Is Brand Distinctiveness a Separate Facet of Brand Knowledge?
Nicole Hartnett, Jenni Romaniuk, Ehrenberg Bass Institute, University of South
Australia
Abstract
Traditionally brand knowledge is measured using image attribute associations in brand equity
research. More recently, attention has moved to distinctive brand elements, which are
elements other than the brand name, such as colour, logos or characters that can identify the
brand. We compare consumer image knowledge of traditional brand associations (e.g., good
value, high quality) to knowledge of distinctive elements to determine how they interlink in
consumer memory. Findings suggest that brand distinctiveness is a separate facet of brand
knowledge, requiring specific marketing strategies to develop this knowledge type.
Furthermore, unlike for image attributes, usage is not a prerequisite for distinctive knowledge,
in that non-users are often just as knowledgeable about brand distinctive assets as brand users.
This has implications for the role that distinctive assets play in consumer choice.
Introduction
Consumer brand knowledge is instrumental in many purchase decisions. Brand perceptions
have a sizeable impact upon the consideration and evaluation of brands in a category
(Nedungadi, 1990). This paper focuses on one part of brand knowledge: distinctive assets. As
competition becomes fiercer and competitor imitation increases, individual brands are finding
it difficult to ‘stand out from the crowd’. To do so, the concept of brand ‘distinctiveness’ has
been gaining momentum in academia, though little empirical research has been done in the
area. Distinctive elements are branding execution tactics that are not the brand name, but aid
consumer recognition of brands. These can include colour, logos, slogans, jingles, etc
(Gaillard, et al., 2005; Keller, 2005). Distinctive elements can be inextricably linked with a
brand, allowing brands to claim ‘ownership’ of them (Keller, 2003). Elements that have long
been trademarked include brand names, logos and slogans, but recent changes in trademark
legislation have enabled colours, shapes and smells to be registered and protected from
competitor replication (Hoek and Gendall, 2006). However, declaring ownership is not easy.
For instance, in 2003 Cadbury attempted to sue Australian competitor Darrell Lea for its use
of the colour purple, but could not legally establish an exclusive claim to the colour (The Age,
2006). Successful colour trademarks include British Petroleum’s (BP) green and ANZ bank’s
blue. The willingness to undertake the arduous trademark process highlights the importance
brands place on these communication assets.
There is a plethora of literature surrounding traditional brand knowledge measurement.
Frameworks developed by Holden and Lutz (1992) and Keller (1993) divide these brand
knowledge associations into four main categories: 1) Attributes; 2) Benefits; 3) Attitudes; and
4) Purchase and consumption situations. Yet comparatively little is known about brand
distinctive associations as a contributory feature of holistic brand memory. The key purpose
of this research is to determine if there is a relationship between more traditionally
acknowledged brand image attributes and brand distinctive elements.
Background and Research Question
Traditionally brand knowledge is measured in terms of brand associations. People are asked if
they consider a brand within a competitive set to pertain to an attribute (e.g., for the whole
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family, innovative) (Winchester and Romaniuk, 2008). As per Keller (1993) brand image is
the overall network of information about a brand in consumer memory (brand name and linked
associations). Brand image perceptions are ideally meant to be strong, favourable and unique
to a brand (Keller, 1993). Recent research uncovered that any single image attribute is rarely
associated with only one brand in a category, with 50% of people not even expressing a
unique association about the brands they use (Romaniuk and Gaillard, 2007). This finding
held irrespective of brand size, bringing to question the assumption that large share brands are
bought because those that use them consider them exceptionally unique. Marketers are now
turning to the brand as a platform to ‘stand out from the crowd’.
A brand is a “name, term, sign, symbol or design, or combination of them which is intended
to identify [the] goods and services of one seller” (Kotler 1991, p. 442). For marketing
communications, it is vital for the brand to be dominant; otherwise messages can be attributed
to a competitor. Communications can contain both direct and indirect branding. Direct
branding involves displaying the brand name visually or aurally. Indirect branding refers to
elements that are not the brand name, but allude to a ‘distinctive’ brand image.
Distinctiveness is defined as cues stored in memory that make a brand identifiable (Gaillard,
et al., 2005). Distinctive cues, or ‘brand elements’ as labelled by Keller (2005), include logos,
colour, graphics, taglines, typeface, packaging, celebrities and music (Gaillard, et al., 2006).
Distinctiveness is separate from traditional brand assets in two dominant ways. Firstly, brand
distinctive elements can be ‘meaningless’, as their purpose is for increasing the likelihood of
brand recognition (Romaniuk and Gaillard, 2007). This is a substantial separation from the
concept of differentiation that calls for a Unique Selling Proposition that is meaningful to
customers (Reeves 1961). At its core, distinctiveness is simply a tool for reducing consumer
confusion by making a brand recognisable (Romaniuk and Gaillard, 2007). Secondly, the
learning process for distinctive elements is different to that of image associations. Often usage
is a dominant influence in developing brand perceptions. Bird, et al., (1970) established that
users are roughly three times more likely to give an image response than former or non-users.
Exceptions are attributes that are descriptive in nature or consistently/continuously stressed
throughout all brand communications (Barwise and Ehrenberg, 1985). Both show higher
levels of agreement between users and non-users. In contrast, distinctive elements are often
external to personal experience and are learned through any form of brand communications
(e.g., advertising, sponsorship, etc). As such, it is possible that distinctive elements would act
differently to traditional brand attributes, and responses would not be so user dominated.
Considering the above information, two core research questions arise:
RQ1: Is image knowledge highly correlated with distinctive knowledge? In this question we
explore how distinctive elements interact with other image associations in consumers’
associative network of brand memory. Is a larger associative network for image attributes
linked to a larger associative network of distinctive elements?
RQ2: Do distinctive elements follow the same response patterns as brand image attributes
when usage is considered? In this question we examine if it would be expected that users of a
brand would have knowledge of a greater number of image attributes and distinctive elements
than non-users, but non-users are more likely to recall distinctive knowledge than other brand
attributes because they have access to distinctive elements without using the brand.
Research Design
The study was conducted in the Australian banking industry, selecting the seven largest banks
for comparison. Each bank uses a number of different visual and verbal identities to be
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distinctive. Across three Australian states, 319 respondents were randomly selected and
interviewed via telephone. Females accounted for almost 66% of the sample. Just under half
of the sample was situated in New South Wales, with remaining respondents divided evenly
between Victoria and Queensland.
Image Attribute Associations – Respondents were first asked a battery of 18 image attribute
questions for the 7 largest banks in Australia. The image data was collected using a free
choice, pick any response approach, where people were given brand lists and asked which
brands they associated with an attribute (e.g., Which bank(s) do you think are community
minded?) (Barnard and Ehrenberg, 1990). Attribute order was randomised across respondents.
Respondents were free to give as many or as few brands as they thought appropriate.
Distinctive Cues Associations – Based on the literature and scrutiny of industry practice, cues
included in the research were colour, logos, slogans, font, characters or celebrity endorsers,
and music. Respondents were described a distinctive cue and then asked which banking
brands they associated with that specific cue. For instance: Which brands do you associate
with a dragon (the logo for St George Bank)? Responses were coded into a yes/no variable for
each distinctive cue, for each brand. Only correct brand/cue links were included.
Brand Usage – Respondents were asked to identify the banks they were using at the time.
Multiple responses were possible for this question.
Results
Correlation Between Measures
The number of image associations and image attributes were collated for each of the seven
brands, creating two variables of collective image knowledge and collective distinctive
knowledge (i.e., Commonwealth Bank distinctive elements include yellow, black, white, grey,
diamond logo, ‘Which Bank?’ etc). The two variables were correlated together, with results
showing that the two variables are weakly correlated (Average = 0.2, see Table 1). Therefore,
we cannot assume that because a person is very knowledgeable about a brand’s portrayed
image, they are also familiar with the visual and aural aspects of the brand, and vice versa.
These results indicate that image perceptions and distinctiveness cues are stored as separate
components of holistic brand knowledge in consumer memory.
Table 1: Spearman’s Correlations for Brand Image and Brand Distinctiveness
Brand
ANZ
Bendigo Bank
Commonwealth Bank
National Australia Bank
*p<0.05; **p<0.01
Correlation Coefficient
0.22*
0.19*
0.12**
0.27*
Brand
St George
Suncorp Metway
Westpac
Average
Correlation Coefficient
0.22*
0.23*
0.20*
0.21*
To further examine the relationship between measures, we classified people in a 2x2 matrix as
high/low image associations and high/low distinctive associations. The mean for each brand
on each measure was used as the cut-off point. Table 2 shows that while there were slightly
higher levels of agreement in low/low and high/high groups, respondents were quite evenly
spread across all four groups. This provides evidence that the low correlation does not mask
high or low knowledge segments that might have higher correspondence between measures.
Table 2: Collective Image Knowledge versus Collective Distinctive Knowledge Matrix (%)
High Image Attributes
High Distinctive Cues
28**
Low Distinctive Cues
18*
3
Low Image Attributes
23*
31*
*p<0.001; **p<0.05. Note: Bendigo was removed from this analysis due to insufficient sample size.
Relationship with Brand Usage
Next we sought to determine the effect of usage on both measures. Respondents were
classified into users or non-users for each brand. This usage variable was cross-tabulated
against each image attribute and distinctive element recalled by respondents. Of the 18 image
attributes, three followed descriptive patterns (as per Barwise and Ehrenberg, 1985) for all
brands: ‘Offers investment products and services’, ‘Offers credit cards’ and ‘Expert in
financial matters’. All other image attributes followed normal evaluative patterns (Barwise
and Ehrenberg, 1985). For each brand, the vast majority of distinctive elements followed
descriptive patterns, with non-users able to identify distinctive elements almost as often as
users. As speculated, a person need not use a brand to learn its distinctive elements, which are
frequently utilised in television, newspaper and radio advertising in the banking industry. As
an example, results for St George bank are displayed in Table 3.
Table 3: Usage Analysis for Image Attributes and Distinctiveness Cues for St George
Users
Non-Users
(%)
(%)
Image Attribute
Is progressive
57
22
Bank you would trust
55
16
Friendly and helpful
55
15
Continuously improves service
49
19
Good partner to increase wealth
47
9
Bank for everyone
43
18
Average (All attributes)
54
25
*Only 6 of the 18 image attributes are displayed.
Distinctive Element
Logo
Colour Red
Celebrity/Character
Colour Green
Font
Slogan
Average
Users
(%)
98
83
51
34
9
4
39
Non-Users
(%)
89
52
41
35
4
4
31
Table 4 shows overall average response levels for each brand on image attributes and
distinctive elements. For the category, users were generally twice as likely as non-users to
give an image attribute response for any one bank. Users were still more likely than non-users
to recall distinctive associations, but at a smaller average ratio of 1.4:1 respectively. These
results were largely consistent across brands, although Commonwealth Bank shows almost
equal agreement between users/non-users on distinctive elements. This is the largest
Australian bank, possibly reflecting the extensive advertising this institution undertakes or the
strength of its distinctive assets. St George is equal highest amongst users in identifying
distinctive elements.
Table 4: Averages for Image Attributes and Distinctiveness Cues Across Category
Bendigo
St George
Suncorp
ANZ
Commonwealth
National
Westpac
Average
Image Attributes
Users (%)
Non-Users (%)
73
31
54
25
52
21
50
24
46
29
45
23
43
24
52
25
Distinctive Elements
Users (%)
Non-Users (%)
14
8
39
31
30
13
36
26
39
36
31
17
21
15
30
21
We also examined the distribution of high and low knowledge for users and non-users
separately. Here we find that while non-users followed the same pattern as the earlier matrix
using all respondents, users had much greater correspondence between those with high
attribute knowledge and high distinctive knowledge (57%). However, this correspondence
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was not evident at the lower knowledge end, with the vast majority of people low in one
dimension but high in the other.
Table 5: User/Non-User Split for Knowledge Matrix (%) Averages Across 6 Brands
Users
Non-Users
High Distinctive
Low Distinctive
High Distinctive
Low Distinctive
High Image
57*
18*
21*
18
Low Image
15*
9*
25*
36*
*Differences between cells are statistically significant at p<0.05, except for non-user high image/low distinctive
knowledge. Note: Bendigo was removed from this analysis due to insufficient sample size.
Conclusion and Implications
In this research we have drawn comparisons between image associations that help consumers
to make brand choices, and distinctive elements that predominantly assist consumers to
identify brands. The first key finding was that image knowledge and distinctive knowledge
are not highly correlated. That is, a brand that has a strongly established image in consumers’
minds does not necessarily have a strong range of distinctive elements that it can claim
‘ownership of’. This could be because of how these two separate pieces of information are
derived or learned. As such, integration of brand distinctiveness into brand equity measures is
important, as current categories of brand knowledge are not indicative of the strength of
distinctive elements. The second phase of analysis sought to establish the effect of usage on
responses to questions about brand distinctive elements. Previous research identified that
brand users are more likely to assign image attributes to it, most likely because experiential
learning enables firmer judgments than those that have not interacted with the brand (Bird, et
al., 1970). However, the same ‘usage effect’ is not evident for distinctive elements. Non-users
are almost as likely as users to assign a distinctive element to a brand. We suspect this is
because non-users have multiple opportunities each day to encounter the colours, logos,
slogans and celebrities that signify any one brand. If a brand does not gain descriptive
patterns for distinctive elements when undertaking usage/image analysis, they are most likely
under-utilising these assets in marketing campaigns, but further research is needed to explore
this. The results suggest that these are two separate components of brand knowledge and so
need separate consideration. Marketers cannot assume that consumers (largely users) who
have strong image attribute networks in memory will also strongly identify with the
distinctive elements associated with the brand. Therefore, care is needed when using
distinctive elements to supplement or replace the brand name in communications, as it may
negatively affect correct brand identification by customers. There is limited evidence attesting
that ‘standing out’ contributes to brand choice; Chandon and Wansink (2002) found visual
distinctiveness could increase product consumption related to stockpiling. Ultimately, further
research in understanding how consumers use distinctive elements is needed, as the results
suggest that different consumers may rely on each type of knowledge in different ways.
Understanding this further will allow marketers to better leverage their distinctive assets.
Limitations and Future Research
This research was conducted via telephone and dependent on respondent recall of brand
distinctive attributes. No visual stimulus was provided, which would be recommended for
future studies to find if this has an impact on results. Further replication and extension into
other markets is encouraged, particularly FMCG categories where the marketplace is very
cluttered with SKUs and the importance of distinctive elements for brand identification
increases. Further research should examine the antecedents of the formation of distinctive
brand elements. How much is advertising based, how much repetition/time is needed before
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they are learned, and at what points of the marketing process are they most important? Further
research is also needed into the efficacy of different distinctive element strategies.
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