Journal of Consumer Research, Inc.

Brand Credibility, Brand Consideration, and Choice

Author(s): TlinErdem and JoffreSwait
Source: Journal of Consumer Research, Vol. 31, No. 1 (June 2004), pp. 191-198
Published by: The University of Chicago Press
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Brand Credibility, Brand Consideration, and

Choice
LIN ERDEM
TU
JOFFRE SWAIT*
We examine the role of brand credibility (trustworthiness and expertise) on brand
choice and consideration across multiple product categories that vary in regard to
potential uncertainty about attributes and associated information acquisition costs
and perceived risks of consumption. We find that brand credibility increases probability of inclusion of a brand in the consideration set, as well as brand choice
conditional on consideration. We also find that although credibility impacts brand
choice and consideration set formation more and through more constructs in contexts with high uncertainty and sensitivity to such uncertainty, credibility effects are
present in all categories. Finally, our results indicate that trustworthiness, rather
than expertise, affects consumer choices and brand consideration more.

ne of the most important roles played by brands (understood to be a name, term, sign, symbol or design,
or a combination of them which is intended to identify the
goods and services of one seller or a group of sellers and
to differentiate them from those of competitors; Kotler
1997, p. 443) is their effect on consumer brand choice and
consideration.
In this article, we propose and test that one important
mechanism through which brands impact on choice and
consideration materializes is via brand credibility. When imperfect and asymmetric information characterize a market,
economic agents (i.e., consumers and firms) may use signals
(i.e., manipulable attributes or activities) to convey information about their characteristics (Spence 1974). To be effective, such signals must be credible (Tirole 1990). Previous literature has studied the credibility of a brand as a
signal of quality or product positions (Erdem and Swait
1998; Rao and Ruekkert 1994; Wernerfelt 1988). The credibility of a brand as a signal (i.e., brand credibility) has been
conceptualized as the believability of the product position
information contained in a brand.
While previous research has explored the impact of credibility on product utility, we investigate brand credibilitys
effect on consideration and brand choice conditional on consideration. We also research the differential mechanisms
through which credibility exerts its influence on choice and
consideration across categories that vary in regard to po-

tential uncertainty about attributes and associated information acquisition costs and perceived risks of consumption.
This work extends previous work on brand credibility by
explicitly considering the two subdimensions of brand credibility (trustworthiness and expertise).
Our results indicate that brand credibility affects both
conditional brand choice and consideration. These effects
are found in a broad array of product classes we tested,
varying from simple fruit juices to personal computers
(PCs). The products were selected to reflect different degrees
of consumer uncertainty with respect to product attributes
and associated information acquisition costs and to perceived risks of consumption; this uncertainty is confirmed
by this research to underlie the differential role of brand
credibility in consideration and choice. In general, it is found
that trustworthiness, the subdimension of brand credibility
relating to consumers perceptions of firms willingness to
carry through on promises made, exerts a more important
impact than expertise (firms perceived capability to deliver
on promises) on respondents brand consideration and
choice. However, there is considerable variation in the relative importance of trustworthiness vis-a`-vis expertise
across product categories. The mechanisms whereby brand
credibility works its impacts, that is, via perceived quality,
perceived risk, and information costs saved constructs, are
explored in some detail, but specific results are left for later
sections.

*Tulin Erdem is E.T. Grether Professor of Business Administration and

Marketing, Haas School of Business, University of California, Berkeley,
CA 94720-1900; e-mail: erdem@haas.berkeley.edu. Joffre Swait is partner,
Advanis, Inc., and adjunct professor, Faculty of Business, University of
Alberta, 12 W. University Avenue, #205, Gainesville, FL 32601; e-mail:
Joffre_Swait@Advanis.ca.

BRAND CREDIBILITYS IMPACT ON

BRAND CHOICE AND CONSIDERATION
Previous empirical work on how consumers may narrow
attention to a subset of brands out of a bigger set has focused
191
2004 by JOURNAL OF CONSUMER RESEARCH, Inc. Vol. 31 June 2004
All rights reserved. 0093-5301/2004/3101-0016$10.00

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JOURNAL OF CONSUMER RESEARCH

192

on modeling consumer choices as the outcome of a twostage process of consideration set formation and conditional
brand choice (e.g., Andrews and Srinivasan 1995; Roberts
and Lattin 1991; Swait and Ben-Akiva 1987). The main
approach to conceptualizing consideration sets in the literature has been the cost-benefit approach (Hauser and Wernerfelt 1990). This approach employs the expected utility
maximization framework to advance the notion that consumers weigh the cost of brand evaluation for membership
in this subset against the benefits of adding or dropping the
brand. This implies consumer uncertainty about brands.

promises will be kept and (2) demonstrate longer-term commitment to brands (Klein and Leffler 1981). Furthermore,
it has also been shown that the clarity (i.e., lack of ambiguity) of the product information contained in a brand is an
antecedent to brand credibility (Erdem and Swait 1998).
As also suggested by Aaker (1991), higher perceived (or
expected) quality, lower information costs, and risk associated with credible brands may increase consumer evaluations of brands. Indeed, Erdem and Swait (1998) have
shown, using structural equation models, that expected utility is increasing in perceived quality and decreasing in perceived risk and information costs.

Brand Credibility
When consumers are uncertain about brands and the market is characterized by asymmetric information (i.e., firms
know more about their products than do consumers), brands
can serve as signals of product positions (Wernerfelt 1988).
As a signal of product positioning, the most important characteristic of a brand is its credibility. A firm can use various
marketing mix elements besides the brand to signal product
quality: for example, charging a high price, offering a certain
warranty, or distributing via certain channels. Each of these
actions may or may not be credible, depending on market
conditions including competitive and consumer behavior.
However, what sets brands apart from the individual marketing mix elements as credible signals is that the former
embody the cumulative effect of past marketing mix strategies and activities. This historical notion that credibility is
based on the sum of past behaviors has been referred to as
reputation in the information economics literature (see Herbig and Milewicz 1995).
Credibility is broadly defined as the believability of an
entitys intentions at a particular time and is posited to have
two main components: trustworthiness and expertise. Thus,
brand credibility is defined as the believability of the product
information contained in a brand, which requires that consumers perceive that the brand have the ability (i.e., expertise) and willingness (i.e., trustworthiness) to continuously
deliver what has been promised (in fact, brands can function
as signals sinceif and when they do not deliver what is
promisedtheir brand equity will erode). Both the expertise
and trustworthiness of a brand reflect the cumulative impacts
of associated past and present marketing strategies and activities. The credibility of a brand has been shown to be
higher for brands with higher marketing mix consistency
over time and higher brand investments, ceteris paribus
(Erdem and Swait 1998). Consistency refers to the degree
of harmony and convergence among the marketing mix
elements and the stability of marketing mix strategies and
attribute levels over time. The consistency of attribute levels over timefor example, consistency in quality levelsimplies low inherent product variability (Roberts
and Urban 1988), which can be achieved by a dedication
to quality standardization. However, the consistency to
which we refer is that of the brand positioning in general.
Brand investments, on the other hand, are resources that
firms spend on brands to (1) assure consumers that brand

Brand Credibility, Brand Consideration, and

Choice
Such a signaling framework of brand effects on consumer brand utility and choice also implies that when there
is consumer uncertainty about brands and information is
costly to obtain and/or process, the credibility of a brand
may be an important factor underlying the formation of
consideration sets. The cost-benefit approach to consideration and choice set formation suggests that the higher
perceived value and lower perceived risk associated with
a higher credibility brand are anticipated to increase expected benefits (Hauser and Wernerfelt 1990). Additionally, the lower information costs associated with credible
brands are likely to decrease expected costs, while the
credibility of a brand decreases perceived risk because it
increases consumers confidence in a firms product
claims. Credibility also decreases information costs since
consumers may use credible brands as a source of knowledge to save information gathering and processing costs
(e.g., reading Consumer Reports, doing online searches for
product reviews, seeking advice from experts; Erdem and
Swait 1998). Indeed, previous work has suggested that high
levels of cognitive effort needed to evaluate specific brands
may induce negative affect and may lead to lower choice
probabilities (Garbarino and Edell 1997). Consequently,
we expect that
H1: Higher credibility will both increase the probability of a brand being included in the consideration set and the probability of its being chosen
from the consideration set.
However, there is evidence in the literature that different
variables may affect consideration and brand evaluation conditional on consideration (Nedungadi 1990). Given that it
has been shown in previous research that brand credibility
effects on consumer utility materialize through perceived
quality, perceived risk, and information costs saved (Erdem
and Swait 1998), the next question would concern the differential effects of these three brand credibility mediator
constructs on choice versus consideration.
More specifically, perceived risk can be used in the initial
screening before the brand choice stage. Furthermore, the
impact of perceived risk may be much less, or even neg-

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BRAND CREDIBILITY AND BRAND CHOICE

193
TABLE 1

MEASUREMENT MODEL ITEMS AND ESTIMATED COEFFICIENTS

Construct

Item

Cronbachs a

Expertise

This brand reminds me of someone whos competent and knows what

he/she is doing (+)
This brand has the ability to deliver what it promises (+)
This brand delivers what it promises (+)
This brands product claims are believable (+)
Over time, my experiences with this brand have led me to expect it to
keep its promises, no more and no less (+)
This brand has a name you can trust (+)
This brand doesnt pretend to be something it isnt (+)
The quality of this brand is very high (+)
In terms of overall quality, Id rate this brand as a . . . (+)
Id have to try it several times to figure out what this brand is like (+)
I never know how good this brand will be before I buy it (+)
I need lots more information about this brand before Id buy it ()
I know what Im going to get from this brand, which saves time shopping around (+)
I know I can count on this brand being there in the future (+)
This brand gives me what I want, which saves me time & effort trying
to do better (+)

.77

Trustworthiness

Perceived Quality
Perceived Risk
Information Costs Saved

.89

.77
.64
.75

NOTES.All scales formed as simple averages of component items, reverse scored for negative items. All items measured on nine-point agree/disagree scales
except second quality item, which was measured on nine-point scales with 1 p low quality, 9 p high quality. Signs (+/) indicate a priori sign expectation.

ligibly, important at the conditional brand choice stage due

to the exclusion of high-risk brands from the consideration
set during the earlier screening stage of the choice process.
Therefore, we expect
H2: Brand credibilitys impact through perceived risk
to be found mainly at the consideration set formation stage, rather than at the brand choice conditional on consideration stage.
Furthermore, quality and price trade-offs are more likely
to be made explicitly at the brand choice stage rather than
at the choice set formation stage (Nedungadi 1990). Therefore, in the context of brand choice conditional on consideration set, we expect
H3: Brand credibilitys impact through perceived
quality to be more pronounced on brand choice
conditional on consideration set, rather than on
consideration set formation.
The third construct following brand credibility, information costs saved, can have the same screening function
as perceived risk at the consideration set formation stage.
However, in most product categories in which potential information costs are above a certain threshold, or there is
relative brand uncertainty (Moorthy, Ratchford, and Talukdar 1997), information costs saved are likely to continue to
be important at the brand choice stage. Hence, one needs
to test empirically whether brand credibilitys impact
through information costs saved can be found at both the
consideration set formation stage and brand choice conditional on consideration set.

Brand Credibilitys Impact on Choice Processes

and Product-Category Specific Factors
In contexts where uncertainty levels and sensitivity to
uncertainty are higher (e.g., PCs, as compared to orange
juice), one would expect credibility to have a greater impact
on consumer choice processes; additionally, credibilitys impact through perceived risk and information costs saved,
over and above its impact through perceived quality, should
be more pronounced.

EMPIRICAL TEST
In the data collection for this study we have used six
product classes: athletic shoes, cellular telecommunications
services, headache medication, juice, personal computers,
and hair shampoo. The survey we fielded at a major North
American university had eight different versions, each covering three of the six product classes just enumerated. In
each product class, five brands were presented, selected so
that the brand set represents a wide range of market share.1
For each brand, respondents were asked to provide responses
to the items listed in table 1 for the Trustworthiness, Expertise, QUAL (Perceived Quality), RISK (Perceived Risk),
and ICS (Information Costs Saved) constructs. These items
are directly taken from Erdem and Swait (1998), who validated the desired scales; we employed the same nine-point
agree/disagree scales used by them. In addition, three out1
Brands utilized were for athletic shoes: Adidas, Asics, New Balance,
Nike, Reebok; cellular service providers: AT&T, Cingular, Nextel, Sprint
PCS, Verizon; headache medication: Advil, Bayer, Excedrin, Motrin IB,
Tylenol; juice: Dole, Minute Maid, Sunkist, Tropicana, Welchs; personal
computers: Apple, Compaq, Dell, Gateway, IBM; hair shampoo: Alberto
VO5, Herbal Essence, Finesse, Pantene Pro V, Suave.

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JOURNAL OF CONSUMER RESEARCH

194
TABLE 2

BRAND CONSIDERATION BINARY LOGISTIC MODELS

A. Trustworthiness/Expertise models:
Brand 1
Brand 2
Brand 3
Brand 4
Brand 5
Trustworthinessa
Expertiseb
Trust # Uncerc
Exper # Uncer
Trust # Uncer # Famd
Exper # Uncer # Fam
McFaddens r 2
B. Perceived Quality/Risk/Information
Cost models:
Brand 1
Brand 2
Brand 3
Brand 4
Brand 5
Perceived Qualitye
Perceived Riskf
Information Costs Savedg
McFaddens r 2

Athletic
shoes

Cellular
provider

Headache
medication

Juice

Personal
computer

Shampoo

1.44**
1.03**
2.09**
2.68**
.24
1.07**
.63**
.48
.66
.55
.42
.47

2.50**
1.09**
.48*
.77**
1.21**
1.12**
.70**
.06
.52
.12
.13
.45

2.89**
.95**
1.10**
1.45**
2.53**
.96**
.72**
1.02
1.38**
1.23
.46
.49

2.38**
2.19**
.71**
1.52**
1.41**
.83**
.77**
.06
.35
.38
.94
.42

.41
1.18**
2.50**
1.25**
1.56**
1.56**
.38*
1.14
1.14**
1.88**
1.71
.48

.94**
1.35**
.86**
1.73**
.77**
.73**
.62**
.22
.12
.94
.24
.38

1.37**
1.35**
2.16**
2.09**
.03
.77**
.23*
.88**
.45

2.28**
1.38**
.49
.77**
1.19**
1.17**
.08
1.02**
.47

2.39**
1.06**
1.26**
1.72**
1.72**
1.56**
.18
.26
.47

2.23**
2.28**
1.07**
1.68**
1.53**
1.44**
.06
.2
.46

.53*
1.41**
2.45**
1.04**
1.35**
1.09**
.28**
.62**
.44

.97**
1.09**
1.02**
1.35**
1.07**
.88**
.09
.62**
.36

a
Trustworthiness score calculated as the average on nine-point agree/disagree scales of five Trustworthiness items from table 1. This is then centered across
brands in the product category, within each subject.
b
Expertise score calculated as the average on nine-point agree/disagree scales of two Expertise items from table 1. This is then centered across brands in the
product category, within each subject.
c
Uncertainty score is calculated as the sum of Whenever one chooses in this product category, one never really knows whether that is the one that should have
been bought and When one makes purchases in this product category, one is never certain of ones choice, where association of the statement with the product
category results in a score of one, and zero otherwise. This is then centered across product categories, within each subject.
d
Familiarity score is defined as one if I consider myself familiar with this product category is associated with the product class, and zero otherwise.
e
Perceived Quality is calculated as the average of two Perceived Quality items in table 1, centered within subject.
f
Perceived Risk is calculated as the average of two Perceived Risk items in table 1, centered within subject.
g
Information Costs Saved is the average of four Information Costs Saved items in table 1, centered within subject.
*p ! .05.
**p ! .01.

come measures for each brand were elicited: considerationAssuming prices are the same, I would seriously
consider buying this brand. (Check all that apply.);
choiceAssuming prices are the same, this is the one brand
I would be most likely to buy. (Check one only.); allocationAssuming prices are the same, if you were to buy
10 [units of . . .] at one time, how many of each brand
would you buy? (Both choice and allocation measures were
included for convergent validity purposes. We will report
only on the choice results, since the allocation models mirror
them closely.) Note the ceteris paribus equal price condition:
this was done to permit the omission of price (unknown to
us and the participants) from subsequent analyses. Respondents also indicated whether they considered themselves familiar or not with each of the six product categories, as well
as whether or not they viewed their choices in each of the
product categories as being uncertain or not (i.e., the degree
of uncertainty about having made a good selection they
associate with a choice in a product category).
With each respondent providing data on three product

classes, our final sample sizes are 83 for athletic shoes, 84

for cellular services, 83 for headache medication, 83 for
juice, 82 for personal computers, and 83 for shampoo. All
respondents received course credit as incentive for participation.
Construct estimates were obtained as follows for each
brand, product class, and individual: an average score over
the relevant items (as given in table 1) was calculated for
each product class, brand, and individual, and then centered
about the mean brand score of the individual in the product
class. For completeness we report in table 1 the Cronbachs
alpha measures for the constructs thus defined; all constructs
have alpha measures over .6. The definitions of Familiarity
and Uncertainty, as well as the other constructs, are detailed
in the notes of table 2. Centering of constructs is done to
eliminate ambiguity of interpretation: a significant construct
estimate can only mean that within-respondent construct variation underpins the result.
Table 2 presents two sets of models, per figure 1 (and
Erdem and Swait 1998), wherein Trustworthiness and Ex-

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BRAND CREDIBILITY AND BRAND CHOICE

FIGURE 1
STRUCTURAL RELATIONSHIPS BETWEEN BRAND
CREDIBILITY, PERCEIVED QUALITY, PERCEIVED RISK, AND
INFORMATION COSTS SAVED

pertise are posited as antecedents to QUAL, RISK, and ICS.

Thus, in turn, the binary logistic regressions relate brand
consideration (yes/no) to the two sets of constructs in each
of the six product classes: (1) Trustworthiness and Expertise,
the subcomponents of brand credibility, interacted with Familiarity and Uncertainty, and (2) QUAL, RISK, and ICS,
the downstream constructs reflecting brand credibility. Our
motivation in interacting Familiarity and Uncertainty (about
choices) with the core Trustworthiness and Expertise constructs is to aid our understanding about limits that may hold
to the main effects being considered here. We will explain
these in more detail subsequently.
Let us now examine the Trustworthiness/Expertise results
in table 2A. Clearly, across all product classes, as each of
these credibility components increases, so does the probability
that the brand is considered by the respondent. In all product
classes, estimated coefficients for the main effects of these
two constructs are significantly different from zero at the 95%
confidence level. In this population of respondents, undergraduate university students, it seems that Trustworthiness is
generally more impactful than Expertise perceptions; this is
particularly notable in PCs, where the main effect of Trustworthiness is about three times more important than that of
Expertise in terms of increasing brand consideration probabilities (using log odds ratios as the comparison metric). In
two product classes, Headache Medication and Personal Computers, the impact of Expertise on consideration probabilities
is moderated by Uncertainty: the more uncertain a respondent
perceived him/herself to be about choices made in these product categories, the greater role their brand Expertise perception had in determining consideration. No systematic interaction between Uncertainty and Trustworthiness was found
in any of the product classes. However, the role of Trustworthiness is moderated by Uncertainty among respondents
that were familiar with the PC category, as captured through
the significant third-order interaction (Trustworthiness #

195

Uncertainty # Familiarity). These last results reflect the

intuition that individual difference variables (Uncertainty
and Familiarity, among many others, we are sure) are likely
to help establish boundary conditions for the applicability
of the theory presented.
It is noteworthy that in all product categories the main
effects of the centered Trustworthiness and Expertise constructs are statistically significant, indicating that the principal hypothesis 1 holds generally but becomes stronger or
weaker under certain conditions. Note again that the results
hold due to brand credibility differences at the individual
respondent level.
Table 2B also presents the logistic regression results for
brand consideration as a function of QUAL, RISK, and ICS.
These models are interesting to examine because they indicate how brand credibility may work in impacting brand
consideration. (These models do not include individual difference variables since their main intent is to explore the
means whereby credibility impacts are generated.) We find
that QUAL consistently shows up as a statistically significant explanator of brand consideration and is the most impactful variable of the three constructs in all product categories save athletic shoes. The RISK model is statistically
significant in athletic shoes and PCs, marginally significant
in headache medication, and not significant in juice, cellular
services, or hair shampoo. Finally, ICS is statistically significant in athletic shoes, cellular services, PCs, and hair
shampoo; thus, increased brand credibility allows these subjects to simplify decision making by saving time and only
considering brands with high ICS (i.e., implying high Trustworthiness and/or Expertise as well as low RISK). These
combined results seem to suggest that overall perceived risk
and information costs saved play a bigger role in categories
with higher uncertainty and sensitivity to uncertainty.
Table 3 presents estimation results for multinomial logit
models of the choice response variable. Note that all these
models have choice sets that include only those brands that
the individual respondents indicated they would consider
buying, hence they are conditional on stated consideration.
This seemed to us a reasonable procedure to help isolate
the brand credibility effects that are found to those that occur
during product evaluation and not brand consideration (already captured in the models discussed in table 2). This
does, however, imply that the choice sets defined for the
choice models in table 3 contain far fewer observations than
were available for the consideration analyses presented in
table 2 (each respondent provided one choice observation but
five consideration observations in each product category).
Table 3A generally demonstrates that brand credibility
(via Trustworthiness and Expertise) significantly impacts the
stated choice behavior of our respondents across all product
classes used in the study, with the exception of the shampoo
product class. As mentioned above, due to smaller sample
sizes in the choice models compared to the consideration
models of table 2, several of the Trustworthiness and Expertise main effect coefficients are not significantly different
from zero, but all are directionally correct. Almost none of

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JOURNAL OF CONSUMER RESEARCH

196
TABLE 3

BRAND CHOICE MULTINOMIAL LOGIT MODELS (CONDITIONAL ON STATED CONSIDERATION)

A. Trustworthiness/Expertise models:
Brand 1
Brand 2
Brand 3
Brand 4
Brand 5
Trustworthinessa
Expertiseb
Trust # uncerc
Expert # uncer
Trust # uncer # famd
Exper # uncer # fam
McFaddens r 2
B. Perceived Quality/Risk/Information
Cost models:
Brand 1
Brand 2
Brand 3
Brand 4
Brand 5
Perceived Qualitye
Perceived Riskf
Information Costs Savedg
McFaddens r 2

Athletic
shoes

Cellular
provider

Headache
medication

Juice

Personal
computer

1.47**
.32
1.44**
1.36**
0
.54
.72**
.69
.58
1.18
.73
.27

.64*
.15
.42
.38
0
.33
1.15**
.66
1.06**
1.18*
1.07**
.27

.12
1.12
0
.38
0
1.37**
.73
.14
.5
.16
.05
.39

.27
.09
1.36
.79
0
2.48**
1.02
.94
.24
2.11
1.1
.37

3.86**
1.07
.73*
1.55*
0
2.14**
.17
3.67*
6.31
2.29
4.76
.55

1.95**
.80
1.92**
1.66**
0
1.19**
.07
.69**
.34

.60
.25
.60
.20
0
1.29**
.07
.20
.36

.08
.70
.17
.43
0
.93**
.29
1.09**
.36

.14
.23
.77
.12
0
3.09**
.19
.29
.58

2.03
.27
1.17**
.76
0
1.53**
.53
1.56**
.59

Shampoo

.24
.97
.23
1.14
0
.64
.96
.46
.02
1.07
.07
.29
.65
.64
.04
.80
0
1.55**
.35
.65
.41

a
Trustworthiness score calculated as the average on nine-point agree/disagree scales of five Trustworthiness items from table 1. This is then centered across
brands in the product category, within each subject.
b
Expertise score calculated as the average on nine-point agree/disagree scales of two Expertise items from table 1. This is then centered across brands in the
product category, within each subject.
c
Uncertainty score is calculated as the sum of Whenever one chooses in this product category, one never really knows whether that is the one that should have
been bought and When one makes purchases in this product category, one is never certain of ones choice, where association of the statement with the product
category results in a score of one, and zero otherwise. This is then centered across product categories, within each subject.
d
Familiarity score is defined as one if I consider myself familiar with this product category is associated with the product class, and zero otherwise.
e
Perceived Quality is average brand scores calculated using items in table 1, which were then centered at the subject-brand level.
f
Perceived Risk, average brand scores calculated using items in table 1, which were then centered at the subject-brand level.
g
Information Costs Saved is average brand scores calculated using items in table 1, which were then centered at the subject-brand level.
*p ! .05.
**p ! .01.

the Uncertainty and Familiarity interactions with the main

constructs are significant, again likely due to sample size.
Table 3B also yields some information about the likely
routes whereby brand credibility works its impact on final
choice via QUAL, RISK, and ICS. Across the six product
classes we examined, QUAL is always a statistically significant determinant of choice; ICS is found to be statistically significant in athletic shoes, headache medication, and
PCs; and RISK is not found to be statistically significant in
any of the product classes. These results seem again to suggest that information costs saved affects the brand choice
stage more in categories with relatively higher levels of
uncertainty and sensitivity to such uncertainty. That RISK
is not influential in the conditional brand choice stage indicates that this constructs impact is likely limited to the
consideration set formation stage, consistent with hypothesis
2. However, it must be noted that the results from this table
are not very reliable due to small sample sizes, as we said
above.
Interesting comparisons can be made between tables 2

and 3, by product class. For instance, table 2A indicates that

consideration probabilities increase in Expertise for juice;
table 3A, on the other hand, shows that conditional choice
probabilities in this category are essentially unaffected by
Expertise. This may be a sign that in this population segment, and for this product class, perceptions of Expertise
are helpful in getting the brand to be considered, but Trustworthiness is likely the sine qua non of final choice. However, the simple models underlying tables 2 and 3 make such
interpretations something of a guess, mainly because there
is no explicit relationship between consideration, choice set
formation, and final choice.
We have presented above strong statistical evidence,
based on simple models of consideration and overall choice,
that brand credibility works its impact onto these stages of
the choice process through quality and risk perceptions, as
well as through perceptions of information costs saved (i.e.,
decision-making costs). The results from the empirical study
indicate that these results are likely to be found in a relatively
wide range of product classes; they also show that there is

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BRAND CREDIBILITY AND BRAND CHOICE

significant heterogeneity in the overall mechanisms whereby

the impact of brand credibility ultimately works itself out.
For example, we found that brand consideration in the athletic
shoes category is affected by all three constructs (QUAL,
RISK, and ICS), whereas RISK does not seem to have an
impact on the consideration of different cellular providers.
Except for juice, a very low uncertainty and low sensitivity
to uncertainty (low-risk) category, brand credibility affects
consideration set formation (as well as brand choice); RISK
seems to be a factor only at the consideration set stage; if
ICS plays a role, it does so in both decision stages; and QUAL
effects are relatively more pronounced at the brand choice
stage. Finally, RISK determines more strongly the consideration set stage in high-risk categories (PC and athletic
shoes); ICS affects all stages of choice processes in a broader
range of products but affects choice processes less in lowrisk categories (juice and to lesser extent headache medication). These results are consistent with our expectations in
regard to how potential uncertainty and sensitivity to it across
categories may moderate credibility impacts.
While table 2A supports somewhat the contention that
the differentiation of credibility impacts is likely to be a
function of individual differences (herein, familiarity with
and perceptions of uncertainty with respect to making good
choices in the product category), the most notable insight
afforded by that analysis is that across all product classes
examined the main hypothesis holds: brand credibility
(Trustworthiness and Expertise) is an important determinant
of brand consideration. The interactions of the credibility
components with Familiarity and Uncertainty indicate that
brand credibility will play an even greater role in determining consideration for certain product categories (here,
headache medication and PCs) and for certain individuals
(e.g. high Uncertainty and Familiarity). This greater role is
achieved not only by making brand credibility overall a more
important determinant of consideration but also by shifting
the relative importance of Trustworthiness and Expertise
within the overall credibility impact. For example, in PCs
the main effects of these component constructs indicate that
on average Trustworthiness is about three times more important than Expertise in determining brand consideration;
however, for individuals that associate low Uncertainty and
have Familiarity with the product class, Trustworthiness is
only half as important as Expertise perceptions in determining consideration probabilities.

DISCUSSION
In this article we present evidence for brand credibilitys
effect on the formation of consideration sets over and above
its effect on brand choice. We also shed light on the mechanisms through which brand credibility effects materialize
in the consideration set formation and brand choice stages.
In regard to the two subdimensions of credibility, we also
found that trustworthiness, rather than expertise, has the
bigger impact on consumer choices.
Furthermore, we established some boundary conditions
to our results. In the juice category, for example, our em-

197

pirical study indicates that neither perceived risk nor information costs seem to matter in consumer choice processes.
However, we also found that credibility affects consumer
choices through perceived risk, information costs saved, and
perceived quality in most categories, even those with only
moderate levels of uncertainty. This result is found to hold
at the individual respondent level, indicating that it is brand
credibility differences that are driving consumer behavior.
Finally, we also found some evidence for stronger credibility
impacts for individuals who perceive higher uncertainty
when choosing in a given product category.
The simple analysis methods employed in this article
are unable to cleanly isolate and attribute brand credibility
impacts between choice process stages. Future research
should examine brand credibility impacts using structural
choice set formation models to allow a purer attribution of
impacts, leading to a better understanding of the mechanisms
whereby brand credibility impacts choice stages. Future research should also extend our analyses to explore choice dynamics and thus explain the processes by which credibility
and consideration set formation evolve over time. Additionally, and of great practical interest, more detailed analysis of
individual level and product category specific moderators of
credibility effects should be conducted.
[Dawn Iacobucci and David Glen Mick served as editors
and Joel Huber served as associate editor for this
article.]

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