Brand Equity

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Brand equity: extending brand awareness and liking with Signal DetectionTheoryGewei Ye a; W. Fred Van Raaij b

a Department of Marketing and e-Business, Towson University, Baltimore, MD 21252-0001, USA b

Tilburg University, 5000 LE Tilburg, The Netherlands

To cite this Article Ye, Gewei and Van Raaij, W. Fred(2004) 'Brand equity: extending brand awareness and liking withSignal Detection Theory', Journal of Marketing Communications, 10: 2, 95 — 114To link to this Article: DOI: 10.1080/13527260410001693794URL: http://dx.doi.org/10.1080/13527260410001693794

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JOURNAL OF MARKETING COMMUNICATIONS 10 95–114 (June 2004)

Journal of Marketing Communications ISSN 1352–7266 print/ISSN 1466–4445 online© 2004 Taylor & Francis Ltd http://www.tandf.co.uk/journals

DOI: 10.1080/13527260410001693794

Brand equity: extending brand awareness andliking with Signal Detection Theory

GEWEI YETowson University, Department of Marketing and e-Business, 800 York Road, Baltimore, MD21252-0001, USA

W. FRED VAN RAAIJTilburg University, PO Box 90153, 5000 LE Tilburg, The Netherlands

Brand equity, which is a central topic in modern marketing, may be assessed fromthree perspectives: customer mind set, product market outcomes and financial marketoutcomes. Brand awareness (memory) and brand liking are elements of customermind set brand equity. The factors determining brand awareness and likeability arealso determinants of the change in financial brand equity. In order to understandthese factors, Signal Detection Theory is employed for finding the components ofbrand awareness and likeability. Signal Detection Theory has a strong tradition inpsychology, but is under-represented in marketing and consumer behaviour. Thisstudy extended the concept of brand awareness to ‘awareness sensitivity and bias’ andthe concept of ‘brand likeability’ to ‘liking sensitivity and bias’ using Signal DetectionTheory. The effect of divided attention on the extended components was investigatedin three laboratory experiments. It was found that, in the attended mode comparedwith the unattended mode, consumers perform better in preserving a favourable brandawareness and have a conservative reaction tendency. This effect of attention occurs inbuilding brand awareness for short presentations, but not for long presentations. Thesefindings may serve as guidelines for a strategy formulation for enhancing customermind set brand equity.

KEYWORDS: Brand equity; brand awareness; brand liking; Signal Detection Theory;attention; response bias

INTRODUCTION

When shoppers say they don’t like Coca-Cola, should the negative brand liking be attributed to thefailure of branding campaigns, or to the conservative tendency of consumers to say: ‘I don’t like thebrand’? Further, if they say that they are conservative in responding, do they really report the objec-tive tendency? These questions cannot be answered with conventional measures, but with SignalDetection Theory.

A strong brand is a very valuable asset of a firm (Aaker, 1991, 1996; Keller, 1998; Aaker andJacobson, 2001). According to one estimate the brand value of Coca-Cola is $84 billion, that of

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Microsoft is $57 billion and that of IBM is $44 billion (Morris, 1999). In order to build a strongbrand a comprehensive understanding of brand-related consumer behaviour will improvemarketing productivity (Keller, 1993). Hence, a deep understanding of the components of brandequity from a customer-based perspective is vital to the success of brand management.

A variety of definitions of brand equity are offered in the literature (Leuthesser, 1988; Keller,1993, 1998; Aaker, 1996). For example, Aaker (1996) defined brand equity as a set of four cate-gories of brand assets linked to a brand’s name or symbol that add to (or subtract from) the valueprovided by a product or service to a firm and/or to that firm’s customers. These four categoriesare (1) brand awareness, (2) perceived quality, (3) brand associations and (4) brand loyalty(Keller, 2002). Aaker (1996) adopts a definition under Keller’s (1998) framework from aconsumer-based and operational perspective.

Brand equity may be assessed from three aspects: customer mind set, product market out-comes and financial market outcomes (Ailawadi et al., 2003). The measures of customer mind setinclude the awareness, attitude, association, attachment and loyalty that customers have towardsa brand (Aaker, 1991; Keller, 1993, 2003). The product market measures reflect the brand’sperformance in the marketplace. For example, price premium is such a measure that captures theability of a brand for charging a higher price than an unbranded equivalent (Aaker, 1991). Thefinancial market measures assess the value of a brand as a financial asset, including the purchaseprice at the time a brand is sold or acquired and the discounted cash flow valuation of licensingfees and royalties (Ailawadi et al., 2003).

Customer mind set brand equity is the source of the associated financial brand equity (Keller,2003). Keller (1993) defined customer-based brand equity as the differential effect of brandknowledge on the consumer response to the marketing of the brand. He also defined brandknowledge in terms of two core components, brand awareness and brand image. Brand aware-ness relates to the brand recall and recognition performance by consumers. Brand image refers tothe set of associations linked to the brand that consumers hold in their memory. Thefavourability, strength and uniqueness of brand associations (image) are the factors in brandknowledge that determine the differential response that makes up brand equity (Keller, 1993,1998, 2003).

Brand awareness and brand image can be operationalized for the current study. This paperuses ‘brand recognition memory’ for indicating brand awareness and a measure of likeability forindicating the brand image that represents the favourability and strength of brand associations.With favourable brand associations consumers tend to like the brand. In the current study,customer-based brand equity can be operationally defined as a combination of brand awareness,indicated by recognition memory and brand likeability.

Due to the emergent interest in branding, different kinds of information are linked to a brand,including awareness, attributes, benefits, images, thoughts, feelings, attitudes and experiences(Keller, 2003). However, brand awareness and likeability are the core components at the heartof the various brand equity models (Aaker, 1991, 1996; Keller, 1993, 1998). The current studystarts with the two core components of brand equity using Signal Detection Theory. Othercomponents of brand equity may be investigated in a similar way in future research.

Much attention has been devoted to understanding recognition memory with Signal Detec-tion Theory in psychology (Macmillan and Creelman, 1991; Ratcliff et al., 1994; Yonelinas,1994, 2002; DeCarlo, 2002). Signal Detection Theory provides an unbiased strength (sensitivity)measure of recognition memory and a cut-off point measure for indicating people’s tendencyto retrieve information from memory. However, little research has been conducted on therecognition memory of elements of brand equity using Signal Detection Theory. To the best of


97SIGNAL DETECTION THEORY

the authors’ knowledge, no research has been conducted on brand likeability, which is a vitalelement of brand equity, with Signal Detection Theory.

The advantages of using Signal Detection Theory have been well documented in psychologyin the sense that the sensitivity and bias of responses can be measured while conventionalpercentage measures just provide an overall indicator (Green and Swets, 1966; Macmillan andCreelman, 1991; Yonelinas, 1994).

Percentages of correctly retrieved (preferred) items among all presented (marketed) items arethe conventional measures of brand awareness (recognition) and brand likeability. The disadvan-tages of the conventional measures are that (1) they are overall indicators that do not differen-tiate between the strength and tendency of consumer reactions to brands and (2) differentcutting scores (cut-off points) may lead to different results. Thus, the conventional measures canbe biased by the (liberal or conservative) response tendency of individuals (Green and Swets,1966; Snodgrass and Corwin, 1988). Misinterpretations of results may occur using conventionalmeasures if the bias of consumer response tendencies is not considered. Hence, a deeper under-standing of the underlying components of brand awareness and likeability may be reached withSignal Detection Theory than with a percentages method.

In marketing practice using the conventional measures may lead to inaccurate conclusions.On which aspect (strength of consumer memory or feeling or response tendency) shouldresearch be focused? Lacking a comprehensive understanding of brand awareness and likeability,marketing programmes may not be focused on the real issue. For example, conventional mea-sures only tell marketers that the brand likeability may be weak, if a survey shows a poor scoreof consumers’ favourability judgement of the brand associations. In which aspects (sensitivity orbias) is the brand weak? Are consumers unhappy about the brand (product) itself? Or is it theirconservative response tendency (e.g. to think twice) to say that they ‘like’ the brand? Thesequestions cannot be answered in a straightforward manner with conventional measures.

Brand awareness and likeability are vital and central to brand equity. In order to fit with psy-chological theory better and to advance the understanding of consumer-based brand equity, thispaper extends brand awareness and likeability to four components: the sensitivity and bias ofbrand awareness and the sensitivity and bias of brand likeability. A divided-attention procedureis used for investigating the effect of attention on the extended components of brand awarenessand likeability. Principles of branding based on a deeper understanding of brand equity are thendiscussed.

THEORETICAL FRAMEWORK

This section introduces Signal Detection Theory. It then conceptually extends Keller’s (1993,1998) framework of brand awareness and likeability to four core components based on SignalDetection Theory. These components are the sensitivity and bias of brand awareness and thesensitivity and bias of brand likeability, as illustrated in Table 1. Two theoretical models are

TABLE 1. The four components of brand equity

Sensitivity (A) (strength) Response bias (B)

Brand awareness (recognition) Awareness sensitivity Awareness biasBrand likeability Likeability sensitivity Likeability bias


98 YE AND VAN RAAIJ

described along with the four components. One is the global memory model (Gillund andShiffrin, 1984; Ratcliff et al., 1994) for brand awareness (recognition memory). The other is theproposed Signal Detection Theory brand likeability model.

Signal Detection Theory

Signal Detection Theory evolved from the development of communications and radar equipmentin the first half of the twentieth century. It moved to psychology (Green and Swets, 1966),initially as part of sensation and perception, in the 1950s and 1960s as an attempt to understandsome of the features of human behaviour when detecting very faint stimuli (signals) among otherstimuli (noise) that were not being explained by traditional theories of thresholds (Snodgrassand Corwin, 1988; Yonelinas, 1994, 2002; DeCarlo, 2002). Recently, Signal Detection Theorymigrated to marketing and consumer research (Tashchian et al., 1988; Cradit et al., 1994).However, besides the methodology introduction, little research with Signal Detection Theory inmarketing and consumer behaviour has been reported.

A typical Signal Detection Theory paradigm usually encompasses a detection task. With thedetection task, a mix of stimuli contains signals and noise. Signals are stimuli to be detected,while noise is background, which is not to be detected. For example, in a recognition tasksignals are exposed items in a study phase, whereas noise consists of new items never exposedto before. The detection task of a test phase is to find the signals in a mix of signals and noise.When subjects respond to a signal as a ‘signal’ it is called a ‘hit’, otherwise it is a ‘miss’. Whensubjects respond to noise (distracters or signal not present) as a ‘signal’ it is called a ‘false alarm’,otherwise it is a ‘correct rejection’ (Fig. 1).

The sensitivity and bias of an operator’s reactions are measured in signal detection. Sensitivityis specified by the signal-to-noise ratio. More specifically, the sensitivity of strength is thestandardized mean difference of the strength between the signal and noise distributions (Fig. 2).If the sensitivity is high (i.e. the signal is strong relative to the noise), the operator is effective indetecting the presence of signals.

On the other hand, the operator’s response tendency (bias) is distinct from its sensitivity. Itspecifies how certain one must be before one is willing to say ‘yes, a signal is present’. In other

FIGURE 1. The 2 ××××× 2 matrix of Signal Detection Theory.



words, the bias is the cut-off criterion for reporting a signal (e.g. the response bias in Fig. 2).The response bias can change while the sensitivity remains the same. For example, a radiologistmay decide to accept weaker indications of abnormality of X-rays in order to refer patients totreatment.

Brand awareness

Using Signal Detection Theory for measuring recognition memory is well established in psycho-logy (Snodgrass and Corwin, 1988; Yonelinas, 1994, 2002; DeCarlo, 2002). Recognitionmemory is indicated with two measures in experimental psychology: the strength difference(sensitivity) of the standardized means of the signal and noise distributions and the responsetendency (bias) for detecting a signal.

In branding, the widely accepted conceptualization of a memory structure includes brand/node associations and spreading activation (Collins and Loftus, 1975; Wyer and Srull, 1986;Keller, 1993). For example, the associative network memory model views brand knowledge asconsisting of a set of nodes and links. Nodes are stored information such as brands connected bylinks that vary in strength. A spreading activation process from node to node determines theextent of retrieval in memory such as free recall and recognition.

More specifically, the global memory model has been successfully applied to various experi-mental settings for recognition memory (Gillund and Shiffrin, 1984; Yonelinas, 2002). Themodel assumes that a test item presented for recognition will be compared to all itemsin the memory in order to determine the degree of match (familiarity) between the test itemand items in the memory. The familiarity value in turn determines the old–new (presented or

FIGURE 2. Signal Detection Theory. Xc and Xc' are the cut-off points of awareness/likingstrength separating the reported positivity and negativity on the continuum of strength.Non-parametric Signal Detection Theory was used in the current study. According toSnodgrass and Corwin (1988), normal distributions are not required in this case, althoughthis figure uses normal-like distributions for display. The actual distribution of the data maynot necessarily be normal.


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non-presented) judgement. The higher the familiarity of the item, the more likely an ‘old’response will occur (Ratcliff et al., 1994).

Brand awareness can be assessed with brand recognition memory, which in turn is categorizedinto recognition sensitivity and recognition bias. Hence, we can further categorize brandawareness into awareness sensitivity and awareness bias.

The strength (sensitivity) of brand awareness refers to the standardized means differencebetween the signal and noise distributions. In other words, the sensitivity is an objective (unbias-ed) measure of consumer ability for detecting ‘seen before’ brands from ‘unseen’ brands. It is notbiased by consumer motivation or expectancy to respond. On the other hand, the bias of brandawareness refers to the consumer tendency to respond to a mix of seen before and unseenbrands. When the bias is large, consumers are more conservative (e.g. think twice) about report-ing a signal, whereas a small bias indicates that consumers are liberal with responding. Asmentioned, this categorization of brand awareness is based on recognition memory. A similarcategorization may be implemented for recall memory in future research.

The Signal Detection Theory brand likeability model

The use of Signal Detection Theory for modelling brand likeability is not yet documented inmarketing and consumer research. Therefore, this paper first conceptualizes signals and noise ofa liking task on brand names as part of a newly proposed Signal Detection Theory likeabilitymodel, which is followed in the next section by defining the extended two components oflikeability.

The strength of brand liking in the Signal Detection Theory likeability model is assumed tobe in a continuum of strength of favourability of brand nodes, analogous to the continuum ofstrengths of familiarity of brands for recognition memory in the global memory model (Gillundand Shiffrin, 1984; Ratcliff et al., 1994). This assumption is quantitatively valid in the sense thatthe liking response data to various brand nodes can be continuous, regardless of whether one ortwo factors or processes are underlying positive and negative liking.

An experimental setting has been designed that simulates marketing presentations and testing.After being exposed to pairs of brand names, subjects are asked whether they like the presentedand non-presented brands, suggesting that subjects are motivated (signalled) to look for brandswith a positive valence. In general, presented brands should be more likely to be liked than non-presented brands because of the mere-exposure effect, which suggests that presented stimuli arepreferred over non-presented stimuli, even when the presented stimuli are not recognizable(Zajonc, 1968; Bornstein, 1989). Thus, liking for presented (attended and unattended) brandsconstitutes the signal distribution, whereas liking for non-presented brands constitutes the noisedistribution. Hence, a non-presented brand distribution and a presented brand distribution maybe constructed, analogous to the noise and signal-plus-noise distributions of Fig. 2.

The following experiments are designed so that subjects make a yes/no choice for likeabilityof the stimuli (brands). Thus, they are motivated to identify positively valenced stimuli. Thereason underlying this is the mere-exposure effect (Zajonc, 1968; Zajonc and Markus, 1982;Nordhielm, 2002). Hence, presented brands are supposed to evoke a positive liking, whereasnon-presented brands evoke a neutral or baseline liking.

The overlapping area (the noise and signal-plus-noise distributions in Fig. 2) means that theliking of presented and non-presented brands may be overlapping. In other words, the areaindicates that it is possible for a presented and non-presented brand to evoke the same levelof liking.



Extending brand likeability with Signal Detection Theory

Drawing on the Signal Detection Theory model, the likeability of brands is split into two newcomponents: the sensitivity (strength) and bias of likeability. The sensitivity may be the standard-ized means difference between the strength of likeability of the signal and noise distributions. Itis an objective (unbiased) measure of the distance between the positive and neutral favourabilitydistributions. Internal factors such as the consumers’ ability for detecting or situational factorssuch as vividness and baseline familiarity of brands (e.g. famous brands can be easily detectedfrom non-famous brands) may affect the strength of likeability (Loewenstein et al., 2001).In other words, the sensitivity for brand liking is the strength difference between the twodistributions of neutral (noise) and positive likeability (signal).

However, another component of brand likeability, likeability bias, has received little attentionin consumer research. It is the cut-off criterion for concluding (identifying) a positive liking ofthe brand. For example, after consuming some brands of wine, consumers are asked to saywhether they like the brands. The response bias for reporting positively (saying yes) is thelikeability bias. If the bias (criterion) is conservative, consumers think twice before reacting. If aliberal criterion is adopted, it is easy (e.g. think less) to respond.

Summary

This paper has extended ‘brand awareness’ to awareness sensitivity (strength difference) andawareness bias and ‘brand likeability’ to likeability sensitivity (strength) and likeability bias basedon the two measures of Signal Detection Theory (see Table 1). The two underlying models arethe global memory model on brand awareness (recognition) and the proposed Signal DetectionTheory likeability model.

HYPOTHESES

Based on Signal Detection Theory and the core components of customer-based brand equity,brand awareness and brand likeability, this paper extends the two core components to four com-ponents. This section introduces a divided-attention procedure for investigating the effect ofattention on the four components and deriving hypotheses.

In a divided-attention procedure that simulates marketing, subjects participate in two phasesof the procedure: a study phase and a test phase. Subjects first study pairs of brand names pre-sented on a computer screen, with a pair of two brands at a time. One of the two brands isindicated with colour or an asterisk below. The subjects are instructed to pay attention to thecoloured or pointed brand. The other brand of the pair is ignored (unattended). In the test phasethe subjects make recognition judgements on their memory of the brand and liking judgementson their favourability of the brand. In the meantime, the differences in recognition and likingbetween the attended (or unattended) and non-presented brands are measured.

The depth of processing consumers engage in during encoding (study phase) may be indicatedwith various controls, such as repetition (Zajonc, 1968; Bornstein, 1989; Nordhielm, 2002), self-related generation (Craik and Tulving, 1975), stimulus presentation duration (Bornstein, 1989)and level of attention (Ye and Van Raaij, 1997). Here the paper defines two modes of brandinformation processing: deep and shallow information processing. When consumers attend tobrands or are exposed long enough to the brand, a deep processing model is employed, whereasa shallow processing mode is employed when consumers do not attend or are exposed only



briefly to the brand. According to the global memory model (Gillund and Shiffrin, 1984;Ratcliff et al., 1994), in the deep processing mode the strength of familiarity of the brand nodein consumers’ memory is strong, so the match between the brand as a test item and the brandnode memory is facilitated. It is more likely that an ‘old’ response is given to the presented(marketed) brands with higher familiarity. Therefore, subjects in the deep (attended) mode mayperform better in unbiased retrieving of the encoded brand information (brand awareness) thanin the shallow mode.

In addition to the global memory model, empirical data on divided attention in psychologicalliterature have found it to disrupt recognition memory. It has been demonstrated that dividingattention disrupted the recognition (explicit) memory of studied words in experiments (Shapiroand Krishnan, 2001; Wallace et al., 2001). Other supportive evidence is that an attended picturewas recognized more frequently than an unattended one (Goldstein and Fink, 1981). Althoughthe result is obtained with conventional measures (hit rates in the Signal Detection Theory) ofrecognition memory, there is no indication to show that it is biased by respondents’ internalresponse tendency.

In the literature related to brand name recall, it has been found that a brand name explicitlyconveying a product benefit leads to a higher recall of an advertised benefit claim consistent inmeaning with the brand name compared with a non-suggestive brand name (Keller et al., 1998).A suggestive brand name may invoke more associations for the brand nodes in the memoryaccording to the associative network memory model (Keller, 1993) and the global memorymodel, thus leading to deep processing, which may be similar to the effect of attending to thebrand name.

To summarize, the memory models and empirical data in psychology and consumer researchpredict that subjects perform better on brand awareness measures in the deep mode than in theshallow mode. Hence, this paper proposes the following hypothesis.

H1: There is an effect of attention on brand recognition sensitivity. The more attended, the moresensitive the operator’s recognition memory is between seen before and unseen brand names.

There is also an effect of attention on brand recognition bias. When subjects are attending toitems (words or Chinese characters) they tend to adopt a conservative criterion for reportingseen before items (Yang and Ye, 1995). In addition, the sensitivity and bias measures in therecognition memory literature, particularly with the non-parametric Signal Detection Theorythat is used in the current study, seem to be congruent most of the time (Snodgrass and Corwin,1988). Therefore, this paper proposes the second hypothesis.

H2: There is an effect of attention on brand recognition bias. The more attended, the moreconservative the response criterion will be.

The effect of attention may occur for the newly constructed brand liking sensitivity based onthe Signal Detection Theory likeability model. In the continuum of favourability of brandnodes, a deep mode of brand processing (e.g. repetition) is associated with a more favourablestrength of liking, according to the mere-exposure effect (Zajonc, 1968; Bornstein, 1989). Inother words, the signal (marketed brands) distribution is shifted further away from the noise(non-presented brands) distribution (Fig. 2). Thus, the mean difference between the two distri-butions (strength sensitivity of brand liking) is larger.

To elaborate, this theoretical conceptualization is supported with empirical data on the mere-exposure effect suggesting that, the more repetition of a subliminally exposed stimuli, the morethe stimuli will be liked (Zajonc, 1968; Zajonc and Markus, 1982). Although the conventional



measure (percentage of correct identification of preferred stimuli) is used for finding the result,it is believed that the result is not biased by respondents’ internal response tendency. Hence, thispaper proposes the first of the second set of hypotheses.

H3: There is an effect of attention on brand liking sensitivity. The more attended, the more liked thebrands will be.

Since the likeability bias is a new construct in the Signal Detection Theory model, little isknown about it. Thus, a similar effect of attention on the liking bias, analogous to the effect onthe bias of recognition memory, is expected. In this case, it is assumed that the level of brandprocessing (attention) at the encoding (marketing) phase influences the internal responsetendency of consumers to the brand.

H4: There is an effect of attention on the brand liking bias. The more attended, the moreconservative the liking bias will be.

GENERAL METHOD

Three non-parametric Signal Detection Theory experiments for testing the four hypotheses onthe effect of attention on the four new components are reported. This section first introducesnon-parametric Signal Detection Theory followed by the generic method used in all threeexperiments.

Non-parametric Signal Detection Theory

Tashchian et al. (1988) and Cradit et al. (1994) first introduced Signal Detection Theory tomarketing and consumer research. In this study’s brand name experiments the liking bias indi-cator is the response bias index (B� ) of a non-parametric Signal Detection Theory (Grier, 1971;Snodgrass and Corwin, 1988).

Non-parametric Signal Detection Theory analysis is used here rather than parametric SignalDetection Theory because a normal distribution is not required for the data (Snodgrass andCorwin, 1988). Therefore, with a liberal restriction on the data distribution, non-parametricsignal detection analysis can calculate the sensitivity (A) and response (liking and recognition)bias (B). The response bias and the sensitivity are computed with non-parametric formulas.(For H ³ ≥ FA, A� = 0.5 + [(H – FA)(1 + H – FA)]/[4H(1 – FA)] and for H < FA, A� = 0.5 – [(FA – H)(1 + FA – H)]/[4FA(1 – H)]. For H ≥ FA, B � = [H(1 – H) – FA(1 – FA)]/[H(1 – H)+ FA(1 – FA)] and for H < FA, B� = [FA(1 – FA) – H(1 – H)]/[FA(1 – FA) + H(1 – H)].These are non-parametric formulas. In parametric Signal Detection Theory the sensitivity d isthe separation (mean difference) of the two distributions over the variance.) The hit rate (H) isdefined as the conditional probability of responding yes to a signal (e.g. an attended or unat-tended ‘old’ brand) in recognition and likeability judgement tasks. The false alarm rate (FA) isdefined as the conditional probability of responding yes to a noise (e.g. a new brand).

The response bias B� represents an individual’s response bias when they say yes to a brandname in a recognition or liking judgement task of the test phase. A high B� indicates a moreconservative decision criterion, whereas a low B � represents a more liberal decision criterion(Snodgrass and Corwin, 1988). As is known, B � is affected by an individual’s motivationto respond. A high motivation for doing a good job (more hits) is accompanied by a liberaldecision criterion B � . A low motivation is accompanied by a conservative decision criterion.



Stimulus materials

Subjects participated in four tests (tests A, B, C and D) after the brands were presented for 0.5,1 or 2 s in the three experiments, respectively. Test A was a recognition judgement for theattended brand names, test B was a liking judgement for the attended brand names, test C wasa recognition judgement for the unattended brand names and test D was a liking judgement forthe unattended brand names. Each subject participated in all four tests.

Pilot studies were carried out in order to select randomly a list of 80 novel or unfamiliar andmoderately attractive brand names from a book entitled Levensmiddelen Almanak ’93, which con-tains the brand names of the fast-moving consumer goods sold in supermarkets in TheNetherlands. First, 101 brand names were randomly selected from this book according to alpha-betical order. Twenty undergraduate students were asked to rate the familiarity and attractive-ness of each brand name on a four-point scale. With regard to familiarity, the top 16 mostfamiliar brand names were excluded from the list, because over half of the students rated theseas familiar brand names. Regarding the attractiveness rating, five extremely attractive and un-attractive brand names were also excluded from the list (85 brand names) in order to eliminatethe interference of pre-experimental attractiveness towards brand names. This interference wasfurther eliminated using the sensitivity index of the non-parametric Signal Detection Theoryin later analysis (Snodgrass and Corwin, 1988). Thus, a list of 80 unfamiliar and moderatelyattractive brand names was obtained.

To summarize, the homogeneity of baseline attractiveness and familiarity was obtained in thepilot study so that prior brand attractiveness and familiarity could be controlled for. The priorbrand attractiveness might have been the subjects’ individual differences. Although it may becontrolled as a covariate in later analysis, it is thought that a clean homogeneous baseline is moredesirable for investigating the two experimental controls: attention and duration.

The moderately attractive and familiar baselines shift only the means of the noise distributions(non-presented brands), as compared with a non-attractive baseline. The effects of attention andduration are not affected by the choice of the baseline, because the ‘signal’ distribution isassumed to be a signal-plus-noise distribution (Fig. 2).

The subjects were exposed to brand names, first in a study phase and then in four test phases.Twenty pairs of brand names for the study phase were randomly selected from the list of 80brand names. One brand name of each pair of brand names, printed in red, was presented in thecentre of the monitor screen and the other brand name, printed in blue, was presented to theleft of the centre of the screen. The subjects were instructed to memorize the red brand names.Thus, the red brand names were attended to and the blue brand names were not attended to.

All brand names presented in the tests were in black, which is consistent with neither the rednor the blue brand names in the study phase. The context effect of colour was thus controlledfor (Tulving and Thompson, 1973; Murnane and Mathews, 1995). Half of the attended brandnames were randomly assigned to the right side of the screen and half were on the left side ofthe screen in order to eliminate spatial interference.

Each type of test (tests A–D) consisted of 20 brand names with ten ‘old’ (prior exposure)brand names randomly selected from the studied set and ten new brand names randomly selectedfrom the remainder of the list of 80 brand names. With regard to the attended condition, halfof the 20 test brand names were selected randomly from the 20 attented (‘red’) brand namespresented in the study phase. The other half was selected from the rest of the list, to which thesubjects were not exposed during the study phase. Regarding the non-attended condition, halfof the 20 test brand names were randomly selected from the 20 non-attended (‘blue’) brand



names of the study phase and the other half were new brand names from the list that the subjectsdid not contact in the study phase.

EXPERIMENT 1

Subjects and design

Sixteen students of a management school were recruited. In experiment 1 the brands were pre-sented for 0.5 s in the study phase. The experimental design was a 2 × 2 × 2 (attended versusnon-attended × test: recognition versus liking × measure: sensitivity versus bias) mixed factorialdesign with attention, test and measure as within-subject factors.

Procedure

Terminals with colour monitors were used for presenting the stimuli. One to five subjectsattended the experiments at the same time and their responses were transferred from the termi-nals to the central computer after the experiment. The central computer controlled the startingtime of the study phase and each test (tests A–D) for all terminals. The terminals were set in alarge laboratory room and were separated by facing opposite directions. Thus, the interferencebetween subjects was controlled for and minimized. The four tests lasted 10–15 min for eachparticipant.

In the study phase of the experiment 20 pairs of brand names, one printed in red (attendedstimulus) and the other in blue (unattended stimulus) for each pair, were presented for 0.5 s.The subjects were instructed to memorize the brand names printed in red.

In the test phase of the experiment the subjects attended to four tests following a Latin-squaredesign sequence. Twenty brand names were presented in each test. Each brand name was pre-sented for 1000 ms. The subjects were instructed to make a judgement immediately after eachbrand name disappeared. When the participant pressed a key, another brand name was immedi-ately presented. After the four tests were completed the responses were automatically transferredto the central computer.

The counterbalance was performed using a Latin-square design with test sequences. This isequivalent to a different random selection of test items for each subject. Both designs achieve thesame aim of controlling for the differences in the test items, although the individual differencewas addressed once in the pilot study with the baseline homogeneity.

There were four test sequences numbered from 1 to 4. The difference between the testsequences was the order of the four tests presented to a subject. A test sequence included all fourtests. The aforementioned four tests (tests A–D) were constructed with attention (attended andunattended) and test type (recognition and liking). Each test contained ten presented and tennon-presented brands.

A group of 16 subjects was randomly assigned to a test sequence in each of the three experi-ments and went through a test sequence of the four tests, but with the same duration. One testsequence was used just four times because a total of 16 subjects were allocated to the group. Thefour test sequences (1–4) with a Latin-square design (sequence 1 was ADCB, sequence 2was BCAD, sequence 3 was DABC and sequence 4 was CBDA) guaranteed that the test items(presented and non-presented) were counterbalanced.

The instructions for the study phase were identical for all subjects, but the instructions for thedifferent tests varied. In the recognition test the subjects were instructed to make old/newjudgements about the test items. Old items were those that they thought they had seen in the



study phase, while new items were those that they did not think they had seen in the studyphase. In the liking judgement test the subjects were instructed to make a quick likingjudgement (e.g. answer to ‘Do you like the brand?’) about the stimulus according to their firstimpression.

All subjects were instructed to press 1 on the small keyboard if their judgements were yes(‘old’ or ‘like’) or to press 2 if their judgements were no (‘new’ or ‘dislike’). They were told torespond as fast as possible, although their response times were not recorded.

Results for brand awareness (recognition)

A 2 × 2 analysis of variance (ANOVA) with attention (attended versus unattended) and measure(A� versus B� ) as factors for the recognition data revealed a significant main effect of theattention factor (F1,60 = 8.249 and p = 0.006 < 0.05). The main effect of measure yieldedF1,60 = 50.839 and p = 0.001 < 0.05. No significant effect was found for the interaction betweenattention and measure (F1,60 = 0.226, p = 0.636 > 0.05, R2 = 0.497 and adjusted R2 = 0.472).

As a result, both hypotheses 1 and 2 were supported in experiment 1. Table 2 displays themeans of the hit rate, false alarm rate, A� and B � for the brand awareness (recognition) data. Theeffect of attention occurred for both brand recognition sensitivity and recognition bias. In thedeep processing mode (brands are attended to), consumers perform better in retrieving pre-viously encoded brand information than in the shallow processing mode (brands are unattendedto). In the meantime, consumers adopt a more conservative criterion (B� ) for reporting brandawareness in the condition of deep as compared to shallow processing.

Results for brand liking

A 2 × 2 ANOVA with attention (attended versus unattended) and measure (A� versus B � ) asfactors for the recognition data revealed a significant main effect of the attention factor(F1,60 = 19.006 and p = 0.001 < 0.05). The main effect of measure was also significant(F1,60 = 198.104 and p = 0.001 < 0.05). No significant effect was found for the interactionbetween attention and measure (F1,60 = 2.865, p = 0.096 > 0.05, R2 = 0.786 and adjustedR2 = 0.775). Table 2 displays the means of the hit rate, false alarm rate, A� and B� for the brandliking data.

TABLE 2. Experiment 1: brand awareness and liking (presentation duration of0.5 s)

Awareness Liking

Attended A: Unattended C: Attended B: Unattended D:deep mode shallow mode deep mode shallow mode

Hit rate 0.385 0.144 0.235 0.191False alarm rate 0.122 0.085 0.138 0.231A� 0.700 0.595 0.610 0.455B� 0.408 0.262 0.205 0.137



Both hypotheses 3 and 4 were supported in experiment 1. The subjects performed betterin the deep processing mode in preserving the strength of the liking judgement for the brand(high A�) than in the shallow processing mode. The subjects adopted a conservative criterion(high B� ) for reporting brand liking in the deep processing mode and a liberal criterion (low B� )in the shallow processing mode.

EXPERIMENTS 2 AND 3

The study investigated whether hypotheses 1–4 were supported for longer presentations inexperiments 2 and 3. The durations of presentation in the study phases in experiments 2 and 3were 1 and 2 s, respectively. Two groups of 16 students from a management school wererecruited as the participants for experiments 2 and 3. The remainder of the design and procedurewas similar to experiment 1. The data for the two experiments are presented in Tables 3 and 4.

Results for brand awareness

A 2 × 2 ANOVA on the recognition data with attention and measure as the two independentvariables was performed for experiment 2. The main effect of attention was significant(F1,60 = 25.282 and p < 0.05). The main effect of measure was significant (F1,60 = 38.636 andp < 0.05). No significant effect was found for the attention and measure interaction(F1,60 = 0.238, p = 0.627 > 0.05, R2 = 0.517 and adjusted R2 = 0.493).

TABLE 3. Experiment 2: brand awareness and liking (presentation duration of 1 s)

Awareness Liking



TABLE 4. Experiment 3: brand awareness and liking (presentation duration of 2 s)

Awareness Liking





A 2 × 2 ANOVA on the recognition data with attention and measure as the two independentvariables was performed for experiment 3. The main effect of attention was not significant(F1,60 = 1.243 and p = 0.269 > 0.05). The main effect of measure was significant (F1,60 = 40.271and p < 0.05). No significant effect was found for the interaction between attention and measure(F1,60 = 0.015, p = 0.902 > 0.05, R2 = 0.409 and adjusted R2 = 0.379).

The interesting finding of experiment 3 was that divided attention did not influence brandawareness when a long presentation duration was used (2 s) (deep processing). In other words,hypotheses 1 and 2 are not supported in this condition.

Results for brand liking

A 2 × 2 ANOVA on the liking data with attention and measure as the two independent vari-ables was performed for experiment 2. The main effect of attention was significant (F1,60 = 6.644and p = 0.012 < 0.05). The main effect of measure was significant (F1,60 = 121.672 and p ≤ 0.05).No significant effect was found for the interaction between attention and measure (F1,60 = 1.033,p = 0.314 > 0.05, R2 = 0.683 and adjusted R2 = 0.667).

A 2 × 2 ANOVA on the liking data with attention and measure as the two independentvariables was performed for experiment 3. The main effect of attention was significant(F1,60 = 10.118 and p < 0.05). The main effect of measure was significant (F1,60 = 155.498 andp < 0.05). No significant effect was found for the interaction between attention and measure(F1,60 = 0.581, p = 0.449 > 0.05, R2 = 0.735 and adjusted R2 = 0.721).

Summary

The main effects of attention for the 12 conditions × three durations (0.5, 1 and 2 s) × four com-ponents of brand equity (awareness sensitivity and bias and likeability sensitivity and bias) aresummarized in Table 5. Hypotheses 3 and 4 on brand liking were supported across the threepresentation durations, while hypotheses 1 and 2 on brand awareness were supported for theshort (0.5 s) and middle (1 s) presentation durations, but not for the long presentation (2 s).

Conclusions

It was found that attention had a strong effect on the sensitivity (strength) and bias of brandawareness and brand liking in most cases in experiments 2 and 3. In general, the strength andbias of recognition and liking of the brands were stronger in the deep processing mode (e.g.attending to brands) than in the shallow processing mode. However, in the presentation dura-tion of 2 s the effect of attention on the strength and bias of brand awareness was attenuated,although the effect on the strength and bias of brand liking remained the same.

TABLE 5. The effect of attention on the four components

Short (0.5 s) Middle (1 s) Long (2 s)

Awareness sensitivity (H1) Yes Yes NoAwareness bias (H2) Yes Yes NoLikeability sensitivity (H3) Yes Yes YesLikeability bias (H4) Yes Yes Yes



With deep processing due to the long presentation duration, the subjects seemed toremember unattended brands as well as attended brands, as compared with non-presentedbrands. In other words, the shallow processing of brand awareness of unattended brands wasenhanced with deep processing due to the longer presentation. Thus, due to attention, deepprocessing did not generate a difference of brand awareness between attended and unattendedbrands for a longer duration (2 s). However, due to the presentation duration, deep processingdid not affect the effect of attention on brand liking.

DISCUSSION

Customer-based brand equity is defined as consisting of two core components: brand awarenessand brand liking (Keller, 1993, 1998). In the experimental settings of the current study, market-ing management was simulated by a study phase with manipulations of attention and presentationof brands. Extending the two core components, brand equity can be understood as a mix ofthe strength and bias of brand awareness and brand liking. The factors that promote thefour components contribute to building the strength of overall brand equity from a consumerperspective. Theoretically, extending brand awareness and liking fits with psychological theorybecause Signal Detection Theory is a better and more comprehensive measurement than thepercentage measures (Green and Swets, 1966; Yonelinas, 1994). For example, the sensitivity andbias measures can indicate the objective response tendency for a consumer’s brand disliking.Consumers may say they dislike Coca-Cola and are conservative in making this judgement. TheSignal Detection Theory measures can identify the real factors underlying the negative response:a failure of the branding campaigns (using the sensitivity measure) or the consumer is conservative(using the bias measure).

In marketing practice a comprehensive understanding of the core components of brand equityis obtained with the four new components. Hence, marketing programmes can be more focusedon specific components in order to improve marketing productivity. In the meantime, the stra-tegic goals of marketing programmes for building a strong brand may be set, that is to facilitateconsumers (1) in retrieving brand information, (2) in using a liberal criterion for retrieving brandinformation, (3) in liking the brand and (4) in using a liberal criterion for liking the brand. Thesegoals can be viewed as the four principles of strategic branding.

Sensitivity and strength of brand awareness

As found in experiments 1 and 2, when the brand names are being attended to in the deepprocessing mode, the sensitivity and strength of the brand in memory is stronger than in theshallow processing mode. This is consistent with the prediction (hypothesis 1) from the globalmemory models in the sense that the strength of familiarity of the brand nodes in memory isenhanced in the deep processing mode. Thus, the match between the test brand and thememory is facilitated, which in turn generates more sensitivity in detecting the presented(marketed) brand.

In general, it is suggested that marketers induce consumers into a deep processing mode onbranding and marketing by attracting their attention. In this way, a strong brand will be builtin the memory that can be easily retrieved when a buying or repurchasing decision has to bemade. This suggestion has one exception, as implied by the result of experiment 3. Attractingconsumers’ attention works best when branding material is exposed to consumers only briefly.



When the exposition is long (2 s) the difference between the deep and shallow mode disappears.Then it is not needed to attract consumer’s attention explicitly.

Bias of brand awareness

Attracting consumers’ attention in order to build strong brand awareness in their memory isimportant for building a strong brand equity. However, the minus side of the deep processingmode is the conservative criterion associated with it. With Signal Detection Theory (Fig. 2), aconservative bias reduces the hit and false alarm rates (Fig. 1). In other words, consumers tendto be less willing to retrieve encoded (marketed) brand information in the deep processing mode.They may think twice before retrieving the brand information (e.g. remembering the brand).

An optimal treatment of balancing the sensitivity (strength) and the bias of brand awareness isenhancing the sensitivity (strength) of recognition memory while keeping the bias unchanged.Theoretically, this can be done. Signal Detection Theory suggests that the bias remainsunchanged if the expectancy and motivation of consumers can be controlled. The expectancycan be represented by the consumers’ perceived percentage of the presented (branded) materialin the mix of test brands. The motivation can be represented by the weights (e.g. rewards) forreporting a signal (branded material). It can be inferred from the current study that attention hasinfluenced either expectancy or motivation or both in the sense that the bias of recognitionjudgement is affected. Future research may search for a method of manipulating the level ofprocessing (e.g. using famous versus non-famous brand names such as the strategy of brandextension), but leaving the expectancy or motivation unchanged. In this way, the sensitivitystrength of brand awareness may be increased with a stable liberal bias.

Strength of brand liking

It was found that hypothesis 3 was supported in all three experiments in the sense that the sen-sitivity of brand liking is higher in the deep processing mode. Why hypothesis 3 was supportedmay be explained by employing the Signal Detection Theory likeability model. In a nutshell,the strength of liking of presented (marketed) brands was enhanced in the deep processing modeduring the study (marketing) phase, according to the mere-exposure effect. The strength loca-tions of the marketed brands (signals) move to the right of the continuum (Fig. 2). Thus, thedistance between the signal (marketed brand) distribution and the noise (non-marketed brand)distribution in the deep processing mode was larger than in the shallow processing mode.Note that the strength sensitivity is the mean distance (difference) between the strength of thepresented brands (in deep or shallow processing modes) and non-presented brands. Thus,hypothesis 3 was supported.

The goal of treating the sensitivity of brand liking is to facilitate consumers in liking thebrand. This goal is associated with building a strong sensitivity of brand liking that is usually theobjective of conventional marketing programmes. The current research suggests that marketersshould introduce consumers to a deep information-processing mode, the deeper the better.Attracting consumers’ attention always leads to a sensitivity improvement of brand liking acrossthe three levels of short to long presentations of brand names.

As a result of the current study, it was found that the effect of attracting attention in order tobuild a strong brand liking is not attenuated by long exposures of the brand. This suggests thatmarketers may introduce consumers to a deep processing mode as much or as long as possible inorder to increase brand liking.



Bias of brand liking

Similar to the bias of brand awareness, a conservative bias of brand liking was associated with thedeep processing mode. An optimal treatment of brand liking in marketing programmes willfacilitate consumers (1) in liking the brand and (2) in using a liberal criterion for liking thebrand. In this way, the sensitivity of liking will be increased, whereas the bias is unchanged orthe bias will be shifted to be liberal, whereas the sensitivity is unchanged or both are improved.This is particularly useful in studying consumer repurchasing decisions.

Consumer repurchasing decisions may be determined by the strength and bias of brand liking.After a satisfactory consumption of the branded product, the strength of liking may be high,leading to a strong brand loyalty for repurchasing. However, the repurchasing decision may alsobe affected by the criterion for differentiating favourably from a baseline (neutral) liking for thebrands. In other words, the brand liking bias may be a determinant of the brand repurchasingdecisions. For example, according to the Signal Detection Theory likeability model (Fig. 2),given the same liking strength to the brand, the cut-off point for identifying liking may be setdifferently based on the level of brand processing, as is found in the study. With differentcut-off points (from Xc to Xc' in Fig. 2), the brand with the same liking strength (point P) maybe judged as either ‘liked’ or ‘disliked’, which would lead to a positive or negative repurchasingdecision for the branded product. Hence, brand equity, the most valuable asset of the brandedproduct, may be influenced by the liking bias.

As is now known, brand liking consists of two components, brand likeability bias and brandlikeability sensitivity. Marketers should employ a different strategy for enhancing the liking biascompared with the conventional approach to enhancing liking sensitivity. For example, promo-ting Coca-Cola should build a liberal criterion for consumers to like the brand, in addition tobranding and packaging for enhancing brand liking sensitivity. Hence, having built a strongbrand liking in consumer’s memory, attracting consumers’ attention for the brand may lead to aliberal liking bias in order to achieve brand purchase. In other words, facilitating the brandattention may lead consumers to use a liberal criterion in choosing the brand if the brandmemory is strong. For example, when consumers are making a decision to buy a soft drink inAtlanta (the headquarter city of the Coca-Cola company), the brand of Coca-Cola is more likelyto be chosen with less attention (liberal brand liking bias), compared with scenarios with moreattention to the same brand.

In summary, the level of brand attention becomes a ‘double-edged sword’ in (1) promotinga strong sensitivity of brand liking and (2) facilitating a liberal criterion for consumers to like thebrand. Thus, searching for strategies that only influence the bias but not the sensitivity of brandliking is needed for future research. According to the Signal Detection Theory likeability model,controlling the level of expectancy and motivation is likely to affect the bias only. Hence, infuture research varying the rewards for liking a presented (marketed) brand or telling consumersthe different percentages of presented (marketed) brands in the mix of test brands may be usefulin changing the bias of brand liking only. In this way, both the sensitivity and bias of brandliking can be promoted.

Future research

The advantages of Signal Detection Theory measures over percentages (hit rates) are welldocumented in the psychological literature (Macmillan and Creelman, 1991). Experiments maybe designed for observing the differences between the bias (analogous to consumers’ actual



tendency to make judgements) and hit rates (analogous to consumers’ negative response).As a result, the causes of consumers’ negative responses may be objectively identified andattributed to either bias or sensitivity, whereas the conventional percentage measures (hit rates)cannot do so.

CONCLUSIONS

Branding and brand equity are of vital importance to marketing and marketing communicationsbecause they link customer (consumer) behaviour to firms’ financial metrics. Customer mind setbrand equity, including brand awareness and likeability, is the source for the associated financialbrand equity (Keller, 2003). However, what are the determinants of the mind set elements, suchas awareness and likeability? How can the mind set elements be manipulated in a way that mayultimately affect brand equity? These invoke a further exploration of the customer mind setelements of brand equity such as brand awareness and liking.

In this paper brand awareness and brand likeability of consumer-based brand equity wereextended to four components with Signal Detection Theory: the sensitivity and bias of brandawareness and the sensitivity and bias of brand liking. It was found that the brand liking sensi-tivity and bias were stronger in the attended mode than in the unattended mode. A similar effectwas found for the sensitivity and bias of brand awareness for short presentations, but not for longpresentations. Knowing these determinants (e.g. ‘level of processing’, attention and duration) isinstrumental to marketing programmes endeavouring to enhance brand equity.

ACKNOWLEDGEMENTS

The authors thank Kevin L. Keller and Robert Wyer for their helpful comments on an earlierdraft of this paper.

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BIOGRAPHIES

Gewei Ye is an assistant professor of marketing and electronic business at Towson University,Baltimore, USA. His research interests are consumer decision processes, brand and customerequity, marketing research, e-business and Internet technology.

Fred Van Raaij is a professor of economic psychology at Tilburg University, TheNetherlands. His research interests include consumer decision making, marketing communica-tion, media research and consumer confidence.


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