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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.
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