The effect of consumer confusion
proneness on word of mouth,
trust, and customer satisfaction
Institute for Management, University of Koblenz-Landau,
Koblenz, Germany, and
Cass Business School, City University of London, London, UK
Purpose – Consumer sovereignty assumes that consumers have adequate product information and
are able to understand that information in order to make an informed choice. However, this is not the
case when consumers are confused. Recently, Walsh et al. identiﬁed dimensions of consumer confusion
proneness and developed scales to measure these dimensions. Drawing on their concept of consumer
confusion proneness, this paper seeks to examine consumers’ general tendency to be confused from
marketplace information and its effect on three relevant outcome variables – word of mouth, trust, and
Design/methodology/approach – The reliability and validity of the consumer confusion
proneness scale was tested on the basis of a sample of 355 consumers, using conﬁrmatory factor
analysis. The study employs structural equation modelling to examine the hypothesised relationships.
Findings – The results show that the consumer confusion proneness scale has sound psychometric
properties and that the three dimensions of similarity, overload, and ambiguity have a differential
impact on word of mouth behaviour, trust, and customer satisfaction.
Practical implications – The ﬁndings have implications for marketing theory and management, as
well as consumer education. Marketers may apply the consumer confusion proneness scale to their
customers and assess which dimension is the most damaging in terms of the three marketing
Originality/value – This is the ﬁrst study to test Walsh et al.’s consumer confusion proneness scale
and to extend their work by analysing the effect of the three construct dimensions on three key
marketing outcome variables.
Keywords Consumer behaviour, Customer satisfaction, Trust, Role ambiguity
Paper type Research paper
In competitive environments characterized by a plethora of choice, an abundance of
marketing communications, decreasing inter-brand differences, increasing complexity
of information and its sources as well as increasing search costs, consumers can ﬁnd
information processing for some purchasing tasks confusing and taxing. Indeed, some
authors contend that confusion pervades almost every decision that consumers make
(Snider, 1993) and incidences of consumer confusion have been reported in many
different countries and product markets, such as watches (Mitchell and Papavassiliou,
1997), fashion (Cheary, 1997), telecommunications (e.g., Nanji and Parsons, 1997;
Turnbull et al., 2000), washing powder (Harrison, 1995), health and travel insurance
The current issue and full text archive of this journal is available at
Received January 2008
Revised May 2008,
Accepted August 2008
European Journal of Marketing
Vol. 44 No. 6, 2010
qEmerald Group Publishing Limited
(e.g., Canniffe and McMannus, 1993; Brierley, 1995) own-label brands (e.g., Balabanis
and Craven, 1997; Murphy, 1997) and on the Internet (Mitchell et al., 2004).
Consumer confusion is relevant to marketers because confused consumers are less
likely to make rational buying decisions, to choose products offering the best quality or
best value for money and, to enjoy the shopping experience (Huffman and Kahn, 1998;
Jacoby and Morrin, 1998; Mitchell and Papavassiliou, 1999). In addition, consumer
confusion has been associated with other consequences of economic relevance to
companies, such as negative word of mouth (e.g., Turnbull et al., 2000), cognitive
dissonance (Mitchell and Papavassiliou, 1999), decision postponement (Jacoby and
Morrin, 1998; Huffman and Kahn, 1998; Walsh et al., 2007), dissatisfaction (Foxman
et al., 1990), decreased trust, and decreases in loyalty (Walsh et al., 2007). Word of
mouth, trust, and customer satisfaction are among the most important marketing
outcomes and most measured by companies. Indeed, Ambler (2003) reports that 68 per
cent of ﬁrms use customer satisfaction measures and 64 per cent customer loyalty
measures. Moreover, customer loyalty and (positive) customer word of mouth
communication “are referred to in the marketing literature as key relationship
marketing outcomes” (Hennig-Thurau et al., 2002, p. 231) which are inextricably linked
Despite previous efforts to measure consumer confusion, limited attention has been
given to developing a measure of consumer confusion proneness that captures the
construct’s various sub-domains. A noticeable exception is a recent study which puts
forth a three-dimensional scale of perceived consumer confusion proneness (Walsh
et al., 2007). Here, we examine the effects of different types of confusion proneness on
consumer general word of mouth, marketplace trust, and macro satisfaction. We begin
by discussing the consumer confusion proneness concept before we discuss the
construct dimensions and the related hypotheses after which we elaborate how the
hypotheses were tested against empirical data. Our paper replicates and extends the
work of Walsh et al. (2007) by investigating new outcome variables relevant to
marketers and companies. Finally, the results are discussed with reference to
marketing as well as consumer policy and education.
The concept of consumer confusion proneness and hypotheses
Some authors argue that consumer confusion is predominantly non-conscious which
implies that conscious confusion takes place at least occasionally (e.g., Poiesz and
Verhallen, 1989). Walsh et al. (2002) addressed the conscious/unconscious nature of
confusion in their conceptual piece. They argue that awareness can be seen as an
important aspect because it relates to consumers’ abilities to take measures to reduce it.
Also, Mitchell and Papavassiliou (1999) stress this as an important aspect because it
concerns the consumer’s ability to initiate confusion reduction strategies. In the present
study, the authors treat consumer confusion as something the consumer is conscious of
as a “state” and must deal with.
Drawing on Walsh et al.’s (2007) characterization of consumer confusion as a
conscious condition or “state” that individuals may be prone to, we see this as causing
them to act differently and/or to affect their decision making ability. For example,
when confused, consumers are often in a state of anxiety, frustration, lack of
understanding and indecision. Confusion proneness can be seen how easily/often
The effect of
consumers experience this state of confusion or as “consumers” general tolerance for
processing similar, too much or ambiguous information, which negatively affects their
information processing and decision-making abilities’. This approach from Walsh et al.
(2007) contributes to a more sophisticated understanding of the dimensions and
outcomes of consumer confusion proneness and builds on previous work which has
focused on speciﬁc situations of either stimulus similarity or overload.
Once confused, there are often general negative consequences. For example, when
consumers perceive different brand-related stimuli as similar, overwhelming or unclear
and buy the “wrong” brand, the chosen brand might be “inefﬁcient” because it might
fail to deliver the desired utility (Kamakura et al., 1988). Or, because the state of
confusion is linked with uncertainty, anxiety, a lack of understanding and indecision,
the choice process is inefﬁcient and frustrating. Drawing on Walsh and Mitchell
(2005a), we argue that confusion can result in mistaken purchases, product misuse,
product misunderstanding or misattribution of various product attributes which result
in a non-maximization of utility. We now look at each type of confusion in turn to
develop our hypotheses.
Similarity confusion proneness
Similarity confusion proneness is deﬁned as consumers’ “propensity to think that
different products in a product category are visually and functionally similar” (Walsh
et al., 2007, p. 702). Similarity confusion proneness can be caused by stimuli that are
similar to stimuli the consumer learned in the past. Marketing related examples include
advertisements (e.g., Poiesz and Verhallen, 1989; Keller, 1991; Kent and Allen, 1994),
interpersonal communications, the store environment or products which are very
similar (e.g., Loken et al., 1986; Foxman et al., 1992; Kapferer, 1995; Kohli and Thakor,
1997; Jacoby and Morrin, 1998; Brengman et al., 2001). This is because consumers rely
on visual cues to locate and distinguish brands and when presented with similar
brands or information, can buy a fake or a retailer own-label brand thinking it is the
original. Thus, when faced with similar-looking stimuli, consumers prone to similarity
confusion will potentially alter their choice because of the perceived physical similarity
Possibly because of the legal context of the research, most stimulus-similarity
deﬁnitions tend to imply that a prerequisite of confusion is that the consumer buys the
wrong brand (e.g., Diamond, 1981; Kohli and Thakor, 1997; Jacoby and Morrin, 1998),
which constitutes only one behavioural outcome of confusion and ignores other
behaviour-related consequences such as, engaging in word of mouth, and other
cognitive consequences such as decreased trust and satisfaction.
Consumers who are prone to similarity confusion are likely to have negative
consumption experiences which lead to dissatisfaction. The confusion felt by seeing so
many similar products, can result in indecision, frustration, increased mental
processing, in addition to possibly buying, not necessarily a “wrong” product but one
which might not meet their needs as well as another if they were able to identify the
true differences between the different brands. For our word of mouth concept, we draw
on the market maven idea since they are key market monitors of such information and
deliver it via word of mouth. Mavens are deﬁned as consumers who “initiate
discussions with consumers and respond to requests from consumers for market
information” (Feick and Price, 1987, p. 85) and we conceptualise word of mouth as
being a general concept of marketplace interpersonal interaction and ‘the degree of
product related information which a consumer communicates via speaking to other
consumers’. If people are used to giving their opinions of products, these negative
occurrences as a result of seeing many products as similar, might naturally lead to
consumers wanting to communicate more in order to express their frustration and/or to
warn other consumers.
However, similarity confusion prone consumers may not share their frustrating or
mistaken shopping purchases with others as this would involve admitting the mistake
and could cause them embarrassment. It likely that those who see most brands as similar
and are not able to differentiate between brands will not engage in general word of
mouth about these brands to others and other consumers are less likely to ask their
opinion. It is also possible that when consumers perceive brands in a category as very
similar, they perceive them as more like commodities with little differentiation, and thus
they exhibit little interest to engage in word of mouth to learn about brand differences in
the marketplace. These counter lines of reasoning lead to our ﬁrst hypothesis:
H1. Similarity confusion proneness has no signiﬁcant impact on general
marketplace related word of mouth.
Trust has been deﬁned as consumers’ willingness to rely upon their expectations about
a ﬁrm’s future behaviour (Morgan and Hunt, 1994; Rousseau et al., 1998). Here, we view
it on an aggregate level of the marketplace and conceptualise it as the sum of
consumers’ willingness to rely on many ﬁrms’ future behaviour. In the context of
consumer similarity confusion proneness, trust is likely to be undermined because
when consumers see all brands as being similar or have mistakenly purchased a
copy-cat product, or are confused from similar advertising or messages. This is
because they are likely to think that this is either a deliberate attempt by companies to
dupe them because they see no reason to have so many similar products on the market
when there are few differences. This will raise their suspicions about the companies’
motives and undermine the trust they have in the marketplace. This is also partly
because trust can also be understood as ﬁrms’ intention to “hold consumers’ interest
ahead of their self-interest” (Singh and Sirdeshmukh, 2000, p. 155). Consumers that
notice the similarity between products within a product category may be inclined to
feel that manufacturers and retailers are putting their own interests ahead of
consumers’. This is likely to have a negative impact on consumer trust in the
marketplace. The more times a person is confused by similar stimuli coming from
numerous brands, the less trust they will have in the marketplace. It can also be argued
that similarity confusion prone consumers may not know which products to trust.
Thus, we propose that:
H2. Similarity confusion proneness has a signiﬁcant negative impact on
Drawing on previous studies, van Dolen et al. (2004) argue that customers distinguish
between two kinds of satisfaction, namely encounter and relationship satisfaction (also
referred to as “overall satisfaction”). The former is concerned with an evaluation of the
events and behaviours that occur during a single customer-company interaction. The
latter is a function of satisfaction with multiple experiences and encounters with ﬁrms
(see also Bitner and Hubbert, 1994). In a similar vein, the literature on customer
The effect of
satisfaction distinguishes between micro satisfaction and macro satisfaction. The
former is concerned with customers’ judgments in relation to goods, services and
interaction experiences with a ﬁrm (Renoux, 1974). The latter is a more general
evaluation of a ﬁrm’s (or the “marketplace’s”) behaviour and marketing activities. Since
we conceptualize confusion proneness as something which exists regardless of speciﬁc
products and transactions, using macro satisfaction (as opposed to micro satisfaction) as
an outcome variable seems appropriate. With regard to similarity confusion proneness,
we suggest that a consumer’s inability to differentiate between brands will lead to a
decreasing satisfaction toward the marketplace. Perceived similarity confusion is likely
to negatively inﬂuence consumer satisfaction, regardless of whether the consumer buys
the wrong brand or not. The mere fact that consumers need to invest time and energy
into processing more brand related information (i.e. incurring more transaction costs)
can negatively inﬂuence satisfaction. Therefore we propose:
H3. Similarity confusion proneness has a signiﬁcant negative impact on macro
Overload confusion proneness
Since consumers have limited cognitive abilities, once the amount of stimuli passes a
certain threshold, it overloads and confuses consumers (e.g., Jacoby, Speller, and Kohn,
1974). Although consumers who face a sufﬁciently rich information environment can
feel information anxiety, they are often unable to stop short of information overloading
themselves (Malhotra, 1984; Keller and Staelin, 1987). Overload confusion proneness is
deﬁned as the “consumers’ difﬁculty when confronted with more product information
and alternatives than they can process in order to get to know, to compare and to
comprehend alternatives” (Walsh et al., 2007, p. 704).
Drawing on Sundaram et al. (1998), it is conceivable that overload (and ambiguity)
confusion prone consumers may engage in more general word of mouth behaviour
purely because they have much more to talk about. For market mavens, the overload is
likely to be due to them actively seeking out information in order to process it for the
beneﬁt of others via word of mouth. Thus, the more products and product information
they process, the more and the better the information they have to disseminate and the
more word of mouth occurs. An unfortunate by product of this is that they are more
likely to experience overload confusion as a result of the extra amounts of information
being processed. In addition, the reverse logic also might hold, namely that overload
confusion prone consumers are likely to communicate with reference group members,
whom they trust, as a way of perhaps clarifying some of the information they have
(Wiedmann et al., 2001). Those reference group members can play an important role in
terms of adding decision-making competence and aiding consumers in dealing with
large amounts of decision-relevant information. The fact that they talk more about
products and services as a way of working through some of their confusion, makes
them be perceived as high information disseminators or market mavens as the product
and marketplace information puts them in a more informed position vis-a
consumers who might therefore ask them for advice. Thus, overload confusion prone
consumers might engage in more word of mouth. We therefore suggest that:
H4. Overload confusion proneness has a signiﬁcant positive impact on general
marketplace related word of mouth.
Trust tends to be based on consumer experience and consumers’ evaluation of a ﬁrm
and product-related information (Moorman et al., 1993). As we conceptualise trust as a
general marketplace phenomenon not related to one individual ﬁrm, we are concerned
with the amount of information consumers have to process in their daily lives. To the
extent that consumers can no longer process all the information in the marketplace,
they can feel disempowered and overall less trusting because they know they must be
missing some possibly important information. There might also be an attribution effect
whereby consumers blame the companies as a whole for making the marketplace too
complex and difﬁcult to understand and question their motives. Related to this,
increases in the amount of buying related information and more choices consumers
have to process might lead to more products being chosen and therefore consumers are
able to establish less trust with any individual product. Taken on a macro scale, this
could lead to a decrease in marketplace trust. General insights from the “consumer
resistance” literature also increasingly suggest that “whereas companies want
consumers to trust them, consumers often choose to ignore or avoid them” (Roux, 2007,
p. 603). Others discuss the effects of product and information overchoice on the
consumer’s decision-making quality and argue “[m]ore choice often makes choice
harder not easier for consumers” (Shankar et al., 2006). Indeed, the resistance literature
consistently questions the view that empowerment of consumers through choice is
beneﬁcial and trust enhancing. We can therefore hypothesize that:
H5. Overload confusion proneness has a signiﬁcant negative impact on
Although more information can lead consumers to feel companies are trying to be
transparent and supportive in helping them to make sound buying decisions, if this
information is too much and causes the consumer to become confused, they are likely
to be less satisﬁed with the company for confusing them and could blame the company
for their inability to process all the information. Moreover, confusion from information
overload is likely to cause consumer anxiety, frustration, indecision and stress which
can lead to dissatisfaction. Indeed Beattie et al. (1994) show that choosing from overly
large sets makes consumers feel that the decision is difﬁcult and dissatisfying.
Overload confusion prone consumers who become confused and are unable to process
information satisfactorily will have to give extra time and effort to the decision-making
process. This may involve employing strategies that involve extra effort such as
asking store personnel for help or search for consumer reports which can lead to an
increase in dissatisfaction with the process (Turnbull et al., 2000). Thus we propose
H6. Overload confusion proneness has a signiﬁcant negative impact on macro
Ambiguity confusion proneness
Consumer confusion has been discussed beyond the context of perceived stimulus
similarity and overload (e.g., Mitchell and Papavassiliou, 1999; Turnbull et al., 2000).
For example, some studies stress aspects, such as product complexity (e.g., Boxer and
Lloyd, 1994; Cahill, 1995), ambiguous information or false product claims (e.g.,
Golodner, 1993; Kangun and Polonsky, 1995; Chryssochoidis, 2000) or non-transparent
pricing (e.g., Berry and Yadav, 1996), all of which cause problems of understanding on
The effect of
part of the consumer (e.g., Eagly, 1974) and are related to the concept of cognitive
unclarity (Cox, 1967). According to Cox (1967), consumers perceive unclarity when they
feel uncomfortable from information ambiguity and incongruity (see also MacDonald,
1970). Ambiguity confusion prone consumers are likely to infer things about, or to be
unclear about, product characteristics that are different than the actual product
characteristics. Examples of this might be dubious product claims such as a product
being “nutritious” or “healthy”, or conﬂicting information on the same product from
different sources can lead to confusion (e.g., Golodner, 1993). Therefore, ambiguity
confusion proneness can be largely attributed to consumers’ response to dubious
product claims or conﬂicting information on the same product from different sources.
Walsh et al. (2007, p. 705) deﬁne ambiguity confusion proneness, as “consumers’
tolerance for processing unclear, misleading, or ambiguous products, product-related
information or advertisements”.
When consumers compare two or more complex products and experience confusion
from the ambiguous information they ﬁnd, this could lead to choice deferral because
the consumer tries to cope with what seem to be non-comparable alternatives (Dhar,
1997). Consumers who process lots of product and marketplace information are more
likely to come across ambiguous or misleading information as a result of the sheer
amount of information they are processing. Secondly, market maven type individuals,
who like helping others navigate the marketplace, are more likely to be drawn to
confusing and ambiguous product information and want to deal with it and
understand it more as this presents greater value to their friends and relatives. Thirdly,
it is possible that because they encounter more ambiguous stimuli and sometimes get
confused by it, one way of dealing with the confusion and helping to organise and
understand the information better is to explain it to others. Being more confused by
ambiguous information and using an “explaining it to others strategy” to help them
understand it better means that they are perceived as good marketplace information
providers. We know, for example, that consumers prone to be confused by ambiguous
stimuli are likely to seek help and to get other people to agree on the choice (Greenleaf
and Lehmann, 1995). Once the information has been clariﬁed, they are now in a
position to show off their new knowledge and help others understand the ambiguity or
conﬂicting information which will also increase their general marketplace word of
H7. Ambiguity confusion proneness has a signiﬁcant positive impact on general
marketplace related word of mouth.
Research suggests that when the decision situation offers many equally acceptable
alternatives and none can be easily veriﬁed as best, such as exists when products are
very similar or there are ambiguous information about their differences, this can create
feelings of confusion which lead to a reluctance to commit an action (Ellsberg, 1961;
Scholnick and Wing, 1988). Not knowing which alternative is preferred, while not
being certain that one wants them equally, may result in indecision and a tendency to
avoid commitment (Dhar, 1997). Commitment understood as the customer’s long-term
orientation toward a business relationship that is grounded on emotional bonds (e.g.,
Hennig-Thurau et al., 2002) is likely to wane in the face of ambiguity, as well as trust
(Morgan and Hunt, 1994). Hence, faced with this uncertainty, ambiguity confusion
prone consumers are likely to have less trust in a marketplace which provides them
with ambiguous and sometimes conﬂicting product information. Consequently, we
H8. Ambiguity confusion proneness has a signiﬁcant negative impact on
The relationship between ambiguity confusion and macro satisfaction can be partly
explained by drawing on cognitive categorization theory (e.g., Mervis and Rosch, 1981;
Cohen and Basu, 1987), which suggests that as consumers gain experience with a
variety of products, they cognitively group these products into categories which then
serve as a basis for evaluating new products. Ambiguity confusion results from a new
(or just unfamiliar) product carrying ambiguous or conﬂicting information which
requires extra processing to understand which category it ﬁts into. Because consumers
prefer products that require moderate levels of cognitive effort to categorize (Mandler,
1982), any message or product that involves ambiguous or conﬂicting information, and
hence deﬁes current categories, can be rejected and cause decreased satisfaction
because the consumer has to make more effort to ﬁt the new message/product into
It is also the case that when consumers perceive high levels of ambiguous
information, they are uncertain and anxious as to which information to believe. The
extra processing required to obtain the additional information to reduce the ambiguity,
coupled with the increases in uncertainty and anxiety will contribute to a reduction of
consumers’ satisfaction with the process and companies. In addition, ambiguous
product information can cause consumers not to be able to evaluate and utilize product
features as well as being negatively correlated with perceived user friendliness which
is an important quality dimension for consumers and is associated with customer
satisfaction (Brucks et al., 2000). Thus, we propose that:
H9. Ambiguity confusion proneness has a signiﬁcant negative impact on macro
In the present study, confusion proneness is not measured in a speciﬁc context at a
point-in-time, but as an individual difference characteristic. Walsh et al. (2007)
developed a scale from a mix of original and adapted scale items derived from other
confusion studies to provide an overall assessment of consumers’ confusion proneness
and its three dimensions (i.e. similarity, overload, and ambiguity). Based on
comprehensive validation procedures (exploratory interviews, conﬁrmatory and
exploratory factor analysis, Cronbach alpha, etc.), Walsh et al. (2007) found support for
a three-dimensional, 12-item scale with the following dimensions: Similarity confusion,
Overload confusion, and Ambiguity confusion. They used a 5-point scale (1 ¼strongly
disagree, 5 ¼strongly agree), which we employed for our study. The items to measure
the three outcome variables were adapted from previous studies measuring consumer
word of mouth behaviour (Feick and Price, 1987), marketplace trust (Doney and
Cannon, 1997), and (general) satisfaction (Spreng and Mackroy, 1996). General
satisfaction was measured with a single item, which are increasingly common in
marketing research (e.g., Hurley and Estelami, 1998; van Birgelen et al., 2001). The
most substantive issue is that of whether the item(s) is sufﬁcient to measure the
The effect of
construct. Since we are not looking at transactional satisfaction, that is, satisfaction
with aspects of the product design, use or price, but are more concerned with macro
satisfaction, which is a more overall evaluation, a multi-item scale is not needed to
capture the nature of the macro-concept. This is inline with Bergvist and Rossiter
(2007) who argue that for constructs that consist of a singular object, single-item
measures should be used. Also, multiple-item measurement instruments can
occasionally aggravate respondent behaviour and undermine respondent reliability
(Drolet and Morrison, 2001), supporting the use of a single-item scale. We
face-validated and pre-tested our questionnaire was with a small sample of
For practical reasons, previous consumer confusion studies have predominantly used
student samples, despite contentions that students have speciﬁc attitudes and
behaviours that differ from other consumers not least their higher cognitive abilities
which are of relevance to confusion proneness and that results derived from student
samples have limited generalizability.
In this study, consumers were interviewed to represent the shopping public from a
major northern German city. The on-street interviews were carried out from Monday to
Saturday and conducted by students majoring in marketing as a requirement of their
senior ﬁeld experience. The average interview length was just under 30 minutes. The
interviewers were instructed to interview consumers and to conduct interviews
according to some ﬁxed quota. Using proportional quota sampling, an attempt was
made to represent the gender characteristics of the population by sampling a
proportional amount of each. In terms of gender, the sample is representative of the
current German population. A total of 355 interviews were conducted, representing
approximately a 30 per cent response rate of those individuals asked to respond (see
Efforts were made by the researchers carrying out the research project to control
and avoid non-response error. First, personal interviews were chosen to minimize
participation refusal due to time constraints or inconvenience. Second, the
questionnaire was relatively short.
Measurement and factor structure
The appropriateness of the 12 items for explaining the three consumer confusion
proneness traits was tested in several steps. In the ﬁrst step of the measurement
procedure, the three-factor structure was tested using conﬁrmatory factor analysis
(Table II). Three items showed poor loadings and a more parsimonious model was
estimated. Model identiﬁcation was achieved, and the ﬁt indices suggested that the
model adequately represented the input data, with GFI being 0.963, AGFI being 0.934,
an RMR of 0.087, RMSEA being 0.091, a competitive ﬁt of CFI of 0.943, and
2=df ¼3:08. Reliabilities were calculated for the three scales. All multi-item
measures, except satisfaction, had good reliability, with composite reliabilities larger
than 0.60 (Bagozzi and Yi, 1988) (see Table II). The error of the customer satisfaction
indicator was ﬁxed to 0 because the authors did not expect it to be signiﬁcantly
different from 0. Indeed, many recent multi-item customer satisfaction scales tend to
have very high Cronbach alphas and errors close to 0, justifying ﬁxing the error to 0
(and variance to 1). Further, discriminant validity was found for all possible pairs of
factors, with squared correlations being 0.14 for similarity and overload, 0.05 for
similarity/ambiguity, and .31 for overload/ambiguity, respectively (Fornell and
Larcker, 1981) (see Table III).
Structural model – hypotheses testing
The conceptual model was tested simultaneously with LISREL 8.52. The ﬁt statistics
indicated that the model represents the data reasonably well, with GFI ¼0:944,
AGFI ¼0:921, RMR ¼0:092, RMSEA ¼0:085, CFI ¼0:934,
Standardized residuals and modiﬁcation indices were reviewed with the intent to
pinpoint potential areas of model misspeciﬁcation (e.g., Saris et al., 1987). There were
no unreasonable estimates and all factor loadings were signiﬁcant and hence there was
no need to re-estimate our model. By explaining 25 percent of general word of mouth,
36 percent of marketplace trust, and 38 percent of the overall customer satisfaction
construct, the relevance of consumer confusion proneness for consumer behaviour is
The strongest relationship between the three confusion traits was between overload
and ambiguity (0.45), followed by similarity/overload (0.31) and similarity/ambiguity
(0.15). Table III contains the correlation coefﬁcients, means and standard deviations for
all variables of the model as ﬁnally operationalized in the structural equation
In Table IV, the path coefﬁcients and t-values for each of the nine hypotheses can be
seen. Six of the nine hypotheses are conﬁrmed by the data (see Table IV). The
similarity confusion proneness trait has a strong negative impact on customers’ word
Some high school or less 15.7
High school graduate 26.5
Vocational school/some college 33
College graduate/graduate school 24.8
Demographic proﬁle of
The effect of
Factors and items
Factor 1: Similarity confusion proneness
(adapted from Walsh et al., 2007) 0.76/0.64
Owing to the great similarity of
many products it is often difﬁcult
to detect new products Yes 0.49
Some brands look so similar that it
is uncertain whether they are
made by the same manufacturer or
not Yes 1.00 *
Sometimes I want to buy a product
seen in an advertisement, but
cannot identify it clearly between
scores of similar products No
Factor 2: Overload confusion proneness
(adapted from Walsh et al., 2007) 0.54/0.62
I do not always know exactly
which products meet my needs
best Yes 0.40
There are so many brands to
choose from that I sometime feel
confused Yes 0.67
Owing to the host of stores it is
sometimes difﬁcult to decide
where to shop Yes 0.48
Most brands are very similar and
are therefore hard to distinguish No
Factor 3: Ambiguity confusion proneness
(adapted from Walsh et al., 2007) 0.50/0.63
Products such as CD players or
VCR often have so many features
that a comparison of different
brands is barely possible Yes 0.62
The information I get from
advertising often is so vague that
it is hard to know what a product
can actually perform Yes 0.32
When buying a product I rarely
feel sufﬁciently informed Yes 0.50
When purchasing certain
products, such as a computer or hi-
ﬁ, I feel uncertain as to product
features that are particularly
important for me Yes 0.50
When purchasing certain
products, I need the help of sales
personnel to understand
differences between products No
Item listing, factor
structure and reliability
for model variables
of mouth behaviour and trust, indicating that high degrees of perceived similarity
proneness are associated with low levels of general word of mouth and marketplace
trust, and vice versa. Support was also found for H3, which predicted a negative
relationship between similarity confusion proneness and macro satisfaction.
Factors and items
Items used to operationalize consequences of confusion proneness
Word of mouth
(adapted from Feick and Price, 1987) 0.75/0.90
I like introducing new brands and
products to my friends 0.78
I like helping people by providing
them with information about
many kinds of products 0.71
People ask me for information
about products, places to shop, or
If someone asked me where to get
the best buy on several types of
products, I could tell him where to
My friends think of me as a good
source of information when it
comes to new products or sales 0.61
Think about a person who has
information about a variety of
products and likes to share this
information with others. This
person knows about new products,
sales, stores, and so on, but not
necessarily feel he or she is an
expert on one particular product.
How well you say this description
ﬁts you? 0.47
(adapted from Doney and Cannon, 1997) 0.48/0.68
In general, I trust the products I
In general, I trust the
manufacturers of the products I
In general, I trust the store
personnel that sells me products 0.24
(adapted from Spreng and Mackroy, 1996) 1.00
Overall, I am satisﬁed with the
products I buy 1.00 *
Note: *Fixed parameter Table II.
The effect of
Consistent with H4, overload confusion proneness had a signiﬁcant positive impact on
general word of mouth, supporting the notion that overload-prone consumers involve
others in the decision-making to add expertise or to help narrowing the choice set and
also are seen as information givers because of all the information they have collected.
H5 was not supported, with the relationship of overload confusion proneness to
marketplace trust being non signiﬁcant. Consistent with H6, overload confusion
proneness was found to have a negative impact on macro satisfaction.
As predicted in H7, ambiguity confusion proneness does have a signiﬁcant positive
impact upon general word of mouth. However, contrary to predictions, ambiguity
confusion proneness had a positive impact on marketplace trust (H8), but
non-signiﬁcant impact on macro satisfaction (as predicted in H9), hence H8 and H9
were rejected. Table IV summarizes the results of hypotheses testing.
Discussion and implications
Our ﬁndings offer several key contributions which have implications for research and
managerial practice and public policy.
coefﬁcients t-values Support
H1 Similarity confusion proneness has no signiﬁcant impact
on marketplace-related word of mouth
20.20 (26.61) Not conﬁrmed
H2 Similarity confusion proneness has a signiﬁcant negative
impact on marketplace trust
20.211 (23.31) Conﬁrmed
H3 Similarity confusion proneness has a signiﬁcant negative
impact on macro satisfaction
20.29 (2.36) Conﬁrmed
H4 Overload confusion proneness has a signiﬁcant positive
impact on marketplace-related word of mouth
0.37 (8.94) Conﬁrmed
H5 Overload confusion proneness has a signiﬁcant negative
impact on marketplace trust
0.05 (1.08) Not conﬁrmed
H6 Overload confusion proneness has a signiﬁcant negative
impact on macro satisfaction
20.22 (22.64) Conﬁrmed
H7 Ambiguity confusion proneness has a signiﬁcant
positive impact on marketplace-related word of mouth
0.26 (2.22) Conﬁrmed
H8 Ambiguity confusion proneness has a signiﬁcant
negative impact on marketplace trust
0.24 (5.40) Not conﬁrmed
H9 Ambiguity confusion proneness has a signiﬁcant
negative impact on macro satisfaction
20.01 (21.55) Not conﬁrmed
Summary of hypotheses
Means SD No. of items 1 2 3 4 5 6
1. Similarity 3.30 0.99 2 1
2. Overload 2.77 0.95 3 0.31 1
3. Ambiguity 3.29 0.88 4 0.15 0.45 1
4. Word of mouth 2.84 1.25 6 20.46 0.19 0.10 1
5. Trust 3.41 1.06 3 20.20 0.20 0.33 0.18 1
6. Customer satisfaction 3.19 1.15 1 0.31 0.17 20.22 0.26 0.70
means and standard
deviations of model
The ﬁndings support the validity of Walsh et al.’s (2007) consumer confusion proneness
scale, which exhibits good psychometric properties. In addition, our structural analysis
indicated that the three confusion proneness dimensions behave differently with regard
to their linkage with the three consequences, thus providing support for the nomological
validity of treating consumer confusion proneness as a three-dimensional construct.
Perhaps the most important ﬁnding is that there is an indication of generality of most
scale items. Given this ﬁnding, there is reason to believe that the scale has construct
validity and has potential use across populations. Further, in this study, we have
examined how the three confusion proneness dimensions affect important marketing
outcomes. At a conceptual level, a better understanding is needed on the differential
impact of the three confusion proneness dimensions on outcome variables. We have
tested our hypotheses against empirical data and failed to support four hypotheses.
H1, that similarity confusion proneness had no effect on key information providers’
word of mouth activity is not supported. Given the competing lines of reasoning in the
hypothesis development, the result suggests that the more powerful explanation is that
consumers who are normally asked for their opinions, such as market mavens, are
more reluctant to offer word of mouth when they are faced with seeing many products
as similar in the marketplace. We speculate that this might be an “embarrassment”
effect or perhaps more likely, that because they see products as being similar, this
gives them less to talk about to others.
Unlike anticipated in H5 and postulated in the consumer reactance literature,
overload confusion proneness is not associated with lower levels of marketplace trust.
A plausible explanation of this ﬁnding may lie in the fact that consumers view
information as beneﬁcial whether or not they have the cognitive capacity to utilise it.
Another reason may be that more information reduces the consumer’s perceived
uncertainty (Urbany et al., 1989) which makes a negative information-trust kink less
likely (e.g., Geyskens et al., 1998).
Another key implication for theory is the observed but unexpected positive
ambiguity-trust relationship. There are several possible explanations for this. First, it
is conceivable because when consumers experience ambiguity confusion repeatedly,
they begin to feel more comfortable with ambiguous information. Second, some
research has found that ambiguity confusion is associated with brand loyalty
(Chryssochoidis, 2000). It could be that product-related information or advertisements
perceived as ambiguous cause consumers to increase their blind faith in the
marketplace not to dupe them or provide poor products. For example, mobile phone
manufacturers are often accused of packing too much technology and too many
complex features into their phones, such as Bluetooth capabilities, personal digital
assistants and so forth, but consumers might “trust” in the technology that it is good
and might be useful. Many consumers may not know how to use these features and
could perceive ambiguity confusion, but they may still appreciate those features. Thus
a consumer with a state-of-the-art mobile phone may struggle to understand how
exactly her phone works, but nonetheless puts trust in the manufacturer, and feels
satisﬁed, for devising such a sophisticated product.
The explanation for the non-signiﬁcant effect on macro satisfaction might be due to the
same factor, namely, that as consumers become more comfortable with repeated
exposure to ambiguous product information, its dissatisfaction effect reduces.
The effect of
Our ﬁndings have several implications for marketing management. One implication is
for marketers to identify whether their customers are confusion prone by using the scale
developed. Brand manufacturers and retailers can measure confusion proneness at
different levels of abstraction by either considering individual confusion-proneness
dimensions (lower level of abstraction) or looking at the overall confusion-proneness
score for a given segment (higher level of abstraction). Our results suggest that consumer
confusion proneness should be operationalized as a multidimensional construct.
However, whether as a multidimensional or second-order construct, confusion proneness
can be related to important outcome variables. Essentially, the confusion-proneness scale
could serve as a diagnostic tool that will allow marketers to determine how likely their
customers are to be confused by their own and competing brands and related
communications and even to establish market or market sector norms.
If there are signiﬁcant numbers of confused consumers in their target market, or of
their own customers, marketers need to consider what the cause of consumers’
confusion is and whether they are doing something to exacerbate it. If for example,
stimulus ambiguity was found to be a problem in the marketplace for the customers of
a particular good, then the manufacturer knows that traditional messaging will not
work and must ﬁnd other ways of making the communications clearer and less
ambiguous. For example, companies may need to systematically identify sources of
perceived stimulus ambiguity, such as product claims, and to rectify them. Reducing
confusion causing stimuli can help to reduce the incidence of confusion and conversely
increasing consumers’ decision-making quality, which could be a major source of
competitive advantage in any market, but particularly in those markets where
confusion has already been shown to exist, e.g. telecommunications and ﬁnancial
services. One way of doing this could be to train sales and other personnel to recognize
confusion-prone consumers and to assist them in reducing their confusion.
Alternatively, the company could choose to promote self-help strategies such as
shopping with a friend who could help in making purchase decisions. The most
implications are for retailers who select our brand choices. If for example, many of their
customers see few differences between brands, this would imply that a great range of
product categories could be stocked, with fewer brands per category.
A second implication for marketers comes from looking at the speciﬁc results
presented which are illuminating yet complicated for marketers to act upon. It is clear
that confusion proneness has an important impact on key marketing variables such as
general word of mouth, marketplace trust and overall satisfaction, but that the
relationships are complex. For example, consumers engage in general word of mouth
less when they are prone to similarity confusion, but talk more when they are overload
and ambiguity confused. This emphasizes the importance of our conceptualization and
measuring the different types of confusion proneness as they have different outcomes
for marketers to consider. The conceptual model gives companies guidance on what to
look for and the areas where attention may be required.
Public policy and consumer education implications
Consumers can suffer loss of utility due to unfair marketplace practices or information
asymmetries between ﬁrms and consumers (e.g., Rotfeld and Rotzoll, 1980; Sprott and
Miyazaki, 2002). Discriminating between brands, processing and understanding
available marketplace information is a key consumer ability. Warlop et al. (2005) argue
that consumer welfare depends upon how well consumers learn and remember quality
differences among competing products and demonstrate that consumers have
difﬁculties to learn and remember quality differences between well-established
(manufacturer) brands and lower-priced look-a-likes. It can be argued that confusion
prone consumers suffer a welfare loss because their decision making is impaired. If
they mistakenly buy a look-a-like brand or make a suboptimal choice due to perceived
overload or ambiguity confusion, they suffer a double-whammy effect: First, they
relinquish utility because they do not get the brand they had a satisfactory prior
consumption experience with. Second, consumers are likely to experience
dissatisfaction with the look-a-like brand (Warlop et al., 2005). Moreover, when
consumers struggle to discriminate between manufacturer and look-a-like brands
which look similar they can resort to simplifying decisions (e.g., buying the most
expensive product) or avoid shopping altogether.
The issue of burgeoning amounts of consumer information and consumers’ ability to
process and understand it is one of the major challenges facing consumer policy makers
who have the task of managing the tension between marketers’ interest and those of
consumers (Smith and Cooper-Martin, 1997). Despite the much heralded advent of
“consumer empowerment” it appears that empowered consumers are not always able to
create greater beneﬁts for themselves. The reason for that being that empowerment and
the concomitant increase in choice and information can result in overload and lead to less
consumer control (e.g., Denegri-Knott et al., 2006; Wathieu et al., 2002).
The three traits of consumer confusion proneness can be interpreted as the focal point
of future educational activities. Consumer policy makers could use the confusion
proneness scale to identify the prevalence of confusion-prone consumers within the
population by using it on a national sample. Indeed the scale could be adapted and
distributed to consumers for them to complete by themselves to ascertain their own
confusion proneness score. Before this can be done, however, the scale needs calibrating
on random samples of consumers to establish benchmark scores. The insight gained
from applying the confusion scale might help policy makers educate consumers about
which confusion they are more prone to. The information might also be used to
encourage the development of consumer education programs which analyse consumer
confusion and consumers’ response to it. This could then be used to teach consumers
how to rectify it, for example, by focusing on key information when comparing similar
brands, delaying decisions, asking a friend for advice, or buying the one you know.
Once confusion-prone consumers have been identiﬁed, they could then be proﬁled
and the information brought to the attention of various consumer organizations and
government departments, e.g. health, education, safety, which might need to be aware
of their potential special needs. At a minimum, these organizations should test
important communications and other material on this potentially vulnerable confused
group for ease of understanding. At best, consumer policy makers could consider that
this group may require more targeted help in the form of specialist information and
One consumer policy issue which arises from considering consumer confusion
proneness is whether traditional consumerism, with its calls for “more” information to
be provided to consumers, could actually be undermining consumers’ ability to process
that information. There appears to be the risk that consumers become over-burdened,
The effect of
shopping fatigued, confused and turn off trying to understand the information. It
would appear that consumers need “better”, more targeted, more attuned, and more
comprehendible information which could in some circumstances be less than they have
now thus reducing their information processing burden. In order to reduce confusion
and improve consumers’ rights to be informed and safe, a radical step could be for
marketers to be restricted from developing products, packages and commercial
messages that do not cater for the needs of confused consumers, e.g. products with
larger fonts carrying no redundant information and ambiguous terms, or less
complicated products and product manuals. In certain situations, more ofﬁcial and
well-monitored deﬁnitions for potentially confusing terms particularly on food
products and cosmetics, e.g. “alcohol free”, “hypoallergenic”, “healthy” and “natural”,
would be a major advantage.
Conclusions, limitations, and future research
The purpose of this research was to apply a consumer confusion proneness scale and
provide empirical evidence on the scale’s validity and on how confusion proneness
affects consumers’ behaviour. The results give an indication of generality of most scale
items and support the proposition that consumer confusion proneness is a
multi-dimensional phenomenon that has a signiﬁcant impact on key variables such
as marketplace trust, general marketplace related word of mouth and macro
satisfaction, but not always in ways which could be predicted.
As we seek to understand consumer confusion proneness, there is need for
additional research. Also, our research is not free of limitations, which introduce future
research opportunities. First, consumer confusion proneness was conceptualized here
as a cognitive construct and only three outcome variables were considered. It is likely
that some consumers become frustrated, suggesting that emotions may be associated
with confusion (e.g., anger at his/herself or the retailer). The three outcome variables
considered in this study had not been previously examined in relation to
consumer-confusion proneness. However, other responses may also be useful to
understanding how the construct operates such as, cognitive dissonance, self reproach.
Second, the original confusion proneness scale was developed in Germany. In the
present replication study German data were also used. Future research should focus on
exploration of the dimensions of confusion proneness and its impact on relevant
outcome variables, using data in other countries and across cultures.
Third, despite the confusion proneness scale’s overall good psychometric
properties, there appears to be a need to develop additional items to bolster the
“similarity confusion proneness” dimension. Of the three items measuring “similarity
confusion proneness” one item (“Sometimes I want to buy a product seen in an
advertisement, but cannot identify it clearly between scores of similar products”) does
not perform well and may need to be replaced.
Finally, we must consider the sample limitations in terms of size, representativeness
and country effect. In particular we note that the sample had a large number of 20-29
year olds and was quite well educated. Given that a younger age and higher education
are likely to affect information processing capacity positively and to be negatively
correlated with consumer-confusion proneness (Walsh and Mitchell, 2005b), it is likely
that the results presented are not only not generalizable, but also represent something
of a “best case” scenario.
Ambler, T. (2003), Marketing and the Bottom Line, 2nd ed., Prentice-Hall, London.
Bagozzi, R.P. and Yi, Y. (1988), “On the evaluation of structural equation models”, Journal of the
Academy of Marketing Science, Vol. 16, Spring, pp. 74-94.
Balabanis, G. and Craven, S. (1997), “Consumer confusion from own brand lookalikes:
an exploratory investigation”, Journal of Marketing Management, Vol. 13, pp. 299-313.
Beattie, J., Baron, J., Hershey, J.C. and Spranca, M.D. (1994), “Psychological determinants of
decision attitude”, Journal of Behavioral Decision Making, Vol. 7, pp. 129-44.
Bergvist, L. and Rossiter, J.R. (2007), “The predictive validity of multiple-item versus single-item
measures of the same constructs”, Journal of Marketing Research, Vol. 44, May, pp. 175-84.
Berry, L.L. and Yadav, M.S. (1996), “Capture and communicate value in pricing of services”,
Sloan Management Review, Vol. 37 No. 4, pp. 41-52.
Bitner, M.J. and Hubbert, A.R. (1994), “Encounter satisfaction versus overall satisfaction versus
quality: the customer’s voice”, in Rust, R.T. and Pliver, R.L. (Eds), Service Quality:
New Directions in Theory and Practice, Sage Publications, Thousand Oaks, CA, pp. 72-94.
Boxer, S. and Lloyd, C. (1994), “Too many systems spoil the CD broth”, The Sunday Times,
February, pp. 13-16.
Brengman, M., Geuens, M. and De Pelsmacker, P. (2001), “The impact of consumer
characteristics and campaign-related factors on brand confusion in print advertising”,
Journal of Marketing Communications, Vol. 7 No. 4, pp. 231-43.
Brierley, S. (1995), “A matter of life and death”, Marketing Week,, Vol. 18, July 28, p. 26.
Brooks, M., Zeithaml, V.A. and Naylor, G. (2000), “Price and brand name as indicators of quality
dimensions for consumer durables”, Journal of the Academy of Marketing Science, Vol. 28
No. 3, pp. 359-74.
Cahill, D.J. (1995), “We sure as hell confuse ourselves, but what about the customers?”, Marketing
Intelligence & Planning, Vol. 13, pp. 5-9.
Canniffe, M. and McManus, J. (1993), “33% cut in life policy commissions – shake-up an attempt
to reclaim share of savings market”, The Irish Times, October 8.
Cheary, N. (1997), “Fashion victim”, Marketing Week, Vol. 20, October, pp. 36-9.
Chryssochoidis, G. (2000), “Repercussions of consumer confusion for late introduced
differentiated products”, European Journal of Marketing, Vol. 34, pp. 705-22.
Cohen, J.B. and Basu, K. (1987), “Alternative models of categorization: toward a contingent
processing framework”, Journal of Consumer Research, Vol. 13, pp. 455-72.
Cox, D.F. (1967), “Risk handling in consumer behavior – an intense study of two cases”,
in Cox, D.F. (Ed.), Risk Taking and Information Handling in Consumer Behavior, Division
of Research, Graduate School of Business Administration, Harvard University, Boston,
MA, pp. 34-81.
Denegri-Knott, J., Zwick, D. and Schroeder, J.E. (2006), “Mapping consumer power: an integrative
framework for marketing and consumer research”, European Journal of Marketing, Vol. 40
Nos 9/10, pp. 950-71.
Dhar, R. (1997), “Consumer preference for a no-choice option”, Journal of Consumer Research,
Vol. 24, September, pp. 215-31.
Diamond, S.A. (1981), Trademark Problems and How to Avoid Them, revised ed., Crain
Communications, Chicago, IL.
Doney, P.M. and Cannon, J.P. (1997), “An examination of the nature of trust in buyer-seller
relationships”, Journal of Marketing, Vol. 61 No. 2, pp. 35-51.
The effect of
Drolet, A.L. and Morrison, D.G. (2001), “Do we really need multiple-item measures in service
research?”, Journal of Service Research, Vol. 3 No. 3, pp. 196-204.
Eagly, A.H. (1974), “Comprehensibility of persuasive arguments as a determinant of opinion
change”, Journal of Personality and Social Psychology, Vol. 29, pp. 758-73.
Ellsberg, D. (1961), “Risk, ambiguity, and the savage axioms”, Quarterly Journal of Economics,
Vol. 75, pp. 643-69.
Feick, L.F. and Price, L.L. (1987), “The market maven: a diffuser of marketplace information”,
Journal of Marketing, Vol. 51, January, pp. 83-97.
Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable
variables and measurement error”, Journal of Marketing Research, Vol. 18, February,
Foxman, E.R., Berger, P.W. and Cote, J.A. (1992), “Consumer brand confusion: a conceptual
framework”, Psychology and Marketing, Vol. 9, March-April, pp. 123-40.
Foxman, E.R., Muehling, D.D. and Berger, P.W. (1990), “An investigation of factors contributing
to consumer brand confusion”, Journal of Consumer Affairs, Vol. 24, pp. 170-89.
Geyskens, I., Steenkamp, J-B.E.M. and Kumar, N. (1998), “Generalizations about trust in
marketing channel relationships using meta-analysis”, International Journal of Research in
Marketing, Vol. 15, July, pp. 223-48.
Golodner, L.F. (1993), “Healthy confusion for consumers”, Journal of Public Policy and Marketing,
Vol. 12, pp. 130-4.
Greenleaf, E.A. and Lehmann, D.R. (1995), “Reasons for substantial delay in consumer decision
making”, Journal of Consumer Research, Vol. 22, September, pp. 186-99.
Harrison, K. (1995), “Revolution in the tub”, SuperMarketing, Vol. 17, February, pp. 8-19.
Hennig-Thurau, T., Gwinner, K.P. and Gremler, D.D. (2002), “Understanding relationship
marketing outcomes: an integration of relational beneﬁts and relationship quality”,
Journal of Service Research, Vol. 4 No. 3, pp. 230-47.
Huffman, C. and Kahn, B.E. (1998), “Variety for sale: mass customization or mass confusion?”,
Journal of Retailing, Vol. 74 No. 4, pp. 491-513.
Hurley, R.F. and Estelami, H. (1998), “Alternative indices for monitoring customer perceptions of
service quality: a comparative evaluation in a retail context”, Journal of the Academy of
Marketing Science, Vol. 26 No. 3, pp. 209-21.
Jacoby, J. and Morrin, M. (1998), “‘Not manufactured or authorized by ...’: recent Federal cases
involving trademark disclaimers”, Journal of Public Policy and Marketing, Vol. 17,
Jacoby, J., Speller, D.E. and Kohn, C.A. (1974), “Brand choice behavior as a function of
information load”, Journal of Marketing Research, Vol. 11, February, pp. 63-9.
Kamakura, W.A., Ratchford, B.T. and Agrawal, J. (1988), “Efﬁciency and welfare loss”, Journal of
Consumer Research, Vol. 15 No. 3, pp. 289-302.
Kangun, N. and Polonsky, M.J. (1995), “Regulation of environmental marketing claims:
a comparative perspective”, International Journal of Advertising, Vol. 14 No. 1, pp. 1-24.
Kapferer, J.-N. (1995), “Brand confusion: empirical study of a legal concept”, Psychology and
Marketing, Vol. 12, pp. 551-68.
Keller, K.L. (1991), “Memory and evaluation effects in competitive advertising environments”,
Journal of Consumer Research, Vol. 17, March, pp. 463-76.
Keller, K.L. and Staelin, R. (1987), “Effects of quality and quantity of information on decision
effectiveness”, Journal of Consumer Research, Vol. 14, September, pp. 200-13.
Kent, R.J. and Allen, C.T. (1994), “Competitive interference effects in consumer memory for
advertising: the role of brand familiarity”, Journal of Marketing, Vol. 58, July, pp. 97-105.
Kohli, Ch. and Thakor, M. (1997), “Branding consumer goods: insight from theory and practice”,
Journal of Consumer Marketing, Vol. 14, pp. 206-19.
Loken, B., Ross, I. and Hinkle, R.L. (1986), “Consumer confusion of origin and brand similarity
perceptions”, Journal of Public Policy and Marketing, Vol. 5, pp. 195-211.
MacDonald, A.P. (1970), “Revised scale for ambiguity tolerance: reliability and validity”,
Psychological Reports, Vol. 26, June, pp. 791-8.
Malhotra, N.K. (1984), “Reﬂections on the information overload paradigm in consumer decision
making”, Journal of Consumer Research, Vol. 10, pp. 436-40.
Mandler, G. (1982), “The structure of value: accounting for taste”, in Clark, M.S. and
Fiske, S.T. (Eds), Affect and Cognition, Erlbaum, Hillsdale, NJ, pp. 3-36.
Mervis, C.B. and Rosch, E. (1981), “Categorization of natural objects”, in Rosenzweig, R. and
Porter, L.W. (Eds), Annual Review of Psychology, Annual Reviews Inc., Palo Alto, CA,
Mitchell, V.-W. and Papavassiliou, V. (1997), “Exploring the concept of consumer confusion”,
Marketing Intelligence & Planning, Vol. 15, April-May, pp. 164-9.
Mitchell, V.-W. and Papavassiliou, V. (1999), “Market causes and implications of consumer
confusion”, Journal of Product & Brand Management, Vol. 8, pp. 319-39.
Mitchell, V.-W., Walsh, G. and Frenzel, T. (2004), “Consumer e-confusion on the internet”, Thexis,
Vol. 21 No. 4, pp. 17-21.
Moorman, C., Deshpande
´, R. and Zaltman, G. (1993), “Factors affecting trust in market research
relationships”, Journal of Marketing, Vol. 57 No. 1, pp. 81-101.
Morgan, R.M. and Hunt, S. (1994), “The commitment-trust theory of relationship marketing”,
Journal of Marketing, Vol. 58 No. 3, pp. 20-38.
Murphy, C. (1997), “17% of shoppers take own-label brands in error”, Marketing, March, p. 6.
Nanji, Z. and Parsons, K. (1997), “So many choices”, Telephony, Vol. 233, July, pp. 34-40.
Poiesz, T.B.C. and Verhallen, T.M.M. (1989), “Brand confusion in advertising”, International
Journal of Advertising, Vol. 8, pp. 231-44.
Renoux, Y. (1974), “The interface with consumers”, in Holloway, R.J. and Hancock, R.S. (Eds),
The Environment of Marketing Management, Wiley, New York, NY, pp. 442-8.
Rotfeld, H.J. and Rotzoll, K.B. (1980), “Is advertising puffery believed?”, Journal of Advertising,
Vol. 9 No. 3, pp. 16-20.
Roux, D. (2007), “Ordinary resistance as a parasitic form of action: a dialogical analysis of
consumer/ﬁrm relations”, in Fitzsimons, G. and Morwitz, V. (Eds), Advances in Consumer
Research, Vol. 34, Association for Consumer Research, Provo, UT, pp. 602-9.
Saris, W.E., Satorra, A. and So
¨rbom, D. (1987), “The detection and correction of speciﬁcation
errors in structural equation models”, in Clogg, C.C. (Ed.), Sociological Methodology,
Jossey-Bass, San Francisco, CA, pp. 105-29.
Scholnick, E.K. and Wing, C.S. (1988), “Knowing when you don’t know: developmental and
situational considerations”, Development Psychology, Vol. 24, March, pp. 190-6.
Shankar, A., Cherrier, H. and Canniford, R. (2006), “Consumer empowerment: a Foucauldian
interpretation”, European Journal of Marketing, Vol. 40 Nos 9/10, pp. 1013-30.
Singh, J. and Sirdeshmukh, D. (2000), “Agency and trust mechanisms in consumer satisfaction
and loyalty judgments”, Journal of the Academy of Marketing Science, Vol. 28 No. 1,
The effect of
Smith, N.C. and Cooper-Martin, E. (1997), “Ethics and target marketing: the role of product harm
and consumer vulnerability”, Journal of Marketing, Vol. 61, July, pp. 1-20.
Snider, J.H. (1993), “Consumers in the information age”, The Futurist, January-February,
Spreng, R.A. and Mackroy, R.D. (1996), “An empirical examination of a model of perceived
service quality and satisfaction”, Journal of Retailing, Vol. 72 No. 2, pp. 201-14.
Sprott, D.E. and Miyazaki, A.D. (2002), “Two decades of contributions to marketing and public
policy: an analysis of research published in Journal of Public Policy & Marketing”, Journal
of Public Policy & Marketing, Vol. 21 No. 1, pp. 105-25.
Sundaram, D.S., Mitra, K. and Webster, C. (1998), “Word-of-mouth communications:
a motivational analysis”, Advances in Consumer Research, Vol. 25, pp. 527-31.
Turnbull, P.W., Leek, S. and Ying, G. (2000), “Customer confusion: the mobile phone market”,
Journal of Marketing Management, Vol. 16, January-April, pp. 143-63.
Urbany, J.E., Dickson, P.R. and Wilkie, W.L. (1989), “Buyer uncertainty and information search”,
Journal of Consumer Research, Vol. 16, September, pp. 208-15.
van Birgelen, M., de Ruyter, K. and Wetzels, M. (2001), “Customer evaluations of after-sales
service contact modes: an empirical analysis of national culture’s consequences”,
International Journal of Research in Marketing, Vol. 19 No. 1, pp. 43-64.
van Dolen, W., de Ruyter, K. and Lemmink, J. (2004), “An empirical assessment of customer
emotions and contact employee performance on encounter and relationship satisfaction”,
Journal of Business Research, Vol. 57, pp. 437-44.
Walsh, G. and Mitchell, V.-W. (2005a), “Consumers vulnerable to perceived product similarity
problems: scale development and identiﬁcation”, Journal of Macromarketing, Vol. 25 No. 2,
Walsh, G. and Mitchell, V.-W. (2005b), “Demographic characteristics of consumers who ﬁnd it
difﬁcult to decide”, Marketing Intelligence & Planning, Vol. 23 No. 3, pp. 281-95.
Walsh, G., Hennig-Thurau, T. and Mitchell, V.-W. (2002), “Conceptualizing consumer confusion”,
in Kehoe, W.J. and Lindgren, J.H. (Eds), Proceedings: Enhancing Knowledge Development
in Marketing, AMA 2002 Summer Educators’ Conference, Vol. 13, American Marketing
Association, Chicago, IL, pp. 172-3.
Walsh, G., Hennig-Thurau, T. and Mitchell, V.-W. (2007), “Consumer confusion proneness:
scale development, validation, and application”, Journal of Marketing Management, Vol. 23
Nos 7/8, pp. 697-721.
Warlop, L., Ratneshwar, S. and van Osselaer, S.M.J. (2005), “Distinctive brand cues and memory
for product consumption experiences”, International Journal of Research in Marketing,
Vol. 22 No. 1, pp. 27-44.
Wathieu, L., Brenner, L., Carmon, Z., Chattopadhyay, A., Wertenbroch, K., Drolet, A., Gourville, J.,
Muthukrishnan, A.V., Novemsky, N., Ratner, R.K. and Wu, G. (2002), “Consumer control and
empowerment: a primer”, Marketing Letters, Vol. 13 No. 3, pp. 297-305.
Wiedmann, K.-P., Walsh, G. and Mitchell, V.-W. (2001), “The German Mannmaven: an agent for
diffusing market information”, Journal of Marketing Communications, Vol. 7 No. 4,
Hennig-Thurau, T. and Klee, A. (1997), “The impact of customer satisfaction and relationship
quality on customer retention: a critical reassessment and model development”, Psychology
& Marketing, Vol. 14 No. 8, pp. 737-64.
Inman, J.J., Dyer, J.S. and Jia, J. (1997), “A generalized utility theory model of disappointment and
regret effects on post-choice valuation”, Marketing Science, Vol. 16 No. 2, pp. 97-111.
Lichtenstein, D.R., Netemeyer, R.G. and Burton, S. (1990), “Distinguishing coupon proneness
from value consciousness: an acquisition-transaction utility theory perspective”, Journal of
Marketing, Vol. 54, July, p. 67.
Simonson, I. (1994), “Trademark infringement from the buyer perspective: conceptual analysis
and measurement implications”, Journal of Public Policy and Marketing, Vol. 13 No. 2,
Gianfranco Walsh can be contacted at: firstname.lastname@example.org
The effect of
To purchase reprints of this article please e-mail: email@example.com
Or visit our web site for further details: www.emeraldinsight.com/reprints