with less brand experience than on experienced consumers. These consumers will

be less influenced by negative content found on the Internet.

Fifth, regarding experience with the Internet, and contrary to our hypothesis, our study

showed that, in certain cases, negative UGC can have a larger impact on people with less

Internet experience. This may be because frequent users are more critical regarding Internet

content than occasional users.

Finally, our results suggest that, if the brand already has a DBI, an exposure to negative UGC

will reinforce it. However, if the brand reputation is mostly positive, a single exposure to

negative UGC will not create a DBI. Still, this exposure causes an increase in the frequency of

mentions of negative attributes, suggesting that repetition could lead to the occurrence of a

DBI.

Limitations and future research directions

Several issues would be useful to address in future research. First, while this research focuses

on the risk of DBI for leading corporate brands (brands with a high level of brand equity),

corporate brands with average and low brand equity will have knowledge structures that are

not so well fixed in consumer memory, and the risk of DBI should be different. Second, our

methodology suggests that one exposure to negative UGC can modify the brand image

perception. Future research should study the effect of repetition and time on DBI.

Figure 1: Model

Figure 2 : Design of the experience

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Figure 3: Examples of individual BCM for Nike and Coke (respondent 7)

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Figure 4: Nike’s aggregated BCM for the control group and the 4 treatment conditions Control Group

Group A: (YouTube and journalist)

Group B (YouTube and anti-brand activist) Group C (Blog and anti-brand activist)

Group D (Blog and journalist)

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Figure 5: Coke’s aggregated BCM for the control group and the 4 treatment conditions Control Group Group A:((YouTube and journalist)

Group B (YouTube and anti-brand activist) Group C: (Blog and anti-brand activist)

Group D (Blog and journalist)

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Table 1: Average credibility of the different sources

Consumers Anti-brand activists Journalists

Members of brand communities

Credibility 3,771 2,987 4,494 4,316 Table 2: Average credibility of the different transmission tools

Facebook Blogs E-mails Viewer-created ads You-Tube

Credibility 2,792 2,764 2,972 2,938 3 Table 3: Pre-test: Attributes elicited with ZMET

Coke Nike 1 Fresh, ice, thirst-quenching Just do it 2 Soft drink, bubbles, sparkling Good quality 3 Black Expensive 4 Red Sponsoring worldwide events

(world cup, MBA…) 5 funny advertising, good marketing Global brand, international 6 Santa Claus Advertising, television 7 Worldwide drink, global brand,

international Stars equipment, celebrities (Michael Jordan, Christiano Ronaldo)

8 Good Taste USA 9 Sugar funny advertising, good marketing,

nice logo 10 Fanta young 11 Sprite Barcelona 12 USA Boys 13 Summer, sun Competition 14 Famous Cool 15 Young Comfortable 16 Fast food, Mc Donald’s Extensive distribution 17 Cinema Child labor 18 Cheap Low wages 19 Addiction Sport (football, Jogging, tennis,

basket, dance)

20 Different packaging, different sizes, bottles

Made in China

21 Dietetic Bad rumors 22 Big choice Big choice 23 Caffeine Addidas 24 Extensive distribution Game 25 Pepsi Technologies, innovation,

performance, modern 26 River Coke Clothes, sportswear, shoes, T-shirt,

trousers

23

27 Zero Exploitation

28 Light 29 Break 30 Glass 31 Friends 32 Sponsorship 33 Fat, obesity 34 Whisky 35 Plants 36 Lemon 37 Cherry

24

Table 4: Associations linked to the 2 UGC Coke UGC Nike UGC 1 Power Big profits 2 Anti-union Low wages, underpaid 3 Exploitation, pressure Exploitation, overworked

4 Brutality, violence Made in China (Vietnam, Malaysia and Indonesia)

5 Authority Verbal and physical abuses 6 Low wages Child labor

7 No respect for the environment

8 Irresponsible Table 5: Sample description Control A B C D Total high experience with the brand 16 12 9 14 16 67 Low experience with the brand 44 38 51 46 34 213 high experience with the Internet 25 16 21 26 32 120 Low experience with the Internet 35 34 39 34 18 160 Total 60 50 60 60 50 280 Age max 28 22 22 29 23 29 Age min 19 18 18 21 19 18 Average age 21.98 19.44 19.98 23.16 20.65 21.11 Masculine 25 22 25 34 30 136 Feminine 35 28 35 26 20 144

25

Table 6: Test of the difference in frequency of mention between the control group and the 4 treatment conditions for the brand Nike Frequency of Mention Difference with the control group

Control A B C D Control/A Control/B Control/C Control/D 25 Made in China 0.3 0.64 0.56 0.56 0.43

sig (3.56>1.95) sig (2.94>1.95) sig (2.94>1.95)

26 Child labor 0.41 0.74 0.65 0.6 0.43sig (3.40>1.95) sig (3.56>1.95) sig (2>1.95)

Frequency of First-order

Mention Difference with the control group

control A B C D Control/A Control/B Control/C Control/D 25 Made in China 0.11 0.64 0.43 0.56 0.56 sig (4>1.95) sig (3>1.95) sig (2.65>1.95) sig (1.98>1.95)

28 Big Profits 0.03 0.42 0.34 0.48 0.41sig (2.79>1.95) sig (2.84>1.95) sig (2.64>1.95) sig (2.11>1.95)

29 Verbal and physical abuses 0.1 0.06 0.09 0.11 0.26

sig (2.29>1.95)

30 Exploitation 0.05 0.4 0.22 0.31 0.38 sig (2.48>1.95)

26

Table 7: Test of the difference in frequency of mention between the control group and the 4 treatment conditions for the brand Coke Frequency of Mention Difference with the control group control A B C D Control/A Control/B Control/C Control/D 38 Power 0.08 0.16 0.23 0.13 0.25 sig (2.25>1.95) sig (2.18>1.95)

39 Anti-Union 0 0.12 0.08 0.13 0.09 Sig (2.75>1.95) sig (2.28>1.95) sig (2.92>1.95) sig (2.41>1.95)

40 Exploitation 0.03 0.26 0.15 0.25 0.25 sig (3.44>1.95) sig (2.21>1.95) sig (3.40>1.95) sig (3.18>1.95) 41 Brutality 0 0.02 0.05 0.1 0.06 sig (2.51>1.95) sig (1.95>1.95) 42 Authority 0 0.08 0.06 0.11 0.06 sig (2.23>1.95) sig (2.03>1.95) sig (2.72>1.95) sig (1.95>1.95) 43 Low wages 0.05 0.18 0.15 0.23 0.125 sig (2.17>1.95) sig (2.87>1.95) 44 No respect for the environment 0.02 0.16 0.21 0.1 0.09 sig (2.73>1.95) sig (3.41>1.95)

Frequency of First-Order

Mention Difference with the control group control A B C D Control/A Control/B Control/C Control/D 38 Power 0 0.16 0.25 0.23 0.13 sig (2.50>1.95) sig (2.28>1.95) sig (2.03>1.95) sig (4.05>1.95) 39 Anti-Union 0 0.12 0.09 0.08 0.13 sig (2.03>1.95) sig (1.95>1.95) 40 Exploitation 0.02 0.26 0.25 0.15 0.25 sig (2.64>1.95) 44 No respect for the environment 0 0.16 0.09 0.21 0.1 sig (3.11>1.95) sig (1.95>1.95) Table 8: Effect of the source credibility and media credibility for the brand Nike Frequency of Mention Difference between treatment groups

Control A B C D AB CD AD

BC

26 Child labor 0.41 0.74 0.65 0.6 0.43 sig (2.75>1.95)

27

Table 9: Effect of the source credibility and media credibility for the brand Coke Frequency of First-Order Mention Difference between treatment groups

control A B C D AB CD

AD BC

38 Power 0 0.1 0.08 0.06 0.25 sig (2.48>1.95) Table 10: Tests of the experience with the brand and the experience with the Internet for the brand Nike

Significant difference due to the brand experience

Significant difference due to the internet experience

GROUP A (YouTube and Journalist) Presence as a core attribute Made in China (2.17>1.95)

Verbal and physical abuses (2.56>1.95)

Made in China (2.65>1.95)

Presence as a first order attribute Exploitation (2.33>1.95)

GROUP B (YouTube and anti-brand activist) Presence as a core attribute Presence as a first order attribute

GROUP C (Blog and anti-brand activist) Presence as a core attribute Presence as a first order attribute

GROUP D (blog and Journalist) Presence as a core attribute Low wages (1.956>1.95) Low wages (2.97>1.95) Presence as a first order attribute

28

Table 11: Tests of the experience with the brand and the experience with the Internet for the brand Coke

Significant difference due to the brand experience

Significant difference due to the internet experience

GROUP A (YouTube and Journalist) Presence as a core attribute Low wages (2.03>1.95) Presence as a first order attribute

GROUP B (YouTube and anti-brand activist Presence as a core attribute Brutality (2.33>1.95) Presence as a first order attribute Irresponsibility (2,42>1,95)

GROUP C (Blog and anti-brand activist) Presence as a core attribute Anti-Union (2.48>1.95)

Authority (2.82>1.95) Low salaries (2.7>1.95)

Presence as a first order attribute

Brutality (2.12>1.95) Authority (2.19>1.95)

Authority (2.12>1.95)

GROUP D (blog and Journalist)

Presence as a core attribute Presence as a first order attribute

29

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Appendix 1: Web Usage Measurement

Involvement with online services (Keaveney and

Parthasarathy, 2001)

1. I am very interested in online services

2. My level of involvement with online services is high

3. I am particularly engaged in the online environment

4. I consider myself as an expert on the online electronic environment

5. I consider myself as an Internet expert 6. I purchase products from online vendors

regularly 7. My level of expertise regarding PC is high

Appendix 2: Purchase Frequency

(Dahl, Manchanda and Argo, 2001)

How often do you purchase? Very rarely...very often

When was the last time you purchased? Never...within last month

How familiar are you with purchasing brand X? Not familiar...very familiar

Appendix 3: Source credibility measurement Trustworthiness (Ohanian, 1990)

1. The source is dependable

2. The source is honest 3. The source is reliable 4. The source is sincere 5. The source is trustworthy Expertise (Ohanian, 1990) 6. The source is expert 7. The source is experienced 8. The source is knowledgeable 9. The source is qualified 10. The source is skilled Likeability (Keller, 2003) 11. The source is likeable 12. The source is prestigious 13. The source is dynamic

Appendix 4: Media credibility measurement

(Brakett and Carr, 2001) 1. The medium is credible

2. The medium is trustworthy 3. The medium is believable

Appendix 5: Blog

35

36

Appendix 6: Content of the 2 UGC Coca Cola I want you to understand the situation in Columbia. In Colombia, the government, big companies like Coca Cola and paramilitaries have something in common. They don’t want any social movements and particularly any labor or human rights movement that can challenge their authority, their profits or their brutality. .. Based on extensive investigations, an American university professor wrote: “Unions in Colombia are victims of a calculated and selective strategy carried out by sectors of the state, paramilitaries and some employers to weaken and eliminate trade unions. In an official report: Coca Cola is constantly pressuring unionized workers to resign, Coca Cola is striving for the anti-union company as well as the capacity to drive wages into the ground. It is one of the primary goals of the violence directed against workers. In July 2006, Coca Cola was pointed out in the so called “broad market social index (BMSI) list of socially irresponsible companies” managed by an independent investment research firm which is considered as a world leader in defining the corporate responsibility standards. This report quote that they based their reports on several issues:

• Labor unions rights in Colombia • Environmental issues in India • And jeopardizing children’s health

Nike Nike is one of the largest and most popular sportswear brands in the world with $7,000,000,000 profit per year. The workers are paid 700$ a year in average, while working 17 hours a day. The workers of the Nike factories in China, Vietnam, Malaysia and Indonesia are overworked, underpaid and overall exploited. The minimum age of the Nike factory workers is 8 years old. Reports from the British Broadcasting Corporation on the 22nd February 2001: “The world’s leading sports shoes manufacturer Nike has admitted that its Indonesian workers suffer widespread verbal and physical abuse at its factories”. “Other cases allege that the deaths of two workers were related to the denial of medication”. Vietnamese and Chinese workers still get poverty wages and 4$ a day would be considered as a decent wage. Nike, a company with $8.7 billion in revenue in 1998 that sells its shoes for $150 can well afford to pay its workers such a sum. Nike is a member of the Fair Labor Association and pays $100,000 annually in dues. Do the right thing. Boycott Nike.