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This research focuses on consumer brand usage segments and the responses they give to negative attributes in brand image studies. Analysis was conducted across three markets and four approaches for measuring brand beliefs with respondents who were current users, past users or had never tried a brand. The major finding of this study was that past users of a brand consistently have the highest tendency to elicit negative beliefs about brands. Further, those who have never used a brand typically have a lower propensity than current brand users to elicit negative brand beliefs. These results suggest that negative beliefs about a brand are developed as a result of purchase behaviour, rather than as mechanisms to reject a brand prior to purchase. These findings have implications for the role of negative beliefs in consideration of set formation and the trial of a new brand. They also provide insight into the patterns that may be expected when measuring and interpreting negative brand beliefs across different usage groups.
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Negative brand beliefs and brand usage
Maxwell Winchester
Harper Adams University College and Ehrenberg-Bass Institute,
University of South Australia
Jenni Romaniuk
Ehrenberg-Bass Institute, University of South Australia
This research focuses on consumer brand usage segments and the responses they
give to negative attributes in brand image studies. Analysis was conducted across
three markets and four approaches for measuring brand beliefs with respondents
who were current users, past users or had never tried a brand. The major finding
of this study was that past users of a brand consistently have the highest tendency
to elicit negative beliefs about brands. Further, those who have never used a brand
typically have a lower propensity than current brand users to elicit negative brand
beliefs. These results suggest that negative beliefs about a brand are developed as
a result of purchase behaviour, rather than as mechanisms to reject a brand prior
to purchase. These findings have implications for the role of negative beliefs in
consideration of set formation and the trial of a new brand. They also provide
insight into the patterns that may be expected when measuring and interpreting
negative brand beliefs across different usage groups.
Understanding what a brand means to consumers is important for today’s
marketing managers. One mechanism for gaining insight is to measure the
knowledge that consumers hold about a brand, which consists of all the
thoughts, feelings and beliefs held about any brand (Keller 2003). Consumer
belief measurement is considered an important part of consumer-based
brand equity (CBBE) measurement because of the diagnostic information
it holds (Ailawadi
et al. 2003). As a result, in most large corporations
today, one of the most important aspects of the marketing department’s
activity is to manage and measure CBBE.
Received (in revised form): 21 February 2007
International Journal of Market Research Vol. 50 Issue 3
© 2008 The Market Research Society 355
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Negative brand beliefs and brand usage
Negative brand beliefs are statements about a brand that are considered
undesirable (for example, nominating a fast-food brand as ‘too high in
fat’). In contrast to their positive counterparts, negative brand beliefs
have received very little attention in the academic literature (Winchester
& Romaniuk 2003), and have been highlighted as an area that requires
further research (Hoek
et al. 2000).
Most consumer behaviour theories incorporate the idea that consumers
evaluate brands according to their positive and negative aspects (e.g.
Lussier & Olshavsky 1979; Ajzen & Fishbein 1980; Biehal & Chakravarti
1986; Bagozzi & Warshaw 1990; Kahn & Baron 1995; Moorthy
et al.
1997). In studies where respondents are prompted with a brand and
asked what beliefs they hold (such as in Krishnan 1996), both positive
and negative beliefs are elicited. Similarly, when given negative beliefs
and asked which brands are linked to those, customers are able to elicit
brands, even in a free response context (Bird
et al. 1970; Woodside &
Trappey 1992; Winchester & Romaniuk 2003). While it is evident that
consumers do hold negative beliefs about brands, it is not clear what the
relationship is between purchase behaviour and such beliefs. Therefore,
understanding the contribution these make to the consumer choice process
is an important area of research.
The neglect of negative brand beliefs may be due to the assumption
that negative responses follow polar opposite patterns to non-negative
attributes. Therefore, if brand users are more likely to give positive
beliefs about a brand (as found in studies such as Barwise & Ehrenberg
1985), then they will be less likely to give negative beliefs about brands.
However, explorations of the negative side of constructs in other areas of
marketing research suggest that this assumption may be unfounded. For
example, dissatisfaction is now considered to be a separate construct from
low satisfaction (e.g. LaBarbera & Mazursky 1983) and ad irritation is
considered distinct from likeability (e.g. Greyser 1973; Aaker & Bruzzone
1985). This suggests that the negative belief side of CBBE should be the
subject of a distinct exploration to test the role and contribution that
negative beliefs play in the consumer choice process.
The objective of this paper is to address this by examining the interaction
with past, current and no brand usage experience and consumers’
propensity to associate negative beliefs with brands. Retrieval of beliefs is a
measure of accessibility, and that accessibility is an indicator of past usage
of such consumer memories as it suggests past or recent refreshment and
reinforcement of associative links (Anderson & Bower 1979). Therefore
comparing the negative beliefs held by consumers with differing past usage
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experiences can help understand when negative beliefs are formed and
how they contribute to the choice process. This will provide insight for
those seeking to measure and interpret negative brand image beliefs.
The structure of this paper is as follows: we first review the relevant
literature about the role of negative information, and propose competing
hypotheses about the relationship between usage experiences with the
brand and negative brand beliefs; the research method and empirical
analysis follow; the implications of the results for the different theories are
then discussed, as well as the limitations and future research agenda.
Negative information and consumer decision making
Most consumer behaviour theories have some element of cognition
about them, in that they suggest that consumers utilise memories about
brands in some way, shape or form to select brands from the wide range
of alternatives. Broadly speaking, the consumer choice process can be
considered to consist of two stages prior to purchase. The first is identifying
suitable options of preferred brands, which constitute the consideration set
(consideration); the second is to choose an option from the consideration
set (selection) (Howard & Sheth 1969; Nedungadi 1990). Several theorists
have proposed that negative beliefs may contribute at each of these stages
in a different way, with compensatory and non-compensatory models
commonly used to explain these two processes (Lussier & Olshavsky 1979;
Kahn & Baron 1995; Reed 1996; Moorthy
et al. 1997). For example, non-
compensatory models would suggest that consumers eliminate brands
during the consideration process based on negative beliefs about brands,
or by assessing brands and excluding them based on the fact that they do
not meet selection criteria (Kahn & Baron 1995; Laroche
et al. 2003).
Compensatory models, on the other hand, would suggest that negative
information is utilised in conjunction with positive information to evaluate
a brand (Kahn & Baron 1995; Laroche
et al. 2003). Both types of model
lead to different implications about how negative beliefs will fit into the
process. While non-compensatory and compensatory models explain how
negative perceptions may influence brand consideration and selection,
there is also evidence that negative perceptions are a result of past usage of
a brand. Such feedback may influence either the consideration or selection
stage in the future (e.g. as outlined by Foxall 2002).
et al. (1970) point out that there are three types of usage group for
any brand in the market. The first is ‘current users’; these are consumers
who currently have the brand in their repertoire. The second is ‘past users’;
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these are consumers who have experienced the brand in the past, but no
longer have the brand as part of their repertoire of preferred brands. The
third is those who have not had any actual usage experience with the
brand; Bird
et al. (1970) refer to them as the ‘Never trieds’.
We now discuss these and then establish hypotheses for the level of
negative beliefs in customer groups with different past/current usage
It is suggested that decision makers use non-compensatory models
particularly when there are more than three alternatives (Lussier &
Olshavsky 1979; Reed 1996) or when the motive for extra cognitive effort
is low (such as in low-involvement situations) (Kahn & Baron 1995). Non-
compensatory models assume that the individual does not utilise large
amounts of information and looks to actively reduce options, and that
negative beliefs can act as a primary method for rejecting brands, thereby
culling alternatives.
Negative beliefs, utilised in this way, would remove unacceptable brands
from the choices available, leaving the consumer with a smaller number
of brands in their consideration set (Moorthy
et al. 1997). This has
implications for which customers would be expected to have higher levels
of negative beliefs. If negative beliefs are utilised to reject brands prior to
consideration, the consumer will never get the opportunity to experience
the brand. This would mean that consumers who have never used a brand
are the most likely to hold negative beliefs about that brand (Lynch
al. 1988; Keller 1993). Similarly, brands not selected in a compensatory
model of decision making would also be expected to receive higher levels
of response to negative beliefs than brands selected (Lussier & Olshavsky
1979; Kahn & Baron 1995). This leads to the first hypothesis:
H1: If consumers more commonly use negative beliefs to reject
brands at the consideration stage, then customers who have
never tried a brand will be most likely to express negative brand
In a study of memory processes and retrieval of attribute information,
et al. (1988) observed that their subjects made little attempt to
retrieve and use attribute information. They indicated their findings
were ‘provocative in [their] implication for the degree to which real
world choices involve the sorts of multi-attribute choice rules we have
studied’ (Lynch
et al. 1988, p. 177). In their literature review, Biehal and
Chakravati (1986) discussed ‘rejected’ brands as ones that have been
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negatively evaluated, while in their study they provide no evidence that
such rejection happens. This study suggested that there was relatively
little use of negative information for rejected brands in their brand choice
scenario, a finding that is confirmed by Wänke
et al. (1997).
This leads us to ask what other mechanisms might be utilised to
form negative beliefs. One suggestion is that experiences from using the
brand feed into the negative perceptions that consumers hold, and these
perceptions feed back into the future consumer decision-making process
and lead a consumer to reject a brand they have previously used at the
selection stage. This would lead to the conclusion that past users of a
brand would be the most likely to express negative beliefs about a brand.
This was empirically evident in an early study by Bird
et al. (1970),
although the differences between groups were minor and methodological
concerns raise doubts about their conclusion. They measured negative
beliefs as the polar opposite of positive beliefs and immediately following
the positive belief question (e.g. ‘Which brands are high quality?’ was
then followed by ‘Which brands are low quality’). Such a method may
have inhibited the likelihood of respondents responding to the negative
beliefs. Also, as the study was conducted cross-sectionally in a repertoire
market, past users were simply non-recent users (categorised as ‘used the
brand previously, but just not in the past four weeks’), rather than having
rejected the brand at selection stage. Therefore this relationship needs to
be further investigated in terms of negative beliefs expressed independently
of their polar opposites.
An explanation for this pattern suggests that negative beliefs are likely
to further generate after ceasing to use a brand to rationalise the switching
behaviour and reduce cognitive dissonance (Festinger 1957). Winchester
and Romaniuk (2003) examined the differences in the negative beliefs
between brand users and non-brand users, and found that while brand
users were slightly more likely to express negative beliefs, there was a much
greater agreement between the two usage categories than has previously
been found for positive beliefs (Barwise & Ehrenberg 1985; Hoek
et al.
2000). However, the mixing of the two non-brand user groups of past
users and those who have never used the brand might have confounded
the results. Therefore we would hypothesise the following:
H2: If consumers more commonly use negative brand beliefs to
reject the brand at selection stage, then past brand users should
give more negative beliefs than other user groups.
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Negative brand beliefs and brand usage
Accounting for past usage in brand image data was acknowledged as
important as early as 1961, when Franklin Evans noted: ‘brand image
statements are meaningless unless the brand owned by the respondent is
taken into account’ (Evans 1961, p. 21). A study by Biehal and Chakravati
(1986) noted that previously chosen brands were highly accessible in
memory and were retrieved and chosen in a subsequent choice. This is
because retrievals of recently used brands reinforce the associative links in
memory, which then subsequently maintains or increases the salience of
the brand and its associative links upon future occasions (Collins & Loftus
1975; Romaniuk & Sharp 2004).
This suggests that brands currently being bought are more easily
retrieved for most stimuli, and such retrieval is likely to inhibit the retrieval
of information for other (not currently used) brands. Further, given that
most companies do not communicate negative information about their own
brand, and comparative advertising that makes direct, explicit negative
claims about competitor brands is still relatively rare, the source of most
negative information is likely to be either past experience or negative word
of mouth (WOM). Recent research into the relative incidence of negative
WOM has found it to be rare relative to positive WOM (East
et al. 2007),
which leaves direct experience with the brand as the primary potential
source of negative beliefs. This suggestion is in line with authors who
argue that consumer responses are a function of behavioural history (e.g.
Foxall 2002).
This line of thought would suggest that, if negative beliefs are a product
of consumer experiences with the brand and that the overall experience
inhibits memories (positive or negative) for other brands not currently
used, then current brand users will be the most likely to express a negative
belief about that brand. This leads to the following hypothesis:
H3: If the current usage experience is the dominant influence on
giving responses, then those that currently use the brand will be
the most likely to give negative beliefs about that brand.
Research method
One key criticism of many studies conducted in the marketing discipline
that has been put forward is that there is very little replicated, generalisable
research conducted (Lindsay & Ehrenberg 1993; Hubbard & Armstrong
1994). The absence of replication studies is seen to be impeding knowledge
development in marketing (Hubbard
et al. 1992). Hubbard et al. (1992)
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outline a number of consequences that can arise from not replicating
studies in marketing. These consequences include type one error bias,
perpetuating erroneous results. In line with the arguments put forward
by authors such as Ehrenberg and Bound (1993) and Barwise (1995), this
study is conducted in the empirical generalisationalist tradition: instead
of relying solely on inferential statistical tests on a single sample (which
increases the likelihood of promoting exceptional one-off results), this
research tests the hypotheses across a number of independent markets and
conditions to triangulate findings.
Table 1 details the key information from
each of the studies utilised in this research.
In all studies, brand usage was self-reported. Respondents were asked
which of the following categories applied to their usage of each brand:
(a) Currently use the brand (Current users); (b) Have used in the past but
no longer do so (Past users); (c) Have never used the brand (Never tried).
This method does assume that people realise when they have ceased using
a brand. The specifics of each of the individual studies are discussed prior
to the results.
Study 1: personal banking
This study involved a split-sample approach, where respondents were
randomly allocated to one of three treatment groups. The study was
designed to specifically explore response patterns to negative attributes
for each measurement technique; as a result, no positive perceptions were
collected. Each treatment group had a different method for measuring
negative brand perceptions:
a 5-point Likert rating scale
ranking brands from highest to lowest on that quality
Table 1 Outline of studies included in research
Study Sample Industry Respondents
No. of brands/No. of
negative attributes
1 n = 404 Financial services Personal 3 different
5 brands/5 attributes
2 n = 230 Irrigation products Business Open-ended 2 brands/3 attributes
3 n = 368 Fast food Personal Free-choice ‘pick any’ 5 brands/5 attributes
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Negative brand beliefs and brand usage
a free-choice, ‘pick any’ approach where respondents were asked
which, if any, brands they linked with a particular attribute and so
were free to respond for any, all or no brands.
Table 2 presents an example of how the three questioning styles may be
worded for a particular attribute.
Depending on which group respondents were allocated to, they were
asked to rank, rate or nominate brands to five negatively worded attributes:
‘Doesn’t care about smaller customers’; ‘Stuck in the past’; ‘Bureaucratic’;
‘Poor customer service’; ‘High fees and charges’.
These three particular methods were chosen for their common use
in academia and industry measurement (Barnard & Ehrenberg 1990;
Driesener & Romaniuk 2006). The sample sizes were: rating = 182;
ranking = 192; pick any = 230. The difference in sample size probably
reflects the relative speed of the ‘pick any’ approach when compared to
other measurement approaches (Driesener & Romaniuk 2006). The same
five brands and five negative attributes were included in each treatment.
The attributes were derived from past research conducted in the industry
by the authors, and circulated to colleagues to ensure face validity that
they were undesirable qualities for this market. For example, one potential
attribute – ‘old-fashioned’ – was not included in the research as there was
concern that as some had expressed a desire to return to old-fashioned
service, the belief of being old-fashioned was not necessarily negative.
All respondents were recruited randomly from an electronic telephone
directory by trained market research interviewers.
Table 2 Forced-choice vs free-choice examples
Forced-choice – Scale Forced-choice – Rank Free-choice – Pick any
We would like to know if you agree
or disagree with the statements on
a scale of 1–5, where 1 is that you
strongly disagree with the statement
and 5 is that you strongly agree with
the statement
‘Empire Bank is a bank that has high
fees and charges’
We are going to give you a list
of banks. We would like you to
rank the banks from the one that
is most closely associated with
the statement to the one least
associated with the statement.
‘Is a bank that has high fees and
(list of brands)
Which banks would
you say have high fees
and charges?
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Results: personal banking
For the rating and ranking, usage group means for each brand were
calculated and compared. Table 3 presents the results. The findings of
this study suggest that, regardless of which technique is used to collect
brand image data, in general past users of a brand rank, rate or respond
at the highest levels when presented with negative attributes. Specifically,
the results indicate that, by using a scale or rank to measure responses
to brand image attributes, there is far less sensitivity to differences in
The results are presented by brand for each attribute across all three
methods to allow direct comparison of the results.
Table 3 presents the
results for the attribute ‘High fees and charges’. The table presents the
average response level for the pick any technique for each brand at the left
of the table, the average rating given for the rating technique in the middle
and the average place each brand was ranked on the right. Values that
are significantly different from the highest value (shaded) are identified
using a chi-squared test for the pick any method and a one-way ANOVA
with Tukey’s post-test for the other two methods (* =
p < 0.05 and ** =
p < 0.01).
The results show that, regardless of measurement technique, Past users
tend to respond, rate or rank brands higher than the other groups. This
is supportive of Hypothesis 2. Regardless of measurement technique, the
Never tried group tended to respond, rate or rank at the lowest levels
across brands, not supporting Hypothesis 1. For one brand, Current users
were equal or higher than Past users, suggesting that there can be brand-
specific exceptions to the general pattern of a higher response level from
Past users. The results presented in the table suggest that differences across
Table 3 Comparison of results for three measurement methods for attribute ‘High fees and
Pick any Rating Ranking
Empire Bank 56 52 43* 4.1 3.9 3.7 4.1 4.2 4.1
Federal Bank 50 65 28** 4.0 4.0 3.6 3.5 3.5 3.4
Western Pacific Bank 63 46* 28** 3.9 3.4 3.7 3.4 3.0 2.7*
Oceanic Bank 48 37 35* 4.5 3.8 3.7* 2.2 2.2 2.4
Knight’s Bank 43 52 30* 3.9 3.5 3.5 2.1 1.9 1.6
Mean 52 50 33 4.1 3.7 3.6 3.0 2.9 2.8
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Negative brand beliefs and brand usage
brands and groups are more notable when using the pick any technique,
while the other two methods tend to reduce the differences across brands
and groups. This is in line with what was expected for these techniques,
given what has been found with positive attributes in the past (Barnard &
Ehrenberg 1990; Driesener & Romaniuk 2006).
The same analysis was conducted for the remaining four attributes,
with the results shown in
Table 4. The first observation is that two
previously identified patterns with positive attributes are evident in the
results presented. The first of these patterns shows that brands with more
users (i.e. bigger brands) tend to get higher levels of response (e.g. Barwise
Table 4 Comparison of results for three measurement methods (highest responses shaded)
Pick any Rating Ranking
Poor customer service
Empire Bank 52 51 31** 4.0 3.5* 3.6 4.2 4.5 4.7
Federal Bank 41 32 21** 3.5 3.0 3.4 4.0 2.8* 3.4
Western Pacific Bank 34 34 25* 4.1 3.1* 3.1* 3.6 2.9 3.0
Oceanic Bank 35 37 22** 4.3 3.2* 3.4* 3.6 3.0 2.8
Knight’s Bank 31 39 23** 3.6 2.9 2.9 2.8 3.4 2.2
Average response 39 39 24 3.9 3.1 3.3 3.6 3.3 3.2
Stuck in the past
Empire Bank 44 23** 16** 3.5 2.6* 2.7* 4.1 4.3 4.5
Federal Bank 14 13 8 2.4 2.6 2.8 3.9 3.3 3.8
Western Pacific Bank 31 20** 10** 3.1 2.5 2.7 3.6 2.3 3.4
Oceanic Bank 13 14 10 3.1 2.6 2.9 3.3 3.1 3.2
Knight’s Bank 19 13 10** 2.9 2.2 2.7 3.2 2.4 2.4
Average response 24 17 11 3.0 2.5 2.7 3.6 3.1 3.5
Empire Bank 54 45 39* 3.8 3.7 3.4 4.6 4.4 4.4
Federal Bank 46 26** 20** 4.2 3.6 3.4 3.7 3.7 3.6
Western Pacific Bank 34 37 21** 3.8 3.3 3.5 3.6 3.5 2.8*
Oceanic Bank 52 23** 20** 4.0 3.5 3.7 3.6 3.5 2.7
Knight’s Bank 31 26 19* 4.0 3.3 3.2* 2.1 2.5 1.8
Average response 43 31 24 4.0 3.5 3.4 3.5 3.5 3.1
Doesn’t care about smaller customers
Empire Bank 67 51* 46** 3.9 3.5 3.5 4.3 4.5 4.4
Federal Bank 50 48 34** 3.6 3.1 3.6 3.6 3.7 3.9
Western Pacific Bank 66 42** 37** 4.0 3.3 3.4 3.6 3.9 3.2
Oceanic Bank 52 28** 34** 4.0 3.4 3.5 2.8 2.8 2.5
Knight’s Bank 45 33* 30** 3.7 3.1 3.3 2.5 2.5 1.9
Average response 56 41 36 3.8 3.3 3.4 3.3 3.5 3.2
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& Ehrenberg 1985). The second pattern shows that some attributes
are more typical to the market in question (e.g. Loken & Ward 1990).
Values that are significantly different from the highest value (shaded)
are identified (* =
p < 0.05 and ** = p < 0.01). Across five brands × five
attributes × three techniques (75 different analyses) the results show that,
in 61 instances (81%), Past users gave the highest (or equal) response.
The next most common cases were 17 instances where Current users were
the most common (23%). These tended to be concentrated in the ranking
methodology. In only three instances were those who never tried the brand
the most likely to elicit a negative association. This suggests strong support
for Hypothesis 2, limited support for Hypothesis 3 and no support for
Hypothesis 1. Negative beliefs are most likely to be given by those who
have used the brand in the past, but no longer do so, or from current users
of the brand. This initial analysis suggests that negative beliefs are more
likely to arise as a consequence of using a brand rather than being used to
reject brands prior to purchase.
Study 2: agricultural irrigation products
One of the limitations of Study 1 is that the five attributes chosen may
not have represented the actual negative beliefs that consumers held. In
this study, respondents were prompted for brands and asked to write in
their perceptions of the positive and negative aspects of the brand (as per
Krishnan 1996). Therefore the respondent rather than the researchers
created the negative perceptions. The context for this research was the
agricultural sector. Respondents were dealers of agricultural products, who
were asked to nominate brands (from a list) they currently stock or have
stocked in the past. The remaining brands were classed as never stocked.
This study was conducted via a mailed self-completion survey to dealers
of agricultural products. It should be noted that, due to the dominance of
two major brands in the market, only these two brands were considered
in the analysis.
Experienced coders classified the attributes and three groups of beliefs
emerged. Positive beliefs tended to describe ‘good quality’, ‘good customer
service’ and ‘good value for money’. The negative beliefs tended to describe
the polar opposite attributes: ‘poor quality’, ‘poor value for money’
and ‘poor customer service’. The results (see Table 5) offer support for
Hypothesis 2, in that the Past user group was the highest response group in
five out of six cases. Values that are significantly different from the highest
value (shaded) are identified (* =
p < 0.05 and ** = p < 0.01).
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Negative brand beliefs and brand usage
For comparison and validation purposes, the results for positive percep-
tions for each user group are presented, as well as the average response
for positive attributes. The pattern is consistent with studies conducted
previously, where the current user group responds at the highest level (Bird
& Channon 1969; Bird & Ehrenberg 1970).
Study 3: fast food
We recognise that a potential influence on our results is the method used
to categorise Past users. Therefore this third study included two important
modifications to the measurement of current users and past users. The
definition of current usage was narrowed to having bought from the brand
in the last three purchases, which is likely to exclude very light/non-recent
brand buyers. This allows us to see the effect of more recent brand usage
experience on negative beliefs. The self-reported Past usage measure was
modified to include ‘used previously but would not go back to’; this extends
the Past usage categorisation to have both a behavioural and attitudinal
component. Both these changes polarise the three groups into current users
being regular and recent users only; Past users having both behaviourally
and attitudinally rejected the brand; the Never trieds consisting of those
who have never used the brand, or have not used it recently. Therefore we
might expect to see greater differences between the three groups.
The context for this study was retail fast food, where 368 people were
randomly recruited and interviewed by telephone about their perceptions
of fast-food brands. Respondents were asked a series of brand attribute
statements whose order was rotated by the interviewers, to which
respondents were able to reply in a free-choice format (Joyce 1963).
Table 5 Summary results for the agricultural irrigation study
Poor quality
Poor value for money
Poor customer
service (%)
Poly Products 42 11** 15** 10 10 15 10 5* 8
Agricorp 14 8* 7** 12 7 3** 8 6 7
Mean response 28 9 11 11 9 9 9 6 7
Mean positive response
39 62 24 5 6 3 9 14 9
For opposing attributes (e.g. good quality polar opposite to ‘poor quality’)
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The attributes included in the study were: ‘Too high in fat’; ‘Pre-
prepared’; ‘Boring range of products’; ‘More expensive’; ‘Inconsistent food
quality’. Across five brands by five attributes, Past users were the highest
response group in 20 instances (80%). The remaining five instances
were split between recent users and non/light users (see Table 6). This is
strong support for Hypothesis 2. The attributes of more expensive and
inconsistent quality contained the most variable responses.
There were also inconsistent results when the negative beliefs of recent
users and non-recent users were compared. This is probably an outcome of
the less distinct classification between someone who would regularly buy
the brand (but hadn’t within the last three purchases) and someone who
has never bought the brand. This shows the importance of clearly defined
usage groups when analysing the relationship between brand beliefs and
usage status, and the benefit of using subscription markets for such studies
et al. 2002).
Interestingly, unlike some previous research on negative attributes (Bird
& Channon 1969; Bird & Ehrenberg 1970; Woodside & Trappey 1992),
high response levels could be achieved for some attributes, especially when
considering the response levels of Past users. It could be that respondents
view the fast-food industry poorly, or that the attributes selected for this
study were more prototypical for this market (Rosch 1978; Romaniuk &
Sharp 2000).
For comparison and validation purposes, the results for positive
perceptions for each user group across attributes were explored. As there
Table 6 Results for the fast-food study
Too high in fat (%) Pre-prepared (%)
Boring product
range (%)
O’Burgers 77 71 68 77 62* 67 58 30** 36**
Royale Burgers 78 63* 67 56 52 59 53 23** 35**
Dr Sub Sandwiches 35 13** 18** 65 22** 24** 50 14** 14**
Big Chook 96 80 79 69 66 54* 49 13** 30**
Pizza House 65 61 60 50 42 35** 50 29** 28**
Average 70 57 58 63 49 48 54 22** 29**
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were no polar opposite attributes (unlike the previous study), average
response levels for positive attributes were taken across all attributes.
Recent users responded on average 50% of the time, non-recent users
responded on average 41% of the time and those who were past users
responded 33% of the time. Again, the pattern found is consistent with
studies conducted previously, where the current user group (in this case,
‘recent user group’) responds at the highest level (Bird & Channon 1969;
Bird & Ehrenberg 1970).
This paper has investigated the relationship between usage status (current,
past or never used the brand) and the propensity to have negative brand
beliefs. The data used in this study were vastly diverse, which may lead
one to question the ability to compare results. However, these data sets
have enabled the hypotheses to be tested over three markets, four brand
association measurement techniques and two data collection methods,
therefore improving the generalisability of the findings. Regardless of
how one measures the negative brand beliefs, whether one prompts or
not, whether positive beliefs are included in the study or not, and whether
the study is conducted in a subscription or repertoire market, the results
remain largely consistent.
The results show that, contrary to common beliefs about how negative
information may work in decision making (e.g. Lussier & Olshavsky
1979; Kahn & Baron 1995; Reed 1996), this series of studies provides
evidence that consumers are less likely to respond to negative attributes if
they haven’t used a brand. This does not support Hypothesis 1.
Table 6 Results for the fast food study (continued)
More expensive (%) Inconsistent quality (%)
Past user Recent user
user Past user Recent user
O’Burgers 7 2** 11 33 25 21**
Royale Burgers 0** 6 8 41 17** 20**
Dr Sub Sandwiches 30 25 25 15 5** 6**
Big Chook 30 39 29 31 36 14**
Pizza House 23 11** 24 23 26 16*
Average 18 16 20 29 22 15
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Further, while current usage does seem to have an influence (as this
group frequently had a higher response level than the Never trieds), the act
of behaviourally rejecting a brand seems to be the trigger for more negative
beliefs, either as a prior trigger to defect or a post hoc rationalisation after
the event.
These findings extend the knowledge that usage of a brand increases the
propensity for a response to a positive brand attribute (Bird & Channon
1969; Bird & Ehrenberg 1970; Barwise & Ehrenberg 1985; Romaniuk &
Sharp 2000), but also increases the likelihood that a negative attribute will
receive a response as well. On the contrary, past studies suggest that, when
a respondent ceases to use a brand (as when a ‘Current user’ becomes a
‘Past user’), their propensity to respond to a positive attribute decreases
(e.g. Bird & Channon 1969; Romaniuk 2001). The research presented
in this paper indicates that the opposite pattern occurs with responses to
negative attributes; they appear to increase after ceasing to use a brand.
These results extend research by Romaniuk (2003), suggesting that
like positive attributes, negative attributes are subject to a salience effect,
in that there may be a propensity for a respondent to nominate them
largely because they have experience with a brand. This is the most likely
explanation for those who have never tried a brand responding generally
at the lowest level, and current users responding at a higher level.
Conclusions and managerial implications
It is accepted within marketing circles that ‘the consumer may eliminate
some products based strictly upon recalled (negative) overall evaluations’
et al. 1988, p. 182). Such a theoretical basis is supported by the
widely subscribed non-compensatory and compensatory decision-making
theories (Lussier & Olshavsky 1979; Wright & Kriewall 1980; Kahn &
Baron 1995; Reed 1996) on which some choice modelling procedures
are based (Johnson
et al. 1989; Swait & Adamowicz 2001). Such models
assume that consumers rationally evaluate brands available prior to
purchasing them, and it is these models that are usually found in marketing
and consumer behaviour books (e.g. Engel
et al. 1993; Solomon 1994;
et al. 2001). For marketing practitioners, the results of this study
suggest that consumers who have not tried a brand are not likely to hold
negative beliefs about the brand. Our research shows that this common
perception of how negative beliefs work is unfounded. Negative beliefs
do not appear to be the barriers to purchase thought in the past (e.g.
et al. 1988). Complementing research which indicates that positive
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Negative brand beliefs and brand usage
brand beliefs are driven by usage, the findings of this paper suggest that
negative attributes are driven largely by usage as well, and therefore the
development of negative brand beliefs by consumers is more likely to occur
after purchase than prior to it. We believe that some of the past confusion
in this area might have been due to the mixing up of Past users with those
who have never tried the brand.
The findings in this paper highlight the complexity of the influence of
past experiences with a brand on current perceptions. It would be logical
to assume that because negative attributes are the polar opposite to
positive attributes, the responses to negative attributes will be the polar
opposite to those for positive attributes. However, this is not the case. For
positive attributes the propensity to give an image response is typically
ordered (from highest to lowest) as Current user, Past user, Never used the
brand. For negative attributes, the order (again from highest to lowest)
is Past user, Current user, Never used the brand. This is why larger-share
brands, with more current and past users, typically gain more responses
for attributes in brand health studies.
For both types of attribute a common factor is the low response level
from those who have never tried the brand. It is not that non-users are
rejecting brands or considering brands and deciding they are not good
enough, but that they barely think about the brands they don’t use. This
is why one of the biggest challenges in marketing is breaking through and
building up brand associations and salience in non-users (Romaniuk &
Sharp 2004). This is the main barrier that needs to be overcome when
marketing to non-users rather than redressing negative perceptions.
From a measurement perspective, the high level of agreement in the
results across the different measures and markets suggests that negative
perceptions can be measured by a variety of mechanisms. The results of
this study confirm that a non-response to a positive attribute in a brand
image survey cannot be interpreted as a negative evaluation, nor can the
reverse patterns be assumed (a brand that scores high on positive beliefs
will score low on negative beliefs). Another contribution of this research
is to highlight that, when including negative beliefs in brand knowledge
measurement instruments, it is important to include (and use in analysis) a
measure to separate out those who have never used the brand from those
who have in the past but do not do so currently.
This also has implications for when both positive and negative attributes
are used in multivariate analyses, and suggests that the two types of
attribute should be considered and analysed separately.
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Limitations and future research
A possible explanation is that responses to negative image attributes could
be indicative of a consumer having a bad experience with the brand and
then, at some opportunity, ceasing to use it, as would be expected given
the dissatisfaction literature (e.g. LaBarbera & Mazursky 1983). Further
longitudinal research is required to understand whether a higher level
of response to negative beliefs occurs prior to or after ceasing to use a
An observation from these findings confirms the earlier research
suggesting that consumers generally do not recall many negative beliefs
about brands (e.g. Bird & Channon 1969; Bird
et al. 1970; Woodside &
Trappey 2001). It should be noted, however, that some negative attributes
were responded to at quite high levels (for example, when presented with
the attribute ‘Too high in fat’, the largest burger chain in the fast-food study
had a 70% response rate). Further research needs to differentiate between
negative attributes that might be category characteristics (i.e. shared across
brands) and those that might be specific to a particular brand.
In this research we can see the systematic effect of heterogeneity in past
experiences on the broad ‘user/non-user’ categorisation on image attribute
responses. There may be further benefit in exploring this heterogeneity
within the group of brand users to further provide context for positive
image attribute results.
Finally, this research also leads to the questions ‘What is the effect of
current negative perceptions from Past users on their future propensity
to buy the brand previously used?’ and ‘Do these negative perceptions
hamper long-term “win-back” efforts?’ This is an important area for
future research.
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About the authors
Maxwell Winchester is a Senior Lecturer in Strategic Management and
Marketing at Harper Adams University College, Newport, Shropshire and
a Research Associate at the Ehrenberg-Bass Institute for Marketing Science,
University of South Australia. His main research interests have been in the
area of understanding negative brand attributes and consumer behaviour.
Maxwell’s academic research interests have led to the publication of
papers in the areas of wine marketing, brand management and research
methodology. Aside from his academic career spanning three continents,
Maxwell has held the position of Manager of Market Research for one
of Australia’s largest companies and worked as a consultant throughout
Europe, North America, Asia and Australasia.
Jenni Romaniuk heads the Brand Equity Research group at the
Ehrenberg-Bass Institute, based at the University of South Australia. As
well as publishing in
IJMR, Jenni has published internationally in a wide
range of marketing journals including
Journal of Advertising Research and
Australasian Marketing Journal. Jenni regularly speaks at international
IJMR_50_3.indb 374 04/04/2008 12:06:09
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conferences and consults to a wide range of companies around the
world. Her current research interests are Brand equity, Brand salience,
Advertising effectiveness and the influence of Word of Mouth on consumer
Address correspondence to: Maxwell Winchester, Ehrenberg-Bass
Institute, University of South Australia, GPO Box 2471, SA 5001
IJMR_50_3.indb 375 04/04/2008 12:06:10
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... Existing studies veri ed the presence of unmet expectations as an in uential factor for the behavioural intention of brand avoidance (Kim et al., 2013) and even suggested that negative previous experiences are the most important in anti-consumption behaviour (Lee et al., 2012;Nenycz-iel & Romaniuk, 2011;Winchester & Romaniuk, 2008). Moreover, Winchester and Romaniuk's (2008) research on the relationship between negative brand beliefs and purchase behaviour showed that negative beliefs about the brand are usually formed not prior to the purchase but a er it. ...
... Existing studies veri ed the presence of unmet expectations as an in uential factor for the behavioural intention of brand avoidance (Kim et al., 2013) and even suggested that negative previous experiences are the most important in anti-consumption behaviour (Lee et al., 2012;Nenycz-iel & Romaniuk, 2011;Winchester & Romaniuk, 2008). Moreover, Winchester and Romaniuk's (2008) research on the relationship between negative brand beliefs and purchase behaviour showed that negative beliefs about the brand are usually formed not prior to the purchase but a er it. erefore, past consumers of the brand are more likely to have negative beliefs than current users or those who have never tried the brand (Winchester & Romaniuk, 2008). ...
... Moreover, Winchester and Romaniuk's (2008) research on the relationship between negative brand beliefs and purchase behaviour showed that negative beliefs about the brand are usually formed not prior to the purchase but a er it. erefore, past consumers of the brand are more likely to have negative beliefs than current users or those who have never tried the brand (Winchester & Romaniuk, 2008). ...
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The article analyses one of the parts of anti-consumption phenomenon – brand avoidance. Most of the research on the topic of brand avoidance is usually qualitative, analysing only singular brand avoidance reasons, whereas this article employs a quantitative method to analyse brand avoidance, including different brand-related stimuli (unmet expectations, symbolic incongruence, unacceptable trade-off, ideological incompatibility) and the negative emotions (dislike, anger, worry, embarrassment) consumers feel towards brands. What is more, this research is the first to analyse brand avoidance in the context of emerging markets, as previous studies were carried out in Western countries. However, it was found that different brand-related stimuli have positive relations mainly with dislike emotion, which was the only one influencing brand avoidance.
... Keller (1993) found people who had never used a brand were more likely to have negative views of that brand after a negative brand information exposure. However, Winchester and Romaniuk (2008) found that, after exposure to negative publicity, current consumers were more likely to have negative evaluations of a brand and to share negative views than were non-consumers. Consumers also often receive positive updates and messages from a company through mobile or internet sources, whereas non-consumers have less opportunity to receive such positive information. ...
... Consumers also often receive positive updates and messages from a company through mobile or internet sources, whereas non-consumers have less opportunity to receive such positive information. Therefore, current consumers are likely to be exposed to more positive brand information, and this may impact on their responses to negative brand information (Winchester and Romaniuk, 2008). Current consumers were chosen as the focus in this study, as they are more likely to affect a brand's sales than non-consumers, especially in a frequently purchased category. ...
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Purpose This study aims to understand the influence of negative publicity on brand image, brand attitude and brand purchase intention. Specifically, the study examines the role of attribution (or brand blame) and information characteristics in Chinese consumers’ responses to negative publicity. Design/methodology/approach The study used a quasi-experimental approach involving two negative publicity scenarios (mild and high severity) and a sample of 203 young and educated Chinese consumers. Partial least squares was used to test the hypotheses. Findings A common assumption is that negative brand information has a negative influence on all aspects of a brand. However, this study finds that brand blame and information severity have differential effects on consumer evaluations of the affected brand. Specifically, brand blame negatively impacted attitudes and purchase intentions, but not brand image. In contrast, information severity negatively impacted brand image, but not attitudes or intentions. Further, the relations between brand image and brand attitudes and intentions depended on the level of information severity. In the mild-severity condition, brand image positively influenced attitudes and intentions, but not in the high-severity condition. Research limitations/implications Future research should examine consumer responses to negative publicity across different media and product categories. Cross-cultural studies should also be explored in the future. Practical implications When a brand encounters negative publicity, its marketer or brand manager should assess to what extent various brand equity components are influenced by negative publicity before adopting any cognitive-based or imagery-based communication strategies. Originality/value This paper contributes to the limited and fragmented literature on consumer response to negative publicity by examining the impact of consumer’s attributions of blame to the brand under conditions of mild and severe negative information on a range of important brand-related outcomes. Specifically, the authors find that negative publicity has a different impact on brand image, brand attitudes and intentions to purchase. The authors suggest that brand managers use this information to guide their marketing communications.
... Current consumers' responses to negative publicity may be different from non-current (or potential) consumers. Winchester and Romaniuk (2008) claimed that after a negative publicity exposure, current consumers of the affected brand may be more likely to have negative evaluations of that brand and to share negative views as compared to non-current consumers. However, Keller (1993) claimed the opposite, in that consumers who have never used a brand before are more likely to have negative evaluations of that brand after being exposed to the negative information than current consumers. ...
... This is consistent with de Mooij and Hofstede's (2002) finding that consumers from collectivist cultures tend to share ideas and views with relatives, friends and family members as opposed to those from individualistic cultures. However, Winchester and Romaniuk (2008) suggest that current consumers may be less likely to engage in negative word-of-mouth behaviour. ...
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Negative publicity is believed to have detrimental impacts on a brand; however, consumers’ responses to negative publicity may not always be negative, and the responses may vary in different cultural contexts. The current study aims to understand the influence of internalised aspects of culture on Chinese consumers’ responses to negative publicity. Our results suggest that Chinese consumers with higher levels of collectivism and uncertainty avoidance are more likely to search for further information and to spread negative word-of-mouth when confronted with negative publicity, than those with lower levels. Further, consumers with higher levels of power distance are more likely to search for further information when the negative information is severe and to participate in negative word-of-mouth when the negative information is less severe, than those with lower levels of power distance. Finally, when exposed to negative publicity, further information search is positively related and negative word-of-mouth negatively related to brand attitudes and purchase intentions.
... It has been long acknowledged that prior brand experience influences consumers' responses to questions about brands (Bird et al., 1970;Romaniuk et al., 2012). Past experience, particularly direct experience, can influence brand health metrics (Winchester and Romaniuk, 2008;Driesener and Romaniuk, 2006;Hoek et al., 2000;Barwise and Ehrenberg, 1985) and, relevant to this research, assessments of advertising memorability (Romaniuk and Wight, 2009;Harrison, 2013;Vaughan et al., 2016). The evidence points to a positive relationship between a person's prior brand experience and their probability of noticing any promotional material from that brand. ...
Retailers often feature manufacturer brands in their advertising with the aim to drive sales for those brands and, at the same time, increase in-store traffic. The adoption of such cooperative advertising strategies should reach an expanded audience base comprising of store and brand buyers. Based on empirical evidence in non-retail settings, these buyers are more likely to remember the advertising than non-buyers. This usage-bias effect implies that cooperative advertisements achieve greater cut through than would be the case if either brand advertised alone. Our paper tests this hypothesis in the context of retailer advertisements that feature manufacturer brands in the US, UK and Australia. Our results confirm that a retail brand's shoppers are more likely to recall its advertisements than non-shoppers, extending the usage-bias generalisation to a retailing context. However, while cooperative advertising does expand the buyer-base reach, any uplift in ad-memorability is negated by a reduced ability for buyers of only one of the two brands to recall the advertisement. Information overload on cognitive processing is a possible explanation for this finding, and has implications for extracting value from any investment in cooperative advertising.
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New marketing strategies and growing technological advances have not been ignored by large and even medium-sized companies. This context, in turn, has allowed some companies to make the decision to introduce their own brands in the market. In this scenario, augmented reality (AR) is a technology that allows virtual reality to be added to a real physical environment, which makes it a novel tool for discovering new uses, forms and habits of consumption. Through the AR, the aim is to surprise the client and obtain their complete attention and interest. Of course, the main objective of AR sales is to awaken the consumer's desire and then motivate them to purchase the final product that they have already visualized through an app (usually through a mobile phone). This approach, without a doubt, originates a novel marketing strategy and very few companies currently use or know, since they assume that their cost is high, which is not necessarily the case.
... One has to take into account that not all employees identify with corporations on such a positive level. Variations are likely to occur as a result of individuals' negative beliefs or commitment to a corporation [42]. Future studies should investigate the effect of brands regarding different levels of identification, and on wider spectrum of differences -such as effects of personality and effects of gender. ...
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Brand identification has the potential of shaping individuals' attitudes, performance and commitment within learning and work contexts. We explore these effects, by incorporating elements of branded identification within gamified environments. We report a study with 44 employees, in which task performance and emotional outcomes are assessed in a real-world assembly scenario - namely, while performing a soldering task. Our results indicate that brand identification has a direct impact on individuals' attitude towards the task at hand: while instigating positive emotions, aversion and reactance also arise.
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Understanding how to more effectively advertise is an important area for practitioners, many of whom are struggling to stay in business. Nowadays, both product and corporate branding are important and given the increase in online advertising, additional research to better understand advertising avoidance is needed. Most advertising avoidance studies to date measure either task attention or attention to advertising. This paper contributes to a more holistic view of attention by examining two distinct online tasks, such as reading information and buying products, and two forms of advertising, such as product brand advertisements and corporate brand advertisements. An eye-tracking experiment with 121 respondents show that when reading, users look more at advertisements that increase the memory of the ads. Regardless of the task performed, the corporate brand ads are more memorable than the product brand ads even if the product ads attract more attention. The longer the user stays on a website, the more the user will look at the ads and the easier it will be to remember them. However, ads are considered to be distractors for online users, therefore, to increase their effectiveness they must be more engaging and provide value for the audience.
New technologies have changed the way today's own label products are being offered. Today the Internet and even more the so-called social networks have played key roles in dispersing any particular product in a more efficient and dynamic sense. Also, having a smartphone and a wireless high-speed network are no longer a luxury or a temporary fad, but rather a necessity for the new generations. These technological advances and new marketing trends have not gone unnoticed by the medium and large stores. The augmented reality applied to interactive catalogs is a new technology that supports the adding of virtual reality to a real environment which in turn makes it a tool for discovering new uses, forms, and in this case, spending habits. The challenge for companies with their private labels in achieving their business objectives, is providing customers with products and services of the highest quality, thus promoting the efficient and streamlined use of all resources that are accounted for and at the same time promoting the use of new information technologies as a strategic competitive.
Three measures of consumers’ beliefs about different attributes of competitive brands of packaged goods—free choice, scaling, and ranking—are found to place the brands in the same relative positions on each attribute dimension. In addition, gradients by the recency or frequency of claimed brand usage that have been observed previously in free-choice belief data now are found also in the scaling and ranking data.
Derived and reported utility functions for members of a high school junior's family were used to predict actual college applications made the next year. The manipulations were whether or not advance deliberations were prompted before the utility measurement and whether or not subjects imagined an imminent commitment deadline. The predictions were poorer when subjects did not deliberate in advance or imagine a commitment was imminent. The reported utilities gave better predictions than the derived ones.
The proportion of people who express an attitude about a given brand generally depends on how recently they have used the brand.
Linear compensatory models, which involve tradeoffs between product attributes, have been argued to provide reasonably good predictions of choices made by noncompensatory heuristics, which do not involve tradeoffs. This robustness to misspecification of functional form may fail, however, when there are negative correlations among attributes in a choice set. A Monté Carlo simulation demonstrates that certain noncompensatory rules are poorly fit by linear models, even in orthogonal environments, and that this fit diminishes further in nonorthogonal environments. Two laboratory experiments assess the extent to which such model failure might arise in natural contexts. The first, a process-tracing analysis, examines the decision strategies consumers use in nonorthogonal choice environments. The second explores the ability of a compensatory choice model calibrated on actual choices to predict decisions made in orthogonal and nonorthogonal contexts. The authors conclude with a discussion of the work's implications for current research in applied choice modeling.
Sixteen copy characteristics or advertising approaches that appear to either increase or decrease irritation emerge from a study of 524 television commercials. The results also show how irritation levels vary by product class and by socioeconomic level.
The author presents a conceptual model of brand equity from the perspective of the individual consumer. Customer-based brand equity is defined as the differential effect of brand knowledge on consumer response to the marketing of the brand. A brand is said to have positive (negative) customer-based brand equity when consumers react more (less) favorably to an element of the marketing mix for the brand than they do to the same marketing mix element when it is attributed to a fictitiously named or unnamed version of the product or service. Brand knowledge is conceptualized according to an associative network memory model in terms of two components, brand awareness and brand image (i.e., a set of brand associations). Customer-based brand equity occurs when the consumer is familiar with the brand and holds some favorable, strong, and unique brand associations in memory. Issues in building, measuring, and managing customer-based brand equity are discussed, as well as areas for future research.
A simplified cognitive model is proposed to assess the dynamic aspect of consumer satisfaction/dissatisfaction in consecutive purchase behavior. Satisfaction is found to have a significant role in mediating intentions and actual behavior for five product classes that were analyzed in the context of a three-stage longitudinal field study. The asymmetric effect found demonstrates that repurchase of a given brand is affected by lagged intention whereas switching behavior is more sensitive to dissatisfaction with brand consumption. An attempt to predict repurchase behavior on the basis of the investigated cognitive variables yielded weak results. However, repurchase predictions were improved when the model was extended to a multipurchase setting in which prior experience with the brand was taken into account.